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omoju/Fundamentals

CS/Part_1_Complexity_RunTimeAnalysis.ipynb

1

62076

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display\n", "from IPython.display import HTML\n", "import IPython.core.display as di # Example: di.display_html('<h3>%s:</h3>' % str, raw=True)\n", "\n", "# This line will hide code by default when the notebook is exported as HTML\n", "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n", "\n", "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n", "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "0e0e180b-0ab4-4487-ab26-961766d551d8" } }, "source": [ "\n", "# Runtime Analysis \n", "## using *Finding the nth* Fibonacci numbers as a computational object to think with\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import libraries\n", "from __future__ import absolute_import, division, print_function\n", "\n", "import math\n", "from time import time\n", "import matplotlib.pyplot as pyplt" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "854e3099-a83f-49e7-bf2f-00f8d4cd9210" } }, "source": [ "## Fibonacci\n", "Excerpt from [Algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani](http://cseweb.ucsd.edu/~dasgupta/book/) \n", "\n", "Fibonacci is most widely known for his famous sequence of numbers\n", "\n", "$0,1,1,2,3,5,8,13,21,34,...,$\n", "\n", "each the sum of its two immediate predecessors. More formally, the Fibonacci numbers $F_n$ are generated by the simple rule\n", "\n", "\n", "$F_n = \\begin{cases} \n", "F_n−1 + F_n−2, & \\mbox{if } n \\mbox{ is} > 1 \\\\ \n", "1, & \\mbox{if } n \\mbox{ is} = 1 \\\\ \n", "0, & \\mbox{if } n \\mbox{ is} = 0\n", "\\end{cases}$\n", "\n", "No other sequence of numbers has been studied as extensively, or applied to more fields: biology, demography, art, architecture, music, to name just a few. And, together with the powers of 2, it is computer science’s favorite sequence.\n", "\n", "\n", "\n", "### Tree Recursion\n", "A very simple way to calculate the *nth* Fibonacci number is to use a **recursive** algorithm. Here is a **recursive** algorithm for computing the *n*th Fibonacci number.\n", "\n", "```python\n", "def fib(n):\n", " if n == 0 or n == 1:\n", " return n\n", " else:\n", " return fib(n-2) + fib(n-1)\n", "```\n", "\n", "This algorithm in particular is done using tree recursion. \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/ls0GsJyLVLw\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x106e7ddd0>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('ls0GsJyLVLw')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "nbpresent": { "id": "4cfca957-0bd7-49d1-9ad1-a043299e3e41" } }, "outputs": [], "source": [ "def fib(n):\n", " if n == 0 or n == 1:\n", " return n\n", " else:\n", " return fib(n-2) + fib(n-1)\n", " " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "nbpresent": { "id": "8eccd566-9e6f-408f-ae2b-206490413f7d" } }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib(5)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "e6d9eb6e-90f3-4121-a105-af0d36889c7e" } }, "source": [ "Whenever we have an algorithm, there are three questions we always ask about it:\n", "\n", "1. Is it correct?\n", "2. How much time does it take, as a function of n? \n", "3. And can we do better?\n", "\n", "### 1. Correctness \n", "For this question, the answer is yes because it is almost a line by line implementation of the definition of the Fibonacci sequence.\n", " \n", "### 2. Time complexity as a function of n \n", "Let $T(n)$ be the number of computer steps needed to compute $fib(n)$; what can we say about this function? For starters, if $n$ is less than 2, the procedure halts almost immediately, after just a couple of steps. Therefore,\n", "\n", "$$ T(n)≤2 \\, \\mbox{for} \\, n≤1. $$\n", "\n", "For larger values of $n$, there are two recursive invocations of $fib$, taking time $T (n − 1)$ and $T(n−2)$, respectively, plus three computer steps (checks on the value of $n$ and a final addition).\n", "Therefore,\n", "\n", "$$ T(n) = T(n−1) + T(n−2)+3\\, \\mbox{for} \\,n>1. $$\n", "\n", "Compare this to the recurrence relation for $F_n$, we immediately see that $T(n) ≥ F_n$.\n", "This is very bad news: the running time of the algorithm grows as fast as the Fibonacci numbers! $T(n)$ is exponential in $n$, which implies that the algorithm is impractically slow except for very small values of $n$.\n", "\n", "Let’s be a little more concrete about **just how bad exponential time is**. To compute $F_{200}$,\n", "the $fib$ algorithm executes $T (200) ≥ F_{200} ≥ 2^{138}$ elementary computer steps. How long this actually takes depends, of course, on the computer used. At this time, the fastest computer in the world is the NEC Earth Simulator, which clocks 40 trillion steps per second. Even on this machine, $fib(200)$ would take at least $2^{92}$ seconds. This means that, if we start the computation today, it would still be going long after the sun turns into a red giant star." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "nbpresent": { "id": "43cfc47f-7817-488c-b7df-42fcebcf63c4" } }, "outputs": [], "source": [ "# This function provides a way to track function calls\n", "\n", "def count(f):\n", " def counted(n):\n", " counted.call_count += 1\n", " return f(n)\n", " counted.call_count = 0\n", " return counted" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "nbpresent": { "id": "0a9dad88-c785-4548-9768-32f910919779" } }, "outputs": [], "source": [ "fib = count(fib)\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "nbpresent": { "id": "d37d70ec-2a1f-4f34-8669-e685f13cb795" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This recursive implementation of fib( 5 ) took 0.0002 secs\n", "And 15 calls to the function\n" ] } ], "source": [ "t0 = time()\n", "\n", "n = 5\n", "fib(n)\n", "\n", "print ('This recursive implementation of fib(', n, ') took', round(time() - t0, 4), 'secs')\n", "print ('And {0} calls to the function'.format(fib.call_count))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "nbpresent": { "id": "a7c27b04-5057-457e-bed9-860a856734e7" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This recursive implementation of fib( 30 ) took 1.2692 secs\n", "And 2692552 calls to the function\n" ] } ], "source": [ "t0 = time()\n", "\n", "n = 30\n", "fib(n)\n", "\n", "print ('This recursive implementation of fib(', n, ') took', round(time() - t0, 4), 'secs')\n", "print ('And {0} calls to the function'.format(fib.call_count))" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "5ae5088b-0ec2-47c7-a812-57b9a2dec026" } }, "source": [ "### 3. Can we do better?\n", "#### A polynomial algorithm for $fib$\n", "Let’s try to understand why $fib$ is so slow. `fib.call_count` shows the count of recursive invocations triggered by a single call to $fib(5)$, which is 15. If you sketched it out, you will notice that many computations are repeated!\n", "A more sensible scheme would store the intermediate results—the values $F_0 , F_1 , . . . , F_{n−1}$ as soon as they become known. \n", "\n", "Lets do exactly that through **memoization**. Note that you can also do this by writing a polynomial algorithm.\n", "\n", "\n", "#### Memoization\n", "Tree-recursive computational processes can often be made more efficient through memoization, a powerful technique for increasing the efficiency of recursive functions that repeat computation. A memoized function will store the return value for any arguments it has previously received. A second call to `fib(30)` would not re-compute the return value recursively, but instead return the existing one that has already been constructed.\n", "\n", "Memoization can be expressed naturally as a **[higher-order function](http://www.composingprograms.com/pages/16-higher-order-functions.html)**, which can also be used as a [decorator](http://programmingbits.pythonblogs.com/27_programmingbits/archive/50_function_decorators.html). The definition below creates a cache of previously computed results, indexed by the arguments from which they were computed. The use of a dictionary requires that the argument to the memoized function be immutable." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "nbpresent": { "id": "229ca1b3-4797-41e4-92ba-ddd73d62af2b" } }, "outputs": [], "source": [ "def memo(f):\n", " cache = {}\n", " def memoized(n):\n", " if n not in cache:\n", " cache[n] = f(n) # Make a mapping between the key \"n\" and the return value of f(n)\n", " return cache[n]\n", " return memoized" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "nbpresent": { "id": "f28aa18b-3e01-45a5-9393-f13da56ae779" } }, "outputs": [], "source": [ "fib = memo(fib)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "nbpresent": { "id": "8487fc19-3985-4e42-8798-cf194be354f6" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This memoized implementation of fib( 400 ) took 0.0019 secs\n" ] } ], "source": [ "t0 = time()\n", "\n", "n = 400\n", "fib(n)\n", "\n", "print ('This memoized implementation of fib(', n, ') took', round(time() - t0, 4), 'secs')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "nbpresent": { "id": "fc2797f9-e574-41c6-ba66-49b1c6916c38" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This memoized implementation of fib( 300 ) took 0.0001 secs\n" ] } ], "source": [ "t0 = time()\n", "\n", "n = 300\n", "fib(n)\n", "\n", "print ('This memoized implementation of fib(', n, ') took', round(time() - t0, 4), 'secs')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "nbpresent": { "id": "4210b44b-d6c2-4d1d-8292-551d9e24f051" } }, "outputs": [], "source": [ "# Here is the polynomial algorithm for fibonacci sequence\n", "def fib2(n):\n", " if n == 0:\n", " return 0\n", " \n", " f = [0] * (n+1) # create an array f[0 . . . n]\n", " f[0], f[1] = 0, 1\n", " \n", " for i in range(2, n+1):\n", " f[i] = f[i-1] + f[i-2]\n", " \n", " return f[n] " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "nbpresent": { "id": "729599f0-c732-4a2c-a75e-a7b4cd49e69e" } }, "outputs": [], "source": [ "fib2 = count(fib2)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "nbpresent": { "id": "27a41e36-3fa9-4c46-8b10-51401c34e66b" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This polynomial implementation of fib2( 3000 ) took 0.0025 secs\n" ] } ], "source": [ "t0 = time()\n", "\n", "n = 3000\n", "fib2(n)\n", "\n", "print ('This polynomial implementation of fib2(', n, ') took', round(time() - t0, 4), 'secs')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "nbpresent": { "id": "a6bb1ef1-3ed4-45c7-92fd-f0444cab2204" } }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib2.call_count" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "cfbd7dad-6414-4c2e-abe7-eb3cb0650593" } }, "source": [ "How long does $fib2$ take? \n", "- The inner loop consists of a single computer step and is executed $n − 1$ times. \n", "- Therefore the number of computer steps used by $fib2$ is linear in $n$. \n", "\n", "From exponential we are down to polynomial, a huge breakthrough in running time. It is now perfectly reasonable to compute $F_{200}$ or even $F_{200,000}$" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "nbpresent": { "id": "6cf5797b-e592-4f09-b074-0a81fd5732ec" } }, "outputs": [ { "data": { "text/plain": [ "280571172992510140037611932413038677189525L" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib2(200)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "246b8801-0e8d-415e-9a03-4b8483cb6897" } }, "source": [ "Instead of reporting that an algorithm takes, say, $ 5n^3 + 4n + 3$ steps on an input of size $n$, it is much simpler to leave out lower-order terms such as $4n$ and $3$ (which become insignificant as $n$ grows), and even the detail of the coefficient $5$ in the leading term (computers will be five times faster in a few years anyway), and just say that the algorithm takes time $O(n^3)$ (pronounced “big oh of $n^3$”).\n", "\n", "It is time to define this notation precisely. In what follows, think of $f(n)$ and $g(n)$ as the running times of two algorithms on inputs of size $n$.\n", "\n", "> Let $f(n)$ and $g(n)$ be functions from positive integers to positive reals. We say $f = O(g)$ (which means that “$f$ grows no faster than $g$”) if there is a constant $c > 0$ such that \n", "> ${f(n) ≤ c · g(n)}$.\n", "\n", "Saying $f = O(g)$ is a very loose analog of “$f ≤ g$.” It differs from the usual notion of ≤ because of the constant c, so that for instance $10n = O(n)$. This constant also allows us to disregard what happens for small values of $n$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example:\n", "For example, suppose we are choosing between two algorithms for a particular computational task. One takes $f_1(n) = n^2$ steps, while the other takes $f_2(n) = 2n + 20$ steps. Which is better? Well, this depends on the value of $n$. For $n ≤ 5$, $f_1(n)$ is smaller; thereafter, $f_2$ is the clear winner. In this case, $f_2$ scales much better as $n$ grows, and therefore it is superior." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "nbpresent": { "id": "82e0f68f-ef84-4909-85cc-61229bc91777" } }, "outputs": [ { "data": { "image/png": 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JjrBn0+jIiIiI5Ly1a+HEE+Hll/1gVTUiuUXNiIiI5DTn4JxzYNw4f9t//6gr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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112b253d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = arange(0, 15, 1)\n", "f1 = t * t \n", "f2 = 2*t + 20\n", "\n", "pyplt.title('Exponential time vs Linear time')\n", "plot(t, f1, t, f2)\n", "pyplt.annotate('$n^2$', xy=(8, 1), xytext=(10, 108))\n", "pyplt.annotate('$2n + 20$', xy=(5, 1), xytext=(10, 45))\n", "pyplt.xlabel('n')\n", "pyplt.ylabel('Run time')\n", "pyplt.grid(True)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "630a6eff-3a1d-4408-8695-5cf79987f963" } }, "source": [ "Is there a faster way to compute the nth Fibonacci number than by `fib2`? One idea\n", "involves matrices.\n", "We start by writing the equations $F_1$ = $F_1$ and $F_2$ = $F_0$ + $F_1$ in matrix notation:\n", "\n", "$$\n", "\\begin{bmatrix} F_1\\\\F_2 \\end{bmatrix} = \\begin{bmatrix} 0&1\\\\ 1&1 \\end{bmatrix} \\cdot \\begin{bmatrix} F_0\\\\F_1 \\end{bmatrix}\n", "$$\n", "\n", "similarly,\n", "\n", "$$\n", " \\begin{bmatrix} F_2\\\\F_3 \\end{bmatrix} = \\begin{bmatrix} 0&1\\\\ 1&1 \\end{bmatrix} \\cdot \\begin{bmatrix} F_1\\\\F_2 \\end{bmatrix} = \\begin{bmatrix} 0&1\\\\ 1&1 \\end{bmatrix}^2 \\cdot \\begin{bmatrix} F_0\\\\F_1 \\end{bmatrix}\n", "$$\n", "\n", "and in general \n", "\n", "$$\n", "\\begin{bmatrix} F_n\\\\F_{n+1} \\end{bmatrix} = \\begin{bmatrix} 0&1\\\\ 1&1 \\end{bmatrix}^n \\cdot \\begin{bmatrix} F_0\\\\F_1 \\end{bmatrix}\n", "$$\n", "\n", "So, in order to compute $F_n$, it suffices to raise this 2 × 2 matrix, call it $X$, to the nth power.\n", "\n", "Thus the number of arithmetic operations needed by our matrix-based algorithm, call it `fib3`, is\n", "just $O(log n)$, as compared to $O(n)$ for `fib2`. Have we broken another exponential barrier? \n", "\n", "The catch is that our new algorithm involves multiplication, not just addition; and multiplications of large numbers are slower than additions. We have already seen that, when the complexity of arithmetic operations is taken into account, the running time of `fib2` becomes $O(n^2)$.\n", "\n", "In conclusion, whether `fib3` is faster than `fib2` depends on whether we can multiply n-bit integers faster than $O(n^2)$.\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

YubinXie/Computational-Pathology

Prostate_Length.ipynb

2

708

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " The mpp_x and mpp_y is same in each slide." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-2.0

hbutler/InverseCCP

1 - Generate coupon probabilities - part 1.ipynb

1

11538

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import scipy.stats as stat\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Calculating a Distribution for the Inverse Coupon Collector's Problem With Non-Uniform Coupon Probabilities\n", "To find results for the InverseCCP via simulation, we actually need to simulate the regular CCP with non-uniform probabilities and logging the results. By \"transposing\" the results into a lookup table, we will see the results for the InverseCCP. We can't simulate an InverseCCP directly because we don't know how.\n", "\n", "###Generating Uniform Probabilities\n", "The probability of collecting a coupon from a collection of $n$ coupons where all coupons have the same uniform probabilitiy is just $\\frac{1}{n}$. There is little ambiguity about what that means and it is easily interpreted and understood how this probability is generated. But how does this translate to a non-uniform probability distribution such as a normal distribution?\n", "\n", "###Generating Normal Probabilities\n", "To create a simulation of the coupon collector's problem with non-uniform probabilities, we must first agree on how to generate non-uniform probabilities. If I say that I have a coupon collector's problem where the probabilities obey a normal distribution, what does that really mean? Intuitively, I think that means that most coupons have a similar probability of collection, while a few have a much lower than normal probability of collection, and a few others have a much higher probability of collection. But I struggle with what that actually means with an inherently finite problem. Normal distributions have non-zero probabilities on the open interval ($-\\infty$ to $+\\infty$), but we only have a finite number of coupons. Also, normal distributions are continuous, and we only allow integers as inputs to our probability function. How do we deal with this? I really think that normal distributions are the most applicable to the coupon collector's problem aside from the uniform distribution, but I can't move forward until we formalize what is intuitively understood. Also, the answers we come up with for normal distributions will likely translate to other distributions that are popular but not discrete such as exponential distributions.\n", "\n", "##Option 1: Binomial Approximation\n", "One way is to generate a binomial approximation (with $p=0.5$) of the normal distribution. This has the advantage of already being computed, and there is an $n$ embedded in the calculations so the total coupon probabilties will sum to 1 (a requirement since we assume that there is no option to collect something outside of the set of $n$ coupons). The downside is that this doesn't actually translate well to other distributions and there is no way to adjust the standard deviation like you can with a true normal distribution (changing $p$ shifts the centrality of the distribution along with the standard deviation). It also doesn't make as much sense because the binomial distribution already has a well defined definition (the number of successes in a sequence of $n$ independent yes/no experiments, each of which yields success with probability p) that doesn't really mesh with how we are thinking of this problem. Anyway, an example application of this option would look like this with $n=20$ coupons. This is actually generated with a percent point function (quantile function), and then scaled to make everything sum to 1 (this contradicts one of my earlier statements about the \"advantage\" of this method, but it works). This makes something that fits with my intuition of \"normal.\" Most coupons have a probability between 0.04 and 0.06, with a few above or below that range." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean prob = 0.0500000000000000 , sum of all probs = 1.0000000000000000\n" ] }, { "data": { "text/plain": [ "[0, 20, 0, 0.1]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x70e74e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 20 #number of coupons\n", "p = 0.5 #this is to make the distribution central, where most coupons are middle of the road probabilities\n", "x = np.arange(n)+0.5 #arange goes from 0 to n-1, and I want it to go from 1 to n\n", "p_x = stat.binom.ppf(x/n, n, p) # ouptputs whole numbers because it's a discrete distribution\n", "p_x = p_x/np.sum(p_x) # change whole numbers into \n", "print('mean prob = %1.16f' % np.mean(p_x), ', sum of all probs = %1.16f' % np.sum(p_x) )\n", "plt.bar(x, p_x, align='center')\n", "plt.axis([0,n,0,2/n])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Option 2: Normal Distribution\n", "After playing with the ppf of the last example, I think I know how to find a consistent way to create coupon probabilites that intuitively fits a normal distribution. Look at the notebook for GenerateProbsPart2 for this option." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

aitatanit/metatlas

docs/example_notebooks/Prototype_Notebook_Using_IPython_NERSC_Interface.ipynb

1

6569

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import metatlas\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# myPath = '/global/homes/b/bpb/ExoMetabolomic_Example_Data/'\n", "myPath = '/project/projectdirs/metatlas/original_data/tls/150330_bundle_timecourse/'\n", "%system ls $myPath\n", "# myFile = 'MEDIA-1.mzML'\n", "myFile = '150405_2A_1day_neg.mzML'\n", "metatlas.mzml_to_hdf('%s%s'%(myPath,myFile))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tables\n", "fid = tables.open_file('%s%s'%(myPath,myFile.replace('mzML','h5')))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polarity = 0\n", "# mz_theor = 205.0971541 #tryptophan\n", "# mz_theor = 150.0583 #methionine\n", "mz_theor = 386.999943 #Leu-Leu\n", "rt,intensity = metatlas.get_XIC(fid, 0, 1500, 1, polarity)\n", "metatlas.plot_XIC(rt,intensity)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polarity = 0\n", "# mz_theor = 205.0971541 #tryptophan\n", "# mz_theor = 150.0583 #methionine\n", "mz_theor = 133.051136\n", "rt,intensity = metatlas.get_XIC(fid, mz_theor - mz_theor*50/1e6, mz_theor + mz_theor*50/1e6, 1, polarity)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "metatlas.plot_XIC(rt,intensity)\n", "plt.xlim(3,7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# get coarse 2d hist\n", "mzEdges = np.linspace(80, 620,500)\n", "rtEdges = np.linspace(2,15,500)\n", "ms_level = 1\n", "polarity = 0\n", "hMap = metatlas.get_heatmap(fid,mzEdges,rtEdges,ms_level,polarity)\n", "metatlas.plot_heatmap(np.log10(hMap['arr']+1),hMap['rt_bins'],hMap['mz_bins'],title='entire file')\n", "plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)\n", "fig = plt.gcf()\n", "fig.set_size_inches(18.5, 10.5)\n", "# fig.savefig('test2png.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "\n", "ms_level=2\n", "rt_min = 3.0\n", "rt_max = 3.5\n", "data = metatlas.get_data(fid, ms_level, polarity,\n", " min_mz=0,\n", " max_mz=mz_theor+10,\n", " min_rt=rt_min,\n", " max_rt=rt_max,\n", " min_precursor_MZ=mz_theor - mz_theor*10/1e6,\n", " max_precursor_MZ=mz_theor + mz_theor*10/1e6)\n", "# min_precursor_intensity=0,\n", "# max_precursor_intensity=0,\n", "# min_collision_energy=0,\n", "# max_collision_energy=0)\n", "plt.vlines(data['mz'],0,data['i'],color='r',linestyles='solid')\n", "plt.xlabel('m/z')\n", "plt.ylabel('intensity')\n", "# plt.xlim(0,225)\n", "# plt.plot(data['mz'],data['i'])\n", "plt.show()\n", "idx = np.argsort(data['i'])\n", "print data['mz'][idx]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "from metatlas import xcms\n", "import os\n", "myFileList = []\n", "for file in os.listdir(myPath):\n", " if file.endswith(\".mzML\"):\n", " if 'neg' in file:\n", " myFileList.append('%s%s'%(myPath,file))\n", "print myFileList\n", "xset = xcms.get_xmcs_set(myFileList)\n", "xset = xcms.group(xset)\n", "df = xcms.peak_table(xset)\n", "print(df.head())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# print df['mz'][0]\n", "# print df['STRAIN2.1'][0]\n", "myKeys = np.sort(df.keys()[8:])\n", "M = np.zeros((len(df),len(myKeys)))\n", "for i,k in enumerate(myKeys):\n", " for j,d in enumerate(df[k]):\n", " M[j,i] = float(d)\n", "M = np.nan_to_num(M)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m_idx = np.argmax(M,axis = 1)\n", "m = np.max(M,axis = 1)\n", "idx = np.argsort(m)[::-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print df['mz'][idx[:5]]\n", "# df['rt'][idx[:5]]\n", "# missing = 170.15" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print np.sort(abs(df['rt'][idx[0]] - df['rt']))[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.to_csv('saving_dataframe.xls')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

chenleo/ipynotebook

Poisson.ipynb

1

51965

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy import random" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "random.poisson?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "s = random.poisson(1,1000)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "s.view()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "array([0, 1, 1, 2, 1, 1, 3, 1, 1, 1, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1,\n", " 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0, 4, 0, 0, 0, 2, 1, 2, 3, 0, 1, 3,\n", " 1, 2, 3, 0, 1, 1, 1, 1, 1, 0, 1, 3, 2, 1, 0, 2, 0, 1, 1, 1, 2, 1, 3,\n", " 0, 1, 1, 0, 0, 1, 1, 0, 3, 1, 1, 0, 2, 1, 2, 1, 2, 0, 1, 1, 0, 0, 0,\n", " 1, 1, 0, 2, 2, 0, 2, 0, 2, 1, 1, 0, 0, 0, 2, 3, 1, 2, 0, 2, 1, 0, 2,\n", " 1, 1, 2, 2, 2, 1, 0, 2, 1, 1, 2, 0, 0, 1, 1, 0, 2, 0, 1, 0, 0, 1, 0,\n", " 1, 0, 2, 0, 1, 2, 0, 1, 1, 1, 1, 1, 1, 2, 0, 2, 1, 0, 1, 1, 3, 3, 3,\n", " 2, 1, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 1, 0, 1, 0, 0, 2, 1, 1, 0, 0, 1,\n", " 0, 3, 1, 3, 2, 2, 2, 0, 0, 4, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 2, 4, 0,\n", " 0, 1, 1, 2, 2, 1, 0, 1, 0, 0, 0, 1, 3, 0, 0, 0, 0, 1, 1, 0, 2, 1, 1,\n", " 0, 1, 0, 0, 1, 0, 0, 3, 2, 2, 0, 0, 2, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n", " 3, 2, 0, 0, 0, 2, 1, 0, 2, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 0, 1, 2,\n", " 1, 0, 2, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 1, 0, 1, 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"text": [ "<matplotlib.figure.Figure at 0x106ca17d0>" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "s.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "0.99199999999999999" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "s.std()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "0.99996799948797799" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "#Calculate Auto-correlation function based on fft\n", "#only support 1D\n", "#for cross correlation, in optimisation\n", "#input data suggest to be numpy.ndarray(1d) or pandas.core.series.Series\n", "def acf_fft(data):\n", " \"\"\" Series or 1darray ---> Series \n", " Calculate the auto correlation function of input data\n", " \"\"\"\n", " data_mean = data.mean()\n", " \n", " #Step 1: calculate fft for data\n", " fft_data = fft.fft(data)\n", " \n", " #Step 2: Calculate the pow spectrom,\n", " S_f = fft_data.__mul__(fft_data.conj())\n", " \n", " #Step 3: calculate auto correlation function, result as g(\\tau)\n", " acf = ifft(S_f)/fft_data[0]/data_mean - 1;\n", " \n", " #Clean up, return half of data\n", " acf = acf.real[0:int(len(acf)/2)]\n", " return acf" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(acf_fft(s),'o')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "[<matplotlib.lines.Line2D at 0x106ca6190>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x106cbc310>" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "import os" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "os?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

YuriyGuts/kaggle-quora-question-pairs

notebooks/unused/feature-oofp-nn-lstm-with-activations.ipynb

1

16407

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature: Out-Of-Fold Predictions and Feature Layer Activations from an LSTM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the output of the final network layer, the model will also output the activations of the intermediate feature layer.\n", "\n", "To achieve this, we'll create a multi-output network (target output + activations output), and supply dummy ground truth and a dummy loss function to the second output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This utility package imports `numpy`, `pandas`, `matplotlib` and a helper `kg` module into the root namespace." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pygoose import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import gc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.metrics import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from keras import backend as K\n", "from keras.models import Model, Sequential\n", "from keras.layers import *\n", "from keras.callbacks import EarlyStopping, ModelCheckpoint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Config" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Automatically discover the paths to various data folders and compose the project structure." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "project = kg.Project.discover()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Identifier for storing these features on disk and referring to them later." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_list_id = 'oofp_nn_lstm_with_activations'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make subsequent NN runs reproducible." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RANDOM_SEED = 42" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(RANDOM_SEED)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Word embedding lookup matrix." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "embedding_matrix = kg.io.load(project.aux_dir + 'fasttext_vocab_embedding_matrix.pickle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Padded sequences of word indices for every question." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train_q1 = kg.io.load(project.preprocessed_data_dir + 'sequences_q1_fasttext_train.pickle')\n", "X_train_q2 = kg.io.load(project.preprocessed_data_dir + 'sequences_q2_fasttext_train.pickle')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_test_q1 = kg.io.load(project.preprocessed_data_dir + 'sequences_q1_fasttext_test.pickle')\n", "X_test_q2 = kg.io.load(project.preprocessed_data_dir + 'sequences_q2_fasttext_test.pickle')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_train = kg.io.load(project.features_dir + 'y_train.pickle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Word embedding properties." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "EMBEDDING_DIM = embedding_matrix.shape[-1]\n", "VOCAB_LENGTH = embedding_matrix.shape[0]\n", "MAX_SEQUENCE_LENGTH = X_train_q1.shape[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(EMBEDDING_DIM, VOCAB_LENGTH, MAX_SEQUENCE_LENGTH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define models" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def zero_loss(y_true, y_pred):\n", " return K.zeros((1,))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_model_question_branch():\n", " input_q = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n", " \n", " embedding_q = Embedding(\n", " VOCAB_LENGTH,\n", " EMBEDDING_DIM,\n", " weights=[embedding_matrix],\n", " input_length=MAX_SEQUENCE_LENGTH,\n", " trainable=False,\n", " )(input_q)\n", "\n", " timedist_q = TimeDistributed(Dense(\n", " EMBEDDING_DIM,\n", " activation='relu',\n", " ))(embedding_q)\n", "\n", " lambda_q = Lambda(\n", " lambda x: K.max(x, axis=1),\n", " output_shape=(EMBEDDING_DIM, )\n", " )(timedist_q)\n", " \n", " output_q = lambda_q\n", " return input_q, output_q" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_model(params): \n", " embedding_layer = Embedding(\n", " VOCAB_LENGTH,\n", " EMBEDDING_DIM,\n", " weights=[embedding_matrix],\n", " input_length=MAX_SEQUENCE_LENGTH,\n", " trainable=False,\n", " )\n", " lstm_layer = LSTM(\n", " params['num_lstm'],\n", " dropout=params['lstm_dropout_rate'],\n", " recurrent_dropout=params['lstm_dropout_rate'],\n", " )\n", "\n", " input_q1 = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n", " embedded_sequences_1 = embedding_layer(input_q1)\n", " x1 = lstm_layer(embedded_sequences_1)\n", "\n", " input_q2 = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n", " embedded_sequences_2 = embedding_layer(input_q2)\n", " y1 = lstm_layer(embedded_sequences_2)\n", "\n", " features = Concatenate(name='feature_output')([x1, y1])\n", " dropout_feat = Dropout(params['dense_dropout_rate'])(features)\n", " bn_feat = BatchNormalization()(dropout_feat)\n", "\n", " dense_1 = Dense(params['num_dense'], activation='relu')(bn_feat)\n", " dropout_1 = Dropout(params['dense_dropout_rate'])(dense_1)\n", " bn_1 = BatchNormalization()(dropout_1)\n", "\n", " output = Dense(1, activation='sigmoid', name='target_output')(bn_1)\n", "\n", " model = Model(\n", " inputs=[input_q1, input_q2],\n", " outputs=[output, features],\n", " )\n", " \n", " model.compile(\n", " loss={'target_output': 'binary_crossentropy', 'feature_output': zero_loss},\n", " loss_weights={'target_output': 1.0, 'feature_output': 0.0},\n", " optimizer='nadam',\n", " metrics=None,\n", " )\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict(model, X_q1, X_q2):\n", " \"\"\"\n", " Mirror the pairs, compute two separate predictions, and average them.\n", " \"\"\"\n", " \n", " y1 = model.predict([X_q1, X_q2], batch_size=1024, verbose=1).reshape(-1) \n", " y2 = model.predict([X_q2, X_q1], batch_size=1024, verbose=1).reshape(-1) \n", " return (y1 + y2) / 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Partition the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NUM_FOLDS = 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "kfold = StratifiedKFold(\n", " n_splits=NUM_FOLDS,\n", " shuffle=True,\n", " random_state=RANDOM_SEED\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BATCH_SIZE = 2048" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "MAX_EPOCHS = 200" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Best values picked by Bayesian optimization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_params = {\n", " 'dense_dropout_rate': 0.075,\n", " 'lstm_dropout_rate': 0.332,\n", " 'num_dense': 130,\n", " 'num_lstm': 300,\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_output_size = model_params['num_lstm'] * 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create placeholders for out-of-fold predictions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_train_oofp = np.zeros_like(y_train, dtype='float32')\n", "y_train_oofp_features = np.zeros((len(y_train), feature_output_size), dtype='float32')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_test_oofp = np.zeros((len(X_test_q1), NUM_FOLDS), dtype='float32')\n", "y_test_oofp_features = np.zeros((len(X_test_q1), feature_output_size), dtype='float32')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The path where the best weights of the current model will be saved." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_checkpoint_path = project.temp_dir + 'fold-checkpoint-' + feature_list_id + '.h5'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the folds and compute out-of-fold predictions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%%time\n", "\n", "# Iterate through folds.\n", "for fold_num, (ix_train, ix_val) in enumerate(kfold.split(X_train_q1, y_train)):\n", " \n", " # Augment the training set by mirroring the pairs.\n", " X_fold_train_q1 = np.vstack([X_train_q1[ix_train], X_train_q2[ix_train]])\n", " X_fold_train_q2 = np.vstack([X_train_q2[ix_train], X_train_q1[ix_train]])\n", "\n", " X_fold_val_q1 = np.vstack([X_train_q1[ix_val], X_train_q2[ix_val]])\n", " X_fold_val_q2 = np.vstack([X_train_q2[ix_val], X_train_q1[ix_val]])\n", "\n", " # Ground truth should also be \"mirrored\".\n", " y_fold_train = np.concatenate([y_train[ix_train], y_train[ix_train]])\n", " y_fold_val = np.concatenate([y_train[ix_val], y_train[ix_val]])\n", " \n", " print()\n", " print(f'Fitting fold {fold_num + 1} of {kfold.n_splits}')\n", " print()\n", " \n", " # Compile a new model.\n", " model = create_model(model_params)\n", "\n", " # Train.\n", " model.fit(\n", " # Create dummy ground truth values for the activation outputs.\n", " [X_fold_train_q1, X_fold_train_q2],\n", " [y_fold_train, np.zeros((len(y_fold_train), feature_output_size))],\n", " \n", " validation_data=(\n", " [X_fold_val_q1, X_fold_val_q2],\n", " [y_fold_val, np.zeros((len(y_fold_val), feature_output_size))],\n", " ),\n", "\n", " batch_size=BATCH_SIZE,\n", " epochs=MAX_EPOCHS,\n", " verbose=1,\n", " \n", " callbacks=[\n", " # Stop training when the validation loss stops improving.\n", " EarlyStopping(\n", " monitor='val_loss',\n", " min_delta=0.001,\n", " patience=3,\n", " verbose=1,\n", " mode='auto',\n", " ),\n", " # Save the weights of the best epoch.\n", " ModelCheckpoint(\n", " model_checkpoint_path,\n", " monitor='val_loss',\n", " save_best_only=True,\n", " verbose=2,\n", " ),\n", " ],\n", " )\n", " \n", " # Restore the best epoch.\n", " model.load_weights(model_checkpoint_path)\n", " \n", " # Compute out-of-fold predictions.\n", " y_train_oofp[ix_val] = predict(model, X_train_q1[ix_val], X_train_q2[ix_val])\n", " y_test_oofp[:, fold_num] = predict(model, X_test_q1, X_test_q2)\n", " \n", " # Clear GPU memory.\n", " K.clear_session()\n", " del X_fold_train_q1, X_fold_train_q2\n", " del X_fold_val_q1, X_fold_val_q2\n", " del model\n", " gc.collect()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cv_score = log_loss(y_train, y_train_oofp)\n", "print('CV score:', cv_score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_names = [feature_list_id]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features_train = y_train_oofp.reshape((-1, 1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features_test = np.mean(y_test_oofp, axis=1).reshape((-1, 1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "project.save_features(features_train, features_test, feature_names, feature_list_id)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ioos/comt_notebooks

admin/Untitled0.ipynb

1

7050

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"text": [ "<matplotlib.figure.Figure at 0x7f72f367b150>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

queirozfcom/python-sandbox

python3/notebooks/subprocess-post/main.ipynb

2

23180

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:38.959003Z", "start_time": "2021-08-09T04:02:38.951775Z" } }, "outputs": [ { "data": { "text/plain": [ "'3.6.9'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from platform import python_version\n", "\n", "python_version()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:39.119682Z", "start_time": "2021-08-09T04:02:39.113968Z" } }, "outputs": [], "source": [ "import subprocess" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## call example" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:39.589493Z", "start_time": "2021-08-09T04:02:39.579745Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# returns return code only\n", "subprocess.call([\"ls\", \"-lha\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "note that no exception is raised if the underlying command errors:\n", "\n", "`bash-script-with-bad-syntax` is a shell script with bad syntax." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:39.934591Z", "start_time": "2021-08-09T04:02:39.915493Z" } }, "outputs": [ { "data": { "text/plain": [ "127" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.call([\"./bash-script-with-bad-syntax\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## call() example using shell=True" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:40.513494Z", "start_time": "2021-08-09T04:02:40.248650Z" } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.call(\"ls -lha\", shell=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## call example capture standard output and error" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:40.754326Z", "start_time": "2021-08-09T04:02:40.732686Z" } }, "outputs": [ { "data": { "text/plain": [ "127" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.call([\"./bash-script-with-bad-syntax\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## call example, force exception if called process causes error" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:41.081291Z", "start_time": "2021-08-09T04:02:41.066321Z" } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if there's no error in the underlying process,\n", "# this is just the same as subprocess.call\n", "subprocess.check_call([\"ls\",\"-lha\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:41.268539Z", "start_time": "2021-08-09T04:02:41.247856Z" } }, "outputs": [ { "ename": "CalledProcessError", "evalue": "Command '['./bash-script-with-bad-syntax']' returned non-zero exit status 127.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-e87152a9901c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# but unlike call, this throws a Called\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"./bash-script-with-bad-syntax\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36mcheck_call\u001b[0;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mCalledProcessError\u001b[0m: Command '['./bash-script-with-bad-syntax']' returned non-zero exit status 127." ] } ], "source": [ "# but unlike call, this throws a Called\n", "subprocess.check_call([\"./bash-script-with-bad-syntax\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## call example, capture stfout and stderr" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:41.939712Z", "start_time": "2021-08-09T04:02:41.926888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "output:\n", "total 44K\n", "drwxr-xr-x 3 felipe felipe 4,0K ago 9 01:02 .\n", "drwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\n", "-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\n", "-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\n", "-rw-r--r-- 1 felipe felipe 0 ago 9 01:02 err.txt\n", "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\n", "-rw-r--r-- 1 felipe felipe 0 ago 9 01:02 out.txt\n", "\n", "error:\n", "./bash-script-with-bad-syntax: line 4: foo: command not found\n", "\n" ] } ], "source": [ "import subprocess\n", "import sys\n", "\n", "# create two files to hold the output and errors, respectively\n", "with open('out.txt','w+') as fout:\n", " with open('err.txt','w+') as ferr:\n", " out=subprocess.call([\"./bash-script-with-bad-syntax\"],stdout=fout,stderr=ferr)\n", " # reset file to read from it\n", " fout.seek(0)\n", " \n", " print('output:')\n", " print(fout.read())\n", " \n", " # reset file to read from it\n", " ferr.seek(0) \n", " print('error:')\n", " print(ferr.read()) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## store output in variable" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:47.328484Z", "start_time": "2021-08-09T04:02:47.320184Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 52K\n", "drwxr-xr-x 3 felipe felipe 4,0K ago 9 01:02 .\n", "drwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\n", "-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\n", "-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\n", "-rw-r--r-- 1 felipe felipe 62 ago 9 01:02 err.txt\n", "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\n", "-rw-r--r-- 1 felipe felipe 464 ago 9 01:02 out.txt\n", "\n" ] } ], "source": [ "output = subprocess.check_output([\"ls\",\"-lha\"],universal_newlines=True)\n", "print(output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## run() examples" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:48.630672Z", "start_time": "2021-08-09T04:02:48.617192Z" } }, "outputs": [ { "data": { "text/plain": [ "CompletedProcess(args=['ls', '-lha'], returncode=0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cp = subprocess.run([\"ls\",\"-lha\"])\n", "cp" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:49.113404Z", "start_time": "2021-08-09T04:02:49.102572Z" } }, "outputs": [ { "data": { "text/plain": [ "CompletedProcess(args=['ls -lha'], returncode=0)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cp = subprocess.run([\"ls -lha\"],shell=True)\n", "cp" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:49.717359Z", "start_time": "2021-08-09T04:02:49.674083Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CompletedProcess(args=['ls', '-lha'], returncode=0, stdout='total 52K\\ndrwxr-xr-x 3 felipe felipe 4,0K ago 9 01:02 .\\ndrwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\\n-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\\n-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\\n-rw-r--r-- 1 felipe felipe 62 ago 9 01:02 err.txt\\ndrwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\\n-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\\n-rw-r--r-- 1 felipe felipe 464 ago 9 01:02 out.txt\\n', stderr='')\n" ] } ], "source": [ "cp = subprocess.run([\"ls\",\"-lha\"], universal_newlines=True,stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", "print(cp)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:51.587874Z", "start_time": "2021-08-09T04:02:51.578240Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stdout: \n", "stderr: ls: cannot access 'foo bar': No such file or directory\n", "\n" ] } ], "source": [ "cp = subprocess.run([\"ls\",\"foo bar\"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n", "print(\"stdout: \", cp.stdout)\n", "print(\"stderr: \", cp.stderr)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:52.573015Z", "start_time": "2021-08-09T04:02:52.557645Z" } }, "outputs": [ { "ename": "CalledProcessError", "evalue": "Command '['ls', 'foo bar']' returned non-zero exit status 2.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-17-f2920e44b5b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ls\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"foo bar\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mretcode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m raise CalledProcessError(retcode, process.args,\n\u001b[0;32m--> 438\u001b[0;31m output=stdout, stderr=stderr)\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCompletedProcess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ls', 'foo bar']' returned non-zero exit status 2." ] } ], "source": [ "subprocess.run([\"ls\",\"foo bar\"], check=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:53.992641Z", "start_time": "2021-08-09T04:02:53.986050Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Errno 2] No such file or directory: 'xxxx': 'xxxx'\n" ] } ], "source": [ "try:\n", " cp = subprocess.run([\"xxxx\",\"foo bar\"], universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", "except FileNotFoundError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## popen" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:10.765247Z", "start_time": "2021-08-09T04:03:10.756308Z" } }, "outputs": [], "source": [ "from subprocess import Popen" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:11.194881Z", "start_time": "2021-08-09T04:03:11.183790Z" } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = Popen([\"ls\",\"-lha\"])\n", "p.wait()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:11.681372Z", "start_time": "2021-08-09T04:03:11.671094Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 52K\n", "drwxr-xr-x 3 felipe felipe 4,0K ago 9 01:02 .\n", "drwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\n", "-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\n", "-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\n", "-rw-r--r-- 1 felipe felipe 62 ago 9 01:02 err.txt\n", "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\n", "-rw-r--r-- 1 felipe felipe 464 ago 9 01:02 out.txt\n", "\n", "\n" ] } ], "source": [ "p = Popen([\"ls\",\"-lha\"], stdout=subprocess.PIPE, stderr= subprocess.PIPE, universal_newlines=True)\n", "\n", "output, errors = p.communicate()\n", "\n", "print(output)\n", "print(errors)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:12.118854Z", "start_time": "2021-08-09T04:03:12.096048Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n", "\n", "total 52K\n", "drwxr-xr-x 3 felipe felipe 4,0K ago 9 01:02 .\n", "drwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\n", "-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\n", "-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\n", "-rw-r--r-- 1 felipe felipe 62 ago 9 01:02 err.txt\n", "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\n", "-rw-r--r-- 1 felipe felipe 464 ago 9 01:02 out.txt\n", "\n" ] } ], "source": [ "path_to_output_file = '/tmp/myoutput.txt'\n", "\n", "myoutput = open(path_to_output_file,'w+')\n", "\n", "p = Popen([\"ls\",\"-lha\"], stdout=myoutput, stderr= subprocess.PIPE, universal_newlines=True)\n", "\n", "output, errors = p.communicate()\n", "\n", "print(output)\n", "print(errors)\n", "\n", "with open(path_to_output_file,\"r\") as f:\n", " print(f.read())\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:51.454412Z", "start_time": "2021-08-09T04:03:51.427234Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "output: None\n", "errors: None\n", "output_file: ls: cannot access 'foo bar': No such file or directory\n", "\n" ] } ], "source": [ "path_to_output_file = '/tmp/myoutput.txt'\n", "\n", "myoutput = open(path_to_output_file,'w+')\n", "\n", "p = Popen([\"ls\",\"foo bar\"], stdout=myoutput, stderr= myoutput, universal_newlines=True)\n", "\n", "output, errors = p.communicate()\n", "\n", "print(\"output: \", output)\n", "print(\"errors: \", errors)\n", "\n", "with open(path_to_output_file,\"r\") as f:\n", " print(\"output_file: \", f.read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pipe commands together" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:03:52.396505Z", "start_time": "2021-08-09T04:03:52.367540Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 22K ago 9 01:02 main.ipynb\n", "\n" ] } ], "source": [ "from subprocess import Popen,PIPE\n", "\n", "# this is equivalent to ls -lha | grep \"ipynb\"\n", "p1 = Popen([\"ls\",\"-lha\"], stdout=PIPE)\n", "p2 = Popen([\"grep\", \"ipynb\"], stdin=p1.stdout, stdout=PIPE, universal_newlines=True)\n", "\n", "p1.stdout.close()\n", "\n", "output = p2.communicate()[0]\n", "\n", "print(output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## async" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:04:21.284611Z", "start_time": "2021-08-09T04:04:21.270764Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[return code: 0]\n", "\n", "[stdout]\n", "total 52K\n", "drwxr-xr-x 3 felipe felipe 4,0K ago 9 01:04 .\n", "drwxr-xr-x 73 felipe felipe 4,0K ago 8 20:00 ..\n", "-rwxr-xr-x 1 felipe felipe 41 jun 20 2020 bash-script-with-bad-syntax\n", "-rw-r--r-- 1 felipe felipe 337 jun 20 2020 cmd.py\n", "-rw-r--r-- 1 felipe felipe 62 ago 9 01:02 err.txt\n", "drwxr-xr-x 2 felipe felipe 4,0K jun 20 2020 .ipynb_checkpoints\n", "-rw-r--r-- 1 felipe felipe 23K ago 9 01:04 main.ipynb\n", "-rw-r--r-- 1 felipe felipe 464 ago 9 01:02 out.txt\n", "\n", "stderr is empty\n" ] } ], "source": [ "import asyncio\n", "\n", "proc = await asyncio.create_subprocess_exec(\n", " 'ls','-lha',\n", " stdout=asyncio.subprocess.PIPE,\n", " stderr=asyncio.subprocess.PIPE)\n", "\n", "\n", "# if proc takes very long to complete,\n", "# the CPUs are free to use cycles for \n", "# other processes\n", "stdout, stderr = await proc.communicate()\n", "\n", "print('[return code: '+ str(proc.returncode) +']')\n", "if stdout:\n", " print('\\n[stdout]\\n'+str(stdout.decode()))\n", "else:\n", " print('stdout is empty')\n", " \n", "if stderr:\n", " print(f'\\n[stderr]:\\n'+str(stderr.decode()))\n", "else:\n", " print('stderr is empty')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

exe0cdc/ipython-d3networkx

examples/demo simple.ipynb

5

2804

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html import widgets\n", "from IPython.display import display\n", "from d3networkx import ForceDirectedGraph, EventfulGraph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = EventfulGraph()\n", "d3 = ForceDirectedGraph(G)\n", "display(d3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code creates an animation of some of the plot's properties." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Redisplay\n", "display(d3)\n", "\n", "import time\n", "G.node.clear()\n", "G.add_node(1, fill=\"red\", stroke=\"black\", color='black', label='A')\n", "time.sleep(1.0)\n", "\n", "G.add_node(2, fill=\"gold\", stroke=\"black\", color='black', r=20, font_size='24pt', label='B')\n", "time.sleep(1.0)\n", "\n", "G.add_node(3, fill=\"green\", stroke=\"black\", color='white', label='C')\n", "time.sleep(1.0)\n", "\n", "G.add_edges_from([(1,2),(1,3), (2,3)], stroke=\"#aaa\", strokewidth=\"1px\", distance=200, strength=0.5)\n", "time.sleep(1.0)\n", "\n", "G.adj[1][2]['distance'] = 20\n", "time.sleep(1.0)\n", "\n", "G.adj[1][3]['distance'] = 20\n", "time.sleep(1.0)\n", "\n", "G.adj[2][3]['distance'] = 20\n", "time.sleep(1.0)\n", "\n", "G.node[1]['r'] = 16\n", "time.sleep(0.3)\n", "G.node[1]['r'] = 8\n", "G.node[2]['r'] = 16\n", "time.sleep(0.3)\n", "G.node[2]['r'] = 20\n", "G.node[3]['r'] = 16\n", "time.sleep(0.3)\n", "G.node[3]['r'] = 8\n", "\n", "G.node[1]['fill'] = 'purple'\n", "time.sleep(0.3)\n", "G.node[1]['fill'] = 'red'\n", "G.node[2]['fill'] = 'purple'\n", "time.sleep(0.3)\n", "G.node[2]['fill'] = 'gold'\n", "G.node[3]['fill'] = 'purple'\n", "time.sleep(0.3)\n", "G.node[3]['fill'] = 'green'\n", "time.sleep(1.0)\n", "\n", "G.node.clear()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

machow/siuba

docs/draft-old-pages/intro_sql_interm.ipynb

1

75470

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import matplotlib.cbook\n", "\n", "import warnings\n", "import plotnine\n", "warnings.filterwarnings(module='plotnine*', action='ignore')\n", "warnings.filterwarnings(module='matplotlib*', action='ignore')\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Querying SQL (advanced)\n", "\n", "**NOTE: THIS DOC IS CURRENTLY IN OUTLINE FORM**\n", "\n", "In this tutorial, we'll use a dataset of television ratings.\n", "\n", "* copying data in, and getting a table from SQL\n", "* filtering out rows, and aggregating data\n", "* looking at shifts in ratings between seasons\n", "* checking for abnormalities in the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from siuba.tests.helpers import copy_to_sql\n", "from siuba import *\n", "from siuba.dply.vector import lag, desc, row_number\n", "from siuba.dply.string import str_c\n", "from siuba.sql import LazyTbl\n", "\n", "data_url = \"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-08/IMDb_Economist_tv_ratings.csv\"\n", "tv_ratings = pd.read_csv(data_url, parse_dates = [\"date\"])\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "db_uri = \"postgresql://{user}:{password}@localhost:5433/{db}\".format(\n", " user = \"postgres\",\n", " password = \"\",\n", " db = \"postgres\"\n", " )\n", "\n", "# create tv_ratings table\n", "tbl_ratings = copy_to_sql(tv_ratings, \"tv_ratings\", db_uri)\n", "\n", "# can also access an existing table\n", "tbl_ratings = LazyTbl(db_uri, \"tv_ratings\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><pre># Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", "</pre><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>titleId</th>\n", " <th>seasonNumber</th>\n", " <th>title</th>\n", " <th>date</th>\n", " <th>av_rating</th>\n", " <th>share</th>\n", " <th>genres</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>tt2879552</td>\n", " <td>1</td>\n", " <td>11.22.63</td>\n", " <td>2016-03-10</td>\n", " <td>8.4890</td>\n", " <td>0.51</td>\n", " <td>Drama,Mystery,Sci-Fi</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>tt3148266</td>\n", " <td>1</td>\n", " <td>12 Monkeys</td>\n", " <td>2015-02-27</td>\n", " <td>8.3407</td>\n", " <td>0.46</td>\n", " <td>Adventure,Drama,Mystery</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>tt3148266</td>\n", " <td>2</td>\n", " <td>12 Monkeys</td>\n", " <td>2016-05-30</td>\n", " <td>8.8196</td>\n", " <td>0.25</td>\n", " <td>Adventure,Drama,Mystery</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>tt3148266</td>\n", " <td>3</td>\n", " <td>12 Monkeys</td>\n", " <td>2017-05-19</td>\n", " <td>9.0369</td>\n", " <td>0.19</td>\n", " <td>Adventure,Drama,Mystery</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>tt3148266</td>\n", " <td>4</td>\n", " <td>12 Monkeys</td>\n", " <td>2018-06-26</td>\n", " <td>9.1363</td>\n", " <td>0.38</td>\n", " <td>Adventure,Drama,Mystery</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div><p># .. may have more rows</p></div>" ], "text/plain": [ "# Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", " titleId seasonNumber title date av_rating share \\\n", "0 tt2879552 1 11.22.63 2016-03-10 8.4890 0.51 \n", "1 tt3148266 1 12 Monkeys 2015-02-27 8.3407 0.46 \n", "2 tt3148266 2 12 Monkeys 2016-05-30 8.8196 0.25 \n", "3 tt3148266 3 12 Monkeys 2017-05-19 9.0369 0.19 \n", "4 tt3148266 4 12 Monkeys 2018-06-26 9.1363 0.38 \n", "\n", " genres \n", "0 Drama,Mystery,Sci-Fi \n", "1 Adventure,Drama,Mystery \n", "2 Adventure,Drama,Mystery \n", "3 Adventure,Drama,Mystery \n", "4 Adventure,Drama,Mystery \n", "# .. may have more rows" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl_ratings\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspecting a single show" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>titleId</th>\n", " <th>seasonNumber</th>\n", " <th>title</th>\n", " <th>date</th>\n", " <th>av_rating</th>\n", " <th>share</th>\n", " <th>genres</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>tt0118276</td>\n", " <td>1</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>1997-04-14</td>\n", " <td>7.9629</td>\n", " <td>11.70</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>tt0118276</td>\n", " <td>2</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>1997-12-31</td>\n", " <td>8.4191</td>\n", " <td>19.41</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>tt0118276</td>\n", " <td>3</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>1999-01-29</td>\n", " <td>8.6233</td>\n", " <td>17.12</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>tt0118276</td>\n", " <td>4</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>2000-01-19</td>\n", " <td>8.2205</td>\n", " <td>16.19</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>tt0118276</td>\n", " <td>5</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>2001-01-12</td>\n", " <td>8.3028</td>\n", " <td>11.99</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>tt0118276</td>\n", " <td>6</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>2002-01-29</td>\n", " <td>8.1008</td>\n", " <td>8.45</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>tt0118276</td>\n", " <td>7</td>\n", " <td>Buffy the Vampire Slayer</td>\n", " <td>2003-01-18</td>\n", " <td>8.0460</td>\n", " <td>9.89</td>\n", " <td>Action,Drama,Fantasy</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " titleId seasonNumber title date av_rating \\\n", "0 tt0118276 1 Buffy the Vampire Slayer 1997-04-14 7.9629 \n", "1 tt0118276 2 Buffy the Vampire Slayer 1997-12-31 8.4191 \n", "2 tt0118276 3 Buffy the Vampire Slayer 1999-01-29 8.6233 \n", "3 tt0118276 4 Buffy the Vampire Slayer 2000-01-19 8.2205 \n", "4 tt0118276 5 Buffy the Vampire Slayer 2001-01-12 8.3028 \n", "5 tt0118276 6 Buffy the Vampire Slayer 2002-01-29 8.1008 \n", "6 tt0118276 7 Buffy the Vampire Slayer 2003-01-18 8.0460 \n", "\n", " share genres \n", "0 11.70 Action,Drama,Fantasy \n", "1 19.41 Action,Drama,Fantasy \n", "2 17.12 Action,Drama,Fantasy \n", "3 16.19 Action,Drama,Fantasy \n", "4 11.99 Action,Drama,Fantasy \n", "5 8.45 Action,Drama,Fantasy \n", "6 9.89 Action,Drama,Fantasy " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "buffy = (tbl_ratings\n", " >> filter(_.title == \"Buffy the Vampire Slayer\")\n", " >> collect()\n", " )\n", "\n", "buffy" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>avg_rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>8.239343</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " avg_rating\n", "0 8.239343" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "buffy >> summarize(avg_rating = _.av_rating.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Average rating per show, along with dates" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><pre># Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", "</pre><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>avg_rating</th>\n", " <th>date_range</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Friends from College</td>\n", " <td>6.875100</td>\n", " <td>2017 - 2017</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Better Things</td>\n", " <td>8.133150</td>\n", " <td>2017 - 2016</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>How to Get Away with Murder</td>\n", " <td>8.762340</td>\n", " <td>2018 - 2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Dexter</td>\n", " <td>8.582400</td>\n", " <td>2013 - 2006</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Queen of the South</td>\n", " <td>8.574733</td>\n", " <td>2018 - 2016</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div><p># .. may have more rows</p></div>" ], "text/plain": [ "# Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", " title avg_rating date_range\n", "0 Friends from College 6.875100 2017 - 2017\n", "1 Better Things 8.133150 2017 - 2016\n", "2 How to Get Away with Murder 8.762340 2018 - 2014\n", "3 Dexter 8.582400 2013 - 2006\n", "4 Queen of the South 8.574733 2018 - 2016\n", "# .. may have more rows" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_ratings = (tbl_ratings \n", " >> group_by(_.title)\n", " >> summarize(\n", " avg_rating = _.av_rating.mean(),\n", " date_range = str_c(_.date.dt.year.max(), \" - \", _.date.dt.year.min())\n", " )\n", " )\n", "\n", "avg_ratings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Biggest changes in ratings between two seasons" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><pre># Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", "</pre><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>max_shift</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Third Watch</td>\n", " <td>4.8500</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Law &amp; Order: Special Victims Unit</td>\n", " <td>2.0508</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Greek</td>\n", " <td>1.9068</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Roseanne</td>\n", " <td>1.7177</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div><p># .. may have more rows</p></div>" ], "text/plain": [ "# Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", " title max_shift\n", "0 Third Watch 4.8500\n", "1 Law & Order: Special Victims Unit 2.0508\n", "2 Greek 1.9068\n", "3 Roseanne 1.7177\n", "# .. may have more rows" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_4_shifts = (tbl_ratings\n", " >> group_by(_.title)\n", " >> arrange(_.seasonNumber)\n", " >> mutate(rating_shift = _.av_rating - lag(_.av_rating))\n", " >> summarize(\n", " max_shift = _.rating_shift.max()\n", " )\n", " >> arrange(-_.max_shift)\n", " >> head(4)\n", " )\n", "\n", "top_4_shifts" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (294189085)>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "big_shift_series = (top_4_shifts\n", " >> select(_.title)\n", " >> inner_join(_, tbl_ratings, \"title\")\n", " >> collect()\n", " )\n", "\n", "from plotnine import *\n", "\n", "(big_shift_series\n", " >> ggplot(aes(\"seasonNumber\", \"av_rating\"))\n", " + geom_point()\n", " + geom_line()\n", " + facet_wrap(\"~ title\")\n", " + labs(\n", " title = \"Seasons with Biggest Shifts in Ratings\",\n", " y = \"Average rating\",\n", " x = \"Season\"\n", " )\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do we have full data for each season?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><pre># Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", "</pre><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>titleId</th>\n", " <th>seasonNumber</th>\n", " <th>date</th>\n", " <th>av_rating</th>\n", " <th>share</th>\n", " <th>genres</th>\n", " <th>row</th>\n", " <th>mismatch</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7th Heaven</td>\n", " <td>tt0115083</td>\n", " <td>1</td>\n", " <td>1996-08-26</td>\n", " <td>7.700</td>\n", " <td>0.10</td>\n", " <td>Drama,Family,Romance</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>7th Heaven</td>\n", " <td>tt0115083</td>\n", " <td>10</td>\n", " <td>2006-05-08</td>\n", " <td>6.300</td>\n", " <td>0.01</td>\n", " <td>Drama,Family,Romance</td>\n", " <td>2</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ABC Afterschool Specials</td>\n", " <td>tt0202179</td>\n", " <td>25</td>\n", " <td>1996-09-12</td>\n", " <td>3.300</td>\n", " <td>0.10</td>\n", " <td>Adventure,Comedy,Drama</td>\n", " <td>1</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>American Gothic</td>\n", " <td>tt5257744</td>\n", " <td>1</td>\n", " <td>2016-08-05</td>\n", " <td>7.535</td>\n", " <td>0.07</td>\n", " <td>Crime,Drama,Mystery</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>American Gothic</td>\n", " <td>tt0111880</td>\n", " <td>1</td>\n", " <td>1995-09-22</td>\n", " <td>7.800</td>\n", " <td>0.08</td>\n", " <td>Drama,Horror,Thriller</td>\n", " <td>2</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div><p># .. may have more rows</p></div>" ], "text/plain": [ "# Source: lazy query\n", "# DB Conn: Engine(postgresql://postgres:***@localhost:5433/postgres)\n", "# Preview:\n", " title titleId seasonNumber date av_rating \\\n", "0 7th Heaven tt0115083 1 1996-08-26 7.700 \n", "1 7th Heaven tt0115083 10 2006-05-08 6.300 \n", "2 ABC Afterschool Specials tt0202179 25 1996-09-12 3.300 \n", "3 American Gothic tt5257744 1 2016-08-05 7.535 \n", "4 American Gothic tt0111880 1 1995-09-22 7.800 \n", "\n", " share genres row mismatch \n", "0 0.10 Drama,Family,Romance 1 False \n", "1 0.01 Drama,Family,Romance 2 True \n", "2 0.10 Adventure,Comedy,Drama 1 True \n", "3 0.07 Crime,Drama,Mystery 1 False \n", "4 0.08 Drama,Horror,Thriller 2 True \n", "# .. may have more rows" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mismatches = (tbl_ratings\n", " >> arrange(_.title, _.seasonNumber)\n", " >> group_by(_.title)\n", " >> mutate(\n", " row = row_number(_),\n", " mismatch = _.row != _.seasonNumber\n", " )\n", " >> filter(_.mismatch.any())\n", " >> ungroup()\n", " )\n", "\n", "\n", "mismatches" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " n\n", "0 54" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mismatches >> distinct(_.title) >> count() >> collect()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }

mit

wolf9s/doconce

doc/pub/admon/admon_quote.ipynb

2

4335

{ "metadata": {}, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo of admonition styles in DocOnce\n", "**May 2, 2015**\n", "\n", "**Summary.** This note demonstrates how admonitions look like in the output format\n", "**ipynb**.\n", "\n", "\n", "\n", "## The four main types of admonitions\n", "\n", "Key options when compiling this document were" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " --ipynb_admon=quote\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the warning admon:\n", "\n", "> **Division by zero is illegal!**\n", ">\n", "> Most math systems will give fatal errors if you divide by zero.\n", ">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Terminal> python -c 'print 4/0'\n", " Traceback (most recent call last):\n", " File \"<string>\", line 1, in <module>\n", " ZeroDivisionError: integer division or modulo by zero\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question admon (without title).\n", "\n", "> **Question.**\n", ">\n", "> What are the admon options for `doconce format html`?\n", "> \n", "> <!-- Answer: `--html_admon=`, `--html_admon_shadow`, `--html_admon_bg_color=`, -->\n", "> <!-- `--html_admon_bd_color`. There is also `--html_box_shadow` for boxes. -->\n", "\n", "\n", "\n", "\n", "\n", "Summary admon:\n", "\n", "> **Summary.**\n", ">\n", "> The most popular methods for solving algebraic equations\n", ">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "f(x) = 0\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> are\n", "> \n", "> * Newton's method\n", "> \n", "> * The Bisection method\n", "> \n", "> * The Secant method\n", "> \n", "> * The Fixed-Point method ($f(x) = x - g(x)$)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Notice admon:\n", "\n", "> **Tip: follow well-established conventions for variable names!**\n", ">\n", "> For example, in Python, variable and function names use\n", "> lower case letters separated by underscore, as in\n", "> `vibration_with_damping` (while Java typically would\n", "> have `vibrationWithDamping`). Class names apply cap words,\n", "> as in `ProblemClass`.\n", "\n", "\n", "\n", "\n", "\n", "## The block, quote and plain box environment\n", "\n", "DocOnce features a `block` environment with or without title.\n", "\n", "> Blocks are often used in slides to frame a collection of things.\n", "\n", "\n", "\n", "\n", "\n", "> **Block with title.**\n", ">\n", "> Blocks can contain text, math, code, figures, movies.\n", "\n", "\n", "\n", "\n", "\n", "Here is a quote environment (`quote`):\n", "\n", "> Sayre's law states that\n", "> \"in any dispute the intensity of feeling is inversely\n", "> proportional to the value of the issues at stake.\" \n", "> By way of corollary, it adds: \n", "> \"That is why academic politics are so bitter.\" \n", "> *Source*: [wikipedia](http://en.wikipedia.org/wiki/Sayre's_law)\n", "\n", "\n", "\n", "\n", "\n", "Boxes are very simple frames (without any icons, background color,\n", "or stash, except for a shadow)\n", "used for important results like\n", "\n", "> **Box.**\n", ">\n", "> The world most famous equation:\n", ">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "E = mc^2\n", "$$" ] } ], "metadata": {} } ] }

bsd-3-clause

jpilgram/phys202-2015-work

assignments/assignment10/ODEsEx03.ipynb

1

96234

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Ordinary Differential Equations Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "from scipy.integrate import odeint\n", "from IPython.html.widgets import interact, fixed" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Damped, driven nonlinear pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The equations of motion for a simple [pendulum](http://en.wikipedia.org/wiki/Pendulum) of mass $m$, length $l$ are:\n", "\n", "$$\n", "\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta\n", "$$\n", "\n", "When a damping and periodic driving force are added the resulting system has much richer and interesting dynamics:\n", "\n", "$$\n", "\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta - a \\omega - b \\sin(\\omega_0 t)\n", "$$\n", "\n", "In this equation:\n", "\n", "* $a$ governs the strength of the damping.\n", "* $b$ governs the strength of the driving force.\n", "* $\\omega_0$ is the angular frequency of the driving force.\n", "\n", "When $a=0$ and $b=0$, the energy/mass is conserved:\n", "\n", "$$E/m =g\\ell(1-\\cos(\\theta)) + \\frac{1}{2}\\ell^2\\omega^2$$" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Basic setup" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Here are the basic parameters we are going to use for this exercise:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "g = 9.81 # m/s^2\n", "l = 0.5 # length of pendulum, in meters\n", "tmax = 50. # seconds\n", "t = np.linspace(0, tmax, int(100*tmax))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `derivs` for usage with `scipy.integrate.odeint` that computes the derivatives for the damped, driven harmonic oscillator. The solution vector at each time will be $\\vec{y}(t) = (\\theta(t),\\omega(t))$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "nbgrader": { "checksum": "c7256bdd25791dfa8322d3b828cec74d", "solution": true } }, "outputs": [], "source": [ "#I worked with James A and Hunter T.\n", "def derivs(y, t, a, b, omega0):\n", " \"\"\"Compute the derivatives of the damped, driven pendulum.\n", " \n", " Parameters\n", " ----------\n", " y : ndarray\n", " The solution vector at the current time t[i]: [theta[i],omega[i]].\n", " t : float\n", " The current time t[i].\n", " a, b, omega0: float\n", " The parameters in the differential equation.\n", " \n", " Returns\n", " -------\n", " dy : ndarray\n", " The vector of derviatives at t[i]: [dtheta[i],domega[i]].\n", " \"\"\"\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " theta = y[0]\n", " omega = y[1]\n", " dtheta =omega\n", " dw = -(g/l)*np.sin(theta)-a*omega-b*np.sin(omega0*t)\n", " return [dtheta, dw]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3509b75989fc0ec30fa07c7a9331e14e", "grade": true, "grade_id": "odesex03a", "points": 2 } }, "outputs": [], "source": [ "assert np.allclose(derivs(np.array([np.pi,1.0]), 0, 1.0, 1.0, 1.0), [1.,-1.])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "nbgrader": { "checksum": "eb552816913899d79298c64989e872d4", "solution": true } }, "outputs": [], "source": [ "def energy(y):\n", " \"\"\"Compute the energy for the state array y.\n", " \n", " The state array y can have two forms:\n", " \n", " 1. It could be an ndim=1 array of np.array([theta,omega]) at a single time.\n", " 2. It could be an ndim=2 array where each row is the [theta,omega] at single\n", " time.\n", " \n", " Parameters\n", " ----------\n", " y : ndarray, list, tuple\n", " A solution vector\n", " \n", " Returns\n", " -------\n", " E/m : float (ndim=1) or ndarray (ndim=2)\n", " The energy per mass.\n", " \"\"\"\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " if y.ndim==1:\n", " theta = y[0]\n", " omega = y[1]\n", " if y.ndim==2:\n", " theta = y[:,0]\n", " omega = y[:,1]\n", " E = g*l*(1-np.cos(theta))+0.5*l**2*omega**2\n", " return (E)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3eda6ae22611b37df76850d7cdc960d0", "grade": true, "grade_id": "odesex03b", "points": 2 } }, "outputs": [], "source": [ "assert np.allclose(energy(np.array([np.pi,0])),g)\n", "assert np.allclose(energy(np.ones((10,2))), np.ones(10)*energy(np.array([1,1])))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Simple pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use the above functions to integrate the simple pendulum for the case where it starts at rest pointing vertically upwards. In this case, it should remain at rest with constant energy.\n", "\n", "* Integrate the equations of motion.\n", "* Plot $E/m$ versus time.\n", "* Plot $\\theta(t)$ and $\\omega(t)$ versus time.\n", "* Tune the `atol` and `rtol` arguments of `odeint` until $E/m$, $\\theta(t)$ and $\\omega(t)$ are constant.\n", "\n", "Anytime you have a differential equation with a a conserved quantity, it is critical to make sure the numerical solutions conserve that quantity as well. This also gives you an opportunity to find other bugs in your code. The default error tolerances (`atol` and `rtol`) used by `odeint` are not sufficiently small for this problem. Start by trying `atol=1e-3`, `rtol=1e-2` and then decrease each by an order of magnitude until your solutions are stable." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "#raise NotImplementedError()\n", "y0 = [np.pi,0]\n", "solution = odeint(derivs, y0, t, args = (0,0,0), atol = 1e-5, rtol = 1e-4) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f126eb3d550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "#raise NotImplementedError()\n", "plt.plot(t,energy(solution), label=\"$Energy/mass$\")\n", "plt.title('Simple Pendulum Engery')\n", "plt.xlabel('time')\n", "plt.ylabel('$Engery/Mass$')\n", "plt.ylim(9.2,10.2);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f126e9ab080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "#raise NotImplementedError()\n", "theta= solution[:,0]\n", "omega = solution[:,1]\n", "plt.plot(t ,theta, label = \"$\\Theta (t)$\")\n", "plt.plot(t, omega, label = \"$\\omega (t)$\")\n", "plt.ylim(-0.5,5)\n", "plt.legend()\n", "plt.title('Simple Pendulum $\\Theta (t)$ and $\\omega (t)$')\n", "plt.xlabel('Time');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "afb5bca3311c3e9c7ac5070b15f2435c", "grade": true, "grade_id": "odesex03c", "points": 3 } }, "outputs": [], "source": [ "assert True # leave this to grade the two plots and their tuning of atol, rtol." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Damped pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a `plot_pendulum` function that integrates the damped, driven pendulum differential equation for a particular set of parameters $[a,b,\\omega_0]$.\n", "\n", "* Use the initial conditions $\\theta(0)=-\\pi + 0.1$ and $\\omega=0$.\n", "* Decrease your `atol` and `rtol` even futher and make sure your solutions have converged.\n", "* Make a parametric plot of $[\\theta(t),\\omega(t)]$ versus time.\n", "* Use the plot limits $\\theta \\in [-2 \\pi,2 \\pi]$ and $\\theta \\in [-10,10]$\n", "* Label your axes and customize your plot to make it beautiful and effective." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "nbgrader": { "checksum": "82dc6206b4de351b8afc48dba9d0b915", "solution": true } }, "outputs": [], "source": [ "def plot_pendulum(a=0.0, b=0.0, omega0=0.0):\n", " \"\"\"Integrate the damped, driven pendulum and make a phase plot of the solution.\"\"\"\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " y0 =[-np.pi+0.1,0]\n", " solution = odeint(derivs, y0, t, args = (a,b,omega0), atol = 1e-5, rtol = 1e-4)\n", " theta=solution[:,0]\n", " omega=solution[:,1]\n", " plt.plot(theta, omega, color=\"k\")\n", " plt.title('Damped and Driven Pendulum Motion')\n", " plt.xlabel('$\\Theta (t)$')\n", " plt.ylabel('$\\omega (t)$')\n", " plt.xlim(-2*np.pi, 2*np.pi)\n", " plt.ylim(-10,10);" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Here is an example of the output of your `plot_pendulum` function that should show a decaying spiral." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "data": { "image/png": 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VN28+xo+fSGhoCMOGDUyX5WR79OiNra0tP/+8QJavFZmWJHghEtixYyt79uyi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\n1KgJXbokvrpcQu7cucOiRQvw9899d9peR7BarSxfvpRHH81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LqVKlGmPGjEt3Zy9EQAEv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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f126e8ac2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_pendulum(0.5, 0.0, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use `interact` to explore the `plot_pendulum` function with:\n", "\n", "* `a`: a float slider over the interval $[0.0,1.0]$ with steps of $0.1$.\n", "* `b`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$.\n", "* `omega0`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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QQTz//EuoUiVOdRxHY4En1/HeO3r44XYKkxCZbd683Wg78bK548eP491330FM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\n+k888ajCNETWsHr1N6a+7zk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"text/plain": [ "<matplotlib.figure.Figure at 0x7f126e911e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "#raise NotImplementedError()\n", "interact(plot_pendulum, a=(0.0,1.0,0.1), b=(0.0,10.0,0.1), omega0 = (0.0,10.0,0.1));" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use your interactive plot to explore the behavior of the damped, driven pendulum by varying the values of $a$, $b$ and $\\omega_0$.\n", "\n", "* First start by increasing $a$ with $b=0$ and $\\omega_0=0$.\n", "* Then fix $a$ at a non-zero value and start to increase $b$ and $\\omega_0$.\n", "\n", "Describe the different *classes* of behaviors you observe below." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "40364759d02737525e2503b814608893", "grade": true, "grade_id": "odesex03d", "points": 3, "solution": true } }, "source": [ "Increasing a (with b=0 and $\\omega_0=0$) decreases the number of spirals and makes the densest part of the spirals more central,this means that a increases damping so it spirals to 0 faster with higher a. b is the amplitude of the driving force, as you increase b the spirals start overlapping with eachother. Once b gets high enough, the driving force overcomes the pendulum motion and the graph no longer looks like pendelum motion. $\\omega_0$ is angular fequency of the driving force. As $\\omega_0$ increases, the spot at which the spirals bunch together moves, starting from the middle. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

madjelan/Data-Science-45min-Intros

adaboost/adaboost_tutorial.ipynb

2

1774341

unlicense

mne-tools/mne-tools.github.io

0.18/_downloads/7df5cd97aa959dd7e2627aba5e552081/plot_forward.ipynb

1

13976

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\nHead model and forward computation\n==================================\n\nThe aim of this tutorial is to be a getting started for forward\ncomputation.\n\nFor more extensive details and presentation of the general\nconcepts for forward modeling. See `ch_forward`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path as op\nimport mne\nfrom mne.datasets import sample\ndata_path = sample.data_path()\n\n# the raw file containing the channel location + types\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\n# The paths to Freesurfer reconstructions\nsubjects_dir = data_path + '/subjects'\nsubject = 'sample'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the forward operator\n------------------------------\n\nTo compute a forward operator we need:\n\n - a ``-trans.fif`` file that contains the coregistration info.\n - a source space\n - the :term:`BEM` surfaces\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute and visualize BEM surfaces\n----------------------------------\n\nThe :term:`BEM` surfaces are the triangulations of the interfaces between\ndifferent tissues needed for forward computation. These surfaces are for\nexample the inner skull surface, the outer skull surface and the outer skin\nsurface, a.k.a. scalp surface.\n\nComputing the BEM surfaces requires FreeSurfer and makes use of either of\nthe two following command line tools:\n\n - `gen_mne_watershed_bem`\n - `gen_mne_flash_bem`\n\nOr by calling in a Python script one of the functions\n:func:`mne.bem.make_watershed_bem` or :func:`mne.bem.make_flash_bem`.\n\nHere we'll assume it's already computed. It takes a few minutes per subject.\n\nFor EEG we use 3 layers (inner skull, outer skull, and skin) while for\nMEG 1 layer (inner skull) is enough.\n\nLet's look at these surfaces. The function :func:`mne.viz.plot_bem`\nassumes that you have the the *bem* folder of your subject FreeSurfer\nreconstruction the necessary files.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.viz.plot_bem(subject=subject, subjects_dir=subjects_dir,\n brain_surfaces='white', orientation='coronal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualization the coregistration\n--------------------------------\n\nThe coregistration is operation that allows to position the head and the\nsensors in a common coordinate system. In the MNE software the transformation\nto align the head and the sensors in stored in a so-called **trans file**.\nIt is a FIF file that ends with ``-trans.fif``. It can be obtained with\n:func:`mne.gui.coregistration` (or its convenient command line\nequivalent `gen_mne_coreg`), or mrilab if you're using a Neuromag\nsystem.\n\nFor the Python version see :func:`mne.gui.coregistration`\n\nHere we assume the coregistration is done, so we just visually check the\nalignment with the following code.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The transformation file obtained by coregistration\ntrans = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'\n\ninfo = mne.io.read_info(raw_fname)\n# Here we look at the dense head, which isn't used for BEM computations but\n# is useful for coregistration.\nmne.viz.plot_alignment(info, trans, subject=subject, dig=True,\n meg=['helmet', 'sensors'], subjects_dir=subjects_dir,\n surfaces='head-dense')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nCompute Source Space\n--------------------\n\nThe source space defines the position and orientation of the candidate source\nlocations. There are two types of source spaces:\n\n- **source-based** source space when the candidates are confined to a\n surface.\n\n- **volumetric or discrete** source space when the candidates are discrete,\n arbitrarily located source points bounded by the surface.\n\n**Source-based** source space is computed using\n:func:`mne.setup_source_space`, while **volumetric** source space is computed\nusing :func:`mne.setup_volume_source_space`.\n\nWe will now compute a source-based source space with an OCT-6 resolution.\nSee `setting_up_source_space` for details on source space definition\nand spacing parameter.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "src = mne.setup_source_space(subject, spacing='oct6',\n subjects_dir=subjects_dir, add_dist=False)\nprint(src)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The surface based source space ``src`` contains two parts, one for the left\nhemisphere (4098 locations) and one for the right hemisphere\n(4098 locations). Sources can be visualized on top of the BEM surfaces\nin purple.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.viz.plot_bem(subject=subject, subjects_dir=subjects_dir,\n brain_surfaces='white', src=src, orientation='coronal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute a volume based source space defined with a grid of candidate\ndipoles inside a sphere of radius 90mm centered at (0.0, 0.0, 40.0)\nyou can use the following code.\nObviously here, the sphere is not perfect. It is not restricted to the\nbrain and it can miss some parts of the cortex.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sphere = (0.0, 0.0, 40.0, 90.0)\nvol_src = mne.setup_volume_source_space(subject, subjects_dir=subjects_dir,\n sphere=sphere)\nprint(vol_src)\n\nmne.viz.plot_bem(subject=subject, subjects_dir=subjects_dir,\n brain_surfaces='white', src=vol_src, orientation='coronal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute a volume based source space defined with a grid of candidate\ndipoles inside the brain (requires the :term:`BEM` surfaces) you can use the\nfollowing.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "surface = op.join(subjects_dir, subject, 'bem', 'inner_skull.surf')\nvol_src = mne.setup_volume_source_space(subject, subjects_dir=subjects_dir,\n surface=surface)\nprint(vol_src)\n\nmne.viz.plot_bem(subject=subject, subjects_dir=subjects_dir,\n brain_surfaces='white', src=vol_src, orientation='coronal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the surface-based source space only sources that lie in the plotted MRI\nslices are shown. Let's write a few lines of mayavi to see all sources in 3D.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np # noqa\nfrom mayavi import mlab # noqa\nfrom surfer import Brain # noqa\n\nbrain = Brain('sample', 'lh', 'inflated', subjects_dir=subjects_dir)\nsurf = brain.geo['lh']\n\nvertidx = np.where(src[0]['inuse'])[0]\n\nmlab.points3d(surf.x[vertidx], surf.y[vertidx],\n surf.z[vertidx], color=(1, 1, 0), scale_factor=1.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nCompute forward solution\n------------------------\n\nWe can now compute the forward solution.\nTo reduce computation we'll just compute a single layer BEM (just inner\nskull) that can then be used for MEG (not EEG).\n\nWe specify if we want a one-layer or a three-layer BEM using the\nconductivity parameter.\n\nThe BEM solution requires a BEM model which describes the geometry\nof the head the conductivities of the different tissues.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conductivity = (0.3,) # for single layer\n# conductivity = (0.3, 0.006, 0.3) # for three layers\nmodel = mne.make_bem_model(subject='sample', ico=4,\n conductivity=conductivity,\n subjects_dir=subjects_dir)\nbem = mne.make_bem_solution(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the :term:`BEM` does not involve any use of the trans file. The BEM\nonly depends on the head geometry and conductivities.\nIt is therefore independent from the MEG data and the head position.\n\nLet's now compute the forward operator, commonly referred to as the\ngain or leadfield matrix.\n\nSee :func:`mne.make_forward_solution` for details on parameters meaning.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fwd = mne.make_forward_solution(raw_fname, trans=trans, src=src, bem=bem,\n meg=True, eeg=False, mindist=5.0, n_jobs=2)\nprint(fwd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore the content of fwd to access the numpy array that contains\nthe gain matrix.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "leadfield = fwd['sol']['data']\nprint(\"Leadfield size : %d sensors x %d dipoles\" % leadfield.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To extract the numpy array containing the forward operator corresponding to\nthe source space `fwd['src']` with cortical orientation constraint\nwe can use the following:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fwd_fixed = mne.convert_forward_solution(fwd, surf_ori=True, force_fixed=True,\n use_cps=True)\nleadfield = fwd_fixed['sol']['data']\nprint(\"Leadfield size : %d sensors x %d dipoles\" % leadfield.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is equivalent to the following code that explicitly applies the\nforward operator to a source estimate composed of the identity operator:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_dipoles = leadfield.shape[1]\nvertices = [src_hemi['vertno'] for src_hemi in fwd_fixed['src']]\nstc = mne.SourceEstimate(1e-9 * np.eye(n_dipoles), vertices, tmin=0., tstep=1)\nleadfield = mne.apply_forward(fwd_fixed, stc, info).data / 1e-9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save to disk a forward solution you can use\n:func:`mne.write_forward_solution` and to read it back from disk\n:func:`mne.read_forward_solution`. Don't forget that FIF files containing\nforward solution should end with *-fwd.fif*.\n\nTo get a fixed-orientation forward solution, use\n:func:`mne.convert_forward_solution` to convert the free-orientation\nsolution to (surface-oriented) fixed orientation.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise\n--------\n\nBy looking at\n`sphx_glr_auto_examples_forward_plot_forward_sensitivity_maps.py`\nplot the sensitivity maps for EEG and compare it with the MEG, can you\njustify the claims that:\n\n - MEG is not sensitive to radial sources\n - EEG is more sensitive to deep sources\n\nHow will the MEG sensitivity maps and histograms change if you use a free\ninstead if a fixed/surface oriented orientation?\n\nTry this changing the mode parameter in :func:`mne.sensitivity_map`\naccordingly. Why don't we see any dipoles on the gyri?\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

igmhub/lyaforecast

examples/plot_camb_linPk.ipynb

1

36689

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Playing with CAMB" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import numpy as np\n", "import camb\n", "from camb import model, initialpower" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Setup cosmological model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<camb.initialpower.InitialPowerParams at 0x1150296a8>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pars = camb.CAMBparams()\n", "#one massive neutrino and helium set using BBN consistency\n", "pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06)\n", "pars.InitPower.set_params(As=2e-9,ns=0.965, r=0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: redshifts have been re-sorted (earliest first)\n", "[ 0.16997738 0.33506916 0.80266669]\n" ] } ], "source": [ "pars.set_matter_power(redshifts=[0.,2.0,5.0], kmax=10.0)\n", "#Linear spectra\n", "pars.NonLinear = model.NonLinear_none\n", "results = camb.get_results(pars)\n", "kh, zs, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=10, npoints = 200)\n", "s8 = np.array(results.get_sigma8())\n", "print(s8)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot matter power spectrum " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1167bac88>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AXrV60T2kO+0C2+HuZNuzmC+mXGTFuRVsubYFKSX9QvsxMWwiTfyalJ8TUftg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\nU0ZSBo91fIyCogLGfDQm8IHVavqaIR47DHN/C8e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"text/plain": [ "<matplotlib.figure.Figure at 0x115041400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, z in enumerate(zs):\n", " plt.loglog(kh, pk[i,:],label='z = '+str(int(z)))\n", "plt.xlabel('k [h/Mpc]')\n", "plt.ylabel('P(k) [Mpc/h]')\n", "plt.legend(loc='lower left')\n", "plt.title('Matter power at different redshifts')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "229.79162843739516" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z=2.25\n", "results.hubble_parameter(z)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1734.611130325552" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.angular_diameter_distance(z)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "angular_diameter_distance2() missing 1 required positional argument: 'z2'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-3ccf96d5ca9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangular_diameter_distance2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: angular_diameter_distance2() missing 1 required positional argument: 'z2'" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

magenta/magenta-demos

jupyter-notebooks/Sketch_RNN_TF_To_JS_Tutorial.ipynb

1

47281

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, I will show how to train the TensorFlow version of Sketch-RNN on a new dataset, and convert the weights of the TF model to a JSON format that is usable by Sketch-RNN-JS so that interactive web demos can be built.\n", "\n", "For the purpose of this tutorial, I will be training on the dataset file called `kanji.rdp25.npz` which is available inside the repo `https://github.com/hardmaru/sketch-rnn-datasets/` under the `kanji` subdirectory. If you have a custom dataset, you will need to convert it over to an .npz file using the stroke-3 format as done for these datasets. Please study the README.md in Sketch-RNN to understand how the file format that Sketch-RNN can work with work, in the section called [\"Creating Your Own Dataset\"](https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/README.md).\n", "\n", "After cloning the TensorFlow repo for the Sketch-RNN [model](https://github.com/tensorflow/magenta/tree/master/magenta/models/sketch_rnn), below is the command that I ran to train the TensorFlow model:\n", "\n", "```\n", "python sketch_rnn_train.py --data_dir=kanji --hparams=data_set=['kanji.rdp25.npz'],num_steps=200000,conditional=0,dec_rnn_size=1024\n", "```\n", "\n", "I store the `kanji.rdp25.npz` inside the subdirectory called `kanji` but you can use whatever you want. The important thing to note here is that I'm trainining a decoder-only model by setting `conditional=0` and I'm training a 1 layer LSTM with hidden size of 1024, which should be good enough for most datasets in the order of 10K size. Using 200K steps should take around half a day on a single P100 GPU, so it should cost around USD 10 dollars using the current prices for Google Cloud Platform to train this model.\n", "\n", "After the model is trained, I run the remaining commands for this IPython notebook to generate a file call `custom.gen.json`, which can be used in the Sketch-RNN-JS repo for interactive work:\n", "\n", "https://github.com/tensorflow/magenta-demos/tree/master/sketch-rnn-js\n", "\n", "This `json` format created will also work for future TensorFlow.js and ML5.js versions of sketch-RNN." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import the required libraries\n", "import numpy as np\n", "import time\n", "import random\n", "\n", "import codecs\n", "import collections\n", "import os\n", "import math\n", "import json\n", "import tensorflow as tf\n", "from six.moves import xrange\n", "\n", "# libraries required for visualisation:\n", "from IPython.display import SVG, display\n", "import svgwrite # conda install -c omnia svgwrite=1.1.6\n", "import PIL\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", "\n", "# set numpy output to something sensible\n", "np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:TensorFlow Version: 1.8.0\n" ] } ], "source": [ "tf.logging.info(\"TensorFlow Version: %s\", tf.__version__)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import our command line tools\n", "'''\n", "from magenta.models.sketch_rnn.sketch_rnn_train import *\n", "from magenta.models.sketch_rnn.model import *\n", "from magenta.models.sketch_rnn.utils import *\n", "from magenta.models.sketch_rnn.rnn import *\n", "'''\n", "\n", "# If code is modified to remove magenta dependencies:\n", "from sketch_rnn_train import *\n", "from model import *\n", "from utils import *\n", "from rnn import *" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# little function that displays vector images and saves them to .svg\n", "def draw_strokes(data, factor=0.2, svg_filename = '/tmp/sketch_rnn/svg/sample.svg'):\n", " tf.gfile.MakeDirs(os.path.dirname(svg_filename))\n", " min_x, max_x, min_y, max_y = get_bounds(data, factor)\n", " dims = (50 + max_x - min_x, 50 + max_y - min_y)\n", " dwg = svgwrite.Drawing(svg_filename, size=dims)\n", " dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\n", " lift_pen = 1\n", " abs_x = 25 - min_x \n", " abs_y = 25 - min_y\n", " p = \"M%s,%s \" % (abs_x, abs_y)\n", " command = \"m\"\n", " for i in xrange(len(data)):\n", " if (lift_pen == 1):\n", " command = \"m\"\n", " elif (command != \"l\"):\n", " command = \"l\"\n", " else:\n", " command = \"\"\n", " x = float(data[i,0])/factor\n", " y = float(data[i,1])/factor\n", " lift_pen = data[i, 2]\n", " p += command+str(x)+\",\"+str(y)+\" \"\n", " the_color = \"black\"\n", " stroke_width = 1\n", " dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\"none\"))\n", " dwg.save()\n", " display(SVG(dwg.tostring()))\n", "\n", "# generate a 2D grid of many vector drawings\n", "def make_grid_svg(s_list, grid_space=10.0, grid_space_x=16.0):\n", " def get_start_and_end(x):\n", " x = np.array(x)\n", " x = x[:, 0:2]\n", " x_start = x[0]\n", " x_end = x.sum(axis=0)\n", " x = x.cumsum(axis=0)\n", " x_max = x.max(axis=0)\n", " x_min = x.min(axis=0)\n", " center_loc = (x_max+x_min)*0.5\n", " return x_start-center_loc, x_end\n", " x_pos = 0.0\n", " y_pos = 0.0\n", " result = [[x_pos, y_pos, 1]]\n", " for sample in s_list:\n", " s = sample[0]\n", " grid_loc = sample[1]\n", " grid_y = grid_loc[0]*grid_space+grid_space*0.5\n", " grid_x = grid_loc[1]*grid_space_x+grid_space_x*0.5\n", " start_loc, delta_pos = get_start_and_end(s)\n", "\n", " loc_x = start_loc[0]\n", " loc_y = start_loc[1]\n", " new_x_pos = grid_x+loc_x\n", " new_y_pos = grid_y+loc_y\n", " result.append([new_x_pos-x_pos, new_y_pos-y_pos, 0])\n", "\n", " result += s.tolist()\n", " result[-1][2] = 1\n", " x_pos = new_x_pos+delta_pos[0]\n", " y_pos = new_y_pos+delta_pos[1]\n", " return np.array(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define the path of the model you want to load, and also the path of the dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# you may need to change these to link to where your data and checkpoints are actually stored!\n", "# in the default config, model_dir is likely to be /tmp/sketch_rnn/models\n", "data_dir = './kanji'\n", "model_dir = './log'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Loaded 10358/600/500 from kanji.rdp25.npz\n", "INFO:tensorflow:Dataset combined: 11458 (10358/600/500), avg len 63\n", "INFO:tensorflow:model_params.max_seq_len 133.\n", "total images <= max_seq_len is 10358\n", "total images <= max_seq_len is 600\n", "total images <= max_seq_len is 500\n", "INFO:tensorflow:normalizing_scale_factor 14.4871.\n" ] } ], "source": [ "[train_set, valid_set, test_set, hps_model, eval_hps_model, sample_hps_model] = load_env(data_dir, model_dir)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "[hps_model, eval_hps_model, sample_hps_model] = load_model(model_dir)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Model using gpu.\n", "INFO:tensorflow:Input dropout mode = False.\n", "INFO:tensorflow:Output dropout mode = False.\n", "INFO:tensorflow:Recurrent dropout mode = True.\n", "WARNING:tensorflow:From /Users/hadavid/devel/test/sketch_rnn/model.py:287: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n", "WARNING:tensorflow:From /Users/hadavid/devel/test/sketch_rnn/model.py:297: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "\n", "Future major versions of TensorFlow will allow gradients to flow\n", "into the labels input on backprop by default.\n", "\n", "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n", "\n", "INFO:tensorflow:Model using gpu.\n", "INFO:tensorflow:Input dropout mode = 0.\n", "INFO:tensorflow:Output dropout mode = 0.\n", "INFO:tensorflow:Recurrent dropout mode = 0.\n", "INFO:tensorflow:Model using gpu.\n", "INFO:tensorflow:Input dropout mode = 0.\n", "INFO:tensorflow:Output dropout mode = 0.\n", "INFO:tensorflow:Recurrent dropout mode = 0.\n" ] } ], "source": [ "# construct the sketch-rnn model here:\n", "reset_graph()\n", "model = Model(hps_model)\n", "eval_model = Model(eval_hps_model, reuse=True)\n", "sample_model = Model(sample_hps_model, reuse=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.InteractiveSession()\n", "sess.run(tf.global_variables_initializer())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def decode(z_input=None, draw_mode=True, temperature=0.1, factor=0.2):\n", " z = None\n", " if z_input is not None:\n", " z = [z_input]\n", " sample_strokes, m = sample(sess, sample_model, seq_len=eval_model.hps.max_seq_len, temperature=temperature, z=z)\n", " strokes = to_normal_strokes(sample_strokes)\n", " if draw_mode:\n", " draw_strokes(strokes, factor)\n", " return strokes" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Loading model ./log/vector-199000.\n", "INFO:tensorflow:Restoring parameters from ./log/vector-199000\n" ] } ], "source": [ "# loads the weights from checkpoint into our model\n", "load_checkpoint(sess, model_dir)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# randomly unconditionally generate 10 examples\n", "N = 10\n", "reconstructions = []\n", "for i in range(N):\n", " reconstructions.append([decode(temperature=0.5, draw_mode=False), [0, i]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if our model kind of works by sampling from it:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<svg baseProfile=\"full\" height=\"94.98375825191033\" version=\"1.1\" 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0.7647537440061569,0.6554336100816727 l0.7215210795402527,0.38239896297454834 \" fill=\"none\" stroke=\"black\" stroke-width=\"1\"/></svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stroke_grid = make_grid_svg(reconstructions)\n", "draw_strokes(stroke_grid)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_model_params():\n", " # get trainable params.\n", " model_names = []\n", " model_params = []\n", " model_shapes = []\n", " with sess.as_default():\n", " t_vars = tf.trainable_variables()\n", " for var in t_vars:\n", " param_name = var.name\n", " p = sess.run(var)\n", " model_names.append(param_name)\n", " params = p\n", " model_params.append(params)\n", " model_shapes.append(p.shape)\n", " return model_params, model_shapes, model_names\n", "\n", "def quantize_params(params, max_weight=10.0, factor=32767):\n", " result = []\n", " max_weight = np.abs(max_weight)\n", " for p in params:\n", " r = np.array(p)\n", " r /= max_weight\n", " r[r>1.0] = 1.0\n", " r[r<-1.0] = -1.0\n", " result.append(np.round(r*factor).flatten().astype(np.int).tolist())\n", " return result" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_params, model_shapes, model_names = get_model_params()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['vector_rnn/RNN/output_w:0',\n", " 'vector_rnn/RNN/output_b:0',\n", " 'vector_rnn/RNN/LSTMCell/W_xh:0',\n", " 'vector_rnn/RNN/LSTMCell/W_hh:0',\n", " 'vector_rnn/RNN/LSTMCell/bias:0']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_names" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scale factor converts \"model-coordinates\" to \"pixel coordinates\" for your JS canvas demo later on.\n", "# the larger it is, the larger your drawings (in pixel space) will be.\n", "# I recommend setting this to 100.0 and iterating the value in the json file later on when you build the JS part.\n", "scale_factor = 200.0\n", "metainfo = {\"mode\":2,\"version\":6,\"max_seq_len\":train_set.max_seq_length,\"name\":\"custom\",\"scale_factor\":scale_factor}" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_params_quantized = quantize_params(model_params)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_blob = [metainfo, model_shapes, model_params_quantized]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"custom.gen.full.json\", 'w') as outfile:\n", " json.dump(model_blob, outfile, separators=(',', ':'))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "After you dump the `custom.gen.full.json`, you should save the below code as `compress_model.json`, and run:\n", "\n", "```\n", "node compress_model.js custom.gen.full.json custom.gen.json\n", "```\n", "\n", "To get to the final file you can use for Sketch-RNN-JS\n", "\n", "Below is the entire code for `compress_model.js` which will be run using node:\n", "\n", "```\n", "/*\n", "compress_model.js\n", "Compress JSON model to b64 encoded version to save bandwidth. only works for decoder-only sketch-rnn model.\n", "*/\n", "\n", "const assert = require('assert');\n", "const fs = require('fs');\n", "\n", "/**\n", " * deals with decompressing b64 models to float arrays.\n", " */\n", "function btoa(s) {\n", " return Buffer.from(s, 'binary').toString('base64');\n", "}\n", "function string_to_uint8array(b64encoded) {\n", " var u8 = new Uint8Array(atob(b64encoded).split(\"\").map(function(c) {\n", " return c.charCodeAt(0); }));\n", " return u8;\n", "}\n", "function uintarray_to_string(u8) {\n", " var s = \"\";\n", " for (var i = 0, len = u8.length; i < len; i++) {\n", " s += String.fromCharCode(u8[i]);\n", " }\n", " var b64encoded = btoa(s);\n", " return b64encoded;\n", "};\n", "function string_to_array(s) {\n", " var u = string_to_uint8array(s);\n", " var result = new Int16Array(u.buffer);\n", " return result;\n", "};\n", "function array_to_string(a) {\n", " var u = new Uint8Array(a.buffer);\n", " var result = uintarray_to_string(u);\n", " return result;\n", "};\n", "\n", "var args = process.argv.slice(2);\n", "\n", "try {\n", " assert.strictEqual(args.length, 2);\n", "} catch (err) {\n", " console.log(\"Usage: node compress_model.js orig_full_model.json compressed_model.json\")\n", " process.exit(1);\n", "}\n", "\n", "var orig_file = args[0];\n", "var target_file = args[1];\n", "\n", "var orig_model = JSON.parse(fs.readFileSync(orig_file, 'ascii'));\n", "\n", "var model_weights = orig_model[2];\n", "var compressed_weights = [];\n", "\n", "for (var i=0;i<model_weights.length;i++) {\n", " compressed_weights.push(array_to_string(new Int16Array(model_weights[i])));\n", "}\n", "\n", "var target_model = [orig_model[0], orig_model[1], compressed_weights];\n", "\n", "fs.writeFileSync(target_file, JSON.stringify(target_model), 'ascii');\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

lexieheinle/jour407homework

ChartSecondHomework/VisualizingPresidentialCommercials.ipynb

1

8506025

mit

emreyamangil/Convex.jl

examples/optimal_advertising.ipynb

6

652070

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "srand(1);\n", "using Distributions\n", "m = 5;\n", "n = 24;\n", "SCALE = 10000;\n", "B = rand(LogNormal(8), m) + 10000;\n", "B = round(B, 3);\n", "\n", "P_ad = rand(m);\n", "P_time = rand(1,n);\n", "P = P_ad * P_time;\n", "\n", "T = sin(linspace(-2*pi/2, 2*pi-2*pi/2, n)) * SCALE;\n", "T += -minimum(T) + SCALE;\n", "c = rand(m);\n", "c *= 0.6*sum(T)/sum(c);\n", "c = round(c, 3);\n", "R = [rand(LogNormal(minimum(c)/c[i]), 1) for i=1:m];" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Form and solve the optimal advertising problem.\n", "using Convex, SCS;\n", "D = Variable(m, n);\n", "Si = [min(R[i]*P[i,:]*D[i,:]', B[i]) for i=1:m];\n", "problem = maximize(sum(Si),\n", " [D >= 0, sum(D,1)' <= T, sum(D,2) >= c]);\n", "solve!(problem, SCSSolver(verbose=0));" ] }, { "cell_type": "code", "execution_count": 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element.attr(\"visibility\",\n", " bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " if (xscalable) {\n", " var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n", " var xtrans = new Snap.Matrix().translate(tx, 0);\n", " root.selectAll(\".yfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(yfixed_t);\n", " });\n", "\n", " root.select(\".xlabels\")\n", " .transform(yfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1/scale, 1, cx, cy).add(st);\n", "\n", " element.transform(unscale_t);\n", "\n", " var x = cx * scale + tx;\n", " element.attr(\"visibility\",\n", " bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry *\";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val * old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe\");\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-4\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-12\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-12\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-12\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-14\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-14\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-14\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-15\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-15\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-15\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-15\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-15\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-16\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-16\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-0ac8e972215b48d6a3ca72af4c6b43fe-element-16\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot traffic.\n", "using Gadfly\n", "plot(x=1:length(T), y=T, Geom.line, Guide.XLabel(\"Hour\"), Guide.YLabel(\"Traffic\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", " fill=\"#000000\"\n", " stroke-width=\"0.3\"\n", " font-size=\"3.88\"\n", ">\n", "<g class=\"plotroot xscalable yscalable\" id=\"fig-bd231a1dd9e14c899b9e999333019f41-element-1\">\n", " <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" 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});\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry *\";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val * old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-e5d1be1df52645109ee9bd399079b552\");\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-9\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-12\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-12\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-13\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-13\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-17\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-17\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-17\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-19\")\n", " .data(\"max_pos\", 111.42)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-19\")\n", " .data(\"min_pos\", 94.42)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-19\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-20\")\n", " .data(\"max_pos\", 111.42)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-20\")\n", " .data(\"min_pos\", 94.42)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-20\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-20\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-20\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-21\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-21\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e5d1be1df52645109ee9bd399079b552-element-21\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot P.\n", "spy(P)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", " fill=\"#000000\"\n", " stroke-width=\"0.3\"\n", " font-size=\"3.88\"\n", ">\n", "<g class=\"plotroot xscalable yscalable\" id=\"fig-938c7359d1204f4787c7284d38f46574-element-1\">\n", " <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-938c7359d1204f4787c7284d38f46574-element-2\">\n", " <text x=\"66.95\" 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});\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry *\";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val * old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-7c0ab464e62e4f1eac606775cf54095b\");\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-9\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-12\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-12\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-13\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-13\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-17\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-17\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-17\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-19\")\n", " .data(\"max_pos\", 104.19)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-19\")\n", " .data(\"min_pos\", 87.19)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-19\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-20\")\n", " .data(\"max_pos\", 104.19)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-20\")\n", " .data(\"min_pos\", 87.19)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-20\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-20\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-20\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-21\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-21\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-7c0ab464e62e4f1eac606775cf54095b-element-21\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot optimal D.\n", "spy(evaluate(D))" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.9", "language": "julia", "name": "julia-0.3" }, "language_info": { "name": "julia", "version": "0.3.9" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-2-clause

outlace/Machine-Learning-Experiments

VariableOutput.ipynb

1

9212

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### A recursive neural network that decides how many times to run itself\n", "Produces variable-length outputs for static-length inputs." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n", "y = np.array([[0],[0,0],[0,0,0],[0,0,0,0]])" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(x):\n", " return np.matrix(1.0 / (1.0 + np.exp(-x)))\n", "\n", "def relu(x):\n", " alpha = 0.01\n", " return np.maximum(x, (alpha * x))" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#initialize random weights\n", "numIn, numHid, numOut = 2, 3, 2\n", "theta1 = np.array( 0.5 * np.sqrt ( 6 / ( numIn + numHid) ) * np.random.randn( numIn + 1, numHid ), dtype=\"float32\" )\n", "theta2 = np.array( 0.5 * np.sqrt ( 6 / ( numHid + numOut ) ) * np.random.randn( numHid + 1, numOut ), dtype=\"float32\" )" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta = np.append(theta1.flatten(), theta2.flatten()) #unroll vectors in a one long vector" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def nn(x, theta):\n", " i = 0\n", " theta1 = np.array(theta[:9]).reshape(3,3)\n", " theta2 = np.array(theta[9:]).reshape(4,2)\n", " #print(theta1.shape)\n", " #print(theta2.shape)\n", " outputs = []\n", " def comp(x):\n", " #print(x)\n", " a1 = np.array(np.concatenate((x.reshape(1,2), np.ones((1,1))), axis=1))\n", " z2 = a1 @ theta1\n", " a2 = np.concatenate((relu(z2), np.ones((1,1))), axis=1)\n", " z3 = a2 @ theta2\n", " a3 = sigmoid(z3)\n", " return a3\n", " \n", " a3 = comp(x)\n", " outputs.append(a3[0,1])\n", " while a3[0,0] > 0.5 and i < 3: #prevent an infinite loop; constrain output length\n", " i += 1\n", " input = np.array([[a3[0,1],0]])\n", " a3 = comp(input)\n", " outputs.append(a3[0,1])\n", " return np.array(outputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The neural network accepts an input vector of length 2. It has 2 output nodes. One node is used to control whether or not to recursively run itself, the other is the real data output. We simply threshold > 0.5 to trigger a recursive call to itself." ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.62431196]\n", "[ 0.71118059]\n", "[ 0.60979257]\n", "[ 0.69732337]\n" ] } ], "source": [ "###example output with random initial weights\n", "print( nn(X[0], theta) )\n", "print( nn(X[1], theta) ) \n", "print( nn(X[2], theta) ) \n", "print( nn(X[3], theta) ) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cost Function\n", "Arbitrarily assign a high cost to mismatches in the length of the output, then also assess MSE" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def costFunction(X, Y, theta):\n", " cost = 0\n", " for i in range(len(X)):\n", " y = Y[i]\n", " m = float(len(X[i]))\n", " hThetaX = nn(X[i], theta)\n", " if len(y) != len(hThetaX):\n", " cost += 3\n", " else:\n", " cost += (1/m) * np.sum(np.abs(y - hThetaX)**2)\n", " \n", " return cost" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Genetic Algorithm to Solve Weights:" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brandonbrown/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:16: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/Users/brandonbrown/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: RuntimeWarning: overflow encountered in exp\n", " from ipykernel import kernelapp as app\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Sol'n:\n", "[[ 13.55 14.58 62.0889891 -6.4966617 7.16086276\n", " -1.80259996 8.53635902 8.6205749 -10.54 -5.27 -13.5479922\n", " -0.16212416 -9.13629965 8.46666441 10.39147747 27.13608853\n", " -23.26767004]]\n", "Cost:0.835819518334\n" ] } ], "source": [ "import random as rn, numpy as np\n", "# [Initial population size, mutation rate (=1%), num generations (30), solution length (13), # winners/per gen]\n", "initPop, mutRate, numGen, solLen, numWin = 100, 0.01, 500, 17, 20\n", "#initialize current population to random values within range\n", "curPop = np.random.choice(np.arange(-15,15,step=0.01),size=(initPop, solLen),replace=False)\n", "nextPop = np.zeros((curPop.shape[0], curPop.shape[1]))\n", "fitVec = np.zeros((initPop, 2)) #1st col is indices, 2nd col is cost\n", "for i in range(numGen): #iterate through num generations\n", " #Create vector of all errors from cost function for each solution\n", "\tfitVec = np.array([np.array([x, np.sum(costFunction(X, y, curPop[x].T))]) for x in range(initPop)])\n", "\t#plt.pyplot.scatter(i,np.sum(fitVec[:,1]))\n", "\twinners = np.zeros((numWin, solLen))\n", "\tfor n in range(len(winners)): #for n in range(10)\n", "\t\tselected = np.random.choice(range(len(fitVec)), numWin/2, replace=False)\n", "\t\twnr = np.argmin(fitVec[selected,1])\n", "\t\twinners[n] = curPop[int(fitVec[selected[wnr]][0])]\n", "\tnextPop[:len(winners)] = winners #populate new gen with winners\n", "\tduplicWin = np.zeros((((initPop - len(winners))),winners.shape[1]))\n", "\tfor x in range(winners.shape[1]): #for each col in winners (3 cols)\n", " #Duplicate winners (20x3 matrix) 3 times to create 80x3 matrix, then shuffle columns\n", "\t\tnumDups = ((initPop - len(winners))/len(winners)) #num times to duplicate to fill rest of nextPop\n", "\t\tduplicWin[:, x] = np.repeat(winners[:, x], numDups, axis=0)#duplicate each col\n", "\t\tduplicWin[:, x] = np.random.permutation(duplicWin[:, x]) #shuffle each col (\"crossover\")\n", " #Populate the rest of the generation with offspring of mating pairs\n", "\tnextPop[len(winners):] = np.matrix(duplicWin)\n", " #Create a mutation matrix, mostly 1s, but some elements are random numbers from a normal distribution\n", "\tmutMatrix = [np.float(np.random.normal(0,2,1)) if rn.random() < mutRate else 1 for x in range(nextPop.size)]\n", " #randomly mutate part of the population by multiplying nextPop by our mutation matrix\n", "\tnextPop = np.multiply(nextPop, np.matrix(mutMatrix).reshape(nextPop.shape))\n", "\tcurPop = nextPop\n", "best_soln = curPop[np.argmin(fitVec[:,1])]\n", "print(\"Best Sol'n:\\n%s\\nCost:%s\" % (best_soln,np.sum(costFunction(X, y, best_soln.T))))" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.]\n", "[ 0. 0.]\n", "[ 0.77 0. 0. ]\n", "[ 0.99 0.3 0. 0. ]\n" ] } ], "source": [ "#Demonstrate variable output after training\n", "print( np.round(nn(X[0], best_soln.reshape(17,1)), 2) )\n", "print( np.round(nn(X[1], best_soln.reshape(17,1)), 2) )\n", "print( np.round(nn(X[2], best_soln.reshape(17,1)), 2) )\n", "print( np.round(nn(X[3], best_soln.reshape(17,1)), 2) )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jerkern/pyParticleEst

docs/example/BasicModel.ipynb

1

45516

{ "metadata": { "name": "", "signature": "sha256:14e723ea85ca9dc3dbcf17a8950cb60dfcd8fe1c7bc561fa16ccb902d9b504fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Basic Model\n" ] }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Demonstrates a simple integrator model. Start by imported the interface classes from pyparticleest and the main simulator class\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy\n", "import pyparticleest.utils.kalman as kalman\n", "import pyparticleest.interfaces as interfaces\n", "import matplotlib.pyplot as plt\n", "import pyparticleest.simulator as simulator\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "First we need the generate a dataset, it contains a true trajectory x and an array of measurements y. The goal is to estimate x using the data in y." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def generate_dataset(steps, P0, Q, R):\n", " x = numpy.zeros((steps + 1,))\n", " y = numpy.zeros((steps,))\n", " x[0] = 2.0 + 0.0 * numpy.random.normal(0.0, P0)\n", " for k in range(1, steps + 1):\n", " x[k] = x[k - 1] + numpy.random.normal(0.0, Q)\n", " y[k - 1] = x[k] + numpy.random.normal(0.0, R)\n", "\n", " return (x, y)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "We need to specify the model which estimation is based upon, for this example we implement it directly on top of the interface specifications. More commonly one would use one of the base classes for a specific class of model to reduce the amount of code needed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Model(interfaces.ParticleFiltering):\n", " \"\"\" x_{k+1} = x_k + v_k, v_k ~ N(0,Q)\n", " y_k = x_k + e_k, e_k ~ N(0,R),\n", " x(0) ~ N(0,P0) \"\"\"\n", "\n", " def __init__(self, P0, Q, R):\n", " self.P0 = numpy.copy(P0)\n", " self.Q = numpy.copy(Q)\n", " self.R = numpy.copy(R)\n", "\n", " def create_initial_estimate(self, N):\n", " return numpy.random.normal(0.0, self.P0, (N,)).reshape((-1, 1))\n", "\n", " def sample_process_noise(self, particles, u, t):\n", " \"\"\" Return process noise for input u \"\"\"\n", " N = len(particles)\n", " return numpy.random.normal(0.0, self.Q, (N,)).reshape((-1, 1))\n", "\n", " def update(self, particles, u, t, noise):\n", " \"\"\" Update estimate using 'data' as input \"\"\"\n", " particles += noise\n", "\n", " def measure(self, particles, y, t):\n", " \"\"\" Return the log-pdf value of the measurement \"\"\"\n", " logyprob = numpy.empty(len(particles), dtype=float)\n", " for k in range(len(particles)):\n", " logyprob[k] = kalman.lognormpdf(particles[k].reshape(-1, 1) - y, self.R)\n", " return logyprob\n", "\n", " def logp_xnext_full(self, part, past_trajs, pind,\n", " future_trajs, find, ut, yt, tt, cur_ind):\n", "\n", " diff = future_trajs[0].pa.part[find] - part\n", "\n", " logpxnext = numpy.empty(len(diff), dtype=float)\n", " for k in range(len(logpxnext)):\n", " logpxnext[k] = kalman.lognormpdf(diff[k].reshape(-1, 1), numpy.asarray(self.Q).reshape(1, 1))\n", " return logpxnext\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Define length of dataset and some parameters for the model defined above\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "steps = 50\n", "num = 50\n", "P0 = 1.0\n", "Q = 1.0\n", "R = numpy.asarray(((1.0,),))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Generate the dataset, but first set the seed for the random number generator so we always get the same example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "numpy.random.seed(1)\n", "(x, y) = generate_dataset(steps, P0, Q, R)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Instantiate the model and create the simulator object using the model and measurement y. This example does not use an input signal therefore set u=None\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = Model(P0, Q, R)\n", "sim = simulator.Simulator(model, u=None, y=y)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Perform the estimation, using 'num' as both the number of forward particle and backward trajectories. For the smoother simply use the ancestral paths of each particle at the end time." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sim.simulate(num, num, smoother='ancestor')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "33" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Extract filtered and smoothed estimates" ] }, { "cell_type": "code", "collapsed": false, "input": [ "(vals, _) = sim.get_filtered_estimates()\n", "svals = sim.get_smoothed_estimates()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Plot true trajectory, measurements and the filtered and smoothed estimates\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(range(steps + 1), x, 'r-')\n", "plt.plot(range(1, steps + 1), y, 'bx')\n", "plt.plot(range(steps + 1), vals[:, :, 0], 'k.', markersize=0.8)\n", "plt.plot(range(steps + 1), svals[:, :, 0], 'b--')\n", "plt.plot(range(steps + 1), x, 'r-')\n", "plt.xlabel('t')\n", "plt.ylabel('x')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "<matplotlib.text.Text at 0x7f654682bc10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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z/b/9VqoFe/QI5+GH46lVS8ZU+PlBdLRUX6Wl3aBQoWssXBjC9OmyHOqePdaf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\nKosICT+hlH7k5UHRsHFYlKLOG28ArjWEDH744Q67c/bFF12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"text": [ "<matplotlib.figure.Figure at 0x7f6546874850>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 } ], "metadata": {} } ] }

lgpl-3.0

Deltares/hydro-engine

notebooks/generate_index.ipynb

1

231702

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Generate spatial index for catchments based on their topology\n", "\n", "Save parent catchment info for every child catchment so that it can be quickly queried, O(n), eventually it is much faster with column value indices.\n", "\n", "![Catchments Index](catchment_index.png \"Catchments Index\")\n", "\n", "So, for every parent of a given catchment, a new feature is generated, where an id of its child catchment is saved. This way, all parent catchment ids can be found using a single query." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, that rivers index is generated using QGIS: __Join attributes by location__ tool. \n", "\n", "Before join, HydroBASINS layer was converted to contain only a single field HYBAS_ID - changed to text to avoid int overflow. Used __Refactor field__ tool.\n", "\n", "hydro-engine\\data\\HydroBASINS\\l05\\hybas_lev05_v1c_id.dbf " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import glob\n", "import os\n", "import logging\n", "import sys\n", "import json\n", "\n", "import math\n", "\n", "import shapefile\n", "\n", "import shapely.geometry, shapely.wkt\n", "import shapely as sl\n", "import numpy as np\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import pylab" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pylab.rcParams['figure.figsize'] = (17.0, 15.0)\n", "logging.basicConfig(stream=sys.stderr, level=logging.INFO)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def generate_index(src, dst):\n", " shp = shapefile.Reader(src)\n", " \n", " # 1. fill directed graph\n", " graph = nx.DiGraph()\n", " \n", " list_hybas_id = [r[0] for r in shp.records()]\n", " list_next_down = [r[1] for r in shp.records()]\n", " list_shape_box = [s.bbox for s in shp.shapes()]\n", "\n", " edges = zip(list_hybas_id, list_next_down)\n", "\n", " edges = [e for e in edges if e[1] != 0]\n", " edges = [e for e in edges if e[0] != 0]\n", "\n", " graph.add_nodes_from([(hybas_id) for (hybas_id) in list_hybas_id])\n", " graph.add_edges_from([(node_from, node_to) for (node_from, node_to) in edges])\n", "\n", " # 2. traverse parent nodes\n", " index = []\n", " for r in zip(list_hybas_id, list_shape_box):\n", " hybas_id = r[0]\n", " bbox = r[1]\n", " x0 = bbox[0]\n", " x1 = bbox[2]\n", " y0 = bbox[1]\n", " y1 = bbox[3]\n", " \n", " poly = [[[x0, y0], [x1, y0], [x1, y1], [x0, y1], [x0, y0]]]\n", " \n", " parents = nx.bfs_predecessors(graph.reverse(), hybas_id)\n", " \n", " for parent in zip(parents.keys(), parents.values()):\n", " index.append([hybas_id, parent[0], parent[1], poly])\n", " \n", " # endorheic\n", " if len(parents) == 0:\n", " index.append([hybas_id, 0, hybas_id, poly])\n", " \n", " return index\n", "\n", " # 3. write\n", " \n", " print('{0} -> {1}'.format(src, dst))\n", " print(len(shp.shapes()))\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing ../data/HydroBASINS\\hybas_lev09_v1c.shp ...\n" ] } ], "source": [ "files = glob.glob('../data/HydroBASINS/*lev09*.shp')\n", "\n", "import geojson\n", "\n", "l = None\n", "for src in files: \n", " file_dir = os.path.dirname(src)\n", " file_name = os.path.basename(src)\n", " \n", " dst = '../data/HydroBASINS_indexed/' + file_name\n", "\n", " print('Processing {0} ...'.format(src))\n", " \n", " index = generate_index(src, dst)\n", "\n", " w = shapefile.Writer(shapeType=shapefile.POLYGON)\n", " w.field('HYBAS_ID', 'N', 16)\n", " w.field('PARENT_FROM', 'N', 16)\n", " w.field('PARENT_TO', 'N', 16)\n", " \n", " features = []\n", " for i in index:\n", " w.poly(i[3])\n", " \n", " w.record(i[0], i[1], i[2])\n", " \n", " w.save(dst)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "w = shapefile.Writer(shapefile.POINT)\n", "w.point(1,1)\n", "w.point(3,1)\n", "w.point(4,3)\n", "w.point(2,2)\n", "w.field('FIRST_FLD', 'N',16)\n", "w.field('SECOND_FLD','C','40')\n", "w.record(1,'Point')\n", "w.record(1,'Point')\n", "w.record(2,'Point')\n", "w.record(3,'Point')\n", "w.save('s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DONE" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [], "source": [ "shp = shapefile.Reader('../data/HydroBASINS\\hybas_af_lev05_v1c.shp')\n", "\n", "# 1. fill directed graph\n", "graph = nx.DiGraph()\n", "\n", "# 2. traverse all parent nodes\n", "list_hybas_id = [r[0] for r in shp.records()]\n", "list_next_down = [r[1] for r in shp.records()]\n", "\n", "edges = zip(list_hybas_id, list_next_down)\n", "\n", "edges = [e for e in edges if e[1] != 0]\n", "edges = [e for e in edges if e[0] != 0]\n", "\n", "graph.add_nodes_from([(hybas_id) for (hybas_id) in list_hybas_id])\n", "graph.add_edges_from([(node_from, node_to) for (node_from, node_to) in edges])" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1028" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(graph.nodes())" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "550" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(graph.edges())" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x20d111b3f28>" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AQOcT1j4JYS0A0E69GuR0epjXyy40TO6UavNzacb47ecAwFNpRVirZy0AwCr1cl/M5cm0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\nUbB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o3RGXG/r9fjo6Oujo6KC1tZVx48YRCATo6+sD9hV+A4EAY8eOxeFwDHh/DQYDOp2O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A2hkBuvdwUGg4LRaGSgJcK+anJloqOjIxJ0jIqKwm63A5CYmMjq1a9z443f4447Fg35tS/U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\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1yVYPSZMvgFX67Gc/G7XaH0RX16/E4OAXoqfn16Jev69pX8KtnLeffPIjzttAx0htwcPp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\nSICwFgAAAAAgAcJaAAAAAIAECGsBAAA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kSZIkSZKkIWCxVpIkSZIkSZKGgMVaSZIkSZIk\nSRoC/wMuqrrhMDo2zQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x20d106b74e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pos = nx. (nx.Graph(graph), iterations=50, scale=1.0)\n", "\n", "nx.draw_networkx_nodes(graph, pos, node_color='b', alpha=0.85, node_size=10)\n", "nx.draw_networkx_edges(graph, pos, alpha=0.2)\n", "\n", "\n", "# Niger: https://code.earthengine.google.com/0a846953681587649ed9bc6deee974cf\n", "start_node = 1050022420\n", "\n", "# traverse up, DFS\n", "parent_nodes = graph.subgraph(nx.dfs_predecessors(graph.reverse(), start_node))\n", "\n", "nx.draw_networkx_edges(parent_nodes, pos, edge_color='r', alpha=0.2)\n", "\n", "nx.draw_networkx_nodes(parent_nodes, pos, node_color='r', alpha=0.85, node_size=30)\n", "\n", "nx.draw_networkx_nodes(graph.subgraph([start_node]), pos, node_color='r', alpha=0.85, node_size=30)\n", "\n", "nx.draw_networkx_nodes(graph.subgraph(graph.predecessors(start_node)), pos, node_color='r', alpha=0.85, node_size=30)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1050909890, 1050909900]" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.predecessors(start_node)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predecesors = nx.bfs_predecessors(graph.reverse(), start_node)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1050635010,\n", " 1050741380,\n", " 1050737110,\n", " 1050874630,\n", " 1050839560,\n", " 1050680300,\n", " 1050722570,\n", " 1050635020,\n", " 1050513090,\n", " 1050870030,\n", " 1050827040,\n", " 1050874640,\n", " 1050668760,\n", " 1050715340,\n", " 1050669460,\n", " 1050827030,\n", " 1050736280,\n", " 1050694020,\n", " 1050870170,\n", " 1050669470,\n", " 1050680480,\n", " 1050839280,\n", " 1050668450,\n", " 1050513230,\n", " 1050888070,\n", " 1050887720,\n", " 1050515500,\n", " 1050603890,\n", " 1050742280,\n", " 1050741300,\n", " 1050761270,\n", " 1050710380,\n", " 1050515770,\n", " 1050747470,\n", " 1050677950,\n", " 1050722720,\n", " 1050909890,\n", " 1050846020,\n", " 1050641670,\n", " 1050677960,\n", " 1050742220,\n", " 1050909900,\n", " 1050710240,\n", " 1050845560,\n", " 1050682900,\n", " 1050747640,\n", " 1050690130,\n", " 1050893000,\n", " 1050696660,\n", " 1050736270,\n", " 1050577750,\n", " 1050760280,\n", " 1050715100,\n", " 1050689850,\n", " 1050577760,\n", " 1050760290,\n", " 1050652110,\n", " 1050603880,\n", " 1050651900,\n", " 1050737100,\n", " 1050755820,\n", " 1050514030,\n", " 1050761260,\n", " 1050909040,\n", " 1050908780,\n", " 1050694130,\n", " 1050892990,\n", " 1050513910,\n", " 1050755960,\n", " 1050683010,\n", " 1050641660,\n", " 1050696830]" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(predecesors.keys())" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

lgpl-3.0

rjw57/vagrant-ipython

notebooks/matlab-example.ipynb

1

1067

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MATLAB integration\n", "\n", "This is a test of using MATLAB from the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "% A simple test:\n", "A = rand(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "t = linspace(0,6*pi,100);\n", "plot(sin(t))\n", "grid on\n", "hold on\n", "plot(cos(t), 'r')" ] } ], "metadata": { "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab_kernel" }, "language_info": { "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-matlab", "name": "matlab" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

sueiras/training

tensorflow/00-basics/data_estimator_iris_example.ipynb

1

23092

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples of use tf.data and tf.estimator with the iris dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from http://download.tensorflow.org/data/iris_training.csv\n", "8192/2194 [================================================================================================================] - 0s\n", "Downloading data from http://download.tensorflow.org/data/iris_test.csv\n", "8192/573 [============================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s\n" ] }, { "data": { "text/plain": [ "'/tmp/datasets/iris_training.csv'" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TRAIN_URL = \"http://download.tensorflow.org/data/iris_training.csv\"\n", "TEST_URL = \"http://download.tensorflow.org/data/iris_test.csv\"\n", "\n", "CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',\n", " 'PetalLength', 'PetalWidth', 'Species']\n", "\n", "train_path = tf.keras.utils.get_file(fname=TRAIN_URL.split('/')[-1],\n", " origin=TRAIN_URL, cache_dir='/tmp')\n", "\n", "test_path = tf.keras.utils.get_file(fname=TEST_URL.split('/')[-1],\n", " origin=TEST_URL, cache_dir='/tmp')\n", "train_path" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "_CSV_COLUMNS = ['SepalLength', 'SepalWidth',\n", " 'PetalLength', 'PetalWidth', 'Species']\n", "\n", "_CSV_COLUMN_DEFAULTS = [[0], [0], [0], [0], [0]]\n", " \n", "\n", "def input_fn(data_file, num_epochs, shuffle, batch_size):\n", "\n", " def parse_csv(value):\n", " print('Parsing', data_file)\n", " columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS)\n", " features = dict(zip(_CSV_COLUMNS, columns))\n", " labels = features.pop('Species')\n", " return features, labels\n", "\n", " # Extract lines from input files using the Dataset API.\n", " dataset = tf.data.TextLineDataset(data_file)\n", "\n", " if shuffle:\n", " dataset = dataset.shuffle(buffer_size=100)\n", "\n", " dataset = dataset.map(parse_csv, num_parallel_calls=5)\n", "\n", " # We call repeat after shuffling, rather than before, to prevent separate\n", " # epochs from blending together.\n", " dataset = dataset.repeat(num_epochs)\n", " dataset = dataset.batch(batch_size)\n", " return dataset" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing /home/jorge/.keras/datasets/iris_training.csv\n" ] }, { "data": { "text/plain": [ "<BatchDataset shapes: ({PetalWidth: (?,), SepalWidth: (?,), PetalLength: (?,), SepalLength: (?,)}, (?,)), types: ({PetalWidth: tf.int32, SepalWidth: tf.int32, PetalLength: tf.int32, SepalLength: tf.int32}, tf.int32)>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input_fn(train_path, 2, True, 5)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing /home/jorge/.keras/datasets/iris_training.csv\n" ] }, { "ename": "ValueError", "evalue": "features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-24-49deb9779651>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m model.train(input_fn=lambda: input_fn(\n\u001b[0m\u001b[1;32m 3\u001b[0m train_path, 2, True, 5))\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps, saving_listeners)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0msaving_listeners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_listeners_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss for final step: %s.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36m_train_model\u001b[0;34m(self, input_fn, hooks, saving_listeners)\u001b[0m\n\u001b[1;32m 709\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mglobal_step_read_tensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m estimator_spec = self._call_model_fn(\n\u001b[0;32m--> 711\u001b[0;31m features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)\n\u001b[0m\u001b[1;32m 712\u001b[0m \u001b[0;31m# Check if the user created a loss summary, and add one if they didn't.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 713\u001b[0m \u001b[0;31m# We assume here that the summary is called 'loss'. If it is not, we will\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36m_call_model_fn\u001b[0;34m(self, features, labels, mode, config)\u001b[0m\n\u001b[1;32m 692\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'config'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_fn_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'config'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m \u001b[0mmodel_fn_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_fn_results\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_fn_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEstimatorSpec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py\u001b[0m in \u001b[0;36m_model_fn\u001b[0;34m(features, labels, mode, config)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m config=config)\n\u001b[0m\u001b[1;32m 254\u001b[0m super(LinearClassifier, self).__init__(\n\u001b[1;32m 255\u001b[0m \u001b[0mmodel_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_model_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py\u001b[0m in \u001b[0;36m_linear_model_fn\u001b[0;34m(features, labels, mode, head, feature_columns, optimizer, partitioner, config)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m raise ValueError('features should be a dictionary of `Tensor`s. '\n\u001b[0;32m--> 100\u001b[0;31m 'Given type: {}'.format(type(features)))\n\u001b[0m\u001b[1;32m 101\u001b[0m optimizer = optimizers.get_optimizer_instance(\n\u001b[1;32m 102\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_get_default_optimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>" ] } ], "source": [ "for n in range(5):\n", " model.train(input_fn=lambda: input_fn(\n", " train_path, 2, True, 5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jorge/.keras/datasets/iris_training.csv'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_path" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = tf.data.TextLineDataset(train_path)\n", "dataset = dataset.shuffle(100)\n", "\n", "def parse_csv(value):\n", "columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS)\n", " features = dict(zip(_CSV_COLUMNS, columns))\n", " labels = features.pop('Species')\n", " return features, labels\n", "\n", "dataset = dataset.map(parse_csv, num_parallel_calls=5)\n", "dataset = dataset.repeat(2)\n", "dataset = dataset.batch(10)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "({'PetalLength': <tf.Tensor 'DecodeCSV:2' shape=() dtype=int32>,\n", " 'PetalWidth': <tf.Tensor 'DecodeCSV:3' shape=() dtype=int32>,\n", " 'SepalLength': <tf.Tensor 'DecodeCSV:0' shape=() dtype=int32>,\n", " 'SepalWidth': <tf.Tensor 'DecodeCSV:1' shape=() dtype=int32>},\n", " <tf.Tensor 'DecodeCSV:4' shape=() dtype=int32>)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parse_csv('/home/jorge/.keras/datasets/iris_training.csv')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "\n", "wide_columns = [\n", " tf.feature_column.numeric_column('SepalLength'),\n", " tf.feature_column.numeric_column('SepalWidth'),\n", " tf.feature_column.numeric_column('PetalLength'),\n", " tf.feature_column.numeric_column('PetalLength')\n", "]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using default config.\n", "INFO:tensorflow:Using config: {'_service': None, '_keep_checkpoint_every_n_hours': 10000, '_keep_checkpoint_max': 5, '_num_ps_replicas': 0, '_is_chief': True, '_model_dir': 'tmp/model', '_save_checkpoints_steps': None, '_master': '', '_task_id': 0, '_tf_random_seed': None, '_session_config': None, '_save_summary_steps': 100, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f9ca2329ac8>, '_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_num_worker_replicas': 1, '_task_type': 'worker'}\n" ] } ], "source": [ "model = tf.estimator.LinearClassifier(\n", " model_dir='tmp/model',\n", " feature_columns=wide_columns)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-21-f3d0aec955be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps, saving_listeners)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0msaving_listeners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_listeners_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss for final step: %s.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36m_train_model\u001b[0;34m(self, input_fn, hooks, saving_listeners)\u001b[0m\n\u001b[1;32m 709\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mglobal_step_read_tensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m estimator_spec = self._call_model_fn(\n\u001b[0;32m--> 711\u001b[0;31m features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)\n\u001b[0m\u001b[1;32m 712\u001b[0m \u001b[0;31m# Check if the user created a loss summary, and add one if they didn't.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 713\u001b[0m \u001b[0;31m# We assume here that the summary is called 'loss'. If it is not, we will\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36m_call_model_fn\u001b[0;34m(self, features, labels, mode, config)\u001b[0m\n\u001b[1;32m 692\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'config'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_fn_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'config'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m \u001b[0mmodel_fn_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_fn_results\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_fn_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEstimatorSpec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py\u001b[0m in \u001b[0;36m_model_fn\u001b[0;34m(features, labels, mode, config)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m config=config)\n\u001b[0m\u001b[1;32m 254\u001b[0m super(LinearClassifier, self).__init__(\n\u001b[1;32m 255\u001b[0m \u001b[0mmodel_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_model_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tf14/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py\u001b[0m in \u001b[0;36m_linear_model_fn\u001b[0;34m(features, labels, mode, head, feature_columns, optimizer, partitioner, config)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m raise ValueError('features should be a dictionary of `Tensor`s. '\n\u001b[0;32m--> 100\u001b[0;31m 'Given type: {}'.format(type(features)))\n\u001b[0m\u001b[1;32m 101\u001b[0m optimizer = optimizers.get_optimizer_instance(\n\u001b[1;32m 102\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_get_default_optimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>" ] } ], "source": [ "for n in range(5):\n", " model.train(input_fn=lambda: dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:tf14]", "language": "python", "name": "conda-env-tf14-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

kootsoop/DSP.SE

Python/SO70768384 Right method for finding 2-D Spatial Spectrum from cross spectral densities.ipynb

1

64961

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of CSD data (1156, 257)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Spatial Spectrum @10Hz')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Code from https://stackoverflow.com/questions/70768384/right-method-for-finding-2-d-spatial-spectrum-from-cross-spectral-densities\n", "\n", "import numpy as np\n", "from scipy import signal\n", "import matplotlib.pyplot as plt\n", "import cmath\n", "\n", "# Set up data\n", "# Distance[Meter] between sensors \n", "x = [2.1,2.1,-0.7,-2.1,-2.1,-0.7,-0.7,0.6,-5.7,-8.5,-11.4,-7.7,-6.3,-3.5,-2.1,-3.4,5.4,-5.2,-8.9,-10,-10,5.4,5.4,-0.8,-3.6,-6.2,-6.8,-12.2,-17.1,-19,-18.6,-13.5,14.8,14.8]\n", "y = [6.65,4.15,3.65,5.05,7.25,8.95,11.85,8.95,-2,-0.6,-0.9,1.25,2.9,0.9,-0.1,-1.4,9.2,5.2,4.8,6.1,8.9,13.3,17.1,17.9,13.8,-9.3,-5.2,-3.6,-3.6,-0.9,3.7,3.7,-1.8,5.7]\n", "\n", "if (len(x) != len(y)):\n", " raise Exception('X and Y lengthd differ')\n", "\n", "n = len(x)\n", "dx = np.array(x); M = len(dx)\n", "dy = np.array(y) ; N = len(dy)\n", "\n", "np.random.seed(12345)\n", "raw_data=np.reshape(np.random.rand(10000*n),(10000,n))\n", "\n", "f = 10 # frequency in Hz\n", "c = 50 # wave speed 50, 80, 100, 200 m/s\n", "k = 2.0*np.pi*f/c # wavenumber\n", "kx = np.linspace(-20*k,20*k,n*10) # space vector\n", "ky= np.linspace(-20*k,20*k,n*10) # space vector\n", "\n", "\n", "# Finding cross spectral density (CSD)\n", "fs=500\n", "def csdMat(data):\n", " rows, cols = data.shape\n", " total_csd = []\n", " \n", " for i in range(cols):\n", " for j in range(cols):\n", " f, Pxy = signal.csd(data[:,i], data[:,j], fs, nperseg=512)\n", " #real_csd = np.real(Pxy)\n", " total_csd.append(Pxy) # output as list\n", " \n", " return np.array(total_csd)\n", "\n", "## Spatial Spectra:- DFT of the csd along two dimension\n", "\n", "def DFT2D(data):\n", " #data = np.asarray(data)\n", " dft2d = np.zeros((len(kx),len(ky)), dtype=complex)\n", " for k in range(len(kx)):\n", " for l in range(len(ky)):\n", " sum_matrix = 0.0\n", " for m in range(M):\n", " for n in range(N):\n", " e = cmath.exp(- 1j * ((kx[k] * dx[m]) / len(dx) + (ky[l] * dy[n]) / len(dy)))\n", " sum_matrix += data[m,n] * e\n", " dft2d[k,l] = sum_matrix\n", " return dft2d\n", "\n", "\n", "# Call the seismic array\n", "#** Open .NPY files as an array \n", "#with open('res_array_1000f_131310.npy', 'rb') as f:\n", "# arr= np.load(f)\n", "#raw_data = arr[0:10000, :]\n", "\n", "#CSD of the seismic data\n", "csd = csdMat(raw_data)\n", "print('Shape of CSD data', csd.shape)\n", "\n", "# CSD data of a specific frequency\n", "csd_dat=csd[:, 11] \n", "fcsd = np.reshape(csd_dat, (-1, n))\n", "\n", "dft = DFT2D(fcsd) # Data or cross-correlation matrix\n", "spec = np.abs(dft) #dft.real # Spectrum or 2D_DFT of data[real part]\n", "\n", "spec = spec/spec.max()\n", "\n", "plt.figure()\n", "c = plt.imshow(spec, cmap ='seismic', vmin = spec.min(), vmax = spec.max(),\n", " extent =[kx.min(), kx.max(), ky.min(), ky.max()],\n", " interpolation ='nearest', origin ='lower')\n", "plt.colorbar(c)\n", "plt.rcParams.update({'font.size': 18})\n", "plt.xlabel(\"Wavenumber, $K_x$ [rad/m]\", fontsize=18)\n", "plt.ylabel(\"Wavenumber,$K_y$ [rad/m]\", fontsize=18)\n", "plt.title(f'Spatial Spectrum @10Hz', weight=\"bold\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

tata-antares/tagging_LHCb

Stefania_files/track-based-tagging-experiments.ipynb

1

821806

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas\n", "import numpy\n", "from sklearn.metrics import roc_curve, roc_auc_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading initial data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import root_numpy\n", "data_full = pandas.DataFrame(root_numpy.root2array('datasets/tracks.root', 'tracks', \n", " branches=['run', 'event', 'N_sig_sw', 'signB', 'signTrack', 'IPs',\n", " 'diff_eta', 'diff_phi', 'partPt']))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>run</th>\n", " <th>event</th>\n", " <th>N_sig_sw</th>\n", " <th>signB</th>\n", " <th>signTrack</th>\n", " <th>IPs</th>\n", " <th>diff_eta</th>\n", " <th>diff_phi</th>\n", " <th>partPt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>115839</td>\n", " <td>204997902</td>\n", " <td>0.59521</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>0.816143</td>\n", " <td>0.201112</td>\n", " <td>-0.136253</td>\n", " <td>0.300418</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>115839</td>\n", " <td>204997902</td>\n", " <td>0.59521</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>1.375382</td>\n", " <td>0.684863</td>\n", " <td>-0.072971</td>\n", " <td>1.103876</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>115839</td>\n", " <td>204997902</td>\n", " <td>0.59521</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>4.338812</td>\n", " <td>0.749864</td>\n", " <td>-4.177158</td>\n", " <td>1.182519</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>115839</td>\n", " <td>204997902</td>\n", " <td>0.59521</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.287509</td>\n", " <td>-0.644510</td>\n", " <td>-0.599056</td>\n", " <td>0.905010</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>115839</td>\n", " <td>204997902</td>\n", " <td>0.59521</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.562424</td>\n", " <td>0.736161</td>\n", " <td>-3.499841</td>\n", " <td>0.516123</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " run event N_sig_sw signB signTrack IPs diff_eta \\\n", "0 115839 204997902 0.59521 1 -1 0.816143 0.201112 \n", "1 115839 204997902 0.59521 1 -1 1.375382 0.684863 \n", "2 115839 204997902 0.59521 1 -1 4.338812 0.749864 \n", "3 115839 204997902 0.59521 1 1 2.287509 -0.644510 \n", "4 115839 204997902 0.59521 1 1 0.562424 0.736161 \n", "\n", " diff_phi partPt \n", "0 -0.136253 0.300418 \n", "1 -0.072971 1.103876 \n", "2 -4.177158 1.182519 \n", "3 -0.599056 0.905010 \n", "4 -3.499841 0.516123 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_full.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "event_id_column = 'event_id'\n", "event_id = data_full.run.apply(str) + '_' + data_full.event.apply(str)\n", "data_full[event_id_column] = event_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Remove rows with NAN from data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "27156190" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_full = data_full.dropna()\n", "len(data_full)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tagging track sign independence checking" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### assign $p(B+) = -\\sum signTrack_i$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### assign $p(B+) = -\\sum signTrack_i - signB$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_sum_of_charges(data, name, bins=60, use_pt=False):\n", " result_event_id, event_positions, data_ids = numpy.unique(data[event_id_column].values, return_index=True, return_inverse=True)\n", " used_weights = data.signTrack.values\n", " if use_pt:\n", " used_weights *= data.partPt.values\n", " used_weights /= sum(data.partPt.values)\n", " result_probs = -numpy.bincount(data_ids, weights=used_weights)\n", " result_label = numpy.bincount(data_ids, weights=data.signB.values) / numpy.bincount(data_ids)\n", " result_weight = numpy.bincount(data_ids, weights=data.N_sig_sw.values) / numpy.bincount(data_ids)\n", " \n", " min_max = 10\n", " result = {} \n", " result['ROC $-\\sum_i charge_i$'] = [roc_auc_score(result_label, result_probs, sample_weight=result_weight)]\n", "\n", " figure(figsize=(16, 7))\n", " subplot(1, 2, 1)\n", " fpr, tpr, _ = roc_curve(result_label, result_probs, sample_weight=result_weight)\n", " plot(fpr, tpr)\n", " plot([0, 1], [0, 1], 'k--')\n", " grid(True), xlim(0, 1), ylim(0, 1), title('ROC $-\\sum_i charge_i$')\n", " subplot(1, 2, 2)\n", " \n", " hist(result_probs * (result_label == 1), bins=bins, weights=result_weight * (result_label == 1), \n", " range=(-min_max, min_max), alpha=0.2, normed=True, label='$B^+$')\n", " hist(result_probs * (result_label == -1), bins=bins, weights=result_weight * (result_label == -1), \n", " range=(-min_max, min_max), alpha=0.2, normed=True, label='$B^-$')\n", " legend(), title(name + ', $-\\sum_i charge_i$'), xlim(-min_max, min_max)\n", " \n", " plt.savefig('img/assymetry_tracks_{}.png'.format(name), format='png')\n", " \n", " show()\n", " \n", " figure(figsize=(16, 7))\n", " subplot(1, 2, 1)\n", " fpr, tpr, _ = roc_curve(result_label, result_probs - result_label, sample_weight=result_weight)\n", " plot(fpr, tpr)\n", " plot([0, 1], [0, 1], 'k--')\n", " grid(True), xlim(0, 1), ylim(0, 1), title('ROC $-\\sum_i charge_i$ - signal track sign')\n", " subplot(1, 2, 2)\n", " \n", " hist((result_probs - result_label) * (result_label == 1), bins=bins, weights=result_weight * (result_label == 1), \n", " range=(-min_max, min_max), alpha=0.2, normed=True, label='$B^+$')\n", " hist((result_probs - result_label) * (result_label == -1), bins=bins, weights=result_weight * (result_label == -1), \n", " range=(-min_max, min_max), alpha=0.2, normed=True, label='$B^-$')\n", " legend(), title(name + ', $-\\sum_i charge_i$ - signal track sign'), xlim(-min_max, min_max)\n", "\n", " plt.savefig('img/assymetry_tracks_with_sig_part_{}.png'.format(name), format='png')\n", " \n", " show()\n", " \n", " result = {}\n", " for mask, bname in zip([result_label == 1, result_label == -1], ['$B^+$', '$B^-$']):\n", " result[bname] = [numpy.sum(result_probs * result_weight * mask) / sum(result_weight * mask)]\n", " result[bname + ', with signal part'] = [numpy.sum((result_probs - result_label) * \\\n", " result_weight * mask) / sum(result_weight * mask)]\n", " result['ROC AUC'] = roc_auc_score(result_label, result_probs, sample_weight=result_weight)\n", " result['ROC AUC, with signal part'] = 1 - roc_auc_score(result_label, result_probs - result_label, \n", " sample_weight=result_weight)\n", " result['name'] = [name]\n", " return pandas.DataFrame(result)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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Dhw9n6dKlfPTRR/57Xo9/v2VlZcyaNQtJkpR9Icaq680Qwr7AgBhj54rjS4EV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\nJEklpY5aKjt3TCVW7dw1qcRR/hTfpD5t1PqjKB9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933bbd34d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IpJUjjjiCL774gqysrFDjeO45GD0a5syBvfYKNRTJUM65IjPrB0zF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\n7/KXQwgPhxBm1OfwjAzCLGohhKEhhAUhhH+sYxvPKeshhPC9EMLLIYS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933b912c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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QIbn+3Z0999yT//73v9SrVw+ACRMm0LdvX+bPn892222XUgwV/dxlXk96bNacURERicSH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\nSZIgIuoi4tmImBcRZ65lm/Etrz8REXu1PLd9RMyKiL9ExNM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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93366ce510>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9N7hEV0RERKQ6KkZFRHLERx99RNu2bTnmmGPYZptt4g6nxsaNg5YtYf/9445E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\negAFwCDn3LJDjffcc89xyy23MHPmTJzzej9ffBFeegl+8hOvAF2wAJo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93361b4fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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cUaZrs0TRpOmTqH9E/UDGWr14Na3Pbh3IWACTJs2nfv1GgYy1evV8WrcOZiyR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\nkiXShAmJ3090yZIlmjZtWsbevgUAAAAAoiCre0aT7ROVpOH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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93361802d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Pn8+/50qPd9yxSPvsI/33v1W/vvn7uXPnCgAAJJ+5V19vmtnRkga5e9fU4+sl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\ngFJZezoO/aE8ioiISNRl9TLdZ599lo0bNwYdhgRo2zYYNgz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9335ea82d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "means = [compute_sum_of_charges(data_full[mask], name, bins) for mask, name, bins in \\\n", " zip([data_full.signB > -100, \n", " (data_full.IPs > 3) & ((abs(data_full.diff_eta) > 0.6) | (abs(data_full.diff_phi) > 0.825)), \n", " (abs(data_full.diff_eta) < 0.6) & (abs(data_full.diff_phi) < 0.825) & (data_full.IPs < 3)], \n", " ['full', 'OS', 'SS'], [21, 21, 21])]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "means = pandas.concat(means)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>$B^+$</th>\n", " <th>$B^+$, with signal part</th>\n", " <th>$B^-$</th>\n", " <th>$B^-$, with signal part</th>\n", " <th>ROC AUC</th>\n", " <th>ROC AUC, with signal part</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.443411</td>\n", " <td>-0.556589</td>\n", " <td>-0.572163</td>\n", " <td>0.427837</td>\n", " <td>0.571579</td>\n", " <td>0.569146</td>\n", " <td>full</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.111173</td>\n", " <td>-0.888827</td>\n", " <td>-0.157273</td>\n", " <td>0.842727</td>\n", " <td>0.529527</td>\n", " <td>0.684603</td>\n", " <td>OS</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.175970</td>\n", " <td>-0.824030</td>\n", " <td>-0.178102</td>\n", " <td>0.821898</td>\n", " <td>0.567696</td>\n", " <td>0.777694</td>\n", " <td>SS</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " $B^+$ $B^+$, with signal part $B^-$ $B^-$, with signal part \\\n", "0 0.443411 -0.556589 -0.572163 0.427837 \n", "0 0.111173 -0.888827 -0.157273 0.842727 \n", "0 0.175970 -0.824030 -0.178102 0.821898 \n", "\n", " ROC AUC ROC AUC, with signal part name \n", "0 0.571579 0.569146 full \n", "0 0.529527 0.684603 OS \n", "0 0.567696 0.777694 SS " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "means.to_csv('img/track_signs_assymetry_means.csv', index=False, header=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/moosefs/miniconda/envs/ipython_py2/lib/python2.7/site-packages/pandas/computation/expressions.py:190: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead\n", " unsupported[op_str]))\n" ] }, { "data": { "image/png": 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GGVlXI0lSeYkxMmHCBLbbbrtvP/fYY4/x2Wef0b179wwrK3BmVJLUIIYOfZnDDutB375X\n0a+f1+VKktSYJk2axH333cfChQu54447APj444958803eeqpp1h11VUzrhBCjLFxXiiE2Fiv1VSV\n+3MJ02CGyZlhOpp6jnff/SLHHdeL00+/iRtuOLRBXiOEQIzRLjcB12ZJSqa4Fn3ncyNGvNjgj3bp\n0aPhjl8XNf13V/t8nddmz4xKklJ1660T6N//AC644I9ceeV+WZcjSVKjatmy/s8Cre/xmwrPjEqS\nUjNmDOy11+Fceulx/PrXPRr0tTwzmpxrsyQls7QzhE1dWmdGbUYlSal4/HH4xS/g/vsjFRUN3yPa\njCbn2ixJydiM1vj5Oq/N3k03R8rtuYQNwQyTM8N0NLUcH3oI+vSBYcNolEZUkiTln82oJCmRe++F\n//kfGD4cdtwx62okSVJeeJmuJGm5/fKXI7j33gpGjVqJzTZr3Nf2Mt3kXJslKRkv063x895NV5LU\nsA4//A8MHXolI0Y8xWabtcu6HEmSlDNeppsjTW3GLAtmmJwZpiPvOe6334088MA1jB49jm7d2mVd\njiRJyiHPjEqS6ixG2GOP/2X8+Dt5+unxbL/9+lmXJEmScsqZUUlSncQIvXv/mVGjrua550az5ZY/\nyrQeZ0aTc22WpGScGa3x886MSpLSEyOcdRa89dbBTJmyFx07rp11SZIklaSnnn2KufPnNtjxW7Zo\nyS477JLKsT766CPGjRv3nc+1adOGioqKVI5fG5vRHKmsrGy0N0ZTZYbJmWE68pRjVVXh0S1TpkBl\n5SqsueYqWZckSVLJmjt/Lmtv3HC/tP1o5kf12v/FF1/kkksuYd68eRx11FEAvPzyy7Ru3ZoBAwZw\n0EEHNUSZdWIzKklaqoUL4fjjYc4cGDUKWrXKuiJJklQfW2+9Na1ataJ///7svffeAHz55ZesscYa\nnHvuubRs2TKz2pwZlSTVaO7cBRx99AK+/LIlDz8MGa5VNXJmNDnXZklKpqbZyRHjRzT4mdEeu/ao\n1/e0a9eO6dOns/LKKxNj5KKLLuKLL77gpptuWq4anBmVJDWYzz//ho4dD2P11bdiypRLWXnlrCuS\nJEnLY9q0abRp04Zx48YRY+SRRx6hS5cunHjiiVmX5nNG8yTvzyUsBWaYnBmmo5Rz/PjjefzkJ/vT\nrFlzXnzxAhtRSZJybOzYsRx00EH06NGDnj17csMNN3D11Vczc+bMrEuzGZUk/df773/Jxhv3olWr\ntZg5cwirrdYi65IkSVIC48aNY+edd/52u0WLFrRq1Ypp06ZlWFWBM6OSJADeeutzNttsL3784014\n+eVBrLhis6xLWiZnRpNzbZakZEp9ZjTGyHrrrcesWbNYuXip02OPPcapp57KK6+8wqqrrrpcNTgz\nKklKzccfw377rcy22/6CkSNPoFkzL5yRJCnPJk2axH333cfChQu54447APj444958803eeqpp5a7\nEU2TZ0ZzJE/PJSxVZpicGaajlHL817+ge3fo1QuuugpCTs41emY0OddmSUqmpjOETz37FHPnz22w\n12zZoiW77LBLgx2/LjwzKklK7J13Co3okUfCxRfnpxGVJKlUZd0o5olnRiWpTM2eDd26wcknwznn\nZF1N/XlmNDnXZklKZmlnCJu6tM6MOhQkSWVo9OiZbLrp0ZxxxqJcNqKSJCn/bEZzpJSfS5gXZpic\nGaYjyxyHDXuVHj0qOPjgrpx+emnfMVeSJDVdNqOSVEaGDJnMAQd0o1+/q7nrrhOzLkeSJJUxZ0Yl\nqUwMHvw8J5ywL7/85S1cd93BWZeTmDOjybk2S1IyzozW+Pk6r802o5JUBsaPhx49TuHcc/fmssv2\nybqcVNiMJufarHI3cNDtfDb369SOt0bLlTm13wmpHU+lz2a0xs/7aJemqJSeS5hXZpicGaajMXMc\nPbrw6JZHH/093bo1yktKUi58NvdrNtxix9SON2fqM6kdS/kRfC7acrMZlaQm7NFH4fjj4YEHYBcf\neyZJUqrK8axomryBUY54Nio5M0zODNPRGDkOHQp9+xYaUhtRSZJUamxGJakJOvvsx+nf/zNGjIDt\ntsu6GkmSpO+zGc0Rn++YnBkmZ4bpaMgc+/QZzA03nMjgwR/QpUuDvYwkSVIizoxKUhNyyCG38NBD\n1/DEE2PZc8+fZl2OJEnSUtmM5oizesmZYXJmmI6GyLFXr+sYOfL3jB07jl122Sj140uSJKXJZlSS\nci5GOOKIvzNq1G1MmDCebbddL+uSJEmSauXMaI44q5ecGSZnhulIK8cY4fzzYdq0Xkye/A8bUUmS\nlBueGZWknKqqgjPOgAkToLKyOW3arJ11SZIkSXVmM5ojzuolZ4bJmWE6kua4aBGcfDJMmwZPPgmt\nW6dTlyRJUmPxMl1Jypmvv17IEUd8xsyZMHKkjWg5CiEMDiH8K4Tw8jL2uSmEMCOEMCWEsFVj1idJ\nUl3YjOaIs3rJmWFyZpiO5c3xyy/ns/HGR/D885fx2GOw2mrp1qXcuBPoubQvhhD2BjaOMXYATgJu\nbazCJEmqK5tRScqJTz/9mvbtD2bRom+YMuV/adky64qUlRjjU8Any9ilN3BXcd/ngNYhhHUbozZJ\nkurKZjRHnNVLzgyTM8N01DfHDz+cS/v2vWnRYmVmzhzKGmus3DCFqaloC7xdbfsdwFstS5JKijcw\nkqQS98EHc+nYcS/WXntDXn11MCut5D/dqpOwxHasaacBAwZ8+3FFRYW/cJIk1VllZWWiES5/osmR\nyspKf0hIyAyTM8N01DXHTz6B3r1XZuut+zJy5NE0b+4FLaqTd4H1q22vV/zc91RvRiVJqo8lf4l5\n2WWX1ev7/alGkkrUhx/C7rvDz3++Ak8+eayNqOpjGHAsQAhhB+DTGOO/si1JkqTv8sxojng2Kjkz\nTM4M01Fbju+/D927w/77wxVXQFjygkuVtRDCvUBXYO0QwtvApcCKADHGQTHGx0MIe4cQZgJfAcdl\nV60kSTWzGZWkEvPWW9CtG/TpAxdemHU1KkUxxiPqsM+pjVGLJEnLy2u+csTnOyZnhsmZYTqWluO4\ncW/SqdP+nHjiNzaikiSpSbMZlaQS8cQT/6Rbt6707r0n5567UtblSJIkNSib0RxxVi85M0zODNOx\nZI4PPvgK++yzG8ceO4AhQ07JpihJkqRGZDMqSRm7555JHHLIHpxyynUMHnx81uVIkiQ1ilqb0RBC\nzxDC9BDCjBDCeTV8fY0QwiMhhMkhhFdCCH0apFI5q5cCM0zODNOxOMdnnoGTTnqAs8++hZtvrvWe\nNJIkSU3GMu+mG0JoBgwEulN4WPbEEMKwGONr1XbrD7wSY9w3hLA28HoI4S8xxoUNVrUkNQGVlXDo\nofDAA1fQs2fW1UiSJDWu2s6MbgfMjDHOjjEuAIYA+y2xTxWwevHj1YGPbUQbhrN6yZlhcmaYjm++\nqeDQQ2HIEGxEJUlSWaqtGW0LvF1t+53i56obCHQOIbwHTAHOSK88SWp6hg2DY46Bhx6C3XfPuhpJ\nkqRsLPMyXSDW4Rg9gZdijLuFENoDo0IIW8YYv1hyxz59+tCuXTsAWrduTZcuXb49y7J4fsrtpW9P\nnjyZM888s2TqyeP24s+VSj153F4yy6zrydv2eec9yh/+MJ++fd/i5z/373N9thd/PHv2bCRJUv6F\nGJfeb4YQdgAGxBh7FrcvAKpijNdU2+dR4KoY4z+K208C58UYX1jiWHFZr6XaVVZWfvvDmZaPGSZn\nhsuvX7+/cPvt53DffSNp0+Zjc0wohECMMWRdR565NqvcXXnDQDbcYsfUjjdn6jNceNapqR1Pypv6\nrs21nRl9AegQQmgHvAccBix5u8e3KNzg6B8hhHWBjsAbdS1AdecPrsmZYXJmuHyOPvo2hgy5jL//\n/Un22adz1uVIkiRlbpnNaIxxYQjhVGAE0Ay4I8b4WgihX/Hrg4DfAH8KIUwFAnBujPE/DVy3JOXG\ngQfexLBhv2XEiEq6dds463IkSZJKQq3PGY0xPhFj7Bhj3DjGeFXxc4OKjSgxxvdjjD1ijFvEGDeP\nMf61oYsuV9XnprR8zDA5M6yfE04Yy6OP3sT48eO+04iaoyRJKne1XaYrSVoOMcIll8A//lHB5MkT\n6dx5zaxLkiRJKik2oznirF5yZpicGdYuRjjnHBg9GsaNC/zgB99vRM1RkiSVO5tRSUpRVRWcdhpM\nnAhjxsBaa2VdkSRJUmmqdWZUpcMZs+TMMDkzXLr58xdx9NEfMnVq4azoshpRc5QkSeXOM6OSlIJ5\n8xayySa/YP78lZkx4w5WXTXriiRJkkqbzWiOOGOWnBkmZ4bf98UX8+nU6QgWLJjL9OkP1qkRNUdJ\nklTuvExXkhL45JOvad/+AGKsYtash1lrrVWyLkmSJCkXbEZzxBmz5MwwOTP8r//8Zz7t2+9Dy5ar\nM2vWfbRqtVKdv9ccJUlSubMZlaTl8NlnsO++K9Kly8n8859/YZVVVsy6JEmSpFyxGc0RZ8ySM8Pk\nzBD+8x/o3h222iowevTBtGjRrN7HMEdJklTubEYlqR7+/W/YbTeoqICbb4YV/FdUkiRpufhjVI44\nY5acGSZXzhm+9x507Qr77w/XXgshLP+xyjlHSZIksBmVpDqZMOEtNt54Dw4//AsuuyxZIypJkiSb\n0Vxxxiw5M0yuHDMcM+YNdt21Kz179uLSS1ulcsxyzFGSJKk6m1FJWobHHpvOnnt25fDDz+fBB8/M\nuhxJkqQmw2Y0R5wxS84MkyunDO+/fyq9e+/O8cdfwV/+0i/VY5dTjpIkSTWxGZWkGkycCMcfP5rT\nT7+BP/7xF1mXI0mS1OQ0z7oA1Z0zZsmZYXLlkOHTT8OBB8I99/yS3r0b5jXKIUdJkqRlsRmVpGrG\njIHDDoN77oE998y6GkmSpKbLy3RzxBmz5Mwwuaac4eOPFxrRoUMbvhFtyjlKkiTVhc2oJAEXXvgY\nxxwzk2HDoGvXrKuRJElq+mxGc8QZs+TMMLmmmOHpp/+Nq6/uy003fc6OOzbOazbFHCVJkurDmVFJ\nZa1v37v4058u4P77R3HggZtnXY4kSVLZ8MxojjhjlpwZJteUMjz88D9w110X8eijYxq9EW1KOUqS\nJC0Pz4xKKktnnfUSDzxwDaNGVbLbbu2zLkeSJKns2IzmiDNmyZlhcnnPMEa44gp47LGfMWXKFDp3\nXj2TOvKeoyRJUlI2o5LKRoxw4YUwbBiMHw8//GE2jagkSZKcGc0VZ8ySM8Pk8pphjHDWWfDEE1BZ\nCT/8Ybb15DVHSZKktHhmVFKTt3BhFX36vMfMmesxZgysuWbWFUmSJMlmNEecMUvODJPLW4bffLOI\nTTc9gc8++5I33rifVq2yrqggbzlKkiSlzWZUUpM1d+4COnU6hq+++ojp0/9eMo2oJEmSnBnNFWfM\nkjPD5PKS4eeff0P79ofyzTdfMmvWo6yzzqpZl/QdeclRkiSpodiMSmpyvvyyivbtD6BZs2bMmvUg\nrVuvnHVJkiRJWoLNaI44Y5acGSZX6hl+8QXsu+8KbL75acycOYTVVmuRdUk1KvUcJUmSGprNqKQm\n49NPoUcP6NABRo/ei5VXdixekiSpVNmM5ogzZsmZYXKlmuHHH0O3brDttjBoEKxQ4v+6lWqOkiRJ\njaXEf1yTpNp98EGkogL22ANuvBFCyLoiSZIk1cZmNEecMUvODJMrtQwnTnyXn/ykK3vv/SFXXZWf\nRrTUcpQkSWpsNqOScuvpp+ew005dqajoxTXXrJObRlSSJEk2o7nijFlyZphcqWQ4evRMKip2Zf/9\nz+Dxx8/Lupx6K5UcJUmSsmIzKil3hg17lR49KjjqqIu4//7Tsi5HkiRJy8FmNEecMUvODJPLOsNJ\nk+CYY56nX7+rueuuEzOtJYmsc5QkScqaD+GTlBvPPQe9e8PgwX046KCsq5EkSVISnhnNEWfMkjPD\n5LLKcPx42HdfGDyYJtGI+l5UEiGEniGE6SGEGSGE7w1NhxDWCCE8EkKYHEJ4JYTQJ4MyJUlaJptR\nSSVv9Gg4+GC4917o1SvraqRshRCaAQOBnkBn4IgQwiZL7NYfeCXG2AWoAK4PIXg1lCSppNiM5ogz\nZsmZYXKN0MYFAAAgAElEQVSNneGAAU9wyCEv8sAD0K1bo750g/K9qAS2A2bGGGfHGBcAQ4D9ltin\nCli9+PHqwMcxxoWNWKMkSbWyGZVUss4550Euv7wP11+/kF12yboaqWS0Bd6utv1O8XPVDQQ6hxDe\nA6YAZzRSbZIk1ZmX7ORIZWWlZ1MSMsPkGivDU075K4MG/Yq//nU4hx++VYO/XmPzvagEYh326Qm8\nFGPcLYTQHhgVQtgyxvjFkjsOGDDg248rKip8X0qS6qyysjLRfTBsRiWVnD59BvPnP1/MQw+Npnfv\nTbMuRyo17wLrV9ten8LZ0er6AFcBxBhnhRDeBDoCLyx5sOrNqCRJ9bHkLzEvu+yyen2/l+nmiL+t\nTs4Mk2voDC++eAZ/+ctveOKJsU26EfW9qAReADqEENqFEFoAhwHDltjnLaA7QAhhXQqN6BuNWqUk\nSbXwzKikknHttfDXv3bg5ZensckmLbMuRypJMcaFIYRTgRFAM+COGONrIYR+xa8PAn4D/CmEMBUI\nwLkxxv9kVrQkSTXwzGiO+FzC5MwwuYbIMEYYMKDwDNHx4ymLRtT3opKIMT4RY+wYY9w4xrj4ctxB\nxUaUGOP7McYeMcYtYoybxxj/mm3FkiR9n2dGJWUqRjj/fHjiCRg3DtZdN+uKJEmS1BhsRnPEGbPk\nzDC5NDNctChy3HFv8Oqr7Rk7Ftq0Se3QJc/3oiRJKnc2o5IysWBBFZtvfjL/+tdbzJ49nDXWyLoi\nSZIkNSZnRnPEGbPkzDC5NDL8+uuFdOzYhw8+eJ1XX72/LBtR34uSJKnceWZUUqP66qsFdOx4FF9/\n/SmzZj1BmzZN/2ZFkiRJ+j6b0Rxxxiw5M0wuSYbz5kU23vhwYAGzZg1jjTVWTq2uvPG9KEmSyp2X\n6UpqFF99Bb17Bzp1Op2ZM4eWdSMqSZIkm9FcccYsOTNMbnky/Pxz2GsvaNsWRo/uyqqrtki/sJzx\nvShJksqdzaikBvXJJ7DHHrDppjB4MDRrlnVFkiRJKgU2oznijFlyZphcfTL8978ju+8OO+0Ev/89\nrOC/ON/yvShJksqdPxpKahBTpnxAu3Y78vOfz+G3v4UQsq5IkiRJpcRmNEecMUvODJOrS4bPPfcO\n227blZ122oeBAze0Ea2B70VJklTubEYlpWrcuDfZeedd6dnzJEaPvijrciRJklSibEZzxBmz5Mww\nuWVlOHz4P+nWrSsHHfQrhg37VeMVlUO+FyVJUrmzGZWUipdfhiOPfJ1jjx3AkCH9sy5HkiRJJc5m\nNEecMUvODJOrKcOXXio8vuX3v9+XwYOPb/yicsj3oiRJKnfNsy5AUr498wzstx/88Y+w//5ZVyNJ\nkqS8qPXMaAihZwhheghhRgjhvKXsUxFCmBRCeCWEUJl6lQKcMUuDGSZXPcPKykIjevfdNqL15XtR\nkiSVu2WeGQ0hNAMGAt2Bd4GJIYRhMcbXqu3TGrgF6BFjfCeEsHZDFiypNFx11SiuuSbw4IPd2X33\nrKuRJElS3tR2ZnQ7YGaMcXaMcQEwBNhviX2OBB6IMb4DEGP8KP0yBc6YpcEMk6usrOSiix7hwguP\n4qqrVrERXU6+FyVJUrmrbWa0LfB2te13gO2X2KcDsGIIYSzQCvhdjPHP6ZUoqZQMHFjJQw/dyp13\nPsYvfrFt1uVIkiQpp2prRmMdjrEi8DOgG9ASeCaE8GyMccaSO/bp04d27doB0Lp1a7p06fLt3NTi\nswRuL3t7sVKpx+3y2r733nd46KFBXHzxlWy44VcsVir15W17sVKpp9S3F388e/ZsJElS/oUYl95v\nhhB2AAbEGHsWty8AqmKM11Tb5zxglRjjgOL27cDwGOPQJY4Vl/VakkrbNde8x4UX7sLDDz/CPvt0\nzrociRACMcaQdR155tqscnflDQPZcIsdUzvenKnPcOFZp6Z2PClv6rs21zYz+gLQIYTQLoTQAjgM\nGLbEPn8Hdg4hNAshtKRwGe+r9SladbPk2RTVnxkunxtvhFtv/TFTp77Kaqv9O+tymgTfi5Ikqdwt\n8zLdGOPCEMKpwAigGXBHjPG1EEK/4tcHxRinhxCGA1OBKuC2GKPNqNREXHUVDB4M48fDBhusxL/t\nRSVJkpSCZV6mm+oLeSmQlCsxwiWXwAMPwOjR8OMfZ12R9F1eppuca7PKnZfpSumq79pc2w2MJJWh\nqqpI376vMWlSZyor4Qc/yLoiSZIkNTW1zYyqhDhjlpwZ1m7hwiq23PI0hg49iSefjN9rRM0wHeYo\nSZLKnWdGJX1r/vxFbLZZPz744DVeffVx2rTxCkhJkiQ1DJvRHFn8zD0tPzNcunnzFrLJJr/gs8/e\nZ8aMEay77mo17meG6TBHSZJU7mxGJfHNN9Cx43F8/fV/mDXrMdZaa5WsS5IkSVIT58xojjhjlpwZ\nft+8eXDAAbDxxmcwa9bDtTaiZpgOc5QkSeXOZlQqY19+Cb16wZprwsiR29Cq1UpZlyRJkqQyYTOa\nI86YJWeG//XZZ9CzJ2y0Edx9NzSv40X7ZpgOc5QkSeXOZlQqQx99VEX37tClC9x2GzRrlnVFkiRJ\nKjc2oznijFlyZgivvvohG2ywA5tu+go33wwr1PNfATNMhzlKkqRyZzMqlZFJk95nq60q2GabHgwe\nvCnBx4hKkiQpIzajOeKMWXLlnOGECW+x/fa7UlFxFOPH/4YVVli+TrScM0yTOUqSpHJnMyqVgSef\nnMWuu3Zln336M2LEr7MuR5IkSbIZzRNnzJIrxwxfew0OP/x9Dj/8Ah588MzExyvHDBuCOUqSpHJX\nx4c5SMqjKVMKj2/57W935phjds66HEmSJOlbNqM54oxZcuWU4cSJsM8+MHAgHHJIesctpwwbkjlK\nkqRyZzMqNUFPPw0HHgh33AH77pt1NZIkSdL3OTOaI86YJVcOGV5//Vh69bqfe+5pmEa0HDJsDOYo\nSZLKnc2o1IRcfvlwzjnnMC67bB322CPraiRJkqSl8zLdHHHGLLmmnOH55z/MtdeexKBBf+fEE3ds\nsNdpyhk2JnOUJEnlzmZUagJOP/1v3HLLGfz5z09w1FFbZ12OJEmSVCsv080RZ8ySa4oZ3nzzJ9x6\n66Xcd9/IRmlEm2KGWTBHSZJU7jwzKuXYLbfA//3fmkye/AqbbupfZ0mSJOWHP73miDNmyTWlDK+/\nvvAM0cpK+MlPGu+vclPKMEvmKEmSyp3NqJRDV1wBd98N48fD+utnXY0kSZJUf86M5ogzZsnlPcOq\nqkjfvi8xZEh2jWjeMywV5ihJksqdZ0alnKiqimyzza+YPn0cb7zxHD/8oX99JUmSlF/+NJsjzpgl\nl9cMFy6sYsst+zNnzktMmzY600Y0rxmWGnOUJEnlzmZUKnHffLOITTc9gQ8/nMnrr4+ibdvVsy5J\nkiRJSsyZ0Rxxxiy5vGW4YAF07tyfTz55m5kzh5dEI5q3DEuVOUqSpHJnMyqVqG++gYMPhvXXP41Z\nsx5lnXVWzbokSSUihNAzhDA9hDAjhHDeUvapCCFMCiG8EkKobOQSJUmqlZfp5ogzZsnlJcO5c+GA\nA2D11WHkyE1p0SLriv4rLxmWOnPU8gohNAMGAt2Bd4GJIYRhMcbXqu3TGrgF6BFjfCeEsHY21UqS\ntHSeGZVKzBdfQK9e8IMfwL33UlKNqKSSsB0wM8Y4O8a4ABgC7LfEPkcCD8QY3wGIMX7UyDVKklQr\nm9EcccYsuVLP8KOPFtKjB3ToAHfdBc1L8NqFUs8wL8xRCbQF3q62/U7xc9V1ANYKIYwNIbwQQjim\n0aqTJKmOSvBHXak8zZjxMV267EWvXr9j0KAdCSHriiSVqFiHfVYEfgZ0A1oCz4QQno0xzlhyxwED\nBnz7cUVFhZeQS5LqrLKyMtEv2EOMdVnTkgshxMZ6LSlvXnnl32yzTXe23LInzzxzDSusYCcq1SaE\nQIyx7P6yhBB2AAbEGHsWty8AqmKM11Tb5zxglRjjgOL27cDwGOPQJY7l2qyyduUNA9lwix1TO96c\nqc9w4VmnpnY8KW/quzZ7ma6UsYkT32Xrrbuy444H2ohKqosXgA4hhHYhhBbAYcCwJfb5O7BzCKFZ\nCKElsD3waiPXKUnSMtmM5ogzZsmVWoZPPz2HnXbqSrdufRg7dkAuGtFSyzCvzFHLK8a4EDgVGEGh\nwfxbjPG1EEK/EEK/4j7TgeHAVOA54LYYo82oJKmkODMqZWTGDDjkkM856KCzGTLk5KzLkZQjMcYn\ngCeW+NygJbavA65rzLokSaoPm9Ec8aYSyZVKhtOmwZ57whVXbE7fvptnXU69lEqGeWeOkiSp3NmM\nSo1s0iTYe2+4/no48sisq5EkSZKy4cxojjhjllzWGT73HPTsCQMH5rcRzTrDpsIcJUlSubMZlRrJ\nwIFP0737IAYPhoMOyroaSZIkKVs2oznijFlyWWV47bVPcvrpB3LxxT+hV69MSkiN78N0mKMkSSp3\nzoxKDWzAgMe5/PI+/O53QznttF2zLkeSJEkqCZ4ZzRFnzJJr7AzPOedBLr/8OG6//ZEm04j6PkyH\nOUqSpHLnmVGpgdxxx1xuvPFy/vrX4Rx++FZZlyNJkiSVFJvRHHHGLLnGyvD222HAgJZMmvQSm23W\ntC5A8H2YDnOUJEnlzmZUStnNN8N118HYsdChQ9NqRCVJkqS0+JNyjjhjllxDZ3jttXDjjTBuHHTo\n0KAvlRnfh+kwR0mSVO48MyqloKoqcsIJE5gw4eeMHw9t22ZdkSRJklTabEZzxBmz5Boiw6qqyE47\n/ZrJkx9h+vSJtG27SuqvUUp8H6bDHCVJUrmzGZUSWLQo8rOfncmMGU8xdWol7do17UZUkiRJSosz\noznijFlyaWa4YEEVm27ajzfeeJ7XXhvDT3+6dmrHLmW+D9NhjpIkqdx5ZlRaDgsXwpZbnssHH7zO\nP/85kh/9qFXWJUmSJEm5YjOaI86YJZdGhvPnw5FHwjrr9Oepp9alTZuWyQvLEd+H6TBHSZJU7mxG\npXr4+ms4+GBo3hxGjtyIlVbKuiJJkiQpn5wZzRFnzJJLkuFXX8G++8Jqq8H991O2jajvw3SYoyRJ\nKnc2o1IdfPTRfPbaC9ZbD+65B1ZcMeuKJEmSpHyzGc0RZ8ySW54MZ8/+lHbtutKq1RPccQc0a5Z+\nXXni+zAd5ihJksqdzai0DNOnf0Tnzrvz059uzyOP9GQF/8ZIkiRJqfBH6xxxxiy5+mQ4ZcoHdOmy\nG1tt1ZMXXriBFVYIDVdYjvg+TIc5SpKkcmczKtXg+effYdttu7Lzzofy9NNX2ohKkiRJKbMZzRFn\nzJKrS4ZvvAEHHbSQ/fY7i9GjLyYEG9HqfB+mwxwlSVK5sxmVqnn9daiogF//uh33339y1uVIkiRJ\nTZbNaI44Y5bcsjJ8+WXYbTe4/HL4n/9pvJryxvdhOsxRkiSVu+ZZFyCVgpdegr33hhtvhMMPz7oa\nSZIkqenzzGiOOGOWXE0Z3nbbs+y669X84Q82onXh+zAd5ihJksqdzajK2o03jqNfv30599wt2H//\nrKuRJEmSyketzWgIoWcIYXoIYUYI4bxl7LdtCGFhCOHAdEvUYs6YJVc9w//935H88pcHc+21Q7jk\nkr2zKypnfB+mwxwlSVK5W2YzGkJoBgwEegKdgSNCCJssZb9rgOGAz8FQybvooke46KKjGTjwIc4+\nu1vW5UiSJEllp7Yzo9sBM2OMs2OMC4AhwH417HcaMBT4MOX6VI0zZslVVFRw770Lufbaq7nzzsc4\n5ZSdsy4pd3wfpsMcJUlSuavtbrptgberbb8DbF99hxBCWwoN6u7AtkBMs0ApTXffDeef35yJE59m\nyy09iS9JkiRlpbYzo3VpLG8Ezo8xRgqX6PoTfgNxxiyZQYPgV7+qZMwYbEQT8H2YDnOUJEnlrrYz\no+8C61fbXp/C2dHqtgaGhBAA1gb2CiEsiDEOW/Jgffr0oV27dgC0bt2aLl26fHup2uIfzNxe+vbk\nyZNLqp48bZ96aiVDhxaeI9qpU/b1uO22f5/rv73449mzZyNJkvIvFE5oLuWLITQHXge6Ae8BzwNH\nxBhfW8r+dwKPxBgfrOFrcVmvJTWUvn1HM25cN8aMCWywQdbVSEpLCIEYo5c5JODarHJ35Q0D2XCL\nHVM73pypz3DhWaemdjwpb+q7Ni/zzGiMcWEI4VRgBNAMuCPG+FoIoV/x64MSVSs1oKqqSEXFAJ57\n7m9MnvwsG2zQOuuSJEmSJBXV+pzRGOMTMcaOMcaNY4xXFT83qKZGNMZ4XE1nRZWO6peqadmqqiLb\nb38eEyc+xIsvjmOTTQqNqBkmZ4bpMEdJklTuapsZlXJn4cIqttrqdN544zleeaWS9u3XyrokSZIk\nSUuwGc2RxTfz0NItWgTbbvsb5syZxPTpo1l//TW+83UzTM4M02GOkiSp3NV6ma6UFwsWwNFHw6qr\n9mPGjBHfa0QlSZIklQ6b0RxxxmzpvvkGDjsMPv8cRo36Ieuuu1qN+5lhcmaYDnOUJEnlzmZUuTdv\nHhxwAIQADz0Eq6ySdUWSJEmSamMzmiPOmH3fhx/OY++9I2uuCX/7G7Rosez9zTA5M0yHOUqSpHJn\nM6rcevvtz9l44x7EOIS774bm3o5LkiRJyg2b0Rxxxuy/Zs36D5tssgcbbLAZo0cfRrNmdfs+M0zO\nDNNhjpIkqdzZjCp3Xn31QzbbbHc6ddqZKVNuoXlz38aSJElS3vhTfI44YwaTJr3PVlt1Zdtt9+X5\n569jhRVCvb7fDJMzw3SYoyRJKnc2o8qNOXPgwAObs/feZzB+/G/q3YhKkiRJKh02ozlSzjNmM2dC\n165w1lnr8NBD/Zb7OOWcYVrMMB3mKEmSyp3NqErea69BRQX8+tdw+ulZVyNJkiQpDT4MI0fKccZs\nyhTo2ROuvRaOOSb58coxw7SZYTrMUZIklTvPjKpk3XXXC+y003ncdFM6jagkSZKk0mEzmiPlNGN2\n660TOO64vTnzzJ045JD0jltOGTYUM0yHOUqSpHJnM6qSc/31Y+nff3+uvPLPXHnlflmXI0mSJKkB\n2IzmSDnMmF1++XDOOedQfvvb+7jggh6pH78cMmxoZpgOc1QSIYSeIYTpIYQZIYTzlrHftiGEhSGE\nAxuzPkmS6sJmVCXjwQcjV175O/7wh79z5pkVWZcjSSUphNAMGAj0BDoDR4QQNlnKftcAwwEfzCxJ\nKjk2oznSlGfM7r0X+vcPTJjwOCedtFODvU5TzrCxmGE6zFEJbAfMjDHOjjEuAIYANc00nAYMBT5s\nzOIkSaorm1Fl7s474eyzYdQo2Hprf3kvSbVoC7xdbfud4ue+FUJoS6FBvbX4qdg4pUmSVHc+ZzRH\nmuKM2e9/D1dfDWPHwk9/2vCv1xQzbGxmmA5zVAJ1aSxvBM6PMcYQQsDLdCVJJchmVJk54YTHePLJ\nnowb14yNNsq6GknKjXeB9attr0/h7Gh1WwNDCn0oawN7hRAWxBiHLXmwAQMGfPtxRUWFvyiRJNVZ\nZWVlotGjEGPjXLkTQoiN9VpNVWVlZZP5IaF79ysZP/5PPP/8P+jS5QeN9rpNKcOsmGE6zDG5EAIx\nxrI74xdCaA68DnQD3gOeB46IMb62lP3vBB6JMT5Yw9dcm1XWrrxhIBtusWNqx5sz9RkuPOvU1I4n\n5U1912bPjKpRVVVFdtnlIl588WEmThzPlls2XiMqSU1BjHFhCOFUYATQDLgjxvhaCKFf8euDMi1Q\nkqQ68syoGk1VVWSbbX7F9OljeOmlUXTqtE7WJUnKsXI9M5om12aVO8+MSunyzKhKUlUV7LzzDfzz\nn//g1VfH0q7dmlmXJEmSJClDPtolR/L6XMKFC+G44yCE43n99VGZNqJ5zbCUmGE6zFGSJJU7z4yq\nQS1YAEcfDZ98AqNGtaZly6wrkiRJklQKbEZzJG933vzmGzj00MIlusOGwcorZ11R/jIsRWaYDnOU\nJEnlzst01SA+/nge++yzgBYt4IEHSqMRlSRJklQ6bEZzJC8zZu+//yUbb9yLzz+/nXvvhRYtsq7o\nv/KSYSkzw3SYoyRJKnc2o0rVnDmf0bFjD9Zd9yc8/fRJNPdCcEmSJEk1sBnNkVKfMZsx42M6d+7G\nRhv9jGnT/siKKzbLuqTvKfUM88AM02GOkiSp3NmMKhXTpn3I5pvvxmab7c6kSTfRrJlvLUmSJElL\nZ8eQI6U6Y/bOO3DAAavQo8cZPPPMNaywQsi6pKUq1QzzxAzTYY6SJKnc2YwqkdmzoWtXOPHE1fj7\n3/uWdCMqSZIkqXTYjOZIqc2YzZhRaETPOgvOOSfrauqm1DLMIzNMhzlKkqRy571OtVymTYM994TL\nL4e+fbOuRpIkSVLeeGY0R0plxmzIkMlsu+1JXHttzF0jWioZ5pkZpsMcJUlSubMZVb0MHvw8Rx7Z\ng1NO2ZOjjnI+VJIkSdLysRnNkaxnzAYOfJoTTtiHiy++g+uuOzjTWpZX1hk2BWaYDnOUJEnlzmZU\ndXLttU9y+ukHcvXV93DZZftkXY4kSZKknLMZzZGsZswefRQuueR2fve7oZx77h6Z1JAW5/SSM8N0\nmKMkSSp33k1XyzR0KPTvD+PH38t222VdjSRJkqSmwjOjOdLYM2Z/+QucdhqMGEGTaUSd00vODNNh\njpIkqdzZjKpGt98O558PTz4JXbpkXY0kSZKkpsZmNEcaa8bsxBMf5vLLv2bsWOjcuVFestE4p5ec\nGabDHCVJUrlzZlTf0avXdYwc+XvGj9+GDh3Wy7ocSZIkSU2UzWiONOSMWVVVpFu33/CPf9zDhAnj\n2XbbptmIOqeXnBmmwxwlSVK5sxkVVVWRnXb6NZMnP8KLL45j881/mHVJkiRJkpo4Z0ZzpCFmzKqq\noHv3O5g6dQRTp1Y2+UbUOb3kzDAd5ihJksqdzWgZW7QI+vWDefOO5rXXxvDTn66ddUmSJEmSyoSX\n6eZImjNmCxdCnz7w7rswatTKrLbayqkdu5Q5p5ecGabDHCVJUrmzGS1D8+fDkUfCV1/B44/DKqtk\nXZEkSZKkcuNlujmSxozZp59+Te/eX7FwITz8cPk1os7pJWeG6TBHSZJU7mxGy8iHH86lffv9eP/9\nm7n/flhppawrkiRJklSubEZzJMmM2bvvfkGHDnuz5po/5Pnnz2bFFdOrK0+c00vODNNhjpIkqdzZ\njJaB2bM/pVOnPWnbthPTp9/JSis5KixJkiQpWzajObI8M2YzZnxC586706HD9rz88q00b17e/8ud\n00vODNNhjpIkqdyVd2fSxL3/Puy336p0734mL7xwAyusELIuSZIkSZIAm9Fcqc+M2dtvQ9eucNRR\nLRg27Fgb0SLn9JIzw3SYoyRJKncODzZBb7wB3bvDaafBWWdlXY0kSZIkfZ9nRnOkLjNmr78OFRVw\n7rk2ojVxTi85M0yHOUqSpHJnM9qEPPjgK3TpchiXXVbFySdnXY0kSZIkLZ3NaI4sa8bsnnte4pBD\nunPiiftz3HH+b10a5/SSM8N0mKMkSSp3di1NwG23Pcsxx/TknHN+z003HZF1OZIkSZJUK5vRHKlp\nxux3vxtPv369GTDgT1x99YGNX1TOOKeXnBmmwxwlSVK58266OTZiBJx//t/4v/+7l1/9qlvW5UiS\nJElSndXpzGgIoWcIYXoIYUYI4bwavn5UCGFKCGFqCOEfIYQt0i9V1WfMhg2DY46B0aNvsRGtB+f0\nkjPDdJijJEkqd7U2oyGEZsBAoCfQGTgihLDJEru9AewaY9wC+A3wx7QL1X/ddx+cdBI8/jj8/OdZ\nVyNJkiRJ9VeXM6PbATNjjLNjjAuAIcB+1XeIMT4TY/ysuPkcsF66ZQoKM2Z33w1nngkjR8I222Rd\nUf44p5ecGabDHCVJUrmry8xoW+DtatvvANsvY/++wONJilLNrrtuHJMmbcWYMWvQqVPW1UiSJEnS\n8qtLMxrrerAQwm7A8YAXj6bswANvYvjwwYwadQydOq2RdTm55ZxecmaYDnOUJEnlri7N6LvA+tW2\n16dwdvQ7ijctug3oGWP8pKYD9enTh3bt2gHQunVrunTp8u0PZIsvWXP7+9s9e17D6NE3ceON17Pb\nbj/JvB633Xbb7Sy2F388e/ZsJElS/oUYl33iM4TQHHgd6Aa8BzwPHBFjfK3aPhsAY4CjY4zPLuU4\nsbbX0ndVVUUqKgbw3HP3MWHCaL74Ysa3P5xp+VRWVpphQmaYDnNMLoRAjDFkXUeeuTar3F15w0A2\n3GLH1I43Z+ozXHjWqakdT8qb+q7Ntd7AKMa4EDgVGAG8CvwtxvhaCKFfCKFfcbdLgDWBW0MIk0II\nzy9H7aomRthvv6FMnPgwL700jq23bpt1SZIkSZKUmlrPjKb2Qv72tc6qquC00+D55xdx331fsNFG\nrbMuSZJKjmdGk3NtVrnzzKiUrvquzXWZGVUjWrQITjwRZsyAJ59sxuqr24hKkiRJanrq8pxRNZIF\nC+Doo2HOHBg+HFZf/btfr34TDy0fM0zODNNhjpIkqdx5ZrREfPHFfI444itiXJNHH4VVVsm6IkmS\nJElqODajJeCTT76mY8eDWXPNzXn55ato0aLm/bzzZnJmmJwZpsMcJUnS/7d370F2lGUex78PCRAi\n18gWKpeVS0hBsRK1NBYIkxiIA5HF7JIYYhYSAwaD4AqCUVgWgS1kCxaKBVIEwiLu4sjVjJgQEshl\nkXBbSbgkY4gSBMGISMBFcBN4949zkGGczJxJ95w+M/39VKVyzpyenqd+c868/XT32112nqZbsPXr\nX2fffT/H4ME7sGLFBZttRCVJai8imiOiLSKejohvdvL6FyNiZUQ8HhE/rd4PXJKkhmEzWqDnnnuN\n/fdvZtdd92TNmv9ku+227nJ555hlZ4bZmWE+zFFZRMQA4CqgGTgQOD4iDuiw2C+Bw1NKHwEuBGbX\nt2eqGIoAAA+LSURBVEpJkrpmM1qQZ5/9AwcccCR77fU3rFo1h222GVB0SZKkvuOTwNqU0rqU0kag\nBTi2/QIppeUppVerTx8C9qhzjZIkdclmtAC//S0cc8z7GDXqTFauvJqBA2v7NTjHLDszzM4M82GO\nymh34Ll2z5+vfm1zpgHzerUiSZJ6yGa0zl54AZqaYNy4rWhtncBWW3m/dklSj6VaF4yIUcCXgL+Y\nVypJUpG8mm4dPfssjB4NJ50EM2f2/PuXLFni0ZSMzDA7M8yHOSqjXwN7tnu+J5Wjo+9RvWjRdUBz\nSumVzlZ0/vnn//nxyJEjfV9Kkmq2ZMmSTNfBsBmtk7Vr4Ygj4Iwz4PTTi65GktTHPQoMjYgPAy8A\nXwCOb79AROwF3AFMTimt3dyK2jejkiT1RMedmN/5znd69P2eplsHP/lJGwcddBRnn/1/mRpR91Zn\nZ4bZmWE+zFFZpJQ2AV8FFgCrgB+mlFZHxPSImF5d7DxgF2BWRDwWEQ8XVK4kSZ3yyGgvu/XWx5k4\nsZlp0y5mxgxvIipJykdKaT4wv8PXrm33+CTgpHrXJUlSrTwy2ou+971HmThxDKeffjmzZ5+YeX3e\nlzA7M8zODPNhjpIkqexsRnvJrFkPMHXq0cyceS2XX/6FosuRJEmSpIbiabq94L774BvfmMdFF93E\nt7/dnNt6nWOWnRlmZ4b5MEdJklR2NqM5mzcPpkyBefMuoqmp6GokSZIkqTF5mm6O7rwTpk6F1lZ6\npRF1jll2ZpidGebDHCVJUtnZjOakpQVmzID58+FTnyq6GkmSJElqbJ6mm4MZM27ljjs+zaJFH+Sg\ng3rv5zjHLDszzM4M82GOkiSp7DwymtHEibOYPfsMbrzxtV5tRCVJkiSpP7EZzeDYYy/n9tv/lUWL\nltDcPKzXf55zzLIzw+zMMB/mKEmSys7TdLfQEUf8C8uW3cj99y9lxIi9ii5HkiRJkvoUm9EeSgkm\nTVrA/fffzCOPLOPggz9Yt5/tHLPszDA7M8yHOUqSpLKzGe2BlOCMM6CtbQyrVy9n7713LLokSZIk\nSeqTnDNao7ffhq98BR58EBYvjkIaUeeYZWeG2ZlhPsxRkiSVnUdGa7BpE0ybBuvWwT33wA47FF2R\nJEmSJPVtNqPd+OMfNzJx4su8+eYHmD8fBg8urhbnmGVnhtmZYT7MUZIklZ3NaBdee+1PDBs2kUGD\nPsTq1VczaFDRFUmSJElS/+Cc0c14+eU32GefzzNgwACeeOLyhmhEnWOWnRlmZ4b5MEdJklR2NqOd\nePHF/2W//cayww5DWLu2he2336bokiRJkiSpX7EZ7eA3v3mTYcM+y2677cOaNTcxaFDjnMnsHLPs\nzDA7M8yHOUqSpLKzGW3n5Zfh6KO3palpJk89NZuttx5QdEmSJEmS1C/ZjFatXw8jR8KYMUFr6zEM\nGNB40TjHLDszzM4M82GOkiSp7Bqv4yrA889DUxOMHw8XXwwRRVckSZIkSf1b6ZvRZ55JNDXBtGlw\n3nmN3Yg6xyw7M8zODPNhjpIkqexK3YwuXPg0BxzQxIwZr3PWWUVXI0mSJEnlUdpmtLV1Fc3No5gw\nYTJnnvm+osupiXPMsjPD7MwwH+YoSZLKrpTNaEvLCsaNG8306d/lppu+XHQ5kiRJklQ6pWtGb7jh\nYSZN+ixf//q/c801k4sup0ecY5adGWZnhvkwR0mSVHalakaXLYPTT7+fc8+9nksvPa7ociRJkiSp\ntErTjC5aBMcdB3PnnsEFFxxTdDlbxDlm2ZlhdmaYD3OUJEllV4pm9K67YNIkuP12GD266GokSZIk\nSf2+Gb3ttso9RO+6Cw47rOhqsnGOWXZmmJ0Z5sMcJUlS2Q0suoDedNppt9LSMpyFC4cyfHjR1UiS\nJEmS3tFvj4xOmXID11zzj1x//Z/6TSPqHLPszDA7M8yHOUqSpLLrl0dGx4+/mjvvvIT58xczZsz+\nRZcjSZIkSeqg3zWjY8deyj33XMPixUs57LC9iy4nV84xy84MszPDfJijJEkqu37TjKYEJ5/8EIsW\nXc8DDyzjE5/Yo+iSJEmSJEmb0S/mjKYEM2fCww+PoK3tZ/22EXWOWXZmmJ0Z5sMcJUlS2fX5I6Nv\nvw1f+xosXw6LF8P73z+46JIkSZIkSd3o083oW2/BKafAqlVw772w005FV9S7nGOWnRlmZ4b5MEdJ\nklR2fbYZffPNTUyc+GteffWvWbAAtt++6IokSZIkSbXqk3NGX399I/vtN4lHHvkn5s0rTyPqHLPs\nzDA7M8yHOUqSpLLrc0dGN2x4k2HDJrDVVsHPf/59ttuu6IokSZIkST3Vp46MvvTSH9l332PZdttB\n/OIXt7HjjtsWXVJdOccsOzPMzgzzYY6SJKns+kw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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933b981390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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NM2PGDI0bN05VVVU7NKLkCAAAkq6p03QBAAGUlpbqb3/7m/bYY4+oSwEAAMgr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\nwlK+D78haVdEdEq6UNJttptdWK5ufqJGhn1iTBm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9336213f50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Ms2vLGd5+e10jev31Ld+ItuUcJUmSmsJmVJKA44+/jR/+cDq33AK9exddjSRJ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\nO5tRNckZZ9zNMcfswRlnXMnJJw8suhx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933b843a10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Q1NTU6JxzzlG7du109913R10OAABA3qMZjRHmmAVHhsGR4Rdt3LhRgwYN0rp1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\nV/JwqxpXScRxad6HknSNpHWSXiyyuBaRJsO5ktZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93364daed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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1qiFHSZKkxXFmVJIauOUWOPJIGDGCFmtEJUmSZDNaKq4xy84Ms2vLGf7tb3DC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\n/5PLLtuHIUOu5YQTdi+0liVVdIZtgRn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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9336420a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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f4O43px+7M92Iyt2XuXuZux/m7oe6+1+zXXRS1Z43hZYhw+DIsHlmzJihcePG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\n1LNnTx199NG644471KpVsv/JmacXHBm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9335eac750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "means_pt = [compute_sum_of_charges(data_full[mask], name, bins, use_pt=True) for mask, name, bins in \\\n", " zip([data_full.signB > -100, \n", " (data_full.IPs > 3) * ((abs(data_full.diff_eta) > 0.6) | (abs(data_full.diff_phi) > 0.825)), \n", " (abs(data_full.diff_eta) < 0.6) & (abs(data_full.diff_phi) < 0.825) * (data_full.IPs < 3)], \n", " ['full_pt', 'OS_pt', 'SS_pt'], [21, 21, 21])]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "means_pt = pandas.concat(means_pt)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>$B^+$</th>\n", " <th>$B^+$, with signal part</th>\n", " <th>$B^-$</th>\n", " <th>$B^-$, with signal part</th>\n", " <th>ROC AUC</th>\n", " <th>ROC AUC, with signal part</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.089463e-08</td>\n", " <td>-1</td>\n", " <td>-3.419350e-08</td>\n", " <td>1</td>\n", " <td>0.500021</td>\n", " <td>1</td>\n", " <td>full_pt</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>2.455944e-08</td>\n", " <td>-1</td>\n", " <td>-3.564637e-08</td>\n", " <td>1</td>\n", " <td>0.500034</td>\n", " <td>1</td>\n", " <td>OS_pt</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>1.521839e-07</td>\n", " <td>-1</td>\n", " <td>-1.682846e-07</td>\n", " <td>1</td>\n", " <td>0.500587</td>\n", " <td>1</td>\n", " <td>SS_pt</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " $B^+$ $B^+$, with signal part $B^-$ \\\n", "0 2.089463e-08 -1 -3.419350e-08 \n", "0 2.455944e-08 -1 -3.564637e-08 \n", "0 1.521839e-07 -1 -1.682846e-07 \n", "\n", " $B^-$, with signal part ROC AUC ROC AUC, with signal part name \n", "0 1 0.500021 1 full_pt \n", "0 1 0.500034 1 OS_pt \n", "0 1 0.500587 1 SS_pt " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means_pt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## check on MC" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32154831" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mc = pandas.read_csv('datasets/MCTracks.csv', sep='\\t')\n", "print len(data_mc)\n", "\n", "event_id = data_mc.run.apply(str) + '_' + data_mc.event.apply(str)\n", "data_mc[event_id_column] = event_id\n", "data_mc['N_sig_sw'] = 1." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mNtHMFpvZUjPrGpdIJeXW6oVBOfQvyjmcOBFOOw3OOQfeey/cQrRoHt944w2G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\nq6ury/QBe9U1oxdcAJ/9LCS4ue277r77bg4++GB23333dEG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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9336080590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hWqvnn3Lon3IYjKrmcdu2bXTq1KnSGwSU54Yb4MgjoU+fwJoUERERqZCm6YqI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\nSVLL1dRzcMcsnytJUqoaW013G2DZWrffAfbP4phOwPK6wfr3n8Zvf9u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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9335f670d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JJ8KbbyYbFW2yyerF+etf/8qVV15JRUUFW2yxRbaNlCRJyqlVPmc0xjgyxvjj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\nfDi/+tWvsr+AJEli2rT3GD1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YDSE0BQYDXYBtgCNCCFuv4LhBwCjABzIqtU8/TUZEjzkG\n+vXLPv7DDz/MUUcdxf3338/ee++d/QUkSZIkrdSqRkZ3AqbGGKfFGBcBQ4EDl3NcX+AeYEbG7dNS\nSmWN2ZdfJmtDy8vh/POzjV1WVkZVVRWXX345w4cPp3379tleoASUyn1YaOZRkiSVulU9Z3RT4P2l\nXk8Hdl76gBDCpiQF6l7Ar4CYZQNVWubNgwMPhO22gz/9CUIBxtmbNWvGY489RihEcEmSJEm1sqqR\n0doUllcD58YYI8kUXX/CL5DGvsZs0SI47DDYeGP4298KU4guyaGF6Opr7PdhfTGPkiSp1K1qZPQD\nYLOlXm9GMjq6tB2BoTU/3LcF9g0hLIoxPrRssF69etGuXTsA2rRpw3bbbff1VLUlP5j5esWvX3jh\nhaJqT5avH320gksvhbXXLmPIEJg0qTDXW6Khv19f+7ox/30u1Oslf542bRqSJCn/QjKguYIPQ2gG\nvAnsDfwXeBo4Isb4+gqOvxV4OMZ433I+iyu7lkpXjHDyyfCf/8CIEdCiRbbxx40bx9577+1oqNTI\nhBCIMfoXOwX7Zq3IpCcnMXfh3Mzi3fPASDp2PTqTWEOuH8Sxp6zwAQ8NFivreFnGevelJ/j9madl\nEktambr2zSsdGY0xVoUQTgNGA02Bm2OMr4cQetd8fkOq1qrkxQhnnw0vvwxjx2ZbiMYYGTBgAMOG\nDePJJ5+kTZs22QWXJKkRm7twLm23aJtZvEVxYWaxJDUeq3zOaIxxZIzxxzHGLWKMA2veu2F5hWiM\n8bjljYoqG8tONW0MLr44KUJHjIDWrbOLG2Okf//+3H///VRWVn5diDbGHNY3c5gN8yhJkkrdqtaM\nSgVz9dXwz3/CxInwne9kF7e6uprTTz+dp556ioqKCr6TZXBJkiRJmbAYzZElm3k0BrfcAlddBZMm\nJbvnZuniiy/m+eefZ9y4cay77rrf+Kwx5bChmMNsmEdJklTqVjlNV8raXXfB+ecn03O///3s4/fu\n3ZvRo0d/qxCVJEmSVDwsRnOkMawxGzEC+vaFUaNgq60Kc42NN96Ytddee7mfNYYcNjRzmA3zKEmS\nSp3TdFVvKiuhVy946CH4+c8bujWSJEmSGpIjozmS5zVmzzwDv/41DB0Ku+ySXdx58+ZRl2fk5TmH\nxcIcZsM8SpKkUmcxqoJ75RXo1g1uvhn22iu7uLNnz6a8vJyhQ4dmF1SSJElSvbAYzZE8rjGbOhXK\ny5Odc7t1yy7up59+SufOnfnZz35Gjx49an1eHnNYbMxhNsyjJEkqdRajKpjp06FzZ7jwQjjiiOzi\nzpgxg7322ov27dtz3XXX0aSJt7EkSZKUN/4UnyN5WmP2ySfQqRP06QMnn5xd3A8//JCOHTvSrVs3\nrrjiCkIIdTo/TzksVuYwG+ZRkiSVOnfTVeY+/zyZmnvYYXD22dnGbtasGf369aN3797ZBpYkSZJU\nrxwZzZE8rDH76ivo2hU6dICLLso+/gYbbJCqEM1DDoudOcyGeZQkSaXOYlSZWbAADj4Yttoq2bCo\njjNoJUmSJJUQi9EcKeY1ZlVVcPjhsO66cOONUKx7ChVzDvPCHGbDPEqSpFJXpCWD8qS6Go4/HubP\nhzvvhGYZrUR+9tln6d+/fzbBJEmSJBUVNzDKkYqKiqIbTYkR+vaFadNg1Cho3jybuI8//jgHHXQQ\nN954YzYBaxRjDvPGHGbDPEqS6su0ae8xevSUTGK1agV77LFjJrEki1Gl8rvfwVNPwfjxyT9OWZgw\nYQI9evTgjjvuoLy8PJugkiRJJWrRwia0bZtNATlzZjZFrQQWo7lSbKMoAwfCQw9BZSWss042MUeN\nGsXRRx/N3XffXZDvt9hymEfmMBvmUZIklTqLUa2W666Dm26CSZOgbdtsYsYYueaaa3jwwQfZbbfd\nsgkqSZIkqSi5gVGOFMtzCW+/HS6/HMaNg002yS5uCIERI0YUtBAtlhzmmTnMhnmUJEmlzpFR1cn9\n90P//ska0R/8IPv4wYeTSpIkSSXBYjRHGnqN2Zgx0Lt3smvu1ls3aFNWW0PnsDEwh9kwj5IkqdQ5\nTVe1Mnky9OwJ990HO+yQTczhw4ezePHibIJJkiRJyhWL0RxpqDVmzz0HhxwCd94J7dtnE/PSSy/l\njDPOYNasWdkErCXX6aVnDrNhHiVJUqlzmq5W6vXXoWtX+PvfYZ990seLMXL++efzwAMPMHHiRDbc\ncMP0QSVJUqZefuUtWsyYkVm8jz+amVksSY2HxWiO1Pcas3feSQrQQYOSkdG0YoycddZZjB8/noqK\nCjbYYIP0QevIdXrpmcNsmEdJxWzBAti4TXYbRFRVPZRZLEmNh8Woluu//4VOneDcc+GYY7KJedVV\nVzF58mQmTJjAeuutl01QSZIkSbnkmtEcqa81ZrNmJSOiJ5wAffpkF/f4449n7NixDVqIuk4vPXOY\nDThwlOQAAB+2SURBVPMoSZJKnSOj+obZs6FLF9h/fzjvvGxjt2nTJtuAkiRJknLLkdEcKfQas7lz\noVs3+NWvYOBACKGgl2sQrtNLzxxmwzxKkqRSZzEqABYuhEMPhe9/HwYPTl+Izps3j0WLFmXTOEmS\nJEmNjsVojhRqjdnixXDUUdC8Odx6KzRJeVfMmTOHrl27ctNNN2XTwAy5Ti89c5gN8yhJkkqdxWiJ\nq66Gk06Czz6Dof+/vXsPs6o87z7+vRmrCNFMLKlYz1ZN42WN0UZiG6OIyEEBRUWtGEk0krexiVI1\nxuTy1aBtMKINATWKBk2NJ2ItJgIe4gCmIDGCaJVXbIsiKgXjEYQZ4Hn/2KMZcZjZM2vNPsz+fq7L\nC/beaz1z85sZ1773Ws967oJtMs4ifvvttxk0aBD77LMP5557bj5FSpIkSep2bEarSN5zzFKCceNg\n6VK4/37o2TPbeG+88QYDBgzgkEMO4aabbqKuri6fQnPkPL3szDAf5ihJkmqdzWgNu/xymDMHHnwQ\nevfONtbq1avp378/Rx99NJMmTaJH1mt9JUmSJHVrdgxVJM85ZhMnwt13w+zZkMeKK9tvvz3f/va3\nmTBhAlHBt+F1nl52ZpgPc5QkSbXOdUZr0E03Fe6YO3cu/Nmf5TPmJz7xCc4+++x8BpMkSZLU7dmM\nVpE85pjdeSdccUXh8tzdd89eU7Vxnl52ZpgPc5QkSbXOZrSGPPAAXHABPPII7LtvuauRJEmSVMuc\nM1pFsswx+81v4OyzCw3pgQdmq2Px4sWce+65pJSyDVQGztPLzgzzYY6SJKnWeWa0BixYAKedBvfc\nA1/4QraxFi5cyLBhw5gyZUpF36hIkiRJBavWrGD+4tm5jLXhvRUMGnRoLmNJNqNVpDNzzJYsgREj\nYNo0yDpF7fHHH2fkyJHceuutHH/88dkGKxPn6WVnhvkwR0lSqWykifq9+uQy1ktLluUyjgReptut\nvfACDB4MkybB0KHZxnr00UcZOXIkd9xxR9U2opIkSZIqh81oFenIHLOXX4aBA2H8eDj11Oxfe+rU\nqUyfPp2BAwdmH6yMnKeXnRnmwxwlSVKt8zLdbmjVKjjmGDj//MJNi/Jw55135jOQJEmSJOGZ0apS\nzByzN9+EY4+FM84oLOOij3KeXnZmmA9zlCRJtc5mtBt5773C3NABA+Cyy8pdjSRJkiRtnc1oFWlr\njtn69YW75h54IEycCFlWXbn//vtZv3595weoYM7Ty84M82GOkiSp1tmMdgNNTTBqFHz603Djjdka\n0WuuuYZx48axZs2a/AqUJEmSpC14A6Mq0tocs02bYMwY2LwZbr8d6uo6N3ZKifHjx3PHHXcwd+5c\ndtttt0y1Virn6WVnhvkwR0mSVOs8M1rFUoK//3t49VW4917YdtvOjpO49NJLueeee5gzZ063bUQl\nqdpExOCIWBoRyyLiO1vZZlLz609HxOdbPL88IpZExKKIWFi6qiVJKo7NaBVpOccsJbj4Yli8GGbM\ngO237/y4t9xyC7Nnz6ahoYG+fftmL7SCOU8vOzPMhzmqPRFRB0wGBgMHAKdHxGe32GYosG9KaT/g\nXOCGFi8n4KiU0udTSoeVqGxJkopmM1qlrroKZs2CmTNhhx2yjTV69Gh+85vf0KdPn3yKkyTl4TDg\nxZTS8pRSE3AXMGKLbYYDtwGklJ4A6iNi5xavZ7iLgCRJXctmtIp8MMfsxhth2jR46CHYaafs4/bs\n2ZP6+vrsA1UB5+llZ4b5MEcVYVdgRYvHrzQ/V+w2CXgkIp6MiK93WZWSJHWSNzCqMvffDz/4Acyb\nB7vsUu5qJEldKBW53dbOfn4ppfRqRHwaeDgilqaU5m250eWXX/7h34866ig/KJEkFa2hoSHT1COb\n0SoyeXIDV1xxFDNnwl/8RefGWL9+PZs2baJ37975FlclGhoafKOVkRnmwxxVhJXA7i0e707hzGdb\n2+zW/BwppVeb/1wdEf9G4bLfNptRSZI6YssPMa+44ooO7e9lulVi6VK47DL4+c/hr/+6c2OsW7eO\nESNG8JOf/CTf4iRJXeFJYL+I2CsitgVOBWZssc0M4CsAEfFF4K2U0qqI6BUROzQ/3xs4FnimdKVL\nktQ+z4xWgddegyFD4Nprj2Lw4M6N8e677zJs2DD23HNPLrzwwnwLrCKeicrODPNhjmpPSmljRJwH\nzAbqgFtSSs9HxNjm13+aUnowIoZGxIvAWuCrzbv3Be6LCCgc6+9IKT1U+n+FJElbZzNa4d55B4YO\nhbPPhjFjOjfGW2+9xZAhQ/jc5z7H9ddfT48enhCXpGqQUpoJzNziuZ9u8fi8Vvb7b+Dgrq1OkqRs\n7EoqWGMjnHQS9OsH3/te59YlfPPNNzn66KPp168fN9xwQ803oq7tmJ0Z5sMcJUlSrfPMaIVKqXA2\ntFcvmDwZopMrxfXu3Zvzzz+fM888k+jsIJIkSZKUM5vRCnXppfDii/Doo7BN83epM3PMtt12W77y\nla/kW1wVc55edmaYD3OUJEm1zma0Ak2eDPfdB7/9beHMqCRJkiR1N7U9gbAC3Xcf/PM/w6xZ0KfP\nR19zjll2ZpidGebDHCVJUq2zGa0gjz8O3/gGPPAA7L13x/d/9tlnOfXUU9m8eXP+xUmSJElSjmxG\nK8RzzxXunPuv/wqHHNL6Nm3NMXvqqac45phjOOGEE2r+jrltcZ5edmaYD3OUJEm1zjmjFeDVVwtr\nif7oR3DssR3ff8GCBQwfPpwbb7yRkSNH5l+gJEmqePMWzGNd47pcxlq+4kX2POjwXMaSpK2xGS2z\nt9+GIUNg7Fho76a3DQ0NHzubMnfuXE4++WSmTZvG0KFDu67QbqK1DNUxZpgPc5SUt3WN6+izb5/2\nNyxCU2rMZRxJaovNaBk1NsLIkfClL8Ell3RujLvvvps777yTAQMG5FucJEmSJHWhoiYXRsTgiFga\nEcsi4jutvH5GRDwdEUsi4rcRcVD+pXYvmzfDV78KO+4IkyZBRPv7tHYWZcqUKTaiHeCZqOzMMB/m\nKEmSal27Z0Yjog6YDBwDrAR+FxEzUkrPt9jsv4Evp5TejojBwE3AF7ui4O7ikktg+XJ45BGoqyt3\nNZIkSZJUWsWcGT0MeDGltDyl1ATcBYxouUFKaX5K6e3mh08Au+VbZvfy4x8Xlm+ZMQO23774/VyX\nMDszzM4M82GOkiSp1hXTjO4KrGjx+JXm57bmbODBLEV1Z/feW7hr7qxZ8Kd/2rF958yZw9tvv93+\nhpIkSZJU4YppRlOxg0VEf+BrwMfmlQrmzIFvfhN+9SvYc8+O7Ttp0iRuvfVW3njjja4prkY4Ty87\nM8yHOUqSpFpXzN10VwK7t3i8O4Wzox/RfNOim4HBKaU3WxtozJgx7LXXXgDU19dz8MEHf/iG7INL\n1rrr45/9rIELLoDp04/i4IM7tv+ECROYNGkSEydOZJ999qmIf4+PfexjH5f68Qd/X758OZIkqfpF\nSm2f+IyIbYD/BwwAXgUWAqe3vIFRROwB/AYYnVJasJVxUntfq7t65RX4m7+Bf/onGD26+P1SSlx+\n+eXcc889PPLIIyxbtuzDN2fqnAbXdszMDPNhjtlFBCmlIu5Frq2p5WNzdzR77uzc1hn9yY9u4Zjj\nz85lLIDbbpzAWd/I58K5Sh0r7/EqdayXlsznexecl8tY6n46emxu9zLdlNJG4DxgNvAccHdK6fmI\nGBsRY5s3uwz4FHBDRCyKiIWdqL1beustGDIEzjuvY40owPTp07n//vuZM2cOu+7a1jRdSZIkSaou\nxVymS0ppJjBzi+d+2uLv5wDn5Fta9duwAU48Efr3h4su6vj+I0eOZODAgdTX1wPOMcuDGWZnhvkw\nR0mSVOuKuYGROmHzZjjrrMIdc6+7DqITF5LV1dV92IhKkiRJUndS1JlRddxFF8Grr8JDD0FdXT5j\nOscsOzPMzgzzYY6SpGq0fPnLzJ79+9zG69ULjjji0NzGU3WxGe0C115bWEf08cehZ8/i9mlsbGTt\n2rV86lOf6triJEmSpE5qauxBnz75NY9r1uTX2Kr6eJluzu66q3BZ7syZUGxfuX79ekaOHMnVV1/d\n5naeRcnODLMzw3yYoyRJqnU2ozl67DH41rfg17+GPfYobp+1a9dy/PHHs8MOO/CDH/ygawuUJEmS\npArhZbo5eeYZOPXUwpnRgw4qbp933nmH4447jn333ZepU6dS187kUueYZWeG2ZlhPsxRUt6eeXYZ\nPVevzmWsVa+vyWUcSWqLzWgOVqyAoUPhxz+Go48ubp93332XgQMHcuihhzJ58mR69PAktSRJ6rwN\nG6Bv/WdzGWvjxhm5jCNJbbEZzejNN2HIEDj/fDj99OL36927N//4j//IKaecQhS57otnUbIzw+zM\nMB/mKEmSap3NaAbr18MJJ8DAgTBuXMf27dGjB6NGjeqawiRJkiSpwnltaCdt3gxnngl9+8LEiVDk\nyc1MGhoauv6LdHNmmJ0Z5sMcJUlSrfPMaCekBBdcAKtXF9YTdbqnJEmSJHWMbVQnTJwIjz4K998P\nPXu2v/3SpUsZMmQIjY2Nmb6uc8yyM8PszDAf5ihJkmqdzWgH/eIXhbvmzpwJ9fXtb79kyRKOPvpo\nTjvtNLbddtuuL1CSJEmSqoDNaAc8+mjhrrkPPgi7797+9k8++STHHnss1113HWeddVbmr+8cs+zM\nMDszzIc5SpKkWuec0SI9/XRh6ZZ77oG/+qv2t/+P//gPTjjhBG6++WZGjBjR9QVKkiRJUhWxGS3C\nSy/BccfB5MlQ7DSvBx98kNtvv53BgwfnVodzzLIzw+zMMB/mKEmSap3NaDv+8AcYMgQuvBA6sizo\nlVde2XVFSZIkSWWwas0K5i+endt4G95bwaBBh+Y2nqqLzWgb3n8fhg+HoUMLc0XLraGhwbMpGZlh\ndmaYD3OUJFWjjTRRv1ef3MZ7acmy3MZS9fEGRluxaROMHl24UdHVV5e7GkmSJEnqXmxGW5ESfPvb\n8OabMG0a9GgnpXvvvZfXXnuty+vyLEp2ZpidGebDHCVJUq2zGW3FtdfCnDlw332w3XZtb3vDDTcw\nbtw43nnnndIUJ0mSJEndgM3oFv7932HiRPj1r6G+vu1tr7vuOq6++moaGhr4zGc+0+W1uS5hdmaY\nnRnmwxwlSVKt8wZGLSxaBOecU2hE99ij7W2vuuoqpk2bxpw5c9ijvY0lSZIkSR9hM9ps5crCnXOv\nvx4OO6ztbWfPns0vfvEL5s6dyy677FKaAnGOWR7MMDszzIc5SpKkWmczCqxdW2hEv/ENOOWU9rc/\n9thjmT9/PjvuuGPXFydJkiRJ3VDNzxndvLmwhMuBB8Kllxa3T0SUpRF1jll2ZpidGebDHCVJUq2r\n+TOj3/0uvPEG3HUXRJS7GkmSJEmqDTV9ZvSWW+CXv2x7CZempiZef/310ha2Fc4xy84MszPDfJij\nJEmqdTXbjD72WOGs6K9+BX36tL7Nhg0bGDVqFOPHjy9tcZIkSZLUzdXkZbovvACnnQZ33gl/+Zet\nb/P+++8zcuRIevfuzXXXXVfaAreioaHBsykZmWF2ZpgPc5QEMG/BPNY1rstlrOUrXmTPgw7PZSxJ\nKoWaa0bfeAOOOw6uvBIGDGh9m/fee4/hw4ezyy67cNttt7HNNjUXkyRJKoF1jevos+9WLtHqoKbU\nmMs4klQqNXWZbmMjnHQSjBgBX/9669usX7+eQYMGsc8++3D77bdXVCPqWZTszDA7M8yHOUqSpFpX\nOZ1WF0upsI7oJz8JEyZsfbvtttuOSy65hOOOO44ePWqqV5ckSZKkkqmZbuvqq2HRIrjjDqir2/p2\nEcGwYcMqshF1XcLszDA7M8yHOUqSpFpXE2dG77sPfvITWLAAPvGJclcjSZIkSer2zeiTT8LYsTBr\nFuy228dfTykREaUvrBOcY5adGWZnhvkwR0mSVOsq71rUHL3yCpxwAtx0Exx66MdfX7ZsGUceeSRr\n164tfXGSJEmSVMO6bTP63nswbBh861tw4okff/25556jf//+jB49mt69e5e+wE5wjll2ZpidGebD\nHCVJUq3rlpfpbtoEf/d3cMghcNFFH3998eLFDBkyhB/96EeMHj269AVKkiRJYvnyl5k9+/e5jNWr\nFxxxRCuXQ6pidctm9OKLC2dGp0+HLaeDLly4kGHDhjFlyhROPvnk8hTYSc4xy84MszPDfJijJIBn\nnl1Gz9Wrcxlr1etrchlHKqWmxh706ZNPA7lmTT5NrUqn2zWjN90Ev/oVzJ8P22778dcff/xxpk6d\nyrBhw0pfnCRJUgsbNkDf+s/mMtbGjTNyGUeSSqVbzRl95BG47LJCM7rTTq1vM27cuKptRJ1jlp0Z\nZmeG+TBHSZJU67rNmdHnny/ME733Xthvv3JXI0mSJElqS7c4M7pmDRx/PFx9NRx5ZLmr6TrOMcvO\nDLMzw3yYoyRJqnVV34xu2FBYumXUKBgz5qOv3XvvvSxbtqwsdUmSJEmStq6qm9GU4JxzYOed4aqr\nPvrarbfeyvnnn8+GDRvKU1wXcI5ZdmaYnRnmwxwlSVKtq+o5oz/8ISxdCnPmQI8WbfWUKVOYMGEC\njz32GPvvv3/5CpQkSZK0VavWrGD+4tm5jLXhvRUMGuQ6o9WkapvRGTNgyhR44onCArcfuOaaa7j+\n+uuZM2cOe++9d/kK7ALOMcvODLMzw3yYoyRJsJEm6vfqk8tYLy1xel61qcpm9D//E84+u7CEy667\n/vH5J554gqlTpzJ37lx222238hUoSZIkSWpT1c0ZfeMNGD4crr0W+vX76Gv9+vXjqaee6raNqHPM\nsjPD7MwwH+YoSZJqXVU1o01NcMopMHIknHlm69v0annNriRJkiSpIlVVMzpuHGy3XeHGRbXIOWbZ\nmWF2ZpgPc5QkSbWuauaM3nwzPPwwLFgAdXWwceNGVq5cyZ577lnu0iRJUo2Yt2Ae6xrX5Tbe8hUv\nsudBh+c2niRVk6poRufNg+9/v/BnfT00NTVxxhln0LNnT26//fZyl1cyDQ0Nnk3JyAyzM8N8mKNU\nndY1rqPPvvnc+ROgKTXmNpYkVZuKb0ZfeglGjYLbb4f994f169czatQoIoKf//zn5S5PkiRJktQJ\nFT1ndO1aGDECLroIBg2CdevWMWLECHr27Mn06dPZbrvtyl1iSXkWJTszzM4M82GOkiSp1lXsmdHN\nm+Gss+Dgg+GCC2DTpk0cd9xx7L777tx6661ss03Fli5JkiS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9335fe2ad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Fi6Rx47wNUFx6b2CUcPTNAIAgVbZvZtJ+hOTm5oYdQuSRQ//IYTDIY/mKNqd44oknlJeX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\n4F7gp8Co2MhepVYS+0GcNZQ6xPm7vNbM3gJWECzEM9o5p2Y0Js734VB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9335ef2c90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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sUSGaHrm2Vi8dlMPUVeUcTp4Mxx4LnTvDCy+ktxCtynkUERERSUZ5u+k2AlYU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\nhkwfsNuM1pCmbxCeeQZOOQUeeQS+8IXy7P+JJ55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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93338d4ed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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nP/9Ru3btdNJJJ2nbbbeNO5xYUYwCAFD82MAoQVhjFh45DI8cRqNyHmfMmKHO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\nkYiISOoopoh6Tf1ZZLuEYl/aEZHgha4YXbx4Md26dWPUqFF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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933643e310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "means_mc = [compute_sum_of_charges(data_mc[mask], name, bins) for mask, name, bins in \\\n", " zip([data_mc.signB > -100, \n", " (data_mc.IPs > 3) & ((abs(data_mc.diff_eta) > 0.6) | (abs(data_mc.diff_phi) > 0.825)), \n", " (abs(data_mc.diff_eta) < 0.6) & (abs(data_mc.diff_phi) < 0.825) & (data_mc.IPs < 3)], \n", " ['full_mc', 'OS_mc', 'SS_mc'], [21, 21, 21])]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "means_mc = pandas.concat(means_mc)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>$B^+$</th>\n", " <th>$B^+$, with signal part</th>\n", " <th>$B^-$</th>\n", " <th>$B^-$, with signal part</th>\n", " <th>ROC AUC</th>\n", " <th>ROC AUC, with signal part</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.317782</td>\n", " <td>-0.682218</td>\n", " <td>-0.770055</td>\n", " <td>0.229945</td>\n", " <td>0.584897</td>\n", " <td>0.571588</td>\n", " <td>full_mc</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.047280</td>\n", " <td>-0.952720</td>\n", " <td>-0.284975</td>\n", " <td>0.715025</td>\n", " <td>0.540302</td>\n", " <td>0.694391</td>\n", " <td>OS_mc</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.155203</td>\n", " <td>-0.844797</td>\n", " <td>-0.184142</td>\n", " <td>0.815858</td>\n", " <td>0.568065</td>\n", " <td>0.788316</td>\n", " <td>SS_mc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " $B^+$ $B^+$, with signal part $B^-$ $B^-$, with signal part \\\n", "0 0.317782 -0.682218 -0.770055 0.229945 \n", "0 0.047280 -0.952720 -0.284975 0.715025 \n", "0 0.155203 -0.844797 -0.184142 0.815858 \n", "\n", " ROC AUC ROC AUC, with signal part name \n", "0 0.584897 0.571588 full_mc \n", "0 0.540302 0.694391 OS_mc \n", "0 0.568065 0.788316 SS_mc " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means_mc" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "means_mc.to_csv('img/track_signs_assymetry_means_mc.csv', index=False, header=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kSZI6nM5FF6CWc8YsOzPMrhYyvO8+2GUXuOKKI9lhh7Z5jVrIUZIkaX5sRiWp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\nwp57DuDgg7dmn32cD5UkSZK0YGxGS6T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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9336205e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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mTVK7du2iLgkAACAWaEZjhDlmwZFhcGT4X5s2bdLJJ5+sTp066ZFHHtGuu+6a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\nDAc5AgCApOPKaMw9/PDDeuihh3TiiSd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9336480150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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wxz9CxMIfq5pzlCRJAptRSWqS++9/ie7dt2T33T/k5JOzNaKSJEmyGS0UZ8yy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\n1pIkSZI0f3YMBVKpM2Yvvww77rgEW29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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93360803d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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YL19onJkdotSN7k9y9y8bWi/qf0e+tvxK+mtDkDGef5dkffFvkp9fzRVGM/qkpLMlycyO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\nx2zNmjX6+uuvgxcTU8zTC44Mw0GOAAA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933b981c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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kSRLYjEpSk9x//2t06bIVv/jFx/z2t0VXI0mSVH42oyXijFl2ZphdLWY4YsTL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\nCEaOPIuo4mtzqzXDMjHDfJijJEmqdTa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f933b8bd790>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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m9EVrHpU0xN0XRV0PkIkg4zuAeGryf7uZPSBptqT9zexDMzvbzIaZ2TBJcvepkt41s0WS\nbpOU83spIlk2btyorl27aunSrN4lBUAeeeaZZ7R5c6DbGhaUpsZmSVdJ2knSLWY2z8zmRFYskCHG\ndyB5mpwz6u6DMljnonDKQWOGDh0adQl5Yfny5erYsWOL5pOQYXBkGA5yzNz48eN10003afbs2dp9\n992jLicvNDU2e+r2FM29RQXyQGlpadQlRCbI+J5tSf53yVf8mxSGjG7tEsqOzDxX+wIAFIaxY8fq\n9ttv19NPP6299977S8+ZmZwLGAXC2AwACFNzx2ZOyo+RysrKqEuIPTIMjgzDQY6N23pxinvuuUdV\nVVVfaUQBAMgHZpbYrzCEdWsXAABCc8stt2jSpEmqqqrS1772tajLAQCgQUk8wySsZpTTdAEAeefz\nzz/X5s2btcsuuzS4DqfpBsfYDADBpMeiqMvIuYZ+7+aOzTSjAIBYohkNjrEZAIKhGa33ceaMFiLm\nmAVHhsGRYTjIEQAAJB3NKAAgUhs3btSmTZuiLgMAgMT7+OOPc7o/TtMFAERmw4YNOvXUU3Xcccdp\nxIgRzfpZTtMNjrEZAIIptNN077vvPg0ePLjJ9ThNFwAQa2vXrtXxxx+vTp066cILL4y6HAAAkGPc\n2iVGKisrVVpaGnUZsUaGwZFhOJKe46pVq9S/f3/tu+++uuOOO1RUVBR1SQAAhOLZZ+dq3brsbb9D\nB+mYYw7PeP25c+fqqquu0vr167cd9XzjjTfUuXNnjR49WgsWLNDcuXMlSbNnz5aUOsI5cODArI/P\nNKMAgJz67LPPVFZWpu9///u6+eab1aoVJ+kAAArHunVScXHmzWJzVVfPbdb6hx9+uDp16qThw4er\nX79+kqQ1a9Zoxx131GWXXaYDDjhABxxwwLb1MzlNNyz8BRAjST6KEhYyDI4Mw5HkHFu3bq0zzjhD\nEyZMoBEFACAHXnzxRfXs2VOS5O66/vrrNXz4cHXo0CHSujgyCgDIqU6dOumiiy6KugwAABLhzTff\n1C677KKqqiq5uyZPnqxu3brpvPPO+8q63/rWt3JaGx9Jxwj3JQyODIMjw3CQIwAAyIWZM2fq1FNP\nVVlZmcrLyzVu3DjdcMMNWrRo0VfWPeqoo3JaG80oAAAAABSoqqoqde/efdtymzZt1KlTJ7355psR\nVpVCMxojSZ5jFhYyDI4Mw5GUHBcsWKALL7ywoO7BBgBAXLi7Zs+erSOOOGLbY1OmTNHKlSvVu3fv\nCCtLYc4oACAr3njjDZWVlen666+XWcb3vwYAACGYN2+eHnroIdXU1OjOO++UJK1YsULvvfeenn32\nWXXs2DHiCiXL1afVZuZ8Mh5M0u9LGAYyDI4Mw1HoOc6dO1f9+/fX+PHjdfrpp2dlH2Ymd6fLDYCx\nGQCCSY9FX3qsomJu1m/tUlaWve1nor7fu9bjGY/NHBkFAIRq9uzZOuWUU/TnP/9ZJ510UtTlAACQ\nUx06NP9eoM3dfqHgyCgAIFQ/+clPdNZZZ6msrCyr++HIaHCMzQAQTENHCAtdWEdGaUYBAKFy95zM\nEaUZDY6xGQCCoRmt9/GMx2auphsj3JcwODIMjgzDUcg5crEiAACQCZpRAAAAAEDOcZouAKDFKioq\nVFpaqrZt2+Z835ymGxxjMwAEw2m69T7OaboAgOy69dZbde6552rZsmVRlwIAAGKIZjRGCnmOWa6Q\nYXBkGI645/j73/9eY8eOVVVVlbp27Rp1OQAAIIa4zygAoFl+85vf6O6779asWbPUpUuXqMsBAAAx\nxZxRAEDG/vrXv+qGG27QjBkz9I1vfCPSWpgzGhxjMwAEw5zReh/nPqMAgPCtX79ea9euVXFxcdSl\n0IyGgLEZAIKpryl79sVntW7juqzts0ObDjrmqGNC2VZ1dbWqqqq+9Nguu+yi0tLSRn8urGaU03Rj\npLKysskXBhpHhsGRYTjimmP79u3Vvn37qMsAACBvrdu4TsX7Zu9D2+pF1c1af+7cubrqqqu0fv16\nDR48WJL0xhtvqHPnzho9erROPfXUbJSZEZpRAAAAAChQhx9+uDp16qThw4erX79+kqQ1a9Zoxx13\n1GWXXaYOHTpEVhun6QIA6rVp0yZt2rQp0kGqMZymGxxjMwAEU9/pqhWzKrJ+ZLSsR1mzfqZr165a\nsGCB2rVrJ3fXlVdeqdWrV2v8+PEtqoHTdAEAWfPFF19o4MCBOvTQQ3X11VdHXQ4AAGihN998U7vs\nsouqqqrk7po8ebK6deum8847L+rSuM9onMT9voT5gAyDI8Nw5HOO69ev18knn6zWrVvriiuuiLoc\nAAAQwMyZM3XqqaeqrKxM5eXlGjdunG644QYtWrQo6tJoRgEA/2fNmjXq37+/dt55Z02cOFFt2rSJ\nuiQAABBAVVWVunfvvm25TZs26tSpk958880Iq0qhGY2ROF55M9+QYXBkGI58zHHVqlUqKyvTPvvs\no3vvvVetWzOTAwCAOHN3zZ49W0ccccS2x6ZMmaKVK1eqd+/eEVaWwl8aAABJUrt27XTmmWfq3HPP\nVatWfFYJAECczZs3Tw899JBqamp05513SpJWrFih9957T88++6w6duwYcYVcTTdW4npfwnxChsGR\nYTjIMTiuphscYzMABFPfVWWfffFZrdu4Lmv77NCmg4456pisbT8TXE0XAAAAAPJM1I1inHBkFAAQ\nSxwZDY6xGQCCaegIYaEL68gok4IAIIEWLVqkIUOGaPPmzVGXAgAAEopmNEby+b6EcUGGwZFhOKLM\n8a233lJpaalKSkpUVFQUWR0AACDZmDMKAAny6quvqm/fvvrf//1fDRkyJOpyAABAgjFnFAASYs6c\nOTrhhBM0YcIEnXbaaVGXExhzRoNjbAaAYJgzWu/jGY/NNKMAkBAXXnih+vXrp+OPPz7qUkJBMxoc\nYzPi6Nln52pdSHfN6NBBOuaYw8PZmMK9pUc+3L4DTaMZrfdxbu1SiLgvYXBkGBwZhiOKHP/0pz/l\ndH8AkA3r1knFxeE0kNXVc0PZzlbrNq5T8b7FoWyrelF1KNtB9pnxuWhL0YwCAAAAQAsk8ahomLia\nboxwNCo4MgyODMNBjgAAIOloRgGgAE2dOlUrV66MugwAAIAG0YzGCPd3DI4MgyPDcGQzx7vuukvn\nnXeePv7446ztAwAAICjmjAJAAZkwYYLGjh2rmTNn6tvf/nbU5QAAADSIZjRGmGMWHBkGR4bhyEaO\nN954o/70pz+pqqpK3/zmN0PfPgAAQJhoRgGgADz22GO6/fbbNWvWLO25555RlwMAANAk5ozGCHP1\ngiPD4MgwHGHn2L9/fz3//PM0ogAAIDZoRgGgALRu3VrFxeHcaB0AACAXaEZjhLl6wZFhcGQYDnIE\nAABJRzMKADFTU1PDPUQTzszuMrP/mNkbjawz3swWmtlrZnZoLusDACATNKMxwly94MgwODIMR0tz\n3LhxowYNGqRrrrkm3IIQN3dLKm/oSTPrJ2lfd99P0vmSbslVYQAAZIpmFABiYsOGDTrttNP0xRdf\n6De/+U3U5SBC7v6spM8aWeVESX9Jr/uSpM5m9vVc1AYAQKZoRmOEOWbBkWFwZBiO5ua4bt06nXji\niWrXrp0eeeQRtWvXLjuFoVDsIenDWstLJHGpZQBAXuE+owCQ59atW6e+fftq77331l133aXWrXnr\nRkaszrLXt9Lo0aO3fV9aWsoHTgCAjFVWVgaawsVfNDFSWVnJHwkBkWFwZBiO5uTYrl07nXPOORoy\nZIhateKEFmRkqaQutZb3TD/2FbWbUQAAmqPuh5jNvaYFf9UAQJ5r1aqVzjjjDBpRNMfjks6QJDM7\nStLn7v6faEsCAODLODIaIxyNCo4MgyPDcJAjgjCzBySVSCo2sw8lXS1pO0ly99vcfaqZ9TOzRZLW\nSjorumoBAKgfzSgAADHj7oMyWOeiXNQCAEBLcc5XjHB/x+DIMDgyDEdDOb733ns6+eST9cUXX+S2\nIAAAgByjGQWAPPGvf/1LJSUlOu6449S2bduoywEAAMgqTtONEeaYBUeGwZFhOOrmOH/+fJWVlWnM\nmDE6++yzoykKAAAgh2hGASBi8+bNU79+/XTTTTdp0KAmpwICAAAUhCZP0zWzcjNbYGYLzWxUPc/v\naGaTzexVM5tvZkOzUimYqxcCMgyODMNRO8e///3vmjBhAo0oAABIlEaPjJpZkaSbJfVW6mbZ/zSz\nx9397VqrDZc0391PMLNiSe+Y2d/cvSZrVQNAAbn22mujLgEAACDnmjoyeoSkRe6+2N03SZoo6aQ6\n62yRtEP6+x0kraARzQ7m6gVHhsGRYTjIEQAAJF1Tzegekj6stbwk/VhtN0s60Mw+kvSapBHhlQcA\nAAAAKERNXcDIM9hGuaRX3P1YM/uWpKfM7HvuvrruikOHDlXXrl0lSZ07d1a3bt22HR3YOn+K5YaX\nX331VV1yySV5U08cl7c+li/1xHG5bpZR1xO35SeeeEIbN27UBx98wP/nFvz/rays1OLFiwUAAOLP\n3BvuN83sKEmj3b08vXyFpC3uPrbWOk9Iut7dn08vPy1plLu/XGdb3ti+0LTKysptf5yhZcgwODJs\nub/97W/65S9/qSeffFIrVqwgx4DMTO5uUdcRZ4zNiKOKirkqLj48lG1VV89VWVk425KkilkVKt63\nOJRtVS+qVlmPslC2BeRKc8fmpo6MvixpPzPrKukjSQMl1b3c4wdKXeDoeTP7uqT9Jb2baQHIHH+4\nBkeGwZFhy9x+++265ppr9PTTT+vAAw+MuhwAAIDINdqMunuNmV0kqUJSkaQ73f1tMxuWfv42SWMk\n3WNmr0sySZe5+6dZrhsAYmP8+PG66aabVFlZqX333TfqcgAAAPJCk/cZdfdp7r6/u+/r7tenH7st\n3YjK3Ze5e5m7H+LuB7v7/dkuOqlqz5tCy5BhcGTYPDNnztT48eNVVVX1pUaUHAEAQNI1dZouACCA\n0tJS/fOf/9ROO+0UdSkAAAB5pckjo8gfzNULjgyDI8PmMbN6G1FyBAAASUczCgAAAADIOZrRGGGO\nWXBkGBwZNmzz5s1avnx5RuuSIwAASDrmjAJACGpqanTmmWeqXbt2uvPOO6MuBwAAIO/RjMYIc8yC\nI8PgyPCrNm7cqEGDBmndunV69NFHM/oZcgQAAEnHaboAEMCGDRt0yimnaMuWLZo0aZLat28fdUkA\nAACxQDMaI8wxC44MgyPD/7Nx40Ydf/zx2mGHHfTQQw+pbdu2Gf8sOQIAgKTjNF0AaKHttttOF1xw\ngU455RQVFRVFXQ4AAECs0IzGCHPMgiPD4Mjw/5iZTjvttBb9LDkCAICk4zRdAAAAAEDO0YzGCHPM\ngiPD4MgwHOQIAACSjmYUADLwwQcfqE+fPlq9enXUpQAAABQEmtEYYY5ZcGQYXBIzfPfdd1VSUqL+\n/furU6dOoWwziTkCAADURjMKAI1YsGCBSkpKdPnll+uSSy6JuhwAAICCQTMaI8wxC44Mg0tShq+/\n/rp69uypa6+9VsOGDQt120nKEQAAoD7c2gUAGjBjxgyNGzdOAwcOjLoUAACAgkMzGiPMMQuODINL\nUoaXXnpp1radpBwBAADqw2m6AAAAAICcoxmNEeaYBUeGwZFhOMgRAAAkHc0oAEiaMmWKFi1aFHUZ\nAAAAiUEzGiPMMQuODIMrxAwffPBBnXPOOVq1alXO9lmIOQIAADQHzSiARPvLX/6in//853rqqad0\n2GGHRV0OAABAYtCMxghzzIIjw+AKKcNbb71VV155pZ555hkdfPDBOd13IeUIAADQEtzaBUAivfLK\nKxo7dqwqKyv1rW99K+pyAAAAEodmNEaYYxYcGQZXKBkedthheu2117TDDjtEsv9CyREAAKClOE0X\nQGJF1YgCAACAZjRWmGMWHBkGR4bhIEcAAJB0NKMACt6WLVu0ZMmSqMsAAABALcwZjRHmmAVHhsHF\nLcPNmzfr3HPP1Zo1a/Twww9HXc42ccsRAAAgbDSjAArWpk2b9NOf/lTV1dV67LHHoi4HAAAAtXCa\nbowwxyw4MgwuLhl+8cUXOv3007VmzRo98cQT6tixY9QlfUlccgQAAMgWmlEABWfLli065ZRTVFRU\npEcffVQfbPXdAAAgAElEQVTt2rWLuiQAAADUwWm6McIcs+DIMLg4ZNiqVStdfPHF6tOnj1q3zs+3\nuTjkCAAAkE35+VcaAATUt2/fqEsAAABAIzhNN0aYYxYcGQZHhuEgRwAAkHQ0owBiz92jLgEAAADN\nRDMaI8wxC44Mg8u3DJcuXaqSkhItX7486lKaJd9yBAAAyDWaUQCx9f7776ukpET9+/fXrrvuGnU5\nAAAAaAaa0RhhjllwZBhcvmS4aNEi9ejRQyNGjNCoUaOiLqfZ8iVHAACAqNCMAoidt956S6Wlpbry\nyit18cUXR10OAAAAWoBbu8QIc8yCI8Pg8iHDOXPm6IYbbtCQIUOiLqXF8iFHAACAKNGMAoidoUOH\nRl0CAAAAAuI03RhhjllwZBgcGYaDHBGEmZWb2QIzW2hmX5k0bWY7mtlkM3vVzOab2dAIygQAoFE0\nowAAxIiZFUm6WVK5pAMlDTKz79RZbbik+e7eTVKppN+ZGWdDAQDyCs1ojDDHLDgyDC7XGU6bNk1z\n587N6T5zgdciAjhC0iJ3X+zumyRNlHRSnXW2SNoh/f0Okla4e00OawQAoEk0owDy1qOPPqqhQ4eq\npoa/oYFa9pD0Ya3lJenHartZ0oFm9pGk1ySNyFFtAABkjFN2YqSyspKjKQGRYXC5yvD+++/XyJEj\nNX36dB166KFZ31+u8VpEAJ7BOuWSXnH3Y83sW5KeMrPvufvquiuOHj162/elpaW8LgEAGausrAx0\nHQyaUQB556677tL//M//aMaMGTrooIOiLgfIN0sldam13EWpo6O1DZV0vSS5+7/N7D1J+0t6ue7G\najejAAA0R90PMa+55ppm/Tyn6cYIn1YHR4bBZTvDhQsXasyYMZo5c2ZBN6K8FhHAy5L2M7OuZtZG\n0kBJj9dZ5wNJvSXJzL6uVCP6bk6rBACgCRwZBZBX9ttvP7355pvq0KFD1KUAecnda8zsIkkVkook\n3enub5vZsPTzt0kaI+keM3tdkkm6zN0/jaxoAADqwZHRGOG+hMGRYXC5yDAJjSivRQTh7tPcfX93\n39fdt56Oe1u6EZW7L3P3Mnc/xN0Pdvf7o60YAICvohkFAAAAAOQczWiMMMcsODIMLswM3V3//ve/\nQ9tenPBaBAAAScecUQCR2LJliy644AJ98MEHmj59etTlAAAAIMc4MhojzDELjgyDCyPDmpoaDR06\nVO+8844efvjh4EXFEK9FAACQdBwZBZBTmzZt0uDBg/X5559r2rRpibhYEQAAAL6KZjRGmGMWHBkG\nFyRDd9dPfvITbdq0SY8//rjatWsXXmExw2sRAAAkHc0ogJwxM/3sZz/T0UcfrTZt2kRdDgAAACLE\nnNEYYY5ZcGQYXNAMS0pKaETFaxEAAIBmFAAAAACQczSjMcIcs+DIMLjmZOju2Ssk5ngtAgCApKMZ\nBZAVH3/8sY4++mi9//77UZcCAACAPEQzGiPMMQuODIPLJMMlS5aopKRExx9/vPbee+/sFxVDvBYB\nAEDS0YwCCNV7772nHj166Pzzz9eVV14ZdTkAAADIUzSjMcIcs+DIMLjGMvzXv/6lkpISjRw5UiNH\njsxdUTHEaxEAACQd9xkFEJp33nlHo0eP1tlnnx11KQAAAMhzHBmNEeaYBUeGwTWW4QknnEAjmiFe\niwAAIOloRgEAAAAAOddkM2pm5Wa2wMwWmtmoBtYpNbN5ZjbfzCpDrxKSmGMWBjIMjgzDQY4AACDp\nGp0zamZFkm6W1FvSUkn/NLPH3f3tWut0ljRBUpm7LzGz4mwWDCA/PPXUUzIz9e7dO+pSAAAAEENN\nHRk9QtIid1/s7pskTZR0Up11/kvS3919iSS5e3X4ZUJijlkYyDC4yspKTZ48WYMHD1b79u2jLie2\neC0CAICka6oZ3UPSh7WWl6Qfq20/STub2Uwze9nMfhpmgQDyS2Vlpc4991xNmTJFP/rRj6IuBwAA\nADHV1K1dPINtbCfpMEm9JHWQ9IKZvejuC+uuOHToUHXt2lWS1LlzZ3Xr1m3bvKmtRwlYbnx5q3yp\nh+VkLS9ZskS33XabrrvuOq1du1Zb5Ut9cVveKl/qyfflrd8vXrxYAAAg/sy94X7TzI6SNNrdy9PL\nV0ja4u5ja60zSlJ7dx+dXr5D0nR3f6TOtryxfQHIbx999JGOOeYYTZ48WQceeGDU5QAyM7m7RV1H\nnDE2I44qKuaquPjwULZVXT1XZWXhbEuSKmZVqHjfcC6fUr2oWmU9ykLZFpArzR2bmzpN92VJ+5lZ\nVzNrI2mgpMfrrPOYpO5mVmRmHSQdKemt5hSNzNQ9moLmI8OW23333fXWW2/pk08+ibqUgsBrEQAA\nJF2jp+m6e42ZXSSpQlKRpDvd/W0zG5Z+/jZ3X2Bm0yW9LmmLpNvdnWYUKEBt27aNugQAAAAUiEZP\n0w11R5wKBAAIEafpBsfYjDjiNF0gf4V9mi6ABHJ3vfUWJzgAAAAge2hGY4Q5ZsGRYdO2bNmiiy++\nWOeff77qO2JChuEgRwAAkHRN3doFQIJs3rxZw4YN09tvv62pU6fKjDMgAQAAkB00ozGy9Z57aDky\nbFhNTY3OPPNMLVu2TBUVFdp+++3rXY8Mw0GOAAAg6WhGAUiSzjrrLH366aeaMmWK2rdvH3U5AAAA\nKHDMGY0R5pgFR4YNGzFihCZNmtRkI0qG4SBHAACQdBwZBSBJ+v73vx91CQAAAEgQjozGCHPMgiPD\n4MgwHOQIAACSjmYUSKAtW7ZEXQIAAAASjmY0RphjFhwZSsuXL9dRRx2l+fPnt+jnyTAc5AgAAJKO\nZhRIkGXLlqm0tFRlZWU66KCDoi4HAAAACUYzGiPMMQsuyRl+8MEH6tGjhwYPHqwxY8bIzFq0nSRn\nGCZyBAAAScfVdIEE+Pe//63evXtrxIgRuuSSS6IuBwAAAODIaJwwxyy4pGa4bNkyXXHFFaE0oknN\nMGzkCAAAko4jo0ACdO/eXd27d4+6DAAAAGAbjozGCHPMgiPD4MgwHOQIAACSjmYUAAAAAJBzNKMx\nwhyz4JKQ4cyZM/Xwww9nbftJyDAXyBEAACQdzShQQKZPn66BAwdq1113jboUAAAAoFE0ozHCHLPg\nCjnDSZMm6YwzztBjjz2W1d+zkDPMJXIEAABJRzMKFIAHH3xQF1xwgaZNm6ajjz466nIAAACAJtGM\nxghzzIIrxAw/++wzXX311XryySd1+OGHZ31/hZhhFMgRAAAkHfcZBWJup5120vz589W6Nf+dAQAA\nEB8cGY0R5pgFV6gZ5rIRLdQMc40cAQBA0tGMAgAAAAByjmY0RphjFlzcM3R3vfLKK5HWEPcM8wU5\nAgCApKMZBWLC3TVy5Eidd955qqmpibocAAAAIBCueBIjzDELLq4ZbtmyRcOHD9crr7yiGTNmRHqx\norhmmG/IEQAAJB3NKJDnNm/erHPPPVeLFi3SU089pR122CHqkgAAAIDAOE03RphjFlwcMxw+fLg+\n/PBDTZ8+PS8a0ThmmI/IEQAAJB3NKJDnLr74Yj3xxBPq2LFj1KUAyBNmVm5mC8xsoZmNamCdUjOb\nZ2bzzawyxyUCANAkTtONEeaYBRfHDA866KCoS/iSOGaYj8gRLWVmRZJultRb0lJJ/zSzx9397Vrr\ndJY0QVKZuy8xs+JoqgUAoGEcGQUAIF6OkLTI3Re7+yZJEyWdVGed/5L0d3dfIknuXp3jGgEAaBLN\naIwwxyy4fM8wDrdsyfcM44IcEcAekj6stbwk/Vht+0na2cxmmtnLZvbTnFUHAECGOE0XyBMrVqxQ\n37599Yc//EFHH3101OUAyF+ewTrbSTpMUi9JHSS9YGYvuvvCuiuOHj162/elpaWcQg4AyFhlZWWg\nD9hpRmOEPxCCy9cMP/nkE/Xu3Vvl5eU66qijoi6nUfmaYdyQIwJYKqlLreUuSh0dre1DSdXuvl7S\nejObJel7khptRgEAaI66H2Jec801zfp5TtMFIrZ06VKVlJRowIABGjt2rMws6pIA5LeXJe1nZl3N\nrI2kgZIer7POY5K6m1mRmXWQdKSkt3JcJwAAjaIZjRHmmAWXbxm+//77Kikp0dChQzV69OhYNKL5\nlmFckSNayt1rJF0kqUKpBvNBd3/bzIaZ2bD0OgskTZf0uqSXJN3u7jSjAIC8wmm6QIRWrVqlX/zi\nF7rggguiLgVAjLj7NEnT6jx2W53lGyXdmMu6AABoDprRGGGOWXD5luHBBx+sgw8+OOoymiXfMowr\ncgQAAEnHaboAAAAAgJyjGY0R5pgFR4bBkWE4yBEAACQdzSiQI88995xuu+22plcEAAAAEoBmNEaY\nYxZcVBk+/fTTGjBggPbZZ59I9h8mXofhIEcAAJB0NKNAlk2dOlWDBg3SI488oj59+kRdDgAAAJAX\naEZjhDlmweU6w0cffVRnnXWWJk+erB49euR039nC6zAc5AgAAJKOW7sAWbJu3Tr9+te/1vTp03Xo\noYdGXQ4AAACQV2hGY4Q5ZsHlMsMOHTrolVdeUatWhXUCAq/DcJAjAABIusL6KxnIM4XWiAIAAABh\n4S/lGGGOWXBkGBwZhoMcAQBA0tGMAiFwdz3//PNRlwEAAADEBs1ojDDHLLhsZOju+tWvfqVhw4Zp\n/fr1oW8/3/A6DAc5AgCApOMCRkAA7q5LLrlEzz77rCorK9W+ffuoSwIAAABigSOjMcIcs+DCzHDL\nli0aNmyY5syZo2eeeUbFxcWhbTuf8ToMBzkCAICk48go0EKXXXaZ3nnnHT355JPq1KlT1OUAAAAA\nsUIzGiPMMQsuzAyHDx+ur3/96+rQoUNo24wDXofhIEcAAJB0NKNAC33zm9+MugQAAAAgtpgzGiPM\nMQuODIMjw3CQIwAASDqaUSADGzdujLoEAAAAoKDQjMYIc8yCa0mGn3/+uUpKSjRt2rTwC4ohXofh\nIEcAAJB0NKNAI6qrq9WzZ08deeSRKi8vj7ocAAAAoGDQjMYIc8yCa06GH3/8sY499liVl5dr3Lhx\nMrPsFRYjvA7DQY4AACDpaEaBeixZskQlJSU6/fTTdd1119GIAgAAACHj1i4xwhyz4DLNsKamRj//\n+c91wQUXZLegGOJ1GA5yBAAASceRUaAeXbt2pREFAAAAsohmNEaYYxYcGQZHhuEgRwAAkHQ0owAA\nAACAnKMZjRHmmAVXX4YvvviibrjhhtwXE1O8DsNBjgAAIOloRpFoVVVVOuGEE3TIIYdEXQoAAACQ\nKE02o2ZWbmYLzGyhmY1qZL0fmFmNmQ0It0RsxRyz4Gpn+OSTT+q0007TxIkT1a9fv+iKihleh+Eg\nRwAAkHSNNqNmViTpZknlkg6UNMjMvtPAemMlTZfEDRmR9yZPnqwhQ4boH//4h3r16hV1OQAAAEDi\nNHVk9AhJi9x9sbtvkjRR0kn1rHexpEckLQ+5PtTCHLPgSktLVVNToxtuuEFTpkxR9+7doy4pdngd\nhoMcAQBA0rVu4vk9JH1Ya3mJpCNrr2BmeyjVoPaU9ANJHmaBQNhat26t5557TmYcxAcAAACi0tSR\n0Uway99LutzdXalTdPkLP0uYYxbc1gxpRFuO12E4yBEAACRdU0dGl0rqUmu5i1JHR2s7XNLE9B/3\nxZL6mtkmd3+87saGDh2qrl27SpI6d+6sbt26bTtVbesfZiw3vPzqq6/mVT1xXN4qX+phObnL/H9u\n2f/fyspKLV68WAAAIP4sdUCzgSfNWkt6R1IvSR9JmiNpkLu/3cD6d0ua7O6P1vOcN7YvIFtmzJih\nXr16cTQUKDBmJnfnP3YAjM2Io4qKuSouPjyUbVVXz1VZWTjbkqSKWRUq3rc4lG1VL6pWWY+yULYF\n5Epzx+ZGT9N19xpJF0mqkPSWpAfd/W0zG2Zmw4KVCmSXu+vqq6/WRRddpJUrV0ZdDgAAAIBamrzP\nqLtPc/f93X1fd78+/dht7n5bPeueVd9RUYSj7qmmaJi7a9SoUfrHP/6hqqoqde7cWRIZhoEMw0GO\nAAAg6ZqaMwrEzpYtW/Szn/1ML730kiorK7XzzjtHXRIAAACAOmhGY2TrxTzQuDFjxmjevHmaMWOG\ndtxxxy89R4bBkWE4yBEAACRdk6fpAnEzbNgwVVRUfKURBQAAAJA/aEZjhDlmmdltt920/fbb1/sc\nGQZHhuEgRwAAkHQ0owAAAACAnKMZjRHmmH3V+vXr1Zx75JFhcGQYDnIEAABJRzOK2Fq1apXKyso0\nceLEqEsBAAAA0Ew0ozHCHLP/8+mnn6pPnz767ne/q4EDB2b8c2QYHBmGgxwBAEDS0YwidpYvX66e\nPXuqe/fumjBhglq14mUMAAAAxA1/xccIc8ykZcuWqaSkRCeccIJuvPFGmVmzfp4MgyPDcJAjAABI\nutZRFwA0R+vWrTVixAgNGzYs6lIAAAAABMCR0Rhhjpm06667BmpEyTA4MgwHOQIAgKSjGQUAAAAA\n5BzNaIwwxyw4MgyODMNBjgAAIOloRpG3Xn75ZY0aNSrqMgAAAABkAc1ojCRpjtns2bPVr18//fCH\nPwx1u0nKMFvIMBzkCAAAko6r6SLvzJw5UwMHDtRf//pXlZWVRV0OAAAAgCzgyGiMJGGO2fTp03X6\n6afroYceykojmoQMs40Mw0GOCMLMys1sgZktNLMG5zOY2Q/MrMbMBuSyPgAAMkEzirzh7vrDH/6g\nxx57jD/UAaABZlYk6WZJ5ZIOlDTIzL7TwHpjJU2XZDktEgCADNCMxkihzzEzM02dOjX0eaK1FXqG\nuUCG4SBHBHCEpEXuvtjdN0maKOmketa7WNIjkpbnsjgAADJFM4q8YsaH9wDQhD0kfVhreUn6sW3M\nbA+lGtRb0g95bkoDACBzNKMxwqmrwZFhcGQYDnJEAJk0lr+XdLm7u1Kn6PJJHwAg73A1XURmypQp\nKi8vV1FRUdSlAECcLJXUpdZyF6WOjtZ2uKSJ6bNNiiX1NbNN7v543Y2NHj162/elpaV8UAIAyFhl\nZWWgqUeW+tA0+8zMc7WvQlVZWVkwfyRcd911uueee/T888/ra1/7Ws72W0gZRoUMw0GOwZmZ3D1x\nR/zMrLWkdyT1kvSRpDmSBrn72w2sf7ekye7+aD3PMTYjdioq5qq4+PBQtlVdPVdlZeFsS5IqZlWo\neN/iULZVvahaZT24xR3ipbljM0dGkVPuriuvvFKTJk3SrFmzctqIAkAhcPcaM7tIUoWkIkl3uvvb\nZjYs/fxtkRYIAECGaEZjJO5HUdxdI0eO1DPPPKPKykrtuuuuOa8h7hnmAzIMBzkiCHefJmlancfq\nbULd/aycFAUAQDPRjCJnxo0bp+eff14zZ87UTjvtFHU5AAAAACLE1XRjJO73JTz77LP11FNPRdqI\nxj3DfECG4SBHAACQdBwZRc507tw56hIAAAAA5AmOjMYIc8yCI8PgyDAc5AgAAJKOZhRZsX79em3a\ntCnqMgAAAADkKZrRGInLHLM1a9aof//+uuOOO6Iu5SvikmE+I8NwkCMAAEg6mlGEauXKlSorK9M+\n++yj888/P+pyAAAAAOQpmtEYyfc5ZitWrFCvXr102GGH6c9//rOKioqiLukr8j3DOCDDcJAjAABI\nOppRhGL58uU69thj1bNnT40fP16tWvHSAgAAANAwOoYYyec5Zu3bt9eIESM0duxYmVnU5TQonzOM\nCzIMBzkCAICk4z6jCMX222+vc845J+oyAAAAAMQER0ZjhDlmwZFhcGQYDnIEAABJRzMKAAAAAMg5\nmtEYyZc5Zq+++qrOP/98uXvUpTRbvmQYZ2QYDnIEAABJRzOKZpkzZ47Kysp03HHH5fWFigAAAADk\nNy5gFCNRzzF77rnnNGDAAN111106/vjjI62lpaLOsBCQYTjIEQAAJB1HRpGRp59+WgMGDNB9990X\n20YUAAAAQP6gGY2RKOeY3XHHHXrkkUfUp0+fyGoIA/P0giPDcJAjAABIOk7TRUYeeOCBqEsAAAAA\nUEA4MhojzDELjgyDI8NwkCMAAEg6mlEAAAAAQM7RjMZIruaYTZo0SRs2bMjJvnKNeXrBkWE4yBEA\nACQdzSi+5MYbb9Sll16q6urqqEsBAAAAUMC4gFGMZHOOmbtrzJgxuu+++zRr1iztueeeWdtXlJin\nFxwZhoMcAQBA0tGMQu6uX/3qV5o8ebKqqqq02267RV0SAAAAgALHaboxkq05ZnfeeacqKipUWVlZ\n8I0o8/SCI8NwkCMAAEg6mlFoyJAheuaZZ1RcXBx1KQAAAAASgtN0YyRbc8zatWundu3aZWXb+YZ5\nesGRYTjIEQAAJB1HRgEAAAAAOUczGiNhzDHbsGGD1q5dG7yYmGKeXnBkGA5yBAAASUczmiDr1q3T\nSSedpD/+8Y9RlwIAAAAg4WhGYyTIHLPVq1erX79+2m233fSLX/wivKJihnl6wZFhOMgRAAAkHc1o\nAnz++ec67rjjdMABB+juu+9W69Zctwo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f93338c6a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "means_mc_pt = [compute_sum_of_charges(data_mc[mask], name, bins, use_pt=True) for mask, name, bins in \\\n", " zip([data_mc.signB > -100, \n", " (data_mc.IPs > 3) & ((abs(data_mc.diff_eta) > 0.6) | (abs(data_mc.diff_phi) > 0.825)), \n", " (abs(data_mc.diff_eta) < 0.6) & (abs(data_mc.diff_phi) < 0.825) & (data_mc.IPs < 3)], \n", " ['full_mc_pt', 'OS_mc_pt', 'SS_mc_pt'], [21, 21, 21])]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>$B^+$</th>\n", " <th>$B^+$, with signal part</th>\n", " <th>$B^-$</th>\n", " <th>$B^-$, with signal part</th>\n", " <th>ROC AUC</th>\n", " <th>ROC AUC, with signal part</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.174432e-08</td>\n", " <td>-1</td>\n", " <td>-3.615733e-08</td>\n", " <td>1</td>\n", " <td>0.500015</td>\n", " <td>1</td>\n", " <td>full_mc_pt</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>3.207049e-08</td>\n", " <td>-1</td>\n", " <td>-5.181189e-08</td>\n", " <td>1</td>\n", " <td>0.500032</td>\n", " <td>1</td>\n", " <td>OS_mc_pt</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>1.202127e-07</td>\n", " <td>-1</td>\n", " <td>-1.382307e-07</td>\n", " <td>1</td>\n", " <td>0.500353</td>\n", " <td>1</td>\n", " <td>SS_mc_pt</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " $B^+$ $B^+$, with signal part $B^-$ \\\n", "0 2.174432e-08 -1 -3.615733e-08 \n", "0 3.207049e-08 -1 -5.181189e-08 \n", "0 1.202127e-07 -1 -1.382307e-07 \n", "\n", " $B^-$, with signal part ROC AUC ROC AUC, with signal part name \n", "0 1 0.500015 1 full_mc_pt \n", "0 1 0.500032 1 OS_mc_pt \n", "0 1 0.500353 1 SS_mc_pt " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pandas.concat(means_mc_pt)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## random track strategy for event" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result_event_id, event_positions, data_ids = numpy.unique(data_full[event_id_column].values, return_index=True, return_inverse=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result_probs = -numpy.bincount(data_ids, weights=data_full.signTrack.values * data_full.partPt.values / sum(data_full.partPt.values))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result_label = numpy.bincount(data_ids, weights=data_full.signB.values) / numpy.bincount(data_ids)\n", "result_weight = numpy.bincount(data_ids, weights=data_full.N_sig_sw.values) / numpy.bincount(data_ids)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0e14d729d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(result_probs[result_label == 1], bins=60, normed=True)\n", "hist(result_probs[result_label == -1], bins=60, normed=True)\n", "pass" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_ids # event indices\n", "track_ids = numpy.arange(len(data_ids))\n", "selected_ids = numpy.zeros(numpy.max(data_ids) + 1)\n", "selected_ids[data_ids] = track_ids # assigns last track in each event\n", "permutation = numpy.random.permutation(track_ids)\n", "selected_ids[data_ids[permutation]] = track_ids[permutation]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(27156190, 27156186)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data_full), max(selected_ids.astype(int))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "signs_random = data_full.signTrack.values[selected_ids.astype(int)]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.48935024021414875" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roc_auc_score(result_label, signs_random)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+Pkcf3Ye//hWuvRYWLYKePb8/T3lLLPVxiYjUoaioiPz8fGbMmMHxx/fjnHNg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\nFBUuERFJKSpcIiKSUlS4REQkpfw/cS7da2BOOE0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fac5f4b9410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figsize(7, 6)\n", "plot(*roc_curve(result_label, signs_random)[:2])\n", "plot([0, 1], [0, 1], 'k--')\n", "grid()\n", "title('Random track choose, \\n ROC AUC using track sign %1.4f' %(1 - roc_auc_score(result_label, signs_random)), fontsize=14)\n", "plt.savefig('img/assymetry_tracks_random.png', format='png')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

hktxt/MachineLearning

sorting.ipynb

1

8872

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### sorting" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "strings = [\"Beryl\", \"Magage\", \"Clafe\", \"Stfg\", \"Bargs\", \"Rafdg\"] # sort lsit" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "strings.sort()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Bargs', 'Beryl', 'Clafe', 'Magage', 'Rafdg', 'Stfg']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strings" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "strings.sort(reverse=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Stfg', 'Rafdg', 'Magage', 'Clafe', 'Beryl', 'Bargs']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strings" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "stringst = (\"Beryl\", \"Magage\", \"Clafe\", \"Stfg\", \"Bargs\", \"Rafdg\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'tuple' object has no attribute 'sort'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-8-a8de71d9d838>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstringst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'sort'" ] } ], "source": [ "stringst.sort() # tuple sort not work" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method_descriptor:\n", "\n", "sort(...)\n", " L.sort(key=None, reverse=False) -> None -- stable sort *IN PLACE*\n", "\n" ] } ], "source": [ "help(list.sort)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### reverse = False, so ascending order default\n", "##### in-place, that return sorted values without copy " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#### key = <sort function>" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "planets = [\n", " (\"Mercury\", 2440, 5.43, 0.395),\n", " (\"Venus\", 6052, 5.24, 0.723),\n", " (\"Earth\", 6378, 5.52, 1.000),\n", " (\"Mars\", 3396, 3.93, 1.530),\n", " (\"Jupiter\",71492, 1.33, 5.210),\n", " (\"Saturn\", 60268, 0.69, 9.551),\n", " (\"Uranus\", 25559, 1.27, 19.213),\n", " (\"Neptune\", 24764, 1.64, 30.070)\n", "]\n", "# name, radius, density, distance from sun" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Jupiter', 71492, 1.33, 5.21),\n", " ('Saturn', 60268, 0.69, 9.551),\n", " ('Uranus', 25559, 1.27, 19.213),\n", " ('Neptune', 24764, 1.64, 30.07),\n", " ('Earth', 6378, 5.52, 1.0),\n", " ('Venus', 6052, 5.24, 0.723),\n", " ('Mars', 3396, 3.93, 1.53),\n", " ('Mercury', 2440, 5.43, 0.395)]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sort by size\n", "size = lambda planets: planets[1]\n", "planets.sort(key=size, reverse=True)\n", "planets" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Earth', 6378, 5.52, 1.0),\n", " ('Mercury', 2440, 5.43, 0.395),\n", " ('Venus', 6052, 5.24, 0.723),\n", " ('Mars', 3396, 3.93, 1.53),\n", " ('Neptune', 24764, 1.64, 30.07),\n", " ('Jupiter', 71492, 1.33, 5.21),\n", " ('Uranus', 25559, 1.27, 19.213),\n", " ('Saturn', 60268, 0.69, 9.551)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sort by density\n", "density = lambda planets: planets[2]\n", "planets.sort(key=density, reverse=True)\n", "planets" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# lsit.sort() changes thes list\n", "# can you create a sorted copy?\n", "# how do you sort a tuple?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function sorted in module builtins:\n", "\n", "sorted(iterable, /, *, key=None, reverse=False)\n", " Return a new list containing all items from the iterable in ascending order.\n", " \n", " A custom key function can be supplied to customize the sort order, and the\n", " reverse flag can be set to request the result in descending order.\n", "\n" ] } ], "source": [ "help(sorted)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "strings = [\"Beryl\", \"Magage\", \"Clafe\", \"Stfg\", \"Bargs\", \"Rafdg\"]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Bargs', 'Beryl', 'Clafe', 'Magage', 'Rafdg', 'Stfg']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_strings = sorted(strings);sorted_strings" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Beryl', 'Magage', 'Clafe', 'Stfg', 'Bargs', 'Rafdg']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strings # original does not change, not like sort() which is in-place" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "data = (7, 2, 5, 6, 1, 3, 9, 10, 4, 8)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(data) # output lsit" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7, 2, 5, 6, 1, 3, 9, 10, 4, 8)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A', 'a', 'a', 'b', 'c', 'e', 'h', 'i', 'l', 'l', 'p', 't']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(\"Alphabetical\") # intersting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

machow/siuba

docs/backends.ipynb

1

41711

{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "pd.set_option(\"display.max_rows\", 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Backends\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quick examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### pandas (fast grouped) _" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><p>(grouped data frame)</p><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cyl</th>\n", " <th>mpg</th>\n", " <th>hp</th>\n", " <th>avg_mpg</th>\n", " <th>hp_per_mpg</th>\n", " <th>demeaned</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6</td>\n", " <td>21.0</td>\n", " <td>110</td>\n", " <td>19.742857</td>\n", " <td>5.238095</td>\n", " <td>-12.285714</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>21.0</td>\n", " <td>110</td>\n", " <td>19.742857</td>\n", " <td>5.238095</td>\n", " <td>-12.285714</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>8</td>\n", " <td>15.0</td>\n", " <td>335</td>\n", " <td>15.100000</td>\n", " <td>22.333333</td>\n", " <td>125.785714</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>4</td>\n", " <td>21.4</td>\n", " <td>109</td>\n", " <td>26.663636</td>\n", " <td>5.093458</td>\n", " <td>26.363636</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>32 rows × 6 columns</p>\n", "</div></div>" ], "text/plain": [ "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11882d320>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pandas fast grouped implementation ----\n", "from siuba.data import cars\n", "from siuba import _\n", "from siuba.experimental.pd_groups import fast_mutate, fast_filter, fast_summarize\n", "\n", "fast_mutate(\n", " cars.groupby('cyl'),\n", " avg_mpg = _.mpg.mean(), # aggregation\n", " hp_per_mpg = _.hp / _.mpg, # elementwise \n", " demeaned = _.hp - _.hp.mean(), # elementwise + agg\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SQL _" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><pre># Source: lazy query\n", "# DB Conn: Engine(sqlite:///:memory:)\n", "# Preview:\n", "</pre><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cyl</th>\n", " <th>avg_mpg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>26.663636</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>19.742857</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>8</td>\n", " <td>15.100000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div><p># .. may have more rows</p></div>" ], "text/plain": [ "# Source: lazy query\n", "# DB Conn: Engine(sqlite:///:memory:)\n", "# Preview:\n", " cyl avg_mpg\n", "0 4 26.663636\n", "1 6 19.742857\n", "2 8 15.100000\n", "# .. may have more rows" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from siuba import _, mutate, group_by, summarize, show_query\n", "from siuba.sql import LazyTbl\n", "from sqlalchemy import create_engine\n", "\n", "# create sqlite db, add pandas DataFrame to it\n", "engine = create_engine(\"sqlite:///:memory:\")\n", "cars.to_sql(\"cars\", engine, if_exists=\"replace\")\n", "\n", "# define query\n", "q = (LazyTbl(engine, \"cars\")\n", " >> group_by(_.cyl)\n", " >> summarize(avg_mpg=_.mpg.mean())\n", ")\n", "\n", "q" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SELECT cars.cyl, avg(cars.mpg) AS avg_mpg \n", "FROM cars GROUP BY cars.cyl\n" ] } ], "source": [ "res = show_query(q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supported methods\n", "\n", "The table below shows the pandas methods supported by different backends. Note that the regular, ungrouped backend supports all methods, and the fast grouped implementation supports most methods a person could use without having to call the (slow) `DataFrame.apply` method.\n", "\n", "> 🚧This table is displayed a bit funky, but will be cleaned up!" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/html" }, "source": [ "<style>\n", "\n", "iframe.method-support {\n", " border: none;\n", " width: 100%;\n", " height: 1800px;\n", " \n", "}\n", "</style>\n", "<iframe class=\"method-support\" src=\"http://mchow.com/siuba-doc-method-table/\"></iframe>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pandas (ungrouped)\n", "\n", "In general, ungrouped pandas DataFrames do not require any translation.\n", "On this kind of data, verbs like `mutate` are just alternative implementations of methods like `DataFrame.assign`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>g</th>\n", " <th>x</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>a</td>\n", " <td>1</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>a</td>\n", " <td>2</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>b</td>\n", " <td>3</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " g x y\n", "0 a 1 2\n", "1 a 2 3\n", "2 b 3 4" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from siuba import _, mutate\n", "\n", "df = pd.DataFrame({\n", " 'g': ['a', 'a', 'b'], \n", " 'x': [1,2,3],\n", " })\n", "\n", "df.assign(y = lambda _: _.x + 1)\n", "\n", "mutate(df, y = _.x + 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Siuba verbs also work on grouped DataFrames, but are not always fast. They are the potentially slow, reference implementation." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><p>(grouped data frame)</p><div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>g</th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>z</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>a</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>-0.5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>a</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>b</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div></div>" ], "text/plain": [ "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1188709b0>" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mutate(\n", " df.groupby('g'),\n", " y = _.x + 1,\n", " z = _.x - _.x.mean()\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pandas (fast grouped)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that you could easily enable these fast methods by default, by aliasing them at import.\n", "\n", "```python\n", "from siuba.experimental.pd_groups import fast_mutate as mutate\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Architecture (1)\n", "\n", "Currently, the fast grouped implementation puts all the logic in the verbs. That is, `fast_mutate` dispatches for DataFrameGroupBy a function that handles all the necessary translation of lazy expressions.\n", "\n", "See TODO link this ADR for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SQL\n", "\n", "### Architecture (2)\n" ] }, { "cell_type": "raw", "metadata": { "jupyter": { "source_hidden": true }, "raw_mimetype": "text/html" }, "source": [ "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" width=\"811px\" viewBox=\"-0.5 -0.5 811 623\" content=\"&lt;mxfile host=&quot;app.diagrams.net&quot; modified=&quot;2020-04-29T22:00:18.274Z&quot; agent=&quot;5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36&quot; etag=&quot;Tp_dICBLt3o_wYUzd_51&quot; version=&quot;13.0.2&quot; type=&quot;google&quot; pages=&quot;3&quot;&gt;&lt;diagram id=&quot;prtHgNgQTEPvFCAcTncT&quot; 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id=&quot;t5tLW42HWwBpjyBLeSh1&quot; 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SqlTbl</text><text x=\"5.5\" y=\"474.5\">shape_call(Call, ...) : Call</text><text x=\"5.5\" y=\"488.5\">track_call_windows(Call):  </text><text x=\"5.5\" y=\"502.5\">get_ordered_col_names()</text></g><path d=\"M 360 501 L 360 475 L 540 475 L 540 501\" fill=\"#ffffff\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 360 501 L 360 619 L 540 619 L 540 501\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 360 501 L 540 501\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" font-weight=\"bold\" text-anchor=\"middle\" font-size=\"12px\"><text x=\"449.5\" y=\"492.5\">WindowReplacer</text></g><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-364-506-172-84-0)\" font-size=\"12px\"><text x=\"365.5\" y=\"518.5\">columns: Mapping[sqla.Column]</text><text x=\"365.5\" y=\"532.5\">group_by</text><text x=\"365.5\" y=\"546.5\">order_by</text><text x=\"365.5\" y=\"560.5\">window_cte</text><text x=\"365.5\" y=\"574.5\">windows: List</text></g><path d=\"M 360 589 L 540 589\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-364-598-172-26-0)\" font-size=\"12px\"><text x=\"365.5\" y=\"610.5\">+ method(type): type</text></g><path d=\"M 360 257 L 360 231 L 540 231 L 540 257\" fill=\"#ffffff\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 360 257 L 360 431 L 540 431 L 540 257\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 360 257 L 540 257\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" font-weight=\"bold\" text-anchor=\"middle\" font-size=\"12px\"><text x=\"449.5\" y=\"248.5\">CallTreeLocal</text></g><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-364-262-172-84-0)\" font-size=\"12px\"><text x=\"365.5\" y=\"274.5\">local: dict</text><text x=\"365.5\" y=\"288.5\">call_sub_attr: set</text><text x=\"365.5\" y=\"302.5\">chain_sub_attr: Bool</text><text x=\"365.5\" y=\"316.5\">dispatch_cls: Type</text><text x=\"365.5\" y=\"330.5\">result_cls: Type</text></g><path d=\"M 360 345 L 540 345\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-364-354-172-82-0)\" font-size=\"12px\"><text x=\"365.5\" y=\"366.5\">create_local_call(str, ...) : Call</text></g><path d=\"M 650 370 L 650 344 L 810 344 L 810 370\" fill=\"#ffffff\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 650 370 L 650 574 L 810 574 L 810 370\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 650 370 L 810 370\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" font-weight=\"bold\" text-anchor=\"middle\" font-size=\"12px\"><text x=\"729.5\" y=\"361.5\">CallListener</text></g><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-654-375-152-26-0)\" font-size=\"12px\"><text x=\"655.5\" y=\"387.5\">generic_visit: type</text></g><path d=\"M 650 400 L 810 400\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g fill=\"#000000\" font-family=\"Helvetica\" clip-path=\"url(#mx-clip-654-409-152-170-0)\" font-size=\"12px\"><text x=\"655.5\" y=\"421.5\">enter(Call): Call</text><text x=\"655.5\" y=\"435.5\">exit   (Call): Call</text><text x=\"655.5\" y=\"449.5\">generic_enter(Call): Call</text><text x=\"655.5\" y=\"463.5\">generic_exit   (Call): Call</text><text x=\"655.5\" y=\"477.5\">enter_if_call(T): T</text><text x=\"655.5\" y=\"505.5\">-- enter, exit methods format</text><text x=\"655.5\" y=\"519.5\">enter_&lt;Call.func&gt;(Call)</text><text x=\"655.5\" y=\"547.5\">-- eg</text><text x=\"655.5\" y=\"561.5\">enter___get_item__(Call)</text></g><rect x=\"170\" y=\"51\" width=\"110\" height=\"30\" fill=\"#ffffff\" stroke=\"#000000\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 66px; margin-left: 225px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; white-space: nowrap; \">group_by(...)</div></div></div></foreignObject><text x=\"225\" y=\"70\" 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font-family=\"Helvetica\" font-size=\"12px\" text-anchor=\"middle\">arrange(...)</text></switch></g><rect x=\"520\" y=\"51\" width=\"150\" height=\"30\" fill=\"#ffffff\" stroke=\"#000000\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 66px; margin-left: 595px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; white-space: nowrap; \">mutate/filter/summarize(...)</div></div></div></foreignObject><text x=\"595\" y=\"70\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"12px\" text-anchor=\"middle\">mutate/filter/summarize(....</text></switch></g><path d=\"M 225 81 L 225 121 Q 225 131 215 131 L 105 131 Q 95 131 95 141 L 95 218.76\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 91.5 212.88 L 95 219.88 L 98.5 212.88\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe flex-end; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 108px; margin-left: 150px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 11px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; background-color: #ffffff; white-space: nowrap; \">copy(group_by = Tuple[str])</div></div></div></foreignObject><text x=\"150\" y=\"108\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"11px\" text-anchor=\"middle\">copy(group_by =...</text></switch></g><path d=\"M 395 81 L 395 121 Q 395 131 385 131 L 105 131 Q 95 131 95 141 L 95 218.76\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 91.5 212.88 L 95 219.88 L 98.5 212.88\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe flex-end; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 108px; margin-left: 350px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 11px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; background-color: #ffffff; white-space: nowrap; \">copy(order_by = Tuple[sqla.Clause])</div></div></div></foreignObject><text x=\"350\" y=\"108\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"11px\" text-anchor=\"middle\">copy(order_by =...</text></switch></g><path d=\"M 595 81 L 595 121 Q 595 131 585 131 L 105 131 Q 95 131 95 141 L 95 218.76\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 91.5 212.88 L 95 219.88 L 98.5 212.88\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe flex-end; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 108px; margin-left: 550px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 11px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; background-color: #ffffff; white-space: nowrap; \">append_op(...)</div></div></div></foreignObject><text x=\"550\" y=\"108\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"11px\" text-anchor=\"middle\">append_op(...)</text></switch></g><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 12px; margin-left: 410px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: center; \"><div style=\"display: inline-block; font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; white-space: nowrap; \"><font style=\"font-size: 20px\">SQL Architecture</font></div></div></div></foreignObject><text x=\"410\" y=\"16\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"12px\" text-anchor=\"middle\">SQL Architecture</text></switch></g><path d=\"M 700 151 L 786 151 L 800 165 L 800 221 L 700 221 L 700 151 Z\" fill=\"#ffffff\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><path d=\"M 786 151 L 786 165 L 800 165\" fill=\"none\" stroke=\"#000000\" stroke-miterlimit=\"10\" pointer-events=\"none\"/><g transform=\"translate(-0.5 -0.5)\"><switch><foreignObject style=\"overflow: visible; text-align: left;\" pointer-events=\"none\" width=\"100%\" height=\"100%\" requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: flex; align-items: unsafe flex-start; justify-content: unsafe flex-start; width: 98px; height: 1px; padding-top: 152px; margin-left: 702px;\"><div style=\"box-sizing: border-box; font-size: 0; text-align: left; \"><div style=\"display: inline-block; font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; white-space: normal; word-wrap: normal; \">Note<br /><br />sqla = sqlachemy</div></div></div></foreignObject><text x=\"702\" y=\"164\" fill=\"#000000\" font-family=\"Helvetica\" font-size=\"12px\">Note...</text></switch></g></g><switch><g requiredFeatures=\"http://www.w3.org/TR/SVG11/feature#Extensibility\"/><a transform=\"translate(0,-5)\" xlink:href=\"https://desk.draw.io/support/solutions/articles/16000042487\" target=\"_blank\"><text text-anchor=\"middle\" font-size=\"10px\" x=\"50%\" y=\"100%\">Viewer does not support full SVG 1.1</text></a></switch></svg>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SQL implementation consists largely of the following:\n", " \n", "* **LazyTbl** - a class that holds a sqlalchemy connection, table name, and list of select statements.\n", "* **Verbs** that dispatch on LazyTbl - eg. mutate takes a LazyTbl, and returns a LazyTbl that has a new select statement corresponding to that mutate.\n", "* **CallListeners** for (1) translating lazy expressions to SQL specific functions, and (2) adding grouping information to OVER clauses.\n", "\n", "See TODO link this ADR for more details." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

MarekKZielinski/Predicting-Blood-Donations

.ipynb_checkpoints/Predict Blood Donations CNN-checkpoint.ipynb

1

16553550

mit

balarsen/pymc_learning

Distributions/TruncatedNormal.ipynb

1

321787

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TruncatedNormal\n", "how does this compare to a bounded variable?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import stats\n", "import seaborn as sns\n", "import pymc3 as pm\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", "sns.set(font_scale=1.5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.14568013, 2.54837666, 0.77564356, 0.2037265 , 1.36044847,\n", " 2.49665803, 0.80871428, 2.05183848, 3.00514862, 1.66909572,\n", " 3.0813126 , 1.45238379, 1.16459861, 1.73612704, 1.22713344,\n", " 6.56894626, 2.15293012, 1.71000748, 1.32382677])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = np.random.normal(2, 2, size=20)\n", "data = data[data > 0]\n", "data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1c2576d0d0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [norm, sd, mean]\n", "Sampling 2 chains: 100%|██████████| 3000/3000 [00:02<00:00, 1054.77draws/s]\n", "There were 111 divergences after tuning. Increase `target_accept` or reparameterize.\n", "There were 81 divergences after tuning. Increase `target_accept` or reparameterize.\n", "The acceptance probability does not match the target. It is 0.7193673915433556, but should be close to 0.8. Try to increase the number of tuning steps.\n", "The estimated number of effective samples is smaller than 200 for some parameters.\n" ] } ], "source": [ "with pm.Model() as model:\n", " mean = pm.Uniform('mean', 0, 100)\n", " sd = pm.Uniform('sd', 0, 100)\n", " norm = pm.TruncatedNormal('norm', mu=mean, sigma=sd, lower=0)\n", " trace = pm.sample(1000)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c29f03990>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x111629690>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x1c26042950>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1c26069dd0>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x1c26098650>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1c260cc910>]],\n", " dtype=object)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [sd, mean]\n", "Sampling 2 chains: 100%|██████████| 12000/12000 [00:06<00:00, 1956.53draws/s]\n" ] } ], "source": [ "with pm.Model() as model:\n", " mean = pm.Uniform('mean', 0, 100)\n", " sd = pm.Uniform('sd', 0, 100)\n", " norm = pm.TruncatedNormal('norm', mu=mean, sigma=sd, lower=0, observed=data+10)\n", " trace = pm.sample(5000, tune=1000, target_accept=0.9)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c2a4781d0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1c2f9b57d0>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x1c2f92fe90>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1c2f8f94d0>]],\n", " dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([12.05673274, 11.67734871, 12.04520494, ..., 11.48027536,\n", " 12.008448 , 11.14983553])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trace.get_values('mean')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1c2fbdacd0>" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(trace.get_values('mean')-10)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/pymc3/stats.py:991: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n", " axis=1, join_axes=[dforg.index])\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mean</th>\n", " <th>sd</th>\n", " <th>mc_error</th>\n", " <th>hpd_2.5</th>\n", " <th>hpd_97.5</th>\n", " <th>n_eff</th>\n", " <th>Rhat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mean</th>\n", " <td>11.869117</td>\n", " <td>0.350850</td>\n", " <td>0.003859</td>\n", " <td>11.126730</td>\n", " <td>12.524384</td>\n", " <td>6677.368375</td>\n", " <td>0.999928</td>\n", " </tr>\n", " <tr>\n", " <th>sd</th>\n", " <td>1.515014</td>\n", " <td>0.279144</td>\n", " <td>0.003222</td>\n", " <td>1.047906</td>\n", " <td>2.084130</td>\n", " <td>6297.734771</td>\n", " <td>0.999980</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean sd mc_error hpd_2.5 hpd_97.5 n_eff \\\n", "mean 11.869117 0.350850 0.003859 11.126730 12.524384 6677.368375 \n", "sd 1.515014 0.279144 0.003222 1.047906 2.084130 6297.734771 \n", "\n", " Rhat \n", "mean 0.999928 \n", "sd 0.999980 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.summary(trace)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1c30387e10>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1c307bbd50>],\n", " dtype=object)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 993.6x331.2 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.plot_posterior(trace)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

abnowack/PyTracer

scripts/tests/raytrace.ipynb

1

250347

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def raytrace(line, extent, pixels, debug=False, display_pixels=False):\n", " # Fixed issue, alphax[0] in Filip Jacob's paper means first alphax in siddon array, not alphax at zero.\n", "\n", " from math import ceil, floor\n", "\n", " p1x, p1y, p2x, p2y = line\n", " bx, by, = extent[0], extent[2]\n", " Nx, Ny = np.size(pixels, 0) + 1, np.size(pixels, 1) + 1\n", " dx, dy = (extent[1] - extent[0]) / np.size(pixels, 0), (extent[3] - extent[2]) / np.size(pixels, 1)\n", "\n", " p12x = lambda a_: p1x + a_ * (p2x - p1x)\n", " p12y = lambda a_: p1y + a_ * (p2y - p1y)\n", "\n", " alphax = lambda i_: ((bx + i_ * dx) - p1x) / (p2x - p1x)\n", " alphay = lambda j_: ((by + j_ * dy) - p1y) / (p2y - p1y)\n", "\n", " if p1x == p2x:\n", " alphaxmin = 0\n", " alphaxmax = 0\n", " else:\n", " alphaxmin = min(alphax(0), alphax(Nx - 1))\n", " alphaxmax = max(alphax(0), alphax(Nx - 1))\n", "\n", " if p1y == p2y:\n", " alphaymin = 0\n", " alphaymax = 0\n", " else:\n", " alphaymin = min(alphay(0), alphay(Ny - 1))\n", " alphaymax = max(alphay(0), alphay(Ny - 1))\n", "\n", "\n", " if p1x == p2x:\n", " alphamin = max(0, alphaymin)\n", " alphamax = min(1, alphaymax)\n", " elif p1y == p2y:\n", " alphamin = max(0, alphaxmin)\n", " alphamax = min(1, alphaxmax)\n", " else:\n", " alphamin = max(0, alphaxmin, alphaymin)\n", " alphamax = min(1, alphaxmax, alphaymax)\n", "\n", " phix = lambda a_: (p12x(a_) - bx) / dx\n", "\n", " if p1x < p2x:\n", " if alphamin == alphaxmin:\n", " imin = 1\n", " else:\n", " imin = ceil(phix(alphamin))\n", "\n", " if alphamax == alphaxmax:\n", " imax = Nx - 1\n", " else:\n", " imax = floor(phix(alphamax))\n", "\n", " if p1x == p2x:\n", " alphax_ = np.inf\n", " else:\n", " alphax_ = alphax(imin)\n", "\n", " else:\n", " if alphamin == alphaxmin:\n", " imax = Nx - 2\n", " else:\n", " imax = floor(phix(alphamin))\n", "\n", " if alphamax == alphaxmax:\n", " imin = 0\n", " else:\n", " imin = ceil(phix(alphamax))\n", "\n", " if p1x == p2x:\n", " alphax_ = np.inf\n", " else:\n", " alphax_ = alphax(imax)\n", "\n", " phiy = lambda a_: (p12y(a_) - by) / dy\n", "\n", " if p1y < p2y:\n", " if alphamin == alphaymin:\n", " jmin = 1\n", " else:\n", " jmin = ceil(phiy(alphamin))\n", "\n", " if alphamax == alphaymax:\n", " jmax = Ny - 1\n", " else:\n", " jmax = floor(phiy(alphamax))\n", "\n", " if p1y == p2y:\n", " alphay_ = np.inf\n", " else:\n", " alphay_ = alphay(jmin)\n", "\n", " else:\n", " if alphamin == alphaymin:\n", " jmax = Ny - 2\n", " else:\n", " jmax = floor(phiy(alphamin))\n", "\n", " if alphamax == alphaymax:\n", " jmin = 0\n", " else:\n", " jmin = ceil(phiy(alphamax))\n", "\n", " if p1y == p2y:\n", " alphay_ = np.inf\n", " else:\n", " alphay_ = alphay(jmax)\n", "\n", " Np = (imax - imin + 1) + (jmax - jmin + 1)\n", "\n", " alphamid = (min(alphax_, alphay_) + alphamin) / 2\n", "\n", " i = floor(phix(alphamid))\n", " j = floor(phiy(alphamid))\n", "\n", " if debug:\n", " draw_alpha(line, alphamin, color='blue')\n", " draw_alpha(line, alphamax, color='orange')\n", "\n", " if p1x == p2x:\n", " alphaxu = 0\n", " else:\n", " alphaxu = dx / abs(p2x - p1x)\n", " if p1y == p2y:\n", " alphayu = 0\n", " else:\n", " alphayu = dy / abs(p2y - p1y)\n", "\n", " d12 = 0\n", " dconv = ((p2x - p1x)**2 + (p2y - p1y)**2)**0.5\n", " alphac = alphamin\n", "\n", " if debug:\n", " draw_alpha(line, alphac)\n", "\n", " if p1x < p2x:\n", " iu = 1\n", " else:\n", " iu = -1\n", "\n", " if p1y < p2y:\n", " ju = 1\n", " else:\n", " ju = -1\n", "\n", " for k in range(Np):\n", " if display_pixels:\n", " pixels[i, j] = 1\n", "\n", " if alphax_ < alphay_:\n", " lij = (alphax_ - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", " i = i + iu\n", " alphac = alphax_\n", " alphax_ = alphax_ + alphaxu\n", " else:\n", " lij = (alphay_ - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", " j = j + ju\n", " alphac = alphay_\n", " alphay_ = alphay_ + alphayu\n", "\n", " if debug:\n", " draw_alpha(line, alphac)\n", "\n", " # have to think about this for case of line in and outside of image\n", " # alphamax == 1 means last point is in image\n", " # alphamin == 0 means first point is in image\n", " # print(alphamax, alphamin, alphaxmin, alphaxmax)\n", " if alphamax == 1:\n", " if display_pixels:\n", " pixels[i, j] = 1\n", "\n", " lij = (alphamax - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", "\n", " if debug:\n", " draw_alpha(line, alphamax)\n", "\n", " if debug:\n", " draw_algorithm(extent, pixels)\n", " draw_line(line)\n", "\n", " return d12" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.28.2 -->\n", "<html>\n", 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class=\"\">006</span>: </pre>\n", "<pre class=\"cython line score-107\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">007</span>: <span class=\"k\">cpdef</span> <span class=\"kt\">double</span> <span class=\"nf\">c_raytrace_fast</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">line</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ex1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ex2</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ey1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ey2</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:,</span> <span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span 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__pyx_v_alphay;\n", " double __pyx_v_alphamid;\n", " int __pyx_v_i;\n", " int __pyx_v_j;\n", " int __pyx_v_iu;\n", " int __pyx_v_ju;\n", " int __pyx_v_imax;\n", " int __pyx_v_imin;\n", " int __pyx_v_jmax;\n", " int __pyx_v_jmin;\n", " int __pyx_v_Np;\n", " double __pyx_v_alphamin;\n", " double __pyx_v_alphamax;\n", " double __pyx_v_alphaxu;\n", " double __pyx_v_alphayu;\n", " double __pyx_v_dconv;\n", " double __pyx_v_alphac;\n", " CYTHON_UNUSED int __pyx_v_k;\n", " double __pyx_v_lij;\n", " double __pyx_r;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"c_raytrace_fast\", 0);\n", "/* … */\n", " /* function exit code */\n", " __pyx_L1_error:;\n", " <span class='pyx_c_api'>__Pyx_WriteUnraisable</span>(\"_cython_magic_66b3bd080e40ad88ea24f32fdf408264.c_raytrace_fast\", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0);\n", " __pyx_r = 0;\n", " __pyx_L0:;\n", " <span 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class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", " values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", " values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n", " }\n", " __pyx_v_line = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[0], PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_v_line.memview)) __PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_v_ex1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_ex1 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_v_ex2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_ex2 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_v_ey1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[3]); if (unlikely((__pyx_v_ey1 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_v_ey2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[4]); if (unlikely((__pyx_v_ey2 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_v_pixels = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[5], PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_v_pixels.memview)) __PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " }\n", " goto __pyx_L4_argument_unpacking_done;\n", " __pyx_L5_argtuple_error:;\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast\", 1, 6, 6, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L3_error)</span>\n", " __pyx_L3_error:;\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_66b3bd080e40ad88ea24f32fdf408264.c_raytrace_fast\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return NULL;\n", " __pyx_L4_argument_unpacking_done:;\n", " __pyx_r = __pyx_pf_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast(__pyx_self, __pyx_v_line, __pyx_v_ex1, __pyx_v_ex2, __pyx_v_ey1, __pyx_v_ey2, __pyx_v_pixels);\n", "\n", " /* function exit code */\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "static PyObject *__pyx_pf_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_line, double __pyx_v_ex1, double __pyx_v_ex2, double __pyx_v_ey1, double __pyx_v_ey2, __Pyx_memviewslice __pyx_v_pixels) {\n", " PyObject *__pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"c_raytrace_fast\", 0);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " if (unlikely(!__pyx_v_line.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"line\"); <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L1_error)</span> }\n", " if (unlikely(!__pyx_v_pixels.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"pixels\"); <span class='error_goto'>__PYX_ERR(0, 7, __pyx_L1_error)</span> }\n", " __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_f_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast(__pyx_v_line, __pyx_v_ex1, __pyx_v_ex2, __pyx_v_ey1, __pyx_v_ey2, __pyx_v_pixels, 0));<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_r = __pyx_t_1;\n", " __pyx_t_1 = 0;\n", " goto __pyx_L0;\n", "\n", " /* function exit code */\n", " __pyx_L1_error:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_66b3bd080e40ad88ea24f32fdf408264.c_raytrace_fast\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " __pyx_r = NULL;\n", " __pyx_L0:;\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_v_line, 1);\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_v_pixels, 1);\n", " <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">008</span>: <span class=\"c\"># Fixed issue, alphax[0] in Filip Jacob&#39;s paper means first alphax in siddon array, not alphax at zero.</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">009</span>: <span class=\"k\">cdef</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">010</span>: <span class=\"n\">double</span> <span class=\"n\">d12</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_d12 = 0.0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">011</span>: <span class=\"n\">double</span> <span class=\"n\">alphaxmin</span><span class=\"p\">,</span> <span class=\"n\">alphaxmax</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">012</span>: <span class=\"n\">double</span> <span class=\"n\">alphaymin</span><span class=\"p\">,</span> <span class=\"n\">alphaymax</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">013</span>: <span class=\"n\">double</span> <span class=\"n\">p1x</span><span class=\"p\">,</span> <span class=\"n\">p1y</span><span class=\"p\">,</span> <span class=\"n\">p2x</span><span class=\"p\">,</span> <span class=\"n\">p2y</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">014</span>: <span class=\"n\">double</span> <span class=\"n\">bx</span><span class=\"p\">,</span> <span class=\"k\">by</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">015</span>: <span class=\"nb\">int</span> <span class=\"n\">Nx</span><span class=\"p\">,</span> <span class=\"n\">Ny</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">016</span>: <span class=\"n\">double</span> <span class=\"n\">dx</span><span class=\"p\">,</span> <span class=\"n\">dy</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">017</span>: <span class=\"n\">double</span> <span class=\"n\">alphax</span><span class=\"p\">,</span> <span class=\"n\">alphay</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">018</span>: <span class=\"n\">double</span> <span class=\"n\">alphamid</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">019</span>: <span class=\"nb\">int</span> <span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">iu</span><span class=\"p\">,</span> <span class=\"n\">ju</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">020</span>: <span class=\"nb\">int</span> <span class=\"n\">imax</span><span class=\"p\">,</span> <span class=\"n\">imin</span><span class=\"p\">,</span> <span class=\"n\">jmax</span><span class=\"p\">,</span> <span class=\"n\">jmin</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">021</span>: <span class=\"nb\">int</span> <span class=\"n\">Np</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">022</span>: </pre>\n", "<pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">023</span>: <span class=\"n\">p1x</span> <span class=\"o\">=</span> <span class=\"n\">line</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_1 = 0;\n", " __pyx_t_2 = -1;\n", " if (__pyx_t_1 &lt; 0) {\n", " __pyx_t_1 += __pyx_v_line.shape[0];\n", " if (unlikely(__pyx_t_1 &lt; 0)) __pyx_t_2 = 0;\n", " } else if (unlikely(__pyx_t_1 &gt;= __pyx_v_line.shape[0])) __pyx_t_2 = 0;\n", " if (unlikely(__pyx_t_2 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_2);\n", " <span class='error_goto'>__PYX_ERR(0, 23, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_p1x = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_line.data) + __pyx_t_1)) )));\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">024</span>: <span class=\"n\">p1y</span> <span class=\"o\">=</span> <span class=\"n\">line</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_3 = 1;\n", " __pyx_t_2 = -1;\n", " if (__pyx_t_3 &lt; 0) {\n", " __pyx_t_3 += __pyx_v_line.shape[0];\n", " if (unlikely(__pyx_t_3 &lt; 0)) __pyx_t_2 = 0;\n", " } else if (unlikely(__pyx_t_3 &gt;= __pyx_v_line.shape[0])) __pyx_t_2 = 0;\n", " if (unlikely(__pyx_t_2 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_2);\n", " <span class='error_goto'>__PYX_ERR(0, 24, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_p1y = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_line.data) + __pyx_t_3)) )));\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">025</span>: <span class=\"n\">p2x</span> <span class=\"o\">=</span> <span class=\"n\">line</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_4 = 2;\n", " __pyx_t_2 = -1;\n", " if (__pyx_t_4 &lt; 0) {\n", " __pyx_t_4 += __pyx_v_line.shape[0];\n", " if (unlikely(__pyx_t_4 &lt; 0)) __pyx_t_2 = 0;\n", " } else if (unlikely(__pyx_t_4 &gt;= __pyx_v_line.shape[0])) __pyx_t_2 = 0;\n", " if (unlikely(__pyx_t_2 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_2);\n", " <span class='error_goto'>__PYX_ERR(0, 25, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_p2x = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_line.data) + __pyx_t_4)) )));\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">026</span>: <span class=\"n\">p2y</span> <span class=\"o\">=</span> <span class=\"n\">line</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_5 = 3;\n", " __pyx_t_2 = -1;\n", " if (__pyx_t_5 &lt; 0) {\n", " __pyx_t_5 += __pyx_v_line.shape[0];\n", " if (unlikely(__pyx_t_5 &lt; 0)) __pyx_t_2 = 0;\n", " } else if (unlikely(__pyx_t_5 &gt;= __pyx_v_line.shape[0])) __pyx_t_2 = 0;\n", " if (unlikely(__pyx_t_2 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_2);\n", " <span class='error_goto'>__PYX_ERR(0, 26, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_p2y = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_line.data) + __pyx_t_5)) )));\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">027</span>: <span class=\"n\">bx</span><span class=\"p\">,</span> <span class=\"k\">by</span> <span class=\"o\">=</span> <span class=\"n\">ex1</span><span class=\"p\">,</span> <span class=\"n\">ey1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_6 = __pyx_v_ex1;\n", " __pyx_t_7 = __pyx_v_ey1;\n", " __pyx_v_bx = __pyx_t_6;\n", " __pyx_v_by = __pyx_t_7;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">028</span>: <span class=\"n\">Nx</span><span class=\"p\">,</span> <span class=\"n\">Ny</span> <span class=\"o\">=</span> <span class=\"n\">pixels</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">pixels</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_8 = ((__pyx_v_pixels.shape[0]) + 1);\n", " __pyx_t_9 = ((__pyx_v_pixels.shape[1]) + 1);\n", " __pyx_v_Nx = __pyx_t_8;\n", " __pyx_v_Ny = __pyx_t_9;\n", "</pre><pre class=\"cython line score-10\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">029</span>: <span class=\"n\">dx</span><span class=\"p\">,</span> <span class=\"n\">dy</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">ex2</span> <span class=\"o\">-</span> <span class=\"n\">ex1</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">pixels</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">],</span> <span class=\"p\">(</span><span class=\"n\">ey2</span> <span class=\"o\">-</span> <span class=\"n\">ey1</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">pixels</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-10 '> __pyx_t_7 = (__pyx_v_ex2 - __pyx_v_ex1);\n", " if (unlikely((__pyx_v_pixels.shape[0]) == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 29, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_6 = (__pyx_t_7 / ((double)(__pyx_v_pixels.shape[0])));\n", " __pyx_t_7 = (__pyx_v_ey2 - __pyx_v_ey1);\n", " if (unlikely((__pyx_v_pixels.shape[1]) == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 29, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_10 = (__pyx_t_7 / ((double)(__pyx_v_pixels.shape[1])));\n", " __pyx_v_dx = __pyx_t_6;\n", " __pyx_v_dy = __pyx_t_10;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">030</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">031</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">==</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x == __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L3;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">032</span>: <span class=\"n\">alphaxmin</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphaxmin = 0.0;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">033</span>: <span class=\"n\">alphaxmax</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphaxmax = 0.0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">034</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">035</span>: <span class=\"n\">alphaxmin</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"mf\">0</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">),</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", "/* … */\n", " __pyx_t_6 = ((__pyx_v_bx + (0.0 * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_10 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 35, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_12 = (__pyx_t_6 / __pyx_t_10);\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">036</span>: <span class=\"p\">((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">Nx</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_bx + ((__pyx_v_Nx - 1) * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_6 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_6 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 36, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_7 = (__pyx_t_10 / __pyx_t_6);\n", "/* … */\n", " if (((__pyx_t_7 &lt; __pyx_t_12) != 0)) {\n", " __pyx_t_10 = __pyx_t_7;\n", " } else {\n", " __pyx_t_10 = __pyx_t_12;\n", " }\n", " __pyx_v_alphaxmin = __pyx_t_10;\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">037</span>: <span class=\"n\">alphaxmax</span> <span class=\"o\">=</span> <span class=\"nb\">max</span><span class=\"p\">(((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"mf\">0</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">),</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_7 = ((__pyx_v_bx + (0.0 * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_10 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 37, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_6 = (__pyx_t_7 / __pyx_t_10);\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">038</span>: <span class=\"p\">((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">Nx</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_bx + ((__pyx_v_Nx - 1) * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_7 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_7 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 38, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_12 = (__pyx_t_10 / __pyx_t_7);\n", "/* … */\n", " if (((__pyx_t_12 &gt; __pyx_t_6) != 0)) {\n", " __pyx_t_10 = __pyx_t_12;\n", " } else {\n", " __pyx_t_10 = __pyx_t_6;\n", " }\n", " __pyx_v_alphaxmax = __pyx_t_10;\n", " }\n", " __pyx_L3:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">039</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">040</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">==</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y == __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L4;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">041</span>: <span class=\"n\">alphaymin</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphaymin = 0.0;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">042</span>: <span class=\"n\">alphaymax</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphaymax = 0.0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">043</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">044</span>: <span class=\"n\">alphaymin</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"mf\">0</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">),</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", "/* … */\n", " __pyx_t_12 = ((__pyx_v_by + (0.0 * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_10 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 44, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_7 = (__pyx_t_12 / __pyx_t_10);\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">045</span>: <span class=\"p\">((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">Ny</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_by + ((__pyx_v_Ny - 1) * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_12 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_12 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 45, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_6 = (__pyx_t_10 / __pyx_t_12);\n", "/* … */\n", " if (((__pyx_t_6 &lt; __pyx_t_7) != 0)) {\n", " __pyx_t_10 = __pyx_t_6;\n", " } else {\n", " __pyx_t_10 = __pyx_t_7;\n", " }\n", " __pyx_v_alphaymin = __pyx_t_10;\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">046</span>: <span class=\"n\">alphaymax</span> <span class=\"o\">=</span> <span class=\"nb\">max</span><span class=\"p\">(((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"mf\">0</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">),</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_6 = ((__pyx_v_by + (0.0 * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_10 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 46, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_12 = (__pyx_t_6 / __pyx_t_10);\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">047</span>: <span class=\"p\">((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">Ny</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_by + ((__pyx_v_Ny - 1) * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_6 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_6 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 47, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_7 = (__pyx_t_10 / __pyx_t_6);\n", "/* … */\n", " if (((__pyx_t_7 &gt; __pyx_t_12) != 0)) {\n", " __pyx_t_10 = __pyx_t_7;\n", " } else {\n", " __pyx_t_10 = __pyx_t_12;\n", " }\n", " __pyx_v_alphaymax = __pyx_t_10;\n", " }\n", " __pyx_L4:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">048</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">049</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">==</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x == __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L5;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">050</span>: <span class=\"n\">alphamin</span> <span class=\"o\">=</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">alphaymin</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_10 = __pyx_v_alphaymin;\n", " __pyx_t_13 = 0;\n", " if (((__pyx_t_10 &gt; __pyx_t_13) != 0)) {\n", " __pyx_t_7 = __pyx_t_10;\n", " } else {\n", " __pyx_t_7 = __pyx_t_13;\n", " }\n", " __pyx_v_alphamin = __pyx_t_7;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">051</span>: <span class=\"n\">alphamax</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">alphaymax</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_7 = __pyx_v_alphaymax;\n", " __pyx_t_13 = 1;\n", " if (((__pyx_t_7 &lt; __pyx_t_13) != 0)) {\n", " __pyx_t_10 = __pyx_t_7;\n", " } else {\n", " __pyx_t_10 = __pyx_t_13;\n", " }\n", " __pyx_v_alphamax = __pyx_t_10;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">052</span>: <span class=\"k\">elif</span> <span class=\"n\">p1y</span> <span class=\"o\">==</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y == __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L5;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">053</span>: <span class=\"n\">alphamin</span> <span class=\"o\">=</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">alphaxmin</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_10 = __pyx_v_alphaxmin;\n", " __pyx_t_13 = 0;\n", " if (((__pyx_t_10 &gt; __pyx_t_13) != 0)) {\n", " __pyx_t_7 = __pyx_t_10;\n", " } else {\n", " __pyx_t_7 = __pyx_t_13;\n", " }\n", " __pyx_v_alphamin = __pyx_t_7;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">054</span>: <span class=\"n\">alphamax</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">alphaxmax</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_7 = __pyx_v_alphaxmax;\n", " __pyx_t_13 = 1;\n", " if (((__pyx_t_7 &lt; __pyx_t_13) != 0)) {\n", " __pyx_t_10 = __pyx_t_7;\n", " } else {\n", " __pyx_t_10 = __pyx_t_13;\n", " }\n", " __pyx_v_alphamax = __pyx_t_10;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">055</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">056</span>: <span class=\"n\">alphamin</span> <span class=\"o\">=</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">alphaxmin</span><span class=\"p\">,</span> <span class=\"n\">alphaymin</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_t_10 = __pyx_v_alphaxmin;\n", " __pyx_t_7 = __pyx_v_alphaymin;\n", " __pyx_t_13 = 0;\n", " if (((__pyx_t_10 &gt; __pyx_t_13) != 0)) {\n", " __pyx_t_12 = __pyx_t_10;\n", " } else {\n", " __pyx_t_12 = __pyx_t_13;\n", " }\n", " __pyx_t_6 = __pyx_t_12;\n", " if (((__pyx_t_7 &gt; __pyx_t_6) != 0)) {\n", " __pyx_t_12 = __pyx_t_7;\n", " } else {\n", " __pyx_t_12 = __pyx_t_6;\n", " }\n", " __pyx_v_alphamin = __pyx_t_12;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">057</span>: <span class=\"n\">alphamax</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">alphaxmax</span><span class=\"p\">,</span> <span class=\"n\">alphaymax</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_12 = __pyx_v_alphaxmax;\n", " __pyx_t_10 = __pyx_v_alphaymax;\n", " __pyx_t_13 = 1;\n", " if (((__pyx_t_12 &lt; __pyx_t_13) != 0)) {\n", " __pyx_t_7 = __pyx_t_12;\n", " } else {\n", " __pyx_t_7 = __pyx_t_13;\n", " }\n", " __pyx_t_6 = __pyx_t_7;\n", " if (((__pyx_t_10 &lt; __pyx_t_6) != 0)) {\n", " __pyx_t_7 = __pyx_t_10;\n", " } else {\n", " __pyx_t_7 = __pyx_t_6;\n", " }\n", " __pyx_v_alphamax = __pyx_t_7;\n", " }\n", " __pyx_L5:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">058</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">059</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">&lt;</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x &lt; __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L6;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">060</span>: <span class=\"k\">if</span> <span class=\"n\">alphamin</span> <span class=\"o\">==</span> <span class=\"n\">alphaxmin</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamin == __pyx_v_alphaxmin) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L7;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">061</span>: <span class=\"n\">imin</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_imin = 1;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">062</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">063</span>: <span class=\"n\">imin</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">ceil</span><span class=\"p\">(((</span><span class=\"n\">p1x</span> <span class=\"o\">+</span> <span class=\"n\">alphamin</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"n\">bx</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dx</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_p1x + (__pyx_v_alphamin * (__pyx_v_p2x - __pyx_v_p1x))) - __pyx_v_bx);\n", " if (unlikely(__pyx_v_dx == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 63, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_imin = ((int)ceil((__pyx_t_7 / __pyx_v_dx)));\n", " }\n", " __pyx_L7:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">064</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">065</span>: <span class=\"k\">if</span> <span class=\"n\">alphamax</span> <span class=\"o\">==</span> <span class=\"n\">alphaxmax</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamax == __pyx_v_alphaxmax) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L8;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">066</span>: <span class=\"n\">imax</span> <span class=\"o\">=</span> <span class=\"n\">Nx</span> <span class=\"o\">-</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_imax = (__pyx_v_Nx - 1);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">067</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">068</span>: <span class=\"n\">imax</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1x</span> <span class=\"o\">+</span> <span class=\"n\">alphamax</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"n\">bx</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dx</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_p1x + (__pyx_v_alphamax * (__pyx_v_p2x - __pyx_v_p1x))) - __pyx_v_bx);\n", " if (unlikely(__pyx_v_dx == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 68, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_imax = ((int)floor((__pyx_t_7 / __pyx_v_dx)));\n", " }\n", " __pyx_L8:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">069</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">070</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">==</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x == __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L9;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">071</span>: <span class=\"n\">alphax</span> <span class=\"o\">=</span> <span class=\"n\">INFINITY</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphax = NPY_INFINITY;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">072</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">073</span>: <span class=\"n\">alphax</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"n\">imin</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_bx + (__pyx_v_imin * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_12 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_12 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 73, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphax = (__pyx_t_7 / __pyx_t_12);\n", " }\n", " __pyx_L9:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">074</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">075</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">076</span>: <span class=\"k\">if</span> <span class=\"n\">alphamin</span> <span class=\"o\">==</span> <span class=\"n\">alphaxmin</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_t_11 = ((__pyx_v_alphamin == __pyx_v_alphaxmin) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L10;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">077</span>: <span class=\"n\">imax</span> <span class=\"o\">=</span> <span class=\"n\">Nx</span> <span class=\"o\">-</span> <span class=\"mf\">2</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_imax = (__pyx_v_Nx - 2);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">078</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">079</span>: <span class=\"n\">imax</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1x</span> <span class=\"o\">+</span> <span class=\"n\">alphamin</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"n\">bx</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dx</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_p1x + (__pyx_v_alphamin * (__pyx_v_p2x - __pyx_v_p1x))) - __pyx_v_bx);\n", " if (unlikely(__pyx_v_dx == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 79, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_imax = ((int)floor((__pyx_t_12 / __pyx_v_dx)));\n", " }\n", " __pyx_L10:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">080</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">081</span>: <span class=\"k\">if</span> <span class=\"n\">alphamax</span> <span class=\"o\">==</span> <span class=\"n\">alphaxmax</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamax == __pyx_v_alphaxmax) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L11;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">082</span>: <span class=\"n\">imin</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_imin = 0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">083</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">084</span>: <span class=\"n\">imin</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">ceil</span><span class=\"p\">(((</span><span class=\"n\">p1x</span> <span class=\"o\">+</span> <span class=\"n\">alphamax</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"n\">bx</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dx</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_p1x + (__pyx_v_alphamax * (__pyx_v_p2x - __pyx_v_p1x))) - __pyx_v_bx);\n", " if (unlikely(__pyx_v_dx == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 84, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_imin = ((int)ceil((__pyx_t_12 / __pyx_v_dx)));\n", " }\n", " __pyx_L11:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">085</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">086</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">==</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x == __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L12;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">087</span>: <span class=\"n\">alphax</span> <span class=\"o\">=</span> <span class=\"n\">INFINITY</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphax = NPY_INFINITY;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">088</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">089</span>: <span class=\"n\">alphax</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">bx</span> <span class=\"o\">+</span> <span class=\"n\">imax</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_bx + (__pyx_v_imax * __pyx_v_dx)) - __pyx_v_p1x);\n", " __pyx_t_7 = (__pyx_v_p2x - __pyx_v_p1x);\n", " if (unlikely(__pyx_t_7 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 89, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphax = (__pyx_t_12 / __pyx_t_7);\n", " }\n", " __pyx_L12:;\n", " }\n", " __pyx_L6:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">090</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">091</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">&lt;</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y &lt; __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L13;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">092</span>: <span class=\"k\">if</span> <span class=\"n\">alphamin</span> <span class=\"o\">==</span> <span class=\"n\">alphaymin</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamin == __pyx_v_alphaymin) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L14;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">093</span>: <span class=\"n\">jmin</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_jmin = 1;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">094</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">095</span>: <span class=\"n\">jmin</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">ceil</span><span class=\"p\">(((</span><span class=\"n\">p1y</span> <span class=\"o\">+</span> <span class=\"n\">alphamin</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"k\">by</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_p1y + (__pyx_v_alphamin * (__pyx_v_p2y - __pyx_v_p1y))) - __pyx_v_by);\n", " if (unlikely(__pyx_v_dy == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 95, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_jmin = ((int)ceil((__pyx_t_7 / __pyx_v_dy)));\n", " }\n", " __pyx_L14:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">096</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">097</span>: <span class=\"k\">if</span> <span class=\"n\">alphamax</span> <span class=\"o\">==</span> <span class=\"n\">alphaymax</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamax == __pyx_v_alphaymax) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L15;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">098</span>: <span class=\"n\">jmax</span> <span class=\"o\">=</span> <span class=\"n\">Ny</span> <span class=\"o\">-</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_jmax = (__pyx_v_Ny - 1);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">099</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">100</span>: <span class=\"n\">jmax</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1y</span> <span class=\"o\">+</span> <span class=\"n\">alphamax</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"k\">by</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_p1y + (__pyx_v_alphamax * (__pyx_v_p2y - __pyx_v_p1y))) - __pyx_v_by);\n", " if (unlikely(__pyx_v_dy == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 100, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_jmax = ((int)floor((__pyx_t_7 / __pyx_v_dy)));\n", " }\n", " __pyx_L15:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">101</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">102</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">==</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y == __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L16;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">103</span>: <span class=\"n\">alphay</span> <span class=\"o\">=</span> <span class=\"n\">INFINITY</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphay = NPY_INFINITY;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">104</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">105</span>: <span class=\"n\">alphay</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"n\">jmin</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_7 = ((__pyx_v_by + (__pyx_v_jmin * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_12 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_12 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 105, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphay = (__pyx_t_7 / __pyx_t_12);\n", " }\n", " __pyx_L16:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">106</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">107</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">108</span>: <span class=\"k\">if</span> <span class=\"n\">alphamin</span> <span class=\"o\">==</span> <span class=\"n\">alphaymin</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_t_11 = ((__pyx_v_alphamin == __pyx_v_alphaymin) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L17;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">109</span>: <span class=\"n\">jmax</span> <span class=\"o\">=</span> <span class=\"n\">Ny</span> <span class=\"o\">-</span> <span class=\"mf\">2</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_jmax = (__pyx_v_Ny - 2);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">110</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">111</span>: <span class=\"n\">jmax</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1y</span> <span class=\"o\">+</span> <span class=\"n\">alphamin</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"k\">by</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_p1y + (__pyx_v_alphamin * (__pyx_v_p2y - __pyx_v_p1y))) - __pyx_v_by);\n", " if (unlikely(__pyx_v_dy == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 111, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_jmax = ((int)floor((__pyx_t_12 / __pyx_v_dy)));\n", " }\n", " __pyx_L17:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">112</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">113</span>: <span class=\"k\">if</span> <span class=\"n\">alphamax</span> <span class=\"o\">==</span> <span class=\"n\">alphaymax</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamax == __pyx_v_alphaymax) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L18;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">114</span>: <span class=\"n\">jmin</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_jmin = 0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">115</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">116</span>: <span class=\"n\">jmin</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">ceil</span><span class=\"p\">(((</span><span class=\"n\">p1y</span> <span class=\"o\">+</span> <span class=\"n\">alphamax</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"k\">by</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_p1y + (__pyx_v_alphamax * (__pyx_v_p2y - __pyx_v_p1y))) - __pyx_v_by);\n", " if (unlikely(__pyx_v_dy == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 116, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_jmin = ((int)ceil((__pyx_t_12 / __pyx_v_dy)));\n", " }\n", " __pyx_L18:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">117</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">118</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">==</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y == __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L19;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">119</span>: <span class=\"n\">alphay</span> <span class=\"o\">=</span> <span class=\"n\">INFINITY</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphay = NPY_INFINITY;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">120</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">121</span>: <span class=\"n\">alphay</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"k\">by</span> <span class=\"o\">+</span> <span class=\"n\">jmax</span> <span class=\"o\">*</span> <span class=\"n\">dy</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_12 = ((__pyx_v_by + (__pyx_v_jmax * __pyx_v_dy)) - __pyx_v_p1y);\n", " __pyx_t_7 = (__pyx_v_p2y - __pyx_v_p1y);\n", " if (unlikely(__pyx_t_7 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 121, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphay = (__pyx_t_12 / __pyx_t_7);\n", " }\n", " __pyx_L19:;\n", " }\n", " __pyx_L13:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">122</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">123</span>: <span class=\"n\">Np</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">imax</span> <span class=\"o\">-</span> <span class=\"n\">imin</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">jmax</span> <span class=\"o\">-</span> <span class=\"n\">jmin</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_Np = (((__pyx_v_imax - __pyx_v_imin) + 1) + ((__pyx_v_jmax - __pyx_v_jmin) + 1));\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">124</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">125</span>: <span class=\"n\">alphamid</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"nb\">min</span><span class=\"p\">(</span><span class=\"n\">alphax</span><span class=\"p\">,</span> <span class=\"n\">alphay</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">alphamin</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"mf\">2</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_7 = __pyx_v_alphay;\n", " __pyx_t_12 = __pyx_v_alphax;\n", " if (((__pyx_t_7 &lt; __pyx_t_12) != 0)) {\n", " __pyx_t_10 = __pyx_t_7;\n", " } else {\n", " __pyx_t_10 = __pyx_t_12;\n", " }\n", " __pyx_v_alphamid = ((__pyx_t_10 + __pyx_v_alphamin) / 2.0);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">126</span>: </pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">127</span>: <span class=\"n\">i</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1x</span> <span class=\"o\">+</span> <span class=\"n\">alphamid</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"n\">bx</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dx</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_p1x + (__pyx_v_alphamid * (__pyx_v_p2x - __pyx_v_p1x))) - __pyx_v_bx);\n", " if (unlikely(__pyx_v_dx == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 127, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_i = ((int)floor((__pyx_t_10 / __pyx_v_dx)));\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">128</span>: <span class=\"n\">j</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">int</span><span class=\"p\">&gt;</span><span class=\"n\">floor</span><span class=\"p\">(((</span><span class=\"n\">p1y</span> <span class=\"o\">+</span> <span class=\"n\">alphamid</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"k\">by</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">dy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_10 = ((__pyx_v_p1y + (__pyx_v_alphamid * (__pyx_v_p2y - __pyx_v_p1y))) - __pyx_v_by);\n", " if (unlikely(__pyx_v_dy == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 128, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_j = ((int)floor((__pyx_t_10 / __pyx_v_dy)));\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">129</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">130</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">==</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x == __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L20;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">131</span>: <span class=\"n\">alphaxu</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphaxu = 0.0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">132</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">133</span>: <span class=\"n\">alphaxu</span> <span class=\"o\">=</span> <span class=\"n\">dx</span> <span class=\"o\">/</span> <span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_10 = fabs((__pyx_v_p2x - __pyx_v_p1x));\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 133, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphaxu = (__pyx_v_dx / __pyx_t_10);\n", " }\n", " __pyx_L20:;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">134</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">==</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y == __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L21;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">135</span>: <span class=\"n\">alphayu</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphayu = 0.0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">136</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">137</span>: <span class=\"n\">alphayu</span> <span class=\"o\">=</span> <span class=\"n\">dy</span> <span class=\"o\">/</span> <span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> /*else*/ {\n", " __pyx_t_10 = fabs((__pyx_v_p2y - __pyx_v_p1y));\n", " if (unlikely(__pyx_t_10 == 0)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_ZeroDivisionError, \"float division\");\n", " <span class='error_goto'>__PYX_ERR(0, 137, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_alphayu = (__pyx_v_dy / __pyx_t_10);\n", " }\n", " __pyx_L21:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">138</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">139</span>: <span class=\"n\">dconv</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">p2x</span> <span class=\"o\">-</span> <span class=\"n\">p1x</span><span class=\"p\">)</span><span class=\"o\">**</span><span class=\"mf\">2</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">p2y</span> <span class=\"o\">-</span> <span class=\"n\">p1y</span><span class=\"p\">)</span><span class=\"o\">**</span><span class=\"mf\">2</span><span class=\"p\">)</span><span class=\"o\">**</span><span class=\"mf\">0.5</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_dconv = pow((pow((__pyx_v_p2x - __pyx_v_p1x), 2.0) + pow((__pyx_v_p2y - __pyx_v_p1y), 2.0)), 0.5);\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">140</span>: <span class=\"n\">alphac</span> <span class=\"o\">=</span> <span class=\"n\">alphamin</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphac = __pyx_v_alphamin;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">141</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">142</span>: <span class=\"k\">if</span> <span class=\"n\">p1x</span> <span class=\"o\">&lt;</span> <span class=\"n\">p2x</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1x &lt; __pyx_v_p2x) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L22;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">143</span>: <span class=\"n\">iu</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_iu = 1;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">144</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">145</span>: <span class=\"n\">iu</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_v_iu = -1;\n", " }\n", " __pyx_L22:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">146</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">147</span>: <span class=\"k\">if</span> <span class=\"n\">p1y</span> <span class=\"o\">&lt;</span> <span class=\"n\">p2y</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_p1y &lt; __pyx_v_p2y) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L23;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">148</span>: <span class=\"n\">ju</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_ju = 1;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">149</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">150</span>: <span class=\"n\">ju</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_v_ju = -1;\n", " }\n", " __pyx_L23:;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">151</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">152</span>: <span class=\"k\">for</span> <span class=\"n\">k</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">Np</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_2 = __pyx_v_Np;\n", " __pyx_t_14 = __pyx_t_2;\n", " for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {\n", " __pyx_v_k = __pyx_t_15;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">153</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">154</span>: <span class=\"k\">if</span> <span class=\"n\">alphax</span> <span class=\"o\">&lt;</span> <span class=\"n\">alphay</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphax &lt; __pyx_v_alphay) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " goto __pyx_L26;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">155</span>: <span class=\"n\">lij</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">alphax</span> <span class=\"o\">-</span> <span class=\"n\">alphac</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dconv</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_lij = ((__pyx_v_alphax - __pyx_v_alphac) * __pyx_v_dconv);\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">156</span>: <span class=\"n\">d12</span> <span class=\"o\">=</span> <span class=\"n\">d12</span> <span class=\"o\">+</span> <span class=\"n\">lij</span> <span class=\"o\">*</span> <span class=\"n\">pixels</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">j</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_16 = __pyx_v_i;\n", " __pyx_t_17 = __pyx_v_j;\n", " __pyx_t_18 = -1;\n", " if (__pyx_t_16 &lt; 0) {\n", " __pyx_t_16 += __pyx_v_pixels.shape[0];\n", " if (unlikely(__pyx_t_16 &lt; 0)) __pyx_t_18 = 0;\n", " } else if (unlikely(__pyx_t_16 &gt;= __pyx_v_pixels.shape[0])) __pyx_t_18 = 0;\n", " if (__pyx_t_17 &lt; 0) {\n", " __pyx_t_17 += __pyx_v_pixels.shape[1];\n", " if (unlikely(__pyx_t_17 &lt; 0)) __pyx_t_18 = 1;\n", " } else if (unlikely(__pyx_t_17 &gt;= __pyx_v_pixels.shape[1])) __pyx_t_18 = 1;\n", " if (unlikely(__pyx_t_18 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_18);\n", " <span class='error_goto'>__PYX_ERR(0, 156, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_d12 = (__pyx_v_d12 + (__pyx_v_lij * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_pixels.data + __pyx_t_16 * __pyx_v_pixels.strides[0]) )) + __pyx_t_17)) )))));\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">157</span>: <span class=\"n\">i</span> <span class=\"o\">=</span> <span class=\"n\">i</span> <span class=\"o\">+</span> <span class=\"n\">iu</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_i = (__pyx_v_i + __pyx_v_iu);\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">158</span>: <span class=\"n\">alphac</span> <span class=\"o\">=</span> <span class=\"n\">alphax</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphac = __pyx_v_alphax;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">159</span>: <span class=\"n\">alphax</span> <span class=\"o\">=</span> <span class=\"n\">alphax</span> <span class=\"o\">+</span> <span class=\"n\">alphaxu</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphax = (__pyx_v_alphax + __pyx_v_alphaxu);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">160</span>: <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">161</span>: <span class=\"n\">lij</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">alphay</span> <span class=\"o\">-</span> <span class=\"n\">alphac</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dconv</span></pre>\n", "<pre class='cython code score-0 '> /*else*/ {\n", " __pyx_v_lij = ((__pyx_v_alphay - __pyx_v_alphac) * __pyx_v_dconv);\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">162</span>: <span class=\"n\">d12</span> <span class=\"o\">=</span> <span class=\"n\">d12</span> <span class=\"o\">+</span> <span class=\"n\">lij</span> <span class=\"o\">*</span> <span class=\"n\">pixels</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">j</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_19 = __pyx_v_i;\n", " __pyx_t_20 = __pyx_v_j;\n", " __pyx_t_18 = -1;\n", " if (__pyx_t_19 &lt; 0) {\n", " __pyx_t_19 += __pyx_v_pixels.shape[0];\n", " if (unlikely(__pyx_t_19 &lt; 0)) __pyx_t_18 = 0;\n", " } else if (unlikely(__pyx_t_19 &gt;= __pyx_v_pixels.shape[0])) __pyx_t_18 = 0;\n", " if (__pyx_t_20 &lt; 0) {\n", " __pyx_t_20 += __pyx_v_pixels.shape[1];\n", " if (unlikely(__pyx_t_20 &lt; 0)) __pyx_t_18 = 1;\n", " } else if (unlikely(__pyx_t_20 &gt;= __pyx_v_pixels.shape[1])) __pyx_t_18 = 1;\n", " if (unlikely(__pyx_t_18 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_18);\n", " <span class='error_goto'>__PYX_ERR(0, 162, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_d12 = (__pyx_v_d12 + (__pyx_v_lij * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_pixels.data + __pyx_t_19 * __pyx_v_pixels.strides[0]) )) + __pyx_t_20)) )))));\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">163</span>: <span class=\"n\">j</span> <span class=\"o\">=</span> <span class=\"n\">j</span> <span class=\"o\">+</span> <span class=\"n\">ju</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_j = (__pyx_v_j + __pyx_v_ju);\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">164</span>: <span class=\"n\">alphac</span> <span class=\"o\">=</span> <span class=\"n\">alphay</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphac = __pyx_v_alphay;\n", "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">165</span>: <span class=\"n\">alphay</span> <span class=\"o\">=</span> <span class=\"n\">alphay</span> <span class=\"o\">+</span> <span class=\"n\">alphayu</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_alphay = (__pyx_v_alphay + __pyx_v_alphayu);\n", " }\n", " __pyx_L26:;\n", " }\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">166</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">167</span>: <span class=\"c\"># have to think about this for case of line in and outside of image</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">168</span>: <span class=\"c\"># alphamax == 1 means last point is in image</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">169</span>: <span class=\"c\"># alphamin == 0 means first point is in image</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">170</span>: <span class=\"c\"># print(alphamax, alphamin, alphaxmin, alphaxmax)</span></pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">171</span>: <span class=\"k\">if</span> <span class=\"n\">alphamax</span> <span class=\"o\">==</span> <span class=\"mf\">1</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_11 = ((__pyx_v_alphamax == 1.0) != 0);\n", " if (__pyx_t_11) {\n", "/* … */\n", " }\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">172</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">173</span>: <span class=\"n\">lij</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">alphamax</span> <span class=\"o\">-</span> <span class=\"n\">alphac</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">dconv</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_lij = ((__pyx_v_alphamax - __pyx_v_alphac) * __pyx_v_dconv);\n", "</pre><pre class=\"cython line score-2\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">174</span>: <span class=\"n\">d12</span> <span class=\"o\">=</span> <span class=\"n\">d12</span> <span class=\"o\">+</span> <span class=\"n\">lij</span> <span class=\"o\">*</span> <span class=\"n\">pixels</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">j</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_21 = __pyx_v_i;\n", " __pyx_t_22 = __pyx_v_j;\n", " __pyx_t_2 = -1;\n", " if (__pyx_t_21 &lt; 0) {\n", " __pyx_t_21 += __pyx_v_pixels.shape[0];\n", " if (unlikely(__pyx_t_21 &lt; 0)) __pyx_t_2 = 0;\n", " } else if (unlikely(__pyx_t_21 &gt;= __pyx_v_pixels.shape[0])) __pyx_t_2 = 0;\n", " if (__pyx_t_22 &lt; 0) {\n", " __pyx_t_22 += __pyx_v_pixels.shape[1];\n", " if (unlikely(__pyx_t_22 &lt; 0)) __pyx_t_2 = 1;\n", " } else if (unlikely(__pyx_t_22 &gt;= __pyx_v_pixels.shape[1])) __pyx_t_2 = 1;\n", " if (unlikely(__pyx_t_2 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_2);\n", " <span class='error_goto'>__PYX_ERR(0, 174, __pyx_L1_error)</span>\n", " }\n", " __pyx_v_d12 = (__pyx_v_d12 + (__pyx_v_lij * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_pixels.data + __pyx_t_21 * __pyx_v_pixels.strides[0]) )) + __pyx_t_22)) )))));\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">175</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">176</span>: <span class=\"k\">return</span> <span class=\"n\">d12</span></pre>\n", "<pre class='cython code score-0 '> __pyx_r = __pyx_v_d12;\n", " goto __pyx_L0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">177</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">178</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">179</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-112\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">180</span>: <span class=\"k\">cpdef</span> <span class=\"kt\">void</span> <span class=\"nf\">c_raytrace_fast_bulk</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:,</span> <span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">lines</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ex1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ex2</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ey1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">ey2</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:,</span> <span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">pixels</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">cache</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-112 '>static PyObject *__pyx_pw_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_3c_raytrace_fast_bulk(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n", "static void __pyx_f_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast_bulk(__Pyx_memviewslice __pyx_v_lines, double __pyx_v_ex1, double __pyx_v_ex2, double __pyx_v_ey1, double __pyx_v_ey2, __Pyx_memviewslice __pyx_v_pixels, __Pyx_memviewslice __pyx_v_cache, CYTHON_UNUSED int __pyx_skip_dispatch) {\n", " int __pyx_v_i;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"c_raytrace_fast_bulk\", 0);\n", "/* … */\n", " /* function exit code */\n", " goto __pyx_L0;\n", " __pyx_L1_error:;\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_t_4, 1);\n", " <span class='pyx_c_api'>__Pyx_WriteUnraisable</span>(\"_cython_magic_66b3bd080e40ad88ea24f32fdf408264.c_raytrace_fast_bulk\", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0);\n", " __pyx_L0:;\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", "}\n", "\n", "/* Python wrapper */\n", "static PyObject *__pyx_pw_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_3c_raytrace_fast_bulk(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n", "static PyObject *__pyx_pw_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_3c_raytrace_fast_bulk(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n", " __Pyx_memviewslice __pyx_v_lines = { 0, 0, { 0 }, { 0 }, { 0 } };\n", " double __pyx_v_ex1;\n", " double __pyx_v_ex2;\n", " double __pyx_v_ey1;\n", " double __pyx_v_ey2;\n", " __Pyx_memviewslice __pyx_v_pixels = { 0, 0, { 0 }, { 0 }, { 0 } };\n", " __Pyx_memviewslice __pyx_v_cache = { 0, 0, { 0 }, { 0 }, { 0 } };\n", " PyObject *__pyx_r = 0;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"c_raytrace_fast_bulk (wrapper)\", 0);\n", " {\n", " static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_lines,&amp;__pyx_n_s_ex1,&amp;__pyx_n_s_ex2,&amp;__pyx_n_s_ey1,&amp;__pyx_n_s_ey2,&amp;__pyx_n_s_pixels,&amp;__pyx_n_s_cache,0};\n", " PyObject* values[7] = {0,0,0,0,0,0,0};\n", " if (unlikely(__pyx_kwds)) {\n", " Py_ssize_t kw_args;\n", " const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n", " switch (pos_args) {\n", " case 7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n", " CYTHON_FALLTHROUGH;\n", " case 6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n", " CYTHON_FALLTHROUGH;\n", " case 5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", " CYTHON_FALLTHROUGH;\n", " case 4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", " CYTHON_FALLTHROUGH;\n", " case 3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n", " CYTHON_FALLTHROUGH;\n", " case 2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n", " CYTHON_FALLTHROUGH;\n", " case 1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n", " CYTHON_FALLTHROUGH;\n", " case 0: break;\n", " default: goto __pyx_L5_argtuple_error;\n", " }\n", " kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n", " switch (pos_args) {\n", " case 0:\n", " if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lines)) != 0)) kw_args--;\n", " else goto __pyx_L5_argtuple_error;\n", " CYTHON_FALLTHROUGH;\n", " case 1:\n", " if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_ex1)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast_bulk\", 1, 7, 7, 1); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " }\n", " CYTHON_FALLTHROUGH;\n", " case 2:\n", " if (likely((values[2] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_ex2)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast_bulk\", 1, 7, 7, 2); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " }\n", " CYTHON_FALLTHROUGH;\n", " case 3:\n", " if (likely((values[3] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_ey1)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast_bulk\", 1, 7, 7, 3); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " }\n", " CYTHON_FALLTHROUGH;\n", " case 4:\n", " if (likely((values[4] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_ey2)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast_bulk\", 1, 7, 7, 4); <span class='error_goto'>__PYX_ERR(0, 180, 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class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " }\n", " } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 7) {\n", " goto __pyx_L5_argtuple_error;\n", " } else {\n", " values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n", " values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n", " values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n", " values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", " values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", " values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n", " values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n", " }\n", " __pyx_v_lines = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[0], PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_v_lines.memview)) __PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_ex1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_ex1 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_ex2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_ex2 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_ey1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[3]); if (unlikely((__pyx_v_ey1 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_ey2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[4]); if (unlikely((__pyx_v_ey2 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_pixels = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[5], PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_v_pixels.memview)) __PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_v_cache = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[6], PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_v_cache.memview)) __PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " }\n", " goto __pyx_L4_argument_unpacking_done;\n", " __pyx_L5_argtuple_error:;\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"c_raytrace_fast_bulk\", 1, 7, 7, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L3_error)</span>\n", " __pyx_L3_error:;\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_66b3bd080e40ad88ea24f32fdf408264.c_raytrace_fast_bulk\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return NULL;\n", " __pyx_L4_argument_unpacking_done:;\n", " __pyx_r = __pyx_pf_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_2c_raytrace_fast_bulk(__pyx_self, __pyx_v_lines, __pyx_v_ex1, __pyx_v_ex2, __pyx_v_ey1, __pyx_v_ey2, __pyx_v_pixels, __pyx_v_cache);\n", "\n", " /* function exit code */\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "static PyObject *__pyx_pf_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_2c_raytrace_fast_bulk(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_lines, double __pyx_v_ex1, double __pyx_v_ex2, double __pyx_v_ey1, double __pyx_v_ey2, __Pyx_memviewslice __pyx_v_pixels, __Pyx_memviewslice __pyx_v_cache) {\n", " PyObject *__pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"c_raytrace_fast_bulk\", 0);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " if (unlikely(!__pyx_v_lines.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"lines\"); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L1_error)</span> }\n", " if (unlikely(!__pyx_v_pixels.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"pixels\"); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L1_error)</span> }\n", " if (unlikely(!__pyx_v_cache.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"cache\"); <span class='error_goto'>__PYX_ERR(0, 180, __pyx_L1_error)</span> }\n", " __pyx_t_1 = __Pyx_void_to_None(__pyx_f_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast_bulk(__pyx_v_lines, __pyx_v_ex1, __pyx_v_ex2, __pyx_v_ey1, __pyx_v_ey2, __pyx_v_pixels, __pyx_v_cache, 0));<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 180, 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class=\"\">182</span>: <span class=\"nb\">int</span> <span class=\"n\">i</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">183</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">184</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]):</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_1 = (__pyx_v_lines.shape[0]);\n", " __pyx_t_2 = __pyx_t_1;\n", " for (__pyx_t_3 = 0; __pyx_t_3 &lt; __pyx_t_2; __pyx_t_3+=1) {\n", " __pyx_v_i = __pyx_t_3;\n", "</pre><pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">185</span>: <span class=\"n\">cache</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">c_raytrace_fast</span><span class=\"p\">(</span><span class=\"n\">lines</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">],</span> <span class=\"n\">ex1</span><span class=\"p\">,</span> <span class=\"n\">ex2</span><span class=\"p\">,</span> <span class=\"n\">ey1</span><span class=\"p\">,</span> <span class=\"n\">ey2</span><span class=\"p\">,</span> <span class=\"n\">pixels</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_4.data = __pyx_v_lines.data;\n", " __pyx_t_4.memview = __pyx_v_lines.memview;\n", " __PYX_INC_MEMVIEW(&amp;__pyx_t_4, 0);\n", " {\n", " Py_ssize_t __pyx_tmp_idx = __pyx_v_i;\n", " Py_ssize_t __pyx_tmp_shape = __pyx_v_lines.shape[0];\n", " Py_ssize_t __pyx_tmp_stride = __pyx_v_lines.strides[0];\n", " if (0 &amp;&amp; (__pyx_tmp_idx &lt; 0))\n", " __pyx_tmp_idx += __pyx_tmp_shape;\n", " if (0 &amp;&amp; (__pyx_tmp_idx &lt; 0 || __pyx_tmp_idx &gt;= __pyx_tmp_shape)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_IndexError, \"Index out of bounds (axis 0)\");\n", " <span class='error_goto'>__PYX_ERR(0, 185, __pyx_L1_error)</span>\n", " }\n", " __pyx_t_4.data += __pyx_tmp_idx * __pyx_tmp_stride;\n", "}\n", "\n", "__pyx_t_4.shape[0] = __pyx_v_lines.shape[1];\n", "__pyx_t_4.strides[0] = __pyx_v_lines.strides[1];\n", " __pyx_t_4.suboffsets[0] = -1;\n", "\n", "__pyx_t_5 = __pyx_v_i;\n", " *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_cache.data) + __pyx_t_5)) )) = __pyx_f_46_cython_magic_66b3bd080e40ad88ea24f32fdf408264_c_raytrace_fast(__pyx_t_4, __pyx_v_ex1, __pyx_v_ex2, __pyx_v_ey1, __pyx_v_ey2, __pyx_v_pixels, 0);\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_t_4, 1);\n", " __pyx_t_4.memview = NULL;\n", " __pyx_t_4.data = NULL;\n", " }\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython --annotate\n", "\n", "cimport cython\n", "\n", "from libc.math cimport floor, ceil\n", "from numpy.math cimport INFINITY\n", "\n", "cpdef double c_raytrace_fast(double[::1] line, double ex1, double ex2, double ey1, double ey2, double[:, ::1] pixels):\n", " # Fixed issue, alphax[0] in Filip Jacob's paper means first alphax in siddon array, not alphax at zero.\n", " cdef:\n", " double d12 = 0\n", " double alphaxmin, alphaxmax\n", " double alphaymin, alphaymax\n", " double p1x, p1y, p2x, p2y\n", " double bx, by\n", " int Nx, Ny\n", " double dx, dy\n", " double alphax, alphay\n", " double alphamid\n", " int i, j, iu, ju\n", " int imax, imin, jmax, jmin\n", " int Np\n", " \n", " p1x = line[0]\n", " p1y = line[1]\n", " p2x = line[2]\n", " p2y = line[3]\n", " bx, by = ex1, ey1\n", " Nx, Ny = pixels.shape[0] + 1, pixels.shape[1] + 1\n", " dx, dy = (ex2 - ex1) / pixels.shape[0], (ey2 - ey1) / pixels.shape[1]\n", "\n", " if p1x == p2x:\n", " alphaxmin = 0\n", " alphaxmax = 0\n", " else:\n", " alphaxmin = min(((bx + 0 * dx) - p1x) / (p2x - p1x),\n", " ((bx + (Nx - 1) * dx) - p1x) / (p2x - p1x))\n", " alphaxmax = max(((bx + 0 * dx) - p1x) / (p2x - p1x),\n", " ((bx + (Nx - 1) * dx) - p1x) / (p2x - p1x))\n", "\n", " if p1y == p2y:\n", " alphaymin = 0\n", " alphaymax = 0\n", " else:\n", " alphaymin = min(((by + 0 * dy) - p1y) / (p2y - p1y),\n", " ((by + (Ny - 1) * dy) - p1y) / (p2y - p1y))\n", " alphaymax = max(((by + 0 * dy) - p1y) / (p2y - p1y),\n", " ((by + (Ny - 1) * dy) - p1y) / (p2y - p1y))\n", "\n", " if p1x == p2x:\n", " alphamin = max(0, alphaymin)\n", " alphamax = min(1, alphaymax)\n", " elif p1y == p2y:\n", " alphamin = max(0, alphaxmin)\n", " alphamax = min(1, alphaxmax)\n", " else:\n", " alphamin = max(0, alphaxmin, alphaymin)\n", " alphamax = min(1, alphaxmax, alphaymax)\n", "\n", " if p1x < p2x:\n", " if alphamin == alphaxmin:\n", " imin = 1\n", " else:\n", " imin = <int>ceil(((p1x + alphamin * (p2x - p1x)) - bx) / dx)\n", "\n", " if alphamax == alphaxmax:\n", " imax = Nx - 1\n", " else:\n", " imax = <int>floor(((p1x + alphamax * (p2x - p1x)) - bx) / dx)\n", "\n", " if p1x == p2x:\n", " alphax = INFINITY\n", " else:\n", " alphax = ((bx + imin * dx) - p1x) / (p2x - p1x)\n", "\n", " else:\n", " if alphamin == alphaxmin:\n", " imax = Nx - 2\n", " else:\n", " imax = <int>floor(((p1x + alphamin * (p2x - p1x)) - bx) / dx)\n", "\n", " if alphamax == alphaxmax:\n", " imin = 0\n", " else:\n", " imin = <int>ceil(((p1x + alphamax * (p2x - p1x)) - bx) / dx)\n", "\n", " if p1x == p2x:\n", " alphax = INFINITY\n", " else:\n", " alphax = ((bx + imax * dx) - p1x) / (p2x - p1x)\n", "\n", " if p1y < p2y:\n", " if alphamin == alphaymin:\n", " jmin = 1\n", " else:\n", " jmin = <int>ceil(((p1y + alphamin * (p2y - p1y)) - by) / dy)\n", "\n", " if alphamax == alphaymax:\n", " jmax = Ny - 1\n", " else:\n", " jmax = <int>floor(((p1y + alphamax * (p2y - p1y)) - by) / dy)\n", "\n", " if p1y == p2y:\n", " alphay = INFINITY\n", " else:\n", " alphay = ((by + jmin * dy) - p1y) / (p2y - p1y)\n", "\n", " else:\n", " if alphamin == alphaymin:\n", " jmax = Ny - 2\n", " else:\n", " jmax = <int>floor(((p1y + alphamin * (p2y - p1y)) - by) / dy)\n", "\n", " if alphamax == alphaymax:\n", " jmin = 0\n", " else:\n", " jmin = <int>ceil(((p1y + alphamax * (p2y - p1y)) - by) / dy)\n", "\n", " if p1y == p2y:\n", " alphay = INFINITY\n", " else:\n", " alphay = ((by + jmax * dy) - p1y) / (p2y - p1y)\n", "\n", " Np = (imax - imin + 1) + (jmax - jmin + 1)\n", "\n", " alphamid = (min(alphax, alphay) + alphamin) / 2\n", "\n", " i = <int>floor(((p1x + alphamid * (p2x - p1x)) - bx) / dx)\n", " j = <int>floor(((p1y + alphamid * (p2y - p1y)) - by) / dy)\n", "\n", " if p1x == p2x:\n", " alphaxu = 0\n", " else:\n", " alphaxu = dx / abs(p2x - p1x)\n", " if p1y == p2y:\n", " alphayu = 0\n", " else:\n", " alphayu = dy / abs(p2y - p1y)\n", "\n", " dconv = ((p2x - p1x)**2 + (p2y - p1y)**2)**0.5\n", " alphac = alphamin\n", "\n", " if p1x < p2x:\n", " iu = 1\n", " else:\n", " iu = -1\n", "\n", " if p1y < p2y:\n", " ju = 1\n", " else:\n", " ju = -1\n", "\n", " for k in range(Np):\n", "\n", " if alphax < alphay:\n", " lij = (alphax - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", " i = i + iu\n", " alphac = alphax\n", " alphax = alphax + alphaxu\n", " else:\n", " lij = (alphay - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", " j = j + ju\n", " alphac = alphay\n", " alphay = alphay + alphayu\n", "\n", " # have to think about this for case of line in and outside of image\n", " # alphamax == 1 means last point is in image\n", " # alphamin == 0 means first point is in image\n", " # print(alphamax, alphamin, alphaxmin, alphaxmax)\n", " if alphamax == 1:\n", "\n", " lij = (alphamax - alphac) * dconv\n", " d12 = d12 + lij * pixels[i, j]\n", "\n", " return d12\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cpdef void c_raytrace_fast_bulk(double[:, ::1] lines, double ex1, double ex2, double ey1, double ey2, double[:, ::1] pixels, double[::1] cache):\n", " cdef:\n", " int i\n", " \n", " for i in range(lines.shape[0]):\n", " cache[i] = c_raytrace_fast(lines[i], ex1, ex2, ey1, ey2, pixels)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "_cache = np.zeros((10000), dtype=np.double)\n", "\n", "def raytrace_fast(line, extent, pixels):\n", " return c_raytrace_fast(line, extent[0], extent[1], extent[2], extent[3], pixels)\n", "\n", "def raytrace_bulk_fast(lines, extent, pixels):\n", " c_raytrace_fast_bulk(lines, extent[0], extent[1], extent[2], extent[3], pixels, _cache)\n", " return _cache[:np.size(lines, 0)]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def draw_algorithm(extent, pixels, draw_lines=True):\n", "\n", " plt.imshow(pixels.T, extent=extent, origin='lower')\n", "\n", " if draw_lines:\n", " # vertical lines\n", " for i in range(np.size(pixels, 0) + 1):\n", " x = extent[0] + (extent[1] - extent[0]) / np.size(pixels, 0) * i\n", " plt.plot([x, x], [extent[2], extent[3]], 'g')\n", "\n", " # horizontal lines\n", " for i in range(np.size(pixels, 1) + 1):\n", " y = extent[2] + (extent[3] - extent[2]) / np.size(pixels, 1) * i\n", " plt.plot([extent[0], extent[1]], [y, y], 'g')\n", "\n", "\n", "def draw_line(line, draw_option='c-'):\n", " plt.plot([line[0], line[2]], [line[1], line[3]], draw_option, lw=1)\n", "\n", "\n", "def draw_alpha(line, alpha, color='red'):\n", " point_x = line[0] + alpha * (line[2] - line[0])\n", " point_y = line[1] + alpha * (line[3] - line[1])\n", "\n", " plt.scatter(point_x, point_y, color=color)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def test_outside(seed=8675309, nruns=5000, npixels=50, debug=False):\n", " import random\n", "\n", " def distance(line):\n", " return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\n", "\n", " def outside_intersection_distance(line, extent):\n", " x1, y1, x2, y2 = line\n", " x3, x4, y3, y4 = extent\n", "\n", " def intersection(x1, x2, y1, y2, x3, x4, y3, y4):\n", " denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)\n", " if denom != 0:\n", " t = (x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)\n", " t /= denom\n", " u = - ((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3))\n", " u /= denom\n", "\n", " if 0 <= t <= 1 and 0 <= u <= 1:\n", " return [x1 + t * (x2 - x1), y1 + t * (y2 - y1)]\n", "\n", " return None\n", "\n", " top_intersection = intersection(x1, x2, y1, y2, x3, x4, y4, y4)\n", " bottom_intersection = intersection(x1, x2, y1, y2, x3, x4, y3, y3)\n", " left_intersection = intersection(x1, x2, y1, y2, x3, x3, y3, y4)\n", " right_intersection = intersection(x1, x2, y1, y2, x4, x4, y3, y4)\n", "\n", " intersections = [i for i in [top_intersection, bottom_intersection,\n", " left_intersection, right_intersection] if i is not None]\n", "\n", " true_line = [intersections[0][0], intersections[0][1],\n", " intersections[1][0], intersections[1][1]]\n", "\n", " return distance(true_line)\n", "\n", " image = np.ones((npixels, npixels))\n", " extent = [-5, 5, -5, 5]\n", "\n", " plt.figure()\n", " draw_algorithm(extent, image)\n", "\n", " random.seed(seed)\n", "\n", " differences = []\n", "\n", " for i in range(nruns):\n", " if random.choice([True, False]):\n", " line = [extent[0] - 1,\n", " random.uniform(extent[2], extent[3]),\n", " extent[1] + 1,\n", " random.uniform(extent[2], extent[3])]\n", " true_value = outside_intersection_distance(line, extent)\n", " else:\n", " line = [random.uniform(extent[0], extent[1]),\n", " extent[2] - 1,\n", " random.uniform(extent[0], extent[1]),\n", " extent[3] + 1]\n", " true_value = outside_intersection_distance(line, extent)\n", "\n", " d12 = raytrace(line, extent, image, debug)\n", " difference = abs(d12 - true_value)\n", " if difference > 1e-12:\n", " # print(d12, distance(line), abs(d12 - distance(line)))\n", " draw_line(line, 'r-')\n", " else:\n", " draw_line(line)\n", " differences.append(difference)\n", "\n", " plt.figure()\n", " plt.hist(differences)\n", " print()\n", " \n", " plt.show()\n", "\n", "\n", "def test_innoutside(seed=8675309, nruns=5000, npixels=50, debug=False):\n", " import random\n", "\n", " def distance(line):\n", " return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\n", "\n", " image = np.ones((npixels, npixels))\n", " extent = [-5, 5, -5, 5]\n", "\n", " plt.figure()\n", " draw_algorithm(extent, image)\n", "\n", " random.seed(seed)\n", "\n", " differences = []\n", "\n", " for i in range(nruns):\n", " inside_pt = [random.uniform(extent[0], extent[1]),\n", " random.uniform(extent[2], extent[3])]\n", " side = random.choice(['left', 'right', 'top', 'bottom'])\n", " if side == 'left':\n", " outside_pt = [extent[0]-1, random.uniform(extent[2], extent[3])]\n", " elif side == 'right':\n", " outside_pt = [extent[1]+1, random.uniform(extent[2], extent[3])]\n", " elif side == 'top':\n", " outside_pt = [random.uniform(extent[0], extent[1]), extent[3]+1]\n", " elif side == 'bottom':\n", " outside_pt = [random.uniform(extent[2], extent[3]), extent[2]-1]\n", "\n", " if random.choice([True, False]):\n", " line = [inside_pt[0], inside_pt[1], outside_pt[0], outside_pt[1]]\n", " else:\n", " line = [outside_pt[0], outside_pt[1], inside_pt[0], inside_pt[1]]\n", "\n", " def intersection(x1, x2, y1, y2, x3, x4, y3, y4):\n", " denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)\n", " if denom != 0:\n", " t = (x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)\n", " t /= denom\n", " u = - ((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3))\n", " u /= denom\n", "\n", " if 0 <= t <= 1 and 0 <= u <= 1:\n", " return [x1 + t * (x2 - x1), y1 + t * (y2 - y1)]\n", "\n", " return None\n", "\n", " if side == 'left':\n", " intersect = intersection(line[0], line[2], line[1], line[3],\n", " extent[0], extent[0], extent[2], extent[3])\n", " elif side == 'right':\n", " intersect = intersection(line[0], line[2], line[1], line[3],\n", " extent[1], extent[1], extent[2], extent[3])\n", " elif side == 'top':\n", " intersect = intersection(line[0], line[2], line[1], line[3],\n", " extent[0], extent[1], extent[3], extent[3])\n", " elif side == 'bottom':\n", " intersect = intersection(line[0], line[2], line[1], line[3],\n", " extent[0], extent[1], extent[2], extent[2])\n", "\n", " true_value = distance([inside_pt[0], inside_pt[1], intersect[0], intersect[1]])\n", " d12 = raytrace(line, extent, image, debug)\n", " difference = abs(d12 - true_value)\n", " if difference > 1e-12:\n", " # print(d12, true_value, difference)\n", " draw_line(line, 'r-')\n", " else:\n", " draw_line(line)\n", " differences.append(difference)\n", "\n", " plt.figure()\n", " plt.hist(differences)\n", " print()\n", " \n", " plt.show()\n", "\n", "\n", "def test_inside(seed=8675309, nruns=5000, npixels=50, debug=False, display_pixels=False):\n", " import random\n", "\n", " def distance(line):\n", " return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\n", "\n", " if display_pixels:\n", " image = np.zeros((npixels, npixels))\n", " else:\n", " image = np.ones((npixels, npixels))\n", " extent = [-5, 5, -5, 5]\n", "\n", " plt.figure()\n", " draw_algorithm(extent, image)\n", "\n", " random.seed(seed)\n", "\n", " differences = []\n", "\n", " for i in range(nruns):\n", " line = [random.uniform(extent[0], extent[1]),\n", " random.uniform(extent[2], extent[3]),\n", " random.uniform(extent[0], extent[1]),\n", " random.uniform(extent[2], extent[3])]\n", "\n", " d12 = raytrace(line, extent, image, debug, display_pixels)\n", " true_value = distance(line)\n", " difference = abs(d12 - true_value)\n", " if difference > 1e-12:\n", " draw_line(line, 'r-')\n", " else:\n", " draw_line(line)\n", " differences.append(difference)\n", "\n", " plt.figure()\n", " plt.hist(differences)\n", " print()\n", " \n", " plt.show()\n", "\n", "\n", "def test_special_cases(nruns=100, npixels=50, run_cython=False):\n", " def distance(line):\n", " return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\n", "\n", " image = np.ones((npixels, npixels))\n", " extent = [-5, 5, -4, 4]\n", "\n", " plt.figure()\n", " draw_algorithm(extent, image)\n", "\n", " def test_line(line, extent, image, true_value):\n", " if run_cython:\n", " d12 = raytrace_fast(line, extent, image)\n", " else:\n", " d12 = raytrace(line, extent, image)\n", " difference = abs(d12 - true_value)\n", " if difference > 1e-12:\n", " print(d12, true_value, difference)\n", " draw_line(line, 'r-')\n", " else:\n", " draw_line(line)\n", "\n", " # outside lines\n", " line = [-6.2, -7.1, -5.4, 7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [-5.4, -7.1, -6.1, 7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [-5.4, -7.1, 6.1, -7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [-5.4, -7.4, 6.1, -7.1]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [6.2, -7.1, 5.4, 7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [5.4, -7.1, 6.1, 7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [-5.4, 7.1, 6.1, 7.4]\n", " test_line(line, extent, image, 0)\n", "\n", " line = [-5.4, 7.4, 6.1, 7.1]\n", " test_line(line, extent, image, 0)\n", "\n", " # horizontal lines\n", " line = [-5.4, 2.11, 6.1, 2.11]\n", " test_line(line, extent, image, 10)\n", "\n", " line = [5.4, -2.11, -6.1, -2.11]\n", " test_line(line, extent, image, 10)\n", "\n", " # vertical lines\n", " line = [2.11, 6.1, 2.11, -5.4]\n", " test_line(line, extent, image, 10)\n", "\n", " line = [-2.11, -6.1, -2.11, 5.4]\n", " test_line(line, extent, image, 10)\n", "\n", " # horizontal line inside\n", " line = [-4.4, 1.11, -4.4+3, 1.11]\n", " test_line(line, extent, image, 3)\n", "\n", " # horizontal line inside / outside\n", " line = [-6.4, 3.5, -5 + 3, 3.5]\n", " test_line(line, extent, image, 3)\n", "\n", " # horizontal line outside / inside\n", " line = [6.4, 3.5, 5-3, 3.5]\n", " test_line(line, extent, image, 3)\n", "\n", " # vertical line inside\n", " line = [1.11, -4.4, 1.11, -4.4+3]\n", " test_line(line, extent, image, 3)\n", "\n", " # vertical line inside / outside\n", " line = [3.5, -6.4, 3.5, -5 + 3]\n", " test_line(line, extent, image, 3)\n", "\n", " # vertical line outside / inside\n", " line = [3.5, 6.4, 3.5, 5-3]\n", " test_line(line, extent, image, 3)\n", "\n", " # ray within a single pixel\n", " line = [1.1, 1.1, 1.12, 1.12]\n", " test_line(line, extent, image, distance(line))\n", "\n", " # ray crosses the origin only\n", " line = [0.1, 0.1, -0.1, -0.1]\n", " test_line(line, extent, image, distance(line))\n", " \n", " plt.show()\n", "\n", "\n", "def test_object(nrays=100):\n", " import pytracer.transmission as transmission\n", " import pytracer.geometry as geo\n", " from scripts.assemblies import shielded_assembly\n", "\n", " xs = np.linspace(-11, 11, 200)\n", " ys = np.linspace(-6, 6, 200)\n", "\n", " assembly = shielded_assembly()\n", " assembly_flat = geo.flatten(assembly)\n", "\n", " # truth\n", " mu_image, extent = transmission.absorbance_image(xs, ys, assembly_flat)\n", " mu_image = mu_image.T\n", "\n", " draw_algorithm(extent, mu_image, draw_lines=False)\n", "\n", " radians = np.array([0.]) #np.linspace(0, np.pi, 100)\n", " arc_radians = np.linspace(-np.pi / 8, np.pi / 8, nrays)\n", " start, end = geo.fan_beam_paths(60, arc_radians, radians, extent=False)\n", " for (s, e) in zip(start[:, 0], end[:, 0]):\n", " plt.plot([s[0], e[0]], [s[1], e[1]], color='blue', ls='-', lw=0.2)\n", "\n", " d12_values = np.zeros(nrays)\n", " d12_index = 0\n", "\n", " for (s, e) in zip(start[:, 0], end[:, 0]):\n", " line = [s[0], s[1], e[0], e[1]]\n", "\n", " d12_values[d12_index] = raytrace(line, extent, mu_image)\n", " d12_index += 1\n", "\n", " plt.figure()\n", " plt.plot(arc_radians, d12_values)\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def test_speedup(npixels=50, nruns=100, seed=8675309, run_cython=True):\n", " import cProfile\n", " import random\n", "\n", " random.seed(seed)\n", "\n", " image = np.ones((npixels, npixels), dtype=np.double)\n", " extent = [-5, 5, -5, 5]\n", "\n", " lines = np.zeros((nruns, 4), dtype=np.double)\n", "\n", " for i in range(nruns):\n", " lines[i] = [random.uniform(extent[0], extent[1]),\n", " random.uniform(extent[2], extent[3]),\n", " random.uniform(extent[0], extent[1]),\n", " random.uniform(extent[2], extent[3])]\n", "\n", " integral_python = 0\n", " integral_cython = 0\n", "\n", " def test_python_raytrace(lines, extent, image):\n", " integral = 0\n", " for line in lines:\n", " integral += raytrace(line, extent, image)\n", " print(integral)\n", " print()\n", "\n", " def test_cython_raytrace(lines, extent, image):\n", " integral = 0.\n", "# for line in lines:\n", "# integral += raytrace_fast(line, extent, image)\n", " integral = np.sum(raytrace_bulk_fast(lines, extent, image))\n", " print(integral)\n", " print()\n", "\n", " g = globals()\n", " l = locals()\n", " if run_cython:\n", " cProfile.runctx('test_cython_raytrace(lines, extent, image)', globals=g, locals=l, sort='time')\n", " else:\n", " cProfile.runctx('test_python_raytrace(lines, extent, image)', globals=g, locals=l, sort='time')\n", " # do cython and compare stats, also compare answers" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "7.999999999999984 10 2.000000000000016\n", "7.999999999999984 10 2.000000000000016\n", "2.6000000000000005 3 0.39999999999999947\n", "2.0 3 1.0\n", "2.0 3 1.0\n" ] } ], "source": [ "# test_inside(nruns=1, debug=True, display_pixels=True)\n", "# test_outside(nruns=100)\n", "# test_innoutside(nruns=100)\n", "test_special_cases()\n", "# test_object()\n", "\n", "# test_speedup(nruns=10000, run_cython=True)\n", "# test_speedup(nruns=10000, run_cython=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

mne-tools/mne-tools.github.io

0.18/_downloads/c92aa91c680730c756234cdbc466c558/plot_introduction.ipynb

1

24435

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nOverview of MEG/EEG analysis with MNE-Python\n============================================\n\nThis tutorial covers the basic EEG/MEG pipeline for event-related analysis:\nloading data, epoching, averaging, plotting, and estimating cortical activity\nfrom sensor data. It introduces the core MNE-Python data structures\n:class:`~mne.io.Raw`, :class:`~mne.Epochs`, :class:`~mne.Evoked`, and\n:class:`~mne.SourceEstimate`, and covers a lot of ground fairly quickly (at the\nexpense of depth). Subsequent tutorials address each of these topics in greater\ndetail.\n :depth: 1\n\nWe begin by importing the necessary Python modules:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\nimport numpy as np\nimport mne" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading data\n^^^^^^^^^^^^\n\nMNE-Python data structures are based around the FIF file format from\nNeuromag, but there are reader functions for `a wide variety of other\ndata formats <data-formats>`. MNE-Python also has interfaces to a\nvariety of :doc:`publicly available datasets <../../manual/datasets_index>`,\nwhich MNE-Python can download and manage for you.\n\nWe'll start this tutorial by loading one of the example datasets (called\n\"`sample-dataset`\"), which contains EEG and MEG data from one subject\nperforming an audiovisual experiment, along with structural MRI scans for\nthat subject. The :func:`mne.datasets.sample.data_path` function will\nautomatically download the dataset if it isn't found in one of the expected\nlocations, then return the directory path to the dataset (see the\ndocumentation of :func:`~mne.datasets.sample.data_path` for a list of places\nit checks before downloading). Note also that for this tutorial to run\nsmoothly on our servers, we're using a filtered and downsampled version of\nthe data (:file:`sample_audvis_filt-0-40_raw.fif`), but an unfiltered version\n(:file:`sample_audvis_raw.fif`) is also included in the sample dataset and\ncould be substituted here when running the tutorial locally.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample_data_folder = mne.datasets.sample.data_path()\nsample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',\n 'sample_audvis_filt-0-40_raw.fif')\nraw = mne.io.read_raw_fif(sample_data_raw_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, :func:`~mne.io.read_raw_fif` displays some information about the\nfile it's loading; for example, here it tells us that there are four\n\"projection items\" in the file along with the recorded data; those are\n:term:`SSP projectors <projector>` calculated to remove environmental noise\nfrom the MEG signals, plus a projector to mean-reference the EEG channels;\nthese are discussed\nin a later tutorial. In addition to the information displayed during loading,\nyou can get a glimpse of the basic details of a :class:`~mne.io.Raw` object\nby printing it; even more is available by printing its ``info`` attribute\n(a :class:`dictionary-like object <mne.Info>` that is preserved across\n:class:`~mne.io.Raw`, :class:`~mne.Epochs`, and :class:`~mne.Evoked`\nobjects). The ``info`` data structure keeps track of channel locations,\napplied filters, projectors, etc. Notice especially the ``chs`` entry,\nshowing that MNE-Python detects different sensor types and handles each\nappropriately.\n\n.. TODO edit prev. paragraph when projectors tutorial is added: ...those are\n discussed in the tutorial `projectors-tutorial`. (or whatever link)\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(raw)\nprint(raw.info)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":class:`~mne.io.Raw` objects also have several built-in plotting methods;\nhere we show the power spectral density (PSD) for each sensor type with\n:meth:`~mne.io.Raw.plot_psd`, as well as a plot of the raw sensor traces with\n:meth:`~mne.io.Raw.plot`. In the PSD plot, we'll only plot frequencies below\n50 Hz (since our data are low-pass filtered at 40 Hz). In interactive Python\nsessions, :meth:`~mne.io.Raw.plot` is interactive and allows scrolling,\nscaling, bad channel marking, annotation, projector toggling, etc.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw.plot_psd(fmax=50)\nraw.plot(duration=5, n_channels=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preprocessing\n^^^^^^^^^^^^^\n\nMNE-Python supports a variety of preprocessing approaches and techniques\n(maxwell filtering, signal-space projection, independent components analysis,\nfiltering, downsampling, etc); see the full list of capabilities in the\n:mod:`mne.preprocessing` and :mod:`mne.filter` submodules. Here we'll clean\nup our data by performing independent components analysis\n(:class:`~mne.preprocessing.ICA`); for brevity we'll skip the steps that\nhelped us determined which components best capture the artifacts (see\n:doc:`../preprocessing/plot_artifacts_correction_ica` for a detailed\nwalk-through of that process).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# set up and fit the ICA\nica = mne.preprocessing.ICA(n_components=20, random_state=97, max_iter=800)\nica.fit(raw)\nica.exclude = [1, 2] # details on how we picked these are omitted here\nica.plot_properties(raw, picks=ica.exclude)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we're confident about which component(s) we want to remove, we pass them\nas the ``exclude`` parameter and then apply the ICA to the raw signal. The\n:meth:`~mne.preprocessing.ICA.apply` method requires the raw data to be\nloaded into memory (by default it's only read from disk as-needed), so we'll\nuse :meth:`~mne.io.Raw.load_data` first. We'll also make a copy of the\n:class:`~mne.io.Raw` object so we can compare the signal before and after\nartifact removal side-by-side:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "orig_raw = raw.copy()\nraw.load_data()\nica.apply(raw)\n\n# show some frontal channels to clearly illustrate the artifact removal\nchs = ['MEG 0111', 'MEG 0121', 'MEG 0131', 'MEG 0211', 'MEG 0221', 'MEG 0231',\n 'MEG 0311', 'MEG 0321', 'MEG 0331', 'MEG 1511', 'MEG 1521', 'MEG 1531',\n 'EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006',\n 'EEG 007', 'EEG 008']\nchan_idxs = [raw.ch_names.index(ch) for ch in chs]\norig_raw.plot(order=chan_idxs, start=12, duration=4)\nraw.plot(order=chan_idxs, start=12, duration=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detecting experimental events\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThe sample dataset includes several :term:`\"STIM\" channels <stim channel>`\nthat recorded electrical\nsignals sent from the stimulus delivery computer (as brief DC shifts /\nsquarewave pulses). These pulses (often called \"triggers\") are used in this\ndataset to mark experimental events: stimulus onset, stimulus type, and\nparticipant response (button press). The individual STIM channels are\ncombined onto a single channel, in such a way that voltage\nlevels on that channel can be unambiguously decoded as a particular event\ntype. On older Neuromag systems (such as that used to record the sample data)\nthis summation channel was called ``STI 014``, so we can pass that channel\nname to the :func:`mne.find_events` function to recover the timing and\nidentity of the stimulus events.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "events = mne.find_events(raw, stim_channel='STI 014')\nprint(events[:5]) # show the first 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting events array is an ordinary 3-column :class:`NumPy array\n<numpy.ndarray>`, with sample number in the first column and integer event ID\nin the last column; the middle column is usually ignored. Rather than keeping\ntrack of integer event IDs, we can provide an *event dictionary* that maps\nthe integer IDs to experimental conditions or events. In this dataset, the\nmapping looks like this:\n\n\n+----------+----------------------------------------------------------+\n| Event ID | Condition |\n+==========+==========================================================+\n| 1 | auditory stimulus (tone) to the left ear |\n+----------+----------------------------------------------------------+\n| 2 | auditory stimulus (tone) to the right ear |\n+----------+----------------------------------------------------------+\n| 3 | visual stimulus (checkerboard) to the left visual field |\n+----------+----------------------------------------------------------+\n| 4 | visual stimulus (checkerboard) to the right visual field |\n+----------+----------------------------------------------------------+\n| 5 | smiley face (catch trial) |\n+----------+----------------------------------------------------------+\n| 32 | subject button press |\n+----------+----------------------------------------------------------+\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,\n 'visual/right': 4, 'smiley': 5, 'buttonpress': 32}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Event dictionaries like this one are used when extracting epochs from\ncontinuous data; the ``/`` character in the dictionary keys allows pooling\nacross conditions by requesting partial condition descriptors (i.e.,\nrequesting ``'auditory'`` will select all epochs with Event IDs 1 and 2;\nrequesting ``'left'`` will select all epochs with Event IDs 1 and 3). An\nexample of this is shown in the next section. There is also a convenient\n:func:`~mne.viz.plot_events` function for visualizing the distribution of\nevents across the duration of the recording (to make sure event detection\nworked as expected). Here we'll also make use of the :class:`~mne.Info`\nattribute to get the sampling frequency of the recording (so our x-axis will\nbe in seconds instead of in samples).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.plot_events(events, event_id=event_dict, sfreq=raw.info['sfreq'])\nfig.subplots_adjust(right=0.7) # make room for the legend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For paradigms that are not event-related (e.g., analysis of resting-state\ndata), you can extract regularly spaced (possibly overlapping) spans of data\nby creating events using :func:`mne.make_fixed_length_events` and then\nproceeding with epoching as described in the next section.\n\n\nEpoching continuous data\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nThe :class:`~mne.io.Raw` object and the events array are the bare minimum\nneeded to create an :class:`~mne.Epochs` object, which we create with the\n:class:`mne.Epochs` class constructor. Here we'll also specify some data\nquality constraints: we'll reject any epoch where peak-to-peak signal\namplitude is beyond reasonable limits for that channel type. This is done\nwith a *rejection dictionary*; you may include or omit thresholds for any of\nthe channel types present in your data. The values given here are reasonable\nfor this particular dataset, but may need to be adapted for different\nhardware or recording conditions. For a more automated approach, consider\nusing the `autoreject package`_.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reject_criteria = dict(mag=4000e-15, # 4000 fT\n grad=4000e-13, # 4000 fT/cm\n eeg=150e-6, # 150 \u03bcV\n eog=250e-6) # 250 \u03bcV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also pass the event dictionary as the ``event_id`` parameter (so we can\nwork with easy-to-pool event labels instead of the integer event IDs), and\nspecify ``tmin`` and ``tmax`` (the time relative to each event at which to\nstart and end each epoch). As mentioned above, by default\n:class:`~mne.io.Raw` and :class:`~mne.Epochs` data aren't loaded into memory\n(they're accessed from disk only when needed), but here we'll force loading\ninto memory using the ``preload=True`` parameter so that we can see the\nresults of the rejection criteria being applied:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.2, tmax=0.5,\n reject=reject_criteria, preload=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll pool across left/right stimulus presentations so we can compare\nauditory versus visual responses. To avoid biasing our signals to the\nleft or right, we'll use :meth:`~mne.Epochs.equalize_event_counts` first to\nrandomly sample epochs from each condition to match the number of epochs\npresent in the condition with the fewest good epochs.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conds_we_care_about = ['auditory/left', 'auditory/right',\n 'visual/left', 'visual/right']\nepochs.equalize_event_counts(conds_we_care_about) # this operates in-place\naud_epochs = epochs['auditory']\nvis_epochs = epochs['visual']\ndel raw, epochs # free up memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like :class:`~mne.io.Raw` objects, :class:`~mne.Epochs` objects also have a\nnumber of built-in plotting methods. One is :meth:`~mne.Epochs.plot_image`,\nwhich shows each epoch as one row of an image map, with color representing\nsignal magnitude; the average evoked response and the sensor location are\nshown below the image:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "aud_epochs.plot_image(picks=['MEG 1332', 'EEG 021'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><h4>Note</h4><p>Both :class:`~mne.io.Raw` and :class:`~mne.Epochs` objects have\n :meth:`~mne.Epochs.get_data` methods that return the underlying data\n as a :class:`NumPy array <numpy.ndarray>`. Both methods have a ``picks``\n parameter for subselecting which channel(s) to return; ``raw.get_data()``\n has additional parameters for restricting the time domain. The resulting\n matrices have dimension ``(n_channels, n_times)`` for\n :class:`~mne.io.Raw` and ``(n_epochs, n_channels, n_times)`` for\n :class:`~mne.Epochs`.</p></div>\n\n\nTime-frequency analysis\n^^^^^^^^^^^^^^^^^^^^^^^\n\nThe :mod:`mne.time_frequency` submodule provides implementations of several\nalgorithms to compute time-frequency representations, power spectral density,\nand cross-spectral density. Here, for example, we'll compute for the auditory\nepochs the induced power at different frequencies and times, using Morlet\nwavelets. On this dataset the result is not especially informative (it just\nshows the evoked \"auditory N100\" response); see `here\n<inter-trial-coherence>` for a more extended example on a dataset with richer\nfrequency content.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "frequencies = np.arange(7, 30, 3)\npower = mne.time_frequency.tfr_morlet(aud_epochs, n_cycles=2, return_itc=False,\n freqs=frequencies, decim=3)\npower.plot(['MEG 1332'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimating evoked responses\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nNow that we have our conditions in ``aud_epochs`` and ``vis_epochs``, we can\nget an estimate of evoked responses to auditory versus visual stimuli by\naveraging together the epochs in each condition. This is as simple as calling\nthe :meth:`~mne.Epochs.average` method on the :class:`~mne.Epochs` object,\nand then using a function from the :mod:`mne.viz` module to compare the\nglobal field power for each sensor type of the two :class:`~mne.Evoked`\nobjects:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "aud_evoked = aud_epochs.average()\nvis_evoked = vis_epochs.average()\n\nmne.viz.plot_compare_evokeds(dict(auditory=aud_evoked, visual=vis_evoked),\n show_legend='upper left',\n show_sensors='upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also get a more detailed view of each :class:`~mne.Evoked` object\nusing other plotting methods such as :meth:`~mne.Evoked.plot_joint` or\n:meth:`~mne.Evoked.plot_topomap`. Here we'll examine just the EEG channels,\nand see the classic auditory evoked N100-P200 pattern over dorso-frontal\nelectrodes, then plot scalp topographies at some additional arbitrary times:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# sphinx_gallery_thumbnail_number = 13\naud_evoked.plot_joint(picks='eeg')\naud_evoked.plot_topomap(times=[0., 0.08, 0.1, 0.12, 0.2], ch_type='eeg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evoked objects can also be combined to show contrasts between conditions,\nusing the :func:`mne.combine_evoked` function. A simple difference can be\ngenerated by negating one of the :class:`~mne.Evoked` objects passed into the\nfunction. We'll then plot the difference wave at each sensor using\n:meth:`~mne.Evoked.plot_topo`:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evoked_diff = mne.combine_evoked([aud_evoked, -vis_evoked], weights='equal')\nevoked_diff.pick_types('mag').plot_topo(color='r', legend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inverse modeling\n^^^^^^^^^^^^^^^^\n\nFinally, we can estimate the origins of the evoked activity by projecting the\nsensor data into this subject's :term:`source space` (a set of points either\non the cortical surface or within the cortical volume of that subject, as\nestimated by structural MRI scans). MNE-Python supports lots of ways of doing\nthis (dynamic statistical parametric mapping, dipole fitting, beamformers,\netc.); here we'll use minimum-norm estimation (MNE) to generate a continuous\nmap of activation constrained to the cortical surface. MNE uses a linear\n:term:`inverse operator` to project EEG+MEG sensor measurements into the\nsource space. The inverse operator is computed from the\n:term:`forward solution` for this subject and an estimate of `the\ncovariance of sensor measurements <tut_compute_covariance>`. For this\ntutorial we'll skip those computational steps and load a pre-computed inverse\noperator from disk (it's included with the `sample data\n<sample-dataset>`). Because this \"inverse problem\" is underdetermined (there\nis no unique solution), here we further constrain the solution by providing a\nregularization parameter specifying the relative smoothness of the current\nestimates in terms of a signal-to-noise ratio (where \"noise\" here is akin to\nbaseline activity level across all of cortex).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load inverse operator\ninverse_operator_file = os.path.join(sample_data_folder, 'MEG', 'sample',\n 'sample_audvis-meg-oct-6-meg-inv.fif')\ninv_operator = mne.minimum_norm.read_inverse_operator(inverse_operator_file)\n# set signal-to-noise ratio (SNR) to compute regularization parameter (\u03bb\u00b2)\nsnr = 3.\nlambda2 = 1. / snr ** 2\n# generate the source time course (STC)\nstc = mne.minimum_norm.apply_inverse(vis_evoked, inv_operator,\n lambda2=lambda2,\n method='MNE') # or dSPM, sLORETA, eLORETA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, in order to plot the source estimate on the subject's cortical\nsurface we'll also need the path to the sample subject's structural MRI files\n(the ``subjects_dir``):\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# path to subjects' MRI files\nsubjects_dir = os.path.join(sample_data_folder, 'subjects')\n# plot\nstc.plot(initial_time=0.1, hemi='split', views=['lat', 'med'],\n subjects_dir=subjects_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The remaining tutorials have *much more detail* on each of these topics (as\nwell as many other capabilities of MNE-Python not mentioned here:\nconnectivity analysis, encoding/decoding models, lots more visualization\noptions, etc). Read on to learn more!\n\n\n.. LINKS\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

ProfessorKazarinoff/staticsite

content/code/webscrape/webscrape_html_table_with_pandas.ipynb

1

7004

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import urllib3,certifi\n", "import requests\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "system: win32\n", "python version: 3.6.3 |Anaconda, Inc.| (default, Oct 27 2017, 12:22:41) [MSC v.1900 64 bit (AMD64)]\n", "pandas version: 0.20.3\n", "urllib3 version: 1.22\n", "certifi version: 2017.07.27.1\n", "requests version: 2.18.4\n" ] } ], "source": [ "print('system: ', sys.platform)\n", "print('python version: ', sys.version)\n", "print('pandas version: ', pd.__version__)\n", "print('urllib3 version: ', urllib3.__version__)\n", "print('certifi version: ', certifi.__version__)\n", "print('requests version: ', requests.__version__)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Force certificate check and use certifi to handle the certificate. \n", "https = urllib3.PoolManager( cert_reqs='CERT_REQUIRED', ca_certs=certifi.where(),) " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "page = 'https://www.pcc.edu/schedule/default.cfm?fa=dspCourse2&thisTerm=201801&crsCode=ENGR&subjCode=ENGR&crsNum=114&topicCode=GE&subtopicCode=%20'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ " #x = requests.get(url=url, certs= certs).content" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = https.urlopen('GET', page)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "No text parsed from document: <urllib3.response.HTTPResponse object at 0x000001BDEFDC5160>", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-19-bb72e7838443>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdf_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Continuum\\Anaconda3\\envs\\webscrape\\lib\\site-packages\\pandas\\io\\html.py\u001b[0m in \u001b[0;36mread_html\u001b[1;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, tupleize_cols, thousands, encoding, decimal, converters, na_values, keep_default_na)\u001b[0m\n\u001b[0;32m 904\u001b[0m \u001b[0mthousands\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mthousands\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 905\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconverters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconverters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_values\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m keep_default_na=keep_default_na)\n\u001b[0m", "\u001b[1;32m~\\AppData\\Local\\Continuum\\Anaconda3\\envs\\webscrape\\lib\\site-packages\\pandas\\io\\html.py\u001b[0m in \u001b[0;36m_parse\u001b[1;34m(flavor, io, match, attrs, encoding, **kwargs)\u001b[0m\n\u001b[0;32m 741\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 742\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 743\u001b[1;33m \u001b[0mraise_with_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretained\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 744\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 745\u001b[0m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Continuum\\Anaconda3\\envs\\webscrape\\lib\\site-packages\\pandas\\compat\\__init__.py\u001b[0m in \u001b[0;36mraise_with_traceback\u001b[1;34m(exc, traceback)\u001b[0m\n\u001b[0;32m 342\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mEllipsis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 344\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 345\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[1;31m# this version of raise is a syntax error in Python 3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: No text parsed from document: <urllib3.response.HTTPResponse object at 0x000001BDEFDC5160>" ] } ], "source": [ "df_list = pd.read_html(url)\n", "df_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

VandyAstroML/Vanderbilt_Computational_Bootcamp

notebooks/Week_05/05_Numpy_Matplotlib.ipynb

1

92420

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Week 5 - Numpy & Matplotlib\n", "\n", "## Today's Agenda\n", "* Numpy\n", "* Matplotlib" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## [Numpy](http://www.numpy.org/) - Numerical Python\n", "\n", "From their website ([http://www.numpy.org/](http://www.numpy.org/)):\n", "\n", "> NumPy is the fundamental package for scientific computing with Python. \n", "> * a powerful N-dimensional array object\n", "> * sophisticated (broadcasting) functions\n", "> * tools for integrating C/C++ and Fortran code\n", "> * useful linear algebra, Fourier transform, and random number capabilities" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "You can import __\"`numpy`\"__ as" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy arrays\n", "\n", "In standard Python, data is stored as lists, and multidimensional data as **lists of lists**. In `numpy`, however, we can now work with arrays. To get these arrays, we can use `np.asarray` to convert a list into an array. Below we take a quick look at how a list behaves differently from an array." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 5 6 7 8 9 10]\n" ] } ], "source": [ "# We first create an array `x`\n", "start = 1\n", "stop = 11\n", "step = 1\n", "\n", "x = np.arange(start, stop, step)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also manipulate the array. For example, we can:\n", "\n", "- Multiply by two:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x * 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Take the square of all the values in the array:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x ** 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Or even do some math on it:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6.33333333, 14.66666667, 25. , 37.33333333,\n", " 51.66666667, 68. , 86.33333333, 106.66666667,\n", " 129. , 153.33333333])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(x**2) + (5*x) + (x / 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to set up an array in numpy, we can use range to make a list and then convert it to an array, but we can also just create an array directly in numpy. `np.arange` will do this with integers, and `np.linspace` will do this with floats, and allows for non-integer steps." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n" ] } ], "source": [ "print(np.arange(10))\n", "\n", "print(np.linspace(1,10,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last week we had to use a function or a loop to carry out math on a list. However with numpy we can do this a lot simpler by making sure we're working with an array, and carrying out the mathematical operations on that array." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[ 0 1 4 9 16 25 36 49 64 81]\n" ] } ], "source": [ "x=np.arange(10)\n", "print(x)\n", "\n", "print(x**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In numpy, we also have more options for quickly (and without much code) examining the contents of an array. One of the most helpful tools for this is `np.where`. `np.where` uses a conditional statement on the array and returns an array that contains indices of all the values that were true for the conditional statement. We can then call the original array and use the new array to get all the **values** that were true for the conditional statement.\n", "\n", "There are also functions like `max` and `min` that will give the maximum and minimum, respectively." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5\n", " 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5\n", " 12. 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5\n", " 18. 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5\n", " 24. 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5\n", " 30. 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5\n", " 36. 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5\n", " 42. 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5\n", " 48. 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5\n", " 54. 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5\n", " 60. 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5\n", " 66. 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5\n", " 72. 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5\n", " 78. 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5\n", " 84. 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5\n", " 90. 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5\n", " 96. 96.5 97. 97.5 98. 98.5 99. 99.5 100. ]\n" ] } ], "source": [ "# Defining starting and ending values of the array, as well as the number of elements in the array.\n", "start = 0\n", "stop = 100\n", "n_elements = 201\n", "\n", "x = np.linspace(start, stop, n_elements)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can select only those values that are divisible by *5*:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120,\n", " 130, 140, 150, 160, 170, 180, 190, 200]),)\n", "[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55. 60. 65.\n", " 70. 75. 80. 85. 90. 95. 100.]\n" ] } ], "source": [ "# This function returns the indices that match the criteria of `x % 5 == 0`:\n", "x_5 = np.where(x%5 == 0)\n", "\n", "print(x_5)\n", "\n", "# And one can use those indices to *only* select those values:\n", "print(x[x_5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or similarly:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.,\n", " 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[x%5 == 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can find the `max` and ``min`` values of the array:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The minimum of `x` is `0.0`\n", "The maximum of `x` is `100.0`\n" ] } ], "source": [ "print('The minimum of `x` is `{0}`'.format(x.min()))\n", "\n", "print('The maximum of `x` is `{0}`'.format(x.max()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy also provides some tools for loading and saving data, loadtxt and savetxt. Here I'm using a function called transpose so that instead of each array being a row, they each get treated as a column.\n", "\n", "When we load the information again, it's now a 2D array. We can select parts of those arrays just as we could for 1D arrays." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2D-array from file `myfile.txt`:\n", "\n", " [[ 0.000000e+00 3.000000e+00]\n", " [ 2.000000e-01 2.000400e+00]\n", " [ 4.000000e-01 1.001600e+00]\n", " ...\n", " [ 9.960000e+01 -3.957984e+02]\n", " [ 9.980000e+01 -3.963996e+02]\n", " [ 1.000000e+02 -3.970000e+02]] \n", "\n", "Selecting certain elements from `data`:\n", "\n", " [[0. 3. ]\n", " [0.2 2.0004]\n", " [0.4 1.0016]] \n", "\n" ] } ], "source": [ "start = 0\n", "stop = 100\n", "n_elem = 501\n", "\n", "x = np.linspace(start, stop, n_elem)\n", "\n", "# We can now create another array from `x`:\n", "y = (.1*x)**2 - (5*x) + 3\n", "\n", "# And finally, we can dump `x` and `y` to a file:\n", "np.savetxt('myfile.txt', np.transpose([x,y]))\n", "\n", "# We can also load the data from `myfile.txt` and display it:\n", "data = np.loadtxt('myfile.txt')\n", "print('2D-array from file `myfile.txt`:\\n\\n', data, '\\n')\n", "\n", "# You can also select certain elements of the 2D-array\n", "print('Selecting certain elements from `data`:\\n\\n', data[:3,:], '\\n')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Resources\n", "* Scientific Lectures on Python - __Numpy__: [iPython Notebook](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb)\n", "* Data Science iPython Notebooks - __Numpy__: [iPython Notebook](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## [Matplotlib](http://matplotlib.org/)\n", "\n", "Matplotlib is a Python 2D plotting library which\n", "* produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms\n", "* Quick way to visualize data from Python\n", "* Main plotting utility in Python\n", "\n", "From their website ([http://matplotlib.org/](http://matplotlib.org/)):\n", "\n", "> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n", "\n", "A great starting point to figuring out how to make a particular figure is to start from the [Matplotlib gallery](http://matplotlib.org/gallery.html) and look for what you want to make." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "## Importing modules\n", "%matplotlib inline\n", "\n", "# Importing LaTeX\n", "from matplotlib import rc\n", "rc('text', usetex=True)\n", "\n", "# Importing matplotlib and other modules\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now load in the data from `myfile.txt`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt('myfile.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest figure is to simply make a plot. We can have multiple figures, but for now, just one. The plt.plot function will connect the points, but if we want a scatter plot, then plt.scatter will work." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(8,8))\n", "plt.plot(data[:,0],data[:,1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also pass the `*data.T` value instead:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(8,8))\n", "plt.plot(*data.T)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can take that same figure and add on the needed labels and titles." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Creating figure\n", "plt.figure(figsize=(8,8))\n", "plt.plot(*data.T)\n", "plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\n", "plt.xlabel('x value', fontsize=20)\n", "plt.ylabel('y value', fontsize=20)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a large number of options available for plotting, so try using the initial code below, combined with the information [here](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) to try out a few of the following things: changing the line width, changing the line color" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.plot(data[:,0],data[:,1])\n", "plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\n", "plt.xlabel('x value', fontsize=20)\n", "plt.ylabel('y value', fontsize=20)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

google/earthengine-api

python/examples/ipynb/UNET_regression_demo.ipynb

1

31375

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNET_regression_demo.ipynb","provenance":[{"file_id":"https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb","timestamp":1586992475463}],"private_outputs":true,"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"esIMGVxhDI0f","colab_type":"code","colab":{}},"source":["#@title Copyright 2020 Google LLC. { display-mode: \"form\" }\n","# Licensed under the Apache License, Version 2.0 (the \"License\");\n","# you may not use this file except in compliance with the License.\n","# You may obtain a copy of the License at\n","#\n","# https://www.apache.org/licenses/LICENSE-2.0\n","#\n","# Unless required by applicable law or agreed to in writing, software\n","# distributed under the License is distributed on an \"AS IS\" BASIS,\n","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","# See the License for the specific language governing permissions and\n","# limitations under the License."],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aV1xZ1CPi3Nw","colab_type":"text"},"source":["<table class=\"ee-notebook-buttons\" align=\"left\"><td>\n","<a target=\"_blank\" href=\"http://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb\">\n"," <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a>\n","</td><td>\n","<a target=\"_blank\" href=\"https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td></table>"]},{"cell_type":"markdown","metadata":{"id":"_SHAc5qbiR8l","colab_type":"text"},"source":["# Introduction\n","\n","This is an Earth Engine <> TensorFlow demonstration notebook. Suppose you want to predict a continuous output (regression) from a stack of continuous inputs. In this example, the output is impervious surface area from [NLCD](https://www.mrlc.gov/data) and the input is a Landsat 8 composite. The model is a [fully convolutional neural network (FCNN)](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf), specifically [U-net](https://arxiv.org/abs/1505.04597). This notebook shows:\n","\n","1. Exporting training/testing patches from Earth Engine, suitable for training an FCNN model.\n","2. Preprocessing.\n","3. Training and validating an FCNN model.\n","4. Making predictions with the trained model and importing them to Earth Engine."]},{"cell_type":"markdown","metadata":{"id":"_MJ4kW1pEhwP","colab_type":"text"},"source":["# Setup software libraries\n","\n","Authenticate and import as necessary."]},{"cell_type":"code","metadata":{"id":"neIa46CpciXq","colab_type":"code","colab":{}},"source":["# Cloud authentication.\n","from google.colab import auth\n","auth.authenticate_user()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jat01FEoUMqg","colab_type":"code","colab":{}},"source":["# Import, authenticate and initialize the Earth Engine library.\n","import ee\n","ee.Authenticate()\n","ee.Initialize()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8RnZzcYhcpsQ","colab_type":"code","colab":{}},"source":["# Tensorflow setup.\n","import tensorflow as tf\n","print(tf.__version__)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"n1hFdpBQfyhN","colab_type":"code","colab":{}},"source":["# Folium setup.\n","import folium\n","print(folium.__version__)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iT8ycmzClYwf","colab_type":"text"},"source":["# Variables\n","\n","Declare the variables that will be in use throughout the notebook."]},{"cell_type":"markdown","metadata":{"id":"qKs6HuxOzjMl","colab_type":"text"},"source":["## Specify your Cloud Storage Bucket\n","You must have write access to a bucket to run this demo! To run it read-only, use the demo bucket below, but note that writes to this bucket will not work."]},{"cell_type":"code","metadata":{"id":"obDDH1eDzsch","colab_type":"code","colab":{}},"source":["# INSERT YOUR BUCKET HERE:\n","BUCKET = 'your-bucket-name'"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wmfKLl9XcnGJ","colab_type":"text"},"source":["## Set other global variables"]},{"cell_type":"code","metadata":{"id":"psz7wJKalaoj","colab_type":"code","colab":{}},"source":["# Specify names locations for outputs in Cloud Storage. \n","FOLDER = 'fcnn-demo'\n","TRAINING_BASE = 'training_patches'\n","EVAL_BASE = 'eval_patches'\n","\n","# Specify inputs (Landsat bands) to the model and the response variable.\n","opticalBands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n","thermalBands = ['B10', 'B11']\n","BANDS = opticalBands + thermalBands\n","RESPONSE = 'impervious'\n","FEATURES = BANDS + [RESPONSE]\n","\n","# Specify the size and shape of patches expected by the model.\n","KERNEL_SIZE = 256\n","KERNEL_SHAPE = [KERNEL_SIZE, KERNEL_SIZE]\n","COLUMNS = [\n"," tf.io.FixedLenFeature(shape=KERNEL_SHAPE, dtype=tf.float32) for k in FEATURES\n","]\n","FEATURES_DICT = dict(zip(FEATURES, COLUMNS))\n","\n","# Sizes of the training and evaluation datasets.\n","TRAIN_SIZE = 16000\n","EVAL_SIZE = 8000\n","\n","# Specify model training parameters.\n","BATCH_SIZE = 16\n","EPOCHS = 10\n","BUFFER_SIZE = 2000\n","OPTIMIZER = 'SGD'\n","LOSS = 'MeanSquaredError'\n","METRICS = ['RootMeanSquaredError']"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hgoDc7Hilfc4","colab_type":"text"},"source":["# Imagery\n","\n","Gather and setup the imagery to use for inputs (predictors). This is a three-year, cloud-free, Landsat 8 composite. Display it in the notebook for a sanity check."]},{"cell_type":"code","metadata":{"id":"-IlgXu-vcUEY","colab_type":"code","colab":{}},"source":["# Use Landsat 8 surface reflectance data.\n","l8sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\n","\n","# Cloud masking function.\n","def maskL8sr(image):\n"," cloudShadowBitMask = ee.Number(2).pow(3).int()\n"," cloudsBitMask = ee.Number(2).pow(5).int()\n"," qa = image.select('pixel_qa')\n"," mask1 = qa.bitwiseAnd(cloudShadowBitMask).eq(0).And(\n"," qa.bitwiseAnd(cloudsBitMask).eq(0))\n"," mask2 = image.mask().reduce('min')\n"," mask3 = image.select(opticalBands).gt(0).And(\n"," image.select(opticalBands).lt(10000)).reduce('min')\n"," mask = mask1.And(mask2).And(mask3)\n"," return image.select(opticalBands).divide(10000).addBands(\n"," image.select(thermalBands).divide(10).clamp(273.15, 373.15)\n"," .subtract(273.15).divide(100)).updateMask(mask)\n","\n","# The image input data is a cloud-masked median composite.\n","image = l8sr.filterDate('2015-01-01', '2017-12-31').map(maskL8sr).median()\n","\n","# Use folium to visualize the imagery.\n","mapid = image.getMapId({'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3})\n","map = folium.Map(location=[38., -122.5])\n","folium.TileLayer(\n"," tiles=mapid['tile_fetcher'].url_format,\n"," attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n"," overlay=True,\n"," name='median composite',\n"," ).add_to(map)\n","\n","mapid = image.getMapId({'bands': ['B10'], 'min': 0, 'max': 0.5})\n","folium.TileLayer(\n"," tiles=mapid['tile_fetcher'].url_format,\n"," attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n"," overlay=True,\n"," name='thermal',\n"," ).add_to(map)\n","map.add_child(folium.LayerControl())\n","map"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gHznnctkJsZJ","colab_type":"text"},"source":["Prepare the response (what we want to predict). This is impervious surface area (in fraction of a pixel) from the 2016 NLCD dataset. Display to check."]},{"cell_type":"code","metadata":{"id":"e0wHDyxVirec","colab_type":"code","colab":{}},"source":["nlcd = ee.Image('USGS/NLCD/NLCD2016').select('impervious')\n","nlcd = nlcd.divide(100).float()\n","\n","mapid = nlcd.getMapId({'min': 0, 'max': 1})\n","map = folium.Map(location=[38., -122.5])\n","folium.TileLayer(\n"," tiles=mapid['tile_fetcher'].url_format,\n"," attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n"," overlay=True,\n"," name='nlcd impervious',\n"," ).add_to(map)\n","map.add_child(folium.LayerControl())\n","map"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CTS7_ZzPDhhg","colab_type":"text"},"source":["Stack the 2D images (Landsat composite and NLCD impervious surface) to create a single image from which samples can be taken. Convert the image into an array image in which each pixel stores 256x256 patches of pixels for each band. This is a key step that bears emphasis: to export training patches, convert a multi-band image to [an array image](https://developers.google.com/earth-engine/arrays_array_images#array-images) using [`neighborhoodToArray()`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray), then sample the image at points."]},{"cell_type":"code","metadata":{"id":"eGHYsdAOipa4","colab_type":"code","colab":{}},"source":["featureStack = ee.Image.cat([\n"," image.select(BANDS),\n"," nlcd.select(RESPONSE)\n","]).float()\n","\n","list = ee.List.repeat(1, KERNEL_SIZE)\n","lists = ee.List.repeat(list, KERNEL_SIZE)\n","kernel = ee.Kernel.fixed(KERNEL_SIZE, KERNEL_SIZE, lists)\n","\n","arrays = featureStack.neighborhoodToArray(kernel)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F4djSxBRG2el","colab_type":"text"},"source":["Use some pre-made geometries to sample the stack in strategic locations. Specifically, these are hand-made polygons in which to take the 256x256 samples. Display the sampling polygons on a map, red for training polygons, blue for evaluation."]},{"cell_type":"code","metadata":{"id":"ure_WaD0itQY","colab_type":"code","colab":{}},"source":["trainingPolys = ee.FeatureCollection('projects/google/DemoTrainingGeometries')\n","evalPolys = ee.FeatureCollection('projects/google/DemoEvalGeometries')\n","\n","polyImage = ee.Image(0).byte().paint(trainingPolys, 1).paint(evalPolys, 2)\n","polyImage = polyImage.updateMask(polyImage)\n","\n","mapid = polyImage.getMapId({'min': 1, 'max': 2, 'palette': ['red', 'blue']})\n","map = folium.Map(location=[38., -100.], zoom_start=5)\n","folium.TileLayer(\n"," tiles=mapid['tile_fetcher'].url_format,\n"," attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n"," overlay=True,\n"," name='training polygons',\n"," ).add_to(map)\n","map.add_child(folium.LayerControl())\n","map"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZV890gPHeZqz","colab_type":"text"},"source":["# Sampling\n","\n","The mapped data look reasonable so take a sample from each polygon and merge the results into a single export. The key step is sampling the array image at points, to get all the pixels in a 256x256 neighborhood at each point. It's worth noting that to build the training and testing data for the FCNN, you export a single TFRecord file that contains patches of pixel values in each record. You do NOT need to export each training/testing patch to a different image. Since each record potentially contains a lot of data (especially with big patches or many input bands), some manual sharding of the computation is necessary to avoid the `computed value too large` error. Specifically, the following code takes multiple (smaller) samples within each geometry, merging the results to get a single export."]},{"cell_type":"code","metadata":{"id":"FyRpvwENxE-A","colab_type":"code","cellView":"both","colab":{}},"source":["# Convert the feature collections to lists for iteration.\n","trainingPolysList = trainingPolys.toList(trainingPolys.size())\n","evalPolysList = evalPolys.toList(evalPolys.size())\n","\n","# These numbers determined experimentally.\n","n = 200 # Number of shards in each polygon.\n","N = 2000 # Total sample size in each polygon.\n","\n","# Export all the training data (in many pieces), with one task \n","# per geometry.\n","for g in range(trainingPolys.size().getInfo()):\n"," geomSample = ee.FeatureCollection([])\n"," for i in range(n):\n"," sample = arrays.sample(\n"," region = ee.Feature(trainingPolysList.get(g)).geometry(), \n"," scale = 30,\n"," numPixels = N / n, # Size of the shard.\n"," seed = i,\n"," tileScale = 8\n"," )\n"," geomSample = geomSample.merge(sample)\n","\n"," desc = TRAINING_BASE + '_g' + str(g)\n"," task = ee.batch.Export.table.toCloudStorage(\n"," collection = geomSample,\n"," description = desc,\n"," bucket = BUCKET,\n"," fileNamePrefix = FOLDER + '/' + desc,\n"," fileFormat = 'TFRecord',\n"," selectors = BANDS + [RESPONSE]\n"," )\n"," task.start()\n","\n","# Export all the evaluation data.\n","for g in range(evalPolys.size().getInfo()):\n"," geomSample = ee.FeatureCollection([])\n"," for i in range(n):\n"," sample = arrays.sample(\n"," region = ee.Feature(evalPolysList.get(g)).geometry(), \n"," scale = 30,\n"," numPixels = N / n,\n"," seed = i,\n"," tileScale = 8\n"," )\n"," geomSample = geomSample.merge(sample)\n","\n"," desc = EVAL_BASE + '_g' + str(g)\n"," task = ee.batch.Export.table.toCloudStorage(\n"," collection = geomSample,\n"," description = desc,\n"," bucket = BUCKET,\n"," fileNamePrefix = FOLDER + '/' + desc,\n"," fileFormat = 'TFRecord',\n"," selectors = BANDS + [RESPONSE]\n"," )\n"," task.start()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rWXrvBE4607G","colab_type":"text"},"source":["# Training data\n","\n","Load the data exported from Earth Engine into a `tf.data.Dataset`. The following are helper functions for that."]},{"cell_type":"code","metadata":{"id":"WWZ0UXCVMyJP","colab_type":"code","colab":{}},"source":["def parse_tfrecord(example_proto):\n"," \"\"\"The parsing function.\n"," Read a serialized example into the structure defined by FEATURES_DICT.\n"," Args:\n"," example_proto: a serialized Example.\n"," Returns:\n"," A dictionary of tensors, keyed by feature name.\n"," \"\"\"\n"," return tf.io.parse_single_example(example_proto, FEATURES_DICT)\n","\n","\n","def to_tuple(inputs):\n"," \"\"\"Function to convert a dictionary of tensors to a tuple of (inputs, outputs).\n"," Turn the tensors returned by parse_tfrecord into a stack in HWC shape.\n"," Args:\n"," inputs: A dictionary of tensors, keyed by feature name.\n"," Returns:\n"," A tuple of (inputs, outputs).\n"," \"\"\"\n"," inputsList = [inputs.get(key) for key in FEATURES]\n"," stacked = tf.stack(inputsList, axis=0)\n"," # Convert from CHW to HWC\n"," stacked = tf.transpose(stacked, [1, 2, 0])\n"," return stacked[:,:,:len(BANDS)], stacked[:,:,len(BANDS):]\n","\n","\n","def get_dataset(pattern):\n"," \"\"\"Function to read, parse and format to tuple a set of input tfrecord files.\n"," Get all the files matching the pattern, parse and convert to tuple.\n"," Args:\n"," pattern: A file pattern to match in a Cloud Storage bucket.\n"," Returns:\n"," A tf.data.Dataset\n"," \"\"\"\n"," glob = tf.io.gfile.glob(pattern)\n"," dataset = tf.data.TFRecordDataset(glob, compression_type='GZIP')\n"," dataset = dataset.map(parse_tfrecord, num_parallel_calls=5)\n"," dataset = dataset.map(to_tuple, num_parallel_calls=5)\n"," return dataset"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Xg1fa18336D2","colab_type":"text"},"source":["Use the helpers to read in the training dataset. Print the first record to check."]},{"cell_type":"code","metadata":{"id":"rm0qRF0fAYcC","colab_type":"code","colab":{}},"source":["def get_training_dataset():\n","\t\"\"\"Get the preprocessed training dataset\n"," Returns: \n"," A tf.data.Dataset of training data.\n"," \"\"\"\n","\tglob = 'gs://' + BUCKET + '/' + FOLDER + '/' + TRAINING_BASE + '*'\n","\tdataset = get_dataset(glob)\n","\tdataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()\n","\treturn dataset\n","\n","training = get_training_dataset()\n","\n","print(iter(training.take(1)).next())"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j-cQO5RL6vob","colab_type":"text"},"source":["# Evaluation data\n","\n","Now do the same thing to get an evaluation dataset. Note that unlike the training dataset, the evaluation dataset has a batch size of 1, is not repeated and is not shuffled."]},{"cell_type":"code","metadata":{"id":"ieKTCGiJ6xzo","colab_type":"code","colab":{}},"source":["def get_eval_dataset():\n","\t\"\"\"Get the preprocessed evaluation dataset\n"," Returns: \n"," A tf.data.Dataset of evaluation data.\n"," \"\"\"\n","\tglob = 'gs://' + BUCKET + '/' + FOLDER + '/' + EVAL_BASE + '*'\n","\tdataset = get_dataset(glob)\n","\tdataset = dataset.batch(1).repeat()\n","\treturn dataset\n","\n","evaluation = get_eval_dataset()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9JIE7Yl87lgU","colab_type":"text"},"source":["# Model\n","\n","Here we use the Keras implementation of the U-Net model. The U-Net model takes 256x256 pixel patches as input and outputs per-pixel class probability, label or a continuous output. We can implement the model essentially unmodified, but will use mean squared error loss on the sigmoidal output since we are treating this as a regression problem, rather than a classification problem. Since impervious surface fraction is constrained to [0,1], with many values close to zero or one, a saturating activation function is suitable here."]},{"cell_type":"code","metadata":{"id":"wsnnnz56yS3l","colab_type":"code","colab":{}},"source":["from tensorflow.python.keras import layers\n","from tensorflow.python.keras import losses\n","from tensorflow.python.keras import models\n","from tensorflow.python.keras import metrics\n","from tensorflow.python.keras import optimizers\n","\n","def conv_block(input_tensor, num_filters):\n","\tencoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)\n","\tencoder = layers.BatchNormalization()(encoder)\n","\tencoder = layers.Activation('relu')(encoder)\n","\tencoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)\n","\tencoder = layers.BatchNormalization()(encoder)\n","\tencoder = layers.Activation('relu')(encoder)\n","\treturn encoder\n","\n","def encoder_block(input_tensor, num_filters):\n","\tencoder = conv_block(input_tensor, num_filters)\n","\tencoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)\n","\treturn encoder_pool, encoder\n","\n","def decoder_block(input_tensor, concat_tensor, num_filters):\n","\tdecoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)\n","\tdecoder = layers.concatenate([concat_tensor, decoder], axis=-1)\n","\tdecoder = layers.BatchNormalization()(decoder)\n","\tdecoder = layers.Activation('relu')(decoder)\n","\tdecoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\n","\tdecoder = layers.BatchNormalization()(decoder)\n","\tdecoder = layers.Activation('relu')(decoder)\n","\tdecoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\n","\tdecoder = layers.BatchNormalization()(decoder)\n","\tdecoder = layers.Activation('relu')(decoder)\n","\treturn decoder\n","\n","def get_model():\n","\tinputs = layers.Input(shape=[None, None, len(BANDS)]) # 256\n","\tencoder0_pool, encoder0 = encoder_block(inputs, 32) # 128\n","\tencoder1_pool, encoder1 = encoder_block(encoder0_pool, 64) # 64\n","\tencoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) # 32\n","\tencoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) # 16\n","\tencoder4_pool, encoder4 = encoder_block(encoder3_pool, 512) # 8\n","\tcenter = conv_block(encoder4_pool, 1024) # center\n","\tdecoder4 = decoder_block(center, encoder4, 512) # 16\n","\tdecoder3 = decoder_block(decoder4, encoder3, 256) # 32\n","\tdecoder2 = decoder_block(decoder3, encoder2, 128) # 64\n","\tdecoder1 = decoder_block(decoder2, encoder1, 64) # 128\n","\tdecoder0 = decoder_block(decoder1, encoder0, 32) # 256\n","\toutputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)\n","\n","\tmodel = models.Model(inputs=[inputs], outputs=[outputs])\n","\n","\tmodel.compile(\n","\t\toptimizer=optimizers.get(OPTIMIZER), \n","\t\tloss=losses.get(LOSS),\n","\t\tmetrics=[metrics.get(metric) for metric in METRICS])\n","\n","\treturn model"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uu_E7OTDBCoS","colab_type":"text"},"source":["# Training the model\n","\n","You train a Keras model by calling `.fit()` on it. Here we're going to train for 10 epochs, which is suitable for demonstration purposes. For production use, you probably want to optimize this parameter, for example through [hyperparamter tuning](https://cloud.google.com/ml-engine/docs/tensorflow/using-hyperparameter-tuning)."]},{"cell_type":"code","metadata":{"id":"NzzaWxOhSxBy","colab_type":"code","colab":{}},"source":["m = get_model()\n","\n","m.fit(\n"," x=training, \n"," epochs=EPOCHS, \n"," steps_per_epoch=int(TRAIN_SIZE / BATCH_SIZE), \n"," validation_data=evaluation,\n"," validation_steps=EVAL_SIZE)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"U2XrwZHp66j4","colab_type":"text"},"source":["Note that the notebook VM is sometimes not heavy-duty enough to get through a whole training job, especially if you have a large buffer size or a large number of epochs. You can still use this notebook for training, but may need to set up an alternative VM ([learn more](https://research.google.com/colaboratory/local-runtimes.html)) for production use. Alternatively, you can package your code for running large training jobs on Google's AI Platform [as described here](https://cloud.google.com/ml-engine/docs/tensorflow/trainer-considerations). The following code loads a pre-trained model, which you can use for predictions right away."]},{"cell_type":"code","metadata":{"id":"-RJpNfEUS1qp","colab_type":"code","colab":{}},"source":["# Load a trained model. 50 epochs. 25 hours. Final RMSE ~0.08.\n","MODEL_DIR = 'gs://ee-docs-demos/fcnn-demo/trainer/model'\n","m = tf.keras.models.load_model(MODEL_DIR)\n","m.summary()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"J1ySNup0xCqN","colab_type":"text"},"source":["# Prediction\n","\n","The prediction pipeline is:\n","\n","1. Export imagery on which to do predictions from Earth Engine in TFRecord format to a Cloud Storge bucket.\n","2. Use the trained model to make the predictions.\n","3. Write the predictions to a TFRecord file in a Cloud Storage.\n","4. Upload the predictions TFRecord file to Earth Engine.\n","\n","The following functions handle this process. It's useful to separate the export from the predictions so that you can experiment with different models without running the export every time."]},{"cell_type":"code","metadata":{"id":"M3WDAa-RUpXP","colab_type":"code","colab":{}},"source":["def doExport(out_image_base, kernel_buffer, region):\n"," \"\"\"Run the image export task. Block until complete.\n"," \"\"\"\n"," task = ee.batch.Export.image.toCloudStorage(\n"," image = image.select(BANDS),\n"," description = out_image_base,\n"," bucket = BUCKET,\n"," fileNamePrefix = FOLDER + '/' + out_image_base,\n"," region = region.getInfo()['coordinates'],\n"," scale = 30,\n"," fileFormat = 'TFRecord',\n"," maxPixels = 1e10,\n"," formatOptions = {\n"," 'patchDimensions': KERNEL_SHAPE,\n"," 'kernelSize': kernel_buffer,\n"," 'compressed': True,\n"," 'maxFileSize': 104857600\n"," }\n"," )\n"," task.start()\n","\n"," # Block until the task completes.\n"," print('Running image export to Cloud Storage...')\n"," import time\n"," while task.active():\n"," time.sleep(30)\n","\n"," # Error condition\n"," if task.status()['state'] != 'COMPLETED':\n"," print('Error with image export.')\n"," else:\n"," print('Image export completed.')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zb_9_FflygVw","colab_type":"code","colab":{}},"source":["def doPrediction(out_image_base, user_folder, kernel_buffer, region):\n"," \"\"\"Perform inference on exported imagery, upload to Earth Engine.\n"," \"\"\"\n","\n"," print('Looking for TFRecord files...')\n","\n"," # Get a list of all the files in the output bucket.\n"," filesList = !gsutil ls 'gs://'{BUCKET}'/'{FOLDER}\n","\n"," # Get only the files generated by the image export.\n"," exportFilesList = [s for s in filesList if out_image_base in s]\n","\n"," # Get the list of image files and the JSON mixer file.\n"," imageFilesList = []\n"," jsonFile = None\n"," for f in exportFilesList:\n"," if f.endswith('.tfrecord.gz'):\n"," imageFilesList.append(f)\n"," elif f.endswith('.json'):\n"," jsonFile = f\n","\n"," # Make sure the files are in the right order.\n"," imageFilesList.sort()\n","\n"," from pprint import pprint\n"," pprint(imageFilesList)\n"," print(jsonFile)\n","\n"," import json\n"," # Load the contents of the mixer file to a JSON object.\n"," jsonText = !gsutil cat {jsonFile}\n"," # Get a single string w/ newlines from the IPython.utils.text.SList\n"," mixer = json.loads(jsonText.nlstr)\n"," pprint(mixer)\n"," patches = mixer['totalPatches']\n","\n"," # Get set up for prediction.\n"," x_buffer = int(kernel_buffer[0] / 2)\n"," y_buffer = int(kernel_buffer[1] / 2)\n","\n"," buffered_shape = [\n"," KERNEL_SHAPE[0] + kernel_buffer[0],\n"," KERNEL_SHAPE[1] + kernel_buffer[1]]\n","\n"," imageColumns = [\n"," tf.io.FixedLenFeature(shape=buffered_shape, dtype=tf.float32) \n"," for k in BANDS\n"," ]\n","\n"," imageFeaturesDict = dict(zip(BANDS, imageColumns))\n","\n"," def parse_image(example_proto):\n"," return tf.io.parse_single_example(example_proto, imageFeaturesDict)\n","\n"," def toTupleImage(inputs):\n"," inputsList = [inputs.get(key) for key in BANDS]\n"," stacked = tf.stack(inputsList, axis=0)\n"," stacked = tf.transpose(stacked, [1, 2, 0])\n"," return stacked\n","\n"," # Create a dataset from the TFRecord file(s) in Cloud Storage.\n"," imageDataset = tf.data.TFRecordDataset(imageFilesList, compression_type='GZIP')\n"," imageDataset = imageDataset.map(parse_image, num_parallel_calls=5)\n"," imageDataset = imageDataset.map(toTupleImage).batch(1)\n","\n"," # Perform inference.\n"," print('Running predictions...')\n"," predictions = m.predict(imageDataset, steps=patches, verbose=1)\n"," # print(predictions[0])\n","\n"," print('Writing predictions...')\n"," out_image_file = 'gs://' + BUCKET + '/' + FOLDER + '/' + out_image_base + '.TFRecord'\n"," writer = tf.io.TFRecordWriter(out_image_file)\n"," patches = 0\n"," for predictionPatch in predictions:\n"," print('Writing patch ' + str(patches) + '...')\n"," predictionPatch = predictionPatch[\n"," x_buffer:x_buffer+KERNEL_SIZE, y_buffer:y_buffer+KERNEL_SIZE]\n","\n"," # Create an example.\n"," example = tf.train.Example(\n"," features=tf.train.Features(\n"," feature={\n"," 'impervious': tf.train.Feature(\n"," float_list=tf.train.FloatList(\n"," value=predictionPatch.flatten()))\n"," }\n"," )\n"," )\n"," # Write the example.\n"," writer.write(example.SerializeToString())\n"," patches += 1\n","\n"," writer.close()\n","\n"," # Start the upload.\n"," out_image_asset = user_folder + '/' + out_image_base\n"," !earthengine upload image --asset_id={out_image_asset} {out_image_file} {jsonFile}"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LZqlymOehnQO","colab_type":"text"},"source":["Now there's all the code needed to run the prediction pipeline, all that remains is to specify the output region in which to do the prediction, the names of the output files, where to put them, and the shape of the outputs. In terms of the shape, the model is trained on 256x256 patches, but can work (in theory) on any patch that's big enough with even dimensions ([reference](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)). Because of tile boundary artifacts, give the model slightly larger patches for prediction, then clip out the middle 256x256 patch. This is controlled with a kernel buffer, half the size of which will extend beyond the kernel buffer. For example, specifying a 128x128 kernel will append 64 pixels on each side of the patch, to ensure that the pixels in the output are taken from inputs completely covered by the kernel."]},{"cell_type":"code","metadata":{"id":"FPANwc7B1-TS","colab_type":"code","colab":{}},"source":["# Output assets folder: YOUR FOLDER\n","user_folder = 'users/username' # INSERT YOUR FOLDER HERE.\n","\n","# Base file name to use for TFRecord files and assets.\n","bj_image_base = 'FCNN_demo_beijing_384_'\n","# Half this will extend on the sides of each patch.\n","bj_kernel_buffer = [128, 128]\n","# Beijing\n","bj_region = ee.Geometry.Polygon(\n"," [[[115.9662455210937, 40.121362012835235],\n"," [115.9662455210937, 39.64293313749715],\n"," [117.01818643906245, 39.64293313749715],\n"," [117.01818643906245, 40.121362012835235]]], None, False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lLNEOLkXWvSi","colab_type":"code","colab":{}},"source":["# Run the export.\n","doExport(bj_image_base, bj_kernel_buffer, bj_region)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KxACnxKFrQ_J","colab_type":"code","colab":{}},"source":["# Run the prediction.\n","doPrediction(bj_image_base, user_folder, bj_kernel_buffer, bj_region)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uj_G9OZ1xH6K","colab_type":"text"},"source":["# Display the output\n","\n","One the data has been exported, the model has made predictions and the predictions have been written to a file, and the image imported to Earth Engine, it's possible to display the resultant Earth Engine asset. Here, display the impervious area predictions over Beijing, China."]},{"cell_type":"code","metadata":{"id":"Jgco6HJ4R5p2","colab_type":"code","colab":{}},"source":["out_image = ee.Image(user_folder + '/' + bj_image_base)\n","mapid = out_image.getMapId({'min': 0, 'max': 1})\n","map = folium.Map(location=[39.898, 116.5097])\n","folium.TileLayer(\n"," tiles=mapid['tile_fetcher'].url_format,\n"," attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n"," overlay=True,\n"," name='predicted impervious',\n"," ).add_to(map)\n","map.add_child(folium.LayerControl())\n","map"],"execution_count":0,"outputs":[]}]}

apache-2.0

Cyb3rWard0g/HELK

docker/helk-jupyter/notebooks/sigma/win_susp_powershell_empire_launch.ipynb

1

3455

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Empire PowerShell Launch Parameters\n", "Detects suspicious powershell command line parameters used in Empire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Empire PowerShell Launch Parameters\n", " id: 79f4ede3-402e-41c8-bc3e-ebbf5f162581\n", " description: Detects suspicious powershell command line parameters used in Empire\n", " status: experimental\n", " references:\n", " - https://github.com/EmpireProject/Empire/blob/c2ba61ca8d2031dad0cfc1d5770ba723e8b710db/lib/common/helpers.py#L165\n", " - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/lib/modules/powershell/persistence/powerbreach/deaduser.py#L191\n", " - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/lib/modules/powershell/persistence/powerbreach/resolver.py#L178\n", " - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/data/module_source/privesc/Invoke-EventVwrBypass.ps1#L64\n", " author: Florian Roth\n", " date: 2019/04/20\n", " tags:\n", " - attack.execution\n", " - attack.t1086\n", " logsource:\n", " category: process_creation\n", " product: windows\n", " service: null\n", " detection:\n", " selection:\n", " CommandLine:\n", " - '* -NoP -sta -NonI -W Hidden -Enc *'\n", " - '* -noP -sta -w 1 -enc *'\n", " - '* -NoP -NonI -W Hidden -enc *'\n", " condition: selection\n", " level: critical\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-*', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='process_command_line.keyword:(*\\ \\-NoP\\ \\-sta\\ \\-NonI\\ \\-W\\ Hidden\\ \\-Enc\\ * OR *\\ \\-noP\\ \\-sta\\ \\-w\\ 1\\ \\-enc\\ * OR *\\ \\-NoP\\ \\-NonI\\ \\-W\\ Hidden\\ \\-enc\\ *)')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

TomAugspurger/pyowa

flights.ipynb

1

2604828

mit

ES-DOC/esdoc-jupyterhub

notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb

1

209021

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmos \n", "**MIP Era**: CMIP6 \n", "**Institute**: TEST-INSTITUTE-2 \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Atmos \n", "**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \n", "**Properties**: 156 (127 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:45" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-3', 'atmos')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Overview](#1.-Key-Properties---&gt;-Overview) \n", "[2. Key Properties --&gt; Resolution](#2.-Key-Properties---&gt;-Resolution) \n", "[3. Key Properties --&gt; Timestepping](#3.-Key-Properties---&gt;-Timestepping) \n", "[4. Key Properties --&gt; Orography](#4.-Key-Properties---&gt;-Orography) \n", "[5. Grid --&gt; Discretisation](#5.-Grid---&gt;-Discretisation) \n", "[6. Grid --&gt; Discretisation --&gt; Horizontal](#6.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[7. Grid --&gt; Discretisation --&gt; Vertical](#7.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[8. Dynamical Core](#8.-Dynamical-Core) \n", "[9. Dynamical Core --&gt; Top Boundary](#9.-Dynamical-Core---&gt;-Top-Boundary) \n", "[10. Dynamical Core --&gt; Lateral Boundary](#10.-Dynamical-Core---&gt;-Lateral-Boundary) \n", "[11. Dynamical Core --&gt; Diffusion Horizontal](#11.-Dynamical-Core---&gt;-Diffusion-Horizontal) \n", "[12. Dynamical Core --&gt; Advection Tracers](#12.-Dynamical-Core---&gt;-Advection-Tracers) \n", "[13. Dynamical Core --&gt; Advection Momentum](#13.-Dynamical-Core---&gt;-Advection-Momentum) \n", "[14. Radiation](#14.-Radiation) \n", "[15. Radiation --&gt; Shortwave Radiation](#15.-Radiation---&gt;-Shortwave-Radiation) \n", "[16. Radiation --&gt; Shortwave GHG](#16.-Radiation---&gt;-Shortwave-GHG) \n", "[17. Radiation --&gt; Shortwave Cloud Ice](#17.-Radiation---&gt;-Shortwave-Cloud-Ice) \n", "[18. Radiation --&gt; Shortwave Cloud Liquid](#18.-Radiation---&gt;-Shortwave-Cloud-Liquid) \n", "[19. Radiation --&gt; Shortwave Cloud Inhomogeneity](#19.-Radiation---&gt;-Shortwave-Cloud-Inhomogeneity) \n", "[20. Radiation --&gt; Shortwave Aerosols](#20.-Radiation---&gt;-Shortwave-Aerosols) \n", "[21. Radiation --&gt; Shortwave Gases](#21.-Radiation---&gt;-Shortwave-Gases) \n", "[22. Radiation --&gt; Longwave Radiation](#22.-Radiation---&gt;-Longwave-Radiation) \n", "[23. Radiation --&gt; Longwave GHG](#23.-Radiation---&gt;-Longwave-GHG) \n", "[24. Radiation --&gt; Longwave Cloud Ice](#24.-Radiation---&gt;-Longwave-Cloud-Ice) \n", "[25. Radiation --&gt; Longwave Cloud Liquid](#25.-Radiation---&gt;-Longwave-Cloud-Liquid) \n", "[26. Radiation --&gt; Longwave Cloud Inhomogeneity](#26.-Radiation---&gt;-Longwave-Cloud-Inhomogeneity) \n", "[27. Radiation --&gt; Longwave Aerosols](#27.-Radiation---&gt;-Longwave-Aerosols) \n", "[28. Radiation --&gt; Longwave Gases](#28.-Radiation---&gt;-Longwave-Gases) \n", "[29. Turbulence Convection](#29.-Turbulence-Convection) \n", "[30. Turbulence Convection --&gt; Boundary Layer Turbulence](#30.-Turbulence-Convection---&gt;-Boundary-Layer-Turbulence) \n", "[31. Turbulence Convection --&gt; Deep Convection](#31.-Turbulence-Convection---&gt;-Deep-Convection) \n", "[32. Turbulence Convection --&gt; Shallow Convection](#32.-Turbulence-Convection---&gt;-Shallow-Convection) \n", "[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \n", "[34. Microphysics Precipitation --&gt; Large Scale Precipitation](#34.-Microphysics-Precipitation---&gt;-Large-Scale-Precipitation) \n", "[35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation---&gt;-Large-Scale-Cloud-Microphysics) \n", "[36. Cloud Scheme](#36.-Cloud-Scheme) \n", "[37. Cloud Scheme --&gt; Optical Cloud Properties](#37.-Cloud-Scheme---&gt;-Optical-Cloud-Properties) \n", "[38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution](#38.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Water-Distribution) \n", "[39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Ice-Distribution) \n", "[40. Observation Simulation](#40.-Observation-Simulation) \n", "[41. Observation Simulation --&gt; Isscp Attributes](#41.-Observation-Simulation---&gt;-Isscp-Attributes) \n", "[42. Observation Simulation --&gt; Cosp Attributes](#42.-Observation-Simulation---&gt;-Cosp-Attributes) \n", "[43. Observation Simulation --&gt; Radar Inputs](#43.-Observation-Simulation---&gt;-Radar-Inputs) \n", "[44. Observation Simulation --&gt; Lidar Inputs](#44.-Observation-Simulation---&gt;-Lidar-Inputs) \n", "[45. Gravity Waves](#45.-Gravity-Waves) \n", "[46. Gravity Waves --&gt; Orographic Gravity Waves](#46.-Gravity-Waves---&gt;-Orographic-Gravity-Waves) \n", "[47. Gravity Waves --&gt; Non Orographic Gravity Waves](#47.-Gravity-Waves---&gt;-Non-Orographic-Gravity-Waves) \n", "[48. Solar](#48.-Solar) \n", "[49. Solar --&gt; Solar Pathways](#49.-Solar---&gt;-Solar-Pathways) \n", "[50. Solar --&gt; Solar Constant](#50.-Solar---&gt;-Solar-Constant) \n", "[51. Solar --&gt; Orbital Parameters](#51.-Solar---&gt;-Orbital-Parameters) \n", "[52. Solar --&gt; Insolation Ozone](#52.-Solar---&gt;-Insolation-Ozone) \n", "[53. Volcanos](#53.-Volcanos) \n", "[54. Volcanos --&gt; Volcanoes Treatment](#54.-Volcanos---&gt;-Volcanoes-Treatment) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Overview \n", "*Top level key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of atmospheric model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"AGCM\" \n", "# \"ARCM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Basic approximations made in the atmosphere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"primitive equations\" \n", "# \"non-hydrostatic\" \n", "# \"anelastic\" \n", "# \"Boussinesq\" \n", "# \"hydrostatic\" \n", "# \"quasi-hydrostatic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Resolution \n", "*Characteristics of the model resolution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Horizontal Resolution Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Range Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Number Of Vertical Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels resolved on the computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. High Top\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping \n", "*Characteristics of the atmosphere model time stepping*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the dynamics, e.g. 30 min.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Timestep Shortwave Radiative Transfer\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestep Longwave Radiative Transfer\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for the longwave radiative transfer, e.g. 3 hours.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Orography \n", "*Characteristics of the model orography*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of the orography.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.orography.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"modified\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Changes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *If the orography type is modified describe the time adaptation changes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.orography.changes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"related to ice sheets\" \n", "# \"related to tectonics\" \n", "# \"modified mean\" \n", "# \"modified variance if taken into account in model (cf gravity waves)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid --&gt; Discretisation \n", "*Atmosphere grid discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of grid discretisation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Atmosphere discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"spectral\" \n", "# \"fixed grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"finite elements\" \n", "# \"finite volumes\" \n", "# \"finite difference\" \n", "# \"centered finite difference\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Scheme Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation function order*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"second\" \n", "# \"third\" \n", "# \"fourth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Horizontal Pole\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal discretisation pole singularity treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"filter\" \n", "# \"pole rotation\" \n", "# \"artificial island\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal grid type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gaussian\" \n", "# \"Latitude-Longitude\" \n", "# \"Cubed-Sphere\" \n", "# \"Icosahedral\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Discretisation --&gt; Vertical \n", "*Atmosphere discretisation in the vertical*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Coordinate Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Type of vertical coordinate system*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"isobaric\" \n", "# \"sigma\" \n", "# \"hybrid sigma-pressure\" \n", "# \"hybrid pressure\" \n", "# \"vertically lagrangian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Dynamical Core \n", "*Characteristics of the dynamical core*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of atmosphere dynamical core*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the dynamical core of the model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Timestepping Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestepping framework type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Adams-Bashforth\" \n", "# \"explicit\" \n", "# \"implicit\" \n", "# \"semi-implicit\" \n", "# \"leap frog\" \n", "# \"multi-step\" \n", "# \"Runge Kutta fifth order\" \n", "# \"Runge Kutta second order\" \n", "# \"Runge Kutta third order\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of the model prognostic variables*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"surface pressure\" \n", "# \"wind components\" \n", "# \"divergence/curl\" \n", "# \"temperature\" \n", "# \"potential temperature\" \n", "# \"total water\" \n", "# \"water vapour\" \n", "# \"water liquid\" \n", "# \"water ice\" \n", "# \"total water moments\" \n", "# \"clouds\" \n", "# \"radiation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Dynamical Core --&gt; Top Boundary \n", "*Type of boundary layer at the top of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Top Boundary Condition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sponge layer\" \n", "# \"radiation boundary condition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Top Heat\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary heat treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Top Wind\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary wind treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Dynamical Core --&gt; Lateral Boundary \n", "*Type of lateral boundary condition (if the model is a regional model)*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Condition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Type of lateral boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sponge layer\" \n", "# \"radiation boundary condition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Dynamical Core --&gt; Diffusion Horizontal \n", "*Horizontal diffusion scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal diffusion scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal diffusion scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"iterated Laplacian\" \n", "# \"bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Dynamical Core --&gt; Advection Tracers \n", "*Tracer advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Tracer advection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heun\" \n", "# \"Roe and VanLeer\" \n", "# \"Roe and Superbee\" \n", "# \"Prather\" \n", "# \"UTOPIA\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Scheme Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Tracer advection scheme characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Eulerian\" \n", "# \"modified Euler\" \n", "# \"Lagrangian\" \n", "# \"semi-Lagrangian\" \n", "# \"cubic semi-Lagrangian\" \n", "# \"quintic semi-Lagrangian\" \n", "# \"mass-conserving\" \n", "# \"finite volume\" \n", "# \"flux-corrected\" \n", "# \"linear\" \n", "# \"quadratic\" \n", "# \"quartic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Conserved Quantities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Tracer advection scheme conserved quantities*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"dry mass\" \n", "# \"tracer mass\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Conservation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracer advection scheme conservation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"conservation fixer\" \n", "# \"Priestley algorithm\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamical Core --&gt; Advection Momentum \n", "*Momentum advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Momentum advection schemes name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"VanLeer\" \n", "# \"Janjic\" \n", "# \"SUPG (Streamline Upwind Petrov-Galerkin)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Scheme Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Momentum advection scheme characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"2nd order\" \n", "# \"4th order\" \n", "# \"cell-centred\" \n", "# \"staggered grid\" \n", "# \"semi-staggered grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Scheme Staggering Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Momentum advection scheme staggering type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa D-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Conserved Quantities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Momentum advection scheme conserved quantities*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Angular momentum\" \n", "# \"Horizontal momentum\" \n", "# \"Enstrophy\" \n", "# \"Mass\" \n", "# \"Total energy\" \n", "# \"Vorticity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Conservation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Momentum advection scheme conservation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"conservation fixer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Radiation \n", "*Characteristics of the atmosphere radiation process*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Aerosols\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Aerosols whose radiative effect is taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.aerosols') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sulphate\" \n", "# \"nitrate\" \n", "# \"sea salt\" \n", "# \"dust\" \n", "# \"ice\" \n", "# \"organic\" \n", "# \"BC (black carbon / soot)\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"polar stratospheric ice\" \n", "# \"NAT (nitric acid trihydrate)\" \n", "# \"NAD (nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particle)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Radiation --&gt; Shortwave Radiation \n", "*Properties of the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of shortwave radiation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Spectral Integration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Shortwave radiation scheme spectral integration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"wide-band model\" \n", "# \"correlated-k\" \n", "# \"exponential sum fitting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Transport Calculation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Shortwave radiation transport calculation methods*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"two-stream\" \n", "# \"layer interaction\" \n", "# \"bulk\" \n", "# \"adaptive\" \n", "# \"multi-stream\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Spectral Intervals\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Shortwave radiation scheme number of spectral intervals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Radiation --&gt; Shortwave GHG \n", "*Representation of greenhouse gases in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Greenhouse Gas Complexity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CO2\" \n", "# \"CH4\" \n", "# \"N2O\" \n", "# \"CFC-11 eq\" \n", "# \"CFC-12 eq\" \n", "# \"HFC-134a eq\" \n", "# \"Explicit ODSs\" \n", "# \"Explicit other fluorinated gases\" \n", "# \"O3\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. ODS\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CFC-12\" \n", "# \"CFC-11\" \n", "# \"CFC-113\" \n", "# \"CFC-114\" \n", "# \"CFC-115\" \n", "# \"HCFC-22\" \n", "# \"HCFC-141b\" \n", "# \"HCFC-142b\" \n", "# \"Halon-1211\" \n", "# \"Halon-1301\" \n", "# \"Halon-2402\" \n", "# \"methyl chloroform\" \n", "# \"carbon tetrachloride\" \n", "# \"methyl chloride\" \n", "# \"methylene chloride\" \n", "# \"chloroform\" \n", "# \"methyl bromide\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Other Flourinated Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HFC-134a\" \n", "# \"HFC-23\" \n", "# \"HFC-32\" \n", "# \"HFC-125\" \n", "# \"HFC-143a\" \n", "# \"HFC-152a\" \n", "# \"HFC-227ea\" \n", "# \"HFC-236fa\" \n", "# \"HFC-245fa\" \n", "# \"HFC-365mfc\" \n", "# \"HFC-43-10mee\" \n", "# \"CF4\" \n", "# \"C2F6\" \n", "# \"C3F8\" \n", "# \"C4F10\" \n", "# \"C5F12\" \n", "# \"C6F14\" \n", "# \"C7F16\" \n", "# \"C8F18\" \n", "# \"c-C4F8\" \n", "# \"NF3\" \n", "# \"SF6\" \n", "# \"SO2F2\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Radiation --&gt; Shortwave Cloud Ice \n", "*Shortwave radiative properties of ice crystals in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with cloud ice crystals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud ice crystals in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bi-modal size distribution\" \n", "# \"ensemble of ice crystals\" \n", "# \"mean projected area\" \n", "# \"ice water path\" \n", "# \"crystal asymmetry\" \n", "# \"crystal aspect ratio\" \n", "# \"effective crystal radius\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Radiation --&gt; Shortwave Cloud Liquid \n", "*Shortwave radiative properties of liquid droplets in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with cloud liquid droplets*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud droplet number concentration\" \n", "# \"effective cloud droplet radii\" \n", "# \"droplet size distribution\" \n", "# \"liquid water path\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"geometric optics\" \n", "# \"Mie theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Radiation --&gt; Shortwave Cloud Inhomogeneity \n", "*Cloud inhomogeneity in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Cloud Inhomogeneity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for taking into account horizontal cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Monte Carlo Independent Column Approximation\" \n", "# \"Triplecloud\" \n", "# \"analytic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Radiation --&gt; Shortwave Aerosols \n", "*Shortwave radiative properties of aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of aerosols in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"number concentration\" \n", "# \"effective radii\" \n", "# \"size distribution\" \n", "# \"asymmetry\" \n", "# \"aspect ratio\" \n", "# \"mixing state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to aerosols in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Radiation --&gt; Shortwave Gases \n", "*Shortwave radiative properties of gases*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with gases*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Radiation --&gt; Longwave Radiation \n", "*Properties of the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of longwave radiation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the longwave radiation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Spectral Integration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Longwave radiation scheme spectral integration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"wide-band model\" \n", "# \"correlated-k\" \n", "# \"exponential sum fitting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Transport Calculation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Longwave radiation transport calculation methods*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"two-stream\" \n", "# \"layer interaction\" \n", "# \"bulk\" \n", "# \"adaptive\" \n", "# \"multi-stream\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Spectral Intervals\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Longwave radiation scheme number of spectral intervals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiation --&gt; Longwave GHG \n", "*Representation of greenhouse gases in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Greenhouse Gas Complexity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CO2\" \n", "# \"CH4\" \n", "# \"N2O\" \n", "# \"CFC-11 eq\" \n", "# \"CFC-12 eq\" \n", "# \"HFC-134a eq\" \n", "# \"Explicit ODSs\" \n", "# \"Explicit other fluorinated gases\" \n", "# \"O3\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. ODS\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CFC-12\" \n", "# \"CFC-11\" \n", "# \"CFC-113\" \n", "# \"CFC-114\" \n", "# \"CFC-115\" \n", "# \"HCFC-22\" \n", "# \"HCFC-141b\" \n", "# \"HCFC-142b\" \n", "# \"Halon-1211\" \n", "# \"Halon-1301\" \n", "# \"Halon-2402\" \n", "# \"methyl chloroform\" \n", "# \"carbon tetrachloride\" \n", "# \"methyl chloride\" \n", "# \"methylene chloride\" \n", "# \"chloroform\" \n", "# \"methyl bromide\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Other Flourinated Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HFC-134a\" \n", "# \"HFC-23\" \n", "# \"HFC-32\" \n", "# \"HFC-125\" \n", "# \"HFC-143a\" \n", "# \"HFC-152a\" \n", "# \"HFC-227ea\" \n", "# \"HFC-236fa\" \n", "# \"HFC-245fa\" \n", "# \"HFC-365mfc\" \n", "# \"HFC-43-10mee\" \n", "# \"CF4\" \n", "# \"C2F6\" \n", "# \"C3F8\" \n", "# \"C4F10\" \n", "# \"C5F12\" \n", "# \"C6F14\" \n", "# \"C7F16\" \n", "# \"C8F18\" \n", "# \"c-C4F8\" \n", "# \"NF3\" \n", "# \"SF6\" \n", "# \"SO2F2\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Radiation --&gt; Longwave Cloud Ice \n", "*Longwave radiative properties of ice crystals in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with cloud ice crystals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Physical Reprenstation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud ice crystals in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bi-modal size distribution\" \n", "# \"ensemble of ice crystals\" \n", "# \"mean projected area\" \n", "# \"ice water path\" \n", "# \"crystal asymmetry\" \n", "# \"crystal aspect ratio\" \n", "# \"effective crystal radius\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Radiation --&gt; Longwave Cloud Liquid \n", "*Longwave radiative properties of liquid droplets in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with cloud liquid droplets*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud liquid droplets in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud droplet number concentration\" \n", "# \"effective cloud droplet radii\" \n", "# \"droplet size distribution\" \n", "# \"liquid water path\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"geometric optics\" \n", "# \"Mie theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Radiation --&gt; Longwave Cloud Inhomogeneity \n", "*Cloud inhomogeneity in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Cloud Inhomogeneity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for taking into account horizontal cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Monte Carlo Independent Column Approximation\" \n", "# \"Triplecloud\" \n", "# \"analytic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Radiation --&gt; Longwave Aerosols \n", "*Longwave radiative properties of aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of aerosols in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"number concentration\" \n", "# \"effective radii\" \n", "# \"size distribution\" \n", "# \"asymmetry\" \n", "# \"aspect ratio\" \n", "# \"mixing state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to aerosols in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Radiation --&gt; Longwave Gases \n", "*Longwave radiative properties of gases*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with gases*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Turbulence Convection \n", "*Atmosphere Convective Turbulence and Clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of atmosphere convection and turbulence*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Turbulence Convection --&gt; Boundary Layer Turbulence \n", "*Properties of the boundary layer turbulence scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Boundary layer turbulence scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Mellor-Yamada\" \n", "# \"Holtslag-Boville\" \n", "# \"EDMF\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Boundary layer turbulence scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TKE prognostic\" \n", "# \"TKE diagnostic\" \n", "# \"TKE coupled with water\" \n", "# \"vertical profile of Kz\" \n", "# \"non-local diffusion\" \n", "# \"Monin-Obukhov similarity\" \n", "# \"Coastal Buddy Scheme\" \n", "# \"Coupled with convection\" \n", "# \"Coupled with gravity waves\" \n", "# \"Depth capped at cloud base\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Closure Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Boundary layer turbulence scheme closure order*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Counter Gradient\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Uses boundary layer turbulence scheme counter gradient*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Turbulence Convection --&gt; Deep Convection \n", "*Properties of the deep convection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Deep convection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Deep convection scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mass-flux\" \n", "# \"adjustment\" \n", "# \"plume ensemble\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Deep convection scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CAPE\" \n", "# \"bulk\" \n", "# \"ensemble\" \n", "# \"CAPE/WFN based\" \n", "# \"TKE/CIN based\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical processes taken into account in the parameterisation of deep convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vertical momentum transport\" \n", "# \"convective momentum transport\" \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"penetrative convection\" \n", "# \"updrafts\" \n", "# \"downdrafts\" \n", "# \"radiative effect of anvils\" \n", "# \"re-evaporation of convective precipitation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.5. Microphysics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"tuning parameter based\" \n", "# \"single moment\" \n", "# \"two moment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Turbulence Convection --&gt; Shallow Convection \n", "*Properties of the shallow convection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Shallow convection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *shallow convection scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mass-flux\" \n", "# \"cumulus-capped boundary layer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *shallow convection scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"same as deep (unified)\" \n", "# \"included in boundary layer turbulence\" \n", "# \"separate diagnosis\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical processes taken into account in the parameterisation of shallow convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"convective momentum transport\" \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"penetrative convection\" \n", "# \"re-evaporation of convective precipitation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Microphysics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Microphysics scheme for shallow convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"tuning parameter based\" \n", "# \"single moment\" \n", "# \"two moment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Microphysics Precipitation \n", "*Large Scale Cloud Microphysics and Precipitation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of large scale cloud microphysics and precipitation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Microphysics Precipitation --&gt; Large Scale Precipitation \n", "*Properties of the large scale precipitation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name of the large scale precipitation parameterisation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Hydrometeors\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"liquid rain\" \n", "# \"snow\" \n", "# \"hail\" \n", "# \"graupel\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics \n", "*Properties of the large scale cloud microphysics scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Large scale cloud microphysics processes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mixed phase\" \n", "# \"cloud droplets\" \n", "# \"cloud ice\" \n", "# \"ice nucleation\" \n", "# \"water vapour deposition\" \n", "# \"effect of raindrops\" \n", "# \"effect of snow\" \n", "# \"effect of graupel\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Cloud Scheme \n", "*Characteristics of the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of the atmosphere cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Atmos Coupling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Atmosphere components that are linked to the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"atmosphere_radiation\" \n", "# \"atmosphere_microphysics_precipitation\" \n", "# \"atmosphere_turbulence_convection\" \n", "# \"atmosphere_gravity_waves\" \n", "# \"atmosphere_solar\" \n", "# \"atmosphere_volcano\" \n", "# \"atmosphere_cloud_simulator\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Uses Separate Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Processes included in the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"bulk cloud\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.6. Prognostic Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the cloud scheme a prognostic scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.7. Diagnostic Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the cloud scheme a diagnostic scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.8. Prognostic Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List the prognostic variables used by the cloud scheme, if applicable.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud amount\" \n", "# \"liquid\" \n", "# \"ice\" \n", "# \"rain\" \n", "# \"snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Cloud Scheme --&gt; Optical Cloud Properties \n", "*Optical cloud properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Cloud Overlap Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Method for taking into account overlapping of cloud layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"random\" \n", "# \"maximum\" \n", "# \"maximum-random\" \n", "# \"exponential\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Cloud Inhomogeneity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Method for taking into account cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution \n", "*Sub-grid scale water distribution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.2. Function Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution function name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.3. Function Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution function type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.4. Convection Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Sub-grid scale water distribution coupling with convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"coupled with deep\" \n", "# \"coupled with shallow\" \n", "# \"not coupled with convection\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution \n", "*Sub-grid scale ice distribution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.2. Function Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution function name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.3. Function Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution function type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.4. Convection Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Sub-grid scale ice distribution coupling with convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"coupled with deep\" \n", "# \"coupled with shallow\" \n", "# \"not coupled with convection\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Observation Simulation \n", "*Characteristics of observation simulation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of observation simulator characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Observation Simulation --&gt; Isscp Attributes \n", "*ISSCP Characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. Top Height Estimation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Cloud simulator ISSCP top height estimation methodUo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"no adjustment\" \n", "# \"IR brightness\" \n", "# \"visible optical depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. Top Height Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator ISSCP top height direction*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"lowest altitude level\" \n", "# \"highest altitude level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 42. Observation Simulation --&gt; Cosp Attributes \n", "*CFMIP Observational Simulator Package attributes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 42.1. Run Configuration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP run configuration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Inline\" \n", "# \"Offline\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.2. Number Of Grid Points\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of grid points*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.3. Number Of Sub Columns\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.4. Number Of Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of levels*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 43. Observation Simulation --&gt; Radar Inputs \n", "*Characteristics of the cloud radar simulator*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 43.1. Frequency\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar frequency (Hz)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.2. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"surface\" \n", "# \"space borne\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.3. Gas Absorption\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar uses gas absorption*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.4. Effective Radius\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar uses effective radius*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 44. Observation Simulation --&gt; Lidar Inputs \n", "*Characteristics of the cloud lidar simulator*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 44.1. Ice Types\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator lidar ice type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice spheres\" \n", "# \"ice non-spherical\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 44.2. Overlap\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Cloud simulator lidar overlap*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"max\" \n", "# \"random\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 45. Gravity Waves \n", "*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 45.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of gravity wave parameterisation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.2. Sponge Layer\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rayleigh friction\" \n", "# \"Diffusive sponge layer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.3. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background wave distribution*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"continuous spectrum\" \n", "# \"discrete spectrum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.4. Subgrid Scale Orography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Subgrid scale orography effects taken into account.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"effect on drag\" \n", "# \"effect on lifting\" \n", "# \"enhanced topography\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 46. Gravity Waves --&gt; Orographic Gravity Waves \n", "*Gravity waves generated due to the presence of orography*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 46.1. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the orographic gravity wave scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.2. Source Mechanisms\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Orographic gravity wave source mechanisms*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear mountain waves\" \n", "# \"hydraulic jump\" \n", "# \"envelope orography\" \n", "# \"low level flow blocking\" \n", "# \"statistical sub-grid scale variance\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.3. Calculation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Orographic gravity wave calculation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"non-linear calculation\" \n", "# \"more than two cardinal directions\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.4. Propagation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Orographic gravity wave propogation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear theory\" \n", "# \"non-linear theory\" \n", "# \"includes boundary layer ducting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.5. Dissipation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Orographic gravity wave dissipation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"total wave\" \n", "# \"single wave\" \n", "# \"spectral\" \n", "# \"linear\" \n", "# \"wave saturation vs Richardson number\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 47. Gravity Waves --&gt; Non Orographic Gravity Waves \n", "*Gravity waves generated by non-orographic processes.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 47.1. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the non-orographic gravity wave scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.2. Source Mechanisms\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Non-orographic gravity wave source mechanisms*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"convection\" \n", "# \"precipitation\" \n", "# \"background spectrum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.3. Calculation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Non-orographic gravity wave calculation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"spatially dependent\" \n", "# \"temporally dependent\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.4. Propagation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Non-orographic gravity wave propogation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear theory\" \n", "# \"non-linear theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.5. Dissipation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Non-orographic gravity wave dissipation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"total wave\" \n", "# \"single wave\" \n", "# \"spectral\" \n", "# \"linear\" \n", "# \"wave saturation vs Richardson number\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 48. Solar \n", "*Top of atmosphere solar insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 48.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of solar insolation of the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 49. Solar --&gt; Solar Pathways \n", "*Pathways for solar forcing of the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 49.1. Pathways\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Pathways for the solar forcing of the atmosphere model domain*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SW radiation\" \n", "# \"precipitating energetic particles\" \n", "# \"cosmic rays\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 50. Solar --&gt; Solar Constant \n", "*Solar constant and top of atmosphere insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 50.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of the solar constant.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"transient\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 50.2. Fixed Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 50.3. Transient Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *solar constant transient characteristics (W m-2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 51. Solar --&gt; Orbital Parameters \n", "*Orbital parameters and top of atmosphere insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 51.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of orbital parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"transient\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.2. Fixed Reference Date\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Reference date for fixed orbital parameters (yyyy)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.3. Transient Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of transient orbital parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.4. Computation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used for computing orbital parameters.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Berger 1978\" \n", "# \"Laskar 2004\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 52. Solar --&gt; Insolation Ozone \n", "*Impact of solar insolation on stratospheric ozone*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 52.1. Solar Ozone Impact\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does top of atmosphere insolation impact on stratospheric ozone?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 53. Volcanos \n", "*Characteristics of the implementation of volcanoes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 53.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of the implementation of volcanic effects in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.volcanos.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 54. Volcanos --&gt; Volcanoes Treatment \n", "*Treatment of volcanoes in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 54.1. Volcanoes Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How volcanic effects are modeled in the atmosphere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"high frequency solar constant anomaly\" \n", "# \"stratospheric aerosols optical thickness\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

winpython/winpython_afterdoc

docs/installing_julia_and_ijulia.ipynb

2

13034

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Installating Julia/IJulia \n", "\n", "### 1 - Downloading and Installing the right Julia binary in the right place" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import io\n", "import re" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import urllib.request as request # Python 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# get latest stable release info, download link and hashes\n", "g = request.urlopen(\"https://julialang.org/downloads/\")\n", "s = g.read().decode()\n", "g.close;\n", "\n", "r = r'<a href=\".current_stable_release\">([^<]+)</a></h2> ' + \\\n", " r'<p>Checksums for this release are available in both <a href=\"([^\"]*)\">MD5</a> and <a href=\"([^\"]*)\">SHA256</a> formats.</p>' + \\\n", " r'[^W]*Windows <a href=\"/downloads/platform/.windows\">.help.</a> <td colspan=3 > <a href=\"[^\"]*\">64-bit .installer.</a>, <a href=\"([^\"]*)\">64-bit .portable.</a>' + \\\n", " r' <td colspan=3 > <a href=\"[^\"]*\">32-bit .installer.</a>, <a href=\"([^\"]*)\">32-bit .portable.</a>'\n", "\n", "release_str, md5link, sha256link, ziplink64bit, ziplink32bit = re.findall(r,s)[0]\n", "julia_version=re.findall(r\"v([^\\s]+)\",release_str)[0]\n", "print(release_str)\n", "print(julia_version)\n", "print(ziplink64bit)\n", "print(ziplink32bit)\n", "print(md5link)\n", "print(sha256link)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "overwrite links, since v1.5.3 installation does not work properly due to\n", "https://github.com/JuliaLang/julia/issues/38411" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if julia_version=='1.5.3':\n", " julia_version='1.6.0-rc1'\n", " ziplink64bit='https://julialang-s3.julialang.org/bin/winnt/x64/1.6/julia-1.6.0-rc1-win64.zip'\n", " md5link='https://julialang-s3.julialang.org/bin/checksums/julia-1.6.0-rc1.md5'\n", " sha256link='https://julialang-s3.julialang.org/bin/checksums/julia-1.6.0-rc1.sha256'\n", " print(julia_version)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# download checksums\n", "g = request.urlopen(md5link)\n", "md5hashes = g.read().decode()\n", "g.close;\n", "\n", "g = request.urlopen(sha256link)\n", "sha256hashes = g.read().decode()\n", "g.close;" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# downloading julia (may take 1 minute or 2)\n", "\n", "if 'amd64' in sys.version.lower():\n", " julia_zip=ziplink64bit.split(\"/\")[-1]\n", " julia_url=ziplink64bit\n", "else:\n", " julia_zip=ziplink32bit.split(\"/\")[-1]\n", " julia_url=ziplink32bit\n", " \n", "hashes=(re.findall(r\"([0-9a-f]{32})\\s\"+julia_zip, md5hashes)[0] , re.findall(r\"([0-9a-f]{64})\\s+\"+julia_zip, sha256hashes)[0])\n", " \n", "julia_zip_fullpath = os.path.join(os.environ[\"WINPYDIRBASE\"], \"t\", julia_zip)\n", "\n", "g = request.urlopen(julia_url) \n", "with io.open(julia_zip_fullpath, 'wb') as f:\n", " f.write(g.read())\n", "g.close\n", "g = None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#checking it's there\n", "assert os.path.isfile(julia_zip_fullpath)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# checking the hashes\n", "import hashlib\n", "def give_hash(of_file, with_this):\n", " with io.open(julia_zip_fullpath, 'rb') as f:\n", " return with_this(f.read()).hexdigest() \n", "print (\" \"*12+\"MD5\"+\" \"*(32-12-3)+\" \"+\" \"*15+\"SHA-256\"+\" \"*(40-15-5)+\"\\n\"+\"-\"*32+\" \"+\"-\"*64)\n", "\n", "print (\"%s %s %s\" % (give_hash(julia_zip_fullpath, hashlib.md5) , give_hash(julia_zip_fullpath, hashlib.sha256),julia_zip))\n", "assert give_hash(julia_zip_fullpath, hashlib.md5) == hashes[0].lower() \n", "assert give_hash(julia_zip_fullpath, hashlib.sha256) == hashes[1].lower()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# will be in env next time\n", "os.environ[\"JUPYTER\"] = os.path.join(os.environ[\"WINPYDIR\"],\"Scripts\",\"jupyter.exe\")\n", "os.environ[\"JULIA_HOME\"] = os.path.join(os.environ[\"WINPYDIRBASE\"], \"t\", \"julia-\"+julia_version)\n", "os.environ[\"JULIA_EXE_PATH\"] = os.path.join(os.environ[\"JULIA_HOME\"], \"bin\")\n", "os.environ[\"JULIA_EXE\"] = \"julia.exe\"\n", "os.environ[\"JULIA\"] = os.path.join(os.environ[\"JULIA_EXE_PATH\"],os.environ[\"JULIA_EXE\"])\n", "os.environ[\"JULIA_PKGDIR\"] = os.path.join(os.environ[\"WINPYDIRBASE\"],\"settings\",\".julia\")\n", "os.environ[\"JULIA_DEPOT_PATH\"] = os.environ[\"JULIA_PKGDIR\"] \n", "os.environ[\"JULIA_HISTORY\"] = os.path.join(os.environ[\"JULIA_PKGDIR\"],\"logs\",\"repl_history.jl\")\n", "os.environ[\"CONDA_JL_HOME\"] = os.path.join(os.environ[\"JULIA_HOME\"], \"conda\", \"3\")\n", "\n", "\n", "# move JULIA_EXE_PATH to the beginning of PATH, since a julia installation may be present on the machine\n", "os.environ[\"PATH\"] = os.environ[\"JULIA_EXE_PATH\"] + \";\" + os.environ[\"PATH\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if not os.path.isdir(os.environ[\"JULIA_PKGDIR\"]):\n", " os.mkdir(os.environ[\"JULIA_PKGDIR\"])\n", " \n", "if not os.path.isdir(os.path.join(os.environ[\"JULIA_PKGDIR\"],\"logs\")):\n", " os.mkdir(os.path.join(os.environ[\"JULIA_PKGDIR\"],\"logs\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if not os.path.isfile(os.environ[\"JULIA_HISTORY\"]):\n", " open(os.environ[\"JULIA_HISTORY\"], 'a').close() # create empty file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# extract the zip archive\n", "import zipfile\n", "try:\n", " with zipfile.ZipFile(julia_zip_fullpath) as z:\n", " z.extractall(os.path.join(os.environ[\"WINPYDIRBASE\"], \"t\"))\n", " print(\"Extracted all files\")\n", "except:\n", " print(\"Invalid file\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# delete zip file\n", "os.remove(julia_zip_fullpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2 - Initialize Julia , IJulia, and make them link to winpython" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# connecting Julia to WinPython (only once, or everytime you move things)\n", "# see the Windows terminal window for the detailed status. This may take \n", "# a minute or two.\n", "import julia\n", "julia.install()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext julia.magic" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "info = julia.juliainfo.JuliaInfo.load()\n", "print(info.julia)\n", "print(info.sysimage)\n", "print(info.version_raw)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from julia.api import Julia\n", "jl = Julia(compiled_modules=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# sanity check\n", "assert jl.eval(\"1+2\") == 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Print julia's versioninfo()\n", "The environment should point to the usb drive and not to C:\\\\ (your local installation of julia maybe...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "jl.eval(\"using InteractiveUtils\")\n", "jl.eval('file = open(\"julia_versioninfo.txt\",\"w\")') \n", "jl.eval(\"versioninfo(file,verbose=false)\")\n", "jl.eval(\"close(file)\")\n", "\n", "with open('julia_versioninfo.txt', 'r') as f:\n", " print(f.read())\n", " \n", "os.remove('julia_versioninfo.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Install julia Packages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%julia\n", "using Pkg\n", "\n", "Pkg.instantiate()\n", "Pkg.update()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%julia\n", "# add useful packages. Again, this may take a while...\n", "Pkg.add(\"IJulia\")\n", "Pkg.add(\"Plots\")\n", "Pkg.add(\"Interact\")\n", "Pkg.add(\"Compose\")\n", "Pkg.add(\"SymPy\")\n", "\n", "using Compose\n", "using SymPy\n", "using IJulia\n", "using Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fix the kernel.json to allow arbitrary drive letters and modify the env.bat\n", "\n", "the path to kernel.jl is hardcoded in the kernel.json file\n", "this will cause trouble, if the drive letter of the usb drive changes\n", "use relative paths instead\n", "rewrite kernel.json and delete the one created from IJulia.jl Package" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kernel_path = os.path.join(os.environ[\"WINPYDIRBASE\"], \"settings\", \"kernels\", \"julia-\"+julia_version[0:3])\n", "assert os.path.isdir(kernel_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open(os.path.join(kernel_path,\"kernel.json\"), 'r') as f:\n", " kernel_str = f.read()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "new_kernel_str = kernel_str.replace(os.environ[\"WINPYDIRBASE\"].replace(\"\\\\\",\"\\\\\\\\\"),\"{prefix}\\\\\\\\..\")\n", "print(new_kernel_str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open(os.path.join(kernel_path,\"kernel.json\"), 'w') as f:\n", " f.write(new_kernel_str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# add JULIA env variables to env.bat\n", "inp_str = r\"\"\"\n", "rem ******************\n", "rem handle Julia {0} if included\n", "rem ******************\n", "\n", "if not exist \"%WINPYDIRBASE%\\t\\julia-{0}\\bin\" goto julia_bad_{0}\n", "set JULIA_PKGDIR=%WINPYDIRBASE%\\settings\\.julia\n", "set JULIA_DEPOT_PATH=%JULIA_PKGDIR%\n", "set JULIA_EXE=julia.exe\n", "set JULIA_HOME=%WINPYDIRBASE%\\t\\julia-{0}\n", "set JULIA_HISTORY=%JULIA_PKGDIR%\\logs\\repl_history.jl\n", ":julia_bad_{0}\n", "\n", "\"\"\".format(julia_version)\n", "\n", "# append to env.bat\n", "with open(os.path.join(os.environ[\"WINPYDIRBASE\"],\"scripts\",\"env.bat\"), 'a') as file :\n", " file.write(inp_str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3 - Launching a Julia Notebook \n", "\n", "choose a Julia Kernel from Notebook, or Julia from Jupyterlab Launcher\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4 - Julia Magic \n", "or use %load_ext julia.magic then %julia or %%julia" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

hakonsbm/nest-simulator

doc/topology/examples/grid_iaf.ipynb

1

1796

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nNEST Topology Module Example\n\nCreate layer of 4x3 iaf_psc_alpha neurons, visualize\n\nBCCN Tutorial @ CNS*09\nHans Ekkehard Plesser, UMB\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nest\nimport pylab\nimport nest.topology as topo\n\npylab.ion()\n\nnest.ResetKernel()\n\nl1 = topo.CreateLayer({'columns': 4, 'rows': 3,\n 'extent': [2.0, 1.5],\n 'elements': 'iaf_psc_alpha'})\n\nnest.PrintNetwork()\nnest.PrintNetwork(2)\nnest.PrintNetwork(2, l1)\n\ntopo.PlotLayer(l1, nodesize=50)\n\n# beautify\npylab.axis([-1.0, 1.0, -0.75, 0.75])\npylab.axes().set_aspect('equal', 'box')\npylab.axes().set_xticks((-0.75, -0.25, 0.25, 0.75))\npylab.axes().set_yticks((-0.5, 0, 0.5))\npylab.grid(True)\npylab.xlabel('4 Columns, Extent: 1.5')\npylab.ylabel('2 Rows, Extent: 1.0')\n\n# pylab.savefig('grid_iaf.png')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

mathnathan/notebooks

mpfi/probability blog post.ipynb

1

124466

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "Machine learning literature makes heavy use of probabilistic graphical models\n", "and bayesian statistics. In fact, state of the art (SOTA) architectures, such as\n", "[variational autoencoders][vae-blog] (VAE) or [generative adversarial\n", "networks][gan-blog] (GAN), are intrinsically stochastic by nature. To\n", "wholesomely understand research in this field not only do we need a broad\n", "knowledge of mathematics, probability, and optimization but we somehow need\n", "intuition about how these concepts are applied to real world problems. For\n", "example, one of the most common applications of deep learning techniques is\n", "vision. We may want to classify images or generate new ones. Most SOTA\n", "techniques pose these problems in a probabilistic framework. We frequently see\n", "things like $p(\\mathbf{x}|\\mathbf{z})$ where $\\mathbf{x}$ is an image and\n", "$\\mathbf{z}$ is a latent variable. What do we mean by the probability of an\n", "image? What is a latent variable, and why is it necessary[^Bishop2006] to pose\n", "the problems this way?\n", " \n", "Short answer, it is necessary due to the inherent uncertainty of our universe.\n", "In this case, uncertainty in image acquisition can be introduced via many\n", "sources, such as the recording apparatus, the finite precision of our\n", "measurements, as well as the intrinsic stochasticity of the process being\n", "measured. Perhaps the most important source of uncertainty we will consider is\n", "due to there being sources of variability that are themselves unobserved.\n", "Probability theory provides us with a framework to reason in the presence of\n", "uncertainty and information theory allows us to quantify uncertainty. As we \n", "elluded earlier the field of machine learning makes heavy use of both, and\n", "this is no coincidence.\n", "\n", "\n", "## Representations\n", "\n", "How do we describe a face? The word \"face\" is a symbol and this symbol means\n", "different things to different people. Yet, there is enough commonality between\n", "our interpretations that we are able to effectively communicate with one\n", "another using the word. How is that? What are the underlying features of faces\n", "that we all hold common? Why is a simple smiley face clip art so obviously\n", "perceived as a face? To make it more concrete, why are two simple ellipses\n", "decorated underneath by a short curve so clearly a face, while an eye lid,\n", "lower lip, one ear and a nostril, not? \n", "\n", "\n", "**Insert Image of Faces**\n", "*Left: Most would likely agree, this is clearly a face. Middle:\n", "With nearly all of the details removed, a mere two circles and\n", "curve are enough to create what the author still recognizes\n", "as a face. Right: Does this look like a face to you? An ear, \n", "nostril, eyelid, and lip do not seem to convey a face as clearly\n", "as the eyes and the mouth do. We will quantify this demonstration\n", "shortly.*\n", "\n", "Features, or representations, are built on the idea that characteristics of the\n", "symbol \"face\" are not a property of any one face. Rather, they only arise from\n", "the myriad of things we use the symbol to represent. In other words, a\n", "particular face is not ascribed meaning by the word \"face\" - the word \"face\"\n", "derives meaning from the many faces it represents. This suggests that facial\n", "characteristics can be described through the statistical properties of all\n", "faces. Loosely speaking, these underlying statistical characteristics are what\n", "the machine learning field often calls latent variables.\n", "\n", "\n", "## Probability of an Image\n", "\n", "Most images are contaminated with noise that must be addressed. At the\n", "highest level, we have noise being added to the data by the imaging device. The\n", "next level of uncertainty comes as a consequence of discretization.\n", "Images in reality are continuous but in the process of imaging we only measure\n", "certain points along the face. Consider for example a military satellite\n", "tracking a vehicle. If one wishes to predict the future location of the van,\n", "the prediction is limited to be within one of the discrete cells that make up\n", "its measurements. However, the true location of the van could be anywhere\n", "within that grid cell. There is also intrinsic stochasticity at the atomic\n", "level that we ignore. The fluctuations taking place at that scale are assumed\n", "to be averaged out in our observations.\n", "\n", "The unobserved sources of variability will be our primary focus. Before we\n", "address that, let us lay down some preliminary concepts. We are going to assume\n", "that there exists some true unknown process that determines what faces look\n", "like. A dataset of faces can then be considered as a sample of this process at \n", "various points throughout its life. This suggests that these snapshots are a\n", "outputs of the underlying data generating process. Considering the many\n", "sources of uncertainty outlined above, it is natural to describe this process\n", "as a probability distribution. There will be many ways to interpret the data as\n", "a probability, but we will begin by considering any one image to be the result\n", "of a data generating distribution, $P_{data}(\\mathbf{x})$. Here $\\mathbf{x}$ is considered to be\n", "an image of a face with $n$ pixels. So $P_{data}$ is a joint distribution over\n", "each pixel of the frame with a probability density function (pdf),\n", "$p_{data}(x_1,x_2,\\dots,x_n)$.\n", "\n", "To build intuition about what $p_{data}(\\mathbf{x})$ is and how it relates to\n", "the assumed data generating process, we will explore a simple example. Take an\n", "image with only 2 pixels... [$x_1$,$x_2$] where both $x_1$ and $x_2$ are in\n", "[0,1]. Each image can be considered as a two dimensional point, in\n", "$\\mathbb{R}^2$. All possible images would occupy a square in the 2 dimensional\n", "plane. An example of what this might look like can be seen in Figure\n", "\\ref{fig:images_in_2dspace} on page \\pageref{fig:images_in_2dspace}. Any one\n", "point inside the unit square would represent an image. For example the image\n", "associated with the point $(0.25,0.85)$ is shown below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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x6rPn6hpEuQk9FgTBo5N8wrixHD92jOVpK1AktULV9HZMBRmY8o6R+/nzKBvc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f50b041bb00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = np.random.uniform(size=500)\n", "x2 = np.random.uniform(size=500)\n", "fig = plt.figure();\n", "ax = fig.add_subplot(1,1,1);\n", "ax.scatter(x1,x2, edgecolor='black', s=80);\n", "ax.grid();\n", "ax.set_axisbelow(True);\n", "ax.set_xlim(-0.25,1.25); ax.set_ylim(-0.25,1.25)\n", "ax.set_xlabel('Pixel 2'); ax.set_ylabel('Pixel 1'); plt.savefig('images_in_2dspace.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any one point inside the unit square would represent an image. For example the image associated with the point $(0.25,0.85)$ is shown below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADHCAYAAAAu5CnUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f50b041b780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "im = [(0.25, 0.85)]\n", "plt.imshow(im, cmap='gray',vmin=0,vmax=1)\n", "plt.tick_params(\n", " axis='both', # changes apply to the x-axis\n", " which='both', # both major and minor ticks are affected\n", " bottom='off', # ticks along the bottom edge are off\n", " top='off', # ticks along the top edge are off\n", " left='off',\n", " right='off'\n", ")\n", "plt.xticks([])\n", "plt.yticks([])\n", "plt.xlabel('Pixel 1 = 0.25 Pixel 2 = 0.85')\n", "plt.savefig('sample_2dspace_image.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now consider the case where there is some \n", "process correlating the two variables. This \n", "would be similar to their being some rules behind\n", "the structure of faces. We know, that this must be\n", "the case because if it weren't then faces would\n", "be created randomly and we would not see the \n", "patterns that was do. In \n", "this case, the pixels would be correlated in \n", "some manner due to the mechanism driving the\n", "construction of faces. In this simple case, \n", "let's consider a direct correlation of the \n", "form $x_1 = \\frac{1}{2} \\cos(2\\pi x_2)+\\frac{1}{2}+\\epsilon$ \n", "where $\\epsilon$ is a noise term coming from\n", "a low variability normal distribution \n", "$\\epsilon \\sim N(0,\\frac{1}{10})$. We see \n", "in Figure \\ref{fig:structured_images_in_2dspace}\n", "on page \\pageref{fig:structured_images_in_2dspace}\n", "that in this case, the images plotted\n", "in two dimensions resulting from this \n", "relationship form a distinct pattern." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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c2S92a91UVedaIKvZ5JKONx/PJTMz06uIlkLDcX7/PvvsM9TpfQiJ0KDfsozU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\nERhmgAzDCAwzQIZhBMb/AWekUT7WaapgAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f50a8ae96d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = lambda x2: 0.5*np.cos(2*np.pi*x2)+0.5\n", "x2 = np.linspace(0,1,200)\n", "eps = np.random.normal(scale=0.1, size=200)\n", "fig = plt.figure();\n", "ax = fig.add_subplot(1,1,1);\n", "ax.scatter(x2,x1(x2)+eps, edgecolor='black', s=80);\n", "ax.grid();\n", "ax.set_axisbelow(True);\n", "ax.set_xlim(-0.25,1.25); ax.set_ylim(-0.25,1.25); plt.axes().set_aspect('equal')\n", "ax.set_xlabel('Pixel 2'); ax.set_ylabel('Pixel 1'); plt.savefig('structured_images_in_2dspace.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will refer to the structure suggested by \n", "the two dimensional points as the 'manifold'.\n", "This is a common practice when analyzing images.\n", "A 28 by 28 dimensional image will be a point in\n", "784 dimensional space. If we are examining \n", "images with structure, various images of the\n", "number 2 for example, then it turns out that \n", "these images will form a manifold in 784 \n", "dimensional space. In most cases, as is the \n", "case in our contrived example, this manifold \n", "exists in a lower dimensional space than that\n", "of the images themselves. The goal is to 'learn'\n", "this manifold. In our simple case we can describe\n", "the manifold as a function of only 1 variable \n", "$$f(t) = <t,\\frac{1}{2} \\cos(2\\pi t)+\\frac{1}{2}>$$ \n", "This is what we would call the underlying data \n", "generating process. In practice we usually \n", "describe the manifold in terms of a probability\n", "distribution. We will refer to the data \n", "generating distribution in our example as \n", "$p_{test}(x_1, x_2)$. Why did we choose a \n", "probability to describe the manifold created \n", "by the data generating process? How might this\n", "probability be interpreted?\n", "\n", "Learning the actual distribution turns out to \n", "be a difficult task. Here we will use a\n", "common non parametric technique for describing\n", "distributions, the histogram. Looking at a \n", "histogram of the images, or two dimensional points,\n", "will give us insight into the structure of the \n", "distribution from which they came. Notice here \n", "though that the histogram merely describes the \n", "distribution, we do not know what it is.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f50850a1cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.colors import LogNorm\n", "x2 = np.random.uniform(size=100000)\n", "eps = np.random.normal(scale=0.1, size=100000)\n", "hist2d = plt.hist2d(x2,x1(x2)+eps, bins=50, norm=LogNorm())\n", "plt.xlim(0.0,1.0); plt.ylim(-0.3,1.3); plt.axes().set_aspect('equal')\n", "plt.xlabel('Pixel 2'); plt.ylabel('Pixel 1')\n", "plt.colorbar();\n", "plt.savefig('histogram_of_structured_images.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As our intuition might have suggested, the data\n", "generating distribution looks very similar to \n", "the structure suggested by the two dimensional\n", "images plotted above. There is high probability\n", "very near the actual curve \n", "$x_1 = \\frac{1}{2} \\cos(2\\pi x_2)+\\frac{1}{2}$ \n", "and low probability as we move away. We imposed\n", "the uncertainty via the Gaussian noise term \n", "$\\epsilon$. However, in real data the \n", "uncertainty can be due to the myriad sources\n", "outlined above. In these cases a complex \n", "probability distribution isn't an arbitrary \n", "choice for representing the data, it becomes \n", "necessary. \n", "\n", "Hopefully we're now beginning to understand how\n", "to interpret $p_{test}(x_1, x_2)$. One might say\n", "$p_{test}$ measures how likely a certain \n", "configuration of $x_1$ and $x_2$ is to have \n", "arisen from the data generating process $f(t)$.\n", "Therefore if one can learn the data generating\n", "distribution, then they have a descriptive\n", "measure of the true underlying data generating\n", "process. This intuition extends to the \n", "$p_{data}(x)$ for faces that was presented \n", "above. A sample from the LFW dataset is shown in \n", "Figure \\ref{fig:Agnelo_Queiroz_0001} on page\n", "\\pageref{fig:Agnelo_Queiroz_0001}. \n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ESMG/ESMG-configs

CCS1/preprocessing/CreateFMSgridTopo.ipynb

1

156388

{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import scipy.io\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Open ROMS grid file\n", "if 'nc' not in vars(): nc = scipy.io.netcdf_file('CCS_7k_0-360_fred_grd.nc')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nj=480, nj=180\n" ] } ], "source": [ "nj,ni = nc.variables['lon_rho'].shape\n", "nj -=2; ni -=2\n", "print('nj=%i, nj=%i'%(nj,ni))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Supergrid shape\n", "snj,sni = 2*numpy.array([nj,ni]) # Smallest useful super-grid has a multiplier of 2\n", "\n", "# Declare shapes\n", "lon = numpy.zeros((snj+1,sni+1))\n", "lat = numpy.zeros((snj+1,sni+1))\n", "area = numpy.zeros((snj,sni))\n", "dx = numpy.zeros((snj+1,sni))\n", "dy = numpy.zeros((snj,sni+1))\n", "angle = numpy.zeros((snj+1,sni+1))\n", "\n", "# Copy in data from ROMS file\n", "lon[::2,::2] = nc.variables['lon_psi'][:,:] # Cell corners\n", "lon[1::2,1::2] = nc.variables['lon_rho'][1:-1,1:-1] # Cell centers (drop outside row and column)\n", "lon[1::2,::2] = nc.variables['lon_u'][1:-1,:] # U-points (drop outside row)\n", "lon[::2,1::2] = nc.variables['lon_v'][:,1:-1] # V-points (drop outside column)\n", "lat[::2,::2] = nc.variables['lat_psi'][:,:] # Cell corners\n", "lat[1::2,1::2] = nc.variables['lat_rho'][1:-1,1:-1] # Cell centers (drop outside row and column)\n", "lat[1::2,::2] = nc.variables['lat_u'][1:-1,:] # U-points (drop outside row)\n", "lat[::2,1::2] = nc.variables['lat_v'][:,1:-1] # V-points (drop outside column)\n", "\n", "def angle_p1p2(p1, p2):\n", " \"\"\"Angle at center of sphere between two points on the surface of the sphere.\n", " Positions are given as (latitude,longitude) tuples measured in degrees.\"\"\"\n", " phi1 = numpy.deg2rad( p1[0] )\n", " phi2 = numpy.deg2rad( p2[0] )\n", " dphi_2 = 0.5 * ( phi2 - phi1 )\n", " dlambda_2 = 0.5 * numpy.deg2rad( p2[1] - p1[1] )\n", " a = numpy.sin( dphi_2 )**2 + numpy.cos( phi1 ) * numpy.cos( phi2 ) * ( numpy.sin( dlambda_2 )**2 )\n", " c = 2. * numpy.arctan2( numpy.sqrt(a), numpy.sqrt( 1. - a ) )\n", " return c\n", "# Approximate edge lengths as great arcs\n", "R = 6370.e3 # Radius of sphere\n", "dx[:,:] = R*angle_p1p2( (lat[:,1:],lon[:,1:]), (lat[:,:-1],lon[:,:-1]) )\n", "dy[:,:] = R*angle_p1p2( (lat[1:,:],lon[1:,:]), (lat[:-1,:],lon[:-1,:]) )\n", "\n", "# Approximate angles using centered differences in interior\n", "angle[:,1:-1] = numpy.arctan( (lat[:,2:]-lat[:,:-2]) / ((lon[:,2:]-lon[:,:-2])*numpy.cos(numpy.deg2rad(lat[:,1:-1]))) )\n", "# Approximate angles using side differences on left/right edges\n", "angle[:,0] = numpy.arctan( (lat[:,1]-lat[:,0]) / ((lon[:,1]-lon[:,0])*numpy.cos(numpy.deg2rad(lat[:,0]))) )\n", "angle[:,-1] = numpy.arctan( (lat[:,-1]-lat[:,-2]) / ((lon[:,-1]-lon[:,-2])*numpy.cos(numpy.deg2rad(lat[:,-1]))) )\n", "\n", "def spherical_angle(v1, v2, v3):\n", " \"\"\"Returns angle v2-v1-v3 i.e betweeen v1-v2 and v1-v3.\"\"\"\n", " # vector product between v1 and v2\n", " px = v1[1]*v2[2] - v1[2]*v2[1]\n", " py = v1[2]*v2[0] - v1[0]*v2[2]\n", " pz = v1[0]*v2[1] - v1[1]*v2[0]\n", " # vector product between v1 and v3\n", " qx = v1[1]*v3[2] - v1[2]*v3[1]\n", " qy = v1[2]*v3[0] - v1[0]*v3[2]\n", " qz = v1[0]*v3[1] - v1[1]*v3[0]\n", "\n", " ddd = (px*px+py*py+pz*pz)*(qx*qx+qy*qy+qz*qz)\n", " ddd = (px*qx+py*qy+pz*qz) / numpy.sqrt(ddd)\n", " angle = numpy.arccos( ddd );\n", " return angle\n", "def spherical_quad(lat,lon):\n", " \"\"\"Returns area of spherical quad (bounded by great arcs).\"\"\"\n", " # x,y,z are 3D coordinates\n", " d2r = numpy.deg2rad(1.)\n", " x = numpy.cos(d2r*lat)*numpy.cos(d2r*lon)\n", " y = numpy.cos(d2r*lat)*numpy.sin(d2r*lon)\n", " z = numpy.sin(d2r*lat)\n", " c0 = (x[:-1,:-1],y[:-1,:-1],z[:-1,:-1])\n", " c1 = (x[:-1,1:],y[:-1,1:],z[:-1,1:])\n", " c2 = (x[1:,1:],y[1:,1:],z[1:,1:])\n", " c3 = (x[1:,:-1],y[1:,:-1],z[1:,:-1])\n", " a0 = spherical_angle( c1, c0, c2)\n", " a1 = spherical_angle( c2, c1, c3)\n", " a2 = spherical_angle( c3, c2, c0)\n", " a3 = spherical_angle( c0, c3, c1)\n", " return a0+a1+a2+a3-2.*numpy.pi\n", "# Approximate cell areas as that of spherical polygon\n", "area[:,:] = R*R*spherical_quad(lat,lon)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a mosaic file\n", "rg = scipy.io.netcdf_file('ocean_hgrid.nc','w')\n", "# Dimensions\n", "rg.createDimension('nx',sni)\n", "rg.createDimension('nxp',sni+1)\n", "rg.createDimension('ny',snj)\n", "rg.createDimension('nyp',snj+1)\n", "rg.createDimension('string',5)\n", "# Variables\n", "hx = rg.createVariable('x','float32',('nyp','nxp',))\n", "hx.units = 'degrees'\n", "hy = rg.createVariable('y','float32',('nyp','nxp',))\n", "hy.units = 'degrees'\n", "hdx = rg.createVariable('dx','float32',('nyp','nx',))\n", "hdx.units = 'meters'\n", "hdy = rg.createVariable('dy','float32',('ny','nxp',))\n", "hdy.units = 'meters'\n", "harea = rg.createVariable('area','float32',('ny','nx',))\n", "harea.units = 'meters^2'\n", "hangle = rg.createVariable('angle_dx','float32',('nyp','nxp',))\n", "hangle.units = 'degrees'\n", "htile = rg.createVariable('tile','c',('string',))\n", "# Values\n", "hx[:] = lon\n", "hy[:] = lat\n", "hdx[:] = dx\n", "hdy[:] = dy\n", "harea[:] = area\n", "hangle[:] = angle\n", "htile[:5] = 'tile1'\n", "rg.close()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a topography file\n", "rg = scipy.io.netcdf_file('ocean_topog.nc','w')\n", "# Dimensions\n", "rg.createDimension('nx',ni)\n", "rg.createDimension('ny',nj)\n", "rg.createDimension('ntiles',1)\n", "# Variables\n", "hdepth = rg.createVariable('depth','float32',('ny','nx',))\n", "hdepth.units = 'm'\n", "# Values\n", "hdepth[:] = nc.variables['hraw'][0,1:-1,1:-1]\n", "rg.close()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0xa966df4c>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmUHcd1Jvgl3qt6taBQhSqsBYIsiIAIbiLZtCTaVMuU\n", "ZEuyuiXLPfLS0/YZjz1bt7vdY7d7LM3MOSP/sNvW8Rmf03Omx93HP7y02/LSlkdteSRKtGGJtLVQ\n", "JiSIFEiCYpFFYiMKqEKtr/Be5fyI+2VE3ozI5b1XKIB895w6US+XyMjIzO/e+O6NG1Ecx+hLX/rS\n", "l768vmXHdjegL33pS1/6svXSB/u+9KUvfXkDSB/s+9KXvvTlDSB9sO9LX/rSlzeA9MG+L33pS1/e\n", 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"output_type": "stream", "text": [ "/home/mjh/anaconda/lib/python2.7/site-packages/scipy/io/netcdf.py:287: RuntimeWarning: Cannot close a netcdf_file opened with mmap=True, when netcdf_variables or arrays referring to its data still exist. All data arrays obtained from such files refer directly to data on disk, and must be copied before the file can be cleanly closed. (See netcdf_file docstring for more information on mmap.)\n", " ), category=RuntimeWarning)\n" ] }, { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0xa97296ec>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXucJVV5LvwUe09vuoeZ6bkwNxgcZICBoKIgGFQclXjB\n", "SBSv0ZiDUY/GJMYYEzHmO16+6FGO55gENZIf+QQRlEvEgxcCQhgjEIhcJkCGAWaYhmGuzKVneqa7\n", "d0/vqe+P9T61Vr21VlXt3d3T0zP1/n79W11Vq1atql31rHc972VFcRyjkkoqqaSSw1uOmuwOVFJJ\n", "JZVUMvFSgX0llVRSyREgFdhXUkkllRwBUoF9JZVUUskRIBXYV1JJJZUcAVKBfSWVVFLJESClwD6K\n", "or4oih6JoujhKIr+Q/bNiaLoF1EUPRlF0e1RFPU69T8bRdFTURStiaLoDRPV+UoqqaSSSspJWc0+\n", "BrAijuOXxnF8juy7FMAv4jg+BcCdso0oik4H8B4ApwN4E4BvR1FUzSAqqaSSSiZR2gHhSG1fBOBq\n", "+f9qAG+T/38HwA/iON4fx3EfgLUAzkEllVRSSSWTJu1o9ndEUfRAFEUfkX0L4jjeKv9vBbBA/l8M\n", 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{ "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

ES-DOC/esdoc-jupyterhub

notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb

1

99839

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: EC-EARTH-CONSORTIUM \n", "**Source ID**: EC-EARTH3-CC \n", "**Topic**: Seaice \n", "**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \n", "**Properties**: 80 (63 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:59" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-cc', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Model](#1.-Key-Properties---&gt;-Model) \n", "[2. Key Properties --&gt; Variables](#2.-Key-Properties---&gt;-Variables) \n", "[3. Key Properties --&gt; Seawater Properties](#3.-Key-Properties---&gt;-Seawater-Properties) \n", "[4. Key Properties --&gt; Resolution](#4.-Key-Properties---&gt;-Resolution) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Key Properties --&gt; Key Parameter Values](#6.-Key-Properties---&gt;-Key-Parameter-Values) \n", "[7. Key Properties --&gt; Assumptions](#7.-Key-Properties---&gt;-Assumptions) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid --&gt; Discretisation --&gt; Horizontal](#9.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Seaice Categories](#11.-Grid---&gt;-Seaice-Categories) \n", "[12. Grid --&gt; Snow On Seaice](#12.-Grid---&gt;-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --&gt; Energy](#14.-Thermodynamics---&gt;-Energy) \n", "[15. Thermodynamics --&gt; Mass](#15.-Thermodynamics---&gt;-Mass) \n", "[16. Thermodynamics --&gt; Salt](#16.-Thermodynamics---&gt;-Salt) \n", "[17. Thermodynamics --&gt; Salt --&gt; Mass Transport](#17.-Thermodynamics---&gt;-Salt---&gt;-Mass-Transport) \n", "[18. Thermodynamics --&gt; Salt --&gt; Thermodynamics](#18.-Thermodynamics---&gt;-Salt---&gt;-Thermodynamics) \n", "[19. Thermodynamics --&gt; Ice Thickness Distribution](#19.-Thermodynamics---&gt;-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --&gt; Ice Floe Size Distribution](#20.-Thermodynamics---&gt;-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --&gt; Melt Ponds](#21.-Thermodynamics---&gt;-Melt-Ponds) \n", "[22. Thermodynamics --&gt; Snow Processes](#22.-Thermodynamics---&gt;-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Model \n", "*Name of seaice model used.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Variables \n", "*List of prognostic variable in the sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the sea ice component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Seawater Properties \n", "*Properties of seawater relevant to sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant seawater freezing point, specify this value.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Resolution \n", "*Resolution of the sea ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning applied to sea ice model component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any observed metrics used in tuning model/parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Which variables were changed during the tuning process?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Key Parameter Values \n", "*Values of key parameters*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *What values were specificed for the following parameters if used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P*) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Assumptions \n", "*Assumptions made in the sea ice model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General overview description of any *key* assumptions made in this model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the sea ice component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Provide a general description of conservation methodology.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in sea ice by the numerical schemes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does conservation involved flux correction?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Sea ice discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Grid on which sea ice is horizontal discretised?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the type of sea ice grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the advection scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model thermodynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model dynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional horizontal discretisation details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Sea ice vertical properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using multi-layers specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional vertical grid details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Seaice Categories \n", "*What method is used to represent sea ice categories ?*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Set to true if the sea ice model has multiple sea ice categories.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify each of the category limits.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the sea ice thickness distribution scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --&gt; Snow On Seaice \n", "*Snow on sea ice details*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow on ice represented in this model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels of snow on ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the snow fraction on sea ice is determined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional details related to snow on ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "*Sea Ice Dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of horizontal advection of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which method of sea ice strength formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which processes can redistribute sea ice (including thickness)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Rheology, what is the ice deformation formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --&gt; Energy \n", "*Processes related to energy in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the energy formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of thermal conductivity is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of heat diffusion?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method by which basal ocean heat flux is handled?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which the heat content of precipitation is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --&gt; Mass \n", "*Processes related to mass in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which new sea ice is formed in open water.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs the vertical growth and melt of sea ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice lateral melting?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs sea ice surface sublimation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of frazil ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --&gt; Salt \n", "*Processes related to salt in sea ice thermodynamics.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does sea ice salinity impact the thermal properties of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --&gt; Salt --&gt; Mass Transport \n", "*Mass transport of salt*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the mass transport of salt calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --&gt; Salt --&gt; Thermodynamics \n", "*Salt thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the thermodynamic calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --&gt; Ice Thickness Distribution \n", "*Ice thickness distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice thickness distribution represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --&gt; Ice Floe Size Distribution \n", "*Ice floe-size distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice floe-size represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Please provide further details on any parameterisation of floe-size.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --&gt; Melt Ponds \n", "*Characteristics of melt ponds.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are melt ponds included in the sea ice model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What method of melt pond formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What do melt ponds have an impact on?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --&gt; Snow Processes \n", "*Thermodynamic processes in snow on sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has a snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has snow ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow ice formation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the impact of ridging on snow cover?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "*Sea Ice Radiative Processes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used to handle surface albedo.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method by which solar radiation through sea ice is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

greenca/diy-spectrometer

peak-detection.ipynb

1

75028

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Detecting Peaks in a Spectrum" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import spectrumlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filename = 'shear.png'\n", "spectrum = spectrumlib.getSpectrum(filename)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2857b09950>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JDaELgPsi+FST+zoD2Bs4PIJnW6mjl43psM8tyohobv3z/PhdgCdrv0x5YaSv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\nADjszcwGwP8HSpEwZ+lAfvkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f28887564d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(spectrum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, find the relative maxima of the spectrum." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.signal import argrelextrema\n", "maxes = argrelextrema(spectrum, np.greater, order=2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5 14 20 32 49 58 71 74 94 119 125 155 159 188 194 204 210 217\n", " 223 229 238 249 263 270 282 308 329 358 370 380]\n" ] } ], "source": [ "print maxes[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f284edf3d90>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0NbE6snnGBHwkYNHveU8EXB/wQMBLkT4cH8xq/07AW9rzPlj3u3Qs7Neuf01k\n/977NbJNh32V+WBswDsDfh7wdMANAT/K/v9M9vf0tYAzsr+dt1VcL0wLmBOpcbJ7mW3tGKkhN6Zm\nbekDfkXAlq28Zxrd190e9spWkCtJERFa9zzVUnn+ka+PmCa9BngAOIiI+6otW2YDE0nn5k8ENgH+\n8loGH1rG5NOAE4EtgadJl5Z/S8TiteuUtiONsPkO4EhgDemYwY+AfxOxOtAYIkKKCMZMIXUnHEC6\nuOf3wIPAcrKDytVLreP3qbzw+4ELgIeAZ4A9VjNmh7GsWULq0lsIfF7En0t+P5EOim0FPEDqF22L\n0t+lpd+rwW1Vm9bscq1Ma0dNzcxf73rq/BvaEHg3MBVYAPyFNEBb/etNXTlfAD5NuijyHOB6YDxp\nhNNHifhcXbVJ5wG7AUcS8VKNX7H+GluYtx3v8eHZ2dCyhYe9NEasWR1RuR99vZ2UTv3afgueeGRF\nbDE07R3Ad4A/E/HZiss2VGO2XDpne1LpG7fKG2wcMIaIV8rN255/7BbXkf6gDiWNJ3PnAK/c/yrj\ntyedabEofSh1NnjXleKwr2d7zcxTz/xtDfsmVPk7Gg8cQzrd8ynSB8jNwHFEvFrnOjYAfkpqxJ0D\nXElEwze36KWwb+6IejtI55OubJ1+C/uBbvsA6ZSqx0gt7DeTrhTd9FS+ATr926Sr494KPPcwO4Ge\nXUYKrSeBbwAXtr3O1Nqu2kIpmbdtrd6OSWfNDA0vwEoBEYuKK8hsmNRYugjpEtKpxQ8RUel0y0rr\nWIX0EdJ1Kp8Azkf6I/Aw6ZaWfyGNnPk2Ug5tNexnS0D/xVTQovtJVyD/gXSW3ARgaAiW3UlnCW16\nE/uD/vSvwCJStv6VdCLD64Bdsxw7kHSRZO6DXBUX9mmn/wE45WKOe3BfbjuJ9LVrW9KOXUC69dyy\nLXkCUuB+nezr4RZaFasYtyfwKvBUETvPzDooYhVwdQvLB6lhcxXStqRQ3pk0dtCupEy5g9SNOg9Y\nPuxnzQHcPPgorzsc2Ix0jcHpwEvASmAsqYH6z8CyMzn7lhs58CrSab9B6tb9Ginr7pvMIMB3gQlI\n04Y64fNSfDcObeqzb2DZ+mqsvFyzX+26ohunjvW5G6fx5dyN07hu/HtoZf11/9tKm9HkMOCtdOP0\n4nn2ZmbdK9/7PazlsDcz6wMOezOzPuCwNzPrAw57M7M+4LA3M+sDHQl7SYdKmi/pQUmVbiRhZmY5\naXvYKw29lJrHAAAEmElEQVQv8APSJfnTgGMl7dru7eRB3XEbtppGQ52joUZwne3mOrtHJ1r204EF\nEbEw0vABl5MuWR6NZhRdQJ1mFF1AHWYUXUCdZhRdQJ1mFF1AnWYUXUCdZhRdQKd1Iuy3I40NMWRx\nNs3MzArSibDPf/wFMzOrqu1j40jaF5gZEYdmz88g3fbpmyXz+APBzKwJXTOevdI40vcDBwOPA7cD\nx0bJDUXMzCxfbR/iOCJWSfo06Q5MY4ELHfRmZsUqZIhjMzPLV65X0HbzxVaSFkq6S9IcSbdn0zaX\nNEvSA5KulTSpgLp+ImlQ0t0l0yrWJemMbP/Ol3RIwXXOlLQ426dzJB1WZJ2Spkq6QdK9ku6R9Nls\nelftzyp1dtv+nCDpNklzJc2T9PVserftz0p1dtX+LNn22Kye32TP27M/m71TeaM/pC6dBaRbgI0D\n5gK75rX9Oup7BNh82LRzgFOzx6cB3yigrncAewF316qLdBHb3Gz/7pDt7zEF1nkWcEqZeQupE9ga\n2DN7PJF0bGnXbtufVersqv2ZbXuj7P8bALcCB3Tb/qxSZ9ftz2z7pwCXAFdnz9uyP/Ns2Y+Gi62G\nH+U+HLgoe3wR6TZjuYqIm0g3XS5Vqa4jgMsiYmVELCT9408vsE4YuU+hoDojYmlEzM0ePw/cR7oG\npKv2Z5U6oYv2Z1bf0E28B0gNuqfosv1ZpU7osv0paQrpvrs/LqmtLfszz7Dv9outArhO0h2SPp5N\nmxwRg9njQWByMaWNUKmubUn7dUg37OPPSLpT0oUlXz8Lr1PSDqRvIrfRxfuzpM5bs0ldtT8ljZE0\nl7Tfboh0Y/Cu258V6oQu25/Ad4AvAmtKprVlf+YZ9t1+JHj/iNgLOAw4SdI7Sl+M9L2p636HOuoq\nsuYLgB2BPYElwLlV5s2tTkkTgSuBkyPiufWK6KL9mdX5K1Kdz9OF+zMi1kTEnsAU4J2SDhr2elfs\nzzJ1zqDL9qekDwDLImIO5b9xtLQ/8wz7x4CpJc+nsv6nUqEiYkn2/+XAr0lfhwYlbQ0gaRtgWXEV\nrqdSXcP38ZRsWiEiYllkSF9Lh75iFlanpHGkoL84Iq7KJnfd/iyp8xdDdXbj/hwSEc8AvwX2pgv3\nZ5k69+nC/fl24HBJjwCXAe+SdDFt2p95hv0dwM6SdpA0ABwNXJ3j9iuStJGkTbLHGwOHAHeT6js+\nm+144Krya8hdpbquBo6RNCBpR2Bn0kVthcjemEM+SNqnUFCdkgRcCMyLiO+WvNRV+7NSnV24P7cc\n6vqQtCHwHmAO3bc/y9Y5FKCZwvdnRJwZEVMjYkfgGOAPEXEc7dqfeR1hzo4eH0Y6s2ABcEae265R\n146ko9pzgXuGagM2B64DHgCuBSYVUNtlpCuRXyUd8/hotbqAM7P9Ox94b4F1/gPwc+Au4M7sDTq5\nyDpJZ2Csyf6d52Q/h3bb/qxQ52FduD/fDPw1q/Mu4IvZ9G7bn5Xq7Kr9OazmA1l3Nk5b9qcvqjIz\n6wO+LaGZWR9w2JuZ9QGHvZlZH3DYm5n1AYe9mVkfcNibmfUBh72ZWR9w2JuZ9YH/D4IBU/w3/Jda\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f284edf3dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for x in maxes[0]:\n", " plt.axvline(x)\n", "plt.plot(spectrum, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This clearly gives us way too many local maxima. \n", "\n", "So, next, we try the `find_peaks_cwt` function from `scipy.signal`, which uses wavelets." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2507: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "from scipy.signal import find_peaks_cwt\n", "cwt_peaks = find_peaks_cwt(spectrum, np.arange(10,15))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8, 33, 97, 159, 202, 233, 330, 374]\n" ] } ], "source": [ "print cwt_peaks" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f284e4f3910>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f284e4f3950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for x in cwt_peaks:\n", " plt.axvline(x)\n", "plt.plot(spectrum, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is better, in that we lost all the spurious values, but it doesn't match that well, and we don't get the\n", "double-peak (near 150) anymore.\n", "\n", "https://gist.github.com/endolith/250860 has a python translation of a matlab peak-detection script. Downloaded as `peakdetect.py`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import peakdetect\n", "peaks, valleys = peakdetect.peakdet(spectrum, 3)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 32. 33.5952381 ]\n", " [ 94. 27.52380952]\n", " [ 155. 172.04761905]\n", " [ 159. 214.54761905]\n", " [ 194. 63.82142857]\n", " [ 204. 73.08333333]\n", " [ 210. 63.13095238]\n", " [ 229. 143.82142857]\n", " [ 249. 66.58333333]\n", " [ 270. 33.80952381]\n", " [ 282. 26.75 ]\n", " [ 308. 21.11904762]\n", " [ 329. 32.79761905]]\n" ] } ], "source": [ "print peaks" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f284e452b90>]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f284e452c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for index, val in peaks:\n", " plt.axvline(index)\n", "plt.plot(spectrum, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a pretty decent result, which we should be able to use for matching with known spectra." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sample spectrum above is for a fluorescent lamp. This is a known spectrum, that we can use for calibration. Here is a labelled plot of the spectrum: \n", "\n", "\n", "\n", "\"Fluorescent lighting spectrum peaks labelled\". Licensed under CC BY-SA 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Fluorescent_lighting_spectrum_peaks_labelled.gif#/media/File:Fluorescent_lighting_spectrum_peaks_labelled.gif" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visually, this appears to match pretty well with our spectrum. We calibrate the x-axis by matching two points with the known spectrum. Let's use the strongest two peaks: 5, at 546.5 nm (from Mercury) and 12, at 611.6 nm (from Europium). In our spectrum, peak 4 has a higher intensity than peak 12, but we'll use peak 12 anyway, because peaks 4 and 5 are too close together to get an accurate calibration." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "159.0 229.0\n" ] } ], "source": [ "intensities = sorted([intensity for index, intensity in peaks])\n", "peak5 = [index for index, intensity in peaks if intensity == intensities[-1]][0]\n", "peak12 = [index for index, intensity in peaks if intensity == intensities[-3]][0]\n", "print peak5, peak12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linear scale between index numbers and wavelengths:\n", "<br>`wavelength = m*index + b`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.93 398.63\n" ] } ], "source": [ "peak5_wl = 546.5\n", "peak12_wl = 611.6\n", "m = (peak12_wl - peak5_wl)/(peak12 - peak5)\n", "b = peak5_wl - m*peak5\n", "print m, b" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wavelengths = [m*index + b for index in range(len(spectrum))]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f284e348250>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BayMa97SSmEfqKvuiXGU41MZz7nSi6BES7wDeF8Ebm7w+iXQDnTfmny1J7RkP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\n9NwRX/8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f284e399590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelengths, spectrum)\n", "plt.xlabel('Wavelength (nm)')\n", "plt.ylabel('Intensity')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

tanghaibao/goatools

notebooks/cell_cycle.ipynb

1

115266

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cell Cycle genes\n", "Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Download Ontologies, if necessary" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " EXISTS: go-basic.obo\n" ] } ], "source": [ "# Get http://geneontology.org/ontology/go-basic.obo\n", "from goatools.base import download_go_basic_obo\n", "obo_fname = download_go_basic_obo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Download Associations, if necessary" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " EXISTS: gene2go\n" ] } ], "source": [ "# Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz\n", "from goatools.base import download_ncbi_associations\n", "gene2go = download_ncbi_associations()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Read associations\n", "Normally, when reading associations, GeneID2GOs are returned. We get the reverse, GO2GeneIDs, by adding the key-word arg, \"go2geneids=True\" to the call to read_ncbi_gene2go." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HMS:0:00:05.295771 323,107 annotations, 19,649 genes, 18,246 GOs, 1 taxids READ: gene2go \n", "12285 IDs in loaded association branch, BP\n", "12285 GO terms associated with human NCBI Entrez GeneIDs\n" ] } ], "source": [ "from goatools.anno.genetogo_reader import Gene2GoReader\n", "\n", "objanno = Gene2GoReader(\"gene2go\", taxids=[9606])\n", "go2geneids_human = objanno.get_id2gos(namespace='BP', go2geneids=True)\n", "\n", "print(\"{N:} GO terms associated with human NCBI Entrez GeneIDs\".format(N=len(go2geneids_human)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Initialize Gene-Search Helper" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "go-basic.obo: fmt(1.2) rel(2020-01-01) 47,337 GO Terms; optional_attrs(comment def relationship synonym xref)\n" ] } ], "source": [ "from goatools.go_search import GoSearch\n", "\n", "srchhelp = GoSearch(\"go-basic.obo\", go2items=go2geneids_human)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Find human all genes related to \"cell cycle\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5a. Prepare \"cell cycle\" text searches\n", "We will need to search for both *cell cycle* and *cell cycle-independent*. Those GOs that contain the text *cell cycle-independent* are specifically **not** related to *cell cycle* and must be removed from our list of *cell cycle* GO terms." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import re\n", "\n", "# Compile search pattern for 'cell cycle'\n", "cell_cycle_all = re.compile(r'cell cycle', flags=re.IGNORECASE)\n", "cell_cycle_not = re.compile(r'cell cycle.independent', flags=re.IGNORECASE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5b. Find NCBI Entrez GeneIDs related to \"cell cycle\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2062 human NCBI Entrez GeneIDs related to 'cell cycle' found.\n" ] } ], "source": [ "# Find ALL GOs and GeneIDs associated with 'cell cycle'.\n", "\n", "# Details of search are written to a log file\n", "fout_allgos = \"cell_cycle_gos_human.log\" \n", "with open(fout_allgos, \"w\") as log:\n", " # Search for 'cell cycle' in GO terms\n", " gos_cc_all = srchhelp.get_matching_gos(cell_cycle_all, prt=log)\n", " # Find any GOs matching 'cell cycle-independent' (e.g., \"lysosome\")\n", " gos_no_cc = srchhelp.get_matching_gos(cell_cycle_not, gos=gos_cc_all, prt=log)\n", " # Remove GO terms that are not \"cell cycle\" GOs\n", " gos = gos_cc_all.difference(gos_no_cc)\n", " # Add children GOs of cell cycle GOs\n", " gos_all = srchhelp.add_children_gos(gos)\n", " # Get Entrez GeneIDs for cell cycle GOs\n", " geneids = srchhelp.get_items(gos_all)\n", "print(\"{N} human NCBI Entrez GeneIDs related to 'cell cycle' found.\".format(N=len(geneids)))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Print the \"cell cycle\" protein-coding gene Symbols\n", "In this example, the background is all human protein-codinge genes. \n", "\n", "Follow the instructions in the `background_genes_ncbi` notebook to download a set of background population genes from NCBI." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from genes_ncbi_9606_proteincoding import GENEID2NT" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RNF212B ring finger protein 212B\n", "TTYH1 tweety family member 1\n", "CABLES2 Cdk5 and Abl enzyme substrate 2\n", "SEH1L SEH1 like nucleoporin\n", "KIF18A kinesin family member 18A\n", "CHAF1B chromatin assembly factor 1 subunit B\n", "SDE2 SDE2 telomere maintenance homolog\n", "ABL1 ABL proto-oncogene 1, non-receptor tyrosine kinase\n", "CLTCL1 clathrin heavy chain like 1\n", "AICDA activation induced cytidine deaminase\n", "USP9X ubiquitin specific peptidase 9 X-linked\n", "SMC1A structural maintenance of chromosomes 1A\n", "HIPK1 homeodomain interacting protein kinase 1\n", "ACTA1 actin alpha 1, skeletal muscle\n", "ACTB actin beta\n", "SPC25 SPC25 component of NDC80 kinetochore complex\n", "NAA10 N-alpha-acetyltransferase 10, NatA catalytic subunit\n", "ACVR1 activin A receptor type 1\n", "ACVR1B activin A receptor type 1B\n", "TUBGCP5 tubulin gamma complex associated protein 5\n", "BIRC6 baculoviral IAP repeat containing 6\n", "ADARB1 adenosine deaminase RNA specific B1\n", "LIN9 lin-9 DREAM MuvB core complex component\n", "ESCO1 establishment of sister chromatid cohesion N-acetyltransferase 1\n", "ADCYAP1 adenylate cyclase activating polypeptide 1\n", "MYORG myogenesis regulating glycosidase (putative)\n", "AXIN2 axin 2\n", "BAP1 BRCA1 associated protein 1\n", "CDC7 cell division cycle 7\n", "CDC45 cell division cycle 45\n", "CNOT6 CCR4-NOT transcription complex subunit 6\n", "FZD9 frizzled class receptor 9\n", "GFI1B growth factor independent 1B transcriptional repressor\n", "GMNC geminin coiled-coil domain containing\n", "SYCE1L synaptonemal complex central element protein 1 like\n", "NLN neurolysin\n", "MTA3 metastasis associated 1 family member 3\n", "HECW2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2\n", "RPTOR regulatory associated protein of MTOR complex 1\n", "KLHL13 kelch like family member 13\n", "HACE1 HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1\n", "MAD1L1 mitotic arrest deficient 1 like 1\n", "AHR aryl hydrocarbon receptor\n", "KLHL42 kelch like family member 42\n", "AIF1 allograft inflammatory factor 1\n", "TP53I13 tumor protein p53 inducible protein 13\n", "AKT1 AKT serine/threonine kinase 1\n", "AKT2 AKT serine/threonine kinase 2\n", "MICAL3 microtubule associated monooxygenase, calponin and LIM domain containing 3\n", "TAOK1 TAO kinase 1\n", "UXT ubiquitously expressed prefoldin like chaperone\n", "CEP126 centrosomal protein 126\n", "RAD54L RAD54 like\n", "ALOX15B arachidonate 15-lipoxygenase type B\n", "DYRK3 dual specificity tyrosine phosphorylation regulated kinase 3\n", "DYRK2 dual specificity tyrosine phosphorylation regulated kinase 2\n", "CUL4B cullin 4B\n", "CUL4A cullin 4A\n", "CUL3 cullin 3\n", "CUL2 cullin 2\n", "CUL1 cullin 1\n", "TICRR TOPBP1 interacting checkpoint and replication regulator\n", "KLF11 Kruppel like factor 11\n", "BIN1 bridging integrator 1\n", "FKBP6 FKBP prolyl isomerase 6\n", "RAE1 ribonucleic acid export 1\n", "OFD1 OFD1 centriole and centriolar satellite protein\n", "ANK3 ankyrin 3\n", "C6orf89 chromosome 6 open reading frame 89\n", "PPM1D protein phosphatase, Mg2+/Mn2+ dependent 1D\n", "ANXA1 annexin A1\n", "USP28 ubiquitin specific peptidase 28\n", "KLHL40 kelch like family member 40\n", "KNSTRN kinetochore localized astrin (SPAG5) binding protein\n", "PKP4 plakophilin 4\n", "APAF1 apoptotic peptidase activating factor 1\n", "USP29 ubiquitin specific peptidase 29\n", "APBB1 amyloid beta precursor protein binding family B member 1\n", "CDKL4 cyclin dependent kinase like 4\n", "APC APC regulator of WNT signaling pathway\n", "APBB2 amyloid beta precursor protein binding family B member 2\n", "APEX1 apurinic/apyrimidinic endodeoxyribonuclease 1\n", "BIRC2 baculoviral IAP repeat containing 2\n", "GAS7 growth arrest specific 7\n", "BIRC5 baculoviral IAP repeat containing 5\n", "TRIM71 tripartite motif containing 71\n", "CAMK1 calcium/calmodulin dependent protein kinase I\n", "BARX2 BARX homeobox 2\n", "USP37 ubiquitin specific peptidase 37\n", "APP amyloid beta precursor protein\n", "FAS Fas cell surface death receptor\n", "FASLG Fas ligand\n", "MAPKAPK5 MAPK activated protein kinase 5\n", "PIAS1 protein inhibitor of activated STAT 1\n", "CDC14B cell division cycle 14B\n", "CDC14A cell division cycle 14A\n", "KIAA1614 KIAA1614\n", "CDK10 cyclin dependent kinase 10\n", "GADD45GIP1 GADD45G interacting protein 1\n", "THOC5 THO complex 5\n", "ARF1 ADP ribosylation factor 1\n", "MADD MAP kinase activating death domain\n", "ARF6 ADP ribosylation factor 6\n", "SPC24 SPC24 component of NDC80 kinetochore complex\n", "RHOA ras homolog family member A\n", "RHOB ras homolog family member B\n", "RHOC ras homolog family member C\n", "ARL2 ADP ribosylation factor like GTPase 2\n", "ARL3 ADP ribosylation factor like GTPase 3\n", "ARNTL aryl hydrocarbon receptor nuclear translocator like\n", "CCDC124 coiled-coil domain containing 124\n", "CNOT6L CCR4-NOT transcription complex subunit 6 like\n", "RUVBL1 RuvB like AAA ATPase 1\n", "KASH5 KASH domain containing 5\n", "HAUS1 HAUS augmin like complex subunit 1\n", "CDC26 cell division cycle 26\n", "ASCL1 achaete-scute family bHLH transcription factor 1\n", "CDK13 cyclin dependent kinase 13\n", "TP63 tumor protein p63\n", "STK33 serine/threonine kinase 33\n", "ASNS asparagine synthetase (glutamine-hydrolyzing)\n", "MARK4 microtubule affinity regulating kinase 4\n", "SSNA1 SS nuclear autoantigen 1\n", "ASPH aspartate beta-hydroxylase\n", "PHACTR4 phosphatase and actin regulator 4\n", "BRINP2 BMP/retinoic acid inducible neural specific 2\n", "DDRGK1 DDRGK domain containing 1\n", "LAMTOR3 late endosomal/lysosomal adaptor, MAPK and MTOR activator 3\n", "POLD4 DNA polymerase delta 4, accessory subunit\n", "CCAR2 cell cycle and apoptosis regulator 2\n", "DYNLL1 dynein light chain LC8-type 1\n", "ZFHX3 zinc finger homeobox 3\n", "TNKS tankyrase\n", "ATM ATM serine/threonine kinase\n", "CCNB1IP1 cyclin B1 interacting protein 1\n", "RBM24 RNA binding motif protein 24\n", "BECN1 beclin 1\n", "PEA15 proliferation and apoptosis adaptor protein 15\n", "ATP2B4 ATPase plasma membrane Ca2+ transporting 4\n", "BOLL boule homolog, RNA binding protein\n", "CDC23 cell division cycle 23\n", "TRADD TNFRSF1A associated via death domain\n", "TPRA1 transmembrane protein adipocyte associated 1\n", "EED embryonic ectoderm development\n", "GBF1 golgi brefeldin A resistant guanine nucleotide exchange factor 1\n", "ATR ATR serine/threonine kinase\n", "ATRX ATRX chromatin remodeler\n", "CRADD CASP2 and RIPK1 domain containing adaptor with death domain\n", "TNFSF14 TNF superfamily member 14\n", "RIPK1 receptor interacting serine/threonine kinase 1\n", "TNFSF10 TNF superfamily member 10\n", "AVPR1A arginine vasopressin receptor 1A\n", "STAC3 SH3 and cysteine rich domain 3\n", "BACH1 BTB domain and CNC homolog 1\n", "BAD BCL2 associated agonist of cell death\n", "RAB11A RAB11A, member RAS oncogene family\n", "ADGRB1 adhesion G protein-coupled receptor B1\n", "MEIOB meiosis specific with OB-fold\n", "ADGRB2 adhesion G protein-coupled receptor B2\n", "BAK1 BCL2 antagonist/killer 1\n", "RIPK2 receptor interacting serine/threonine kinase 2\n", "FADD Fas associated via death domain\n", "BAX BCL2 associated X, apoptosis regulator\n", "BARD1 BRCA1 associated RING domain 1\n", "BBS4 Bardet-Biedl syndrome 4\n", "BCAT1 branched chain amino acid transaminase 1\n", "CCND1 cyclin D1\n", "BCL2 BCL2 apoptosis regulator\n", "BCL2L1 BCL2 like 1\n", "BCL3 BCL3 transcription coactivator\n", "TNFRSF10B TNF receptor superfamily member 10b\n", "BCL6 BCL6 transcription repressor\n", "TNFRSF10A TNF receptor superfamily member 10a\n", "BCL9 BCL9 transcription coactivator\n", "BCR BCR activator of RhoGEF and GTPase\n", "CCNK cyclin K\n", "CDKL1 cyclin dependent kinase like 1\n", "BANF1 BAF nuclear assembly factor 1\n", "BDNF brain derived neurotrophic factor\n", "BGLAP bone gamma-carboxyglutamate protein\n", "IQGAP1 IQ motif containing GTPase activating protein 1\n", "BID BH3 interacting domain death agonist\n", "BLM BLM RecQ like helicase\n", "CXCR5 C-X-C motif chemokine receptor 5\n", "CFLAR CASP8 and FADD like apoptosis regulator\n", "BMI1 BMI1 proto-oncogene, polycomb ring finger\n", "BMP2 bone morphogenetic protein 2\n", "BMP4 bone morphogenetic protein 4\n", "BMP7 bone morphogenetic protein 7\n", "KAT2B lysine acetyltransferase 2B\n", "CDK5R1 cyclin dependent kinase 5 regulatory subunit 1\n", "BOK BCL2 family apoptosis regulator BOK\n", "BRCA1 BRCA1 DNA repair associated\n", "PER2 period circadian regulator 2\n", "BRCA2 BRCA2 DNA repair associated\n", "BRDT bromodomain testis associated\n", "ZFP36L1 ZFP36 ring finger protein like 1\n", "ZFP36L2 ZFP36 ring finger protein like 2\n", "CDC123 cell division cycle 123\n", "SPHK1 sphingosine kinase 1\n", "BTC betacellulin\n", "KLF5 Kruppel like factor 5\n", "CDC16 cell division cycle 16\n", "ZPR1 ZPR1 zinc finger\n", "NAE1 NEDD8 activating enzyme E1 subunit 1\n", "BTG1 BTG anti-proliferation factor 1\n", "ANGEL2 angel homolog 2\n", "BUB1 BUB1 mitotic checkpoint serine/threonine kinase\n", "BUB1B BUB1 mitotic checkpoint serine/threonine kinase B\n", "WRAP73 WD repeat containing, antisense to TP73\n", "CCNA1 cyclin A1\n", "TIMELESS timeless circadian regulator\n", "OSGIN2 oxidative stress induced growth inhibitor family member 2\n", "EME2 essential meiotic structure-specific endonuclease subunit 2\n", "PHOX2B paired like homeobox 2B\n", "UHRF2 ubiquitin like with PHD and ring finger domains 2\n", "CDK5R2 cyclin dependent kinase 5 regulatory subunit 2\n", "BTRC beta-transducin repeat containing E3 ubiquitin protein ligase\n", "PTTG1IP PTTG1 interacting protein\n", "SENP5 SUMO specific peptidase 5\n", "USP17L2 ubiquitin specific peptidase 17 like family member 2\n", "NUDT16 nudix hydrolase 16\n", "CALM1 calmodulin 1\n", "CALM2 calmodulin 2\n", "CDKL2 cyclin dependent kinase like 2\n", "CALM3 calmodulin 3\n", "CALR calreticulin\n", "CAMK2A calcium/calmodulin dependent protein kinase II alpha\n", "CAPN2 calpain 2\n", "CAPN3 calpain 3\n", "BRSK2 BR serine/threonine kinase 2\n", "RNF8 ring finger protein 8\n", "CASP1 caspase 1\n", "CASP2 caspase 2\n", "HIP1R huntingtin interacting protein 1 related\n", "CASP4 caspase 4\n", "CASP5 caspase 5\n", "CASP3 caspase 3\n", "TM4SF5 transmembrane 4 L six family member 5\n", "CASP8 caspase 8\n", "CASP9 caspase 9\n", "CASP10 caspase 10\n", "WBP2NL WBP2 N-terminal like\n", "WHAMM WASP homolog associated with actin, golgi membranes and microtubules\n", "CTDSP1 CTD small phosphatase 1\n", "UBA3 ubiquitin like modifier activating enzyme 3\n", "CAV2 caveolin 2\n", "CAV3 caveolin 3\n", "PRC1 protein regulator of cytokinesis 1\n", "RUNX3 RUNX family transcription factor 3\n", "CCK cholecystokinin\n", "DIRAS3 DIRAS family GTPase 3\n", "CCNA2 cyclin A2\n", "CCNB1 cyclin B1\n", "CCNC cyclin C\n", "CCND2 cyclin D2\n", "PKMYT1 protein kinase, membrane associated tyrosine/threonine 1\n", "CCND3 cyclin D3\n", "CCNE1 cyclin E1\n", "CCNF cyclin F\n", "CCNG1 cyclin G1\n", "CCNG2 cyclin G2\n", "CCNH cyclin H\n", "UNC119 unc-119 lipid binding chaperone\n", "CCNT1 cyclin T1\n", "CCNT2 cyclin T2\n", "DNAJA3 DnaJ heat shock protein family (Hsp40) member A3\n", "USP2 ubiquitin specific peptidase 2\n", "USP10 ubiquitin specific peptidase 10\n", "USP8 ubiquitin specific peptidase 8\n", "LATS1 large tumor suppressor kinase 1\n", "MS4A3 membrane spanning 4-domains A3\n", "CNOT9 CCR4-NOT transcription complex subunit 9\n", "SMC3 structural maintenance of chromosomes 3\n", "AIFM1 apoptosis inducing factor mitochondria associated 1\n", "CD28 CD28 molecule\n", "CCNB2 cyclin B2\n", "CCNE2 cyclin E2\n", "CTDP1 CTD phosphatase subunit 1\n", "CD44 CD44 molecule (Indian blood group)\n", "CD53 CD53 molecule\n", "EXO1 exonuclease 1\n", "CD74 CD74 molecule\n", "CDK1 cyclin dependent kinase 1\n", "CDK11B cyclin dependent kinase 11B\n", "CDC5L cell division cycle 5 like\n", "ARHGEF2 Rho/Rac guanine nucleotide exchange factor 2\n", "CDC6 cell division cycle 6\n", "ZW10 zw10 kinetochore protein\n", "CDC20 cell division cycle 20\n", "CDC25A cell division cycle 25A\n", "CDC25B cell division cycle 25B\n", "CDC25C cell division cycle 25C\n", "CDC27 cell division cycle 27\n", "BUB3 BUB3 mitotic checkpoint protein\n", "CDC34 cell division cycle 34, ubiqiutin conjugating enzyme\n", "CDC42 cell division cycle 42\n", "DCUN1D3 defective in cullin neddylation 1 domain containing 3\n", "CDH13 cadherin 13\n", "CDK2 cyclin dependent kinase 2\n", "CDK3 cyclin dependent kinase 3\n", "CDK4 cyclin dependent kinase 4\n", "CDK5 cyclin dependent kinase 5\n", "AURKB aurora kinase B\n", "CDK7 cyclin dependent kinase 7\n", "CDK6 cyclin dependent kinase 6\n", "PHF13 PHD finger protein 13\n", "CDK9 cyclin dependent kinase 9\n", "CDKN1A cyclin dependent kinase inhibitor 1A\n", "CDKN1B cyclin dependent kinase inhibitor 1B\n", "CDKN1C cyclin dependent kinase inhibitor 1C\n", "CDKN2A cyclin dependent kinase inhibitor 2A\n", "CDKN2B cyclin dependent kinase inhibitor 2B\n", "CDKN2C cyclin dependent kinase inhibitor 2C\n", "CDKN2D cyclin dependent kinase inhibitor 2D\n", "CDKN3 cyclin dependent kinase inhibitor 3\n", "NOLC1 nucleolar and coiled-body phosphoprotein 1\n", "TMEM67 transmembrane protein 67\n", "PTTG1 PTTG1 regulator of sister chromatid separation, securin\n", "CCPG1 cell cycle progression 1\n", "TBRG4 transforming growth factor beta regulator 4\n", "CEACAM5 CEA cell adhesion molecule 5\n", "CEBPA CCAAT enhancer binding protein alpha\n", "CENPA centromere protein A\n", "CENPC centromere protein C\n", "CENPE centromere protein E\n", "CENPF centromere protein F\n", "CETN1 centrin 1\n", "CETN2 centrin 2\n", "CETN3 centrin 3\n", "CFL1 cofilin 1\n", "PIWIL1 piwi like RNA-mediated gene silencing 1\n", "RCC1 regulator of chromosome condensation 1\n", "CHD3 chromodomain helicase DNA binding protein 3\n", "CHEK1 checkpoint kinase 1\n", "FOXN3 forkhead box N3\n", "KLF4 Kruppel like factor 4\n", "TRIP13 thyroid hormone receptor interactor 13\n", "RHOU ras homolog family member U\n", "NLRC4 NLR family CARD domain containing 4\n", "CNOT8 CCR4-NOT transcription complex subunit 8\n", "RPRD1B regulation of nuclear pre-mRNA domain containing 1B\n", "TAOK2 TAO kinase 2\n", "RPL23 ribosomal protein L23\n", "BOD1 biorientation of chromosomes in cell division 1\n", "CKS1B CDC28 protein kinase regulatory subunit 1B\n", "CKS2 CDC28 protein kinase regulatory subunit 2\n", "H1-8 H1.8 linker histone\n", "SLC9A3R1 SLC9A3 regulator 1\n", "KIF3B kinesin family member 3B\n", "WIZ WIZ zinc finger\n", "RRAGD Ras related GTP binding D\n", "CLIC1 chloride intracellular channel 1\n", "SUN2 Sad1 and UNC84 domain containing 2\n", "RECQL5 RecQ like helicase 5\n", "CLTA clathrin light chain A\n", "RAD54B RAD54 homolog B\n", "CLTC clathrin heavy chain\n", "FBXO7 F-box protein 7\n", "EIF2AK3 eukaryotic translation initiation factor 2 alpha kinase 3\n", "NIPBL NIPBL cohesin loading factor\n", "PLK3 polo like kinase 3\n", "HOMER1 homer scaffold protein 1\n", "ANAPC13 anaphase promoting complex subunit 13\n", "CATSPERZ catsper channel auxiliary subunit zeta\n", "ROCK2 Rho associated coiled-coil containing protein kinase 2\n", "COL4A3 collagen type IV alpha 3 chain\n", "ANKK1 ankyrin repeat and kinase domain containing 1\n", "PSMF1 proteasome inhibitor subunit 1\n", "KIF23 kinesin family member 23\n", "POC1A POC1 centriolar protein A\n", "ADAMTS1 ADAM metallopeptidase with thrombospondin type 1 motif 1\n", "INTS7 integrator complex subunit 7\n", "GDF15 growth differentiation factor 15\n", "MAP3K8 mitogen-activated protein kinase kinase kinase 8\n", "CNOT10 CCR4-NOT transcription complex subunit 10\n", "EEF1E1 eukaryotic translation elongation factor 1 epsilon 1\n", "ANAPC15 anaphase promoting complex subunit 15\n", "AHCTF1 AT-hook containing transcription factor 1\n", "VPS4B vacuolar protein sorting 4 homolog B\n", "RTTN rotatin\n", "RMI2 RecQ mediated genome instability 2\n", "NEK7 NIMA related kinase 7\n", "NOX4 NADPH oxidase 4\n", "SYCP3 synaptonemal complex protein 3\n", "NSL1 NSL1 component of MIS12 kinetochore complex\n", "MACROH2A1 macroH2A.1 histone\n", "SIN3A SIN3 transcription regulator family member A\n", "ZNF385A zinc finger protein 385A\n", "EPGN epithelial mitogen\n", "SYF2 SYF2 pre-mRNA splicing factor\n", "KANK2 KN motif and ankyrin repeat domains 2\n", "CLOCK clock circadian regulator\n", "BABAM2 BRISC and BRCA1 A complex member 2\n", "ATF2 activating transcription factor 2\n", "CREBL2 cAMP responsive element binding protein like 2\n", "KIF20B kinesin family member 20B\n", "SH2B1 SH2B adaptor protein 1\n", "MAD2L1BP MAD2L1 binding protein\n", "WTAP WT1 associated protein\n", "CHMP2B charged multivesicular body protein 2B\n", "CRY1 cryptochrome circadian regulator 1\n", "HINFP histone H4 transcription factor\n", "ULK3 unc-51 like kinase 3\n", "RNF167 ring finger protein 167\n", "CTCFL CCCTC-binding factor like\n", "MAPK14 mitogen-activated protein kinase 14\n", "L3MBTL1 L3MBTL histone methyl-lysine binding protein 1\n", "FAM32A family with sequence similarity 32 member A\n", "ARHGEF10 Rho guanine nucleotide exchange factor 10\n", "KLHDC3 kelch domain containing 3\n", "CSNK1A1 casein kinase 1 alpha 1\n", "CSNK1D casein kinase 1 delta\n", "CSNK1E casein kinase 1 epsilon\n", "CSNK2A1 casein kinase 2 alpha 1\n", "CSNK2A2 casein kinase 2 alpha 2\n", "MDC1 mediator of DNA damage checkpoint 1\n", "CEP135 centrosomal protein 135\n", "CUZD1 CUB and zona pellucida like domains 1\n", "MARF1 meiosis regulator and mRNA stability factor 1\n", "SENP6 SUMO specific peptidase 6\n", "ANKRD17 ankyrin repeat domain 17\n", "GIGYF2 GRB10 interacting GYF protein 2\n", "NKX2-5 NK2 homeobox 5\n", "APPL1 adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1\n", "CTBP1 C-terminal binding protein 1\n", "CCN2 cellular communication network factor 2\n", "ZNF830 zinc finger protein 830\n", "SLFN11 schlafen family member 11\n", "POLDIP2 DNA polymerase delta interacting protein 2\n", "CTNNB1 catenin beta 1\n", "CROCC ciliary rootlet coiled-coil, rootletin\n", "PUM1 pumilio RNA binding family member 1\n", "ESPL1 extra spindle pole bodies like 1, separase\n", "CEP57 centrosomal protein 57\n", "CTSH cathepsin H\n", "UTP14C UTP14C small subunit processome component\n", "TDRD12 tudor domain containing 12\n", "RAB11FIP3 RAB11 family interacting protein 3\n", "CCDC69 coiled-coil domain containing 69\n", "CYLD CYLD lysine 63 deubiquitinase\n", "CDCA3 cell division cycle associated 3\n", "HDAC9 histone deacetylase 9\n", "KNTC1 kinetochore associated 1\n", "CYP1A1 cytochrome P450 family 1 subfamily A member 1\n", "CCP110 centriolar coiled-coil protein 110\n", "NLRP12 NLR family pyrin domain containing 12\n", "ZFP42 ZFP42 zinc finger protein\n", "RIPOR2 RHO family interacting cell polarization regulator 2\n", "ROMO1 reactive oxygen species modulator 1\n", "HDAC4 histone deacetylase 4\n", "PCLAF PCNA clamp associated factor\n", "RASSF2 Ras association domain family member 2\n", "MCIDAS multiciliate differentiation and DNA synthesis associated cell cycle protein\n", "LIN54 lin-54 DREAM MuvB core complex component\n", "CYP27B1 cytochrome P450 family 27 subfamily B member 1\n", "DLGAP5 DLG associated protein 5\n", "CKAP5 cytoskeleton associated protein 5\n", "DACH1 dachshund family transcription factor 1\n", "MAML1 mastermind like transcriptional coactivator 1\n", "DAP death associated protein\n", "OR1A2 olfactory receptor family 1 subfamily A member 2\n", "DAPK3 death associated protein kinase 3\n", "DAZL deleted in azoospermia like\n", "BRINP1 BMP/retinoic acid inducible neural specific 1\n", "NUF2 NUF2 component of NDC80 kinetochore complex\n", "SFI1 SFI1 centrin binding protein\n", "CUL7 cullin 7\n", "RB1CC1 RB1 inducible coiled-coil 1\n", "SPAG8 sperm associated antigen 8\n", "LIN52 lin-52 DREAM MuvB core complex component\n", "DCTN1 dynactin subunit 1\n", "MELK maternal embryonic leucine zipper kinase\n", "NEK9 NIMA related kinase 9\n", "DDB1 damage specific DNA binding protein 1\n", "GINS1 GINS complex subunit 1\n", "GADD45A growth arrest and DNA damage inducible alpha\n", "FBXL3 F-box and leucine rich repeat protein 3\n", "DDIT3 DNA damage inducible transcript 3\n", "PHGDH phosphoglycerate dehydrogenase\n", "MYEF2 myelin expression factor 2\n", "DDX3X DEAD-box helicase 3 X-linked\n", "DDX5 DEAD-box helicase 5\n", "CABLES1 Cdk5 and Abl enzyme substrate 1\n", "KIAA0753 KIAA0753\n", "DDX11 DEAD/H-box helicase 11\n", "CDCA2 cell division cycle associated 2\n", "PSMD6 proteasome 26S subunit, non-ATPase 6\n", "CHMP7 charged multivesicular body protein 7\n", "MAGI2 membrane associated guanylate kinase, WW and PDZ domain containing 2\n", "SLC26A8 solute carrier family 26 member 8\n", "TLK1 tousled like kinase 1\n", "TAS2R13 taste 2 receptor member 13\n", "FBXO22 F-box protein 22\n", "FBXO6 F-box protein 6\n", "FBXO5 F-box protein 5\n", "FBXO4 F-box protein 4\n", "PARD6A par-6 family cell polarity regulator alpha\n", "WASHC5 WASH complex subunit 5\n", "KLHL21 kelch like family member 21\n", "ANKRD2 ankyrin repeat domain 2\n", "DHCR24 24-dehydrocholesterol reductase\n", "DHFR dihydrofolate reductase\n", "NCAPD2 non-SMC condensin I complex subunit D2\n", "KIF14 kinesin family member 14\n", "DLG1 discs large MAGUK scaffold protein 1\n", "DCLRE1A DNA cross-link repair 1A\n", "SESN2 sestrin 2\n", "DMPK DM1 protein kinase\n", "DMRT1 doublesex and mab-3 related transcription factor 1\n", "DNA2 DNA replication helicase/nuclease 2\n", "USP3 ubiquitin specific peptidase 3\n", "TNFSF15 TNF superfamily member 15\n", "RHNO1 RAD9-HUS1-RAD1 interacting nuclear orphan 1\n", "DYNC1H1 dynein cytoplasmic 1 heavy chain 1\n", "FAM162A family with sequence similarity 162 member A\n", "DYNC1I2 dynein cytoplasmic 1 intermediate chain 2\n", "DNM2 dynamin 2\n", "RBX1 ring-box 1\n", "DNMT3A DNA methyltransferase 3 alpha\n", "THOC1 THO complex 1\n", "REC8 REC8 meiotic recombination protein\n", "DMTF1 cyclin D binding myb like transcription factor 1\n", "CASP8AP2 caspase 8 associated protein 2\n", "DRD2 dopamine receptor D2\n", "DRD3 dopamine receptor D3\n", "ARID3A AT-rich interaction domain 3A\n", "MARVELD1 MARVEL domain containing 1\n", "HDAC5 histone deacetylase 5\n", "PDCD6 programmed cell death 6\n", "BCL2L10 BCL2 like 10\n", "BCL2L11 BCL2 like 11\n", "RCAN1 regulator of calcineurin 1\n", "PDCD6IP programmed cell death 6 interacting protein\n", "L3MBTL2 L3MBTL histone methyl-lysine binding protein 2\n", "DUSP1 dual specificity phosphatase 1\n", "CHAF1A chromatin assembly factor 1 subunit A\n", "DUSP3 dual specificity phosphatase 3\n", "PARP3 poly(ADP-ribose) polymerase family member 3\n", "TOM1L1 target of myb1 like 1 membrane trafficking protein\n", "DYRK1A dual specificity tyrosine phosphorylation regulated kinase 1A\n", "SMC4 structural maintenance of chromosomes 4\n", "E2F1 E2F transcription factor 1\n", "E2F2 E2F transcription factor 2\n", "E2F3 E2F transcription factor 3\n", "E2F4 E2F transcription factor 4\n", "E2F5 E2F transcription factor 5\n", "E2F6 E2F transcription factor 6\n", "E4F1 E4F transcription factor 1\n", "CCNQ cyclin Q\n", "USH1C USH1 protein network component harmonin\n", "ECT2 epithelial cell transforming 2\n", "NUPR1 nuclear protein 1, transcriptional regulator\n", "GMNN geminin DNA replication inhibitor\n", "ACTR3 actin related protein 3\n", "ACTR2 actin related protein 2\n", "EDN1 endothelin 1\n", "EDN3 endothelin 3\n", "HEXIM2 HEXIM P-TEFb complex subunit 2\n", "PHC1 polyhomeotic homolog 1\n", "RPS27L ribosomal protein S27 like\n", "CTDSP2 CTD small phosphatase 2\n", "RAD50 RAD50 double strand break repair protein\n", "KIF20A kinesin family member 20A\n", "ESCO2 establishment of sister chromatid cohesion N-acetyltransferase 2\n", "USP26 ubiquitin specific peptidase 26\n", "FEM1B fem-1 homolog B\n", "ACTR1B actin related protein 1B\n", "ACTR1A actin related protein 1A\n", "SETDB2 SET domain bifurcated histone lysine methyltransferase 2\n", "MLXIPL MLX interacting protein like\n", "CNTD1 cyclin N-terminal domain containing 1\n", "OPTN optineurin\n", "YAF2 YY1 associated factor 2\n", "SH3GLB1 SH3 domain containing GRB2 like, endophilin B1\n", "LATS2 large tumor suppressor kinase 2\n", "AKAP9 A-kinase anchoring protein 9\n", "EGF epidermal growth factor\n", "HMCN1 hemicentin 1\n", "TBC1D10A TBC1 domain family member 10A\n", "EGFR epidermal growth factor receptor\n", "SBDS SBDS ribosome maturation factor\n", "CNEP1R1 CTD nuclear envelope phosphatase 1 regulatory subunit 1\n", "EIF4E eukaryotic translation initiation factor 4E\n", "EIF4EBP1 eukaryotic translation initiation factor 4E binding protein 1\n", "EIF4G1 eukaryotic translation initiation factor 4 gamma 1\n", "EIF4G2 eukaryotic translation initiation factor 4 gamma 2\n", "HASPIN histone H3 associated protein kinase\n", "RBM7 RNA binding motif protein 7\n", "DYNC1LI1 dynein cytoplasmic 1 light intermediate chain 1\n", "ING4 inhibitor of growth family member 4\n", "MRNIP MRN complex interacting protein\n", "AATF apoptosis antagonizing transcription factor\n", "SENP2 SUMO specific peptidase 2\n", "PSME3 proteasome activator subunit 3\n", "NME6 NME/NM23 nucleoside diphosphate kinase 6\n", "EMD emerin\n", "EML1 EMAP like 1\n", "CKAP2 cytoskeleton associated protein 2\n", "RASSF4 Ras association domain family member 4\n", "TOPORS TOP1 binding arginine/serine rich protein, E3 ubiquitin ligase\n", "FLOT1 flotillin 1\n", "PSMD14 proteasome 26S subunit, non-ATPase 14\n", "TUBD1 tubulin delta 1\n", "TUBE1 tubulin epsilon 1\n", "ENG endoglin\n", "CTDSPL CTD small phosphatase like\n", "ENSA endosulfine alpha\n", "EP300 E1A binding protein p300\n", "EPB41 erythrocyte membrane protein band 4.1\n", "EPB41L2 erythrocyte membrane protein band 4.1 like 2\n", "HERC5 HECT and RLD domain containing E3 ubiquitin protein ligase 5\n", "NIN ninein\n", "NUSAP1 nucleolar and spindle associated protein 1\n", "ANKRD31 ankyrin repeat domain 31\n", "DUSP13 dual specificity phosphatase 13\n", "RIDA reactive intermediate imine deaminase A homolog\n", "EPS8 epidermal growth factor receptor pathway substrate 8\n", "SPRY1 sprouty RTK signaling antagonist 1\n", "SPRY2 sprouty RTK signaling antagonist 2\n", "ERCC1 ERCC excision repair 1, endonuclease non-catalytic subunit\n", "CCDC8 coiled-coil domain containing 8\n", "EREG epiregulin\n", "BRIP1 BRCA1 interacting protein C-terminal helicase 1\n", "CDK2AP2 cyclin dependent kinase 2 associated protein 2\n", "ERCC4 ERCC excision repair 4, endonuclease catalytic subunit\n", "ERCC2 ERCC excision repair 2, TFIIH core complex helicase subunit\n", "ERCC3 ERCC excision repair 3, TFIIH core complex helicase subunit\n", "ERCC6 ERCC excision repair 6, chromatin remodeling factor\n", "ZMPSTE24 zinc metallopeptidase STE24\n", "AKAP8 A-kinase anchoring protein 8\n", "ERH ERH mRNA splicing and mitosis factor\n", "ERF ETS2 repressor factor\n", "CENPW centromere protein W\n", "STAG1 stromal antigen 1\n", "ERN1 endoplasmic reticulum to nucleus signaling 1\n", "SHISA5 shisa family member 5\n", "ESRRB estrogen related receptor beta\n", "MAEA macrophage erythroblast attacher, E3 ubiquitin ligase\n", "PAK4 p21 (RAC1) activated kinase 4\n", "KATNB1 katanin regulatory subunit B1\n", "CDKL3 cyclin dependent kinase like 3\n", "CCNO cyclin O\n", "TFDP3 transcription factor Dp family member 3\n", "MECOM MDS1 and EVI1 complex locus\n", "EVI2B ecotropic viral integration site 2B\n", "IKZF1 IKAROS family zinc finger 1\n", "MCMDC2 minichromosome maintenance domain containing 2\n", "KLHL41 kelch like family member 41\n", "RRAGB Ras related GTP binding B\n", "RXFP3 relaxin family peptide receptor 3\n", "MND1 meiotic nuclear divisions 1\n", "EZH2 enhancer of zeste 2 polycomb repressive complex 2 subunit\n", "F2R coagulation factor II thrombin receptor\n", "HORMAD1 HORMA domain containing 1\n", "CNTROB centrobin, centriole duplication and spindle assembly protein\n", "F3 coagulation factor III, tissue factor\n", "RAB6C RAB6C, member RAS oncogene family\n", "NPM2 nucleophosmin/nucleoplasmin 2\n", "HMG20B high mobility group 20B\n", "WAC WW domain containing adaptor with coiled-coil\n", "PYHIN1 pyrin and HIN domain family member 1\n", "SYCE2 synaptonemal complex central element protein 2\n", "FANCA FA complementation group A\n", "FANCD2 FA complementation group D2\n", "CITED2 Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2\n", "USP44 ubiquitin specific peptidase 44\n", "TUBA1B tubulin alpha 1b\n", "DACT1 dishevelled binding antagonist of beta catenin 1\n", "PCGF6 polycomb group ring finger 6\n", "TUBB3 tubulin beta 3 class III\n", "TUBB4A tubulin beta 4A class IVa\n", "FZR1 fizzy and cell division cycle 20 related 1\n", "SCGB3A1 secretoglobin family 3A member 1\n", "TUBB4B tubulin beta 4B class IVb\n", "BTN2A2 butyrophilin subfamily 2 member A2\n", "FAP fibroblast activation protein alpha\n", "SYCP2 synaptonemal complex protein 2\n", "TAOK3 TAO kinase 3\n", "NOD1 nucleotide binding oligomerization domain containing 1\n", "ANAPC10 anaphase promoting complex subunit 10\n", "DLC1 DLC1 Rho GTPase activating protein\n", "NDRG1 N-myc downstream regulated 1\n", "ATRIP ATR interacting protein\n", "RACK1 receptor for activated C kinase 1\n", "NDC80 NDC80 kinetochore complex component\n", "CEP78 centrosomal protein 78\n", "ABRAXAS1 abraxas 1, BRCA1 A complex subunit\n", "STRADA STE20 related adaptor alpha\n", "DUX4 double homeobox 4\n", "CRLF3 cytokine receptor like factor 3\n", "PRMT5 protein arginine methyltransferase 5\n", "WNT16 Wnt family member 16\n", "TUBGCP3 tubulin gamma complex associated protein 3\n", "FEN1 flap structure-specific endonuclease 1\n", "RWDD1 RWD domain containing 1\n", "RBM14 RNA binding motif protein 14\n", "FER FER tyrosine kinase\n", "FES FES proto-oncogene, tyrosine kinase\n", "PPME1 protein phosphatase methylesterase 1\n", "FGF8 fibroblast growth factor 8\n", "FGF10 fibroblast growth factor 10\n", "FGFR1 fibroblast growth factor receptor 1\n", "FGFR2 fibroblast growth factor receptor 2\n", "GPNMB glycoprotein nmb\n", "MAD2L2 mitotic arrest deficient 2 like 2\n", "TACC3 transforming acidic coiled-coil containing protein 3\n", "PRKAG2 protein kinase AMP-activated non-catalytic subunit gamma 2\n", "PIBF1 progesterone immunomodulatory binding factor 1\n", "FHL1 four and a half LIM domains 1\n", "FHIT fragile histidine triad diadenosine triphosphatase\n", "SNX9 sorting nexin 9\n", "ANAPC5 anaphase promoting complex subunit 5\n", "ANAPC7 anaphase promoting complex subunit 7\n", "TADA3 transcriptional adaptor 3\n", "YTHDF2 YTH N6-methyladenosine RNA binding protein 2\n", "FOXG1 forkhead box G1\n", "FOXC1 forkhead box C1\n", "ARAP1 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1\n", "LCMT1 leucine carboxyl methyltransferase 1\n", "FOXE3 forkhead box E3\n", "FOXM1 forkhead box M1\n", "CARM1 coactivator associated arginine methyltransferase 1\n", "CHMP4C charged multivesicular body protein 4C\n", "TRIM72 tripartite motif containing 72\n", "FLNA filamin A\n", "MYBBP1A MYB binding protein 1a\n", "FLT3LG fms related receptor tyrosine kinase 3 ligand\n", "CIB1 calcium and integrin binding 1\n", "SLF1 SMC5-SMC6 complex localization factor 1\n", "KAT5 lysine acetyltransferase 5\n", "IPO7 importin 7\n", "SPOUT1 SPOUT domain containing methyltransferase 1\n", "ZNRD2 zinc ribbon domain containing 2\n", "UBD ubiquitin D\n", "BATF basic leucine zipper ATF-like transcription factor\n", "TRIAP1 TP53 regulated inhibitor of apoptosis 1\n", "DCTN2 dynactin subunit 2\n", "ANP32B acidic nuclear phosphoprotein 32 family member B\n", "LAMTOR5 late endosomal/lysosomal adaptor, MAPK and MTOR activator 5\n", "RTF2 replication termination factor 2\n", "SLC25A33 solute carrier family 25 member 33\n", "CHMP5 charged multivesicular body protein 5\n", "GTSE1 G2 and S-phase expressed 1\n", "DTL denticleless E3 ubiquitin protein ligase homolog\n", "ANAPC11 anaphase promoting complex subunit 11\n", "ZC3HC1 zinc finger C3HC-type containing 1\n", "HBP1 HMG-box transcription factor 1\n", "PDCD2L programmed cell death 2 like\n", "TACC2 transforming acidic coiled-coil containing protein 2\n", "SIRT7 sirtuin 7\n", "CHORDC1 cysteine and histidine rich domain containing 1\n", "CINP cyclin dependent kinase 2 interacting protein\n", "SMC2 structural maintenance of chromosomes 2\n", "ERN2 endoplasmic reticulum to nucleus signaling 2\n", "USP16 ubiquitin specific peptidase 16\n", "AKAP8L A-kinase anchoring protein 8 like\n", "P3H4 prolyl 3-hydroxylase family member 4 (inactive)\n", "HEXIM1 HEXIM P-TEFb complex subunit 1\n", "SPAG5 sperm associated antigen 5\n", "CYFIP2 cytoplasmic FMR1 interacting protein 2\n", "STAMBP STAM binding protein\n", "TXNIP thioredoxin interacting protein\n", "CNPPD1 cyclin Pas1/PHO80 domain containing 1\n", "BEX3 brain expressed X-linked 3\n", "GAS2L1 growth arrest specific 2 like 1\n", "RGS14 regulator of G protein signaling 14\n", "MLH3 mutL homolog 3\n", "KHDRBS1 KH RNA binding domain containing, signal transduction associated 1\n", "SND1 staphylococcal nuclease and tudor domain containing 1\n", "CTCF CCCTC-binding factor\n", "MTOR mechanistic target of rapamycin kinase\n", "CGRRF1 cell growth regulator with ring finger domain 1\n", "CGREF1 cell growth regulator with EF-hand domain 1\n", "RRAGA Ras related GTP binding A\n", "ANKRD1 ankyrin repeat domain 1\n", "SSX2IP SSX family member 2 interacting protein\n", "NR5A2 nuclear receptor subfamily 5 group A member 2\n", "ECRG4 ECRG4 augurin precursor\n", "CHMP3 charged multivesicular body protein 3\n", "CDK5RAP1 CDK5 regulatory subunit associated protein 1\n", "MRGPRX2 MAS related GPR family member X2\n", "MTBP MDM2 binding protein\n", "NR5A1 nuclear receptor subfamily 5 group A member 1\n", "RAB11FIP4 RAB11 family interacting protein 4\n", "USP39 ubiquitin specific peptidase 39\n", "POLD3 DNA polymerase delta 3, accessory subunit\n", "DOT1L DOT1 like histone lysine methyltransferase\n", "LZTS2 leucine zipper tumor suppressor 2\n", "BRSK1 BR serine/threonine kinase 1\n", "NFAT5 nuclear factor of activated T cells 5\n", "NUDC nuclear distribution C, dynein complex regulator\n", "BBC3 BCL2 binding component 3\n", "PLK4 polo like kinase 4\n", "STAG3 stromal antigen 3\n", "STAG2 stromal antigen 2\n", "HECA hdc homolog, cell cycle regulator\n", "SLX4 SLX4 structure-specific endonuclease subunit\n", "SMC1B structural maintenance of chromosomes 1B\n", "PTTG2 pituitary tumor-transforming 2\n", "KCNH5 potassium voltage-gated channel subfamily H member 5\n", "GIPC1 GIPC PDZ domain containing family member 1\n", "CIDEB cell death inducing DFFA like effector b\n", "CAB39 calcium binding protein 39\n", "UIMC1 ubiquitin interaction motif containing 1\n", "NES nestin\n", "PLK2 polo like kinase 2\n", "ZMYND11 zinc finger MYND-type containing 11\n", "GAK cyclin G associated kinase\n", "SPIRE2 spire type actin nucleation factor 2\n", "ARPP19 cAMP regulated phosphoprotein 19\n", "WWOX WW domain containing oxidoreductase\n", "NEK6 NIMA related kinase 6\n", "MCM8 minichromosome maintenance 8 homologous recombination repair factor\n", "RTEL1 regulator of telomere elongation helicase 1\n", "TUBG2 tubulin gamma 2\n", "TMEM8B transmembrane protein 8B\n", "ZNF268 zinc finger protein 268\n", "CDK12 cyclin dependent kinase 12\n", "VPS4A vacuolar protein sorting 4 homolog A\n", "STRA8 stimulated by retinoic acid 8\n", "SDCCAG8 SHH signaling and ciliogenesis regulator SDCCAG8\n", "ENTR1 endosome associated trafficking regulator 1\n", "GAS1 growth arrest specific 1\n", "GAS2 growth arrest specific 2\n", "GAS6 growth arrest specific 6\n", "MAP3K20 mitogen-activated protein kinase kinase kinase 20\n", "GATA3 GATA binding protein 3\n", "DNER delta/notch like EGF repeat containing\n", "GATA6 GATA binding protein 6\n", "PARD6G par-6 family cell polarity regulator gamma\n", "SIK1 salt inducible kinase 1\n", "KAT2A lysine acetyltransferase 2A\n", "SIX4 SIX homeobox 4\n", "TUBGCP2 tubulin gamma complex associated protein 2\n", "TUBGCP4 tubulin gamma complex associated protein 4\n", "OPN1MW opsin 1, medium wave sensitive\n", "TUBA8 tubulin alpha 8\n", "LILRB1 leukocyte immunoglobulin like receptor B1\n", "CHMP2A charged multivesicular body protein 2A\n", "GEM GTP binding protein overexpressed in skeletal muscle\n", "C10orf99 chromosome 10 open reading frame 99\n", "GFI1 growth factor independent 1 transcriptional repressor\n", "PDCD4 programmed cell death 4\n", "JMY junction mediating and regulatory protein, p53 cofactor\n", "USP19 ubiquitin specific peptidase 19\n", "PARD6B par-6 family cell polarity regulator beta\n", "GJA1 gap junction protein alpha 1\n", "TUBB6 tubulin beta 6 class V\n", "JTB jumping translocation breakpoint\n", "TTBK1 tau tubulin kinase 1\n", "BLCAP BLCAP apoptosis inducing factor\n", "AFAP1L2 actin filament associated protein 1 like 2\n", "TXNL4A thioredoxin like 4A\n", "SUGT1 SGT1 homolog, MIS12 kinetochore complex assembly cochaperone\n", "GADD45G growth arrest and DNA damage inducible gamma\n", "APEX2 apurinic/apyrimidinic endodeoxyribonuclease 2\n", "EHMT2 euchromatic histone lysine methyltransferase 2\n", "DBF4 DBF4 zinc finger\n", "SPIN1 spindlin 1\n", "GLI1 GLI family zinc finger 1\n", "EHD1 EH domain containing 1\n", "SPDYC speedy/RINGO cell cycle regulator family member C\n", "RPS6KA6 ribosomal protein S6 kinase A6\n", "BTG3 BTG anti-proliferation factor 3\n", "UBE2S ubiquitin conjugating enzyme E2 S\n", "PRPF19 pre-mRNA processing factor 19\n", "INSM2 INSM transcriptional repressor 2\n", "GML glycosylphosphatidylinositol anchored molecule like\n", "PPP1R9B protein phosphatase 1 regulatory subunit 9B\n", "GNAI1 G protein subunit alpha i1\n", "GNAI2 G protein subunit alpha i2\n", "SPATA22 spermatogenesis associated 22\n", "GNAI3 G protein subunit alpha i3\n", "SGSM3 small G protein signaling modulator 3\n", "BEX2 brain expressed X-linked 2\n", "CCNI cyclin I\n", "COPS5 COP9 signalosome subunit 5\n", "GOLGA2 golgin A2\n", "PSRC1 proline and serine rich coiled-coil 1\n", "SFN stratifin\n", "KIF2C kinesin family member 2C\n", "TLK2 tousled like kinase 2\n", "HORMAD2 HORMA domain containing 2\n", "GPR3 G protein-coupled receptor 3\n", "RAB35 RAB35, member RAS oncogene family\n", "TDRKH tudor and KH domain containing\n", "PIM2 Pim-2 proto-oncogene, serine/threonine kinase\n", "GPER1 G protein-coupled estrogen receptor 1\n", "HTRA2 HtrA serine peptidase 2\n", "TENT4A terminal nucleotidyltransferase 4A\n", "EML4 EMAP like 4\n", "KMT5A lysine methyltransferase 5A\n", "TUBA1C tubulin alpha 1c\n", "GRK5 G protein-coupled receptor kinase 5\n", "CNTRL centriolin\n", "UBE2C ubiquitin conjugating enzyme E2 C\n", "GPX1 glutathione peroxidase 1\n", "TOPBP1 DNA topoisomerase II binding protein 1\n", "TRIOBP TRIO and F-actin binding protein\n", "PLPP7 phospholipid phosphatase 7 (inactive)\n", "PARD3B par-3 family cell polarity regulator beta\n", "TMEM119 transmembrane protein 119\n", "GRIN2A glutamate ionotropic receptor NMDA type subunit 2A\n", "NR3C1 nuclear receptor subfamily 3 group C member 1\n", "MEI1 meiotic double-stranded break formation protein 1\n", "KATNA1 katanin catalytic subunit A1\n", "PRDM7 PR/SET domain 7\n", "PRDM5 PR/SET domain 5\n", "CIT citron rho-interacting serine/threonine kinase\n", "CEP43 centrosomal protein 43\n", "GSPT1 G1 to S phase transition 1\n", "KIF3A kinesin family member 3A\n", "ZWINT ZW10 interacting kinetochore protein\n", "ZNF503 zinc finger protein 503\n", "KLHL22 kelch like family member 22\n", "CDC37 cell division cycle 37, HSP90 cochaperone\n", "DMC1 DNA meiotic recombinase 1\n", "PLAAT3 phospholipase A and acyltransferase 3\n", "ZBTB49 zinc finger and BTB domain containing 49\n", "SULT1A4 sulfotransferase family 1A member 4\n", "GTF2H1 general transcription factor IIH subunit 1\n", "NUDT6 nudix hydrolase 6\n", "WDHD1 WD repeat and HMG-box DNA binding protein 1\n", "FAM107A family with sequence similarity 107 member A\n", "TBRG1 transforming growth factor beta regulator 1\n", "LZTS1 leucine zipper tumor suppressor 1\n", "WDR6 WD repeat domain 6\n", "RASSF1 Ras association domain family member 1\n", "CEP250 centrosomal protein 250\n", "CHEK2 checkpoint kinase 2\n", "TTL tubulin tyrosine ligase\n", "MASTL microtubule associated serine/threonine kinase like\n", "H2AX H2A.X variant histone\n", "ZFYVE19 zinc finger FYVE-type containing 19\n", "MAEL maelstrom spermatogenic transposon silencer\n", "AVPI1 arginine vasopressin induced 1\n", "WEE2 WEE2 oocyte meiosis inhibiting kinase\n", "AJUBA ajuba LIM protein\n", "LSM10 LSM10, U7 small nuclear RNA associated\n", "PMF1 polyamine modulated factor 1\n", "HCFC1 host cell factor C1\n", "EDA2R ectodysplasin A2 receptor\n", "HTT huntingtin\n", "DCTN3 dynactin subunit 3\n", "HELLS helicase, lymphoid specific\n", "HGF hepatocyte growth factor\n", "TREX1 three prime repair exonuclease 1\n", "HHEX hematopoietically expressed homeobox\n", "NANOGP8 Nanog homeobox retrogene P8\n", "HIC1 HIC ZBTB transcriptional repressor 1\n", "HIP1 huntingtin interacting protein 1\n", "HINT1 histidine triad nucleotide binding protein 1\n", "SMYD1 SET and MYND domain containing 1\n", "ECD ecdysoneless cell cycle regulator\n", "HLA-G major histocompatibility complex, class I, G\n", "PHB2 prohibitin 2\n", "TUSC2 tumor suppressor 2, mitochondrial calcium regulator\n", "CBX3 chromobox 3\n", "OIP5 Opa interacting protein 5\n", "HMGA1 high mobility group AT-hook 1\n", "HMMR hyaluronan mediated motility receptor\n", "NR4A1 nuclear receptor subfamily 4 group A member 1\n", "PPP1R10 protein phosphatase 1 regulatory subunit 10\n", "FOXA1 forkhead box A1\n", "HNRNPU heterogeneous nuclear ribonucleoprotein U\n", "HAUS8 HAUS augmin like complex subunit 8\n", "RINT1 RAD50 interactor 1\n", "USP51 ubiquitin specific peptidase 51\n", "HPGD 15-hydroxyprostaglandin dehydrogenase\n", "HRAS HRas proto-oncogene, GTPase\n", "PRMT2 protein arginine methyltransferase 2\n", "PRMT1 protein arginine methyltransferase 1\n", "HES1 hes family bHLH transcription factor 1\n", "LSM11 LSM11, U7 small nuclear RNA associated\n", "POC5 POC5 centriolar protein\n", "HSF1 heat shock transcription factor 1\n", "INCA1 inhibitor of CDK, cyclin A1 interacting protein 1\n", "HSPA1A heat shock protein family A (Hsp70) member 1A\n", "HSPA1B heat shock protein family A (Hsp70) member 1B\n", "HSPA2 heat shock protein family A (Hsp70) member 2\n", "SYCE1 synaptonemal complex central element protein 1\n", "HSP90AA1 heat shock protein 90 alpha family class A member 1\n", "HSP90AB1 heat shock protein 90 alpha family class B member 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MIIP migration and invasion inhibitory protein\n", "HSPD1 heat shock protein family D (Hsp60) member 1\n", "HSPE1 heat shock protein family E (Hsp10) member 1\n", "HUS1 HUS1 checkpoint clamp component\n", "HYAL1 hyaluronidase 1\n", "ICAM1 intercellular adhesion molecule 1\n", "ID1 inhibitor of DNA binding 1, HLH protein\n", "ID2 inhibitor of DNA binding 2\n", "ID3 inhibitor of DNA binding 3, HLH protein\n", "ID4 inhibitor of DNA binding 4, HLH protein\n", "SNX33 sorting nexin 33\n", "PRKAB2 protein kinase AMP-activated non-catalytic subunit beta 2\n", "IFI16 interferon gamma inducible protein 16\n", "PRKAG1 protein kinase AMP-activated non-catalytic subunit gamma 1\n", "TUBGCP6 tubulin gamma complex associated protein 6\n", "IFNG interferon gamma\n", "IFNW1 interferon omega 1\n", "MISP mitotic spindle positioning\n", "NANOS2 nanos C2HC-type zinc finger 2\n", "IGF1 insulin like growth factor 1\n", "IGF2 insulin like growth factor 2\n", "CCNB3 cyclin B3\n", "LRRCC1 leucine rich repeat and coiled-coil centrosomal protein 1\n", "TNKS1BP1 tankyrase 1 binding protein 1\n", "MYOCD myocardin\n", "CEP295 centrosomal protein 295\n", "IK IK cytokine\n", "IL1A interleukin 1 alpha\n", "IL1B interleukin 1 beta\n", "IL4R interleukin 4 receptor\n", "CXCL8 C-X-C motif chemokine ligand 8\n", "IL10 interleukin 10\n", "TRNP1 TMF1 regulated nuclear protein 1\n", "IL12A interleukin 12A\n", "IL12B interleukin 12B\n", "BRINP3 BMP/retinoic acid inducible neural specific 3\n", "ILK integrin linked kinase\n", "INCENP inner centromere protein\n", "ING1 inhibitor of growth family member 1\n", "ING2 inhibitor of growth family member 2\n", "INHA inhibin subunit alpha\n", "INHBA inhibin subunit beta A\n", "TUBB8 tubulin beta 8 class VIII\n", "CXCL10 C-X-C motif chemokine ligand 10\n", "INS insulin\n", "PLK5 polo like kinase 5 (inactive)\n", "INSM1 INSM transcriptional repressor 1\n", "INSR insulin receptor\n", "PPP2R3B protein phosphatase 2 regulatory subunit B''beta\n", "IRF1 interferon regulatory factor 1\n", "RAD21L1 RAD21 cohesin complex component like 1\n", "IRF6 interferon regulatory factor 6\n", "TUBB2B tubulin beta 2B class IIb\n", "ZNF385B zinc finger protein 385B\n", "ITGB1 integrin subunit beta 1\n", "JAK2 Janus kinase 2\n", "JUN Jun proto-oncogene, AP-1 transcription factor subunit\n", "JUNB JunB proto-oncogene, AP-1 transcription factor subunit\n", "JUND JunD proto-oncogene, AP-1 transcription factor subunit\n", "MEI4 meiotic double-stranded break formation protein 4\n", "CCNYL1 cyclin Y like 1\n", "KCNA5 potassium voltage-gated channel subfamily A member 5\n", "PPP1R1C protein phosphatase 1 regulatory inhibitor subunit 1C\n", "SGO2 shugoshin 2\n", "KEL Kell metallo-endopeptidase (Kell blood group)\n", "KIF2A kinesin family member 2A\n", "KIF11 kinesin family member 11\n", "KIFC1 kinesin family member C1\n", "KIF25 kinesin family member 25\n", "KIF22 kinesin family member 22\n", "KPNB1 karyopherin subunit beta 1\n", "IPO5 importin 5\n", "CCSAP centriole, cilia and spindle associated protein\n", "ACTR8 actin related protein 8\n", "KRT18 keratin 18\n", "C10orf90 chromosome 10 open reading frame 90\n", "STMN1 stathmin 1\n", "LCK LCK proto-oncogene, Src family tyrosine kinase\n", "LEP leptin\n", "LFNG LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase\n", "IHO1 interactor of HORMAD1 1\n", "LIF LIF interleukin 6 family cytokine\n", "LIG1 DNA ligase 1\n", "LIG3 DNA ligase 3\n", "LIG4 DNA ligase 4\n", "LIMS1 LIM zinc finger domain containing 1\n", "LMNA lamin A/C\n", "LRP6 LDL receptor related protein 6\n", "LRP5 LDL receptor related protein 5\n", "MAD2L1 mitotic arrest deficient 2 like 1\n", "SMAD3 SMAD family member 3\n", "TMPRSS11A transmembrane serine protease 11A\n", "MAGEA2 MAGE family member A2\n", "CACUL1 CDK2 associated cullin domain 1\n", "SPRED2 sprouty related EVH1 domain containing 2\n", "TP53INP1 tumor protein p53 inducible nuclear protein 1\n", "MAP4 microtubule associated protein 4\n", "MAPT microtubule associated protein tau\n", "MAX MYC associated factor X\n", "MCM2 minichromosome maintenance complex component 2\n", "MCM3 minichromosome maintenance complex component 3\n", "MCM4 minichromosome maintenance complex component 4\n", "MCM5 minichromosome maintenance complex component 5\n", "MCM6 minichromosome maintenance complex component 6\n", "MCM7 minichromosome maintenance complex component 7\n", "SKA2 spindle and kinetochore associated complex subunit 2\n", "SGO1 shugoshin 1\n", "MDM2 MDM2 proto-oncogene\n", "MDM4 MDM4 regulator of p53\n", "MECP2 methyl-CpG binding protein 2\n", "PSMA8 proteasome 20S subunit alpha 8\n", "MEF2C myocyte enhancer factor 2C\n", "MEIS2 Meis homeobox 2\n", "MEN1 menin 1\n", "AGO4 argonaute RISC component 4\n", "MIF macrophage migration inhibitory factor\n", "CXCL9 C-X-C motif chemokine ligand 9\n", "MKI67 marker of proliferation Ki-67\n", "MLF1 myeloid leukemia factor 1\n", "MLH1 mutL homolog 1\n", "FOXO4 forkhead box O4\n", "MME membrane metalloendopeptidase\n", "MMP14 matrix metallopeptidase 14\n", "MNAT1 MNAT1 component of CDK activating kinase\n", "KLHDC8B kelch domain containing 8B\n", "MNT MAX network transcriptional repressor\n", "DEUP1 deuterosome assembly protein 1\n", "MOS MOS proto-oncogene, serine/threonine kinase\n", "MRE11 MRE11 homolog, double strand break repair nuclease\n", "LAMTOR2 late endosomal/lysosomal adaptor, MAPK and MTOR activator 2\n", "HUS1B HUS1 checkpoint clamp component B\n", "GIT1 GIT ArfGAP 1\n", "BTBD18 BTB domain containing 18\n", "RGCC regulator of cell cycle\n", "MCTS1 MCTS1 re-initiation and release factor\n", "HIPK2 homeodomain interacting protein kinase 2\n", "PIWIL4 piwi like RNA-mediated gene silencing 4\n", "MSH2 mutS homolog 2\n", "MSH3 mutS homolog 3\n", "MSH4 mutS homolog 4\n", "MSH5 mutS homolog 5\n", "NUPR2 nuclear protein 2, transcriptional regulator\n", "PRKAG3 protein kinase AMP-activated non-catalytic subunit gamma 3\n", "MSX1 msh homeobox 1\n", "MSX2 msh homeobox 2\n", "SETD2 SET domain containing 2, histone lysine methyltransferase\n", "MAPK15 mitogen-activated protein kinase 15\n", "CHMP4A charged multivesicular body protein 4A\n", "BABAM1 BRISC and BRCA1 A complex member 1\n", "LAMTOR4 late endosomal/lysosomal adaptor, MAPK and MTOR activator 4\n", "PYCARD PYD and CARD domain containing\n", "BRD7 bromodomain containing 7\n", "RACGAP1 Rac GTPase activating protein 1\n", "UHRF1 ubiquitin like with PHD and ring finger domains 1\n", "CENPV centromere protein V\n", "FLCN folliculin\n", "PLD6 phospholipase D family member 6\n", "MUC1 mucin 1, cell surface associated\n", "GEN1 GEN1 Holliday junction 5' flap endonuclease\n", "TRIM37 tripartite motif containing 37\n", "MAGEA2B MAGE family member A2B\n", "MX2 MX dynamin like GTPase 2\n", "MAJIN membrane anchored junction protein\n", "MYB MYB proto-oncogene, transcription factor\n", "MYBL1 MYB proto-oncogene like 1\n", "MYBL2 MYB proto-oncogene like 2\n", "CDC14C cell division cycle 14C\n", "MYC MYC proto-oncogene, bHLH transcription factor\n", "THAP5 THAP domain containing 5\n", "ACER2 alkaline ceramidase 2\n", "GADD45B growth arrest and DNA damage inducible beta\n", "MYF5 myogenic factor 5\n", "MYF6 myogenic factor 6\n", "NUGGC nuclear GTPase, germinal center associated\n", "MYH9 myosin heavy chain 9\n", "MYH10 myosin heavy chain 10\n", "MYO6 myosin VI\n", "MYOD1 myogenic differentiation 1\n", "NEK10 NIMA related kinase 10\n", "MYOG myogenin\n", "PPP1R12A protein phosphatase 1 regulatory subunit 12A\n", "PPP1R12B protein phosphatase 1 regulatory subunit 12B\n", "NASP nuclear autoantigenic sperm protein\n", "NUBP1 nucleotide binding protein 1\n", "NBN nibrin\n", "SPICE1 spindle and centriole associated protein 1\n", "DRG1 developmentally regulated GTP binding protein 1\n", "NEDD9 neural precursor cell expressed, developmentally down-regulated 9\n", "NEK1 NIMA related kinase 1\n", "NEK2 NIMA related kinase 2\n", "NEK3 NIMA related kinase 3\n", "NEUROD1 neuronal differentiation 1\n", "NEUROG1 neurogenin 1\n", "PELO pelota mRNA surveillance and ribosome rescue factor\n", "NF2 neurofibromin 2\n", "NFATC1 nuclear factor of activated T cells 1\n", "NFATC2 nuclear factor of activated T cells 2\n", "NFATC3 nuclear factor of activated T cells 3\n", "NFATC4 nuclear factor of activated T cells 4\n", "NFE2L1 nuclear factor, erythroid 2 like 1\n", "BHLHA15 basic helix-loop-helix family member a15\n", "NGF nerve growth factor\n", "NGFR nerve growth factor receptor\n", "ANAPC16 anaphase promoting complex subunit 16\n", "NKX3-1 NK3 homeobox 1\n", "SLC39A5 solute carrier family 39 member 5\n", "CNOT2 CCR4-NOT transcription complex subunit 2\n", "CNOT3 CCR4-NOT transcription complex subunit 3\n", "CNOT4 CCR4-NOT transcription complex subunit 4\n", "NOTCH1 notch receptor 1\n", "NOTCH2 notch receptor 2\n", "CCN3 cellular communication network factor 3\n", "NPAT nuclear protein, coactivator of histone transcription\n", "NPM1 nucleophosmin 1\n", "NPPC natriuretic peptide C\n", "NPR2 natriuretic peptide receptor 2\n", "MIS18A MIS18 kinetochore protein A\n", "NUMA1 nuclear mitotic apparatus protein 1\n", "NUP88 nucleoporin 88\n", "NUP43 nucleoporin 43\n", "ARL8A ADP ribosylation factor like GTPase 8A\n", "POLE3 DNA polymerase epsilon 3, accessory subunit\n", "ODF2 outer dense fiber of sperm tails 2\n", "CHAMP1 chromosome alignment maintaining phosphoprotein 1\n", "OPA1 OPA1 mitochondrial dynamin like GTPase\n", "SEPTIN7 septin 7\n", "ORC1 origin recognition complex subunit 1\n", "ORC2 origin recognition complex subunit 2\n", "ORC4 origin recognition complex subunit 4\n", "ORC5 origin recognition complex subunit 5\n", "OVOL1 ovo like transcriptional repressor 1\n", "P2RX1 purinergic receptor P2X 1\n", "PAFAH1B1 platelet activating factor acetylhydrolase 1b regulatory subunit 1\n", "UHMK1 U2AF homology motif kinase 1\n", "SERPINE1 serpin family E member 1\n", "CYCS cytochrome c, somatic\n", "PAX6 paired box 6\n", "PBX1 PBX homeobox 1\n", "PCM1 pericentriolar material 1\n", "PCNA proliferating cell nuclear antigen\n", "PCNT pericentrin\n", "CHMP1A charged multivesicular body protein 1A\n", "CDK16 cyclin dependent kinase 16\n", "CDK17 cyclin dependent kinase 17\n", "CDK18 cyclin dependent kinase 18\n", "PDCD2 programmed cell death 2\n", "PDE3A phosphodiesterase 3A\n", "DCDC1 doublecortin domain containing 1\n", "REC114 REC114 meiotic recombination protein\n", "CDK8 cyclin dependent kinase 8\n", "PDGFB platelet derived growth factor subunit B\n", "PDGFRB platelet derived growth factor receptor beta\n", "PDK2 pyruvate dehydrogenase kinase 2\n", "TMOD3 tropomodulin 3\n", "E2F7 E2F transcription factor 7\n", "NCAPH2 non-SMC condensin II complex subunit H2\n", "WNT4 Wnt family member 4\n", "SYCE3 synaptonemal complex central element protein 3\n", "CDK14 cyclin dependent kinase 14\n", "PGGT1B protein geranylgeranyltransferase type I subunit beta\n", "ABCB1 ATP binding cassette subfamily B member 1\n", "PNPT1 polyribonucleotide nucleotidyltransferase 1\n", "SENP1 SUMO specific peptidase 1\n", "MIS12 MIS12 kinetochore complex component\n", "FBXL22 F-box and leucine rich repeat protein 22\n", "PIK3C3 phosphatidylinositol 3-kinase catalytic subunit type 3\n", "ANLN anillin actin binding protein\n", "PIM1 Pim-1 proto-oncogene, serine/threonine kinase\n", "NUP37 nucleoporin 37\n", "ZNF655 zinc finger protein 655\n", "PIN1 peptidylprolyl cis/trans isomerase, NIMA-interacting 1\n", "MOV10L1 Mov10 like RISC complex RNA helicase 1\n", "ANAPC2 anaphase promoting complex subunit 2\n", "CNOT7 CCR4-NOT transcription complex subunit 7\n", "NABP2 nucleic acid binding protein 2\n", "FBXW5 F-box and WD repeat domain containing 5\n", "PKD1 polycystin 1, transient receptor potential channel interacting\n", "PKD2 polycystin 2, transient receptor potential cation channel\n", "ETAA1 ETAA1 activator of ATR kinase\n", "SPIN2A spindlin family member 2A\n", "ASPM assembly factor for spindle microtubules\n", "PKHD1 PKHD1 ciliary IPT domain containing fibrocystin/polyductin\n", "PSMC3IP PSMC3 interacting protein\n", "TERB1 telomere repeat binding bouquet formation protein 1\n", "NLE1 notchless homolog 1\n", "GPSM2 G protein signaling modulator 2\n", "PLAGL1 PLAG1 like zinc finger 1\n", "PIMREG PICALM interacting mitotic regulator\n", "TMEM109 transmembrane protein 109\n", "PLK1 polo like kinase 1\n", "DSCC1 DNA replication and sister chromatid cohesion 1\n", "PLRG1 pleiotropic regulator 1\n", "GPR132 G protein-coupled receptor 132\n", "RPA4 replication protein A4\n", "IQGAP3 IQ motif containing GTPase activating protein 3\n", "DDX4 DEAD-box helicase 4\n", "PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1\n", "ANAPC4 anaphase promoting complex subunit 4\n", "PML PML nuclear body scaffold\n", "DNMT3L DNA methyltransferase 3 like\n", "SERTAD1 SERTA domain containing 1\n", "CDIP1 cell death inducing p53 target 1\n", "DDIT4 DNA damage inducible transcript 4\n", "DONSON DNA replication fork stabilization factor DONSON\n", "SLC2A8 solute carrier family 2 member 8\n", "PHF23 PHD finger protein 23\n", "NOP53 NOP53 ribosome biogenesis factor\n", "POLA1 DNA polymerase alpha 1, catalytic subunit\n", "POLD1 DNA polymerase delta 1, catalytic subunit\n", "POLD2 DNA polymerase delta 2, accessory subunit\n", "POLE DNA polymerase epsilon, catalytic subunit\n", "POLE2 DNA polymerase epsilon 2, accessory subunit\n", "FBXL15 F-box and leucine rich repeat protein 15\n", "RAD9B RAD9 checkpoint clamp component B\n", "BRCC3 BRCA1/BRCA2-containing complex subunit 3\n", "POU4F1 POU class 4 homeobox 1\n", "POU4F2 POU class 4 homeobox 2\n", "FSD1 fibronectin type III and SPRY domain containing 1\n", "INO80 INO80 complex ATPase subunit\n", "CCNJ cyclin J\n", "PPARG peroxisome proliferator activated receptor gamma\n", "MED1 mediator complex subunit 1\n", "PAF1 PAF1 homolog, Paf1/RNA polymerase II complex component\n", "PPAT phosphoribosyl pyrophosphate amidotransferase\n", "MAP10 microtubule associated protein 10\n", "PPM1A protein phosphatase, Mg2+/Mn2+ dependent 1A\n", "PPM1G protein phosphatase, Mg2+/Mn2+ dependent 1G\n", "PPP1CA protein phosphatase 1 catalytic subunit alpha\n", "PPP1CB protein phosphatase 1 catalytic subunit beta\n", "PPP1CC protein phosphatase 1 catalytic subunit gamma\n", "PPP2CA protein phosphatase 2 catalytic subunit alpha\n", "PIM3 Pim-3 proto-oncogene, serine/threonine kinase\n", "PPP2R1A protein phosphatase 2 scaffold subunit Aalpha\n", "PPP2R2A protein phosphatase 2 regulatory subunit Balpha\n", "PPP2R2B protein phosphatase 2 regulatory subunit Bbeta\n", "PPP2R2C protein phosphatase 2 regulatory subunit Bgamma\n", "PTPA protein phosphatase 2 phosphatase activator\n", "PPP2R5B protein phosphatase 2 regulatory subunit B'beta\n", "PPP3CA protein phosphatase 3 catalytic subunit alpha\n", "PPP3CB protein phosphatase 3 catalytic subunit beta\n", "PPP3CC protein phosphatase 3 catalytic subunit gamma\n", "PPP3R1 protein phosphatase 3 regulatory subunit B, alpha\n", "PPP5C protein phosphatase 5 catalytic subunit\n", "PPP6C protein phosphatase 6 catalytic subunit\n", "MEIOC meiosis specific with coiled-coil domain\n", "PRCC proline rich mitotic checkpoint control factor\n", "PRELP proline and arginine rich end leucine rich repeat protein\n", "PRIM1 DNA primase subunit 1\n", "PRIM2 DNA primase subunit 2\n", "PRKAA1 protein kinase AMP-activated catalytic subunit alpha 1\n", "PRKAA2 protein kinase AMP-activated catalytic subunit alpha 2\n", "PRKAB1 protein kinase AMP-activated non-catalytic subunit beta 1\n", "PRKACA protein kinase cAMP-activated catalytic subunit alpha\n", "PRKACB protein kinase cAMP-activated catalytic subunit beta\n", "CEP41 centrosomal protein 41\n", "PKIA cAMP-dependent protein kinase inhibitor alpha\n", "PRKAR1A protein kinase cAMP-dependent type I regulatory subunit alpha\n", "MPLKIP M-phase specific PLK1 interacting protein\n", "PRKAR2B protein kinase cAMP-dependent type II regulatory subunit beta\n", "PRKCA protein kinase C alpha\n", "PRKCD protein kinase C delta\n", "PRKCE protein kinase C epsilon\n", "PRKCB protein kinase C beta\n", "PKN2 protein kinase N2\n", "PRKDC protein kinase, DNA-activated, catalytic subunit\n", "MAPK1 mitogen-activated protein kinase 1\n", "MAPK3 mitogen-activated protein kinase 3\n", "MAPK4 mitogen-activated protein kinase 4\n", "MAPK6 mitogen-activated protein kinase 6\n", "MAPK7 mitogen-activated protein kinase 7\n", "MAPK13 mitogen-activated protein kinase 13\n", "MAP2K1 mitogen-activated protein kinase kinase 1\n", "MAP2K6 mitogen-activated protein kinase kinase 6\n", "BTG4 BTG anti-proliferation factor 4\n", "PRNP prion protein\n", "PROX1 prospero homeobox 1\n", "ALKBH4 alkB homolog 4, lysine demethylase\n", "DAB2IP DAB2 interacting protein\n", "BHLHE41 basic helix-loop-helix family member e41\n", "TET2 tet methylcytosine dioxygenase 2\n", "HMGN5 high mobility group nucleosome binding domain 5\n", "LGMN legumain\n", "HAUS6 HAUS augmin like complex subunit 6\n", "KLK10 kallikrein related peptidase 10\n", "NDE1 nudE neurodevelopment protein 1\n", "ERCC6L ERCC excision repair 6 like, spindle assembly checkpoint helicase\n", "SLC38A9 solute carrier family 38 member 9\n", "PSMA1 proteasome 20S subunit alpha 1\n", "PSMA2 proteasome 20S subunit alpha 2\n", "PSMA3 proteasome 20S subunit alpha 3\n", "PSMA4 proteasome 20S subunit alpha 4\n", "PSMA5 proteasome 20S subunit alpha 5\n", "PSMA6 proteasome 20S subunit alpha 6\n", "PSMA7 proteasome 20S subunit alpha 7\n", "PSMB1 proteasome 20S subunit beta 1\n", "PSMB2 proteasome 20S subunit beta 2\n", "PSMB3 proteasome 20S subunit beta 3\n", "PSMB4 proteasome 20S subunit beta 4\n", "PSMB5 proteasome 20S subunit beta 5\n", "PSMB6 proteasome 20S subunit beta 6\n", "PSMB7 proteasome 20S subunit beta 7\n", "PSMB8 proteasome 20S subunit beta 8\n", "FBXL12 F-box and leucine rich repeat protein 12\n", "PSMB9 proteasome 20S subunit beta 9\n", "PSMB10 proteasome 20S subunit beta 10\n", "PSMC1 proteasome 26S subunit, ATPase 1\n", "PSMC2 proteasome 26S subunit, ATPase 2\n", "PSMC3 proteasome 26S subunit, ATPase 3\n", "PSMC4 proteasome 26S subunit, ATPase 4\n", "PSMC5 proteasome 26S subunit, ATPase 5\n", "PSMC6 proteasome 26S subunit, ATPase 6\n", "PSMD1 proteasome 26S subunit, non-ATPase 1\n", "PSMD2 proteasome 26S subunit, non-ATPase 2\n", "PSMD3 proteasome 26S subunit, non-ATPase 3\n", "PSMD4 proteasome 26S subunit, non-ATPase 4\n", "PSMD5 proteasome 26S subunit, non-ATPase 5\n", "HAUS3 HAUS augmin like complex subunit 3\n", "PSMD7 proteasome 26S subunit, non-ATPase 7\n", "PSMD8 proteasome 26S subunit, non-ATPase 8\n", "PSMD10 proteasome 26S subunit, non-ATPase 10\n", "PSMD11 proteasome 26S subunit, non-ATPase 11\n", "PSMD9 proteasome 26S subunit, non-ATPase 9\n", "PSMD13 proteasome 26S subunit, non-ATPase 13\n", "PAGR1 PAXIP1 associated glutamate rich protein 1\n", "PSME1 proteasome activator subunit 1\n", "PSME2 proteasome activator subunit 2\n", "PSMD12 proteasome 26S subunit, non-ATPase 12\n", "CNTLN centlein\n", "PTCH1 patched 1\n", "PTEN phosphatase and tensin homolog\n", "BOD1L2 biorientation of chromosomes in cell division 1 like 2\n", "NSUN2 NOP2/Sun RNA methyltransferase 2\n", "NCAPG2 non-SMC condensin II complex subunit G2\n", "KLLN killin, p53 regulated DNA replication inhibitor\n", "PTGS2 prostaglandin-endoperoxide synthase 2\n", "TTC19 tetratricopeptide repeat domain 19\n", "PTK6 protein tyrosine kinase 6\n", "SPDL1 spindle apparatus coiled-coil protein 1\n", "CHTF8 chromosome transmission fidelity factor 8\n", "PTPN3 protein tyrosine phosphatase non-receptor type 3\n", "PTPN6 protein tyrosine phosphatase non-receptor type 6\n", "HAUS4 HAUS augmin like complex subunit 4\n", "PTPN11 protein tyrosine phosphatase non-receptor type 11\n", "CEP120 centrosomal protein 120\n", "PTPRC protein tyrosine phosphatase receptor type C\n", "PTPRK protein tyrosine phosphatase receptor type K\n", "TXNL4B thioredoxin like 4B\n", "RAD1 RAD1 checkpoint DNA exonuclease\n", "TIPIN TIMELESS interacting protein\n", "OR2A4 olfactory receptor family 2 subfamily A member 4\n", "BANP BTG3 associated nuclear protein\n", "MAMSTR MEF2 activating motif and SAP domain containing transcriptional regulator\n", "PINX1 PIN2 (TERF1) interacting telomerase inhibitor 1\n", "AURKAIP1 aurora kinase A interacting protein 1\n", "CDC73 cell division cycle 73\n", "LAMTOR1 late endosomal/lysosomal adaptor, MAPK and MTOR activator 1\n", "MUL1 mitochondrial E3 ubiquitin protein ligase 1\n", "CEP97 centrosomal protein 97\n", "PHIP pleckstrin homology domain interacting protein\n", "WDR62 WD repeat domain 62\n", "USP47 ubiquitin specific peptidase 47\n", "RAD9A RAD9 checkpoint clamp component A\n", "RAD17 RAD17 checkpoint clamp loader component\n", "RAD21 RAD21 cohesin complex component\n", "RAD23A RAD23 homolog A, nucleotide excision repair protein\n", "RAD51 RAD51 recombinase\n", "RAD51C RAD51 paralog C\n", "CCNJL cyclin J like\n", "RAD51B RAD51 paralog B\n", "RAD51D RAD51 paralog D\n", "RAD52 RAD52 homolog, DNA repair protein\n", "RNASEH2B ribonuclease H2 subunit B\n", "MOK MOK protein kinase\n", "RALA RAS like proto-oncogene A\n", "RALB RAS like proto-oncogene B\n", "RAN RAN, member RAS oncogene family\n", "RANBP1 RAN binding protein 1\n", "ZWILCH zwilch kinetochore protein\n", "EGLN3 egl-9 family hypoxia inducible factor 3\n", "RARA retinoic acid receptor alpha\n", "CHMP6 charged multivesicular body protein 6\n", "ASZ1 ankyrin repeat, SAM and basic leucine zipper domain containing 1\n", "MCPH1 microcephalin 1\n", "RASA1 RAS p21 protein activator 1\n", "RB1 RB transcriptional corepressor 1\n", "RBBP4 RB binding protein 4, chromatin remodeling factor\n", "IFT57 intraflagellar transport 57\n", "RBBP7 RB binding protein 7, chromatin remodeling factor\n", "RBBP8 RB binding protein 8, endonuclease\n", "RBL1 RB transcriptional corepressor like 1\n", "RBL2 RB transcriptional corepressor like 2\n", "SMC6 structural maintenance of chromosomes 6\n", "OPN1LW opsin 1, long wave sensitive\n", "RDX radixin\n", "MZT1 mitotic spindle organizing protein 1\n", "PIWIL2 piwi like RNA-mediated gene silencing 2\n", "CEP192 centrosomal protein 192\n", "C11orf80 chromosome 11 open reading frame 80\n", "UPF1 UPF1 RNA helicase and ATPase\n", "RFC1 replication factor C subunit 1\n", "RFC2 replication factor C subunit 2\n", "RFC3 replication factor C subunit 3\n", "RFC4 replication factor C subunit 4\n", "FIGN fidgetin, microtubule severing factor\n", "CHMP4B charged multivesicular body protein 4B\n", "RFC5 replication factor C subunit 5\n", "RFPL1 ret finger protein like 1\n", "HAUS2 HAUS augmin like complex subunit 2\n", "CDCA8 cell division cycle associated 8\n", "THAP1 THAP domain containing 1\n", "SUV39H2 suppressor of variegation 3-9 homolog 2\n", "RGS2 regulator of G protein signaling 2\n", "MSTO1 misato mitochondrial distribution and morphology regulator 1\n", "E2F8 E2F transcription factor 8\n", "RFWD3 ring finger and WD repeat domain 3\n", "RHEB Ras homolog, mTORC1 binding\n", "CEP55 centrosomal protein 55\n", "RING1 ring finger protein 1\n", "PRMT6 protein arginine methyltransferase 6\n", "RIF1 replication timing regulatory factor 1\n", "PBRM1 polybromo 1\n", "RNF2 ring finger protein 2\n", "APPL2 adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 2\n", "RNF4 ring finger protein 4\n", "ARL8B ADP ribosylation factor like GTPase 8B\n", "RCBTB1 RCC1 and BTB domain containing protein 1\n", "FANCI FA complementation group I\n", "FBXO31 F-box protein 31\n", "SNX18 sorting nexin 18\n", "EHMT1 euchromatic histone lysine methyltransferase 1\n", "STEAP3 STEAP3 metalloreductase\n", "ROBO1 roundabout guidance receptor 1\n", "ROCK1 Rho associated coiled-coil containing protein kinase 1\n", "SPRED1 sprouty related EVH1 domain containing 1\n", "YY1AP1 YY1 associated protein 1\n", "EIF2AK4 eukaryotic translation initiation factor 2 alpha kinase 4\n", "KDM8 lysine demethylase 8\n", "RPA1 replication protein A1\n", "RPA2 replication protein A2\n", "RPA3 replication protein A3\n", "CSPP1 centrosome and spindle pole associated protein 1\n", "NUDT15 nudix hydrolase 15\n", "NEK11 NIMA related kinase 11\n", "TUBAL3 tubulin alpha like 3\n", "BORA BORA aurora kinase A activator\n", "FBXW7 F-box and WD repeat domain containing 7\n", "CCNI2 cyclin I family member 2\n", "RPL24 ribosomal protein L24\n", "RPL26 ribosomal protein L26\n", "MAP9 microtubule associated protein 9\n", "SMIM22 small integral membrane protein 22\n", "MCMBP minichromosome maintenance complex binding protein\n", "MIS18BP1 MIS18 binding protein 1\n", "MNS1 meiosis specific nuclear structural 1\n", "EXD1 exonuclease 3'-5' domain containing 1\n", "FBXL8 F-box and leucine rich repeat protein 8\n", "ATAD5 ATPase family AAA domain containing 5\n", "RPS6 ribosomal protein S6\n", "RPS6KA1 ribosomal protein S6 kinase A1\n", "RPS6KA2 ribosomal protein S6 kinase A2\n", "RPS6KA3 ribosomal protein S6 kinase A3\n", "RPS6KB1 ribosomal protein S6 kinase B1\n", "HJURP Holliday junction recognition protein\n", "CCNP cyclin P\n", "RPS15A ribosomal protein S15a\n", "PIDD1 p53-induced death domain protein 1\n", "TUBA3E tubulin alpha 3e\n", "CEP76 centrosomal protein 76\n", "JADE1 jade family PHD finger 1\n", "MCM10 minichromosome maintenance 10 replication initiation factor\n", "RRM1 ribonucleotide reductase catalytic subunit M1\n", "RRM2 ribonucleotide reductase regulatory subunit M2\n", "RTKN rhotekin\n", "WDR76 WD repeat domain 76\n", "CLIP1 CAP-Gly domain containing linker protein 1\n", "DSN1 DSN1 component of MIS12 kinetochore complex\n", "SPRED3 sprouty related EVH1 domain containing 3\n", "RYR1 ryanodine receptor 1\n", "EHD2 EH domain containing 2\n", "ANKRD53 ankyrin repeat domain 53\n", "SORT1 sortilin 1\n", "PIK3R4 phosphoinositide-3-kinase regulatory subunit 4\n", "S100A8 S100 calcium binding protein A8\n", "S100A9 S100 calcium binding protein A9\n", "PHC3 polyhomeotic homolog 3\n", "STRADB STE20 related adaptor beta\n", "CAMK2N1 calcium/calmodulin dependent protein kinase II inhibitor 1\n", "FBXL18 F-box and leucine rich repeat protein 18\n", "MAPK12 mitogen-activated protein kinase 12\n", "CCL2 C-C motif chemokine ligand 2\n", "CCL8 C-C motif chemokine ligand 8\n", "TUBA4B tubulin alpha 4b\n", "SMPD3 sphingomyelin phosphodiesterase 3\n", "TRIM36 tripartite motif containing 36\n", "TERB2 telomere repeat binding bouquet formation protein 2\n", "TUBB8B tubulin beta 8B\n", "SDCBP syndecan binding protein\n", "RBM38 RNA binding motif protein 38\n", "VCPIP1 valosin containing protein interacting protein 1\n", "HAUS7 HAUS augmin like complex subunit 7\n", "ZNF703 zinc finger protein 703\n", "CNOT11 CCR4-NOT transcription complex subunit 11\n", "SETMAR SET domain and mariner transposase fusion gene\n", "SFPQ splicing factor proline and glutamine rich\n", "SFRP1 secreted frizzled related protein 1\n", "CENPT centromere protein T\n", "ATF5 activating transcription factor 5\n", "SRSF5 serine and arginine rich splicing factor 5\n", "PHLDA1 pleckstrin homology like domain family A member 1\n", "CTC1 CST telomere replication complex component 1\n", "DBF4B DBF4 zinc finger B\n", "MYO19 myosin XIX\n", "CEP290 centrosomal protein 290\n", "TUBB tubulin beta class I\n", "VASH1 vasohibin 1\n", "PRR5 proline rich 5\n", "C14orf39 chromosome 14 open reading frame 39\n", "SHH sonic hedgehog signaling molecule\n", "MUS81 MUS81 structure-specific endonuclease subunit\n", "SHOX2 short stature homeobox 2\n", "SIAH1 siah E3 ubiquitin protein ligase 1\n", "SIAH2 siah E3 ubiquitin protein ligase 2\n", "NLRP1 NLR family pyrin domain containing 1\n", "NAA50 N-alpha-acetyltransferase 50, NatE catalytic subunit\n", "STIL STIL centriolar assembly protein\n", "SIPA1 signal-induced proliferation-associated 1\n", "SIX1 SIX homeobox 1\n", "SIX3 SIX homeobox 3\n", "SKI SKI proto-oncogene\n", "SKIL SKI like proto-oncogene\n", "SKP1 S-phase kinase associated protein 1\n", "SKP2 S-phase kinase associated protein 2\n", "KHDC1L KH domain containing 1 like\n", "PRPF40A pre-mRNA processing factor 40 homolog A\n", "URGCP upregulator of cell proliferation\n", "CEP164 centrosomal protein 164\n", "CARD8 caspase recruitment domain family member 8\n", "CEP63 centrosomal protein 63\n", "SLC6A4 solute carrier family 6 member 4\n", "MAPRE1 microtubule associated protein RP/EB family member 1\n", "MAPRE3 microtubule associated protein RP/EB family member 3\n", "PLA2R1 phospholipase A2 receptor 1\n", "SIRT2 sirtuin 2\n", "YJU2 YJU2 splicing factor homolog\n", "CDK5RAP3 CDK5 regulatory subunit associated protein 3\n", "NDC1 NDC1 transmembrane nucleoporin\n", "SNW1 SNW domain containing 1\n", "SLC16A1 solute carrier family 16 member 1\n", "SLF2 SMC5-SMC6 complex localization factor 2\n", "P2RX2 purinergic receptor P2X 2\n", "TRIM32 tripartite motif containing 32\n", "CEP72 centrosomal protein 72\n", "INTS13 integrator complex subunit 13\n", "CHTF18 chromosome transmission fidelity factor 18\n", "EPC1 enhancer of polycomb homolog 1\n", "TPX2 TPX2 microtubule nucleation factor\n", "SNAI2 snail family transcriptional repressor 2\n", "CHFR checkpoint with forkhead and ring finger domains\n", "CEP70 centrosomal protein 70\n", "PAXIP1 PAX interacting protein 1\n", "CCAR1 cell division cycle and apoptosis regulator 1\n", "SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1\n", "NINL ninein like\n", "DMRTC2 DMRT like family C2\n", "CDK5RAP2 CDK5 regulatory subunit associated protein 2\n", "CEP295NL CEP295 N-terminal like\n", "SMARCD3 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 3\n", "SMO smoothened, frizzled class receptor\n", "CEP131 centrosomal protein 131\n", "CEP152 centrosomal protein 152\n", "SNAI1 snail family transcriptional repressor 1\n", "REEP4 receptor accessory protein 4\n", "PRR11 proline rich 11\n", "SNCA synuclein alpha\n", "CLSPN claspin\n", "KIF13A kinesin family member 13A\n", "RIOK2 RIO kinase 2\n", "CDCA5 cell division cycle associated 5\n", "CNOT1 CCR4-NOT transcription complex subunit 1\n", "TXLNG taxilin gamma\n", "FIGNL1 fidgetin like 1\n", "MYO16 myosin XVI\n", "PCID2 PCI domain containing 2\n", "KDM1A lysine demethylase 1A\n", "PIWIL3 piwi like RNA-mediated gene silencing 3\n", "SOD1 superoxide dismutase 1\n", "SON SON DNA and RNA binding protein\n", "MITD1 microtubule interacting and trafficking domain containing 1\n", "SOX2 SRY-box transcription factor 2\n", "SOX4 SRY-box transcription factor 4\n", "SOX9 SRY-box transcription factor 9\n", "PDS5B PDS5 cohesin associated factor B\n", "SOX11 SRY-box transcription factor 11\n", "SOX15 SRY-box transcription factor 15\n", "SP100 SP100 nuclear antigen\n", "WAPL WAPL cohesin release factor\n", "CENPJ centromere protein J\n", "DIS3L2 DIS3 like 3'-5' exoribonuclease 2\n", "SPAST spastin\n", "PPP2R2D protein phosphatase 2 regulatory subunit Bdelta\n", "TOPAZ1 testis and ovary specific PAZ domain containing 1\n", "TRIM35 tripartite motif containing 35\n", "ACTR10 actin related protein 10\n", "SPTBN1 spectrin beta, non-erythrocytic 1\n", "CDK19 cyclin dependent kinase 19\n", "SRC SRC proto-oncogene, non-receptor tyrosine kinase\n", "TSPYL2 TSPY like 2\n", "HDAC8 histone deacetylase 8\n", "PBK PDZ binding kinase\n", "PERP p53 apoptosis effector related to PMP22\n", "SRF serum response factor\n", "CUL9 cullin 9\n", "SRPK2 SRSF protein kinase 2\n", "TRIM21 tripartite motif containing 21\n", "CLASP2 cytoplasmic linker associated protein 2\n", "POGZ pogo transposable element derived with ZNF domain\n", "STOX1 storkhead box 1\n", "PHF8 PHD finger protein 8\n", "SMOC2 SPARC related modular calcium binding 2\n", "KMT2E lysine methyltransferase 2E (inactive)\n", "SMC5 structural maintenance of chromosomes 5\n", "NEDD1 NEDD1 gamma-tubulin ring complex targeting factor\n", "SSTR5 somatostatin receptor 5\n", "BIN3 bridging integrator 3\n", "ANKLE2 ankyrin repeat and LEM domain containing 2\n", "CENPK centromere protein K\n", "RCC2 regulator of chromosome condensation 2\n", "SEPTIN6 septin 6\n", "STAT3 signal transducer and activator of transcription 3\n", "STAT5B signal transducer and activator of transcription 5B\n", "RRAGC Ras related GTP binding C\n", "CCNY cyclin Y\n", "GJD4 gap junction protein delta 4\n", "TTLL12 tubulin tyrosine ligase like 12\n", "NEK4 NIMA related kinase 4\n", "ABRAXAS2 abraxas 2, BRISC complex subunit\n", "AURKA aurora kinase A\n", "LPIN1 lipin 1\n", "CDKL5 cyclin dependent kinase like 5\n", "CEP68 centrosomal protein 68\n", "STK10 serine/threonine kinase 10\n", "AURKC aurora kinase C\n", "STK11 serine/threonine kinase 11\n", "LIN37 lin-37 DREAM MuvB core complex component\n", "KLHL9 kelch like family member 9\n", "NCAPG non-SMC condensin I complex subunit G\n", "FBXL7 F-box and leucine rich repeat protein 7\n", "UBXN2B UBX domain protein 2B\n", "PSME4 proteasome activator subunit 4\n", "NSFL1C NSFL1 cofactor\n", "SULT1A3 sulfotransferase family 1A member 3\n", "RRS1 ribosome biogenesis regulator 1 homolog\n", "DPEP3 dipeptidase 3\n", "SUV39H1 suppressor of variegation 3-9 homolog 1\n", "SVIL supervillin\n", "SYCP1 synaptonemal complex protein 1\n", "PLCB1 phospholipase C beta 1\n", "PDS5A PDS5 cohesin associated factor A\n", "BOP1 BOP1 ribosomal biogenesis factor\n", "TACC1 transforming acidic coiled-coil containing protein 1\n", "ADAM17 ADAM metallopeptidase domain 17\n", "TAF1 TATA-box binding protein associated factor 1\n", "TAF2 TATA-box binding protein associated factor 2\n", "TAF6 TATA-box binding protein associated factor 6\n", "MLST8 MTOR associated protein, LST8 homolog\n", "TAF10 TATA-box binding protein associated factor 10\n", "MGA MAX dimerization protein MGA\n", "TAL1 TAL bHLH transcription factor 1, erythroid differentiation factor\n", "KLHL18 kelch like family member 18\n", "TBX1 T-box transcription factor 1\n", "TBCD tubulin folding cofactor D\n", "TBCE tubulin folding cofactor E\n", "FBXW11 F-box and WD repeat domain containing 11\n", "TBX2 T-box transcription factor 2\n", "UBR2 ubiquitin protein ligase E3 component n-recognin 2\n", "TBX3 T-box transcription factor 3\n", "NCAPD3 non-SMC condensin II complex subunit D3\n", "TCF7L2 transcription factor 7 like 2\n", "TENT4B terminal nucleotidyltransferase 4B\n", "USP22 ubiquitin specific peptidase 22\n", "TTC28 tetratricopeptide repeat domain 28\n", "CLASP1 cytoplasmic linker associated protein 1\n", "SUN1 Sad1 and UNC84 domain containing 1\n", "HAUS5 HAUS augmin like complex subunit 5\n", "RNF212 ring finger protein 212\n", "OBSL1 obscurin like cytoskeletal adaptor 1\n", "PPP1R13B protein phosphatase 1 regulatory subunit 13B\n", "ESX1 ESX homeobox 1\n", "DYNLT3 dynein light chain Tctex-type 3\n", "DYNLT1 dynein light chain Tctex-type 1\n", "RRP8 ribosomal RNA processing 8\n", "AZI2 5-azacytidine induced 2\n", "SPECC1L sperm antigen with calponin homology and coiled-coil domains 1 like\n", "MAU2 MAU2 sister chromatid cohesion factor\n", "TEX15 testis expressed 15, meiosis and synapsis associated\n", "TEX14 testis expressed 14, intercellular bridge forming factor\n", "TEX12 testis expressed 12\n", "TEX11 testis expressed 11\n", "TEP1 telomerase associated protein 1\n", "NCAPH non-SMC condensin I complex subunit H\n", "TERF2 telomeric repeat binding factor 2\n", "TDRD1 tudor domain containing 1\n", "TERT telomerase reverse transcriptase\n", "TERF1 telomeric repeat binding factor 1\n", "CTDNEP1 CTD nuclear envelope phosphatase 1\n", "TFAP4 transcription factor AP-4\n", "NR2F2 nuclear receptor subfamily 2 group F member 2\n", "TFDP1 transcription factor Dp-1\n", "SIRT1 sirtuin 1\n", "TFDP2 transcription factor Dp-2\n", "KHDC1 KH domain containing 1\n", "ITGB3BP integrin subunit beta 3 binding protein\n", "TGFA transforming growth factor alpha\n", "TGFB1 transforming growth factor beta 1\n", "TGFB2 transforming growth factor beta 2\n", "RYBP RING1 and YY1 binding protein\n", "TGFBR1 transforming growth factor beta receptor 1\n", "DDIAS DNA damage induced apoptosis suppressor\n", "TGM1 transglutaminase 1\n", "LMOD3 leiomodin 3\n", "TARDBP TAR DNA binding protein\n", "THBS1 thrombospondin 1\n", "TIMP2 TIMP metallopeptidase inhibitor 2\n", "NACC2 NACC family member 2\n", "CBX5 chromobox 5\n", "SUSD2 sushi domain containing 2\n", "BRD4 bromodomain containing 4\n", "PES1 pescadillo ribosomal biogenesis factor 1\n", "NR2E1 nuclear receptor subfamily 2 group E member 1\n", "MEPCE methylphosphate capping enzyme\n", "TAS1R2 taste 1 receptor member 2\n", "NANOS3 nanos C2HC-type zinc finger 3\n", "KIF4B kinesin family member 4B\n", "ZFYVE26 zinc finger FYVE-type containing 26\n", "TNF tumor necrosis factor\n", "TNFAIP3 TNF alpha induced protein 3\n", "SCRIB scribble planar cell polarity protein\n", "SUZ12 SUZ12 polycomb repressive complex 2 subunit\n", "MORC3 MORC family CW-type zinc finger 3\n", "PARD3 par-3 family cell polarity regulator\n", "SKA1 spindle and kinetochore associated complex subunit 1\n", "TOP2A DNA topoisomerase II alpha\n", "TOP2B DNA topoisomerase II beta\n", "TOP3A DNA topoisomerase III alpha\n", "TP53 tumor protein p53\n", "TP53BP1 tumor protein p53 binding protein 1\n", "TP53BP2 tumor protein p53 binding protein 2\n", "TP73 tumor protein p73\n", "TPD52L1 TPD52 like 1\n", "CDK20 cyclin dependent kinase 20\n", "TPR translocated promoter region, nuclear basket protein\n", "GTPBP4 GTP binding protein 4\n", "TRAF2 TNF receptor associated factor 2\n", "METTL3 methyltransferase like 3\n", "CASP14 caspase 14\n", "CCNDBP1 cyclin D1 binding protein 1\n", "ORC6 origin recognition complex subunit 6\n", "ORC3 origin recognition complex subunit 3\n", "CD2AP CD2 associated protein\n", "STXBP4 syntaxin binding protein 4\n", "PHLDA3 pleckstrin homology like domain family A member 3\n", "SPO11 SPO11 initiator of meiotic double stranded breaks\n", "TSC1 TSC complex subunit 1\n", "TSC2 TSC complex subunit 2\n", "TSG101 tumor susceptibility 101\n", "NUP62 nucleoporin 62\n", "HEPACAM2 HEPACAM family member 2\n", "RABGAP1 RAB GTPase activating protein 1\n", "PPP1R15A protein phosphatase 1 regulatory subunit 15A\n", "POLA2 DNA polymerase alpha 2, accessory subunit\n", "TTK TTK protein kinase\n", "TTN titin\n", "TUBA4A tubulin alpha 4a\n", "TUBB2A tubulin beta 2A class IIa\n", "TUBG1 tubulin gamma 1\n", "CASP12 caspase 12 (gene/pseudogene)\n", "HIRA histone cell cycle regulator\n", "TWIST1 twist family bHLH transcription factor 1\n", "TYMS thymidylate synthetase\n", "TUBB1 tubulin beta 1 class VI\n", "HEPACAM hepatic and glial cell adhesion molecule\n", "UBB ubiquitin B\n", "UBE2B ubiquitin conjugating enzyme E2 B\n", "UBE2D1 ubiquitin conjugating enzyme E2 D1\n", "RPRM reprimo, TP53 dependent G2 arrest mediator homolog\n", "GSPT2 G1 to S phase transition 2\n", "UBE2E2 ubiquitin conjugating enzyme E2 E2\n", "UBE2E1 ubiquitin conjugating enzyme E2 E1\n", "UBE2I ubiquitin conjugating enzyme E2 I\n", "UBE2L3 ubiquitin conjugating enzyme E2 L3\n", "ANAPC1 anaphase promoting complex subunit 1\n", "EID1 EP300 interacting inhibitor of differentiation 1\n", "SPIN2B spindlin family member 2B\n", "BCL2L13 BCL2 like 13\n", "MTCH1 mitochondrial carrier 1\n", "TAF1L TATA-box binding protein associated factor 1 like\n", "UVRAG UV radiation resistance associated\n", "SMYD3 SET and MYND domain containing 3\n", "TEX19 testis expressed 19\n", "VCP valosin containing protein\n", "TOM1L2 target of myb1 like 2 membrane trafficking protein\n", "AEN apoptosis enhancing nuclease\n", "VRK1 VRK serine/threonine kinase 1\n", "CEP85 centrosomal protein 85\n", "CYP26B1 cytochrome P450 family 26 subfamily B member 1\n", "DIABLO diablo IAP-binding mitochondrial protein\n", "WEE1 WEE1 G2 checkpoint kinase\n", "ASAH2 N-acylsphingosine amidohydrolase 2\n", "WNT5A Wnt family member 5A\n", "WNT10B Wnt family member 10B\n", "WNT9A Wnt family member 9A\n", "WRN WRN RecQ like helicase\n", "XBP1 X-box binding protein 1\n", "BCCIP BRCA2 and CDKN1A interacting protein\n", "XDH xanthine dehydrogenase\n", "MARCHF7 membrane associated ring-CH-type finger 7\n", "POLE4 DNA polymerase epsilon 4, accessory subunit\n", "XK X-linked Kx blood group\n", "YTHDC2 YTH domain containing 2\n", "XPC XPC complex subunit, DNA damage recognition and repair factor\n", "NIBAN2 niban apoptosis regulator 2\n", "DCLRE1B DNA cross-link repair 1B\n", "XRCC1 X-ray repair cross complementing 1\n", "XRCC2 X-ray repair cross complementing 2\n", "XRCC3 X-ray repair cross complementing 3\n", "NABP1 nucleic acid binding protein 1\n", "XPO1 exportin 1\n", "NSMCE2 NSE2 (MMS21) homolog, SMC5-SMC6 complex SUMO ligase\n", "YWHAE tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon\n", "YWHAG tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma\n", "OPN1MW2 opsin 1, medium wave sensitive 2\n", "ZNF16 zinc finger protein 16\n", "FBXO43 F-box protein 43\n", "FMN2 formin 2\n", "MRPL41 mitochondrial ribosomal protein L41\n", "FAM9A family with sequence similarity 9 member A\n", "FAM9B family with sequence similarity 9 member B\n", "FAM9C family with sequence similarity 9 member C\n", "KIF18B kinesin family member 18B\n", "EME1 essential meiotic structure-specific endonuclease 1\n", "PCGF2 polycomb group ring finger 2\n", "ZBTB17 zinc finger and BTB domain containing 17\n", "TDRD9 tudor domain containing 9\n", "CDK15 cyclin dependent kinase 15\n", "RNF112 ring finger protein 112\n", "MDM1 Mdm1 nuclear protein\n", "TCIM transcriptional and immune response regulator\n", "MEIKIN meiotic kinetochore factor\n", "CDK11A cyclin dependent kinase 11A\n", "KIF4A kinesin family member 4A\n", "SPIRE1 spire type actin nucleation factor 1\n", "ZNF207 zinc finger protein 207\n", "SMARCAD1 SWI/SNF-related, matrix-associated actin-dependent regulator of chromatin, subfamily a, containing DEAD/H box 1\n", "ZNF318 zinc finger protein 318\n", "TIPRL TOR signaling pathway regulator\n", "KCTD11 potassium channel tetramerization domain containing 11\n", "INTS3 integrator complex subunit 3\n", "SMYD2 SET and MYND domain containing 2\n", "PTP4A1 protein tyrosine phosphatase 4A1\n", "EVI5 ecotropic viral integration site 5\n", "GDPD5 glycerophosphodiester phosphodiesterase domain containing 5\n", "PRDM9 PR/SET domain 9\n", "PRDM11 PR/SET domain 11\n", "EFHC1 EF-hand domain containing 1\n", "BTG2 BTG anti-proliferation factor 2\n", "PSMG2 proteasome assembly chaperone 2\n", "NDEL1 nudE neurodevelopment protein 1 like 1\n", "ALMS1 ALMS1 centrosome and basal body associated protein\n", "KIF15 kinesin family member 15\n", "RSPH1 radial spoke head component 1\n", "TUBA1A tubulin alpha 1a\n", "LMLN leishmanolysin like peptidase\n", "CCNL1 cyclin L1\n", "PDXP pyridoxal phosphatase\n", "FAM83D family with sequence similarity 83 member D\n", "CAB39L calcium binding protein 39 like\n", "CDT1 chromatin licensing and DNA replication factor 1\n", "SHCBP1L SHC binding and spindle associated 1 like\n", "NLRP2B NLR family pyrin domain containing 2B\n", "PCBP4 poly(rC) binding protein 4\n", "BAG6 BAG cochaperone 6\n", "DDX39B DExD-box helicase 39B\n", "PCNP PEST proteolytic signal containing nuclear protein\n", "CCNL2 cyclin L2\n", "FZD3 frizzled class receptor 3\n", "CHMP1B charged multivesicular body protein 1B\n", "KAT6A lysine acetyltransferase 6A\n", "NR4A3 nuclear receptor subfamily 4 group A member 3\n", "GJC2 gap junction protein gamma 2\n", "PSMB11 proteasome subunit beta 11\n", "NUP214 nucleoporin 214\n", "ZMIZ1 zinc finger MIZ-type containing 1\n", "SAPCD2 suppressor APC domain containing 2\n", "SELENON selenoprotein N\n", "REEP3 receptor accessory protein 3\n", "NLRP3 NLR family pyrin domain containing 3\n", "FOSL1 FOS like 1, AP-1 transcription factor subunit\n", "CUL5 cullin 5\n", "M1AP meiosis 1 associated protein\n", "AAAS aladin WD repeat nucleoporin\n", "YEATS4 YEATS domain containing 4\n", "HMGA2 high mobility group AT-hook 2\n", "CDK2AP1 cyclin dependent kinase 2 associated protein 1\n", "TMEM14B transmembrane protein 14B\n", "MED25 mediator complex subunit 25\n", "SASS6 SAS-6 centriolar assembly protein\n", "SPDYA speedy/RINGO cell cycle regulator family member A\n", "LACRT lacritin\n", "SKA3 spindle and kinetochore associated complex subunit 3\n", "KAT14 lysine acetyltransferase 14\n" ] } ], "source": [ "for geneid in geneids: # geneids associated with cell-cycle\n", " nt = GENEID2NT.get(geneid, None)\n", " if nt is not None:\n", " print(\"{Symbol:<10} {desc}\".format(\n", " Symbol = nt.Symbol, \n", " desc = nt.description))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2016-present, DV Klopfenstein, Haibao Tang. All rights reserved." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-2-clause

AkshanshChahal/BTP

Baseline 2.ipynb

1

18442

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Establishing a Baseline for the Problem\n", "## Using variety of regression algorithms (non linear)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", "from math import sqrt\n", "\n", "import pprint\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn import metrics\n", "\n", "from sklearn.svm import SVR\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>State_Name</th>\n", " <th>ind_district</th>\n", " <th>Crop_Year</th>\n", " <th>Season</th>\n", " <th>Crop</th>\n", " <th>Area</th>\n", " <th>Production</th>\n", " <th>phosphorus</th>\n", " <th>X1</th>\n", " <th>X2</th>\n", " <th>X3</th>\n", " <th>X4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>15</td>\n", " <td>Andhra Pradesh</td>\n", " <td>anantapur</td>\n", " <td>1999</td>\n", " <td>kharif</td>\n", " <td>Rice</td>\n", " <td>37991.0</td>\n", " <td>105082.0</td>\n", " <td>0.0</td>\n", " <td>96800.0</td>\n", " <td>75400.0</td>\n", " <td>643.720</td>\n", " <td>881.473</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>16</td>\n", " <td>Andhra Pradesh</td>\n", " <td>anantapur</td>\n", " <td>2000</td>\n", " <td>kharif</td>\n", " <td>Rice</td>\n", " <td>39905.0</td>\n", " <td>117680.0</td>\n", " <td>0.0</td>\n", " <td>105082.0</td>\n", " <td>96800.0</td>\n", " <td>767.351</td>\n", " <td>643.720</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>17</td>\n", " <td>Andhra Pradesh</td>\n", " <td>anantapur</td>\n", " <td>2001</td>\n", " <td>kharif</td>\n", " <td>Rice</td>\n", " <td>32878.0</td>\n", " <td>95609.0</td>\n", " <td>0.0</td>\n", " <td>117680.0</td>\n", " <td>105082.0</td>\n", " <td>579.338</td>\n", " <td>767.351</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>18</td>\n", " <td>Andhra Pradesh</td>\n", " <td>anantapur</td>\n", " <td>2002</td>\n", " <td>kharif</td>\n", " <td>Rice</td>\n", " <td>29066.0</td>\n", " <td>66329.0</td>\n", " <td>0.0</td>\n", " <td>95609.0</td>\n", " <td>117680.0</td>\n", " <td>540.070</td>\n", " <td>579.338</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>21</td>\n", " <td>Andhra Pradesh</td>\n", " <td>anantapur</td>\n", " <td>2005</td>\n", " <td>kharif</td>\n", " <td>Rice</td>\n", " <td>25008.0</td>\n", " <td>69972.0</td>\n", " <td>0.0</td>\n", " <td>85051.0</td>\n", " <td>44891.0</td>\n", " <td>819.700</td>\n", " <td>564.500</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 State_Name ind_district Crop_Year Season Crop Area \\\n", "0 15 Andhra Pradesh anantapur 1999 kharif Rice 37991.0 \n", "1 16 Andhra Pradesh anantapur 2000 kharif Rice 39905.0 \n", "2 17 Andhra Pradesh anantapur 2001 kharif Rice 32878.0 \n", "3 18 Andhra Pradesh anantapur 2002 kharif Rice 29066.0 \n", "4 21 Andhra Pradesh anantapur 2005 kharif Rice 25008.0 \n", "\n", " Production phosphorus X1 X2 X3 X4 \n", "0 105082.0 0.0 96800.0 75400.0 643.720 881.473 \n", "1 117680.0 0.0 105082.0 96800.0 767.351 643.720 \n", "2 95609.0 0.0 117680.0 105082.0 579.338 767.351 \n", "3 66329.0 0.0 95609.0 117680.0 540.070 579.338 \n", "4 69972.0 0.0 85051.0 44891.0 819.700 564.500 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing the dataset we prepared and saved using Baseline 1 Notebook\n", "ricep = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/BTP/ricep.csv\")\n", "ricep.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ricep = ricep.drop([\"Unnamed: 0\"],axis=1)\n", "ricep[\"phosphorus\"] = ricep[\"phosphorus\"]*10" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ricep[\"value\"] = ricep[\"Production\"]/ricep[\"Area\"]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X = ricep[[\"X1\",\"X2\",\"X3\",\"X4\",\"phosphorus\"]]\n", "y = ricep[[\"value\"]]*1000" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \n" ] } ], "source": [ "# Z-Score Normalization OR try using the sklearn internal normalizing by setting mormalize flag = true !!!\n", "\n", "cols = list(X.columns)\n", "for col in cols:\n", " col_zscore = col + '_zscore'\n", " X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>X1_zscore</th>\n", " <th>X2_zscore</th>\n", " <th>X3_zscore</th>\n", " <th>X4_zscore</th>\n", " <th>phosphorus_zscore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-0.285176</td>\n", " <td>-0.374714</td>\n", " <td>-0.457800</td>\n", " <td>0.021735</td>\n", " <td>-0.837691</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.247120</td>\n", " <td>-0.276111</td>\n", " <td>-0.198113</td>\n", " <td>-0.496827</td>\n", " <td>-0.837691</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.189232</td>\n", " <td>-0.237950</td>\n", " <td>-0.593035</td>\n", " <td>-0.227176</td>\n", " <td>-0.837691</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-0.290648</td>\n", " <td>-0.179903</td>\n", " <td>-0.675518</td>\n", " <td>-0.637250</td>\n", " <td>-0.837691</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-0.339162</td>\n", " <td>-0.515288</td>\n", " <td>-0.088153</td>\n", " <td>-0.669613</td>\n", " <td>-0.837691</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " X1_zscore X2_zscore X3_zscore X4_zscore phosphorus_zscore\n", "0 -0.285176 -0.374714 -0.457800 0.021735 -0.837691\n", "1 -0.247120 -0.276111 -0.198113 -0.496827 -0.837691\n", "2 -0.189232 -0.237950 -0.593035 -0.227176 -0.837691\n", "3 -0.290648 -0.179903 -0.675518 -0.637250 -0.837691\n", "4 -0.339162 -0.515288 -0.088153 -0.669613 -0.837691" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_ = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\", \"phosphorus_zscore\"]]\n", "X_.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X_, y, test_size=0.2, random_state=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First checking the avg RMSE for Linear Regression" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1030.92314374 1109.37929379 972.36266895 1487.52744177 491.48595541]\n", "\n", "\n", "Avg RMSE is 1018.33570073\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", " warnings.warn(mesg, RuntimeWarning)\n" ] } ], "source": [ "clf = LinearRegression()\n", "scores = cross_val_score(clf, X_, y, cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])\n", " \n", "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Epsilon-Support Vector Regression (SVR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RBF Kernel" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# 5 Fold CV, to calculate avg RMSE\n", "clf = SVR(C=500000.0, epsilon=0.1, kernel='rbf', gamma=0.0008)\n", "scores = cross_val_score(clf, X_, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 904.09013921 940.99998887 981.97853142 1616.00179024 568.93419484]\n", "\n", "\n", "Avg RMSE is 1002.40092892\n" ] } ], "source": [ "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Just the 4 original features (no soil data)\n", "X_old = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\"]]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 903.93008696 753.88394413 765.69751566 1574.251674 636.95214188]\n", "\n", "\n", "Avg RMSE is 926.943072526\n" ] } ], "source": [ "# 5 Fold CV, to calculate avg RMSE\n", "clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)\n", "scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])\n", "\n", "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### SVR : 927\n", "#### LR : 1018\n", "#### SVR (RBF kernel) works better than Linear Regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Also, the soil feature, for now, does more harm than good (Phosphorous content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lets check the importance of Rain Data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Just 2 features (no rain data)\n", "X_nr = X[[\"X1_zscore\", \"X2_zscore\"]]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1039.57563055 863.77364865 944.40471 1492.31174906 672.96822263]\n", "\n", "\n", "Avg RMSE is 1002.60679218\n" ] } ], "source": [ "# 5 Fold CV, to calculate avg RMSE\n", "clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)\n", "scores = cross_val_score(clf, X_nr, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])\n", "\n", "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Rain data does helps us" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lets try for SVR with other kernels ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Degree 3 Polynomial" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 906.20976415 837.77643762 1049.76326739 1568.88777167 504.49443066]\n", "\n", "\n", "Avg RMSE is 973.426334297\n" ] } ], "source": [ "# 5 Fold CV, to calculate avg RMSE\n", "clf = SVR(kernel='poly', gamma='auto', degree=3, coef0=2)\n", "scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])\n", " \n", "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Polynomial Kernel also does better than Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Degree 4 Polynomial" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 907.10874357 787.20784909 848.64917648 1570.06140194 557.83575489]\n", "\n", "\n", "Avg RMSE is 934.172585194\n" ] } ], "source": [ "# 5 Fold CV, to calculate avg RMSE\n", "clf = SVR(kernel='poly', gamma='auto', degree=4, coef0=2)\n", "scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\n", "for i in range(0,5):\n", " scores[i] = sqrt(-1*scores[i])\n", " \n", "print(scores)\n", "avg_rmse = scores.mean()\n", "print(\"\\n\\nAvg RMSE is \",scores.mean())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

rbiswas4/Cadence

LSSTmetrics/readingLightCurves.ipynb

1

185222

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sncosmo\n", "import analyzeSN as ans\n", "from analyzeSN import LightCurve" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lc_field309_mjd_49923.ascii lc_field744_mjd_49923.ascii\r\n" ] } ], "source": [ "!ls *.ascii" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fname = 'lc_field744_mjd_49923.ascii'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lcdf = pd.read_csv(fname, delim_whitespace=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>time</th>\n", " <th>band</th>\n", " <th>flux</th>\n", " <th>fluxerr</th>\n", " <th>zp</th>\n", " <th>zpsys</th>\n", " <th>SNR</th>\n", " <th>finSeeing</th>\n", " <th>airmass</th>\n", " <th>filtSkyBrightness</th>\n", " <th>fiveSigmaDepth</th>\n", " <th>propID</th>\n", " <th>night</th>\n", " <th>DetectionEfficiency</th>\n", " <th>modelFlux</th>\n", " <th>deviation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>416373</td>\n", " <td>49921.234141</td>\n", " <td>r</td>\n", " <td>8.922394e-10</td>\n", " <td>3.173156e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.118357</td>\n", " <td>0.705636</td>\n", " <td>1.286271</td>\n", " <td>21.094100</td>\n", " <td>24.579716</td>\n", " <td>366</td>\n", " <td>568</td>\n", " <td>0.987746</td>\n", " <td>8.922394e-10</td>\n", " <td>1.764052</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>416372</td>\n", " <td>49921.233713</td>\n", " <td>r</td>\n", " <td>8.922386e-10</td>\n", " <td>3.177184e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.082686</td>\n", " <td>0.706165</td>\n", " <td>1.288225</td>\n", " <td>21.092869</td>\n", " <td>24.578092</td>\n", " <td>366</td>\n", " <td>568</td>\n", " <td>0.987726</td>\n", " <td>8.922386e-10</td>\n", " <td>0.400157</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>416371</td>\n", " <td>49921.233285</td>\n", " <td>r</td>\n", " <td>8.922378e-10</td>\n", " <td>3.181244e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.046824</td>\n", " <td>0.706698</td>\n", " <td>1.290191</td>\n", " <td>21.091633</td>\n", " <td>24.576458</td>\n", " <td>366</td>\n", " <td>568</td>\n", " <td>0.987706</td>\n", " <td>8.922378e-10</td>\n", " <td>0.978738</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>416370</td>\n", " <td>49921.232857</td>\n", " <td>r</td>\n", " <td>8.922370e-10</td>\n", " <td>3.185325e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.010859</td>\n", " <td>0.707233</td>\n", " <td>1.292167</td>\n", " <td>21.090392</td>\n", " <td>24.574818</td>\n", " <td>366</td>\n", " <td>568</td>\n", " <td>0.987686</td>\n", " <td>8.922370e-10</td>\n", " <td>2.240893</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>416369</td>\n", " <td>49921.232428</td>\n", " <td>r</td>\n", " <td>8.922362e-10</td>\n", " <td>3.189429e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>27.974791</td>\n", " <td>0.707771</td>\n", " <td>1.294155</td>\n", " <td>21.089148</td>\n", " <td>24.573172</td>\n", " <td>366</td>\n", " <td>568</td>\n", " <td>0.987666</td>\n", " <td>8.922362e-10</td>\n", " <td>1.867558</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index time band flux fluxerr zp zpsys \\\n", "0 416373 49921.234141 r 8.922394e-10 3.173156e-11 0.0 ab \n", "1 416372 49921.233713 r 8.922386e-10 3.177184e-11 0.0 ab \n", "2 416371 49921.233285 r 8.922378e-10 3.181244e-11 0.0 ab \n", "3 416370 49921.232857 r 8.922370e-10 3.185325e-11 0.0 ab \n", "4 416369 49921.232428 r 8.922362e-10 3.189429e-11 0.0 ab \n", "\n", " SNR finSeeing airmass filtSkyBrightness fiveSigmaDepth propID \\\n", "0 28.118357 0.705636 1.286271 21.094100 24.579716 366 \n", "1 28.082686 0.706165 1.288225 21.092869 24.578092 366 \n", "2 28.046824 0.706698 1.290191 21.091633 24.576458 366 \n", "3 28.010859 0.707233 1.292167 21.090392 24.574818 366 \n", "4 27.974791 0.707771 1.294155 21.089148 24.573172 366 \n", "\n", " night DetectionEfficiency modelFlux deviation \n", "0 568 0.987746 8.922394e-10 1.764052 \n", "1 568 0.987726 8.922386e-10 0.400157 \n", "2 568 0.987706 8.922378e-10 0.978738 \n", "3 568 0.987686 8.922370e-10 2.240893 \n", "4 568 0.987666 8.922362e-10 1.867558 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lcdf.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "banddict = dict((x, 'lsst_' + x) for x in 'ugrizy')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lightcurve = LightCurve(lcdf[['time', 'flux', 'band', 'fluxerr','zp', 'zpsys', 'SNR']], bandNameDict=banddict)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mjd</th>\n", " <th>flux</th>\n", " <th>band</th>\n", " <th>fluxerr</th>\n", " <th>zp</th>\n", " <th>zpsys</th>\n", " <th>SNR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>49921.234141</td>\n", " <td>8.922394e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.173156e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.118357</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>49921.233713</td>\n", " <td>8.922386e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.177184e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.082686</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>49921.233285</td>\n", " <td>8.922378e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.181244e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.046824</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>49921.232857</td>\n", " <td>8.922370e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.185325e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.010859</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49921.232428</td>\n", " <td>8.922362e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.189429e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>27.974791</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mjd flux band fluxerr zp zpsys SNR\n", "0 49921.234141 8.922394e-10 lsst_r 3.173156e-11 0.0 ab 28.118357\n", "1 49921.233713 8.922386e-10 lsst_r 3.177184e-11 0.0 ab 28.082686\n", "2 49921.233285 8.922378e-10 lsst_r 3.181244e-11 0.0 ab 28.046824\n", "3 49921.232857 8.922370e-10 lsst_r 3.185325e-11 0.0 ab 28.010859\n", "4 49921.232428 8.922362e-10 lsst_r 3.189429e-11 0.0 ab 27.974791" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lightcurve.lightCurve.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mjd</th>\n", " <th>flux</th>\n", " <th>band</th>\n", " <th>fluxerr</th>\n", " <th>zp</th>\n", " <th>zpsys</th>\n", " <th>SNR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>49921.234141</td>\n", " <td>8.922394e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.173156e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.118357</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>49921.233713</td>\n", " <td>8.922386e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.177184e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.082686</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>49921.233285</td>\n", " <td>8.922378e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.181244e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.046824</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>49921.232857</td>\n", " <td>8.922370e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.185325e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>28.010859</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>49921.232428</td>\n", " <td>8.922362e-10</td>\n", " <td>lsst_r</td>\n", " <td>3.189429e-11</td>\n", " <td>0.0</td>\n", " <td>ab</td>\n", " <td>27.974791</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mjd flux band fluxerr zp zpsys SNR\n", "0 49921.234141 8.922394e-10 lsst_r 3.173156e-11 0.0 ab 28.118357\n", "1 49921.233713 8.922386e-10 lsst_r 3.177184e-11 0.0 ab 28.082686\n", "2 49921.233285 8.922378e-10 lsst_r 3.181244e-11 0.0 ab 28.046824\n", "3 49921.232857 8.922370e-10 lsst_r 3.185325e-11 0.0 ab 28.010859\n", "4 49921.232428 8.922362e-10 lsst_r 3.189429e-11 0.0 ab 27.974791" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lightcurve.coaddedLC().head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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blX0EaALKO2kzHYhMrylTpkRXpkyZkvEaCxcuzNh+1apVGdsDsWrVqozXWLhw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\n5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"text/plain": [ "<matplotlib.figure.Figure at 0x10b0e2390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = sncosmo.plot_lc(lightcurve.snCosmoLC(), color='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to plot the model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lsst.sims.catUtils.supernovae import SNObject" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astropy.cosmology import FlatLambdaCDM\n", "cosmo = FlatLambdaCDM(H0=73., Om0=0.25)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "snobject = SNObject(ra=0., dec=np.degrees(0.794553))\n", "paramDict = dict(x1=0., z=0.5, c=0., t0=49923.)\n", "mwebv = snobject.ebvofMW\n", "#snobject.set_MWebv(0.)\n", "snobject.set(**paramDict)\n", "snobject.set_source_peakabsmag(-19.3, 'BessellB', 'AB')\n", "snobject.set_MWebv(0.)\n", "sncosmoModel = snobject.equivalentSNCosmoModel()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Model at 0x10f54c710>\n", "source:\n", " class : SALT2Source\n", " name : 'salt2-extended'\n", " version : 1.0\n", " phases : [-20, .., 50] days\n", " wavelengths: [300, .., 18000] Angstroms\n", "effect (name='host' frame='rest'):\n", " class : OD94Dust\n", " wavelength range: [909.09, 33333.3] Angstroms\n", "effect (name='mw' frame='obs'):\n", " class : OD94Dust\n", " wavelength range: [909.09, 33333.3] Angstroms\n", "parameters:\n", " z = 0.5\n", " t0 = 49923.0\n", " x0 = 1.0068661711630977e-05\n", " x1 = 0.0\n", " c = 0.0\n", " hostebv = 0.0\n", " hostr_v = 3.1000000000000001\n", " mwebv = 0.0\n", " mwr_v = 3.1000000000000001\n" ] } ], "source": [ "print sncosmoModel" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2eR8CFmfNT2sfEcdljqnLvDNJ+5AMa90nIpaQDKW5jiTb0L1VnKsyxTInzcys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\nfvbv31+u0RG5b+/eveHnocfr168PP4/cH5r+tzaaNWtGs2bNaN68Oc2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/xmfVqlWMGTMm\n0WGEpFz9XNn/TDL8n6TyXYva/M7j/b7U9HqV7Z84cWLS3iEISbW/nWRTl/o5Kg2Cyrj7v8zsNuBZ\n4LhYvU68uXuxmS0B8oHZAGZmwee/q+Kwt4FzK6R9N5hepV69enHyySc3LGDBzKjNnbDp06fr9x1j\nu3fv1hgCiZlUrJ8r1l9mlvB6Kjc3t9zzwYMHN7r+4JH1UMW6pza/82R4XyJfb/fu3Uk/hiAyRo0h\nSD4xaxAElRBYDj7VPAhMD37whKa1ywamA5jZPUAXdw/NZT0VGBe8bf0HAh9OFwLnxTnutLRr165y\nFWVkRfSXv/yFuXPnMnz48KSrPFOVvvhLjKVU/RxZfyXLl6iKF1gqu+DSoUMHioqKaNq0KVu3HjLb\na0JVvCjRunXrcvVSbX7n8X5fKl5MqaweTYa/jZo0hhjTVbQGFRdUTAI6AzcA/4jGayQTd3/RzDoA\ndxG4tbwMGObuW4JZ8oDDI/J/aWbDCcxa8WPga+Bqd684s4XESFWV0AUXXKCGgEgKScX6Odm+RFV2\ndTxSqDEAUFRURIcOHZKuUVCT2vzO4/2+6GKKxFK07hD8b4XnDmwB/g7cHKXXSCruPoXAQjaV7buq\nkrRFBKbDExGRGFL9HFs1XR0PNQaqei4iySdag4ozQo/NLCOYVhaNc4uIiEhyicbV8UR1hapN9xuR\ndJNRc5baMbOrzWwFcAA4YGYrzOxH0Tp/sjCztmb2nJntMrMdZvakmeXUcMwfzayswvbXeMUsIpIO\nVD8nh7Fjx1b7HMovouXuhwxUjrXdu3eHNxGJ3hiCu4CfAA/z75kZBgCTzay7u/8iGq+TJJ4n0C81\nH2hKYKDa48BlNRz3KnAlgfEVAFVOZyciIvWi+jkJTJoUWNIhNGFD6Hmk2gxMFpH4idYYgrHAGHef\nEZE228yWE2gkpESDwMyOA4YRWODhg2DajcBcM7vF3aua5xrgYMSgNhERiSLVz8ll0qRJlTYERCQ5\nRavLUBbwfiXpS4j91KbxNADYEfqwCXqdwCDqM2o4doiZbTKzT81sipmlzOrNIiJJQPVzIzJ9+vRq\nn4tIfEXry/ozBO4S/KRC+jXAc1F6jWSQB2yOTHD3UjPbHtxXlVeBPwNfAEcD9wB/NbMBrvukIiLR\noPq5EQlN96x1YESSQzSv3l9tZt8F3gk+PwPoDjxtZg+GMrl7xUZDwgUXqvlZNVkc6F3f87v7ixFP\nPzazj4DPgSHAgqqOu+222w4ZaHXhhRcyYsSI+oYiIlJvM2fOZNasWeXSYj07jOrn1KV1YESip6H1\ns0XjAoiZVVlpVuDuPrTBLxhlZtYeaF9DtjXA5cD/uHs4r5llAoXAhe7+ch1eczPw/9x9WiX7TgWW\nLFq0KOFL1IuIVGfZsmUMGjQIAn33l0b7/KqfRUTqpy71c7TWITg7GudJFHffBmyrKZ+ZvQ20MbNT\nIvqp5hOYmeLd2r6emXUj8AH3TT3CFRFJG6qfRURiL2rrEKQDd/8UmAdMM7PTzGwggVmUZkTOYBEc\nmPYfwcc5ZnafmZ1hZj3MLJ/Ays6rg+cSEZEGUv0sIlJ/ahDU3SXApwRmr5gDLAKurZCnJxDqXFoK\n9AVeBlYB04B/AoPcvTgeAYuIpAnVzyIi9ZBKU4LGhbvvpIZFbtw9M+JxIfC9WMclIpLuVD+LiNSP\n7hDIIWbOnJnoEGIu1cuY6uUDlVHSUzr8TaR6GVO9fKAyNkZqENSRmd1uZv8ws33B+a1re9xdZrbB\nzPab2XwzOyaWcTZExWmrUlGqlzHVywcqoxxK9XNqSPUypnr5QGVsjNQgqLss4EXgsdoeYGY/A24g\nsFDb6cA+YJ6ZNY1JhCIi6Un1s4hIPWgMQR25+y8BzGxUHQ4bD9zt7nOCx14BbAJ+QODDS0REGkj1\ns4hI/egOQYyZ2ZFAHvBGKM3ddxOYF3tAouISEUl3qp9FRAJ0hyD28gAncMUp0qbgvso0B1i1alUM\nw6rarl27WLZsWUJeO15SvYypXj5QGZNFRD3VPJFx1JPq5ySU6mVM9fKBypgs6lQ/u3vab8A9QFk1\nWylwbIVjRgHba3HuAcHjO1VIf4HAgjmVHXMJgQ8pbdq0aWss2yWqn7Vp06YtKbca62fdIQj4H+CP\nNeRZU89zbwQM6ET5q1CdgA+qOGYecCnwJVBYz9cVEYmH5sARxG5lX9XPIiL1U+v6WQ0CwN23Adti\ndO4vzGwjkA8sBzCz1sAZwKPVxPN8LOIREYmBxbE6sepnEZEGqVX9rEHFdWRmh5vZSUAPINPMTgpu\nORF5PjWz/4g47LfAHWb2fTM7EXga+Bp4Oa7Bi4ikMNXPIiL1ozsEdXcXcEXE86XBn2cDi4KPewK5\noQzufp+ZZQOPA22At4Bz3b0o9uGKiKQN1c8iIvVgwUFSIiIiIiKShtRlSEREREQkjalBICIiIiKS\nxtQgEBERERFJY2oQiIiIiIikMTUIRERERETSmBoEIiIiIiJpTA0CEREREZE0pgaBiIiIiEgaU4NA\nRERERCSNqUEgIiIiIpLG1CAQEREREUljahCIiIiIiKQxNQhERERERNKYGgQiIiIiImlMDQIRERER\nkTSmBoGIiIiISBpTg0BEREREJI2pQSAiIiIiksbUIBARERERSWNqEESBmZ1lZrPNbL2ZlZlZQSV5\n7jKzDWa238zmm9kxiYhVRETKM7OWZvZbM/syWEf/n5n1T3RcIiLxogZBdOQAy4DrAa+408x+BtwA\nXAOcDuwD5plZ03gGKSIilfo9kA9cCvQB5gOvm1nnhEYlIhIn5n7I91dpADMrA37g7rMj0jYA97v7\n5ODz1sAmYJS7v5iYSEVExMyaA3uA77v73yLS3wf+6u6/SFhwIiJxojsEMWZmRwJ5wBuhNHffDbwL\nDEhUXCIiAkATIBM4WCH9APDt+IcjIhJ/ahDEXh6BbkSbKqRvCu4TEZEEcfe9wNvAf5tZZzPLMLPL\nCFywUZchEUkLTRIdgBzKzNoDw4AvgcLERiMiUq3mwBHAPHffluBY6usy4A/AeqAEWAo8D/SrmFH1\ns4g0IrWun9UgiL2NgAGdKH+XoBPwQRXHDAOei3FcIiLRdCmBL9GNjrt/AZxtZi2A1u6+ycz+BKyp\nJLvqZxFpbGqsn9UgiDF3/8LMNhKYwWI5hAcVnwE8WsVhXwI8++yz9O7dOx5hljNhwgQmT54c99eN\np1QvY6qXD1TGZPHJJ59w2WWXQbDeaszc/QBwwMzaEvjif0sl2b4E1c+xlOplTPXygcqYLOpSP6tB\nEAVmlgMcQ+BOAMBRZnYSsN3d1wG/Be4ws88IvCl3A18DL1dxykKA3r17c+qpp8Yy9Erl5uYm5HXj\nKdXLmOrlA5UxCTXa7jNm9l0C9fcqoCdwH7ASmF5JdtXPMZbqZUz18oHKmIRqrJ/VIIiO/sACAoOH\nHXggmP4UMNrd7zOzbOBxoA3wFnCuuxclIlgRESknF7gH6ApsB2YBd7h7aUKjEhGJEzUIosDdF1LD\njE3uPhGYGI94RESk9tx9JjAz0XGIiCSKph0VERGRuDCzGjcRiT81COQQI0eOTHQIMZfqZUz18oHK\nKOmpsf9NNGvWrMb9jb2MNUn18oHK2BiZuyc6BqnAzE4FlixZsqQxDVgRkTS0dOlS+vXrB9DP3Zcm\nOp5YU/0cPZF3A/RdRCT66lI/awyBiIiISC3UpkuTGjfSGKnLkKSU7t27V9s3tXv37okOUUREGil3\nD28tW7YEoGXLluXSRRoj3SGQlLJ27dpqr+CsW7cOM1OlLSKShNydTZs2sW/fPrKzs8nOzqZ169Ya\nbCwSY7pDIClnxYoV3HrrrXTq1CmcZmZceumlTJ06lT179iQwOhERqejuu+9myJAhdOzYkc6dO3PM\nMcfQpUsX2rRpQ+fOnfnBD37ApEmT+OyzzxIdqkhKUoNAUsZHH33ERRddxIknnshTTz1FRsa//7zN\njDVr1nD99dfTrVs3brnlFtavX5/AaEUkGZhZhpndbWZrzGy/mX1mZnckOq50c++999KxY0fGjx/P\nn//8Z/7+978zZ84cZsyYwY9+9CP27t3L3XffTc+ePcnPz+eFF16gtDT+68b17duXjIwMMjIy2Lt3\nLwB79+4Np/Xt2zfuMYlEg7oMSaOSlZVFSUlJtXmeeOIJRo0aRYsWLcJpZWVlLF68mLVr1/Loo48y\nbdo0Hn/8cSZNmsR1111XrvEgEg1nn302p5xyCg8++GCiQ5Hq3QZcC1wBrCSw8vx0M9vp7o8kNLJG\nprYDbsvKyvjtb39bLn39+vW0adOm2mMPHDjArFmzmDZtGv/1X/9F3759eeCBBzjnnHMaFHddLF++\nPPw49HnUpEkTiouL4xaDRE8i6umvvvqKI488kmXLliVVA1LfgqRRqc0VoTFjxtC0aVPKysoO2de9\ne3cmTZrEF198waWXXsq4ceMYMmQIn3/+eSzCFWmQq666igsuuCDRYaS6AcDL7v43d1/r7n8BXgNO\nT3BcjU5VA2sj0zdu3Mg555zDzTffXC5PTY0BgBYtWnD55ZezaNEi3nvvPVq1asV3vvMdCgoK2LBh\nQ1TLIlJbda2nu3fvzsaNG+nTp08Mo6o7NQikUSkrK6v0A+eVV145JL26q/65ublMnTqVBQsWsH79\nevr378+8efNiFrdIotR0R01YDOSbWU8AMzsJGAj8NaFRpaB169YxaNAgPv30U954440Gneu0007j\nrbfe4sUXX+T999+nb9++zJ49O0qRisSOmXHYYYclXc+E5IpGpJa2bdtW7vn5559/SJ7IFntVq2MO\nGTKEJUuWMGDAAM477zzuv/9+zUAkUTdlyhSOPfZYWrRoQV5eHv+fvTuPs7Hu/zj++s6MdtzoAAAg\nAElEQVQYM8MMhiyRZawZ+5KiEhEV2XejUEKpuO+SbhXu9l+lboQk64yxE9mVLKVoJrJUyjK47UwY\nzGY+vz9m5twzzD5nznXOmc/z8bgezrmu61zX+8J853yv67v06tXLtm3p0qXUr1+fIkWKcMcdd9Cu\nXTtu3LjBhAkTmDt3Ll999RUeHh54enqybdu2TM8TGRmJh4cHixcvplWrVhQpUoQFCxbk9+W5uveB\nRcDvxpg4IBz4VEQWWhvLvRw+fJgHH3yQ+Ph4duzYwcMPP5znYxpj6NmzJ7/++iv3338/nTt3ZsSI\nEcTFxdkhsSpoHF1Op25+5gy0D4FyOVeuXOHRRx+1vc/rcHQlSpRg9erVvPnmm4wePZojR47w2Wef\nOV3tXbmm8PBwXnrpJUJDQ2nevDmXLl1i+/btAJw5c4Z+/frx0Ucf0aVLF65evcr27dsREV5++WV+\n++03rl69ypw5cxARSpYsma1zvvbaa0ycOJGGDRvi4+OTn5fnDnoD/YA+JPUhaAj8xxhzSkTmZ/Sh\nUaNGUbx48TTr+vbtS9++ffMzq0s6evQoLVu2xM/Pj82bN1OxYkW7Hv+OO+5g5cqVTJs2jZEjR7J/\n/36WLVtGqVKl7Hoe5b4cXU7nxzC6YWFhhIWFpVl3+fLlbH9eKwTKpSQkJNC1a1f+/PNP27pb7+in\n94MWGxubZv2tn/H09OSdd96hWrVqPPPMM1y/fp0vv/ySQoX0R0TlzfHjx/Hz86NDhw4ULVqUihUr\n0qBBAwBOnz7NzZs36dq1q+1LUp06dWyf9fX1JS4ujtKlS+fonKNGjaJz5872uwj39n/AeyKyJPn9\nAWNMFeA1IMMKwSeffELjxo3zP50beOKJJ/D19WXr1q2UK1cuX85hjOG5556jXr16dOvWjWbNmrF6\n9WqCgoJs+6T+whQTE0NkZCSVK1e2VZrXrVuXaRM7b29vYmJi8iW/spajy+n8aImQ3g2JiIgImjRp\nkq3P67cd5VLGjx/P1q1b+eabb2jVqtVt27OaibhixYocP348w+2DBw+mSJEiBAcHc+PGDUJDQ/Hy\n8sprbFWAtWvXjkqVKhEYGMijjz7Ko48+SteuXfH19aVBgwa0adOGunXr0r59e9q1a0ePHj2y1cEy\nM9n9BaAAKALcOlpBItqk1m5OnDjBjz/+mG+VgdQefPBBdu3aRadOnbj//vtZu3YtzZs3B6Bfv363\n7X/o0KE071O+qNWpU4eDBw8SFBTEgQMH8j23spYV5bSz0QJPuYzNmzfz7rvvMmHCBB566KF09zl+\n/HiaES1uXTKrDKTo06cPy5YtY+XKlTz99NPpjlakVHYVLVqUX375hYULF1K+fHnGjRtHgwYNuHLl\nCh4eHmzcuJH169dTp04dJk+eTK1atYiMjMzzOVW2rQZeN8Y8boypbIzpCowCllucy+kYY7Jc0rN4\n8WJq167tsJyBgYHs2LGDevXq0bZtWzZu3AhkPQqSt7e37ToOHjwIwMGDB23rtPmd+7KinHY2WiFw\nAJ34Ju/Onj1LcHAwDz/8MGPGjAH+1zQoP9ride7cmXnz5jF//nzb+ZTKLQ8PDx5++GHef/999u7d\ny7Fjx/j2229t25s3b864ceP45ZdfKFy4MCtWrACgcOHCOZ58KT9+HtzcCGAp8BlJfQj+D5gGvGll\nKGeU+gt1ypNTLy+vDL9op2jfvr0jYwJJI8mtX7+e1q1b07FjR5YtW5blZ2rWrJluxSZlXc2aNfMr\nrnICBb2c1iZDjqET32RTVj8kZ8+exdPT0yFZ+vTpw7lz53jppZcoW7bsbeNmK5Uda9as4ciRI7Rs\n2ZKAgADWrFmDiFCrVi127drFN998Q7t27ShTpgw//vgjFy5csLV7rlKlChs3buTQoUOUKlWK4sWL\nZ9mvRUfJyhkRuQb8I3lRmahUqRInTpxIsy4+Pt5Wbqc0yXSWOQGKFCnCihUrePLJJ+nduzeLFy/O\ndLz41KO+eHh4ICIYY/QpcQGg5XQeKgTGGC+gHEntL8+LyCW7pXI/tolvkt8fN8b0Qye+uc2CBQts\nnb5Wr16dZlvTpk35xz8c+zv7xRdf5MyZM7z88stUq1aNLl26OPT8ynWlfEkKCAhg+fLlTJgwgZiY\nGGrUqMHChQupXbs2v//+O9u2beM///kPV65coXLlykycOJF27doBSZPsbd26laZNm3Lt2jW2bNlC\ny5Yts3VepewtdZPLjAZpSExM5KmnnnJorsx4eXkxf35S3/DevXuzZMmSDMvx1J2OU65JROjUqROg\no0i5Iy2nU8msvfWtC+APDAe2AjdI6oiVmPxnJPAFcE9OjlkQFpJGqzgC1Eh+3wA4DfTJYP/GgISH\nh0tBZowRQADp0KGDJCYm2t5nttjbzZs3pXv37uLn5yf79++3+/GVcmXh4eEpP3uNxQnK2/xetHxO\nklGZO3HixGyXyflZbt8qPj5eevbsKV5eXvL1119nee7sZCtUqJAAUqhQofyKrVSe5KR8znYfAmPM\nP4BjwCBgM9CFpPGaa5J0B3wCSU8cNhpj1qfM+qgAnfgmzyZNmoQxJru/sO3Kw8ODOXPmEBgYSJcu\nXYiKirL7OZRSytUdOXKE1157jVGjRlkd5TaFChUiNDSUDh060KNHD6vjKOV0THa/QBljwoC3RSTT\n8beMMd4kVRriRGRW3iO6PmNMH+AD4GVSTXwDjJJ0Jr4xxjQGwlu2bFmgJ77JbN4AKxw5coR77rmH\ne+65hzVr1jisL4NSKd577z3efffddLe1bNmSNWvW5Ov5M5r4JnlmziYiEpGvAZxASvkcHh5eoOch\nSK987tSpE3v27OG3337Dz8/vtu23fi4j+Vnex8TE8Nhjj/Hdd99ler7s/P7x8vIiISGBQoUKER8f\nb/esyjVZXU6nlmoegizL52xXCFTuGWOOkzTxzbRU68YC/UUkKJ399RcOzlchANi0aRPt27dnwoQJ\nvPHGG1bHUQXM33//zaVL6XfX8vX15c4773Rwopz9wnEHWj4nubV8XrNmDR07dmTJkiX06NEjw/K7\nfv367N+/P8221PvWrVs3Tefe/HDlypU0N9u0QqDsyZnK6ZyUz3npVFwaqAOUAC4AkSJyIvNPFVg6\n8U0Off/997bXztT55pFHHmHcuHGMGzeOFi1a0KZNG6sjqQKkRIkSbjcZjnJ9MTExvPjii7Rp04bu\n3btnum9+f9nPjmLFiqV5HxUVRUBAgEVplLtx1XI6x19IjTFVjDFbgOPAYmASsAo4Yoz5PnnKd5WW\nTnyTAyLC2LFj07x3Jq+//jpt2rShX79+nD592uo4SillqY8++ojjx48zefJkp7qBk109evQgLi7O\n6hhKWSo3d6hfS16KikgZEakkIiWBoiR1LNYJt26nE9/kwLfffsvWrVutjpEhT09PQkND8fT0pF+/\nfjmekEQp5TyMMUeNMYnpLJOtzuYqPvjgA1588UWHzkZsT9u3b2fYsGFOd/NJKUfKTYXgexH5UUTS\nzNQhInEishHYZp9o7kNEronIP0QkUESKikgNERknIglWZ3M2IsLrr7/OPffcY3WUTJUpU4awsDC2\nbt3K+++/b3UcpVTuNSVpTp2U5RGShulbbGUoVyIiLj2j+6xZs5g9ezYTJ060OopSlslNhaCRMeaO\n9DYYY8oD9+YtkirI1q5dy48//sjbb79te/TsrI+gH3roIcaOHcu4cePYuXOn1XGUUrkgIhdF5FzK\nAjwBHBaR7VZncxUvvfQSpUuXtjpGrgUHB/Pqq68yevRoNm3aZHUcpSyRm07FocBuY8xl4BIQAxig\nTPLiPFMUKpfz/vvv06JFCx555BGro2TLuHHj+Oabb+jXrx979uy5bZhYpZTrMMZ4Af2Bj6zO4kr+\n+c9/Wh0hz9555x327t1L79692b17t9VxlHK4HD8hEJGfgVokjam/gKQmQquS31cRkW/tmlAVGD/+\n+CM7duzglVdecdqnArdKmezm0qVLPPfcc1bHUUrlTVegODDX6iCupGTJklZHyDNPT08WLFhAqVKl\n6NKli9VxlHK4XA07KiJxJM1WrJTdfPjhh9SoUYNOnTpZHSVHAgMDmTp1KsHBwXTq1InevXtbHUkp\nlTuDgXUiciarHUeNGlWgJ45MT0Y3cpxxTpn0BAQEsHLlSu69V1s+K9eT0cSR2ZXreQiUsqe//vqL\nFStWMG3aNDw8XG96hn79+rFq1SqGDx/OAw88QIUKFayOpJTKAWNMJaAtkK3bw5988kmBnZgsoy8Z\n9erVy3LSMWdXp04dvvzyS/r06WN1FKVyJL0bEqkmJsuS633zUm5p4sSJ3HHHHTz//PMYYzDG2O4k\npfxSMcbg5eVlcdL0GWOYNm0aUVFR3HXXXba8ty7Oml8pxWDgLLDW6iDObubMmemu//XXX0lMTLQt\nIpLmvTNMSpYdmT3lbd++Pd7e3nh7e5OQkDRQYEJCgm1d+/btHRVTKbuya4XAGFPfGOO6Y48pS5w/\nf57Zs2fzwgsvkJiYmOm+zjzmf3ba0TpzfqUKKpN0G3sgMOfWIbVVWgkJCUyaNMnqGA519uxZ2+sN\nGzYQGxtLbGxsmpHwUtZt2LDBqphK5Ym9nxCUBprZ+ZjKzaXcbRo+fLjtrlJGS1YVBquJCM8//zxF\nihSxrUt52uEK+ZUqoNoCFYHZVgdxdsuXL+f48eNWx8iV1E9rs7M+Rd++ffVmjnJ7du1DICLfAN/Y\n85jKvd28eZPPP/+cPn36cMcd6U5v4XLef/991q1bx5EjR6yOopTKBhHZBHhancMVfPLJJ7Ru3Zot\nW7ZYHSXHFixYYOt0GRMTQ2RkJJUrV8bHxwcgww7hW7duZdy4cbz99tsOy6qUo9mlQmCMqQ/Eicjv\n9jieKjjWr19PZGQkw4YNszqK3fj5+TF79mweeughq6MopZTd7Ny5kx9//JFVq1a5ZIUgt6NAvfPO\nO7z22ms0b96cDh065EMypayXoyZDxph7jTHVU72/2xjzG7AHOGCM2Z7RLMZKpWf69Ok0atSIZs3c\nq6VZy5Ytba+deZg9pZTKrqlTp1KtWrUC96V49OjRdOzYkQEDBhAZGWl1HKXyRU77EJwgaabiFK8A\nI4BAoAEQArxnn2jK3UVGRrJmzRqGDx/uMhOR5Zb2HVBKubJLly6xZMkSnn32WZccGjovPDw8mDt3\nLsWKFaN3797ExcVZHUkpu8vpT/UZoKkxpnzy+80i8o2IRIrIfhH5HPjTvhGVu5oxYwb+/v7069fP\n6ij5bvr06VZHUEqpXAsJCeHmzZs89dRTVkexRMmSJVm8eDERERG8+uqrVsdRyu5yWiGoABggpbt9\n4ZQNxpiU19oVX2UpLi6OmTNn8uSTT1K0aFGr4+SL1E89Xn31VX3UrJRySSLCjBkz6NKlC2XLlrU6\njmWaNWvGxx9/zKeffsry5cutjqOUXeW0QnAnMBcYaowZB9QxxtxjjPEELhpj3gD+sHdI5X6+/vpr\nzp07x9ChQ62O4hAlSpRg2LBh2p9AKeVydu7cyYEDBxgyZIjVUSw3YsQIevToweDBg7U8V24lR6MM\nicguYFd624wxbYGTIvJfewRT7m3u3Lk0bdrUJaayzytjDNOnT6djx47Zanurv2SUUs7kiy++oEqV\nKrRt29bqKJYzxjBz5kwaN27M5cuXrY6jlN3kqmeQMeZOY0yP5OFGU5wB7jLG+NknmnsxxpQ3xsw3\nxlwwxlw3xuw1xjS2OpcVzp07x9q1axk4cKDVURymQ4cO9OrVC0/PpKHOM5oYp169elbEU6pA0/I5\nY3///TeLFi3imWeeKXCdiTNSvHhxFi1aZHUMpewqx/MQGGNaAusAX0CMMZ+IyMskVQgaAj+gE7yk\nYYwpAXxP0qRt7YELQA0gyspcVlmwYAHGGPr06WN1FLtLb7QkEUmzfvDgwXz55Zd4eHjYtukoREpZ\nQ8vnzC1atIjY2FgGDRpkdRSn0rRpU9trfaqr3EFuqvv/Ap4EigH1gTLGmA9EJBb4iaROxyqtMcBx\nEXlGRMKTR2XaLCJHrQ5mhTlz5tCpUydKlSpldRS7E5FMl+nTpzNr1iy2bt1qdVSlVBItnzMREhJC\nu3btKF++fNY7F2AnTpywOoJSeZKbCsFOEVkmItEickBEngR+N8Y8DUjyotJ6AvjZGLPYGHPWGBNh\njHnG6lBW2LNnD3v37i2wQ9cNGTKEFi1aMHToUL2rpJRz0PI5A0ePHmXHjh0EBwdbHcXp9e/fn4SE\nBKtjKJVruakQXAUwxlRNWSEis4FTQEc75XI3VYHhJI3A1A6YBkwyxgywNJUF5s6dS5kyZXj00Uet\njmIJDw8PZsyYweHDh62OopRKouVzBkJDQylatChdunSxOorT+/7773n77betjqFUruW4DwGwwxjz\nLvCqMeZ+EfkRQETWJfcviLZrQvfgAewSkTeS3+81xtQFhgHzM/rQqFGjKF68eJp1ffv2pW/fvvkW\nND/Fx8cTGhpKcHAwXl5eVsexTJ06dRg9ejTvvvuu1VGUypGwsDDCwsLSrHODkVa0fE6HiBASEkK3\nbt3cdq6YvDLG2PqBjR8/nvHjx9O6dWseeughq6OpAiiv5bPJTbMFY4wvUF1E9qWzLVDbXqZljDkG\nbBSRZ1OtGwaMFZGK6ezfGAgPDw+ncWP3Gehi3bp1PP7440RERNCoUSOr41jqxo0bFClSxPZemw8p\nVxUREUGTJk0AmohIhNV5ckrL5/Tt3r2bZs2asWHDBtq1a3fb9tQDJbhj+ZWd60s9MER8fDxt2rTh\n8OHD/PrrrwQEBDgqqlIZykn5nKsxxETkRnqVgeRtWhm43fdArVvW1QIK1NS1CxYsoHbt2jRs2NDq\nKJbz9fW1OoJSKomWz+kICQmhXLlytGnTxrauVKlStiGSU0tZ5+oDRYSFhdGpUyc6deqUZn3Kulvv\nvqbm6enJ/PnziY6O1j5iyiXlpsmQyrlPgO+NMa8Bi4F7gWeAAjPt4/Xr11mxYgWvvfZaukNzFnTn\nzp2jTJkyVsdQqiAq8OXzreLj4wkLC2PAgAG2uVMALl26lOnnstru7FI3+Up993/VqlXZ+nzFihWZ\nMWMGvXr14rHHHtOhWpVL0VlGHEBEfga6An2BfcBY4CURWWhpMAdavXo1165dc5v2tfY2cuRIqyMo\nVSBp+Xy7b7/9lvPnz9O/f/8067MaVtnV74rXr18fDw8PW2UAkq45ZV39+vWzOAL07NmTQYMG8cIL\nL/DXX3/ld2Sl7EYrBA4iImtFpL6IFBGROiIyy+pMjhQaGsp9991H1apVs965gEj9pCQsLIx169ZZ\nmEapgqugl8+3WrJkCdWrVy9wfb0OHTqUbsUmZd2hQ4eydZxJkyZRrlw5BgwYoEORKpeRpwqBMeaN\n9F4rldrFixdZt24d/fr1szqK03rkkUcYNmwY0dE6SJdSyjrx8fGsWLGCHj16FLjmnTExMbYv/0FB\nQQAEBQXZ1sXExGTrOH5+fsyfP59du3bx/vvv52dkpewmr08IimTwWimbZcuWkZiYSK9evayO4pSM\nMUyfPp3z588zduxYq+MopQqwLVu2cOnSJXr27Gl1FIdL3Wn64MGDABw8eDBXnaabN2/Ov/71LyZM\nmMDPP/+cX5GVspu8Vggkg9dK2SxYsIC2bdtStmxZq6M4rapVq/LWW28xefJkfvjhB6vjKKUKqKVL\nl1K1atUC11wIoF+/fpQtW5ayZctSsmRJvLy8KFmypG1dTp9yv/nmmzRo0IDg4GCuX7+eT6mVsg/t\nQ6Dy1cmTJ9m2bZt2Js6GkSNHcs899/DAAw/Y7kilt/j4+FgdVSnlhhISElixYgU9e/YscM2FACZP\nnsyZM2c4c+YMFy9eJC4ujosXL9rWTZ48OUfH8/LyIiQkhMjISMaMGZNPqZWyj7xWCApeiaFyZNGi\nRRQuXJhu3bpZHcXpeXp6MmvWrCxH6oiNjXVQIqVUQfLdd99x4cKFAtlcKL/cfffdfPDBB0yePJnN\nmzdbHUepDOkTApWvwsLC6NChA8WKFbM6ikuoU6cOb7/9Np6enuzevdt2l84Y4zZD+ymlnNOSJUsI\nDAx06xmYrTBixAjatGnDoEGD+Pvvv62Oo1S67NmHQKk0Dh06RHh4uI4ulErqpj+px7lOvX706NHU\nr1+fQYMG6Zd/pZRDpDQXKoijC+VE+/bt8fb2xtvbO00ZnrKuffv2t33Gw8OD2bNnc/XqVUaMGOHo\nyEpliz4hUPkmLCwMf39/Hn/8caujOA1vb+8st3t5eTFnzhz++OMPB6VSquAyxowzxiTeshy0Opej\nff/995w/f54ePXpYHcWpbdiwgdjYWGJjYylUqBAAhQoVsq3bsGFDup+rWLEiU6ZMITQ0lIULC+yc\nd8qJaYVA5QsRISwsjK5du+Lr62t1HKeRepzr9JaUca7r16/Pm2++aXFapQqM/UBZoFzy8oC1cRxv\nxYoVVKhQgaZNm1odxW3179+f3r17M2zYMI4fP251HKXSyGuF4MNUrz/K47GUG9mzZw9//PGHji6U\nB6+++qrttTYdUipfJYjIeRE5l7xcsjqQI4kIK1eupHPnznh46H3C/GKMYdq0aRQrVownn3ySmzdv\nWh1JKZs8/eSLSFSq1wWqAFWZCwsLo3Tp0rRp08bqKC7Ly8vL6ghKFRQ1jDH/NcYcNsaEGGMqWh3I\nkfbs2UNkZCRdu3a1OorbCwgIYN68eWzbto2PP/7Y6jhK2eitAGV3N2/eJCwsjJ49e+qXWjvasWOH\n1RGUckc/AgOB9sAwIBDYZowpamUoR1qxYgUlSpTgoYcesjpKgdCqVStGjx7N2LFjdRZj5TQK2eMg\nxpgqgL+I7LPH8ZRr27JlCydPnuTJJ5+0Oopbeeqpp9i7dy9+fn5WR1HKbYhI6l6g+40xu4BIoBcw\nO6PPjRo1iuLFi6dZ17dvX5dsJrly5Uo6duyoN3Ac6N///jfffPMNffv2JSIiAn9/f6sjKRcXFhZG\nWFhYmnWXL1/O9uftUiEAJgCFjDETgf8D/gaeEpFoOx1fuZB58+ZRs2ZNmjVrZnUUl5d6eNIzZ87w\nyiuvMG3aNItTKeW+ROSyMeYQUD2z/T755BO3GK//8OHD7Nu3j/Hjx1sdpUApXLgwYWFhNGrUiBEj\nRjB37lyrIykXl94NiYiICJo0aZKtz9urydB6YADwNPAKsBh4107HVi4kOjqaZcuW8dRTT+lY1nZk\njOHDDz9k+vTprF+/3uo4SrktY4wfSZWB01ZncYSVK1fi4+OT7vj5Kn9Vr16dqVOnMm/ePBYsWGB1\nHFXA2atCECsiiUCoiESIyCLggJ2OrVzI8uXLuX79OsHBwVZHcTvDhw+nXbt2DB48mIsXL1odRym3\nYIz50BjT0hhT2RjTAlgBxANhWXzULaxYsYJ27dpRtGiB6TLhVAYMGEBwcDBDhw7lzz//tDqOKsDs\nVSEYZYzpA6Sedem8nY7tdowxY5Inv5lodRZ7mzdvHq1bt6ZSpUpWR3E7xhhmzZpFTEwMw4YN06FI\nlbKPu4AFwO/AQpJ+d90nIm5f6z579iw//PADXbp0sTpKgTZ16lTuvPNOevfuTWxsrNVxVAFlrwrB\nSsAPGGyMWWmMeQtoYadjuxVjzD3As8Beq7PY24kTJ/j222+1M3E+qlChAtOnT2fp0qWEhoZaHUcp\nlycifUXkLhHxFZFKItJPRI5ancsRvv76a4wxdOzY0eooBZq/vz+LFy/mwIEDvPLKK1bHUQWUXSoE\nIvKxiMwUkWCgG/A1UN4ex3YnyW1TQ4BnSOp47VZCQkLw8fGhe/fuVkdxacYY25LyFEBEbOt69+5N\n//79ef7553W2S6VUrq1cuZIHHniA0qVLWx2lwGvYsCETJ05k8uTJLF++3Oo4qgCy+zwEIpIoIj8B\nH9j72G7gM2C1iHxrdRB7ExFmz55Nt27ddPi0PPL29s5y+5QpUyhevLjOdqmUypXo6Gg2bdqkzYWc\nyHPPPUf37t0ZNGgQhw8ftjqOKmDybWIyEXG7JjF5kdzHoiHwmtVZ8sMPP/zAn3/+ydNPP211FJcX\nExODiGS4xMTEUKJECebOncu2bduYONHtuqIopfLZhg0biI2NpXPnzlZHUcmMMXz55ZeULl2anj17\nEhMTY3UkVYDYax6CNIwxRYBBIvJZfhzf1Rhj7gI+BdqKSHx2P+dKE9/MmjWLwMBAnenSgVq3bs3L\nL7/M2LFjeeSRR2jYsKHVkZSby+vEN8p5fPXVV9SrV4+qVataHUWlUrx4cZYuXcp9993HyJEj+eKL\nL0hMTMxwfw8PD31KrOzC2GOkEmPMZKAGUBYoAkQBJUTk7jwf3A0YYzoDy4GbQMrg/J6AJK/zllT/\nEMaYxkB4eHi4S0x8Ex0dTbly5Rg9ejRvvvmm1XEKlNjYWO69917i4+P5+eef8fX1tTqSKmBSTXzT\nREQirM6T31ytfE5PfHw8ZcuWZcSIEfz73/+2Oo7L8vLyIiEhgUKFChEfn+17fdnyxRdf8Oyzz2a5\nnzEm0wqDKthyUj7bq8nQ9OSlCTBaRO4D+tvp2O5gM1CPpCZDDZKXn0nqYNxA7FErs9CSJUu4fv06\nTz31lNVRChxvb29CQ0M5cuQIL7/8stVxlFIuYPv27URFRWn/ASf2zDPPMHDgQHx9fdmzZ89tw0yn\nNCHVyoCyF3uNMnQAWANUIunONyISbo9juwMRuSYiB1MvwDXgooj8ZnW+vJo1axZt27alcuXKVkcp\nkOrUqcPHH3/M1KlT+eqrr6yOo5RycitXrqRixYo0atTI6igqA8YYpk6dyt133023bt2IioqyOpJy\nczmuEBhjPI0xTxtj+hhjUpq/ICLxInIM2GWMqWGMqW3PoG7IpZ8KpDh06BA7duxg8ODBVkcp0IYP\nH07nzp0ZPHgw//3vf62Oo5TLcueJIwESExNZsWIFXbp0IdWvcOWEfH19WbZsGdohLksAACAASURB\nVFFRUQQHB1sdR7m53DwhGAd8SNLMjrfNjCQiJ4HTwEd5i+beRORhEfmH1TnyatasWZQoUUIfPVvM\nGMPMmTPx8fEhODhYO5kplQvuPHFkit27d3Py5El69OhhdRSVDYGBgSxYsIB169ZZHUW5udxUCCqI\nSEmgDBBtjLltWBkRiQYm5DWccm7x8fHMmTOH4OBgfHx8rI5T4N1xxx2EhISwbds2xo8fb3UcpVyK\nu08cmWLp0qWUKVOG+++/3+ooKpseffRR3nnnHatjKDeXmwrBUQARuQA8D7RIbycR2ZWHXMoFrF69\nmrNnzzJkyBCro6hkrVu35q233uLtt99m7dq1VsdRypW47cSRKUSEpUuX0q1bNzw9Pa2Oo3JgzJgx\nVkdQbi43FYK4lBfJY+pH2y+OciVffPEFzZo1o379+lZHUamMGTOGDh06EBwcTGRkpNVxlHJ67j5x\nZIqIiAiOHTumzYVc0K39PS5cuGBREuWucjMxWUNjTCkRuZj8PtaegZRriIyMZMOGDcyYMcPqKOoW\nHh4ezJs3jyZNmtC9e3e2b9+u8xMolYGCMHFkimXLllGqVCmdQNIN9OjRg40bN1K4cGGroygnkdeJ\nI3NTIegC9DTG7AU2AkWMMX7J/QYwxjwuItpWwc3NmjWLokWL0qdPH6ujqHSULFmS5cuXc//99/P0\n008TGhqqI4oolb4mQGkgItXIeZ5AS2PMCG6ZODLFJ5984lITk4kIS5YsoUuXLhQqlJtf/Qqgfv36\n7N+/H8A2N0BCQgIeHkkNLurWrcuvv/6a7zl++OEHXnjhBR566CEWLlwIQExMDJGRkVSuXNnWr8/Z\nK6nKftL7t041MVmWclMqvAdMAx4G2gJtgOeMMXuA74C7Aa0QuLGbN28ya9Ys+vbti5+fn9VxVAYa\nNWrE3Llz6dWrF/Xq1eO119y6NYRSuZUycWRqc4DfgPddfeLIFPv27eOvv/5i8uTJVkdxaam/7Pv7\n+xMdHY2fnx9Xr151aI7p06fz9NNPp/uU/tChQ7bXq1ev1gqBypbcVAgmichlYHHygjEmkP9VDlrb\nL55yRuvXr+fkyZPamdgF9OzZkzfffJOxY8cSFBRE586drY6klFMRkWvAwdTrjDFuM3FkiqVLl1Ki\nRAkefvhhq6MoOxg8eDAHDhzg008/5Z///Ce///47q1evtm1/5JFHbE8JwsLCtFKgspTjCkFyZeDW\ndUeBL4AvjDFv2yOYcl6ff/45jRo1omnTplZHUdkwbtw4Dhw4QL9+/diyZQvNmjWzOpJSzs4tngqk\nEBEWL15M586dtc25G/m///s//vrrL6ZNm8aOHTv4+uuvbc2Y3n//fZdq0qasl5uZirP6zOJcZlEu\n4Pjx46xZs4Zhw4Zpm3QX4eHhwfz582nQoAEdO3bkr7/+sjqSUk7NXSaOTLFv3z7++OMPevfubXUU\nl2eMsS3R0UmDLEZHR6dZ7yienp6EhoZSo0YNOnbsiJu0blMWyc2wo78YY0pntFFE8r83jbLMzJkz\nKVq0qD5+dDG+vr6sXr2agIAAHnvsMc6fP291JKWUgyxatIiAgADatGljdRSXJyJZLo7k5+fH6tWr\ntTKg8iw3FYKbwHZjTMWUFcaYFsaYocnDtyk3FR8fz8yZMwkODsbf39/qOCqHSpUqxfr167l69Srt\n2rUjKirK6khKqXwmIixatIhu3bppcyE3VaFCBdasWWN1DOXiclMhWAS8A2w1xtQAEJEfgCXA+8aY\n7XbMp5zI6tWrOX36NEOHDrU6isqlwMBANm3axPHjx3nsscccPjKGUsqxfvnlFw4fPqzNhVxURk2R\nbl3foEGDNNtnzZrlsIzKPeRmlCEjIvOTR2H4xhjTUUR+FZFLxpinuGW0BuU+pk+fTvPmzW8reJRr\nqVevHhs3buThhx+mQ4cOrFu3jqJFi1odSymVDxYtWsQdd9xB69Y6AKArWrBggW2yqYzmGUjP3Llz\nmTJlisNyKteXmwpBOQARWW6MiQbWGGN6ichOEblpjPnGvhGVM/jrr7/YtGkTc+fOtTqKsoMmTZqw\nfv162rVrR7t27VizZg0lSpSwOpZSyo5SRhfq3r27TkbmonI7sVh0dDSTJ0/mhRdeyIdUyh3lpslQ\nR2OMF4CIbAT6AEuNMW2Tt2vDZDf0+eefExAQQM+ePa2OouykefPmbN68mZ9++omAgACMMXh4eNj+\nTFnq169vdVSlVC7s3r2bY8eO0atXL6ujKAdI3awoODiYF198kdDQUAsTKVeSm1sGW4EFxpi3kpsK\nfW+M6QisMsa8RFKnY+VGYmJimD17NoMGDcLX19fqOMqO7r33Xm7e/N+PbMpIFalHrNi3b5/Dcyml\n8m7hwoWULVuWhx56yOooysFGjhyJl5cXAwcOpESJEnTo0MHqSMrJ5fgJgYg8TdJTgRKp1v0CPAJ8\nDHS0Wzo3YYx5zRizyxhzxRhz1hizwhhT0+pc2bVkyRIuXryonYndlIhw5MgRatSocdt6K4bRU8qR\njDHDjDF7jTGXk5cfjDGPWp0rr27evMmiRYvo1asXnp6eVsdRDmaMYcaMGTzxxBN0796djRs3Wh1J\nObncNBlCRG6KyLZb1v0OtAZ0PMrbPQhMBu4F2gJewEZjjEvcbp8+fTpt2rShZk2XqcOoHAoMDOSn\nn36yOoZSVjgBvAo0BpoA3wJfGWNqW5oqj7Zv386pU6d0zpgCrFChQixcuJC2bdvSuXNntmzZYnUk\n5cRyVSHIiIgcA+rZ85juQEQeF5H5IvKbiOwDBgKVSPrl49R+/fVXfvjhB4YPH251FJXPAgIC0rx/\n9tlniYmJsSiNUo4hImtEZL2IHBaRv0TkdSAauM/qbHkRFhZGlSpVuO8+l74MlUeFCxdm6dKltGzZ\nko4dO7J161arIyknle0KgTGmUnb2E5GY5P0r5DZUAVACEOCS1UGyMm3aNO688046depkdRTlYPPm\nzaNFixYcOXLE6ihKOYQxxsMY0wcoAuy0Ok9uxcXFsXTpUvr06XPb+PXKvaSejyB1884mTZrY1q9Y\nsYIVK1bQvHlzHnvsMTZv3mxhYuWscvKEYLcx5nNjzD0Z7WCMKW6MGWKM2Q90z3s892OSSudPgR0i\n4tRzNly5coWQkBCGDBmCl5eX1XGUg+3cuZMrV65QrVq1NL900luUcmXGmLrGmKtALDAV6JrcDNYl\nbdq0iUuXLmlzoQKgZMmSWW7v27cvRYoUYfXq1bRq1YqOHTuydu1aByVUriInowwFAWOBTcaYGCAc\nOAXEAAHJ2+sAEcBoEdH/bembStLf1f1Z7Thq1CiKFy+eZl1uxyTOjXnz5nHjxg2effZZh5xPOZdG\njRoRHh7OiBEjCAkJoU+fPixcuBBIuiuVmJhocULlaGFhYbZJklJcvnzZojR29TvQACgO9ADmGWNa\nZlYpsLp8zkxYWBhBQUHUq6cteN3dxYsXs72vr68vK1asoHfv3nTp0oUFCxbQo0ePfEynHCmv5bPJ\n6QgiyR1hOwAPAJUBX+AC8AuwQUT25+iABYgxZgrwBPCgiBzPZL/GQHh4eDiNGzd2WL7URITatWtT\nv359Fi9ebEkG5RjZucMfFhbGsGHDbIWLVghUioiICJo0aQLQREQirM5jD8aYTcBfInJb5ylnKJ8z\nc/36dcqUKcOYMWN4/fXXrY6jnFB8fDwDBw4kLCyM6dOn600/N5aT8jnH8xCIyA1gafKisim5MtAZ\neCizyoCz+Oabb/jjjz+YMWOG1VFUPsvuTYH777+fSpUq2T5z6tQpypcvn5/RlLKKB+BtdYjc+Prr\nr7l27Rp9+vSxOopyUl5eXsyfP5+SJUsydOhQzp8/z7/+9S9t/lnA2XWUIZU+Y8xUoD/QD7hmjCmb\nvPhYHC1DU6ZMoX79+jz44INWR1FOomLFimne16xZk3feeYcbN25YlEipvDPGvGuMedAYUzm5L8F7\nwENAiNXZciM0NJRmzZpRvXp1q6MoJ+bh4cGkSZOYMGECr7/+Os899xwJCQn5cq5SpUpl2getVKlS\n+XJelTNaIXCMYUAx4DuS+l2kLE45n/yxY8dYvXo1I0aM0DsGKkNDhw5lwoQJ3H333cyZM4f4+Hir\nIymVG2WAuST1I9hM0nDQ7UTkW0tT5cKlS5dYt24d/fv3tzqKcgHGGN58802+/PJLZs6cSadOnbh6\n9ardz3PpUuYDKma1XTmGVggcQEQ8RMQznWWe1dnSM23aNIoVK0a/fv2sjqKcTEoF0RjDxx9/zIED\nB2jatCmDBg2iZs2azJgxQ+cuUC5FRJ4Rkaoi4isi5UTEJSsDkDSrfGJiIr1797Y6inIhgwcPZu3a\ntezYsYOWLVty/Lh9WzWnnvU+9e+Q1OuV9bRCoNK4du0aM2fOZPDgwRQtWtTqOMrJ1ahRg2XLlrF3\n716aNWvGsGHDqFixIv/61784ceKE1fGUKlBCQ0Np27YtZcuWtTqKcjGPPPII33//PVFRUTRt2pQd\nO3ZYHUk5mFYIVBqzZs3i8uXLvPjii1ZHUU4ivYlvUu70pCz169dn0aJF/PHHH/Tr148pU6ZQpUoV\nOnXqxKpVq/KtbapSKklkZCTbt2/X5kIq1+rVq8fu3bsJCgri4YcfZsaMGXr3vgDJVYXAGNM6k21D\ncx9HWSkhIYGJEyfSu3dvKleubHUc5SRSP9bNaElRo0YN/vOf//Df//6XKVOmcOrUKTp37kzFihV5\n+eWX2bdvn4VXopT7WrhwIb6+vnTp0sXqKMqFlS5dmk2bNjFkyBCGDh3KoEGDuH79utWxlAPk9gnB\nemPMh8YY2/S1xpg7jDGrgfftE03lt1t7+nt5eXHs2DEWLFigM9CqPPH392f48OH8/PPPRERE0LNn\nT+bMmUP9+vVp2LAhH3zwAZGRkXY7n6enZ6ajWHh6etrtXEo5o9DQUDp16oS/v7/VUZSL8/Ly4rPP\nPmPevHksWbKE++67jz/++CPXxwsLC6NTp0506tQpzVPmlHW3TqalrJHbCkFroCuw2xgTZIzpAOwn\naSSdhvYKp/JXRh16tKOPsqdGjRoxadIkTp06xcqVK6lZsybjx4+nSpUqtGjRgo8//pijR49aHVMp\nl7Vv3z727dunzYWUXQ0YMICffvqJuLg4GjduzMyZM3P1vaBv376sWrWKVatWpVmfss4ZZvdWuawQ\niMgPJH3x3w9EACuAT4BWImK/235KKbdRuHBhOnfuzOLFizl37hzz58+nTJkyvP7661StWpWGDRvy\nxhtvsGvXrhzPgnzz5s1MR7G4efNmflySUk4hJCSEUqVK0b59e6ujKDdTt25dfv75Z/r27cuQIUPo\n2bMnFy9etDqWygd56VRcE2gKnAQSgFpAEXuEUkq5N39/f4KDg1m5ciXnz59n0aJF1K1bl88++4x7\n772XcuXKERwcTEhICKdPn7Y6rlJOKzExkbCwMHr16kXhwoWtjqPckJ+fHzNnzmTp0qV8++231KlT\nh6+++srqWMrOctupeAywE9gE1AWaAY2AX40xze0XTznCli1brI6gCjA/Pz969epFSEgI586dY+vW\nrTzzzDMcPHiQAQMGUL58eYKCghgxYgSLFi3i1KlTVkdWbsQY85oxZpcx5oox5qwxZoUxpqbVubJr\n+/btnDhxQpsLqXzXvXt39u/fzz333EOXLl0IDg7WpwVuJLdPCF4CuojICyISIyL7SaoULCdpNl7l\nIkSEMWPGWB1DKQAKFSpEy5Yteffdd4mIiODs2bMsXLiQBx98kA0bNtCnTx8qVKhA1apV6du3L//5\nz3/YuXOnjoKh8uJBYDJwL9AW8AI2GmN8LU2VTSEhIbb+OErlt/Lly7Nq1SrmzZvHmjVrqFWrFnPm\nzNE+h26gUC4/V09ELqReISLxwCvGmK/zHks5ysqVK9m1a5ftvY4spJxJmTJl6N27t23m1TNnzrBj\nxw6+//57fvrpJ1asWEFsbCweHh4EBQXRqFGjNKNYpO5ToFR6ROTx1O+NMQOBc0ATwKlnZ4qNjWXp\n0qU8//zz+v9cOYwxhgEDBtCuXTv++c9/MmjQIObMmcPkyZOpV6+e1fFULuWqQnBrZeCWbVtzH0c5\nUkJCAmPHjqVdu3Zs2rRJa/jK6ZUrV44ePXrQo0cPAOLi4ti3bx8RERGEh4ezZ8+eNPuXKlWKoKAg\n7r77bmrVqkWtWrWoWbMmgYGBeHt7W3EJyvmVAAS4ZHWQrKxdu5a///5bmwspS5QtW5aQkBAGDhzI\n888/T8OGDXnuueeYMGECJUuWtDqeyqFcVQiMMW9mtl1E/p27OMqR5s2bx2+//UZISAibNm2yOo5S\nOVa4cGGaNGlCkyZNGDJkCJD2Kdc///lPDhw4wC+//MLChQu5du2abZ9KlSoRGBhIlSpVCAwMpGLF\nilSqVImKFStSvnx5/Pz8LLkmZR2T9J/nU2CHiBy0Ok9WQkJCaNy4MbVr17Y6iirA2rZty759+5g0\naRL//ve/WbBgAW+88QbDhw/XGy8uxOTmrrAx5pdbVnkBgSSNNnRYRBrbIVuBZYxpDISHh4fTuHH+\n/FWePXuWcuXKZbmfPjVQrsbDw8PWVCj18KUiwqlTp/jrr79sy9GjRzl27BhHjx7l3LlzaY5TrFgx\nypcvT7ly5ShXrhxlypShdOnSlC5dmlKlStmWEiVKEBAQgJ+fX4FsthEREUGTJk0AmohIhNV58sIY\nMw1oD9wvIukOb5VSPrds2ZLixYun2da3b1+Hjan+999/U65cOd59913+8Y9/OOScSmXlzJkzvPHG\nG8yaNYvKlSvzzjvv0Lt3bzw8krqspi4j9fuFfYWFhd02ydvly5fZtm0bZKN8zlWFIN0DGVMMmAOs\nEJH5djloAeWICkHPnj357rvvOHjwIKVLl86XcyhlhYwqBFmJiYnh5MmTnDhxglOnTtmWs2fPcubM\nGc6dO8f58+e5cOFCusf18PCgWLFiFCtWDH9/f/z9/fHz86No0aIUKVLEtvj4+NiWwoUL4+3tjbe3\nd5rXPj4+tj9TXvv6+uLr64uPjw9FihTB29vbKSog7lIhMMZMAZ4AHhSR45nsl+/lc3Z88cUXDBs2\njJMnT3LnnXdalkOp9Bw8eJAxY8awevVq6tWrx4QJE+jSpYutYgBaIXCEnJTPue1UfBsRuWKMGQes\nBrRC4MSWLl3K0qVLWbRokVYGlErm4+ND9erVqV69eqb7JSYmcvnyZS5dusTFixf5+++/iYqKIioq\niqtXr3LlyhWuXLlCdHS0bTl9+jTXr1/n+vXrxMTEcOPGDWJjY4mLiyM2NpbY2Ngc5zXG4OPjg6+v\nr62ykfp1SkUksz9TL+mtKyjj2idXBjoDD2VWGXAm8+bN45FHHtHKgHJKQUFBrFq1ip07d/LGG2/Q\nrVs3GjVqZHUslQm7VQiSFU9elJO6cOECzz33HN26daNnz55Wx1HK5Xh4eBAQEEBAQADVqlWzyzFF\nhISEBFslISYmhpiYGGJjY9NUIG7cuJHuklLZuH79OteuXbOtO3v2bJr1165ds73OztOTQoUKZVmx\nuHHjhl3+DqxijJkK9AU6AdeMMWWTN10WkRjrkmXsyJEj7Nixg9DQUKujKJWp5s2bs3nzZr777jve\neuutNNvi4+Px8vKyKJm6VW47Fb946yrgTmAAsC6vodyVMeZ54GWgHLAXeEFEdjvq/DExMfTq1Yub\nN28ydepUp2huoJRKutvv5eXlsF+OIkJsbGyaisKtS0bbUq8/e/Ys58+fd0jmfDSMpFGFvrtl/SBg\nnsPTZENISAh+fn506dLF6ihKZUurVq1o1apVmu8dVatWZeTIkQwZMoRixYpZmE5B7p8QjLrlfSJw\nHpgLvJenRG7KGNMb+Bh4FthF0t/hBmNMzcyGcbWXmzdv0r9/f3bu3MnGjRspW7Zs1h9SykWkV7m9\ndQ4Cba/6PynNjXx8fPI8PGCqNqouSURyO0GnJUSEefPm0aNHD4oUKWJ1HKVyrU2bNrz22muMHz+e\nQYMGMWLECGrWdJlJwt1OrgpCEQm8ZakmIveJyL9E5Kq9Q7qJUcDnIjJPRH4n6a7UdWBwfp9YRHju\nuef46quvWLx4MQ8++GB+n1Iph8pqaDsd+k65GmNMuouHhweHDx9mzpw5VkdUKk/mzJnD0aNHGTly\nJGFhYdSqVYv27duzcuVKEhISrI5X4LjUnRFXZYzxImnWy29S1knS7crNQPP8Oq+IsGnTJh544AFm\nzJjBzJkzeeKJJ/LrdEpZJiYmxjYzcXpLTIxTNgVXKkOp//+mzInh5+fHsGHDuOuuu7h586bFCZXK\nuwoVKvDWW29x4sQJZs+ezeXLl+natStVqlThjTfe4OjRo1ZHLDCy3WTIGDMxu/uKiA6KnNYdgCdw\n9pb1Z4FaGX1ozJgxBAQE3Lbew8ODQoUKUaZMGVu745SlUKFCREVFce7cOX788UcOHDhA3bp1+eyz\nz6hfvz4REf8bderOO+/MdISKGzdu8Ntvv2V6YbVr18bX1zfD7adPn+b06XSH8waSRnYJCgrK9BwH\nDx7M9AudXsf/6HUk0ev4H0dch3IMEWHRokUMHTo0zfCNSrk6Hx8fBg4cyMCBA/nll1/4/PPPmTRp\nEm+//TatW7fmySefpHv37vj7+1sd1X1ldlct9QJEAduBLcC3yX+mt3yb3WMWlIWkDteJwL23rP8A\n2JnO/o1J6uSW4eLt7S21atWSqlWrSsWKFaVcuXJSqlQpKV68uFSpUkWaNWsm/v7+mR5j3Lhxkpn9\n+/dn+nlA9u/fn+kxxo0bl+nng4KCMv28iEhQUJBeh16HXocTXMeCBQvkiSeeSLO0bNkyZb/G4gTl\nbX4vJJfP4eHhWf7b2Iufn5+QXO4DcujQIYedWyl7Sl2uZOXatWsyd+5cadWqlRhjxNfXV3r37i3L\nly+X69evOyCt6wsPD892+ZzticmMMYlAORE5Z4w5AtwjIhez9eECLrnJ0HWgu4isSrV+DlBcRLre\nsn9jIDwkJCTDKeld5c6hu9wB1ev4H72OJHodSdxlYrLssmJiMn9/f6Kjo/H09OSBBx7gu+++c8h5\nlbK33A70cOLECUJDQwkLC+PXX3/Fz8+PJ554gq5du/Loo4/qk4MM5KR8zkmF4CLwuIj8lFw5KCsi\nLj/enKMYY34EfhKRl5LfG+A4MElEPrxlX6eYCVMppbKiFYL8l1IhgKQhR/v37++Q8yplb/YY+e2P\nP/5g8eLFLF++nD179uDt7c3DDz/M448/TocOHQgMDLRXXJeXk/I5J40QlwHbjDFHSXr88LMx5kh6\nS+6ju7WJwBBjzJPGmLuB6UARYI6lqZRSSrmMbt26WR1BKUvVqlWLN954g19++YUjR47w3nvvERsb\ny6hRo6hatSq1atXihRdeYPXq1Vy+fNnquC4j252KReRZY8xyoDowCfgC0CFGs0lEFhtj7gD+DZQF\n9gDt9SmLUkpZxxjzIPAKSSPB3Ql0Sd200xmk3En18vLSzt1KpRIYGMioUaMYNWoUV65cYfPmzWzc\nuJGvv/6aKVOm4OHhQZMmTWjVqhUtW7akRYsWeZ57xV3laGIyEVkPYIxpAvxHdM6BHBGRqcBUq3Mo\npZSyKUrSDZovgeUWZ0lXyhCjhQrldi5RpdxfsWLF6NatG926dUNEOHz4MFu2bOG7774jJCSEDz9M\nap1dp04d7rvvPu677z7uvfdeateurT9b5HKmYhEZZO8gSimllKMl3+hKudl1+5TXTiCl0/iNGzcs\nTqKUazDGUL16dapXr86QIUMQEY4dO8aOHTvYsWMHP/30E7NnzyYxMZEiRYrQqFEjGjduTMOGDWnU\nqBFBQUEFbkJLrRIppZRSTiqrEaCUUlkzxhAYGEhgYCADBgwAIDo6mp9//pnw8HB+/vlnNm7cyJQp\nUxARPD09qVGjBnXr1iUoKIigoCBq165NjRo13LbZnlYIlFJKKSf16aefWh1BqTwJCwsjLCzstvWd\nOnUCoG/fvvTt29fRsfDz86NVq1a0atXKti46Opp9+/bZlv379zN9+nTOnTtn26dixYrUqFGDatWq\nUa1aNapWrUrlypWpUqUKpUuXxkkfNGYp28OOKsfRYUeVUq7CnYYdTR5SO9NOxSnlc8uWLSlevHia\nbfb+YnP+/HkqVapEYmIicXFx+Pn5cfWqdt1TrsvDwyNpEixjSExMtDpOtl26dInff/+dQ4cO8eef\nf3Lo0CEOHz7M4cOHuXLlim0/Hx8f7rrrLipWrEiFChWoUKEC5cuXp1y5cpQtW5Zy5cpRpkwZSpQo\nYfeKQ3oVr8uXL7Nt2zbIRvmsTwiUUkqpHPrkk0/y/YbNtGnTMMbg5eVFXFxcvp5LKZWxkiVL0qJF\nC1q0aJFmvYgQFRVFZGQkx44dIzIykpMnT3Ly5EmOHj3KDz/8wKlTp26bPLJQoUKULl2aUqVK2ZaA\ngAACAgIoUaIEJUqUoHjx4hQrVoxixYrh7++Pv78/RYsWxc/Pj6JFi97WETq9GxKpbthkSSsESiml\nlJOJiYnhs88+Y9CgQcybN8/qOEqpdBhjKFmyJCVLlqRRo0bp7iMiXL58mbNnz3L69GnOnz9vWy5e\nvMiFCxe4ePEiJ0+eJCoqiqioKC5fvkx8fHym5/by8qJIkSL4+vraFh8fH7y9vW1/Xr9+PdvXohUC\npZRSBZYxpihJ8+ukPL+vaoxpAFwSkRNW5QoNDeX8+fOMHDlSKwRKuTBjjO2uf61atbL1GREhJiaG\nK1eucPXqVduf165dIzo6muvXr3P9+nWuXbvGjRs3bEtsbCwxMTHExsYSFxfHtWvXsp1TKwRKKaUK\nsqbAFkCSl4+T188FBlsRKDY2lrfffpuuXbtSo0YNKyIoZTfptZVP6UeQ+r36H2OM7a5/2bJlc30c\nbTKklFJKZYOIbAU8rM6R2ueff87x48dZu3at1VGUyrOSJUty6dKlTLcrazZDqwAAIABJREFU62mF\nQCmllHISV65c4a233mLQoEHUrl3b6jhK5dnFixetjqCywanuiiillFIF2UcffUR0dDTjx4+3OopS\nqgDRCoFSSinlBM6cOcPHH3/MSy+9xF133WV1HKVUAaJNhpRSSimLiQgjR47E29ubV199Nd2OmNHR\n0doRUymVL7RCoJRSSlksJCSERYsWERYWRkBAgH7ZV0o5lDYZUkoppSx09OhRnn/+eYKDg+nTp4/V\ncZRSBZBWCNRtwsLCrI6Q79z9Gt39+kCvUbmHhIQEBgwYQMmSJZkyZUqW+xeE/xPufo3ufn2g1+iK\ntEKQz4wxlY0xM40xR4wx140xfxpjxhtjvKzOlhF3+0+eHne/Rne/PtBrVPZljHneGHPUGHPDGPOj\nMeae/D7n5cuX6dGjBzt37iQkJITixYtn+ZmC8H/C3a/R3a8P9BpdkfYhyH93AwYYAhwG6gIzgSLA\naAtzKaWUAowxvUmaofhZYBcwCthgjKkpIhfy45y//fYbXbt25fTp03z11Vc88MAD+XEapZTKFn1C\nkM9EZIOIPC0i34jIMRH5GvgI6GZ1NqWUUkBSBeBzEZknIr8Dw4DrwGB7niQhIYHNmzfzzDPPcM89\n9+Dp6cnu3bvp2LGjPU+jlFI5pk8IrFECyHgeb6WUUg6R3HyzCfBuyjoREWPMZqB5bo6ZmJjImTNn\nOHLkCIcPH2bfvn3s3buX8PBwoqKiqFatGqNGjWL06NH4+/vb6UqUUir3tELgYMaY6sAI4B+Z7OYD\nSY+UrXD58mUiIiIsObejuPs1uvv1gV6js0hVTvlYmSMP7gA8gbO3rD8L1Epnfx+Ap556iqJFi5KY\nmEhcXBxxcXFcu3aNK1eucOXKFRITE20fuPPOO6lVqxY9e/bkwQcfpHbt2hw9epRVq1ZlHOqOOyhd\nuvRt61P+T8TExHD06NFMLywwMBAfn4z/Wc6fP8+FCxm3iPL29qZq1aqZnuPIkSPExsZmuD2j60iR\n3nWcPHmS0NBQ23tXvY5bpVxHRj/XrnYdGTl//vxt/4apudJ1ZPbvcerUqSzLZ6uvIyfls9GxjnPH\nGPMe8GomuwhQW0QOpfpMBeA74FsRGZrJsfsB6f8kKaWUc+ovIgusDpFTxpg7gf8CzUXkp1TrPwBa\nikjzW/bX8lkp5WqyLJ/1CUHufQTMzmKfIykvjDHlgW+BHZlVBpJtAPoDx4CYPGRUSqn85gNUIanc\nckUXgJtA2VvWl+X/2bv3OJur/fHjr/cMhhmMSy4pikilEMpBKbeRRGF0IVToqt9Jp1xOVFKn6KJy\nTiUq9zsVSaILHZTvmSESKnKPU4jQDGbevz/2nn32jLnu2Xt/9uX9fDz2o9nrc3t/pm3tWZ+11nvB\ngVz2t/rZGBMuCl0/Ww9BELh7Bj4H/g/oo/ZLN8aYkCEiXwPfqOpf3e8F2A28rqovOhqcMcYEgfUQ\nBJi7Z+BL4GdcaUarur5rQFVzjlk1xhgTfK8Ak0Ukhf+lHY0HJjsZlDHGBIs1CAKvA1DH/drjLhNc\ncwxinQrKGGOMi6rOFZFzgGdwDRXaAHRU1V+djcwYY4LDhgwZY4wxxhgTxWxhMmOMMcYYY6KYNQiM\nMcYYY4yJYtYgMMYYY4wxJopZg8AYY4wxxpgoZg2CIhKRa0VkkYjsE5FMEelaiGOuF5EUEUkTkR9E\npF8wYjXGmGhi9bMxxvjGGgRFl4ArJd2DuFKH5ktELgQ+Aj4DGgGvAZNEpEPgQjTGmKhk9bMxxvjA\n0o4Wg4hkAreo6qJ89hkDdFLVhl5ls4BEVb0xCGEaY0zUsfrZGGMKz3oIAu8vwIocZcuAFg7EYowx\n5n+sfjbGGGyl4mCoDhzMUXYQKC8icaqanvMAEakMdAR2AmkBj9AYY3xXGrgQWKaqhxyOpaisfjbG\nRLJC18/WIAhNHYEZTgdhjDFF0BuY6XQQQWD1szEm3BRYP1uDIPAOANVylFUDjuX29MltJ8DEiROp\nX79+AEPL3bBhw3jhhReCft1givR7jPT7A7vHULFt2zYGDhwI7norzFj9HIIi/R4j/f7A7jFUFKV+\ntgZB4K0FOuUoS3KX5yUNoH79+jRu3DhQceUpMTHRkesGU6TfY6TfH9g9hqBwHD5j9XMIivR7jPT7\nA7vHEFRg/WyTiotIRBJEpJGIZH0K6rjf13Rvf15Epngd8pZ7nzEiUl9EHgSSgVeCHLoxxkQ0q5+N\nMcY31iAoumbAeiAFV57rl4FUYJR7e3WgZtbOqroT6Ay0x5UfezDQX1VzZrYwxhhTPFY/G2OMD2zI\nUBGp6kryaUip6t25lK0CmgYyLmOMiXZWPxtjjG+sh8CcJTk52ekQAi7S7zHS7w/sHk10iobPRKTf\nY6TfH9g9hiNbqdhHIvIQ8BiuLuhvgYdV9f/y2Pc64IscxQqcq6r/zWX/JkDKqlWrwmnCijEmCm3Y\nsIHWrVsDNFXVVKfjAaufjTEGilY/Ww+BD0TkNlxjU58CrsT1hbNMRM7J5zAF6uH6gqpOHl82xhhj\nfGf1szHGFJ01CHwzGJigqlNVdStwP3ASuKeA435V1f9mvQIepTHGRB+rn40xpoisQVBEIlIS1wS0\nz7LK1DXuagXQIr9DgQ0isl9EPhWRloGN1BhjoovVz8YY4xtrEBTdOUAscDBH+UFcXc25+QW4D+gB\ndAf2AF965co2xhhTfFY/G2OMDyztaBCo6g/AD15FX4vIRbi6tvvlddywYcNITEzMVpacnEzPnj0D\nEqcxxuRn3rx5zJ8/P1vZ0aNHHYrGP6x+NsZEguLWz5ZlqIjcXdIngR6qusirfDKQqKrdCnmesUAr\nVW2VyzbLYmGMCQuhlGXI6mdjjPkfyzIUQKp6GtcqmO2yykRE3O/XFOFUjXF1VRtjjPEDq5+NMcY3\nPg8ZEpHyhd1XVY/5ep0Q9QowWURSgHW4upbjgckAIvI8UENV+7nf/xX4GdgMlAYGAm2ADkGP3Bhj\nIpvVz8YYU0TFmUPwO67czfkR9z6xxbhOyFHVue6c1s8A1YANQEdV/dW9S3WgptchpXDlxa6Bqzt7\nI9BOVVcFL2pjjIl8Vj8bY0zRFadB0MZvUYQhVX0DeCOPbXfneP8i8GIw4jLGmGhn9bMxxhSNzw0C\nVV3pz0CMMcYYY4wxwefzpGIRaVjYlz8DDhUi8pCI/Cwif4rI1yJyVQH7Xy8iKSKSJiI/iEie6eyM\nMcb4zupnY4wpmuIMGdqAa36AFLBfxM0hEJHbcI05vZf/TVpbJiIXq+pvuex/IfARri7sXkB7YJKI\n7FfV5cGK2xhjIp3Vz8YYU3TFaRDU9lsU4WcwMEFVpwKIyP1AZ+AeYGwu+z8A7FDVIe7320TkGvd5\n7AvHGGP8x+pnY4wpouLMIdjlz0DChXvhm6bAP7LKVFVFZAXQIo/D/gKsyFG2DBgXkCCNMSYKWf1s\njDG+KU4PgYeI9M1ve9aTmghxDq4hUAdzlB8E6udxTPU89i8vInGqmp7bQdu2bStOnMZEJfeqjACs\nWmWZIwMtxOqpiKyf7TMdmuz/S9HZ7yy4ilJP+aVBALyW431JXAvBnMKV1zmSGgRBM3DgQKdDMCas\neX/5GONPTtXP9pkOTfb/pejsdxZa/NIgUNWKOctEpB7wJpGX3/k3IAPXgjfeqgEH8jjmQB77H8vr\n6RPAxIkTqV8/r4da/pPbP0pruZvchPrTHfssB9+2bdtC6eGF1c9BEup1QXG0bduWM2fOeN6XKFGC\nzz//PNs+Tvx/Keh3Hur/T0L1sxzJilI/+6uH4Cyq+qOIDAOmA5cE6jrBpqqnRSQFaAcsAhARcb9/\nPY/D1gKdcpQlucvzVL9+fRo3bly8gH3k1HWjSfny5T0/Hzt2zOd9ghWT9zZwVe7BiKm47LMcPax+\nDo5wrQsKy7sxkPW+ML/zQP5/Keh3Hq7/T5z+LJv/8XkdgkI6g2s5+EjzCjBQRPqKyCXAW7iGSE0G\nEJHnRWSK1/5vAXVEZIyI1BeRB4Fk93lMFMpZeed8X9h9gh1TqMv5BRgOX4jG7yKqfg7Xz3T58uU9\nr+LsEyjFvXZ8fHy+70PBwoULnQ4hm3D9LEcLf00q7pqzCDgXGASs9sc1QomqzhWRc4BncHUtbwA6\nquqv7l2qAzW99t8pIp1xZa34f8BeoL+q5sxs4Yhjx44F/Ul0YYRiTCb02WclukVa/Qzh95nO7eFC\nQT2Oue0TKP64dqtWrVi+fHm296FmyZIldO/e3ekwsgm3z3I08deQoQ9yvFfgV+Bz4G9+ukZIUdU3\ncC1kk9u2u3MpW4UrHV5ICrV/pE5+WfhLpDVoQrXhaExOkVY/h5pIrwsKc38LFiygR48erF69mlat\nWrFgwQJHY8q5vWLFinTu3DmgMZnI4q9JxZ6hRyIS4y7L9Me5jQlHBTVoOnTokO3pUocOHc46R7C/\ndAtzvUj74jfG+CbS64LC3F+gGwE5FRTTsWPHWLhwIUuWLKFz584h1ztgQpvf5hCISH8R+Q74E/hT\nRL4TkQH+On+oEJGKIjJDRI6KyBERmSQiCQUc856IZOZ4fRysmE3oWbBgAR06dCA+Pp4OHTrk+cVy\n7NgxzysYgn09Y/zJ6ufQUJix4k6OJ4/ksezdu3fnnXfescaAKTJ/zSF4BngUGM//MjO0AMaJSC1V\nfdIf1wkRM3GNS20HlMI1UW0CcGcBxy0F7sI1vwIgz3R2BmJjY8nIyMj2PqdWrVrx/fffc9lll7F6\ndfhNVQn20yVjooDVzyGiMH9kO/mHeCQ1AozxB3/NIXgAGKiqs7zKFonIRlyNhIhoELgzVnQEmqrq\nenfZw8ASEXlMVfPKcw2Q7jWpzRTgyJEjVKxYkYyMDGJjYzly5Ei27a1atWLTpk0AbNq0iVatWoVU\no6BSpUocPnw423tjTOBY/WyMMb7z15ChksB/cilPIYBrHTigBXAk68vGbQWuSdTNCzj2ehE5KCJb\nReQNEbG/EAtw5MgRjh07dlZjAPA0BvJ6DzB06FAaNGjA0KFDAxZjXnbu3OlpBFSqVImdO3cGPQZj\noozVz8YY4yN/NQim4eolyOleYIafrhEKqgP/9S5Q1QzgsHtbXpYCfYG2wBDgOuBj94I5JgCGDh3K\nm2++yZ49e3jzzTcdyXO9c+dOjh07Zo0BY4LD6mdjjPGRP5/e9xeRJOBr9/vmQC1gqoh4FnhR1Uf9\neE2/EJHngfweIytwqa/nV9W5Xm83i8gmYDtwPfBFXscNGzaMxMTEbGXJycn07NnT11CixjvvvHNW\nWTimLjUmlMybN4/58+dnKzt69GhAr2n1szHGFKy49bOoarGDEJE8K80cVFXbFvuCfiYilYHKBey2\nA+gDvKSqnn1FJBZIA5JV9cMiXPO/wBOqOjGXbU2AlFWrVtmy3vnIL0Vm1vyDnKxBYIx/bdiwgdat\nW4Nr7H6qv89v9bMxxvimKPWzv9YhaOOP8zhFVQ8BhwraT0TWAhVE5EqvcartcGWm+Kaw1xOR83F9\nwf3iQ7jGLb8/7tu2bZstz78xJjxZ/WyMMYHnt3UIooGqbgWWARNF5CoRaYUri9Is7wwW7olpN7t/\nThCRsSLSXEQuEJF2uFZ2/sF9LhMAWXn+vVnvgDGRy+pnY4zxXSRlAAqWXsA/cWWvyATmA3/NsU89\nIGtwaQbQENektQrAflxfNE+q6ulgBBytLM+/MVHH6mdjjPGBNQiKSFV/p4BFblQ11uvnNOCGQMdl\njDHRzupnY4zxjQ0ZMsYYY4wxJopZg6CIROTvIrJaRE6IyOGCj/Ac94yI7BeRkyKyXETqBjLO4pg3\nb57TIQRcpN9jpN8f2D2as1n9HBki/R4j/f7A7jEcWYOg6EoCc4E3C3uAiAwFBuFaqO1q4ASwTERK\nBSTCYsqZxzYSRfo9Rvr9gd2jyZXVzxEg0u8x0u8P7B7Dkc0hKCJVHQUgIv2KcNhfgdGq+pH72L7A\nQeAWXF9exhhjisnqZ2OM8Y31EASYiNQGqgOfZZWp6jFcebFbOBWXMcZEO6ufjTHGxRoEgVcdUFxP\nnLwddG8zxhjjDKufjTEGGzIEgIg8DwzNZxcFLlXVH4IUUmmAbdu2Bely2R09epQNGzY4cu1gifR7\njPT7A7vHUOFVT5UOxPmtfs4uHD4TxRXp9xjp9wd2j6GiKPWzqGpgowkDIlIZ11L1+dmhqme8jukH\njFPVSgWcuzawHWisqhu9yr8E1qvq4FyO6QXMKPwdGGOM43qr6kx/n9TqZ2OMKbYC62frIQBU9RBw\nKEDn/llEDgDtgI0AIlIeaA78K4/DlgG9gZ1AWiDiMsYYPykNXIir3vI7q5+NMcZnha6frUFQRCJS\nE6gEXADEikgj96afVPWEe5+twFBV/dC97VVghIj8hOtLZDSwF/iQXLi/AP3+pM0YYwJkjdMBgNXP\nxhiTi0LVz9YgKLpngL5e71Pd/20DrHL/XA9IzNpBVceKSDwwAagAfAV0UtVTgQ/XGGOihtXPxhjj\nA5tDYIwxxhhjTBSztKPGGGOMMcZEMWsQGGOMMcYYE8WsQWCMMcYYY0wUswaBMcYYY4wxUcwaBMYY\nY4wxxkQxaxAYY4wxxhgTxaxBYIwxxhhjTBSzBoExxhhjjDFRzBoExhhjjDHGRDFrEBhjjDHGGBPF\nrEFgjDHGGGNMFLMGgTHGGGOMMVHMGgTGGGOMMcZEMWsQGGOMMcYYE8WsQWCMMcYYY0wUswaBMcYY\nY4wxUcwaBMYYY4wxxkQxaxAYY4wxxhgTxaxBYIwxxhhjTBSzBoEfiMi1IrJIRPaJSKaIdM1ln2dE\nZL+InBSR5SJS14lYjTEmmhSmfvba9y33Pv8vmDEaY4zTrEHgHwnABuBBQHNuFJGhwCDgXuBq4ASw\nTERKBTNIY4yJQvnWz1lEpBvQHNgXpLiMMSZklHA6gEigqp8AnwCIiOSyy1+B0ar6kXufvsBB4BZg\nbrDiNMaYaFOI+hkROQ94DegIfBy86IwxJjRYD0GAiUhtoDrwWVaZqh4DvgFaOBWXMcYYTyNhKjBW\nVbc4HY8xxjjBGgSBVx1XN/XBHOUH3duMMcY4ZxhwSlX/6XQgxhjjFBsyFIJEpDKuruudQJqz0Rhj\nTL5KAxcCy1T1kMOxFImINAX+H3BlEY6x+tkYEy4KXT9bgyDwDgACVCN7L0E1YH0ex3QEZgQ4LmOM\n8afewEyngyiia4AqwB6v6QWxwCsi8oiq1snlGKufjTHhpsD62RoEAaaqP4vIAaAdsBFARMrjymbx\nrzwO2wkwffp0Lr300mCEmc3gwYMZN25c0K8bTJF+j5F+f2D3GCq2bNnCnXfeCe56K8xMBZbnKPvU\nXf5eHsfsBKufAynS7zHS7w/sHkNFUepnaxD4gYgkAHVx9QQA1BGRRsBhVd0DvAqMEJGfcP1PGQ3s\nBT7M45RpAJdeeilNmjQJZOi5SkxMdOS6wRTp9xjp9wd2jyEoJIfPFKJ+PpJj/9PAAVX9MY9TWv0c\nYJF+j5F+f2D3GIIKrJ+tQeAfzYAvcE0eVuBld/kU4B5VHSsi8cAEoALwFdBJVU85EawxxkSRfOvn\nXPbPc60CY4yJVNYg8ANVXUkBGZtU9Wng6WDEE+1Kly5Nenp6ntvj4uJISkoKYkTGGKcUpn7OsX9u\n8waMMSaiWdpRE3HyawwUZrsxxhhjTDSxBoE5yx133OF0CMVSqVKlAreH+z0WJNLvD+weTXSKhs9E\npN9jpN8f2D2GI1G14ZKhRkSaACkpKSnhNGElJHmlEsQ+68b4X2pqKk2bNgVoqqqpTscTaFY/R67C\nDDdNSwvJufPG5Koo9bP1EBhjjDEm6qWlpaGqqCqXXXYZAJdddpmnzBoDJpJZg8AYY4wxxpgoZlmG\njDHGGBMUs2bNYtasWYDrifyuXbu44IILKF26NOAal+3U2OzKlStz+PDhbGXff/+9Z+hppUqVOHTo\nkBOhGRNw1iAwxhhjTFAMGjTorD+6f/jhB8/Pq1evdqxB0KtXL+bNmwfAwYMHPeXVqlUDoGfPno7E\nZUww2JAhY4wxEUtErhWRRSKyT0QyRaSr17YSIjJGRDaKyHH3PlNE5FwnYw5XpUuXRkTyfJUuXZoT\nJ07ke46CtgfS+PHjOXDgAAcOHKBkyZIAlCxZ0lM2fvx4x2IzJtCsQWCMMX7Wpk0bHn30UafDMC4J\nwAbgQc5ehTgeaAyMAq4EugH1gQ+DGWCkKMykXO994uLiAFf2Hpu4a4LB6ua82ZAhY4wJQXfffTdH\njx5l4cKFTocS1lT1E+ATAPHOQ+zadgzo6F0mIoOAb0TkfFXdG7RAw4A/0nLWqlWLPXv2ZCtLT0/3\njNOvWbMmu3fvLn6wxgRIpNbN1kNgwkp+3dFZL2OMKYYKuHoSfnc6kFDjj7Scu3fv9uyfVV+LiKfM\nGgPGOMMaBCasFGYV4qI6c+YMaWlpZGRk2OJlxu/eeOMNLr74YsqUKUP16tW59dZbPdvmz59Pw4YN\niY+P55xzziEpKYk///yTUaNGMWXKFD788ENiYmKIjY1l1apV+V5n1KhRnn1jYmI8r6lTpwb6FiOG\niMQBLwAzVfW40/GEG+8HM99//z3wvyw9ofDAplatWvk+TKpVq5aj8ZngClbd3K5dOx5++OFsZb/9\n9htxcXF88cUXAbk3X9iQIRNSCtMlnfVHu6+rEO/atYuZM2fyzTffsGXLFrZv305GRgYAMTEx1K1b\nl8aNG9OkSRNuueUW6tevX+hzd+zYkS+//NIT0+nTpylZsqQn1tOnT+cba1xcHBkZGZw5cybPfUqU\nKMHp06cLHZNxTkpKCn/961+ZMWMGLVq04PDhw3z11VcAHDhwgF69evHSSy9xyy238Mcff/DVV1+h\nqjz22GNs2bKFP/74g8mTJ6OqBTZ2H3/8cR544AHP++nTp/PUU0/RrFmzgN5jpBCREsA8XL0DDxa0\n/+DBg0lMTMxW5mTKzFDgXbfFxMR4egEyMzMdjOp/vHsfevbsyfz580lOTvZkFjLRI5h184ABA3j4\n4Yd55ZVXPJPVp02bxvnnn0+bNm38dk/eKX2zHD16tNDHW4PAhJSEhIR8GwQJCQk+n/uDDz7g1Vdf\nZeXKlcTHx3PNNdfQqVMnLrnkEsqWLcuZM2f4888/2bp1Kxs2bGD06NEMGzaMJk2a0KdPHwYMGEDZ\nsmXzvcayZcs8Pz/77LOMHDmSJ598khEjRgDZ/8F+/vnnnDhxgoSEBNq2bQuc/QdFnTp1+Pnnn6ld\nuzY7duzw+d6NM3bv3k3ZsmXp3LkzCQkJ1KxZk0aNGgHwyy+/kJGRQbdu3ahZsyYADRo08BxbpkwZ\nTp06RZUqVQp1rfj4eOLj4wH4+uuvGTFiBNOmTfMM7TB582oM1ATaFqZ3YNy4cTRp0iTgsZng8a6f\nsx7KnDlzhq5dXYmpor3BF0mCWTd3796dQYMG8eGHH5KcnAzAlClTuPvuu/16T7l9PlNTU2natGmh\njrcGgQkp//znPz0V8pIlS8jMzCQmJobOnTsDFKsy7tatG9dddx1Tpkyhe/fuBf5xn5aWxscff8zM\nmTMZMmQIzz77LI8//jgPPfRQnsfm1sMxcuRIRo4c6Xmf1VuQ9TTtxIkTfPTRRwDs3LmTvn37ntVD\n8PPPP3uOsx6C8JGUlEStWrWoXbs2N9xwAzfccAPdunWjTJkyNGrUiHbt2nH55ZfTsWNHkpKSSE5O\npkKFCsW65u7du+nWrRtDhgyhR48efrqTyOXVGKgDtFHVIw6HFLJKlix5Vt3kvXBXuNdNkydPztbD\nm/XfrAc96enp1iCIEMGsm+Pi4ujTpw/vvvsuycnJpKamsnnzZhYvXuznuyoem0NgQsrkyZNZtmwZ\ny5Yt83QzZ2ZmesomT57s87k//PBDvvjiC/r27VtgYwBcf9x3796d+fPn89NPP3HrrbcycuRILr74\nYpYvX57rMSVK5N/GTkhIIDMzk8zMTE8MZcuW9ZRt3LjRk4ovLwVtN6EjISGB9evXM3v2bGrUqMFT\nTz1Fo0aNOHbsGDExMXz66ad88sknNGjQgPHjx1O/fn127drl8/VOnjxJ165dadWqFU8//bT/biSM\niUiCiDQSkcbuojru9zXdjYEFQBPgTqCkiFRzv0o6FnSIatu2LaVKlaJUqVLZyrPKsno6Q5X3Ognz\n588HXGPFs8pWrlxJenp6tqxHIuIp8+4BNuEt2HXzgAEDWL58Ofv37+e9996jbdu2nt6HUGENAhNS\nVq5cyalTpzh16lS28qyylStXFuo8mZmZDBkyJFtZ165dfZ7UVqtWLd544w1++uknLr/8cpKSknj8\n8cfPijO/sf/g6g3I+vI5ftw1KuH48eOestjYWCZOnEiXLl3o0qVLtmOzyiZOnOjTPRhnxMTE0LZt\nW1544QW+/fZbdu7cyeeff+7Z3qJFC5566inWr19PqVKleP/99wHXH1lZc1sKq3fv3ogI06ZN8+s9\nhLlmwHogBdf8gJeBVFxrD5wHdAHOx7VWwX7gF/d/WzgRbChbtmxZWP/B7J0lyfuBjK2BEJ2CWTdf\nfvnlNGvWjLfffptZs2bRv39/v96LP9iQIRNSvCtkXycNnzp1irvuuovZs2f7NTZwNQw++eQTXnnl\nFf7+97+zZs0alixZ4ulKLCh+70nH3o2JrCdu119/fbZxgN7nWLRokd/vxwTWkiVL2LFjB61bt6Zi\nxYosWbIEVaV+/fqsW7eOzz77jKSkJKpWrcrXX3/Nb7/95hnzf+GFF/Lpp5/yww8/ULlyZRITE/Pt\ngXrqqaf47LPPWL58OceOHePYsWMAJCYmUrp06aDcbyhS1ZXk//DLHowZE2WCWTdn6d+/P4MGDaJs\n2bLccsstgb7FIvO5IhSRku4u1/oiUvRcj8YEQGZmJv369WPBggXMnTs3INeIiYnhscceY9WqVWzZ\nsoX27dtz6NChQh1711130bFjRzp2zLYWkqfsrrvuCkDEJtiyGnL8OQG2AAAgAElEQVQVK1Zk4cKF\ntGvXjssuu4y3336b2bNnc+mll1K+fHlWrVpF586dqV+/Pk8++SSvvPIKSUlJAAwcOJD69evTrFkz\nqlatypo1a/K95qpVqzhx4gQtW7akRo0anleg/h0YY0y4caJuznLHHXdQokQJevXqddawu1AgRXny\nKiLlcI2zvB24GigFCK5u2L3Ap8Dbqvp//g81eohIEyAlJSUl6rJYeGd58J5wkzV8Jq+n51mf4+HD\nhzNmzBjmzp1LcnKyz70MhbVhwwY6dOjAueeey4oVK6hatapnW0HXLkxsgY7fmOLyymLRVFVTnY4n\n0KK5fvZWUFrRwqQddTI1ably5Th+/Dhly5bljz/+CKnYTGTauXMndevWJSUlxZPRKNCKUj8XesiQ\niDwKPAFsBxYD/8A1zvJPoBJwOXAt8KmIfAM8rKo/+nQHJmoVZ7jMW2+9xQsvvMDLL7/sSe0VaI0b\nN+bLL7+kffv2dOrUiVWrVhUrNaoxxhhjIseZM2f47bffGDFiBC1atAhaY6CoijJk6Cqgtaperaqj\nVXWZqm5S1Z9UdZ2qvquqdwPVgQ9wNQ4MICIxIjJaRHaIyEkR+UlERjgdVyRZuXIlDz30EA8//DCD\nBw8O6rUbNGjA0qVL2bp1K3fddZc9TTIB8fzzz1OuXLlcX1lpeY0xeatcuXK+SR0qV67scIQmHBVU\nN69evZoaNWqQmprKW2+95XS4eSp0D4GqFir5rqqmA6F7x84YBtwH9AW+x5X1YrKI/K6q/3Q0sgjR\nu3dvWrduzbhx43zOJFQcjRs3Zvr06XTv3p1nnnnGUj4av3vggQe47bbbct1WpkyZIEdjTPjxnusV\nGxvrWeemqBljjPFWUN187rnnhsWDQp+zDIlIFaABUAH4Ddilqnv8FViEaQF8qKqfuN/vFpFeuOZh\nGD/4888/mT59OrGxsY7F0K1bN5577jmeeOIJrrjiCsfiMJGpQoUKxV60zBhjjH9FSt1c5AaBiFwI\nvAf8BfgDSAPKAuVEZB3QW1V3+i/EiLAGGCgi9VT1RxFpBLQCgju2JYK9++67nHfeeU6HwfDhw1m/\nfj0DBgxwOhRjjDHGmELxJe3ocPcrQVWrqmotVa0EJOBa6MXGxp/tBWAOsFVETuFaIOdVVfV/ovwo\ndfPNNzsdAuCaCD1hwgSbWGxMiBCRa0VkkYjsE5FMEemayz7PiMh+9xyv5SJS14lYTWDNmjWLrl27\n0rVrV88QjszMTE9ZVoY7Y6KRL0OGVqvq1zkLVfUUrgxD1YsfVsS5DeiFK13r90Bj4DUR2a+qtqSo\nD0I5BWelSpWYNm0abdu2dToUY4zrYdUG4B1gYc6NIjIUGIRrjtdO4FlgmYhc6v5eMxHCO4udd1pR\nW/TRGN8aBFeKyMeq+lvODSJSA2gOTC12ZJFlLPC8qs5zv9/sHno1HMizQTB48GASExOzlXlXaNFs\nxowZToeQrzZt2jgdgjF+571OSJajR486FE3huOdufQIguWcc+CswWlU/cu/TFzgI3ALYqm7GmKjg\nS4NgBvB/InIUOIxrDoEAVd2vfv4LL2LEAznTGGRSwJCtcePGRfXCN3k5dOhQ0FOLFldGRoajE56N\n8YfcHkh4LXwTdkSkNq5U2Z9llanqMfdaOi2wBoExJkoUuUGgqv8RkfpAa+BC4BzgKLAVWKWqlr/r\nbIuBESKyF9gMNME1oXiSo1GFqSFDhnDmzBmnwyiSiRMncv/99zsdhjEmu+qA4uoR8HbQvc0YY6KC\nT2lH3eMqV/g5lkg2CBgN/AtXL8p+4E13mSmC1NRU3n33Xd544w0efPBBp8MptOHDh9OtWzeqVavm\ndCjGGD+wIZ3GmFBS3CGdEsqTM6OViDQBUlJSUqJ6yJD3cF9VRVVp3749v/zyCxs3bqRkyZLZthfm\nHMHkfe3KlSvTqVMnpk2bluv2UIzfmMLwGjLUVFVTnY4nPyKSCdyiqovc72sD24HGqrrRa78vgfWq\netbYRKufXbwn5ea26FJB2wu7T6D4I35jQl1R6mdf0o4a44hly5bx+eefM2bMGEqU8HlNPUe8+OKL\nTJ8+na+++srpUIwxbqr6M3AAaJdVJiLlcSXHWONUXMZEkpIlSyIieb68H+4Z5/i1QSAiDUVkmD/P\naQy4JuUOGTKE1q1bc9NNNzkdTpH169ePpk2b8vjjj9uTfmOCSEQSRKSRiDR2F9Vxv6/pfv8qrjle\nXUTkClxZ8vYCHzoRrzGRpqCEGpZwIzT4u4egCnC1n89pDNOmTWPTpk28+OKL2YbRhIuYmBjGjh3L\nN998w4IFC5wOx5ho0gxYj2tBSAVeBlJxLaSJqo4FxgMTgG+AMkAnW4PAGP8YOHAg1apVO2sOXVbZ\nwIEDHYrMePPruAtV/Qyv9G3G+MvTTz9Nz549ufrqgtubJUuWzDULUVZDokSJEpw+fdrvMRakbdu2\n3HDDDQwfPjxkVlY2JtKp6koKePilqk8DTwcjHmOiTcuWLdm1axcAixcv9pRnfZ+3bNnSkbhMdn5p\nEIhIQ+CUqm71x/mMyWnXrl189NFHhdo3IyP/zLcFbQ+kMWPG0LhxY95++23HYjDGGGOCxVaIDg9F\nGjIkIs1FpK7X+0tEZAuuZeE3i8hXInKOv4M0plu3blx++eWF2rdixYrF2h5IDRs2pG/fvowaNcqx\nGIwxxhhjvBV1DsEeXCsVZ3kcV4792kAjYDrwvH9CM+Z/nnjiiULve+jQIU+a0txehw4dCmCkBRs9\nejTHjh1zNAZjjDHGmCxFbRAcAJqJSA33+xWq+pmq7lLV71R1AvCjf0M0hqw8uhGhZs2aDBo0yOkw\njDHGGGOAojcIzgMEyBqEXSprg4hk/ezcAG1jwsTw4cOdDsEYY4wxBij6pOJzgSnAfeJK2ZIgIlfh\nSuF2SETG4krvZkxUyislas4VhytXrhyskIwxxhhj8lWkBoGqrgPW5bZNRNoDe1V1nz8CM9HtP//5\nj9Mh+CQuLo709PR8txtjjDHGhBKfFiYTkXNFJNmdbjTLAeB8ESnrn9BMNHvttdecDsEnaWlpnsnL\no0ePBlyTiLPK0tLScj1u48aNwQzTGOMmIjEiMlpEdojISRH5SURGOB2XMeDKTBcTE+N5iUi29w0b\nNiz4JMYUQpEbBCLSGvgJmAusF5GX3JsOANWBo/4Lz0SjX375hTlz5jgdhk86duxIXFwccXFxPPPM\nMwA888wznrKOHTvmetyQIUOCGaYx5n+GAfcBDwKXAEOAISJiM/9NSFHVbP81xp986SH4O9AXKA80\nBKqKyBhVTce17Hvug6iNKaQJEyZQqlSpXLdVrlwZETlrrH5WmdNj85ctW0Z6ejrp6emcOnUKVeXU\nqVOesmXLluV53PLly4McrTEGaAF8qKqfqOpuVV0IfAoUvCy6MQG2ZcsWTw+zt6yyLVu2OBSZiTS+\nNAjWquoCVT2uqptVtS+wVUT6A+p+GeOT9PR03nzzTe66665ctx8+fDjf4wvaHqpatmzJkCFDyMzM\ndDoUY6LNGqCdiNQDEJFGQCvgY0ejMgY4ffq054//EiVc0z5LlCjhKTt9+rTDEZpI4UuD4A8AEamT\nVaCq7wH7gZv8FJeJUnPnzuW///0vDz/8cK7b81twLLenKOHixRdfZMOGDcyYMaPgnY0x/vQCMAfX\ng61TQArwqqrOdjYsY7IPQz1z5gwAZ86cKXAYqjFFVdS0owD/FpF/AENFpJWqfg2gqkvd8wuO+zVC\nE1XeeustOnToQP369Z0OJahatmxJjx49GD58ON27dychIcHpkIyJFrcBvYDbge+BxsBrIrJfVafl\nddDgwYNJTEzMVnbHHXdwxx13BDJWE2W8h5nGxMSgqohIvtnsTHSaNWsWs2bNylZ29Gjhp/UWuUGg\nqutEZBMwS1U35di2SkQaF/WcxgBs3ryZNWvWMG/ePKdDccTYsWO57LLL+Mc//sFzzz3ndDjGRIux\nwPOqmlXxbBaRC4HhQJ4NgnHjxtGkSZPAR2eMMYWQ2wOJ1NRUmjZtWqjjfekhQFX/BDblse1nX85p\nzMSJE6lSpQpdu3Z1OhRH1KlThyFDhjBmzBjuvvtu6tat63RIxkSDeCAjR1kmPqblNkWnquzYsYPv\nvvuOHTt2sH37dn799VeOHTvG8ePHiYmJoUyZMsTHx1OjRg0uuOACateuTaNGjbjooovyXBDSGFN4\nPjUIjPG3tLQ0pk6dysCBA/PMMBQNhg0bxpQpU3j00UdZtGiR0+EYEw0WAyNEZC+wGWgCDAYmORpV\nBMvMzGTjxo3Z5nxddNFFAJQpU4Y6depQvXp1ypUrR+XKlVFVTp48yfHjx/niiy/YtWsXx4+7Ricn\nJibSrFkz2rdvT1JSEo0bNyYmxtpyxhSVNQhMSFiwYAFHjhxhwIABTofiqPj4eF5++WV69uzJ0qVL\nnQ7HmGgwCBgN/AuoiitBxpvuMuMnGRkZrFq1ijlz5vDBBx9w8ODBbNuXLFlC48aNOffccwt84q+q\n/Prrr6SmppKSksLatWt59tlnGT58OOeeey79+vWjf//+1stqTBFYM9qEhIkTJ9KmTRvq1avndCiO\n69GjB+3atWPQIFsXyZhAU9UTqvqoqtZW1QRVraeqT6nqGadjiwSqytChQzn//PNp27Ytn3zyCXfe\neSefffZZtv1uvPFGatSoUajhPyJC1apVueGGG3jiiSf46KOPOHz4MCtXrqRHjx689dZb1KtXj44d\nO7Ju3bpA3VrIKF26tGctntxepUuXdjpEEwaK1SAQkZG5/WzOJiI1RGSaiPwmIidF5FsRsRlpwLZt\n21i5ciUDBw50OpSQICK88cYb7N271+lQjDGmyDIyMrINB3r77bfp2bMnX3/9NT///DMvvfQSbdu2\n9es1S5UqRevWrRk/fjz79+9n2rRp7N27l+bNm9O9e3e2bt3q1+uFkoEDB1KtWjWqVatGbGwsALGx\nsZ4y+241hVHcHoL4PH42XkSkArAaSAc6ApcCfwOOOBlXqJgyZQoVK1akW7duTocSMi6++GKeeOIJ\np8MwxphCS09P5/XXXz9rqM7+/ft5/fXXad68ebYegKyf/T0puEyZMtx5551s3LiRqVOnsmHDBho1\nasRzzz0XkQt5TZw4kYMHD3Lw4EEyMlzz4zMyMjxlEydOdDhCEw6K2yDQPH422Q0DdqvqAFVNUdVd\nqrrCMjK5JpdNmzaN22+/3bo1cxg6dKjTIRhjTIFUlQULFnDZZZcxePBgWrVqlW17mTJlHIkrNjaW\nPn36sHnzZh599FGeeuoprrrqKjZu3OhIPIHi3UPgzXoITFHYHILg6AL8R0TmishBEUkVkeiePev2\n5ZdfsnfvXvr27et0KCEnLi7O6RCMMSZfqkpSUhLJycnUr1+fjRs3Mn36dKfDyqZMmTI8//zzfPPN\nN2RmZtK8efOwXdU+N+PHj+fAgQMcOHAgW69LVtn48eMdjtCEg+I2CCz5b+HUAR4AtgFJuDJYvC4i\nfRyNKgRMnTqVevXq0bx5c6dDCXm7du1yOgRjjDnL5s2b+fjjj/n4449p0KABELjhQMXRtGlTvvnm\nG3r37u0pi6SGgTHFYWlHgyMGWKeqWROvvxWRy4H7yWclzMGDB5OYmJitLLeV6MLViRMnmD9/PsOG\nDQupL41QNWDAAD799FP7XRnHzJo1i1mzZmUrO3r0qEPRGKeoarY/pL/99luqVKniYESFV6ZMGSZN\nmsQ777zjKTt06BCVK1d2MCpjnFfcBoE1rQvnF2BLjrItQPf8Dho3bhxNmkRuIqL333+fEydOcOed\ndzodSlhYsWIFkyZNsvGgxjG5PZBITU2ladOmDkVkgi0zM/OslMjh0hjIS6tWrVi6dCm1a9d2OhRj\nHGNzCIJjNVA/R1l9IKrHgEydOpXrrruOCy+80OlQwkL//v3529/+xu7du50OxZiIYmmhC+f06dP0\n6dOHCRMmOB1KsXn3tJ45c4YWLVqwadMmByMyxlnWIAiOccBfRGS4iFwkIr2AAcA/HY7LMfv27WPF\nihX06RP10ygK7eWXX6ZChQr069fPk1rOGFM8lha6cDIzM+nTpw/z5s1jzpw5TofjNyLC2rVrOffc\nc2nbti3fffed0yEZ44jiNghe9Pr5pWKeK2Kp6n+AbsAdwCbgCeCvqjrb0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du2benRowfd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161i7di1z585ly5YtBfWqVatGkyZNaNasGU2bNqVp06Y0adKExo0b07hxYxo1ahRxutdkYWbN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\nBGBmj5rZS2H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"text/plain": [ "<matplotlib.figure.Figure at 0x10f53e8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = sncosmo.plot_lc(lightcurve.snCosmoLC(), color='k', model=sncosmoModel)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

HWNi/data-512-a1

hcds-a1-data-curation.ipynb

2

173304

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A1 Data Curation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step1: Data Acquisition" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import packages that will be used in this assignment\n", "import requests\n", "import json\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "import seaborn as sns\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the monthly traffic data on English Wikipedia from January 2008 through September 2017, we need to use 2 API endpoints, the Pagecounts API and the Pageviews API. The Pagecounts API provides monthy desktop and mobile traffic data from January 2008 through July 2016, and the Pageviews API provides monthy desktop, mobile-web, and mobile-app traffic data from July 2015 through September 2017. Once the user finishes the parameter settings for the API request, the traffic data will be returned in JSON format. The codes below will get you all pagecounts for English Wikipedia accessed through desktop from January 2008 through July 2016." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Collect desktop traffic data from January 2008 through July 2016 using the Pagecounts API\n", "endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'\n", "\n", "params_pc_desktop = {\n", " 'project' : 'en.wikipedia.org',\n", " 'access' : 'desktop-site',\n", " 'granularity' : 'monthly',\n", " 'start' : '2008010100',\n", " 'end' : '2016080100'#use the first day of the following month to ensure a full month of data is collected\n", " }\n", "\n", "api_call = requests.get(endpoint_pagecounts.format(**params_pc_desktop))\n", "response_pc_desktop = api_call.json()\n", "with open('pagecounts_desktop-site_200801-201607.json', 'w') as outfile:\n", " json.dump(response_pc_desktop, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The codes below will get you all pagecounts for English Wikipedia accessed through mobile from January 2008 through July 2016." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Collect mobile traffic data from January 2008 through July 2016 using the Pagecounts API\n", "endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'\n", "\n", "params_pc_mobile = {\n", " 'project' : 'en.wikipedia.org',\n", " 'access' : 'mobile-site',\n", " 'granularity' : 'monthly',\n", " 'start' : '2008010100',\n", " 'end' : '2016080100'\n", " }\n", "\n", "api_call = requests.get(endpoint_pagecounts.format(**params_pc_mobile))\n", "response_pc_mobile = api_call.json()\n", "with open('pagecounts_mobile-site_200801-201607.json', 'w') as outfile:\n", " json.dump(response_pc_mobile, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The codes below will get you all pageviews for English Wikipedia accessed through desktop from July 2015 through September 2017. Note that the data doesn't count traffic by web crawlers or spiders." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Collect desktop traffic data from July 2015 through September 2017 using the Pageviews API\n", "endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\n", "\n", "headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\n", "\n", "params_pv_desktop = {\n", " 'project' : 'en.wikipedia.org',\n", " 'access' : 'desktop',\n", " 'agent' : 'user',\n", " 'granularity' : 'monthly',\n", " 'start' : '2015070100',\n", " 'end' : '2017100100'\n", " }\n", "\n", "api_call = requests.get(endPoint_pageviews.format(**params_pv_desktop))\n", "response_pv_desktop = api_call.json()\n", "with open('pageviews_desktop_201507-201709.json', 'w') as outfile:\n", " json.dump(response_pv_desktop, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The codes below will get you all pageviews for English Wikipedia accessed through mobile website from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Collect mobile web traffic data from July 2015 through September 2017 using the Pageviews API\n", "endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\n", "\n", "headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\n", "\n", "params_pv_mobile_web = {\n", " 'project' : 'en.wikipedia.org',\n", " 'access' : 'mobile-web',\n", " 'agent' : 'user',\n", " 'granularity' : 'monthly',\n", " 'start' : '2015070100',\n", " 'end' : '2017100100'\n", " }\n", "\n", "api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_web))\n", "response_pv_mobile_web = api_call.json()\n", "with open('pageviews_mobile-web_201507-201709.json', 'w') as outfile:\n", " json.dump(response_pv_mobile_web, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The codes below will get you all pageviews for English Wikipedia accessed through mobile app from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Collect mobile app traffic data from July 2015 through September 2017 using the Pageviews API\n", "endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\n", "\n", "headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\n", "\n", "params_pv_mobile_app = {\n", " 'project' : 'en.wikipedia.org',\n", " 'access' : 'mobile-app',\n", " 'agent' : 'user',\n", " 'granularity' : 'monthly',\n", " 'start' : '2015070100',\n", " 'end' : '2017100100'\n", " }\n", "\n", "api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_app))\n", "response_pv_mobile_app = api_call.json()\n", "with open('pageviews_mobile-app_201507-201709.json', 'w') as outfile:\n", " json.dump(response_pv_mobile_app, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Data processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we have 5 JSON files containing the traffic data we're interested in. In this step, we first iterate these 5 JSON files one by one and combine the data into a Python dictionary. Eventually, the key of the dictionary will be the list of time stamps (from January 2008 to September 2017). For each key (time stamp), we will append a list which contains 5 values: pagecounts accessed through desktop, pagecounts accessed through mobile, pageviews accessed through desktop, pageviews accessed through mobile web, and pageviews accessed through mobile app. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_cleaned = {}\n", "\n", "for item in response_pc_desktop['items']:\n", " timeStamp = item['timestamp']\n", " data_cleaned[timeStamp] = [item['count'], 0, 0, 0, 0]\n", " \n", "for item in response_pc_mobile['items']:\n", " timeStamp = item['timestamp']\n", " if timeStamp in data_cleaned:\n", " data_cleaned[timeStamp][1] = item['count'] \n", " else:\n", " data_cleaned[timeStamp] = [0, item['count'], 0, 0, 0]\n", " \n", "for item in response_pv_desktop['items']:\n", " timeStamp = item['timestamp']\n", " if timeStamp in data_cleaned:\n", " data_cleaned[timeStamp][2] = item['views'] \n", " else:\n", " data_cleaned[timeStamp] = [0, 0, item['views'], 0, 0]\n", " \n", "for item in response_pv_mobile_web['items']:\n", " timeStamp = item['timestamp']\n", " if timeStamp in data_cleaned:\n", " data_cleaned[timeStamp][3] = item['views'] \n", " else:\n", " data_cleaned[timeStamp] = [0, 0, 0, item['views'], 0]\n", " \n", "for item in response_pv_mobile_app['items']:\n", " timeStamp = item['timestamp']\n", " if timeStamp in data_cleaned:\n", " data_cleaned[timeStamp][4] = item['views'] \n", " else:\n", " data_cleaned[timeStamp] = [0, 0, 0, 0, item['views']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After we get the dictionary, we could convert it into a Pandas dataframe and save the dataframe to a csv file" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>pagecount_all_views</th>\n", " <th>pagecount_desktop_views</th>\n", " <th>pagecount_mobile_views</th>\n", " <th>pageview_all_views</th>\n", " <th>pageview_desktop_views</th>\n", " <th>pageview_mobile_views</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2008010100</th>\n", " <td>2008</td>\n", " <td>01</td>\n", " <td>4930902570</td>\n", " <td>4930902570</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008020100</th>\n", " <td>2008</td>\n", " <td>02</td>\n", " <td>4818393763</td>\n", " <td>4818393763</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008030100</th>\n", " <td>2008</td>\n", " <td>03</td>\n", " <td>4955405809</td>\n", " <td>4955405809</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008040100</th>\n", " <td>2008</td>\n", " <td>04</td>\n", " <td>5159162183</td>\n", " <td>5159162183</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008050100</th>\n", " <td>2008</td>\n", " <td>05</td>\n", " <td>5584691092</td>\n", " <td>5584691092</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008060100</th>\n", " <td>2008</td>\n", " <td>06</td>\n", " <td>5712104279</td>\n", " <td>5712104279</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008070100</th>\n", " <td>2008</td>\n", " <td>07</td>\n", " <td>5306302874</td>\n", " <td>5306302874</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008080100</th>\n", " <td>2008</td>\n", " <td>08</td>\n", " <td>5140155519</td>\n", " <td>5140155519</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008090100</th>\n", " <td>2008</td>\n", " <td>09</td>\n", " <td>5479533823</td>\n", " <td>5479533823</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008100100</th>\n", " <td>2008</td>\n", " <td>10</td>\n", " <td>5679440782</td>\n", " <td>5679440782</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008110100</th>\n", " <td>2008</td>\n", " <td>11</td>\n", " <td>5415832071</td>\n", " <td>5415832071</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2008120100</th>\n", " <td>2008</td>\n", " <td>12</td>\n", " <td>5211708451</td>\n", " <td>5211708451</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009010100</th>\n", " <td>2009</td>\n", " <td>01</td>\n", " <td>5802681551</td>\n", " <td>5802681551</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009020100</th>\n", " <td>2009</td>\n", " <td>02</td>\n", " <td>5547320860</td>\n", " <td>5547320860</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009030100</th>\n", " <td>2009</td>\n", " <td>03</td>\n", " <td>6295159057</td>\n", " <td>6295159057</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009040100</th>\n", " <td>2009</td>\n", " <td>04</td>\n", " <td>5988817321</td>\n", " <td>5988817321</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009050100</th>\n", " <td>2009</td>\n", " <td>05</td>\n", " <td>6267516733</td>\n", " <td>6267516733</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009060100</th>\n", " <td>2009</td>\n", " <td>06</td>\n", " <td>5818924182</td>\n", " <td>5818924182</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009070100</th>\n", " <td>2009</td>\n", " <td>07</td>\n", " <td>5801646978</td>\n", " <td>5801646978</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009080100</th>\n", " <td>2009</td>\n", " <td>08</td>\n", " <td>5790850384</td>\n", " <td>5790850384</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009090100</th>\n", " <td>2009</td>\n", " <td>09</td>\n", " <td>4057515768</td>\n", " <td>4057515768</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009100100</th>\n", " <td>2009</td>\n", " <td>10</td>\n", " <td>6016107147</td>\n", " <td>6016107147</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009110100</th>\n", " <td>2009</td>\n", " <td>11</td>\n", " <td>5768486910</td>\n", " <td>5768486910</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2009120100</th>\n", " <td>2009</td>\n", " <td>12</td>\n", " <td>5426505977</td>\n", " <td>5426505977</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010010100</th>\n", " <td>2010</td>\n", " <td>01</td>\n", " <td>5703465285</td>\n", " <td>5703465285</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010020100</th>\n", " <td>2010</td>\n", " <td>02</td>\n", " <td>5762451418</td>\n", " <td>5762451418</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010030100</th>\n", " <td>2010</td>\n", " <td>03</td>\n", " <td>6661347946</td>\n", " <td>6661347946</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010040100</th>\n", " <td>2010</td>\n", " <td>04</td>\n", " <td>6618552152</td>\n", " <td>6618552152</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010050100</th>\n", " <td>2010</td>\n", " <td>05</td>\n", " <td>6410578775</td>\n", " <td>6410578775</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2010060100</th>\n", " <td>2010</td>\n", " <td>06</td>\n", " <td>4898035014</td>\n", " <td>4898035014</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2015040100</th>\n", " <td>2015</td>\n", " <td>04</td>\n", " <td>9421035574</td>\n", " <td>6198945657</td>\n", " <td>3222089917</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2015050100</th>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>9657871297</td>\n", " <td>6323801814</td>\n", " <td>3334069483</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2015060100</th>\n", " <td>2015</td>\n", " <td>06</td>\n", " <td>8203576103</td>\n", " <td>5165413640</td>\n", " <td>3038162463</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2015070100</th>\n", " <td>2015</td>\n", " <td>07</td>\n", " <td>8483698717</td>\n", " <td>5229226022</td>\n", " <td>3254472695</td>\n", " <td>7665421980</td>\n", " <td>4376666686</td>\n", " <td>3288755294</td>\n", " </tr>\n", " <tr>\n", " <th>2015080100</th>\n", " <td>2015</td>\n", " <td>08</td>\n", " <td>8304022031</td>\n", " <td>5035534449</td>\n", " <td>3268487582</td>\n", " <td>7634815221</td>\n", " <td>4332482183</td>\n", " <td>3302333038</td>\n", " </tr>\n", " <tr>\n", " <th>2015090100</th>\n", " <td>2015</td>\n", " <td>09</td>\n", " <td>8582061182</td>\n", " <td>5409631355</td>\n", " <td>3172429827</td>\n", " <td>7655695037</td>\n", " <td>4485491704</td>\n", " <td>3170203333</td>\n", " </tr>\n", " <tr>\n", " <th>2015100100</th>\n", " <td>2015</td>\n", " <td>10</td>\n", " <td>8781786976</td>\n", " <td>5535704471</td>\n", " <td>3246082505</td>\n", " <td>7746031887</td>\n", " <td>4477532755</td>\n", " <td>3268499132</td>\n", " </tr>\n", " <tr>\n", " <th>2015110100</th>\n", " <td>2015</td>\n", " <td>11</td>\n", " <td>8515190628</td>\n", " <td>5296956116</td>\n", " <td>3218234512</td>\n", " <td>7524321290</td>\n", " <td>4287720220</td>\n", " <td>3236601070</td>\n", " </tr>\n", " <tr>\n", " <th>2015120100</th>\n", " <td>2015</td>\n", " <td>12</td>\n", " <td>8651858036</td>\n", " <td>5264446173</td>\n", " <td>3387411863</td>\n", " <td>7476287344</td>\n", " <td>4100012037</td>\n", " <td>3376275307</td>\n", " </tr>\n", " <tr>\n", " <th>2016010100</th>\n", " <td>2016</td>\n", " <td>01</td>\n", " <td>9309261244</td>\n", " <td>5569632502</td>\n", " <td>3739628742</td>\n", " <td>8154016303</td>\n", " <td>4436179457</td>\n", " <td>3717836846</td>\n", " </tr>\n", " <tr>\n", " <th>2016020100</th>\n", " <td>2016</td>\n", " <td>02</td>\n", " <td>8680940753</td>\n", " <td>5347709361</td>\n", " <td>3333231392</td>\n", " <td>7585859457</td>\n", " <td>4250997185</td>\n", " <td>3334862272</td>\n", " </tr>\n", " <tr>\n", " <th>2016030100</th>\n", " <td>2016</td>\n", " <td>03</td>\n", " <td>8827529692</td>\n", " <td>5407676056</td>\n", " <td>3419853636</td>\n", " <td>7673274617</td>\n", " <td>4286590426</td>\n", " <td>3386684191</td>\n", " </tr>\n", " <tr>\n", " <th>2016040100</th>\n", " <td>2016</td>\n", " <td>04</td>\n", " <td>8873620523</td>\n", " <td>5572235399</td>\n", " <td>3301385124</td>\n", " <td>7408147859</td>\n", " <td>4149383857</td>\n", " <td>3258764002</td>\n", " </tr>\n", " <tr>\n", " <th>2016050100</th>\n", " <td>2016</td>\n", " <td>05</td>\n", " <td>8748968139</td>\n", " <td>5330532334</td>\n", " <td>3418435805</td>\n", " <td>7586811330</td>\n", " <td>4191778094</td>\n", " <td>3395033236</td>\n", " </tr>\n", " <tr>\n", " <th>2016060100</th>\n", " <td>2016</td>\n", " <td>06</td>\n", " <td>8347710510</td>\n", " <td>4975092447</td>\n", " <td>3372618063</td>\n", " <td>7243630656</td>\n", " <td>3888839711</td>\n", " <td>3354790945</td>\n", " </tr>\n", " <tr>\n", " <th>2016070100</th>\n", " <td>2016</td>\n", " <td>07</td>\n", " <td>8864627560</td>\n", " <td>5363966439</td>\n", " <td>3500661121</td>\n", " <td>7834439589</td>\n", " <td>4337865827</td>\n", " <td>3496573762</td>\n", " </tr>\n", " <tr>\n", " <th>2016080100</th>\n", " <td>2016</td>\n", " <td>08</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8210865519</td>\n", " <td>4695046216</td>\n", " <td>3515819303</td>\n", " </tr>\n", " <tr>\n", " <th>2016090100</th>\n", " <td>2016</td>\n", " <td>09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7528292279</td>\n", " <td>4135006498</td>\n", " <td>3393285781</td>\n", " </tr>\n", " <tr>\n", " <th>2016100100</th>\n", " <td>2016</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7871021581</td>\n", " <td>4361737690</td>\n", " <td>3509283891</td>\n", " </tr>\n", " <tr>\n", " <th>2016110100</th>\n", " <td>2016</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7983113161</td>\n", " <td>4392068236</td>\n", " <td>3591044925</td>\n", " </tr>\n", " <tr>\n", " <th>2016120100</th>\n", " <td>2016</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7986152433</td>\n", " <td>4209608578</td>\n", " <td>3776543855</td>\n", " </tr>\n", " <tr>\n", " <th>2017010100</th>\n", " <td>2017</td>\n", " <td>01</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8753941940</td>\n", " <td>4521980398</td>\n", " <td>4231961542</td>\n", " </tr>\n", " <tr>\n", " <th>2017020100</th>\n", " <td>2017</td>\n", " <td>02</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7738463562</td>\n", " <td>4026702163</td>\n", " <td>3711761399</td>\n", " </tr>\n", " <tr>\n", " <th>2017030100</th>\n", " <td>2017</td>\n", " <td>03</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8223465891</td>\n", " <td>4319971902</td>\n", " <td>3903493989</td>\n", " </tr>\n", " <tr>\n", " <th>2017040100</th>\n", " <td>2017</td>\n", " <td>04</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7591080111</td>\n", " <td>3951456992</td>\n", " <td>3639623119</td>\n", " </tr>\n", " <tr>\n", " <th>2017050100</th>\n", " <td>2017</td>\n", " <td>05</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7874558299</td>\n", " <td>4187870579</td>\n", " <td>3686687720</td>\n", " </tr>\n", " <tr>\n", " <th>2017060100</th>\n", " <td>2017</td>\n", " <td>06</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7123934190</td>\n", " <td>3604550997</td>\n", " <td>3519383193</td>\n", " </tr>\n", " <tr>\n", " <th>2017070100</th>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7290503797</td>\n", " <td>3565444544</td>\n", " <td>3725059253</td>\n", " </tr>\n", " <tr>\n", " <th>2017080100</th>\n", " <td>2017</td>\n", " <td>08</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7196978615</td>\n", " <td>3575572313</td>\n", " <td>3621406302</td>\n", " </tr>\n", " <tr>\n", " <th>2017090100</th>\n", " <td>2017</td>\n", " <td>09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7079052261</td>\n", " <td>3547447892</td>\n", " <td>3531604369</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>117 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " year month pagecount_all_views pagecount_desktop_views \\\n", "2008010100 2008 01 4930902570 4930902570 \n", "2008020100 2008 02 4818393763 4818393763 \n", "2008030100 2008 03 4955405809 4955405809 \n", "2008040100 2008 04 5159162183 5159162183 \n", "2008050100 2008 05 5584691092 5584691092 \n", "2008060100 2008 06 5712104279 5712104279 \n", "2008070100 2008 07 5306302874 5306302874 \n", "2008080100 2008 08 5140155519 5140155519 \n", "2008090100 2008 09 5479533823 5479533823 \n", "2008100100 2008 10 5679440782 5679440782 \n", "2008110100 2008 11 5415832071 5415832071 \n", "2008120100 2008 12 5211708451 5211708451 \n", "2009010100 2009 01 5802681551 5802681551 \n", "2009020100 2009 02 5547320860 5547320860 \n", "2009030100 2009 03 6295159057 6295159057 \n", "2009040100 2009 04 5988817321 5988817321 \n", "2009050100 2009 05 6267516733 6267516733 \n", "2009060100 2009 06 5818924182 5818924182 \n", "2009070100 2009 07 5801646978 5801646978 \n", "2009080100 2009 08 5790850384 5790850384 \n", "2009090100 2009 09 4057515768 4057515768 \n", "2009100100 2009 10 6016107147 6016107147 \n", "2009110100 2009 11 5768486910 5768486910 \n", "2009120100 2009 12 5426505977 5426505977 \n", "2010010100 2010 01 5703465285 5703465285 \n", "2010020100 2010 02 5762451418 5762451418 \n", "2010030100 2010 03 6661347946 6661347946 \n", "2010040100 2010 04 6618552152 6618552152 \n", "2010050100 2010 05 6410578775 6410578775 \n", "2010060100 2010 06 4898035014 4898035014 \n", "... ... ... ... ... \n", "2015040100 2015 04 9421035574 6198945657 \n", "2015050100 2015 05 9657871297 6323801814 \n", "2015060100 2015 06 8203576103 5165413640 \n", "2015070100 2015 07 8483698717 5229226022 \n", "2015080100 2015 08 8304022031 5035534449 \n", "2015090100 2015 09 8582061182 5409631355 \n", "2015100100 2015 10 8781786976 5535704471 \n", "2015110100 2015 11 8515190628 5296956116 \n", "2015120100 2015 12 8651858036 5264446173 \n", "2016010100 2016 01 9309261244 5569632502 \n", "2016020100 2016 02 8680940753 5347709361 \n", "2016030100 2016 03 8827529692 5407676056 \n", "2016040100 2016 04 8873620523 5572235399 \n", "2016050100 2016 05 8748968139 5330532334 \n", "2016060100 2016 06 8347710510 4975092447 \n", "2016070100 2016 07 8864627560 5363966439 \n", "2016080100 2016 08 0 0 \n", "2016090100 2016 09 0 0 \n", "2016100100 2016 10 0 0 \n", "2016110100 2016 11 0 0 \n", "2016120100 2016 12 0 0 \n", "2017010100 2017 01 0 0 \n", "2017020100 2017 02 0 0 \n", "2017030100 2017 03 0 0 \n", "2017040100 2017 04 0 0 \n", "2017050100 2017 05 0 0 \n", "2017060100 2017 06 0 0 \n", "2017070100 2017 07 0 0 \n", "2017080100 2017 08 0 0 \n", "2017090100 2017 09 0 0 \n", "\n", " pagecount_mobile_views pageview_all_views \\\n", "2008010100 0 0 \n", "2008020100 0 0 \n", "2008030100 0 0 \n", "2008040100 0 0 \n", "2008050100 0 0 \n", "2008060100 0 0 \n", "2008070100 0 0 \n", "2008080100 0 0 \n", "2008090100 0 0 \n", "2008100100 0 0 \n", "2008110100 0 0 \n", "2008120100 0 0 \n", "2009010100 0 0 \n", "2009020100 0 0 \n", "2009030100 0 0 \n", "2009040100 0 0 \n", "2009050100 0 0 \n", "2009060100 0 0 \n", "2009070100 0 0 \n", "2009080100 0 0 \n", "2009090100 0 0 \n", "2009100100 0 0 \n", "2009110100 0 0 \n", "2009120100 0 0 \n", "2010010100 0 0 \n", "2010020100 0 0 \n", "2010030100 0 0 \n", "2010040100 0 0 \n", "2010050100 0 0 \n", "2010060100 0 0 \n", "... ... ... \n", "2015040100 3222089917 0 \n", "2015050100 3334069483 0 \n", "2015060100 3038162463 0 \n", "2015070100 3254472695 7665421980 \n", "2015080100 3268487582 7634815221 \n", "2015090100 3172429827 7655695037 \n", "2015100100 3246082505 7746031887 \n", "2015110100 3218234512 7524321290 \n", "2015120100 3387411863 7476287344 \n", "2016010100 3739628742 8154016303 \n", "2016020100 3333231392 7585859457 \n", "2016030100 3419853636 7673274617 \n", "2016040100 3301385124 7408147859 \n", "2016050100 3418435805 7586811330 \n", "2016060100 3372618063 7243630656 \n", "2016070100 3500661121 7834439589 \n", "2016080100 0 8210865519 \n", "2016090100 0 7528292279 \n", "2016100100 0 7871021581 \n", "2016110100 0 7983113161 \n", "2016120100 0 7986152433 \n", "2017010100 0 8753941940 \n", "2017020100 0 7738463562 \n", "2017030100 0 8223465891 \n", "2017040100 0 7591080111 \n", "2017050100 0 7874558299 \n", "2017060100 0 7123934190 \n", "2017070100 0 7290503797 \n", "2017080100 0 7196978615 \n", "2017090100 0 7079052261 \n", "\n", " pageview_desktop_views pageview_mobile_views \n", "2008010100 0 0 \n", "2008020100 0 0 \n", "2008030100 0 0 \n", "2008040100 0 0 \n", "2008050100 0 0 \n", "2008060100 0 0 \n", "2008070100 0 0 \n", "2008080100 0 0 \n", "2008090100 0 0 \n", "2008100100 0 0 \n", "2008110100 0 0 \n", "2008120100 0 0 \n", "2009010100 0 0 \n", "2009020100 0 0 \n", "2009030100 0 0 \n", "2009040100 0 0 \n", "2009050100 0 0 \n", "2009060100 0 0 \n", "2009070100 0 0 \n", "2009080100 0 0 \n", "2009090100 0 0 \n", "2009100100 0 0 \n", "2009110100 0 0 \n", "2009120100 0 0 \n", "2010010100 0 0 \n", "2010020100 0 0 \n", "2010030100 0 0 \n", "2010040100 0 0 \n", "2010050100 0 0 \n", "2010060100 0 0 \n", "... ... ... \n", "2015040100 0 0 \n", "2015050100 0 0 \n", "2015060100 0 0 \n", "2015070100 4376666686 3288755294 \n", "2015080100 4332482183 3302333038 \n", "2015090100 4485491704 3170203333 \n", "2015100100 4477532755 3268499132 \n", "2015110100 4287720220 3236601070 \n", "2015120100 4100012037 3376275307 \n", "2016010100 4436179457 3717836846 \n", "2016020100 4250997185 3334862272 \n", "2016030100 4286590426 3386684191 \n", "2016040100 4149383857 3258764002 \n", "2016050100 4191778094 3395033236 \n", "2016060100 3888839711 3354790945 \n", "2016070100 4337865827 3496573762 \n", "2016080100 4695046216 3515819303 \n", "2016090100 4135006498 3393285781 \n", "2016100100 4361737690 3509283891 \n", "2016110100 4392068236 3591044925 \n", "2016120100 4209608578 3776543855 \n", "2017010100 4521980398 4231961542 \n", "2017020100 4026702163 3711761399 \n", "2017030100 4319971902 3903493989 \n", "2017040100 3951456992 3639623119 \n", "2017050100 4187870579 3686687720 \n", "2017060100 3604550997 3519383193 \n", "2017070100 3565444544 3725059253 \n", "2017080100 3575572313 3621406302 \n", "2017090100 3547447892 3531604369 \n", "\n", "[117 rows x 8 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame.from_dict(data_cleaned, orient='index')\n", "df_result = pd.DataFrame\n", "df['timestamp'] = df.index\n", "df['year'] = [t[0:4] for t in df['timestamp']]\n", "df['month'] = [t[4:6] for t in df['timestamp']]\n", "df['pagecount_all_views'] = df[0] + df[1]\n", "df['pagecount_desktop_views'] = df[0]\n", "df['pagecount_mobile_views'] = df[1]\n", "df['pageview_all_views'] = df[2] + df[3] + df[4]\n", "df['pageview_desktop_views'] = df[2]\n", "df['pageview_mobile_views'] = df[3] + df[4]\n", "df = df.loc[:, 'year' : 'pageview_mobile_views']\n", "df.to_csv('en-wikipedia_traffic_200801-201709.csv', index=False)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 3: Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the final step, we make a time series plot for the data we processed before. The X-axis of the plot will be a date range, and Y-axis of the plot will be the amount of traffic times 1 million(The author downscaled the traffic data by 1 million)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZGUVRiIqKwtHRkZycHIKCgsjJycHPzw+ovdH39vYGICsrizVr1rB+/XpUVUWn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bPqT6tVk4mKEVuePso3vvAUfSNjdaXMyWSFJEnSDIrFojz/wjfxKwa9jjJuWPvQlI4TSQoURSEQ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\nOXk7l15xRtgR0Amu76lZ5erbKyq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"text/plain": [ "<matplotlib.figure.Figure at 0x10d3a86a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dateRange = pd.date_range('2008-01', '2017-10', freq='M')\n", "scale = 1e-6\n", "sns.set_style(\"whitegrid\")\n", "fig = plt.figure(figsize=(18, 12))\n", "plt.plot(dateRange, df['pagecount_all_views'] * scale, linestyle = ':')\n", "plt.plot(dateRange, df['pagecount_desktop_views'] * scale)\n", "plt.plot(dateRange, df['pagecount_mobile_views'] * scale)\n", "plt.plot(dateRange, df['pageview_all_views'] * scale, linestyle = ':')\n", "plt.plot(dateRange, df['pagecount_desktop_views'] * scale)\n", "plt.plot(dateRange, df['pagecount_mobile_views'] * scale)\n", "plt.legend()\n", "plt.xlabel('Year')\n", "plt.ylabel('Amount of Traffic (* 1,000,000)')\n", "fig.savefig('en-wikipedia_traffic_200801-201709.jpg')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

gregcaporaso/Extensible-Evaluation-Framework-Presentation

1-evaluation-framework-results.ipynb

1

129671

{ "metadata": { "name": "1-evaluation-framework-results" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Taxonomic assignment method evaluation framework\n", "================================================\n", "\n", "This [IPython Notebook](http://ipython.org/notebook.html) illustrates how to apply the evaluation framework described in (Bokulich, Rideout, et al. (in preparation)). Given a set of precomputed taxonomic assignment method evaluation results (this will likely be the ones included in the [short-read-tax-assignment GitHub repository](https://github.com/gregcaporaso/short-read-tax-assignment/)) and a set of results generated by the user for a new method, the results from the new method are analyzed in the context of the precomputed results. This allows users to rapidly determine how a new method performs, relative to the precomputed results. \n", "\n", "The following sections present different evaluations with an example usearch-based taxonomic assigner. A parameter sweep was performed using this new taxonomic assigner (as described in the [usearch-tax-assigner notebook](http://nbviewer.ipython.org/urls/raw.github.com/gregcaporaso/short-read-tax-assignment/master/demo/eval-demo/0-usearch-tax-analyzer.ipynb)), and the evaluations that are run here allow us to determine how the new assigner performs relative to the precomputed results and summarize the results in a report, which will be this notebook after all cells have been executed. This provides a convenient interactive framework for analyzing new methods. \n", "\n", "This notebook could easily be applied to the results of a different taxonomic assigner, and it is therefore intended to serve as a template for report generation. Only one change would need to be made to support that, and that change is discussed inline below.\n", "\n", "This code and other components of this framework can be found in the [short-read-tax-assignment GitHub repository](https://github.com/gregcaporaso/short-read-tax-assignment/).\n", "\n", "Requirements\n", "------------\n", "\n", "To run this notebook, you'll need to have the following installed:\n", "\n", " * [IPython](http://ipython.org/) >= 1.0.0\n", " * [QIIME](http://qiime.org/)\n", " * [PyCogent](https://github.com/pycogent/pycogent)\n", " * [short-read-tax-assignment](https://github.com/gregcaporaso/short-read-tax-assignment)\n", " * [matplotlib](http://matplotlib.org/)\n", " \n", "Terminology\n", "-----------\n", "\n", "Throughout the notebook and associated code, we refer to the precomputed results obtained from GitHub as the *subject results*, and the results generated by the user as the *query results*.\n", "\n", "Structuring query results for comparison to subject results\n", "-----------------------------------------------------------\n", "\n", "To prepare results from another classifier for analysis, you'll need to have [BIOM](http://www.biom-format.org) files with taxonomy assignments as an observation metadata category called ``taxonomy``. These should be generated for all analyses (mock community, natural community), datasets, reference databases, methods, and parameter combinations of interest. An example of how to generate these is presented in the [usearch-tax-assigner notebook](http://nbviewer.ipython.org/urls/raw.github.com/gregcaporaso/short-read-tax-assignment/master/demo/eval-demo/0-usearch-tax-analyzer.ipynb)).\n", "\n", "Your BIOM tables should be called ``table.biom``, and nested in the following directory structure:\n", "\n", "```\n", "query_results_dir/\n", " analysis/\n", " dataset-id/ \n", " reference-db-id/\n", " method-id/\n", " parameter-combination-id/\n", " table.biom\n", "```\n", "\n", "``query_results_dir`` is the name of the top level directory, and you will set this value in the first code cell of this notebook. You can name this directory whatever you want to.\n", "\n", "This directory structure is identical to that for the [subject results](https://github.com/gregcaporaso/short-read-tax-assignment/tree/master/data/eval-subject-results). You can review that directory structure for an example of how this should look.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configure local environment-specific values\n", "-------------------------------------------\n", "\n", "**This is the only cell that you will need to edit to generate reports locally.**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## short_read_tax_dir should be the directory where you've downloaded (or cloned) the \n", "## short-read-tax-assignment repository. \n", "short_read_tax_dir = \"/home/jrideout/dev/short-read-tax-assignment/\"\n", "\n", "## query_results_dir should be the directory where the results of the method(s) to be\n", "## compared to the pre-computed results can be found. \n", "query_results_dir = \"/home/jrideout/dev/short-read-tax-assignment/usearch_parameter_sweep/\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prepare the environment\n", "-----------------------\n", "\n", "First we'll configure IPython to add matplotlib plots inline. Then we'll import various functions that we'll need for generating the report. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "from os.path import join, exists\n", "from taxcompare.eval_framework import (get_expected_tables_lookup, \n", " find_and_process_result_tables,\n", " compute_prfs,\n", " compute_pearson_spearman,\n", " compute_procrustes,\n", " generate_pr_scatter_plots,\n", " generate_prf_box_plots,\n", " generate_precision_box_plots,\n", " generate_recall_box_plots,\n", " generate_fmeasure_box_plots,\n", " generate_prf_table,\n", " generate_correlation_box_plots,\n", " generate_pearson_box_plots,\n", " generate_spearman_box_plots,\n", " generate_pearson_spearman_table,\n", " generate_procrustes_table)\n", "from cogent.draw.distribution_plots import generate_box_plots" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Define the subdirectories where the query mock and natural community data should be, and confirm that they exist.\n", "mock_query_results_dir = join(query_results_dir,\"mock\")\n", "natural_query_results_dir = join(query_results_dir,\"natural\")\n", "\n", "assert exists(mock_query_results_dir), \"Mock community query directory doesn't exist: %s\" % mock_query_results_dir\n", "assert exists(natural_query_results_dir), \"Natural community query directory doesn't exist: %s\" % natural_query_results_dir\n", "\n", "# Define the subdirectories where the subject mock and natural community data should be, and confirm that they exist.\n", "mock_subject_results_dir = join(short_read_tax_dir,'data/eval-subject-results/mock/')\n", "natural_subject_results_dir = join(short_read_tax_dir,'data/eval-subject-results/natural/')\n", "\n", "assert exists(mock_query_results_dir), \"Mock community subject directory doesn't exist: %s\" % mock_subject_results_dir\n", "assert exists(natural_query_results_dir), \"Natural community subject directory doesn't exist: %s\" % natural_subject_results_dir" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find mock community pre-computed tables, expected tables, and \"query\" tables\n", "----------------------------------------------------------------------------\n", "\n", "Next we'll use the paths defined above to find all of the tables that will be compared. These include the *pre-computed result* tables (i.e., the ones that the new methods will be compared to), the *expected result* tables (i.e., the tables containing the known composition of the mock microbial communities), and the *query result* tables (i.e., the tables generated with the new method(s) that we want to compare to the *pre-computed result* tables)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "query_results = find_and_process_result_tables(mock_query_results_dir)\n", "subject_results = find_and_process_result_tables(mock_subject_results_dir)\n", "expected_L6_tables = get_expected_tables_lookup(mock_subject_results_dir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evalution 1: Compute and summarize precision, recall, and F-measure for mock communities\n", "----------------------------------------------------------------------------------------\n", "\n", "In this evaluation, we compute and summarize precision, recall, and F-measure of each result (pre-computed and query) based on the known composition of the mock communities. We then summarize the results in two ways: first with a scatter plot, where pre-computed results are represented by blue points, and query results are represented by red points; and second with a table of the top twenty-five methods based on their F-measures. \n", "\n", "This is a qualitative evaluation, effectively telling us about the ability of the different methods to report the taxa that are present in each sample. These metrics are not concerned with the abundance of the different taxa." ] }, { "cell_type": "code", "collapsed": false, "input": [ "subject_prf = list(compute_prfs(subject_results,expected_L6_tables))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "query_prf = list(compute_prfs(query_results,expected_L6_tables))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_pr_scatter_plots(query_prf,subject_prf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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RZcuWnZNcBQUFMrh3b/HabOKx2WTowIFSVFR0zvNSXF6Ude1UVkaKSwYR4eDBgzgcjpOC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\nfn3c3Bx+ZdzpYmNjqVevXrmfV7bfdGpCUM8s/KkibVHWZ599xsCBA2s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"text": [ "<matplotlib.figure.Figure at 0x507cf90>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_precision_box_plots(query_prf,subject_prf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x35fc190>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_recall_box_plots(query_prf,subject_prf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x35f5e50>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_fmeasure_box_plots(query_prf,subject_prf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x4fbe0d0>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_prf_table(query_prf,subject_prf)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Data set | Precision | Recall | F-measure | Method | Parameters \n", "ITS2-SAG1 | 0.615 | 0.889 | 0.727 |rtax |single \n", "ITS2-SAG1 | 0.571 | 0.889 | 0.696 |rdp |0.0 \n", "ITS2-SAG1 | 0.571 | 0.889 | 0.696 |mothur |0.0 \n", "ITS2-SAG1 | 0.533 | 0.889 | 0.667 |mothur |0.2 \n", "ITS2-SAG1 | 0.533 | 0.889 | 0.667 |mothur |0.1 \n", "ITS2-SAG1 | 0.500 | 0.889 | 0.640 |rdp |0.2 \n", "ITS2-SAG1 | 0.500 | 0.889 | 0.640 |rdp |0.1 \n", "ITS1 | 0.600 | 0.667 | 0.632 |rdp |0.0 \n", "ITS1 | 0.600 | 0.667 | 0.632 |mothur |0.0 \n", "S16S-2 | 0.500 | 0.826 | 0.623 |blast |1.0 \n", "S16S-2 | 0.500 | 0.826 | 0.623 |blast |100.0 \n", "S16S-2 | 0.500 | 0.826 | 0.623 |blast |0.0001 \n", "S16S-2 | 0.500 | 0.826 | 0.623 |blast |1e-06 \n", "S16S-2 | 0.500 | 0.826 | 0.623 |blast |1e-10 \n", "S16S-2 | 0.487 | 0.826 | 0.613 |blast |1e-30 \n", "S16S-2 | 0.475 | 0.826 | 0.603 |rdp |0.0 \n", "S16S-2 | 0.475 | 0.826 | 0.603 |mothur |0.0 \n", "ITS1 | 0.545 | 0.667 | 0.600 |rdp |0.1 \n", "ITS1 | 0.545 | 0.667 | 0.600 |mothur |0.1 \n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c1.000000\n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c0.750000\n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c0.750000\n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c0.510000\n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c0.510000\n", "S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c1.000000\n", "S16S-2 | 0.462 | 0.783 | 0.581 |usearch |e0.001000_qf0.500000_ma3_c0.510000\n", "S16S-2 | 0.462 | 0.783 | 0.581 |usearch |e1.000000_qf0.500000_ma3_c0.510000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c0.510000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c1.000000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c1.000000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c0.510000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c0.750000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c0.750000\n", "S16S-2 | 0.442 | 0.826 | 0.576 |rdp |0.1 \n", "S16S-2 | 0.442 | 0.826 | 0.576 |mothur |0.1 \n", "ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1.0 \n", "ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |100.0 \n", "ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |0.0001 \n", "ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1e-06 \n", "ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1e-10 \n", "ITS2-SAG1 | 0.421 | 0.889 | 0.571 |mothur |0.3 \n", "S16S-2 | 0.432 | 0.826 | 0.567 |mothur |0.2 \n", "S16S-2 | 0.459 | 0.739 | 0.567 |usearch |e1.000000_qf0.500000_ma5_c0.510000\n", "S16S-2 | 0.459 | 0.739 | 0.567 |usearch |e0.001000_qf0.500000_ma5_c0.510000\n", "S16S-2 | 0.439 | 0.783 | 0.562 |usearch |e1.000000_qf0.750000_ma3_c0.510000\n", "S16S-2 | 0.439 | 0.783 | 0.562 |usearch |e0.001000_qf0.750000_ma3_c0.510000\n", "S16S-2 | 0.422 | 0.826 | 0.559 |rdp |0.3 \n", "S16S-2 | 0.422 | 0.826 | 0.559 |rdp |0.2 \n", "S16S-2 | 0.447 | 0.739 | 0.557 |usearch |e0.001000_qf0.750000_ma5_c0.510000\n", "S16S-2 | 0.447 | 0.739 | 0.557 |usearch |e1.000000_qf0.750000_ma5_c0.510000\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluation 2: Compute and summarize correlations between observed and known mock community structure\n", "----------------------------------------------------------------------------------------------------\n", "\n", "In this evaluation, we compute and summarize the correlation between each result (pre-computed and query) and the known composition of the mock communities. We then summarize the results in two ways: first with a series of boxplots of correlation coefficients by method; and second with a table of the top twenty-five methods based on their Pearson correlation coefficient. \n", "\n", "This is a quantitative evaluation, which tells us about the ability of the different methods to report the taxa that are present in each sample and accurately assess their abundance. Because many factors can affect the observed abundance of taxa beyond the accuracy of the taxonomic assigner (e.g., primer bias), the correlation coefficients are frequently low, but we expect that their relative values are informative in understanding which taxonomic assigners are more correct than others." ] }, { "cell_type": "code", "collapsed": false, "input": [ "subject_pearson_spearman = list(compute_pearson_spearman(subject_results,expected_L6_tables))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "query_pearson_spearman = list(compute_pearson_spearman(query_results,expected_L6_tables))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_pearson_box_plots(query_pearson_spearman,subject_pearson_spearman)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x4fb7f90>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_spearman_box_plots(query_pearson_spearman,subject_pearson_spearman)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TdeTIEWvQoEFWQUGBdcMNN1iWZVn333+/9corr1iWZVkVFRXWFVdcYVVXV1uL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\nkAIAGIuQAgAYi5ACABjrfyDkidXp707fAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x30faa10>" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_pearson_spearman_table(query_pearson_spearman,subject_pearson_spearman)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Data set | r | rho | Method | Parameters \n", "ITS2-SAG1 | 0.679 | 0.219 |rdp |0.5 \n", "ITS2-SAG1 | 0.678 | 0.181 |rdp |0.6 \n", "ITS2-SAG1 | 0.632 | 0.096 |mothur |0.7 \n", "ITS2-SAG1 | 0.588 | 0.383 |mothur |0.5 \n", "ITS2-SAG1 | 0.586 | 0.178 |rdp |0.4 \n", "ITS2-SAG1 | 0.574 | 0.089 |mothur |0.6 \n", "Turnbaugh-2 | 0.562 | -0.223 |rdp |1.0 \n", "ITS2-SAG1 | 0.557 | 0.357 |mothur |0.3 \n", "Turnbaugh-2 | 0.532 | -0.422 |mothur |1.0 \n", "Turnbaugh-3 | 0.524 | 0.053 |mothur |0.9 \n", "ITS1 | 0.513 | 0.025 |blast |1.0 \n", "ITS1 | 0.513 | 0.025 |blast |100.0 \n", "ITS1 | 0.513 | 0.025 |blast |0.0001 \n", "ITS1 | 0.513 | 0.025 |blast |1e-06 \n", "ITS1 | 0.513 | 0.025 |blast |1e-10 \n", "ITS2-SAG1 | 0.493 | -0.531 |mothur |0.7 \n", "ITS2-SAG1 | 0.483 | -0.532 |rdp |0.7 \n", "ITS2-SAG1 | 0.477 | 0.373 |rdp |0.3 \n", "ITS1 | 0.473 | -0.402 |rdp |0.5 \n", "ITS1 | 0.472 | -0.019 |blast |1e-30 \n", "Broad-2 | 0.465 | 0.427 |mothur |0.9 \n", "ITS1 | 0.464 | -0.391 |rdp |0.6 \n", "Broad-2 | 0.464 | 0.336 |rdp |0.9 \n", "Turnbaugh-1 | 0.463 | 0.227 |blast |1e-10 \n", "Turnbaugh-1 | 0.462 | 0.227 |blast |1.0 \n", "Turnbaugh-1 | 0.462 | 0.227 |blast |100.0 \n", "Turnbaugh-1 | 0.462 | 0.227 |blast |0.0001 \n", "Turnbaugh-1 | 0.462 | 0.227 |blast |1e-06 \n", "Turnbaugh-2 | 0.459 | 0.158 |mothur |0.8 \n", "ITS2-SAG1 | 0.458 | 0.285 |mothur |0.2 \n", "Turnbaugh-1 | 0.458 | 0.232 |blast |1e-30 \n", "ITS1 | 0.455 | 0.187 |mothur |0.8 \n", "Turnbaugh-2 | 0.453 | 0.154 |blast |1.0 \n", "Turnbaugh-2 | 0.453 | 0.154 |blast |100.0 \n", "Turnbaugh-2 | 0.453 | 0.154 |blast |0.0001 \n", "Turnbaugh-2 | 0.453 | 0.154 |blast |1e-06 \n", "Turnbaugh-2 | 0.453 | 0.154 |blast |1e-10 \n", "Broad-3 | 0.448 | 0.392 |rdp |0.9 \n", "ITS2-SAG1 | 0.447 | 0.137 |rdp |0.8 \n", "ITS1 | 0.447 | -0.405 |rdp |0.7 \n", "ITS2-SAG1 | 0.445 | 0.058 |rdp |0.7 \n", "ITS1 | 0.443 | -0.416 |mothur |0.7 \n", "ITS2-SAG1 | 0.442 | -0.071 |blast |1e-30 \n", "ITS1 | 0.441 | 0.087 |rdp |0.3 \n", "Turnbaugh-2 | 0.440 | 0.107 |rdp |0.9 \n", "Broad-3 | 0.439 | 0.373 |mothur |0.9 \n", "ITS2-SAG1 | 0.436 | 0.029 |blast |1.0 \n", "ITS2-SAG1 | 0.436 | 0.029 |blast |100.0 \n", "ITS2-SAG1 | 0.436 | 0.029 |blast |0.0001 \n", "ITS2-SAG1 | 0.436 | 0.029 |blast |1e-06 \n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find natural community pre-computed tables, expected tables, and \"query\" tables\n", "-------------------------------------------------------------------------------\n", "\n", "Next we'll use the paths defined above to find all of the natural community tables that will be compared. These include the *pre-computed result* tables (i.e., the ones that the new methods will be compared to), the *expected result* tables (i.e., the OTU table), and the *query result* tables (i.e., the tables generated with the new method(s) that we want to compare to the *pre-computed result* tables)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "query_results = find_and_process_result_tables(natural_query_results_dir,filename_pattern=\"table.biom\")\n", "subject_results = find_and_process_result_tables(natural_subject_results_dir)\n", "expected_pcs = get_expected_tables_lookup(natural_subject_results_dir,\n", " filename_pattern='bray_curtis_pc.txt')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluation 3: Compute and summarize Procrustes analysis results based on real community data\n", "--------------------------------------------------------------------------------------------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "query_procrustes = list(compute_procrustes(query_results,expected_pcs))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "subject_procrustes = list(compute_procrustes(subject_results,expected_pcs))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "generate_procrustes_table(query_procrustes,subject_procrustes)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Data set | M^2 | Method | Parameters \n", "study_449 | 0.047 |rtax |single \n", "study_449 | 0.050 |usearch |e0.001000_qf0.500000_ma3_c0.510000\n", "study_449 | 0.050 |usearch |e1.000000_qf0.500000_ma3_c0.510000\n", "study_449 | 0.054 |usearch |e1.000000_qf0.750000_ma3_c0.510000\n", "study_449 | 0.054 |usearch |e0.001000_qf0.750000_ma3_c0.510000\n", "study_449 | 0.104 |blast |1.0 \n", "study_449 | 0.104 |blast |100.0 \n", "study_449 | 0.104 |blast |1e-30 \n", "study_449 | 0.104 |blast |0.0001 \n", "study_449 | 0.104 |blast |1e-06 \n", "study_449 | 0.104 |blast |1e-10 \n", "study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c0.510000\n", "study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c1.000000\n", "study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c1.000000\n", "study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c0.510000\n", "study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c0.750000\n", "study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c0.750000\n", "study_449 | 0.120 |rdp |1.0 \n", "study_449 | 0.121 |usearch |e1.000000_qf0.750000_ma3_c1.000000\n", "study_449 | 0.121 |usearch |e0.001000_qf0.750000_ma3_c1.000000\n", "study_449 | 0.122 |usearch |e0.001000_qf0.750000_ma3_c0.750000\n", "study_449 | 0.122 |usearch |e1.000000_qf0.750000_ma3_c0.750000\n", "study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c1.000000\n", "study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c0.750000\n", "study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c0.750000\n", "study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c0.510000\n", "study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c0.510000\n", "study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c1.000000\n", "study_449 | 0.126 |usearch |e0.001000_qf0.750000_ma5_c0.510000\n", "study_449 | 0.126 |usearch |e1.000000_qf0.750000_ma5_c0.510000\n", "study_449 | 0.127 |usearch |e1.000000_qf0.500000_ma3_c1.000000\n", "study_449 | 0.127 |usearch |e0.001000_qf0.500000_ma3_c1.000000\n", "study_449 | 0.128 |usearch |e1.000000_qf0.500000_ma3_c0.750000\n", "study_449 | 0.128 |usearch |e0.001000_qf0.500000_ma3_c0.750000\n", "study_449 | 0.134 |usearch |e1.000000_qf0.500000_ma5_c0.510000\n", "study_449 | 0.134 |usearch |e0.001000_qf0.500000_ma5_c0.510000\n", "study_449 | 0.134 |mothur |1.0 \n", "study_449 | 0.156 |usearch |e0.001000_qf0.750000_ma5_c0.750000\n", "study_449 | 0.156 |usearch |e1.000000_qf0.750000_ma5_c0.750000\n", "study_449 | 0.158 |usearch |e0.001000_qf0.500000_ma5_c1.000000\n", "study_449 | 0.158 |usearch |e1.000000_qf0.500000_ma5_c1.000000\n", "study_449 | 0.160 |usearch |e1.000000_qf0.750000_ma5_c1.000000\n", "study_449 | 0.160 |usearch |e0.001000_qf0.750000_ma5_c1.000000\n", "study_449 | 0.163 |usearch |e1.000000_qf0.500000_ma5_c0.750000\n", "study_449 | 0.163 |usearch |e0.001000_qf0.500000_ma5_c0.750000\n", "study_449 | 0.195 |rdp |0.8 \n", "study_449 | 0.196 |mothur |0.8 \n", "study_449 | 0.234 |rdp |0.1 \n", "study_449 | 0.235 |mothur |0.1 \n", "study_449 | 0.237 |rdp |0.5 \n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

luwei0917/awsemmd_script

notebook/Optimization/hybrid_simulation_analysis_dec07.ipynb

1

9580790

mit

mayankjohri/LetsExplorePython

Section 2 - Advance Python/Chapter S2.05 - REST API - Server & Clients/requests.ipynb

2

9881

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "endpoint = 'https://raw.githubusercontent.com/rickiepark/python-tutorial/master/tutorial-4/sample.json'\n", "r = requests.get(endpoint)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.status_code" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Content-Length': '285', 'X-XSS-Protection': '1; mode=block', 'Content-Security-Policy': \"default-src 'none'; style-src 'unsafe-inline'; sandbox\", 'X-Cache-Hits': '0', 'X-Frame-Options': 'deny', 'Access-Control-Allow-Origin': '*', 'X-Served-By': 'cache-ams4140-AMS', 'X-GitHub-Request-Id': '7D8A:2331:98B583:A5FBE6:59F3328C', 'Expires': 'Fri, 27 Oct 2017 13:25:13 GMT', 'X-Fastly-Request-ID': '6feb490ce8d25b7c4b31f43ffd3c17f6439fd3e8', 'Date': 'Fri, 27 Oct 2017 13:20:13 GMT', 'Source-Age': '0', 'X-Cache': 'MISS', 'Accept-Ranges': 'bytes', 'Strict-Transport-Security': 'max-age=31536000', 'Connection': 'keep-alive', 'X-Geo-Block-List': '', 'Via': '1.1 varnish', 'X-Content-Type-Options': 'nosniff', 'Content-Encoding': 'gzip', 'X-Timer': 'S1509110413.026402,VS0,VE125', 'Vary': 'Authorization,Accept-Encoding', 'ETag': '\"d2ca30f0a367151e8325dce8f425b35cb93be8d9\"', 'Cache-Control': 'max-age=300', 'Content-Type': 'text/plain; charset=utf-8'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.headers" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "u'{\\n \"data\": [{\\n \"type\": \"articles\",\\n \"id\": \"1\",\\n \"attributes\": {\\n \"title\": \"JSON API paints my bikeshed!\",\\n \"body\": \"The shortest article. Ever.\",\\n \"created\": \"2015-05-22T14:56:29.000Z\",\\n \"updated\": \"2015-05-22T14:56:28.000Z\"\\n },\\n \"relationships\": {\\n \"author\": {\\n \"data\": {\"id\": \"42\", \"type\": \"people\"}\\n }\\n }\\n }],\\n \"included\": [\\n {\\n \"type\": \"people\",\\n \"id\": \"42\",\\n \"attributes\": {\\n \"name\": \"John\",\\n \"age\": 80,\\n \"gender\": \"male\"\\n }\\n }\\n ]\\n}'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.text" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'included': [{u'attributes': {u'gender': u'male', u'age': 80, u'name': u'John'}, u'type': u'people', u'id': u'42'}], u'data': [{u'relationships': {u'author': {u'data': {u'type': u'people', u'id': u'42'}}}, u'attributes': {u'body': u'The shortest article. Ever.', u'updated': u'2015-05-22T14:56:28.000Z', u'created': u'2015-05-22T14:56:29.000Z', u'title': u'JSON API paints my bikeshed!'}, u'type': u'articles', u'id': u'1'}]}\n" ] } ], "source": [ "obj = r.json()\n", "print(obj)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'dict'>\n" ] }, { "data": { "text/plain": [ "{u'data': [{u'attributes': {u'body': u'The shortest article. Ever.',\n", " u'created': u'2015-05-22T14:56:29.000Z',\n", " u'title': u'JSON API paints my bikeshed!',\n", " u'updated': u'2015-05-22T14:56:28.000Z'},\n", " u'id': u'1',\n", " u'relationships': {u'author': {u'data': {u'id': u'42',\n", " u'type': u'people'}}},\n", " u'type': u'articles'}],\n", " u'included': [{u'attributes': {u'age': 80,\n", " u'gender': u'male',\n", " u'name': u'John'},\n", " u'id': u'42',\n", " u'type': u'people'}]}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(obj.__class__)\n", "obj\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = requests.post(endpoint, data={'key':'value'})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = requests.get(endpoint, params={'key1': 'value1', 'key2': 'value2'})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://raw.githubusercontent.com/rickiepark/python-tutorial/master/tutorial-4/sample.json?key2=value2&key1=value1'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.url" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = requests.get(endpoint, headers={'user-agent': 'my-app/0.0.1'})" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = requests.get('https://github.com', timeout=5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<RequestsCookieJar[Cookie(version=0, name='logged_in', value='no', port=None, port_specified=False, domain='.github.com', domain_specified=True, domain_initial_dot=True, path='/', path_specified=True, secure=True, expires=2140262600, discard=False, comment=None, comment_url=None, rest={'HttpOnly': None}, rfc2109=False), Cookie(version=0, name='_gh_sess', value='eyJzZXNzaW9uX2lkIjoiZjg3MjgxNDllNDZmNzJkODg2OGZiOGZkMWE1MmJmMzYiLCJsYXN0X3JlYWRfZnJvbV9yZXBsaWNhcyI6MTUwOTExMDYwMDcxOCwiX2NzcmZfdG9rZW4iOiJYYlJCdEhmMXdJbVZRU0d4ZjEyRXltQWpPVGxoR0Fydnc4UDlSbktyaGtzPSJ9--4162b236fee11ffc769c3cc7b42ff4e50d92871c', port=None, port_specified=False, domain='github.com', domain_specified=False, domain_initial_dot=False, path='/', path_specified=True, secure=True, expires=None, discard=True, comment=None, comment_url=None, rest={'HttpOnly': None}, rfc2109=False)]>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.cookies" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "requests.cookies.RequestsCookieJar" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.cookies.__class__" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(r.cookies)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'eyJzZXNzaW9uX2lkIjoiZjg3MjgxNDllNDZmNzJkODg2OGZiOGZkMWE1MmJmMzYiLCJsYXN0X3JlYWRfZnJvbV9yZXBsaWNhcyI6MTUwOTExMDYwMDcxOCwiX2NzcmZfdG9rZW4iOiJYYlJCdEhmMXdJbVZRU0d4ZjEyRXltQWpPVGxoR0Fydnc4UDlSbktyaGtzPSJ9--4162b236fee11ffc769c3cc7b42ff4e50d92871c'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.cookies['_gh_sess']" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'no'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.cookies['logged_in']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_gh_sess': 'eyJzZXNzaW9uX2lkIjoiZjg3MjgxNDllNDZmNzJkODg2OGZiOGZkMWE1MmJmMzYiLCJsYXN0X3JlYWRfZnJvbV9yZXBsaWNhcyI6MTUwOTExMDYwMDcxOCwiX2NzcmZfdG9rZW4iOiJYYlJCdEhmMXdJbVZRU0d4ZjEyRXltQWpPVGxoR0Fydnc4UDlSbktyaGtzPSJ9--4162b236fee11ffc769c3cc7b42ff4e50d92871c',\n", " 'logged_in': 'no'}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.cookies.get_dict()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

giraph/data-sci

hobart-temp/coffs-harbor-temp.ipynb

1

827314

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coffs Harbor temperature\n", "7/28/2017 *coffs-harbor-temp.ipynb*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "from urllib.request import urlretrieve\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('seaborn')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#https://stackoverflow.com/questions/11936967/text-file-parsing-with-python\n", "def clean_data(filename): \n", "\n", " inputfile = open(filename + '.txt')\n", " outputfile = open(filename + '.csv', 'w')\n", " \n", " outputfile.writelines('Date,Temp\\n')\n", " for line in inputfile.readlines()[1:]:\n", " outputfile.writelines(','.join(line.split()).replace('99999.9', '') + '\\n') \n", "\n", " inputfile.close()\n", " outputfile.close()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_data(url, filename, force=False):\n", " if force or not os.path.exists(filename + '.txt'): \n", " urlretrieve(url, filename + '.txt')\n", " if force or not os.path.exists(filename + '.csv'):\n", " clean_data(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#http://www.bom.gov.au/climate/change/acorn-sat/#tabs=Data-and-networks\n", "\n", "minURL = 'http://www.bom.gov.au/climate/change/acorn/sat/data/acorn.sat.minT.059040.daily.txt'\n", "minFile = 'coffs-harbor-min'\n", "get_data(minURL, minFile)\n", "\n", "maxURL = 'http://www.bom.gov.au/climate/change/acorn/sat/data/acorn.sat.maxT.059040.daily.txt'\n", "maxFile = 'coffs-harbor-max'\n", "get_data(maxURL, maxFile)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "minData = pd.read_csv('coffs-harbor-min.csv', index_col='Date', parse_dates=True)\n", "maxData = pd.read_csv('coffs-harbor-max.csv', index_col='Date', parse_dates=True)\n", "#data = pd.DataFrame.join(maxData, minData, how='outer', lsuffix='_Max', rsuffix=\"_Min\" )\n", "data = minData.merge(maxData, suffixes=('_Min', '_Max'), left_index=True, right_index=True)\n", "data['Temp_Diff'] = data['Temp_Max'] - data['Temp_Min']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(26504, 3)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Temp_Min</th>\n", " <th>Temp_Max</th>\n", " <th>Temp_Diff</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1943-02-02</th>\n", " <td>21.8</td>\n", " <td>30.5</td>\n", " <td>8.7</td>\n", " </tr>\n", " <tr>\n", " <th>1943-02-03</th>\n", " <td>21.0</td>\n", " <td>29.7</td>\n", " <td>8.7</td>\n", " </tr>\n", " <tr>\n", " <th>1943-02-04</th>\n", " <td>19.8</td>\n", " <td>28.3</td>\n", " <td>8.5</td>\n", " </tr>\n", " <tr>\n", " <th>1943-02-05</th>\n", " <td>19.8</td>\n", " <td>28.2</td>\n", " <td>8.4</td>\n", " </tr>\n", " <tr>\n", " <th>1943-02-06</th>\n", " <td>20.9</td>\n", " <td>28.5</td>\n", " <td>7.6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Temp_Min Temp_Max Temp_Diff\n", "Date \n", "1943-02-02 21.8 30.5 8.7\n", "1943-02-03 21.0 29.7 8.7\n", "1943-02-04 19.8 28.3 8.5\n", "1943-02-05 19.8 28.2 8.4\n", "1943-02-06 20.9 28.5 7.6" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Temp_Min</th>\n", " <th>Temp_Max</th>\n", " <th>Temp_Diff</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-08-22</th>\n", " <td>14.1</td>\n", " <td>24.9</td>\n", " <td>10.8</td>\n", " </tr>\n", " <tr>\n", " <th>2015-08-23</th>\n", " <td>12.5</td>\n", " <td>26.2</td>\n", " <td>13.7</td>\n", " </tr>\n", " <tr>\n", " <th>2015-08-24</th>\n", " <td>16.0</td>\n", " <td>22.0</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>2015-08-25</th>\n", " <td>14.3</td>\n", " <td>27.6</td>\n", " <td>13.3</td>\n", " </tr>\n", " <tr>\n", " <th>2015-08-26</th>\n", " <td>7.5</td>\n", " <td>22.1</td>\n", " <td>14.6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Temp_Min Temp_Max Temp_Diff\n", "Date \n", "2015-08-22 14.1 24.9 10.8\n", "2015-08-23 12.5 26.2 13.7\n", "2015-08-24 16.0 22.0 6.0\n", "2015-08-25 14.3 27.6 13.3\n", "2015-08-26 7.5 22.1 14.6" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Temp_Min</th>\n", " <th>Temp_Max</th>\n", " <th>Temp_Diff</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>23406.000000</td>\n", " <td>23485.000000</td>\n", " <td>23339.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>14.063779</td>\n", " <td>23.268993</td>\n", " <td>9.228587</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>5.179292</td>\n", " <td>3.841212</td>\n", " <td>3.533022</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-3.200000</td>\n", " <td>12.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>10.400000</td>\n", " <td>20.200000</td>\n", " <td>6.600000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>14.800000</td>\n", " <td>23.300000</td>\n", " <td>8.800000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>18.200000</td>\n", " <td>26.000000</td>\n", " <td>11.600000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>26.700000</td>\n", " <td>43.300000</td>\n", " <td>27.300000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Temp_Min Temp_Max Temp_Diff\n", "count 23406.000000 23485.000000 23339.000000\n", "mean 14.063779 23.268993 9.228587\n", "std 5.179292 3.841212 3.533022\n", "min -3.200000 12.000000 0.000000\n", "25% 10.400000 20.200000 6.600000\n", "50% 14.800000 23.300000 8.800000\n", "75% 18.200000 26.000000 11.600000\n", "max 26.700000 43.300000 27.300000" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory visualisation" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def apply_common(title=''):\n", " ax.set_ylim(-5,45)\n", " ax.set_title(title)\n", " ax.set_xlabel('Date')\n", " ax.set_ylabel('°Centrigrade')\n", " ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002ACBB6A7940>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002ACBB85BAC8>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x000002ACBB8C1860>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002ACBBB786A0>]], dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbb6ad898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.hist()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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VajsVPPD9f6LAiVl48HzhwFIs2P6wKjUs2P4wXjn0luTze5xv7a7AvjPBorbm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\niIiIiCLg/wOtwSDkFWzQ9gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbbb27470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.plot()\n", "apply_common('All data')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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7tA9fMO66O3BPK5cGZvD4gpztnsTrj/fjra+w0Vxrp6N7kjl/GF9AKzAhAJtb\nSqkpK+KtcyMEwzFK7RKSUaR/fPnCDzrXN0ZRILxQujYojTESdQHKHGa+8T9u17iIr5VM9cCphI02\nN5clLQ8sa03xanb7yqzma6ALnhQw6hvzxIVRfvSbTmU2/acf2qSUXalfPFbJoLTyA5Tf5VmOjs5S\nYrMYCUUiSqMOQGllKceVUwmT3Le9AQH4r5MDSdv80L4WPnTHmqTPFzuIzqQymMqIn+t1JS0PLKtS\n4Wo2UjKr+RpkMtR696wC4kjHsCY+faRjWPlOHQP3BSNKpyyr+arR1o20zlJTU2alpc5OJKQd34ci\nMSSTiFmKJ5qmqpkvtUscO5+6xeoLb/Zw4oL2u1Qdq1Kh7p4lk6kUSnZP37+9gfUNTo50DGvKreSQ\nUoXTojxnhRZvTjznVNfgRuFGPrds0ZtyFBB752Nx8ox679ZaIH6jTnvi8We3N6S4uydm/Jp4W7yR\nQTRls4TlIl0zCnnGpXN9Ipfmjbh8abXkFxooHjo1kFbONhiGf33+AjVlRUB8YDrjCSyYoJmukU42\nseJj50eV4zl+fpQHdzbGQ0qBCD9/uQuIq3NZJQMH7mktmHhz4jkfuKdVeQdcSzOhQiRfjZKud3RD\nXUDIbm51Aoz6RnXYTEqcT32z1pQVaTJlz7w/ztvvjSmyoU3VDm5bX8HprnGGxr1sbS0HBIbGvdRV\n2bBbTbzVObpgl62FKLObcHlSv4h1I319k0UIekFtefdcUFM2lYgvGOFoxzAnu8aV+109OE01o81U\nbZFJZTCVeND5vmnFO6X+TlY4KxQSz/lIx3DBK54tlutBzW050A11gZEoB6q+Ud3eEE67OeWNKr/c\njp8fxa+SWAyGobnWoXQcAhTpxjKHmZuqinn69Z681EanM9I6Nzat9XasZhO+QIieQQ/FViPBUJhg\nwrivurQIAUHJBE+kzGEmBpr7fffGKrz+MG1NJZpWlTKLrbZIbHXpsJloayrh0uAMvkBEM+goNLd3\n4jknlp4V0rFeK3q5WxzdUBc42dyoicZcjdVsIEbq3sEud4A33h0ueJWxa0UykmQ0dPJHYoezdJ6Z\nTJUE2zdU8fAd8UStU6oZ9dmeeBXDuR4XMeClt6+wq62aUruk1EGn0/JWt7FM7G51pGOYpupixqf9\nSAaR3ZureeXUoOLqPnjfeqWcK7HqYqXKrtT7P3BPa8rSs+U4tqW8Dr1DMxw+3a9sWy93i6NnfWfJ\nUt2c2WTJV2HHAAAgAElEQVQ5qvcNpHwpqd3jEDfYRoPAR/Y1s6m5nO89eTrJWDtsJpqqiznXM5Uy\nrnyjohvuwuTzH96khHvO9broH/Vw7L2xjOskVkdA/Hn4wVPvaGbLXzpwq2Js1d/JJFZMJGZ4p8oU\nh+Rn8VrI9C5I94wvdyvKpWyDqW5StBItNlcaPev7Gsk2+3SpaKyyKy+NHzz1Dk+/1s33njzDL17p\nUoRNZCH9Lx24lYP3rUcyiYQjMV58ux+AP3lwA7J8ukUS2NpShj8Y5mzPFGZJ0DRHKLObUh6HaX79\n6/2m0Y308mMUFl5GrnKQ73d7kbTgOonVEZC5eU3idzLBUDRjhndirPRox/CyvhMSvWbyOWTTzGep\njiPf+861SdFq4np/5y4LS3lz5sLRjmHlAfX6w7x8ckB5Scgvt8YqO90DM8rswO0NcbRjmIkZP7Ja\nqD8Y473LU0odrD8Y05TTpE8Ii/+vF4HpLITRAFtaSqkps9JYaWN7W+WCLxu5ykH9u80SN54WSWD3\npiplNikjCNDWVKIp31F3sAJtF6vE79TLfOqhDWm7RiWWe6lj6fI7YSlLptT7t1kMWObFYZY7bruU\nHcA2N5dRUWJZkm1f7xi+8Y1vfGOlDyKRQmscbpEMdHRP5r35POTWKP1szyQ9Q27NZ6mauycu11Ln\nYPuGKk53jRMIRbGYRIKqLGzJJADCDR+r1lk+ojFoqbVzsX+GGW+IgfG5lOEVp82EWRL5xAM301Bh\n481zI1gkA06bma7+aU5eHCMShSKLiT95YAN3bK6hssTK5uZSRFFg/621vHJqkFMXx+nonqStqXTe\nyMYYdnlZV+/kM3+4UTG8TpuZTc1l+ANhRqfi3bsko8B/f2gDOzZUs66hJOWz7bSZaWsqpbLEygf3\nrOGmarvmnbCzrYofP9vJma4JTneNU+608NPnzmuOa6F3RqZ3gbx/ySDSPzaLLxiXcf3kAzfT1rR8\nBi3xOuTTNe20mbn9lnqKLYactt0/5tHcN9crtgzHrhvqLMj15szlxsnFUJcUS4qxlZFfEh3dk1gk\nA2+dG+bouWEi4QiRWHz0/fF71zPimuPEeyNEYmA0gtlkJBSOYhThj/a1UGw1MpAm2ccqxd3o14pR\ngE0tpYxNFU6pi87SMTI5t2BZVygSxR+McnnIzZvnRjjTNUFH9ySldjP/+vwF5V4PhKJUlljZdnMV\n6xpKaG0o4fZNNbw/OMOpi/EqBnnQOj0b4N9e6sIXiDA27WNDUyn1FVeli502M2PTPqVhTSQar4xQ\nD3ZT4bSZFUOe+E549/0J3uubVo51xhNgcGJOc1wLbX+hd4HTZuZczyQXrszMH3eMsmIzm1vKM243\n36ivQ76pr3FQV2rNettyWDKXAVGhkslQ61nfWZKpJlPNUhboN1bZ+dKBW5XGAomCJ79783JSjarB\nIDLimuOnz50nqHJ9G8R4oDYchWeP9hIIpn+j+hboE5wtMQHeH5jJuEw6wRSIi26U2iWMBoHRqdT9\ntHWWF3FerzvV36y2vIiBibmMnho55CK3hoSrtcEamVyzgc3NZUlJnamqIp55vTtJ4U+dbAb5KftR\nvxNiaOPk5U4Lk/OCLfl04yZeytXuBFstddb6jDrPvHluJGmEn2kkncuMGq6OZusrilnXUEJH96Sy\nv1Sz3kAoSiAYYXhSO1tWvzyXq9NlLJb6GLNen/g1XUnltRuZxiobs3OhrF7+VkkkGo0pojqRhLGc\nKEBbUykWSSQSjWm8QDJGERCu3otyvpkogs8f0qxz9211XBmd5clX3ufdS5OcOD/Glpa4sZZdwrXl\nNm6qtlPmsCjPhCDAR/a3UF9RzIkLo/zq0CWMBoFiq4lwOEprnYNH724FWNAL1j/m4fm3+ujsmcRZ\nLCnL9Y95uDI6y6jLSygcQzIJfOD2Ndy7rSHJC5fK2yZ/5ig2IxkyZ92pvWo2i4HGKjslqmNZDIXi\nOu4f8/BW5wgisayPYynDksuN7vpeRnK9cXI11Jn2ZzUbkgxhmcPMrrYq3h+cVl6mFknEbBIJzfcl\ntEgCkXnbJxB/gWajRKWzcmxZghCC25udkYb4gEteNtFIQ3xQNTDuxeUJpjTSEL/HUs22YzGS1hkY\n9/L+wIxyf4fCUfzBMNtursLtDfL0692c75vmdNc49+9oZENTKYFghI/sb6GmrIinXn2f3xy+zOiU\nj1MXxnn7/CgXrswwNRtgTY09ZTxZbcDc3iA/eOodzvdN0z3kVgYKbm+Qf3img87eKSSjiCAIBEMx\neobc7N5Yzbabq5RtPf9WH0+/3q2499uaSpX1T10c58T5ETbclNmlLMfYJYPI0OQc5/umr8nlWyiu\nY/k43jo7ktNxLGXMfLnRXd/LyEoU6G9bX4nHF8RulRhxeTl/eYoiiwHJaOC29RX89s0+gqFYvHfw\nxipuW1eptMW0WyVK7RJvvDPErD9Mc60dXyCcJGKxEEYRTQvNQqOxsoj6qmJOXhhbNg/CUtLVnzmE\ncKORSkdcLuVRV0PIVQ4fu3d9kgSvjFoAKJ0EJ6AJYW1fX6kp6/L6wxztGMZpNyvrqj09ajdsqmNQ\n70f+fGLan5XrtrHKjtPuSirRWsy7plBcx9dyHNmGJa9ndEO9BCzXjaN+AQiCdnbinosAEV4+Oah8\nFo3B5LRfiWmXOczcu62ep1/rUWZHZ3umFnUshWykAcZnfAxPzhX8cWZLullqrlSXmgs+3m80xJMf\nvX5tAfx9OxqBzHFbtQGQEQCzJOAPxnDYTCklOBMNR4x4dYS6tWcMbaw7nexoqmNQfy+vX1FiyUkC\nNR/SmoUi0Vkox1Go6K7vFeZaXN/qeHi2+AIhPHPh+Z8jTLr919yM43ogHNHd+QC7N1YyNjWnuKvV\ns0CLaeEBl1HQ1tGLQuqEptZ6O+sbSxAE2NhUSltTCXP+MKXFEv5ghObaYlwe7X1vNRtoayohGoli\nMgrctq6cyZkAwXAMq9nA/lvqqCwxMz7tp9gaD/NUlVq5qdquxG0dNhMf+4N1ittUHRoyCmC3mbjz\nllpGXD4CoShmycD9OxrZvbFa4z5NDGH98d2trG8soePSBJEoyn7kGHlliZUP721WysfUblj1thw2\nE/tvqePRu+PduNSu2z/5b5uodFiy+jvmy+VbKK5j+Tia60t4cEfjDT9DTkUm17cuIZonFiMx2j/m\n4fKYlzVVtpxuTHlf6ozvxBl1Ou7bXs+prglc7gBWs4G9W2p45eTgimWPFrrL/EYjUSpzqRCAIosB\nr1/b4EK+T42iQFg1cnIUmUDQLmOVDJrM792bqjjWqZUUtVkM/OUn2oGrcp7qnxur7Jy4MBqvepg/\n74XkQmUSn+lM+uELoX5mJ2b8SRLA53pd3NneSLFpYQ2qldYcX0qykVS+UckkIarPqPPAYhIyFps8\nod5Xz5CbA/e00lzrYN8tdTTXOtjcXEogFKHMIVHusHDgD1ppqbUz6fbzgd038Uf7Wim1m+noniQQ\njDIx42d9o1OTmOQoMhAI5Wa6rZLImprkWdJC5GOWm4U6pc48kWV0K8jJioFQNMlVrz4MQYDbt9Ry\n8cq0ZplwJKYkSJY5zMRi4PJoXcihcExTX61OzpKfq8sjHs52X1UTjES1202X8KmuF5afu87eKaZm\nA+zeWJ1T0pXTZsYiGZKS1nJNJiuU5K+l4lqTa69n9GSyJWYxiRCLTZ5IXG9ixp80G3hw15qk9dSf\nJUqMJsal4/Ht3PAFozknoOUDs0nMW7x2NSAKyxMCiMeBRfzBKDaLkUg0LmwiI+dHnO+bpq2phEAE\npfe0XEvvsJk4eN96ZQY64prj0mCnZj9GAU08M9Vzlaql5YM7G5XuW4t57haTdJVOijibZDJ5Fj0z\nX5t9Lcehc/2ha33ngcXo3y5WMzcfWrsFF+u4BnQjnRuLMdLpmrQkIgrx7Pp19Q4evbsFab6LSyAQ\n1hhpUYB7t9Xz4K41PHLXWl58u5/fvNFDJBJl96Yqiub1vQFqyooUDfsdG6q5b3u9Zp9ms4HO3kn+\n7lfvcOLCaIImtpEzXeOMuOb40oFbuX97A/dtb+Dgfet58e1+zva4+PnLXSm1uBN1unN57tJpfKfa\nhvqzdMlk6qZAx86PKqGEVMeRT33xxW4zH8ewFOeRT5b7+HTXdx5YTELGYpMnEvcFWqGG/jEPL7zV\nx9meybRCCGrRBMkAFrNBcVPeiJQ7JCocZtxzqZuN6GQmEI5mlf8QA9xzIcLRKBUOqyKpmTiUigE9\nQ262ri3naMewslwoHMMqGRiZD8PIsqGyYFD/mIf/PNyrUd8LhWN0Xp5ibMrHqYvjbGgq5d5tDfiD\nYXqHPbg8AeXzB3Y2saWlnMPvDGnkPiWDqJHhTOVeVieNZXrGM7mmU70nskkmUyeNBkJR9t1Sx7ab\nK5OOYync4rlu81qPwWYzc/HyZEG795cq/KC7vpeBxZRkNVbZad9Ul5Q8kSqJRf27/C9RrvTAPa38\n/OUuxcX35rlh/vIT7cqyRzuGiRHvSnTwvvX86/MX8AUjWCwi92+vpcQu8VbnKP1j3nxdloJg0h1k\nkutr8FdIGEXIRUVWFk6xmg1JkrYyvmCEc72uJO9OJunNVGVOJoNAaF4ERZYL/eIf34pXVcmQKCO6\nkAxnOjd3Ns/4Qi7yVNuQP0uXSJVYupTOXb8UNdG5bnOpQgSF5N5fiePTXd8FRmLv6xMXRtP2vU28\nYY6ohB8gXnpztGOY/jEPP3jqHf7r5AAvnxzgB0+9Q/fAjJJR6/aGcNrNPLhrDSXFhTNy1dFiXIGn\n1WYx8OE7W3Jer9Qu8amHNiDNZzEbDcSVu+a/t5oNCMTwzAWR5j3dDpuJh3Y3ceCeVra0lHHgnri0\n5y9e6eKXr3RR4bQobmLJFG97+Uf7mhHmNyoIV1tl7t1am/Jz+TvZfSzXUau5lvDSUrSBVPebz9Q7\nYCn2nes283EMS9lKMx+sxPHpru8VJjHLMVErPBCM0Dcyq/yudgUm1np+YHcT57onNWUvLXUO3HMh\nznRNKJ8FQlFa6hxMzwaSpE6NBoGTF3KrzdZZHvKVBJZKmzsVVklkbb2TyhIrnZdzE8LpvDxFS62d\n7sEZQuG4JriccW4yCgRCUd67PMXAuJdILC5r+ycPbqDYauKnz52nb2SWrv5pjnQMceHKDN1Dbrr6\np7l/RwM9Q24CoShzgTAP72nWyIXKs+b6imLqKoqSPoe4C7rcaSEQjPCHd6xJahOZLpSVjSb2tdQl\nL9TmcqGOVUtRE53rNq/1GGw2M8Z5nfiVru1Ox1LVnut11AVAOnd2Yu2k2p1d5jBz4J5WpVbaYTOx\nq61a4/pK3O6Lxy/zzO97iEbBIMA92+oZcc3ReXlK6VQkZ9Me6RhieMJLbYWNoQkvdRU2Hrmrlc7e\nSX59uIfgja+DopMFoni1y1UuVDgkJtzZD7ofvXstAE+/1p12mS0tZUp7SnmdVDXQmUh8xrLpcLeY\ndXJlNdcQy6zma5CpjlqPUS8DqWLJsvF97cwg/+sjW5SHXq0VLosjHLinle6BGY6dH+XlkwOc6hpP\nelGMuOY40jHMm+dGlJdqJIZGQhTis6RdbVX824sXFFUq+WU66Q5yvu8EZpNRN9I6Cosx0gC1FbaU\nhto2L4SibmlaUiwlSWo6bCaCoYiSMZ5O7jNXlrOcUkcnH+iGehlIFUvOVDsp/6wewaubAqRqHJCt\nMpkvGE0y3mrCEQhHdCutk54tLaWa2vvdGysZnJjD64vfnzariT+8Yw0AF/umUImLYbMY+JMHNyi9\n1LsHZogBD+9vVTxLB+5p5UjHMG1NJbz4dj/+YBTJJHLwvnijjZqyIiUxcjEsRlda16LWWUl0Q70M\npMralGcF6WonE417z7BbEYQoc5ipcFp45vVuZZlcAxjq2Uwii3V16qwOpmcTZsmCgNcfxuUJUuYw\n89k/3AjAD556R2OkIZ7geKRjmEfuWqvURvePeThzcYw1VTYAxdt0SZXwGAzFVfRkTnaN43IHUnqX\nFpLYXEyHu5XoiqejI6Mb6mWgscquzBL2bq1VEluOdAzzgb3NKR96tZqSAFwajBvq+7c3sLbBeVXj\nm9QGVwTqq2zUVxTx7qUJfEHtUvYiA+65SNL6BgHqyovoH5/L09lfpcxuwuXJvpZZMqK74AsMgwjD\nE9ryvYlpf0rFLXUFgowgwNkeF4MTXv7ika1AsudI3pYvGFFKvNJ1o0p0QyeGmdLFkhdbTqkbaJ2V\nICtD/e677/K3f/u3/OxnP6Ozs5Ovf/3rSJJEW1sbX/va1xBFkX/6p3/iueeeo7i4mM9+9rPcfffd\nTE9P8+Uvf5nZ2VlKSkp47LHHKC8vX3iHNxj9Yx7FsA7Ov+Tk34ddc5oYdSpkQyqXUU3MXH0xppsV\nR4Hx6bm0NdGyTGji+pEYS2KkgZyMNOhGeimQ0xajEC+LEjJf58SBXF25jf7xq/eUZBK4b0cjY9Px\n+n2bxci0J0Brg1PxAEE8q7uxspj3B93AVQObKIkZI17yok6mTGxikckNfTRFb+l8Nc/ItLw+y9ZZ\nShY01P/8z//Ms88+i9VqBeCv//qv+d//+3/T3t7O3/3d3/Hb3/6WDRs28Lvf/Y6nn34agI997GPs\n3r2bH//4x2zbto0//dM/5c033+SJJ57g8ccfX9ozKkByjVHL6yTOSFL1sM2EP1hwCf2rjkILI6gP\nJdEtDfH4c2fvlFIKVlVqZmo2SDAUQzJAMKxdqbTYzJmuMbzzqm9ef1hJeHxwZyP/8UYP4QgYRIF7\ndzQy6bnaC73CaeGlt68o23LYTLQ2OAGUDlUAlwZmONoxzJ6tV2e16oRLeQYPcOz8qGZ7aiO+kFFN\n1d8906w829m7js61sqCEwk033cQPf/hD5ffR0VHa2+Nt5drb2zl16hTd3d3s3LkTs9mM2WymqamJ\nixcvcunSJfbt26dZdjWSWCC/d2vtgvq+6nVkl7f8IpBd6evqHVik9L2jUn1nugbVjFzX1Ltawdpa\nx0ofQk6c7ZnS1GuPTgUIzndSC0ZgVNVlLf69n2PvjRNJGBO63AHOdE0g23WvP0L3wIxGuGNixq8Z\njG5sKuWpQ5d4+eQAJ+c1un/w1Du8fHKA/5oX6pEFfxqr7GxuLuOpQ5d4+rVufvDUO/y/ly5qtrer\nrTrJJZ5KOEhGPaCWcz7UrvxMy2daLlt6h2YKWt9aZ+VYcEb9wAMPMDAwoPze2NjI22+/zc6dO3nt\ntdfw+XzcfPPN/NM//ROzs7OEQiHOnDnDgQMHaGtr49ChQ2zcuJFDhw7h9/sz7OkqpaVFGI2GhRdc\nYXqHZjhzcYzbbq6iuc6p+R1Qfm7fVMc3Sm2aZdvWVmp+T6Sy0q6sAzHOXppkLhylstLOkXcH+LcX\nL+L1hym2GjEawsoL0SqJbGqpYM4f4uF9LYy55vj31y4RDIYIxwRC4SjFRSaikQhzAbk/r4Cz2Myc\nP0yJzYzXH2B6NtkfmuvEMJv5vEEg6SV/I/H+oHtV9tyuKLFQX21XXN0A1iKJ9k11tG+qA6C01MZr\nZwaZmPZTUWKhuqKYY+/F+0273AFOXBjXGF63N8TlMa+y/uFzI4qhdHtDuL0hpTtYRYmFh/e3KrWp\n6mVd7oBmOzJ3tjcqxyMjCtDSWJKyxlW9fEWJhTvbGzPWwmaid2iGb/70GBPTfl47M8jffGZ3yvfC\namCx1/BGJudksm9/+9s8/vjj/N//+3/Zvn07kiSxdu1aDh48yGc/+1nq6uq45ZZbKC0t5XOf+xyP\nP/44Bw8eZP/+/dTU1GS1j6mppYmR5hO12+s3b3RraqP//bX3gfjL4zdvdCsz4Ts3x89/fNxDsUnk\nzs01GQv8i00iFqPAj37zHrEYnLo4Rm//FL892qdkw876tAbVF4zS2TOJLxhhYLyDUDiq0luOW8TZ\nhOYUwVCM8fmZkte3vIHhG9lIy9yIRtpkgFAK17lFgsoSGyXFEsfPDWMQ4ypoFklkbi7I6c4hZZY7\nNeWltc6BURQospgwG662unTYTLTUFtPx/pjiordZjKypsvH84UtK+ZbNYsTrv3rPRmOwrt5BudPC\nky+8h6NIYs/WWtZU2ZTYt8Nm4nzPBD8ccbNHJR5UbBL5Xx/ZwjOvdyuiKtEY9PRPc7PKaKpd6P/r\nI1uUn4tN4qLFOg6f7lcGCBPTfg6f7tcIIa0WdMGT1ORsqH//+9/zt3/7t5SWlvKtb32Lffv24XK5\n8Hq9/PKXv8Tj8fDpT3+adevWcfjwYR599FHa29t56aWXFJf5jUCmuLN6FnCt4ghHOoYVN1wsBm+8\nO6wYaYjPhk0Gg/KykkyCRsNbRycXss20T2WkAfxB6B/zJiUx+oNRXj45wJvnRrhjcw2tDU5NAxnw\n0TPkRpyPl0QiUX77Zl9CHD1GZ+8kT7/WQww0CmUyNouR3hG3ZiZ/7PwoB+9bz7b1lXh8Qc52Tyoz\n92PnR/nSgVsVtcAjHcPYLEZNKaQcmuof8/DCsT7eeX8CfyiqxKVzVUZLxebmMmV2rtdp6ySSs6Fu\namriU5/6FFarlV27drF//35isRg9PT189KMfxWQy8Zd/+ZcYDAaam5v5yle+AkBVVRXf/va3834C\nK0Wm2mhZ8D/Vg36kY1iTKHP43AhrqmyaWJq6y9XerbXxLkOKsY4pswirZODhPU1MeYJ4fEHsVolW\nVemWw2ZKmFHHZxv947P4g1EMAlSWWhmf8uVtZrsa3bw3EkudaS8nmx3p0A44ZeT4uKyap103wqFT\nA2lDKhUOKd5mM2FVtzekdIqzSgbNft3ekBJb/sFT7ygDB5vFwP3bG5QZt9zYJtdBeLZZ4Y1Vdv7m\nM7s5fLpfzyDXSULX+r4GMrWjBJK++96Tp5UXkM1iwGAQFWMu15SqXwYOm4kvHbiVEdccv3vzsmaW\nsntTFbetq1SMsnobRzqGFcMNMY0SWSahE53l4UaPy2eLQRSURh1J36W4Rg6biabqYo0qmowgwK62\nKmWmrMZiEvGHro4eJZOgJMjJz9i5XleSxrhaR/yF431J31vNBr56sH3B3tTZ6oOvZrevzGq+BrrW\n9xKRKICQ6neZox3DmllC/Of47+lEIuTR/kO7mjjSMawx1F5fWFNP7XIHONoxrCg2yeUlRlGbe63b\nh5VB7pssClBRYmZ0KnNp3fWEZEhd6pWJMoeZe7fV8+sjlwmGoogiSEYBfzCmeIqee6sPrz+CKMDO\njVWK0ewZig94jQbYfnMll0dm2XdLLZuay+kamMHlDiAZBW6+qYTaMptGIEiuzb40MKN4tuTn9Pm3\nLqsG0vF68P4xj5JhLgsQAUgmkU89tGHJezPr6IBuqJeNRAMpGcBiSY6DqV8G6jpQtQtc7q9bU1ak\ncb/HIKm8JJyv3og610RofnoYjVFQRlok92z+RLIx0hZJYN/WekrsEm91jkIMyp1WvvbJbUoXuakp\nr8Yj9eLb/UCE4iITD+1qUozcnzy4gZ8+d55gKMrx98aJAa+cGmRTc3lamc+asiLN5+q2lzIGgwhE\nMBnjfqeXTw5w5Owwn3poAzs2VPOlA7fywvE+Jqb93LejMeU21GSrD56uk971ji4Gkz/0ftTLhD8Y\npqN7gkg0Phr/zB9u5P4djTTXl/DgjkYaq+w4bWY2NZchGURa6hzcdVs9l0c8WCQDbU1lmE0Ck24/\nt2+qJhqDqlIruzdWIxlEastttDY46eqfJqBy89ksRpqqbbi9QSX+Z5EEbBaTZrnFYJVEwroP97pm\nuf564QisrXPw6ulBJmYCuOdCnLwwTltTKftvrae+xoFRQOm5/Oa5EaWHeiAU1fRhP/zOEF0DM5rt\ny73at91cxbqGEtzeoKZ39EL9nNX7i0YhFI7NH3eMzh4XW9fGFRWfP3aFwYk5eobctDWVXnN/aNk9\nfuriOCfOj7Dhpsw9p68X1OfV0T254LWSydST+0YnUz9qfUa9DMgSosFQ3K33qQ9sUEbj7ZvqNDGZ\nxio7H7vXnrI15iunBnG5A4y6BokRn30fvG+94u4+fn6UYEI6rtcf5tKgNuYTCsXwB3PPCE9U2fIF\n9ayx1YDRADaLiZl5T89i8hxsFiM9w25NKRXE8ylSzUzVruZEhbFU+05M2sxVMUw9+3XYTPiDYSWO\n7QtGlNBUrq7shfTB1e7xdCqF1yO62z+/3Dh+lgJGfdP6ghFNF6Bs1kks/1Jrf79yol9TFubPwngu\ndhJcSFKYOotDXIRc3JoaO8H5VH6rZGDXxkrNi8NoAKdNO+avLjXTWGXDaTNSbDUSCoW5pCqZkqkp\ns/LC8T56h67OkOXqCHnQGYlEGXHNKapde7fWYrPEBZEskqBR7YPURqJ/zKOsr/5ZRpYlvX97A7va\nqvnw3masUnwf8iAgUWEwkys7W4Ux9TbTqRTmk1yO7VrY3FymVL8kDrR0ckefUS8D+eh/qy7/UlPu\ntDA539jAYTMRicSSZi2JyIlNuaJnK1+/lDskAqFokkBONqg9Mr5ghLcvjGvi2uEIzHi1243H4ReO\nxR/pGMEXjPDamUEevWstlwZmOH5+VJNU6fVHlJj0S29fYWNTqfKdZDJqEsL6xzxMewJJLWHlGbas\nLe72hlLOtmXvVJnDzKc+sCGpMcdCrS5znc2rdcszxajzEe/VtcmvX3RDvUhyeXCy7WWb2LlH7hwk\n/75tfQVvnx/D7Q0RI15mctv6Sh7a3cS/v97NwMQsVklUDLXRAHffVs9rZwY1taV33VbH798ZVGpm\nZdlFGaMIFrORcCSMXxUuisTicemKEitmk0j3oCd+HEbYcFMZ1WVWTSmYTuEw6c5f3C9fnhWLSVRq\nmiem/UqtcyqC8/kUbm9IU4IlV0bIJZCyIZL18ffMJ2FmI0aUOBOfmPEniZnk4srO1T2erjQpXwZ2\nOd3R6qZC6r+RzuLQDfUiWMyDs9ADnqpzj1xKIpeWJBIMxXjq0CW2ra+gI4VKUzgCl4c9SQIQicY0\nMTAqyS8AACAASURBVDE8HE2WJpXxBaNJqlPBMHQNTNHVP532/NJRbBGZ9es+9VxprCxiYsaPQWTB\n61eotfO3rqtQyqkSpUDVSAYwmQwpRVDS9al2e0O4fUHlmVPHn+XvE71bi/F8JZKPbSSSLwO7FMdW\nCPtaDehZ34vgzXMjnLo4DlzNNpUzUnNFznJUb1PGF4gQCEboG5lNu74vEGFsyqfEEBNZ3+hkeHKO\npZa1CUcWVwpmMRuuOft8NRJX4IoRDC/+DysZRQQhljRQWwibxYCjyKRRvEuFSPoBgsNm4v6dN2Ex\nGWitc/Chu9ZyvteFLxDBZjGwptrOtpsrGJ6YIxCOYTUbuW19Be65IIFQFIfNxP5b6nj07lZlNt3V\nP83Q+KxyHw6MezGbBLbdXK1kX394bzN3bK5JmYmdTZY2xAfV6oxyNU6bmVK7mUAwwgd2N9HWlL2B\nGpvx8+rJK1gkgyZrvarUSkf3JL5AhDKHmQ/uWbOozPBszy8fLHZfetZ3anRlskWQq+JQJmR312Jm\n1BB/4VWVWJIyu2X++O4WTneNp/0+n+Tae9lhM7GxqYRj740vvLBOQaAWJlmIdfUOjea202YiEArj\nD8aSlPm+8T9uZ2rKy9GOYY7Nx6gT5T4fvXstm5vLkkJIqeQ9ZYyiwF9/anvejNJCz/5i3w39Yx5+\n+B9nmZj2J836ZcXB1VCTrCuTpUZ3fS+CdDHnxBhzLg+VepuJ68tiDQIxznRNUO60cNv6SroHZjh2\nfpRLgx4skkCxxUSJ3Uz/mFeZpT79ek/K2fTujZV0DcxQbrfQPejOWfTCYgJ/wntxISOduI5VEnUj\nfZ0RjZKVkQaY9GirGypLrErmd6Iy35mLY9y5uQan/Wps0xeMYDUblJmk/DwkPlNHO4bTNqAJR2N5\njY8u5IZerJv6XK9L6Z6VKo6uFnzRWX3ohnqRJL4wUs2Ic038SBfHVn/+4K41yucTM37lofYHYzy8\np5GHdjVpdInT+UveveTCF4ww6wsvSpkq0UgvZp1CUugqRAolfr+lpZTzfdOEI7H5yoJoynixGkGA\ne7c1KDKgFkmkwmlhYGwWfyiKZBKwSEZl1ij3cE+snz543/qMg94TF0Y52ZWs7y2Ta3x0oSTRhWKv\ni43NqrtnZYqj66xOdEOdJ9Qjadk4LnVmZbqXgvrzVJiMV7Ntg3p8uGApBCMNKE0wJJPIwfvWU1NW\nxP976aLGrS1jFKG51sG9OxqpKSvi10d6gXibS3W2djAUo31dCY3VDqY9fr77byfYs7maTc3lRCLx\n85b/T8eJC6P86DedmsGoZIS7bm1gbYNTMfAjrjmeeb2btqYSYgjXVFqVqYJDNvJytUauHjV19yxY\nOVf3ckh/Frq8aKEdn55MlicskkFJ+BDmRSWySfxQJ5DkmiDitJmJRqNMuv3cfVsd226OKzy5vUHC\n4SitdQ623VxBz5Bbkfosthq5c2sN07NBfIEIFpMIqoSixsoi/KEwC7wjlwVHkYFAqOBSKFYtkWiM\nsmIze7bU8cLxvpSVAdFYvFHGAzub+JfnzjM06Uu7vaHxOeoqinj55CCeuRCdl6cYHJtlZCruAg6F\nY3R0T3K225VShvJXhy4xOqXdfiQK9++M63Cvayihq3+aH/2mk9EpH52Xp3jv8hQd3ZOU2s10dE9q\nnrtsk0RTyZGqJTN7htyLStaqr3FQV2rNSvJ0qVis9Ge+9lEIyWTLcQ1SkSmZTDfUeUKd5bjvljqa\nax0LPqz9Yx6+/+RpTl5If0MkZpiqf+/qn+bfXupi1hfmvb4p6iqKiEZj/MMzHXT2TjE1G+DhPc20\nNjgZGJtl1hcmGI7SO+ShvqKIWX+YQCiqyfp1z4UKwkgDaY20UQDE9G59naWjpc7BlpZyQuEInZeT\n203Ky5QUS/z6je6M91IMkioWfIGQRj9eboOZynAaDQKnLiTnOASCEW7fVAOkNua+QITOHhfvdk9q\nnjv1YDvX7Op8VIIUgpHKZ0XLYvaxWq5BKnSt72VioVrpRNQJJKnc5Kn0vuUM8FdPDVBuNyvGKhaL\n6yZvaCpNan15LEHpKQbLkgW+VIRjFGZh8HWIo8iAey59vLnYYsAfjCDb0t7hGV48fplj50axWURC\nYTSGVgBGXF7+z89PE0iYcJc7JIwGQclNsJoNSEatEleF08Koa45gRNuz3Wo2UOG0KMv1j3m4NDCD\nWdJmoMud5WTUXedk5AQ10D532QoTpeJGqRtejvMo9GtViMenz6hXEItk4Fyvizl/OOUIPnFkp66p\n9gUirG90MjgRFx8RBPjI/hZa6hyaWUFtuY3zfbkLkeisDhYKLYQjMY1srMsTpPPyFDNzIULhmDLj\nVTM25U/ZVc0XiGiS0MKRWJISmXsuRCQWj4d/cM8aJKOBsWkfgWBU6Vjl9gYVr5FazKfCIfHfH9qg\nafJRX1FMXUURgWCEW9aWYbOY2LOlhmHXXMqZ82JdzvmoUS6E2eRy1Fpn2sdquQap0OuoC5jZUDRt\nAom6PtQgCuzYUME770/inxd8+NKBWxlxzfHKiX7KnRYe2t2krCdvZ8Q1x0+fe0/pBARxadHw/Iyl\nzGHRKI0ZxXh9s8uTOa27zL7wMvnodayzekmso4Z4LTWgVDWokZ+JhRLFZH2CXBO+lprVXEMss5qv\ngV5HXcA01zkpNokpM06Bqx2EojFNzbGcEVtTVsSkJ8D7g266BmaUTFXZYMvtNQ0iSrwwHIGtLWV0\nD80kyYGGoyxogCG7ZXQjrbNY1DrgMmo3pOyalEyCMgjNpCmdjY63jk6hohvqAiGVUAKQtm2l179w\nj1z1NhOTeoYmZheshdXJP5IRpRnKSlBdas65fn1LSynnL08RBVpq7bSvr+T37wwxNRtEMgg01zm4\nMjqLQYyL7ngDIdrXVzLimqN32INkEpnzh/AHY0gmkQ/vXUMMAYEY5/umqSmzMjUboqW2mCujs0zO\n+LltfQXPvXUFrz+MZAC7TeIPttUndbGqcFqUvI1M8cRCjDvq6GSLbqgLhHQvkuff6kvZrEDd4zXd\nC0i9TXXTA0GA2gobE3nsqKSTHStppCE3kRlRhJ0bKjWenOHJOaY8Acan/URjEAzF66xtFiMz3hAu\nT/ye0jR+8V3tG61uSQnw4K74/5WVdp4/fInfvtmHLxBhaNKrDCSDkXj3r1+91jO/zhpN4qas3JfJ\njX0tiWI6OiuNHqNeYdQxmcQi+/4xDy8c6+PyiAfJILJ7czVTniACKC+8/jEPRzqGlc8gOc59tGMY\n91yQiRkf054At62vxDOnbRe4HEiG+Ev3WnDYjDRV2xURDp2lpcIh5W1A9+jda9O6m2dDUb7yw8Np\n21zK5Fu7u5BYzfFZmdV8DfQY9XWCepaQ2GjAYTOxqbk8YwOAtQ1OTfmWHOdOLM9aqZ7R12qkAdze\nsG6klwkxB8+LySAQSpHpLWOzGBCI8Z2fndQkPsqcuTimMdLpWnMupN2draJUNssVmjqVzupFN9QF\nirrxOqROlEmMax/pGE4Z507XsEBHJxPRGEx7snOVZzLSRhG2tJQpruv3B90ce2+MLS2lDE54Kbdb\nqK+2a8IzMeIDhVQtOCuclpQNcABl4PrS21fY1VbN3gRXO2QnFaoeKL/09hW+dOBW4PqW9dQHHtcv\neh11CjL1m833dtLVDVokA6e7xpUuWJJJYO+WWuorijXLqGumP7C7iZ4htyINWmQ2srbeSVf/dMae\nz5JRm2xmFFO/IGWcNqPeQ3qV4J679kFeNBaXC028pcam/PgCEVyeAJeHPYQSeqqnuwWDwQgvnxzg\n1MVxTl0cp7M3LgsaCUc51xv3tgRC8brrVIp/2ShPvfBWH+/N6w8EQlH8wTDPH7uypLKS6d4F+ZC0\nXClZzFwphDrqlUKXEM2BfN3Q2Wynf8zDW50jiMRSNqDf1FyGPxhmbGqOYDhGz5CbaDTKb9+8zMys\nn7FpP2vr7IiiwAd2N7FjQzWldjPvXpogGI4xMO7lXPckW1rKGBifU7adaIhjMe1LMZORhrhQRWmx\nCV+ajPR8YjMLhHJ0mUtGoWBkUHXmZ8Z53F4oEsGV4I73BSK01DmYng0oqmPy54mGOBup0LM9k/QM\nuTXrDE7Mpd1mPkhnpPIhablSspi5ohvq1Oiu7wQW20821+1k02Bejlkf6xxTtiO7D8/2xN3ackvN\nwQkvNf8/e28e3cZ53vt/ZwYYDAgCIEES3MVFFC2KEiVRkiVbtGQlsi3FWVzHspu4Sd2b1nHP7T09\nqU+Se+q0zi+JfJtepz2tr0+a5jht7Wb3zY1X2bUseZFkrZREiRJFUVwEriAJkgBB7DO/P4YznBnM\nYCHBfT7n+FgEZt4ZADPzvO+zfB9HFm71TiASnbG0wQgb1/NZsXBJapiVxFgupTrqTOAPpZ/rGI4u\nufzIjFNTal02MrB31hWgvder2c0tGYxCJnT/tjIcvdAnaylrNlGoKbOjqaFYJpsrVEJI3b4AsK22\nQJaUqaSpoRhnpsewWYy4b0c5PL7kZWDzQSZKy/TytOWNvqJWMBdh/nTGSXWGKx3HSBGaRlUYIxiO\nyVYCOrPDlrW03ftCGdRcSBbiEGBoAmxM2xVtIPnvKxxjVRuleKciuH9HGW72TcRJjuZYDCh3ZuOe\nraXItRhEzw9JAvu3lWLbHU58ce9a1FXkijKg3YOT2FnnxIYqB0rysnB7aBLhaVf3rg2F2L2pBPVV\nDlECEgB+9OtLuNg+gnPXh3D62hCu94xjbDKEXRsKxftSGqoqd1rFMR5qqkJdhWPeZSW1VpOZkLRc\nLFnMdNFX1OrohlpBqhd0svhzsnHUDLnXH47rlHW1yyO6tzdWO9DVL19FTXfUhM1ixENNVaCNFM6r\ndBSSUlNqTfqgd1jTd22TWDm9Mpaykc4UqXpSogmMtDBOjOMQ0whRhCIsSJLAwOhU3Ht7Npfgz/9g\nEwiKwK+Pdoivcxyw7Q4nDu6sgN1iQml+NgwUgd8cv4WhsQCu9YyhvjIXR8/3iU1BpBNeqWa3NN4c\niXLibyvdXi1UVe60ynS/57v1ZCIjlYljL1brzHTQDbU6Kbm+L1++jOeffx6vvPIKWltb8eyzz4Km\nadTV1eGZZ54BSZL42c9+hjfffBMEQeCpp57Cfffdh/HxcXzzm9/E5OQkcnJy8IMf/AB5eXkZ+2Dz\nRbIuWKlkjSYbRxBg6Hb7Uem0AIBmpyzBvdczZIRRomxlNBCgSIhuwUHPFF5+50bSz5eKy3Q2ru2V\nb9pWL8kU1SIJwg02ixFNDcW46RpHMKKeMPb+WZfsdSNFxLlnT7QMyLrFfXR5QFbSZTZRqi5d5Zkx\nRhLBCCtzAWcq5KWjMx+QyTb46U9/iu985zsIhfiL+G/+5m/w13/91/jFL36B7OxsvPHGG/B6vXj5\n5Zfxq1/9Cj/72c/w3HPPAQB+8pOfYNu2bfjlL3+Jr3zlK/iHf/iH+f00C4SW3Ge6lDuteHjfOpQ7\nrQlLrYSHk9cfkT0sI1FONNJefwQnWgZUVcx0dObK3i1l2LXBiXWlNuzaUCB7jzbwddJSGJrAulIb\n7BYjKgqzUeTIwp88WAdpV0sLQ4ktKT99ZzkIYua99RV8GMjl9uHImR643D40NRSL2xAEsGdzMRw2\nfhVipik8cXC9ZrxZOD8LQ+FPHqzDoX1rZRPsjVUOcSw9hquz1EhqqNesWYMXXnhB/HtoaAiNjY0A\ngMbGRly4cAFmsxklJSUIBAIIBAIgpu+mjo4O7NmzR7btSmA+bmrlmE0NMw8h4eFksxhhYWacIBaG\ngs1ilO0jfX8lQhv4ldNCYFrZX6UmawossFuMsrBKTZkdtiwalcU2TCk04vduKcODd8kVx8oKsnGz\nz4sJfwRXOsfw979oBgBkmfnrlSSAB++a6fbm9gRw6N5qrCu1gaEJXOkcw9/9vBmHXzmP3x6/hf/1\nygVcbB/GoXursanagae+UI8DOyvxl4804NC+tfiff9SIHesLZYZdjUgkhtGJQNzrgodLacDnm2Tn\nu5xI57OspM+9ECR9FD3wwAPo7e0V/y4vL8fZs2dx55134vjx4wgE+Iu+uLgYDz74IGKxGL7+9a8D\nAOrq6nDs2DFs2LABx44dQzAYTOmkcnOzYDAszMN4NhQUWPHdXAsu3nBj6x1OVJXYZzVOV/8EPj5+\nE1vvcKKxviRuzLq1Bbh4ww2nwwy3J4CtdzgBAO+fu42JyRDs2Sasr8xFWzdfO1q3tgD/678X4HfH\nb6K1axShYBRRjot7sCaDNgB2K4PhMfXfiyKAQocZ/aPxD7x0oQgggVZGHOEosK0mFxdvjKSdrZ4u\noVXqnOgd9svCGCzL4T/fa4dPRTjHajHic3tr8PJb12SvK8Mr/mAM59qGRfEdlgPe/OQ2qspz8dLr\nrRgZDyI/h8HG6jzc7OOTIaVlVnz1ghttt8fxva/fLd5zBQVWNNaXAODvpxd+dwUj40Ecv9iHv/3a\nLlSV2PHaqW6ZbrhQOSHdRjnWQhB3vrmWWT9LFhut73422yaS0lytpL1meO6553D48GG8+OKL2L59\nO2iaxkcffQS32433338fAPC1r30NjY2NePLJJ3H48GE8/vjj2Lt3L4qKilI6xthYfNLJUiPbSOKe\njfznmY02rTTO/dpHt8RZvHRM6THukFzIjTV54r7HL/ArFK8/go8v9eEvH2mA027CBxIju67Uhq5B\nL6IxfiVDG/lyFzNN4YnPrMfF9mGZ7nc4Ck0jDfCGNRNGWhgrXS60jWTk2DrqKHMNJgPaM5bcLBrZ\nRhI71hfgfJu2djxNATvWF6C1a1Q0wFPBKN4+0YWRcf5aGxkPonco8b00PhnGx80uZBvjnYEfN7tk\nYwnbaSUnSbdZaFxuH1794JbsfC/ecC/KuWQCre8+3W11rW910r4qPvzwQzz//PP4j//4D4yPj2P3\n7t2w2+1gGAY0TcNkMsFqtcLr9eL8+fM4dOgQfv7zn6OiokJ0mevMLc4t3dfrj4irFI83hJMtA/jd\nh12y7W/18UYa4FcyQlw7EI5hZCIIaxY914+zrKBIQhYrXU0YCIAxZm68fo8fvzx6A2+e7NbchiKB\nh/ZUY2QiiM/dXQF6+qHMh2k4MNN/O2wmNNbmgzYSmmNZGAr5dkbmNhXcqPl2RgwX2SxGjPtCYmxb\nGUMXjpdO2CpT7lphkn6l0yOGtRw2k+gxW2gy8bnSCQfq+QDpk/aKuqKiAk888QTMZjN27tyJvXv3\nAgBOnTqFRx99FCRJorGxEbt378bt27fx7W9/GwDgdDrFJDOd9AQIlBq9+XYGZhOFQCgmxqgFcQcO\nfOMCKdIVEkUSYGgS/mBMPO6gZwrHL/aKxnylo6zlXU1EOSA6B62awlw+JCJcU9GYepOXTdW5KHZY\nwAGokTSLsVmMMFIEwhHAH5xpsGIgCWyrzcfRC30IR3hvz+d2V4h9q39/ohvh6Yzxl9+5AX8winfP\n3sbj99XK+lE/9qka3OqdwOnrQ7zMaPsw/vKRBnz1wHq89NZ1hCMsLAyF3RuLNcVO1Ei10iMVpBNt\njuN10B+5dy2qSuwLvprM1OdKp42o3nI0ffQ2l4uIy+2TlWepXbjS5gAWxoBN1Q5c6xmD1x8BSfCq\nT9YsE3yBMKxmGrlWWuzpCwCFuSaMTIRlxok2EnBYGbF15luf3JZlixPT/2UxJIIhFmqVN4W5prR6\nG+usDGpKbejoSy6oc9/2Mnxpfy0A4MiZHvz2+K2k+xhIQjbJFNpiJtp/XalNjGkL+wCQba/1mlbL\nTTWU55Du/lK0VAkXw+2byc+VCXTXtzqrNK91aVDu5JNXmlv7NWe1J1sGRNe2PxiVxZJZDqI8qFBr\nLfxfQM2YhiMcBj18jPn2dGKNFG76v8mgdmW0bqRXD0INNUOTCIVjojdHC4rkV9ECG6scePNUNwKh\nGGgjAYY2qHZ0i7KcOLbUyyT1PtFGAuHIzAXOmCjVfYTtzSbeVV7kyErJg6XVYSqTEpxLaUWpS4su\nD3RlskXGYjHhvbM9mnKiyuYAOqsPZXez+cSg0kCDIACTkUQwzMI7FUE0SQYgxwHtrnEAHN441Y2e\nQS+6BycB8J9jz+ZiOHPN6B32xx3nD+6pwoYqh0zNz24xIddqQigcw8YqB3qHJxFj+Xj1mC+EqVAM\ntJHEVx64A9lmI060DIAk+MSzUHhGWnTXhsI4pUCpwqDXHxalRpvbh1Ff5ZCpkmVSglNNJUypypWJ\nLn5zVVBcaHRlMnX0FXUGmW2/10Sz2poyOz683CdbRSSCwMLIeFLkwhmP1U4iRbBMoxbmiMaAqMaP\nnUWTmFKRmvX6I2IZlJIrnR4U5JjjXue7uBFxrleX2yfGoa92TTfhoClUl9jEGHc4wuLizWFc6x6L\nW60LyZoHd1ZoNsZ5/0Ivasvt4r5efwQnWwbwh/tntldTGpyvHs+ZiB1nQkFRZ2mwSnNfM49wU/z2\n+C3806stqhmUWtmV5U4rHvtUDdaV2lArcRn+8ugN/OS1VjG5RqkIlW+jwdDyDNkshsL928uwa0MB\nsuj4nzfbbACpnVSbMrqR1gEQ10M6FfZsLpapjAlouV6VyVcAX7GgdL+PjAdVXeqpjOvxhsSSIYFk\nE95U7vnZkgn1w0wpKOosPvqKOkOk09ZSObt1uX34+Xvt8PojuNnnxbWeMeysc8qyaQPhmOg+FPB4\nw3FuSkHYob3Xq7rSSVQTq6OTLhXFVrhVDKTDSqs2fiEB5NnN2LG+EACv311XkQOLhQFjIHCyZQAc\nBtAkyciWepwEj5HQetI9zt83tJFAY20+Bkb94j2QLLtb6cmSjifokydiPvXB9daWOlL0GHWGmG1b\nSyFGfbF9RsQjFGHhHguIXYEAXqjk4K41aJ1WIQP4B5ay9aXZRGFNoRWtXTPb6ejMBwxN4L8/3ACW\nZeFy+8BK5oUxNqbqdeEAtNwaxeaaPGSbjWA5YGNVHqrKcvFPv7mEaz3j6Oz3orl9GHl2Bi23RsFy\nHBgjBWcugzFfEJEoBxNN4f4d5bBbjLjhGkckysE1PIlojEU0xsFoIHBPQzEGPQE4bAxYlouL1Srj\ns3UVDllry2RGN1MtcQWk8dnV1NpSih6jVkcvz8ogieJViUoymlv78fe/uCiWSNksxrgV9X3bS/Gl\n/Xfgl0dv4P3zfWAl2x2/2I9ojHePNzUUoeXWKIbHg2A5flVhoil4vKld/CQAkkLCmmqhLKy5fQTh\nBF2TlPuolS+T0LtuLTdqSm3YWluAjVUOtHaNasaiE3H/9jKcbx8W74d7tpTitY/k45hpCoFwTKxk\nEP6WjvFxi7yDlhp8qSElagdkUss7kzHq1VyaJLCavwO9PGuBSKWtpdZNTVF8wI42EHj8vlrsWF+I\nXKsJH10ewJ7NxTiwsxIutw9nrrtFwxaORHHmultmpJXiE4FgTHQFpgILgE2yubQsLOVxNez5XI10\nudOCPrd/wYy9gQRmEZZdUdy3o1x0Xf/491fT3t9mMYIDZG5j4XXBhU4bCdEAS+PS0lIsbvo1AS3d\neA4zIaFMu6j1RCydhUA31AuI1k19tcsjPqDCUQ4jE3xSy4GdlTiws1J1O4CXAg2G+b8D4Rgutscb\nz5VuUxbSSANAXWWumGm81HFYjbK+4gaS92xIF6CJhGuEvs3KMQGIkp0FOYxYky9FWX1QU2pFVbFd\nFOapKbPjgmRF/ekda1DiMOP/fdSJYCiCHXWFuNA+IotLM0YSn7ubVyvLtzPo6J0QjbugZPbOWRe8\n/ggYmkAozIGDfEUt1FVnkvnK/F7oY+gsXXTX9yIjuL7V3OJKpCplAO/WpihSlA8tzc+KMyL6ClAn\nEQxNosDOwDWcfiMcwSXN0AQYIwWrxQSX26+6rYWh8NUD6+PkPkcmgsi3M+j3BHDkky6xDJEAcGhf\nNcZ9YXxwqVcsUbNZjDLZUJvFiF11hWLCmNSgDXqmcKJlQEwK+/e32xAIz939LT0GAPGetFmMePqx\nLbMeV8vtqxU2W4norm919GQyFZKJBKi9PxtxApfbh09aB5FjoWViDABUx7JbTKivciAYjoKhKTy8\ndy3u31Eu7reh0oFz14cQmY4bGyhgX2Mp1pbY0Nk/c/E7rEYEFBnhdosRsRirGUvWWZlEYxy8U3MQ\n/wafz/C5pipMBiJwj6l3VYtEOYTCMfRMVy4EQjFUFduwscqBl966jkvtI3HJZyRJwGo2or13RvAn\nFGFl44QiLBrvKMC26YYWUiGR0vxs3FVfhNL8bLTcGsXlW6PisaWiQukgGM0LN4bRcmsUY74gbk23\n9AxFWNAUiY3VeWmPC2gnUmkloq5E9GQydfQ6agXJaiPV3p9NPaWwz7+9eQ3/9GoLAIhCD8nGandN\noKPPi18f6xD3E9zq3/pyIxqq+Zm+0DDBp7jwpe5QgclABFFWX31L0W+OxAh10EKnqrqKnLjaaAGh\n3EnomiS4oKUlTkqaGorjapkZIykbJ9Wyo0x1bJpr7fVs0LtN6egxagXJaiO1RATSrafUOs5sji99\nv9xphXtcvqppbh9N+rl1AZN4VvpXYqYJBMLapkUImyi9LLs2FMAfjKHIYUb3gA+u4Um8d74XjJHE\nzroCdA9OorIoG/3DUwjHWFQWWXFwFz8JrS2zo/nmCAKhGH59rAOPfaoGDptJZqxJEnhkb7WYsPbe\n+V7xvc83VWLH+kIUObISVlgo3xNEhaSu8CNnejRjvqlqfqdbez0blpI2uM7ioBtqBclEArTeT1dY\nQGuc2R5f+mDZVO3AoGcm+7swN3kMUpcEXX0kMtIA+EAx4kMhZ9uGwbLAFUVVVjDCitUA0gSzqVAU\nW2sLRFEfAY83hJGJIP7ykQZ0u/3w+4O43jOOpoZi0UgLiZUC3PRJaSVmagkLSWVIe4Z4L5XXH1GV\n1kw0xtUujxhbF4xmoklDMqT37WSExcfNLtVx9Ozy1Y2eTKZCsgxLtfeVr6WSpSltc6l8UKiNlYmK\ntgAAIABJREFUJWS6+qbCsGXRYgLNubYh/PuRNgRCMTA0iXCUlYlPCNgtRtizaNA0iY6+xN9xsiQ0\nmpJnD9stBnj90QXRGddZfijbUQKQJUZNRlh896efxCVMpZtIpdW2MVGrTGVrR7UxNlY5Mp7QJf1s\nNosRJEli3LfyE8YSoSeTqaOvqFVINntVe1/6Wjpi+I31JXEXptZYUmwWI3Y3FMPl9onZrAAQVJEN\nFZjwRzChooWsRrJYtVJjYsKvS5OuVCiCX1xHpzO82RgX9/snwsJQyLMzMkO9rtSGP3rgDvE6v3jD\nLQvpvPrBLTxy79q03b5qHieX24dxX0gs5bIwBkRiMYQjXMpeq/mQC5WOqfQ0ZLLWW2f5oxvqeSCT\nN7VWso3XHxHj48mUmXR05oJURIQiiaRtLgWMBgKRKAeKIrGmMBvNN4cRjnCwMBQqi20AZrxH1eU5\nYqyaIPgOW30jfnGSm+r9ozTsAGSr1l31TlzrHoM/yMFAEdi/rVR10q02OUg3vJXMqyadEChX1HrC\nmI4UvTxrHkhHA9g9EcT7529jfDKEllujcSVZ45MhXL01iqgiUMgYSezeVIzqEpt4LKOBBKvXVunM\nI5Eol1L5nsNGwx/gJ5ChCN8TOhRhQRtJUCSBttsTaG4fxietg7jYPoIbPWN4dF8NQuEYhqZLvLRK\nkVLpsSyUaElLm0IRFrnWmVpvlgM6+71oWJsXN46yX3S6utnKMq66ilzVYwhjPtRUhc80VSOboZaN\nLvd8oJdnqaMb6nkg1Zva5fbhf/+iGefbhnHhxjBau8ZkN7XL7cNPXm9VlQCNshzaXeO4e2ORWIP9\nyL1rUVeRC/foFPzByJzqoUksTF9rnZXJ5+6uwIBnCoHQtOzntNcnxnJinX8owiI0rXw2FYyiqtiG\ne7eWJpzkpmIApSgnzZ/ZVYHWLo/oFYjGuJTrkpXGOxGp1j7L6r6LbCjJNc+pscdyRzfU6uiu73ki\nFXfd1S6PWIcppPQpS7XU+usKzDS3r5WVoYxMBDUTZ1JFTwBfOiy2upwti4J3Sj5ZTNRM5b7tpaiv\nysOYLwwCwNoyu5jxzdAkwPEZ4tJYcX4OAwIc/uX3VxEKR9BQ7cAXp2PUUlINK0ndzmpubKlKmbRy\n4kTLAAhAszVmqugtJnUyib6iXkQYmsLVLg+mglFRKEK6imBoCs3tw+KqQ43qEhs2KZSQpKsIneXP\nYkczQpH4E1BqeUvJZow4cuY2rveMo3/Uj173JHxTYURjHP8fy4Gc1h0PRTgYDSTMRhLn2kYwGYgi\nEuUwNBaAM4dBjWIVmkpYSbnqriyyYioUhXN6tVqan42GtXkyj5cgz3u9Zxy3+r04d92NTdWOWa9u\nZ9NiUlhNzkblcKWwmCvqxf7e9TaXSwS1GbtQO5lvZ2S1mdJ9TrYMgANQU2bHrd4JnLw6ILrDd20o\ngC2L7yTU1FCMI6e70dw+ijvW2FHssOD09aG4VbmB5EUlwhqJ2ou9gtPRAYAihxnPPXlX3OvJkrSU\n5VVCe8xEZU9q5Vv3by/DH+6vzcAnSY10dP9XKotVnrUU9NT18qwlgLKhxunrQ3j6sS1orC9BtlFb\nrLLcacUf7p/5AXesLwQHTmxnKW03+f75XtEdeaVzDBbGgAN3lsf1CyZJIJKgmkqtBltHR4qB4Mu1\n5pM9m9VVvpKFlaRuZ6EtJpDYVb6xyoHXTnSKTUGAxcnRmI8yMJ3kLPXvXZczXiCU8WZpeVW6XOlU\n309pX5vbR3GxfSRuu3A08UNIt9M6UhiagN3Ct7ckwXtx/uwL9ciz0TAZgPKCLBgp+T6UypOFVOiA\nWxgKRQ6z6jFrSq2or1JvbuFy+3DkTI+osy/8W0Aorzq0by2eOLg+Tl9cjXKnFV97cANoA3+S8yUH\nmgxd13txWOrfu+76XiCUK2qhJZ6a4Eky3jnTHbdKBuITfHZtKABA4PQ1t2w7hiYRCrN6VrfOrKAN\nABub24o630YjEI6pVjQIKBXKrnZ5QIDDG6d6EAjFYJuePAhtXv/ykQYAiHOLn2sbEpPHjBSBe7eW\nIMfKqIabFqvvs1SlUO0zpLJ/ps57MXtfL6Yy2WL3/E7k+tYN9QKiFqNOdmGqyYmebBlA58AExn0h\nFOdb0D/iR0m+BY/cWyPGqBtr8/Dk5zfB5fbh73/RLD4Qy50W3FVfiNtDk+gb8aM034KRiYAoKWqg\ngH1bS3H0fJ/MkFtMBPyhJXeppISuY758Ucp3JuL+7WU43z4cF2fUkg8V+mkvdix4rvHRTMZXFztW\nq0uIqqPHqBeQcqcVX9o/uxvw/Qu9eOxTNbLGBgxN4GrnGDgAHl8YTQ1TePLzm+KO+a0vN+JkywBO\nXx+Cy+1H73AnOI5/UAniDwLRGPDhpb641fZyNdLAwhlp2qCdoKeTPoIL8mTLgKqRpo0EGNogrqg5\nqHexy7czqn3W1Uoik5HJEi4BrY58qa7u5ksJcSnGalcruqFewihvmhMtA7I4d1DS/YjjgBMtA2LX\nISnlTivs1pkYufCA0vKl6MZmdqzm7402EGBMFLwqmu8kyVcShKNqzVyMKMhh4B4PoqIwGwCBuooc\ncCCQb2dwomUAH17qjRuTNpJoXJePrbUFovsaAM5MVzkYDSTaejxwDflwrWdMtcRNKDGzWYzItzNx\nbS+l3iwAOHK6B8033OL5n7w6iG99eWtKKmXJpETfPXtbbJWZb2dkE/T920rjuool2n8u8dVk9d8L\n5R5ebDf0UkM31EsY5U3T1FCMniGfbEUdCnPgwK+Om6abdKhd4Pl2RsyATVQDCySukV0IZpNRnEiA\nYzaojae2KtPhCUc5hKPqMxWWBUgDf1XFN3OZaRTTNzKl2jVLSbnTggl/GKevudHeOyHbJzbtPolE\nWVzpHIvbt8hhxp7NxRj3hfFRSx+CYQ7hSEz0VAlNdIAZjfB3z95GLMbGxdP9wei04JC2IUm1QY+U\nW70Tsgm6kI8irLTVjHWmSNQEZTafZTYs1HGWE7qhXmDSmSlq3TRHz7mQZ2dwcFcFWrtG8dHlAVQW\nZeO9cy4MjE7BH4zijRNd2DK94ujoncCZ60MIhGKgjSSikZlEMgKAVaI8ZaD4FY3aQ26hmE2SUqa9\n22rj6UZ69pO4YLLe14hX5dOKSZuMlDhZVe6TLDntzx/aiHKnFb862i6eUzDMil3npK5ntc5WSpJ9\nqlRcydKKEK8/Am76XD3eEIwUgci03KmW10y5/1zd1VrlbwvlFtfd7/GkZKgvX76M559/Hq+88gpa\nW1vx7LPPgqZp1NXV4ZlnnsGNGzfw3HPPidtfunQJL774IhoaGvDNb34Tk5OTyMnJwQ9+8APk5amX\nXKwG1GaKiRIIgPiWl78+1gGPN4RRXwhbPVM4eqEPHm8Ig56AbL9ghMXpa+64jO+wQuWMA2TykNEY\nt6hGWofHQPH5AvPBbL0PBICN1bmzuj4YmkzYghXgjZPggs63M6KxkmJhKNy3oxweX4foaZLuI7Sy\nVB57T0MJ1pbZRSOsNLC0kYhreyntbKW2ok6lhCsVKVE1z1lTQ7GY5f7bD2ZyStSOt1BypSvtOMuJ\npFnfP/3pT/H666/DbDbjN7/5DR5++GF85zvfQWNjI/7xH/8R1dXV+MIXviBuf+TIERw9ehQ/+tGP\n8MMf/hB2ux1PPfUUTp06hbfeeguHDx9OelIrNetPrSH9Vz+7MeXPq9x/U7VDs6ZaC4okENOXhrMi\n0+71RGyapUGcbwgA+7eXonvAB9ewD8Ewx5drsfFqdkW5DAbHeC17m8WIykIrrnV5EOX4MMKddQXw\nB2Ooq8iBxcKAMRDiRNRhM+GxT9XIlPhoI4mvPViHHesLRc9Uvp2J2+di+zDOt7nF82FoAltqCnCt\nZ0xMPJMmZtosRjx+X23CUi0AYhLZ2jK7qoqgFql40aTlWcptzrUN4UTLgGaMOtVjZIL5Po6Q9b0a\nY9Rzyvpes2YNXnjhBXzrW98CAAwNDaGxsREA0NjYiPfff1801FNTU3jhhRfwn//5nwCAjo4OfOMb\n3xC3/d73vje3T7IMkT5QpM3rU03UkN6k0qQRo4Hvb2VhDPAHk2cxUSSQbTairiJHpmamhnL1s67U\nBsZEiRnmix3DXiwWssJrKRppgP/duwd82FJbgJt9XgAzSXTKiYxgpIFpl2y3RwwfsBzQfHMUz3xl\nm1im+PKbV2Uuz5GJIOxWk7iSDUdYjEzwYwqepiNneuL2KS+0yjxJwTAn+1vY7unHtiQ0BkoXcDoV\nG4nG0dpGS1Nhx/rCpHHpdHp2z4WVdpzlQlJD/cADD6C3dybrsry8HGfPnsWdd96J48ePIxCYcbm+\n+uqrOHDgABwO3gDV1dXh2LFj2LBhA44dO4ZgMBg3vhq5uVkwGKjkGy5xuvon8MLvrmBkPCgmItEG\nAtWlNhz69Do01pegq38CF2+44XSY8dLrrRgZD+L4xT787dd2oW/Yh5+81gqWA1q7PPjjB+vEB51W\nsowSAwXEYnyJ0oQ/gtPXhpNqeStdlMIDWaCyOBtdA5Npfx86K4PSQivuaSzH8Yt9Yvc3IPFExmgg\nEVFcdOEIi263H431JQAgGzM/h8E9jeUAEPeadOWhtc9753sx7lOPcQvbVZXYxWMvFZKFwlYD+ncQ\nT9rJZM899xwOHz6MF198Edu3bwdN0+J7b7zxBv75n/9Z/PvJJ5/E4cOH8fjjj2Pv3r0oKipK6Rhj\nY1PpntaS5ONml/ggEwxsOMqhs8+LH//fFni9Qfz2g1sYGQ+KjQMAYGQ8iI+bXWiTlJWwHHDkVDcm\np7QTW9RQi3POteGGbqQXn4XQ2tY6npEEfvHONZiNFBqqHWhzjck0stXYVpuHCzdGxMQoALAwBlQ6\nLXj74w6caxvGjvUF+B8PbxJXudlGEi63D1tr8sW65WwjKVt1jo35494HgG8casDJlgF4p8Kiy5s2\nEmisLcDBnRXINpJobu0XG940Zagmei6sZrEPgdX8HWRU8OTDDz/E888/j9zcXHz/+9/Hnj17AAA+\nnw/hcBjFxTPJDufPn8ehQ4fQ2NiId999V3SZrxakSRFKvP4ITrQMiIY8EI6J5VOCWzzfzuBql0dM\nJNmzuRjvnHUlzEJNhdUWp16JQiQLaaSVxxMawgCAa9ivsnU86uEWDq9+0CGGVC7ccOOpL9Tj4M4K\nfmyFStbu6fLDI6d7MDoRxNbafDGZUnhfoNxpxe4GPoN4TWE23jjZg0A4hnbXBA7ujJf0PTPdJGex\njfVisBrjwcuNtA11RUUFnnjiCZjNZuzcuRN79+4FAHR1daG0tFS2bVVVFb797W8DAJxOpywzfDUg\nLa8iwOH3J7rFrGshY3TAM4WR8aCY4CJNUil3WjE6EcBHlwewZ3Mx6qvyMOYLo3vAK3NHF+YyGJqO\nB9IGgCL5VUVZQTZ6Br1i7SoJ4JF91QCAVz/oFFfrUle4mSaRb2fgGp5/rwZtIBCeo8WxmEj4Q4ld\nBCvNSC8XkiXf+YMxWfhGWX6kLNM52TIga/EqvQeUZTxSIy/1VknLr9Sa5Kw2Q6XXLC8PdK3vBUTa\nW7pJ0Y9abTYrvYmUDQi21ebjSqcHm6odOHPdnfIq+9F91eIqRGCx+k8vZBa1zsJTU2qFxxdOqtEt\nQADYucEpqo1JM7ptFiOcOWZ0KPIlhDpjZVOOCV8I/3V+JrdG6q0StlNrkrOYRmox3L5qlSiCR2Mx\n0F3f6uiGepFJdGFqNRMAIK4SpKuFVDCQBKKryO2tE89cJ0gGEsjPMYOmSOzaWIjXT3bFCZoYSODP\nPl+Pjt6J6SoBTuYyNxoIUCSBYJiFgQKMFIkYy8q8HzaLERsqcgECuNY9pjoZfXRfNcZ9YXAAasrs\nomG3MBRiMQ7BCKvqrQLUJ87zjbQKRHk+WpP2+XRNS0MAWpOVhXSN64ZaHV2ZbAkjjXFLV9TC6gDg\nY9uCWEMqRFkuZSlRnZVJOkbaTJMIKKoAoiww6AnAYTPhtntSXXWMgKxOOaiYTEaiHDiK3y8aA6Iq\nnVO8/ghOX3NrTkYtDIU8u1n0EJ1oGRC3E9zjZprCY5+q0dTATyT/OVu0DJvUQyZ07pJKlgoVIlIX\n9GK7phf7+Do8uqFeYii780glRAGImaxXOj3wB6OiWIOwcsmx0vjwUj+Gx4NgOV7swUhR8AeiYMG7\nAJs2FeFKpydOzQwAjBQQmSdFrExgIABbNg2PL7zYp7IqUBppKR5vSFaeJSUag0zWUmub1M5hJtFS\nOkn1B2M4IemsJd1Ouq9Qe70QJDJs0pi7snMXAPG71JJSnQ85zWTyo7qc59KAXOwT0JlBcEO9d74X\n/3W+Fz/69SUAwMGdFeLNcb59GKevuWUiJ0WOLHxpfy12NxTj6IU+DI0FxUSxYJjDgV0V+PpD9fzK\nJBTDe+f7VI00sLSNNAAQJHQjvcgwNAGAl/y8b0c5LEy85oHNYhRftzCU6BEiifSP57CZ8MTB9Ti0\nby2eOLgeDptJfL2poVj29xMH1+P+7WXi8RZaglKrZSXAe8iEcyWmvwfh/DZWOZCfw8Sds3Sf+fgs\nycaf7+PrpAb13e9+97uLfRJKpqZWz4PYYjGJn/fU1UFcbB8R3wtFWBTkmLGuLEd8/8INeZmLdBu1\n9x02Ez6/uxLdgz5cvjU6z59m/sk2GxCK6ClocyGbIcVsewK88lwgFIaRIsHGONVwyJoCC4wGAp+9\nuwJ3bypBKBzDZ3ZVYMf6QkxMhtDZz8cVLQyFfVtKsXdrKa52eRCKsDCbDHj8vloYKAL+QETmxt5V\n78TdGwthZoxo2lSI1u6ZLHBhrHs2l4jx3GyzEdEoi5oSGw7tq4n7u67CgY3VeaivcqAgx4zP765c\n0BUgQ1NouTUqJq59fncl7Bbe0NktJuRaTQiFY9i7pRgbqhzi+dktJty1uRTZDCU7Z7vFhLqK3Fl9\nFpfbh1NXB8HQlHgOSpKNP5fjzwbp83C1YdH4jQDd9b2gpNKX9u1PusX4mrJPrjRmLcS4bBYjxn0h\nuNw+2ftScQfhWIL86GxIJw4+n0yF9FqruTIZnJnocABu9XnBAjBQnGb8un/UjygLvPXJbVAUAa8/\ngr4RP0YnArIkMX8wBrvVhJGJoMylevHmMM60uuMmAf5AFPVVefjKZzehubUftmmdANpA4KsH1qPI\nkSVrNymM57CZsFaSPKZWR70YLtpkbSKF8+0b8cfFe6tK7KJgi3LMdD9LOrHlZOPrcp6Lj76iTpFU\nZqdKzrUN4TfHOmCgCLAsh396tQUXbgyj5dYo6ipyYbeYZDNIrz+M09eGEIqwoI0kPntXBd441SPu\ns2tDISqLrAiFY2hY6wBFEBjzhdDumkBz+zDu3lgEq9mAG65xRKIcRr1B1JTZ0T/ix//7qBNjvhCi\nMQ4MTYBlZxLJakqt8PrDorucJORJZjWlVoxNhJZEKZWesJ55hK800Xcrla4VPBqBUAyj3iAmAzOT\nJyNF4JF718KZa5atLDkO8KhIerrHAmi5NYottQU4f31I9CjFWKCq2Ab3eED0EoUi8mOHwjH0DE6K\nf0u9T4uJ3WLCurKcuOeE1OOldr6ZXE0mO9ZSRV9Rq6Mb6hQQZqdKI5uIc21D+JfXWjE0xj9oAuEo\nbvbyNaDSG0fL9R1jOZAkIXsQ0RSJ9873omdwEl0DPox6ecML8A+xYDiKjy4NIDxdFB2NcbjcMYKz\n19zw+Ga2jcbkhtg7FZHVUSuf1x5feEkYaZ2lAU0BsWlvzqe3lcrc1fVVudhQya8kpS7TQkcWLrTN\nhGWkoepAKIaivCzUltnR3D6MUISFzWLEQ01VMoNvsxhhoimEpkuuPrOrAp39XjHJ7O6NRWBZLu0J\n9XwineArJy9StziQWSOVyAW/VHG5ffikdRAkuCV/rvOBbqjnyGxmp7851oGhsZmELYamAAJxN470\n5lTeXNIHkcNmQnGeBa1d2o04GJqCW5GFy6pYWOkDz2yi4npU66xeLAyFfBuDqWBUNVZNAKAofsVL\nEADLcXBLumS5x4LiZHbQM4VTVwbQN+LH1nUFKMhhcL2HlwtlaAJZjFE0ul+6fz2CwQg+vNSHaIwD\nQQAOqwkfXR7AzjonrBYjCBC4p2EmtltX4UCu1YSWW6MIhVlc7x7D6Wv8qjyVCXU6XrLZeNSkE/wz\n14ZQU2bH/m1lmvHeTBrqVGPLs/lc84HwXX1yZTDlxdBKQzfUc2Q2s1MDRYjGnSCA/dtKkWtlxKQX\n4caR3pzKm6uuwiH7mzZSaO3yiCtjKRaGwsN714qGfebcSdn25U4LttTkIRJlUbvGjoM7KzTHnCsO\nqzFheU95QRa8aTYZ0Zk/yp0W/I8vNoAxGRJOCIWS52hMbqQFAqEYguEoXvu4G6O+EHqH/Th3fQiM\nyQCX2z+9L7B1XT7u2liEz++uxIa1Bfg/v72EvpEpcezW7jG4xwJo7R5D77AfHl8I13rGsGdzCeoq\n+Ozjjy/1o713AkC8Wz7RhDodL9lsPGqAfIIfjXFo7fSgqaEY2+5wqu6fabevlgteYLafaz5Yrq76\nTKInk82RRAkiWggCCydaBlBXkaPZPEDtWNLxhb+FRBSpEQZmuhpRFIkiRxa21ebLknuUq2WX2y8+\nLDv6vOhz+1FTapuX/sceX2Ij3De6MrqkLWVoCkhVuG79mlzx2nvjRBeCs/S0OGwm9Lv9shW5PxhT\nrbkW5Cq7+idwtdMT974SpR64cnopJD0mKyVKpz54trXEG6scePNUt0ycaCnVIS+lGmlpIqxeBhaP\nXkedIuVOqyyDOhV2rC/ENx7dAg6EZm1lqkhvKilCTwtBrOCK4mGXLPnKNeyfFyOdCmpueZ3Z47Aa\nQRtJWa1yqkaaNhJoaijGO2e68fwvL4LlWHGcVB8SJPhV+f5tpXBPxE/CGmvzZTXX13rG4HLzZV0X\nb7hTyoMgwMt9CjQ1FIs10zaLEV97cAMO7VuLxz5Vg6tdHnF8JenUB8+2lrjcacUTB9fDTFNp77sQ\nLKUaaWEx9Cef3aCrn6mgu74XgESu81TdXdIx1BDGzTJRsuQeAzWjgqSzsgmEWcRY9TroZMRY4MTl\nflzpGkM4yiImqQqQjkeA79AmuL9JElhbYsXYtN621x9Be++4aimf1WIERZJi9regAcDQFDoHfOgd\n8iESZUGSwKF7q7FnC1+vfe+WYnQPehGJcrAwFB64c42sNlmomX6oqQp1FQ4wNIWX3rqe0KWbTn3w\nXGqJS/Oz0bA2L6V9FzrjeaFrpFM5n+31xTDMQhRnJaC7vheZ2bjOE43R1uORrYIbqh344r1rZW7z\n9y/0oiTfgkfurcGgZwpHz7ngGvYhGOb4lRIxtxWtw2oUXdsk+Fi4wUDAO5WetFlhrglDY6l1V5ot\n60ptspaImSbbbJCVKC1XUumgxkHeNpRlgY4++ao1otG6tPnGMMJRTtQAcNhMyLczYr2vQLbZiPqq\nPJQ7rdixvhBHzvSI2gL+YLz7WBkuStWlm0598FxqiZdyHfJSPjedGXRDvUBk4oaQGmGAN5Cf3l6K\nL+2/Q7bdgZ2VOLCzUvbaltoC7N9RjpMtA2jp9IjLpGKHGRazIe5hW1NqjXtNijT+zAKYCrPALBYD\n822kAWD/jnJUFo/LYveZxL8CjHSqUCTA0AaZhK0SwYuj7LMhqKFxHD952r+jXKbVLeD1R3CyZUBs\nmEGAk7WzTOai1eOdOisN3VAvM6SrBRZAjpVJuL1UochhM4FUOEYj0Ri6B+KTfDoTGOnlxomWAQTn\nUdFsNUUWCA64e2OhZlMXgM/oZmgCbFjbDd8/6se/vX0dQY2qgA8u9yHHSuP20CROX3OLr2+rzRdz\nPIQkS6WnSvA+CS0sBz1Tmq0l02Eh2z3qzJ6V+DvphnqZke5qQekG3FSdixHvzNK3ON+CEW98MlmO\nxLW93GlqKMaJlv7FPo20oA1yF/NisKk6F8FQTBY2iHJIyTOh2vpSguDK1iIc4fCb451xr7/f3AeW\n5eVwH7+vVpTkfPNUN544uF7WzvJ8+zA83hCOXugFxyGutWS6utl6u8elz0r9nXRDPY8kmtkJ71WX\n56DTNS4aXOnMX20FoFwtaB1XeD/XSout/yyMAYFQDLs2FKB7cBJ7NhejvioPfSPyGKFDxUgzRiAY\n0f47GSSA0oIsuIZnsoEX0hhZGOPCHChNDKR6bJihDQhHF9dSD48HUVlknXN8v8hhjlt9C0YzXYS8\nCq8/gqPnXDMtLkMx/PvbbShyZMW1hxSOo2wtmc4DfCmVMulos1J/J91QzxOJZnbS90iCL6GSNhwQ\nHmKJVgDCauFC+3Dc2D/69aW45htGAwl/MIqOPi9u9QNPfaFeXH0oa6/VVtJKo5yOkQZ4N73USAML\nZ6R/9tZ1cEu0FsxmUe+t7Z1a/Lh3tnl2jwfhmhbYVJ2HPZtp/O7DLkRZDmaaQlNDkeyaMxgImAyk\nbKWtNOaCZoBAnp1B74hftU5ZrYGNNIkt3bi1HvdeHqzU30k31PNEopmdLM4sqYMWSLYCSDa2Woes\niGTZphSNUNZerzSWclvMheyt7bDSKC2wpFw339Hnw8DoVJznQ/jbaCCwfk0OOvu98AdjoEigNM+C\nXRsL8frJbgTDLBiaFOuex3xh+AJhWM00mhqKUVOWg6PnXMizM/jywQ0YG/OrepSk///5e+3w+iOw\nWYw4uKsCW2sL8O9vtyEQjskezNIqCS0PVTqxzExUbswnWp9lJcZrE7HUf6fZQnDc0quyHR5efolM\nyhtCmcSVbEUtiDaoragT7e+wmfDYp2rEBxAA1RW1hZFn6pYXWHDXxkJwIOAa8uL0NXkf66WCchVF\nGwAOpGzioZMaBpK/1pQiOAQSJ8Tt2uDE+TY3oiyf0f3wnmpwIGTX+pEzPWhuH0Y4wtc6S1fGj+6r\nFpX5hGPZLEY8/dgW8ZouKLAmvO+F+4sAh+s942hqKBYnmukaI5fbhxMtAzhzfUhsmTlEkROTAAAg\nAElEQVQfscxUQl/S95J9B4mOo/asSfQMWqrM9jtYCRQUaP82+ooaqd3oyW46NTe32sxOGEcwrtIY\n9aBnSpQc5UAkzFLdVlsw/dDj8K+vX0OM5fDu2dt4+rEtePqxLThypgcj40FUFVvRdnscE5PyEhjX\nsB8ulWSduWLLMsAfiGK20uEOq9wVrCzJ5Vd2upGeDVpzG6MBiETVjbXNYoQ1ixb3jcaAN0724H/+\nUaPsmrx8c1QUOVEmin10eaYESziGsgQrEdL7S5i89o34xXh0OqWP0rEE5iOWmWroKxMJT1oetpUa\nr12NrHpDncpNk2wbrRtC+QBRm+E21pfgjhJ70qbyamMoVy7Cw293QzHaXRPweEPomEehDzXmGlv1\n+MJgjOSsdaZ1tGFoAiGVkinBrZ1tNqDIYcbtQR84AHl2M/5gTzWKHFn4+HK/+JsoNauvdnkQkGiV\nGiggNt1KlSCAPZuL8c5ZV5yXJ9W5nFpiWCqGR21yrSbFOx+xzFRDX5kwoFpx2ZUar12NrHqtb7Wb\nJt1tUtXMTTROKueh3E6txIWDti74cqHMaZHpVetkhgK7OaFxnAxE0dHnQzgGRGLAoCeAf3v7OgY9\nU2A5+cSJkIyUb2fAGPlHidFAYvsdThzaV411pTbsrHMiz25GOCK/Vm0WI2rK7PjV0Xb88mg7uvon\nVM/J5fZh3BcSQ0PE9HWRzPAIE9rfHr+Ff3q1RdT8lt6rNosR928vS2tFK7j5tTTEBRI9EzKtsS14\n7w7tWyv7LFqv6yw/Vv2KOpVZZ7JtUk1gUBvnxOVevH2iC3UVOXDYTLL3lHG5uoocjPvCYrmVMrYo\nJO60do2CJFOTCM02U5gMpCf7Od8kUkTTmT3KrPtUCIZZHD3nisvQv9g+ggM7K+Fy+/Dz99rF1XYk\nyuL0NTeu9fAJazf7vLjQPiyTFS13WvDZuyvFxDAAONfmxjce3azpgaKNBHbVO7F1XUFKoiWJvFyz\nTTZKx2Wd6DjzkfCk5f7XJUJXBqveUKdy06S6TbqZo4OeKfzktVawHP9g2b+tFIOegJglq4ylKbOz\nOfDJVUW5FpQ4LTi4swKDnqk4oYiaUitCERbD41NxQhRaRjoT+tXp1lovN5IlYa0U8uwMuocmZQl8\nwXBMnEiqVRlIX1Nqf5uMFEYmgrJtxifDcS5gqbENRzhcvjmKgzsrZKImWrkjiSbXszVe6bqsEx1H\nN6A66bDqDTWQupHN9Mz31Q9uiRm4HAccb+5HlOXQN+LH9tqClNzX4Siwc2OR2Nf31Q9uxW1DgMCu\n+iL89nj8e1o4sk1zNtQr2UgDy89IWxgDss1UWvrqBgJYU5gNfzAiK+tyDfvx//3bOZTmZ8VVFAAA\nRQAgCMRU+qzm2xnk2xnYLEbRWOdk0zIv0sYqh2Y/Z2BGGEjI61CucFOZXKebla3HfHUWC73N5SJi\noAg03xgWH/jCMy0QiqG6xIbxyZBmW0sBhiZQmJuFnGwadosJBorA+TZ5qVVtuR27NxUnbJOppK4y\nF33D/nQ/0rJlXalNbL8ohTaSqsZmORKJsphKIt2phAXQ2j0G91i8HjwHwDsVAcexKC2wyLUAEK88\nlm+jQRBA18AkOvu9uH9HGcKRGEoLsrD1jkIQgKw95a4Nhagps6O104PodEOOO+uc4jatnR74pvhj\nBkIxFOSYsa4sRzye3WLCurKcuBaXAHCubQj/53dXcLljNK4VpiAadLF9BKevDsLpMKM0P3ve20Iu\nRJtLl9uHU1cHwdCU6vey2Cx0q8+lhN7mMkNkWjygyJGFPVtL0Tfkw9bafDEz1mYxoqmhGE0NxbIY\ndZHDjO4BHzhwCEVY5GTT6BmaxHvne3Hm+hB21vEPtnKFVOfW2gKUO60ozc/CuDckFjeRAOqrc3Gz\nd1zmEqcI4OCuCgDckq2vziQkwXfY6h1pi5vIhFdY9vl8TDmiMcDrT/5wDUZYMQHS4w3hjZM9CIRj\nIAjgSucYzDQlZo8LruWDOytQ5MgS7zup+1maaU4Q/Eo9FVxunyiSIj2WcE+fbBkQJx3BCIuX3ro+\nq1KwpcZK1cFeDaRkqC9fvoznn38er7zyClpbW/Hss8+CpmnU1dXhmWeeAUmS+PDDD/Hiiy+C4zjU\n19fj2WefxcTEBL75zW9icnISOTk5+MEPfoC8vLz5/kzzQroXeTKjrizVyrOb47aRPhTqq+TSoIyR\nlK1ivP4I3jvfi6Pne+Mexrd6J3Cx3R2nSMWCjw0q49YxDnj1gw60pqhgtdxhOeDizeG0M83VNKxX\nKwxNYUIlVi3lrvpCXGgfgccbEhMigZmVdyAcE19XqoxJ7yHB/awcY2QiftUvIL0fleVkZhMlc2PH\nla9F2BVRg6zXVS9fkrq+f/rTn+LHP/4xCILAoUOH8NRTT+GZZ57BX/zFX6C5uRnDw8MoKyvDX/3V\nX+Gll17CH//xH+PmzZuoqqrCv/7rv6Kqqgp/93d/B4fDgZdffhmf/vSnk57UUnR9nLo6iAs3+NWl\nmptNimCEBRee1K2mNV4oHIPLzbuaQxE2bvxTVwdxsX1E/DvKcnCPTcFiNiaVyKwuseF0q1vVhWvP\nohGOxBBVvDcyFlxVsiIkgJE0S9rmGsNfaBijtuhJImgDv2IVLpHygiw4cxlRmIahSawpzJa5xw0k\nsLbEBoeNxmQgih3r8/HEwQ2oq8gFTZHItZrgnQojFGFlJVd/dH8tHNkmFOdZQBsptNwalblpBfcz\nTZGgjSSGJcfcWJWLGsk9I7h5xydDMpf6nXVOtLvGEYqwoI0k/ttn6lBXMWOoc7JpnLs+JCbBWRgD\n/mBPtaarWHoc5fkqt0nkcp5vty9DU2L4y2Ez4fO7K5ec+1t3fauTdEW9Zs0avPDCC/jWt74FABga\nGkJjYyMAoLGxEe+//z4cDgdqa2vxwx/+EC6XC4cOHYLD4UBHRwe+8Y1viNt+73vfy8TnWRTSSSRJ\nZeYqHc9solBXkYO+Eb/m+BurHHj37G1ZHDAc5bB3Cx/bOz0th6hVsjXo8atqPH92dyUA4Me/b5W9\nbjaR8Ie0n+pqHbaWMxwgW6Etd4wUXwstZbbJfeEoYLcYMOHnJybKMq9gmI27tqIsEIzE0Ov2gwNw\n5vowttYOociRJTaUEeqY15bZEYxyYAwEOnonRGnP9873AgDe/qQH3/ryVtWmNFLeONmD+qo8UT5T\n8EDRRlIMYXi8IdzqnanZZmgKRY4s2TjlTiu+emA9XnrzGsJRDhSl7WpRU02bbyWydFB69jJRFraU\n9cPVzm0+znehv4OkhvqBBx5Ab2+v+Hd5eTnOnj2LO++8E8ePH0cgEMDY2BjOnDmD3//+98jKysLj\njz+OLVu2oK6uDseOHcOGDRtw7NgxBIParikpublZMBio2X+qeaCgwIrv5lpw8YYbW+9woqrEHrdN\nV/8ELt5wo7o8B/k5DEbGg8jPYXBPY3mcjutkhEVDTT5OXx1EIBTDsYv9+LOHNsLtCYjjn7jcizc+\n7oLZRKHMacWff7EBR8/exgVJsliEZTE5FcUX99UAIODxBvDaR10z70dYvH6qG+2umYeTmQaiLAmO\nZdHvmYqTFwUQZ6TNNBCQTHSrSu3wtI1AyXItWXKtoMQ5kgCys2iMZajhBwGIRjodBA8RwBuwc23D\n2FSTLxpYrz+C0iIbPnNPDbr6J/C9l05jZDz+GeEPRnH8Uj+efnw7AODjq4OqFRGBcAzdbj8a60vw\n2qlucVIbjrBgaArBcAz5OQzMWbQsZCTsIyV4dRDh6RW11jbKc5Gqpkm3l26jfE9JIr3ndOnqn8AL\nv7uCkfEgjl/sw99+bRca60s0jz3bMdWehXNhtt+B2rkByPj5LsR3oCTtZLLnnnsOhw8fxosvvojt\n27eDpmnk5ORg06ZNKCgoAABs374d169fx5NPPonDhw/j8ccfx969e1FUVJTSMcbG0hdmWAiyjSTu\n2ch/BqVwfKJGGdlGUra9mt7wyHgQna5xsczq7Y878C+/bxWN3oW2YRy/YERlofwi/qC5n3//hhtP\nfaEeJy/1yd6PcZAZdkAwuLwhlhr1RAQUz/wLKkYaWJ5GOhVILA+F8WyzARurctHcnpkkwEQ9w9OZ\nlBEEUF2cjd5Br1jOZbMYUem04O2PO/D6yW5VIy3QO+QT76FKp0UUB5JiNlGodFowPOyLc59uqclD\neaFV9FR9fKlP9GYxBiLufpYew2EzieMqkW4nbaIjbO9y+9A76BVL0RKNlemGFB83u8TvdGQ8iI+b\nXcg2zk2Mcj7GlDKX70Dt3IR/S19bqt9BRptyfPjhh3j++eeRm5uL73//+9izZw/q6+vR3t4Oj8cD\nm82Gy5cv49FHH8X58+dx6NAhNDY24t133xVd5isRpbt7ZCIoGt1E2woo3d0nWgbiHoJefwS9I5Oq\nYwqtK72rNL4z3zhzGQyqlCjNlsJcE8YnIzAZyYz2np4MRDUz9bMZEsEwm1acWstI2y1G+IMRRJNE\nChiaQDbDy4W+caonLrRwoqVf1pdai1A4hnNtQ2IG+P5tpfjo8oAsma9pU5Ho9uaPzX9eC0Ph4K4K\nmYtS6MEeCMXw8js3xKxugWRNdYTX1NppqnXQE9z8uxuKF8xdPB9130u5llzr3FbCd5C2oa6oqMAT\nTzwBs9mMnTt3Yu/evQCAp59+Gn/6p38KADhw4ABqa2thMpnw7W9/GwDgdDrx3HPPZfDUlxbp/HjS\nbXOyady53hl3Azc1FONqp0dmrC0Mhf3byuKUxwTGfSHcsSY35X7DyxGS4Fdys+3ONVsyaaQBiKIj\nC9krezKYuWMly/DeVJ2LYocFp68PYcQbxojK5IGPQyc20naLERP+CFzDfvzL71uRNd2IRli9Shn0\nBFS9VRQlX+2caxuSHdcfjOLImR48+bmNsu0SNdWRxpq1SrakE3KvPwK71bSgMd35kipdqv2etc5t\nJXwHej/qDJJOgoGw7T2N5aLbRLn/O2e68eqHnaJmt4Wh8K0vN8pWIcKKZcSrvZJeV2pD95BvJovV\nRMAfSv1nT3f7TEERC2+QdTKDzWLErrpC/Nf5Xs1ttLqkSV3IDqsppQ5wBAE89YV6jEwEVRX4Du1b\nK3q4/vE3l+LkeGtKbfjrr2xPeIwjZ3pkY0vHVCPdftCruRezwGr+DvR+1AtEOmIIwrbChak2W+dA\nyBpr+IMxnGwZwImWQfG1YJhDOJzY3e0LRGR6y+ka3cUw0oBupJcKFoZCKBzTdJkzNAGWI2TiMF5/\nBBwwE78FH882GkisX2NHscMC71QYp6+5ZWOtK7Vha20+OBDYWOVAa9eozFBbFCtqC0PBYWNQmm8R\ns7fFpjUSgy/1cNVV5MQZ6vt2lCf9HtJ1eS611edSztbWSYxuqJcIaiVdypIsm8UIDnJFJiNFIKKw\naMqVyqZqB0Yn+uO209FJhtFA4sG7KkTVPHLa4orqdiSwriwnLtzisJlEdb2TLQM4eXUA/mAMkShf\nytU3MoX920plhpo28N22ekf8eOLgegDAO2ddsve/emA9RiaCYjw4387g5XduwOX24/KtURgNJAKh\nGMw0hc/trhANvjTGfPTCjNu72GHGQ3uqZY0+tJiN4V0qSma6KtnyRjfUC8i5tiGcaBlAU0Mxdqwv\nhMvtw8dXB1HptKjO1sudVjz92BacnE4sqymzo6N3QswgFR5GRy/wGawGksDDe6sw5gvheDP/MKqr\nzMWJK4Or3kjft700paQlHTmRKIvrPePiZFGpmcOywI3b4+I1Kbi8pTkXdqsnrne6xxvC9Z5x2WtC\n4logFMO/v92GexqKFboBvMreH+6vFV/71zeuig1BAqGYrIEHByLONa1M5GzaXJKSkRZYKoY3XXRV\nsuWNbqgXiHNtQ/iX11rBTbe0HJ0IiAZWiF+pzdbLnVb84X4+g/Tvf3ER/mAUDE1iV70TVjON+qo8\n5NnN4gRgdCIgM0jJEssM5OzUqpYbbbfHk2+kEwdJ8q7ia91jms1JBOGdHKtJlvF85EyP2AVLmIRK\n3dFNDcXo6JtQFZnhDW28d0h5BsqSLiGvYba95efKUnUvL+VsbZ3k6N2zFojfHOvA0NhMKcmoN4jh\n6YeMIEm67Q6nZrefXx+7ia4BPskiGuMwNBbAzd4JNLcP40rnKFxuPzr7vbjZO5FWJvEKaQyVlKlA\nZNV81kQYCHk9OAEgz0ZrKrJtrMrF2etu2TXF0CTsFqO4j4UxICebxuDoFC7cGIY/GMYv3++QdcHa\ntaEQBTlm1FfmgiQJfGYX31famWvG5Y5RsIofx2yi8OX7arGuPActt0YRYznQRgL7t5eDZTlRjtNA\nEWjtnpmMHtpXjQ1VDs3uVnaLCblWE0LhGHbWOeEeD2rKeqbbaSqRdHAqMqPzKZ85352/MoUuIaqO\nvqJeIIROWBzHZ6ju2VwsW1Enm+EqVw6R6WWw1DXo8YZQmMskLZ2RYiCA6CowYKvBa5AMEvETMw7A\naIKKATWPTDjMIihJYPQHo7JYszT5S9oFC4AYJ+0b8YvJXxGVH+dzd1fI3MxCt6uX37mBSCyGcITD\nu2dvIyzRSWVoQiYhKqzoAYirXABiD2vhflSL2c4mpnuyZSDOvQzw+gaCLKqWzOhsSWcFr+a2X6oe\nAB05uqFOEeUFnUp3LOn7QhxMGqOur8pDt9uPSqdF8yZxuX040TIAs0ldUtVsohBjWYQjfL/eh/eu\nxUtvtorxPhIACPWVM0kCD++txu0h36poZ7naydRcJZVxhCRH6SRULU56SUNBbVwifzoyERQTKIV4\nNCCfpAJ8BYRgHAUj++7Z2+K271/oxfbaAlXJT2XMNt2Yrsvtw+nrQ+LfNosR+XYmrqY70THTZa4J\nYnqC2fJBN9QpoLygH/tUjTgrT2c2vmN9oSxxpdxpRWN9iWbdoLSxgBaC+9FMU3jsUzXYsb4QRY4s\nvqfuVBiXOobj2lgCvHAIywJvfdITl+ijs/rItEb7H+ypkmVcu9w+jPtCM4mQJgr5dgZ5dgY3Veqk\nhXMR9lNrmKKss2eMZFzPaqXHSVYyplG+BaQf073a5ZEda2ddIUYmgnEKhImOmS5zTRDTE8yWD7qh\nTgHlBX1CxcU1l9l4ouMmMtJSAuGY2I9XSEA7cqYnrk5VQFhh60Z66UFqeEDmExNNYE9DKU5e6U/Y\nNU2Jw0bD6wvLwiflBVkyF/Qvj7bj1NVB+INR0AbekxMIxfDz99px4M7yuGvUZjGiqaFYVWVMQKh4\nEMrGaCOJP3mwTrzPBCPLGElQFAF/MCYrGVOT/JSSbimW0rA3NRTLzkPIhl9bZtc8ZrrMNUFsKSeY\n6S55OXoyWQoo+7h+ZlcFOvu9mn1dpdvbLEbk2xjkZNOqCSmJkicYmkJz+7CYyKPdbA+a5yHtqyuF\npvjViIWhVN9fKCgyXgpSComV2+hDi7l83kTXSCKiMaAgl0HXgLqWvBaBUCzOFe6diqC5fRh5dgYv\nvXUdrV1jYhw6xs783qEIC+9UWDYZ3VTtwJ9+dgPKnVZZz3YpRQ4zvvHoZmy7oxD1VQ4U5JjxyN61\nYk9pIWGstdODYISF2WTA3s0lOLSvBuVOK+wWExiaQvegL6EhsFtMmsmdatsqk7Wkrz3UVIXdm0pQ\nmp+tOWa6iVRzTRBbiglmFosJN7pHNZPyVjKJksl0CdEUmU2M+mTLgNgnWikhKNRUf6apCncoWqRJ\nxx70TOG9cy7k5zDYuq4AIxNBtPV4cK17DBbGgAM714ADIROAuHhzGH1uP0qdFljNRpy5PgSW5Rs2\nCOza4IQti8buhmIMeqbwyrs3ZO8D898xypZlQDAc1Wz8wNAEskyGFdX3eilTU2pLSa4zVTZVO+IU\nwJRIywNtFiOefmyLah9nAUEqNFntcyK5z3SlPReK1SyfKVBQYMXLb15NS6p1paBLiGYAZcZkMuGD\ncqcVduuM61rqApfWVLd2efB1yYNH+hCRJsJ4fCEc3FmBIkcW3j17GzEW8E5F8c5ZFx6/r1aMmUtJ\n1GP59DU3HDYTdjcUo8iRhalQvLVkARgoJO2QNFuSdY0KhjkEw7qRXggsDIX7dpSjb6RNFgs2myhs\nXutIO9mQNhLoGfKqxpYFlNfWzrpCzQ5W474grnR6sGdzscxIa02YE7l19djs0mYpu+QXC931PY8o\nXeaCa1paU82Bb+F3Vz3f51rq7gtFWNHtLdRau8cDuNg+0ws6FGERCsfQM5iey1I5ZmuXujCKXnu8\nfDBQyX+v+7aXwh+IyLKnAYDgOIx6g3AruoRFYxzu3liE+qpctLvGUr4eYix/bUZjHBqqHXh4bzUM\nFIFeyeTx09tKMTIRRCjCgqEpVBRm4+j5XhgoAqX52QAguql/+8EtjHpDcA1Por7KAa8/jLc/6cFv\nP7iFi+0jcS5SqVv3zjonLt8cwZXOUeRk03DmmsX70myicPfGIvF4i8lcaojTrfleqlgsJhgILDmX\n/EKQyPWtG+p5RCsGZKAI0RiTBF8iJTwolPFtE00hFGHhsJlwZ50T/SN+9A9PIjr9xGRoEmuc2fBO\nhdNumSg8pLIYAy7fHIl7CBOYiWUrmW0sVGf+YIwkKJIQrw01+Ezk+PsrxgEen/p9d+P2OIrysnCz\nb3ZuWYIAvvrAenx0uR9uiegPRRDw+sOIRDlEYxw6+31wjwVwoW0YJflZ4j1x5JMeXJuWGw1FWATD\nUbx9+jZau8biJrLrynLE8QUj/5PXW3GtZxyd/V40tw/j7o1FqCyyorXTg0A4hs5+75KIg87WUCcS\nWlluCN9BOvkBKwVd8GSBUGp5AzMuckGAQYgl799WiiudHhy8uzKuZEuabQpAzFD9+XvtKrWjrJg1\n21DtAAdOJlKhFmfetaEAzTf5ycDL79xAjI2paoFzAMIabm99ob30IEhgXYk9oWxsqlUEUqIsh09a\nhzTfT1batWcznwGtFP1JVJp19JxLvC/UZEOVYR4tF6mycsLrj4i11kJt9nJ3f+uu/JWPbqgzhFLL\nG4Bq3FmooxT+/8aJLlQ6sxPGv8udVvzqaLyRVtKikrijtsY+e21YfF3pAtVZvmSbjXANJV71mk0U\nYtGYbAKWzNBaGAMsDBWXbAgARgOwrbYAU8GY7PrbtaEA3YOT2LO5GAd2VsLl9skmqHs2FyPPbsaF\n9hFVZbI8OyP+u6mhGKemu2/9/+2deXwb5Z3/PzO6LeuwbMmn4thxnNhxnPugcRwSkkLYBmiakKZZ\nKLRAt7/tLuz2VdhCod1Nym4Le7BZyvJi21+3lLNsYYGS8AMSjiTkDjFxnDiOj/iUD9mWLFv3/P6Q\nZzwzGh225Vi2nvfrlbwsaeaZZx49mu/zfE/Wlm53NgjCnvhFQPhIVaBjBTr7Pv+9mQix6c5+iKBO\nEEdqOrmwE4YJvWYFNX/Fyz8GCO0O4lkBJ3IHS7JpJj8TKZZi6w+PNxYj5djFAFAqaBRa0iV3uC63\nP+KCzucHjl/sgVIxZgyhACwrteCB2xZzcdTiFJoHT7YiEGAkhbRWLcPWtUIvX5mMBhCATEYjx5QW\nd4yzuAIdG998pKYTgUD4tWdi/G6y1b0mJB4iqBOEWK1XVmgUVA9iV+/s7oV9YOm0Cpyr7wEFJqx2\n7qlLNnx4qhWZBjWWlZq5XYVSDpj0Gnj9AUl7Ix9x9iYxWnXITj2eEpAmvRImnQoNE7RZEmJzvXOT\ne31B9DvdsQ+MeP7YJGMA/PpPdQAQFo3ALlAjaYcWF5uw48Z5YQmE2ONZ1fXWNYVxCyQ2ARAgHfLF\nV4fP1JSaM7X8JiE+iKBOEPxc3mWFRq7gBptylCVNLcO6ilzMKzDg3JUeHK/thtPl4+JX3z95DT/c\ntRRd9mH851u1YABcaXfgi4YeyGgaAEDTFAacHkH5PxZ56BDuQR+rDLXHF8BnNR3jutfBIW/MBQJh\nZmHSq5CblYbeCX6v4gWh1xcUZPBjYReo+tHqW/wdtVpBhwlpILGqXXE9amDMvk1svYRkhQjqBMLm\n8j5woiUs5Si7I3C5AzDoVFi1MBtHajrD2nC4fDha04lO+7BA3R3K1x3g/S0tgce7E/MHQiE440FC\nY0hIcmQUYM7QwGxUC5zNsjNUqF5agCyDGg1tg6hr6hekA9UoaSwpyYwYR52mpPG1dXMBAK8fbuTe\nZ9OAtticcLh8UCtpWM3pWFaaxWmOuuzD+PWf6uD1BaFW0rj31jLJZEJ81W6WQc3tfvmJUcRqXynH\nTkAo9LVqGfIytdi8yhqWepTYegnJBBHUU4BU3t/2XlfYA6CqMhe1TXbJ0oNVlbm40GjnxLFaSUFG\n0wnPza1UhLY4kbKDEWYHapUMX68uxn+9Wyd43+70IsuglkyYAwAj3mDUZCdyRaiq25ufNXPvKeQU\n9mwp5cpYAqHohCvtDtgGRrCmLCQ42QIyF5rsWL/cinQFLVnQBhiLfBAXwwHC1dVd9uGIjp2s0Gez\nBl5pd6DP2YAcUxqx9RKSFhJHPQWI46fLCk2S8dT5WelYUGxCd68Lwx4v/IHQTuSbN81HWaEJNvsQ\nOvqGoVXRmJdvRL/TE5aX22pOQzAYhFciX3e6Rg4gGHUHvGl5PlpsQ+PeJWdnqEhBjxmEz89g0OlB\nz6DQDh0MYsIJc4BQXHPdtX4EeFqZYBAoytWHJedhj2/scHDxvlaLDvMLjMjP0WN42CtI+DPiCUAp\no/HmZ004c7kHtY12OId93Gdssh7+8WajBmfre7iEQoAwoRAQ+n229gxxfePHYE9n/O5kEp7MFlJ5\nDEgc9TQQb8rRqiUFWJBnCFPfHTzRzO1khtzBiLGxrT3DEfsgFU4jZjxOZHzi8TAmJBeZBjU6+oYF\nHtxqJTUafiWfUKieQk6HeW4rFWOqbTaFKGubZolkAxZro5jRY4FQ3DPbHlsmM8eUFqauZtXjrGMn\n6+kd7TpEzU1IZsiOepqJlInnN+/VxSVoCQSWWJXIHC4PbliUjWG3H2AYzMlOhwBpxYUAACAASURB\nVNsXxNUOp2SYFIucFqYm5Vc0U8rDc8GXz82ASR9SUzuHfdAoZfh6dRHys7ToGRzhMu3xq73xfwd8\n7ZNSIUNtkx3+AAOTXoWta6yhynWjGcXWlmdjbXm2QFuVn5WOvKw0eLwBbN9QLFnAI1krR6XSs0+K\nVB4DsqOegVQvyRU45wCh3U8giISWpZRTwDRWuZw2YiX5mInEMl84hv0CDUqLbSiuuSSW4fyXIcdG\nIV829uNy6yC8bHpPbwAMKHxzcynWjYYxRqs6x8/K99qhhtAOWinDrk0l6B10h2UUkwrVYh07o0FC\nmggzhVkvqKU8Qq9nUgPW+7Ss0Ih+pxcUEDGLkrBvmdji9HAPVqWcwm3rinC2vkcQv5ydoeLU0DSA\n9DQZHMNjWxylgsICq3HU+zZ8h55pVKWcGlutpCQFTKrh8zNQK2jJML94kdHSCwSvL8ipqfmq5WjC\nUexItrLULFB79w66icqakJLMakEdyYP0eiU14KcV5dflPV5nE9TdjdTflaVm7jOvn8GbnzWHqSj5\nQjYICIQ0EEpEES33c6oJaaWcwtISM5cfPdVZOj8LoIDjteMfD7mMAk0xkoJar1Vgz5ZSTrjy53qk\nhbI4jplBKMZZLJTzs9JAgcFNK/LDwrkACLKQsZ+L34uEuBa8OMRrJmYuI8x8ZrWglkpgwP7Nf2+q\nfnD8tKJ82Fhpg24sLEWqv/wHVbS6vjOJ6Va1e/0M+gYnnoErmSnJ1+FalxPeQEi1L5NIQ8ralxmM\nVl7LTse5+l7oRZqYeJCKvzdo5dBrVfjaaLGZU5dseOPjqygrNGLA6YVj2IuLLf1wuHxhC+WKIhPe\n+7wFLrcfWrUcVZW5XMY/Vgj/8uVznNMbaxo6eLIVDpcP733eDIDiPj9RZ8OeLaWCYjafnG/Hd/+s\nXFItzl8os/0AxkK8ckxpU7rIb+oYxGdnW8kigBDGrHYmk6oHza9FK3ZoSTSDQ27UNofvZtUKGp39\nwzhX34tTdV1YOMfIleQ7W98Djy8IvVaBG5flQ62QoSRPj00rCjinmnhZW26GY9g37vKXU4VJp0B6\nmnLaC4HYnbNTi2B3egXZwfgOYOkaORbMMWDQ5eXmkD/AoLa5H3anBx7f5FZPKgUNhRxwuYNwuHyo\nbx0AwOB379fD1j+C2uZ+XO1woK3HJVmaUqtV4dxlG45dCFXp8vmDKCvMQFmhiXOyPHahC1809Amu\na7O70D/kGz1HmDucrdXe2j1WAzsQBGob7aiclxn2u+eHhok1Vx5vAEEGYaFg/LKak6G124mnXj6L\n05dmfqnKyUCcyaShr2M/rjtWiw67NpVgcbEJuzaVcKvUFaVmfHVlwZSqvVu7nfjwTMi+LKcpLC7O\n4D5z+4LcCp8tyiEmEAjipQ/q8cHpNnx6vgNHajpAi4pAq5UU7txYDKM2XDGiVlLYunYu9mwphVKe\nHNWj7U4fbP2zczeb7AyN+NHQ5oDbOzWLNotRI7D7O1w+fHpeWqPEIrYxizP1iV9XFJmgVQvnem6W\nVvBaIR97pLHZ0fRaheCYEW9A8jdXUWSCST/qhc67Dhvixf880fbxC0129A6Efht87R+BAMSp+j5/\n/jyefvppvPjii6itrcVPf/pTKJVKlJWV4bHHHgNN09i3bx/Onj0LrTb0w/nVr36FQCCAH/3oRxga\nGoLRaMS+ffuQmZk5pTfEp7XbyWUyau8NrarZ1ya9Cusk4isTBV+N7Q8y6BkQCiiFjIIvwCDLqBbU\nneanGuVShvqk46jdXgYDTi9WleXgg9NtYZ+F1OsqyWQohNRjJFJx8UmiVlL42rq5AhWzWknDbFTD\nZh8J865XKylYzTosK83iBJLZrBMUtgFChW34WC06PPytZThwogW9A25sWWVFjikNLbYvuHKVe7aU\n4mrboMAenWNKw4ETLTh7uQdePxNRyIozk0nZqKcqc1lFkQmHz7Wjd8BNnOQIYcRUfb/wwgt47rnn\nQFEUdu7cib/4i7/AY489hh/84Ac4e/Ysenp6sHDhQjz33HN4/vnnsWfPHmzfvh0qlQrPPPMMioqK\n8E//9E8wmUz43e9+h5tuuilmpxKl+hBnOeJnYEq06kqMWO2+cVkeLraEhC1FAd/YUIzyIhPu/rNF\nMOvVYefotQowkHbU4VOcp0dVZS6nMhd/tnKhhWuTQBDDj4meKCsXmHHbumIADOpa+sEgpFbv7ndD\nraRg1CqxbnEOLBka0ADcvgC6+t242NKP2qZ+1Fztw9JSM4qydVApKFy+NoAgA3Tah8NUwAatCisW\nWDA3V4fmLicsGRp8pSIHZqMGd1QVoazQhIriTCwuHlNtG7Sq0LUpCiV5eqxfkofmLifUSlmYepnN\nZ+BwedHc5cSNy/JRVmgK+zyaWrq124ljF7ok24+EQavCDUvyka6WJU1c93RAVN/SxNxRz5kzB/v3\n78fDDz8MALDZbFi+fDkAYPny5fjoo4+wbds2tLS04IknnkBvby927NiBHTt2oKGhAX/zN3/DHfsP\n//APcXU4IyMNcrksrmOjsX65lVulZhnVuLWqCJ32Ye71+uVWmM1T84Mwm3X4WYYW5y53Y9kCC4ry\nDCiyZuCjk624abUVVUsKJM+53x/kjgGAZ149x6krFTIgEBiLY01Ty1CQo8M7x1rwjY0lsDs8OHi8\nCR4vAxkNHLvQibQ0Be6/owJ/+OgKGiVqDZfNzUC2SYNPv+hAMMqiYCL1kVOVDJ0S/c6Z8bBJxFd6\n/GIPyoo78fbRlrC89W4vA7fXi1OXewGGwcDQ2LiwO2e7w8P9Tho7h+AfbcTu8KC524Xli/IEbTZ1\nDGL/H79E74Abh8+144nvrsXdomMiHW9MV+Lk5R4MOD3cuUV5hpjti4+J51rjPdcMxH3sbGaqnskz\nmZiC+uabb0Zb25ha1Wq14uTJk1i9ejUOHz6MkZERDA8P48///M9x7733IhAI4O6770ZFRQXKyspw\n6NAhlJeX49ChQ3C747NP9vdHTos5HtIVNP5q+2KBqor/Ol1Bo6dn6moqpytozLVo8dnZVvT3u7Ag\nz4AFd4R+iOx1zWYd93drtxMvvHUBdocHjR2DeHBHJW5bN5fzbvWJNsV+fwC/fbcODIDTl7qxttwM\nz6idMBAMqc//99MmHDjaBFbrSVOhf6zQrWvuR52Ew5uYubm6WVV/Wqui4PbF1liMFwpAuloxYwR1\nojhwrBnuKKr1AQkHPjatqEmvgsWkwc9e+Bx2h0fw/lyLNuw3+tnZVs6e2zvgxrufNMCgU0VUR/OP\n5y8Uegfc+OxsKxd1Eal9qWMiMZlz+c+CVCWVxyDaAmXczmRPPvkknn/+eXz7299GZmYmMjIyoNFo\ncPfdd0Oj0SA9PR1r167FpUuX8MADD6C9vR179uxBW1sbcnJyYl8gwVgtOkHmIvHrqYQN9/jD4at4\n5o0atHZHn4BS4WR1LQMRj/f6hWrLSFWO+M/PIDOxnfFsEtIA4PIkXkgDQEVxBhYWTo05ZbooyddB\nGWNJPzA0Ingtp4G1iyzQqkOaMa1aDhnvaZOukWPzinysXWSBSafCiQtd3NxnGGBxsUnS2bO124lW\nm5NzGtOq5TheZ5P8jbV2O3HgRAuyDGqBQxnrWhnJFsx3GtNrFRhwemL+dqXOnQ22ZnYM473/69VW\nqjFuQf3JJ5/g6aefxn//939jYGAA69atQ3NzM3bv3o1AIACfz4ezZ89i0aJFOH36NHbu3ImXXnoJ\nhYWFnMo8VYgUxx0JqR95VWUuqAhO22plfN7cyslbEQhxUtvcj8NnJ1boJFlpaHfGLIPqFikQggyw\nbL4ZslHpPOL2CxZGQyOhdKbHa7vR0O7Ax2fbOU9rk16FHTfOkxTS//zaFzh+sZsLn/L4/JwDm93h\nwdGaThw40YJTl2zcIvm1Qw0oLxyLumAA5Jg0gkgQtv0DJ1oAhJzGvrqyAF5fAB+cbsMvXz4XJmCk\nBA/rkLZz47yIUSXJKrDE/RrvRiNW24lqK1J/ZzPjTnhSWFiIe+65BxqNBmvWrMGGDRsAALfffjvu\nvPNOKBQK3H777Zg/fz6USiUeeeQRAIDFYsGTTz6Z2N6PE3FWoURlGYrUDj/dIX9lHulaYq9TAOgd\ndGPnjcWoaxlAWaER17qH0NHtQp5Zi61rC3Hg+FiVLYWcxo1Lc9Hc6USfYwQOlw+5mWnoHXSDDgS5\nRBdi+Hmv0zUyDI1MjeOZnAZoGrO69nUwmBi770wnyITCq1ghGs+Y5GamYVmpOeLvkR8VwcIvCEIB\nOHqhEy53KDc4Pye4Lk3JJQ+iKKDLPoLXDo3VoZbKYugY8XL+IS63HwdOtOCBbRUApLMe8rV28aZJ\nncoQ0fEg1S+pjcZE+5rItiL1NxnGcaqIS1AXFBTg9ddfBwBs2rQJmzZtCjvmvvvuw3333Sd4r7Cw\nEK+++moCujl5xF/srk0lYUXoJ/JFx/rB8ovUf3C6DWfqe6Jei/2R89s16VVc+tMPz7TD7vBgyOPH\nnKY+gbrb5w/is5pOyGiaSyoSrQwmC194T5WQBkIq91yjBp32kdgHTwKlnEYwGEwp5zeaBoxaBexO\nX+yDrwNqBY2yQiMa2gcx4gnE5YyYZYjuIV1RZML7J68JhDU/VzkDcDXS+SUxTXoVl+XsjY+vcul8\n+QJDSpD0ikIq+a8nKngSLbAShVS/EplXPdE52pN1HKeKWZ3whI/4iz1S0zkutXS87YrbsVp0MOhU\nAvVcPNeSalf83qfnO8POc3uD0575KxrO4anPCub1p5aQBkI7eb6Q1qfJsGVlfthxNMY0G/q08dtE\npB4Yku1QDA6ebA0JaRmFjcvzIRfZqLeszEdJ/tjD9fjFnqiqUatFJ0jgo1TQWL8kFxqVbPQ1JVCf\n37N1oUAFbbXosOPGeQL7c6vNiVc+rEeWQR1mdtqyyiq4Pv/1RG3R8Z53vdW6Uv2KR40fL4lsq7Xb\niQGnh/M7mA2+ALGY1SlE+Yjjmm9dWxiqazvJVKJSaUrF7YhTg95RVRRWhzeedi0ZGkE7N63ID0tR\nqlRQoGkaQXGcTJIwJzsd9hTziJ4OPD4GzR3OMFMHg5BammEgmTZUr5VHTTkrNat8ASYsA5k/AK6d\nIANc6xoSLJ68/iCaOp2wO8LnwognAKWMRkVxeHKkmqt93I44EGTQ2TvMqbgDQUCjkmHDkjzs3Fgi\nSD/KwtahVspotNicaOocQmOHA/WtA9i9eT6KcvWSda2/IaprbdCqkKFTweMN4Na1hYJY62hEq4PN\nPgtYbdqZy9cvnWikfsUTNz6ea8RqK1YcNTs2tU39UCnHvuvZsJsm9agRbv9lMxZNxkbN2qbZOrlS\nVYKO1HTCOexFQORizJ5bbDWisXUg7Fyp/rZ2O7l23G4fMg0aWM1pnHqbpgCFjIbLHQBNAWajGko5\nHVP9TY/+Fy2OWsxEi2tcL+9xGU0hkKSLlevFRJQKUqVQY14nGJp74uHmfwf+IBNWWCZaetHjdTbJ\ncrB8FapUoRqHyweDThX19xzSctk5NTl7Xu+gG1vXFAqOjVTXWpz1kLV1x0OsOtjTpdadCfW5+WMT\nz3c9W0gZQQ2ET8TJTEwpG7JYSP/za1+EOb+wlbNO1/fA7vBwDzgpO7m4f0drOrmHizcAPPdWraDt\nIDNmowsyiDuvdpD7L36SPSvpqoVmnLjYPemsWzMZGolxbOMvBqXQqGTIz0oTLMKUcmDBHCMutw7C\n6wvCpFdh16YSXG0bxPE6GxwuHxcvrVXLUJhrgIwaK8nqcPkiCqgVpWZQAOYVGPC7g5cFpp541KCs\n6lSrlnPn6rWKcalPownTyTqpkprbkUnVsUkpQZ1IYq16pTxUgdCDhMFYqU12FxLPyjmVhc54UCto\nuNy+lBkvmgbSlDSG3EKxHEtI0xhNfhNloNRKCjdU5KB1NOkOEIqrtjs8sDu9oGkg4A+EaUoUchm+\nbOwPCWGLHstKs7gc3GydagoMF82g1aqhllNo7x2O+BAWL46NOmEltrWLLFg238z5gEQKjWLb0GsV\nqCw2Ydjjx5ZV1nEJ1UgCIx5v5FiCXEqbRgiRqmNDBPUEibWyE3uoatUyrKvI5QqBnBHtqCM9mPgT\nsqTAgE/OtSFabQWrOQ1tPcPjElIyGlOS/GO6iFTEZLYSDCJMSMd1HsLV1WLcXibMaZEvlINBQMqi\nyGp2XO4ArrQ70NDu4OYkWyeaVR2zhThMehU2r8hHXcsAV1CDTyxnyr4Bd8xIDrHq9ErbIEa8Adid\nDeNWX0sJjFgL+HjDimaCGnq6SMWxSRlnskQTzSmE/XxRkQkKGY2SPD12by7FusV5MGhVgnO/tr4Y\neaY0ro3Wbife+7wFn1/oxJufNuGLhj6cre9BpkGN1w41YIhnV9OqZSgUOWc5hscfnqNS0OOqc51o\ntGoavmTXpacoagWNr662StZVnyhsnWi2QA7LiCeAxg4H2nuH0djhCHOiilXoZn6BAVfaHFxbUkV3\n+G1oVGOx1hMp0iPlHBXLuVRcKIh/zVQuSMGSymMQzZmMCOpJEMuL0aBVYXFxJiqKw4vUs+eWFZuR\nl6GBQavi7Np1LQNo63Hxsi8FMej0oL1XaCf0+RkMuf2TdpqaTiENgAhpCdRKSpDMYyqQ0+E76uwM\nFbcbltPA8gVmFOcb4PH60TvgjltTs7bcjPJCE3oGR+DxBcHPoSejATAMGICb4wDCBKdSRuPLxj7U\nNvbBkK6E1aITLI5XLMjmvLK3byjGsvnmiNEVLPxF8lcqcsYd+RGrMlasBXw0QZ7KQoollceAeH3P\nECLZtQEg06BGn9PDqdVYvFFCaQgzF4shDR1215QKa6lYc1u/R/D58YvdOFHXHdVDWykHli+w4GRt\nN2cXX1ZqQY4pDQzAOX6du9KDU3XdCASBrlFHR9b0k6aW42s3zOES+ui1Ci7LGBDyAv/hrqVhak++\nV3a8Mcf8NsYT+ZEItXWq2lgJkyMlBHWiUoUmug+t3U58dqELcy3a0GubE0oZwmzQSgWFZaXmUMrQ\nEy04fakb/kAoh7dCIQuFYwFQKGioFBQcw8IGNEoaBeZ0uL1+2Prd8MbIBqJWAO7kSHCVsnROsZAe\nD9GENBBKCds34BY4r/3XuxehUcnhcPlg0qu4UKvjtd2Cc9kd/bDbDwYUJ8QGnB58cHqsal80L3AW\n/kI3nuOB8dk7ExU2lYo2VsLkmPWCOhlywkr1AYDA+zQQCI6pHGXAyoUWzLGk452jLRjxBvDaoQbs\n2lSC+tZB7gHuDQAKxVitaI8vCI9IwMppYMQbxBWJWtSRSJSQ1qpo6LWqKU8ZOhsRlzSdTtgwqkiY\n9CpkGtSCOebzM/D5hdn4sgxqyGWIuAChwAjy3J8YDeMCxsKnWrudOFrTCQYIczgbT+jORBbv0xUa\nlAwbDcL0Mutt1NGcN64XUn3oHhjh3vP4ggI7bZABbqjIAUDh/NU+7jwpBxyfn4nquTudOT98AQZD\nI8mbzjTVoRE75M9q0aKs0AjnsE8yY5lGKcPWNVYMurxo73EJPtOq5fD5QzHU8/J0ePmDK5yQ1ijD\nHRgbOxw4f7UPNVf7sLY8G8V5egw4PSidY8DdNy8EAPzy5XO4dG0AjR0OHKvpgFxO4Ur7INRKWZgN\nm6+54tuVT12y4T/++CXON/SNK/NXLPuzFLFs2oLxkrDPTkeWsumE2KilmfWCOp4Un7EYz48t3j5Y\nMjTce3qtAnIZxQlr1hGGf4w47Slb+lKtoOGPIo0V8uRNJ0qYXuKZFQ6XD209wxHTivoDDK60D6LF\nNhT22dzsdHj9AZQVGvHRmXaBTVwspBVymrvGiCeAc/U9OHelF7b+EQx7/Fhbno2jNZ24dG2sPnuA\nCZUVvdjczwkxq0WH+QVGOFxeHLvQhYEhD379pzpO0GXoVPjte5cm7O0dzYFUakGw/40a1Fy142x9\nDxYVmbh+ST1LpITU9dxoTPY5lwiIoJZm1gvqiayC+YxnRRtpokv1gX2vKN+IW9fMwbrFuVwo166b\n5guOYc8bGvGhs9eF0jkGbPvKXBTl6rFwjhGXrvWDYUKJLyhG+ABevTALIx5/WKrFiWCIkQd6orA7\nO5oCLDyv40Ri0ikw4iWOd4mGpiJHDdidHnj9QbT1DMfU7Bi0CigVY8I6pGUaE9xmowZubyh8Swq+\nEOP/Zmsb7XCOhiyyWil+9IRGJcP26uJJCybxcyJDp8Kv/1QnuB+314/3jl+L+CyRElKJ2GhMpP/T\ntXMnglqaWW+jBibnvBGvA0ksW7hUH6wWHZYvykNPT8hbdffmyB6kpy7Z8J//WwuGAa52OLBsfqhu\n7y9fPsslK5HK1c0vgzlZBieQBzoe2G6H0p5OTXWtZCn/mEgmmm+dhV+HfKLctCIfxy50TXpxdceG\neWjrcgocyFg0KhmyDGpUFJlw+FybpI2bbzM+yquMJ1Xusr3XFcoVrpThnq0LE2L3larOJ47I6B1w\nj9sZ7Xp5iada2ciZRkoI6skQrwPJVE/0IzWdnEMPw4ReLyzMmJLdJ2FyJCrHdjTiqe8cCylBnZ2h\nEiyWpK5DAzDpldi0ogCLijLxWU14uVVx9MLi4gzUNvYjiJDmZ/VCC3RpCnzZaEf1klxs31iKF9/9\nErSoOAxNhXbCL31QjzVl2di4LB8fnWlHkAmVubyjai4YUILCNcfrbNz5eq2CS1kaqxjPZJy2+JkI\n9VoFqipz0WJzCjITblllhd3ZMG5ntER4ice6t1TNoT1TIII6BvGuaKdyord2O6FVy7kHK0WFPF5z\nTGl47/NmgbCWCu8Ss7g4Q5BiMxE7q+lEowRGkkhbVmDW4prIsSrRJKLetlQTYo2G1HWCAHodXhw8\n2Ypr3UNwS5gUaFFe2vbeYezYWMzl9maF6+7NCwAAR8634Q8fN3KL0Uy9EoNDXu76DpcPH5xu4zzQ\n5TIKd1TNxS1r5gquK85FsKYsvPqVlOCLpBGbqPDOMaXhh7uW4khNJyiAC0+bbMW+iRBP5AuJ705u\niKCOg3hWtFaLDrs2leBITadknuKJwJbJZMNUtGoZ8jK12LzKyj18Hv7Wchw43oL2XheM6Uo0djjg\nDYQktXh3AgD6NDkaRKFaYiE9WZXq9SaZhDQAGHRKYIoFdTLgcPnQ0S19n2LhbXd4uFBDNrc3X2h8\ndLJVEAKmkMskFwnsMf4Ag3eOtmBRUWbUEK2q0dz6sZDSiAGIO7RTKoZ765rCMHPWdMRQx6vtI/Hd\nyQs93R2YLbD1ab9stOO1Qw1xZ0mK1t4zb9Tgg9Nt3APA5Q5gaalZsEOwWnTYurYQLrcfXzb2C3bX\nUjZrx3Bsx7KZJKSTke7+mRE3TlOhmP3J4PUHoIyy3GejE/jpQVlha3d48Pv3L+PVD+uxuCSTSzNK\nAahekguTPuRco1TQ0KrDOzriDeCoSO3O7gy/urIAK0rNUfve2u3EgRMtaO12oqLIxF2P1YhFEt5S\nSJ2fLCRz32LB/45SGbKjThCJtlHz22OJ9COTOpYwfcRbB3y6uWlFAZzDnkk5HHZFuFe9VoHyuRkA\nA+jTlDDqlHidVyqT5Uq7A1faHdCq5VApKbi9DGQyINOg4VSxWQY1GtoGR000DA6fHQv1Ol5n4yrS\n8Y9ltVBn6nsk1dgAuHrx75+8hh/uWhqmEeuyDwsc0bIMahw40RKWXZBtczKqY36WwkTvaq+3WjtR\nCVqSIVlVsjDrw7OuFxMNo4gUjsBvT69VYMOSPKxfkofmLmdY+Bf/WK1aBiYYPQmKmOwMFTy+QNRz\n1pabEZhkAhOlHJDJ6IhFRJSyUGwsYeox6VW4c2MJDh5vwXAMDcudG4tB01TcmgI5TaGqMgenLvWg\nuWsI/UMeyOU02qKYA3z+IOfNHWSAmqt9qF6SB0uGBr/+Ux1qm/rRMziC9l6XIMzO4wtCKaPx5mdN\nOHO5B2cu9+Bqh0MQk202aqBWygThR/1ON66OlutkQ6c+Pd+Jlq4hNHY4kKFT4bVDDXAO+7ikLu8c\naxGELzlcXkGba8uzsWKBZUJ5Gp55owaff9k1ZaFRsQoIJYrJhnnxn4fJkKzqepLScdTXi4nGa0cS\n1Pz27qgqwpxsnSBxA/8HwD/269XF2LA0H26PHxqlDKVWPdp6hFW31EoKZYUZ6B7dDbncAawuM6N7\nwC0pRLVqGW5ZU4jzo4sBOU1hdZk5rF0xa8vN6LKPxdDK6MiVspQKGhuW5qGxY0zFVVlsgiVDzfWT\nkBjm5+vxwG2LYLXocKGpL+b4lheZcOOyfG4xGIsgA1zrGuJyyo94AlArZbA7o2t9+BW2AkEGShkN\nx7BPkMFPHMdv0quQm6lFbZN0GU6KAqpHF7j8h764P2qljIuvFmcB9AcY0DTFvZbKLjgZQTKbBNJk\n74X/PLxeMeTJQjRBTWzUCcRq0WHrmsKEqWf47cWyl/GPtVp0eOC2Cvz4rpWwZuvD2nV7GTR1Cm0+\nF5r6I1bicrkDOMKLTfUHmbjCwpq7hgQxr15/ZO9yry+ILxuF98QA6BlIvJCOZFNVKxL/c5ifHz7+\niaAkP/ocW1tuhlYdfqNry82heTE6R3fcWAJ5lNtmc2yz6tOdG+dJXruy2ASlPCRqNSqZIFueRhUK\nTVIrqbDzZKPXNqYrUVGcIfiMgdC+qtcqoNcquL+/urIAD+6oRFXlmD2bEl2CYcCFZvHttFtWWQVt\nbVllFXzOb1PqdUWRKWG235lsQxaTyHvhz7lUVnsDZEc97cSbiWeiq0u1UsbV6GXRaxVYW54t2L1u\nWJqLgSGv5I5JnL6UfX2hsS9iVioKwK1r56C1Z4i7tlpJIRBBvpv0KmxcloeLLaGdEUUB2zcUw2JU\no7Z5bLckVUNZCqU89KBnmJBGgL+T/8aGYlztGBAsIvRaBe6+ZSFM6Sp02V3w+Rlo1TIo5TR8fgZK\nBY1VC7PCtAhry83Ytq4INvswjGlKfOurpVi10AKPN4BvbCjGrptKkZeVuoHAVQAAD4dJREFUhnP1\nPWCY0Bhsry6G1xfA0IgHgWDovaol+VDQFLfL06plmJutw4DLA4YJ9c9iVMPvD+DGZXn4P1+vhEpB\n4eLo2NAAdm4sRnmRCbs2lWDTcisWF5vg9vpBASgvysD928qxablV0H+DVoWl87M4DczmlfnIz9LC\nbFSjrDAD3xzNksceO7/AiLm5epyqs8HnZyCnge/dvgjf2FCCJSVZYXWeNUoZ7rl1IVYtzEblvNB1\nfP7QHNqzpRR/dsNcmI0a3LOtAsU5OkE9afbafM3SVypyuL/XLc6DQasSaJSql+TBlK7i6mCzvxVx\nHvCyQhMWFZm4tsoKTWGfR3stlTlwooKEn6XwllXWGS2QJjsm4ufh9VLZJwPRdtQUw8QqYnf9YTN1\npQJmsy7u+52okwZbccgx7IU+TcnFdB480YxPz3eiekkublkzl2ufAhMW7yoVU9ra7cQbHzfgmm0I\nc7LTUVWZh3P1PegbdHMhZGyIGRtL2mUfxpGaTq7tLIMabj/DOdGcumTjHHpY7/aDJ5px6EwbcrO0\n2HFjCddGjkmDLvsItGoZmruGUL0kF4uKMgUOQ+zf7Dlsu+y9ZBnUgmQY4nHmtxGpfxP97vjvsRnq\npMY42nc+XZWVEt0v9neQSEekmRYTPJ5nwWwllcfAbI48T4mgnmZSeWKykDEgY5Dq9w+QMQBSewyi\nCWpioyYQCAQCIYkhgppAIBAIhCSGCGoCgUAgEJKYuAT1+fPncddddwEAamtrsWPHDnzrW9/C3r17\nEeTlqQwGg7jvvvvwyiuvAAAGBgZw//33Y/fu3fj+97+Pvr6+KbgFAoFAIBBmLzEF9QsvvICf/OQn\n8HhCYSOPP/44Hn30Ubz88stIT0/HO++8wx37b//2b3A4xgo+PP/881ixYgVeeeUV3HXXXfiXf/mX\nKbgFAoFAIBBmLzEF9Zw5c7B//37utc1mw/LlywEAy5cvx5kzZwAABw8eBEVRWL9+PXdsQ0MDqqur\nw44lEAgEAoEQHzGLctx8881oa2vjXlutVpw8eRKrV6/G4cOHMTIygvr6erz77rv493//dzz77LPc\nsWVlZTh06BDKy8tx6NAhuN3xZZnKyEiDfLJlfWYQ0dzyUwUyBmQMUv3+ATIGABkDKcZdPevJJ5/E\nz3/+czz77LNYuXIllEol3nrrLdhsNnz7299Ge3s7FAoF8vPz8cADD+DnP/859uzZgw0bNiAnJyeu\na/T3R88hPZtI5bhBFjIGZAxS/f4BMgZAao9BtAXKuAX1J598gqeffhoZGRnYu3cvqqursWHDBu7z\n/fv3IysrC9XV1fj444+xc+dOLF++HO+//z6nMicQCAQCgRAf4xbUhYWFuOeee6DRaLBmzRqBkBZT\nVFSERx55BABgsVjw5JNPTrynBAKBQCCkICSF6DSTyqoeFjIGZAxS/f4BMgZAao8BSSFKIBAIBMIM\nhQhqAoFAIBCSGCKoCQQCgUBIYoigJhAIBAIhiSGCmkAgEAiEJIYIagKBQCAQkhgiqAkEAoFASGKI\noCYQCAQCIYkhgppAIBAIhCSGCGoCgUAgEJIYIqgJBAKBQEhiiKAmEAgEAiGJIYKaQCAQCIQkZtxl\nLmcDrd1OXGiyI8ugRu+gGxVFJlgtkSuXJOoaAHChyS74e/1yK9IVtOAcfn9OXbLhw1OtyDSosazU\njKttg2AAVFXmwmrRobXbiV+9WYPufg8sGSpsWJqPs/W9yDKqsXVNIQDgjY8bcM02hDnZ6dhxYwms\nFh1OXbLhtUNXMDzix/oludi9eQF3fQoM6loGkGPSoKnTiSyjGnMs6ahrGYBWLUNz1xAWF5tg1KlB\ngcHntTaAAW6oyEZdSz86e13YtKIAi4oyBffT2u3EGx83oKnTCSYYhFwuw+oyC9LSVLD1DkGfpsQ6\n3n2Jzz1a0wkGQIZOibqWAVRV5mLVwmxuzE9dsuFITSf3vtR4ir+jIzWdoADuuuP9fqdi7sTT/niu\nzx+7kgLDlM35eJnqsSMQZhspV+aytduJZ96ogd3hAUUBDAOY9Co8uKNyUg8N/sMHQNg19FoFAMDh\n8gn+zjKq8VfbFwvO0WsVWFOWjQydEn843AipL0hGAZtW5OOj0+0IRuiTjKZAMQz8TPh5H5xuFxyb\nnaFC76AXgWDipoNaScHtZUDTgFGrhGPIK+iL5DkKGmajBr0ON0Y8AWjVciwuNuGLK71w+8LvdMvK\nfOzevACnLtnw3Fu13Psl+Tq09w5jxBOARiXDPVsXom9wBJ+e70T1klz0Oz2CMdBrFdizpRTn6nvQ\nN+jG5lXWiMK+tduJf37tCzhcPqgVNNI0cmxeUYBb1syNuAhkFynsIuaWNXMF98Ev78efo+xcqOIt\nJMTXv61qLhhQkgtP/rF8EjHnWca7aGDvjd+HVC5vyELGILXHIFqZy5QT1AdOtOAPh6+Gvb9z4zxu\nBzpexA+flaVm/L/TbXGfv3PjPAAI65ecp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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbd65f5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(data['Temp_Max'], data.index, marker='.')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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sgRF/bNbhg9nkd6gmxwJSwjTxBI9RfwxsgkeU5XC124fLXT70jYbRNRTCeDCu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deBt08P4Ge/uQI2zcHMUCr60DNXJ/CLw1dxZdCPtbWlcDsYXBsKymHhMqcFdguNTU0e3L+xEkmW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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbb875278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(data['Temp_Min'], data.index, marker='.')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZkWaNFOqrnagps+OPbhfqTo839mGJwwIzqx6MzrXMrGdCZ34RT3Dwz3KOQSKZ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vtlr3mmQzJ31tsnrgi/lygoiYw4p4n77M2P+lX7/0prSCuLTfjMrSuwaXUJTl4YhS8YJRPg5oZi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\n1ARBEASRxpChJgiCIIg05v8DEAvVFj8F+dEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbd4605c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(data['Temp_Diff'], data.index, marker='.')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbc0864a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtered_data = data.dropna()\n", "boxplot_data = [filtered_data['Temp_Max'], filtered_data['Temp_Min'], filtered_data['Temp_Diff']]\n", "plt.boxplot(boxplot_data)\n", "plt.xticks([1, 2, 3], ['maximum', 'minimum', 'difference'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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TdjT2xDYD0u49CXtim4E9s927Q5uFBA4dNIVk/e53v1O/T5kyBRdffDGuvvpqzJ07F5Mm\nTcIzzzyDj33sY80oWiAQCAQCwS6IK664AosWLcLatWtRKpWw7777Yvjw4bjuuuuaWu6KFSvw5S9/\nGYcccgjCMESlUsEJJ5yAU089FZ2dnbj55ptxwQUXYNasWfjFL36B008/Hb7v4+6778a3vvUtHHvs\nsdtc9k5L4XDeeefhggsuwC9+8QtMmDABxxxzzM4qWiAQCAQCwSBj2rRpACKXorfeegvnnHPOTiv7\noIMOwh133AEAqFQq+O///m/ss88++NSnPqWSjD711FM4//zz8alPfQqnnXYabrjhBnWcz7ai6SSL\nGgUAd955Z7OLEwgEAoFAsAW8PeM2rJ/z/A6958iPH4H3n3XGVn/vqquuwvz58xEEAc4++2xMnjwZ\np5xyCiZOnIilS5eira0Nhx12GObMmYPe3l7MmDEDs2bNwuzZs9HX14fu7m5MnToVRx11VEPlZTIZ\nnH766fjzn/+M/fffH9OmTcPXvvY1PPvss1iyZAlee+01LFmyBNOmTcO11167XTm2JFGVQCAQCASC\nQcFTTz2Fzs5O3HXXXbjttttw/fXXo6+vDwDw4Q9/GLfffrvy454xYwbGjx+PF198EQBQKpUwc+ZM\nTJ8+HZdffjl832+43JEjR6K7u1v9ffTRR+PjH/84pk2bhv/5n//BQQcdhJ/97GfbncRUMr4LBAKB\nQLCH4f1nnbFNqtOOxrJly7Bw4UJMmTIFAOD7PlavXg0AOOSQQwBEZwuS2W7YsGEol8sAgEmTJiGV\nSmHMmDEoFAro6enBiBEjGip39erVGDt27I5uThVEyRIIBAKBQDAomDBhAo444gjccccdmDlzJo49\n9liMGzcOAFTap3pYuHAhgOig6FKphI6OjobKrFQquOOOO/D5z39++yrfAETJEggEAoFAMCg4+uij\n8cILL+DUU0/FwMAAjjnmGBQKhYa+29XVhTPOOAO9vb245JJLNntU37JlyzBlyhSkUil4nocTTjgB\nkyZNwooVK3ZUU2oiFYZh2NQStgO7Yi6T3SHHyo7GnthmQNq9J2FPbDOwZ7Z7d2iz5MnaMu69916s\nWrUK3/nOdwa7KpuFKFkCgUAgEAh2e1x33XWYN29e1fUrr7xyux3YtxVCsgQCgUAgEOxWOOmkk6qu\nTZ06dRBqsnmI47tAIBAIBAJBEyAkSyAQCAQCgaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBoAoRkCQQC\ngUAgEDQBQrIEAoFAIBAImgAhWQKBQCAQCARNgJAsgUAgEAgEgiZASJZAIBAIBAJBEyAkSyAQCAQC\ngaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBoAoRkCQQCgUAgEDQBQrIEAoFAIBAImgAhWQKBQCAQCARN\ngJAsgUAgEAgEgiZASJZAIBAIBAJBEyAkSyAQCAQCgaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBoAoRk\nCQQCgUAgEDQBQrIEAoFAIBAImgAhWQKBQCAQCARNgJAsgUAgEAgEgiZASJZAIBAIBAJBEyAkSyAQ\nCAQCgaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBoAoRkCQQCgUAgEDQBQrIEAoFAIBAImgAhWQKBQCAQ\nCARNgJAsgUAgEAgEgiZASJZAIBAIBAJBEyAkSyAQCAQCgaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBo\nAoRkCQQCgUAgEDQBQrIEAoFAIBAImgAhWQKBQCAQCARNgJAsgUAgEAgEgiZASJZAIBAIBAJBEyAk\nSyAQCAQCgaAJEJIlEAgEAoFA0AQIyRIIBAKBQCBoAuxm3dj3ffzoRz/C22+/jVQqhUsuuQTZbBbT\npk1DKpXCgQceiIsuugjptPA8gUAgEAgEQw9NI1lPP/00AODuu+/G3Llz8ctf/hJhGOLb3/42Jk2a\nhAsvvBBPPvkkjj766GZVQSAQCAQCgWDQ0DQZ6aijjsKll14KAFi9ejXa29uxaNEifPSjHwUAfPKT\nn8ScOXOaVbxAIBAIBALBoKKptjrbtnHeeefh0ksvxfHHH48wDJFKpQAALS0t6O3tbWbxAoFAIBAI\nBIOGVBiGYbMLWbt2Lf7jP/4DfX19mDdvHgDgiSeewJw5c3DhhRfW/Z7n+bBtq9nVEwgEAoFAINjh\naJpP1oMPPojOzk7813/9F/L5PFKpFCZOnIi5c+di0qRJeOaZZ/Cxj31ss/fo7h5oVvW2GaNHt2Ht\n2j1LgdsT2wxIu/ckDFabX127CCGAfx79oZ1eNiDPelfF6NFtg10FwQ5C08yFkydPxuLFi3Haaafh\n7LPPxg9/+ENceOGFuP766/HVr34VruvimGOOaVbxAoFAsMvjptduw82v3TbY1dilsbJ3Faa/dgeK\nXmmwqyIQbDWapmQVCgVce+21VdfvvPPOZhUpEAgEgiGGGxZMR787gHFte+PY/Y8c7OoIBFsFSVIl\nEAg2Czfw0F3aONjVEOyhKHvl6KdfGeSaCARbDyFZAoFgs7hryf340ZzLsbpvzWBXZZdAxa/gjY1v\nIwiDHXbPnRB/tMtgQ6kbl879OZZ1v9HQ5610FPzkBV4zqyUQNAVCsgQCwWYxd81LAIBF65cMck12\nDVz94g345cs34sXOBTvsnn7o77B77epYvH4p1vR34tr5Nzf0eSsVkaw9qY8EQwdCsgQCA2EY7lCV\nYndHi1MAAHQNrBvkmuwaWN0fKXqdA2u36z58jLl7kEpjp7fOFZiULD9oHskKwxDv9XfuUYqiYOdA\nSJZAYODql27ABXN+OtjV2GUwOj8KALCuuH6Qa7Jroae8abu+7zHS4Abu9lZn0LCg6zX85tUZDZvz\nKsy3qpHNTKJkNW/j8/c1L+Enc3+Op975W9PKEOyZEJIlEBhYsekdbCz3DHY1dhm0ZVoBAH1u/yDX\nZNcAKTHbT7ISUuL6zSFZJa+E2xbfjZe7Xm3K/QHgtwvvwGvrXsc7vasb+jx3YK800G4iWV4TlayF\n6xYDAOasfqHqfy91LsDvXr+vYZUrCANRxAQKQrIECMIAMxfdjeffe3GwqyLYgXADb4eaWHaXPEVF\nr6ipJTsadrzoby8R5z5GzTIXvrpuMV5Y8zJuWXhnQ4Rme9CoksVJViMKnh2bC4Mm+mTlrBwAoOSX\nq/5366LfY857L6C73FiE7fnPXYaLn79yh9ZPsPtCSJYA64vdmNf5Mu58/Z7BrsouhfsXPTbYVdhm\nuL6Lb8/+IX7z6sztvhcRtQGv9gkMfZVdS+E655mLcMnfr27a/b14sd9U2b6s4ZqS1SRz4ds9K9Tv\njZKErcGAm4yJeuPDRGUrSRYpWS91vdI0Mpq1swAi5a8eGlXSNlV6sa60oeGyOwfW4vHlT4v6NUQh\nJEuAnsrWmz02lnvw/56ehufenduEGu0a+MPCRwa7CtuMgXixWLxhac3/P/Tmn/HQm39u6F6kuJT9\nSpUPzTOrnsd5z16CJRv+sR213XGg+jXT3JuQzmLDC+Of3nq8ymTn7gSS9S5Lu9GMXGd9jGT1u8WG\nvlMOGMmqoa6ZY8xKJcvU8zXMeTsCfvwsailZhJK/ZSV3W8y+P/771XjorT9j+aaVW/1dwa4PIVmD\ngOdXz8M3nzq3obxDJa+Eq+Zdj7+umtO0+mwodW/1dxasXYggDPD7pfc3oUYJttbc9fCbs3DtyzcN\nWvmNouK7mL7wTry+fllT7r+lRfvxFU/j8RVPN3QvbtYaMBbSPyz7IwBg4brXt7KGzQE3iW2JAIVh\niHlr5qOn3LgiFYQBQoTq90oD5KjsV/Dn5U/gloV3an5tmrnQb45CU2akoRkkSxsbDSpZZY/5ZBnK\n1O9evw8/fPYnKHlJvdOxuRAA0il9ydpY7sG6YuOqUT00Ygove/UJGKGf9UEjBJybTjdH8AS7L4Rk\nDQLuXHIvADSUZ6dzYC1W9L6De5Y92DSfmG0hWZl0Zqu/U/ErmLdmfsO7vYfe/DOmzv7BVqkSf1nx\nFJZtfHOHJS6sBLpvz9qB9TvESXnJhmWY3/UqbnhlelPMBJxkmSYQ/ncj0V2caNZTPduzu8aBtrzd\nW3pfXt+wDDMX34Vr5/+m4fubJqOit2X1hpvHVve9x+61Y5WsMAyrxhIve4NhLvQDf7vHHu+PfrdB\nc2FQ21zYXdqIOe+9gF63T3vnuZIVGPW95O9X46Lnr2j4fR9wi3hh1YKqcV/0a78T/L6NkCBuOjdN\nmwvXvY5Zy5/Srm1iBP+uJQ9I6pghCCFZg4iMtWWiwsOW7132EFZsemeH12NTpW+rv2Oz3WWj+OMb\nj2Hm4rswa/mTDX2elJZ/dL+11WXtqF0h32luKHXj4r9fiV+9cst23/ed3ne1++5ocCK4wVAwNrKo\nOFOZqgWuVnAfH75Am4Tm8eVP4zevztzpzvJcydqSGZyiA+vlu/IDHy91LsCa/s7kWqgvnI20r1In\nmo4v4I0oYsDmSfFFz19R5XDNxy9XYgbcIqbO/gFueenuhsqtB94fAwbJGnCL+Nu7z1fVWfPJYv2x\nnr0HXIHjc6C5YaB7Let+s6H6/nn5E/jZczfhvn88rF3n9+UqGjeBNvKsuVJZNuagG1+dgUfemoVe\nNt9y9W99aQNeXbuogVYIdicIyRpENLKL9NlEPHfNS/jta3fs8Hp4bIJv9CXfluitN3veBgCsYrv5\nhsoKtr6sRqT9WjCfCb8PTY7/2Lj1pM8EJbQEgFV9jYW+bw34om06aHOVoM/dMsHmagVfzPgCzheg\niu/iobf+jNfWLcaz7/5du1cYhnhm1RyNZG4O64obcP5zl1X5fP2j+y08+MZjVc/LZWPFTLGwsneV\nRtitLWwUnl71LG5d9Hvc8fq96to2KVnsWfDf+b0aUWIefetx/L+np9VNHbG+1F3lcF32K8rE5jFC\ntLJ3FQDg8Tef2WK5hO7SxioXh80pWXctvR93L/0jnlypl8HJB1eyOPngYytgZRTr+EWtbHA8kV/c\nUoOU8fE7wJ5p/2ZIk+u7VZtenWTVnrfWFpOkvuYmZ0tjUrD7QUjWIKLf23JUlpmAb1sUpC2By9o3\nvXZb1Y60FrblsFZqi9mGolfEza/ehrd7ajt+bkvo+bYqWeauu1wnceJ6ww/knd53cfNrtzfUd+Z9\nVzWQX8gLPLy2bnHDPmJcITDzW3GS1cvMG32Vflw571o88eaz2uf9MEDezgPQF9K1LDkpJxvcgdc0\n9a7uX4M/LHsQV8y7tqF2/O+Kp7Gx3INbF/5Ou37N/N/gf1fOxk/nXaM9Fz5WBgwCdOW863ANMw1S\n1Fo9kCmHt8c82sVcJN/pfRfPrdaDQeopN5z0NGKCfmz5EwCivE0mONmk8sIwRCWooBA/O07k3mPq\nXKP45cs34rIXfqH5sPG0Cv1Gf6/cFBE5rn4CRp6sOpsBTmg8Vka96L9NDQTvVPyKGo/8PQ3DUDPb\ncXWJj/eSsXG7+bXbcdWL12tEi48HTY1j720nOznB9GPb2mz4gl0fQrJ2MvhEZ+78NpZ7qhZvc4c7\nKj9S+/vv772I7/71R1Xf2xqYvgONKE3aJNigPwSpcubi9pflT+OVdYtwy8I7a9dvW0iWp09wty2+\nu65Kd/HziQnQMxbReu181lhIp792B15ZuxD/u/KvDdWP7+BNc14tvNz1Kn7z6syGUzLwxWtzJIvv\n1Ff1rcbK3ndx84u/08iKH3jIWhmkU2lNVbxi3jXqd05oeJ+ZJpalTJFqJPWDog6p2v9/t+89vMUW\n8Yrmi5bUoxbR2ZJqYCoXQC0lS2/fFfOuxe+X3K/1sWYuZP3HF17TXPj6hmX43xWza9br/jf+hHeN\nd5R/n0iQF/oIwkAdi8TrvmagS/3eiKIehIEy583rfFld5/c0Nxh5JyJ35vjj/cHfKZ1k1e6nInum\nvN6NBC/we/ZW+hQ576ls0hzWOVHi102CR5G7/FnzMcPbydvWxczT5kZADsEeehCStZPBFRZz8jn/\nuctw4fNXaNe2dJTEHa/fg7JfaejwXi/wcMfr91RJ3J4xwTeilvB2NKpq0YRsLm7vxaazej5q5Rrm\nQj/wVYQjgU+6PNz6vf5OvLDmZdz02m01HINdrC2ux+L1S9V9tbK1nD7JBGia+OhzjUZw8XuZi3lP\neROeWvn2teXKAAAgAElEQVSM1jYi5Is3LK1S217qXIC7l/5Ra5vL6n3vsoc0E1O3RrKSRaRcZ1Hw\nwwBWyoKTtusS3qKb9DfvQ3Onzgl8d0MBDVGbUvVYllEeX9jMMUCg985KbX76qzWuaaNAySvrmQt5\nv2rmwjo+Wabj+w0LpuPBNx+rSx5e36BHpXICQL5oNK4KNpGs2spZIw7ra5n6wjd0XNkz70Nkpc+4\nXk/Z42qSqWSRklryi9p19V1DyQrCoGrzyJ9niFCpuERYiYzWMxfyOY+bNn1NSWU+cOx3PtY5yTLT\nXjQzq71gcCAkayejrDlV1p7c+GJpmif4RMkXFzPqphYWrV+Cv7/3Iq568Xrtuhk+zs0YPeVe3DB3\nJl6Lj50gbG3W5qiOUX1tQ8miHfLo/AjtupN2AFSbZOZ3vYZL5/4Mv33tdsxe9VzNevBFh+801xs+\nK4+8NUv9HoZhVX/XU7JM5ZCSGTYaCekFnuoHczG/dO7PcP8bf1LEz8Sa/i7t71sX/R5/e/d5zdHb\nDI3nPk09XGVhfcbbyhctP/RhpdNw0o76vEn0+MKkp3woGZ9LxryZwXtjuUdbvIBk0Uml6pMsXna9\nMcDPXaRFf0sbGG1Rjt8v+k4hVmn0I2KS3/nizIlVfZKV/M6JW1cdp3xTQeN/E0Gm+rQ41eZC/ozm\nr32tZhkc3DTcwwi4V4dQ+4Gvkp+WDCJaz1yo+TNpSrSHlphk8XbyjYRJRu/7xyP49uwfanOH6UdK\nY5yU5P3axsVlcJKVtInGVskrY9qzP1bX+TjTAg00UsaVrGpz4bBMu2qrYGhBSNZOBt8N9TNzCSdW\n9Wz5gD4Zc7WhViTVuuIGbUEm0mKC7jm+bV8A+sT5wpqX8MzyuVVmKi3XTYNKFi1QaUPJIoJo0sSM\nFdW313DOnr7wDjXprx1IJv96OWe4Oa5kmBv4gbBFr1Ql15fq+IasL3UrohGGoZpETSfzdcUNNRdK\nN3CRd/JIIaU970Xrl6iFhD9rXq96qmWtBYFApAAANpZ6an5OI1ls0fIDH3bKRsbKKJJg7rh1MsrN\nO/oCy3fu5j3Of+4ybfECkoU3vRklq68OoeHPmi/O5Oy/pXD5cg21ljYgeTtSsnj/ra1B5KI61TYX\nelqerOQ+v1qQRK9eM/83NZVlc4PGVTsy21G5hVihccPaG7RaOc5MxVcz62ljgyuyFfW5jeVNqn8H\nWN+HYahv0DRFrbbTuBf6cCwHOSurm4C507wxR/w13nwt21g7UANIxg095+HZjqi+bm2SReO137BA\naCSLPV9tPLC5vqu4TvUNfXfSXocDANwmHh0kGBwIydrJ4JM9t/fzybqeKmB+ji/otY74uOj5K3Dp\n3J+pv/mkxCcSN3DhpB0csff/jcqsYxbTzTLc9yC5rxt4+M5ff4Q7WUSW2ZbQWNxI0TB9HshE8I/u\nN1XZ5sLYkW2vWQ8+GfPjRDSlw4hU6q30Vqkbv1+SJFt1DRWC+twPfTWBmybgi56/Apf8/erqKDjf\nQybtIGtltMn/FeY3xscKJyR/e/d51AJXgUziy8vYyAg5b1N9c6EPKxUpWdR/polZ84FjfWgqLnzR\n8htw/CbyurnjVDTVKOBEOylbI1nxgqcRjc6lmzUV073oM0Sy+Jjj/c8X1UqdDOd8LGpRdsYYMv12\ngGpFlvc/1buslKxqnyxO8Mw55h/db+J7z1yAu5f+kdWPb+6S8WP6MFJdN7D6uYGryJcXeAgRIhNv\n+LRNp+bXp/tk2SkLOTunPUduwnR9V71j/F17g0WT0juRd/RnR2UNzw3T2gDofmb0jMyEqEVOxtnm\nkwdA0DPNWVl4gadMlVR2reAEwdCAkKydDNMp2Fe7o9qOl9VKVjIZ83xHmwy5nOeiokmHL7x8AvAC\nD07aVpEtfDfFI9/4brFUJwx7U7kXFb+C59+bBxM0yVf82r4SZkQgtb3P7Vc+NeZi61iJOqcvsMm9\nuD9SRds564vXpkpfTbme6kEqRjb2HUsUJ5281lJITKXRDVzYaQdZK6tn5S5z1Y37n9QOmOALCl/k\nqU4ffd9HAOgRZ/3ugPJx0nzD2EKtmYRCH+m0hYyVmAvpe4eP+WccMGx/lP0yM6nVNn0B+qLFF31O\n6nj/0Rgf8Iras8sy/z2N0NQh2rV8ITm5+PHsa/Dgm/pZlfy50AbAUyQrWhTdOuYunhpDMxcG3JxU\n298vb2WRtTLsGVUTUNNcXdJSaOgkK2flkEKqrrnQXNjf7FmOsl/RyDz/TE+lN3nWcX+QPyU933VG\n7jd6V6hOHTGhKTaQLoHGX87Oae28Zn5yskOIEF4YJVed1zlfXeepHeielDiX+pX6SylZdcyF9OzN\njZje91EZwzLtWuQuBd3s07oXAKCnEqnJRLoLNUy6gqEBIVk7GaZaQ2oWf7H5QahVShYjKJw8mA7M\nj7z1F/U3LYx84uKOwJGSZSsfISIaFb+iJWrk5em+KLVVOL5Y+oFfXwWJ62X2DV9cKsaESNASOnLl\nQUsuyPw4Ak6yokmQFrM+t7+mn07RWGDJkZjK46QxRFgzwecvXvp1Vb2dtI2sndEIAHecH9CUrNqm\nQ67WcBJM46fVaYnr6Kq2BGGA4bloQdGi8Vg9OBkiJcFJO0oxoPs7MVEMEaq+9YNAuyctxGEYauot\nH9uchFL/RTv+pE3dbOGmZwDoBI0rRTynEh8DtLCbZJibjgEzD1hZq3Mtc6FOsri5sLZPVr2Dkt3A\ng5N28PFYWaY2cV/G9cUNWv2LNfyC6P5ZKwM7bdf15zTNtrWSE3OS7wWeUrCoP1ri50H9RCSQiEsV\nyTIITRhG782wTJt2H6qrnbKQt3Io+qW60ZCuX8HS7jdw2+Ikweq7fe9VbTLbMy3x53UlSxG/OglI\nvTpzci2frPZsmyrvH91vYkVvFGy0T+veAJK5lN6/WsEJgqEBIVk7GbS7pp047cL5ovb4iqfxh1iq\np8nsqP0+hfZMm7GjZGYfNkkvNRI3lowJDqhekO20o6L+qMzV/WvUOW2Arpxpocpsd87bwQnDku6k\nTryuQRioycg0LdWKhjL9KugzYRjizZ7lrM21ox/1UPeoPcNik6Mf+tpiotoUEwOqt+n0bCoNtNhq\nebVK3Xr0H5EsK6tHIbE+47t8amfezqnQfADoqyT9zaNGqU8TkqWbM4dnowWlnrlwgBGRECGslIVM\n2lGKARFl27KV07/pt6TqEiRKpUm8CdxxmcZmT7lXG3/8WBgv8DAmPwrpVFrzuauXwqHoVZsOzcWS\n+iMIA/zsxV9pmfgTJUv3ydKU0UptJYa/H3qgQX0zopN2lA8lBTFwauGFvrbp6WIJLk1zYS2SRcQq\na2Wqnlct1wOTiLnqWUfXW2OTJLWbEm6Ob9edyakvaPzROCv7Ffihj9GFUfH1aFz3lDdF5kUrg5yd\njaMGa5uWK4GrJfoEok0ItYf8pdqyrVEbqpSsanNhOaiolDPJJsJI42H47qVTaRTsPPzQhx/42rMZ\nR0oWkSy/ghRSyFnROyTRhUMPQrJ2MkgtGJmLIulITTET+c2PMxPTC31gxwS0Z9oM0xyPJkuuk7N7\nsiusJlncxKLMhUrJisp8tzcKbZ445mAAeoRMPSWLKxVrmArGlTMe+aaZZJjJyWyTOSEm16N7vdmz\nHPf/45Ga99UyTMd1DcMQN712GwCgPdOq2s1VGIKpZLUYxKUeyarnFxWEAfzQj1WgDCp+BUEYoOSV\nUfLLGFsYrZULJIsZpQ7wlCk1Icsvdb6iFjPqF/LHoQWGxhspWfUc32mhobFgpS1lmnV9lylZttow\nEKkJ4j4kkxq128yj5NVRski9IpNKW/x8OOlxAxcZK4MRueFYV0ocjDWTMVcza7YtedZEFLvLGzHg\nFfH2JiOBZnxfpWRZtZQs5r9TR93Vj9ipbS4kAk4ki8ogwkzgpmWeQT9RWKOfGSsDO2VppDIII3XI\ntuwq0kDvOU+bQQSNxl/FIBz0TiiSNbAedsrC3i3vA8DnoOj/rZkWpFNppRqREj88Oxx22lbvEJn+\n/mnUhxSxrXe8TcWvwEpVJ/NM5o7oJ5EsU+FvcVpgpywj51sFOTuLdCrNzIX1laxKUEHWyijzadmv\nqPdi37Z9MDKOoOZKVsZyVIJm89gmwe4PIVk7GfRCjswPB8B2ckY2YVOOV3mK2GRMDsztmTYtnJmi\nWsa17ROV6esTHKCbNpS5MPbJogmVdmAfGLl/fB/u4FnbJ4svpJ0s4aEuu9dWG/gu1Q98TfWgdtMi\nelDHAVpdeSQPoJuKdCWLiFHSj+mYXBL5McHNVwBUOLnyTzKctonomD5mtJDQfWwrUrKAaAGg5zQi\nNzy+T7WpghYa6kOuZoQIVX/S5ymyrFrJ2jzJorxXfPxllLJSUd9z0o7ahdP3SRkpGCSLCDiRMr64\ncwJFdSSz1fg4tJ63lYjIqNwI9Fb6ElWR+2TV8Kniv/Nn/eH3fShqt1eqDnNFMt5poc3VUrI0B/wt\nK1z10qBEyrINR72P0f9SqRRG5Ubgiwd8DkAyLtcVN+CNjW+pfiVCWOYky1SyQh9W2oqu11GyQoTq\nHUzGUzz2SclSJCsaZzT+uorrMCo/Uo0/GsvKhJnOoGDnlUmc5sGCk0er06L6jHxNx7ePU2O/5NU2\nGVZ8VztNgkgi1Z36QylZvn49a2WRd/KaubDiV5BJZ+CwfjLVJs1c6JWRjX3qgOhdoU3Dfxx0giLK\n9C5UfBeZdIbNvaJkDTUIydrJoIl/WCxN06RUzwzGlQQ7bcNnpqKe8iZkrQzaMq3apE45gcjJ0izD\nSTuGk3RkLrQNcyERl/07xmn3ie5VO7qQ+yN19nOSxaIZ/doLO4Aqx+qkjrqSlURMRZ+j882+OOFz\nUVqEOnWlsrkv0wc63g8gNhfW8Ikg04VJXJTvS1w304RknjFHxyjp/kzVO97EbMGdbmMlIVZc3Li9\nRLSJ6CR+b0QITZIV3ZP8T2qZ11qcvHpeNBZsTcny1ALlpG1VNo1t8mtrUWbV6DqRzPZYYeUkZ0OR\nHQ4c14OeVWusZFG/Ehm20zZGxIrcxthkSJ/JpB2NpJa8sooKo4Wd3qNJ7zsc+3VEvjJFr1STaNOz\npv/lavhkaVGsNY41yjNSEdW1NsnyyFwY93eSNiMiX5xsAMCr6xbBDTwcvd+n47qWtftH5kKryieL\nNm7mwq5HqZrjKa9dJ+WlhZkLXd9F0SuiIztMbSKoTpz4FZy8GuPUlrydQ4tTUOPUYyRfJYD1S9r8\n8Jl9/y2qU+BqkX9UJ3pXlE+WUrISMppCCk7aRsEuVCmPWTsLm21wzfFhmgszloNMOnmvibS2Oa3a\nO0T3j5QsfYMrGDoQkrWTQZNJR5x8Tk2IdQ5BpgXLSiWLHL2IPeVNGJZtRyZ2SCZsKHWjPdOmFjNz\nFzkyNxxu4OKNjW8rxchJ20pqJ2K3dmAdslYGY1tHa3Xljq9m3fu1U+WThZMnpeTmQlPtSXbItUmW\nCnk2SBbdZ2zLGOTsbN2M9EQq6DkcOuoQlYQwWryrzYUDhj9Oi6EOKV8tu6D9bSZ9JZKRqEC2tggR\n8Ss4BbQ6LdpiZyooppJFZghaCOj5JITQjetgKFk8BYHbj4KdR1u2tcqkxpUsN3A1JctcSGn8UH9Q\nWPuAIlntWpsAfayUDbWxYCiH9Bwcy4ETk1RT6WzPtmuKR8kvocUuIJN2VNQmLZb/d+yHlUJT8kpa\nvYjQUNuo3Zm0AydtaxuMroF1GJ0fiRRSVUk2W+wCWp0CijxAJa5rCilVfwoqcCxuLkzSH9hpW5kq\naVzSWN5/2H5R2eSTFSQKjW2QKV8pWZYWURsRpOpAEcqxlaexH9RRsvyyqlfByatNQckgWVkri7yd\nV2XR2M/bObQ6LSj5Uc46qhsnl0WvpOr1z6M+lIyP2OxOMDdiVFZHrl1rW9kvR9GcqZQWQav+l87A\nTiVKoJlEVymjgY8+tx9tTluiZPkV5WPYlmlV6iQ9Uzdw4VgZFtktJGuoQUjWTkaiZLVrf5smJyV1\nhzTJWMwR1lUv9LBMO5y0Ay92sgSiyaTFKagF2XR8P2h4ZGp7Yc3LiVJh2YmSFU8Amyq96Mh2IE8T\npVe9Q47qnkwM3Fyo+8REC1vOymm7djMUnRYt+oyafNTOL7qe7FJJ8k8CCrJW1oj4qaFkxW0ZnR+p\njlfxw0BbcD6y96EAkmgj1zCDKZJl1Mk80DrJXD+gtcVmKlDZL6s65awchmXbtezxylyofIESoh21\nY5TWbwkhJIISK1mxGaYt0wo7ZWmKQG+lF22ZNrRkCsy3K1E/lbISJEkn7XS147uvCJ6pZEX3pBD6\nJG9aqI0Dk2gr8urr5MtJO4kfYXwvan97pg0hQuZ3E/nKcDUpUYnTqq4DhpLVbmyGSGXL2llk0hlV\nXr87gD63H2MKo+FYjma+76v0oyVTiMuu9s9qcQpqDHmsbRnDJ4t8J/OMEPJ+z1k5OJZTwycrUkpc\nLUIwSjBrKllmZGFSNhFnU8kyyLxXRjHeaOXtfEI2PN1cnbdzyKQdeIGHIAwUAcpZ2cSk5g6ocu20\npam+1DbHcpT/E8/HFfVri9anRHZGFeINiZ+o43RvnUxFR/NkLCfup82bC3vdPoQI0ZFtV+9K2a+g\n3x2I3nU7q+YC2iRF5kgHdkqUrKGKhkhWpVLBjTfeiHPPPRd9fX244YYbUKnUVl4EmwctpGQupEmb\nFgOS/MkfgpyI07G0D0QvIi2aOTurMqO7QRReX/RLyFnZKjMOTUwkr5uKhJKs44nTC3zYaQs5R9/N\n0/3aHF12B3QHfj0lQAkppNCWadFI1suxg/8Hhx+o3Us5yMYT5ZbMhXyHnLNziekq8OEGXpXDrprU\n7ZwyMfihr5Ssf59wLI4/+CitbM83TSO6kpUQP51k8YWY/11tLqQ6ZTEs265dS8yFOe3vRMmK/Lhc\nRVJ1gkL9RkpWq1OIyADzget3B9CeaY0W/cCD67uaecdJJX4j3PGdO/lSP0ZlGz5ZdcyFnQNd6PcG\nVKCGilJU/Wr4wDElkPuyuL6Lt3tWxmVEY5MTkYyVQZ7lWiLVI52ymJJVNEhWq1YnGlc5K6sRGjoq\nZUxhFDJpR6m1QRig3xtAq9OCgp1X/UrPJBNHESrFiJFXHmgQhAG82ESqFB1fJ1lZK4NsOkluqzYe\nhhJDfW+l0lU+WWZkoWkuNDcYZgoHTcmy80rlLPllvL5+Ge5d9lDUf7EJjp6d2mDYOTXGi15J1c1K\nWbDSidLOFUXlK+hXtDbS3EH36KsQyRqu9XXRK6mNgp22onk3DLQ5hfu0VTm+x1GztCkalm3XlCwi\nx9H9ScmK5mrT8V1I1tBDQyTrxz/+MYrFIhYvXgzLsrBy5Uqcf/75za7bkETJj8iGWgQMGf1fxh6G\n/zPiIADRZMJ9Yvhuh+/mHeVk6aqdYc7OaaQsKiPyS0kSKXps52yrUGWfTSZWykp8QIy6ElHk6hUt\n4h3ZYUYyyFJMCDOa8rWmvwsFO4/926MjfcwszCbJIhNIYpozSVYGeXb8BpG2tjg3jjIXknnCyqrU\nFUGQKFkFJ1/lo6Ycug1zIS2o9ZQsMkeaju+muZB8O3J2Dh2ZqG+JRFHZiU9WvED4JdgpKyFTzLSU\nQipRG+N2k69Lq9Oi8l4ByS6/PdOGrJ2MJzPXEt27pppk7PRN3zXyvyGSRZ9b1h0dfXLo6A9p/aqI\notGvXAm0WFTW3Uv/qJSSxBE7MZUnSlYxPqeSTKGJksUTBPO6mhuMrJWNA1GiOlHqgDH50dEZj0TK\nvGgBbnEKmlpG/ZKxMmhxCthQ2ojOgbUaebWZaclXio6t3l9SxZK0MNH7ZaZwyMQ+WUQeomflw0rb\nsbkwaW+vQbJMZbRgEF46oYCbC0kF5SSr7JfxLEuAnLNy2njiPll83uLtTiLwGMm3+PyXjEtep0TJ\nisy2KuN74ML1XfS5/SpvFyd+9fKM1TrmqOxXtJQwGY1k+WruzjATsCKK7N2qdW/B7o2GSNaiRYvw\n3e9+F7ZtI5/P48orr8Trr1efdyXYMkpeGTk7p+3wgMQUQhMiAM0EaKXSsNKx4hL4zPGYmRV8V1sE\nrLRuSinHkTLJROJqC5apZBHJokU3caCPfu7TGoVn88i+AbeIvJ1Hwc7rKQHcInJWZCLQjxDpQ1um\nLZkojQUiIVmxudCrp2QRQYkWGnJiVxFFGYoo0s2FupIVMCXBqZr4TOdf5TtEWZvt2krWSQd9EUBC\nskrMtJmxayhZVhbDYpMapTbw4oSMGcPcUPbKselKj0Qj/51UKqX54/S5/VFeHjunHfisnHMzrUm7\n2XFBJslSebIY0TEjYhMly3R8p5QZHjr7u/CHZQ8CAPZr3Uf1Be9vUz2h+2XSGUXw3MDH3DUvqT4n\n5ZKSofqhr5SsIAwikztzqtZIFlMqktQEFdXfAJCPxxmRWjqfckxhVLSRMNIDkA9SVEaSGyprZXDE\n3v8XIUIsXr9U9atpLuRuA6bje4WNfW7WIlJKSkzUp7RhiFRq23JUpnQgGQcdlEetbiCFi/XFDUq9\naWHmQgp+yTt5TU2nekd1ZSQr9JQqp5Gv0NPabac4AarRT4aSZR4n1FfpQ2umJTEv+hWVe40CKPgY\n5y4IPLKbj4+Dhn9APQtFsjLtyDLHd3oXgehIHvLBS4I0+JwvStZQQ0MkK5VKoVKpIJWK/IS6u7vV\n74KtQ8kvR6Y88mMxzIUZy9Ec0JOFgClNLNUBNytUApeZwbLq8zTJ0KSumVg0c2EiWYdhZK600mmk\nU+n4jD3d7NjqtKAjO0w7Vb7fHUBLvIOlvFclrxz7+7RqEZLcRJVhbYjqSpFuOnGh3XtbplVzGDal\nfeon6l9SJIjgcHOhxfx6aJJzjJ1z1G5Xlc37wTQXVoLEAffAjgkYFvv1kIpUZoqV2ul7zCfLzmqp\nHeiZaEQ4fqbReMpppiX+eUD3M+l3B9DiFJBOpTVzYT9TuFS7A99QQ5jyELeB51ejCE/l+G4kbTWf\nqR8G+M1rM0EgP0VTycrZufhZkxoXmzwzLclGIvadIdD7VfRKWhsS5+liomSlLRQyCcniPjfqPa2p\nZCW+V/QOjC2MRiZtV51QkLWyiixGRwRFykdHtgOj8yOj9hr5x7gPpiL/KVsRSJ7IMyojAyttsU0V\nJwkJOX9j49soekWkU2ltjBe9Ip585xkASWCEGn+UH4z5+PEgF3p2buAyJSunNmiUb4qQt3OaMs/H\nPh/jyicrxch84Gm+ghnm/6SRLJZFPQgD9LsDaHX4XOOp1CGUNoUTPAoWobGv5q143Jx68IlaTjv1\nDjEiR/6LVPdUKhUTYVep7Bkr8clyfSFZQw0NkazTTz8dZ511FtauXYvLLrsMJ554Is4444xm121I\nouSVkLdzNfylau9qFMlKpzUyQJOu6ZOgFnArW0USyn4ZWZvv/hNHUd1cmJA7evlzVhKxx6X6MflR\n6C5vVPUf8AbQ4rSo7Mxe4GHxhqXwQh8fGnlwQgYCT4u6oZ2f6bTbmtGVLJrIWpyCJuErh2Qro5mv\n1OGv2Q6kkFJ+GSU/UY1IyQqYkuXUUGhoUiRFgsqmw3oVIfR5vzrqOpnLiODxfDplv6LViZuKqA6c\nZBHhiJTRLGwzEi3kfiAWI1n9Sp3JpG3mH0SqR4YtZonJRFNYAw+vx6cK7NO6d10ly0wfkRyGW1B1\n7K/whXqY9nnNCZxFfRHJanNakoU6VqoI/P2qBAkJyddQrNKpdEKmgoqmVCQkWCeKOTsbOW7HC2/X\nwFo4aRvDsu1wrIw6fkgzYzNfo/f6OxEixLi2vermH9OSv5JyYyWkIjkFoRwlFo3HR6JkVWJfpsSf\nsxK4+OXLNwKIEphy09zMRXcrsshJE38WRBSjOkXXDhl5sNpIuIHHfLIKSWCHV1bkkPrPYeOpyMyF\ninDEAT5AnMKGzX88VQeN5z63X43/o/b7FNpiNdgLfRS9EkKEaHUKsNIW0qk0XN9VJyyMIHNhqpaS\nxTZujPhZXFX0S4p0tjgF1q/Rxo2+T89WV7Ii1ZxvJARDB9XpcWvghBNOwMSJEzF37lz4vo8bb7wR\nH/zgB5tdtyGHMAxR8stqF5xCqkodipSshOzwnVw6nThok7xvp20VHecGHsJ4l6UpNCxPTEemXe2m\nXNO3hpspySk4LjNrZ1WCSv6dloye6dkNPBQc3eGVIsfGt++bHPTsuypFQVumtUqJocWJJlCXkTgg\nWsC5027Zr6iFhi/6XDVqdVoUsePKF3d892qYT3n/ZbgZMVbj/rpqjmoHkPjG0X2I+PUbSlbeCE7g\nvixm3hxSpnhYf7SIR+MpY5Av12dKlrHw0q6dJvswDJUi41gZ2CG1TzeZUD+4oY+3epZjn9a9MDI/\nXPkj+XWVrFgFinNVEaHxAx85O6cWp44sRfLpCqGTtpFJZ9SilPhdtWiZ6bPpDMijSKU74c776YyK\nzix6JaW8Wak0csxczf1irFQaTtpOHN+Z/xPPY9VVXIfR8TE//PghM+qVylgdm+X2bnkf899hilVa\nz/ieKN2ZSIFKJcSZ5hQA8XVdueb9YSol3EXgnd5V6npCsjys6e/C8jiggKuT9Hze376fZgZT5txY\nQaN5TlMaDRNmSTMXJuTLCz2kU5GaritZyUaPVOreSp+q3+Fj/xld/WvV58m0Sg7u5LbAo20BaMRP\n32Aw/zgtQW/y7MhM2mIXsCndq56dF3hw2LmTlHMrUbIybE4WkjXUsFmS9eCDD2p/t7REC96SJUuw\nZMkSnHDCCc2r2RCEq5zSs0ilUtq5dZWgEsv3tkF2aihZQaCSADqMlFX8ZBeuTWLxbrviu8jQZBwv\nvFqkFtvFJb5gVvx/B71hNCF5dGAtMxV5gacWvPZMm0pBUfbLLAxbV2LUBOe0JhFqRrbqViMMm4gK\nOchS/XvdPqUwcXMrVxLaMq3ojn1IXGUKcBRh5RnnubnQCxJzYcbKaAECPKfOgXEW+sjskwQspFIp\nFE02CeoAACAASURBVJyCCgoosTQA3DGYUhwUYpWOt9v13cjJV4swdREi1JQslUsq9BShsFMWc973\n1b0dTgZowk/zIz4CcOffCojARSYQUqr4OFs7sB6vrFsUPyPd8Z0IISep3ITU4hTiHFN6DiYyiauE\nqmphbGHt1ZUspYaEugNzgflF8Rx05BvH3yEA7D1NzIWZtKPIFBApmWW/gjGx6YhvGDiZT1TLsgoW\nacu0MdMSU6zStkaceaZ06hNuKqd72GkbIUL4sQpJ1xUZMPLx2VYynhzWfxTpWfEruHTuz6K+QEqN\nV33ucDQzmG+MfXIdCFiWdt0H1NMia01zIc0x3FdQEaC0o3z8NlV6k35I6SdYJFGhOfWM3MDVMs3z\nMlymgmsuFqGnqWuOlTwjmpsKTkHbDEVR2kzJsqKAE65kARHZMoNmBLs/Nkuy5s6NokFWrlyJFStW\n4NOf/jTS6TSeffZZfOADHxCStZVQuzWbXvSEJNDxCoBOEpJJ16lpLnTStlJiuKN35JNFjvLRzi9E\nmOSDiUkWV1z4TpHv1ugnRY/VioDyAh/vbIp2wuNa98K62Neh5JUT59WUrTnzql1+bHqhfgCqSRYt\npv3uAPJ2DlY6cgKv+NECtL64QWVuJ5LAd9VZK4vWTCtW969RBIX61WdmriSaM4mao+SDlTgTNydf\n1H+HjT6UHaWhK1lARCDIMZZP+NxcSAtvwc4nZCqMlKZ+tx/75PY2IrIS03DiEJ9E+BHRsNM2Sm5Z\nSzwL6GRATfhWBrafjAPul0KLJJl2aIHmiunvl9wHgun4Xo79Efm45M7Qke9fto6S5SgFK0lD0aoO\nlvYDT7WHvhO1QU9DwdMDcHNhOlasKr6rJdod37avVicKNOD9917fGgBQvlXc/MfL5vnEKozkc3N/\niSmv3LSe3Ce6xtXJkldCe6w86SpuRZEHTgY4uP8d1QOAuh//fIhQc2VQzudxnSLi4rF3KCozG0c8\n8hx0ADSf0VKcEJQ2mtF1L06aSr6FyeeVmhn7P2WtDHorfUqBM+czbualZ1TxXaWMF8wNA1OyyPGd\n14nqz+etfncgUmqtbNLfvqtOJyA4aRtFt8hMwLHaaAQFCYYGNkuyfvrTnwIApkyZgocffhgjRsSH\nW/b04Jvf/GbTK9dd2oj7/vEIvvSB4zAqnsB2Z/DoMSB+qdT5WYmcbjGzYNEvqV2fRrIY+UoSl/oa\nqVBkzVAkgIjwmObCdCqNVqcFG0rdbCKJ6mKnEzWklt+SH3pY2RcdUrtv2zj0xpNgmZlftNw/jOhk\n0iyhoOEkTakXaEEZ8IpqQszaWfSXurGmvwshQuwVH0bLoyo1JYv5bvDopDAIVT/VzMEUJubCFqdF\nd5RXZMrSfGU4saRnvtaIUMuZSpY3gJyVU0coUT+V/DK80I98kJhvDffhquX4Trtpii7kz436PSrD\nZcpeRpEn3zB3mZmzk/snCy+PNK12fK+g1WlR49ILfY1kReU4VdGFUS4uB5WSntCyJfavoXvR53/y\n8R9iRWz6Mv3KCnUc3+n/5SAhA/+690dx4PAJyFoZFUVX9itKTaL201hPNk+JaY6PP+rvsuEnxpUs\nrugoNcR3tTZEfR69v10D6zDgFXFgnGDYURs0D+Wggg4r8nPjEcgcfIxzJZCeS9XntVxpuhJDKUHU\nOxG/Jzk7iwE36e+vfehUvezYJ4tUJj2K1VPjy2LXeSQfECmCvZVebczUysOVZXNvn9uvTHymkuUF\nydiPDtiuNhfyBNGRKjaAgp2PInrjzxNpNkmWazi+009+LJlgaKAhx/euri50dHSov/P5PNauXdu0\nShHe6lmOBWtfw+L1y5pe1s5AydhNUbi1H/joLm1UYdN8Iit5ZUXKtBQO2mRiVV3PGIoLJ19AtLM1\nzYUAsF/7OKwvdauDWWkBslJJnp0krNpWk7ob+GriG5UfUTVRRp9nWet9PRFqdXRh4iQdHRWSKFnk\nSE4Kw7rY8XxMYVTcf7zd8WJmZ5V/V787wHLUOIpIBqz/tLw85M8UJw5MpVKwYt8XbtIiX5laSlbk\nPxbV5+lVz0bjwMqpHa8XeBhwiyrxJjfDknms1WlVJroBd4CRtSS3UCVOcsgXJ/JdS+qULIrRs3Nr\nmwsDP1kI0slCQ+H2VCY3Y3O/G24eAxLfIX5/7gwdPY9MEl3Ix1k6o9rGI/Z4P1X8CoZl2jA818GO\nL9FzfSnHd5f7ZFmqjZG5MCID49v2VeUo4hd6sNmiCCQm7Iwir4lpjtc1IdQVtknKMDJV0VIZaG1g\nPlnU917gYVn3GwCgcutZTMV1fVc9g6ROOmniCg1XshI1LlGfJr3vcO295u8vPSee04tIEUUm07xB\nR0CZebIUSU0lpF1TZNnmhlKbkB9fq9NiZIhn7g8hNxcmKmQlcFXyZFJd+XjiBJm/p9yVgj8jPjdR\n28jPkh9cbce+kImvICOpomQNOTTk+P7pT38aZ511FiZPnowgCDBr1ix87nOfa3bd2Es4NAYePzYF\nSCal9aVueKGP97WMAaArMXzy0VM4JApUgEiJ8YIkm3S06CcqU9nYCTtpB71Bv5YKAohyFS1evxTv\nxKqUZfpDBNw53NGULPJvyNv5JDt46BmLJV/8WMQUyysT/dQje7w4GscNXEayMtGRHPHujyYrXqcS\nizpURM7Xo7jKKcpeHSDww/i6vhOOnMPdpIxUGr6WxycxwbkaydL7b8mGZMOQs7OJChl46PcGVEg4\nX4D6Ymf9lkxBRVv2uf1V6QSifo38/iLTTkKC+KJIdeKO2+YRLNGz81VUHY8upP52avQ3dxpPp9Iq\nOaYfm1azPLVI6CMf3+PQUf9HlUMBETwIgZ4djQOqK88CTj5zADTloWaEn19Kgjtikp2xMuh3+7Xo\nMfoeqZZukERtquOSYpNTYjZLxjhXQ5J0BmVNxciw51BSebhyLIUDX5ATJWvALSpVb1ROJy4UTafe\nd6u2ksVJFimYvG30vvW7AzjlgyeqZKWUYZ/aQPcqukUtt1XUf1lt42EpM3ZCaEp+WVkrbKZ2c1Mb\nj3rdWO6Jkzq3qXtFaibzF61hWlem3lh1K7oDmmM7971auP71uP4Z3V+Vqfy6/10ZI+PnQP1H5dL3\nqZ9ChOp/ym/OODdRMDTQEMn6wQ9+gL/85S944YUXkEql8LWvfQ1HHnlks+sGMyx9dwc3BQCIz+5y\n0TnQBQAYW4hIFt9NlfyScuzUfbISgqKSZYa+lm+LOzDzhSYqI3JS5VE6Ud0Sn5WozMRcSGXUO9ak\n6JZUTi0+geo7vxoZj9P6QgMgidRx8kpeT/wn8nFbon4kJYHnhaI6cbLGzWNaNKeXmGcJ3KeDFli+\naFmxCU6ZJ1hGZ57vqGryZiQkb+eYX005MkfGShXvpyRlQavyUetz+7XxxFUjs2xaSKkvnLSjXedK\nSVZz7PcVkcvbOeULZSpZXDkkE+CPJn1P3Y+nFtHMY3FyXAD46kFfUv8v+xV1ULKVshRZAyhnlKuu\n24ywVYKKIqGaQsjIP8+WbvodZi0H3aXE8Z3uzX2pvMBj2bujOtH4o37lEWfakTcsHQQf++SHpJsL\nc1qqAVfdJ3l2buhVj7O4zvSuJKZN3XwFAN87/JtYUVoeXfeSAJXj9j9KM30HYYB9WvcyTOieUrn4\neIo2GHr6F2o3vdPJnJLUyQs8pTLx+c8LPDUnJY7vkbmw1WnRlHZ6RlQXTrLKhpJFEaB0KDqBvvPK\n2kV4Y+Pbqv7cfO8xpY7aXo7f+YxBtJX/YrqawCoFlKmNlD9QMHTQEMkCgH333RcjR46MjqPwfdx3\n3334yle+0sy6sUVgiJAs40UnE1KSZVl3XnVjp12lZKW5WUs38wH6mV6aozxTuIhMKT8dX7+PKju+\nnkxizBmfmyqN6ELlk1DDXOhoPlmu5ldGCxMlGew3MpO7AQuRZkoWAPR5/XGbyHejOplmJPlzU6UH\nO16oSfHjiQYpYiqdSke+SYHhE5OyDJ+sRN2oZy4EEvPBJ/f5uDbx9hl+PZykJubCFkayBrTxpIgA\nDzRQZccKlKeTI+60y81XtqGkUr3Uomj4ZJlktD3TrkzfZGrTnkM8bopeSS1wSk1KZ6KIxyDKKl9d\n14oy29Z61lzpof4rKpKV07Kl83EJxKZK9uzSTMmi/tMDB6Kf1SSr2pHdjC4kP79kgXW0z1M9aSNm\nKtFm4ArVhdpdtYDHP0klPHTUIZgwbDzW+lFKlZJfghvn1fr8hMlYHTvzV2LSpBRt7Wgv01wYO76T\nYpVOfLIAqFQdSkmN70W560wy5QVkLqSymaO8V1Z+VEBCsgZUklVbS8fAk51G/RXVuae8SVkQeNlr\n4lQz1Hd8Q/K/K2fHZaar1ExTSSW/STO6EEgCOJS50CKTvx4BKti90RDJOu+88zB//nz09PRgwoQJ\nWLJkCT7ykY/sNJLlDRWSxXxogGRyMnc7ajcakwqaGPjxL+YiDsSmOU6yeBRcqC8oJFmXDcdMc0eY\nmAu5n0Qtc2GUMVr5NjDlhtdVTVZM7YlMJsnuH4gOmi44eRX15QaempQKzCcLqFayHOZYrStZ+pl8\njlqoeTJSnXRS3iFlGmHEwg90x/eoLRkUy5uqFnDlzxQ/671axmr9S23g0Z/UfzzijBIg9lX6tN05\nz7dl1pWbkLR+YoS3XMtcGJdtx74n5n1MnywvTmLL/U+ysQmuyII+UqkU8nY+dj4fpt1DEZGgoqI5\no7J49nN+PV6Y/CjLftaqNheCJbrkShYRbTq9gsYHkUilZKlxRmS+tpKVqBiJCkkZxYdnh6l3pczS\nRGjh+yxbOg+OqbBIt4REOtohxibhpbmDO3oDyRmV5LJQcBLlulZ/J9Fx1W4DprmQ1CFSRXl0Ia+T\n6YJAgQP5KpLlww+TbOl6niwXTjralPL/Fb1SkhuMjZlaShYAzawelU3EMPEV5EeRrYtz/gFkDjf9\n8mq/cxZ7JzLGs6hKsyFZ34cUGnJ8nzdvHh599FEcc8wxuPTSS3HPPfegUmk+2yZz4ZAhWb4ZXagr\nA/Ri0g5aqRuW6ZNVO7WDpkwxh26e8sFUVgbMSDHKuRXoJEsro6a5MFKy8lUkKwnp1s2FiYnKSSf5\nn2jR6I8Pc6W2uMxJtUWZC/VFzvTd8APd4ZWHVbuB7l9F/cT9gKJ72XpeHivpp8gHRA9XjxbFSlUY\nu2OqSfF9SPUzd8I8BYFZduTk26/5mdixOmSqRrwOZlSg/iwSpUQnzolPoEkUbbWgcCXL1/xPyPyX\nZPSOnl3eyWHAK6rjhxK/qMSkNuAV1WG+3Jxc8RMyQOPSjOJy2NzByybzXMkrwQ1c1Qbe92RetAwl\nq88k83GdBjajZK0rrkfOyqHFKWhKVsWvIIUUI2zRET3mRsxO29q7kihZNJ6K6nP8GZFqlDXGE2XY\np/7kZza6vltl7qIjoqo2W4EemczrUPJ0s2BiLhzQ+pXuRUotDwiiMnTHdzIX+pqaGZWVkEsiK+Sv\ntancW9MnlcDHq80Ue0LWzrC2ldT1UfkRzPTXb9zfUH2NdyL6jk7MkvdRlKyhhIZI1pgxY+A4Dg44\n4AAsXboUBx54IPr7+5tdN02iHQqoNYECbNEyZHHThFQvTxaPguNpESj/T0QeDIWmyvSjEzylZBkT\noq9Nrom5MPLp8Kp2o26o5+LSslurLOORaS7Kwhz540SRdgVVjuu7SR6pOkqWIoTMf6yvEpkdC05e\nU7IiB2Z9ofbjyEnqt+h/6SgNgOrXxCfLZwqhWiwpVxAFICgnX1ItyWlcn9jNSKPEB6S67LydQ9Er\nVSVYzNpR0kfubM3rkJguYnMNUyu42ZhH//HAC3Nhd1SerGRhivIacSUrOl6JHKZJ6YyUrJLKQZZW\nCzKpDxUU3SRdh0rxER89k5i9TZJFTv3J3MEz6UcqWg4D8RmF/DlkTZJlqGu0kCYmTCI0eiAAP0t0\nXXEDRudHKNOznY5ycfFIVfoOpeWIkn4mztA8hYOp0pjvr9o8Geoa9R+RLxpnRLJKhpKVqHplrS/o\n3TBVc16HolfS3iH1npK5MJVsYIBqdY3aUA4qWm4urgRGZltOsqKySj5TsqzoSKvuco/mEM+fUXSN\nR/7FbfATMpVlStYAM/fn7XyVf5XDzNgppGr6ZNGzIDMpT+EAJGq+YGigIXPh2LFjcdNNN+GII47A\n1VdfDQAYGBjYwre2H9zZcCjANEXVMxfS5GOakBKFJtAmODI9eBr5YoqLlppA3zVVO43HSpYyF+pO\nqp7hh0TXya8sb6QgME1qPFEjJ4RAkvG47Jfhh74iU3T8Cz+3EICK1qrXBj/w0e8NJGZHZh6r+BUU\nYr8hdXZhEGjRY3RPflYaX9x5f9PCoZ6p4YtR7RdlTPLxxpknm4z626tW0dJOHPmn+5lkrSzKsQM9\n/Q0k44YWzGoyEDnX5+2cngwy9LXAC7X4GSaqJPlr5CRN/c+/s8nwuynYeRVVBiTjLFmA+uGFviJl\npv9YokLGdTLIAPcdGmDmwuhnHkW3CJvlI+N1VRFhzFcLqB5ndN0knVS37tJGuIGLEXHKgqjPKJ2G\nr4hM9J2MSlLKEwxHY3+Tdt5l1D59PJnjjMiAqdyQyY4+r5QsP1Ky8lk911ctc1cyp9A7oW8kSl5Z\nGwM0PpW5MDbPq81kxTAXqo2b2TZzY8hIFjfHsXxfHdlhWFdcDzcYF39Hf0b8vnoZ0RgYlR8Jh5nQ\nEyXa1n7ShjjLnqmTtpnptLp+vYq0689oqIgKgggNKVmXXXYZxo0bh3/6p3/C5MmT8ac//QkXX3xx\nk6s29HyyfNOEZOm7UZMkKEXCMGsFBpni0WCmg7uVtqqULyCZXDfF+WZMx3ciFaa50HR8t4zJPlvD\n8dgPfbWzrXVOm8N225XARS9z9KY2hgjVIkfm1noKg8VIQn+lX92HJ2Tkfj1pw6yq75CtqnxRyXU9\nB1jUBtNHQ18gaipZNXa5nCRUm4psBGGgFlJSAOgQ7y2ZCxMTVaL2bCh1qzMNuUmIB17wemptCFPx\n+XS6+hm1I/qd1IoCI1lAYoIzTXOU/JMW3iRooaI9IzutvyvV0ZyR43uG+Q9GSmCxJqHm96qKgjPI\nF+VdIyQOzHobWuNNAX2Xcnpxc1fGIh+rsjb+aINB9xoWH3psbtBMJSvZoGXV/aPP6+OPfLJMJctO\nRUqMUj8ZabLiNvCIUX5PSqBMoDqEcaqZJIWDoWTZWe161SYzfh6myd2sn0myyn5FpUGppWRZNTYF\nZPL84gGf09tmqJYZY7PKn6lTh8jRd/qUiVGUrKGMhpSsqVOn4tZbbwUQZX+fMmVKUytFGGrRhSr0\nV50HSJOSbupIlAdD4dLyZCW7SDtIVA9TLSOSYDq+k18TJRA1TWeVKnNhdRl2OtnhmeYa7ZDX2MEY\nSCbMCkvImGETVp/bj9X9UWTTWHUWnD4Z81w3tfopiUJy0e8NYHScpDRJhBov1PF96XBbPwzg+W6V\nI+yAlxyDQuYsK/bJ8mv4ZAF8AtWJjukDx/8X9YHpk+VV9ZNTZ3GiM/aqzIXGeDIX5N5KH8p+BcOz\nHdrnFak1fGV4vV9c0oVfP7gQrZPSjOhUL1pKrXDIXBgt7v3xdcoXljEICqmZ1C8DXjFOpaE7eleZ\nC9ncUXSLWmb5XJy3yQosRcD5dxOSpZvvzUgxOt+PkCy80f/p3crzFAGp5HDgNvZ9Ts7NSDQ/jJIV\nt7KM/6Yyahvvb+L4rqskZiQkbbYGYh81qgedRUj3T2vP1IIXRr5u/DxDnjaD93eB/R7VMR33S3R9\nY2kjAG4u1N8huj9t1IjocDWK1y9rkCwAWBefRGAqzrwvo+txv6p0J+YmSX+HkuhCqhM7P7PORorq\nRxs0U22UXFlDCw0pWaVSCe+9916z61KFJE/W0Bh09cL6addkZjYuevqLXtsnS3d8rwSuSk1AZdRy\n6KZdeE9s5jMnb3rRazm+c/VGLUxkrjGVhNgZ2jImpQpztub+TBW/glV9qwEA49r2jv8fT7rkIGtl\nq8rg/UYLb1+lH0EYKPMiLSADxm6UvhNls9fPGVNKlvIfS4hLwJLC8jbwuqoJVDmN66Yl/l3+eR5J\nZR6p4hjEhRahnB35P/WbC2zKXCB0k+Ta4joAwPBch3a9v0bgBY0rIFrkHno2yiUUBmkWkcr6L60r\nWbSwZllYfzqVTiL8TJJF5kKrNklIHIyJGCWKbDqVjpKRBhVVHqCbwjRCY/aTYbo1SWqbQbLMdAnK\nhM5IlmNF/oUlr4y8ldSJk0hOZomwrSttUISBt7/Kn5OenRFIYY59SjxKpFf5B3Fzl+VURVpGv0f+\niFEkX1aNCZNMEgpMyUshpT5P7yW9v2psGOkmOGmi1B9UP4JVV8mKIhApKtBUcfk1/ju9c9Vzsj53\n2GlbbRDMOtVS88z6RffSzc+/fuUWCIYOGlKyNmzYgM9+9rMYOXIkstkswjBEKpXCk08+2dzKsYVm\nKICfeQXojqL8OhGSZOdMu3Z+rE51hB+ZC/XJJ10VEQhAyzHDr1cpWUYy0mRyjQ5zrY7uqvaJ8cLk\niBddyfJga/4nkbmwayA6sul9hSjNQSLJ66SilrISlR2VtTE2hbY4eoJPtdCwHSwFCLiBq+3Cydxa\nCXTH9CTVRe2otmQXHpMyqzbRAaCOEQGSxZD61q1jLqQyKMUF7xcyASef101F5jNaHy9AHepw3dqB\nF6lUpFrwiDbPj6IDU2FCsmorWUSy4gOL47JNB2ZqQ5W5sI5ZRvkBGU79UfsspQTSGXeAQSRqLLBV\nJrg6EYwZy1FBCPzzVFdSsniySyftYIO/EX7oa2kC6J0NwkBlPOf3ApLnw+tQ8ktw0rYiqUn6FzO6\nUN+wmrm+1MbDKLvfMOdGZVuoxBGj/F1p0ZJ6Jp/n7bcYoW5h5AtIxlnWyiCFlOo/TkqiSNxqJcvS\nyD8nWdHGgUyV5mkH0Xd1AslhBomYz5qig6lfOSHkZfDrvD10Ziy/p2BooaGnesstg8OsKaOzF3iY\ns3oe1hfX4/gDjh2UuuwIVJsLazu+0y5rwNX9J5JjcgJNmeI5rLivERBNjq7Hs4BH/zP9SUxzYXWe\nrMRcGJ3R1aJdV2TD8Lcww7B5tm+eRgFIJiJykiYimJjHDOJiTEqWQVJpgVCJHS0zD5KuWJEypUf+\nkZJVO4Q+abel/b/KXJgyFjOultUwJdB3K36FLd563/ZV+lTeKSBR+HricydNf5zumLgkR63odTId\n5fuM/qP68d28IllIV40B3i+bKrqSpauFyQJJ7af8UjTOEiWL/O903yH+Dj35/9l782jPivJc+Nnz\nbzxTn54HmoZG5lEmBwQRFIcb/ZKriXpNhJjEIQk3EodoMFFzMxgzmOHzfmbQoATHJBCTXBRRBBRp\nmkFmmu4Geu4+fcbftMfvj6q36q3a+9d0soR1OevUWr36nH32UFW7dr1PPe/zvnXPLkyMRKAktoMs\nNgzbs+lmbO2VfZy/u5FwpAScVV3VDgW6/0iTBZRzMek6lZlNAGjJAATAZkzKbbD1TPw749cQmO8m\nZeBSlSKBntFNeojzFOOMXeNzCh/TFCEqjjNBvCcCLSiNB41fAeZDI5HrjbfvwPKxOkIvrARZJmtU\nDUw5E2u69cqLAvv3sq6R9ZOr9xzkdaox9ioyfq4eizbDtVQWRzkqkHX33XeXjtVqNXQ6HZxwwgk/\n8UrxQlmEv7/7B9jXPfCCBlmZvc9diVkRx2nyJRdLtbtQuwXNHFamcNt3fGRFt5Qcs8mYLNdxSwn/\nVJ4slfGdRewlHayUWZJtA2SnfBDb6qQlV4qdbBLQhmPBchMMy0VTXnWazMPAjqYb0t+ifa6KhPQt\nkJpW5MnSbKPNZNmuPBMQxlZkHu8r+zjlmKI8Yq4lxI7zxDBs5BIjN1Vk9fnT87sAABvkxsflvFem\nQdHuwmpjEXgBUrnXo+t4SKWx5IaUfp6VAM8G5+Ja0wUJAAelhoaE3mQUFyx3oc0keI6LL31L7A+5\n5uUhuklXaLjcYcCqQvScVbtVq7J303fEv6HANY1lPah2o9XZ5tgm8GMuzCFsCGeBjpSGgmfAd+Ao\nRsd4husrQGiL7lW9rX5KCsEQ1mVSXcBkx7l7sSozOyDeXdNvlFzJoh2R+rZCN8RXpFt6wyvK84W4\nLxtDFZosuw3D3Hr8HPG76ZauWiQFXgCk9LN+Nv9uahWuYfvnwALCS2VxlKMCWbfccgsefvhhvOpV\nrwIAfPe738WKFSvQ7Xbxhje8Ab/wC7/wnFWQsn37rmd8SC/Eovf0Eh/yqFxl0UqOJmCabJT2ycpM\nTposOxlkP+1jdjCHTaPHqGcKd6G5STOAyv26eN3iEpNFoeEiCokShdqMTmAxWUkuNFmNwAQhlJsp\nqDAuC0kHoXRHAqaGhvJ/ieMWyGJ6HDqftymyWCZbi0H5dwydTknYGhj3HKRHdhfazJe6rxdg6+MH\nsXndqGF4uYGIvAidpCMy4g9hOmyWCSiDLG6cV9QnFXCxtUY2yNJJIvkzzPdFTJYHSxid5ciyQo2H\nfjZAzauV2DjANLx8CxtAJ5RUWrekrNMJGMhy2ZQW+RH2dw6Uzg+HskDWO3LN8VS92a/JAIv7m+PS\n0GSxdtd8bnir6xQNMchcsD/snqIu4hobjNpuUjvSza6T7VKj+cFwFxpBBNVj1LP6uBk0SgEc9POs\nzMnpwQcgfnGK6gVJ1RgCoPaytOsUDBl/5Qhak8nX2swhgSvsOG8PpZuxzxn281JZPOWoUMvBgwfx\nT//0T/jwhz+MD3/4w/j617+Ooijw5S9/Gd/4xjee0woKej1BxvbPeqEWe0+v8dqo8XcdYn5kvRTl\nyVL5aeSkv2thDwoUWNtara+VyTHtPQpDr9pol1I4KCZLnDNn6ZxUnqLMZrK0ezEpdHQhaYhUQsYK\nyn8+XjAmymFMis1k6bZVC9xJRzY3MMO56Rq9mmfHh4AmG8gFro8v3vwYduwW52n3gcnGUTkw3kDW\ndgAAIABJREFUNcBffuPH+Mjn7jLrUcFkxVli6lKM7NHl1TJpWWwmCxAupyzP8cCTU8r9HFuGl7Ks\nz8p3PZTJcn3FZHnQz/AcD398w3149598Tz0DANpDDJ47hIUAhDuOH7cZQl5vQLgtVV3lPoiAaRSH\narKGMqMm44fCxY137EC3z5nYovL+gHAX3r/tEHYf6hgLAw4+hn2PBsPFfm4NATT8/di/DweXZdBo\n/2wCEebyG6LJ4ue7jqvaas/hnJGLhowzp2DvJS/rF+37Gq5ht/q7GRbdW+UunO3E2H+4bxyvmiNE\nvfm8ZTJzVIz3PqS/l8riKUfFZE1PT6PZZG6JKMLs7Cx8Xwsu7ZIkCX7rt34Lu3fvRhzHePe7343j\njz8eH/rQh+A4DjZv3oyPfexjcN0j47zA9dHL+tIt9sJmsihf1PY98/jardvw9tdtNP6u3IXWROk7\nPp7YNYMfbd+j7kNuwX6c4pGnhH6FgMCa1ip1Le3sbru1+KRUNZmScQottoz0Pk2Vw8pa5VuGiYTH\ngcEkhBhYmbt5PQoUpmH3q3/2DeOqo5ZKe81Jw+Y4Dhp+nTFZbPXrhsyV52Pr4weRb5tS9TvQPaTq\nLton+0OyRm4e4Ttbn4A3OYVwk7otE2ibxiVNxHez0EuMv9nMVCL3XRvmHqpV9A2BI53+wGQnbrpj\nJ268Yycuf+mkUSe674QF/kMvxJ985T6ccdyksSLnmizfMY3W48+IsPxeP1fHW0HLOIeKW6HJAsQ7\nJWA2LKCA/w0AULigzK6261W359kNLNfv2O7Ch3fMYMsPeti+Zw6tk0zDeHiujwPTPUNr5CPCn39t\nKwDgJa/X59f9On7w0D6gAIKxanehyWxWM1lUvz+6fiuycB7QuU/R6xX4vb+7E8evG0UwwcdBdbuH\ngbphyT7rfh1plmOuE6NR4/nAzPFOGf4jC0Rz9ouPA/79OzkbK6gGSsPqN+xdG8cpl1s3Ri/RYJmu\n+c2/vAOp00f9bH08GApMQ7mAsb5Nw4U5ps/3Qsx1YmzbPYuJVUsgazGWowJZl19+OX7+538eV1xx\nBfI8x80334xLL70U//zP/4zly5dXXnPjjTdibGwMn/rUpzAzM4M3vvGNOPHEE3H11Vfj/PPPx7XX\nXotbbrkFl1122RGfHXgB5pMFZG5grIpfiIXyRf35V+9Hp5/ijnsPqz3wAP2xe66nRM+AcIt98otb\n4URd1M7QmqyGX8fn//1R/OiRA2icp/UWFFr+z9/fjgODGHDL+6sNE3vaK81RFXEmziEDTgkWVeRa\nVh1K3k/7KFCYRs4NMZDb8JgrOQYehugZhjEYvqs3+rXdgvy8ZtDQIMvxsevgAmqBV4pg+stv/BgA\ncMl/E9dun90p6y7qS1oPOt7rSAORm+DVBn50/yTTk3mecZAR4dq/vQsnb5xANBmpdkzWlxn3fbZ+\nAnj6Ax7x5GPbbqGPeuypOWCNPr/TzXHlH3wHV/23U9Dw64oJ7HaAB7cfxoPbD+PC15psSJZLTVYR\ngCLZ+RiaW9CRwW0u3B4qfNdtaAVNtqdhNaMIwIjGQ6EXfW5hsleDJEOeF5ZRrAYb9tgCtJg8i8Xf\nHnhyChefYhrGa/76TgDA2IWBZndRzabUvAifu+lhAMAvvkOngzCZTf7dMCYr5O5CcfzRp2eAoA+W\nYB7P7Ovh0Gwfh2b7WH8Rb1OA//Ojp3Huqast8FENXrnGakN7LR6aehSAWBR847bt+I+7nsZ73niq\nOoe/a4C7riPMLgzwt998BFdccEwpwlCdz92e7JtymbuwMYQ54y5WwZwHcp/KYQBNHP/1z9wOOBnq\n5+pHe44n2NqSG5GDURO8/dlX7wcAvOnN1UyWiEqtqyS5f3j9Vuyd6uKqn15b1RVL5QVejooaev/7\n34+rrroKO3bswK5du/CLv/iLuPrqq7Fx40Z8+tOfrrzmNa95DX79138dAFAUBTzPw0MPPYTzzjsP\nAHDRRRfhzjvvfNZnC4Mk3YXuCx1kiXxR0i4hywoFiDzHM1hBLopVk4M0ICJVg2CyfrxdhN+bWgUf\nRVHgxjt24sBh2nDZdIV5TtmgpFlu5HwBgF7Hw6/+8a1Y6AggOC0TBzYDk2FQq3aluTHTAHBw1A5b\nmB7MGNfbP4ccPFTojox+sdpju0y4a6RhhZlf+7c/wgc++4MSyKKSJuYnQuedteJ0fb7jYXpWgIki\nr2ZlDLeIGyKO9Qa0TqGvyRIPuw52cPPdzxjX2/VW9/KrQVbdF1sJ/eudO/HM3p46HrgBaqF08cbm\nqv2ZfWKM/O2NDxmC4YUFPSayxBxnVNyiDEoAYHZB57gb5uIaxkKYwNwEM7bbUhXWl7xOoRfig5/9\nAd77p7cdgcmqXnjYLqQiqQ7T54VH2RYMRBt6OiYIjyoYXaq3flaIp/fP4/pvP46aq681wHVuvp/Z\nBb3ZMGcbe/0cX/7ONlzzme8PZWXCIUzWcaPH6jZ4Ndz9iNC93fFjnUuRdg+w2xF5IR55ahoP7jiM\nT/3jvSWQ9cOH9+FPvnwfAlZXL2esFuMFRiINTD13uMvZzkgP2Bo4Dws9OU4L83uneQ+5a8yN/pB5\nK7BAtF0HKm2aPz0fe6fEdzffybFUFl85IpP10EMP4ZRTTsHdd9+NVquFV7/61epvd999N84999yh\n15J7cWFhAb/2a7+Gq6++Gn/4h3+oc6Q0m5ifnz9i5cbHG2hGNaRzGXJkiPwGli9vH/Ga56P8l+vg\nFgj9AIknV+eRj3atienBDALPN+7bqjUUa7Rymcz1konXlbkJ0iJFPYrQbgToDVK4CJDLEJfJ8TaC\nmvyoJdXezcSHvHrFOFzXRZ7rD7oeRpiYaOKNH7gJZ5zUBmQ1HDj45u37sHPvHL63NQHGgZlYgKPV\nyyaw42AHjzx90Gji+FhTtSNwfXSlzmmkoY9vXr5RRbq16nV1fPSANsL8/JWZptdXjCxTx+sxm1h9\n3X95bqanWDbWUn8ba44Ac/IZrQYAMbm263qyH2nwid/8RNatmkToBZhY1oDzI8Eetmst9FIJWJiR\nqweReq7b1IxOLYwQ1pnrp6ZX/bWGBpSjrRYg7BcmWiPqXuOz+vzxVpv1kzZsI1ETUSPCN27bDqfW\nQU1iwnajgbAtJv8kMQH1imUjAMT7Wt6eUJn3k5S1KdLGfc1K/Tzf1QZltNUAIIDdXCcD5OuYaOu6\nLktYtnP27sYy3f56qPsvYccBYMXEKCYnRT80ohogp5JmvQ4SSUcMnI+PtDDXieXP+tkTIyN48OkZ\nnBb4mBzX4f419u6yuqnHGfQDABk818EoySgcx/h+Qz9EJ+2gGTbQbOt6tJu6/160ZgPoBU+Mjhrn\n0L0me/qek2Mj+ORfbkGaFTjrRB3V1240VF8gY0Y+qGHAGNOQuXpbzQY7Tx9fNqrfEf8ORtsNbH3y\nMHYdmMcVl6wDBFmD5eOjcBzRP2Gkn716fJnZHzLwpR5FyBkgmmjrPl++vI3/78bvAAAuOV300+rW\nCqwaXQNAzBc8cnPjqlVY3pTjZl5/8ysmRuGGPvZNdXHKpmWoB5HYmzMMVZ1WLddjd6zdxEEFRh2R\ngFh6F6Ce5wiZg2QnV06OYrkcR626fqfj7D0uG9VtW73SBJ0TzVEc6B3CocEUABGo1Go0gMNYKous\nHBFk3XDDDfjEJz6Bz3zmM6W/OY6Df/iHfzjizffu3Yv3vve9eOtb34o3vOENanNpAOh0OhgZGTnC\n1cD0dBeQq8Be0kc7aOPgwSMDs+e6LF/+X6/DIInhFi6IsOp0YwRNKaSGZ9w3ZC6G+VnxYSPz4cLF\nrpl9QkSfOQh8V/2N3mZnPsEDT+8HABSZTMzZn0PohZia6uDRp6axfmVLh3TnLh7fITRH9z8+hfo5\n4j6tsAlJUGHQF5Um0XhntsAffuGuEr3eXUhVO0I3VOcXqauOLw9WqPOL1MHBg/PI8hxxT7M7bqb7\no7+gAeGoO6aOkzsVAAInNPvPDZSgu8fq5Od61dnv6ucVqQYcKWN45uY0E/PqY16JR5+YwvY9c3jJ\nqavQCpqYTxbQ8BrYvkuATw6yPCdgddVt8BHg0NSC+j0f6Do9RfcBkMe6Tl6u79XrsHonLnbvmcG/\n3LED6zfqutbcOp58SqRBoDEAAFkCEJXaZe0HgKSn61iDNhz7Dwz0OQxvTE9phiyLXdCQXWDs1YGp\ngQJZWezgwIE5/PtdT+ORqSdAj8hzqLYVRaH0TE7hGsd5CoLeQoY/vf4e3Lp1N057lX5fHfbsRFfb\naNvMtP5DZ8rHdTduRbsR4H3/Q7trXLh4etc0/uyr9+OY9eY0eXhK3yvuyz4sCjyze1odd+Q4qLkR\n9h/Q47Lf1dcGAw1ierP6/umgUO0eMHaj30lVoMH+g3r8OJmH3XvpBo5yjwVOgN379bNzNsanpvSL\nHAx0//W7mXo2+7zQ66T431+9DwDw4hP1vJ32HAVeFzoxILFeryu+uQeePIRGFCBNRD/FcYo9s7pO\n8wv6XfDv97T2WRiPmnjFupfi4W0d/Ty2+Ucy7+BgV1zTZe+9t5DhPX/5HXQHKT793pfK6ESgyMVc\ns3x5G3Mz+rm9bopb73ta/e5LkOXAwb79up99hBhAXNedS/Hk9BSiwDPmjr37dF3nZqvbBgCvWnsJ\nHj30JE6bOAXb5bHDhwdYKouvHBFkfeITnwAAXHHFFXjrW9/6n7rxoUOHcOWVV+Laa6/FhRdeCAA4\n+eSTcdddd+H888/HbbfdhgsuuOBZ70N0fFosDndh4AXwXPFR5nmhNmilvQWp8C07tLvCQeQ2cEBu\ng7Jz7hl4/dPEnxjI8l0f0/Pyg81NDcPjz8zgj/7xXhy/dhTOOgdFUSBwffyv6+4RJzG6fCRsI5Cs\nW2qxHnOSDbLpda7dCLwARVreOJUnCAzcANfd/Bi2PHoAV7xOP8OI0mEuMdqH0OwXff6Pt0/hwHQP\nkdyfzj7P2OKjcAHIZJp5tXsoTXX71rRW4e///VFs2zWLLC/QCOqYTxbQCpo4MC0Ah5FCwMqBQ4ko\nQy/AIGGgK9dMh3JbwHQB84SO3O0xFo3gy9/Zhlvv3Y3jDjjAJLWzrl1FPCLLDdCTgK8fZ2gwgXbB\n9EzcPZknzLXHwD/psQANKgAgcGoAhHGK40JdEXkhnt6/gK9990m4rVlEJ8t7yjFz+wN7MdIMEbqh\nzGSux4zjOKLf1ObXEW7dug0AkDIjx921PCrNKQJQMqOEgde2OwFgL+a7Ce59bEq3wQ3wvfv24Ild\ns3hi30CJniMvRByT275QoA+A/uYAZdgbfh0xe9dxytzETK3h5fz9DnEdutV9H3kR4iRjv4dyH8LQ\nqBPXM+XM7QvWZ+RG++Mb7sXC6AwgPz3h1hP32r2f3zNAnIr2xUmGXz3zXbj5qVtx3qqzkBcF/uyr\nDwAATn6VOL9AoUAZAOQM5+eFbtO4P4kz1wm35CCZYydV57ritiHyQnQH4l0fnuvriOdh0YWOp4KH\nxL18II+xrD6h7iPOY+/CqeF9n7kdq5c1cNLL2MKNBXqsrR+Dta3V+Onj3wC7nLTsBPzeSz+CdtDC\nv+B7AIQmcqksvnJUmqzrr7/+P33jz372s5ibm8Nf//Vfq02lr776avzFX/wF3vKWtyBJEsP9OKwM\ny2fyQiyZ3BdPYizkeaHSNdibYNM+coA10TraIL9s7QV6wuIgwfHR6cv7cfeVX8POfWJFtW33LAo5\nqTnwMEMGmYGm5fVJhIFMKhqbQ6VINPAzMnwPieDhgMPcyy3ArVt3Y76b4OEds8Y5eV6g008MbcOq\nhg60oA1jAQ3E/vQr9+NL33p8qECegweH5XbKh+hmUmaQa16EQzMCTP14+5RKJzAajeCgPM77j7ef\nIhsBLcJW7ch0++a72gD1Y8ZMDYmq3Dy2CU9KIbvDWLpm0MDMQhlo+66PgdSDFYXZVs54ha4eZykb\nml6h69FnujJkbIwyFqwoTI3avU8cLB33HBfbds3i7/7tEfzZV+8H38qkKAo8/swMsjw3QMYwbV6e\ns8VAztumn1ekug2TNQ3a/8+Pdhv3VN8Wq2s7bKuISvE8DQy6fd1R9G3V/BoGDFjt74j2b2ivRZoy\nkJpWZyAfFhXJAUnNi4zxFErXbeSGWOjrMeRDv1MOnHn7CLg8vHMaew5qtotr9Lbv0axMwVzJgyTH\niROb8Wtn/RLqfh0z85yZ0e9ljo3xyBHgcrI2YejHeB/3GdDh75GXYSkcpucHao7wXA95XmDvoU5p\nzuoOdD+RxGFda43xTj2IvgndQM0Xe6e6xvzcYedHRRu/dd7/xIsmjgcA3PPYAXzqH+9Vkbdj0agR\nUTnfWRzbxy0VsxxVdOGqVavwjne8A2eccQaiSE9Q73vf+4Ze89GPfhQf/ehHS8e/+MUv/qcqaOy0\n/gJP4UDRha5EWVlRoGYlIKUybmQqZiswNlG+dv1r8G/598UvmblK6/bFRMENZ92vC0rfKh4fBozN\nWNGYxH75a5aafZ8mpniYtvkZFuUT+WLPy5375hG1TEHz6mUN7J3qYueeDrBO1/WGW57At+/ZhY9d\ndaY6f0N7Xan+QFnszrUbXKTaLDFZ5faJ/hZGLEkcyLkVNb+GeuRjZiFGlhUqr9by+jLFGGSJS6eX\nxK5c/MuZh5y9O85kba6fgR9ABIfQZrkAsEyKisejMaxsrECBpwAAKXM7joRtzBySRq4wmQpukEXA\ngBgTHKC4DLAlaV55vB9ro5BzIJezPG/snqEXYs8h6U5h7InruHj06Wnjd0CIgh99ahqfuuE+nLV5\nEuHqgCR0Rt/y8Vuw53HQ6WQ1ADK5ajKBd5/+ToyEbcwfrmZ0AtfX/cSOr2qswEHWH5xR4u80KfQ2\nK/z4WaMXAn6CXzj555AwINEd6HMoxH/PoQ5cvxo8cHDnu57BlpFoPPJC7GPjqQ49p3CAzNm/pt9A\nQqCQvdOmp5n1bbtnAelZ9aG/O87SAdALDwA80w9nsk5onYI3HZ/g7BWnY25mCMhi/dcrBKtlRy8O\n27vw8PxA5Sd04ODWe3fjS996HOeeuAKQhLrruOgPzLoDAmR1ZnT/Eciq+3Xjm+DzM/9+ewMTNP3j\nLU/g8NwAq5c1cMJ68Y75e7h/2xRwRqkaS+UFXo4KtZx55pk477zzDID1fJVhWye8EAu5PCk3WJ4X\nKkzejrIZltGZIm08xzMnyoyvzLxKJqvh17DQE8ebNV9vmmokYeQga7mafOIBZx48DPT8aa0KOZ1v\npm34xm3b8YkvbMFjOxbYOToNQLenDUc9qOHb9wix68NPzuPE8c24YuOlJZcxubpKgMZKOLlz3xxu\numOHCWaHgCy+Mk0YJo28SEWG9uMUbzvxv6Ph1/Hi5efoW3LXi6xTkuZIs1wtGFzHVWwSAHiJeNfH\njx1rrITzQR3XnPNerGyswMvXatf6hpF1+NgFH8DHLvhNOI6DrmQr5thKeDQa0ewkK4FnGn0ONDmb\n52S6/xJuPNlxPv6cmIXTs2gwGExWhBnFvJogy2AMVBqQAAdnBZty7xOHzEzoHFQzN1iRMR0bE8sX\nsa73Qi/BqZMnYcPIOqTMWBqMDgej7PiGkXWGgSXGqgAwYMfnUgEal9UnDAA07q7E+895D5bVx437\ndPsJzlpxOlpBEy9eeSbue+IQPvo3d+EH92lXGR+XudSoUR1MN6ROBcPHk5vq/ugZcwdn6lpqjuBj\nmQPnp/ZpJsuDrhMfVwBwcMYMGABEP8Ws3YM4x6s2vAITtXHDNZcywb4BViRjeuZyIZP44s2P4b1/\n+j3sOcgiaJlb7/BcHwdljrvl9WXYJyP5tj5+ECeMHy8WKtEamHG2oqxrrzbGJaUE8V3P+Ca4Toyf\n37VAFrWJvxMOOHsVQG+pvPDLUTFZa9euxZve9Cbj2Je+9KXnpEJ2GZZ1+IVWiqJAJjdKJndhlhXo\nyxQHLWvD5mNGxP5yL1t7ATjwCfMRwBGuR/4R56nNZMkvn4Gsml9TK61mPaCArFKm6421ExF7M3jR\n+HH4fiz2DOsNMoxIQe1I2DYmCm+Yu9BKBvnNHwjGZWo6Nc6JK4xZw68jClMM4gyHZvv41Ve/C0cq\nkRcpgyfaxHMnefj457cAAN65Ztw4TiUeMOPsVB+vMe1LL87wkjXn4iVrzkWHuWTMjNTCsH/kcz9E\nkubY8FJhqHppHzXOZCUhfu+lH0HDr+Mrt+xQx2cXYlw4egyuveAaAMCdD+7FXCfB5eeuxwqmTSOj\nOLsQK/3TWDiCxxfKQlqRwJZN5hxkMZuQsNQV3CgiZUwWMwpeX7u33SEgK/RCzFBEMX/XQcPoQ+Xe\ncVwT0GSuug/fD4+nauDPdtKGWkY67DhnRjhjYoAKzmSx729Dex2yXKcqyIcwWbSAWd1chfiQPs5Z\nRG6oB3GGK896K7IiR+D6uOlOMQ7uf2wWr738Mtyz/z7DZZekORxHaCpzFAZoJ61XmmVI0hz1yENv\nkKGRiSTFr1z/cvT365edseHQDlvYOyfBERvLfSaOT7MCozKPWuAwJisxNUUzFeMPRYGMs1Ss3l02\nBjjLx0HWWOd0XHrqiXjZ2vMBAN/ZKly8+w4ygMY0et1BirQmnrGyuRyPMLfgr5/1SwCAqdkyGAQE\nk/XD/h59INc7JCTGN6HBKx8DO/bO4azNWt7gezIBMXOX2n20prYOe/q7KuuzVF6Y5Ygg6/Of/zwW\nFhZwww03YPdurVfIsgw33XQT3va2tz3nFRyW1feFVvIiR4FC7MEoUVaS5bhk/cvxxMx2vGbjpcb5\na1qr8MmX/BbGolE8uEPH9Y7Gx+KZ6F6cteJ0Y/Lh2gjf9dWKijNcDb+OQxJktRjI8si9EHgYJBku\naL0GLz9dZKnsDbbp+zo+EiQYi0bRmakGWcP2EOM/j9ZbpIuWwmDSDmkjV/drWDGW4pkDC9h9SEfs\nULnxjh1osJDxyItw2/16QhwJtNGfnWcGvKgGAPHAVV8Ddz/xyKuaH6k+58bBYENys/29QYpDchKn\n/R47aRcN7gIZZMqAckAzZ7l2/+ZfHxH3qfvq/SRprgw3d101wyZmOtVRsIYejOnSMmac+NgygE5a\n7S7MUh91v4Ysz5BzcojnDXMDxa7x42PRCA5yxkCCrKIoDMNLGrUN7bWGW3ttcAIec+7DcaMbUS/G\noPJeJDUl3Obsmgl0WGXtbyUpMwvHNDcA0CArY8Ce3/e08GLs9X6MM5efih/s1YJ6/n7tn0ljWBSF\nyp20bLSG1x17IV53rJm0eZBkcOEgr7gXgaxYuvDHWhF6gy78rIlPX/xxhF6Irz7zpG5Dqse+7/oK\nBOTzEzhxfDMu3XAROuwbAoAPnvM/ERd9dGb0dz2w+osvAs9Yfiq2zz6F05afjF2Mpeon+hy+cEsN\nlo8dH3i4eP1LYZd0vo1NqzdiPp5Hv6/vn6Q5fum0n8dtu+7EeavOwT39R0rX9mLaI9ZFmuVYGx6L\nA+kzGItG0e0/pc5r5ssBZxv6ad8AgWu848T1lnfhhw/tx/9z0XHqd/o+55lLcVZ+4616gIVegtev\nfjOSxr5SHZfKC7ccEWQdc8wxeOihh0rHwzDEH/zBHzxnlTKetUiYrByC3g+9UOUKG8QZzlh+Bv78\n4v9VSngIaPH7n37lfnXMT9v43Ys/hFbQxONPaSOaJXqL3sD10ZETHE+cWPNrmO9SxB1jbiTl36iJ\n1Ts3PD1mSGk/w7HaqKE94KDE0I9x4Ttz0yVM8Lu8MYk4leHTuakfc10BrrbvmUOSZgiYPuWfvy9W\n+uMXRmrLoC/8x2Pq75OhThPB3ZA+A1lcvzPoeSo/WMNvghIv9eZryqceupGaRPtDQIjBOrohntjF\nxfwSZMUdjLJVP7+er4RnO4PKcx7YNqVAlsGiQeyX1s/6QpM1fwh2GaQDxGxMhE4DgHBtZVyTFQvN\nywljx+HQdib0TnUUqwE0swKffMlHABS473GdhoK/0zzzNHNUcJA1ip2sHQQSsiIzDG8d45jDFE4Y\nPx633KNX+618OX77/PejFbRw21adty1PfAWyOPCLY9b3sj6nbFqGh7br/moGDexh7RsJRlHzA3iO\n6ZaeDAVTcdLEZoPJWVWciF+58LXieRZjpZ495L0fmOmpvp2t0FDSfRqNBubieeG+r0huq77XVoS9\nU12kaa4S+3LXVJbBsAQLtEAbNPCeM66C57q4d8rMh1dzm5isj+GRQ3oBmOUF0iyHLyOSOVC/ZN3L\ncOL4ZqxprcK/5joJtclkcXdhmclynLLuKwxcxEmOw3MDXPO6X0FRFHiKpVGIkwxnLD8dZyw/xXhG\nnhfIiwKu46j7j7dDHJzp44LGG3DxWWvgOI5Rv2XpCZhcP8A5K87A//66totpHOI3zn4PGkEd3/uh\ndu/aoJPu1WFzJy06lo0IL4OHAGesPBNLZfGUI4KsSy65BJdccgmuuOIKHHfccUc69Tkri0WTFbg+\n3nnKz2FVcyX++i6xOlIrqAqANawkWYFJuW9Gd6DFwlkcKJDFNS5FokHFWDSq6Gm+ElMgKxKpH7ix\n4BNlJkPgXTiY77EwbJm4L3QDQ5DKdVGj3jiAZwCYBmWytgxpJvqjsNyFNCmlWY69U11sWNlWv1Np\nBU30s4Hcp1CnhpjwdbLGXleDLLfQdeJsSr+jQVbd1a7bpFvDb7/4VzE9mDWZHgY+eV/yEnkhHmOh\n4ROhcPFN1MYNY8Hbww0vN7CH57VLY9seDdz4hA0Av3LSu9FxprCmuQqzHQ06KcdUPxugH7NNm50J\nTGF36dkYtHDt+ddgrDaGj/3oHl3X2MG5a8/ChpF16B3mQDNTEZB0nyjwMOjrvsx49nwGvgRjoO+l\nQVZusCEnFK/AL5z7WqxprsInv7tVVzXJsEJGnXIXXJzkOH7sWNS8CAkb04PEBIcAMNIMwQFyK2ga\naTau3PQebFo9goWuqbM5sX0aVp02iheNH4fv3nOg8hnDUjgkQ1gt7r6atXR1jiOiQgd8G4BNAAAg\nAElEQVRxhvdceCX+bce3ccn6l+H+x1iiLfkdpTI/wpgMNOHP4P0UDVYB0cM4ddmJAICHGHOepgW8\nEEaEJKBZmTQ31UxJqkEWf6d57mBde41xLWC6nHmEnxkUkML3XAS+gywzn0dE4mGKInQsptL6NmlR\nUkAslBq1QM1xo80IB2f6SCSrCJjvK09dXHnyz2Jqto/90xooLnRjHDcm0k30Y/G9e65Z17zQLl2T\nyRLz8cRIhKf2z5fat1Re+OWorPuePXvwgQ98ALOzs4bu5ZZbbnnOKkbFEH2/gN2FAHCOXKGkGemc\nyiG7O/bOoTtIccpGvQHZaCtUk21mrPB4Mk22sa7jaIaDsRZj4RgWepTFm4Msysbsy7+ZriwqLwrO\nx2PJXTh+bBMe4wAgEe6QC9ecV9rgWD0j0xqaQZIprUzN0Ua4GOhz6n7NYIu4geDsRjNs4lD/MOYT\n06U44izHVae+HYHrY/eTPKlnhGW1CWRFhrY7DkDUPemz/QBdHYSQFwXWt9fhmJH1hvGLkxxZnsNz\nXYvJ0iXwAjzxjDZ+Z4+/GJ5f4PxVZ+P/fVS7a6qMreOYRsp+NpUFC2Q13RFsnlyN3iA17ntS82w8\n3LkHx48di39NtLur5Yg9EU+eeBGS/WY9VjZXyJ8z1ELhChmkOd5zys8BAG7e90yp3oAGLlHoGQav\nkBo433OQZi4ip4ZB0UczaKDT14bdka7AvMhNQ505Krp0pFntpuJGdZBkuObsdwMA/v2upyrPJ7cU\nvx9AIIszUDk810OSmqAnywucuVzs2RcbQK6apRpW12SIe2yhlxjskOc6SLMC/STD+vZa/PLpPw8A\n6A1Y/8EEWc16UHoGf19RfyU+dOnVajPuLY9qsJhkOSJ4Jb0VzUOpBWLiJFPziO1ypsTJXNTO+8Zw\nF7JzuoMMjZqPLMsN92xeFKpNfEFiRHla9eZ92+kLkEVAvt0ISm3i7zGVdKjLvACA1kTyZzdrvnEt\nZxrjJEecZAgDTy2SRltigZLlSyBrsZWjAlmf/OQn8aEPfQibN2829td7PgpF3wEv/BQOVGhi4B88\nlU98QQi0/+5Dr1THJkdrCmRVCUIdmG5B895MeJy3AZRBlkh6WaBRE8OBJmCKiqNyrHs23vji87Gm\ntQr/2LlDHS+kMmRVY4W8LoPrOshhTpRU4iTH+1/yHgyy2NgoGWmIN274GRyUeoj+ENcK1+lcvuFi\nfO7B6/DytRfgfmh3T5zmOE/uL/jwA0/oR2QFfvfCD6JAga2P6fOzmOf0MtNpZFkB13cMDRIgXTY1\n1zAIvHSTHuY6HAwUeM3GV8r6iW1ZyMWi+yaDA6BdD4wIMJ5UkgPtjjWG6F62q+LMxsvxM6e9UjKH\nWl+5Gi/CGScux9krTseXdlcDvyTN0aoLwTw3YBzgcWNGwKUWeJgDcNb4uXiqt03lz6qFPhZ6CV49\n+nYEk/tw0sQJ6A6+z2ormaw8Q4+9a86a8L0fbQOmjleAKfs4fU82yIr8CAMG3OkZNjOSZtX3Nepk\nRdOpZw8BPXZUWj/O0KqLPhG6oazEYPJ0CS1HyAzG/AnsAdRelfY71W0osF6yTEVRmCwtSzQKiIVY\nb5CqdtvMC4+wNEAW66csr253b4gmq9dPUJeME3+e0YYh79cI2oD5vdAcSWOmVQFGB3GG0HcRp7l6\nBicaAGChx3PbyX6qBeiyEGwj2AQi+CIMPDV31GQ+wiWQtfjKUYGs8fFxXHLJJc91XSoLj6ihcP0X\nelEga5CK7UIkcDUnPr16TdOi9KEDmvoeaYWYS8yQ+yp2xc+0K49PekKbVCghOV1LoKLdCDDfTZDl\nBTaMrMMgzkpGHBB5tZI0x9V/cQc2rRnBa151Ih47/ATedtLPYGFKG4U4ybBpdCMA0zgAwIvaJ+Gy\nlechzwvLrVMNLE6ZOAWfvujjqPk1NGu3qb9xYzvX4c8WUVkOHHPiZ6H/eWYuJLI8RwDXWJkC4v01\nakFpNb8sXIGp+ADiPFarX8BkBeMkRz0SYMM2tkHgohb5BqgzjZSeiAnojDRDzHVidZ5ixCBcI3kB\nrGwsV4alWRMJa/MCeMkasS9SOsSAJWmOybEAh2b7BnggQx+FXqX7MwqF4XjF8suwed1/x2MyF1ZD\ntjtymrh4w0XI88IMXx/CZJmG9MigCTABzTD2hO7Zbpggyz6PjKT9XSUWQH7Wn4e5C9k51ObAFywp\nB9WUhPTwvBmVtv9wV/18QngeTl6/Co3OsXgYT6Au30MVc+Y4JuhJsxwcQ9A1dH4j8iTIKtT5ANSC\nwWiHwWRxDVih2sb7w9T4cdCZYdloDXGSDY3mzPJCMcvxEL3jIMmseSSR9ZGLAgKjFnBu1HzEC7Fq\nsw2EuPtvwJisA9PMLVqxQENDtzMMXKMuS2XxlKOihs455xz8/u//Pm6//Xbcfffd6t/zUV7MRIBp\nXmZ+XoiFPuKiMCeW/dN6ouRAIslyhIEH13GM1fz0nNysdKxuMFk2Q3bl5qvwrtPeYQi9TWMhhkHd\nYrJoJTrSjIx6z8roI1eCw5e134ALVr0Ym8c2YaGXoDdI8dCOw1gbbcRvX3ANNo1uxK1bNXsyGGKA\nAD2B2Ss/Xt+ONamRmNeeUPW11T+nFpC4YuOleO2xl5WYKaqTXVeazLkGCQBevfyn8eKVZ+I1Gy81\nVt49y+hEgSddZ6axDX0P9dA39Cq8rlleqNU0GYoxycTQeZx5ADTjQP1CrOUwYEXXF9IlE/kuAt81\njCIBvIl2hDQrlLFOlNHyjGfT/alO1G6buQnkrgahF6LTT1UiSw5YeRoG410PcdMNY5xsJmssE5v1\nrmutqRyndD59STxr+zAWLT6KcWkwOpb7isZfwdxj0/MDg1HZP60XK0Xu4tINF6ktkGjXBhPMCyY1\nYmwKUP7uaDwN1HgyXWp0rf1OARtkaRYoywsF/HifVS0q0kyw6bXQl0CuGkCJuppjXJyjf7ajdenZ\nNKfSosD+DpS7VdYpt0AW77NBksH3HIS+i6LQoNjuVxURnJnPXmKyFl85KibrgQfE/lMPP/ywOnY0\nG0T/JIrruNjQXoun53eXtp55IRY+UQJiQqUJihLlAQJIjDLD6XuOMMjs2kOzfbiOg+VjdTy+S+vV\nbBfS6vo6rJls4qGdWreRpLkyFJTAUTFZlAtKGvnRVojdB/UkNi8nq4mRCIdm+1jhHotLT345AKAf\na+3QroMLGJNag/u2MVfeEeh8mnTsld8wzcogztCqB6V+HfYMzoDwFWua5Xj9JrHN05NMWC7+JkGW\nJf6l3xWoiIQGqeG28M5T3qruS4WDrEGSo90I1IqetzMMXNSkninPC7hSh8NLlhfwPUcBnbF2hKcP\nLJSYh3rkoztI1eStQFYUAOgPdb/Qz2kmMj4FgSdTfOhzOMjaO9WV7JxbYrIUSFV1Mo/bEZJnN16B\nyZEG3nTca3Htdx7ESFNoElPr/VLYuxGxx5iB4Vot870D2lW0rvdy/MZlb8VYNGqCD3mv1OpXAyCn\nZVBW/rnaVZawcwictOoBDs8NFAi4+9EDimVK0hwLvUQxcFyTRECE3q3vOeVxlgiNlOe5BntiM9TK\nXUhMlgTnSvjOADVnZfOiMNx/dJyuq0c+5rqJmSusAhTTdaHvwnMd9IcAWYDpx4aAV+ojyhtG1xNo\nokUBvfe8KBCnOZpqoWKCMvUMy3UdBTpND0kNBkNAlvpWltyFi7YcFci67rrrnut6HLFQpufFALIE\nC6F/7/ZTTMigOD5RdqzEfIHvSj2GvvjQbA/j7UgaMwfnTJyL1aPjJSaLJhE+mSZprhJX5tI9Y2uy\nCOiQKJMmBCXurAsXkrEFBptMDs+VkxE6jqnbsCcfMhCkR6LcNQaT1S/T82KzXu22GJYLKRniruH9\nmpaAH63myWUiDKxaCZMGKfQxi9gALnxC7hlC/gyhX4PvuSWXU70WKODdj4W7gvqYosuyrIDvAZ0e\n5UIiQG6ybnXLQFB/KyYrP3Lf0P+B5yIK3JK7MAo91Gtay1KPdB1IZ6LGTWrWSTFZfYruCjHbiREW\nLfziqW+XbsQUy0dbAmRZjMtEOxIgqwLQtOqBlRKA5bOq0Po1JMjKUgfL6vobUoJ/S5NFIMt0T3LA\nUA2meF05U2losvrEZIl3Su/uC//xKHg5PDdAuxGiKAozZxl97/J/13UQeK7FZOUIfReu6w7tG6D8\nzTesd0f/21KDXQcWTBBJGi4Gsux2m7nnTPYz8F2h88yrFwX892FpMkjXumpZEzv2zKk2UZ0ixfhl\nRptrkWDRhjFZA6sNtdCD55GuUEgNqG32eEqtZy+5CxdfOSp34e7du/HOd74Tl19+OQ4ePIh3vOMd\n2LXr+ctKSxGGaZ48y5n/9xd7YuCU+pwBssxVoO+58H3XmNxmF2IsG60p7dYlK1+NK459VYkZoEmE\n56fiKyav0CkceB2JySJXSmpNlLYRBcyJkiLiyK1xwvoxRIFnrPxos9gRFdkjmawjRPxQri9AG62y\nK2qIG2yo+DdX9bRXqra7sCXrZLuQVH8w4FLFFBVFgSQRjJXNMMTSNVeTbA8ZT84YiDqJ38l1Soxh\nUgKEVCeTyWrWTDciIIAt1UkHP4jzA99FGJgswUI/QasWIJJRY3Z/2EwWufJI+0LPVi5P2QZ6dle5\nq83xVxRC+1OLPAR+NWPVqottcdQ7lff0XKeSMWnWTNEztZOerdKJpAQSPOO+VF8HYszawM+BcDEa\n4vghWq2Su1D20+Z1OsEuvz5JhY6KdD1aOyTq5rtynFmLrMAvu6tt3aEWvld/X0q4bc0djz0tcqVN\njtaMfsoy8z7DXKyJBcwD34PnusZ4tSMebT0irw8AzMl0CauWNY3zMuvbUmAtIVbUE4u91OxXXQ/z\nPYacyVLyB72Q4G2lZ0eB+Z0ulcVTjgpkXXvttbjqqqvQaDQwOTmJ17/+9fjgBz/4XNdNFQJZi4HJ\nsqOT+Gqb707PdUdpliPwXGNCjBMRu9eIfBUabUct0qRBugB7cviNs96H1258FUYLsd1GQxoamthU\n/hiLyTqShoGvqA/L7TnoOjLUfOVHbNfysbpRRzJqIw29/x+VedZPZYahWn+ifjY0IOK4Ep3mpkGm\nYoMsMn42wLM1SIW1hQgHyAWECyTwKoxf4CogQoyeSotgTcYlIGKBoxKTpTRZgXEcENFd9chHGOi9\n2RL73fGQ+16KZt1Xmh96rwoQBqZrSbmcyFBTG/omG0fPHgayhP6rQFThwiQg16wFKIoy42KDL1p4\nELNHz6Z2jlhGMbFAgh2Q0aj5aucEKnEiNJVh6JnpHDjjUqFNatVMTRa9+1eevdZ4No3jpjrfXAx5\n0l2YWtrEwBcLN77gGrDs57y99A1pd2Fu/F+35gISg6+caBjHU4vJsrcWInBiuwuFa9Mx62q57zU4\n0mM/TvW7Jk8B1WkYk0XfGt0n8s2513bp8feYZgU81zXchYB+RzbIEuc78Dzz/KWyeMpRgazp6Wm8\n7GUvAyC0WG9+85uxsLDwLFf95Aq5CxeD8N024NzAcibLzn6s3YW5cZ0vDTWdB2hmgMBArpgs89kr\na6vxuk2XM8rahcPO6ytQIY0frZBp9VoRtcSZLNoMmAwZuZy4oaHUBApkyXvbq3mjnyqYLK6V4X1h\nt5tP6ip60grdtvvJdhe26zK5Y2Ia8JrF3OSFdmGKNphgIww8wU4ycJLlhRS+SyZrYDJZ2q1QqH5x\nHUe7OhgIB3QwgzIcyiCbAAiQ0ZKRj5Cxa3Sd77mIfBG5lReFiv40Qb4FOiPTBTLsOLG2Y20zmSml\n6mjWAsNdo4xf4EkXpimY9j1HvQt6Z3TtxEiEJM2NRLeAWDB4TPtG/TQqx74C8xZ45SlIOr0EzXog\noi1L7Ibov0EFmBLnMDCeF3Ac8W2L383+s9kk+k5tdpL+99yyJovchX5Jk0VgVD7DYrIaQ5gs3R/m\nN9Gw3IJUp0ACF9tdSCLz1PoWSZPFx6udA8sO+mjVfeFatxYky0brsq0myKpZCzS6Txh6xtxrAyG+\niMvyHJ7nMHehCbJG5IJ1wMaf5znwXO1eXCqLqxwVyKrVati3b59KNbBlyxaEYTnk+bkqi4rJsgw4\nn4w5k0UTZ14USLNCuAuZJku5caQbEShHaylNhyVStevCDSlfLaqJsmZOuDQRRBVRS1W5rTggDAPT\nAFEm88kx6VawJiUFsoYxWRab1LAme/taM4qL3H8mU0IrXjL6SvhuuQtpJU39otxj1mRs6y3IYAUW\nk6Umdd9VIKEKDPB7pWkO33dYokeTeVBMli18r5lgtCiE/qkhmSzqJ2pb4LkIGVtBuZSI+eLtskGn\nHTjQsOrUHeIu7LB0E3zsUz9FoYcoNJM+xolwg1E/kTGjd0EGlnY+4IDNZ0CE2mK7C4dFSBZFgU4/\nRbMWVDBZImLUHvv0jXuuU0pxUMWG2FGbismS33vDYr5KmqwKxtTWedJegi0roo6zQ6JO1e/aXqjQ\nfWz3mAB+uj9ogUFMT9/6rgMFsthck+gxyM/VQJHc+uZYbjfM42UmqwzmOcjimqxjV7eNyNosK+C7\nDnt3xDba7kI99/quq5msJXfhoitHBbI+/OEP45d/+Zexc+dO/NRP/RSuueYafPSjH32u66bKZF1k\npZ6sTTzLmf/3lyOBgYWumUsK0B+pb1HWhiBUgl+af4gFo0kmz+1VJ63yTQPue66he8isVWpquSHs\nyQ2wwrBtXY/nlgzQ7EIM13EwThmPZV16sQkUOUA0mKxnMX6AMNpqsmcg0GayqH/IAK8cbxjtHSQm\n8IstloRcfGW9SjXY0EwWsXfSkNX8EnAelrQwUa5k02VcZh5Mw0GuJQ3ahZHT7kIbIGu2bJBkimms\nhT6iwNJkEehUdbXYNatOHdtdmJjgoRb5xtgnwKOYLEvLE/iuUVfeDtIIkQh6ZmGAMHBRj3wDiNjv\neqj2T2m4RKqBZk0wgdzwxqnQ39kM14AtJDhQzPMCnueod8oBNfU577+Szk6BLAI0pvYvz4W7VUkQ\neHLQIYk5VcoHCzgrkGXNBXbUJr0z5cJ0HbHvYGq+03G5sKHxZYMsnhbh0IxYoK2aqMtzTcG6nXqB\n7tUmoCPPp/mR5lLVZjbOfN/VTL48/00vP1bJGeIkV+kpPA6yrEUjjXHNosnIcXfJXbhYy1FFF552\n2mn42te+hp07dyLLMmzatOl5ZbIuP+ZieI6Hl6w573l75nNV6INv1GR0Et8nsCJzNXe1Ge5CK+oG\nKIfEK+FsYU6IjSgQIcwWS2KvFum4bVByC2QZq3MWMWUzZYHvGgbIc4VR831mUGhSst2FnMnqVDBZ\nQ+pK147LSDQeaEATX8tySZILc8VEA489PV0CCdpdOESTZRk5e4VM9yFNVl6I/iDDUo98+K4pYtZM\nlhXdlebCZfxfZLJsV0o98tFLspKx9D0XodygO44z1XfCvWi65vi2OvwZVCcbdJaZLDKW1H9y7Kfl\nsR9JQEipLpJUZOi2QRZdu2xEgKwZKYKenh9gvF2D45BLzdRk1SXAKzNZpnuWNJTNeqDG7iDO0agJ\nF2vY9uBaonul02lF2H1QZ5fP8hyeUzbUwhVaftdKk6UWC+ZiSGmyskKmJdAgH2luGHb1Xi1WjHRl\nvqWZUgu3IUwWfb92AIcYTxr4aTd2AN9zNciy5iZAzD+u52CfTMC6fkULO/bOlxhhBRStOUJ9vxbL\n6VkBAoNEf7+B52A+NfvVZaBzkGRK2+l5rnIXambe1BdyBs/zynP4Ulk85VmZrK9//et44IEHEAQB\nNm/ejG9+85u46aabno+6qRJ6Ia449lKMRu3n9bnPRVFMVq3MZPXjVBlqOweTz1I4FEVRPfkUppCY\nWCDNZIlrmnXfeAYxKcPchRElQrVWcs8Whm2vIInJAhhVT64Rr3rl17aE7+TWonJ00YUZ6qGH0HeN\na3uD1DDIZIiJyVoxburEaOVru0DKIMtinyz3DjdyPGiBgE4j8ll/mGCnFLH3LExWCWRZImm6Lwd4\noe8pwTB3FyqDwupaizxlXNR4IsBrRRHaLK6KkFRMlqnJov4Ofc9ylefqeImxSnMl0hf9wIyZ6ygm\nYXYhRpLmmO8mmJDsCWd7uKso8MuLG9WGzPzmmjXfMLwUCRkGQo/IGa5+LMTnjUik6ODgSOh6CNDo\ndvPv3daPKaG8xUT7rqOY0STN1ffKhe80d+g8auWghTBwS4shO1rQ1mTRAoYWXwaTxdyF5B6shR4a\nkae24dKudZYWQbZr3+EuPNfBahktyN2Fvucq1je2gJ9291N/E+Nn5iLUY0CApjS3z3fZAiMzNHBl\nd6ElfGcBLf6SJmtRlyOCrOuuuw433HADWi29HctFF12E66+/Htdff/1zXrnFWGgSIiMXM9o4TnL1\nEcbWCjyQyUjFuabxc9kKDzC1LHQ+oCdlerbNAvmeA5eJS+l/X7oVVK4bNbGWszZPS4Die5oV4MyD\nbfwEo6UnGWX0h2iyBkmGAmWXpw2yEuYKSOU2HpTbiEqnn0hXlPns2U6MRuRr0bhiskzmi9qthcem\nC64Unj2EyaJr+kznZNdJRxeWIyEDdp9yMtJqnYkNvhTAk9FxJBgmF4nvu4ZbkOrKhe+KacrySuE2\ntdtO4aDyZEkApIyiCqE305fosH4N/MiIxSm5C12jvYIxdRV46MeZAtPjzwKyPJc/2wRZ1N+cCeTv\nTkWSBl5Ju9aPU0SBx0T6Goi4btnwiijjsv6OXOvlyD/N0BAzmmV67iDhO38Xdh41rSMUDKEGfhaT\nZX13mskyNW2cXePuQnpuFHpqf0TRv/pdkyyC7jG7EGO0FeooZ1bXKHANYFlVJzu6UAUIWN9KGHgI\nWAqHnJ3PF438PsPchcQ2cpAqZBpL7sLFWo4Isr72ta/h7//+77Fp0yZ17Nxzz8XnPvc53HDDDc95\n5RZjGabJslc6tog48E22wtRkiXvTx9/tJ0pjAgCFzWTJCZT2dlPP8Fz4rqM+dEXtS3eencKBmAru\nLjw004PvuZgcrZVdThVuHFq1+54d8izpdUuTpbLQNylKxwRAul9NsXDge2jU9OT9w4f34eBMH3Od\n2AA6gDDuUegZhqnyHZFbyzZMlpGztVrJszBZdZn8kD/bTovAo0xFDjVp/CgZKbkLLUCjXCAymk6t\ntAeaSQgUmNLsii+FytTXXUMvZRr9JM3VWOLPpnbrRKjavR0Fnh5P1K9cu1ahR+QsJF+UmGBeg4HA\ncxGwYA3KWE9A3ghCiM1nl7YGsgIHhgHkmC2G7LE/SETiShXkQGyPFE/7FrurmSxzvA6GGHCaD1zD\n6FtMFkuaueXRA/i3Hz4l7mVFpQo2zis9WwvfTU2Wds0Ndxf6HncXyvEUWCCLRxda7K7oPz3PcYAc\nBp5imUpi/Ia9wNXgyKiTIXx3VFStARRDDeb5orTEzMscajaLm8igpiXh++ItRwRZrusaLBaViYkJ\nuO5RaeaXilVSa5Km1braJ5BCfG13oafZCsEwMJBlrZooFN8+rqMFzUmGgyDPdUvuLp0I1XRDkNaD\nhzAfmu1jcrSGMHBLk1sgV6/i2bm6l2es2mlSqmayqJ9sASnl5WnVg6EBAjR5F0WBb939jKqzAigG\ncNEuFh0Fl8J1HCZ2NfU7TctdY7v4qpgszgxQm+syZBzQQCSz7qWNfmEwWXZeKNuFScYsCuxoVe2C\nC3xtePnYCBkgVHWNyoY3SYV+xw5jL+XJkr93ZX4pAnf0TfB+qqqrLXDP8lwJukOLxYiTzOinmG2i\nTuCRmKyi0DsGRKF8tkqjYIJ5agPP6M2NflpRVzVuZHZw0tnxiGIDGLF2+76HwDddUcMSzKbMDUbj\nLM/1Js6UjJTe3XU3PwYqDSvnlkj54DHgZz6bUj6ohdUwJqvCpZYXhWp7FIrFUCIDCWzhu3i2XvRE\nbKHCUzhEgafGAJde+DKggKeP0KDJDBCIGZOlWLFMM1YUtUntpm/FSMlA7Hg3QasRlBZ0mawTB8FL\nZXGVIyIlz/MwNTVVOn7o0CFkWVZxxVJ5tjKUybJAhb35MF/t2EyWrckSoeR+6bh2F0omy6L2bU1W\nZgA8p5TCQWgS9K73vUGKhV6CybGaMVlxY1ZmsnIzGZ/Sq1SHkpNrhPQ7dB/KMTbSDA2RNE+LUI98\npJnY4/CE9SJ79jGr2qXEi0kq2aGKgIJGzS+vUktpEUx3q53k0GRoNBDpVTBZGvgV8hp9flEUSDO9\nG4DZBpNFs5msWigE3famzoHvGqJ7PjbIaA0SnWeqFvjK6GsQKTRItuEYJoam8eo6jmH8FPAjd00F\ncObuwpgLla3+INZIZUVPc91m+T59z1VuUi56rgJ4jcgclxxkadBu9V+g+09shZMh4kyW7I8sy2UK\nhzJwDlgOJvvZSvhekYyUL7g4O8QjGIn5AczAiJw2CWeaLDthpx2NmEgNHM1z9N1y1shxNPCz3YWA\nWCxyPScXvtMCwAa1gNBahYGrGVk2nmhc8DmCgybfc0sAMgr4N5Eb7kKjTgxA2nPHXCfGaDMsLTzS\nrDCE8kvuwsVXjgiy3v72t+Nd73oXtmzZgjiOMRgMsGXLFrz73e/GW97yluerjouqaE2Wua1Ej2lc\nQpawk7sLOVvBJx+uyUqzHAO5391wJks+28omTisqzcQU6tlVeWKErkKnZFAJROtixZblhaoT3aes\nySqkZsQU8/YGeosXACW3Ful3aHKmHGOjEmQpsMGeTZN3b5DCkdtj/4/LX6T7NeXGjE98knGxGELq\nhzgW26bULVfAUOE7Z7IYmKrS9WTWitfU+8j34zmlNqjIxrBakxVJEFTKu+a7jMHTq3PT1Zap+/vM\n6HPdkhAqm++U0gAEvgA7vVgIw/tsk/TA95ixZHWS4D/PWXQcA+1xkjH2yVPsFI9e4wY5ZgsVai93\n3dquIp6qQbwjEzirdBOh6S7k44/XlbLW1wJPCbQHTLfEhe+2u9BmQ3TG9+HJSMylrAUAACAASURB\nVL0KkMXfdZoVaqzY99LaOMZkZbpfPdcpCd9poRIGLhynnO/Nt0ATF76rfTs5yAoY6MwLJROwAXVR\nFIiJ4bK+CXJj0/NtjSn1E/VbzFzrvJ84UORzLF988ncXy5QnAmTp7z2XANbnoKxYAlmLrRwxhcMb\n3/hGxHGM3/zN38S+ffsAAOvXr8eVV16Jn/3Zn31eKrjYio4utBgapokR0V0m7R5YfnvDXcgEoWSo\nm7WA5c8qjHs1reg4PvFx4Xua6+MCZCXqOQDgOQJkEbgaMDcEgamEAULf47mWNGAzMyRrJqse+iU3\nonYXDmeyjGgwlvhTdge6g1Sxe6TD4P2QSpG0b4GEXj/F2GRUEuD2pQbE1tBwcOnABCHUTxxMDRgb\nwgMDqB89i13jDGGZycrVvXidBnEmROkSRNosE49UzLJCA3DfRVhotx/X+2gmUOt3xpphSdOWJJpJ\nqIe+YJ9SIQwnxo0Lj7lx5wzoMHehZi214Y3TTAKzXBhepsni3xbdj96bIXw33IXm92sHanAgZwJh\ns67EWtVCv5R4NssLeI6Z1qSQbBJntG09WDkZqQke6G9ck6XqmueKqQTM3Hg6ypOlJmBMVsTdaWqh\nUshvTmTfr4ouVIuVgo39gO0gkBWGpo0vbkxAbbrsSoEGjLHnruFSqguXtKcmSxex1BVZxgXuGvjZ\nWi2PjX09N0WGTjFj8+uS8H3xliOCrK1bt+KJJ57AF77wBbTbbbiui9HR0eerbouylFI4UAgzm3T5\n1jM8hYPL/Pzc0FDJi0JFanEmS4Es+X9LRRdqA+6AJmMXnMoGoBIjJvak5DmIfBezC6bbMQxcg8Ln\ngNDOmZTmZvI+BWhikXbBdR04jq47gSzSXsUWyGpL3YMWTxNr5ClwxCOBXNdkDAorw744nqvQd+6G\npUXnQArlbUbHiM70KzKWB8xg5mY/2cYsk8Jt7m7Q4LjMZJX2OrTcZo7Mw1Slc+JGnAPkotDv1Iik\nstgNpcmy3IW0lQsAaXhT7SaiHENMjB8z404sS2yDrAp3IWdAbVaqalxWMVlcjxN4OkGv3lbHZMr4\n98sTyfLgkZCBLO4eK2Wnp4WHZdgLlBltfl2Nghlyi3FmYECAcw1GfY/YnELVD4ABtA1tUgngZZVB\nIkmaqXNDn+2FydhDvgjss/5QrjmmczLE4VmOfqHHt2+86zLDpdyFWa7cl0K3aQYI0DyUF4Wpyws8\nYy61mS/qV84cumzs0w4So83QeA/mThvmYnKpLJ5yRHfh7/zO7+DNb34zPv7xj2N8fHwJYP0Eii2c\npYl+vqsjnfj2GzwZKf+guaCWTwCUiLTBwIBKRmoBPJ5V3perTo/pdGgyCWT0WtkN4RrZwbUbzFxd\ncsZF5ZVhQli6D6DZi36cqizSPAs9ZwyiwFNuhoVeinrklQBNlRsszRkT48Byt2oXHJ/4eA4rm8lS\nIeOWezZjIMjY3NvQ02njZLhxLGMmmCwORnPr/GrhO2n2+Oqc+vrZ9sLkQE6IyTUzQEyg4zDxrwQV\nWV4YYE27uzLFJNQiH704NQyZeNcO61c9njhA0cDZdMER42KDKc56kC4qSbMSk8WjdzX487SrUrJJ\njjzO2UlDk6XuY2qyIhb0wYXekaXJosSq6l1z1tIrA3CtjxMuWlsTaGiyMhZdGLgGIIwYyDKYL+4u\ntMAUMVl8sUB1M/RPJXe1BuF5YQJFn93LAFnsu6P7GJqsjINjttBj+lZeJ+pTg4FiY5bqZCwYZIQh\n9RFn40yGS9+H5uRm3TcWMKZQ3lyoLJXFU44IsprNJrZs2YJly5Y9X/VZ9IVcKoJN0AaX9uNrN0KE\nvseyZ2vjV6mr8MwVYa+vXQclJisjPYmZRDRJC2UYfFdvXcFXWkpjVRRG1FIYuMoYa0E3z1GT6TZw\n1qPQLg3P05mT+0kKyhlGWaQ58IvZJB0Gnp5AM+0K4Dm9KoFImiuQ4LqOMXlrcML1J4XKSm4whIUZ\n5VROu6AnXW5ohjEDHIx6ljHLstzc4ywrDANE9+FpM4id5HvyDWRdqU+q0iJwcTN/Bo/+M5ksznpo\nJqFKq0Ugpx56iBOd5T5S77rMdopEntrNXBVI0U8yI+2CBlN5ielxZF04cwjAAMk8utBonzTUjuMY\nubvM6EL2Tiv0iMJdWOFelEyq0ikyTaAJgi02Kcngyoz1VdtiDdVkGcEMOmM5vW/7fCMalonDKR0I\nfya5NqnONO+lMzOiXwMPDqtTn4Mv1t8qoeoQAGRHFw6M+aHsQqdzeSAFARvXMd18cQUgzJgmy06N\nkRo6RYfdJy/fx2KyNLBcYrIWWzkiyPqrv/orTE5O4uMf//jzVZ9FX1QiQDk5EEig/fhGGgF8v5yC\nwJ5keP4nHkXIE5HaIc90T5rsddRhriZuPplwgMcBipEnhuVOGsZkmVoF6WqTgK0ohHHj4l9tFCW1\n75az0JOOwUjKKdsQ8OhCZiC0caoWr+asXw1BtwR91HeUvSTPC+S5WD1Xuwv1StU3XE7MaA0xfjaT\nNUhkxm2WoJIbXschl67uJzrODQp32YnV/JHdhYYmi7lfTE2WBHhZrl1RFcxezDVZEuiTK4XAkm+4\nC3MZccgj87LKZKSDOFMZvG13td7r0Gfb55Q1WdyVI7KGO1ZQRq72RrT7b5jw3ciYz1k3xj65FQbc\nBkacubZZy/5AjD9HRmdWJSPlInMetUmboM91EkN6wMcAT8rJx3heCADBwUN7Zh/2/d3fIE9iA9Ak\naY65O+/Auuv+CGfPPKoAL9VJ5cliIJWYUepr8/0wkGq4efkcpPubInEDBvzUd1oUKtqRs2iDJFcR\nr5XaK2vu4KltvCrAHmg3fZbnmunmukaWc3CpLI5yRJA1MTGByy67DFEUPV/1WfSFG9KACWppP762\nFAzzEH3Adi1ZyUjZB01RivXQNyZvQDMroe/CLXL43Xl5PFfMExegc+0Bn9g5Xc7F4Zx5UHmKLKEo\nd7XxlbZmsjIdBBAxFxIBFEukz4GiWjkz1k1HJ3mVbXD5HnFsGxkOLLnx81kbjtu5Bdt+6UrU4w5C\n36sAteZK1d5bLbTqVKXJ4jmBaiF3N5igTFynwTlt10H3066RXN2bmKyiMF2VR+UulF4Nz9XuwizL\nFdAx8hplYizHaabuQQB6Ru4TqZgsw12o2RUuGq/OPZUbzEOlJivU7a7SZBm5pFLN+HGXGhfvV7mA\nDRbXAglVTFYUmqlWDH1Q1aLKM5lGcT+9HRfvv0rdXF4YIJUCSOa6sXr2mcdPGu8uVm5YEzjHDOgQ\neLj4nq9g7s7b8aLD20rjcu5HPwQAnDr/pFisyECUPNcpM4y0Jgzk2y7PPmMtubtwkFTMQUzbZbow\nJXMoc/VRX1G7id1zHDuKsAIIZ6a7kC+GuLua2pJmhWKtPE88I/R1BvylsnjKUW0QvVR+coVvE+G5\njprY5roxHEeI0odOrhWshy0grQQJbFsOmhjesufbOObJfUgvOwEJ02MYICgXhtpY4WXDBJ6aFrez\n0ys63tAwcKZHTIgOBCPBDTv9Pctz5P0ecgkifV+0o88YLt8Xgn7u5lMGxXORWqvwUp1yyz3mV4Ay\nueJ1HODUbXcAAI7t7oHjry9FF6oJX/bHIBZsJWfXOJDjrKVvsEk6J5ApnC0bDu4upHvzTOYZB19D\nAJtK4cDdVJ7Oa5SkuTIirsPzNhVqJW4nIxWLBl1XAtAzFpPF87Rxl5MCU7GpySrIZctSOHAWI8k4\nk2UmHbVBKo2DNBcuRq5dU/3EmKyFXoL5boKd++YMw8uDEPh3qrf6yZlQ3nynpgFn4M74rk39Tj/O\nVJ4q4S4sM6n8u+YRt5rJitV3/c7Xnsh0XTn7rj2jLwZWv3qeAxey/nlazknlkStMRBxyt7vJpOpn\n8Ag8n32n3AVcrf+0gx/Mb8VmtWnO5WOW9GbUNvsd8bkjK3gKB4cBc5M51O/IDFyhvy+BrMVXjshk\nLZWffLEZKJrY5rsJWvXA2rOMgSbPFBJrA6E/9Pqubdjw2d/Gqv4hmDmVNLPiy1XTMT2RkiPevx8p\nM2b8GZQoDzBX80Y+GO6+SssaBsoFI853DEDIDYrjCDZrEGfGCo/+nmcZtr3v3dj87X9Q/eEykJow\nUOEyaj8dwjBUJRS0XXbmvmR6AuX/AxCsoGcyjQAwK/fGG22JqCKe4kDUSfdTKiMYld6HJQQ1tCEs\naSsX5gJljZUCWb7OZE56H+pDeqdmclumyWLGXbkLkxwnfevvcfmBH8qs12VRdcSYvTTjbhxX/R3Q\nWsTIMkD0PsgADU3hwNyFVdGFPH8WsWe0AXaJyWJjNmYGlofviz38xHGKLr3+209g2eFnUEOq3LbU\nf3yRxHPEVUWu8fQA3A3LtXHGZuCGC1jcu842VzY0WWzBRdeFvouxdg2ABFmciWELlaqAjCwz9U+i\nn7g5KYzxVwAKEBfUbr44NNpX4a72TMa+P4S11K54M7qQ781J96Nn5Hmh6sIF6AbIYtqrnM1PlclI\n2SLJYFIZAyo8Bbpt1E/xkrtw0ZUlkPU8FyO7usvzFFV80JmZD8uIRKvQZK3ecjMA4ILphyp1PYLd\n0KBM3sxwLRmarDxXBoZP7EOjcRJeVy3krGK+8sI8Dgg6fZBkBgCi/2u9BQBA6/Ae8QwZbaRYurRg\n2ho9IcYVBiJNNcBzSkyWnviUAU/LdaJJGQBc6H7l6SZmVOh2pNJyCLdZWf9Exiwogd3cjFxzteHl\nEY9UZ55mg9orhMemWJ2eD9hpNnTG8qHJSJMUrem9OHvuccOA8+guwyDnOm1AYDEDJeG7q0PouRvH\ncBdWiMnjNGNuLTNBJa8T/S80WRRJao2bTIxlzWLoe9FG07yctLATr7j/n3DZgbtkf3NNUaGOcU1W\nlS7PiFDzXLVx9b7D3VICUddxVN9RolpA5I/rydQYmQQPDnOJ54xxDn1Puws7sQ5o8cyF3rDIxgED\ntfydUuFMlni2OL+QCyre7kTq73jeNSMCz60WvpvRnLnhCuWpLlRAkMzUb7h0c7aYZAL0OMkZ+8QX\nh6xOFUJ2z3WN8acYVu7SZcDZW2KyFnVZchc+j8XWvnieg4ue+Bb2X7cNaXac1iB5DpwiR9rvlYTe\ngDR+FZosWloXjk4gCphpAHxPbygNAEWWGoJQzkwR80XPB6TwnYEjnlyPbxdDk1Y+N4ti0AdA9Drk\n/U2DAggRcD/JSqyR77lo92aMviSNRpYXOnOyDRTzwgJZsg358K0xNLuhjWKS5CoakvqaA1Wv4JoO\nzcTMLkiQ1Qox0gyRZxkO3XEnil6hdG58uw5bVA0Amx68FXP5dgAt4VpiehW9DY9ksjwXHbmHY5Lm\nqIWBarsdsCDaWM1k8YziCQOdyn0q3Z7UD1qTVRjaJM7scRcff0eU163aLaMZGq1n0oBQ9IV2g/Lg\nBB5xRu3mSUcrNVmW3ojce0Z/pGbSTgBYe/BJAMC63n7zfEtcz7fV4WkU+HNpXPqug3YjxKqJBp7c\nPasT/XoCYI22QszMD1ReNxoXBJpmOgMVuWu0LSuw93BH9UNT7jnYj1PIR6vvCDDTKNipRXjmfXGd\n7heH/W5vVA+UowvNsc+lCeailM7nOidiD0uMLHP9dadn4BS5YrICYz7Lje9X9BMFtNiu5CMI39m8\nRffH7AyO/84NWNs402RreXoK+czQdzHXWWKyFltZYrKep1LkOZ669iM47rHbAWi25/iZHZj93q2K\nZQLER/q6A3di3zW/iuDAHnW+a01KgJnCgQf/miCLpQFgtDsA5EkqWY+yu5CvkI2Jr0I3Yod6U52i\nz/wuTv2Xv9DnU11zPYGqcwPhLrQZLs91MDKYNfqT8oaZwKjcBsN9wCZWLnzn/VoZDZaW62QwWUVu\nrISVu7AzgOc6aNUDjLUinLjwFKY//zlc+OC/KYNiuGfTTB+Xz9m44x7E3/yG6J/QFNf31CbNvmo/\n9QUHzpR+Q7Gormn8bH0SjyDj7mqqWx7Huu2OZvC4/qkmAy8cxwq5l6t5dxjIYmwt18qQsRskGXOz\nOJWGNwxcFRXGRc90r3J0oQmc+3Gqsobz6wZJZujKPvS2s0XdZg8BAKajMdVX1H88UKOKyeKZzDMG\nKujYuhUt9OMM03MDVXcAGG9HmFmIGfMqmay22G5qZn4gmBhrPM12Yjy4/TAA2iOT6cHY92iw6WwM\nKG2XDALgdeIudLB+ov9td6FKnTI3i5HZ/Wyu0UCHC9+NiL1n0dnxdBPo9+B9+rfxc7u/ZbC+9I7y\noix8jyU4t70LXObAUzjYGd9pLpr48e1oTe/DT++9VbtVSfhuvTuxndoSk7XYyhLIer5KUSDeuwdj\nM3tZdnW+YiyU8fNcF6fObwcAjOx4EAAZvzJ4IAE4PYOK75viaUAIk7lQHgCyJJHnS5DA3YUZi0Rj\nk5IRGs4isrjw2HMFGwcAfjKQ5zNAk2YlZkVpsiyj6HkOWvGC0Z2UN8yOgAPM5KJGMlIuqK3IkzV8\nn0VT+M7/BwC/yDRIdbVwe64To90IFPMwkgoGYeXcnmpRdVpmsngxNFkV7kKRukLr73gUIaDTDNhM\nFk+9wPvJFNfrceZwkEWuR08IrlU+MZZIlrs2aU88utfAcuVxtpa7C3mqkCTL4TgyWlW9azO6kO4Z\np6auh/4vAOZaMgEeAUX7XVD/af2T1HgtCJZ1PmoZ5yeZyTjz6EJ93Ks21KpfCfhlxr3HWxGyvMCM\n1P3R8dGmZLIWYpXDit/v8LxglVcva2C8HQl22XEUQ0MBDlybxCULFAST5iZryZ8BAA7KLHghmR6K\n1qN3N/ija/G6H38NNScz3kMqJQuk5eRjgydzpbolGZcHaNd60JkDAGzo7zcWJOIdFchSxmTJ/qYE\nolXgP60AyM7cNOq33oQwT+C7mg3O5R6pXsFcj/JbSfvmuwt9z/jmlsriKEsg63kqjufBCQJ4aawm\nK4+DHYs1Shy54u93xTErJD5JKVKQuwvpP6fSXUjP4AAhlYbDBiikMSi5C7nwnd2LhNuAdBV5DrzC\nnCw8OSlt6O7DKV/8JPr3bxX3ZpqsAjqruxajugjT2LgXMXJFYercxPlMV1Ghyfre/XsqszZzhpBr\nsmIOCCtAlphY9SqcNFmDOFOT+lgrQuJo77zNMJAxMzRZ1maxtdDX7sWcuwv1ViG5jHLiuc/ongv3\nbMH63j5mwE13IfUFT8mgAa82sEWm3YXaOImcUTa7RolkibGqM/Al+tYKKGCaGC7SNwXG1YEa3F1I\n7ePRrTZboYGLxWJIA27rdAgoqgUJXSfHJk2mRmQZG08qgpbl+jJypRVlkEV1oNQsBPwoKvDQTM84\nn6IMO/3EEMSr+/z/7L1nuGVHdSb8VtUOJ9zUt/t2zlILSa0ECqCAQIAQYIHBGIQwtsEejG38MR7G\nHmNwnPGMMMGAGWMbYfAYjD/ApAFjBEiAZERGAZTVrc59u2++96Qda35UrQp7nxb+vnmM1f30+tO3\nz9ln76rae1e9613vWjVQ173ozDUgE4I0Qna8bT0nX+JA58o9YF4PF7rH03hQ2Q/KUjXvkM64bjI/\n246E73SPXcbPrTMG6HudFci00+hqWGVhQ3BG+K7P1b/5PXjNfR/GmnRJj6P6TZ1hHRJFCGwV+nVf\n/zRad38DV87fAyGYDVfrp0LAhp8DzvDcA7eh+c7fRVyk5t64JSdO26ljp0HWT9B4owFR2KJ/IbeL\nqMs8CMGRa5AlEjWJBoL5hShzK8C1C749X3WjX8DVZLkga2DOD9hJhjQGBjw4rMfQStKFnwnJOYOQ\nvr6AvOZLl+4HAKRf/aJ3TVrcaQJ1swvjUnl9BbPHVtkQC7JsH4ZVMj8238P9+xZMm8zxlSr3pINL\ns9LLkHT/BYC4zDzWjRb1xFnkxtoRMu6ArCqTVfpMFmcMAauCLOGBihrICvzxq+7Jl/7D3+DnDn/J\ny2YCbAZUNTxGIMHNChWCA1mdyaKMqV5iK+MDMIVk+xXWzdy7tHrvnNCZqynyWFwL/jlzQpKVLC4T\nFix8EF5lpqqhW5PAUQlf9SsOSRVUCEjvd+7ee8axEtwvmRFUwoUOS0zXWJ0uonX/d722jmowtaSz\nM+vOkPTqjNH9I6Do7lOohNjKeXKfa6EZqyrIont6Ip0dWVVjReFC5pT/uHThfnN8xChJwHHoitJh\n+J1wYYXJCgKOZ+35Es742/8OSOmFCwsXZFXelXLPw2gUKab6s15b7ThVwXzpZdzSvaN5eiLreJqs\ngkCWx2QxnLW8DwAwnnfMNUMjTzgNsk4lOy18/wkajxsIBz3zgkcVEOJ61IUGWUHSB0Z0WMFdaBzW\no8pkAfAy/6o1ktxM62KQAGjU9Ex5WXrAz83M66e58soZG+rhETgJKv0TFCYghov7izpNygY0OexG\nXKjFpBAhGCMRvQ8qwsAHQH7RVlHLCgPqmixTXsFZqNPMMlkmXMhUKIBDIipTlA5IoCKH7iLXCIXH\nZFXDY5kOa7lVt2MHZDHpV9b2he++mJfYJHfhFaW9F25CAWC3IzGeduCECx1AQ/eEZXUmi2p01Rkr\n5oc29ebkdM+J0akJjyvhQr9+m2V9zTXce2fChQK9QVbTvtSZLP89SmpMlj+udE8DwU1IHAAEfD2V\nF9Z3no+ywoYQ81k6wncT0uUMrz3wv4EDwPrNL7ChJd1HypqrayeHA+d+BTy44+dmpNK9cLPgXHBZ\nBYruNQBb1sT9XhoqC2acnj33PfObBifNoAX5XuazK02oli8RHDuX9qvfy8JLKHCZrKajyXLvXQz/\nOaRxpfP7EgRHv0jPa6iYxUaZKgeNgKJ2Chl8Zp6shM2mNFsc9Qc4baeOnWayfoLGGg0EZWYFyXBA\niJTeREwAJUiVhxSKqhjVEUkb4bsVllJFeTqe0sNdoTzgMFlVb1FPrlUxal6UWFxJTLjCrb9jNlcO\nCGRVw4VaW6E/l9yn4k0ISS9y3Pm8Uaa6b9ZLPDGTVQdNbtacaU+FlSqd48lDprRqYu9cTVapJ9C4\nzD2wQYJdKe0krf51sqsqep8q+wQAkcN0Rrq4o8tO1rILAz+05C5ycWnZp6pWS6WZl44w195r9xmg\n71juC9/V5wzt/hLOueWDWD+YtYyVDi3VtVoEsur6OwDofPaTuPb4t4beI7fQqvqO1zIbqX8qRd/N\nRrTXGKSF1nb513Az+dzjq0yWEMw8lwAgpM9ClWWJQuvXXFBdLQhq9860bTXPvjP243nXnMcUNp2f\nx7NmvotQ+u/AIFUi/bgC5qkIKjGsNC55ScyhA6g1O+kWQjWfF2UN1Hq/lXZ+MtmFzqbiALwsZwCI\n4QNSev4so23B/0A7MPT8ec6JnmPJCWRueNuE0BlCmZvPI0l6RXUecgp+bLiQQG3Y0NdO/f0UZaWT\n8LM3peOo0jguveNPar85bSevnQZZP0HjcYywyKxWBo4XLH3xZVDqCZ1AVuCnMHuhpRqTxbzK0Fnh\n6FL05ENWDnS4wZmMd3YPQ771TZhMFu1CTWAgK7Dcy7BKp4pXhfKAzfoaFi4U3NFqVZgsaq9hEpzF\njxYzJt2io8NB1jCNlZdt5LSnenzi1BCi36V5UQsXcsbAqLq1LLzQZl64SQDWS+VuOLfKMKT+Ag74\nICuUeX2T8MLq8gAHZCU+u6FAll1oarXPcumJpD2tVqUuVCAYWG4XJxcIX3L4uxhdOobrj33DhAsJ\nVNS0WgSoh4SGASC9/SuqDpcTQqd+u+yGe400K7yMQwoXFjUmy4LR0HkfyNGwTNYJwoVOaYdmkdh2\nwNd+7bjva7j8M+/EWGZDQqrsiC8m97ZsmT6KLf1jNbYRAJpFYu4xLcgb/+lvcdnSA9j02F1eH6tM\nDJ2vGgaj31AhVC9cqFnIE2uy6mFEsisWfgi6RFADWcPDixH895gymYeVZknSwuix3H4DSiPpMXju\n80r3OuCIyiEgq8Jy1hMy1Hgw/RmdLycmq0hV4WPqgx/xr/VbSDuPq+3OCsjZ4/UfnbaT1k6DrJ+g\n8UYDHBKx3l7CBVlnd/eb/wsAgf47zAdgmuVydQFDQZZj6ng9QfS7OPKud2Bjf6bG5pQJZbioc4y2\nIjz/+J1gyQCXLt5fW2jmlhTzRYUSqwyDuXY1XCilCjU54ULpZFO61yCg4zIPBBJ4WXiAMCwzJGk9\nbLGtdxT5176EzGGIqn3nFQEuMVm7OgcQP6hE+VEodMV3n6XgnIHpRUNlF9oF3GXQTKZbyMEdIXtV\nx1LVBwFWowIAYZnXyys4omB1TnWt3uDHMVm+958VBdJ0+BYibikI9R0Hy4cI3zkD9H1lkM4+bWo8\nqlotE8rR5wm4v5Ca8zuhJdWmssZkEXBxK3Sr8fDDWoF5zuxCWj0PYIGfCbfqz8Mj+7B+MOuEzZgH\nssyWMvr82/eqUNiO3hGPffVCToGfoYv3vRU/d/gW0293PJrFoBYubKyocgwIaUsp9X038bPjTHmK\nxH8u6RrVHR7U59wHhG74NEux/nMfxBndQ0M1WRwS43vuNX0EnDpZFDZzQA4ARMxPRMgr91q9cyWK\nIvdCodVrx2XqtImDZXWnIBAckeN4EKtF10prjl7dwXXlEnkQOde25WJQ+I4m4DN+XJbm3FHot+m0\nnRr2hAdZUkoMDuy38fyT2HhDUcpN7bGFDgh54bF/wZmPfrP2OZcSEyyppFX7miw7wdhwYSBs2vNZ\ne7+F5KEH8KJjd3geHwAUqa6/o8915fnrrR5MWtEpVbGe0dlM9XChFb6TIF5U9CqGycIJwoWCYXvv\nCKa+cwvg1q5hykNU42HDVxuOPID/vPcfgB/pLEVnPF529FbIWz+PqZm9RuxfFSrzysJelBJJXuCl\n01+D+OxHAThMVjXrC6VdVGXhLeC5W3wzJC9VWC0agLgCXo3n7LQxZPb4SGZw9zTcsO8eXLjnX7yt\nTGgxM1lwzqLogSynrUGZQ3zzq2gUg1q4kK8sYcPCAV+TJTi4E35hLhhw9ZSXrgAAIABJREFUwkGG\nMeBM68c0kxX5QM62yV/MbL81w1Aps+GFCwUzlczdhZfAOIXIqvvTuY6K24dtd38Fuzq233SeC+/4\nKF596AteeMwbV+nXfSMbKfqe5lGJxgvDQgrO0CwGCLu2FhwttoHgSLWWr+UyWRV9oYyb5njAAm1b\nXsF/Nrxwod7vUInMHZZFC9/Hju7F2SuPeazO9sV9aB/bj5cdva2WwUhGLHydydLPhv7e9LkSLqSw\nu3leOcMbHvs4Nn3mZi/DtHrtuMw8R8wNF1L3wirIKn0my2a9+nMsPX9VB5ekGo1SMVk018ncB5Lu\nuQD1zFjGWXiM82k7NewJD7L6Dz6AA//1D7Hy3W//ezfl/9pYpCll8prgeznjC7rwqPRfzNWFqq9k\nvKZaJpo+kGgBZkMmQjC0e2ryTnlYAxqyp0tE6M8bUYCSwI/D0NDiT1vFUL0jr4SDszcirzBZQVlo\net0uRqRpcsHAK458Bese/DYms2XT35BJKyqGNAvBpv33AACie7+lf28XatKDrZ47aBakKuNnQn8u\nk5X49yQKVIHAapHIyAFMgfQLybrFEq0mi4M7zGW70Kn3DqsC+ExW6DQ3LHNTU+lJnX3Yfd9XcO7R\ne+CutVHIASkR//MnsLN7yKtw3nDDhcxq5566eB/Gv3kLrj/2Da++FACc87n34YWP3YLxzNYoCzgD\ndxYOWjCFYLbiBKsv1PVNciv3oqJ/IqNFp7qn5rBwYZIXHvg4YSKAc21f28UwWvSwdd8P8NLpr2Fk\n7pA5JnLAlEk4YczoiAAYEE1tSwP1vo9nHe83pMky2cGM4Vf3fQrP+NoHbL/zxPS7LxRL0iwTc54o\nFCYZBLDMN41TFUzVNFmu8F1Y4buojE1RSFz83U/ixcfu8Bg81+kdWowUAA8D73tyVKjSe6jL05BR\nuNAk7OR+QWTBgGaZojF7xCsvU712XGbmOaiFt42G0A8XVkFWY/YwdnUO1B0xHSat6j+ZTuBgcPZy\nDDhQ1EGW+46rLbnU/+NKCPO0nRr2hAdZ+aJKtZdJ+mOOfOKb1CBrrNQeXkWzRItTUPqfT5YEssgD\n9ytPV0s4uHNdwDmayQoAYDloW+aEq4kbRw9iW++Ip7sh8CMc/ZPJZqqKfwmg9AfYcvR+21bhl3AI\nZA6q6UWLEZVjoO2E3IUzdMTkYTULE37YgfZEq2VbAmBOyQwSXZOZCdTJXMv7/sRvMh7zCsiCnQw9\nTZZhpnzhrGKybPivnXa9NlPxQ0/47jBZYZkbvdFLpm83nzeZHZsoEJhKFzHy4Pfx8qO3OSJfn3Ex\noWjBMaEB1FS6YO5xW5cH4HqBaDJXOOwzWWQUygHggywjSq9qXPypZ1hpDABo6HBctSp/dYG1W+FY\nhiasMFnVMgfuMapvNgQMANv/6YPqeM4wnnXtb0L3HjlZm5XswlKX7GiUqcesLKwkOHC844GWuOJY\nRblNSCFmOSxzc544FNg8sNodeqcMsKQ6T5VipJScUg215UPDhXZvUMCy2RQCNm0N/WfftEn4Gj+b\nXajnuTzxjo9kVZPlb+3FHQCSD8kwNecp7TsvGEPTKWRswoWBL3wnDSy9M5ff+VHFaFfD1VJi0+we\nTBYd73M4c7ZXn25IuNALv7tMVii89/S0nRr2hAZZeVFi30GlOWB6H7aT2eQqVQBw58G7AdTBg2EF\n9Mtfjk8CAJ587B7kS4u2js8JagtZ7OF4owFHI7O0PL3QJNpmC3O48chXMDJ32BxDTBZ3xPjkGRq9\nT8V7bd/6aVzx6G24fEb1TbAKkyVVJpebXUiebaMSpgKA7f2jtpq4AzYAYHa+Qx1V/+YFzlveY3UV\nHsiyWU5xJPDCK7YDAHZ2D2P1QD1bBBDO/fancdGtH/KudSKNRsPR4gSycEAC0Mr7DpNgF1LhsB5N\nDbIakVqIO31bRJEsdLMR9cJRZXqazqQcBRylc+/dbU1iBxgFlcXMXEMvyLT/nfncvY+CIczrC4Hg\nVtYv4Sx+Toaam8lXZRWr4mbTJ83WuKUuZKXtVgfnhwuJxSEmq5rBWB2DKvtq+8BNtX7AXyRjuKF9\nP1xIoCCUhV9njNpHNayGaCqjzNavM7XhZO4I3znWJgu2H9LvY7efgklnW53Kc+Myfm64MKwAl8Kp\n2eSG7dxyMXbvwso9LfykGhNy1+NU1WRRHwgo99Pc25OUp/adKypMVuBcuiEz09bn7LsVlxz9vvmO\n5sZquDAobHjWa1MFfPGFOTz3sa/gZT/6mB6TOsgKnGQl5jBZJRVKdcfY2ZIrOs1knZL2hAZZ991/\nCOLLnwEAsDD6d27N/73xy64CAIx25gDUGSszAegXrZicAgCsGizg8LvfWcsQosnIgiqfjgeAkEtE\n2mM8s3cIzUR7YJXyCnHHTtguk2UmAH2tqt7HCONnVKizVQ7059zTZMUoja6MGK4yy3Bm9yAaqC/6\n18z9AEFHbYdRXfjauQaNup3NmUO4/vg3MPG921TfnAmXFbkHXEZaIS5YfgQvP3orbnhI7QkoOMM5\nnX2Ymn4Ezf6yOVZK6eh6/LISzdzWshHOtjo7D92LN+z7BMKPfwC/tu+TWHf3V1U7GPNCe42BYhdp\nT76VHk3AbrjQGT9df6eq92nC0ZWEwnumXOF7o7QLFIF7d1wAWxYgCgVGHJ+mIZ3FSHBMZCt2jJxN\ncc0z5TGpKoyYZAWiQHjhRddseNtvUxVkpVn9WRGcmVpmHkitZltW9DWiLHDVo19Bf8+jpg3DQFYo\nmAey3GtHQ8KF5jknkOUCMRcEOyHE2jVz+x4RaKXkB0Ddo8BhYnhFz/Tihz6LN+79B1ONvQrkXMaP\n9tKTABoO2ysE89gmutdBwI2TBpw4XCgyKpSqmT0CocT4VeY/Gqc4EqoyfqeLTf3jliVO7Tv3qv2f\nx8TAbhofOWPcdFjmHQt7vWucSPguKCxd7QN9ru8R6+n5E75Gkzl9oWNCwT3wVayo98YtQu1qTKNQ\neGHp03Zq2BMaZDUO7zELAo9OfpCVlMDxaALxQL2EVU0WVQmlwpG5ZrIAIDl40LzQXc16NCqb7dK8\nZ6dPYCzve6UDdv3LP6rfVEDWyJ4fmb8LzWTt6B/FxIJiuMIKk2VSzKnNXTWBkBaqygpQ6MsFgO39\nD+Jnj34Vjdu/oM9VmeA0W1Rd+F53QAFvVBanQE9ugeMN8iL3RL5BkeEFx7/p/Y5zhm39aVRN5hag\nGfZQtzF2QJabXbj1oNaJPfYQxvMuJu6yob3IaW6U2BDwGck0Wrn1fsnc7NNmmdRAkfrcLhRRwL0Q\nSOiIp5sV5o0+d81deDeEdrKPpRsuZJjMLBCFqUHlZIA65zTh06Tw2u+xjcxZ/CrPQJjbfS8Bm3la\nDRVV90B0/x5fOIzNfbsBMbXpnM4+bJ95GAdv+hNznqrjQ8d7C7IbmvKYLAd4oDTvsctYMwaM5l2c\nt7wHMbdC8JQFWGlOoNSlAAJdv85NIHHDYHHgA0JR+iBr/WAWoSxAtzSoJhScIDOv6YCsgHPDqAEw\nIu64AvCqOy2Y8cj8Ta2hQ8Y0TlWQRf3hjCGOBC6/9/P4+cNfxOpl9W5yB2RtSOawbumQ+b87xucu\n70HRtaDYa5PuarWEg8iHM1n0OT0zVSG72bvQCQuWHTUPRaEwfQaAYkW9Ny77KZzkotNM1qlpT2iQ\nNTLWNn+zUwBkDZICK0EbIktQ9PteFW7ACRdq7z1vjNjv4oZ50bsVvQVtLUJTvBuWGi/8yaaxPAsp\npdkCxHy+70H0Hn4IgA0XAsC5t30YgJ2U3W1CADVBR2VqJsDI8QhdTVZYqajsmpg9qv6tTnBCV2ce\nwi4APmADALlaMX+0ITWgPExXQ1MFl9SmjYPZ2ueysAzVv/xQtZEm1UZeDRdqHYkuSjjMAseDJZYj\nPTaNlx38El5z8PMAYJgHAAgcJqtRpLUFAAAa0hFkVxZeAvNhwNFy6zkRwxJw7ylwF94Jh8nyamwx\nhlWZy/ZpkMVsLbNgSE2uXuIziu6C7P7dS3KvEndIWaWPx2Q5ovthequX7vk8XnX4FkeTRVo/X1sm\nOKslndA5PUDjls3wMoGd7VtQD88CwDmH7sLr930S1x//Bs47dq93TBI2MP2sl6lrpBQutIxwKKtM\nlrtY++UPzBjo8Rtt+XILKmCr+mPHjLI/qZ9NhwGVuXXuQo8x1QC5sg0U10xWKDiunfk22sszuq3D\nmSx3PmzGATZ21Ds3onWDLPEroUduCNwBLquSJUz/zfsxzNwNzSPn/pPO0HU0ABtGJEnB5js+7bfZ\nhAvtM1tokDXeDj3hOzFZ3jMDu3VUVKlnd9pODXtCg6zRiVHz96kQLhxkBVaCFgAgX1iogQcCDYHe\nBysJ7IIdjI6aFPO0q75vRL43aopjOkunu7gCQNFoDRVjAoDUmgcKF7oWV1gUt17UmCsKdrJ0XFaA\nvDe3iKfp2/qN6t9qsVBaOIYAIwA1JsuIfx2WKS5TLwTHK9cGACYLrE6X0GuMep/LvKjX1jIgy9G5\nydKEAoshIIu0GK5uhBaUYllNvKPFMCbLttXNLHOtUbiaLH/hjRwmoVn64U1ALTSmSdIPVY46tFvk\nXoMVXqYiNOieXJ7GVKrCN6Gz+IUBx1jWweqFQ54OyA1fufd9fnng3SMaJwqp2cKz9jecM7TzvtLl\nuckCwfB7Z57dygbcVfa10ExyIJh5rgGfKQ6HaLIAYNIJqbrnPO+w3UZmTUcJ12VRgEMiZwIp19sO\n5TbURuE1JXyn0JIP/Gxmo9/nUD8DY207f3LGanWyyLxwIffBudSsTCMMfNE4AT/tRMyPKGeHNFSB\n4Lh46SFzPKuArMM7nqKOc8bYndsifRxLKiUfinoInKz/6CMYZm52oXtPKWM2jnyQJQrrNG4ezCDs\nr3jfGybLmesIjI61Y08yQUyWy35etni/cUSiUHjA77SdGvaEBllRq2n+5qeA8H2Q5hZkLS4YL4nM\n1I8ZKNAyCJ3+j4xAcIbnzXwLv3DXBzGS97yJknPrzbtMVlTxzGXc9Pby8q4fqDGWvL6lpcuwqP9b\nbU27sAt46DBZ7pYjG/szpp2iApqCll/jh8x48ENCOKrB1eM1W+Z4vJsHM7jg0Ttq53RNLsyDQ2Jh\nfIP/eUXPRf0CrDA5j1TbCdgWkS8aB4CyrwGU4+kbL97p8+b+MQ/oBKgwWYEPKgE/lBeF/sJBYLNa\nmTwoLJPlZYCeILPRZQwaFU9b6n6M9qymLyxSUxOpnffw6/s/hRsP3YJRF+g5C7v7d1FKD6xQ+0jn\nVCSJtwWV+j3HJYsP4Prj38CF3/2MbceQ8CpgAVoVcFcL6DJh97nzWKNK1ieZ2+41qdUL+WDILTCr\nxlJqEF4wYWpiEbMiKkyWrdvEPedDVEoQmGtklhEb0VmjrUbgJcd4IKviuDWLIUxWLPx+U/KLngeW\nAxWBYNVwoRkDfU/1ORJd6kI4YH6UO6xWrjJ+Weo7jHFeD4Gbfq9dh2FGPQ0E80LA1JaowmQR2FWF\nj4fXvLp++g6smT9oPqOQ4sRI5M03+bICWY3EOqXrk3k0j6vfVndmOG2nhj2hQRZv2AXrVGCykrRA\nVyimo1hZMaJKMrOnV1+9hF3RwGfWXQ0AEO02woDjomXloa1P5kxmGqAzfGjrF2cip9Tw5Izd6hqc\nQw6p3QLYyd4NF5rzhxWw4egwWg7IIuDIIHH1/N3m820rSj/BGauF7Ey2UZXJMszc8PayKpOlwWOQ\n+CHSHft/YK/lLKwr8RgAIJ9RjMJya9L7XZnbcCGTpQqLUnKCXhCyuKX7oCf5Ic8pgSx3rRH6Hrga\nj1cdvgWN43aydsMvzTJVGXyVorzx4zBZAS2wQWX7F6e6tdGAMF8P5Z4ndK9RFebSdk0VnYnUe/Zd\ncpvN1hwvbHkMutfNYoALFx40/XrFs3d5z4f790TewWt/9He4fOGHNeH7WK5CNJPHH0M2r7JGq8wf\nAT8jVq4wWaICpki0HAg/jEj6p94D92PHzMPmczfMOZksOscXQ48hhrDUYbWMBRZkGY2QfV+iMjd9\nAIazaG7RYgAIHB3T+IiutxVXgIQXLnQ1WcC6ZN78X2ZWkzUsrDrSU30+GkygBAM0KHKB/ICH4LQ/\nI41jpEBZ4DBVo3A0gTpbrxYudBjrKpMVrV1bax8AcCoEHXDjgA54aGppxbrWnDk+syCLatu5xiBx\nXucx7zNi/Mbbkdd3ChfGy3Pe8aUuU3Ra+H5q2hMcZNnQyymhyUoL9A3IWkaQV0AWhTE0yOqwGA+O\nbAPgi7ABlSbvep2tODBVh7nHZKlrrDzpKVgK2uBJ3zBZfeGzLmWWYfDYXuycs1S7dAqGullQZhsU\nJvGC43faz4nJqvQtLqyA2WXaAOvZ1kJzJtV7CPskpZ9GCEvZi/5w0SugQoPm/LodxZya9DqNMWTM\njqkscjPp/dzhW/DGvf+vYaAotJHp8KAJa+l/B7HV0xHIcr1aSu+XFe88mjtq/nYXfNLGEBAm84Tu\nAfcFyRSu4czXZOWWySJvnjmZlNVru+E/F9QBlskK9D2i8SPhcegIp8ecoqYEBG48/GVcffhOLH9T\nPUMTIzEuP8cukC5A2dk7Ag6JZ8zf7Vcm5/5m5NnxYwDUovWkzn7bVgrbUlZf5TlUIW6n0KojJndD\n3/S8Hnrn2wwblbDAgAYABtR2RaMCsuwCHmiWhNqVM45Ejx83DIplsjikB8o91s2rZ+XcR6d8y7jW\nZY2UCXoP3G+P8cKFTvLD9EO4dOkB839yzuLIZ7IIIDc7CpAdF6PIWACQaFw/P3vHtyPloQ0X6vN1\nA81kOxXgR5mzr2CqmawKk+Q+W82uAiqPtjarD0TdUQRgtFNudmFfNEypBXZ4P96058PmcGKyKCRd\nNTakorsJF1aYLAOylnz9Z9HRWq3TwvdT0v5NQdY999yDn//5nwcA7N+/HzfeeCNe+cpX4g//8A9N\nAcnHMxZbkMXDkz9c6DFZy8tm8iFr7HsQ+dIiuM6SW2ExwBhKLiCzrAKyfJFmIxZmAne9f3pp+xAY\n8Ag86RtNVsb9MZVZhkPvfqffaCmN9xyFHJct3IfnHb/TVqs+csB4kQUYRFlA5rnxAFfG12EpGrOL\nOatrYag9tWKGFP4b4jXLPBvCZOmQSb9TO57MnXoJvJUDNVkPRIS/OfuV2Lf6DADA8v0P4uyP/imu\nmL8Xmwc63KlDIMKALJ0NRtu/6M+/9tQbccfkher8usCpG56ibKSyUmTX9eZdMEoTfDW7yQUFKlzo\nMEp6EQpl4Z1LZFYrQyDNFVUDvpDYTeOPKjWyJDFZ+l51hVowy14d6I6mdZC1NlWLY+lkgzWdaLXL\nBLhZjZMLlvGrhqZpkQsFx0umv24/1+yaqXlUZbKq4cLSPpe+yLyu60t45LFUNH4DHiFwGChPb5bX\nw4WJ1Mypw2S5C7VMhuuQCLT3Hn4I23o2U5YcjnxpCdff9j9x8eID+Kl7P4FD73wb0mPqOCpPAvhz\nytrpR70+0rPXCDm29a0zkGkmuKHLwCyEo8i5ACNQq+eCAQtQghswSsCmz0KkLPDC/G7iwMjsIcii\n8DL41Hnt8ZN7FGt+9/gu1dZsOFhhw0AWj8EKdY+Sb97hH6+f9zgUGC38QsUAvM2nyWicxtuxB+RJ\nkxWsLHjH5wuWyTodLjz17N8MZN188834vd/7PSR6Urjpppvwm7/5m/joRz8KKSVuvfXWH9+42AkX\nngJMVpIV6GmQla8sm5CAa0f+4s/B+l0UYOiUasKTIoDMbIE9QLEiLpPVjAIzgdCkLIsCO/cpoW1P\nCgxEDJYmZmHvhDZ7E1CTPS1EZMzxnqOA41lz38dFy4+aujEuY7UcKvamTBNAL+Qr42uR8sCyIVLW\ntDB0/kAwj0miyXhYWr1MUrCKJos8XQKpw4zLOiNRasYnZwJ9FiDTCQfz3/4OAOCq+Xuc3xC7pn6b\naDbQfK4Xjl7BkGgQWxCT5dZUonBhVhnvgS+oJ2sVA5RZZgCEOab0F0gPDNDecTn1T4d4CXwFDCNa\n79IoM4QdR0fkMiMOsIoqiRQUUiMw1NGsRNGrL0htD2RVAHXTOlQNB2S5GWiTqd3bb+KgFVJzzrxK\n2bTAVrVApX62TdmRSoiptqk5lWCohgtlUQO7KQ99Nqm0IItBDk02oftCICtjAgm5AU6Yyn1uaEP3\nbH7e35xaX/vQ227Cz+z7Z9vWu9V2ZN0f/RBclrh29rvmnlMWXGdgn6nSqeRexFYT6raztTSD8dze\n34VbvqiupZ+rvoiRscA82wTq+1IgZwIsV5o9ArFpyTAQEURiz9lysmajziIO/dnbAQ3CvrTjOVgM\nRhA6odBg0EOPxzjUUCzoiSQRNE5hoPSLOThSHqgQYVGgqnrkVEA04NjaP1Y7X/V9BGDu9Xg7QiAL\npCxAwYXRZLn1vgAgX1AMYKSZ5TqEP20ns/2bgaytW7five99r/n/fffdh8suuwwAcPXVV+POO+88\n0U+NMXciDupi7JPNilL6mqwh25MM9u4FG/SR8AgDXW2ZQJZrgSy8TJhmJIw3Twv+4m1fMWCgWwi7\n6GvWYD4ax0NX34DbVqvsHpllXsbe4VhVqKfsuOVeff875oTmjker1PFJahifTERIeYSwzCDL0pv8\nvrX1SnVdAlncL2BqKmgPKeGQHDnshf4A6xnzxwkXugCPzk/AMmWB2qOQGB025DfEuulrpXpfOWKw\nDKOQMyR66yLDZDkMCAHCsgJqpQMQiaXqaHaoWFw0eg97jL0nzTjwRdZ6UaIsr6VAgWCh2TI26Jus\nRgAID1ttiRd2dEpiULV3AsOGySorTNaQOkWuULmqv2NOwoCbyeqCLDdEFSWuvot7WZYuaHetymQZ\nJsipxu6Jmx2GtRouJIBClvHAex4paSHRz0eZ1d910i+WqQVZaclQgBkWqFpqpVhZgSwKPPZf3og1\nA6uXGvaOAACOK8aJhfX5k+k5leruAcAlZ9tQbaH1hmQkM4h1+O6gBjSlfs4oPD3gEXIemGebBPAJ\nDzEbjUOkA+Rzs+ZdGpQMfR6DD1yQpdo0G44DAPoPPYjO7aqw7xxvK1DmhBd5liDloamOTwxTj/uS\nCJp/AsEQyQwpD5HTs5xnNZDFCCguL2IqXcSyUGMitfZyGGNG93p8JEJcpEh4iDRqGSarqi2jcSLh\neyZOfkLhtFn7N0Mu1113HQ4dssXipJQmvNNut7GysnKinxpbtcq+5GvWjNbCSf9eNjU1+uMPGmJx\nI1QLL2MQ6cATrrrGswQJD23plSAAKwtMTY2CZLZhmWPrpgnTljVN6QjFJaamRtFzMs/SIDLp4SNc\nT+pcINm8CwsPKw+tFauUfprWM51lKLMMU1PjXhvXjIeYmhrFrMgxD+DuJ18PuUexCxMtAd4UmANQ\nRrG57uRoBCDCowCOrt6Ohyd34WkHvoFIqDGdWU6w4Cwq4yMRpqZG0dI7JX9u3VW4eu4ujOddHHrb\nTfCXACDi6jyPpgNUg9E0TqNtu+tgUGRoJ8tYFuqapQiQF9IJTdez+VaNqn4HKJAxgVxnoE00Baam\nRhGiRAGGpITykAG0Q6Z+4/oMssTU1CiyyL9GkCWmrVS5ejYax0i/jxEkCMfG4cpsI5TmeCmlx7hE\nvWVMTY2iOMKwAGApaGN1towmyzE1NYrlWVVodjqexPpkHq1kxV7byWQMysx83uY6xCNihHkPqyYa\naE2NgqQ85ES0hHpeH2iPQXR1VhXseaKmv5CMtgLVpgcfwvaPv8d8Hgt779xsy7gY2DbFASZyC3pG\nGupeTB1bhgv1JkZCtKdGsWqmi12dA7hsUemSyJnbuH7MY7dYqca2KEpvXCdGIowKf3EtwcBLey8C\nWaAEQ8r0sz8WI5qw7685V1QiGglwEIpJZYIj4wFGSnWPur0BDjvHt/IuxkdtmD8HR4DSjFP1/JAS\nayZb4JOjqJbbXTWh3iDaxeG6p23DhvX2PY8q5QzG2yEmpkYxEQM9APtaG7BlcByhfgYfkxkyKFav\nEAGQ9TE1NQrWEjiuPz/cWIuzuwcQzhxGHAC57ndfxOD9BayeaICHIca5ZtuDJtZkmsHUEyKBMpHM\nIzp2AOPn7YbIUyS8afZ5DPRcIDnHvBjFkcYUzlvZi1Xj6vncsH4cUZkj44EBWZNjMZYalXpioTpP\nZ3kGhwA8NLINGwcz2JQvqjl2sISqtWKBNZMtdD7891iVd3AsWoV2M0bZWcDU1CgeyRMPOoeysO+F\nzJAHp0HWqWQ/MXqIO6xUt9vF2NjYj/3NwoL1bKaPLdfSa/89bGpqFDMzPx4gDrNOJ1FecxAi6Q0g\nB2oirobPyn4PiRhBp6+zj3iAPEm9675g5psQnRsxo9fCyGFvWFlgZmYFaWRhyHyvQKjBztwB5d2m\nCLAw3zOTTGex47WEwFGZZpiZWcFTzpoCtExjZXYZM80WBnMqxLSQcbTp/Efn0ZtVOoOBFIDOmJo5\nPGPKROSSoa+ZukE3wczMClYW/XHtLvcwM7OCrDdACKWdmI5XYzwfzlTlA3WeotdFykKvvAGNXX+l\nD3cavev1b8DYVU9X/c/09h8aXFWLnQLA8kIH4cwKWJYhZwKLQoVcl/cfxMyOnUCWImcCvX6GMd3v\n5bllBDMrXsioTHSf5/0+h4Vzn3XocC4ax/b+NGYfO4xok98elvnPRexkJOYPP4DHvvQ15BrsLYUj\nQB9Il1YwM7OChbsVyDjYWIf1yTyypWXMzCigVTraH5YMzDWkZuUGPMYYetj3uVsw9bMvNywRiZiX\njs2Dz6wAeYaZaBxr0iXw1J6nN/CZnaX5DtjMCvb/5c3e52Vq+yfKAgkLIBlD0FF9kFJi8723+eda\nWAGbWUF3wV8A544toDeyGse//X28dPprziCq+7yy1DfsVsoCREVuruEyhJ2lLsTyontqlIyDyRLH\njy+rDL+yQME4cp2pOzs9jzCrO4lHfvSIYakHJUevlyJjAfJ+X7+ri8e+AAAgAElEQVQTPmM28+Ae\npGs2mv+nPERQJigq84Nrx4/Mo7tYD9/Ozy5j5Exgpauuv/3Oz+Kh5GFMXvd8AL7+CwAWZpeRzawg\nW9ZhRs1aDla66r3rdJGwEJJxlDxA2U9w/PgyOjOLekxDLGuJwvyBo8j6CRiAbiaNjGJ672GIVgvt\nO7/sXcO1gQ4vAsCP3vIH2HXzh8CSBGljzLy7aV+9XyMhQ7cQhrV++IMfxgVv+s+Yn+sgKjOsBC0z\n/81ML6C/7I932lXPbP+46kPOhAJyRY7jx5aQHPefA0DNW/tu/Rcc+fRnASiHJAibKJNpHDs0C1kJ\npSfdvrl3cZkhCaru42k7me0nRg2de+65+Pa3lT7g9ttvxyWXXPKv+t1DZ12FO1ed5+36frIa9YGF\nIWSWgecZUh7gn6ee5h+YKoqZCi8iqIcLAUWhk42UbhmF1FyHbDljJj28WKRJL8BKL0Wp2ZhqSCNj\n9Lk63yufs8t8R5oipin+JRma85fJwNS0SVmAhD4fDAxdXzCOQaFBDFH4ldIODY2pKWSUM+HtIVe1\n9JGHkB6bBksSJKIi6iex7bA6WQQQSg2uTpSZBBuSFGWOnAksRMrzL2eOme9zHmCQFnb80gQr3/k2\nNh5RmVoFmOlzWcku5E4ogbRQs/oa+eJCLTxRrbXW0uzXAzordfG2W9H7ttpGiMKFVNSRCjY+0t6i\nPnf0YNytg+S0kWpsDXQodOGLX9DH64xVHZ6hEAjPM2QsRMJDL+yoKpnXCzhWzQ0XcpQomEBPNExo\n6dgHP4Atj3zH+w2FC6t1jeg+nzFeAc9OYd2mpIyzGChLw8C7TBYvSxQVYb8JU2kgzWWBnAkbVh3y\n/gJAPjdrw9WSo5eo56dMUsiiwOCTHwUALOh7N/eZTyGbtdlp5Aixsq4TM/3Ospr2D7DjdM1TNqGV\n97Fq772Y/cTHzPcUAv/B2Fne8aTL6wZNlGA25J30MdDvXSEsC17q50oxXLoWX5ra0HoGr0hzf6/d\nb3A2nqi1u2BW+gAAMhmAQarPKFGIxqLIARGYbbNWdBYrpFSAhkdGiiKzvLYdDzMhXR0m54HRNsoi\nH3pfZZ6ZjEFAyQRyHXrNl5aAZGDAoHtuWRSIyszc09N2athPDGT9zu/8Dt773vfihhtuQJZluO66\n6/5Vvztw5lNx++qnmO1cTmYzolINmniuvNZ7xs+qHZvy0OyXhyCAzLP6vllO9mV7YDOvoqSr9E+O\nfqeXFublzZcUyEpYgOVeiqipFsbqhEHhLtKMNCNHeE9iaL3YLBShPT4ZANoL7klbxVqBLMpE5Ehz\nVXSC+lUr7UAbyTrAbCaqT7rv2/Yz5u+jN/81kPS9SRgA+o+oQMqwbXVokutQ9zX7UAyS2rGU4cQ1\nmDrK1eJX6LIBvFDgqyilDbemKY6+/y/NOVIemvR2WckuLPqO1kiP8Vyo+pwvLBgw8p2Jc1XNoQrI\nWjOYR8oCfH7tVapvgwE63/kWALWQlWCGIUsOHsBAxJhuqPpgzNHEkMYqYwI8S+xG0LpNBRueCUoa\nJJkpDR7T45HwyANZUSjwiis3m/+fCCC4IIvYoT6PwfpdSCmx/M1vmO+/PnmRuTbgZ14CjvalIjsg\ndhUARks1NlRQk0CTV4dpaR7SeTYk4D3j1NacCeTGwag/S4BKiiAnJucci50UuQgh0wSdu+9Ceo+q\n8TbrPPfJYSvDyLhADg7Z7QzVfQHKeaJ32DUa8xuedSZ+7/mba98TyCLWyIDXnDRWEQoRGMZLai0p\nAJSBBVOUBDEQERptrdlLEiN875fAih7vfGEeg8cUyPrMuqvtfXAsZ34h1nxJMZZ0D6QGWeVgAJmm\nKDnH/uZ67xxFtwMOxaCZIsx5hrJbSZrR95+AcM6E2dtVZicCWYV3LyKZmXp62cxxQErDnqnzpJBl\niUde98sQslSM82k7ZezfFGRt3rwZH//4xwEAO3bswEc+8hF87GMfw0033QTxOGyBayRSPTWYLM2m\naCaLZalZiKuW8MgBWep4AkdkbuX2tXf+k7oGOLiUWPjyLYYt+frkRegNcgdk6UmJBVjpZYh1Zld1\nwiBxJ00Y3p5benGXesFezLgpCSEHian/tFJwy3B5IIuhkPC8zmrVZjqWykGkPMSXpy6rjdWyOymV\nJZAMkPAIhxpT5uOl27+mzjUk/Z6ATq/Ur4N+NmnBdG3m3W/D/Bc+D1HmyJjAsVT3WQuhCWQBVhxe\nZasUyBoufC+dUILIEpRgWIiUXiNfXDBjlbIAOQuMAB9QC9d4fwHH4kkUXIA1m94zE8gCAx6ZcEXZ\n62EQNpGyUIMvF2RplkGHcSh8KJxis65ROwjcKvbE0f7x0GSfkT19l9X/0LG1shwuyJKFYbJYWZqE\nArLFUGvTCLRXAGj3h2qvwEI/s82zz9FZy7Yv7XyAAbf6RVmoBdOtD7b8dx/w7qkEMzXnSBBv20qf\nDw/lzXz0Izj+4f8FAEh5hOWuYpbLNDUMEKCYtT1nqGefUv4BxeoUTCCfPor+Iw9hmP04JisQHPHi\n8dr39p4SyNLjSSBdM1NlqkC47FuQJQOSGqQmCWLAIwQN+zxZ50lgmZis+QUDsg411xqQ6lrBhFet\nPdN17gwDJARkUeDQn70NANDMB7hFRwvE+ASKJDHbWfVEwzD+MstQ9tUzSnMWqzDOroZLVrJ97x09\nw4yrm6U94BEGWrqR6bIZ7txUpikKB9w9MLaz1ufTdvLaE0NJ/jhG9XSK4lQAWTZcWGYpWDowE8P+\nnZd6xybc2bkuCAEpMfe5z3rHeIu3BnD3j+4AAMx+4mMGpMxFE+gnFmQVGmRlOqzVbDkgy91uI/ZD\nPy4Iozo+cjCABJBAeOFCAihLGfe9fN2mXGr9kwOyqP4QhRfpWMpOSnmIjIdo7j4fJzIWRUBZIuUh\n/n7TdfirrS9W19FlFKoZiTSOJRhK/TpQuJBKL1Rt9lP/qMFUgEIqMEWgkuUOyCImq8JWpdwWaqwC\nsLLft6yRzpjqiCYkGPLFRTOp50wg44FXBiQ7fgwM0rB9vNkygBoAHmttRE80IPWCXyYD5EKFWPoi\nBhzQwk22oHo2unffhe4P70WoN/OugizDZHGHyTLef4CER+Bp6lWs79x9l/n7X8VkyVKxYpSxV9G2\nDLjPuIhKTa+lryntFj0Lk897PqKNm7znupX30BFNlPo9kEWOwZ5Haw7A3GftRsESzIRJabEkJqvn\nZBPLIQDfbY8J5wcRUBSeJqpgHHnTgm2ynAkcbqoFO9lvC6+6JvN6aRb1uQYQSYKV736n9j05UnZc\ntYPUJ5AVoRChYov0dkcGZGuQtfDFfzJj0hcxhN7FQyaJYYUVyFKMVTY/h8HePeATq9AJWl5JFzsW\nAvOh1fTmc7O6PT6TNdBhx/FkGYmI0AlaKJYW8cM3vcVk+tVA1mCAYP0GvG/7S/XJ9TxH4UIWGHG9\nzC2T9ZU1l+D21U+ufd6JR/FPa680RajTY4rxXojG8M6dNyIbX6PGTwPRftTGQ7HV3J22k99OApCl\nmlj8K4qXPtGNwoUsCFF2OmBZZlLeD130LIw/45nmWJrY1PFq8q1uekohC5nnYLLEvgolboscchSl\nRK4Xp5w0WXpSarTccKGjFXCyCwFfCEsp5uWgr87DmGGyyiQxk0ZHCmTc9fw0E6NBlnRAFmk0DDgh\nJiu1KeAAIBr1TZhNu/TzknAlwF0MRwEuUHS7KLMU8u/fX/tNORgoDQnzNVkuyCJdirmOw1ilPIRM\nErWIZokJm7revGsZU0yWlBLFSiU8IaVhMCjLVDKOpDmCdHrabn3EODImEPZXDHDJNMNBoRfebBqg\n+sDqJ2E5HEFPNFB2O2os0lQt6IDHcAFONW79fE7/zftx+D1/BrEwCwk/7Crz3Bw/cFgPu12MChcy\nSHR+8H3V/iTB7Cc/4Z1jmDGHqaNwIT0HZQUEuwAPqDNZZPQ73myBh6FaFKWqB9fIB+iJBgpQiYoC\ng32PeX2rXlsyu3sCAQpe5iq06YCsrgMqhxm1vwjpXFYflPIQUssDXCYr5SHun1BaSRdQuyazrPYM\nAnbMpz/wfgycucW8jxpg2HChfvYo/McjxbolCUrN4A30OJAebvG2W00/+jxG0KQ6al2wItfODTMh\nsv6jj6BYXkZzu3IW88oWXxIqMeWO1RehbKnfZBpkWSYrGKrxMwVz9z5mqq93RcNkE5daPyYaDRvO\nqzDOGRdWk5VnhuVXgnir1aLjv/2ka7ESti3IOnrEjEXGQ8hIgVQao0MbzsYpwCecNsee+CDrlAoX\n6qrPTmFVKt7YjANvf0ZX/EiFWLPpaSwGznYtWiTtvujfmLzAfJ8vq0mXXv5cn59CSCTMFtHwWj5S\nT3D0+WD/PvudnkTK/sDUdSFwsXTH7cZT7Iqm1aWkqQFOWTkEZOmJMakwEkZEr8fE3dOyZg7IAlSN\nI9Fuo+h2kB2vh0QAFeIpHaG8AVmajTvYWItj8WTtdwQeVU2gBDJNwaRESosx1dKphQutVqtYri+M\nc5/736oraWL6sTy1FcXKMvqPPGLOMa63qVm87SuqHYsEsvR+ik2bmUX96+pCq9mMWpgIZPVFjLLX\nhSwKrDzyKOL9D3uLn2sM8Lb/KAcDW1iS2wVLah1QzgPMRYp56Hz/u+r7CkA6kTBcHjmI/h6V0so1\nyCKwU2UaC24XPwBDi/2SVgcAeKOpHBgpIQtV+4rpMSqIycoLC8qG6PkAn8ki54IbJsuGC3sPDw/n\nkRlNY6TBlPNsJDyCbKj7SU4SoN5hcp6GPUsAaYdODLI6d33f+9yEybMUOePm/ZWp0tkNHtuLQdBE\nwkOtH0uRO6AFAEa7toaXCReKCFI/b92770K691EFTpkev0YTiQa0wSpVc68aLpTaGSqYQHH5s9S4\nTSt2iO6BFKJWTw7wt7XKDZMVGyZrsHePchgaDTNnsop2MmeBcpL0OLnV+g3IcsKzNHfTPJ8cPqyv\nqx3FIPLChdUCsKft5LcnPsg6RcOFZJSi3IiEB74yd3Jp2pTeI401+NT6ZwCwOhk3LLMYjuL4GYq2\nJo+XwmA0wVFokSZ1HhNdXtEHBVb4vvKD7+PoX/2F+Y4m+nKgQ06AqQmUHj5kwg89Yb1CmWdWk0UT\nEhe2QGlXTdQEEozmLB0gYwIl4xCceYL/r1/8cgDAcqSrzevJioBOoEFW2ekO1VgBanGiDEvAMoeF\n1mdsfPVr8NTz6xQ+LQAZ195832pVAAChZfZc65D+ZHHRW0jJFr98ixKNpwMDODvrlWdPuqK+aGB/\na4Pq67TSeeSGyVLPFG84IEuzav3QEeDCgqyFcAwoS2QzMzj4sU+AD3r41qrdQ4XHALztP/KFeTSO\n7FPndfQqbiX9r+tQCi3Grt4IOHF2IQBM/83NkFIanRON75H3vdc7zmplNDgfAizKwcBhshrmXpep\nzYJLeIjSZAtaUHbL2qfVzgcokEVsT9HpqLaWVj+mPl9BPj839PdkBqASyJqzxyc8BIsJZFkmK2PC\n1FWiiuK1PmcpFr50S73dJ6iKTuNQZhkyFnhZssmhgyiWl3F8ajvAVMayzHNks+p5ov72Rq1T0nvg\nfhSMo2SixkyZ/VMZA1vj7FnZauGPXnMpLj638t4572lrlQLuvft+CMC+V0qTVe+bcBwDAnOdoKWA\nG1TmJqD2zJWMowA3TLDRZLHAAMl8acnZd1J47Cc5GOQ4r1BBYe3gmn6HIVCWRsKRhidm6U/byWkn\nAciicOHJD7JKw2Q5ICsgkBV4Wwe5gnjuMBJ9Eduq2gSyTHaSZmCaCnAYkKW98rziJdE1mJ7Uy8HA\nlWSZcFcx6ONoZUGb+fg/QOrQVh7EtTYDQBI0UDJuNQxparxnA7KEMIui1N4lgSyjyUqsdo0x5m0c\nnoytBgB8ftf1qg26z30NTsKAg4+MoOh2UJxgEYKUw5ksvRifsXU1dp+5rvYzWuxzHS50F2kAEEEA\nFgS1tHAC1vve8jumnIZr8dZtKHs9MCnNZFyOKc/eDTfcukbp+CiMS4svLTai5YAsPdknofqMNlGm\nz6lMRHr0CDI94d8++WQvROYalX0AgGMf+Tvzd+6Ghp3tYkomIBtNw7ZUAe+J9ppTB5fmWSgYN1X2\ny0rVdVcrAwDFwjyqpvSCGmQ1muadk1lq2pTyyIAsWVgm63BjCntam2rn9ITv3Y6jm+OGycpXVoxA\n+0RmwLl2IrL5eee7CGjWq+nnPDBA+UTP99LXvwoMS/igEG0l2eDozX+tPtYga2yVmk9kkpgwWz6u\n3rtmT93P6ff/FQALHh654LneJs3EIrmlCwB/AWJT9h3jrRa2rhvF9c840zverVw/ceEF4M2muT9m\n3hCBCvPpfs2tUpmTbvX85TtVVupiOApZScKi+SVnwtxLN4mD3q9iaXE4k5XnJlROc/0Ki73xoOeC\nNIfH/teHAAAdnC5EeqrZEx9k6XBhfgposky40GOy1AvbjAW4s7WIt4efw2RlLHAE1WpxdbflAICi\nSenQatGll7+IfJBFzFMYBuCNhtrU15lwM+1V5Z3htamyY8cg89xM8lWQlWpQR+3q/OD7OPS2m1Sb\niSngAWSRI1tYwMyH/xaA1RR17voBDv/5uyA7Kxa4cB9k0SRG3igtvMs6Iy8QDKLdBqREery+9xhZ\n6YRqeaDHXlrmcdi2JHvaavLOhdL1EJgiQBgKDhbFZmEio4KdZGLUinjF+LjSiJFYmEIgo37F/b6I\nnbCZ0sjkCwqw0WLjMlnEYg4iNbYzH/sH9bnW/8w5ICtfWYFsjwKMGZF51b665iloPuls9Zsjtia5\nl3lFDCs9F+0xw7bUwoV6wR+qzWKAK/hP2PA6QgXzw4XE8H1pzWUYe/rV6rouk9WwomeVzWczywo4\nICuxn1f3OwSUJssIt2dnDWAsuMCAq3DYYO9eJPseq91714xEgBb5OVsPK+EhWKNepDJjAXJ9D6ua\nLPlUxXh3vv+9odeb+ehHsPLwIzUANtDh2SZTFdGf/TSV7VY6jkTUVm1hpT8eJgzWaJqipgDQaahn\nvKxcy60/Fmzdbv4WLX3+0H/+hOOITqyfwtgVV5n/03PPhJIgBKsUm3b3pS8Z2n9AOTx5ZV6k96Zg\nHCA9qmFlA+MYq0QUe6/BGCTjXnYhj7WUopAIxu07TBotceyId+0VebpG1qlmT3yQdQqGC12QNR0r\nj7ARBWCxnUByR5PFWzZks3bduA0rVJksyk5q6Swk7ckToCkbTR9E6cUvCBh4u42i62drFQZkDd9w\ned/vvUkfp3U9lX3C8li1mxgGqlUFOCFMPSESfQ/YyTI5eADde+8BSFwPVTDSBVlGT1YJQ3QaakIL\nA45gQrFAbvHWqlGIBgCY8AEVD0OvlhLZgabyvKnwIoVQKcTXiAR4HNXS981CpI30JwC0Z94zpQCI\nGeDjfn2wvoh1BiozQuR8cQEyjEyo1N10mfRh3UZlSygdLqM94pKjR5B3OpD6mTsRk1UygdbZ56i/\nHcBU6v0L0uPH7GbG5DCMjKLsdDxd1OQLFAMph2Rb0r5z2fy8eTYLLaIfZrnDZGXz8+h87zvImMAP\nxp8E0R4xbS0HA7C4Aca5cWwKJ9ybeuHCwmG4Qnxj8sLadSWYAllCKMfDCSGBMfD2iAkTRRP2Pm7+\nrd9B+wJ7PgrLMb3Iu+Oa8Ai8VQdoGRcoiY1zCtlGGzdi3Vk7asdv+6P/hlXPe4H5/72//abaMYDa\n4JsnA2zaMoVzz1IJNWWamHpfsQZZJF0go1CaEBzCARV37laAq6xEJNwQXrTdli7gGmS5EgoAEM7c\nyRjD6FMvr10bQaDAcZog2rDRmy9qxpipYWWuTSCXWyare9+PAKg5kyIJgwP7VakcOE6e1piaWm36\n+cry0sxDgH2vWeZLCRaLk3+P3tPm28kDsk6BcGFRlhCceZqsFb3NRDMW4I7XVjjhK9G2k8ClF2zF\nH7z2CgAKZMmytAJjYrJG/QWZvPIgCMzkBVhNViA4RKutmCz3dxS2WNK7x09MYNf7P4ixK670jiu1\nJ52ICL1f/32jcxloXUZWAUAAjLA4F6GekOz9XRmyrQSxboIzb/Im0FhWgFG3OW761j5PlXzo3nuP\n+f5Ho34tGumwiCzyARULAu+eAQA4t2n7VOV6qZK1GQmwOPa20wHqIZPAWXh5Q4U/3LR3AAgaMfiI\nFaH3NUPCGg3DoOWLCx7j5W66TCBrJfJBVnOgAOBSOAIWBEgPH0be6YJp9rRa1BWw9YDcMDYAfGDL\nC1WbAOSzsyZ7kMA/14xdvrJiGBEx6te2coHFF9ZdoQBBUaB3v1rkCsaxbZPP6pFdfI7SqMksw9Id\nX1djMDaOX/np3aatxGQRAKVwYZlYJitxNhqWea7KajCOnAkcbK7Drps/5F1XQjEYYs0U0mPTZmHe\nsmECb/75ixGM2TE/8/W/Zv5unX0O2uc7oE0/yy5zTZbyACKug4UHR7Z7GkVjXJj3kEyMjSHauAnt\n3efVj6c26e+SgweUCLzVMkB05Zt3YrBHJV5s3qycw7Mvv8gDbeRABIJBjNh+r55SAGP9ar9vj7Zs\nEdRogw3FCg3yq+8dC0O8/iXn4zdfpsatuXMn1rzsBoTXPA+SxOpCJzMMBmBRZDYK/8JGtX1WY71y\nju7YpthNcgbJvHBhlqNz7z3IdOmFlAVmfure9QMTol3RTB0rCyT795m55rILVP+e99StCFevro1T\n/tJf9K49J0+HC081e+KDLHFqlXDgnBlQxCfXmO+qmqzSmSCFw2SJRoyx1RNgQYDO976DR/+fX0N6\nTNUuIj1MOeaDLPLKg4BB6IW6gBKiAiqsxdttr1goAOSa3UlnZ1Qbd+wE4xysMqm7AIWPT2D9f3gd\n4i1bceR8NYkVrA6y6Nqz4RggJQYHDpjvhomtjYaBMwQOq0O1h0pXw9ZumyydUHC0zt3t6SEApSnK\nrnupPY/Tp3zK192wMPTGBdDgQC+KFC6tlcaIA6+tZA+PbPX+T2G3iWuvg2i2IPPcnIuAXBRwxBus\nCJiqTrNmC2W/hzJLVVkQ597z2AFZlOUU+mObjKtnUDKOcP0GJAf2A1KC6WdOVgDhxC/+B3xhnQLZ\nje2WKYm2bsNsvMo7lvRj9FxSWLRYXrKV0fVC3L33HqTTR1EsL4M3GvjHDdfg0dZmtM5WY0OalbN3\nTuGF15yNYfaaFykwLbMcic6E3fXmN+Np5643C2c5GOg0fZ0cQCDLDRfy0Ix7NjenfhNGtsQHY5i4\n1u5YcVgXlgym1qLs9ZDNqPdl85Y1OHPTuAc2wvUbvDZHG9T/g01W4xa26yCrZByhs3fr+NXPwOef\n9Rs4Hk8ijKMaGBm/8iov9BqsmsTOt79Lvb/BcLZkx9vfhfaFqmr+obe/FYAK27nz0tLtCryuWTuO\n97zhKrzy2l1mTgGcMBjn3ucvvnY3XvHsXbjhmjOx6Y2/jQ2/+nps+k+/hS9NPdW2seGE7JsULqw7\nPBc/aQoXnGEBy+R1z0f7p37aOwbQpUWiCKEO/983shNnfeBvcfFfvw9nfeBvsfN6dQ8vvHCbdw0e\nx1i3qmk0WVTIOHvKFegFTQxEDFFllmN1j1emnHdbCJyzcw3e98arce0lWxBOWWE/zYli90Von28z\nwn/lZZdg3arTGYankj3hQVZwioULBWdGoOwyMo1IKO2QNumEp4IRO+nyKAYPQ+NxyiRB90cqu8aE\nZZptb2IkrzwU3IRNhFMGQTFZOqPPqadTaq3C4PiM/g1lrfkgK3T6EQqG0UsuxbY//K/gY+O6XfVJ\nndicdbu2AwDmdaHVL0xd7m05QUaZdJzBAy5EcHJnM+fG1u1mQQoDDh7HiLf4wKYE8+ttucBxfALh\nlK7IzJjSeFTqDImRUQM/iMkiTUymQVczDrwQAVnBBNa+8lXm/9Hatdh184ew9oYbTUgo1/vTmbpD\nAUe8bbvqq8MgiXYLZa9n0/rH7L1wGQ6q3s+CwLt/M+fYjLlw0maEEcg6Hq/CyGXOQuiEIBs7dprn\n7PFql5lSIeMEspYNY+UuxLOfUQU+V7/oJXi0vQVgDMHkau9cY0GJiSedhckX/rT3eeOMMwEhwJtN\nZDPHMdi/H8GqSYSrFYikPmezMyhWVswYWiYr8UDW/pYKkR39q79AeuQwZOT3b+0NN2LXX30A89e8\nBJ/XoDPUm95TWDxYpdpObB2gQs/rf/m12PSffguAYrN2vP1d2PqW3zfHtM+oV/xeCVoIAztd81Yb\nA10GJQr80NzG3/iPmHjOc70tYrb/jz+1e3KK+vsYbdqMcNWqmlPAmz7IMp/HDYy2ohqYooKqgWBe\nvxujbTz30i2IQoH2ubsxesmlaO8+z+r1YKMWgL0vjHNs/5ObzOduaN01d6sk5oBaFkWIw+HJU9c8\neRPe98arsfscf0uheOs2/Mlrn4rN6ycgswzZ9DR4q43GT99gj3Hmk3jbdjznUgWSo1/6DRsC1o5Z\nI9LP/5jVXpr+CA7hMNnn7liNm153ee2403by2hMeZJ1a4UINsvRi7IaJGnHgTVZutlvoTtIaHE3+\n1IvMZ1T/icIyQnBvMiL9UxgIcw3uhOeCgHu6LzIZRiosdlDtlUYhFo8hATCy3jJygbMQxBroVPe5\nA1QJgCjk2PXkJ3mfZzwYevxDbTWpCc4gJpztWDSTxR2tWbx1q5l0A/3v+JVXeYxCyTiiEQtWmCMS\nDzgzolma7NvnX4Cxq55uWDzeaBiWlYTHVBSRmK1GJGqLQq7vhRsSEmPjZjsZWvzn/1ltk0Q6kygQ\nWPWca9E6dze2/JfftW1tj6AcDJAcPAgAaK5RC/umNW3vPlEV/UYkjA5r9NLLEESUtekvAlwDBMk4\nNv6KDXG5GVpMCAPkeKPhgQCvz8SYaiBw+D1/hlmTLt9E40xVTLP/sNLMxVu24G2/ejne+rqnee8I\nYDPuSMsFAOt+8TXY8ttvAmMMrXN3KyC1tIh4m2UoyLmY/UL6bCYAAB5eSURBVMePm+sClikpktTL\nDp2OV6Ox8wz7+yGJDywIMDj3EiQEhPX4UTX4QINW97kDgLHLr/RCduGqVQgcILNm7QRWv+jF5v+f\nfvLPoS8aHpAQ7RGkegP5KBSYuPqZ5rt461a1qfUa5Si0L7jQ04HySl/GLr8SW970FtXmynjzVqu2\n1RHgO1quc0hsnxDc6zcVCa7a5bttAWXBObb98Z9g6pWvQrzRsrYu8xat8wsuk3kAdK1lC3kUm3lo\nmDWioNa25q6zVFa7rpSfTh9FODWFnRvHcMOzzsQfveZSNLZakLXlt9+EF16xHe/6jStx4ZlrjDNU\nNXIYok0W1AWCo3nGrhO277Sd/PbEB1liuBdyMhqFC4k2buywHmszEuDOpCQdfVbksCGks2nu3Int\n/+1/ALAbxlJYhnPmhRgJtCidhBYAO8UfA8EQDPGyhNZqkfETMFmrN9nU68BZCGjiqzJTx7adjx+O\nnYFWHHiTKaBA1ooTLgzXr8emt78HPar9xJmnXaNkJXcdCNeuNYXrqQ0T1zwbZ7zblqHgssTEpAMq\nXHZIh08BuziwIMD6V/8yRnQ4JVq7DmGgLtJrq/sz2LsHgK3YzRirMVnfmzinfj0X3BD40h0j/Ucj\nFgjXTGHzG3/b86JHn6zqT81+SumfRtdP4Y9ecyl+91UXe4kUHZBOLDDCfhZGFtwxX0Mjn3q1125a\nIMJ2G8+8aCN+4ToNjvX48EYDa08Q5jAJFq6WriwBxhCuncJqzUqRviWYXI01E02sXdWqMSmUceeC\nhmj9BrMQt84513ze2GpBVrTOL8EhdUkB0htVmSzJOLa++fex8Q2/CTExgZELLsAwc9kXl7kBgFCP\nWUuHg91w0ePZ6rGG1+d+yyZxkPFGA2mm+hCHwgBVwALKkUsvw8bf+I/Y8Lpf9y9Q0UjGW7aawrXe\nPYLN8quaKxng7XrBWneueTx77QvPxXMv3YJNU200IoF402asetZz/Gu5IGv9cJAVuWPjHMOiCNHj\ngCyyDb/ya5h6xSux8x3vMu98wwFL4dRaMMZw3WVbsXXdKGIHZPFGA4wxjI+oZ2nkwidDjI5h9PIr\nvGuMPPkpWPfqX8LmN/6W/S1nGLlIvcNjV/nv3Gk7NewJn8pgswtPfk0WMVkbXvNarHzvOxi7+pnA\nvUrj0IgCCEcvo4pHqj6HTUfz5LATwZophS6oijpR9Zx5AnfSZIWB4106GqMw4Bh/+jMw/4XPe+0V\ngkOMjprsOCqcWQVZU1vWA1DHuN42/Z1XSjs8/OTno/Ojaax2GCMyKjpK1ti+A5HL5OkJcOsf/DFY\nEEJ+Uy26rrcdjE+AQYX3XNDnGkeJ5qgd72jE/t2KA4gh4mMAWPvKV6F93vlo7d4N/iFVGLQ3qlkf\nHVIswgjIgbwoEa63G8HOXv4CfP246q8LskInJFZd1KgmTzPyx/Dl15yJI3NdjD99J+Y+91lkulxB\nMLEKW9dpnZOzEK6UAYBcifG1RkXK0mR7Ce6Hd87YsRbXX9HFxWep9m998++j98D9aJ1zLn7hHNsO\nJqzYePf2SRyeqZf7ePWLzseR1jqMNXtwq4K1zj4XwfiE2ayXLJh0nAqmFutcM1jNsyzzOfGc52Lx\nK19CtNFq6Fz2Kd6+3fxdBTiUkUlg5uF3vceEytzsxZELLsLIO94NAGi/+3ZsWOMzvlRiBqgzVoEW\nOo9cehnWDQaINm/B41kgGPJCYqQZYsl5DoIKKwsA4AyJZrLiUHggkuYIxuwC7lq1ur4YtWBIVEAW\nPwHI8sqoVJNCoEutDAkzDrNXPPvHMDmOnjJcW69XB/gMerBuA2h243H0uEwW2agTEidb95pfhhgd\nxcKXvojmLr+N8abNtePJGtu344x3/Xntc8YYxitASkoJMTKKM//nX9bKVZy2U8NOHpB1CjBZRSHN\nYjZxzbO975SX6jA0QQhApfe6k6urs+FhiGDVKrMAmY2JC+mFo1yQxUV90gwFRzg1hfWvfR2mdSFC\nQLEbzAF1ZtKshA/i1RYoudqoYAiTJcbHzb0UnNdCEbRlBZ72TOBbX8PEs56DQDCEAUeWl6DTE0sh\n5axpq7nuxASA48OaatshS2+haIy0QaB2tBXafldOIEZGTHYltb034uuGyrAB5MoxaO6yex72dp0P\nOaOSFOjaLIqsVgaoef9075qx/6o+76nWk26dsxud731H990pB+Hcu+VCAMjRiAPLDOS53YWAM69O\nG2MMP3O1ZVqDiQmMVTxzdRGdhVeW+NlnnoF1q5pYdfHPYeFjf28OWbtmHFu3rzc1snizifGrrsaq\nF/yU6rOzsIuxMY+pBJwFnTGse/Uvm8+nbrgRUy9/hRfuiTdtVgCWcbSdjcRZEECMjpntnkytLHqm\ny1KVPGHMVMyv2nve8PTa88RPwGSNP+Mao3FjjGH8ar/UwTB7x+uvRJ6XYIxh7PIr0H/4IUxc82wE\n31TOjReOZcwJF3KIseEZl8MsXLPG+7/LRFXHfhjDDfjPFoUlW7vPoynLFJHe+gd/XJMX/H81t6SK\nm6Hntce5MeHq1dQMhOvWIwr//wVsGOeYevkrsPpFL/bmQTrv+NXPRPOss07w63+dERPv1rQ7baeW\nnQThwlMHZJVSepPy45nQCyGD76Whwsy4HjpltXX7mceIUAmHUPB6KQJYEFf1zgRnHgha/WKVjVc6\n9bS2vOktEK02Vo+pSagROV6nPq/LTO146ztsvTA9FlvebEW/FPIMr3shdrztnWjuPEN55E1bwsE1\nKm7oLn7BxIQBJSv94du1vOCyLR5gba6x4GS0FcGWlPjx94tHkUf1Fxqs5IWEaLUwdsVVaJ13PtCy\ni1lj23as/6XXYvt//1PvXFU2xLQvPrE/5DIRrgbMXRh6uepPMxJm0ZJFYZksxobub/fjjFHoSZYI\nBMc1T9mMqWuv9Y/Ruq9gbAw73vp2/J/27j0uyjrfA/jnmSsDMyBXuQjIRcwrhoa1JOq2bZR6EBUO\noKCnNd12T2Z5acvWLFm2UmzLMtcur/XV1m6rp7XdXh1fq2bySk9tkunJQkvRtPVoIsgdhPmdP4YZ\n5oY8ms/AzHzefwk+wzxfBng+8/t9f78nef3vEPnvRdB0rzZUG422F9C50d3yOUso0EXHONyPUZIk\nl34aSa1G4pO/QdJvn3UIrwAQt2Sp7d/WkOXc1K0KDoFZUjtctG3/5/T7AFj2P7Kdp13QCbl9ksvj\n+xIcqENYcHePm1aHmIWLYUhJtbVMaDUqBHd/XUNKKm7uHmUcnhAKSZIQfd9i16lBN9RGI1Jf3Gz7\n2Pn7lPRMhe3f1tG3sBm5ME28FabMidBGRjp839QmE1Ke24jY/3yw52vavRnqrY9KLvvpQncLSZxp\n7HrOAhKHQqfpeyTraqzTgQ7npFJhcOkCBN/q5o3HNdD00sdIvsMLRrK6e7J8ZLqwr6FrSR8A0d5m\nCxOBARqoJAnGjPFo+qzKpTFVGxll22SzIygEEEBz2xWn6cLuC5hGBZWbfY+sv+j2F/jtMVMRp5Zs\nfy0NN42wBTdD97L6sHumw9DdC7Lm3kx8X99qu0gAcNsIrdJqEdQdmEJN1v6ynike6+okrV4PbVhP\neDAFalHX2O4SUnMyE3DkRC1yb08CPrJ8Tm0KRkSIAV+fvYyL9Y47i1sNiTQ6vMM2xcUAqLU9V4uM\njGUNi3qdGsaMDDR8VAkAmDk1DS/t+BJ3jLeE1uh7FwIAjn921uHxzvuNAY4jWZFLlgHvd2+foev9\n50ZtNxJhP/JgX98dGUPwTuVJZI4YDOnTnk07rSFVpZIQfFsWLu//CCn/UQLXfc170f16CKctVvQJ\niZbtIOC4oaR11MOedVsBceWKwwpHK0NqKlq+OOIyldUbd18DsPRADv3tszjz2zJE/2yRy7kBgC48\nHMsKx8leRt/W3vOd0g+xa2juZRXc9bCfLhw8bz4iZs6GZtAglAw2I3tsLFLiLK958ET5q9JUAQGI\nf+QxtH/6P7aeMSv70SLr6syI3N53TQdc+9HkvZWUxz5k9bb9hD2NSkJozj24XLkPAUOToDnd2Odj\nPG1V6Xh8eaoOseHup2PJdwz4kBU5yABJAkJN3n/jzK4us8tIzJ0T4hEY0PMypFT8DsLcBfW2LwH0\njGDELP4FRFeX60UhpmcljTEmCvhXEzRqlUPPj7DbwsHdzuXWM3KYqkobjX/LSkLjoe7/tbuIBiQk\nIuV3LzoEuaAALYKiHb+2/TSnfnQ6Arp7bfImWfZXmn6b4/40gF3/ltNfacvokutWHjclhuLVR6ZC\nJUloWPRzdNZegqRWI3KQ5eelvslxdGbIykdRv2cXjBNucRgFMUX1TKEEGbRocddR78Qa/PVaNdTG\nnp/P8TdF4/Vfub57l3PhUdmt1DKOHAW8/6HlOa4WsuxeN/tRCfuQdc9tiZg0NgYhRj0uDL8J7We+\nhT5xqMOootpkwtAnyxAWacL338u7MNm+h04hK/5Xq/DNLyxBRk6viaRWQ1y54nKxBoCwnHsAIRCc\nde2jQ850kVFI2WDXL+M0iqMJC8eooe5DmjutHT17UUkaDeIffRxtNSdljbjIZf2bodVYwqj1jZZW\no0LqEPnThM4Mw9KQ8KPxbl/rsBm5ljsd9LIq0JOu9Rw0ahXC5hQgco7l5vFajfs7VvSnlNgQpMRe\n/2tH3mPAh6zk2GC89FC2ba8Rb+ZuurDoJ44Nldb+E+sIXmB3yJLUapdhfQAOPT+LZ47F+x+fxvQf\nDUXnx9+6HKvVqGyNwyGTpwDdt5yzDdpoNIhe9HNowyPwWIrlxqyGuSU498JziCwsdvhaclYO2Y9k\nRd3/gC0wmgJ1PavTnNhuLeL0eVOgJcC1tLve2846tROc2bPn04/GxOBv+0+hyKmpNjBtOALTXJ9b\nbd/3JkkIHDESl/ftvep0gDWgWPY46/v7IWfG2340UWPXVO1u+sr2mF6e235KR2W3+ilyTgEMqakw\n3jwe5p2WPZ2cw79c2ohIdPzrXw73XwQcR4jc7bPkwvqz7eZGxpJGY1uBqDR9/NWb051ZFxlkjbGE\nakNKKgwpqVd7yDWz/hz0tkWGEvoaufK0+Ecfl/U3B3D8vQEcF+MQeZpXJBdfCFhAz+pCOQL03Red\nPg63NoAHJKcgLDgA835qCRANbva9Gp0cDt3gEKS88JKl0fLZD12OsQ8q1q+fufU12SMb9uwvCn1d\nIAJHjkLLl0dtt/px7kGy9mRZm337EjXIgNcemep2jx97Q5attI0e5U1Ksh1vmnALop5bh5bA3kc1\nrCFLp1U79EX1xvm+be44jEr1ce49B7r/tMoQiJApP3a56EsaDUwTMgEA2emx+Oh/zzk0uV+L6Hvv\nw6X/fg/hM2b2eoycVWaBI0ai6eCn0MXE9nnsjWQYloaI2QW4+F+W/bPsp67lSE8JxxMLbkFcpOvv\n241i/d3ReVP/jtyfXZmuJbiqnftWf2BPFtEP4RvpxUtYVxfKYR3BanUzcmNP0miQ8vxLLr0K1s0O\nNaGhWFY4DhfqWpHafc83tXMAU2hNgf10YV91xz20HKKzE2UNHTj9f40OvV0AMCY5HFXHvsePM+J6\n+Qqu5IQU+32VZmQ53lDXmJyMVhnhUqOW3N5vzplwM0rjTKXTIW7pw7bdwuWwbj2gddoLSpIkDJ5X\netXHpg4JwSsrp9hGTq+V2mhEZH6h2/9LXv8cupqa3I7AOote8DM0jh7rtk9NSZIkIezuexB3+y34\n7sBBGG4a0feDnB6fGO1+scKN8pMJ8YgOD7L1MHqDGxuxro3LSJY3hVPyOQxZHmTdjFQOa8hqabt6\nyAKcdly2fs5oRHLF8xCdndCGh2HUUNfHTc2Iw97PvkP8YHnD8NfK/o9bX4FHkiw3zo4J1yIm3LWe\nMcnhqPilZy/AcllXYSasWu1253wruQtkg0b3bHw5e3IygoOuPhIUOGIkYhbdb7sH4rW63oDVF82g\nUNm9SaqAgOtakXejGJOTEWZybcofCJJigpEU434rBeoRHhyA2oY2l6l1a6sBUX9gyPIQsxAQkN/7\nYp0uc9eDJJfz7s3OSn46HEV3DOt1w84fyl/eQVq/f/Y7+LtjljGS5WzabUNlHeduM0Uif1K+6FZ0\ndHa5vKELCw7AL/NGY0iUMm8mia7GP66CA4B1VZzckDXl5jjotWr8PHd03wf/AEoFLMB/Gk7lvqZy\npguJfEI/zBdqNSoEBbgftRo/PAqDQ7ldAnkeR7I8RkCC/CbMyEEGvLys712iBzJ/GclybrTtjZzG\ndyJvFhSgQXNbp63dgcjf8TfBQ7QaNebffRPi/WjI2l92M5Y7ksWMRb7u0Xnjsf+Lc5g40v09Bon8\njX9cBQeI7PRYv2pg9fXpwvQUywrAoTJXl3G6kHxdbEQQ8qekKtqGQORNOJJFivH16cJf5I3Bxcut\nbldDupORFom/7T+Fgqk3drNKIiIamBiySDHXu4u4t9BqVLIDFmDZHXzLiil8l09E5CcYskgxkiSh\nYGqq7Jvt+gMGLCIi/8GQRYrKmZjQ36dARETUL/i2moiIiEgBDFlERERECmDIIiIiIlIAQxYRERGR\nAhiyiIiIiBTAkEVERESkAIYsIiIiIgUwZBEREREpgCGLiIiISAEMWUREREQKYMgiIiIiUgBDFhER\nEZECGLKIiIiIFMCQRURERKQAhiwiIiIiBTBkERERESmAIYuIiIhIAQxZRERERApgyCIiIiJSAEMW\nERERkQIYsoiIiIgUwJBFREREpACNJ5/MbDZjzZo1OHbsGHQ6HcrKypCYmOjJUyAiIiLyCI+OZO3e\nvRsdHR14++23sWzZMjz99NOefHoiIiIij/FoyKqqqsKkSZMAAOPGjcMXX3zhyacnIiIi8hiPThc2\nNTXBaDTaPlar1ejs7IRG4/40QkMDodGoPXV6skVGmvr7FDzOH2sGWLc/8ceaAf+s2x9rpv7h0ZBl\nNBrR3Nxs+9hsNvcasACgrq7FE6d1TSIjTfj++8b+Pg2P8seaAdbtT/yxZsA/6/aGmhkCfYdHpwsz\nMjJQWVkJAPj888+RlpbmyacnIiIi8hiPjmTdeeed2L9/PwoLCyGEQHl5uSefnoiIiMhjPBqyVCoV\nnnrqKU8+JREREVG/4GakRERERApgyCIiIiJSAEMWERERkQIYsoiIiIgUwJBFREREpACGLCIiIiIF\nMGQRERERKYAhi4iIiEgBDFlERERECmDIIiIiIlIAQxYRERGRAhiyiIiIiBTAkEVERESkAIYsIiIi\nIgUwZBEREREpgCGLiIiISAEMWUREREQKYMgiIiIiUgBDFhEREZECGLKIiIiIFMCQRURERKQAhiwi\nIiIiBTBkERERESmAIYuIiIhIAQxZRERERAqQhBCiv0+CiIiIyNdwJIuIiIhIAQxZRERERApgyCIi\nIiJSAEMWERERkQIYsoiIiIgUwJBFREREpACGLDuHDx9GSUkJAODo0aOYM2cOiouLsXbtWpjNZttx\nZrMZCxcuxJ/+9CcAQH19Pe677z4UFRXh/vvvR21tbb+c//WQU3NZWRlmzZqFkpISlJSUoLGx0atr\nBuTVvW/fPhQUFCA/Px9r1qyBEMLn6/7qq69sr3NJSQnGjBmDyspKr65bzmv9+uuvY9asWZg9ezZ2\n7doFwLt/rwF5dW/ZsgW5ubmYO3cu9u7dC8A7675y5QpWrFiB4uJizJkzB3v27MHp06dRVFSE4uJi\nPPHEE7aa//KXv2DWrFkoKCjw6prJSwgSQgixZcsWMX36dJGfny+EECIvL09UVVUJIYTYsGGD2LFj\nh+3YiooKkZ+fL9566y0hhBBPP/20ePnll4UQQuzfv1889thjHj776yO35sLCQlFbW+vwWG+tWQh5\ndTc2Nopp06bZ6t6yZYuora31+brtvf/+++Lhhx8WQnjv6y2n5suXL4vJkyeL9vZ2UV9fL6ZMmSKE\n8N6ahZBXd3V1tZgxY4Zoa2sTbW1tYubMmaKlpcUr696+fbsoKysTQghRV1cnJk+eLBYvXiw+/vhj\nIYQQv/71r8U//vEPceHCBTF9+nTR3t4uGhoabP/2xprJO3Akq1tCQgI2btxo+/j8+fPIyMgAAGRk\nZKCqqgoAsHPnTkiShEmTJtmO/eabb5Cdne1y7EAnp2az2YzTp09j9erVKCwsxPbt2wF4b82AvLoP\nHTqEtLQ0PPPMMyguLkZERATCwsJ8vm6rlpYWbNy4EatWrQLgva+3nJoNBgNiY2PR2tqK1tZWSJIE\nwHtrBuTVfeLECWRmZkKv10Ov1yMxMRHHjh3zyrpzcnLw4IMPAgCEEFCr1Th69CgyMzMBANnZ2Thw\n4ACOHDmCm2++GTqdDiaTCQkJCaiurvbKmsk7MGR1u+uuu6DRaGwfx8fH45///CcAYO/evWhtbcXx\n48fx3nvv2X6ZrUaMGIEPPvgAAPDBBx+gra3Ncyf+A8ipuaWlBfPmzcO6devw6quv4q233kJ1dbXX\n1gzIq7uurg6ffPIJli9fjldeeQVbt25FTU2Nz9dttX37duTk5CAsLAyAb/+MA0BMTAymTZuGvLw8\nlJaWAvDemgF5dQ8fPhwHDx5EU1MT6urqcOjQIbS2tnpl3UFBQTAajWhqasKSJUuwdOlSCCFsgTko\nKAiNjY1oamqCyWRyeFxTU5NX1kzegSGrF+Xl5fj973+P+fPnIzw8HKGhodixYwfOnz+P+fPn469/\n/Sv+8Ic/oLKyEosWLcJ3332HuXPn4uzZs4iOju7v078u7mo2GAwoLS2FwWCA0WjErbfeiurqap+p\nGXBf96BBgzBmzBhERkYiKCgIEyZMwFdffeXzdVv9/e9/R35+vu1jX6nbXc2VlZW4cOEC9uzZgw8/\n/BC7d+/GkSNHfKZmwH3dKSkpmDt3LhYuXIi1a9ciPT0doaGhXlv3uXPnUFpaitzcXMyYMQMqVc/l\nrbm5GcHBwTAajWhubnb4vMlk8tqaaeBjyOrFvn37sH79emzduhX19fXIysrCypUrsW3bNrzxxhvI\ny8vDggULkJ2djYMHDyI/Px9vvvkmEhMTbcPy3sZdzadOnUJRURG6urpw5coVfPbZZxg1apTP1Ay4\nr3vUqFE4fvw4Ll26hM7OThw+fBipqak+XzcANDY2oqOjAzExMbZjfaVudzWHhIQgICAAOp0Oer0e\nJpMJDQ0NPlMz4L7uS5cuobm5GX/+85/x5JNP4ty5cxg2bJhX1n3x4kXce++9WLFiBebMmQMAGDly\nJD755BMAQGVlJSZMmICxY8eiqqoK7e3taGxsxIkTJ5CWluaVNZN30PR9iH9KTEzEggULYDAYMHHi\nREyePLnXY5OSkvDII48AAKKiolBeXu6p07yheqs5NzcXBQUF0Gq1yM3NxbBhw6DT6XyiZqD3upct\nW4aFCxcCsPR8pKWlQa/X+3zdNTU1iIuLczjW13/GDxw4gIKCAqhUKmRkZCArKwvffvutT9QMuK9b\nCIGTJ09i9uzZ0Gq1WLlyJdRqtVe+1ps3b0ZDQwM2bdqETZs2AQBWrVqFsrIybNiwAcnJybjrrrug\nVqtRUlKC4uJiCCHw0EMPQa/Xe2XN5B0kIYTo75MgIiIi8jWcLiQiIiJSAEMWERERkQIYsoiIiIgU\nwJBFREREpACGLCIiIiIFcAsHIj9y9uxZ5OTkICUlBQDQ1taG4cOHY/Xq1YiIiOj1cSUlJXjjjTc8\ndZpERD6BI1lEfiYqKgrvvvsu3n33XezcuROJiYlYsmTJVR9jvSULERHJx5EsIj8mSRIeeOABZGVl\nobq6Gn/84x/x9ddf4+LFi0hKSsKLL76I9evXAwDy8/Oxbds2VFZW4oUXXkBnZyeGDBmCtWvXOtyS\nh4iILDiSReTndDodEhMTsXv3bmi1Wrz99tvYtWsX2tvbsW/fPjz++OMAgG3btuHSpUuoqKjAa6+9\nhh07duD222+3hTAiInLEkSwigiRJGDlyJOLj4/Hmm2/i5MmTOHXqFFpaWhyOO3z4sO1GvABgNpsR\nEhLSH6dMRDTgMWQR+bmOjg7U1NTgzJkzeP7551FaWopZs2ahrq4Oznfd6urqQkZGBjZv3gwAaG9v\nR3Nzc3+cNhHRgMfpQiI/ZjabsXHjRqSnp+PMmTO4++67MXv2bERERODTTz9FV1cXAECtVqOzsxPp\n6en4/PPPUVNTAwDYtGkTnn322f4sgYhowOJIFpGfuXDhAnJzcwFYQtaIESNQUVGB8+fPY/ny5di5\ncyd0Oh3GjRuHs2fPAgDuuOMO5Obm4p133kF5eTmWLl0Ks9mMwYMHY926df1ZDhHRgCUJ5/kAIiIi\nIvrBOF1IREREpACGLCIiIiIFMGQRERERKYAhi4iIiEgBDFlERERECmDIIiIiIlIAQxYRERGRAhiy\niIiIiBTw//0Azh2WQcCkAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbd7373c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.resample('m').mean().plot()\n", "apply_common('Monthly mean')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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fy+ZtpXjLtiVr587UM4qzZ88SGhpK+/btcXBwYPv27dSpU6dQkqzSYPXRrzkS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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbd7cb0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.resample('d').mean().rolling(365).mean().plot()\n", "apply_common('Rolling 365-day mean')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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JA02HEsfi0EGW6SJ1sbDZpq979GIt/vbpFZzqvyO9Ecuy2PtDMXr6dNiwPAYP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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbd4ffda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.loc['2007-3-1':, 'Temp_Max'].resample('m').mean().plot()\n", "apply_common('Ten year monthly mean')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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AmICQBQAAYAJCFgAAgAkIWQAAACbwNOuNHQ6HHnroISUnJ8tisehvf/ubfHx8dP/998ti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\nSUmJVq5cqfT09JrtDodDH3/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WU3UA4D7oyQKaMIvForvuukvDhg3ToUOH9Pbbb+vHH39U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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbbd3fcc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.loc['2014-3-1':, 'Temp_Max'].resample('m').mean().plot()\n", "apply_common('One year monthly mean')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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hCP/6sajNx4lFVpL3As0/b3DMwFRcfmEvVNZZ8c9NpyK/WKLHEdciKy1Jh/pG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\nAGAAIQsAAMAAQhYAAIABhCwAAAAD/j/yOughw1H8zAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbbdd6b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.loc['2007-2-28':, 'Temp_Max'].resample('w').mean().plot()\n", "apply_common('One year weekly mean')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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MdmQ6ORfLIgDHDsnXvvcMrpk1qxXGnCw5xXBhrJIbBEF0D3qPyOImbH6iZxOU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\nqU/51sDEm1VOBNdthfqwrE4OherCxyvVcjgqJLnTHeHlCMZ2G28+jSN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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbbecb278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = data.loc['2014-3-1':,'Temp_Max'].plot()\n", "apply_common('One year daily')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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yKvbgY/JhXNpormk3+rjJud/vd72zgSUHlvFl3loMDFLDkhmePIwBCX3wvwBG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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2acbd0cdcf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#https://www.kaggle.com/miguelferia/visualization-of-temperature-data\n", "#http://benalexkeen.com/resampling-time-series-data-with-pandas/\n", "ax = data.resample('AS').mean().plot()\n", "apply_common('Annual summary mean')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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Dwf6YohUOF0ova2jikvM7PZJyiTQNvteRr8SISRLcRgkJtO2MYd52ysT1Oa2b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0O0adD3BGRYSl8QCBdPe6jxPsVC0jQOlZYEsUT4XUIT4qQGqTRZs4wW9gLNZnyaobvFNjtIC8dE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"text/plain": [ "<matplotlib.figure.Figure at 0x2acbbfdc128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['day#'] = data.index.dayofyear\n", "fig, (ax1, ax2, ax3) = plt.subplots(nrows = 3, ncols = 1)\n", "for key, grp in data.groupby(pd.TimeGrouper(freq='AS'), group_keys=False):\n", " ax1.plot(grp['day#'], grp['Temp_Max'], alpha=0.35)\n", " ax2.plot(grp['day#'], grp['Temp_Min'], alpha=0.35)\n", " ax3.plot(grp['day#'], grp['Temp_Diff'], alpha=0.35)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

unlicense

yingjun2/project-spring2017

part2/bin/FP+Q2+Xiaoliang+-+v1.0.ipynb

1

28036

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import plotly\n", "import plotly.plotly as py\n", "from plotly.graph_objs import *\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plotly.tools.set_credentials_file(username='jxljiang221', api_key='RgXxTYxCam4vBca8DHDq')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/xiaoliangjiang/work'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>LogNumber</th>\n", " <th>Institution</th>\n", " <th>Program</th>\n", " <th>ProgramType</th>\n", " <th>ProjectTitle</th>\n", " <th>ProjectType</th>\n", " <th>AwardDate</th>\n", " <th>InstAddr1</th>\n", " <th>InstAddr2</th>\n", " <th>InstAddr3</th>\n", " <th>...</th>\n", " <th>FIPSState</th>\n", " <th>FIPSCounty</th>\n", " <th>CensusTract</th>\n", " <th>CensusBlock</th>\n", " <th>FIPSMCDCode</th>\n", " <th>FIPSPlaceCode</th>\n", " <th>CBSACode</th>\n", " <th>MetroDivisionCode</th>\n", " <th>Description</th>\n", " <th>Level</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>IA-00-00-0001-00</td>\n", " <td>Museum of the Aleutians</td>\n", " <td>Conservation Assessment Prog.</td>\n", " <td>IA</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3/30/2000</td>\n", " <td>P.O. Box 648</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>16.0</td>\n", " <td>200.0</td>\n", " <td>2014.0</td>\n", " <td>1615.0</td>\n", " <td>80770.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>IA-00-00-0002-00</td>\n", " <td>Depot Museum, Inc.</td>\n", " <td>Conservation Assessment Prog.</td>\n", " <td>IA</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3/30/2000</td>\n", " <td>P.O. Box 681420</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>49.0</td>\n", " <td>961000.0</td>\n", " <td>2013.0</td>\n", " <td>91206.0</td>\n", " <td>27616.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>IA-00-00-0003-00</td>\n", " <td>National Voting Rights Museum and Institute</td>\n", " <td>Conservation Assessment Prog.</td>\n", " <td>IA</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3/30/2000</td>\n", " <td>6 Highway 80 East</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>47.0</td>\n", " <td>957200.0</td>\n", " <td>1005.0</td>\n", " <td>92883.0</td>\n", " <td>NaN</td>\n", " <td>42820.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>IA-00-00-0004-00</td>\n", " <td>Bob Jones Museum</td>\n", " <td>Conservation Assessment Prog.</td>\n", " <td>IA</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3/30/2000</td>\n", " <td>P.O. Box 613</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>IA-00-00-0005-00</td>\n", " <td>Coronado Museum of History and Art</td>\n", " <td>Conservation Assessment Prog.</td>\n", " <td>IA</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3/30/2000</td>\n", " <td>1100 Orange Avenue</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>73.0</td>\n", " <td>10900.0</td>\n", " <td>1021.0</td>\n", " <td>92780.0</td>\n", " <td>16378.0</td>\n", " <td>41740.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 44 columns</p>\n", "</div>" ], "text/plain": [ " LogNumber Institution \\\n", "0 IA-00-00-0001-00 Museum of the Aleutians \n", "1 IA-00-00-0002-00 Depot Museum, Inc. \n", "2 IA-00-00-0003-00 National Voting Rights Museum and Institute \n", "3 IA-00-00-0004-00 Bob Jones Museum \n", "4 IA-00-00-0005-00 Coronado Museum of History and Art \n", "\n", " Program ProgramType ProjectTitle ProjectType \\\n", "0 Conservation Assessment Prog. IA NaN NaN \n", "1 Conservation Assessment Prog. IA NaN NaN \n", "2 Conservation Assessment Prog. IA NaN NaN \n", "3 Conservation Assessment Prog. IA NaN NaN \n", "4 Conservation Assessment Prog. IA NaN NaN \n", "\n", " AwardDate InstAddr1 InstAddr2 InstAddr3 ... FIPSState \\\n", "0 3/30/2000 P.O. Box 648 NaN NaN ... 2.0 \n", "1 3/30/2000 P.O. Box 681420 NaN NaN ... 1.0 \n", "2 3/30/2000 6 Highway 80 East NaN NaN ... 1.0 \n", "3 3/30/2000 P.O. Box 613 NaN NaN ... NaN \n", "4 3/30/2000 1100 Orange Avenue NaN NaN ... 6.0 \n", "\n", " FIPSCounty CensusTract CensusBlock FIPSMCDCode FIPSPlaceCode CBSACode \\\n", "0 16.0 200.0 2014.0 1615.0 80770.0 NaN \n", "1 49.0 961000.0 2013.0 91206.0 27616.0 NaN \n", "2 47.0 957200.0 1005.0 92883.0 NaN 42820.0 \n", "3 NaN NaN NaN NaN NaN NaN \n", "4 73.0 10900.0 1021.0 92780.0 16378.0 41740.0 \n", "\n", " MetroDivisionCode Description Level \n", "0 NaN NaN 1.0 \n", "1 NaN NaN 1.0 \n", "2 NaN NaN 1.0 \n", "3 NaN NaN 1.0 \n", "4 NaN NaN 1.0 \n", "\n", "[5 rows x 44 columns]" ] }, "execution_count": 274, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.read_csv('DiscGrants96to13-2017_04_10_19_27_08.csv',encoding='iso-8859-1',sep='\\t')\n", "df2.head()" ] }, { "cell_type": "code", "execution_count": 282, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df3=df2[(df2[\"Longitude\"]>-95)&(df2[\"Latitude\"]<24)]\n", "#df2=df2[(((df2[\"Longitude\"]>-95)&(df2[\"Latitude\"]<24))==False)]\n", "df2=df2[((df2[\"InstState\"]==\"GU\")|(df2[\"InstState\"]==\"VI\")|(df2[\"InstState\"]==\"PR\")|(df2[\"InstState\"]==\"MP\")|(df2[\"InstState\"]==\"FM\")|(df2[\"InstState\"]==\"MH\")|(df2[\"InstState\"]==\"AS\"))==False]\n", "#VI,GU,PR,MP,FM,MH\n", "# since some points (Saint Thomas, U.S. Virgin Islands) could not be shown on the US map, so I tried to remove them by using the codes above." ] }, { "cell_type": "code", "execution_count": 283, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df2.columns" ] }, { "cell_type": "code", "execution_count": 284, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df2.describe()" ] }, { "cell_type": "code", "execution_count": 285, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "#colors = [\"#ffcccc\",\"#ffddcc\",\"#ffeecc\",\"#ffffcc\",\"#eeffcc\",\"#ddffcc\"]\n", "colors = [\"e0e0e0\",\"#66b2ff\",\"#66ff66\",\"#ffff66\",\"#ffb266\",\"#ff6666\"]\n", "institutions = []\n", "scale = 6000\n", "\n", "for i in range(len(limits)):\n", " subdf2=df2[((df2['AwardTotal']/1e3)<limits[i][1])&((df2['AwardTotal']/1e3)>limits[i][0])]\n", " institution = dict(\n", " type = 'scattergeo',\n", " locationmode = 'USA-states',\n", " lon = subdf2['Longitude'],\n", " lat = subdf2['Latitude'],\n", " text = subdf2['Text'],\n", " marker = dict(\n", " size = subdf2['AwardTotal']/scale,\n", " color = colors[i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\n", " institutions.append(institution)" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/0.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 286, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layout = dict(\n", " title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " ),\n", " updatemenus=list([\n", " dict(\n", " x=-0.05,\n", " y=1,\n", " yanchor='top',\n", " buttons=list([\n", " dict(\n", " args=['visible', [True, True, True, True, True, True]],\n", " label='All',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [True, False, False, False, False, False]],\n", " label='0-10 thousand dollar',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [False, True, False, False, False, False]],\n", " label='10-100 thousand dollar',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [False, False, True, False, False, False]],\n", " label='100-200 thousand dollar',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [False, False, False, True, False, False]],\n", " label='200-500 thousand dollar',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [False, False, False, False, True, False]],\n", " label='500-1000 thousand dollar',\n", " method='restyle'\n", " ),\n", " dict(\n", " args=['visible', [False, False, False, False, False, True]],\n", " label='1000+ thousand dollar',\n", " method='restyle'\n", " )\n", " ]),\n", " )\n", " ]),\n", " )\n", "\n", "fig = dict( data=institutions, layout=layout )\n", "py.iplot( fig, validate=False, filename='q2testworldmap' )" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/0.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "#colors = [\"#ffcccc\",\"#ffddcc\",\"#ffeecc\",\"#ffffcc\",\"#eeffcc\",\"#ddffcc\"]\n", "colors = [\"#ff6666\",\"#ffb266\",\"#ffff66\",\"#b2ff66\",\"#66ff66\",\"#66ffb2\",\"#66ffff\",\"#66b2ff\",\"#6666ff\",\"#ff66ff\",\"#b266ff\",\"#ff66b2\",\"#000000\",\"#404040\",\"#808080\",\"#c0c0c0\",\"#ffffff\"]\n", "pt=[\"IL\",\"ST\",\"ML\",\"RE\",\"IC\",\"IM\",\"IS\",\"MN\",\"LE\",\"MP\",\"MH\",\"LT\",\"LI\",\"IG\",\"MA\",\"IA\",\"IG\"]\n", "institutions = []\n", "scale = 5000\n", "\n", "for i in range(len(colors)):\n", " subdf2=df2[df2[\"ProgramType\"]==pt[16-i]]\n", " institution = dict(\n", " type = 'scattergeo',\n", " locationmode = 'USA-states',\n", " lon = subdf2['Longitude'],\n", " lat = subdf2['Latitude'],\n", " text = subdf2['Text'],\n", " marker = dict(\n", " size = subdf2['AwardTotal']/scale,\n", " color = colors[16-i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name =pt[i])\n", " institutions.append(institution)\n", "layout = dict(\n", " title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " )\n", " )\n", "\n", "fig = dict( data=institutions, layout=layout )\n", "py.iplot( fig, validate=False, filename='q2testworldmap' )" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/0.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "#colors = [\"#ffcccc\",\"#ffddcc\",\"#ffeecc\",\"#ffffcc\",\"#eeffcc\",\"#ddffcc\"]\n", "colors = [\"#ff6666\",\"#ffb266\",\"#ffff66\",\"#b2ff66\",\"#66ff66\",\"#66ffb2\",\"#66ffff\",\"#66b2ff\",\"#6666ff\",\"#ff66ff\",\"#b266ff\",\"#ff66b2\",\"#000000\",\"#404040\",\"#808080\",\"#c0c0c0\",\"#ffffff\"]\n", "pt=[\"IL\",\"ST\",\"ML\",\"RE\",\"IC\",\"IM\",\"IS\",\"MN\",\"LE\",\"MP\",\"MH\",\"LT\",\"LI\",\"IG\",\"MA\",\"IA\",\"IG\"]\n", "institutions = []\n", "scale = 6000\n", "\n", "for i in range(len(colors)):\n", " subdf2=df2[df2[\"ProgramType\"]==pt[i]]\n", " institution = dict(\n", " type = 'scattergeo',\n", " locationmode = 'USA-states',\n", " lon = subdf2['Longitude'],\n", " lat = subdf2['Latitude'],\n", " text = subdf2['Text'],\n", " marker = dict(\n", " size = subdf2['AwardTotal']/scale,\n", " color = colors[16-i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name =pt[i])\n", " institutions.append(institution)\n", "layout = dict(\n", " title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " )\n", " )\n", "\n", "fig = dict( data=institutions, layout=layout )\n", "py.iplot( fig, validate=False, filename='q2testworldmap' )" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/0.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 272, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + 'Program Type: ' + df2['ProgramType'] + '<br>'+ 'Total Award: ' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "#colors = [\"#ffcccc\",\"#ffddcc\",\"#ffeecc\",\"#ffffcc\",\"#eeffcc\",\"#ddffcc\"]\n", "colors = [\"e0e0e0\",\"#66b2ff\",\"#66ff66\",\"#ffff66\",\"#ffb266\",\"#ff6666\"]\n", "institutions = []\n", "scale = 6000\n", "\n", "for i in range(len(limits)):\n", " subdf2=df2[((df2['AwardTotal']/1e3)<limits[i][1])&((df2['AwardTotal']/1e3)>limits[i][0])]\n", " subdf2=subdf2[1:50]\n", " institution = dict(\n", " type = 'scattergeo',\n", " locationmode = 'USA-states',\n", " lon = subdf2['Longitude'],\n", " lat = subdf2['Latitude'],\n", " text = subdf2['Text'],\n", " marker = dict(\n", " size = subdf2['AwardTotal']/scale,\n", " color = colors[i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\n", " institutions.append(institution)\n", "layout = dict(\n", " title = 'Temp Scatter Plot of Top 50 for Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " )\n", " )\n", "\n", "fig = dict( data=institutions, layout=layout )\n", "py.iplot( fig, validate=False, filename='q2testworldmap' )" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jxljiang221/4.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\n", "limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "#colors = [\"#ffcccc\",\"#ffddcc\",\"#ffeecc\",\"#ffffcc\",\"#eeffcc\",\"#ddffcc\"]\n", "colors = [\"e0e0e0\",\"#66b2ff\",\"#66ff66\",\"#ffff66\",\"#ffb266\",\"#ff6666\"]\n", "institutions = []\n", "scale = 6000\n", "\n", "\n", "for i in range(len(limits)):\n", " subdf2=df2[((df2['AwardTotal']/1e3)<limits[i][1])&((df2['AwardTotal']/1e3)>limits[i][0])]\n", " institution = dict(\n", " type = 'scattergeo',\n", " locationmode = 'ISO-3',\n", " lon = subdf2['Longitude'],\n", " lat = subdf2['Latitude'],\n", " text = subdf2['Text'],\n", " marker = dict(\n", " size = subdf2['AwardTotal']/scale,\n", " color = colors[i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\n", " institutions.append(institution)\n", "layout = dict(\n", " title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " )\n", " )\n", "\n", "fig = dict( data=institutions, layout=layout )\n", "py.iplot( fig, validate=False, filename='q2testworldmap' )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

baumanab/noaa_requests

NOAA_sandbox.ipynb

1

26222

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas.io import json\n", "import requests\n", "import os\n", "import sys\n", "import string\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "NOAA_Token_Here= 'enter as string'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Play with some basic functions adapted from [tide data functions](https://github.com/baumanab/seattle_tides)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Query Builder" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def query_builder(start_dt, end_dt, station, offset= 1):\n", "\n", " \"\"\"Function accepts: a start and end datetime string in the form 'YYYYMMDD mm:ss'\n", " which are <= 1 year apart, a station ID, and an offset. \n", " Function assembles a query parameters/arguments dict and returns an API query and the \n", " query dictionary (query_dict). The relevant base URL is the NCDC endpoint \n", " 'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'.\"\"\"\n", "\n", " import urllib\n", " \n", " # API endpoint\n", " base_url= 'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'\n", "\n", " # dict of NOAA query parameters/arguments\n", "\n", " query_dict = dict(startdate= start_dt, enddate= end_dt, stationid= station,\n", " offset= offset, datasetid= 'GHCND', limit= 1000)\n", "\n", " # encode arguments\n", "\n", " encoded_args = urllib.urlencode(query_dict)\n", " \n", " # query\n", " query = base_url + encoded_args\n", " \n", " # decode url % (reconvert reserved characters to utf8 string)\n", " query= urllib.unquote(query)\n", "\n", " # create and return query from base url and encoded arguments\n", " return query, query_dict" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2015-01-01&offset=1&limit=1000&datasetid=GHCND\n" ] } ], "source": [ "query_1, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174')\n", "print(query_1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2015-01-01&offset=1001&limit=1000&datasetid=GHCND\n" ] } ], "source": [ "query_2, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174', offset= 1001)\n", "print(query_2)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Offset Generator" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def offsetter(response):\n", " \n", " \"\"\"\n", " Function accepts a restful query response (JSON)\n", " Function returns a dictionary of offsets to pull the entire query set\n", " where the set is limited to 1000 records per query. Function also \n", " returns a record count for use in validation.\n", " \"\"\"\n", " \n", " # get repeats and repeat range\n", " import math\n", " count= response['metadata']['resultset']['count']\n", " repeats= math.ceil(count/1000.)\n", " repeat_range= range(int(repeats))\n", " \n", " # get offsets dictionary\n", " \n", " offset= 1\n", " offsets= [1]\n", " for item in repeat_range[1:]:\n", " offset += 1000\n", " offsets.append(offset)\n", " \n", " \n", " # zip up the results and convert to dictionary\n", " offset_dict= dict(zip(repeat_range[1:], offsets[1:])) # the first call has been done already to get meta\n", " \n", " return offset_dict, count # for quality control \n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Query Generator\n", "\n", "#### TODO\n", "- refactor with a decorator\n", "- make key an attribute that can be hidden" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def execute_query(query):\n", " \n", " \"\"\"\n", " Function accepts an NOAA query for daily summaries for a specfic location\n", " and executes the query.\n", " Function returns a response (JSON)\n", " \"\"\"\n", " url = query\n", " # replace token with token provided by NOAA. Enter token as string\n", " headers = {'token': NOAA_Token_Here} # https://www.ncdc.noaa.gov/cdo-web/token\n", " response = requests.get(url, headers = headers)\n", " response = response.json()\n", " \n", " return response" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'query_1' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-09badf1aeec9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mworking_1\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mexecute_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery_1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'results'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mworking_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecute_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery_2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'results'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'query_1' is not defined" ] } ], "source": [ "working_1= execute_query(query_1)['results']\n", "working_2 = execute_query(query_2)['results']" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Extract Results" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def extract_results(response):\n", " \n", " \"\"\"\n", " Function accepts a NOAA query response (JSON) return the results\n", " key values as well as the number of records (for use in validation).\n", " \"\"\"\n", " data= response['results']\n", " # for quality control to verify retrieval of all rows\n", " length= len(data)\n", " \n", " return data, length" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def collator(results):\n", " \n", " \"\"\"\n", " Functions accepts the results key of an NOAA query response (JSON)\n", " and returns a tidy data set in PANDAS, where each record is an \n", " observation about a day.\n", " \"\"\"\n", " \n", " df= pd.DataFrame(results) \n", " df= df.drop(['attributes','station'], axis=1)\n", " df= df.pivot(index= 'date',columns= 'datatype', values= 'value').reset_index()\n", " \n", " return df" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_ncdc(start_dt, end_dt, station):\n", " \n", " \"\"\"\n", " Function accepts a start date (MM-DD-YYY) an end date (MM-DD-YYYY)\n", " and a NOAA station ID. Date limit is 1 year.\n", " Function returns a tidy dataset in a PANDAS DataFrame where\n", " each row represents an observation about a day, a record count\n", " and a query parameters dictionary.\n", " \"\"\"\n", " \n", " \n", " # count for verifying retrieval of all rows\n", " record_count= 0\n", " # initial query\n", " query, query_dict= query_builder(start_dt, end_dt, station)\n", " response= execute_query(query)\n", " \n", " # extract results and count \n", " results, length= extract_results(response)\n", " record_count += length\n", " \n", " # get offsets for remaining queries\n", " off_d, count= offsetter(response)\n", " \n", " # execute remaining queries and operations\n", " for offset in off_d:\n", " query, _= query_builder(start_dt, end_dt, station, off_d[offset])\n", " print(query)\n", " response= execute_query(query)\n", " next_results, next_length= extract_results(response)\n", " \n", " record_count += next_length\n", " \n", " # concat results lists\n", " results += next_results\n", " \n", " assert record_count == count, 'record count != count'\n", " \n", " collated_data= collator(results)\n", " \n", " return collated_data, record_count, query_dict\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=1001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=2001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=3001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=4001&limit=1000&datasetid=GHCND\n" ] } ], "source": [ "test, qc, params = get_ncdc('2014-01-01', '2014-12-31', station= 'GHCND:USW00023174')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "0 2014-01-01T00:00:00\n", "1 2014-01-02T00:00:00\n", "2 2014-01-03T00:00:00\n", "3 2014-01-04T00:00:00\n", "4 2014-01-05T00:00:00\n", "Name: date, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.date.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "360 2014-12-27T00:00:00\n", "361 2014-12-28T00:00:00\n", "362 2014-12-29T00:00:00\n", "363 2014-12-30T00:00:00\n", "364 2014-12-31T00:00:00\n", "Name: date, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.date.tail()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 365 entries, 0 to 364\n", "Data columns (total 16 columns):\n", "date 365 non-null object\n", "AWND 365 non-null float64\n", "PRCP 365 non-null float64\n", "SNOW 357 non-null float64\n", "SNWD 360 non-null float64\n", "TAVG 365 non-null float64\n", "TMAX 365 non-null float64\n", "TMIN 365 non-null float64\n", "WDF2 365 non-null float64\n", "WDF5 354 non-null float64\n", "WSF2 365 non-null float64\n", "WSF5 354 non-null float64\n", "WT01 94 non-null float64\n", "WT02 5 non-null float64\n", "WT03 1 non-null float64\n", "WT08 37 non-null float64\n", "dtypes: float64(15), object(1)\n", "memory usage: 45.7+ KB\n" ] } ], "source": [ "test.info()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>datatype</th>\n", " <th>date</th>\n", " <th>AWND</th>\n", " <th>PRCP</th>\n", " <th>SNOW</th>\n", " <th>SNWD</th>\n", " <th>TAVG</th>\n", " <th>TMAX</th>\n", " <th>TMIN</th>\n", " <th>WDF2</th>\n", " <th>WDF5</th>\n", " <th>WSF2</th>\n", " <th>WSF5</th>\n", " <th>WT01</th>\n", " <th>WT02</th>\n", " <th>WT03</th>\n", " <th>WT08</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [date, AWND, PRCP, SNOW, SNWD, TAVG, TMAX, TMIN, WDF2, WDF5, WSF2, WSF5, WT01, WT02, WT03, WT08]\n", "Index: []" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test[test.date.isnull()]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "y1, qc, params = get_ncdc('2014-05-03', '2015-05-02', station= 'GHCND:USW00023174')\n", "y2, qc, params = get_ncdc('2015-05-03', '2016-05-02', station= 'GHCND:USW00023174')\n", "y3, qc, params = get_ncdc('2016-05-03', '2017-05-02', station= 'GHCND:USW00023174')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "y1.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "years= pd.concat([y1, y2, y3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "years.date.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "years.date.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "years.to_csv('LAX_3years.csv', index= False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CSV Generator" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gen_csv(df, query_dict):\n", " \"\"\"\n", " Arguments: PANDAS DataFrame, a query parameters dictionary\n", " Returns: A CSV of the df with dropped index and named by dict params\n", " \"\"\"\n", " \n", " # extract params\n", " station= query_dict['stationid']\n", " start= query_dict['startdate']\n", " end= query_dict['enddate']\n", " \n", " # using os.path in case of future expansion to other directories\n", " path= os.path.join(station + '_' + start + '_' + end + '.' + 'csv')\n", " \n", " # remove problem characters (will add more in future)\n", " exclude_chars= ':'\n", " path= path.replace(exclude_chars, \"_\")\n", " \n", " # export to csv\n", " \n", " my_csv= df.to_csv(path, index= False)\n", " \n", " return my_csv, path\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stuff, path= gen_csv(test, query_dict)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'GHCND_USW00023174_2014-01-01_2015-01-01.csv'" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Volume in drive C has no label.\n", " Volume Serial Number is CE97-BE73\n", "\n", " Directory of C:\\Users\\Andrew\\Documents\\noaa_requests\n", "\n", "05/12/2017 12:35 AM 30,420 GHCND_USW00023174_2014-01-01_2015-01-01.csv\n", " 1 File(s) 30,420 bytes\n", " 0 Dir(s) 685,094,088,704 bytes free\n" ] } ], "source": [ "ls *csv" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check dt format('DD-MM-YYYY', and whether dates span <= 1 year from a current or past date\n", "If dates exceed one year, NCDC query returns a null object\n", "Need a token take a token, have a token, keep it to yourself @ https://www.ncdc.noaa.gov/cdo-web/token\n", "start_dt: -f\n", " end_dt: C:\\Users\\vhim98198\\AppData\\Roaming\\jupyter\\runtime\\kernel-9420aae1-29a1-4c51-ae89-41b7fd679e89.json\n" ] } ], "source": [ "#!/usr/bin/env python\n", "# coding: utf-8\n", "\n", "\n", "\"\"\"Python code for querying NOAA daily summary weather and returnig a CSV per year\n", "for a specfic station. Code is intended to be executed from CLI.\"\"\"\n", "\n", "import sys\n", "\n", "# set path to tools library and import\n", "sys.path.append(r'noaa_weather_tools')\n", "import noaa_weather_tools\n", "\n", "NOAA_Token_Here= 'enter token as string'\n", "\n", "print(\"Check dt format('DD-MM-YYYY', and whether dates span <= 1 year from a current or past date\")\n", "print(\"If dates exceed one year, NCDC query returns a null object\")\n", "print(\"Need a token take a token, have a token, keep it to yourself @ https://www.ncdc.noaa.gov/cdo-web/token\")\n", "print('start_dt: {}\\n end_dt: {}'.format(sys.argv[1], sys.argv[2]))\n", "\n", "\n", "def noaa_dailysum_weather_processor(start_dt, end_dt, station):\n", "\n", " \"\"\"Function accepts a station ID, and beginning/end datetime as strings with date format as\n", " 'MM-DD-YYYY' which span <= 1 year from a current or past date, passing them to the query_builder function. \n", " Function creates a .csv file of NOAA (NCDC) Daily Summary data for a specific station.\"\"\"\n", " \n", " print(15 * '.' + \"reticulating splines\" + 5* '.' + \"getting records\") \n", " df, record_count, query_parameters= noaa_weather_tools.get_ncdc(start_dt, end_dt, station)\n", " \n", " print(15* '.' + \"exporting to csv\")\n", " my_csv, my_path= noaa_weather_tools.gen_csv(df, query_parameters)\n", " \n", " print(\"spines reticulated\")\n", " return my_csv\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...............reticulating splines.....getting records\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=1001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=2001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=3001&limit=1000&datasetid=GHCND\n", "http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=4001&limit=1000&datasetid=GHCND\n", "...............exporting to csv\n", "spines reticulated\n" ] } ], "source": [ "noaa_dailysum_weather_processor('2014-05-03', '2015-05-02', station= 'GHCND:USW00023174')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Volume in drive C is Acer\n", " Volume Serial Number is 3829-CAE6\n", "\n", " Directory of C:\\Users\\vhim98198\\Documents\\noaa_requests\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "File Not Found\n" ] } ], "source": [ "ls *csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discarded Functions" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "```python\n", "def collator(response):\n", " \n", " data= pd.DataFrame(response['results'])\n", " # for quality control to verify retrieval of all rows\n", " length= len(data)\n", " \n", " data= data.drop(['attributes','station'], axis=1)\n", " data= data.pivot(index= 'date',columns= 'datatype', values= 'value').reset_index()\n", " \n", " return data, length\n", "\n", "def get_ncdc(start_dt, end_dt, station):\n", " \n", " \n", " # count for verifying retrieval of all rows\n", " row_count= 0\n", " # initial query\n", " query, query_dict= query_builder(start_dt, end_dt, station)\n", " response= execute_query(query)\n", " \n", " # collate and count \n", " collated_data, length= collator(response)\n", " row_count += length\n", " \n", " # get offsets for remaining queries\n", " off_d, count= offsetter(response)\n", " \n", " # execute remaining queries and operations\n", " for offset in off_d:\n", " query, _= query_builder(start_dt, end_dt, station, off_d[offset])\n", " print(query)\n", " response= execute_query(query)\n", " next_data, next_length= collator(response)\n", " \n", " row_count += next_length\n", " \n", " # stack DataFrames\n", " collated_data= pd.concat([collated_data, next_data])\n", " \n", " assert row_count == count, 'row count != count'\n", " \n", " return collated_data, row_count\n", "``` \n", " \n", " \n", " \n", " \n", " \n", " \n", " " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

peterwittek/ipython-notebooks

Parameteric and Bilevel Polynomial Optimization Problems.ipynb

1

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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Relaxations of parametric and bilevel polynomial optimization problems\n", "========================================\n", "Suppose we are interested in finding the global optimum of the following constrained polynomial optimization problem:\n", "\n", "$$ \\min_{x\\in\\mathbb{R}^n}f(x)$$\n", "such that\n", "$$ g_i(x) \\geq 0, i=1,\\ldots,r$$\n", "\n", "Here $f$ and $g_i$ are polynomials in $x$. We can think of the constraints as a semialgebraic set $\\mathbf{K}=\\{x\\in\\mathbb{R}^n: g_i(x) \\geq 0, i=1,\\ldots,r\\}$. [Lasserre's method](http://dx.doi.org/10.1137/S1052623400366802) gives a series of semidefinite programming (SDP) relaxation of increasing size that approximate this optimum through the moments of $x$.\n", "\n", "A [manuscript](http://arxiv.org/abs/1506.02099) appeared on arXiv a few months ago that considers bilevel polynomial optimization problems, that is, when a set of variables is subject to a lower level optimization. The method heavily relies on the [joint+marginal approach](http://dx.doi.org/10.1137/090759240) to SDP relaxations of parametric polynomial optimization problems. Here we discuss both methods and how they can be implemented with [Ncpol2sdpa](https://ncpol2sdpa.readthedocs.io/)>=1.10 in Python.\n", "\n", "Constraints on moments\n", "------------------------------\n", "\n", "Let us consider the plainest form of the SDP relaxation first with the the following trivial polynomial optimization problem:\n", "\n", "$$ \\min_{x\\in \\mathbb{R}}2x^2$$\n", "\n", "Let us denote the linear mapping from monomials to $\\mathbb{R}$ by $L_x$. Then, for instance, $L_x(2x^2)= 2y_2$. If there is a representing Borel measure $\\mu$ on the semialgebraic set $\\mathbf{K}$ of the constraints (in this case, there are no constraints), then we can write $y_\\alpha = \\int_\\mathbf{K} x^\\alpha \\mathrm{d}\\mu$. The level-1 relaxation will be\n", "\n", "$$ \\min_{y}2y_{2}$$\n", "such that\n", "$$\\left[ \\begin{array}{cc}1 & y_{1} \\\\y_{1} & y_{2}\\end{array} \\right] \\succeq{}0.$$\n", "\n", "We import some functions first that we will use later:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from math import sqrt\n", "from sympy import integrate, N\n", "from ncpol2sdpa import SdpRelaxation, generate_variables, flatten\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not surprisingly, solving the example gives a very accurate result:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.5990354545454662e-08 -1.5990354545454556e-08\n" ] } ], "source": [ "x = generate_variables('x')[0]\n", "sdp = SdpRelaxation([x])\n", "sdp.get_relaxation(1, objective=x**2)\n", "sdp.solve()\n", "print(sdp.primal, sdp.dual)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that even in this formulation, there is an implicit constraint on a moment: the top left element of the moment matrix is 1. Given a representing measure, this means that $\\int_\\mathbf{K} \\mathrm{d}\\mu=1$. It is actually because of this that a $\\lambda$ dual variable appears in the dual formulation:\n", "$$max_{\\lambda, \\sigma_0} \\lambda$$\n", "such that\n", "$$2x^2 - \\lambda = \\sigma_0\\\\\n", "\\sigma_0\\in \\Sigma{[x]}, \\mathrm{deg}\\sigma_0\\leq 2.$$\n", "\n", "In fact, we can move $\\lambda$ to the right-hand side, where the sum-of-squares (SOS) decomposition is, $\\lambda$ being a trivial SOS multiplied by the constraint $\\int_\\mathbf{K} \\mathrm{d}\\mu$, that is, by 1.\n", "\n", "We normally think of adding some $g_i(x)$ polynomial constraints that define a semialgebraic set, and then constructing matching localizing matrices. We can, however, impose more constraints on the moments. For instance, we can add a constraint that $\\int_\\mathbf{K} x\\mathrm{d}\\mu = 1$. All of these constraints will have a constant instead of an SOS polynomial in the dual. To ensure the moments are not substituted out while generating the SDP, we enter them as pairs of moment inequalities. Solving this problem gives the correct result again:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0000006249546196 0.9999997167810939\n" ] } ], "source": [ "moments = [x-1, 1-x]\n", "sdp = SdpRelaxation([x])\n", "sdp.get_relaxation(1, objective=x**2, momentinequalities=moments)\n", "sdp.solve()\n", "print(sdp.primal, sdp.dual)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dual changed, slightly. Let $\\gamma_\\beta=\\int_\\mathbf{K} x^\\beta\\mathrm{d}\\mu$ for $\\beta=0, 1$. Then the dual reads as\n", "\n", "$$max_{\\lambda_\\beta, \\sigma_0} \\sum_{\\beta=0}^1\\lambda_\\beta \\gamma_\\beta$$\n", "such that\n", "$$2x^2 - \\sum_{\\beta=0}^1\\lambda_\\beta x^\\beta = \\sigma_0\\\\\n", "\\sigma_0\\in \\Sigma{[x]}, \\mathrm{deg}\\sigma_0\\leq 2.$$\n", "\n", "Indeed, if we extract the coefficients, we will see that $x$ gets a $\\lambda_1=2$ (note that equalities are replaced by pairs of inequalities):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.0, 2.0] [0.9999997167810939, 2.00000000000000]\n" ] } ], "source": [ "coeffs = [-sdp.extract_dual_value(0, range(1))]\n", "coeffs += [sdp.y_mat[2*i+1][0][0] - sdp.y_mat[2*i+2][0][0]\n", " for i in range(len(moments)//2)]\n", "sigma_i = sdp.get_sos_decomposition()\n", "print(coeffs, [sdp.dual, sigma_i[1]-sigma_i[2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moment constraints play a crucial role in the joint+marginal approach of the SDP relaxation of polynomial optimization problems, and hence also indirectly in the bilevel polynomial optimization problems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Joint+marginal approach\n", "-------------------------------\n", "In a parametric polynomial optimization problem, we can separate two sets of variables, and one set acts as a parameter to the problem. More formally, we would like to find the following function:\n", "\n", "$$ J(x) = \\inf_{y\\in\\mathbb{R}^m}\\{f(x,y): h_j(y)\\geq 0, j=1,\\ldots,r\\},$$\n", "\n", "where $x\\in\\mathbf{X}=\\{x\\in \\mathbb{R}^n: h_k(x)\\geq 0, k=r+1,\\ldots,t\\}$. This [can be relaxed](http://dx.doi.org/10.1137/090759240) as an SDP, and we can extract an approximation $J_k(x)$ at level-$k$ from the dual solution. The primal form reads as\n", "\n", "$$ \\mathrm{inf}_z L_z(f)$$\n", "such that\n", "$$ M_k(z)\\succeq 0,\\\\\n", "M_{k-v_j}(h_j z)\\succeq 0, j=1,\\ldots,t\\\\\n", "L_z(x^\\beta) = \\gamma_\\beta, \\forall\\beta\\in\\mathbb{N}_k^n.$$\n", "\n", "Notice that the localizing matrices also address the polynomial constraints that define the semialgebraic set $\\mathbf{X}$. If the positivity constraints are fulfilled, then we have a finite Borel representing measure $\\mu$ on $\\mathbf{K}=\\{h_j(y)\\geq 0, j=1,\\ldots,r\\}$ such that $z_{\\alpha\\beta}=\\int_\\mathbf{K} x^\\alpha y^\\beta\\mathrm{d}\\mu$. \n", "\n", "The part that is different from the regular Lasserre hierachy is the last line, where $\\gamma_\\beta=\\int_\\mathbf{X} x^\\beta\\mathrm{d}\\varphi(x)$. This establishes a connection between the moments of $x$ on $\\mathbf{K}$ in measure $\\mu$ and the moments of $x$ on $\\mathbf{X}$ in measure $\\varphi$. This $\\varphi$ measure is a Borel probability measure on $\\mathbf{X}$ with a positive density with respect to the Lebesgue measure on $\\mathbb{R}^n$. In other words, the marginal of one measure must match the other on $\\mathbf{X}$.\n", "\n", "The dual of the primal form of the SDP with these moment constraints is\n", "\n", "$$ \\mathrm{sup}_{p, \\sigma_i} \\int_\\mathbf{X} p \\mathrm{d}\\varphi$$\n", "such that\n", "$$ f - p = \\sigma_0 + \\sum_{j=1}^t \\sigma_j h_j\\\n", "p\\in\\mathbb{R}[x], \\sigma_j\\in\\Sigma[x, y], j=0,\\ldots,t\\\\\n", "\\mathrm{deg} p\\leq 2k, \\mathrm{deg} \\sigma_0\\leq 2k, \\mathrm{deg}\\sigma_j h_j \\leq 2k, j=1,\\ldots, t.\n", "$$\n", "\n", "The polynomial $p=\\sum_{\\beta=0}^{2k} \\lambda_\\beta x^\\beta$ is the approximation $J_k(x)$. Below we reproduce the three examples of the paper.\n", "\n", "*Example 1*\n", "\n", "Let $\\mathbf{X}=[0,1]$, $\\mathbf{K}=\\{(x,y): 1-x^2-y^2\\geq 0; x,y\\in\\mathbf{X}\\}$, and $f(x,y)= - xy^2$. We know that $J(x) = -1(1-x^2)x$. First we declare $J(x)$, a helper function to define a polynomial, and we set up the symbolic variables $x$ and $y$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def J(x):\n", " return -(1-x**2)*x\n", "\n", "\n", "def Jk(x, coeffs):\n", " return sum(ci*x**i for i, ci in enumerate(coeffs))\n", "\n", "x = generate_variables('x')[0]\n", "y = generate_variables('y')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define the level of the relaxation and the moment constraints:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "level = 4\n", "gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\n", "marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we define the objective function and the constraints that define the semialgebraic sets, and we generate and solve the relaxation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.25003374095299485 -0.2500337409529949 optimal\n" ] } ], "source": [ "f = -x*y**2\n", "inequalities = [1.0-x**2-y**2, 1-x, x, 1-y, y]\n", "\n", "sdp = SdpRelaxation([x, y], verbose=0)\n", "sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\n", " inequalities=inequalities)\n", "sdp.solve()\n", "print(sdp.primal, sdp.dual, sdp.status)\n", "coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\n", "coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\n", " for i in range(len(marginals)//2)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the correctness of the approximation, we plot the optimal and the approximated functions over the domain." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6317ad3ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_domain = [i/100. for i in range(100)]\n", "plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\n", "plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Example 2*\n", "\n", "The set $\\mathbf{X}=[0,1]$ remains the same. Let $\\mathbf{K}=\\{(x,y): 1-y_1^2-y_2^2\\geq 0\\}$, and $f(x,y) = xy_1 + (1-x)y_2$. Now the optimal $J(x)$ will be $-\\sqrt{x^2+(1-x)^2}$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.8116610109835365 -0.8116610109835365 optimal\n" ] }, { "data": { "image/png": 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L+bMt2DlkvBYG9Z+0qHcD4+9dDecdTxD+YN/zjPpqpcWpVG5ocVAZ7HbDrW90JjZiGQCR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\nanEi99LikE9mrtv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6317a96e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def J(x):\n", " return -sqrt(x**2+(1-x)**2)\n", "\n", "x = generate_variables('x')[0]\n", "y = generate_variables('y', 2)\n", "\n", "f = x*y[0] + (1-x)*y[1]\n", "\n", "gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\n", "marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])\n", "inequalities = [1-y[0]**2-y[1]**2, x, 1-x]\n", "sdp = SdpRelaxation(flatten([x, y]))\n", "sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\n", " inequalities=inequalities)\n", "sdp.solve()\n", "print(sdp.primal, sdp.dual, sdp.status)\n", "coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\n", "coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\n", " for i in range(len(marginals)//2)]\n", "plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\n", "plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Example 3*\n", "\n", "Note that this is Example 4 in the paper. The set $\\mathbf{X}=[0,1]$ remains the same, whereas $\\mathbf{K}=\\{(x,y): xy_1^2+y_2^2-x= 0, y_1^2+xy_2^2-x= 0\\}$, and $f(x,y) = (1-2x)(y_1+y_2)$. The optimal $J(x)$ is $-2|1-2x|\\sqrt{x/(1+x)}$. We enter the equalities as pairs of inequalities." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.535187880633569 -0.535188348622651 near_optimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/pwittek/.local/lib/python3.5/site-packages/mosek/__init__.py:5463: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if barxj_ != None:\n", "/home/pwittek/.local/lib/python3.5/site-packages/mosek/__init__.py:5498: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if barsj_ != None:\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f63178ad8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def J(x):\n", " return -2*abs(1-2*x)*sqrt(x/(1+x))\n", "\n", "x = generate_variables('x')[0]\n", "y = generate_variables('y', 2)\n", "f = (1-2*x)*(y[0] + y[1])\n", "\n", "gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\n", "marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])\n", "\n", "inequalities = [x*y[0]**2 + y[1]**2 - x, - x*y[0]**2 - y[1]**2 + x,\n", " y[0]**2 + x*y[1]**2 - x, - y[0]**2 - x*y[1]**2 + x,\n", " 1-x, x]\n", "sdp = SdpRelaxation(flatten([x, y]))\n", "sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\n", " inequalities=inequalities)\n", "sdp.solve(solver=\"mosek\")\n", "print(sdp.primal, sdp.dual, sdp.status)\n", "coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\n", "coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\n", " for i in range(len(marginals)//2)]\n", "plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\n", "plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bilevel problem of nonconvex lower level\n", "------------------------------------------------------\n", "We define the bilevel problem as follows:\n", "$$ \\min_{x\\in\\mathbb{R}^n, y\\in\\mathbb{R}^m}f(x,y)$$\n", "such that\n", "$$g_i(x, y) \\geq 0, i=1,\\ldots,s,\\\\\n", "y\\in Y(x)=\\mathrm{argmin}_{w\\in\\mathbb{R}^m}\\{G(x,w): h_j(w)\\geq 0, j=1,...,r\\}.$$\n", "\n", "The more interesting case is when the when the lower level problem's objective function $G(x,y)$ is nonconvex. We consider the $\\epsilon$-approximation of this case:\n", "\n", "$$ \\min_{x\\in\\mathbb{R}^n, y\\in\\mathbb{R}^m}f(x,y)$$\n", "such that\n", "$$g_i(x, y) \\geq 0, i=1,\\ldots,s,\\\\\n", "h_j(y) \\geq 0, j=1,\\dots, r,\\\\\n", "G(x,y) - \\mathrm{min}_{w\\in\\mathbb{R}^m}\\{G(x,w): h_j(w)\\geq 0, j=1,...,r\\}\\leq \\epsilon.$$\n", "\n", "This approximation will give an [increasing lower bound](http://arxiv.org/abs/1506.02099)) on the original problem. The min function on the right of $G(x,y)$ is essentially a parametric polynomial optimization problem, that is, our task is to find $J(x)$. We have to ensure that the parameter set is compact, so we add a set of constraints on the coordinates of $x$: $\\{M^2-x_l^2\\geq 0, l=1,\\ldots,n\\}$ for some $M>0$.\n", "\n", "The idea is that we relax this as an SDP at some level $k$ and fixed $\\epsilon$ to obtain the following single-level polynomial optimization problem:\n", "$$ \\min_{x\\in\\mathbb{R}^n, y\\in\\mathbb{R}^m}f(x,y)$$\n", "such that\n", "$$g_i(x, y) \\geq 0, i=1,\\ldots,s,\\\\\n", "h_j(y) \\geq 0, j=1,\\dots, r,\\\\\n", "G(x,y) - J_k(x)\\leq \\epsilon.$$\n", "\n", "Then we relax this is an another SDP at level $k$.\n", "\n", "Consider a test problem defined as follows:\n", "$$ \\min_{(x,y)\\in\\mathbb{R}^2} x+y$$\n", "such that\n", "$$x\\in[-1,1], \\\\\n", "y\\in \\mathrm{argmin}_{w\\in\\mathbb{R}^m}\\{\\frac{xy^2}{2}-\\frac{y^3}{3}, y\\in[-1,1]\\}$$.\n", "\n", "This is clearly a bilevel problem. We set up the necessary variables and constraints, requesting a level-3 relaxation, and also fixing $\\epsilon$ and a choice of $M$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = generate_variables('x')[0]\n", "y = generate_variables('y')[0]\n", "\n", "f = x + y\n", "g = [x <= 1.0, x >= -1.0]\n", "G = x*y**2/2.0 - y**3/3.0\n", "h = [y <= 1.0, y >= -1.0]\n", "epsilon = 0.001\n", "M = 1.0\n", "level = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the relaxation of the parametric polynomial optimization problem that returns an approximation of $J(x)$ from the dual:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def lower_level(k, G, h, M):\n", " gamma = [integrate(x**i, (x, -M, M))/(2*M) for i in range(1, 2*k+1)]\n", " marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*k+1)])\n", " inequalities = h + [x**2 <= M**2]\n", " lowRelaxation = SdpRelaxation([x, y])\n", " lowRelaxation.get_relaxation(k, objective=G,\n", " momentinequalities=marginals,\n", " inequalities=inequalities)\n", " lowRelaxation.solve()\n", " print(\"Low-level:\", lowRelaxation.primal, lowRelaxation.dual, lowRelaxation.status)\n", " coeffs = []\n", " for i in range(len(marginals)//2):\n", " coeffs.append(lowRelaxation.y_mat[len(inequalities)+1+2*i][0][0] -\n", " lowRelaxation.y_mat[len(inequalities)+1+2*i+1][0][0])\n", " blocks = [i for i in range(len(inequalities)+1)]\n", " constant = lowRelaxation.extract_dual_value(0, blocks)\n", " return constant + sum(ci*x**(i+1) for i, ci in enumerate(coeffs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we put it all together:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Low-level: -0.3506176102365808 -0.3506180235695789 unknown\n", "High-level: -0.0027997294093268232 unknown\n", "Optimal x and y: -1.0 0.9972\n" ] } ], "source": [ "Jk = lower_level(level, G, h, M)\n", "inequalities = g + h + [G - Jk <= epsilon]\n", "highRelaxation = SdpRelaxation([x, y], verbose=0)\n", "highRelaxation.get_relaxation(level, objective=f,\n", " inequalities=inequalities)\n", "highRelaxation.solve()\n", "print(\"High-level:\", highRelaxation.primal, highRelaxation.status)\n", "print(\"Optimal x and y:\", highRelaxation[x], highRelaxation[y])\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "These values are close to the analytical solution." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

nilbody/h2o-3

h2o-docs/src/booklets/v2_2015/source/DeepLearning_Vignette.ipynb

15

4853

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#---------------------------------------------------------------------\n", "#\n", "# Include and run all the Python code snippets from the H2O Deep Learning Vignette.\n", "#\n", "# The snippets are broken out into separate files so the exact same\n", "# piece of code both shows up in the document and is run by this\n", "# script.\n", "#\n", "# Currently these scripts replicate the GLM ipynb version, but are not finished.", "#\n", "#---------------------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import h2o" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "print(os.getcwd())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h2o.init()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_gaussian_example.py\")\n", "\n", "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_binomial_example.py\")\n", "\n", "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_poisson_example.py\")\n", "\n", "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_gamma_example.py\")\n", "\n", "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_tweedie_example.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/coerce_column_to_factor.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_cross_validation.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_stopping_criteria.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_grid_search_over_alpha.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Not supported right now.\n", "#\n", "# h2o.remove_all()\n", "# execfile(\"deeplearning/deeplearning_grid_search_over_lambda.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_model_output_10.py\")\n", "execfile(\"deeplearning/deeplearning_model_output_20.py\")\n", "execfile(\"deeplearning/deeplearning_model_output_30.py\")\n", "execfile(\"deeplearning/deeplearning_model_output_40.py\")\n", "execfile(\"deeplearning/deeplearning_accessors.py\")\n", "execfile(\"deeplearning/deeplearning_confusion_matrix.py\")\n", "execfile(\"deeplearning/deeplearning_scoring_history.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_binomial_predictions_with_response.py\")\n", "execfile(\"deeplearning/deeplearning_binomial_predictions_without_response.py\")\n", "execfile(\"deeplearning/deeplearning_recalculate_predict.py\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h2o.remove_all()\n", "execfile(\"deeplearning/deeplearning_download_pojo.py\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

izaid/dynd-python

docs/notebooks/SciPy2013Intro.ipynb

8

16177

{ "metadata": { "name": "", "signature": "sha256:59d5d6f4be6a184d13faea3e888d526f35876a3a49d4feb01c62a7e6e4682589" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys, dynd\n", "print('Python %s' % sys.version)\n", "print('DyND Python Bindings %s' % dynd.__version__)\n", "print('LibDyND %s' % dynd.__libdynd_version__)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Python 3.3.3 |Continuum Analytics, Inc.| (default, Dec 3 2013, 11:56:40) [MSC v.1600 64 bit (AMD64)]\n", "DyND Python Bindings 0.6.5.post024.g5f07540\n", "LibDyND 0.6.5.post150.g8e9d756\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standard dynd import: The `nd` namespace is for array operations, and the `ndt` namespace is for types. We'll import numpy as well to show some comparisons." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from dynd import nd, ndt\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic usage is quite similar to numpy, the data type is deduced from python types when converting." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nd.array([1,2,3,4,5]) # dynd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "nd.array([1, 2, 3, 4, 5],\n", " type=\"5 * int32\")" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "np.array([1,2,3,4,5]) # numpy" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([1, 2, 3, 4, 5])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "nd.array([[1, 1.5], [-1.5, 1]]) # dynd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "nd.array([ [ 1, 1.5], [-1.5, 1]],\n", " type=\"2 * 2 * float64\")" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "np.array([[1, 1.5], [-1.5, 1]]) # numpy" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "array([[ 1. , 1.5],\n", " [-1.5, 1. ]])" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see some differences with numpy at this level already, such as the array dimensionality being included in the type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default string is variable-length in dynd. In numpy, you either choose a maximum size, or use object arrays with lower performance." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nd.array([\"this\", \"is\", \"a\", \"test\"]) # dynd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "nd.array([\"this\", \"is\", \"a\", \"test\"],\n", " type=\"4 * string\")" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "np.array([\"this\", \"is\", \"a\", \"test\"]) # numpy" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "array(['this', 'is', 'a', 'test'], \n", " dtype='<U4')" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dynd has a variable-length dimension type, which supports ragged arrays. If you give this kind of data to numpy, it uses object arrays which are slower and the array programming functionality in the ragged dimension." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nd.array([[1,5,2], [1], [7,9,10,20,13]]) # dynd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "nd.array([ [1, 5, 2], [1], [ 7, 9, 10, 20, 13]],\n", " type=\"3 * var * int32\")" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "np.array([[1,5,2], [1], [7,9,10,20,13]]) # numpy" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "array([[1, 5, 2], [1], [7, 9, 10, 20, 13]], dtype=object)" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a part of blaze, dynd is based on its datashape type system notation. This provides a convenient way to create arrays of structs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = nd.array([('XDress - Type, But Verify', 'Anthony Scopatz', '2013-06-26T11:55', 204),\n", " ('matplotlib: past, present and future', 'Michael Droettboom', '2013-06-27T10:15', 106),\n", " ('The DyND Library', 'Mark Wiebe', '2013-06-27T14:35', 203),\n", " ('The advantages of a scientific IDE', 'Carlos Cordoba', '2013-06-26T11:30', 203)],\n", " dtype=\"\"\"{\n", " title: string,\n", " presenter: string,\n", " time: datetime,\n", " room: int32\n", " }\"\"\")\n", "x" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "nd.array([[\"XDress - Type, But Verify\", \"Anthony Scopatz\", 2013-06-26T11:55, 204],\n", " [\"matplotlib: past, present and future\", \"Michael Droettboom\", 2013-06-27T10:15, 106],\n", " [\"The DyND Library\", \"Mark Wiebe\", 2013-06-27T14:35, 203],\n", " [\"The advantages of a scientific IDE\", \"Carlos Cordoba\", 2013-06-26T11:30, 203]],\n", " type=\"4 * {title : string, presenter : string, time : datetime, room : int32}\")" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Field access can be done directly via python attributes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x.room" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "nd.array([204, 106, 203, 203],\n", " type=\"4 * int32\")" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, equivalently, by indexing into the dimension of the struct." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x[:,3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "nd.array([204, 106, 203, 203],\n", " type=\"4 * int32\")" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "x[:, :2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "nd.array([[\"XDress - Type, But Verify\", \"Anthony Scopatz\"],\n", " [\"matplotlib: past, present and future\", \"Michael Droettboom\"],\n", " [\"The DyND Library\", \"Mark Wiebe\"],\n", " [\"The advantages of a scientific IDE\", \"Carlos Cordoba\"]],\n", " type=\"4 * {title : string, presenter : string}\")" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a preliminary groupby operation implemented. This is very basic compared to advanced stats packages like pandas, but demonstrates how the results of operations like this can be represented well within the dynd array." ] }, { "cell_type": "code", "collapsed": false, "input": [ "g = nd.groupby(x, x.room)\n", "g.groups" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "nd.array([106, 203, 204],\n", " type=\"3 * int32\")" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "g.eval()[1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "nd.array([[\"The DyND Library\", \"Mark Wiebe\", 2013-06-27T14:35, 203],\n", " [\"The advantages of a scientific IDE\", \"Carlos Cordoba\", 2013-06-26T11:30, 203]],\n", " type=\"var * {title : string, presenter : string, time : datetime, room : int32}\")" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another simple groupby, this time by date." ] }, { "cell_type": "code", "collapsed": false, "input": [ "g = nd.groupby(x, x.time.date)\n", "g.groups" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "nd.array([2013-06-26, 2013-06-27],\n", " type=\"2 * date\")" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "g.eval()[1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "nd.array([[\"matplotlib: past, present and future\", \"Michael Droettboom\", 2013-06-27T10:15, 106],\n", " [\"The DyND Library\", \"Mark Wiebe\", 2013-06-27T14:35, 203]],\n", " type=\"var * {title : string, presenter : string, time : datetime, room : int32}\")" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluation of expressions is deferred, which in some cases can be used to make interesting views of data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = nd.array([\"2011-07-11\", \"2012-07-16\", \"2013-06-24\"], dtype=\"string[10,'A']\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "a[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "nd.array(\"2011-07-11\",\n", " type=\"string[10,'ascii']\")" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "b = a.ucast(ndt.date)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "b[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "nd.array(2011-07-11,\n", " type=\"convert[to=date, from=string[10,'ascii']]\")" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "b[0] = b[0].replace(month=3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "a[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "nd.array(\"2011-03-11\",\n", " type=\"string[10,'ascii']\")" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ascii size-10 strings we created is compatible with numpy. Whenever dynd data is compatible with numpy, we can create views." ] }, { "cell_type": "code", "collapsed": false, "input": [ "c = nd.as_numpy(a)\n", "c" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "array([b'2011-03-11', b'2012-07-16', b'2013-06-24'], \n", " dtype='|S10')" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "c[1] = \"2010-07-16\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "b[1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "nd.array(2010-07-16,\n", " type=\"convert[to=date, from=string[10,'ascii']]\")" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-2-clause

Vizzuality/gfw

docs/Update_GFW_Layers_Vault.ipynb

2

95954

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Layer Config Backup\n", "\n", "This notebook outlines how to run a process to create a remote backup of gfw layers.\n", "\n", "Rough process:\n", "\n", "- Run this notebook from the `gfw/data` folder\n", "- Wait...\n", "- Check `_metadata.json` files in the `production` and `staging` folders for changes\n", "- If everything looks good, make a PR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, install the latest version of LMIPy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 819 }, "colab_type": "code", "id": "Ar8773SCSXJG", "outputId": "f2fdde1e-33cc-41c1-997b-095456301bb2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LMI ready!\n" ] } ], "source": [ "!pip install LMIPy\n", "\n", "from IPython.display import clear_output\n", "clear_output()\n", "\n", "print('LMI ready!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, import relevent modules" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "b4EuD29XSenY" }, "outputs": [], "source": [ "import LMIPy as lmi\n", "\n", "import os\n", "import json\n", "import shutil\n", "\n", "from pprint import pprint\n", "from datetime import datetime\n", "from tqdm import tqdm" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a-hIn6Rvaj95" }, "source": [ "First, pull the gfw repo and check that the following path correctly finds the `data/layers` folder, inside which, you should find a `production` and `staging` folder." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "QPqD_2_HUoAD" }, "outputs": [], "source": [ "envs = ['staging', 'production']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "Knr1BGlKSvig", "outputId": "39bebc0a-74ee-45ac-fdf6-7295612db84c" }, "outputs": [], "source": [ "path = './backup/configs'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/Users/vizzuality/Workspace/gfw/data/backup/archived/archive_2019-06-21@10h-06m-39s.zip'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create directory and archive previous datasets\n", "with open(path + '/metadata.json') as f:\n", " date = json.load(f)[0]['updatedAt']\n", " \n", "shutil.make_archive(f'./backup/archived/archive_{date}', 'zip', path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 164 }, "colab_type": "code", "id": "AXicSZaGSykM", "outputId": "d2fd4d38-3017-4ad2-8270-9e4eaeffed42" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Good to go!\n" ] } ], "source": [ "# Check correct folders are found\n", "\n", "if not all([folder in os.listdir(path) for folder in envs]):\n", " print(f'Boo! Incorrect path: {path}')\n", "else:\n", " print('Good to go!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following to save, build `.json` files and log changes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Update record" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/43 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saving to path: ./backup/configs/staging\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 43/43 [02:50<00:00, 3.97s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Save complete!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/493 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saving to path: ./backup/configs/production\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 493/493 [28:07<00:00, 3.42s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Save complete!\n", "Done!\n", "CPU times: user 23.7 s, sys: 2.05 s, total: 25.7 s\n", "Wall time: 31min 8s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%%time\n", "for env in envs:\n", " \n", " # Get all old ids\n", " old_ids = [file.split('.json')[0] for file in os.listdir(path + f'/{env}') if '_metadata' not in file]\n", " \n", " old_datasets = []\n", " files = os.listdir(path + f'/{env}')\n", " \n", " # Extract all old datasets\n", " for file in files:\n", " if '_metadata' not in file:\n", " with open(path + f'/{env}/{file}') as f:\n", " old_datasets.append(json.load(f))\n", " \n", " # Now pull all current gfw datasets and save\n", " col = lmi.Collection(app=['gfw'], env=env)\n", " col.save(path + f'/{env}')\n", " \n", " # Get all new ids\n", " new_ids = [file.split('.json')[0] for file in os.listdir(path + f'/{env}') if '_metadata' not in file]\n", " \n", " # See which are new, and which have been removed\n", " added = list(set(new_ids) - set(old_ids))\n", " removed = list(set(old_ids) - set(new_ids))\n", " changed = []\n", " \n", " # COmpare old and new, logging those that have changed\n", " for old_dataset in old_datasets:\n", " ds_id = old_dataset['id']\n", " old_ids.append(ds_id)\n", " with open(path + f'/{env}/{ds_id}.json') as f:\n", " new_dataset = json.load(f)\n", " \n", " if old_dataset != new_dataset:\n", " changed.append(ds_id)\n", " \n", " # Create metadata json\n", " with open(path + f'/{env}/_metadata.json', 'w') as f:\n", " \n", " meta = {\n", " 'updatedAt': datetime.today().strftime('%Y-%m-%d@%Hh-%Mm-%Ss'),\n", " 'env': env,\n", " 'differences': {\n", " 'changed': changed,\n", " 'added': added,\n", " 'removed': removed\n", " }\n", " }\n", " \n", " # And save it too!\n", " json.dump(meta,f)\n", " \n", "print('Done!')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, 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"fb5dab00-6ff9-4bc7-81be-289d36e758ec\n", "f6c67028-d820-46f3-9190-97cc56e7fef0\n", "88ba8c39-ec02-4140-89f5-1bdeb43400cf\n", "69ec0d19-7192-4ab9-a01e-7cb1bd4b7295\n", "c92b6411-f0e5-4606-bbd9-138e40e50eb8\n", "a90c116c-d43a-4c22-ba30-ff34620ec1fc\n", "101fb2b9-5a0a-4ec4-9f86-96c0819b97cd\n", "f65452a0-ff07-4390-a3d0-a7a154a658a3\n", "619341a4-459d-49df-9fa9-4d25ad458180\n", "b05ad94a-0084-461d-897a-6b8b9d5b20ca\n", "e7b55b31-06b6-4053-8c2f-196b2794bdaf\n", "a09e5ca8-5c50-42d0-a362-553a7e04a613\n", "0064ccfb-efb0-4bd4-befa-b2ba034197d1\n", "c1685dd6-905a-4842-8c1a-4940f24ec851\n", "f6b81da7-a846-45c1-88b2-dad95a26e523\n", "7acca489-9398-416e-a877-944a2075d9e4\n", "11f1cbc4-6c69-4ce6-b680-a4ec542b6f88\n", "d9c4d140-5dad-4b8d-9a45-656013e691d0\n", "8813dcc7-76bd-40e9-8aaf-a772a82f0631\n", "f649ed25-a0e3-4224-aeef-5d2ddb517745\n", "60afae8c-2949-4b24-ba89-bd63e990ee8c\n", "e71a3cad-5168-4f2a-a370-6690752128be\n", "98ad8429-c6fa-43b7-8fc2-fbd4d0cb6ee0\n", "e8b066e7-0f85-47be-ae20-bc7ae3615d8a\n", "22f6d217-63c0-4d53-9112-b7467c5479f3\n", "e6ab46fb-fdfe-4dc6-bcac-86f951d7b167\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "100%|██████████| 2/2 [04:29<00:00, 134.82s/it][A\u001b[A\u001b[A\u001b[A\n" ] } ], "source": [ "# Generate rich metadata\n", "\n", "metadata = []\n", "for env in tqdm(envs):\n", " with open(path + f'/{env}/_metadata.json') as f:\n", " metadata.append(json.load(f))\n", " \n", "for env in tqdm(metadata):\n", " for change_type, ds_list in env['differences'].items():\n", " tmp = []\n", " for dataset in ds_list:\n", " # generate Dataset entity to get name etc...\n", " print(dataset)\n", " tmp.append(str(lmi.Dataset(dataset)))\n", " env['differences'][change_type] = tmp\n", " \n", "with open(path + f'/metadata.json', 'w') as f:\n", " \n", " # And save it too!\n", " json.dump(metadata,f)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'differences': {'added': ['Dataset 09557278-4449-41ba-8f56-535c98f7489f '\n", " '[Test] Wood fiber concessions (LM v3) (v20200725)',\n", " 'Dataset cc7a506e-b2f4-4f3f-8852-a1c5f7d19e8e '\n", " 'Brazil land cover (LM v3) [REMOVE]',\n", " 'Dataset 6dc39298-7db4-415e-b0f5-531a1f198629 '\n", " '[Test]VIIRS Vector Tiles (LM v3)',\n", " 'Dataset f1b4b202-5a2a-421e-9a12-54888e86e140 USA '\n", " 'Land Cover (LM v3) [REMOVE]',\n", " 'Dataset ac739f3d-8d36-4ff7-90cf-6243f67a95a7 '\n", " 'Gross Carbon Emissions',\n", " 'Dataset 2831761c-86d8-4a5c-8bba-76c4f5211cc5 Test '\n", " 'Dataset',\n", " 'Dataset bc099e2e-0292-4e22-9445-9d428ecf7a56 '\n", " 'Potential carbon sequestration rate (LM v3)',\n", " 'Dataset 0366e6d1-3d1e-4758-9703-f8ca57d1a894 Net '\n", " 'Carbon Flux 2001-2015',\n", " 'Dataset 79bc3d81-2326-4513-9d4f-3ed6f5003231 '\n", " 'Carbon dioxide emissions from tree cover loss '\n", " '2020 (LM v3)',\n", " 'Dataset 603eace3-6e9e-4c85-ad30-936007b40321 '\n", " 'Global biodiversity significance 2019 (LM v3)',\n", " 'Dataset 0e251685-9d1f-4641-95f4-d401810a9ad3 '\n", " 'Birdlife Key Biodiversity Areas (v20191211)',\n", " 'Dataset 77446273-5fd3-4d7a-9811-1b0986772328 '\n", " '[Test] Indonesia forest area (LM v3) (v201709)',\n", " 'Dataset e11856c6-9ebd-46ba-a651-5b38f1ccf55a '\n", " '[Test] Alliance for Zero Extinction sites (LM v3)',\n", " 'Dataset 24363416-2ebc-4e71-9514-d831a1b4de4d '\n", " 'Template Test',\n", " 'Dataset 1580b62a-35ac-458b-bbdf-5f658920faf0 '\n", " '[Test] Oil palm concessions (LM v3) (v20191031)',\n", " 'Dataset 1e24c6d9-6735-45ad-b2d3-53419af35eb3 Tree '\n", " 'cover loss by dominant driver 2020 (LM v3)',\n", " 'Dataset d6a0de48-b00b-48b9-b41d-abac3203f990 '\n", " '[Test] RSPO oil palm concessions (LM v3) '\n", " '(v20200114)',\n", " 'Dataset 1ddacdc5-b05e-4ba0-91e6-9b99c66f70b2 '\n", " 'Global biodiversity intactness 2019 (LM v3)',\n", " 'Dataset f7b77b94-b48a-4acf-9fcc-5e677648586f '\n", " 'Template Test',\n", " 'Dataset f84f8889-6bb7-494d-b95f-c0b90d798cbf '\n", " 'Cumulative carbon gain 2001-2015 v3',\n", " 'Dataset 1b5d6aeb-8ad4-45da-8b46-06c489b2c54b Soil '\n", " 'carbon density (LM v3) [REMOVE]',\n", " 'Dataset 8959e698-48b9-403b-a9d1-d4429cc3f5a2 '\n", " 'Population density (LM v3) [REMOVE]',\n", " 'Dataset 9e494e8d-808f-4179-aed8-edf753738997 '\n", " 'Potential carbon sequestration rate error (LM v3)',\n", " 'Dataset c461cc5b-2eaf-4a66-8f85-2b5c47514ed4 '\n", " 'Cumulative Carbon Gain 2001-2015'],\n", " 'changed': [],\n", " 'removed': []},\n", " 'env': 'staging',\n", " 'updatedAt': '2020-08-10@14h-19m-50s'},\n", " {'differences': {'added': ['Dataset 55eee72a-14c1-409c-94e7-39a55dc485ff Glad '\n", " 'Alerts Whitelist - GADM v3.6 Adm1 level - '\n", " 'v20191213',\n", " 'Dataset 97e5daa8-0858-47fd-9ba3-8ad0023eb292 Tree '\n", " 'Cover Loss 2018 Whitelist - Geostore - v20191213',\n", " 'Dataset cb00f471-63d5-4531-9177-17b4c7acaccf Glad '\n", " 'Alerts Daily Change - WDPA - v20200429',\n", " 'Dataset 6dea3bff-de18-4c86-b08d-0a124148e705 Glad '\n", " 'Alerts - Daily - Geostore - User Areas',\n", " 'Dataset 770cc7b1-1a40-47cf-8f12-978fe3ae91a2 Glad '\n", " 'Alerts Weekly Change - WDPA - v20191213',\n", " 'Dataset 47b50ffa-9a82-4ad9-94ea-5eae3e2e8209 Glad '\n", " 'Alerts Weekly Change - GADM v3.6 Adm1 level - '\n", " 'v20191213',\n", " 'Dataset f3abc54b-dfd7-48d5-bafb-cf3f2f97a50f Tree '\n", " 'Cover Loss 2018 Whitelist - GADM Iso level - '\n", " 'v20200222',\n", " 'Dataset f3b49d45-57aa-4b64-9560-fbbc2245eae6 Tree '\n", " 'Cover Loss 2019 Change - GADM Adm2 level - '\n", " 'v20200429',\n", " 'Dataset a8360d91-06af-4f2d-bd61-4e50c8687ad8 '\n", " 'Protected Areas Vector Tiles (LM v3)',\n", " 'Dataset d138179b-d2ea-41fb-b72b-6a4b8346c925 '\n", " 'Brazil biomes (LM v3)',\n", " 'Dataset 19035378-b7af-45c9-917b-22d48ce16e9e '\n", " 'Subnational Political Boundaries_copy',\n", " 'Dataset ead25cb4-0c9d-464e-8d88-85d286ab9628 '\n", " 'VIIRS Fire Alerts Daily Change - GADM Adm2 level 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'Dataset e7b55b31-06b6-4053-8c2f-196b2794bdaf '\n", " 'MODIS Fire Alerts Whitelist - GADM Adm1 level - '\n", " 'v20200807',\n", " 'Dataset a09e5ca8-5c50-42d0-a362-553a7e04a613 Glad '\n", " 'Alerts Summary - Geostore - v20191213',\n", " 'Dataset 0064ccfb-efb0-4bd4-befa-b2ba034197d1 Glad '\n", " 'Alerts Daily Change - Geostore - v20200429',\n", " 'Dataset c1685dd6-905a-4842-8c1a-4940f24ec851 Glad '\n", " 'Alerts Summary - WDPA - v20200222',\n", " 'Dataset f6b81da7-a846-45c1-88b2-dad95a26e523 '\n", " 'Population Density - 2015 (LM v3)',\n", " 'Dataset 7acca489-9398-416e-a877-944a2075d9e4 Tree '\n", " 'Cover Loss 2018 Change - Geostore - v20200222',\n", " 'Dataset 11f1cbc4-6c69-4ce6-b680-a4ec542b6f88 RTRS '\n", " 'Guides for Responsible Soy Expansion (LM v3)',\n", " 'Dataset d9c4d140-5dad-4b8d-9a45-656013e691d0 Tree '\n", " 'Cover Loss 2019 Whitelist - WDPA - v20200429',\n", " 'Dataset 8813dcc7-76bd-40e9-8aaf-a772a82f0631 '\n", " 'River basins (LM v3)',\n", " 'Dataset f649ed25-a0e3-4224-aeef-5d2ddb517745 '\n", " 'Example NLCD Dataset',\n", " 'Dataset 60afae8c-2949-4b24-ba89-bd63e990ee8c Glad '\n", " 'Alerts Summary - GADM Adm1 level - v20200222',\n", " 'Dataset e71a3cad-5168-4f2a-a370-6690752128be GLAD '\n", " 'Alerts Weekly Change - geostore - latest',\n", " 'Dataset 98ad8429-c6fa-43b7-8fc2-fbd4d0cb6ee0 Tree '\n", " 'Cover Loss 2019 Change - GADM Adm1 level - '\n", " 'v20200429',\n", " 'Dataset e8b066e7-0f85-47be-ae20-bc7ae3615d8a Tree '\n", " 'Cover Loss 2019 Summary - GADM Iso level - '\n", " 'v20200429',\n", " 'Dataset 22f6d217-63c0-4d53-9112-b7467c5479f3 '\n", " 'VIIRS Fire Alerts Whitelist - GADM Adm1 level - '\n", " 'v20200429',\n", " 'Dataset e6ab46fb-fdfe-4dc6-bcac-86f951d7b167 '\n", " 'Indonesia forest moratorium (LM v3)'],\n", " 'changed': ['Dataset 63e88e53-0a88-416e-9532-fa06f703d435 '\n", " 'Summarized GLAD alerts for admin stats',\n", " 'Dataset 098b33df-6871-4e53-a5ff-b56a7d989f9a '\n", " 'Subnational Political Boundaries',\n", " 'Dataset 3d170908-043f-49db-b26b-9e9bfaaa40ce '\n", " 'GFW - Climate: Insights - Glad Alerts Countries '\n", " 'Data',\n", " 'Dataset 461e6f3f-c03c-40b2-8a40-47d1354c93bf '\n", " 'Deforestation alerts (Terra-i)',\n", " 'Dataset a20e9c0e-8d7d-422f-90f5-3b9bca355aaf '\n", " 'country page data for admin level 2',\n", " 'Dataset 01e90557-91f1-4da2-a810-a1bdd38e7824 '\n", " 'Test Data A Thomas',\n", " 'Dataset a705fce9-601c-455c-b97b-6237da5cedba '\n", " 'AGB Gains',\n", " 'Dataset 391ca96d-303f-4aef-be4b-9cdb4856832c '\n", " 'GLAD alerts summary stats grouped by year, week '\n", " 'and iso',\n", " 'Dataset 044f4af8-be72-4999-b7dd-13434fc4a394 '\n", " 'Tree cover',\n", " 'Dataset 93e67a77-1a31-4d04-a75d-86a4d6e35d54 '\n", " 'Wood fiber concessions',\n", " 'Dataset c7a1d922-e320-4e92-8e4c-11ea33dd6e35 '\n", " 'GLAD alerts summary stats grouped by year, '\n", " 'week, iso, adm1 v2',\n", " 'Dataset e663eb09-04de-4f39-b871-35c6c2ed10b5 '\n", " 'Deforestation alerts (GLAD)',\n", " 'Dataset ff289906-aa83-4a89-bba0-562edd8c16c6 '\n", " 'Fire alerts summary stats',\n", " 'Dataset 428db321-5ebb-4e86-a3df-32c63b6d3c83 '\n", " 'GLAD alerts summary stats grouped by year, '\n", " 'week, iso, adm1 and adm2',\n", " 'Dataset 9b26177b-1a28-4078-a4b9-8267ac4df669 '\n", " 'Soil carbon density',\n", " 'Dataset 4145f642-5455-4414-b214-58ad39b83e1e '\n", " 'Fire alerts (MODIS and VIIRS) summary stats '\n", " 'grouped by date, polyname, iso, adm1 and adm2',\n", " 'Dataset c36c3108-2581-4b68-852a-c929fc758001 '\n", " 'dis.007 Landslide Susceptibility',\n", " 'Dataset 5bc5cd49-706f-409c-b10d-77fdfecb010f '\n", " 'Fire alerts summary stats',\n", " 'Dataset 9cd1da2d-ab39-4fd9-9487-beea1d56dbac '\n", " 'Forma Activity',\n", " 'Dataset 134caa0a-21f7-451d-a7fe-30db31a424aa '\n", " 'Political boundaries (GADM)',\n", " 'Dataset 7cc6ac21-c8ef-4dd8-a181-8967721a15a4 '\n", " 'Political boundaries Admin 2 level (GADM 3.6)',\n", " 'Dataset 85f82851-e16e-4126-a630-93bb63d4ef42 '\n", " 'Terra I alerts summarized by admin 1 boundary '\n", " 'from GADM2.8',\n", " 'Dataset 916022a9-2802-4cc6-a0f2-a77f81dd0c09 '\n", " 'Global Forest Watch - Home page news',\n", " 'Dataset 9c0dfd21-53dd-40a2-9239-6cf292bd80c0 '\n", " 'ISO test data 2 Thomas',\n", " 'Dataset b67fc529-af07-4443-85a9-24b5cf6f2eae '\n", " 'Mangrove biomass density',\n", " 'Dataset f56a1761-d6be-40ec-9cd3-df16d3588480 '\n", " 'Tree cover loss 2018 - GADMv3.6 ADM2 summary - '\n", " 'v20190429',\n", " 'Dataset 9b9e56fc-270e-486d-8db5-e0a839c9a1a9 '\n", " 'Fire alerts summary stats - adm1',\n", " 'Dataset 3dd68cff-a4b8-4d45-8799-a5d433e75e60 '\n", " 'NDC stats for countries',\n", " 'Dataset 3f633a05-a3c9-44a5-939c-aecae35fe63e '\n", " 'NDC stats for countries',\n", " 'Dataset e10f4382-c7c4-4205-a484-17b9d60a68f5 '\n", " 'Test Data Rows',\n", " 'Dataset 60db4603-84fd-487b-b0b8-2db9e13df0f5 '\n", " 'Mongabay Stories',\n", " 'Dataset 091cab6a-3a78-4015-a7b4-7a5d46ccf50b '\n", " 'Tree plantations by type - 2013-2014 CLONE',\n", " 'Dataset acee82c1-e621-4ba6-8e37-0e7075aa73ff '\n", " 'Global Forest Watch - Countries config',\n", " 'Dataset 0f24299d-2aaa-4afc-945c-b614028c12d1 '\n", " 'Fire alerts summary stats',\n", " 'Dataset bd42375f-0983-4e4f-9602-806eb2c26401 '\n", " 'Tree cover loss 2018 - GADMv3.6 ADM2 summary - '\n", " 'v20190423',\n", " 'Dataset 4fc24a03-cb3e-4df3-a2ee-e2a8dca342b3 '\n", " 'Logging concessions',\n", " 'Dataset 2b247346-2a1c-4dbf-a934-dd529deed869 '\n", " 'CMR 9.1 test data NULL Thomas',\n", " 'Dataset 8f22dec5-2aea-49d6-8a7b-c494dbb8095c '\n", " 'Political boundaries Admin 1 level (GADM 3.6)',\n", " 'Dataset cdc5217b-09b7-461d-961d-dc262ba2b4be '\n", " 'Tree cover loss 2018 - GADMv3.6 ADM1 summary - '\n", " 'v20190423',\n", " 'Dataset 97546f05-3dce-4dd0-9abf-80fd1bff9cee '\n", " 'Tree cover loss 2018 - GADMv3.6 ISO summary - '\n", " 'v20190423',\n", " 'Dataset b3d076cc-b150-4ccb-a93e-eca05d9ac2bf '\n", " 'soc.064.02 Political Boundaries (Second '\n", " 'Subnational Level)',\n", " 'Dataset 64632828-d5fa-4b94-be53-92b9e7b069a5 '\n", " 'Test Data C Thomas',\n", " 'Dataset a8dc9474-ba42-4ae3-a7d3-d8df5f1e78df '\n", " 'Political boundaries (GADM 3.6)',\n", " 'Dataset fe80bbb1-90e5-4ab6-ae10-3bce6abcc0fb '\n", " 'Global Mangrove Forests'],\n", " 'removed': []},\n", " 'env': 'production',\n", " 'updatedAt': '2020-08-10@14h-48m-03s'}]\n" ] } ], "source": [ "pprint(metadata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "name": "Update-GFW-Layers-Vault.ipynb", "provenance": [], "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

pastas/pasta

examples/notebooks/02_fix_parameters.ipynb

1

287265

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Analysis with Pastas\n", " \n", "*Developed by Mark Bakker, TU Delft*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Required files to run this notebook (all available from the `data` subdirectory):\n", "\n", "* Head files: `head_nb1.csv`, `B58C0698001_1.csv`, `B50H0026001_1.csv`, `B22C0090001_1.csv`, `headwell.csv`\n", "* Pricipitation files: `rain_nb1.csv`, `neerslaggeg_HEIBLOEM-L_967.txt`, `neerslaggeg_ESBEEK_831.txt`, `neerslaggeg_VILSTEREN_342.txt`, `rainwell.csv`\n", "* Evaporation files: `evap_nb1.csv`, `etmgeg_380.txt`, `etmgeg_260.txt`, `evapwell.csv`\n", "* Well files: `well1.csv`, `well2.csv`\n", "* Figure: `b58c0698_dino.png`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pastas\n", "Pastas is a computer program for hydrological time series analysis and is available from the [Pastas Github](https://github.com/pastas/pastas) . Pastas makes heavy use of `pandas` `timeseries`. An introduction to `pandas` `timeseries` can be found, for example, [here](http://nbviewer.jupyter.org/github/mbakker7/exploratory_computing_with_python/blob/master/notebook8_pandas/py_exploratory_comp_8_sol.ipynb). The Pastas documentation is available [here](http://pastas.readthedocs.io)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 3.8.2 (default, Mar 25 2020, 11:22:43) \n", "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", "Numpy version: 1.21.2\n", "Scipy version: 1.7.1\n", "Pandas version: 1.3.3\n", "Pastas version: 0.19.0b\n", "Matplotlib version: 3.4.3\n" ] } ], "source": [ "import pandas as pd\n", "import pastas as ps\n", "import matplotlib.pyplot as plt\n", "\n", "ps.set_log_level(\"ERROR\")\n", "ps.show_versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the head observations\n", "The first step in time series analysis is to load a time series of head observations. The time series needs to be stored as a `pandas.Series` object where the index is the date (and time, if desired). `pandas` provides many options to load time series data, depending on the format of the file that contains the time series. In this example, measured heads are stored in the csv file `head_nb1.csv`. \n", "The heads are read from a csv file with the `read_csv` function of `pandas` and are then squeezed to create a `pandas Series` object. To check if you have the correct data type, use the `type` command as shown below. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The data type of the oseries is: <class 'pandas.core.series.Series'>\n" ] } ], "source": [ "ho = pd.read_csv('../data/head_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\n", "print('The data type of the oseries is:', type(ho))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The variable `ho` is now a `pandas Series` object. To see the first five lines, type `ho.head()`. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "date\n", "1985-11-14 27.61\n", "1985-11-28 27.73\n", "1985-12-14 27.91\n", "1985-12-28 28.13\n", "1986-01-13 28.32\n", "Name: head, dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ho.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The series can be plotted as follows" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ho.plot(style='.', figsize=(12, 4))\n", "plt.ylabel('Head [m]');\n", "plt.xlabel('Time [years]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the stresses\n", "The head variation shown above is believed to be caused by two stresses: rainfall and evaporation. Measured rainfall is stored in the file `rain_nb1.csv` and measured potential evaporation is stored in the file `evap_nb1.csv`. \n", "The rainfall and potential evaporation are loaded and plotted." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The data type of the rain series is: <class 'pandas.core.series.Series'>\n", "The data type of the evap series is <class 'pandas.core.series.Series'>\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rain = pd.read_csv('../data/rain_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\n", "print('The data type of the rain series is:', type(rain))\n", "\n", "evap = pd.read_csv('../data/evap_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\n", "print('The data type of the evap series is', type(evap))\n", "\n", "plt.figure(figsize=(12, 4))\n", "rain.plot(label='rain')\n", "evap.plot(label='evap')\n", "plt.xlabel('Time [years]')\n", "plt.ylabel('Rainfall/Evaporation (m/d)')\n", "plt.legend(loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recharge\n", "As a first simple model, the recharge is approximated as the measured rainfall minus the measured potential evaporation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "recharge = rain - evap\n", "plt.figure(figsize=(12, 4))\n", "recharge.plot()\n", "plt.xlabel('Time [years]')\n", "plt.ylabel('Recharge (m/d)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First time series model\n", "Once the time series are read from the data files, a time series model can be constructed by going through the following three steps:\n", "\n", "1. Creat a `Model` object by passing it the observed head series. Store your model in a variable so that you can use it later on. \n", "2. Add the stresses that are expected to cause the observed head variation to the model. In this example, this is only the recharge series. For each stess, a `StressModel` object needs to be created. Each `StressModel` object needs three input arguments: the time series of the stress, the response function that is used to simulate the effect of the stress, and a name. In addition, it is recommended to specified the `kind` of series, which is used to perform a number of checks on the series and fix problems when needed. This checking and fixing of problems (for example, what to substitute for a missing value) depends on the kind of series. In this case, the time series of the stress is stored in the variable `recharge`, the Gamma function is used to simulate the response, the series will be called `'recharge'`, and the kind is `prec` which stands for precipitation. One of the other keyword arguments of the `StressModel` class is `up`, which means that a positive stress results in an increase (up) of the head. The default value is `True`, which we use in this case as a positive recharge will result in the heads going up. Each `StressModel` object needs to be stored in a variable, after which it can be added to the model. \n", "3. When everything is added, the model can be solved. The default option is to minimize the sum of the squares of the errors between the observed and modeled heads. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit report head Fit Statistics\n", "==================================================\n", "nfev 10 EVP 92.02\n", "nobs 518 R2 0.92\n", "noise True RMSE 0.13\n", "tmin 1985-11-14 00:00:00 AIC -2592.26\n", "tmax 2010-01-01 00:00:00 BIC -2571.01\n", "freq D Obj 1.70\n", "warmup 3650 days 00:00:00 ___ \n", "solver LeastSquares Interp. No\n", "\n", "Parameters (5 optimized)\n", "==================================================\n", " optimal stderr initial vary\n", "recharge_A 749.010453 ±4.79% 215.674528 True\n", "recharge_n 1.049137 ±1.53% 1.000000 True\n", "recharge_a 134.483793 ±6.73% 10.000000 True\n", "constant_d 27.547665 ±0.07% 27.900078 True\n", "noise_alpha 58.973443 ±12.37% 15.000000 True\n" ] } ], "source": [ "ml = ps.Model(ho)\n", "sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\n", "ml.add_stressmodel(sm1)\n", "ml.solve(tmin='1985', tmax='2010')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `solve` function has a number of default options that can be specified with keyword arguments. One of these options is that by default a fit report is printed to the screen. The fit report includes a summary of the fitting procedure, the optimal values obtained by the fitting routine, and some basic statistics. The model contains five parameters: the parameters $A$, $n$, and $a$ of the Gamma function used as the response function for the recharge, the parameter $d$, which is a constant base level, and the parameter $\\alpha$ of the noise model, which will be explained a little later on in this notebook.\n", "The results of the model are plotted below." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ml.plot(figsize=(12, 4));" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit report head Fit Statistics\n", "==================================================\n", "nfev 10 EVP 92.02\n", "nobs 518 R2 0.92\n", "noise True RMSE 0.13\n", "tmin 1985-11-14 00:00:00 AIC -2592.26\n", "tmax 2010-01-01 00:00:00 BIC -2571.01\n", "freq D Obj 1.70\n", "warmup 3650 days 00:00:00 ___ \n", "solver LeastSquares Interp. No\n", "\n", "Parameters (5 optimized)\n", "==================================================\n", " optimal stderr initial vary\n", "recharge_A 749.010453 ±4.79% 215.674528 True\n", "recharge_n 1.049137 ±1.53% 1.000000 True\n", "recharge_a 134.483793 ±6.73% 10.000000 True\n", "constant_d 27.547665 ±0.07% 27.900078 True\n", "noise_alpha 58.973443 ±12.37% 15.000000 True\n" ] } ], "source": [ "ml = ps.Model(ho)\n", "sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\n", "ml.add_stressmodel(sm1)\n", "ml.solve(tmin='1985', tmax='2010', solver=ps.LeastSquares)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit report head Fit Statistics\n", "===================================================\n", "nfev 23 EVP 91.52\n", "nobs 518 R2 0.92\n", "noise True RMSE 0.13\n", "tmin 1985-11-14 00:00:00 AIC -2584.62\n", "tmax 2010-01-01 00:00:00 BIC -2567.62\n", "freq D Obj 1.74\n", "warmup 3650 days 00:00:00 ___ \n", "solver LeastSquares Interp. No\n", "\n", "Parameters (4 optimized)\n", "===================================================\n", " optimal stderr initial vary\n", "recharge_A 776.738694 ±5.08% 215.674528 True\n", "recharge_n 1.000000 ±nan% 1.000000 False\n", "recharge_a 153.740242 ±5.46% 10.000000 True\n", "constant_d 27.534802 ±0.08% 27.900078 True\n", "noise_alpha 64.122503 ±12.81% 15.000000 True\n" ] } ], "source": [ "ml = ps.Model(ho)\n", "sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\n", "ml.add_stressmodel(sm1)\n", "ml.set_parameter('recharge_n', vary=False)\n", "ml.solve(tmin='1985', tmax='2010', solver=ps.LeastSquares)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ml.plot(figsize=(10, 4));" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

Sz593/coursera_ml_notes

Jupyter Notebooks/LaTeX Notes Sandboxes/Coursera ML Notes Tex 2 - Logistic Regression.ipynb

1

350317

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import datetime as dt\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, constants\n", "from scipy.special import comb, perm, factorial, expit\n", "import statsmodels.api as sm\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True True\n" ] } ], "source": [ "fp_list_master = ['C:', 'Users', 'szahn', 'Dropbox', 'Statistics & Machine Learning', 'coursera_ml_notes']\n", "fp = os.sep.join(fp_list_master)\n", "fp_fig = fp + os.sep + 'LaTeX Notes' + os.sep + 'Figures'\n", "print(os.path.isdir(fp), os.path.isdir(fp_fig))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RERHauHGjk0MDAAAEHUeBLS0tTR07dlTDhg0r3S4sLEwdOnTQjz/+6FlWv359xcbG6tCh\nQ04ODQAAEHQcBbamTZsqMzNTJSUllW5XUlKizMxMNWjQoMzygoICnXPOOU4ODQAAEHQcBbYePXro\n6NGjeumllyrd7m9/+5uOHDminj17epZlZmZq7969io2NdXJoAACAoONolujUqVO1bt06LViwQLt2\n7dL111+vLl26KDw83DNL9P3339fq1asVGhqqqVOnSpI+/fRTPfvssyopKdHIkSO92ggAAECgsmzb\ntp18cdWqVXrggQdUUFBQ7sQD27bVsGFDPfbYY7rmmmskSaNHj9b27dsVHx+vd955R2FhYTWrvgoZ\nGRlKTEyUy+XijB4AAPBbjs6wSdLw4cPVq1cvvfbaa/r000+VkZHhWRcdHa3LL79cEydOVLt27TzL\nu3Tpoquvvlo33XRTrYc1AACAQOH4DNvp3G63fv31VzVq1EhNmjTxxi5rjDNsAAAgEDg+w3a6sLAw\nRUVFeWt3AAAA+K8qA9tTTz0ly7I0efJkNW/e3LPsbFiWpZkzZzqrEAAAIMhVGdjeeOMNWZalsWPH\negJb6bLqsG2bwAYAAFADVQa2UaNGybKsMg+6LV0GAACA2ue1SQcmYtIBAAAIBI7edAAAAIC6U+Ul\n0RMnTnjlQOHh4V7ZDwAAQLCpMrD16tWrxgexLEvbt2+v8X4AAACCUZWBzRu3uAXwbXIAAAC1rsrA\n5nK56qIOAAAAVKDKwNa2bdu6qAMAAAAVqJNZopmZmXVxGAAAgIDk+F2iOTk5Wr58uXbv3q2CggKV\nlJSUWV9cXKzCwkIdPHhQu3fv1vfff1/jYgEAAIKRo8B2+PBhjR07VgcOHPBMKLAsq8zkgtI3Idi2\nrXr1vPaOeQAAgKDj6JLoa6+9pv379ys8PFxjx47VrbfeKtu21bt3b02dOlUjR45URESEbNvWxRdf\nrI0bN3q7bgAAgKDh6NRXSkqKLMvSwoUL1bt3b0nSxx9/LMuydPfdd0uSsrOzNWnSJG3YsEHff/+9\n+vTp472qAQAAgoijM2xZWVlq3bq1J6xJUteuXbV161bPvWwtWrTQ3LlzZdu2Fi9e7J1qAQAAgpCj\nwFZcXKyWLVuWWXbuueeqsLBQP//8s2dZQkKCYmNjtWXLlppVCQAAEMQcBbbIyEhlZ2eXWdauXTtJ\n0o8//lhmedOmTXXkyBGH5QEAAMBRYOvWrZuysrK0adMmz7JOnTrJtu0yEwzcbrcyMjIUERFR80oB\nAACClKPANnr0aNm2ralTp+r5559XUVGRevfuraZNm2rZsmX68MMPtWvXLj3yyCPKyclRXFyct+sG\nAAAIGo4C2+DBgzVmzBgdP35cb7zxhkJDQxUeHq7x48erqKhI//d//6eRI0fqww8/lGVZmjx5srfr\nBgAACBqOn2g7Z84cJSYm6quvvvI8JPeOO+5QQUGBFi1apBMnTigiIkJ33nmnBg4c6LWCAQAAgo1l\nn/p6Ai8pKirSkSNH1KJFC4WGhnp799WWkZGhxMREuVwuxcbG+qwOAACAmqjyDNu+ffsc7/zAgQOe\nzzExMY73AwAAEMyqDGyXX36555KnU5Zlafv27TXaBwAAQLCq9j1sNblyWgtXXQEAAIJGlYEtJCRE\nJSUlsixLCQkJGjZsmIYOHaqoqKi6qA8AACDoVRnYvvjiCyUnJ+uTTz7Rxo0btWPHDs2bN0+9evUi\nvAEAANSBs5ol+uuvvyo5OVmrV6/Whg0bVFRUpJCQEPXs2dMT3qKjo2uz3rPCLFEAABAIHD/WIzc3\nV2vXrtXq1av11Vdf6bffflNISIh69OihYcOGKSkpyefhjcAGAAACgVeew5aXl6e1a9fqk08+0Zdf\nfim3262QkBBdeOGFGjZsmIYNG6bWrVt7o96zQmADAACBwOsPzs3Pz9enn36qNWvWKCUlRQUFBT57\nrAeBDQAABALHr6aqyKFDh5SVlaWDBw/K7XbzSA8AAIAa8kpg27lzp9asWaPk5GTt3r1b0slnr7Vq\n1UpXXHGFhg4d6o3DAAAABCXHge0///mPkpOTlZycrMzMTEknQ1pMTIyuuOIKJSUlqVevXl4rFAAA\nIFhVO7AVFxdrw4YNWrNmjVwulw4fPuy53NmhQwcNHTpUQ4cOVbdu3WqtWAAAgGBUZWBzuVxKTk7W\n+vXrlZub6wlpnTt39oS0+Pj4Wi8UAAAgWFUZ2O666y5ZliXbttW1a1dPSIuLi6uL+gAAAIJetS+J\n1qtXT1lZWXr77bf19ttvn9VBLMtSamrqWRcHAACAagY227ZVVFSko0ePOjqIZVmOvgcAAIBqBLZF\nixbVRR0AAACoQJWBrW/fvnVRBwAAACoQ4usCAAAAUDkCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4\nAhsAAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEI\nbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOw\nAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAG\nAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsA\nAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEIbAAA\nAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAA\nGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYrp6vC6hI\nbm6uXnzxRblcLh08eFCRkZG69NJLdddddykmJsbX5fnerFll//ZX9GGeQOoFAAKEkYEtNzdXN9xw\ng/bs2aMmTZooPj5ev/zyi9577z0lJydryZIlOv/8831dpu/MmiXNnl32Z39EH+YJpF4AIIAYeUn0\noYce0p49ezRo0CClpKRo+fLl+vzzzzV69Gjl5ubqnnvukW3bvi7TN04fUGfP9s9BlT7ME0i9AECA\nMS6wpaenKzk5WY0bN9ZTTz2lRo0aSZLCwsL0+OOPq1OnTkpLS1NycrKPK/WB0wfUUv42sNKHeQKp\nFwAIQMYFtn/961+ybVuDBw9WREREmXUhISEaPXq0bNvWqlWrfFShj1Q0oJbyl4GVPswTSL0AQIAy\nLrB99913sixLPXv2LHd99+7dJUmbN2+uy7J8q6oBtZTpAyt9mCeQegGAAGZcYNu7d68kKTY2ttz1\nbdu2lSRlZ2frxIkTdVaXz1R3QC1l6sBKH+YJpF4AIMAZF9iOHDkiSWrevHm565s2ber5fPTo0Tqp\nyWfOdkAtZdrASh9m9SEFVi8AEASMC2yFhYWSpAYNGpS7vmHDhp7PBQUFdVKTTzgdUEuZMrDSx0mm\n9CEFVi8AECSMC2whIZWXVFJS4vlsWVZtl+MbNR1QS/l6YKWPsnzdhxRYvQBAEDEusJU+xqP0TNvp\n3G635/OpZ9sChrcG1FK+Gljpo3y+DDqB1AsABBnjAluzZs0kSTk5OeWu//XXXz2fIyMj66QmAAAA\nXzIusMXFxUmSMjMzy12/b98+SVKrVq0qvM/Nr82aJT36qPf29+ijvjszRR9n8lUfUmD1AgBBxrjA\ndsEFF8i2bW3ZsqXc9d9++62k/z2PLSB5a2D19YBKH2X5ug8psHoBgCBiXGC74oorJElr165Vbm5u\nmXUlJSVasWKFLMvSyJEjfVFe3anpwGrKgEofJ5nShxRYvQBAkDAusHXp0kWDBg3SsWPH9Ic//MFz\nz5rb7daDDz6otLQ0xcXFaciQIT6utA44HVhNG1Dpw6w+pMDqBQCCQD1fF1Ce2bNn6+abb9bGjRs1\nePBgxcXFKSMjQzk5OWratKnmz5/v6xLrTungWN3ZfaYOqPRhnkDqBQACXOisWeb9D9ykSRONGjVK\nhYWF2r9/v37++WeFh4dryJAheuaZZ3TuuedWaz+5ublatGiRxo0bd8aL5P3KoEEn//7ss8q3M31A\npQ/zBFIvABDALNu2bV8XUVsyMjKUmJgol8tV4btJ/Uplz9HypwGVPswTSL0AQAAy7h42VKKi+478\nbUClD/MEUi8AEIAIbP7m9IHVXwdU+jBPIPUCAAHGyEkHqMKpg6g/D6j0YZ5A6gUAAgj3sAEAABiO\nS6IAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYj\nsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABguHq+LqA2FRcXS5L279/v40oAAACq\np3Xr1qpXr2xEC+jAdujQIUnSLbfc4uNKAAAAqsflcik2NrbMMsu2bdtH9dS6goICbdu2Ta1atVJo\naKivywEAAKhSeWfYAjqwAQAABAImHQAAABiOwAYAAGC4gJ50AKD2zZ8/X/Pnzz/r761bt04xMTG1\nUJF/+eyzz/Thhx/q22+/VXZ2tsLCwhQVFaV+/fppzJgx+t3vfnfGd1asWKH7779fF1xwgZYvX+6D\nqgHUNQIbgBpp06aNLrroojOWb9u2TW63Wx06dFCLFi3KrLMsSw0aNKirEo1UXFysGTNmaPXq1bIs\nS61bt1Z8fLxyc3OVmZmpZcuWadmyZZowYYLuu+++M75vWZYsy/JB5QB8gUkHAGrF5ZdfrqysLM2d\nO1ejRo3ydTnGeea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"text/plain": [ "<matplotlib.figure.Figure at 0x1b373fc1da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list = [1, 2, 3, 4, 7, 8, 9, 10]\n", "y_list = [0, 0, 0, 0, 1, 1, 1, 1]\n", "\n", "with sns.axes_style('white'):\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plt.plot(x_list, y_list, 'rD', markersize=16)\n", " plt.xlabel(\"Tumor Size\", fontsize=24)\n", " plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1], fontsize=24)\n", " plt.xticks([])\n", " plt.ylim(-0.10, 1.10)\n", " plt.xlim(-0.1, 11)\n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor.pdf')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AABVy+LC5iXtiorXet6+0b5957xoA1yKwAQDKLSbGfOLzzBlrPSJC2rHDfCoUgOsR2AAA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\nef5SN9c7HA4lJydr+PDhOnfunNq2bat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"text/plain": [ "<matplotlib.figure.Figure at 0x1b374128e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list = [1, 2, 3, 4, 7, 8, 9, 10]\n", "y_list = [0, 0, 0, 0, 1, 1, 1, 1]\n", "\n", "x_list_reg = list(range(11))\n", "regline = lambda x: (1/6) * (x - 1) - 0.25\n", "y_list_reg = [regline(x) for x in x_list_reg]\n", "\n", "with sns.axes_style('white'):\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plt.plot(x_list, y_list, 'rD', markersize=16)\n", " plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"Tumor Size\", fontsize=24)\n", " plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1], fontsize=24)\n", " plt.xticks([])\n", " plt.ylim(-0.10, 1.10)\n", " plt.xlim(-0.1, 11)\n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1.pdf')" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GTWHvgOp63npQ3/KmyLl0HrtW/xtO7t6IeXwuvNt30swKPZGw8VbDRkR0m5xcYMwjdkg6\noj3T3cVFxIbVVRgew7eXUMP4DJuFcXLzQu/7H0NNtRL7f/5CZ71nuyBUV5Sj8PoVnXXX008CADx8\n/37p/J23MVtTxtG9cHBxR8zjc+Eb0kXTrNVUK7WedWsq74BOKCvKQ0VJoc66nMvn9a7zwrE/ARG4\ne9IsBPUYqBXhcVPzc9XvqhnvhBBZntR0Af2H2es0a+381dj3WyWbNWoSNmwWKHzgaPh0DEN+1gWd\nmZCd+8RChIjETV9pRYCU5F/Hsd9/hI2NHMFRgzTLZTI5RJVxLtPL5bZQVStRVV6qWSaq1UjctBI1\nf90iVTejls59h0IURSRt+Vbr+bf05N0Nvquz0ToVtgCA8mLtRvBaagoyju79q84avT5bZnPronft\n83JEZN4Y20GGwluiZuzOVzPdbuDDz+OXD16A6o7GoXN0DK6cSsLlEwexaelstA/vheqqClw+dQjV\nlRUYMOEZrStGju5eKM7Lxp7vP0S70J7oHN16r38K6X0vTu3+BVs+ehmBkX0hqtTIPHcUxbnX4ODs\nhsrSYlSVF8PBxaNpnxc1GOnJu5Fx5A8UXr8Mv07dUJyXjaunD8PeyQ1VZcUQhPqDeG93+886qOcg\nnNy9GQc2fInr6Sfh4OqJguxLyDp3FPbObqgsKUJlWYk+PwI4unlBhIjjO35EfmYGeo6cbNbvRiWy\nZoztIEPiFTYz1tA7J91926Nb7MQ6t4l9Yj76jX8atvYOSD0Uj6unD8MnKByjnnsHYX/lpNWKHvM4\n3H074FLKAWQk72m0nroqurW07lpv36P3fY+h56gpkAkynNv/Gy6fTISLty9GPPs2ug+dBAC4euZI\nnfvetlDL0BkL0X3YJFSVl+Ls/jiU5F/HPVNfhl/nW/Ebt0eb1Ff/ncfyaheEEc+8Be/2nXD51CGc\nT/wdlSU30Wv0VDz46meAICDz7JF6979z+e23nYOjBiG4xyCU5N/A2f1xKC3MraciIpIqxnZQaxBE\nC56ilpmZidjYWGz7bhH8feueFShVe07m45Siv6nLMGtlN/OgsHfUvKbqdts+X4D8zAw89u+fTVBZ\ny1QrqzDK7QjCAr1NXYrZej/4SJ3L37z5qpErIUvD2A5qMTfdcxbAK2xkwU4kbMTaBY8gO+OU1vIb\nF8/hxsUz8Psr5JaIyBDKy4EJ02x1mjW5XMS3y6vw5nw2a6Q/PsNGFqtz36E4n7gD8Sv/iY7d+sPR\n3Qsl+Tdw+WQibO2dEN2M11wRETWkodiOjWuqMGwIZ4JSy7BhI4vlHRCCMS9+gJSd63Et/SQqS4tg\n7+SKkKh70GP4Q1qTK4iI9JWaLmDURDudmaDt/NWI+5kzQckw2LBJlI2Mf8ANwatdEGIen2vqMgxK\nVVMNB1s+zUAkBfsTZXhgsh0KCrXvdUZGqBG3vgoB7fh3ORkG/9aXKG8noLpK/zR+smAVBWjjXvdD\nqURkPBu22CB2rG6zNvReFf78rZLNGhkUGzaJCu3gCXnxZVOXQRLUVp4PR4e639lKRK2PsR1kCmzY\nJEomkyHEqRCqmmpTl0ISUl1RjHCPysY3JKJWoVIBL8xTYM5rthBF7WbtrflKrFquhIJZ19QK2LBJ\n2MgenvAtPqR5LRNZN2X5TUTKTiL6Li9Tl0JklRjbQabESQcSJpPJMKmfN1IyDiOjyA7XKxxQJdro\n/KuOLJeNIMJOVoMAx3KEtVHjrvZtTF0SkVVibAeZGhs2iZPJZOjZuQ16mroQMhEBgO1f/xGRKTC2\ng6SAt0SJiIjqsT9Rhv7D7HWatcgINRLj2ayR8bBhIyIiqgNjO0hK2LARERHdhrEdJEVs2IiIiP7C\n2A6SKk46ICIiwq3YjikzbbElTvvUKJeL+OpTJaY/qjJRZURs2IiIiBjbQZLHho2IiKwaYzvIHPAZ\nNiIislqM7SBzwYaNiIisEmM7yJywYSMiIqvC2A4yR2zYiIjIajC2g8wVJx0QEZFVYGwHmTM2bERE\nZPEY20Hmjg0bERFZNMZ2kCXgM2xERGSxGNtBloINGxERWSTGdpAlYcNGREQWhbEdZInYsBERkcVg\nbAdZKk46ICIii8DYDrJkbNiIiMjsMbaDLB0bNiIiMmuM7SBrwGfYiIjIbNUX29GtC2M7yLKwYSMi\nIrNUX2zHsCGM7SDLw4aNiIjMSmOxHdt+roKrq4mKI2olbNiIiMhsMLaDrBUnHRARkVlgbAdZMzZs\nREQkeYztIGvHho2IiCSNsR1EfIaNiIgkjLEdRLewYSMiIklibAfR39iwERGRpDC2g0gXGzYiIpIM\nlQqYPbfu2I4351UztoOsFicdEBGRJDC2g6h+bNiIiMjkGNtB1DA2bEREZFKM7SBqHJ9hIyIik2Fs\nB1HTsGEjIiKTYGwHUdOxYSMiIqNibAdR87FhIyIio2kotuOt+UrGdhDVg5MOiIjIKBjbQaQ/NmxE\nRNTqGNtB1DJs2IiIqFUxtoOo5fgMGxERtZr6YjsiIxjbQdQcbNiIiKhVrP+l7tiOofcytoOouZp1\nS/Tq1as4c+YMFAoFevToAU9Pz9aqi4iIzFRtbMcrr9vqrJs+pQYrP+VMUKLmalLDVlJSgoULF2Ln\nzp2aZTY2NnjkkUcwd+5c2Nrq/qEkIiLro1IBL85XYNlXuh3ZW/OVeGNeDQShjh2JqEGNNmxKpRKP\nP/44zp49C1EU4e3tDUdHR1y5cgU//PAD0tLS8J///Af29vbGqJeIiCSKsR1ErafRZ9j++9//4syZ\nM2jTpg2+++477Nu3Dzt27MDq1avh6emJpKQkzJo1C0ql0hj1EhGRBOXkAkPG2Ok0ay4uIuLWV7FZ\nI2qhRhu2bdu2QRAEfPTRR+jXr59med++fbFmzRp4enri4MGDePrpp5GZmdmqxRIRkfSkpgvoP8xe\nJ2Otnb8a+36rZMYakQE02rBduHABfn5+6N27t866kJAQfPfdd/Dy8kJiYiJGjBiBMWPG4B//+Idm\n3/DwcERERBi+ciIiMjnGdhAZR6MNW3V1NRwdHetd37lzZ2zatAmjRo2CIAhIS0vD8ePHNetFUYQo\n8g8sEZGlYWwHkfE0OunAz88Ply5dQnZ2Nvz8/Orcpm3btvj4449RXl6OS5cuoaqqSrN8yZIlhq2Y\niIhMirEdRMbX6BW2e+65BzU1NXjppZeQnZ3d4LaOjo6IiIhAz549AQDOzs548MEH8eCDDxqmWiIi\nMimVCpg9V1Fns/bWfCVWLWezRtQaGm3YnnrqKXh4eCAlJQWjR4/GrFmzcOrUKWPURkREElJeDkyY\nZquTsSaXi/h2eRXenM+MNaLW0mjDVhvn0aFDB1RUVGD37t24fv26MWojIiKJYGwHkWk16U0HoaGh\n2LZtGxISEnDgwAF06tSptesiIiKJSE0XMGqinc5M0Hb+asT9zJmgRMbQ5HeJyuVyjBgxAiNGjGjN\neoiISEL2J8rwwGTdmaCREWrEra/iTFAiI2n0lmhdHnvsMbz77rtN2nb27NkYPny4PochIiITYmwH\nkXQ0+Qrb7ZKSkqBSNe15hfPnz/OZNyIiM8LYDiLpabRhu3DhAj799NM6l7/wwgv17ieKIrKzs3H5\n8uV689uIiEhaVCrgxfkKnZmgwK3YjjfmcSYokSk02rAFBwejqKgIiYmJmmWCIKCwsBC///57kw4y\nefJk/SskIiKjKC8Hpsy01ZkJKpeL+OpTJWeCEplQk26Jvv3229i6davm98uWLYO/vz/Gjx9f7z6C\nIMDJyQmhoaHo379/yyslIqJWk5MLjHnETucF7i4uIjauqeIL3IlMrEkNW2BgIP7v//5P8/tly5bB\nz89PaxkREZmn82kCRk9ibAeRlOk16eDcuXOGroOIiEyAsR1E5kGvWA8iIjJ/jO0gMh96XWEDgBMn\nTmD58uU4fvw4ysrKGoz5EAQBZ86c0fdQRERkQIztIDI/ejVsp06dwrRp06BUKiGKjf8LrCnbEBFR\n62NsB5F50qth+/LLL1FVVYVOnTrh6aefRlBQEOzt7Q1dGxERGRBjO4jMl14NW3JyMuzs7PDdd9/B\n29vb0DUREZGBMbaDyLzp1bBVVlYiJCSEzRoRkRlgbAeR+dNrlmiHDh2Qk5Nj6FqIiMjA9ifKMGC4\nvU6zFhmhRmI8mzUic6FXw/bAAw8gLy8P27dvN3Q9RERkIIztILIcet0SnTFjBg4dOoSFCxciKysL\ngwcPho+PDxQNzAN3cHDQu0giImo6xnYQWR69GrZx48ZBpVKhvLwcH3zwAT744IMGt2cOGxGRcTC2\ng8gy6dWwpaWlaX7NHDYiImlgbAeR5dKrYUtISDB0HURE1AKM7SCybHo1bO3atTN0HUREpCfGdhBZ\nPqO8/L28vNwYhyEisjqM7SCyDnq//L2mpgY7d+5Eeno6KisroVZrX25XqVSoqqpCTk4OkpOTkZSU\n1OJiiYjob+t/scG0Z2xRVaUb27FhdRXc3ExUGBEZnF4NW2lpKaZOnYrz5883uq0oihA4JYmIyGAY\n20FkffS6Jfrtt9/i3LlzEAQB/fr1Q2xsLERRRFhYGEaPHo1evXrBxubWg6/R0dGIi4szaNFERNZK\npQJmz1XU2ay9NV+JVcvZrBFZIr1niQqCgI8//hgjRoyASqVC37594e3tjQ8//BAAkJGRgaeeegrH\njh1DVVWVQYsmIrJGjO0gsl56XWG7evUqvLy8MGLECACAjY0NIiIicOzYMc02ISEhWLJkCWpqavDt\nt98aploiIiuVkwsMGWOn06y5uIiIW1/FZo3IwunVsFVVVcHPz09rWVBQEMrKynD16lXNsr59+6Jt\n27ZITk5uWZVERFbsfJqA/sPsdTLW2vmrse+3SmasEVkBvRo2d3d3FBcXay1r3749AODChQtay9u2\nbYvc3Fw9yyMism6M7SAiQM+GLSIiAleuXNF6RVVwcDBEUdS6LapWq5Gdnc0XvxMR6WH9LzaIHWuH\ngkLd2I4/f6tEQDs2a0TWQq+G7b777oMoinjiiSewfv16qNVq9O7dGw4ODlizZg2Sk5NRVlaGTz75\nBPn5+ejYsaOByyYislyiCHz4uRwPTbfTyVibPqUGceuZsUZkbfRq2MaMGYOBAwciLy8Pb7/9NkRR\nhKurKx566CGUl5dj2rRp6N27N7766isIgoApU6YYum4iIovE2A4iqotesR4ymQwrVqzAunXrcOjQ\nIU3m2pw5c5CXl4e4uDiIogiZTIZHH30UY8eONWjRRESWiLEdRFQfQRRFgz8EcePGDVy7dg2BgYHw\n9PQ09Mc3WWZmJmJjY7Htu0Xw9zVdHUQkLe8HH6lz+Zs3XzVyJX/LyQXGPGKnMxPUxUXExjVVnAlK\nZC3cHOtcrPe7RBvi4+MDHx+f1vhoIiKLk5ouYNREO52ZoO381Yj7mTNBiaiFDZtKpUJGRgZKS0uh\nVqvR0MW66OjolhyKiMgi7U+U4YHJujNBIyPUiFtfxZmgRASgBQ3b6tWrsXz5cpSUlDS6rSAIOHPm\njL6HIiKySOt/scG0Z2x1ZoIOvVeFDas5E5SI/qZXwxYXF4clS5Zofu/g4AA7OzuDFUVEZMlEEfho\nmbzOmaDTp9Rg5aecCUpE2vRq2NauXQvgVh7b3Llz+bwaEVETqVTAi/MVWPaVbkf21nwl3phXA0Go\nY0cismp6NWznzp2Du7s73nvvPSj4z0AioiZhbAcR6UvvHDZ/f382a0RETcTYDiJqCb3edBAaGorL\nly+jpqbG0PUQEVmc1HQB/YfZ6zRr7fzV2PdbJZs1ImqUXg3b9OnTUVZWhi+++MLQ9RARWZT9iTL0\nH2avk7EWGaFGYjwz1oioafS6JdqnTx889thj+PLLL3H69GkMHjwYPj4+Dd4iveeee/QukojIHG3Y\nYoOpTzO2g4haTq+GrX///gAAURSxd+9e7N27t8HtmcNGRNZEFIGPl8vxyusKiKJ2s8bYDiLSh14N\nm5+fn6HrICKyCIztIKLWoFfDtmvXLkPXQURk9hjbQUStpVVe/k5EZG0Y20FErYkNGxFRC6WmCxg1\n0U5nJmg7fzXifuZMUCJqOb0attjY2KYfQC6HnZ0d2rRpg/DwcIwfPx7BwcH6HJaISHL2J8rwwGQ7\nFBRqP5jL38tyAAAgAElEQVQWGaFG3PoqBLRjs0ZELadXw5aVldXsfVJTU3HgwAGsXbsWb7/9NsaN\nG6fPoYmIJIOxHURkLHo1bAkJCXj33Xexa9cuREZG4pFHHkFERAScnJxQVlaG1NRUbNiwAYcPH0Zk\nZCSmT5+O4uJi7N27F7t378brr7+O0NBQhIeHG3o8REStjrEdRGRser3p4NChQ9i9ezcmTJiAn3/+\nGRMmTEB4eDg6dOiA8PBwjB07FmvXrsXjjz+OU6dOQRAETJ48GV9++SVefvllVFdXY+3atYYeCxFR\nq1OpgNlzFZjzmq1Os/bWfCVWLWezRkSGp1fD9v3338PJyQmvv/46hAYChebMmQMXFxd8++23mmVP\nPPEEXF1dkZSUpM+hiYhMprwcmDDNVidjTS4X8e3yKrw5nxlrRNQ69LolmpGRgbvuugv29vYNbmdr\na4vAwECkpaVplikUCgQEBCAjI0OfQxMRmQRjO4jIlPRq2Nzc3JCVlQW1Wg2ZrP6LdGq1GllZWbCz\ns9NaXllZCRcXF30OTURkdIztICJT0+uWaI8ePVBYWIgvvviiwe3+85//oKCgAD179tQsy8rKwuXL\nlxEQEKDPoYmIjGp/ogz9h9nrNGuREWokxrNZIyLj0OsK2zPPPINdu3Zh+fLlSE1NxUMPPYTQ0FA4\nODhoZolu2rQJ27dvh42NDZ555hkAwJ49e/Dhhx9CrVZj7NixBh0IEZGhMbaDiKRCr4atS5cuWLp0\nKRYuXIgdO3YgPj5eZxtRFGFvb49//vOfmitsn332GdLS0hAWFoYJEya0rHIiolbC2A4ikhq9X001\nevRoREVF4euvv8aePXuQmZmpWefj44OYmBjMmDED7du31ywPDQ3F/fffj8mTJ8PW1rZllRMRtQKV\nCnhpgQKfr9TtyN6ar8Qb8zgTlIiMTxBF0SAPYCiVSty8eROOjo5wdnY2xEe2WGZmJmJjY7Htu0Xw\n9/U0dTlEJBHvBx+pc/mr117FlJm22BKn/W9ZuVzEV58qMf1RlTHKIyJr5uZY52KDvfzd1tYWbdu2\nNdTHEREZ3ZAxjO0gImlqtGFbunQpBEHAzJkz4eHhoVnWHIIg4NVXX9WvQiIiI7mzWWNsBxFJRaMN\n26pVqyAIAiZOnKhp2GqXNYUoimzYiMjsREaoEbe+CgHt2KwRkek12rCNGzcOgiBoBd3WLiMiskSM\n7SAiqWm0YXvvvfeatIyIyBIwtoOIpEivNx0QEZkrVQMTPd+ar8Sq5WzWiEh6Gr3CVlFRYZADOTg4\nGORziIj0VV4hwxMvRqE36o71eHN+jZErIiJqmkYbtqioqBYfRBAEnDlzpsWfQ0Skr5w8W0x6KhrJ\nKR7obepiiIiaqdGGzRC5ugbK5iUi0kvaBSc8OKMPLl5xMnUpRER6abRhS0hIMEYdRESt4mCyBx56\nOhoFN/k6PCIyX402bO3atTNGHUREBrc5zg9PvtwDVUqbxjcmIpIwo8wSzcrKMsZhiIgAAKIIfPZ1\nEKY9H6XTrE2dcNVEVRER6U/vd4kWFRVhw4YNSE9PR2VlJdRq7ffsqVQqVFVVIScnB+np6Th9+nSL\niyUiaoxKBcx7pwu+XB2ks27RC+exYHYaPthogsKIiFpAr4YtLy8PEydOxI0bNzQTCgRB0JpcUPsm\nBFEUIZcb7B3zRET1qo3t+DXeV2u5XK7GssUnMG1ipokqIyJqGb06qa+//hrXr1+Ho6MjRo8eDQcH\nB6xduxa9e/dGr169cP36dezZswdFRUXo168fvvjiC0PXTUSk5fbYjtu5OFfjh+VHEDsoz0SVERG1\nnF4N2969eyEIAlauXInevW8lGv36668QBAEvvfQSACA/Px9PPvkkDh06hNOnTyM6OtpwVRMR3aa+\n2A5/3wps+iYJkeElJqqMiMgw9Jp0kJ2dDV9fX02zBgARERE4efKk5lk2Ly8vLFmyBKIoYu3atYap\nlojoDgeTPRAz8W6dZq1LaDH2bNzPZo2ILIJeDZtKpYK3t7fWso4dO6KqqgpXrlzRLAsPD0dAQABS\nUlJaViURUR02x/nhvqn9dDLWhtydi/ifDqCdX6WJKiMiMiy9GjZPT0/k5+drLWvfvj0AIC0tTWu5\nm5sbCgoK9CyPiEhXY7Edm1clwc2V7wUlIsuhV8MWGRmJ7OxsHD58WLMsJCQEoigiKSlJs0ypVCIz\nMxOurq4tr5SICLdiO155uwsWLO4CURS01i164TxWLE2BQsHX4RGRZdGrYRs/fjxEUcQzzzyDjz/+\nGDU1Nejduzfc3Nywbt06bNmyBampqXjjjTdQVFSE4OBgQ9dNRFaovEKGKbN6Y8Ua7Yw1uVyNFUuP\nY+ELaRCEenYmIjJjejVsQ4YMwYQJE1BeXo5Vq1bBxsYGDg4OmD59OmpqajB//nyMHTsWW7ZsgSAI\nmDlzpqHrJiIrk5Nni1FT+utkrLk4V2PTN0nMWCMii6Z3ou27776L2NhYHDx4UBOS++yzz6KyshJr\n1qxBRUUFXF1dMWvWLNxzzz0GK5iIrA9jO4jI2rXoFQQxMTGIiYnR/L42h+35559HQUEBvLy8YGPD\nly4Tkf4OJnvgoaejdWaCdgktxuZVSZwJSkRWodGG7dq1a3p/+I0bNzS/9vf31/tziMg6bY7zw5Mv\n99CZCTrk7lz8sPwIZ4ISkdVotGGLiYnR3PLUlyAIOHPmTIs+g4ishygCn38ThIVLInRmgk6dcBXL\nFp/gTFAisipNviV6+4vdm6sl+xKRdVGpgLn/6qIzExS4FduxYDZnghKR9Wm0YZPJZFCr1RAEAeHh\n4Rg5ciSGDx+Otm3bGqM+IrIi5RUyPPFilM5MULlcjWWLT3AmKBFZrUYbtn379iE+Ph6///47kpKS\ncPbsWXzyySeIiopi80ZEBpOTZ4tJT0UjOcVDa7mLczV+WH4EsYPyTFQZEZHpNdqweXp64uGHH8bD\nDz+MmzdvIj4+Htu3b8ehQ4eQnJyMxYsXo2fPnprmzcfHxxh1E5EFYWwHEVHDmhXr4e7ujkmTJmHS\npEkoLi7Gzp07sX37dhw8eBBHjhzBkiVL0KNHD4wcORIjRoxg80ZEjWJsBxFR4/TOYXN1dcX48eMx\nfvx4lJaWYufOnfj999+xf/9+HD16FO+99x66deuGkSNHYuTIkfD19W38Q4nIqjC2g4ioaVoUnFvL\n2dkZ48aNw7hx41BWVoY9e/Zgx44d2Lt3L1JSUrB06VLGehCRBmM7iIiaxyAN2+1yc3ORnZ2NnJwc\nKJVKRnoQkRbGdhARNZ9BGrZz585hx44diI+PR3p6OoBb2Wtt2rTBsGHDMHz4cEMchojMHGM7iIj0\no3fDdvToUcTHxyM+Ph5ZWVkAbjVp/v7+GDZsGEaMGIGoqCiDFUpE5o2xHURE+mtyw6ZSqXDo0CHs\n2LEDCQkJyMvL09zuDAwMxPDhwzF8+HBERka2WrFEZJ4Y20FE1DKNNmwJCQmIj4/H7t27UVxcrGnS\nOnXqpGnSwsLCWr1QIjJPjO0gImq5Rhu25557DoIgQBRFREREaJq04OBgY9RHRGZsU5wfZjK2g4io\nxZp8S1QulyM7OxurV6/G6tWrm3UQQRBw4MCBZhdHROZJFIHPvgnGwsUROusY20FE1HxNathEUURN\nTQ0KCwv1OojAOfpEVoOxHUREhtdow7ZmzRpj1EFEFoCxHUREraPRhq1Pnz7GqIOIzBxjO4iIWo/B\n33RARNaHsR1ERK2LDRsRtQhjO4iIWp/M1AUQkfnaFOeH+6b202nWhtydi/ifDrBZIyIyEDZsRNRs\nogh8+nUwpv1fL52MtakTrmLzqiRmrBERGRBviRJRszC2g4jI+NiwEVGTMbaDiMg02LARUZMwtoOI\nyHTYsBFRoxjbQURkWmzYiKhBjO0gIjI9zhIlonoxtoOISBrYsBGRjoZiOx5lbAcRkdHxligRaWFs\nBxGR9LBhIyINxnYQEUkTGzYiAsDYDiIiKWPDRkSM7SAikjg2bERWjrEdRETSx1miRFaMsR1EROaB\nDRuRFWootmMqYzuIiCSHt0SJrAxjO4iIzA8bNiIrwtgOIiLzxIaNyEowtoOIyHyxYSOyAoztICIy\nb2zYiCwcYzuIiMwfZ4kSWbDNjO0gIrIIbNiILJAoAp99HYRpz0cxtoOIyALwliiRhWFsBxGR5WHD\nRmRBGNtBRGSZ2LARWQjGdhARWS42bEQWgLEdRESWjQ0bkZljbAcRkeVjw0ZkxjbH+eHJl3vozAQd\ncncuflh+hDNBiSRKpVKhsrISarXa1KVYJEEQYGdnB4VCYepSDIYNG5EZEkXg82+CsHBJBERRe8rn\n1AlXsWzxCSgUoomqI6K6VFdXY9+pi7hYDORUKqCWO0AUmK7VGgRRhFCTDw+FEh1d1RgQGgBXF6fG\nd5QwNmxEZkalAua90wVfrmZsB5G5qK6uxpo/zuKmR0/IXGWwczV1RdahAsAZUUTaoTOYFu0HdzcX\nU5ekN7b2RGakvEKGKbN66zRrcrkaK5Yex8IX2KwRSdEvh9JuNWsynnaNTRAE1Hh1wU+Hr5q6lBbh\nFTYiM8HYDiLzVFNTg8sV9rBxYrNmSgUKX2Tn5sGvjbepS9EL/9dDZAbSLjghZuLdOs2av28F4n86\nwGaNSMJOZ1yF6NrR1GVYPXtXH5y4XGDqMvTGK2xEEsfYDiLzVlhRA7mtnanLIADlKvO9TmW+lRNZ\ngc1xfrhvaj+dZm3I3bmI/+kAmzUiM6BqZMJ28tYvsOLZSKx4NhJH41Y2uO2+Hxdrti0pyG52LSue\njcSGdyZpfn/+4BaseDYSJ3d93+zPkpLyojycO/BLo9upzDhFhQ0bkQSJIvDZ10GY9nyUTsba1AlX\nsXlVEjPWiCyMAAEXj+1scJuLxxMg/PV/huAVEIre989C26DuBvk8U6goKcC6N+7D5ZTdpi6lVfGW\nKJHEMLaDyDo5uHkj7+o5lBRkw8XTT2f99YzjKLuZA1s7J1RXlRvkmN7tw+DdPswgn2UqNcpKg/08\npIxX2IgkhLEdRNZJgICg7jEAUO9VtgtH42Hn4ALfzlHGLE3yRNE6QsJ5hY1IIhjbQWTd2oX1Rfrh\nOFw8loBusdN01l84Fo/A7kOgrCjRWXf+4BacP/AL8jPPo1pZAXsnd7QL7YPosc/D1Tug3mOeP7gF\nu1e/hrsfmofImKma5VnnDiF525fIv3oeMrkCwT1j0XXIo/j5nw+i9/2z0Pv+fwAAtnw4HaUF1zH2\nldVI3PghMs8cQE11FdoEdkH0A8/B/65oreMVXEvHse1fIzs1GeUl+ZDL7eDZrjO6DX0MwVHDNNsd\n3rocR7atwCNv/Q/nD25BWtI2VBTnw7VtB0QOmYKIwQ9p1S9AwMWUXVjxbCSGPP4OQvuPbd4P3wyw\nYSOSgLQLTnhwRh9cvKL96hR/3wps+iYJkeG6f0ETkWWR2cgR2H0I0g9tQ0VJARxcPDXrblw8ibKC\n6wjpNRxn923U2u/ghveRsnMNvAPCEDrgQQgCcC01GWmH45CdcQyT3/4VNgrbOw+ncefzcBeOxmPn\nV69C4eCE4F7DIVfYIf3wb8g8m6izrQAB1VXl2PLB41DYOiB0wFiUF+UhPXk7tn32LCYu+hkefiGa\nMWz98AnY2NohuOcw2Lt4oDjnCi6m7EL8yjkY+dwyBEYO1nyuAAEJq+ajtCAbQVHDIJPZIO3Qr/jz\nv+9AkNkgfOAEeAWEolvMNJzYtRYevkHoFD3a7G/x1ocNG5GJMbaDiGoF9xyG1MT/4VLKLoQPnKhZ\nfuHoDigcnBEQMUCrYSu7mYMTCd/D/65ojHnpGwi3PTMRt2wWrp7ah+z0IwgI71/vMUX8fUuxWlmB\nP9e9A1sHZ4xfsA6ubdoDAHqMmKE1u/R2laWF8OsUhWFPfwiZ7NYkKQ//Tji85XOkJm5F3wdfBAAk\nb10OtajCxHk/wN2no2b/jCM7EP/VHKQnbdM0bLV1VZUV4eG3t8LeyQ0A0Cl6NH55fxrO7d+E8IET\n4N0+DHaOrjixay3cfYLQ675nG/0Zmys+w0ZkQoztIKLbtY8YAIWdIy4cS9BafvHYTnTsPgQ2Ngqt\n5TYKO8TOWIK7H5qn1awBgH/n3gBuzaJsqqun9qGipABdh0zRNGsA4Ozhi25DH9Nq7moJENBt6GOa\nZg0AOnQdBBEiSvKvaZZ1G/oYYmf8W6tZu1VnrzrrFCAg7O7xmmYNAHxDesDOwUXrc60Fr7ARmYAo\nAp9/E4SFSyIgitp/yU6dcBXLFp+AQmEdD9IS0d9sFLYIjByMi8cSoKwsg629E3KvnEFxXibufni+\nzvb2Tm7oFD0aoiii4Fo6CrMvoDgvE/mZ55F19iAAQFSrmnz83MunIUBAm45dddb5hvSsd787mzBb\nh1svWVfVKDXL2kcMAACUF+chP/M8inOvovD6RVxPP/ZXnbohaW4+gTrLFA7OqK4sa3wwFoYNG5GR\nMbaDiBoS1HMY0pO34/KJP9C5z2hcOLIDtva3bofW5cLReBza/AmKcq9AgACFnSO8AyPg1T4MWWcT\nmzWLsrL0JgDA0U33fZtO7m3r3c9Grn2XQHO177ZjlxZkY9+PS3D5xJ6/tpHBzScQvp2ikHf1bJ1X\n7+78XOCvZ+6sZGbo7diwERlReYUMT7wYhV/jfbWWy+VqLFt8AtMmZpqoMiKSig6RgyBX2OPisZ3o\n3Gc0Lh5LQGC3e3RuhwLAjYsnEP/VK3D28MWwpz5Am8Aumlmhx35fhayzic06tq3DrYlPyopSnXXK\nSt1lzRH3+SzcvH4RUaOfRsceMfD06wQbhS0qivNxdt+GFn22NWDDRmQkjO0goqZQ2DqgfcQAXD29\nDzmXTuFmziX0mzCnzm0zDm8HRBGDpryODl0Haq0rzM5o9rG9O3SBCBE5l06hXWgfrXU3Lpxo9ufV\nys88j4LsdIREDUf0A/+nta6gtk59r5pZyS0JTjogMoK0C06ImXi3TrPm71uB+J8OsFkjIi1BUcNQ\nrazA/p+WwNbOCe273F3ndjYKW4gQUV6s/XdI5tlEpB+OAwCoVdVNPm7HHkNg7+iGU7t+QHFelmZ5\naeF1pOz4Vu9XYtko7ADoTiyoLCtC4sYP/6pTv9ftyWxuXXtSNWOc5ohX2IhaGWM7iKgxdz6/1bHb\nvbCxUeDGxRPoHD0aNnLd26EAENJ7JFJ2rsaf//0XrqUehpObN/IzU3H1zAE4OHugoqQAlaVFTa5D\nYeuAgZMXIWHVfGxc/BCCeg6FTCbDhWMJmlZNkDX/Wo9b20C07RiJ7LQj2PL+4/Dt1AMVpTdx6fgu\nqGqUUNg6oLLsptY+dT3TVhd7Z3fYyG1x7fxhHFj/PoJ6xsKvk+W9DYJX2IhaEWM7iKgp7rxyZevg\njHZhfSFAQHCvEXXscGt77/ZhuO/5FWgT2AWXUnbj7J8bUVGcjz5jn8ek1zdCEGS4cvpP7eMIuuG3\nt+sUPQojZ30Gd5+OSD8chwvHEtApehQGTl4EESLktg511lLnmP5aJwgCRs36HKEDxqIkPwsnd/8X\n19OOokPkYExY+DMCwvuj6MZlFOdlau9f7w/s73U2NgoMmvwa7JxcceaPn3DtfFL9+5kxQbTgl3Bl\nZmYiNjYW275bBH9fz8Z3IDIQxnZI2/vBR+pc/ubNV41cCVmDhGNpOIEIU5fRJMrKMlRXltU5I/Tc\n/s3Ys/YNDHvqQ4T0Gm6C6lqufeVZTOzfydRlNMzNsc7FvMJGZGAqFfDqP7tgweIuOs3aohfOY8XS\nFDZrRCRJRTcuYe38WOxZ87rW8hplJU7vWQeZjdwibzeaAz7DRmRA5RUyzHgpClt3MLaDiMyPd4cI\n+HTshvMHtqA4LwttO0aiRlmByyf3ojT/GvqMe6HOjDZqfWzYiAyEsR1EZO4EQcB9L67EifjVyDi6\n49ZVNbkCXu3uwoCJryCo51BTl2i12LARGUDaBSc8OKMPLl5x0lru71uBTd8kITK8xESVEZGp2QhA\nEyc8SoKtvRN6j5mF3mNmmboUg5OZcWQbn2EjaqGDyR6ImXi3TrPWJbQYezbuZ7NGZOVc7GRQ1Vh2\nRpi5cJDrvq/UXLBhI2oBxnYQUWMiQ9pDVXjJ1GVYvcrSfET4u5q6DL2xYSPSgygCn30dhGnPR6FK\naaO1buqEq9i8KglurvqldhORZbG1tUV7+/JmvYSdDM+1Mgsd/HxMXYbe2LARNRNjO4ioucb1DoJD\n/gk2baaSn4rxPX0gmPF7RznpgKgZyitkeOLFKPwaz9gOImo6R0cHPH53EHadOoXLxTa4KXODoHCC\nILNpfGdqNlGthrqmAs41hQh0UWNAzzbw8fJofEcJY8NG1ESM7SCilnB0dMD9fcIgiiJKSkpQXFaC\nahWvuLUGGxng4mgPd7cQs76qdjs2bERNwNgOIjIUQRDg6uoKV1fzfQCejI8NG1EjDiZ74KGno3Vm\ngnYJLcbmVUmcCUpERK2Okw6IGsDYDiIikgI2bER1YGwHERFJCW+JEt1BpQLmvdMFX64O0lm36IXz\nWDA7DRbyDCsREZkJyTZsxcXF+Pzzz5GQkICcnBx4enpi0KBBeO655+Dv72/q8kxG8cl/AQDVL04x\ncSWGI6UxGSq2Q0pjMhRLHBMRkbmQZMNWXFyMhx9+GBcvXoSzszPCwsJw9epVbNy4EfHx8fj+++9x\n1113mbpMo1N88l8oPlun+b0lnDilNCZDxXZIaUyGYoljIiIyJ5J8hu21117DxYsXce+992Lv3r3Y\nsGED/vzzT4wfPx7FxcV4+eWXrS4t+s4TpuKzdZorHuZKSmNKu+CEmIl36zRr/r4ViP/pgN7NGr8n\nIiIyBMk1bBcuXEB8fDycnJywdOlSODo6Arj1LrZ33nkHISEhyMjIQHx8vIkrNZ47T5ia5WZ84pTS\nmA4meyBm4t06GWtdQouxZ+P+JmesSWlMhmKJYyIiMkeSa9j+97//QRRFDBkyRCdUUCaTYfz48RBF\nEXFxcSaq0LjqO2Fq1pvhiVNKYzJUbIeUxmQoljgmIiJzJbmG7cSJExAEAT179qxzfffu3QEAycnJ\nxizLJBo7YWq2M6MTp1TGZMjYDqmMyZAscUxEROZMcpMOLl++DAAICAioc327du0AAPn5+aioqICD\ng4PRajOmpp4wNdv/ta2UHwaXypgMGdshlTEZkiWOiYjI3EmuYSsoKAAAeHh41Lnezc1N8+vCwkKL\nbNiae8LU7CfhE6dUxmSo2A5AOmMyJEscExGRJZBcw1ZVVQUAsLOzq3O9vb295teVlZb3WiB9T5ia\n/SV44pTKmAwV2wFIZ0yGZIljIiKyFJJr2GQyGVQqVb3r1Wq15teChcXNt/SEqfkcCZ04pTKmtAtO\neHBGH52ZoP6+Fdj0TVKTZ4IC0hmTIVnimIiILInkJh3UxnjUXmm7k1Kp1Pz69qtt5s5QJ0zN50ng\nYXCpjKm+2I6uYc2L7QCkMyZDssQxERFZGsk1bO7u7gCAoqKiOtffvHlT82tPT0+j1ETmq77YjpiB\nzYvtICIiMiXJNWzBwcEAgKysrDrXX7t2DQDQpk2bep9zM0fVL05B9ezJhvu82ZNNflvKlGNqLLZj\n0zdJcHVpWmyHVg38nhr/PAmMiYjI0kiuYevatStEUURKSkqd648fPw7g7zw2S2KoE6eUTpimGJNK\nBbz6zy5YsLgLRFH7OcdFL5zHiqUpUCj0f7UZv6cGPkdCYyIisiSSa9iGDRsGANi5cyeKi4u11qnV\namzevBmCIGDs2LGmKK/VtfTEKcUTpjHHVF4hw5RZvXUy1uRyNVYsPY6FLzQ9Y63Bmvg96e4vwTER\nEVkKyTVsoaGhuPfee1FSUoLnn39e88yaUqnEokWLkJGRgeDgYAwdOtTElbYefU+cUj5hGmNMOXm2\nGDWlv07GmotzNTZ9k9SsjLUm1cbv6e/9JDwmIiJLILlYDwB4++23MWXKFCQlJWHIkCEIDg5GZmYm\nioqK4ObmhmXLlpm6xFZXe/Jr6uw9czhhtuaYDBnb0Rz8nsxjTERE5k5yV9gAwMfHB5s2bcK0adPg\n6emJ1NRUyOVyjBkzBuvXr0dQkO4rhSxRU692mNMJszXGZMjYDn3wezKPMRERmTNJXmEDbr2CauHC\nhVi4cKGpSzGpxq52mOMJ05Bj2hznhydf7qEzEzRmYC5+WH5Er5mg+uD3RERErUmSV9hIW31XO8z5\nhNnSMbVWbEdL8HsiIqLWItkrbKTtzqsdlnDC1HdMKhUw919dsGKN7q3xRS+cx4LZhpkJqg9+T0RE\n1BrYsJmR20+SlnLCbO6YyitkeOLFKJ2ZoHK5GssWnzD4TFB98HsiIiJDY8NmZizxZNmc2I5JT0Uj\nOcVDa7mLczX++8URxAzMa43y9GLN3xMRERkeGzYyC6aK7SAiIpICNmwkeQeTPfDQ09E6L3DvGlaM\nTd8k8QXuRERk8ThLlCRtc5wf7pvaT6dZixmYi/ifDrBZIyIiq8CGjSRJirEdREREpsJboiQ5Uo7t\nICIiMgU2bCQp5hDbQUREZGxs2EgyzCm2g4iIyJjYsJEkMLaDiIiofmzYyOQY20FERNQwzhIlk2Js\nBxERUePYsJFJMLaDiIio6XhLlIxOpQLmvdMFX65mbAcREVFTsGEjo2JsBxERUfOxYSOjYWwHERGR\nftiwkVEwtoOIiEh/Ft2wqVQqAMCN3JsmrsS6HT3phucWdMTN4kLI5YWa5XcFl+I/76fAy6MK166b\nsECyOqXy0jqXZ167ZuRKiIjuUGIPX19fyOXaLZogiqJoopJaXXJyMh599FFTl0FERETUZAkJCQgI\nCNBaZtENW2VlJU6dOoU2bdrAxsam8R2IiIiITMzqrrARERERWQIG5xIRERFJHBs2IiIiIomz6Fmi\nRNT6li1bhmXLljV7v127dsHf378VKjIvf/zxB7Zs2YLjx48jPz8ftra2aNu2Lfr27YsJEyagS5cu\nOnLrKOAAAAoWSURBVPts3rwZCxYsQNeuXbFhwwYTVE1ExsaGjYhaxM/PD7169dJZfurUKSiVSgQG\nBsLLy0trnSAIsLOzM1aJkqRSqTBnzhxs374dgiDA19cXYWFhKC4uRlZWFtatW4d169bhiSeewNy5\nc3X2FwQBAt/hRmQ1OOmAiFpFTEwMsrOzsWTJEowbN87U5UjOBx98gK+//hohISH48MMPERYWplmn\nVCqxZs0afPTRRxBFEa+99ppWRFFpaSlyc3Nhb28PPz8/U5RPREbGZ9iIiIysoqICP/zwAwRBwCef\nfKLVrAGAra0tZs6ciX/84x8QRRErVqzQWu/s7IygoCA2a0RWhA0bEZGRXbp0CRUVFbC1tUXnzp3r\n3W7SpEkAgPz8fGRnZxurPCKSIDZsRGRSMTExCAsLwx9//FHn+r59+yIsLAyHDx/WLNu8eTPCwsKw\nZMkS5Ofn44033sCgQYPQvXt33Hffffj+++812/7444944IEH0L17d/Tv3x+vvvoqcnNz6zzW5cuX\n8cYbbyA2NhaRkZHo27cvZsyYge3bt+tsm5WVhbCwMIwZMwYZGRl4+OGH0a1bNwwaNAg//PBDg2Ou\nDcRUKpVITEysdztfX1/88ssvSEhIgK+vr874J06cqFm2YMEChIWFNfrfnRNElEolvvvuO0yYMAFR\nUVHo2bMnxo8fj1WrVkGpVDY4DiIyHk46ICKTa+jh+foerhcEAVlZWRg3bhwKCwvRqVMnyGQyXLhw\nAe+++y7Ky8tx8eJFbN68GW3btkVwcDBSU1OxdetWnD17Flu2bNF6A8rOnTvxyiuvoKqqCo6OjggL\nC0NBQQEOHjyIAwcO4P7778f777+vU0tpaSmefPJJlJSUoFOnTrh48SJCQkIaHG9wcDB8fHxw48YN\nPPfcc3j88ccxZswYBAUF6Wx75+3S+nTs2LHOyR8AUFRUhPT0dAiCoDUzt6ioCDNnzsTJkydhY2OD\ngIAAODg4IDU1FUuXLsW2bduwatUquLm5NakGImo9bNiIyCyJooidO3eic+fOWLdunea9e6+//jrW\nr1+PTz75BAqFAp988glGjhwJAEhJScGjjz6KjIwM7N27F0OGDAFw6xblnDlzoFQqMW3aNMyZM0cz\ni3Xfvn14+eWXsW3bNnTo0AGzZ8/WquP69esIDAzE5s2b4eHhgeLiYri6ujZYu42NDV5//XXMnj0b\n5eXl+OKLL/DFF1/A398fffr0Qd++fTFw4EC0adOmyT+PZ555Bs8884zO8toxCYKAUaNGYfz48Zp1\n8+bNw8mTJ9GrVy/8+9//1vwMb9y4gVdeeQWHDx/GokWL9IptISLD4i1RIjJbgiDgX//6l9ZLkmfO\nnAngVkM3ffp0TbMGAN27d0d0dDQA4OzZs5rlK1euRFVVFQYPHoyFCxdqRY4MHDgQixcvhiiK+Pbb\nb1FUVKRTx5NPPgkPDw8AaLRZqzV06FB8/fXX8Pf311xFvHbtGn755RcsWLAA99xzD2bMmIEzZ840\n4yeia9GiRUhJScFdd92FxYsXa5afOnUKe/bsgaenJ5YvX671M/Tx8cGnn34KR0dHJCQk4Pz58y2q\ngYhajg0bEZktFxcX9OjRQ2vZ7bf8BgwYoLNPbSZcWVmZZtnevXshCAImT55c53GGDh0Kf39/VFZW\n1vnM2Z01NNWAAQMQHx+Pr776Co888ggCAwM1zZsoijhw4AAmTpyIn376Sa/PX7lyJbZu3Qp3d3cs\nX74c9vb2mnUJCQkAgP79+8Pd3V1nX09PT/Tv3x/ArZ8PEZkWb4kSkdmq65ahQqHQ/NrT07Pe9bUR\nlKWlpcjLy4MgCAgPD6/3WOHh4cjOzsalS5eaVEdTyWQyDBw4EAMHDgRw63bkgf9v715C4e3iOIB/\nn0YyTFPkbxYTCisbl3LZyCxIxiUWNjZukdmIGKWsRCkRYTM1G5eURlkQjVIGU7KQKJcyIpfE5DY0\nJM+7eBuvyzPzel2ft76f1TTnzHPmzOrbmd85x27H9PQ0bDYbHh8f0dLSgsTERJ87Sl+bnZ1Fd3c3\nFAoFurq6XqygAcDOzg4AYHl5GcXFxZLPODg4gCiK2N3d/eDsiOirMLAR0f+WUqn02f6emwCer7QF\nBQV57RcYGPimv8dX3tqg0WhQWFiIwsJCLC0tobq6Gm63GxaLBU1NTe96xvb2NoxGI0RRRENDg+RK\no8vlAgCcnp563TUL/P0bevoS0e9hYCMiWfB26Yrb7f7WcZ+HtJubG6hUKsl+19fXAP4Jbp9RX1+P\n1dVVGI1GZGVlee2XkpKCoqIiDAwMYG9v713PPj8/h8FgwO3tLfR6PcrLyyX7KZVKCIKAxsZGlJWV\nfWgeRPRzWMNGRL/q+Zlkr11fX397YFOpVAgNDQUAnwX+nk0KERERnx7z5uYGh4eHXs+ee87z3aTq\nzF57eHhATU3N0xlxzzcZvBYZGQlRFOFwOLz22djYwObmpuSqIhH9LAY2IvpVnl2VUnVSnsL475ae\nng5RFDEyMiLZPjMzg5OTE/j5+SE1NfXT4+n1eoiiiMnJSayvr3vt9/j4CKvVCkEQnmrcfGlpacHy\n8jKCg4PR39/v869anU4HALBarbi4uHjT7nK5UFJSgoKCAsmDg4noZzGwEdGvSkhIgCiKGBwcfLHa\nY7fb0d7e/q46tM+qqKhAQEAA5ufn0dbW9mJVz2azobm5GYIgoLS0VHIjw3+Vk5OD+Ph43N3doays\nDENDQ2/qxHZ2dmAwGLC+vo7Y2FhkZ2f7fObAwABGR0fh7++P3t7eF7tlpSQnJyMpKQmXl5eoqqrC\n/v7+U9vJyQkMBgOurq6g0WiQl5f38ckS0ZdgDRsR/arS0lJMTEzA6XQiPz8fMTExcLlcODg4QGJi\nIpRKJex2+5eO+bpeLioqCh0dHTAajRgaGsLY2Biio6PhdDpxdHQEQRCg1+tRW1v7JeMrFAqYTCbU\n1dXBbrejtbUV7e3tCA8Ph0qlwtnZGY6PjyEIAuLi4tDX1/fiVobXnE7nU7gNDQ2F2WxGf38/7u/v\n38z1z58/6OnpAQB0dnaioqICa2tryMrKenFbxMPDA9RqNUwmE/z9/b9k3kT0cQxsRPRt3rM6ptVq\nYbFY0NfXh4WFBTgcDmi1WtTU1KCystJrSPJ2ZdV7xpb6bGZmJsbHx2E2m7G4uIitrS2o1Wqkp6ej\nqKgIGRkZH56jFLVaDbPZjLm5OVitVqysrMDpdOLw8BAhISHQ6XTIyclBbm6u16u5PO+73e6nYHZ8\nfOzzonitVvv0OiwsDBaLBcPDw5iamoLD4cD9/T00Gg3S0tJQWVn5ryt1RPQzBNHb1iwiIiIikgXW\nsBERERHJHAMbERERkcwxsBERERHJHAMbERERkcwxsBERERHJHAMbERERkcwxsBERERHJHAMbERER\nkcwxsBERERHJHAMbERERkcwxsBERERHJ3F9EigQoVrdSRgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1b37521ec50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list = [1, 2, 3, 4, 7, 8, 9, 10]\n", "y_list = [0, 0, 0, 0, 1, 1, 1, 1]\n", "\n", "x_list_reg = list(range(11))\n", "regline = lambda x: (1/6) * (x - 1) - 0.25\n", "y_list_reg = [regline(x) for x in x_list_reg]\n", "\n", "textstr1 = 'Not malignant'\n", "textstr2 = 'Malignant'\n", "props = dict(boxstyle='round', facecolor='dodgerblue', alpha=0.5)\n", "\n", "with sns.axes_style('white'):\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plt.plot(x_list, y_list, 'rD', markersize=16)\n", " plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"Tumor Size\", fontsize=24)\n", " plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1], fontsize=24)\n", " plt.xticks([])\n", " plt.ylim(-0.10, 1.10)\n", " plt.xlim(0, 10.5)\n", " \n", " plt.axvline(x=5, color='purple', linewidth=6)\n", " plt.axvspan(0, 5, color='wheat')\n", " plt.axvspan(5, 11, color='lavenderblush')\n", " \n", " ax.text(1, 0.95, textstr1, fontsize=20, verticalalignment='top', bbox=props)\n", " ax.text(8, 0.15, textstr2, fontsize=20, verticalalignment='top', bbox=props)\n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1_threshold.pdf')" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cDJeITMqowDZs2DCIoojJkyfjt99+Q0lJCTp37gwXFxesW7cOMTExyM3NxbJl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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1b375224a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list = [1, 2, 3, 4, 7, 8, 9, 10, 15]\n", "y_list = [0, 0, 0, 0, 1, 1, 1, 1, 1]\n", "\n", "x_list_reg = list(range(16))\n", "regline = lambda x: (1/10) * x - 0.25\n", "y_list_reg = [regline(x) for x in x_list_reg]\n", "\n", "\n", "textstr1 = 'Not malignant'\n", "textstr2 = 'Malignant'\n", "props = dict(boxstyle='round', facecolor='dodgerblue', alpha=0.5)\n", "\n", "with sns.axes_style('white'):\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plt.plot(x_list, y_list, 'rD', markersize=16)\n", " plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"Tumor Size\", fontsize=24)\n", " plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1], fontsize=24)\n", " plt.xticks([])\n", " plt.ylim(-0.10, 1.10)\n", " plt.xlim(0, 16)\n", " \n", " plt.axvline(x=7.5, color='purple', linewidth=6)\n", " plt.axvspan(0, 7.5, color='wheat')\n", " plt.axvspan(7.5, 16, color='lavenderblush')\n", " \n", " ax.text(1, 0.95, textstr1, fontsize=20, verticalalignment='top', bbox=props)\n", " ax.text(13, 0.15, textstr2, fontsize=20, verticalalignment='top', bbox=props)\n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1_newpoint.pdf')" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Rov4fL409TpISnT+9pDiqroa33w4W6vzzn4MStW3bkf0XXwyTJsHVV0Pv3uHl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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1b37409e208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list = np.linspace(-15, 15, 150)\n", "y_list = expit(x_list)\n", "\n", "\n", "with sns.axes_style('white'):\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " plt.plot(x_list, y_list, 'b')\n", " plt.xlim(-15, 15)\n", " plt.ylim(-0.05, 1.05)\n", " \n", " ax.spines['left'].set_position('zero')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position('zero')\n", " ax.spines['top'].set_color('none')\n", " ax.spines['left'].set_smart_bounds(True)\n", " ax.spines['bottom'].set_smart_bounds(True)\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.yaxis.set_ticks_position('left')\n", " plt.yticks([0, 0.5, 1], fontsize=18)\n", " plt.xticks(fontsize=18)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg2_sigmoid_func_plot.pdf')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LkrT4gcV53UeYZFXmmzdv1oULF7Rnzx6/9xNq8bi0c6d08KA0PDz2eCwmrVghbdgglZff\n/f3linyFna+YbO3aKknqWteV132ESca3WQ4dOqRDhw4FsZfQchxpxw6pslLau3f8IpCkoSH3eGWl\ne77j3N19Zot8rkLNV2y6znfp2IVjOnbh2OiEjgzK/NatW/r1r3+tn/3sZ4pEIkHuKVQcR2pslF57\nTRoZ8XbNyIh7fmNj+AuBfGMVUr5ilJzK7/x7sfNU5iMjI1q+fLlef/11LV++XLNmzQp6X6HR2ip1\ndGR3bUeHe32YkS+1QshXbJJTeRLT+X95KvPh4WENDg5q165dam1t1eTJk4PeVyjE41JLS25rtLS4\n64QR+dILc75iNN4kznTu8lTmM2bM0NGjR/XUU08FvZ9Q2bnT+z/NUxkZkdra/NmP38iXXpjzFZs7\np/IkpnOX53vmkyYV1yPpAwPuUw9+OHjQXS9MyOddGPMVo4kmcKZzfmgope7u1E89ZGpoSDp50p+1\n/EI+78KYr9ikmsqTmM4p85R6esK9Xq7Il9/1kBkvk3exT+eUeQqJRLjXyxX58rsevEs3lScV+3RO\nmadQUhLu9XJFvvyuB+8ymbiLeTqnzFOoqAj3erkiX37Xgzdep/KkYp7OKfMUqqvdd3X4IRaTqqr8\nWcsv5PMujPmKRTaTdrFO51mXufUf6S8tdV+65IeVK8P3Nj7yeRfGfMUg06k8qVin84jjBPf2ib6+\nPtXV1amzs1NlZWVBfZrAxOPuS5dy+cGTaFQ6fTqcb+EjX3phzmfd4v9bnFWZS9Ljcx8vujcqcptl\nAuXl0pYtua2xZUt4i4B86YU5n2XZTuVJxTidU+ZpvPqqVF+f3bX19e71YUa+1Aohn1V+3Pcutnvn\nlHkakYjU3i5t3+7+k9uLaNQ9v73dvT7MyDdWIeWzKNepPKnYpnPK3INIRNq40b13umZN6qckYjH3\n+OnT7vmFUgTkcxVqPmv8nKiLaTrnG6BZGBhw39Vx5++QrKqy8dQD+YDCQ5kDgAHcZgEAAyhzADCA\nMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcA\nAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhz\nADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADBgSr43UIgGBqTubqmnR0okpJISqaJCqq6WSkvz\nvbvcka9wWc6GNJwAXbx40fnGN77hXLx4MchPc9ecOeM4q1c7ztSpjiON/ROLucfPnMn3TrNDvsLN\nZzkbvKHMPbh1y3G2b3ecaHT8L5Q7/0Sj7vm3buV7596Qr3DzWc6GzFDmady65Tj19d6+UO78U18f\n/i8a8hVuPsvZkDm+AZpGa6vU0ZHdtR0d7vVhRr7Uwp7PcjZkLuI4jhPU4n19faqrq1NnZ6fKysqC\n+jSBicelykppZCT7NaJR6fRpqbzcv335hXzphTWf5WzIDpP5BHbuzO2LRXKvb2vzZz9+I196Yc1n\nORuyw2SewsCAdN990vBw7mvFYlJ/f7geDSOfd2HLZzkbssdknkJ3tz9fLJI0NCSdPOnPWn4hn3dh\ny2c5G7JHmafQ0xPu9XJFvvyulwvL2ZA9yjyFRCLc6+WKfPldLxeWsyF7lHkKJSXhXi9X5Mvvermw\nnA3Zo8xTqKgI93q5Il9+18uF5WzIHmWeQnW1+51+P8RiUlWVP2v5hXzehS2f5WzIHmWeQmmptGKF\nP2utXBm+R7/I513Y8lnOhuxR5hPYsMH9KblcRKNSc7M/+/Eb+dILaz7L2ZAdynwC5eXSli25rbFl\nS3h/XJp86YU1n+VsyFKQb/HirYnhfzMd+Qo3n+VsyByTeRqRiNTeLm3f7v2ftdGoe357u3t9mJFv\nrELJZzkbshDk/1NYmMz/15kzjrNmjftbW8abdmIx93ih/jYX8hVuPsvZ4A0v2srCwID7Pos7f89i\nVZWNJwPIV7gsZ8PEKHMAMIB75gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBg\nAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUO\nAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ\n5gBggOcy7+vr049//GM9+uijevTRR9Xc3KxLly4FuTcAgEdTvJx05coVrVmzRjdu3FBTU5Nu3Lih\n3/72tzp79qwOHTqkKVM8LQMACIinFu7o6NAXX3yhP/7xj3rwwQclSRUVFaqvr9cf/vAHrVixItBN\nAgAm5uk2y9tvv62amprRIpekb33rW3rwwQf19ttvB7Y5AIA3acv86tWrunjxoh566KExx8rLy3Xm\nzJlANgYA8C5tmf/jH/+QJN13331jjs2aNUvXrl1TIpHwf2cAAM/Slvn169clSbFYbMyxqVOnSpL+\n/e9/+7wtAEAm0pa54ziSpEgkkvKciY4BAIKX9mmW6dOnS5KGhobGHBseHpYklZSUjHvtzZs3JUn9\n/f1ZbxAAitXs2bM9P/qd9qw5c+ZIkv75z3+OOfbFF1/onnvuGfcWzP9e86Mf/cjTZgAA/9XZ2amy\nsjJP56Yt8xkzZqisrEzxeHzMsXg8rgULFqS8dsGCBdq3b5/uvfdeTZ482dOGAACu2bNnez7X0/z+\n5JNPas+ePfrkk09GnzV/99139cknn+jFF19MeV0sFlNVVZXnzQAAshNxkt/hnMClS5e0dOlSTZ48\nWQ0NDRoaGlJ7e7seeOAB7d+/X1/60pfuxl4BACl4KnNJOn/+vFpbW9Xd3a1p06bp8ccf109/+lN9\n+ctfDnqPAIA0PJc5ACC8eJ85ABgQWJnz/nM7Nm/erDVr1uR7G/Do+PHj+uEPf6hHHnlElZWVqq+v\n11//+td8bwsenThxQi+88IIWLlyoRYsWaceOHRocHEx7XSBlnnz/eU9Pj5qamtTQ0KB33nlHjY2N\nunHjRhCfEgE5dOiQDh06lO9twKP3339fTU1NSiQSeuWVV/Tyyy/r4sWLWrVqlT788MN8bw9pnDhx\nQo2Njbp165Z+8pOfaPny5XrrrbcmfGpwlBOAX/ziF85DDz3knDt3bvRj7777rjNv3jzn4MGDQXxK\n+OzmzZvOr371K2f+/PnO/PnzndWrV+d7S/Dg2Wefdb7zne84w8PDox/717/+5dTU1DgNDQ153Bm8\n+MEPfuDU1dXd9t9v3759zvz5850///nPE14byGTO+88L28jIiJYvX67XX39dy5cv16xZs/K9JXhw\n9epVnT17Vk8//bSi0ejox2fOnKnq6mqdOnUqj7tDOiMjI5o5c6ZWrlx523+/mpoaOY6j3t7eCa/3\n/fe9Jd9/vmTJkjHHysvLdfz4cb8/JXw2PDyswcFB7dq1S0899ZRqa2vzvSV4UFJSoiNHjmjatGlj\njl2+fJlf7xhy0WhUu3fvHvPx5E/fJ1+tkorv/3W9vv881cu5kH8zZszQ0aNHNWkSDzsVkkmTJun+\n++8f8/GPPvpIp06d0qJFi/KwK2Trs88+03vvvae2tjbNmzdPTzzxxITn+17mXt9/TpmHG0Vuw+Dg\noJqbmxWJRLx9Ew2hMDAwoNraWkUiEcViMW3atOm2Wy/j8f0r1uH950AoDA0N6aWXXtLZs2fV1NTE\ne5IKSCQS0S9/+Uu1tbXp61//utatW6c//elPE17je5nn8v5zAP64du2a6uvr1d3dreeee07r16/P\n95aQgXvuuUff+973tGzZMv3ud7/TnDlz1NraOuE1vpd5Lu8/B5C7S5cuafXq1frggw/0/PPPa9u2\nbfneEnIwdepULV68WJ9//rmuXLmS8jzfyzyX958DyM3169fV0NCg3t5erVu3Tlu3bs33luDRuXPn\nVFtbqzfffHPMsUQioUgkMuF980C+y/Xkk0+Ovu88Kfm/n3nmmSA+JQBJLS0t6u3t1dq1a9Xc3Jzv\n7SADc+fOVSKR0IEDB277SflPP/1UR48eVU1Nzeht7PEE8tZE3n9uS21trcrKyrRnz558bwUT+Pjj\nj/XMM8+otLRUGzZsGPe3ey1btiwPO4NXhw8fVnNzsx5++GEtXbpUly9f1v79+3Xz5k3t379fX/va\n11JeG9grcHn/uR21tbX66le/qjfeeCPfW8EEDhw4oJaWlgnP+dvf/naXdoNsHTlyRLt379bf//53\nTZs2TY899pjWr1+vuXPnTngd7zMHAAP4yRAAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAy\nBwADKHMAMIAyBwAD/h81AvlXCDKD5AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2da5c05e4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list1 = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1.5, 1.5, 2]\n", "y_list1 = [0.5, 1, 1.5, 2, 0.5, 1, 1.5, 0.5, 1, 0.5]\n", "\n", "x_list2 = [2.5, 2.5, 2.5, 2.5, 2, 1.5, 1, 2, 1.5, 2]\n", "y_list2 = [1, 1.5, 2, 2.5, 2.5, 2.5, 2.5, 2, 2, 1.5]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x_list1, y_list1, 'bo', markersize=20)\n", " plt.plot(x_list2, y_list2, 'g^', markersize=20)\n", " \n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " #plt.xlabel(\"Tumor Size\", fontsize=24)\n", " #plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1, 2, 3], fontsize=18)\n", " plt.xticks([0, 1, 2, 3], fontsize=18)\n", " plt.ylim(0, 3)\n", " plt.xlim(0, 3)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_noline.pdf')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2da5b9aca90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list1 = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1.5, 1.5, 2]\n", "y_list1 = [0.5, 1, 1.5, 2, 0.5, 1, 1.5, 0.5, 1, 0.5]\n", "\n", "x_list2 = [2.5, 2.5, 2.5, 2.5, 2, 1.5, 1, 2, 1.5, 2]\n", "y_list2 = [1, 1.5, 2, 2.5, 2.5, 2.5, 2.5, 2, 2, 1.5]\n", "\n", "x_list3 = np.linspace(0, 3, 50)\n", "y_list3 = x_list3[::-1]\n", "\n", "textstr1 = 'y = 0'\n", "textstr2 = 'y = 1'\n", "props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x_list1, y_list1, 'bo', markersize=20)\n", " plt.plot(x_list2, y_list2, 'g^', markersize=20)\n", " plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " #plt.xlabel(\"Tumor Size\", fontsize=24)\n", " #plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks([0, 1, 2, 3], fontsize=18)\n", " plt.xticks([0, 1, 2, 3], fontsize=18)\n", " plt.ylim(0, 3.5)\n", " plt.xlim(0, 3.5)\n", " \n", " ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\n", " ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_withline.pdf')" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2da5d1c2f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\n", "y_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\n", "\n", "x_partial_1 = np.random.uniform(-1, 1, 25)\n", "y_partial_1 = np.random.uniform(2, 3, 12)\n", "y_partial_2 = np.random.uniform(-3, -2, 13)\n", "x_partial_2 = np.random.uniform(2, 3, 13)\n", "x_partial_3 = np.random.uniform(-3, -2, 12)\n", "y_partial_3 = np.random.uniform(-1, 1, 25)\n", "x_partial_4 = np.random.uniform(1, 2, 12)\n", "y_partial_4 = np.random.uniform(1, 2, 12)\n", "x_partial_5 = np.random.uniform(-2, -1, 12)\n", "y_partial_5 = np.random.uniform(1, 2, 12)\n", "x_partial_6 = np.random.uniform(1, 2, 12)\n", "y_partial_6 = np.random.uniform(-2, -1, 12)\n", "x_partial_7 = np.random.uniform(-2, -1, 12)\n", "y_partial_7 = np.random.uniform(-2, -1, 12)\n", "\n", "x_list2 = np.concatenate([x_partial_1, x_partial_2, x_partial_3, x_partial_4, x_partial_5, x_partial_6, x_partial_7], 0)\n", "y_list2 = np.concatenate([y_partial_1, y_partial_2, y_partial_3, y_partial_4, y_partial_5, y_partial_6, y_partial_7], 0)\n", "\n", "textstr1 = 'y = 0'\n", "textstr2 = 'y = 1'\n", "props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\n", "circle = plt.Circle((0, 0), 1.3, color='purple', fill=False, linewidth=4)\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x_list1, y_list1, 'b.')\n", " plt.plot(x_list2, y_list2, 'rd')\n", " #plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " #plt.xlabel(\"Tumor Size\", fontsize=24)\n", " #plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks(fontsize=18)\n", " plt.xticks(fontsize=18)\n", " plt.ylim(-3.2, 3.2)\n", " plt.xlim(-3.2, 3.2)\n", " \n", " #ax.add_artist(circle)\n", " \n", " #ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\n", " #ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\n", " \n", " ax.spines['left'].set_position('zero')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position('zero')\n", " ax.spines['top'].set_color('none')\n", " #ax.spines['left'].set_smart_bounds(True)\n", " #ax.spines['bottom'].set_smart_bounds(True)\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.yaxis.set_ticks_position('left')\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_nonlinear_nocirc.pdf')" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WZYneAnTq2UlVG7SIfboKfVkZ0PpL8kZLC7sARrDEJnJvwPynIkICCk+mINHV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\nPyTEfQ7ayy9rI3ZqPX4S2tC/P3Dnnc5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"text/plain": [ "<matplotlib.figure.Figure at 0x2da5d1fe748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\n", "y_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\n", "\n", "x_partial_1 = np.random.uniform(-1, 1, 25)\n", "y_partial_1 = np.random.uniform(2, 3, 12)\n", "y_partial_2 = np.random.uniform(-3, -2, 13)\n", "x_partial_2 = np.random.uniform(2, 3, 13)\n", "x_partial_3 = np.random.uniform(-3, -2, 12)\n", "y_partial_3 = np.random.uniform(-1, 1, 25)\n", "x_partial_4 = np.random.uniform(1, 2, 12)\n", "y_partial_4 = np.random.uniform(1, 2, 12)\n", "x_partial_5 = np.random.uniform(-2, -1, 12)\n", "y_partial_5 = np.random.uniform(1, 2, 12)\n", "x_partial_6 = np.random.uniform(1, 2, 12)\n", "y_partial_6 = np.random.uniform(-2, -1, 12)\n", "x_partial_7 = np.random.uniform(-2, -1, 12)\n", "y_partial_7 = np.random.uniform(-2, -1, 12)\n", "\n", "x_list2 = np.concatenate([x_partial_1, x_partial_2, x_partial_3, x_partial_4, x_partial_5, x_partial_6, x_partial_7], 0)\n", "y_list2 = np.concatenate([y_partial_1, y_partial_2, y_partial_3, y_partial_4, y_partial_5, y_partial_6, y_partial_7], 0)\n", "\n", "textstr1 = 'y = 0'\n", "textstr2 = 'y = 1'\n", "props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\n", "circle = plt.Circle((0, 0), 1.3, color='purple', fill=False, linewidth=4)\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x_list1, y_list1, 'b.')\n", " plt.plot(x_list2, y_list2, 'rd')\n", " #plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " #plt.xlabel(\"Tumor Size\", fontsize=24)\n", " #plt.ylabel('Malignant?', fontsize=24)\n", " plt.yticks(fontsize=18)\n", " plt.xticks(fontsize=18)\n", " plt.ylim(-3.2, 3.2)\n", " plt.xlim(-3.2, 3.2)\n", " \n", " ax.add_artist(circle)\n", " \n", " #ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\n", " #ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\n", " \n", " ax.spines['left'].set_position('zero')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position('zero')\n", " ax.spines['top'].set_color('none')\n", " #ax.spines['left'].set_smart_bounds(True)\n", " #ax.spines['bottom'].set_smart_bounds(True)\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.yaxis.set_ticks_position('left')\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_nonlinear.pdf')" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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G8TkpAetS2k7JQERkkDhYWIXH62X6uBEBP7eSgYjIILG3oBKAGROUDEREIta+gkoS4qIY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\nF8bnpHDVgtGUnKnn968c6NF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"text/plain": [ "<matplotlib.figure.Figure at 0x2da5d622320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nonconvex = lambda x: x**2 + 10*np.sin(2*x)\n", "x = np.arange(-10, 10, 0.1)\n", "\n", "plt.figure(figsize=(6, 4))\n", "plt.plot(x, nonconvex(x))\n", "plt.xlabel(r'$\\theta$', fontsize=20)\n", "plt.yticks([0, 50, 100], fontsize=14)\n", "plt.xticks([-10, 0, 10], fontsize=14)\n", "plt.title('Non-Convex Curve', fontsize=20)\n", "\n", "plt.savefig(fp_fig + os.sep + 'logreg_eg4_sample_nonconvex_curve.pdf')" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cost_function(hypothesis_function, y):\n", " if y == 1:\n", " return -1 * np.log(hypothesis_function)\n", " elif y == 0:\n", " return -1 * np.log(1 - hypothesis_function)" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.arange(0, 1, 0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fQUS6wZLQQyKRCH99MhBDg1yQdbEcn/4rGU1NzbqORUQGiCWhp4yNRHj7L6EIGmCPk1nF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\n4unpiatXr2ola1fqzHbIzMx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"text/plain": [ "<matplotlib.figure.Figure at 0x2da5ffe74e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 4))\n", " plt.plot(x, cost_function(x, 1))\n", "\n", " ax.spines['left'].set_position('zero')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position('zero')\n", " ax.spines['top'].set_color('none')\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.yaxis.set_ticks_position('left')\n", "\n", " plt.yticks(fontsize=14)\n", " plt.xticks(fontsize=14)\n", " plt.ylim(ymin=-0.5)\n", " plt.xlabel(r'$h_\\theta (x)$', fontsize=20)\n", "\n", " plt.savefig(fp_fig + os.sep + 'logreg_eg5_cost_func_y1.pdf')" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1qgXe3ZOKTJkCo0MGYdGMsD67NsS9YEgQUa8lhMCWfZlIzrqO4X5SvDInktNtdBNDgoh6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\nEQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2da60111f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 4))\n", " plt.plot(x, cost_function(x, 0))\n", "\n", " ax.spines['left'].set_position('zero')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position('zero')\n", " ax.spines['top'].set_color('none')\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.yaxis.set_ticks_position('left')\n", "\n", "\n", " plt.yticks(fontsize=14)\n", " plt.xticks(fontsize=14)\n", " plt.ylim(ymin=-0.5)\n", " plt.xlabel(r'$h_\\theta (x)$', fontsize=20)\n", "\n", " plt.savefig(fp_fig + os.sep + 'logreg_eg5_cost_func_y0.pdf')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1adaf66ad30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [0.6, 1, 0.6, 0.9]\n", "y1 = [0.7, 0.6, 0.9, 1.1]\n", "\n", "x2 = [2, 1.9, 2.4, 2.2]\n", "y2 = [2.2, 1.7, 2.1, 1.8]\n", "\n", "x3 = [0.75, 0.5, 0.9]\n", "y3 = [2.1, 2.5, 2.3]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'bo', markersize=20)\n", " plt.plot(x2, y2, 'g^', markersize=20)\n", " plt.plot(x3, y3, 'rs', markersize=20)\n", " \n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"$x_1$\", fontsize=28)\n", " plt.ylabel('$x_2$', fontsize=28)\n", " plt.yticks([0, 1, 2, 3], fontsize=18)\n", " plt.xticks([0, 1, 2, 3], fontsize=18)\n", " plt.ylim(0, 3)\n", " plt.xlim(0, 3)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg6_multiclass_eg_data.pdf')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1adaecf65c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [0.7, 0.9, 0.8, 1.1]\n", "y1 = [0.4, 0.5, 0.7, 0.9]\n", "\n", "x2 = [2.2, 2.1, 2.4, 2.6]\n", "y2 = [2.2, 1.7, 2.1, 1.8]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'bo', markersize=20)\n", " plt.plot(x2, y2, 'g^', markersize=20)\n", " \n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"$x_1$\", fontsize=28)\n", " plt.ylabel('$x_2$', fontsize=28)\n", " plt.yticks([0, 1, 2, 3], fontsize=18)\n", " plt.xticks([0, 1, 2, 3], fontsize=18)\n", " plt.ylim(0, 3)\n", " plt.xlim(0, 3)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg6_binary_eg_data.pdf')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1adaed286d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [0.6, 1, 0.6, 0.9]\n", "y1 = [0.7, 0.6, 0.9, 1.1]\n", "\n", "x2 = [2, 1.9, 2.4, 2.2]\n", "y2 = [2.2, 1.7, 2.1, 1.8]\n", "\n", "x3 = [0.75, 0.5, 0.9]\n", "y3 = [2.1, 2.5, 2.3]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'bo', markersize=20)\n", " plt.plot(x2, y2, 'kd', markersize=20)\n", " plt.plot(x3, y3, 'kd', markersize=20)\n", " \n", " \n", " #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\n", " plt.xlabel(\"$x_1$\", fontsize=28)\n", " plt.ylabel('$x_2$', fontsize=28)\n", " plt.yticks([0, 1, 2, 3], fontsize=18)\n", " plt.xticks([0, 1, 2, 3], fontsize=18)\n", " plt.ylim(0, 3)\n", " plt.xlim(0, 3)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg6_multiclass_onevall_step1.pdf')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1adaf2d26a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [1, 1.5, 2, 3.2, 4.5, 6]\n", "y1 = [1, 3.5, 5, 5.5, 6, 6.2]\n", "\n", "x2 = np.linspace(0, 7, 50)\n", "fx = lambda x:0.9 * x + 1.5\n", "y2 = [fx(x) for x in x2]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'rd', markersize=16)\n", " plt.plot(x2, y2, 'b-')\n", " \n", " plt.xlabel(\"Size \\n $\\\\theta_0 + \\\\theta_1 x$\", fontsize=28)\n", " plt.ylabel('Price', fontsize=28)\n", " plt.yticks([])\n", " plt.xticks([])\n", " plt.ylim(0, 7)\n", " plt.xlim(0, 7)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_linreg.pdf')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1adaecf6c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [1, 1.5, 2, 3.2, 4.5, 6]\n", "y1 = [1, 3.5, 5, 5.5, 6, 6.2]\n", "\n", "x2 = np.linspace(0, 7, 50)\n", "fx = lambda x:0.9 * x + 1.5\n", "y2 = [fx(x) for x in x2]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'rd', markersize=16)\n", " plt.plot(x2, y2, 'b-')\n", " \n", " plt.xlabel(\"Size \\n $\\\\theta_0 + \\\\theta_1 x$\", fontsize=28)\n", " plt.ylabel('Price', fontsize=28)\n", " plt.yticks([])\n", " plt.xticks([])\n", " plt.ylim(0, 7)\n", " plt.xlim(0, 7)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_linreg.pdf')" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6tQnlUWoqsHIlEBgIvPmm0tUQkTMzOvCPHz9u8Pm8XwCxsbHFPi/LMn8lmGHV\nKuDuXWDcOMDNTelqiMiZlRr4nBtHOTk5ojnH0xMYNEjpaojI2ZUa+Pv27bNHHVSMnTvFBdsBA8Qi\n5URElmAvHQc2f764Hz5c2TqISB0Y+A7q1Clg3z6gdWugQQOlqyEiNWDgOygOtCIia2PgO6DkZDFR\n2uOPA6++qnQ1RKQWDHwHtGKFmAp52DDAwJx1REQmYeA7mJwcYMkSwNsb6NtX6WqISE0Y+A5mxw7g\n8mWgVy+x0AkRkbUw8B1MWJi4/+ADZesgIvVh4DuQP/8E9u4FWrRgV0wisj4GvgNZvFjc8+yeiGyB\nge8g0tKAb78FHn0UeOMNpashIjVi4DuIdeuAlBQxSZq7u9LVEJEaMfAdgCyLi7WurpwVk4hsh4Hv\nAA4dAk6eFE05nI2aiGyFge8A8rpiDhumbB1EpG4MfIUlJgKbNwP164vumEREtsLAV9jy5UB2tuiK\nyRUgiciWGPgKysoCli4FfH2Bnj2VroaI1I6Br6Bt24CEBKBPH8DHR+lqiEjtGPgKWrJE3A8erGwd\nRKQNDHyFXLgA/PSTuFAbHKx0NUSkBQx8hSxbJu7ff1/ZOohIOxj4CtDpgFWrgAoVgK5dla6GiLSC\nga+AiAjgn3/EilaenkpXQ0RawcBXQN7FWs6bQ0T2xMC3s3PngP37gdatgSefVLoaItISBr6d5V2s\nZVdMIrI3Br4dZWQAq1cDlSsDnTsrXQ0RaQ0D3442bQJu3QL69+ciJ0Rkfwx8O1q6VEyQNnCg0pUQ\nkRYx8O3k9GkgOhpo1w547DGlqyEiLWLg28nSpeKeI2uJSCkMfDtITwfWrhXLF772mtLVEJFWMfDt\nYNMmICUFeO89sVA5EZESGPh2sGKFuH/vPWXrICJtY+Db2B9/AAcPAm3b8mItESmLgW9j33wj7gcM\nULYOIiIGvg3pdGJkbUAA8K9/KV0NEWkdA9+Gtm8HbtwAevcGPDyUroaItI6Bb0N5F2v791e2DiIi\ngIFvM1evArt3A02aAPXrK10NERED32ZWrQJkmRdrichxMPBtICdH9M7x8QHeflvpaoiIBAa+DURF\nAVeuAO+8I0KfiMgRMPBtIO9iLZtziMiRMPCt7J9/gK1bxYXaF15QuhoiogIMfCtbuxbIyhJn95Kk\ndDVERAUY+FYky6I5x90d6NlT6WqIiPQx8K0oJgaIixMLlFeooHQ1RET6GPhWtGqVuO/XT9k6iIiK\nw8C3kozodpUVAAAWvUlEQVQMIDxcrGr1yitKV0NEVBQD30q2bhWrWvXuDbi4KF0NEVFRDHwryWvO\n6dtX0TKIiErEwLeCq1eByEigaVOgTh2lqyEiKh4D3wrWrBFdMnmxlogcGQPfQrIMfPst4OUFvPWW\n0tUQEZWMgW+hQ4eA8+eBLl0APz+lqyEiKhkD30Lffivu2ZxDRI6OgW+BtDRgwwagRg2gVSulqyEi\nMoyBb4FNm4B794A+fYAyPJJE5OAYUxbIa87p00fRMoiIjMLAN9PFi8D+/UBoKFCrltLVEBGVjoFv\nptWrxT0v1hKRs2DgmyE3Vwy2KlsW6NpV6WqIiIzDwDdDdDTw998i7LlIORE5Cwa+GdauFfe9eytb\nBxGRKRj4JsrIEH3vq1YFWrZUuhoiIuMx8E20bZuY975HD857T0TOhYFvorzmnF69lK2DiMhUDHwT\n3LgB7NoFNGwI1KundDVERKZh4Jtg/XogJ4cXa4nIOTHwTbB2rWi3f/ddpSshIjIdA99IZ84Ax48D\n7dsDlSsrXQ0RkekY+EZi33sicnYMfCPk5ADr1gG+vsDrrxvxgogIcSMiciCuShfgDPbvB65dAwYM\nEGvXGpSSAgwbJv7cpo34liAicgA8wzeCSc0548YBCQniNm6cTesiIjIFA78UaWliZauaNYFmzUrZ\n+NAhYOnSgr8vWSJmWiMicgAM/FJERIjQ79WrlGUMdTpg0CBAlgsek2Vg4EDxHBGRwhj4pfjuO3Hf\ns2cpG06bBsTFFX08Lg6YPt3qdRERmYqBb8D160BkJBASAjz5pIENz541HOolfRkQEdkRA9+ADRtE\nl8wePQxsJMuiKcdQs41OJ5p2Cjf3EBHZGQPfgHXrRLv9228b2MjYC7PR0foXdImI7IyBX4Lz54Gj\nR0VX+ipVStgoPt60rpdjx4rumkRECmDgl2D9enFvsDln2DAgNdX4N01NLRiURURkZwz8YsiyaM7x\n9ATeeEPpaoiIrIOBX4wTJ4A//gA6dSplZoSwMNOmTvDzAxYtsrg+IiJzMPCLsW6duO/evZQNAwOB\nGTOMf+MZM8RriIgUwMB/SE4OEB4O+PsDHToY8YLBg42YcwFA8+bA++9bXB8RkbkY+A/Zvx9ITAS6\ndQPc3Y14gSQBy5cb3tjdHVi2TGxLRKQQBv5D8ppzDPbOeVjdusD48SU/P2GC2IaISEEM/EIyM4HN\nm4Fq1UQLjElKCvXgYMNfBkREdsLAL2THDtFV/t13S5kZszju7qJpp3CzjSSJphyj2oaIiGyLgV+I\nWc05hTVrpn9h1tgLukREdsDAf+D2bXGGX78+8PTTFrzRzJmi62VQkGldNomIbIxr2j6wZYuY1LLU\nvvel8fUtGFzF9WyJyIEw8B8IDxf3775rhTfjfAxE5IDYpAOx0Mm+fUCTJmLtWiIiNWLgQyxSnpsL\nvPOO0pUQEdkOAx+iOUeSxOhaIiK10nzgX70KHDoEtGzJec2ISN00H/gbNoh7NucQkdppPvDDwwFX\nV6BLF6UrISKyLU0H/vnzwLFjwMsvAxUrKl0NEZFtaTrwf/hB3LM5h4i0QNOBHx4OeHgAnTsrXQkR\nke1pNvBPnxa3jh3FUrNERGqn2cBncw4RaY0mA1+WgfXrgbJlgVdfVboaIiL70GTgHz8OXLgAdOok\nQp+ISAs0Gfh5M2OyOYeItERzgZ+bK9rv/fyAdu2UroaIyH40F/iHDwPXrokp6z08lK6GiMh+NBf4\nGzeKezbnEJHWaCrwc3PF3PcBAUDr1kpXQ0RkX5oK/MOHgYQE0Zzj5qZ0NURE9qWpwM+bCpkLnRCR\nFmkm8NmcQ0Rap5nAj44GEhPZnENE2qWZwM/rncPmHCLSKk0EPptziIg0EvhsziEi0kjg5/XOeest\nZesgIlKS6gM/JwfYvFk057RqpXQ1RETKUX3g5zXndOnC5hwi0jbVBz575xARCaoO/Jwc0TunQgU2\n5xARqTrwo6OBpCT2ziEiAlQe+OydQ0RUQLWBn9c7h805RESCagO/cHOOq6vS1RARKU+1gb95s7jv\n2lXZOoiIHIVDnvvm5OQAAJKSksx6vSwDERGAvz9Qp45Yw5aISCuqVKkC12KaNiRZlmUF6jHo2LFj\n6NGjh9JlEBE5paioKFStWrXI4w4Z+JmZmTh9+jQqVaoEFxcXpcshInIqTnWGT0RE1qfai7ZERKTP\nIS/aEpkjNzcXu3fvRlRUFE6dOoXk5GTk5ubC398fjz32GJo1a4Z//etfqFChQrGvX7RoERYtWgRJ\nkvD777/D3d3dzp+AyLYY+KQK58+fx4gRI/DXX39BkiS955KSkpCUlITDhw8jLCwMH3/8MTsFkCYx\n8MnpJScno1+/fvjnn38QEBCAgQMHomnTpqhSpQpcXFxw48YNxMTEYPny5UhISMDUqVPh5uaGtx6a\nc8PPzw81atQAAJQpw9ZOUh9etCWnN23aNKxZswa+vr6IiIhAUFBQsdvdunULXbp0QVJSEnx9fbFv\n3z74+PjYuVoi5fA0hpzevn37IEkS2rdvX2LYA0BAQABGjx4NALh79y4OHDhgrxKJHAKbdMjp/fPP\nPwCA+/fvl7pts2bNUKdOHfj5+RW5KFvcRdv4+Hi0adPG6FpeeOEFrFmzpsjjhw8fxoYNG/C///0P\nt27dQtmyZfHkk0/i9ddfR5cuXTjehOyCgU9Or1q1ajh//jz27NmDHj164Omnny5x2/Lly+O///2v\nSe//8EVgQ7y9vfX+npWVhQkTJmDbtm1675OSkoLY2FjExMTghx9+wJIlS1CxYkWT6iIyFQOfnF7X\nrl0xc+ZMZGZmonv37mjVqhXat2+Ppk2bIiAgwKL3DgoKwokTJ0p8/v79++jfvz/OnDkDX19fjBs3\nTu/5Tz/9ND/su3btinfffRfVqlXD7du3sWfPHnz99dc4ffo03n//fYSHh8ONK/WQDTHwyen16dMH\nsbGx+Pnnn5GTk4OffvoJP/30EwCgVq1aeP7559G4cWO8+OKLZn0BeHl5lfjc5MmTcebMGbi4uGDO\nnDmoWbNm/nMxMTHYunUrJEnC2LFj0bdv3/znfH19MWjQIDz33HPo3bs3zp49i/Xr16N3794m10dk\nLF60JadXpkwZLF68GGPHjoWfnx8kScq/Xbx4ERs3bsSoUaPQvHlzDBgwAHFxcVbZ77Jly/Djjz9C\nkiR8/PHHaNGihd7z33//PQAgMDBQL+wLCwkJwcsvvwxZlrEhb4k2Ihth4JNq9O3bFwcPHsTixYvx\n1ltvoXr16nrhL8syDh06hK5du2LFihUW7SsyMhLz5s2DJEno1KkT3nvvvSLbxMbGQpIk1KtXD+np\n6SXennnmGQDAhQsXkJKSYlFdRIawSYdUxc3NDa1atUKrB+taJicnIyYmBocPH0ZkZCRSUlKQm5uL\nOXPmICgoCB06dDB5H+fOncvv3vn0009jypQpRbZJS0vDzZs3IUkS9u7di7179xr13omJifDz8zO5\nJiJj8AyfVK1ixYro2LEjpk6div3792PgwIH5zy1atMjk97t58yaGDh2KjIwMVK5cGYsWLSp2zp17\n9+7l/7nwr4zSboVfR2RtPMM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"text/plain": [ "<matplotlib.figure.Figure at 0x1adb0720208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [1, 1.5, 2, 3.2, 4.5, 6]\n", "y1 = [1, 3.5, 5, 5.5, 6, 6.2]\n", "\n", "x2 = np.linspace(0, 6, 50)\n", "fx = lambda x: -0.3545*x**2 + 3.2983*x - 1.1147\n", "y2 = [fx(x) for x in x2]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'rd', markersize=16)\n", " plt.plot(x2, y2, 'b-')\n", " \n", " plt.xlabel(\"Size \\n $\\\\theta_0 + \\\\theta_1 x + \\\\theta_2 x^2$\", fontsize=28)\n", " plt.ylabel('Price', fontsize=28)\n", " plt.yticks([])\n", " plt.xticks([])\n", " plt.ylim(0, 7)\n", " plt.xlim(0, 7)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_quadreg.pdf')" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kz+Lj4aWXzNVD+u9Yv+WWvUJEXE7dTZLVzJnmK4PTp003U0qKuS20Fn+JBAddSUhmSUnn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\nhIiI5EghISIiOVJIiIhIjv4fRFvhkcuAprMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1adaf537550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = [1, 1.5, 2, 3.2, 4.5, 6]\n", "y1 = [1, 3.5, 5, 5.5, 6, 6.2]\n", "\n", "x2 = np.linspace(0, 6, 50)\n", "fx = lambda x: -0.0394*x**5 + 0.5618*x**4 - 2.5949*x**3 + 3.356*x**2 + 4.8908*x - 5.1743\n", "y2 = [fx(x) for x in x2]\n", "\n", "with sns.axes_style('white'):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " plt.plot(x1, y1, 'rd', markersize=16)\n", " plt.plot(x2, y2, 'b-')\n", " \n", " plt.xlabel(\"Size \\n $\\\\theta_0 + \\\\theta_1 x + \\\\theta_2 x^2 + \\\\theta_3x^2 + \\\\theta_4x^4$\", fontsize=28)\n", " plt.ylabel('Price', fontsize=28)\n", " plt.yticks([])\n", " plt.xticks([])\n", " plt.ylim(0, 7)\n", " plt.xlim(0, 7)\n", " \n", " \n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " \n", " plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_quadreg_overfit.pdf')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

kimkipyo/dss_git_kkp

Python 복습/07일차.목_크롤링, 정규표현식/7일차_4T_크롤링(직방_json_api).ipynb

1

675787

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BASE_URL = \"https://www.zigbang.com/search/map?lat=37.52412796020508&lng=127.02295684814453&zoom=5\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = requests.get(BASE_URL)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.status_code" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\r\\n<!DOCTYPE html>\\r\\n<html lang=\"ko\">\\r\\n<head>\\r\\n <meta charset=\"utf-8\" />\\r\\n <meta name=\"viewport\" content=\"width=1140px\">\\r\\n <meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\">\\r\\n\\t<meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\">\\r\\n <title>안심을 잇다, 직방</title>\\r\\n\\t<meta name=\"keywords\" content=\"오피스텔, 원룸, 투룸, 대한민국 No.1 전월세 부동산 앱\" />\\r\\n\\t<meta name=\"description\" content=\"오피스텔, 원룸, 투룸, 대한민국 No.1 전월세 부동산 앱\" />\\r\\n \\r\\n <link rel=\"alternate\" href=\"android-app://com.chbreeze.jikbang4a/zigbang/app/search/map\">\\r\\n <style>\\r\\n .footer {display:none;}\\r\\n </style>\\r\\n\\r\\n \\r\\n <meta name=\"msapplication-TileImage\" content=\"//s.zigbang.com/legacy/icon_zigbang.png\">\\r\\n <meta name=\"msapplication-TileColor\" content=\"#ffd46c\">\\r\\n <link rel=\"shortcut icon\" type=\"image/x-icon\" href=\"//s.zigbang.com/legacy/images/v1/favicon.ico\">\\r\\n <link href=\"//s.zigbang.com/v1/web/css/nanumgothic.css\" rel=\"stylesheet\" type=\"text/css\" />\\r\\n <link href=\"//s.zigbang.com/www2/web.min.css?v20160801\" rel=\"stylesheet\" type=\"text/css\" />\\r\\n <link href=\"//s.zigbang.com/zbee/css/zbeeicons.css\" rel=\"stylesheet\" type=\"text/css\" />\\r\\n <link href=\"//s.zigbang.com/js/cdnjs-jqueryui/1.11.2/jquery-ui.min.css\" rel=\"stylesheet\" />\\r\\n\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-jquery/1.10.2/jquery.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/zbee/zbee.js?636068758171944831\"></script>\\r\\n\\t<!--<script type=\"text/javascript\" src=\"//s.zigbang.com/js/jquery-flexslider/2.3.0/jquery.flexslider-min.js\"></script>-->\\r\\n <script type=\"text/javascript\" src=\"//static.criteo.net/js/ld/ld.js\" async=\"true\"></script>\\r\\n <script>\\r\\n if (BrowserDetect.mobile && location.href.indexOf(\"items1/\") == -1) {\\r\\n var gateway_param = (location.href.split(\"?\")[1]) ? \"?\" + location.href.split(\"?\")[1] : \"\";\\r\\n location.href = \"//s.zigbang.com/mobile/gateway/index.html\" + gateway_param;\\r\\n }\\r\\n </script>\\r\\n</head>\\r\\n<body>\\r\\n <script type=\"text/javascript\">\\r\\n var _gaq = _gaq || [];\\r\\n _gaq.push([\\'_setAccount\\', \\'UA-21390692-9\\']);\\r\\n _gaq.push([\\'_setDomainName\\', \\'zigbang.com\\']);\\r\\n _gaq.push([\\'_trackPageview\\']);\\r\\n\\r\\n (function () {\\r\\n var ga = document.createElement(\\'script\\'); ga.type = \\'text/javascript\\'; ga.async = true;\\r\\n ga.src = (\\'https:\\' == document.location.protocol ? \\'https://ssl\\' : \\'http://www\\') + \\'.google-analytics.com/ga.js\\';\\r\\n var s = document.getElementsByTagName(\\'script\\')[0]; s.parentNode.insertBefore(ga, s);\\r\\n })();\\r\\n\\r\\n window.criteo_q = window.criteo_q || [];\\r\\n window.criteo_q.push(\\r\\n\\t\\t\\t{ event: \"setAccount\", account: 25544 },\\r\\n\\t\\t\\t{ event: \"setSiteType\", type: BrowserDetect.mobile ? \"m\" : \"d\" },\\r\\n\\t\\t\\t{ event: \"viewHome\" }\\r\\n\\t\\t);\\r\\n </script>\\r\\n\\r\\n <div class=\"wrap\">\\r\\n <div class=\"header\">\\r\\n <h1><a href=\"//www.zigbang.com/\"><img src=\"//s.zigbang.com/v1/web/common/new/logo.png\" alt=\"직방\" /></a></h1>\\r\\n\\r\\n <div class=\"gnb\">\\r\\n <a href=\"/search/map\" class=\"gnb-item-map on\">전체 방 보기</a> <em>|</em>\\r\\n <a href=\"/search/officetel\" class=\"gnb-item-officetel \">오피스텔 (도시형 생활주택)</a><em>|</em>\\r\\n <a href=\"/home/AptInfo\" style=\"position:relative\">아파트단지 <img src=\"//s.zigbang.com/v1/web/common/ico_new2.png\" style=\"position:absolute; right:0; top:15px; width:30px;\" alt=\"\" /></a><em>|</em>\\r\\n <a href=\"/item/zzim\">찜한방</a><em>|</em>\\r\\n <a href=\"/my/rooms\">방내놓기</a>\\r\\n\\r\\n </div>\\r\\n\\r\\n <div class=\"top_btn \">\\r\\n <button type=\"button\" class=\"i_login\">로그인</button>/<button type=\"button\" class=\"i_join\">회원가입</button>\\r\\n\\t\\t\\t\\t<em>|</em>\\r\\n <a href=\"/home/RegisterInfo\" class=\"i_link\">중개사무소 가입 및 광고문의 &gt;</a>\\r\\n\\r\\n </div>\\r\\n\\r\\n </div>\\r\\n\\r\\n <div class=\"container\">\\r\\n \\r\\n<div class=\"map-container\">\\r\\n <div class=\"map-area\">\\r\\n <div id=\"map\"></div>\\r\\n\\r\\n <div class=\"map-tab \">\\r\\n <h4 class=\"tab-title tab-officetel \">단지명으로 찾기</h4>\\r\\n <div class=\"tab-item tab-officetel \">\\r\\n <input type=\"text\" class=\"text\" placeholder=\"단지명(건물명) 또는 주소(ex:자곡동)로 검색하세요\" />\\r\\n </div>\\r\\n <h4 class=\"tab-title tab-subway active\">지하철역으로 찾기</h4>\\r\\n <div class=\"tab-item tab-subway active\">\\r\\n <input type=\"text\" class=\"text\" placeholder=\"지하철역명을 입력해주세요\" />\\r\\n </div>\\r\\n <h4 class=\"tab-title\">지역으로 찾기</h4>\\r\\n <div class=\"tab-item\">\\r\\n <select>\\r\\n <option>서울시</option>\\r\\n </select>\\r\\n <select>\\r\\n <option>전체</option>\\r\\n </select>\\r\\n <select>\\r\\n <option>전체</option>\\r\\n </select>\\r\\n <button type=\"button\" class=\"btn-event3 h-30\"><img src=\"//s.zigbang.com/v1/web/search/btn_go.png\" alt=\"바로가기\" /></button>\\r\\n </div>\\r\\n </div>\\r\\n <div class=\"map-tab-state\"><button type=\"button\"><img src=\"//s.zigbang.com/v1/web/search/btn_state.png\" alt=\"\" /></button></div>\\r\\n\\r\\n <img src=\"//s.zigbang.com/v1/web/search/loading2.gif\" id=\"loading\" class=\"loading\" alt=\"\" />\\r\\n </div>\\r\\n <div class=\"map-content\" id=\"map-content\">\\r\\n <!-- search -->\\r\\n <div class=\"map-search\">\\r\\n <div class=\"item-deposit\">\\r\\n <h4>보증금</h4>\\r\\n <select id=\"deposit_s\" name=\"deposit_s\">\\r\\n <option value=\"0\">전체</option>\\r\\n <option value=\"100\">100만</option>\\r\\n <option value=\"500\">500만</option>\\r\\n <option value=\"1000\">1,000만</option>\\r\\n <option value=\"2000\">2,000만</option>\\r\\n <option value=\"3000\">3,000만</option>\\r\\n <option value=\"4000\">4,000만</option>\\r\\n <option value=\"5000\">5,000만</option>\\r\\n <option value=\"6000\">6,000만</option>\\r\\n <option value=\"7000\">7,000만</option>\\r\\n <option value=\"8000\">8,000만</option>\\r\\n <option value=\"9000\">9,000만</option>\\r\\n <option value=\"10000;\">10,000만</option>\\r\\n <option value=\"12000\">12,000만</option>\\r\\n <option value=\"14000\">14,000만</option>\\r\\n <option value=\"18000\">18,000만</option>\\r\\n <option value=\"20000\">20,000만</option>\\r\\n <option value=\"25000\">25,000만</option>\\r\\n <option value=\"30000\">30,000만</option>\\r\\n </select>\\r\\n ~\\r\\n <select id=\"deposit_e\" name=\"deposit_e\">\\r\\n <option value=\"0\">전체</option>\\r\\n <option value=\"100\">100만</option>\\r\\n <option value=\"500\">500만</option>\\r\\n <option value=\"1000\">1,000만</option>\\r\\n <option value=\"2000\">2,000만</option>\\r\\n <option value=\"3000\">3,000만</option>\\r\\n <option value=\"4000\">4,000만</option>\\r\\n <option value=\"5000\">5,000만</option>\\r\\n <option value=\"6000\">6,000만</option>\\r\\n <option value=\"7000\">7,000만</option>\\r\\n <option value=\"8000\">8,000만</option>\\r\\n <option value=\"9000\">9,000만</option>\\r\\n <option value=\"10000;\">10,000만</option>\\r\\n <option value=\"12000\">12,000만</option>\\r\\n <option value=\"14000\">14,000만</option>\\r\\n <option value=\"18000\">18,000만</option>\\r\\n <option value=\"20000\">20,000만</option>\\r\\n <option value=\"25000\">25,000만</option>\\r\\n <option value=\"30000\">30,000만</option>\\r\\n </select>\\r\\n </div>\\r\\n <div class=\"item-deposit\">\\r\\n <h4>월세</h4>\\r\\n <select id=\"rent_s\" name=\"rent_s\">\\r\\n <option value=\"-\">전체</option>\\r\\n <option value=\"0\">0만</option>\\r\\n <option value=\"10\">10만</option>\\r\\n <option value=\"20\">20만</option>\\r\\n <option value=\"30\">30만</option>\\r\\n <option value=\"40\">40만</option>\\r\\n <option value=\"50\">50만</option>\\r\\n <option value=\"60\">60만</option>\\r\\n <option value=\"70\">70만</option>\\r\\n <option value=\"80\">80만</option>\\r\\n <option value=\"90\">90만</option>\\r\\n <option value=\"100\">100만</option>\\r\\n <option value=\"110\">110만</option>\\r\\n <option value=\"120\">120만</option>\\r\\n <option value=\"130\">130만</option>\\r\\n <option value=\"140\">140만</option>\\r\\n <option value=\"160\">160만</option>\\r\\n <option value=\"180\">180만</option>\\r\\n <option value=\"200\">200만</option>\\r\\n <option value=\"250\">250만</option>\\r\\n </select>\\r\\n ~\\r\\n <select id=\"rent_e\" name=\"rent_e\">\\r\\n <option value=\"-\">전체</option>\\r\\n <option value=\"0\">0만</option>\\r\\n <option value=\"10\">10만</option>\\r\\n <option value=\"20\">20만</option>\\r\\n <option value=\"30\">30만</option>\\r\\n <option value=\"40\">40만</option>\\r\\n <option value=\"50\">50만</option>\\r\\n <option value=\"60\">60만</option>\\r\\n <option value=\"70\">70만</option>\\r\\n <option value=\"80\">80만</option>\\r\\n <option value=\"90\">90만</option>\\r\\n <option value=\"100\">100만</option>\\r\\n <option value=\"110\">110만</option>\\r\\n <option value=\"120\">120만</option>\\r\\n <option value=\"130\">130만</option>\\r\\n <option value=\"140\">140만</option>\\r\\n <option value=\"160\">160만</option>\\r\\n <option value=\"180\">180만</option>\\r\\n <option value=\"200\">200만</option>\\r\\n <option value=\"250\">250만</option>\\r\\n </select>\\r\\n </div>\\r\\n <div class=\"item-type\">\\r\\n <h4>방구조</h4>\\r\\n <label><input type=\"checkbox\" name=\"room\" class=\"item_room\" value=\"01\" /> 원룸(오픈형)</label>\\r\\n <label><input type=\"checkbox\" name=\"room\" class=\"item_room\" value=\"02\" /> 원룸(분리형)</label>\\r\\n <label><input type=\"checkbox\" name=\"room\" class=\"item_room\" value=\"03\" /> 원룸(복층형)</label>\\r\\n <label><input type=\"checkbox\" name=\"room\" class=\"item_room\" value=\"04\" /> 투룸</label>\\r\\n <label><input type=\"checkbox\" name=\"room\" class=\"item_room\" value=\"05\" /> 쓰리룸</label>\\r\\n </div>\\r\\n </div>\\r\\n <!-- // search -->\\r\\n <!-- empty -->\\r\\n <div class=\"map-item-empty\" id=\"map-item-empty\">\\r\\n <h4>지도의 표시 영역내에 등록된 방이 아직 없습니다.</h4>\\r\\n <p>\\r\\n · 지도를 좀 더 움직여 다른 곳을 탐색해보세요.<br>\\r\\n · 이 지역의 입주가능한 방을 알고 계시다면 직방에 알려주세요.\\r\\n </p>\\r\\n </div>\\r\\n <!-- list -->\\r\\n <div class=\"map-list\" id=\"map-list\">\\r\\n <div class=\"map-list-wrap\">\\r\\n <h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <span></span> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div id=\"vip-map-list\"></div>\\r\\n <h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> <span></span> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div id=\"premium-special-map-list\"></div>\\r\\n <h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> <span></span> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div id=\"premium-normal-map-list\"></div>\\r\\n <h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> <span></span></h4>\\r\\n <div id=\"zbonly-map-list\"></div>\\r\\n <h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> <span></span></h4>\\r\\n <div id=\"normal-map-list\"></div>\\r\\n </div>\\r\\n </div>\\r\\n <!-- // list -->\\r\\n </div>\\r\\n</div>\\r\\n<div class=\"bg-layer\"></div>\\r\\n<div class=\"trust-layer\">\\r\\n\\t<img src=\"//s.zigbang.com/v1/web/rooms/layer_trust.png\" alt=\"\" />\\r\\n\\t<button type=\"button\">확인</button>\\r\\n</div>\\r\\n\\r\\n\\r\\n </div>\\r\\n\\r\\n\\r\\n <div class=\"footer\">\\r\\n <div class=\"wrap-950\">\\r\\n <p class=\"i-links\">\\r\\n\\t\\t\\t\\t\\t<a href=\"//company.zigbang.com\" target=\"_blank\">회사소개</a> |\\r\\n\\t\\t\\t\\t\\t<a href=\"//s.zigbang.com/agree/user-agreement-last.html\" target=\"_blank\">이용약관</a> |\\r\\n\\t\\t\\t\\t\\t<a href=\"//s.zigbang.com/agree/user-privacy-last.html\" 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All Rights Reserved.</div>\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-jqueryui/1.11.2/jquery-ui.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-modernizr/2.8.3/modernizr.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-cookies/1.1.0/cookies.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-handlebars/3.0.0/handlebars.min.js\"></script>\\r\\n\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-jquery-xdomainrequest/1.0.3/jquery.xdomainrequest.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-jquery-lazyload/1.9.1/jquery.lazyload.min.js\"></script>\\r\\n <!--[if lt IE 10]>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/jquery-placeholder/2.1.0/jquery.placeholder.js\"></script>\\r\\n <![endif]-->\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/js/cdnjs-json3/3.3.2/json3.min.js\"></script>\\r\\n <script type=\"text/javascript\" src=\"/Content/naver_script.js\"></script>\\r\\n\\r\\n <script type=\"text/javascript\">\\r\\n var api_host = \"api.zigbang.com\";\\r\\n var user_type_all = \"\";\\r\\n </script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/www2/js/common.min.js?ver=201606081200\"></script>\\r\\n \\r\\n <script type=\"text/javascript\" src=\"//apis.daum.net/maps/maps3.js?apikey=dedb500c85692ad324afaac2d61ea80a\"></script>\\r\\n <script type=\"text/javascript\" src=\"//s.zigbang.com/www2/js/map.js?v20160801\"></script> \\r\\n\\t<script>\\r\\n\\t\\tfunction trustLayerShow() {\\r\\n\\t\\t\\t$(\".bg-layer\").show();\\r\\n\\t\\t\\t$(\".trust-layer\").show();\\r\\n\\t\\t\\t$(\".trust-layer button\").click(function () {\\r\\n\\t\\t\\t\\t$(\".bg-layer\").hide();\\r\\n\\t\\t\\t\\t$(\".trust-layer\").hide();\\r\\n\\t\\t\\t});\\r\\n\\t\\t}\\r\\n\\t</script>\\r\\n <script id=\"list-item-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-item {{addClass}}\" data-location=\"{{random_location}}\" data-item-id=\"{{id}}\" onclick=\"window.open(\\'/items1/{{id}}\\')\">\\r\\n <div class=\"i-img\"><img src=\"//s.zigbang.com/v2/web/thumbnail.png\" class=\"lazy\" data-original=\"{{img_url}}\" alt=\"\" /><em></em></div>\\r\\n\\r\\n <div class=\"i-tit\">\\r\\n <strong>{{deposit1}}/{{deposit2}}</strong>\\r\\n <b>{{floor}}</b>\\r\\n <img src=\"//s.zigbang.com/v1/web/common/blank.png\" class=\"tag-type tag-{{agent}}\" alt=\"\" />\\r\\n </div>\\r\\n <p class=\"i-info\">{{{info1}}} <em>{{{info_type}}}</em></p>\\r\\n <p class=\"i-txt\">{{{info2}}}</p>\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n\\r\\n<script id=\"item-infowindow-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-item\">\\r\\n <div class=\"i-img\"><img src=\"//d2eznf4yph68t8.cloudfront.net/v1/items/{{id}}/images/1?w=83&h=62\" alt=\"\" /></div>\\r\\n <div class=\"i-tit\">\\r\\n <strong>{{deposit1}}/{{deposit2}}</strong>\\r\\n <b>{{floor}}</b>\\r\\n <img src=\"//s.zigbang.com/v1/web/common/blank.png\" class=\"tag-type tag-{{agent}}\" alt=\"\" />\\r\\n </div>\\r\\n <p class=\"i-info\">{{{info1}}} <em>{{{info_type}}}</em></p>\\r\\n <p class=\"i-txt\">{{{info2}}}</p>\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n<script id=\"list-item-agent-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-agent\" data-userno=\"{{user_no}}\">\\r\\n <div class=\"i-img\"><img src=\"//s.zigbang.com/v2/web/agent_default.png\" class=\"lazy\" data-original=\"{{profile_url}}&w=74&h=74\" alt=\"\" /> <em></em></div>\\r\\n\\t\\t<div class=\"i-tit\"><em class=\"i_agent\">{{user_agent}}</em><span class=\"i_name\">{{user_name}}</span></div>\\r\\n <p class=\"i-info\">{{ad_name}} 전문 중개사무소</p>\\r\\n <p class=\"i-txt\">이 {{type}} 보유 매물 : <em>{{items_count}}</em>개</p>\\r\\n <button type=\"button\" class=\"i-more\">더보기</button>\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n<script id=\"layer-building-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-layer\" id=\"building-list\">\\r\\n <div class=\"list-title\">\\r\\n <h3>{{building_name}} 방 목록 : {{building_count}}개</h3>\\r\\n <p>{{building_name}}에 등록된 방 정보입니다.</p>\\r\\n <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/close.png\" alt=\"\" /></button>\\r\\n </div>\\r\\n <div class=\"map-list\"> \\r\\n <div class=\"map-list-wrap\">\\r\\n <div class=\"map-list-scroll\">\\r\\n <h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"vip-list\"></div>\\r\\n <h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> : {{premium_special_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"premium-special-list\"></div>\\r\\n <h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> : {{premium_normal_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"premium-normal-list\"></div>\\r\\n <h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> : {{zbonly_count}}개</h4>\\r\\n <div class=\"zbonly-list\"></div>\\r\\n <h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> : {{normal_count}}개</h4>\\r\\n <div class=\"normal-list\"></div>\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n\\r\\n <div class=\"list-item-null\">\\r\\n <img src=\"//s.zigbang.com/v1/web/search/icon_null.gif\" alt=\"\"><h4>{{building_name}}에 매물이 존재하지 않습니다.</h4>\\r\\n </div>\\r\\n <!-- // list -->\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n<script id=\"layer-subway-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-layer\" id=\"subway-list\">\\r\\n <div class=\"list-title\">\\r\\n <h3>{{subway_name}} 방 목록 : {{subway_count}}개</h3>\\r\\n <p>{{subway_name}}에서 도보 10분 이내 위치한 방 정보입니다.</p>\\r\\n <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/close.png\" alt=\"\" /></button>\\r\\n </div>\\r\\n <div class=\"map-list\">\\r\\n <div class=\"map-list-wrap\" id=\"subway-list-wrap\">\\r\\n <div class=\"map-list-scroll\">\\r\\n <h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"vip-list\"></div>\\r\\n <h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> : {{premium_special_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"premium-special-list\"></div>\\r\\n <h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> : {{premium_normal_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\\r\\n <div class=\"premium-normal-list\"></div>\\r\\n <h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> : {{zbonly_count}}개</h4>\\r\\n <div class=\"zbonly-list\"></div>\\r\\n <h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> : {{normal_count}}개</h4>\\r\\n <div class=\"normal-list\"></div>\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n\\r\\n <div class=\"list-item-null\">\\r\\n <img src=\"//s.zigbang.com/v1/web/search/icon_null.gif\" alt=\"\"><h4>{{subway_name}} 근처에 매물이 존재하지 않습니다.</h4>\\r\\n </div>\\r\\n <!-- // list -->\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n<script id=\"layer-agent-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"layer-agent\" id=\"agent-list\">\\r\\n <div class=\"agent-title\">\\r\\n {{user_name}}{{#if area}} <span>- {{area}} 전문</span> {{/if}}\\r\\n <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n </div>\\r\\n <div class=\"agent-content\">\\r\\n <div class=\"agent-img\"><img src=\"{{profile_url}}\" id=\"blurImage\" style=\"width: 400px\"></div>\\r\\n <div class=\"agent-info min\">\\r\\n <p>{{user_intro}}</p>\\r\\n <button type=\"button\" class=\"btn-info-more\">[더보기]</button>\\r\\n </div>\\r\\n\\r\\n <div class=\"map-list\">\\r\\n <h4 class=\"list-tit\">{{area}} 주변 보유 매물 : {{count}}개</h4>\\r\\n\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n</script>\\r\\n\\r\\n<script id=\"officetel-info-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"list-item\">\\r\\n <div class=\"i-img\"><img src=\"//zigbang.s3-ap-northeast-1.amazonaws.com/public/buildings/{{id}}.jpg\" alt=\"\"></div>\\r\\n <div class=\"i-tit\">{{name}}</div>\\r\\n <p class=\"i-info\">{{{text}}}</p>\\r\\n <p class=\"i-txt\">입주가능한 방 : {{count}}개</p>\\r\\n </div>\\r\\n</script>\\r\\n;\\r\\n\\r\\n <!-- step1 -->\\r\\n<script id=\"layer-join-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer has-agree\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">회원 가입</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<form>\\r\\n\\t\\t\\t\\t<div class=\"i-mail\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit type-2\">&middot; 이메일</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" style=\"width:305px;\" name=\"username\" placeholder=\"zigbang@zigbang.com\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t<i>※ 사용하실 아이디를 이메일로 입력해 주세요.</i>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t\\t<div class=\"agree-box\">\\r\\n\\t\\t\\t\\t\\t<div class=\"item\">\\r\\n\\t\\t\\t\\t\\t\\t<h4>&middot; 이용약관 (필수)</h4>\\r\\n\\t\\t\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree\" /> 동의 합니다</label>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"i-box\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<div class=\"agree_conainer\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h1>직방 서비스 이용약관</h1>\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h2>제1장 총칙</h2>\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제1조 (목적)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t본 약관은 (주)직방(이하 “회사”라 함)이 운영하는 인터넷 사이트 및 모바일 어플리케이션(이하 “직방”이라 함)에서 제공하는 제반 서비스의 이용과 관련하여 회사와 이용자 및 이용자간의 권리, 의무 및 책임사항, 기타 필요한 사항을 규정함을 목적으로 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제2조 (정의)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 직방: 회사가 컴퓨터 등 정보통신설비를 이용하여 서비스를 제공할 수 있도록 설정한 가상의 영업장을 말하며, 아울러 인터넷 사이트 및 모바일 어플리케이션을 운영하는 사업자의 의미로도 사용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자: 직방에 접속하여 본 약관에 따라 회사가 제공하는 서비스를 받는 회원 및 비회원을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회원: 회사에 개인정보를 제공하여 회원등록을 한 자로서, 직방의 정보를 지속적으로 제공받으며, 회사가 제공하는 직방의 서비스를 계속적으로 이용할 수 있는 자를 말합니다. 회사는 서비스의 원활한 제공을 위해 회원의 등급을 회사 내부의 규정에 따라 나눌 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 비회원: 회원으로 가입하지 않고 회사가 제공하는 서비스를 이용하는 자를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5. 아이디(ID): 회원의 식별과 서비스 이용을 위하여 회원이 설정하고 회사가 승인한 회원 본인의 이메일 주소를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t6. 비밀번호: 회원의 동일성 확인과 회원정보의 보호를 위하여 회원이 설정하고 회사가 승인한 문자나 숫자의 조합을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t7. 서비스: 구현되는 단말기(PC, TV, 휴대형단말기 등의 각종 유무선 장치를 포함)와 상관없이 회원이 이용할 수 있는 직방의 서비스를 의미합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t8. 게시판: 그 명칭, 형태, 위치와 관계없이 회원 및 비회원 이용자에게 공개할 목적으로 부호•문자•음성•음향•화상•동영상 등의 정보 (이하 \"게시물\"이라 합니다)를 회원이 게재할 수 있도록 회사가 제공하는 서비스 상의 가상공간을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t9. 별칭: 인터넷사이트에서 아이디와 함께, 또는 아이디를 대신하여 회원을 식별하기 위하여 이용자의 신청과 회사의 승인에 의하여 설정되는 숫자와 문자의 조합을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t10. 운영자: 회사가 제공하는 서비스의 전반적인 관리와 원활한 운영을 위하여 회사에서 선정한 자를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t위 항에서 정의되지 않은 이 약관상의 용어의 의미는 일반적인 거래관행에 의합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제3조 (약관 등의 명시와 설명 및 개정)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 본 약관의 내용을 이용자가 쉽게 알 수 있도록 직방 인터넷 사이트 및 모바일 어플리케이션에 공지합니다. 다만, 약관의 내용은 이용자가 연결화면을 통하여 볼 수 있도록 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 “약관의 규제에 관한 법률”, “정보 통신망 이용 촉진 및 정보보호 등에 관한 법률” 등 관련법을 위배하지 않는 범위에서 본 약관을 개정할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회사가 약관을 개정할 경우에는 적용일자 및 개정사유를 명시하여 이용자가 알기 쉽도록 표시하여 공지합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 회사가 약관을 개정할 경우에는 변경된 약관은 공지된 시점부터 그 효력이 발생하며, 이용자는 약관이 변경된 후에도 본 서비스를 계속 이용함으로써 변경 후의 약관에 대해 동의를 한 것으로 간주됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제4조 (약관의 해석)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 서비스운영을 위해 별도의 운영정책을 마련하여 운영할 수 있으며, 회사는 직방 인터넷 사이트 및 모바일 어플리케이션에 운영정책을 사전 공지 후 적용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 본 약관에서 정하지 아니한 사항이나 해석에 대해서는 별도의 운영정책, 관계법령 또는 상관례에 따릅니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제5조 (서비스의 제공 및 변경)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사가 제공하는 서비스는 다음과 같습니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 부동산 매물 등에 관한 정보제공 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 부동산 매물 등록 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3) 부동산 중개업소 추천 등 기타 관련 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사가 제공하는 서비스의 내용을 기술적 사양의 변경 등의 이유로 변경할 경우에는 그 사유를 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제6조 (서비스의 중단)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 컴퓨터 등 정보통신설비의 보수점검, 교체 및 고장, 통신의 두절 등의 사유가 발생한 경우에는 서비스의 제공을 일시적으로 중단할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 사업종목의 전환, 사업의 포기, 업체간의 통합 등의 이유로 서비스를 제공할 수 없게 되는 경우에는 회사는 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제7조 (회원에 대한 통지)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이메일, 이동전화 단문메시지서비스(SMS), 푸시알림(App push)등으로 회원에게 통지할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 불특정다수 회원에 대한 통지의 경우 공지사항으로 게시함으로써 개별 통지에 갈음할 수 있습니다. 다만, 회원 본인의 거래와 관련하여 중대한 영향을 미치는 사항에 대하여는 개별통지를 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h2>제 2장 이용계약 및 정보보호</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제8조 (회원가입 및 회원정보의 변경)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 이용자는 회사가 정한 가입 양식에 따라 회원정보를 기입한 후 본 약관 등에 동의한다는 의사표시를 함으로서 회원가입을 신청합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 제1항과 같이 회원으로 가입할 것을 신청한 이용자 중 다음 각 호에 해당하지 않는 한 회원으로 등록합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 등록 내용에 허위, 기재누락, 오기가 있는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 가입신청자가 이전에 회원자격을 상실한 적이 있는 경우 (다만 회원자격 상실 후 회사가 필요하다고 판단하여 회원재가입 승낙을 얻은 경우에는 예외로 합니다.)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3) 회사로부터 회원자격 정지 조치 등을 받은 회원이 그 조치기간 중에 이용계약을 임의 해지 하고 재이용 신청을 하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4) 기타 회원으로 등록하는 것이 직방의 기술상 현저히 지장이 있다고 판단되는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5) 본 약관에 위배되거나 위법 또는 부당한 이용신청임이 확인된 경우 및 회사가 합리적인 판단에 의하여 필요하다고 인정하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회원 가입 계약의 성립시기는 회사의 승낙이 회원에게 도달한 시점으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 회원은 회원 가입 신청 시 기재한 사항이 변경되었을 경우 온라인으로 수정을 하거나 전자우편 기타 방법으로 회사에 그 변경사항을 알려야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5. 제4항의 변경사항을 회사에 알리지 않아 발생한 불이익에 대하여 회사는 책임지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t6. 회원가입은 반드시 본인의 진정한 정보를 통하여만 가입할 수 있으며 회사는 회원이 등록한 정보에 대하여 확인조치를 할 수 있습니다. 회원은 회사의 확인조치에 대하여 적극 협력하여야 하며, 만일 이를 준수하지 아니할 경우 회사는 회원이 등록한 정보가 부정한 것으로 처리할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t7. 회사는 회원을 등급별로 구분하여 이용시간, 이용회수, 서비스 메뉴, 매물 등록 건 수 등을 세분하여 이용에 차등을 둘 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제9조 (이용 계약의 종료)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회원의 해지<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 회원은 언제든지 회사에게 해지 의사를 통지할 수 있고 회사는 특별한 사유가 없는 한 이를 즉시 수락하여야 합니다. 다만, 회원은 해지의사를 통지하기 전에 모든 진행중인 절차를 완료, 철회 또는 취소해야만 합니다. 이 경우 철회 또는 취소로 인한 불이익은 회원 본인이 부담하여야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 회원이 발한 의사표시로 인해 발생한 불이익에 대한 책임은 회원 본인이 부담하여야 하며, 이용계약이 종료되면 회사는 회원에게 부가적으로 제공한 각종 혜택을 회수할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3) 회원의 의사로 이용계약을 해지한 후, 추후 재이용을 희망할 경우에는 재이용 의사가 회사에 통지되고, 이에 대한 회사의 승낙이 있는 경우에만 서비스 재이용이 가능합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4) 본 항에 따라 해지를 한 회원은 이 약관이 정하는 회원가입절차와 관련조항에 따라 신규 회원으로 다시 가입할 수 있습니다. 다만 회원이 중복참여가 제한된 판촉이벤트 중복 참여 등 부정한 목적으로 회원탈퇴 후 재이용을 신청하는 경우 회사는 가입을 일정기간 동안 제한할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5) 본 항에 따라 해지를 한 이후에는 재가입이 불가능하며, 모든 가입은 신규가입으로 처리됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사의 해지<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 회사는 다음과 같은 사유가 발생하거나 확인된 경우 이용계약을 해지할 수 있습니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t①다른 회원의 권리나 명예, 신용 기타 정당한 이익을 침해하거나 대한민국 법령 또는 공서양속에 위배되는 행위를 한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t②회사가 제공하는 서비스의 원활한 진행을 방해하는 행위를 하거나 시도한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t③제 8조 제 2항의 승낙거부 사유가 추후 발견된 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t④회사가 정한 서비스 운영정책을 이행하지 않거나 위반한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t⑤기타 회사가 합리적인 판단에 기하여 서비스의 제공을 거부할 필요가 있다고 인정할 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 회사가 해지를 하는 경우 회사는 회원에게 이메일, 전화, 기타의 방법을 통하여 해지 사유를 밝혀 해지 의사를 통지합니다. 이용계약은 회사의 해지의사를 회원에게 통지한 시점에 종료됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3) 본 항에서 정한 바에 따라 이용계약이 종료될 시에는 회사는 회원에게 부가적으로 제공한 각종혜택을 회수할 수 있습니다. 이용계약의 종료와 관련하여 발생한 손해는 이용계약이 종료된 해당 회원이 책임을 부담하여야 하고, 회사는 일체의 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4) 본 항에서 정한 바에 따라 이용계약이 종료된 경우에는, 회원의 재이용 신청에 대하여 회사는 이에 대한 승낙을 거절할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. “회원”이 계약을 해지하는 경우, 관련법 및 개인정보취급방침에 따라 “회사”가 “회원”정보를 보유하는 경우를 제외하고는 해지 즉시 “회원”의 모든 데이터는 소멸됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제10조 (개인정보보호)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자의 회원가입 시 서비스 제공에 필요한 최소한의 정보를 수집합니다. 다음 사항을 필수사항으로 하며 그 외 사항은 선택사항으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1)이메일주소<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2)비밀번호<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3)휴대폰 번호(부동산 매물등록 서비스 및 신고기능을 이용하는 회원인 경우)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사가 이용자의 개인식별이 가능한 개인정보를 수집하는 때에는 반드시 당해 이용자의 동의를 받습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 제공된 개인정보는 당해 이용자의 동의 없이 목적 외의 이용이나 제3자에게 제공하지 않습니다. 다만, 다음의 경우에는 예외로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 통계작성, 학술연구 또는 시장조사를 위하여 필요한 경우로서 특정 개인을 식별할 수 없는 형태로 제공하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 도용방지를 위하여 본인확인에 필요한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3) 법률의 규정 또는 법률에 의하여 필요한 불가피한 사유가 있는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 회사가 제2항과 제3항에 의해 이용자의 동의를 받아야 하는 경우에는 개인정보관리 책임자의 신원(소속, 성명 및 전화번호, 기타 연락처), 정보의 수집목적 및 이용목적, 제3자에 대한 정보제공 관련사항(제공받은 자, 제공목적 및 제공할 정보의 내용) 등 정보통신망이용촉진등에관한법률 제22조제2항이 규정한 사항을 미리 명시하거나 고지해야 하며 이용자는 언제든지 이 동의를 철회할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5. 회사는 이용자의 개인정보를 보호하기 위해 “개인정보취급방침”을 수립하고 개인정보보호책임자를 지정하여 이를 게시하고 운영합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t6. 이용자는 언제든지 회사가 갖고 있는 자신의 개인정보에 대해 열람 및 오류정정을 요구할 수 있으며 회사는 이에 대해 지체 없이 필요한 조치를 취할 의무를 집니다. 이용자가 오류의 정정을 요구한 경우에는 회사는 그 오류를 정정할 때까지 당해 개인정보를 이용하지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t7. 회사 또는 그로부터 개인정보를 제공받은 제3자는 개인정보의 수집목적 또는 제공받은 목적을 달성한 때에는 당해 개인정보를 지체 없이 파기합니다. 다만, 아래의 경우에는 회원 정보를 보관합니다. 이 경우 회사는 보관하고 있는 회원 정보를 그 보관의 목적으로만 이용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1) 상법, 전자상거래 등에서의 소비자보호에 관한 법률 등 관계법령의 규정에 의하여 보존할 필요가 있는 경우 회사는 관계법령에서 정한 일정한 기간 동안 회원 정보를 보관합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2) 회사가 이용계약을 해지하거나 회사로부터 서비스 이용정지조치를 받은 회원에 대해서는 재가입에 대한 승낙거부사유가 존재하는지 여부를 확인하기 위한 목적으로 이용계약종료 후 5년간 아이디, 전화번호를 비롯하여 이용계약 해지와 서비스 이용정지와 관련된 정보 등의 필요정보를 보관합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t8. 회사는 새로운 업체가 제휴사 또는 제휴영업점의 지위를 취득할 경우 제7조 2항에서 정한 것과 같은 방법을 통하여 공지합니다. 이 때 회원이 별도의 이의제기를 하지 않을 경우 직방 서비스 제공이라는 필수적인 목적을 위해 해당 개인 정보 제공 및 활용에 동의한 것으로 간주 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t9. 모든 이용자는 직방으로부터 제공받은 정보를 다른 목적으로 이용하거나 타인에게 유출 또는 제공해서는 안되며, 위반 사용으로 인한 모든 책임을 부담해야 합니다. 또한 회원은 자신의 개인정보를 책임 있게 관리하여 타인이 회원의 개인정보를 부정하게 이용하지 않도록 해야 합니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h2>제 3장 서비스의 이용</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제11조 (부동산 매물 등에 관한 정보제공 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 부동산 매물 등에 관한 정보제공 서비스는 회사가 이용자 스스로 해당 정보를 확인 및 이용할 수 있도록 관련 정보를 제공하는 것입니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션 내에서 제공하는 모든 정보에 대해서 정확성이나 신뢰성이 있는 정보를 제공하기 위해 노력하지만, 그 과정에서 발생할 수 있는 정보의 정확성이나 신뢰성에 대해서는 어떠한 보증도 하지 않으며, 정보의 오류로 인해 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션을 통해 제공되는 정보의 내용을 수정할 의무를 지지 않으나, 필요에 따라 개선할 수는 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제12조 (부동산 매물 등록 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 부동산 매물 등록 서비스는 회원이 매물정보(부동산 거래정보 및 거래 물건에 대한 다양한 부가정보)와 회원 연락처(회원의 이메일 주소 및 휴대폰 번호)를 직접 직방 인터넷 사이트 및 모바일 어플리케이션에 등록하여 이용자에게 노출할 수 있는 것을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 회원이 등록한 매물정보의 노출순서 및 영역의 추가 등에 대한 결정 권한을 갖고 있습니다. 또한, 회사는 사전통지 없이 회원의 매물정보 등을 직방 인터넷 사이트 및 모바일 어플리케이션 이외의 다른 인터넷 사이트 등에 노출할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 회원이 등록한 매물정보에 대해 등록 후 24시간 이내에 해당 매물정보의 진위 여부를 확인하며, 진위 여부 확인 즉시 해당 매물을 노출합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 회원이 등록한 매물정보가 실제 매물정보와 불일치 하는 경우 회사는 회원이 가입시 제공한 전화번호 또는 이메일을 통해 회원에게 매물정보의 수정을 요청합니다. 회사가 회원이 제공한 연락수단으로 2회 이상 연락하였음에도 불구하고 연락이 되지 않을 경우의 책임은 “회원”에게 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5. 전항에 따른 회사의 정당한 매물정보 수정 요청에도 불구하고 회원이 24시간 이내에 매물정보(거래완료 혹은 노출종료와 같은 매물상태 변경 포함)를 수정하지 않을 경우, 회사는 해당 매물정보의 노출을 중지하고 회원의 서비스 이용을 제한 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t6. 회사는 직방에 등록된 매물 중 사회통념, 관례 및 회사의 합리적인 판단에 의하여 거래가 부적합하다고 판단되는 경우 이의 삭제를 요청하거나 직권으로 삭제할 수 있으며 해당 회원의 서비스 이용을 정지 혹은 탈퇴시킬 수 있습니다. 직방에 거래부적합 부동산 매물을 등록할 경우, 거래부적합 매물에 대한 법적인 책임은 해당 등록자에게 있습니다\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제13조 (부동산 중개업소 추천 등 기타 관련 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자가 원하는 경우 이용자의 편의를 위해 부동산 중개업소를 이용자에게 추천할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자가 회사가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 이용자 스스로의 판단에 따라 결정하는 것으로 회사는 이용자가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제14조 (정보의 제공 및 광고의 게재)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 회원이 서비스 이용 중 필요하다고 인정되는 다양한 정보를 서비스 내 공지사항, 서비스 화면, 전자우편 등의 방법으로 회원에게 제공할 수 있습니다. 다만, 회원은 관련법에 따른 거래관련 정보 및 고객문의 등에 대한 답변 등을 제외하고는 언제든지 위 정보제공에 대해서 수신 거절을 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 서비스의 운영과 관련하여 회사가 제공하는 서비스의 화면 및 홈페이지 등에 광고를 게재할 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h2>제 4장 책임</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제15조 (회사의 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 법령과 이 약관이 금지하거나 공서양속에 반하는 행위를 하지 않으며 이 약관이 정하는 바에 따라 지속적이고, 안정적으로 이용자에게 서비스를 제공하기 위해 최선을 다합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 이용자 상호간의 거래에 있어서 어떠한 보증도 제공하지 않습니다. 이용자 상호간 거래 행위에서 발생하는 문제 및 손실에 대해서 회사는 일체의 책임을 부담하지 않으며, 거래당사자간에 직접 해결해야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제16조 (회원의 아이디 및 비밀번호에 대한 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 아이디와 비밀번호에 관한 관리책임은 회원에게 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회원은 자신의 아이디 및 비밀번호를 제3자에게 이용하게 해서는 안됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회원이 자신의 아이디 및 비밀번호를 도난당하거나 제3자가 사용하고 있음을 인지한 경우에는 바로 회사에 통보하고 회사의 안내가 있는 경우에는 그에 따라야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제17조 (이용자의 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 이용자는 다음 각호의 행위를 하여서는 안됩니다. 만약 다음 각호와 같은 행위가 확인되면 회사는 해당 이용자에게 서비스 이용에 대한 제재를 가할 수 있으며 민형사상의 책임을 물을 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(1)회사 서비스의 운영을 고의 및 과실로 방해하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(2)신청 또는 변경 시 허위 내용의 등록<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(3)타인의 정보 도용<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(4)허위 매물 정보의 등록<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(5)회사가 정한 정보 이외의 정보(컴퓨터 프로그램 등) 등의 송신 또는 게시<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(6)회사 및 기타 제3자의 저작권 등 지적재산권에 대한 침해<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(7)회사 및 기타 제3자의 명예를 손상시키거나 업무를 방해하는 행위<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(8)외설 또는 폭력적인 메시지, 화상, 음성, 기타 공서양속에 반하는 정보를 직방에 공개 또는 게시하는 행위<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(9)사기 및 악성 글 등록 등 건전한 거래 문화 활성에 방해되는 행동<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(10)기타 중대한 사유로 인하여 회사가 서비스 제공을 지속하는 것이 부적당하다고 인정하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 전항의 규정에 의하여 서비스의 이용을 제한하거나 중지할 수 있는 모든 권한을 갖고 있습니다. 회사는 회사 정책에 위반한 행동을 하는 특정 회원의 ID를 삭제할 수 있고, 이용 중지 등의 모든 서비스 제한 조치를 이용자에게 통보 없이 직권으로 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 회사의 정책에 따라서 회원 간의 차별화된 유료 서비스를 언제든지 제공할 수 있습니다. 만약 회원이 비용을 지불하지 않고 사용을 할 경우 회사는 특정 회원에게 서비스 중지 및 특정 서비스 제한을 할 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제18조 (저작권의 귀속 및 이용제한)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 서비스에 대한 저작권 및 지적재산권은 회사에 귀속됩니다. 단, 회원이 직방을 이용하여 작성한 저작물에 대한 저작권은 해당 회원에게 귀속됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자는 서비스를 이용함으로써 얻은 정보 중 회사에게 지적재산권이 귀속된 정보를 회사의 사전 승낙 없이 복제, 송신, 출판, 배포, 방송 기타 방법에 의하여 영리목적으로 이용하거나 제3자에게 이용하게 하여서는 안됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제19조 (책임의 한계 등)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 무료로 제공하는 정보 및 서비스에 관하여 개인정보취급방침 또는 관계법령의 벌칙, 과태료 규정 등 강행규정에 위배되지 않는 한 원칙적으로 손해를 배상할 책임이 없습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 천재지변, 불가항력, 서비스용 설비의 보수, 교체, 점검, 공사 등 기타 이에 준하는 사항으로 정보 및 서비스를 제공할 수 없는 경우에 이에 대한 책임이 면제됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 이용자의 귀책사유로 인한 정보 및 서비스 이용의 장애에 관한 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t4. 회사는 회원이 게재한 정보, 자료, 사실의 신뢰도, 정확성 등의 내용에 관하여는 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t5. “서비스”에서 제공하는 정보에 대한 신뢰 여부는 전적으로 “이용자” 본인의 책임이며, “회사”는 매물정보를 등록한 “회원”에 의한 사기, 연락 불능 등으로 인하여 발생하는 어떠한 직접, 간접, 부수적, 파생적, 징벌적 손해, 손실, 상해 등에 대하여 도덕적, 법적 책임을 부담하지 않습니다. 또한 “서비스”를 통하여 노출, 배포, 전송되는 정보를 “이용자”가 이용하여 발생하는 부동산 거래 등에 대하여 “회사”는 어떠한 도덕적, 법적 책임이나 의무도 부담하지 아니합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t6. “이용자”가 “회사”가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 “이용자” 스스로의 판단에 따라 결정하는 것으로 “회사”는 “이용자”가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제20조 (손해배상 등)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 회원이 서비스를 이용함에 있어 회사의 고의 또는 과실로 인해 손해가 발생한 경우에는 민법 등 관련 법령이 규율하는 범위 내에서 그 손해를 배상합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 회사에 손해가 발생한 경우에는 회사에 그 손해를 배상하여야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t3. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 제3자가 회사를 상대로 민형사상의 법적 조치를 취하는 경우에는 회원은 자신의 비용과 책임으로 회사를 면책시켜야 하며, 이로 인해 발생하는 손해에 대해 배상하여야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제21조 (분쟁해결)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자 상호간 분쟁에서 발생하는 문제에 대해서 일체의 책임을 지지 않습니다. 이용자 상호간 분쟁은 당사자간에 직접 해결해야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자 상호간에 서비스 이용과 관련하여 발생한 분쟁에 대해 이용자의 피해구제신청이 있는 경우에는 공정거래위원회 또는 시·도지사가 의뢰하는 분쟁조정기관의 조정에 따를 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<h3>제22조 (재판권 및 준거법)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t1. 회사와 회원간 제기된 소송은 대한민국법을 준거법으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2. 회사와 회원간 발생한 분쟁에 관한 소송은 민사소송법 상의 관할법원에 제소합니다.<br /><br />\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t부칙<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t제1조 (적용일자)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t이 약관은 2013년 11월 1일부터 적용됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"item\">\\r\\n\\t\\t\\t\\t\\t\\t<h4>&middot; 개인정보 수집 및 이용에 대한 동의 (필수)</h4>\\r\\n\\t\\t\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree2\" /> 동의 합니다</label>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"i-box\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<strong>이메일 가입회원</strong><br /> \\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t개인정보의 수집 및 이용에 대한 안내<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이\\r\\n\\t\\t\\t\\t\\t\\t\\t개인정보를 수집합니다. 정보주체는 본개인정보의 수집 및 이용에\\r\\n\\t\\t\\t\\t\\t\\t\\t관한 동의를 거부하실권리가 있으나, 서비스제공에 필요한\\r\\n\\t\\t\\t\\t\\t\\t\\t최소한의 개인 정보 이므로 동의를 해주셔야 서비스를 이용하실 수\\r\\n\\t\\t\\t\\t\\t\\t\\t있습니다.<br />\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t• 수집하려는 개인 정보 항목: 이메일<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t• 개인정보의 수집 목적: 회원제 서비스 이용, 개인식별,\\r\\n\\t\\t\\t\\t\\t\\t\\t가입의사 확인, 고지사항 전달<br /><br />\\r\\n\\t\\t\\t\\t\\t\\t\\t• 개인정보의 보유기간: 회원 탈퇴 후 바로삭제\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok ladda-button\" data-style=\"zoom-in\">가입하기</button>\\r\\n\\t\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-cancel\">취 소 </button>\\r\\n\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"layer-other-links\">\\r\\n\\t\\t\\t\\t\\t<button type=\"button\" class=\"link1\">기존회원 로그인</button> &nbsp; | &nbsp; <a href=\"/home/RegisterInfo\" class=\"link2 fc-red1\" target=\"_blank\">중개사무소 가입신청</a>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</form>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- step2 -->\\r\\n<script id=\"layer-join2-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer has-agree\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">회원 가입</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<div class=\"agree-box item-left\">\\r\\n\\t\\t\\t\\t<div class=\"item\">\\r\\n\\t\\t\\t\\t\\t<h4>&middot; 이용약관 (필수)</h4>\\r\\n\\t\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree\" /> 동의 합니다</label>\\r\\n\\t\\t\\t\\t\\t<div class=\"i-box\" style=\"height:150px\">\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"agree_conainer\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<h1>직방 서비스 이용약관</h1>\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t<h2>제1장 총칙</h2>\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제1조 (목적)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t본 약관은 (주)직방(이하 “회사”라 함)이 운영하는 인터넷 사이트 및 모바일 어플리케이션(이하 “직방”이라 함)에서 제공하는 제반 서비스의 이용과 관련하여 회사와 이용자 및 이용자간의 권리, 의무 및 책임사항, 기타 필요한 사항을 규정함을 목적으로 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제2조 (정의)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 직방: 회사가 컴퓨터 등 정보통신설비를 이용하여 서비스를 제공할 수 있도록 설정한 가상의 영업장을 말하며, 아울러 인터넷 사이트 및 모바일 어플리케이션을 운영하는 사업자의 의미로도 사용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자: 직방에 접속하여 본 약관에 따라 회사가 제공하는 서비스를 받는 회원 및 비회원을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회원: 회사에 개인정보를 제공하여 회원등록을 한 자로서, 직방의 정보를 지속적으로 제공받으며, 회사가 제공하는 직방의 서비스를 계속적으로 이용할 수 있는 자를 말합니다. 회사는 서비스의 원활한 제공을 위해 회원의 등급을 회사 내부의 규정에 따라 나눌 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 비회원: 회원으로 가입하지 않고 회사가 제공하는 서비스를 이용하는 자를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5. 아이디(ID): 회원의 식별과 서비스 이용을 위하여 회원이 설정하고 회사가 승인한 회원 본인의 이메일 주소를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t6. 비밀번호: 회원의 동일성 확인과 회원정보의 보호를 위하여 회원이 설정하고 회사가 승인한 문자나 숫자의 조합을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t7. 서비스: 구현되는 단말기(PC, TV, 휴대형단말기 등의 각종 유무선 장치를 포함)와 상관없이 회원이 이용할 수 있는 직방의 서비스를 의미합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t8. 게시판: 그 명칭, 형태, 위치와 관계없이 회원 및 비회원 이용자에게 공개할 목적으로 부호•문자•음성•음향•화상•동영상 등의 정보 (이하 \"게시물\"이라 합니다)를 회원이 게재할 수 있도록 회사가 제공하는 서비스 상의 가상공간을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t9. 별칭: 인터넷사이트에서 아이디와 함께, 또는 아이디를 대신하여 회원을 식별하기 위하여 이용자의 신청과 회사의 승인에 의하여 설정되는 숫자와 문자의 조합을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t10. 운영자: 회사가 제공하는 서비스의 전반적인 관리와 원활한 운영을 위하여 회사에서 선정한 자를 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t위 항에서 정의되지 않은 이 약관상의 용어의 의미는 일반적인 거래관행에 의합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제3조 (약관 등의 명시와 설명 및 개정)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 본 약관의 내용을 이용자가 쉽게 알 수 있도록 직방 인터넷 사이트 및 모바일 어플리케이션에 공지합니다. 다만, 약관의 내용은 이용자가 연결화면을 통하여 볼 수 있도록 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 “약관의 규제에 관한 법률”, “정보 통신망 이용 촉진 및 정보보호 등에 관한 법률” 등 관련법을 위배하지 않는 범위에서 본 약관을 개정할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회사가 약관을 개정할 경우에는 적용일자 및 개정사유를 명시하여 이용자가 알기 쉽도록 표시하여 공지합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 회사가 약관을 개정할 경우에는 변경된 약관은 공지된 시점부터 그 효력이 발생하며, 이용자는 약관이 변경된 후에도 본 서비스를 계속 이용함으로써 변경 후의 약관에 대해 동의를 한 것으로 간주됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제4조 (약관의 해석)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 서비스운영을 위해 별도의 운영정책을 마련하여 운영할 수 있으며, 회사는 직방 인터넷 사이트 및 모바일 어플리케이션에 운영정책을 사전 공지 후 적용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 본 약관에서 정하지 아니한 사항이나 해석에 대해서는 별도의 운영정책, 관계법령 또는 상관례에 따릅니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제5조 (서비스의 제공 및 변경)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사가 제공하는 서비스는 다음과 같습니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 부동산 매물 등에 관한 정보제공 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 부동산 매물 등록 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3) 부동산 중개업소 추천 등 기타 관련 서비스<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사가 제공하는 서비스의 내용을 기술적 사양의 변경 등의 이유로 변경할 경우에는 그 사유를 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제6조 (서비스의 중단)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 컴퓨터 등 정보통신설비의 보수점검, 교체 및 고장, 통신의 두절 등의 사유가 발생한 경우에는 서비스의 제공을 일시적으로 중단할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 사업종목의 전환, 사업의 포기, 업체간의 통합 등의 이유로 서비스를 제공할 수 없게 되는 경우에는 회사는 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제7조 (회원에 대한 통지)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이메일, 이동전화 단문메시지서비스(SMS), 푸시알림(App push)등으로 회원에게 통지할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 불특정다수 회원에 대한 통지의 경우 공지사항으로 게시함으로써 개별 통지에 갈음할 수 있습니다. 다만, 회원 본인의 거래와 관련하여 중대한 영향을 미치는 사항에 대하여는 개별통지를 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h2>제 2장 이용계약 및 정보보호</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제8조 (회원가입 및 회원정보의 변경)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 이용자는 회사가 정한 가입 양식에 따라 회원정보를 기입한 후 본 약관 등에 동의한다는 의사표시를 함으로서 회원가입을 신청합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 제1항과 같이 회원으로 가입할 것을 신청한 이용자 중 다음 각 호에 해당하지 않는 한 회원으로 등록합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 등록 내용에 허위, 기재누락, 오기가 있는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 가입신청자가 이전에 회원자격을 상실한 적이 있는 경우 (다만 회원자격 상실 후 회사가 필요하다고 판단하여 회원재가입 승낙을 얻은 경우에는 예외로 합니다.)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3) 회사로부터 회원자격 정지 조치 등을 받은 회원이 그 조치기간 중에 이용계약을 임의 해지 하고 재이용 신청을 하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4) 기타 회원으로 등록하는 것이 직방의 기술상 현저히 지장이 있다고 판단되는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5) 본 약관에 위배되거나 위법 또는 부당한 이용신청임이 확인된 경우 및 회사가 합리적인 판단에 의하여 필요하다고 인정하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회원 가입 계약의 성립시기는 회사의 승낙이 회원에게 도달한 시점으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 회원은 회원 가입 신청 시 기재한 사항이 변경되었을 경우 온라인으로 수정을 하거나 전자우편 기타 방법으로 회사에 그 변경사항을 알려야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5. 제4항의 변경사항을 회사에 알리지 않아 발생한 불이익에 대하여 회사는 책임지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t6. 회원가입은 반드시 본인의 진정한 정보를 통하여만 가입할 수 있으며 회사는 회원이 등록한 정보에 대하여 확인조치를 할 수 있습니다. 회원은 회사의 확인조치에 대하여 적극 협력하여야 하며, 만일 이를 준수하지 아니할 경우 회사는 회원이 등록한 정보가 부정한 것으로 처리할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t7. 회사는 회원을 등급별로 구분하여 이용시간, 이용회수, 서비스 메뉴, 매물 등록 건 수 등을 세분하여 이용에 차등을 둘 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제9조 (이용 계약의 종료)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회원의 해지<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 회원은 언제든지 회사에게 해지 의사를 통지할 수 있고 회사는 특별한 사유가 없는 한 이를 즉시 수락하여야 합니다. 다만, 회원은 해지의사를 통지하기 전에 모든 진행중인 절차를 완료, 철회 또는 취소해야만 합니다. 이 경우 철회 또는 취소로 인한 불이익은 회원 본인이 부담하여야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 회원이 발한 의사표시로 인해 발생한 불이익에 대한 책임은 회원 본인이 부담하여야 하며, 이용계약이 종료되면 회사는 회원에게 부가적으로 제공한 각종 혜택을 회수할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3) 회원의 의사로 이용계약을 해지한 후, 추후 재이용을 희망할 경우에는 재이용 의사가 회사에 통지되고, 이에 대한 회사의 승낙이 있는 경우에만 서비스 재이용이 가능합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4) 본 항에 따라 해지를 한 회원은 이 약관이 정하는 회원가입절차와 관련조항에 따라 신규 회원으로 다시 가입할 수 있습니다. 다만 회원이 중복참여가 제한된 판촉이벤트 중복 참여 등 부정한 목적으로 회원탈퇴 후 재이용을 신청하는 경우 회사는 가입을 일정기간 동안 제한할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5) 본 항에 따라 해지를 한 이후에는 재가입이 불가능하며, 모든 가입은 신규가입으로 처리됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사의 해지<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 회사는 다음과 같은 사유가 발생하거나 확인된 경우 이용계약을 해지할 수 있습니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t①다른 회원의 권리나 명예, 신용 기타 정당한 이익을 침해하거나 대한민국 법령 또는 공서양속에 위배되는 행위를 한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t②회사가 제공하는 서비스의 원활한 진행을 방해하는 행위를 하거나 시도한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t③제 8조 제 2항의 승낙거부 사유가 추후 발견된 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t④회사가 정한 서비스 운영정책을 이행하지 않거나 위반한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t⑤기타 회사가 합리적인 판단에 기하여 서비스의 제공을 거부할 필요가 있다고 인정할 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 회사가 해지를 하는 경우 회사는 회원에게 이메일, 전화, 기타의 방법을 통하여 해지 사유를 밝혀 해지 의사를 통지합니다. 이용계약은 회사의 해지의사를 회원에게 통지한 시점에 종료됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3) 본 항에서 정한 바에 따라 이용계약이 종료될 시에는 회사는 회원에게 부가적으로 제공한 각종혜택을 회수할 수 있습니다. 이용계약의 종료와 관련하여 발생한 손해는 이용계약이 종료된 해당 회원이 책임을 부담하여야 하고, 회사는 일체의 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4) 본 항에서 정한 바에 따라 이용계약이 종료된 경우에는, 회원의 재이용 신청에 대하여 회사는 이에 대한 승낙을 거절할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. “회원”이 계약을 해지하는 경우, 관련법 및 개인정보취급방침에 따라 “회사”가 “회원”정보를 보유하는 경우를 제외하고는 해지 즉시 “회원”의 모든 데이터는 소멸됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제10조 (개인정보보호)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자의 회원가입 시 서비스 제공에 필요한 최소한의 정보를 수집합니다. 다음 사항을 필수사항으로 하며 그 외 사항은 선택사항으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1)이메일주소<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2)비밀번호<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3)휴대폰 번호(부동산 매물등록 서비스 및 신고기능을 이용하는 회원인 경우)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사가 이용자의 개인식별이 가능한 개인정보를 수집하는 때에는 반드시 당해 이용자의 동의를 받습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 제공된 개인정보는 당해 이용자의 동의 없이 목적 외의 이용이나 제3자에게 제공하지 않습니다. 다만, 다음의 경우에는 예외로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 통계작성, 학술연구 또는 시장조사를 위하여 필요한 경우로서 특정 개인을 식별할 수 없는 형태로 제공하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 도용방지를 위하여 본인확인에 필요한 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3) 법률의 규정 또는 법률에 의하여 필요한 불가피한 사유가 있는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 회사가 제2항과 제3항에 의해 이용자의 동의를 받아야 하는 경우에는 개인정보관리 책임자의 신원(소속, 성명 및 전화번호, 기타 연락처), 정보의 수집목적 및 이용목적, 제3자에 대한 정보제공 관련사항(제공받은 자, 제공목적 및 제공할 정보의 내용) 등 정보통신망이용촉진등에관한법률 제22조제2항이 규정한 사항을 미리 명시하거나 고지해야 하며 이용자는 언제든지 이 동의를 철회할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5. 회사는 이용자의 개인정보를 보호하기 위해 “개인정보취급방침”을 수립하고 개인정보보호책임자를 지정하여 이를 게시하고 운영합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t6. 이용자는 언제든지 회사가 갖고 있는 자신의 개인정보에 대해 열람 및 오류정정을 요구할 수 있으며 회사는 이에 대해 지체 없이 필요한 조치를 취할 의무를 집니다. 이용자가 오류의 정정을 요구한 경우에는 회사는 그 오류를 정정할 때까지 당해 개인정보를 이용하지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t7. 회사 또는 그로부터 개인정보를 제공받은 제3자는 개인정보의 수집목적 또는 제공받은 목적을 달성한 때에는 당해 개인정보를 지체 없이 파기합니다. 다만, 아래의 경우에는 회원 정보를 보관합니다. 이 경우 회사는 보관하고 있는 회원 정보를 그 보관의 목적으로만 이용합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1) 상법, 전자상거래 등에서의 소비자보호에 관한 법률 등 관계법령의 규정에 의하여 보존할 필요가 있는 경우 회사는 관계법령에서 정한 일정한 기간 동안 회원 정보를 보관합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2) 회사가 이용계약을 해지하거나 회사로부터 서비스 이용정지조치를 받은 회원에 대해서는 재가입에 대한 승낙거부사유가 존재하는지 여부를 확인하기 위한 목적으로 이용계약종료 후 5년간 아이디, 전화번호를 비롯하여 이용계약 해지와 서비스 이용정지와 관련된 정보 등의 필요정보를 보관합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t8. 회사는 새로운 업체가 제휴사 또는 제휴영업점의 지위를 취득할 경우 제7조 2항에서 정한 것과 같은 방법을 통하여 공지합니다. 이 때 회원이 별도의 이의제기를 하지 않을 경우 직방 서비스 제공이라는 필수적인 목적을 위해 해당 개인 정보 제공 및 활용에 동의한 것으로 간주 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t9. 모든 이용자는 직방으로부터 제공받은 정보를 다른 목적으로 이용하거나 타인에게 유출 또는 제공해서는 안되며, 위반 사용으로 인한 모든 책임을 부담해야 합니다. 또한 회원은 자신의 개인정보를 책임 있게 관리하여 타인이 회원의 개인정보를 부정하게 이용하지 않도록 해야 합니다<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h2>제 3장 서비스의 이용</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제11조 (부동산 매물 등에 관한 정보제공 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 부동산 매물 등에 관한 정보제공 서비스는 회사가 이용자 스스로 해당 정보를 확인 및 이용할 수 있도록 관련 정보를 제공하는 것입니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션 내에서 제공하는 모든 정보에 대해서 정확성이나 신뢰성이 있는 정보를 제공하기 위해 노력하지만, 그 과정에서 발생할 수 있는 정보의 정확성이나 신뢰성에 대해서는 어떠한 보증도 하지 않으며, 정보의 오류로 인해 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션을 통해 제공되는 정보의 내용을 수정할 의무를 지지 않으나, 필요에 따라 개선할 수는 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제12조 (부동산 매물 등록 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 부동산 매물 등록 서비스는 회원이 매물정보(부동산 거래정보 및 거래 물건에 대한 다양한 부가정보)와 회원 연락처(회원의 이메일 주소 및 휴대폰 번호)를 직접 직방 인터넷 사이트 및 모바일 어플리케이션에 등록하여 이용자에게 노출할 수 있는 것을 말합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 회원이 등록한 매물정보의 노출순서 및 영역의 추가 등에 대한 결정 권한을 갖고 있습니다. 또한, 회사는 사전통지 없이 회원의 매물정보 등을 직방 인터넷 사이트 및 모바일 어플리케이션 이외의 다른 인터넷 사이트 등에 노출할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 회원이 등록한 매물정보에 대해 등록 후 24시간 이내에 해당 매물정보의 진위 여부를 확인하며, 진위 여부 확인 즉시 해당 매물을 노출합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 회원이 등록한 매물정보가 실제 매물정보와 불일치 하는 경우 회사는 회원이 가입시 제공한 전화번호 또는 이메일을 통해 회원에게 매물정보의 수정을 요청합니다. 회사가 회원이 제공한 연락수단으로 2회 이상 연락하였음에도 불구하고 연락이 되지 않을 경우의 책임은 “회원”에게 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5. 전항에 따른 회사의 정당한 매물정보 수정 요청에도 불구하고 회원이 24시간 이내에 매물정보(거래완료 혹은 노출종료와 같은 매물상태 변경 포함)를 수정하지 않을 경우, 회사는 해당 매물정보의 노출을 중지하고 회원의 서비스 이용을 제한 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t6. 회사는 직방에 등록된 매물 중 사회통념, 관례 및 회사의 합리적인 판단에 의하여 거래가 부적합하다고 판단되는 경우 이의 삭제를 요청하거나 직권으로 삭제할 수 있으며 해당 회원의 서비스 이용을 정지 혹은 탈퇴시킬 수 있습니다. 직방에 거래부적합 부동산 매물을 등록할 경우, 거래부적합 매물에 대한 법적인 책임은 해당 등록자에게 있습니다\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제13조 (부동산 중개업소 추천 등 기타 관련 서비스)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자가 원하는 경우 이용자의 편의를 위해 부동산 중개업소를 이용자에게 추천할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자가 회사가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 이용자 스스로의 판단에 따라 결정하는 것으로 회사는 이용자가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제14조 (정보의 제공 및 광고의 게재)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 회원이 서비스 이용 중 필요하다고 인정되는 다양한 정보를 서비스 내 공지사항, 서비스 화면, 전자우편 등의 방법으로 회원에게 제공할 수 있습니다. 다만, 회원은 관련법에 따른 거래관련 정보 및 고객문의 등에 대한 답변 등을 제외하고는 언제든지 위 정보제공에 대해서 수신 거절을 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 서비스의 운영과 관련하여 회사가 제공하는 서비스의 화면 및 홈페이지 등에 광고를 게재할 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h2>제 4장 책임</h2>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제15조 (회사의 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 법령과 이 약관이 금지하거나 공서양속에 반하는 행위를 하지 않으며 이 약관이 정하는 바에 따라 지속적이고, 안정적으로 이용자에게 서비스를 제공하기 위해 최선을 다합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 이용자 상호간의 거래에 있어서 어떠한 보증도 제공하지 않습니다. 이용자 상호간 거래 행위에서 발생하는 문제 및 손실에 대해서 회사는 일체의 책임을 부담하지 않으며, 거래당사자간에 직접 해결해야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제16조 (회원의 아이디 및 비밀번호에 대한 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 아이디와 비밀번호에 관한 관리책임은 회원에게 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회원은 자신의 아이디 및 비밀번호를 제3자에게 이용하게 해서는 안됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회원이 자신의 아이디 및 비밀번호를 도난당하거나 제3자가 사용하고 있음을 인지한 경우에는 바로 회사에 통보하고 회사의 안내가 있는 경우에는 그에 따라야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제17조 (이용자의 의무)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 이용자는 다음 각호의 행위를 하여서는 안됩니다. 만약 다음 각호와 같은 행위가 확인되면 회사는 해당 이용자에게 서비스 이용에 대한 제재를 가할 수 있으며 민형사상의 책임을 물을 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(1)회사 서비스의 운영을 고의 및 과실로 방해하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(2)신청 또는 변경 시 허위 내용의 등록<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(3)타인의 정보 도용<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(4)허위 매물 정보의 등록<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(5)회사가 정한 정보 이외의 정보(컴퓨터 프로그램 등) 등의 송신 또는 게시<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(6)회사 및 기타 제3자의 저작권 등 지적재산권에 대한 침해<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(7)회사 및 기타 제3자의 명예를 손상시키거나 업무를 방해하는 행위<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(8)외설 또는 폭력적인 메시지, 화상, 음성, 기타 공서양속에 반하는 정보를 직방에 공개 또는 게시하는 행위<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(9)사기 및 악성 글 등록 등 건전한 거래 문화 활성에 방해되는 행동<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t(10)기타 중대한 사유로 인하여 회사가 서비스 제공을 지속하는 것이 부적당하다고 인정하는 경우<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 전항의 규정에 의하여 서비스의 이용을 제한하거나 중지할 수 있는 모든 권한을 갖고 있습니다. 회사는 회사 정책에 위반한 행동을 하는 특정 회원의 ID를 삭제할 수 있고, 이용 중지 등의 모든 서비스 제한 조치를 이용자에게 통보 없이 직권으로 할 수 있습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 회사의 정책에 따라서 회원 간의 차별화된 유료 서비스를 언제든지 제공할 수 있습니다. 만약 회원이 비용을 지불하지 않고 사용을 할 경우 회사는 특정 회원에게 서비스 중지 및 특정 서비스 제한을 할 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제18조 (저작권의 귀속 및 이용제한)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 서비스에 대한 저작권 및 지적재산권은 회사에 귀속됩니다. 단, 회원이 직방을 이용하여 작성한 저작물에 대한 저작권은 해당 회원에게 귀속됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자는 서비스를 이용함으로써 얻은 정보 중 회사에게 지적재산권이 귀속된 정보를 회사의 사전 승낙 없이 복제, 송신, 출판, 배포, 방송 기타 방법에 의하여 영리목적으로 이용하거나 제3자에게 이용하게 하여서는 안됩니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제19조 (책임의 한계 등)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 무료로 제공하는 정보 및 서비스에 관하여 개인정보취급방침 또는 관계법령의 벌칙, 과태료 규정 등 강행규정에 위배되지 않는 한 원칙적으로 손해를 배상할 책임이 없습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사는 천재지변, 불가항력, 서비스용 설비의 보수, 교체, 점검, 공사 등 기타 이에 준하는 사항으로 정보 및 서비스를 제공할 수 없는 경우에 이에 대한 책임이 면제됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회사는 이용자의 귀책사유로 인한 정보 및 서비스 이용의 장애에 관한 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t4. 회사는 회원이 게재한 정보, 자료, 사실의 신뢰도, 정확성 등의 내용에 관하여는 책임을 지지 않습니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t5. “서비스”에서 제공하는 정보에 대한 신뢰 여부는 전적으로 “이용자” 본인의 책임이며, “회사”는 매물정보를 등록한 “회원”에 의한 사기, 연락 불능 등으로 인하여 발생하는 어떠한 직접, 간접, 부수적, 파생적, 징벌적 손해, 손실, 상해 등에 대하여 도덕적, 법적 책임을 부담하지 않습니다. 또한 “서비스”를 통하여 노출, 배포, 전송되는 정보를 “이용자”가 이용하여 발생하는 부동산 거래 등에 대하여 “회사”는 어떠한 도덕적, 법적 책임이나 의무도 부담하지 아니합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t6. “이용자”가 “회사”가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 “이용자” 스스로의 판단에 따라 결정하는 것으로 “회사”는 “이용자”가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제20조 (손해배상 등)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 회원이 서비스를 이용함에 있어 회사의 고의 또는 과실로 인해 손해가 발생한 경우에는 민법 등 관련 법령이 규율하는 범위 내에서 그 손해를 배상합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 회사에 손해가 발생한 경우에는 회사에 그 손해를 배상하여야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t3. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 제3자가 회사를 상대로 민형사상의 법적 조치를 취하는 경우에는 회원은 자신의 비용과 책임으로 회사를 면책시켜야 하며, 이로 인해 발생하는 손해에 대해 배상하여야 합니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제21조 (분쟁해결)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사는 이용자 상호간 분쟁에서 발생하는 문제에 대해서 일체의 책임을 지지 않습니다. 이용자 상호간 분쟁은 당사자간에 직접 해결해야 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 이용자 상호간에 서비스 이용과 관련하여 발생한 분쟁에 대해 이용자의 피해구제신청이 있는 경우에는 공정거래위원회 또는 시·도지사가 의뢰하는 분쟁조정기관의 조정에 따를 수 있습니다.\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t\\t<h3>제22조 (재판권 및 준거법)</h3>\\r\\n\\t\\t\\t\\t\\t\\t\\t<p class=\"txt\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t1. 회사와 회원간 제기된 소송은 대한민국법을 준거법으로 합니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t2. 회사와 회원간 발생한 분쟁에 관한 소송은 민사소송법 상의 관할법원에 제소합니다.<br /><br />\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t부칙<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t제1조 (적용일자)<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t이 약관은 2013년 11월 1일부터 적용됩니다.<br />\\r\\n\\t\\t\\t\\t\\t\\t\\t</p>\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"item\">\\r\\n\\t\\t\\t\\t\\t<h4>&middot; 개인정보 수집 및 이용에 대한 동의 (필수)</h4>\\r\\n\\t\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree2\" /> 동의 합니다</label>\\r\\n\\t\\t\\t\\t\\t<div class=\"i-box\" style=\"height:220px\">\\r\\n\\t\\t\\t\\t\\t\\t<strong>휴대전화 인증 가입 회원</strong><br /><br />\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t개인정보의 수집 및 이용에 대한 안내<br />\\r\\n\\t\\t\\t\\t\\t\\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이 개인정보를\\r\\n\\t\\t\\t\\t\\t\\t수집합니다. 정보주체는본 개인 정보의 수집 및이용에 관한 동의를 거부하실\\r\\n\\t\\t\\t\\t\\t\\t권리가있으나, 서비스 제공에 필요한 최소한의 개인정보이므로 동의를\\r\\n\\t\\t\\t\\t\\t\\t해주셔야 서비스를 이용하실 수 있습니다.<br /> \\r\\n\\r\\n\\t\\t\\t\\t\\t\\t• 수집하려는 개인 정보 항목: 성명, 이메일, 비밀번호, 휴대전화 번호<br />\\r\\n\\t\\t\\t\\t\\t\\t• 개인정보의 수집 목적: 회원제 서비스 이용, 개인식별, 가입의사 확인,\\r\\n\\t\\t\\t\\t\\t\\t고지사항 전달, 가입 및 가입횟수 제한 불만처리 등 민원 처리, 방내놓기\\r\\n\\t\\t\\t\\t\\t\\t서비스 이용<br />\\r\\n\\t\\t\\t\\t\\t\\t• 개인정보의 보유기간: 회원 탈퇴 후 5년까지\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"item-right\">\\r\\n\\t\\t\\t\\t<form>\\r\\n\\t\\t\\t\\t\\t<h4>&middot; 정보입력</h4>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-box mb-5\">\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">이름:</span>\\r\\n\\t\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" name=\"username\" maxlength=\"4\" placeholder=\"실명을 입력하세요\" />\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">이메일 : </span>\\r\\n\\t\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" name=\"email\" placeholder=\"zigbang@zigbang.com\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호 : </span>\\r\\n\\t\\t\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" name=\"password\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호 확인 :</span>\\r\\n\\t\\t\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" name=\"password1\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"phone-auth\">\\r\\n\\t\\t\\t\\t\\t\\t<h4 class=\"mb-5\">핸드폰인증</h4>\\r\\n\\t\\t\\t\\t\\t\\t<div class=\"mb-10\">\\r\\n\\t\\t\\t\\t\\t\\t\\t<select name=\"phone1\">\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"010\" selected>010</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"011\">011</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"016\">016</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"017\">017</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"018\">018</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t<option value=\"019\">019</option>\\r\\n\\t\\t\\t\\t\\t\\t\\t</select>\\r\\n\\t\\t\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" name=\"phone2\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" name=\"phone3\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t\\t\\t<button type=\"button\" class=\"sendAuthNum\">인증번호 발송</button>\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t\\t<div>\\r\\n\\t\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">인증번호 입력:</span>\\r\\n\\t\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" name=\"authNum\" style=\"width:125px;\" />\\r\\n\\t\\t\\t\\t\\t\\t\\t<button type=\"button\" class=\"sendAuthNum\">재발송</button>\\r\\n\\t\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok\">가입완료</button>\\r\\n\\t\\t\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-cancel\">취 소</button>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t</form>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"layer-other-links\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"show-login\">기존회원 로그인</button> &nbsp; | &nbsp; <a href=\"https://www.zigbang.com/home/registerinfo\" class=\"bold fc-red1\">중개사무소 가입신청</a>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- step3 -->\\r\\n<script id=\"layer-join3-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">추가 정보 입력</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f13\">\\r\\n\\t\\t\\t\\t<strong>사용자 정보가 확인된 회원에게만 제공되는 기능입니다<br /> 회원정보를 입력해주세요</strong>\\r\\n\\t\\t\\t</p>\\r\\n\\r\\n\\t\\t\\t<div class=\"form-box mb-5\">\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">이메일 : </span>\\r\\n\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">이름:</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" placeholder=\"실명을 입력하세요\" />\\r\\n\\t\\t\\t\\t\\t<p>이메일이 입력되지 않았습니다.</p>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호 : </span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t<p>가입된 이메일이 아닙니다. 정확히 입력하셨는지 확인해주세요.</p>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호 확인 :</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\\r\\n\\t\\t\\t\\t\\t<p>가입된 이메일이 아닙니다. 정확히 입력하셨는지 확인해주세요.</p>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"phone-auth\">\\r\\n\\t\\t\\t\\t<h4 class=\"mb-5\">핸드폰인증</h4>\\r\\n\\t\\t\\t\\t<div class=\"mb-5\">\\r\\n\\t\\t\\t\\t\\t<select>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"010\" selected>010</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"011\">011</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"016\">016</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"017\">017</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"018\">018</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"019\">019</option>\\r\\n\\t\\t\\t\\t\\t</select>\\r\\n\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t<button type=\"button\">인증번호 발송</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div>\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">인증번호 입력:</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" style=\"width:125px;\" />\\r\\n\\t\\t\\t\\t\\t<button type=\"button\">재발송</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"align-left\">\\r\\n\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree\" checked /> 이용약관 동의</label>\\r\\n\\t\\t\\t\\t<a href=\"//s.zigbang.com/agree/user-agreement-last.html\" class=\"bold\" target=\"_blank\">[보기]</a>\\r\\n\\t\\t\\t\\t<br />\\r\\n\\t\\t\\t\\t<label><input type=\"checkbox\" name=\"agree2\" checked /> 개인정보 취급방침 및 운영 원칙에 동의</label>\\r\\n\\t\\t\\t\\t<a href=\"//s.zigbang.com/agree/user-privacy-last.html\" class=\"bold\" target=\"_blank\">[보기]</a>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n\\r\\n\\r\\n<!-- login -->\\r\\n<script id=\"layer-login-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">로그인</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f18\">가입된 이메일 주소를 입력하세요 </p>\\r\\n\\r\\n\\t\\t\\t<form>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">이메일</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" name=\"username\" placeholder=\"zigbang@zigbang.com\" />\\r\\n\\t\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t\\t<div class=\"mb-20\">\\r\\n <label><input type=\"checkbox\" /> 자동로그인</label>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"mb-20\">\\r\\n\\t\\t\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t\\t\\t<button type=\"button\" class=\"join ml-15\">[회원가입]</button>\\r\\n <button type=\"button\" class=\"find ml-15\">[아이디 찾기]</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</form>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 비번입력 -->\\r\\n<script id=\"layer-pw-set-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">비밀번호</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f18\">비밀번호를 입력하세요</p>\\r\\n\\r\\n\\t\\t\\t<form>\\r\\n\\t\\t\\t\\t<div class=\"form-type1 mb-20\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" />\\r\\n\\t\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t\\t<div class=\"align-center\">\\r\\n\\t\\t\\t\\t\\t<button type=\"button\" class=\"layer-findpassword\">[비밀번호 찾기]</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</form>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 비번찾기 -->\\r\\n<script id=\"layer-pw-find-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">비밀번호 찾기</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f18\">회원가입 시 등록된 이메일로<br /> 임시 비밀번호를 보내드립니다</p>\\r\\n\\r\\n\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t<span class=\"i-tit\">이메일</span>\\r\\n\\t\\t\\t\\t<input type=\"text\" class=\"text\" name=\"username\" placeholder=\"zigbang@zigbang.com\" />\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 이메일찾기 -->\\r\\n<script id=\"layer-email-find-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"member-layer\" style=\"width:372px;display:none;\">\\r\\n <h3 class=\"layer-title\">이메일(아이디) 찾기</h3>\\r\\n <div class=\"layer-body\" style=\"width:330px;\">\\r\\n <p class=\"l-title f18\">가입할 때 인증한 핸드폰 번호를 입력하세요</p>\\r\\n <p class=\"l-title f13\">이메일 찾기 기능은 핸드폰 인증 회원만 가능합니다.<br />이메일만으로 가입한 회원은 찾을 수 없습니다.</p>\\r\\n\\r\\n <div class=\"form-type1\" style=\"width:330px;\">\\r\\n <div class=\"phone-auth\">\\r\\n <h4 class=\"mb-5\">핸드폰인증</h4>\\r\\n <div class=\"mb-10\">\\r\\n <select name=\"phone1\">\\r\\n <option value=\"010\" selected>010</option>\\r\\n <option value=\"011\">011</option>\\r\\n <option value=\"016\">016</option>\\r\\n <option value=\"017\">017</option>\\r\\n <option value=\"018\">018</option>\\r\\n <option value=\"019\">019</option>\\r\\n </select>\\r\\n - <input type=\"text\" class=\"text\" name=\"phone2\" maxlength=\"4\" style=\"width:66px\" />\\r\\n - <input type=\"text\" class=\"text\" name=\"phone3\" maxlength=\"4\" style=\"width:66px\" />\\r\\n <button type=\"button\" class=\"sendAuthNum\">인증번호 발송</button>\\r\\n </div>\\r\\n <div>\\r\\n <span class=\"i-tit1\">인증번호 입력:</span>\\r\\n <input type=\"text\" class=\"text\" name=\"authNum\" style=\"width:125px;\" />\\r\\n <button type=\"button\" class=\"sendAuthNum\">재발송</button>\\r\\n </div>\\r\\n </div>\\r\\n </div>\\r\\n\\r\\n <div class=\"layer-btn\">\\r\\n <button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n </div>\\r\\n </div>\\r\\n <button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n </div>\\r\\n</script>\\r\\n<!-- 이메일찾기 결과 -->\\r\\n<script id=\"layer-email-result-template\" type=\"text/x-handlebars-template\">\\r\\n <div class=\"member-layer\" style=\"display:none\">\\r\\n <h3 class=\"layer-title\">이메일(아이디) 확인</h3>\\r\\n <div class=\"layer-body\">\\r\\n <p class=\"l-title f13\">고객님의 정보와 일치하는 이메일(아이디) 입니다.<br />확인 후 로그인 또는 비밀번호 찾기 버튼을 눌러주세요.</p>\\r\\n <form>\\r\\n <div class=\"email-results align-center mb-10\">\\r\\n \\r\\n </div>\\r\\n\\r\\n <div class=\"layer-btn\">\\r\\n <button type=\"button\" class=\"btn btn-ok login\">로그인</button>\\r\\n <button type=\"button\" class=\"btn btn-ok pw\">비밀번호 찾기</button>\\r\\n </div>\\r\\n </form>\\r\\n </div>\\r\\n <button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n </div>\\r\\n</script>\\r\\n<!-- 비번변경 -->\\r\\n<script id=\"layer-pw-change-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">비밀번호 변경</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<div class=\"form-box\">\\r\\n\\t\\t\\t\\t<div class=\"form-type1 border\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">현재 비밀번호 : </span>\\r\\n\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" placeholder=\"현재 비밀번호를 입력하세요\" />\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">새로운 비밀번호 : </span>\\r\\n\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" onkeydown=\"return number_validate(event);\"/>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">비밀번호 확인 : </span>\\r\\n\\t\\t\\t\\t\\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" onkeydown=\"return number_validate(event);\"/>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 이름 변경 -->\\r\\n<script id=\"layer-name-change-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">이름 변경</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f18\">변경할 이름을 입력해주세요</p>\\r\\n\\r\\n\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t<span class=\"i-tit\">이름</span>\\r\\n\\t\\t\\t\\t<input type=\"text\" class=\"text\" placeholder=\"실명을 입력해주세요\" />\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 핸드폰 변경 -->\\r\\n<script id=\"layer-phone-change-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">핸드폰 변경</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title f18\">변경할 핸드폰 번호를 입력해주세요</p>\\r\\n\\t\\t\\t<div class=\"phone-auth\">\\r\\n\\t\\t\\t\\t<div class=\"mb-10\">\\r\\n\\t\\t\\t\\t\\t<select>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"010\" selected>010</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"011\">011</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"016\">016</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"017\">017</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"018\">018</option>\\r\\n\\t\\t\\t\\t\\t\\t<option value=\"019\">019</option>\\r\\n\\t\\t\\t\\t\\t</select>\\r\\n\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\\r\\n\\t\\t\\t\\t\\t<button type=\"button\">인증번호 발송</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div>\\r\\n\\t\\t\\t\\t\\t<span class=\"i-tit\">인증번호 입력:</span>\\r\\n\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" style=\"width:125px;\" />\\r\\n\\t\\t\\t\\t\\t<button type=\"button\">재발송</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 공지 레이어 -->\\r\\n<script id=\"layer-notice-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div id=\"newNoticePopup\">\\r\\n\\t\\t<h3></h3>\\r\\n\\t\\t<div class=\"item_body\"><img src=\"\" alt=\"\" /></div>\\r\\n\\t\\t<div class=\"item_check\">\\r\\n\\t\\t\\t<label><input type=\"checkbox\" class=\"i_check\" /> 오늘 하루 더 이상 보지 않습니다.</label>\\r\\n\\t\\t\\t<button type=\"button\" onclick=\"hideNoticePopup();\">[닫기]</button>\\r\\n\\t\\t\\t<a href=\"#\" target=\"_blank\" class=\"zb_btn btn_orange item_link\">자세히보기</a>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" onclick=\"hideNoticePopup();\" class=\"item_close\"><img src=\"//s.zigbang.com/legacy/images/v2/common/close.gif\" alt=\"\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 다운로드 문자 메세지 팝업-->\\r\\n<script id=\"layer-sms-down-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"smsDownLayer\" id=\"smsDownLayer\">\\r\\n\\t\\t<div><img src=\"//s.zigbang.com/v1/web/common/img_msg_1.jpg\" alt=\"\" /></div>\\r\\n\\t\\t<div class=\"sms_info\">\\r\\n\\t\\t\\t<input type=\"text\" class=\"text getPhoneNo\" id=\"app_down_phone\" placeholder=\"휴대폰 번호를 입력하시면 다운로드 주소가 발송됩니다.\" />\\r\\n\\t\\t\\t<button type=\"button\" id=\"btnAppDown\"><img src=\"//s.zigbang.com/legacy/images/btn_ok.png\" alt=\"\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t\\t<div class=\"sms_agree\">\\r\\n\\t\\t\\t<h4>· 개인정보 수집 및 이용에 대한 동의(필수)</h4>\\r\\n\\t\\t\\t<label><input type=\"checkbox\" id=\"sms_check\" /> 동의 합니다.</label>\\r\\n\\t\\t\\t<div>\\r\\n\\t\\t\\t\\t<strong>앱 다운로드</strong><br /><br />\\r\\n\\t\\t\\t\\t개인정보의 수집 및 이용에 대한 안내<br />\\r\\n\\t\\t\\t\\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이 개인정보를 수집합니다. 정보주체는 본 개인정보의 수집 및 이용에 관한 동의를 거부하실권리가 있으나, 서비스제공에 필요한 최소한의 개인 정보 이므로 동의를 해주셔야 서비스를 이용하실 수 있습니다.<br /><br />\\r\\n\\t\\t\\t\\t• 수집하려는 개인 정보 항목: 핸드폰번호<br />\\r\\n\\t\\t\\t\\t• 개인정보의 수집 목적: 앱 다운로드 제공<br />\\r\\n\\t\\t\\t\\t• 개인정보의 보유기간: 사용 후 바로삭제\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"smsLayerClose\"><img src=\"//s.zigbang.com/legacy/images/btn_close.png\" alt=\"\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 일반/클린회원 공지 레이어 -->\\r\\n<script id=\"layer-agent-info-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\" id=\"agent_type1\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">일반회원이란?</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<div class=\"mb-10\">\\r\\n\\t\\t\\t\\t<p class=\"mb-5\"><strong>중개사님은 현재 일반회원입니다. <br />최초로 유료상품을 구매하면 일반회원으로 가입됩니다. 유료이용시작일이 속한 월을 포함해 3개월 동안 중단 없이 유료광고 상품을 이용하고 해당 기간 동안 “허위매물과 관련된 주의 조치”를 받지 않는 경우에는 클린회원으로 전환 가능합니다. </strong></p>\\r\\n\\t\\t\\t\\t<span style=\"font-size:11px\">(단, 특정 상황의 경우 유료이용시작일부터 클린회원으로 전환이 가능합니다)</span>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"txt-box1 mb-10\">\\r\\n\\t\\t\\t\\t<div class=\"mb-5\">일반회원의 경우</div>\\r\\n\\t\\t\\t\\t▶ ‘일반 방 목록’에 매물이 노출<br />\\r\\n\\t\\t\\t\\t▶ 추후 제공될 부가 서비스의 이용이 가능\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"txt-box1\">\\r\\n\\t\\t\\t\\t<div class=\"mb-5\">클린회원의 경우</div>\\r\\n\\t\\t\\t\\t▶ “클린 방 목록”에 매물이 노출 <br />\\r\\n\\t\\t\\t\\t▶ 추후 제공될 클린회원 전용 부가 서비스를 이용 가능<br />\\r\\n\\t\\t\\t\\t▶ 상단에 노출되는 VIP상품(지하철역, 단지형)을 구매 가능\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<a href=\"http://blog.naver.com/zigbangcs/220296997823\" target=\"_blank\" class=\"btn btn-ok\"><b>상세내용 보기</b></a>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n\\t<div class=\"member-layer\" id=\"agent_type2\" style=\"display:none\">\\r\\n\\t\\t<h3 class=\"layer-title\">클린회원이란?</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\r\\n\\t\\t\\t<div class=\"mb-10\">\\r\\n\\t\\t\\t\\t<p class=\"mb-5\"><strong>중개사님은 현재 클린회원입니다.<br /> 최초로 유료상품을 구매하면 일반회원으로 가입됩니다. 유료이용시작일이 속한 월을 포함해 3개월 동안 중단 없이 유료광고 상품을 이용하고 해당 기간 동안 “허위매물과 관련된 주의 조치”를 받지 않는 경우에는 클린회원으로 전환 가능합니다. </strong></p>\\r\\n\\t\\t\\t\\t<span style=\"font-size:11px\">(단, 특정 상황의 경우 유료이용시작일부터 클린회원으로 전환이 가능합니다)</span>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"txt-box1 mb-10\">\\r\\n\\t\\t\\t\\t<div class=\"mb-5\">일반회원의 경우</div>\\r\\n\\t\\t\\t\\t▶ ‘일반 방 목록’에 매물이 노출<br />\\r\\n\\t\\t\\t\\t▶ 추후 제공될 부가 서비스의 이용이 가능\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"txt-box1\">\\r\\n\\t\\t\\t\\t<div class=\"mb-5\">클린회원의 경우</div>\\r\\n\\t\\t\\t\\t▶ “클린 방 목록”에 매물이 노출 <br />\\r\\n\\t\\t\\t\\t▶ 추후 제공될 클린회원 전용 부가 서비스를 이용 가능<br />\\r\\n\\t\\t\\t\\t▶ 상단에 노출되는 VIP상품(지하철역, 단지형)을 구매 가능\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<a href=\"http://blog.naver.com/zigbangcs/220296997823\" target=\"_blank\" class=\"btn btn-ok\"><b>상세내용 보기</b></a>\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n</script>\\r\\n<!-- 공인중개사 회원가입 -->\\r\\n<script id=\"layer-agent-register-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"member-layer\">\\r\\n\\t\\t<h3 class=\"layer-title\">중개사무소 가입 및 광고문의</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p class=\"l-title fs-12\">\\r\\n\\t\\t\\t\\t대표공인중개사만 회원가입이 가능합니다.<br />소속공인중개사 및 중개보조원은 회원가입 후 추가 가능합니다.\\r\\n\\t\\t\\t</p>\\r\\n\\t\\t\\t<form>\\r\\n\\t\\t\\t\\t<div class=\"form-box mb-5\">\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">중개사무소명:</span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"agent_name\" placeholder=\" \" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">대표자명 : </span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"req_user\" placeholder=\"\" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">사무소 주소 : </span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"address\" placeholder=\"상세주소(번지)까지 입력하세요\" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">사무소 유선번호 :</span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"agent_phone\" placeholder=\" \" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">핸드폰 번호 :</span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"tel\" placeholder=\"\" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">대표자 이메일 :</span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"email\" placeholder=\"아이디로 이용되니 정확히 입력하세요\" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t\\t<div class=\"form-type1\">\\r\\n\\t\\t\\t\\t\\t\\t<span class=\"i-tit\">팩스번호 :</span>\\r\\n\\t\\t\\t\\t\\t\\t<input type=\"text\" class=\"text\" id=\"fax\" placeholder=\"\" />\\r\\n\\t\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t\\t<div class=\"align-center\"><b>신청을 하면 직방 담당자가 확인하여 연락드립니다</b></div>\\r\\n\\t\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok ladda-button\" data-style=\"zoom-in\">문의하기</button>\\r\\n\\t\\t\\t\\t</div>\\r\\n\\t\\t\\t</form>\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n</script>\\r\\n\\r\\n\\r\\n<!-- alert 레이어 -->\\r\\n<script id=\"layer-alert-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"alert-layer\">\\r\\n\\t\\t<h3 class=\"layer-title\">{{title}}</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p>\\r\\n\\t\\t\\t\\t{{text}}\\r\\n\\t\\t\\t</p>\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok\">확인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n</script>\\r\\n\\r\\n<!-- confirm 레이어 -->\\r\\n<script id=\"layer-confirm-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"confirm-layer\">\\r\\n\\t\\t<h3 class=\"layer-title\">{{title}}</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<p>\\r\\n\\t\\t\\t\\t{{text}}\\r\\n\\t\\t\\t</p>\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-ok\" id=\"btn_confirm\">확인</button>\\r\\n\\t\\t\\t\\t<button type=\"button\" class=\"btn btn-cancel\" id=\"btn_cancel\">취소</button>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n</script>\\r\\n\\r\\n<script id=\"layer-main-notice-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"main-notice-layer\" data-id=\"{{id}}\">\\r\\n\\t\\t<h3>{{title}}</h3>\\r\\n\\t\\t<div class=\"item_body\"><img src=\"{{img_url}}\" alt=\"\" /></div>\\r\\n\\t\\t<div class=\"item_check\">\\r\\n\\t\\t\\t<label><input type=\"checkbox\" class=\"i_check\" /> 오늘 하루 더 이상 보지 않습니다.</label>\\r\\n\\t\\t\\t<button type=\"button\" class=\"i-hide\">[닫기]</button>\\r\\n\\t\\t\\t<a href=\"{{link_url}}\" target=\"_blank\" class=\"btn btn-orange item_link\">자세히보기</a>\\r\\n\\t\\t</div>\\r\\n\\t\\t<button type=\"button\" class=\"item_close i-hide\"><img src=\"//s.zigbang.com/legacy/images/v2/common/close.gif\" alt=\"\" /></button>\\r\\n\\t</div>\\r\\n</script>\\r\\n\\r\\n\\r\\n<script id=\"layer-sleep-member-template\" type=\"text/x-handlebars-template\">\\r\\n\\t<div class=\"basic-layer\" id=\"sleep-layer\">\\r\\n\\t\\t<h3 class=\"layer-title\">정보 통신망 이용촉진 및 정보보 등에 관한 법률 및 동법 시행령</h3>\\r\\n\\t\\t<div class=\"layer-body\">\\r\\n\\t\\t\\t<div class=\"mb-15\">\\r\\n\\t\\t\\t\\t(주)직방은 정보통신망 이용촉진 및 정보보호에 관한 법률 및 동법 시행령에 따라 1년 동안 저희 서비스에 로그인 하지 않은 고객님의 개인정보를 분리, 보관(휴면계정처리) 하고자 합니다. 휴면을 원하지 않으시면 직방에 로그인 해주시기 바랍니다. 감사합니다.\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"gray-box\">\\r\\n\\t\\t\\t\\t[정보통신망 이용촉진 및 정보보호등에 관한 법률 제29조(개인정보의 파기)] 정보통신서비스 제공자 등은 정보통신서비스를 대통령령으로 정하는 기간 동안 이용하지 아니하는 이용자의 개인정보를 보호하기 위하여 대통령령으로 정하는 바에 따라 개인정보의 파기 등 필요한 조치를 취하여야 한다. <br /><br />[정보통신망 이용촉진 및 정보보호 등에 관한 법률 시행령 제16조(개인정보의 파기)] ①법 제 29조 제2항에서 “대통령령으로 정하는 기간” 이란 1년을 말한다. 다만, 각 호의 경우에는 해당 호에 따른 기간으로 한다. ②정보통신서비스 제공자 등은 제1항의 기간 만료30일 전까지 개인정보가 파기되거나 분리되어 저장•관리되는 사실과 기간 만료일 및 해당 개인정보의 항목을 전자우편•서면•모사전송 또는 이와 유사한 방법 중 어느 하나의 방법으로 이용자에게 알려야 한다.\\r\\n\\t\\t\\t</div>\\r\\n\\t\\t\\t<div class=\"layer-btn\">\\r\\n\\t\\t\\t\\t<button type=\"submit\" class=\"btn btn-ok\">확인</button>\\r\\n\\t\\t\\t</div>\\r\\n\\r\\n\\t\\t\\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\\r\\n\\t\\t</div>\\r\\n\\t</div>\\r\\n</script>\\r\\n \\r\\n\\r\\n <script type=\"text/javascript\">\\r\\n/* <![CDATA[ */\\r\\nvar google_conversion_id = 973803265;\\r\\nvar google_custom_params = 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href=\"//s.zigbang.com/js/cdnjs-jqueryui/1.11.2/jquery-ui.min.css\" rel=\"stylesheet\"/>\n", "<script src=\"//s.zigbang.com/js/cdnjs-jquery/1.10.2/jquery.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/zbee/zbee.js?636068758171944831\" type=\"text/javascript\"></script>\n", "<!--<script type=\"text/javascript\" src=\"//s.zigbang.com/js/jquery-flexslider/2.3.0/jquery.flexslider-min.js\"></script>-->\n", "<script async=\"true\" src=\"//static.criteo.net/js/ld/ld.js\" type=\"text/javascript\"></script>\n", "<script>\n", " if (BrowserDetect.mobile && location.href.indexOf(\"items1/\") == -1) {\n", " var gateway_param = (location.href.split(\"?\")[1]) ? \"?\" + location.href.split(\"?\")[1] : \"\";\n", " location.href = \"//s.zigbang.com/mobile/gateway/index.html\" + gateway_param;\n", " }\n", " </script>\n", "</link></meta></meta></link></meta></meta></meta></head>\n", "<body>\n", "<script type=\"text/javascript\">\n", " var _gaq = _gaq || [];\n", " _gaq.push(['_setAccount', 'UA-21390692-9']);\n", " _gaq.push(['_setDomainName', 'zigbang.com']);\n", " _gaq.push(['_trackPageview']);\n", "\n", " (function () {\n", " var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;\n", " ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';\n", " var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);\n", " })();\n", "\n", " window.criteo_q = window.criteo_q || [];\n", " window.criteo_q.push(\n", "\t\t\t{ event: \"setAccount\", account: 25544 },\n", "\t\t\t{ event: \"setSiteType\", type: BrowserDetect.mobile ? \"m\" : \"d\" },\n", "\t\t\t{ event: \"viewHome\" }\n", "\t\t);\n", " </script>\n", "<div class=\"wrap\">\n", "<div class=\"header\">\n", "<h1><a href=\"//www.zigbang.com/\"><img alt=\"직방\" src=\"//s.zigbang.com/v1/web/common/new/logo.png\"/></a></h1>\n", "<div class=\"gnb\">\n", "<a class=\"gnb-item-map on\" href=\"/search/map\">전체 방 보기</a> <em>|</em>\n", "<a class=\"gnb-item-officetel \" href=\"/search/officetel\">오피스텔 (도시형 생활주택)</a><em>|</em>\n", "<a href=\"/home/AptInfo\" style=\"position:relative\">아파트단지 <img alt=\"\" src=\"//s.zigbang.com/v1/web/common/ico_new2.png\" style=\"position:absolute; right:0; top:15px; width:30px;\"/></a><em>|</em>\n", "<a href=\"/item/zzim\">찜한방</a><em>|</em>\n", "<a href=\"/my/rooms\">방내놓기</a>\n", "</div>\n", "<div class=\"top_btn \">\n", "<button class=\"i_login\" type=\"button\">로그인</button>/<button class=\"i_join\" type=\"button\">회원가입</button>\n", "<em>|</em>\n", "<a class=\"i_link\" href=\"/home/RegisterInfo\">중개사무소 가입 및 광고문의 &gt;</a>\n", "</div>\n", "</div>\n", "<div class=\"container\">\n", "<div class=\"map-container\">\n", "<div class=\"map-area\">\n", "<div id=\"map\"></div>\n", "<div class=\"map-tab \">\n", "<h4 class=\"tab-title tab-officetel \">단지명으로 찾기</h4>\n", "<div class=\"tab-item tab-officetel \">\n", "<input class=\"text\" placeholder=\"단지명(건물명) 또는 주소(ex:자곡동)로 검색하세요\" type=\"text\"/>\n", "</div>\n", "<h4 class=\"tab-title tab-subway active\">지하철역으로 찾기</h4>\n", "<div class=\"tab-item tab-subway active\">\n", "<input class=\"text\" placeholder=\"지하철역명을 입력해주세요\" type=\"text\"/>\n", "</div>\n", "<h4 class=\"tab-title\">지역으로 찾기</h4>\n", "<div class=\"tab-item\">\n", "<select>\n", "<option>서울시</option>\n", "</select>\n", "<select>\n", "<option>전체</option>\n", "</select>\n", "<select>\n", "<option>전체</option>\n", "</select>\n", "<button class=\"btn-event3 h-30\" type=\"button\"><img alt=\"바로가기\" src=\"//s.zigbang.com/v1/web/search/btn_go.png\"/></button>\n", "</div>\n", "</div>\n", "<div class=\"map-tab-state\"><button type=\"button\"><img alt=\"\" src=\"//s.zigbang.com/v1/web/search/btn_state.png\"/></button></div>\n", "<img alt=\"\" class=\"loading\" id=\"loading\" src=\"//s.zigbang.com/v1/web/search/loading2.gif\"/>\n", "</div>\n", "<div class=\"map-content\" id=\"map-content\">\n", "<!-- search -->\n", "<div class=\"map-search\">\n", "<div class=\"item-deposit\">\n", "<h4>보증금</h4>\n", "<select id=\"deposit_s\" name=\"deposit_s\">\n", "<option value=\"0\">전체</option>\n", "<option value=\"100\">100만</option>\n", "<option value=\"500\">500만</option>\n", "<option value=\"1000\">1,000만</option>\n", "<option value=\"2000\">2,000만</option>\n", "<option value=\"3000\">3,000만</option>\n", "<option value=\"4000\">4,000만</option>\n", "<option value=\"5000\">5,000만</option>\n", "<option value=\"6000\">6,000만</option>\n", "<option value=\"7000\">7,000만</option>\n", "<option value=\"8000\">8,000만</option>\n", "<option value=\"9000\">9,000만</option>\n", "<option value=\"10000;\">10,000만</option>\n", "<option value=\"12000\">12,000만</option>\n", "<option value=\"14000\">14,000만</option>\n", "<option value=\"18000\">18,000만</option>\n", "<option value=\"20000\">20,000만</option>\n", "<option value=\"25000\">25,000만</option>\n", "<option value=\"30000\">30,000만</option>\n", "</select>\n", " ~\n", " <select id=\"deposit_e\" name=\"deposit_e\">\n", "<option value=\"0\">전체</option>\n", "<option value=\"100\">100만</option>\n", "<option value=\"500\">500만</option>\n", "<option value=\"1000\">1,000만</option>\n", "<option value=\"2000\">2,000만</option>\n", "<option value=\"3000\">3,000만</option>\n", "<option value=\"4000\">4,000만</option>\n", "<option value=\"5000\">5,000만</option>\n", "<option value=\"6000\">6,000만</option>\n", "<option value=\"7000\">7,000만</option>\n", "<option value=\"8000\">8,000만</option>\n", "<option value=\"9000\">9,000만</option>\n", "<option value=\"10000;\">10,000만</option>\n", "<option value=\"12000\">12,000만</option>\n", "<option value=\"14000\">14,000만</option>\n", "<option value=\"18000\">18,000만</option>\n", "<option value=\"20000\">20,000만</option>\n", "<option value=\"25000\">25,000만</option>\n", "<option value=\"30000\">30,000만</option>\n", "</select>\n", "</div>\n", "<div class=\"item-deposit\">\n", "<h4>월세</h4>\n", "<select id=\"rent_s\" name=\"rent_s\">\n", "<option value=\"-\">전체</option>\n", "<option value=\"0\">0만</option>\n", "<option value=\"10\">10만</option>\n", "<option value=\"20\">20만</option>\n", "<option value=\"30\">30만</option>\n", "<option value=\"40\">40만</option>\n", "<option value=\"50\">50만</option>\n", "<option value=\"60\">60만</option>\n", "<option value=\"70\">70만</option>\n", "<option value=\"80\">80만</option>\n", "<option value=\"90\">90만</option>\n", "<option value=\"100\">100만</option>\n", "<option value=\"110\">110만</option>\n", "<option value=\"120\">120만</option>\n", "<option value=\"130\">130만</option>\n", "<option value=\"140\">140만</option>\n", "<option value=\"160\">160만</option>\n", "<option value=\"180\">180만</option>\n", "<option value=\"200\">200만</option>\n", "<option value=\"250\">250만</option>\n", "</select>\n", " ~\n", " <select id=\"rent_e\" name=\"rent_e\">\n", "<option value=\"-\">전체</option>\n", "<option value=\"0\">0만</option>\n", "<option value=\"10\">10만</option>\n", "<option value=\"20\">20만</option>\n", "<option value=\"30\">30만</option>\n", "<option value=\"40\">40만</option>\n", "<option value=\"50\">50만</option>\n", "<option value=\"60\">60만</option>\n", "<option value=\"70\">70만</option>\n", "<option value=\"80\">80만</option>\n", "<option value=\"90\">90만</option>\n", "<option value=\"100\">100만</option>\n", "<option value=\"110\">110만</option>\n", "<option value=\"120\">120만</option>\n", "<option value=\"130\">130만</option>\n", "<option value=\"140\">140만</option>\n", "<option value=\"160\">160만</option>\n", "<option value=\"180\">180만</option>\n", "<option value=\"200\">200만</option>\n", "<option value=\"250\">250만</option>\n", "</select>\n", "</div>\n", "<div class=\"item-type\">\n", "<h4>방구조</h4>\n", "<label><input class=\"item_room\" name=\"room\" type=\"checkbox\" value=\"01\"/> 원룸(오픈형)</label>\n", "<label><input class=\"item_room\" name=\"room\" type=\"checkbox\" value=\"02\"/> 원룸(분리형)</label>\n", "<label><input class=\"item_room\" name=\"room\" type=\"checkbox\" value=\"03\"/> 원룸(복층형)</label>\n", "<label><input class=\"item_room\" name=\"room\" type=\"checkbox\" value=\"04\"/> 투룸</label>\n", "<label><input class=\"item_room\" name=\"room\" type=\"checkbox\" value=\"05\"/> 쓰리룸</label>\n", "</div>\n", "</div>\n", "<!-- // search -->\n", "<!-- empty -->\n", "<div class=\"map-item-empty\" id=\"map-item-empty\">\n", "<h4>지도의 표시 영역내에 등록된 방이 아직 없습니다.</h4>\n", "<p>\n", " · 지도를 좀 더 움직여 다른 곳을 탐색해보세요.<br>\n", " · 이 지역의 입주가능한 방을 알고 계시다면 직방에 알려주세요.\n", " </br></p>\n", "</div>\n", "<!-- list -->\n", "<div class=\"map-list\" id=\"map-list\">\n", "<div class=\"map-list-wrap\">\n", "<h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <span></span> <button onclick=\"trustLayerShow()\" type=\"button\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", "<div id=\"vip-map-list\"></div>\n", "<h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> <span></span> <button onclick=\"trustLayerShow()\" type=\"button\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", "<div id=\"premium-special-map-list\"></div>\n", "<h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> <span></span> <button onclick=\"trustLayerShow()\" type=\"button\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", "<div id=\"premium-normal-map-list\"></div>\n", "<h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> <span></span></h4>\n", "<div id=\"zbonly-map-list\"></div>\n", "<h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> <span></span></h4>\n", "<div id=\"normal-map-list\"></div>\n", "</div>\n", "</div>\n", "<!-- // list -->\n", "</div>\n", "</div>\n", "<div class=\"bg-layer\"></div>\n", "<div class=\"trust-layer\">\n", "<img alt=\"\" src=\"//s.zigbang.com/v1/web/rooms/layer_trust.png\"/>\n", "<button type=\"button\">확인</button>\n", "</div>\n", "</div>\n", "<div class=\"footer\">\n", "<div class=\"wrap-950\">\n", "<p class=\"i-links\">\n", "<a href=\"//company.zigbang.com\" target=\"_blank\">회사소개</a> |\n", "\t\t\t\t\t<a href=\"//s.zigbang.com/agree/user-agreement-last.html\" target=\"_blank\">이용약관</a> |\n", "\t\t\t\t\t<a class=\"i_privacy\" href=\"//s.zigbang.com/agree/user-privacy-last.html\" target=\"_blank\">개인정보 취급방침</a> |\n", "\t\t\t\t\t<a href=\"//s.zigbang.com/agree/location-agreement-last.html\" target=\"_blank\">위치기반 서비스 이용약관</a> |\n", "\t\t\t\t\t<a href=\"//ceo.zigbang.com\" target=\"_blank\"><strong>중개사 사이트 바로가기</strong></a>\n", "</p>\n", "<em class=\"icon-footer\"></em>\n", "<div class=\"i-copyright\">\n", "\t\t\t\t\t상호 : (주)직방  |  대표 : 안성우<br/>\n", "\t\t\t\t\t주소 : 서울특별시 종로구 청계천로 85 (관철동 10-2) 삼일빌딩 13층 (우:110-748)  |  팩스 : 02-568-4908<br/>\n", "\t\t\t\t\t사업자등록번호: 120-87-61559  |  통신판매업 신고번호 : 제2015-서울종로-0834호<br/>\n", "\t\t\t\t\t서비스 이용문의 : 1661-8734  |  이메일 : <a href=\"mailto:cs@zigbang.com\">cs@zigbang.com </a>  |  서비스제휴문의 : <a href=\"mailto:partnership@zigbang.com\">partnership@zigbang.com</a>\n", "<p>\n", "<a href=\"https://www.facebook.com/zigbangpage\" target=\"_blank\"><img alt=\"\" src=\"//s.zigbang.com/v1/web/common/ico_sns1.png\"/></a>\n", "<a href=\"http://band.us/#!/band/54930059\" target=\"_blank\"><img alt=\"\" src=\"//s.zigbang.com/v1/web/common/ico_sns2.png\"/></a>\n", "<a href=\"https://www.vingle.net/zigbang\" target=\"_blank\"><img alt=\"\" src=\"//s.zigbang.com/v1/web/common/ico_sns3.png\"/></a>\n", "<a href=\"https://instagram.com/zigbang/\" target=\"_blank\"><img alt=\"\" src=\"//s.zigbang.com/v1/web/common/ico_sns4.png\"/></a>\n", "</p>\n", "<div>Copyright © ZIGBANG. All Rights Reserved.</div>\n", "</div>\n", "</div>\n", "</div>\n", "</div>\n", "<script src=\"//s.zigbang.com/js/cdnjs-jqueryui/1.11.2/jquery-ui.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/js/cdnjs-modernizr/2.8.3/modernizr.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/js/cdnjs-cookies/1.1.0/cookies.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/js/cdnjs-handlebars/3.0.0/handlebars.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/js/cdnjs-jquery-xdomainrequest/1.0.3/jquery.xdomainrequest.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/js/cdnjs-jquery-lazyload/1.9.1/jquery.lazyload.min.js\" type=\"text/javascript\"></script>\n", "<!--[if lt IE 10]>\n", " <script type=\"text/javascript\" src=\"//s.zigbang.com/js/jquery-placeholder/2.1.0/jquery.placeholder.js\"></script>\n", " <![endif]-->\n", "<script src=\"//s.zigbang.com/js/cdnjs-json3/3.3.2/json3.min.js\" type=\"text/javascript\"></script>\n", "<script src=\"/Content/naver_script.js\" type=\"text/javascript\"></script>\n", "<script type=\"text/javascript\">\n", " var api_host = \"api.zigbang.com\";\n", " var user_type_all = \"\";\n", " </script>\n", "<script src=\"//s.zigbang.com/www2/js/common.min.js?ver=201606081200\" type=\"text/javascript\"></script>\n", "<script src=\"//apis.daum.net/maps/maps3.js?apikey=dedb500c85692ad324afaac2d61ea80a\" type=\"text/javascript\"></script>\n", "<script src=\"//s.zigbang.com/www2/js/map.js?v20160801\" type=\"text/javascript\"></script>\n", "<script>\n", "\t\tfunction trustLayerShow() {\n", "\t\t\t$(\".bg-layer\").show();\n", "\t\t\t$(\".trust-layer\").show();\n", "\t\t\t$(\".trust-layer button\").click(function () {\n", "\t\t\t\t$(\".bg-layer\").hide();\n", "\t\t\t\t$(\".trust-layer\").hide();\n", "\t\t\t});\n", "\t\t}\n", "\t</script>\n", "<script id=\"list-item-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-item {{addClass}}\" data-location=\"{{random_location}}\" data-item-id=\"{{id}}\" onclick=\"window.open('/items1/{{id}}')\">\n", " <div class=\"i-img\"><img src=\"//s.zigbang.com/v2/web/thumbnail.png\" class=\"lazy\" data-original=\"{{img_url}}\" alt=\"\" /><em></em></div>\n", "\n", " <div class=\"i-tit\">\n", " <strong>{{deposit1}}/{{deposit2}}</strong>\n", " <b>{{floor}}</b>\n", " <img src=\"//s.zigbang.com/v1/web/common/blank.png\" class=\"tag-type tag-{{agent}}\" alt=\"\" />\n", " </div>\n", " <p class=\"i-info\">{{{info1}}} <em>{{{info_type}}}</em></p>\n", " <p class=\"i-txt\">{{{info2}}}</p>\n", " </div>\n", "</script>\n", "<script id=\"item-infowindow-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-item\">\n", " <div class=\"i-img\"><img src=\"//d2eznf4yph68t8.cloudfront.net/v1/items/{{id}}/images/1?w=83&h=62\" alt=\"\" /></div>\n", " <div class=\"i-tit\">\n", " <strong>{{deposit1}}/{{deposit2}}</strong>\n", " <b>{{floor}}</b>\n", " <img src=\"//s.zigbang.com/v1/web/common/blank.png\" class=\"tag-type tag-{{agent}}\" alt=\"\" />\n", " </div>\n", " <p class=\"i-info\">{{{info1}}} <em>{{{info_type}}}</em></p>\n", " <p class=\"i-txt\">{{{info2}}}</p>\n", " </div>\n", "</script>\n", "<script id=\"list-item-agent-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-agent\" data-userno=\"{{user_no}}\">\n", " <div class=\"i-img\"><img src=\"//s.zigbang.com/v2/web/agent_default.png\" class=\"lazy\" data-original=\"{{profile_url}}&w=74&h=74\" alt=\"\" /> <em></em></div>\n", "\t\t<div class=\"i-tit\"><em class=\"i_agent\">{{user_agent}}</em><span class=\"i_name\">{{user_name}}</span></div>\n", " <p class=\"i-info\">{{ad_name}} 전문 중개사무소</p>\n", " <p class=\"i-txt\">이 {{type}} 보유 매물 : <em>{{items_count}}</em>개</p>\n", " <button type=\"button\" class=\"i-more\">더보기</button>\n", " </div>\n", "</script>\n", "<script id=\"layer-building-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-layer\" id=\"building-list\">\n", " <div class=\"list-title\">\n", " <h3>{{building_name}} 방 목록 : {{building_count}}개</h3>\n", " <p>{{building_name}}에 등록된 방 정보입니다.</p>\n", " <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/close.png\" alt=\"\" /></button>\n", " </div>\n", " <div class=\"map-list\"> \n", " <div class=\"map-list-wrap\">\n", " <div class=\"map-list-scroll\">\n", " <h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"vip-list\"></div>\n", " <h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> : {{premium_special_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"premium-special-list\"></div>\n", " <h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> : {{premium_normal_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"premium-normal-list\"></div>\n", " <h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> : {{zbonly_count}}개</h4>\n", " <div class=\"zbonly-list\"></div>\n", " <h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> : {{normal_count}}개</h4>\n", " <div class=\"normal-list\"></div>\n", " </div>\n", " </div>\n", " </div>\n", "\n", " <div class=\"list-item-null\">\n", " <img src=\"//s.zigbang.com/v1/web/search/icon_null.gif\" alt=\"\"><h4>{{building_name}}에 매물이 존재하지 않습니다.</h4>\n", " </div>\n", " <!-- // list -->\n", " </div>\n", "</script>\n", "<script id=\"layer-subway-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-layer\" id=\"subway-list\">\n", " <div class=\"list-title\">\n", " <h3>{{subway_name}} 방 목록 : {{subway_count}}개</h3>\n", " <p>{{subway_name}}에서 도보 10분 이내 위치한 방 정보입니다.</p>\n", " <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/close.png\" alt=\"\" /></button>\n", " </div>\n", " <div class=\"map-list\">\n", " <div class=\"map-list-wrap\" id=\"subway-list-wrap\">\n", " <div class=\"map-list-scroll\">\n", " <h4 class=\"list-tit vip\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>VIP 방</strong> <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"vip-list\"></div>\n", " <h4 class=\"list-tit premium_special\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>추천 방</strong> : {{premium_special_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"premium-special-list\"></div>\n", " <h4 class=\"list-tit premium_normal\"><em class=\"txt-premium\">안심중개사</em> 이 지역 <strong>일반 방</strong> : {{premium_normal_count}}개 <button type=\"button\" onclick=\"trustLayerShow()\"><em class=\"zbIcon icon-question\"></em></button></h4>\n", " <div class=\"premium-normal-list\"></div>\n", " <h4 class=\"list-tit tit-new zbonly\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>최신 방</strong> : {{zbonly_count}}개</h4>\n", " <div class=\"zbonly-list\"></div>\n", " <h4 class=\"list-tit tit-normal normal\"><em class=\"zbIcon icon-location\"></em> 이 지역 <strong>일반 방</strong> : {{normal_count}}개</h4>\n", " <div class=\"normal-list\"></div>\n", " </div>\n", " </div>\n", " </div>\n", "\n", " <div class=\"list-item-null\">\n", " <img src=\"//s.zigbang.com/v1/web/search/icon_null.gif\" alt=\"\"><h4>{{subway_name}} 근처에 매물이 존재하지 않습니다.</h4>\n", " </div>\n", " <!-- // list -->\n", " </div>\n", "</script>\n", "<script id=\"layer-agent-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"layer-agent\" id=\"agent-list\">\n", " <div class=\"agent-title\">\n", " {{user_name}}{{#if area}} <span>- {{area}} 전문</span> {{/if}}\n", " <button type=\"button\" class=\"list-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", " </div>\n", " <div class=\"agent-content\">\n", " <div class=\"agent-img\"><img src=\"{{profile_url}}\" id=\"blurImage\" style=\"width: 400px\"></div>\n", " <div class=\"agent-info min\">\n", " <p>{{user_intro}}</p>\n", " <button type=\"button\" class=\"btn-info-more\">[더보기]</button>\n", " </div>\n", "\n", " <div class=\"map-list\">\n", " <h4 class=\"list-tit\">{{area}} 주변 보유 매물 : {{count}}개</h4>\n", "\n", " </div>\n", " </div>\n", " </div>\n", "</script>\n", "<script id=\"officetel-info-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"list-item\">\n", " <div class=\"i-img\"><img src=\"//zigbang.s3-ap-northeast-1.amazonaws.com/public/buildings/{{id}}.jpg\" alt=\"\"></div>\n", " <div class=\"i-tit\">{{name}}</div>\n", " <p class=\"i-info\">{{{text}}}</p>\n", " <p class=\"i-txt\">입주가능한 방 : {{count}}개</p>\n", " </div>\n", "</script>\n", ";\n", "\n", " <!-- step1 -->\n", "<script id=\"layer-join-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer has-agree\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">회원 가입</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<form>\n", "\t\t\t\t<div class=\"i-mail\">\n", "\t\t\t\t\t<span class=\"i-tit type-2\">&middot; 이메일</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" style=\"width:305px;\" name=\"username\" placeholder=\"zigbang@zigbang.com\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t<i>※ 사용하실 아이디를 이메일로 입력해 주세요.</i>\n", "\t\t\t\t</div>\n", "\n", "\t\t\t\t<div class=\"agree-box\">\n", "\t\t\t\t\t<div class=\"item\">\n", "\t\t\t\t\t\t<h4>&middot; 이용약관 (필수)</h4>\n", "\t\t\t\t\t\t<label><input type=\"checkbox\" name=\"agree\" /> 동의 합니다</label>\n", "\t\t\t\t\t\t<div class=\"i-box\">\n", "\t\t\t\t\t\t\t<div class=\"agree_conainer\">\n", "\t\t\t\t\t\t\t\t<h1>직방 서비스 이용약관</h1>\n", "\n", "\t\t\t\t\t\t\t\t<h2>제1장 총칙</h2>\n", "\n", "\t\t\t\t\t\t\t\t<h3>제1조 (목적)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t본 약관은 (주)직방(이하 “회사”라 함)이 운영하는 인터넷 사이트 및 모바일 어플리케이션(이하 “직방”이라 함)에서 제공하는 제반 서비스의 이용과 관련하여 회사와 이용자 및 이용자간의 권리, 의무 및 책임사항, 기타 필요한 사항을 규정함을 목적으로 합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제2조 (정의)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 직방: 회사가 컴퓨터 등 정보통신설비를 이용하여 서비스를 제공할 수 있도록 설정한 가상의 영업장을 말하며, 아울러 인터넷 사이트 및 모바일 어플리케이션을 운영하는 사업자의 의미로도 사용합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 이용자: 직방에 접속하여 본 약관에 따라 회사가 제공하는 서비스를 받는 회원 및 비회원을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회원: 회사에 개인정보를 제공하여 회원등록을 한 자로서, 직방의 정보를 지속적으로 제공받으며, 회사가 제공하는 직방의 서비스를 계속적으로 이용할 수 있는 자를 말합니다. 회사는 서비스의 원활한 제공을 위해 회원의 등급을 회사 내부의 규정에 따라 나눌 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t4. 비회원: 회원으로 가입하지 않고 회사가 제공하는 서비스를 이용하는 자를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t5. 아이디(ID): 회원의 식별과 서비스 이용을 위하여 회원이 설정하고 회사가 승인한 회원 본인의 이메일 주소를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t6. 비밀번호: 회원의 동일성 확인과 회원정보의 보호를 위하여 회원이 설정하고 회사가 승인한 문자나 숫자의 조합을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t7. 서비스: 구현되는 단말기(PC, TV, 휴대형단말기 등의 각종 유무선 장치를 포함)와 상관없이 회원이 이용할 수 있는 직방의 서비스를 의미합니다.<br />\n", "\t\t\t\t\t\t\t\t\t8. 게시판: 그 명칭, 형태, 위치와 관계없이 회원 및 비회원 이용자에게 공개할 목적으로 부호•문자•음성•음향•화상•동영상 등의 정보 (이하 \"게시물\"이라 합니다)를 회원이 게재할 수 있도록 회사가 제공하는 서비스 상의 가상공간을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t9. 별칭: 인터넷사이트에서 아이디와 함께, 또는 아이디를 대신하여 회원을 식별하기 위하여 이용자의 신청과 회사의 승인에 의하여 설정되는 숫자와 문자의 조합을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t10. 운영자: 회사가 제공하는 서비스의 전반적인 관리와 원활한 운영을 위하여 회사에서 선정한 자를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t위 항에서 정의되지 않은 이 약관상의 용어의 의미는 일반적인 거래관행에 의합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제3조 (약관 등의 명시와 설명 및 개정)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 본 약관의 내용을 이용자가 쉽게 알 수 있도록 직방 인터넷 사이트 및 모바일 어플리케이션에 공지합니다. 다만, 약관의 내용은 이용자가 연결화면을 통하여 볼 수 있도록 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 “약관의 규제에 관한 법률”, “정보 통신망 이용 촉진 및 정보보호 등에 관한 법률” 등 관련법을 위배하지 않는 범위에서 본 약관을 개정할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회사가 약관을 개정할 경우에는 적용일자 및 개정사유를 명시하여 이용자가 알기 쉽도록 표시하여 공지합니다.<br />\n", "\t\t\t\t\t\t\t\t\t4. 회사가 약관을 개정할 경우에는 변경된 약관은 공지된 시점부터 그 효력이 발생하며, 이용자는 약관이 변경된 후에도 본 서비스를 계속 이용함으로써 변경 후의 약관에 대해 동의를 한 것으로 간주됩니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제4조 (약관의 해석)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 서비스운영을 위해 별도의 운영정책을 마련하여 운영할 수 있으며, 회사는 직방 인터넷 사이트 및 모바일 어플리케이션에 운영정책을 사전 공지 후 적용합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 본 약관에서 정하지 아니한 사항이나 해석에 대해서는 별도의 운영정책, 관계법령 또는 상관례에 따릅니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제5조 (서비스의 제공 및 변경)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사가 제공하는 서비스는 다음과 같습니다<br />\n", "\t\t\t\t\t\t\t\t\t1) 부동산 매물 등에 관한 정보제공 서비스<br />\n", "\t\t\t\t\t\t\t\t\t2) 부동산 매물 등록 서비스<br />\n", "\t\t\t\t\t\t\t\t\t3) 부동산 중개업소 추천 등 기타 관련 서비스<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사가 제공하는 서비스의 내용을 기술적 사양의 변경 등의 이유로 변경할 경우에는 그 사유를 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제6조 (서비스의 중단)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 컴퓨터 등 정보통신설비의 보수점검, 교체 및 고장, 통신의 두절 등의 사유가 발생한 경우에는 서비스의 제공을 일시적으로 중단할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 사업종목의 전환, 사업의 포기, 업체간의 통합 등의 이유로 서비스를 제공할 수 없게 되는 경우에는 회사는 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제7조 (회원에 대한 통지)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 이메일, 이동전화 단문메시지서비스(SMS), 푸시알림(App push)등으로 회원에게 통지할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 불특정다수 회원에 대한 통지의 경우 공지사항으로 게시함으로써 개별 통지에 갈음할 수 있습니다. 다만, 회원 본인의 거래와 관련하여 중대한 영향을 미치는 사항에 대하여는 개별통지를 합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h2>제 2장 이용계약 및 정보보호</h2>\n", "\t\t\t\t\t\t\t\t<h3>제8조 (회원가입 및 회원정보의 변경)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 이용자는 회사가 정한 가입 양식에 따라 회원정보를 기입한 후 본 약관 등에 동의한다는 의사표시를 함으로서 회원가입을 신청합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 제1항과 같이 회원으로 가입할 것을 신청한 이용자 중 다음 각 호에 해당하지 않는 한 회원으로 등록합니다.<br />\n", "\t\t\t\t\t\t\t\t\t1) 등록 내용에 허위, 기재누락, 오기가 있는 경우<br />\n", "\t\t\t\t\t\t\t\t\t2) 가입신청자가 이전에 회원자격을 상실한 적이 있는 경우 (다만 회원자격 상실 후 회사가 필요하다고 판단하여 회원재가입 승낙을 얻은 경우에는 예외로 합니다.)<br />\n", "\t\t\t\t\t\t\t\t\t3) 회사로부터 회원자격 정지 조치 등을 받은 회원이 그 조치기간 중에 이용계약을 임의 해지 하고 재이용 신청을 하는 경우<br />\n", "\t\t\t\t\t\t\t\t\t4) 기타 회원으로 등록하는 것이 직방의 기술상 현저히 지장이 있다고 판단되는 경우<br />\n", "\t\t\t\t\t\t\t\t\t5) 본 약관에 위배되거나 위법 또는 부당한 이용신청임이 확인된 경우 및 회사가 합리적인 판단에 의하여 필요하다고 인정하는 경우<br />\n", "\t\t\t\t\t\t\t\t\t3. 회원 가입 계약의 성립시기는 회사의 승낙이 회원에게 도달한 시점으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t4. 회원은 회원 가입 신청 시 기재한 사항이 변경되었을 경우 온라인으로 수정을 하거나 전자우편 기타 방법으로 회사에 그 변경사항을 알려야 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t5. 제4항의 변경사항을 회사에 알리지 않아 발생한 불이익에 대하여 회사는 책임지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t\t6. 회원가입은 반드시 본인의 진정한 정보를 통하여만 가입할 수 있으며 회사는 회원이 등록한 정보에 대하여 확인조치를 할 수 있습니다. 회원은 회사의 확인조치에 대하여 적극 협력하여야 하며, 만일 이를 준수하지 아니할 경우 회사는 회원이 등록한 정보가 부정한 것으로 처리할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t7. 회사는 회원을 등급별로 구분하여 이용시간, 이용회수, 서비스 메뉴, 매물 등록 건 수 등을 세분하여 이용에 차등을 둘 수 있습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제9조 (이용 계약의 종료)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회원의 해지<br />\n", "\t\t\t\t\t\t\t\t\t1) 회원은 언제든지 회사에게 해지 의사를 통지할 수 있고 회사는 특별한 사유가 없는 한 이를 즉시 수락하여야 합니다. 다만, 회원은 해지의사를 통지하기 전에 모든 진행중인 절차를 완료, 철회 또는 취소해야만 합니다. 이 경우 철회 또는 취소로 인한 불이익은 회원 본인이 부담하여야 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2) 회원이 발한 의사표시로 인해 발생한 불이익에 대한 책임은 회원 본인이 부담하여야 하며, 이용계약이 종료되면 회사는 회원에게 부가적으로 제공한 각종 혜택을 회수할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3) 회원의 의사로 이용계약을 해지한 후, 추후 재이용을 희망할 경우에는 재이용 의사가 회사에 통지되고, 이에 대한 회사의 승낙이 있는 경우에만 서비스 재이용이 가능합니다.<br />\n", "\t\t\t\t\t\t\t\t\t4) 본 항에 따라 해지를 한 회원은 이 약관이 정하는 회원가입절차와 관련조항에 따라 신규 회원으로 다시 가입할 수 있습니다. 다만 회원이 중복참여가 제한된 판촉이벤트 중복 참여 등 부정한 목적으로 회원탈퇴 후 재이용을 신청하는 경우 회사는 가입을 일정기간 동안 제한할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t5) 본 항에 따라 해지를 한 이후에는 재가입이 불가능하며, 모든 가입은 신규가입으로 처리됩니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사의 해지<br />\n", "\t\t\t\t\t\t\t\t\t1) 회사는 다음과 같은 사유가 발생하거나 확인된 경우 이용계약을 해지할 수 있습니다<br />\n", "\t\t\t\t\t\t\t\t\t①다른 회원의 권리나 명예, 신용 기타 정당한 이익을 침해하거나 대한민국 법령 또는 공서양속에 위배되는 행위를 한 경우<br />\n", "\t\t\t\t\t\t\t\t\t②회사가 제공하는 서비스의 원활한 진행을 방해하는 행위를 하거나 시도한 경우<br />\n", "\t\t\t\t\t\t\t\t\t③제 8조 제 2항의 승낙거부 사유가 추후 발견된 경우<br />\n", "\t\t\t\t\t\t\t\t\t④회사가 정한 서비스 운영정책을 이행하지 않거나 위반한 경우<br />\n", "\t\t\t\t\t\t\t\t\t⑤기타 회사가 합리적인 판단에 기하여 서비스의 제공을 거부할 필요가 있다고 인정할 경우<br />\n", "\t\t\t\t\t\t\t\t\t2) 회사가 해지를 하는 경우 회사는 회원에게 이메일, 전화, 기타의 방법을 통하여 해지 사유를 밝혀 해지 의사를 통지합니다. 이용계약은 회사의 해지의사를 회원에게 통지한 시점에 종료됩니다.<br />\n", "\t\t\t\t\t\t\t\t\t3) 본 항에서 정한 바에 따라 이용계약이 종료될 시에는 회사는 회원에게 부가적으로 제공한 각종혜택을 회수할 수 있습니다. 이용계약의 종료와 관련하여 발생한 손해는 이용계약이 종료된 해당 회원이 책임을 부담하여야 하고, 회사는 일체의 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t\t4) 본 항에서 정한 바에 따라 이용계약이 종료된 경우에는, 회원의 재이용 신청에 대하여 회사는 이에 대한 승낙을 거절할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. “회원”이 계약을 해지하는 경우, 관련법 및 개인정보취급방침에 따라 “회사”가 “회원”정보를 보유하는 경우를 제외하고는 해지 즉시 “회원”의 모든 데이터는 소멸됩니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제10조 (개인정보보호)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 이용자의 회원가입 시 서비스 제공에 필요한 최소한의 정보를 수집합니다. 다음 사항을 필수사항으로 하며 그 외 사항은 선택사항으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t1)이메일주소<br />\n", "\t\t\t\t\t\t\t\t\t2)비밀번호<br />\n", "\t\t\t\t\t\t\t\t\t3)휴대폰 번호(부동산 매물등록 서비스 및 신고기능을 이용하는 회원인 경우)<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사가 이용자의 개인식별이 가능한 개인정보를 수집하는 때에는 반드시 당해 이용자의 동의를 받습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 제공된 개인정보는 당해 이용자의 동의 없이 목적 외의 이용이나 제3자에게 제공하지 않습니다. 다만, 다음의 경우에는 예외로 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t1) 통계작성, 학술연구 또는 시장조사를 위하여 필요한 경우로서 특정 개인을 식별할 수 없는 형태로 제공하는 경우<br />\n", "\t\t\t\t\t\t\t\t\t2) 도용방지를 위하여 본인확인에 필요한 경우<br />\n", "\t\t\t\t\t\t\t\t\t3) 법률의 규정 또는 법률에 의하여 필요한 불가피한 사유가 있는 경우<br />\n", "\t\t\t\t\t\t\t\t\t4. 회사가 제2항과 제3항에 의해 이용자의 동의를 받아야 하는 경우에는 개인정보관리 책임자의 신원(소속, 성명 및 전화번호, 기타 연락처), 정보의 수집목적 및 이용목적, 제3자에 대한 정보제공 관련사항(제공받은 자, 제공목적 및 제공할 정보의 내용) 등 정보통신망이용촉진등에관한법률 제22조제2항이 규정한 사항을 미리 명시하거나 고지해야 하며 이용자는 언제든지 이 동의를 철회할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t5. 회사는 이용자의 개인정보를 보호하기 위해 “개인정보취급방침”을 수립하고 개인정보보호책임자를 지정하여 이를 게시하고 운영합니다.<br />\n", "\t\t\t\t\t\t\t\t\t6. 이용자는 언제든지 회사가 갖고 있는 자신의 개인정보에 대해 열람 및 오류정정을 요구할 수 있으며 회사는 이에 대해 지체 없이 필요한 조치를 취할 의무를 집니다. 이용자가 오류의 정정을 요구한 경우에는 회사는 그 오류를 정정할 때까지 당해 개인정보를 이용하지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t\t7. 회사 또는 그로부터 개인정보를 제공받은 제3자는 개인정보의 수집목적 또는 제공받은 목적을 달성한 때에는 당해 개인정보를 지체 없이 파기합니다. 다만, 아래의 경우에는 회원 정보를 보관합니다. 이 경우 회사는 보관하고 있는 회원 정보를 그 보관의 목적으로만 이용합니다.<br />\n", "\t\t\t\t\t\t\t\t\t1) 상법, 전자상거래 등에서의 소비자보호에 관한 법률 등 관계법령의 규정에 의하여 보존할 필요가 있는 경우 회사는 관계법령에서 정한 일정한 기간 동안 회원 정보를 보관합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2) 회사가 이용계약을 해지하거나 회사로부터 서비스 이용정지조치를 받은 회원에 대해서는 재가입에 대한 승낙거부사유가 존재하는지 여부를 확인하기 위한 목적으로 이용계약종료 후 5년간 아이디, 전화번호를 비롯하여 이용계약 해지와 서비스 이용정지와 관련된 정보 등의 필요정보를 보관합니다.<br />\n", "\t\t\t\t\t\t\t\t\t8. 회사는 새로운 업체가 제휴사 또는 제휴영업점의 지위를 취득할 경우 제7조 2항에서 정한 것과 같은 방법을 통하여 공지합니다. 이 때 회원이 별도의 이의제기를 하지 않을 경우 직방 서비스 제공이라는 필수적인 목적을 위해 해당 개인 정보 제공 및 활용에 동의한 것으로 간주 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t9. 모든 이용자는 직방으로부터 제공받은 정보를 다른 목적으로 이용하거나 타인에게 유출 또는 제공해서는 안되며, 위반 사용으로 인한 모든 책임을 부담해야 합니다. 또한 회원은 자신의 개인정보를 책임 있게 관리하여 타인이 회원의 개인정보를 부정하게 이용하지 않도록 해야 합니다<br />\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h2>제 3장 서비스의 이용</h2>\n", "\t\t\t\t\t\t\t\t<h3>제11조 (부동산 매물 등에 관한 정보제공 서비스)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 부동산 매물 등에 관한 정보제공 서비스는 회사가 이용자 스스로 해당 정보를 확인 및 이용할 수 있도록 관련 정보를 제공하는 것입니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션 내에서 제공하는 모든 정보에 대해서 정확성이나 신뢰성이 있는 정보를 제공하기 위해 노력하지만, 그 과정에서 발생할 수 있는 정보의 정확성이나 신뢰성에 대해서는 어떠한 보증도 하지 않으며, 정보의 오류로 인해 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션을 통해 제공되는 정보의 내용을 수정할 의무를 지지 않으나, 필요에 따라 개선할 수는 있습니다.<br />\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제12조 (부동산 매물 등록 서비스)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 부동산 매물 등록 서비스는 회원이 매물정보(부동산 거래정보 및 거래 물건에 대한 다양한 부가정보)와 회원 연락처(회원의 이메일 주소 및 휴대폰 번호)를 직접 직방 인터넷 사이트 및 모바일 어플리케이션에 등록하여 이용자에게 노출할 수 있는 것을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 회원이 등록한 매물정보의 노출순서 및 영역의 추가 등에 대한 결정 권한을 갖고 있습니다. 또한, 회사는 사전통지 없이 회원의 매물정보 등을 직방 인터넷 사이트 및 모바일 어플리케이션 이외의 다른 인터넷 사이트 등에 노출할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회사는 회원이 등록한 매물정보에 대해 등록 후 24시간 이내에 해당 매물정보의 진위 여부를 확인하며, 진위 여부 확인 즉시 해당 매물을 노출합니다.<br />\n", "\t\t\t\t\t\t\t\t\t4. 회원이 등록한 매물정보가 실제 매물정보와 불일치 하는 경우 회사는 회원이 가입시 제공한 전화번호 또는 이메일을 통해 회원에게 매물정보의 수정을 요청합니다. 회사가 회원이 제공한 연락수단으로 2회 이상 연락하였음에도 불구하고 연락이 되지 않을 경우의 책임은 “회원”에게 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t5. 전항에 따른 회사의 정당한 매물정보 수정 요청에도 불구하고 회원이 24시간 이내에 매물정보(거래완료 혹은 노출종료와 같은 매물상태 변경 포함)를 수정하지 않을 경우, 회사는 해당 매물정보의 노출을 중지하고 회원의 서비스 이용을 제한 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t6. 회사는 직방에 등록된 매물 중 사회통념, 관례 및 회사의 합리적인 판단에 의하여 거래가 부적합하다고 판단되는 경우 이의 삭제를 요청하거나 직권으로 삭제할 수 있으며 해당 회원의 서비스 이용을 정지 혹은 탈퇴시킬 수 있습니다. 직방에 거래부적합 부동산 매물을 등록할 경우, 거래부적합 매물에 대한 법적인 책임은 해당 등록자에게 있습니다\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제13조 (부동산 중개업소 추천 등 기타 관련 서비스)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 이용자가 원하는 경우 이용자의 편의를 위해 부동산 중개업소를 이용자에게 추천할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 이용자가 회사가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 이용자 스스로의 판단에 따라 결정하는 것으로 회사는 이용자가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제14조 (정보의 제공 및 광고의 게재)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 회원이 서비스 이용 중 필요하다고 인정되는 다양한 정보를 서비스 내 공지사항, 서비스 화면, 전자우편 등의 방법으로 회원에게 제공할 수 있습니다. 다만, 회원은 관련법에 따른 거래관련 정보 및 고객문의 등에 대한 답변 등을 제외하고는 언제든지 위 정보제공에 대해서 수신 거절을 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 서비스의 운영과 관련하여 회사가 제공하는 서비스의 화면 및 홈페이지 등에 광고를 게재할 수 있습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h2>제 4장 책임</h2>\n", "\t\t\t\t\t\t\t\t<h3>제15조 (회사의 의무)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 법령과 이 약관이 금지하거나 공서양속에 반하는 행위를 하지 않으며 이 약관이 정하는 바에 따라 지속적이고, 안정적으로 이용자에게 서비스를 제공하기 위해 최선을 다합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 이용자 상호간의 거래에 있어서 어떠한 보증도 제공하지 않습니다. 이용자 상호간 거래 행위에서 발생하는 문제 및 손실에 대해서 회사는 일체의 책임을 부담하지 않으며, 거래당사자간에 직접 해결해야 합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제16조 (회원의 아이디 및 비밀번호에 대한 의무)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 아이디와 비밀번호에 관한 관리책임은 회원에게 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회원은 자신의 아이디 및 비밀번호를 제3자에게 이용하게 해서는 안됩니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회원이 자신의 아이디 및 비밀번호를 도난당하거나 제3자가 사용하고 있음을 인지한 경우에는 바로 회사에 통보하고 회사의 안내가 있는 경우에는 그에 따라야 합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제17조 (이용자의 의무)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 이용자는 다음 각호의 행위를 하여서는 안됩니다. 만약 다음 각호와 같은 행위가 확인되면 회사는 해당 이용자에게 서비스 이용에 대한 제재를 가할 수 있으며 민형사상의 책임을 물을 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t(1)회사 서비스의 운영을 고의 및 과실로 방해하는 경우<br />\n", "\t\t\t\t\t\t\t\t\t(2)신청 또는 변경 시 허위 내용의 등록<br />\n", "\t\t\t\t\t\t\t\t\t(3)타인의 정보 도용<br />\n", "\t\t\t\t\t\t\t\t\t(4)허위 매물 정보의 등록<br />\n", "\t\t\t\t\t\t\t\t\t(5)회사가 정한 정보 이외의 정보(컴퓨터 프로그램 등) 등의 송신 또는 게시<br />\n", "\t\t\t\t\t\t\t\t\t(6)회사 및 기타 제3자의 저작권 등 지적재산권에 대한 침해<br />\n", "\t\t\t\t\t\t\t\t\t(7)회사 및 기타 제3자의 명예를 손상시키거나 업무를 방해하는 행위<br />\n", "\t\t\t\t\t\t\t\t\t(8)외설 또는 폭력적인 메시지, 화상, 음성, 기타 공서양속에 반하는 정보를 직방에 공개 또는 게시하는 행위<br />\n", "\t\t\t\t\t\t\t\t\t(9)사기 및 악성 글 등록 등 건전한 거래 문화 활성에 방해되는 행동<br />\n", "\t\t\t\t\t\t\t\t\t(10)기타 중대한 사유로 인하여 회사가 서비스 제공을 지속하는 것이 부적당하다고 인정하는 경우<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 전항의 규정에 의하여 서비스의 이용을 제한하거나 중지할 수 있는 모든 권한을 갖고 있습니다. 회사는 회사 정책에 위반한 행동을 하는 특정 회원의 ID를 삭제할 수 있고, 이용 중지 등의 모든 서비스 제한 조치를 이용자에게 통보 없이 직권으로 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회사는 회사의 정책에 따라서 회원 간의 차별화된 유료 서비스를 언제든지 제공할 수 있습니다. 만약 회원이 비용을 지불하지 않고 사용을 할 경우 회사는 특정 회원에게 서비스 중지 및 특정 서비스 제한을 할 수 있습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제18조 (저작권의 귀속 및 이용제한)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 서비스에 대한 저작권 및 지적재산권은 회사에 귀속됩니다. 단, 회원이 직방을 이용하여 작성한 저작물에 대한 저작권은 해당 회원에게 귀속됩니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 이용자는 서비스를 이용함으로써 얻은 정보 중 회사에게 지적재산권이 귀속된 정보를 회사의 사전 승낙 없이 복제, 송신, 출판, 배포, 방송 기타 방법에 의하여 영리목적으로 이용하거나 제3자에게 이용하게 하여서는 안됩니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제19조 (책임의 한계 등)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 무료로 제공하는 정보 및 서비스에 관하여 개인정보취급방침 또는 관계법령의 벌칙, 과태료 규정 등 강행규정에 위배되지 않는 한 원칙적으로 손해를 배상할 책임이 없습니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사는 천재지변, 불가항력, 서비스용 설비의 보수, 교체, 점검, 공사 등 기타 이에 준하는 사항으로 정보 및 서비스를 제공할 수 없는 경우에 이에 대한 책임이 면제됩니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회사는 이용자의 귀책사유로 인한 정보 및 서비스 이용의 장애에 관한 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t\t4. 회사는 회원이 게재한 정보, 자료, 사실의 신뢰도, 정확성 등의 내용에 관하여는 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t\t5. “서비스”에서 제공하는 정보에 대한 신뢰 여부는 전적으로 “이용자” 본인의 책임이며, “회사”는 매물정보를 등록한 “회원”에 의한 사기, 연락 불능 등으로 인하여 발생하는 어떠한 직접, 간접, 부수적, 파생적, 징벌적 손해, 손실, 상해 등에 대하여 도덕적, 법적 책임을 부담하지 않습니다. 또한 “서비스”를 통하여 노출, 배포, 전송되는 정보를 “이용자”가 이용하여 발생하는 부동산 거래 등에 대하여 “회사”는 어떠한 도덕적, 법적 책임이나 의무도 부담하지 아니합니다.<br />\n", "\t\t\t\t\t\t\t\t\t6. “이용자”가 “회사”가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 “이용자” 스스로의 판단에 따라 결정하는 것으로 “회사”는 “이용자”가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제20조 (손해배상 등)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 회원이 서비스를 이용함에 있어 회사의 고의 또는 과실로 인해 손해가 발생한 경우에는 민법 등 관련 법령이 규율하는 범위 내에서 그 손해를 배상합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 회사에 손해가 발생한 경우에는 회사에 그 손해를 배상하여야 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t3. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 제3자가 회사를 상대로 민형사상의 법적 조치를 취하는 경우에는 회원은 자신의 비용과 책임으로 회사를 면책시켜야 하며, 이로 인해 발생하는 손해에 대해 배상하여야 합니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제21조 (분쟁해결)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사는 이용자 상호간 분쟁에서 발생하는 문제에 대해서 일체의 책임을 지지 않습니다. 이용자 상호간 분쟁은 당사자간에 직접 해결해야 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 이용자 상호간에 서비스 이용과 관련하여 발생한 분쟁에 대해 이용자의 피해구제신청이 있는 경우에는 공정거래위원회 또는 시·도지사가 의뢰하는 분쟁조정기관의 조정에 따를 수 있습니다.\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t\t<h3>제22조 (재판권 및 준거법)</h3>\n", "\t\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t\t1. 회사와 회원간 제기된 소송은 대한민국법을 준거법으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t\t2. 회사와 회원간 발생한 분쟁에 관한 소송은 민사소송법 상의 관할법원에 제소합니다.<br /><br />\n", "\n", "\t\t\t\t\t\t\t\t\t부칙<br />\n", "\t\t\t\t\t\t\t\t\t제1조 (적용일자)<br />\n", "\t\t\t\t\t\t\t\t\t이 약관은 2013년 11월 1일부터 적용됩니다.<br />\n", "\t\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t</div>\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"item\">\n", "\t\t\t\t\t\t<h4>&middot; 개인정보 수집 및 이용에 대한 동의 (필수)</h4>\n", "\t\t\t\t\t\t<label><input type=\"checkbox\" name=\"agree2\" /> 동의 합니다</label>\n", "\t\t\t\t\t\t<div class=\"i-box\">\n", "\t\t\t\t\t\t\t<strong>이메일 가입회원</strong><br /> \n", "\n", "\t\t\t\t\t\t\t개인정보의 수집 및 이용에 대한 안내<br />\n", "\t\t\t\t\t\t\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이\n", "\t\t\t\t\t\t\t개인정보를 수집합니다. 정보주체는 본개인정보의 수집 및 이용에\n", "\t\t\t\t\t\t\t관한 동의를 거부하실권리가 있으나, 서비스제공에 필요한\n", "\t\t\t\t\t\t\t최소한의 개인 정보 이므로 동의를 해주셔야 서비스를 이용하실 수\n", "\t\t\t\t\t\t\t있습니다.<br />\n", "\n", "\t\t\t\t\t\t\t• 수집하려는 개인 정보 항목: 이메일<br />\n", "\t\t\t\t\t\t\t• 개인정보의 수집 목적: 회원제 서비스 이용, 개인식별,\n", "\t\t\t\t\t\t\t가입의사 확인, 고지사항 전달<br /><br />\n", "\t\t\t\t\t\t\t• 개인정보의 보유기간: 회원 탈퇴 후 바로삭제\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t\t<button type=\"submit\" class=\"btn btn-ok ladda-button\" data-style=\"zoom-in\">가입하기</button>\n", "\t\t\t\t\t<button type=\"button\" class=\"btn btn-cancel\">취 소 </button>\n", "\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"layer-other-links\">\n", "\t\t\t\t\t<button type=\"button\" class=\"link1\">기존회원 로그인</button> &nbsp; | &nbsp; <a href=\"/home/RegisterInfo\" class=\"link2 fc-red1\" target=\"_blank\">중개사무소 가입신청</a>\n", "\t\t\t\t</div>\n", "\t\t\t</form>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- step2 -->\n", "<script id=\"layer-join2-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer has-agree\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">회원 가입</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<div class=\"agree-box item-left\">\n", "\t\t\t\t<div class=\"item\">\n", "\t\t\t\t\t<h4>&middot; 이용약관 (필수)</h4>\n", "\t\t\t\t\t<label><input type=\"checkbox\" name=\"agree\" /> 동의 합니다</label>\n", "\t\t\t\t\t<div class=\"i-box\" style=\"height:150px\">\n", "\t\t\t\t\t\t<div class=\"agree_conainer\">\n", "\t\t\t\t\t\t\t<h1>직방 서비스 이용약관</h1>\n", "\n", "\t\t\t\t\t\t\t<h2>제1장 총칙</h2>\n", "\n", "\t\t\t\t\t\t\t<h3>제1조 (목적)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t본 약관은 (주)직방(이하 “회사”라 함)이 운영하는 인터넷 사이트 및 모바일 어플리케이션(이하 “직방”이라 함)에서 제공하는 제반 서비스의 이용과 관련하여 회사와 이용자 및 이용자간의 권리, 의무 및 책임사항, 기타 필요한 사항을 규정함을 목적으로 합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제2조 (정의)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 직방: 회사가 컴퓨터 등 정보통신설비를 이용하여 서비스를 제공할 수 있도록 설정한 가상의 영업장을 말하며, 아울러 인터넷 사이트 및 모바일 어플리케이션을 운영하는 사업자의 의미로도 사용합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 이용자: 직방에 접속하여 본 약관에 따라 회사가 제공하는 서비스를 받는 회원 및 비회원을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회원: 회사에 개인정보를 제공하여 회원등록을 한 자로서, 직방의 정보를 지속적으로 제공받으며, 회사가 제공하는 직방의 서비스를 계속적으로 이용할 수 있는 자를 말합니다. 회사는 서비스의 원활한 제공을 위해 회원의 등급을 회사 내부의 규정에 따라 나눌 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t4. 비회원: 회원으로 가입하지 않고 회사가 제공하는 서비스를 이용하는 자를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t5. 아이디(ID): 회원의 식별과 서비스 이용을 위하여 회원이 설정하고 회사가 승인한 회원 본인의 이메일 주소를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t6. 비밀번호: 회원의 동일성 확인과 회원정보의 보호를 위하여 회원이 설정하고 회사가 승인한 문자나 숫자의 조합을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t7. 서비스: 구현되는 단말기(PC, TV, 휴대형단말기 등의 각종 유무선 장치를 포함)와 상관없이 회원이 이용할 수 있는 직방의 서비스를 의미합니다.<br />\n", "\t\t\t\t\t\t\t\t8. 게시판: 그 명칭, 형태, 위치와 관계없이 회원 및 비회원 이용자에게 공개할 목적으로 부호•문자•음성•음향•화상•동영상 등의 정보 (이하 \"게시물\"이라 합니다)를 회원이 게재할 수 있도록 회사가 제공하는 서비스 상의 가상공간을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t9. 별칭: 인터넷사이트에서 아이디와 함께, 또는 아이디를 대신하여 회원을 식별하기 위하여 이용자의 신청과 회사의 승인에 의하여 설정되는 숫자와 문자의 조합을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t10. 운영자: 회사가 제공하는 서비스의 전반적인 관리와 원활한 운영을 위하여 회사에서 선정한 자를 말합니다.<br />\n", "\t\t\t\t\t\t\t\t위 항에서 정의되지 않은 이 약관상의 용어의 의미는 일반적인 거래관행에 의합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제3조 (약관 등의 명시와 설명 및 개정)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 본 약관의 내용을 이용자가 쉽게 알 수 있도록 직방 인터넷 사이트 및 모바일 어플리케이션에 공지합니다. 다만, 약관의 내용은 이용자가 연결화면을 통하여 볼 수 있도록 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 “약관의 규제에 관한 법률”, “정보 통신망 이용 촉진 및 정보보호 등에 관한 법률” 등 관련법을 위배하지 않는 범위에서 본 약관을 개정할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회사가 약관을 개정할 경우에는 적용일자 및 개정사유를 명시하여 이용자가 알기 쉽도록 표시하여 공지합니다.<br />\n", "\t\t\t\t\t\t\t\t4. 회사가 약관을 개정할 경우에는 변경된 약관은 공지된 시점부터 그 효력이 발생하며, 이용자는 약관이 변경된 후에도 본 서비스를 계속 이용함으로써 변경 후의 약관에 대해 동의를 한 것으로 간주됩니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제4조 (약관의 해석)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 서비스운영을 위해 별도의 운영정책을 마련하여 운영할 수 있으며, 회사는 직방 인터넷 사이트 및 모바일 어플리케이션에 운영정책을 사전 공지 후 적용합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 본 약관에서 정하지 아니한 사항이나 해석에 대해서는 별도의 운영정책, 관계법령 또는 상관례에 따릅니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제5조 (서비스의 제공 및 변경)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사가 제공하는 서비스는 다음과 같습니다<br />\n", "\t\t\t\t\t\t\t\t1) 부동산 매물 등에 관한 정보제공 서비스<br />\n", "\t\t\t\t\t\t\t\t2) 부동산 매물 등록 서비스<br />\n", "\t\t\t\t\t\t\t\t3) 부동산 중개업소 추천 등 기타 관련 서비스<br />\n", "\t\t\t\t\t\t\t\t2. 회사가 제공하는 서비스의 내용을 기술적 사양의 변경 등의 이유로 변경할 경우에는 그 사유를 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제6조 (서비스의 중단)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 컴퓨터 등 정보통신설비의 보수점검, 교체 및 고장, 통신의 두절 등의 사유가 발생한 경우에는 서비스의 제공을 일시적으로 중단할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 사업종목의 전환, 사업의 포기, 업체간의 통합 등의 이유로 서비스를 제공할 수 없게 되는 경우에는 회사는 이용자에게 통지하거나, 이용자가 알아볼 수 있도록 공지사항으로 게시합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제7조 (회원에 대한 통지)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 이메일, 이동전화 단문메시지서비스(SMS), 푸시알림(App push)등으로 회원에게 통지할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 불특정다수 회원에 대한 통지의 경우 공지사항으로 게시함으로써 개별 통지에 갈음할 수 있습니다. 다만, 회원 본인의 거래와 관련하여 중대한 영향을 미치는 사항에 대하여는 개별통지를 합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h2>제 2장 이용계약 및 정보보호</h2>\n", "\t\t\t\t\t\t\t<h3>제8조 (회원가입 및 회원정보의 변경)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 이용자는 회사가 정한 가입 양식에 따라 회원정보를 기입한 후 본 약관 등에 동의한다는 의사표시를 함으로서 회원가입을 신청합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 제1항과 같이 회원으로 가입할 것을 신청한 이용자 중 다음 각 호에 해당하지 않는 한 회원으로 등록합니다.<br />\n", "\t\t\t\t\t\t\t\t1) 등록 내용에 허위, 기재누락, 오기가 있는 경우<br />\n", "\t\t\t\t\t\t\t\t2) 가입신청자가 이전에 회원자격을 상실한 적이 있는 경우 (다만 회원자격 상실 후 회사가 필요하다고 판단하여 회원재가입 승낙을 얻은 경우에는 예외로 합니다.)<br />\n", "\t\t\t\t\t\t\t\t3) 회사로부터 회원자격 정지 조치 등을 받은 회원이 그 조치기간 중에 이용계약을 임의 해지 하고 재이용 신청을 하는 경우<br />\n", "\t\t\t\t\t\t\t\t4) 기타 회원으로 등록하는 것이 직방의 기술상 현저히 지장이 있다고 판단되는 경우<br />\n", "\t\t\t\t\t\t\t\t5) 본 약관에 위배되거나 위법 또는 부당한 이용신청임이 확인된 경우 및 회사가 합리적인 판단에 의하여 필요하다고 인정하는 경우<br />\n", "\t\t\t\t\t\t\t\t3. 회원 가입 계약의 성립시기는 회사의 승낙이 회원에게 도달한 시점으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t4. 회원은 회원 가입 신청 시 기재한 사항이 변경되었을 경우 온라인으로 수정을 하거나 전자우편 기타 방법으로 회사에 그 변경사항을 알려야 합니다.<br />\n", "\t\t\t\t\t\t\t\t5. 제4항의 변경사항을 회사에 알리지 않아 발생한 불이익에 대하여 회사는 책임지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t6. 회원가입은 반드시 본인의 진정한 정보를 통하여만 가입할 수 있으며 회사는 회원이 등록한 정보에 대하여 확인조치를 할 수 있습니다. 회원은 회사의 확인조치에 대하여 적극 협력하여야 하며, 만일 이를 준수하지 아니할 경우 회사는 회원이 등록한 정보가 부정한 것으로 처리할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t7. 회사는 회원을 등급별로 구분하여 이용시간, 이용회수, 서비스 메뉴, 매물 등록 건 수 등을 세분하여 이용에 차등을 둘 수 있습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제9조 (이용 계약의 종료)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회원의 해지<br />\n", "\t\t\t\t\t\t\t\t1) 회원은 언제든지 회사에게 해지 의사를 통지할 수 있고 회사는 특별한 사유가 없는 한 이를 즉시 수락하여야 합니다. 다만, 회원은 해지의사를 통지하기 전에 모든 진행중인 절차를 완료, 철회 또는 취소해야만 합니다. 이 경우 철회 또는 취소로 인한 불이익은 회원 본인이 부담하여야 합니다.<br />\n", "\t\t\t\t\t\t\t\t2) 회원이 발한 의사표시로 인해 발생한 불이익에 대한 책임은 회원 본인이 부담하여야 하며, 이용계약이 종료되면 회사는 회원에게 부가적으로 제공한 각종 혜택을 회수할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t3) 회원의 의사로 이용계약을 해지한 후, 추후 재이용을 희망할 경우에는 재이용 의사가 회사에 통지되고, 이에 대한 회사의 승낙이 있는 경우에만 서비스 재이용이 가능합니다.<br />\n", "\t\t\t\t\t\t\t\t4) 본 항에 따라 해지를 한 회원은 이 약관이 정하는 회원가입절차와 관련조항에 따라 신규 회원으로 다시 가입할 수 있습니다. 다만 회원이 중복참여가 제한된 판촉이벤트 중복 참여 등 부정한 목적으로 회원탈퇴 후 재이용을 신청하는 경우 회사는 가입을 일정기간 동안 제한할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t5) 본 항에 따라 해지를 한 이후에는 재가입이 불가능하며, 모든 가입은 신규가입으로 처리됩니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사의 해지<br />\n", "\t\t\t\t\t\t\t\t1) 회사는 다음과 같은 사유가 발생하거나 확인된 경우 이용계약을 해지할 수 있습니다<br />\n", "\t\t\t\t\t\t\t\t①다른 회원의 권리나 명예, 신용 기타 정당한 이익을 침해하거나 대한민국 법령 또는 공서양속에 위배되는 행위를 한 경우<br />\n", "\t\t\t\t\t\t\t\t②회사가 제공하는 서비스의 원활한 진행을 방해하는 행위를 하거나 시도한 경우<br />\n", "\t\t\t\t\t\t\t\t③제 8조 제 2항의 승낙거부 사유가 추후 발견된 경우<br />\n", "\t\t\t\t\t\t\t\t④회사가 정한 서비스 운영정책을 이행하지 않거나 위반한 경우<br />\n", "\t\t\t\t\t\t\t\t⑤기타 회사가 합리적인 판단에 기하여 서비스의 제공을 거부할 필요가 있다고 인정할 경우<br />\n", "\t\t\t\t\t\t\t\t2) 회사가 해지를 하는 경우 회사는 회원에게 이메일, 전화, 기타의 방법을 통하여 해지 사유를 밝혀 해지 의사를 통지합니다. 이용계약은 회사의 해지의사를 회원에게 통지한 시점에 종료됩니다.<br />\n", "\t\t\t\t\t\t\t\t3) 본 항에서 정한 바에 따라 이용계약이 종료될 시에는 회사는 회원에게 부가적으로 제공한 각종혜택을 회수할 수 있습니다. 이용계약의 종료와 관련하여 발생한 손해는 이용계약이 종료된 해당 회원이 책임을 부담하여야 하고, 회사는 일체의 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t4) 본 항에서 정한 바에 따라 이용계약이 종료된 경우에는, 회원의 재이용 신청에 대하여 회사는 이에 대한 승낙을 거절할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t3. “회원”이 계약을 해지하는 경우, 관련법 및 개인정보취급방침에 따라 “회사”가 “회원”정보를 보유하는 경우를 제외하고는 해지 즉시 “회원”의 모든 데이터는 소멸됩니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제10조 (개인정보보호)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 이용자의 회원가입 시 서비스 제공에 필요한 최소한의 정보를 수집합니다. 다음 사항을 필수사항으로 하며 그 외 사항은 선택사항으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t1)이메일주소<br />\n", "\t\t\t\t\t\t\t\t2)비밀번호<br />\n", "\t\t\t\t\t\t\t\t3)휴대폰 번호(부동산 매물등록 서비스 및 신고기능을 이용하는 회원인 경우)<br />\n", "\t\t\t\t\t\t\t\t2. 회사가 이용자의 개인식별이 가능한 개인정보를 수집하는 때에는 반드시 당해 이용자의 동의를 받습니다.<br />\n", "\t\t\t\t\t\t\t\t3. 제공된 개인정보는 당해 이용자의 동의 없이 목적 외의 이용이나 제3자에게 제공하지 않습니다. 다만, 다음의 경우에는 예외로 합니다.<br />\n", "\t\t\t\t\t\t\t\t1) 통계작성, 학술연구 또는 시장조사를 위하여 필요한 경우로서 특정 개인을 식별할 수 없는 형태로 제공하는 경우<br />\n", "\t\t\t\t\t\t\t\t2) 도용방지를 위하여 본인확인에 필요한 경우<br />\n", "\t\t\t\t\t\t\t\t3) 법률의 규정 또는 법률에 의하여 필요한 불가피한 사유가 있는 경우<br />\n", "\t\t\t\t\t\t\t\t4. 회사가 제2항과 제3항에 의해 이용자의 동의를 받아야 하는 경우에는 개인정보관리 책임자의 신원(소속, 성명 및 전화번호, 기타 연락처), 정보의 수집목적 및 이용목적, 제3자에 대한 정보제공 관련사항(제공받은 자, 제공목적 및 제공할 정보의 내용) 등 정보통신망이용촉진등에관한법률 제22조제2항이 규정한 사항을 미리 명시하거나 고지해야 하며 이용자는 언제든지 이 동의를 철회할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t5. 회사는 이용자의 개인정보를 보호하기 위해 “개인정보취급방침”을 수립하고 개인정보보호책임자를 지정하여 이를 게시하고 운영합니다.<br />\n", "\t\t\t\t\t\t\t\t6. 이용자는 언제든지 회사가 갖고 있는 자신의 개인정보에 대해 열람 및 오류정정을 요구할 수 있으며 회사는 이에 대해 지체 없이 필요한 조치를 취할 의무를 집니다. 이용자가 오류의 정정을 요구한 경우에는 회사는 그 오류를 정정할 때까지 당해 개인정보를 이용하지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t7. 회사 또는 그로부터 개인정보를 제공받은 제3자는 개인정보의 수집목적 또는 제공받은 목적을 달성한 때에는 당해 개인정보를 지체 없이 파기합니다. 다만, 아래의 경우에는 회원 정보를 보관합니다. 이 경우 회사는 보관하고 있는 회원 정보를 그 보관의 목적으로만 이용합니다.<br />\n", "\t\t\t\t\t\t\t\t1) 상법, 전자상거래 등에서의 소비자보호에 관한 법률 등 관계법령의 규정에 의하여 보존할 필요가 있는 경우 회사는 관계법령에서 정한 일정한 기간 동안 회원 정보를 보관합니다.<br />\n", "\t\t\t\t\t\t\t\t2) 회사가 이용계약을 해지하거나 회사로부터 서비스 이용정지조치를 받은 회원에 대해서는 재가입에 대한 승낙거부사유가 존재하는지 여부를 확인하기 위한 목적으로 이용계약종료 후 5년간 아이디, 전화번호를 비롯하여 이용계약 해지와 서비스 이용정지와 관련된 정보 등의 필요정보를 보관합니다.<br />\n", "\t\t\t\t\t\t\t\t8. 회사는 새로운 업체가 제휴사 또는 제휴영업점의 지위를 취득할 경우 제7조 2항에서 정한 것과 같은 방법을 통하여 공지합니다. 이 때 회원이 별도의 이의제기를 하지 않을 경우 직방 서비스 제공이라는 필수적인 목적을 위해 해당 개인 정보 제공 및 활용에 동의한 것으로 간주 합니다.<br />\n", "\t\t\t\t\t\t\t\t9. 모든 이용자는 직방으로부터 제공받은 정보를 다른 목적으로 이용하거나 타인에게 유출 또는 제공해서는 안되며, 위반 사용으로 인한 모든 책임을 부담해야 합니다. 또한 회원은 자신의 개인정보를 책임 있게 관리하여 타인이 회원의 개인정보를 부정하게 이용하지 않도록 해야 합니다<br />\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h2>제 3장 서비스의 이용</h2>\n", "\t\t\t\t\t\t\t<h3>제11조 (부동산 매물 등에 관한 정보제공 서비스)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 부동산 매물 등에 관한 정보제공 서비스는 회사가 이용자 스스로 해당 정보를 확인 및 이용할 수 있도록 관련 정보를 제공하는 것입니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션 내에서 제공하는 모든 정보에 대해서 정확성이나 신뢰성이 있는 정보를 제공하기 위해 노력하지만, 그 과정에서 발생할 수 있는 정보의 정확성이나 신뢰성에 대해서는 어떠한 보증도 하지 않으며, 정보의 오류로 인해 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다. 회사는 직방 인터넷 사이트 및 모바일 어플리케이션을 통해 제공되는 정보의 내용을 수정할 의무를 지지 않으나, 필요에 따라 개선할 수는 있습니다.<br />\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제12조 (부동산 매물 등록 서비스)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 부동산 매물 등록 서비스는 회원이 매물정보(부동산 거래정보 및 거래 물건에 대한 다양한 부가정보)와 회원 연락처(회원의 이메일 주소 및 휴대폰 번호)를 직접 직방 인터넷 사이트 및 모바일 어플리케이션에 등록하여 이용자에게 노출할 수 있는 것을 말합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 회원이 등록한 매물정보의 노출순서 및 영역의 추가 등에 대한 결정 권한을 갖고 있습니다. 또한, 회사는 사전통지 없이 회원의 매물정보 등을 직방 인터넷 사이트 및 모바일 어플리케이션 이외의 다른 인터넷 사이트 등에 노출할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회사는 회원이 등록한 매물정보에 대해 등록 후 24시간 이내에 해당 매물정보의 진위 여부를 확인하며, 진위 여부 확인 즉시 해당 매물을 노출합니다.<br />\n", "\t\t\t\t\t\t\t\t4. 회원이 등록한 매물정보가 실제 매물정보와 불일치 하는 경우 회사는 회원이 가입시 제공한 전화번호 또는 이메일을 통해 회원에게 매물정보의 수정을 요청합니다. 회사가 회원이 제공한 연락수단으로 2회 이상 연락하였음에도 불구하고 연락이 되지 않을 경우의 책임은 “회원”에게 있습니다.<br />\n", "\t\t\t\t\t\t\t\t5. 전항에 따른 회사의 정당한 매물정보 수정 요청에도 불구하고 회원이 24시간 이내에 매물정보(거래완료 혹은 노출종료와 같은 매물상태 변경 포함)를 수정하지 않을 경우, 회사는 해당 매물정보의 노출을 중지하고 회원의 서비스 이용을 제한 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t6. 회사는 직방에 등록된 매물 중 사회통념, 관례 및 회사의 합리적인 판단에 의하여 거래가 부적합하다고 판단되는 경우 이의 삭제를 요청하거나 직권으로 삭제할 수 있으며 해당 회원의 서비스 이용을 정지 혹은 탈퇴시킬 수 있습니다. 직방에 거래부적합 부동산 매물을 등록할 경우, 거래부적합 매물에 대한 법적인 책임은 해당 등록자에게 있습니다\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제13조 (부동산 중개업소 추천 등 기타 관련 서비스)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 이용자가 원하는 경우 이용자의 편의를 위해 부동산 중개업소를 이용자에게 추천할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 이용자가 회사가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 이용자 스스로의 판단에 따라 결정하는 것으로 회사는 이용자가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제14조 (정보의 제공 및 광고의 게재)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 회원이 서비스 이용 중 필요하다고 인정되는 다양한 정보를 서비스 내 공지사항, 서비스 화면, 전자우편 등의 방법으로 회원에게 제공할 수 있습니다. 다만, 회원은 관련법에 따른 거래관련 정보 및 고객문의 등에 대한 답변 등을 제외하고는 언제든지 위 정보제공에 대해서 수신 거절을 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 서비스의 운영과 관련하여 회사가 제공하는 서비스의 화면 및 홈페이지 등에 광고를 게재할 수 있습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h2>제 4장 책임</h2>\n", "\t\t\t\t\t\t\t<h3>제15조 (회사의 의무)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 법령과 이 약관이 금지하거나 공서양속에 반하는 행위를 하지 않으며 이 약관이 정하는 바에 따라 지속적이고, 안정적으로 이용자에게 서비스를 제공하기 위해 최선을 다합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 이용자 상호간의 거래에 있어서 어떠한 보증도 제공하지 않습니다. 이용자 상호간 거래 행위에서 발생하는 문제 및 손실에 대해서 회사는 일체의 책임을 부담하지 않으며, 거래당사자간에 직접 해결해야 합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제16조 (회원의 아이디 및 비밀번호에 대한 의무)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 아이디와 비밀번호에 관한 관리책임은 회원에게 있습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회원은 자신의 아이디 및 비밀번호를 제3자에게 이용하게 해서는 안됩니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회원이 자신의 아이디 및 비밀번호를 도난당하거나 제3자가 사용하고 있음을 인지한 경우에는 바로 회사에 통보하고 회사의 안내가 있는 경우에는 그에 따라야 합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제17조 (이용자의 의무)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 이용자는 다음 각호의 행위를 하여서는 안됩니다. 만약 다음 각호와 같은 행위가 확인되면 회사는 해당 이용자에게 서비스 이용에 대한 제재를 가할 수 있으며 민형사상의 책임을 물을 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t(1)회사 서비스의 운영을 고의 및 과실로 방해하는 경우<br />\n", "\t\t\t\t\t\t\t\t(2)신청 또는 변경 시 허위 내용의 등록<br />\n", "\t\t\t\t\t\t\t\t(3)타인의 정보 도용<br />\n", "\t\t\t\t\t\t\t\t(4)허위 매물 정보의 등록<br />\n", "\t\t\t\t\t\t\t\t(5)회사가 정한 정보 이외의 정보(컴퓨터 프로그램 등) 등의 송신 또는 게시<br />\n", "\t\t\t\t\t\t\t\t(6)회사 및 기타 제3자의 저작권 등 지적재산권에 대한 침해<br />\n", "\t\t\t\t\t\t\t\t(7)회사 및 기타 제3자의 명예를 손상시키거나 업무를 방해하는 행위<br />\n", "\t\t\t\t\t\t\t\t(8)외설 또는 폭력적인 메시지, 화상, 음성, 기타 공서양속에 반하는 정보를 직방에 공개 또는 게시하는 행위<br />\n", "\t\t\t\t\t\t\t\t(9)사기 및 악성 글 등록 등 건전한 거래 문화 활성에 방해되는 행동<br />\n", "\t\t\t\t\t\t\t\t(10)기타 중대한 사유로 인하여 회사가 서비스 제공을 지속하는 것이 부적당하다고 인정하는 경우<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 전항의 규정에 의하여 서비스의 이용을 제한하거나 중지할 수 있는 모든 권한을 갖고 있습니다. 회사는 회사 정책에 위반한 행동을 하는 특정 회원의 ID를 삭제할 수 있고, 이용 중지 등의 모든 서비스 제한 조치를 이용자에게 통보 없이 직권으로 할 수 있습니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회사는 회사의 정책에 따라서 회원 간의 차별화된 유료 서비스를 언제든지 제공할 수 있습니다. 만약 회원이 비용을 지불하지 않고 사용을 할 경우 회사는 특정 회원에게 서비스 중지 및 특정 서비스 제한을 할 수 있습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제18조 (저작권의 귀속 및 이용제한)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 서비스에 대한 저작권 및 지적재산권은 회사에 귀속됩니다. 단, 회원이 직방을 이용하여 작성한 저작물에 대한 저작권은 해당 회원에게 귀속됩니다.<br />\n", "\t\t\t\t\t\t\t\t2. 이용자는 서비스를 이용함으로써 얻은 정보 중 회사에게 지적재산권이 귀속된 정보를 회사의 사전 승낙 없이 복제, 송신, 출판, 배포, 방송 기타 방법에 의하여 영리목적으로 이용하거나 제3자에게 이용하게 하여서는 안됩니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제19조 (책임의 한계 등)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 무료로 제공하는 정보 및 서비스에 관하여 개인정보취급방침 또는 관계법령의 벌칙, 과태료 규정 등 강행규정에 위배되지 않는 한 원칙적으로 손해를 배상할 책임이 없습니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사는 천재지변, 불가항력, 서비스용 설비의 보수, 교체, 점검, 공사 등 기타 이에 준하는 사항으로 정보 및 서비스를 제공할 수 없는 경우에 이에 대한 책임이 면제됩니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회사는 이용자의 귀책사유로 인한 정보 및 서비스 이용의 장애에 관한 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t4. 회사는 회원이 게재한 정보, 자료, 사실의 신뢰도, 정확성 등의 내용에 관하여는 책임을 지지 않습니다.<br />\n", "\t\t\t\t\t\t\t\t5. “서비스”에서 제공하는 정보에 대한 신뢰 여부는 전적으로 “이용자” 본인의 책임이며, “회사”는 매물정보를 등록한 “회원”에 의한 사기, 연락 불능 등으로 인하여 발생하는 어떠한 직접, 간접, 부수적, 파생적, 징벌적 손해, 손실, 상해 등에 대하여 도덕적, 법적 책임을 부담하지 않습니다. 또한 “서비스”를 통하여 노출, 배포, 전송되는 정보를 “이용자”가 이용하여 발생하는 부동산 거래 등에 대하여 “회사”는 어떠한 도덕적, 법적 책임이나 의무도 부담하지 아니합니다.<br />\n", "\t\t\t\t\t\t\t\t6. “이용자”가 “회사”가 추천한 부동산 중개업소를 이용할지 여부는 전적으로 “이용자” 스스로의 판단에 따라 결정하는 것으로 “회사”는 “이용자”가 해당 부동산 중개업소를 이용하여 발생하는 모든 직접, 파생적, 징벌적, 부수적인 손해에 대해 책임을 지지 않습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제20조 (손해배상 등)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 회원이 서비스를 이용함에 있어 회사의 고의 또는 과실로 인해 손해가 발생한 경우에는 민법 등 관련 법령이 규율하는 범위 내에서 그 손해를 배상합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 회사에 손해가 발생한 경우에는 회사에 그 손해를 배상하여야 합니다.<br />\n", "\t\t\t\t\t\t\t\t3. 회원이 이 약관을 위반하거나 관계 법령을 위반하여 제3자가 회사를 상대로 민형사상의 법적 조치를 취하는 경우에는 회원은 자신의 비용과 책임으로 회사를 면책시켜야 하며, 이로 인해 발생하는 손해에 대해 배상하여야 합니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제21조 (분쟁해결)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사는 이용자 상호간 분쟁에서 발생하는 문제에 대해서 일체의 책임을 지지 않습니다. 이용자 상호간 분쟁은 당사자간에 직접 해결해야 합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 이용자 상호간에 서비스 이용과 관련하여 발생한 분쟁에 대해 이용자의 피해구제신청이 있는 경우에는 공정거래위원회 또는 시·도지사가 의뢰하는 분쟁조정기관의 조정에 따를 수 있습니다.\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t\t<h3>제22조 (재판권 및 준거법)</h3>\n", "\t\t\t\t\t\t\t<p class=\"txt\">\n", "\t\t\t\t\t\t\t\t1. 회사와 회원간 제기된 소송은 대한민국법을 준거법으로 합니다.<br />\n", "\t\t\t\t\t\t\t\t2. 회사와 회원간 발생한 분쟁에 관한 소송은 민사소송법 상의 관할법원에 제소합니다.<br /><br />\n", "\n", "\t\t\t\t\t\t\t\t부칙<br />\n", "\t\t\t\t\t\t\t\t제1조 (적용일자)<br />\n", "\t\t\t\t\t\t\t\t이 약관은 2013년 11월 1일부터 적용됩니다.<br />\n", "\t\t\t\t\t\t\t</p>\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"item\">\n", "\t\t\t\t\t<h4>&middot; 개인정보 수집 및 이용에 대한 동의 (필수)</h4>\n", "\t\t\t\t\t<label><input type=\"checkbox\" name=\"agree2\" /> 동의 합니다</label>\n", "\t\t\t\t\t<div class=\"i-box\" style=\"height:220px\">\n", "\t\t\t\t\t\t<strong>휴대전화 인증 가입 회원</strong><br /><br />\n", "\n", "\t\t\t\t\t\t개인정보의 수집 및 이용에 대한 안내<br />\n", "\t\t\t\t\t\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이 개인정보를\n", "\t\t\t\t\t\t수집합니다. 정보주체는본 개인 정보의 수집 및이용에 관한 동의를 거부하실\n", "\t\t\t\t\t\t권리가있으나, 서비스 제공에 필요한 최소한의 개인정보이므로 동의를\n", "\t\t\t\t\t\t해주셔야 서비스를 이용하실 수 있습니다.<br /> \n", "\n", "\t\t\t\t\t\t• 수집하려는 개인 정보 항목: 성명, 이메일, 비밀번호, 휴대전화 번호<br />\n", "\t\t\t\t\t\t• 개인정보의 수집 목적: 회원제 서비스 이용, 개인식별, 가입의사 확인,\n", "\t\t\t\t\t\t고지사항 전달, 가입 및 가입횟수 제한 불만처리 등 민원 처리, 방내놓기\n", "\t\t\t\t\t\t서비스 이용<br />\n", "\t\t\t\t\t\t• 개인정보의 보유기간: 회원 탈퇴 후 5년까지\n", "\t\t\t\t\t</div>\n", "\t\t\t\t</div>\n", "\t\t\t</div>\n", "\t\t\t<div class=\"item-right\">\n", "\t\t\t\t<form>\n", "\t\t\t\t\t<h4>&middot; 정보입력</h4>\n", "\t\t\t\t\t<div class=\"form-box mb-5\">\n", "\t\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t\t<span class=\"i-tit\">이름:</span>\n", "\t\t\t\t\t\t\t<input type=\"text\" class=\"text\" name=\"username\" maxlength=\"4\" placeholder=\"실명을 입력하세요\" />\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t\t<span class=\"i-tit\">이메일 : </span>\n", "\t\t\t\t\t\t\t<input type=\"text\" class=\"text\" name=\"email\" placeholder=\"zigbang@zigbang.com\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t\t<span class=\"i-tit\">비밀번호 : </span>\n", "\t\t\t\t\t\t\t<input type=\"password\" class=\"text\" name=\"password\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t\t<span class=\"i-tit\">비밀번호 확인 :</span>\n", "\t\t\t\t\t\t\t<input type=\"password\" class=\"text\" name=\"password1\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"phone-auth\">\n", "\t\t\t\t\t\t<h4 class=\"mb-5\">핸드폰인증</h4>\n", "\t\t\t\t\t\t<div class=\"mb-10\">\n", "\t\t\t\t\t\t\t<select name=\"phone1\">\n", "\t\t\t\t\t\t\t\t<option value=\"010\" selected>010</option>\n", "\t\t\t\t\t\t\t\t<option value=\"011\">011</option>\n", "\t\t\t\t\t\t\t\t<option value=\"016\">016</option>\n", "\t\t\t\t\t\t\t\t<option value=\"017\">017</option>\n", "\t\t\t\t\t\t\t\t<option value=\"018\">018</option>\n", "\t\t\t\t\t\t\t\t<option value=\"019\">019</option>\n", "\t\t\t\t\t\t\t</select>\n", "\t\t\t\t\t\t\t- <input type=\"text\" class=\"text\" name=\"phone2\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t\t\t- <input type=\"text\" class=\"text\" name=\"phone3\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t\t\t<button type=\"button\" class=\"sendAuthNum\">인증번호 발송</button>\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t\t<div>\n", "\t\t\t\t\t\t\t<span class=\"i-tit\">인증번호 입력:</span>\n", "\t\t\t\t\t\t\t<input type=\"text\" class=\"text\" name=\"authNum\" style=\"width:125px;\" />\n", "\t\t\t\t\t\t\t<button type=\"button\" class=\"sendAuthNum\">재발송</button>\n", "\t\t\t\t\t\t</div>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t\t\t<button type=\"submit\" class=\"btn btn-ok\">가입완료</button>\n", "\t\t\t\t\t\t<button type=\"button\" class=\"btn btn-cancel\">취 소</button>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t</form>\n", "\t\t\t</div>\n", "\t\t\t<div class=\"layer-other-links\">\n", "\t\t\t\t<button type=\"button\" class=\"show-login\">기존회원 로그인</button> &nbsp; | &nbsp; <a href=\"https://www.zigbang.com/home/registerinfo\" class=\"bold fc-red1\">중개사무소 가입신청</a>\n", "\t\t\t</div>\n", "\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- step3 -->\n", "<script id=\"layer-join3-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">추가 정보 입력</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f13\">\n", "\t\t\t\t<strong>사용자 정보가 확인된 회원에게만 제공되는 기능입니다<br /> 회원정보를 입력해주세요</strong>\n", "\t\t\t</p>\n", "\n", "\t\t\t<div class=\"form-box mb-5\">\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">이메일 : </span>\n", "\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">이름:</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" placeholder=\"실명을 입력하세요\" />\n", "\t\t\t\t\t<p>이메일이 입력되지 않았습니다.</p>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">비밀번호 : </span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t<p>가입된 이메일이 아닙니다. 정확히 입력하셨는지 확인해주세요.</p>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">비밀번호 확인 :</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상\" onkeydown=\"return number_validate(event);\" />\n", "\t\t\t\t\t<p>가입된 이메일이 아닙니다. 정확히 입력하셨는지 확인해주세요.</p>\n", "\t\t\t\t</div>\n", "\t\t\t</div>\n", "\t\t\t<div class=\"phone-auth\">\n", "\t\t\t\t<h4 class=\"mb-5\">핸드폰인증</h4>\n", "\t\t\t\t<div class=\"mb-5\">\n", "\t\t\t\t\t<select>\n", "\t\t\t\t\t\t<option value=\"010\" selected>010</option>\n", "\t\t\t\t\t\t<option value=\"011\">011</option>\n", "\t\t\t\t\t\t<option value=\"016\">016</option>\n", "\t\t\t\t\t\t<option value=\"017\">017</option>\n", "\t\t\t\t\t\t<option value=\"018\">018</option>\n", "\t\t\t\t\t\t<option value=\"019\">019</option>\n", "\t\t\t\t\t</select>\n", "\t\t\t\t\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t<button type=\"button\">인증번호 발송</button>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div>\n", "\t\t\t\t\t<span class=\"i-tit\">인증번호 입력:</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" style=\"width:125px;\" />\n", "\t\t\t\t\t<button type=\"button\">재발송</button>\n", "\t\t\t\t</div>\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"align-left\">\n", "\t\t\t\t<label><input type=\"checkbox\" name=\"agree\" checked /> 이용약관 동의</label>\n", "\t\t\t\t<a href=\"//s.zigbang.com/agree/user-agreement-last.html\" class=\"bold\" target=\"_blank\">[보기]</a>\n", "\t\t\t\t<br />\n", "\t\t\t\t<label><input type=\"checkbox\" name=\"agree2\" checked /> 개인정보 취급방침 및 운영 원칙에 동의</label>\n", "\t\t\t\t<a href=\"//s.zigbang.com/agree/user-privacy-last.html\" class=\"bold\" target=\"_blank\">[보기]</a>\n", "\t\t\t</div>\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- login -->\n", "<script id=\"layer-login-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">로그인</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f18\">가입된 이메일 주소를 입력하세요 </p>\n", "\n", "\t\t\t<form>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">이메일</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" name=\"username\" placeholder=\"zigbang@zigbang.com\" />\n", "\t\t\t\t</div>\n", "\n", "\t\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t\t<div class=\"mb-20\">\n", " <label><input type=\"checkbox\" /> 자동로그인</label>\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"mb-20\">\n", "\t\t\t\t\t\t<button type=\"submit\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t\t\t</div>\n", "\n", "\t\t\t\t\t<button type=\"button\" class=\"join ml-15\">[회원가입]</button>\n", " <button type=\"button\" class=\"find ml-15\">[아이디 찾기]</button>\n", "\t\t\t\t</div>\n", "\t\t\t</form>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 비번입력 -->\n", "<script id=\"layer-pw-set-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">비밀번호</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f18\">비밀번호를 입력하세요</p>\n", "\n", "\t\t\t<form>\n", "\t\t\t\t<div class=\"form-type1 mb-20\">\n", "\t\t\t\t\t<span class=\"i-tit\">비밀번호</span>\n", "\t\t\t\t\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" />\n", "\t\t\t\t</div>\n", "\n", "\t\t\t\t<div class=\"align-center\">\n", "\t\t\t\t\t<button type=\"button\" class=\"layer-findpassword\">[비밀번호 찾기]</button>\n", "\t\t\t\t</div>\n", "\n", "\t\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t\t<button type=\"submit\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t\t</div>\n", "\t\t\t</form>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 비번찾기 -->\n", "<script id=\"layer-pw-find-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">비밀번호 찾기</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f18\">회원가입 시 등록된 이메일로<br /> 임시 비밀번호를 보내드립니다</p>\n", "\n", "\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t<span class=\"i-tit\">이메일</span>\n", "\t\t\t\t<input type=\"text\" class=\"text\" name=\"username\" placeholder=\"zigbang@zigbang.com\" />\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 이메일찾기 -->\n", "<script id=\"layer-email-find-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"member-layer\" style=\"width:372px;display:none;\">\n", " <h3 class=\"layer-title\">이메일(아이디) 찾기</h3>\n", " <div class=\"layer-body\" style=\"width:330px;\">\n", " <p class=\"l-title f18\">가입할 때 인증한 핸드폰 번호를 입력하세요</p>\n", " <p class=\"l-title f13\">이메일 찾기 기능은 핸드폰 인증 회원만 가능합니다.<br />이메일만으로 가입한 회원은 찾을 수 없습니다.</p>\n", "\n", " <div class=\"form-type1\" style=\"width:330px;\">\n", " <div class=\"phone-auth\">\n", " <h4 class=\"mb-5\">핸드폰인증</h4>\n", " <div class=\"mb-10\">\n", " <select name=\"phone1\">\n", " <option value=\"010\" selected>010</option>\n", " <option value=\"011\">011</option>\n", " <option value=\"016\">016</option>\n", " <option value=\"017\">017</option>\n", " <option value=\"018\">018</option>\n", " <option value=\"019\">019</option>\n", " </select>\n", " - <input type=\"text\" class=\"text\" name=\"phone2\" maxlength=\"4\" style=\"width:66px\" />\n", " - <input type=\"text\" class=\"text\" name=\"phone3\" maxlength=\"4\" style=\"width:66px\" />\n", " <button type=\"button\" class=\"sendAuthNum\">인증번호 발송</button>\n", " </div>\n", " <div>\n", " <span class=\"i-tit1\">인증번호 입력:</span>\n", " <input type=\"text\" class=\"text\" name=\"authNum\" style=\"width:125px;\" />\n", " <button type=\"button\" class=\"sendAuthNum\">재발송</button>\n", " </div>\n", " </div>\n", " </div>\n", "\n", " <div class=\"layer-btn\">\n", " <button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", " </div>\n", " </div>\n", " <button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", " </div>\n", "</script>\n", "<!-- 이메일찾기 결과 -->\n", "<script id=\"layer-email-result-template\" type=\"text/x-handlebars-template\">\n", " <div class=\"member-layer\" style=\"display:none\">\n", " <h3 class=\"layer-title\">이메일(아이디) 확인</h3>\n", " <div class=\"layer-body\">\n", " <p class=\"l-title f13\">고객님의 정보와 일치하는 이메일(아이디) 입니다.<br />확인 후 로그인 또는 비밀번호 찾기 버튼을 눌러주세요.</p>\n", " <form>\n", " <div class=\"email-results align-center mb-10\">\n", " \n", " </div>\n", "\n", " <div class=\"layer-btn\">\n", " <button type=\"button\" class=\"btn btn-ok login\">로그인</button>\n", " <button type=\"button\" class=\"btn btn-ok pw\">비밀번호 찾기</button>\n", " </div>\n", " </form>\n", " </div>\n", " <button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", " </div>\n", "</script>\n", "<!-- 비번변경 -->\n", "<script id=\"layer-pw-change-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">비밀번호 변경</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<div class=\"form-box\">\n", "\t\t\t\t<div class=\"form-type1 border\">\n", "\t\t\t\t\t<span class=\"i-tit\">현재 비밀번호 : </span>\n", "\t\t\t\t\t<input type=\"password\" class=\"text\" placeholder=\"현재 비밀번호를 입력하세요\" />\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">새로운 비밀번호 : </span>\n", "\t\t\t\t\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" onkeydown=\"return number_validate(event);\"/>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t<span class=\"i-tit\">비밀번호 확인 : </span>\n", "\t\t\t\t\t<input type=\"password\" class=\"text\" placeholder=\"영문, 숫자, 특수기호 포함 8자리 이상 입력\" onkeydown=\"return number_validate(event);\"/>\n", "\t\t\t\t</div>\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 이름 변경 -->\n", "<script id=\"layer-name-change-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">이름 변경</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f18\">변경할 이름을 입력해주세요</p>\n", "\n", "\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t<span class=\"i-tit\">이름</span>\n", "\t\t\t\t<input type=\"text\" class=\"text\" placeholder=\"실명을 입력해주세요\" />\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 핸드폰 변경 -->\n", "<script id=\"layer-phone-change-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">핸드폰 변경</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title f18\">변경할 핸드폰 번호를 입력해주세요</p>\n", "\t\t\t<div class=\"phone-auth\">\n", "\t\t\t\t<div class=\"mb-10\">\n", "\t\t\t\t\t<select>\n", "\t\t\t\t\t\t<option value=\"010\" selected>010</option>\n", "\t\t\t\t\t\t<option value=\"011\">011</option>\n", "\t\t\t\t\t\t<option value=\"016\">016</option>\n", "\t\t\t\t\t\t<option value=\"017\">017</option>\n", "\t\t\t\t\t\t<option value=\"018\">018</option>\n", "\t\t\t\t\t\t<option value=\"019\">019</option>\n", "\t\t\t\t\t</select>\n", "\t\t\t\t\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t- <input type=\"text\" class=\"text\" maxlength=\"4\" style=\"width:66px\" />\n", "\t\t\t\t\t<button type=\"button\">인증번호 발송</button>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div>\n", "\t\t\t\t\t<span class=\"i-tit\">인증번호 입력:</span>\n", "\t\t\t\t\t<input type=\"text\" class=\"text\" style=\"width:125px;\" />\n", "\t\t\t\t\t<button type=\"button\">재발송</button>\n", "\t\t\t\t</div>\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\">확 인</button>\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 공지 레이어 -->\n", "<script id=\"layer-notice-template\" type=\"text/x-handlebars-template\">\n", "\t<div id=\"newNoticePopup\">\n", "\t\t<h3></h3>\n", "\t\t<div class=\"item_body\"><img src=\"\" alt=\"\" /></div>\n", "\t\t<div class=\"item_check\">\n", "\t\t\t<label><input type=\"checkbox\" class=\"i_check\" /> 오늘 하루 더 이상 보지 않습니다.</label>\n", "\t\t\t<button type=\"button\" onclick=\"hideNoticePopup();\">[닫기]</button>\n", "\t\t\t<a href=\"#\" target=\"_blank\" class=\"zb_btn btn_orange item_link\">자세히보기</a>\n", "\t\t</div>\n", "\t\t<button type=\"button\" onclick=\"hideNoticePopup();\" class=\"item_close\"><img src=\"//s.zigbang.com/legacy/images/v2/common/close.gif\" alt=\"\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 다운로드 문자 메세지 팝업-->\n", "<script id=\"layer-sms-down-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"smsDownLayer\" id=\"smsDownLayer\">\n", "\t\t<div><img src=\"//s.zigbang.com/v1/web/common/img_msg_1.jpg\" alt=\"\" /></div>\n", "\t\t<div class=\"sms_info\">\n", "\t\t\t<input type=\"text\" class=\"text getPhoneNo\" id=\"app_down_phone\" placeholder=\"휴대폰 번호를 입력하시면 다운로드 주소가 발송됩니다.\" />\n", "\t\t\t<button type=\"button\" id=\"btnAppDown\"><img src=\"//s.zigbang.com/legacy/images/btn_ok.png\" alt=\"\" /></button>\n", "\t\t</div>\n", "\t\t<div class=\"sms_agree\">\n", "\t\t\t<h4>· 개인정보 수집 및 이용에 대한 동의(필수)</h4>\n", "\t\t\t<label><input type=\"checkbox\" id=\"sms_check\" /> 동의 합니다.</label>\n", "\t\t\t<div>\n", "\t\t\t\t<strong>앱 다운로드</strong><br /><br />\n", "\t\t\t\t개인정보의 수집 및 이용에 대한 안내<br />\n", "\t\t\t\t(주)직방은 직방 서비스 제공을 위해서 아래와 같이 개인정보를 수집합니다. 정보주체는 본 개인정보의 수집 및 이용에 관한 동의를 거부하실권리가 있으나, 서비스제공에 필요한 최소한의 개인 정보 이므로 동의를 해주셔야 서비스를 이용하실 수 있습니다.<br /><br />\n", "\t\t\t\t• 수집하려는 개인 정보 항목: 핸드폰번호<br />\n", "\t\t\t\t• 개인정보의 수집 목적: 앱 다운로드 제공<br />\n", "\t\t\t\t• 개인정보의 보유기간: 사용 후 바로삭제\n", "\t\t\t</div>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"smsLayerClose\"><img src=\"//s.zigbang.com/legacy/images/btn_close.png\" alt=\"\" /></button>\n", "\t</div>\n", "</script>\n", "<!-- 일반/클린회원 공지 레이어 -->\n", "<script id=\"layer-agent-info-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\" id=\"agent_type1\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">일반회원이란?</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<div class=\"mb-10\">\n", "\t\t\t\t<p class=\"mb-5\"><strong>중개사님은 현재 일반회원입니다. <br />최초로 유료상품을 구매하면 일반회원으로 가입됩니다. 유료이용시작일이 속한 월을 포함해 3개월 동안 중단 없이 유료광고 상품을 이용하고 해당 기간 동안 “허위매물과 관련된 주의 조치”를 받지 않는 경우에는 클린회원으로 전환 가능합니다. </strong></p>\n", "\t\t\t\t<span style=\"font-size:11px\">(단, 특정 상황의 경우 유료이용시작일부터 클린회원으로 전환이 가능합니다)</span>\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"txt-box1 mb-10\">\n", "\t\t\t\t<div class=\"mb-5\">일반회원의 경우</div>\n", "\t\t\t\t▶ ‘일반 방 목록’에 매물이 노출<br />\n", "\t\t\t\t▶ 추후 제공될 부가 서비스의 이용이 가능\n", "\t\t\t</div>\n", "\t\t\t<div class=\"txt-box1\">\n", "\t\t\t\t<div class=\"mb-5\">클린회원의 경우</div>\n", "\t\t\t\t▶ “클린 방 목록”에 매물이 노출 <br />\n", "\t\t\t\t▶ 추후 제공될 클린회원 전용 부가 서비스를 이용 가능<br />\n", "\t\t\t\t▶ 상단에 노출되는 VIP상품(지하철역, 단지형)을 구매 가능\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<a href=\"http://blog.naver.com/zigbangcs/220296997823\" target=\"_blank\" class=\"btn btn-ok\"><b>상세내용 보기</b></a>\n", "\t\t\t</div>\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "\t<div class=\"member-layer\" id=\"agent_type2\" style=\"display:none\">\n", "\t\t<h3 class=\"layer-title\">클린회원이란?</h3>\n", "\t\t<div class=\"layer-body\">\n", "\n", "\t\t\t<div class=\"mb-10\">\n", "\t\t\t\t<p class=\"mb-5\"><strong>중개사님은 현재 클린회원입니다.<br /> 최초로 유료상품을 구매하면 일반회원으로 가입됩니다. 유료이용시작일이 속한 월을 포함해 3개월 동안 중단 없이 유료광고 상품을 이용하고 해당 기간 동안 “허위매물과 관련된 주의 조치”를 받지 않는 경우에는 클린회원으로 전환 가능합니다. </strong></p>\n", "\t\t\t\t<span style=\"font-size:11px\">(단, 특정 상황의 경우 유료이용시작일부터 클린회원으로 전환이 가능합니다)</span>\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"txt-box1 mb-10\">\n", "\t\t\t\t<div class=\"mb-5\">일반회원의 경우</div>\n", "\t\t\t\t▶ ‘일반 방 목록’에 매물이 노출<br />\n", "\t\t\t\t▶ 추후 제공될 부가 서비스의 이용이 가능\n", "\t\t\t</div>\n", "\t\t\t<div class=\"txt-box1\">\n", "\t\t\t\t<div class=\"mb-5\">클린회원의 경우</div>\n", "\t\t\t\t▶ “클린 방 목록”에 매물이 노출 <br />\n", "\t\t\t\t▶ 추후 제공될 클린회원 전용 부가 서비스를 이용 가능<br />\n", "\t\t\t\t▶ 상단에 노출되는 VIP상품(지하철역, 단지형)을 구매 가능\n", "\t\t\t</div>\n", "\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<a href=\"http://blog.naver.com/zigbangcs/220296997823\" target=\"_blank\" class=\"btn btn-ok\"><b>상세내용 보기</b></a>\n", "\t\t\t</div>\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "</script>\n", "<!-- 공인중개사 회원가입 -->\n", "<script id=\"layer-agent-register-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"member-layer\">\n", "\t\t<h3 class=\"layer-title\">중개사무소 가입 및 광고문의</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p class=\"l-title fs-12\">\n", "\t\t\t\t대표공인중개사만 회원가입이 가능합니다.<br />소속공인중개사 및 중개보조원은 회원가입 후 추가 가능합니다.\n", "\t\t\t</p>\n", "\t\t\t<form>\n", "\t\t\t\t<div class=\"form-box mb-5\">\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">중개사무소명:</span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"agent_name\" placeholder=\" \" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">대표자명 : </span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"req_user\" placeholder=\"\" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">사무소 주소 : </span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"address\" placeholder=\"상세주소(번지)까지 입력하세요\" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">사무소 유선번호 :</span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"agent_phone\" placeholder=\" \" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">핸드폰 번호 :</span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"tel\" placeholder=\"\" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">대표자 이메일 :</span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"email\" placeholder=\"아이디로 이용되니 정확히 입력하세요\" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t\t<div class=\"form-type1\">\n", "\t\t\t\t\t\t<span class=\"i-tit\">팩스번호 :</span>\n", "\t\t\t\t\t\t<input type=\"text\" class=\"text\" id=\"fax\" placeholder=\"\" />\n", "\t\t\t\t\t</div>\n", "\t\t\t\t</div>\n", "\t\t\t\t<div class=\"align-center\"><b>신청을 하면 직방 담당자가 확인하여 연락드립니다</b></div>\n", "\t\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t\t<button type=\"submit\" class=\"btn btn-ok ladda-button\" data-style=\"zoom-in\">문의하기</button>\n", "\t\t\t\t</div>\n", "\t\t\t</form>\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "</script>\n", "<!-- alert 레이어 -->\n", "<script id=\"layer-alert-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"alert-layer\">\n", "\t\t<h3 class=\"layer-title\">{{title}}</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p>\n", "\t\t\t\t{{text}}\n", "\t\t\t</p>\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"submit\" class=\"btn btn-ok\">확인</button>\n", "\t\t\t</div>\n", "\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "</script>\n", "<!-- confirm 레이어 -->\n", "<script id=\"layer-confirm-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"confirm-layer\">\n", "\t\t<h3 class=\"layer-title\">{{title}}</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<p>\n", "\t\t\t\t{{text}}\n", "\t\t\t</p>\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-ok\" id=\"btn_confirm\">확인</button>\n", "\t\t\t\t<button type=\"button\" class=\"btn btn-cancel\" id=\"btn_cancel\">취소</button>\n", "\t\t\t</div>\n", "\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "</script>\n", "<script id=\"layer-main-notice-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"main-notice-layer\" data-id=\"{{id}}\">\n", "\t\t<h3>{{title}}</h3>\n", "\t\t<div class=\"item_body\"><img src=\"{{img_url}}\" alt=\"\" /></div>\n", "\t\t<div class=\"item_check\">\n", "\t\t\t<label><input type=\"checkbox\" class=\"i_check\" /> 오늘 하루 더 이상 보지 않습니다.</label>\n", "\t\t\t<button type=\"button\" class=\"i-hide\">[닫기]</button>\n", "\t\t\t<a href=\"{{link_url}}\" target=\"_blank\" class=\"btn btn-orange item_link\">자세히보기</a>\n", "\t\t</div>\n", "\t\t<button type=\"button\" class=\"item_close i-hide\"><img src=\"//s.zigbang.com/legacy/images/v2/common/close.gif\" alt=\"\" /></button>\n", "\t</div>\n", "</script>\n", "<script id=\"layer-sleep-member-template\" type=\"text/x-handlebars-template\">\n", "\t<div class=\"basic-layer\" id=\"sleep-layer\">\n", "\t\t<h3 class=\"layer-title\">정보 통신망 이용촉진 및 정보보 등에 관한 법률 및 동법 시행령</h3>\n", "\t\t<div class=\"layer-body\">\n", "\t\t\t<div class=\"mb-15\">\n", "\t\t\t\t(주)직방은 정보통신망 이용촉진 및 정보보호에 관한 법률 및 동법 시행령에 따라 1년 동안 저희 서비스에 로그인 하지 않은 고객님의 개인정보를 분리, 보관(휴면계정처리) 하고자 합니다. 휴면을 원하지 않으시면 직방에 로그인 해주시기 바랍니다. 감사합니다.\n", "\t\t\t</div>\n", "\t\t\t<div class=\"gray-box\">\n", "\t\t\t\t[정보통신망 이용촉진 및 정보보호등에 관한 법률 제29조(개인정보의 파기)] 정보통신서비스 제공자 등은 정보통신서비스를 대통령령으로 정하는 기간 동안 이용하지 아니하는 이용자의 개인정보를 보호하기 위하여 대통령령으로 정하는 바에 따라 개인정보의 파기 등 필요한 조치를 취하여야 한다. <br /><br />[정보통신망 이용촉진 및 정보보호 등에 관한 법률 시행령 제16조(개인정보의 파기)] ①법 제 29조 제2항에서 “대통령령으로 정하는 기간” 이란 1년을 말한다. 다만, 각 호의 경우에는 해당 호에 따른 기간으로 한다. ②정보통신서비스 제공자 등은 제1항의 기간 만료30일 전까지 개인정보가 파기되거나 분리되어 저장•관리되는 사실과 기간 만료일 및 해당 개인정보의 항목을 전자우편•서면•모사전송 또는 이와 유사한 방법 중 어느 하나의 방법으로 이용자에게 알려야 한다.\n", "\t\t\t</div>\n", "\t\t\t<div class=\"layer-btn\">\n", "\t\t\t\t<button type=\"submit\" class=\"btn btn-ok\">확인</button>\n", "\t\t\t</div>\n", "\n", "\t\t\t<button type=\"button\" class=\"layer-close\"><img src=\"//s.zigbang.com/v1/web/common/layer_close.gif\" alt=\"레이어닫기\" /></button>\n", "\t\t</div>\n", "\t</div>\n", "</script>\n", "<script type=\"text/javascript\">\n", "/* <![CDATA[ */\n", "var google_conversion_id = 973803265;\n", "var google_custom_params = window.google_tag_params;\n", "var google_remarketing_only = true;\n", "/* ]]> */\n", "</script>\n", "<script src=\"//www.googleadservices.com/pagead/conversion.js\" type=\"text/javascript\">\n", "</script>\n", "<noscript>\n", "<div style=\"display:inline;\">\n", "<img alt=\"\" height=\"1\" src=\"//googleads.g.doubleclick.net/pagead/viewthroughconversion/973803265/?value=0&amp;guid=ON&amp;script=0\" style=\"border-style:none;\" width=\"1\"/>\n", "</div>\n", "</noscript>\n", "</body>\n", "</html>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dom = BeautifulSoup(response.content, \"html.parser\")\n", "dom" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "room_elements = dom.select(\"div.list-item\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(room_elements) #왜? 직방의 경우에는 동작하는 방식이 html을 먼저 내려주고 반짝일 때 빈칸으로 시작한다.\n", " #그 때 자바스크립트를 끄면 정보들 못 불러온다. 즉, html정보를 뿌려주고 나서 자바 스크립트가 매물 정보들을 불러온다.\n", " #네트워크를 확인해야 한다." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BASE_URL = \"https://api.zigbang.com/v1/items?detail=true&item_ids=5624018&item_ids=5608602&item_ids=5631474&item_ids=5642023&item_ids=5630702&item_ids=5644599&item_ids=5444342&item_ids=5644627&item_ids=5463078&item_ids=5555431&item_ids=5595091&item_ids=5567541&item_ids=5563197&item_ids=5519658&item_ids=5596519&item_ids=5647242&item_ids=5626793&item_ids=5512589&item_ids=5646209&item_ids=5490317&item_ids=5533865&item_ids=5610946&item_ids=5519928&item_ids=5655575&item_ids=5629638&item_ids=4774173&item_ids=5590314&item_ids=5526171&item_ids=5586149&item_ids=5589916&item_ids=5615990&item_ids=5169178&item_ids=5644492&item_ids=5608525&item_ids=5602646&item_ids=5608282&item_ids=5600902&item_ids=5441521&item_ids=5630922&item_ids=5644123&item_ids=5634530&item_ids=5652932&item_ids=5605314&item_ids=5641189&item_ids=5623803&item_ids=5641154&item_ids=5619885&item_ids=5508463&item_ids=5423901&item_ids=5642175&item_ids=5537230&item_ids=5644685&item_ids=5628997&item_ids=5595814&item_ids=5646707&item_ids=5593179&item_ids=5608753&item_ids=5600860&item_ids=5512559&item_ids=5594247\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = requests.get(BASE_URL)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "room_information = json.loads(response.text)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 서초구 잠원동',\n", " 'address2': '11-10',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\n", " 'agent_comment': '',\n", " 'agent_email': 'phil1600@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-9830',\n", " 'agent_name': '큰길공인중개사(박상엽)',\n", " 'agent_phone': '02-512-0132',\n", " 'bjd_code': '1165010600',\n", " 'bonbun_code': '11',\n", " 'bubun_code': '10',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 500,\n", " 'description': '▶가까운 지하철역 : 신사역\\n\\n▶편의시설 : 병원, 공원, 은행, 편의점, 음식점, 커피숍 등 \\n\\n▶옵션 : 에어컨, 싱크대, 가스레인지, 냉장고, 세탁기, 신발장\\n\\n▶보증금 조절 가능여부는 확인해야 함!!(궁금하신 사항은 전화로!)\\n\\n▶주차 가능여부도 확인해야 함!\\n\\n\\n큰 원룸방을 원하시는 분들에게 적합한 방!! 여성분들이 선호하는 방!!\\n이 외에도 원룸, 투룸 등 가격대에 비해 퀄리티 높은 위주로 다양한 매물 보유중입니다~ 연락주세요:)\\n전화 부재시 문자, 카톡으로 남겨 두시면 확인 후 바로 답변 해드릴게요!!(카톡 아이디 : fraukenzo)',\n", " 'description_og': '▶가까운 지하철역 : 신사역\\n\\n▶편의시설 : 병원, 공원, 은행, 편의점, 음식점, 커피숍 등 \\n\\n▶옵션 : 에어컨, 싱크대, 가스레인지, 냉장고, 세탁기, 신발장\\n\\n▶보증금 조절 가능여부는 확인해야 함!!(궁금하신 사항은 전화로!)\\n\\n▶주차 가능여부도 확인해야 함!\\n\\n\\n큰 원룸방을 원하시는 분들에게 적합한 방!! 여성분들이 선호하는 방!!\\n이 외에도 원룸, 투룸 등 가격대에 비해 퀄리티 높은 위주로 다양한 매물 보유중입니다~ 연락주세요:)\\n전화 부재시 문자, 카톡으로 남겨 두시면 확인 후 바로 답변 해드릴게요!!(카톡 아이디 : fraukenzo)',\n", " 'elevator': '없음',\n", " 'floor': '3층',\n", " 'floor_all': '4층',\n", " 'id': 5624018,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5624018/2892099a86c36abe1a6c5ae8d97e42c45ae84003.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5624018/ab8362087f4d44058aefe18bbe0bec7e849e9d11.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5624018/ac89ec83fa879c7fa44556f78e7cea9c6817887f.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5624018/730544d545ae1e866e534c01c6fb3f4fbc884b07.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5624018/d3adbb3b22595f22cc167fc47e1c8f35a413f1eb.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5624018/411bdc31a1afeb906e69ee95b4fa6aad381a9a62.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5624018/3246a76c14a9dee97a7387785167440d25285405.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '서초구',\n", " 'local3': '잠원동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '협의입주가능',\n", " 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-5917-2526',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\n", " 'random_location': '37.5172016054739,127.018038583093',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 55,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 12.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '39.67',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '●잠원동●신사역 5번 출구에서 5분거리인 원룸방!!',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'namdoongcom@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-8321',\n", " 'user_name': '중개보조원(김나윤)',\n", " 'user_no': 1545912,\n", " 'user_phone': '0507-1281-8321',\n", " 'view_count': 63},\n", " 'title': '서울시 서초구 잠원동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '65-13',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8, 2층',\n", " 'agent_comment': '',\n", " 'agent_email': 'click3090@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7491',\n", " 'agent_name': '건우공인중개사(김상모)',\n", " 'agent_phone': '010-8456-3090',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '65',\n", " 'bubun_code': '13',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 140,\n", " 'description': '[ 교통/위치 ] - ●\\n\\nㅇ 을지병원 뒷편에 위치한 풀옵션 원룸원거실 입니다.\\n\\nㅇ 학동역, 압구정역, 신사역 7~8분거리에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 거실에 티비, 쇼파, 테이블이 배치 되고도 넓직함을 자랑합니다.\\n\\nㅇ 신축건물로 깔끔한 화이트 톤에 포인트 아트월이 있습니다.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 필로티 구조로1층에 주차공간이 있으며 주차 1대 가능합니다.\\n\\nㅇ 인근에 편의점, 세탁소, 커피숍 등 다양한 편의시설이 있습니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'description_og': '[ 교통/위치 ] - ●\\n\\nㅇ 을지병원 뒷편에 위치한 풀옵션 원룸원거실 입니다.\\n\\nㅇ 학동역, 압구정역, 신사역 7~8분거리에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 거실에 티비, 쇼파, 테이블이 배치 되고도 넓직함을 자랑합니다.\\n\\nㅇ 신축건물로 깔끔한 화이트 톤에 포인트 아트월이 있습니다.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 필로티 구조로1층에 주차공간이 있으며 주차 1대 가능합니다.\\n\\nㅇ 인근에 편의점, 세탁소, 커피숍 등 다양한 편의시설이 있습니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'elevator': '있음',\n", " 'floor': '2층',\n", " 'floor_all': '5층',\n", " 'id': 5608602,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5608602/7e93c257b58d2e1dca6c24aee51271c610966416.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5608602/724ba546e9c5584b6c55e5b45339284553efe8cb.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5608602/45e8bfbe773f9b0c6ea362ffd18c1b3ef5b76722.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5608602/2c5ac87fda79ab4ef466af6948d3f89aa9572706.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5608602/0ced7220dc53fba39a15c606096e7905c4c88140.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5608602/3950271fa98333c5865220e1afa2872550237828.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5608602/46464f3363836628080f086b3639716dbc488292.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5608602/795087a338358ac44550adbd43df860fe02076aa.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5608602/03d47f53d6af934b2d871bc9d1a2db22c6f21273.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5608602/89f6b6b88d77591ec6dbf2e36d5bbde2bb11abd9.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5608602/d9986c8033000ba8343db57b9b8629b5e610030a.JPG'}],\n", " 'images_count': 11,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '하시입주가능',\n", " 'near_subways': '학동역(7호선), 압구정역(3호선), 강남구청역(7호선,분당선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-4622-8144',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/50423.jpg',\n", " 'random_location': '37.519445951968,127.030540130409',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 140,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '➰1룸+1거실➰을지병원 뒷편 깔끔한 최고옵션의 원룸원거실~ ★',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'cpd2108@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-5783',\n", " 'user_name': '중개보조원(최영수)',\n", " 'user_no': 50423,\n", " 'user_phone': '0507-1281-5783',\n", " 'view_count': 125},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '517-21',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 압구정로 2길 62, 1층 ',\n", " 'agent_comment': '',\n", " 'agent_email': 'hasunja@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-8981',\n", " 'agent_name': '청록공인중개사(하순자)',\n", " 'agent_phone': '02-3443-6500',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '517',\n", " 'bubun_code': '21',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 2000,\n", " 'description': '원룸가격에 투룸을!!\\n\\n \\n\\n신사역에서 5분거리!! 완전 가깝고 주변에 편의시설이좋은 현대그린마트와 강남시장의 투룸있어요~\\n\\n주택가라서 저녁에는 조용합니다~~\\n\\n1000/65 2000/60 실평수 11평의 투룸입니다!!\\n\\n이외에도 좋은물건 갖고 있으니 더궁금하신 사항은 연락주세요~\\n\\n청록공인 3443-6500\\n',\n", " 'description_og': '원룸가격에 투룸을!!\\n\\n \\n\\n신사역에서 5분거리!! 완전 가깝고 주변에 편의시설이좋은 현대그린마트와 강남시장의 투룸있어요~\\n\\n주택가라서 저녁에는 조용합니다~~\\n\\n1000/65 2000/60 실평수 11평의 투룸입니다!!\\n\\n이외에도 좋은물건 갖고 있으니 더궁금하신 사항은 연락주세요~\\n\\n청록공인 3443-6500\\n',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5631474,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5631474/96dc0d04fbe477151a5a3d11842b0adfa057d3cc.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5631474/90333385c5ecd58c92f41d29b4b51f338a61ba98.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5631474/261d031fd11f2f10fcaed274c3e3f78eb3e35dd4.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5631474/58ff05d01c1e4b4063cb0c9f1368196539cab824.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5631474/a21497ff72fafd29b3f54bcc6b10b3ec35f564b7.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5631474/886dd1c3bce988c613c4340f50ffdf65dc137cb6.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5631474/a938de9c6e6a5379e7523ef4fa82b21c8eb38a0d.jpg'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '계약후10일이내',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 압구정역(3호선)',\n", " 'options': '에어컨, 가스레인지',\n", " 'original_user_phone': '010-6271-1877',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5196143196011,127.020800893177',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 60,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 11.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '36.36',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '원룸방가격에 투룸을!! +_+ 클릭클릭!!',\n", " 'updated_at': '3일 전',\n", " 'user_email': 'huneloves@gmail.net',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-0534',\n", " 'user_name': '중개보조원(서재훈)',\n", " 'user_no': 935113,\n", " 'user_phone': '0507-1280-0534',\n", " 'view_count': 74},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '73-24',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'artsoup@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6420',\n", " 'agent_name': '즐겨찾기공인중개사(김영한)',\n", " 'agent_phone': '02-540-0124',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '73',\n", " 'bubun_code': '24',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '큰길이라 밤길도 문제 없이 안전하구요 \\n\\n\\n남향이라 밝고 쾌적합니다 \\n\\n베란다가 있어 용이하구요\\n\\n\\n\\n거실은 보시다시피 소파 티뷔 맞벽구로 좋습니다 \\n\\n\\n언제든지 보실수 있습니다 늦은시간이나 일찍오실분들은 미리 연락 부탁드리구요^^\\n\\n\\n언제나 다시 찾고 싶은 부동산과 중개인이 되도록 좋은집으로 보답하겠습니다 ^^',\n", " 'description_og': '큰길이라 밤길도 문제 없이 안전하구요 \\n\\n\\n남향이라 밝고 쾌적합니다 \\n\\n베란다가 있어 용이하구요\\n\\n\\n\\n거실은 보시다시피 소파 티뷔 맞벽구로 좋습니다 \\n\\n\\n언제든지 보실수 있습니다 늦은시간이나 일찍오실분들은 미리 연락 부탁드리구요^^\\n\\n\\n언제나 다시 찾고 싶은 부동산과 중개인이 되도록 좋은집으로 보답하겠습니다 ^^',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '5층',\n", " 'id': 5642023,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5642023/9f50f52c2ca6b1956c2ea18d468e437040cb7cbf.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5642023/3136e93d1d10f3bba0d586bc7a0e15faab157514.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5642023/17174f15e10242041b29b59f6a057951870ff509.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5642023/7cc20089b3848afc372e524f59e4551c40880cae.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5642023/69d127f3147de39536b38e1f28316f2a00e1a6e9.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5642023/b360f12422d05e84e4c7394b40f175d2f514d7fe.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5642023/84a8fd729546107df72ab99ad1f7f28af8b0664a.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5642023/cc1565164fe1d7b1296e0d7fd8c1c1b98af84b30.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5642023/b314d52a48c022ebc6640149807230f22229c26c.JPG'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\n", " 'options': '-',\n", " 'original_user_phone': '010-2446-3484',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5183542989367,127.031831461043',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 115,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 15.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '49.59',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '거실 맞벽구조 제대로 나옵니다 채광 통풍 잘되는 구조구요 집 좋으네요',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'cjpark12@hanmail.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6676',\n", " 'user_name': '중개보조원(박충진)',\n", " 'user_no': 810168,\n", " 'user_phone': '0507-1280-6676',\n", " 'view_count': 44},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '67-17',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 선릉로116길 24',\n", " 'agent_comment': '',\n", " 'agent_email': 'pudu0719__@daum.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-7300',\n", " 'agent_name': '삼성하우스공인중개사(윤미경)',\n", " 'agent_phone': '02-549-7654',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '67',\n", " 'bubun_code': '17',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 10000,\n", " 'description': '층당 1호실씩 있는 집합건물로, 4층 단독으로 1세대입니다. \\n\\n주인분이 거주하고 있는 집으로,\\n집을 아주 잘 꾸며 놓으셨습니다.\\n\\n빨간벽돌 건물이지만, 내부는 으리으리합니다~\\n\\n안방 붙박이장은 물론 방 하나를 붙박이장으로 만들어놓았습니다.\\n아무리 많은 옷도 소화 가능합니다.\\n거실 화장실 호텔식으로 리모델링했습니다. 욕조도 깨끗\\n거실 바닥 고급원목으로 깔았습니다.\\n각 창문마다 블러인드와 커튼 달아놓으셨구요\\n\\n옵션도 많습니다.\\n양문형냉장고 2개 김치냉장고 드럼세탁기 오븐 식기세척기 붙박이장\\n에어콘은 3대~~~\\n다 쓰시면 됩니다. 상태 좋습니다.\\n\\n주인분은 근처 단독주택으로 이사 가는겁니다.\\n전혀 문제 없는 집입니다.\\n\\n거실특대사이즈 (개인적으로 영화관 느낌으로 꾸며도 좋을 듯요^^)\\n거실주방분리형 \\n\\n실평 35평 정확히 나옵니다. \\n넓직합니다.\\n\\n통베란다 있습니다.\\n\\n주차 지정 1대입니다. 빼줄일 없구요\\n\\n외관이 빨간벽돌,\\n엘리베이터 없는 4층 \\n이 두가지가 나올법한 얘기인데, 감안해도 너무 싸게 잘 나온 방입니다.\\n\\n정말이지 에이스입니다\\n광복절 이전에 분명 나갈 매물입니다.\\n서두르세요~^^',\n", " 'description_og': '층당 1호실씩 있는 집합건물로, 4층 단독으로 1세대입니다. \\n\\n주인분이 거주하고 있는 집으로,\\n집을 아주 잘 꾸며 놓으셨습니다.\\n\\n빨간벽돌 건물이지만, 내부는 으리으리합니다~\\n\\n안방 붙박이장은 물론 방 하나를 붙박이장으로 만들어놓았습니다.\\n아무리 많은 옷도 소화 가능합니다.\\n거실 화장실 호텔식으로 리모델링했습니다. 욕조도 깨끗\\n거실 바닥 고급원목으로 깔았습니다.\\n각 창문마다 블러인드와 커튼 달아놓으셨구요\\n\\n옵션도 많습니다.\\n양문형냉장고 2개 김치냉장고 드럼세탁기 오븐 식기세척기 붙박이장\\n에어콘은 3대~~~\\n다 쓰시면 됩니다. 상태 좋습니다.\\n\\n주인분은 근처 단독주택으로 이사 가는겁니다.\\n전혀 문제 없는 집입니다.\\n\\n거실특대사이즈 (개인적으로 영화관 느낌으로 꾸며도 좋을 듯요^^)\\n거실주방분리형 \\n\\n실평 35평 정확히 나옵니다. \\n넓직합니다.\\n\\n통베란다 있습니다.\\n\\n주차 지정 1대입니다. 빼줄일 없구요\\n\\n외관이 빨간벽돌,\\n엘리베이터 없는 4층 \\n이 두가지가 나올법한 얘기인데, 감안해도 너무 싸게 잘 나온 방입니다.\\n\\n정말이지 에이스입니다\\n광복절 이전에 분명 나갈 매물입니다.\\n서두르세요~^^',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '4층',\n", " 'id': 5630702,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5630702/ce2c653600249cf2a80c3980f9cd17a72070d2dd.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5630702/c6a77ed65ec7787af73fd287e67fd43c06cd49b1.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5630702/117bac6ab9220c00003ef6b1dc1a70450f27ddbc.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5630702/a4472a028a18053c84f594c8653b35c5b4928ebe.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5630702/09b032dca3127941ddde98ce958055d315b59b1c.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5630702/b7292ae2be5ff0b2534b4492557316533daa547f.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5630702/e5b5d16e00b2827e40e20322c4e29642fb1ef133.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5630702/b1a9b88fd2a7153cfc861e0a671e491b681fb69f.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5630702/a5bae87ef58104b582aa028fe9bd73e6d8fda853.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5630702/795f77a5bcfadd476e8db49ec78eb07c8dcf7b1a.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5630702/64c389b49e0ce4a9a1ab456c68bc749757982853.JPG'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5630702/65d04f96871fea3daaf54cd8e7f5bcad073be0d5.JPG'},\n", " {'count': 13,\n", " 'index': 12,\n", " 'url': 'http://z1.zigbang.com/items/5630702/5bcbdc310ca29866fbab0550f7b3c582385c4da5.JPG'}],\n", " 'images_count': 13,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '없음',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '협의',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-9045-6470',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1616272/8e717c0ec7333cf0c29d53ce0cf1c3d9371cf8f7.jpg',\n", " 'random_location': '37.5185473925834,127.03087936438',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 190,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '쓰리룸+',\n", " 'secret_memo': None,\n", " 'size': 35.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '115.70',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '완전에이스 4R 포룸+욕실2개 아파트형구조 주인거주중인집 실평 넓고 채광좋고 옵션굿',\n", " 'updated_at': '3일 전',\n", " 'user_email': 'minguk6470@hanmail.net',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-6803',\n", " 'user_name': '중개보조원(송원환)',\n", " 'user_no': 1616272,\n", " 'user_phone': '0507-1281-6803',\n", " 'view_count': 98},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '523-34',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'snws@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6934',\n", " 'agent_name': '나무공인중개사(조민영)',\n", " 'agent_phone': '02-557-1888',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '523',\n", " 'bubun_code': '34',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 100,\n", " 'description': ' ㅇ 위치 및 역세권 정보\\n \\n &#186; 신사동 가로수길 바로 옆에 위치한 풀옵션 투룸입니다.\\n \\n &#186; 신사역 3출구 도보 5분거리, 대로변 버스정류장 도보 3분거리\\n \\n \\n ㅇ 구조 및 옵션 \\n \\n &#186; 신사동에 귀한 투룸이고 가격에 비해 싸게 나와 인기가 매우 많습니다.\\n \\n &#186; 올 화이트 톤으로 깔끔하게 리모델링해서 정말 깔끔합니다.\\n\\n &#186; 벽걸이 티비있으며, 3인용 쇼파, 앉아 안락하게 볼수있습니다.\\n \\n &#186; 남향이라 햇빛도 잘들어오고 겨울에 춥지 않게 사용할수 있습니다. \\n \\n \\n ㅇ 주차 및 편의시설\\n\\n &#186; 주차 1대 협의\\n \\n &#186; 대로변 주변으로 세탁소, 커피숍, 카센터 PC방등 편의시설이 다양합니다.\\n',\n", " 'description_og': ' ㅇ 위치 및 역세권 정보\\n \\n &amp;#186; 신사동 가로수길 바로 옆에 위치한 풀옵션 투룸입니다.\\n \\n &amp;#186; 신사역 3출구 도보 5분거리, 대로변 버스정류장 도보 3분거리\\n \\n \\n ㅇ 구조 및 옵션 \\n \\n &amp;#186; 신사동에 귀한 투룸이고 가격에 비해 싸게 나와 인기가 매우 많습니다.\\n \\n &amp;#186; 올 화이트 톤으로 깔끔하게 리모델링해서 정말 깔끔합니다.\\n\\n &amp;#186; 벽걸이 티비있으며, 3인용 쇼파, 앉아 안락하게 볼수있습니다.\\n \\n &amp;#186; 남향이라 햇빛도 잘들어오고 겨울에 춥지 않게 사용할수 있습니다. \\n \\n \\n ㅇ 주차 및 편의시설\\n\\n &amp;#186; 주차 1대 협의\\n \\n &amp;#186; 대로변 주변으로 세탁소, 커피숍, 카센터 PC방등 편의시설이 다양합니다.\\n',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '4층',\n", " 'id': 5644599,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5644599/ddd39acae052ff14c88b3ff435f03bca389ade6f.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5644599/3c5be037f1430da3950664cd88ab3edac03ebd2c.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5644599/cce2097def2340e37a9fd83e517790933d4a2f5d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5644599/6e05c2145a64c1b8d38b990911196c682e05923b.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5644599/5689199f6538d58d50475e3077d3453085a25a47.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5644599/948a832feb2d9b157d00a7da624e6eb5cbb3612c.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5644599/32753d5d5d4abb0346c7cf4334e46feedbf8aa9d.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5644599/73094f8cf2e1fb9256c78a3ed1c56c9557c46c78.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5644599/89c3534164847dd4fc776c8e5974b92cbf875286.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5644599/0fce439ae36e22973399cafc1e8567bae67d26a4.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5644599/50bb1320cbbc0d1b5516188804420ab4ed2deeee.JPG'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5644599/08c238f4937dc13a48b965f7d247f5c662abed82.JPG'}],\n", " 'images_count': 12,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 논현역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7589-0888',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\n", " 'random_location': '37.5214737984472,127.020739525254',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 16.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '52.89',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '❌신사동❌ 리모델링❌ 귀한 투룸',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'zldzkfn@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-2240',\n", " 'user_name': '중개보조원(김동현)',\n", " 'user_no': 1000906,\n", " 'user_phone': '0507-1281-2240',\n", " 'view_count': 55},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '550-2',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 625-10 커피한잔부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'handstar1018@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6659',\n", " 'agent_name': '커피한잔공인중개사(손별)',\n", " 'agent_phone': '010-9226-9082',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '550',\n", " 'bubun_code': '2',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 3000,\n", " 'description': ' # 100% 실매물 100% 실사진 # \\n차별화된 서비스 전문적으로 책임있게 구해드립니다\\n친절한 미소로 불편한점 없도록 모시겠습니다~^^\\n ★매물담당자 번호로 연락주세요~★\\n\\n▶(신사동)가로수길 압구정역 도보5분거리에 위치합니다.\\n\\n▶방2개 욕실1개 거실맞벽 베란다2개 실16평 \\n\\n▶주차는 1대 무료 가능합니다.\\n\\n▶올수리해서 연식대비 아주 깔끔합니다.\\n\\n▶에어컨,세탁기,냉장고,싱크대,가스렌지,붙박이장,신발장 풀옵션 입니다.\\n\\n▶입주는 현 세입자와 협의(빠른입주 가능합니다.)\\n\\n▶언제든지 볼 수 있습니다.\\n\\n▶자세한사항은 부담없이 24시간 연락주시면 친절히 상담하겠습니다~^^ \\n\\n------------------------------------------------------------------\\n\\n카카오톡 ID happyhouse9 입니다. 카카오톡 상담도 환영입니다~^^',\n", " 'description_og': ' # 100% 실매물 100% 실사진 # \\n차별화된 서비스 전문적으로 책임있게 구해드립니다\\n친절한 미소로 불편한점 없도록 모시겠습니다~^^\\n ★매물담당자 번호로 연락주세요~★\\n\\n▶(신사동)가로수길 압구정역 도보5분거리에 위치합니다.\\n\\n▶방2개 욕실1개 거실맞벽 베란다2개 실16평 \\n\\n▶주차는 1대 무료 가능합니다.\\n\\n▶올수리해서 연식대비 아주 깔끔합니다.\\n\\n▶에어컨,세탁기,냉장고,싱크대,가스렌지,붙박이장,신발장 풀옵션 입니다.\\n\\n▶입주는 현 세입자와 협의(빠른입주 가능합니다.)\\n\\n▶언제든지 볼 수 있습니다.\\n\\n▶자세한사항은 부담없이 24시간 연락주시면 친절히 상담하겠습니다~^^ \\n\\n------------------------------------------------------------------\\n\\n카카오톡 ID happyhouse9 입니다. 카카오톡 상담도 환영입니다~^^',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5444342,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5444342/7a9402b64f3f4cf9a5a0100a3f98fbded37f4de8.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5444342/3de536ca9179af4526a9e0cfde1cafb4d48e6697.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5444342/797475f20508f4ec4eb5de78a39d0089c0c26ab1.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5444342/1992a24baed044c631da8bb16f58db13f2f9a56a.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5444342/46bac53d7f362877fbc6e1109cfd6932f828eab0.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5444342/490c40ed27679b2c002da41b806492ff0e8dadf8.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5444342/e34ed1842ee196e414e2ab54b29987e86aec676f.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5444342/ec9b76b586193cc9a235804026554f9054b33582.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5444342/b9755ce670fc53a0fdf8b7033ac7da6d3f9a6187.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5444342/7d235ca27dd02ce5442752c03963c56b4a9c86c0.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5444342/077ff671571f89b9d6e0c0d69635abe209d794ed.JPG'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5444342/278d9a27cf51c41f25fda0c3c04ae611ea055dd2.JPG'}],\n", " 'images_count': 12,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '7만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '협의입주(빠른입주가능)',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-4551-0717',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5235003949622,127.023868325345',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 18.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '59.50',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '♣♧실매물 리모델링 깔끔한2룸 거실맞벽 베란다2개 풀옵션 A급 추천매물♣♧',\n", " 'updated_at': '20일 전',\n", " 'user_email': 'raymond4860@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-8195',\n", " 'user_name': '중개보조원(김세원)',\n", " 'user_no': 14897,\n", " 'user_phone': '0507-1280-8195',\n", " 'view_count': 269},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'snws@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6934',\n", " 'agent_name': '나무공인중개사(조민영)',\n", " 'agent_phone': '02-557-1888',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 120,\n", " 'description': '- 신사역 도보 약 4분거리에 위치\\n\\n- 복층형 구조로 공간활용성 좋구요.\\n\\n- 신축 대리석바닥에 새집기 모두다 새것입니다.\\n\\n- 1층과 복층 모두 TV가 있어 두분이 지내시기에도 정말 좋구요.\\n\\n- 신축이라서 내부 컨디션은 최상입니다.\\n\\n- 주차 가능\\n\\n- 신사역 인근으로 편의시설 많습니다.',\n", " 'description_og': '- 신사역 도보 약 4분거리에 위치\\n\\n- 복층형 구조로 공간활용성 좋구요.\\n\\n- 신축 대리석바닥에 새집기 모두다 새것입니다.\\n\\n- 1층과 복층 모두 TV가 있어 두분이 지내시기에도 정말 좋구요.\\n\\n- 신축이라서 내부 컨디션은 최상입니다.\\n\\n- 주차 가능\\n\\n- 신사역 인근으로 편의시설 많습니다.',\n", " 'elevator': '있음',\n", " 'floor': '3층',\n", " 'floor_all': '5층',\n", " 'id': 5644627,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5644627/2e5caee1cff5f0d3083edbe3d86f85fe004b552d.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 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'http://z1.zigbang.com/items/5644627/2cc9abca313eed915dae0b783c385aa055447c31.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5644627/b022e378bcf694a46c4f897ba3f63526038d511a.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5644627/d3c557b78b34c65bab433da17609d61ce4788a16.JPG'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5644627/c4e51b2d8881f18406174736987132110ae52810.JPG'}],\n", " 'images_count': 12,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '수도, 인터넷, TV',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7589-0888',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\n", " 'random_location': '37.5165587310278,127.023506978132',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 120,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(복층형)',\n", " 'secret_memo': None,\n", " 'size': 11.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '36.36',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '❌신사역❌ 인근에 위치한 귀한 복층 풀옵션',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'zldzkfn@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-2240',\n", " 'user_name': '중개보조원(김동현)',\n", " 'user_no': 1000906,\n", " 'user_phone': '0507-1281-2240',\n", " 'view_count': 38},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '21-6',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 삼성동41번지 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'scvjcs@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7189',\n", " 'agent_name': '한빛공인중개사(정철수)',\n", " 'agent_phone': '02-515-9639',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '21',\n", " 'bubun_code': '6',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '*강남대로변 바로 이면이며 방사이즈가 큰편이며\\n 앞에 막힌건물이 없어 확트여 채광,통풍좋으면 구조가 아주 좋은 방입니다\\n*올 수리한 깔끔 하며 조용한 학동공원 주변\\n*버스정 류장1분거리이며 신사.논현역 역세권 입니다\\n*항상고객을 먼저 생각하는 부동산 입니다 ',\n", " 'description_og': '*강남대로변 바로 이면이며 방사이즈가 큰편이며\\n 앞에 막힌건물이 없어 확트여 채광,통풍좋으면 구조가 아주 좋은 방입니다\\n*올 수리한 깔끔 하며 조용한 학동공원 주변\\n*버스정 류장1분거리이며 신사.논현역 역세권 입니다\\n*항상고객을 먼저 생각하는 부동산 입니다 ',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5463078,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5463078/42ea80191ee094d0f5663c560a10c12c3d910eb7.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5463078/0be593035eaf1f818d672066c2daa654420dd23f.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5463078/9b292b55e0cbdfec900ffa1815dcc0d9c7ed1850.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5463078/50c57776991e48443626c5e0aa83ad7912a1984c.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5463078/a7b34e645bf54c02120e01ae49a8da7af4575166.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5463078/62299be69f6c2eba5c8726ca4cf99920d71f01ac.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5463078/9911ca0107113e7bfbf62526d7393f5e461a96b6.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '날짜협의',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\n", " 'options': '-',\n", " 'original_user_phone': '010-8983-8111',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5147530100533,127.021129970585',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 60,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '🌺🌺신사초역세권 올 수리 원룸 풀옵션 월세🌺🌺',\n", " 'updated_at': '19일 전',\n", " 'user_email': 'scvjcs@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-2906',\n", " 'user_name': '중개보조원(홍성호)',\n", " 'user_no': 930206,\n", " 'user_phone': '0507-1281-2906',\n", " 'view_count': 480},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '78-3',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\n", " 'agent_comment': '',\n", " 'agent_email': 'phil1600@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-9830',\n", " 'agent_name': '큰길공인중개사(박상엽)',\n", " 'agent_phone': '02-512-0132',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '78',\n", " 'bubun_code': '3',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '【매물 정보】\\n \\n○ 위치:학동역 10번 출구 도보 5분 거리\\n\\n○ 금액:보증금1000/월세50 보증금조정가능,허위매물X\\n\\n○ 입주: 즉시입주\\n\\n○ 층수:102호\\n\\n○ 크기:실10평\\n\\n○ 옵션:에어컨,싱크대\\n \\n○ 비고: YO!! 어차피 지금은 성수기 !\\n\\n #왜모르세요? #강남 #오피스텔 #원룸 #\\n#행운의여보세요 #좋아요',\n", " 'description_og': '【매물 정보】\\n \\n○ 위치:학동역 10번 출구 도보 5분 거리\\n\\n○ 금액:보증금1000/월세50 보증금조정가능,허위매물X\\n\\n○ 입주: 즉시입주\\n\\n○ 층수:102호\\n\\n○ 크기:실10평\\n\\n○ 옵션:에어컨,싱크대\\n \\n○ 비고: YO!! 어차피 지금은 성수기 !\\n\\n #왜모르세요? #강남 #오피스텔 #원룸 #\\n#행운의여보세요 #좋아요',\n", " 'elevator': '없음',\n", " 'floor': '1층',\n", " 'floor_all': '3층',\n", " 'id': 5555431,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5555431/fb65977caf7ad41c5db8c79776d61ca89164c93f.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5555431/6989cc014e3f5c233dcceb45596c3f5d9135d25b.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5555431/aa5bd1149df4ed15a97b5a028b7819997da6319d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5555431/8bc0197de73098490b000ceb625b263a8475d510.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5555431/1f3985b0d6e642f2c1853965a5fb48f0a730cd88.JPG'}],\n", " 'images_count': 5,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 신사역(3호선)',\n", " 'options': '에어컨, 싱크대',\n", " 'original_user_phone': '010-5820-0678',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\n", " 'random_location': '37.516657741281,127.032024827408',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 50,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '#논현동#초A급매물#히트다히트잉~♥',\n", " 'updated_at': '6일 전',\n", " 'user_email': 'cjw4060@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-1909',\n", " 'user_name': '중개보조원(최정웅)',\n", " 'user_no': 1545912,\n", " 'user_phone': '0507-1281-1909',\n", " 'view_count': 486},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '566-11',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 신사동 610-2 1층',\n", " 'agent_comment': '',\n", " 'agent_email': 'suny1209@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7187',\n", " 'agent_name': '열린공인중개사(강광수)',\n", " 'agent_phone': '02-511-0222',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '566',\n", " 'bubun_code': '11',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 8000,\n", " 'description': '분리형원룸 넓은방과 부엌이 넓어요\\n\\n전체 건물 융자없어요\\n\\n반지층 이지만 채광 좋아요\\n\\n실매물 실사진 연락주셔요',\n", " 'description_og': '분리형원룸 넓은방과 부엌이 넓어요\\n\\n전체 건물 융자없어요\\n\\n반지층 이지만 채광 좋아요\\n\\n실매물 실사진 연락주셔요',\n", " 'elevator': '없음',\n", " 'floor': '반지하',\n", " 'floor_all': '3층',\n", " 'id': 5595091,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5595091/a5a9c066c428e7af1775cc184c9d9fd5191492bc.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5595091/1023bb9e37455db5aec4d7eeee6c6d60cdaea4d0.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5595091/14f79f9fda963146dd6a6c2c2e32a31893341825.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5595091/720017b963b2bb82206a3167a6c5dacb638338ef.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5595091/f61188ab5379bc448e6e7bf0ec68ec30b3344e12.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5595091/dcd5a5e6395d1550c317b9395e742a914ddd3a82.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5595091/918ee6b6508c76f9d6c8ecc11bac9fe810512725.jpg'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '7만원',\n", " 'manage_cost_inc': '수도',\n", " 'movein_date': '협의',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 신발장, 싱크대',\n", " 'original_user_phone': '010-9134-5291',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\n", " 'random_location': '37.5223675424642,127.026164124225',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '분리형원룸 베란다 있어요 ^^',\n", " 'updated_at': '6일 전',\n", " 'user_email': 'suny1209@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-7187',\n", " 'user_name': '대표공인중개사(강광수)',\n", " 'user_no': 676867,\n", " 'user_phone': '0507-1280-7187',\n", " 'view_count': 291},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '39-6',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 삼성동41번지 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'scvjcs@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7189',\n", " 'agent_name': '한빛공인중개사(정철수)',\n", " 'agent_phone': '02-515-9639',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '39',\n", " 'bubun_code': '6',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 3000,\n", " 'description': '*풀옵션 방 신축첫입니다\\n*건물수려하며 내부 고급스러운 자재 사용\\n*테라스 있는 방구하기 쉬지 않아용 얼른서두르세요\\n*주변 조용하며 치안좋고 살기 좋은 동네 학동공원 주변입니다\\n*헉동역도보3분 논현역도보5분 이내거리입니다',\n", " 'description_og': '*풀옵션 방 신축첫입니다\\n*건물수려하며 내부 고급스러운 자재 사용\\n*테라스 있는 방구하기 쉬지 않아용 얼른서두르세요\\n*주변 조용하며 치안좋고 살기 좋은 동네 학동공원 주변입니다\\n*헉동역도보3분 논현역도보5분 이내거리입니다',\n", " 'elevator': '있음',\n", " 'floor': '5층',\n", " 'floor_all': '6층',\n", " 'id': 5567541,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5567541/84ce51f62a6676284c931d1dae426c3bef2427b4.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5567541/e50799a7d98e71110d0041db77d644db107d0d71.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5567541/bd70eb23e6ae2358a87ba7b468ed2a261bcda3d6.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5567541/055e6bad8055f3c974ecb0a8569750f74fbb035d.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5567541/2a6073947a5c1bfe9137bcf51356ffd67764af35.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5567541/5705a3cf2f207e720f07102766ca119a949bd4bc.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5567541/a889490ee7ad86eaa3fe6b8f4bdff53b2b08936b.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시',\n", " 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\n", " 'options': '-',\n", " 'original_user_phone': '010-8983-8111',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5139452609109,127.027338094298',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 12.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '39.67',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '**학동역 테라스있는 신축투룸월세**',\n", " 'updated_at': '10일 전',\n", " 'user_email': 'scvjcs@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-2906',\n", " 'user_name': '중개보조원(홍성호)',\n", " 'user_no': 930206,\n", " 'user_phone': '0507-1281-2906',\n", " 'view_count': 627},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '73-5',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'artsoup@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6420',\n", " 'agent_name': '즐겨찾기공인중개사(김영한)',\n", " 'agent_phone': '02-540-0124',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '73',\n", " 'bubun_code': '5',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 2000,\n", " 'description': '- 조용한 동네에 위치하고 있어 주거환경 좋은 방이에요\\n\\n- 분리형 스타일이라서 집에서 요리해도 옷에 음식냄새 걱정없는 추천드릴 방~\\n\\n- 현재 공실로 바로 입주 가능해요~\\n\\n- 주인분이 거주하는 집이라서 집 관리도 잘되고 보안 방범도 좋답니다.\\n\\n- 빨리 계약되는 집이니 서두르셔서 오세요~^^\\n',\n", " 'description_og': '- 조용한 동네에 위치하고 있어 주거환경 좋은 방이에요\\n\\n- 분리형 스타일이라서 집에서 요리해도 옷에 음식냄새 걱정없는 추천드릴 방~\\n\\n- 현재 공실로 바로 입주 가능해요~\\n\\n- 주인분이 거주하는 집이라서 집 관리도 잘되고 보안 방범도 좋답니다.\\n\\n- 빨리 계약되는 집이니 서두르셔서 오세요~^^\\n',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5563197,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5563197/6efcb7ccb86acd4bb2f4826e5eb69030655836b8.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5563197/58d592c99f82d47d0047010a70de94ee980dba02.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5563197/eb3d8d9d66886d0e76ce171f304271a5825a29dd.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5563197/10306fde9c537b35afd8e4013da2a265658d7fdb.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5563197/52c66efbf59f8f584a9033b2bcc68ca8487d498f.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5563197/30fbac752bfe2ae199d450c85c5a99671b9a14b3.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5563197/3329ed644b833099969aa665c9e33a737b1f7f7a.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\n", " 'options': '에어컨, 신발장',\n", " 'original_user_phone': '010-2446-3484',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5181632633649,127.03131882067',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 50,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 9.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '29.75',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '@@ 채광좋고 구조좋은 지상층 분리형 방 @@',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'cjpark12@hanmail.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6676',\n", " 'user_name': '중개보조원(박충진)',\n", " 'user_no': 810168,\n", " 'user_phone': '0507-1280-6676',\n", " 'view_count': 293},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '593-31월담빌딩 5층 503호',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 217-46',\n", " 'agent_comment': '',\n", " 'agent_email': 'mhj06100610@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-4362',\n", " 'agent_name': '한영공인중개사(모형진)',\n", " 'agent_phone': '010-4737-9178',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 12000,\n", " 'description': '안녕하세요 신사동에 위치하고 있고 급하게 나온 매물입니다.\\n\\n일단 현재 여성 세입자가 살고 계신데 방이 너무 깨끗합니다. 일단 실평수 10평정도 인데 그것보다 더큰평수같구요\\n혼자살기에 부담 스러울 정도의 사이즈라고 생각합니다\\n\\n에어컨 , 신발장, 가스레인지등 옵션이 있구요 주차는 달에 10만원 입니다 강남치고는 저렴한 가격입니다.\\n\\n걸어서 5분거리에 바로 압구정역이 있구요 근처 여건이 너무 좋습니다.\\n1층에 어린이집이 있어서 근처에 혐오시설은 없습니다.\\n\\n현재 살고계신 세입자분이 침대, 냉장고, 세탁기등 싸게 양도한다고 하니 이점 참고하시면 될것 같습니다.\\n\\n너무좋은 조건으로 나온 매물이라 금방 계약되오니 바로 연락주세요',\n", " 'description_og': '안녕하세요 신사동에 위치하고 있고 급하게 나온 매물입니다.\\n\\n일단 현재 여성 세입자가 살고 계신데 방이 너무 깨끗합니다. 일단 실평수 10평정도 인데 그것보다 더큰평수같구요\\n혼자살기에 부담 스러울 정도의 사이즈라고 생각합니다\\n\\n에어컨 , 신발장, 가스레인지등 옵션이 있구요 주차는 달에 10만원 입니다 강남치고는 저렴한 가격입니다.\\n\\n걸어서 5분거리에 바로 압구정역이 있구요 근처 여건이 너무 좋습니다.\\n1층에 어린이집이 있어서 근처에 혐오시설은 없습니다.\\n\\n현재 살고계신 세입자분이 침대, 냉장고, 세탁기등 싸게 양도한다고 하니 이점 참고하시면 될것 같습니다.\\n\\n너무좋은 조건으로 나온 매물이라 금방 계약되오니 바로 연락주세요',\n", " 'elevator': '있음',\n", " 'floor': '5층',\n", " 'floor_all': '8층',\n", " 'id': 5519658,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5519658/2442c3ddaa932579aa91304f339967f7c048f9a9.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5519658/5d753ccb675500cfbae35196db1b4ae9654b47e3.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5519658/3a298ff43553ae1da6fe98eb947ca7f8fc89d4c4.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5519658/1abad812840849156d83541485e3b1539d56fc45.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 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'random_location': '37.5219997788444,127.029659203959',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 15,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.1,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.39',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '신사동 가격대비 컨디션 좋은 원룸 방 싸이즈 큽니다.',\n", " 'updated_at': '13일 전',\n", " 'user_email': 'secretk1107@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-0918',\n", " 'user_name': '중개보조원(박민우)',\n", " 'user_no': 112775,\n", " 'user_phone': '0507-1281-0918',\n", " 'view_count': 462},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '',\n", " 'agent_email': 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'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시',\n", " 'near_subways': '압구정역(3호선), 학동역(7호선), 압구정로데오역(분당선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 침대, 신발장, 싱크대',\n", " 'original_user_phone': '010-9134-5291',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\n", " 'random_location': '37.522970287609,127.029636722075',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 70,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '신축원룸 도시형주택 풀옵션 좋아요',\n", " 'updated_at': '어제',\n", " 'user_email': 'suny1209@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-7187',\n", " 'user_name': '대표공인중개사(강광수)',\n", " 'user_no': 676867,\n", " 'user_phone': '0507-1280-7187',\n", " 'view_count': 16},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 서초구 잠원동',\n", " 'address2': '10-43',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 동산로 46 , 1층 (양재동)',\n", " 'agent_comment': '안녕하세요 서초 강남 송파지역 원룸/오피스텔/전세빌라/매매 를 전문으로하고있는 언남부동산 염영규 실장입니다.\\n저희는 허위매물따위는 절대 하지않고 고객께서 쓸데없는 헛걸음 하시는걸 제일싫어하므로 \\n전화나 카톡으로 사전에 꼼꼼히 체크하여 안좋은방이나 원하지 않는방을 보여주는일은 절대없습니다.\\n믿고 맡기시면 됩니다. 감사합니다~!',\n", " 'agent_email': 'lwh2136@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1280-7497',\n", " 'agent_name': '언남공인중개사(이원희)',\n", " 'agent_phone': '02-573-8600',\n", " 'bjd_code': '1165010600',\n", " 'bonbun_code': '10',\n", " 'bubun_code': '43',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, \\n 위치만 말씀해주시면 픽업서비스도 해드립니다. \\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) ※인증된 대형 부동산 !! \\n★ 보증금 / 임대기간 (조정가능) \\n\\n\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\n\\n\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 염실장 입니다^^ \\n저희는 300% 실매물에 실사진으로만 운영합니다. \\n강남 전지역(강남구,서초구,송파구) 원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House\\n★ 구경오세요!!! 직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\n직방내 모든매물 상담가능합니다.^^ \\n\\n\\n■ KAKAO Talk : yyk0410 ■',\n", " 'description_og': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, \\n 위치만 말씀해주시면 픽업서비스도 해드립니다. \\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) ※인증된 대형 부동산 !! \\n★ 보증금 / 임대기간 (조정가능) \\n\\n\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\n\\n\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 염실장 입니다^^ \\n저희는 300% 실매물에 실사진으로만 운영합니다. \\n강남 전지역(강남구,서초구,송파구) 원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House\\n★ 구경오세요!!! 직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\n직방내 모든매물 상담가능합니다.^^ \\n\\n\\n■ KAKAO Talk : yyk0410 ■',\n", " 'elevator': '있음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 5626793,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5626793/1edff41a208a93d7f11f5d70239420ed4549c84f.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5626793/ee1d71ac533db971eaa3f16cefad475369c43720.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5626793/b286a7c12b37b23d105dc1ca091f7c0ce5532d6b.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5626793/3d4ee000d3cbb064b6a71ff1f5ee08d80e7ccf75.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 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False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '서초구',\n", " 'local3': '잠원동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시',\n", " 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-5139-2669',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5161220416603,127.017151886331',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 80,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 9.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '29.75',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '■■신축■첫입주■화이트톤■풀옵션■신사역3분■■',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'jsyyk0410@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-6502',\n", " 'user_name': '중개보조원(염영규)',\n", " 'user_no': 1129550,\n", " 'user_phone': '0507-1281-6502',\n", " 'view_count': 67},\n", " 'title': '서울시 서초구 잠원동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '569-14',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '569',\n", " 'bubun_code': '14',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 65,\n", " 'description': '* 압구정역에서 5분거리에 위치한 깔끔한고 예쁘게 꾸며진 방입니다.\\n\\n* 화이트침구류와 쇼파가 셋팅되어있고 벽걸이TV까지 편리함을 더했습니다.\\n\\n* 컬러벽지로 도보해서 단조로운 원룸을 하나밖에 없는 나만의 원룸으로 꾸몄어요\\n\\n* 압구정역근처 몇없는 매물중에 저렴하면서 가장 깔끔하게 나오는 원룸이에요~!!\\n\\n* 주택가에 위치해서 시끄럽지 않고 조용해서 좋아요\\n\\n* 지하철 3호선을 5분거리로 이용하실수 있고 동호대교로 강북으로의 이동도 편리해요',\n", " 'description_og': '* 압구정역에서 5분거리에 위치한 깔끔한고 예쁘게 꾸며진 방입니다.\\n\\n* 화이트침구류와 쇼파가 셋팅되어있고 벽걸이TV까지 편리함을 더했습니다.\\n\\n* 컬러벽지로 도보해서 단조로운 원룸을 하나밖에 없는 나만의 원룸으로 꾸몄어요\\n\\n* 압구정역근처 몇없는 매물중에 저렴하면서 가장 깔끔하게 나오는 원룸이에요~!!\\n\\n* 주택가에 위치해서 시끄럽지 않고 조용해서 좋아요\\n\\n* 지하철 3호선을 5분거리로 이용하실수 있고 동호대교로 강북으로의 이동도 편리해요',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '5층',\n", " 'id': 5512589,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5512589/39a6dc638c0f1c038b332f7476c2ce9b52bebd1b.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5512589/f29c7b1cf674c94e3d4cb87297f5d994069b8b84.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5512589/023b497c7d4da2e5047264f2b5b0003b0c539aff.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5512589/ab25517fe2f22bf0e436e07b3c65ca37d0a633ca.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5512589/f7f9a8608356a67d64fe0c30f8ce4b4132f28b68.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5512589/2ba1dc72eecff3ca931be7764a22c91f39c39056.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5512589/bb9958412ad51b038b49fa53bd61de04936932ad.jpg'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '6만원',\n", " 'manage_cost_inc': 'TV',\n", " 'movein_date': '즉시 가능',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\n", " 'original_user_phone': '010-6406-9464',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5234972734648,127.027030530475',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 65,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 7.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '23.14',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕ 압구정역 가까운 깔끔하고 아주 예쁜방!!',\n", " 'updated_at': '14일 전',\n", " 'user_email': 'jinyong0223@hanmail.net',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-8860',\n", " 'user_name': '중개보조원(박진용)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-8860',\n", " 'view_count': 449},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '29-7 토미하우스 플러스 2층 205호 ',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\n", " 'agent_comment': '제가 직접 발로 뛰며 찍은 200% 실가격 실사진입니다 \\n절대 양심은 팔지 않습니다 말도 안되는 거짓매물에 현혹 되지 마시길 바랍니다\\n[24시 문의♥카톡ID : mcmax456] \\n',\n", " 'agent_email': 'phil1600@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-9830',\n", " 'agent_name': '큰길공인중개사(박상엽)',\n", " 'agent_phone': '02-512-0132',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 2000,\n", " 'description': '◇방 소 개◇\\n\\n① 분리형 구조 풀옵션 원룸 채광좋고 통풍이 잘되어 있습니다\\n② 관리가 잘된 신축 건물이라 실내가 전체적으로 깨끗합니다 \\n③ 독특한 구조로 만든 원룸입니다 드레스룸 따로 설치되있습니다\\n④ 호텔보다 더 좋은 실내 인테리어로 고급스러움을 자랑합니다\\n⑤ 베란다가 상당히 넓어 수납공간에 용이합니다\\n⑥ 로비룸이 따로있어 간단한 음료등등 마실수있는 공간배치\\n⑦ 현관 입구도 보안키가 있어 보안에 신경을 많이 쓴 건물입니다\\n⑧ 보증금 조절 가능합니다 언제든지 문의주세요 \\n⑨ 고민하는 순간 계약 완료!! 서두르세요 \\n\\n\\n◇관 리 비◇\\n\\n⊙ 10만원 (자세한건 임대인과 협의)\\n\\n\\n◇가 까 운 역◇\\n\\n⊙ 학동역 도보 5분거리\\n\\n\\n◇주 차 시 설◇\\n\\n⊙ 주차 1인 1대 가능합니다(주차시스템 잘되어있습니다)\\n\\n\\n◇편 의 시 설◇\\n\\n① 건물 주변이 대부분 주택가 지역이라 조용하고 아늑합니다\\n② 주변 편의시설 (병원,약국,우체국,편의점,각종음식점등)\\n③ 주변 초등학교 공원등 다양한 편의시설이 있습니다\\n\\n\\n◇한 마 디◇ \\n\\n⊙ 가격대비 좋은 원룸! 언제 빠질지 모릅니다 관심 있으신분들은 빠른 문의주세요\\n⊙ 이 외에도 원룸 투룸등 S급 A급 위주로 다양한 매물 보유중! 언제든지 문의주세요 \\n⊙ 전화 부재시 문자&카톡 문의주시면 무조건~무조건~ 친절상담 해드려요^^♥ \\n⊙ 200% 실사진 실가격 실매물입니다 믿고 보셔도 됩니다\\n⊙ 절대 양심은 팔지 않겠습니다 말도 안되는 거짓 매물에 현혹 되지마시길 바랍니다 \\n\\n[24시 문의♥카톡ID : mcmax456] \\n\\n\\n♠어떠한 조건이라도 내집처럼 마련해 드리겠습니다♠ \\n♠200% 실매물 실가격으로 보답하겠습니다♠\\n',\n", " 'description_og': '◇방 소 개◇\\n\\n① 분리형 구조 풀옵션 원룸 채광좋고 통풍이 잘되어 있습니다\\n② 관리가 잘된 신축 건물이라 실내가 전체적으로 깨끗합니다 \\n③ 독특한 구조로 만든 원룸입니다 드레스룸 따로 설치되있습니다\\n④ 호텔보다 더 좋은 실내 인테리어로 고급스러움을 자랑합니다\\n⑤ 베란다가 상당히 넓어 수납공간에 용이합니다\\n⑥ 로비룸이 따로있어 간단한 음료등등 마실수있는 공간배치\\n⑦ 현관 입구도 보안키가 있어 보안에 신경을 많이 쓴 건물입니다\\n⑧ 보증금 조절 가능합니다 언제든지 문의주세요 \\n⑨ 고민하는 순간 계약 완료!! 서두르세요 \\n\\n\\n◇관 리 비◇\\n\\n⊙ 10만원 (자세한건 임대인과 협의)\\n\\n\\n◇가 까 운 역◇\\n\\n⊙ 학동역 도보 5분거리\\n\\n\\n◇주 차 시 설◇\\n\\n⊙ 주차 1인 1대 가능합니다(주차시스템 잘되어있습니다)\\n\\n\\n◇편 의 시 설◇\\n\\n① 건물 주변이 대부분 주택가 지역이라 조용하고 아늑합니다\\n② 주변 편의시설 (병원,약국,우체국,편의점,각종음식점등)\\n③ 주변 초등학교 공원등 다양한 편의시설이 있습니다\\n\\n\\n◇한 마 디◇ \\n\\n⊙ 가격대비 좋은 원룸! 언제 빠질지 모릅니다 관심 있으신분들은 빠른 문의주세요\\n⊙ 이 외에도 원룸 투룸등 S급 A급 위주로 다양한 매물 보유중! 언제든지 문의주세요 \\n⊙ 전화 부재시 문자&amp;카톡 문의주시면 무조건~무조건~ 친절상담 해드려요^^♥ \\n⊙ 200% 실사진 실가격 실매물입니다 믿고 보셔도 됩니다\\n⊙ 절대 양심은 팔지 않겠습니다 말도 안되는 거짓 매물에 현혹 되지마시길 바랍니다 \\n\\n[24시 문의♥카톡ID : mcmax456] \\n\\n\\n♠어떠한 조건이라도 내집처럼 마련해 드리겠습니다♠ \\n♠200% 실매물 실가격으로 보답하겠습니다♠\\n',\n", " 'elevator': '있음',\n", " 'floor': '2층',\n", " 'floor_all': '3층',\n", " 'id': 5646209,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5646209/d9488a9ef5187e2ebf0711c5e775db07da4a142f.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5646209/203875babde2e2b0672edc290a4b89cbe9bba81c.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5646209/3b1af2a289a04c6ba1e690b20112572641c8012d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5646209/c21fec301bc2ebdf5ca7089bdea9159a9d1a24d0.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5646209/1e186e3750ac48cd45bece9a5629169fc3605df7.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5646209/f5d07ba79dd4749e2bf006e81842fb6e2c2ca44e.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5646209/d9529f582df48a5c8d2a665170c300cb5e28ec9b.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 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'http://z1.zigbang.com/items/5646209/3e4f6acc4ef8bca36d43657b36e5b9430da56118.JPG'},\n", " {'count': 16,\n", " 'index': 15,\n", " 'url': 'http://z1.zigbang.com/items/5646209/76d64e527e99059b8cfc843aca1e7f707f44ebde.JPG'},\n", " {'count': 17,\n", " 'index': 16,\n", " 'url': 'http://z1.zigbang.com/items/5646209/3d0ba2567e040dafeaade5ca5afdd0b93afac233.JPG'},\n", " {'count': 18,\n", " 'index': 17,\n", " 'url': 'http://z1.zigbang.com/items/5646209/286e52fe9459e23abf13a4c6ba561452e8806f71.JPG'},\n", " {'count': 19,\n", " 'index': 18,\n", " 'url': 'http://z1.zigbang.com/items/5646209/da4b569fc3555032168d62b08264b106b5f49b08.JPG'}],\n", " 'images_count': 19,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7378-4343',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\n", " 'random_location': '37.5156004701152,127.028433010821',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 140,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 13.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '42.98',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '♥논현동♥ ⓢ급 매물 오픈형 원룸',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'cda486@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-9333',\n", " 'user_name': '중개보조원(최동안)',\n", " 'user_no': 1545912,\n", " 'user_phone': '0507-1281-9333',\n", " 'view_count': 26},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '39-17',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 반포동 718-7 나라부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'nara5090@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1281-3636',\n", " 'agent_name': '나라부동산공인중개사(강덕귀)',\n", " 'agent_phone': '02-537-5030',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '39',\n", " 'bubun_code': '17',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '◈ 지하철 7호선 학동역 도보 5분이내거리!!\\n\\n◈ 조용한곳에 위치하고 방범 정말 잘되어있어요~\\n\\n◈ 풀옵이라 근처 직장인들에겐 최고의 방은듯싶네요 몸만오시면되요!! (TV 넣어주신다고 합니다)\\n\\n◈ 대로변 인접한 위치라 편의시설도 다양하게 갖춰져 있고 생활하기 너무 편하실듯해요^^\\n\\n◈ 도배 및 수리 전부 완료된 상태이니 언제든지 확인하실수있습니다!!\\n\\n◈ 깔끔하고 좋은 매물 원하시면 부담없이 문의주시기 바랍니다~ \\n\\n◈ 강남 전지역 픽업서비스 가능!! \\n\\n◈ 항상 정직하고 성실하게 상담해드리겠습니다^^',\n", " 'description_og': '◈ 지하철 7호선 학동역 도보 5분이내거리!!\\n\\n◈ 조용한곳에 위치하고 방범 정말 잘되어있어요~\\n\\n◈ 풀옵이라 근처 직장인들에겐 최고의 방은듯싶네요 몸만오시면되요!! (TV 넣어주신다고 합니다)\\n\\n◈ 대로변 인접한 위치라 편의시설도 다양하게 갖춰져 있고 생활하기 너무 편하실듯해요^^\\n\\n◈ 도배 및 수리 전부 완료된 상태이니 언제든지 확인하실수있습니다!!\\n\\n◈ 깔끔하고 좋은 매물 원하시면 부담없이 문의주시기 바랍니다~ \\n\\n◈ 강남 전지역 픽업서비스 가능!! \\n\\n◈ 항상 정직하고 성실하게 상담해드리겠습니다^^',\n", " 'elevator': '없음',\n", " 'floor': '1층',\n", " 'floor_all': '4층',\n", " 'id': 5490317,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5490317/a564bf86c22080479e52385ac9b17d06a98821d6.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5490317/6e355df52950f7da832473421d45cf7edd41c7ad.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5490317/1b118e25a92d612cd00220142e45d1d7a3aeb889.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5490317/aad0225317806f2f23a66a22a236ceb050080f68.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5490317/05045184c48bda4df836add6c98096c09b19fa06.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5490317/5e7f25181dda71f1037404a0165b42f30d58e703.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5490317/418d3e472a09c4de5aa367dd4a274e336cc2fce8.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5490317/a564bf86c22080479e52385ac9b17d06a98821d6.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5490317/6e355df52950f7da832473421d45cf7edd41c7ad.jpg'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5490317/1b118e25a92d612cd00220142e45d1d7a3aeb889.jpg'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5490317/aad0225317806f2f23a66a22a236ceb050080f68.jpg'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5490317/05045184c48bda4df836add6c98096c09b19fa06.jpg'},\n", " {'count': 13,\n", " 'index': 12,\n", " 'url': 'http://z1.zigbang.com/items/5490317/5e7f25181dda71f1037404a0165b42f30d58e703.jpg'},\n", " {'count': 14,\n", " 'index': 13,\n", " 'url': 'http://z1.zigbang.com/items/5490317/418d3e472a09c4de5aa367dd4a274e336cc2fce8.jpg'}],\n", " 'images_count': 14,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '6만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-4965-5090',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1449116/fbd3ca43efd6afc373ea56edebf352ac528862ec.jpg',\n", " 'random_location': '37.5142397489325,127.027435065049',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 60,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 7.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '23.14',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '★☆실매물☆★ 논현동 역세권 깔끔한 풀옵션 원룸#',\n", " 'updated_at': '14일 전',\n", " 'user_email': 'nara5090@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-2205',\n", " 'user_name': '중개보조원(최승우)',\n", " 'user_no': 1449116,\n", " 'user_phone': '0507-1281-2205',\n", " 'view_count': 111},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '75',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '75',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 5000,\n", " 'description': '*대리석건물에 데코타일 건물내외관 럭셔리한 집입니다!\\n\\n*실20평에 12자 화이트붙박이장 잘되어잇고 리모컨 일렬주차100% 주차걱정NO!\\n\\n*거실완벽히넓게잘빠져잇고 쌍둥이방둘다크고 통베란다2개!\\n\\n*저는 가격대비 평범한물건은 아예 광고를하지않고 상위2%내 최상급물건만 취급하고있습니다.\\n\\n*기타 자세한 문의는 문자나 연락주시면 상세설명해드리겟습니다.길게광고올리지않고 여기까지만 . 감사합니다',\n", " 'description_og': '*대리석건물에 데코타일 건물내외관 럭셔리한 집입니다!\\n\\n*실20평에 12자 화이트붙박이장 잘되어잇고 리모컨 일렬주차100% 주차걱정NO!\\n\\n*거실완벽히넓게잘빠져잇고 쌍둥이방둘다크고 통베란다2개!\\n\\n*저는 가격대비 평범한물건은 아예 광고를하지않고 상위2%내 최상급물건만 취급하고있습니다.\\n\\n*기타 자세한 문의는 문자나 연락주시면 상세설명해드리겟습니다.길게광고올리지않고 여기까지만 . 감사합니다',\n", " 'elevator': '있음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5533865,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5533865/7efdb475b03162bfddcbe63ea2abd0a8c99b1f23.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5533865/3fe2431def4dc3200c3de5aac6b8f297623083cd.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5533865/6648a00fe0fecd270b299d7116bddc2f97e10eae.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5533865/19060e78ae2537ebb18fa6df00fd8202769be612.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5533865/81de283e1ae7fc75e2ef8a39f3dd880d7e9f75f9.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5533865/e53170a92a3a5265b1ba6e71028a3f09e2bf4eaa.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 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'-',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 신사역(3호선)',\n", " 'options': '에어컨, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-3897-6929',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5171573873475,127.030370626783',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 160,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 20.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '66.12',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '*럭셔리 화이트톤 투룸의거실대.붙박이짱.주차짱!',\n", " 'updated_at': '13일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6925',\n", " 'user_name': '중개보조원(김명섭)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-6925',\n", " 'view_count': 257},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '518',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 반포동 718-7 나라부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'nara5090@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1281-3636',\n", " 'agent_name': '나라부동산공인중개사(강덕귀)',\n", " 'agent_phone': '02-537-5030',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '518',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '▶ 가로수길 라인 / 신사역 도보5-10분 역세권 위치 최고 !!\\n\\n▶ 도배/장판/싱크대 상태 매우 깔끔하여 즉시입주 가능합니다.\\n\\n▶ 신사역세권 깔끔한 원룸원거실 느낌 분리형 원룸이며 화장실역시 깔끔합니다.\\n\\n▶ 방을 보시면 사이즈도 딱 좋고 깔끔하다고 느낄수 있는 그런 방입니다. \\n\\n▶ 주차는 1대 개별 확인하셔야 합니다.\\n\\n▶ 근처 24시 편의점 , 음식점 등등 다양한 편의시설이 갖추어져있습니다.\\n\\n▶ 위 사진은 광고 게시중인 담당자가 직접 촬영한 실제 사진입니다. \\n\\n▶ 항상 정직하고 밝고 친절하게 양심껏 상담해드리겠습니다. ',\n", " 'description_og': '▶ 가로수길 라인 / 신사역 도보5-10분 역세권 위치 최고 !!\\n\\n▶ 도배/장판/싱크대 상태 매우 깔끔하여 즉시입주 가능합니다.\\n\\n▶ 신사역세권 깔끔한 원룸원거실 느낌 분리형 원룸이며 화장실역시 깔끔합니다.\\n\\n▶ 방을 보시면 사이즈도 딱 좋고 깔끔하다고 느낄수 있는 그런 방입니다. \\n\\n▶ 주차는 1대 개별 확인하셔야 합니다.\\n\\n▶ 근처 24시 편의점 , 음식점 등등 다양한 편의시설이 갖추어져있습니다.\\n\\n▶ 위 사진은 광고 게시중인 담당자가 직접 촬영한 실제 사진입니다. \\n\\n▶ 항상 정직하고 밝고 친절하게 양심껏 상담해드리겠습니다. ',\n", " 'elevator': '없음',\n", " 'floor': '반지하',\n", " 'floor_all': '3층',\n", " 'id': 5610946,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5610946/a7d189678034434391383f885c4f4314ba67a721.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5610946/eff8b06ece3be93f9bdc974d7a6247572be5e548.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5610946/a0f6a40504e0d65d0a7656c0beedaf6e43aedf9e.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5610946/8739de640fc724b7b00bbdab1fedc90982b8cb8c.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5610946/87b64c478b3f3a8254475aa7475ef736f4223473.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5610946/dccb58496ab5dbe9f68d9c2081faab9c0c5ab992.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 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'original_user_phone': '010-5714-5090',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1449116/fbd3ca43efd6afc373ea56edebf352ac528862ec.jpg',\n", " 'random_location': '37.5192588547188,127.021094948653',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 50,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '▶실매물◀가로수길 원룸원거실 느낌 분리형#',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'nara5090@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-1949',\n", " 'user_name': '중개보조원(최진우)',\n", " 'user_no': 1449116,\n", " 'user_phone': '0507-1281-1949',\n", " 'view_count': 72},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '559-11 1층 101호',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 217-46',\n", " 'agent_comment': '',\n", " 'agent_email': 'mhj06100610@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-4362',\n", " 'agent_name': '한영공인중개사(모형진)',\n", " 'agent_phone': '010-4737-9178',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 8000,\n", " 'description': '안녕하세요 신사동에 저렴하게 8000전세로 나온 매물입니다.\\n\\n전세종말시대가 온 시점에서 나온 매물입니다. 현재 방 컨디션은 깔금하고 혼자살기에 나쁘지 않습니다.\\n\\n8000 전세 찾기 힘듭니다. 바로 연락주세요',\n", " 'description_og': '안녕하세요 신사동에 저렴하게 8000전세로 나온 매물입니다.\\n\\n전세종말시대가 온 시점에서 나온 매물입니다. 현재 방 컨디션은 깔금하고 혼자살기에 나쁘지 않습니다.\\n\\n8000 전세 찾기 힘듭니다. 바로 연락주세요',\n", " 'elevator': '없음',\n", " 'floor': '1층',\n", " 'floor_all': '5층',\n", " 'id': 5519928,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5519928/df775983a18eb09e486b3bda61e6eb7b92a7eae5.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5519928/fc084fbf5bdf2ca1cad0f4dab98eff17a0ad4ece.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5519928/c4dcb9e232789930f1c2e3b4039a228256f1d32e.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5519928/208d13732f35608755ddf454d327144611da6cb3.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5519928/969dac4cf1c536e4228870ee26bcdd93eb71946c.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5519928/21234c7e7aab71c6bf34b73782e1b355f2d0a6a1.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5519928/fba7d9ec542af9a0afc35eb1c8cc96f9ff98c48a.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5519928/98cbc7fcc1cd1b30ce6f4770bf5af5a34fca048f.JPG'}],\n", " 'images_count': 8,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '8만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시 가능',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 세탁기, 신발장, 싱크대',\n", " 'original_user_phone': '010-6554-3473',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5198473281445,127.026119028607',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 5.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '16.53',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '전세 멸종시대에 컨디션 좋은 8000전세입니다',\n", " 'updated_at': '13일 전',\n", " 'user_email': 'secretk1107@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-0918',\n", " 'user_name': '중개보조원(박민우)',\n", " 'user_no': 112775,\n", " 'user_phone': '0507-1281-0918',\n", " 'view_count': 1679},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '27-9',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼로34길 14 1층',\n", " 'agent_comment': '',\n", " 'agent_email': 'lee2002top@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-7165',\n", " 'agent_name': '늘파란공인중개사(이기복)',\n", " 'agent_phone': '02-557-8249',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '27',\n", " 'bubun_code': '9',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 35000,\n", " 'description': '★ 실매물/ 실사진 매물없으면 교통비지급해드립니다.\\n\\n★ 3호선 신사역 역세권에 위치했습니다.\\n\\n★ 지하 주차장으로 넉넉한 주차대수 편리한 주차시설입니다.\\n\\n★ 내부 수리 넓은 통 베란다 입니다.\\n\\n★ 학동공원 인근으로 애완견과 산책하시기 최고입니다.\\n\\n★ 거실과 주방 분리구조로 신혼 부부 강추합니다.\\n\\n★ 궁금하신사항 24시간 친절상담해드립니다...',\n", " 'description_og': '★ 실매물/ 실사진 매물없으면 교통비지급해드립니다.\\n\\n★ 3호선 신사역 역세권에 위치했습니다.\\n\\n★ 지하 주차장으로 넉넉한 주차대수 편리한 주차시설입니다.\\n\\n★ 내부 수리 넓은 통 베란다 입니다.\\n\\n★ 학동공원 인근으로 애완견과 산책하시기 최고입니다.\\n\\n★ 거실과 주방 분리구조로 신혼 부부 강추합니다.\\n\\n★ 궁금하신사항 24시간 친절상담해드립니다...',\n", " 'elevator': '있음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5655575,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5655575/8d1ccb5767e9216c56222d27f1d9ef076f3a1766.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5655575/2eb3aeb05a3cc0eb05e8aa97b7372640bcdcdd63.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5655575/b6724bd3b6f3075e3d23d06a68c247a6eb0726a3.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5655575/a33ba2dd3a3fc51e264cb5d91ba30943e570b29a.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5655575/cf2fc64b261aeb6778dcf3b64483d9692eea748c.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5655575/2c182cbd732a31d92547d22a7df3a5bf5d26f096.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5655575/27e3548f4674b8adf66c323fd460f8a8e2e3294c.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5655575/d3648f0400bc88b34923d10db526f306dd717648.JPG'}],\n", " 'images_count': 8,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '3만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '협의가능',\n", " 'near_subways': '신사역(3호선), 학동역(7호선), 논현역(7호선)',\n", " 'options': '신발장, 싱크대',\n", " 'original_user_phone': '010-9924-3243',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5170005176222,127.026435313614',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 18.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '59.50',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '✔실매물/실사진✔지하주차장✔넓은 베란다✔신혼부부강추✔',\n", " 'updated_at': '2시간 전',\n", " 'user_email': 'lee2002top@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7165',\n", " 'user_name': '대표공인중개사(이기복)',\n", " 'user_no': 1351140,\n", " 'user_phone': '0507-1281-7165',\n", " 'view_count': 3},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '588-16',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 735-11 111호',\n", " 'agent_comment': '',\n", " 'agent_email': 'gugu1966@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-8210',\n", " 'agent_name': '강남사무실공인중개사(김지영)',\n", " 'agent_phone': '02-508-4982',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '588',\n", " 'bubun_code': '16',\n", " 'building': {'address': '서울시 강남구 신사동 588-16',\n", " 'address2': '588-16',\n", " 'building_id': 18799,\n", " 'count': 3,\n", " 'elevator': '1',\n", " 'established': '2015.02',\n", " 'floor': '16층',\n", " 'lat': 37.5211,\n", " 'lng': 127.0314,\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'name': '현대썬앤빌',\n", " 'rooms': '120세대'},\n", " 'building_type': '오피스텔·도생',\n", " 'contract': '서울특별시',\n", " 'deposit': 130,\n", " 'description': '신사동 도산공원사거리 인근에 위치한 신축오피스텔 입니다\\n\\n\\n현재 사진에는 침대 티비가 없지만 모두다 셋팅되어 있는 상태입니다.\\n\\n\\n인근에 오피스텔이 없기때문에 위치적 메리트가 꽤 큰편입니다\\n\\n\\n관리비에 일반관리비 공과금 모두 포함입니다~\\n\\n\\n주차는 기계식으로 월 13만원 별도 입니다.',\n", " 'description_og': '신사동 도산공원사거리 인근에 위치한 신축오피스텔 입니다\\n\\n\\n현재 사진에는 침대 티비가 없지만 모두다 셋팅되어 있는 상태입니다.\\n\\n\\n인근에 오피스텔이 없기때문에 위치적 메리트가 꽤 큰편입니다\\n\\n\\n관리비에 일반관리비 공과금 모두 포함입니다~\\n\\n\\n주차는 기계식으로 월 13만원 별도 입니다.',\n", " 'elevator': '있음',\n", " 'floor': '고층/16층',\n", " 'floor_all': '16층',\n", " 'id': 5629638,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5629638/1e8a0ec047021d745a235a9df4f791d2cac25846.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5629638/1367dfc4824b59e609f3e447f71208f2eb681971.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5629638/e360d92facdfdc187e1a2184ef8e9de57d878219.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5629638/a752a0aea3d0d9c7bdbc3d23be934f5e8b4b98e4.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5629638/6f296c2dbfbcecc5890f0f1a62178e87fc0718d7.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5629638/85ac24bd8715b5dc5e4dbc5b2f5f90d60fd51a9d.JPG'}],\n", " 'images_count': 6,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '20만원',\n", " 'manage_cost_inc': '전기세, 가스, 수도, 인터넷, TV',\n", " 'movein_date': '즉시',\n", " 'near_subways': '압구정역(3호선), 학동역(7호선), 강남구청역(7호선,분당선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-6833-0203',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5208684000066,127.031113228511',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 130,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '02',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 18.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '59.50',\n", " 'status': '광고중',\n", " 'title': '신사동 도산대로바로앞 풀옵션 오피스텔',\n", " 'updated_at': '3일 전',\n", " 'user_email': 'jch198412@gmail.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-8722',\n", " 'user_name': '중개보조원(전진현)',\n", " 'user_no': 578453,\n", " 'user_phone': '0507-1280-8722',\n", " 'view_count': 43},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '566-27',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'artsoup@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6420',\n", " 'agent_name': '즐겨찾기공인중개사(김영한)',\n", " 'agent_phone': '02-540-0124',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '566',\n", " 'bubun_code': '27',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 5000,\n", " 'description': '- 압구정역에서 도보거리 5분내에 위치한 신축 첫입주 투룸\\n\\n- 엘리베이터 운행되는 집이라서 계단 소음없구요~\\n\\n- 1층 필로티 주차장으로 주차도 편리하답니다.~\\n\\n- 대로변에서 가까운 위치라서 보안방범도 좋고 임대인분도 좋아서 더 좋아요~^^\\n\\n- 거실 맞벽구조라서 TV소파공간 나오는 구조에요~\\n',\n", " 'description_og': '- 압구정역에서 도보거리 5분내에 위치한 신축 첫입주 투룸\\n\\n- 엘리베이터 운행되는 집이라서 계단 소음없구요~\\n\\n- 1층 필로티 주차장으로 주차도 편리하답니다.~\\n\\n- 대로변에서 가까운 위치라서 보안방범도 좋고 임대인분도 좋아서 더 좋아요~^^\\n\\n- 거실 맞벽구조라서 TV소파공간 나오는 구조에요~\\n',\n", " 'elevator': '있음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 4774173,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/4774173/00a8613c8b1e9ad8d1d960925429ba62a027b511.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/4774173/9343942e4266eb575e569c4659fdb42ffc67e3e4.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/4774173/e23c913d789e043d893b1bdbc2334c622a889ebd.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/4774173/25d57162cc1c262a2814cd6973e33e72e6f88aa3.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/4774173/7b04259806cf2a1e536e884c6e2cfb5c72920f09.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/4774173/34c46f05a2e1bc8e7432be2fcdadba819b33d4d5.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/4774173/bc1a69d65f65bcdc4fe9d5ba4b3aa809f2ce4051.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 신발장, 싱크대',\n", " 'original_user_phone': '010-2446-3484',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5230577630193,127.026885771612',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 14.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '46.28',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '@@ 추천~~압구정역세권에 위치한 신축 첫입주 투룸 @@',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'cjpark12@hanmail.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6676',\n", " 'user_name': '중개보조원(박충진)',\n", " 'user_no': 810168,\n", " 'user_phone': '0507-1280-6676',\n", " 'view_count': 660},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 서초구 잠원동',\n", " 'address2': '23-19',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'snws@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6934',\n", " 'agent_name': '나무공인중개사(조민영)',\n", " 'agent_phone': '02-557-1888',\n", " 'bjd_code': '1165010600',\n", " 'bonbun_code': '23',\n", " 'bubun_code': '19',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 75,\n", " 'description': '잠원동 신사역에서 3분거리 대로변 이면에 위치하고 있습니다.\\n\\n이번에 리모델링 완공하고 아직 잉크도 안말랐습니다.\\n\\n채광, 통풍, 환기가 우수해서 쾌적합니다.\\n\\n화이트톤 넉넉한 수납장\\n\\n베란다에 히노끼 느낌 넓은 테라스가~~~!! 운치가 더해집니다.\\n\\n',\n", " 'description_og': '잠원동 신사역에서 3분거리 대로변 이면에 위치하고 있습니다.\\n\\n이번에 리모델링 완공하고 아직 잉크도 안말랐습니다.\\n\\n채광, 통풍, 환기가 우수해서 쾌적합니다.\\n\\n화이트톤 넉넉한 수납장\\n\\n베란다에 히노끼 느낌 넓은 테라스가~~~!! 운치가 더해집니다.\\n\\n',\n", " 'elevator': '없음',\n", " 'floor': '3층',\n", " 'floor_all': '4층',\n", " 'id': 5590314,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5590314/39e44c290adcfff1a892ad8e0439e22646c8020a.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5590314/fc6eb1918e07057ec5c7e42a91e5019f1ca8ad73.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5590314/560d71ac35e638d855704a56bfab4799603f05ab.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5590314/8f31dbdeaa61e959d460832a7eb6d12b2923c0b5.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5590314/a6940a91451835534bb42571acfdeaa0b3c049bc.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5590314/3f813bcea0e21ca3bbd9dd636ff8c5890a3dcc1d.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 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{'address1': '서울시 강남구 신사동',\n", " 'address2': '523-34',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8, 2층',\n", " 'agent_comment': '',\n", " 'agent_email': 'click3090@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7491',\n", " 'agent_name': '건우공인중개사(김상모)',\n", " 'agent_phone': '010-8456-3090',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '523',\n", " 'bubun_code': '34',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 105,\n", " 'description': '[ 교통/위치 ] - ●\\n\\nㅇ 신사동 신구초등학교 인근에 위치한 분리형 원룸 풀옵션입니다.\\n\\nㅇ 신사역 6번출구 도보 7~8분 거리에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 독립된 방 하나와 주방, 미닫이문으로 분리된 거실이 있습니다.\\n\\nㅇ 리모델링 완료된 매물이라 아주 깔끔합니다.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 주차는 선착순 협의주차 가능합니다.\\n\\nㅇ 가로수길 인근이라 다양한 문화시설과 편의시설이 많습니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'description_og': '[ 교통/위치 ] - ●\\n\\nㅇ 신사동 신구초등학교 인근에 위치한 분리형 원룸 풀옵션입니다.\\n\\nㅇ 신사역 6번출구 도보 7~8분 거리에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 독립된 방 하나와 주방, 미닫이문으로 분리된 거실이 있습니다.\\n\\nㅇ 리모델링 완료된 매물이라 아주 깔끔합니다.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 주차는 선착순 협의주차 가능합니다.\\n\\nㅇ 가로수길 인근이라 다양한 문화시설과 편의시설이 많습니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '4층',\n", " 'id': 5526171,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5526171/b4fe3663a8736797060e70ac96bdb1f7ebbea921.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 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'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': 'TV',\n", " 'movein_date': '입주 협의',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\n", " 'original_user_phone': '010-8886-5877',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5223640629791,127.024459214668',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 70,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕신사동 진짜 귀한 단기 원룸★★ 특aaaa급',\n", " 'updated_at': '7일 전',\n", " 'user_email': 'rjsdn110926@hanmail.net',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6920',\n", " 'user_name': '대표공인중개사(손석진)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1280-6920',\n", " 'view_count': 295},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '518-18',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로26길 30 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'jsgongin@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-4624',\n", " 'agent_name': '정성공인중개사(정문식)',\n", " 'agent_phone': '010-4776-8102',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '518',\n", " 'bubun_code': '18',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 200,\n", " 'description': '♠ 내부구조 및 인테리어 ♠ \\n\\n▷ 내외관 관리가 잘된 풀옵션 분리형~~ \\n\\n▷ 흔하지 않은 단기 입대 가로수길 ~~~\\n\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요? \\n\\n▷ 신축 첫 입주 비품 모두 첫 번째 사용^^ \\n\\n▷ 보안 좋고 채광도 굿~~!\\n\\n♠ 대중교통 및 위치 ♠ \\n\\n▷ 신사역 번 도보5분 거리에 있습니다. \\n\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다. \\n\\n♠ 주차 및 편의시설 ♠ \\n\\n▷ 주차는 1대 가능합니다. \\n\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다. \\n\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★ \\n\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다. \\n\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\n\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\n\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\n", " 'description_og': '♠ 내부구조 및 인테리어 ♠ \\n\\n▷ 내외관 관리가 잘된 풀옵션 분리형~~ \\n\\n▷ 흔하지 않은 단기 입대 가로수길 ~~~\\n\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요? \\n\\n▷ 신축 첫 입주 비품 모두 첫 번째 사용^^ \\n\\n▷ 보안 좋고 채광도 굿~~!\\n\\n♠ 대중교통 및 위치 ♠ \\n\\n▷ 신사역 번 도보5분 거리에 있습니다. \\n\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다. \\n\\n♠ 주차 및 편의시설 ♠ \\n\\n▷ 주차는 1대 가능합니다. \\n\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다. \\n\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★ \\n\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다. \\n\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\n\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\n\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5589916,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5589916/c4e5191c3e9e49aea48a24dd22eb2b97b6ec9c35.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5589916/f9d88c2f083c9e3ef8c3b0744c2a94a9b2c990d3.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5589916/070a227cc06d2340486d8cdc377b967046ab83ed.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5589916/9ed73a48d9ff71b682e42922977462695d4a8daa.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5589916/dbfbe7d8de97f5ffc8f32354c47a943110b50fa5.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5589916/a3b6ad57b654568e92faac240c872bc2215daa28.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5589916/427396b654d2f7baf57f51880402113fde63df0f.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5589916/22cf589130722c5e187ce86fdfb20984ba7e68d4.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5589916/cd2665e1d782793a7d1206888e0d73d515c2b55a.jpg'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5589916/a90bef0c562e766e249e93b8d69c2ab3b54d406c.jpg'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5589916/e44eb0bcd5eb1a4e79f622b6889c45aef346f214.jpg'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5589916/94c83e679ee42189b02a525f29b2e01bebf28396.jpg'},\n", " {'count': 13,\n", " 'index': 12,\n", " 'url': 'http://z1.zigbang.com/items/5589916/351c4c6f4bbccf0220d9c60c065e925b1025dcbf.jpg'}],\n", " 'images_count': 13,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷',\n", " 'movein_date': '즉시 가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 압구정역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7193-9904',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/431435.jpg',\n", " 'random_location': '37.5191962672748,127.021849608438',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 170,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 16.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '52.89',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '☺☺ ~~가로수길 분리형☺☺ 원룸 원거실 가로수길 도보 2분 ☺☺',\n", " 'updated_at': '7일 전',\n", " 'user_email': 'arnani810403@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-5648',\n", " 'user_name': '중개보조원(권혁준)',\n", " 'user_no': 431435,\n", " 'user_phone': '0507-1280-5648',\n", " 'view_count': 94},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '38-6',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 잠원동 13-1 아이미서초빌딩 2층',\n", " 'agent_comment': '',\n", " 'agent_email': 'woopers@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1281-0426',\n", " 'agent_name': '오렌지하우스공인중개사(김승식)',\n", " 'agent_phone': '02-512-4243',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '38',\n", " 'bubun_code': '6',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 28000,\n", " 'description': '★ 전세자급대출가능, 신축 첫입주, 사이즈 완전 큰 거실+투룸입니다^^\\n\\n★ 도보로 학동역5분, 논현역10분거리로 교통이 편리하고 학동공원이 가까이 있어 운동하기 좋아요^^\\n\\n★ 몸만 들어 오세요^^\\n\\n★ 직접 보세요^^\\n\\n',\n", " 'description_og': '★ 전세자급대출가능, 신축 첫입주, 사이즈 완전 큰 거실+투룸입니다^^\\n\\n★ 도보로 학동역5분, 논현역10분거리로 교통이 편리하고 학동공원이 가까이 있어 운동하기 좋아요^^\\n\\n★ 몸만 들어 오세요^^\\n\\n★ 직접 보세요^^\\n\\n',\n", " 'elevator': '있음',\n", " 'floor': '3층',\n", " 'floor_all': '5층',\n", " 'id': 5615990,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5615990/15e6dd167ac118a23def8eccad0264661a83a43d.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5615990/2166a7af929fba04893434d496ba15d9e30411df.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5615990/a3a8683b8f844b61b2257172ef57ad56d4a4290d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5615990/25d56d0767d2916d1d6cc54344b3ecc0f207a29a.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5615990/225b156d616dc283f38f5ad266fcbc3c66ef56a5.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5615990/5e2e128089d5f47e725a2c38806b60939a04e008.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5615990/ad13b5e2a57199e8784b6e7b19f098634f80fc70.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5615990/da20ef2348c3ede0da60cd2ba11ed2ffd64fe61a.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5615990/99aa8a5c83550a278af4961e304cf9bb4ba3d186.JPG'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '7만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 싱크대',\n", " 'original_user_phone': '010-2247-1355',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5144702520298,127.02801180459',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 15.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '49.59',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '◈◈전세자금대출가능!!◈신축첫입주사이즈완~전큰거실+투룸전세!!◈도보학동역5분,논현역10분거리!!강추◈◈',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'pysrose66@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7038',\n", " 'user_name': '중개보조원(박윤숙)',\n", " 'user_no': 327679,\n", " 'user_phone': '0507-1281-7038',\n", " 'view_count': 120},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 85,\n", " 'description': 'ㅇ 논현동 학동공원 블럭에 위치한 신축 복층형 풀옵션입니다.\\n\\nㅇ 신사역 1번 출구 도보 5분 거리 초역세권에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 2015년 8월 신축 매물로 내외부 깔끔함에 아기자기한 복층형구조.\\n\\nㅇ 노출 콘크리트와 대리석 바닥의 감각적인 내부 인테리어^^.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 1층에 주차장이 있으며 1대 주차 가능합니다.\\n\\nㅇ 길건너 가로수길이 있으며 대로변과 가까워 접근성 또한 용이합니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'description_og': 'ㅇ 논현동 학동공원 블럭에 위치한 신축 복층형 풀옵션입니다.\\n\\nㅇ 신사역 1번 출구 도보 5분 거리 초역세권에 위치해 있습니다.\\n\\n\\n[ 인테리어/특징 ] - ●\\n\\nㅇ 2015년 8월 신축 매물로 내외부 깔끔함에 아기자기한 복층형구조.\\n\\nㅇ 노출 콘크리트와 대리석 바닥의 감각적인 내부 인테리어^^.\\n\\n\\n[ 주차/편의시설 ] - ●\\n\\nㅇ 1층에 주차장이 있으며 1대 주차 가능합니다.\\n\\nㅇ 길건너 가로수길이 있으며 대로변과 가까워 접근성 또한 용이합니다.\\n\\n------------------------------------------------------------------------★\\n\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\n\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\n\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\n\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\n", " 'elevator': '있음',\n", " 'floor': '5층',\n", " 'floor_all': '5층',\n", " 'id': 5169178,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5169178/e141653fa319f2abb0a7d4c0f94eda0547515e82.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5169178/0f8b7245a93db0ad51b55350e12acc432739ec8d.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5169178/15bd79b47b869994443e7307c8cd9f4225705a8d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5169178/83782790d1949a89594b1b4e957205d226f33d22.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5169178/f018fd67037b1f00d9d9ec7103448087596328e9.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5169178/474e0dd079de0fe1b72dbf079a5936aa1f1f0c12.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5169178/a6b344a32d5061e7f6374dd9c6b0cc4d02d400db.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5169178/c78e7e16c53318707669051a0b42aa336cb0eaba.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5169178/c0a30bbdbe7af8ea4a3770cf252493d32c84e45c.JPG'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시 입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-3727-0204',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5165492895236,127.02284824381',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 85,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕ 신사역 2분거리*비티지스타일 신축*',\n", " 'updated_at': '14일 전',\n", " 'user_email': 'yegwang6412@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7324',\n", " 'user_name': '중개보조원(한승용)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-7324',\n", " 'view_count': 318},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '553-21',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\n", " 'agent_comment': '',\n", " 'agent_email': 'snws@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6934',\n", " 'agent_name': '나무공인중개사(조민영)',\n", " 'agent_phone': '02-557-1888',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '553',\n", " 'bubun_code': '21',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 70,\n", " 'description': '\\n ㅇ 위치 및 역세권 정보\\n \\n º 신사동 가로수길 인근에 위치한 풀옵션 원룸입니다.\\n\\n º 압구정역 도보 5분거리, 대로변 버스정류장 2분거리\\n \\n \\n ㅇ 구조 및 옵션 \\n \\n º 원룸 오픈형 구조로 되어있으며, 집기 모두 풀옵션 들어가 있습니다\\n\\n º 침대, 세탁기, TV, 냉장고 등 풀옵션으로 몸만 들어오시면 됩니다. \\n\\n º 화장실 샤워부스 설치되어 있어 깔끔합니다.\\n\\n \\n ㅇ 주차 및 편의시설\\n\\n º 지하주차장이 있어 주차 편리하게 가능\\n \\n º 가로수길 도보로 3분거리에 있어 편의시설 정말 많습니다.\\n',\n", " 'description_og': '\\n ㅇ 위치 및 역세권 정보\\n \\n &#186; 신사동 가로수길 인근에 위치한 풀옵션 원룸입니다.\\n\\n &#186; 압구정역 도보 5분거리, 대로변 버스정류장 2분거리\\n \\n \\n ㅇ 구조 및 옵션 \\n \\n &#186; 원룸 오픈형 구조로 되어있으며, 집기 모두 풀옵션 들어가 있습니다\\n\\n &#186; 침대, 세탁기, TV, 냉장고 등 풀옵션으로 몸만 들어오시면 됩니다. \\n\\n &#186; 화장실 샤워부스 설치되어 있어 깔끔합니다.\\n\\n \\n ㅇ 주차 및 편의시설\\n\\n &#186; 지하주차장이 있어 주차 편리하게 가능\\n \\n &#186; 가로수길 도보로 3분거리에 있어 편의시설 정말 많습니다.\\n',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '5층',\n", " 'id': 5644492,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5644492/46a1b6e4e5de1ede94c36fbe6c5409c83b221ac6.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5644492/20dd3e49acd7d381701d448b8b03f3acc4b7a6d9.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5644492/28fa4865fbb2a78c35ddc5eaa8511839897d26b1.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5644492/85b83a9c7d3b690a730b63cbdbf51ff1bae6515d.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5644492/a60be564885a1ae1ac480e73f9d82861939d8809.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5644492/0787c765d915ff6a4c8eb4a11b8c7d7798758818.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5644492/17bc1e468d5fdd1f5a7c17022417368104b98ea4.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5644492/3754b3704e1ebbf57f9cd99edede53310f2b5da6.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5644492/a44edcf34143d97b5b4e96dbcddaf08f27a9345c.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5644492/500e4bd1ef1ec0dd3308d1b2e203832f4cb12cf8.JPG'}],\n", " 'images_count': 10,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '7만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7540-8818',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\n", " 'random_location': '37.5217678573152,127.024994157203',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 65,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '❌압구정역',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'snws@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6934',\n", " 'user_name': '대표공인중개사(조민영)',\n", " 'user_no': 1000906,\n", " 'user_phone': '0507-1280-6934',\n", " 'view_count': 31},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '86-10',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '86',\n", " 'bubun_code': '10',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 72,\n", " 'description': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\n\\n ㅁ 7호선 지하철 역세권 단기임대 사이즈가 넓습니다.\\n\\n ㅁ 저렴한 가격에 역세권 단기임대 매물로 채광이 아주 우수합니다.\\n\\n ㅁ 대로변 바로 이면에 있어서 늦은시간 다니기도 무섭지 않구요.\\n\\n ㅁ 사이즈가 다른 매물에 비해 넓어서 두분이서 지내셔도 넉넉합니다.\\n\\n ㅁ 내부 시설상태 아주 깔끔하여 바로 입주도 가능하십니다.\\n\\n ㅁ 주차는 1대 가능하십니다.\\n\\n \\n\\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\n',\n", " 'description_og': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\n\\n ㅁ 7호선 지하철 역세권 단기임대 사이즈가 넓습니다.\\n\\n ㅁ 저렴한 가격에 역세권 단기임대 매물로 채광이 아주 우수합니다.\\n\\n ㅁ 대로변 바로 이면에 있어서 늦은시간 다니기도 무섭지 않구요.\\n\\n ㅁ 사이즈가 다른 매물에 비해 넓어서 두분이서 지내셔도 넉넉합니다.\\n\\n ㅁ 내부 시설상태 아주 깔끔하여 바로 입주도 가능하십니다.\\n\\n ㅁ 주차는 1대 가능하십니다.\\n\\n \\n\\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &amp;quot; 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\n',\n", " 'elevator': '없음',\n", " 'floor': '3층',\n", " 'floor_all': '5층',\n", " 'id': 5608525,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5608525/f5f55eef67addca608ec74d3ef7cd4fb6787ba5d.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5608525/dffa018fc455417967aec2edaa7f9f1e94ea0f0d.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5608525/acc6bf98d40055e7d48d1809be7884e9482ef022.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5608525/8adff982c3d88bacba8dabd3ccc089ac7f9beb9c.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5608525/b4c06c8590db51b7124cbb05d3ca2bf2302c4450.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5608525/50cb32d429bd5f58d5f2ffcb72fa360d36806e9e.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5608525/9e4190dedfd988d0cac267eb64e91b6c106d405f.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5608525/037adc95c91f5727c483d5c696f112cc5cf248e1.JPG'}],\n", " 'images_count': 8,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '7만원',\n", " 'manage_cost_inc': 'TV',\n", " 'movein_date': '즉시 입주',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 언주역(9호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-2068-9980',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5156337365346,127.030938222767',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 72,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕ ☆7호선 학동역 역세권 단기임대',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'promising14@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-8604',\n", " 'user_name': '중개보조원(박규백)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-8604',\n", " 'view_count': 76},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 서초구 잠원동',\n", " 'address2': '10-43 4층 402호',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 동산로 46 , 1층 (양재동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'lwh2136@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1280-7497',\n", " 'agent_name': '언남공인중개사(이원희)',\n", " 'agent_phone': '02-573-8600',\n", " 'bjd_code': '1165010600',\n", " 'bonbun_code': '',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, 위치만 말씀해주시면 픽업서비스도 해드립니다. \\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) \\n※인증된 대형 부동산 !! \\n★ 보증금 / 임대기간 (조정가능) \\n\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\n\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 장실장 입니다^^ \\n저희는 300% 실매물에 실사진으로만 운영합니다. 강남 전지역(강남구,서초구,송파구) \\n원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House★ 구경오세요!!! \\n직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\n직방내 모든매물 상담가능합니다.^^ \\n\\n■ KAKAO Talk : jangyong1109 ■ ',\n", " 'description_og': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, 위치만 말씀해주시면 픽업서비스도 해드립니다. \\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) \\n※인증된 대형 부동산 !! \\n★ 보증금 / 임대기간 (조정가능) \\n\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\n\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 장실장 입니다^^ \\n저희는 300% 실매물에 실사진으로만 운영합니다. 강남 전지역(강남구,서초구,송파구) \\n원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House★ 구경오세요!!! \\n직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\n직방내 모든매물 상담가능합니다.^^ \\n\\n■ KAKAO Talk : jangyong1109 ■ ',\n", " 'elevator': '있음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 5602646,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5602646/1edff41a208a93d7f11f5d70239420ed4549c84f.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5602646/b286a7c12b37b23d105dc1ca091f7c0ce5532d6b.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5602646/3d4ee000d3cbb064b6a71ff1f5ee08d80e7ccf75.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5602646/2a7f01b2ff1e1cb540223349e18915990a25b97b.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5602646/7bc02f12743ec2947c7af43b2b9b6714b41013d0.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5602646/203d3d4adea05d93a7929b607706d59678e09149.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5602646/e2aada999b08f40da6dab0def8eeb793c0d2011c.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5602646/fb2ea3a53fc24a729a3fa6e17482f133e2aa1667.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5602646/48bb98304dde6182e25d39c65d070ea1cc2eda25.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5602646/ee1d71ac533db971eaa3f16cefad475369c43720.JPG'}],\n", " 'images_count': 10,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '서초구',\n", " 'local3': '잠원동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-5580-0981',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5161092756342,127.017154686615',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 80,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 9.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '29.75',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '■■신사역3분■풀옵션■너무예쁜방■취향저격■■■',\n", " 'updated_at': '6일 전',\n", " 'user_email': 'jangyong1109@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-6246',\n", " 'user_name': '중개보조원(장용석)',\n", " 'user_no': 1129550,\n", " 'user_phone': '0507-1281-6246',\n", " 'view_count': 120},\n", " 'title': '서울시 서초구 잠원동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '568-5',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 도산대로25길 15',\n", " 'agent_comment': '신사동 한솔부동산은 허위매물 근절과 더불어 실매물만 기재할것을 약속드립니다. 감사합니다.\\n\\n카카오톡 ID sanga8949입니다. 24시간 카톡상담도 환영합니다.',\n", " 'agent_email': 'chulwan@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-0864',\n", " 'agent_name': '한솔공인중개사(박철완)',\n", " 'agent_phone': '02-517-2799',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '568',\n", " 'bubun_code': '5',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 10000,\n", " 'description': '가로수길인근 방3 욕실1 통베란다 주차\\n\\n1. 위치 : 압구정역 신사동 가로수길\\n\\n2. 금액 : 1억/200만원, 관리비10만원\\n\\n3. 편의사항 : 주차 통베란다 욕실2\\n\\n 단독층 사용 \\n\\n',\n", " 'description_og': '가로수길인근 방3 욕실1 통베란다 주차\\n\\n1. 위치 : 압구정역 신사동 가로수길\\n\\n2. 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'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\n", " 'options': '신발장, 싱크대',\n", " 'original_user_phone': '010-9498-3916',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5235573828333,127.024663772265',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 200,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '쓰리룸+',\n", " 'secret_memo': None,\n", " 'size': 24.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '79.34',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '압구정역도보 5분 가로수길 방3 욕실2 통베란다 주차',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'sipp1@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1282-0165',\n", " 'user_name': '소속공인중개사(장승일)',\n", " 'user_no': 1531083,\n", " 'user_phone': '0507-1282-0165',\n", " 'view_count': 67},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '595-8',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 도산대로38길 11 1층(논현동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'sm7784@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-4119',\n", " 'agent_name': '청학공인중개사(이석문)',\n", " 'agent_phone': '02-514-1028',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '595',\n", " 'bubun_code': '8',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 53000,\n", " 'description': '◈ 압구정역세권 귀한전세 (융자무)\\n◈ 급 고급 올수리 32평 빌라\\n◈ 편리한지상주차장 \\n◈ 방3개+화장실2개+넓은거실+주방\\n◈ 급급급 !! 가격,크기대비 최고의 매물입니다.... ^^ 8282서두르세요 \\n\\n☆ 강남,서초 전지역 고급빌라만을 고집하여 .. \\n☆ 10년 이상의 노하우와 \\n☆ 2만개이상의 데이터로 \\n★ 항상 고객님의 입장에서 생각하고 고민하여 .. \\n★ 원하시는 평수,가격,스타일을 말해주시면 \\n★ 최고의 매물만을 소개하겠습니다. \\n\\n☞ 청학공인중개사 (24시 친절상담^^) \\n☎ 514-0400 / 010-3232-3782 (대표 이석문)',\n", " 'description_og': '◈ 압구정역세권 귀한전세 (융자무)\\n◈ 급 고급 올수리 32평 빌라\\n◈ 편리한지상주차장 \\n◈ 방3개+화장실2개+넓은거실+주방\\n◈ 급급급 !! 가격,크기대비 최고의 매물입니다.... ^^ 8282서두르세요 \\n\\n☆ 강남,서초 전지역 고급빌라만을 고집하여 .. \\n☆ 10년 이상의 노하우와 \\n☆ 2만개이상의 데이터로 \\n★ 항상 고객님의 입장에서 생각하고 고민하여 .. \\n★ 원하시는 평수,가격,스타일을 말해주시면 \\n★ 최고의 매물만을 소개하겠습니다. \\n\\n☞ 청학공인중개사 (24시 친절상담^^) \\n☎ 514-0400 / 010-3232-3782 (대표 이석문)',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '3층',\n", " 'id': 5600902,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5600902/4d3b1f88a056e1c3b7f96be5b6ab8ecb32ad46c3.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5600902/cf8a66ce1c88cd63ae0975a8e7890936c4e2b29f.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5600902/54c0cd82c36fdea790c8842b35157a130541b8e7.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 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'te2797@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-7013',\n", " 'agent_name': '수호부동산중개(정연진)',\n", " 'agent_phone': '02-555-2249',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '67',\n", " 'bubun_code': '11',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 2000,\n", " 'description': '을지병원 사거리 을지병원 뒷편에 있는 투룸입니다.\\n\\n집 정말 넓어요!!!!\\n\\n이번에 새로 다시 수리 했습니다.\\n\\n집 정말 싸이즈 잘 나왔어요~~~\\n\\n이집 진짜 실가격 실매물입니다.\\n\\n근데... 주차는 안되요... ㅠㅠ\\n\\n주차안되는거 말고는 이가격에 이집 어마어마하게 훌륭한 집입니다.\\n\\n에이스예여~~~\\n\\n빨리 보러오세요~~',\n", " 'description_og': '을지병원 사거리 을지병원 뒷편에 있는 투룸입니다.\\n\\n집 정말 넓어요!!!!\\n\\n이번에 새로 다시 수리 했습니다.\\n\\n집 정말 싸이즈 잘 나왔어요~~~\\n\\n이집 진짜 실가격 실매물입니다.\\n\\n근데... 주차는 안되요... ㅠㅠ\\n\\n주차안되는거 말고는 이가격에 이집 어마어마하게 훌륭한 집입니다.\\n\\n에이스예여~~~\\n\\n빨리 보러오세요~~',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '4층',\n", " 'id': 5441521,\n", " 'images': [{'count': 1,\n", " 'index': 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" 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 침대, 싱크대',\n", " 'original_user_phone': '010-9950-9550',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5160103020122,127.022600606112',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 55,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '◈◈◈ 신사역1번출구 3분 풀옵션 원룸 ◈◈◈',\n", " 'updated_at': '3일 전',\n", " 'user_email': 'todayroomsi@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-0878',\n", " 'user_name': '소속공인중개사(박서인)',\n", " 'user_no': 449807,\n", " 'user_phone': '0507-1281-0878',\n", " 'view_count': 43},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 110,\n", " 'description': '- 전속관리물건 T3하우스 3단복층 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'http://z1.zigbang.com/items/5644123/9180fcf332955d42881fbd5bed260f3ff151cdaf.JPG'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5644123/6c95dc9923708c60b2c685e53e43570e473cf942.JPG'}],\n", " 'images_count': 12,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-8240-5676',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5165686342578,127.022841908394',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 110,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(복층형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '★마지막최저가! 주차 100%일렬1대!럭셔리복층 ★',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-7437',\n", " 'user_name': '중개보조원(강상환)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-7437',\n", " 'view_count': 88},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 서초구 잠원동',\n", " 'address2': '12-4',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 도산대로25길 15',\n", " 'agent_comment': '▼신사동 한솔부동산은 허위매물없는 정직한 중개와 믿을 만한 매물을 약속드리겠습니다. \\n\\n▼보시는 사진 그대로 똑같은 매물로 보여드립니다\\n\\n▼픽업도 가능하니 언제든지 연락주세요. 감사합니다.&amp;quot;\\n\\n카카오톡 ID sanga8949입니다. 24시간 카톡상담도 환영합니다.',\n", " 'agent_email': 'chulwan@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-0864',\n", " 'agent_name': '한솔공인중개사(박철완)',\n", " 'agent_phone': '02-517-2799',\n", " 'bjd_code': '1165010600',\n", " 'bonbun_code': '12',\n", " 'bubun_code': '4',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 15000,\n", " 'description': '[매물지역] 서울 서초구 잠원동 3호선 신사역\\n\\n[건물층수] 총5층 건물 중 해당층 4층 \\n\\n[월세금액] 월세 1.5억/30, 1억/70, 월세금액 조절가능. \\n\\n[월관리비] 월80,000원\\n\\n[옵션내역] 12자붙박이장,씽크대 신발장등 \\n\\n[교통편] 3호선 신사역 도보 3분이내. \\n\\n[매물특징] - 번호키, 엘리베이터, CCTV설치, 지하 주차장. 바닥데코타일시공 깔끔함',\n", " 'description_og': '[매물지역] 서울 서초구 잠원동 3호선 신사역\\n\\n[건물층수] 총5층 건물 중 해당층 4층 \\n\\n[월세금액] 월세 1.5억/30, 1억/70, 월세금액 조절가능. \\n\\n[월관리비] 월80,000원\\n\\n[옵션내역] 12자붙박이장,씽크대 신발장등 \\n\\n[교통편] 3호선 신사역 도보 3분이내. \\n\\n[매물특징] - 번호키, 엘리베이터, CCTV설치, 지하 주차장. 바닥데코타일시공 깔끔함',\n", " 'elevator': '있음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 5634530,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5634530/c10317ff2b094481c0e15cd461fe155c52e35563.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5634530/d3ce87c3652880db9d6fb4e3b52c0e6bb312f24b.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5634530/dd9d2cc7fdee1e3ee36e70ceea5dbac8447530d3.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5634530/d5a08e1005a0e133fde2dea4540732a5df236da1.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5634530/3d0bc9dd59d857a6b53d40bc1f3d411e665139ae.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5634530/23effaf2ca723fc23b070163c1aa349e22186a6a.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5634530/48aab933eee0ca8f6efa670c687337b4b0783ab2.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5634530/10da17c89fa2f44569a8c0b50af26894ec0afe77.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5634530/fecabf2e21d78aef89ae18bbc33b737b0b6ed693.jpg'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5634530/5bf3d319eb8674609b2fa994c2547d8150488930.jpg'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5634530/8bd653010f432b93aa8a92c9a358bf6cc1990c51.jpg'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5634530/7dfa7b38b21e7143300dfc337aa44eb6f4f40394.jpg'},\n", " {'count': 13,\n", " 'index': 12,\n", " 'url': 'http://z1.zigbang.com/items/5634530/5f3a7f4fe5d203b03cce0488ed8f2897d4f5e392.jpg'},\n", " {'count': 14,\n", " 'index': 13,\n", " 'url': 'http://z1.zigbang.com/items/5634530/a833dc798bacb7700151af68db5c52eafc44fbeb.jpg'},\n", " {'count': 15,\n", " 'index': 14,\n", " 'url': 'http://z1.zigbang.com/items/5634530/bcf275020c3aaf2b87d38fb629039df9ed7dbbd1.jpg'},\n", " {'count': 16,\n", " 'index': 15,\n", " 'url': 'http://z1.zigbang.com/items/5634530/e3bcea19f59cfbc34f5c051c1d33bd91bd7339be.jpg'},\n", " {'count': 17,\n", " 'index': 16,\n", " 'url': 'http://z1.zigbang.com/items/5634530/011ff07d6b9b8799c4a1afce1f2f034e3cfba2d8.jpg'}],\n", " 'images_count': 17,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '서초구',\n", " 'local3': '잠원동',\n", " 'manage_cost': '8만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\n", " 'options': '옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-9498-3916',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5172222368081,127.018857805201',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 30,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 17.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '56.20',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '신사역도보3분 방2 욕실1 엘리베이터 지하주차장',\n", " 'updated_at': '3일 전',\n", " 'user_email': 'sipp1@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1282-0165',\n", " 'user_name': '소속공인중개사(장승일)',\n", " 'user_no': 1531083,\n", " 'user_phone': '0507-1282-0165',\n", " 'view_count': 104},\n", " 'title': '서울시 서초구 잠원동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '609-5',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '609',\n", " 'bubun_code': '5',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 70,\n", " 'description': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\n\\n ㅁ 압구정역 3번출구 인근에 있는 너무나 귀한 단기임대 매물입니다.\\n\\n ㅁ 지역 특성상 단기임대 매물이 귀해서 이쪽으로 보시면 이거 꼭 보셔야하는 매물입니다.\\n\\n ㅁ 지하철역과 가깝고 압구정 로데오와 가깝구요 큰 대로변 바로 이면에 있습니다.\\n\\n ㅁ 늦은시간 다니시기 무섭지 않은 곳이구요.\\n\\n ㅁ 시세대비 저렴하게 나온 매물이라 보시는분들마다 좋아하십니다.\\n\\n ㅁ 주차는 1대 가능하십니다\\n\\n \\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 \" 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.',\n", " 'description_og': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\n\\n ㅁ 압구정역 3번출구 인근에 있는 너무나 귀한 단기임대 매물입니다.\\n\\n ㅁ 지역 특성상 단기임대 매물이 귀해서 이쪽으로 보시면 이거 꼭 보셔야하는 매물입니다.\\n\\n ㅁ 지하철역과 가깝고 압구정 로데오와 가깝구요 큰 대로변 바로 이면에 있습니다.\\n\\n ㅁ 늦은시간 다니시기 무섭지 않은 곳이구요.\\n\\n ㅁ 시세대비 저렴하게 나온 매물이라 보시는분들마다 좋아하십니다.\\n\\n ㅁ 주차는 1대 가능하십니다\\n\\n \\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 5652932,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5652932/32b38deea2d8f6b30e0b5c1791d1aeaf0cffa25e.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5652932/98440ef63fb07e0669c099916d919ade75dab7f5.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5652932/1a748b1ac151f71f48e1b585f6ead9d4398f4528.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5652932/ff14b3b25171200bb0c4ef22bb80380bea8a77f2.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5652932/4821498727e4ddf9b14712c1786b7bf61f51d401.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5652932/76f2896bc71be55300c08a829278862a702e4f3c.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5652932/b835a15eccb56782899e24e6eff240831c888e83.jpg'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '6만원',\n", " 'manage_cost_inc': 'TV',\n", " 'movein_date': '즉시 입주',\n", " 'near_subways': '압구정역(3호선), 압구정로데오역(분당선), 학동역(7호선)',\n", " 'options': '싱크대, 에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장',\n", " 'original_user_phone': '010-3727-0204',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5262690640023,127.030077677758',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 70,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 9.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '29.75',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕정말 귀한 신사동 단기임대 오픈형~!!!',\n", " 'updated_at': '5시간 전',\n", " 'user_email': 'yegwang6412@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7324',\n", " 'user_name': '중개보조원(한승용)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-7324',\n", " 'view_count': 6},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '65',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 잠원동 13-1 아이미서초빌딩 2층',\n", " 'agent_comment': '',\n", " 'agent_email': 'woopers@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1281-0426',\n", " 'agent_name': '오렌지하우스공인중개사(김승식)',\n", " 'agent_phone': '02-512-4243',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '65',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 10000,\n", " 'description': '★ 지정주차!! 컨디션 특AA+급 완~전 큰거실+쓰리룸 반전세입니다(보증금조정가능)^^ \\n\\n★ 도보로 학동역 10분거리로 주변 편의시설과 교통이 편리해요^^ \\n\\n★ 현세입자 완전 깔끔한 분으로 집이 새집같아요\\n\\n★ 직접 보세요^^ ',\n", " 'description_og': '★ 지정주차!! 컨디션 특AA+급 완~전 큰거실+쓰리룸 반전세입니다(보증금조정가능)^^ \\n\\n★ 도보로 학동역 10분거리로 주변 편의시설과 교통이 편리해요^^ \\n\\n★ 현세입자 완전 깔끔한 분으로 집이 새집같아요\\n\\n★ 직접 보세요^^ ',\n", " 'elevator': '없음',\n", " 'floor': '3층',\n", " 'floor_all': '5층',\n", " 'id': 5605314,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5605314/ae61e0b0d102267ab1ecc58caf03750a2c095667.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5605314/0967f4401cc4cd793bc8bc6ca954f30ba44c4600.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5605314/991bef6fb54810a2d9064a39500e28c508cc58a7.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5605314/a1d97aed7f35b4f697c81d11f1039ab2bf6614fd.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5605314/e9cbda9e388eaa84cca576c3f299acb745624237.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5605314/8c042f4a790c6ec8824beada506e70ac4a0899a8.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5605314/9ce609eb738730bd7c2b273d97d791431b8d68f7.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5605314/74effba6613f40f6534860cc39bc5e04d1e2a29f.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5605314/758ded84693286782caacf66c20a9d62301a0a76.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5605314/37fcde505f409413e8022b1447da61fa2329e762.JPG'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5605314/f672d7098c748425f8822b4d32f22267b06b23a1.JPG'}],\n", " 'images_count': 11,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '8월23이후',\n", " 'near_subways': '학동역(7호선), 압구정역(3호선), 신사역(3호선)',\n", " 'options': '신발장, 싱크대',\n", " 'original_user_phone': '010-2247-1355',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5193957964846,127.0292793749',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 80,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '쓰리룸+',\n", " 'secret_memo': None,\n", " 'size': 22.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '72.73',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '◈◈지정주차!!◈컨디션특AA+급완~전큰거실+쓰리룸반전세!!◈도보학동역10분!! 강추◈◈',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'pysrose66@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7038',\n", " 'user_name': '중개보조원(박윤숙)',\n", " 'user_no': 327679,\n", " 'user_phone': '0507-1281-7038',\n", " 'view_count': 114},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 110,\n", " 'description': '☆ 신사역 5분 \\n☆ 가로수길 3분 \\n☆ 복층구조라 분리형 느낌 \\n☆ 빈티지 대리석 인테리어로 이국적인느낌 \\n☆ 엘리베이터있구요 \\n☆ 신축 건물이라 내외관 아주깔끔해욧',\n", " 'description_og': '☆ 신사역 5분 \\n☆ 가로수길 3분 \\n☆ 복층구조라 분리형 느낌 \\n☆ 빈티지 대리석 인테리어로 이국적인느낌 \\n☆ 엘리베이터있구요 \\n☆ 신축 건물이라 내외관 아주깔끔해욧',\n", " 'elevator': '있음',\n", " 'floor': '3층',\n", " 'floor_all': '6층',\n", " 'id': 5641189,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5641189/f641d076c981318ca6424bf7b72d8e21eae3d125.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5641189/479ee2025dde62bc0c02b55332c65a7417360c9e.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5641189/37eaef757da7575b5f97ffd21283a22fd8891799.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5641189/bdf801ed360ab2d94f3136b3f26919b4c43fa45a.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5641189/4c1b2bf0da5f3c5e84e1851475cc0813aedb9d20.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5641189/98d426e5290802c1c88a7b260add50ef38c8f68a.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5641189/0f47d74e4138c58eb361d28d207c330fdf9a962e.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5641189/4d73cff6cd94bd4527bc93fa75f312970e79127d.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5641189/9180fcf332955d42881fbd5bed260f3ff151cdaf.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5641189/6c95dc9923708c60b2c685e53e43570e473cf942.JPG'}],\n", " 'images_count': 10,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-9014-9280',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5165445422669,127.022843068526',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 110,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(복층형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '동일매물최저가보장',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6157',\n", " 'user_name': '중개보조원(김하근)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-6157',\n", " 'view_count': 52},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '73-7',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로26길 30 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'jsgongin@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-4624',\n", " 'agent_name': '정성공인중개사(정문식)',\n", " 'agent_phone': '010-4776-8102',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '73',\n", " 'bubun_code': '7',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 165,\n", " 'description': '♠ 내부구조 및 인테리어 ♠\\n\\n▷ 내외관 관리가 잘된 풀옵션 원룸 원거실~~\\n\\n▷ 전체적인 화이트톤의 아늑한 분위기가 물씬 풍기네요 ~\\n\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요?\\n\\n▷ 넓은 원룸 원거실~! 두분이서 살기에 딱 좋으며~\\n\\n▷ 좋은방은 금방금방 나가요~서두르셔야 할거에요~\\n\\n♠ 대중교통 및 위치 ♠\\n\\n▷ 강남을지병원 사거리 도보3분 거리에 있습니다.\\n\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다.\\n\\n♠ 주차 및 편의시설 ♠\\n\\n▷ 주차는 1대 가능합니다.\\n\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다.\\n\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★\\n\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다.\\n\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\n\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\n\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\n", " 'description_og': '♠ 내부구조 및 인테리어 ♠\\n\\n▷ 내외관 관리가 잘된 풀옵션 원룸 원거실~~\\n\\n▷ 전체적인 화이트톤의 아늑한 분위기가 물씬 풍기네요 ~\\n\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요?\\n\\n▷ 넓은 원룸 원거실~! 두분이서 살기에 딱 좋으며~\\n\\n▷ 좋은방은 금방금방 나가요~서두르셔야 할거에요~\\n\\n♠ 대중교통 및 위치 ♠\\n\\n▷ 강남을지병원 사거리 도보3분 거리에 있습니다.\\n\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다.\\n\\n♠ 주차 및 편의시설 ♠\\n\\n▷ 주차는 1대 가능합니다.\\n\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다.\\n\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★\\n\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다.\\n\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\n\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\n\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\n", " 'elevator': '있음',\n", " 'floor': '2층',\n", " 'floor_all': '5층',\n", " 'id': 5623803,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5623803/0076c9cdec0549be670ea070e0a0d73a2e1a14ec.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5623803/365efa15c0825371430644286d321babe721ef92.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5623803/86b480859e484f1fdfdf6158c674612ea2d6610d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 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'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-7193-9904',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/431435.jpg',\n", " 'random_location': '37.5187239445592,127.031664381457',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 165,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 14.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '46.28',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '☺☺ 넓은 원룸 원거실~! 두분이서 살기에 딱 좋으며~ ☺☺',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'arnani810403@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-5648',\n", " 'user_name': '중개보조원(권혁준)',\n", " 'user_no': 431435,\n", " 'user_phone': '0507-1280-5648',\n", " 'view_count': 49},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 115,\n", " 'description': '☆ 신사역 5분\\n\\n☆ 가로수길 3분\\n\\n☆ 복층구조라 분리형 느낌\\n\\n☆ 빈티지 대리석 인테리어로 이국적인느낌\\n\\n☆ 엘리베이터있구요\\n\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\n", " 'description_og': '☆ 신사역 5분\\n\\n☆ 가로수길 3분\\n\\n☆ 복층구조라 분리형 느낌\\n\\n☆ 빈티지 대리석 인테리어로 이국적인느낌\\n\\n☆ 엘리베이터있구요\\n\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\n", " 'elevator': '있음',\n", " 'floor': '3층',\n", " 'floor_all': '6층',\n", " 'id': 5641154,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5641154/0f47d74e4138c58eb361d28d207c330fdf9a962e.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5641154/f641d076c981318ca6424bf7b72d8e21eae3d125.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5641154/479ee2025dde62bc0c02b55332c65a7417360c9e.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5641154/37eaef757da7575b5f97ffd21283a22fd8891799.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5641154/bdf801ed360ab2d94f3136b3f26919b4c43fa45a.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5641154/4c1b2bf0da5f3c5e84e1851475cc0813aedb9d20.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5641154/98d426e5290802c1c88a7b260add50ef38c8f68a.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5641154/4d73cff6cd94bd4527bc93fa75f312970e79127d.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5641154/9180fcf332955d42881fbd5bed260f3ff151cdaf.JPG'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5641154/6c95dc9923708c60b2c685e53e43570e473cf942.JPG'}],\n", " 'images_count': 10,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-9014-9280',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5160556284199,127.023502323327',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 115,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(복층형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '가로수길 걸어서 가요~',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6157',\n", " 'user_name': '중개보조원(김하근)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-6157',\n", " 'view_count': 100},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '68-17',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\n연락주시면 믿음에 보답해 드리겠습니다!!★\\n\\n○ 이광고를 보고 계시다면 아직 방계약전입니다!! 서둘러 연락주세요!!!\\n\\n*24시 전화문자상담 환영~ 성실히 중개해드리겠습니다.\\n',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '68',\n", " 'bubun_code': '17',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 3000,\n", " 'description': '★ 언북중, 서울세관 뒷편 을지병원사거리 도보5분!!\\n\\n★ 거실 5~6인용쇼파와 티비 배치가능한 ㅎㄷㄷ한 크기!!\\n\\n★ 안방 12자 고급붙박이장!!! 슈퍼킹싸이즈 여유있게 들어갑니다!\\n\\n★ 작은방 일반 투룸 안방싸이즈!!! 슈퍼킹 싸이즈 작은방에도 들어갑니다^^\\n\\n★ 베란다또한 굿~!!!!\\n\\n★ 광고그대로 강남에서 손꼽히는 탑클래스 고급형 빌라입니다!!!!!',\n", " 'description_og': '★ 언북중, 서울세관 뒷편 을지병원사거리 도보5분!!\\n\\n★ 거실 5~6인용쇼파와 티비 배치가능한 ㅎㄷㄷ한 크기!!\\n\\n★ 안방 12자 고급붙박이장!!! 슈퍼킹싸이즈 여유있게 들어갑니다!\\n\\n★ 작은방 일반 투룸 안방싸이즈!!! 슈퍼킹 싸이즈 작은방에도 들어갑니다^^\\n\\n★ 베란다또한 굿~!!!!\\n\\n★ 광고그대로 강남에서 손꼽히는 탑클래스 고급형 빌라입니다!!!!!',\n", " 'elevator': '있음',\n", " 'floor': '4층',\n", " 'floor_all': '5층',\n", " 'id': 5619885,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5619885/176d270aa897cf3663f1a7eba35c72c690fda9b4.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5619885/6f6cb90e63c3ada6ace61df823bc64f26c693837.JPG'},\n", " 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'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-6836-4428',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5165591244404,127.022864324202',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 110,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(복층형)',\n", " 'secret_memo': None,\n", " 'size': 10.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '33.06',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '❄신축1룸+복층+엘리베이터❄',\n", " 'updated_at': '14일 전',\n", " 'user_email': 'global704@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-7314',\n", " 'user_name': '중개보조원(이문섭)',\n", " 'user_no': 1316477,\n", " 'user_phone': '0507-1281-7314',\n", " 'view_count': 209},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '559-5',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 신사동 610-2 1층',\n", " 'agent_comment': '신사동 열린부동산 입니다 \\n\\n최선을 다하는 중개사가 되도록 노력할게요',\n", " 'agent_email': 'suny1209@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-7187',\n", " 'agent_name': '열린공인중개사(강광수)',\n", " 'agent_phone': '02-511-0222',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '559',\n", " 'bubun_code': '5',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 24000,\n", " 'description': '2년전 싱크대 및 욕실 도배 장판 올수리한 연립주택 입니다\\n\\n1.5층 정남향 손볼거 전혀 없어요 \\n\\n융자 전혀 없고 주차 협의 1대 가능 합니다\\n\\n임차인 있어 사진을 제대로 못찍어 네요\\n\\n실제와서 보시면 더욱 마음에 들거에요\\n\\n거실 베란다 있고 안방에도 베란다 있어요\\n\\n방3,욕실1개 룸1개는 옷장 및 서립장으로 사용하면 좋을것 같아요\\n\\n입주는 9월20일 고정 입니다\\n\\n실매물 실사진 연락주셔요',\n", " 'description_og': '2년전 싱크대 및 욕실 도배 장판 올수리한 연립주택 입니다\\n\\n1.5층 정남향 손볼거 전혀 없어요 \\n\\n융자 전혀 없고 주차 협의 1대 가능 합니다\\n\\n임차인 있어 사진을 제대로 못찍어 네요\\n\\n실제와서 보시면 더욱 마음에 들거에요\\n\\n거실 베란다 있고 안방에도 베란다 있어요\\n\\n방3,욕실1개 룸1개는 옷장 및 서립장으로 사용하면 좋을것 같아요\\n\\n입주는 9월20일 고정 입니다\\n\\n실매물 실사진 연락주셔요',\n", " 'elevator': '없음',\n", " 'floor': '1층',\n", " 'floor_all': '4층',\n", " 'id': 5423901,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5423901/c3661e422467266774a5849bdbe787d00936ae1b.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5423901/c2e78440323b51a6ddcec75d7a73c0908c49fa78.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5423901/b01196980ecb227bebed4d5c522dfccd08087b7a.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5423901/6017859bc1e0885aaa59b45f35da656420eb059b.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5423901/1ffd92170a01771757f5a910794b946715487b9c.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5423901/fa854b5fd2aea8dff3f72ee3e1f0da126f3f162a.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5423901/19f5c6f16c06f9e98936bb6aa0185f0dfad2d8b8.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5423901/7cd7946f291489204fedd53214fb62beb71d691b.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5423901/07d08315bd67c7a61dfac243c14a11111d2280af.jpg'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5423901/5183962fcdd2949b7e966bcf14a58e8ea698923b.jpg'}],\n", " 'images_count': 10,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '2만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '협의',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\n", " 'options': '신발장, 싱크대',\n", " 'original_user_phone': '010-9134-5291',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\n", " 'random_location': '37.5206003116355,127.025412540907',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '쓰리룸+',\n", " 'secret_memo': None,\n", " 'size': 17.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '56.20',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '2년전 올수리 한 투룸 정남향 입니다',\n", " 'updated_at': '6일 전',\n", " 'user_email': 'suny1209@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-7187',\n", " 'user_name': '대표공인중개사(강광수)',\n", " 'user_no': 676867,\n", " 'user_phone': '0507-1280-7187',\n", " 'view_count': 605},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '16-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 서초구 잠원동 13-1 아이미서초빌딩 2층',\n", " 'agent_comment': '',\n", " 'agent_email': 'woopers@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '서초구',\n", " 'agent_mobile': '0507-1281-0426',\n", " 'agent_name': '오렌지하우스공인중개사(김승식)',\n", " 'agent_phone': '02-512-4243',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '16',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '♥신사역 3분이내 도보권 으로 교통이 편리한 곳에 위치한 저렴한 투룸 입니다.\\n\\n♥실면적 15p, 직접 발품을 팔아 엄선하여 광고등록된 매물로, 시세평수대비 저렴 합니다.\\n\\n♥주거밀집 많은 논현동에 위치하며 강남구에서 보안이 가장 띄어난 동네로 치안에도 안전합니다.\\n\\n♥인근에 세탁소, 편의점, 마트, 은행 등 편의시설이 잘 되어있어 편하게 생활하실 수 있습니다.\\n\\n♥투룸 및 넓은 小거실 구조로 공간활용도가 높습니다.\\n\\n직접 현장에서 DSLR카메라로 찍어온 실매물 입니다~*^^*',\n", " 'description_og': '♥신사역 3분이내 도보권 으로 교통이 편리한 곳에 위치한 저렴한 투룸 입니다.\\n\\n♥실면적 15p, 직접 발품을 팔아 엄선하여 광고등록된 매물로, 시세평수대비 저렴 합니다.\\n\\n♥주거밀집 많은 논현동에 위치하며 강남구에서 보안이 가장 띄어난 동네로 치안에도 안전합니다.\\n\\n♥인근에 세탁소, 편의점, 마트, 은행 등 편의시설이 잘 되어있어 편하게 생활하실 수 있습니다.\\n\\n♥투룸 및 넓은 小거실 구조로 공간활용도가 높습니다.\\n\\n직접 현장에서 DSLR카메라로 찍어온 실매물 입니다~*^^*',\n", " 'elevator': '없음',\n", " 'floor': '반지하',\n", " 'floor_all': '3층',\n", " 'id': 5642175,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5642175/bf727ee9c01381d56fccdd486d7305621e607308.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5642175/a9c20f69c90027e4c85cb12dd3547e7f8d6af131.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5642175/8622a0b45645a2782bb044d6f0de2f62f0d0534c.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5642175/434a9e2b1905b91ac929f7270b73a221f1b34a96.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5642175/db4e65ee83793661c06506b9469901c8272b29b7.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5642175/b9b3e7ac2014a1d5b0316023c96c517273c18cd0.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5642175/9af64291a1e5cc1c34920a631a55a2921b8578f4.JPG'}],\n", " 'images_count': 7,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '수도',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\n", " 'options': '에어컨, 가스레인지, 신발장, 싱크대',\n", " 'original_user_phone': '010-8886-0847',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.51497885071,127.021464593274',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 55,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 15.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '49.59',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '💖💗이가격에💖투룸💟실매물이라니💖💟👰👨💜💙💚💙💜💙💚💙💜💖💚💙💜',\n", " 'updated_at': '2일 전',\n", " 'user_email': 'wogudckacl@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-4233',\n", " 'user_name': '중개보조원(배재형)',\n", " 'user_no': 327679,\n", " 'user_phone': '0507-1281-4233',\n", " 'view_count': 50},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\n연락주시면 믿음에 보답해 드리겠습니다!!★\\n\\n*24시 전화문자상담환영~ 성실히 중개해 드립니다.',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 100,\n", " 'description': '★ 보안, 주차, 생활여건, 방컨디션 모든게 최상급입니다.\\n\\n★ 긴말 않겠습니다. 이가격에서 가장 럭셔리입니다!!!!\\n\\n★ 다른매물 보여드리지도 않습니다^^\\n\\n',\n", " 'description_og': '★ 보안, 주차, 생활여건, 방컨디션 모든게 최상급입니다.\\n\\n★ 긴말 않겠습니다. 이가격에서 가장 럭셔리입니다!!!!\\n\\n★ 다른매물 보여드리지도 않습니다^^\\n\\n',\n", " 'elevator': '있음',\n", " 'floor': '5층',\n", " 'floor_all': '5층',\n", " 'id': 5537230,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5537230/9c5676594d81c8ab656b7c91d30059ac512fe6fd.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5537230/dd10d960a84d33352ccad12904eabb83a10aac6a.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5537230/d3ff921d7029078f773a0decc0716575cb15bd76.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5537230/884607bdf4f95307ab6b1b93a170e78c1424471c.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5537230/35e8052efab37807c689c7afb4812259861ff241.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5537230/46a63ba6ca9d2773b3b9ae4f667e6c0899bc1ea5.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5537230/94e979cae103fabe3274030c8050474603317f14.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5537230/854e67b42c4259550804e514f90137064afc259f.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5537230/61a2ef3137910f5043ee02d87d2b18cccb4d950e.JPG'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션',\n", " 'original_user_phone': '010-4130-9632',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5160789051649,127.022838649819',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 7.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '23.14',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '★ 초특가!!! 신시역1번출구 도보5분 오픈형 원룸!!!',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-8205',\n", " 'user_name': '중개보조원(조관혁)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-8205',\n", " 'view_count': 437},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '16-38',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 664-25',\n", " 'agent_comment': '',\n", " 'agent_email': 'extravvip@gmail.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-5172',\n", " 'agent_name': '퍼스트공인중개사(성민규)',\n", " 'agent_phone': '02-508-8966',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '16',\n", " 'bubun_code': '38',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 2000,\n", " 'description': '신사역 도보 1분거이에 위치한 최고에 신축 풀옵션입니다~~여성분만 계약 가능하실것 같아요~\\n\\n보증금은 1000/65 2000/60 3000/55\\n\\n관리비 5만원(인터넷,케이블 포함) 깍은 금액입니다!!(살짝 변동 될 수도 있습니다)\\n\\n주차비 별도로 있지만 안하시는분으로 구한다고 하세요(방문주차는 좋습니다 주차장이 좋아요)\\n\\n옵션은 에어컨 드럼세탁기 냉장고 전기인덕션 붙박이장(커서 많이 들어갑니다) 책상 책장\\n\\n혼자 사시기 여기보다 좋은곳은 없는것 같네요~~^^신축이여서 깨끗 깔끔하구요~~\\n\\n6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋습니다~~\\n\\n입주는 빠르시면 좋으시구요~~계약하실분 오시면 일주일 안으로 방 구할 예정입니다~~^^\\n\\n가성비 좋은 매물만 올려 계약이 빠르게 되니 서둘러주세요~~!!\\n\\n안녕하세요^^ 반갑습니다!! \\n\\n현재 100% 계약 가능한 매물만 제가 직접가서 찍은 사진입니다.\\n\\n◆ 1년 365일 24시간 언제든 카톡,문자,전화주시면 성심껏 상담해드리겠습니다.\\n\\n◆ 강남 어디든 전화주시면 젭싸게 차량으로 픽업해드리고 가시는 곳 까지 편하고 안전하게 모셔다 드립니다.\\n (점심시간,퇴근시간 때도 모시러가구요 방보시고 다시 모셔다드립니다.)\\n◆ 내 집을 구한다는 마음가짐으로 최선을 다해 손님이 원하는 깨끗한 주거생활 책임지겠습니다.\\n \\n◆ 시간낭비 하시지 않도록 확실하게 중개합니다^^\\n\\n최대한 세입자 입장에 서서 미소를 짓게 만드는 가격에 \\n계약을 성사 시켜드리겠습니다. 감사합니다!\\n\\n:+:세입자의, 세입자에 의한, 세입자를 위한 매물:+:\\n\\n-------------------------------------------정성과 최선을 다하겠습니다-------------\\n\\n직장인과 학생여러분들을 위해 \\n24시간 카톡 상담가능합니다~^ㅡ^*\\n카카오톡ID:2222dragon\\n\\n어떤 중개사를 만나는가에 따라 내가 사는집이 달라집니다~!~!\\n\\n혼자 살기 이보다 좋은곳은 없습니다!! 신축이여서 깨끗 깔끔하구요~~6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋아요~~\\n\\n입주는 빠르면 좋으시구요 계약하시는분 오시면 바로 1주일안으로 방 구할 예정입니다~',\n", " 'description_og': '신사역 도보 1분거이에 위치한 최고에 신축 풀옵션입니다~~여성분만 계약 가능하실것 같아요~\\n\\n보증금은 1000/65 2000/60 3000/55\\n\\n관리비 5만원(인터넷,케이블 포함) 깍은 금액입니다!!(살짝 변동 될 수도 있습니다)\\n\\n주차비 별도로 있지만 안하시는분으로 구한다고 하세요(방문주차는 좋습니다 주차장이 좋아요)\\n\\n옵션은 에어컨 드럼세탁기 냉장고 전기인덕션 붙박이장(커서 많이 들어갑니다) 책상 책장\\n\\n혼자 사시기 여기보다 좋은곳은 없는것 같네요~~^^신축이여서 깨끗 깔끔하구요~~\\n\\n6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋습니다~~\\n\\n입주는 빠르시면 좋으시구요~~계약하실분 오시면 일주일 안으로 방 구할 예정입니다~~^^\\n\\n가성비 좋은 매물만 올려 계약이 빠르게 되니 서둘러주세요~~!!\\n\\n안녕하세요^^ 반갑습니다!! \\n\\n현재 100% 계약 가능한 매물만 제가 직접가서 찍은 사진입니다.\\n\\n◆ 1년 365일 24시간 언제든 카톡,문자,전화주시면 성심껏 상담해드리겠습니다.\\n\\n◆ 강남 어디든 전화주시면 젭싸게 차량으로 픽업해드리고 가시는 곳 까지 편하고 안전하게 모셔다 드립니다.\\n (점심시간,퇴근시간 때도 모시러가구요 방보시고 다시 모셔다드립니다.)\\n◆ 내 집을 구한다는 마음가짐으로 최선을 다해 손님이 원하는 깨끗한 주거생활 책임지겠습니다.\\n \\n◆ 시간낭비 하시지 않도록 확실하게 중개합니다^^\\n\\n최대한 세입자 입장에 서서 미소를 짓게 만드는 가격에 \\n계약을 성사 시켜드리겠습니다. 감사합니다!\\n\\n:+:세입자의, 세입자에 의한, 세입자를 위한 매물:+:\\n\\n-------------------------------------------정성과 최선을 다하겠습니다-------------\\n\\n직장인과 학생여러분들을 위해 \\n24시간 카톡 상담가능합니다~^ㅡ^*\\n카카오톡ID:2222dragon\\n\\n어떤 중개사를 만나는가에 따라 내가 사는집이 달라집니다~!~!\\n\\n혼자 살기 이보다 좋은곳은 없습니다!! 신축이여서 깨끗 깔끔하구요~~6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋아요~~\\n\\n입주는 빠르면 좋으시구요 계약하시는분 오시면 바로 1주일안으로 방 구할 예정입니다~',\n", " 'elevator': '있음',\n", " 'floor': '6층',\n", " 'floor_all': '7층',\n", " 'id': 5644685,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5644685/59dcd1b47381fb2867105a5034dd39a35dbdbfa8.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5644685/e98138f62800645cea547ee3cd8304aa44ebd0fe.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5644685/84e5e60321ee07552bde05c918828de6033bc4fe.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5644685/e7a730aff63377f34e24cd14c7d218654e936f22.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5644685/8e003d560b9cb85968ca63cba6ebc7deb550a9c7.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5644685/c7295caf049ce512c1733f33a74ef7f8bceb15c1.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5644685/213065ef705f43e17e1c094d71d5a09064e56b37.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5644685/70e0c7e6176ec9c0b27ac2948bd8b1b89f2ab09b.JPG'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5644685/36fd8a2970998551e5655fe46264f01df89f1050.JPG'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시입주가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 책장, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-3158-9915',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5147493154384,127.020664946803',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 60,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 7.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '23.14',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '◆■절대 허위아님!! 신사역 도보1분 신축 풀옵션◆■',\n", " 'updated_at': '2일 전',\n", " 'user_email': '2222dragon@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6667',\n", " 'user_name': '중개보조원(이용)',\n", " 'user_no': 158186,\n", " 'user_phone': '0507-1280-6667',\n", " 'view_count': 28},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '545-5',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\n", " 'agent_comment': '',\n", " 'agent_email': 'artsoup@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6420',\n", " 'agent_name': '즐겨찾기공인중개사(김영한)',\n", " 'agent_phone': '02-540-0124',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '545',\n", " 'bubun_code': '5',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 25000,\n", " 'description': '복도식이며 거실 창문과 복도 창문 열면 맞바람 통풍 잘되구요\\n\\n\\n\\n채광 좋습니다 \\n\\n\\n\\n주차가 불편해서 저렴하게 나왔습니다 잠깐 잠깐 대는건 괜찮구요!\\n\\n\\n 저녁 8시부터 다음날 오전10까지는 가능하나 낮에는 안됩니다 \\n\\n\\n\\n가로수길 다들 아시니 따로 말씀 드리지는 않겠지만 골목 들어가지 않아 밤길 귀가도 걱정 없습니다\\n\\n\\n\\n반전세도 가능합니다 22000-20 가능 \\n\\n\\n미리연락 부탁드리며 비번 있으니 언제든지 보러 오세요~~~\\n\\n\\n\\n',\n", " 'description_og': '복도식이며 거실 창문과 복도 창문 열면 맞바람 통풍 잘되구요\\n\\n\\n\\n채광 좋습니다 \\n\\n\\n\\n주차가 불편해서 저렴하게 나왔습니다 잠깐 잠깐 대는건 괜찮구요!\\n\\n\\n 저녁 8시부터 다음날 오전10까지는 가능하나 낮에는 안됩니다 \\n\\n\\n\\n가로수길 다들 아시니 따로 말씀 드리지는 않겠지만 골목 들어가지 않아 밤길 귀가도 걱정 없습니다\\n\\n\\n\\n반전세도 가능합니다 22000-20 가능 \\n\\n\\n미리연락 부탁드리며 비번 있으니 언제든지 보러 오세요~~~\\n\\n\\n\\n',\n", " 'elevator': '있음',\n", " 'floor': '3층',\n", " 'floor_all': '5층',\n", " 'id': 5628997,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5628997/0521923f47d27792e7d489253abb20e699232153.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5628997/5054d2f65972460d2b05e29f8abe84b98c8a91ab.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5628997/821b76570776985a82d84a878dcc90d617acd192.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5628997/d759061df9f0b7b6c4072c3567adc328edf7bd72.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5628997/37adb268efce848259d0ab4c0bdf2b311f1a0a6f.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5628997/47d5d00b42faf11d8161013c30f83ae4deeddf60.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5628997/4a2b95f48bc0f0aed70623f2ecd778b3ca03689b.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5628997/3ba2d66bfca172942b82287d4299ade55f2d5c9a.jpg'}],\n", " 'images_count': 8,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': True,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '수도',\n", " 'movein_date': '즉시또는협의',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\n", " 'options': '-',\n", " 'original_user_phone': '010-2446-3484',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5209045544202,127.022701987347',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 0,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 15.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '49.59',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '전세자금대출가능 가로수길 에 위치한 거실 넓게 빠진 투룸으로 신혼부부나 방보다 넓은 거실 찾는분들에게 딱입니다 가로수길이라도 창문이 반대오 나와 조용합니다',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'cjpark12@hanmail.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-6676',\n", " 'user_name': '중개보조원(박충진)',\n", " 'user_no': 810168,\n", " 'user_phone': '0507-1280-6676',\n", " 'view_count': 152},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '13-20',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 역삼동 639-14',\n", " 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\n연락주시면 믿음에 보답해 드리겠습니다!!★\\n\\n○ 이광고를 보고 계시다면 아직 방계약전입니다!! 서둘러 연락주세요!!!\\n\\n*24시 전화문자상담 환영~ 성실히 중개해드리겠습니다.\\n',\n", " 'agent_email': 'jmk_78@nate.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-5901',\n", " 'agent_name': '우정공인중개사(김성훈)',\n", " 'agent_phone': '02-566-2004',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '13',\n", " 'bubun_code': '20',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 5000,\n", " 'description': '★ 강남 하이클래스 인테리어\\n\\n★ 빈티지스타일 대리석 인테리어!!\\n\\n★ 거실 완벽한 맞벽구조 티비쇼파 배치가능!!\\n\\n★ 침실싸이즈 굿~~ 슈퍼킹싸이즈도 무난하게 소화가능합니다.\\n\\n★ 신사역 1번출구 도보 3분거리 초역세권!! \\n\\n★ 이동네에 유일무이한 스타일입니다!! 서둘러 연락주세요.',\n", " 'description_og': '★ 강남 하이클래스 인테리어\\n\\n★ 빈티지스타일 대리석 인테리어!!\\n\\n★ 거실 완벽한 맞벽구조 티비쇼파 배치가능!!\\n\\n★ 침실싸이즈 굿~~ 슈퍼킹싸이즈도 무난하게 소화가능합니다.\\n\\n★ 신사역 1번출구 도보 3분거리 초역세권!! \\n\\n★ 이동네에 유일무이한 스타일입니다!! 서둘러 연락주세요.',\n", " 'elevator': '있음',\n", " 'floor': '6층',\n", " 'floor_all': '6층',\n", " 'id': 5595814,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5595814/6f78348d00eff1c3721155ddfd83e2877472a5ce.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5595814/ee9387392e949005f58f580dbcc659f647465427.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5595814/bbd3dfd92a61b07781b26110c6e931406c4f119d.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5595814/8ee05f69eddc4c7e0b579e5dcb89007b4d180709.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5595814/5bcf2a196b78a0435b10d904433b468c7718e28a.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 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'http://z1.zigbang.com/items/5595814/c29a8683acecb672552e8e65ff7a370e3dbd5e00.JPG'}],\n", " 'images_count': 13,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '9월초 즉시가능',\n", " 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-4130-9632',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\n", " 'random_location': '37.5165376313724,127.022892016915',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 90,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 14.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '46.28',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '★전속관리★꿈의 원룸원거실★',\n", " 'updated_at': '4일 전',\n", " 'user_email': 'jmk_78@nate.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-8205',\n", " 'user_name': '중개보조원(조관혁)',\n", " 'user_no': 252306,\n", " 'user_phone': '0507-1280-8205',\n", " 'view_count': 130},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '17',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 삼성동 33-18 1층',\n", " 'agent_comment': '',\n", " 'agent_email': 'newmanchoi@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-3522',\n", " 'agent_name': '신화공인중개사(최재호)',\n", " 'agent_phone': '02-3444-6444',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '17',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '안녕하세요 !! 신화부동산입니다!!\\n\\n오늘보실매물은 논현동 학동공원에 위치한 \\n\\n초저렴한 반지하 투룸인데요!!\\n\\n원룸가격이라고해도 정말 저렴한 가격인데!!\\n\\n거기다가 투룸이라니!!\\n\\n반지하지만 반지하느낌 거의 없죠!!\\n\\n구조가 잘나와있고 세탁실처럼 사용할 수 있는 베란다가 깨알같이 있구요!!\\n\\n짐많으신 독신자분들, 단촐하게 2인거주하실분들 정말 강추드립니다!!',\n", " 'description_og': '안녕하세요 !! 신화부동산입니다!!\\n\\n오늘보실매물은 논현동 학동공원에 위치한 \\n\\n초저렴한 반지하 투룸인데요!!\\n\\n원룸가격이라고해도 정말 저렴한 가격인데!!\\n\\n거기다가 투룸이라니!!\\n\\n반지하지만 반지하느낌 거의 없죠!!\\n\\n구조가 잘나와있고 세탁실처럼 사용할 수 있는 베란다가 깨알같이 있구요!!\\n\\n짐많으신 독신자분들, 단촐하게 2인거주하실분들 정말 강추드립니다!!',\n", " 'elevator': '없음',\n", " 'floor': '반지하',\n", " 'floor_all': '4층',\n", " 'id': 5646707,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 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환영합니다.\\n\\n',\n", " 'agent_email': 'chulwan@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1281-0864',\n", " 'agent_name': '한솔공인중개사(박철완)',\n", " 'agent_phone': '02-517-2799',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '560',\n", " 'bubun_code': '12',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '저렴한 분리형원룸입니다.\\n\\n1. 위치 : 신사역 가로수길인근\\n\\n2. 금액 : 1,000/50,5\\n\\n3. 옵션 : 에어컨 베란다\\n\\n도배해 드립니다. 혼자 사시기에 정말 좋아요.....',\n", " 'description_og': '저렴한 분리형원룸입니다.\\n\\n1. 위치 : 신사역 가로수길인근\\n\\n2. 금액 : 1,000/50,5\\n\\n3. 옵션 : 에어컨 베란다\\n\\n도배해 드립니다. 혼자 사시기에 정말 좋아요.....',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '3층',\n", " 'id': 5608753,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5608753/80d818a146e1bd160aa83cba2550780083f16019.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 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'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '-',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 신발장, 싱크대',\n", " 'original_user_phone': '010-9498-3916',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5202204814306,127.025271787105',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 50,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(분리형)',\n", " 'secret_memo': None,\n", " 'size': 7.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '23.14',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '신사역 가로수길 분리형원룸 에어컨 베란다',\n", " 'updated_at': '5일 전',\n", " 'user_email': 'sipp1@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1282-0165',\n", " 'user_name': '소속공인중개사(장승일)',\n", " 'user_no': 1531083,\n", " 'user_phone': '0507-1282-0165',\n", " 'view_count': 205},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '554',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 논현1동 124-33',\n", " 'agent_comment': '',\n", " 'agent_email': 'todayroomik@naver.com',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-8976',\n", " 'agent_name': 'TODAY공인중개사(이인규)',\n", " 'agent_phone': '02-511-0123',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '554',\n", " 'bubun_code': '',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 1000,\n", " 'description': '▒ 100% 실매물, 직접 촬영한 사진으로만 광고합니다.\\n\\n▒ 24시시간 상담가능합니다. 편하게 문의주세요!!\\n\\n▒ 인테리어 및 특징\\n\\n ☞ 건물 내외관 화이트톤으로 리모델링 했습니다!!\\n\\n ☞ TV/침대까지 있는 풀옵션 원룸\\n\\n ☞ 집기상태 매우 양호하고 몸만 오셔도 될정도입니다.\\n\\n ☞ 주변 쇼핑, 커피숍, 미용실 등 편의시설 多多多\\n\\n ☞ 신사역과, 버스노선 다수있어 대중교통이용 편리합니다.\\n\\n▒ 행복한 하루되세요~♬♬♬',\n", " 'description_og': '▒ 100% 실매물, 직접 촬영한 사진으로만 광고합니다.\\n\\n▒ 24시시간 상담가능합니다. 편하게 문의주세요!!\\n\\n▒ 인테리어 및 특징\\n\\n ☞ 건물 내외관 화이트톤으로 리모델링 했습니다!!\\n\\n ☞ TV/침대까지 있는 풀옵션 원룸\\n\\n ☞ 집기상태 매우 양호하고 몸만 오셔도 될정도입니다.\\n\\n ☞ 주변 쇼핑, 커피숍, 미용실 등 편의시설 多多多\\n\\n ☞ 신사역과, 버스노선 다수있어 대중교통이용 편리합니다.\\n\\n▒ 행복한 하루되세요~♬♬♬',\n", " 'elevator': '없음',\n", " 'floor': '4층',\n", " 'floor_all': '6층',\n", " 'id': 5600860,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5600860/cf16282782159f07f02a16f5b1768288cc557426.JPG'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5600860/39bd6e19b2c4fbfdbf2172c303b35a9060f6997f.JPG'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5600860/fe8d21bc264efb4f8b9105845558ee6ae9e93957.JPG'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5600860/eabadad82dd4efaa39a32a27b25676653c6ad865.JPG'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5600860/d28d52a401d241edff29c8dd410d9904be1ac4f0.JPG'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5600860/98ab6c678bdbebbd2666fe2a6fdc9c7ebe13b612.JPG'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5600860/165fbf5bca6c5e67f00529189f477eb1e39473bb.JPG'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5600860/e3f43e1f7765ac4dd7737a37b7599a37c31d36e9.JPG'}],\n", " 'images_count': 8,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '5만원',\n", " 'manage_cost_inc': '수도',\n", " 'movein_date': '즉시입주',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\n", " 'options': '에어컨, 냉장고, 세탁기, 인덕션, 책장, 침대, 옷장, 신발장, 싱크대',\n", " 'original_user_phone': '010-3487-2672',\n", " 'parking': '불가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': None,\n", " 'random_location': '37.5213095416224,127.023417962265',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 68,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 8.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '26.45',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '◆◆리모델링 풀옵션 원룸/가로수길 접근성 우수◆◆',\n", " 'updated_at': '6일 전',\n", " 'user_email': 'todayroommk@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1280-0273',\n", " 'user_name': '중개보조원(지명구)',\n", " 'user_no': 449807,\n", " 'user_phone': '0507-1280-0273',\n", " 'view_count': 44},\n", " 'title': '서울시 강남구 신사동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 논현동',\n", " 'address2': '9-16',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010800',\n", " 'bonbun_code': '9',\n", " 'bubun_code': '16',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 80,\n", " 'description': '\\n ㅁ 도산대로 안쪽에 위치한 신축 풀옵션 입니다\\n \\n ㅁ 신축 건물로 아주 깨끗하여 여성분들에게 인기 좋아요\\n\\n ㅁ 벽걸이TV, 트럼세탁기등 옵션또한 최상으로 셋팅되었습니다\\n\\n ㅁ 화이트톤 싱크대로 셋팅되었고 붙박이장도 아주 좋아요\\n\\n ㅁ 샤워부스 셋팅되었고 도로변이라 밤에도 안전하고 좋아요\\n\\n ㅁ 주차는 1대 편리하게 가능합니다 \\n \\n \\n\\n # 저희 홈페이지는 100% 실사진/실가격/실매물 싸이트입니다\\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 \" 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\n',\n", " 'description_og': '\\n ㅁ 도산대로 안쪽에 위치한 신축 풀옵션 입니다\\n \\n ㅁ 신축 건물로 아주 깨끗하여 여성분들에게 인기 좋아요\\n\\n ㅁ 벽걸이TV, 트럼세탁기등 옵션또한 최상으로 셋팅되었습니다\\n\\n ㅁ 화이트톤 싱크대로 셋팅되었고 붙박이장도 아주 좋아요\\n\\n ㅁ 샤워부스 셋팅되었고 도로변이라 밤에도 안전하고 좋아요\\n\\n ㅁ 주차는 1대 편리하게 가능합니다 \\n \\n \\n\\n # 저희 홈페이지는 100% 실사진/실가격/실매물 싸이트입니다\\n\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\n\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\n\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\n',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5512559,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5512559/c46b9f86b327f93ea63c4b87374a8768d799933c.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5512559/9645fdaa5be01998146d2438363658121121b6c6.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5512559/1e7bea4ac610fe8d5806c2c50fbcca7f31597442.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5512559/047fd27ac909433714485ea60adcc6a047689bf4.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5512559/ab2015e135c271e01b24e00092bf29818fadfac5.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5512559/7b18bcd2626ba764f7ff6d73976f773f7a76337f.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5512559/047293348ff585e706106a56baf2ae78124b45c3.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5512559/261a0e6e596b832cdc42a9331b4e099e973b3c41.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5512559/8f8120a46019b214b49a7f7dfc74330cab46d3b7.jpg'}],\n", " 'images_count': 9,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '논현동',\n", " 'manage_cost': '6만원',\n", " 'manage_cost_inc': 'TV',\n", " 'movein_date': '즉시 입주',\n", " 'near_subways': '학동역(7호선), 신사역(3호선), 논현역(7호선)',\n", " 'options': '싱크대, 에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장',\n", " 'original_user_phone': '010-6406-9464',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5175282814183,127.027387119575',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 80,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '원룸(오픈형)',\n", " 'secret_memo': None,\n", " 'size': 9.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '29.75',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕을지병원 사거리부근 모던한 원룸',\n", " 'updated_at': '14일 전',\n", " 'user_email': 'jinyong0223@hanmail.net',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-8860',\n", " 'user_name': '중개보조원(박진용)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-8860',\n", " 'view_count': 444},\n", " 'title': '서울시 강남구 논현동'},\n", " {'header': False,\n", " 'header_height': 0,\n", " 'item': {'address1': '서울시 강남구 신사동',\n", " 'address2': '523-34',\n", " 'address3': None,\n", " 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\n", " 'agent_comment': '',\n", " 'agent_email': 'rjsdn110926@hanmail.net',\n", " 'agent_local1': '서울시',\n", " 'agent_local2': '강남구',\n", " 'agent_mobile': '0507-1280-6920',\n", " 'agent_name': '도원공인중개사(손석진)',\n", " 'agent_phone': '010-8886-5877',\n", " 'bjd_code': '1168010700',\n", " 'bonbun_code': '523',\n", " 'bubun_code': '34',\n", " 'building': None,\n", " 'building_type': '다세대건물',\n", " 'contract': '서울특별시',\n", " 'deposit': 100,\n", " 'description': '★ 신사역 7분거리 가로수길 3분으로 최고의 위치!!\\n\\n★ 최고급 옵션으로 깔끔하게 셋팅 완료!!\\n\\n★정말 귀한 위치로 서두르셔야 합니다!!\\n\\n★ 투룸 구조로 2~3명이서도 충분히 쓰실수 있는 사이즈!!\\n\\n★ 즉시입주 가능하며 언제든 보실수 있습니다!!',\n", " 'description_og': '★ 신사역 7분거리 가로수길 3분으로 최고의 위치!!\\n\\n★ 최고급 옵션으로 깔끔하게 셋팅 완료!!\\n\\n★정말 귀한 위치로 서두르셔야 합니다!!\\n\\n★ 투룸 구조로 2~3명이서도 충분히 쓰실수 있는 사이즈!!\\n\\n★ 즉시입주 가능하며 언제든 보실수 있습니다!!',\n", " 'elevator': '없음',\n", " 'floor': '2층',\n", " 'floor_all': '4층',\n", " 'id': 5594247,\n", " 'images': [{'count': 1,\n", " 'index': 0,\n", " 'url': 'http://z1.zigbang.com/items/5594247/8109ca56c6e73cdff48d70b6d805b1843a85a20b.jpg'},\n", " {'count': 2,\n", " 'index': 1,\n", " 'url': 'http://z1.zigbang.com/items/5594247/f47987d3494ecbf11debf874ee8409b77b887dc1.jpg'},\n", " {'count': 3,\n", " 'index': 2,\n", " 'url': 'http://z1.zigbang.com/items/5594247/69356c9e345f8e0cfabc2f2298f111146dcd5eb4.jpg'},\n", " {'count': 4,\n", " 'index': 3,\n", " 'url': 'http://z1.zigbang.com/items/5594247/2ad48bbb1bac4af1a2e82e2b1d9ac19decb9af06.jpg'},\n", " {'count': 5,\n", " 'index': 4,\n", " 'url': 'http://z1.zigbang.com/items/5594247/3c0c89159dd16d87ecd1513d580cc2fc81b5882e.jpg'},\n", " {'count': 6,\n", " 'index': 5,\n", " 'url': 'http://z1.zigbang.com/items/5594247/15d45441766f1d59760a382b1e22832d88cb2934.jpg'},\n", " {'count': 7,\n", " 'index': 6,\n", " 'url': 'http://z1.zigbang.com/items/5594247/98970fd542fc9b06260a6d9ecd66fcb4b5b68cfa.jpg'},\n", " {'count': 8,\n", " 'index': 7,\n", " 'url': 'http://z1.zigbang.com/items/5594247/428cb51d51d0f19ab2b18690bd61afb1aa6f0c15.jpg'},\n", " {'count': 9,\n", " 'index': 8,\n", " 'url': 'http://z1.zigbang.com/items/5594247/0baa4c98f45609c835f3c10d1fa3c1b2efd15665.jpg'},\n", " {'count': 10,\n", " 'index': 9,\n", " 'url': 'http://z1.zigbang.com/items/5594247/c3bbdb769a626d5fd616cc16651bae83cef55071.jpg'},\n", " {'count': 11,\n", " 'index': 10,\n", " 'url': 'http://z1.zigbang.com/items/5594247/913cc63fe2a1ddd3f097ce69c2f1f2b6cc0ee5be.jpg'},\n", " {'count': 12,\n", " 'index': 11,\n", " 'url': 'http://z1.zigbang.com/items/5594247/0279c31f0561b0b93ecab3f64a10593e36031e2c.jpg'},\n", " {'count': 13,\n", " 'index': 12,\n", " 'url': 'http://z1.zigbang.com/items/5594247/a8120211213b5b8477c6817272312f9f8d799756.jpg'}],\n", " 'images_count': 13,\n", " 'images_thumbnail': '',\n", " 'is_deposit_only': False,\n", " 'is_direct': False,\n", " 'is_owner': None,\n", " 'is_premium': True,\n", " 'is_premium2': True,\n", " 'is_realestate': True,\n", " 'is_room': False,\n", " 'is_status_close': False,\n", " 'is_status_open': True,\n", " 'is_type_room': False,\n", " 'is_zzim': False,\n", " 'loan_text': '확인필요',\n", " 'local1': '서울시',\n", " 'local2': '강남구',\n", " 'local3': '신사동',\n", " 'manage_cost': '10만원',\n", " 'manage_cost_inc': '인터넷, TV',\n", " 'movein_date': '즉시 가능',\n", " 'near_subways': '신사역(3호선), 압구정역(3호선), 논현역(7호선)',\n", " 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\n", " 'original_user_phone': '010-2068-9980',\n", " 'parking': '가능',\n", " 'pets_text': '확인필요',\n", " 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\n", " 'random_location': '37.5214826313499,127.02071888967',\n", " 'read_updated_at': '1/1/0001 12:00:00 AM',\n", " 'rent': 100,\n", " 'room_direction_text': '확인필요',\n", " 'room_gubun_code': '01',\n", " 'room_type': '투룸',\n", " 'secret_memo': None,\n", " 'size': 14.0,\n", " 'size_contract': 0.0,\n", " 'size_m2': '46.28',\n", " 'size_m2_contract': '-',\n", " 'status': '광고중',\n", " 'title': '⭕ 투룸 ⭕ 가로수길옆 리모델링완료 마지막호실!!',\n", " 'updated_at': '7일 전',\n", " 'user_email': 'promising14@naver.com',\n", " 'user_has_no_penalty': True,\n", " 'user_has_penalty': False,\n", " 'user_mobile': '0507-1281-8604',\n", " 'user_name': '중개보조원(박규백)',\n", " 'user_no': 68880,\n", " 'user_phone': '0507-1281-8604',\n", " 'view_count': 256},\n", " 'title': '서울시 강남구 신사동'}]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "room_information[\"items\"]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'서울특별시 강남구 논현동 173-21번지 '" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#http://m.cafe.naver.com/joonggonara\n", "room_information[\"items\"][0][\"item\"][\"agent_address1\"]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('서울특별시 은평구 진흥로 27', 11500, 0)\n", "('전라남도 목포시 상동 1021-7', 200, 30)\n", "('서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)', 5000, 20)\n", "('경기도 오산시 원동 788-5번지 1층', 4300, 0)\n", "('경기도 성남시 중원구 금광동 4352번지', 300, 30)\n", "('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 500, 53)\n", "('대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가)', 300, 28)\n", "('대구광역시 동구 율하동로23길 84 (율하동)', 300, 27)\n", "('서울특별시 관악구 남부순환로 1795', 1000, 45)\n", "('서울특별시 관악구 신림동 1409-13', 3000, 30)\n", "('광주광역시 광산구 신창동 1229-3', 4000, 0)\n", "('서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층', 3000, 130)\n", "('경기도 오산시 원동 787-3', 1000, 60)\n", "('서울특별시 강북구 도봉로89길 12 1층', 7000, 50)\n", "('경기도 화성시 향남읍 하길리 1456-1', 500, 35)\n", "('서울특별시 관악구 관악로15길 26 (봉천동)', 300, 30)\n", "('서울특별시 서초구 논현로11길 6-7, 1층 102호', 2000, 60)\n", "('경기도 성남시 중원구 성남동 제일로 35번길27', 12000, 0)\n", "('서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)', 4000, 50)\n", "('충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산', 1000, 60)\n", "('서울특별시 강북구 도봉로89길 12 1층', 1000, 40)\n", "('인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소', 300, 36)\n", "('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 200, 30)\n", "('광주광역시 광산구 신창동 1229-3', 2000, 0)\n", "('부산광역시 수영구 광안동 100-26', 1000, 35)\n", "('경기도 안산시 상록구 이화로 44', 200, 33)\n", "('경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌)', 2000, 30)\n", "('서울특별시 송파구 석촌동 282-2 서광빌딩 1층', 55, 55)\n", "('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 500, 27)\n", "('서울특별시 송파구 방이동 69-6', 10000, 50)\n" ] } ], "source": [ "for room_id in range(4461545, 4461545+100):\n", " BASE_URL = \"https://api.zigbang.com/v1/items?detail=true&item_ids=\" + str(room_id)\n", " response = requests.get(BASE_URL)\n", " room_inforation = json.loads(response.text)\n", " \n", " try:\n", " address = room_inforation[\"items\"][0][\"item\"][\"agent_address1\"]\n", " deposit = room_inforation[\"items\"][0][\"item\"][\"deposit\"]\n", " rent = room_inforation[\"items\"][0][\"item\"][\"rent\"]\n", " print((address, deposit, rent))\n", " df.loc[len(df)] = [address, int(deposit), int(rent)]\n", " except:\n", " pass" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>address</th>\n", " <th>deposit</th>\n", " <th>rent</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>서울특별시 은평구 진흥로 27</td>\n", " <td>11500.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>전라남도 목포시 상동 1021-7</td>\n", " <td>200.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)</td>\n", " <td>5000.0</td>\n", " <td>20.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>경기도 오산시 원동 788-5번지 1층</td>\n", " <td>4300.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>경기도 성남시 중원구 금광동 4352번지</td>\n", " <td>300.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)</td>\n", " <td>500.0</td>\n", " <td>53.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가)</td>\n", " <td>300.0</td>\n", " <td>28.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>대구광역시 동구 율하동로23길 84 (율하동)</td>\n", " <td>300.0</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>서울특별시 관악구 남부순환로 1795</td>\n", " <td>1000.0</td>\n", " <td>45.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>서울특별시 관악구 신림동 1409-13</td>\n", " <td>3000.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>광주광역시 광산구 신창동 1229-3</td>\n", " <td>4000.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층</td>\n", " <td>3000.0</td>\n", " <td>130.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>경기도 오산시 원동 787-3</td>\n", " <td>1000.0</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>서울특별시 강북구 도봉로89길 12 1층</td>\n", " <td>7000.0</td>\n", " <td>50.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>경기도 화성시 향남읍 하길리 1456-1</td>\n", " <td>500.0</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>서울특별시 관악구 관악로15길 26 (봉천동)</td>\n", " <td>300.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>서울특별시 서초구 논현로11길 6-7, 1층 102호</td>\n", " <td>2000.0</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>경기도 성남시 중원구 성남동 제일로 35번길27</td>\n", " <td>12000.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)</td>\n", " <td>4000.0</td>\n", " <td>50.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산</td>\n", " <td>1000.0</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>서울특별시 강북구 도봉로89길 12 1층</td>\n", " <td>1000.0</td>\n", " <td>40.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소</td>\n", " <td>300.0</td>\n", " <td>36.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)</td>\n", " <td>200.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>광주광역시 광산구 신창동 1229-3</td>\n", " <td>2000.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>부산광역시 수영구 광안동 100-26</td>\n", " <td>1000.0</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>경기도 안산시 상록구 이화로 44</td>\n", " <td>200.0</td>\n", " <td>33.0</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌)</td>\n", " <td>2000.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>서울특별시 송파구 석촌동 282-2 서광빌딩 1층</td>\n", " <td>55.0</td>\n", " <td>55.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)</td>\n", " <td>500.0</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>서울특별시 송파구 방이동 69-6</td>\n", " <td>10000.0</td>\n", " <td>50.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " address deposit rent\n", "0 서울특별시 은평구 진흥로 27 11500.0 0.0\n", "1 전라남도 목포시 상동 1021-7 200.0 30.0\n", "2 서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층) 5000.0 20.0\n", "3 경기도 오산시 원동 788-5번지 1층 4300.0 0.0\n", "4 경기도 성남시 중원구 금광동 4352번지 300.0 30.0\n", "5 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 500.0 53.0\n", "6 대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가) 300.0 28.0\n", "7 대구광역시 동구 율하동로23길 84 (율하동) 300.0 27.0\n", "8 서울특별시 관악구 남부순환로 1795 1000.0 45.0\n", "9 서울특별시 관악구 신림동 1409-13 3000.0 30.0\n", "10 광주광역시 광산구 신창동 1229-3 4000.0 0.0\n", "11 서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층 3000.0 130.0\n", "12 경기도 오산시 원동 787-3 1000.0 60.0\n", "13 서울특별시 강북구 도봉로89길 12 1층 7000.0 50.0\n", "14 경기도 화성시 향남읍 하길리 1456-1 500.0 35.0\n", "15 서울특별시 관악구 관악로15길 26 (봉천동) 300.0 30.0\n", "16 서울특별시 서초구 논현로11길 6-7, 1층 102호 2000.0 60.0\n", "17 경기도 성남시 중원구 성남동 제일로 35번길27 12000.0 0.0\n", "18 서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층) 4000.0 50.0\n", "19 충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산 1000.0 60.0\n", "20 서울특별시 강북구 도봉로89길 12 1층 1000.0 40.0\n", "21 인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소 300.0 36.0\n", "22 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 200.0 30.0\n", "23 광주광역시 광산구 신창동 1229-3 2000.0 0.0\n", "24 부산광역시 수영구 광안동 100-26 1000.0 35.0\n", "25 경기도 안산시 상록구 이화로 44 200.0 33.0\n", "26 경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌) 2000.0 30.0\n", "27 서울특별시 송파구 석촌동 282-2 서광빌딩 1층 55.0 55.0\n", "28 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 500.0 27.0\n", "29 서울특별시 송파구 방이동 69-6 10000.0 50.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "deposit 2615.166667\n", "rent 35.800000\n", "dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

UCSBarchlab/PyRTL

ipynb-examples/introduction-to-hardware.ipynb

1

10106

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Hardware Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code works through the hardware design process with the the\n", "audience of software developers more in mind. We start with the simple\n", "problem of designing a fibonacci sequence calculator (http://oeis.org/A000045)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyrtl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A normal old python function to return the Nth fibonacci number.\n", "\n", "Interative implementation of fibonacci, just iteratively adds a and b to\n", "calculate the nth number in the sequence.\n", "```>> [software_fibonacci(x) for x in range(10)]\n", "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def software_fibonacci(n):\n", " a, b = 0, 1\n", " for i in range(n):\n", " a, b = b, a + b\n", " return a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attempt 1\n", "\n", "Let's convert this into some hardware that computes the same thing. Our first go will be to just replace the 0 and 1 with WireVectors to see\n", "what happens." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def attempt1_hardware_fibonacci(n, bitwidth):\n", " a = pyrtl.Const(0)\n", " b = pyrtl.Const(1)\n", " for i in range(n):\n", " a, b = b, a + b\n", " return a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above looks really nice does not really represent a hardware implementation\n", "of fibonacci.\n", "\n", "Let's reason through the code, line by line, to figure out what it would actually build.\n", "\n", "* **```a = pyrtl.Const(0)```**\n", "> This makes a wirevector of ```bitwidth=1``` that is driven by a zero. Thus **```a```** is a wirevector. Seems good.\n", "\n", "* **```b = pyrtl.Const(1)```** \n", "> Just like above, ```b``` is a wirevector driven by 1\n", "\n", "* **```for i in range(n):```**\n", "> Okay, here is where things start to go off the rails a bit. This says to perform the following code 'n' times, but the value 'n' is passed as an input and is not something that is evaluated in the hardware, it is evaluated when you run the PyRTL program which generates (or more specifically elaborates) the hardware. Thus the hardware we are building will have The value of 'n' built into the hardware and won't actually be a run-time parameter. Loops are really useful for building large repetitive hardware structures, but they CAN'T be used to represent hardware that should do a computation iteratively. Instead we are going to need to use some registers to build a state machine.\n", "* **```a, b = b, a + b```**\n", "> Let's break this apart. In the first cycle **```b```** is ```Const(1)``` and ```(a + b)``` builds an adder with a ```(Const(0))``` and ```b (Const(1)``` as inputs. Thus ```(b, a + b)``` in the first iteration is: ```( Const(1), result_of_adding( Const(0), Const(1) )``` At the end of the first iteration **```a```** and **```b```** refer to those two constant values. In each following iteration more adders are built and the names **```a```** and **```b```** are bound to larger and larger trees of adders but all the inputs are constants!\n", "* **```return a```** \n", "> The final thing that is returned then is the last output from this tree of adders which all have ```Consts``` as inputs. Thus this hardware is hard-wired to find only and exactly the value of fibonacci of the value N specified at design time! Probably not what you are intending." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attempt 2\n", "\n", "Let's try a different approach. Let's specify two registers (\"a\" and \"b\") and then we can **update those values** as we iteratively compute fibonacci of N **cycle by cycle.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def attempt2_hardware_fibonacci(n, bitwidth):\n", " a = pyrtl.Register(bitwidth, 'a')\n", " b = pyrtl.Register(bitwidth, 'b')\n", "\n", " a.next <<= b\n", " b.next <<= a + b\n", "\n", " return a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is looking much better. \n", "\n", "Two registers, **```a```** and **```b```** store the values from which we\n", "can compute the series. \n", "\n", "The line ```a.next <<= b``` means that the value of a in the next\n", "cycle should be simply be **```b```** from the current cycle.\n", "\n", "The line ```b.next <<= a + b``` says\n", "to build an adder, with inputs of **```a```** and **```b```** from the current cycle and assign the value\n", "to **```b```** in the next cycle.\n", "\n", "### A visual representation of the hardware built is as such:\n", "```\n", "      +-----+      +---------+\n", "      |     |      |         |\n", "  +===V==+  |  +===V==+      |\n", "  |      |  |  |      |      |\n", "  |   a  |  |  |   b  |      |\n", "  |      |  |  |      |      |\n", "  +===V==+  |  +==V===+      |\n", "      |     |     |          |\n", "      |     +-----+          |\n", "      |           |          |\n", "  +===V===========V==+       |\n", "   \\      adder     /        |\n", "   +==============+        |\n", "             |               |\n", "           +---------------+\n", "```\n", "\n", "\n", "Note that in the picture the register **```a```** and **```b```** each have a wirevector which is\n", "the current value (shown flowing out of the bottom of the register) and an *input*\n", "which is giving the value that should be the value of the register in the following\n", "cycle (shown flowing into the top of the register) which are **```a```** and **```a.next```** respectively.\n", "\n", "When we say **```return a```** what we are returning is a reference to the register **```a```** in\n", "the picture above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attempt 3\n", "\n", "Of course one problem is that we don't know when we are done! **How do we know we\n", "reached the \"nth\" number in the sequence?** Well, we need to **add a register** to\n", "count up and see if we are done.\n", "\n", "This is very similliar to the example before, except that now we have a register \"i\"\n", "which keeps track of the iteration that we are on (i.next <<= i + 1).\n", "\n", "The **function now returns two values**, a reference to the register \"a\" and a reference to a single\n", "bit that tells us if we are done. That bit is calculated by comparing \"i\" to the\n", "to a wirevector \"n\" that is passed in to see if they are the same. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def attempt3_hardware_fibonacci(n, bitwidth):\n", " a = pyrtl.Register(bitwidth, 'a')\n", " b = pyrtl.Register(bitwidth, 'b')\n", " i = pyrtl.Register(bitwidth, 'i')\n", "\n", " i.next <<= i + 1\n", " a.next <<= b\n", " b.next <<= a + b\n", "\n", " return a, i == n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attempt 4\n", "\n", "This is now far enough along that we can **simulate the design** and see what happens..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def attempt4_hardware_fibonacci(n, req, bitwidth):\n", " a = pyrtl.Register(bitwidth, 'a')\n", " b = pyrtl.Register(bitwidth, 'b')\n", " i = pyrtl.Register(bitwidth, 'i')\n", " local_n = pyrtl.Register(bitwidth, 'local_n')\n", " done = pyrtl.WireVector(bitwidth=1, name='done')\n", "\n", " with pyrtl.conditional_assignment:\n", " with req:\n", " local_n.next |= n\n", " i.next |= 0\n", " a.next |= 0\n", " b.next |= 1\n", " with pyrtl.otherwise:\n", " i.next |= i + 1\n", " a.next |= b\n", " b.next |= a + b\n", " done <<= i == local_n\n", " return a, done" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

DavidMcDonald1993/ghsom

parameter_tests_density.ipynb

2

148859

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running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "starting ghsom for: 86/1/embedded_network_3.gml\n", "running time of algorithm: 11.6360001564\n", "saved nmi score for network embedded_network_3.gml: [ 0.24378803]\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "starting ghsom for: 88/1/embedded_network_4.gml\n", "running time of algorithm: 12.6510000229\n", "saved nmi score for network embedded_network_4.gml: [ 0.89473409]\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", 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running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading 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times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "writing nmi scores and running times to file\n", "\n", "loading nmi score progress\n", "loading running time progress\n", "\n", "starting ghsom for: 99/1/embedded_network_10.gml\n", "running time of algorithm: 9.88899993896\n", "saved nmi score for network embedded_network_10.gml: [ 0.]\n", "\n", "writing nmi scores and running times to file\n", "\n", "DONE\n", "OVERALL NMI SCORES\n", "[[ 0.54952507 0.56748932 0.62117634 0.8689669 0.90786313 0.61891829]\n", " [ 0.49040801 0.52003907 0.55385055 0.68342899 0.75099239 0.20930235]\n", " [ 0.74189169 0.7927954 0.91430549 0.77401103 0.51242947 0.30388959]\n", " [ 0.86975907 0.90847255 0.94223418 0.76437275 0.49650535 0.26940284]\n", " [ 0.59114813 0.6314837 0.6413286 0.79549181 0.89866097 0.44922438]\n", " [ 0.77742183 0.79794609 0.81828777 0.81413612 0.77374776 0.04583205]\n", " [ 0.56573641 0.65302357 0.66161511 0.76606045 0.85220675 0.2584614 ]\n", " [ 0.67196792 0.70070307 0.76196421 0.78278838 0.90585361 0.54734927]\n", " [ 0.71442567 0.73101836 0.73740175 0.83227564 0.90810815 0.17938423]\n", " [ 0.39265984 0.41185519 0.42299292 0.44501771 0.46549164 0.21294819]\n", " [ 0.51729484 0.52877433 0.57226428 0.68004549 0.85490593 0.34548417]\n", " [ 0.69599862 0.73971083 0.75569862 0.88054738 0.907541 0.53787565]\n", " [ 0.66178272 0.68701664 0.71348357 0.7359724 0.86818984 0.63783515]\n", " [ 0.66916629 0.70154855 0.72716467 0.80823427 0.89660667 0.54878722]\n", " [ 0.6766697 0.74272099 0.80111974 0.85289248 0.89200511 0.53935135]\n", " [ 0.43036159 0.42974014 0.41008955 0.45686715 0.46698894 0.32971662]\n", " [ 0.46224051 0.5239402 0.55973181 0.55407724 0.47730452 0.22830577]\n", " [ 0.43790651 0.44167666 0.46434119 0.45471284 0.45866686 0.19605661]\n", " [ 0.50409803 0.52188909 0.59000313 0.60940083 0.64128171 0.56599709]\n", " [ 0.58249127 0.60712079 0.67087225 0.76685493 0.88429845 0.70463111]\n", " [ 0.73512153 0.80380315 0.79303377 0.86058764 0.76931769 0.14101088]\n", " [ 0.52432465 0.52685064 0.56583541 0.69664756 0.85627336 0.42335175]\n", " [ 0.8685156 0.88487633 0.89449922 0.92202488 0.53305698 0.18598665]\n", " [ 0.43143541 0.45036984 0.49538811 0.65719984 0.72592287 0.38357852]\n", " [ 0.6727648 0.70089606 0.72449522 0.78389157 0.89693662 0.54845905]\n", " [ 0.86578841 0.88629416 0.94215701 0.81481251 0.48257458 0.13512311]\n", " [ 0.71510114 0.72506788 0.74158547 0.78940905 0.86478929 0.66856926]\n", " [ 0.4788749 0.51388749 0.54773472 0.67065553 0.82326338 0.25808119]\n", " [ 0.60175011 0.68068056 0.6955397 0.76101609 0.89916926 0.35968311]\n", " [ 0.67026661 0.71764841 0.72965867 0.85628396 0.89688288 0.44804714]\n", " [ 0.43706169 0.47310536 0.50828678 0.6094256 0.78458666 0.60977995]\n", " [ 0.68472119 0.67462261 0.71686832 0.72909237 0.84626626 0.4092297 ]\n", " [ 0.7558466 0.77093929 0.80673139 0.8535845 0.71086114 0.55441457]\n", " [ 0.79408313 0.79904343 0.85742581 0.8428316 0.62844791 0.34590136]\n", " [ 0.42912769 0.48695157 0.50954179 0.49759907 0.55888392 0.48890269]\n", " [ 0.61534255 0.6520512 0.67686749 0.78657334 0.88408489 0.45062018]\n", " [ 0.61220301 0.64474238 0.67866419 0.76588344 0.89397077 0.72583468]\n", " [ 0.70427041 0.69100481 0.72923623 0.81265725 0.89599084 0.358284 ]\n", " [ 0.65237501 0.67344766 0.67759013 0.75710735 0.89223168 0.62566046]\n", " [ 0.72913538 0.75581506 0.76308175 0.80277497 0.90785414 0.44840138]\n", " [ 0.64361903 0.67975856 0.71380516 0.76328574 0.89303927 0.71472595]\n", " [ 0.66093933 0.70151879 0.73460762 0.78296666 0.8947692 0.64730623]\n", " [ 0.46689171 0.4659103 0.50331961 0.66440175 0.68762023 0.297966 ]\n", " [ 0.56740454 0.61620314 0.64066982 0.80461263 0.90698445 0.52779473]\n", " [ 0.69789711 0.73175138 0.77443267 0.78267117 0.8968744 0.44861129]\n", " [ 0.6842159 0.70155859 0.76323417 0.8116452 0.86406129 0.45815237]\n", " [ 0.56614448 0.57753163 0.62302067 0.64928133 0.69363532 0.42002466]\n", " [ 0.75317611 0.7136595 0.64840915 0.60801226 0.47886036 0.18344557]\n", " [ 0.8625339 0.86324523 0.84857976 0.74212945 0.50580999 0.12299423]\n", " [ 0.70722337 0.75093061 0.77465986 0.78292685 0.85728533 0.36928518]\n", " [ 0.76084206 0.79685743 0.80715672 0.82722595 0.89766646 0.28980628]\n", " [ 0.83731322 0.85010262 0.87957149 0.80641364 0.44375914 0.31934683]\n", " [ 0.79876243 0.80649439 0.82271547 0.83520206 0.69007944 0.34115843]\n", " [ 0.8248597 0.78721335 0.83300509 0.77654594 0.6020407 0.27704426]\n", " [ 0.6399694 0.72260669 0.78404524 0.75524357 0.91796948 0.5486662 ]\n", " [ 0.73435074 0.75213578 0.79013807 0.79614173 0.78898045 0.24545142]\n", " [ 0.85685709 0.8578449 0.82091753 0.79542476 0.63863615 0.29615394]\n", " [ 0.64136285 0.68659274 0.67674475 0.720359 0.74281426 0.42472824]\n", " [ 0.8084302 0.81164128 0.85075465 0.80842885 0.59248826 0.2541637 ]\n", " [ 0.80267374 0.7492153 0.70632592 0.65755461 0.47720788 0.24925608]\n", " [ 0.84590507 0.84649262 0.87274696 0.88658659 0.71091225 0.14746158]\n", " [ 0.76976603 0.78235066 0.75965799 0.7498199 0.59059672 0.2916559 ]\n", " [ 0.60038 0.61252167 0.63584 0.65715981 0.74072097 0.42108186]\n", " [ 0.62638449 0.6530487 0.7319694 0.71705946 0.88892576 0.2801512 ]\n", " [ 0.61641814 0.67391002 0.71501202 0.72437325 0.81318462 0.44912298]\n", " [ 0.60791793 0.63956886 0.71967724 0.7022293 0.89563493 0.44790805]\n", " [ 0.8181593 0.80948791 0.8485548 0.85773862 0.75089196 0.34042436]\n", " [ 0.59856681 0.63139099 0.71782715 0.69740854 0.87191473 0.37839586]\n", " [ 0.72637538 0.74250726 0.77264405 0.77401104 0.80115311 0.31120285]\n", " [ 0.77670652 0.74522905 0.70089113 0.57646704 0.47504081 0.17607441]\n", " [ 0.79174193 0.7555183 0.70000471 0.63272893 0.47746115 0.14575981]\n", " [ 0.8053393 0.7499825 0.73614648 0.64512311 0.48459734 0.09722981]\n", " [ 0.82625864 0.85067941 0.79787576 0.67037047 0.49684078 0.25735227]\n", " [ 0.66688711 0.66666619 0.75028918 0.73468054 0.72370009 0.47098527]\n", " [ 0.59908463 0.57598744 0.51710369 0.45597234 0.37104299 0.1679981 ]\n", " [ 0.7711235 0.76912794 0.68395538 0.56620825 0.45498247 0.20605832]\n", " [ 0.73409469 0.72750921 0.69103044 0.58755294 0.47810317 0.24788601]\n", " [ 0.76725277 0.79915449 0.80136854 0.82909858 0.74453964 0.33343368]\n", " [ 0.69782823 0.67502995 0.65677351 0.59980177 0.40928058 0.10149589]\n", " [ 0.65011018 0.61048631 0.72456986 0.70863219 0.87596625 0.60786757]\n", " [ 0.61088287 0.57505597 0.53767897 0.48511584 0.35918304 0.11345659]\n", " [ 0.58917786 0.605467 0.7250835 0.70696132 0.91650798 0.26839953]\n", " [ 0.69831313 0.6162117 0.61035977 0.4793488 0.3452575 0.13722418]\n", " [ 0.81486034 0.80294798 0.7418346 0.6811155 0.4451345 0.13145958]\n", " [ 0.77144422 0.77953852 0.80482346 0.8117083 0.67209845 0.41409052]\n", " [ 0.81174832 0.79939028 0.80285814 0.69078983 0.55825099 0.16391824]\n", " [ 0.58982492 0.54904596 0.53509968 0.4945495 0.36657169 0.11542566]\n", " [ 0.8288504 0.8221208 0.8578931 0.7863148 0.51187383 0.12900125]\n", " [ 0.66945223 0.67920425 0.70096102 0.71667352 0.78389768 0.52914696]\n", " [ 0.71434567 0.70863833 0.76450924 0.76982958 0.82680669 0.38021429]\n", " [ 0.78990647 0.8146741 0.82189723 0.8168652 0.6989619 0.20465515]\n", " [ 0.68857028 0.67125901 0.65039418 0.55529524 0.41534553 0.21349388]\n", " [ 0.83280633 0.84535158 0.8656412 0.832823 0.6544874 0.12947269]\n", " [ 0.7805793 0.79853232 0.81739112 0.82611417 0.71671765 0.17729986]\n", " [ 0.69434991 0.67756319 0.63619565 0.5451263 0.39579527 0.15423876]\n", " [ 0.76373613 0.76655354 0.82576943 0.84426464 0.64264467 0.21106216]\n", " [ 0.7559443 0.77042208 0.81552976 0.82898259 0.79994685 0.34616979]\n", " [ 0.79544854 0.77184386 0.73012865 0.66283832 0.54984685 0.09519321]\n", " [ 0.71889643 0.66853765 0.65314659 0.59153232 0.38862889 0.22591419]\n", " [ 0.61416811 0.66571283 0.72491661 0.72400041 0.89553419 0.54820093]]\n" ] } ], "source": [ "from __future__ import division\n", "\n", "import os\n", "from shutil import copyfile\n", "import subprocess\n", "from save_embedded_graph27 import main_binary as embed_main\n", "from spearmint_ghsom import main as ghsom_main\n", "import numpy as np\n", "import pickle\n", "from time import time\n", "import networkx as nx\n", "\n", "def save_obj(obj, name):\n", " with open(name + '.pkl', 'wb') as f:\n", " pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n", "\n", "def load_obj(name):\n", " with open(name + '.pkl', 'rb') as f:\n", " return pickle.load(f)\n", " \n", "def graph_measures(gml_filename):\n", " \n", " G = nx.read_gml(gml_filename)\n", " \n", " density = nx.density(G)\n", " \n", " arr = np.array([val for key, val in nx.degree_centrality(G).iteritems()])\n", " arr = np.sort(arr)[::-1]\n", " \n", " k = len(arr)\n", "\n", " k1 = k\n", "\n", " sum = np.sum(arr)\n", " \n", " var = 1\n", "\n", " while var > 0.95:\n", "\n", " k -= 1\n", "\n", " var = np.sum(arr[:k]) / sum \n", " \n", " centrality = k / k1\n", " \n", " assortativity = nx.degree_assortativity_coefficient(G)\n", " \n", " connectivity = nx.node_connectivity(G)\n", " \n", " return np.array([density, centrality, assortativity, connectivity])\n", "\n", "#root dir\n", "os.chdir(\"C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\")\n", "\n", "#save directory\n", "dir = os.path.abspath(\"parameter_tests_density\")\n", "\n", "#number of networks to generate\n", "num_networks = 100\n", "\n", "#number of times to repeat\n", "num_repeats = 10\n", "\n", "#number of nodes in the graph\n", "N = 64\n", "\n", "#make save directory\n", "if not os.path.isdir(dir):\n", " os.mkdir(dir)\n", "\n", "#change to dir\n", "os.chdir(dir) \n", "\n", "#network file names -- output of network generator\n", "network = \"network.dat\"\n", "first_level = \"community.dat\"\n", "\n", "#community labels\n", "labels = 'firstlevelcommunity'\n", "\n", "#mixing factors\n", "mu = 0.3\n", "\n", "parameter_settings = [0.5, 0.6, 0.7, 0.8, 0.9, 1]\n", "\n", "# densities = np.zeros(num_networks)\n", "overall_nmi_scores = np.zeros((num_networks, len(parameter_settings)))\n", "\n", "MAX_EDGES = 2017\n", "MIN_DENSITY = 0.05\n", "MAX_DENSITY = 0.2\n", "\n", "for i in range(num_networks):\n", " \n", " #create directory\n", " dir_string = os.path.join(dir, str(i))\n", " if not os.path.isdir(dir_string):\n", " os.mkdir(dir_string)\n", " \n", " #change working directory \n", " os.chdir(dir_string)\n", " \n", " #make benchmark parameter file\n", " filename = \"benchmark_flags_{}.dat\".format(i)\n", "\n", " if os.path.isfile('density.txt'):\n", " \n", " density = np.genfromtxt('density.txt')\n", " \n", " else:\n", " \n", " #number of edges\n", " num_edges = np.random.randint(MAX_EDGES * MIN_DENSITY, 2017 * 0.8)\n", "\n", " #number of communities\n", " num_communities = np.random.randint(1, 5)\n", "\n", " #number of nodes in micro community\n", " minc = 1\n", " maxc = np.random.randint(16, 30)\n", "\n", " #average number of edges\n", " k = float(num_edges) / N\n", "\n", " #max number of edges\n", " maxk = 2 * k\n", "\n", " ##calculate density\n", " density = 2 * float(num_edges) / (N * (N-1))\n", " with open('density.txt','w') as f:\n", " f.write('{}\\n'.format(density))\n", "\n", " if not os.path.isfile(filename):\n", " print 'density: {}'.format(density)\n", " print '-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}'.format(N, k, maxk, minc, maxc, mu)\n", " with open(filename,\"w\") as f:\n", " f.write(\"-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}\".format(N, k, maxk, minc, maxc, mu))\n", " print 'written flag file: {}'.format(filename)\n", " \n", "# if not os.path.isfile('data.pkl'):\n", "# data = np.zeros((len(parameter_settings), 4, num_repeats))\n", "# else:\n", "# data = load_obj('data')\n", "\n", " data = np.zeros((len(parameter_settings), 4, num_repeats))\n", " \n", " for j in range(len(parameter_settings)):\n", " \n", " #setting fo e_sg\n", " p = parameter_settings[j]\n", " \n", " #ghsom parameters\n", " params = {'w': 0.0001,\n", " 'eta': 0.0001,\n", " 'sigma': 1,\n", " 'e_sg': p,\n", " 'e_en': 0.8}\n", " \n", " #create directory\n", " dir_string_p = os.path.join(dir_string, str(p))\n", " if not os.path.isdir(dir_string_p):\n", " os.mkdir(dir_string_p)\n", " \n", " #change working directory \n", " os.chdir(dir_string_p)\n", " \n", "# if os.path.isfile('nmi_scores.csv') and :\n", "# print 'already completed {}/{}, loading scores and continuing'.format(i, p)\n", "# nmi_scores = np.genfromtxt('nmi_scores.csv', delimiter=',')\n", "# overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)\n", "# continue\n", " \n", " #record NMI scores\n", " if not os.path.isfile('nmi_scores.pkl'):\n", " print 'creating new nmi scores array'\n", " nmi_scores = np.zeros(num_repeats)\n", " else:\n", " print 'loading nmi score progress'\n", " nmi_scores = load_obj('nmi_scores')\n", "\n", " #record running times\n", " if not os.path.isfile('running_times.pkl'):\n", " print 'creating new running time array'\n", " running_times = np.zeros(num_repeats)\n", " else:\n", " print 'loading running time progress'\n", " running_times = load_obj('running_times')\n", "\n", " print\n", " \n", " #copy executable\n", " ex = \"benchmark.exe\" \n", " if not os.path.isfile(ex):\n", "\n", " source = \"C:\\\\Users\\\\davem\\\\Documents\\\\PhD\\\\Benchmark Graph Generators\\\\binary_networks\\\\benchmark.exe\"\n", " copyfile(source, ex)\n", "\n", " #copy flag file\n", " if not os.path.isfile(filename):\n", " \n", " source = os.path.join(dir_string, filename)\n", " copyfile(source, filename)\n", " \n", " print 'copied flag file {} to {}'.format(filename, os.getcwd())\n", "\n", " \n", " #cmd strings\n", " change_dir_cmd = \"cd {}\".format(dir_string_p)\n", " generate_network_cmd = \"benchmark -f {}\".format(filename)\n", "\n", " #output of cmd\n", " output_file = open(\"cmd_output.out\", 'w')\n", "\n", " for r in range(1, num_repeats+1):\n", "\n", " network_rename = \"{}_{}\".format(r,network)\n", " first_level_rename = \"{}_{}\".format(r,first_level)\n", " gml_filename = 'embedded_network_{}.gml'.format(r)\n", " \n", " #generate network and rename\n", " if not os.path.isfile(network_rename):\n", "\n", " process = subprocess.Popen(change_dir_cmd + \" && \" + generate_network_cmd, \n", " stdout=output_file, \n", " stderr=output_file, \n", " shell=True)\n", " process.wait()\n", "\n", " print 'generated graph {}'.format(r)\n", "\n", " os.rename(network, network_rename)\n", " os.rename(first_level, first_level_rename)\n", "\n", " print 'renamed graph {}'.format(r)\n", "\n", " #embed into gml file\n", " if not os.path.isfile(gml_filename):\n", "\n", " ##embed graph\n", " embed_main(network_rename, first_level_rename, gml_filename)\n", "\n", " print 'embedded graph {} as {} in {}'.format(r, gml_filename, os.getcwd())\n", " \n", " ##graph measures\n", "# data[j, :, r-1] = graph_measures(gml_filename)\n", "# print 'graph measures of network {}: {}'.format(i, data[j, :, r-1])\n", "\n", " ##score for this network\n", "# if not np.all(nmi_scores[r-1]):\n", " if nmi_scores[r-1] == 0:\n", "\n", " start_time = time()\n", "\n", " print 'starting ghsom for: {}/{}/{}'.format(i, p, gml_filename)\n", " nmi_score, communities_detected = ghsom_main(params, gml_filename, labels, 1000)\n", " nmi_scores[r-1] = nmi_score\n", "\n", " running_time = time() - start_time\n", " print 'running time of algorithm: {}'.format(running_time)\n", " running_times[r-1] = running_time\n", "\n", " #save\n", " save_obj(nmi_scores, 'nmi_scores')\n", " save_obj(running_times, 'running_times')\n", "\n", " print 'saved nmi score for network {}: {}'.format(gml_filename, nmi_score)\n", " print\n", "\n", " ##output nmi scores to csv file\n", " print 'writing nmi scores and running times to file'\n", " np.savetxt('nmi_scores.csv',nmi_scores,delimiter=',')\n", " np.savetxt('running_times.csv',running_times,delimiter=',')\n", " print\n", " \n", " #odd to overall list\n", " overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)\n", " \n", " ##save data\n", " os.chdir(dir_string)\n", " save_obj(data, 'data')\n", " \n", "print 'DONE'\n", "\n", "print 'OVERALL NMI SCORES'\n", "print overall_nmi_scores" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data=[ 0.1577381 0.90625 -0.14080981 5.7 ]\n", "data=[ 0.075 0.9125 -0.06205974 2.9 ]\n", "data=[ 0.12688492 0.9109375 -0.14340794 5. ]\n", "data=[ 0.18159722 0.9078125 -0.20372251 7. ]\n", "data=[ 0.12668651 0.9109375 -0.06859236 4.7 ]\n", "data=[ 0.17802579 0.9078125 -0.16647651 6.8 ]\n", "data=[ 0.12633929 0.9109375 -0.0606638 4.9 ]\n", "data=[ 0.20917659 0.90625 -0.1398512 7.6 ]\n", "data=[ 0.1749504 0.9140625 -0.13161544 7. ]\n", "data=[ 0.04915675 0.9171875 -0.02349667 1.7 ]\n", "data=[ 0.10059524 0.9125 -0.05323686 4. ]\n", "data=[ 0.14910714 0.9140625 -0.11013808 5.8 ]\n", "data=[ 0.21200397 0.90625 -0.12518589 7.7 ]\n", "data=[ 0.18397817 0.90625 -0.17237283 6.8 ]\n", "data=[ 0.15277778 0.909375 -0.12212552 5.9 ]\n", "data=[ 0.04915675 0.9171875 -0.05022903 1.6 ]\n", 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0.2046627 0.909375 -0.02733698 8. ]\n", "data=[ 0.28467262 0.9078125 -0.11060863 10.5 ]\n", "data=[ 0.21051587 0.90625 -0.04447615 8. ]\n", "data=[ 0.12361111 0.915625 0.1011259 5. ]\n", "data=[ 0.23660714 0.90625 0.05042981 9. ]\n", "data=[ 0.18154762 0.9078125 0.00981767 7. ]\n", "data=[ 0.28501984 0.90625 -0.12098013 10.3 ]\n", "data=[ 0.28318452 0.9078125 -0.0871737 10.9 ]\n", "data=[ 0.33988095 0.90625 -0.19125458 12.3 ]\n", "data=[ 0.3750496 0.9109375 -0.12145297 14.7 ]\n", "data=[ 0.23650794 0.90625 -0.07546205 9. ]\n", "data=[ 0.33328373 0.909375 -0.10597067 12.3 ]\n", "data=[ 0.26170635 0.9078125 -0.09666098 9.5 ]\n", "data=[ 0.13000992 0.9078125 0.0440029 5. ]\n", "data=[ 0.12678571 0.9109375 0.07177607 5. ]\n", "data=[ 0.15297619 0.909375 0.10798277 6. ]\n", "data=[ 0.16185516 0.90625 0.02650372 6. ]\n", "data=[ 0.28864087 0.90625 -0.07852581 10.6 ]\n", "data=[ 0.07202381 0.91875 0.044311 2.9 ]\n", "data=[ 0.1062004 0.90625 0.11925376 3.8 ]\n", "data=[ 0.12678571 0.909375 0.10321317 4.9 ]\n", "data=[ 0.28998016 0.9078125 -0.06676234 10.6 ]\n", "data=[ 0.12683532 0.9109375 0.01839583 5. ]\n", "data=[ 0.35401786 0.9109375 -0.07634657 13.6 ]\n", "data=[ 0.07772817 0.909375 -0.01026146 2.9 ]\n", "data=[ 0.35491071 0.9109375 -0.13573923 13.4 ]\n", "data=[ 0.09791667 0.9171875 0.02145155 4. ]\n", "data=[ 0.15297619 0.909375 0.10603514 6. ]\n", "data=[ 0.28685516 0.9078125 -0.05271674 10.6 ]\n", "data=[ 0.18501984 0.90625 -0.0133152 6.9 ]\n", "data=[ 0.07197421 0.91875 -0.04122683 2.8 ]\n", "data=[ 2.10615079e-01 9.06250000e-01 -7.83400279e-03 7.90000000e+00]\n", "data=[ 0.30828373 0.9078125 -0.09316521 11.4 ]\n", "data=[ 0.31205357 0.90625 -0.04147372 11.6 ]\n", "data=[ 0.2391369 0.90625 -0.03231725 8.9 ]\n", "data=[ 0.09454365 0.91875 0.0361221 3.9 ]\n", "data=[ 0.2078373 0.9078125 0.06683772 8. ]\n", "data=[ 0.26121032 0.9078125 -0.01568238 9.9 ]\n", "data=[ 0.09791667 0.9171875 0.02145155 4. ]\n", "data=[ 0.2421131 0.90625 -0.06022925 9. ]\n", "data=[ 0.26418651 0.90625 -0.043744 10. ]\n", "data=[ 0.15297619 0.909375 0.04801643 6. ]\n", "data=[ 0.10034722 0.9109375 0.10093667 3.8 ]\n", "data=[ 0.32698413 0.9109375 -0.13074014 12.1 ]\n" ] } ], "source": [ "data = np.zeros((num_networks, 4))\n", "best_settings = np.zeros(num_networks)\n", "\n", "for i in range(len(overall_nmi_scores)):\n", " \n", " scores = overall_nmi_scores[i]\n", " idx = np.argsort(scores)[::-1]\n", " \n", " best_settings[i] = parameter_settings[idx[0]]\n", " \n", " os.chdir('C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\\parameter_tests_density\\{}\\{}'.format(i, best_settings[i]))\n", " \n", " measures = np.zeros((num_repeats, 4))\n", " \n", " for r in range(num_repeats):\n", " \n", " gml = 'embedded_network_{}.gml'.format(r+1)\n", " \n", " measures[r] = graph_measures(gml)\n", " data[i] = np.mean(measures, axis=0)\n", " print 'data={}'.format(data[i])\n", " \n", "# print densities[i]\n", "# print best_settings[i]" ] }, { "cell_type": "code", "execution_count": 18, 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0.09791667 0.9171875 0.02145155 4. ]\n", "0.5\n", "\n", "[ 0.15297619 0.909375 0.10603514 6. ]\n", "0.5\n", "\n", "[ 0.28685516 0.9078125 -0.05271674 10.6 ]\n", "0.8\n", "\n", "[ 0.18501984 0.90625 -0.0133152 6.9 ]\n", "0.5\n", "\n", "[ 0.07197421 0.91875 -0.04122683 2.8 ]\n", "0.5\n", "\n", "[ 2.10615079e-01 9.06250000e-01 -7.83400279e-03 7.90000000e+00]\n", "0.7\n", "\n", "[ 0.30828373 0.9078125 -0.09316521 11.4 ]\n", "0.9\n", "\n", "[ 0.31205357 0.90625 -0.04147372 11.6 ]\n", "0.9\n", "\n", "[ 0.2391369 0.90625 -0.03231725 8.9 ]\n", "0.7\n", "\n", "[ 0.09454365 0.91875 0.0361221 3.9 ]\n", "0.5\n", "\n", "[ 0.2078373 0.9078125 0.06683772 8. ]\n", "0.7\n", "\n", "[ 0.26121032 0.9078125 -0.01568238 9.9 ]\n", "0.8\n", "\n", "[ 0.09791667 0.9171875 0.02145155 4. ]\n", "0.5\n", "\n", "[ 0.2421131 0.90625 -0.06022925 9. ]\n", "0.8\n", "\n", "[ 0.26418651 0.90625 -0.043744 10. ]\n", "0.8\n", "\n", "[ 0.15297619 0.909375 0.04801643 6. ]\n", "0.5\n", "\n", "[ 0.10034722 0.9109375 0.10093667 3.8 ]\n", "0.5\n", "\n", "[ 0.32698413 0.9109375 -0.13074014 12.1 ]\n", "0.9\n", "\n" ] } ], "source": [ "os.chdir('C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\\parameter_tests_density')\n", "\n", "save_obj(data, 'data')\n", "\n", "for i in range(num_networks):\n", " print data[i]\n", " print best_settings[i]\n", " print " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GiIgEUcIQEZEgShgiIhJECUNERIIoYYiISBAlDBERCaKEISIiQZQwREQkiBKGiIgEUcIQ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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1d387390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(data[:,2], best_settings, 'x')\n", "plt.xlabel('density')\n", "plt.ylabel('best setting')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.00000000e+00 8.93502361e-01 8.94734085e-01 -2.77555756e-06\n", " 8.93502361e-01 8.94734085e-01 0.00000000e+00 0.00000000e+00\n", " -2.77555756e-06 -2.77555756e-06]\n" ] } ], "source": [ "os.chdir(\"C:\\\\Miniconda3\\\\Jupyter\\\\GHSOM_simplex_dsd\\\\parameter_tests_density\\\\0\\\\1\")\n", "\n", "nmi_scores = load_obj('nmi_scores')\n", "\n", "print nmi_scores" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

amygdala/tensorflow-workshop

workshop_sections/high_level_APIs/mnist_eager_keras-debug.ipynb

1

14437

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Keras with TensorFlow eager mode, on the 'Fashion MNIST' dataset\n", "\n", "In this notebook, we'll use [TensorFlow's new eager execution mode](https://www.tensorflow.org/programmers_guide/eager)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 34 }, "id": "WZfwoVGAa9jd", "outputId": "cb516ba7-baa3-41c8-e01a-91f58e23bee6" }, "outputs": [], "source": [ "import tensorflow as tf\n", "# The TF version should be >= 1.7\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "zX-y0FVXrd_l" }, "outputs": [], "source": [ "from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten\n", "from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enable eager execution for this program. Eager execution makes TensorFlow evaluate operations immediately, returning concrete values instead of creating a [computational graph](https://www.tensorflow.org/programmers_guide/graphs) that is executed later. If you are used to a REPL or the `python` interactive console, you'll feel at home.\n", "\n", "Once eager execution is enabled, it *cannot* be disabled within the same program. See the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager) for more details." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "xU-Wf-vVa-t8" }, "outputs": [], "source": [ "import tensorflow.contrib.eager as tfe\n", "tfe.enable_eager_execution()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the 'Fashion MNIST' dataset, this time via the Keras `datasets` module." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 68 }, "id": "54FtfeNQa0_n", "outputId": "0ef457e2-b3aa-4a45-c684-2b204cfa3abf" }, "outputs": [], "source": [ "from tensorflow.python.keras._impl.keras.datasets import fashion_mnist\n", "\n", "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n", "\n", "y_train = tf.cast(y_train, tf.int32)\n", "y_test = tf.cast(y_test, tf.int32)\n", "\n", "x_train = x_train.reshape(60000, 784)\n", "x_test = x_test.reshape(10000, 784)\n", "x_train = x_train.astype('float32')\n", "x_test = x_test.astype('float32')\n", "x_train /= 255\n", "x_test /= 255\n", "\n", "# convert class vectors to binary class matrices... e.g. based on\n", "# https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py\n", "y_train = tf.keras.utils.to_categorical(y_train, 10)\n", "y_test = tf.keras.utils.to_categorical(y_test, 10)\n", "\n", "print(x_train.shape[0], 'train samples')\n", "print(x_test.shape[0], 'test samples')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a CNN model using Keras." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 306 }, "id": "BNe8xG1Pr0P1", "outputId": "54b4a975-1d09-4600-de63-8c8a8fff1fb5" }, "outputs": [], "source": [ "num_classes = 10\n", "\n", "model = tf.keras.Sequential()\n", "model.add(Dense(512, activation='relu', input_shape=(784,)))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(512, activation='relu'))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(num_classes))\n", "\n", "model.summary()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TensorFlow's [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) handles many common cases for feeding data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\n", "Datasets work with TensorFlow Eager mode." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "CMajrImWbAvZ" }, "outputs": [], "source": [ "BATCH_SIZE = 128\n", "SHUFFLE_SIZE = 1000\n", "\n", "train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) \\\n", " .shuffle(SHUFFLE_SIZE) \\\n", " .batch(BATCH_SIZE)\n", "\n", "test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)) \\\n", " .batch(BATCH_SIZE)\n", "\n", "# View a single example entry from a batch\n", "features, labels = tfe.Iterator(test_dataset).next()\n", "# print(\"example feature:\", features[0])\n", "print(\"example label:\", labels[0])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the loss and gradient function\n", "\n", "Both training and evaluation stages need to calculate the model's *[loss](https://developers.google.com/machine-learning/crash-course/glossary#loss)*. This measures how off a model's predictions are from the desired label, in other words, how bad the model is performing. We want to minimize, or optimize, this value.\n", "\n", "Our model will calculate its loss using the [tf.losses.sparse_softmax_cross_entropy](https://www.tensorflow.org/api_docs/python/tf/losses/sparse_softmax_cross_entropy) function which takes the model's prediction and the desired label. The returned loss value is progressively larger as the prediction gets worse." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "VxmgSrb9118w" }, "outputs": [], "source": [ "def loss(model, x, y):\n", " y_ = model(x)\n", " # import pdb; pdb.set_trace()\n", " return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)\n", "\n", "\n", "def grad(model, inputs, targets):\n", " with tfe.GradientTape() as tape:\n", " loss_value = loss(model, inputs, targets)\n", " return tape.gradient(loss_value, model.variables)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create an optimizer\n", "\n", "An *[optimizer](https://developers.google.com/machine-learning/crash-course/glossary#optimizer)* applies the computed gradients to the model's variables to minimize the `loss` function. You can think of a curved surface (see Figure 3) and we want to find its lowest point by walking around. The gradients point in the direction of steepest ascent—so we'll travel the opposite way and move down the hill. By iteratively calculating the loss and gradient for each batch, we'll adjust the model during training. Gradually, the model will find the best combination of weights and bias to minimize loss. And the lower the loss, the better the model's predictions.\n", "\n", "<table>\n", " <tr><td>\n", " <img src=\"http://cs231n.github.io/assets/nn3/opt1.gif\" width=\"70%\"\n", " alt=\"Optimization algorthims visualized over time in 3D space.\">\n", " </td></tr>\n", " <tr><td align=\"center\">\n", " <b>Figure 3.</b> Optimization algorthims visualized over time in 3D space. (Source: <a href=\"http://cs231n.github.io/neural-networks-3/\">Stanford class CS231n</a>, MIT License)<br/>&nbsp;\n", " </td></tr>\n", "</table>\n", "\n", "TensorFlow has many [optimization algorithms](https://www.tensorflow.org/api_guides/python/train) available for training. This model uses the [tf.train.GradientDescentOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer) that implements the *[stochastic gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent)* (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "i1PcGU6n18zz" }, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll run a training loop, applying the gradients tape as defined above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 187 }, "id": "zNfMvIgK24qL", "outputId": "d1edcb3d-1d59-4508-ec54-661656eb262c" }, "outputs": [], "source": [ "## Note: Rerunning this cell uses the same model variables\n", "\n", "# keep results for plotting\n", "train_loss_results = []\n", "train_accuracy_results = []\n", "\n", "num_epochs = 10\n", "\n", "for epoch in range(num_epochs):\n", " epoch_loss_avg = tfe.metrics.Mean()\n", " epoch_accuracy = tfe.metrics.Accuracy()\n", "\n", " # Training loop - using batches of BATCH_SIZE\n", " for images, labels in tfe.Iterator(train_dataset):\n", " # Optimize the model\n", " grads = grad(model, images, labels)\n", " optimizer.apply_gradients(zip(grads, model.variables),\n", " global_step=tf.train.get_or_create_global_step())\n", "\n", " # Track progress\n", " epoch_loss_avg(loss(model, images, labels)) # add current batch loss\n", " # compare predicted label to actual label\n", " epoch_accuracy(tf.argmax(model(images), axis=1, output_type=tf.int32), labels)\n", "\n", " # end epoch\n", " train_loss_results.append(epoch_loss_avg.result())\n", " train_accuracy_results.append(epoch_accuracy.result())\n", " \n", "# if epoch % 50 == 0:\n", " print(\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(epoch,\n", " epoch_loss_avg.result(),\n", " epoch_accuracy.result()))\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the training loss and accuracy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 558 }, "id": "bsULxWzUaziF", "outputId": "0b7e642a-6853-4292-aeb4-81975c814688" }, "outputs": [], "source": [ "# Skip this cell if you don't have matplotlib installed\n", "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\n", "fig.suptitle('Training Metrics')\n", "\n", "axes[0].set_ylabel(\"Loss\", fontsize=14)\n", "axes[0].plot(train_loss_results)\n", "\n", "axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n", "axes[1].set_xlabel(\"Epoch\", fontsize=14)\n", "axes[1].plot(train_accuracy_results)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at how our model does with the test set:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 34 }, "id": "KOW1QNKud8nh", "outputId": "7107ff56-7512-4126-edb1-772f0c2743c2" }, "outputs": [], "source": [ "test_accuracy = tfe.metrics.Accuracy()\n", "\n", "for (x, y) in tfe.Iterator(test_dataset):\n", " prediction = tf.argmax(model(x), axis=1, output_type=tf.int32)\n", " test_accuracy(prediction, y)\n", "\n", "print(\"Test set accuracy: {:.3%}\".format(test_accuracy.result()))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tcAPS4vVe8Lg" }, "source": [ "<hr>\n", "Copyright 2018 Google Inc. All Rights Reserved.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "ruV-pM0Le9G7" }, "outputs": [], "source": [] } ], "metadata": { }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

LucaCanali/Miscellaneous

Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb

1

14926

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython magic functions for Pyspark\n", "# Examples of shortcuts for executing SQL in Spark" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#\n", "# IPython magic functions to use with Pyspark and Spark SQL\n", "# The following code is intended as examples of shorcuts to simplify the use of SQL in pyspark\n", "# The defined functions are:\n", "#\n", "# %sql <statement> - return a Spark DataFrame for lazy evaluation of the SQL\n", "# %sql_show <statement> - run the SQL statement and show max_show_lines (50) lines\n", "# %sql_display <statement> - run the SQL statement and display the results using a HTML table\n", "# - this is implemented passing via Pandas and displays up to max_show_lines (50)\n", "# %sql_explain <statement> - display the execution plan of the SQL statement\n", "#\n", "# Use: %<magic> for line magic or %%<magic> for cell magic.\n", "#\n", "# Author: Luca.Canali@cern.ch\n", "# September 2016\n", "#\n", "\n", "from IPython.core.magic import register_line_cell_magic\n", "\n", "# Configuration parameters\n", "max_show_lines = 50 # Limit on the number of lines to show with %sql_show and %sql_display\n", "detailed_explain = True # Set to False if you want to see only the physical plan when running explain\n", "\n", "\n", "@register_line_cell_magic\n", "def sql(line, cell=None):\n", " \"Return a Spark DataFrame for lazy evaluation of the sql. Use: %sql or %%sql\"\n", " val = cell if cell is not None else line \n", " return spark.sql(val)\n", "\n", "@register_line_cell_magic\n", "def sql_show(line, cell=None):\n", " \"Execute sql and show the first max_show_lines lines. Use: %sql_show or %%sql_show\"\n", " val = cell if cell is not None else line \n", " return spark.sql(val).show(max_show_lines) \n", "\n", "@register_line_cell_magic\n", "def sql_display(line, cell=None):\n", " \"\"\"Execute sql and convert results to Pandas DataFrame for pretty display or further processing.\n", " Use: %sql_display or %%sql_display\"\"\"\n", " val = cell if cell is not None else line \n", " return spark.sql(val).limit(max_show_lines).toPandas() \n", "\n", "@register_line_cell_magic\n", "def sql_explain(line, cell=None):\n", " \"Display the execution plan of the sql. Use: %sql_explain or %%sql_explain\"\n", " val = cell if cell is not None else line \n", " return spark.sql(val).explain(detailed_explain)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define test tables" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define test data and register it as tables \n", "# This is a classic example of employee and department relational tables\n", "# Test data will be used in the examples later in this notebook\n", "\n", "from pyspark.sql import Row\n", "\n", "Employee = Row(\"id\", \"name\", \"email\", \"manager_id\", \"dep_id\")\n", "df_emp = sqlContext.createDataFrame([\n", " Employee(1234, 'John', 'john@mail.com', 1236, 10),\n", " Employee(1235, 'Mike', 'mike@mail.com', 1237, 10),\n", " Employee(1236, 'Pat', 'pat@mail.com', 1237, 20),\n", " Employee(1237, 'Claire', 'claire@mail.com', None, 20),\n", " Employee(1238, 'Jim', 'jim@mail.com', 1236, 30)\n", " ])\n", "\n", "df_emp.registerTempTable(\"employee\")\n", "\n", "Department = Row(\"dep_id\", \"dep_name\")\n", "df_dep = sqlContext.createDataFrame([\n", " Department(10, 'Engineering'),\n", " Department(20, 'Head Quarter'),\n", " Department(30, 'Human resources')\n", " ])\n", "\n", "df_dep.registerTempTable(\"department\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of how to use %SQL magic functions with Spark" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use %sql to run SQL and return a DataFrame, lazy evaluation " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DataFrame[id: bigint, name: string, email: string, manager_id: bigint, dep_id: bigint]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example of line magic, a shortcut to run SQL in pyspark\n", "# Pyspark has lazy evaluation, so the query is not executed in this exmaple\n", "\n", "df = %sql select * from employee\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use %sql_show to run SQL and show the top lines of the result set" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----+------+---------------+----------+------+\n", "| id| name| email|manager_id|dep_id|\n", "+----+------+---------------+----------+------+\n", "|1234| John| john@mail.com| 1236| 10|\n", "|1235| Mike| mike@mail.com| 1237| 10|\n", "|1236| Pat| pat@mail.com| 1237| 20|\n", "|1237|Claire|claire@mail.com| null| 20|\n", "|1238| Jim| jim@mail.com| 1236| 30|\n", "+----+------+---------------+----------+------+\n", "\n" ] } ], "source": [ "# Example of line magic, the SQL is executed and the result is displayed\n", "# the maximum number of displayed lines is configurable (max_show_lines)\n", "\n", "%sql_show select * from employee" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example of cell magic to run SQL spanning multiple lines" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----+------+---------------+----------+---------------+\n", "| id| name| email|manager_id| dep_name|\n", "+----+------+---------------+----------+---------------+\n", "|1234| John| john@mail.com| 1236| Engineering|\n", "|1235| Mike| mike@mail.com| 1237| Engineering|\n", "|1238| Jim| jim@mail.com| 1236|Human resources|\n", "|1236| Pat| pat@mail.com| 1237| Head Quarter|\n", "|1237|Claire|claire@mail.com| null| Head Quarter|\n", "+----+------+---------------+----------+---------------+\n", "\n" ] } ], "source": [ "%%sql_show \n", "select emp.id, emp.name, emp.email, emp.manager_id, dep.dep_name \n", "from employee emp, department dep \n", "where emp.dep_id=dep.dep_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use %sql_display to run SQL and display the results as a HTML table\n", "Example of cell magic that runs SQL and then transforms it to Pandas. This will display the output as a HTML table in Jupyter notebooks" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>name</th>\n", " <th>email</th>\n", " <th>manager_name</th>\n", " <th>dep_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1234</td>\n", " <td>John</td>\n", " <td>john@mail.com</td>\n", " <td>Pat</td>\n", " <td>Engineering</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1235</td>\n", " <td>Mike</td>\n", " <td>mike@mail.com</td>\n", " <td>Claire</td>\n", " <td>Engineering</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1238</td>\n", " <td>Jim</td>\n", " <td>jim@mail.com</td>\n", " <td>Pat</td>\n", " <td>Human resources</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1237</td>\n", " <td>Claire</td>\n", " <td>claire@mail.com</td>\n", " <td>None</td>\n", " <td>Head Quarter</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1236</td>\n", " <td>Pat</td>\n", " <td>pat@mail.com</td>\n", " <td>Claire</td>\n", " <td>Head Quarter</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id name email manager_name dep_name\n", "0 1234 John john@mail.com Pat Engineering\n", "1 1235 Mike mike@mail.com Claire Engineering\n", "2 1238 Jim jim@mail.com Pat Human resources\n", "3 1237 Claire claire@mail.com None Head Quarter\n", "4 1236 Pat pat@mail.com Claire Head Quarter" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql_display \n", "select emp.id, emp.name, emp.email, emp2.name as manager_name, dep.dep_name \n", "from employee emp \n", " left outer join employee emp2 on emp2.id=emp.manager_id\n", " join department dep on emp.dep_id=dep.dep_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use %sql_explain to display the execution plan" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "== Parsed Logical Plan ==\n", "'Project ['emp.id, 'emp.name, 'emp.email, 'emp2.name AS manager_name#68, 'dep.dep_name]\n", "+- 'Join Inner, ('emp.dep_id = 'dep.dep_id)\n", " :- 'Join LeftOuter, ('emp2.id = 'emp.manager_id)\n", " : :- 'UnresolvedRelation `employee`, emp\n", " : +- 'UnresolvedRelation `employee`, emp2\n", " +- 'UnresolvedRelation `department`, dep\n", "\n", "== Analyzed Logical Plan ==\n", "id: bigint, name: string, email: string, manager_name: string, dep_name: string\n", "Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\n", "+- Join Inner, (dep_id#4L = dep_id#12L)\n", " :- Join LeftOuter, (id#79L = manager_id#3L)\n", " : :- SubqueryAlias emp\n", " : : +- SubqueryAlias employee\n", " : : +- LogicalRDD [id#0L, name#1, email#2, manager_id#3L, dep_id#4L]\n", " : +- SubqueryAlias emp2\n", " : +- SubqueryAlias employee\n", " : +- LogicalRDD [id#79L, name#80, email#81, manager_id#82L, dep_id#83L]\n", " +- SubqueryAlias dep\n", " +- SubqueryAlias department\n", " +- LogicalRDD [dep_id#12L, dep_name#13]\n", "\n", "== Optimized Logical Plan ==\n", "Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\n", "+- Join Inner, (dep_id#4L = dep_id#12L)\n", " :- Project [id#0L, name#1, email#2, dep_id#4L, name#80]\n", " : +- Join LeftOuter, (id#79L = manager_id#3L)\n", " : :- Filter isnotnull(dep_id#4L)\n", " : : +- LogicalRDD [id#0L, name#1, email#2, manager_id#3L, dep_id#4L]\n", " : +- Project [id#79L, name#80]\n", " : +- LogicalRDD [id#79L, name#80, email#81, manager_id#82L, dep_id#83L]\n", " +- Filter isnotnull(dep_id#12L)\n", " +- LogicalRDD [dep_id#12L, dep_name#13]\n", "\n", "== Physical Plan ==\n", "*Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\n", "+- *SortMergeJoin [dep_id#4L], [dep_id#12L], Inner\n", " :- *Sort [dep_id#4L ASC], false, 0\n", " : +- Exchange hashpartitioning(dep_id#4L, 200)\n", " : +- *Project [id#0L, name#1, email#2, dep_id#4L, name#80]\n", " : +- SortMergeJoin [manager_id#3L], [id#79L], LeftOuter\n", " : :- *Sort [manager_id#3L ASC], false, 0\n", " : : +- Exchange hashpartitioning(manager_id#3L, 200)\n", " : : +- *Filter isnotnull(dep_id#4L)\n", " : : +- Scan ExistingRDD[id#0L,name#1,email#2,manager_id#3L,dep_id#4L]\n", " : +- *Sort [id#79L ASC], false, 0\n", " : +- Exchange hashpartitioning(id#79L, 200)\n", " : +- *Project [id#79L, name#80]\n", " : +- Scan ExistingRDD[id#79L,name#80,email#81,manager_id#82L,dep_id#83L]\n", " +- *Sort [dep_id#12L ASC], false, 0\n", " +- Exchange hashpartitioning(dep_id#12L, 200)\n", " +- *Filter isnotnull(dep_id#12L)\n", " +- Scan ExistingRDD[dep_id#12L,dep_name#13]\n" ] } ], "source": [ "%%sql_explain\n", "select emp.id, emp.name, emp.email, emp2.name as manager_name, dep.dep_name \n", "from employee emp \n", " left outer join employee emp2 on emp2.id=emp.manager_id\n", " join department dep on emp.dep_id=dep.dep_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

aitatanit/metatlas

4notebooks/old/examplenotebooks/MetAtlas_005_Find_Data_in_Grouped_Files_For_Given_Atlas.ipynb

1

3608986

bsd-3-clause

adrn/TwoFace

notebooks/figures/HighK-multimodal.ipynb

1

16764

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from os import path\n", "\n", "# Third-party\n", "from astropy.io import fits\n", "from astropy.stats import median_absolute_deviation\n", "from astropy.table import QTable, Table, join\n", "from astropy.time import Time\n", "import astropy.units as u\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "import numpy as np\n", "%matplotlib inline\n", "import h5py\n", "import pandas as pd\n", "from sqlalchemy import func\n", "import tqdm\n", "from sklearn.cluster import KMeans\n", "\n", "from thejoker import JokerSamples\n", "\n", "from twoface.config import TWOFACE_CACHE_PATH\n", "from twoface.samples_analysis import unimodal_P, MAP_sample\n", "from twoface.db import (db_connect, AllStar, AllVisit, AllVisitToAllStar, NessRG,\n", " StarResult, Status, JokerRun)\n", "from twoface.plot import plot_two_panel, plot_phase_fold, _RV_LBL\n", "from twoface.mass import get_m2_min, mf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Session, _ = db_connect(path.join(TWOFACE_CACHE_PATH, 'apogee.sqlite'))\n", "session = Session()\n", "\n", "plot_path = '../../paper/1-catalog/figures/'\n", "table_path = '../../paper/1-catalog/tables/'\n", "\n", "samples_file = path.join(TWOFACE_CACHE_PATH, 'apogee-jitter.hdf5')\n", "mcmc_samples_file = path.join(TWOFACE_CACHE_PATH, 'apogee-jitter-mcmc.hdf5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "run = session.query(JokerRun).limit(1).one()\n", "joker_pars = run.get_joker_params()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "high_K_stars = session.query(AllStar).join(StarResult).filter(StarResult.status_id.in_([1, 4]))\\\n", " .filter(StarResult.high_K).all()\n", "print(len(high_K_stars))\n", "print(session.query(AllStar).join(StarResult).filter(StarResult.high_K).count())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove ones that are already identified as unimodal:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "unimodal = QTable.read(path.join(table_path, 'highK-unimodal.fits'))\n", "mask = np.logical_not(np.isin(np.array([s.apogee_id for s in high_K_stars], dtype='U20'), \n", " unimodal['APOGEE_ID']))\n", "high_K_stars = np.array(high_K_stars)[mask]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Make the catalog:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_n_modal(data, samples, n_clusters=2):\n", " clf = KMeans(n_clusters=n_clusters)\n", " \n", " ecc = samples['e'].value\n", " lnP = np.log(samples['P'].value).reshape(-1, 1)\n", " y = clf.fit_predict(lnP)\n", "\n", " data = star.apogeervdata()\n", " \n", " unimodals = []\n", " means = []\n", " for j in np.unique(y):\n", " sub_samples = samples[y==j]\n", " if len(sub_samples) == 1:\n", " unimodals.append(True)\n", " means.append(sub_samples)\n", " else:\n", " unimodals.append(unimodal_P(sub_samples, data))\n", " means.append(MAP_sample(data, sub_samples, joker_pars))\n", " \n", " return all(unimodals), means" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bimodal = []\n", "MAP_samples = []\n", "nsamples = []\n", "\n", "n = 0\n", "with h5py.File(samples_file, 'r') as f:\n", " for star in tqdm.tqdm(high_K_stars):\n", " samples = JokerSamples.from_hdf5(f[star.apogee_id])\n", " data = star.apogeervdata()\n", " \n", " if len(samples) > 1:\n", " is_bimodal, MAP = is_n_modal(data, samples, n_clusters=2)\n", " bimodal.append(is_bimodal)\n", " MAP_samples.append(MAP)\n", " \n", " else:\n", " bimodal.append(False)\n", " \n", " nsamples.append(len(samples))\n", "\n", "nsamples = np.array(nsamples)\n", "bimodal = np.array(bimodal)\n", "MAP_samples = np.array(MAP_samples)\n", "bimodal.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bimodal.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of these only have a few samples:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(nsamples[bimodal], bins='auto');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bi_stars = np.array(high_K_stars)[bimodal]\n", "bi_MAP_samples = np.array(MAP_samples)[bimodal]\n", "assert len(bi_MAP_samples) == len(bi_stars)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalog = {'APOGEE_ID':[], 'P':[], 'e':[], 'K':[]}\n", "\n", "for samples, star in zip(bi_MAP_samples, bi_stars):\n", " for s in samples:\n", " catalog['APOGEE_ID'].append(star.apogee_id)\n", " catalog['P'].append(s['P'])\n", " catalog['e'].append(s['e'])\n", " catalog['K'].append(s['K'])\n", " \n", "catalog['P'] = u.Quantity(catalog['P'])\n", "catalog['K'] = u.Quantity(catalog['K'])\n", "catalog = Table(catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add Ness masses:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ness_tbl = Table.read('../../data/NessRG.fits')\n", "ness_tbl.rename_column('2MASS', 'APOGEE_ID')\n", "ness_tbl = ness_tbl[np.isin(ness_tbl['APOGEE_ID'], catalog['APOGEE_ID'])]\n", "\n", "# trim the duplicates...\n", "_, unq_idx = np.unique(ness_tbl['APOGEE_ID'], return_index=True)\n", "ness_tbl = ness_tbl[unq_idx]\n", "\n", "tbl_with_ness = join(catalog, ness_tbl, keys='APOGEE_ID', join_type='outer')\n", "assert len(tbl_with_ness) == len(catalog)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.isfinite(tbl_with_ness['lnM']).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rnd = np.random.RandomState(seed=42)\n", "N = rnd.normal\n", "\n", "m1 = np.full(len(tbl_with_ness), np.nan) * u.Msun\n", "m2_min = np.full(len(tbl_with_ness), np.nan) * u.Msun\n", "\n", "for i, row in tqdm.tqdm(enumerate(tbl_with_ness)):\n", " if tbl_with_ness['lnM'].mask[i]:\n", " continue\n", " \n", " m1_ = np.exp(row['lnM']) * u.Msun\n", " mass_func = mf(P=row['P'] * catalog['P'].unit, \n", " K=row['K'] * catalog['K'].unit, \n", " e=row['e'])\n", " \n", " m1[i] = m1_\n", " m2_min[i] = get_m2_min(m1_, mass_func)\n", " \n", "tbl_with_ness['M1'] = m1\n", "tbl_with_ness['M2_min'] = m2_min\n", "\n", "tbl_with_ness['M1'].mask = np.isnan(tbl_with_ness['M1'])\n", "tbl_with_ness['M2_min'].mask = np.isnan(tbl_with_ness['M1'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add APOGEE DR14 info:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allstar_tbl = fits.getdata('/Users/adrian/data/APOGEE_DR14/allStar-l31c.2.fits')\n", "allstar_tbl = allstar_tbl[np.isin(allstar_tbl['APOGEE_ID'], tbl_with_ness['APOGEE_ID'])]\n", "\n", "# trim the duplicates...\n", "_, unq_idx = np.unique(allstar_tbl['APOGEE_ID'], return_index=True)\n", "allstar_tbl = allstar_tbl[unq_idx]\n", "assert len(allstar_tbl) == len(tbl_with_ness)//2\n", "\n", "allstar_tbl = Table(allstar_tbl)\n", "allstar_tbl.rename_column('K', 'KS')\n", "allstar_tbl.rename_column('K_ERR', 'KS_ERR')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output_catalog = join(tbl_with_ness[catalog.colnames + ['M1', 'M2_min']], allstar_tbl, keys='APOGEE_ID')\n", "output_catalog[:1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## By-eye vetting: \n", "\n", "Plot all of the stars, see what orbits look like bad (2) or questionable (1) fits:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# with h5py.File(samples_file, 'r') as f:\n", "# for star in tqdm.tqdm(np.array(high_K_stars)[bimodal]):\n", "# samples = JokerSamples.from_hdf5(f[star.apogee_id])\n", "# data = star.apogeervdata()\n", " \n", "# fig = plot_two_panel(data, samples, \n", "# plot_data_orbits_kw=dict(highlight_P_extrema=False))\n", "# fig.savefig('../../plots/bimodal/{0}.png'.format(star.apogee_id), dpi=200)\n", "# plt.close(fig)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# bimodal:\n", "suspect = ['2M09490802+3649393', '2M09494588+3711256', \n", " '2M10244885+1336456', '2M13412997+2836185',\n", " '2M15125534+6748381']\n", "\n", "check = ['2M18525864-0031500']\n", "\n", "\n", "clean_flag = np.zeros(len(catalog), dtype=int)\n", "clean_flag[np.isin(output_catalog['APOGEE_ID'], check)] = 1\n", "clean_flag[np.isin(output_catalog['APOGEE_ID'], suspect)] = 2\n", "output_catalog['clean_flag'] = clean_flag" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.isin(output_catalog['APOGEE_ID'], suspect).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output_catalog.write(path.join(table_path, 'highK-bimodal.fits'), overwrite=True)\n", "# output_catalog[:4].write('../../paper/1-catalog/tables/bimodal-top.tex', overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "\n", "# Make paper figure:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalog = Table.read(path.join(table_path, 'highK-bimodal.fits'))\n", "len(catalog)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.random.seed(111)\n", "\n", "rc = {\n", " 'axes.labelsize': 18,\n", " 'xtick.labelsize': 14,\n", " 'ytick.labelsize': 14\n", "}\n", " \n", "# rand_subset = np.random.choice(catalog['APOGEE_ID'].astype('U20'), \n", "# size=4, \n", "# replace=False)\n", "rand_subset = ['2M18041328-2958182',\n", " '2M19114515-0725486',\n", " '2M20184780+2023122',\n", " '2M22030551+6844336']\n", " \n", "with mpl.rc_context(rc):\n", " gs = GridSpec(4, 3)\n", " fig = plt.figure(figsize=(8., 9.5))\n", " for j, apogee_id in enumerate(rand_subset):\n", " ax1 = fig.add_subplot(gs[j, :2])\n", " ax2 = fig.add_subplot(gs[j, 2])\n", "\n", " if j == 0:\n", " ax1.set_title('High-$K$, bimodal', fontsize=20)\n", " \n", " star = AllStar.get_apogee_id(session, apogee_id)\n", " data = star.apogeervdata()\n", "\n", " with h5py.File(samples_file, 'r') as f:\n", " samples = JokerSamples.from_hdf5(f[star.apogee_id])\n", "\n", " fig = plot_two_panel(data, samples, axes=[ax1, ax2], tight=False,\n", " plot_data_orbits_kw=dict(n_times=16384, \n", " n_orbits=128,\n", " highlight_P_extrema=False,\n", " xlim_choice='data',\n", " relative_to_t0=True,\n", " plot_kwargs=dict(linewidth=0.2,\n", " rasterized=True)))\n", "\n", " xlim = ax1.get_xlim()\n", " ylim = ax1.get_ylim()\n", "\n", " ax1.text(xlim[0] + (xlim[1]-xlim[0])/20,\n", " ylim[1] - (ylim[1]-ylim[0])/20,\n", " star.apogee_id, \n", " fontsize=15, va='top', ha='left')\n", "\n", " ax1.set_xlabel('')\n", " ax2.set_xlabel('')\n", " \n", " logP = np.log10(samples['P'].to(u.day).value)\n", " span = np.ptp(logP)\n", "# ax2.set_xlim(10**(logP.min()-0.75),\n", "# 10**(logP.max()+0.75))\n", "# ax2.set_xlim(10**(logP.min()-0.5*span),\n", "# 10**(logP.max()+0.5*span))\n", "\n", " ax1.set_xlabel(r'${\\rm BMJD} - t_0$ [day]')\n", " ax2.set_xlabel('period, $P$ [day]')\n", "\n", " fig.tight_layout()\n", " fig.subplots_adjust(left=0.125, right=0.95, hspace=0.2, wspace=0.4)\n", " \n", " fig.savefig(path.join(plot_path, 'highK-bimodal.pdf'), dpi=250)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "(old idea)\n", "\n", "#### Stars with samples that have small dispersion, or PTP lnP:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stats = []\n", "with h5py.File(samples_file, 'r') as f:\n", " for star in tqdm.tqdm(high_K_stars):\n", " logP = np.log10(f[star.apogee_id]['P'][:])\n", " stats.append([np.ptp(logP), np.std(logP)])\n", "stats = np.array(stats)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(6,6))\n", "ax.scatter(stats[:, 0], 3*stats[:, 1], alpha=0.25, linewidth=0)\n", "ax.set_xlim(-0.02, 5)\n", "ax.set_ylim(-0.02, 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "((stats[:, 0] < 1) | bimodal).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "star = np.array(high_K_stars)[(stats[:, 0] < 1) & np.logical_not(bimodal)][11]\n", "\n", "data = star.apogeervdata()\n", "with h5py.File(samples_file, 'r') as f:\n", " samples = JokerSamples.from_hdf5(f[star.apogee_id])\n", " \n", "_ = plot_two_panel(data, samples, plot_data_orbits_kw=dict(highlight_P_extrema=False))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:twoface]", "language": "python", "name": "conda-env-twoface-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

pramitchoudhary/Experiments

notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb

1

15275

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"1000\"\n", " height=\"1000\"\n", " src=\"https://github.com/rhiever/tpot\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x7f3b70e046d0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython import display\n", "URL = \"https://github.com/rhiever/tpot\"\n", "display.IFrame(URL, 1000, 1000)\n" ] }, { "cell_type": "markdown", "metadata": { "_datascience": {} }, "source": [ "TPOT uses a genetic algorithm (implemented with DEAP library) to pick an optimal pipeline for a regression task.\n", "\n", "What is a pipeline?\n", "\n", "Pipeline is composed of preprocessors:\n", "* take polynomial transformations of features\n", "* \n", "\n", "\n", "TPOTBase is key class\n", "\n", "parameters:\n", "\n", "population_size: int (default: 100)\n", " The number of pipelines in the genetic algorithm population. Must\n", " be > 0.The more pipelines in the population, the slower TPOT will\n", " run, but it's also more likely to find better pipelines.\n", "* generations: int (default: 100)\n", " The number of generations to run pipeline optimization for. Must\n", " be > 0. The more generations you give TPOT to run, the longer it\n", " takes, but it's also more likely to find better pipelines.\n", "* mutation_rate: float (default: 0.9)\n", " The mutation rate for the genetic programming algorithm in the range\n", " [0.0, 1.0]. This tells the genetic programming algorithm how many\n", " pipelines to apply random changes to every generation. We don't\n", " recommend that you tweak this parameter unless you know what you're\n", " doing.\n", "* crossover_rate: float (default: 0.05)\n", " The crossover rate for the genetic programming algorithm in the\n", " range [0.0, 1.0]. This tells the genetic programming algorithm how\n", " many pipelines to \"breed\" every generation. We don't recommend that\n", " you tweak this parameter unless you know what you're doing.\n", "* scoring: function or str\n", " Function used to evaluate the quality of a given pipeline for the\n", " problem. By default, balanced class accuracy is used for\n", " classification problems, mean squared error for regression problems.\n", " TPOT assumes that this scoring function should be maximized, i.e.,\n", " higher is better.\n", " Offers the same options as sklearn.cross_validation.cross_val_score:\n", " ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1',\n", " 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted',\n", " 'precision', 'precision_macro', 'precision_micro', 'precision_samples',\n", " 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro',\n", " 'recall_samples', 'recall_weighted', 'roc_auc']\n", "* num_cv_folds: int (default: 3)\n", " The number of folds to evaluate each pipeline over in k-fold\n", " cross-validation during the TPOT pipeline optimization process\n", "* max_time_mins: int (default: None)\n", " How many minutes TPOT has to optimize the pipeline. If not None,\n", " this setting will override the `generations` parameter.\n", "\n", "TPOTClassifier and TPOTRegressor inherit parent class TPOTBase, with modifications of the scoring function." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting deap\n", " Downloading deap-1.0.2.post2.tar.gz (852kB)\n", "\u001b[K 100% |################################| 856kB 722kB/s \n", "\u001b[?25hCollecting update-checker\n", " Downloading update_checker-0.12-py2.py3-none-any.whl\n", "Collecting tqdm\n", " Downloading tqdm-4.8.4-py2.py3-none-any.whl\n", "Requirement already satisfied (use --upgrade to upgrade): requests>=2.3.0 in /usr/local/lib/python2.7/dist-packages (from update-checker)\n", "Building wheels for collected packages: deap\n", " Running setup.py bdist_wheel for deap\n", " Stored in directory: /root/.cache/pip/wheels/c9/9c/cd/d52106f0148e675df35718c0efff2ecf03cc86d5bdcfb91db5\n", "Successfully built deap\n", "Installing collected packages: deap, update-checker, tqdm\n", "Successfully installed deap-1.0.2 tqdm-4.8.4 update-checker-0.12\n", "\u001b[33mYou are using pip version 7.1.2, however version 8.1.2 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!sudo pip install deap update_checker tqdm xgboost tpot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "import pandas as pd \n", "import numpy as np\n", "import psycopg2 \n", "import os\n", "import json\n", "from tpot import TPOTClassifier\n", "from sklearn.metrics import classification_report" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_datascience": {}, "collapsed": true }, "outputs": [], "source": [ "conn = psycopg2.connect(\n", " user = os.environ['REDSHIFT_USER']\n", " ,password = os.environ['REDSHIFT_PASS'] \n", " ,port = os.environ['REDSHIFT_PORT']\n", " ,host = os.environ['REDSHIFT_HOST']\n", " ,database = 'tradesy'\n", ")\n", "query = \"\"\"\n", " select \n", " purchase_dummy\n", " ,shipping_price_ratio\n", " ,asking_price\n", " ,price_level\n", " ,brand_score\n", " ,brand_size\n", " ,a_over_b\n", " ,favorite_count\n", " ,has_blurb\n", " ,has_image\n", " ,seasonal_component\n", " ,description_length\n", " ,product_category_accessories\n", " ,product_category_shoes\n", " ,product_category_bags\n", " ,product_category_tops\n", " ,product_category_dresses\n", " ,product_category_weddings\n", " ,product_category_bottoms\n", " ,product_category_outerwear\n", " ,product_category_jeans\n", " ,product_category_activewear\n", " ,product_category_suiting\n", " ,product_category_swim\n", " \n", " from saleability_model_v2\n", " \n", " limit 50000\n", " \n", "\"\"\"\n", "\n", "df = pd.read_sql(query, conn)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_datascience": {}, "collapsed": true }, "outputs": [], "source": [ "target = 'purchase_dummy'\n", "domain = filter(lambda x: x != target, df.columns.values)\n", "df = df.astype(float)\n", "\n", "y_all = df[target].values\n", "X_all = df[domain].values\n", "\n", "idx_all = np.random.RandomState(1).permutation(len(y_all))\n", "idx_train = idx_all[:int(.8 * len(y_all))]\n", "idx_test = idx_all[int(.8 * len(y_all)):]\n", "\n", "# TRAIN AND TEST DATA\n", "X_train = X_all[idx_train]\n", "y_train = y_all[idx_train]\n", "X_test = X_all[idx_test]\n", "y_test = y_all[idx_test]" ] }, { "cell_type": "markdown", "metadata": { "_datascience": {} }, "source": [ "### Sklearn model: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "sklearn_model = RandomForestClassifier()\n", "sklearn_model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0.0 0.86 0.96 0.91 8260\n", " 1.0 0.60 0.27 0.37 1740\n", "\n", "avg / total 0.82 0.84 0.82 10000\n", "\n" ] } ], "source": [ "sklearn_predictions = sklearn_model.predict(X_test)\n", "print classification_report(y_test, sklearn_predictions)" ] }, { "cell_type": "markdown", "metadata": { "_datascience": {} }, "source": [ "### TPOT Classifier" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 90%|█████████ | 18/20 [09:47<01:56, 58.40s/pipeline]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 1 - Current best internal CV score: 0.647821506914\n", "Generation 2 - Current best internal CV score: 0.647821506914" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " \r", "\r", " \r", "GP Progress: 90%|█████████ | 18/20 [00:00<01:56, 58.40s/pipeline]\r", "GP Progress: 70%|███████ | 21/30 [10:09<06:28, 43.13s/pipeline]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "GP closed prematurely - will use current best pipeline" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Best pipeline: XGBClassifier(input_matrix, 32, 6, 0.48999999999999999, 27.0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r" ] } ], "source": [ "tpot_model = TPOTClassifier(generations=3, population_size=10, verbosity=2, max_time_mins=10)\n", "tpot_model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0.0 0.88 0.93 0.90 8260\n", " 1.0 0.54 0.39 0.45 1740\n", "\n", "avg / total 0.82 0.84 0.82 10000\n", "\n" ] } ], "source": [ "tpot_predictions = tpot_model.predict(X_test)\n", "print classification_report(y_test, tpot_predictions)" ] }, { "cell_type": "markdown", "metadata": { "_datascience": {} }, "source": [ "### Export Pseudo Pipeline Code" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_datascience": {}, "collapsed": true }, "outputs": [], "source": [ "tpot_model.export('optimal-saleability-model.py')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import numpy as np\r\n", "\r\n", "from sklearn.cross_validation import train_test_split\r\n", "from sklearn.ensemble import VotingClassifier\r\n", "from sklearn.pipeline import make_pipeline, make_union\r\n", "from sklearn.preprocessing import FunctionTransformer\r\n", "from xgboost import XGBClassifier\r\n", "\r\n", "# NOTE: Make sure that the class is labeled 'class' in the data file\r\n", "tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)\r\n", "features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)\r\n", "training_features, testing_features, training_classes, testing_classes = \\\r\n", " train_test_split(features, tpot_data['class'], random_state=42)\r\n", "\r\n", "exported_pipeline = make_pipeline(\r\n", " XGBClassifier(learning_rate=0.49, max_depth=10, min_child_weight=6, n_estimators=500, subsample=1.0)\r\n", ")\r\n", "\r\n", "exported_pipeline.fit(training_features, training_classes)\r\n", "results = exported_pipeline.predict(testing_features)\r\n" ] } ], "source": [ "!cat optimal-saleability-model.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "_datascience": {}, "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "_datascience": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

unlicense

camigord/Self-Driving-Car-Nanodegree

Camera_Calibration/camera_calibration.ipynb

1

1691676

mit

efoley/deep-learning

transfer-learning/Transfer_Learning.ipynb

3

23475

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transfer Learning\n", "\n", "Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) trained on the [ImageNet dataset](http://www.image-net.org/) as a feature extractor. Below is a diagram of the VGGNet architecture.\n", "\n", "<img src=\"assets/cnnarchitecture.jpg\" width=700px>\n", "\n", "VGGNet is great because it's simple and has great performance, coming in second in the ImageNet competition. The idea here is that we keep all the convolutional layers, but replace the final fully connected layers with our own classifier. This way we can use VGGNet as a feature extractor for our images then easily train a simple classifier on top of that. What we'll do is take the first fully connected layer with 4096 units, including thresholding with ReLUs. We can use those values as a code for each image, then build a classifier on top of those codes.\n", "\n", "You can read more about transfer learning from [the CS231n course notes](http://cs231n.github.io/transfer-learning/#tf).\n", "\n", "## Pretrained VGGNet\n", "\n", "We'll be using a pretrained network from https://github.com/machrisaa/tensorflow-vgg. Make sure to clone this repository to the directory you're working from. You'll also want to rename it so it has an underscore instead of a dash.\n", "\n", "```\n", "git clone https://github.com/machrisaa/tensorflow-vgg.git tensorflow_vgg\n", "```\n", "\n", "This is a really nice implementation of VGGNet, quite easy to work with. The network has already been trained and the parameters are available from this link. **You'll need to clone the repo into the folder containing this notebook.** Then download the parameter file using the next cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "\n", "vgg_dir = 'tensorflow_vgg/'\n", "# Make sure vgg exists\n", "if not isdir(vgg_dir):\n", " raise Exception(\"VGG directory doesn't exist!\")\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(vgg_dir + \"vgg16.npy\"):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:\n", " urlretrieve(\n", " 'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',\n", " vgg_dir + 'vgg16.npy',\n", " pbar.hook)\n", "else:\n", " print(\"Parameter file already exists!\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Flower power\n", "\n", "Here we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import tarfile\n", "\n", "dataset_folder_path = 'flower_photos'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile('flower_photos.tar.gz'):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:\n", " urlretrieve(\n", " 'http://download.tensorflow.org/example_images/flower_photos.tgz',\n", " 'flower_photos.tar.gz',\n", " pbar.hook)\n", "\n", "if not isdir(dataset_folder_path):\n", " with tarfile.open('flower_photos.tar.gz') as tar:\n", " tar.extractall()\n", " tar.close()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## ConvNet Codes\n", "\n", "Below, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.\n", "\n", "Here we're using the `vgg16` module from `tensorflow_vgg`. The network takes images of size $224 \\times 224 \\times 3$ as input. Then it has 5 sets of convolutional layers. The network implemented here has this structure (copied from [the source code](https://github.com/machrisaa/tensorflow-vgg/blob/master/vgg16.py)):\n", "\n", "```\n", "self.conv1_1 = self.conv_layer(bgr, \"conv1_1\")\n", "self.conv1_2 = self.conv_layer(self.conv1_1, \"conv1_2\")\n", "self.pool1 = self.max_pool(self.conv1_2, 'pool1')\n", "\n", "self.conv2_1 = self.conv_layer(self.pool1, \"conv2_1\")\n", "self.conv2_2 = self.conv_layer(self.conv2_1, \"conv2_2\")\n", "self.pool2 = self.max_pool(self.conv2_2, 'pool2')\n", "\n", "self.conv3_1 = self.conv_layer(self.pool2, \"conv3_1\")\n", "self.conv3_2 = self.conv_layer(self.conv3_1, \"conv3_2\")\n", "self.conv3_3 = self.conv_layer(self.conv3_2, \"conv3_3\")\n", "self.pool3 = self.max_pool(self.conv3_3, 'pool3')\n", "\n", "self.conv4_1 = self.conv_layer(self.pool3, \"conv4_1\")\n", "self.conv4_2 = self.conv_layer(self.conv4_1, \"conv4_2\")\n", "self.conv4_3 = self.conv_layer(self.conv4_2, \"conv4_3\")\n", "self.pool4 = self.max_pool(self.conv4_3, 'pool4')\n", "\n", "self.conv5_1 = self.conv_layer(self.pool4, \"conv5_1\")\n", "self.conv5_2 = self.conv_layer(self.conv5_1, \"conv5_2\")\n", "self.conv5_3 = self.conv_layer(self.conv5_2, \"conv5_3\")\n", "self.pool5 = self.max_pool(self.conv5_3, 'pool5')\n", "\n", "self.fc6 = self.fc_layer(self.pool5, \"fc6\")\n", "self.relu6 = tf.nn.relu(self.fc6)\n", "```\n", "\n", "So what we want are the values of the first fully connected layer, after being ReLUd (`self.relu6`). To build the network, we use\n", "\n", "```\n", "with tf.Session() as sess:\n", " vgg = vgg16.Vgg16()\n", " input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n", " with tf.name_scope(\"content_vgg\"):\n", " vgg.build(input_)\n", "```\n", "\n", "This creates the `vgg` object, then builds the graph with `vgg.build(input_)`. Then to get the values from the layer,\n", "\n", "```\n", "feed_dict = {input_: images}\n", "codes = sess.run(vgg.relu6, feed_dict=feed_dict)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "from tensorflow_vgg import vgg16\n", "from tensorflow_vgg import utils" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "data_dir = 'flower_photos/'\n", "contents = os.listdir(data_dir)\n", "classes = [each for each in contents if os.path.isdir(data_dir + each)]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Below I'm running images through the VGG network in batches.\n", "\n", "> **Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "# Set the batch size higher if you can fit in in your GPU memory\n", "batch_size = 10\n", "codes_list = []\n", "labels = []\n", "batch = []\n", "\n", "codes = None\n", "\n", "with tf.Session() as sess:\n", " \n", " # TODO: Build the vgg network here\n", "\n", " for each in classes:\n", " print(\"Starting {} images\".format(each))\n", " class_path = data_dir + each\n", " files = os.listdir(class_path)\n", " for ii, file in enumerate(files, 1):\n", " # Add images to the current batch\n", " # utils.load_image crops the input images for us, from the center\n", " img = utils.load_image(os.path.join(class_path, file))\n", " batch.append(img.reshape((1, 224, 224, 3)))\n", " labels.append(each)\n", " \n", " # Running the batch through the network to get the codes\n", " if ii % batch_size == 0 or ii == len(files):\n", " \n", " # Image batch to pass to VGG network\n", " images = np.concatenate(batch)\n", " \n", " # TODO: Get the values from the relu6 layer of the VGG network\n", " codes_batch = \n", " \n", " # Here I'm building an array of the codes\n", " if codes is None:\n", " codes = codes_batch\n", " else:\n", " codes = np.concatenate((codes, codes_batch))\n", " \n", " # Reset to start building the next batch\n", " batch = []\n", " print('{} images processed'.format(ii))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# write codes to file\n", "with open('codes', 'w') as f:\n", " codes.tofile(f)\n", " \n", "# write labels to file\n", "import csv\n", "with open('labels', 'w') as f:\n", " writer = csv.writer(f, delimiter='\\n')\n", " writer.writerow(labels)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Building the Classifier\n", "\n", "Now that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# read codes and labels from file\n", "import csv\n", "\n", "with open('labels') as f:\n", " reader = csv.reader(f, delimiter='\\n')\n", " labels = np.array([each for each in reader]).squeeze()\n", "with open('codes') as f:\n", " codes = np.fromfile(f, dtype=np.float32)\n", " codes = codes.reshape((len(labels), -1))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Data prep\n", "\n", "As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!\n", "\n", "> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the labels. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "labels_vecs = # Your one-hot encoded labels array here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use [`StratifiedShuffleSplit`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) from scikit-learn.\n", "\n", "You can create the splitter like so:\n", "```\n", "ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\n", "```\n", "Then split the data with \n", "```\n", "splitter = ss.split(x, y)\n", "```\n", "\n", "`ss.split` returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use `next(splitter)` to get the indices. Be sure to read the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) and the [user guide](http://scikit-learn.org/stable/modules/cross_validation.html#random-permutations-cross-validation-a-k-a-shuffle-split).\n", "\n", "> **Exercise:** Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "train_x, train_y = \n", "val_x, val_y = \n", "test_x, test_y = " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print(\"Train shapes (x, y):\", train_x.shape, train_y.shape)\n", "print(\"Validation shapes (x, y):\", val_x.shape, val_y.shape)\n", "print(\"Test shapes (x, y):\", test_x.shape, test_y.shape)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "If you did it right, you should see these sizes for the training sets:\n", "\n", "```\n", "Train shapes (x, y): (2936, 4096) (2936, 5)\n", "Validation shapes (x, y): (367, 4096) (367, 5)\n", "Test shapes (x, y): (367, 4096) (367, 5)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Classifier layers\n", "\n", "Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.\n", "\n", "> **Exercise:** With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])\n", "labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])\n", "\n", "# TODO: Classifier layers and operations\n", "\n", "logits = # output layer logits\n", "cost = # cross entropy loss\n", "\n", "optimizer = # training optimizer\n", "\n", "# Operations for validation/test accuracy\n", "predicted = tf.nn.softmax(logits)\n", "correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Batches!\n", "\n", "Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batches(x, y, n_batches=10):\n", " \"\"\" Return a generator that yields batches from arrays x and y. \"\"\"\n", " batch_size = len(x)//n_batches\n", " \n", " for ii in range(0, n_batches*batch_size, batch_size):\n", " # If we're not on the last batch, grab data with size batch_size\n", " if ii != (n_batches-1)*batch_size:\n", " X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] \n", " # On the last batch, grab the rest of the data\n", " else:\n", " X, Y = x[ii:], y[ii:]\n", " # I love generators\n", " yield X, Y" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Training\n", "\n", "Here, we'll train the network.\n", "\n", "> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the `get_batches` function I wrote before to get your batches like `for x, y in get_batches(train_x, train_y)`. Or write your own!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "saver = tf.train.Saver()\n", "with tf.Session() as sess:\n", " \n", " # TODO: Your training code here\n", " saver.save(sess, \"checkpoints/flowers.ckpt\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Testing\n", "\n", "Below you see the test accuracy. You can also see the predictions returned for images." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " \n", " feed = {inputs_: test_x,\n", " labels_: test_y}\n", " test_acc = sess.run(accuracy, feed_dict=feed)\n", " print(\"Test accuracy: {:.4f}\".format(test_acc))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "from scipy.ndimage import imread" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Below, feel free to choose images and see how the trained classifier predicts the flowers in them." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'\n", "test_img = imread(test_img_path)\n", "plt.imshow(test_img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Run this cell if you don't have a vgg graph built\n", "if 'vgg' in globals():\n", " print('\"vgg\" object already exists. Will not create again.')\n", "else:\n", " #create vgg\n", " with tf.Session() as sess:\n", " input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n", " vgg = vgg16.Vgg16()\n", " vgg.build(input_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "with tf.Session() as sess:\n", " img = utils.load_image(test_img_path)\n", " img = img.reshape((1, 224, 224, 3))\n", "\n", " feed_dict = {input_: img}\n", " code = sess.run(vgg.relu6, feed_dict=feed_dict)\n", " \n", "saver = tf.train.Saver()\n", "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " \n", " feed = {inputs_: code}\n", " prediction = sess.run(predicted, feed_dict=feed).squeeze()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "plt.imshow(test_img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "plt.barh(np.arange(5), prediction)\n", "_ = plt.yticks(np.arange(5), lb.classes_)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Am3ra/CS

CS4/Metodos/Generación de números aleatorios.ipynb

1

1174

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Alan Macedo Esparza\n", "A01366288" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 3 2 1 2 1 1 2 4 3 2 3 4 2 1 2 3 4 2 4 2 1 4 4 4 2 2 4 1 2 4 4 3 4 4 2 1\n", " 4 1 4 2 2 1 2 4 4 1 4 2 4 4 4 1 4 4 4 1 4 3 4 1 3 3 1 1 2 1 4 2 2 2 1 3 2\n", " 3 4 1 3 1 4 4 1 4 1 3 1 1 1 4 4 4 1 4 4 4 4 1 2 4 1]\n" ] } ], "source": [ "import numpy as np\n", "\n", "a = np.random.choice([1,2,3,4], 100, p=[0.3,0.2,0.1,0.4])\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.7 64-bit", "language": "python", "name": "python37764bit6381e19668b04d93b1afa4267a4f1b24" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

giacomov/astromodels

examples/EBL_attenuation_example.ipynb

2

35446

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EBL Attenuation of a Spectral Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we create a simple *astromodels* spectrum and then apply EBL attenuation, as a function of redshift and energy.\n", "\n", "The `EBLattenuation` class in astromodels relies directly on the *ebltable* package by Manuel Meyer, see [ebltable](https://github.com/me-manu/ebltable) ." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configuration read from /home/rlauer/.threeML/threeML_config.yml\n" ] } ], "source": [ "from threeML import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define power law spectrum\n", "sourceName = 'Mrk421'\n", "spectrum = Powerlaw()\n", "#put it into a PS model, in this context primarily in order to establish units\n", "source1 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrum)\n", "#and set parameters:\n", "spectrum.piv = 1. * u.TeV\n", "spectrum.K = 1.e-11 / (u.TeV * u.cm**2 * u.s)\n", "spectrum.index = -2.2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ul>\n", "\n", "<li>description: (Powerlaw{1} * EBLattenuation{2})</li>\n", "\n", "<li>formula: (no latex formula available)</li>\n", "\n", "<li>parameters: \n", "<ul>\n", "\n", "<li>K_1: \n", "<ul>\n", "\n", "<li>value: 1e-20</li>\n", "\n", "<li>desc: Normalization (differential flux at the pivot value)</li>\n", "\n", "<li>min_value: 1e-30</li>\n", "\n", "<li>max_value: 1000.0</li>\n", "\n", "<li>unit: cm-2 keV-1 s-1</li>\n", "\n", "<li>is_normalization: True</li>\n", "\n", "<li>delta: 0.1</li>\n", "\n", "<li>free: True</li>\n", "\n", "</ul>\n", "\n", "</li>\n", "\n", "<li>piv_1: \n", "<ul>\n", "\n", "<li>value: 1000000000.0</li>\n", "\n", "<li>desc: Pivot value</li>\n", "\n", "<li>min_value: None</li>\n", "\n", "<li>max_value: None</li>\n", "\n", "<li>unit: keV</li>\n", "\n", "<li>is_normalization: False</li>\n", "\n", "<li>delta: 0.1</li>\n", "\n", "<li>free: False</li>\n", "\n", "</ul>\n", "\n", "</li>\n", "\n", "<li>index_1: \n", "<ul>\n", "\n", "<li>value: -2.2</li>\n", "\n", "<li>desc: Photon index</li>\n", "\n", "<li>min_value: -10.0</li>\n", "\n", "<li>max_value: 10.0</li>\n", "\n", "<li>unit: </li>\n", "\n", "<li>is_normalization: False</li>\n", "\n", "<li>delta: 0.2</li>\n", "\n", "<li>free: True</li>\n", "\n", "</ul>\n", "\n", "</li>\n", "\n", "<li>redshift_2: \n", "<ul>\n", "\n", "<li>value: 1.0</li>\n", "\n", "<li>desc: redshift of the source</li>\n", "\n", "<li>min_value: None</li>\n", "\n", "<li>max_value: None</li>\n", "\n", "<li>unit: </li>\n", "\n", "<li>is_normalization: False</li>\n", "\n", "<li>delta: 0.1</li>\n", "\n", "<li>free: False</li>\n", "\n", "</ul>\n", "\n", "</li>\n", "\n", "</ul>\n", "\n", "</li>\n", "\n", "</ul>\n" ], "text/plain": [ " * description: (Powerlaw{1} * EBLattenuation{2})\n", " * formula: (no latex formula available)\n", " * parameters: \n", " * K_1: \n", " * value: 1e-20\n", " * desc: Normalization (differential flux at the pivot value)\n", " * min_value: 1e-30\n", " * max_value: 1000.0\n", " * unit: cm-2 keV-1 s-1\n", " * is_normalization: True\n", " * delta: 0.1\n", " * free: True\n", " * piv_1: \n", " * value: 1000000000.0\n", " * desc: Pivot value\n", " * min_value: None\n", " * max_value: None\n", " * unit: keV\n", " * is_normalization: False\n", " * delta: 0.1\n", " * free: False\n", " * index_1: \n", " * value: -2.2\n", " * desc: Photon index\n", " * min_value: -10.0\n", " * max_value: 10.0\n", " * unit: \n", " * is_normalization: False\n", " * delta: 0.2\n", " * free: True\n", " * redshift_2: \n", " * value: 1.0\n", " * desc: redshift of the source\n", " * min_value: None\n", " * max_value: None\n", " * unit: \n", " * is_normalization: False\n", " * delta: 0.1\n", " * free: False\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#define attenuated spectrum:\n", "ebl = EBLattenuation()\n", "spectrumEBL = spectrum * ebl\n", "source2 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrumEBL)\n", "#show new parameter names:\n", "spectrumEBL.display()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#set redshift:\n", "spectrumEBL.redshift_2 = 0.031*u.dimensionless_unscaled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently, the 3ML implementation selects the `Dominguez` model for optical depth by default.\n", "In the next cell, we also define EBL attenuation for a different model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#new EBL with different model:\n", "ebl2 = EBLattenuation()\n", "ebl2.set_ebl_model('gilmore')\n", "spectrumEBL_Gil = spectrum*ebl2\n", "spectrumEBL_Gil.redshift_2 = 0.031*u.dimensionless_unscaled\n", "source3 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrumEBL_Gil)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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8iWttGwbfLkh4xNO5ymJTiUdRimfOxGMjpcyr6DqWRiUe8zhz8SBxW9/lh0t7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\nM6sqB4+ZmVWVg8fMzKrqfwEcDxXvpLp7zAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x9017d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#use matplotlib to plot both spectra:\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "energies = np.logspace(-1.,2.,100)*u.TeV\n", "#factor 1e9 to convert to TeV^-1\n", "plt.loglog(energies,spectrum(energies)*1e9,label=\"intrinsic\")\n", "plt.loglog(energies,spectrumEBL(energies)*1e9,label=\"with Dominguez EBL attenuation\")\n", "plt.loglog(energies,spectrumEBL_Gil(energies)*1e9,label=\"with Gilmore EBL attenuation\")\n", "plt.legend(loc=1)\n", "\n", "plt.xlim(0.2,100.)\n", "plt.ylim(1e-19,1e-9)\n", "plt.xlabel(\"Energy [TeV]\")\n", "plt.ylabel(r\"Flux (ph cm$^{-2}$ s$^{-1}$ TeV$^{-1}$)\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

parrt/msan501

notes/sound.ipynb

1

2087627

mit

jmhsi/justin_tinker

data_science/courses/cs231/simple_net_spiral_data.ipynb

1

6985

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "N = 100 # number of points per class\n", "D = 2 # dimensionality\n", "K = 3 # number of classes\n", "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n", "y = np.zeros(N*K, dtype='uint8') # class labels\n", "for j in range(K):\n", " ix = range(N*j,N*(j+1))\n", " r = np.linspace(0.0,1,N) # radius\n", " t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n", " X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n", " y[ix] = j\n", "# lets visualize the data:\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Softmax Linear Classifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Train a Linear Classifier\n", "\n", "# initialize parameters randomly\n", "W = 0.01 * np.random.randn(D,K)\n", "b = np.zeros((1,K))\n", "\n", "# some hyperparameters\n", "step_size = 1e-0\n", "reg = 1e-3 # regularization strength\n", "\n", "# gradient descent loop\n", "num_examples = X.shape[0]\n", "for i in range(200):\n", " \n", " # evaluate class scores, [N x K]\n", " scores = np.dot(X, W) + b \n", " \n", " # compute the class probabilities\n", " exp_scores = np.exp(scores)\n", " probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]\n", " \n", " # compute the loss: average cross-entropy loss and regularization\n", " corect_logprobs = -np.log(probs[range(num_examples),y])\n", " data_loss = np.sum(corect_logprobs)/num_examples\n", " reg_loss = 0.5*reg*np.sum(W*W)\n", " loss = data_loss + reg_loss\n", " if i % 10 == 0:\n", " print( \"iteration %d: loss %f\" % (i, loss))\n", " \n", " # compute the gradient on scores\n", " dscores = probs\n", " dscores[range(num_examples),y] -= 1\n", " dscores /= num_examples\n", " \n", " # backpropate the gradient to the parameters (W,b)\n", " dW = np.dot(X.T, dscores)\n", " db = np.sum(dscores, axis=0, keepdims=True)\n", " \n", " dW += reg*W # regularization gradient\n", " \n", " # perform a parameter update\n", " W += -step_size * dW\n", " b += -step_size * db" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# evaluate training set accuracy\n", "scores = np.dot(X, W) + b\n", "predicted_class = np.argmax(scores, axis=1)\n", "print( 'training accuracy: %.2f' % (np.mean(predicted_class == y)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training a Neural Network" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pdb" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# initialize parameters randomly\n", "h = 100 # size of hidden layer\n", "W = 0.01 * np.random.randn(D,h)\n", "b = np.zeros((1,h))\n", "W2 = 0.01 * np.random.randn(h,K)\n", "b2 = np.zeros((1,K))\n", "\n", "# some hyperparameters\n", "step_size = 1e-0\n", "reg = 1e-3 # regularization strength\n", "\n", "# gradient descent loop\n", "num_examples = X.shape[0]\n", "for i in range(10000):\n", " \n", " # evaluate class scores, [N x K]\n", " hidden_layer = np.maximum(0, np.dot(X, W) + b) # note, ReLU activation\n", " scores = np.dot(hidden_layer, W2) + b2\n", " \n", " # compute the class probabilities\n", " exp_scores = np.exp(scores)\n", " probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]\n", " \n", " # compute the loss: average cross-entropy loss and regularization\n", " corect_logprobs = -np.log(probs[range(num_examples),y])\n", " data_loss = np.sum(corect_logprobs)/num_examples\n", " reg_loss = 0.5*reg*np.sum(W*W) + 0.5*reg*np.sum(W2*W2)\n", " loss = data_loss + reg_loss\n", " if i % 1000 == 0:\n", " print(\"iteration %d: loss %f\" % (i, loss))\n", " \n", " # compute the gradient on scores\n", " dscores = probs\n", " dscores[range(num_examples),y] -= 1\n", " dscores /= num_examples\n", " \n", " # backpropate the gradient to the parameters\n", " # first backprop into parameters W2 and b2\n", " dW2 = np.dot(hidden_layer.T, dscores)\n", " db2 = np.sum(dscores, axis=0, keepdims=True)\n", " # next backprop into hidden layer\n", " dhidden = np.dot(dscores, W2.T)\n", " # backprop the ReLU non-linearity\n", " dhidden[hidden_layer <= 0] = 0\n", " # finally into W,b\n", " dW = np.dot(X.T, dhidden)\n", " db = np.sum(dhidden, axis=0, keepdims=True)\n", " \n", " # add regularization gradient contribution\n", " dW2 += reg * W2\n", " dW += reg * W\n", " \n", " # perform a parameter update\n", " W += -step_size * dW\n", " b += -step_size * db\n", " W2 += -step_size * dW2\n", " b2 += -step_size * db2\n", " pdb.set_trace()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# evaluate training set accuracy\n", "hidden_layer = np.maximum(0, np.dot(X, W) + b)\n", "scores = np.dot(hidden_layer, W2) + b2\n", "predicted_class = np.argmax(scores, axis=1)\n", "print( 'training accuracy: %.2f' % (np.mean(predicted_class == y)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

Myllyenko/incubator-toree

etc/examples/notebooks/meetup-streaming-toree.ipynb

8

29770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Streaming Meetups Dashboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of this notebook is to give an all-in-one demo of streaming data from the [meetup.com RSVP API](http://www.meetup.com/meetup_api/docs/stream/2/rsvps/#websockets), through a local [Spark Streaming job](http://spark.apache.org/streaming/), and into [declarative widgets](https://github.com/jupyter-incubator/declarativewidgets) in a dashboard layout. You can peek at the meetup.com stream [here](http://stream.meetup.com/2/rsvps)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example is a Scala adaptation of [this](https://github.com/jupyter-incubator/dashboards/blob/master/etc/notebooks/stream_demo/meetup-streaming.ipynb) notebook from [jupyter_dashboards](https://github.com/jupyter-incubator/dashboards).\n", "\n", "On your first visit to this notebook, we recommend that you execute one cell at a time as you read along. Later, if you just want to see the demo, select *Cell > Run All* from the menu bar. Once you've run all of the cells, select *View > View Dashboard* and then click on the **Stream** toggle to start the data stream.\n", "\n", "**Table of Contents**\n", "\n", "1. [Initialize DeclarativeWidgets](#Initialize-DeclarativeWidgets) <span class=\"text-muted\" style=\"float:right\"></span>\n", "2. [Define the Spark Streaming Job](#Streaming-Meetups-Scala) <span style=\"float:right\" class=\"text-muted\">create stream context, custom receiver, filter by topic, top topics, venue metadata</span>\n", "3. [Create a Dashboard Interface with Widgets](#Streaming-Meetups-Dashboard)<span style=\"float:right\" class=\"text-muted\">charts, interactive controls, globe</span>\n", "4. [Arrange the Dashboard Layout](#Arrange-the-Dashboard-Layout-Top)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\" role=\"alert\" style=\"margin-top: 10px\">\n", "<p><strong>Note</strong><p>\n", "\n", "<p>We've condensed all of the demo logic into a single notebook for educational purposes. If you want to turn this into a scalable, multi-tenant dashboard, you'll want to separate the stream processing portions from the dashboard view. That way, multiple dashboard instances can pull from the same processed data stream instead of recomputing it.</p>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize DeclarativeWidgets<span style=\"float: right; font-size: 0.5em\"><a href=\"#Initialize-DeclarativeWidgets\">Top</a></span>\n", "\n", "In Toree, declarativewidgets need to be initialized by adding the JAR with the scala implementation and calling `initWidgets`. This is must take place very close to the top of the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<span id=\"__jar_url__\"></span>\n", "<script>\n", "(function(){\n", " var thisUrl = this.location.toString();\n", " var jarUrl = thisUrl.substring(0,thisUrl.indexOf(\"/notebooks\"))+\"/nbextensions/urth_widgets/urth-widgets.jar\";\n", " document.getElementById(\"__jar_url__\").innerHTML = jarUrl;\n", "})();\n", "</script>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\" role=\"alert\" style=\"margin-top: 10px\">\n", "<p><strong>Note</strong> The output of the cell above is the URL to use below.\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%AddJar http://localhost:8888/nbextensions/urth_widgets/urth-widgets.jar" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import urth.widgets._\n", "initWidgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Spark Streaming Application<span style=\"float: right; font-size: 0.5em\"><a href=\"#Streaming-Meetups-Scala\">Top</a></span>\n", "\n", "With the frontend widgest in mind, we'll now setup our Spark Streaming job to fulfill their data requirements. In this section, we'll define a set of functions that act on a `SparkStreamingContext` or `RDDs` from that context." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Install external dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%AddDeps eu.piotrbuda scalawebsocket_2.10 0.1.1 --transitive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Custom WebSocker Receiver\n", "\n", "We create here a custom receiver that can connect to a WebSocket. That is how we will stream data out of the Meetup API." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import scalawebsocket.WebSocket\n", "import org.apache.spark.storage.StorageLevel \n", "import org.apache.spark.streaming.receiver.Receiver\n", "\n", "class WebSocketReceiver(url: String, storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER) extends Receiver[play.api.libs.json.JsValue](storageLevel) {\n", " @volatile private var webSocket: WebSocket = _\n", "\n", " def onStart() {\n", " try{\n", " val newWebSocket = WebSocket().open(url).onTextMessage({ msg: String => parseJson(msg) })\n", " setWebSocket(newWebSocket)\n", " } catch {\n", " case e: Exception => restart(\"Error starting WebSocket stream\", e)\n", " }\n", " }\n", "\n", " def onStop() {\n", " setWebSocket(null)\n", " }\n", "\n", " private def setWebSocket(newWebSocket: WebSocket) = synchronized {\n", " if (webSocket != null) {\n", " webSocket.shutdown()\n", " }\n", " webSocket = newWebSocket\n", " }\n", "\n", " private def parseJson(jsonStr: String): Unit = {\n", " val json: play.api.libs.json.JsValue = play.api.libs.json.Json.parse(jsonStr)\n", " store(json)\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Spark Meetup Application\n", "\n", "We then put all our functionality into an `object` that is marked as `Serializable`. This is a great technique to avoid serialization problems when interacting with Spark. Also not that anything that we do not want to serialize, such as the `StreamingContext` and the `SQLContext` should be marked `@transient`.\n", "\n", "The rest of the call are methods for starting and stopping the streaming application as well as functions that define the streaming flow." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case class TopicCount(topic: String, count: Int)\n", "\n", "object MeetupApp extends Serializable { \n", " import play.api.libs.json._\n", " import org.apache.spark.storage.StorageLevel \n", " import org.apache.spark.Logging\n", " import org.apache.spark.streaming._\n", " import org.apache.spark.sql.functions._\n", " import org.apache.spark.streaming.dstream.DStream\n", " import org.apache.spark.rdd.RDD\n", " import urth.widgets.WidgetChannels.channel\n", " import org.apache.spark.sql.SQLContext\n", " \n", " @transient var ssc:StreamingContext = _\n", " @transient val sqlContext:SQLContext = new SQLContext(sc)\n", " \n", " import sqlContext.implicits._\n", " \n", " var topic_filter = \"\"\n", " \n", " //Resetting the values on the data channels\n", " channel(\"status\").set(\"streaming\", false)\n", " channel(\"stats\").set(\"topics\", Map())\n", " channel(\"stats\").set(\"venues\", List())\n", "\n", " def create_streaming_context(sample_rate: Int): StreamingContext = {\n", " return new StreamingContext(sc, Seconds(sample_rate))\n", " }\n", " \n", " /**\n", " Creates a websocket client that pumps events into a ring buffer queue. Creates\n", " a SparkStreamContext that reads from the queue. Creates the events, topics, and\n", " venues DStreams, setting the widget channel publishing functions to iterate over\n", " RDDs in each. Starts the stream processing.\n", " */\n", " def start_stream():Unit = {\n", " ssc = create_streaming_context(5)\n", " ssc.checkpoint(\"/tmp/meetups.checkpoint\")\n", " val events = get_events(ssc, sample_event)\n", " get_topics(events, get_topic_counts)\n", " get_venues(events, aggregate_venues)\n", " ssc.start()\n", " \n", " channel(\"status\").set(\"streaming\", true)\n", " }\n", "\n", " /**\n", " Shuts down the websocket, stops the streaming context, and cleans up the file ring.\n", " */\n", " def shutdown_stream():Unit = {\n", " ssc.stop(false)\n", " channel(\"status\").set(\"streaming\", false) \n", " }\n", " \n", " /**\n", " Parses the events from the queue. Retains only those events that have at\n", " least one topic exactly matching the current topic_filter. Sends event\n", " RDDs to the for_each function. Returns the event DStream.\n", " */\n", " def get_events(ssc: StreamingContext, for_each: (RDD[play.api.libs.json.JsValue]) => Unit): org.apache.spark.streaming.dstream.DStream[play.api.libs.json.JsValue] = {\n", "\n", " val all_events = ssc.receiverStream( new WebSocketReceiver(\"ws://stream.meetup.com/2/rsvps\"))\n", "\n", " // Filter set of event\n", " val events = all_events.filter(retain_event)\n", "\n", " // Send event data to a widget channel. This will be covered below.\n", " events.foreachRDD(for_each)\n", "\n", " return events\n", " }\n", " \n", " /**\n", " Returns true if the user defined topic filter is blank or if at least one\n", " group topic in the event exactly matches the user topic filter string.\n", " */\n", " def retain_event(event: play.api.libs.json.JsValue):Boolean = {\n", " val topics = (event \\ \"group\" \\ \"group_topics\").as[play.api.libs.json.JsArray].value\n", " val isEmpty = topic_filter.trim == \"\"\n", " val containsTopic = topics.map(topic => topic_filter.\n", " equals((topic\\\"urlkey\").as[play.api.libs.json.JsString].value)\n", " ).reduce( (a,b) => a || b)\n", "\n", " isEmpty || containsTopic \n", " }\n", " \n", " /*\n", " Takes an RDD from the event DStream. Takes one event from the RDD.\n", " Substitutes a placeholder photo if the member who RSVPed does not\n", " have one. Publishes the event metadata on the meetup channel.\n", " */\n", " def sample_event(rdd: org.apache.spark.rdd.RDD[play.api.libs.json.JsValue]):Unit = {\n", " \n", " try {\n", " val event = rdd.take(1)(0)\n", "\n", " // use a fallback photo for those members without one\n", " val default_event: play.api.libs.json.JsObject = play.api.libs.json.Json.parse(\"\"\"{\n", " \"member\" : {\n", " \"photo\" : \"http://photos4.meetupstatic.com/img/noPhoto_50.png\"\n", " }\n", " }\n", " \"\"\").as[play.api.libs.json.JsObject]\n", " val fixed_event = default_event ++ (event).as[play.api.libs.json.JsObject] \n", "\n", " channel(\"meetups\").set(\"meetup\", fixed_event.value)\n", " } catch {\n", " case _ => print(\"No data to sample\")\n", " }\n", " }\n", " \n", " /**\n", " Pulls group topics from meetup events. Counts each one once and updates\n", " the global topic counts seen since stream start. Sends topic count RDDs\n", " to the for_each function. Returns nothing new.\n", " */ \n", " def get_topics(events:DStream[JsValue], for_each: (RDD[((String,String),Int)]) => Unit) = {\n", " //Extract the group topic url keys and \"namespace\" them with the current topic filter\n", " val topics = events.\n", " flatMap( (event: JsValue) => {\n", " (event \\ \"group\" \\ \"group_topics\").as[JsArray].value\n", " }).map((topic: JsValue) => {\n", " val filter = if(topic_filter.equals(\"\")) {\n", " \"*\"\n", " } else { \n", " topic_filter \n", " }\n", " ((filter, (topic \\ \"urlkey\").as[JsString].value), 1) \n", " })\n", "\n", " val topic_counts = topics.updateStateByKey(update_topic_counts)\n", "\n", " // Send topic data to a widget channel. This will be covered below.\n", " topic_counts.foreachRDD(for_each)\n", " }\n", " \n", " /**\n", " Sums the number of times a topic has been seen in the current sampling\n", " window. Then adds that to the number of times the topic has been\n", " seen in the past. Returns the new sum.\n", " */\n", " def update_topic_counts(new_values: Seq[Int], last_sum: Option[Int]): Option[Int] = {\n", " return Some((new_values :+ 0).reduce(_+_) + last_sum.getOrElse(0))\n", " }\n", " \n", " /**\n", " Takes an RDD from the topic DStream. Takes the top 25 topics by occurrence\n", " and publishes them in a pandas DataFrame on the counts channel.\n", " */\n", " def get_topic_counts(rdd: RDD[((String,String),Int)]){\n", " //counts = rdd.takeOrdered(25, key=lambda x: -x[1])\n", " val filterStr = if (topic_filter.equals(\"\")) \"*\" else topic_filter\n", " \n", " /*\n", " keep only those matching current filter\n", " and sort in descending order, taking top 25\n", " */\n", " val countDF = rdd.filter((x:((String,String),Int)) => filterStr.equals(x._1._1)).\n", " map(tuple => TopicCount(tuple._1._2, tuple._2)).toDF().\n", " sort($\"count\".desc).limit(25)\n", " \n", " channel(\"stats\").set(\"topics\", countDF)\n", "\n", " }\n", " \n", " /**\n", " Pulls venu metadata from meetup events if it exists. Sends venue \n", " dictionaries RDDs to the for_each function. Returns nothing new.\n", " */\n", " def get_venues(events:DStream[JsValue], calculate: (DStream[JsValue]) => Unit):Unit = {\n", " \n", " val venues = events.\n", " filter(hasVenue).\n", " map((event:JsValue) => {event \\ \"venue\"})\n", "\n", " calculate(venues)\n", " }\n", " \n", " /**\n", " Checks if there is a venue in the JSON object\n", " */\n", " def hasVenue(event:JsValue):Boolean = {\n", " (event \\ \"venue\") match {\n", " case e:play.api.libs.json.JsUndefined => false\n", " case _ => true\n", " }\n", " }\n", " \n", " /**\n", " Aggregating the venues by lat/lon\n", " */\n", " def aggregate_venues(venues:DStream[JsValue]): Unit = {\n", " //build a (lat, long, mag) tuple\n", " val venue_loc = venues.map((venue:JsValue) => {\n", " ( \n", " (venue \\ \"lat\").asOpt[Int].getOrElse(0),\n", " (venue \\ \"lon\").asOpt[Int].getOrElse(0)\n", " )\n", " }).\n", " filter((latLon:(Int,Int)) => {latLon != (0,0)}).\n", " map((latLon:(Int,Int)) => {\n", " ((\"\"+latLon._1+\",\"+latLon._2), (latLon._1, latLon._2, 1))\n", " })\n", " \n", " val venue_loc_counts = venue_loc.updateStateByKey(update_venue_loc_counts)\n", " \n", " venue_loc_counts.map{\n", " _._2\n", " }.foreachRDD((rdd:RDD[(Int,Int,Int)]) => {\n", " val venue_data = rdd.collect()\n", " \n", " if( !venue_data.isEmpty ){\n", " val total = venue_data.reduce((a,b)=> (0, 0, a._3+b._3))._3 * 1.0\n", " \n", " val venue_data_list= venue_data.map( (x:(Int,Int,Int)) => List(x._1,x._2,x._3/total)).toList\n", " \n", " channel(\"stats\").set(\"venues\", venue_data_list)\n", " }\n", " })\n", " }\n", " \n", " /**\n", " Function to count the lat, lon\n", " */\n", " def update_venue_loc_counts(new_values: Seq[(Int,Int,Int)], last_sum: Option[(Int,Int,Int)]): Option[(Int,Int,Int)] = {\n", " if( new_values.isEmpty )\n", " last_sum\n", " else{\n", " val partial = new_values.reduce((a,b)=> (a._1, a._2, a._3+b._3))\n", " Some((partial._1, partial._2, partial._3 + last_sum.getOrElse((0,0,0))._3))\n", " }\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a Dashboard Interface with Widgets <span style=\"float: right; font-size: 0.5em\"><a href=\"#Streaming-Meetups-Dashboard\">Top</a></span>\n", "\n", "Now that we have a streaming job defined, we can create a series of interactive areas that can control and display the data is being produced." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Control to start and stop the Stream\n", "\n", "Here we will use a switch widget and tie it to a function that can start and stop the stream job." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val start_stream = () => {\n", " MeetupApp.start_stream()\n", "}:Unit\n", "\n", "val shutdown_stream = () => {\n", " MeetupApp.shutdown_stream()\n", "}:Unit" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<link rel=\"import\" href=\"urth_components/paper-toggle-button/paper-toggle-button.html\"\n", " is=\"urth-core-import\" package=\"PolymerElements/paper-toggle-button\">\n", " \n", "<template is=\"urth-core-bind\" channel=\"status\">\n", " <urth-core-function id=\"start\" ref=\"start_stream\"></urth-core-function> \n", " <urth-core-function id=\"shutdown\" ref=\"shutdown_stream\"></urth-core-function> \n", " <paper-toggle-button checked=\"{{streaming}}\" onChange=\"this.checked ? start.invoke() : shutdown.invoke()\">Stream</paper-toggle-button>\n", "</template>\n", "\n", "<style is=\"custom-style\">\n", " paper-toggle-button {\n", " --default-primary-color: green;\n", " }\n", "</style>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Topic Bar Chart\n", "\n", "Here we insert a `<urth-viz-chart>` to show the top 25 meetup topics by occurrence in the stream. Take note of the `<template>` element. We use it to specify that the HTML within will make use of a `counts` channel. We will put data on the `counts` channel later in this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<link rel=\"import\" href=\"urth_components/urth-viz-chart/urth-viz-chart.html\" is=\"urth-core-import\">\n", "\n", "<template is=\"urth-core-bind\" channel=\"stats\">\n", " <urth-viz-chart type='bar' datarows='[[topics.data]]' columns='[[topics.columns]]' rotatelabels='30'></urth-viz-chart>\n", "</template>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Topic Filter\n", "\n", "Next we create an `<urth-core-function>` which that binds the value of a `<paper-input>` widget to a Python function that sets a global variable. The function will set a string that we'll use to filter the incoming events to only pertaining to a certain topic.\n", "\n", "Notice that the `<link>` tag here is different than what we specified above. `<urth-viz-chart>` is already loaded within the notebook, but here we are using a third-party [Polymer](https://www.polymer-project.org/1.0/) element which needs to download first. To handle that automatically, we specify `is=\"urth-core-import\"` and set the [bower](http://bower.io/) package name as the `package` attribute value." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val set_topic_filter = (value: String) => {\n", " MeetupApp.topic_filter = value\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<link rel=\"import\" href=\"urth_components/paper-input/paper-input.html\"\n", " is=\"urth-core-import\" package=\"PolymerElements/paper-input\">\n", " \n", "<template is=\"urth-core-bind\" channel=\"filter\" id=\"filter-input\">\n", " <urth-core-function auto\n", " id=\"set_topic_filter\"\n", " ref=\"set_topic_filter\"\n", " arg-value=\"{{topic_filter}}\">\n", " </urth-core-function>\n", " \n", " <paper-input label=\"Filter\" value=\"{{topic_filter}}\"></paper-input>\n", "</template>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### User Card\n", "\n", "Now we add a simple `<paper-card>` element showing the name and photo of one user who RSVPed recently in the event stream. We add some custom styling and a bit of custom JavaScript in this case to format the datetime associated with the RSVP event." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<link rel=\"import\" href=\"urth_components/paper-card/paper-card.html\"\n", " is=\"urth-core-import\" package=\"PolymerElements/paper-card\">\n", "\n", "<style is=\"custom-style\">\n", " paper-card.meetups-card {\n", " max-width: 400px;\n", " width: 100%;\n", " \n", " --paper-card-header: {\n", " height: 100px;\n", " border-bottom: 1px solid #e8e8e8;\n", " };\n", "\n", " --paper-card-header-image: {\n", " height: 80px;\n", " width: 80px !important;\n", " margin: 10px;\n", " border-radius: 50px;\n", " width: auto;\n", " border: 10px solid white;\n", " box-shadow: 0 0 1px 1px #e8e8e8;\n", " };\n", " \n", " --paper-card-header-image-text: {\n", " left: auto;\n", " right: 0px;\n", " width: calc(100% - 130px);\n", " text-align: right;\n", " text-overflow: ellipsis;\n", " overflow: hidden;\n", " };\n", " }\n", " \n", " .meetups-card .card-content a {\n", " display: block;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " white-space: nowrap;\n", " }\n", "</style>\n", "\n", "<template is=\"urth-core-bind\" channel=\"meetups\" id=\"meetup-card\">\n", " <paper-card\n", " class=\"meetups-card\"\n", " heading=\"[[meetup.member.member_name]]\"\n", " image=\"[[meetup.member.photo]]\">\n", " <div class=\"card-content\">\n", " <p><a href=\"[[meetup.event.event_url]]\" target=\"_blank\">[[meetup.event.event_name]]</a></p>\n", " <p>[[getPrettyTime(meetup.event.time)]]</p>\n", " </div>\n", " </paper-card>\n", "</template>\n", "\n", "<!-- see https://github.com/PolymerElements/iron-validator-behavior/blob/master/demo/index.html -->\n", "<script>\n", " (function() {\n", " var dateStringOptions = {weekday:'long', year:'numeric', month: 'long', hour:'2-digit', minute:'2-digit', day:'numeric'};\n", " var locale = navigator.language || navigator.browserLanguage || navigator.systemLanguage || navigator.userLanguage;\n", "\n", " var scope = document.querySelector('template#meetup-card');\n", " scope.getPrettyTime = function(timestamp) {\n", " try {\n", " console.log('The date is', timestamp)\n", " var d = new Date(timestamp);\n", " return d.toLocaleDateString(locale, dateStringOptions);\n", " } catch(e){\n", " return ''\n", " }\n", " }\n", " })();\n", "</script>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Map Venues\n", "\n", "Finally, we add a [WebGL globe](https://github.com/dataarts/webgl-globe) showing the location of meetup venues to which users are RSVPing in the stream. On the globe we render bars to represent the number of recent RSVPs in a geographic area." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "<link rel=\"import\" href=\"urth_components/webgl-globe/webgl-globe.html\"\n", " is=\"urth-core-import\" package=\"http://github.com/ibm-et/webgl-globe.git\">\n", "\n", "<template is=\"urth-core-bind\" channel=\"stats\">\n", " <webgl-globe data=[[venues]]></webgl-globe>\n", "</template>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arrange the Dashboard Layout <span style=\"float: right; font-size: 0.5em\"><a href=\"#Streaming-Meetups-Dashboard\">Top</a></span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before toggling the stream on/off switch, we should switch to dashboard view. Otherwise, we'll need to scroll up and down this notebook to see the widgets updating. For convenience, this notebook already contains metadata to position our widgets in a grid layout.\n", "\n", "Select *View > View Dashboard* from the menu bar to see the dashboard view now. Then toggle the stream switch in the top right of the dashboard to begin stream processing. To return to the regular notebook view, select *View > Notebook*.\n", "\n", "If you want to arrange the notebook cells differently, select *View > Layout Dashboard*. Then, hover your mouse over the main notebook / dashboard area. When you do, you'll see icons appear that allow you to:\n", "\n", "- Drag cells to new locations\n", "- Resize cells\n", "- Show / hide cells in the dashboard view\n", "- Flip to editing mode for a cell\n", "\n", "Save the notebook to save your changes to the layout within the notebook file itself.\n", "\n", "<div class=\"alert alert-info\" role=\"alert\" style=\"margin-top: 10px\">\n", "<p><strong>Note</strong><p>\n", "\n", "<p>in a fresh notebook, the dashboard will only show cells with non-empty output. All other cells can be found in the *Hidden* section at the bottom of the dashboard layout page. You can quickly add all cell outputs or remove all cell outputs from the dashboard using the show / hide icons that appear in the notebook toolbar when you are in layout mode.</p>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Toree - Scala", "language": "scala", "name": "toree_scala" }, "language_info": { "name": "scala" }, "urth": { "dashboard": { "cellMargin": 10.0, "defaultCellHeight": 20.0, "layoutStrategy": "packed", "maxColumns": 12.0 } } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

IgorWang/MachineLearningPracticer

basic/Tree-Based Methods.ipynb

2

2650485

gpl-3.0

shngli/Data-Mining-Python

Google Scholar network/google scholar.ipynb

1

938395

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Google Scholar Visualization\n", "Credit: chengjun's scholarNetwork script " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize Justin Wolfers' Google Scholar network" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from scholarNetwork import scholarNetwork\n", "import matplotlib.pyplot as plt\n", "import networkx as nx" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set the scholar\n", "scholar = 'https://scholar.google.com/citations?user=x6fNSxcAAAAJ&hl=en&oi=ASCII'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of nodes\n", "nodes = 52" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n", "52\n", "183\n", "more than 52 people now, break\n" ] } ], "source": [ "# Get the graph g\n", "g = scholarNetwork.getGraph(scholar, nodes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the network" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PHnZlkpKS5O+//9aU0TvwYlBQkNx5552aNlsPhlWRtmzZIoWFher00KFDPVre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\nk4ULF6o35QoKCuTBBx+UJUu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6dW+6zwUFBaSmpvLUU0+RnZ3N1atX\nSU5OZvny5cyePRuLxUJWVtbtHhMRERERkf8oBblFRERE/kZBQUH4+PiQnJyM1Xr9V7GYmBjatGlD\naGgop0+ftrWNiYnB09MTDw+PEn1kZGTQsWNHateuzbJly9i9ezePP/44AJ6enlSoUAFPT0/s7e2p\nUKECAGknTvDBT+cX/fTfS5cu0bt3b7p06QJcrxlujCEgIAB3d3dCQ0NZt24dAQEBeHp6AjBhwgSW\nLFlCamoqcL2+c3GJi+Jgdl5eHk5OTrRo0YJOnTrRunVrLBYL48aNY+7cubaxAWbPnk3lypV5++23\nb1qr7OxsW83qU6dOsX79ep5++mkcHBywWCysWrWKCRMm0LNnT15//fXr11ZUVCLI3bhxY7Zs2WKr\ni21nZ2crBeLr68sHH1xflUOHDtGsWTO2bt1KVFQUubm5NG7cmPyCAvoD9sBeYA9w6epVW/8XL15k\n6NChwPUvMAD8/f1JTk6mdu3avPHGG0ydOtW2RjeWRbkxkF/8XlFREbt27WL27Nls27YNgHvuuYfD\nyclEf/IJxhhCQkLIycnBxcUFZ2dnLBYLVquVTz/9lMTERD766CMCAwNtXx6MGTOG+fPn28b5+ZcS\nN45fpUoV6tSpQ0ZGBg0aNGDatGm89tpr/7F63D+vUS8iIiIicisKcouIiIj8Dunp6VitVnbv3n3H\n52zcuBGr1Up4eDiHDx8ucTwyMpJWrVqVCHJv3LiR0qVL39RPQkIC+fn5TJ06lSZNmhAYGMiFCxd+\ncewxb7zBNa5nQC8BnnNwoKy/P8uXL6d9+/YAhISE4O7uTv369UlMTKRHjx50796d5OTkEn1NnTqV\nZs2aYbFY6N+/v20TxZFDhgDgB5SyWqlYsSIHDx7kzJkzAAwfPpwxY8ZgtVopKipi1KhRBAcHU65c\nOd5++22mT59O7969yczMtF1f/fr1+fLLLykqKmLFihXs2rWLl19+mZo1a2KxWJgyZQrdunXjjTfe\nwNHREXt7e65duwbA559/TnR0NCtWrOD48eNYLBZatGjB/v37ee2113jhhRf497//TadOnQgLC6Nu\n3bq0b9+e2NhYZs+ezahRo7BYLMz7ad3aAo5WK76+vgBYrVYWLFjACy+8wMiRI9m3bx/ly5cnPDyc\nkJAQsrOz2bFjB+3ataNy5cp4eHjg5uaGv78//v7+VKpUybZBaLGTJ0+ycuVKLl++DFwP2p86dYrA\nwEBOnjwJXK99npeXx4wZM0oEzSMjI9m/fz/Z2dkcPnyYmTNnUlBQwJNPPsmcOXPw9/dnzJgxpKSk\nlAhaz5s3DycnJ6pVqwZA2bJlefTRR8nOzmbTpk1cunQJR0fHEnW9AcaNG4f1p+xyOzs7vL29b/m8\n/BE31qhv1aoVTz/99J/W9+/x5ZdfEhQUhIODA48//jibNm3CarVy/vx5gJvup4iIiIj8PRTkFhER\nkf83bpcVmpCQgNVq5dixY3fcV5UqVTh9+jT169e/43OaN2/O6dOnuf/++22Z0Dk5OWzfvp1WrVrR\nsmVLTp8+zZUrV+jYsSOnTp0iPT2dhQsXMmrUKFsgu3r16hQVFTF16lTS0tJYsmQJ3377LQA1a9bE\nw8ODp556iitXrhAREQFAjx49qFOnDgBrWrXi4xUrqF69OlarlczMTJYuXUpwcDDR0dFs3bqVYcOG\nERoaSq1atfjnP//JwYMHKSq6ngN+//338+qrr1K5cmUyMjIAmPOvf+GZl4cF+AHom51NZmoqGRkZ\nHDhwAGMMq1atYvTo0Xh4eFCuXDkmTpxIbGwsRUVF+Pj4MGHCBNq2bUu9evU4d+4cYWFhNG7cmNGj\nR9OoUSOCgoJYv349gC0Lvnbt2nTt2pUKFSrYgtxbtmwBrme8e3t7c99991GpUiUAW0D/6tWrzJo1\ni0mTJuHn54erqyuzZs2iYsWKeHh4sGjRIjIyMjDG4FGqFEUWCyne3rzw8su256RRo0Y88sgjfPbZ\nZzz11FO2zRufe+45Nm3axOuvv86RI0eIjo7mxIkTNGzY8LbPhjGGrKwsTpw4QaVKlRg8ePBN93ja\ntGm/+ozl5OQwbNgwNm3aRHp6Ojt37iQ2NpbatWsDMHToUDIzMxk6dChJSUm2ezJixAhbEPvEiROc\nPn3aNu6bb75JtWrVyM3NZejQoVStWhVnZ2cmT56Mi4sLn376KSdPnuSzzz7j+++/p1OnTr86zzt1\nYzB+xYoVvPPOO7+rn+IvpX7pZ/z48b/az8CBA+nZsyfHjh1j2rRphIeHc/r0acqUKfO75iUiIiIi\nfw4FuUVEROT/jRuzQv8oq9VKhQoVsLOzu+NzHBwcqFChAq1btyY/P99Wm7p8+fIEBAQQHh7OpUuX\n2LJlC8ePH8fZ2ZkmTZrQsmVLVq9eTXh4OBcvXqRu3bpMmzaN9957j9q1azNv3jwefvhhANauXcu+\nffsYP3487u7ufPrpp7bxe/XqBcCH0dE88MADxMfH06tXL0JDQ0lOTmbKlClER0fj6+vL+vXr6dCh\nA3v37mXOnDlkZmbSoEED3N3dqVevHg4ODixdutSWXXzuwgUeAizAfcCHwPa9e2nXrp2t1MnAgQPp\n06cPVqvVFhQsW7YsGzZsoLCwkJMnT3L+/Hm+/PJL7OzsiImJoXXr1vj4+LBixQpbzeob72OFChXY\nt28fcP3+li9fnrNnz9quNycnh6pVqzJo0CDgeu1pgO+//578/HyaN29u689qtdKsWTNq1arFlStX\nGDx4MADh4eGULVuWi9nZTJo0ybae//jHPwDo2LEj//73vxk8eDCLFy8mJCSEzz77jM8//5y6devy\n8ssvU6VKFerVq3fbZ2PZsmVcvnwZe3t7oqOjadu2LQ4ODrz00kvUrFmTefPmMWDAAIwxbNy4kYYN\nG/Lkk0+SnZ3NyZMn2bBhA/Xq1aNcuXJ8+eWX9O/fnxo1atC9e3cCAwNJTU2lfPny1KxZk4CAALZs\n2UJISIjtnkyePJmPPvqInj17kpqayrJlywgODua1115jyJAhhIWFkZCQQEJCAvPmzSMlJYU+ffrg\n6emJMYZ77rmHiIgIBg0axPfff2/LbAb48MMPCQwMxMnJiaCgIObMmVPi2mfOnEn16tVxcXGhfPny\ntG/f3vaFisViwRjDtGnTqFOnDpUrV+bxxx8nOzvbdv6aNWto0aIFZcqUoWzZsrRv377EX0qkp6cT\nEBDA3LlzadGiBc7OzpQrV47y5ctz+vRp289zzz132/sD8OOPP3L+/HnatWuHt7c3Hh4ets+0iIiI\niPyH/e1bXYqIiIj8h0RFRZnOnTvfdDw+Pt5YLBaTkZFhjDEmJibGWCwWk5WVZWuTlpZmLBaL2bVr\n1y1fG2PM6tWrTXBwsHF2djYtWrQw0dHRt+3X39/fjBs3zowdO9Y8+uijxhhjioqKjIuLiwkICDC9\nevUy7du3t/WdmZlpXF1dzbBhw2zH/Pz8zOTJk40xxsyfP9+4u7ubli1bmuHDh9/y+nNyckzp0qXN\nkiVLzJkzZ4yDg4OJiYmx9d+yZUtTqlQp079/f7Ns2TKTkpJiwsPDzYABA2x9+Pv7mylTppTot1Wr\nVubBBx80FV1czAIwC8CUsbc3Tz/9tLly5YptbsUaNmxoXn311RJ9fPvtt8ZisZgrV6784jgjo/+4\nfAAAIABJREFURoywvbZYLCYqKspMnTrVFBUV3fK8nJwcs2rVKvPss8+aKlWqmDp16pirV6+affv2\nGYvFYo4ePVpijL59+5qHHnqoxP0aPXq0ycvLM8bc+r7fiZ/PvdhHH31knJ2dzbhx48yKFStMnTp1\nzKOPPmqSk5PN999/b1asWGG2b99eYj5NmjQxsbGxJjEx0dSpU8eEh4ebyMhIExcXZxISEkzVqlXN\nP/7xD9sYGzZsMIsXLzaHDx82ycnJZvjw4aZ06dIlnm+LxWIqVapkoqOjzffff29Gjx5tHB0dzbFj\nx4wxxixbtswAJiI01KxZs8YYY8zYsWNNnTp1bH2cOnXK9gxVqlTJuLq6msDAQGNvb29mzJhhUlJS\nzNNPP20AM3bsWBMWFmbs7e2NnZ2dmTdvnunevbtxcXExnp6eZuLEiaZjx46mWrVqxsvLyzz55JMm\nLCzMPPjgg6ZUqVLmnXfeMefPnzf9+/c3bm5uxtHR0YSHh5sVK1aYXr16mcDAwJvuWY0aNczKlStN\namqqCQ0NNVar1fa8/fwZvXG9s7KybP++8WfTpk03/b/i5/2cP3/ehIeHm/bt25urV6/+pmdGRERE\nRO6cMrlFRETk/xXzF22gd/z4cbp27cr999/Pvn37aNGiBQMffxxjDJs2bbqpfWRkJDExMcTExNCq\nVSsA9u7dS3Z2Nv7+/mzatMm2kSGAt7c3ffv2ZenSpbZjt8pM/6VsdScnJ3r27El0dDSffvop3t7e\ntrG9vb05f/48ffr0oW7duhw4cIA1a9Zw5MiRO7p+Pz8/Fn7xBV/ddx9ftW3LmIkTKVOmDLNmzeLi\nxYsl2tasWZOtW7eWOBYbG0vlypVxc3O7o/GKVatWjYsXL9621IyTkxMPPPAA7733HvHx8Rw8eJBt\n27ZRrVo1HB0diY2NtbUtLCxk+/bttk0q8/LybGM4ODj8pnndiWvXrrFlyxaKioqIiIjgwQcf5Nix\nY7Rt25bq1asTEBBAly5daNq0aYnz3njjDZo3b07dunV56qmn2L59O++99x5hYWGEhoYSFRVlK+sC\n15+1vn37EhwcTPXq1Zk+fTrOzs6sXr26RL/9+/enT58+BAQE8MYbb9hKv6xdu5ah/frhDLjs2kX/\nrl1Zu3YtAElJSbZa4z4+Phw4cICAgACWLFliK3FTfH2BgYF069YNgHfffZe3336bDz74ADc3N2Jj\nY9m1axdff/0127dvZ8+ePcTGxmKxWPDy8mLWrFm4ubnh5+dHz549Wb9+PY899hgbN24ktGZNWoSG\nUlRUxIgRI5g5cyZpaWnEx8eXuL6RI0fSsWNHqlWrRocOHSgqKrL9FcCvad68OQcPHgRg+fLlnD59\nmmbNmv3iOZmZmdx7771UqVKFr7/+GldX1zsaS0RERER+O/tfbyIiIiLyv2PNmjU3bQxXVFT0h8uY\n/Otf/8Lf359p06axdu1a5k6dygO5uXwBjHziCSpUqICTk5OtfWRkJNHR0VgsFubPnw/AkSNHsFgs\n7Nixg+zsbCIjIwGIi4ujf//+dOjQgfPnz5OVlUXZsmVvmoMxhry8PK5du1ZiA8sKFSrYalj369eP\n1q1bk5aWRu/evUucX716dWJjY4mKiuLgwYNMnjyZy5cvk56ezuXLl2+7oZ4xBmMM999/P/fff7/t\neHJyMsuWLWP37t228hNwvWZ1WFgYr7/+Or179yY+Pp733nvvV+stF49zo6pVq3L06FH27NmDn59f\nifcXLFhAYWEhjRs3tpVucXR0JCgoCDc3N4YMGcKLL75IuXLl8Pf3Z+rUqfzwww8MHToUgGPHjmGM\noWbNmr84r99r9+7dXLx4EXt7e9q0aQNcD8QOGjSIhQsX0qZNG3r06EFwcHCJ824se1JcKqNu3bol\njhWXbAE4e/Ysr776Khs3buTMmTMUFhaSnZ3N8ePHb9uvnZ2drfTLZ/PmMSknBw/gCeByTg59Hn6Y\n4Fq18PX1JSYmhtzcXObOncuUKVOYMWOGrRb8hQsXqFevHh9++CEzZsyw9W+1Wmnbti1NmzZlxowZ\nzJs3j4iICE6ePEmTJk2YO3eurY56rVq1Snw+vb292bx5M0eOHKGUgwOdjh1jORBnsVBkDL6+vhQV\nFXHs2DHCw8NveX3FJXRuXKdf4uDgQPny5QEoU6bMr5YoSU1N5b777qNDhw7MnDnzjsYQERERkd9P\nmdwiIiLy/0rLli3Zt29fiZ9PPvnkD2d4JyUl2TJuZ0+ZwsTsbIb+9N7o3FxmT5lSon1kZCT5+flU\nrFiRgICAEu/l5eXh5eVFaGgocD3jNyUlhfz8fADs7W/OU7BYLFy7do1t27Yxd+5cfHx88PHxwdfX\nl/T0dFu7Fi1aUKlSJZKSkujXr1+JPt577z1bzfCxY8fSt29f6tSpw48//si0adPYvHnzLa/9dtnj\nwcHBDBo0CDs7O4qKiti2bRvGmFvWrB49ejTDhg27/QLfZhxXV1d69uxJlSpVbG2KlS5dmrlz53Lv\nvfdSt25dvvjiC5YvX46fnx8AEydO5OGHH2bAgAGEhITYstcrVqwIQFpaGhaLhWrVqt00j9/K09PT\ntnEoXM/8d3d3JzQ0lNKlS9uOjx07lkOHDtG1a1e2bdtGvXr1bF+CFLsxq7x4LjfWhrdYLCW+VIiK\nimLXrl28//77bN++nb1791KpUiVbpvqt+oXrX/4cOHCAjJ+en+5AJvAMULFcOY4fP86xY8f49NNP\nqVmzJk2aNAGgd+/euLu74+Hhwfnz59m/fz9Hjx695Vju7u4sXLgQuB6Efuedd6hRowYXL160bZT6\n8+fdYrFcr8ltDFPy8pgHeAKjjKGUhwd9+/bF3t7+F6+veN1uXKc/S15eHi1atKBTp04KcIuIiIj8\nTZTJLSIiIv+vuLi43BRUvnGTPMCW9Xxj4Ls4wHw7xRvk3U5GRgZxcXHA9c0PK1WqdFOArXr16gAs\nXLiQPn362I63atWKwsJCnnjiCcqXL4+Xl5dtnsVjRkVFERUVRfPmzQkNDWX69Om3nUtaWtotj1ep\nUoVvv/22xLGXX36Z48ePs2rVKmJiYhg+fDidO3cu0SYmJua2Y1WsWJGaNWsSGxvLt99+y9mzZ+nc\nuTPdunWzla640zn+fJwb12/nzp03ndelSxe6dOly2zEcHR2ZOnUqU6dOvem9goICXF1d+eSTT2xB\nbwB/f38KCwtv2+ftBAcH88033wDXN73My8ujQYMGvPfee9SoUaNE28DAQEaMGMGIESMYOnQoc+bM\nYcCAAb95zGJbt27lgw8+oEOHDgCcOXOGU6dO3bJtXl4eBw4cIC4ujsuXL3P8+HHu7dSJF2bNgtxc\nAD52cWHhjBls376d6dOnM27cOJ5//nnbX0RYrVYmTpxIhw4d6NWrFwEBAUybNq3EODdmx9vZ2WGx\nWHj++ed5//33qVChAqtWrbI92xaLhfz8fFv2+dGjR7l48SIGuAIkA7OA9J/6ys/Pt20weqdu/CwV\n+7XP/O04ODhw//33s2rVKp5//nnbFzAiIiIi8tdRJreIiIjIzxSXJcjMzLQd27t37y+eU7NmTVug\n9cnnnuNFFxeKczjfcnAgrHVrDh06hDGG999/n+joaBISEkpk94aEhFCrVi2mTJlyU8AtMzOT6Oho\noqKiSszzxjnm5ORw+PDh33XNv6Ry5coMHjyYrl27UlhYyP79+7l06dKvnnf16lXi4+NZsmQJDz30\nELVr12bfvn0sWLCAa9eu/alzrFixYokSLX+Uvb09rVq1on379n9Kf0OGDOHo0aP07duXw4cPY7Va\nmTp1KkuXLuX5558Hrt+/YcOGsWnTJtLT09m5cyexsbHUrl37D41dvXp1Fi1aRFJSEvHx8TzyyCM4\nOjra3i9+1uLi4nj33Xf5+uuvuXDhAk5OTkRERPD+++/z8ZdfXq+3ft99LPziC1tZGicnJwoKCsjJ\nySEkJAS4Xr/6448/JiAggFdffZUVK1bwxRdfkJKSwvLlywEYMWIEACtXrmTt2rXY2dmxatUqoqOj\nuXz5Mn5+fiQmJnLt2jUyMjKYMGECZ86cITMzk7y8PFxdXbFYLLzm4IA78BIw0tGRqzk5xMbG3vKv\nHX5J+fLluXbtGpcvX7Yd+7XP/O1YLBYWLFhAREQEkZGRN5WFEREREZE/n4LcIiIiIj8TGBhI5cqV\nGTduHCkpKaxbt45evXr94jlPPfUU6enpPPPMM/j7+zNo5EhWOztjgPfnzuVf//qXrXyCn58faWlp\nrFq1imnTpjF9+nTWrVtHWloac+bMISUlhS5durBjxw5bFnXbtm0JDg5m7NixtjFbt25NdHQ0mzZt\n4uDBgzz++OO/K8v4TlgsFurXr88zzzxDly5dSE9PJyEh4RezXd99910eeOABGjVqxPjx4+nRowet\nWrXixIkTfPjhh5w7d+5Pm5+fnx8ZGRl/Wn/Xrl3D09OTUqVK/Sn9Va1alY8++oiTJ08ycOBAmjRp\nwr///W+WLVtmCxjb2dlx4cIFHnvsMWrUqEH37t0JDw/nvffes/Vzq1Ipv7b56Lx587hy5QqhoaH0\n6dOHQYMG4e/vT0FBAXFxccyYMQNjDGlpafj4+PDQQw8xatQoXFxcbBuBNmrUiB8LCugeFYW3tzdp\naWkcOnSIc+fO0bZtW9zd3alevTp9+/blyJEjJCQksGTJEnx9fencuTNvvvkmtWvX5osvvgCgXbt2\nwPWSMmvWrMHe3p4RI0bw8ssv06VLF5577jlyc3PJyckhPz+fGjVqULZsWerXr094eDjlypWjS5cu\neHp7U7V6dfa7uPBjfj6FhYVMnz69RP37263bjZo2bYqbmxujR48mNTWVzz///A+XGlm4cCHh4eG0\natVKgW4RERGRv5jF/NEClCIiIiJ3iQEDBpCVlcVXX31lO3bmzBmeffZZlixZgpOTE15eXgQGBtK0\naVO+++47UlJSCAkJYdu2bVitVuLj42nYsCHp6elUq1bN9hrgm2++YeTIkWRkZNCoUSOGDBlCv379\nSE9Pp0qVKjz22GMsXLiQrKwsPD09OXbsGCkpKSQlJXHx4kXgegaxk5MT3333Hbt27eL8+fMYY3jo\noYdYuHAhLi4utrlfvnyZwYMH88033+Dh4cGYMWP49NNPqVu37m3LlWzcuJHWrVtz7tw5ypQp84fW\nMzs7m8TERLy8vAgODr7jWtWHDh1i+fLleHl5MXz48D+86WexXbt2Ubdu3RJZyr/Xvn37CA4OxtnZ\n+Q/3ZYwhLi6OqlWr/uqGhX81YwzHjx8nPj6epKQkCgsLcXFxITQ0lIYNG5aoD36jvLw8Xn/9db79\n9ltSU1PJzc3F19eXBx98kFdeecX2ZUBBQQFvvfUWH3/8MSdOnKBMmTI0adKEsWPHEhISwsaNG2nT\npg1nz54lPz+ftLQ0UlNTSU1NZcWKFSQlJeHs7EzHjh35/vvvqVOnDnPnzsVisRAZGVni2b5w4QLP\nPPMMX331FTk5OURERDBt2rQ72ii0eIPMG2uFf/XVVzz//PMcP36cli1b0q9fP/r3788PP/xAmTJl\nOHfuHBUrViQmJoZ7770XwHY9xW0WLFjA008/bftLh6KiIqKiotixYwcxMTG2zTRFRERE5M+lILeI\niIj8zxo3bhyff/45+/fvv+X76enpNG/enFKlSjF27Fjq1auHi4sLBw4cYM6cOfTq1YvevXsD12v2\nLlu2jO7du9/x+AkJCTRu3NgW5P6l+fz444+kpqZy+PBhMjIybBnZZcqUYcuWLaxZs4Z169bZNrf8\nvf7MIHexc+fOkZycTLVq1bjnnnvu6JzMzEzOnTtHfn4+DRs2/FMC3VevXuXo0aPUrVv3D/cVFxdH\n48aN/3A/xRtu1q1b11ZL/T/h2rVr7Nu3j7i4OFuJnICAABo3bkxQUJCtDv1fxRhDVlaWLaidnp5u\n+8sGe3t7qlSpQmBgIFWrVqVixYrk5eXh5+fHiy++yLPPPvuXzk1ERERE7n7aeFJERET+MsUB1dtp\n1aoVGzZsuOP+fk+g+ZcMGTIEe3t7EhISSmRI+/n50bFjx5vaZ2Vl0bNnT1atWkV2drYtMOvm5oav\nry+1a9fm5MmTJCYm4uLiQnh4+C+Ov3//fp555hkSEhIoKiqiWrVqvP/++/Tu3ZuMjAw+//xzJk+e\nbKu73TYykjrVqvHokCH079+fzp07U7t2bby8vPjoo4+wWq3079+fSZMm/eag8fjx4/nss89uCsA3\nb96cRo0aMW3aNIqKinjrrbeYPXs2P/zwA9WrV+fNN9/kwQcfpFy5cmzatAkfHx8WL17M/Pnz2bZt\nG/7+/kybNo22bduW6NfHxwcfHx9+/PFHtm7dStOmTX9zHeWfc3Nz+1NqfV+7dq3E8/B75efns337\ndho1aoSrq+sf7u/3OHv2LBs3biQ5OZmioiLc3Nxo2bIlDRs2xNPT8y8b1xjDhQsXSEtL4/vvv+fo\n0aPk5OQA18uy+Pr6EhQURNWqVfH29iYxMZFDhw5Rvnx5Tp06xcSJE7l69SoPP/zwXzZHEREREfnf\noSC3iIiI/GWaN29+y80AV6xYwZAhQxg2bNh/YFbXZWVlsW7dOt555507DmiOHz+eiRMnMmLECFq2\nbImdnR1xcXGUKVOG3bt307t3b/Ly8pg0aRKNGzfm0UcfvWkDyRv16dOHkJAQ/vWvf2Fvb8/+/ftx\ndnbG3t4eZ2dnXn/9daKioggODua1UaOw5ORQ+uBBXnvmGXbs2MHJkydJSEhg6NChbN++nT179tCn\nTx9CQ0N55JFHftN6DBw4kDfeeIP4+HjCwsIASE5OZvv27cyaNQuAadOmMXnyZD788EMaNWrEokWL\n6N69O7t27aJ+/fr4+fkBMGbMGJ555hlmzJjB22+/zSOPPEJGRoatvvONSpcuTaNGjdi2bRsNGzbE\n3d39N8375ypUqMCZM2eoWLHi7+4jJSWF4ODgPzSP7Oxs4uPjadq06Z9SPuW3MsZw4sQJDh48yOHD\nhwkKCiIsLIyAgIC/PGt79erVHDx4kKtXrwLXv5zy9va2ZWr7+vre8guNqVOnkpycjL29PSEhIWze\nvBkfH5+/dK4iIiIi8r9BG0+KiIjIX8bBwYEKFSqU+MnKymLUqFGMGTOGHj16/KH+jx07Rrdu3fD0\n9MTT05MePXpw8uTJm9otXbqUatWq4enpSbdu3cjKyiI1NRVjDGvWrKFz585MmzaNSpUqYbVacXBw\nwN3dnSFDhpTop3///vTp04cqVapgsViwt7cnKSkJPz8/zp07h7OzMz179uStt96ifv36fPzxxwAM\nGzaMypUr89Zbb5GSksKCBQts83d2diY8PJxKlSrRpUsXWzmS+++/H4vFwsyZM9m8ahUDCgooBawB\nrAUFbPr2W4qKiihTpgwRERHk5+fz4IMPEhkZyfr163/zWvr6+tK+fXvmzZtnOzZv3jwaNWpkK/8x\nefJknn/+eR555BECAwN5/fXXadGiBZMnTy7R18svv8ygQYPIyspi0KBBnD9/nn379t12bGdnZyIi\nIjhw4ABnzpz5zXO/kb+/P+np6X+oj9zc3D9Ui/vy5cskJCTQvHnzvz3AnZ2dzZ49e4iLi8NqtdKu\nXTuee+45evfuTWBg4F8S4C4sLOTYsWPEx8cTHx/P8ePHcXd3p3nz5jz66KO89NJLDBo0iFatWuHn\n53fLAHeDBg2Ij4/n0qVLnD9/nvXr1xMSEvKnz1VERERE/jcpyC0iIiJ/mwsXLtClSxdat27N+PHj\n/1BfRUVFdOnShR9++IGNGzcSExNDZmYmXbt2LdEuPT2dzz77jC+//JJ169axZ88exowZU6LNli1b\nWLt2LbX8/AitVQtHR0cqVqxIbm5uiXb16tUr8bpUqVKcPXsWgKSkJOrXr89LL73ExYsX+e6772jW\nrBlWq5UKFSqwatUqhg0bRtmyZRk8eDAbNmxg5MiRzJ8/n4sXLxIVFUVycjIAFy9e5PDhw7aa0GfO\nnWM2MPSncR8Ezp4/z6VLl2jUqBEtWrSgUqVKHD58GAcHB44cOcLZs2d/MYv8Vp544gmWLl1Kbm4u\nhYWFLFq0iIEDBwJw6dIlTp06RfPmzUucExERwaFDh25aJzc3N5o1a0ZgYCBwPTv6l1itVpo2bcrZ\ns2dJTU39TfO+UfGXD/n5+b/r/Ozs7D8U4M7KyuLAgQNERERgZ2f3u/v5LYwxpKens3PnTlJSUqhV\nqxZNmjTB19cXq9V6ywz6P6KoqIjMzEwSEhKIj49n7969ODo60qhRI8LCwnjiiSd46qmnaNu2LQEB\nATg4OPyp44uIiIiI/JzKlYiIiMif6nZ1s4uKiujTpw+Ojo5ER0f/4XHWr1/P/v37OXr0KFWqVAHg\nk08+ITAwkA0bNthqgRcUFLBgwQI8PDwAePLJJ3nzzTcpLCzEYrFw8eJFnJyc2BUTw6SfagYPsbPj\n3LlzN43582CdxWKhqKjI9toYQ82aNQFIS0uztenevTv16tWjVKlSlClThnvvvZclS5bw0Ucf0bdv\nXwYOHMj69etZvnw5s2bNIicnBwcHB3x9fQG4mJ+P1d4eCgqwAF84ONC6bVvWrFnDyZMnmT59OmXK\nlCEwMBBHR0dbbeqEhASMMXh6ehIQEPCrWcUPPPAArq6uLFu2DE9PTy5evEifPn1+8RxjzE31v29c\np+KNKC9dusSOHTuoX7/+L5aHqVu3Lunp6ezZs4cGDRr8rg0pg4ODSU5Opk6dOr/53JSUFIKCgn7z\neXB9M81Tp07RtGnTP2UjzV9z9epVDh8+TEFBAX5+fjRp0uQvGccYw9mzZzlx4gRFRUVYLBa8vb1p\n2LDhLTPD/45rFxERERG5kYLcIiIi8quMMbRs2ZJSpUrx1Vdf2Y5fu3aNkJAQ2rRpw8yZMwE4ffo0\npUqVuqmPl19+mZ07dxIXF/enZJYmJSXh4+NjC3ADVK1aFR8fHw4dOmQLcvv5+dkC3ADe3t7Y29vz\n/vvvc+LECTZv3oy7szOTcnKI+qnN1MJC9l26xMGDB/nuu+8oV64cAIsWLeLpp5/mm2++uWk+tWrV\nYv78+Vy+fBm4Hujbtm0bhYWFbNiwgdGjR5Oamkpubi5HjhwhMjISgMDAQD744AMaNmxI3759mTNn\nDnl5eTRs2JCdO3dijOHo0aMUWK2MtloxRUXk2NuzadMmjDFUqlSJsLAwUlJSiIuLIy0tjezsbFav\nXk1QUBABAQF4eXmRlJREXl6eLVv8Vlne9vb2PPbYY8ybNw8vLy969OhhWztPT098fHyIjY21zR0g\nNjaW2rVr/+r98vX1pVGjRiQmJmJnZ0fdunVvWzrD398fDw8Ptm3bRtOmTX9zRrS7u7utHvRvlZOT\n87s2nUxPT+fy5cuEhob+rnHvVPHzcO7cOdzc3KhXr96fniltjCErK4vjx49TUFAAQMWKFWnQoMHf\nlp0uIiIiIvJbKMgtIiIiv8pisbBw4ULq1avH/PnzGTBgAAAvvvgixhimTJlia1uhQoWbzl+6dClT\npkzhm2++oVq1an/LfIvdKvvaGIObmxszZ86kdu3aZF24wA4gjOu/HBX/gnTo0CG2bt0KXA/8ffnl\nlwwYMIBVq1YBkJ+fz7lz57hw4QJ9+vRh7Nix9OrVC7he9mLw4MHUqVOHBQsWMH36dGJjY9mwYQMN\nGjTg9OnTDBs2jF69euHn50dwcDBr164lIiKC5cuXs3nzZtq3b8/QoUNtWfArV66ka9eujB49GoDe\nvXtzzz338MADDwDXa0Hv3r2bzMxMrl69ytatW9m6datt47+goCC8vLwA+Oyzz3B3d8fd3R1fX18c\nHByoX78+gwYNYsKECdjZ2fHtt9+WWLvnn3+e1157jaCgIBo2bMjixYuJjY3lgw8+uKP7Ym9vT8OG\nDbl8+TI7d+7knnvuoWrVqrdsW7ZsWRo2bEhsbCxhYWG4urre0RjFypUrxw8//ED58uXv+JzfW6rk\nyJEjALba5X+FS5cuceTIEYqKiqhateqf/jm6cOECGRkZ5OXlAdfXv06dOio1IiIiIiJ3BQW5RURE\n5I5UrVqVyZMn8+yzz9KmTRtSUlKYNWsWmzZtKpH5+vNyJQMGDGDhwoXY2dnx5JNP0qtXL8aPH4+T\nk5PtnA8//JB3332X48ePU6VKFV588UUGDRoEwEsvvcS+fftYvXo1AN999x0PPfQQo0ePJjMzk4yM\nDPr27UuHDh3o3bs3mZmZXLhwgdDQUPbt24fVauWVV15h7NixtoBddnY2I0aM4IMPPqBz585s3ryZ\nj86cYY4x2AO5gJeXF1arFWdnZ7p06cK4ceMICQnh3nvvJTk5GWMM58+fZ/HixTg5OWG1Wunfvz9T\npkzBGMOkSZN44IEH+PHHH2nUqBF9+/YlNTUVR0dHkpOTKV26NBcuXOCxxx7j1KlTODs7Y4zhnnvu\nISIigoiICFavXs3zzz9Pbm4uS5cuZejQoUyYMMF2Ha6uriUyaz08PChTpgzGGEaNGsWPP/5IWloa\n33//PUePHuXkyZO2TRmHDh3KjSwWC5s3byY4OJiWLVty/PhxWrZsWaLN008/zeXLl3nhhRc4c+YM\nNWrUYPny5SWCu3dSqsLDw4NmzZpx8uRJtm7dSs2aNSlTpsxN7VxcXIiIiGDnzp0EBQX9poB1QEAA\ncXFxv+mc31OqZP/+/Xh4eODv7/+bzrsTRUVFpKSkcOHCBTw8PGjQoMEtN238PS5fvkx6ejo5P5Xo\nKVWqFDVq1CjxuRQRERERuVsoyC0iIiJ3bPDgwXzxxRf069ePjIwMnnvuOcLDw2/b/ty5c6xYsYJm\nzZoxffp0Dh8+zIsvvkh+fj4vvPACANu3b2fEiBFUrVqVxx9/nNq1azN06FDuueceOnXqRGRkJDNn\nzrTVvj5w4ACenp4s+fhj3F1d6dChA6mpqQwYMIC+ffsSGBjIpEmTmD59Ort37+brr7+E0dr0AAAg\nAElEQVRm2bJl5Obm8u6779rmVhyMdXFxoVGjRgwfPpzZP2Wku1SowL59+3j55ZcZOHAgcXFxVKtW\njS1btuDq6kp6ejoTJkygRo0aBAcHExwcTGJiIitXrqSgoABvb28GDx4MwNq1a/niiy/w9vbG39+f\nGjVqsHbtWkJCQli8eLFtHleuXMHb25s5c+bw4YcfAtCiRQt27NjBunXr6NSpE+7u7iQnJ2Nvb8+B\nAwdo3LgxEydOLLHm8+fPt/27dOnSlC5dmoYNG2KM4YcffiAtLY22bduSkZFBfn4+9vb2dOvWjapV\nq+Lg4MCxY8dIS0ujQ4cOpKSk4O/vbwuqWywWXnnlFV555ZVb3m9/f38KCwtvOn5j3fIb+fr64uPj\nQ1JSEikpKTRo0OCmIKudnR3NmjUjMTGRy5cvExAQcMu+fs5isWBnZ0dBQcEdB4Z/S6kSYwy7du3C\n19cXb2/vOzrnTv3444+2jToDAwMJDg7+w31eu3aN9PR0WxkXDw8PAgMDf1dpFhERERGR/zYWc6uC\njCIiIiK3kZ6eTkBAAEFBQRw4cOCmcgY3ZnIvXLiQAQMG2EqEFCverNBisdC0aVNq1qzJ+vXriYyM\nZN68eQwYMIDU1FS2bNnClStXKFOmDJs2baJ58+Z4eHhQdO0a7gUF/8fencdFVe9/HH/NsMyw77LJ\nopBrLom5hBpukWlZmmZaQiaWmuJuSu4/UxPX0sxMMNMsxSWXtFJxyd2U3DU2wY1NEGaGgWHO7w/u\nnMsIqKl1W77Px+M+ZOacOcuXA3Q/53PeX8YC7ysUlEoSDg4OdO7cmczMTF566SViYmKYNm0aCQkJ\nTJ8+nTfffJPCwkLi4+MZOHAgQ4YMYfHixbz11lvk5uaaZY2bPvfrr78SFhbG/v372bNnD2FhYWZj\nYDonW1tbatasSbt27cjKykKv17NkyRJycnJITU1l7ty5nDt3DktLS5o2bUpJSQnZ2dkMHDgQV1dX\nPD09qVu3LvPmzWPjxo3cuHGjUvHxxx9/ZMaMGZw8eRJLS0vq1q1LZGRkpY7sB2U0Grl58yYFBQX4\n+vqSnp7OjRs32L17N5988gnJycmoVCrS0tLkYnhgYGCVHdePQ0lJCUlJSajVaho2bFhlXndKSgqF\nhYU0btz4gTrGCwsLuXr16gNlhut0Oq5cuULjxo3vu64kSRw5coQ6derg5uZ23/UfRFlZGZcvX+bO\nnTs4OzsTHBz8SPnXxcXFpKenc+fOHaD8Gg0ICMDe3v6xHK8gCIIgCIIg/JWIIrcgCIIgCL/LpEmT\nWLBgAZIk8csvv1TqMr07rmTDhg0sXLiQ5ORkioqKKCsrw2g0yjEJbm5uxMbGyjnfACtWrGD8+PHk\n5uYC0Lp1a9q3b8/s2bNRKpUsLStjBJACDAISbWz4YOpU7O3tGTlyJFDeAWwqhEqShF6vJzMzE29v\nb9q3b0+jRo1YvHjxPc/13LlzhISEYGVlxZgxY5gyZYrZ8qq2ExkZSW5uLlu3bq20Pb1eT05Ojvy/\nrKwssrKyKCgoIDAwkGXLluHl5cUHH3yAq6sr7u7u2NnZPVBB93FQKpV4eHgwffp0WrZsSWlpKVCe\ns+7p6UlmZiZ5eXkAuLq6EhgY+NjiM0wKCgo4d+4cNWvWNJtU1CQ7O5srV67QsmXLByoCHz16lJYt\nW953vV9//ZUnnnjivp3NBoOBw4cP07RpU7MJTR9Wbm4uycnJKJVKs8z036ukpISMjAz5+6NSqQgI\nCHjo7QmCIAiCIAjC34mIKxEEQRAE4YEdP36cOXPmsHXrVpYuXUpERASHDh2qsusW4MiRI7z++utM\nnTqV559/HmdnZ7Zs2cKYMWPuuy9TYddoNNK0aVNWr16No6MjaktLVLm5tAT2ApcAVycnPDw8KCws\nRJIknn32WRo0aFBpm8uWLcPGxobMzEwsLS3ZvHkzjo6O2Nvb4+DgYPYvQP/+/XnllVfo3bs3ffr0\noXv37jRt2lTenlKp5O5+AVNhuCoqlQpfX198fX3N3s/NzSUxMZFjx47x66+/UrduXfLy8sjIyJDj\nJUzs7e1xd3fHxcXlkTp9q1JVrIgkSWRlZXH+/HnKyspQKpX4+PhgbW3Nr7/+isFgwMrKisDAQFxc\nXB75GJycnHjmmWe4evUqP//8M08++aRZodbDwwM7OzsOHjxIixYt7luUdnNzIycnB3d393uu9yBR\nJXq9nqNHj9KiRYuHmqDSxGAwcOnSJYqKinBzc6N58+bV/gzdaxuZmZlkZ2cD5ROs+vv7U7t27T/t\npoggCIIgCIIg/FWIIrcgCIIgCA+kuLiY/v3789ZbbxEeHk7Tpk1p2LAhH330Ee+//36Vn/n555/x\n9fUlJiZGfs808aFJ/fr1OXjwoFkn98GDB+WIiatXr7Jy5UoMBgNhYWEEBQUx/quvaK3TsRi4DMwf\nN07+/LfffouPjw/Tp0+nqKiIwsJC+d/CwkLy8/NRKBRotVqSkpKqPd/9+/eTnJxMZGQkkiTRpk0b\nevXqxbfffourqyv29va4ublx/fp1s88lJSU9cG60SUhICPn5+cyaNUsuznt4eFSaNFGSJDQaDTk5\nOVy9etWsKG1paYm7uztubm6PNWdZoVDg6emJp6cnUF4Iv3HjBmlpaUiShKWlJZ6ennKHNYC7uzv+\n/v6P1OXt7++Pn58f586d4/LlyzRp0gRra2ugPHqjTZs2HDlyhHr16t0zMqR27dqcOHHinkXu4uLi\n+xatNRoNJ0+epHXr1pUieh5UVlYWqampctzM74kOKSsr4/r169y6dQtJkrCwsMDPz4+AgABR1BYE\nQRAEQRD+9USRWxAEQRCEBzJhwgRKSkqYP38+AJ6enixZsoSIiAheeumlKjun69aty7Vr11i7di2t\nWrVi165drFu3zmydsWPH0qtXL0JCQujcuTM7d+5k7dq1bNq0CSifzDAvLw9nZ2cOHDjA3Llz6dmz\nJzNjYjj6yy/YqNUMGzZM3t7kyZPp1q0bgYGB9OrVC0tLS9LS0uQudICVK1fSqFEjJk2ahFarlQvg\npmL46dOn2bdvH4MGDaK4uJizZ8/y9NNPc/z4caKjo+nYsSNQnuO8a9cuBg4cSEBAADdu3CAzM/N3\nF7nvLvxXR6FQYG9vj729PYGBgWbLSktL5egLUxSMiZOTE+7u7jg5Of3ujuG7KZVKs250U0dxQUEB\nCoVCLgCburytra0JDAzE2dn5d+9LoVDw5JNPotfrOX36NPb29tSvX1+eVPKZZ57h9OnTFBYWVhqP\niserVCrvOQHllStXCA4OrvY48vPzOXfuHG3atPnd41dSUsLFixfR6XTUqFGDFi1aPFBR2mg0cuvW\nLa5du4YkSXIHfUhIiChqC4IgCIIgCMJdRCa3IAiCIAj3tX//fjp27Mju3btp166d2bLevXuTlpbG\nkSNH5IJixUzuiRMnsmLFCnQ6HeHh4XTq1ImhQ4dSVlYmb+Ozzz4jNjaW9PR0AgMDGT9+PG+//bbZ\nflq3bs2lS5fIzc1FoVBQXFyMi4sLbdu25YcffjBb936TNN4rk1uv1xMSEkLLli354osvgPIOap1O\nx6ZNm3jrrbeIj4/H39+f/Px8Pv74Yw4dOoQkSbzxxhtcu3aNgoIC+WaAiZWVFQ4ODnIcilqt/lOK\nlZIkUVBQQE5ODgUFBWbxKiqVSu7+NnVJPyq9Xs/Vq1fJz88Hys9boVCg1+uB8g51f3//h4paycvL\n48KFCwQGBppFvvz222/odDoaNWpU5efu3LlDZmZmlTdiAI4dO0aLFi2qXJaVlUVaWhpPP/307/p+\nXb9+nYyMDKysrKhXrx62trb3XF+SJLKzs8nMzKSsrAyFQoGXlxfe3t6PPZZGEARBEARBEP5pRJFb\nEARBEAThT1BSUmIWm3J3t7WJpaWl3K3t4OCAra3tH1YMLy4uJjc3l5ycHLMscYVCIU98aW9v/0j7\n12q1pKWlodFo5AK70WhEqVRibW1N7dq1cXR0/F3bTE1N5caNGzRq1Eie/PHWrVukpKTQsmXLKrut\nq5uAsri4mEuXLtGkSZNKyzIzM8nJyTHLYb+X4uJiLl68SHFxMT4+Pvj5+VU7dpIkybnrprH38PCg\nZs2aj30yT0EQBEEQBEH4pxNFbkEQBEH4G0pLS5Ozhps1a/a/PhzhMTIYDGZZ4lqtttLkllAew2Fn\nZyd3htvb2z9yFIlJWVkZ+fn55OTkUFhYaLbMzs5OnvjyYYqxd+7cIT09neLiYgwGA3q9HpVKhYWF\nBTVq1MDPz++BOpeNRiNnzpzBYDDQpEkTLC0tKSoq4uTJk7Rs2bJSxvaVK1dwdXWtlN995swZgoKC\nKnVaJycno9frq+3+NpEkiYyMDK5fv45araZevXrV5nsXFBSQnp4ud7W7urri5+f32LroBUEQBEEQ\nBOHfShS5BUEQBOEvJDExkQ4dOlS7PCwsjD179vyti9xarZannnqKjh07snTpUrNlkyZNIi4ujrNn\nzz5UhvO/SVlZGRqNRi6IazQas4koTRQKBba2tmbF8IftFJYkCa1WS05ODnl5eWaRMxYWFri5ueHu\n7n7faI6K27t9+zZXr16VO90NBgNOTk6o1Wpq164td2pXR6fTkZSUhIuLC3Xq1KGsrIwjR47QsGFD\nXFxc5PWMRiMnTpyoFEtSVVTJ+fPnUalUBAUFVbtfrVbLpUuXKCkpwc/PD29v70pd20VFRaSlpaHT\n6YDybHR/f//7TnIpCIIgCIIgCMLvI56FFARBEIS/kNDQUG7evFnp/c2bNzN48GCGDh36px/T1KlT\nSUhI4MyZM49le7a2tqxevZo2bdrQo0cPOnXqBMCJEyf46KOP2LZt2/+swP13unlgYWGBo6PjfaM+\nTIXpwsJCcnJySE1NNStOV2RjYyPHpNjb21fqMFYoFNjZ2WFnZ0dAQIDZMoPBQG5uLmlpaWi1WrNl\njo6OuLu74+zsbNZtbopFcXV1lY81KyuLa9euodFo2LdvHyqVCkdHR7y9valZs6b8+Yrfq1atWpGd\nnc3PP/9McHAwoaGh/PLLLxQXF9O2bVv5+6lQKCgrK5M7xYuLi1GpVGbHevr0adzd3alZs2aVY5mW\nlkZWVhY2NjY0bNjQbIy0Wi3p6ekUFRXRsmVLFixYQFRU1AMX/QVBEARBEARBeDiik1sQBEEQ/uIu\nXLhAy5YtGTFiBNOnTwf+W+DbsGEDn376KYcOHSIwMJBFixbJRWMo70gdO3YsBw4cwMbGho4dO1JW\nVsb69esZMGCAXJjs1KkTc+fOJScnB71eT5cuXdixYwdffvklI0eO5Pz583h6esrb7devH0VFRWzZ\nsqXKInh8fDzDhg2rFHVR0eTJk4mPj+fs2bOoVCqaNWtG+/btmT59OkOHDuXgwYPk5uZSu3ZtxowZ\nQ2RkpPzZsLAwGjZsiJOTE59//jlKpZL+/fvz0Ucf3Tc/OjAwkGHDhjF69OhKy4xGIzk5Obi5uf3r\nJvuTJIni4mI5JqWwsNAsp7sia2truRDu4OCASqW6Z/a0qcB++/Zts+gVa2treeJLU7H5fk8zNGnS\nhPj4eCwtLWnUqJHZDQlJkkhOTiY7O5vGjRuTkZFBVlYWoaGhWFhYUFBQwPXr16lfvz5Qfg3Onz+f\n/Px8LCwsOHbsGL6+vtSpU4egoCD5mi4qKuKnn36iR48erF+/np49ewLlk2ymp6dTUFAAlN8kCAgI\nwMHBgaysLJydnUUUiSAIgiAIgiD8CUQntyAIgiD8heXn59O9e3c6dOggF7griomJITY2lmXLljFj\nxgz69OlDeno6dnZ23Lhxg3bt2hEVFcX8+fMpLS1l4sSJHD58GD8/P7799lteeuklDhw4gI+PD7t2\n7aJdu3aUlJSQlpYGQO/evRkxYgT79+/H0dGR5fPmUVpayg+HD7N+/fpHOrfJkyezY8cOhg8fjoeH\nB2VlZcTGxpKXl0fz5s2ZMGECjo6O/Pjjj7zzzjv4+/ubFT/XrFnDiBEjOHz4MKdOnaJv376EhITQ\np0+fe+5XoVBUW5BVKpXUqFHjkc7r70qhUGBjY4ONjc19x0Cv18uF8Fu3bt13Ek0HBwe8vLyoVauW\n2djr9Xpyc3Pl2A8AtVrNrl27cHJywtXVFQcHBxQKhfw0w6BBg9BoNJw7dw6AkydP4uHhga+vL0ql\nkuDgYGrVqsWvv/4KQFBQECdOnODpp5/GycmJixcvyvtv3LgxWq2Ww4cPo1QqadiwIWfOnMHZ2Znf\nfvuNY8eOIUkSdnZ23Lx5E7VaTcOGDTl+/DgAKpUKf39/6tSpU+nc/63XkSAIgiAIgiD8L4gityAI\ngiD8RRmNRvr27Yu1tTVr1qypcp1Ro0bRtWtXAD788EO+/PJLkpKSeOaZZ/j0009p2rQps2bNktdf\ntWoVrq6utGnTBnd3d9LS0nBycmLZsmV89913ODk54ebmRnZ2NlBecAwODiYiIgJHYI5OxwxAD3z3\n3XcMHjyY3Nxc1Go1Go0GW1tbFAoFer2e4uJiHBwccHBwYNSoUSQmJuLh4UFcXBzTp09n/fr1fPvt\ntzRr1gxJkti/fz8dO3akefPmLFq0iOPHjzNo0CBOnTqFJEn07duXzZs306pVKwAaNmzIgAEDiI6O\nZvfu3UiSxIQJE2jbti2+vr4PNeZ/p7iS/yWVSoVKpao0iePdSktLKSoqMsumrm4STUdHR7k73MbG\nhjt37pCTk0NGRgapqamMGjWKt956i3bt2uHu7i4XkfV6PS+//DLnzp2jRo0ajB8/nsjISJ566inO\nnz+Pn58fX3/9NXq9npYtW+Ls7Mzt27extbUlODgYHx8fVq1axeLFi7G1tWXnzp00bdqUa9eu8euv\nv9K5c2eysrLYtGkTjRo1wsPDgzp16hAbG8vy5cu5fv06wcHBjB8/nn79+pmd04YNG+jRo8fjHXxB\nEARBEARBECoRRW5BEARB+IuaOHEiR48e5dixY9jZ2VW5TuPGjeWvvb29AcjKygLKO1z3799f5cR9\nWq2Wt99+m2nTpslZxStXrmTAgAFs2LDBLBc8JCSE48eP8yEQAYym/D8gNn7zDX0jIti3bx9nz56l\ne/futG3bFiiPKzEYDLz66qs4OjqyevVqLl++TMOGDZkzZw56vZ5z587xSng4tlZW2Dk7s2nTJg4f\nPkxoaCgvvvgihw8fpqioCKPRiNFopKSkhI4dO/Lhhx9y8+ZNvL29adu2LWq1mnHjxpGQkMCVK1d4\n9tlnmT17NkqlEqVSSbNmzfDz87tvjInw+FlZWeHi4mI2AWRVysrK5GL4tWvX0Gq18iSahYWFjBkz\nhubNmzN48GAMBgOZmZny0wbz5s0jOjqa2rVrExcXR0xMDO7u7iiVSjlGxNnZGUmS2Lt3L0VFRSyb\nOxcLCwsGjhxJgwYNSE5OJiMjg7y8PL7//nteeOEFvLy8+OGHH3jppZdo3rw5Z86cYciQIbi5uRET\nE8PGjRtZunQpdevW5dChQ0RFReHi4sILL7zwh47pP8m9ooP+af5N5yoIgiAIgvC/IIrcgiAIgvAX\ntG7dOubNm8eOHTsICgqqdj0rKyv5a1MR11QclCSJbt26ERsba/aZsWPHotPp6Nu3L8OHD0ev13Pz\n5k127drFkiVL2LBhg9n6np6eKJVKDhiNdAJyAWfgyeBgunXrRn5+PqmpqeTm5tKsWTM0Gg1Xr17F\nysqKrl27YjQaCQkJYciQIdjZ2eHj40NOTg6WgE9GBu7AsaIifvzxR/z9/bly5Qp79+7l1VdfxcvL\nC2tra7Zu3Sp39h47dgyDwUB2djaZmZmMGzcOS0tLFAoFNWrUICUlhe3bt1O7dm0kSUKhUMiFf4CS\nkhIyMjI4ceJEpfG8fv06UJ5lbhrHvwulUolCoZD/re7r+y3/Pes+yLYehIWFBU5OTjg5OZm9bzQa\n6datGw4ODmzfvh0bGxt5Es07d+4A0LdvX9q0aUNZWRl9+/Zly5Yt/PbbbzRu3Fgucv/888888cQT\nnD9/nhXz5xP7n2iUoSdP0qZrV7Zv305mZiY2NjZcuHCBb7/9ltTUVKKjo6lRowYXL17k5s2bdOjQ\nAY1Gw4IFC/jxxx8JDQ0FICAggKNHj7JkyZK/VZE7OzubKVOm8P3333Pjxg2cnZ158sknef/99+Vs\n/z+yOPt7rpHHrbrzio2NZcmSJaSmpj7wth5kHP+X5yoIgiAIgvBvIIrcgiAIgvAXc/r0aQYOHMic\nOXPo3LnzQ2+nWbNmfPvtt/j7+2Np+d8/+Q4ODpSWluLs7Iy/vz8ZGRmsWrWK9u3bU7NmzSq35eLi\nwpa8PIokCUdAq1QycdYswsPDSUlJISEhAaPRyIsvvkhSUhJGoxG1Ws2rr74qb2PFihUEBQXx5ptv\n0vO55xgiSawCugGFksSvSUn0ef11kpKSaNKkCY0bN2bLli1cunRJLlIrFAp0Oh0WFhaUlJTg5ORE\nQEAAUB6hYW9vj5OTExqNRo7SyMzMJDMzE/jv5Irp6ekcPnxYfs8kJydH/h7k5OQQHBzMk08+KS+v\nuG5VXz/q8kdZV5IkjEYjBoNBfl3x/Xu9V3EbptcVb5ZUt96DrPsovvrqKw4cOMDs2bPZuXOn2TLT\nNWFvb096ejrw3xs9t2/fpqCggNzcXAC5Q3zn+vXElpQQYdpISQlxGRmUlpZiaWmJXq/Hw8OD4OBg\nvL29SU5O5tatW+zduxdbW1tatmzJL7/8QnFxMeHh4WZFy9LSUmrVqvVI5/tn69mzJ8XFxaxcuZLg\n4GBu3brFvn375HED/rGF2cdZdH6QcfwjGAwGs9/tgiAIgiAI/2biv4oEQRAE4S8kJyeHl19+mbCw\nMPr162cWG2Li5eX1QNsaOnQon3/+Oa+99hrjx4/H3d2dlJQUDh8+LHeHP/HEE+zbt4+4uDhmzJhR\n7bY8PT3RarX8pNfj4uhI/bp1CQ8PB6B9+/bodDpu3bpFcnIymzZtqnIbdxc8GwO2QDpwh/LIirp1\n63L16lVOnz5NZmYmer0eV1dXbt++jZeXF4WFheh0OsrKyuRiql6vR6FQyLEmpn1V7MQ2FbNMhS2F\nQoGFhYXZ+1BeKDf9a2Njg62tLXZ2dmbFsPt9/TjX/bO39b8mSRIGg4Hi4mK+/vprtm3bxueff07z\n5s0pKSkxi64xTXZpbW0NlJ+H6RorKSnBYDDITzr4+/vTrFkz9m3bBsnJZvt0c3EhICCAxMREJEki\nLCwMADs7O0JCQkhMTCQxMZG2bdtiYWEhX1fbtm3D39/fbFsVn6z4q8vPz+fgwYP89NNPtG/fHgA/\nPz+aN28urxMWFkZ6ejpjx45l7NixKBQKysrKADh06BATJkzgxIkTuLi48NJLLzFnzhw5HiksLIz6\n9etjbW3N6tWrAeSbdxWvOZ1OxzvvvMO6detwdHQkOjqaMWPGyMsLCgoYO3YsW7ZsQafT0axZM+bN\nm0dISAhQHo00bNgwtmzZwvDhw0lLS6NFixasXLmSwMBAAHbt2sXyefMAGDR6tPy7688axwc916tX\nr8pzDAB07tyZxYsXy3MMTJ06lYSEBEaPHs2MGTNIT0/nzp07lJaW3nOMBEEQBEEQ/g1EkVsQBEEQ\n/kK2b9/O1atXycjIkDO2K6pYZLpfcdLb25uff/6ZCRMm8Pzzz1NcXIy/v79ZcdfHxwcLCwtyc3N5\n+eWXq92WUqmkT58+bNy4kS7duskxEAD16tWjW7du7Nq1iyZNmtClSxezYiCUZ4CfPXuWJ554Aigv\nNEUcPMjTOh0HKC9yd+jQgZiYGIYOHcrbb7/Nxo0bcXBwIDIyksLCQpKSkjh+/DitW7emuLgYJycn\ntm3bxssvv0xAQACnTp3i6tWrXLhwgSFDhsgFp7vFxsYSGhrKkCFDKi1LS0tj0qRJ9OzZU0w8+RDK\nysrkSUeLi4vNvq543ZqK0aavTZ8zFaevXLnCuHHjGDhwILVr16awsFCOpLG0tMTKygp7e3sA3N3d\nycnJYfDgwXJxsEWLFvTr14/09HTGjBlDu3btuHPnDrsOHeKkWg3FxVwA5gDrBw3CaccONm3aRFJS\nEosWLZLPJywsjN27d7Nv3z451qJBgwaoVCrS0tLkgvj/SmRkJLm5uWzduvV3f9be3h57e3u2bNlC\naGiofIMnMTGRDh06kJOTw6ZNm6hbty7Z2dlcvHhRzlY/c+YM4eHhTJ8+nZUrV5Kbm8uIESMYMGAA\n69evl/exZs0a3nrrLY4cOUJSUhJRUVF4e3szcuRIoPymxoIFC5g+fTrjx49nx44dDB8+nDZt2tCq\nVSskSaJr1664uLiwfft2XF1diY+Pp0OHDly6dEm+4afX65k9ezbx8fGoVCoiIiJ499132blzJ7t2\n7SLilVeYo9MBEHHwIKv+cyPuUZ80uNc43u1+52o0GunevTt2dnbyDZf33nuPl19+mePHj8vbSU1N\nZd26dSQkJGBtbY21tTXPPffcfcdIEARBEAThn04UuQVBEAThLyQiIoKIiIj7rhcYGCgXDSu6O0c6\nODjYrOgE/y2MAcTFxfHxxx8D/+1Cbdq0KX5+fpW2fePGDV577TX0en2l4lBISAhpaWmcOXMGgMGD\nB7Nz50727NmDl5cX//d//yfnYwOEh4ezatMmFvzf/1Fw8CCWlpZMmjQJKJ8kMCEhgebNm+Ps7ExE\nRARFRUWMGzdO7trdu3cvUB7J0q9fPxYtWsTQoUMZNmwYISEh1Ra4obzYdO3aNU6fPm32flXn/G9i\n6oqvqkhd8p8M66oK1BW/trCwQKVSoVKp5O56U2HadL1WFbeiVqtxd3fHzs6O4uJi3n33XcLCwhg0\naBB5eXnMmDFDLmBbWFjg7e1NWFgYkiTRqFEjOZ+7SZMmANja2la6CRQaGsrNmzc5deoUS2bP5vyl\nSyj+k7Ot0+n46quvsLa2plu3bvJnnn32WXr16kVRUZF8TTk4ODBmzBjGjBmDJMpcypEAACAASURB\nVEm0bduWoqIijhw5goWFBVFRUY/8vYiPj2fAgAHya3d3d5o2bcqMGTNo2bKl/P7HH3/80IVaS0tL\n4uPjiYqKYvny5Tz11FOEhoZSu3ZteR0XFxc5X93DwwNXV1cA5s6dy2uvvSYXq4OCgli6dCnNmjUj\nJyeHV199lStXruDj4yPfNKhTpw6XL19m/vz58ueg/HeB6YbTe++9x+LFi9m9ezetWrVi7969JCUl\nkZ2djVqtBmD69Ols3bqV1atXM3bsWKA8tmPJkiXyTbQxY8bI47d83jzm6HT/jajR6Vg+bx6SJBET\nE8PUqVPNxqW0tBQfH59HHsdevXrRokULs3Xvda67d+/mzJkzpKSkyE8IrF27luDgYPbs2UOHDh2A\n8qcUVq9ejYeHBwB79ux5oDESBEEQBEH4pxNFbkEQBEH4l7k7i9bUEVvd8uLiYgoLC/nxxx/59ddf\nmTt3bqUC4t2fiY2NRaPR8NJLL+Hg4MCIESPIysqSizBQXvAJDw+nQ4cOZGRk8Oyzz5ptc+XKlQwa\nNIiQkBB8fX2ZOnWqnJltYoooMBUgO3fuLBft73X+CxYsYMGCBWbvf/LJJ3Tt2vUvFd/xoCrGfNzd\nQV3xpkTFwrSJ6T2FQiEXqNVqNba2tri4uKBWq7GyskKSJLRaLUVFRWg0GjQajVz8Nm2zrKwMrVaL\nVqvFxsYGOzs7nJycsLe3R61WVxrb0tJS8vLyyM3N5fbt29y+fZtt27bJTzPs2rXLbPumfcyZM4e3\n3377gcbGtE8rKytq1KhBeHg4nTt3ZuDAgaTExwPlkTsGgwE/Pz+zIm+bNm3kpwYqRj/MmDEDT09P\nYmNjGTx4MI6Ojjz11FOMGzfugY7pQdja2pKSkoIkSfIEq126dCEjIwM7OzsAORrkYfXo0YOuXbty\n4MABDh8+zM6dO4mNjb3vz8DJkydJTk7mm2++kd8zXUPJFeJgWrVqZfa5Vq1aMWnSJIqKirC3t0eh\nUNC4cWOzdXx8fMjOzpb3o9Vq5YKuSXFxMSkpKfJrlUolF7ih/CmWkpIS8vPzqz0HhULB6NGjK11H\nK1as4Ouvv77n+d+tqnGcN28eM2fOZMKECfL+7nWuFy5cwMfHxywCp1atWvj4+HD+/Hm5yF2zZk2z\n8ahujPR6vdkYCYIgCIIg/NOJIrcgCIIg/MvExcX9ruXr1q0jPz+fWbNm0aBBgyo/P2XKFKZMmSK/\ntrOz48svv+TLL78EygsuCxYsoGvXrpU+e/PmTbOuVZPGjRtz5MgRs/f69etn9trPz6/aDPDqpKam\n3nN5VR3yf7SHjfkwfQ3lHaVqtRq1Wo1KpcLd3R21Wo21tTVKpbLK/RoMBrOitUajobi42CyOxkSp\nVGJra4u9vT2urq74+fnJnfX3YjQaKSgo4Nq1axQUFJgVrC0tLXF1dSUgIEDuvm7evHml7tqqYjkS\nExPZtm0bzZo1IzExESifMPTpp5/mzTffZNasWSxfvlwev4oxHK6urnKhGMoLh6Zu2by8PDnyYdiw\nYWzcuJFRo0Zha2tL69at+eKLL6hVqxbvvfceOTk5JCQkEB0dTVRUFImJibzwwgusWLHC7KmKuLg4\n5s6dS2pqKv7+/gwePJjo6Gh++OEHZsbEcODkSdavX282UatCoaBGjRpAeSb+iBEj6N69O5cuXZKj\ndEzj0rNnT4YNG8bNmzcZPHgwmzZtwsHBgVGjRpGYmIiHh4f8c3v79m1GjBjB1q1bKS4uJjQ0lEWL\nFjFp0iQmTZpE165d2bFjBwaDodrvqWli19u3b+Pk5ETHjh0ZN24c9vb2TJkyhf379yNJEqtWreLL\nL78kLS3NrHh74MABunbtip+fX6Ucc9MTAKZrx9PTk4MHD1Y6BkdHR7PryCQ2NpbY2Fj581GjRvFG\nYiKUlgIw3saGVaNH8+677+Lm5mZ2UwOQu9V/L5VKRadOnejUqROTJk0iKiqKqVOnMnbsWPn47nWu\n91LxpkPF6xYefIwEQRAEQRD+6USRWxAEQRD+YKZiWWFh4d9yn2lpab/7M6dPn+b8+fO0aNGCwsJC\n5syZg0aj4bXXXpPXyc7OZsOGDVy9epV33nnnkY/zf+VeMR+lpaXVdk5X/NoU82EqUjs6OlKjRg3U\najUWFha/+1g0Gg137tzh+vXr6HS6aiMtLC0tsbe3x87ODi8vL+zs7H7X/iruV6PRkJubS15entmN\nAqVSiZOTE+7u7gQFBT10p/zdn1OpVHInucnEiRP56KOP8PLyIjo6mn79+nH+/Pkqt+fp6Sl3wFdH\nr9czffp0FixYwJo1a9i0aRNBQUH4+PjQuHFjXFxcSEtLY+3atUD5zZ61a9cyYMAAtmzZAsDnn3/O\nlClT+OSTTwgJCeHMmTNERUVx5coVEuLimKnT0Qt4r39/HBwcqpwUMT8/n7Vr1+Li4kJISAgJCQn0\n6NFDfoKiT58+dOvWjdGjR7N//342b96Mt7c3M2bM4ODBg/To0UPeVmRkJFeuXOG7777D2dmZmJgY\nnn/+eS5fvoxarSYgIABAntizYmFWqVRWupZ0Oh1r1qxhzZo1SJLEkiVLSElJISUlBTs7Ow4dOoS7\nuzsAR44cwdfXFxsbm2rHvKJmzZpx69YtFAoFtWrVqnY9jUbD/PnzGTVqVKVlQUFBdHntNeIvXMDV\n1ZV+jRrx6quvysf0uNSqVYthw4bJx1C/fn356Yq7n5Yx/X6uODll/fr1uX79Ounp6fL3ICUlhevX\nr9OgQYNq9xsSEnLPMVIqlWzYsMHsGhAEQRAEQfgnEkVuQRAEQXhEkZGRcseypaUlfn5+9OjRg2nT\npmFraysXoP4twsLC8PLyIjk5mUuXLmFpaclTTz3F5MmTqVmzpty56OnpiYeHB5999tlDd08+qoox\nHxWL1PeK+bg78uN+MR+PGn9iNBrR6XRoNBq561qv11e7vlqtxs7ODjs7O9zd3bGxsXlsESx6vZ7c\n3Fxyc3MrHYO9vT1ubm74+vqaddY+LhXH/NixY6xZs4bnnnvObJ0ZM2bIsTeTJ0+mTZs2XL9+vcqM\nZdM1l5OTI3dM381gMPD+++8zaNAgnJycmD9/PsOGDWPmzJkUFRUxceJEdDodX375Jf7+/tSrV4/P\nPvuMtm3bkpycTFBQEDNmzGDu3LlykTEgIIDx48cz+YMPWKjTYXqGwfE/WdGmIrdGo8HBwUGOiXni\niSdITEykSZMm8lhIkoQkSajVagwGA3FxcaxevZqOHTsC8MUXX1CzZk35fK5cucLWrVvZv38/9evX\np1evXrz++uvs3buXhQsXEhQUxLp164D/xhh5eXmRmZnJjRs3OHfuHFOmTEGj0bB371569+5Nhw4d\neOeddyguLiYiIoJVq1aRnJzMrVu3gPLoEF9fX5o2bcr+/fuZPHnyfb/PGRkZKJVKcnJyCA0NpXv3\n7nz00UfUrVuXmzdvsnPnTjp37kybNm2A8miXwYMHV7mtn376ifr167N8+XJsbGyI/09EzYP65JNP\niImJqfKGYatWrdi6dSu9evVi/PjxNGvWjNTUVE6cOMFHH31Ep06dKhW47z4+0/eyc+fONG7cWJ5j\nQJKkB5pjoFOnTvccI0EQBEEQhH8LUeQWBEEQhEekUCjo3Lkzq1evprS0lP379zNw4EC0Wi1LliyR\nu3P/LUwxC6ZimcmGDRvMXt/rMf3q4jv8/PzMikaPGvMhSRJWVla/O+bjURkMBrOIkKKiompjUhQK\nBba2ttjZ2eHs7Iyvry/W1tZ/WHa4wWDg9u3b5ObmUlRUZLZMpVLh5uZGnTp1UKlUf8j+q7Nz504c\nHBwwGAyUlpby8ssvV8pfr5h57O3tDUBWVlaVRW4XFxcAORO5Kkqlks8//xylUsmJEyewtbVl1qxZ\nFBYW8t5775GZmcnatWvlQnJubq7c0RwaGsqUKVPIzMxk0KBBvPPOO2g0GrkDXZIkEoHaQAfAdCYF\nBQUsX74cKJ9ksEaNGnTr1o3NmzfLxc5evXoB5dEVYWFhxMfHM2TIEEpLS2nRogXJycmMGjWKY8eO\nUVBQwHfffcf27dspKytDqVTSunVrateuTa1ateQC7oQJE3B3d6dz585mk9W+9dZbnDhxgpCQEEpL\nS6lXrx7JyclYWFjw1VdfsWrVKnndil3fpqcT9Ho9mZmZZGRk0LRpU0aMGMH+/fvl81u4cCGTJk2i\nQYMGLF++3Czf39/fn2+++Yb+/fvzwgsvyN/XZ599lsjISEpKStixYwdarZYaNWrQo0cPuZvZlA+e\nlZWFRqMhJCSE1NRUnJycKP1PdEnF7/Mnn3zCTz/9xPbt2806zXNzc7lz5w7PPfccq1evNvuctbW1\nHGOzcuVKJk6ciF6vx9fXlzfeeIMPPvig2mvLdIwVf47vN8fA3eub7Nixgw8++ICoqCiysrLw9PSk\nTZs2REZG3nP/D8tgMPwhN7L+6G0LgiAIgvDPJv4LQhAEQRAekSRJWFtby92gr7/+OomJiWzevJkl\nS5ZUig6ZOnUqCQkJxMTEEBMTQ3Z2Nh07dmTFihW4ubkB5f9Hf+zYsaxatQqFQsGAAQPQaDRcuHCB\nvXv3yvnCd3v22WfZu3ev/HrPnj0MGDCAzMxMXB0dmbtgAREREQBmhbDCwkLq1q3L9OnTzXKzq8vv\nvdfj86bjLygoMCs+p6enA+V5vMXFxWzfvp3PPvuMTz/9VF7vwoULfP7554wdOxZbW9tK223evDme\nnp73jflQqVR/aqFEkiRKSkrMitZarbbamBALCwvs7Oywt7enRo0a1KpV608/3oKCAnJzc8nPzzc7\nTgsLC1xdXalZsyZ2dnZ/mYk4n332WZYvX46VlRU+Pj5VxqpULLKajru6mykPUuQG2LVrFx9++GGV\n1+Pdhf7p06czc+ZMtmzZQtu2bYmOjpbfLykpYeLEiVhbW9OxY0eUSiXbd+3C5z8F12lqNatHj+aD\nDz4gMzMTGxsbLl68SEpKCtnZ2TRo0EDu8P3iiy/o1q0b0dHRlW5EQHkXeNeuXfnwww+JjIzEaDTS\no0cPFi5caDY+Z8+eZfr06axcuRJnZ2f27NmDh4cHkiQR0bMn773/Pg0aNEChUHD9+nVcXV1p0KAB\nUVFRjBw5kmHDhnHmzBk2b96Ms7OzfDOhoKAAo9FIly5diI2NZfv27QwfPpxr166hVCr58ccfkSSJ\nrKwsnJycaNeuHTqdjn79+rFjxw45J1uj0dCtWzdefPFFZs6cyTPPPIOFhQU+Pj60bNkSJycnrl69\niqurK1FRUTg4ODB79mz59XPPPUdhYSEKhYLo6GiGDx/OggULWLx4MWPGjGHo0KHcvn0bSZIYOXIk\nCxcuZP78+WZjOWXKFFJTU8nLy6u243/mzJmsWbOGmJgYRo8eLY/B+++/z5YtW7h9+za1atUy66oH\nmDRpEsOHD8fe3p4WLVqwcuVKszkGtm7dyosvvsj58+fx9vamb9++nDx5Ul4eGBhIVFQUV69eZd26\ndTg6OjJz5kzGjBljtp8bN27QtWtXOZ995syZZvMbXLt2jdGjR/PDDz8A8Mwzz7Bw4UKCg4OB//7N\nGj16NDNmzCA9PZ07d+6QmZlJVFQUx44do1atWsyfP59XX32VJUuWyH9nHnbbVf28CYIgCIIg3Iso\ncguCIAjCY/AgecEVpaWlsX79erZs2UJRURF9+vQhJiaGZcuWAeWTp61atYovvviCJ598kiVLlrB2\n7Vp5wrnQ0FBu3rwpby8zM5NOnTqZPdau1+sZO3YshTdvMrWsjBW3bzNowAC8vLwIDw83K4TZ2Niw\nbt06evTowcmTJwkICECv19O3b19SUlJYtGgR1tbWfPLJJ4SFhckT+mm1WnQ6nVkGdVpaGlqt1qyg\nBnDu3DkkSWLPnj3yGBiNRrKzs7G2tsbGxkaOkGjatCkeHh5yd7WpiO3j41Np4rU/iiRJlWJCTDnF\nVbG2tpbzrf38/LC1tf3DOsEfhCnmwpSTXTF7WqFQ4OTkhJubG7Vq1fqfHueDsrGxqTRJ4KNQq9Uo\nFAoyMzOrXcdoNCJJEvXr1wfg6tWrXL9+XX4N5UU80zb69+9PUFAQRqORN954g61bt6JWq9m6dStN\nmzZFoVAQERHB5MmTcXd3lyeelE6eZOnq1YSHh7N06VICAwO5ffs2fn5++Pn5AVBYWCgXYJ2dneWb\nOaYit1KpxMrKimPHjtG7d28aN26MVqvlt99+45VXXsHa2prLly9jNBo5dOgQAOHh4bzxxhtMnDiR\nBQsWcPHiRb749FMUwPOJiUQcPcqouyYBbdasGWfPnuWbb75h//79HDp0iEaNGsnLk5OTKS4uxtra\nGm9vbwIDAxk6dCjz5s3jxo0bAPK1GB8fT7169Rg/fjwFBQVcvHgRS0tLEhIS6NmzJwCHDx+mYcOG\nODg44Ofnx4ULF1AoFBw8eJDXXnuNyMhIfvjhBzw8PBg1ahR79uzh4MGD7NmzhxdffJHvv/+egQMH\n8swzzxAQEEDTpk2xtrZm6dKlvPLKK3I8zKBBg6qMPDGp7oaVScUua0mSeOGFFygoKCA+Pp66deuy\ndu1aFnz4Id+sWEFgo0bo9Xpmz55NfHw8KpWKiIgI3n33XXbu3AmU31x54403WLx4Me3atSM9PZ13\n330XvV7P3Llz5f0uWLCA6dOnM378eHbs2MHw4cNp06YNrVq1kteZMmUKs2bNYtGiRXz77bf079+f\nevXqERISglarpX379rRp04b9+/djbW3N3Llz6dSpExcuXJC72lNTU1m3bh0JCQlYW1tjaWnJK6+8\ngo+PD0ePHkWr1RIdHU1JSYk8Dg+77T/7KRFBEARBEP4ZRJFbEARBEB6DB8kLrshgMBAfH4+DgwNQ\nXmCJi4uTly9atIj333+fV155BYCFCxfKxQ8o71g1dRXqdDoGDRpEhw4dzPJuDQYD7mo1w/V6IiiP\nRYgwGvlwwgR0Oh1arRYLCwsSExPR6/UolUpq1KhBdHQ07dq1Izc3lx9//JG33nqLlJQUANq0acPx\n48eJi4ujefPmWFlZyXnUpvxnOzs7Tp06xZkzZ8yK/0ajEaVSyfDhw1Gr1Xz99dfs3r2bsWPHyusk\nJiYyZ84cwsPD/5CcblNh3lS01mg0laILTBQKhXw+Dg4OeHt7o1Kp/jKdzSYlJSVyTvbdRXg7Ozvc\n3Nxo2LChWZez8F85OTnVLrOwsMBgMHDp0iVOnz7NyJEjefLJJ+XMaygvvkdERMidya+99hp16tTh\n9OnT2NjYULduXY4cOUKrVq2QJInmzZvz/fffc/36dd5//31UKhUdOnSQn8wYPHgw3bt3l5/mePHF\nF2nXrh0ODg4MHDiQefPmVTlZpumJj/Hjx2NnZ0dCQgIJCQkUFhbKMRtNmjShe/fuvPPOO+j1etzd\n3enVqxc2NjY4OTlxJy+PhkYjvwCvA/Y6HV98+63ZfsaPH8/TTz/Nvn37mDNnDjY2Nmzbto1t27ax\nbNkyfvnlF6D8uly2bBlxcXGUlZVRXFyMl5cXAF26dGHu3Lk899xzeHp6snTpUho0aIAkSeTk5Mhd\n9iqVyqxYa21tDUBMTAzOzs6kp6cTHR0tdwlDeda/Xq/n9ddfx2AwkJeXx8cff8ySJUsAKC0tRa/X\n89tvvxEaGkqXLl04d+4cLVq0uOd1YorLqei9995j1qxZldb96aefOHLkCOfPn6du3brs2rWLRTNm\nMEenA2B4YiIGg4ElS5bwxBNPADBmzBgGDBggb2PmzJmMGzdO7oiuVasWs2fP5s033zQrcoeHhzNk\nyBD5eBYvXszu3bvNxq1nz55ERUUB5ZOzmjLYV69eLcdKrVy5Ul5/2bJleHp6sm3bNjkap6SkhNWr\nV+Ph4QGUF+EvX77MTz/9JEcDLVy4kNDQUHk7D7ttQRAEQRCEhyGK3IIgCILwGDxIXnBFAQEBZgUT\nb29vsrKygPLH3G/dulWp6NKiRQsyMjLM3pMkicjISCRJqpQXq1KpsK/Q9ewNlAF5eXkkJSUhSRL7\n9u3j4sWL3LlzB6PRSGlpKY0aNaJdu3YcO3YMCwsLRo4cia2trdxNvW/fPoKCgpg0aVKV5/bZZ58R\nEhLClClTzN7//vvvGTZsmFzAelzdw6WlpWZFa41GU21EhYWFhZxv7e7uTkBAwN+i+FtWVkZ+fr6c\nD1yRtbU1rq6uBAUFmWUJ/5NUl0V89zr3e6+q16brp6rlKpWKsrIyPvroIyZPnkzr1q3ZuHEjUP6z\nd+fOHdzd3eUbTnFxcdStW1cuBMfFxdG7d29GjBjBzJkzARg+fDhNmjThvffeq/I8nn/+eebNm8fY\nsWPJycmha9eu9OrVi5UrVxIdHc28efM4cOAAvXv3rjQusbGxaDQaXn75ZSRJ4vXXX+fKlSsEBweT\nmppKQUEBo0aNYtq0aVy8eJFPPvmEwMBAevbsyZkzZ8rPC7h7JCvuQ6VSYWVlRc2aNfm///s/pk2b\nRu3ateWJNSv+7BkMBgwGAxYWFjz//PNycfby5ctIksTTTz9dKS6n4ufvjvC5XwQNwM2bN1EqlSiV\nSkJCQoDy30nPPPMMUD43wKRJk3BycqJbt258/PHHLFu27L5PiJjicipycnKqct1Tp07h7e1N3bp1\nAVg+bx5zdDoi/rP8QGkpcUqlXOCG8r8BJSUl5Ofn4+zszMmTJzl+/DizZ8+W1zEajRQXF3Pr1i05\ntqliDj2Aj49PpQie1q1bm71u1aoVO3bsAODkyZOkpqZWKuDrdDr55iZAzZo1zYrQFy9exMfHRy5w\nQ3mcVMXf6w+7bUEQBEEQhIchityCIAiC8Bg8SF5wRXcXVhUKxT0LN1D14/LTp0/n4MGDHD9+vFKB\n09LSkkGjRxNx8CDodFygvIA1cdYs+vTpw5AhQ0hPT2fp0qU88cQT2NjY0L9/f7y8vGjfvr2cId6g\nQQOz81Eqlfc9Pycnp0rREp6enmavlUplpXMydVXrdDpycnLkqJDi4uJq4wKsrKzkfGsfHx9sbW3v\ne3x/RZIkUVhYSG5uLrdv3za7HpRKJS4uLnh5efHEE0/85brJ/2gVn3KoSlhYWKWJOwMDA83eu3ud\nsLAwTp06xaZNm8jKyqpyGwqFgueff56kpCTS09NRqVSkpqby3XffcfHiRY4dO4ZWq6VFixZ88803\nLFmyhKioKLnQZ/o+9enTh1atWlG7dm327t0rxw5V57333pOL4M8//zx9+/bls88+w8/PD2tra9q1\na2c2LvHx8UB55/6XX37J6dOn6dKlC7169eK5557D19eXpKQktFote/bsoW3btpw6dYpu3brx7rvv\n4uPjg5eXFxs3buTCuXOsLCtjKzDexoZVM2fKkR537tyhe/fudOzYkc2bN1d57E899RRQHmuyfv36\nSr8TNRoN48aNA8q7eoODg8nOzqZt27b3HJOq1K9fn8OHD5u9d/XqVaD898ILL7yAj48Pv/32G2+8\n8QYANWrUkJdt377d7EmSe3nccTnKam7AmH7uJUli6tSpcrdzRe7u7vLXD/O35O79NW3alG+++abS\nOqYbksBDxUT9kdsWBEEQBEG4myhyC4IgCP8In332GaNHjyY/P1/u/ispKcHZ2ZmgoCC5SxHgt99+\no06dOuzevdssw/pRPM4CiJOTE15eXhw7doywsDCgvOBx/PhxeWI3KO9InDt3LomJiWbvVxQeHs6q\nTZtYPm8e2Xl5KH75hfDwcBQKBT///DMRERG88sorlJWVMWvWLI4ePcrRo0fZvHkzvr6+cn6vqQB1\n584dzp49y9tvv11pX6YM69LSUnm9innW+/btA8ozezUaDbdv30ar1bJv3z65yLFt2zagPK/b19cX\nOzs7PDw8sLGx+ccUdnU6nRwvUjEqRaFQ4ODggJubG/7+/n/LQv3fjenGy61bt6r9+Y2NjSUsLIw6\nderQtm1beWLGvLw8Tp8+jb+/PyNHjmT06NF4eHg88hMKkydPJiQkhAYNGmAwGNi4cSNBQUFyMTMw\nMJCffvqJtm3bolKpcHZ2pqioCKPRyKeffsovv/yCTqdjxYoVJCQkUFRUxPnz55EkCR8fH/r374+3\ntzfx8fE0adLErMvXxcWFzl278t1/YjVWjR4tF7gB+vXrh06nIzY21mxOABM3Nzfq1KlD7dq1uXDh\nAqdOneKpp57i1KlTJCYmEhQUhL+/v/w0QqtWrXB1deXs2bNm2zHFktxPdHQ0/fv3x97enuzsbGbN\nmkVmZiZGo5GaNWuiVquZNm0aw4YNw9nZmS5dupCZmUlZWRn16tXDaDTSvn37++ZtQ9VPClTnqaee\n4saNG1y8eJF69eoxaPRo3ty3D/4zT8N6Kyus7jPRbLNmzbhw4cJj+bty+PBhIiMj5ddHjhyRc+VD\nQkJYt24dbm5u1XamV6VevXpcv36dGzduyN3cJ06cMCuwP+y2BUEQBEEQHoYocguCIAj/CB06dECr\n1XL06FE5E/To0aM4Ozvz22+/kZOTI3e/7d27F7VabZYd+lcTHR3NRx99RJ06dahfvz6fffYZN2/e\nxNfXF4CzZ88SERHBrFmzqFmzplxwMkVXVBQeHk54eDiJiYly7i9AnTp12LhxIy+99BLLli2TJ0B7\n4YUXmDp1Kr/88guzZs0iKiqK2bNnY2Vlxdy5c1Gr1Xh4eLBx40YKCgrQaDRotVq52zozM5PS0lIS\nEhLMjiMvLw8oL6SZJtT74IMPWL9+PSNGjCApKYnvvvsOKO/Q/CMyuf8spaWl5OXlkZubi1arNVtm\nY2ODm5sb9evXf+BinvDHMP1OuH79eqVlGo0Gg8HA+vXr6d+/Pz///DN79+4lPz8fNzc3GjduTLdu\n3Th//vwD76+6QmnF99VqNTExMaSmpqJWq2ndujVbt24Fym8kTZs2jffff5+VK1fi7OzMyJEjOXbs\nGAaDgaSkJHbu3El2djYGg4H8/Hzc3d2Jiopi7969eHh4UKtWrXseX0BAIEfFGwAAIABJREFUAIsX\nL660LD09ne3bt6NQKKhTp06Vn927dy/t2rUjNDSUo0ePMm7cODIzM3F1daVly5Z07NiRmjVrYmVl\nRUlJCWlpaRw+fFiOPjKNQ0BAAFAe05OdnS1HDN1djO7duzcpKSnExMQwf/58evXqRfv27fnhhx+Y\nPXs2Xl5edOjQgREjRvDxxx8zYcIElEolpaWl1K5dm/Hjx9O/f39SUlLumc0OyDEhFY/BwsKiypiN\nTp060bJlS3r27MmCBQuwtLSkcdu2zLpyhYZ16jCwceNK0Sd3mzx5Mt26dSMgIIBevXphaWnJ2bNn\nOX78OHPmzKn2c5IkVRqnTZs28fTTT/Pss8+yYcMG9uzZw7Fjx4DyGxexsbF0796d6dOn4+fnR0ZG\nBt999x3vvvsuwcHBVe7nueeeo27dukRERBAbG4tWq2XUqFFYWlrK38eH3bYgCIIgCMJDkQRBEATh\nH8LX11eaMWOG/HratGnSG2+8IYWGhkrr16+X33/99delDh06SJIkSUajUZozZ44UFBQk2djYSI0a\nNZK++uored3U1FRJoVBICQkJUqdOnSRbW1upQYMG0o8//iivExkZKbVo0UKqU6eOpFarpbCwMGnd\nunWSQqGQ0tPTpbi4OEmtVkv29vaSJEnS1KlTpUaNGkl79+6VFAqFlJubK8XFxUn29vZSnz59pJo1\na0pqtVpydXWVbG1tJRcXF2nUqFFSZGSk5OrqKg0ZMkTq2rWrRHn6iARICoVCUigUUvv27aWbN29K\nTZs2lQApMDBQWrVqldSwYcP/Z+++w6K42j4A/2ZBlmVZOqxU6SgKFooFUVAURWON2BsG60usRCMW\nFGMsWEhsr0FBFLsodowKImIMNooCSpcgXaXXPd8ffDuvK6hYMXru69or7JRzzswOS3zmmeeQyZMn\nEw6HQ3Jzc0lxcTH566+/SI8ePQiPxyPS0tKkc+fOpEuXLsTOzo5s2bKFrF27lixevJh07NiRyMrK\nEoZhiJycHJkzZw7x9vYmq1atIk5OTkRTU5MEBgaSkJAQ0q9fP6KgoEC6dOlChEIhUVJSIhMmTCBl\nZWXk2LFjhMPhSHxmoaGhxNTUlPB4PDJgwABy4MABwuFwSFFR0Se5Rj6m+vp6UlRURB49ekRu375N\nYmJi2Ne9e/dIVlYWKS8vJyKRqKWHSr3Bpk2byNatWwkhDd8H2dnZ5NatWyQ6Opps3LiRnDx5kiQl\nJZGamprPOi6RSERevHhBEhMTyZUrV0hgYCD59ddfibe3N/tat24dCQoKIhEREeTRo0ektLT0s47x\ndaZMmUK+++67164/cuQIMTIyIrKysqRr164kLCyMcDgccu3aNXYbHx8foqmpSTgcDpk6dSohhBAH\nBwfi4eHRqD19fX2yadMmkpiYSLy9vcm5c+eIu7s7UVdXJ7KysqR9+/bs34CAgAAiEAjYfevr68mE\nCROIsbExefLkyWuPR/wd+/JLV1e30RjEnj9/Ttzd3YmamhqRlpYmQqGQ/dvy6hgIISQ8PLzRd9+l\nS5eIvb09kZOTIwoKCsTGxoZs3779tX02dY4YhiHbt28nAwYMIDwej7Rp04YEBQVJ7JOXl0emTp1K\nNDQ0CJfLJQYGBmTatGnsWMR/s1716NEj0qtXL8Llcknbtm3JmTNniIyMDDl69OgHt01RFEVRFPWu\nGEKa8XweRVEURf0LTJw4ETk5Obhy5QqAhuzuCRMmIC0tDc+ePcP27dsBNEzMNXv2bCxbtgxeXl4I\nCQmBn58fzMzMEB0dDXd3dxw9ehQuLi7IyMiAoaEhzMzM4Ovri7Zt28LHxwdnz55FZmYm+Hw+srKy\nYGJiAg8PD8yYMQNxcXFYsGABsrOzkZ6eDj09PQQGBsLDw4Otcw2AzawuLCyEiooKcnJy4O3tjbT4\neEhLS6OtlRV27tyJCxcuoE+fPujcuTMKCgpQVlaGefPmYcKECbh37x7GjRuHPXv2wMXFBeXl5Rg/\nfjwKCgowd+5c1NXVYffu3UhNTUWvXr3Qq1cv1NXVNTp3Bw4cQE1NDcaNGwc1NTXw+XwIBAIoKChA\nXl4efD4fv/76K8rKynDo0CHw+Xy2FMCJEyfYcjBTpkzBqVOnMHr0aCxYsABZWVlwdXXF4sWLsWTJ\nks9wFXxchBCUlZWxdbJfrtnM4XCgpKQEVVVVKCgofDXlVL41hw8fxqNHj9C/f39ISUlBU1MT2tra\nYBgGhJDP9rlWVVUhKysLOTk5ePLkCXJyclBVVcWu53K50NTUhK6uLrS0tKClpQWBQECvu/9HCMGO\nHTtQUlKCBQsWgMvltvSQQAjBvn37kJmZiUmTJr0xi/5rEBsbi86dO+POnTtsbXaKoiiKoqjPhZYr\noSiKor4aDg4O8PDwQG1tLerr63Hz5k34+/tDV1cXc+fOBQAkJSUhNzcXffr0QXl5ObZs2YI///yT\nLV3Spk0b3Lp1C9u3b4eLiwvb9oIFCzBo0CAAwNq1axEUFITY2Fj06NEDO3fuhLGxMXx9fQEAJiYm\nePToEby8vN5p/PHx8Th94ADWV1aiCID3rVswb98eGzZswJ49exAfHw8DAwPIy8vDwMAAYWFhqKio\nQJs2beDn54f09HQUFhbixo0b+OGHH1BQUAAAGDx4MFtmxNjYGAoKCuDz+exkjXw+H87OznBzc8OG\nDRvQrl07dO/eHS4uLvjuu+/Y8SkqKqKurk5i0rOmKCoqYteuXWAYBmZmZhg1ahSuXLnyRQe5q6qq\n2DrZNf9fN1dMXCdbR0eHrfdO/btVVFQgNTUVlZWV7ASoRkZGjUpPfI4AckVFBXJycvDo0SPExMQA\naCg71Lp1a4mAtqKiIg1ov0FSUhIKCwvh6Oj4RQS4ASA6OhqZmZmws7P7KgPcJ0+eBJ/Ph4mJCTIy\nMrBgwQJ06tSJBrgpiqIoimoR9F9qFEVR1FejT58+qKqqQnR0NEQiEdTV1WFoaAihUIjU1FTk5eUh\nPDwccnJy6Nq1K+7evYuqqip2Ikax2traRgEJS0tL9mfxJFv5+fkAGoIrNjY2Etvb2tq+8/j/6+sL\nu8pKbALwBEBZbS3u378PhmGgra2NcePG4fr161BSUkJeXh7k5OSgpqYGHR0dVFdXw8nJCTExMZCS\nkoK3tzcUFBQgJycHKSkpHDx4EFZWVhg9enSTfevo6ODBgwe4c+cOoqKiEBkZCVdXV/Tv3x9nz559\np+Caubm5xPaampq4devWO5+Pj62urg7Pnj1DUVERysrKJNZxuVyoqqrCzMzsiwmQUR9XcXEx0tPT\nUV9fDx6PByMjI8jJyUFZWRmJiYnIz89vsr7yx1RVVYWcnBwUFxezdZPl5OSgpaUFR0dH6OnpQUtL\nC8rKyjSg/Q4IIbhy5QpkZGTQtWvXlh4OALBPFbVu3fqjTXD8pSkrK8OSJUvw5MkTKCsrw9HREVu2\nbGnpYVEURVEU9Y2iQW6Koijqq2FgYIA2bdogIiIChBA4ODgAAPh8PqysrBAREYGIiAjY29tDSkoK\nIpEIAHD27Fno6elJtNWqVavXvhcHn8T7i8savIk4W/RltbW1Eu8fZWQgA8B/AVgACAPgq6KCNkZG\nOHLkCOTl5fH999/D0tISP/30E7vftWvXUFxcDDs7O3byNE1NTXA4nDeO6VUMw8Da2hrW1taYN28e\ngoODMXHiRFy/fh29evVq1jEAaJTtzDAMe64+NZFIhJKSEhQVFeH58+cS45WWloaysjJ0dHTA5/Np\nEPErRwjBP//8w04qqaysjI4dOza6PoVCIQAgLy8P7du3/2j9V1dXIzc3F4WFhexkgLKystDS0oKB\ngUGT11+HDh0+Wv/fkqSkJBQVFaFPnz5fxE2q6upqHDlyBFJSUnB1dYWUlFRLD+mTmDhxIiZOnNjS\nw6AoiqIoigJAg9wURVHUV8bR0RHh4eEghGDy5MnscgcHB1y5cgXXrl3DwoULATRkHHO5XGRkZLAB\n8ffRtm1bhIaGSiz7+++/Jd6rq6ujoqICpaWlEAgEAID79+9LbCOvqor69HTU1dfjHgBfWVnIKymx\n5UmAhmD5m4Kzbdu2hUgkwu3bt9ls8uzsbDbQ9y7atWsHAGzWs7q6OmJjYyW2EWeav+xTB48JIaio\nqEBRURGKi4slaowzDANFRUWoqqrCwMDgnQP91L9bbW0t0tPT8fz5czAMAy0tLdjY2LzxmhQIBJCW\nlkZWVtYH9Zubm4v8/Hz2xoqMjAw0NTXRuXNneh1+QoQQXL58GVwu972eoPkUzp07h5KSEgwfPhzK\nysotPRyKoiiKoqhvAg1yUxRFUV8VR0dHBAcHg2EYBAQEsMt79+6NUaNGoaysjH10XCAQYNGiRVi0\naBEIIbC3t0dZWRn++usvSElJwd3dvVl9zpw5E5s3b4anpyd++OEHPHjwALt37wbDMGxwrWvXruDz\n+fj5558xb948xMbGYseOHRLt9OzZE6mpqQgwMIBMq1awVVHB9evXJbLMxRmhrxIvMzMzg7OzM2bO\nnImdO3eCy+XC09MTPB7vjYG+77//Hj179kT37t3RunVrpKen4+eff0br1q3Ro0cPAEDfvn2xceNG\nBAQEwN7eHiEhIYiOjoaOjk6TY/lQNTU1bJ3slyfgAxqy81VVVdGhQwdaJ/sbV1FRgZSUFFRVVUFa\nWhoGBgYwNTVt9v4Mw0BFRQV5eXnN2r6urg55eXnIy8tjn1CQlpaGpqYmOnXq9NVm7X6pEhMTUVxc\n/MVkccfHxyM+Ph4dOnSQKHNFURRFURRFfVr0X4UURVHUV8XR0RG1tbXQ1dWFoaEhu7xnz56oqqqC\noqIirKys2OU+Pj4QCoXw9fXFrFmzoKCggM6dO0uUA3lbZrKenh5OnDiBBQsWYNu2bbC1tcWKFSsw\nbdo0yMrKAgBUVFQQHBwMT09P7N27F71798aaNWswadIktp1ly5YhPT0df/75J3g8HqZOnQo9PT0k\nJiZKjKWpzOmXlwUGBsLd3R0ODg4QCoVYtWoV0tPT2bE0ZcCAAThy5AjWrVuH58+fQ0NDAz179sTe\nvXuhpKQEAOjfvz9WrlwJLy8vVFRUYMKECZg9ezbOnDnzTuN7WX19PVsnu7S0VGKdjIwMVFVVYWRk\nBB6P99qxU9+eoqIiZGRkoL6+HnJycjAxMfmga0RPTw+3b99GVVWVxO9JfX098vPzkZubyz4xIC0t\nDaFQiI4dO9KAdgsT1+LmcrlfRC3uZ8+e4cyZM1BQUMDgwYNbejgURVEURVHfFIZ8rHQriqIoiqJY\nfn5+8Pb2xrNnz1p6KCgsLIS2tjYOHz6M4cOHf/b+CSESdbJfrs8tJSUFZWVlqKqqQl5entbJpppE\nCEF2djaePn0KoOGmkb6+/kfL4r9z5w7Onj2LoUOHAgAb0OZwONDQ0IBQKGxUp59qeQ8ePMDx48fR\nt29f9OzZs0XHUl9fD39/f+Tl5WHatGnQ1tZu0fFQFEVRFEV9a2gmN0VRFEV9BNu3b4eNjQ3U1dXx\n119/Yc2aNZgyZUqLjCU8PBwlJSWwsLBAfn4+vLy8oK6ujgEDBnzSfl+uk/3yhJQMw0BBQQGqqqrQ\n19en9YmpZqmtrUVaWhpevHgBhmGgo6Pz1vrazSUSiVBUVISnT5+ipqaGvRmVn58PBwcHyMjIfHAf\n1Kf1chb3l1CLOyIiArm5uejbty8NcFMURVEURbUAGuSmKIqiqI8gNTUVv/76K4qKiqCjo4NZs2Zh\nxYoVLTKW2tpaLF++HGlpaZCTk0P37t0RGRn5XuUcqqurUVxczNbGVlNTg5qaGoqKilBRUSGxLY/H\ng6qqKtq1a0eDhNR7KS8vR0pKCqqrq9GqVSsYGBjAzMzsg9okhKC4uBg5OTlsbXcOhwM1NTWYmZmB\ny+WipqYG0dHRqK6uptfuv8TDhw/x7NkzODk5tfhnlpGRgaioKOjp6cHOzq5Fx0JRFEVRFPWtouVK\nKIqiKOob93Jd7KKiIhQWFiI/Px/FxcWorKyU2FZPTw9OTk5QVVWFnJxcC42Y+poUFhYiMzMT9fX1\n4PP5MDIyemP9+DchhOD58+fIyclhb8KIJ5bU1NR8442eDRs2QF5eHrNnz36vvqnPRyQSYdu2baio\nqMCCBQtaNMhdUVGB7du3o76+HnPmzIFAIGixsVAURVEURX3LaCY3RVEURX0DXq6LLX7l5+ejsLAQ\nZWVlePWeN5/Ph7q6OoRCIVRVVdmXoqIiLTdCfRCRSITs7Gzk5uYCAFRVVdGpU6d3nsRRfE3n5OSg\nrKyMXa6srAwDA4N3vgmjqamJjIwMEEJobfgv3JeSxU0IwalTp1BRUYGxY8fSADdFURRFUVQLokFu\niqIoivrCREREoE+fPigsLISKikqj9wAQGhqKRYsWISMjAxMnTsTevXsRGhqKhQsXIjMzE4MGDYKH\nhwcKCgpQUFCAFy9eoL6+XqIfLpcLFRUVGBoaQk1NjQ1kq6iofLQJ/SgKAGpqapCWloaSkhIwDANd\nXd13rq9dWlqKnJwclJSUsMsUFRWhq6sLeXn5Dx6jrq4uWwNcSUnpg9ujPg2RSISrV69CVlYWNjY2\nLTqW27dv4/Hjx7C2toapqWmLjoWiKIqiKOpbR/8FS1EURX2RpkyZgqKiIpw5c+aT9hEUFAQ3Nzf4\n+/tLrFu8eDE2btyIQYMGvfMYNm/ejEWLFmHp0qVYs2bNB4/Tzs4Oubm5bIC7trYWbm5uGDFiBFxc\nXFBRUYFdu3Zh4cKF6NKlC4YNGwYZGRlERUVBSkoKSkpKMDU1hbq6ukRW9vuWhKCo5igrK0Nqaipb\nX9vQ0BBt27Zt1r7l5eXIycnB8+fP2WUCgQBaWlowNTX9JJnWGhoaAIC8vDwa5P6CibO4+/Xr16JZ\n3Pn5+bh48SJUVVXh7OzcYuOgKIqiKIqiGtAgN0VRFPVFYhjmk5cMEGeUHj16FL/99htb3qCurg5B\nQUHQ09N7rzHs2bMHXbt2RWBgIFavXv3e5T2aqpMtfl9cXIza2lrExcWBYRhIS0ujsrIS3333Hfr2\n7csGsuXl5WnpBeqzIISw9bVFIhH4fD7MzMzeejOloqICT58+RXFxMbuMz+dDS0sLxsbGn+36FQqF\nABqC3B862SX1aYhEIly5cqXFs7jr6upw9OhRMAyD0aNH0ydfKIqiKIqivgC0qCZFURT1RSKEsHWi\nRSIRfHx8oKurC1lZWVhaWuL06dPsthkZGeBwOAgJCUG/fv3A5/PRvn17XL58+a39WFpawsTEBEeP\nHmWXnTt3DjweDw4ODo1qVe/btw8WFhaQlZVF69atMWXKFIn1N2/eRFpaGo4fP47y8nJcuHBBYn1g\nYCBbt5UQgtLSUhw+fBgcDgfHjx/H/v37cezYMQDA7t27cfDgQezatQvfffcdnjx5goKCAmzYsAEM\nwyAoKAirV6+Gvb09li1bBgBYsmQJbGxskJWVBYFAgJs3b6J3797g8/nQ0dHB7NmzUVpa2sxPgaLe\nTCQSITMzE3///TdiYmJQVlaGzp07w9bWFu3bt28U4K6qqkJ6ejpu376NmJgYxMTEIC0tDcrKyrC2\ntoaNjQ1sbGxgbm4OJSWlz3qDRllZGRwOB9nZ2Z+tT+rdPHjwAM+fP0evXr3QqlWrFhtHWFgYioqK\nMGDAAKirq7fYOCiKoiiKoqj/oUFuiqIo6oslDnD5+fnB19cXGzduREJCAoYPH44RI0YgNjZWYnsv\nLy/MmzcPcXFxsLGxwZgxY1BeXv7WfqZNm4a9e/ey7/fu3Qs3N7dGAbb//ve/mDlzJqZNm4aEhASs\nWLECd2/exMj+/REWFgYA8Pf3x/fffw9tbW2MHz8e/v7+qKqqwj///IO4uDgkJiairq4O27Ztwy+/\n/ILNmzfj4sWLIIQgJiYG2dnZ4HK5AIBevXph1KhRGDJkCBiGgYeHB3755Rc8ePAAABASEoKnT5+i\nZ8+eEstyc3PRvXt3xMfHw9nZGcOGDUNcXBxCQkJw//59uLm5vecnQr3M29sbFhYWH7XNKVOm4Lvv\nvvuobX7svmpqapCYmIi///4bd+/eZbNqbW1tYWBgwE4gWV1djczMTImA9uPHjyEQCGBlZcUGtDt0\n6AAVFZUWf+KAw+FASUkJOTk5LToOqmkv1+K2trZusXEkJyfj9u3bMDU1hZWVVYuNg6IoiqIoipJE\nn62jKIqivni+vr7w9PTEmDFjAACrVq1CZGQkfH19sX//fna7BQsWYNCgQQCAtWvXIigoCLGxsejR\no0eT7RJCwDAMxo0bh0WLFiE1NRV8Ph9hYWHYvn07mx0t5uPjg/nz52PevHkICwvD6kWLsL6yEnj0\nCJOjorB+504cOXIE69atw6lTp6CiooJdu3Zh+fLl7MR4ycnJEIlEIITA2NgYGhoa0NbWRlBQEDw8\nPKCjo4OIiAhs3boV3bt3h4qKCvLz89kxtGrVis0cVFFRYesIN7Vs48aNGD16NObPnw8AMDIywo4d\nO9ClSxcUFhZCTU3twz6Yf7GCggKsXLkSFy5cwNOnT6GkpIQOHTpgyZIlcHJyarFx/f77742eHngf\nhBDs2bMHe/bswYMHD8AwDIyNjTFmzBjMnDkTAoHgnUoClZaWIjU1FTU1NWjVqhWMjIygoKDArq+t\nrUVubi7y8/MhEokAADIyMtDU1ESXLl3eu2TP56arq4vY2FjU1dXREhRfmISEBDx//hz9+/dvsSzu\n0tJSnDx5Enw+H8OGDWvxGzMURVEURVHU/9D/e6coiqK+aKWlpXj69Cns7Owklvfs2RPnz5+XWGZp\nacn+rKmpCQASAeLXUVJSwvDhw7Fnzx4oKirC0dEROjo6Etvk5+cjJycHffv2BQDs3rQJ6ysrMVm8\nQWUlli1aBFlZWbZmtpKSEvT19VFcXAxXV1eoqqpCXV0dV65cgYeHB9u2OAAorgn+sdy5cwepqak4\ncuQIu0wc2E9NTf2mg9wjR45EVVUV9u7dC2NjY+Tl5eHatWsSdaFbgriUzYeaOHEiQkJC4OXlhd9/\n/x0aGhpISEjAtm3bIBQKMWnSpDcG0wkhKCgoQFZWFkQiEeTl5dGuXTtwuVzU1dUhLy8PKSkpqK+v\nB9Bw86V169bo1KkTm8n9b6SpqYnY2FgUFBSw3yFUyxNncfN4vBbL4iaE4Pjx46iursaYMWPA4/Fa\nZBwURVEURVFU0/4daTUURVEU9QpxsPZlL2f3ideJs0rfxs3NDfv27UNAQMB7l/MoqazEs2fPsGbN\nGvj4+GDx4sVIT09HVFQUbGxsYGhoCHl5+UbBxdra2vfq720IIXB3d0dsbCz7iouLw+PHj9GxY8dP\n0ue/wfPnzxEVFYV169bB0dERurq6sLa2xsKFC+Hq6goAWL16dZOlSOzs7DBv3rxGyy9dugQul9so\nSL506VL2XBcVFWHs2LHQ1dWFnJwcOnTogMDAQIntXy0h4uDggDlz5mDp0qVQV1eHUCiEp6fnGwPU\nR48excGDB3Hw4EF4eXnB2toaenp6cHFxwfnz5zF06FAADb8jhBD4+flBR0cHKioqcHV1RWRkJGJi\nYtgJIT08PNC9e3doaGige/fuOH36NBiGgaWlJWxsbHDhwgUMGzYM+vr60NHRweTJ7K0fREZGolu3\nbhAIBFBSUkLXrl3Z0jrFxcVvPR/NOf68vDwMGTIEcnJyMDAwQFBQEDp06IBVq1a99hy9zsuTT1Jf\njoSEBLx48aJFa3HfuHEDWVlZ6NmzJ/T19VtkDBRFURRFUdTr0SA3RVEU9UUTCATQ0tJCVFSUxPKo\nqCi0b9/+g9sXB8v69u0LLpeLoqIiDBs2rNF24rIi4skspy9ciMU8HvYB2AdgIZeL0ooK/PnnnxJB\n5Vu3biEjIwPXr18H0FBWpKKiQmLyx/v373/wcTSlS5cuSEhIgKGhYaPXqxMCfkvk5eUhLy+P0NBQ\nVFdXN7nNtGnTkJSUhJiYGHZZcnIybt68iWnTpjXavm/fvlBTU2MnDQUarq2DBw9i4sSJABpqVFtb\nW+PcuXN4+PAh5s6dixkzZuDq1avsPk2VEAkODoaMjAxu3ryJbdu2YevWrRLZ+a8KDg6GmZkZhg0b\nhrCwMIzs31+ibryioiI7vuvXr+PGjRvYsmULVq1ahbCwMBw/fhwMw6CgoADJycn44YcfcOvWLdy4\ncQN6enpYvHgx1NXVIS0tjRMnTmDTpk3YuXMnUlJScPbsWXTt2hUAUFdXh6FDh6JXr16Ii4vD33//\njfnz57OZ3lVVVW89H805/smTJ+PJkycIDw/HqVOnEBQUhKysrPcqJSEu9ZObm/vO+1KfxstZ3C1V\nA/uff/7B1atXoampCUdHxxYZA0V97bKysrBmzZpmzaVCURRFUU2h5UooiqKoL56npydWrFgBExMT\ndOnSBQcOHEBUVBR+//33j9pPXFwcALw2U9DLywvz58+HUCiEi4sLVvj6YpOfH0zatEEveXlkZWWh\nT58+jfbr27cv/P39YW9vj65du4LP5+Pnn3/GvHnzEBsbix07dnzU4xBbvHgxunXrhlmzZmH69OkQ\nCARISkrC2bNnsWvXrk/S57+BtLQ0AgMD4e7ujt27d6Nz586ws7PDqFGjYGtrCwDQ1tbGgAEDsHfv\nXtjY2ABomJDU2tq6yQxvKSkpjBkzBsHBwZgxYwaAhszP7OxsjBs3DgCgpaWFhQsXsvu4u7vj6tWr\nOHToEHvdEEIaZWm3b98e3t7eAABjY2P88ccfuHLlCluj/lWPHz9G27ZtERYWhsnDhzfUjQcwOSoK\n+06eRI8ePZCSkoKnT59CRkYGHh4ekJWVhaGhIQYNGoQHDx6wxyz+r9jevXuhqKiImJgY9OjRA5mZ\nmdDU1ES/fv0gLS0NHR0dNhBZUlKCFy9eYPDgwTAwMAAAmJqasm0153y87fiTk5Nx6dIl/PXXX+xn\nFxgY+N6ZtnJycpCRkcGTJ0/ea3/q44uPj8eLFy/g7OzcIlnc1dXDtU1PAAAgAElEQVTVOHLkCKSl\npeHq6vqvqS9PfVwGBgbw8PDAggULWnooX6WamhqMGjUKU6dOBZ/Pb+nhUBRFUf9S9P/SKIqiqC+S\nSCRiJ3778ccf4enpiZ9++gkWFhYIDQ1FSEiIRLDxfbI2X82aFWf4vm79zJkzsX37dvzxxx+wsLCA\nj48PbO3tcfjcOURFReH7779vsp9Ro0bhxIkTKC0thYqKCoKDg/Hnn3/C0tIS/v7+WLNmTaPxv+v7\nppZZWFggMjISGRkZcHBwQKdOnbB06VK0bt36LWfm6zdixAjk5OTgzJkzGDhwIKKjo9GtWzf8+uuv\n7Dbu7u44fPgwqqurUV9fj/379zeZxS02YcIE3Lhxgw2QBgcHw8HBAVpaWgCA+vp6/PLLL7C0tISa\nmhoEAgFCQkLeGFAVlwV5maamZrNqzb9cN34ygPWVlfD56SdERESgpqYGAoEAnTt3hr29PWxsbGBl\nZQUTExMUFhaybaSmpmLcuHEwNjaGoqIiWrduDZFIhKysLACAq6srqqqqYGBggB9++AHHjx9HTU0N\ngIZJUKdMmQJnZ2cMHjwYW7ZskTjW5pyPtx1/UlISOByORJ1mHR0d9py/Dw0NDRQUFLz3/tTH8yXU\n4j5z5gxKS0sxZMgQKCkptcgYqE8nLy8P8+fPh6mpKXg8HoRCIezs7LBt2zaJjOLbt29j1qxZLTjS\nLxOHw0FISMgHt+Pp6Ym+ffti5syZH2FUFEVR1LeKZnJTFEVRX6Tc3FyYmJgAaAh0LVu2DMuWLWty\nW319fXYCvJe9rR53QEDAO693c3Nrsmb3m4KOU6dOxdSpU9n3Q4YMwZAhQyS2GT9+PPuzg4ODxPG8\n+l5NTa3R8Ta1DACsrKxw4cKF147tW8blcuHk5AQnJycsX74c7u7u8Pb2hqenJ6SlpeHi4gI5OTkc\nP34cCgoKePHiBZuV3ZTOnTujbdu2CA4OxsKFC3Hs2DH4+vqy6319fbF582b89ttvsLCwgLy8PH7+\n+ee3BqxfzV5lGOaN17apqSkePnyIDm3aNFonFArZmt/y8vKNyrW82vbgwYOhp6eH3bt3Q1tbG1JS\nUjA3N2cD2To6OkhOTsaVK1dw+fJlLFy4EKtWrcKtW7cgJyeHvXv3Yt68ebh48SJOnz4NLy8vnDp1\nCv3792/2+Wjq+N9Uk/xD6erqIjs7G2VlZRI3vajPLy4uDiUlJRg4cCB70/Nz9//gwQNYWFigQ4cO\nn71/6tPKyMiAnZ0dlJSUsGbNGlhaWoLH4yEhIQH+/v5QU1Njn5hRVVVt4dF+mLq6uhb5HWouPz+/\nlh4CRVEU9RWgmdwURVHUF6WwsBChoaGIjIxEv379Wno41DekXbt2qKurQ1VVFYCGsiZTpkzB3r17\nERAQgJEjR0IgELyxjQkTJiA4OBgXL15ERUWFRHZ/VFQUhgwZgvHjx8PS0hIGBgZITk5+76cQXmfc\nuHF4/Pgx2nfrJlE3/icuF9MXLsSLFy+a1U5RURGSk5OxdOlS9OnTB2ZmZigpKUFdXZ3EdlwuFy4u\nLti8eTNiYmLw4MEDREdHs+stLS3x008/ITw8HA4ODggKCvpo56Nt27YQiUS4ffs2uyw7Oxs5OTnN\nbuNV4icd6OSTLUskEiE8PBxycnLo0qXLZ++/uLgYZ86cgaKiIgYPHvzZ+6c+vVmzZkFaWhq3b9+G\nq6sr2rZtizZt2mDQoEE4efKkREkofX19bNq0iX2/efNmdOzYEfLy8tDR0YG7u7vEd2tgYCAEAgGu\nXr2KDh06QF5eHn369EFGRga7jbe3NywsLLBv3z7o6+tDXl4ebm5uqK2txe+//w5dXV2oqanB09NT\nYtwHDhyAjY0NFBQUIBQK4erqKvGdt2HDBjAMA7suXWBmZgYul4tLly4BADZt2gQTExPIyspCV1cX\nS5cuZfeLj4+Hk5MT5OTkoKqqiqlTp6KkpOSDznFAQADMzc3B4/FgZmaGrVu3StykzMrKwvDhw6Gg\noAAFBQWMHDkS//zzT6NzdPjwYRgZGUFBQQHDhw9HUVHRB42LoiiK+jrRIDdFURT1RXF1dYWHhwcW\nL17c5ASQFPWhioqK0KdPHwQHByMuLg7p6ek4duwYNmzYACcnJ4ns3R9++AERERE4e/bsG0uViI0f\nPx4PHz7EihUrMGTIEIm2zMzMcPnyZdy4cQNJSUn4z3/+g4yMjDdmJTdVo1u8/HVcXV0xevRo+Pr6\nwnnUKOyxssJ6XV24jB2L33//HaGhoc1qR1lZGWpqati9ezdSUlJw7do1zJw5UyIbMDAwEHv27EF8\nfDzS09Oxd+9eyMjIwMTEBBkZGViyZAlu3ryJzMxMhIeHIy4uDubm5s0+H287fjMzMzg7O2PmzJm4\ndesW7t+/j6lTp4LH473XzQOgIdsdePPTGdSnJ87i7t2792fPQK2vr8fRo0dRX18PV1dXyMjIfNb+\nqU+vqKgIly5dwpw5c8Dj8d66/avly6SkpODn54eHDx/i4MGD+Pvvv+Hh4SGxT3V1NdatW4fAwEDc\nvHkTz58/b1SOIyMjA2fOnMH58+cREhKCY8eOYdCgQbh//z4uX74Mf39/+Pn54dSpU+w+tbW18PHx\nQVxcHM6ePYvCwkKMHTsWABAWFoZ1y5eDAfDk3j3kZ2Rg9+7dsLW1xc8//4w1a9bAy8sLiYmJCAkJ\nQZv/f+KnvLwczs7OUFBQQExMDE6ePIno6Ogmn1xrrj/++ANeXl5Ys2YNkpKSsGnTJqxfv56dh0Qk\nEmHo0KEoKChAREQEwsPDkZOT0+j//TIyMnDs2DGEhobi0qVLuHfvHry8vN57XBRFUdTX68t9Zomi\nKIr6Jl29erWlh0B9IhwOB8ePH8eIESPeab+IiAj06dMHhYWFUFFRea++AwMD4eHhgdLSUggEAnTv\n3h1+fn5ISUlBdXU1tLW1MWHChEYlcQwMDNC7d288efIEvXv3llj3atADAPT09NCzZ09ERUXBx8dH\nYt2yZcuQnp6OgQMHgsfjYerUqRg/fjwSExNf22ZTfTS17FWHDh3CH3/8gT179uBBcjJqa2uRd/o0\nysrKcPjw4Wa1zeFwcOTIEfz444+wsLCAiYkJfH19MXLkSHZ7ZWVlrF+/HosWLUJtbS3at2/PBk7y\n8/Px+PFjjBo1CoWFhRAKhZgwYQIWL1783uejqWXiSUQdHBwgFAqxatUqpKenQ1ZW9o3n6HXU1NQA\n4IOywakPU19fj6tXr7ZYFvfVq1eRl5cHJyenD6rvTn25UlJSQAiBmZmZxHIdHR02I3vChAnYuXNn\nk/vPnTuX/VlPTw/r16/HsGHD2CdVgIYSIdu3b2dLry1atKhR0Li+vh4BAQEQCAQwNzfHgAEDEBkZ\nifPnz0NaWhpmZmaws7PDlStX2ODvy+XP9PX1sWPHDpibmyMnJwe7N23C9JoabADgB6Ckpgang4Mx\ncuRIbN26FX5+fpgyZQqAhr9v4smFDx48iIqKCuzfv5+d+HH37t1wdHREWloaDA0N3/EMAz4+Pti4\ncSP7N79NmzZYvHgxduzYgTlz5uDKlSuIj49HWloa9PT02HEYGxvj6tWr7ATEdXV1bGY8AEyfPv2t\n5eYoiqKobxMNclMURVEU9cHy8vKwdu1anDt3DtnZ2VBTU4OlpSU8PDwwcODAD2rbzs4Oubm5zQ5w\nNxVMHzNmDFtyQEZGBr/88gt++eWXZrWXm5vbZDbbypUrsXLlSoll9fX1cHFxQX5+PkaPHg0ulwsj\nIyNMmjQJHh4eOHHiBKZMmYKioiKsW7euUZuv/sM9PDz8rdu8jru7O9zd3QE0BCsuXbqEsLAwNru8\nqXZePSZHR0fEx8dLbFNaWsr+PHToUDx79gweHh4oKyuT2E5DQwMnTpx47fiUlJTeuB5o3vELhUKc\nPn2afV9YWIjp06fD2Nj4jW2/jpSUFAQCwRsnBKU+rbi4OJSWlsLFxeWzZ3Gnp6cjOjoa+vr66NGj\nx2ftm2p5N27cQF1dHaZPn95ozoKXXb16Fb/++iuSkpLw4sUL1NfXo7a2Frm5uWzJIy6Xywa4gYZJ\nc2tqavD8+XN2ElM9PT2JMlgaGhowNTWVuO6FQqHEkyV3797FqlWrEBsbi+LiYvbJFvGEwGLWAMRp\nAw8fPkR1dTX69u3b5PEkJiaiY8eObIAbALp37w4Oh4OHDx++c5C7oKAA2dnZmD59ukT2+svlrhIT\nE6GlpcUGuIGGwLuWlhYePnzIBrnbtGkjcY6aO/kyRVEU9e2hQW6KoiiKoj6IePIuRUVFrFu3Dh07\ndoRIJMLly5cxc+ZMZGZmvnfbdXV1aNWqFTQ0NN5pv1dLXMjKyr5zZm9BQQGOHz+OrKwszJgxo1n7\nrFq1Cjt37sT27dtha2uL8vJy3L17t1Hw4XPT19dvVEv7Y/gUbb6r8PBwlJSUwMLCAvn5+fDy8oK6\nujoGDBjw3m1qa2sjOTkZIpEIHA6t7vc51dfXs7W4O3fu/Fn7rqiowLFjx8DlcjFy5Mj3LnnzvjIy\nMmBoaIjbt2+3SAb7t8TY2BgMwyAxMRFDhw5ll4vLd8jJyb1238zMTAwaNAgzZszAmjVroKqqijt3\n7mDs2LHspLwAGt2gEV9PL0/u29TEuk3d2BHvIy4r0r9/fxw4cAAaGhooKCiAvb09ampqMH3hQoy9\ndg2oqcFpACtbtcKu6dObdU5eV77qfX4PxOP973//+143i17u810nX6YoiqK+XfT/2imKoiiK+iCz\nZ88Gh8PB7du38f3338PExARmZmaYM2dOoyzgoqIijBo1CvLy8jAyMkJwcDC7LiMjAxwOB4cPH0af\nPn0gJyeH3bt3IyIiAhwOB8XFxQCAFy9eYOLEiRAKheDxeDAyMoKfnx+AhmAuAIwaNQocDofNPnv5\nUWcASE1NxdChQ6GpqQl5eXlYWVnh3LlzEmPV0NCAp6cnbG1tYWBgAF1dXfj6+r7xXJw+fRqzZs2C\nq6sr9PX10b59e0ycOJGtH+rt7Y2goCCcO3cOHA4HHA4HkZGRAJo36dfbJvF68eIFZs2aBS0tLfB4\nPJibm+Po0aNsZuHL2z579gx2dnYYOHAgKisrQQjBhg0bYGxsDDk5OVhaWr718/nvf/8LNzc3lJeX\ns8ezevXqN56jj622thbLly+HpaUlWwc9MjKyWXV2X0dbWxuEEDx//vwjjpRqDnEWt4ODw2fL4p4y\nZQq+++47hISEoLKyEiNHjpSop/8p+/xSiH+/xS9FRUVYWVnhwIEDH70v8WSCLUlVVRX9+/fHtm3b\nUF5e3mj96+YDAIDbt2+jtrYWW7ZsQdeuXWFsbCwxWeKnIA76JiUloaioCGvXrkXPnj1hamoqMUmu\ns7Mzlvj4gAA42bUrXMaMQUZGBrS0tMDlcnH58uUm2zc3N0d8fLzEEznR0dEQiURo167dO49XKBRC\nS0sLKSkpMDQ0bPQCGiZ7zsnJkbgRnpaWhpycHHbuBoqiKIp6FzSTm6IoiqKo91ZcXIywsDD88ssv\nTWa+KSgoSLxfvXo11q9fj/Xr18Pf3x9ubm7o1asXdHV12W1+/vlnbNq0CQEBAZCWlsbjx48l2li2\nbBkSEhJw7tw5CIVCpKWloaCgAEBD8EFDQwP+/v4YPHgwpKSkmhx3eXk5Bg0ahLVr14LH4+Hw4cMY\nMWIE4uLi2Bqtbdq0QVlZGb7//nv4+/vj/Pnz+PHHH9GzZ09069aNbSssLAy7N20C0JBxFh4ejvz8\n/Cazzz09PZGUlIRnz55h//79ABrqWouz87p164aYmBgUFRXB3d0dbm5uOH78OICGSbxWrlyJbdu2\nwcrKCvHx8XB3d0erVq0wZ84cEELg4uKCFy9eIDAwEGZmZnj06BEqKyvZILc4+y0nJwfOzs7o0KED\n9u/fD2lpaXh5eSEkJAQ7duyAmZkZoqOj4e7uDmVlZbi4uDT5+XA4HNTX12Pp0qVIS0sDAInH3T+H\n/v37Iy4u7qO2aWtri6dPn753DXjq/YizuPl8/mfN4mYYBi9evEBqaipsbW0lSkx8a8LCwtCxY0c8\ne/YMO3bswKRJk6Cvr4+ePXu29NAaEX+fve/TFjt27ICdnR2srKzg7e0NS0tLSEtL486dO4iLi4Oz\ns3OT+5mYmEAkEmHLli0YPnw4/vrrL/ZG66ciDrjr6emBy+Xi999/x+zZs5GYmIjly5dLbGtrawuG\nYXD0/HkUFRXh0KFDOHbsGGbNmoWff/4ZXC4X9vb2KCoqwt27dzFz5kyMHz8eK1euxKRJk7B69WoU\nFxdjxowZGDly5FtLlaSnp+P+/fsSy4yMjLBq1Sp4eHhASUkJAwcORG1tLe7evYucnBwsWbIE/fr1\ng6WlJcaPHw8/Pz8QQuDh4QErKys4Ojp+3BNIURRFfRsIRVEURVHUe7p16xZhGIacOnXqrdsyDEOW\nLl3Kvq+rqyNycnIkODiYEEJIeno6YRiGbN68WWK/8PBwwjAMKSoqIoQQMmTIEOLm5vbGfk6cOCGx\nLCAggMjLy79xfN26dSNr1qxh37dp04aMGzdOYhsTExOJbS5evEiEPB4JBEggQFS5XKKnp0c4HA5p\n3749+eGHH0hISIhEG5MnTyaDBw+WWLZ7926iqKhIysrK2GURERGEYRiSmppKCCFEV1eXHDhwQGK/\nLVu2EHNzc0IIIZcuXSIcDockJSU1Orb6+noybNgwIisrSx4/fkz09fXJrFmz2PVlZWWEx+ORqKgo\nif3mzp1LXFxcCCGv/3yac27/jY4dO9bSQ/jm3Llzh3h7e5OYmJjP2q+rqysxNTUl27ZtI9XV1WT1\n6tVER0eHcLlcYmFhQUJDQ9ltxb8HJ06cIE5OTkROTo6Ym5uTP//8k92mvr6euLm5EQMDA8Lj8YiJ\niQnZsGEDEYlEhBBCVq5cSRiGkXhdu3bto7RNyP++Y7Zu3Uq0tbWJsrIymTp1KqmoqHjtORD3fefO\nHXZZSUkJYRiGbNq0iV2WnZ1NRo8eTZSVlYmysjIZNGgQefz4Mbt+5cqVpEOHDhJtv/wdERAQ0OjY\n9+3bRwgh5Pnz58Td3Z1oaGgQgUBAevfuTW7fvt2onfPnz5P27dsTaWlp8uDBg7d8um+Wm5tL5s6d\nS4yNjQmXyyXy8vLE1taWrFu3TuL7WF9fX+I8/Pbbb0RbW5vweDzi5OREjh49SjgcDsnMzGTHKhAI\nJPoKDw8nHA6H/Vvm7e1NLCwsJLb5z3/+QxwdHSWWjRkzhowaNYp9f+TIEWJkZERkZWVJp06dJK6h\npvp58OAB8fb2Jn5+fmT16tXE0NCQyMjIEF1dXbJs2TK23fj4eNK3b1/C4/HYa6akpOSN5+/Vz5Jh\nGMLhcMi5c+cIIYQcOnSIdOnShcjKyhJlZWVib29Pjhw5wu6flZVFhg0bRgQCAREIBGTEiBHkn3/+\nYdc3dY6aOrcURVEURUjDY1gURVEURVHv5a+//nqnIPfhw4cllrVp04Zs2bKFEPK/IEtkZKTENq8G\nuS9cuED4fD7p2LEjWbRoEfsP+5f7eVuQu6ysjHh6ehJzc3OirKxM5OXlibS0NJkxYwa7jb6+Plm3\nbp1EO7179yZz585l34/o148EAoT8/ysQIMOdnEhMTAzZsmULGT58OJGWliYuLi5sEKqpIPf8+fNJ\nr169JJZVV1cTKSkpcubMGZKfn08YhiFycnJEXl6efcnKyhJZWVlCCCHr168n2traTZ16Qggh48aN\nI1JSUqR169bkP//5j8S6v//+mzAMQ/h8vkT7XC6XtG3blhDy+s/naw1yX7p0ieTm5rb0ML4phw4d\nIhs3biR1dXWfrc+amhpia2tLzMzMSEFBAdm8eTNRUFAghw4dIo8fPyYrVqwgUlJS5P79+4SQ//0e\ntG3blpw9e5akpKSQyZMnE1VVVTYoWltbS1asWEFu375NMjMzydGjR4mSkhLZs2cPIaTh+2f06NGk\nf//+JC8vj+Tl5ZGampqP0jYhDd8xfD6f6Gtrk349epC1a9cSJSUl8uuvv772PIj7FgeVa2pqyKZN\nmwiHw2G/Y8vLy4mJiQmZOnUqiY+PJ8nJyeSHH34gbdq0YQPobwtyV1ZWkkWLFpG2bduyx15ZWUlE\nIhGxs7MjgwcPJjExMSQ1NZUsX76cKCgokKdPn7LtSEtLkx49epDo6Gjy+PFjUlpa+mEXwBeoqb8R\nr9PUzYmm3Lt3j3h7e5MdO3aQysrKZrXdu3dvwjAM8fHxabTO1dWVMAzT6G8JRVEURbUkWq6EoiiK\noqj3ZmJiAoZh8PDhQ4nJu16nORNIva3cxYABA5CZmYkLFy7gypUrGDRoEEaNGoW9e/c2e9yLFi1C\nWFgYNm3aBBMTE/B4PEyaNEli0rDmjrcp1tbWsLa2xrx58xAcHIyJEyfi+vXr6NWrF9vOq8gbJv36\n0Em8gIYatBwOB/369cO5c+fg6ekJPT09AP977P/s2bPsMrFXz8HnLkfSUoyMjJCSkgKhUNjSQ/lm\n6OnpYejQoa8tM/QpXLx4EdXV1dDQ0ICamhp8fX3h6emJMWPGAGiYTDYyMhK+vr5siSEAWLBgAQYN\nGgQAWLt2LYKCghAbG4sePXpAWloaq1atkjiuO3fu4NChQ3BzcwOfz4esrCxkZGSaLGv0IW0DwD//\n/IPKigqsLC8H888/WHzvHro7OODKlStYsmTJG89Hr169wOFwUFlZCVlZWRw9epT93jp8+DAASHzX\n7tq1C0KhEGfPnsWoUaPeer5lZWXB5/MhLS0tcexXr15FbGwsCgoK2EmCV69ejTNnzmD//v3w9PQE\n0FDSZtu2bZ99UtLPiWGYjz7paadOnVBTU4MLFy5g//79mDx5MmRkZN46Dl1dXQQGBmLZsmXs8qKi\nIoSGhkJPT++Dx1lXV/fZau9TFEVRXz868SRFURRFUe9NRUUFzs7Or52861NN3KeqqooJEyYgICAA\n/v7+2LdvH2prawE0BGXr6+vfuP+NGzcwefJkDB8+HB06dIC2tjZSUlLeeRzTFy7EYh4P+wDsA7BA\nWhrGnTujrq6O3UY8aZd4Qi8ZGRmJ9cDbJ/1qziReXbp0wdOnT5GUlNTkWJWVlcEwDDthmaOjI548\necL2z+VykZGR0ajtl+ulN0VGRuat5/vfSF9fH7m5uS09jG/GkydPoKur+0EThr6rpKQk3L17F4qK\nilBSUkJpaSmePn0KOzs7ie169uyJhw8fSiyztLRkf9bU1AQA5Ofns8t27doFa2traGhoQCAQYOvW\nrezv29t8aNuPHjxAe0IwBcBkAOsrK5GenCzRxuscOnQI9+/fx+nTp6Gmpobz58+z6+7cuYP09HQI\nBAL2paSkhOfPn7M1+d/XnTt3UFFRAXV1dYn2ExISJNqWlpZGp06dPqivLx15adJLkUgEHx8f6Orq\nQlZWFpaWljh9+nSjfTIyMtCvXz/w+Xy0b99eYoJJ8eTNZWVlOHLkCObMmQMzMzPExMS8dSwDBw5E\nWVkZIiIi2GUHDhxAt27dYGBgIHFztrq6GvPmzUPr1q3B4/HQvXt33Lhxo9E4Lly4AFtbW3C5XFy6\ndAnl5eWYNGkSBAIBtLS04Ovri8GDB2Pq1KkSfdrY2EBBQQFCoRCurq7Iyclp1PbVq1fRtWtX8Pl8\n2NjY4N69e8076RRFUdRXgQa5KYqiKIr6INu3bwchBNbW1jh+/DiSk5ORlJSEnTt3omPHjh+9vxUr\nViA0NBSPHz9GYmIiQkJCYGRkxGYc6+vr4/Lly8jNzcWzZ8+abMPU1BQhISG4d+8e4uPjMWHCBFRX\nV782m1rs5eADADg7O2PfyZM43a8fTjs5QdPMDHFxcVi9ejVSUlIQERGBOXPmoHXr1mwGtoGBARIS\nEvDo0SMUFhairq4O48ePh5ycHCZNmoSEhARERkY2mvRr1apV2LBhA7Zu3Yrk5GQkJCQgKCgI69at\nAwD07dsXXbt2xciRI3Hp0iWkp6fjzz//RGhoKABAUVERAJCbm4t9+/ahR48ecHBwwJMnTyAQCLBo\n0SIsWrQIAQEBSElJwf3797Fr1y788ccfbzwn+vr6qKqqwuXLl1FYWIjKysq3fYT/ChwO563XA/Xx\niIPcn0tJSQlOnjwJPp/f6OmFVxFCGmWsvvyEg3id+ImII0eOYP78+XBzc8OlS5cQGxuL2bNno7q6\nWqKN12XBfoy2m/pHXnOeQtHR0YGRkRFcXFwQFBSEgIAANlApEonQqVMnxMbGSrwePXqE6dOnN/Tb\nxO+N+Abkm4hEIgiFwkZtJycnw8fHh92Oy+V+9CznL5H4GP38/ODr64uNGzciISEBw4cPx4gRIxAb\nGyuxvZeXF+bNm4e4uDjY2NhgzJgxjW48L126FDt37sQff/wBKSkpDBs27K03KFu1aoVJkyax2fth\nYWFYuWwZyouKUFxcLPFZ/PTTTzh69CgCAgJw//59WFhYYMCAAY1uFi5ZsgRr165FcnIybG1tsXDh\nQkRGRuLUqVO4fPky7ty5g6ioKIm2a2tr4ePjg7i4OJw9exaFhYUYO3Zso/EuXboUGzZswN27d6Gq\nqorx48c342xTFEVRXwsa5KYoiqIo6oMYGBjg7t276NevHxYvXoyOHTuib9++CA0NxdatW9+prdcF\nL15eLisrCy8vL3Tq1Ak9e/ZEeXk5zpw5w67ftGkTwsPDoaenBysrqybb2Lx5MzQ0NGBvb49Bgwah\nR48esLe3f2vwpKnHyJ2dnXHi0iWc+PNPzJ07FwUFBdi8eTPatWuHiRMnwsDAAFeuXIGSkhIAwN3d\nHe3atYO1tTWEQiGio6PB4/EQFhaGkpIS2NraYtiwYbCzs5MoCzBt2jTs3bsX+/fvR6dOndCrVy/4\n+/uzQXCGYXDhwgXY2dlhwoQJMDc3x/z589kAk4KCAgAgJycHDMOwge4+ffogOzsbPj4+8Pb2hq+v\nLzp06ID+/fvj5MmTbPuv+3x69OiBmTNnYuzYsdDQ0MDGjXqW3YUAACAASURBVBvfeA4p6lVZWVlv\nDTR/TCKRCMeOHUNNTQ1cXV3Z8ijiTNKoqCiJ7aOiotC+fftmtx8VFYWuXbti9uzZ6NSpEwwNDZGS\nkiLx+9PUEx0fq23T9u2RyOGwT5h4crkwf4/s5169eqF379749ddfAQBWVlZISUmBqqpqoyc+lJWV\nAQDq6urIy8uTaOf+/fsS75t6+sPKygp5eXlgGKZR22pqau889q/Fy+VzjI2NsWrVKtjb28PX11di\nO3GJGyMjI6xduxbFxcWNAuE+Pj7o3bs3Jk2ahNmzZyMnJwf+/v5vvPnBMAzc3NwQEhKCU6dOYdzQ\noagpK8P0hAQkJiQgMzMTAFBeXo5du3Zhw4YNGDhwIMzMzNhSNtu3b5do09vbG05OTtDX14esrCwC\nAgKwYcMG9O3bF+bm5tizZw84HMkwxdSpUzFgwADo6+vDxsYGO3bswPXr1yWyuV8+RjMzM6xYsQJJ\nSUmNtqEoiqK+Yi1UC5yiKIqiKOqrFRkZSby9vcmuXbuaPcnX57Bx40by22+/tfQw/jVOnDhBamtr\nW3oYX70bN2581v6uXbtGvL29ydWrVwkhDRP9fffdd4QQQrZu3cpOPJmcnEyWL19OpKSkSFxcHCHk\n9RP9vTzh7e+//04EAgG5cOECefToEVm9ejVRVFQk+vr67PZr164lurq6JDk5mRQUFJDa2tqP1vbk\nyZNJu3btSEdDQ+JobU0uXrxIZsyYQczMzF57Tl7X95kzZwiHwyEJCQmkoqKCmJmZkd69e5Nr166R\ntLQ0cu3aNbJw4ULy+PFjQgghiYmJhMPhkF9++YWkpKQQf39/IhQKJSanPXjwIJGTkyN3794lBQUF\npLq6mhBCiL29PbGwsCAXLlwgaWlpJDo6mqxYsYJcv36dEPL1TnL7KvH1WFJSQhiGYa9TsWXLlpEu\nXboQQv73uf3111/sepFIRBiGISdPniSE/G/y5pcn0k1NTSUMw5AZM2aQkydPshMjv8zBwYF4eHgQ\nQgjp0aMH6dyuHXEEyMz/n2jZDCBGurqEEEJiY2MJwzAkLS1Noo0JEyaQESNGSIwjKyuLXX///n3C\nMAxJT0+X2M/e3p5MnTqVfX/nzh0yZMgQ0qZNGyIQCIi8vDxhGIbcvHnztceYlpZGGIYh9+7de9Pp\npiiKor4iNJOboiiKoijqI7O3t0f//v2Rm5uLvXv3oqKioqWHBADQ1tbGs2fPmlW2gAK0tLSQmpra\n0sP4qmVmZn7WLO7s7GxERERAS0sLvXv3BtCQ2S2e/O7HH3+Ep6cnfvrpJ1hYWCA0NBQhISGwsLBg\n23jbEx8zZsyAq6srxo0bB1tbW2RlZWHhwoUS+zX1RMfHaruurg4KCgoYPmkSdh44AGdnZ2hqakIk\nErGZt01pqu/BgwfD1NQUvr6+4PF4iIyMhKGhIUaNGvV/7N13VBRX+8Dx76z0LmVBQARUEEHAXlCD\n2JIYW+zGCNgSyatpltdoLNEYY4kpvxiSqIgt9ha7YkHEhgqIgICIHbBiRAVk5/eHYV9XkJJQLPdz\nzp7jzty595nZFfSZO8/Fzc2NgIAA7t69q57JXa9ePX755Rd+++03vLy8CAsL44svvtDou1evXrz9\n9tu0b98epVKpXtBy+/bt+Pn5MXz4cOrVq0e/fv1ITk7Gzs6u2BhfN3IZy+cU1aZgprSzszMxMTHs\n2LGj2PJMQ4YMIfXKFY4BQwrGKWWsz87KLs3ixU/Hkp2dTefOnTEyMmL58uVERUWxc+dOgGIXi37e\ndRAEQRBeXWIpY0EQBEEQhArQsmVLtLS02L59O4sWLSIwMBAjI6MqjalmzZokJSVx69YtrKysqjSW\nl4GLiwvHjh3D1dW1qkN5ZV29elVdr74y5Obm4ujoSLdu3dTJt/T0dOrWrQs8SYxNmjSJSZMmFXm8\no6NjkXWMn06kaWtrs3DhQhYuXKjR5ssvv1T/2dLSkl27dhXqpzz6DgoK4uHDh0RERGBjYwPAlClT\nmDJlCgkJCaSmpmqUISruvAASEhLUf1YqlRpllIoyYsQIdY3uAqNHj1b/WUdHh7Vr1xY6zsjIiO+/\n//65Za4CAgIICAgoduxXydPlc9q1a6feXtbyOcXx8/MjOTmZEydOoKurS/v27Yts169fP0aNGkWe\nJBEvy8QDKZJEKycnAGrXro2Ojg4RERE4/b0tPz+fI0eOMGjQoOeOX7CexvHjx3F0dATgwYMHxMXF\nqf9OJiYmcuvWLWbOnEmtWrUAiIuLK5fzFwRBEF4tYia3IAiCIAhCBWnatCndu3fn9u3bLFq0iHv3\n7lVpPAUJr2cXAhOKZm5u/sLMwn8VXbx4UZ20qgw5OTlkZGQwePBgzMzMuHnzJps3byY8PJyOHTtW\nWhwV6fr161hbW5OWloZCoVDX4i/g5ubG48ePSUlJqaIIhbIYO3Ysc+fOZdWqVSQlJTF58mQiIiIY\nM2ZMufSvUCjo06cPjo6OREREcOjQIfU++amFlo2MjMjIyGDd3wstb2rfHmWNGjx69Ijk5GQMDQ0Z\nOXIk48ePZ8eOHSQkJDBy5Ehu3LhBUFDQc8c3MjJiyJAhjB8/nn379hEfH8+wYcM0Zqs7ODigq6vL\nTz/9RGpqKtu2bdO4qSMIgiAIBUSSWxAEQRAEoQJ5e3vTq1cvsrKyWLhwIXfv3q2yWAqS3NevX6+y\nGAShwNWrVzVKUVS0qKgomjRpon7ft29fRo0axfjx4+nRo0elxVFRZFkmNTWV2rVrk5GRgbm5eZHl\nPVxcXJAkicTExCqIUihJeZfPeV6bgm1aWloMHDgQOzs79u3bx7Fjx9T7nz7O2NiY7t27s373bjbu\n3auehb1mzRquXbvGt99+S79+/QgMDKRhw4bExcWxc+dOrK2ti41j7ty5tGnThm7dutG+fXu8vLxo\n0qQJenp6wJPFTENDQ9m0aRPu7u5Mnz6d+fPnF+qruHMUBEEQXg+SXFzxLUEQBEEQBKFcJCYmsmbN\nGgwMDBgyZAjm5uZVEsc333yDpaUlw4cPr5LxXzbr1q2jd+/eVR3GKyctLQ1tbe1KS3InJCRgbm6u\nkXB71SQnJ2NqaoqhoSFz586lWbNmvPXWW89tn5aWRnZ2drmVvhDKR6dOnahbty4///xzpY6bk5ND\nSEgIGRkZdOvWjYYNG5Z4TGZmJgsXLkRLS4sRI0ZgZmZWLnHUqlWL8ePH8+mnn/7r/gRBEITXh5jJ\nLQiCIAiCUAnq1avHgAEDePjwIQsXLuTGjRtVEodSqeTGjRvFLjIm/I++vj5ZWVlVHcYr59q1a5WW\n4L5z5w65ubmvdII7Pz+fmzdvolQqycjIACjx+jo6OmJiYkJsbGxlhCiUoKrL5+jq6uLv74+FhQVb\ntmzh7NmzJR6jVCoZMGAAjx49YunSpTx8+LDM40ZHR7Ny5UpSUlI4ffo0/v7+ZGdn069fv39yGoIg\nCMJrTCS5BUEQBEEQKkndunUZNGgQubm5LF68uEpqY9eqVYu8vDz++uuvSh/7ZeTk5ERSUlJVh/FK\nSUtLUy8yV9Hy8/M5c+YMnp6elTJeVYmLi1OXsCj4uVJQnqg4NWvWxNLSktOnT1dofELJXoTyOfr6\n+gQEBGBqasr69etL9bPPycmJHj16cOfOHVauXMnjx4/LPO78+fNp1KgR7du358aNG4SHh2Nra/tP\nTkEQBEF4jYkktyAIgiAIQiVycnJi8ODBPH78mJCQEK5evVqp49eoUQMQi0+WVp06dSr9M3qVybLM\ntWvXKi2BdfLkSRo3bvxK1+Z99OgReXl5GBkZAXD58mUALCwsSnW8ra0ttra2REVFiSc8qtC+ffu4\ndOkS06ZNq9I4jIyMCAwMxNDQkNWrV5OamlriMZ6enrRr144rV66wcePGMn2PvL29OXHiBPfu3eP2\n7duEhYWVqlSKIAiCIDxLJLkFQRAEQRAqmYODAwEBAQCEhoZy6dKlShu7YHanSHKXjo6Ozj+amSgU\nrTJncaelpWFlZYWhoWGljFdVYmJi8PLyUr+/fPkyhoaGVKtWrdR9WFtb4+joyPHjx0WiW8DU1JTA\nwEB0dXVZuXJlqX5HtWnTBm9vb+Lj4wkLC6uEKAVBEARBk0hyC4IgCIIgVAE7OzsCAwNRKBQsXbqU\nCxcuVMq45ubmKBQK9WxPQagssixz/fr1SpnF/eDBAzIyMnBycqrwsarS3bt3MTQ0RFtbG4C8vDyy\ns7OpVatWmfuytLTExcWFo0ePikS3gLm5OYGBgWhpabF8+XKuX79ebHtJknjnnXdwdHTk8OHDREVF\nVVKkgiAIgvCESHILgiAIgiBUERsbG4YOHYqOjg7Lly8nJSWlwseUJAlzc3NRgqMMJElCpVJVdRgv\nvbS0tEpJOsuyzMmTJ2nSpEmFj1XVzp49i7u7u/p9ZmYm8ORpkX+ievXquLu7ExkZKb7zAlZWVvj7\n+wNPnjoq+H49T7Vq1ejfvz+WlpZs376d5OTkyghTEARBEACR5BYEQRAEQahSVlZWDB06FH19fVau\nXEliYmKFj1mrVi0ePnxITk5OhY/1KrCysuLKlStVHcZLrWAWd0FN+Ip05swZ3N3dS12uw9fXl9Gj\nR1dwVOXvypUr2NnZadQbLyhDVKNGDQICAujatWuZ+zUxMcHLy4vDhw+Tn59fbvH+U//0PITyUaNG\nDd5//33y8/NZsmQJt2/fLra9rq4ugwcPxsDAgDVr1pQ4A1wQBEEQyotIcguCIAiCIFQxCwsLhg0b\nhpGREWvWrCEuLq5CxytINGZkZFToOK+KOnXqVMos+1fZhQsXcHZ2rvBxMjMz0dLS4rPPPkOhUKBQ\nKNDR0cHa2ho/Pz8WLFhQqMb6pk2b+Oabbyo8tpKkpaWpY1YoFBgbG1OvXj2GDx/OmTNnNNrKsszF\nixcL1TdPS0sDQKlUIklSqRbc3LRpEx06dMDCwgIDAwNcXV35z3/+gyRJREZGVnlN+p9++okVK1ZU\naQyvu5o1azJw4EBycnIICQkhKyur2PbGxsYMHjwYSZJYtmwZd+/eraRIBUEQhNeZSHILgiAIgiC8\nAMzMzBg2bBimpqasX7+e6OjoChurIMktZtiVjo2NTYmzF4Xnk2WZ9PR09aKnFSUvL4+kpCTq16+P\nJEl07NiR9PR0Ll68yJ49e+jatStTpkyhTZs2PHjwQH2cmZnZC7U45a5du0hPTycuLo758+eTmZlJ\n48aNWb16tbpNUlISrq6uhY5NSkoCniyYKstyibW1J06cSJ8+ffDy8mLz5s2cO3eO1atXU79+fb78\n8kuaNm1KZGQkeXl55XuSf8vNzS2xjbGxMSYmJhUy/qumIme9Ozk50a9fP7KzswkJCeH+/fvFtlcq\nlQwYMIBHjx6xdOlSHj58WOYxU1NTGTZsGI6Ojujp6WFnZ4efnx9Lly4t9++kQqFgw4YNGtumTp1K\ngwYNynUcQRAEoeKIJLcgCIIgCMILwsTEhKFDh2Jubs7mzZsrbOEuKysrAFGXu5QUCkWpZsQKRUtN\nTa2UWdxRUVE0bdoUeJJY19HRQalUUqNGDTw9Pfn00085cOAAp06dYvbs2erjfH19GTVqlPr98uXL\nadq0KSYmJlhbW9O3b1+uXbum3n/gwAEUCgX79u2jefPmGBoa0rRpU06fPq0RT2RkJG+88QaGhobY\n29sTFBTEX3/9VeJ5WFhYoFQqqVWrFm+99RabN2+mT58+fPjhh2RlZZGfn8+dO3dISkrS6H/kyJH8\n9ddfaGtro1CU/N+8Y8eO8c033zB//nzmzZtH69atqVmzJt7e3owbN479+/ejp6dHixYtGDFiBB4e\nHhrHL1myBGNjY41tf/75J40bN0ZfXx9nZ2cmTZqkkYx0dHRk2rRpDBkyhOrVq/P+++8DcPToUfz8\n/DAyMsLMzIz27durb8I9m7jNycnhk08+wcbGBn19fVq2bMnhw4dLPN/XQWln75fWszchXFxc6NWr\nF1lZWYSEhGjcLCqKk5MTPXr04M6dO6xcubJMTwVERUXRsGFD4uPj+b//+z/i4uI4dOgQQUFBhIaG\nVsjvR7HgqiAIwstNJLkFQRAEQRBeIEZGRgwdOhQrKyu2bdvGkSNHyn0MbW1tjI2NuXz5crn3LQhP\nk2WZjIyMCp/FnZycjIODA7q6usW2c3d3580332T9+vXqbc8mBvPy8pg+fTqxsbFs3bqVmzdvMmDA\ngEJ9ffHFF8yePZtTp05hYWHBe++9p9535swZOnfuTI8ePYiNjWXDhg1ER0czZMiQf3R+Y8aMISsr\ni7179xIbG4skSRr9jxs3jlUrV7Lgxx/VdblLsnLlSoyNjQkKCiq2nY6ODvb29jx48IBHjx49t92u\nXbsYNGgQo0ePJj4+nsWLF7Nu3Tq++OILjXbfffcd9evX5+TJk8ycOZOYmBjatWuHi4sLkZGRHDt2\njIEDB6oTos9+PuPGjWPNmjWEhIQQHR1NgwYNePPNN0t93q+ykmbvh4eH07x5c/T19bGxseGzzz7T\nuAnh6+tLUFAQY8aMQalU0qZNGwDi4+Pp0qULJiYm+Pn5cfToUS5dukRoaCg5OTnqGxE//PAD9vb2\nmJubM2TIEB4+fIinpyempqYMGzYMHR0djZI87dq1e+55+Pv74+rqSmRkJO+88w516tTB2dmZ3r17\nExYWRsuWLdXtz5w5Q4cOHTAwMMDCwoLAwEDu3bun0WdISAj169dHX18fV1dXvv/+e/W1Kij706dP\nHxQKBU5OToSGhvLVV19x9uxZdbxLly4FICsrixEjRmBtbY2JiQm+vr6cPHmy7B+YIAiCUK5EklsQ\nBEEQBOEFY2BgwJAhQ7CxsWH37t0cOnSo3MeoWbOmelaoUDJtbe0SZy0KhaWmplK7du0KHePevXvc\nu3cPOzu7UrV3c3MjNTX1ufsDAwN58803cXR0pGnTpixYsIBDhw5pzOYGmD59Om+88Qaurq5MnjyZ\nxMREdZs5c+bQr18/Pv30U2rXrk2zZs1YsGAB69ev5+bNm2U+Rzc3N+BJORKVSsXPP/+s7j8lJYWZ\n//0vn967x73sbFYEB7Nr164S+0xKSsLZ2Vlj1veCBQswNjZWvwoWXK1WrRqGhoacOHHiuX8Pvv76\na8aNG4e/vz9OTk74+voya9YsgoODNdr5+voyZswYnJ2dqV27NrNnz6ZRo0YEBwfj6emJq6srQ4cO\npWbNmoBm4jY7O5vg4GBmz57NW2+9haurK8HBwVhbW/Pzzz+X+bq+Tq5evcpbb71F48aNiY6OZtGi\nRfzxxx9MmDBBo93y5cuRJImIiAiWLl3K9evXadu2LZ6enpw4cYKwsDC0tbXZsWMHGRkZLF26FJVK\nxaFDh4iPjycsLIzVq1ezceNGfvjhBwCCgoJYsmQJn3/+OatXryYqKgozMzN1knvXrl306tSJXp06\nsWvXLqKjo0lISGDMmDElnld2djadO3fGxMSEEydOsHHjRiIjIzVuKP3+++9MnDiRGTNmkJiYyLx5\n8/j2229ZsGABgHpW+MKFC0lPTycqKop+/frx+eef4+rqSnp6Ounp6fTt2xdZlunSpQvXr19n27Zt\nREdH07ZtW/z8/MSNFkEQhComktyCIAiCIAgvID09PQICArCzs2Pfvn3s27evXB+ltrOzQ5blf5Rw\nex3VrFmT5OTkqg7jpVIwi9va2rrCxlCpVMTExNCwYcMyxVVcSYdTp07RvXt3HB0dMTExUZdAuXTp\nkkY7T09P9Z8L6txnZmYCcPLkSZYvX66RMG7dujWSJHH+/PlSx/p0zPCkjr6Xl5dG/+906ULWw4fM\nBiTgo7w8fps3r0z9Fhg0aBAxMTEsX76c7OxsVCqVxn4fHx9OnTpVZNmVkydPMmPGDI1zfu+993jw\n4IF6kVtJkmjSpInGcdHR0fj5+ZUq3vPnz5OXl4ePj496m0KhoGXLlsTHx5eqj9fVggULsLe3Z8GC\nBbi6utKlSxdmzZrF//3f/2nM0Hd2dmbOnDm4uLjg6urKL7/8gre3N9988w2urq54eHgQGhpKfHw8\n9vb2XLt2jdTUVExNTQkODsbV1ZWOHTvSp08fwsLCgCdPAwwaNAgPDw9iYmIYNGgQfn5+TJ48mV27\nduHfsyfd9uyh2549+Pfsydq1awE06s5nZWVhZGSk/m4VLBa7cuVKHjx4wLJly3B3d6dt27b89ttv\nbNiwQX0za/r06cyZM4d3332XWrVq8c477zB+/Hh1ktvS0hJ4Up9fqVRiYWGBnp4ehoaGaGlpoVQq\nUSqV6OnpsX//fmJiYli7di1NmjTB2dmZr776CmdnZ5YtW1bxH6QgCILwXFpVHYAgCIIgCIJQNF1d\nXfz9/VmxYgWHDh0iLy+PTp06lUvN1YLyEenp6RWahHxV1K1bl/379+Pl5VXVobw0zp8/X+GzuKOj\no/Hy8ipVDeoC8fHxz42rYFZop06dWL58OUqlkhs3btCmTZtC9Ym1tbXVfy74O1mQFJZlmeHDh/Pp\np58WGsPW1rbUsT4dM0CdOnXQ0tLS6D9o8GDaHz5Mr7/bhgFnS9Gnq6srERERPH78GC2tJ/8tNDEx\nwcTEpNBCqwqFAlmWUSgU+Pj4cOTIkULlIGRZZurUqfTp06fQWAVJRKDIRT7/7Q28gtiE50tISKBF\nixYa23x8fMjNzSUlJUVdc71x48YabU6ePEl4eHih+uuSJKlv3mzcuBEzMzNUKhXVqlUDntz4OXbs\nmLp9tWrV6NevHz4+Pty9e5cpU6YA8Nu8eXz78CH+BQ0fPmTB1q2F4jcxMSE2NhZZlnn77bfVZVYS\nEhLw8vLS+F61bNkShUJBfHy8+omEESNG8OGHH6rblKU++LPX48GDB+q1LQrk5OQU+4SIIAiCUPFE\nklsQBEEQBOEFpq2tzaBBg1i1ahVHjx4lLy+PLl26/OtEd0GSu2BmqFA8IyOjQklO4flkWSYzM5M6\ndepU2BhXr17F2NgYExOTIvcX9XckLi6OXbt28eWXXxZ5TGJiIrdu3WLmzJnUqlVLfUxZNWrUiLi4\nuHJbcHPu3LkYGRkREBBQqP9Pv/wS/549UT58CMCX+vqEfv45f/zxR7E/JwYMGMCPP/7ITz/9VGQy\n/mlWVlYas7FbtmzJvHnzNJLTjRo1IiEhoczn3LBhQ/bt21eqtrVr10ZHR4eIiAicnJwAyM/P58iR\nIwwaNKhM475uJEl67s2Egu+JJEmFbkLIssw777zD3LlzCx2nVCoxNDTEysqKS5cusWHDBnr16qVe\nrPfZJwG+/fZbrl+/ztChQ9myZYvGzY+nGRkYAP9LYBfEVvDd0tHRKRTj886rIIZff/2VVq1aFdmu\nLFQqFdbW1kRERBTa97yfRYIgCELlELe7BUEQBEEQXnBaWloMGDAAV1dXTp48yebNmwslD8rKwMAA\nXV1d0tLSyidIQXjK+fPnKzTBnZOTw6VLl6hbt+5z2zx69IiMjAyuXbtGTEwM3333He3ataNJkyYa\ntX6frvlcsHjlTz/9RGpqKtu2bXtuQrw448eP5/jx44wcOZLTp0+TkpLC1q1bNWaSPs/NmzdJT0/n\nwoUL7Nixg27durFhwwZmzZqlTqI93b9SqWTKvHlMs7ZmgqUloRs30rlzZ/W5PU/z5s0ZN24cY8eO\n5ZNPPuHQoUNcvHiR48ePExwcjCRJ6lm57dq14/bt28ycOZPz58+zePFiDh8+jCzL3LhxA4DJkyez\ncuVKpkyZQlxcHImJiaxbt47x48cXe75jx47l9OnTfPDBB8TGxnLu3DkWLlxY5MK4hoaGjBw5kvHj\nx7Njxw4SEhIYOXIkN27cKHEBzdfF825suLm5cfToUY3vREREBDo6OsU+cVFwQ8XBwQFnZ2eNl5GR\nEZIkYWdnh6mpKfHx8fz5559Ffu/WrVvHnDlz2LJlC6NGjUKSJJYtW8bADz5gvL4+oUAo8JmWFoH/\n+Q9ubm7Mnj27yN91T/dfv359zpw5w/3799XbIiMjUalUuLm5YW1tja2tLSkpKYXif/qGjLa2dqE1\nKnR0dApta9y4MRkZGeqk+9Ov5yXtBUEQhMohktyCIAiCIAgvgWrVqtG3b1/c3d2JiYlh/fr1/zrR\nbWNjw82bN8u11rcgFMziViqVFTZGVFRUodrOT5Mkib1791KjRg1q1apFhw4d2Lp1K9OmTSM8PBx9\nfX2NtgWJQSsrK0JDQ9m0aRPu7u5Mnz6d+fPnF0ocFpVIfHpbgwYNCA8PJy0tDV9fX7y9vfniiy/U\nT1AU580338TW1hYPDw8++eQTlEolv//+u0YS99n+x4wZQ7Ys0713b3WC++nzep5Zs2axZs0azpw5\nQ/fu3albty69evXiwYMHhIeHqxfzrFevHr/88gu//fYbXl5ehIWF8cUXX1CtWjUuX75Meno6nTp1\nYtu2bezfv5/mzZvTvHlzZs+erZ4R/zxeXl7s3buXxMREWrRoQYsWLVizZo16tu6z5/Htt9/Sr18/\nAgMDadiwIXFxcezcuVOUXfpbVlYWMTExREdHq18XL14kKCiIa9euERQUREJCAtu2bWPChAmMGjUK\nPT09QPOGT4GPPvqIrKws+vXrx/Hjx0lNTWXv3r188MEHGollpVJJ/fr1iY6OZufOnRr9xMXF4e/v\nz8yZM7G3t0elUtGhQwdu377N1atX+X31arZ07Mh6X1+6DRpEeno6wcHBnD9/npYtW7JlyxaSkpJI\nSEhg4cKFXL16VX0D5r333sPAwIDBgwcTFxdHeHg4H3zwAb169VInsadNm8bs2bP5/vvvOXfuHHFx\ncSxdupRZs2apY3R0dGTv3r2kp6dz584dAJycnLh48SKnT5/m5s2b5Obm0qFDB3x8fOjevTs7d+7k\nwoULHDlyhClTphQ5u1sQBEGoPJIs/lcjCIIgCILw0lCpVPz5559ER0fj4uJC37591f/ZL6v9+/cT\nHh7Oxx9/jJmZWTlH+urZs2cPnp6eIplWguTkZExNHgIAgwAAIABJREFUTSssyR0fH4+lpWWFJtFf\nJPHx8djY2GBubv7cNhEREYSFhREUFFSoVnBliImJwdLSUp0UF6pGYGAgoaGhhbb37t2bNWvWcOjQ\nIcaOHUt0dDRmZma89957zJo1S11fvl27djRo0IAff/xR4/iUlBQmTJhAWFgYjx49wsHBgc6dOzN3\n7ly0tbUJDAzk1q1bbNy4kVWrVpGSksKFCxc4deoUsbGxLFmyhCFDhhSKq0mTJnTp0gV7e3v8/f3R\n0tIiMTGR1atXU6dOHZo3b86sWbPYs2cP6enp6Ovr4+XlRf/+/Rk2bJg67ri4OD755BMiIyPR09Oj\nR48e/PDDDxp1xFetWsWcOXOIj49HX18fDw8P/vOf/9C3b18Atm7dymeffUZaWhr29vakpqaSm5vL\ne++9R1hYGHfv3mXJkiUMHjyY+/fvM2nSJNavX09mZibW1ta0bt2ar7/+Wl1GRxAEQah8IsktCIIg\nCILwkpFlme3btxMVFYWzszMDBgxQLxxXFgkJCaxZs4Z+/fpRr169Coj01ZKamsq1a9do3bp1VYfy\nwpJlmSNHjpRL7dui3L59mytXruDp6Vkh/b9oHj9+zMmTJ2nevHmx7VatWkVSUhKTJk2qsgUY4+Li\nMDExwcHBoUrGF14Mjx8/ZsWKFaSlpdG+ffsSf14ePHiQAwcO4O7uTq9evZAkiR07dnD8+HE6depE\ny5YtKylyQRAE4WUnypUIgiAIgiC8ZCRJ4u2336Zly5akpqayfPnyf7QoYkHphPT09PIO8ZXk6Oio\nXnxPKFpKSkqxdbL/jfz8fM6ePUuDBg0qpP8XUWxsbKkS+leuXMHMzKzKEtwAHh4eZGdnc+HChSqL\nQah6WlpaDBw4EFtbW8LCwjh+/Hix7du2bYuXlxdnz55VL0DaqVMnlEole/bs4erVq5URtiAIgvAK\nEEluQRAEQRCEl5AkSXTs2JG2bdty8eJFli5dSk5OTpn6MDMzo1q1aly6dKmCony1VGUC8WVQsAhh\nRZXLOHnyJI0bNy6xzvSrIjs7G0mSNOqHFyU3N5fs7OwSa19XBjc3N/Ly8khJSanqUIQqpK2tzfvv\nv49SqWTHjh2cPn36uW0lSaJr1644OjoSERHByZMnqVatGv3790dLS4tVq1bx6NGjSoxeEARBeFmJ\nf6kLgiAIgiC8pCRJol27dvj5+XH16lWWLFlSpmSAJEk0adIEU1PTCoxSeF0kJydX2CzuCxcuoFQq\nMTAwqJD+X0SlncWdmZkJgK2tbUWHVCouLi5IksS5c+eqOhShAqSlpaFQKDh16lSx7fT09PD398fc\n3JwtW7Zw9uzZ57YtSGpbWlqybds2UlJSqF69Oj179uT+/fts3LixVAskT5069bV60kMQBEHQJJLc\ngiAIgiAIL7k2bdrQuXNn0tPTWbx4MQ8ePCj1sZ06deLx48cVGN2rRZIkcb2KIMsyN2/erJBZ3NnZ\n2dy4cQNHR8dy7/tFdePGDczNzUu1qGxBuaGC8kMvgtq1a6Orq0t8fDz379+v6nCEIgQEBKBQKAq9\nSqqn7+DgQHp6Ol5eXiWOYWBgQGBgIKampqxfv57IyEi0tbVZvnx5oba6urokJyczf/58Vq9ezfXr\n13Fzc6NJkyYkJSVx7Nixf3yugiAIwutBJLkFQRAEQRBeAS1atKBLly7cvHmTuLi4Uh+nUChKNUNO\neMLW1pbz589XdRgvnOTkZFxcXMq9X1mWOXXqFI0bNy73vl9kSUlJpb6e165dA8Da2roiQyozR0dH\nbt68yffffy9mdb+ACkpepaena7y2b9/+3GPy8vJQKBQolcpS3YABMDIyIjAwEENDQ8LCwvDz82Px\n4sWF2j1+/JjVq1czdOhQJEli2bJlZGVl0blzZ6ysrNi9e7f6u16Uf7IuhSAIgvBqEUluQRAEQRCE\nV0STJk346KOPqFatmlggsYK4uLiQmppa1WG8UApmcVtaWpZ737GxsXh4eJQ6ofYquHDhAo6OjqWu\nPX7x4kUMDQ3R0dGp4MjKrkGDBujr67N69eoy3XwTKp4sy+jq6qJUKjVeZmZm6jYKhYIFCxbw7rvv\nYmRkxMSJE4ssV5KYmEi3bt0wMzPD2NiYVq1aaXzepqamBAYGoquri7W1NQcPHiy0QOm2bdvIzMxk\n9OjR9O/fnyNHjuDq6oqxsTHfffcdx44d448//lCX5CoqtoK/M6tWraJ27dqYmJjQs2dPbt26pXHe\n06dPp2bNmujp6eHp6cmWLVs0Yjlz5gwdOnTAwMAACwsLAgMDuXfvnnr/48eP+fTTTzE3N8fCwoKx\nY8cSFBREu3btNPqZPXs2derUwcDAAE9PT1asWKHeV3AdN2zYQMeOHTE0NMTd3Z29e/eW+bMUBEEQ\nnhBJbkEQBEEQhFeIhYUFjRs35tq1a8XOenuajo5OmUqcvM7Mzc3FtXpGWWYdl0VGRgba2tpUr169\n3Pt+UcmyzPXr17GzsytVe5VKxd27d1+YetzPql69OsOHD8fExIT169cTHR1d1SEJTynNUzzTpk3j\nnXfeIS4ujo8++qjQ/mvXrtG6dWuqVavG3r17iYmJ4eOPPyY/P1+jnbm5OQEBAdSrVw9jY2N+/PFH\njf2LFi2iQ4cOODg4EBYWRkREBG+88QbTpk1j3rx5HD9+nH379rFp0yZ13BMnTuTiuXO0btgQDw8P\nZFkmLS2NtWvXsnnzZnbv3s3p06eZOHGiepzvv/+euXPnMmfOHOLi4ujZsyfvvvsuMTExwJPySJ07\nd8bExIQTJ06wceNGIiMjGTJkiLqPuXPnEhoayqJFizh69Ch5eXmsXLlS48bUxIkTCQkJYcGCBSQk\nJDBhwgQ++OCDQjPlJ06cyCeffEJsbCxNmzalf//+ZGdnl/i5CIIgCIVJsng+VRAEQRAE4ZUUGxuL\nubk59vb2xbaLiYlBpVLRsGHDSors5bZu3Tp69+5d1WG8EGRZ5ujRo7Rs2bJc+83Ly+PYsWO0bt26\nXPt90cXFxWFvb68xm7Y4N2/e5Oeff8bPz482bdpUcHT/3P379wkJCeH27du8/fbbNG3atKpDeu0F\nBASwYsUK9PT0NLb/5z//4ZtvvgGezJYeNWoUP/zwg3p/Wloazs7OREVF0ahRIyZOnMjKlStJTk5G\nS0urxHGvX7/OwIEDOX36NElJSSiVStLT03FwcGDFihX06dMHBwcHvvnmG+zs7Dh48CDu7u5cvnyZ\n+fPnM3ToUN566y1atGiBnkLBL38n08fr69Ph3XdZv349mZmZGBsbAzBz5kxCQkJITk4GwM7OjpEj\nRzJp0iR1TO3atcPe3p5ly5bx+++/M3bsWK5evYqhoSEABw8epF27dqSkpODs7EyNGjX49NNPGTdu\nnLqPevXqYWtry759+8jOzsbKyoo9e/bg4+OjbvPJJ5+QnJzMtm3b1Nfx119/Zfjw4cCTGwb29vZE\nRESUWBtdEARBKKzk30KCIAiCIAjCS8nT05OzZ8+Sn59PrVq1ntvO1dWVXbt2iSS3UGYVNYv7xIkT\nr10iNC8vjwcPHpQ6wQ3/fNHJZxOVFc3IyIihQ4cSGhrK9u3buXTpEn369PnX40+dOpX169dz5syZ\ncoz29fHGG2/w22+/aWwzNTXVeN+kSZNi+zh9+jStW7cuVYIboEaNGsyYMYM2bdowYcIE5syZQ2ho\nKGZmZvTo0YMbN25w5coVRowYgUKh4PHjx6hUKuBJ0t3S0pJdu3YhyzKD8vPxL+j44UNmHTlCrVq1\n1AnugvEyMzMBuHfvHtevX9dIPAP4+PiwY8cOABISEvDy8lInuAFatmyJQqEgPj4eCwsLMjIyaNas\nmUYfzZo148qVKwDEx8fz6NEjOnfurDG7Oy8vDycnJ43jPD09NWIF1PEKgiAIZSOS3IIgCIIgCK8w\nd3d3EhISSE1NxdnZucg2enp65OXlVXJkLy8DAwOysrIKJYNeN7Isc/v2bVxdXcu136SkJGrVqoWu\nrm659vuii4mJ0Uh4lUZBSSIbGxsCAgK4desWf/75Z0WEVybnz59n5syZ7Nmzh8zMTGxsbGjatCkf\nffQRWlpaHDp0CChduQyh4ujr6z/390KBp5O9RZEkqcyfo4+PDy1btuT48eNs3bqVxYsXM2jQILS1\ntdUJ7V9//ZVWrVqRn5/Pxo0buXr1Ku3bt8fHx4fg4GAAivoJoa2tXSi+gj6Lo1D8r5Lr886nuDr5\nTx9TMN7WrVtxcHAoNr6n3xf0X5p4BUEQhMJETW5BEARBEIRXnJubG48fPyYlJaWqQ3klODk5kZSU\nVNVhVLlz586V+yzue/fucf/+/VLXpH5V/PXXX2hraxcqHVGSS5cuoa2tjZGREZIklXqxyopUMDs7\nISGB4OBgEhIS+PPPP2ncuDFjxozB398fOzs7ZFnm2LFjL2WiW6VSvRKJyPL4vjRs2JCIiIgy3ygd\nNWoUSUlJZGRkkJyczLBhwwCwtrbG1tZWXRqkbt26fPzxx9StW5fo6Gju3LlDt27dAAjV0iIUCAU+\n19bGq4SnP0xMTLC1tSUiIkJje0REBPXr1wee/L48c+YM9+/fV++PjIxEpVLh5uaGqakpNjY2HD9+\nXL1flmVOnDihfl+/fn10dXXVT0w8/apZs2aZrpMgCIJQeiLJLQiCIAiC8BooSEaeO3euyP2SJPH4\n8ePKDOmlVbt2bfVj6a8rWZa5c+cOFhYW5danSqUiJibmtSybExcXR4MGDcp0jCzLZGZmYm1trZ5N\nW5AwVqlUTJ8+nZo1a6Knp4enpydbtmwp1EdaWhodO3bE0NAQd3d39u7dq9534MABFAoF+/bto3nz\n5hgaGtK0aVNOnz5dbEwBAQHUqVOHw4cP8/bbb+Pk5ESDBg3473//y759+9DR0aFHjx5IksTBgwdp\n1KhRkeOrVCqGDh2Ks7MzBgYGuLi4MGfOnGKT4gEBAXTt2pVvv/2WGjVqYGZmxoQJE1CpVHz55Zco\nlUpq1KjBvHnzNI7LyspixIgRWFtbY2Jigq+vLydPnlTvX7JkCcbGxuzYsQMPDw90dXVJTEzkzJkz\ntG/fHlNTU4yNjfH29ubAgQMlfnYvikePHpGRkUF6err6dePGjTL1ERQUxP379+nbty9RUVGkpKTw\nxx9/qBdyfJ6ePXtiZGTErFmz8PDwQKlUqvdNmzaN2bNn8/3333Pu3DmSk5PR1tbm6NGjrF69Gisr\nKyRJomGrVixv1ozVPj681a8fjx49KjHesWPHMnfuXFatWkVSUhKTJ08mIiKCMWPGADBo0CAMDAwY\nPHgwcXFxhIeH88EHH9CrVy/1rPePP/6Y2bNns2nTJs6dO8fnn39Oenq6+qaBsbExY8aMYcyYMYSE\nhJCSkkJ0dDTBwcH8/vvvZbq+giAIQumJJLcgCIIgCMJrok6dOujo6BAfH19on62tLRcuXKiCqF4+\nOjo65P+92Nnr6ty5c+VepuT06dN4e3u/ELORK1NGRgaWlpYa5RJK4/79++Tl5WmUQyi4dj/88ANz\n585lzpw5xMXF0bNnT959991CiceJEyfyySefEBsbS9OmTenfvz/Z2dkabb744gtmz57NqVOnsLCw\n4L333ntuTNHR0cTHxzN27NgiP0cTExPgfyUaCmbQDhkyhPt37tDl7bfZvHkz8CTJbW9vz9q1a0lM\nTOTrr79WLyJYnPDwcC5evMjBgwcJDg5m9uzZvPnmm6hUKiIjI5k6dSpjx44lOjoaeJKY79KlC9ev\nX2fbtm1ER0fTtm1b/Pz81DXP4UlCeMaMGfz+++8kJCTg4ODAwIEDsbOz48SJE8TExDBt2rQyz8av\nKpIksXfvXmrUqIGtra361bhx41IdW8DW1pbw8HByc3Np164djRo14ueffy5UluNZurq6DBo0iLt3\n7zJ69GhSUlK4c+cOAEOHDmXx4sUsW7YMb29v2rZty4oVK9Q3R5YuXQpA3bp1afvOOyxetw5/f3+y\nsrJ4+PBhsfGOHj2asWPHMm7cOBo0aMDmzZvZsGGD+iaTvr4+u3bt4t69ezRr1owePXrg4+PD4sWL\n1X2MGTOG999/n8DAQFq2bIkkSfTs2VOjxNL06dOZOnUqc+fOxcPDg06dOrFx40aN8jCv2886QRCE\nCicLgiAIgiAIr5WLFy/KZ86c0dh269Ytefv27VUU0ctn3bp1VR1ClVGpVHJkZGS59nn58mU5OTm5\nXPt8WURERMgqlarMxyUlJclTp05V/1329/eXu3btKsuyLNva2srTp0/XaO/r6ysPGjRIlmVZvnDh\ngixJkvzbb7+p91+9elWWJEk+fPiwLMuyvH//flmSJHn37t3qNocPH5YlSZKvXr1aZEyrV6+WJUmS\no6Oji429YPzg4GB54sSJsrmWljwfZAnk6jo68s6dO4s8bvz48XKHDh3U76dMmSJ7eHio3/v7+8sO\nDg4a17NJkyayt7e3Rj+Ojo7y3LlzZVmW5bCwMNnIyEh++PChRhtvb2959uzZsizLckhIiCxJknzq\n1CmNNiYmJnJoaGix5yqUjkqlkg8fPizfu3ev2Hbnz5+Xp02bJv/444/ylStX5BkzZsjz58+Xs7Oz\n5eDgYHnatGnylStXKinq//H29pZHjx5d6eMKgiAI/yNmcguCIAiCILxmHBwcMDMz05jVaW5uXmgG\np1C8V6Em7z+RmJhYrrO4Hz16xJUrV6hTp0659fmyOH/+PM7Ozv9oRmfBLGMbGxuN7X/99RfXr1/H\nx8dHY3vr1q0LPcXx9EKXNWrUACAzM7PMbQrIZayv7e3tTcKxY3z3+DEf/71tUG4uv/1dTiQ4OJgm\nTZqgVCoxNjbm+++/5/Lly8X2Wb9+fY3raW1tjYeHh0Yba2trdVmOkydP8uDBA6ysrDA2Nla/4uLi\nSE1NVR+jpaWFt7e3Rj+fffYZw4YNo3379sycOfO55aBeZ76+vowePbrEdpIk0bJlS2JiYor9XeTs\n7Ey3bt24ffs2u3fvpkuXLmRlZbFt2zb69OmDlpYWq1evJicnpzxPQ8OlS5f47bffOHfuHHFxcXz8\n8cfExcXh7+9fYWMKgiAIJRNJbkEQBEEQhNeQvb09SqWSU6dOVXUoLyUrKysuXbpU1WFUOlmWuXv3\nLubm5uXWX1RUFE2aNCmX/l4mKpWKjIwMdeK4rC5fvoxCoSj1ZyHLcqFk+tMlJQr2PXvzpjRtChTU\n/i+qJFJRtLW14e8+CyIrSJOvXr2aTz/9lCFDhrB7925iYmIICgoqMXmppaWl8V6SpEKlMyRJUp+D\nSqXC2tqamJgYjde5c+eYPn26+hhdXd1C12/KlCnEx8fTo0cPIiMj8fT0LLGcysuooNb507Zu3Yqh\noSGTJ08u9thNmzbxzTfflGqcadOmMXLkSE6dOlVsfe2CEiaXLl3i/PnzeHl5ER8fz4ULF+jWrRt/\n/fUXW7ZsKfGmi6OjIwqFguXLlxfa17x5cxQKRaH67QAKhYJly5bRvHlzWrVqxfHjx9mxYweNGjUq\n1XkKgiAIFUMkuQVBEARBEF5TNWrUwN7enqioKI0F64SS1a1bV2OW5+siMTGRevXqlVt/8fHxuLq6\nFkpMvg7Onj1baIZxWVy9ehUzM7NCtbyNjY2xtbUlIiJCY3tERATu7u7/eLzSaNiwIfXr12fOnDlF\n/iy5e/duoW0jPv+c8fr6hP79fpmODiM+/5yIiAiaN29OUFAQ3t7eODs7k5KSUuKs97LOim/UqBEZ\nGRlIkoSzs7PGy9LSssTj69Spw6hRo9i6dStDhw5l4cKFZRr/ZSBJksZ1XbZsGb1792bWrFl89dVX\nRR6Tm5sLgJmZGYaGhmUaryBxXNBHUXx9ffH09CQuLg5DQ0PMzc3Zvn07VlZW6qR3SYtfSpJEzZo1\nNeptw5OFYM+ePYulpWWR3yd7e3sOHTrE3bt3uXfvHkeOHKFDhw5lOkdBEASh/IkktyAIgiAIwmtM\nqVTi6OjI8ePHMTMzIyMjo6pDeilYW1urF0l7XahUKu7evUv16tXLpb9bt26hUqmwsrIql/5eJjk5\nOTx69Ei9EGNZPX78mEePHlGrVq0i948dO5a5c+eyatUqkpKSmDx5MhEREYwZM+bfhF0qISEhnD9/\nntatW7Nt2zbOnz/PmTNnmD17Nh07dizUvnPnzoRu3MiWjh2RgVHjxtG5c2dcXV05deoUO3fuJDk5\nmenTpxMeHl7i7Nxn98uyXOy2jh074uPjQ/fu3dm5cycXLlzgyJEjTJkypdCNgqc9evSIjz76iIMH\nD5KWlsaxY8cq5UZCVXj6+s2fP5/hw4ezePFiRo0apd5eMNv722+/xd7eXr0gqq+vr0a7DRs24Onp\niYGBARYWFvj6+pKZmcmSJUv46quvOHv2LNra2vj6+jJ16lTy8vLIyspixIgRWFtbY2Jigq+vL6dO\nnaJbt27UqlWLn3/+mf/+979cuHABb29vBg0axIoVK1i2bBk3b94s9twGDhzIkSNHNBZeXrRoEb17\n9y6UnL9z5w7+/v6Ym5tjYGBAx44dNZ5aWLJkCcbGxuzbtw8PDw+MjIzw8/MjLS1N3eb8+fN0796d\nGjVqYGRkROPGjdm2bZvGOLm5uXzxxRc4Ojqip6dH7dq1+emnn9T7w8PDad68Ofr6+tjY2PDZZ5+R\nl5dX7HkKgiC8DkSSWxAEQRAE4TVnaWmJi4sLubm5oqZsKSkUijLXH37Zlecs7vz8fOLj4//VTOaX\nWWxsLF5eXv/4eC0tLdzc3OjcubN6m0qlUs+IHz16NGPHjmXcuHE0aNCAzZs3s2HDBho0aKBuX5oZ\nz0W1Kem4pk2bcvLkSerVq8eHH35I/fr16dq1K0eOHGHu3LlF9tO5c2fW796NJEk0bNgQgA8++IC+\nffsycOBAmjVrxqVLl/j88881jnt2hvGz70u7bfv27fj5+TF8+HDq1atHv379SE5Oxs7O7rnnXa1a\nNe7evUtAQAD16tXj3XffpVWrVnz33XfFXp+XkSRJyLLMpEmTmDRpEps2bWLgwIGF2h08eJC4uDh2\n795NWFiY+tiCa5eenk7//v0JDAwkMTGR8PBwBg8eDED//v35/PPPcXV1JT09nfT0dMaPH09kZCRv\nv/02169fZ9u2bURHR9O2bVv8/Py4ceMGAwYMoF69euTm5pKQkEDXrl356quv0NXVZcuWLaxZs4bH\njx8DsGvXLnp16kSvTp3YtWsX8OT3X9euXdVlZnJzc1mxYgVDhw4tdH4BAQGcOHGCLVu2cPz4cQwM\nDHjzzTc1Sqvk5OQwa9YslixZwpEjR7h79y4ffvihen92djZdunRh7969xMbG0qtXL959912N373+\n/v4sW7aM+fPnk5iYSGhoqPrm4tWrV3nrrbdo3Lgx0dHRLFq0iD/++IMJEyb88w9YEAThVVEFi10K\ngiAIgiAIL6Dbt2/Lv/zyi5yfn1/VobwU1q5dW9UhVJr8/Hw5MjKy3Po7evSo/ODBg3Lr72WSlZUl\nx8bG/ut+nv3+dezYUQ4KCvrX/QrCs/z9/WVdXV1ZkiR5+/btz22jVCrl3Nxcje2+vr7yqFGjZFmW\n5ZMnT8qSJMkXL14sso8pU6bIHh4eGtu2b98u6+vry9nZ2Rrbvb295dmzZ8uyLMshISGyJEnyjh07\n5PXr18tTp06Vv/76a1lHR0eeOnWqvH37dnnnzp2ytb6+vATkJSBb6+vL1tbW8rx58+QdO3bIDg4O\nskqlkteuXSu7uLjIsizLjo6O8rx582RZluWkpCRZkiT50KFD6hiysrJkU1NTeeHChRpxJCUlqdus\nWLFC1tXVLfb6tmjRQp4xY4bGOLt27Sqy7RdffKGOr8CSJUtkXV1d+eHDh8WOIwiC8KoTM7kFQRAE\n4QUxdepUjVl2z76vDAcOHEChUHD79m3gf4/eCq+H6tWrY2pqSmRkpKjNXQo6Ojo8ePCgqsOoFImJ\nibi5uZVLXxcuXMDGxgZ9ff1y6e9lExcX969LWuTm5lKtWjUAbt68yebNmwkPDy+yHIgg/FuSJOHh\n4UGdOnWYOnUqWVlZRbbz8PAotMjn07y9venQoQMeHh707t2b4ODgEsuJxMXFkZOTg6WlJcbGxupX\nXFycxroIurq6ODk54eXlRfXq1UlJSSEvL48aNWpw/Phxvv/qK759+BB/wB/49uFD/vr7PDp37ows\ny+zZs4dFixYxZMiQQnEkJCSgUCho2bKlepuJiQkNGjQgISFBI466deuq39eoUYPc3Fx1Pfrs7GzG\njRuHu7s75ubmGBsbExUVxeXLlwE4ffo0CoWCdu3aFXk9EhISaNGihcY2Hx8fcnNzSUlJKfZaCoIg\nvOpEklsQBEEQ/oWAgAAUCgUzZszQ2P5ssrg0xo4dS3h4eJnGVygUKBSKQnVD8/PzsbOzQ6FQsH79\n+jL1WREUCgUbNmyo6jCEUtDW1qZhw4YcPnyY/Pz8qg7nhebg4EBycnJVh1HhVCoVWVlZmJmZ/eu+\nsrOzuXnz5nNrSb/qrl+/jo2NTaHFIssqJSUFe3t7APr27cuoUaMYP348PXr0KI8wBUGDLMvY2tpy\n4MABsrKy6NChQ5GLiBoYGBTbj0KhYPfu3ezevRtPT08WLVpE3bp1iY2Nfe4xKpUKa2trjhw5QkhI\nCNHR0cTExHDu3DmmT5+ubqelpYWrqyv37t2jc+fO6hIpHTt2xMDAgIzMzOeOIUkS/v7+fP311+zf\nvx9/f/+SLomaLMsapWyeXUS3YF/BjeMxY8awbt06ZsyYQXh4ONHR0TRr1qzYRTaf7U9+Tqmssi66\nKgiC8KoRSW5BEARB+BckSUJPT485c+aUOBupJIaGhv9oQTcHBwcWL16ssW3Hjh3q2VQvyn96nvef\nsgIFNTOFqqWrq0t+fj5NmjTh8OHD4nMphouLCxcvXqzqMCpcQkJCuczilmWZU6dO0bhx43KI6uUj\nyzKpqak4Ozv/675SU1NxdXUFYN++fVy6dIl0KM8ZAAAgAElEQVRp06b9634F4XmeTnRnZ2fTvn37\nMt3If1qLFi2YPHkyJ06cwNbWljVr1gBPno559uZq48aNycjIwMTEhPbt23Pz5k2cnJxwdnbG0tKy\nUN/e3t7cunWLRo0aIcsyhw4dok+fPri3aMEYbW1CgVDgMy0tDIyM1McNGTKEiIgIOnXqhI2NTaF+\n3dzcUKlUREZGqrfdu3ePuLg46tevX+pzP3z4MP7+/vTs2RMPDw/s7Ow0ZmB7e3ujUqnYt29fkce7\nublx9OhRjX9TRUREoKOjQ+3atUsdhyAIwqtIJLkFQRAE4V9q164djo6OGjOKihIfH0+XLl0wMTHB\n2tqagQMHkpGRod7/T8uT+Pv7s3btWrKzs9XbFi1aREBAQKG23333HV5eXhgZGWFvb8/w4cOf+9jx\n0/bt24eHhwdGRkb4+fmRlpamsf/XX3+lTp066sd0Fy5cqN7n6OgIQJ8+fVAoFOoET8H5LlmyhNq1\na6Ovr8+DBw/YuXMnbdq0wdzcHAsLC958800SExPLfF2Ef6Z27dqcO3cOfX19mjdvTmRkJHl5eVUd\n1gvJwMCg1LPvXlYqlYp79+6Vyyzu2NhYGjRo8K9nMb+MHj58yKFDhzTKGPwbOTk5mJiYlEtfglAW\nNjY2HDhwgNzcXPz8/Lh161ax7WVZVidkjx49yowZM4iKiuLSpUts3ryZy5cvq5PEjo6OXLx4kdOn\nT3Pz5k1yc3Pp0KEDPj4+dO/enWPHjqGlpcWSJUuYMmVKoafY4MmN/RYtWpCXl4ckSZw9e5asrCwC\nAwN5s18/Qps0YUO7dnQbNIj8/Hz17zcnJydu3bqlTrg/q27dunTv3p0PPviAiIgIzpw5w6BBgzA1\nNS1yEc7ncXFxYcOGDZw+fVrdR05Ojvoaubi40LdvX4YNG8aGDRu4cOEChw4dYvny5QAEBQVx7do1\ngoKCSEhIYNu2bUyYMIFRo0ahp6dX6jgEQRBeRa/fvzAFQRAEoRzJsoxCoWDWrFkEBwdr1Id82vXr\n12nbti2enp6cOHGCsLAw7t+/T/fu3Uuc4VwST09P3NzcWL16NQCZmZns3LmTwMBAjXa7du0i9Lff\nMNXW5pdffmHlypUcP36cUaNGFerTwsKCS5cuAU+SKbNmzWLJkiUcOXKEu3fv8uGHH6rbbty4kVGj\nRvHZZ59x9uxZPv74Y4KCgti6dSsAUVFRACxcuJD09HROnDihPvbChQusWrWK9evXExMTg66uLg8e\nPOCzzz7jxIkTHDx4EFNTU7p27fpKJFpfhrItderU4cqVK8CTWd0tWrTgyJEj5OTkVHFkL6YX5UmJ\nilJes7jT09PR1dUtl2T5yyg8PJz9+/e/Ej/HhNePJEkaP+uUSiX79+8HwM/Pjxs3bhRqU9SxZmZm\nREZG8s477+Di4sLYsWOZPHmyOkncu3dv3n77bdq3b49SqWTVqlUAbN++HT8/P4YPH06rVq2YNGkS\nx48fx87OTmOcAgqFAnd3dyRJwsTEhD///BN3d3fatm2Lb9eu/F9oKGPHjkWWZeLj49XHmZmZFZso\nDgkJoVmzZnTr1o3mzZvz6NEjdu7cia6ubpFxFLXtu+++Q6lU0qZNG7p06UKrVq1o06aNRpulS5cy\ncOBARo8ejZubG4GBgdy7dw8AW1tbduzYwenTp2nYsCFDhw5l4MCBzJw587lxC4IgvDYqf61LQRAE\nQXh1+Pv7y127dpVlWZbbtWsn9+/fX5ZlWd6/f78sSZJ869YtWZZl+csvv5Tbt2+vcezt27dlSZLk\nEydOyLIsy1OmTJE9PDzU+599XxRJkmRAdnZ2ln18fGRZluU5c+bIHTt2VO9fv369vHPnTtlaX19e\nAvISkK319eWdO3fKO3bskHV1ddX9FcQNyBcvXpRDQkJkSZLkpKQkdZsVK1ZoHNOqVSt56NChGnEF\nBATIrVu31ohz/fr1Gm2mTJkia2try5mZmYXOSZIk+dChQ7Isy/L9+/flatWqyeHh4bKtra0sSZK8\nbt26Yq/L00JCQmQjI6NC22vVqiXPnTu31P2Uh4yMDDknJ6dSx/wn1q5dq/E+NzdXPnjwoPzw4cMq\niujFtXbtWjk/P7+qw6gQ+fn5cmRk5L/uJycnR46IiCiHiF5Ot2/flr/66is5JCREVqlU/7q//Pz8\nMv0M/H/27jyupvz/A/jr3OrWbUVJu7ZpoZJSiUYZsoxC9qVRGMsgY4Y0li/ZGdnGGM2g+Nm1EYYI\nbdKkVakUlWgxSNKm5X5+f/h2vl2VFqXi83w87uOhe875fN7nlLt8Pu/z/lBUZ9Oczzd1NfQZ4smT\nJyQpKemDx71584YEBQWRjRs3kt9++408f/6cbN26lXh4eJDy8nISEBBA3N3dyb179wghhFhbW5Ml\nS5a0/IQ6WFeNm6Ioqq3RTG6KoiiK+kjkv5nYO3bsgI+PD+Li4urtExsbi7CwMEhJSbEPNTU1MAyD\nR48efVTf2trayM7ORlxcHNLT0+Hl5YW5c+cK7PfXrl3YUV4OVQAnAFSVl8NuzBhMnDgRVVVVKCgo\naLSP2hIktXr16oXKykp20am0tDQMHjxY4JjBgwcLZEc1RkVFBT179qx3TjweD99++y1kZGSgoKAA\nPp+Pixcvtmmd8Y7IwJWXlweXy/3k/X4sERERDBo0CHfv3kVZWVlHh9OpyMjI4Pnz5x0dRrtITU1t\nUa3ZxsTExMDMzKwNIuqarl27Bj6fj1GjRrXJ687Tp08hKyvbBpFRVNuoXYT7+++/r7fNzc0NHA4H\n9vb27HOtWWj7fSoqKpCSkkJqamqj+0hKSsLExAQGBgYoLCzE7du3MX78eJSUlOD8+fMYPXo0m+ld\nVFTUaDZ6rQ8tKm5gYNDiuvi1i4dzOBxwuVxoaGhg1apVLV70uam4KYqivhR0kJuiKIqi2oiZmRkm\nTpyIlStX1vuyQQiBnZ0dEhMTBR4ZGRkYM2ZMq/orLy8HAEybNg3Dhg2DhoYGFixYgH///RcODg7I\nzs4GIQRRUVGIiI3FPADDAUgBWAxAV1UV27ZtAyEEgwcPBo/Hw9KlSxvsS0pKCleuXIGBgQFGjBgB\nAKioqICbmxtbvsTc3BzXrl1jj2EYBnPnzoWmpiYIIViyZAl27twpUJ5FQkKiwf4UFBRQVVWFkJAQ\nxMfHQ1hYGDdu3GhxnfGQkBDMmTMHpaWl7BfJDRs2YOjQoXj8+DFcXV3B4XAgJCTEtufv7w9DQ0OI\niYlBTU1N4Bbgo0ePCnwprX3MmTMHABrcVrf+cFcoVwK8i/P9WtPCwsIYNGgQYmNjUVJS0kGRdT7a\n2tpIT0/v6DDaXG0tbhkZmY9q58GDB1BXV++Skztt4cmTJ0hLS0O/fv0aXMyuNTIyMthFJymqM2AY\nBqqqqjh37pzARGh1dTX+7//+j53Ur9Xahbbf17t3b4iKiiIjI6PRfeTk5GBiYgJVVVUkJCSguroa\nJiYmePDgAZKTkzF58mRUV1fD19f3o2Jp7UBzbSm3rKws7N27FwcPHsSuXbs+Kpa29rmvPUFR1OeD\nDnJTFEVRVBvaunUrwsPDcfXqVYHnTUxMkJycDDU1NWhqago8JCUlW9VX7Rey/v37Y/78+cjLy0No\naChmzJghMKB06tQpLP7xR4gKC4MAuAbgIJcLMxsb+Pn5sYPciYmJmDBhAggh9b6oVVRUYPPmzTh0\n6BD+7//+DwCwZMkShIeHo2/fvhg/fjycnJxgb2+Pe/fuISIiAn369IGKigp8fHzA5XIxbdo0bN26\nFd7e3g2eT1BQEOxsbAC8ywQ3NDREfHw8iouLUV1djaSkpHp1xgFASEgI+/btQ0pKSr0644MHD8be\nvXshLi6OgoICFBQUwNXVFf7+/lBRUcH69etRUFCA/Px8AO8y7qdMmYJJkyYhOTkZ27dvx7Zt2/D7\n778DeDehUNtOQUEBgoKCwOVyYW1tDQAC2548eQJTU1PY/PecuhIVFZUG7zAQEhJi/1Zq64N+6Xr3\n7o1///23o8NocykpKR+dxf369WuUlZVBSUmpjaLqWggh+PvvvyEkJIRhw4a1WbuvXr1Cr1692qw9\nimoLRkZG+OqrrwQWbrx8+TJ4PB5sbGwEJrgbWmj72LFj7ASzgoJCvUntly9fYvLkyZCUlISWlhZO\nnjwJAOxE+sKFC6GnpwdxcXFoaGjAzc2NXUuid+/eMDMzw+PHjzFq1ChMmTIF+/fvx/Lly8HhcDBk\nyBDk5uY2ayHuttatWzfIy8tDWVkZ48aNg62tbb07Ai9evAhTU1PweDxoampi7dq1jdb3b2wyvu7n\np6baU1dXx4YNGzBnzhx0794d3333XfucPEVRVBujg9wURVEU1Ya0tLQwf/587N27V+D5xYsX4/Xr\n15g6dSqio6ORmZmJ4OBgLFiwoNVZsUeOHGEHo8ePHw9RUVF4eXnBw8NDYD97e3usW7cOv/72GwCg\nFMACV1cMGTKEXQRSWVkZlZWVsLa2BsMw9RbDrKmpwe+//w5LS0t2oSd/f3+cPXsWmzZtQkBAABiG\nwddff43Fixfj1KlTcHNzw4YNG2Bqagp1dXWUlpZi5syZ7CB5XUFBQXBycMDE0FAAwJmTJ6Gnp4c9\ne/Zg4cKF4HA40NHRQe/evesd++OPP8LGxgZqamoYMmQIduzYwX7JFhERgbS0NBiGgby8POTl5SEu\nLo7u3btDSEgIUlJS7PPAu6xwGxsbrF+/Htra2pgxYwZWrFiBHTt2AADExMQE9p83bx4WLVoEJycn\nAGC3ycvLY8uWLXj9+jX8/Pxa+JvteHp6eo0uosrhcDBo0CAkJyezJWu+ZHUz9T8XfD4fJSUlH5XF\nzefzkZiYCGNj4zaMrGtJSUlBQUEBrKysICUl1WbtMgzzWf7dUV3f3Llz4eXlxf7s5eWFOXPmNJnh\n/Oeff2LhwoWYO3cukpOTcfXqVfTr109gn40bN8LBwQH37t3D1KlTMWfOHDx58gQAoKOjAy6Xi23b\ntiEtLQ1//PEHzpw5gy1btrDH5+Xl4dy5c7CwsICbmxs8PT1x//59zJo1C5aWllBSUsKrV69QWlra\nYIxBQUFYt2IFCCG4ceNGve3vf25qrrrHpaSkIDIyEgMHDhTo19HREUuXLkVKSgq8vLzg6+uL1atX\nN9heY5PxtRPuzW1v9+7d6NOnD2JjY+milhRFdRn00xFFURRFfYSGbk9dt24dREREBJ5XVFTE7du3\nweFwMGrUKBgYGGDJkiUQExODqKhog2196NbXhw8f4vbt2+zPwsLCcHJywrlz5+qVBagdGP7hhx+w\nb98+AMDOnTtx6tQpGBgYAHg3UOfn54fr16832KewsHC9wSpCCPr06QNHR0dwOBwsWbIEN27cQFxc\nHA4ePIgxY8bA09MTAwYMQEFBAQ4dOoQDBw4gMjKy3vnV1gyvzTOS4vNx8uRJ3L9/HwsXLgQhhM2W\nft/Nmzdha2sLVVVVSEtLN6vOeGMaqy+em5srMBlRWVmJCRMmoG/fvg3eVnzgwAGcPn0aFy9ebJNb\nsj81aWlpthxOQxiGgaWlJdLS0hqsTfqlae3gRmfVFlnccXFxMDEx+WLrxFZXVyMoKAg8Hg+DBg3q\n6HAoql3V3gE2Y8YMxMTE4NGjR+wAq7Ozc5OvkZs2bcJPP/2EZcuWQVtbG8bGxvjpp58E9pk1axZm\nzJgBTU1NbNq0CcLCwggPD2e3//bbb9DW1oawsDBGjx6NVatW4fTp0+z2LVu24JdffsHUqVPBMAy4\nXC7c3NwQHh6O69evY9KkSWAYBhkZGfXKc9ROxA+KjQUDYOqUKRAXFxdYZyUtLa1V1+67776DlJQU\neDweDAwMYGVlJVA6bsuWLVi5ciWcnJygoaEBGxsbbN++HZ6eng2219RkfHPbs7GxwYoVK6CpqQkt\nLa1WnRtFUdSnJtzRAVAURVFUV9ZQ6Y2ePXs2WMpBW1sbPj4+jba1fv16rF+/vtGf6zp8+DBqamog\nJCSEqVOnAvjfQNvTp0+hoqIC4N1gZG0NbQBYunQpli1bhtOnT2PChAmYMGEC9PX1sX79ety4cQPR\n0dFwcnLC0aNHwefz2duFXVxc2MEqGxsbnD59mv0yW7sYZC0ejwdFRUWcPXsWP/30E3bt2oVBgwZB\nWloav//+OwICAj54fgyAGQByrK0hrayM48ePo3v37tizZ0+9fR8/fowxY8ZgwYIF2Lx5M2RlZREb\nG4vp06e3uoZkY1/G6w7WLViwAK9fv8bVq1frDeLduHEDrq6uuHDhwmddN5dhGFhYWCAmJgY1NTX1\nFhD9ktTWMP8c6k7XZnFLS0u3uo0nT56ge/furS7F9Dn4559/8ObNG4wdO7ZN/y6KiorA4/HarD2K\nakvdunWDg4MDjhw5AhkZGQwdOpT9PNKYf//9F3l5eU2W9DEyMmL/LSQkhJ49ewqUivL19cXevXvx\n4MEDlJeXg8/ng8/ns9tjY2Nx9+5dCAsLo6qqCoQQCAkJoaqqCqGhodDW1oasrCzevn2LK1euYNy4\nceyxtRPxvQH8CmA9gDsmJvjjv3emEULw7bffNv9C1eHh4YFRo0ahpqYGGRkZ+Pnnn+Hk5ITjx48L\nxL19+3b2GD6fj4qKCjx79qzR0kWNTcY3pz2GYTBgwIBWnQ9FUVRHooPcFEVRFNXFVFdX49ixY9i+\nfTvs7OzY5wkh+O677+Dt7Y3//Oc/zWpLX18ffn5+4HK5GD16NIyMjNjM6dOnT2Pu3LkNHte/f38Q\nQpCfn99ozemIiAhYWFhg0aJF7HMPHz5sMLNz/vLlcIqIAP6bPXyUw8E0LS2MGjUKkydPxpIlSxoc\nKIqJiUFVVRX27NnDthsYGCiwD5fLRU1NTb1jG3peX19fIEO+9jxUVVXZRTI9PDzw999/Izo6ut4g\nXkZGBqZMmYKdO3fC1ta2wevSlfD5/A+WRWAYBmZmZoiNjUVNTU2bLazX1SgrKyMrK+uzmNS4f//+\nR2VxV1RUIDc3V+B2+y9NWVkZQkNDISsrW6/kwsdKS0ujWZVUpzZnzhzMmjULUlJS2LRpU5u1+/6E\nOsMw7CB2VFQUpk+fDnd3d+zbtw9PnjxBUlKSwEQ6IQTu7u6YPHkySkpKcOLECdTU1GDSpEkIDw9H\nQEAAxMTEICkpiYSEBOjq6kJPT6/BWOQA8MTEoKmpyT7X2sksBQUFtp2vvvoKJSUlmDZtGjZu3AgN\nDQ2BuOvFISfXaLuNTcY3t73GFganKIrqzGi5EoqiKIrqYi5fvoyXL19i3rx56NOnD/vo27cvpk2b\n1ujCjg1ZuHAhsrOzsWzZMjx48AB37txBYmIiGIbBixcv4OnpiZSUlHrH6ejoYObMmXB2doafnx8y\nMzMRExMDDw8PNlNbV1cXcXFxuHr1KjIyMrBp0yaEhYU1GMfIkSNxLCAAgba2IAC+W7AA6urqSE1N\nRURERL0647W++uor8Pl87NmzB1lZWTh9+jRbkqWWuro6KioqEBwcjBcvXrBlONTV1REWFoa8vDy8\nePECALB8+XKEhoZiw4YNSE9Px8mTJ7F7926sXLkSABAcHIw1a9bgwIEDEBUVZWteFhcXo7y8HGPH\njsXw4cMxadIkgZqYXZGsrCxyc3Obta+pqSkKCgqQl5fXzlF1Tjo6Oo3WMO9KampqUFpa2uosbkII\nYmJivvgMwJCQEFRVVeHbb79t89rZeXl5dJCb6pRq74IaNmwYREVF8fLlS4wfP77J42oXXQwODm51\n37dv34aysjLWrFkDU1NTjBs3DklJSQL7mJiYIDU1FZqamjAyMsKCBQsgIyOD+Ph4TJo0CVVVVXj+\n/Dk0NDQgISGB8+fP482bNwDeTcS78XioXVJ8rZAQ+llZtTreD6kdkC4rK6sX9/sPISGhBtuonYy/\ndOlSvcn41rRHURTVVdBBboqiKIrqYry8vPDNN980WOt50qRJePz4Mftlsal6uKqqqvD398fVq1dh\nbGyMffv2Yfv27WAYBrNmzYKysjLS09NRVVWFnJwcgWO9vb0xe/ZsrFy5Evr6+rC3t0dERATU1dUB\nvMsimjJlCmbMmAFzc3Pk5ORg+fLljcYycuRI+F27BoZhMHz4cMyfPx8SEhK4fv06wsPDGywjYmRk\nhH379mH37t3o27cvu/Bm3fMeNGgQFi5ciOnTp0NeXh47d+4E8G4RqydPnkBLS4u93bd///7w8fGB\nn58fDA0NsXr1aqxatQqLFy8G8O6LdFVVFaZMmQIlJSX2sWzZMvz777948OABfH19oaioyG6rXaiz\nq9HV1UVGRkaz9zc2NsbLly/ZhcC+JN27d2cHJLqylJQU9O3bt9XH379/H3p6ehAWbv7NokePHm2z\nRRk5HA78/f3b9Zjs7GxwOBzExcU1uP3ly5eIiYlhB40+RkPXhs/nC1xfDw8PaGhofFQ/LWFjYyNQ\nL5iiGnLv3j1kZWXVy75uzJo1a7B3717s3bsX6enpSEhIwO7duz94TN3PBLq6usjNzcWpU6eQmZkJ\nT09PhISEAAA7UL1u3TqcOnUK69evR3JyMqqqqlBTU4NTp04hJSUF1tbWqKioQH5+PiZPnoy3b9/C\nz88PhBB2Ij7S1BQEgNOSJWAYRmBy8/3PKMOGDWt0cci6Xr16xU4Sh4aGYuPGjdDV1YW+vn6Dcael\npcHX1xdubm4Cfdf2/6HJ+Oa2R1EU1WURiqIoiqKoRvD5fBITE0O2bNlC3N3dSWBgICkvL/9k/ZeV\nlRFvb2/i7u5OTpw4QSoqKj5Z31+6mpoa4uPj0+LjkpOTSVZWVtsH1Mn5+vp2dAgfpbq6mty5c6fV\nxz9//pwkJSXVe97JyYkwDEMYhiEiIiJEU1OTrFixgpSWlhJCCCkvLyfPnz9vdb91MQxD/Pz8WnTM\ns2fPyNu3b5u9f01NDXn27Bmprq5ucPvJkyeJsbExe84MwxA5OTliZ2dH0tLSWhSbt7c3kZSUFHju\n/b+znTt3EnV19Ra1+zFevXpFSkpKPll/VNfg7OxM7O3tm73d3d2dGBoaCuxz5MgR0qdPH8LlcomC\nggKZO3cuu62h/9vq6upk165d7M+rVq0iPXv2JJKSkmTixInk4MGDhMPhkPDwcPZv9tq1a+Trr78m\n4uLiRFpampiZmRFHR0fi7u5OkpKSiK6uLjE3NyfZ2dnk+vXrxN3dnURGRrJ93Lp1i3A4HJKTk0O2\nbdtGdu7cyX4uMTAwIBs2bBCIb/bs2R+8bnVfJzgcDlFSUiLTp0+v9x7aUNwHDhxgt9vY2BAXFxf2\n2tZtt/ZRN5am2nv/2lIURXUVDCGf2XLwFEVR1Bfl6NGjcHFxYTN1vnQcDge+vr6YMGFCm7b75s0b\nXLp0Cenp6RAXF4ednR2bZdTe+Hw+goKCEB0djR49esDR0bHBLHaq7fn5+WHixIktPi4tLQ1cLvej\nM1m7ktZeq84iKSkJ6urqrcqqrq6uRlRUFAYPHlzv7pHZs2cjLy8Px48fR1VVFcLCwvD999/D2dkZ\nBw4caJPYq6qqICIi0m6vf82VnZ2NY8eOITw8HEJCQuzCcbm5uXB1dUVeXl6D5Z8a8/77W2VlZb0F\n8Tw8PHDgwAFkZWW17clQ1GeCz+cjIiIC5ubmEBMTq7e9oqICv/32GyorKzFr1iycOnUKwsLCWLhw\nIY4fP44XL15g/vz59RZ4vH//Pnx9fWFiYgJ7e/tPdToURVHUB9ByJRRFUVSn5uzsDA6HAw6HAy6X\nCy0tLbi6urKlAaZNm9biL/fq6uoCK823h7S0NEyfPh0KCgoQ++/iRCtWrEBRUVGTxzZ1O3xHkJKS\nwvTp0zF16lQAwLlz53D69OlPMrnA4XAwevRojB07FkVFRfD09KQDOp9Ia3Mh9PT0UF1d3aJyJ10d\nj8dr1v/vzqi2Fndry4bExsbC1NS0wfJIhBBwuVy27u706dPh6OiI8+fPA2i4JMfFixdhamoKHo8H\nTU1NrF27FlVVVex2dXV1bNiwAXPmzEH37t3h6OhYr9/GXkffL0/y/s///PMPTExMwOPxMGDAAFy9\nehUcDoddT6CxdgkhuHLlCoSFhaGgoABRUVHIy8tDXl4e/fv3x7Jly5CWloa3b9+yx/zyyy/Q09OD\nuLg4NDQ04ObmJrD9fenp6bh58yYUFBQgJSUFJycnlJSUCOzj7Oxcb8DN3d0dhoaGAvG//6gteWJj\nY9Pg9trzt7GxgYuLS6MxUlRnw+FwMGjQIERHR6OysrLedjExMTg6OoLP5+PChQsYO3YsSktLERgY\niMmTJ4NhGJw7d07gNQgA+vTpAx0dHcTFxdHPJBRFUZ0EHeSmKIqiOjWGYWBra4uCggJkZWVh8+bN\n+OOPP+Dq6grg3ZeTD60u31ibTeHz+eDz+a2KOTo6GmZmZigtLcWFCxfw8OFD7N+/H1euXMGgQYPw\n+vXrRo+t+yWqM95spaenBxcXF5iYmCA9PR379+9HTEzMJ4m1f//+7KTH8ePHER0d3Smv0edEXFy8\n1QO3Ojo64HA4SEtLa+OoOicNDQ2kp6d3dBitcv/+/VbX4s7MzISioiJ4PF6j+7z/misqKtrgYBMA\nBAUFwdHREUuXLkVKSgq8vLzg6+tbr7bt7t270adPH8TGxmLr1q2tiv19JSUlsLOzQ58+fRAXF4ft\n27djxYoVH3zPCAoKwsQRIzBy0CBERkZiyJAhEBYWFnhtevPmDc6ePQsjIyOIioqyz0tKSsLb2xtp\naWn4448/cObMGWzZsqXRvo4fPw5PT09s2rQJ8fHx0NXVxe7duwXiYxjmg/GqqakJLIqbnp6O3r17\nY+jQoQCAgIAAdlt+fj4WLFgABQUF6OnpNat9iuqMhIWFYWlpiTt37qC6urrediUlJdja2qKwsBAZ\nGRkYMGAAMjIykJWVhdGjR6OwsBDXr18XOIZhGNjb24PL5cLf37/R1zSKoijq06GD3BRFUVSn1tZZ\ngDY2Nnj8+DFcXV3B4XDYleRr27ly5QuMRXMAACAASURBVAoMDAwgKiqKtLQ0vHr1Ck5OTujRowfE\nxcVha2v7wdvNCSGYM2cO9PT0EBgYCAsLC6ioqGDMmDEIDg7G48ePsWbNGnb/9zMSZ86cyZZ4MDMz\nA4fDwTfffAPg3cD7pk2boKqqCjExMRgZGSEwMPCD1y83NxfTpk1Djx490KNHD9jZ2eHhw4cA3mUF\ncjgcJCcnCxzz119/oWfPnqipqQGfz8fcuXOhqakJcXFx6OjoYP/+/bCzs8Ps2bMhISGBy5cv48iR\nI3jx4kWTv8+PpaqqioULF6Jnz564cuUKAgMDUVNT0+79fqm0tLQ+apBaS0sLYmJiLSrR0FVpaWkh\nNze3o8NosZqaGpSVlbUqi7ukpASFhYVQU1P74H51B3yjo6Nx8uRJDB8+vMF9t2zZgpUrV8LJyQka\nGhqwsbHB9u3b4enpKbCfjY0NVqxYAU1NTWhpabU49oacPHkSfD4fR44cgb6+PoYPH441a9Y0OpkW\nFBQEJwcHjL1+HTOjonDl7Fl2cberV69CSkoKUlJSkJGRQVhYGE6ePClw/Nq1a2FpaQk1NTWMHj0a\nq1atwunTpxuN7/z583B2dsa8efOgra2N1atXw8LCQmAfUmcBuoZwOBw2w1xOTg7Lli2DkpISe327\nd+/Obg8JCcGxY8dw/vx5yMvLN+saUlRnJSIigoEDByIyMrLBzw0DBw6EtrY2EhISoKysDDk5OVy9\nehXKysrQ1tbG3bt32c9PtSQlJWFnZ4eSkpJ6g+AURVHUp0cHuSmKoqhOry2zAAMCAqCiooL169ez\nmWq1KioqsHnzZhw6dAipqalQU1ODs7Mz7t69i8DAQERHR0NcXByjRo1CRUVFg/0nJCQgJSUFy5cv\nF4hp4ogRWOLkBGtr63qDGHUzErdt24bo6Gj2uIKCAvZW+n379sHDwwM7d+5EcnIyHBwcMGHCBCQm\nJjYYS1lZGYYOHQpxcXGEhYUhKioKioqKGD58OCoqKqCjowMzM7N6Ay8nT57E1KlTISQkBD6fDxUV\nFfj4+CAtLQ1btmzB1q1b4e3tDTU1NSxevBhff/018vLycPDgQYSFhbX7oLOMjAy+//576OvrIyEh\nAV5eXigtLW3XPr9UWlpayMvL+6g21NXVISkpiaSkpDaKqnPicrmtvvujI92/fx8GBgYtPo4Qgvj4\neJiYmDS5b+2AL4/Hw6BBgzB06FDs37+/wX1jY2OxefNmdoBYSkoKM2fORFlZGZ49ewbg3XvCgAED\nWhxzU9LS0mBoaCiQbW1ubt7o/n/t2oUd5eVwAuAEYFdVFbz27QMAWFtbIzExEYmJiYiOjsawYcMw\nYsQIPH36lD3e19cXVlZWUFRUhJSUFH7++Wc8efKk0f5yc3NhaWkp8NzAgQNbfUeLm5sbkpOTcf78\neXC5XIFtMTExmDt3Lry8vD54DSiqKxEVFYWZmRkiIyPrvV4zDIMJEyZAUlISFy9exMiRI9lSJXZ2\nduDxePDz86v3ecPAwADa2tqIiYlBdnb2JzwbiqIo6n10kJuiKIrq9NoyC7B79+4QEhKClJQUm61W\nq6amBr///jssLS2hra2N/Px8XLx4EX/99ResrKxgYGCA48ePo7i4uN7AcK3acgW1izLWzfQbe/06\nwoOD8erVK4Gs5/czEmvLr8jKykJeXh7dunUD8G6BMVdXV0ybNg3a2trYsGEDvv76a3h4eDQYy5kz\nZwAAXl5eMDAwgI6ODjw9PVFSUoKLFy8CABwdHQUG3XNychAREcHWuBUWFsaGDRtgamoKNTU1TJ48\nGQsWLGCPERYWxjfffIOFCxeiV69euHXrFv744w+BgZz2ICIigsmTJ+Obb75BXl4ePD09UVBQ0K59\nfomEhYXbZOBWTU0NPXr0QEJCQhtE1Xl1tfI5NTU1KC8vh6SkZIuPTUxMhKGhITicpr9O1A74pqen\n4+3bt/D19W20zBQhBO7u7uwAcWJiIpKSkpCRkSFwjISExAf7rI2r7u/k/Zq6jfXfFmrvJNLU1MSA\nAQNw+PBhFBcX46+//gIAREVFYfr06Rg9ejQuXbqEhIQEbN68udEJXD6f36wyIRwOp945NHTex44d\nw59//omLFy+iZ8+eAtvy8vIwbtw4LF++HNOmTWvuKVNUl8Dj8dC/f39ERkbW+7/C4/EwdepUEEJw\n4cIFjBo1Cq9evUJISAgmTpyIiooKnD9/XuA4hmEwduxYcLlcBAQE0LIlFEVRHYgOclMURVGdXltn\nATZGWFgYxsbG7M+pqangcDgCmXPS0tIwNDREampqs2J/P9PPvqqq3pej5mQkFhcXIz8/H4MHDxZ4\n3srKqtFSELGxscjKyhK4Ft26dUNRUREyMzMBAFOnTkVeXh7Cw8MBAKdPn4ampiYGDhzItuPp6YkB\nAwZAXl4eUlJS2Lt3b71sQ3l5ecybNw+jR49GcXExjhw5gr///rtdv+wxDIOvv/4a06ZNQ0VFBQ4f\nPvxFlMXoqpSVlaGgoNCpFlRtawzDdKls7tbW4s7PzwePx2Mn4JpSO+CrqqrKlohqjImJCVJTU9kB\n4rqPpo6tq3bgtu6dCE1Nsujr6yM5OVngTp3aO2saMn/5crjxeDgG4BgANx4P8/97F09jA9Ll5eUA\ngNu3b0NZWRlr1qyBqakptLS0PpgFmpOTA3V1ddy5c0fg+aioKIG+5OXlBe5QAt6dd919IiMjsWjR\nIpw8eZJdkLJWRUUFxo8fDysrK2zYsKHReCiqK5OUlISRkRGioqLqDXSrqKhg+PDhKCkpQUpKCnvX\nWEVFBczNzfHw4UPExsYKHCMlJYUxY8aguLgYN27c+JSnQlEURdVBB7kpiqKoTq89sgAbIioq2qxM\nOUJIo/vp6OgAeDd4BLxbcKyuXADCQkICmXVNZSS2NhY+nw9jY2OBa1F7HefPnw/g3YCIra0tm5l+\n8uRJzJw5k23j7Nmz+OmnnzBnzhxcu3YNiYmJWLRoEd6+fVuvP4ZhYG5ujiVLlkBLSwt3797Fb7/9\nhoyMjFafX3Po6upi3rx5kJCQgI+PD27evNnlMmo7M2Fh4UbL87SUgoICVFRUcPfu3c/yd9SzZ0/k\n5OR0dBjNUl1d3aos7srKSmRlZUFXV7dd4lq3bh1OnTqF9evXIzk5GWlpafD19YWbm1uL2uHxeBg4\ncCB27NiBlJQUREZGYsWKFR88ZsaMGRASEsK8efOQkpKC4OBgdlHLhl5nR44ciWMBAQi0tUWgrS2O\nBQRg5MiRAN4NFj979gwFBQVITU2Fi4sLysvLYW9vD+Dd61Zubi5OnTqFzMxMHDx4kL37piEZGRlw\ncXHBsWPHcPjwYWRkZAiUt6r1zTffID4+Ht7e3nj48CF+/fVXREZGstsLCgrg4OCARYsWwdzcnF1k\n8vnz5wCABQsWoLi4GNu3bxdYoLL2Paupmt8U1RzZ2dngcDgdOukpLS0NPT29Bt+PLC0toaWlhays\nLMjIyEBKSgoXLlyAmZkZevTogatXr9Zbh8TQ0BBaWlqIjo5u1vtA7domH8vd3b3eZFVbaK92KYqi\n2hMd5KYoiqI6vbbOAuRyuc2qG62vrw8+ny8wQFBcXIzk5GT06dOnwWP69+8PfX19bN26FSdPnoTF\n8OFYKSaGYwD2AggDoKWtjb/++gt//vknqt7L7K6ND4BAjNLS0lBSUkJERITAvhEREY1mYpqamuLh\nw4eQlZWtdy26d+/O7ufo6AgfHx/ExsYiOTmZLVVS276FhQUWLVoEY2NjaGpq4uHDhx+cDJCRkcHM\nmTMxadIk1NTU4NSpU/Dx8WnXutny8vJYuHAh1NTUEB4ejjNnztBbhtuImppam05UyMvLQ1NTE9HR\n0Z/dYNlXX32FR48edXQYzdLaWtwxMTEtqofNMEyTk4d1t48YMQKXL1/GrVu3YGFhAQsLC/z666/o\n3bt3i2P18vIC8G4R3x9++AFbtmz54P61tXjv378PExMTuLm5sdnMYmJiDcY7cuRI+F27Br9r19gB\nboZhEBwcDEVFRSgpKWHgwIGIjY2Fj48PhgwZAuDdAJerqyuWLVuGfv364caNG9i4cWO9a1X7c3Fx\nMebPnw93d3esWbMGJiYmuH//Pn7++WeB/UeMGIH169djzZo1GDBgAHJycrBo0SJ2e1paGp4/f45d\nu3ax8SkpKbELWIaFhSEjIwNaWlrsNmVlZTaDvDm/T+rz5uzsDA6HAw6HAxEREaioqMDJyaneHQQd\nSV1dHbt27WpyPwcHBwwcOBBCQkIQFRWFuro6XFxcUFpaiokTJ0JCQgIxMTEwMzNDdXU1/P39MWnS\nJBBCcO7cOYHPaQzDYNy4cRAREYG/v3+T5ZHo/yWKoqh2QCiKoiiqE3NyciJ2dnaNbvf29iaSkpLs\nz0FBQURERISsW7eOJCUlkdTUVOLj40NWrlzJ7jNixAhiZ2dHcnNzyfPnzxtsp9b48eOJvr4+CQ8P\nJ/fu3SP29vZETU2NVFRUNBhPdnY22bRpE+HxeOTrr78mFy5cIMePHyeWxsZESkKCqKmpkezsbHL5\n8mWyadMm0q1bNzJmzBhy+/ZtUl5eTgghpKqqioiLi5ONGzeSgoICUlRURAghZO/evURaWpqcPn2a\nPHjwgPznP/8hQkJC5N69e2z/DMMQPz8/QgghZWVlRFdXl1hbW5PQ0FCSmZlJQkNDyfLly0lGRgZ7\nTFlZGZGSkiL9+vUjFhYWAuezf/9+IiUlRa5cuULS09PJxo0biYyMDNHQ0Gj0d1JXWVkZCQgIIO7u\n7mTr1q0kPj6e8Pn8Zh3bGjU1NeTy5cvE3d2d7N+/nxQWFrZbX1+KN2/ekAsXLrR5u4WFhSQyMrJd\n/x4+tZqaGuLj49PRYTSpqqqKREVFtfi4tLQ0kpeX1w4RdV7nz58nHA6HvHz5skPj6Ap/V9SXwdnZ\nmYwYMYI8e/aM5ObmkmvXrhFVVVUyfPjwZreRlZVFGIYhsbGxbRrb27dvCSGEqKurEw8Pjyb3t7Gx\nIXPnziVJSUkkKCiI+Pn5ERkZGfL9998TQgjJyckhmzZtIn5+fiQoKIi4u7uT4OBgcufOHeLu7k6u\nXbtWr834+Hji7u5Orly58sG+7ezsyOzZs1txlv9TVVVF1q9fTwwMDD6qnU/VLkVRVHujmdwURVFU\np9YeWYAbN27EkydPoKWlhV69ejXYTi1vb2+Ym5tj7NixsLCwQEVFBa5evQpRUVGB/QoKCnD27Fmk\npaXBzc0NR48ehaysLObNm4e5c+civ6gI8xcuxL1799C7d298++23cHV1BY/HA8MwuH79Onbu3InL\nly+juLgYv/32Gw4fPgxlZWU4ODgAAJYuXQpXV1esXLkShoaGuHDhAvz9/Ru9nZTH4yEsLAyampqY\nPHky9PX14ezsjKKiIoFMbh6PBwcHByQlJQlkcQPvbl2fMmUKZsyYAXNzc+Tk5GD5f2vONgePx8P4\n8eMxa9YsiImJ4cKFCzh69ChevXrV7DZagsPh4Ntvv4W9vT0KCwvx559/frDOLdU0SUnJZi3Y11Ld\nu3dH3759ERkZ2aXqWH9IcxZh7Axak8VdVFSE8vJyKCoqtlNUncOxY8cQHh6O7OxsXLp0CcuWLcPY\nsWPRo0ePjg6NojoFQghERUUhLy8PJSUl2NraYvLkyYiKihLYZ9OmTVBVVYWYmBiMjIwQGBhYr60H\nDx7AysoKPB4P+vr6uH79usD2lJQUjBkzBtLS0ujVqxdmzJghsL6Ks7Mz7O3tsWPHDqiqqkJVVRVD\nhw7F48eP4erqCg6H0+QdgOLi4jAwMECfPn2go6ODYcOGseeiqqqKxYsX4/Tp05g+fTq2bNkCZ2dn\ntkZ+ZGQksrOzERYWBgsLC/B4PIwaNQp37txBZGQku35JWVkZnJ2dISUlBQUFBWzbto29TrUqKyvh\n5uYGVVVVSEhIwNzcHNeuXWO3h4SEgMPh4MqVKzA3N4eoqCiCgoLqnU9OTg709PQwe/Zs1NTUtFu7\nFEVRnVIHD7JTFEVRVJf24sUL4uPjQy5dukQqKioIn88nkZGRLcr6q6mpIWlpaeTPP/8k7u7uxN3d\nnRw9epRkZmZ+VlmulZWV5Nq1a2TDhg1k06ZNJCIigtTU1LRbf48fPybbt28nGzZsINHR0e3Wz5eg\nPbNI37x5Q8LCwtr1b+FT8vX17egQPqg1Wdw1NTUkLCzss3o9asyvv/5K1NXViaioKOnduzdZvHgx\nKSkp6dCYXrx40WRWKEV9Ku/fYffo0SPSp08fMnToUPa53bt3s3eeZWRkkHXr1hEhISGSkJBACPlf\nJreKigrx8fEhDx48IC4uLoTH45Hc3FxCCCF5eXlEVlaW/PLLLyQtLY0kJSURe3t7YmFhwb4WOTk5\nESkpKeLo6Eju379PkpOTSWFhIVFVVSXu7u7k2bNn5NmzZ42ei42NDVmyZAn7899//03k5OQEMqyX\nLl1KFBQUyPz584lZnz5EVlqaiIiIkKSkJLJt2zaydu1aIi4uTn744QeSlpZGLl26RHr16kWsrKzI\n3r17SWVlJfnhhx+IsrIyuXbtGklOTiaTJ08m0tLSAv3MmDGDWFpakvDwcJKVlUV+//13wuVySWJi\nIiGEkFu3bhGGYYiRkRG5fv06ycrKIs+fPxfIuE5JSSEqKipk+fLl7d4uRVFUZ0QHuSmKoiiqFYqK\nioi/vz85f/48efPmDSHk3UBQeHg4ef36davbzc/PJ76+vmTDhg3E3d2d7Nmzh8TFxZGqqqq2Cr3D\n5efnkz/++IO4u7uTAwcOtGv5g6KiInLgwAHi7u5OAgMDSXV1dbv19Tnz8fFp10Ho0tJSEhoa+ln8\nfi5cuEBKS0s7OoxGxcfHt3jQNjo6mn2doz6927dvC5SYoqiO5OTkRISFhYmkpCTh8XiEYRhiZ2cn\nMLmvpKRENm3aJHCcjY0NcXR0JIT8b5B769at7HY+n090dHTI2rVrCSGE/Oc//yHDhg0TaKOwsJAw\nDEPu3r3LxiIvL08qKysF9lNXVye7du1q8lysra0Jl8slkpKSRFRUlDAMQyZNmkSSkpIIIYSUlJQQ\nLpdLXF1dibyYGDkKEC+AcABiaWlJUlNTyddff00UFRUFJgGPHj1KuFwuWbNmDTl//jwRFRUlp06d\nYreXlJSQbt26sYPcDx8+JBwOh+Tk5AjEN27cOLJo0SJCyP8Go/39/QX2qR2MjoqKInJycgLXtL3a\npSiK6qy6xj2VFEVRFNVJlJSU4OLFiwgODoa1tTXGjRsHSUlJVFdXIyIiAv3794e0tHSr21dQUMDE\niROxfPlyWFtbo7y8HIGBgfj1119x8+ZNvHnzpg3PpmMoKChgwYIFsLW1RWFhIQ4dOoRr1661S0kM\nGRkZzJs3D3p6eoiLi8PRo0fbdQHMz5W8vDxycnLarX1xcXGYmZnh9u3bqK6ubrd+PoW2XqizLVVX\nV+Pt27eQkJBo9jE5OTmQlZWFpKRkO0ZGfUhBQQHU1dU7OgyKYllbWyMxMRHR0dFwcXFBaGgoW0ak\nuLgY+fn5GDx4sMAxVlZWSElJEXjO0tKS/TfDMLCwsEBqaioAIDY2FmFhYZCSkmIfampqYBhGYIFf\nAwMDiIiItOo8GIbBtGnTkJiYiDt37mDKlCmIjIzEmzdvkJWVhUePHqGqqgr37tzBrxUVcAIwG4AF\ngJR791BUVISqqirIy8vj3r17bLuDBw9GVVUVeDwerl69isrKSoFzlZCQECg1FxcXB0II+vTpI3C+\nf//9NzIzMwVibmjh39zcXNja2uKXX37BqlWr2r1diqKozkq4owOgKIqiqK6gvLwcoaGhKCkpweDB\ngwXq0lZWVuLOnTuwsLCAmJhYm/QnISEBGxsbWFlZ4f79+wgNDUV4eDgiIiKgr6+PwYMHQ0lJqU36\n6ggcDgeDBg2Cvr4+AgMDcefOHSQnJ2P8+PHQ1NRs075EREQwZcoUhIWFISQkBJ6ennB0dBSox059\nmK6uLhITE9t1oI3H48HCwgK3b9+GpaUluFxuu/XVnnR0dBAcHIx+/fp1dCj1JCcnN1rDvyHl5eXI\ny8vDwIED2zEqqimEEAgL069tVOfB4/HY9+p9+/YhKSkJP/74o0Ct5/cRQppcY4XUqVHN5/NhZ2cH\nDw+PevvJy8uz/xYXF29p+AJkZGTYczlx4gT69u0Lb29vLFu2DPn5+Y0eV7ueioyMDKqrq3Hp0iWo\nqakJrHkybNgwnDhxAgDqTeC+f64MwyAmJqbegD2PxxP4uaFJSjk5OWhoaOD06dOYO3cuunXr1q7t\nUhRFdVY0k5uiKIqiPuDt27e4efMmLl26BD09PUyaNElggLuiogJ37tzBoEGD2myAuy5hYWH069cP\nLi4ucHZ2hqamJlJSUnDo0CF4enoiJSWlSy/a1717d8yaNQsODg7sZEFlZWWb98MwDKytrTF16lRU\nVFTg8OHDbLYY1bRevXqhuLi43fsRFRWFpaUloqKi8Pbt23bvrz2Ii4u3y10JH6u6uhqVlZXNHhAi\nhCAmJqbB7D6Koqi61q9fj+DgYMTGxkJaWhpKSkqIiIgQ2CciIgJ9+/YVeO7OnTvsvwkhiI6Ohr6+\nPgDA1NQUycnJUFNTg6ampsCjqTtLuFxuqxZHFBYWxurVq3Hs2DF0794d3bp1A5fLheHAgXDj8XAM\ngDeAfwAYmZiAYRjw+Xy8fPkS1dXV8PHxAZ/PR0REBLhcLoyNjTF16lRwOBx4eXmx/ZSWliI5OZn9\nuX///iCEID8/v965NmexXzExMQQGBqJ79+6wtbXF69ev27VdiqKozooOclMURVFUA6qqqhAREYEL\nFy5AUVERkyZNqpfFWlpaiujoaAwePLjVt8o2F8Mw6N27NxwdHfHjjz/CwsICL168gI+PDzw8PHD7\n9m1UVFS0awzthWEYGBkZwcXFBQ4ODoiLi6t3G21b0dPTw7x588Dj8XDu3DncunVLIJuK6nhcLheD\nBg3CP//802X/pjvj31RycjIMDAxatL++vj7NIO5gFRUV9HdAdXrW1tYwMTHBjh07AACurq7w8PDA\nmTNnkJ6ejnXr1iEiIgIrVqwQOM7T0xN+fn548OABli1bhidPnuCHH34AACxevBivX7/G1KlTER0d\njczMTAQHB2PBggUoKSn5YDzq6uoICwtDXl4eXrx40eh+5N0aZQLPzZgxA3JyctizZw8sLCwwceJE\nHD9+HEtWr8YJS0tsVFaGkIgILC0tYWRkBENDQ+Tn5yM+Ph737t3Djh07sGrVKri4uEBMTAxWVlYY\nMmQIDhw4gDNnzuD+/fuYM2eOQIKCjo4OZs6cCWdnZ/j5+SEzMxMxMTHw8PBAQEAAAODq1asCsR49\nehRSUlLseYiKiuLixYuQkZFhB6Sb0+6HNNbuh9jZ2WH27NnszzY2Nli6dGmTfVEURbUFOshNURRF\nUXVUV1fjn3/+QUBAACQlJTF58mTo6+vXu8X29evXSEhIgJWV1ScfgOjWrRtGjRoFV1dXjB49GhwO\nB8HBwdi5cycuXbqEly9fftJ42oqEhATExcUxcOBAcLlc3L59G2VlZW3ej7y8PBYuXAhVVVWEhYXh\nzJkz7ZI9TrWesLAwBg8ejLt377bL38Cn0JnusKiqqmpRFvfz58/BMAzk5OTaOTKqKQ8ePICamlpH\nh0FRLIZhGiw7snz5cgQEBCArKwtLly6Fq6srVq5cCUNDQ1y4cAH+/v4wNDSEs7Mz5s6dC4ZhsH37\nduzevRuGhobYv38/HBwc2FJsioqKuH37NjgcDkaNGgUDAwMsWbIEYmJiEBUV/WAsGzduxJMnT6Cl\npfXB0mQNHS8iIoIlS5bg0KFDePPmDby9vTF06FDs378f4XFxUFRTw5kzZ8Dj8VBcXIxLly5BQ0MD\nmZmZ+PPPP7F161bY29tj69atbB8nTpyApqYmnJ2dMWzYMBgZGWHIkCEC/Xp7e6NXr16YNGkSdHR0\nYG9vj4iICDbB4ptvvmnwXOueg5iYGC5dugRpaWmMGDECxcXF8Pb2xuzZs7Fy5Uro6+vXa7e2jZa0\n+6GB7vev6fnz57Ft27ZG96coimpLDOmMqSYURVEU9YnV1NQgISEBmZmZUFNTw4ABAyAkJNTgvi9f\nvkRGRgYsLCyarC/5KRBCkJGRgbCwMOTm5gIAevfujSFDhkBDQ6NTxNgatb8THo/X4ERDW7R/9epV\nJCYmYvr06dDQ0GjT9j83QUFBMDExQc+ePT9Zn3w+H5GRkTA2Nu5SCx8GBwfD0NCw09R9j4+Ph66u\nbrMGuaurqxEVFYXBgwd32deOz0lgYCCGDx/+0XWHKaqzmD17Nl6+fInAwEAAwPHjxzFv3jzs3LkT\nLi4uHRxdwwgh+Oeff6Cnp8fWpQ4MDER8fDwuX74MYWFhjBkzBt999x1OnDgBMTExLF68mB2MB4C7\nd+/i77//hpWVFYYNG1avj7dv30JJSQk6OjoghCAqKuqDMR09ehQuLi6dbkFye3t79OzZU6A8C0VR\n1KdCM7kpiqKoLxqfz8e9e/dw/vx5VFRUYNy4cbCwsGh0gPvZs2fIzMzsNAPcwLusGR0dHXz//fdY\nuHAhDA0NkZOTg+PHj2Pv3r2Ii4vrlDWCmyIkJARTU1PIy8sjIiICRUVFbd7+mDFjsGTJErx+/brd\nSqR8LrS1tZGenv5J+6xdoDQxMfGT1ARvKx1xrRpTVVWFqqqqZg+S1tbh7iyvb1+6lvzuKKorqJtj\nt2fPHsybNw9eXl7sAHfdMhy1QkJCwOFwUFhYCODd3XTfffcdevXqBR6PBy0tLezbt4/dn8PhwN/f\nX6ANdXV17Nq1S2AfT09PjB07FhISEtDV1UVISAhycnIwYsQISEpKwsTEBPfu3QPDMLCwsEBKSgo7\nqGxrawsxMTG8ePECOjo6qK6uRlJSEsaOHYs3b97g8uXLAN5NEE8cMQLbV6/G69evcfv27QYXtPT3\n9wePx8OpU6cQExOD+/fvC2xvnJuAhwAAIABJREFU6LrU9ejRI4wbNw6KioqQlJSEqakpGwPwrjRM\nbb1z4N1kLIfDYUvMAICjoyPmzZsH4F1Cx/Tp06GqqgpxcXEYGBjg6NGjAn2WlZXB2dkZUlJSUFBQ\nYDO26/6ObWxsOu3kBUVRnx86yE1RFEV9kQghuH//PgIDA1FYWIhRo0Zh8ODB4HK5jR7z9OlT5Ofn\nw8zMrNMOAPXq1QsTJkzA8uXLYWNjg4qKCly8eBG//vorbty40ekyfppDTk4OVlZWePLkCeLj49u8\nDIS0tDSMjY3BMAzi4+M7ZT3lzkBDQwMFBQWfvN/age7k5GS8evXqk/ffGmpqavj33387OgwA72pr\nGxoaNmvfR48eQVlZuV0W0aUoigLeTcwTQrB27VqsXbsW58+fx4wZM1rUxtq1a5GcnIzLly8jPT0d\nXl5eUFZWbrLf9z+7bd68GTNnzkRiYiIGDBiA6dOnY86cOXBxcUF8fDwUFRXh5OTEHm9paYnExESU\nlZWBx+NhzJgxIITg2bNnmDt3Ll68eAFdXV0YGBggKSkJf/31F5wcHDD2+nWMDQ7GqYMH8ejRI/j5\n+dVbGPPw4cOYPXs2NDQ0MGrUKBw+fLhF16S0tBRjxoxBcHAw7t27h4kTJ2LChAl48OABgHeDzQ8e\nPGDfm0JCQiAnJ4eQkBC2jbCwMAwdOhTAu8zyAQMG4PLly0hJScGPP/6IBQsW4ObNm+z+K1asQHBw\nMPz9/XHjxg3Ex8cjLCxM4Do3VlKGoiiqPdBBboqiKOqLQgjBgwcPcPHiRRQUFMDGxgY2NjaQkJD4\n4HHZ2dkoKiqCsbHxJ4r040hISMDa2horV66Eg4MDpKWlERERgd27d+Ps2bPIy8vr6BBbhGEYGBoa\nQltbG5GRke0y2KqhoQE1NTVERkaiurq6zdvv6jgcTodNANQOLqSnp3eJmvMdea3qqqqqQnV1NXg8\nXpP7vnnzBq9evYKqquoniIxqjs5U152i2gohBNevX8fWrVvh6+uLUaNGtbiNnJwcmJiYYMCAAVBV\nVYW1tTUmTZrU4nacnJwwdepUaGtrY/Xq1Xj27Bns7Oxgb2+Pr776CitXrkRiYiKbQc4wDAYNGoSY\nmBhUVFSgb9++EBMTQ2FhId6+fYvRo0cjICAAdnZ2kJSUhOfOndhRXg4nAE4AdlRUIDctDS9fvkRo\naCgbR1ZWFsLCwjB37lwAwPz583HixIkWrRdiZGSE+fPno2/fvtDU1MTq1athYmICX19fAO8W3lZQ\nUMCtW7cAAKGhoVixYgUiIiLA5/Px8OFDPH36FDY2NgAAJSUlLF++HEZGRlBXV8e8efMwYcIEnD59\nGgBQUlICLy8v7Ny5E7a2tujbty+8vb3B4dAhJoqiOg59BaIoiqK+CIQQPHr0CFeuXMHjx48xcOBA\nDBs2jK2t+CEPHz7E27dvYWBg8AkibVtCQkIwMjLCkiVLMHv2bGhrayMtLQ2HDh3CwYMHkZKS0qUG\nUqSkpGBlZYXi4mJER0e3eRkWWVlZmJqaIjIysktmvX/OGIaBubk5MjMzO02W9Id0hsy15OTkZr1u\nEUKQkJAAExOTTxAV1VzZ2dmdpq47RbUVhmFgYGAAbW1tuLu7f3ARw8b88MMPOHv2LIyNjeHq6oqw\nsLBWxWJkZMT+W15eHgAE7nypfa7ue07t3UW1n0Hk5OTA4XAQEBCAHj16QEdHB+Hh4Zg8eXKDn69k\npKWhoqKCiIgIdsL+yJEjGDp0KLsY5JgxYyAqKorz5883+1xKS0uxcuVK9O3bFz169ICUlBRiYmLw\n5MkTdh9ra2vcunULZWVluHv3LpydnSEnJ4fo6GiEhIRAW1ubXfizpqYGW7ZsgZGREeTk5CAlJQV/\nf3+2vUePHqGyshKWlpZs+xISEs2+c4iiKKo90EFuiqIo6rP3+PFjXL9+HRkZGTA0NMSIESPYLy5N\nSU1NBcMw0NXVbeco2xfDMFBTU8PMmTPx448/YuDAgXj58iV8fHywc+dOREREoLy8vKPDbDYdHR0Y\nGRkhJiYG2dnZbdq2mJgYrKyskJqa2uUy3tsbl8tFWVlZh/XPMAzMzMzw9OnTDimd0hIcDqdFWXht\nrSVZ3AkJCTAyMqIZeJ3Mw4cPu/x7D0W9jxACJSUlhISE4PXr1xg+fLjAmhsN3Qnz/oT2qFGj8Pjx\nY6xYsQIvXrzAmDFjMGfOHHZ7bUmUD7UBACIiIgLHNPbc+4PVwsLCsLS0RFRUFISEhKCsrIzS0lL4\n+/vD2NgY1dXVKCoqwvT58/GzsDCOATgGYLmwML5bvBgODg7gcDjw8/NDZWUljh49ihs3bkBERAQi\nIiIQExNDfn5+i0qWrFixAr6+vti8eTPCwsKQkJAAc3NzgfchGxsb3Lp1C3fu3IG2tjbk5eXZ50JD\nQ9ksbgDw8PDA7t274ebmhps3byIxMRHjx49v8n2tM9zFRFHUl4t+kqUoiqI+W0+fPsWNGzeQkpIC\nTU1NjBw5skW34iclJUFcXBxaWlrtGOWn161bN4wcORIrV67Et99+C2FhYdy4cQMeHh4IDAzEixcv\nOjrEZhETE4OlpSU4HA4iIyPbdJCew+HA3Nwcr1+/Rmpqapu129X17t2bre/ZkUxMTPDs2TPk5uZ2\ndCiNkpWVRXR0dIf1n5SU1Kws7ry8PEhISEBGRuYTREW1xJs3b9CzZ8+ODoOi2lzdge7S0lIMGzaM\nLQnSs2dPlJWVCdxNlZCQUK8NWVlZODo6wtvbG4cPH8axY8fYgeyePXsKTFI/e/aswcUeP4aIiAgs\nLCzw+vVr9OrVC7KyskhPT0dycjJsbW3x8OFDTJs2DTN/+AF7NTVxwsICo6dNA5/PR48ePTB8+HC8\nePECu3btQmFhIWJjY5GYmMg+Ll26hBs3biAnJ6dZ8dy+fRtOTk5wcHCAgYEBlJWV8fDhQ4F9bGxs\nkJGRgZMnT7K1t21sbHDz5s16g9wREREYO3YsZs6cCSMjI2hoaAi8/2tpaUFERAR37txhnystLUVy\ncvJHXFWKoqiPQwe5KYqiqM9OQUEBQkJCkJSUBAUFBYwcORLa2totKh8QFxcHWVlZ9O7dux0j7Vhc\nLhdmZmb4+eefMWPGDCgqKiI+Ph4HDhyAt7c3Hj161CUyctTU1GBubo7k5GQ8ePCgTWPW19eHjIwM\noqOju1RZl/aiq6vb7C/c7a1fv34oLCzsNPG8LycnB7du3eqQ/0OVlZWoqalpMou7srIS2dnZ0NHR\n+USRURRF/Y+CggJCQkJQWVmJb775f/bOOyyq4+vj37ssZYGlSZEqTZqAKCgoqBQjosQakWgixIYx\nahINMXbsGksw0cTECGosWLBixAoqTRABQaUKCkYEFVRgAWHn/YOX+3MF6U2dz/Pc52Hnzj1z5u6y\nO/fMKc549uwZbG1tISMjg0WLFiEzMxPBwcH4/fffRa5bvnw5Tp06hYyMDNy7dw/Hjx9nja4A4Ozs\njB07diA+Ph4JCQnw9vZul4K6kpKS4PP5SEtLg4GBAfLy8nDw4EHcuHEDZmZmSEhIwM2bNyGnpQX3\nzz+Hu7s7kpOT8ejRI9ja2kJDQwP79u2Ds7MzrKysYGZmxh5ubm4wNjbG7t27m6SLkZERjh8/joSE\nBCQnJ+OLL75ARUWFSB9jY2Ooqalh//79Ikbu8PBwPHr0SMTIbWxsjEuXLiEyMhKpqamYM2cOcnJy\n2N80WVlZTJs2DQsXLsSlS5dw584dTJ06tc5aiRDyXqwlKRTKhwE1clMoFArlgyErKwsnTpxAYmIi\n5OXl2UI4zQnBJ4QgNjYWWlpabF7CDx2GYdCzZ09Mnz4dX3/9NXr37o3c3Fzs378f/v7+iI+Pb/Pc\n120Nl8tFv379oKioiMjISLx8+bLNZGtoaMDU1BQRERF1Hhg/NqSkpLrUZ8HCwgIlJSVtnrKmLdDW\n1oZQKBQJw+8oUlJSmpQXNS4uDv369esAjSgUCqUGhmFEnA5UVVXZYojOzs6orq7GgQMHcPHiRVha\nWuLvv//GmjVrRK6RkpLCkiVLYGVlBQcHB5SWluLMmTPs+S1btkBfXx+Ojo7w8PDAjBkzmpSmrj5n\niMYcJMTExHDlyhWMHDkSf/31F9avXw8HBwfs3r0bJiYmuH//Pvh8PoqLiyEuLg4xMTGEhIQAABwc\nHJCRkQFVVVVUV1fXkT1hwgTs2bOHNRK/rcubr7du3QpVVVUMGjQII0eOxMCBAzFo0KA6Mh0dHSEU\nCjFkyBAANRFaWlpaMDAwEFn3Ll26FP3794ebmxuGDBkCPp+PyZMni4y5efNmODk5YezYsXBxcYGl\npSUGDx5cR8euUKOCQqF8HDCEbqtRKBQKpY3x8/PDH3/8gcLCQgQGBiI7OxvBwcFITk5ul7GCgoLg\n6+uLR48e4erVq8jPz8edO3eaLYsQgujoaJiamkJRUbHNdX2fKCsrw82bNxEVFYWKigoYGhpi4sSJ\n4HK5na1aowiFQvazZmlp2WYPV1VVVYiJiYGZmRmUlJTaROb7SHBwMMaPH9/ZaoiQmpoKcXHxLpVa\nKDU1FYcPH8bEiRNhYmLSYeNWVlYiKSmpUeN1amoqFBQU0L179w7SjNIcCgsLkZCQgGHDhnW2KhQK\npQm8fPkSiYmJiImJAQAMHjwYdnZ2iIyMhEAgQEFBATIyMmBubo6UlBSMHTsWlpaWiI6OxoULF+Dk\n5FTHQEyhUCiU5kE9uSkUCoXSZLy9vfHpp5+KtIWEhEBGRgbLly8HUONBuGrVKuzatQv5+fmYOHEi\nfH1bVvk+PDwcHA6n3iM9PR0FBQVISkpCUVER8vLyoKuri127diEiIqLZY1VXVyMiIgLm5uYfvYEb\nAKSlpTF48GD4+vpi3LhxsLGxQXBwME6dOtWmXtLtAYfDQe/evaGrq4vIyEgUFha2iVwulwt7e3s8\nfPgQ2dnZbSLzfaWqqqqzVRDBxMQEQqEQ6enpna0Ki5qaGoCaXLAdSVO8uIuKilBRUUEN3F2Y9PR0\nGBoadrYaFArl/9HV1cWWLVveeV5OTg5ffvklUlNTIRAI8ODBA2hrayMqKgoSEhJQU1PDtm3bEBAQ\nABkZGYSGhqKyshK2trZQV1dHeHg4CgoKOnBGFAqF8uFBjdwUCoVCaTJvhxz+888/+Oyzz7Bhwwas\nWrUKANgiN6NGjYKqqiqkpKQgIyPToOG4sUrtd+/eRX5+Pnukpqbi5s2b2LVrF4qKisDlcuHl5YUp\nU6ZAX1+/2UbqqqoqREZGwtraGnJycs269kNHTEwMFhYWMDY2xsSJE9kCRYcPH+6SKSLeRF5eHvb2\n9nj69Clu3rzZJoZZhmFgZWUFQgiSkpI+yjyT6urqXdLI37NnT3C5XKSmpna2KgBqCrxyOBzk5eV1\n2JiVlZUQCoUN5p4VCoVISUmBpaVlh+lFaT75+fnQ1dXtbDUolI+CwsJCzJ49G3p6epCSkkL37t0x\ndOhQXLp0ie3TlLQbt27dwooVK6CsrIzU1FRs27YN3t7eMDY2hqKiIn7//XdMnToVzs7OEAgEuH79\nOjgcDsaNGweGYRAcHNxo/Q9HR0fMnTu3TeZNoVAoHxrUyE2hUCiUJvOmQe+XX37BjBkzEBAQwC62\n/fz8MG7cOAA13rRiYmJs+5uehbUe4Rs3boSWlhZ0dHQaHFdVVRWqqqrg8XiIiIjA8ePHkZGRAT6f\nD3NzcygpKbHGgLfHiouLw7Bhw6CiogJ5eXkMGjSIDSUFgGXLlkFCQgKOjo6QlZVlPcVXrlzZupv1\ngSIvL48xY8Zg/PjxyMrKQlBQEGJiYrpsUUaGYWBqaopevXohNjYWubm5bSJXX18fmpqaiIqK6nJe\nze2NiYkJu5nV1dDX14eUlFSL0hW1NQzDQFFREY8fP+6wMZOTk2Fubt5gn/j4ePTt25fmSH0PaE49\nCQqF0nLGjx+PmzdvIiAgABkZGQgJCYGbmxuePXvWLDm1Bcs/+eQTcDgcNn2eqqoqxMTEYGNjAz09\nPZSUlLBriKKiIigrK8PZ2RkFBQWIjIxscAya45pCoVDeDV05USgUCqXJMAwDQgiWLl2KpUuX4uTJ\nk5g0aRJ73tfXF7t27QJQ44XWkHHn6tWrSElJwYULF3D58uU658+fP4/lP/wAQgj+/fdfhISE4M8/\n/0RycjK4XC5GjRqFOXPmQFlZuUGdS0pK4OXlhYiICMTFxcHKygojRozA8+fPIRAI4ODggNzcXNZL\nfO/eveByufUW66H8Dy6XCxcXF3h6ekJGRgZHjx7FuXPnUF5e3tmq1QuPx8PAgQNRVVWF6OjoNikg\nqaysDGtra0RFRaGkpKQNtHw/UFJSQmlpaWer8U50dXUhJyeH27dvd7Yq0NbWRmlpaYcU66ysrAQh\npEEv7gcPHkBZWRkyMjLtrg+FQqG8DxQXFyMiIgIbNmyAk5MTtLW1YWNjgwULFmDixIkifQUCAXx8\nfCAvLw9tbW1s3rxZ5HxtShMjIyNYW1tj8eLFOHz4MG7dugVLS0sMGjQIx44dg7S0NIyNjVFWVobx\n48dDTU0Nbm5uOHDgAA4ePNjiNGt79uwBn88XaatN/ff8+fMWyaRQKJT3ia5fPYpCoVAoXQZCCC5e\nvIizZ8/i7NmzGD58uMh5GRkZyMvLA0CjVex5PB4CAgIgLi5e59z58+fhNXYsvAUCRAD48ssvweVy\nWe9wZWVl/PDDD03S2cnJSeT1r7/+iuDgYJw4cQJGRkYYOnQo63GelpaGefPmYfPmzXB2dm6SfApg\nYWEBCwsLPHnyBCEhIQCAQYMGsTmJuxJ6enrQ0tJCQkICFBUV0bNnz1bJk5KSgoODA+Li4qCjowN1\ndfU20pTSGrS1tcHhcJCYmAgrK6tO00NdXR2JiYkoKCiApqZmu46VnJzcYC5ugUCA/Px82Nratqse\nlNZTVlZW728jhUJpe2RlZSErK4tTp07B3t4ekpKS9fYjhOCXX37BqlWrsHDhQvz777+YN28eHBwc\nYGdnB0DUy9rV1RViYmLIzs6GuLg4Hj58CKFQiGP79+NWeDicx4zBiRMnQAjBX3/9BQsLC+zYsQM7\nduyArq4ufH19aTQHhUKhNBP6rUmhUCiUJsMwDMzNzWFoaAg/Pz+8ePGixbLMzc3f+RC/c9MmbBQI\nUGtC9wNgbWyMxMREpKSk4Pr1600ep6CgAD4+PjA2NoaCggLk5ORQUFCAuLg42Nvbswbu4uJijBo1\nCp6enpg3b16L5/Uxo6amhs8++wzu7u6Ij49HUFAQUlJSOlutOoiLi6N///6QlZVFREREq72wORwO\nbG1tUVRU1GXyQVMATU1NdO/eHfHx8Z2mQ+1GT3sXE6uoqAAhpEHjzM2bN2FjY9OuelDahvT0dPTo\n0aOz1aBQPgq4XC727NmD/fv3Q0FBAQMHDoSvry9iY2Pr9HV1dcXs2bOhr6+POXPmwNDQsN5oRKCm\npkllZSViY2Nx48YNnDp1Ck8LC2Gdm4vPo6OxbulSZGdnY9KkScjNzWW9wHV0dHD58mVER0ezsprr\njU3zdlMolI8VauSmUCgfBDk5OeBwOLh161Znq9Iotfmom0JXmxchBBoaGggPD8eLFy8wdOhQFBcX\nt0iWtLR0nbaqqiokJibi1atXIu3KADQ1NGBqagp9ff1mPfx7eXkhPj4e/v7+iI6OxpUrV6CiogJ1\ndXXWQ6aqqgoTJkyAtrY2tm/f3qL5UP6HlJQURowYAQ8PD5SUlCAoKAhXrlzpcrmr1dXVMXDgQGRk\nZCA5ObnVRSTNzMzA5/MRGxv7wRekVFBQ6NBc0y2le/fu0NHR6bT3pDaiJT8/v13HSUlJadCLOzk5\nGWZmZuymHqVr8+DBAxgbG3e2GhTKB4u3tzdbg4XD4eCzzz7D8+fPsW3bNri5uSEqKgp2dnZYv349\new3DMHUK9mpoaDSYWoRhGEhJSeG///7DiX37oEQI+gHwAuD6+jUqKyuxYcMGLFiwAHw+H3w+Hzk5\nOSgvL8eVK1fw9OlTAIC9vT3y8/OhpKTUpPm1NG/31atXRQrHGxgYYMKECbhy5UqzZVEoFEpnQI3c\nFAql3XlzISkhIQEDAwP4+vqirKys0/QQFxeHlpYWvLy8OtxQ89tvv+HAgQMdOmZb8qahu7S0FC4u\nLq3O81ddXY3bt28jPj4ePXv2hO+qVVjI4yH0/8+vlJLCzAULWiQ7MjISc+fOhZubGxQVFVFUVITn\nz5+LLP6/++47PHz4EMeOHaNGoDaEw+HAzs4Onp6e0NPTw/Hjx3Hq1Cm8fPmys1Vj4XA46NOnD7S0\ntBAREdHsIlNvo6mpCRMTE1y/fr1N8n53VYyMjJCent7ZajQJFRUVGBgY4MaNGx1u6ObxeJCUlMTD\nhw/bbYzGvLgLCgogJiaGbt26tZsOlLalqqqqwdzqHUlX22yvj7cLTlMojcEwDD755BO2Hkt+fj6e\nPHmCadOmYdmyZYiMjMS0adPg5+cn8rzydgQiwzCNFt7m8XiQlpauE/1IAEhJSGDLli2YP38+vvnm\nG9y4cQOpqan4888/AQDHjx9HZWUlxMXFG00DCNSsad7+nWtqTYg///wTLi4uUFJSwpEjR5Ceno4z\nZ87AycmJRjhSKJT3BmrkplAo7c6bC8ns7GysWbMGv//+O3x9fTtNjwcPHiAwMBBhYWGYMmVKh+rB\n5/MhJyfXoWO2B927d0d4eDgqKyvh7OzcIuOgUChESkoK4uLioKenB1tbW8jIyMDV1RV7T5xAlLU1\nCIC1v/6K3r17izyMNHXRbmRkhH/++QdXrlzB5cuXsX79ekhISLDnAwMDERgYiF27dqG8vJyV35UL\n672P6OnpwcPDA05OTggLC8Phw4dx//79zlaLRVFREQ4ODsjPz0d8fDyqq6tbLEtOTg4DBgxAXFzc\nB1voSUtLi/Uwex/o1q0bTExMEB0d3eGGblVVVTx9+rTdxm0oF3dVVRXS09NhZmbWLmNT2o/mRH29\nz2M2hKOjo4i37dtHbeoyX19fXLt2rZO1pbxP1G4MqqqqihxiYmJwdHTE7NmzkZGRgcrKSgwePBhA\njbF4165dkJOTg5qaGiZNmoTKykpWZnV1NZ4/f46lS5dCSUkJ33//PYCa5w93d3f0srPDcwD7AKgB\nOAqgvLISpaWl0NPTg4yMDG7cuAFDQ0MkJSXhxIkT+Oabb7B27dp605XExMTA2dkZsrKyUFBQgIuL\nC8TExFBWViYSOZeYmAigZh32119/1Xs/cnNzMW/ePHz33XfYs2cPHB0doaOjAzMzM8yePVsk9Vxj\nxS1LS0shJyeH4OBgkT4XL16EhIREi4tqUigUSlOgRm4KhdLuEEIgISEBVVVVaGpq4vPPP8cXX3yB\nkydPAgD279+Pfv36sYtGDw8P/Pfff+z19S3sWuJZ9OaCVkNDA5988gkmTJiAmJgYkX6BgYEwMzMD\nj8eDsbEx/P39RYwTf/75J4yMjMDj8aCiooLhw4ezXhy1D4jbtm2DlpYWlJSUMHXqVAgEAvb6+h4i\nt2zZgp49e0JKSgra2tpYvHhxvXMQCoX45ptvoK+vj6ysrCbPva14O/xRVVUVYWFhAABnZ2d24fp2\niOSb11VUVKCsrAylpaW4ceMGtLS0YGdnV2fB7OrqitVbtoBhGPj4+EBDQ4M9NDU12Yfbt3V6+3VA\nQACePXuGkSNHws/PD9OnT4euri57/tq1aygvL4ejo6PIGFu2bGmDO0Z5Gzk5OYwePRrjx49HTk4O\ngoKCEB0d3agnVEfAMAx69eoFExMTxMTEiHwPNRdxcXHY29vjwYMHyMnJaTsluwjvYzEsBQUFmJub\nIyoqqkM/b9ra2qiqqmp17vf6qKioAMMw7/TijouLg42NTYvC1imdQ1VVFfs79rG/bydOnBDZ3K51\nUjA3N0e/fv3YIqoyMjJQVFTsZG0p7xu1a/tnz57B2dkZBw4cwO3btyEQCBAYGIi4uDg4ODjgwIED\nePz4MR4/fgwNDQ3ExcXh8uXLKCkpQXJyMvt7smXLFrx69Yp9tqiurma/901MTGoKUYqL4y6XC1V9\nfXz22WdQUVHBwoUL2RQloaGhIITg+++/x+LFi3H69GkMGjSI1dlr/HikJCUhOjoaQ4YMgZKSEgIC\nArBnzx6MGDEClpaWkJGRwf3791FcXIzg4GBs2rQJhBD4+/tj5syZ9d6LY8eO4fXr1/jxxx9bfV9l\nZGQwadIkBAQEiLQHBATg008/hYqKSqvHoFAolHdCKBQKpZ3x8vIin376qUjb3LlzibKyMiGEkICA\nAHLu3DmSnZ1NYmNjiZOTExk8eDDbNywsjDAMQ549e8a2ZWdnE4ZhSHx8fL2v36WHu7s7+zorK4uY\nmZkRJycnEhoaSsZ98gnpY2pKlJSUSHBwMMnJySFnzpwh3bt3J9u3byeEEBIXF0e4XC45ePAgefjw\nIUlKSiL+/v6kqqqKHUNeXp7MnDmTpKamkgsXLhAFBQWyfv16dlxvb2+R+/HTTz8RBQUFEhgYSO7f\nv09iY2PJzp0768yrsrKSeHp6EgsLC/L48ePmvQldgMrKShIREUHWrVtH1q1bR/Lz8ztk3PT0dJKW\nltYhY1FaRnJyMgkKCiJnz54lAoGgs9VhyczMJDExMaSioqLVchITE4lQKGwjzboGR48e7WwVWsSr\nV6/ItWvX2O/t9ub27dvEz8+PZGRktLnsuLg4Ul5eXu+5jIwMkpub2+ZjUtqXjIwMEhkZKbJmqa6u\nJqtWrSJaWlpEUlKSWFhYkFOnTrHX1K4VgoODydChQ4m0tDQxMzMjFy9eZPtUV1eTqVOnEj09PcLj\n8UjPnj3Jzz//zH4vrVixgjAMI3JcvXq1TWQT8r81mL+/P9HU1CSKiorkq6++ImVlZc26P9OnTyca\nGhrk0aNHbNuKFSuIubl5s8YaMmQI+frrr8n8+fOJkpISUVFRIdu2bSMCgYD4+PgQeXl5oqOjQw4e\nPCgy/sKFC4mxsTHh8Xhk5Gp1AAAgAElEQVREV1eX/PjjjyL/g7W6HDp0iOjr6xM+n0/GjBlDnj59\n2qx5UtoXLy8vwuVyiaysLJGRkSHi4uJEXl6eKCoqEg6HQyQkJMiCBQtIUVERIYSQZcuWESkpKbJl\nyxZWxvPnzwkAMnHiREIIIerq6kRRUZHtIxQKCZfLJQYGBoQQQh49ekQAkMGDB5OoqCgSFBREFi1a\nRDQ0NIiUlBQRFxcnsrKyBABZvHgxO05CQgJZuXIlAUC2A8SkJtMJASDy/+rr60sIIeTUqVOEx+MR\nLpdLevXqRXg8HuFwOCLPUW8za9YsoqCgINJ25swZIisryx7Xr18nhBASGBhIZGVlRfq+/ax28+ZN\nwuVy2f/T58+fEx6PR86ePdvMd4pCoVCaBzVyUyiUdudt4/KNGzeIkpIS8fT0rLf/vXv3CMMw7MKo\nLY3ctQtaHo9HGIYh7u7u5OjRo0SNxyN7AKIEEDlxcRIaGspe98svvxAzMzNCCCHBwcFEXl6evHr1\n6p1j6OjoiDzYzZgxgwwdOrTe+/Hq1SsiJSVF/vzzz3rl1c7r6tWrxNXVlQwYMIBdcL8vvH79mkRH\nR5MNGzYQPz8/8uuvv5J79+51iMHvzp07JCsrq93HobQN+fn55NixY+To0aMdtgnSGBUVFSQmJqbV\nn6PCwkISGRnZYYbVjuD06dPkxYsXna1GiygtLSVXr14lr1+/bvexnjx5Qvz8/EhERESbyhUIBOTm\nzZv1nnv58uU7z1G6NufOnSNPnz4VcRDYunUrkZOTI4cOHSIZGRlk+fLlRExMjCQmJhJC/rdWMDEx\nISEhISQzM5N4eXmRbt26kZKSEkJIzW/x8uXLyc2bN8mDBw/IkSNHiIKCAtm9ezchhJCSkhIyceJE\nMmzYMPLkyRPy5MkTUllZ2SayCanrBLBu3ToizuWSXoaGImuuhtixYweRlJQk0dHRIu31GbkbczgY\nMmQIkZOTIytXriSZmZlky5YthGEYMmzYMPLrr7+SrKws1rD55u/R6tWrSVRUFHnw4AH5999/iY6O\nDlm2bJmILrKysmTcuHEkOTmZREdHkx49ehAfH58mzZHSMXh5eREXFxeSlZXFHv/99x8hhBBHR0cy\ndepUkf4jRoxgjdBvHhwOhwQFBZHi4mLCMAwJCwsTuY7H4xFjY2NCCCGxsbGEYRgiLi5OeDwe4fF4\nREpKioiJiZHu3buT6upqsm7dOgKA7N+/n5UhFAqJlZERYQDyDCAEIBoAMdbTe+f8hgwZQrS0tIi4\nuDiJiYlp9H7MmjWLyMvLi7SVlpaSrKwsEhcXxz6LENI0IzchhPTp04esW7eOEELI9u3biZaW1ge3\n2U+hULoe71+sKYVCeS8JDQ0Fn88Hj8fDwIED4eTkhN9++w0AcOvWLYwePRq6urqQk5NDv379AKBd\nCnUNGTIESUlJiI2Nxdy5c3H16lX8uXUrNgoEGAGgCED569dwHzmSrXK+aNEiNnfwsGHD0KNHD+jp\n6eGLL77Avn376oSgm5mZiYQYq6uro6CgoF597t69i4qKCri4uDSo9xdffIGioiJcvnwZCgoKrboH\nHUV1dTXi4+Pxyy+/4Pz585CSksL48eMxZ84cmJiYtHsYdlJSEvh8PvT19dt1HErboaamhvHjx8Pd\n3R23bt1CUFAQkpOTO1UnCQkJ2NraQlJSEpGRkS3O1a6srIw+ffq0SkZXQ19f/70pPvk20tLS6N+/\nP6Kiopqc37+ldOvWDQzDtCr9TX2kpKTUm4ubEIKEhAT06dOnTcejdAylpaV1ioRu3rwZvr6+8PT0\nhKGhIVauXIlBgwZh8+bNIv3mz5+PkSNHwsDAAOvWrcPz58+RlJQEAOByuVi5ciWsra2ho6ODCRMm\nwMfHB4cOHQJQk2JASkqKTS+nqqoqUmSvNbJrkZeXx86dO5GTk4Ntq1djYFUVkJkJr7Fjcf78+Qbv\ny7Vr1/D999/j999/h52dXaP3sXYsY2NjNj3d5cuXRfqYm5tj+fLlMDAwwPz586GsrAwej4e5c+dC\nX18fy5cvh1AoRGRkJHvN0qVLMWDAAOjo6MDNzQ2LFi2qM8+qqirs2bMH5ubmsLOzw8yZM+uMTel8\neDwe9PX12UNdXZ09JyMjI9KXEAJ3d3ckJSWJHBkZGRg5cmQd2QKBABcvXkR5eTkrqzatyezZszFr\n1ixERkbC398fa9euxeeff46QkBCMHTsWDMMgNjaWzavNMAx4PF6z5sYwDHr37g0NDQ38/fffjfY3\nNjbGy5cvkZ+fz7ZJS0tDX19fJMUf0PTiltOnT8eePXsA1KQq8fLy+uhTMFEolPaHGrkpFEqHUGtc\nTk9PR0VFBY4dOwZlZWWUlpbC1dUVsrKy2L9/P27evInQ0FAAYIu51OZ+fXNB1VKjRO2C1tzcHNu2\nbYONjQ1up6UBAGoztHoDcLGzYxewd+7cwZ07dwAAsrKyuHXrFo4cOQIdHR2sX78eJiYmePz4MTsG\nl8sVGbMpldcbw93dHSkpKYiIiGiVnI5AKBQiKSkJ27ZtQ0hICMTExDB69GjMnTsX5ubm7b7AJYQg\nPj4eqqqq0NbWbtexKO2DlJQU3Nzc4OHhgbKyMgQFBeHSpUsihZQ6Gk1NTdjZ2SE1NRV3795tURFB\nHo8HBwcHJCcnizxIvq/07NkTubm5na1Gi5GSkoKtrS2ioqJEioe1NWJiYuDz+cjLy2szmeXl5WAY\nRqSIbi0JCQmwsrJ6L/OmU+ry6tUrPH78GPb29iLtDg4OuHv3rkibpaUl+3etwe7NTfadO3fCxsYG\nqqqq4PP58Pf3b/L/cFvIrnUC+GvLFmwUCDAEgBiAjQIB/mqgDsbDhw/x2WefwcfHB1OnTm2Svo05\nHDAMIzInoKbOyJsbR1wuF4qKiiLXHTt2DA4ODlBXVwefz8f8+fPrzLNHjx4idUYacnagvB/07dsX\nKSkp0NHRETGM6+vrQ1ZWFvLy8lBXV0d0dDSAmvo9n3/+ORQUFCAvLw+g5jMpKSmJHj16QF5eHikp\nKZg+fTo0NTWhq6uLhIQEtoZHeXk5W/MGAMZMmgQC4BCAvQCecTjgSEk1qLO+vj7CwsJw4cKFd+bi\nruWzzz6DuLg41q9f3+i9UFFRQVlZGV69esW21Ra3fJNJkyYhLy8P27dvR0JCAr766qtGZVMoFEpr\noatfCoXSIdQal7W1tSEmJsa2p6am4tmzZ1i3bh0cHBxgZGSEJ0+eiFxbW6DkTS+4+hZTLWHFihUo\nLCrCt2JiCAWgAOAAl4vvly2rs4itRUxMDE5OTli3bh1u376N0tJSnD17lj3fHCOuqakpJCUlcenS\npQb7TZ8+Hf7+/hgzZkyjfZtDfUU9WwohBCkpKfjtt99w8uRJVFdXY+TIkfj22287zOBCCEFsbCx6\n9Ogh4pFDeT/hcDiwtbVlvRePHz+OkydP4sWLF52ij5iYGKytraGmpobIyEgUFRU1WwaHw4GdnR2e\nPXuGtP/fYHtfkZCQQHV1dWer0SokJSUxcOBAREdHo6Kiot3G0dLSwqtXr9rsfr3Li/vRo0fg8/mQ\nk5Nrk3EoXRdCSJ31xpue17XnajfZDx8+jO+//x5Tp07FhQsXkJSUhNmzZ9f53L9rDdMWsmudAGqv\nY/A/B4N3IRAIMHbsWFhYWMDf37+R3nXHelPntx0O3pxTbZ/62mqvi4mJweeffw43NzeEhIQgMTER\na9asqbNJ1pAMSteH1KR0FWn75ptv8OLFC0ycOBGxsbG4f/8+Ll26BB8fHzai89tvv8XPP/+M4OBg\nuLm5YfLkySLf+Xw+Hz/88APWrl2L4uJixMXF4eTJkyguLkZ8fDwkJCTYNb6srCyio6PZzZH+/fuD\nYRgEWVnhDzMzDHVzQ3Z2Nnx8fHD79m2kpaXh77//Zjdcauegp6eHsLAwhIaGwsfH551z1tLSgr+/\nP7Zv344vv/wSYWFhyMnJQUJCArZu3QqGYdjnN1tbW8jIyGDRokXIzMxEcHAwfv/99zoyFRQUMGHC\nBPzwww8YMmQIDAwMWvGuUCgUStOgRm4KhdKp6OjoQFJSEr/99hvu37+Ps2fPYtmyZSJ9DA0Noa2t\nDT8/P2RkZODChQtYs2ZNm4w/ZMgQ6OrqQl5TE0cHD4aemRmqxMRw7949pKWlISUlBfv27cOGDRsA\nACEhIdi2bRsSEhLw4MEDHDhwAK9evYKpqSkrszkennw+H99++y0WLVqEPXv2ICsrC7GxsRgwYAA4\nHA4WLlzI9p0xYwZ++eUXjBgxAgzD4NNPP22Te9Bc/Pz8wOFwwOFwIC4ujm7duqFv374YPXo0Dh06\nhPLycri6uuL777+HjY2NyKZGeyIUChEdHQ1jY2MoKyt3yJiUjkNXVxceHh5wdnbG1atXcfjwYWRl\nZXWKLt26dYO9vT3y8vKQkJDQIuNFr169ICMjg7i4uBZ5hVPaDnFxcdjb2+PGjRsQCATtMoampiYI\nIXj27FmrZZWXl4PD4dTx4q6oqMDDhw/Rs2fPVo9B6RyePHlSZ4OCz+dDQ0OjTiRXREQEevXq1WTZ\nERERsLW1xezZs2FlZQV9fX1kZmaKGLUlJCRaFDHTFNlAjbG3qqoKBlZWmM/lIhFAMYAfpaQwc8GC\nemVPnz4dxcXFOHr0aLM2y9sjaiwyMhKamppYsmQJrK2tYWBgwHreUt4vGIZ552ekvnPq6uqIjIwE\nh8PB8OHDYW5ujjlz5kBKSgqSkpIAgAULFuCrr77C9OnT2ZQ6kydPFpGzevVq+Pn54fz58/jjjz/g\n5eWFU6dOYfTo0VBTU2ON4kOGDAEhBMeOHWOjVxmGQcCRI/DfvRu2traYM2cOUlNTYWdnBzs7Oxw5\ncoT9XXhzDvr6+ggPD8e5c+cwa9asd96Tr7/+GpcvX0ZxcTE8PDxgZGQENzc33Lt3D6dPn2ajSZSU\nlHDgwAFcvHgRlpaW+Pvvv7FmzZp67+fUqVNRWVmJadOmNfyGUCgUShvBbbwLhUKhtI6GFpIqKirY\nu3cvFi9ejB07dqB379745Zdf4ObmxvYRFxdHUFAQZs+ejd69e6NPnz5Yv359HSNvYw809enx4MED\n9O3bFydPnkR4eDj09PQQFBSETZs2YdGiReDxeOxCFgAUFRVx6tQprF69GmVlZTA0NMTu3bvZhV99\nY7zd9vbr9evXQ1FREatXr0ZeXh7U1NSgqKgIbW1tnD17VqTv1KlT4evri6qqKjx9+rTB+TZGa/LQ\nmpiYICwsDNnZ2fj333+RkJCAiIgIJCQkICoqqsPThFRXVyMqKgpWVlYiIcKUDw85OTmMGjUKVVVV\nuH79OuLi4qCtrc1uDHUUDMPAwsICr169QlRUFAwNDdG9e/dmydDS0oKcnByuX7/O5v1+3+BwOKiq\nqqrjNfm+weVyYW9vj6ioKPTt27dOPtbWoqqqCqDGiFn7d0tJTk5G796967TfvHkT/fv3b5VsSueS\nlpZW7yaFr68vli9fjp49e6Jv377Yv38/IiIi2NomTcHY2Bh79+5FaGgoDAwMEBQUhGvXrkFRUZHt\no6enh9DQUKSnp0NJSanJNUCaIhuo2Yw+dOgQ+Hw+fFevxt5du/CyoAB+q1Zh2LBhdeRu2rQJx44d\nw5kzZ1BZWVknzROfz3/n/2pjm4f1ees2do2xsTEePXqEgwcPws7ODufPn0dQUFCD11C6JoGBge88\n92aakFry8/Mxe/ZsREVFQSAQoLq6Gvn5+ZgyZQoUFRXZtq1bt2Lr1q0Njj1nzhzWQH348GFYW1vD\n3d0d9+7dAyEEK1asQFFREYyNjZGWlobz589jxIgRrAH81atXGDhwICIiIrBhwwYMGDCg0Tno6+s3\nqdaRo6MjHB0dG+03atQojBo1SqTtbYM+ADx+/Bjy8vL47LPPGpVJoVAobQH15KZQKO1OYGAgTp8+\n/c7zHh4eyMzMhEAgQExMDIYNG4bq6moMHjyY7TNgwAAkJCSgrKwMkZGR7GKvb9++AGq8PN983RQ9\nCCE4e/Ys+vTpg+LiYujp6QEAPD09ER8fD4FAgOfPn+PatWvw8PAAANjb2+PKlSt4+vQpysrKcPv2\nbXh5eTU41xUrVuD27dvv7MMwDBYuXIisrCzWE69v376wtLSEsbExdu/ezc7r7Nmz6NatG6ZMmSLi\nrRwXF4dhw4ZBRUUF8vLyGDRoEGJiYkT04HA4+P333zFu3DjIyspiyZIlde5RRUUFxo4dC2tr6waN\n6EKhEKdPn2YLSi5YsAAJCQkQCAR1PPF//vlnGBoaQlpaGpaWljhw4IDI+Z9++gkmJiaQlpaGnp4e\nFi5cKBLi7OfnBwsLCwQFBcHAwABycnIYO3Ys6xH5+vVrREREwMbGhhq4PyK4XC6cnJzg6ekJRUVF\nHDlyBGfPnkVZWVmH6sHn8+Hg4ICXL18iNja22ZtHcnJyGDBgAOLi4lqU/qSz0dLSQkZGRmer0SaI\niYnB3t4eCQkJIrlG2wI1NTUAaHUu9vLycoiJidXx4r537x4MDQ3rpEmgvF8UFhZCR0cHQM3vbO3m\n0bx58+Dr64sff/wRFhYWOHXqFI4fPy6SsqaxjX4fHx94eHhg0qRJ6N+/Px4+fIgFCxaIXDdjxgyY\nmprCxsYGampqiIqKajPZQE3aufv372PgwIFYuHAhPKdMQQ89PXz55ZfIzMysI/ePP/5AVVUVhg8f\nDg0NjTrHlv/P492YM0Fz+jSEu7s7fH198d1336F37964fPkyVq1a1ajcpsimdCze3t7NiojcvHkz\n8vPzkZSUxNbhqa+tOZiYmKBnz56Ij49HXl4eTE1NYWRkBBUVFeTk5EBBQQEcDgeZmZm4ceMGe52V\nlRW4XC5UVFTw999/w9XVFSoqKpCWloapqSnmzZuHBw8eNFuftkQgEOD+/ftYt24dZs6cCakG8oc3\n972gUCiUhmAIjZGlUCgfKQkJCTh9+jRcXFzg4ODQ2eqI8NVXX+Hp06dwc3NjPaIAYPTo0ejXrx+y\nsrLw9OlTnDlzBkCNx8Z///0HGxsbMAyD3377DQcOHEBmZiaUlJQA1Bi5VVRUsH79ejg7OwMAcnJy\n4OzsjKdPn4LL5WL06NEAgDNnzkBWVhYAcP78ebYg1NgpU3D06FHExMRg7ty5sLOzg729PVv1/dtv\nv8XevXtRXFwMAFiyZAmOHz+Obdu2wdjYGFFRUZgxYwaOHDmCESNGAADWrFkDFxcXaGpq4s6dO5g1\naxa8vLywatUqADVG7i1btmDYsGFYuXIlSkpK4OnpieHDh2Pbtm2IiYmBnZ3de+kFS2lbCgoKcP36\ndRBCYG9v3+F52cvLy5GQkAB1dXXo6uo261pCCBISEtCtWzf06NGjfRRsB168eIGIiAiMHDmys1Vp\nM2pTH5mbm7MFw1oLIQTr1q2Durp6kwvn1UdcXBx69+4tYuQuKirCw4cP6/XuprxfBAcHY/z48QCA\nYcOGoWfPntixY0cna9V6hEIhDh8+jPT0dNja2sLV1bWO0Tc6Ohp2dnbUGEzpEL766is8e/asQSec\nNxkzZgwUFBSwZ8+eBtve5vXr1w1uPr548QLbt2+HvLw8vv76axBCEB4ejsTERJSWlsLCwgLJycmw\nt7eHiooK+z1fWFiITZs2YcuWLRgwYADWrFkDfX195OXl4eDBg6ioqMCuXbuaNLe3qaysrLeocXPw\n8/PDunXrMGjQIJw6dYp9pqgPb29vPHv2jH2maSkfQlQZhUJpAwiFQqF8hJSXl5MNGzaQLVu2kNev\nX3e2OnXw8vIin376KSkqKiI8Ho9kZmaSx48fE0lJSZKbm0u8vLyIu7v7O68XCoVEXV2d7N+/n21j\nGIbMmzdPpF9YWBhhGIbcvXuX9O3bl4wePZpUVFSw50NDQ4kaj0f2AGQPQJS4XGJpaUl0dXVJSUlJ\nnXH/+OMPwjAMKSwsJCUlJYTH45GIiAiRPt9++y0ZMWLEO3X/448/iKGhIft6xYoVREpKirx8+ZJt\nW7t2LTEwMCBXr17tku9fV2bTpk1EV1eXfb1ixQpibm7e4DXZ2dmEYRgSHx/f3uq1CRUVFeTcuXPk\n0KFDJDExscPHf/DgAYmMjCRlZWXNvjYjI4MkJSW1g1btx9GjRztbhTZHKBSSqKgo8vz58zaTuXPn\nTrJ+/foWX19WVlbnf7Cqqopcu3aNCIXC1qpH6QIcO3aMFBYWkpMnTxJJSUly4sSJzlap1VRXV5PD\nhw8TPz8/EhIS8s7P6uPHj0lWVlYHa0f5WHl7HR0QEEBMTU2JlJQUMTIyIr/88gv7We3RowdhGIY9\nvL29ia6urkjbV199RQipWWvv2LGDjB07lsjIyBBfX19CCCGnT58mffv2JVJSUkRPT48sWbKEVFZW\nEkIImT59OlFVVWXXy8+fPyfh4eFER0eH2NnZkY0bN5K1a9eStLQ0kp6eTgghJDc3l4iLi5PRo0cT\nPz8/cuvWLZH5FRcXk9DQUOLu6Ei01NSIsrIy4fF4pFevXiQwMFCk75AhQ8jXX39NFixYQFRUVEj/\n/v3ZuWzfvp2MGDGCSEtLkx49eog8VxBCSF5eHpk4cSJRVFQkioqKZOTIkSQjI4M9X7vGPHToENHX\n1yd8Pp+MGTOGPH36lD3/5n1kGIZcvXq1WbIDAwOJvr4+ERMTI6Wlpc39KFAolA8Mmq6EQqF8lFy7\ndg3l5eVwc3Pr0rv+CgoKGDt2LHbv3o29e/fCyckJWlpadfoVFBTAx8cHxsbGUFBQgJycHAoKCtgq\n67XY2NjUO46rqyu0tbVx/PhxEe+Nv7ZswUaBAF4AvABsrapC+cuXkJWVrTcPJvn/4CCGYXD37l22\nCCWfz2ePnTt34v79++w1x44dg4ODA9TV1cHn8zF//vw6evfo0UMkFYmioiIePHgAR0fHeovoLFy4\nEBwOp1PCH8PDw8HhcCAvL1+niN29e/fYop3Pnz/vcN3qw9fXl40UAD6MsFEJCQkMHz4cHh4eqKys\nRFBQEC5evNiigmotQUdHB/3790dKSgpSU1ObVVjS0NAQ6urqiIqKYvNvdnUIIe+Nrk2FYRjY2dkh\nPT291fUPatHR0UFFRUWLi1umpKSIpKcAgPj4eFhbW1Pv1w+AkpISSEhIwMPDA3PnzsXChQsxZsyY\nzlarVQiFQpw4cQL37t1Dnz592MLZ9dG9e3c8efKkgzWkfMzUfhZ37dqFJUuWYM2aNUhNTcWWLVuw\nceNG/P777wBq6h0MHToUEydORH5+Pn799VfExcWJtG3bto2Vu3LlSri7uyMlJQWzZ8/G+fPn8cUX\nX2DevHm4e/cuAgICcOzYMSxevBgAsHz5cjx9+hT//PMPioqKoKioiLKyMuTm5qJPnz4AajzCb968\nidevXyMvLw9Hjx5FVVUVpkyZAnl5eZw7d04kzVZMTAy8xo6FS3g47J88gfDVK/zxxx/49ttv4ePj\ngytXrojci/3794NhGERERGDfvn1s+4oVKzBmzBgkJSVh5syZmDJlCuLj4wEAZWVlcHJygrS0NK5d\nu4aYmBioq6tj6NChIr9zOTk5OHr0KE6dOoULFy4gISGBTZno6+sLDw8PfPLJJ8jPz0d+fj4GDBjQ\nZNnZ2dkICgpCcHAwbt++TaM6KRQKzclNoVA+PoqKihATEwMtLS2YmJh0tjqNMnXqVOzduxeBgYHv\nDHP38vJCfHw8/P39ER0djcTERGhpaaGyslKk37sKNH366aeIiIhAcnJyo/o0VNzv7t27kJeXR7du\n3SAUCgEAISEhSEpKYo+7d+/iwoULAGoW4Z9//jnc3NwQEhKCxMRErFmzpo7eb4Z6FhUVIS8vDwCg\nra2NI0eOiORhrqqqwr59+6Cjo9Nqw09rinMqKiri6NGjIm27d+9uE73evj+tQUZGpk5xsA8FDoeD\nfv36wdPTE8bGxjh+/DhOnDjBptNpT7hcLvr16wclJSVERkbixYsXTb5WRUUFffr0QWRkJEpLS9tR\ny9ZTXl6OtLQ0XL58ubNVaXMYhkH//v2Rk5ODgoKCVsurLUzaElkCgQBiYmIi34U5OTlQVVWFtLR0\nq3WjdD7p6enQ09PDlStX8PDhQ6xcubKzVWoVhBCcPn0aKSkpsLS0xKefftrob5+2tnaTiuNRKG3J\n6tWrsWnTJowbNw49evSAu7s7Fi5cyBq5lZWVISEhAR6PB1VVVfD5/HrbavH09MTUqVOhq6sLXV1d\nrF27Fj/++CO8vLygp6cHR0dHbNiwATt37gRQ87l3dnZGfHw8zpw5w6YsMTExwfjx4yEQCCApKYnM\nzEyIi4vjyZMnSE5Ohry8PBwcHGBtbY3Xr1+z1wL/c1L5DkAQgK0VFTh94ABmzJiBcePG4dChQyL3\nQF9fH5s2bYKRkRGMjY3Z9vHjx2PGjBkwNDTE4sWL4ezsDH9/fwBgi64GBATA3NwcRkZG2LlzJ0pK\nShASEsLKqKqqwp49e2Bubg47OzvMnDmTXTPIyMhASkoKEhISUFVVhaqqKsTFxZssu7KyEv/88w+s\nrKxgZmYGMTGxtvhIUCiU9xhq5KZQKB8doaGhEAqFGDlyZJf2fqtdqLq4uEBSUhLPnj17p1dXZGQk\n5s6dCzc3N5iamkJWVrZZRXBWr16NWbNmwcXFBUlJSWz7zAULsJDHw14AewEs5PFgXU8Vd6CmgvrB\ngwcxbtw4AICZmRkkJSWRk5MDfX19kUNbW5vVW1NTE0uWLIG1tTUMDAyQk5PzTj0LCwuRnp4OQ0ND\nAIClpSV69uyJI0eOsH3Onj0LHo8HR0dHEQ/a1hTn/Pfff2FrawtpaWkoKytj1KhRIsUx68Pb2xsB\nAQHs69evX2P//v3w9vau49l79+5djBw5EnJyclBTU8OkSZNEPNpqvas3btwILS0ttjDZo0eP4Onp\nCSUlJSgpKcHd3b1O8a6ff/4Z3bt3B5/Ph5eXF0pKSkTO1xb2rP173759OHv2LOtx/qaXd05ODj75\n5BPIyMigV69euD0z0zQAACAASURBVHTpUoP3oCuho6PDegtdu3YNhw8frrfQWVujqqqKgQMHIicn\nB0lJSU326ubxeHBwcEBycnKrixW2J1JSUuBwOB+sYYphGNjY2CAvL69FhcXepLb4ZEu8VZOTk0W8\nuMvKylBQUNDs3O+Urktubi6MjIw6W402gRCCM2fOICkpCebm5hg9enST1ltaWlp49OhRB2hIodTw\n9OlT5OXlYebMmSJRh4sWLRKJOmxOMdG3oybj4+OxZs0aEfmTJ09GWVkZ+3swd+5cpKamIiMjA8nJ\nyfjnn38wZ84cSEhIwMTEBBUVFSCE4MKFC7CwsEBRUREIIexGZ+/evZGRkYF79+6JjF0NYC2ApQBC\nwsPB5/Nx/PhxkYhJhmFgbW1d71wGvLXmt7Ozw927d9l5ZWdni8xLQUEBxcXFIvfu7WhMdXX1Rjd7\nmypbS0sLKioqDcqiUCgfF9TITaFQPipycnKQnp4OKysr1qvufeD27dvIzs5+Z/EaIyMj/PPPP7h3\n7x7i4uLg6enZ7KIxa9asgY+PD4YOHYrbt28DqEljsvfECZz+5BOc/uQT7D1xAoaGhqiqqsKTJ0/w\n+PFj3LlzB3/99RcGDBgAZWVlrF+/HgDA5/Pxww8/4IcffkBgYCAyMzORmJiInTt3ssVwjI2N8ejR\nIxw8eBD379/HH3/8wXpvvM3jx4+Rm5sLW1tbkQeLadOmiRiTAwICMHXq1DoPHyUlJfDy8kJERATi\n4uJgZWWFESNG1Ekb8naYaWhoKEaPHg1XV1fcunULV69ehbOzM+up/i4mT56M2NhYdjEeEhICPp8P\nR0dHAIDX+PEYP2wYDh48iMGDB8PS0hJxcXG4fPkySkpKMHr0aBGD6NWrV5GSkoILFy7g8uXLTQrl\nPHLkCJYtW4bVq1cjISEBxsbG2Lp16zsfzN4VNlrLkiVL8N133+H27dush3RX9zR+G1lZWYwaNQoT\nJkxAXl4eDh8+jMjIyEbfz9bA4XDQu3dv6OrqIjIyEoWFhU2+zs7ODs+ePUN6enq76dda1NTUUFBQ\n0Ky0LO8bffv2RUFBARtF0hJqH8SbaywXCAQQFxdnv/8JIWyaEsqHQ1VVVauLvXUFCCH4999/kZCQ\nAFNTU4wdO7bBCLC3UVdXx3///deOGlIo/6P2t//PP/8UiTq8c+cO7ty50yKZb0dNEkLg5+cnIj85\nORkZGRlQVlYGAIwYMQJycnLIysrC1q1b8eLFC3z55Zfo1asXjIyM2Ii7p0+f4uLFixg0aBBevnyJ\nhw8fwsrKCgoKCpCRkcGZM2cgEAhYJ5XPAawH8ILLhf+2bUhKSsKYMWPqOGq8K9KzPmrXkEKhEFZW\nViLzSkpKQnp6OmbOnMn2f/vZhWGYOmuut9elTZXdHL0pFMrHQddNREuhUChtjFAoREhICMTFxTF0\n6NDOVqdBGIYRWfC9XZX87fMBAQGYOXMmrK2toampCT8/vybnkX1Tztq1a0EIgYuLC65cuQILCwu4\nurrC1dWV7RMTE4O0tDSoq6uDw+FATk4OpqammDVrFubOnSuy4Fy9ejXU1NSwefNmfP3115CTk0Of\nPn3w448/AgDc3d3h6+uL7777DgKBAK6urli1ahW++eYbEf2qqqpQWFiIvn371rkHkyZNwg8//ICs\nrCzIyMjg/Pnz2LFjB5YuXSoyTycnJ5HXv/76K4KDg3Hu3DlMnjyZba8NM61l8uTJmDBhAlatWsW2\n9erVq9H7qqSkhFGjRiEgIABr1qzB7t278dVXXyE2NhaEEAwPD4csgJlhYehpbs5uDgDA3r170a1b\nN8THx7MeQTweDwEBAezDQq1h/00D/86dO6GmpoaQkBBMmDAB/v7+8Pb2xowZMwAAixcvRlhYGLKy\nsurV+e2w0beZP38+Ro4cCQBYt24d9u3bh6SkJAwcOLDR+9HV4HA47IbDvXv3cPToUcjIyMDZ2bnd\n0j/Iy8vD3t4eaWlpyMnJQZ8+fZpUE6BXr17Izc1FXFwcbGxsulwEio6ODvLy8lBSUiLirfWh0bt3\nbyQnJ0MoFLLRFM1BXFwcMjIydWoONEZycjKbl7X2da9evWhYNqXLQQjB+fPncfPmTRgZGWH8+PHN\nMnADNV6fMTEx0NDQaCctKZT/oaqqCg0NDWRmZuKLL75olzH69u2Le/fuQV9f/519uFwuvL29ceXK\nFRQXF8PW1pb9PS0oKMDIkSMRFBSEqqoqpKamon///pCQkMD8+fNx9OhRmJqaghCCa9eu4dy5c3Bx\nccHeEyfg/cUX4L1+DbdRo+Dp6QkFBQWkpaVBSUmpSbpHR0fD29ubfR0TEwNTU1MAgLW1NYKCgtCt\nWzfIy8u3+P5ISEjUqZnSVrIpFMrHBzVyUyiUj4aEhAQ8e/YMQ4cO7fI7/4GBgc06b2lpWSf1xpuG\nWwD1eqo6OjrWKRi3bt06rFu37p1jr1ixAitWrGhQvzeZM2cO5syZ887z9Y33ZjHJL7/8EhMmTICZ\nmRnb5u3tjfDwcDx//lykOKe8vHyDxTmXLVuG8PBwPHnyBNXV1RAIBI0W50xMTHxnLvSGYBgG06ZN\nw7Rp0+Dj44NLly7hr7/+gueoUWAAfA5ACTXFPJOSkuoYBxmGQVZWFquPubm5iDfMm6GcbyIQCFjv\n8dTUVBGPF6Am1LSlaTosLS3Zv9XV1QG0LL9wV8PU1BSmpqYoLCzEuXPnIBQKYW9v3y5GFoZhYGJi\nAoFAgNjYWJH0Mw2hra0NOTk5REREwNbWtkt5fNZ+FvLz8z9oIzcAWFhY4O7du8jOzoaenl6zr1dX\nV8f9+/dBCGnSZkVZWZmIF3dBQQG4XG6TDRSU94Oqqqout3nVXAghuHjxIm7cuAFDQ0N4eHi0aCOG\nYRioqKigoKCg3s1WCqWtWblyJebOnQsFBQW4ubnh9evXuHXrFv777z/89NNPAGo+329HK9XXVh/L\nly+Hu7s7evTogQkTJoDL5SIlJQVxcXHYuHEj22/69OnYsGEDW/g4NzcX2traMDExQVRUFCZNmoR9\n+/ahpKQE//33HzZs2IAFCxbA3d0dP/74I0pKSsDhcLBx40YcOXIEQUFBmOzlhYMHD0JcXBx///03\ncnJykJOTI1KLpaF5nDhxAv369cOQIUNw7NgxXLlyBbGxsQBqnjM2b96M0aNHY9WqVdDW1kZubi5O\nnz6NWbNmsakFG0NPTw+hoaFIT0+HkpISFBQU2kw2hUL5+KBGbgqF8lFQXl6OixcvQk5ODnZ2dp2t\nDqWJpKeng2EYEQN3fUydOhVTpkwBn8/H6tWr6+3j5eWFwsJC+Pv7Q1dXFxISEnBxcWlycc6W4OLi\nAg6HgylTpsDFxeWdRlM5Hg8zZs2CsrIy1NXVoaKiAlVVVZH8rG97F9eGch4+fLiOvNYawN5lbHnT\nyP5muOqHgoqKCsaPH4/KykqEhYXh2rVrMDY2FvGibSt4PB6bqzs6Ohp9+/aFpKRkg9fIy8vDzs4O\nMTExsLCwgIKCQpvr1RLeNHL37Nmzk7Vpf8zMzJCWlobMzMxmP2hraWkhMzMTRUVFTfo/TUlJYT9/\nr1+/RkZGBuzt7VukN6Xrkp2d/d57LoeFhSE6Ohr6+vqYOHFiqyINDAwMEBMTQ43clHZDKBSykVTT\npk2DjIwMNm3ahEWLFoHH48Hc3FzEQePtCMp3tdXHsGHDcPbsWaxevRqbN28Gl8uFsbGxiIc0UGPs\nHTJkCB48eABDQ0OcPHkSs2fPhpiYGPr374+YmBgMHz4coaGhSEtLg729PS5cuIANGzZgzJgxKC8v\nh4qKCrp37w59fX1UVFRg6dKlyM7OxqFDh8DhcDBp0iRMnjxZJHd3Q/Pw8/NDcHAw5s2bB1VVVezZ\ns4dNlcXj8XDt2jX89NNPmDBhAl68eAENDQ04Ozuzv2/vkv1m24wZMxAeHg4bGxuUlpYiLCwMgwcP\nbrFsCoXycUON3BQK5aPg6tWrqKiowNixY2mI93vCnTt3IC0t3aC3ZHOLc/72229wc3MDADaneGP0\n6dMHly5dwrRp05o9Bw6HA29vb6xevRpHjx4FALh7eOB6fDwOAZAFkCkmBmlpaZiZmaGgoADZ2dnI\nzs4GUBO+qqqqikePHqGyshJPnjyBiooKOBxOk0I5TU1N6w01beihoL6w0Y8NCQkJuLq6QigU4tat\nWzh8+DCUlJTg5OTUpPQizUFXVxeamppISEiAoqJio0ZicXFxODg44NatW1BWVkaPHj3aVJ+WoKio\n+EEXn6wPY2NjZGRkID09vVnFAt8sPtmYkbusrAwSEhLs5tLNmzfrRJpQPgwyMzPf6w348PBwXL9+\nHT169Pg/9u47rIorf/z4+166VKX3oiBFUKoFbFEsURNl12iMWYkxuinspljWmKJx0813NZtk4yY/\nbDEb16ibRGOJsSC9g4iCdFFAQERBOvP7g2XWK9hpmvN6Hp7HOzP3zLlzvXPvfOacz4cnn3zyvs+T\nCoWCAQMGUFlZibGxcRf1UhD+58absnPnzmXu3Lk33f6nn366o2U3u/EfHBxMcHDwHfVr0aJFPPLI\nIxw4cIDo6GhGjx6Nuro6bm5unD9/Xh7VfOrUKcaNG8cvv/xCQUEBTU1N6Ovrk5GRQVRUFIcPH2ba\ntGns2rWLuro6NmzYgJaWFmFhYSqf0aNHj960PxYWFuzfv/+m683MzFTS5t2os9mfoaGhKr9LTUxM\nOHjwYJe0LQiCIApPCoLw0KusrCQuLg47O7u7CkYIvSc1NRVDQ8O7SgfQXcU5V61axc6dO3nzzTfJ\nzMzk1KlTrF+/Xi7ueDtvvPEG5eXlzJo1C4CAgAAUCgUHxo3jx+Bgvtq6FaVSyd69exk/fjyzZs3C\n3t6epKQkLC0tqa6upry8nNLSUr788kveffddvvjiC4yMjDA0NGTKlCkcPXqU/Px8IiIiWLp0qZyO\n5M9//jNbtmzh66+/5uzZs7z//vtyTvCbcXR0JCMjg+zsbCoqKn7TAW+lUomfnx9z5sxh8ODB7Nmz\nh927d1NVVdWl+9HQ0CAgIAB9fX0iIyO5evXqLbdXKBT4+vrS2NjIyZMnu7Qv90KpVNK/f//fXLE4\nZ2dn1NXVVUbE3U57kPtO0vxkZGQwZMgQAM6ePYudnd1tR/sLD6Zr166ppA94kJw4cYLjx49ja2vL\nU0891WU3Al1cXPp0wV3hwVRRUcEPP/xARETEHQWde0p5eTn/+Mc/KCoqYsmSJfj7+2Nubs6xY8fk\n3xzGxsZoaGgwZcoU1NXVqaqq4uTJk5SXl+Pg4EBTUxMtLS0YGBjg6OhIYmKinJJPR0eHSZMmceXK\nFaKjo3vzpQqCIHQrEeQWBOGhd+DAASRJYtq0aWJaWx8nSRKJiYlYWFh0mlf7ep0V57y+QGdnxTlr\namrw9fVl3rx5LFq0CAcHh9v2aerUqezZs4f9+/fj4+PDuHHjOHbs2C2LaV2/3/b8udcvUygUbNm1\ni12HDjFv3jyioqJQKpVMmTKFESNG8MEHH+Do6MjTTz/N0qVL8fT0xNbWluDgYFxdXWlsbOTUqVPM\nnj2b+vp6ZsyYgYuLC0888QTZ2dnU1tbS0NDAE088werVq1m1ahU+Pj6cOnWKV199tUNfbpw26ubm\nhp+fH+bm5vLF0G/9s2NnZ8fs2bOZNGkSJ06cYMeOHZw9e7ZL92FhYcGoUaPIycnh5MmTt8316ezs\nLL9HN+bW72kODg5cu3aNhoaGXu1HT3NycqJfv35kZGTc0fZGRkYolcrbFp+sra1FU1MTdXV1rly5\nwpUrV7C2tu6KLgtCl4mOjubIkSNYW1szf/78m95kvhcKhQIDAwOqq6u7rE1BeOKJJwgLC2PFihU3\nnfnXG8zNzVm9ejUbN25kwIABKJVKZs6ciSRJ/Pjjj/LvATc3NwoKCuRR5zk5ORw/fpzm5mbc3d0p\nLS3F0dERGxsbNDQ02L17tzxYwdvbG3NzcyIiIrh8+XKvvVZBEITupJDupFqCIAjCAyovL49t27bh\n6+vL9OnTe7s7wi1IkkRcXBzOzs5ievIdaGxspLS0lJKSEi5cuEBxcTFVVVUqgVEDAwOsra2xtrbG\n0tISCwuLDrm9hXvT2tpKZGQkJSUlWFlZERgYeMsbH3erqqqKjIwM3N3db/t5uHbtmpzKorfe36Sk\nJPbu3cszzzxzR4U0Hzbnzp2jqqpKpTjrzXz22WfU1dWxbNmym24THx+Pj48PSqWSyMhIRo8e/Zu/\n0fSwam1tZffu3fz+97/v7a7clbi4OA4cOIClpSULFizollkGra2txMfHP9CpXAThfhw8eJDY2FhC\nQkLw9PQE2uozxMXFUVxcTFZWlnzDe8KECUiSRHR0NJaWlmRlZREfH09QUBATJkwA4MKFC3z11Ve4\nuLjw5JNP9uZLEwRB6BZqq1evXt3bnRAEQegOra2tbN++nebmZp588skuHWEkdK3W1lZiYmLw8PB4\nYKds9zQ1NTUMDQ2xsbHBzc2N4cOHExgYiKurK1ZWVujr63Pt2jXOnTtHbm4u6enpREdHk5iYSH5+\nPlVVVTQ2NqKpqYmmpqYIoN0lhUKBvb09Q4YMoa6ujiNHjpCfn4+trW2XnGt0dHSws7OjoKCAoqIi\nzM3NbxpE19DQwMbGhuTkZDQ1NVVmNPQUSZJITk7G0tLyNzni2NDQkJaWFnJycuRCnDdTUlLCuXPn\nCAwM7LRGRG1trVxkKzk5mSFDhog0JQ+x0tJSampqcHJy6u2u3LHExET279+Pubk5CxYsQFtbu1v2\no1AoqKqqol+/fuIzIPwm2dnZkZqaSlZWFr6+vmhoaKD231ouampq5OfnU11djZGREQ0NDVhYWGBt\nbU1GRgb9+vWjoaGBzMxMBg8ejJ6eHvr6+ly5coVTp05ha2vboTZEYmIiNjY2PPPMMzet9yIIgtCX\nicKTgiA8tJKSkrh06RKTJk0So1f7sJaWFqKjo/H29u6V4NzDRF1dHSsrK6ysrORlra2tVFRUUFJS\nIgfXCgoK5LzdANra2nKKGEtLSywtLTEyMhKB7zvk6uqKq6srFRUVHDhwgJaWFkaNGnXfwV6FQoG7\nuzu1tbXExsbKRSo7o6amxsiRI8nIyKC6urrH6w+YmZkB/Obycl/PysoKNTU1EhMT8fX1vennx9LS\nktTUVC5evNjp+5mRkYGvry/FxcUYGBigr6/f3V0XelF2dvZtC872JZIkcf78+W4PcLdzd3cnMTGR\n4cOHd+t+BKErhIaGUllZ2WlRynuhqanJY489xrfffsuhQ4d4/PHHgbZijceOHeONN97AxsaGd955\nh7q6OszNzTE3N2fEiBHExMRgb29PaWkpe/bsYcmSJSiVSiZOnMipU6f46aefeOmll7q8oLYgCEJv\nEmc0QRAeSnV1dfz6668YGRkREBDQ290RbqKpqYno6GgCAgLQ0dHp7e48lJRKJWZmZpiZmTF06FCg\nLUhRVVWlkuqkpKSEgoIC+XkaGhqYm5urBL6NjY27NCXHw8bExISQkBAaGxs5evQoJ06cwMXFBR8f\nn/tqV1dXl8DAQHJzc4mNjcXHx+emRVOHDBlCUVHRbQOtXU1DQwN9fX0KCwt7ZH99VfuI+4SEBPz9\n/Ts9/u3FJ8vKyjoEuWtra9HS0qK5uZlz584xcuTIHum30HsqKioYPXp0b3fjjkiSREJCAkFBQfTv\n379Hvg/U1NTQ0tLi2rVrYsCC0OfdWOekKzg7OzN48GDKyspobW2VP3dHjhzB1dWVrKwskpOTefzx\nxzl8+DCPPfYY+vr6+Pj4EBsbi5eXFykpKcTExBAYGEi/fv0IDg5m3759xMTEdPn5p32WoCAIQm8Q\nV6qCIDyUjh8/TkNDA48++min08GF3tfQ0EB0dDQjR44UAe4eplAoGDBgAB4eHgQHB/PMM8+wYsUK\nXn75ZebOncvYsWOxs7Pj0qVLxMbGsmfPHr744gvee+89vvzyS44fP97bL6FP09TUZPLkyXJhqB07\ndnDo0CEaGxvvq92BAwfi4+NDcnIyubm5N93Ozs4OZ2dnIiMj73ufd8PGxobq6upeL4LZ20xNTRk0\naBBxcXGdFg+9Psh9o4yMDDw8POQc68Jvw4Ny8zApKQlHR8cev+Hp4eHBqVOnemx/gnCvJEmSz/ut\nra2sXbsWW1tbtLW18fLy4scff5S3LSgoQKlUsnv3boKDg9HV1cXDw4PDhw93aHf27NmMHTuWiooK\noG0wz7/+9S8++ugjvLy8SEpK4uzZszg5OXHo0CFOnz6NkZERxcXFvPnmm7z33nvMmjWL3bt3A1Be\nXs6+774jdM4crK2t0dHRYcyYMWRnZ3fYd3R0NGPHjkVXVxcbGxteeOEFrl69Kq8fN24cL7zwAkuX\nLsXMzEwOmm/cuBEXFxd0dHQwNTVlypQp8u8DSZK67NgIgiBcT4zkFgThoVNRUUF8fDwODg4P1BTg\n35L2QnmBgYFimmQfoVAoMDQ0xNDQkMGDB8vLa2trKSkpobS0lOLiYi5cuEB2drY8oq59xLeZmZm4\nodQJHx8ffHx8KC4u5ocffkBNTY1x48Z1yIN5pzQ1NRkxYgTnz58nKiqKYcOGoaur22E7Q0NDhg8f\nTlxcHJ6enhgZGd3vS7ktGxsbTp8+TUVFhRzI/a0aMGAArq6uxMTEMHLkSJWRfdra2mhpaVFUVKTy\nnNraWrS1tcnOzsbFxUXUkRD6lOTkZOzs7DA1Ne3xfbfnIa6vr+/29CiCcL/az/cbNmxg3bp1bNy4\nET8/P7Zt20ZISAhJSUnyzDqAVatWsW7dOr788kvWrl3L3LlzKSwsVPluV1NTw8XFhaioKExNTfn+\n++8xNDRk+vTpXLx4kZdffpkLFy7IN6Hi4+MB+Pjjj3nttdeora1l8+bNLFiwAKVSyR/nzWN5XR0r\nAHWlks/++U8MDAx45ZVXVL6vTp48yeTJk3nnnXcIDw+nsrKSl19+mYULF7Jz5055u2+++YYlS5YQ\nGRmJJEkkJiby0ksvsXXrVoKCgqiqquLo0aPy9uvXr++yYyMIgnA9hdTZEBNBEIQH2LZt28jPz+f5\n55/vlYsx4dauXLlCeno6o0aNemBGrwmqrp8u29TURFlZGRcvXpRH6KipqWFqaoq5ubmYsnqDmpoa\njh49yrVr1xg2bJjKDYW71dLSQmpqKtra2ri7u3c6Rbq9IKSpqSl2dnb30/XbysvLY9u2bcycOVPl\nIrWvcHBwICwsjNdee63H9nmz892mTZu4cOECr7/+uvy+xcXF4ejoSGlpKV5eXj3WR6H3XLlyhYiI\nCKZPn97bXbmllJQULC0tsbCw6LU+NDY2cvLkSXx9fXutD4JwO6GhoVy6dIkff/wRa2trnn/+ed54\n4w15/fjx47GxsWHbtm0UFBTg5OTExo0bee6554C2uhY2NjZERkYyatSoDu1fvnyZc+fOERYWxiOP\nPMJbb71Fc3Mz5ubmBAcHExAQQEBAAHl5eYSGhvL555/z/PPPc+LECVJTU/nTn/6En5sbL50+TRaw\nG1gJ7AgM5OfISN59913efPNNCgoKsLOz4w9/+AOampp8/fXXch9SU1Px8fHh4sWLmJiYMG7cOC5f\nvkxqaqq8ze7du1m4cCHFxcWd1tvpjmMjCIIAIl2JIAgPmZycHPLy8vDz8xMB7j6oqqqKjIwMAgMD\nRYD7AXb9e6ehoYGNjQ0+Pj74+/vj7+/P0KFD0dDQIDMzk4SEBBISEkhMTCQ/P5/6+vpe7Hnv09PT\nY8aMGcyePZuysjK+++47IiIiaG1tveu21NTU8PX1xcLCgsjISKqqqjpso1Ao8PX1pb6+noyMjK54\nCTfVHgArKSnp1v10pry8nBdeeAFHR0e5kOrEiRNVpjZ3R67U2zEwMMDb25uoqCiVNC62trY0NzdT\nU1MDwP79+xk5ciQJCQl4enr2aB+F3pOdnY2Tk1Nvd+OW0tLSsLCw6NUAN7TNYpEkqUdTMAnCvbp6\n9SolJSUEBgaqLA8KCiIzM1Nl2fU3NS0tLQG4ePFip+0aGRmRl5dHVFQUzzzzDNBWdHzRokWcPn2a\nq1evUlZWJv9OUygUNDc3M3r0aExNTVEoFFT897fCaWDEf9stLy+npaWFESNGqOwvKSmJb775Bn19\nffkvKCgIhUKhkjbtxptPkyZNwt7eHkdHR+bPn8/WrVvl77srV650y7ERBEEAka5EEISHTElJCbq6\nuowfP763uyLc4OLFixQUFHSYui88fNTU1DoERVpbW7l06RJnz55VCXT3798fCwuLTkf6PMyUSiVj\nxowBICsri507d9KvXz/Gjx9/18fC2NiYoKAgMjIyKCgoYOjQoR1uIrm4uFBWVkZMTAwBAQHdklqm\nX79+aGlp9Urxyd/97nfU19cTHh7OoEGDKCsr4/jx41RWVvZ4X26kq6uLr68vUVFRjBo1CnV1dZW8\n3Pr6+uTn5wMwdOhQcX78DSkuLubRRx/t7W7cVHp6OiYmJnJwqbcNGTKEU6dO4e3t3dtdEYR7IklS\nh3P89amp2tfd6sZ3dHQ0LS0tKjfI2ifnT5kyhZycHIYMGQKAlZUV0dHRBAYGEhISAoCdiwtLL13C\nvrGRcmCvhgZTAwKIjY3ttL/PPfccr7zySod1VlZWcp9vTB+ip6dHcnIyERER/PLLL7z//vu8/vrr\nJCQk3DTVSFccG0EQBDGMThCEh4YkSSiVSl555RVRyLCPuXDhAufPnycgIEAEcH6jlEolJiYmeHp6\nyiO+/fz8MDY25ty5c/KI74SEBM6cOcPly5c7Ldr3MBo8eDBz5swhMDCQQ4cOsXPnToqLi++qDYVC\ngaenJ4MGDSI6OprS0tIO25ibmzN06FCioqK4du1aV3VfhYWFBRUVFT363l2+fJnIyEg++OADxo8f\nj62tLX5+frz22mvMmTNHZdu6ujqWLFmCoaEhtra2rFu3TmX9//3f/zF06FD09PSwsbHhueeeo7q6\nWl6/efNmZsvTMgAAIABJREFU9PX1OXLkCEOGDEFPT49HHnmEgoIClXZ++uknfH190dHRwcnJiffe\ne08e0d3U1KQS5K6pqaGurg5AznUPkJmZybRp0zAwMMDc3Jx58+apFKsMDQ1lxowZbNiwARsbGwYM\nGMDChQvltgAiIiIYMWIE+vr6GBkZMXz4cJUCfndSUOzFF1/k9ddfl1MQLVu27Dfz2exuLS0tfTal\nU0ZGBv3798fa2rq3uyLT1tamqamJ5ubm3u6KINySvr4+VlZWREZGqiyPjIzEw8Pjntttbm5m69at\nrFy5kr1795KWlib/eXl5cenSJerr6zl37hzQNlvE09OT6OhouQaOs7MzU554gktWViRraTF1zhx8\nfHw4evQox44dU9mfj48PGRkZODk5dfi7XX58NTU1xo8fz3vvvUd6ejq1tbXs27cPAwODbjk2giAI\nIILcgiD0sGPHjqFUKrl06VKXt52Tk4Ozs3OXjVAcN24cYWFhXdLWb1lhYSGVlZVi5JXQgUKhoH//\n/ri5ucmBb39/f6ytrbl48SKJiYly4DsjI4PKysqHOrg2YMAAQkJCePzxx8nMzOS7774jKSmJlpaW\nO07z0j6V+OrVq8TFxdHU1KSyvl+/fgQGBpKWltYtU37t7e1pbm5WCQx3Nz09PfT09Pjhhx9oaGi4\n6XaSJPG3v/2NoUOHkpKSwooVK1i+fLnK6DU1NTU2bNhAZmYm3377LfHx8R2+BxoaGvjggw/YvHkz\nMTExXL58mT/+8Y/y+oMHDzJ//nz+9Kc/kZmZSXh4ON9//z3vvPMOI0eOJDo6mtjYWP6zdSsrnn+e\njRs3drgxW1JSwpgxY/Dy8iIhIYFff/2VmpoaHn/8cZXPwIkTJ8jMzOTXX39lx44d7Nmzhw0bNgBt\nwZDHH3+cMWPGkJ6eTnx8PK+88or8HdleUGzmzJmkp6eze/duUlNTWbhwoUpftm/fjqamJjExMXz2\n2WesX7+eHTt23OW7JDxITp06hYGBAba2tr3dlQ48PDxUbtQIQl+1bNky1q1bx3fffUd2djZvvfUW\nkZGRLF269J7b3LdvH5WVlSxduhRDQ0OcnZ1xd3fHw8ODuXPn8uuvv+Ln5ycXt7azsyMmJgZvb29i\nYmKAtvoU7u7uPPH007QCBQUFFBcXk56ezj/+8Q+V/a1YsYL4+Hief/55UlJSyMnJYe/evSrfeZIk\ndfhttm/fPjZs2EBKSgqFhYVs376dq1ev4ubm1m3HRhAEAQBJEARBkqQFCxZICoVCevbZZzusW758\nuaRQKKTp06ff936OHj0qKRQKqbKy8o6f8/bbb0tDhgy55TZNTU1STEzM/XZPxbhx46SwsLAubfO3\npLi4WPrnP/8ppaWl9XZXhIfAtWvXpNzcXCkhIUGKj4+X4uPjpdTUVKm0tFRqbm7u7e51m6SkJOnr\nr7+W3nnnHWnv3r13de6sq6uToqOjpfz8/E7Xp6enS9nZ2V3U0zaZmZnS6tWrpdOnT3dpu7eza9cu\nacCAAZK2trY0cuRIaenSpVJcXJzKNvb29tK8efNUljk7O0t//etfb9ru/v37JS0tLfnxpk2bJIVC\noXLctm/frrLN6NGjO7S5Z88eSU9PT5IkSdq7d69kqqUlbQZpM0gmmprS+++/r/Ld+Oabb0oTJkxQ\naePSpUuSQqGQEhISJElq+962s7OTWltb5W2ee+45aeLEiZIkSVJlZaWkUCik48ePd/rann766Q7f\n+SkpKZJCoZDKy8slSZKksWPHSqNGjVLZJjg4WFq0aNHNDplwh5qamqRdu3b1djc6yMzMvOk5o6+I\njY19qM/7woPr6aeflmbNmiVJkiS1trZKa9eulWxtbSVNTU3Jy8tL+uGHH+Rt8/PzJaVSKSUlJam0\noVAobnpueOyxx6TJkydLkiRJ9fX1Kt9zubm5klKplA4dOiR9+umnklKplLZu3SodPnxYSk5Oli5f\nviwpFAppx44d0q5du6TVq1dLK1eulBwdHSV1dXXJyclJCgkJkZRKpVRYWCi3m5iYKE2ZMkUyMDCQ\ndHV1JU9PT+ntt9+W13d2vRQZGSmNHz9eMjY2lnR0dCRPT09p8+bN8vruODaCIAiSJEkiJ7cgCEDb\niEpbW1v+/e9/8+mnn8pTptunxdnZ2d13mokbRxR2pfT0dJXiJELvKigoYPv27SiVSszMzHq7O8JD\noD3tw/U5KBsbGyktLSU1NVXO0aiuro6ZmRlmZmYquRwfVD4+PpiamrJ//34SExNJTEzEwcGBMWPG\n4ODgcMvzsra2NiNHjqSoqIjo6Gi8vb1VRgx7enpSVFREUlISPj4+XZJKqD0Pe2lpKa6urvfd3p0K\nCQlh2rRpnDhxgpiYGA4cOMAnn3zCu+++y8qVK4G277kbvyesrKwoLy+XHx85coT333+fM2fOUF1d\nTUtLC01NTZSWlsqvTUtLC2dnZ/k5lpaWNDY2cvnyZYyMjEhKSiIhIYEPPvhA3qa1tZX6+nrKysoI\n37CBjxsaWNC+srGR//f99yr9SkpKIiIiAn19fZXl7cW+/Pz8AHB3d1d53ywtLYmLiwPaZgaEhoYy\nefJkJkyYwIQJE/j9738vj85NSkoiNzdXZVS29N+cqLm5uZiYmHR6zCwtLUXhry6Qk5ODjY1Nb3dD\nRVZWFtra2jg4OPR2V27J3d2d06dPy3mHBaGvKC0tlb8fFAoFb7zxBm+88Uan2zo4OKgUJG53q5zT\nP/zwg/xvLS0tjIyMKCsrw9zcHCcnJ7m9UaNGYWJiwogRI1AoFMTFxWFnZ0dFRQW5ubmYm5tTXV1N\nY2MjOTk5bN++nby8PNTV1Rk7dqxKmiJfX1/2799/0z4dPXq0w7LAwECOHDly0+d0x7ERBEEAka5E\nEITreHl54ezszL///W952b59+9DR0WHcuHEqU9ESEhKYNGkSpqamGBoaMnr06A4FS5RKJV988QUh\nISHo6emxatWqDvtsaGhg1qxZ+Pr6UlFRcU/9Pnv2LCtXrpRzkk6fPp2cnBx5/erVq/H09GTLli04\nODigp6fHwoULaWpq4u9//zu2traYmJiwbNmyW+6nsbGRFStWYGtri66uLgEBARw6dAhoCwwMGjSI\nTz75pEPflEolqamp9/TaHkTZ2dls27YNdXV1Fi5cqFJ8UBC6kqamJnZ2dvj6+sqpTjw9PVEoFGRk\nZMipTpKSkigsLLxlKou+zNbWlsWLF7NkyRKGDBlCYWEhW7dulacC3y4/rZ2dHQEBAWRkZHD69GmV\nc7mdnR0DBw4kMjKyS25EGhkZoaamRlFR0X23dbe0tLSYOHEib775JlFRUTz77LOsXr1a5fjceOND\noVDIF82FhYVMmzYNDw8Pvv/+e5KTkwkPD0eSJBobG+XntOc1vb4N+N/FtyRJrF69WiVX6smTJzl7\n9iwmJiZ39FokSWL69OkqbaSlpXH27FmmTZt2y75cHwQIDw8nLi6OMWPG8OOPPzJ48GCV763nnntO\npf309HTOnj3L0KFD7+iYCfcuLy+PwYMH93Y3ZNnZ2airq+Po6NjbXbktfX19ampqxP9Doc+oqKjg\nhx9+ICIiguDg4B7br7OzM2fPnu2QLkRXVxdHR0fKysq4ePEiEydOZP/+/RgaGmJvb09FRQVubm64\nu7sTGxvL9OnTUVNTQ0dHh6qqKvlmqSAIwoNGBLkFQVDx7LPPEh4eLj8ODw9n4cKFHUb41dTUsGDB\nAiIjI0lISGDYsGE8+uijHXJtr1mzhunTp5ORkcELL7ygsu7KlStMmTKFy5cvc/z48dte/B88eJDf\nTZrE7yZN4uDBgwBcu3aNCRMmYGVlRUREBLGxsVhaWjJx4kSV4lsFBQX89NNP/Pzzz+zevZudO3cy\nbdo0UlNTOXz4MF9//TUbNmzgP//5z033/8wzz3DixAn+9a9/cerUKRYsWMCMGTNIT09HoVCwaNEi\nNm3apPKc8PBwvL29GTZs2C1f28MiIyOD7777jn79+rFo0SK5uJog9BR1dXWsrKzw9vaWA9/tI5iz\nsrLkwHdiYiK5ubndVoCxO1hYWPC73/2OV199lbFjx1JXV8ePP/7IRx99xJEjR1QKBt5IXV0df39/\njI2NiYyMVMmZ3V6QMDY29r5zaSsUCoyNjSkpKbmvdrqCm5sbzc3Nd5zPPDExkaamJv72t78xfPhw\nBg0axPnz5+96vz4+Ppw+fbrTQl1qamosfu01VujosAXYAizT0mL6E090aCMjIwM7O7sObejp6cnb\n3cnoey8vL5YvX87Ro0cZN24cW7ZsUdnHvRQUEwWE709FRQUpKSl3XWC2u+Tk5KBQKBg4cGBvd+WO\nubq6cubMmd7uhiAA8MQTTxAWFsaKFSuYOXNmj+23vej0yZMnO6wbPnw4xcXF2Nvbk5mZSWBgIHv3\n7sXMzAxLS0s0NTVRV1enrKyMK1euMHbsWK5evYquri5Hjx695W8KQRCEvkoEuQVBAP43RXnevHly\n8Ke0tJSDBw8SGhraYYTA+PHjeeqppxg8eDAuLi58+umnaGtrd5jONnfuXBYuXIiDg4PK9NeysjLG\njx+PoaEhBw8eVLlo70xNTQ0LZs3isV9+4bFffmHBrFkcPHiQjRs3oqamxqZNmxgyZAguLi58+eWX\n1NTUsHfvXvn5LS0tbNq0CXd3dyZNmsSUKVNIS0tj48aNDB48mJkzZxIYGMivv/7a6f5zc3P57rvv\n2LFjB0FBQTg4OPDiiy8ydepUNm7cCEBoaCjZ2dny6IeWlha2bt3Ks88+e8fvw4MsMTGRXbt2YWho\nyKJFizA2Nu7tLgkCgJw2x8vLSw58+/r60r9/f/Lz8+XAd0JCAllZWVy5cqVPF7jU09Nj3LhxLFu2\njJkzZ6Knp8eJEyf429/+xs6dO7lw4cJNn2tmZkZQUBAFBQWkpaXJIyE1NTUJCgri7NmznDt37r76\n5+DgQH19vcqNxu5UWVnJI488wvbt20lPTyc/P5+dO3fy0UcfMXHixFt+v0jXFcxydnamtbWVv/3t\nb+Tn5/Ovf/1LLuJ4N9566y2+/fZb3n77bTIyMjhz5gzff/89K1asAGDy5Mls3r2brf7+rHdy4vkV\nKwgICFBp48UXX6S6upo5c+YQHx9PXl4ehw8fZsmSJdTU1Kj0/2by8/P5y1/+QkxMDIWFhRw9epT0\n9HQ8PDyAey8odrv9CrdXWlpKc3Mzmpqavd0V8vLyaG1tVUnB8yAwMjKiurpa/F8U+oQjR45QVFTE\nmjVr7vg5FRUVKJVKIiIi7mvfhoaGtLa2dghKKxQKAgMDiY2NxdXVlStXrmBoaEhSUhKWlpaYmprS\n0NCAh4cHCQkJuLm50b9/fxoaGmhubpYHFF3PwcGhw6zVGzk6OvJ///d/9/WaBEEQ7pXIyS0Iggoj\nIyNmzZrF//t//w9DQ0PGjx/fac7Iixcv8uabb3Ls2DHKyspoaWmhrq6uQ3CkPW/ojSZPnoyPjw+7\nd+9Gqbz9/bZLFRV8Wlf3vxymdXX8c906JH19iouLO+QtraurIy8vT35sZ2enso2ZmRkuLi4qU73N\nzc1vmmc0OTkZSZJwd3dXWd7Q0MCECROAtlGW06dPJzw8nOHDh3PgwAGqqqp46qmnbvv6HnRRUVEc\nPnwYExMTQkND0dXV7e0uCcItKRQKBgwYwIABA+RlkiRx9epVSkpKyMrKkpfr6upiYWFB//79+9QI\nVnV1dYYOHYqXlxdFRUWcOHGCzMxMMjMzMTc3Z8yYMbi6unY4xyoUCoYOHUp1dTVRUVEMHjwYMzMz\nFAoFfn5+ZGdnk5GRcc/5bi0tLYG2QF5PpD7Q19dn5MiRbNiwgZycHBoaGrC2tmb+/Pk3zffZTqFQ\nyO+pl5cXGzZs4MMPP+SNN94gMDCQdevWMXfu3A7P6ayddpMmTWLfvn2sXbuWdevWoa6uzuDBgwkN\nDZW3cXV1ZfuPP7Jz504kSVLJKQ9txzAqKoqVK1cyZcoU6uvrsbOzY/LkyWhpaXXoe2evR1dXl7Nn\nzzJ79mwqKiowNzdn/vz5crDd09OTiIgI3njjDcaNG0dLSwtOTk6EhIR02t6tlgl3p32mQ2+n88rP\nz6exsbFH8+d3pfZUDS4uLr3dFeEhERoaytatW4G287GtrS0hISGsWbNGrlfUF3l5eRETE0NgYKDK\ncktLSwYMGEBWVhYmJia4uroSERGBnZ0dtra2tLS0kJ6ejru7O8ePH+fMmTPs3LmT6upqNDU1+eST\nT/jzn//MnDlz5HP/7c7/iYmJd3WsCgoKcHJyIjExER8fn3t6/d3t2LFjPPLII1RUVKj8bhQEoe8R\nQW5BEDpYuHAhf/jDH9DX12ft2rWdbrNgwQLKy8tZv349Dg4OaGpqMmHCBJW8pcBNg50zZsxgx44d\nnDx5UiX3592ovXYNC2trhg0bplI4q13//v3lf3eWU/TGXKZw84Imra2tKBQKEhMTO7R1fSG3RYsW\nMW/ePNavX094eDghISEYGhre1et6kEiSxJEjR4iMjMTS0pI//OEPt53mLgh9lUKhwMDAAAMDA5Xl\ntbW1lJaWkpeXJ48a1NLSwtLSEmNj4zu6UdedFAoF9vb22Nvby7k0ExMT2blzJzo6OgQGBuLr69vh\ns2loaEhQUBBZWVkUFhbi7e2Nuro6Li4ulJaWEhMTw/Dhw+/69V1ffLIngtyampq8++67vPvuu7fc\nLj8/v8OyGwtmhYWFERYWprJs9uzZ8r9DQ0NVgtWAHCC+XnBw8C3zspaWljJixAjs7e3ltBU6Ojoq\n//cGDRrEzp07b9rGjemxAN5++23efvttoO1m7q5du276fLi3gmKd7Ve4O0VFRWhoaNx2Flt3Kiws\npL6+Hjc3t17rw/0yMTGR8xGLGy9CV1AoFAQHB7Nt2zaampqIiIhg0aJFXLt2jc8//7zD9s3NzZ1e\nT/Q0pVKJo6Mjubm5HdIO+fj4kJCQgCRJ5ObmMnXqVH788UeeeOIJHBwcqKurIy0tjddff52amhrm\nzp2LJEloampy8eJF1q5dy6hRo7Czs7ujvtzrTM4HYVbGg9BHQfitE+lKBEGQtX9xT5gwAS0tLSor\nK2+aVy4qKoqwsDCmTp2Km5sbenp6d5WDde3atfzxj39kwoQJpKWl3Xb7ASYmLNPSknOYrtDRYcrs\n2QQFBZGTk4OxsXGHnKLXB7nv1M0ukry9vZEkiZKSkg77aR+1CG0j1A0MDPjHP/7B3r17Wbhw4V33\n4UEhSRI///wzkZGRODg4EBoaKgLcwkNJV1eXgQMH4ufnJ6c7cXFxoba2lpSUFDnVSWpqKiUlJbct\nBNmd+vfvz5QpU1i2bBlTpkxBoVBw+PBhPv74Y/bu3UtlZaXK9gqFAldXV4YMGUJ8fLxcMNLCwgIv\nLy8iIyPvOu2IqakpCoXinvJZ/xbU1NTIwU1TU1PKyspYv359jxYrE3pXWVmZPHuiNxQVFVFTU/NA\nB7jbOTk5qczcE4T70R7cNTMzw9ramieffJL58+fLNXvai9lv3ryZgQMHoqOjw7Vr1zhw4ACjR49m\nwIABGBsbM2XKlA454xMSEvD19UVHRwcfH59OiztmZmYybdo0DAwMMDc3Z968eZSVlals055+UUdH\nh8GDB7N+/XokScLKyory8nKUSiWff/4506ZNQ1dXFw8PD6Kiorh69SpOTk6kp6cTGBjIvn37kCQJ\nNzc3vvvuO0pLSwkPD2fmzJlYW1tjbm6Os7MzX375JWlpafxu0iQulpaSnp7OkiVLMDQ0xNbWlnXr\n1qn078aUJkqlkq+++orZs2ejp6fHwIED2b59u7zeyckJAH9/f5RKJY888oh8vCZNmoSpqSmGhoaM\nHj2a2NhYlX1lZ2czduxYdHR0cHd358CBA+jp6cm1JwoKClAqlSQnJ6s8T6lUsnv3bpVtdu/eTXBw\nsHzMDh8+LK9v75OpqSlKpVK+vruT910QhJ4lgtyCIHSqPa/pjaOW27m4uLBt2zZOnz5NQkICc+fO\nvevckn/9619ZsmQJEydOJD09/ZbbqqurM3raND6ws2NLQAChf/oTEyZM4KmnnsLc3JzHH3+ciIgI\n8vPziYiIYOnSpeTk5NxVf0D1Dv31uUhdXFx46qmnCA0NZdeuXeTl5ZGYmMi6devYs2eP/Bw1NTUW\nLlzIypUrsbGxkX8UPWxaW1vZs2cPiYmJ8rHpC7lFBaGnaGtr4+DggK+vrxz49vDwkKf+tge+k5OT\nOXfuXIdZLt1NS0uL4cOHs3TpUp588kksLCxISkris88+Y8uWLSqj0qFtRsqoUaNobW0lJiaG+vp6\ndHV1CQwMJCUlhfLy8jvet5qaGgYGBved2/thdebMGQYPHgxAfHw84eHhaGtr89lnn/Vyz4SeUFNT\nQ1NTE/b29r2y/+LiYqqrq+Xc7A+6W6WaE4R7cePNJy0tLZXv8Pz8fL777jt27dpFWloaWlpaXLt2\njVdffZWEhASOHz+OoaEhM2bMoKmpCWj73E+bNo1BgwaRlJTEBx98wNKlS1X2U1JSwpgxY/Dy8iIh\nIYFff/2VmpoaHn/8cfn7+quvvmLVqlX89a9/5cyZM3zyySd8+OGHfPHFF0DboBxom9Uzc+ZM0tLS\nWLx4MW+99RYZGRnk5ORgb29PfX09AwYMIDY2ltbWViIiIhg9ejS6uroMGjQIFxcX6uvr0dHRYdOm\nTTw3Zw6P/fILOvX1bN2yBQ0NDVJSUlixYgXLly9XCT53ltLknXfeYdasWaSnpzNnzhwWLlwo/0aI\nj48H4ODBg5SWlsrB55qaGhYsWEBkZCQJCQkMGzaMRx99lEuXLgFt1yKzZs1CU1OTuLg4wsPDefvt\nt2lsbLynG4irVq3i5ZdfJj09HX9/f+bOnUttbS12dnbyrKjMzExKS0vleh23e98FQeh5vT+3RhCE\nPuHGHyQ3TqG9cX14eDiLFy/G19cXa2trVq9eTUVFxR3vq927776LJElMmDCBI0eO4Onp2en2ubm5\n5OTkoFAoyDp3jmMJCRw5coT4+HgiIiL4y1/+wuzZs6mursbKyopHHnlEzpl2pzlFb1x24+NNmzbx\n7rvvsnz5coqLixkwYADDhw+Xc3K3W7hwIe+88w7PPPPMHR2PB01zczM7d+4kOzsbT09PZs6c2evp\nGgShL9DQ0MDGxkaljkFLSwvl5eVkZmbKFz0KhQJjY2MsLCxU0h11B4VCgYuLCy4uLpSVlREVFUVG\nRgYFBQUYGhoyZswYPD095RuaDg4OWFtbk5KSgpGRES4uLowaNYr09HSqq6sZNGjQHe3X1taWjIyM\nPjOVu69obW2lpaVFPt5hYWFcvXqVgQMHdlr/Qnj4lJaWAqjMAuspFy5c4NKlS3h5efX4vruTnZ0d\nhYWFvXbjQHi4XH8DOD4+nu3btzNp0iR5WWNjI9u2bcPU1FRedn0tA2i7TjI0NCQhIYFRo0bx7bff\n0tTUxKZNm+jXrx/u7u688cYbPP300/Jz/vGPfzBs2DDef/99edmWLVswNjYmKSkJPz8/1q5dy8cf\nfyzvz97enhUrVvDFF1/w4osvyjUbpk6dynPPPQfA66+/ztGjR/nxxx9Zv349586do1+/fnh4eBAZ\nGUliYiKXL19m0qRJXLp0ibq6OkaNGkVxcTFXrlwhJyWFD/9bF+ltwBkoyc7GycmJl156iU8//ZRf\nf/2VESNG3PSY/uEPf2DevHlA22zeDRs2cOLECebNm4eJiQnQlubEzMxMfs748eNV2vj000/ZtWsX\n+/fv56mnnuKXX34hOzubw4cPy+fT9evXd8hLfqdeffVVpk2bBsB7773H1q1bSUtLY9SoUfLsYDMz\nM5Wc3Ld73wVB6HniqkMQBOD2OTZvXO/l5dVhytiNBRY7y2/dWe7S9957j/fee++m+/7LX/6ClpYW\n/fr146WXXiIuLk4lT6yZmRnh4eE3ff71OUrb/f3vf++w3b/+9S+VxzfmIlVXV++0rRuVlJSgpqbW\nIW/rw6CxsZFvv/2WwsJC/Pz8ePTRR0UeTEG4BTU1NSwsLFQKzLW2tnLp0iVycnKor6+XlxsZGWFp\nadlteXrNzc0JCQlh8uTJJCYmEh0dzU8//cT+/fsZMWIEAQEB6Ovro6GhQUBAAKWlpURGRsrFLQsL\nC0lOTsbb2/u2n3tra2syMjIoLy/vlWBeX3VjvlSlUomFhQXFxcW92CuhJ7UHuXu66GRJSQkXL15k\n2LBhPbrfnmBtbU1MTIwIcgtd4sCBA+jr69Pc3ExTUxMzZ85UuW6wsbFRCXBD27n9zTffJD4+nvLy\nclpbW2ltbaWoqIhRo0Zx+vRphg4dqlKQ8cagcFJSEhEREejr66ssbx/sY29vT3FxMYsXL+aPf/yj\nvL6zFGl2dna0trbK10ojRozg559/pri4GCcnJ65cuUJmZiZBQUHs27cPaLvO8fHx4dSpU+Tm5jJ6\n9Gh++uknlVHsCsAWuP5Krj1Nyq1cf2NNTU0NU1PT287AuHjxIm+++SbHjh2jrKyMlpYW6urq5BHg\nZ86cwcrKSuU3hp+f3z0PvLm+j+1t3q6Pt3vfBUHoeSLILQhCnxcdHU1DQwOPP/64nMeyL44cbmxs\nlH+QhYSEPHSj8urr69m6dSslJSWMHj2a8ePHiwC3INwDpVKJiYmJPHoJ2kaOVVdXc+7cOWpqauTl\nenp6WFpaYmho2GWfN11dXcaOHUtQUBCnTp3i+PHjREZGEhkZiZubG0FBQVhZWWFhYYGZmRlpaWmo\nq6szZMgQDAwMiIqKYvjw4TdNZwX/u0AsLS0VQe7rVFZW4uzsrLLMzs6O8+fPc/Xq1Q7BDeHhc+7c\nOZRKpcpowO5WVlZGSUkJPj4+PbbPnmZlZcX58+extrbu7a4ID7ixY8fyz3/+Ew0NDaysrFBTU1NZ\nr6ur2+E506dPx87Ojn/+859YW1ujpqaGu7u7SoD4dkULJUli+vTpHXJcQ9uAntraWgA2btx42wCq\njY0u8lAPAAAgAElEQVQNJ0+eZOjQoSrL3dzcuHjxIo2NjTg5OZGbm4uPjw+6urqcPHmSP//5z5SU\nlFBSUoKfnx/Ozs7k+PiwLCcHGhupAfYplXz/yitymwqFotOBTde78ffCnTxnwYIFlJeXs379ehwc\nHNDU1GTChAl3lf6t/Xrx+mN/s1Qi1/ex/ffW7fp4J++7IAg9SwS5BUHo02pra4mOjsbc3JzBgwcT\nExNzz9PQutu3337LokWLGDZs2G1Hxj9oamtr2bx5MxUVFQQHB4vRCYLQxRQKBUZGRhgZGaksv3r1\nKqWlpeTk5MgXaTo6OlhYWGBsbHxfgW81NTW8vLzw9PTk3LlznDhxgtOnT3P69GnMzMwYO3Ysrq6u\neHt7U1VVJQfB/f39iYmJYdiwYRgYGHTatrm5OdCWHqE9R+hvXXV1NYaGhh2Wt98QPX/+PK6urj3d\nLaGHnT9/HiMjox67WX/x4kWKi4vx9fXtkf31Fnt7e2JiYkSQW7hvOjo6cjHEO1FZWUlWVhZffvkl\nY8eOBSA5OVllhLW7uztbtmzh2rVr8mjuG2fE+vj48O9//xs7O7tO03zp6elhZWVFTk4O8+fPv2Wf\nUlNTGTlypHzzNDY2Fnd3d4yNjcnJycHb25u4uDisrKxQKpVMmDCB7du3s3r1anx9fUlOTiYqKorg\n4GBycnJQzp3L5lOnqDl5EvchQ3BwcLjj43M77TV9bpzpGxUVxd///nemTp0K/O9mXTtXV1cuXLhA\nSUmJfDM9MTFRJTDdPuL+woUL8jkwNTW1S/p4J++7IAg9TwS5BUHo0yIiImhubmbKlClkZ2fLxbr6\notDQ0IcyRUl1dTWbNm2iurqa6dOnP/QXyoLQl+jr63cY3VtXV0dpaSkFBQVy4FtTUxNzc3NMTU07\njDq7HYVCgZ2dHU899RSXL18mLi6OhIQEdu7ciba2NkFBQfj4+BAUFMTp06cpLCxk1KhRpKSkYGlp\n2emsFW1tbXR0dCgsLLz3F/+Qyc7O7nQkbXtQTgS5H36NjY3U1tbi4uLSI/urqKigsLAQf3//Htlf\nbzMzM6OsrEy+ySYIPaF///6YmJjIo3nPnz/PsmXLVALV8+bNY9WqVSxcuJC33nqL8+fP8+6776q0\n8+KLL/LVV18xZ84cVqxYgYmJCXl5eezcuZNPPvkEPT091qxZQ1hYGEZGRkydOpWmpiaSk5O5cOEC\nf/nLX+S29uzZg6+vL2fOnCE/P1+uYwQwdOhQ0tLS8Pf3JyUlBXV1dT755BNSU1Px9fXlww8/xMLC\nAiMjI9auXcvPP//MjBkzeHTZMrJffRVzc3NOnDiBm5sb0DZK+naj1G/FzMwMHR0dDhw4gJ2dHTo6\nOhgYGODi4sK2bdsICAigpqaG5cuXqxS5nzRpEoMHD2bBggWsW7dOLgKprq4uDwDQ0dFhxIgRfPjh\nhwwcOJDLly+zcuXKu+6jvb09CoWCvXv3Mn36dPr163dH77sgCD2v7833FwRB+K/Lly+TmJgoF0Kr\nrq5Wmd4vdL/Kykq++uorrly5wu9+9zsR4BaEPkBHRwdHR0f8/Pzw9/fH398fNzc3GhsbSUtLIyEh\ngYSEBJKTkzl//vxNp+Z2xsjIiMmTJ7N8+XKmTp2Kmpoahw8fZt26dezduxczMzNcXV3lEWC1tbWc\nOnWq07YsLS2prKy8r4vfh0X76K/ObkAYGBigra0tbgj8BpSVlQFtqTW6W2VlJXl5efj5+XX7vvoK\nJycn8vLyersbwgOss8L0t1uvVCrZsWMH6enpeHp6EhYWxl//+le5CCS0pTjZu3cvZ8+excfHh+XL\nl/PRRx+ptGVpaUlUVBRKpZIpU6YwZMgQXnrpJbS1teW2nn32WcLDw9m2bRvDhg1jzJgxfP311x1G\nnq9evZo9e/bwzDPP8Pnnn7N582b5N7y2tjba2to0NDRgZ2eHvr4+58+fZ+/evUydOpW1a9cya9Ys\n5s+fT2RkJLNmzWLo0KGcOXOGlpYWbGxsKCkpkXNj3+6Y3Y66ujqffvopX3/9NdbW1sycORNoK+JY\nU1ODr68v8+bNY9GiRSojyBUKBXv27KGhoYGAgACeeeYZVq1ahUKhQFtbW96uvW6Tv78/zz//fIeb\nC+1t3Yq1tTVr1qxh1apVWFhYEBYWdkfvuyAIPU8hiSsPQRD6qF27dpGRkcHixYu5cOECHh4eKj9a\nHmSOjo6EhYXx6quv3nQbPT09Pv/8cxYsWHDf+wsNDaWyspKffvrpjh5D28X45s2baWxsZM6cOXc9\n8uzYsWM88sgjVFRU9GjuUUEQ2uTk5ODi4sI333yDs7MzSUlJvPDCC6SkpODq6npHF2KSJHH27Fki\nIiI4f/480JZDesyYMUiSREVFhTyCKSAgQCUFw/Hjxzl27BhhYWFdfg5wcHAgLCyM1157rUvb7S6n\nT5/Gysqq03QlAN988w2FhYW8/vrrotbBQywhIYGff/6ZZ599tlvrdlRVVZGVlcXw4cN/c/+fsrKy\nMDExwdjYuLe7Igi9QqlU8v333xMSEgJAXFwc3t7eKqOgW1tb5RSQqamp9O/fn0uXLsk3xZ2cnNDT\n0yMlJYXm5mbq6uqIj49HS0uLgIAAjh8/jrOzM08++WSvvMabSUtLw9vbm6SkJJEqTRB+o8RcCkEQ\n+qSLFy+SkZGBm5sbenp6qKurd2uAOzQ0lK1btwJtIwr69++Ph4cHv//971m8eHGXTz1LTExUqbDe\nmduNjFi9ejXvvPMO0DY60MDAAFdXV2bMmEFYWJhKYZy///3vKqMpb2z7xsfFxcVs27aN1tZWnn76\n6S7NvScIwt25/vx0vREjRhAdHX3T5zk5OVFaWoqxsTFqampy0SptbW2ysrJoaGgA2j7//fv3x9LS\nssN5qa6ujs2bN7Nz506Ki4vR1NTEyMgIf39/AgMDCQwMpKioCCMjIyIjI/H390dHRwcACwsLoK34\nZFcHue935FhPq66ulqd2d8be3p7c3FwqKyvFjKWH2IULFwC6NZ3G5cuXOXPmDCNGjHigPiNdxcXF\nhdjYWEaOHNnbXRGEPsHb25uUlBSGDx8uL1Mqldja2lJUVMTQoUOJjIzE2NgYU1NT8vPzOXPmDH5+\nfpiZmVFTU4OpqSllZWXk5eWRnZ3NoEGDyM7O5tKlS706kGXPnj3o6uri7OxMQUEBr776KsOGDRMB\nbkH4DRPpSgRB6JMOHTqEQqFg4sSJnDx5Ek9Pz27dn0KhIDg4mNLSUgoLC/nll1+YMWMGb7/9NqNH\nj+batWtduj9jY2M5EHQ/XF1dKS0tpbi4mMjISBYsWMDGjRvx9vaWp0VDW17f6wvE3Zg/7/rH+fn5\nbNmyBWgLrvW1AHd3ViwX1dCFvuj689P1fz///PNNn9PU1IRSqcTMzKxDigxTU1O8vLzkVCe+vr70\n79+f/Px8OdVJQkICWVlZPPvss/z73/9mw4YNZGVlcfz4cV577TXs7OxoaGhg//79HDt2jOzsbBob\nG4mLi6O8vBxALgRVWlrafQenC7W2tqoUrOoqlZWVtx1Ven1ebuHhVVRUhK6uLhoaGt3S/pUrV8jM\nzPzNBrih7XxpaGjI5cuXe7srgtAnaGpqYmxsrFK0EdpmZZ07dw5JkvDz8+PKlSsUFRUxaNAg9PX1\niY+Px8nJiZqaGqqqqpg+fTo6OjqUlZXJv5djYmJ64yXJampqCAsLw8PDg/nz5+Ph4cHBgwd7tU+C\nIPQuEeQWBKHPKSoqIjc3F29vb+rq6rC0tFSZAt8dJElCU1MTMzMzLC0t8fLy4pVXXuHYsWMkJyfz\n0Ucfydt+8803+Pv7Y2BggLm5OU888YQ8Oqu1tRVbW1s+++wzlfazs7NRKpVyRW8HBwc++eQTeX1O\nTg7jxo1DR0cHV1dX9u7de0f9VlNTw8zMDHNzc9zc3Fi8eDExMTFcunSJFStWyNuFhoYyY8aMTts4\nePAgRw8dIjE2lq+//ppvvvkGDQ0N6urqGDt2LP369cPLy4vt27fLzykoKECpVLJ7926Cg4PR1dXF\nw8ODw4cPd2g/JiaGYcOGoaOjg5+fH8nJySrro6OjGTt2LLq6utjY2PDCCy9w9epVef24ceN44YUX\nWLp0KWZmZowePRqAffv2MXjwYHR0dBg/fjw7duxAqVRSVFR0320LQl8iSRJaWlqYmZmp/BkZGcnb\nKJVKvvjiC0JCQtDT02PVqlXy5/TGz1y7hoYGZs2ahZ+fH62trXh4eMiBbz8/PywtLfn555958skn\n5VFcGhoazJ8/n08++YRly5YREhLC5s2bef/991mzZg0zZszA2dmZRYsWERUVxX+2beP1sDC+//57\nFixYwIABA+jXrx/BwcFkZmbKfbG0tGTHjh3y46CgIAwMDORc1jk5OSiVSvlcC3D16lXmz5+Pvr4+\nlpaWKudUaBs9vXjxYszNzTEwMGDcuHEkJSXJ6zdv3oy+vj779+9nyJAhaGlpcebMGcrKynjsscfo\n168fjo6ObN26lSFDhrBmzZp7ev9ycnIYOHDgLbdpz9FcXFx8T/sQ+r7W1laqqqq6LU3J1atXOXny\nJCNHjvzNBrjbubq6cubMmd7uhiD0itbWVjlVSbtBgwaRl5fX4UbukCFDyMjIQEdHBxsbG/r3709e\nXh5KpRJ7e3sSExPlgslnzpwhJCSElpYWGhoaMDQ0JCUlhbq6uh57bTd6+umnycrK4tq1a5w/f55v\nvvkGU1PTXuuPIAi9TwS5BUHoUyRJ4uDBg6ipqTF27FgKCgpwdHTstf54eHgwZcoUdu3aJS9rampi\n7dq1pKens3fvXioqKuScdEqlknnz5qkEhAG2b9+Ou7s7w4YNA1Sn2re2tjJr1iwAYmNjCQ8PZ82a\nNXIqgbtlYWHBU089xX/+8x952c2m9h88eJAFs2ZhW1KCeUUFK55/nnPnzlFWVsbOnTv54osvOH36\nNCtXrmTJkiUdRo6uWrWKl19+mfT0dPz9/Zk7d66cEqHd0qVL+fjjj0lMTMTJyYnp06fLP4hPnjzJ\n5MmTmTlzJunp6ezevZvU1FQWLlyo0sY333yDQqEgMjKSrVu3UlRUREhICDNmzCA9PZ2XXnqJ5cuX\nq7zGe21bEPqiOymhsmbNGqZPn05GRgb/n707D4uq+h84/p5h35VVEJBFAUVEXEAFFTTEMnczU3PN\nXVssv5ZZaeZSKalpi5lrX/VXmlruG+4CIgiigiKCyCaLggsDDDO/P/xyH0ZA0VRIz+t55nmcO+ee\ney7IzNzPPefzmTRp0kPbFhYW0r17d27dusWRI0cqpciQyWSYmppiZ2dHQkIC7u7utG3bFicnJ27e\nvMmZM2eIjo5GoVBgZmZGQkICVlZWjBw5km7durFq1Sre7NmT969cYcKFCwx9800OHz7MX3/9RWRk\nJIaGhnTv3h2FQgHcv+F0+PBhAO7du8fp06fR19cnKioKuJ/jv3HjxlIwWK1WExoaiqenJzExMcye\nPZsZM2awdetW6fUePXqQmZnJzp07OXv2LJ06daJLly4aM8sVCgVfffUVv/zyCxcvXsTR0ZHhw4eT\nlpZGWFgY27Ztk95zniRwWFpaipaW1iNv1Orr62NmZiaKT77AyouwPosg9507d4iNjaVDhw4vfYAb\n7n8XMzIy0ripLQgvuxYtWhAXF6exzczMjJKSEhQKBfb29igUCmxsbKRJI9bW1qSlpWFlZYWenh7a\n2tp4enqSlZWFgYEBZWVlnD59upbOSBAEoTIR5BYEoU5JSkoiIyODdu3akZ6e/tAcps9L06ZNSU5O\nlp6PHDmS7t274+TkRNu2bfnhhx84duyYNMNw6NChREREaOyzYcMGhg4dWmX/Bw4c4OLFi/z22294\ne3vToUMHFi9ejFKp/EdjLiwsJDc3F6icnqTcikWL+LqoiMaAAxCqVJKemMiPP/7IypUr6datG40a\nNeKtt97inXfeYfny5Rr7T506lR49euDq6sq8efPIz88nNjZWo83nn39OcHAwnp6erF69mqKiIjZs\n2ADAt99+y5tvvskHH3yAq6srvr6+/PDDD2zZskUaO9zPLfztt9/i5uaGu7s7P/74I40bN2bhwoU0\nadKE/v37M378eI1zfNK+BaEu2rNnDyYmJhqPTz75RKPNoEGDGDVqFE5OTjRq1KjavrKzswkKCsLM\nzIy9e/dibGxcbdsVK1YQERGBpaUlrVu35uOPP+bq1au0adNGmvVtaGiIm5sbn376KQMGDOD111/H\nWF8fn7IyhgMdgBKVCntzczp06EDz5s1Zv349hYWF0g3BwMBAwsLCgPsrMFxdXenRo4e07fDhwwQG\nBmqMrV27dnzyySc0btyYsWPHMmzYMEJDQwEICwsjNjaWP/74gzZt2uDi4sKXX36Ji4sL69evl/oo\nKytj2bJltG/fnsaNG5Oens6+ffv4+eef8fPzw9vbmzVr1jxxyqrExEQ8PDxq1NbR0ZHc3Nx/9N4v\n1F3lN1fKc9U/LXfv3iUmJkYEuB/QrFkzjdUigvCiK1+dVN1zExMTZDIZhYWFGvt5e3tL391btmxJ\nZmYmpaWlODs7S6lJTExMKCoqIj09neDgYNzc3LCxsUFXV5eTJ0+Kzy1BEOoMEeQWBKHOKJ/Fraur\ni5+fH4WFhbVazKTiuCpeOEZHR9O7d2+cnJwwNTWlbdu2AFKaDC8vL7y8vKTgTXnAe8iQIVX2f/Hi\nRRo2bKgxu8vX1/cfpWgpD/Y+yQXvPYUChUJBSEiIRkDtp59+0gjcw/1ZIeXK8+/euHFDo03F4k9G\nRkZ4eXlx8eJFAM6cOcNvv/2mcZyAgABkMhlXrlyR9mvdurVGnwkJCdLPvZyvr6/G8yftWxDqos6d\nOxMbG6vx+OijjzTatGnTpkZ9hYSE4ODgwJ9//omuru5D23bs2JHk5GQOHTrEwIEDuXTpEt26dWP8\n+PFSGy0tLSm3d6dOnRg2bBj1zMwon0N5EZABdwoK+Oqrr/j222/ZvHkz9vb2HDp0iMuXL+Pt7c2l\nS5fIysri8OHDBAUFaczuPnLkiEaQWyaTVSos165dOymodebMGe7du4eVlZXGe0B8fLzG+5i2tra0\nwgbuv7fI5XKNn6W9vb00g/xx3blz56E3ESqyt7dHrVb/a3KYC4+nPB/u0wxyFxUVER0djb+//zNP\n6/Zvo6WlhZ6e3lOvqSIIz0J2djbvvfcejRs3Rl9fH3t7e1577TV27979xH0OGjSIq1evamzz8vLi\n3LlzGtt0dHQwNTUlNzcXmUxGmzZtUCgUXL16FXNzc1q0aIGtrS0dO3bklVdewdPTkx07dpCSkoK1\ntTXFxcXVpkWraObMmdjb21fKl3/hwgX09fX5448/nvhcBUEQymnX9gAEQRDKnTt3jry8PF555RUS\nExPx9vau7SEB9798ledTvXv3LiEhIXTr1o3ffvsNa2trcnJy6Nixo0bRwqFDh/Lrr7/y2Wef8d//\n/peOHTvi4ODwXMdsZmb2yGJnYz/8kOHHj9O4qIg7wHQDAz4ZPJgjp0+zY8cOHB0dNdo/WCyr4vOK\n6Vce5sGCl2PGjOGDDz6o1K48qCSTyTAyMtJ4TSaTPTJ9w5P2LQh1kYGBAS4uLg9tU9P/yz179uT/\n/u//OHfuXI3eZ7W1tQkICCAgIIDp06czd+5cPvvsM2bMmCG9Rzz43uDevDlHb9xgrUpFDKAC3ho7\nliZNmkiFMwsLC0lJSZFWdhgZGTF16lROnz7NG2+8gampKceOHePUqVOkp6dXmsn9MCqVChsbG44f\nP17ptYpFePX09J7Z7NesrKzHCmiW3+hMT09/ZnmbhdqTmpqKrq5ujW96PIpCoeD06dMiwP0QzZs3\nJzo6utJNcEGoS1JSUvD398fMzIwFCxbg7e2NSqXiwIEDTJgwgZSUlCfqV19fH319fY1tcrkcV1dX\nLl++TJMmTaTtHh4enDx5EktLSwwMDGjYsCGFhYXk5eUBsHz5cuzs7DAzM+PixYscPnyYDz/8kHHj\nxmFra0tYWBg+Pj4PLao7a9Ysdu/ezeTJk/ntt98AUCqVDB8+nAEDBvDGG2880XkKgiBUJL4RCYJQ\nZ9y5cwdra2uaNm2Knp5epS9mz1pVgY74+Hj27t3LgAEDgPuz/PLy8pg3bx4BAQG4ubmRnZ1dab+3\n3nqLpKQkIiIi+P3336tNVQL3U4ukp6drFByLjIx8ZLC4OpmZmWzYsKFS0ZmqhISEsHbrVtJsbcm2\ntGTt1q2MGjUKPT09UlJScHFx0Xg8SaC+YuX1u3fvcv78eSkNTatWrYiPj690HBcXl4f+/j08PKRc\nveUiIyM1nj9p34JQFz3NQOycOXMYP348Xbt2rZReqCbK/37v3LlTbZuGDRvi07YtfwUHc+Z/M6X1\n9PTo378/kyZNYvLkyRQWFjJgwAAGDBhAly5daNmyJUlJSaSkpFBWVkZcXBy6urpMnDiR+vXrs379\nen755Rf+/vtvFAoFBw4cICcnR1omHR4eTrNmzYD7f//Z2dnIZLJKf/8P5h+vyMPDA5VKpfH+cv36\ndY2ClzWVkpLy0LQxDyrPg5qWlvbYxxLqNrVaTU5ODjY2Nk+lP4VCQWRkJP7+/mhpaT2VPl9E2tra\naGtrS7n/BaEumjhxInK5nKioKAYMGECTJk1wd3dn0qRJGjm0r127Rt++fTE1NcXU1JT+/fuTnp5e\nbb8PpiuZNWsWXl5e0mopU1NT+vbtS15eHjKZDFdXV5KSkgBwcHCguLhY2j8/P5+uXbtiYGCAl5eX\nVAvn559/Zttvv7FxxQq++uorzp8/T48ePTA1NcXGxobBgwdL10na2tqsX7+eP//8kz///BOA+fPn\nk52dzfLlyx95fuXjr8k5btq0CVdXV41zLKdUKvnggw8wNzfHwsKCadOmMXHiRIKCgh77dycIQt0j\ngtyCINQJ5bNyJ0yYQEJCAp6ens99DAqFguzsbDIyMoiNjSU0NJSgoCDatGkjpQVwdHRET0+P77//\nnuTkZHbu3Mlnn31WqS97e3s6d+7MuHHjKCwsfOjshODgYDw8PBg2bBixsbGcOnWKDz74AG3tRy+2\nUSqVZGdnk5mZyfnz51mxYgXt27fH0tKS+fPn1+i8Q0JCCOrWjTbt2kkpSj766CM++ugjVq9eTVJS\nEmfPnuWnn37il19+qVGfFc2dO5cDBw5w/vx5KYA+ePBgAKZPn05kZCQTJkwgJiaGpKQkduzYoZEK\noap84uPHj+fKlStMmzaNxMRE/vzzT1asWKFRYPNJ+xaEuqj8/al8FnRWVhY5OTlP3N9XX33FuHHj\neOWVVyoVoqooMDCQFStWcObMGVJSUti1axczZsygadOmUrC7ur8jKysrtuzbx9HoaLy9vZk/fz57\n9+7l3LlzDB06FDMzMyZOnIinpycdO3Zk6NChxMTE0LRpUykQHxgYSHx8PK1bt8bMzIy8vDyio6Mp\nKioiKiqKt956i6lTp9KvXz/WrFlDQEAAJ06cwMHBAV9fX3r16sWePXu4evUqp06d4osvvqhydnc5\nd3d3QkJCGD9+PBEREZw9e5aRI0diYGDwWDcaiouL0dXVfax9tLS0sLS0lFJfCS+O27dvU1paWml1\n1JMoLi4mIiKCDh06iAB3DTRv3pz4+PjaHoYgVCk/P5+9e/cyadIkDA0NK71evvJIpVLRu3dvcnJy\nOHz4MGFhYWRkZNCnT5/HOl5KSgp//PEHf//9N6GhocTExPDpp58C91Mp5eTkUFZWBoCPj49Uw8bc\n3Jy0tDQcHBwwNjZGLpdjZmaGSqWiyaVLfJCczPfz5uHn50eLFi04ffo0Bw8e5M6dO/Tu3Vv6jtCs\nWTPmzp3LhAkT2LdvH3PnzmXVqlWYmJg8lfOreI7bt29n3759GucIsHDhQtauXcuvv/5KeHg4paWl\nbNiwQdQ0EIQXhEhXIghCnZCcnIyLiwvXr1+nYcOGz33prUwm48CBA9ja2qKlpUW9evXw8vJi9uzZ\njB07Vgo4W1lZsXbtWmbMmMHy5cvx9vbmu+++49VXX63U59ChQxk9ejT9+vXDzMzsocfeu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99syOKwjC4xOFJwVBeOZUKhXOzs4oFAr09fVrvF/FZWdWVlYaxToUCsUji7qM/fBDphsY\nsBZYC0zV1qZdcLD0urGxMXv37qVhw4Z07txZSqdSPrOq4vHOnj1bozGfOHGC4cOH07dvX5o3b07D\nhg1JSkqq0b6CIAgvs1atWmFiYsLBgwdRKBS1PZxHSkxMfOq5uCuytLREW1v7X1uQ83HI5XIcHBzo\n0KEDvXv3pn///vTu3Zvu3btz7NgxZs6cyeTJkxkwYACdO3fGy8sLX19fFixYwLx581i2bBmbN2/m\nyJEjnDt3jvT0dKysrMjKytJYSfU4RowYgVwur/ZRHtAaNGgQV69elfabNWsWXl5eVQa5Y2Njsba2\nrjJFwNNU3ZiEqtnY2Ei/L0F4XCtXrqSsrAwXFxd0dHTQ0dFh0aJF7Nu3r1Lx4IelsJDJZJVSPFW8\nFjI2NkYmkzFmzBhiY2Pp3bs3gYGBxMXFcfnyZalgcbkxY8ZgZGAAgC9wEtADOrVujaGhIadOncLL\ny4vZs2dLE3yqqncUHByMUqlk9erVjBo1infeeQdfX1+uXbtGy5Yt6d27NwDjxo0jNjaWXbt2YWlp\nyaBBg1i8eLFG0Fomk+Hs7IyxsTE2NjYkXrrEnr176dKlC/r6+iQkJFQ7u7zc9u3bsbKyIigoiC5d\numBnZ8e2bds02lSVuqQmtZ0etc3Kyoq1a9eybds2PD09mTNnDt99912V+zzq+IIgPBtiJrcgCM9U\n+Re3a9eu0apVK3bu3FnjfSt+0evSpQurVq2iV69eWFpaMnfuXI2lzlUJCQlh7datrFi0CJVKxfAW\nLbh3754UzIb7Xxj37NlDjx496Ny5M2FhYTRu3BgHBwdmzZrFggULuHr1Kl999VWNxuzm5saff/5J\nr1690NbWZvbs2RQXF1dbAb3iuT6qjSAIwotMLpfTo0cPNm3axNGjR+nWrVttD+mhCgsLn+nyaLlc\njo2NTaUgycvCxMQEPz8/HBwcCA4OZv369ZSVlVFYWMitW7e4c+cOJSUlZGVlkZeXx4ULF6TP0bKy\nMrS0tNDW1sbU1BQbGxusra0xNzeXHgYGBg8NOixdupRvvvlGY5tarWbo0KEkJydLKQf09fWrvIF/\n7do15HI59evXB+DcuXNYWlo+l6Jj1Y1JqJqzszOnTp165jcfhBePUqlk7dq1LFiwgNdff13arlar\nefvtt1m9enWNZ/p6eHiwfft26fnq1auZP38+4eHh0rZWrVoRHx8vFbGsaM6cOcyZM4cRI0YA92+U\nrlq7lj59+jAauMr9CT+fDxhA4tKlfPLJJ8yePVujjy+++IIvvviCESNGSO+n33zzDX/88QfZ2dlS\nraTytGJHjhxBS0sLe3t7LCwscHFxwc7OjtDQUJKTk8nKysLGxoYtW7YwcOBALCwsUCgU2Nrakp2d\njUwmIzg4mAsXLtCxY0esrKw0UloClVakODg4sPV/NZaq0qZNm0rXiFVtKz/Xir7//vtK/W3cuFHj\n+cCBAxk4cKDGtop9BwYGVjqWk5PTI69bBUF4OsRMbkEQnpqqLhbT09ORy+WYmJigo6PzRHfNAT75\n5BO6dOkizezq1KkTPj4+jxxTSEgIW/btY+uBAyxYsAB7e3tSU1O5d++e1MbQ0JBdu3bh5uZGYGAg\n165dY9OmTSQnJ+Pt7c3s2bOZP39+je7Sh4aGYm1tTceOHenRowcdOnSgY8eOj7x7X9XPQBAE4WXj\n5uaGra0t4eHhFBQU1PZwqpWbm/tcCgY2atSIe/fuvdRpJ9RqNXp6elhbW2Nra4u7uzt+fn507dqV\nV199lZEjR/Kf//wHCwsLoqKi+Oabb7h69SpOTk589tlnJCcnk5CQwJEjR1ixYgVdu3bF3NwcfX19\nnJ2dWbhwIWFhYcTGxpKWlsbdu3dRq9WYmppibW2t8Vi5ciXh4eFs27YNc3Nz4H5qkPKgzJo1a/jy\nyy85f/48w4cP54svvmDdunVMmjQJb29vHBwcNGaDVwwwrV69mmbNmmFgYIC7uzuLFy+Wgkwff/wx\nr776qtR25cqVyOVy/u///k/aFhAQwNy5cx86pvLjrl27Vpq9+eDjwaDXy6C8KGpubm5t7gJRZAAA\nIABJREFUD0X4l9m5cyd5eXmMGTOGZs2aSQ9PT08GDRrE6tWra9zX+PHjuXLlCtOmTSMxMZE///yT\nFStWaFwjTJ8+ncjISCZMmEBMTAxJSUns2LGD8ePHV9ln79696du3L+t1dfnZ25tuAwawfv16bty4\nwcSJE2s8tnPnzrFnzx7u3LkjbTM1NSUuLg6A2bNn88033zBlyhQ+/vhjTp48yYULF0hKSqJly5YE\nBQUhl8sxNjYmLS0NR0dHaeXOnj17qF+/PteuXWP8+PEcP368xuMSBEF4kJjJLQjCUzFgwIBKd6hL\nS0sJCQmhS5cuNG3aFHjyu+YmJiZs2LBBY9uDX+gqLs2tiq6uLm+//TYA169f59ixY3Ts2BEAAwMD\n9u/fL7V1dXUlJiZGY/9H3aWH+7naKvYD94vMPGqcYWFhDx27IAjCy0Amk/H666/zyy+/sG/fPt54\n443aHlKVrly5gq+v7zM/TsW83B4eHs/8eHVVTVY6ffnll8yfP5/vv/9eY9n/22+/TcuWLUlMTMTf\n3x9vb2+mTJmCQqEgNjaW5ORk7t69q9GXlpYWJiYmUnDbwsKCmJgYvvjiCzZt2lRt6o9BgwaxY8cO\n9u7ejaOVFT0GDqRFixb4+vpqfPfZu3cvo0ePlr6D/PLLL3zxxRcsW7aM1q1bc+7cOcaMGYOOjg6T\nJk0iKCiIH374AZVKhVwu5/Dhw1haWnL48GHefPNN7t27JwX4qxrT+fPn2bFjB0eOHAHuB6fKysqY\nMGFCtWN62TRp0oTw8PDncvNKeHGsWrWKLl26SCs2KhowYACffPIJBw4coHHjxo+czOLo6MiWLVuY\nOnUqy5Ytw9fXl88//5zRo0dLKzO8vLw4evQoM2fOlK5FXFxc6Nevn9TPgxNnNm3axPTp09m4cSPR\nCQnY2NgwbNgwjI2Nqx1LValSfH19OXHiBP7+/hr1lG7dukXTpk3p1asXW7ZsITc3F0NDQ5o1a0bX\nrl1RKpWkpqZiYGDArVu38PLyIiEhgfnz57N582bGjBnDjRs3MDIywt7evsqfpSAIQk3J1GJ9vCAI\nz0h0dDSGhoaYmJhIF+p1QUlJCevXr+f69et06dLlpb2gEwRBqKs2bdpEYmIiY8eOlYpz1RWlpaXE\nxcXRunXrZ36sgoICFi9ejL+/P6+88sozP15dNGLECP773/9WSr8xefJk5s+fD9xP7TJlyhSWLFki\nvf5g4clPP/2UDRs2cPnyZY1i12q1mtu3b5Ofn09eXh75+flkZ2eTk5PDnTt3UKlU5OTksHLlSvz8\n/HjllVcwNjbG2toaGxsbjh8/zsKFC8nMzOTEiRMM6NmT+kolXwHTdHX5dsUKhg8fLh0vMTGRdu3a\nMXv2bN59913gfnBr/vz5DBkyRGq3ePFifvnlF86fP8+dO3cwNzfn2LFj+Pn54ejoyOTJk1m1ahUJ\nCQkcOHCAPn36cOvWLbS1tSsVnpw1axZbtmzh3LlzVf6MqxrTy+jixYs0aNBABNmEOmPJkiXMmjWL\nmzdvPrU+L1++zIYNG7C3t2fkyJFSPu6ayMnJIS8vDw8PD5RKJWfOnGH//v2UlZVhampKSEgITZs2\nlQLkV65cITk5GV1dXXx9fdm/fz/a2trk5eVhYmJCly5dMDU1BWD37t1ERkbSvn37Op+uTBCEukvM\n5BYE4Zkor/idn59f52aflc/oXrduHYcOHUImkxEQEFDbwxIEQRD+p3v37ly6dImdO3cyevToOpXO\nKSEh4bl9rpmammJgYPBSFJ98mM6dO7NixQqNbWZmZhrP27Rp89A+YmJiCAgI0Ahww/0Zi6amppia\nmuLk5KTxmlqtJj09nc6dO9O+fXtmzpxJVlYWOTk5JCcnk5SUxNmzZyktLSU0NJRt69fzilLJFWA4\nQEkJ23/7TQpy37p1i169ejFo0CApmJyTk8P169cZO3asxgo1pVIp/dvY2JjWrVsTFhaGhYUFBQUF\nTJo0iVmzZpGVlcXhw4fp0KFDpXOriarG9LJyd3cnMjKSdu3a1fZQhJfU8uXLadu2LVZWVoSHh/PV\nV19JObafliZNmtC5c2eOHDnC/v37CQkJqfG+VlZWJCcnExERwZEjRygqKkJfX59OnToREBBQKWDu\n6upKbm4uBQUFREVFERwczObNm/Hy8uLYsWOcOXOGoKAgqW8bGxsiIiIICAjA0NDwqZ63IAgvBxHk\nFgThmYiLi0NbW5vmzZvX9lCqpKury7Bhw1i3bh0HDx5EJpPh7+9f28MSBEEQgHr16tG2bVsiIyNJ\nSkqiSZMmtT0kyd27dzEyMnoux5LJZDRs2JCUlBTUanWdCvY/TwYGBri4uDy0zaN+JxVTmNSUWq1m\n3LhxGBgYsGPHDo1jqNVq7t69y88//8z+/fvp2LEj+x4oBAf3U5Rt3LgRNzc3pk2bhoODA8uWLZNe\nLy/Q/fPPP9OhQ4dqxxIYGEhYWBhWVlZ06tQJIyMj/Pz8CAsL48iRIxo5u2tKqVTyxhtvVBrTy6o8\nZ3BhYaE0u1QQnqcrV64wf/588vLysLe3Z8KECXz++edP/TidO3fm+vXrhIeHY29vj6en5yP3UalU\nxMfHc/ToUe7du4eBgQG9evXC29ubkydPVvv51KZNGw4fPkxpaSnJyck4ODiQmpqKv78/ERERODk5\n4ezsjLOzMwqFgoMHD3LixAmCg4Of9mkLgvASEIUnBUF46m7fvo2WlhYqlapOXySUB7rt7Ow4cOAA\nJ06cqO0hCYIgCP8TGBiItrY2O3fulAKBtS0zM/O5p09xdHREqVS+1EXxnkZw38fHh+PHj1NaWlrj\nfWbOnEl4eDjbt2+vFESXyWQYGxtjYWGBXC6nS5cufL5wIQe1tckH1gLT9PQI7NmTy5cvM2HCBOLi\n4ggODubo0aOkp6ejVquxsbHBzs6OpKQkXFxcKj3KBQYGcuLECfbv309gYKC0bceOHZw+fVraVhVd\nXd0q64i8//77XLt2jc2bN6OlpVXjn8uLrGnTply8eLG2hyG8pEJDQ7l+/TpFRUVcvnyZL7/88olW\naDyKTCZjwIABmJqasm3bNm7cuFFtW7Vazfnz51m8eDFbt26lrKwMPz8/Bg4ciI+PD3K5HA8PDxIS\nEqrcX0tLizZt2lBaWkp2djYuLi5oa2uTn59PvXr1OH78OGVlZVhYWKCjo4O1tTXh4eEUFRU99fMW\nBOHFJ4LcgiA8defPn6ekpARvb+/aHsojPRjoPnnyZG0PSRAEQeD+7N2goCAKCgo4e/ZsbQ8HgNTU\nVBwdHZ/rMSsWn3xZKRQKsrOzycrKkh45OTmP1cfEiRO5c+cOAwcOJCoqiqSkJDZu3EhsbGyV7X//\n/Xe+/vprvvvuO4yMjDSOnZWVRWFhYaV9QkJC6D9kCFkyGT+3aMGydev4+uuvsbW1JT4+ng8//JDb\nt2+zc+dOvvvuO2bPns3mzZsZO3Ys33zzDYsXLyYxMZH4+HjWrVvHggULpL4DAgIoLi7mzz//lJb3\nBwYG8vvvv6Ojo/PQQqjOzs6kpqYSExNDbm4uJSUlrF69mtWrV/PLL7+gUCik83qwCOfLRktLC319\n/Zf+5yC8+PT19Rk8eDBqtZoNGzagUCg0Xler1SQmJrJ06VI2b96MQqGgW7dufPjhh3Tv3p2MjAzp\n5pmlpSWFhYXV3kQ0MzPD1dWV4uJikpKSqF+/PiUlJTRr1gyFQsH+/fuB+6spunbtikqlEtdkgiA8\nERHkFgThoVJSUpDL5URHR9eofV5eHnK5nHr16qGjo/OMR/dkAgMDmTJlivRcT09PCnTv379ffKkS\nBEGoI3x9fTE0NGTfvn2UlJTU6lgUCgV6enrPPWWInZ0d8PIGuWUyGQcOHMDW1hY7OzvpUZPCnxV/\nV3Z2dhw9epSSkhKCgoJo1aoVy5cvr/a7yk8//QTcL3xZ8bjlj/fff7/K4/j6+tK0WTMS0tJ46623\n2LhxIydPnkShUPDpp58yb948QkNDCQ0NJTo6mvPnz6NWq3nttdcIDQ2lRYsWdOzYkZUrV2rM5DYy\nMqJNmzYYGxvj4+MDgJ+fH9ra2rRv377KXOPl+vfvz2uvvUbXrl2xsbFh48aNHD16lKKiIgIDAzXO\na9GiRY/8ub7oPD09OX/+fG0PQxCeORsbG3r37k1BQQFbtmxBrVajVqu5cuUKy5cvZ9OmTdy5c4cu\nXbrw0Ucf0b59e+k909vbW+Mm4YPPH9S4cWPq1atHSUkJRUVFUiouNzc3MjIySElJwdnZGS0tLSwt\nLTl16lSlwLsgCMKjyNSPm5xOEIR/jREjRrBu3Trg/swUGxsbunbtyoIFC2q83DolJQUXFxeioqJo\n1arVI9ufPHkSlUqFv79/jQIBTk5OTJkyhQ8//PCh7QIDA/Hy8uL777/X2L5582YGDhz4WEvZg4KC\n8PLyYunSpRrbi4uLWbt2LZmZmXTr1o327dvXuE9BEATh2YiPj2fLli107tz5oSkZnrWzZ8/i4eGB\nvr7+cz/24sWL0dHRYdKkSc/92ELN3b59WwpU9+3bt0b7KBQKkpOTSUhIIDExUbqZo6enh7u7Ox4e\nHri4uKCnp/cshy48ICYmBg8PDwwMDGp7KILwzO3evZvIyEh8fHzIyMggOzsbLS0t/P39ad++fbWf\ne/Hx8djZ2WFubg7AuXPncHR0rFQYuFxZWRkHDhxAJpOhUqmwtrbm5s2bZGVloVKpeOONN4iLi8PK\nyoq9e/cyevToOjtpShCEuknM5BaEF5hMJiM4OJisrCxSU1NZvXo1YWFhDBs27JkcLzMzE7VajYuL\nyyMD3OUXcTWdESeTyZ757Dk9PT2GDx+Ora0t+/bt49SpU8/0eIIgCMKjeXp6YmFhwbFjx7hz506t\njEGtVqNQKGolwA3383Ln5eWhVCpr5fhCzcTHxwPQsmXLGu+jr69Ps2bN6NevHx9//DFjx44lMDAQ\nExMT4uLipLQpK1as4NSpU+Tm5j52AU3h8YnZ3MLLxNPTEx0dHWJiYrhx4wYdOnTgww8/JCgo6KGf\ne56enly4cEF6T3rU342WlhZ+fn4olUoKCwuxsrJCoVDQpEkTZDIZ+/btA6BRo0ZS/QJBEITHIYLc\ngvACU6vV6OnpYW1tjZ2dHcHBwbzxxhuEh4drtJkzZw4ODg7o6+vTokUL/vrrr0p9JSYmEhAQgIGB\nAU2bNpVyp5W7cOEC/fv3p1u3bvj4+DB48GCys7Ol10eMGEHPnj35+uuvcXBwwMHBgaCgIFJTU5k2\nbRpyufypFD3Kz8/nrbfewsHBAUNDQ5o3b86aNWsqtSsrK2PGjBlYWVlhY2PDtGnTpJ/X8OHDWbp0\nKTNnzqRPnz6YmZnh4ODAwoUL//H4BEEQhMcjk8no2bMnKpWKgwcP1soY0tLSnnsu7oocHBxQq9Vk\nZWXV2hiER4uKikJXV5dGjRo90f4ymQxbW1s6d+7MpEmTmDZtGv369cPDw4OcnBz27dvH8uXL+fbb\nb/n777+5fPnyYxXSFGpOV1cXmUxGcXFxbQ9FEJ6ZzMxM1qxZw+rVqyktLUVbWxsdHR3atm1bo1UM\nMplMo+ikXC7H3t6ea9euVbtPvXr1WLlyJQsXLuTo0aO0a9eOjIwMTE1NUalUFBYWcu3aNVxdXblx\n48ZDV+uWX18KgiCUE0FuQXjBVZztk5yczJ49e2jbtq20bfHixSxcuJBvv/2W+Ph4+vbtS79+/Srl\nVPvPf/7D+++/T2xsLMHBwfTu3ZuMjP9n777Dorq2h49/Z+gd6dIEpQooICiWKBZsxA7qNYkaYolG\nk6jRXFP1F29iYmw3VZNgixojYuwae0EMTZoIAtIUxIIFBKTMvH9wmTcT7FJ1f56HJzoz5+w1J+qc\nWXvvtfKBmhukHj16YG9vz8mTJzl8+DAlJSUMGzZMafzjx4+TnJzMgQMHOHLkCG+99RaaGhq42Nuz\nefNmCgoKHvu9PEh5eTk+Pj7s2bOHlJQU3nnnHaZOncqRI0eUzrNx40bU1dWJjIzk22+/ZcWKFWzZ\nsgWoWdGtq6vLX3/9RWVlJaGhobz//vvMmzdPaYJAEARBaBxt2rTB3t6e+Ph4rl+/3ujj5+fnK2pj\nN4Xa5pOXLl1qshiEhysqKqKoqAhPT0+k0vr5iqWtrY2HhwejR49m/vz5vPHGG7z00ktoamoSFxfH\npk2b+OKLLwgNDSUqKoqbN2/Wy7hCDbGaW3ieFRUVsW7dOnJzc/Hy8mLWrFmMHz+eyspKNm3a9NgT\naCYmJty9e5eysjIAPvjgA/r161dngujw4cOoq6tz5swZ1q1bx6JFi5DJZOTm5mJlZYWOjg537txR\nTGxLpVJeeeUVVFVVkUqlSj8nT55UnLex+2QIgtC8iZrcgvAcmzhxIhs3bkRTU5Pq6mrKy8sJDAxk\n3bp1itppVlZWTJs2jY8++khxXO/evbG2tmbDhg2Kmtz/+c9/mD9/PlCTJHZxcWH06NF89tlnfPzx\nx+zdu5fVq1crGkHdvHkTY2NjoqKi8PHxYeLEiezbt49Lly6hpqbGgQMHmDBiBNVlZfQDjmppsW77\ndgYMGHDf9+Lv709kZCTq6upKj1dXV3Pv3j1Fd+/7+de//oWuri4//fST4lyVlZVEREQoXtO/f3/a\ntGmjeI2dnR1+fn707t2bK1euMHDgQF577TUmTJjAhx9++IT/JwRBEIRndf36db777jvs7e0brOzW\n/ZSWlpKRkUGHDh0abcx/qq6u5vPPP8fFxYXg4OAmi0N4sGPHjnH8+HEmTZqkmJRoSCUlJaSnp5OS\nkkJWVpbiPkhHRwc3NzecnZ2xtbWt05BSeDLR0dF4enqKusDCc+POnTscPHgQFRUVPDw8MDY2xtDQ\nUPF8VFQU+/btw8PDgxEjRjxWErmqqoqoqCi6devG7du3cXd3Z9CgQaxevVoxpoeHB6+99hqLFi0C\naj7X9u3bx/Xr1xk2bBgnT55ET0+PwsJCysvL6dSpE+bm5oSHhzN8+HAqKysJDAxES0uLkydPoq6u\nzsSJEykqKrrvLmRBEF5MYiW3IDznevXqRUJCAlFRUcycOZPjx48ryojcuXOHgoICunfvrnRMjx49\nSElJUXrs700YJRIJXbp04fz58wCcPHmSxMRERQ1JPT09bG1tkUgkZGZmKo5zd3dXfElYvXQpX5aV\noQv4Al+WlbF66dIHvg+JRMLYsWNJSEhQ+lmyZInSCu/q6mr+85//0KFDB0xMTNDT0yM8PJy8vDyl\nc/0zWdG6dWuuXr2q9BovLy8mTJiAubk5+/fvR1dXl2vXrj3scguCIAgNxMTEBE9PT7KyssjJyWm0\ncVNTU3FxcWm08e5HRUUFU1NTpc8yofmQy+XExcWhra3daCv+dXV18fLy4pVXXmH+/PlMmDCBrl27\noqKiQlRUFBs2bOCLL75gw4YNxMbGcufOnUaJ63kjVnMLz4vS0lK2b9/OwYMHCQgIYPjw4bRr104p\nwQ3g6+uLu7s7SUlJxMTEPNa5VVVVsbKyIjc3FwMDA0UJlFOnTgEwa9YsjI2NWbBgAVCzEGv48OF0\n796d6upqzpw5g5+fH7du3UIul2NsbExeXh6RkZH8sWED44YMYcqUKdy4cYPt27crFj1JJBLkcjkr\nV67E2toaIyMjQkJCFKvKoWZx08yZM5XiFWVOBOH5Jab2BeE5p6WlRdu2bQFYuXIlSUlJvPPOO4rG\nHvcjl8sfOWtfm1iWyWSUlpbi7+/PqlWr6rzOzMxM8Wttbe2HnvNRDbUMDAwU76WWubm50u+//vpr\nli1bxn//+188PDzQ1dVl/vz5SglsoM6KnNou3/98jaamJhMnTmTt2rXcvHnzkSVVBEEQhIbTt29f\nEhMT2bVrF2+99VaDb1OWy+VUVlbW2UXUFNq0aUNUVBSlpaWP/DwVGldhYSHFxcX06NGjSbbOq6io\nYGdnh52dHf379+f27dukp6dz7tw5srOzuXjxIlBzH+Xm5oaTkxM2Njb1Vlbleaatra3YMVgfvWME\nobGVl5dz4MABKisr6devX52k9j9JJBKGDh3KlStX2LdvHxYWFtjY2DxynDZt2nD69GmsrKzo27cv\nb775Jq+99hpLlixh06ZNxMTEKHaWSCQSJBIJrVq1onPnzkRFRWFqaoqlpSXFxcVcunSJy5cvs/Kz\nz1hy7x6HgQ3A8uXLlSYS5XI5J0+exNLSksOHD5Obm8vo0aNxcnLi3//+t9JY/3yPosyJIDyfxJ2N\nILxgPv30Uw4dOkRsbCz6+vpYWloqZtlrnTp1Cjc3N6XHIiMjFb+Wy+VERUXh6urKuXPncHR0JCcn\nB1tbW9q2bav0o6ure984psyZw/taWpQDfwGzVVUxtrNTWvn9NE6dOsXQoUN55ZVX6NChA/b29qSl\npT3TjUxtoltNTY3s7Gyio6OfKUZBEATh6ejq6vLSSy9x48aNRlldefHiRezt7Rt8nMdhbW0NoOiH\nITQfiYmJAE1a0ubvDAwM8PHxYcKECcyfP59XX30VX19fqqqqOH36NGvXruXzzz9n8+bNxMfHU1JS\n0tQhN2tiNbfQElVUVLB792527dpF9+7dCQoKemSCu5aamhrjxo1DTU2N33777bH/jfD09CQ+Ph6A\nJUuWIJVKGTt2LIsWLVL6bimXyxULpjw8PNDU1CQvLw9ra2uKi4sxNjZm58aNLLl3D3tgCxACnNy7\nt86YBgYG/Pjjjzg7OxMQEEBwcPAjm1T/fXxBEJ4vIsktCC+YXr164e3tzZdffgnA3Llz+frrr/nt\nt9+4cOECn3zyCadOneK9995TOu7HH39k27ZtpKWl8e6775KXl8fkyZPJzc1l7ty53L59mzFjxhAV\nFcXFixc5dOgQU6dOfeBN0YABA1i3fTsyY2NOmJjw8ZIl2NjY8Ouvv3LkyJE6q6of92bE2dmZQ4cO\nERERQWpqKjNmzCA7O1vp2Ke5sdHU1MTCwgItLS327t0rEt2CIAhNpFu3bmhoaLBv375H7gB6Vteu\nXVPakdSUaus8X758uYkjEf5OLpdz9uxZDAwMMDU1bepw6lBVVaVdu3YMHjyY9957jxkzZjBw4EBa\nt25Neno6O3bsYOnSpXzzzTccPXqUy5cvi+TPP+jq6lJaWlrn3lQQmqOqqir27dvH9u3b8fX1JTg4\nGBMTkyc+T6tWrQgODqa0tJTffvvtof2Pamlra6OpqUlRURGamprMnz8fNTU15syZ89DjBgwYQHFx\nMWfPnsXX15ebN29SVVnJDSAImAr0hPv+29S+fXulxUz/LEEpCMKLRZQrEYTn2IO2Ys2ZM4fx48eT\nlZXF22+/TXFxMfPmzaOwsBAXFxfCw8Px8PBQOs/ixYtZtmwZcXFx2NnZsX37dq5du4aenh6enp5E\nREQwf/58Bg4cSHl5Oba2tgwYMAANDY0HxjJgwAB27tnD1KlTmT9/PhUVFfz888+cPHmSnJwcgoOD\nFSvBH7at7O+Pf/TRR2RlZTFo0CC0tLR4/fXXeeWVVxT1wx90rsfZtqaiooKrqytmZmbs3bsXiUSC\nj4/PQ48RBEEQ6pe6ujr9+/dn165dikZXDeHOnTvo6ek1yLmfRqtWrVBXV2/UeuTCo+Xl5VFeXl6n\nv0lzZWxsjLGxMV26dKGiooLs7GxSU1M5f/48J06c4MSJE6ipqeHo6Iirqyvt2rVDS0urqcNucq6u\nrpw/f77OTkdBaC5kMhmHDh2iqKiIHj16KHb/PAsHBwd69+7N0aNH+fPPPxk0aNAjj2nfvj0RERF0\n794dVVVVVFRUyMzMpF27dg88xsjICB0dHTIzMzl79iwymQwnHx/mZWTgBHgB76mr88HgwXWO/Wdz\n3X+WoJRKpXWS45WVlY98H4IgtEwSuZiqFwThKZSXl7N//3769ev3wJIkT0Mmk3Hs2DFOnjyJlpYW\no0ePxs7Ort7OXx/KyspYs2YN165dIzAwUCS6BUEQGplMJuO///0vd+/eZfbs2Q2ShIuJicHT07PO\nF+imFB0dTUVFRYtJqL4Idu3aRVxcHO+8885jlwJojuRyOdeuXSM9PZ3k5GSuXLkCgK2tLf7+/s2m\nbE9TioyMxM/PT9TyFZqV2u9OhYWFdO3atd6/N8nlcjZt2kRGRgYjR45UWgj1IDdu3KCwsJCoqChm\nzpzJgQMH6NKli6Ku/cSJE7lx4wbLly8nKSmJlJQUxc4sdXV13Nzc+PLLL0lOTsbVxobysjK8e/Ui\nICCAf/3rX4pxJk6cSFFRETt37lQ8tmDBArZt20ZSUhIAY8eOpbKykm3btile4+7uTtu2bZWOEwTh\n+dB87toFQWhRYmNjsbS0rNcEN9TMtvfp0wdbW1vCwsJYt24dffr0abJmTvdTu0J8zZo17NmzB0Ak\nugVBEBqRVColMDCQTZs2cezYscdaXfYkZDIZMpmsWSW4AXx9fUW5rGakurqapKQkzMzMWnSCG2pW\nP5qZmWFmZkb37t0pLy/n4sWLWFpakpiYSGxsLHK5nNatW+Pj44OmpmZTh9zonJ2dSUtLw8XFpalD\nEQRkMhmnTp3i8uXL+Pj40KdPnwYZRyKRMGrUKH788Ud27NiBmZkZ5ubmDz3G2NiYrKws7t27B9T0\nK0hMTMTDw4OLFy+Sk5NDQUEBGzduBMDCwgJPT0/KysqQyWSsXbuWmJgY3n77bYyMjCgrK6O0tBQ1\nNTX27NmDv78/Ojo6wP1LmPxdnz59ePfdd9m1axdOTk6sWrWKS5cu0bZt23q4OoIgNDfN685dEIQW\noaSkhMLCQoYOHdpgYzg4ODB9+nS2bNnCkSNHyMrKIigoCG1t7QYb80nUJrpDQ0PZs2cPEomETp06\nNXVYgiAILwwHBwesrKyIjo7Gz8+PVq1a1du509PTcXR0rLfzCc+nrKwsKisrn8vPf01NTdq3bw9A\nz549FY9nZmayf/9+qqqqUFdXx8vLCxsbm6YKs1EZGRmRmpqKXC5vNgsvhBdTZGSA80tCAAAgAElE\nQVQkWVlZeHl5Kf39bCiampqMGzeO1atXs2nTJqZNm/bIiS5PT0+2b9+ORCLh0qVLREdHs2fPHqqr\nq7l16xYaGhoEBgbi4uKiWDSVlpZGq1atOHDgAFVVVSxdulRxvr//vVuwYAGffPLJY5WgDAkJITEx\nkZCQEABmzJjBiBEjuHHjRr1cG0EQmhdRrkQQhCd26NAhrK2tG2UlS3V1NYcPHyYyMhIdHR3GjBnT\nrL5MlZaWsmbNGq5fv86QIUPw9vZu6pAEQRBeGAUFBaxevRpnZ2fGjh1bb+c9c+YMfn5+9Xa++hQT\nE0OnTp1Ekq0ZCAsL49y5c7z33nuKVYUvkpKSEqKioigqKkIul2NjY4O3tzfq6upNHVqDuXr1Knfu\n3MHBwaGpQxFeQNHR0aSnp+Pm5kbHjh0bffxz585x8OBBXnnllYc22q2oqCA9PZ0zZ84oNbM1NDTk\npZdewsXF5b4Ll+RyOX/99ReOjo78+eefODo6Ehsbi4qKiqLp8qBBg5BIJPj6+jbMmxQEoUUTK7kF\nQXgiRUVF3LlzB2dn50YZT0VFhf79+9OmTRu2bdvGmjVrCAgIaDY1EbW1tRWlS3bt2gUgEt2CIAiN\npHXr1rRv356UlBTy8/OxtLR85nPevHmzXleF1zdjY2Nu3LiBiYlJU4fyQqusrCQ1NRUbG5sXMsEN\noKurqyiRIJPJSEtLY/fu3chkMjQ1NfH19X1kWYOWxszMjIyMDNq1a9cs7kOFF0N8fDwpKSk4Ozsz\nbty4JovDzc0NJycnIiMjMTY2RiqVKp67d+8eFy5cICEhgaysLGQyGRKJBENDQ3r06IGrqyuXL19G\nV1f3gTtzJRIJWlpaaGlp4eHhQUJCAi4uLmRlZaGurk5FRQUVFRXk5uaKJLcgCPelsmDBggVNHYQg\nCC3HwYMH8fPzQ09Pr1HHNTExwcPDg6ysLBITEykoKMDR0bFZ1EtVU1PD3d2dtLQ04uPjMTAwoHXr\n1k0dliAIwgvB2tqav/76i/z8fLy9vZ858ZSUlISHh0ezTWBpa2uTnZ393CUPW5q0tDSSk5Pp3bs3\nFhYWTR1Ok5NIJJiamuLq6kr79u0xMzMjLi6Os2fPkpKSQnl5OaampkpJsZZKRUWFoqKiFl+HXWj+\nUlJSOHjwIHp6egwaNKheJnKflL29PTKZjK5duwI1f/6NjIxISkrC2NiYc+fOceDAAfbs2cP58+e5\ndesWCxYsYOjQocyaNQtvb2/s7Ozw9vame/fuxMfHY2Nj88DPWGNjY5KTk/H09OTKlSuKnSJSqZS7\nd+9SUFBAp06duHLlSpNcD0EQmremzw4JgtBi5OfnI5VKm+zLnKGhIZMmTeLAgQPExMTw/fffM3bs\n2GZxg6OtrU1ISAihoaGKTt1eXl5NHJUgCMLzz8DAAD8/PyIjI7lw4cIz7TSqqqpCIpE060SchoaG\nopmX0HTi4uKQSCS4uro2dSjNkqGhIf379wdqVnmfO3eOHTt2IJPJ0NXVpXPnzhgbGzdxlE/H0tKS\nyMhI7OzsmjoU4TmVlpZGbGwsNjY2jB07tkE/kyZOnMiNGzcUO1L/KSYmRmnldWlpKRkZGcTGxrJ3\n716gphm0g4MDHTt2xNHRkQULFtCmTRs0NDQAOH78uKLEj4uLS50Grq+88gppaWmcOXMGNTU15HI5\nFRUVLFq0iOLiYmbNmoWBgQFXr16lvLwcCwsLTp069Vz2QxAE4dmIJLcgCI8tIiKCQYMGNWkMqqqq\nBAYGYmdnxx9//MEvv/zCwIED8fHxUVoRkJ2dTdu2bYmJiWm08iF/L12yc+dOJBIJnp6ejTK2IAjC\ni6xnz56KplaOjo5PnRBIS0trtHJcQstVXl7OxYsXadeunSKJIzyYVCrFw8MDDw8PAK5fv05UVBQl\nJSVIpVKcnJxwc3Nr1pNL/2RlZUVeXl6z6hMjtHxZWVlERkbSunXrBk9u17pf88a/MzY2pqSkhJiY\nGOLj4xW1sVVUVGjdujWenp54eXmhpqb2wHP06NGDiIgILCwsMDExISMjg8rKSsUx3333He7u7nz2\n2WcsXLiQ9u3bM3/+fFJSUjh27BinTp3C0dGRgoICSkpKOHbsGM7OzsTHx4vvWoIgKGk5dxKC0ExN\nnDiRIUOGNHUYDS4tLQ1jY2NF9+vHJZVKCQ8Pr/d43NzcePPNNzEyMmLv3r2EhYUprWyztbXlypUr\njd6URUdHh9dffx0jIyN27NhBfHx8o44vCILwItLU1KRv374UFxcTFxf31OcpLi5GX1+/HiNrGKqq\nqlRVVTV1GC+s1NRU5HK52LH1lExMTBg0aBDBwcEMGzaMiooKwsPDCQsL4+DBg9y+fbupQ3wkW1tb\n8vLymjoM4TmRl5fHb7/9RkZGBmPHjqV3796NNukjl8sVjSH/rri4mKioKIyNjRk1ahR79uzhypUr\nLFy4EKlUSnx8PLNnz2bEiBFs2rTpoWOoqKiQmZlJSkoKAJ6eniQkJCieNzQ05Oeff+aLL74gJiaG\nnJwcvvvuO7799lvc3d3x8fEhPj6euXPnkpKSQlZWFu7u7pw/fx47OzuWLl1avxdFEIQWSyS5BeEZ\nPWr2u6FVVFQ0+BhyuZy4uDh69erV4GM9CWNjY6ZMmYKnpycpKSn8+OOPFBYWAjXJdTMzM1RUVBo9\nLh0dHUJCQhSJ7r/fxAmCIAgNw9fXFx0dHQ4ePPhU5TyuXr2KqalpA0RW/ywsLLhy5UpTh/HCio2N\nRUVFBUdHx6YOpcVTVVWlU6dOBAUFERQUhLu7OydPniQsLIzw8HBSU1ORyWRNHeZ9WVhYUFBQ0NRh\nCC1YQUEBW7Zs4dy5cwQFBREQENAsdjRkZGSwYsUK9u3bh0wmw9LSkldeeYX58+cDsHr1akaOHEli\nYiKvvvoqkydPJjc3t855Dhw4wKj+/ZHL5WRkZFBeXk5paSmampqoq6srTWgNHDiQ119/nfHjxzN+\n/HgGDRqkKEdibm5OSUkJgOL6nDlzhjZt2lBZWdlse2gIgtD4mv5fUEFo4R40+11r2bJldOzYEV1d\nXaytrZk8ebLSB3rr1q3ZsmWL4vc9evRAX1+f6upqoOYmQyqVkp+fD4CdnR0LFy4kJCSEVq1a8dpr\nrwFw+vRpevXqhY6ODtbW1kyfPp3i4mLFef39/Xnrrbf44IMPMDU1xdzcnLlz5yrFXlFRwQcffICd\nnR2ampq0a9eOb775hujoaBwdHUlLSyMwMBB9fX3Mzc0ZN26cIqn8tNasWUP79u3R0tLC2dmZFStW\nKMW0atUqnJyc0NLSwtTUlIEDByquTVJSEgMHDmT8+PF89dVXLF68mA8++ID4+Hiys7ORSqVKK/r2\n7NmDs7MzWlpa9O7dmy1btiCVSu97U/as/p7o/uOPP0hMTKz3MQRBEIT/T0VFhcGDB1NRUUFERMQT\nH5+VlUXbtm0bILL6Z2ZmxtWrV5s6jBdSSUkJly5dwsXF5aHb84Wn07p1a15++WWCgoJ4+eWXuXXr\nFuHh4WzdupUjR44oEl3Ngb29PVlZWU0dhtACXbt2jS1bthAXF8eIESMYOHAgqqpNW0k2PT2dbdu2\nsXXrVi5fvszAgQN57bXXMDQ0xMPDAwcHB8XiofHjxzNu3Djatm3L559/jpqaGr/++qvS+c6ePcuE\nESMYevAgEuCbr76isLBQscvV3d2dc+fOKR2zdOlSLl26RFZWFqGhody4cQOoaTCtpaUFgJGREVDz\n3bdLly5UVlY25GURBKGFEUluQWhgKioqrFy5kpSUFDZt2kRUVBQzZ85UPO/v78+xY8eAmkYe0dHR\naGpqEhMTA8CxY8dwcHBQaq64bNky2rdvT2xsLJ9//jlJSUkMGDCA4cOHk5iYSHh4OPHx8YSEhCjF\nsnHjRtTV1YmMjOTbb79lxYoVSgn2CRMmsGHDBpYvX86qVauwNjJi3apVhIeHY2lpSc+ePenQoQPR\n0dEcPnyYkpIShg0b9tAk/8P89NNPfPjhhyxatIjU1FSWLl3Kl19+yffffw/UNDqZMWMGCxcu5MKF\nCxw+fFipJvi4ceOwsrIiOjqapKQkFi9ejIGBATt27ODAgQNKY+Xm5jJy5EiGDBlCYmIiM2bMYN68\neQ0681+b6G7VqhXbt28XiW5BEIQG5urqiqmpKREREUoTvY9SUVGBqqpqi1kNpqKiopjwFRrX37fb\nCw1LXV0dPz8/goKCCA4OxsHBgSNHjhAWFsYff/zBxYsXmzQ+iUSCqakp165da9I4hJbj5s2bbN26\nlcjISIYNG0ZgYCDq6upNEotMJiM1NZXs7Gzy8/MpLCxkyJAhBAcH06tXL3x9fR848duhQwfFr1VU\nVDA1NaW0tJScnBzF4wd37uTLsjIm/O/3r1VU8MuKFdjY2CgWI1lZWSktNtqyZQvV1dXcuXOHhIQE\nxW4JiUSCu7u7oiFlbfyXLl1CVVVV7GwSBEFBNJ4UhAb2zjvvKH5ta2vLl19+yfDhw1m/fj1Qk+Re\nvnw5UDMj3a5dO7p06cLRo0fp0qULx44dw9/fX+mc/v7+vPfee4rfjx8/njFjxjBr1iwA2rVrx/ff\nf4+3tzfXr1/HxMQEqKljvWDBAgAcHBz46aefOHz4MGPHjiU9PZ0tW7awf/9+5HI50155hS/LygCY\nl5lJXl4enp6efPHFF4px161bh7GxMTExMfj6+j7xtfnss89YsmQJI0eOBKBNmza8//77fP/997z1\n1lvk5uaio6PDkCFD0NXVxcbGRummKjc3l7lz5+Lk5ARA27ZtqaioYOfOnZw6dQqouZkE+OGHH3Bw\ncODrr78GwNHRkQsXLvDhhx8+cdxPojbRHRoayvbt25FIJIrGS4IgCEL9kkgkDBkyhNDQUA4dOsSI\nESMe67jU1FRcXV0bODrheRATE4OamlqLWfX/PLG1tcXW1haoaf4ZHR2t2LFnbm6Or68vmpqajRqT\ng4MDZ86caTGljoSmcefOHQ4ePIiqqiqBgYFoa2s3SRwymYyUlBTOnz8PgI2NDba2tty8eZMePXo8\n9nn+uYtFIpFgZGTEtWvXaNWq1UOPtbGxITIyEmtra9q0aUNERAQ2Njbk5uYye/Zsli1bRkpKCiEh\nISQmJpKcnEzr1q1xc3NDIpGgr6+Puro6FRUV7N69W2nHsyAIgljJLQgN7MiRIwQEBGBjY4O+vj6j\nRo2isrJSMePcq1cvLly4wJUrVzh27Bi9e/dWWt19/PhxpSS3RCLBx8dHaYzY2Fh+/fVX9PT0FD89\nevRAIpGQmZmpOO7vCWKo2RJau9357NmzSKVSevfuzeqlSxUz7xOAr8rLOfznn5w4cUJpDFtbWyQS\nyVOtpLl27RqXLl1iypQpSuecP3++4nz9+/enTZs22Nvb8+qrr7J+/XqlbaqzZ89m0qRJ9O3bl88/\n/5y0tDTU1dUZNWoUffv2BSA8PJzk5GRSU1PrJOI7d+78xHE/DV1dXUJCQjA0NCQ8PJykpKRGGVcQ\nBOFFZGNjQ7t27UhMTHzskh6lpaVNlnR4WlpaWpT9bzJaaBy3bt3i2rVreHh4NIu6uS8yTU1NXnrp\nJUUtb0tLS/bv309YWBg7d+5stKaQEokEQ0NDxaIKQfi7kpISwsPDOXjwIAEBAQwbNqzRP2tkMhnx\n8fFs3bqVbdu2UVZWxqhRowgODsbPzw+pVIpEIqnTwNHe3p5ly5Y90VidOnVSlCMJGDqU97W0WPe/\n574FnP/3HdbT01PxOjc3N5KSknj99dfp2rUrU6dOZfHixaiqqvLee++hr6/P7du3sbGxQUdHh8uX\nLysWcF26dImCggI2btzIf//732e7UIIgPBfE3ZkgNKCcnBwCAwNxc3MjLCyMuLg4QkNDlbZaubi4\nYGFhwdGjRzl+/Dh9+vTB39+fiIgIUlNTuXz5cp2V3Do6Okq/l8vlTJ48mYSEBMVPYmIi6enpdOzY\nUfG6+826P26pkarKSgIDA5XGSEhIID09ncDAwCe+NrVNhFatWqV0vnPnzinqs+nq6hIXF8fvv/+O\nra0tX3zxBS4uLoomP59++ikpKSkMHz6c06dP06FDB9asWaO0WlpTU5Nt27Zx5cqVJt3arauryxtv\nvKFIdP+zBp0gCIJQfwYNGoSamtpj9VzIz89XKgnWUlhZWXH58uWmDuOFUjtJLUqVND/t2rVj+PDh\nBAUF0adPH9LT0wkLCyMsLIwzZ840aKN2Z2dn0tLSGuz8QstTXl7Ojh072L9/P3369GHUqFHo6+s3\n2vgymYzY2FhFYvvjjz9mzJgxjBkzhi5duqCqqopUKqVbt24A3L59m8rKSvLz84mPjyc+Pp7t27cz\nbdq0JypLKZFIFIuIvLy8WLd9OzsDAqg9Q+25tLS00NHR4fr16xgaGrJu3Tri4+MJDQ1VPL927Vp+\n+eUXLl26RGpqKlKpFCMjI8LCwrCxsaGgoICwsDBFqbEnrY+/YMECsbtWEJ5DolyJINSDB9XwjImJ\nobKykuXLlytes3Pnzjqv69WrF7t37yYmJgZ/f3+MjY0xMTHhq6++qlOP+368vb1JTk5+pq2znp6e\nyGQyjhw5wpQ5c5hw6hT8b4XYHDU1WtvYEBERQUVFBS4uLk89Ti1zc3MsLS3JyMjg1VdffeDrVFRU\n6N27N71792bhwoWYmZmxZ88eJk2aBNRsE505cyYzZ85k+vTp/Pzzz7z++uuK44OCgsjJyeHQoUMc\nPnyYmzdvKrbRRUVFPfP7eBK1K7o3b94s6jcKgiA0IGNjY95991127dpVZ/fTP+Xm5tKlS5dGiqz+\nGBoaKnZrCY0jNjYWTU1NrK2tmzoU4SF0dXXp06cPUJPsu3DhArt370Ymk6GpqUmnTp1o3bp1vY0n\nlUrR1dXlzp07jZrIFJqfiooKDhw4QHl5OX369MHY2LjRxq6qqiI2Npbc3FwkEgnOzs6MGjUKqVTK\n3r17CQgIYMOGDUrHqKurM2vWLE6ePIlcLmfZsmWKMppBQUH8/vvvT9yrorZkUG5uLu+++y4DBgxA\nKpUil8u5ePEi6enpODo64uLiQkREBKampvz444/MnTtX6Ttvt27dmDVrFlOmTGHjxo2Ul5cr3sOo\nUaPQ1dXF39+fGzducPPmTQwNDRXnFgThxSVWcgtCPbh9+zYJCQmKme/4+HhycnJwcnJCJpOxfPly\nsrKy2Lx5MytXrqxzvL+/P7///juOjo6KmyF/f39+/fXXOqu47+f9998nKiqKadOmcfbsWTIyMti9\nezdvvvmm4jVyufy+M/G1jzk5OTF69GgmTZrE3bt3+fKHHwj18eFbd3fW//EHX3zxBaWlpYwcOZKP\nP/6YmJgYDh06xNSpUx/Z6T4rK0vp2sTHx1NcXMzChQv56quvWLFiBWlpaSQnJ7N+/XoWL14MwO7d\nu1m5ciVnz54lJyeHjRs3UlxcjKurK+Xl5bz11lscP36c7Oxs/vrrL06dOoWbm5vS2BoaGowZM4b3\n3nuPa9euMXToUPbt20d4eDirV69GIpE0aqMxPT09Jk+ejKurq6KOpCAIglD/tLW1MTIyemgiuLy8\nHE1NzRbTcPLvWmLMLdnVq1e5ffs23t7e4tq3IFKpFBcXF0aOHElQUBAvvfQSycnJilXesbGxVFVV\nPfM4rq6uiqakwounqqqKffv2sX37djp37kxwcHCjJLirqqo4ffo0W7du5Y8//kBPT49Ro0YRFBSk\nVFZJLpejoaGBmZmZ0o+hoSFr1qxBJpMpypXIZDJkMhlRUVEsXbqUrKws7ty5g1QqVZwvKCgIqVTK\nwoULAfj999/Zv38/pqamGBgY0L17d0XTSKiZbJJIJNy7d48hQ4ago6ODvb09KSkpVFZWcvfuXcaM\nGUNycjJjx47FyMgIIyMjUlJSOHToED4+PixevJjQ0FAqKiooLS3l6tWrXLp0iTlz5iCRSFBVVeXV\nV19FV1eXdu3asXHjxqe6pn369GHmzJlKj925cwdtbW3++OMPoKbn04QJEzAyMkJbW5uAgAClv/9r\n165FT0+PI0eO4O7urph4y87OVrwmLy+PYcOGYWxsjI6ODq6urmzZsuWpYhYE4f8TK7kF4RlJJBJO\nnjyJl5eX0uO1s98rV67kyy+/5KOPPqJ79+58/fXXjB07Vum1/v7+VFdXKyW0/f39Wb9+/WMluT08\nPDhx4gQfffSR4lxt27ZVNHSsjfOfX8r++dj69ev5+OOPefvtt7l+/TrW1tbMnj2bwYMHA3DmzBne\nfPNNvv76axYvXoyFhQVDhw5FQ0PjofHNnTu3zri7du3ijTfeQEdHhyVLljB//ny0tLRwd3dnxowZ\nALRq1YodO3bw2WefUVpaioODA7/88gvdu3ensrKSW7duMXHiRAoKCjA2NmbIkCGKxpK149T+d9iw\nYfzyyy+89957DB06FBcXFz766CMmT57c6E2KJBIJ5ubmAMTFxeHt7d2o4wuCILwo+vbty+bNm2nX\nrl2d5+RyOefPn6d9+/ZNEFn9qJ3AFknXhldbquSf/U2ElsXAwICAgACgJvF27tw5duzYgUwmQ1dX\nl86dOz9VclJFRQVtbW1KSkrQ1dWt77CFZkomk3Ho0CGKiop46aWXsLKyavAxKyoqOHPmDIWFhUil\nUtzd3RVlRx7mScqOgPL3xLlz5zJ9+nTFcwcOHOCNN97gpZdeAmpqj0+YMIFvvvkGiUTCN998w/Tp\n09m8eTMGBgaKOuQnTpygR48eTJkyhbKyMqZNm8b69euxt7fH1tYWFxcXAgICOHHiBOrq6ixZsoR+\n/fpx/vx5+vbtS1JSEjvCw1n+vzgWRkUpdhh//fXXBAYGMnDgQCoqKggJCaFnz57Y2Ng80fueMmUK\nb731FkuXLkVdXR2AzZs3o6+vz5AhQwCYOHEi6enp7Ny5E0NDQz788EMGDhzIhQsXFN8r7927x+LF\ni1m7di0aGhpMmDCBN998k/379wMwffp0KioqOHbsGPr6+qSmpj5RnIIg3J9E/qT/2gmC8MK7fv06\nu3fvJicnB1VVVfr27Yuvry8qKipNHdojlZWVsW3bNjIzM0lJSeHAgQPcunWryeK5cuUK+fn5ItEt\nCILQQKKjo5FKpXTq1Enp8ZMnT5KUlERISEijT3bWl/T0dExNTTE0NGzqUJ5rcrmcr7/+GhUVFWbN\nmiUmFZ5T169fJzo6mrt37yKRSHBycsLNze2xm4xWVVURFxfXaI3NhaYjk8k4evQoV69epWvXrtjZ\n2TXoeOXl5URGRnL9+nVUVFTo2LHjfSdvH2TixIls3LixzmfdjBkz+OKLL4CaRpMzZ85k9uzZ9/19\nrbS0NPz8/Fi4cCFvv/32fceTy+VYWVnx5Zdf0qZNG3r06IGqqiqTJ09m6NChxMTEMHToUObNm4e5\nuTnTp08nNTWVRYsWcfDgQcV7q66uxtzcnB9++IEBAwbg5eZGxaVL1LaVXQes9vAgMjmZPn368K9/\n/YtLly4xZswYOnXqxE8//cS4cePuG+OCBQvYtm2bYgKz1r1797C2tubbb79lzJgxAHTp0oVevXrx\n1VdfkZ6ejrOzsyJhDzUrvW1tbVm6dClvvPEGa9euJSQkhLS0NEX5lE2bNhESEkJ5eTkAHTt2ZNSo\nUXzyySeP+t8nCMITECu5BUF4YiYmJkycOJGMjAx27drFgQMHOH36NC+//DKOjo7N9svfd999h6+v\nL926dVOUdPHy8mrS+m0WFhYAnD17ts5uAEEQBOHZ+fr6snHjRry8vBTJKplMxl9//YWamtojdyM1\nZ1ZWVmRlZYkkdwPLz8+ntLQUf3//ZnuPIzw7ExMTBg0aBNQkrBMTEwkPDwdAX1+fLl26YGBg8MDj\nVVVVUVNTo6ysDC0trUaJWWhcMpmMU6dOcfnyZXx9fenbt2+DjVVaWkpkZCRFRUWoqKjg7e1N7969\nn/p8vXr1YvXq1UqPPezP8/3cunWLoUOHMnbsWKUE99WrV/n44485duwYhYWFVFdXU1ZWxuXLlxkx\nYgQxMTEAdO3alf79+5ORkcHevXvp2LEjR48exdbWlh9++IG8vDw8PDyUFk6VlZVx8eJF9PX1kclk\nD4zN0tJSMQlw+PBhTE1NuXr16hO9P6gpdfnaa68RGhrKmDFjOHfuHNHR0axfvx6A8+fPI5VK6dq1\nq+IYfX19PDw8OH/+vNJ5/v79snXr1lRUVHDr1i0MDQ155513FCu7+/bty4gRI8SiJ0GoByLJLQjC\nU3NwcOCdd95R1OfevHkz1tbWDBkyBDMzs6YOr47MzEy++OILbty4gbW1NZMmTaJ169Zs2rSJ7t27\n06dPn8derVOfRKJbEAShYXXu3Jljx44pmtFlZGRw9+5dBgwY0KKTltra2pSWljZ1GM+9hIQEoKY8\nnPBiUFVVxdvbW5F0Kiws5NSpU5SVlSGRSGjfvj3Ozs517hvd3d2Jj4/H19e3KcIWGohMJuPMmTNk\nZ2fj5eVFz549G2SckpISTp8+ze3bt1FTU8PHx6feGt1qaWnRtm3bpz6+qqqK4OBgbGxs+Pbbb5We\nmzBhAteuXWPFihXY2dmhrq5O3759qaioQFdXV6mhpJqaGsHBwfz888+kpqYikUiwtrbm7t27dOzY\nkTVr1pCTk6NUSqxVq1YAePv5sWP7dtb9rxjB+1pafDBpEpHvvou2tjZSqRRNTU2uX79OdXX1Q5Pi\nDzNp0iQ6dOhAXl4eoaGhdOvWDWdn54ce88/SYaqqyqm22udqYwoJCWHAgAHs3buXQ4cO0a1bN+bP\nn8+nn376VDELglBDJLkFQXgmUqmUzp0706FDB44ePUp0dDQ//PADXl5e9OvXT1GDrTlYtmwZy5Yt\nU3rs7t27bN26lYiICHJychg9ejR6enqNHpuFhQVyuVwkugVBEBqAo6MjsbGxVFRUoK6uzl9//aXY\n8i0IDyOTyUhISMDY2BgjI6OmDkdoIubm5gQGBgI1NZHj4uIUq7yNjIzo3BbuD1MAACAASURBVLkz\nurq6qKmpIZVKuXfvXoveJSL8f9HR0aSnp+Ph4fHA0hfP4s6dO5w+fZri4mLU1dXp3LkzrVu3rvdx\nnnVC99133yU3N1fx+fl3ERERfPPNN4qdEIWFhYqmk4AiUX/06FEmTpyIpaUl/v7+rFu3DgcHBwAC\nAgJ4//33sbW1pbKyEnNzc3R0dJTG6dSpExEREez8X2+EdXPmEBAQwLv/S3IbGhpy7do1JBIJZWVl\nT1yHvFb79u3p0qULq1evZuPGjXz++eeK51xdXZHJZJw+fVpRk/zOnTskJyfzxhtvPNE4VlZWTJ48\nmcmTJ/PVV1+xcuVKkeQWhGckktyCINQLTU1NBg0aROfOndmzZw9nz54lMTGRPn360KVLl2Zbr1tH\nR4fx48dz4sQJjh8/zvfff8/o0aOxt7dv9Fhqb2jj4+Px9PRs9PEFQRCeZ71792b//v307NmTixcv\n0qFDh+eipIBUKkUmkzXJTqQXQXZ2NhUVFfj4+DR1KEIzoa6ujp+fn+L3eXl5HDlyhIqKClRUVHB1\ndSU5OblOHwChZYmPjyclJQUXF5d6T27funWL06dPc/fuXTQ0NPDz82vwXbDl5eUUFhYqJX5VVFQw\nNTW97+v//ro1a9awZs0a9u3bR3l5OVeuXAFAT08PHR0dnJyc2LBhA507d6akpIR58+Ypmjb+3c6d\nO/nhhx/o168fp06dIisri4CAAAoKCpg4cSJLlixh8ODBLFq0iP3792NqasrOnTt58803cXBwwN7e\nnjt37vDWv/9Nhw4d0NfXV3z2GRgY0LVrV/bv349MJqO6upqioqJHXpOEhASl96qjo4OjoyOTJ09m\n6tSpaGhoKGpzQ82k+bBhw5g6dSqrV6/GwMCADz/8EAMDgyf6c/LOO+8wePBgHB0duXPnDvv27cPN\nze2xjxcE4f5EklsQhHplbGzM+PHjuXjxIrt27eLgwYOcPn2aIUOG4OTk1Cy3hUulUvz9/bG1tWXr\n1q2sX78ef39/evbs2ejxikS3IAhCwzA3N6eyspLTp08DPDfN4czMzLh69aqi9JVQv2pLlYjkg/Ag\nNjY22NjYADVJs5iYGFJSUsjMzMTCwoLOnTu32Oa2L6Jz586RkJBA27Zt6zW5fePGDSIjIykrK0Nb\nWxs/Pz+MjY3r7fwPI5FIOHToUJ0V4tbW1uTm5j7wmFonTpygvLwcf39/pdcsWLCATz75hNDQUKZM\nmUKnTp2wsrJiwYIFXL9+vc45/+///o9169YxZ84czMzM+O677ygqKmLr1q1MmzaNyMhIQkJCGDt2\nLLdu3cLCwoKAgABFuZJRo0YRHh7O8OHDKSkpYe3atYwfPx6oSdinpKQwZMgQtm7dCsCFCxceek0y\nMzPr7KD18fEhKiqK0aNH8/bbbzN69Og6K8rXrFnDu+++y9ChQykvL6dHjx7s379faffG/b5D/v0x\nuVzOzJkzycvLQ09Pj379+rF06dIHxisIwuORyJ92D4cgCMIjyGQyzp49y4EDB6isrMTKyoohQ4Zg\nbm7e1KE9UHFxMVu2bOHy5cvY2dkRFBRU58amMeTn53Pt2jWxlV4QBKEeFRcXs2LFCoyNjZk2bVqz\nnHh9UpWVlSQnJ4tSVw2gqqqKxYsXY25uzuTJk5s6HKEFKS0tJS0tDQMDA5KSkqisrERdXR1PT09s\nbW2bOjzhPtLS0oiNjcXW1pZu3brVy+6YwsJC/vrrL+7du4eOjg7dunV74RsF37x5U1HbHGoaRnp4\nePD2228TGBjIrVu3yMvLw93dnYiICLp3717nszopKQl7e3t0dXUVj/3++++oqqrSv39/Dhw4QHJy\nMlBTL9zOzu6J48zPz6dNmzacOHFCqclkY/P398fDw4NvvvmmyWIQhJZE7GsUBKHBSKVSOnXqxOzZ\ns+natSv5+fn8+OOPbN++nbt37zZ1ePelp6dHSEgI3bt3Jzs7m++///6BKxwakqWlJaampooVZIIg\nCMKzy83NRSaTNdudRU9DTU2N6urqpg7juZSRkUF1dbUoOyE8MW1tbSoqKrC1tWXYsGEEBQXRp08f\nMjMzCQsLY+vWrZw5c4aKioqmDvWFd/HiRTZt2kRBQQFjx46lR48ez5TgLigoYMeOHWzdupX4+Hj8\n/f0JDg5m8ODBzTLBfe3aNaZPn469vT2amppYWFjQr18/Dh06pHiNnZ1dva0ybtWqFQYGBmRnZwOg\noaGBiYkJMTExZGZmYmhoSGVlJXfv3sXZ2Zm0tLQ653B1deX8+fNKj6mrq+Pj48Px48cZOXKkohzZ\nwYMH6xxfUVGBiYkJixYtqvNc7eSmra0tHTt2fGSCWyqVKn7U1dWxt7dn/vz59fa5LJFInpv7FUFo\nDKJciSAIDU5TU5P+/fvj4+PD3r17SUxM5Ny5c/j7++Pn51en+3RTk0ql9OvXjzZt2hAWFsbatWvp\n27cv3bp1a9SbjNpO5AkJCWJFtyAIQj04c+YMqqqqinqizwuxMbNhnD17FolEgqura1OHIrRAbm5u\npKSk0OF/TfJ0dXXp3bs3ULPbMT09nd27dyOTydDQ0MDHx6dBGg4KdRUVFVFSUsLp06cxMTFh7Nix\nz5TYzsvLIyYmhqqqKgwNDenbt6/SKuPmbNSoUZSXlxMaGoqDgwOFhYUcP36cGzduKF5T399/2rZt\nS2xsrKIMiYeHBxKJhPDwcGbOnEnHjh05c+YM3bt3JzMzk8rKStTU1BTHq6qqIpFIFM2kAbp27Up8\nfDwlJSVIJBKmTZvGsmXLyM/PJzc3V2kHhbq6OuPHj2ft2rV89NFHSrGdOnWK+fPno6+vT2ho6GO9\nn59//pmXX36ZyspKYmJimDBhAq1atWLevHlPfY2qqqqabU8rQWjOxEpuQRAajZGREa+++ioTJkxA\nX1+fw4cPs3z5cs6fP98sv6A7Ojoyffp0zM3NOXToEJs3b270OC0tLTExMRErugVBEJ7R9evXuXTp\nEp6enrRp00axlfl5oK6uzr1795o6jOdKRUUFGRkZ2NnZPRcNSoXGp6urS2lpKTKZrM5zUqkUZ2dn\nRo4cSVBQEL169SI5OZmwsDDCwsIUCVOhfuXl5fHTTz/xzTffEBcXR1BQEP369XuqBHd2djbh4eFs\n3bqV9PR0BgwYQHBwMAEBAS0mwX3r1i1OnTrF4sWL6d27NzY2Nvj4+DBnzhxFs0V/f39ycnKYO3cu\nUqlUKfF6+vRpevXqhY6ODtbW1kyfPp3i4mLF8/7+/kybNo133nkHIyMjjIyMmDdvHnK5HG9vbxIT\nEwGorq7m7NmzfPrppzg4OLB8+XLs7Oy4ePEinp6eREREMGXKFMzNzdHX18ff35979+6RkpICwNq1\na3FwcOD06dN88MEHaGtrM2zYMBwcHMjIyGD0oEGM+l8Zk1qTJk3i4sWLHDt2TOmatGrVColEwp49\nexQTVI9iaGiImZkZVlZWDBs2jICAAOLi4hTPL1iwAA8PD6Vj1q5di56eXp3XrF27lnbt2qGlpUVp\naWmd756HDx+mVatWrF69Gqgp3dK3b18MDAzQ09PD09OzznsShBeJSHILgtDo7OzsmDFjBkOHDqWq\nqorff/+dn376iYKCgqYOrQ4DAwMmTZpEly5dkMvl5OfnN3oMVlZWmJiYKG4EBUEQhCcXHR0NgK+v\nL927dychIeG+yaeWyNLSsll+hrZkqampyGQyvL29mzoUoQW7X1mF+9HX1ycgIICgoCBGjhyJhoYG\nO3bsICwsjH379ima+D0v/2Y1tvz8fNasWUNoaCgFBQV07tyZ/v37P/Fu0oyMDLZt28bWrVvJzs5m\n8ODBBAcH06dPH7S1tRso+oajq6uLrq4uO3bseOBE6fbt27G2tubTTz/lypUris+apKQkBgwYwPDh\nw0lMTCQ8PJz4+HhCQkKUjt+4cSNQs5Nq1apVrF69mhUrViCRSPD19aW8vJzly5fTs2dPzp49y+jR\no5k3bx55eXlcvXoVqVTKrFmzyMvLY8+ePcTHx9OzZ08GDx5Mfn6+4u/EvXv32LVrF99//z0LFy7k\n1q1bLF++nH2//ca0lBSGHjzIhBEjFInu9u3b06VLlzqrtX/55RecnJzo0aPHY1/HvyeiU1JSOH36\nNH5+fo99fK2srCx+++03tm3bRkJCApqamkqr6MPCwhg5ciQ//fQTU6ZMAWDcuHFYWVkRHR1NQkIC\nCxcuFI1uhRda86oRIAjCC0MqleLl5UX79u05ceIEkZGRrF69Gg8PD/r379+sVkCoqKgwcOBA5HI5\n6enpJCQk0KFDh0YtXWJlZQVAYmLiY68qEARBEGpUVlZy9uxZLC0tMTMzA6Bjx45ERETw0ksvNXF0\nz87ExIScnJynaq4l3F9cXJxita0gPC0DAwNSUlKQy+WPfd8olUrx8PBQrPy8ceMGUVFR3L17l4KC\nAqqqqnBzc8PJyQkbGxtR0uAhCgsL+fPPP7l48SISiQRvb2/8/f2VVtA+SlpaGklJScjlciwsLBgy\nZIiiREZLp6qqytq1a5k8eTKrV6/Gy8uL7t27ExwcTOfOnYGalc0qKiro6ekpPj8BlixZwpgxY5g1\naxYA7dq14/vvv8fb25vr169jYmIC1EzCrly5EgAnJycuXLjAsmXLmDVrFpqamqipqeHt7c306dMB\nmDdvHuHh4Rw+fJg5c+bw888/k5GRwdKlS/Hx8QHg//7v/9i1axfR0dG0bdsWqCnvERoaSk5ODs7O\nzkyePJkZb71FqFzOhNqgy8pYvXQpAwYMAGpWc7/99tt8++236Ovrc+/ePTZu3Mi///3vJ7qOr732\nGhMnTqSqqop79+4RHBzM22+//cT/PyoqKtiwYQOmpqZKj8vlclavXs28efPYtm0b/fr1UzyXm5vL\n3LlzcXJyAlBcD0F4UYkktyAITUpDQ4OAgAB8fX3Zu3cvSUlJnDt3jl69etGtW7dmVa9bIpHg5ORE\nUVERERER+Pr6oqGh0Wjj1ya6k5KS6mx5EwRBEB7s3LlzVFZW0qVLF8Vj7u7ubNq0iaqqqmb1WfM0\nJBJJsyz71VKVlpaSk5ODi4uLUh1YQXgatc3zXFxcnup4Y2NjBg0aBMCJEyeIiooiMjKSyMhIVFRU\nsLe3p3379jg4ODxR8vZ5dv36dQ4dOqRoWtihQwd69+79WI0fZTIZ58+fV5RTtLa2Zvjw4S3+c+JB\nRo4cSWBgICdPniQyMpL9+/ezdOlS/vOf/zB//vwHHhcbG0tmZiZbtmxRPFY7mZOZmalIcv9zRbOf\nnx8ff/wxJSUl6OrqoqKigqurK/n5+VhaWmJhYYGJiQmXL19GU1OT7OxsSktLGTp0qNJk0b1797C3\nt8fe3p64uDhUVVU5ceIEt2/fRi6Xc/HiRWRyOXcf8t7Hjh3LrFmz2Lx5M1OnTuWPP/6guLiYCRMm\nPOSour7++msGDhxIdXU16enpzJ49mwkTJrBhw4YnOo+1tfV9E9x//PEHq1at4uTJk0r3MQCzZ89m\n0qRJrFu3jr59+zJq1CgxOSu80J7Pf6kFQWhxDA0NGTduHLm5uezcuZOjR49y5swZAgMDad++fbPq\nKm1kZISfnx9RUVG0bdsWCwuLRhvbysoKuVwuEt2CIAhP4MyZM2hoaNC+fXulx3v27Mmff/7J4MGD\nmygyoTmqrfPq5eXVxJEIzwMjIyPS0tKeaDX3g/Ts2ZOePXtSVFRERkYGycnJZGZmkpGRAdSsuq1d\n5W1lZfVMzRRbops3b3L48GHOnTsH1JSL6devH0ZGRvd9fe3kYO299YULFwBo06bNc53Y/icNDQ36\n9etHv379+Pjjj5k8eTILFixg7ty5D7wGcrmcyZMnK1Zy/52lpSXw6AnYyspKZDIZOjo6xMbGkpub\nS2VlJZWVlaSkpLBq1SpSUlLQ0dHh9ddfv2/cWVlZ5ObmIpFIqKqqQltbm1b/j73zDqvi2v73e+i9\nCEiXJkhHEbFLUTCWWKIxJvZuYjReazTGrtfk2vM1MUbFGpPYEo1Rr4ioNCk2uhSxixQV6eWc3x9c\n5ucRUDRYM+/znOdhZvbsWbMPZ8raa32Wvj45OTlIJBLmKyujWV4OwGx1dbZPny7sr6mpyaBBg9i6\ndSsTJkxgy5Yt9O7dWy5ivSGYmJgIEdT29vYUFhYyePBgFi9ejI2NDQoKCrXGoaKiolY/mpqatdZJ\nJBI8PDxISEhg8+bNtZzcCxYsYMiQIRw9epTjx4+zaNEiNm7cWOd4iYj8E/hnXLVFRETeGpo1a8ak\nSZO4fPkyR48eZd++fRgbG9OnTx/hgelNQElJiQ4dOpCcnExubi4uLi6vzBFvYWEBiBHdIiIiIg3h\nzp07ZGdn0759+1ov6xYWFkRGRgoRZW8z2traPHr0SIzkbATi4uJQUlLCzs7udZsi8o5QUwDP3t6+\nUfpr0qQJ3t7eeHt7U1FRQVZWFikpKSQnJxMWFkZYWBhKSko0b94cZ2dn7Ozs3krN6Iby8OFDQkND\nuXjxIlDtaAwICKgVFQvVztm7d++SmJjIxYsXMTIyQkNDAzs7OwYMGPCPmxioCycnJyorKyktLUVL\nSwsVFRWqqqrk2nh6epKQkCAnjyGTySgpKeHRo0fcvXuXwsJCTp8+TUhICIWFhTx48IC9e/eio6PD\nmjVrkEqlFBQUEBUVJTfuBQUFqKurk5eXh5WVFYWFhZibm+Pq6kpZWRnNmzdHS0sLTU1NNDU1+fe/\n/83JkyeZNm0alZWV/P7777Ru3RqAjbt3s2fTJnJzcvhu7lxBqqSGsWPH0qFDB/78809CQkI4dOjQ\n3x6/mnfC4uJiAIyMjMjOzpZrU/O/2hBsbW357rvv8PX1Zfz48ULRyRqaN2/O5MmTmTx5Mp999hmb\nN28Wndwi/1hEJ7eIiMgbR82MtZOTE2fPniU8PJyffvoJZ2dnunfvjo6Ozus2UcDJyYmcnBwiIiJo\n06bNK9Pos7CwQCaTkZCQgKur6ys5poiIiMjbSE3ByRotzycJDAzk+PHjDBgw4FWa1eiYm5tz69at\nF5ZEEKmmoKCAu3fv0rJlS1HrWKTRMDIyIi0tjebNmzd6UISysjL29vbY29vTu3dv8vLySEtLIyEh\ngdTUVFJSUoBq7X5XV1ccHBwwMTF5o7IkX5QaJ2pcXBwymQwbGxsCAwNrZVnKZDJu3bpFYmIily5d\noqSkBKieHGzXrt0/Vt4hLy+PDz/8kDFjxuDm5oa2tjaxsbF8++23dOvWTZj8tba25syZMwwZMgQV\nFRUMDQ2ZPXs27dq1Y9iwYfTq1YuKigqOHz9OSkoK77//PgA5OTncuXOHuXPn0qZNG3Jzczl58iQ9\ne/bEzs4OPT091NTUcHZ25qOPPkJRUZFbt24REhKCu7s7ffv2xdjYmPj4eH766Se+/fZbJBIJ+fn5\n7Nu3j4CAADp16oSmpqYQKa2kpIRMJqNZs2YA+Pv7M3DgQK5fvy5kPDxOu3btcHZ2Zvjw4ZiamgrS\nQABdu3albdu2LF++/KnjeP/+fe7evYtUKiUtLY3FixfTokULnJycAPDz8yM/P5/ly5fz0UcfERoa\nyv79+xv0HclkMuF/+9SpU/j6+jJhwgR+/PFHSkpKmDFjBoMGDcLKyors7GzCwsJeqOiliMi7gujk\nFhEReWNRUVGha9eueHl5cezYMZKSkkhJSaFz58507NjxjdHJNDIyQldXl5iYGBwcHOqMGnkZWFpa\nAoiObhEREZF6KCsr4/Lly1hZWdWbrq6rq4uysjLZ2dkYGxu/Ygsbj5pIbpG/R05ODoqKinh4eLxu\nU0TeMWxsbMjKysLGxualHUMikWBoaIihoSHt27enrKyMzMxMUlJSSE1NJTQ0lNDQUFRUVHBwcMDJ\nyQlbW1vU1NRemk0vg+LiYsLCwjh37hxSqRRLS0u6d+8u1K+BaufgjRs3SExM5PLly5SWlgLV13xv\nb29cXFxe2TP7m4q2tjbt27dn3bp1pKenU1ZWhqmpKb169WLw4MEcP36cwsJCfH192bRpEzY2NlRW\nVvLbb78BsGTJEn799Vd+//13pFIpTZs2pVOnTgQGBqKpqcnRo0fx9fVFVVWVXbt2IZFImDRpEt98\n840Qua2mpoalpaUwQauhoUFpaSkSiQRnZ2fCwsI4fPgw8+fPZ+LEiTx48AB9fX38/f0ZOXIkAMbG\nxnJyIA4ODpw7dw6JRMLdu3dp0qQJFhYWREdH1zkOY8aMYcaMGUyaNElu8iczMxMrK6tnjuO4ceOA\n6t+fiYkJPj4+LF++XDhHR0dHfvjhB5YvX87y5cvp06cPc+fOZd68eUIfEomkzomnx9fb2toSGhqK\nr68vEydOZP369Tx48ICRI0dy584dDAwMeP/991m5cuUzbRYReVeRyMQqNSIiIk/BxsaGyZMnM23a\ntHrbaGlpsWHDhucu0tFQsrKysLW15c8//yQrK4ucnBzU1NTo2bMnrq6uSCQSQkND8ff3Jzc3t15H\nxovQu3dvjIyMCAoKemZbmUxGYmIiCgoKtXRfXyY3btzg4cOHoqNbRERE5AliYmL466+/GDRokBBR\nVRfl5eUcOHCAwYMHv0LrGp+YmBjatGnzus14q0lNTeXevXt06tTpnYh0FXmziIiIoEOHDq/l2DKZ\njOzsbCHK+969e8I2ExMTXF1dsbe3x8jI6I393y8tLSU8PJyIiAikUikmJib06NFDiNqVSqVcv36d\n+Ph4EhISKP+fFrO+vj6enp44OTlhYGDwOk/hlVNeXk5ubi55eXncv3+fBw8e1KkHDdUOVU1NTXR1\ndTEwMMDAwAA9Pb0XlnDx8/PDzc2N9evXP9d+8fHxmJiYYGRkRHFxMYmJibRp04bbt29TUlJCWVkZ\nxsbGct/lxYsXadGiBerq6kilUvbv38/777/PkSNHhEyt33//nR49eqCqqvpC5yMiIvLmI0Zyi4i8\nZYwcOZIdO3YA1elYNQVmBg4cyPjx4xu9QEpsbOwzNfzqm3muobi4mKVLl7J3715u3ryJlpYWLVq0\n4PPPP38uh0LNg2xCQgJ//fUXBw4c4OzZs/Tp06fBfTwvzzq3J9u6urpy9+5dQb7kVUSb10R0JyYm\n4uLi8tKPJyIiIvI2IJPJiIqKQl1d/Zmp6CoqKjRt2pS0tLRG08x9HdQU+XpTHVRvA/Hx8fTr108c\nQ5GXgqWlJTdu3BCe3V4lNVGmJiYmdO7cmZKSEjIyMkhJSeHKlSsEBwcTHByMmpoaTk5OtGjRAhsb\nm1cmxfc0ysrKiIqKIiwsjMrKSoyMjHjvvfewsbFBKpWSmZlJfHw8iYmJggPXwMBAcGzr6+u/5jNo\nPKRSKYWFheTk5JCfn8+DBw949OhRvQUelZSU0NHRQV9fH2trawwNDV+ZPnuN1Mbz4urqSkREBDo6\nOmhoaGBgYCD8bqKiovD09CQmJoaOHTsK+7i4uHD58mVat26NgoKC8JFKpVRUVKCsrIyhoSHp6eni\n+5KIyDuM6OQWEXnLkEgkBAQEsHPnTqqqqsjJyeHkyZMsWLCAnTt3cvLkyUZ9cGmMaIeJEycSERHB\n+vXrcXV1JT8/n6ioKO7fv//cfUkkEtzc3HB0dCQ8PJyzZ8+yZcuWWsVQXicmJibo6+sTFRWFs7Pz\nK4kYsbS05Pr166KjW0REROR/3Lhxg/z8fLp06dKgKDRfX1/27NnzVju5DQwMyMvLw9DQ8HWb8lYi\nlUqRyWSNHjAgIlKDpaUlkZGRr8XJ/STq6uq4urri6uqKTCbj9u3bXLlyhYSEBC5cuMCFCxeQSCSY\nm5vj4uKCvb39K4+CrqioIDo6mtOnT1NRUYG+vj49evTAxsaGq1evcvDgQZKTk6msrASgadOmtGrV\nCicnJ3R1ddm2bRvdu3cXpJy2bdvG5MmT611+XVRWVpKXl0deXh75+fk8fPhQkFepC3V1dfT09NDX\n1xfkuN7E69bzBAs9uZ+3tzdRUVF06tQJW1tbIiMjhe/34sWL2NjYkJGRIRQIVlZWRiqVUllZiZKS\nEq1btyY8PBw9PT3Wrl3L1KlTsbCwIDMzU3xXEhF5h3nzroQiIiJPRSaTCRFnAKampri7uxMYGIin\npyfffvstCxcuBGDXrl2sW7eO1NRU1NXV8fHxYe3atZiZmSGVSrGysmL27Nl8/vnnQv9XrlzB0dGR\n8+fP07JlS6ytrZk8eTLTp08HID09nbFjx3Lu3DmsrKwapPl1+PBhVq1aRc+ePQFITk7mxIEDQLW2\nWE2V61WrVrFx40Zu3LiBkZERw4YNkyv0kZWVxezZs4mIiMDa2pp169bxxRdfcPz4cY4cOQLA6dOn\n6dWrFzKZjMGDB3P9+nWOHz+OgoICkyZNIiwsjLy8PGxtbZkxY4ag5QbVEeefffYZ+/fvR1NTky++\n+EIY8xrKy8v5+uuv+fnnn8nPz8fFxYWlS5cSGBgod86qqqp06tSJy5cvk5ub+0oK2jRr1kx0dIuI\niDQaNVJRsbGxeHp6Nnr/T95fGpvo6GgkEgmtW7duUHsFBQUcHR2Jjo7G29v7pdj0sjEzMyM1NVV0\ncr8gly9fFhwmIiIvCxMTE27duoWZmdkrzRh4PBsUqifF2rVrx8qVK2nRogXm5uaYm5vj5+dHYWEh\nGRkZJCUlkZGRwc2bNzl+/DgaGhq4uLjg4OCAtbW1nGM1NDSUlStXcu7cOYqKirCysiIgIIDp06c3\nSNf4cSorK4mNjeXUqVOUl5ejo6NDnz59UFJS4uLFi/z6669CgIuJiYng2NbW1pbrZ/DgwfTu3ftv\njNqLU1hYSG5urhBtXVBQUG9QjoKCAjo6Oujp6WFhYUHLli3R0NB4YZmQN4VTp0698L7Kysq4ublx\n4cIFPD098fLyIiYmhg4dOqCnp4eioiLZ2dlYW1sLRYKdnJxITk7Gzc0Na2troqOjcXR0FCRPXFxc\nuHDhAlKp9K0fWxERkboRndwiIu8ILi4uvPfee+zfv19wcldUVLBkipCCqgAAIABJREFUyRIcHR3J\nyclh9uzZfPzxx5w+fRoFBQU++eQTdu/eLefk3r17N87OzrRs2RKQn4GXSqX0798fAwMDoqKiKCoq\n4osvvqCsrOyptpmYmHD06FEGDhxIZGQkI/r355v/VTUfERbG9oMHCQ0NZePGjaxZswYfHx9yc3M5\nf/68XD9fffUVK1euZOPGjSxZsoTBgwdz7do1PvzwQwB27NhBVFQUly5d4siRI2hpaXH69Gm0tLS4\nffs2Xl5ezJkzBx0dHU6cOMGECRNo1qwZ/v7+AMyYMYPg4GAOHDiAmZkZixYt4syZM4KOG8CoUaO4\nevUqe/bswcLCgiNHjvD+++8TExODu7u7nL0SiQQPDw9u375NVFQUbdq0ER7CXhbNmjXj2rVrJCUl\nvVJdcBERkbeT8+fP4+XlRYcOHQgLC3ulx37RCK+GUFxcTHJyMnZ2dujo6DyzvampKZ9//jlfffUV\nu3fvxsvLi+XLlzN//nz27t0rdx8YOnQo169f58yZMy/F9r+DqqrqM+/JIvWTlpYm912LiLwMNDU1\n2b59Oz169Hil9VQezwYFuHXrFjNnzqR///4kJSXJtdXS0sLDwwMPDw+kUik3b94UorxjYmKIiYlB\nQUGBZs2a4eLiwtmzZ5k5cybDhg1j37592NjYcPPmTX7++WeWLl3KTz/91CAbq6qquHjxIsHBwZSW\nlqKpqYmnpyf5+fkcPHgQqVQKgLm5Oa1atcLR0RFNTc16+1NTU3uuopoymYyqqqo6o6KlUin5+fnk\n5uYK2tbFxcX19qWqqoquri5NmjQRtMDfBOmXt4maaPWrV69iY2ODtbU1aWlpODg4EBYWhoeHB/Hx\n8cJ7q5aWFkVFRYJsl5qaGhYWFigpKREWFkbLli3R1dXl+vXrWFtbv96TExEReSmITm4RkXcIJycn\ngoODheVRo0YJf1tbW/P999/j7OzM7du3MTMzY+jQofznP/8hMzMTW1tbAH7++WfGjBlTZ//BwcEk\nJyeTlZWFhYUFAGvXrqVz585PtWvTpk0MGTIEQ0NDNNXUaF1SgjnQDaCkhO+/+Yb/Rkaybt06IbLa\nxsamVvGsadOm0atXLwCWL1/Ojh07uHTpEh06dBCqo3fs2JHPPvsMLS0thgwZQn5+PlpaWpiZmclF\nC44bN46QkBD27NmDv78/hYWFbN26laCgIAICAgAICgoSzhMgIyODX375haysLCHNdNKkSZw4cYIf\nf/yRDRs21Hn+ZmZm6OvrExERgZubG3p6ek8dr7+LlZWV6OgWERFpEJs3b6ZNmzZERUWRkpKCo6Pj\n3+6zJlX4dVITqdW2bdsGtff39yc0NJSvvvqKdu3acfLkSU6dOkWzZs0IDQ2Vc3yeOnWK8ePHvyzT\nG0R5ebnoLGlkKisrkUgkYnSfyEtHT0+PqqoqTp06hYuLyyuL5pbJZKiqqgrZoE2bNmXq1Kn06dOH\nsrIyoRhffHw8//rXv4iIiEBdXZ0+ffqwbt06unXrRrdu3SgoKCA9PZ3ExESysrK4dOkS69evp0OH\nDnz88cdYWVlhbm5Os2bN6NChAw8fPhRsOHDgAAsWLCAtLY2mTZsyceJE5s6di1QqZf78+fz00098\n9tlnqKiokJ+fz6JFi+jatSudO3fG0tKSvXv3YmxszIIFCwDYunUrCxcuJC8vj8DAQAIDA5k0aZLg\nDK+RI7l16xZFRUVkZ2cjlUoJDw+nqKiImJgYKioqWLt2LSUlJZSXl6Ovr4+5uXmt8ZNIJGhpaaGv\nr4+RkRFOTk7o6OiI14yXjI2NDefPn0dfXx8zMzNiY2MpLCzEzc2NzMxMpFIpRUVFwmSHnZ0dGRkZ\nNG/enI4dOxIZGYmXlxdRUVHcuXMHLS0t7t27Jzq5RUTeUcQrsojIO8STxabOnz9P3759sba2RkdH\nR3AaX79+HQA3Nzfc3NzYvXs3AOfOnSMzM5MhQ4bU2X9ycjLm5uZyjl9vb+9nPtx17tyZzMxMQkJC\nMDc2JhsIBCb+b3v+w4eUlZXRtWvXp/bzeKS0qakpgFxleIDPP/8cT09PNm7cyKNHjwgKCuLmzZtU\nVVWxbNky3N3dMTQ0RFtbmwMHDnDjxg2g2oFdXl5O+/bthb40NTVxc3MTls+fP49MJsPZ2RltbW3h\n89dff5GZmflU29XV1enUqRNXr14lPT39qW0bAysrKzQ1NWtF5oiIiIjUUFJSwp49e1i0aBH+/v5s\n2bKlznapqal06tQJdXV1nJycOHHihLAtNDQUBQUFjh49ire3N6qqqvz3v/8lMzOTvn37YmpqipaW\nFq1btxZkpZ60YcKECejq6mJpaVlLAmv16tV4eHigpaWFhYUF48aNk3OYbNu2DW1tbY4dO4ajoyNq\namqYGRnx+YgRnDlzhp49e6Knp8fIkSOfGuHs6+tLREQElZWV2NnZkZ2dTWRkJHPmzJFLt75y5Qp3\n7tzB39+fM2fOoKKiQnZ2tlxfX331FR4eHnL2hYSE4OrqipaWFv7+/mRlZcntc/jwYVq3bo26ujq2\ntrbMmzdPKJ4G1RPVixYtYvTo0ejr6zNs2LB6z0VJSUnQpxVpODExMa80qlbkn4uKigodOnQgPz+f\nK1euvNJjPy7B9+jRI3799Vfc3d0FB3dRURHdu3dHR0eHmJgYDh48SEREBKNHjxb209HRwdPTk2HD\nhjFnzhw0NDSQSqX4+fkRGRnJ9u3b+fe//83u3bu5cOGC8J4QFxfHhx9+SFVxMT5eXnzyySf8+9//\nZu7cuaxZs4YHDx6Qk5NDYWEh5eXlXLx4ES0tLUpKSpg1axajRo0iISGBbt26UVZWxrFjxxg3bhyD\nBg1i165dNG/enDlz5gDwww8/sHLlSg4dOkR5eTlr1qxh06ZNREZGUllZSXBwMJGRkdy+fVsYk6ZN\nm+Lq6kqXLl0YMGBArc8HH3xAYGAgbdq0oXnz5ujp6YkO7ldEq1atiI+Pp7KyEk9PTy5cuICOjg4y\nmQxbW1suX74stDUyMiI3NxeoluQpKioS3oGjoqLQ09OjpKTkhQpiioiIvPmIV2URkXeIpKQkQUuy\n5iFVS0uLXbt2ERsby7Fjx4DqCLAahg4dKji5d+/eLURKNDZKSkp06tSJVf/3f+Soq9Mf2AR8oaiI\ntrExAA8ePHhqH8rKysLfj0uoPM77779PREQE+vr6TJ06lR49enDu3DnGjh3L6tWrmT17NiEhIVy6\ndIl+/frJjUVdPP4AJJVKkUgkxMbGcunSJeGTkpLC1q1bnzkGEomEVq1aoaqqyrlz5156sUwrKys0\nNDRER7eIiEid7Nu3D11dXd577z3Gjx/Pjh076nSOzpo1i6lTp3Lp0iUCAgLo27cvt2/flmvz5Zdf\nsnz5clJTU/H29qawsJBevXoRHBzM5cuXBQdBamqqsI9MJmPNmjV4eHhw4cIFZs+ezaxZs4iKihLa\nKCoqsm7dOpKSkvj555+Jjo5m8uTJcscuKytj9erVTJ48GQ2gJDeX8owMwkJDmT59Or///juHDh3i\nhx9+qHcs/Pz8KCkpITIyEgBtbW20tLQYNmwY6enp5OTkANVR3Orq6rRr144uXbpgZ2cnp3ErlUrZ\nsWMHY8eOlbNvxYoVbNu2jcjISB48eMDEiROF7cePH2fo0KFMmTKFpKQktm7dyr59+5g7d66cjatX\nr8bZ2Zm4uDi5ehVPYmJiwt27d+vdLlI3N2/ebJRMBhGRhuDt7Y2ysjKnTp16pc62Y8eOCUEaurq6\nnDlzRngPgOqMzuLiYnbu3ImLiwtdunRh06ZNHDhwoM6ADiUlJfLz89HV1WXRokVMmTKFHj16YG5u\nTkZGBocOHWL16tWsW7eOMWPGoAjMzszkk/Bwgtauxd7eng0bNlBYWIiRkRGampo8ePCANm3a8ODB\nAz766CPi4uLYtWsX8+bN4+bNm1y4cIEVK1Ywc+ZMbG1t0dbW5vLly2hpadG8eXOgWg9bXV0dQ0ND\nFBUV8fHxoWfPnrRp0wZlZWUmTZrE7Nmz6dOnDyoqKkydOpUxY8YwYMAAQfpC5M2hphDluXPnkEgk\nuLi4kJCQgLu7O4mJiRgaGsrd94yNjYVlPT09SktLsbS0JCEhgaZNm1JZWSk4wkVERN4tRCe3iMhb\nSF1pjQkJCRw/fpyBAwcCkJKSQl5eHsuXL6dTp044ODjUijYD+Pjjj0lPT+fcuXP89ttvDB06tN7j\nOjk5cevWLW7evCmsi46OruVofhrdu3dn+8GD3HJ3RwbMXb6cAQMGoKioyMKFC9m3b5/gTHgRlixZ\nwsSJE+natSuZmZl4e3szYMAAbt68iaurK7a2tri7u2NjYyPnbLGzs0NZWVlwcED1REFCQoKw3KpV\nK2QyGXfu3MHW1lbuUxNZ3hAsLS1xdXUlPDycgoKCFz7XhmBtbY2GhgbJyckv9TgiIiJvH1u2bBGi\n8/r164dEIuGPP/6o1e6zzz5j4MCBODg4sG7dOiwtLWs5jBcuXEi3bt2wtrbG0NAQd3d3xo8fj4uL\nC7a2tsydOxdPT0/27dsnt1/37t357LPPsLW15fPPP6d58+acPHlS2P7FF1/g6+tLs2bN6NKlC998\n8w2//fabXB+VlZVs2LCBkD/+YE1ZGWOBVGC1VMp/9+/H19eXvn37yvX7JM2bN8fS0lKI2r5w4QLO\nzs6UlZXRunVrYf2pU6fo2LGjMOk6duxYgoKChH6OHz9OTk6O3L20xj4vLy/c3NyYMWMGoaGhwvZl\ny5Yxa9YsRowYgY2NDb6+vqxYsYKNGzfK2ejr68uMGTOwtbV9anHEpk2b1nm/F6mf8vLyl14zQ0Tk\ncdTU1GjXrh3Z2dnPzAZsTHx8fIQgjejoaLp27UpgYKDwbJ+cnIyHh4ecznX79u1RUFCoN2hCJpMJ\njnp9fX28vb0ZNWoUc+bM4ZNPPsHLy4uysjKuJCfTUyplBDAC+LasjId37lBQUCBk2lhbW3PhwgXC\nw8NJTExET08PNTU1YmNjhaK6np6eeHt7U1ZWhr+/P5988gnjxo1j6tSpgpTUzJkzmTRpEh06dEBR\nURFfX1/atGmDubk5EokEQ0ND1NTUXmnhz3eRhQsXymW9vkxUVVVxcHAgISGBJk2aIJFIuH//viC5\nmZ6eLvwfWltbCxlTnTp1Iioqio4dOyKVSlm7di0zZ87k2rVrr8Tuupg2bRr/+te/XtvxRUTeZUQn\nt4jIW0hpaSnZ2dncvn2bS5cusXr1avz8/PDy8mLGjBlAdQFCVVVVvvvuOzIzMzly5Ahff/11rb4s\nLCzw8fFhwoQJFBQUCEUc6yIgIABHR0eGDx/OpUuXiIyM5F//+tcztVd9fX3ZtGkTcXFxZGVlUVVV\nxYOyMpydnZk5cyZjxoxh4sSJnDp1il27drF06VKWLl3KN99880Ljs3TpUiZMmEC3bt2E9DUPDw8y\nMzNJTU3lP//5D8OGDSMrK0t4GNLS0mLMmDHMnj2b4OBgEhMTGT16tJwD38HBgSFDhjBy5Ej2799P\nZmYmsbGxrFy5koMHDz6XjZqamnTu3JkrV65w9erVFzrPhmJtbY26urro6BYRERFIT08nPDxcqN2g\npKTEiBEj6pQseVzGSSKR0LZt21rODi8vL7nloqIiZs2ahYuLC02aNEFbW5vY2FhBIqqmrycL9pqZ\nmclNdIaEhBAQEIClpSU6OjoMGDCAiooKuYgtVVVV7O3theWmgAmg9Vi/TZs2rSVv9SR+fn5yzuyP\nPvqIEydO4OvrK6wPDQ3Fz89P2Gf48OFkZmYK0edbt26lf//+6Ovr12ufqakp5eXlQvZSXFwcS5cu\nlZPBGjJkCMXFxYKzWiKR1Brj+lBUVHyuyWcRCA8Pb/D4iog0Fu3atUNRUVFOEullUyOJZGtri5eX\nF5s3b6agoECuMGR9keX1OYRbtGhBQUEBd+7ckVuvrKyMvb09vXr1YsaMGSgpKVFfzHpN3zXOyZs3\nb9KkSRO0tLSwtrYmPT2dK1euYGlpSVpaGtHR0Tx69IgrV67w22+/sWPHDjZt2kRISAhQXQ+oJnum\nsrKSI0eO8N///pfk5GSqqqqIiooiLi6OGzduIJVKSU9P59q1a9y6dYt79+5RWFj4nCP7bjBy5EgU\nFBTkspFqmD17NgoKCrz//vvCupkzZ77SIsxGRkaoqKhw69YtXFxcSEpKwtjYmLy8PGxtbQX5H4lE\ngra2NgUFBWhoaFBZWYm9vT3Kysp89913cu94CxcuREFBgW7dutU63g8//ICCgkKjO/JnzZrFli1b\n5ALH6mLkyJFy4y0iIvJsxMKTIiJvGRKJhODgYExNTVFUVERPTw83NzcWLVrE+PHjBYezkZER27dv\nZ+7cuWzYsAEPDw/WrFlDjx49avU5dOhQxowZwwcffICuru5Tj33w4EHGjRtH27ZtsbKyYuXKlXzy\nySdPtfm9995j586dfPXVVxQWFmJiYkJgYCDz588XHmrXr1+PhYUFGzdu5MiRI2hqauLh4YG5uTnN\nmzdvUKTF422WLVuGTCaja9euhISEMG/ePK5evcqUKVNQV1fHz8+Ptm3bUlJSIuyzcuVKioqK6N+/\nP5qamkyePLlW1fSgoCAh6q7mAbxt27bP1BOvz14vLy+ysrKIiYmhdevWL03bz9ramqtXrzZaYTkR\nEZG3m82bN1NVVSVEQMH/d2zcvHlTrvbCkzxZ/wGQi/oDmDFjBsePH2fVqlXY29ujrq7O8OHDa0lE\nPS5DBdXXxZoXz2vXrtGrVy8mTJjA0qVLMTAwIC4ujo8//liun5r73vjp0xkRFoZvSQllwAwVFXb9\nr+Dw4/3Wh6+vL59++ikPHjwgOjqaoKAgrly5gpOTE8uWLSMlJYV79+7h7+8v7GNkZESfPn3YsmUL\n9vb2HD58mD///FOu3ycngp+U25LJZCxcuLDOSWZDQ0Ph7yfHWKTxyM3NlZu8EBF5FWhoaODt7U1k\nZCTXrl3DysrqpR+zvufpmuddZ2dngoKCKCwsREureqowIiICqVSKk5NTnfsOHDiQL7/8khUrVrBu\n3bpa2y9fvkxkZCQGTZvy17VrbP/fvWaWmhpenp48ionB2tqa3NxcrK2t+fPPP7l8+TI2Njaoqqri\n7u7O+fPnuXv3LmPGjKFDhw5UVlYSEhLCgwcPaNasGWVlZVRUVAh1h+7fv09VVRU5OTlIpVJiY2OB\n6hoTVVVVHD9+HKjO2qmsrJSTbIFqiYvH74NKSkqoqKigqqoqfNTU1FBVVUVdXV34qKmpvdU63RKJ\nBEtLS3777TfWr1+PhoYGUJ2RtGPHDpo1ayb3P6SpqfnK700tWrTg3Llz6Onp4enpyfnz5wWdboCK\nigqUlZVxdHQkLi4Ob29vLC0tSUlJQSqVUllZiYuLC0pKShQVFSGRSDAxMSE8PLzW73DLli21zrkx\nMDExoUuXLmzevJmFCxfW204ikYjZBiIiz4no5BYRecsICgqSS41+GoMGDWLQoEFy6+rSgR41apQQ\nzfckT0YZ29vby6VZQ3Xhmqfx5Zdf8uWXXz61jUQiYfbs2cyePRuofjg9c+YMly5dIiMjgy1btmBg\nYCC3z+MOC19f31rntnz5cjnd0v3798ttLy4u5tixYxw+fJgePXqgoaHB9u3b2b59e712KikpsWDB\nAqGqe2NgbW2NgYEBYWFheHp6Ci8VjY2NjY3o6BYREaGyspLt27ezYsUKevfuLayXyWQMGzaMoKAg\nucyfyMhIfH19hTbR0dG17i1PEh4ezogRI+jfvz9QnYGUnp5OixYtGmxnbGwsFRUVrFmzRnjJO3To\nUL3ta+SwZkyezKNr1/hw0KDnqjHh5+dHWVkZq1atwsjICFtbW6ytrdm2bRsZGRns3r0bLS0toYBV\nDePGjWPgwIHY2NhgampaZzTY0/D09CQ5OVluwuHvoq6uTklJCerq6o3W57tKYWEhKioqr9sMkX8o\nHTp04Ny5c4SGhjJixIiXfryabFCZTMb9+/f5v//7P0pKSoRo0SFDhrBgwQKGDx/O4sWLyc/PZ8KE\nCQwYMKDea5SFhQVr1qzh888/5+HDh4waNUqQHVm/fj0PHz6kX79+zJw5k8mTJ/ONjQ0WJiaM7NSJ\nH374gX//+9+MGzcOqC5G/Ouvv5KQkMC0adPQ1NTE0NCQpKQkZDIZFRUVnDt3jiZNmvD+++/z5Zdf\nkpqayscff0xUVBQZGRnCOwVUF/89ceIE8+bNo7KykqCgIE6ePMmnn35KRUUFe/bsITg4mEGDBlFR\nUSF8DAwMhAycGsdocXExJSUllJaWUlJSQllZGYWFhZSWllJeXi442v8ujzvTVVRUUFNTEz6PO9Nf\n1nXL3d2d27dv89tvvzFy5EgAjhw5grq6Ol26dCEvL09ou3DhQvbv3098fDxQHXmcl5dHt27d+M9/\n/kNxcTH9+vVjw4YNwv2oqKiITz/9lIMHD6Ktrc20adMIDQ3FyMiIoKAgFi9ezN69e4U+a+jYsSNt\n2rRh7dq1fP/996SlpREWFoaenh45OTns2rWLEydOsGvXLry8vFBUVERJSYmysjLatm3L/v37OX/+\nPC1atODGjRsYGBiQkZGBTCbDwMCADh06EBQUJDidL1++TGpqKhMmTBAmRQAyMjKYNm2akE3QokUL\nFi9eTK9evYDqiZTWrVvz/fffM3z4cKBaC79v376cOXOGtm3bAtC3b19Wrlz5VCf341JANctLly5l\n06ZN5OTk4ODgwNKlS+nTp4/QJj4+nn/9619ERESgrq5Onz59WLduHTo6Os/zbyAi8tYiOrlFRF4x\nNjY2TJ48mWnTpj2z7bZt25g8efIznciNxb59+xg0aNDfSnNuLJv79++Pm5sbS5cuJTw8nLi4OLZt\n24a5uTn+/v7Y2Nj87ZltDQ0NPvjgA+7cucPevXtp1qwZHTt2/Ft9vija2tp06tSJuLg4jI2Nadas\n2Us5jujoFhEROXLkCHl5eYwbN05OVgNg8ODBbNy4Uc7JvXHjRhwcHHB1deX777/nxo0bfPrpp089\nhoODAwcOHKBPnz4oKSmxaNEiysrKnllg7fEXOnt7e6RSKWvWrKF///5ERUXVGSX4ON27dyd+/Hj+\n7//+Dw8PDw4dOtTg66mVlRU2NjasX7+evn37AqCgoICjoyNOTk6sX7+eLl261IrSCwgIwMDAgMWL\nFzNnzpwGHetx5s+fT+/evbGysuLDDz9ESUmJhIQEYmJiXli2y9zcnFu3bglF2ETqJywsjHbt2r1u\nM0T+oWhpadGqVSvi4uK4desW5ubmL+1Yj2eDQvWzp5OTE3v37qVLly5A9QTZ8ePHmTp1Kt7e3qip\nqdGvX79nXns//fRTWrRowapVq+jfvz+FhYXo6OhgbW3N559/zqBBg9DU1MTU1JQFCxZwOjaW1Js3\nmTNnDpMmTRL6UVdXp2vXruzdu5dZs2ZhYGCAVCpl3759SCQSevfuzY0bN7h79y4lJSX07t2b7777\njm+++QZ7e3u6du3KgQMHBBmLmswjRUVFFBUVBR3upk2bAtXZMgoKCvVGqUP1fUBFRQUVFRX09PT+\n7tfwVKRSKcXFxYIjvbS0lNLSUh49ekRubq7gTC8vL/9bBexlMhnKysooKyvLRaffv3+foqIiPvjg\nA3788UcGDRqEmpoaW7duZfTo0WRkZDyz77Nnz2JmZsZff/3F7du3+fjjj3FwcBACnqZPn86ZM2f4\n/fffMTU1ZcmSJYSFhfHBBx8AMGbMGJYsWUJMTIwwqZyamkpkZKRQq0JBQQF9fX1iYmJo27YtERER\n6OnpCVHcBQUF6OjoCJImrVq1Aqon7T09PVm9aBFNmjSh37Bhgt2jR49m0qRJgtN5y5YtfPTRR2hr\na8udX1FREb169WL58uWoq6vzyy+/8MEHH3D58mVatGhBixYtWLNmDZMnT6Zz585oaWkxcuRI5s2b\nJzi4Adq0aUNaWhp3797FxMSkQd/b2rVrWblyJT/++CNeXl7s3LmTDz74gLi4ODw8PCgqKqJ79+60\na9eOmJgY4Vlv9OjRtWqiiIi8q4hObhGRRmLkyJHs2LEDqNbDNDY2pmvXrqxYsUKuKGFsbKyQ+vU2\nERoaKpeiXRcNjTBvCDXpWbq6uvTs2ZMuXboQERFBdHQ0O3fuxMTEBD8/P+zt7f+2s9vU1JSPP/6Y\n+Ph4du/ejbe3t5x+6qtCQUGBNm3akJGRwfnz52nVqtVLSVGzsbEhMzNTdHSLiPxD2bp1K/7+/rUc\n3FCddj5nzhyCg4MFqagVK1awevVqzp8/j7W1NQcPHsTMzEzYp67r1OrVqxkzZgydO3emSZMmTJ06\nlbKysmde0x5PzXV3d2fdunV88803zJs3j44dO7Jy5UoGDx5ca58nlxUUFBgwYAA7duzg999/r9fO\nJ/Hz8yMoKEiIXIfqSEsrKysSEhLqvQ+OHDmSRYsW1ZkVVddxH18XGBjIkSNHWLJkCStXrkRJSYkW\nLVoIEXQvgp6eHunp6S+8/z+JR48eYWxs/LrNEPkH07lzZ86fP09oaChDhgx5acdpaDaoq6srwcHB\nz92/k5MTgwYNwtvbG4lEgqenJz4+PnJOwv79+wsZPvWxZ88e9uzZIywrKCjUWSSwrKyMe/fukZ2d\nzd27d7l+/Tq7du1CT0+PvXv3CvsuW7aMP/74A1NTU/z8/OQK844cOfJvXWsbGwUFBbS0tF5aVmcN\n9UWnS6VSKioq8PDwYMmSJezatQtFRUWOHj1Kr169OHnyJI8ePRKyY5OTkykoKGD//v1IJBKuXbuG\nsrIygYGBHD58mMrKSmxtbfnpp5+oqqqisrKSzZs3M2TIEJKTk8nIyMDX15fDhw9z7do1jhw5grKy\nMl5eXixbtow5c+YIOtqurq6oq6tz7do1ioqKqKioQFdXl0uXLuHm5sbmzZtRU1NDKpUSHx9Px44d\nUVVVpaKigqqqKqytrcnOzibixAnWVlRAZiYz4+Pp9sEHSCQYdGn/AAAgAElEQVQS3nvvPSoqKggO\nDqZz587s3r2bQ4cO8d///ldu7Nzd3eXqicydO5fDhw+zb98+vvrqK6A6w+vo0aN88sknNGnSBHt7\ne+bNmyfXT80EfFpaWoOd3CtXrmTmzJnCc9CiRYs4c+YMK1euZOfOnfz8888UFxezc+dOQUZm06ZN\n+Pn5kZmZ2agZYyIibyqik1tEpJGQSCQEBASwc+dOKisrSUxMZMyYMQwfPpwTJ04I7Z6U3HgdlJeX\nP3eKW8eOHYVCXzKZjLlz55KamsqBAweENjo6Ovzyyy+NamsNWlpaBAYG0qlTJyIjIzl37hx79uzB\n0NAQf39/HB0dazkSKisrn1kU83Hc3NxwcXEhNDSU8+fPExAQQJMmTRr7VJ6JnZ0dDx8+5OzZs3h5\neb2USRFbW1uhEOfzyAeIiIi8/fzxxx/1brO1tZWLDqv5++OPP66zfV1SUVD98vb4vQ+olcFUV9Hd\nJwuwTZ48mcmTJ8ute1y7ui4HxfTp05n+Py1ub29voqOj+eijj1ixYkWd5/A4mzdvZvPmzbXWr1ix\ngunTp+Pj41Pnfnfu3KFbt261osbrsq+uMQsICCAgIKBeu563QLGo4dkw8vPzRa1zkdeOrq4u7u7u\nXLp0iezs7Ldu0iU7O5sTJ04IUiEtW7bEz8/vpcsjqKqq8ssvvxAQEICjoyM3b97k4sWLzJ8/n/79\n+5Odnc3Nmze5ffs2Fy9e5OLFi3L7GhsbY2lpiYmJCcbGxhgYGLzVetrPQ33R6TXvPb1792bAgAFk\nZWWhq6tL165dGT9+PBEREeTl5TFgwACgWhojKSlJWD506BBqamoMGDCA0NBQSkpKSE5OJjIyEktL\nSzIyMpBKpVhZWVFWVkZRURFVVVUYGhqSn58vaKdbW1vz+++/4+bmhoKCAvv27cPX11fQTr9y5Yog\nbVNDZmYmubm5HD58GAUFBc6ePYuysjKKioqEh4dTVVWFTCZjSkUFgjBQWRlLzp5FXU8PBQUFRowY\nwdatW8nLy6Np06Z06NChlpO7qKiIRYsWceTIEe7cuUNFRQWlpaV4eHjItdu8eTMODg6Ul5cTHx9f\n675c8/t4+PBhg76zmuKuT2Ydd+zYkaNHjwLVkw4eHh5y97X27dujoKBAUlKS6OQW+UcgOrlFRBoJ\nmUyGqqqqkP5mZmbGhx9+yKZNm+TaWVtbM3nyZOEF/OHDh3z55Zf88ccf3L9/HxsbGxYuXCindxoS\nEsKUKVPIysrC29ubrVu3Ym1tDTxbF6zmmKNGjeLatWscPHiQwMBAfv31V3bs2MHXX39Nbm4u/v7+\nvPfee/Wen7KysnBuUJ1O+OS6x/m7Nj9JeXk5X3/9NT///DP5+fk4OTkxePBgCgoK+O2338jNzWXD\nhg0cPnyYRYsWcenSJQ4ePEjPnj3r7bMuFBQU8Pf3p7S0lKNHj6KoqMh77733yvU6dXV16dixI7Gx\nsZibmz+1CNyLIjq6RURE3nW6devGlStX+Ouvv7CxsXnhVHNnZ2f27NlTa/L04cOHJCUlsXPnTiFy\n8E2iriKhIv+f8PBwQaZBROR10qVLFy5dusTp06efWfPgTSEnJ4cTJ06QlpYGVAeL+Pv7v3RJj8eJ\ni4tj1apVPHz4EFtbW1asWMGUKVMA5J5tKyoqyMnJkYv6vn37tlCoEqonB3V0dLCwsMDc3Fxwgj9Z\nIPmfwujRoxk+fDja2tosWbKkwfvV3CNrMqLOnTtHYmIiw4cP59KlSyxZsoTRo0cL74UAR48epXnz\n5sybN4+KigpKSkoIDQ3F1NQUDQ0NqqqqWLx4sRCZHRcXR15eHv7+/lRUVHDt2jUMDQ1RUlLC2tqa\n/Px8pFIpysrKVFZWUlJSIhSsLn3C3sdlOkeNGoWbmxtZWVn11qtqaGHt+Ph4CgoKkEgk3Lx5s1Zh\n2YKCAoBG+b08PjlTnyyc+Cwg8k9BdHKLiDQij99UMjMzOXbsWK0CVY+nYstkMnr27MnDhw/Ztm0b\nLVq04MqVK0KFc6hOxVuxYgXbtm1DVVWVESNGMHHiRI4dOwY8WxeshtWrV/P1118zb948ZDIZ586d\nY9SoUSxdupQPP/yQkJAQZsyYgUwmY0BgIOOnT6d79+4vNA6NZfPjjBo1iqtXr7Jnzx4sLCw4cuQI\n06ZNIzw8nLKyMrZv345MJmPcuHHMnz+fgIAAdHV1X8h+ADU1NSEK5MCBA5iZmdGpU6dXGuGhqKhI\n27ZtSUtL4+LFi3h4eDT6A4qtrS0ZGRlcuXIFBweHRu1bRERE5HWjrKzMwIED2bJlC/v372f06NEv\nfB318fHh+PHjchOyffv2JSYmhrFjx9KjR4/GMrtR0NfX58GDB3XK0ohUU1pa+reeFUREGosmTZrg\n7OxMUlISeXl5b0TmZ33k5eURHBxMSkoKAC4uLvj7+7+W7MeGZpAqKytjZmYmJ7Ulk8koLCwkOzub\n7Oxsbt26xa1bt0hMTCQxMRGA5s2bY2pqSvPmzbGwsPhHRHrXvM927doVVVVV8vLy6NevX4P3f9o9\n1s7ODmVlZaKjowUnd3FxMQkJCdjb28tpp48ePZr9+/ejq6vLwIEDBV1tqJ54PnHiBJ07dwaqs3c3\nbdpEkyZNsLGxISAggLt376KsrIyrqyt37tyhrKyMtWvX8kNREc6VlQDMUFHBy92dmzdvAtXfd9u2\nbYmMjBSkzp6kIYW1Hzx4wLBhw5g5cybFxcUMGzaMS5cuyUn31EjwNLR2ho6ODmZmZoSFheHn5yes\nDwsLw9nZGaiWDAoKCqKwsFCQvImIiEAqlT5Vd15E5F1CdHKLiDQix44dQ1tbm6qqKkpLS+nVqxfb\nt2+vt31wcDBRUVEkJSUJN8YnZ3krKyvZsGGDoBE9Y8YMRo8eLWxviC4YVM+mz5gxQ1j++uuv6dat\nm1AkKyMjg8riYiRAnxMnGBEWxvaDB1/I0d1YNteQkZHBL7/8QlZWFpaWlgBMmjSJEydOEBQUxIYN\nGygpKWHz5s0EBASQnZ3N77//jo+PD3p6es8lWfIkxsbGDB48mISEBPbs2UPr1q1fuY61vb099+/f\nJywsjDZt2qCmptao/dvZ2YmObhERkXcWc3NzOnXqxNmzZzl37twLFxk0MzMjIiJC7uUxNDS0ES1t\nXMzMzMjMzBSd3PVw+/btVxpxKiLyLHx8fEhKSuLMmTPP1K1+Hdy/f5+QkBASEhKA6kjpbt26YWho\n+JotezEkEgna2tpoa2vLORqrqqrIzc0lOzsbFxcX7t69S1paGjExMYIDWElJCUtLS+zt7V+6LMvr\n5PLlywDPFc3+tALTWlpajB49mtmzZ2NoaIiJiQlLly6tM+to7NixrFixAkVFxVryZ/7+/nz77bcE\nBQXRuXNnDhw4QHJyMkZGRjg5OXH37l3Ky8spLS2lrKwMU1NToqKiCAwMJD09nUMGBmRevcrAgADU\n1dXJysoS+j569Cjl5eX1ToA2pLD2xIkTMTY2ZvHixVRVVRESEsKkSZOE+l0A0dHR2NvbN1iPG2Dm\nzJnMnz8fe3t7PD092bVrF2FhYXz33XcADB06lIULFzJ8+HAWL15Mfn4+EyZMYMCAAaJUicg/BtHJ\nLSLSiPj4+LBp0yaKi4v56aefCAoKIjs7u97IhgsXLmBqavpUqQhVVVW5IoimpqaUl5fz4MED9PT0\nGqQLJpFI8PLykus3JSWFPn36CMubVq1icFUV26Bap6ykhE2rVr2Qk7sxbH6c8+fPI5PJhFnqGsrK\nyujatSvw/1PjFi5cSF5eHqdPn+bIkSOEhITQpUsXWrdu/bfSDV1dXXF2dub06dNcvHjxlT/U6+vr\n0759e2JiYrC2tpYrZtoYiI5uERGRdxkfHx9SUlI4ceIEzZs3f+Hrd2BgIMeOHWPgwIGNbGHjo6Gh\nQUlJyes2440lOjqawMDA122GiIhA06ZNsbe3Jz4+Hj8/vzdmEubBgwecOnVKcHja29vTrVu3eiUL\n33YUFRUxNjYWtNHNzc0xNzeXa1MTvXv69GnKysqE9bq6utjZ2WFtbf1WRn0/nnEM1CqA+eT2Zy3X\ntW7lypUUFRXRp08ftLW1mTp1Kvfu3asVxGNjY4OPjw83btyoVQ8jMDCQBQsW8NVXX1FcXMzQoUOZ\nNGkS+/fvp6CgAKlUio2NjZAN27ZtWwwNDenZsyezZs3ixIkTHDlyhKysLCorK6n8X2Q3VEtyqqur\n12v/swpr79y5kz///JPz588Lkek///wzbdq0oXfv3oIc0aFDh+qtd1JDjeRKDVOmTOHRo0fMmjWL\n7OxsHB0dOXDgAG5uboLtx48fZ+rUqXh7e6Ompka/fv1Yt27dU48jIvIuITq5RUQaEXV1dWGWdN26\ndcTHx/PFF1/UKljxPDwZhVxzA63RD2uoLtiLFFa6ffs2ly9fxtHR8bk0qRvL5hqkUikSiYTY2Nha\njurHH0Kg+uHSxsaGVq1aER8fT2hoKMePH+f06dN07NiRNm3aoKqq2uBzeRwFBQX8/PwoLS3l2LFj\nSCQSevTo8cr0upWUlGjfvj0pKSnk5OTg5ubWqPIldnZ2pKenk5aWJjdJISIiIvK2o6ioyMCBA/nx\nxx/Zt28f48ePfyEHhI6ODqqqqty5c6fRJxtFXi0VFRUvpbCziMjfwdfXl7S0NMLCwujdu/drtaWg\noIDQ0FAuXryITCbD1taWgICA54o8fVdRU1PD1dUVV1dXufXZ2dmkp6dz4cIFYZ1EIsHc3BwHB4c3\nPrMmKCjoubYvWLCABQsWPHX/J9toamqyY8cOIaq5rKyMNWvW1Fmb6e7du3LZwE/rF2DZsmVcuHAB\na2trEhISMDEx4d69e+Tl5WFnZ8fdu3dRV1dnw4YNBAQEcPXqVfr37//U39qTx3lWYe1hw4YxbNgw\nue0uLi5ycqR37tzh7Nmz/Pjjj/Uet+b8H38nk0gkzJs3j3nz5tW7j6urK8HBwU/tV0TkXUZ0couI\nvEQWLFiAn58fsbGxtSKpAVq1asWdO3dISUl5YQmMhuiC1YWTkxORkZHC8vjp0+kfEgJVVWynWqOs\nZ+vWHDx4EEVFRRwdHWnZsiW2trZ/OzLheW1u1aoVMpmMO3fuCEVMnoWioiItW7bE3d2dpKQkQkJC\nOHnyJGfPnqV9+/a0a9fuhWU/ambFc3JyOHDgACYmJnTp0uWVRWw4OjqSm5tLeHj433La10Xz5s1F\nR7eIiMg7SdOmTenatSsnTpzgzJkzDb6fPEn37t05cOAAgwcPblwDXwIKCgpUVVWhqKj4uk15o8jI\nyBCiNEVE3iTMzMywsbHhwoUL+Pj4yGn4vioKCws5ffo0cXFxyGQyrKysCAwMlNOzFqmbxyPAaygv\nL+fq1atERUXJOTo1NDSwtbXFzs7ub0krvm1cvHiRpKQkvL29efToEd988w1FRUV89NFHQpucnBz2\n7dvH9evXmTBhwnP137JlS8LCwnBxceHq1avIZDKSkpLo3LkzOjo6TJ8+nXXr1tGxY0cA0tLS8PDw\nEDKOXwUrV65k7NixWFhY1Lm95j3vzJkzfPbZZ6/EJhGRd4V/ztVUROQ14OPjg6enJ99++y2//fZb\nre3dunWjbdu2DBgwgDVr1mBvb096ejrFxcX07du3QcdoiC5YXUyZMoUOHTqwYsUKBgwYwPXr11HR\n0qLk4UMOBQSwa/p0/Pz8SE1N5cKFCyQlJZGYmIiamhru7u4UFRW90Jg01GaZTCYsOzg4MGTIEEaO\nHMmqVato1aoV+fn5hIaGYmdn91TdQgUFBVxdXXFxcSElJYVTp05x+vRpIiIi8Pb2pkOHDi8cyWVk\nZMTgwYNJTk5mz549tGrVqpakysvC0NCQtm3bCnpujZkyKjq6RURE3lXatWsnaN46ODi8kNNGRUUF\nY2PjvzVB/aowNjbm3r17YtT5E1y8eJH333//dZshIlInvr6+BAUFER4eznvvvffKjltUVERYWBjR\n0dFIpVIsLCzo3r17vY44kYahoqJCixYtagXz3L9/nytXrnDo0CG5dyATE5NGf7Z/01izZg2pqako\nKSnRqlUrzpw5I3c/NjY2xsjIiB9//PG5C5pKJBK8vb2JjY3F0NAQHR0dbt26RWZmJs7OzpSUlDB6\n9GhSU1NxdHREV1cXW1tbEhIS5IpbvkxWrVr11O2DBg0iPT2d2bNnP1fRTxEREdHJLSLSaNSlQQYw\nffp0hg8fztWrV7Gxsam1z9GjR5k5cyZDhw7l0aNH2NnZsXDhQrk2dR2rhmfpgtVH27Zt2bJlCwsW\nLGDx4sX4+fmxbNkypkyZwv7H5FXc3Nxwc3OjqKiIxMREzp8/T3R0NElJSeTn53P69Gnc3d3l0u8a\nw+YnxzMoKIhly5Yxa9Ys/h97dx5XVZ0/fvx1L5usgsgmiiyyIwiyKqmgSKamZTYulVpp5dS0aDlN\nvxltKictM9Nx0krT3BWz0RK1FBVR9kV2UUAFRQVF9uXe8/vD4X69QgoKgvR5Ph48Ht1zP+fzed9L\ncs55n895fy5evEivXr0ICAhQ1eT+vXFvf8/V1RUXFxfy8vI4fPgwJ06c4NSpU/j5+TF06NBmdeda\ny9XVFWdnZ6Kjo9m2bRuhoaEP5cRUS0uLoUOHkpGRwdWrV3F3d2+3vpsS3Xl5ea1e9VsQBKGrk8vl\nTJo0idWrV7Nr1y7mzp17XzPohg8fztatW7t8ktvS0pLTp0+LJPdtlEolCoXioZUaE4S2srGxoW/f\nviQkJDBs2LAOL6tTXV2tOidWKpVYWVnx+OOPY2Nj06Hj/tGZmJgQEBCgtk2pVFJQUEBqaio3b95U\nbdfW1sbOzo4BAwa0+wL0D9ugQYOIj4+/a5umEpf3S0dHB2dnZ4qLi6moqMDIyIjCwkL69++PhoYG\nHh4epKSkEB4ezokTJ9DU1KShoeGBxmxPhw8f7uwQBOGRJZPuNd1TEAThDqWlpaSlpZGSkqI6AbOy\nssLb2xt3d/dHosalJEnk5+dz5MgRLl68iFwux8fHh+Dg4N9dTbs16uvriYyMRKFQMGbMmId2ItpU\nA9Df3/+BFti805kzZ5DJZCLRLQhCt5KYmMi+ffsIDAy8rwWW4dbi0XV1dQQGBrZzdO0rPj4ePz+/\nzg6jy0hPT6empkZ8J0KXdu7cOX744QeCg4PVJnS0p9raWmJiYoiJiUGhUGBubs7jjz/ebFKO0Plu\n3rxJXl4eFy5cUFsk0dTUFEdHR6ysrB7JhS47Wm5uLpqampSUlFBTU0OvXr1wcnIiNjZWVbasoKCA\nxx9/nMuXL+Ps7PzI30QQhD86keQWBOG+SZJEUVERqampnD59WjUb28HBgUGDBuHk5NSuCdeOcv78\neY4cOUJBQQEymQwvLy+GDRv2QIvDlJaWcujQIczNzRkxYsRDOfGsq6sjPj4eFxcXevfu3W79ikS3\nIAjdjSRJ/PDDD+Tn5zNz5kz69+9/X/1s2bKFKVOmdOnkgkhyq9u5cyeTJk3q0r8zQZAkiTVr1lBW\nVsY777zTrom3uro6Tp48SXR0NAqFgt69e/P4449jb2/frguaCx1LqVRSVFREXl4eZWVlqu2amprY\n2Njg6Oh430+pdidxcXEYGRlRVVXFlStXGD58OGlpaRw/fhy5XE7fvn1xcnLC0dGR8+fPP7TSk4Ig\ndAyR5BYEoV0oFArOnj1LamoqOTk5KBQKNDU1cXNzY9CgQfTv37/LX1AWFRURFRVFXl4eMpkMd3d3\nhg8f/kAJ45ycHBISEvD09GTgwIHtGG3LJEkiPT0dLS2tdn2MPjc3Fw0NDRwcHNqtT0EQhM5UUVHB\nqlWr0NHR4c9//vN9LeJbUFBAbm4uo0eP7oAI20dqaiouLi7tukjxo0qpVBIREcHkyZM7OxRBuKfc\n3Fy2bt1KSEgIw4YNe+D+6uvrVTNYGxsb6dWrF48//jgDBgwQye1upLq6mrNnz1JQUEB9fT1w6/rA\n2NiYAQMGYGNj0+WvydqTUqkkOjoabW1t6uvrVdd4hw8fJiMjAw8PD0xMTAgNDSUuLg5/f//ODlkQ\nhAcgktyCILS7uro6srOzSU5OprCwELi1griXlxdeXl7NVh3vai5fvkxUVBQ5OTkAuLi4MG7cOPT1\n9e+7z+PHj3Px4kVCQ0Mfyue/dOkSBQUF+Pn5tduK7SLRLQhCd5Oenk5ERASDBg1q9YLPd9q+fTsT\nJkzoso84X716laqqKmxtbTs7lE4XHx+Prq4uHh4enR2KINyTJEn8+9//pqKignnz5t13HfmGhgbi\n4uI4evQoDQ0NGBsbEx4ejrOzs0hu/0EolUquXr1Kbm4uV65cUW2/fSbzg5Rr7Oqqq6tJTU2lrq6O\nmzdvEhAQQHp6OtHR0Xh4eFBVVcXzzz9PYmIi3t7eaGhodHbIgiDcJ5HkFgShQ1VUVJCenk5SUhLX\nrl0DoFevXnh7ezNw4MAufUJ19epVjh07Rl5eHsOHD8fT0/OB6o3X19dz4MABGhoaeOKJJzo8IVJb\nW0t8fLxqhkJ7EIluQRC6mx07dpCVlcW0adNwdHRs8/5lZWUcO3aMiRMndkB0D06SJBITE/H19e3s\nUDqdKFUiPGoyMjLYtWsXYWFhDBkypE37NjY2kpCQwJEjR6ivr8fIyIjw8HBcXV1FclsAbl2bnD17\nlvz8fKqrq1XbDQwMcHBwwM7Ort0my3S24uJiLly4QGVlJXV1dXh4eLB7925qamqwtLRk4sSJNDQ0\nUF1dLW4KC8IjTCS5BUF4aK5evUpaWhrJyclUVVUB0LdvX7y9vXFzc+uys+CaVtvOyMigvr4ed3f3\nB5rVff36dQ4ePIipqSmhoaEderEtSRKpqano6+vfV/KmJU2LuNjb27dLf4IgCJ2ppqaGlStXAvD6\n66/f183MPXv28Nhjj2Fqatre4bULUZf7VjJn7969TJo0qbNDEYRWUyqVrFy5krq6Ot55551WJRwV\nCgVJSUn89ttv1NXVYWBgwOjRo3F3dxc3eIRWuXbtGmfOnOHSpUsolUoAZDIZVlZWODk5tevaPw9T\nRkYGpaWllJeXM2DAANLS0sjOzqZfv374+voycOBA4uPjRckSQXiEaSxatGhRZwchCMIfg76+Pvb2\n9gQFBakWtykoKCArK4uTJ09SXFyMlpYWxsbGXeokXENDAw0NDSwtLbGwsCA3N5f8/HyMjIzuq8Zp\n06PSCoWCAwcOIEkSlpaWHRD5rRNSS0tLampqyMzMbJfV101NTbly5Qo3b95stxnigiAInUVLSwtz\nc3OSk5O5fv067u7ube7D1taW/fv3d9kyGMXFxVhbW3d2GJ3qxIkTODs7i+OW8EiRyWTo6uqSnp6O\noaHhXf8dKxQKUlJS2Lx5M1lZWejo6DB27FgmTJiApaWlmL0ttJqenh79+vXD1dUVNzc33NzccHJy\nor6+nszMTFJSUsjMzCQrK4uCggIaGhro2bNnu5T5sLOzQ6lUEhQU1A6f5Jbvv/+exx57jOXLl1NU\nVERjYyNFRUU4ODioJu8YGhpiZ2dHUVERffr0Ef9eBOERJWZyC4LQqRobG8nLyyMlJYUzZ86gVCrR\n1tbG3d0dLy8vbGxsuuRJhkKhICsri8rKSlxcXDA2Nr7vvk6cOEFhYSEhISFYWVm1Y5TqqqurSUxM\nxNPTs13KxOTk5KClpSVmdAuC0C389NNPpKSkMGnSpPtKVh88eBBHR0fs7Ow6ILoHk52djbW1NYaG\nhp0dSqfZuXOnWHBSeCQpFApWrFiBUqnk7bffbpZIVCqVpKWlcejQIaqrq9HV1WXUqFF4eXmJ2sJC\ni2bOnElpaSl79+594L7Ky8vJzc2lqKgIhUKh2m5mZoajoyMWFhbI5XIaGhqQy+W89NJLdx27tLQU\nPT09dHV1Hzi2Jt9//z1vvPEGFRUVNDY2EhUVRXl5OWZmZqSkpHDjxg369+/PjBkzyM/Px8DAADMz\ns3YbH24tVG1vb09CQgI+Pj7t2rcgCP+nexRYEgThkaWpqYmLiwsuLi7U1taSmZlJcnKy6kdfXx9v\nb288PT3b/WTjQWhoaODh4YFSqSQnJ4esrCycnJzu61H1oUOHEhAQQGRkJHV1dYwZM+aBan//Hj09\nPYKDg0lOTqZnz54PXFfb2dmZ7Oxs8vPzu2RSRxAEoS3GjBnD2bNn2bt3L/37929zQnjUqFFs27at\nS/49tLa2pqioCBcXl84OpVNUV1d3m7qywh+PhoYGw4cPZ9++faSlpeHt7Q3cSm5nZGRw8OBBKisr\nVTO3xcJ5wr3IZLIHmkRUX1+vWgi1Z8+e+Pn5qZXEUiqVnD9/nszMTGJiYoBbJVBKSkrIyspCoVAQ\nExODhYUFFhYWGBgYqPbt6LJfmpqa+Pv7c+zYMQoLC+nbty/Xr1/n5s2bVFVVYWNjQ3Jycoddd4o5\npoLQsbpOPQBBEP7wevTogY+PDy+99BJvvfUWI0eOREdHh+joaFavXs3q1as5efIkFRUVnR2qilwu\nx9XVlcDAQK5du8bJkyfVVi1vLU1NTcaNG8fIkSPZt28fhw4dUtXAa08ymQwfHx80NTWJi4t74DFc\nXFyoq6sjPz+/nSIUBEHoHNra2kyaNIn6+np+/PHHNl+IyuVy7O3tSUpK6qAI75+hoWGXOnY+bCdO\nnCAwMLCzwxCE++bl5YWenh5RUVEoFAoyMjJYsWIFu3fvpr6+nvDwcObPn4+vr69IcAv3dPvxbebM\nmYwfP17t/UWLFjFw4MBmbZYsWULfvn2xsbEBYNOmTfj5+WFkZISFhQXPPvssxcXFyOVybG1tGTly\nJJMmTWLSpEmMHDkSV1dXJEmioqKCQ4cOsWnTJpYtW8bixYv55ptvOHDgAH369GHhwoU0NjYC8MUX\nX+Dl5YWBgQF9+/Zl9uzZlJeXq2L7/vvvm92UjoqKQi6XU1ZWBsD8+fNVayzBracjFi1axLJly6ip\nqUGhULB9+3b69++PgYEBs2bN4sSJE836O3z4MAEBAfFfj/YAACAASURBVOjr6+Pn50dycjIAVVVV\nGBkZERERoRbHoUOH0NbW5sqVK6onX/38/JDL5YSGhqrarV+/HjMzM+RyOc7Oznz55ZcsXLiwxd+B\nIAh3J5LcgiB0ST179iQ4OJg33niDV199laCgIKqqqjh48CBffPEF33//PampqdTV1XV2qMCt5LGz\nszOBgYHcvHmTkydPcunSpTb3Y2xszLPPPouDgwNbt27tsGRJ//79cXNzIzo6+oETHyLRLQhCd9G/\nf38CAwPJz88nMTGxzfsHBgaSlZV1zxuICQkJyOVyzp8/f7+htllHlf6Sy+Xs3r271e3vTD48DDdu\n3OjQcmCC0NE0NTUJDg7m5s2bfPHFF+zatYuamhrCwsKYP38+gYGB4mkF4b7d7fgwc+ZMfvvtN44e\nPUp6ejoHDx7k73//O/r6+uzYsYOPPvqItLQ09u3bx7Vr15g6dWqL/QwYMIDJkyfj5uaGo6Mjr776\nKk899RRBQUFYWFhw7do1Tp06RU1NDadOneKTTz5h2bJlpKam8tJLL7Fv3z7Wrl1LXFwcb7zxBgAH\nDhxg1eefU1tTw4EDB1pMeN+puLiYYcOG4ejoyOrVq6murubo0aOkpqYyefJkUlJScHFx4fHHH+f8\n+fN89tlnvPzyy0iSxOjRo6msrGTBggWYmJgwffp04Na6U9OmTWPdunVYWVnxySefALBu3TrGjx/P\n2rVrVTcWDhw4wOXLl9m9ezfPPfccjo6OfPDBB6xYsYKUlBSWLVvGkiVLiI+Pb/Y76oolPAWhqxFH\nQkEQujwLCwtGjx7NqFGjKCwsJDU1lYyMDAoLC9HQ0MDJyYlBgwbh4ODQ6bNXZDIZAwYMwMHBgYKC\nAk6ePEm/fv3o27dvm/qxt7fH3t6eU6dOsWXLFoYNG9bmPu7FwMCA4OBgEhMTMTMzw9bW9r77cnFx\nISsrS5QuEQThkTdy5Ehyc3OJjIzEwcGB/Px8fH19GTJkCNHR0ffc39fXl6NHjxISEvIQom0bSZLU\nLpLbo0bo5cuXH2hdio5WXl5Ojx49OjsMQbhvkiRx5swZVdKrurqakJAQAgMDVSUjBOFB3O3JpaZj\nhq6uLuvWrWPbtm3MmzePzz77TJVshlsLMK9evRo3NzeKi4vp06fPXftsKlXi6emp2l5dXc23336L\ni4sLAwcO5OLFizg4OHD9+nWOHj0KwKBBg9iyZQsDBgxg1eLFPFlXRwYw46mnmP7aa3f9nHl5eYSF\nhTFmzBhWr16NJElcuHCBEydO8OSTT2JlZYWTkxMbNmzA2dmZYcOGUV5ezgsvvMDKlSv5/vvv0dHR\n4YsvvuD555/n9ddfV33W2bNnExgYyLhx44iKimLu3Ln89NNP7Nq1i2XLlqnKhpmammJubg7AkSNH\nqKysZPXq1UybNg0AT09PFixYwOLFi7GwsGjV7+hB3F56RhC6AzGTWxCER4ZcLsfOzo6JEyfy3nvv\n8cwzz2BnZ0d2djZbt27ls88+4+eff+bixYudXu9MJpNhZ2dHUFAQSqWSkydPUlBQ0OZ+AgMDefbZ\nZ0lLS2PXrl1UVla2a5xyuRw/Pz+USiWJiYkP9L25urpSW1t7X59TEAShq9DU1OSZZ55BqVQSERHB\nN998g5+fH6dOnSI7O/ue+zs7O1NSUkJ9ff19x6BUKtu9ZFXv3r25du1au/YJYG5u3qUvkE+cOMHQ\noUM7OwxBaDNJkjh79iyrV69m69atVFRUqCYSdPV/d0L30XRt4OHhwapVq5g9ezbr1q3jjTfeICkp\niQkTJmBlZYWmpqaqHMnrr7+u9qToiBEj+POf/8zf/vY3tm3bxsGDB3n33XfVrjtsbW1Zvnw55eXl\nrFmzhjfffJP6+nqGDBnCkSNH+M9//sOSJUvYtGkTjY2NLFq4kF51dfTl1szNmTU1fPHFF1RVVRES\nEoIkSSxZskTVf2NjI56enhQVFbF3714+//xz1eQkpVKJjY0NkiRRVFSEnp4e+vr6FBYW8ttvv/HU\nU08BEBYWxuTJk4mJieGxxx4DUJWpHDx4MAMHDkQmkxETE8MPP/yAqakpoaGhnDx5ktfuSMDn5uZS\nXFxMRUUFc+bMQUdHBw0NDQwNDXn//fe5fv26WnuZTIYkSXz88cdYWlpiaGjIiy++SG1trdr3fPuN\nB2he5mTEiBHMnTuX+fPnY25urvocgtBdiCS3IAiPJC0tLdzd3Zk+fTrz589n7Nix9OrVi4SEBL77\n7jt+++23Tk90N7GxsSEoKAhtbW1OnjzJ2bNn2xSbpqYmTzzxBKNHj2b//v0cOHCg3ZMf9vb2ODo6\ncvz4caqrq++7H1dXV2pqakSiWxCER5qVlRXDhw+noKCATZs28eGHHxIaGsp3332n1q6goEBVriMs\nLAx9fX3c3d3R0NDgwIEDqnaRkZG4uLigq6vLsGHDyM3NVeun6RHr/fv34+HhgY6ODtnZ2dTX17Ng\nwQL69euHvr4+/v7+HDx4ULVfYGCg2kX8c889h1wup6SkBLg1K05HR4eYmBj69OlzX2W0li5dyoAB\nA9DT08PT05PNmzervX9nuZLY2Fh8fHzQ1dXF19eXyMhI5HI5x44dU9svJSWlxdqm7a2qqopevXp1\nSN+C0FHy8/NZs2YNmzZt4vr16wwbNox58+bxpz/9CS0tLY4cOdJlznOFR5tcLm/2/9Lt9aubZnKf\nP3+e//f//h979uxh2rRpVFVVER4eTmNjI+Xl5bz77rts374dmUxGbm4uL774olqfmzdvRltbmyee\neAIPDw++/PJLtm/frtZm+fLlaGtrM2/ePBYsWMC7777L2LFjGTRoEHv27MHV1ZVBgwYhk8kY6uOD\nM7AEUAKOQB9TU9WNarhVe3vlypXU19ejUChwcXHBwsKC2bNn895773Hq1Cm0tLRUn+/HjRuZMnYs\nBw4coLi4GAsLC7WnnJraymQyVVmU26/JXn75ZVJSUqipqeHf//43M2bMIDY2FjMzM55++mkAVfL6\nyJEj6OrqArBmzRpeffVVBgwYoHpiee7cuWrfjSRJHD16lNOnT3P48GEiIiI4ePAgCxYsUPtd3VnS\npKVtmzZtQiaTER0dzcaNGxGE7kQkuQVBeOTp6enh6+vLnDlz+Mtf/kJISAj9+/cnPj6e+Ph4SktL\nOztEAPr06UNQUBAGBgacOnWK3NzcNl2gGBkZMXnyZJydndm6dSsJCQntGp+RkRFDhw7l9OnTXLhw\n4b77cXV1pbq6msLCwnaMThAE4eF67LHHKCoqQlNTEx8fH+bMmcPGjRtVi2Hd7oMPPuCtt94iLS0N\nPz8/XnvtNW7evMnNmze5cOECEydOJDw8nNTUVN544w3ee++9ZhedtbW1fPzxx3zzzTdkZWVhY2PD\nrFmzOH78OFu3biUjI4MZM2Ywfvx40tLSAAgJCSEqKgq4Vedz965daGtpsWLFCgBiYmLQ0tLC398f\nbW3tNq9j8cEHH7B+/XpWr15NVlYW77//Pq+88gq//PJLi+0rKysZN24cbm5uJCUl8emnnzJ//vwW\n64j+7W9/Y+nSpSQlJWFqaqqqbdqeSkpK7lmfVRC6kvPnz7N27Vo2btzI1atXGTJkCPPmzSMkJIQe\nPXqgo6NDUFAQV65c4ezZs50drtANmJmZNbsBmpKSovq7LUkSxcXF5OXlsWvXLh5//HEAsrOzKS0t\nRUdHh2nTpvGvf/1LVRf+7bffJiIiQu3pIXd3dxYtWoSRkRHW1taEhITw22+/qY0bHh6OoaEhpqam\nvP7661haWtLY2Mjy5cuprq4mNzeXZ599FoA5b71FrK4uhkA18H6PHjw5eTJaWlro6+sjk8no168f\nlZWVNDY2IpfLmTBhAr169WLFihWYmpqydu1aamtr0dTU5NB//8vb584xOy2NFyZOpKKiAktLyzZ9\nl9OmTaOkpARjY2POnDnDrFmzOHLkCMOHD6dnz57ArRvBcCvJHRwcTJ8+fcjLy8PExARtbW1VyUoT\nE5Nm/WtqarJ+/Xrc3NwYPXo0S5YsYc2aNdTU1PxuTJIkNbvetLe357PPPsPJyQlnZ+c2fUZB6OpE\nklsQhG7FxMREtZiIv78/gwcP5vr168TFxZGcnExVVVVnh4iFhQVBQUH06tWLU6dOkZWV1aZkt62t\nLdOnT0epVLJly5Z2XbhMQ0ODgIAA6urqSE5Ovu9ZQm5ublRVVYlEtyAIjyy5XE5ubi7e3t7s2rWL\n8ePHI5PJ+Omnn5q1feeddxg7diwODg4sXryYsrIyLCwsOHjwIP/5z3+wtbVlxYoVODk5MXnyZF57\n7bVmf18VCgWrVq0iKCiIAQMGUFJSwrZt29i+fTvBwcHY2try5z//mTFjxrBmzRoAhg8fTnR0NPv3\n72f6hAlQV0d4fT1fLl3KgQMHiIqKYsiQIfe1IF1VVRXLly/n22+/ZfTo0fTv35+pU6fy8ssv8+9/\n/7vFfTZv3oxSqeS7777D1dWVUaNG8cEHH7R4LPnoo48YPnw4zs7O/OMf/yA7O5vi4uI2x3k3p06d\nIjg4uF37FISOcPHiRb777jvWr19PSUkJgYGBvPPOO4SFhalmezZpWmTyyJEjnRSt0J2MHDmS5ORk\n1q9fT15eHkuXLiUmJkb1d1smk2FiYoK+vj6LFi2ivLwcuPWkqo6ODkePHuWHH35AV1eXyZMnq8qV\nyGQy1Y0YmUymVnu7vLwcHR0dzpw5Q0pKCikpKSgUCjw9PdWOF9bW1iiVSpYvX86hQ4eoqqri/fff\nR5IkXn31VW4CV//Xf2BYGLa2tiiVSlX97ueff573338fPT09NDU1GT9+PB999BH29vbcuHGD5ORk\n9u/fT09dXTQVCswBf8D1fyVA2rrOkLGxMZMnT6a8vJyePXvi4ODAkSNHGDFiBObm5mhpabFnzx5K\nSko4fPgwISEhfPjhhyxdupRTp05RV1dHeno6GzdubHENEE9PT/T09FSvAwMDqa+vb9MNL5lMxuDB\ng9v0uQThUSKS3ILwiLK1tWXZsmWdHUaXJ5fLGTBgAP7+/ri7u5Ofn09sbCzp6ekPVC+1PfTu3Zug\noCAsLS2JjY0lIyOjTWVI/P39mTJlChkZGezcubNd63UPGDAAOzs7oqOj7zo74G5EolsQhEdZXl4e\ncXFxvPnmm1y9epUTJ04wY8aMZiVLALWLdysrK+DWrGZdXV2Sk5MJDAxUa3/na7g1Q2vQoEGq10lJ\nSUiShJubG4aGhqqfX375hXPnzgEQHBxMXV0dSxcu5Mm6OkKBd4CeCgVrly0jKiqKESNGqI3R0kz0\nlmRmZlJbW6uaWdf08/XXX6vGv1N2djYDBw5ER0dHtc3f37/Fti19Z021TdtLfX09BgYG7dqnILSn\n4uJi1q9fz3fffUdRURF+fn68/fbbhIeHo6+v3+I+urq6+Pn5UVxcLM6xhPuiVCpVNz9Hjx7NwoUL\n+eCDD/D19eX8+fPMnTtXbSa3np4eQ4YMoby8nFGjRnHjxg3MzMzYsGEDFRUVqlIg69atQy6Xs379\nes6cOYOXl5dqzNtLfRw/fpx9+/Zx9OhRfHx88PHxoaysDC0tLbUnfwwNDRk2bBhffPEFy5YtQ0tL\ni6+++gq5XM6BAwdIT0/n7NmzbNy4kZycHP7xj3/Q0NDAxx9/3GLZDh8fH5588kliY2MxNTXl3Llz\nhIaG4j5gAP7ALMAbKAL0dXW5dOmSWrL/Ti1te/HFF5EkiaqqKm7cuEFcXBwjRoxAU1OT119/neTk\nZKytrbl69SqhoaG89NJLrFu3jrS0NPLy8hg2bBjffvttizO57zX56F6lZ5r83t8WQegORJJb+EO6\nevUqc+fOxc7Ojh49emBpacmoUaP49ddfOzu0Vmupvtbtmup73i4vLw97e3vGjBnzQHWXH9UEu7a2\nNh4eHgQEBGBra0tGRgZxcXHk5eW1e43rtjAxMSEwMJC+ffsSFxfH6dOnUSgUrdpXLpczZswYxowZ\nQ2RkJPv372+3z2JsbExQUBApKSn3PbvOzc2NysrKdp1tLgiC8DB8++23KBQKnnnmGT766CPCwsJY\ntmwZBw8e5OLFi2ptmy7e4f8uepVKJeHh4ZSWlrbqqRgdHR2147pSqUQmk5GQkEBqaqrqJzs7m3Xr\n1gFgYGDA4MGDuXr9OtlACBAIlAGV1dUkJCSoJbmtrKxaXZe76Viyb98+tfEzMzPV6oLfqbVPAP3e\nd9Zezp8/T+/evdutP0FoT5cvX2bjxo188803XLhwAR8fH9566y2eeOKJVt2YGTJkCHK5/KHN5n5U\nz/2Fll2+fFl1cxFg4cKFFBcXc+PGDVatWsUnn3yiKosF4OXlxcGDB4mKiqKqqoqRI0dSVlbGs88+\ny7PPPstjjz1GcnIyM2bMQKFQMGXKFOzt7enRo0ezsdevX49SqWTGjBmMGzdOtdCyubk5cKse/Tvv\nvKM29sWLF/n5559pbGxk7NixKBQKgoODVaU9nnvuOXJycli/fj3a2tpMnz4dhUKhWo/B0NCQxYsX\nq/qUy+W4uroyY8YMHnvsMd5fvJhUXV2WAGuAmz16MGXaNOLj44mMjGTEiBFq/SmVSkxNTVEoFGo1\nuwEuXbqEoaEhDQ0NLFu2DDMzM+zt7QH45z//iUwm4/3338fQ0BA/Pz8ApkyZwpw5c3B3d6esrIxj\nx47h7u7e7Ls7ffq02jX8qVOn0NbWxsHBAbhVeubOa7bU1NS75gwEobsRSW7hD2nSpEkkJCSwbt06\nzpw5w759+xgzZgxlZWWdHVqHSUlJITg4mMDAQPbt26f2qFNrNc187g4HSgMDA7y9vfH398fY2JjE\nxERiY2MpLi7utIV8evbsSWBgILa2tiQkJJCSktLqGXcGBgY888wzuLu7s23bNuLi4tolJk1NTYKC\ngqioqCA1NfW+vht3d3cqKipEolsQhEdGY2MjGzZs4NNPPyU1NZUTJ07wxhtvMH/+fAYOHMj69etb\n1Y+mpibOzs4cP35cbfupU6fuua+3tzeSJHHp0iXVhXzTz+3JiREjRqBtaEisTEYlsA2QZDIkPT1V\nPe4m5ubmrZ4t7ebmho6ODgUFBc3G79evX4v7uLq6kp6eTu3/HvUG2u141FYJCQkMHTq0U8YWupeZ\nM2cil8uRy+Voa2tjYWFBaGgoq1evbvV5WpMrV66wefNm1qxZQ35+Pl5eXrz55puMHz8eIyOjVvfT\ndIOrsLCQoqIi1SK4SUlJqjbV1dWMGTMGe3v7B67ffa/JNcKj4dq1a/z0008cO3aMsLCwNu9vaWlJ\nVFQU9fX1hIaGUlpayoIFC4iLi+O1114jOTmZvLw89u3bx6uvvqrar6W60K1x+35hYWEMHTqUCRMm\nEBkZSX5+PidPnmThwoWq0h62trbU1tby66+/cu3atVbXqg4PD2fDjz+ybcgQvrS3572PP2b16tUE\nBwczZcoUVq5cSUpKCvn5+ezevVuV1L9dTU0N586dY/Hixbz66qvY29vz1Vdfqd1obvp3+9VXXzFs\n2DDk8ral4xobG3nxxRfJzMzk0KFD/PWvf2XOnDmqkkahoaHs37+fvXv3kpOTwzvvvMPFixfVvvv7\n/V0IwqNCJLmFP5wbN24QHR3Np59+SkhICP369cPX15d58+apFrKAW6sO+/n5YWRkhIWFBc8++6za\nndGoqCjkcjmRkZH4+Pigp6fHsGHDKCoq4vDhw3h6emJoaMiTTz6pWkUZbp0ojx8/Xi2mRYsWMXDg\nQLVtTYtK6Orq4uzszJdffnnfB6Rjx44xYsQIJk+ezJYtW9DQ0ABu3Q0eNWoUenp6mJqaMmvWLG7e\nvNks1iVLltCvXz/69etHSEgIhYWFvPvuu8jlclVfLc0cb/qOmm4eNLWJjIzExcUFfX19JkyYwM2b\nN9m+fTtOTk4YGxszc+bMNi+O9SB69+6Nn58f/v7+KBQK4uPjSUhIUPu9PUyGhoYEBATg5OREcnIy\nycnJrS6tYmNjw7Rp05DL5WzevJmCgoJ2icnZ2Zm+ffsSHR19X78bkegWBOFR8vPPP1NaWsrs2bNx\nc3MjICCAmTNnoquri7e3d6uT3AAff/wxFy9e5K233iInJ4ddu3apamrfjZOTE9OnT2fmzJlERERw\n7tw5EhIS+Pzzz/nxxx9V7UaMGMHp06fR09cndeRIvnJ0xNvPjyNHjhAUFKRWj7ulR5kBcnJyVHVR\nm360tbWZP38+8+fPV9VqTUlJ4euvv+abb75pMeZp06ahoaHB7NmzyczM5Ndff1XNnnvYCTKFQoG2\ntvZDHVPonmQyGWFhYVy+fJnCwkIOHTrE+PHjWbhwIY899lirn47cvXs3//nPf8jLy8PDw4O//OUv\nTJw4UbUgXVsNHToUmUymWnz2dtevX2fUqFEUFxcTExOjmunZVi2VOhAeXc8++yxvvPEGCxYsYOLE\nia3a584bHObm5qonCEJDQ7GysuLYsWMUFBQwYsQIBg0axN/+9je1RRtbuknSmhsnd7b55ZdfCA0N\nZfbs2bi4uPCnP/2JM2fOYG1tDdx6wuHVV19l6tSpmJub89lnn7W67/DwcD5asYJJs2ZhYWGBtrY2\nhw4d4oUXXuC7775jyJAhDB48mKVLlzJ58uRmZceWLFmCi4sLvXv35u9//zshISFUVlaqJbnh1jG7\nsrKS0NDQu8bT0usRI0bg7u5OSEgITz/9NKNGjWLp0qWqNi+++KLqJzg4mJ49e/LUU0/dtV9B6HYk\nQfiDaWhokAwNDaW//OUvUm1t7e+2W7dunbR//34pPz9fiouLk0JCQqRhw4ap3j9y5Igkk8mkgIAA\nKTo6WkpLS5M8PDykIUOGSCEhIVJcXJyUkJAg2dnZSW+++aZqv5kzZ0rjx49XG2vhwoWSh4eH6vXa\ntWslKysrKSIiQiooKJD27t0rWVpaSqtWrVK1sbW1lZYtWyZFRkZKT4eFSU+HhUmRkZGq99evXy8Z\nGBhI//3vfyU9PT1p0aJFamNWVlZKVlZW0lNPPSWlp6dLR48elZycnKRJkyap2syYMUMyNDSUnnvu\nOSkjI0NKT0+XysrKpH79+kmLFi2SSkpKpJKSErXxbtf0HZWWlqraaGlpSWFhYVJSUpJ08uRJqU+f\nPtLIkSOl8ePHS6dPn5aOHDkimZiYSMuXL//9X+JD0NjYKOXk5EixsbFScnKyVFVV1Wmx1NTUSPHx\n8VJCQsJd/5+9k0KhkCIjI6UdO3ZI5eXl7RJLfX29dOLECenSpUv3tf/p06el8+fPt0ssgiAIHeXJ\nJ5+UwsPD1bYplUrpp59+kr777jtJLpdLhw4dkvLz8yW5XC4lJiaqtZXJZFJERITq9cqVKyUbGxup\nR48eUnBwsLR582ZJLpdLhYWFkiTdOj4aGho2i6OhoUFatGiRZG9vL2lra0uWlpbShAkTpKSkJFWb\niooKSUtLS3VusWfPHmnmzJmSTCaTPvnkk2Z9xsXFqf47Pz9fkslkzX7kcrmUkZGhit3NzU3S0dGR\nzMzMpNGjR0u//vrr737WU6dOSd7e3pKOjo7k4+MjRURESDKZTDXukSNHJLlcrjo3aIqjpe/xfmVm\nZkoxMTHt0pcgzJgxQxo3blyz7enp6ZK2tra0cOFC1ba6ujrpvffek/r27Svp6elJfn5+0p49e6SI\niAhp9erV0tatW6Xp06dLdnZ2kq6uruTo6CgtXbpUUiqVzcb78ssvJWtra8nExESaNWuWVF1d3SyG\nPXv2SIsWLZLi4uIkmUwmJSYmSkVFRZK7u7sUHBws3bhxQ9W2tLRUmjJlitS3b19JV1dXcnd3l9av\nX6/W3/Dhw6XXXntNmjdvnmRmZib5+/tLkvR/1x1Nli1bJnl6ekr6+vqStbW19PLLL6uN1XRd8Ntv\nv0nu7u6Svr6+FBISIuXn57f16xeEhyY+Pl5avHixtGrVKkmhUEiSJEnZ2dnS9evXOzkyQRBaSyS5\nhT+kiIgIqVevXlKPHj2koKAgaf78+VJsbOxd98nKypJkMplUVFQkSdL/JXAPHjyoarNq1SpJJpNJ\nycnJqm2LFi1SS2C3dKJ8Z5K7X79+0qZNm9TaLF++XHJzc1O9trW1lWbPni1Z6OpK34P0PUgWurqq\nRPf69eslDQ0NSUtLS3rvvfeafZ61a9dKPXv2lCorK1XboqKiJJlMJp09e1YVq7m5uVRfX6+2750n\nuk3jtSbJLZPJpNzcXFWb+fPnSxoaGmoXuzNnzmzxYqKz1NbWSmlpaVJsbKyUmZnZ7Pt4mHEkJiZK\ncXFxLV7o/J6Kigpp586d0s8//yw1NDS0SywZGRnS6dOn1S7KWkskugVBeJTl5eXd19+wzZs3qy6a\nO1JmZqa0aNEiKS0trcX3T58+/VBv3O7Zs6dZUruj7dy5s92Od4Lwe0luSbp1Q+z2c/hp06ZJQUFB\n0vHjx6XMzExp9uzZkqampnTixAlJqVRKDQ0N0j/+8Q8pISFBKiwslHbs2CEZGxtL3333ndp4PXv2\nlObMmSNlZ2dLBw8elIyNjaV//etfzcYvLS2VnnvuOcnH2VkCpL/+9a+Sra2tNHbsWKmmpkatbVFR\nkfT5559LqampUn5+vrR27VpJW1tb+u2331Rthg8fLhkaGkrz58+XcnJypOzsbEmSmp/7f/nll9KR\nI0ekwsJC6ejRo5Knp6f0/PPPq96/fWJLfHy8lJaWJnl7eze7eSgIXUl8fLy0ZMkSaeXKlaoJPfX1\n9e12A1YQhI4nypUIf0hPP/00xcXF7N27lzFjxhATE0NgYCD/+te/VG2SkpKYMGECtra2GBkZqRaG\nuLPcgqenp+q/mxbMuL30SFvqX8KtRTEvXrzInDlzMDQ0VP28//77nDt3Tq3tqaNHWVJTwwxgBrCk\npoa1ty0Ko6Ojw5gxY1i/fr3a4iEAWVlZeHl5qa2uHBQUhFwuJzMzU7XNw8NDbXGoB6Wjo4Ojo6Pq\ntbm5OZaWlqqFPJq2teU762g6OjoMHDgQf39/QGsEzwAAIABJREFUrK2tOX36NHFxceTn5z/UmmY6\nOjr4+PgwaNAgsrOziYuLo6qq6p77NdXr9vT0ZMeOHa2qBXsvbm5uWFhYEBMT0+pSKk08PDwoLy/n\nwoULDxyHIAjCw+bg4MDFixfb/LcvODj4rgs2thd7e3tkMhm5ubktvm9tbX3fiwm3xoYNGzh+/DgF\nBQXs27ePt956iyeffFLtON+RlEolkiSplWkRhI7i6uqqOj8/e/Ys27ZtY8OGDZSVlZGRkcGSJUsY\nO3YsmzdvRiaToampyYcffsjgwYOxsbFh8uTJvPLKK2zdulWt3549e/L111/j7OxMWFgYkydP5rff\nfms2fnx8PJE7djA1JwcZ8Omnn6Kvr89PP/3UbNG/Pn36MG/ePDw9PbG1tWX27Nk8/fTTzca2t7fn\ns88+w8nJCWdn5xY/95tvvsmIESOwsbFh2LBhLFmyhB07dqi1aWxs5N///je+vr4MHDiQ+fPnt1ha\nRRC6kqZjR1Mdey0trTbX3hcEofOIJLfwh6Wjo8OoUaP4+9//zokTJ3jppZdYtGgRjY2NVFVVER4e\njoGBAZs2bSIhIYHIyEiAZhe1tyeAm+pbNdWpbtqmVCpVr1uqh3l7vbumtmvWrCE1NVX1k5GRQUZG\nRps+o1wuJyIiguDgYEJDQ0lJSVF7//cStLfX6WrtApX3+lxN7rzolMlkzZLod35nXYmRkRE+Pj74\n+/ujr69PQkICcXFxlJSUPLQYtLS08Pb2xsfHh7y8PGJjY6moqLjnfn379mXatGloa2uzefPmZjdN\n2srMzAw/Pz/i4uK4evVqm/b18PDgxo0bXLx48YFiEARB6AyDBw8mMTGxTfvY2Nhw8+bNVtfvvV86\nOjr06dOHM2fOtHicNzY27tA1J65cucILL7yAi4sLr7/+OmPHjmXTpk0dNt6dUlJS1G6mC0JHkiRJ\ntXhcXFwckiTh6enJtGnTmDVrFjY2Nvzyyy9q51xff/01vr6+mJubY2hoyJdfftnsxr+bm5va+biV\nlVWLE0DWLlvG5/X1PPO/1/5AZmZms8Q13KpT/8knn+Dp6Unv3r0xNDRk9+7damPLZDIGDx58z899\n+PBhwsLC6NevH0ZGRkyaNImGhgYuX76sanPnxBYrKyvq6+u5cePGPfsXhIetoaEBTU1N9PT00NTU\nVLtG0dHRUVtQWRCErkskuQXhf1xdXWlsbKS2tpbs7GxKS0tZvHgxwcHBODk5tVsS09zcnEuXLqlt\nS0lJUZ3IWlhY0KdPH/Ly8rC3t2/2c7vA4cNZoKvLBmADsEBXlznz5qm10dTUZMeOHYSGhjJy5EjV\nqutubm6cPn2ayspKVduYmBiUSiWurq53/Qza2tooFAq1bWZmZlRXV6slW+9Mqnc35ubm+Pn54efn\nR01NDXFxcSQmJlJeXv5QxtfU1MTLywtfX18KCws5depUqy4cfHx8mDp1KmfPnmXHjh0PFK+2tjZD\nhw6lpKSErKysNu07cOBArl+/LhLdgiA8crS1tbG2tiY/P79N+4WFhalumnckFxcX6urqmp1vQMcv\nAPnuu++Sn59PbW0tBQUFrFq1Su2psY529uxZtafsBKEjZWZmYmdnx88//0xsbCwymYyUlBTS0tJU\nE1Wys7NZt24dANu3b+ftt9/mxRdf5ODBg6SmpjJ37txmi3q3NCmkNRNAngDcBwxg5syZbNiwQe29\nzz//nC+++IIFCxZw+PBhUlNTmThxYrOx7/XvtbCwkLFjx+Lu7s6uXbtISkpi3bp1SJKkNhmopc8A\ndNmJLMIfW1lZGaamphgZGaFQKKioqFDdKHZwcCAtLa3Z9a8gCF2PSHILfzilpaWEhoayefNm0tLS\nyM/PZ+fOnSxdupRRo0ZhYGCAjY0NOjo6rFy5knPnzvHzzz/z97//vV3GDw0NJTk5mfXr15OXl8fS\npUuJiYlRa/Phhx+ydOlSvvzyS3JyckhPT2fjxo18+umnau1cXFzY8OOPfOPpyZcODqzbtYvw8PBm\nY2pqarJ161bCw8MZNWoU8fHxTJs2DT09PV544QXS09M5duwYr7zyCpMmTWqWTL+Tra0tx44do7i4\nmGvXrgEQEBCAvr4+77//Pnl5eURERLB69eoH/LYeDTKZDFtbW/z9/fHy8uLSpUvExsaSlpb2UO76\na2ho4OHhgb+/P8XFxZw8eZKysrK77iOXywkLC2PcuHH8+uuv7N27974fxZPJZHh4eGBiYkJMTEyb\n+hGJbkEQHlU2NjZcuXKFmpqaVu9jYmKChoZGm59+aaum2ZNnzpz53TYPs9zWw9LY2IhMJlPNrBWE\n9tLSzaGUlBQiIyNxcXHBx8eHuXPnIkkSly5dajZJxcrKCoDo6GgCAgKYO3cugwYNwt7enry8vGb9\nt/Zm1Jx581igq8uu/73+SkeHz1eu5PPPP+ell15SJdebxn7yySeZPn06np6e2NnZkZOT0+YbXwkJ\nCTQ0NLB8+XICAgIYMGAARUVFbepDELqa0tJSevXqhYmJCfX19cjlctVTT/n5+ezfv5+CgoLODVIQ\nhHsSZ4DCH46hoSFBQUGsWLGCESNG4OHhwQcffMBzzz3H9u3bgVuzkjds2MCePXtwd3fno48+Yvny\n5a06AW2pze3bRo8ezcKFC/nggw/w9fXl/PnzzJ07V22fppPSH374gUGDBjFs2DC+/fbbFpPP4eHh\nfPb110x8/nm8vb1/NxYNDQ02bdrEuHHjGD16NOnp6Rw4cICbN2/i7+/PxIkTGTp0qNrJ8J2xN/nn\nP//JhQsXcHBwwMLCAoBevXqxefNmDh06hKenJ99++y0ff/zxPb+zlsb4vXEfBZqamri4uBAQEICj\noyM5OTnExcWRk5PT4Xf/5XI5bm5uBAYGcvXqVU6ePHnPRIqenh6TJk3C19eXnTt3cuLEifse39LS\nEh8fn1Yl2W/XlOgWF0iCIDxqBg8erHpCqrXCw8M5fPhwB0V0i7m5OXp6emRnZ7f4vomJSbcsGRAb\nG6u2LoogtJfa2lpKSkooLi4mMTGRV155hWHDhuHl5cWGDRuwsrLCycmJ6dOnM3PmTCIiIjh37hwJ\nCQl8/vnn/PjjjwA4OzuTlJREZGQkZ86c4aOPPuLYsWPNbjq19iZUeHg4G378kV+Dg5GADz//nPDw\ncN566y1WrFjBnDlzWLt2rWrsX3/9lRMnTpCdnc3rr79OQUGB2liSJN1zbEdHR5RKJcuXLyc/P5+t\nW7eyYsWKNnybgtD1VFdXo6enh6GhIQC6urqqtS369+8P8LvHVEEQug6Z1B2ncQjCH0xhYSHff/89\n48aNa1UdPeHhu3HjBnl5eSgUCqysrOjXr1+HJ/IlSSIvL49r165hZ2eHpaXlPfdJTU0lPT0df3//\n+65pKkkSaWlp6Orq4uTk1Or90tLSMDU1xdra+r7GFQRB6AyXLl3i5s2bv7tAW0sOHTqEvb09Dg4O\nHRbXnj17SE1N5b333kNXV1ftvZqaGs6ePYuHh0eHjd8Zdu7cyeTJkzs7DKGbmTVrlqr0h1wuR09P\nDw8PD5577jnmzJmjVpajsbGRTz75hI0bN3Lx4kV69epFQEAACxcuxNvbm4aGBl577TV2796NJEk8\n88wz2NjYsH79elXd7lmzZlFaWsp///tfVb8ffvghERERzRaSb1JQUICDgwPx8fH4+Piotq9Zs4Y/\n//nPrFq1iilTpvDSSy9x6NAhdHV1mTVrFhUVFWRlZaluvIWEhDBw4EC++uortf7t7Ox44403eOed\ndwBYuXIlS5YsoaysjKFDhzJnzhymTJlCfn4+NjY2fP/99/zlL3/h5s2bqj6ioqIYOXIkV69efWgL\n0QpCayUkJODr60t8fDzR0dE4ODhQX1/PM8/cqni/evVqqqurmTdv3iM7GUsQ/ghEklsQuoH6+nr+\n9a9/4e3tzZNPPtnZ4Qh3IUkSJSUlnD9/XlXmxMzMrMPHzM/Pp6SkBBsbm3smkZVKJYcPH6a0tJTR\no0djYmJyX+MWFRVx4cIF/Pz81BZjvRuR6BYE4VGUkJCAi4sLBgYGrWqvVCrZunUr06dP77CYMjMz\n2blzJ5MmTWoxmR0fH4+fn1+Hjf+w1dbW8ssvv/D00093dihCN6NUKomKiuLKlSsEBARgZ2fX2SEJ\ngtDOmpLcCQkJREdHM3jwYPLy8pg1axZw6ybN0aNHeeWVV1o1cUgQhM4hypUIQjegra2NsbFxs5XZ\nha5HJpNhaWmJv78/gwcPpqKigtjYWJKSktQW7WzvMe3t7QkKCkKhUHDy5EkKCwt/t71cLmfUqFFM\nmDCBI0eOsHfvXrWFhFrL2toaLy8vTpw40erH4j09Pbl27RrFxcVtHk8QBKGz+Pj4kJyc3OoSA3K5\nHCcnJxITEzssJnt7e2Qymepx6+4uJiamWyXthc6nVCo5evQoO3bswMbGhilTpogEtyB0c02ztHV1\ndZEkSXV95uLiAkBOTk6nxSYIwr2JJLcgdBN9+/altLT0vhcPFB4+uVyOvb09AQEBDBw4kAsXLhAX\nF0d6enqzle7bi42NDUFBQWhqanLy5EnOnTv3u0mZHj168PTTT+Pv709ERATHjx9v83i6uro89thj\nnDt3jry8vFbt4+XlxdWrV0WiWxCER4ZcLsfV1ZXMzMxW7+Pn50d2djZKpbJDYurRowdWVlacOXOm\nxb/zcrm8w9eKeJhKS0vp169fZ4chdANKpZLo6Gi2b9+OhYUFU6ZMYcCAAZ0dliAIHaS2thYdHR3V\na5lMhkKhwNTUVHVct7CwQE9Pj4yMjM4KUxCEVhBJbkHoJvr27YskSVy5cqWzQxHug5aWFm5ubvj7\n+2NnZ0dWVhZxcXGcOXOmQ5IQ1tbWBAUFoaenx6lTp343CQK3TuqmTp2KsbExmzdvbvMMBplMho+P\nD9ra2sTFxbUqoSMS3YIgPGp69+5NQ0NDmxZ09Pf3JyoqqsNicnFxoba2lsuXLzd7z8LCotucM1RW\nVqolKAThfp08eZJt27bRq1cvpk6dqpq9KQhC91VaWoqpqSlwq8yjhoYGCoUCMzMzLl68CNy6nnFz\nc+Pq1atqteYFQehaRJJbELqJPn36AIikYDegr6/PoEGD8Pf3x9TUlOTkZGJjY7l48WKrH4VvLUtL\nS4KCgjA2NubUqVNkZ2f/7hgDBw5k6tSpFBUVsW3bNkpLS9s0lo2NDe7u7kRHR7eqNEtTovvSpUtt\nGkcQBKGzeHl5cfr06Vb/rXZ0dOTKlSv3VRKqtf0DnDlzptl7lpaWLSa/H0XHjx8nKCios8MQHmHx\n8fFs3boVPT09pk2bhpubW2eHJAjCQ1JWVqZKcsOtUqBNi8xWVVWptjfd9PqjlAEThEeRSHILQjfR\ntABG091moXvo1asXvr6++Pv7I5PJiI+PJz4+nrKysnYdx8zMjKCgIMzNzYmNjSUzM7PFGddyuZzQ\n0FAmTpzIsWPH+Omnn9qUnNHX1yc4OJicnBzy8/Pv2d7Ly4uSkhKR6BYE4ZEgk8nw9PQkNTW11fuE\nhIQQGRnZIfFYWFigq6tLdnZ2s/c0NTW7TYmzysrKDl/EWeiekpKS2LJlCxoaGkydOhUvL6/ODkkQ\nhIesrq5O9TSQTCajR48e1NTUoFQqaWhooLa2FoD+/fujqalJVlZWZ4YrCMJdiCS3IHQTWlpamJiY\niCR3NyWTybC2tsbf3x8fHx9KS0uJjY0lOTlZbYbBg+rVqxeBgYH06dOHuLg4Tp8+3WK5lB49evDU\nU08RFBTE7t27OXr0aKvrysrlcnx9fYFbK5nfa79BgwaJRLcgCI+Mnj17oqWlxbVr11rV3sLCgoaG\nBsrLy9s9FplMhqOjI5cuXaKmpqbd++8KSktL0dfX7+wwhEdMamoqW7duRaFQMG3aNHx8fDo7JEEQ\nuggDAwPKy8tRKpX07t2b9PR04NbNYQcHBwoKCjrsCSxBEB6MSHILQjfSr18/ysrKaGho6OxQhA6k\noaGBo6MjAQEBuLu7k5+fT1xcHBkZGe32uzc2NiYwMBBbW1sSEhJITU1tccafubk5U6ZMwczMjK1b\nt7ZpZoOdnR3Ozs5ER0ffM1E/aNAgLl++3G0erRcEoXtzc3MjKyur1Tf/xowZw8GDBzskFicnJwDO\nnTvX7D0dHZ0OW+j4YYmJiSE4OLizwxAeEenp6WzZsoWamhqmTp2Kn59fZ4ckCEIXY2pqqiqtaGFh\nQWFhoeo9Nzc3lEpli8dUQRA6n0hyC0I3Ym1tjSRJlJSUdHYowkOira2Nh4cH/v7+9O/fn9OnTxMb\nG8vZs2dbnVy5G0NDQwICAhgwYABJSUkkJye3mEh3c3Nj6tSplJSUsG3bNq5evdrq/oODg8nIyOD8\n+fN3bevt7c2lS5dEolsQhC6vacHdpKSkVrXX09NDX1+/Q57GcnBwQCaTtVhDtE+fPo/8Wh61tbUY\nGRl1dhhCF5ednc2WLVsoLy9nypQpBAYGdnZIgiC0o6ioKORyeZtKOs6cOZPx48c32967d29ViRJz\nc3PVf8OttS5kMpkoWSIIXZRIcgtCNyIWn/xjMzAwwMfHh4CAAHr27EliYiJxcXFcunTpgRes1NfX\nx9/fHxcXF1JSUkhMTGw2+08ulzNixAiefvppYmJi2LNnT6se5ZPL5fj7+9PQ0EBSUtJdYxWJbkEQ\nHhX6+voYGRm1utTS6NGjOXbsWLvH0aNHDywtLcnNzW3299XU1LTNiwh3JUVFRZiYmHR2GEIXdubM\nGbZs2cKVK1eYMmUKQ4cORS4Xl8CC0NFmzpyJXC5HLpejra2NhYUFoaGhrF69ukPWgxg6dCiXL1+m\nV69erd5HJpOhUCjUSl7JZDJMTEyor69HV1cXExMTamtrVdc0urq69OnTh5ycHJRKJYsWLUIul/Py\nyy+r9V1QUIBcLm/1zW5BENqHOMILQjdiYWGBTCajqKios0MROlnv3r3x8/PDz8+PhoYG4uLiSEhI\n4MaNGw/Ur66uLn5+fnh4eJCenk5CQoLa7Aa4Nbt8woQJBAcH8+OP/5+9O4+rol4fOP4557Avh1UW\nRUAQBBVlUdwNV1xzKzU1Q00ty67XvNliqVlWbi03y6xcUq9aicvNXDEXRHZFVhdQUBBFkH2H+f3h\nZX4cWVJzQfu+X6/zesXM98x8ZzDOmWeeeZ6d/PHHH1RXV/9poN3Z2RlnZ2dOnDhBcXFxg+NqAt3i\niQVBEJo6V1dXUlJS7uqCXktLi5YtW8q1Px8kd3d3SktL69wgVCgUf/km6OMUHh4uSpUI9UpJSWHr\n1q1cvXqV8ePH07t3bxHcFoRHSKFQMGDAADIzM0lNTeXQoUMMHz6chQsX0qtXr0a/698PbW1trKys\n7uk9kiRRXl6OhYWFvExLSwsjIyOqqqqwsrIiLy8PCwsLjc/mdu3aUVZWJl9z6+npsXHjRpHdLQhN\ngPikF4SnSE3zyStXrjzuqQhNhEKhwN7eni5duuDl5cX169cJDw8nJibmLzUh09XVxcfHhw4dOpCY\nmEh4eHidL6uWlpaMGzcOW1tbNmzYwBdffFFvJmFtJiYm9OjRg7NnzzZ6s8bLy4v09HQR6BYEocnz\n8fEhKirqrsb26NGDmJiYB1JuqrbWrVsDcPHixQe63cetsrISPT29xz0NoQlJS0tj27ZtXLp0iXHj\nxtGnTx8R3BaEx0CSJHR0dLCyssLW1pYOHTrwz3/+k6NHjxIdHc2yZcvksZs3b6Zz586o1Wqsra0Z\nO3asxpPJNaVIjhw5QpcuXTA0NKRz586cPn26zpja5UpCQkJ45plnMDQ0xM7OjlmzZsm1tmtUVFRo\nZH9v3LgRLy8v5s2bR9++fdm8eTO2trZcunRJHuPq6srFixd5ceRItm/ahLW1NYMGDeLtt99u9JzE\nxsbSv39/DAwMsLCwYMqUKeTn52us79evHyYmJhgbG+Pp6cnRo0fl9QkJCQwdOlQ+TxMmTNC4Fqop\nv/Lll19iZ2eHubk5U6dOla/5as7Rna8+ffrI2wgMDMTDwwM9PT3s7e1ZunRpo8ckCE2N+MQXhKdM\ny5YtuXXrlmg+KdShUqlo06YNvr6+tGnThgsXLhAeHk5iYuJ9Pzaoo6ODl5cX3t7eXLhwgbCwsDpf\nHt3c3OjevTslJSVs3bqVdevWcePGjUbn2bVrV4qKijhz5kyDQXFvb28R6BYEocnT09PDysrqT/sO\nwO3yTR07duTkyZMPdA42Njbo6emRlJRUZ52xsbHGRfaT4sKFC9jY2DzuaQhNxNWrV9m2bRtJSUk8\n99xz9OvXTwS3BaEJateuHYMGDWLHjh3ysoqKCpYsWcLZs2f57bffuHnzJi+88EKd97777rssW7aM\n6OhoLCwsmDhxYoP7iY2Nxd/fn5EjR3L27FkCAwM5c+YMU6dO1RhXXV2NlpYWAO+99x67du1i1apV\nLFiwgH/84x8sXbqUqKgojWvryMhI9m3fzpToaNqkpHA1NZVhw4axd+9egoOD651PUVER/v7+qNVq\nIiIi2LlzJyEhIRrzmTBhAi1atCAiIoKYmBgWL14s38i9du0avXv3pkOHDkRERBAUFERhYSEjRozQ\nuFY6ceIECQkJBAUFsX37dnbu3MmXX34J/H9Jl5pXZGQkpqamcpA7KiqKsWPH8txzzxEXF8enn37K\nJ598wtdff93geRaEJkcSBOGJ88cff0gKhULKzs6usy4iIkJatGiRlJaWprH8pZdekoYNG/bQ5/bM\nM89Ir7/++kPfj/Dg5OXlSZGRkVJYWJh06dIlqbq6+r63VVlZKZ09e1Y6deqUlJubq7GupKRE+v33\n36XFixdLixYtknbv3i0VFRU1ur3s7Gzp+PHjUklJSYNjIiMjpevXr9/3nAVBEB6FkJAQqbS09K7G\nbtmyRaqoqHig+w8MDJQWL15c5+9pfn6+lJCQ8ED39Sj88ssvD/wcCU+ejIwMadu2bdK+ffvEvwdB\naEIau/acP3++ZGBg0OB7ExMTJYVCIaWnp0uS9P/XvgcPHpTHnDx5st4xNdfHL774ojRt2jSN7Z4+\nfVpSKBRSVlaWPMeePXtKkiRJhYWFkr6+vrRt2zbp1q1b0urVq6XLly9L48ePlwYOHCitX79eKi8v\nlyRJkkYPGCBtAEkCaSFIdiCNHjBAmjJlitStWzdJkiTp0qVLkkKhkKKioiRJkqS1a9dKJiYmUmFh\noTyfo0ePSgqFQkpOTpYkSZLUarW0cePGes/J+++/L/Xr109jWU5OjqRQKKSIiAj5eOzt7TWu5aZP\nny7179+/zvaKi4slHx8facyYMfKyCRMm1NnHokWLJDs7u3rnJAhNkbi9LQgPQFNqrGFvb4+dnR06\nOjoayxUKBQqFosFt1tcco7i4mMGDB+Pk5ERycvJdze3P9iM0PWq1Gh8fH3x9fdHX1yciIoLw8PBG\ns60bolKp8PDwwNfXl6tXr3Lq1Clu3boF3M5mHDx4MK+99hqtWrXi9OnTrFq1ipCQEKqqqurdnrm5\nOd26dSM6OrrB5m0+Pj5cuXLlvuYrCILwqHTq1Omuy5b07t2bgwcPPtD9u7i4IEkSKSkpGsuNjY0p\nLCx8oPt62Gr6PNRk3wl/P9evX2f79u2cPn2aUaNGMWjQIPHvQRCeEJIkaVwvRkdHM2LECBwdHVGr\n1XTu3BmgzhNQHTp0kP/b1tYWoMHv/1FRUWzevBljY2P51bNnTxQKRb3XtQkJCZSWljJ16lTs7OyY\nO3cubdu2JTAwkLS0NExMTIiPj2/0uD788EPOnDnDzp0766xLTEykY8eOGk0uu3XrhlKpJCEhAYC5\nc+fy8ssv069fP5YuXcq5c+c0juf48eMax2Nvb1/neNq2batxbm1tbeucI0mSCAgIQJIkNm3aJC9P\nSkqiR48eGmN79OhBenr6E/c9Qfj7Et8EBOEBqGmssWnTJqqqqsjKyiIoKIiFCxeyadMmgoKCMDAw\neGD7a6yxhpWVFT4+PnUC4JIk3VNzqVu3bjF06FCKiooICQkRjwT/TVhbW2NtbY0kSaSmphIWFoaW\nlhYuLi6o1eq73o5SqaRdu3ZIksS5c+dISkrCxcUFS0tLLCwsmDx5MikpKfz3v//l0KFDnDp1imHD\nhuHq6lrnJomWlhbdu3cnMTGRmzdv0r59+zpjate8vdemM4IgCI+CtrY29vb2JCcn4+zs3OhYOzs7\nTp06RWFhIUZGRg9k/87Ozri7u2Ntbf1Atvc4xcfH06pVq8c9DeExuHnzJn/88Qf6+vqMGjWqTlKH\nIAhNX0JCgvw5WFPGY+DAgWzevBkrKyuysrLo1asX5eXlGu/T1taW/7vmWqChHhaSJDF9+nT++c9/\n1lnXvHnzBrezfv16HB0dOXHiBNbW1lhZWWFvb09eXh4pKSl4enoy4803eSk4GEpKOANkKhTMePNN\n7OzsmD17Nu+88w579+6td071qZnDwoULmThxIvv27ePAgQMsXryYNWvWMGXKFCRJYtiwYaxYsaLO\n+2tf+9x5s0+hUNQ5Rx9++CHBwcFERESgr69/T3MUhKZOZHILwgMgNbHGGkOGDGHatGl1aiPfrYyM\nDHr16oVKpeL48eNygDsnJwc/Pz8M9PTQUqlwdHRkw4YNdd5fVVXFu+++S7NmzbC2tuZf//qXxgem\no6MjH3/8MTNnzsTExISWLVvW+cDOy8tjxowZWFtbo1ar8fPz08iA27BhA8bGxuzfvx83NzcMDQ0Z\nMWIE+fn5bN++HVdXV0xNTQkICKCsrEx+3/79++nVqxfm5uZYWFgwaNAgjRqlNRntgYGBDBgwAEND\nQ9q1a8fhw4fv61w+qRQKBY6OjnTp0oWOHTuSnp5OeHg4Z8+epbS09J624+bmRteuXbl16xanTp2S\na2g7OTkxe/Zshg0bRllZGdu2bePHH39ssMa2u7s7tra2nDx5ss6XXvj/jO6srKz7O2hBEISHzM7O\njpycnDqNeuszcOBADhw48MD2ra+vT7+8JAgxAAAgAElEQVR+/cjNza2zrr6L4KYsKSkJb2/vxz0N\n4RHKycnh119/JSQkhOHDhzNs2DAR4BaEJq6+wGhcXBwHDhzgueeeA27/Pc/Ozmbp0qX07NkTV1fX\nB9Jvx9vbm7i4OJycnOq8aupcl5eXy0Hhtm3boqurS3p6OnZ2drRo0QJzc3N8fHzQ0dFBW1tbfkLb\n39+fjTt3smfAAM45OWHn4IC/vz8A77zzDllZWXz//fca82nbti2xsbEaGdEhISFUV1fj7u4uL2vd\nujWzZ8/mt99+Y9q0afzwww8ax2Nvb1/neGrfDP+zYPSvv/7K8uXL2b17t0awH25fa93ZEyQ4OJiW\nLVtqZKALQlMmgtyC8BA9rsYaO3bsICYmpk5jjbtx/vx5evTogaOjI4cOHcLExERet3//fqJOnuTd\nsjI+ra4mJyODGTNmcOTIEXmMJEls2bIFHR0dTp06xddff80XX3zB9u3bNfbz+eef07FjR06fPs38\n+fN56623CA0NlbcxdOhQrl27xt69ezlz5gy9e/emb9++ZGZmytsoKytj1apVbN26laCgICIjIxk9\nejRbtmwhMDCQXbt2sWfPHr799lv5PcXFxcydO5eIiAiOHTuGiYkJw4cPr9Oo87333mPOnDmcPXuW\nzp07M378eIqKiu75fD4NtLS0cHd3x9fXFxcXF5KSkggLC+PcuXMNlhm5k0KhwMXFha5du1JYWEhI\nSAgZGRkolUp8fHyYO3cu3bp1IyMjgzVr1rBz5856z7elpSVdunQhIiKi3scTfXx8SE1NFYFuQRCa\nLG9vb43SYA0xMTFBW1v7gTbXtbCw0LhBXsPS0pLs7OwHtp+Hqbq6GoVCIZoK/k3k5eWxY8cOTpw4\nwZAhQ3j22WflAJUgCE1baWkp169fJyMjg5iYGFatWkWfPn3o1KkT8+bNA26X2tTV1eXf//43KSkp\n7N27l/fff/8v73v+/PmEh4fz6quvcvr0aS5evMhvv/3GK6+8Io8pKyuTb5YZGxszb948PvroI7Zu\n3Upubi7x8fFs376ddevWYWtrS1lZmRyk9vf3Z8fBg4x78UWNILOpqSnvvvuu3OyxxsSJEzEwMGDy\n5MnExcVx/PhxZs6cyZgxY3BycqKkpITXXnuNY8eOcfnyZcLCwggODqZdu3YAvPbaa+Tl5TFu3DjC\nw8NJSUnh8OHDzJw5UyNw3tiT23Fxcbz00kssXboUOzs7uQFlzfeCN998k2PHjrF48WLOnz/Pli1b\nWLVqFW+99dZf/G0IwiP0qIuAC8LTpKahxLBhw5pcY43NmzfXaazRWOPJmmPR1dWVevbsKVVWVtYZ\nU7vJhgTSBpBaWFlp7PuZZ56RunfvrvG+AQMGSC+//LL8s4ODgzRhwgSNMS4uLtJHH30kSZIkBQUF\nSUZGRnWaY3l6ekrLli2TJEmS1q9fLykUCun8+fPy+nnz5kkqlUqjIWdAQECjx11YWCipVCrp5MmT\nGudh7dq18pj09HRJoVDIY4Tbbt26JUVEREihoaFSamrqPTesvHz5shQSEiKlpqbKy7Kzs6VNmzZJ\nixYtkpYsWSKdOHGiwUZScXFxUnx8fL3rIiIipBs3btzTfARBEB6VzMxMKTEx8U/HlZWVSVu3bn2g\n+w4PD693P2fOnHmg+3lYQkNDG/zbLzw98vLypB07dkiBgYFSQUHB456OIAj3KCAgQFIoFJJCoZC0\ntLQkS0tLqU+fPtLq1avrfLffvn275OzsLOnp6UldunSRDhw4ICmVSunYsWOSJN2+9lUqlRrXeJcu\nXZKUSqXc2LG+MZGRkdKgQYMktVotGRoaSh4eHtLChQvl9cOHD69znfjpp59KLi4ukra2tmRsbCwN\nHDhQWr16tVRVVSV99tln0r///W+N8YsWLZI8PDw0lpWVlUkODg4a85MkSYqNjZX69esn6evrS2Zm\nZtKUKVOk/Px8SZIkqby8XJowYYLk6Ogo6erqSs2bN5dmzpyp8ffvwoUL0nPPPSeZmZlJ+vr6Ups2\nbaQ33nhDbogZEBAgDR8+vMH51Vw/3/nq06ePPD4wMFDy8PCQdHR0JHt7e2np0qV1freC0JSJmtyC\nwO3GkT/99FOd5V27diUkJKTB99nb25OZmcm8efPk5np3kupprLF48WJiYmLIycmRS4rY2dlhZGRE\n8+bNkSSpwcYadz5WBLcbUSQnJ2tkS1dVVcmNKCwtLf/kDPy/kSNHsmPHDrZu3cqkSZPqHMseYCWQ\nARQBZTducOzYMVatWoWxsTE3b97E0dGR0NBQjI2NUavVWFhYaGSiKRQKjeOD27XRarJvo6KiKC4u\nplmzZhpjSktLNRpm6erq4uLiIv9sZWWFjY2NRj1yKysruZkHQHJyMu+//z7h4eFkZWVRXV1NdXU1\naWlpdO/eXR53L41N/q5MTU3p1KkTkiSRmZlJREQECoWCVq1a3dW/OQcHBxwcHOQGlTY2Njg6OjJp\n0iQuX77Mnj17CAoKkut1u7m5afy/1K5dO65fv87Jkyfx9fXVqNPn4+NDZGQkCoXinv79C4IgPArW\n1takp6dTUFCAsbFxg+NqSqFduHBB4/PurzA1NeXWrVuYmZlp7Ke+MlBNUVpaGmPGjHnc0xAeksLC\nQg4dOoQkSfTv3/+e+oEIgtB0rF+/nvXr19/V2LFjxzJ27FiNZbWfFvXz86vz9Kijo+OfjvHx8WHf\nvn317rOgoIDJkyfTv39/jeU12dW3bt0iNjaWqVOnEhkZiVKpxMTEhOvXr1NZWSmXOVm4cCELFy7U\n2IaOjg6XL1+us8/27ds3WAJTW1ubLVu21LuuRuvWrfnll18aXF/f+a49v4CAAAICAhrdx6hRoxg1\nalSjYwShKRNBbkFAs3FkbY3V+quoqJAbQDb2yOyfNdZwc3NDkiR27tyJm5sbc+fO5dy5c8TExDBw\n4EB5fnBvjTUSExNxdnbG0dHxrs5BjbfeegsfHx8CAgKoqqripZdekteZ2duzHpgKOAKf6+jg0qYN\n1dXVGBsbU1BQQFFREVlZWRp1RBMTEykpKeHTTz/FyMiIwsJCzp8/z7Fjx1Cr1RgbG1NRUUF5eTmS\nJFFdXY21tTXBwcF15lf7Yqe+xhq1A501y2qft2HDhmFvb8/atWtp0aIFKpWKtm3b/qXGJn93CoUC\nW1tbbG1tqa6u5tKlS6SkpKCtrY2Li8ufNk2zs7PDzs6Oa9euERoaSrNmzXB2dub1118nJiaG/fv3\n8/PPP2Nra8vw4cPlmw5wO1BkampKWFgY7u7uWFhYyHPq1KkTkZGRACLQLQhCk+Pp6cnJkyfp2bNn\nozU0/fz82Lp16wMLcjs6OhIXF6cR5H5SlJeXo1QqRamSp1BxcTGHDh2ioqKC/v37Y2pq+rinJAjC\nU+zq1avEx8fj4eGh8fdGS0uLqqoqzM3NKS8vl6//JEnCwsKCzMxM0tPTcXBweFxTFwShEeIboiBw\n+0NLV1cXKysrjVftDzylUsk333zD6NGjMTIy4r333pObFGZnZ8sXqElJSTz77LOYmppiaGjI3r17\n6dWrl7zuzsYaNczMzGjTpg1z584FkIPEsbGxTJo0CUmS6Nu3L1OmTNGoVRwbG0tOTg7ffvstHTt2\nZPTo0aSlpdGpUydiYmIwMDDgyJEj7N27l3379tVpYgm3m16MGzcOSZIYPHgwly5d4qOPPmLatGms\nW7cOPz8/Zs2axdGjR9HS1iZQreb0gAFs2b0bpVKJlZUV06dPZ+7cuTg6OuLj48Mrr7zCxIkTGT58\nODY2NpiYmMhZ6FVVVVy9epWjR4+yZ88etmzZQkZGBlFRUXz88cekpKSQmZnJvn37SE5O5saNG5SW\nlqKtrY22tvZd14G+U3Z2NufOnePdd9+lb9++tGnThvz8fLmJiPDXKZVKnJ2d8fX1pV27dqSmphIe\nHk5cXNyfZgna2trSrVs31Go1oaGhXLhwAU9PT+bOnUv37t3JzMxk7dq1BAYGatSe09XVpUePHqSn\np3Pu3Dl5eU2g+9KlS9y8efOhHbMgCML9UCqVtGvXjri4uD8d5+bmRnh4+APZb+3mWbVpaWnV6U/R\n1ISEhIiGk0+Z0tJS9uzZw++//06vXr147rnnRIBbEISHrqYOde0ngAFUKhWVlZWYmZkhSRIFBQWo\n1WoKCgpo1aoVAJcuXXrk8xUE4e6ITG5B+B+pkSYNNRYvXswnn3zCqlWrUCgUGu8pLS0lJiaGPn36\n4OnpyZQpU9i4cSMtWrTgxRdfBDQba8yaNYvExMQ6+1WpVADs+uUXLp45Q/CZM3Tu3BmAFStWsHz5\nco2SHRMmTKBz584cPnyYZ599lk6dOpGVlUV+fj6rV68Gbjex9PHxoaysDF1dXSZOnCiX8KhpWjln\nzhzCw8NZvnw5a9asISsriy+//JIZM2bg7OxMdHQ0bdq0oaioiM8//xwvLy/+/e9/c/nyZY1sMEmS\nUKlUWFtbY21tDYCNjQ3a2tpMnjwZgJUrV9K/f3+mT59Ofn4+BQUF7N69GxsbG1xdXbGysuLQoUMs\nWbKE/v37Y2lpSWFhIRcvXsTJyQkHBwfi4uKoqKhg06ZNmJmZoVarSU9Pp7Kykhs3bqBWq9HV1dU4\nt2ZmZlhaWspZ3Onp6fzrX/+qkxEuPBg6Ojpys5SioiLi4+OpqKjA3NwcJyenBjPxam4yZWdnExYW\nhlqtpl+/fnTu3Jnff/+d2NhY4uPjeeaZZ+jevTtaWlpyCZyMjAxOnTpF586d5eWdOnWSS6nUZHoL\ngiA0Bebm5ly9erVO+ZA7+fj4sGXLFjp16vRAsphrLuJrf/7Z2tqSmZlJy5Yt//L2H5asrCz8/Pwe\n9zSEB6C8vJyDBw9SUlKCn59fnRJ1giAID1NNAsydn70qlYqqqioMDQ1RKBTk5+fTrFkzbty4Qbt2\n7di3bx9JSUnis0gQmiiRyS0I/7N//36MjY01Xu+8847GmPHjxzN16lQcHR01HlFSKBQcPnwYT09P\nbt26xdmzZ4mJieGjjz4iISGBLl26ANCsWTM2btzIrl27aNeuHUuWLEGhUMhZ4GVlZSxduhSAQVeu\nYH7kCLdychg9ejRKpRJvb2/Wrl3LiRMn5H2npaXx/PPPExwcTE5ODosWLWLKlCksWLBALu2xZMkS\nbG1tMTY25oMPPiApKYmMjAwAli9fzrhx45g2bRpKpZL27dvzzTffsGPHDsaNG8fq1as5f/48JiYm\nHDp0iB49ejBz5kyeeeYZjI2NmThxosZj1rWPp7FlKpUKU1NT7O3tadeuHSYmJjg4ODB27FhmzJjB\n2bNnGTduHEePHuXbb7/l999/R09PjwEDBuDl5SVn+aSnpxMVFcUff/xBQkIChYWFfPvtt3z22Wd8\n/PHHREREcPPmTX799VeCgoJYtGgRp0+fxsPDg9mzZ/PRRx/VCYY39ti4cH8MDQ3x8vLC19cXMzMz\noqOjCQ8PJz09vcEbTBYWFnTt2pXmzZsTHh7O1atXGTduHFOmTMHMzIw//viDVatWER8fL2+jefPm\neHp6curUKblOvkKhoHPnziQnJ5Odnf3IjlkQBOFueHh4EBcX96clsbp27UpQUNAD2aeDgwNpaWka\ny6ysrJp074ni4uI6JcmEJ09lZSW///47O3fupHPnzjz//PMiwC0Iwl+2YcOGRntc3OnGjRsYGRnV\nSXbS0tKisrISHR0dFAoFeXl5mJqakpubi56eHmZmZnJfpwctICCA4cOHP/Dt1lbzJHp0dHSDY1q1\nasWqVase6jwE4WFRSHeTvioIT7mAgACuXr3K2rVrNZabmJjImZ9KpZKNGzfKWdlw+0PCycmJyMhI\nvL29GTJkCBYWFnVqezdGqVSip6eHSqWipKQElVLJmIoK/gPMBX4H7Hx9+fyHH9DT00OpVNKmTRv+\n85//MGrUKD7++GOWLl1Kr1696NevH2PGjKFNmzYAfPfdd7z66qtcu3ZNzqq+dOmSnJnt6elJu3bt\nSE5O1rhwlCSJkpISQkJC6NKlC35+fjg7O/Pjjz/e3wl+yCorKykoKKCgoEDODM/Pzyc3N5fc3FwK\nCgooKSmRv4x06tRJPh9KpRIjIyO5SaaBgYEIcj8ikiSRnp5Oenq6XObkzkcGa8vPzycxMRF9fX3c\n3d1JSEhg3759lJWVYW1tzbPPPiuXxJEkiTNnzmBkZCTXsZUkiYiICJydnUVGtyAITUp+fj7Jycl4\neXk1Om779u2MGDECPT29v7Q/SZKIjIyUnxSrERERUWdZU3Hw4EE8PDw0+jIIT47KykoOHz5MXl4e\nvXr1qreRuiAID1ZAQAA//fQTcDt427JlS0aPHs3ixYsxMDB4zLN7sDZs2MDs2bMpKCi4q/GfffYZ\n1tbW9TZijIqKwsfHh3Xr1uHm5kb37t2JjIykU6dObN++naSkJGbMmMELL7yAh4cH//73vx/IMRQU\nFCBJ0kNtuHtn/KI+2dnZGBgYoK+v/9DmIQgPi3hGXxD+R19fHycnp0bHGBoaNrr+zhImd2vFihUM\nGjQItVrNzAkT8D90SF4ncftxqsDAQOD2RUJ1dTW7d+8mMTERlUrFG2+8QUpKClu3bmXhwoW8/PLL\nDBkyhCtXrgC3S5LcuHEDPT09srKygNsfohUVFfU2raxRcwGiUCj+9NgfJy0tLczMzBp91FuSJIqL\niykoKMDU1FQOEFRXV1NYWEh+fj5paWkUFxfX+zvU1dWVm2QaGxuLbLIHQKFQyE0nq6qqSElJ4eLF\ni+jo6ODq6lrny7daraZLly4UFhZy+vRpdHR0mD17NqGhoZw8eZLvv/+etm3b4u/vj1qtxsvLiytX\nrhAWFkanTp1QqVR07tyZ8PBwFApFowF1QRCER0mtVqOnp8eNGzewsrJqcFy/fv04cOAAI0aM+Ev7\na+hmblO+yZuXlycC3E+g6upqDh8+TE5ODj169GjS5XAE4WmjUCgYMGAAmzZtoqKiguPHj/Pyyy9T\nXFwsl7Ws7c4yVk+rsrIySktL5aSnhmhra1NaWqqxzMbGhqSkJJKTk+t9WvmvuJdM9IY8iN+hSAYS\nnmSiXIkg/M+D+IDy8vIiODj4nhs32djY4OTkhKWlJTPefJP5+vpsBHKBC8A/Fixg8uTJjB07Fnt7\newCGDBkiN/fz9PRk8ODBzJgxQ65bHB0dzdWrV5EkiUOHDhEYGMh//vMffv75ZyRJYvPmzSxduhRt\nbW1+//13du/ezf79+zl27Bjh4eHEx8dz4sQJjh8/Tn5+Pjdv3uT8+fOkpaVx48YN8vPzKS8vv6+g\n/uNQE6i3sbHRyIBTKpWo1Wrs7Oxwd3fHx8eHTp06abx8fHxwcXHBwMCAW7duER8fT2RkZJ1XdHQ0\n58+fJzMzk6Kioifm3DQFKpUKFxcXfH19cXd3Jzk5mbCwMBISEur8/2RkZISvry+urq4kJCRgZmbG\nq6++ipubGwkJCXz55ZccPXqUiooKWrZsiYeHBydPniQ/Px+FQoGvry8XLlyQG84IgiA0BW5ubpw/\nf77R5sqWlpZUVVXJ5Zj+ChMTE3JzczWW6evrU1xc/Je3/aDl5uaKjLInTHV1NUFBQfz888+4uroy\nfvx4EeAWhEdMkiR0dHSwsrKiRYsWvPDCC0yaNIldu3YBsGjRIjw8PNiwYQPOzs7o6emxd+9elEpl\nnVefPn3k7YaEhPDMM89gaGiInZ0ds2bN0sig9vPzY9asWbz55ptYWFhgZWXFV199RWlpKa+88gqm\npqY4ODiwdetW+T19+/Zl9uzZGvPPz8/HwMBAnm9gYCAdOnTAwMAACwsL/Pz8GiyzlZyczIgRI7C1\ntcXIyAgfHx/27t0L3G46GRERwaxZs+Txhw8fRqlU8tlnn8nLvvvuO5YtW6ZxPtu3b49CoSApKane\n/S5btozWrVtjYGBAhw4d2LJli7wuICCg3nNbk21/Z7mSsrIy5syZg42NDfr6+nTr1o2TJ0/K648e\nPYpSqWTfvn34+vqiq6vLwYMHuXLlCiNGjMDCwgJDQ0Pc3d3Zvn17vfOtrq7mtddew8nJieTkZAAc\nHR1ZuXJlveMFoal7+m/TCcJdKi0t5fr16xqBSZVKdU91AmfNmsWaNWsYO3Ys7733HqampkRERNC2\nbVs6dux4V9vw9/dn486drF25kmuZmehdvMjPP/9M586dycnJYcWKFYwZM4aJEydSUlLCvHnzGDt2\nLA4ODly/fp3vv/8ef39/3n//fQ4cOMBPP/3ESy+9hL6+PqWlpaSkpPDVV1/h7e2Nvb09arWat99+\nm927d+Pr64tSqSQjI4OEhAT5QzY7O5vk5GSNLyI1FAoFOjo66OjooKenh56eHgYGBhgYGMg/N/bS\n1tZu0pljcPsYa+bb2L+HqqoqCgsLKSgoIDU1lZKSknoD3Xp6enJ5FGNj479FxsS90NXVxcPDA7j9\nxEFsbCyVlZVYWlri6OgoN10zMDCgc+fOlJaWEh8fj7OzM506deLAgQMcO3aMsLAwhgwZQvv27enV\nqxfR0dGYmZnh5OSEr68v4eHhuLi4iIxuQRCaBIVCgbe3N9HR0Y2WDBk0aBC//fYbY8eO/Uv7c3R0\nJCEhAU9PT3lZTVPmmjJPTUVwcDA9evR43NMQ7kJ1dTXHjh0jMzMTX19f+vXr97inJAh/a3deZ+nq\n6lJeXi7/fOnSJbZt28aOHTvQ0dHBxcWFzMxMef3Vq1fp37+/HOSOjY3F39+fDz/8kHXr1pGdnc2c\nOXOYOnUqv/zyi/y+LVu28OabbxIeHs7u3buZM2cOe/fuZdiwYURHR7NhwwamTp1K3759sba2ZsaM\nGbz22musXLkSHR0dALZu3YparWb48OFkZmYyfvx4PvvsM8aMGUNBQQFhYWENHndRURFDhw5l6dKl\n6Ovrs23bNkaPHs3q1av5Zd06Uq9cITU9XX6C6ujRo1haWnL06FH69+8PQFJSklyq1NTUlLy8PMzN\nzTExMeH69et19vnee+8RGBjIN998Q5s2bQgJCWH69OmYmZkxZMgQvvrqK42g+Q8//MAnn3xCp06d\n5N9V7d/XW2+9xS+//ML69etxcnJi5cqVDBo0iAsXLmBjYyOPe/vtt1m5ciWtW7fGyMiIKVOmUF5e\nztGjR1Gr1Q0G5CsqKpg8eTLx8fGEhITI23zQGeqC8CiJyIog8P+NI+98DNbOzq5OY6b63lujefPm\nHD9+nH/961/06dMHhUJBhw4d6tT6/jP+/v74+/uTl5fHe++9x5EjR/D19UVPT4+RI0fy5ZdfArfL\ndOTm5hIQEMC1a9ewsLBg+PDhrFixAqVSib6+PgqFAhsbGzmQV7Osc+fOeHt7M3jwYHr37s2CBQv4\n7rvvqKqqwsnJiVGjRjF//nxKS0s5ePAgLi4ujBs3jtLSUo1XSUkJxcXFFBcXU1paSk5ODpmZmXed\nzd6jRw9atGhRJ/itq6srBzOfFCqVChMTE0xMTBocI0kSZWVl5Ofnk5OTQ2pqKpWVlXXGKZVKuTSK\nWq2Wf29/N8bGxnK9uKysLKKiopAkCXt7e6ytreUbED4+PpSXlxMXF4ePjw8qlYqgoCACAwM5ceIE\nzz77LD4+Ply+fJmIiAh8fHzw9fUlLCyMNm3aNFrqRhAE4VExMDDAzMyM9PR0WrRo0eAYtVpNWlqa\n/HTX/dDR0anzWW1iYsLFixfve5sPS0lJifg73cRVV1cTHBxMeno63t7eGlmfgiA8PrUTbsLDw9my\nZQsDBw6Ul5WXl7Np0yaNRJ6aslklJSXMmDGDvn378sEHHwCwfPlyxo0bJ5e6dHZ25ptvvsHb25ub\nN29iaWkJQPv27eX3zJ07l08//RR9fX05W/uDDz7gs88+4+TJk4wePZpRo0Yxe/Zsdu7cybhx4wBY\nt24dkydPRqVSkZGRQWVlJWPGjJE/+9q1a9fgcXfo0IEOHTrIP7/77rts2rSJOa++yur/XXtNBb74\n4guWLl3KsWPHmDdvHkuWLKG6upqLFy+Sk5Mj97pq1qwZ169fx9TUFEtLS3JzczU+Q4uKivj88885\ndOiQfFPWwcGBsLAwVq9ezZAhQ1Cr1XK97RMnTrBkyRK2bdtG27Zt5d9Vze+rqKiINWvW8OOPPzJ4\n8GAA1qxZw5EjR1i9ejVLliyR971o0SI5MA+QlpbGmDFj5KQhBweHOuensLCQ4cOHk5+fz/HjxzE1\nNW3wXArCk0QEuQUBWL9+PevXr290TH0dlB0dHes8Vty2bVv5Uai70VhnZhMTE5599lmaNWvGhAkT\n6mRWaWtrazwCdSc/Pz/CwsI0MlXrm7OPjw/79u2rdxt6enoaj0XdrZpg7p1B8Ttf3t7e6OrqUlpa\nSllZGbdu3ZLX/Vm5D0mS5Mad9b2aYpC8dlZ4Y3VXa7LC8/PzuXz5coOPj+vp6WnUCn+as8KbNWtG\ns2bNkCSJK1euEBERgUqlonXr1piYmKCjo4O3tzcVFRXEx8fTq1cvCgsLCQsL48cff8TNzY1BgwZh\naWlJcHAw3t7edOnSRQS6BUFoUlq3bk1ISAhWVlYN9n8YOHAg27ZtY8KECX9pXyqVSqN+5/32FnmY\nrl+/3ujNY+Hxqq6u5tSpU6SlpdGxY0d69+79uKckCEIt+/fvx9jYmMrKSioqKhg5cqRGo0Q7O7t6\nn1SVJImAgAAkSWLTpk3y8qioKJKTkzXKX0iShEKhIDk5GUtLSznRqzYrKys56Ar/31OpptyIrq4u\nL774IuvWrWPcuHHEx8cTEREhl/Lw9PSkf//+tG/fnoEDB9K/f3+ee+45Oah+p6KiIhYvXszevXu5\ndu0aFRUVFBUV8Ywk8dL/xnwLbPvpJxYsWEBERAQ7duzg22+/JT4+nsrKSpo3b46JiQnV1dWo1Wou\nXLgA3C4ddvHiRUpKSuT9JSQkUFpair+/v0ZiUkVFBa1atdKY2+XLlxkzZgwLFy5ssMdGcnIyFRUV\nGk8xKZVKunXrRkJCgsbYmkzwGm2rgUQAACAASURBVP/4xz945ZVX2L9/P/369WPUqFF1mkxOmjQJ\nW1tbjh49KsqBCU+VpzcaIghPiV69ehEVFcWBAwdwdna+58BtTQC5dh3qR6F2MPdu3G9jy6qqKo1g\nem5u7l0Hye+c552Z5CqV6r7m9CDcbVZ4aWkpBQUFZGdnc/ny5XpruapUKoyMjORg+JOeFa5QKLC3\nt8fe3p7KykqSk5M5d+4curq6uLq6oq+vj6enJ1VVVcTHx9O7d2/S09NJSkri/PnzdO/enR49ehAb\nG4uVlZUIdAuC0OT4+PgQFRVF165d612vVCpxdHQkJibmrsuh1cfe3p60tLQ6jbdrAhZNQWhoKAMG\nDHjc0xDqERYWRkpKCu3bt+eFF1543NMRBKEezzzzDGvXrkVbW5vmzZvXub5p6Brsww8/JDg4mIiI\nCI0gqCRJTJ8+Xc7krq158+byf995k1ahUNS7rHbC18svv0yHDh24cuUK69ato3v37nImtVKp5ODB\ng4SGhnLw4EF+/PFH3nnnHY4dO1YnoA4wb948Dhw4wMqVK3FxcUFfXx9fHx+q8vLkMW5A/K1bnDp1\nitatW2NlZYWfnx+RkZHk5eXh5eUF3A6Y124K6ebmRmhoqEYSUs1x/Pbbb3Wesqp93IWFhTz77LMM\nHjyYt99+u868/0xNkldtd/4Op06dir+/P7///juHDx+me/fuvPPOOyxcuFAeM2zYMDZu3EhwcLD4\njBWeKiLILQhNnL6+Pr179+bw4cPExsbe88Wsra0tmZmZODo6PpwJPmYqlUquAX4/qqurNTLLawfJ\n78yyr++C/3EGyRUKBfr6+ujr6/9pVnhBQQEFBQVcunRJI+ugNn19fY1a4Y8zyH+3tLS05C+/paWl\nnD9/ntLSUtRqNa1bt6ZDhw5UV1eTmJiIhYUFFy5ckL+wDx48mMrKSqKjo+Ua3W5ubuJxPUEQHjtd\nXV1sbW25fPlyg5/f3bt3Z8uWLXh4eNz3k0sWFhZcunRJI8htbm5Obm5uk7npV15eft+f8cLDERkZ\nyYULF3B3dxfBbUFo4vT19evcyPwzv/76K8uXL+fo0aMagWsAb29v4uLi7nmbd6Nt27Z06dKFtWvX\nsmXLFpYuXVpnTNeuXenatSsffPAB7dq14+eff643yH3y5EleeuklRo0aBdy+TqhWKIhQqdj4v6Sg\n33R1KSopYcuWLXKJJT8/P7755huuXbvGrFmzqKqqIj8/XyPI3bJlS4yNjTVqm7dt2xZdXV0uX76M\nn59fvcdXXV3NxIkTMTEx4Ycffmj0XDg7O6Ojo0NwcLCcCV5VVcWpU6eYNGlSo++F2z02pk+fzvTp\n01m2bBlffvmlRpD75ZdfxsvLi5EjR7J7926NcieC8CQTQW5BeAJ06dKFU6dOcejQIdq1a3dPJSks\nLCwavUj+u1MqlX85SF47kzwvL4/r169TWlpab1Z1jZqAuUKhQFdXt95A+YMKMqtUKkxNTRsN3tZk\nhefn53Pz5k0uXbrUYFZ47Vrhenp6TSbbT09PT/6Sm5eXR0xMDJWVldjY2NC2bVvc3d1p2bIl586d\n4/z58+zatQsLCwv69etHcHAwPj4+xMbGikC3IAhNgoODA6GhodjY2DT4VJSXlxcnTpzgmWeeua99\n1Pf3u3nz5iQnJzeJIPfly5cbfBRdePROnz5NYmIibdq0EcFtQXhKxcXF8dJLL/HJJ59gZ2cnN6HU\n0dHB3Nyc+fPn07VrV1599VVmzJiBsbExSUlJ/Pbbb6xZswbQrC1d425LYU2fPp2ZM2eiq6sr1+aG\n20+OHDp0iEGDBmFlZcXp06e5cuWKXM/6Tq6urgQGBvLss8+ipaXF4sWLkSSJ3v36sed/c9ny5psE\nBASwefNmtm3bBtwOck+bNg1Jkujfvz/x8fHk5+fTokULOfNcqVRiaWlJdXW1HOg2NjZm3rx5zJs3\nD0mS5LKJoaGhqFQqpk+fzuLFiwkNDeXw4cNkZ2fLczU1Na3zOW9oaMirr77K/PnzsbS0xNHRkc8/\n/5ysrCxmzZrV6Dn8xz/+wZAhQ3BxcSE/P599+/bVW798+vTpSJLEyJEj2bVrlwh0C08FEeQWhCeA\nlpYW/fv3Z/fu3URERNCtW7e7fm9TCUA+rWoafN5vLbM7g+T5+fncuHGjTiZ5Q18MG8okv9cgee2s\ncGtr6wbHVVZWyrXCU1JSGs0Kr10r/FFnhZuYmODj4wPcrucaEREB3K5J7+7uTmJiIqdPnyYlJYWf\nf/4ZFxcXqqurcXJyIjExEXd39ycq0O3n50eHDh346quvHvdUBEF4gHx8fAgPD9eoyVlb27Zt2bp1\nq0Zd7XulVqvJy8uTy2Pp6+s3+Lf9UYuKimL48OGPexp/e7GxscTGxtK6deu/XAdeEIRHR6FQNHot\nWN/6qKgoSkpKmDNnDnPmzJGX+/n5ceTIETw8PDh+/DgLFizAz8+PqqoqnJycGD16dKPbvdtr0nHj\nxvHGG28wduxYjTIcJiYmhISE8PXXX5Obm4u9vT0ffPCBxt+k2vtYtWoV06ZNo1evXpibmzNnzhzK\nyspo1qwZ69at0ziuX375Rb5Z7ODggLW1NYaGhrRu3Zq4uDiKiooAMDMz49atW1hYWGBubo4kSRQU\nFMjbWrJkCdbW1qxYsYJXX30VtVqNl5cXb731FgDHjx/n5s2bdZ7M3rBhA5MnT65z3j777DMApkyZ\nQm5uLt7e3uzfv1/jWq2+8ypJErNnz+bKlSsYGxvTv39/Vq5cWe97ZsyYIQe6d+/eTb9+/Rr9/QhC\nU6eQmlp3GUEQ6lVdXc3q1aspLCzkn//85z3V2I6MjKzTkEJ4OtQOkt/Z6LOxTPKaP/2NZZLfb8BE\nkiRKSkrIz8+Xy6TUDthnZ2ezfv16QkJCuH79OhYWFnTo0IE33niDIUOG3Nc+70Z1dTWpqalkZWWh\npaVF69atSU1NJSQkhNDQUI4dO8bNmzepqKjAwsKC7t278+OPP2o8nviwKZVKfv31V40LhbuRm5uL\ntrb2fde2FwSh6crIyKCoqKhO8+na60+fPs3QoUPva/vl5eUkJCTg6ekpL2sq3xt++eUXnn/++cc9\njb+thIQEzpw5g6OjI127dm2SDb0FQXi6ZGRk4ODgwPHjx+8psetBioqKwsfHh4qKCrZs2YKrqyvd\nu3enoKCA9PR03Nzc5L4/AwYMkLPABUF4/EQmtyA8IZRKJf7+/mzdupXg4OB7epxIT0+PkpIS0Tn5\nKfQgMsnLy8vlwHhhYSE3b9780yB5bQ0FyW1sbLCxsdEYe/nyZUaMGIGJiQnLly/H2dmZ/Px8goKC\nePnll9mzZ0+d7RsYGMhZ4UZGRvedFa5UKmnVqhWtWrWioqKCCxcuUFxcjJWVFf/5z3/o1q0bgwcP\nxsDAAF1dXeLj48nKynqkQW64+8c5a3uSss4FQbg3zZs3JzIykqKionpvZDVv3pyQkBAKCwsxMjK6\n5+3r6OhQUVGhsUypVFJVVfVYezMkJCTQsmXLx7b/v7Nz584RFRVFy5YtGT9+vAhuC4Lw0FVWVnLz\n5k3effddvL29H1uAuzZtbW1UKhWlpaUAGBkZUVhYSGZmJidOnODWrVvY2to+5lkKglCb+MYiCE8Q\nFxcXWrRoQWhoKPn5+Xf9PltbW65du/YQZyY8qZRKJXp6epiammJjY4OjoyNubm54enri4+Pzpy9v\nb29at26NpaUlWlpaFBYWcvXqVeLi4oiMjNR4RUVFMXHiRKqqqvj+++9p3bo1CoUCKysrpkyZQlBQ\nkLz/P/74g2nTpuHn58eAAQOYP38+Fy9eJCYmhsjISI4ePcqQIUOwsLBAT0+Pli1b8v7775OVlUVp\naSlr1qzB1dUVfX19mjVrxqBBgzSC9tra2nJzmwsXLmBmZsZnn31Gv379MDMzQ1tbm27dusnd1Zct\nW4ZCoWB4nz4cOHAAuB2wVyqVREdHA3D06FGUSiVHjhyhS5cuGBoa0rlzZ06fPi3vNy8vjxdffBFr\na2v09fVxdnbmyy+/BJDr5j///PMolUqNhj7//e9/8fHxkZsHLViwQCMo5efnx+zZs+WfHR0d+fjj\nj5k5cyYmJia0bNmSFStWPPh/QIIgPBJeXl6cPn26wZtgAwcOZP/+/fe9/Zqgdg0rKytu3Lhx39t7\nEOLj4/H19X2sc/i7SU5OZuvWrVy7do3x48fTq1cvEeAWBOGRCA4Opnnz5oSGhvL9998/0G3f+Z39\nXmhra1NWVgb8f5mPnj17snLlSiZMmICpqakcBH/Y/spxCMLfhcjkFoQniEKhYNCgQezcuZPi4mLU\navVdvc/c3JxLly49lC7Ywt9b7ZrgfyYnJ4fQ0FA+/vhjub6sJEkameRFRUVkZ2fLTVVatGhBRkYG\nK1as4IMPPmDx4sUAfPvttyQnJ/PFF19gY2PDtWvXyMnJISMjgzNnzvD666+zePFiOnbsSEFBgRxk\nr90008jICKVSiYODA7m5uZSUlNCnTx9atGhBQkICV65cITExkcOHD/PLhg0ogEFHj/JSWBgbd+6k\nTZs29R7nu+++y7Jly7CxseEf//gHEydOJCEhAYAFCxYQFxfH3r17sba2JiUlhaysLOB2eQArKyt+\n+OEHhg0bJmdQHjhwgEmTJvHVV1/Ru3dvUlNTeeWVVygrK2P58uXy7+HOmnyff/45H374IfPnz+f3\n33/njTfeoGfPnnTt2vXef9GCIDxWKpWKNm3akJiYWG+TLbVaja6uLteuXbuvrDJ7e3vS0tJo1aoV\nADY2NsTGxj62DLWaElciwPpoXL58mVOnTmFtbc24cePEeRcE4ZHz8/PTKG9Yn4CAAH766ac6y7t2\n7UpISEiD77O3tyczMxMLC4t7nlftTG4/Pz+sra25ePEiALt27WLdunWo1WpWr17N9OnT73n7giA8\nWCLILQhPGDs7O15++WUSExPrlIJoiGg+KTQFFy9eRJIk3N3d5WU1NcF1dXXlpmcAy5Yt03ivvb09\nI0eOZO/evQCUlpbSs2dPJk2apBEkr2neqa+vT+/evTEwMMDW1hYXFxckSZID4TU1zCVJQkdHh969\ne9O3b19MTU1p164dPXv2pH///iQlJbHzp5+YWVHBMuAFwKikhLUrV7Jy7dp6j3PJkiVy85oPPviA\nnj17kpGRQfPmzUlLS8Pb21uudVv7UXxLS0vgdukRKysrefnHH3/MW2+9xUsvvQRAq1at+PTTT3nx\nxRflIHd9/P395e7rr7/+Ol999RVBQUEiyC0IT6hmzZpx9epVjSaRtfn7+xMYGMj48ePveduWlpZc\nvnxZDnJraWlRWVn5l+d8v06fPo2rq+tj2//fRVpaGiEhIVhYWIjgtiAITZ5CoWDAgAFs2rRJY7mO\njk6D76moqEBbW1vju/W9UCqVSJKEJElyUklNOa+DBw+yb98+Xn/99QYD3OXl5Y3OTxCEB0t8kxGE\nJ1BN/eWSkpLHPBNBuHv3Umv6yJEjDBgwgJYtW6JWqxkzZgwVFRVkZmYC8Oqrr7J9+3a8vLxYsGAB\nMTExWFtb4+DgwJQpU+Qu759//jnx8fG4ubnRuXNnevToQb9+/Rg6dCijR49m9OjRDBkyhF9//ZUz\nZ87wzjvvYGlpyZo1axgxYoScqXGn1NRUdu3aBcCFCxfIzMyUH/Xv0KGDPK4mC7Lmsf+aeXt6evKv\nf/2L48eP/+m5iIqK4qOPPpKz0I2NjZk4cSLFxcVcv3693vcoFAqNecDtur01WePCoxEQEIBSqZRf\nzZo1Y/jw4Zw7d+6etrNo0SI8PDwe0izvza5du+jWrRtmZmYYGxvj7u6ucWG3YcOGR17H/u/E09OT\ns2fP1vv3VEdHB4VCwXffffen2XB3amo3w1NSUprMv/mnUUZGBtu3bycxMZHnnnuOAQMGiAC3IAhN\nniRJ6OrqYmVlpfGq3ZtGqVTyzTffMHr0aIyMjHjvvffuq8RgDZVKhUqlori4GLjd6yonJ4c333yT\nzZs3ExAQgL29vTzez8+PWbNmMW/ePKysrOjVq5c8r8DAQI1tOzo6snLlSo25r169mqFDh2JoaIij\noyNbtmxp8HxUV1fz2muv4eTkRHJyMtXV1UybNg0nJycMDAxwdXVl+fLl99XvRxCeVOLbjCA8oTp0\n6MDZs2fvery+vr784SwIj4OLiwsKhUIu3dGQ1NRUhg4dSrt27fj111+Jjo5m3bp1cmkTgEGDBpGa\nmsq8efO4efMmQ4cOZerUqcDtpjDR0dH8/PPP2Nvb88knn+Dm5qZRl768vJz09HTOnDnDH3/8wbZt\n2/jtt98oLi7G09OTmTNnIkkSwcHBDBwzhjXa2gBsBebp6NDd35/U1FQkSeLIkSN89913bN68GYA9\ne/bwxx9/EBcXR3Z2NvD/j943Nu+GSJLEokWLiImJkV+xsbFcuHBBzv6uj/b/5lxDoVDcc+BL+Gtq\nMo4yMzPJzMzk4MGDlJSUMGrUqMc9tfsSFBTE2LFjGTFiBGFhYZw5c6bRpwmEB0+hUODh4dHg53/L\nli3JzMzkzJkz97xttVpNXl6e/LOurq5ch/RRqqysRKFQiKDrQ3D9+nW2b99OTEwMY8aMwd/fHy0t\n8WCvIAhPjrsJ2C5evJhhw4YRFxfHa6+91uC4mhKD0dHRWFhYMHHixDpjdHV1UalUcj8sLS0tpk+f\nzubNmzly5AgGBgb89PXXjOrfX+7bs3nzZhQKBcHBwfWWV6lRX7nBhQsXMnLkSGJiYpgxYwaTJ08m\nKiqqznsrKiqYOHEiJ06cICQkBGdnZ6qrq7Gzs+OXX34hKSmJjz/+mKVLl7J+/fo/PWeC8LQQ3x4F\n4QmlpaWFoaGhxgVpY0TzSeFxMzc3x9/fn6+//pqioqI663Nzc4HbtakrKir4/PPP6dKlC61btyY9\nPb3OeAsLCyZNmsT69ev54Ycf2Lhxo9yMUaVS0adPHz766COCgoIoKChgxYoV/PTTTyxbtoxPPvmE\nH374gT179hAWFkZ2djbW1tZ07dqVESNG8MYbb+Dk5ESrVq1YtmwZy1evRgJ2du3K5j17+Oqrr+jW\nrRsKhYIhQ4YwcOBAnJ2dgdu1TY8fP86OHTvkwPeWLVvYtm0bR48eJTMzE39/f3744Yc689bW1tZo\n/gbg7e1NYmIiTk5OdV41dbuFpunOjCMvLy/mzJlDUlKSRvAwPT2d8ePHY25ujrm5OcOGDZOfItiw\nYQMffvgh8fHxckZ4zQXTd99912iD1fXr19O2bVv09fVp06YNX3zxhXxxOHXqVIYPH64x3+rqauzt\n7fniiy/qPZ7//ve/dO3albfffhtXV1ecnZ0ZNmyY3CBq2bJlTJkyhcLCQnmuH374IXD7xtL8+fNp\n2bIlhoaG+Pr6cvDgQXm/LVu25Ouvv9bY3/nz51EqlXLANi8vjxkzZmBtbY1arcbPz0/jwq8mi/zI\nkSO0b98eIyMj+vbty+XLl+UxV65cYcSIEVhYWGBoaIi7uzvbt2+X18fGxtK/f38MDAywsLBgypQp\nGo2eAwICGD58OF9++SV2dnaYm5szderUR/pklampKSqVSr6JVpuvry8GBgYcPHhQvil4txwdHTXO\nVfPmzcnIyPir071noaGhdOzY8ZHv92mWlZXFzz//TGRkJKNGjWLw4MEiuC0IwhNp//79Gk83Ghsb\n884772iMGT9+PFOnTsXR0REHB4cGt1VTYrBNmzZ88MEHJCUl1fnc09XVRaFQkJ+fjyRJbN68md9+\n+42goCCys7P5z5o1zElJYWRQEC+NGkVOTg5OTk4sX74cV1fXBnv4NGTMmDFMnz6d1q1b8+6779K3\nb98638sKCwsZPnw4qampHD9+XC5hqqWlxeLFi/Hx8cHe3p7nn3+emTNnsnXr1nuagyA8ycS3G0F4\ngrVt25b/Y+/Ow2s614ePf9feSXbmOTvzKBFCgsScRIghaqiD0lK/Umro4HU6OtXB1B5VnNKWQ2sq\nh2pRR1sqhsYcQ8QYUZkTIYMImWXY6/0jzTq2xNQi6PO5LtclK89e+9krZK11r/u578OHD9OpU6c7\njrWxsSE1NfUhzEoQbm3hwoWEhobStm1bZs6cSWBgILIsExMTwyeffEJGRgZ+fn7odDo+++wzBg4c\nyKFDh1iwYIHefj788ENCQkIICAigurqaH374AW9vb9LT0/nhhx9ITEzEzs4OWZZJTU2luLiYq1ev\nkpmZia2tLc2aNcPJyQlHR0d+/PFHEhMTadu2LT4+PlRUVLBgwQISEhKYMmUKUBvY+uijj7ByccHH\nx4ft27fz8ccfA7WZk8HBwUrQ8o033sDAwIC8vDxOnTrF559/jqGhIcnJyfz73//GxcUFBwcHJVNc\nq9Vy8OBBHBwccHd3Z8eOHYSHh6PRaLCxseHDDz+kX79+eHp6MmTIEAwMDDhz5gxHjx5l9uzZAEqt\nwNu5mzHC/XfjMS8uLua7774jKCgIjUYDQFlZGd26dSMsLIy9e/diZGTEnDlz6NGjB4mJiTz33HMk\nJCTw888/s2fPHqA24zYuLo7XXnuNVatWERYWRmFhITExMcp7ff3110ydOpUvv/ySkJAQTp8+zdix\nYzE0NOTVV19l3LhxhIeHk5OTo9wc7dixg9zcXP7v//6vwc/i7OxMYmIip06dqlcOJzo6mnlTp/I8\nsAGw1Gj4YuVKJZD+4osvkpaWxrfffoubmxtbtmyhf//+HD16lKCgIIYPH86aNWt47bXXlH2uWbOG\ngIAAWrdujSzL9O3bFxsbG7Zs2YKtrS0rV64kMjKS3377TfkM169f55NPPmHlypVoNBpGjhzJhAkT\n2LZtGwCvvPIKlZWV7N69G0tLS86dO6e8X2lpKVFRUXTs2JGjR49SUFDA2LFjGT16NBs2bFDG7du3\nDxcXF3bt2kVmZiZDhw6ladOm/OMf/7infxt/RosWLdi/fz+hoaF6Gc8GBgY89dRTbNy4kYMHD9K1\na9e73qdGo9ELjNvZ2enV6X5YLl26RFhY2EN9zydVQUEBv/76K8bGxjz99NN31SRaEAThURYREcFX\nN/XFublPRV3vmzu5XYnBOmZmZhQXF1NSUoIkSYSGhnL8+HGmTJmCuqyMuZWVjKwbXF7OWxcu8PSf\nWLF38319x44d2bp1q962ESNG4OzszO7du5UypnUWL17M0qVLyczMpLy8nKqqKry8vP7wfAThcSOC\n3ILwGFOpVNjb25Obm4ujo+Ntxz5q9TaFvyZvb2/i4+P55z//yeTJk8nOzsbOzo7AwEAlSyEoKIgF\nCxYwe/Zs3n//fUJDQ5k7d67STK28vJyKigreeOMNsrOzUavVuLm58dRTT7F27VoyMzPZu3cveXl5\nVFVV4ebmxqxZs5gwYQKWlpb1/i+Eh4dz7NgxXn75ZS5evKjUsFu9ejXDhg0DajOs161bxyuvvEKr\nVq1o06YNs2bNqpcJW7fs0MrKCisrKwwNDZEkiaFDh9KmTRuuX7/Od999R1ZWFgYGBnh6ejJ06FB2\n794N1GZh/vDDDyxfvhw7OzvWr1+Pu7s73377LQsWLGDu3LkYGBjg7+/PqFGj6r3v7dzNGOH+q8s4\ngtogqru7u97Nyrp16wBYvny5sm3x4sU4Ojry888/M2TIEMzMzDAwMNBrmpSZmYmZmRn9+/fH3Nwc\nd3d3vZu1mTNnMmfOHAYNGgSAp6cnkydPZtGiRbz66qt07NiRZs2a8c033zB58mRlDnVZzg2ZOHEi\n+/bto3Xr1ri5udGhQwd69OjBiBEj+GrePD6tqEAGNgNzKir4ftkynn32WVJSUli3bh3p6elKs9VX\nX32VHTt2sGTJEhYuXMjzzz/PnDlzSE1NxcfHB4C1a9cyZswYAGJiYjh58iT5+flKoG7GjBn89NNP\nrF69mrfffhuoLXWxcOFC/Pz8AHjrrbf0SgJlZmYyePBgpd7zjRlea9eupaysjNWrV2NmZgbAV199\nRbdu3fTmZWVlxeLFi5EkCX9/f4YMGcKuXbseapBbkiTatGnD8ePHCQkJ0fteixYt2LNnD/v27aNt\n27aYm5vf9X5VKpXSUKsxfl9UVFSIFSr3wdWrV9m5cyeGhob0799fBLcFQXhimJiYKOfjW6k7h9/J\njaX96s55N5f2s7W1JT09XdneokULxo0bx6RJk6Cmhn53+f6SJNVLNqlbyXmv+vXrxzfffMP+/fvp\n2bOnsv27777j9ddfZ968eXTu3BlLS0u+/PJLNm3a9IfeRxAeRyLILQiPOV9fX2JjY+8Y5BaER4WT\nkxOff/45n3/++S3HTJw4kQkTJpCfn09eXh65ubksWbKENWvWUFlZiZmZGS+88AJQe7Hr5OSEm5sb\njo6OaLVabG1t7zpQ0rp1a1auXHnHcZ06darXkObG0hBdu3atV2rEy8tLb9vMmTOZOXOm3hhZliks\nLFQ+66BBg8jJyamXmduzZ0+GDh2qZKBrtVquXLmCjY2N3jiAtLS0evO/eYzwcNyYcXTlyhUWLVpE\nr169OHz4MG5ubhw7doy0tLR6zRrLy8tvu/qmV69eeHp64u3tTVRUFL169VKaLOXn53PhwgXGjRvH\nhAkTlNdUV1fr7WPs2LEsWrSIyZMnc+XKFX788UeloWpDTE1N+fnnn0lNTSUmJoZDhw7x7rvvMmvW\nLFo0kCVUVFREZWUl8fHxyLJMQECA3vevX79O9+7dgdqHW4GBgaxZs4YPPviAw4cPk5qaqtTHPHbs\nGGVlZTg4OOjto6KiQu84aTQaJcANtZlZlZWVXL16FWtrayZNmqRkdnfv3p2BAwcSHBwMQGJiIq1a\ntdK7Oe3UqRMqlYqzZ88qN9UBAQF6AWBnZ2cOHz58y+P2oJibm2Nubq6XjQ+1N9L9+vVj5cqV7Nq1\niwEDBtz1Pt3d3cnKylKyvh726o/9+/fTvn37h/qeT5KioiJ27NiBWq2mT58+mJqaNvaUBEEQ7quH\n/QDW3NwcWZb1AtJNmzZlcuVRUAAAIABJREFU586dRERE8IpKhazTYQhMNjHB5feH+TdzcHDQK4WS\nm5vbYCnR2NhYvUSWQ4cO1bt+eumll2jTpg1/+9vf2Lx5Mz169ABqz6EdOnTglVdeUcYmJyeLJBfh\nL0UEuQXhMSdJEh4eHmRkZNy25hjUBihKS0vv+um2IDwMOp2OK1eukJeXR15eHhcuXODSpUv1GqUa\nGhoqJT3qgrwODg4YGRk10szvD0mSlFrMN9bt0+l0esHv3NxccnJySExMJCEhQRmnVquxsbHRC347\nODhgbW0tLmofATdmHPn4+LB06VKsrKz4+uuvmT59OjqdjtatW+vVha5jY2Nzy/3WNVjdu3cvO3bs\nYNasWUyZMoWjR48q5SuWLFlC586db7mPESNGMHnyZA4cOEB8fDxarZaoqKg7fqa6mvBjxozhvffe\no2nTpoSHhzPZxIR+5eVUA28aGPCUnx+ffvop165dQ5Ik4uLi6jVEvXGZ7YgRI1i2bBkffPABa9as\nITw8XMn81ul0ODo6sn///nrzsbS0VP5+c53hmzOzRo8eTVRUFFu3bmXnzp107tyZd999l6lTpwK3\nDure+H+pofdorKau/v7+HDhwAHt7e7151T0AOXHiBKGhobdtUnsjBwcH4uLilCC3paUlRUVFesf4\nQamurqawsBA3N7cH/l5PmpKSEqXGfVRU1D1l7wuCIDxOKioqyM3N1Ttfq9Xqeg/B75e6++br168r\npf/s7OzQaDQcOnSIzp07M7myknYtWzJ/4kQWL17c4LVEZGQkCxcupHPnzqhUKqZMmdLgKptNmzbR\nrl07IiIi2LBhA7/++itHjhypN27s2LHIsszf/vY3/vvf/9KjRw/8/f355ptv2LZtG02aNGHdunXs\n3bsXW1vb+39gBOERJYLcgvAEcHNz48CBA3h4eNw2qFXXROrGLDdBeFhkWaa4uFgJZl+8eJHs7Gyu\nXbumdzGoUqmwsbGhadOmODk5KU37/moPZ1QqFXZ2dtjZ2dGsWTNle91DgZuD3wkJCZw5c0YZp1ar\nsbW1xdnZWTmGDg4OWFlZieD3Q3SrY133ECckJIR169ZhZ2dXr6ZkHSMjo3qrBOB/DVa7devG9OnT\n0Wq1bNmyhZdeegkXFxeSk5MZMWLELedma2vLoEGDWLZsGSdOnGDkyJG3HHsrnp6emJiY4OzszDeb\nNvHBW29RdfYsqzZvxsPDg9jYWHJzc9HpdCxcuJCRI0fSsmVLpSb5jYYNG8a7777L4cOH+f777/no\no4+U74WEhJCbm4skSX+6RrSrqytjx45l7NixfPrppyxYsICpU6fSvHlzVqxYQUlJiRIkPHjwIDqd\njubNmyuvf9T+/wQHBxMfH18vA7pPnz4sXLiQrVu3Kitf7uTmz+bq6kpWVtZDCXJv2LCBrKwsdDqd\nXp1x4dbKysrYvn07NTU19OzZ86H8nARBEBqLJEns3LlTqZ9dx83NjczMzDu+9nZf37xNlmUkScLU\n1BRJkqioqFBK/2m1WpKSkggMDOTQoUOEhYVRZWyMu7v7LcsDzps3jzFjxtC1a1ecnJyYPXu2Xl+Q\nOtOmTWPjxo38v//3/9BqtaxcuVKvLNmN+x43bpwS6N68eTPjx4/nxIkTDB8+HFmWeeaZZ3jzzTdZ\nsWLFbY+NIDxJRJBbEJ4Q/v7+nD9//rYdnK2srEhOTn6IsxIE2L59OykpKVy5cqVeuQQLCwv8/f1x\ncXFBq9Xi6OgogrB3UFeL397eXi/wVlNToxf8zsnJITc3l9OnT+s9RDAwMKgX/NZqtVhYWIjj/gDc\nmHFUWFjIl19+SXl5uVLP/fnnn2fu3LkMGDCAGTNmKOUifvzxRyZMmICvry/e3t5kZGRw/Phx3N3d\nsbS0ZMeOHSQnJ9OlSxdsbW2JiYmhuLhY+Tcxffp0Jk6ciLW1NU899RRVVVXEx8dz8eJFvdrRY8eO\nJSoqipqaGn744YfbfpZp06ZRXl5Onz598PDw4OrVq3z++eeUlZXx9NNPExYWhoWFBWFhYRgZGeHk\n5MSIESMoKysjMTGR1atXk5ubi4uLC66urhQVFdGmTRsG/t6gyc3NjYiICMaPH09RURFDhgxR3rtH\njx6EhoYyYMAAPv30U/z9/cnJyWHbtm307NnzrhsVTpo0iT59+uDn50dRURG//PILLVq0AGozyadN\nm8YLL7zAjBkzuHLlCuPHj2fw4MF69T8ftQauJiYm2Nvbk5WVpWS+A9jb2yt1u+9mtVcdCwsLJXvb\n3NyckpKSBzV1RWVlJUlJSXh4eIgA912oqKhg+/btVFZW0r1799uu+hAEQXhSrFix4o4B24ZWVt1c\nPvBOJQaPHz+OTqdDrVZjYGCASqXi+vXr/Prrr8q1cl3D+SZNmrBv3z7s7OxITk6+ZXlAZ2fneg0k\n6/qm3MjJyYlffvmlwX3c/DkAxo8fz/jx45Wvly5dytKlS/XGfPDBBw3uTxCeRCLILQhPCHt7e5KS\nkm6bASUCWMKDpNPpyM3NJTk5Wa8zeWpqKjqdDldXV9zd3ZVgtp2dnWgwdh/VLdV0cHDQq91XU1ND\nQUEBeXl55OfnK8HvU6dO1Qt+29nZ4ezsjKOjIw4ODmi1WszNzcXvjj/o5owjCwsLmjdvzvr16+nS\npQtQG6Dcu3cv//jHPxgyZAjXrl3DxcWFyMhIJXA1ePBgfvjhB7p3787Vq1dZuXIlTZo0YfPmzcyc\nOZOysjJ8fX1ZtmwZoaGhAIwZMwYzMzPmzJnDu+++i4mJCS1btuS1117Tm2PXrl1xd3fHy8tLKVFx\nK127dmXRokWMHDmS3NxcLC0tadmyJT/++KMSZO7cuTMTJkxg2LBhFBQUMG3aND788EO2bdvGzJkz\nWb58OTk5ORgbG+Pq6kpFRQXu7u4EBgai0WgYMWIEY8aMYdCgQfUy27du3cr777/P2LFjycvLw9HR\nkbCwsHpNWBv6OdSRZZmJEyeSlZWFhYUFPXr0YN68ecrPIjo6mr///e+0b98eY2Nj/va3v7FgwQK9\nfTWUDdbY/0d8fHyU/hw3lnCKjIzk5MmT/PTTT7z66qt3NU9vb2/OnTtHq1atHuSU9fz222/odLp6\nTTQFfRUVFezYsYOKigoiIyNv2SRWEARB+OPUarXShBlqE0zUajXl5eUN9jpwc3Pj/PnzSvky8bBW\nEBqPJD9q6SiCIPxhxcXFpKWlERQUdMsxcXFxtG3b9iHOSngSXbt2jaSkJLKzs/Wys62trWnSpIle\nNt6NF4nCo6O6ulov+H3p0iXy8vIoKirSG2doaIi9vb2S+V0X/DYzM2v0wJ7w55WXl+Pm5saXX37J\nsGHDHtr7Xrlyhfj4eI4ePUplZSUqlYoWLVrQoUMHXFxcxL+tP6CyspJjx47RqVMnve179uxh9+7d\nDB48mJYtW97Vvo4ePUq7du2A2uuG4ODgB3rTvnLlSrKyspg8efJj32fhQaisrGTHjh2UlpbStWtX\ntFptY09JEAThiZWQkICPj4/SN2Tjxo1YWloSGBioNHq++Z766NGjuLi4ALWlvv4IlUrFhg0bGszw\nFgTh7ohMbkF4glhYWFBRUUFlZeUtbxLNzMz06o0Kwu1UVFSQkpJCeno6FRUVynYTExO8vLyIiopq\nsGnKjUSA+9FkYGCAo6Mjjo6OeturqqoaDH7Hx8frjTMyMrpl8Ft49MmyTH5+PgsWLMDU1JShQ4c+\n1Pe3tbWlR48edOvWjfPnzxMbG8vp06c5ffo01tbWdO7cmcDAwDv+fhH+x8jICDc3N1JTU/XKq3Tu\n3JnY2Fi2bt1Ks2bN6jXObIhKpVIeUDo4OHD58uUHFlgtLy8nMzOTpk2bigD3Taqrq9mxYwdFRUV0\n6dKlXh1aQRAE4f6rrq6mqKhICXKrVCo0Gg1FRUVKkNvAwICqqiq9htpFRUVER0fz0ksv/aF77cZq\nYi0ITxIR5BaEJ0yrVq04derULbO165pPNm3a9CHPTHiU6XQ6MjIySE1NpbCwUNluaGiIq6srYWFh\nt2yKJzxZDA0NcXJyUi7i61RVVXH58mWlcWhd2ZOLFy/qjdNoNHrB77oAeEPLO4XGk5GRgY+PD+7u\n7qxYsaLRHkap1WqaN29O8+bNuXr1KseOHePIkSNs3bqVbdu2ERAQQPv27XFzcxPZ3XfB3d2dI0eO\n4OzsrNycGxoaEhUVxY8//siRI0fo3LnzHffj5ubGhQsX8PT0xNnZmcTExAcW5D579iyyLBMcHPxA\n9v84qq6u5tdff6WwsJCwsLA/nBUoCIIg3Lvt27djb2/Piy++CNRe20qSpNejwt7ensuXLysPH42N\njamqquLatWukp6ff9copQRDuLxHkFoQnjEajQa1WU1ZW1mBQydLSkqSkpEaYmfCoqKubnZubq2QM\n1HUKDwgIwNHRUdSSE+oxNDTE2dm5XiZhZWUl+fn5SsPLS5cukZ+fT3Z2tt44Y2Nj7O3t8fDwoGfP\nng9z6kIDvLy8HrmMIWtra7p3766X3X3mzBnOnDmDpaUlnTt3JigoSAneCg0LDg7m8OHDSn12qH0A\nvmfPHmJiYmjTps0dj6FWqyUuLg5PT0+MjIyorKx8YPONi4vDwMCAJk2aPLD3eFzodDp27dpFQUEB\nnTt3xsPDo7GnJAiC8JdjYGCgV46x7jx447nTwcGB3377TbkudnNzUxI/UlNTRZBbEBqJCHILwhMo\nMDCQY8eO0aFDh3rfE5lwfx0lJSWcP3+erKwsqqqqgNqfv6WlJd7e3nTo0OGulq0Lwu0YGRnh6upa\nL9Pw+vXrDQa/i4uLlYaKAFZWVjg4OGBlZSV+PwlA7bLgZs2a0axZM65du6Zkd2/bto3o6GiaN29O\nhw4dcHd3F/9mGlAXMP7tt9/w9/cHao9pv379WLNmDbt37+app5667T4e1nEtKioiJyeHVq1a/aVL\nW+l0Onbv3k1eXh4dOnQQDwIFQRAaUV3jyToajYbi4mK9hu0ajUbvAbC1tTXJycnY29uTnJz8UOcr\nCML/iOiGIDyBDAwMMDc3p7CwUC+YJDyZKisrSU1NJS0tjbKyMmW7kZERXl5e9OzZU5SKEB46jUaD\nm5sbbm5ueturq6uVhyuyLHPt2jXy8/Pr3RBYW1vj4OCApaWlCGT+hVlZWREZGUnXrl1JTk4mNjaW\ns2fPcvbsWSwsLOjcuTOtWrUS2d03cXJyIjs7W68HR5MmTXB1deXo0aN07NjxjtcH5ubmFBcXY2Fh\n0WDt0fshISEBgNatW9/X/T4udDod+/bt49KlS7Rt25bIyMjGnpIgCMJf3s1BbktLSwoLC5WkoYbU\nXav6+fkRGxvLtWvXRKlHQWgEIsgtCE+ogIAAYmNjG6y9aWFhody4Co8PnU7HhQsXSElJ4cqVK8p2\ntVqNi4sLHTp0wNbWthFnKAh3duPqAUmSsLa2xtraWm+MLMtcvXqV3NzceuWVbGxscHBwwMLCQgS/\n/0JUKhVNmzaladOmFBUVER8fz+HDh4mOjmb79u00a9aMDh064OHhIf5d/K5NmzYcOHCAsLAwJElC\nkiT69evHkiVLiI6O5rnnnrvt6318fDh37hytWrXC2dmZnJwc3N3d7+sc4+Li0Gg0f7myHDqdjoMH\nD5KZmUlwcDARERGNPSVBEAThd2q1muvXrytfW1hYIMuy3raGqFQqPD09iY2NJT09nVatWj3oqQqC\ncBMR5BaEJ5QkSTg6OnLp0qV6NXTrmk/WLWMWHj0FBQUkJSVx8eJFvaVx9vb2+Pn54eLiIupmC08s\nSZKwsbGpl2mq0+m4evUqly5d4vz58/XGa7VazMzMRJDzCWdpaUnXrl3p0qULKSkpHDp0iMTERBIT\nEwkODqZ///6NPcVHgkqlIiAggISEBKU2qJOTEwEBAZw9e5aLFy/i4uJyy9ffuBRbq9Vy/Pjx+xrk\nLigo4MqVK7Rv3/4vdT6LjY0lLS2N1q1bExYW1tjTEQRBEG5iYGCgtzrWzMwMlUpFRUUFsiwr15l1\ntbqNjIyA2nNsZWUlkiSRkpIigtyC0AhEkFsQnmA+Pj7ExsbWC3LXZXILja+srIykpCQyMjL0lsCZ\nm5vj5eVF27ZtRd1sQfidSqXC1ta23ooFnU5HYWEhWVlZlJaWKtslScLW1lYJfgtPFpVKhZ+fH35+\nfhQXFxMfH4+hoSHfffcdpqamREREYGlp2djTbFR2dnZcuHCBq1evKismevXqRWJiIj///DNjx469\n7UMhSZLQ6XSoVKr73qj09OnTAH+ZIMCRI0dITk6mRYsWDB8+vLGnIwiCINzCzeVKTE1NkWUZnU7H\n9evXMTY2BmqTj/Lz85W+NE5OTpw4cQJHR0dSUlL0AuKCIDwcInIiCE8wSZLw8vIiLS0Nb2/vxp7O\nX1p1dTVpaWmkpqYqDxgkScLQ0BAPDw8iIyOVuqmCINwblUqFnZ0ddnZ2ett1Oh1XrlwhIyNDLyOn\nLliu1WpFvfonhIWFhV7Jh8LCQmJiYqioqMDf35+goKC/VLbwjYKCgti/f79StsTKyopOnTpx8OBB\nzp8/f9tVXW5ubly4cOG+lxORZZljx45hZmZW70H8kyY+Pp5z587RrFkzEdwWBEF4DBgYGNQLcldX\nV2NsbExRUZES5HZwcCAhIUEJcqvVanQ6Hb6+vuzfv5/CwkJRSlIQHjIR5BaEJ5yLiwsHDx7Ey8ur\n3pNk8XT5/tPpdFy6dInk5GQuX76sHGNJknB2diYkJAR7e/vGnqYg/CWoVCrs7e3r/Z+rqanhypUr\npKWlUV5eXm+8g4ODaGT4mLOxsWHAgAHodDqOHz/O+vXrMTExISIi4i/XCEqSJIKCgjh58qTS4DE8\nPJyjR4+yZcsW/Pz8bvkAwNHRkbi4ODw8PDA1NaW0tPS+rIrIzc2lpKSE8PDwJ+46RKfTIUkSp06d\n4uzZs/j6+orgtiAIwmPE0NBQb/WSWq1GlmVMTEwoKipCq9Uq46qrq+u93svLi/3795OWliaC3ILw\nkIkgt/BE8vb2ZuLEibzxxhtA7Ylm4sSJvPnmm0BtIGPDhg0MGjSoMaf50DRr1oxz587RvHlzZZul\npSVFRUUP7WZ/5cqVTJw4Ua9MyldffcVHH31EdnY2U6dO5cMPP3woc7lfCgsLSUpKIjs7W+9CyMbG\nBl9fX8LDw/+ymYOC8ChTq9U4ODjg4OCgt72mpoaCggJSUlKoqKjQG18X/K7L3hEeDyqVipCQEEJC\nQrh69Sp79uyhvLwcPz8/Wrdu/Zf5HW1lZYWhoSGXL1/G3t4eY2NjIiMjiY6OJj4+nrZt2zb4uhsD\n0K6urly8eBE/P78/PZ+TJ08CEBgY+Kf39aiQZZmEhAS2b9+Oubk5QUFBDBs2rLGnJQiCINwjAwOD\nBkt0aTSaO5b8tLa2xtLSEpVKRUpKCiEhIQ9qmoIgNEAEuYXHSm5uLp988glbtmwhKysLS0tLfH19\nGTZsGC+++KKSXRQXF6e3BL0uk/Zh6Nq1K4GBgXzxxRe3HTdq1ChWrVpVb3vHjh05ePDgXb+fl5cX\nmZmZrFq1ihEjRuh9r0OHDhw9epQ5c+YQGhpKTU0NarUaqM3wzsrKqhfk3r17N5GRkVy+fPmBPnku\nLCzk1VdfZf78+TzzzDOPdKmOiooKkpKSSE9P5/r168q/JVNTU7y8vOjbt6/ScEQQhMeXWq1Gq9Uq\nGTp1qquruXz5MklJSVy/fl3ZbmBgoAS/NRrNw56ucI+sra15+umn0el0nDx5kvXr12NsbEyXLl3q\nNTl9EgUEBHDgwAE6deqEWq2mXbt27N+/nx07dhAYGHjLf8NmZmaUlJRgZWVFUlLSn56HLMucOHEC\na2vreg+aHkeyLJOYmEh0dDRFRUUYGRnRo0cPgoKCGntqgiAIwh9wc7kS+F884ebtN3N3dycpKQkX\nFxfS0tLEymlBeMhEkFt4bKSnpxMaGoq1tTUfffQRQUFBmJiYcObMGZYuXYq9vT3PPfccQL26rA9D\ndXW1EkC+G5Ik0bNnT1avXq23/V6DpZIk4e7uzvLly/WC3GfOnCEhIQF7e3skSSIwMJAzZ84oDZ7M\nzc31GrTdTJble5rHvcrIyKCmpoa+ffvi6Oj4QN/rblVXV5ORkUFqairXrl1TLkgMDAxwd3cXTcwE\n4S/KwMAAJycnnJyc9LZXVVVx+fJlfvvtNyorK/XG12WKiwdgjx6VSkWbNm1o06YNRUVF7Nmzh7Ky\nMnx8fAgJCXlis7slSaJNmzYcP36ctm3bolar6dOnD+vXr2f//v107969wdf5+Phw/vz5+xa0zcrK\noqKigrCwsPuyv8YiyzLnz58nOjqawsJCDA0N6d69O+3btxf/7wVBEB5jBga1YbK6xstQe49eWVlZ\n74GwRqOhoqJCWelnbGxMRUUFvr6+XLhwgcuXLz8RD3QF4XHxZF7FC0+kl19+GQMDA+Li4hg6dCjN\nmjXD09OTvn37smnTJiXADbXZzfPmzbvt/i5dukTfvn0xMzPDy8uLNWvW6H0/Ozub5557DltbW2xt\nbenXrx/JycnK96dNm0ZgYCArV66kSZMmGBsbM3ToUPbu3cvChQtRqVSoVCoyMzMbfH9ZltFoNErW\nYN2fw4cPM7hXLwb36kV0dPRdHZvhw4cTGxtLWlqasm3ZsmU888wzSna7mZkZVVVVFBcXM3nyZNzd\n3QkPD6d9+/Zs374dqH2QEBkZCdQ20lCpVIwePVqZ77x58/Dz88PY2Bh3d3emTJmivN8//vEPmjVr\nhqmpKd7e3kyePFkv4/FGK1euJDg4GKi9eb7dcXoQ6upm79u3j40bNyp/fvzxR3JycggKCuKZZ55h\n8ODBDB48mAEDBhAcHCwC3IIg6DE0NMTZ2ZmgoCDatm2r/AkICECSJM6dO0dcXJzy5+TJk1y8eJGq\nqqrGnrrwO0tLS/r378+zzz6LkZER69evZ/PmzVy5cqWxp/ZAmJmZYWVlxcWLFwFo3rw5Wq2WgwcP\n3nIJtrGxsd75/M8+BD9x4gQALVu2/FP7aSyyLJOUlMTChQtZt24dxcXFdOvWjTfffJOwsDAR4BYE\nQXjM1QW5b6y3rdFoqKysrHcO1Gq15Ofn19uHt7c3gN79uSAID57I5BYeCwUFBWzfvp1Zs2bdVTOw\nuylPMnXqVGbNmsWCBQv4/vvveeGFF2jWrBkhISGUlZXRrVs3wsLC2Lt3L0ZGRsyZM4cePXqQmJio\nzCEtLY1169axceNGjIyMcHNz4+LFizRv3px//vOfALdtMnjzSTI6OpqRAwcy+/dGaCP37+ebTZuI\nioq67Wext7enf//+rFixghkzZlBZWcmaNWvYuHEje/bsUcYFBQXx9NNPU1RUxLfffsuVK1fIzMyk\nf//+HD16lJYtW7Jx40YGDx7M2bNnsbW1VT7rlClTWLx4MZ999hkRERFcvnyZ+Ph4Zd/m5uasWLEC\nV1dXEhISmDBhAhqNhhkzZtSb73PPPYeLiwu9e/fm6NGjuLu7P7BmjEVFRZw/f57s7Gy9CxUrKyua\nNGlCaGjoE5u1JwhC4zAyMsLFxQUXFxe97ZWVleTn55OQkKD3+8jIyAgHBwfs7e0xNDR82NMVfteq\nVStatWqll93t5eVFu3btnqjzhJ+fHwcOHECr1WJgYEC/fv1Yvnw5O3fuZODAgQ2+RpIkdDodtra2\nFBYW/uFyZjU1NZw5cwatVvvYNQCVZZm0tDS2bdtGfn4+arWaiIgIOnbsKGr1C4IgNJL09HR8fHyI\ni4tTkqj+rLrV2TeWJtFoNFRXV9drNGlnZ8fp06dxd3dXtkVERDB//nzUajUpKSm0b9/+nucwbdo0\nNm7cyOnTp//gp7h3DfXQEoTHjQhyC4+F5ORkZFnG399fb7ubmxvXrl0DYMSIEfz73/++630OHjyY\nsWPHArUB3JiYGObPn8/q1atZt24dAMuXL1fGL168GEdHR37++WeGDBkC1AYsVq9erbcEycjICFNT\n03o1XRuybds2LCwslK8rysuJqqlhZN2G8nK+mjfvjkFuSZIYPXo048ePZ/r06fz444/Y2NgQHh6u\nNy4rK4sdO3aQkJBAs2bNSEpKokuXLuzcuZMlS5awcOFCpS6pVqtVbmJLSkqYP38+CxYsYNSoUUDt\n0+l27dop+37//feVv3t4ePDuu+8yb968BoPcxsbGyr4dHBzu6lg1pKSkhLy8PPLy8rh06RIXLlzA\n2dlZOSZ1XbC9vLyIiooSN6GCIDQqIyMjXF1dcXV11dt+/fr1BoPfGo1GCX7XZRUJD15ddjfA6dOn\nWb9+PUZGRoSHhz+wB7IPW0hICMeOHaNDhw64u7vj6+vLqVOnCA0NbfCc7ObmxoULF3BxcSE5OfkP\nB7nT0tKoqqq6ZaPLR1V6ejrR0dHk5OSgUqkIDQ0lNDT0rhIvBEEQnjT3GoBVqVRs2LCBQYMG/aH3\nq2uQ3LlzZ/bv33/LcXfbG+tObhfkvrExOTRcv1utVnPt2jXc3NxIT0+vV5e7oeN35MgR+vTpw9NP\nP83XX3/9p+Z/t24+Xs899xz9+vV7KO8tCA+KuGMSHmsHDhygurqacePG3bI0xq106tRJ7+uOHTuy\ndetWAI4dO0ZaWppeABqgvLyc1NRU5Ws3N7c/VWMrIiKCr776Svn6lRde4KkDB/TG5ObmcubMGZo0\naXLbm6moqChkWWbHjh0sW7ZMKTNyo/j4eGRZ1qs5qtPpqKqqumUtToCzZ89y/fr1247ZsGED8+fP\nJyUlhZKSEmpqahrsSv1HVFZWKsHs3NxcMjMzKSgoqLfk38zMjObNm9OiRYv78r6CIAgPg0ajwc3N\nDTc3N73tFRUV5Ofnc/r0ab0bKGNjY7RaLXZ2dvfUC0K4d4GBgQQGBlJSUsKePXsoKSnBw8ODDh06\nPNbZ3cbGxjg6OpKRkYGnpye9e/fmyy+/ZMuWLbz44ov1xjs6OhIXF4eHh0e9G/x7cfz4caC2Cebj\nICsri23btnHx4kXxT/X9AAAgAElEQVRUKhWdOnUiLCxMr7m5IAjC427UqFGsWrWK0aNHs3TpUr3v\nTZ48mTlz5tC3b19++uknAN5++20mTZr00Oa3dOlS2rVrx6FDhzh37hzNmjVrcNzdrOa+k7pjIcsy\nH3/8MTY2NrRo0YLQ0FDCw8NRqVRcv379jg3Hy8rK8PX1JSMjg5ycHCURqyE7d+5k0KBBjB8/njlz\n5vyp+d+Lm4+XsbGxSAoTHnsiyC08Fnx9fZEkicTERAYMGKBs9/T0BLhvNxt1v+R1Oh2tW7fmu+++\nqzemLtMZUOpd/1EmJib4+PgoX7/+wQeMHDgQ89/LlbxlaMhTrVqxceNGJEnCxcWFwMBAOnTo0ODc\nR44cyccff8zhw4dZsWJFvTE6nQ5Jkli/fj1OTk5YW1tz6tQppYnnH3Xo0CGGDRvGtGnT6N27N9bW\n1mzevJm33nrrnvZTU1NDQUGBEszOysoiLy+P8t+PRx2NRoOTkxNubm44OTmh1WpFpqMgCE+cuv4H\nNy6BhdoHrvn5+Zw8eVLvYaKJiYmyCkcEv+8vc3Nz+vbtC9Q++N2wYQMGBgaEhYX94dVIjc3Ly4tD\nhw7h5OSEnZ2dkt2dlpam1BKt82eDBlDbqPXcuXO4u7v/6eunBy07O5vo6GiysrKQJIn27dvTpUuX\nR37egiAIf4QkSbi7u/P999/z+eefK/fW1dXVrFq1Cg8PD73zgJmZ2UP7fVheXs63337Lt99+y7/+\n9S+WLVvWYCA4PT2dkydPsn//fnbt2sWCBQvo0aMHALt37yYyMpKtW7cyZcoUzp07R9u2bfn222/5\n7bff+Pvf/05aWhrdunXD3Nycdu3a0aVLF0aNGsXGjRtZtGgRMTExGBkZMXLkSFq1aqUkuiUnJzN+\n/HjOnj2Lh4eHXl+wG+ty3yrI/f333zNy5EimT5/OO++8U+/769at47333iM/P5/u3buzdOlS7Ozs\nOHfuHAEBAeTk5KDVaikrK8PGxobIyEh++eUXoPbhwOzZs0lKSgJqe2j997//JTMzE0dHR2pqavQe\nOt9criQlJYU33niDI0eOUFxcjL+/PzNmzFCuhwThUSQiQsJjwc7Ojl69evHll18yceLEeidVWZbv\nuRFSbGysUnoDagO1zZs3B2qX8a5btw47O7t7rhlpZGRUr1bXrdx80xgVFcU3mzbx1e8nR/WpU7Ro\n0YL+/ftz7tw5zp8/T1xcHJIkKUHejIwMjh49CsDo0aP55z//Sd++fXFycqr3fm3atFFKeFRUVODj\n48OVK1f0Au11DZNuzBps3rw5Go2GnTt30qRJE6D2pD1x4kTeeOMNDhw4gKurK++9957ymvT0dOXv\no0aNUhpNQe3Pq+7keeTIEWJjY8nOzqakpETv56hWq7G1tSUgIEAJZmu12gafMDdG3TJBEITGYGJi\ngoeHBx4eHnrby8rKyMvLIysrSwl+y7KMmZmZEvx+nLOPHxUBAQEEBARQVlbG7t27KS4uxs3NjU6d\nOj12xzckJISjR4/SuXNnIiMjOXHiBD/99BMTJ06sd41iZmZGSUkJarWampqae36QkpSUhE6nIyQk\n5H5+hPvq0qVLbN++nfT0dCRJIiQkhIiIiHor+wRBEJ40QUFBXLx4ke+//165R96yZQsmJiZ06dKF\ngoICZWxD913ffPMNc+fOJSkpCWtra3r37s3KlSuV7xcUFDBkyBB++eUXHB0dmTFjBs8//7zy/ZsD\nsEOHDmXGjBls2LABKysrevfuze7du5k3bx5ffPEFLi4uDB8+nJEjawt9vvfee7i5udGmTRskSeK5\n557jk08+YcmSJSQkJCDLMs8//zzLly/Hx8eH4cOHM3ToUDQaDcuWLUOlUjFkyBClrKa5uTnbtm1j\n8eLFLFq0iOrqaoYPH85//vMfXF1dmTp1Kp6enlRUVFBTU4OhoSFubm5Mnz6dsrIyvvjiC2bPno1a\nrWbJF1/QvnVrXn77baUMaX5+PpaWlhQXFxMaGkpFRQXe3t5Ko8rs7GwSExMZOXIkhoaGNGnShEOH\nDvHee++xePFimjVrhizLvP/++xQWFvLzzz9TXV3Nnj170Ol0qFQqdu/ejZWVFV5eXuTk5GBoaEh4\neDjbt28nISGBgQMHKnEEqH2IX1JSwldffcW4ceP47rvvOHnyJEVFRZiYmFBZWcmgQYM4depUvTKy\ngvCoEEFu4bGxaNEiQkNDCQkJYdq0aQQFBWFgYMCxY8c4derUHetW32zTpk20a9eOiIgINmzYwK+/\n/sqRI0cAeP7555k7dy4DBgxgxowZuLu7k5WVxcsvv8zZs2eVpT2SJBEZGckzzzzDuHHjMDAwwMvL\niyNHjpCRkYGZmRl2dna3zICqqKggNzdXL7AbHBzMxu3bAXj22Wc5ffo0kydPJiAgAJ1OR3l5Oaam\npuTm5iqNH69du6acGAsKCpQgcHR0NOnp6Urd8qZNm/L888/Tq1cvDA0N2b59O6mpqcTExODu7s7I\nkSOZNWsWkiTx888/069fP0xNTbGwsGDSpEm8++67aDQawsPD+eqrr0hMTATA39+f7Oxs1q5dS8eO\nHYmOjlbqmpeVlVFSUkJZWRnV1dV8+eWXFBYWkpWVhSzLHDhwABsbGywtLQkICMDFxUUJZltYWNyX\n7DFBEIS/AlNTU7y8vPDy8tLbXlpaSl5eHhkZGcr5RpZlzM3N0Wq12NjYPHbB2UeBqakpffr0ASAx\nMZH169cr2d2Ojo6NPLu7Y2hoiKenJ8nJyfj6+hIREcGvv/7K6dOnCQoK0hvr7e1NcnIyjo6O5Obm\n1museifHjh1DkqRbLjNvTLm5uco1kSRJtG7dmm7dumFpadnYUxMEQXhoxowZw/Lly5Ug9/Llyxk9\nejQpKSm3fd2SJUv4+9//zqxZs+jXrx8lJSXExMTojZkxYwazZ89m9uzZLF26lNGjR9OlSxdltZq5\nuTkrVqzA1dWVhIQEJkyYgEajYe/evYwePZro6GgWLVqEqakps2bNIiAggAkTJpCXlwfAG2+8wdq1\na7GysuIf//gHq1atIi0tjZkzZ3L16lWGDx+Ou7s7//rXv9izZw8TJkxg4sSJxMfH07p1awBGjhzJ\nnDlzyL90iZJLl7hw7RoLFixg0KBBlJaWEhISQkpKCqtWrWLq1KlUVFSQl5fH66+/zsCBA3FxcSEn\nJ4fw8HBGjBiBj48PH7z5JpUlJci7djHy4EG+2bSJM2fOkJubiyRJTJs2DY1Gw+zZs/VWjFdWVqJS\nqTh48CAWFhZ88cUXLF26lB07dugd17Vr1/LVV1+h1WqJjY3l+PHj/PTTTwwYMIDo6GhKS0vZuHEj\ngYGB5ObmcvjwYSVRwsPDg/PnzwO1pUe/+OILjI2NGTduHADOzs5KQD0/P5/JkyeTkZHBhg0b9JLb\nBOGRIgvCYyQnJ0eeNGmS7OvrK2s0Gtnc3Fxu3769/Mknn8glJSXKOC8vL3nevHm3/FqSJHnhwoVy\n7969ZRMTE9nT01NetWqV3nvl5ubKL774oqzVamWNRiN7e3vLfn5+crdu3eTc3Fz5zTfflP38/OR/\n/etfsr29vdyxY0e5tLRUPn/+vNypUyfZ1NRUVqlUckZGRoOfZdSoUbIkSfX+uLu7K2O6du0qd+vW\n7bbHRJIkeciQIfLJkyflw4cPy3FxcXJ+fr6s0+nkkpISGZCHDx+ujE9NTZXVarWsVqtlAwMD2cHB\nQe7Tp4+8aNEiWZIkOT09XZ45c6bs7Owsq1Qq+cUXX5RlWZZ1Op38ySefyD4+PrKRkZHs7u4uv//+\n+8p+33nnHdnOzk42NTWVQ0ND5eeff16WJEmeNm2a3KpVK9nR0VE2MjKSP/30U3n16tXy4sWLZZVK\nJR8/flyuqam57We8G1OnTpVbtmz5p/cjCILwV6DT6eTi4mI5JSVFjouLk48ePar8OXfunFxQUCDr\ndLrGnuZjp7S0VN66dau8bt06ee/evffl/PYwHDlyRC4tLZUrKyvlTz75RJ49e7ZcVVXV4Liqqio5\nPj7+nvZfXl4uT58+Xf7Pf/5zv6Z8X+Tl5clr1qyRp02bJk+bNk3euHGjXFhY2NjTEgRBeKhGjhwp\n9+/fXy4sLJRNTEzk5ORk+dKlS7JGo5GzsrLkkSNHyv369VPG33zf5erqKr/77ru33L8kSfKUKVOU\nr6urq2VTU1N5zZo1t3zNv//9b9nDw0M2MDCQMzMz5fDwcPmjjz6S33nnHfmpp56SZVmWN23aJJua\nmsqSJMmHDh2Su3btKk+cOFHW6XSyJEnypk2bZFmW5ZiYGFmSJHnfvn2yJElydna2/P3338uSJMnV\n1dXKe7722msyILcC+XOQASXmYG5uLhsYGCjbZFmWbWxsZBMTE7m6ulo+duyYLMuyXFlZKavVannF\nihVyZPv28kqQ/w2yL8grQR7Us6fs6uoqW1hYyB06dJB9fX3lzMxMuVevXrK3t7feMfb391e+1ul0\nspWVlWxiYqJsA2RbW1tZlmU5NDRUXrdunaxWq+Vnn31WTkpKkiVJkps0aaKcz9evXy+HhobKTk5O\nsrm5uaxSqWS1Wi0vWbJEtrKykt966y3Z3Nxc2X9JSYn89ttvywEBAbKNjY1samoqA/KIESNu+XMT\nhMYmMrmFx4qjoyPz589n/vz5tx1Xt8znVl/XLeN+5ZVXbrkPrVbL8uXL9baNGjWKgoICtFotc+fO\nZe7cuQD06tWL4OBgPv30U6ZNm8bBgwf5z3/+w4IFC2jZsiUmJiZEREQwf/58XFxc0Ol07Ny5k88/\n/5zXXntN2f/58+dp1qwZJ06coHXr1qSlpTFx4kTl+8nJybz00kscPnwYT09P5s6di5mZGf369VMy\nrmpqasjIyFAaZIaEhOiV99i3bx+dOnXC09OTJk2a8OyzzyLLMmvWrMHb2xtPT0/ef/99XF1dmTNn\nDt9++y0HDhzg5Zdf5p133uHtt9+moKCA1q1bc/nyZVavXs2lS5c4e/YssixTWVnJ8ePHycvLY/Hi\nxXh6ehIfH4+DgwNhYWHMmzeP0tJShgwZQmlpqTK3bdu28fHHH5OQkIAkSbRr14758+frZXxdvHiR\nt99+m+joaMrLy2natCmfffYZXbt2Vcbcqm6ZIAiC8D+SJGFubo65ubnedvn3clL5+fmkpaXprTSy\ntLTEwcEBa2trscrmFkxNTXnqqacA+O2339iwYQNqtZrOnTvftulUYwsODiY2NpawsDB69+7Nf//7\nXw4dOkRYWJjeOEmSUKlUeiXN7sa5c+eQZZng4OD7Oe0/rKCggJ07d3Lu3DkAWrRoQWRkJLa2to08\nM0EQhMZjbW3NwIEDWbZsGVZWVnTr1q1eU+yb5eXlcfHiRbp3737bcTeuDlKr1Tg4OChZ2FCbSTx/\n/nxSUlIoKSmhpqaGyspKdDodPj4+VFdXs2/fPmV8XfnSut5NhoaGyvfqrlGSk5MZMGAAhw8fRpZl\nevfuDUBmZqYy5sbSW/t37sQAcAeGApOAQD8/vtu8GYDx48ezb98+ZcWyJEkYGRmhVqv1eqTIssy0\nadPIvnCBg4AE/O+7tecgOzs7du7cSVRUFF27dqVv375KVjX8bxWev78/ubm51NTUUFpaqvc5AQoL\nC0lJSeHYsWN0794da2trTpw4wZ49e/D09KS6uhpvb2/atGnD1q1b+fDDD+nbty/W1tb06tWL1NRU\nXnvtNfbt26es0q4zcuRIfvnlF8zMzLh+/breewrCo0oEuQXhPmjRogW9e/dm48aNTJs2DahtsDRz\n5ky95T3Dhg1jz549qFQqhg8fzpo1a/SC3GvWrCEgIEBZMnVjx2OdTsfAgQOxs7Pj0KFDlJaWMmnS\nJL0TDtSeqH18fPDx8UGWZSIiIvjuu+84cuQIxsbG7Nixg27duuHh4cGmTZuwtrZmy7p1HP/tN9p1\n7AjA119/zdSpU5k9ezbu7u4cPnyY6dOns2/fPlq1aoUsy5SVlZGamoqrqytFRUX88ssvTJ06le7d\nu6NWqzl06BBjxoxBrVbz7bffEhMTg62tLb/++isXLlxg9OjRTJ48mQULFgC1ZU3eeOMNgoKCKC8v\nZ+bMmfTv35+zZ89iaGhIaWkpERERODk5sXnzZlxdXTl16pTeZ09PT2f9+vVs3ryZkpISnnvuOaVu\nmSAIgnBnkiRhaWlZr0yDLMsUFRWRn59fb9mylZUVDg4OWFlZieD3Dfz9/fH396eiooLdu3ezd+9e\nnJycCA0NfeQaJavVapo2bUpiYiJBQUHs2bOH3bt3ExwcrNfc29XVlezs7HvugxIXF4darcbPz+9+\nT/2eFBYWsmvXLhISEoDan1GPHj2wt7dv1HkJgiA8KkaPHs0LL7yAhYUFM2fOvG/7vTk4K0mSEhg+\ndOgQw4YNY9q0afTu3Rtra2t++OEH3nnnHWbPnk2/fv0IDg7mlVdeoVevXrz11lv07NmTiRMncuHC\nBb2EpxvNmDGD/v3789577zFp0iTWr19P3759qaysvON8HQFroKSsTOlfVVhYiIeHhxL4NzQ05OrV\nq1y4cEF53fLly9HpdAQFBREQEMDhXbvoVVnJd8BkExO+efNNtv4erDc3Nyc6Opo+ffqwatUqvcSD\nTZs2UV5ezvz58/Hy8sLIyIgOHToofa3qjqG1tTUff/wxvr6+2NvbY2xsTFpaGjt27KBnz5588cUX\n7Nq1i9mzZwOwfv163nrrLUxNTZU4gouLC0uXLiU0NFTZd2lpKf/9738JCAhg0aJFaLVaLly4QPfu\n3fUC+oLwqHm0rrAF4THWvHlzdu7cqXz94osvKn/38vJi0aJFBAQEcPHiRVxcXBgxYgRz5swhNTVV\nOXGuXbuWMWPGNLj/nTt3kpiYSHp6unJinT9/PuHh4beckyRJ9OnTh88++wxnZ2ccHByIiYmhU6dO\n2NjYEBMTQ9yuXcy4fp3dwMFdu5g+fToLFiyge/fupKamKhnhHTt25MCBAzz77LO4uLjw9ddf06VL\nF6ZMmcKmTZswNzfn9ddfV07OHTp00JuLgYEBK1aswNTUlICAAGbPns2YMWP45JNPMDExYdCgQXrj\nly9fjpWVldIQa+3atUodsbpMq5vrzlZXV7Ny5UqlQdS4ceNYsWLFLY+PIAiCcHckScLKyqpeM2ZZ\nlrl27Rr5+fkkJyfXGy/6K4CxsbGSPZaUlMSmTZtQqVR06tTpnutaP0h1N7AlJSX069eP1atXExMT\nQ9++fZUxTk5OxMXFodFoqKioaLAR9M1KSkrIzs6mZcuWjRbcv3r1KjExMcrDcV9fX3r27IlWq22U\n+QiCIDxq6h5edu/eHY1GQ0FBAX/729/u+DqtVourqys7d+68Yzb3rRw4cABXV1e9Os979uwBYOzY\nsdjY2NC2bVuuXLlC7969OXXqFIsXL+azzz67bU+R4uJi/vnPfyqrum/MHG9IeM+enDh3jgvAN0CN\noSFp2dnMnz+fJk2acOLECcLCwli5ciVt27bF2NgYrVbLCy+8wEsvvURlZSUff/wxAIMHDyYnJwd/\nf39+2rgROSuLbzZtIioqCnt7e8rKygCUBpdubm7k5OQosYGsrCy0Wq2yOiw3N5erV6/Wa/ocEBDA\nf/7zHyZMmADUBt7NzMz44YcfWLlyJRqNhj59+qDT6Th48CBnzpxh48aNlJSUKMcjJiaGrl27kpSU\npOz33Llz1NTUUFVVpWRyizrcwuNABLkF4T6RZVnvJj4+Pp7p06dz8uRJrly5olw4ZGZm4uLiQmBg\nIIGBgaxZs4YPPviAw4cPk5qaqtdl+kaJiYm4urrqLRlr3779HZuFhYaGotFoiImJITw8nMuXLzN6\n9GiMjIzQ1dQwvrISD2qXUM2srubrFSu4evUqP/74I1u2bFGyyeuWJg8dOhSoPYGamJggSRK9evXC\n09MTb29voqKi6NWrF4MGDdJ7Gh0UFKSXDdaxY0cqKytJSUmhZcuWpKSk8MEHH3DkyBHy8/PR6XTo\ndDoyMzPp3Lkzx48fp1WrVrddSuzp6akEuKG2WcadLmYEQRCEP64ui8ja2lpvuyzLXL16lUuXLukt\nv60br9VqMTc3/8sFv/38/PDz86OiooK9e/eyb98+tFot4eHhj0R2d+vWrTlw4ABhYWG4u7tz7Ngx\nOnXqpJx7635erq6uXLp0CW9v7zvusy5rum6V2sNUVFTE7t27OXHiBLIs4+PjQ8+ePXFycnrocxEE\nQXhc1D0QvDn7+lbee+89Xn/9dRwdHenTpw9lZWX8+uuvvPHGG7d8zY0rgvz9/cnOzmbt2rV07NiR\n6OhopcFiXTPGDz/8kH79+uHp6UnHjh3JyMjgww8/JC8vTzk3ybKst19DQ0O++OILgoODkSSJWbNm\n6c3h5muQFi1aYGhoyEULC75v2ZJ/jx9PVlYW8+bN48KFC6hUKgoLC5VziCRJjBo1itjYWF588UU8\nPDwYNWoUM2fO5NChQ/j7+5OSkkJRRQWSJBEVFQXU3gdv3LiRFStWEBYWxqZNm/4/e/cdFcXVPnD8\nuwsCSxOQIipFsCF2wI4UEUzs9bVFxW7UaCyx5LUQE2M3msQYo7EkpipWjAWjEhELghQBDQKK2AAr\nRdrO7w/fnZ8roJio0Xg/5+w5YebOzN3BMDPP3Ps8qNVq9PT08Pb25vfff6dKlSrcuXOHxMREcnJy\n+OCDD8q8T2jQoAERERHyaHZJkqhVqxZnzpzhxo0brF+/nubNm+Pq6krbtm05cuQIY8eOpWPHjjg6\nOpKcnEzNmjU5fPgwHh4e8uhue3t79PX1ycvLo23bthgZGYki5cJr4Z+/mxaEf4mEhAScnZ2Bh9N7\nNMHe77//HmtrazIzM/H09NSaHjVo0CDWr1/P7Nmz2bJlC56ennKF6efFwMCAli1bcvjwYdRqNR4e\nHhgYGFBQUICZiQlJ2dkUAHV5OCXLzs6OhMuXWb9+Pa1bt67QMYyNjYmKiiIsLIyDBw/y6aefMmvW\nLE6fPi3nIH3a1ObOnTtjb2/P2rVrqV69Ojo6OtSvX1/rfD1tH0+aBicIgiC8PAqFAnNzc/nhVEOt\nVnPnzh0yMjLIyckp1d7a2hojI6N/ffDbwMAAf39/AFJSUti+fTsKhYKWLVs+Nf/pi6RUKmnQoAHx\n8fF06tSJNWvWsG/fPgYMGCC3MTQ0lEf5VSTIfebMGSpVqlShts9LTk4OR48e5cyZM0iShIODA/7+\n/q/UyHlBEIRXxaMpMoFS9ToeX//4z2PGjEFPT49ly5Yxffp0LCwstGYBlXdMjc6dOzNt2jQmTZpE\nfn4+AQEBrFy5knHjxslt/P39CQkJYf78+SxdulQeAT106FB5MNbjqT5/+eUXZs2axZdffomHhwcf\nffSRPDK6d+/epepLjB49mhMnTrBp0yZ+++MPDhw/jpmZGQ0bNmTmzJnUrVsXIyMjCgsLKSoqAsDK\nyoojR46QnZ3N3bt35fzh69atIzc3FwcHByZPnqw1CvrXX3/l008/ZcaMGeTl5dGrVy/Gjh3Lzp07\nSUhIAB7Wqxo1ahRubm5Ur16defPmkZWVRZ8+fbT67O/vr5WaU6FQ0K9fP06dOsXOnTtZtGgRU6dO\npaioCFdXV0JCQnj77bcB8PHxkWeaOTk58f777zN37lzGjBnDmjVr2Lx5M7NmzZLzomvO34wZM574\nuxWEf5JCetakeoLwBhs6dCi3bt1i165dWsvj4+Np1qwZs2fPZvbs2Zw5cwYPDw9SU1NxcHAAIDg4\nmN69e3PkyBHatWsHwJUrV3B0dCQ8PJxu3brx8ccfM2LECHm/NWvWZMKECUyePJkDBw7w9ttva6Ur\nCQ8Px9PTk40bNzJ48OBy+/3RRx/x7bff4uXlha2tLT179kRXV5evv/6aDd98g50k4QicU6nYtH07\nw4cPZ8SIEXJ+8bI82rfHFRUVYW1tzZIlSxgxYgRDhw5l165dXLlyRR7NvWXLFoYPH87t27fJy8uT\nU6l4eXkBD0fCu7u7y99t3bp1TJ48mdTU1DILSc6bN49t27YRFxcnL9u4cSMTJkzQyl0mCIIgvHrU\najW3b9/m5s2b5ObmysuVSiUWFhZYWVnJRab+rQoLCzl69Ci3bt3CysqKdu3a/WOju2NjY6lRowZH\njhwhLi6O4cOHy/ce+fn5JCcn8+DBAzw8PJ64n9u3b7Nq1Src3Nzo3LnzC+93bm4uf/zxB6dPn0at\nVlOjRg0CAgL+0RcHgiAIwr/DiRMnACgpKaFevXpaz6RqtZro6Gjc3NzkZVeuXGH9+vV07NixVCrP\nx/Xo0QO1Ws3O/xW5fNnS09MZP348+fn5HDhw4B/pgyA8D2IktyA8owcPHsgVjjMzMzl06BCffvop\n7u7uTJ06Ffj/6T2ff/457777LomJicyePbvUvmrUqIGXlxejR4/m3r17pd7MPqpDhw7Uq1ePwYMH\ns2LFCvLy8nj//fcr9ADs4+MjB4FXrFhBs2bNAPD09GTDhg2kFhdj1bAhmxYvJiAggKCgICZMmICZ\nmRlvvfUWRUVFREVFcfXqVfnN7aPvx/bs2cPFixdp164dFhYWHD58mPv37+Pi4iK3KS4uZtiwYcyZ\nM4eMjAxmzJjBqFGjUKlU6OvrY2lpKY/izsjIYNq0aVrfbcCAASxcuJBu3bqxcOFCqlWrRnx8PKam\npuUWGxEEQRBeD0qlkipVqpR6ialWq7l16xaXLl2S81c+2t7KykorFdbrTE9Pjw4dOgAPCylv374d\neFjjwt7e/qX2pWHDhhw7dgxfX1/OnTvH7t27GTNmDAqFApVKxYP/Tb1+Gs2L58aNG7/Q/ubl5REe\nHs6JEydQq9VUq1ZNTqUmCIIgCM9DpUqV5BHc9+7d07pnUSqVpWYd29raolQqSUtL0wpy5+fns3r1\najp27Iiuri7btm1j165dBAcHv5wvUgY3Nzdq1KjBxo0b/7E+CMLzIILcgvAMFAoFoaGh2NraoqOj\nI09fCgoKYoGWIIsAACAASURBVNSoUXJQ1srKik2bNsnToxo3bsyKFSvk6VGPGjRoEMOHD6dnz56l\nCno9fuzt27czcuRIWrRogYODA0uXLtWaQlyWa9euAQ+nRpeUlPDOO++go6PD8ePH8fPzw8zMjFu3\nbhHyv9xfAMOHD8fIyIglS5Ywc+ZMVCoVDRo0YPz48Vr90TA3N2fnzp3Mnz+fvLw8atWqxfr16+UK\nzQqFAm9vb1xdXfHx8SEvL4/evXuzePFi4OFNwc8//8x7771Hw4YNqV27NkuXLqVXr17yMQwNDTl6\n9ChTpkyhS5cuFBYWUq9ePVasWCEfo6wH7n/7lHdBEIR/M6VSiaWlJZaWllrLS0pKyM7OJjU1lfz8\nfHm5jo6OHPxWqVQvu7vPjaOjI46OjhQWFhIWFkZERASWlpZ4enqip6f3wo+vUCho0qQJf/75J61a\ntSI8PJykpCT55bVCocDIyIh79+5hampa7n6ioqJQqVQvbCT1gwcPOH78OMePH6ekpARra2s6duz4\nUlOjCIIgCG8GTSqROnXqcO/evae219HRoWrVqly+fFmrfpdCoWDfvn18+umn5OfnU6dOHbZs2UK3\nbt1eaP+fRNSxEv4tRLoSQfgXkiSJtLQ0rl+/TtWqVXF0dNQK9kZHR+Pg4MCff/6Jh4eHKCIhCIIg\n/CsUFxeTnZ1NZmYmDx48kJfr6OhgaWmJlZUVBgYG/2AP/7rLly9z6tQpJEnCw8MDR0fHF37MxMRE\nTExM2Lx5M/r6+kyaNAkdHR2uXr1Kbm4uxcXFWrO2HnXz5k2++uor2rRpg5+f33PtV0FBARERERw7\ndoySkhIsLS3p2LEjTk5O4uW2IAiC8EJoBld5enoC4O7urrU+MjISNzc3retQaGgo4eHhTJw4sVSR\nbkEQnj8xklsQ/kXUajUXLlzg9u3bODo60qpVq1JtLl68iLm5OYaGhujp6YkAtyAIgvCvoauri42N\nDTY2NlrLi4uLycrK4s8//6SgoECrvSb4ra+v/7K7+0zs7e2xt7enuLiYsLAwTp48iYWFBV5eXi9s\ndLeLiwvHjh3Dz8+P3377jcjISFq0aIGtrS2RkZFPDCjHxsYCD1OfPC+FhYWcPHmSsLAwiouLsbCw\noGPHjtSqVUsEtwVBEIQXysjIiNu3b5e73sTEhPv372vNcLK3tyc8PJz09HQR5BaEl0AEuQXhX6C4\nuJiEhATy8vKoU6cO9erVK7NdZmYmubm5NGrUiKioKFxdXV9yTwVBEATh5dPV1aVq1apUrVpVa3lR\nURFZWVmcP3+ewsJCeXmlSpXk4PfLSA/yLHR1dfH19QUeForatWsXkiTh4uJCUVERrq6uz7XPzZo1\nIy4uDhMTEw4dOkSTJk3kFwLlTQiVJIno6GhMTU1LvXD4K4qKijh16hRHjx6lqKgIMzMzAgICqFu3\nrghuC4IgCC+FiYnJE9N6WFtbc/PmTa0gt52dHfBwNtbzfOkrCELZRJBbEF5jBQUFxMfHU1JSQv36\n9TE2Ni63bV5eHsnJybRq1Qq1Wk1RUdErP2pNEARBEF6kSpUqYWtri62trdbywsJCsrKySExMlItM\nadpbWVlhZWVFpUqVXnZ3S7Gzs8POzo7i4mK2bNlCWloaISEhNGzYkObNm5f6Xn+FoaEhlpaWtGzZ\nkoMHD/LHH3/g5+eHSqXi7t27qNXqUrPCMjIyyMvLw8fH528du7i4mMjISA4fPkxhYSGmpqZ0794d\nFxcXEdwWBEEQXioTExNKSkrKvO4BmJmZcfHiRa1lKpUKMzMzUlNTX1Y3BeGNJoLcgvAaysnJISEh\nAV1dXRo0aPDUYHVJSQmRkZFyIcjz589Tt27dl9FVQRAEQXjt6OnpUa1aNapVq6a1vKCggKysLM6d\nO0dxcbG8XF9fHysrKywtLeUi1C+Trq4uAwcOJCkpiYiICM6ePcvZs2exsLCgdevWFbpXeBJnZ2eu\nX7+OjY0NERERNG/eHCcnJ06ePElWVhbW1tZa7f9uqpLi4mKio6M5dOgQBQUFGBsb07lzZ1xdXUWa\nNUEQBOEfYWRkBDx8EV7WNbW8l681a9YkOjqagoICMchMEF4wEeQWhNfI7du3OX/+PIaGhjRr1qzC\nD9KnTp3Cw8MDHR0dAO7cuVNuoShBEARBEMqmr69P9erVqV69utbygoICMjMziYuLo6SkRKu9tbU1\nVapUeeHBb82L7wYNGpCdnc2ZM2eIjIxkz5497N27Vx7d/XjgvqLc3d25f/8+N27c4MCBA/Tu3RsD\nAwOuXbumFeRWq9XExMRgaWmJubn5Mx2jpKSEmJgYQkNDyc/Px9DQkO7du9OwYUMR3BYEQRD+UZog\nd0lJCZIkUVxcXKFru729PdHR0Vy5cgVnZ+cX3U1BeKOJILcgvAauX79Oamoq5ubmNG/e/Jke9GJj\nY6lVqxYqlQqAK1eulHo4FwRBEAThr9PX16dGjRrUqFFDa3l+fn6ZwW8DAwM5+K15Af08ValSBX9/\nf9q3by+P7o6JiSEmJgZzc3Nat25Nw4YNn2lEmb6+Pi4uLmRkZHDu3Dk8PT3R1dXVKuQJkJaWRmFh\nIe7u7hXet1qtJjY2loMHD5KXl4dKpaJLly40btz4hZwfQRAEQXhWmiB3UVGRXGSyrJe5kiRpjeq2\nt7cHHublFkFuQXixRJBbEF5RkiRx6dIlrl27RtWqVWnZsuUz559MS0vDyMgIKysreVl6ejotW7Z8\n3t0VBEEQBOExKpUKe3t7+QFXIz8/n5s3bxITE4NardZqb21tjYWFxXMJ7uro6ODq6oqrqyu3bt0i\nKiqK06dPExISwm+//YarqystWrSgWrVqFbrHcHBwwMnJiYyMDEJCQggICCAxMVGrzdmzZwEqVNxa\nrVYTHx/PwYMHycnJQV9fn06dOtG0aVMR3BYEQRBeKY+mK1EoFNy7d69UkLty5crcvXsXMzMzeZm5\nuTn6+vqkpqb+7VoVgiA8mQhyC8JfcOXKFTZs2MB7771H5cqVn+u+JUniwoUL3Lp1CwcHB1q1avWX\n9pOdnc2dO3do0qSJvOzu3btUrlxZFGsSBEH4G44cOYKvry9ZWVlYWFhUaJuhQ4eSnZ3N7t27X3Dv\nypeWloaTkxORkZE0a9bsH+uH8DCY7eDggIODg9byvLw8bt68SXp6uhz8liQJY2NjrKyssLCw+Mtp\nOywsLPDz88PHx4cLFy4QERFBXFwccXFxmJmZyaO7DQwMnrgfT09PUlJSSE9PJz8/n/v371NUVESl\nSpUoLi4mISGBatWqPbEYtiRJJCQksH//fu7fv4+enh4BAQG4u7v/IznNBUEQBOFpDA0NAXjw4AFq\ntZp79+6VamNtbc2NGze0gtwKhQJ7e3tSUlLKLVopCMLzIf7vEv4VjI2N2bRp00s5VlFREX379sXE\nxOS5BrhLSkqIi4vj5MmTWFlZ0apVq7+cN/PBgwckJSXRuHFjreWi4KQgCP9GQ4cORalUolQq0dPT\nw8bGBl9fX1avXq1VHPB5adOmDdevX69wgBsePuA87QXjjh07aNWqFebm5piYmODi4sLIkSP/bneF\n14ihoSGOjo40a9YMd3d3+WNnZ8f9+/eJjo4mMjKSyMhITp8+TVJSEtnZ2VqjwZ9GR0cHFxcXwsLC\niIiIoG3btuTl5bF3716WLFnCtm3bSE9PR5KkMrfX1dXF19cXpVJJSEgIJiYmXLt2DYDk5GRKSkrK\nTVUiSRKJiYmsXLmSrVu38uDBAzp06MDUqVNp2bKlHOD29vZmwoQJ8nZ5eXn07t0bMzMzlEolly9f\nrvD3FQRBEITnQTOSOz8/H0mSSqXrAjA1NS0z+O3o6EhJSQk3btx44f0UhDeZGCrxBho6dCibN28u\ntfzs2bM0atToH+jR31eR4MHTPPpG1djYmLp16zJr1ix69Oih1W7GjBm0bt2aSZMm/a3jAdy4cYM/\n/viD4uJinJyccHFxwcTE5KnbFRYWsnLlSn744QcuXLiAgYEBderUITAwkMGDB3Pq1Clat26tdU4K\nCwvR0dF5Zab/vgqjGgVB+HdQKBR06NCB7777jpKSEjIzMzl06BBz587lu+++49ChQ/Lom+ehUqVK\nWoX2KkKSpHKDhgCHDh2ib9++fPTRR2zatAkdHR0SExPZuXPn3+2u8Bp69BqpUCgwNjbG2NiYmjVr\nym0kSSInJ4fMzEzS0tLkf1+SJGFqaoqVlRXm5uYcPXq0zJkHn3/+udz20dHd8fHxxMfHY2pqSuvW\nrWnUqJFc10OjZs2a2NvbM2zYMN5//32MjIywt7cnKiqKXbt2ERQUxOnTp3Fzc5P75O7ujo6ODp06\ndZID5S1atEBPT6/U93/8vu7bb7/ljz/+IDw8HCsrKywtLZ/r+RYEQRCEp9FcCx88eFBum/JiEo/m\n5ba1tX3+nRMEARAjud9ImmDA9evXtT4VyZ1YlsLCwufcw3/OunXruH79OqdPn6Zx48b06dOHkydP\narVZtmwZS5cufeq+yhs9qMm1vXnzZtasWcO5c+dQqVQ0b968wgHugIAAFixYwPDhwzl+/DhRUVFM\nnjyZDRs2sHHjRtzc3EpN901ISKB+/fp/qc+CIAivMkmS0NPTw9raGltbWxo1asT777/PkSNHiIqK\nYvHixXLb77//Hg8PD0xNTbGxsaFv375cvXpVXn/kyBGUSiW///47LVq0wMjICA8PD6Kjo0u1uXXr\nlrzs+PHjeHl5YWRkRI0aNXj33Xe5f/++Vj8zMzPp5e9PL39/9u/fr7Vu9+7dtGzZkhkzZlCnTh2c\nnZ3p3Lkz33zzjdzm1q1b9O/fHzs7OwwNDWnQoAEbN24sdT6WLVtG7dq1MTAwwM7OjlmzZpV53tRq\nNePGjcPJyYmLFy8C8PXXX1OnTh1UKhVWVlZ07NhRq2Ci8HJU5OW9QqHAxMQEJycn3NzctEZ+29ra\ncvv2baKiojh//jwAUVFRXLhwgTt37iBJEiYmJpiamgIPX/TXq1ePwMBAJk2ahKenJwUFBezbt48l\nS5bw66+/cvnyZa0XNf369aNGjRqEhYVx/fp1CgsLuXjxIunp6djb23PkyBEkSSI5OZkVK1YQExOD\nnZ0d3t7eTJ06FU9PzzID3GVJTk7GxcUFV1dXrK2t//JUb3GfIwiCIPxVSqUSfX39Jwa5y2Nra4tS\nqeTSpUsvoGeCIGiIIPcbSJIk9PX1sba21vpoRvju3r0bNzc3VCoVTk5O/Pe//6WoqEje3tHRkaCg\nIIYNG4a5uTmDBg1i48aNmJiY8Pvvv9OgQQOMjY3x9fUlLS1N3u7ixYt069YNW1tbjI2NcXNzIyQk\nRKtvjo6OfPLJJ4wePZrKlStjZ2dXKqCcnJyMt7c3KpWKevXqsWfPnlLfMS4uDj8/PwwNDalSpQqB\ngYFlTht6nJmZGdbW1tStW5evv/4aAwMDeaRxRkYG/fr1w8LCAgsLC9566y2SkpLkbefNm0fDhg3Z\nuHEjzs7OGBgYkJeXx927dxk1ahQ2NjbyCPEFCxZw6dIlmjZtyvr16+nRo4c81V7zKW8q7meffUZY\nWBiHDh1i/PjxNG7cGAcHB/r06cM333yDn5+fPJVq8eLF1KpVC0NDQ3r37k1wcLC8n7S0NJRKJT/9\n9BO+vr4YGhry1VdfYWhoWOqcHjhwAD09PbKysoCHo9nr1auHoaEhNWvWZPr06VrTtTTn4qeffsLZ\n2RlTU1N69OhBdna2vH7z5s2EhITI3zcsLKzM89y5c2eSk5Plfaenp9OtWzeqVKmCkZERLi4u/Pzz\nz0/93QqC8OZxdXWlY8eObNu2TV5WVFTE/PnziY2NZc+ePWRlZdG/f/9S286aNYvFixcTFRVFlSpV\nGDhwYLnHiYuLIyAggO7duxMbG0twcDBnz55l2LBhcpuMjAyiT5+m68GDdD14kCE9emgFum1tbUlM\nTCQ2Nrbc4zx48AB3d3dCQkJISEhg4sSJjB49mt9//11uM3PmTD7++GM+/PBDEhMTCQ4OLpX3WXMe\nBg4cyB9//MHx48dxdnYmMjKS8ePHExQUxIULFzh06BBvvfVW+SdYeGEeDSYPHTqULl26aK3XXGc1\n4uLiaN++PZUrV8bU1JR27dqRnp5OlSpVGDt2LAD+/v7Uq1ePESNGcObMGTp37oynpyeRkZH8+eef\ntG3blnfffZdFixbxn//8hxUrVnD58mXs7e1JSEhgw4YNrFixghMnTpCXl4e+vj7e3t4kJydz/fp1\nkpKSuHXrFnfv3mXKlCmEhISwZs0atmzZQmxsLGq1moULF+Ll5UVISIic/9ve3p4FCxaUey68vb1Z\ntWoVYWFhKJVKfH19gYcv/adPn46dnR1GRkY0b96cAwcOyNtpXkb99ttvNG/eHH19fQ4cOCDuIwRB\nEIS/TKVSlZmm5FEKhaJUGjEdHR2qVq0qgtyC8IKJdCVvqPKmTO/fv59BgwaxatUq2rVrx6VLlxgz\nZgwFBQUsWbJEbrd8+XJmz57Nf//7X9RqNceOHaOgoICFCxeyceNG9PX1GTJkCGPGjGHfvn0A5Obm\n0qlTJxYsWIBKpeKnn36iZ8+exMbGauWJXrFiBR999BHTp09n7969vPfee7Rt25aWLVuiVqvp0aMH\nVapU4cSJE+Tm5jJx4kStC01ubi4BAQG0bNmS06dPk52dzciRIxk2bBhbt26t8DnSpPYoKCggPz8f\nHx8fvL29CQsLQ19fn2XLluHn50diYqI8Ajs1NZWffvqJKVOmsP377xnYtSsXrl3D0tKSd955h6Ki\nIuLi4vj++++JjY3F2dmZNm3ayKPkJElixIgRpKSkYGNjU2a/tmzZQocOHUoVDUtPT0dfX59atWoB\n8OGHHxIcHMzq1avR1dUlOTmZ0aNHY25uzttvvy1vN3PmTJYtW8aGDRvQ1dXl2LFjbNmyhc6dO2sd\n09/fX54ebGxszIYNG6hevTrnzp1jzJgx6Ovr89FHH8nbpKWl8euvv7Jz505ycnLo168fH374IWvW\nrGHatGkkJSVx+/ZtvvvuO+Bh1em8vDx8fHxo27YtYWFh6OnpsWTJEvz8/EhKSsLAwIB3332XwsJC\njhw5gqmpqdaLBkEQhMe5uLgQGhoq/xwYGCj/t6OjI6tXr6Z+/fpcvXpVqw7C/Pnz8fLyAmDOnDm0\nbdu2VBuNJUuW8J///If3338fAGdnZ1avXk2zZs3IysrC0tKSC+fO4aJWM0SzUX4+y+bNk/fn6+vL\n3r17adKkCTY2NjRu3JhWrVrRrVs3jIyM5JG9PXr0kEf4+vv7ExAQwDfffEPt2rXJy8uTr6EBAQFy\noSMHBwcyMzPlkedXr15l2rRp3L9/n927d6NSqbh37x7nz5/HyMgIX19fTExMsLS0pE6dOvLo11cl\n3dWb6GmjugcMGEDTpk356quv0NXVJS4uTg4gb9u2jV69epGQkICFhQUqlQoTExOsrKxQKpW4ublx\n9+5dioqK+O677+jfvz9r167l/PnzzJ49mzZt2jBp0iSio6M5efIk+/fv58CBA9SrVw8/Pz82b97M\nFwsXstHICF1zcxo3bszdu3fl2Q2tW7cmJycHJycnateuzZkzZ+jbty+zZ89m4MCBnDp1itGjR2Nq\nasr48eNLfbft27czdepUzp8/T3BwsDz6OzAwkNTUVH788Udq1KhBSEgIXbp04fTp01rp92bMmMGy\nZcuoVasWxsbGBAYGivsIQRAE4S8xMjKSB36Vx9zcnDt37pSq3VKzZk3Cw8O5e/fuX67t9XjKzxeR\nAvSvFFgXhFeFCHK/ofbt26eVGqNdu3aEhITwySef8MEHHzBkyMPH8Jo1a7Jw4ULeeecdrSC3Zqqp\nxrFjxyguLubLL7+kdu3aAEydOlVrFFujRo20HjpmzZrF7t272bp1Kx9++KG8PCAggHfffReA8ePH\ns2rVKg4dOkTLli0JDQ0lMTGRtLQ0atSoATwc2ezp6Slv/8MPP5CXl8d3330nj2heu3YtPj4+pKSk\n4OTkVO550QT/CwoKWLx4Mffv38fPz48ff/wRpVLJ2rVr5barV68mODiYkJAQ+vXrBzwcVRQYGMjE\nwEAW5eeTAOwA+vfvj7W1Na1bt2bRokW0atWK4OBgpk2bRpUqVeR9Llq0iBMnTnDq1Cn09fXL7GNy\ncrI8iknjzp073Lx5U859mZuby4oVKzh48CBt2rQhIiKCUaNGkZCQwJdffqkV5H7vvffo2bOn/POg\nQYPo168fOTk5GBsbk5+fz44dO/j666/lNv/973/l/7a3t5cD5Y8GuYuLi+UR/gCjRo1iw4YNwMOb\nAwMDAzm9gIYm4P3tt9/Ky9asWYONjQ179uyhd+/eXL58mV69eskj2MoaoSgIgqAhSZJWgDAqKoqg\noCBiYmK4deuW/Hf/8uXLWgHsR69XmtyJN2/eLDPIfebMGS5evKg1GlRz3IsXL5abP/jmzZtaM2x8\nfX1p0qQJaWlpXLlyhYULF7JixQpGjhyJsbGx/FI5Pj6e+/fvU1JSQklJCY6Ojnz77bdcuXKFgoIC\nMjIytK5XGrdv30aSJAYOHIiJiQlDhw7VSndSUFCAgYEBTk5OODs74+zsjIuLC/r6+ri5uWnlkHz0\nZbkmAF/Rz1/Z5p/YVtP+VfCkfO7w8N/vtGnTqFOnDoDWvY65uTkA1tbWWg+rmjzxCoUCMzMzVCoV\njRo1kv/tSJLE/v372bdvH40aNZJn6WVmZpKenk5iYqIcIG5z+TJtgNEKBXUaNJDTofj4+NCuXTtm\nz54t37ssX74cb29v5s6dC0CtWrX4888/WbRoUZlBbnNzc1QqlVYu/IsXL/LTTz+RlpaGnZ0dAOPG\njePgwYN8/fXXfPnll/L28+bNw8/PT+tcifsIQRCE18ONGzdYuHAhISEhpKenY2pqSq1atejfvz+B\ngYHy8/7LYmJiwt69e5k1axZ2dnakpKRoDQL47bff6NSpEyqVitzcXK1t7e3tCQ8P5/Lly1qzsR7X\nuHFj4uLiOH/+vBxb0Xg8ndnzqE0mCP8mIsj9hvLy8tJ6ANYUUThz5gynT59m4cKF8jq1Ws2DBw+4\nceMGNjY2KBQK3N3dS+1TX19f64+wra0thYWF3LlzBzMzM3JzcwkKCiIkJIRr165RVFTEgwcPaNy4\nsbyNQqEoVfyyWrVqZGZmApCYmEj16tXlADdA8+bNtXIzJiYm0rhxY60LXqtWrVAqlSQkJDwxyP3O\nO+8wdOhQ8vPzMTMzY9myZQQEBDBu3DguXLhQKgekQqEgJSVF/rlGjRr8sn49i/LzGQIsARTALz//\nrPV2taCgQGs7eJgmZt68eRw4cECrsNTjHn/QLSwsJD4+njZt2sjLEhISePDgAQEBAVp9LSoqKrXv\nx3+XHTt2xNDQkO3bt/POO++wa9cuJEmie/fucputW7fy2WefcfHiRXJycigpKSk1JcvBwUHrRYqt\nrS03b94s93vBw39/qamppXKT5+fny/liJ06cKM8QaN++PT169Cg1ql0QBEEjISEBZ2dn4P9n+vj7\n+/P9999jbW1NZmYmnp6epepLVKpUSf5vzcPD43/nNCRJYuTIkfJI7kdpguJ1XF0Ju3GDTf/bxwcG\nBiyZO5d27drJwcbHP+np6bz11lvk5+czbNgwvvnmG86cOcOMGTOoXbs2KpWKlStXcvv2bbp27Upc\nXBzr16/Hx8eH6tWraxUilCSJq1evsmrVKnx9fdm3bx8WFha4u7trHdPHx4fY2FjOnDnDsWPHOHbs\nGKtWrcLDw6PMGUaPH6Osz9PWP+3z+PZqtfpvbf8sH83vvrwgc0XWPylAXd76rKws7t27R2RkJFlZ\nWdy9e5fIyEh5/dWrV8nPz5eX9evXj+HDh/P555/TvHlzfHx8cHR0BJBzckdHR2uNHHt8vzk5OTg6\nOmodx8jIiBs3bmj118rKCiMjI6ysrAj56SfqAkXAEGCqJEFeHu+//z5JSUlERETQtGlToqKimDhx\nIgBJSUlas8UA2rRpQ1BQkPyC/WmioqKQJKlUrZGCggLat2+vtezx+xxxHyEIgvB6SEtLo02bNpiZ\nmfHxxx/LhZDj4+NZt24dlpaW8mCzxxUVFWndyz0vJiYmqNVq9PX1ycnJYdeuXfTo0UNev379euzt\n7csc7a2JYTwpyH3q1CmSkpLktKaPxmWgdCHzpxU2F4Q3jQhyv6E0+bYfJ0kS8+bNo0+fPqXWPToS\nraw3po8XOnw8KDB16lT2798vF8RSqVQMHjz4iYEFzX7KCyyU52kPo+VZunQpHTt2xNTUVOv7qtVq\n2rRpwx9//PHE7R8/L2qgMtCiVStWb96stU5T7AkgPj6eQYMGsXr1aq1R6WWpU6cOCQkJwMPvefLk\nSVq2bKn13TTnS5NvtmnTpvL6x8/v432uVKkSffv2ZcuWLbzzzjts2bKFnj17YmBgAMCJEyfo378/\n8+bNo2PHjpiZmbFz506tkf1lHaes3+Pjvw+1Wk2TJk3KzI2pGYk2bNgwAgIC2Lt3L6GhobRu3ZqZ\nM2fKI8IEQXgzlfX3PT4+nv379zN79mzgYXAtOzubBQsWyKM34+Pj//axmzVrRnx8/BNfolavXp2m\nHh7s+t/f/s1Tpmi9iCyL5lppaGiIi4sLf/75Jz169GDGjBnAw2vArFmzsLCwoGnTptSqVYvRo0dz\n48YNrReTGpo6GbNnz+btt99m0qRJ7Ny5U2uUK0CHDh2Ahw+I1tbW5ObmlptC61Ub8fxvYWlpiVKp\nxN3dXR69/Giwdvv27ahUKnmZu7s706ZN47fffmP//v0MHDiQNWvWEBgYSE5ODgBNmzbVGsltaWmp\nNXDBxMSEatWqaR3n8TaP27JmDeqkJI4AacAdoI2DA8bGxnh5ebF9+3YaNWpEcXGx1iy0v3qfpqFW\nq1EoFERGRpa639AM3NB4/D5H3EcIgiC8HsaOHYuuri6RkZFaf9sdHBzo1KmTVlulUskXX3xBaGgo\nBw4cIrkdpgAAIABJREFU4N1332Xx4sXyQLKEhARsbW0ZMGAAc+fOla8dN27cYOTIkYSGhmJjY0NQ\nUBCLFy+mT58+ZV4XNNcUpVJJkyZN+PTTT+Ugd1ZWFiEhIXzwwQel6ooBGBoaUrlyZVJTU8v9zpp6\nXV27dmXq1Kl88sknz5wubvHixaxdu5arV69Sq1Ytpk+fLteWSUtLw8nJia1bt/LVV19x/PhxHB0d\nWblyZan7wYiICD788EPOnz+Pq6sra9eupVmzZuTm5mJra8uGDRvo1auX3P7gwYN06tSJjIwMrKys\nnqnPgvC8iMKTgpZmzZqRmJiIk5NTqc/fzcUZHh7OkCFD6NGjBw0aNKB69epaBQUrwsXFhYyMDK5c\nuSIvO3XqlFbwtH79+sTFxckPdQDHjx9HrVbj4uLyxP1XrVoVJyenUlPL3dzciImJkQsnPsmoKVOY\nrlKxCcjm4QNf/5EjS51PzTGysrLo0qULo0aN0soVW54BAwYQGhrKmTNniIyMpEmTJvJFWq1Wc//+\nferXr4++vj5JSUnUrVsXZ2dn+biaab1PMmjQIA4dOkRiYqKcp10jPDyc6tWr8+GHH+Lm5oazs7NW\ngdGK0tPTk/O8ari5uZGcnEyVKlVKnS9NkBseBotGjhzJzz//zEcffVTmtHxBEN4smhlHV69eJSYm\nhuXLl+Pj44O7u7v8Es7e3h59fX0+//xzUlJSCAkJkQPgf8f06dM5deoUY8eOJTo6muTkZPbs2cOY\nMWO02llZWbHtwAG2HThQKsA9b948pk+fztGjR0lNTSU6Opphw4aRl5dH165dAahbty6hoaGEh4eT\nlJTE+PHjSUtLkwOGJiYmTJw4kZkzZ7Jx40YuXrzIqVOnWLNmTak+jxw5khUrVtC9e3c5Z/mePXtY\nuXIl0dHRXLp0iS1btnD//v2nXjuFF8vKyopr165pLTt79mypgHCtWrWYMGECe/bsYfjw4axbtw5A\nzmGtqf/xrJ4UeB41ZQqHdXW5BLz3v7ZjP/gAeJja7tixYxw8eBAXFxf5RYmLiwvh4eFa+zl27Jhc\nQLIimjZtiiRJXLt2rdT9wqNpdcoj7iMEQRBebdnZ2Rw4cIBx48aVenlZnqCgIDp37kx8fDzvvvuu\n/Bz73nvvkZCQwLfffsvWrVuZNWuWvM2QIUNIT0/n8OHD7Nixg82bN3P58uVyr32PXqe8vb2JiYmR\n05J89913tGnT5omDHm7fvs26FSvo4eenVYAcHs44/Pnnnxk5ciS9e/emsLCQPXv2VOi7a3z44Yds\n2LCB1atXk5iYyMyZMxk9ejR79+4t1W7SpEnExsbi4eFBv379SqVXmTp1KkuWLCEyMhInJyc6d+5M\nfn4+RkZGDBgwQCvFKDxMOdqlSxcR4Bb+USLILWiZM2cOP/zwA3PnziU+Pp6kpCS2bt3K9OnT//a+\n69SpQ3BwMNHR0cTFxTFo0CAKCgqeOr3m0Sk4HTp0oF69egwePJiYmBgiIiJ4//33tUaRDxw4EEND\nQwYPHkx8fDxhYWGMHj2aXr16PfGC8yQDBw6kWrVqdO3alSNHjpCamkpYWBjvvfeePA1YIyAggE3b\nt7OrQwcuduhAgwYNWLZsGfv27SM1NZWIiAjmzp3LsWPHAOjVqxc1atRg8uTJXL9+Xf6UN3p90qRJ\ntG3bFl9fX/bv38/FixdJTU0lODgYT09PoqOjMTExYerUqcyYMYOTJ0+SnJzM2bNnWbNmDd98881T\nv2+rVq1wcHCgf//+WFlZaU39rVu3LhkZGfzwww+kpKTw1Vdf8dNPPz3zOa1Zsybx8fFcuHCBrKws\niouLGThwIDY2NnTr1o2wsDD5PE+dOlV+ITJx4kT2799PSkoKZ8+e5bfffsPV1fWZjy8Iwr+HQqEg\nNDQUW1tbHBwc8PPzY8+ePQQFBREWFiY/HFlZWbFp0yZ27NiBq6sr8+fPZ8WKFaUeZMp6sHlSm4YN\nGxIWFkZaWhre3t40adKEWbNmUbVqVa32TwoWent7k5qaypAhQ6hfvz4dO3bk8uXL7Nq1i7Zt2wIP\n6yE0b96ct956Cy8vL0xMTBg4cKDWfj/99FOmT5/O/PnzqV+/Pr179yYjI6PMfo8aNYply5bRvXt3\nDh06hLm5OTt37qRDhw64uLiwfPly1q9fr5UOS3j52rdvT3R0NBs2bCA5OZnFixdz/PhxeX1+fj7j\nxo3j6NGjpKWlcfLkSY4dOyZfGx0cHFAoFOzZs4fMzMxSD7Ea5U15ftJ9WpMmTXi7Tx9QKAhRKmnU\nrJn8AqdOnToYGxvLKXQ0pkyZwtGjRwkKCuLChQts2bKF5cuX88H/guNP6otGnTp1GDhwIEOHDmXb\ntm2kpKQQGRnJ0qVL2b59e7nbgbiPEARBeB0kJycjSRJ169bVWl6jRg1MTEwwMTFh7NixWuv69evH\nsGHDcHR0xNHRUaveWM2aNfH29mbhwoXyy//z589z4MABvv76a1q0aEHjxo3ZuHEjeXl55fbL0NAQ\neHidqlmzJlZWVmz+34zt9evXM2zYMPn69fjz/P79+/lq0SLeT0mh+6FDDOnRQyvQ/euvv2JpaUn7\n9u3R09Nj8ODB8gvritDU5Vq3bh3+/v7y8/yIESO0alUATJ48mU6dOuHs7MyCBQu4desWMTExWm3m\nzJlDhw4dcHV1ZcOGDeTn5/PDDz8ADwdLHDhwgKtXrwIPg/c7d+5k+PDhFe6vILwQkvDGGTp0qNSl\nS5dy1x84cEDy9PSUDA0NJVNTU8nDw0P68ssv5fWOjo7SsmXLtLbZsGGDZGJiorXs8OHDklKplLKz\nsyVJkqRLly5Jfn5+kpGRkWRnZyctW7ZM6ty5sxQYGPjEfXt7e0sTJkyQf75w4YLk5eUl6evrS3Xq\n1JF27dolGRsbS5s2bZLbxMXFSe3bt5dUKpVkbm4uBQYGSvfu3XvieVEoFNK2bdvKXX/jxg0pMDBQ\nsra2lvT19aWaNWtKw4cPl7/fvHnzpIYNG5ba7v79+9LEiROlGjVqSHp6epKdnZ3Uv39/KSUlRT6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SUlJXIaEPj/VCAZGRlUr169QvuZOnUq+/fvZ9myZdSuXRuVSsXgwYMpLCzUavf4zGaFQqG1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\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ammJvb19t/bfeeovt27fz9ttvM3HiRMLCwqhduzbXr1/n559/1hLMHBwcOHXqFO179WJm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DL42vfB9evXSVVVlU6fPk0pKSm0atUq4v3VH5JJCdy6dYtUVFRoy5Yt7POyf/9+9r1Xt+2m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\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rUw41z1HR0dENtung4MC5c+c0lp09e5Zu3brVKYhLSEhI3I8kcktISEj8D1FYWEhUVJTo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HsXLlSpGSkiJiY2PF1KlTRVRUlBBCiFmzZglTU1Oxfv16ceLECTF+/HhRunRptX8bNmwQbZo1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\nSwi5AQAAgD8xQm4AAPDYuXjxouLi4vTSSy8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"text/plain": [ "<matplotlib.figure.Figure at 0x10c923a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (26, 26))\n", "pos = nx.spring_layout(g) \n", "nx.draw_networkx_labels(g, pos, font_color='k', font_size = 14)\n", "nx.draw(g, pos, node_size = 20, edge_color = 'grey', width = 0.4, arrows = True)\n", "\n", "plt.title(\"Justin Wolfers' Google Scholar Network\", fontsize=40)\n", "plt.xticks([])\n", "plt.yticks([])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the important network nodes" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot graph g with important nodes according to PageRank\n", "def important(G):\n", " rank = nx.betweenness_centrality(G).items()\n", " r = [x[1] for x in rank]\n", " # Mean centrality m\n", " m = sum(r)/len(r)\n", " # Threshold t, keep only the nodes with 3 times the mean m\n", " t = m*3\n", " Gt = G.copy()\n", " for k, v in rank:\n", " if v < t:\n", " Gt.remove_node(k)\n", " return Gt\n", "\n", "Gt = important(g) " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xmKrHE41GYRgGQqHQRNaaV19dRq1WRL0u8NhjL+B97ytet3YnoVAI2WwWb3rT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\nphqXs4Gu6yQSCZm2u9Cp1+tks1mZLhXF7n6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"text/plain": [ "<matplotlib.figure.Figure at 0x10299ef90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 8))\n", "pos = nx.spring_layout(g)\n", "# plot all the nodes and the edges\n", "nx.draw_networkx_nodes(g, pos, node_color = 'b', alpha = 0.2, node_size = 8)\n", "nx.draw_networkx_edges(g, pos, alpha = 0.1)\n", "\n", "# plot the important nodes and their labels\n", "nx.draw_networkx_nodes(Gt, pos, node_color = 'r', alpha = 0.4, node_size = 250)\n", "nx.draw_networkx_labels(Gt, pos, font_size = 12 , font_color = 'b')\n", "\n", "plt.title(\"Justin Wolfers' Google Scholar Network\", fontsize=20)\n", "plt.xticks([])\n", "plt.yticks([])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histogram of Degree Distribution" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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czwWOGio7BVhfVYcAl/TzJDkUOB44tF/nrCT2zCVJmqc5w7OqLgW+N1R8NLC2n14LHNtP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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109aa8e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = nx.degree(g)\n", "plt.figure(figsize = (8, 5))\n", "plt.hist(d.values())\n", "plt.title(\"Degree Distribution\", fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Degrees of each node" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Degrees of each node: Linli Xu \t1\n", "Degrees of each node: Michael Gelman \t1\n", "Degrees of each node: Marc Meredith \t1\n", "Degrees of each node: Betsey Stevenson \t2\n", "Degrees of each node: Vincent Nijs \t1\n", "Degrees of each node: Kenneth C Wilbur \t21\n", "Degrees of each node: Thomas M. Eisenbach \t1\n", "Degrees of each node: Zhenyu Yan \t1\n", "Degrees of each node: Justin Wolfers \t17\n", "Degrees of each node: Daniele Nosenzo \t1\n", "Degrees of each node: Daniel Kahneman \t1\n", "Degrees of each node: Yi Zhu \t7\n", "Degrees of each node: Robert Hall \t1\n", "Degrees of each node: Sergio Tufik \t1\n", "Degrees of each node: Andrea M. Buffa \t1\n", "Degrees of each node: Pradeep Chintagunta \t1\n", "Degrees of each node: Fabio Schiantarelli \t1\n", "Degrees of each node: Daniel Sacks \t1\n", "Degrees of each node: Jeffrey Carpenter \t1\n", "Degrees of each node: Richard Thaler \t1\n", "Degrees of each node: John Chalmers \t1\n", "Degrees of each node: Angelo Mele \t1\n", "Degrees of each node: Andrew Caplin \t1\n", "Degrees of each node: Daniel L. Greenwald \t1\n", "Degrees of each node: Eric Bradlow \t1\n", "Degrees of each node: John Van Reenen \t1\n", "Degrees of each node: Eric Verhoogen \t1\n", "Degrees of each node: Greg M. Allenby \t1\n", "Degrees of each node: Robin Greenwood \t1\n", "Degrees of each node: Florentino Felgueroso \t1\n", "Degrees of each node: Danny Yagan \t1\n", "Degrees of each node: Diane Del Guercio \t1\n", "Degrees of each node: Christopher Ré \t1\n", "Degrees of each node: Carles Boix \t1\n", "Degrees of each node: Phillip Swagel \t1\n", "Degrees of each node: Esteban Rossi-Hansberg \t1\n", "Degrees of each node: Gianmarco León \t1\n", "Degrees of each node: Michaela Draganska \t1\n", "Degrees of each node: Xiaowei Xu \t1\n", "Degrees of each node: Steven Huddart \t1\n", "Degrees of each node: luis serven \t1\n", "Degrees of each node: Samuel Kortum \t1\n", "Degrees of each node: Lasse Heje Pedersen \t1\n", "Degrees of each node: Vasco Cúrdia \t6\n", "Degrees of each node: Dean Karlan \t1\n", "Degrees of each node: David Weil \t2\n", "Degrees of each node: Christian Stoeckert \t1\n", "Degrees of each node: Timur Kuran \t1\n", "Degrees of each node: Maciej H. Kotowski \t1\n", "Degrees of each node: James J. Choi \t1\n", "Degrees of each node: Przemyslaw Jeziorski \t1\n", "Degrees of each node: Philip Lane \t3\n", "Degrees of each node: Usman Roshan \t1\n", "Degrees of each node: Robert C. Feenstra \t1\n", "Degrees of each node: Tobias Adrian \t1\n", "Degrees of each node: Stephen P. Zeldes \t12\n", "Degrees of each node: Andrew Abel \t1\n", "Degrees of each node: David THESMAR \t1\n", "Degrees of each node: Markus Brunnermeier \t23\n", "Degrees of each node: Pierluigi Balduzzi \t1\n", "Degrees of each node: Paul Heaton \t1\n", "Degrees of each node: Wouter Dessein \t1\n", "Degrees of each node: Sylvain Chassang \t4\n", "Degrees of each node: Colin DeYoung \t1\n", "Degrees of each node: Mitchell Hoffman \t7\n", "Degrees of each node: Abraham Wyner \t2\n", "Degrees of each node: W. Evan Johnson \t1\n", "Degrees of each node: Frederick Guy \t1\n", "Degrees of each node: Marco Del Negro \t1\n", "Degrees of each node: Gholson Lyon \t1\n", "Degrees of each node: Brigitte Madrian \t1\n", "Degrees of each node: Huiqi Qu \t1\n", "Degrees of each node: Hakon Hakonarson \t1\n", "Degrees of each node: Olivia S. Mitchell \t1\n", "Degrees of each node: Costas Meghir \t1\n", "Degrees of each node: Andrea Tambalotti \t1\n", "Degrees of each node: Cass Sunstein \t11\n", "Degrees of each node: Kathryn M. E. Dominguez \t1\n", "Degrees of each node: Andrew Leigh \t1\n", "Degrees of each node: Adam Rennhoff \t1\n", "Degrees of each node: Dan Silverman \t1\n", "Degrees of each node: Fernando Pérez Cervantes \t1\n", "Degrees of each node: Hyun Song Shin \t1\n", "Degrees of each node: Lawrence Lessig \t4\n", "Degrees of each node: Jonathan Parker \t1\n", "Degrees of each node: Ricardo Reis \t11\n", "Degrees of each node: Bo Cowgill \t14\n", "Degrees of each node: David Laibson \t1\n", "Degrees of each node: Jonathan Skinner \t1\n", "Degrees of each node: Luigi Pascali \t1\n", "Degrees of each node: Christian Zehnder \t1\n", "Degrees of each node: Kai Wang \t1\n", "Degrees of each node: Ignacio Palacios-Huerta \t1\n", "Degrees of each node: Shane Jensen \t7\n", "Degrees of each node: Boaz Barak \t1\n", "Degrees of each node: John Morgan \t1\n", "Degrees of each node: Joshua Rauh \t1\n", "Degrees of each node: Michael Cafarella \t1\n", "Degrees of each node: Yuriy Gorodnichenko \t1\n", "Degrees of each node: Z. John Daye \t1\n", "Degrees of each node: Karen Dynan \t1\n", "Degrees of each node: Stijn Van Nieuwerburgh \t3\n", "Degrees of each node: Robert Hahn \t1\n", "Degrees of each node: Erik Snowberg \t11\n", "Degrees of each node: Wenguang Sun \t1\n", "Degrees of each node: Laura Veldkamp \t1\n", "Degrees of each node: Gary Gorton \t1\n", "Degrees of each node: Mark Dean \t1\n", "Degrees of each node: Joshua W. Elliott \t1\n", "Degrees of each node: Michael Elsby \t1\n", "Degrees of each node: Jon Anderson \t1\n", "Degrees of each node: Joel Slemrod \t1\n", "Degrees of each node: John Ameriks \t2\n", "Degrees of each node: David Dillenberger \t1\n", "Degrees of each node: Stephen V. Burks \t14\n", "Degrees of each node: Mark Lemley \t1\n", "Degrees of each node: Jonathan Reuter \t5\n", "Degrees of each node: Dimitri Vayanos \t14\n", "Degrees of each node: Mingyu (Max) Joo \t6\n", "Degrees of each node: Lorenz Goette \t1\n", "Degrees of each node: Susanto Basu \t7\n", "Degrees of each node: PATRICK BOLTON \t1\n", "Degrees of each node: Benito Arruñada \t1\n", "Degrees of each node: Zhi Wei \t18\n", "Degrees of each node: Luis Rayo \t1\n", "Degrees of each node: Andrea Mattozzi \t1\n", "Degrees of each node: Erin Krupka \t1\n", "Degrees of each node: Luis Garicano \t23\n", "Degrees of each node: John Beshears \t1\n", "Degrees of each node: Robert Barsky \t3\n", "Degrees of each node: Andrea Prat \t1\n", "Degrees of each node: Rong Ge \t1\n", "Degrees of each node: Yiran Guo \t1\n", "Degrees of each node: Jon McAuliffe \t1\n", "Degrees of each node: Shijie Lu \t1\n", "Degrees of each node: Nick Bloom \t1\n", "Degrees of each node: Eric Zitzewitz \t9\n", "Degrees of each node: Pol Antras \t1\n", "Degrees of each node: Steven Tadelis \t1\n", "Degrees of each node: John Fernald \t2\n", "Degrees of each node: Stefan Nagel \t1\n", "Degrees of each node: David Schkade \t1\n", "Degrees of each node: Eric Posner \t2\n", "Degrees of each node: Martin Oehmke \t1\n", "Degrees of each node: Avinash Persaud \t1\n", "Degrees of each node: Mingyao Li \t1\n", "Degrees of each node: Robert Rooderkerk \t1\n", "Degrees of each node: David Weisbach \t8\n", "Degrees of each node: Carl Mela \t1\n", "Degrees of each node: Kfir Eliaz \t1\n", "Degrees of each node: Raffaella Sadun \t1\n", "Degrees of each node: Paul Resnick \t1\n", "Degrees of each node: Steven Berry \t1\n", "Degrees of each node: Arvind Krishnamurthy \t1\n", "Degrees of each node: Andrea Ferrero \t1\n", "Degrees of each node: Stephen H. Shore \t1\n", "Degrees of each node: Wei Wang \t1\n", "Degrees of each node: William Fuchs \t1\n", "Degrees of each node: N. Gregory Mankiw \t16\n", "Degrees of each node: Philippe Rigollet \t1\n", "Degrees of each node: Kristen Monaco \t1\n", "Degrees of each node: Valerie Ramey \t1\n", "Degrees of each node: Elizabeth Lyons \t1\n", "Degrees of each node: Michael H. Belzer \t1\n", "Degrees of each node: Neil Malhotra \t1\n", "Degrees of each node: Gilbert E. Metcalf \t1\n", "Degrees of each node: Matthew D. Shapiro \t21\n", "Degrees of each node: Pietro Ortoleva \t5\n", "Degrees of each node: Dolan Antenucci \t1\n", "Degrees of each node: Jonathan Zinman \t2\n", "Degrees of each node: Ahmed Khwaja \t1\n", "Degrees of each node: Pierre-Olivier Weill \t1\n", "Degrees of each node: Patrick Eichenberger \t1\n", "Degrees of each node: lawrence summers \t1\n", "Degrees of each node: Vicente Cuñat \t1\n", "Degrees of each node: Kusum L. Ailawadi \t1\n", "Degrees of each node: John Y. Campbell \t1\n", "Degrees of each node: Jura Liaukonyte \t1\n", "Degrees of each node: Peter Kondor \t1\n", "Degrees of each node: Tyler Shumway \t1\n", "Degrees of each node: Margaret Levenstein \t1\n", "Degrees of each node: Pingzhao Hu \t1\n", "-------------------------------------------\n", "Sum of edges: 470\n" ] } ], "source": [ "degrees = g.degree()\n", "for each in degrees.items():\n", " print 'Degrees of each node: ', each[0], '\\t', each[1]\n", "sum_of_edges = sum(degrees.values())\n", "print '-------------------------------------------'\n", "print 'Sum of edges: ', sum_of_edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Degree Centrality" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Degree Centrality: Linli Xu \t0.00552486187845\n", "Degree Centrality: Michael Gelman \t0.00552486187845\n", "Degree Centrality: Marc Meredith \t0.00552486187845\n", "Degree Centrality: Betsey Stevenson \t0.0110497237569\n", "Degree Centrality: Vincent Nijs \t0.00552486187845\n", "Degree Centrality: Kenneth C Wilbur \t0.116022099448\n", "Degree Centrality: Thomas M. Eisenbach \t0.00552486187845\n", "Degree Centrality: Zhenyu Yan \t0.00552486187845\n", "Degree Centrality: Justin Wolfers \t0.0939226519337\n", "Degree Centrality: Daniele Nosenzo \t0.00552486187845\n", "Degree Centrality: Daniel Kahneman \t0.00552486187845\n", "Degree Centrality: Yi Zhu \t0.0386740331492\n", "Degree Centrality: Robert Hall \t0.00552486187845\n", "Degree Centrality: Sergio Tufik \t0.00552486187845\n", "Degree Centrality: Andrea M. Buffa \t0.00552486187845\n", "Degree Centrality: Pradeep Chintagunta \t0.00552486187845\n", "Degree Centrality: Fabio Schiantarelli \t0.00552486187845\n", "Degree Centrality: Daniel Sacks \t0.00552486187845\n", "Degree Centrality: Jeffrey Carpenter \t0.00552486187845\n", "Degree Centrality: Richard Thaler \t0.00552486187845\n", "Degree Centrality: John Chalmers \t0.00552486187845\n", "Degree Centrality: Angelo Mele \t0.00552486187845\n", "Degree Centrality: Andrew Caplin \t0.00552486187845\n", "Degree Centrality: Daniel L. Greenwald \t0.00552486187845\n", "Degree Centrality: Eric Bradlow \t0.00552486187845\n", "Degree Centrality: John Van Reenen \t0.00552486187845\n", "Degree Centrality: Eric Verhoogen \t0.00552486187845\n", "Degree Centrality: Greg M. Allenby \t0.00552486187845\n", "Degree Centrality: Robin Greenwood \t0.00552486187845\n", "Degree Centrality: Florentino Felgueroso \t0.00552486187845\n", "Degree Centrality: Danny Yagan \t0.00552486187845\n", "Degree Centrality: Diane Del Guercio \t0.00552486187845\n", "Degree Centrality: Christopher Ré \t0.00552486187845\n", "Degree Centrality: Carles Boix \t0.00552486187845\n", "Degree Centrality: Phillip Swagel \t0.00552486187845\n", "Degree Centrality: Esteban Rossi-Hansberg \t0.00552486187845\n", "Degree Centrality: Gianmarco León \t0.00552486187845\n", "Degree Centrality: Michaela Draganska \t0.00552486187845\n", "Degree Centrality: Xiaowei Xu \t0.00552486187845\n", "Degree Centrality: Steven Huddart \t0.00552486187845\n", "Degree Centrality: luis serven \t0.00552486187845\n", "Degree Centrality: Samuel Kortum \t0.00552486187845\n", "Degree Centrality: Lasse Heje Pedersen \t0.00552486187845\n", "Degree Centrality: Vasco Cúrdia \t0.0331491712707\n", "Degree Centrality: Dean Karlan \t0.00552486187845\n", "Degree Centrality: David Weil \t0.0110497237569\n", "Degree Centrality: Christian Stoeckert \t0.00552486187845\n", "Degree Centrality: Timur Kuran \t0.00552486187845\n", "Degree Centrality: Maciej H. Kotowski \t0.00552486187845\n", "Degree Centrality: James J. Choi \t0.00552486187845\n", "Degree Centrality: Przemyslaw Jeziorski \t0.00552486187845\n", "Degree Centrality: Philip Lane \t0.0165745856354\n", "Degree Centrality: Usman Roshan \t0.00552486187845\n", "Degree Centrality: Robert C. Feenstra \t0.00552486187845\n", "Degree Centrality: Tobias Adrian \t0.00552486187845\n", "Degree Centrality: Stephen P. Zeldes \t0.0662983425414\n", "Degree Centrality: Andrew Abel \t0.00552486187845\n", "Degree Centrality: David THESMAR \t0.00552486187845\n", "Degree Centrality: Markus Brunnermeier \t0.127071823204\n", "Degree Centrality: Pierluigi Balduzzi \t0.00552486187845\n", "Degree Centrality: Paul Heaton \t0.00552486187845\n", "Degree Centrality: Wouter Dessein \t0.00552486187845\n", "Degree Centrality: Sylvain Chassang \t0.0220994475138\n", "Degree Centrality: Colin DeYoung \t0.00552486187845\n", "Degree Centrality: Mitchell Hoffman \t0.0386740331492\n", "Degree Centrality: Abraham Wyner \t0.0110497237569\n", "Degree Centrality: W. Evan Johnson \t0.00552486187845\n", "Degree Centrality: Frederick Guy \t0.00552486187845\n", "Degree Centrality: Marco Del Negro \t0.00552486187845\n", "Degree Centrality: Gholson Lyon \t0.00552486187845\n", "Degree Centrality: Brigitte Madrian \t0.00552486187845\n", "Degree Centrality: Huiqi Qu \t0.00552486187845\n", "Degree Centrality: Hakon Hakonarson \t0.00552486187845\n", "Degree Centrality: Olivia S. Mitchell \t0.00552486187845\n", "Degree Centrality: Costas Meghir \t0.00552486187845\n", "Degree Centrality: Andrea Tambalotti \t0.00552486187845\n", "Degree Centrality: Cass Sunstein \t0.060773480663\n", "Degree Centrality: Kathryn M. E. Dominguez \t0.00552486187845\n", "Degree Centrality: Andrew Leigh \t0.00552486187845\n", "Degree Centrality: Adam Rennhoff \t0.00552486187845\n", "Degree Centrality: Dan Silverman \t0.00552486187845\n", "Degree Centrality: Fernando Pérez Cervantes \t0.00552486187845\n", "Degree Centrality: Hyun Song Shin \t0.00552486187845\n", "Degree Centrality: Lawrence Lessig \t0.0220994475138\n", "Degree Centrality: Jonathan Parker \t0.00552486187845\n", "Degree Centrality: Ricardo Reis \t0.060773480663\n", "Degree Centrality: Bo Cowgill \t0.0773480662983\n", "Degree Centrality: David Laibson \t0.00552486187845\n", "Degree Centrality: Jonathan Skinner \t0.00552486187845\n", "Degree Centrality: Luigi Pascali \t0.00552486187845\n", "Degree Centrality: Christian Zehnder \t0.00552486187845\n", "Degree Centrality: Kai Wang \t0.00552486187845\n", "Degree Centrality: Ignacio Palacios-Huerta \t0.00552486187845\n", "Degree Centrality: Shane Jensen \t0.0386740331492\n", "Degree Centrality: Boaz Barak \t0.00552486187845\n", "Degree Centrality: John Morgan \t0.00552486187845\n", "Degree Centrality: Joshua Rauh \t0.00552486187845\n", "Degree Centrality: Michael Cafarella \t0.00552486187845\n", "Degree Centrality: Yuriy Gorodnichenko \t0.00552486187845\n", "Degree Centrality: Z. John Daye \t0.00552486187845\n", "Degree Centrality: Karen Dynan \t0.00552486187845\n", "Degree Centrality: Stijn Van Nieuwerburgh \t0.0165745856354\n", "Degree Centrality: Robert Hahn \t0.00552486187845\n", "Degree Centrality: Erik Snowberg \t0.060773480663\n", "Degree Centrality: Wenguang Sun \t0.00552486187845\n", "Degree Centrality: Laura Veldkamp \t0.00552486187845\n", "Degree Centrality: Gary Gorton \t0.00552486187845\n", "Degree Centrality: Mark Dean \t0.00552486187845\n", "Degree Centrality: Joshua W. Elliott \t0.00552486187845\n", "Degree Centrality: Michael Elsby \t0.00552486187845\n", "Degree Centrality: Jon Anderson \t0.00552486187845\n", "Degree Centrality: Joel Slemrod \t0.00552486187845\n", "Degree Centrality: John Ameriks \t0.0110497237569\n", "Degree Centrality: David Dillenberger \t0.00552486187845\n", "Degree Centrality: Stephen V. Burks \t0.0773480662983\n", "Degree Centrality: Mark Lemley \t0.00552486187845\n", "Degree Centrality: Jonathan Reuter \t0.0276243093923\n", "Degree Centrality: Dimitri Vayanos \t0.0773480662983\n", "Degree Centrality: Mingyu (Max) Joo \t0.0331491712707\n", "Degree Centrality: Lorenz Goette \t0.00552486187845\n", "Degree Centrality: Susanto Basu \t0.0386740331492\n", "Degree Centrality: PATRICK BOLTON \t0.00552486187845\n", "Degree Centrality: Benito Arruñada \t0.00552486187845\n", "Degree Centrality: Zhi Wei \t0.0994475138122\n", "Degree Centrality: Luis Rayo \t0.00552486187845\n", "Degree Centrality: Andrea Mattozzi \t0.00552486187845\n", "Degree Centrality: Erin Krupka \t0.00552486187845\n", "Degree Centrality: Luis Garicano \t0.127071823204\n", "Degree Centrality: John Beshears \t0.00552486187845\n", "Degree Centrality: Robert Barsky \t0.0165745856354\n", "Degree Centrality: Andrea Prat \t0.00552486187845\n", "Degree Centrality: Rong Ge \t0.00552486187845\n", "Degree Centrality: Yiran Guo \t0.00552486187845\n", "Degree Centrality: Jon McAuliffe \t0.00552486187845\n", "Degree Centrality: Shijie Lu \t0.00552486187845\n", "Degree Centrality: Nick Bloom \t0.00552486187845\n", "Degree Centrality: Eric Zitzewitz \t0.0497237569061\n", "Degree Centrality: Pol Antras \t0.00552486187845\n", "Degree Centrality: Steven Tadelis \t0.00552486187845\n", "Degree Centrality: John Fernald \t0.0110497237569\n", "Degree Centrality: Stefan Nagel \t0.00552486187845\n", "Degree Centrality: David Schkade \t0.00552486187845\n", "Degree Centrality: Eric Posner \t0.0110497237569\n", "Degree Centrality: Martin Oehmke \t0.00552486187845\n", "Degree Centrality: Avinash Persaud \t0.00552486187845\n", "Degree Centrality: Mingyao Li \t0.00552486187845\n", "Degree Centrality: Robert Rooderkerk \t0.00552486187845\n", "Degree Centrality: David Weisbach \t0.0441988950276\n", "Degree Centrality: Carl Mela \t0.00552486187845\n", "Degree Centrality: Kfir Eliaz \t0.00552486187845\n", "Degree Centrality: Raffaella Sadun \t0.00552486187845\n", "Degree Centrality: Paul Resnick \t0.00552486187845\n", "Degree Centrality: Steven Berry \t0.00552486187845\n", "Degree Centrality: Arvind Krishnamurthy \t0.00552486187845\n", "Degree Centrality: Andrea Ferrero \t0.00552486187845\n", "Degree Centrality: Stephen H. Shore \t0.00552486187845\n", "Degree Centrality: Wei Wang \t0.00552486187845\n", "Degree Centrality: William Fuchs \t0.00552486187845\n", "Degree Centrality: N. Gregory Mankiw \t0.0883977900552\n", "Degree Centrality: Philippe Rigollet \t0.00552486187845\n", "Degree Centrality: Kristen Monaco \t0.00552486187845\n", "Degree Centrality: Valerie Ramey \t0.00552486187845\n", "Degree Centrality: Elizabeth Lyons \t0.00552486187845\n", "Degree Centrality: Michael H. Belzer \t0.00552486187845\n", "Degree Centrality: Neil Malhotra \t0.00552486187845\n", "Degree Centrality: Gilbert E. Metcalf \t0.00552486187845\n", "Degree Centrality: Matthew D. Shapiro \t0.116022099448\n", "Degree Centrality: Pietro Ortoleva \t0.0276243093923\n", "Degree Centrality: Dolan Antenucci \t0.00552486187845\n", "Degree Centrality: Jonathan Zinman \t0.0110497237569\n", "Degree Centrality: Ahmed Khwaja \t0.00552486187845\n", "Degree Centrality: Pierre-Olivier Weill \t0.00552486187845\n", "Degree Centrality: Patrick Eichenberger \t0.00552486187845\n", "Degree Centrality: lawrence summers \t0.00552486187845\n", "Degree Centrality: Vicente Cuñat \t0.00552486187845\n", "Degree Centrality: Kusum L. Ailawadi \t0.00552486187845\n", "Degree Centrality: John Y. Campbell \t0.00552486187845\n", "Degree Centrality: Jura Liaukonyte \t0.00552486187845\n", "Degree Centrality: Peter Kondor \t0.00552486187845\n", "Degree Centrality: Tyler Shumway \t0.00552486187845\n", "Degree Centrality: Margaret Levenstein \t0.00552486187845\n", "Degree Centrality: Pingzhao Hu \t0.00552486187845\n" ] } ], "source": [ "centrality = nx.degree_centrality(g)\n", "for each in centrality.items():\n", " print 'Degree Centrality: ', each[0], '\\t', each[1]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Avergage Degree Centrality: 0.0142675004553\n" ] } ], "source": [ "avg_centrality = sum(centrality.values())/len(centrality)\n", "print 'Avergage Degree Centrality: ', avg_centrality" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OmGOZ/eew71I3av8PHCyQ5GDgR4BPTbyFC2ec5x7gzcDvAg8uVAMX2DjP/eOB\n25Ocl+TTSf4myR4L2trJG/n5r6o7gTcCX6H7ZsndVfXRBWzrQphL/xdi36ViIn3Yic99s5nXuW+S\ngT3Xq9daHEHNxaj937JfkkcC7wd+q3+32YpR+54kzwU2VdU1M2xvxTjP/QrgMOBtVXUY8E3gjAm2\nbUcY+W8/yROAV9FNKe4PPDLJz0+uaTvEOFfu7gxX/Y7dh2Vw7nuIUc59kwzsW4GDBpYPonu3MVuZ\nA/syc9l3qRu1/7cCJHkY8AHg76vq4gVs50IYp+8/BpyQ5EvABcCzk7xrAdu6EMbp/3pgfVVt/om8\n99MFeEvG6f/hwMer6o6qegD4IN1roiXjnL+Wy7lvm5bBuW9b5n3um2RgXw08McnBSXYDXgBcMlTm\nEuAlsOVX0e6uqo1z3HepG7n/SQK8E1hbVW/ZkY2ekFH7vqGqfr+qDqqqxwMvBP6lql6yIxs/ASM/\n91W1AbglySF9uWOAz++gdk/KOH/7XwCOTPKI/u/gGGDtjmv6RMzn/DU8klou577Ntur/Mjn3bbZV\n30c69034irnn0P0B3gSc2a97JfDKgTJ/0W//LHDYbPu2dhu1/8BP0H2G8Rngmv523GL3Z0c99wPb\nj6bBq8TH7T/wVOCqfv0Haewq8Qn0/3S6NynX0l2Y9rDF7s+k+093NfQtwD3AXXSf2T9yW/u2dhu1\n/8vh3Dfbcz9Qx5zOff5wiiRJDZj0/4ctSZIWgIEtSVIDDGxJkhpgYEuS1AADW5KkBhjYkiQ1wMCW\nJKkBBrYkSQ34//9F6kPDlN/KAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10443ebd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (8, 5))\n", "plt.hist(centrality.values())\n", "plt.title(\"Degree Centrality Distribution\", fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Closeness Centrality" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Closeness Centrality: Linli Xu \t0.0\n", "Closeness Centrality: Michael Gelman \t0.0\n", "Closeness Centrality: Marc Meredith \t0.0\n", "Closeness Centrality: Betsey Stevenson \t0.273413897281\n", "Closeness Centrality: Vincent Nijs \t0.0\n", "Closeness Centrality: Kenneth C Wilbur \t0.24659400545\n", "Closeness Centrality: Thomas M. Eisenbach \t0.0\n", "Closeness Centrality: Zhenyu Yan \t0.0\n", "Closeness Centrality: Justin Wolfers \t0.375518672199\n", "Closeness Centrality: Daniele Nosenzo \t0.0\n", "Closeness Centrality: Daniel Kahneman \t0.0\n", "Closeness Centrality: Yi Zhu \t0.241978609626\n", "Closeness Centrality: Robert Hall \t0.0\n", "Closeness Centrality: Sergio Tufik \t0.0\n", "Closeness Centrality: Andrea M. Buffa \t0.0\n", "Closeness Centrality: Pradeep Chintagunta \t0.0\n", "Closeness Centrality: Fabio Schiantarelli \t0.0\n", "Closeness Centrality: Daniel Sacks \t0.0\n", "Closeness Centrality: Jeffrey Carpenter \t0.0\n", "Closeness Centrality: Richard Thaler \t0.0\n", "Closeness Centrality: John Chalmers \t0.0\n", "Closeness Centrality: Angelo Mele \t0.0\n", "Closeness Centrality: Andrew Caplin \t0.0\n", "Closeness Centrality: Daniel L. Greenwald \t0.0\n", "Closeness Centrality: Eric Bradlow \t0.0\n", "Closeness Centrality: John Van Reenen \t0.0\n", "Closeness Centrality: Eric Verhoogen \t0.0\n", "Closeness Centrality: Greg M. Allenby \t0.0\n", "Closeness Centrality: Robin Greenwood \t0.0\n", "Closeness Centrality: Florentino Felgueroso \t0.0\n", "Closeness Centrality: Danny Yagan \t0.0\n", "Closeness Centrality: Diane Del Guercio \t0.0\n", "Closeness Centrality: Christopher Ré \t0.0\n", "Closeness Centrality: Carles Boix \t0.0\n", "Closeness Centrality: Phillip Swagel \t0.0\n", "Closeness Centrality: Esteban Rossi-Hansberg \t0.0\n", "Closeness Centrality: Gianmarco León \t0.0\n", "Closeness Centrality: Michaela Draganska \t0.0\n", "Closeness Centrality: Xiaowei Xu \t0.0\n", "Closeness Centrality: Steven Huddart \t0.0\n", "Closeness Centrality: luis serven \t0.0\n", "Closeness Centrality: Samuel Kortum \t0.0\n", "Closeness Centrality: Lasse Heje Pedersen \t0.0\n", "Closeness Centrality: Vasco Cúrdia \t0.243935309973\n", "Closeness Centrality: Dean Karlan \t0.0\n", "Closeness Centrality: David Weil \t0.0\n", "Closeness Centrality: Christian Stoeckert \t0.0\n", "Closeness Centrality: Timur Kuran \t0.0\n", "Closeness Centrality: Maciej H. Kotowski \t0.0\n", "Closeness Centrality: James J. Choi \t0.0\n", "Closeness Centrality: Przemyslaw Jeziorski \t0.0\n", "Closeness Centrality: Philip Lane \t0.0\n", "Closeness Centrality: Usman Roshan \t0.0\n", "Closeness Centrality: Robert C. Feenstra \t0.0\n", "Closeness Centrality: Tobias Adrian \t0.0\n", "Closeness Centrality: Stephen P. Zeldes \t0.259312320917\n", "Closeness Centrality: Andrew Abel \t0.0\n", "Closeness Centrality: David THESMAR \t0.0\n", "Closeness Centrality: Markus Brunnermeier \t0.261183261183\n", "Closeness Centrality: Pierluigi Balduzzi \t0.0\n", "Closeness Centrality: Paul Heaton \t0.0\n", "Closeness Centrality: Wouter Dessein \t0.0\n", "Closeness Centrality: Sylvain Chassang \t0.223181257707\n", "Closeness Centrality: Colin DeYoung \t0.0\n", "Closeness Centrality: Mitchell Hoffman \t0.241333333333\n", "Closeness Centrality: Abraham Wyner \t0.0\n", "Closeness Centrality: W. Evan Johnson \t0.0\n", "Closeness Centrality: Frederick Guy \t0.0\n", "Closeness Centrality: Marco Del Negro \t0.0\n", "Closeness Centrality: Gholson Lyon \t0.0\n", "Closeness Centrality: Brigitte Madrian \t0.0\n", "Closeness Centrality: Huiqi Qu \t0.0\n", "Closeness Centrality: Hakon Hakonarson \t0.0\n", "Closeness Centrality: Olivia S. Mitchell \t0.0\n", "Closeness Centrality: Costas Meghir \t0.0\n", "Closeness Centrality: Andrea Tambalotti \t0.0\n", "Closeness Centrality: Cass Sunstein \t0.0565042692115\n", "Closeness Centrality: Kathryn M. E. Dominguez \t0.0\n", "Closeness Centrality: Andrew Leigh \t0.0\n", "Closeness Centrality: Adam Rennhoff \t0.0\n", "Closeness Centrality: Dan Silverman \t0.0\n", "Closeness Centrality: Fernando Pérez Cervantes \t0.0\n", "Closeness Centrality: Hyun Song Shin \t0.0\n", "Closeness Centrality: Lawrence Lessig \t0.0388466850829\n", "Closeness Centrality: Jonathan Parker \t0.0\n", "Closeness Centrality: Ricardo Reis \t0.317543859649\n", "Closeness Centrality: Bo Cowgill \t0.308347529813\n", "Closeness Centrality: David Laibson \t0.0\n", "Closeness Centrality: Jonathan Skinner \t0.0\n", "Closeness Centrality: Luigi Pascali \t0.0\n", "Closeness Centrality: Christian Zehnder \t0.0\n", "Closeness Centrality: Kai Wang \t0.0\n", "Closeness Centrality: Ignacio Palacios-Huerta \t0.0\n", "Closeness Centrality: Shane Jensen \t0.0658503807675\n", "Closeness Centrality: Boaz Barak \t0.0\n", "Closeness Centrality: John Morgan \t0.0\n", "Closeness Centrality: Joshua Rauh \t0.0\n", "Closeness Centrality: Michael Cafarella \t0.0\n", "Closeness Centrality: Yuriy Gorodnichenko \t0.0\n", "Closeness Centrality: Z. John Daye \t0.0\n", "Closeness Centrality: Karen Dynan \t0.0\n", "Closeness Centrality: Stijn Van Nieuwerburgh \t0.0\n", "Closeness Centrality: Robert Hahn \t0.0\n", "Closeness Centrality: Erik Snowberg \t0.285039370079\n", "Closeness Centrality: Wenguang Sun \t0.0\n", "Closeness Centrality: Laura Veldkamp \t0.0\n", "Closeness Centrality: Gary Gorton \t0.0\n", "Closeness Centrality: Mark Dean \t0.0\n", "Closeness Centrality: Joshua W. Elliott \t0.0\n", "Closeness Centrality: Michael Elsby \t0.0\n", "Closeness Centrality: Jon Anderson \t0.0\n", "Closeness Centrality: Joel Slemrod \t0.0\n", "Closeness Centrality: John Ameriks \t0.0\n", "Closeness Centrality: David Dillenberger \t0.0\n", "Closeness Centrality: Stephen V. Burks \t0.243606998654\n", "Closeness Centrality: Mark Lemley \t0.0\n", "Closeness Centrality: Jonathan Reuter \t0.23264781491\n", "Closeness Centrality: Dimitri Vayanos \t0.257834757835\n", "Closeness Centrality: Mingyu (Max) Joo \t0.241655540721\n", "Closeness Centrality: Lorenz Goette \t0.0\n", "Closeness Centrality: Susanto Basu \t0.0276243093923\n", "Closeness Centrality: PATRICK BOLTON \t0.0\n", "Closeness Centrality: Benito Arruñada \t0.0\n", "Closeness Centrality: Zhi Wei \t0.0974585635359\n", "Closeness Centrality: Luis Rayo \t0.0\n", "Closeness Centrality: Andrea Mattozzi \t0.0\n", "Closeness Centrality: Erin Krupka \t0.0\n", "Closeness Centrality: Luis Garicano \t0.261183261183\n", "Closeness Centrality: John Beshears \t0.0\n", "Closeness Centrality: Robert Barsky \t0.0\n", "Closeness Centrality: Andrea Prat \t0.0\n", "Closeness Centrality: Rong Ge \t0.0\n", "Closeness Centrality: Yiran Guo \t0.0\n", "Closeness Centrality: Jon McAuliffe \t0.0\n", "Closeness Centrality: Shijie Lu \t0.0\n", "Closeness Centrality: Nick Bloom \t0.0\n", "Closeness Centrality: Eric Zitzewitz \t0.299668874172\n", "Closeness Centrality: Pol Antras \t0.0\n", "Closeness Centrality: Steven Tadelis \t0.0\n", "Closeness Centrality: John Fernald \t0.0\n", "Closeness Centrality: Stefan Nagel \t0.0\n", "Closeness Centrality: David Schkade \t0.0\n", "Closeness Centrality: Eric Posner \t0.0\n", "Closeness Centrality: Martin Oehmke \t0.0\n", "Closeness Centrality: Avinash Persaud \t0.0\n", "Closeness Centrality: Mingyao Li \t0.0\n", "Closeness Centrality: Robert Rooderkerk \t0.0\n", "Closeness Centrality: David Weisbach \t0.0497237569061\n", "Closeness Centrality: Carl Mela \t0.0\n", "Closeness Centrality: Kfir Eliaz \t0.0\n", "Closeness Centrality: Raffaella Sadun \t0.0\n", "Closeness Centrality: Paul Resnick \t0.0\n", "Closeness Centrality: Steven Berry \t0.0\n", "Closeness Centrality: Arvind Krishnamurthy \t0.0\n", "Closeness Centrality: Andrea Ferrero \t0.0\n", "Closeness Centrality: Stephen H. Shore \t0.0\n", "Closeness Centrality: Wei Wang \t0.0\n", "Closeness Centrality: William Fuchs \t0.0\n", "Closeness Centrality: N. Gregory Mankiw \t0.337057728119\n", "Closeness Centrality: Philippe Rigollet \t0.0\n", "Closeness Centrality: Kristen Monaco \t0.0\n", "Closeness Centrality: Valerie Ramey \t0.0\n", "Closeness Centrality: Elizabeth Lyons \t0.0\n", "Closeness Centrality: Michael H. Belzer \t0.0\n", "Closeness Centrality: Neil Malhotra \t0.0\n", "Closeness Centrality: Gilbert E. Metcalf \t0.0\n", "Closeness Centrality: Matthew D. Shapiro \t0.266961651917\n", "Closeness Centrality: Pietro Ortoleva \t0.223733003708\n", "Closeness Centrality: Dolan Antenucci \t0.0\n", "Closeness Centrality: Jonathan Zinman \t0.00552486187845\n", "Closeness Centrality: Ahmed Khwaja \t0.0\n", "Closeness Centrality: Pierre-Olivier Weill \t0.0\n", "Closeness Centrality: Patrick Eichenberger \t0.0\n", "Closeness Centrality: lawrence summers \t0.0\n", "Closeness Centrality: Vicente Cuñat \t0.0\n", "Closeness Centrality: Kusum L. Ailawadi \t0.0\n", "Closeness Centrality: John Y. Campbell \t0.0\n", "Closeness Centrality: Jura Liaukonyte \t0.0\n", "Closeness Centrality: Peter Kondor \t0.0\n", "Closeness Centrality: Tyler Shumway \t0.0\n", "Closeness Centrality: Margaret Levenstein \t0.0\n", "Closeness Centrality: Pingzhao Hu \t0.0\n" ] } ], "source": [ "close = nx.closeness_centrality(g) \n", "for each in close.items():\n", " print 'Closeness Centrality: ', each[0], '\\t', each[1]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Avergage Closeness: 0.0328750762923\n" ] } ], "source": [ "avg_closeness = sum(close.values())/len(close)\n", "print 'Avergage Closeness: ', avg_closeness" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kwAUuu9RG7d+6geeX8z0E8/avqr5WVZ8Abl3ssktsnL7NaP3YfbiqbupnPwoc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\nktQAA1uSpAb8f5gUXQYVDM0lAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b6aeb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (8, 5))\n", "plt.hist(close.values())\n", "plt.title(\"Closeness Centrality Distribution\", fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Betweeness Centrality" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Betweeness Centrality: Linli Xu \t0.0\n", "Betweeness Centrality: Michael Gelman \t0.0\n", "Betweeness Centrality: Marc Meredith \t0.0\n", "Betweeness Centrality: Betsey Stevenson \t0.0\n", "Betweeness Centrality: Vincent Nijs \t0.0\n", "Betweeness Centrality: Kenneth C Wilbur \t0.00920810313076\n", "Betweeness Centrality: Thomas M. Eisenbach \t0.0\n", "Betweeness Centrality: Zhenyu Yan \t0.0\n", "Betweeness Centrality: Justin Wolfers \t0.0805862492327\n", "Betweeness Centrality: Daniele Nosenzo \t0.0\n", "Betweeness Centrality: Daniel Kahneman \t0.0\n", "Betweeness Centrality: Yi Zhu \t0.00061387354205\n", "Betweeness Centrality: Robert Hall \t0.0\n", "Betweeness Centrality: Sergio Tufik \t0.0\n", "Betweeness Centrality: Andrea M. Buffa \t0.0\n", "Betweeness Centrality: Pradeep Chintagunta \t0.0\n", "Betweeness Centrality: Fabio Schiantarelli \t0.0\n", "Betweeness Centrality: Daniel Sacks \t0.0\n", "Betweeness Centrality: Jeffrey Carpenter \t0.0\n", "Betweeness Centrality: Richard Thaler \t0.0\n", "Betweeness Centrality: John Chalmers \t0.0\n", "Betweeness Centrality: Angelo Mele \t0.0\n", "Betweeness Centrality: Andrew Caplin \t0.0\n", "Betweeness Centrality: Daniel L. Greenwald \t0.0\n", "Betweeness Centrality: Eric Bradlow \t0.0\n", "Betweeness Centrality: John Van Reenen \t0.0\n", "Betweeness Centrality: Eric Verhoogen \t0.0\n", "Betweeness Centrality: Greg M. Allenby \t0.0\n", "Betweeness Centrality: Robin Greenwood \t0.0\n", "Betweeness Centrality: Florentino Felgueroso \t0.0\n", "Betweeness Centrality: Danny Yagan \t0.0\n", "Betweeness Centrality: Diane Del Guercio \t0.0\n", "Betweeness Centrality: Christopher Ré \t0.0\n", "Betweeness Centrality: Carles Boix \t0.0\n", "Betweeness Centrality: Phillip Swagel \t0.0\n", "Betweeness Centrality: Esteban Rossi-Hansberg \t0.0\n", "Betweeness Centrality: Gianmarco León \t0.0\n", "Betweeness Centrality: Michaela Draganska \t0.0\n", "Betweeness Centrality: Xiaowei Xu \t0.0\n", "Betweeness Centrality: Steven Huddart \t0.0\n", "Betweeness Centrality: luis serven \t0.0\n", "Betweeness Centrality: Samuel Kortum \t0.0\n", "Betweeness Centrality: Lasse Heje Pedersen \t0.0\n", "Betweeness Centrality: Vasco Cúrdia \t0.0024554941682\n", "Betweeness Centrality: Dean Karlan \t0.0\n", "Betweeness Centrality: David Weil \t0.0\n", "Betweeness Centrality: Christian Stoeckert \t0.0\n", "Betweeness Centrality: Timur Kuran \t0.0\n", "Betweeness Centrality: Maciej H. Kotowski \t0.0\n", "Betweeness Centrality: James J. Choi \t0.0\n", "Betweeness Centrality: Przemyslaw Jeziorski \t0.0\n", "Betweeness Centrality: Philip Lane \t0.0\n", "Betweeness Centrality: Usman Roshan \t0.0\n", "Betweeness Centrality: Robert C. Feenstra \t0.0\n", "Betweeness Centrality: Tobias Adrian \t0.0\n", "Betweeness Centrality: Stephen P. Zeldes \t0.00520257826888\n", "Betweeness Centrality: Andrew Abel \t0.0\n", "Betweeness Centrality: David THESMAR \t0.0\n", "Betweeness Centrality: Markus Brunnermeier \t0.00957642725599\n", "Betweeness Centrality: Pierluigi Balduzzi \t0.0\n", "Betweeness Centrality: Paul Heaton \t0.0\n", "Betweeness Centrality: Wouter Dessein \t0.0\n", "Betweeness Centrality: Sylvain Chassang \t0.0012277470841\n", "Betweeness Centrality: Colin DeYoung \t0.0\n", "Betweeness Centrality: Mitchell Hoffman \t0.00184162062615\n", "Betweeness Centrality: Abraham Wyner \t0.0\n", "Betweeness Centrality: W. Evan Johnson \t0.0\n", "Betweeness Centrality: Frederick Guy \t0.0\n", "Betweeness Centrality: Marco Del Negro \t0.0\n", "Betweeness Centrality: Gholson Lyon \t0.0\n", "Betweeness Centrality: Brigitte Madrian \t0.0\n", "Betweeness Centrality: Huiqi Qu \t0.0\n", "Betweeness Centrality: Hakon Hakonarson \t0.0\n", "Betweeness Centrality: Olivia S. Mitchell \t0.0\n", "Betweeness Centrality: Costas Meghir \t0.0\n", "Betweeness Centrality: Andrea Tambalotti \t0.0\n", "Betweeness Centrality: Cass Sunstein \t0.0102823818293\n", "Betweeness Centrality: Kathryn M. E. Dominguez \t0.0\n", "Betweeness Centrality: Andrew Leigh \t0.0\n", "Betweeness Centrality: Adam Rennhoff \t0.0\n", "Betweeness Centrality: Dan Silverman \t0.0\n", "Betweeness Centrality: Fernando Pérez Cervantes \t0.0\n", "Betweeness Centrality: Hyun Song Shin \t0.0\n", "Betweeness Centrality: Lawrence Lessig \t0.00141190914672\n", "Betweeness Centrality: Jonathan Parker \t0.0\n", "Betweeness Centrality: Ricardo Reis \t0.0408839779006\n", "Betweeness Centrality: Bo Cowgill \t0.0407612031921\n", "Betweeness Centrality: David Laibson \t0.0\n", "Betweeness Centrality: Jonathan Skinner \t0.0\n", "Betweeness Centrality: Luigi Pascali \t0.0\n", "Betweeness Centrality: Christian Zehnder \t0.0\n", "Betweeness Centrality: Kai Wang \t0.0\n", "Betweeness Centrality: Ignacio Palacios-Huerta \t0.0\n", "Betweeness Centrality: Shane Jensen \t0.0130141190915\n", "Betweeness Centrality: Boaz Barak \t0.0\n", "Betweeness Centrality: John Morgan \t0.0\n", "Betweeness Centrality: Joshua Rauh \t0.0\n", "Betweeness Centrality: Michael Cafarella \t0.0\n", "Betweeness Centrality: Yuriy Gorodnichenko \t0.0\n", "Betweeness Centrality: Z. John Daye \t0.0\n", "Betweeness Centrality: Karen Dynan \t0.0\n", "Betweeness Centrality: Stijn Van Nieuwerburgh \t0.0\n", "Betweeness Centrality: Robert Hahn \t0.0\n", "Betweeness Centrality: Erik Snowberg \t0.0164211172498\n", "Betweeness Centrality: Wenguang Sun \t0.0\n", "Betweeness Centrality: Laura Veldkamp \t0.0\n", "Betweeness Centrality: Gary Gorton \t0.0\n", "Betweeness Centrality: Mark Dean \t0.0\n", "Betweeness Centrality: Joshua W. Elliott \t0.0\n", "Betweeness Centrality: Michael Elsby \t0.0\n", "Betweeness Centrality: Jon Anderson \t0.0\n", "Betweeness Centrality: Joel Slemrod \t0.0\n", "Betweeness Centrality: John Ameriks \t0.0\n", "Betweeness Centrality: David Dillenberger \t0.0\n", "Betweeness Centrality: Stephen V. Burks \t0.0061387354205\n", "Betweeness Centrality: Mark Lemley \t0.0\n", "Betweeness Centrality: Jonathan Reuter \t0.00184162062615\n", "Betweeness Centrality: Dimitri Vayanos \t0.00405156537753\n", "Betweeness Centrality: Mingyu (Max) Joo \t0.0\n", "Betweeness Centrality: Lorenz Goette \t0.0\n", "Betweeness Centrality: Susanto Basu \t0.00225598526703\n", "Betweeness Centrality: PATRICK BOLTON \t0.0\n", "Betweeness Centrality: Benito Arruñada \t0.0\n", "Betweeness Centrality: Zhi Wei \t0.0108041743401\n", "Betweeness Centrality: Luis Rayo \t0.0\n", "Betweeness Centrality: Andrea Mattozzi \t0.0\n", "Betweeness Centrality: Erin Krupka \t0.0\n", "Betweeness Centrality: Luis Garicano \t0.00957642725599\n", "Betweeness Centrality: John Beshears \t0.0\n", "Betweeness Centrality: Robert Barsky \t0.0\n", "Betweeness Centrality: Andrea Prat \t0.0\n", "Betweeness Centrality: Rong Ge \t0.0\n", "Betweeness Centrality: Yiran Guo \t0.0\n", "Betweeness Centrality: Jon McAuliffe \t0.0\n", "Betweeness Centrality: Shijie Lu \t0.0\n", "Betweeness Centrality: Nick Bloom \t0.0\n", "Betweeness Centrality: Eric Zitzewitz \t0.0115561694291\n", "Betweeness Centrality: Pol Antras \t0.0\n", "Betweeness Centrality: Steven Tadelis \t0.0\n", "Betweeness Centrality: John Fernald \t0.0\n", "Betweeness Centrality: Stefan Nagel \t0.0\n", "Betweeness Centrality: David Schkade \t0.0\n", "Betweeness Centrality: Eric Posner \t0.0\n", "Betweeness Centrality: Martin Oehmke \t0.0\n", "Betweeness Centrality: Avinash Persaud \t0.0\n", "Betweeness Centrality: Mingyao Li \t0.0\n", "Betweeness Centrality: Robert Rooderkerk \t0.0\n", "Betweeness Centrality: David Weisbach \t0.00352977286679\n", "Betweeness Centrality: Carl Mela \t0.0\n", "Betweeness Centrality: Kfir Eliaz \t0.0\n", "Betweeness Centrality: Raffaella Sadun \t0.0\n", "Betweeness Centrality: Paul Resnick \t0.0\n", "Betweeness Centrality: Steven Berry \t0.0\n", "Betweeness Centrality: Arvind Krishnamurthy \t0.0\n", "Betweeness Centrality: Andrea Ferrero \t0.0\n", "Betweeness Centrality: Stephen H. Shore \t0.0\n", "Betweeness Centrality: Wei Wang \t0.0\n", "Betweeness Centrality: William Fuchs \t0.0\n", "Betweeness Centrality: N. Gregory Mankiw \t0.031599140577\n", "Betweeness Centrality: Philippe Rigollet \t0.0\n", "Betweeness Centrality: Kristen Monaco \t0.0\n", "Betweeness Centrality: Valerie Ramey \t0.0\n", "Betweeness Centrality: Elizabeth Lyons \t0.0\n", "Betweeness Centrality: Michael H. Belzer \t0.0\n", "Betweeness Centrality: Neil Malhotra \t0.0\n", "Betweeness Centrality: Gilbert E. Metcalf \t0.0\n", "Betweeness Centrality: Matthew D. Shapiro \t0.00980662983425\n", "Betweeness Centrality: Pietro Ortoleva \t0.00184162062615\n", "Betweeness Centrality: Dolan Antenucci \t0.0\n", "Betweeness Centrality: Jonathan Zinman \t0.000644567219153\n", "Betweeness Centrality: Ahmed Khwaja \t0.0\n", "Betweeness Centrality: Pierre-Olivier Weill \t0.0\n", "Betweeness Centrality: Patrick Eichenberger \t0.0\n", "Betweeness Centrality: lawrence summers \t0.0\n", "Betweeness Centrality: Vicente Cuñat \t0.0\n", "Betweeness Centrality: Kusum L. Ailawadi \t0.0\n", "Betweeness Centrality: John Y. Campbell \t0.0\n", "Betweeness Centrality: Jura Liaukonyte \t0.0\n", "Betweeness Centrality: Peter Kondor \t0.0\n", "Betweeness Centrality: Tyler Shumway \t0.0\n", "Betweeness Centrality: Margaret Levenstein \t0.0\n", "Betweeness Centrality: Pingzhao Hu \t0.0\n" ] } ], "source": [ "btwn = nx.betweenness_centrality(g, weight='weight')\n", "for each in btwn.items():\n", " print 'Betweeness Centrality: ', each[0], '\\t', each[1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Avergage Betweenness: 0.00179743522285\n" ] } ], "source": [ "avg_betweenness = sum(btwn.values())/len(btwn)\n", "print 'Avergage Betweenness: ', avg_betweenness" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b617950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (8, 5))\n", "plt.hist(btwn.values())\n", "plt.title(\"Betweeness Centrality Distribution\", fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

gklambauer/SelfNormalizingNetworks

SelfNormalizingNetworks_CNN_MNIST.ipynb

1

56613

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Tutorial on self-normalizing networks on the MNIST data set: convolutional neural networks\n", "\n", "*Author:* Guenter Klambauer, 2017\n", "\n", "tested under Python 3.5 and Tensorflow 1.1 \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "\n", "from __future__ import absolute_import, division, print_function\n", "import numbers\n", "from tensorflow.contrib import layers\n", "from tensorflow.python.framework import ops\n", "from tensorflow.python.framework import tensor_shape\n", "from tensorflow.python.framework import tensor_util\n", "from tensorflow.python.ops import math_ops\n", "from tensorflow.python.ops import random_ops\n", "from tensorflow.python.ops import array_ops\n", "from tensorflow.python.layers import utils\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from scipy.special import erf,erfc\n", "\n", "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (1) Definition of scaled exponential linear units (SELUs)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def selu(x):\n", " with ops.name_scope('elu') as scope:\n", " alpha = 1.6732632423543772848170429916717\n", " scale = 1.0507009873554804934193349852946\n", " return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (2) Definition of dropout variant for SNNs\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def dropout_selu(x, rate, alpha= -1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0, \n", " noise_shape=None, seed=None, name=None, training=False):\n", " \"\"\"Dropout to a value with rescaling.\"\"\"\n", "\n", " def dropout_selu_impl(x, rate, alpha, noise_shape, seed, name):\n", " keep_prob = 1.0 - rate\n", " x = ops.convert_to_tensor(x, name=\"x\")\n", " if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1:\n", " raise ValueError(\"keep_prob must be a scalar tensor or a float in the \"\n", " \"range (0, 1], got %g\" % keep_prob)\n", " keep_prob = ops.convert_to_tensor(keep_prob, dtype=x.dtype, name=\"keep_prob\")\n", " keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())\n", "\n", " alpha = ops.convert_to_tensor(alpha, dtype=x.dtype, name=\"alpha\")\n", " keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())\n", "\n", " if tensor_util.constant_value(keep_prob) == 1:\n", " return x\n", "\n", " noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x)\n", " random_tensor = keep_prob\n", " random_tensor += random_ops.random_uniform(noise_shape, seed=seed, dtype=x.dtype)\n", " binary_tensor = math_ops.floor(random_tensor)\n", " ret = x * binary_tensor + alpha * (1-binary_tensor)\n", "\n", " a = tf.sqrt(fixedPointVar / (keep_prob *((1-keep_prob) * tf.pow(alpha-fixedPointMean,2) + fixedPointVar)))\n", "\n", " b = fixedPointMean - a * (keep_prob * fixedPointMean + (1 - keep_prob) * alpha)\n", " ret = a * ret + b\n", " ret.set_shape(x.get_shape())\n", " return ret\n", "\n", " with ops.name_scope(name, \"dropout\", [x]) as name:\n", " return utils.smart_cond(training,\n", " lambda: dropout_selu_impl(x, rate, alpha, noise_shape, seed, name),\n", " lambda: array_ops.identity(x))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (3) Scale input to zero mean and unit variance" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scaler = StandardScaler().fit(mnist.train.images)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parameters\n", "learning_rate = 0.025\n", "training_iters = 50\n", "batch_size = 128\n", "display_step = 1\n", "\n", "# Network Parameters\n", "n_input = 784 # MNIST data input (img shape: 28*28)\n", "n_classes = 10 # MNIST total classes (0-9 digits)\n", "keep_prob_ReLU = 0.5 # Dropout, probability to keep units\n", "dropout_prob_SNN = 0.05 # Dropout, probability to dropout units\n", "\n", "# tf Graph input\n", "x = tf.placeholder(tf.float32, [None, n_input])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability for ReLU)\n", "dropout_prob = tf.placeholder(tf.float32) #dropout (dropout probability for SNN)\n", "is_training = tf.placeholder(tf.bool)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create some wrappers for simplicity\n", "def conv2d(x, W, b, strides=1):\n", " # Conv2D wrapper, with bias and relu activation\n", " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", " x = tf.nn.bias_add(x, b)\n", " return tf.nn.relu(x)\n", "\n", "def conv2d_SNN(x, W, b, strides=1):\n", " # Conv2D wrapper, with bias and relu activation\n", " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", " x = tf.nn.bias_add(x, b)\n", " return selu(x)\n", "\n", "def maxpool2d(x, k=2):\n", " # MaxPool2D wrapper\n", " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", " padding='SAME')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create model\n", "def conv_net_ReLU(x, weights, biases, keep_prob):\n", " # Reshape input picture\n", " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", "\n", " # Convolution Layer\n", " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", " # Max Pooling (down-sampling)\n", " conv1 = maxpool2d(conv1, k=2)\n", "\n", " # Convolution Layer\n", " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", " # Max Pooling (down-sampling)\n", " conv2 = maxpool2d(conv2, k=2)\n", "\n", " # Fully connected layer\n", " # Reshape conv2 output to fit fully connected layer input\n", " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", " fc1 = tf.nn.relu(fc1)\n", " \n", " # Apply Dropout\n", " fc1 = tf.nn.dropout(fc1, keep_prob)\n", "\n", " # Output, class prediction\n", " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", " return out" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create model\n", "def conv_net_SNN(x, weights, biases, dropout_prob, is_training):\n", " # Reshape input picture\n", " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", "\n", " # Convolution Layer\n", " conv1 = conv2d_SNN(x, weights['wc1'], biases['bc1'],)\n", " # Max Pooling (down-sampling)\n", " conv1 = maxpool2d(conv1, k=2)\n", "\n", " # Convolution Layer\n", " conv2 = conv2d_SNN(conv1, weights['wc2'], biases['bc2'])\n", " # Max Pooling (down-sampling)\n", " conv2 = maxpool2d(conv2, k=2)\n", "\n", " # Fully connected layer\n", " # Reshape conv2 output to fit fully connected layer input\n", " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", " fc1 = selu(fc1)\n", " \n", " # Apply Dropout\n", " fc1 = dropout_selu(fc1, dropout_prob,training=is_training)\n", "\n", " # Output, class prediction\n", " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", " return out" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# RELU: Store layers weight & bias\n", "## Improved with MSRA initialization\n", "\n", "weights = {\n", " # 5x5 conv, 1 input, 32 outputs\n", " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32],stddev=np.sqrt(2/25)) ),\n", " # 5x5 conv, 32 inputs, 64 outputs\n", " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64],stddev=np.sqrt(2/(25*32)))),\n", " # fully connected, 7*7*64 inputs, 1024 outputs\n", " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024],stddev=np.sqrt(2/(7*7*64)))),\n", " # 1024 inputs, 10 outputs (class prediction)\n", " 'out': tf.Variable(tf.random_normal([1024, n_classes],stddev=np.sqrt(2/(1024))))\n", "}\n", "\n", "biases = {\n", " 'bc1': tf.Variable(tf.random_normal([32],stddev=0)),\n", " 'bc2': tf.Variable(tf.random_normal([64],stddev=0)),\n", " 'bd1': tf.Variable(tf.random_normal([1024],stddev=0)),\n", " 'out': tf.Variable(tf.random_normal([n_classes],stddev=0))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (4) Initialization with STDDEV of sqrt(1/n)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# SNN: Store layers weight & bias\n", "weights2 = {\n", " # 5x5 conv, 1 input, 32 outputs\n", " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32],stddev=np.sqrt(1/25)) ),\n", " # 5x5 conv, 32 inputs, 64 outputs\n", " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64],stddev=np.sqrt(1/(25*32)))),\n", " # fully connected, 7*7*64 inputs, 1024 outputs\n", " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024],stddev=np.sqrt(1/(7*7*64)))),\n", " # 1024 inputs, 10 outputs (class prediction)\n", " 'out': tf.Variable(tf.random_normal([1024, n_classes],stddev=np.sqrt(1/(1024))))\n", "}\n", "\n", "biases2 = {\n", " 'bc1': tf.Variable(tf.random_normal([32],stddev=0)),\n", " 'bc2': tf.Variable(tf.random_normal([64],stddev=0)),\n", " 'bd1': tf.Variable(tf.random_normal([1024],stddev=0)),\n", " 'out': tf.Variable(tf.random_normal([n_classes],stddev=0))\n", "}\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Construct model\n", "pred_ReLU = conv_net_ReLU(x, weights, biases, keep_prob)\n", "pred_SNN = conv_net_SNN(x, weights2, biases2, dropout_prob,is_training)\n", "\n", "# Define loss and optimizer\n", "cost_ReLU = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred_ReLU, labels=y))\n", "cost_SNN = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred_SNN, labels=y))\n", "\n", "optimizer_ReLU = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost_ReLU)\n", "optimizer_SNN = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost_SNN)\n", "\n", "# Evaluate ReLU model\n", "correct_pred_ReLU = tf.equal(tf.argmax(pred_ReLU, 1), tf.argmax(y, 1))\n", "accuracy_ReLU = tf.reduce_mean(tf.cast(correct_pred_ReLU, tf.float32))\n", "\n", "# Evaluate SNN model\n", "correct_pred_SNN = tf.equal(tf.argmax(pred_SNN, 1), tf.argmax(y, 1))\n", "accuracy_SNN = tf.reduce_mean(tf.cast(correct_pred_SNN, tf.float32))\n", "\n", "\n", "# Initializing the variables\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "training_loss_protocol_ReLU = []\n", "training_loss_protocol_SNN = []" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RELU: Nbr of updates: 1, Minibatch Loss= 2.008754, Training Accuracy= 0.35156\n", "SNN: Nbr of updates: 1, Minibatch Loss= 2.451197, Training Accuracy= 0.39844\n", "RELU: Nbr of updates: 2, Minibatch Loss= 1.732718, Training Accuracy= 0.37500\n", "SNN: Nbr of updates: 2, Minibatch Loss= 2.160526, Training Accuracy= 0.65625\n", "RELU: Nbr of updates: 3, Minibatch Loss= 1.752255, Training Accuracy= 0.39844\n", "SNN: Nbr of updates: 3, Minibatch Loss= 1.601260, Training Accuracy= 0.53125\n", "RELU: Nbr of updates: 4, Minibatch Loss= 1.605729, Training Accuracy= 0.53906\n", "SNN: Nbr of updates: 4, Minibatch Loss= 0.978341, Training Accuracy= 0.70312\n", "RELU: Nbr of updates: 5, Minibatch Loss= 1.555425, Training Accuracy= 0.51562\n", "SNN: Nbr of updates: 5, Minibatch Loss= 0.645711, Training Accuracy= 0.82031\n", "RELU: Nbr of updates: 6, Minibatch Loss= 1.313229, Training Accuracy= 0.67969\n", "SNN: Nbr of updates: 6, Minibatch Loss= 0.401476, Training Accuracy= 0.90625\n", "RELU: Nbr of updates: 7, Minibatch Loss= 1.203895, Training Accuracy= 0.77344\n", "SNN: Nbr of updates: 7, Minibatch Loss= 0.453578, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 8, Minibatch Loss= 1.089910, Training Accuracy= 0.85938\n", "SNN: Nbr of updates: 8, Minibatch Loss= 0.297481, Training Accuracy= 0.95312\n", "RELU: Nbr of updates: 9, Minibatch Loss= 1.017870, Training Accuracy= 0.79688\n", "SNN: Nbr of updates: 9, Minibatch Loss= 0.365949, Training Accuracy= 0.91406\n", "RELU: Nbr of updates: 10, Minibatch Loss= 1.070305, Training Accuracy= 0.76562\n", "SNN: Nbr of updates: 10, Minibatch Loss= 0.405422, Training Accuracy= 0.90625\n", "RELU: Nbr of updates: 11, Minibatch Loss= 0.985618, Training Accuracy= 0.79688\n", "SNN: Nbr of updates: 11, Minibatch Loss= 0.460914, Training Accuracy= 0.88281\n", "RELU: Nbr of updates: 12, Minibatch Loss= 0.875668, Training Accuracy= 0.72656\n", "SNN: Nbr of updates: 12, Minibatch Loss= 0.349492, Training Accuracy= 0.90625\n", "RELU: Nbr of updates: 13, Minibatch Loss= 1.041480, Training Accuracy= 0.76562\n", "SNN: Nbr of updates: 13, Minibatch Loss= 0.436600, Training Accuracy= 0.89062\n", "RELU: Nbr of updates: 14, Minibatch Loss= 0.836483, Training Accuracy= 0.83594\n", "SNN: Nbr of updates: 14, Minibatch Loss= 0.356240, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 15, Minibatch Loss= 0.824995, Training Accuracy= 0.81250\n", "SNN: Nbr of updates: 15, Minibatch Loss= 0.407508, Training Accuracy= 0.87500\n", "RELU: Nbr of updates: 16, Minibatch Loss= 0.739613, Training Accuracy= 0.85156\n", "SNN: Nbr of updates: 16, Minibatch Loss= 0.289174, Training Accuracy= 0.92969\n", "RELU: Nbr of updates: 17, Minibatch Loss= 0.782138, Training Accuracy= 0.80469\n", "SNN: Nbr of updates: 17, Minibatch Loss= 0.314916, Training Accuracy= 0.91406\n", "RELU: Nbr of updates: 18, Minibatch Loss= 0.687675, Training Accuracy= 0.85156\n", "SNN: Nbr of updates: 18, Minibatch Loss= 0.243602, Training Accuracy= 0.94531\n", "RELU: Nbr of updates: 19, Minibatch Loss= 0.647239, Training Accuracy= 0.82812\n", "SNN: Nbr of updates: 19, Minibatch Loss= 0.205704, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 20, Minibatch Loss= 0.673955, Training Accuracy= 0.78906\n", "SNN: Nbr of updates: 20, Minibatch Loss= 0.293074, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 21, Minibatch Loss= 0.643871, Training Accuracy= 0.84375\n", "SNN: Nbr of updates: 21, Minibatch Loss= 0.305403, Training Accuracy= 0.92969\n", "RELU: Nbr of updates: 22, Minibatch Loss= 0.577555, Training Accuracy= 0.91406\n", "SNN: Nbr of updates: 22, Minibatch Loss= 0.225528, Training Accuracy= 0.96875\n", "RELU: Nbr of updates: 23, Minibatch Loss= 0.539012, Training Accuracy= 0.90625\n", "SNN: Nbr of updates: 23, Minibatch Loss= 0.207042, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 24, Minibatch Loss= 0.595193, Training Accuracy= 0.85938\n", "SNN: Nbr of updates: 24, Minibatch Loss= 0.297265, Training Accuracy= 0.89844\n", "RELU: Nbr of updates: 25, Minibatch Loss= 0.610190, Training Accuracy= 0.83594\n", "SNN: Nbr of updates: 25, Minibatch Loss= 0.255643, Training Accuracy= 0.95312\n", "RELU: Nbr of updates: 26, Minibatch Loss= 0.708689, Training Accuracy= 0.69531\n", "SNN: Nbr of updates: 26, Minibatch Loss= 0.161673, Training Accuracy= 0.98438\n", "RELU: Nbr of updates: 27, Minibatch Loss= 0.702952, Training Accuracy= 0.79688\n", "SNN: Nbr of updates: 27, Minibatch Loss= 0.215801, Training Accuracy= 0.94531\n", "RELU: Nbr of updates: 28, Minibatch Loss= 0.470672, Training Accuracy= 0.88281\n", "SNN: Nbr of updates: 28, Minibatch Loss= 0.269345, Training Accuracy= 0.91406\n", "RELU: Nbr of updates: 29, Minibatch Loss= 0.554051, Training Accuracy= 0.83594\n", "SNN: Nbr of updates: 29, Minibatch Loss= 0.296727, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 30, Minibatch Loss= 0.504638, Training Accuracy= 0.84375\n", "SNN: Nbr of updates: 30, Minibatch Loss= 0.227030, Training Accuracy= 0.93750\n", "RELU: Nbr of updates: 31, Minibatch Loss= 0.566984, Training Accuracy= 0.85938\n", "SNN: Nbr of updates: 31, Minibatch Loss= 0.212100, Training Accuracy= 0.96875\n", "RELU: Nbr of updates: 32, Minibatch Loss= 0.505076, Training Accuracy= 0.86719\n", "SNN: Nbr of updates: 32, Minibatch Loss= 0.224962, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 33, Minibatch Loss= 0.487980, Training Accuracy= 0.87500\n", "SNN: Nbr of updates: 33, Minibatch Loss= 0.192593, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 34, Minibatch Loss= 0.377008, Training Accuracy= 0.93750\n", "SNN: Nbr of updates: 34, Minibatch Loss= 0.164228, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 35, Minibatch Loss= 0.468827, Training Accuracy= 0.89062\n", "SNN: Nbr of updates: 35, Minibatch Loss= 0.222637, Training Accuracy= 0.92969\n", "RELU: Nbr of updates: 36, Minibatch Loss= 0.456475, Training Accuracy= 0.90625\n", "SNN: Nbr of updates: 36, Minibatch Loss= 0.223814, Training Accuracy= 0.92969\n", "RELU: Nbr of updates: 37, Minibatch Loss= 0.521786, Training Accuracy= 0.83594\n", "SNN: Nbr of updates: 37, Minibatch Loss= 0.289590, Training Accuracy= 0.91406\n", "RELU: Nbr of updates: 38, Minibatch Loss= 0.512233, Training Accuracy= 0.80469\n", "SNN: Nbr of updates: 38, Minibatch Loss= 0.254801, Training Accuracy= 0.92188\n", "RELU: Nbr of updates: 39, Minibatch Loss= 0.462405, Training Accuracy= 0.84375\n", "SNN: Nbr of updates: 39, Minibatch Loss= 0.192647, Training Accuracy= 0.95312\n", "RELU: Nbr of updates: 40, Minibatch Loss= 0.398073, Training Accuracy= 0.89844\n", "SNN: Nbr of updates: 40, Minibatch Loss= 0.127224, Training Accuracy= 0.97656\n", "RELU: Nbr of updates: 41, Minibatch Loss= 0.454393, Training Accuracy= 0.85156\n", "SNN: Nbr of updates: 41, Minibatch Loss= 0.204394, Training Accuracy= 0.92969\n", "RELU: Nbr of updates: 42, Minibatch Loss= 0.455688, Training Accuracy= 0.88281\n", "SNN: Nbr of updates: 42, Minibatch Loss= 0.198009, Training Accuracy= 0.95312\n", "RELU: Nbr of updates: 43, Minibatch Loss= 0.402138, Training Accuracy= 0.89062\n", "SNN: Nbr of updates: 43, Minibatch Loss= 0.170651, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 44, Minibatch Loss= 0.430634, Training Accuracy= 0.89062\n", "SNN: Nbr of updates: 44, Minibatch Loss= 0.216837, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 45, Minibatch Loss= 0.389273, Training Accuracy= 0.91406\n", "SNN: Nbr of updates: 45, Minibatch Loss= 0.180505, Training Accuracy= 0.96094\n", "RELU: Nbr of updates: 46, Minibatch Loss= 0.409469, Training Accuracy= 0.91406\n", "SNN: Nbr of updates: 46, Minibatch Loss= 0.193067, Training Accuracy= 0.94531\n", "RELU: Nbr of updates: 47, Minibatch Loss= 0.368824, Training Accuracy= 0.89062\n", "SNN: Nbr of updates: 47, Minibatch Loss= 0.158238, Training Accuracy= 0.97656\n", "RELU: Nbr of updates: 48, Minibatch Loss= 0.388534, Training Accuracy= 0.89844\n", "SNN: Nbr of updates: 48, Minibatch Loss= 0.229685, Training Accuracy= 0.93750\n", "RELU: Nbr of updates: 49, Minibatch Loss= 0.321354, Training Accuracy= 0.94531\n", "SNN: Nbr of updates: 49, Minibatch Loss= 0.143143, Training Accuracy= 0.96875\n", "RELU: Nbr of updates: 50, Minibatch Loss= 0.356414, Training Accuracy= 0.90625\n", "SNN: Nbr of updates: 50, Minibatch Loss= 0.160477, Training Accuracy= 0.96094\n", "Optimization Finished!\n", "\n", "ReLU: Testing Accuracy: 0.859375\n", "SNN: Testing Accuracy: 0.916016\n" ] } ], "source": [ "# Launch the graph\n", "gpu_options = tf.GPUOptions(allow_growth=True)\n", "with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:\n", " sess.run(init)\n", " step = 0\n", " # Keep training until reach max iterations\n", " while step < training_iters:\n", " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", " batch_x_norm = scaler.transform(batch_x)\n", " # Run optimization op (backprop)\n", " sess.run(optimizer_ReLU, feed_dict={x: batch_x, y: batch_y,\n", " keep_prob: keep_prob_ReLU})\n", " sess.run(optimizer_SNN, feed_dict={x: batch_x_norm, y: batch_y,\n", " dropout_prob: dropout_prob_SNN,is_training:True})\n", " \n", " \n", " if step % display_step == 0:\n", " #batch_x, batch_y = mnist.test.next_batch(batch_size)\n", " #batch_x_norm = scaler.transform(batch_x)\n", " # Calculate batch loss and accuracy\n", " loss_ReLU, acc_ReLU = sess.run([cost_ReLU, accuracy_ReLU], feed_dict={x: batch_x,\n", " y: batch_y,\n", " keep_prob: 1.0})\n", " training_loss_protocol_ReLU.append(loss_ReLU)\n", " \n", " loss_SNN, acc_SNN = sess.run([cost_SNN, accuracy_SNN], feed_dict={x: batch_x_norm,\n", " y: batch_y,\n", " dropout_prob: 0.0, is_training:False})\n", " training_loss_protocol_SNN.append(loss_SNN)\n", " \n", " print( \"RELU: Nbr of updates: \" + str(step+1) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss_ReLU) + \", Training Accuracy= \" + \\\n", " \"{:.5f}\".format(acc_ReLU))\n", " \n", " print( \"SNN: Nbr of updates: \" + str(step+1) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss_SNN) + \", Training Accuracy= \" + \\\n", " \"{:.5f}\".format(acc_SNN))\n", " step += 1\n", " print(\"Optimization Finished!\\n\")\n", "\n", " # Calculate accuracy for 256 mnist test images\n", " print(\"ReLU: Testing Accuracy:\", sess.run(accuracy_ReLU, feed_dict={x: mnist.test.images[:512],\n", " y: mnist.test.labels[:512],\n", " keep_prob: 1.0}))\n", " print(\"SNN: Testing Accuracy:\", sess.run(accuracy_SNN, feed_dict={x: scaler.transform(mnist.test.images[:512]),\n", " y: mnist.test.labels[:512],\n", " dropout_prob: 0.0, is_training:False}))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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W53Xwd2HWpkT2J2cS7d8wKhglCWRPvbrjFQk5aZB5xtiRNEraJKcbacwUHu4a\nwMReQfy6K5mpq48bKLLrftySxN7kTN4a2BZnW+37MXfwFyNCOc0nNTQyQdUVz0jxei7euHFINfJs\n31YMa+/DF6uO8+suw/1ykZSazSf/HqVna3cGhddsuzVXOytauNuyU+5KLDUw2rQ6qmjOIKt0d11J\nW01DwMwczu2BtoOMHY2kJUVR+HBoO1Ky8nht6X6aOVjTXQ/Vclfzi9iRmMaWE2lsPpnG4fNXsLHU\nMOXuUJ2mEmMCXPhj33mKS9Q6L+qQJEPR5hnUbsAXuIyo4HMCLiiKchF4VFXVOAPG13BYWINHMJyX\nIyhTY2luxtf3t2f4t9t4Yl4cvz7eiRCvmi8ST88uYM7mRDafTGPvmQyKSlQszc2I8nPmhdtbcWeo\nJz7OunVl7+DvwoIdZzh6IYu2Xg46XUOS6httEtR/wGJVVf8BUBTldmAYosPE10Cs4cKrmEm1OrqR\nVyQcXiELJUyQvbUFs8d2YMjXmxk3eye/TeiCt1MTrc/PyClg1HfbOHYxizAfJ8Z3D6RLkBtRzZ2v\nlbrXRofS4oidSekyQUkNhjbPoDqWJSeA0q02Oqmqug2wMlhkVTCpVkc38oyA3MuQcdrYkUg6aOZo\nzZxxMeQWFjP2hx1k5BRodd6VvEJG/7CDhEvZzBkXw+8TuvDSnW3oEuSml+QE4OPcBE9Ha1koITUo\n2iSo84qivKwoSvPSr5eAi4qiaAC5CU9NeJUVSuwxbhySzlo3s+fbB6NISstm4Jeb2H36cpXHZ+cX\nMW72Tg6du8LX97fXy/OriiiKQgd/F3YmplfY3kmSTJE2CWoU4AP8XvrlV/qeBrHbrqStpiFgZiGf\nQ5m4zi3cWDi+IyUlcO+MrXy19gTFJeWTQl5hMY/8uIs9py8zbWQkfQy8FUiHABdSsvI5nZ6j92sX\nFZfw5Pw4Hp6zU24OKdUZbZrFpqqq+rSqqpGlX0+pqnpJVdUCVVVP1EWQDYa5FTRtK0dQDUBUcxf+\nnNSNu0Kb8b9/jvLA99u5kJl37fP8omIemxvHtsQ0Ph0eTr92ngaPKab0OdSOasrNi4pLiDt1uUYj\nrf/76wh/7r/A6iMpvL38gBylSXWi2gSlKEorRVFmKoryr6Ioa8q+6iK4BskzQqyFkv+DmzzHJhZM\nHxnJx/eEsTc5gzunbuDfgxcoLC7hqZ/3sP7YJT4c0o4hkXWzk3JLDzscm1iws5rnUJ/9d4xh32zh\n3T8OaZVOugtGAAAgAElEQVRoFu06w/ebEhnb2Z8Jt7VgwY4z/LglSU9RS1LltKniWwTMAL7n5o0L\nJV14RcLuH+FyErjIdoemTlEUhkf7Et3cmYkL9zB+bhwtPew4nnKVdwaFcF+MX53FYmam0MHfmZ1J\nlT8XS0rN5vuNiXg5WjN7cxKFxSW8Oyi00k4bcacu8/pvB+ga5MYb/YMxUxSOXbzKeysP08LDTut+\ngZKkC22eQRWpqvqNqqo7VFWNK/syeGQNlVeEeJXPoRqUQHc7ljzRmUe7BXDi0lVevasNYzr713kc\nHfxdSEzNJiUrr8LPp6w8hIVG4fcJXXisRyDztp3mtd/2U1LBM7Tzmbk8NjcOTydrvhwVibnGDDMz\nhc9HRNDSw44J83eTcKl+bvYoNQzaJKgViqI8qSiKp6IoLmVfBo+sofJoCxpL+RyqAbIy1/B6/7Yc\nfOcOHuvRwigxdCjd7n5XBaOotUdTWHU4hYm9W+LhYM0rd7bh6V5BLNx5hpeW7Lup0CO3oJhHf9pF\nXmEx34+Oxsnmem9AOytzvhsdjbnGjEd+2qX17sV1obhErbBgRTJN2kzxjSl9ffGG9+R+ULoytxJJ\nSvbka7BsLI23SUColyPWFmbsSEy/qTCjoKiE91YcItDNlnFdxNSyoig8f3trzM3M+HzVMQqLS/j0\n3nA0ZgovLt7LwXNX+H50NC2b2pe7j6+LDd/c354HZm3n6QV7+GFMdLlt6eva1fwiBn+5ifOZebT1\ndCDEy4EQb0dCvRxp2dSuzrZMkfSn2v+T5L5QBuAVCQeXyo4Skt5ZmpsR6etcrlBizpZEElKzmT2u\nA5bmN/+gntSnJeYahf/9c5SiYpVWTe35Y995Xr6zDb2DKy+Njw105b3BobyydD8f/nWENwe0Ncj3\npK23lx0kMTWbe6N8OXnpKovikvlx6ykALDVmtPG05+XSBdKSaahqy/deqqquURRlaEWfq6q61HBh\nNXBeERA3Gy4ngosciEr61SHAhS/XHCcrrxB7awtSruQxddVxerfx4LbWHhWeM+G2ICw1Zrz/52FW\n7j/P4AgvHu9R/X+b98X4cfRiFrM2JZJfVMx9HfwI8XIw+N5Zt1qx9xxLdiczsVcQz93eGoCSEpXE\ntGwOnrvCwbOZ/HPwAo/8uIt5j8QQ1Vw+pTAFVY2gegBrgIEVfKYCMkHp6saOEjJBSXoW4+9CiSoq\n8Hq29uCjv49SWKxWO8J5tHsgtlbmbEtIq9EGja/3CyYnv5hfdp5h3rbTtPSwY0h7bwZHeNeoX6Gu\nki/n8Npv+4n0c2Ji75bX3jczU2jhbkcLdzsGhXvxSLdAhn+7lbGzd/LL+E6yZ6EJUExxwd0NzWIf\nPX7c8BvJ6V1RAXzoDbGPw+3vGTsaqYHJzi8i7J1/ebxHIL2DmzL06y080bMFL9/ZxqD3zcgpYOX+\n8/y+5+y1UvfYABeGtvfmnihfg2wDUlyiMnLmNg6dv8KfE7vh51p1N/jkyzncO2MrhcUlLHq8MwFu\ntnqPSaqeoihxqqpGV3tcdQlKURQrRPdyf24Ycamq+m4tY6y16OhoddeuXcYOQzczbwNLWxj7h7Ej\nkRqgwV9uwkJjRkFxCRev5LHm+Z7YWtVd8cbptByWxZ/ltz1nSUjN5o3+wTzSTf+zBdNXH+fT/47x\n2fBwhrbXbkH0iZSrDP92K00sNCx+ohOejoYf5Uk30zZBaVPWsgwYDBQB2Td8SbXhFQHn90GJ7Gsm\n6V8Hfxd2nbrMvuRMXrmrTZ0mJwA/Vxue7t2S1c/3INLPiUW7kvXeHmn36ct8sfo4g8K9GBLprfV5\nQR52/DguhszcQh74fjtpV/P1GpekP9okKB9VVUeoqvqxqqqfln0ZPLKGzisS8jNFoYQk6VnZeqj2\nfk7cHaH9D299UxSFYe19OHoxi4Pnrmh93qWsfJbuTuZMJY1vs/IKeWZhPM0crHlPh12I2/k4MmtM\nNMmXcxk7eydZefVnLZd0nTYJaouiKO0MHklj41naUaI+LdjNugCFucaOQtKDLkFu3BnSjA+Hal/s\nYCgDw7ywNDdjcVyy1ue8v/IQz/26l24fr6XXp+t4Z8VB1h5NIbdAdFt7e/lBki/n8MV9ETg2sdAp\nrthAV755oD2Hz1/h4R/FomSpftFm3N8VGKsoSiKQj9j2XVVVNcygkTV0HsGgsRIJqt09lR9XXASa\nOpieST0OM3tC2AgY8Jnh7ycZlJ2VOTMejDJ2GAA42ljQN7gpy/ee47V+weXWYd3qQmYef+w7z9BI\nb0K8Hdlw7BI/bz/N7M1JWJqbEerlwO7TGUzsFXRtJ2Fd9WrTlE+HhzNpYTzTVh/npRoUkhQUlXDg\nXCb5hSXkFxWTX1RCQVHJtdcuQa40d5VFGLWhzU++uwweRWOksYBmoXB+b+XHJKyDn++D+xdBQDfD\nxVKYC4vGQsFVsYD4zv8Dc8tqT5MkbQ1t783K/edZdzSF20OaVXns3G1JFKsqz/RphZ+rDQ93DSCv\nsJgdiemsP3aJDccu0a2l200l5bUxOMKbDcdSmbkhgYHhXgR7Vl9+XlRcwgPfb69yB2M3Oyv+nNgV\nDwdrvcTZGFW1UNdBVdUrQFYdxtO4eEXC3l9EoYTZLb9VZpyBxQ9BUS6cWGXYBPX3q3DxAHR4FHZ+\nBwlrodUdhruf1Oh0b+WOm50lS3YnV5mgcguK+Xn7afoGN72pZNzaQkP3Vu4G25H4jf7BrDuawitL\n9rH0yS7VlsR/ufYEO5LSeeWuNoT7OGFpboaVuRnWFmZYajSkZOVdawM1/5FYo7eBMlVV/a39XPoa\nB+wqfY274c9SbXlGQEEWpCfc/H5RPvw6WqyXcvaHMzsMF8OBJaKrRZdJcMcHYO0o3tNWcREkrJf7\nW0lVstCYMTjCmzVHUricXVDpcb/Hn+VyTiEPd63bDmvOtpa8NbAte5MzmVPNXlc7k9KZtvo4QyK9\nebxHCzq1cCWquTOh3o4Eedjj52pDtL8LHwxpx/bEdD7971jdfBMNUKUJSlXVAaWvAaqqBpa+ln3J\n9gf6cGNHiRv99RKc2w1DvoHW/cU/F1X+P7XO0k7C8kngEwO93hTTesGD4MhKKNBy2/Cd38NPg+CI\nXM8lVW1Yex8Ki1VW7DtX4eeqqvLDpkRCvByICaj7VkSDwr3o2dqdT/89SvLliv/7z8wR1YM+zja8\nOzikyusNbe/DyBg/vll3ktWHL1Z7/8LiEtYeSSG/SBZrlNFq3KkoirOiKDGKonQv+zJ0YI2Cexsw\nt755b6jdcyFuDnR9FoIHgm8MFOXBhf36vXdRPiweB2YauOcH8UwMRMFGwVU4/m/11ygpgR3fin/e\n+JkcRUlVauvlQLCnA0sqqebbeDyV4ylXeahLgFEqDxVFYcrdoQC88Xv5be1VVeW13/Zz8Uoe00ZG\nYm9dffXg2wPbEuLlwLO/xFdaMg9iI8l7Zmxl3JydTJi/h8JiuT4StNvy/RFgA/AP8E7p62TDhtVI\naMyhWbvrI6hze2Dl8xDYU4xoAHxjxeuZ7fq9979vigKNu78BJ9/r7/t3A1sP7ab5TqwS05OBPcUo\nL3GDfmOUGpxh7b3Zm5zJiZTyj7Z/2JyIm50VA8I9Kzizbvg42/DC7a1Zd/QSy/fePNL7ddcZVu4/\nz3O3tyLC10mr61lbaPjm/ihU4Mn5u8uNjlRVZXFcMv2nbSTx0lXuj/Vj1eGLvLBor9zXCu1GUJOA\nDsApVVVvAyKBDING1Zh4RohEkZ0Kv4wGW3cYNkuMbAAcPMHRT78J6tByMfLp+CS06XfzZ2YaCBkC\nx/6BvGoWVm6fAXbNYMQ8sGsKmz7XX4xSgzQ4whuNmcLiuLM3vX8i5Srrjl5idKfmWJlrjBSdMKaz\nP+G+Try74tC152UnUq4yefkhOrdw5fHuNduM0s/Vhk/vDWf/2Uze++PQtfczcwt5esEeXli0lxBv\nR/5+pjvvD2nHy3e2YVn8uQpHcY2NNgkqT1XVPBB9+VRVPQK0NmxYjYhXpJhS++luuHoBRvwEtrfs\nV+MbIxKUPv5jvZwEy54Cr/bQ552KjwkdBsX5cPTPyq9z6RicXA0dHgYre5HsEtZqv/D4wgFIlrU2\njY27vRU9Wrnz257km0YIc7YkYmluxqhYPyNGJ2jMFP5vaDsycwuZsvIw+UXFTFywB2sLMz4fEYGZ\nDk1vbw9pxmPdA5m37TTL4s+yMymdflM38teBC7x4R2sWPNoRr9LO70/0bMGE21qwYMdpPvjzcJVJ\nKiOngI/+PsILi/Y2yIXG2qyDSlYUxQn4HfhPUZTLwCnDhtWIeJV2lLi4HwZOBe8KFlf6xsKBxZCZ\nfPN0XE3lZsAvD4h/vnd25WudfDqAoy/sXwzh91V8zI6ZYuv6qLHiz9EPiedQm76A4T9WHUfWRVFY\nobGC5w7JTRsbmWHtfVhzJIXNJ1Lp3sqdjJwClsSd5e4IL9zsrIwdHgDBng481iOQr9ae5OKVPA6d\nF7sLN63FmqYX7mjNntMZvLR4H4XFJfg427D48U5E+jmXP/b21mTnF/PdxkTsrCyY1OfmNV85BUXM\n3pzEjPUnuZpfhKqKLvZfjmpvkK7xxlLtCEpV1SGqqmaoqjoZeBOYBdxt6MAaDbfW4plP+zHiqyK+\nMeK1NtN8BTmw4D5IOSKKIpz9Kz/WzAxCh4oRUXZa+c/zMiH+ZzHSsivdAM/aQYymDi2D1BOVX7uk\nBH5/AnLSIOscpBzW/XuSTFLvYA8crM1ZslsUSyzYcYbcwmIequPS8uo83aslAW62bDqRyuhOzenT\ntvLdhbVhoTFj+qhIvJ2aMLS9D39O6lZhcgJRsPHWgLbcE+XD56uO8f1GsRSloKiEn7Ym0f3jdfzv\nn6PEBrjy16RuvDmgLX8duMCbyxrWtGCVIyhFUTTAQVVV2wCoqrq+TqKqxg37QRk7lNrTmMMz+0Q1\nX2UjiaahYGEj1kNV1RapMkUFYl3V6W0iObXsU/05ocNg81Q4vByix9382Z75UJgNsY/d/H7HJ2Db\n17BlKgyaXvF1d8wUU4NdnxXPrE6uhqbG3SpcqlvWFhoGhnuxZHcyGTkF/LQ1iS5BrrRpVr82ELS2\n0DB9ZCSLdp3h1X7BerlmUwdr1rzQU6tjzUqnGnMKipiy8jDJl3NZfeQiZ9JziQlw4dsH21/bGbhN\nMwfSrubz9bqTuNlaXttVuCpliczYvRqrUuUISlXVYuCooijGnxi+gaqqK1RVHe/o6GjsUPTDoknV\n01waczH1p8sIqqQYfnsMTvwHA78QIyNtNAsD15blq/lKSkSS8Y29vo6rjJ0HRD4A8QvgSgVrXS4e\nhP/egpZ3QO+3RZn9idU1/54kkzcsyoe8whImLYznfGYeD3WpX6OnMqHejrwzOBRrC+MUbphrzPhi\nRCQ9W7szZ0sSDtYWzBnXgV/Gdyy3bf2Ld7RmRLQv09acYM7myndJyCss5rsNCbR/7z+mra5itqMe\n0KZIwhk4qCjKakVRlpd9GTow6Ra+sWItVEENtuJSVVj5nOiv1/fd68+LtKEoYhSVtAmunL/+/on/\nxBYht46eynR+GtQSMZK6UWEeLHlUTAUO/kpcv0VvOLVF+0XBUoMR6etEoJst649dIsDNlttaexg7\npHrL0tyMbx+MYvHjnVjxVFd6tvaocNSjKArvDwnl9rZNmbziEMvib66ULClR+W1PMr0/Xc/7fx5G\nURRmrD9Zr/fD0iZBvQkMAN4FPr3hS6pLvrGgFsPZ3dqfs2py6aLf50Qro5oKHQaocOj36+9tnwH2\nnqLjREWc/cV5u2ZD7uXr769+B1IOinVXdqX91IJ6iWrBU5trHptk0hRFYViU2AF3XBd/nSrjGhMr\ncw3R/i7V/j2Za8yYNjKS2AAXnv91L+uPXQJg4/FLDJi+iWd/2YuzrQXzH4nl18c6kV9UzMwNCVVe\n05i0SVD9VFVdf+MX0K/asyT98indHVnbab5Nn8PmL0R1Xe+3dLuneyuxkHj/YvHnS0fh5BpRDKGp\nYhV912dE6fyO78WfT6wSI6qY8dCy7/XjmncRz97kNF+j9EDH5jzbpxXDo2tRmSqVY22h4bsx0bRs\nas/jc+MYOXMbD87awZW8QqbeF8HyCV3pEuRGkIcdg8K9+GnrKVLr6ShKmwTVt4L35BYcdc3GRVT8\nadM4dv9iMXoKHQb9PqldGXfoMDi7S6yf2jFTlIZHjav6nKYh4jnT9m9EV/bfnxTPm/q+e/NxFk1E\nkjopE1Rj5NhElE8b6/lOQ+ZgbcGPD3XAw8GKwxeu8OaAtqx+vgeDI7xvGoVN7N2S/KJivl1/0ojR\nVq7SBKUoyhOKouwHWiuKsu+Gr0RgX92FKF3jGwPJO0ShQmWKi2D1u6JDxZBvr3ek0FVIaVHFrtmi\n+KHdPeUXElek67OilPy7XmKqb9j3IiHdKqg3pB6DjNO1i1OSpJt42Fvz16RubH2lNw93DaiwQ0eg\nux13R3gzd9spUrLytLpudn6RvkOtVHXbbQwElpe+ln1Fqar6QB3EJt3KN1b8sE+rovLm8DLIOAXd\nX6h6Gk5bzs1Ft/PNU0Vpecx47c5r3gn8OkF2CvSZLKYKKxJUWvIup/kkSe9sLM1pYln1L6lP925J\nYbHKt+urfxb136GL9PjfWg6fr6YNmp5Utd1GpqqqSaqqjlRV9dQNX5VvISkZVnWNY1VVJBLXILFN\nh760uwdQRcIp63yhjQFfiHZKsU9UfoxbK3DwkdN8kmQkAW623B3hzbxtp0i5UvkoaltCGhN+3o23\nUxN8XWwqPU6f5DaPpsQ1CJo4V56gEteLxrOdJ5bfobc2QoaI1kfdnq/ZeR5tRMFEVbEoiqjmS1gP\nxYW1i1OSJJ083SuIohKVbyp5FnXgbCaP/LgLX+cmzB4Xg52VNl3yak8mKFNiZiam2yorlNg8VXQV\nDxuh3/vaecCzB26uwNOnFr0h/4psHitJRuLvZsuQSG9+3n663Cgq4dJVxvywAwdrc+Y+HIuLbSU9\nPA1AJihT4xsDqUch55aZ1vP7RAl47ONgoXtDS6MI7AmKRk7zSZIRlY2ivl53fRR1ITOPB2ftQAXm\nPhJ7reN6XZEJytSUPYe6dbSxZRpY2ol1T6amiZNY5yULJSTJaJq72jKsvTc/7zjNhcw8MnIKeHDW\ndjJyCvhxXAwt3O3qPCaZoEyNd3sx2rjxOdTlU3BgqWhl1ES7nT7rnRa9xV5SFXVPlySpTjzdqyUl\nJSqf/XeUcXN2cio9h+/GRNPOxzh9T2WCMjWWtqJk+8YEtfUrUMzEpoGmKqgPoIotPiRJMgpfFxvu\nifLh113J7D2TwfSRkXRuocW6RwORCcoU+cbC2TixKDc7DXb/BGHDwdHb2JHpzitCVChWN81X0vB2\nDZWk+uSpXkG0cLfl43vCuSOkmVFjkQnKFPnGQGEOXDwAO7+HolzRRdyUmWkg8DZRKFHZhmtH/4KP\n/GHvL3UamiQ1Jj7ONqx+vif3lDbzNSaZoExRWaFEwjrY8S20uhM89LOhmlEF9YGrF0XivVXcHFg4\nSpSjb59R56FJklT3ZIIyRU6+4OANGz4R/e502UqjPmrRS7zeOM2nqrD2Q1gxSRRS3PY6nNstOqsb\nSlEB7FsEs/vD5mmGu48kSVWSCcpU+cZAQRb4dBAtiBoCB0/wCLm+Hqq4CJY/Dev/DyIegJELRKWi\nooH4n/V//6spsP5j+KIdLH0EzmwTBShVNeeVJMlgTDJBKYoyUFGUmZmZmcYOxXjKpvm6TKrddhr1\nTVBvOLUVrl4SU3p75kL3F2Hwl6L5rZ2H6Gix7xf9FUyci4ffnoDPQ2Dt+9AsFO5fDIO/hqsXxHYj\n+nByDUxrD5nJ+rmerk6sggUjITfDuHFIUjVMMkGpqrpCVdXxjo7Gqc2vFyIfgCEz9dsUtj4I6g0l\nhTCjq9hevv9n0OuNm5Nw+EjIOq+fkvQNn8DMHnBoGbQfAxN2wgNLRBJsdQeYmcORP2p/H4D9SyD9\nJKx8ofJCEEM7sRoWjIKjf8KBJcaJwZCKC433dyvpnUkmKAmwsofwEfptClsf+HUCCxvIy4AR88Tu\nvbdqfRdYO4n9qWprz1yxaeJzh6D/J2IX4TJNnCCgOxz+o/Y/9FRVFLVYOcCxv+Dw8tpdTxcn14pR\nqVsr0Xh4/6K6j8GQVBW+7w2/PiinZRuIBvbTTTJ55lYwciE8sgraVDI6NLcSW4Ac+QPyajHNm54o\ndgpuO7jyDhxtBohRz6Ujut8HID0BriRDrzehWRj8+WLdTrElrBfTei4tYPQyMQo9vVV0IWkoTm8V\n3fwPr4Ct040djaQHMkFJ9U9gj8o3OCwTPgqK8uDgb7rfp2yKMPC2yo9p3U+81naar+xeQb1h0DTI\nvgSrJtfumtpK2gQ/jwBnfxizHGxdod294rOGNIqK/1n0o2zdD1a9U3nXf8lkyAQlmSbv9mKqqjbT\nfCfXiHJ9t5aVH+PgKSolD9c2Qa0Te2q5BIJXpGhLFTdbFIQY0qktMP9ecPIrTU6lbWucm4vp1P2L\nGsYzm4IcOPi7GA0PmQGOPrBoXPmu/5JJkQlKMk2KAhGjRCl4WsWbrFWppBgSN0CL26qvgmwzAM7H\nQ8YZ3WItKYbEjWJkWHavnq+Co59Y31WUr9t1q3N6G8y7RyThMStEBeSN2t0rpi4v7DfM/evSkZVi\n2UX4SLB2hHvniEXfvz/ZMBJwIyUTlGS6wkaIJrl7F9b83HN7xPOrqqb3ygQPFK9HVtb8PiCei+Rl\nQEDP6+9Z2cGAz8TeXpu+0O26Vck8K5KTfTORnOyblj8mZAiYWYiSfVO392eR8Jt3EX/2bg+3vycK\nUrZ+ZdzYJJ3JBCWZLgcvsdnh3gU1r9o6Wfb8qWf1x7q2APc2uj+HSlhXeq8eN7/fsi+E3gMbP4FL\nx3S7dmV2fgeF2XD/IjFNWREbFxHDgSWm3YT3yjnxdxx+381VrbGPi9Hvqrflbs0mSiYoybSFj4LM\nM3BqU83OS1grqulstdxKoM0A8TxHl2caietFh4xbp9gA7vxQlNWvmKS/0ujCXNG7sE1/kVyrEjZc\nrClL2qifexvDvl9ALREJ6kaKIhZ423uJ51G5l40Tn6QzmaAk09amv1hbVJNiifyrosKrhRbTe2WC\nB4BaLDqq10RhriiEuHX0VMbOA26fAqe3wJ6fanbtyuz7Vfwwjn28+mNb3QmW9qL3oClSVfHv3je2\n4mTcxBnunQ1Z52DZU/J5lImRCUoybZY2EHK36ASRf1W7c05tFt0qtHn+VMYzAhx8aj7Nd2Y7FOdX\nPZUY+YB4drL2A/0sCN7+LTQNvf48pioWTaDtILFwuDC3dvc2hnO7xXO88JGVH+MTDX0mi393+36t\nq8gkPZAJSjJ94aPE8xZtuzOcXAvm1jVrsqsoYrR2cg0UZGt/XsJ60S6peeeqrx06TFSdZdRy4WzS\nJkg5KEZP2vZoDBsutjE59nft7m0M8QtAYyUKPqrS6SlR4h8/v27ikvRCJijJ9Pl1BOcA7TucJ6wt\nbalkXbP7BA8Qi4Or2/X3pnutA+9o0ZqqKt7txevZ3TWL6VbbZ0ATF9FpQ1v+3cCumelN8xXlw4HF\n4heHyjqBlCn7JSBpo+haL5kEmaAk01e2Jippo2gpVJUr58Tan5o8fyrj11k809B2mi/3slg/Fdiz\n+mM9QkBjKaasdHU5STSBjR4npu60ZaYRCe34v6a1sPXYP+LvOGKUdseHDBXFFIeWGTYuSW9kgpIa\nhsgHwLwJrHm/6uNOatHeqDIac2h1l5gKKy6s/vikTeIHYmUFEjcytxRVhbUZQe34DlAguoIGu9UJ\nGy6eyx36Xff717W9C8Cuqfb/Lpu2BffghtnFvYGSCUpqGBy8oPPTYsrnzM7Kj0tYC7buoohAF8ED\nxALfJC3K2hPWgYWtmOLThnd7sTeVLmuS8q/C7rmi4MHRu+bnNwsDt9amM82XnSpGfGHDxS8O2god\nJprKZp41XGyS3sgEJTUcXSaJZyn/vFpxNVxJiUgagT1136akRS+xbkmbab6E9eDfRYyOtOEdJYo9\ndNnOft8vkJ8JsU/U/FwQ06Rhw0W5e8Zp3a5Rl/YvgpIiUSBTE6FDxWttmgxLdUYmKKnhsLKD3m9C\n8s6Kp3FSDoou4rpM75WxaCI6kh9ZWfXC2sxkSDsOAVpM75XxKi2UqOlzqLLScs8I8I2p2bk3MqUO\n53sXgGe4mLarCdcW4ryDSw0TlynZPRdSDhs7iirJBCU1LOEjxVYdqyaXX9dT9vxJlwKJG7UZILov\nnNtT+TEJ68VrYE/tr+saJBYd1/Q5VMJasRao4xPal5ZXxLk5+HYUP7iyLtTs3KL8utsk8OIh0d+w\npqOnMiFD4Wyc2A+sscpOg+VPGaYPpB7JBCU1LGYauOND0f7o1iahCWvFcxYHr9rdo9UdYh3VH5Mg\n62LFxySuF8+6PGrwG76ZGXhFiB+eNbFtBth6VL8WSBvdXxTJ6ZvOcOTP6o8vKYbtM+F/QfDHM7W/\nf5ncy7Bnnvh3uO7/4J/XYfnTsGgsLHlYrC2rSSn9jcr+nhrzNF9Za6vz8caNoxoyQUkNT0A3McrZ\n9Pn1BFKYJ3rp1Xb0BKLUfMR8SEuAWX0g9cTNn5dt7x7QvebPurzaw8WD2m/BkXYSjv8D0Q+JnYZr\nq2UfeGy9SOILR8Ifz4q9lipydjd81wv+elGM/Hb/CEmbax8DwPqPYdkE+Oc1WPch7JoNx/4VfzcW\nTaDHy9r3UbyVc3Oxx1djnuZL3CBeU49p34HFCGSCkhqmvu+KH/Jrp4g/n9kmFtnW5vnTjVr2gbEr\nxA/vWX1v7pZ96ajoChHYs+bX9Y4S5d4XDmh3/I7vxJYZ0eNqfq/KuLeGR1ZD54mw6weY2UNMqZXJ\nyxRb1n/XS0x13vMDPLVTbIq48jkoKqjd/VVVbNse1BdePgVvpcPr5+CFo+I+j66BHi/V7h6hw8Q+\nWH5rXPAAABRZSURBVKnHa3cdfSougt0/1axTia6SNordh9WSer0fmExQUsPk2gJixovnKRf2i+dP\nZuaiqk5fvKPg4X/B2gHmDICjpa2Crm2v0VOHa5Z1lNBimq8wV7TuCblb7PukT+ZWYj+l0csgPwu+\n6w2bp8L+xfBlB5EYY8aLhBE6TPRE7PeJWAS99cva3fvCPjFFG3K36BBhptHP93SjtncDChyoR6Oo\ng7+JacwNnxj2PlfOi5FT+zHiz1U9SzUymaCkhqvHi+IH3D+vi+dPPjHVtxyqKdcW8PB/4N4KFo4S\nvwEnrBOtl5z8an49B2/xPEmbSr4Tq0UPvaoapdZWYE94Yot47vbfW+L5j72nGMX0+1jsXlum1R1i\nanX9x3C5Fj0FD/8hNqJsdWdto6+cg6dopntgSf3pcB43R7xun2HYdkxla/jC7hX/LmWCkiQjaOIs\ntlZPXC+mqFr0Msx97Dxg7ErRMWL503DiP91GTyCq8LyjtBtBHVomvseA7rrdS1s2LjBiHgz9DgZO\nFcmpbKR3q7s+Esnlr5d0/8F/ZKXolajrMyZthQ4R1Y8phwx7H22kHhd7mkU+KKamN35quHslrhe/\nWDQLA6/Iel0oIROU1LBFPwRurcQ/66NAojJW9jDyFwi7TywgbdlX92t5txc/sPKuVH5MYZ7Ym6rN\nANBY6H4vbZUt5I0aW/WUm6MP3PaqaAd1ZGXN75OeINartRmgc6haCx4Mikb31ke5GWIdkT5GYHFz\nxBR077dEb8FdP0DGmdpftyKJG0SDYDONWDtX3X9rRiQTlNSwaSxg0HSxZsYr0rD3MreEITPg8c3Q\nup/u1/FqD6hV/2Z7cg0UZJU+S6lnYh8XraT+eqnmFWJlSa1Nf/3HdSs7dzH6PLBUtyTz96vwdUeY\nFgn/vS2mynS5TmGe6MTfpr8Yjfd4Wby//qOaX6s6l0+JLV38u4k/e0UCqnjuVw/JBCU1fH4dYcg3\nhnnYfitFgWahtVswq02hxKFlYO2kXSPauqaxgP6fwZWzsP7/anbukZViobVzc8PEdqvQYXA5Ubfn\nMGe2i0TsEgBbpsPMnjAtQjyrq0myOvIH5KaL0SmAk68Y+cf/LJYR6FPZ+qeyaWGvCPF6rn5O85lk\nglIUZaCiKDMzMzONHYok6Z+NCzj7V95RoihfbKtRV9N7uvCLFVViW78Wa5e0cTUFTm+rm+m9MsED\nRJl+TddE5WZA+klRafjgb/DiCTFSd2khFhfP7Am/PabdteLmgFNzCOh5/b1uz4tKyrUf1Cyu6iRu\nABs38AgWf7bzEDtF19NCCZNMUKqqrlBVdbyjo2P1B0uSKfKOqvyHxsm1onovpB5O792oz2RRRfnH\ns9q1QTr6F6DWzfRemSbOorfigd9q1qqpbF1Y2bSxjQu0Hw0PLoUXjkPHCaKB7+EVVV8n9YQY1USN\nuXlRt52HmCo9sLj6NXGFeaJ1UXVUFRI3ioXsN47wvSJkgpIkqQa82ou1QBWVGx/6XVRh1aQRrTHY\nuMDtU8RUWPy86o8/8ocYSei6FYquQobCleSaNektez7oWcFzTRsX6PuOmKpc+YIYbVVm9xxRHBHx\nQPnPukwEK0dYW8UeZ2d2wNex8GV09YUOaSch61z5qk+vCDEazKt/M1IyQUlSfVTZFvBFBaJHXuv+\n2m/jYUzhI0XJ+KrJVe/Wm58l1o+1GVC753e6KKvu1GaPrzLn9oCjH9i6Vvx5WXFOdop4JlWRonzx\nnKn1XWDftPznTZyhy9NiOvfWPc6KC8X03w93iOvkpsPO76uOOam0vdGtv9iUjQJv7BZST8gEJUn1\nkWe4WE90a6FEwjqx71N9n94royiiw0RuBqyZUvlxJ1ZBcUHdTu+VsfMAl0Ax0tPWufjrBQaV8YqE\nThNEj8LEjeU/P7wCctKuF0dUJPYJ8cxozXvX30s7CT/cKar8wkbAhO3Qord49lVZ30QQz5/svcT3\neqOyUWA9nOaTCUqS6iNLW7E9+a3TTod+F41ZA3saIyrdNAsVbZF2/VD5D8HDf4CNq6i4NAbfjiJB\naVN5l3tZVP5Vl6AAer4mCl5WTCy//UvcHNFtJLCKBeRWdtDtObG4NmE9xP0IM7qJvcbumS2WNVg7\nQvcXICdVdDKpyLXnT93Lj1BtXcVoUCYoSZK05t1eTPGV/dAsKhDPaVr300/n8rp026ti+5GVL5Qv\nRigqENu3t76rbpYCVMQvVoxmtCnrvrVAoiqWNqL7RnrCzeuayooj/r+9ew+2qjzvOP79cVHwBiqo\nXMUqQjDKxQve6yUy3lqrzQSNRttkmtRGY401JfaPTB3TaklrmqTTNlPT2AZtaIyRxFw01gTqFUVE\nIjpJBFGPikaloNVyefrH+25Z57AP58DZ7LVg/T4zzF5r7XX2evc77P3s97Ked9plPWe8P+oTKQXW\n7RelQDf6SLj8oU2rAwMceDyMPR4e/ErzZL2rlqUA1l3WkYpOlHCAMquqUdPS2MKbK9L+8vlpIHtH\n6d4rGjQkJZ996bGU4LZoxfw0K3Hi75RTNkgtKEhZ73vS+CIf0YsWFKTW7tRL4IGvbApui25NWSym\nNpkc0dXAQSnDRGxIk04+dhcMGbX5eSdfk+49e/L2zZ9rLK9x0EnNrzFyavp/9r9v9uINtY8DlFlV\ndV0C/uk7YZc9W7dkSLsdMTNPmPhC5wkTz9wNA3cv96bjYYemG59X9jJADT0wzdbrrRk3pC7MeVem\ncaLFc/LkiF5moZ98IVzXAcdf2X2L6+DTU9D875vT0h1Fy+enrsbuEhg3WoMVu2HXAcqsqvY/DPrv\nmrr5NqxLX+QTzkq/qHdExQkTjanTGzemWYmHnJ4WIixLv34wZnrvJkp0LN76tFmD94azZ6cW1G0f\nyZMjtnINr566P6U0FvXm8s6rBW/ckBLRbimp8IjJ6bFi3XwOUGZV1X8gjDgiBajlP0/dLzti917R\nAR+EY/4IFt6SvgxfehzWvgIfKLF7r2Hs9LRO0pamw7/zRsplty15HSedl6bRr1iQJiVsj+TFE85J\nk2sW/O2msb5XlqSu4XFbCFCN7CUVy2zuAGVWZSOnpS+Npd9NK6BuryVD2umUwoSJZfPSjap9yf7e\nKu+PQ22hFdX4Au/NDL6upNSK2m1fmP6p7TMhpF+/NOvvtWXp/inYNMW9u/GnhpFT3YIys60w6khY\n9w4smZsW8CuzG6xVBg+FM65PEyYe+aeUWXvw3mWXKk1K6Tdwy+NQ70+QmLxt19hrJFzzbLo/ans5\n7ILUGlrwpTy9fD4Mm9DzeNfIqfDWyt6lTWoTByizKmtklNi4bsfv3iuafGGaMFHWzbnNDBycAs+W\nWlAdi9NqyX0JqP0Hbt9sGf0HwIlXp2D6y3vh+Qd7bj1BIaNEdVpRDlBmVbbPwSkf28Dd4ZAPlV2a\n1pHg3C+n93TY+WWXZpOxx6Yxv/XvNX9+WyZIlGHyRSlrxPc/A+ve7t2qy+9PlOhhHOqtlX0vXy85\nQJlVWb9+MOUimP7JnaN7r2i/iXDJHdt/afetMWY6bHiveV66t38Dq1fuGAFqwK4p2eyal9P+uF60\noAYNST+ItjQO9fit8NUj4cXHWlPOHjhAmVXdWTelpSts+2ukWmo2DtXo+tqWCRJlmHZZyuO3/+G9\nv2dr5NTuW1DLF8Ddn4VxJ/b+JuU+GtCWq5iZ7Qj22C+NMTUbh+rrBIl222U3uHhumvjRWyOnpjWo\n1r4GewzfdPyN52Dux1Ki2Q//axrnagO3oMzMisYem1pQXRPHdixOXWCDdqCFUkcdme6l661G67B4\nP9S7q+G2mWn7o99OszDbxAHKzKxozPSUWPWN5zof31EmSPTFAUcA2tRa3LAe/vMPU13M/NbmS3Vs\nZw5QZmZFzcah1r6WVt3d2QPUoL1g2PhNAeqev4Bf3wfn3pzGntrMAcrMrGjYhNSNV8xs3pcMEjua\nxkSJhbekG6mPuwKmXVpKURygzMyKGoljVxYmSnQ8ASh3ge3kRkyBNR3ww2th/IyU9aMkDlBmZl2N\nmQ6vP7spcWzH4tT1NWivcsvVDo1uzGGHwu/fUt4ikjhAmZltrjEO9cKj6bHjibbd+1O60UfDb89K\nU9RLDsi+D8rMrKuR01KW9RceTi2KNR07/wSJhv4D4NTPl10KwC0oM7PN7bJbuiF35SP1miBRMQ5Q\nZmbNjDkWOhblbr6aTJCoGAcoM7Nmxk6H9e/C4jkwfALsukfZJaodBygzs2bGTE+Pa16uzwSJinGA\nMjNrZs8DYOiBabsuEyQqxgHKzKw7jenmDlClcIAyM+vOpPNSBvMDDi+7JLXk+6DMzLoz8Zz0z0rh\nFpSZmVWSA5SZmVWSA5SZmVWSA5SZmVWSA5SZmVWSA5SZmVWSA5SZmVWSA5SZmVWSA5SZmVWSIqLs\nMmwzSa8Bz/fxZYYBr7egODsL10dnro/OXB+duT466219HBgRw3s6aYcOUK0g6bGIOKrsclSF66Mz\n10dnro/OXB+dtbo+3MVnZmaV5ABlZmaV5AAFXy+7ABXj+ujM9dGZ66Mz10dnLa2P2o9BmZlZNbkF\nZWZmleQAZWZmlVTbACXpTEnPSvqVpFlll6cMkr4haZWkpYVj+0i6V9Iv8+PeZZaxXSSNkXS/pKcl\n/ULSVfl4LesDQNIgSY9KejLXyV/m4wdJeiR/dr4taZeyy9oukvpLekLSD/J+besCQNIKSU9JWizp\nsXysZZ+ZWgYoSf2BfwDOAiYBF0maVG6pSvFN4Mwux2YB90XEeOC+vF8H64FrImIScCzw6fx/oq71\nAfAecFpETAamAGdKOha4Cbg5Ig4B3gQ+UWIZ2+0qYFlhv8510XBqREwp3P/Uss9MLQMUcAzwq4h4\nLiL+D/gP4LySy9R2ETEfeKPL4fOAW/P2rcDvtbVQJYmIlyNiUd5eQ/oSGkVN6wMgkrV5d2D+F8Bp\nwHfy8drUiaTRwDnAv+R9UdO66EHLPjN1DVCjgBcK+y/mYwb7R8TLefsVYP8yC1MGSeOAqcAj1Lw+\ncpfWYmAVcC/wa+CtiFifT6nTZ+fLwOeAjXl/X+pbFw0B3CPpcUmfzMda9pkZ0NfS2c4rIkJSre5D\nkLQHcAfwpxHxP+lHclLH+oiIDcAUSUOBO4GJJRepFJLOBVZFxOOSTim7PBVyYkS8JGk/4F5JzxSf\n7Otnpq4tqJeAMYX90fmYwauSRgDkx1Ull6dtJA0kBac5EfHdfLi29VEUEW8B9wPHAUMlNX7c1uWz\ncwLwu5JWkIYETgP+nnrWxfsi4qX8uIr0A+YYWviZqWuAWgiMzzNwdgEuBOaVXKaqmAdclrcvA+4q\nsSxtk8cTbgGWRcTfFZ6qZX0ASBqeW05IGgycQRqbux/4cD6tFnUSEZ+PiNERMY70ffFfEXExNayL\nBkm7S9qzsQ3MAJbSws9MbTNJSDqb1KfcH/hGRHyx5CK1naTbgVNIKfJfBb4AfA+YC4wlLWXykYjo\nOpFipyPpRGAB8BSbxhiuI41D1a4+ACQdQRrk7k/6MTs3Iq6X9FukVsQ+wBPAJRHxXnklba/cxfdn\nEXFunesiv/c78+4A4LaI+KKkfWnRZ6a2AcrMzKqtrl18ZmZWcQ5QZmZWSQ5QZmZWSQ5QZmZWSQ5Q\nZmZWSQ5QVguSHsyP4yR9tMWvfV2za7X4GiMk3dOi11rbw/NDJf1JK65l1hcOUFYLEXF83hwHbFWA\nKmQK6E6nAFW4ViudCfxkO7xuM0MBBygrnQOU1UKh1XAjcFJev+bqnAx1tqSFkpZI+lQ+/xRJCyTN\nA57Ox76Xk2L+opEYU9KNwOD8enOK11IyW9LSvGbOzMJr/0zSdyQ9I2lOzmSBpBuV1qRaIulLhbdw\nJvCj/Lc/KLyvr0n6g7y9QtLf5Gs9KumQfPwgSQ/l4zcU/nYPSfdJWpSfa2T0vxE4OL+n2fncawt1\n1FgXandJdyutF7W08f7MWsXJYq1uZpGzAADkQLM6Io6WtCvwQKErbRrwwYhYnvc/HhFv5LQ/CyXd\nERGzJF0REVOaXOsC0jpKk0nZOhZKmp+fmwocBnQADwAnSFoGnA9MzEk2G2mG+gMTIuLpnJRzS1ZH\nxOGSLiVlSjmXlDPuHyPi3yR9unDuu8D5OSnuMODhHJBn5fc9JV9/BjCelGdNwDxJJwPDgY6IOCef\nN6SHspltFbegrO5mAJcqLSnxCGkJhfH5uUcLwQngM5KeBB4mJRsez5adCNweERsi4lXg58DRhdd+\nMSI2AotJXY+rSUHjFkkXAO/kc6fnsvXG7YXH4/L2CYXj/144V8BfSVoC/JS0VESzpRFm5H9PAItI\nGc3Hk9JCnSHpJkknRcTqXpbRrFfcgrK6E3BlRHQa38n51t7usv8h4LiIeEfSz4BBfbhuMV/bBmBA\nRKyXdAxwOikB6RWkrNlnAT/O566n8w/LrmWIXmw3XExqBR0ZEeuUMnU3e08C/joi/nmzJ6RpwNnA\nDZLui4jrm/y92TZxC8rqZg2wZ2H/J8DlSkttIOnQnJm5qyHAmzk4TSQtC9+wrvH3XSwAZuZxruHA\nycCj3RVMaS2qIRHxQ+BqUtcgpID107z9PDBJ0q65C/D0Li8zs/D4UN5+gJSBG1JQKr6nVTk4nQoc\nmI83q6OP5/IhaZSk/SSNBN6JiG8Bs0ldomYt4xaU1c0SYEPuqvsmaXxmHLAoT1R4jeZLVP8Y+OM8\nTvQsqZuv4evAEkmL8hIMDXeSutmeJLVgPhcRr+QA18yewF2SBpFaLZ/Nge3dvAw9EfGCpLmkZQ2W\nk7rdivbOXXbvARflY1cBt0n6czovfTAH+L6kp4DHgGfyNX4j6QFJS4EfRcS1kj4APJTncqwFLgEO\nAWZL2gisAy7v5n2ZbRNnMzerMEmXAKMj4sZenLsCOCoiXt/uBTNrA7egzCosd5+Z1ZJbUGZmVkme\nJGFmZpXkAGVmZpXkAGVmZpXkAGVmZpXkAGVmZpX0//UXd1IiCybCAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f90c83deda0>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot( training_loss_protocol_ReLU, label='Loss ReLU-CNN')\n", "ax.plot( training_loss_protocol_SNN, label='Loss SNN')\n", "ax.set_yscale('log') # log scale\n", "ax.set_xlabel('iterations/updates')\n", "ax.set_ylabel('training loss')\n", "fig.tight_layout()\n", "ax.legend()\n", "fig" ] } ], "metadata": { "kernelspec": { "display_name": "tf-alpha", "language": "python", "name": "tf-alpha" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

PhilHarnish/forge

src/puzzle/examples/mscpc/yr2016/journal_logs.ipynb

1

1708

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import forge\n", "from puzzle.puzzlepedia import puzzlepedia\n", "\n", "puzzle_dates = puzzlepedia.parse(\"\"\"\n", "01110 01111 00001 01001 10010\n", "\"\"\")\n", "\n", "puzzle_lyrics = puzzlepedia.parse(\"\"\"\n", "problems\n", "out\n", "rain\n", "thing\n", "car\n", "out\n", "doing\n", "eyes\n", "soundtrack\n", "\"\"\", hint='acrostic')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "puzzlepedia.interact_with(puzzle_dates)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "puzzlepedia.interact_with(puzzle_lyrics)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "widgets": { "state": { "636329e07b884a22917147e679c77793": { "views": [ { "cell_index": 2 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ZwickyTransientFacility/ztf_sim

notebooks/analyze_sim.ipynb

1

294354

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_context('talk')\n", "sns.set_style('ticks')\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = (10,6)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sim_name = 'test_schedule_v7'\n", "outdir = f'fig/{sim_name}'\n", "if not os.path.exists(outdir):\n", " os.mkdir(outdir)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: OldEarthOrientationDataWarning: Your version of the IERS Bulletin A is 14.8 days old. For best precision (on the order of arcseconds), you must download an up-to-date IERS Bulletin A table. To do so, run:\n", "\n", ">>> from astroplan import download_IERS_A\n", ">>> download_IERS_A()\n", " [astroplan.utils]\n", "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Simulation Name\ttest_schedule_v7\n", "Number of Nights\t274\n", "Nights completely weathered out\t65\n", "Average Hours of Darkness\t9.315612904074852\n", "Total Science Time (h)\t1463.544238795485\n", "Average Science Time per night (h)\t5.341402331370383\n", "Fraction of time usable\t0.5733817394917663\n", "Average Number of Exposures per hour\t81.54656131079717\n", "Open Shutter Fraction\t0.6782594738763953\n", "Mean Time Between Exposures (s)\t14.23086024666003\n", "Mean Slew Distance (deg)\t9.310018197994042\n", "90% Time Between Exposures (s)\t16.478572003543377\n", "90% Slew Distance (deg)\t15.078197937363033\n", "Median Airmass\t1.1140699322383127\n", "90% Airmass\t1.5544640387341593\n", "Program Fraction\t{1: 0.39995140221371295, 2: 0.39571166430660176, 3: 0.20433693347968529}\n", "Filter Fraction\t{'g': 0.37815780874257415, 'i': 0.051212012032141568, 'r': 0.57063017922528425}\n", "Average Nightly Filter Exchanges\t10.669856459330143\n", "Average Filter Exchanges per hour\t1.523698389763276\n", "Sequence Completion Fraction by Program\t{1: 0.83883382539013074, 2: 0.90517599958405071, 3: 0.88486937590711179}\n", "Average Summed Figure of Merit per Science Hour\t129.44324265249082\n" ] } ], "source": [ "%run ../bin/analyze_sim.py test_schedule_v7" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df_read_from_sqlite(sim_name, tablename='Summary', directory='sims')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['obsHistID', 'requestID', 'propID', 'fieldID', 'fieldRA', 'fieldDec',\n", " 'filter', 'expDate', 'expMJD', 'night', 'visitTime', 'visitExpTime',\n", " 'FWHMgeom', 'FWHMeff', 'airmass', 'filtSkyBright', 'lst', 'altitude',\n", " 'azimuth', 'dist2Moon', 'solarElong', 'moonRA', 'moonDec', 'moonAlt',\n", " 'moonAZ', 'moonPhase', 'sunAlt', 'sunAz', 'slewDist', 'slewTime',\n", " 'fiveSigmaDepth', 'totalRequestsTonight', 'metricValue', 'subprogram'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAAAqrtSS16RJkxJjeXl5mj59uj788EMFBwfr3XffVcuWLd0aEAAAAOVn6sYL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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x113bd88d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grp_night = df.groupby('night')\n", "nobs_night = grp_night['expDate'].agg(len)\n", "plt.bar(nobs_night.index, nobs_night.values)\n", "plt.xlabel('Night of Year')\n", "plt.ylabel('Number of Observations')\n", "plt.xlim(0,360)\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/nobs_bynight.png')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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CREfSTXDNZiyLeBA5ob4/pzOR0S3fWik6gk9gwQJoaG/AmvI1omPopqilDHc5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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a0b0a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "grp_filter = df.groupby('filter')\n", "nobs_perfilter = grp_filter['expDate'].agg(len)\n", "plt.pie(nobs_perfilter, labels=nobs_perfilter.index, autopct='%.0f%%')\n", "plt.savefig(f'fig/{sim_name}/nobs_byfilter.png')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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CRfJLZGcXyr/gMYVLpylcOlX+9x+Vwqw7SASfVTXphy+tMB3DFSjYePj3j6XKD/Z5yAq1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\nkXoO3bdMe4+W/OmmkwEJg4IFjkRL3Z6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"text/plain": [ "<matplotlib.figure.Figure at 0x11a244978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "grp_program = df.groupby('propID')\n", "nobs_perprogram = grp_program['expDate'].agg(len)\n", "plt.pie(nobs_perprogram, labels=nobs_perprogram.index, autopct='%.0f%%')\n", "plt.savefig(f'fig/{sim_name}/nobs_byprogram.png')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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erb59+5rfP/nkE3Xu3FmxsbHmZ/78+ZIkwzCUnZ2t3r17Kz4+XllZWaqpqTHX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\nrvHdHRodAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a5510f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(df.loc[df.slewTime.notnull(),'slewTime'],bins=np.linspace(10,25,50))\n", "plt.xlabel('Time Between Exposures (s)')\n", "plt.ylabel('Number of Slews')\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/slew_time_hist.png')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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+Mp/IyEiNHTtWfn5+2rp1q6ZPn67Jkydr9+7dkqRFixZp48aNWrZsmVatWqVd\nu3Zp7ty5kqSDBw9q3LhxSklJUWZmpgIDAzVq1ChXHiIAAIDbc5tweODAAYWFhdm1Xbx4UWvXrtXQ\noUPl7e2t8PBwxcbGKj09XZL06aefKjExUdWrV1dQUJAGDx6spUuXSpKWL1+umJgYNWnSRD4+Pho+\nfLg2bdrE7CEAAMAv8HJ1AZKUl5cni8WiBQsWaMSIEfrDH/6gJ598Ug888IC8vLxUp04ds2xwcLDW\nrFkjScrJyVH9+vXt+iwWi2w2m3JyctS0aVPTFxAQIH9/f1ksFgUGBjpU19mzZ3Xu3Dm7ttzc3N9z\nqAAAAG7NLcKh1WpVs2bN1LNnT02fPl179+5VUlKSBgwYIB8fH7tlfXx8lJ+fL+lKqLy239fXVyUl\nJSosLCzVd7U/Ly/P4brS0tI0Y8aM33FkcGf3jlxZZt/RSV2cWAkAAO7DLcJhnTp1lJaWZr5HRkYq\nPj5eWVlZKigosFs2Pz9ffn5+kq4ExWv78/Ly5OXlJW9vb7sQeW3/1XUd0adPH8XGxtq15ebmqn//\n/g5vAwAA4FbiFuFw//792rJliwYNGmTaCgoKVLNmTRUVFenkyZO65557JEkWi8WcSg4NDZXFYlGT\nJk1MX0hIiF3fVWfOnNH58+cVGhrqcF0BAQEKCAiwa6tcufJvO0gAAIBbgFvckOLn56cZM2Zo9erV\nKikp0bZt27Ry5Ur17t1bMTExmjJlivLy8rR3716tWLFCcXFxkqSuXbsqNTVVubm5slqtmj17tuLj\n4yVJsbGxWrNmjZl9TElJUbt27UqFPQAAAPwvt5g5DA4O1tSpU/XPf/5TI0eOVI0aNTRx4kQ1atRI\nycnJGjdunKKjo+Xn56cRI0aYmcJevXrJarUqISFBRUVFiouL04ABAyRJYWFhSk5O1pgxY3Tq1ClF\nRkZq4sSJrjxMAAAAt+dhs9lsri7iVnL8+HHFxMRo3bp1ql27drnt53a9WeKXjtuZKvJvDADAL3GL\n08oAAABwD4RDAAAAGIRDAAAAGIRDAAAAGIRDAAAAGIRDAAAAGIRDAAAAGIRDAAAAGIRDAAAAGIRD\nAAAAGIRDAAAAGIRDAAAAGIRDAAAAGF6uLgDAzXfvyJVl9h2d1MWJlQAAbjXMHAIAAMAgHAIAAMAg\nHAIAAMAgHAIAAMAgHAIAAMAgHAIAAMAgHAIAAMAgHAIAAMAgHAIAAMDgDSnAdfCGEQDA7YqZQwAA\nABiEQwDkne3vAAAUy0lEQVQAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiE\nQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABheri4AgHPdO3JlmX1HJ3Vx\nYiUAAHfEzCEAAAAMwiEAAAAMwiEAAAAMrjkEYJR1PSLXIgLA7YNwCNygm31DBzeIAADcCaeVAQAA\nYBAOAQAAYHBaGbiJuGYPAHCrIxwCbozrEQEAzsZpZQAAABiEQwAAABicVgac4JdODwMA4E6YOQQA\nAIDBzCFwi3LmbGRFvjGmIh8bAPwWhEM4HadYAQBwX5xWBgAAgEE4BAAAgMFpZQC/C9fsAUDFQjgE\n4HYInADgOoRDAOWmPG4+cmZw/K37ItwCuJVV+HCYnZ2t1157Td98843q1aun119/XREREa4uC8At\nriLfdU+4dRy/FSqiCh0OCwoKlJSUpKSkJHXv3l2ffvqpnnnmGa1du1ZVq1Z1dXkAbrJbIbD9lhrd\nKWT8ljBEgAJuLRU6HGZmZsrT01O9evWSJCUkJGj+/PnKyMhQ586dXVwdADimPEJveYSy31LnrXJs\nv4Wzj62s/bnL7yHdGjWigodDi8Wi0NBQu7bg4GDl5OQ4tP7Zs2d17tw5u7YTJ05IknJzc29OkWW5\neKbMruPHj5fvvsvbLxwbAOe49/mFri6h3HBsv38dZ7sVaiwPm1992Gn7uvvuu+Xl5Vjsq9Dh8NKl\nS/L19bVr8/HxUX5+vkPrp6WlacaMGdft69279++u75d4/0JfzJoJ5brv8vZLxwYAwO3Cmf89X7du\nnWrXru3QshU6HPr6+pYKgvn5+fLz83No/T59+ig2NtaurbCwUCdPnlRISIgqVap002q91rFjx9S/\nf3+9//77qlOnTrnsAzeGMXFPjIv7YUzcE+Pifpw9JnfffbfDy1bocBgSEqK0tDS7NovFUirwlSUg\nIEABAQGl2hs2bHhT6itLUVGRpCsD6WjKR/liTNwT4+J+GBP3xLi4H3cekwr9+rxWrVqpsLBQCxcu\nVFFRkZYsWSKr1ao2bdq4ujQAAAC3VKHDYZUqVfTee+9p5cqVat68udLS0jRz5kyHTysDAADcbir0\naWVJuv/++/Xhhx+6ugwAAIBbQqXx48ePd3URKM3Hx0fNmzcvdbc1XIcxcU+Mi/thTNwT4+J+3HVM\nPGw2m83VRQAAAMA9VOhrDgEAAHBjCIcAAAAwCIcAAAAwCIcAAAAwCIcAAAAwCIcAAAAwCIcAAAAw\nCIduJjs7WwkJCYqIiFB8fLx2797t6pJua3v37rV7F/f58+c1ZMgQNWvWTO3bt9fixYtdWN3tJSsr\nS927d1ezZs3UsWNH8+YjxsS1Vq1apU6dOqlp06bq0qWL1q5dK4lxcQdWq1WtWrXShg0bJDEmrpSa\nmqrGjRuradOm5pOVleW+Y2KD28jPz7e1bdvWtmjRIlthYaFt8eLFtpYtW9ouXLjg6tJuOyUlJbbF\nixfbmjVrZmvevLlpf/75523Dhw+35efn2/bs2WNr3ry57auvvnJhpbeHc+fO2aKiomzLli2zFRcX\n2/bt22eLioqybdmyhTFxoZycHFuTJk1s//nPf2w2m822ZcsWW6NGjWynT59mXNzAoEGDbPfff79t\n/fr1NpuNv1+u9NJLL9n+9a9/lWp31zFh5tCNZGZmytPTU7169VLlypWVkJCgwMBAZWRkuLq0286s\nWbO0YMECJSUlmbaLFy9q7dq1Gjp0qLy9vRUeHq7Y2Filp6e7sNLbw8mTJxUdHa24uDh5enqqUaNG\natGihXbt2sWYuFBwcLC2bNmiBx98UJcvX5bValXVqlVVpUoVxsXFPvjgA/n6+qpmzZqS+PvlagcO\nHFBYWJhdmzuPCeHQjVgsFoWGhtq1BQcHKycnx0UV3b4ef/xxffrpp/rjH/9o2r799lt5eXmpTp06\npo3xcY6wsDC99dZb5vv58+eVlZUlSYyJi1WtWlXHjh1TeHi4XnnlFQ0bNkzfffcd4+JCFotF8+bN\n0/jx400bf79cJy8vTxaLRQsWLFDr1q3VqVMnLVmyxK3HhHDoRi5dulTq5ds+Pj7Kz893UUW3r+rV\nq8vDw8Ou7dKlS/Lx8bFrY3yc7+eff1ZSUpKZPWRMXK9mzZras2eP5s2bpzfffFPr169nXFzk8uXL\neuWVVzRmzBjdeeedpp2/X65jtVrVrFkz9ezZUxs2bFBycrImTZqkDRs2uO2YeLm6APwvX1/fUv9Q\n5Ofny8/Pz0UV4Vq+vr4qKCiwa2N8nOvYsWNKSkpSnTp1NHXqVB05coQxcQNeXlf+U9KqVSs98sgj\n2rdvH+PiIu+++67CwsIUHR1t187fL9epU6eO0tLSzPfIyEjFx8crKyvLbceEmUM3EhISIovFYtdm\nsVhUv359F1WEa9WrV09FRUU6efKkaWN8nGf//v164okn1KZNG7377rvy8fFhTFwsIyND/fv3t2sr\nKipS3bp1GRcXWbVqlVauXKnIyEhFRkbq5MmTeumll7Rx40bGxEX279+vOXPm2LUVFBSoZs2abjsm\nhEM30qpVKxUWFmrhwoUqKirSkiVLZLVa7R6lAtepVq2aYmJiNGXKFOXl5Wnv3r1asWKF4uLiXF1a\nhWe1WvXUU09pwIABGjVqlDw9r/zpYkxc64EHHtC+ffuUnp6ukpISZWRkKCMjQz169GBcXGT16tX6\nz3/+o6ysLGVlZemee+5RSkqKhgwZwpi4iJ+fn2bMmKHVq1erpKRE27Zt08qVK9W7d2/3HRNX3y4N\newcOHLD16NHDFhERYYuPj3eLW9pvZ5mZmXaPsjl79qxt6NChtqioKFt0dLRt8eLFLqzu9jFz5kxb\ngwYNbBEREXaflJQUxsTFdu7caevWrZutadOmtm7dutm2bdtms9n4d8VdPPzww+ZRNoyJ66xbt84W\nGxtra9Kkie2RRx6xffbZZzabzX3HxMNms9lcHVABAADgHjitDAAAAINwCAAAAINwCAAAAINwCAAA\nAINwCAAAAINwCAAAAINwCMCtXL58WbNmzdKjjz6qxo0b66GHHtIrr7yiEydOmGVGjhypYcOGlWsd\nx48fV8OGDc0nLCxMUVFRevLJJ7V//367ZRs2bKgvv/zyV7d58eJFLVmypLxK/t2Kior02GOP6dix\nY9ftf/vtt/XEE0/clH39+OOP6tatmwoLC2/K9gDcPIRDAG4lJSVFS5cu1ZgxY7R69Wq9++67On36\ntPr27au8vDyn17No0SJt3rxZGzdu1Lx583TnnXeqb9++OnLkiFlm8+bNatmy5a9ua968efr444/L\ns9zf5f3339eDDz6oOnXqlPu+qlevrtatW5d6rRgA1yMcAnAr/+///T8NHTpU7dq1U+3atRUREaFp\n06bpxx9/VEZGhtPrCQgIUFBQkGrUqKHGjRvrrbfeUoMGDZSSkmKWCQoKUpUqVX51W+78zoH8/Hz9\n61//Ut++fZ22zz59+mj+/Pm6ePGi0/YJ4NcRDgG4FQ8PD23dulWXL182bdWqVdPy5cvLfM/4hg0b\nFBcXp/DwcMXFxWnFihWSpLV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"text/plain": [ "<matplotlib.figure.Figure at 0x11a53eda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.degrees(df.loc[df.slewTime.notnull(),'slewDist']),bins=np.linspace(0,50,75))\n", "plt.xlabel('Slew Distance (deg)')\n", "plt.ylabel('Number of Slews')\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/slew_distance_hist.png')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sqlalchemy import create_engine\n", "engine = create_engine('sqlite:///../data/ptf.db')\n", "ptf_df = pd.read_sql('Summary', engine)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from astropy.time import Time\n", "iptf_start = Time('2013-01-01').mjd\n", "wiptf = ptf_df.expMJD >= iptf_start" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['obsHistID', 'propID', 'fieldID', 'filter', 'expMJD', 'night',\n", " 'visitExpTime', 'airmass', 'lst', 'altitude', 'azimuth', 'moonRA',\n", " 'moonDec', 'moonAlt', 'moonPhase', 'wind', 'humidity', 'finSeeing',\n", " 'filtSkyBrightness', 'fiveSigmaDepth', 'sessionID', 'fieldRA',\n", " 'fieldDec', 'expDate', 'rotSkyPos', 'ditheredRA', 'ditheredDec'],\n", " dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ptf_df.columns" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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fwILEruUMWFtr84g3qV8aThfOkF0aU+63AqgMAgsqyEhxNlJ0XQipa7mU6PEUAMWTKbBe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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11aacd160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(ptf_df[wiptf].airmass,bins=np.linspace(1,3.5,100),histtype='step',normed=True, label='iPTF')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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FhobK6XRaruvkyZNyOp0eP0ePHr3kfgEAAOqqaj9z+Oqrr5p/7tq1q6ZPn66D\nBw+qS5cuatSokQ4fPqzMzEz95je/ueQiiouL9e6776pXr1564403dODAAY0ePVqLFi2SzWbz2NZm\ns6mkpESS5Ha7PeaDg4NVWVmpsrKyKnPn5t1ut+W6MjMzNX/+/EvoDAAAoH6pNhwuXLjQ4/HPf/5z\nbdu2Tdu2bTPH7Ha71q5dq9/+9reXVETjxo0VGhqq5ORkSVJsbKzuuOMOzZ07V6WlpR7blpSUKCQk\nRNL3QfH8ebfbrYCAAAUFBXmEyPPnzz3XimHDhikuLs5jrLCwUCNGjLiY9gAAAOqNasPh+SHQ28LD\nw1VRUaGKigrzSuOKigpdf/31ys/P1/Hjx3XttddKkpxOp3m6ODIyUk6nU9HR0eZcRESEx9w5RUVF\nKi4uVmRkpOW67Ha77Ha7x1hgYOBPbxQAAKCOs/SZQ0n66quv9N577+mtt97y+Hn77bcvuYg+ffrI\nZrNp/vz5Onv2rHbv3q1//vOf+uUvfymHw6H09HS53W7t379f2dnZio+PlyQNHDhQixcvVmFhoVwu\nlxYuXKiEhARJUlxcnLZs2aL8/HyVlpYqIyND/fr1qxL2AAAA8B+W7nP4t7/9Tc8884wqKiqqzPn5\n+emDDz64pCJsNpuWLVumP/3pT7rpppvUtGlT/eEPf1BMTIzS0tKUmpqq/v37KyQkRJMmTTJXCpOS\nkuRyuZSYmKjy8nLFx8dr5MiRkqSoqCilpaVp2rRpOnHihLp3766ZM2deUp0AAAANnZ9hGMaPbXTT\nTTfp/vvv1+jRoxUUFOSLuuqsY8eOyeFwKCcnR61atfLafi5002lfudDNrbkJNgAAlwdLp5UrKyt1\n1113XfbBEAAAoKGzFA5HjBihF154QWfOnPF2PQAAAKhFlj5z2K9fPy1ZskTdu3eX3W6Xn5+fx/xb\nb73lleIAAADgW5bC4WOPPabIyEjFx8crODjY2zUBAACgllgKh0ePHlVWVpauu+46b9cDAACAWmTp\nM4e9e/fW3r17vV0LAAAAapmllcPY2FilpqZq8+bNuu6666p8S8iECRO8UhwAAAB8y1I43LFjhzp3\n7qxvvvlGBw4c8Jj774tTAAAAUH9ZCofLli3zdh0AAACoAyyFw127dv3gfI8ePWqkGAAAANQuS+Hw\ngQceuOB4YGCgQkNDuc8hAABAA2EpHO7fv9/j8dmzZ/X555/rz3/+s+655x6vFAYAAADfs3Qrm8aN\nG3v8hIScOCwuAAAaMUlEQVSEqGPHjpo6darS09O9XSMAAAB8xFI4rI7b7dbJkydrqhYAAADUMkun\nlTMyMqqMfffdd9qyZYtuvvnmGi8KAAAAtcNSONyzZ4/HYz8/PwUGBioxMVGjRo3ySmEAAADwPe5z\nCAAAAFO14dDpdFp+kfDw8BopBgAAALWr2nB45513ys/PT4ZhXHD+/K/N++CDD2q+MgAAAPhcteEw\nJyen2id99NFHmjFjhr788kuNHDnSK4UBAADA96oNhy1btqwyVlpaqnnz5umVV15Rly5d9OKLL6p9\n+/ZeLRAAAAC+Y+mCFEnKzc3Vn/70J3333XdKTU3V0KFDvVkXAAAAasGPhsOvvvpKTz31lDZv3qz4\n+HhNmTJFP/vZz3xRGwAAAHzsB8NhZmamnn/+eYWFhenll19W7969fVUXAAAAakG14TAxMVEHDx5U\ny5Ytdf/99+vzzz/X559/fsFt77nnHq8VCAAAAN+pNhwWFRWpRYsWqqys1Msvv1ztC/j5+REOAQAA\nGohqw+G2bdt8WQcAAADqAP/aLgAAAAB1B+EQAAAAJsIhAAAATIRDAAAAmAiHAAAAMBEOAQAAYCIc\nAgAAwEQ4BAAAgKnOhUOXy6XevXvrjTfekCQVFxdr3Lhx6tatmwYMGKCVK1ea2xqGofT0dPXq1Us9\nevTQjBkzVFFRYc5nZ2fL4XAoJiZGycnJcrlcPu8HAACgPqn2G1Jqy7Rp03Tq1Cnz8RNPPKGQkBDt\n3LlTH374ocaMGaP27dsrJiZGy5cv1/bt27Vhwwb5+fkpOTlZS5Ys0ZgxY3T48GGlpqZqyZIl6tCh\ng9LS0jRlyhS99NJLtdgdzmn7+OtVxj6ddXctVAIAAM5Xp8Lh3//+dwUHB6tFixaSpNOnT2vr1q3a\nvHmzgoKC1KVLF8XFxWndunWKiYnR+vXrNXz4cDVv3lySlJycrDlz5mjMmDHKysqSw+FQdHS0JGni\nxInq3bu3XC6XwsLCaq3H+uBCwQ0AAFwe6kw4dDqdevnll/Xaa69p8ODBkqTPPvtMAQEBat26tbld\neHi4tmzZIkkqKChQu3btPOacTqcMw1BBQYG6du1qztntdoWGhsrpdFoOhydPnvRYxZSkwsLCn9wj\nAABAXVcnwuHZs2f12GOPadq0abrqqqvM8TNnzshms3lsa7PZVFJSIklyu90e88HBwaqsrFRZWVmV\nuXPzbrfbcl2ZmZmaP3/+T2kJAACgXqoT4fCFF15QVFSU+vfv7zEeHBys0tJSj7GSkhKFhIRI+j4o\nnj/vdrsVEBCgoKAgjxB5/vy551oxbNgwxcXFeYwVFhZqxIgRll8DAACgPqkT4XDjxo06ceKENm7c\nKEn67rvvNGHCBI0ePVrl5eU6fvy4rr32Wknfn34+dyo5MjJSTqfT/Fyh0+lURESEx9w5RUVFKi4u\nVmRkpOW67Ha77Ha7x1hgYOBPbxQAAKCOqxO3svnHP/6h999/X/n5+crPz9e1116rjIwMjRs3Tg6H\nQ+np6XK73dq/f7+ys7MVHx8vSRo4cKAWL16swsJCuVwuLVy4UAkJCZKkuLg4bdmyRfn5+SotLVVG\nRob69etXJewBAADgP+rEyuEPSUtLU2pqqvr376+QkBBNmjTJXClMSkqSy+VSYmKiysvLFR8fr5Ej\nR0qSoqKilJaWpmnTpunEiRPq3r27Zs6cWZutAAAA1Hl+hmEYtV1EfXLs2DE5HA7l5OSoVatWXttP\nfbidzKXcl5D7HAIAUDfV+ZVDXD6qC8SERgAAfKdOfOYQAAAAdQPhEAAAACbCIQAAAEyEQwAAAJgI\nhwAAADBxtTJ+Mm5HAwBAw8PKIQAAAEyEQwAAAJgIhwAAADARDgEAAGAiHAIAAMBEOAQAAICJcAgA\nAAAT4RAAAAAmwiEAAABMfEMKahTfmgIAQP3GyiEAAABMhEMAAACYCIcAAAAwEQ4BAABgIhwCAADA\nRDgEAACAiXAIAAAAE/c5hNdd6N6HAACgbmLlEAAAACbCIQAAAEyEQwAAAJgIhwAAADARDgEAAGDi\namXUeRe62vnTWXfXu30AAFAfsHIIAAAAE+EQAAAAJsIhAAAATIRDAAAAmOpMOMzPz9fQoUPVrVs3\n3XrrrVqxYoUkqbi4WOPGjVO3bt00YMAArVy50nyOYRhKT09Xr1691KNHD82YMUMVFRXmfHZ2thwO\nh2JiYpScnCyXy+XzvgAAAOqTOhEOi4uL9dBDD+nXv/61du3apTlz5igjI0M7d+7UE088oZCQEO3c\nuVNz587Vs88+q71790qSli9fru3bt2vDhg3auHGjdu/erSVLlkiSDh8+rNTUVGVkZCgvL09hYWGa\nMmVKbbYJAABQ59WJcHj8+HH1799f8fHx8vf3V6dOndSzZ0/t3r1bW7du1fjx4xUUFKQuXbooLi5O\n69atkyStX79ew4cPV/PmzdWsWTMlJydr7dq1kqSsrCw5HA5FR0fLZrNp4sSJ2rFjB6uHAAAAP6BO\n3OcwKipKs2fPNh8XFxcrPz9fHTp0UEBAgFq3bm3OhYeHa8uWLZKkgoICtWvXzmPO6XTKMAwVFBSo\na9eu5pzdbldoaKicTqfCwsIs1XXy5EmdOnXKY6ywsPAn9QgAAFAf1IlweL5vv/1WKSkp5urh0qVL\nPeZtNptKSkokSW63WzabzZwLDg5WZWWlysrKqsydm3e73ZZryczM1Pz58y+hGwAAgPqlToXDo0eP\nKiUlRa1bt9bzzz+vTz75RKWlpR7blJSUKCQkRNL3QfH8ebfbrYCAAAUFBXmEyPPnzz3XimHDhiku\nLs5jrLCwUCNGjLjIzgAAAOqHOhMODx48qNGjR2vgwIGaPHmy/P391aZNG5WXl+v48eO69tprJUlO\np9M8lRwZGSmn06no6GhzLiIiwmPunKKiIhUXFysyMtJyTXa7XXa73WMsMDDwkvoEAACoy+rEBSku\nl0ujR4/WyJEjNWXKFPn7f19W06ZN5XA4lJ6eLrfbrf379ys7O1vx8fGSpIEDB2rx4sUqLCyUy+XS\nwoULlZCQIEmKi4vTli1blJ+fr9LSUmVkZKhfv35Vwh4AAAD+o06sHK5atUpFRUVasGCBFixYYI7/\n+te/VlpamlJTU9W/f3+FhIRo0qRJ5kphUlKSXC6XEhMTVV5ervj4eI0cOVLS9xe5pKWladq0aTpx\n4oS6d++umTNn1kp/qJ/aPv56lbFPZ91dC5UAAOA7foZhGLVdRH1y7NgxORwO5eTkqFWrVl7bz4WC\nCf6jpkOa1d834RAA0NDVidPKAAAAqBsIhwAAADARDgEAAGCqExekABeLi0UAAPAOVg4BAABgIhwC\nAADAxGllNBicagYA4NKxcggAAAAT4RAAAAAmTiujQeNUMwAAF4dwCFyE6r5mj8AJAGgoCIe47PC9\n1QAAVI/PHAIAAMBEOAQAAICJcAgAAAAT4RAAAAAmLkgBGhiuqAYAXApWDgEAAGBi5RCoAdxsGwDQ\nULByCAAAABMrh4APscIIAKjrCIeAl1j9JhYCIwCgLuG0MgAAAEysHAJ1kDdWE1mhBABYQTgE6glf\nBcYLIUQCwOWDcAjgsseqKgD8B585BAAAgImVQ6Aes3pa2Bv7YWUNABomwiGAn4TACAANE+EQQI2p\nDxe4+Gq1FQDqKz5zCAAAABMrhwB87mJW72prlZHT5gAuV4RDAHXapZwGrukwV10thEYADQnhEECD\nxecLAeDiEQ4B4BLVhwtxAMAqwiEA+AghEkB90ODD4aFDh/Tkk0/qyJEjatOmjf74xz8qJiamtssC\ngGoRIgHUpgYdDktLS5WSkqKUlBQNHTpU69ev19ixY7V161Y1adKktssDgEvCFdUAvKFBh8O8vDz5\n+/srKSlJkpSYmKi//e1vys3N1V133VXL1QFAzavNi3AuFEzrw22LAHhq0OHQ6XQqMjLSYyw8PFwF\nBQWWnn/y5EmdOnXKY+yLL76QJBUWFtZMkdU5XeTd1weAGtb2d8tq9fl13VuT/6fKWN+n3/jJz63O\nhV7T6r4vZj+oX6655hoFBFiLfQ06HJ45c0bBwcEeYzabTSUlJZaen5mZqfnz519w7v7777/k+n5I\nkFdfHQDga44tM6qMWf1v/YWeW50LvabVfV/MflC/5OTkqFWrVpa2bdDhMDg4uEoQLCkpUUhIiKXn\nDxs2THFxcR5jZWVlOn78uCIiItSoUaMaq/V8R48e1YgRI/TKK6+odevWXtlHXXU59y5d3v1fzr1L\nl3f/9H559i5d3v37uvdrrrnG8rYNOhxGREQoMzPTY8zpdFYJfNWx2+2y2+1Vxjt06FAj9VWnvLxc\n0vdvpNWU31Bczr1Ll3f/l3Pv0uXdP71fnr1Ll3f/dbl3/9ouwJt69+6tsrIyLVu2TOXl5Vq1apVc\nLpf69u1b26UBAADUSQ06HDZu3FgvvfSSXn/9dd14443KzMzUggULLJ9WBgAAuNw06NPKktSxY0et\nWLGitssAAACoFxpNnz59em0XgapsNptuvPHGKldbXw4u596ly7v/y7l36fLun94vz96ly7v/utq7\nn2EYRm0XAQAAgLqhQX/mEAAAABeHcAgAAAAT4RAAAAAmwiEAAABMhEMAAACYCIcAAAAwEQ4BAABg\nIhzWgv379//g9ztnZ2fL4XAoJiZGycnJcrlc5tyhQ4eUmJiomJgYJSQkaO/evb4oucb8WO+vvfaa\nbr/9dsXGxmrIkCHKz8835xYvXqzOnTura9eu5s/58/XBj/WfnJysLl26ePR4TkN+75988kmPnmNi\nYtShQwdlZWVJqr/vfX5+voYOHapu3brp1ltvrfbbmhrqMW+1/4Z43FvtvaEe81b6b6jH/caNG3Xn\nnXeqa9euuvvuu7V169YLblenj3sDPlNZWWmsXLnS6Natm3HjjTdecJsPPvjAiI2NNfbu3Wu43W5j\n6tSpxujRow3DMIySkhLj5ptvNpYvX26UlZUZK1euNHr16mV89913vmzjJ7HS+zvvvGP07NnTOHTo\nkFFRUWGsWbPG6Natm1FUVGQYhmFMmDDB+Otf/+rLsmuMlf4NwzD69u1r7N+/v8p4Q3/v/9vzzz9v\nDBs2zCgrKzMMo36+96dOnTJ69OhhbNiwwaioqDAOHDhg9OjRw3j77bc9tmuox7zV/hvicW+1d8No\nmMf8xfR/voZw3BcUFBjR0dHG+++/bxiGYbz99ttGp06djK+//tpju7p+3LNy6EMvvviili5dqpSU\nlGq3ycrKksPhUHR0tGw2myZOnKgdO3bI5XIpLy9P/v7+SkpKUmBgoBITExUWFqbc3FwfdvHTWOm9\nsLBQv/nNbxQVFSV/f38NGjRIjRo10pEjRyRJH3zwgaKionxVco2y0v/XX3+toqIi/eIXv6gy19Df\n+/MdOHBAy5Yt0zPPPKPAwEBJ9fO9P378uPr376/4+Hj5+/urU6dO6tmzp3bv3u2xXUM95q323xCP\ne6u9N9Rj3mr/52sox314eLjefvttxcbG6uzZs3K5XGrSpIkaN27ssV1dP+4Jhz40ZMgQrV+/Xjfc\ncEO12xQUFKhdu3bmY7vdrtDQUDmdTjmdTkVGRnpsHx4eroKCAq/VXFOs9P6///u/GjNmjPn4/fff\n1+nTpxUZGSm32y2n06mlS5eqT58+uvPOO7Vq1SpflF4jrPR/6NAhNWnSRMnJyerVq5fuvfde7dmz\nR5Ia/Ht/vpkzZ+rBBx9UixYtJKnevvdRUVGaPXu2+bi4uFj5+fnq2LGjx3YN9Zi32n9DPO6t9t5Q\nj3mr/Z+voRz3ktSkSRMdPXpUXbp00WOPPaZHHnlETZs29dimrh/3AT7bE9S8efMf3cbtdstms3mM\nBQcHy+1268yZM1W+nNtms6mkpKRG6/QGK72f78iRIxo/frzGjx+vn/3sZzp69Ki6deum++67T3Pn\nztX+/fuVkpKiZs2aqX///l6quuZY6b+0tFQxMTGaNGmS2rRpo1WrVmnMmDHatGnTZfPev//++zpy\n5IgWLVpkjrlcrnr93kvSt99+q5SUFHXq1Em33HKLx1xDPebP90P9n6+hHffSD/feUI/581l57xvi\ncd+iRQvt27dP+fn5euihh9SmTRv17t3bnK/rxz0rh3XMhf4CuN1uhYSEKDg4uMpcSUmJQkJCfFmi\n17311lu67777dP/99+vBBx+UJLVu3VqZmZnq37+/GjdurO7duyshIUE5OTm1XG3NufXWW7Vo0SK1\nb99ejRs3VlJSklq0aKF33333snnv16xZo4EDB6pJkybmWH1/748ePap7771XoaGhmj9/vvz9Pf+z\n29CP+R/r/5yGeNz/WO8N/Zi3+t43xOM+ICBAgYGB6t27t26//fYqddf1455wWMdERkbK6XSaj4uK\nilRcXKzIyEhFRER4zEnfn3o4f2m6vlu9erXGjx+v1NRUPfTQQ+b4wYMHPf5VKX3/r+7//hxHffaP\nf/xDGzdu9BgrLS1VUFDQZfHeS9Ibb7yhO++802OsPr/3Bw8e1K9+9Sv17dtXL7zwQpWVAqlhH/NW\n+pca5nFvpfeGfMxbfe+lhnXc5+bmasSIER5j5eXluuKKKzzG6vxx77NLX2DKy8ur9qrNQ4cOGbGx\nscauXbuMkpISY9q0acaYMWMMwzCM0tJSo2/fvsbSpUs9rmA6ffq0L8u/JD/U+86dO40bbrjB2LVr\nV5W5goIC44YbbjA2bdpkVFRUGDt37jRiYmKMAwcOeLvkGvVD/a9evdq46aabjI8//tgoKyszXnrp\nJePmm282Tp8+3eDfe8MwjM8//9zo1KmTUVpa6jFeX9/7EydOGL169TIWLlz4g9s11GPeav8N8bi3\n2ntDPeat9m8YDe+4/+qrr4xu3boZa9euNSoqKozt27cbsbGxxpEjRzy2q+vHPeGwFvz3/ySfeOIJ\n44knnjAfv/7668btt99udO3a1RgzZozhcrnMuQ8++MC45557jJiYGCMhIcHYs2ePT2u/VD/U+8iR\nI42OHTsaMTExHj+5ubmGYRhGTk6OERcXZ0RHRxu33367sWnTplrp4VL82Hv/4osvGv379zeio6ON\n++67zzh8+LA515Dfe8P4/pYmN9100wWfWx/f+wULFhi/+MUvqvx9zsjIuCyOeav9N8Tj/mLe+4Z4\nzF9M/w3tuDcMw9i1a5cxaNAgo2vXrsagQYOMd955xzCM+vX/ej/DMAzfrVMCAACgLuMzhwAAADAR\nDgEAAGAiHAIAAMBEOAQAAICJcAgAAAAT4RAAAAAmwiEAXKTc3Fx16NBBTz/9tMf4vHnz9Ktf/aqW\nqgKAmsF9DgHgIk2cOFH79+/X6dOnlZubq4CAAEnS6dOnVV5erquuuqqWKwSAn46VQwC4CGfOnFFO\nTo4eeughnTx5Urm5ueZckyZNCIYA6j3CIQBchJycHJWVlcnhcKhr165as2aNOXf+aeU1a9ZoyJAh\nmjBhgrp166aXX35Z8+bN08MPP6xnnnlGsbGx6tu3r9auXavt27frtttuU9euXTVp0iSdPXtWknT2\n7FnNnj1bAwYMUKdOndS3b18999xz5v4++ugj3X///YqJidFNN92kGTNmqKysTJL05ZdfasyYMYqN\njdWNN96oSZMm6dtvv/XhbwpAfUU4BICLkJWVpZ49e+qKK67QbbfdptzcXBUVFV1w2wMHDuhnP/uZ\nVq9erTvvvFOStHXrVp09e1br1q3THXfcoenTp+uFF17Qc889p2effVabNm3Sli1bJEmLFi3Spk2b\n9Oyzz2rz5s0aN26cFi5cqN27d0uSJk2apDZt2igrK0tz587Vpk2b9Pe//12S9Mc//lF+fn5atWqV\nXn75ZR08eFDz5s3zwW8IQH1HOAQAi4qKivT222/rtttukyTddtttKi8v14YNG6p9zrhx49S2bVtd\nc801kr4/9Tx58mRdd911uu+++1RSUqKxY8eqc+fOcjgcioqK0pEjRyRJv/jFLzRr1ix1795drVq1\n0n333afmzZub88eOHdNVV12la6+9Vt27d9eiRYt0yy23mHNXXHGFWrVqpU6dOmnevHkaOnSoN389\nABoIwiEAWLRp0yZVVlbq1ltvlSS1bNlSnTt31urVqy+4fdOmTWW32z3GWrZsqUaNGkmSbDabJKl1\n69bmvM1mM08N33rrraqsrNQzzzyjlJQUDRgwQF9++aUqKiokSWPHjtXLL7+s3r1769FHH9W///1v\n87UefPBBbd68WT179tS4ceP0r3/9SxERETX42wDQUBEOAcCirKwsVVZWqn///rr++ut1/fXX6+DB\ng/roo4904MCBKtufC3/nO3dl8/n8/PwuuL+5c+fq97//vQzD0F133aWlS5eaK5CSNHr0aOXk5Oh3\nv/udiouLNX78eD377LOSpLi4OL355puaOnWq/P399eSTT2rixIk/tXUAlxHCIQBYcPToUe3Zs0cT\nJkzQunXrzJ8VK1YoMDDQ48KUmvLyyy9rypQpmjx5sgYOHKirrrpKX3/9tQzDUGlpqZ566ilVVlbq\ngQce0F//+lc9/PDD2rhxoyTpueee0xdffKGhQ4dq3rx5mjlzpv7xj3+Iu5cB+DFV/wkLAKgiKytL\nISEhGjZsmJo0aeIxd8cddyg7O1v33HNPje7z6quvVm5urmJjY3Xq1CllZGSovLxcZWVlCgoK0vvv\nv6/PP/9ckyZNUmVlpXJzc9W5c2dJktPpVFpamlJTUxUSEqLNmzcrKiqq2lVKADiHlUMAsCA7O1tx\ncXFVgqEk3X///SouLtbWrVtrdJ+zZs2S0+lUXFycHn74YXXu3Fl33HGHDh48KEmaM2eOKisrde+9\n9+ree+9V8+bNNX36dEnS9OnT1bJlS40aNUqDBg2S2+3WnDlzarQ+AA0T35ACAAAAEyuHAAAAMBEO\nAQAAYCIcAgAAwEQ4BAAAgIlwCAAAABPhEAAAACbCIQAAAEyEQwAAAJgIhwAAADD9P5OIvH2tUJqN\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11abb2c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(df.airmass,bins=np.linspace(1,3,100))\n", "plt.xlabel('Airmass')\n", "plt.ylabel('Number of Images')\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/airmass_hist.png')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.68601092516\n" ] } ], "source": [ "print(df.airmass.max())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "121\n" ] } ], "source": [ "print(np.sum(df.airmass > 2.5))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAAC4hqm1PgEAAOB6BDUAAAA3RVADAABwU2UGteTk5CovGwUAAIDKKzOoPf7448rMzJQk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\ntWrVUkBAgKQrU3E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"text/plain": [ "<matplotlib.figure.Figure at 0x119eb9048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bins = np.linspace(1,3,100)\n", "_ = plt.hist(df.airmass,bins=bins,normed=True,label='ZTF')\n", "_ = plt.hist(ptf_df[~wiptf].airmass,bins=bins,histtype='step',normed=True, label='PTF',linewidth=2)\n", "_ = plt.hist(ptf_df[wiptf].airmass,bins=bins,histtype='step',normed=True, label='iPTF',color='black',linewidth=2)\n", "plt.xlabel('Airmass')\n", "plt.ylabel('Normalized Number of Images')\n", "plt.legend()\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/airmass_hist_vs_ptf.png')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.\n", " out=out, **kwargs)\n", "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] } ], "source": [ "intranight_grp = df.groupby(['night','propID','fieldID'])\n", "intranight_gap = intranight_grp['expMJD'].agg(lambda x: np.median(np.diff(x)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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OqyrM7nNeeYs1gOSylBolLt64aHafXHUuawB/Jqjlr6ysDKmpqbh06RJ0Oh3u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NdeQmqHd3W1l5xU4PAytVGgFrIK9HbMwzuy/+FmVUucEQFqD3k464SUtlGjdIKLZzIc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\ntGnTEBISovoKCgqCv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"text/plain": [ "<matplotlib.figure.Figure at 0x119eca710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gap = intranight_gap*24.\n", "gap = gap.reset_index()\n", "grp_np = gap.groupby(['night','propID'])\n", "intranight_gap_night_prop = grp_np.agg(np.median)\n", "intranight_gap_night_prop = intranight_gap_night_prop.reset_index()\n", "for prop in [1,2,3]:\n", " w = intranight_gap_night_prop['propID'] == prop\n", " plt.scatter(intranight_gap_night_prop.loc[w,'night'], intranight_gap_night_prop.loc[w,'expMJD'], label=prop)\n", "plt.legend()\n", "plt.xlabel('Day of Year')\n", "plt.ylabel('Median Time Between Observations (hours)')\n", "plt.savefig(f'fig/{sim_name}/intranight_gap_by_doy.png')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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g888/rzp16qigoEA9e/bUtGnT5O3tbe53cXGRl5eXAgICHFIoAAAAbOOm9wFs2rSpJCkp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\n5s2bZ+Tn55u3XbZsmdGqVSvDMAzj5Mm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"text/plain": [ "<matplotlib.figure.Figure at 0x11a237198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(gap.loc[gap['expMJD'].notnull(),'expMJD'],bins=np.linspace(0.2,4,100))\n", "plt.xlabel('Median Time Between Observations of a Field (hours)')\n", "plt.ylabel('Number of Request Sets')\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/intranight_gap_hist.png')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "night_min = intranight_grp['expMJD'].agg(np.min)\n", "night_max = intranight_grp['expMJD'].agg(np.max)\n", "night_max = night_max.reset_index('night')\n", "night_min = night_min.reset_index('night')\n", "\n", "# not quite right--need to subtract night from night\n", "#intranight_gap = night_max.values[:-1] - night_min.values[1:] " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/pandas/core/indexing.py:1325: PerformanceWarning: indexing past lexsort depth may impact performance.\n", " return self._getitem_tuple(key)\n", "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.\n", " out=out, **kwargs)\n", "/Users/ebellm/anaconda3/envs/ztf_sim/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] } ], "source": [ "intranight_gap = {}\n", "for idx in night_min.index:\n", " intranight_gap[idx] = np.median(night_min.loc[idx,'expMJD'].values[1:] - night_max.loc[idx,'expMJD'].values[:-1])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intranight_df = pd.DataFrame.from_dict(intranight_gap,orient='index')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intranight_df = intranight_df.reset_index()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intranight_df['propID'] = intranight_df['index'].apply(lambda x: x[0])\n", "intranight_df['fieldID'] = intranight_df['index'].apply(lambda x: x[1])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>0</th>\n", " <th>propID</th>\n", " <th>fieldID</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>(1, 257)</td>\n", " <td>3.937569</td>\n", " <td>1</td>\n", " <td>257</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>(1, 258)</td>\n", " <td>4.937213</td>\n", " <td>1</td>\n", " <td>258</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>(1, 259)</td>\n", " <td>4.000025</td>\n", " <td>1</td>\n", " <td>259</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>(1, 260)</td>\n", " <td>1.967431</td>\n", " <td>1</td>\n", " <td>260</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>(1, 307)</td>\n", " <td>3.002759</td>\n", " <td>1</td>\n", " <td>307</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index 0 propID fieldID\n", "0 (1, 257) 3.937569 1 257\n", "1 (1, 258) 4.937213 1 258\n", "2 (1, 259) 4.000025 1 259\n", "3 (1, 260) 1.967431 1 260\n", "4 (1, 307) 3.002759 1 307" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intranight_df.head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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HAABgQoQ8AAAAEyLkAQAAmBAhDwAAwIQIeQAAACZEyAMAADAhQh4AAIAJEfIA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\nAKCwubm5aePGjfLx8bFpX79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"text/plain": [ "<matplotlib.figure.Figure at 0x11a5d2be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for prop in [1,2,3]:\n", " w = (intranight_df['propID'] == prop) & intranight_df[0].notnull()\n", " plt.hist(intranight_df.loc[w,0], bins=np.arange(0,30),label=f'{prop}',histtype='step',linewidth=2)\n", "plt.legend()\n", "plt.xlabel('Median Nights between Observations of a Field')\n", "plt.ylabel('Number of Fields')\n", "sns.despine()\n", "plt.savefig(f'fig/{sim_name}/internight_gap_hist.png')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 3515., 6041., 13031., 21191., 24033., 21242., 15928.,\n", " 8744., 4177., 1445.]),\n", " array([ 18.61669562, 34.72008075, 50.82346589, 66.92685102,\n", " 83.03023615, 99.13362128, 115.23700641, 131.34039154,\n", " 147.44377668, 163.54716181, 179.65054694]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11a6a3400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.degrees(df.dist2Moon))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

royalosyin/Python-Practical-Application-on-Climate-Variability-Studies

ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb

1

295878

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 130%;\n", " line-height: 1.5;\n", "}\n", "\n", ".rendered_html h1 {\n", " margin: 0.25em 0em 0.5em;\n", " color: #015C9C;\n", " text-align: center;\n", " line-height: 1.2; \n", " page-break-before: always;\n", "}\n", "\n", ".rendered_html h2 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #26465D;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html h3 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #002845;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html li {\n", " line-height: 1.5; \n", "}\n", "\n", "/*.prompt {\n", " font-size: 120%; \n", "}*/\n", "\n", ".CodeMirror-lines {\n", " font-size: 110%; \n", "}\n", "\n", "/*.output_area {\n", " font-size: 120%; \n", "}*/\n", "\n", "/*#notebook {\n", " background-image: url('files/images/witewall_3.png');\n", "}*/\n", "\n", "h1.bigtitle {\n", " margin: 4cm 1cm 4cm 1cm;\n", " font-size: 300%;\n", "}\n", "\n", "h3.point {\n", " font-size: 200%;\n", " text-align: center;\n", " margin: 2em 0em 2em 0em;\n", " #26465D\n", "}\n", "\n", ".logo {\n", " margin: 20px 0 20px 0;\n", "}\n", "\n", "a.anchor-link {\n", " display: none;\n", "}\n", "\n", "h1.title { \n", " font-size: 250%;\n", "}\n", "\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext load_style\n", "%load_style talk.css" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset\n", "\n", "**Trend** and **anomaly** analyses are widely used in atmospheric and oceanographic research for detecting long term change.\n", "\n", "An example is presented in this notebook of a numerical analysis of Sea Surface Temperature (SST) where the global change rate per decade has been calculated from 1982 to 2016. Moreover, its area-weighted global monthly SST anomaly time series is presented, too. In addition, all of calculating processes is list step by step.\n", "\n", "* Data Source\n", "> NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2 is downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html.\n", ">\n", "> Spatial Coverage:\n", "> * 1.0 degree latitude x 1.0 degree longitude global grid (180x360).\n", "> * 89.5N - 89.5S, 0.5E - 359.5E.\n", ">\n", "> Because oisst is an interpolated data, so it covers ocean and land. As a result, have to use land-ocean mask data at the same time, which is also available from the webstie.\n", ">\n", "> We select SST from 1982 to 2016." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load basic libs" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "% matplotlib inline\n", "\n", "from pylab import *\n", "import numpy as np\n", "import datetime \n", "\n", "from netCDF4 import netcdftime\n", "from netCDF4 import Dataset as netcdf # netcdf4-python module\n", "from netcdftime import utime\n", "\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.basemap import Basemap\n", "import matplotlib.dates as mdates\n", "from matplotlib.dates import MonthLocator, WeekdayLocator, DateFormatter\n", "import matplotlib.ticker as ticker\n", "\n", "from matplotlib.pylab import rcParams\n", "rcParams['figure.figsize'] = 15, 6\n", "\n", "import warnings\n", "warnings.simplefilter('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Read SST data and pick variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Read SST" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ncset= netcdf(r'data/sst.mnmean.nc')\n", "lons = ncset['lon'][:] \n", "lats = ncset['lat'][:] \n", "sst = ncset['sst'][1:421,:,:] # 1982-2016 to make it divisible by 12\n", "nctime = ncset['time'][1:421]\n", "t_unit = ncset['time'].units\n", "\n", "try :\n", " t_cal =ncset['time'].calendar\n", "except AttributeError : # Attribute doesn't exist\n", " t_cal = u\"gregorian\" # or standard\n", "\n", "nt, nlat, nlon = sst.shape\n", "ngrd = nlon*nlat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Parse time" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(420L,)\n" ] }, { "data": { "text/plain": [ "array([datetime.datetime(1982, 1, 1, 0, 0),\n", " datetime.datetime(1982, 2, 1, 0, 0),\n", " datetime.datetime(1982, 3, 1, 0, 0),\n", " datetime.datetime(1982, 4, 1, 0, 0),\n", " datetime.datetime(1982, 5, 1, 0, 0)], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "utime = netcdftime.utime(t_unit, calendar = t_cal)\n", "datevar = utime.num2date(nctime)\n", "print(datevar.shape)\n", "\n", "datevar[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Read mask (1=ocen, 0=land)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(420L, 180L, 360L)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmfile = 'data\\lsmask.nc'\n", "lmset = netcdf(lmfile)\n", "lsmask = lmset['mask'][0,:,:]\n", "lsmask = lsmask-1\n", "\n", "num_repeats = nt\n", "lsm = np.stack([lsmask]*num_repeats,axis=-1).transpose((2,0,1))\n", "lsm.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Mask out Land" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sst = np.ma.masked_array(sst, mask=lsm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Trend Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Linear trend calculation" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#import scipy.stats as stats\n", "sst_grd = sst.reshape((nt, ngrd), order='F') \n", "x = np.linspace(1,nt,nt)#.reshape((nt,1))\n", "sst_rate = np.empty((ngrd,1))\n", "sst_rate[:,:] = np.nan\n", "\n", "for i in range(ngrd): \n", " y = sst_grd[:,i] \n", " if(not np.ma.is_masked(y)): \n", " z = np.polyfit(x, y, 1)\n", " sst_rate[i,0] = z[0]*120.0\n", " #slope, intercept, r_value, p_value, std_err = stats.linregress(x, sst_grd[:,i])\n", " #sst_rate[i,0] = slope*120.0 \n", " \n", "sst_rate = sst_rate.reshape((nlat,nlon), order='F')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Visualize SST trend" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xc79c748>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5ObYiGcncdErLK9l/LI29qSfYd/QkB7KKqaioMIbaq6+vp66yjHoJdXX1PHvX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0dn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EBh/QaIqxfG1YbVQrkRFx5nEgNl05k7fwG8JQlvvvQ85i5ajFnvvIVbbh2HwMCgynNPnUrF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\nuBjNW7RA++ZJC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"text/plain": [ "<matplotlib.figure.Figure at 0x82dcba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = Basemap(projection='cyl', llcrnrlon=min(lons), llcrnrlat=min(lats),\n", " urcrnrlon=max(lons), urcrnrlat=max(lats))\n", "\n", "x, y = m(*np.meshgrid(lons, lats))\n", "clevs = np.linspace(-0.5, 0.5, 21)\n", "cs = m.contourf(x, y, sst_rate.squeeze(), clevs, cmap=plt.cm.RdBu_r)\n", "m.drawcoastlines()\n", "#m.fillcontinents(color='#000000',lake_color='#99ffff')\n", "\n", "cb = m.colorbar(cs)\n", "cb.set_label('SST Changing Rate ($^oC$/decade)', fontsize=12)\n", "plt.title('SST Changing Rate ($^oC$/decade)', fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Anomaly analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Convert sst data into nyear x12 x lat x lon" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(35L, 12L, 64800L)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sst_grd_ym = sst.reshape((12,nt/12, ngrd), order='F').transpose((1,0,2))\n", "sst_grd_ym.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Calculate seasonal cycle" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(12L, 64800L)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sst_grd_clm = np.mean(sst_grd_ym, axis=0)\n", "sst_grd_clm.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Remove seasonal cycle" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(420L, 180L, 360L)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sst_grd_anom = (sst_grd_ym - sst_grd_clm).transpose((1,0,2)).reshape((nt, nlat, nlon), order='F')\n", "sst_grd_anom.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 Calculate area-weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.4.1 Make sure lat-lon grid direction" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 89.5 88.5 87.5 86.5 85.5 84.5 83.5 82.5 81.5 80.5 79.5 78.5]\n", "[ 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5]\n" ] } ], "source": [ "print(lats[0:12])\n", "print(lons[0:12])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.4.2 Calculate area-weights with cos(lats)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(180L, 360L)\n" ] } ], "source": [ "lonx, latx = np.meshgrid(lons, lats)\n", "weights = np.cos(latx * np.pi / 180.)\n", "\n", "print(weights.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.4.3 Calculate valid grids total eareas for Global, NH and SH" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sst_glb_avg = np.zeros(nt)\n", "sst_nh_avg = np.zeros(nt)\n", "sst_sh_avg = np.zeros(nt)\n", "\n", "for it in np.arange(nt):\n", " sst_glb_avg[it] = np.ma.average(sst_grd_anom[it, :], weights=weights)\n", " sst_nh_avg[it] = np.ma.average(sst_grd_anom[it,0:nlat/2,:], weights=weights[0:nlat/2,:])\n", " sst_sh_avg[it] = np.ma.average(sst_grd_anom[it,nlat/2:nlat,:], weights=weights[nlat/2:nlat,:]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Visualize monthly SST anomaly time series" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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RERFJj7DENF0ZxPYCxPvvh1degXPPhVdf7Vrb+gkFiCIiIiIikh7xSky7MtNA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v398dznXkmWbNmxYzvo5JWXNe6Vru69unrOc87nfAQBDEcQW7ByNEUkunlLrfziB4Ig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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c1ce5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1 , figsize=(15,5))\n", "\n", "ax.plot(datevar, sst_glb_avg, color='b', linewidth=2, label='GLB')\n", "ax.plot(datevar, sst_nh_avg, color='r', linewidth=2, label='NH')\n", "ax.plot(datevar, sst_sh_avg, color='g', linewidth=2, label='SH')\n", "\n", "ax.axhline(0, linewidth=1, color='k')\n", "ax.legend()\n", "ax.set_title('Monthly SST Anomaly Time Series (1982 - 2016)', fontsize=16)\n", "ax.set_xlabel('Month/Year #', fontsize=12)\n", "ax.set_ylabel('$^oC$', fontsize=12)\n", "ax.set_ylim(-0.6, 0.6)\n", "fig.set_figheight(9)\n", "\n", "# rotate and align the tick labels so they look better\n", "fig.autofmt_xdate()\n", "# use a more precise date string for the x axis locations in the toolbar\n", "ax.fmt_xdata = mdates.DateFormatter('%Y')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## References\n", "\n", "http://unidata.github.io/netcdf4-python/\n", "\n", "http://www.scipy.org/\n", "\n", "Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.\n", "\n", "Matplotlib: A 2D Graphics Environment by J. D. Hunter In Computing in Science & Engineering, Vol. 9, No. 3. (2007), pp. 90-95\n", "\n", "Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

spacedrabbit/PythonBootcamp

Advanced Modules/Collections Module.ipynb

1

14356

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({' ': 2,\n", " 'a': 1,\n", " 'g': 1,\n", " 'h': 1,\n", " 'i': 2,\n", " 'n': 1,\n", " 'r': 1,\n", " 's': 1,\n", " 't': 2,\n", " 'w': 1})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter('with a string')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({'a': 1, 'string': 1, 'with': 1})" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter('with a string'.split())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c = Counter([1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 100, 'test'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({1: 3, 2: 2, 3: 8, 5: 1, 6: 1, 100: 1, 'test': 1})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_items([(1, 3), (2, 2), (3, 8), (100, 1), (5, 1), (6, 1), ('test', 1)])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.viewitems()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "key: 1 value: 3\n", "key: 2 value: 2\n", "key: 3 value: 8\n", "key: 100 value: 1\n", "key: 5 value: 1\n", "key: 6 value: 1\n", "key: test value: 1\n" ] } ], "source": [ "for k, v in c.iteritems(): \n", " print \"key:\", k, \"value:\", v" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3, 8), (1, 3), (2, 2), (100, 1), (5, 1), (6, 1), ('test', 1)]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.most_common() # descending order of most common" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3, 8)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.most_common(1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3, 8), (1, 3), (2, 2)]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.most_common(3)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({1: 3, 2: 2, 3: 8, 5: 1, 6: 1, 100: 1, 'test': 1})" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 100, 5, 6, 'test']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(c)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 5, 6, 100, 'test'}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(c)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1: 3, 2: 2, 3: 8, 5: 1, 6: 1, 100: 1, 'test': 1}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict(c)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('test', 1), (6, 1), (5, 1), (100, 1)]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.most_common()[:-4-1:-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## defaultdict\n", "\n", "The whole point is that it will always return a value even if you query for a key that doesnt exist. That value you set ahead of time is called a **factory object**. That key also gets turned into a new key/value pair with the factory object" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = {'k1':1}" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"k1\"]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyError", "evalue": "'k2'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-44-d5a5b2fb6795>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"k2\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'k2'" ] } ], "source": [ "d[\"k2\"] # this will get an error because the k2 key doesnt exist" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = defaultdict(object)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<object at 0x1023693f0>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['one'] # this doesn't exist, but in calling for it it will create a new element {'one' : object}" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<object at 0x102369410>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['two'] # same, this will add {'two' : object}" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "key: two item: <object object at 0x102369410>\n", "key: one item: <object object at 0x1023693f0>\n" ] } ], "source": [ "for k, v in d.items():\n", " print \"key:\", k, \"item:\", v" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "e = defaultdict(lambda: 0) # lambda just returns 0 here" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e['four']\n", "e['twelve']" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def error():\n", " return 'error'\n", "\n", "f = defaultdict(error) #returned item must be callable or None" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'error'" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f['new']" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('new', 'error')]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.items()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## orderedDict\n", "\n", "dictionary subclass that remembers the order items were added" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1\n", "c 3\n", "b 2\n", "e 5\n", "d 4\n" ] } ], "source": [ "d_norm = {}\n", "d_norm['a'] = 1\n", "d_norm['b'] = 2\n", "d_norm['c'] = 3\n", "d_norm['d'] = 4\n", "d_norm['e'] = 5\n", "\n", "# order isn't preserved since a dict is just a mapping\n", "for k,v in d_norm.items():\n", " print k,v\n" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import OrderedDict" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d_ordered = OrderedDict()\n", "d_ordered['a'] = 1\n", "d_ordered['b'] = 2\n", "d_ordered['c'] = 3\n", "d_ordered['d'] = 4\n", "d_ordered['e'] = 5" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1\n", "b 2\n", "c 3\n", "d 4\n", "e 5\n" ] } ], "source": [ "for k,v in d_ordered.items():\n", " print k, v" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import namedtuple" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this is kind of like creating a new class on the fly\n", "# the first parameter of a namedtuple is the name of the class/tuple type\n", "# the second parameter is a space-delimeted list of properties of the tuple\n", "Dog = namedtuple('Dog','age breed name')\n", "sam = Dog(age=2, breed='Lab', name='Sammy')" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "Lab\n", "Sammy\n" ] } ], "source": [ "print sam.age\n", "print sam.breed\n", "print sam.name" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class '__main__.Cat'>\n", "<type 'type'>\n", "fuzzy\n", "sharp\n", "Mittens\n", "1\n", "1\n" ] } ], "source": [ "Catzz = namedtuple('Cat', 'fur claws name')\n", "mittens = Catzz(fur='fuzzy', claws='sharp', name='Mittens')\n", "print type(mittens)\n", "print type(Catzz)\n", "print mittens[0]\n", "print mittens.claws\n", "print mittens.name\n", "print mittens.count('fuzzy')\n", "print mittens.index('sharp')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

percyfal/bokeh

examples/howto/Range update callback.ipynb

1

3562

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "\n", "import numpy as np\n", "\n", "from bokeh.models import ColumnDataSource, CustomJS, Rect\n", "from bokeh.plotting import output_notebook, figure, show\n", "from bokeh.layouts import row\n", "\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N = 20\n", "img = np.empty((N,N), dtype=np.uint32)\n", "view = img.view(dtype=np.uint8).reshape((N, N, 4))\n", "for i in range(N):\n", " for j in range(N):\n", " view[i, j, 0] = int(i/N*255)\n", " view[i, j, 1] = 158\n", " view[i, j, 2] = int(j/N*255)\n", " view[i, j, 3] = 255" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "source = ColumnDataSource({'x':[], 'y':[], 'width':[], 'height':[]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xrange_callback = CustomJS(args=dict(source=source), code=\"\"\"\n", " var data = source.data;\n", " var start = cb_obj.start;\n", " var end = cb_obj.end;\n", " data['x'] = [start + (end - start) / 2];\n", " data['width'] = [end - start];\n", " source.'change.emit();\n", "\"\"\")\n", "\n", "yrange_callback = CustomJS(args=dict(source=source), code=\"\"\"\n", " var data = source.data;\n", " var start = cb_obj.start;\n", " var end = cb_obj.end;\n", " data['y'] = [start + (end - start) / 2];\n", " data['height'] = [end - start];\n", " source.change.emit();\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p1 = figure(title='Box Zoom Here', plot_width=400, plot_height=400,\n", " x_range=(0,10), y_range=(0,10), tools ='box_zoom,wheel_zoom,pan,reset')\n", "p1.image_rgba(image=[img], x=[0], y=[0], dw=[10], dh=[10])\n", "p1.x_range.callback = xrange_callback\n", "p1.y_range.callback = yrange_callback\n", "\n", "p2 = figure(title='See Zoom Window Here', plot_width=400, plot_height=400, \n", " x_range=(0,10), y_range=(0,10), tools=\"\")\n", "p2.image_rgba(image=[img], x=[0], y=[0], dw=[10], dh=[10])\n", "rect = Rect(x='x', y='y', width='width', height='height', fill_alpha=0, line_color='black')\n", "p2.add_glyph(source, rect)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "show(row(p1, p2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

tensorflow/docs-l10n

site/en-snapshot/io/tutorials/orc.ipynb

2

9413

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "qFdPvlXBOdUN" }, "source": [ "# Apache ORC Reader" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/io/tutorials/orc\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/io/blob/master/docs/tutorials/orc.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/io/blob/master/docs/tutorials/orc.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/orc.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "xHxb-dlhMIzW" }, "source": [ "## Overview\n", "\n", "Apache ORC is a popular columnar storage format. tensorflow-io package provides a default implementation of reading [Apache ORC](https://orc.apache.org/) files." ] }, { "cell_type": "markdown", "metadata": { "id": "MUXex9ctTuDB" }, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "1Eh-iCRVBm0p" }, "source": [ "Install required packages, and restart runtime\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "g7cxbf1-skn6" }, "outputs": [], "source": [ "!pip install tensorflow-io" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "IqR2PQG4ZaZ0" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import tensorflow_io as tfio" ] }, { "cell_type": "markdown", "metadata": { "id": "EyHfC3nEzseN" }, "source": [ "### Download a sample dataset file in ORC" ] }, { "cell_type": "markdown", "metadata": { "id": "ZjEeF6Fva8UO" }, "source": [ "The dataset you will use here is the [Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris) from UCI. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. It has 4 attributes: (1) sepal length, (2) sepal width, (3) petal length, (4) petal width, and the last column contains the class label." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "zaiXjZiXzrHs" }, "outputs": [], "source": [ "!curl -OL https://github.com/tensorflow/io/raw/master/tests/test_orc/iris.orc\n", "!ls -l iris.orc" ] }, { "cell_type": "markdown", "metadata": { "id": "7DG9JTJ0-bzg" }, "source": [ "## Create a dataset from the file" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "ppFAjXAYsj-z" }, "outputs": [], "source": [ "dataset = tfio.IODataset.from_orc(\"iris.orc\", capacity=15).batch(1)" ] }, { "cell_type": "markdown", "metadata": { "id": "4xPr3f4LVdeN" }, "source": [ "Examine the dataset:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "id": "9B1QUKG70Lzs" }, "outputs": [], "source": [ "for item in dataset.take(1):\n", " print(item)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "03qncHJPVNK3" }, "source": [ "Let's walk through an end-to-end example of tf.keras model training with ORC dataset based on iris dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "tDkpKRMVcPfb" }, "source": [ "### Data preprocessing" ] }, { "cell_type": "markdown", "metadata": { "id": "nDgkfWFRVjKz" }, "source": [ "Configure which columns are features, and which column is label:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "id": "R1OYAybz07dr" }, "outputs": [], "source": [ "feature_cols = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n", "label_cols = [\"species\"]\n", "\n", "# select feature columns\n", "feature_dataset = tfio.IODataset.from_orc(\"iris.orc\", columns=feature_cols)\n", "# select label columns\n", "label_dataset = tfio.IODataset.from_orc(\"iris.orc\", columns=label_cols)" ] }, { "cell_type": "markdown", "metadata": { "id": "GSYMP48vVvV0" }, "source": [ "A util function to map species to float numbers for model training:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "id": "TQvuE7OgVs1q" }, "outputs": [], "source": [ "vocab_init = tf.lookup.KeyValueTensorInitializer(\n", " keys=tf.constant([\"virginica\", \"versicolor\", \"setosa\"]),\n", " values=tf.constant([0, 1, 2], dtype=tf.int64))\n", "vocab_table = tf.lookup.StaticVocabularyTable(\n", " vocab_init,\n", " num_oov_buckets=4)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "lpf0w41iWAZ4" }, "outputs": [], "source": [ "label_dataset = label_dataset.map(vocab_table.lookup)\n", "dataset = tf.data.Dataset.zip((feature_dataset, label_dataset))\n", "dataset = dataset.batch(1)\n", "\n", "def pack_features_vector(features, labels):\n", " \"\"\"Pack the features into a single array.\"\"\"\n", " features = tf.stack(list(features), axis=1)\n", " return features, labels\n", "\n", "dataset = dataset.map(pack_features_vector)" ] }, { "cell_type": "markdown", "metadata": { "id": "R1Tyf3AodC2Y" }, "source": [ "## Build, compile and train the model" ] }, { "cell_type": "markdown", "metadata": { "id": "oVB9Q0B-WDn4" }, "source": [ "Finally, you are ready to build the model and train it! You will build a 3 layer keras model to predict the class of the iris plant from the dataset you just processed." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "id": "tToy0FoOWG-9" }, "outputs": [], "source": [ "model = tf.keras.Sequential(\n", " [\n", " tf.keras.layers.Dense(\n", " 10, activation=tf.nn.relu, input_shape=(4,)\n", " ),\n", " tf.keras.layers.Dense(10, activation=tf.nn.relu),\n", " tf.keras.layers.Dense(3),\n", " ]\n", ")\n", "\n", "model.compile(optimizer=\"adam\", loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[\"accuracy\"])\n", "model.fit(dataset, epochs=5)" ] } ], "metadata": { "colab": { "collapsed_sections": [ "Tce3stUlHN0L" ], "name": "orc.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

ledeprogram/algorithms

class5/Simple_Linear_Regression.ipynb

1

73478

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)\n", "import statsmodels.formula.api as smf # package we'll be using for linear regression" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(\"data/heights_weights_genders.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f819e1a2438>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Jfatx3APpxHWWuD2WEdeG4wIvo6FZSmGIKCuVhW5nCZzvjnm11ryUS2fkRNuE\nZl3oabrEqqrJ0FFGYgL73VJOUje4kE9JtBiuRVT6IifKFfgx2kT5Ec91hHzFnJR7XiNa2Ldd4irw\nksRhsY5gX3LMVomJ72sFnpB8vqWiV7Xh+ILxxkkA56EJ4d93i//3gHeh3UseQR9NHgZagmtudsbB\nUmANxx3d3d0Jg1AKFsTkwjtL4l7USySuQPaaS6e715wbs9EZibTCulnOGLwzuFdJ4APumE9FjXth\nxwV1t0l5dlTR3ctXUtdK5fx9uurH3L7bpFzmw5PvYYGe15wKJc23HvqtjIcYfxiJkbAe1wZDgIGB\nAfr7+2lsbGTXrl08++yzXHzxB9EciteBT6BhnDtQh3gm+kxzJ7AYeB/KP4RhKAG+i4ZqvoFyFU+j\nXMP5wG3u+hPQIrn3oYqqs9AQ0CzgLnedb0P6PlSp9Q1oOKoOdb6fBXahIaxu4kI53wVuJspT1Lh5\nnYY2/LnPfad+dz8f5d2P8hEz3H3+EuUwLndjfJ64WHApynnoPa0X9fjDSHpcj3m4aSw2zJMwjAF8\n3n4up3pFhcIcqatrcE/QM9zT86dERfG8V7FRNNQjokqpyTBUp3vC982Blrgn8FXu+CnuSd0/5U+T\n8lqKHnf/NIkOL3MRZj496M732Upp6qxeZsOntnZLLLUxSyASeL/AlSkehy/E6xT4D4G3URC41B3X\nz0eqxWQ4dmC8hZvGajMjYRhtlEolVz0dLohe3yhUZV2QWDh7gs9p9Qv1ouGetKrnyqY7+jlZ9dzo\nFuVw32JnsGrdsTQj5O/T4/aVEtf/oZRzHRdJzCv4kNIZifv6QrxmicNTGyVuQqQpvI2NCy3UNA4x\nEiNh2k2GKQuvuwTw/PPPs29fO+UZO9PQLKCvoOGde4C5aCroUjR99N+Bt7vPbWha6TLirKP9aGjm\n64mxTwD+mMomQ6eiVN02N842N4bvL+3DWD6Dai3wZ6hu0zbKs5WuRLOO3oWGo36AhopeB37s5hdm\nOC1FKcTH3L7r3fjhfZ9Ce3D/Bfn8FxD5PJnMKeza9ePgvBc5cOAFS3mdLDgSSwKcdyT7jtWGeRKG\no0S57pIvdEsjkM8UuNkd8+c2ispgbBSV0gj7K3RKuSpqu8QZT+HYLQIPpNyzzXktedFsoox79Z6C\n7//QFHgGq1I8jfCJvxBspwbXJ6/pFA2ricQSId6D8j0o/G/SLHV1DdLX1ye9vb2ybt16k9iYAGCs\nwk3A945ue4YBAAAgAElEQVRk37HazEgYjgbpukutbmH0FcNFt8B7A5CWCfSQaIjJ6yqVpFygz8t1\nL3Tj+Iroevf5Q1Iu3NccXJORuP90XjSr6ZNucfdiel4WvCdljl5zab0zLMk03paUfT7z6TYpT3Ht\nkTgTq/z8kHcwiY3xj1E3EmhKxPWoHvDHgu1O4Inh3my0NjMShqNBuu5Sl8QNe04UDslV1EtlZ7ez\n3SKalTidVUSf9n1zHf/knlyE16Ys7CHhnBPlCjw/st6992OulnJD4MX2/lji9FS/f31gEM6Scg9n\nscQprV6nKTRQSdmPeVJJyi+SXK5oRmECYSRG4nCyHFlUa6kOrfP326+A3z/6YJfBMHwMDAzw2GOP\nHZLIONJrHn74YR5++GEaGxvZv/85ymUt+tFynvPQf+IFNJ5/IpoOGp77I3esFuUgXkD5gKvQyunv\noOU+Sb5hJvBGVO6iARUUWOQ+16IppScD/4D+l3sEuA74Nppm6+UvBtx1c919ssAKN5dXUC4iR1yV\n/VmUg+h0552GpuBOB+ahaaxPATe4ORbRNNnwO/+U2trKfZnMKfT39x/m1zdMaByJJQFmD9f6jOWG\neRJTFoeT8E7DunXrXSrrHIklrr3YXmfw9JymYtrgwi/VnuYLomGgBnfcd3ZLZkGFSq29onxG6En4\n8E4okuc5gfDpfYG71he7dQXfqUniPhXrJS6A8z0tzk54DL7wLzlHH6byHEi91Nbm5e6711R4HtYD\nYmKBMeQk5gPr0erob/ptuDcbrc2MxNTESNqIrlu3PrFA+jTPM91i2C5xY5y0Gge/WLcI1EilnIWX\nzjhHYoLa3+tSiesZWgTeJuXkcbM77ySJCe9qzYMkWNz99d5YeYORxkmkhb28xEaNVDYzqnPfpzwM\n5rmHdevWSy5XlMbGhUZQT0CMpZF4As2xOxf1l98IvHG4NxutzYzE1MSRSHiHKJVKksslCdi0BbNH\nYumKHin3JLx2kX/yDp/+17pFdrpbkJNEcJszPrlgEU4agI3uvs1uQU42CDrDze2MKnP3xHmSY5kb\n3LeSS9BjHe6+1wl8VJS0zguskLAo7u6711T8rkZQT0yMpZF4fLgDj+VmRmJqYrieRG9vrzQ2Lg4W\nx16B+YkFs0s0hbReYm2lE0TDT5+UmOidL3FDnlAXyWcqTXdjhWMvlPipvDdlIT9dYh2lkyTWQ0oa\nklMkFvwLr+8UbfhzmqSL+J3pFvuGlGO3BZ9bnLE50/0W2uMik2m0qulJhpEYiSGJ6yiK2qIoagO+\nGkXR1VEUzfD73H6D4ZjBS3gXCsspFpdQKCwfUoK6o6ODwcF+YrL1dTRRLyRfnwU+g5LNT6Lk8G/Q\nwrjPAJeh0dZnUV2lLEoyP4qSyd9x+16lvAfETrTQzpPXHSn37kcJ6jxauJcD9qJFbWeiUuEfcfe9\nEyWlw+tfATagct7NwXVL3fn/hvaIqHP7FrrXE4BPuXEWofpP57j5tKMFdYPU1tZRLBZTf1vDFMJQ\nFgT9V+9LM5Pbj4drkUZrwzyJKY3hhDtiTsITyVkXYvHhmxapLCo7I+XJ3NdLPJjiESwU1Vyqc55A\nh/MKPiYxx9AnWuuQC+79BknXXSpKnLbaIhqyut55DaHy6vqE59Hjxp+VmN8i0ZBWt8TeUFrIrZK/\nMGJ6coGxCjeNt82MhGEoJI3IH/3RH4uGdVrcouqL2+aKitklQzxFqQwddYr2iEj2jPZksucc3iAx\nudwisUieJ7C9obpNYk2mUHepWn1FTjSklZO4WO/BFAN3tqQXyYU1E9dL3FOi6I6J5HKnSTY7S0KN\nJ5P7nlwYMyMBvD9l+y3gxOHecDQ2MxKGakimyJ5//jsSi3iyGC0nlRk+vuI4bLrjs4WekJjD8JXP\nQxHjfoEPxypITJI3JIxO2sI/OzAyTe6as9x8GqXSK/Dek2985IvjQlXYE6U8nTXMxGp155knMdkw\nlkbi/6AB0C+77RdoOuzTwH8e7k2PdjMjMXkwmpkypVJJ8vkW0ayjrQJ/lLJoe6lrEfUomtzCvF1U\nVdWngBYkDhvVu0VzdbA/L3CjW2T9Yt4rlZXKM901S4JF2neNWyWx4qo/vySVIaS09NYGZ8i8NlNn\nsLgnvZvIGYqHqoyZfo98vsVSXCcZxtJIdAPTgs/T3L424N+Ge9Oj3cxITA6MpDAuDd7Q3HhjKMTn\nC+QWJhbtRW4x75HYi/Bx+rMk7hS32i3kZ0scrhHReP/bJE55HWpBDz/7lFnfn8LzFz1SWRvh6y0W\niYaDvAy33xZILNDn6y1yEosBpnkzcyQ2cEUpz5RKM26dctNNt4zyX9xwvDGWRqIv8Tny+4Adw73p\n0W5mJCY+RlIYlwZvaBoaFqYsjqH6akgM+1CLT2HNpiyqnsz1BqVZ4li+9zByEosAdog+yV8rcXW2\nbxm6JTAofmE/T7QSuk/itFPPY6x2C35O0onmsMDOf/Z6TSdIJZ/iDaPnJDbK4T2JVsnnWyzUNMkw\nlkbiPrRX4n9x2z+6fQ1Az3BverSbGYmJj+EWxolUhqbKDU2vlAvx+bDN6pQFOmkUkg15ZrqtSbSW\noTUwLKGH4QX+fG/rNRKL/M2SuECvWdLlL/xT/xyJw0bb3XfpEPU87nX3aXWLf7NoJlXSCDRJ7D0l\n7xWG2E4VyEkU5ZxxXeS+/8WS7FltpPXkw1gaiQgV9Psrt/0+aH/s47GZkZj4GK4nkRaa6u3tlaYm\n/9SclLR4UDSkkiZ1kTQKHaJ8RJ+7znMSSa2jU4KxPDnsDcRpEj/Rh5lPdaJZSacl7rlQ4qf7sHuc\nN0B5gVtEuRX/PbxKbRrPslDUsGWlkogPyXptUbpy5SoplUrS3d3teJweSZL1RlpPPlgKrGFCwS/8\nh2tUU82gbN++3Qn3fVa0BsBrGHW4hTIv6VIXYb/n1e68E6T86T5N66hOYsntluB+oTHxBsgbJ39O\nedZQLPkhok/v3YGB2SJx/UZR4qwqPx9vxEKJ71Z33gclboQ0Q9LkwJOLv/875PP63QsF02WarBh1\nIwFsd6+/RuXB/fZr4FfDvdlobWYkJg/SQkjJbKe00FQ+P0dqaxvc4u4zhEKBOr/Yp6m7Ft0xL5vR\nHJyTDFuJxFpHPtNprSh5nCy488ZkrRvHy2IkvZi8qEJr+HR/o7u2JHEvCX/cfw+/0G8R5UL89/S8\nyjSBgmQyswTyks3OkHy+RVasuOKwonz+d/ed5syDmJwwT8IwoVEt26nSk+hxC2RL4gndh4WSuf8h\nUR3WQdzoFljvbZREQzxpRHGdxF6B5yLC9NfQmORFw1BpekvzJO46588PQ0MrUq7pdPe/UZSzUAL+\nkksulZoaL3vec2i+uVyLbN++/bDG1zD1MKZGAngr8Cfu/RuAOcO92WhtZiQmH9IMQS5XlL6+PhEp\nD01lMg2S3vuhKJVFa7c5g3GmW5C9vMbJErcTLYkSv97oNLin+bMk7q9wqnv1Pa4/XMWY+MpmT1on\nSeR6d6+8aI/rZPZVmshfuaegrxpyymbPkLiOQ/9HG+FsqIaRGInDdaYDIIqiO4Ab0TZYoIpm/+NI\nrjUYjgT9/f1ksx2o4NxW4CL27p1OV9dbuP/+B5g3by6PP76dT3zi9zl4cD8wg/Kub22oON4a4HRU\nrG868NfA51Chu160e9s0NGr6l2hXuR+gXeG+hdaH/l80V+NK9HloA/AvwO+561uBTeh/gzej3d3e\njAr/Fd18pqOifmuB5cASd86twHY3/tVoJ7on3ZzXuDH2oh3llrjXGrQsaRD4mnvNA4+yb98PUJHB\nj6Ad63YyOPgcHR0dw/4bGAypOBJLgvZ1jAhqIoCdw7VIo7VhnsSkQ+xJPCRpstdNTWdJNtsoUeQ7\nsKVJavdIuWfhC9bitE71HGrc0/i73L1yzrMIs486A+/ht5xn4ENcvsd1ncTkuPdefKaSJ4yThXTb\nJc5u8lXTrVJek+GzpE52x72X0OXOWSSaLSUSzrehYb4RzoYhwQg8ibojtCX7RESiKBKAKIoaRt9c\nGaYy2tvbueSS3+cLX/hD4BTKvYROfv3r6ehTfh36JP4k+pTdBvwclcpeFlyTcdt3UK/jG2gP6t1u\n/6/deK+7MevQHtBrgDehyjOfRR3ofvTpvRb1NhahPa3fiXoCl/pv4ebzLtTjeAcq990AvObOvRB4\nHyobPgeV9v4WcBHqefSj3sgvUc/g6+577QSec/P9KbDP7VsE7CSTGeDv//5/0tXVVVU63WAYEY7E\nkgAfB+5HZcOvRP/nfXS4Fmm0NsyTmHSIJb17JM4ISsbpr5FyUrckWoj2keBa3z865574Q4XVevf0\n73kCX3MQFsjlJO7QVgzm8VHRTCKRuI91WjV0s2izomaJ5UHSuIsPSmW3u/C45zNC9VjPSfh6CF9g\n1yqQlcsu++AhDsfDCGtDCMYgBfa/oi1L69BHoLvRQO6Fw73RaG5mJCYehlqstM1o2ILT90nIuwWx\nwS3IPjQTFqslJbh9/+cTU873C7CvdbhdygvkPiSxzHdBNKzlJTMWuX2+R3ayj7UnuT8QLPpejiOp\ni+T7YhdEjWAy5XZhcM6HRbOX7hU1gA9JnDXlC+xKEkuZF+Saa64VkdHTxjJMHoyFkfhLlMV7BfWJ\nPw28B2gb7o1GczMjMbFwuMVKK6fPkrj4rEW0EMwvvq3OACyQ2DNI02ryBWreGFRLJy24cxe7hfhM\nUUmN5Hg+m2kojaM2UemOjc7g1Io2FfLFdKFnFBqq5c54pFWEt7h7/7HEUhq9wXc4XSp5m/Lvvn37\n9lHRxjJMLoy6kTh0kqZxvMWFnb6MpoT0Dfdmo7WZkZg4OBL5jfic90tlyMkvgqEQX0lURiNpADyx\n65/GG6WS4G6UuO+0J5R9aqz3ZPwTeoeUd6FLU0v1Sq2+hsJ3nQsrvb1h8x5PKEfuPY5WiQ2Y72pX\nFPUc0gT9fIX46e47rpfYqzhVbr/99mFrYxkmP8bSSDSjbNxK4BHg/wf+drg3G63NjMTEwZEK+d10\n0y0S8wi9ki6lsULi0I+X6q72NO0zpTyf4Rfvme7zlRJzFb5y2UtchPpJRRnak/CKseF+L/WR1lr0\nVGeQOkUVY314yzcH8t3iGiUOe71JwpBXFOUkkylKLne6xBLhre67qNdlnoQhDWMRblqPJoh/Hfgk\n8DtA63BvMtqbGYnxh2qcw+GK5Dy6u7vdAjlUiMZrIvWKegqeDwglv73Q3ilu8cxLHK5aK5WFduHi\n772BpOfhdZR874aQtyhKLJkRGrU5oqGnsNJ7tVQamz7RSuqcVDd4ze68XsnlTnP8TXXDVVfXJKVS\n6Yi1sQxTB2NhJL7uvIaNwIeAsziO6q/BvEb3lzMcFQ7HOcQCctr4plA4q+K8Uqkk2WyzaAZT2BrU\nVxTXugXRh4i8HIWvU+hz+xsFvipwvsQy3H6xDrvD+TCRr404ReKn93CxP1ViGY2HAiMW1jT4LKSk\nUWt319a4eYTjTnfGp0sqmwCJlIfOOkUFAJ+QTKbo+Bt/Xq/A/LJrQ0/NspsMIcYk3IQW0S10RmKj\nMxoPA58c7s1GazMjcfwRCsINFdbw523fvl1yuZBsLfcoSqWSrFy5SnI5/6TuF37fKMg3+gk9h4x7\nvz5YcOuD83zoKE2BtcUZoFCue7ukFfLBG9x90sJg86VcgTbUh/KGJCysC8d9SOKMpaFI+HqBuQL1\nctllH0zRsSq/1sJKhmoYM05Cx+Zk4GJU5+DfgdeGe7PR2sxIHF+EnkMuV5RCoTyFs6lpsWzcuFHu\nvnuN5HIt0tTUlTjPE7nzJZdrkcsvv1JyuaI0NCyQmhofmw9F726TWJ47XEib3LnJBdanzCZDVr75\nTqeox+KJX29QSgK/LbGqrA9hvVeqtxr1WkxbRENgPjPL8x1tovUNIZ9SEM1cKkq5wfPV4c0SezX+\n/VqBvPT19VWEka655loLKxmOCGMRbroW2AI87wzDf0dFYs4Gag47uIrevEwg4QHcgZaMfs9t7wqO\n3YyWwT4JvHOIccfydzQMgXRF1sqnZCVV0+LwaQttQVQ11bcbPVtij2C2W0xvkfJMI5FYHbWa0mry\n/C5Rz8RLb/vai4vcOBk3j4IzIiF/4YvXfBisU2LJjCfcHJskFhBMfr+ixBlIHRIbsnpnMEIPqiQx\n0Z0Tr2Lr6x98s6Du7m5TeTUMC2NhJO5B9QJmDHdgd/1bgcUpRuJjKecuAHa4wr0O4Jlq/IcZieOH\n9N4OHc5jWCyVoZa4dWYuN9t5Cp2JxXuBxMVrPjW1WeKaCZ/N1JCy+HpiOrnfVycnn/p9D+km0TBR\njzMQBdHQUUGUEG8W5TZ6Rb2ND0hc3Jd311znxihJXPw2P+X7LXSLv+czQnVa71mslrj1qfcuGiST\nOVluv/32CjVcK5AzjARjGm4a6QbMTjES16ecdxNwY/D5n4A3VRlztH87wxGiWt1DX1+fbNy4UfL5\n2VIeajnFLaQqr1FXF8pnl0Sf2PNukfWS171SKXnRI5VSFP5p3FdFe05ihbvmY1LemGeVu+cW9/l0\niRsQJY3MKRL3r/aeR060PmONxDUY8yROWfUGINk0yBvKOc64FCW9YVGfJFuIQqGMt7G0VsPRYCRG\n4oikwscA10RR9P0oij4fRVGz2zcTVT3z+JnbZxhHaG9vZ8OG+ygUllMsLqFQWM6GDfexYMECzj33\nXPbsKaGy3I+71wHgPwF/DsD+/U3u/VvQ54eHgBxwFyoJ9iE0QvkyMJdY6K/Bnf9DVEbshyhN9hLw\nd6ig3nmAAH+Byo1vBGa5sfajYgGgEdPvoFHNj7txQkHBk1GBv6+jkuJfQ+W6a918/9zd5ztodPSf\n3fEz0X+yN1AuD3498Ldurh1uLr8B/rf7ffw9e913vBQVC1xEodDJrl27gKScus41k5lNf39/tT+X\nwXD0GK5VGe5GpSfRjgsjoSvD5937zwGXBud9Hnh/lTFH38QahgXvOYT1Dr29vQE5HVYtf1Riee9O\n5znkpLwArNFdM1cgEjgpxZNIe+JvkLjRj3/i96Gc8Nxm9wQ/U8o5jL4q466RmPfYKumNgPqCcRZJ\nzHf0SCxO6LvUJYvtvOZUW/C5UjAwmSlmnoThaMAIPIkjlQofTaM0EHx8APiqe/8z9LHP42S3LxV3\n3nnnoffLli1j2bJlozZHw9DYvHkrK1ZcTTbbwb59/WzYcB+XXHKxa3TzM1RiezX653wJfYquBdah\nOpFfRp+ut+GlrvWJe6O7vg54FWgBlqKS2i+gjXaWo01/XkKf3juA69Cku7nANWi+xWzKvYO5wJ+4\neb1CLLM9iHohS9F/cj8Ffgv4mDvnRTS/4qTEeCehkuIPoHTaM8AVaEOjdwOnAc+6cb4QzGfAzeHR\n4Lsv5f3vf7fz0r7E/v1vBmaQzf6cDRvuPyT97b24FSuWk8nMZnDwOTZsuM+kwQ1VsW3bNrZt23Z0\ngwzXqgx3Q/8X/2vweXrw/s+ATe79GShxnUVXBSOuxyEO9zQbS3774w9KrDN0mmjM/WaJyd3Q4/Aq\nrjmJtYq86mm9G+OzEpPSXol1tfMUPLmdJuHtZTrC+omFEvMgfaIifbNEvR7Pe+ScB5LGM/QE8/Jp\nrNvd+R+VcnkPP59eSWZdNTaeLd3d3cHvql5IPt9SVTXXMpkMIwHjjbhGezy+gPZjfB59lPsS+vj0\nfeArwLTg/JudcbAU2HGKw2kxqaJrl5SHc5KhJS8DHuokFQTe7RbYWc6YXOteT5K4F0Ra/4U2iYvt\n5rox3i3lgnu+duFKN+YcZ1AaJT0s5TOdvMHaInGPCF+1LW78eyXWiZobGI5wXG9sOiqOFQpt0t3d\nbYJ8hjHHuDMSY7WZkTh+OJwnUX68T+AySY/nF6Wy9WfWLdrzRZ/ma92rz3zKiGYuLSxbTJUPaHDX\nN4hKf3tl15y7PtRu6nH37xPlM3wGVFhU96AzJuudUeiUuA7CezlhJpTnGApu3sk02E53/273HQrS\n2Hj2oTRW4xsMxwJmJAxjCh/mWLdu/ZAVvps2bZEo8iGWk6osmLVu4fepsldI7HHMlUopix6JFU/T\nFmrfiKfRbd47yYlWTDdKeRX1amcAmt3+oqiX0Srpgny+W53vcxFWg6cVB1Yj2bUuoq5uhmzcuLHM\nCJggn2GsYUbCMGZIFnGtW7e+qurrFVdcGSySJamU2/by3eEimpVy6Yu1EmcheRkPr7w6LXgtSmWR\nXah7lKab5DOt0sJWfaKezHXu+ILg3B4p7w43R9L1nBaJ1mR4g9MicbhplcAZkssVjW8wHHOYkTCM\nCpILValUknw+LPKqFOfzHkZtbb2o6uncYNH0xWvTg6dwTxj7c05yRsEvurcEC3OaBlOPG2NryiId\nKqieIJWejOcvkrIdZ4oS495IhbLgyfn68FS1+XlZ85OdEfPyHdp1bt269cfzT2yYojAjYThqpMk+\nrFy5SuLQkBecU3G+a665TgqFNkdWe2J5jlSGa3JSmSHUIpoN9ClRT6LJndsisXyGJ5HTjECnaJ9q\nX73suYRmibkHL9vRI8oHrJXYQ0gT6/ONhtI0mEIPxYe4Qs8mGaZKq6folN/+7Xcd7z+zYYrCjITh\nqJBGnubzLZLPJ7WR/GLpF+EHBW6QyrRU388hJ1rElnzi956FJ3xr3KLqiWKRWKqimifheQIfwvIL\n9XS32J8lGjoKj/sQlM+u8vpJYVFeJHE4yfec6BT1ePKiHEWYmnui+/yxxJi+5Wr821VLbTUYxhoj\nMRLHS5bDMA6RJvtQU3MitbWnUF5I1oGWtPxPtN3I9cC9aIHY91E5jtXAdKCEymT8Ci0u2+nG2Qb8\n0l3ztHvNAR8FdgM/d+cuQDOjlwKd7jUDvA8tpvsu8ARQQGUynnFj/dLN6SdokV59cPyf0AK6O4Ai\nKvFR494vcvOuQQv2rgRORwsEX0CL+SJUgqMOOODm8mv3u2wEPoMKBkxDJTbejEp0LAfWks3OMSkN\nw8TBcK3KeNgwT2JMkOZJQF4ymWJin2/pmXOvPkV1feAlLArCMV4h1ZPTncFr6Fn4rKd5EpPVXrTv\nFrf/o6Jx/u7AM6nWDCgjGsIKvZgkCR5KgreJZisl+1N77+KPJT2TKS/VPZ2tkhTts9RWw/ECFm4y\nHC3iiulF4sNGmUyj5POt0tCwyKm4FkRj+2kL6ZohFs8mF5bpFrhH0mP+DcF1fRLzFZ8VDR9tlUrC\nuCTpcuHzJa7HaJWhSWaR2Jg9mGJ0zhCtyE6S3fOcEQr3nSpxTUVJfOitqWmxpbYajivMSBiOGlox\nfZbEsfiSZLMnSTZblFzuJImlM7qlstmPl9TIiHoEZ0ksu1ESWOyOey7gBIm7sfkq5oWi7UI9cewl\nPTzX4AvXMqL8gC+E816L9xAuDQyBr43ISWUP60UStzD1Xk93ijEpSNzF7nA1EfVSV9cgmUzjoZqH\nainDBsOxhBkJwxGjWj5+nO66VjTE42sKekTDJmvd68dSFkcfZsmLegz1Ui7H0eCMR1hD0SjJ/gkx\n2e17NqTdw3exC4noHklrdqTGKvRGkov8HPedWiUm3xslbgDkjdIcd68mKS+mW+Wu75RcrkVWrlwl\npVLJah4M4w5mJAxHhKG6m23atEXq6vwiWO+2OaKifP6pv9EturdJ/PQe6hl1im+5Wbkge09E3CJe\nkHIPICcaZmp1xiPUgRL3+ZbE2A9KJb+R9BAWuP0+nOY1nWa5e344Zb6+A11eYi2oU91n3wu7RdQI\ntgicIPfee+9x/MsaDEPDjIThsBhKI6ivr0+y2WQtQ6vEoZxkTUFJlIMIVVu9SN71Uhna8fpF/v7d\nEnspvpI5557MlwQLetKTaJBybqAk6fxIh5vLGinnLHzHunDOucCQhPPNS+xphFXjXojwwbJ7bt++\n/Xj/iQ2GqjAjYTgsyhsD6VYoLJSVK1e5aum0/sw592SeXED9Auk9ijA049+nGRafYRTqOoVZR80S\nh6qStQwnukU7NHQ9EoecQpG9S0W9njMkDk8tcgv8GYnvU6nOqnPwISr//f08vUpsnNGVzS4w1VbD\nuIYZCcNh0deX3oktm210i3OycK1RlEhOXuOzhs5yRiQvqm6aF33KbxFYHhiPZlEP4EHRcNJaicnp\nHkknijsk9hR6Jc4+CgvhZkt5+KjGGQRfBe2NxmJRg9fr7p+8X6tb8JN6SyWJPZq0ecZV2Jbaahjv\nGImROOad6QzHBgMDA+zYsQOArq6uQ93Ldu3aRaEwnd27l6Pd0p4jm22npgbgDWh/5re79y8A+9CC\nsTvRYrCZaEHaTWiv6R+gxW0z0OK676LtQK5w76eh3d4OuPOuRwvdTkIL2nLAe915YcHeKUA/WnTX\nALyOdq2L0KK3O9Git5dIdnmDE9G+1t8N9r/NzeF14BzgRnfuXLTI7wvAxWg/7iXuvAjtTHclWpT3\nLio73rXR0PBWDh78uXWJM0xODNeqjIcN8ySGxKZNWyST8VlJ8ySbbT5ETsecRI/4TKBCoU1yOd99\nzT/p3y5K2tZLHP9f7zwIr83kn9Zvc17BPPdU7TOFkp7BQylP4nlR/iKZddQkscCeD+3USXmIKclN\niPMC0gT5OkXrGbyH0ipxWmyynsPLfVwscQe7nKiUeJKzKcjWrVvNgzBMCGDhJkOcwlq+SIehkLS+\nBddcc51bGGdKeXppwS2MPSkLvG8JWhStRi6IFr91ihLPYY3EPNFitJAP8eqw/j5ZUSXWVmcQ0rKj\nwqruhSmLdhz+qf6+RWJJ8EbRqusWN49WiRVbWwU+6YxRh8SNj5olru2YJt3d3cf5r24wHBnMSBik\nt7dXGhpOk2TFcEPDojJSNczhL5VKzvNodYtfUsm0QfTJPpmt5LN+fFpoVlRsLy9xlbOvkcg6IxHW\nSCS9jRbRjKeSaNFcWlprU7DY+0W7IHHb0KT30JGyv0vUY/ICfT6F9SSJaytEYgHCpGjhQxLXY9Sb\nkW4tBMoAABroSURBVDBMGIzESJjA3yRDR0cHBw68TLmY3k4OHvwpHR0dgPIV/f39dHR00N7ezo4d\nOxgcPIDG/7+HxvhXAwMo19CCiu79jHKBvpeAbwGfQOP3M4FX3PvInfO4e61BeYosygW8BTiB8vj+\nyUArygO8hHIi8XfQNuktwH9wY7wbFe77c+AXKMexIDj/BeA/unuG+38IfAQV6PuOm+O3gFdRjsV/\nPy9AGIoWTgP+ELgEeC+ZTA1dXV0pfwmDYZJguFZlPGyYJzEklJPw1cqdZZxEWoe5e++9VyolNtK6\nq10bfPYSF56D8NlGvm1ou1SGlc4KjlWTvvD8w3TRTKW8xJlLs92r5xZyEoeG8s7jaXFzb5E4VBZm\nOhVFCwNvTPFUOiXmVnJDeDJ9zhOx5kGGiQUs3GTwKJVK0t3dLd3d3dLX1ye9vb3S19eXKKTT+ob6\n+jMlvdo4WaDWLPBVt3jWSUx0z6+y4PdIdfG9HrfAe6NTEA33FN28vCRGk2hYqCe43hPvXk+pRTT0\ntcW994amzc1xlWiIqEHiKvK0uoiCwMVSV9cg99xzT0ofjXJOpKlpsdVFGCYUzEgYKhB6DrlcixQK\ncyTOVAr5gWsECpLJnCZxdk950V38hJ0XrZ3IuvdprUDPcAv9vJQn8oXu2GJ3/RvdGDlR4jqtY1zI\nFXRJXDdxlcQyHv67PCgxEZ6XWDZkfWJszzEsdvunC+QkinKyadOWQ79dY+PZkskUpa6uvJe21UUY\nJhrMSBjKUCnB0eMW4ttFs3oWSVxB7Cuaa905l6Y8afvez94wLBT1KE6qcm6fxNIbyQwkH7Jplpj0\nnu/GTRqnsLo76Un4sJAvzPOhqXY37oUSS3D0VjFmG4PxHhJoPtQ9LiT407LCDIaJBDMShjL09vZK\nc7PPclrjDMMcKc8KapTKkEreLbxZiWP0vrf1End95AyKDxFd785J6/Psq6R99XNOyuXB50rcFjTN\nOHnPJmxNWhANRRVFw0kl0RDSiRK3F/W8yHqJQ1JpIaaFbi4dzpAsloaG+amhJFN2NUxkjMRIWHbT\nJMLAwACPPfYYAwMDgGY67dvXD/w+mgE0F83g+R00s8dXPT/pRljk9v0VWlW9EhhEs4oA/gXNBPoO\nkEersl9119yHZhE9B+wHPgnMA84DBM1uOujGiYCPA0+hWUc/Bz7gznkKqHXXzUOzmPJujOfctT9H\nq673Af8A3IJmRP0S+De00rqAthTdiVZMPwT8nCg6SKGwnKamLjf2x9Fq6y+jLVZfB57jwIHSoWyw\nEO3t7ZxzzjlWWW2YOhiuVRkPG+ZJVKCa/Pfdd6+p8vTcE3xulvLag26B7e6J/GPOo0jyCmdIpc5T\nk7vufNHMpJPcOQ1Snv2UDbwa34kuOccwDJUTuE7iLKUkp+Dbp4a1EGeJVkz7osB6iaK8rFy56hCR\nv27deikU2iSf98T9NIF6yWQaLZRkmJTAwk1TE9Xkv7dv3y7nnfdWqUxvnefCKv6zj+kX3aJ8kZT3\neLhNKsNSRans9eDlMPy1LQIfFM1ASpLRvqp7scQd58KxQpG9rMTps9MT5y2QONMqabBKAg9JXV1B\nrrjiQ6lG1IeP+vr6DmWDWSjJMFlhRmKKopx70C2bXeAW2Gpx+J7gc6t7as9IXC3dI3FVcVHi5jsL\n3fUrUhZ+n3KaxifMDuZXrf/DWok9mkZnJHzdxfoqcw+VX73Ehzd2Wk9x991rqvbQMBimEkZiJIyT\nmASIuYe4Olk/fx34MaqYuhRY6F7PA34XVTtdDqxFK51rUWVV0ErlDwO/h/ISs1E+4EmUH9iGxu/f\nBMwCzkcVXjspr6I+DdgAlNw1AN9AK7nD804CVgEdqErrAbQK+vto1fMN7pqTUNXY+aiyaw3wNyh/\n8RDKjTwDnA78koaG2bS3n0A221F2v0xmNv39/RW/ZZLXMRimOsxITAK0t7ezYcN9FArLKRaXkM2+\nHZWvWObOuAGV3n7KfS651/PcvmmonEYBaHOvAtyL/hN5FF14H0XJ6U+4z59CDUfOz4SkHIiSzRei\ni/u7gFOBFVRKbryCSoJ8BzVWcyg3Ih2ocXkRJeFfAh5AjcNFbrzpwDvdOT8FTmH//hc499xzK4zo\n4OBzFcT05s1bmT37dC688MPMnn06mzdvTf/BDYaphOG6HuNhY5KGm442vdJf/9WvfjUlxNRUJey0\nUOJK5GSYqF60dkGC7UwXztmaEm7yoSVPFoeKqr5j3Ynufjl3fJGkC/NlU8Zudtc1SazM6r+TLw5c\nJKEYn5fNOFyNw1BtXQ2GyQKMk5i4qJadNBSSRqVUKsnKlaukUGiTKJrpFuyTJa4bSKq4Lha4V7TO\nIE3H6LMphsAXqp0ilYS472G9PVjMF4tyC8lFv9kZG18fkWZsfKGcl9goSFwTUZL6+vly2WUfdOd3\nOcPRIDBHstnmCl2loYxwGq9TLHaZ7IZhUsGMxATFSJ5iQ6OSz7fIBz5wsWsc5D2CLRIXtxUlLp4L\nF+NGt/iekXKsIOX9qOe5Rd/3s94u6ZpMaYV4DSlGqMsZlJzAWyTd+6h3177BGbnyxkS5XIv09fW5\n/hkPuvn2SC5XlL6+vjH/GxgMEw1mJCYoqj3Fdnd3pz75li9ooUie7xpXkrQOanF4Zp5U1ib4mgPf\njzpMee1xi/kCZzBmiWY+eXVXX2dws6isRUbijKNZEldcV8t6KrjvcJqUazR1iteU0jFvk1BC5O67\n15TVOxytXIbJbhgmO8xITFCkPcVmMk1Vw0/d3d2usVCfpIeDPiuVoSAvZTFHYm2mZJ3DGQIfkXJ5\n7blSKbNRcPf2arG1ol7GEok1lHyNxbUS10X48FdBKlNZ81JZnOdlQ2YIZJzY3kLJ5YqyYsUVFZLn\noyGXYbIbhskMMxITGJs2bZFsNtQ+Kn/y9qEP/7QbazAtSCz0/vqQiO5JeZJPkwL30tt5t+jnRInm\njsQ9Otxi3yJx+MkrsKaFoPoCI+XnNztl3r5TXIczNieJ5yUymeIhQ1ApeW6hIYPhSDASIzGmKbBR\nFG2IoujlKIp2Bvtaoyh6OIqiH0ZR1B1FUXNw7OYoip6OoujJKIreOZZzG2+44IJ3UFMToWmlX0Hr\nAMrz+nfs2MGKFVeze/eNqE7RHDTldBPaRc53Y3sA1U9ahtZCvAftGhemlM4E3o7WN5yF1k9c7a7f\nDGSAXlQL6ZfE6aOfBV5GU2VrUF2lk92Y/VSmrp4MfJG4y5tPpQ3rJnaineVywKXAa2i9xato+u4z\nDA7+M9de+wk6OjrYtWvXEdc9VIPVQxgMR4jhWpXhbMBbgcXAzmDfauAG9/5G4DPu/RnADlR5rgNd\nTaIq446JlT2eKOclSpIMIxUKbdLd3S1NTWcFx7ZI3JynXuKeDB+Q8v7TXgIjjWRe4MZok7jbXK+U\n98j23ENa9XaLxNlJ1TwJL6sReg6nSUxs+zBYQ8r4bRLzFJ2HZDOOxpMYSSaZwTAZwHgMN6GluqGR\neAqY5t5PB55y728CbgzO+yfgTVXGHP1f7zijcuFTIrmpafGhhaxUKrkMprNTDYlyCQ+5RbtOtE/C\nRrcgXxuEe1okXSK8yRmDnpSxG9yYZ0hlmKhW4jCRN0h+8T/fzSdpPIqiXe58rwcRzVBKaxnaK57s\n7u7uFpGRk8yWxWSYypgoRuKVxPFX3OvngEuD/Z8H3l9lzNH+7cYFkgtfkowtlUpy7bXXuUX3QSl/\n2heJ1VCzEtdHhEV0W90ifIuk1zjMdNcWJc6E6gzGfH9wb6+x5Ano9c5IeePwdlEuokfidFivrVQQ\n7T/RK5oZ5edQTdNpoUCrZDKNZYv5SEhmq4cwTGWMxEjUjWLkaqSQkVx05513Hnq/bNkyli1bNkrT\nOX645JKLueCCd9Df309HR0dZz4L773+A6667gZqaWSgXcKU7shONzXtpi+uBv0QlMnx/hFaUc5iO\nSlb8jRsjvPbnwNPu+JuA36D9Hz6Fymq8iEYPs8Ad7v77UUmPM1FpjG8H4y1F9aFK7rwaN4+foFpQ\nXwC6UW0pP48Xqa2NOHDgzajm04/dda9RVzfIF7/4+bLfpL29fdh9Hcp1rnSuaRIdBsNkwLZt29i2\nbdvRDTJcqzLcjUpP4knKw01PSnq46etMoXDTUFi3LuxHHYZr2tyTd1oP57B+Ii9wnjv3IrcvLLbz\nRWteGsMXtYUpsmlP+U1VvJqS80oiiaU0im6eRYGcZDKNUl8/X+rqGiSbbS7znkajOG4oWD2EYaqC\ncRpu6gD+Nfi82hsD0onrLJoiM6WI6yTCPgfKQyxKCQ956YsmKe/hHPIVobHwvR58iChcjD1J3CPK\nV3htJW8U0vgCX+zWIuWV3q0Sh52ykqYLtXXr1kOhomTY6Fgs4lYPYZiKGHdGAs3NfAHYi/bA/BM0\n5vAI8EPgYaAlOP9mZxyeBN45xLhj8wuOE4TZN7lci+Tzp0hloVmbaE+HbLAQex7hQdFsou6U61ol\n7vaW1HKaJ3GGVMa9eh7B8xvJqumS21ZJZX2G5xTmVBgXT0BXQ7iI24JuMIwOxp2RGKttMhuJtOyb\nmOj1lcxt7ol9vcSSF2udUbhe4gyjM6VSYXWxqDjf7ZKezvqQu2aFW/zXimY1nSixF+K9hLrE9Y2i\nJHPS22goOy+TKVq6qsFwHGBGYhIgLfsmnz9TcrmiZDLT3BP+RmcQmpzXEHIH3SmLf1HKs5HmOEPj\nlVXnBYbHL+zdwZi+WrpFyjvWhYJ+BclkZlbcO5ttlrq6OLMpkylauqrBcJwwEiMxHrKbDAHi7Jtt\nQAPwOnv2/JhLL/19vvzlr5LJdDI4+BG0qdB+4H+i0TmfrfME2uAnrHpuR7ONXkWzhb6LZistdcd+\nguYJLCOu2v6Nu34nNTW/5uDBQbRKe1kw25PQ5j+/Qy73Jb70pXvYseP7/PVfLyeTmc3g4HNs2HA/\nF1zwDnbs2AFAV1fXEWck9ff3k812sHt3ZWX1cLOaDAbDCDFcqzIeNiaBJzFUnP3yyz/knsjnu9f3\np3gHTe7JvtO9bxQlt73+UZI7uNd5E13OExDnQWyUWAF2gXudJbFqbL2sXLlK7rjjk1XCU33O25kj\nuVzLIenylStXjYrYnnkSBsPoAQs3TQz4OHtTU5fkci1lzXG0qtqTzSUXTmqUcpI5LR21TbRIriBx\nZlKXe20Iwk2tidBTnxvjdHdeTzBms+RyMX+wbt16yeVapLHxbClXhu2pMCCjtZhbuqrBMHowIzEB\nUI2Y9oait7dXmpq6JG72s0QqVWEflMqK6cUSZx/5GoiZzsB4I9MoSmp74vnSYA6+C1w4ZqesXLmq\nbP59fX2yceNGufvuNYcW71yuKIVCuTbTaFYxW3aTwTA6GImRME7iGKO/v5+6utmUcwancu21H2PO\nnNnMmjWLwcFngauA+/DVzlG0FJE3A3NxJSSUV0z/CDjRfV4EvAN4C1rZnAXei6q64sZcDDwEnAr8\nDK2CfiEx5gtlsf/Nm7eyYsXVZLPKm3zqU7fS3n4C8+bN48ILf4+xqmIeSWW1wWAYJQzXqoyHjQnu\nScThJP8Ur8J6DQ1nS6HQJm9601tcOGmJ+KyjpqbFksk0OC/CC+M1i/IQzRJFvqYhTfcorWnQXIG8\nvO1ty+SBBx6Q7u5uufvuNe7YInff1YfCRtU8oKams6RQaJNrrrnWwkIGwzgHFm6aGIhlNhaJkr9h\n7+aeivi+its1uVRSbwymS9zpLS+XX36lZDKNjnNY5M5ZI0pMrxGol/r6syoMRsgdaKjrLFFiu1QW\nNkpLzQ0VWguFNunr67OwkMEwjjESIzGmTYcM6bjqqitZt+6vyeX6KRTaiMNEoGmvsygPR7Vx8OB+\n9u//H2gK6/8B9qGprD8BvsvmzV/mc5+7h3xeKBR2oemxdwH3AndRVxfxmc9cSVPTPLSRj44dNuvp\n6Ohg//6foc1/2gnDRuXCeLjXn6JptTPIZGaza9cuzjnnHAsNGQyTCGYkjhOuuupKfvKTZ/jKV9ZS\nKLxCvPi+ji788WKcy/2C+vp5aHe3Wagh6SDZmW3JksU8//yP+MpX1pLJ5NFai8eBbdTU1HHBBRc4\nIxCPHXIH7e3tbNhwH4XCcorFJRQKy9mw4b5DnIA/1tTUhdZY7EWVVE5j9+6nTUnVYJiMGK7rMR42\nJni4KYlkmqeP7zc1LZZcruVQJpFKZnhxvur1A0P1TDiSlNKhsolKpZJ0d3dLJlOu45TNNluYyWAY\n58A4ifGNwy2+4TGtSShWEMN1de3OUKhURja7oGKxP1wR2tGmlFrjHoNhYsKMxDjGcITq4kW+R7xO\nkieGV65cJblcs+Tzp0ouV6xa2ZzmMYxWvYFVQhsMExNmJMYphruo9vb2SqEwV+JiujbJ5zuku7t7\nWOOERmG01VStEtpgmHgYiZGwYrpjgOEK1TU2NrJ794vAo/jitD17lvLaa68NaxxPOA8MDLBixdXs\n3t3jrt3JihXLueCCd4w4E2moVqsGg2HywLKbjgHS0keHqkjetWsXhcI8wuylQqGTlpaWYY3j4Y1U\nMhvKp76OFO3t7ZbyajBMcpiROAYYKrU0Dbrol6eqwgt0dXUNOc7AwACPPfYYAwMDFeONxLgYDAbD\ncecXRrIxwTgJj+EQx0PF/NPGORznYByCwWBgBJxEpNdNLERRJBNl3gMDAyOO2x/ptQMDA8yefTq7\nd/fgOYxCYTnPPfdU2XVHMxeDwTDxEUURIhIN5xoLN40hNm/eyuzZp3PhhR9m9uzT2bx567CuP9KY\n/5FyDsYhGAyG4cI8iTHCkT7dJ68ZyZP+SO5lMBimHsyTGEcYbkbR0XgdwyXGDQaD4UhhnsQYYThP\n96PlCRjnYDAYhoJ5EuMIw3m6H606BuMcDAbDaMM8iTFA+EQPHPbp3jgFg8FwLGCexDhAklt45JFv\nHvbp/v+1d2chdpRpGMf/j8at4xbRUQRxA7cbYzImGhUFg6jgipioF2pQcjEZM5kLR+cmDN64oCLR\nuRiVuKDivoFo3CK4LzFqHPFiQjRRk4mOtnHBifp6Ud/pnK4+deI5drrqM88Pmq5TXd399Mvpfvur\nOt9XvqZgZk3lkcQo+q0jAl9TMLNNqZ+RhBf4G0W9LuRX1lqQz8ysKXy6aRR5jSQz+71xkxhFvrZg\nZr83viaxCfjagpk1UT/XJNwkzMw2E34JrJmZjSo3CTMzq+QmYWZmlWqbJyFpBTAI/Aysj4gpkiYA\n9wJ7AyuAsyNisK6MZmabuzpHEj8Dx0XEYRExJe27DHgmIg4EngMury3dJrR48eK6I/wmzl+vnPPn\nnB3yz9+POpuEOnz/04Db0/btwOljmmiM5P5Ec/565Zw/5+yQf/5+1NkkAnha0huSLkr7do+INQAR\nsRr4Q23pzMys1rWbjoqIzyTtBiyS9CFF42jnyRBmZjVqxGQ6SfOBb4CLKK5TrJG0B/B8RBzc4fj6\nQ5uZZSiLVWAlDQBbRMQ3ksYDJwD/AB4DLgCuAs4HHu30+b3+kGZm1p9aRhKS9gUepjidNA64KyKu\nlLQLcB+wF/ARxUtgvxrzgGZmBjTkdJOZmTVTFjOuJa2Q9I6ktyW9nvZNkLRI0oeSnpK0U905q1Tk\nny9plaQl6e3EunN2ImknSfdL+kDS+5KmZlb7Tvlzqf0B6TmzJL0flHRJLvXvkj+L+gNImidpmaR3\nJd0laeuM6l/Ovk0/tc9iJCFpOTA5Ir5s23cV8EVEXC3pb8CEiListpBdVOSfD6yLiOvqS7Zxkm4D\nXoiIhZLGAeOBv5NP7W9jZP6/kEHt20naAlgFTAXmkEn9W0r5Z5FB/SXtCbwIHBQR/5d0L/AEcAgN\nr3+X7PvQY+2zGEmQ/8S7Tvlb+xtL0o7AMRGxECAifkzLpGRR+y75oeG172A68J+IWEkm9S9pzw/5\n1H9LYHz6B2M74BPyqX979gGK7NBj7XNpErlPvGvPf3Hb/jmSlkq6paFD1n2BzyUtTEPTf6VXpuVS\n+6r80Pzal80A7k7budS/3QzgnrbHja9/RHwKXAt8TPEHdjAiniGD+nfI/lXKDj3WPpcmcVRETAJO\nBv4k6RjymnhXzn808E9gv4iYCKwGmjj0HgdMAm5K+b+lWF8rl9qX839HkT+H2g+RtBVwKnB/2pVL\n/YGO+bOov6SdKUYNewN7UvxXfh4Z1L9D9u0lnUsftc+iSUTEZ+n9WuARYAqwRtLuACom3v23voTd\nlfI/DEyJiLVtt9e7GTi8rnxdrAJWRsSb6fGDFH90c6l9Of8DwGGZ1L7dScBbEfF5epxL/Vta+ddC\n8XuQSf2nA8sj4n8R8RPF7+408qh/OftDwLR+at/4JiFpQNL2abs18e49Nky8gy4T7+pWkX9ZenK1\nnAksqyNfN2lIvVLSAWnX8cD7ZFL7ivz/zqH2Jecw/FRNFvVvMyx/RvX/GDhC0raSRHr+kEf9O2X/\noJ/aN/7VTcp84l2X/HcAEymWTF8BzG6d52wSSYcCtwBbAcuBCykuiDW+9lCZfwEZ1B6GVif4iOIU\nwbq0L4vnPlTmz+K5D0OvQpwJrAfeplg6aAcyqH8p+xLgYuBWeqx945uEmZnVp/Gnm8zMrD5uEmZm\nVslNwszMKrlJmJlZJTcJMzOr5CZhZmaV3CTMSiStKz0+X9KCjXzOKZIu3cgxx0p6vOJjcyVt23ta\ns03LTcJspE6Th7pOKIqIxyPi6j6/NhTLlw9UfMysNm4SZj2QtKukByS9lt6OTPuHRhuS9pP0ioob\nTV1RGpnsoA03QbozHf9nikXYnpf07Jj/UGZdjKs7gFkDDUhakrYFTKBYrwfgBuC6iHhZ0l7AUxQ3\noYENo4QbgOsj4j5Jsxk+epiYjl8NvCRpWkQskDQPOK79xlRmTeAmYTbSd2lpcaAYJQCT08PpwMFp\n0TQolmAunyY6kmKZZijuAXFN28deb60KLGkpxZ3CXqZoRrnciMc2I24SZr0RMDUi1g/bqWF/36N0\nfLsf2rZ/wr+D1nC+JmE2Urf/6BcBc4cOLFaZLXsVOCttz/yV3/NrYMdfeazZmHGTMBup2yuZ5gJ/\nTBellwGzOxwzD/hrOp20PzDY4Zjy97kZeNIXrq1pvFS42SiTtF1EfJ+2ZwAzI+KMmmOZ9cXnQ81G\n32RJN1KctvoSmFVzHrO+eSRhZmaVfE3CzMwquUmYmVklNwkzM6vkJmFmZpXcJMzMrJKbhJmZVfoF\nRRGCYHwsivoAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f819e186be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind=\"scatter\",x=\"Height\",y=\"Weight\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lm = smf.ols(formula=\"Weight~Height\",data=df).fit() #notice the formula regresses Y on X (Y~X)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept -350.737192\n", "Height 7.717288\n", "dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.params #get the parameters from the model fit" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intercept, slope = lm.params #assign those values to variables" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f819bb43438>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sZ9Asqy+hBDARdQi4vS8WorkotwPvwRJVOv1XZLMzI9lgHscW27ZtY9u2bUd3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\nPv1E7rpra6mf3MNj7KCvpsdw2Bil7qajTa+0199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"text/plain": [ "<matplotlib.figure.Figure at 0x7f819bdf0cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind=\"scatter\",x=\"Height\",y=\"Weight\")\n", "plt.plot(df[\"Height\"],slope*df[\"Height\"]+intercept,\"-\",color=\"red\") #we create the best fit line from the values in the fit model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>Weight</td> <th> R-squared: </th> <td> 0.855</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.855</td> \n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>5.904e+04</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Tue, 26 Jul 2016</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>16:09:00</td> <th> Log-Likelihood: </th> <td> -39219.</td> \n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 10000</td> <th> AIC: </th> <td>7.844e+04</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 9998</td> <th> BIC: </th> <td>7.846e+04</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> -350.7372</td> <td> 2.111</td> <td> -166.109</td> <td> 0.000</td> <td> -354.876 -346.598</td>\n", "</tr>\n", "<tr>\n", " <th>Height</th> <td> 7.7173</td> <td> 0.032</td> <td> 242.975</td> <td> 0.000</td> <td> 7.655 7.780</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> 2.141</td> <th> Durbin-Watson: </th> <td> 1.677</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.343</td> <th> Jarque-Bera (JB): </th> <td> 2.150</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td> 0.036</td> <th> Prob(JB): </th> <td> 0.341</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 2.991</td> <th> Cond. No. </th> <td>1.15e+03</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Weight R-squared: 0.855\n", "Model: OLS Adj. R-squared: 0.855\n", "Method: Least Squares F-statistic: 5.904e+04\n", "Date: Tue, 26 Jul 2016 Prob (F-statistic): 0.00\n", "Time: 16:09:00 Log-Likelihood: -39219.\n", "No. Observations: 10000 AIC: 7.844e+04\n", "Df Residuals: 9998 BIC: 7.846e+04\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept -350.7372 2.111 -166.109 0.000 -354.876 -346.598\n", "Height 7.7173 0.032 242.975 0.000 7.655 7.780\n", "==============================================================================\n", "Omnibus: 2.141 Durbin-Watson: 1.677\n", "Prob(Omnibus): 0.343 Jarque-Bera (JB): 2.150\n", "Skew: 0.036 Prob(JB): 0.341\n", "Kurtosis: 2.991 Cond. No. 1.15e+03\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.15e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

ray-project/ray

doc/source/ray-contribute/docs.ipynb

1

20550

{ "cells": [ { "cell_type": "markdown", "id": "8c7dab40", "metadata": {}, "source": [ "(docs-contribute)=\n", "\n", "# Contributing to the Ray Documentation\n", "\n", "There are many ways to contribute to the Ray documentation, and we're always looking for new contributors.\n", "Even if you just want to fix a typo or expand on a section, please feel free to do so!\n", "\n", "This document walks you through everything you need to do to get started.\n", "\n", "## Building the Ray documentation\n", "\n", "If you want to contribute to the Ray documentation, you'll need a way to build it.\n", "You don't have to build Ray itself, which is a bit more involved.\n", "\n", "You just have to [clone the Ray repository](https://docs.ray.io/en/master/ray-contribute/development.html#clone-the-repository), then [prepare the Python environment](https://docs.ray.io/en/master/ray-contribute/development.html#prepare-the-python-environment).\n", "\n", "Next, change into the `ray/doc` directory:\n", "\n", "```shell\n", "cd ray/doc\n", "```\n", "\n", "**Note**: If you are on an Apple Silicon (M1) read the instructions below for installing the dependencies.\n", "\n", "Make sure you activate the Python environment you are using (e.g. venv, conda, etc.) and then to install the documentation dependencies, run the following command:\n", "\n", "```shell\n", "pip install -r requirements-doc.txt\n", "```\n", "\n", "Additionally, it's best if you install the dependencies for our linters with\n", "\n", "```shell\n", "pip install -r ../python/requirements_linters.txt\n", "```\n", "\n", "so that you can make sure your changes comply with our style guide.\n", "Building the documentation is done by running the following command:\n", "\n", "```shell\n", "make html\n", "```\n", "\n", "which will build the documentation into the `_build` directory.\n", "After the build finishes, you can simply open the `_build/html/index.html` file in your browser.\n", "It's considered good practice to check the output of your build to make sure everything is working as expected.\n", "\n", "Before committing any changes, make sure you run the \n", "[linter](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)\n", "with `../scripts/format.sh` from the `doc` folder,\n", "to make sure your changes are formatted correctly.\n", "\n", "## Building Docs for Apple Silicon (M1)\n", "\n", "If you are using an Apple Silicon (M1) some of the dependencies required for building the docs don't have binary packages available by default (not available in PyPI).\n", "\n", "The simplest way to install those dependencies is with `conda` (https://docs.conda.io/en/latest/miniconda.html) first, that way `pip` won't try to install them by building them from scratch.\n", "\n", "To do that, make sure you create and/or activate the conda environment, and then install the dependencies with:\n", "\n", "\n", "```shell\n", "conda install -c conda-forge xgboost lightgbm\n", "```\n", "\n", "After that you can install the other dependencies as described above.\n", "\n", "## The basics of our build system\n", "\n", "The Ray documentation is built using the [`sphinx`](https://www.sphinx-doc.org/) build system.\n", "We're using the [Sphinx Book Theme](https://github.com/executablebooks/sphinx-book-theme) from the\n", "[executable books project](https://github.com/executablebooks).\n", "\n", "That means that you can write Ray documentation in either Sphinx's native \n", "[reStructuredText (rST)](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html) or in\n", "[Markedly Structured Text (MyST)](https://myst-parser.readthedocs.io/en/latest/).\n", "The two formats can be converted to each other, so the choice is up to you.\n", "Having said that, it's important to know that MyST is\n", "[common markdown compliant](https://myst-parser.readthedocs.io/en/latest/syntax/reference.html#commonmark-block-tokens).\n", "If you intend to add a new document, we recommend starting from an `.md` file.\n", "\n", "The Ray documentation also fully supports executable formats like [Jupyter Notebooks](https://jupyter.org/).\n", "Many of our examples are notebooks with [MyST markdown cells](https://myst-nb.readthedocs.io/en/latest/index.html).\n", "In fact, this very document you're reading _is_ a notebook.\n", "You can check this for yourself by either downloading the `.ipynb` file,\n", "or directly launching this notebook into either Binder or Google Colab in the top navigation bar.\n", "\n", "## What to contribute?\n", "\n", "If you take Ray Tune as an example, you can see that our documentation is made up of several types of documentation,\n", "all of which you can contribute to:\n", "\n", "- [a project landing page](https://docs.ray.io/en/latest/tune/index.html),\n", "- [a getting started guide](https://docs.ray.io/en/latest/tune/getting-started.html),\n", "- [a key concepts page](https://docs.ray.io/en/latest/tune/key-concepts.html),\n", "- [user guides for key features](https://docs.ray.io/en/latest/tune/tutorials/overview.html),\n", "- [practical examples](https://docs.ray.io/en/latest/tune/examples/index.html),\n", "- [a detailed FAQ](https://docs.ray.io/en/latest/tune/faq.html),\n", "- [and API references](https://docs.ray.io/en/latest/tune/api_docs/overview.html).\n", "\n", "This structure is reflected in the\n", "[Ray documentation source code](https://github.com/ray-project/ray/tree/master/doc/source/tune) as well, so you\n", "should have no problem finding what you're looking for.\n", "All other Ray projects share a similar structure, but depending on the project there might be minor differences.\n", "\n", "Each type of documentation listed above has its own purpose, but at the end our documentation\n", "comes down to _two types_ of documents:\n", "\n", "- Markup documents, written in MyST or rST. If you don't have a lot of (executable) code to contribute or\n", " use more complex features such as\n", " [tabbed content blocks](https://docs.ray.io/en/latest/ray-core/walkthrough.html#starting-ray), this is the right\n", " choice. Most of the documents in Ray Tune are written in this way, for instance the\n", " [key concepts](https://github.com/ray-project/ray/blob/master/doc/source/tune/key-concepts.rst) or\n", " [API documentation](https://github.com/ray-project/ray/blob/master/doc/source/tune/api_docs/overview.rst).\n", "- Notebooks, written in `.ipynb` format. All Tune examples are written as notebooks. These notebooks render in\n", " the browser like `.md` or `.rst` files, but have the added benefit of adding launch buttons to the top of the\n", " document, so that users can run the code themselves in either Binder or Google Colab. A good first example to look\n", " at is [this Tune example](https://github.com/ray-project/ray/blob/master/doc/source/tune/examples/tune-serve-integration-mnist.ipynb).\n", "\n", "## Fixing typos and improving explanations\n", "\n", "If you spot a typo in any document, or think that an explanation is not clear enough, please consider\n", "opening a pull request.\n", "In this scenario, just run the linter as described above and submit your pull request.\n", "\n", "## Adding API references\n", "\n", "We use [Sphinx's autodoc extension](https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html) to generate\n", "our API documentation from our source code.\n", "In case we're missing a reference to a function or class, please consider adding it to the respective document in question.\n", "\n", "For example, here's how you can add a function or class reference using `autofunction` and `autoclass`:\n", "\n", "```markdown\n", ".. autofunction:: ray.tune.integration.docker.DockerSyncer\n", "\n", ".. autoclass:: ray.tune.integration.keras.TuneReportCallback\n", "```\n", "\n", "The above snippet was taken from the\n", "[Tune API documentation](https://github.com/ray-project/ray/blob/master/doc/source/tune/api_docs/integration.rst),\n", "which you can look at for reference.\n", "\n", "If you want to change the content of the API documentation, you will have to edit the respective function or class\n", "signatures directly in the source code.\n", "For example, in the above `autofunction` call, to change the API reference for `ray.tune.integration.docker.DockerSyncer`,\n", "you would have to [change the following source file](https://github.com/ray-project/ray/blob/7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065/python/ray/tune/integration/docker.py#L15-L38).\n", "\n", "## Adding code to an `.rST` or `.md` file\n", "\n", "Modifying text in an existing documentation file is easy, but you need to be careful when it comes to adding code.\n", "The reason is that we want to ensure every code snippet on our documentation is tested.\n", "This requires us to have a process for including and testing code snippets in documents.\n", "\n", "In an `.rST` or `.md` file, you can add code snippets using `literalinclude` from the Sphinx system.\n", "For instance, here's an example from the Tune's \"Key Concepts\" documentation: \n", "\n", "```markdown\n", ".. literalinclude:: doc_code/key_concepts.py\n", " :language: python\n", " :start-after: __function_api_start__\n", " :end-before: __function_api_end__\n", "```\n", "\n", "Note that in the whole file there's not a single literal code block, code _has to be_ imported using the `literalinclude` directive.\n", "The code that gets added to the document by `literalinclude`, including `start-after` and `end-before` tags,\n", "reads as follows:" ] }, { "cell_type": "code", "execution_count": null, "id": "ba88d95f", "metadata": { "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "# __function_api_start__\n", "from ray import tune\n", "\n", "\n", "def objective(x, a, b): # Define an objective function.\n", " return a * (x ** 0.5) + b\n", "\n", "\n", "def trainable(config): # Pass a \"config\" dictionary into your trainable.\n", "\n", " for x in range(20): # \"Train\" for 20 iterations and compute intermediate scores.\n", " score = objective(x, config[\"a\"], config[\"b\"])\n", "\n", " tune.report(score=score) # Send the score to Tune.\n", "\n", "\n", "# __function_api_end__" ] }, { "cell_type": "markdown", "id": "6971eeb0", "metadata": {}, "source": [ "This code is imported by `literalinclude` from a file called `doc_code/key_concepts.py`.\n", "Every Python file in the `doc_code` directory will automatically get tested by our CI system,\n", "but make sure to run scripts that you change (or new scripts) locally first.\n", "You do not need to run the testing framework locally.\n", "\n", "In rare situations, when you're adding _obvious_ pseudo-code to demonstrate a concept, it is ok to add it\n", "literally into your `.rST` or `.md` file, e.g. using a `.. code-cell:: python` directive.\n", "But if your code is supposed to run, it needs to be tested.\n", "\n", "## Creating a new document from scratch\n", "\n", "Sometimes you might want to add a completely new document to the Ray documentation, like adding a new\n", "user guide or a new example.\n", "\n", "For this to work, you need to make sure to add the new document explicitly to the \n", "[`_toc.yml` file](https://github.com/ray-project/ray/blob/master/doc/source/_toc.yml) that determines\n", "the structure of the Ray documentation.\n", "\n", "Depending on the type of document you're adding, you might also have to make changes to an existing overview\n", "page that curates the list of documents in question.\n", "For instance, for Ray Tune each user guide is added to the\n", "[user guide overview page](https://docs.ray.io/en/latest/tune/tutorials/overview.html) as a panel, and the same\n", "goes for [all Tune examples](https://docs.ray.io/en/latest/tune/examples/index.html).\n", "Always check the structure of the Ray sub-project whose documentation you're working on to see how to integrate\n", "it within the existing structure.\n", "In some cases you may be required to choose an image for the panel. Images are located in\n", "`doc/source/images`. \n", "\n", "## Creating a notebook example\n", "\n", "To add a new executable example to the Ray documentation, you can start from our\n", "[MyST notebook template](https://github.com/ray-project/ray/tree/master/doc/source/_templates/template.md) or\n", "[Jupyter notebook template](https://github.com/ray-project/ray/tree/master/doc/source/_templates/template.ipynb).\n", "You could also simply download the document you're reading right now (click on the respective download button at the\n", "top of this page to get the `.ipynb` file) and start modifying it.\n", "All the example notebooks in Ray Tune get automatically tested by our CI system, provided you place them in the\n", "[`examples` folder](https://github.com/ray-project/ray/tree/master/doc/source/tune/examples).\n", "If you have questions about how to test your notebook when contributing to other Ray sub-projects, please make\n", "sure to ask a question in [the Ray community Slack](https://forms.gle/9TSdDYUgxYs8SA9e8) or directly on GitHub,\n", "when opening your pull request.\n", "\n", "To work off of an existing example, you could also have a look at the\n", "[Ray Tune Hyperopt example (`.ipynb`)](https://github.com/ray-project/ray/blob/master/doc/source/tune/examples/hyperopt_example.ipynb)\n", "or the [Ray Serve guide for RLlib (`.md`)](https://github.com/ray-project/ray/blob/master/doc/source/serve/tutorials/rllib.md).\n", "We recommend that you start with an `.md` file and convert your file to an `.ipynb` notebook at the end of the process.\n", "We'll walk you through this process below.\n", "\n", "What makes these notebooks different from other documents is that they combine code and text in one document,\n", "and can be launched in the browser.\n", "We also make sure they are tested by our CI system, before we add them to our documentation.\n", "To make this work, notebooks need to define a _kernel specification_ to tell a notebook server how to interpret\n", "and run the code.\n", "For instance, here's the kernel specification of a Python notebook:\n", "\n", "```markdown\n", "---\n", "jupytext:\n", " text_representation:\n", " extension: .md\n", " format_name: myst\n", "kernelspec:\n", " display_name: Python 3\n", " language: python\n", " name: python3\n", "---\n", "```\n", "\n", "If you write a notebook in `.md` format, you need this YAML front matter at the top of the file.\n", "To add code to your notebook, you can use the `code-cell` directive.\n", "Here's an example:\n", "\n", "````markdown\n", "```{code-cell} python3\n", ":tags: [hide-cell]\n", "\n", "import ray\n", "import ray.rllib.agents.ppo as ppo\n", "from ray import serve\n", "\n", "def train_ppo_model():\n", " trainer = ppo.PPOTrainer(\n", " config={\"framework\": \"torch\", \"num_workers\": 0},\n", " env=\"CartPole-v0\",\n", " )\n", " # Train for one iteration\n", " trainer.train()\n", " trainer.save(\"/tmp/rllib_checkpoint\")\n", " return \"/tmp/rllib_checkpoint/checkpoint_000001/checkpoint-1\"\n", "\n", "\n", "checkpoint_path = train_ppo_model()\n", "```\n", "````\n", "\n", "Putting this markdown block into your document will render as follows in the browser:" ] }, { "cell_type": "code", "execution_count": null, "id": "78cac353", "metadata": { "tags": [ "hide-cell" ], "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "import ray\n", "import ray.rllib.agents.ppo as ppo\n", "from ray import serve\n", "\n", "def train_ppo_model():\n", " trainer = ppo.PPOTrainer(\n", " config={\"framework\": \"torch\", \"num_workers\": 0},\n", " env=\"CartPole-v0\",\n", " )\n", " # Train for one iteration\n", " trainer.train()\n", " trainer.save(\"/tmp/rllib_checkpoint\")\n", " return \"/tmp/rllib_checkpoint/checkpoint_000001/checkpoint-1\"\n", "\n", "\n", "checkpoint_path = train_ppo_model()" ] }, { "cell_type": "markdown", "id": "d716d0bd", "metadata": {}, "source": [ "As you can see, the code block is hidden, but you can expand it by click on the \"+\" button.\n", "\n", "### Tags for your notebook\n", "\n", "What makes this work is the `:tags: [hide-cell]` directive in the `code-cell`.\n", "The reason we suggest starting with `.md` files is that it's much easier to add tags to them, as you've just seen.\n", "You can also add tags to `.ipynb` files, but you'll need to start a notebook server for that first, which may\n", "not want to do to contribute a piece of documentation.\n", "\n", "Apart from `hide-cell`, you also have `hide-input` and `hide-output` tags that hide the input and output of a cell.\n", "Also, if you need code that gets executed in the notebook, but you don't want to show it in the documentation,\n", "you can use the `remove-cell`, `remove-input`, and `remove-output` tags in the same way.\n", "\n", "### Testing notebooks\n", "\n", "Removing cells can be particularly interesting for compute-intensive notebooks.\n", "We want you to contribute notebooks that use _realistic_ values, not just toy examples.\n", "At the same time we want our notebooks to be tested by our CI system, and running them should not take too long.\n", "What you can do to address this is to have notebook cells with the parameters you want the users to see first:\n", "\n", "````markdown\n", "```{code-cell} python3\n", "num_workers = 8\n", "num_gpus = 2\n", "```\n", "````\n", "\n", "which will render as follows in the browser:" ] }, { "cell_type": "code", "execution_count": null, "id": "8412103e", "metadata": { "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "num_workers = 8\n", "num_gpus = 2" ] }, { "cell_type": "markdown", "id": "1d8cc54e", "metadata": {}, "source": [ "But then in your notebook you follow that up with a _removed_ cell that won't get rendered, but has much smaller\n", "values and make the notebook run faster:\n", "\n", "````markdown\n", "```{code-cell} python3\n", ":tags: [remove-cell]\n", "num_workers = 0\n", "num_gpus = 0\n", "```\n", "````\n", "\n", "### Converting markdown notebooks to ipynb\n", "\n", "Once you're finished writing your example, you can convert it to an `.ipynb` notebook using `jupytext`:\n", "\n", "```shell\n", "jupytext your-example.md --to ipynb\n", "```\n", "\n", "In the same way, you can convert `.ipynb` notebooks to `.md` notebooks with `--to myst`.\n", "And if you want to convert your notebook to a Python file, e.g. to test if your whole script runs without errors,\n", "you can use `--to py` instead.\n", "\n", "## Where to go from here?\n", "\n", "There are many other ways to contribute to Ray other than documentation.\n", "See {ref}`our contributor guide <getting-involved>` for more information." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 5 }

apache-2.0

phoebe-project/phoebe2-docs

2.2/tutorials/requiv_crit_detached.ipynb

1

6075

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Critical Radii: Detached Systems\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install -I \"phoebe>=2.2,<2.3\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As always, let's do imports and initialize a logger and a new Bundle. See [Building a System](building_a_system.ipynb) for more details." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import phoebe\n", "from phoebe import u # units\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "logger = phoebe.logger()\n", "\n", "b = phoebe.default_binary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detached Systems\n", "-----------------------------\n", "\n", "Detached systems are the default case for default_binary. The requiv_max parameter is constrained to show the maximum value for requiv before the system will begin overflowing at periastron. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Parameter: requiv_max=2.013275176537638 solRad | keys: description, value, quantity, default_unit, limits, visible_if, copy_for, advanced>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b['requiv_max@component@primary']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ConstraintParameter: {requiv_max@primary@component} = requiv_L1({q@binary@component}, {syncpar@primary@component}, {ecc@binary@component}, {sma@binary@component}, {incl@primary@component}, {long_an@primary@component}, {incl@binary@component}, {long_an@binary@component}, 1) (solar units) => 2.013275176537638 solRad>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b['requiv_max@constraint@primary']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the default system is well within this critical value by printing all radii and critical radii." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ParameterSet: 4 parameters\n", " requiv@primary@component: 1.0 solRad\n", "* requiv_max@primary@component: 2.013275176537638 solRad\n", " requiv@secondary@component: 1.0 solRad\n", "* requiv_max@secondary@component: 2.013275176537638 solRad\n" ] } ], "source": [ "print(b.filter(qualifier='requiv*', context='component'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we increase 'requiv' past the critical point, we'll receive a warning from the logger and would get an error if attempting to call [b.run_compute()](../api/phoebe.frontend.bundle.Bundle.run_compute.md)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Wed, 11 Dec 2019 13:08 BUNDLE WARNING primary is overflowing at periastron (requiv=2.2, requiv_max=2.013275176537638). Use contact model if overflowing is desired. If not addressed, this warning will continue to be raised and will throw an error at run_compute.\n", "Wed, 11 Dec 2019 13:08 BUNDLE WARNING primary is overflowing at periastron (requiv=2.2, requiv_max=2.013275176537638). Use contact model if overflowing is desired. If not addressed, this warning will continue to be raised and will throw an error at run_compute.\n" ] } ], "source": [ "b['requiv@primary'] = 2.2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Run Checks Report: FAIL\n", "ERROR: primary is overflowing at periastron (requiv=2.2, requiv_max=2.013275176537638). Use contact model if overflowing is desired. (3 affected parameters)\n" ] } ], "source": [ "print(b.run_checks())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the value of requiv was exactly the critical value, we'd have a semi-detached system. Alternatively, we could [use a constraint to enforce that a system remains semi-detached](./requiv_crit_semidetached.ipynb).\n", "\n", "If the value of requiv is over the critical value, the system is overflowing and will raise an error. If we intentionally want a contact system, we can [explicitly create a contact system](./requiv_crit_contact.ipynb)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

hide-tono/python-training

python-system-trade/sandbox/point_and_figure.ipynb

1

70519

{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "\n", "import matplotlib.finance as mpf\n", "import matplotlib.pyplot as plt\n", "import pandas\n", "from matplotlib.dates import date2num\n", "\n", "df = pandas.read_csv('DAT_MT_EURUSD_M1_201710.csv',\n", " names=['date', 'time', 'o', 'h', 'l', 'c', 'v'],\n", " parse_dates={'datetime': ['date', 'time']},\n", " ) # type DataFrame\n", "df.index = df['datetime']\n", "df['d'] = df.index.map(date2num)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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X5A+ARxCyfHtTuuBfbnk6pvJEm3hev71799Lb64ud/s2GbD4GNOoH2LJ9O8dq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\nIHMnHHMnxti/NMTIfj6obrcSHLlhhoOFECKF0qn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"text/plain": [ "<matplotlib.figure.Figure at 0x1caec228e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# df = df[-240:]\n", "plt.grid()\n", "plt.xticks(df['d'][::10], df.index[::10], rotation=45, size='small')\n", "ax = plt.subplot()\n", "# y軸のオフセット表示を無効にする。\n", "ax.get_yaxis().get_major_formatter().set_useOffset(False)\n", "data = df[['d', 'o', 'h', 'l', 'c']].values\n", "# print(data)\n", "# ローソク足は1日分の太さが1である。1日分の分足で割ってさらにその1/3の太さにする\n", "wdth = 1.0 / (60 * 24) / 3\n", "mpf.candlestick_ohlc(ax, data, width=wdth, colorup='g', colordown='r')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, -5, 4, -2, 2, -2, 2, -3, 6, -2, 3, -2, 4, -3, 3, -4, 2, -3, 2, -3, 3, -2, 11, -2, 4, -2, 4, -2, 3, -2, 4, -2, 7, -4, 2, -3, 6, -4, 2, -4, 3, -2, 2, -2, 6, -2, 7, -2, 3, -3, 2, -2, 2, -3, 2, -2]\n" ] } ], "source": [ "import numpy as np\n", "from math import fabs\n", "\n", "p_and_f = []\n", "\n", "points = 0.0001\n", "pips = 10\n", "pivot = 2\n", "\n", "buffer = 0\n", "for idx, data in df.iterrows():\n", " diff = ((data['c'] - data['o']) / (pips * points))\n", " buffer += diff\n", "# print(buffer)\n", " if fabs(buffer) > 1:\n", " if len(p_and_f) == 0:\n", " p_and_f.append(int(buffer))\n", " else:\n", " if buffer > 0 and p_and_f[-1] > 0:\n", " p_and_f[len(p_and_f) - 1] += int(buffer)\n", " buffer = buffer % 1\n", " elif buffer < 0 and p_and_f[-1] < 0:\n", " p_and_f[len(p_and_f) - 1] += int(buffer)\n", " buffer = buffer % 1\n", " elif fabs(buffer) >= pivot:\n", " p_and_f.append(int(buffer))\n", " buffer = buffer % 1\n", "# print(buffer)\n", "print(p_and_f)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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+s+Ribi+aIiILgAzsC4JBggkMAs9ajrx2Su86Mc/o0d1TxvGjqjr/ZRYBBJZk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\naa07lVJTgD8B6cBu4BatdciVkunDE/jRdZ9DKXV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"text/plain": [ "<matplotlib.figure.Figure at 0x1cae8f22278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# https://stackoverflow.com/questions/8750648/point-and-figure-chart-with-matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "BOX = 0.001\n", "START = 1.18\n", "changes = p_and_f[0:40]\n", "\n", "# one way to force dimensions is to set the figure size:\n", "fig = plt.figure(figsize=(5, 10))\n", "\n", "# another way is to control the axes dimensions\n", "# for axes to have specific dimensions:\n", "# [ x0, y0, w, h] in figure units, from 0 to 1\n", "#ax = fig.add_axes([.15, .15, .7*.5, .7])\n", "ax = fig.add_axes([.15, .15, .7, .7])\n", "\n", "def sign(val):\n", " return val / abs(val)\n", "\n", "pointChanges = []\n", "for chg in changes:\n", " pointChanges += [sign(chg)] * abs(chg)\n", "\n", "symbol = {-1:'o',\n", " 1:'x'}\n", "\n", "chgStart = START\n", "for ichg, chg in enumerate(changes):\n", " x = [ichg+1] * abs(chg)\n", " y = [chgStart + i * BOX * sign(chg) for i in range(abs(chg))] \n", " chgStart += BOX * sign(chg) * (abs(chg)-2)\n", " ax.scatter(x, y,\n", " marker=symbol[sign(chg)],\n", " s=175) #<----- control size of scatter symbol\n", "\n", "ax.set_xlim(0, len(changes)+1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

jesserobertson/pynoddy

docs/notebooks/Sensitivity-Analysis.ipynb

1

255933

{ "metadata": { "name": "", "signature": "sha256:bfe0404ce8becc0369bb874843c773476bb9b85480c3ba4ef61b1c3a5bf5d5e3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensitivity Analysis\n", "\n", "Test here: (local) sensitivity analysis of kinematic parameters with respect to a defined objective function. Aim: test how sensitivity the resulting model is to uncertainties in kinematic parameters to:\n", "\n", "1. Evaluate which the most important parameters are, and to\n", "2. Determine which parameters could, in principle, be inverted with suitable information.\n", "\n", "## Theory: local sensitivity analysis\n", "\n", "Basic considerations:\n", "\n", "- parameter vector $\\vec{p}$\n", "- residual vector $\\vec{r}$\n", "- calculated values at observation points $\\vec{z}$\n", "- Jacobian matrix $J_{ij} = \\frac{\\partial \\vec{z}}{\\partial \\vec{p}}$\n", "\n", "Numerical estimation of Jacobian matrix with central difference scheme (see Finsterle):\n", "\n", "$$J_{ij} = \\frac{\\partial z_i}{\\partial p_j} \\approx \\frac{z_i(\\vec{p}; p_j + \\delta p_j) - z_i(\\vec{p};p_j - \\delta p_j)}{2 \\delta p_j}$$\n", "\n", "where $\\delta p_j$ is a small perturbation of parameter $j$, often as a fraction of the value.\n", "\n", "\n", "## Defining the responses $\\vec{z}$\n", "\n", "A meaningful sensitivity analysis obviously depends on the definition of a suitable response vector $\\vec{z}$. Ideally, these responses are related to actual observations. In our case, we first want to determine how sensitive a kinematic structural geological model is with respect to uncertainties in the kinematic parameters. We therefore need calculatable measures that describe variations of the model.\n", "\n", "As a first-order assumption, we will use a notation of a stratigraphic distance for discrete subsections of the model, for example in single voxets for the calculated model. We define distance $d$ of a subset $\\omega$ as the (discrete) difference between the (discrete) stratigraphic value of an ideal model, $\\hat{s}$, to the value of a model realisation $s_i$:\n", "\n", "$$d(\\omega) = \\hat{s} - s_i$$\n", "\n", "In the first example, we will consider only one response: the overall sum of stratigraphic distances for a model realisation $r$ of all subsets (= voxets, in the practical sense), scaled by the number of subsets (for a subsequent comparison of model discretisations):\n", "\n", "$$D_r = \\frac{1}{n} \\sum_{i=1}^n d(\\omega_i)$$\n", "\n", "\n", "Note: mistake before: not considering distances at single nodes but only the sum - this lead to \"zero-difference\" for simple translation! Now: consider more realistic objective function, squared distance:\n", "\n", "$$r = \\sqrt{\\sum_i (z_{i calc} - z_{i ref})^2}$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting up the base model\n", "\n", "For a first test: use simple two-fault model from paper" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sys, os\n", "import matplotlib.pyplot as plt\n", "# adjust some settings for matplotlib\n", "from matplotlib import rcParams\n", "# print rcParams\n", "rcParams['font.size'] = 15\n", "# determine path of repository to set paths corretly below\n", "os.chdir(r'/Users/flow/git/pynoddy/docs/notebooks/')\n", "repo_path = os.path.realpath('../..')\n", "import pynoddy.history\n", "import pynoddy.events" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(pynoddy.history)\n", "reload(pynoddy.events)\n", "nm = pynoddy.history.NoddyHistory()\n", "# add stratigraphy\n", "strati_options = {'num_layers' : 8,\n", " 'layer_names' : ['layer 1', 'layer 2', 'layer 3', 'layer 4', 'layer 5', 'layer 6', 'layer 7', 'layer 8'],\n", " 'layer_thickness' : [1500, 500, 500, 500, 500, 500, 500, 500]}\n", "nm.add_event('stratigraphy', strati_options )\n", "\n", "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_W',\n", " 'pos' : (4000, 3500, 5000),\n", " 'dip_dir' : 90,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)\n", "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_E',\n", " 'pos' : (6000, 3500, 5000),\n", " 'dip_dir' : 270,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)\n", "history = \"two_faults_sensi.his\"\n", "nm.write_history_tmp(history)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "output_name = \"two_faults_sensi_out\"\n", "# Compute the model\n", "pynoddy.compute_model(history, output_name) \n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot output\n", "reload(pynoddy.output)\n", "nout = pynoddy.output.NoddyOutput(output_name)\n", "nout.plot_section('y', layer_labels = strati_options['layer_names'][::-1], \n", " colorbar = True, title=\"\",\n", " savefig = False)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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i7WkUJwE7Aie3218AjgS+9JyOVQ+1AXxNVd3ddeypJHu351tEs5zeD4HrgUvXq9CqS5Mc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\ny4H7gP8BXD/eOR2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"text": [ "<matplotlib.figure.Figure at 0x10a74f990>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define parameter uncertainties\n", "\n", "We will start with a sensitivity analysis for the parameters of the fault events. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "H1 = pynoddy.history.NoddyHistory(history)\n", "# get the original dip of the fault\n", "dip_ori = H1.events[3].properties['Dip']\n", "# dip_ori1 = H1.events[2].properties['Dip']\n", "# add 10 degrees to dip\n", "add_dip = -20\n", "dip_new = dip_ori + add_dip\n", "# dip_new1 = dip_ori1 + add_dip\n", "\n", "# and assign back to properties dictionary:\n", "H1.events[3].properties['Dip'] = dip_new\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " STRATIGRAPHY\n", " FAULT\n", " FAULT\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(pynoddy.output)\n", "new_history = \"sensi_test_dip_changed.his\"\n", "new_output = \"sensi_test_dip_changed_out\"\n", "H1.write_history(new_history)\n", "pynoddy.compute_model(new_history, new_output)\n", "# load output from both models\n", "NO1 = pynoddy.output.NoddyOutput(output_name)\n", "NO2 = pynoddy.output.NoddyOutput(new_output)\n", "\n", "# create basic figure layout\n", "fig = plt.figure(figsize = (15,5))\n", "ax1 = fig.add_subplot(121)\n", "ax2 = fig.add_subplot(122)\n", "NO1.plot_section('y', position=0, ax = ax1, colorbar=False, title=\"Dip = %.0f\" % dip_ori)\n", "NO2.plot_section('y', position=0, ax = ax2, colorbar=False, title=\"Dip = %.0f\" % dip_new)\n", "\n", "plt.show()\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10c5e2850>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate total stratigraphic distance\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# def determine_strati_diff(NO1, NO2):\n", "# \"\"\"calculate total stratigraphic distance between two models\"\"\"\n", "# return np.sum(NO1.block - NO2.block) / float(len(NO1.block))\n", "\n", "def determine_strati_diff(NO1, NO2):\n", " \"\"\"calculate total stratigraphic distance between two models\"\"\"\n", " return np.sqrt(np.sum((NO1.block - NO2.block)**2)) / float(len(NO1.block))\n", "\n", "\n", "\n", "diff = determine_strati_diff(NO1, NO2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 154 }, { "cell_type": "code", "collapsed": false, "input": [ "diff" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 155, "text": [ "5.6516369310138801" ] } ], "prompt_number": 155 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function to modify parameters\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set parameter changes in dictionary\n", "\n", "changes_fault_1 = {'Dip' : -20}\n", "changes_fault_2 = {'Dip' : -20}\n", "param_changes = {2 : changes_fault_1,\n", " 3 : changes_fault_2}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 156 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(pynoddy.history)\n", "H2 = pynoddy.history.NoddyHistory(history)\n", "H2.change_event_params(param_changes)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " STRATIGRAPHY\n", " FAULT\n", " FAULT\n", "{2: {'Dip': -20}, 3: {'Dip': -20}}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 157 }, { "cell_type": "code", "collapsed": false, "input": [ "new_history = \"param_dict_changes.his\"\n", "new_output = \"param_dict_changes_out\"\n", "H2.write_history(new_history)\n", "pynoddy.compute_model(new_history, new_output)\n", "# load output from both models\n", "NO1 = pynoddy.output.NoddyOutput(output_name)\n", "NO2 = pynoddy.output.NoddyOutput(new_output)\n", "\n", "# create basic figure layout\n", "fig = plt.figure(figsize = (15,5))\n", "ax1 = fig.add_subplot(121)\n", "ax2 = fig.add_subplot(122)\n", "NO1.plot_section('y', position=0, ax = ax1, colorbar=False, title=\"Original Model\")\n", "NO2.plot_section('y', position=0, ax = ax2, colorbar=False, title=\"Changed Model\")\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10f4fb250>" ] } ], "prompt_number": 158 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Full sensitivity analysis\n", "\n", "Perform now a full sensitivity analysis for all defined parameters and analyse the output matrix:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import copy\n", "new_history = \"sensi_tmp.his\"\n", "new_output = \"sensi_out\"\n", "def noddy_sensitivity(history_filename, param_change_vals):\n", " \"\"\"Perform noddy sensitivity analysis for a model\"\"\"\n", " param_list = [] # list to store parameters for later analysis\n", " distances = [] # list to store calcualted distances\n", " # Step 1:\n", " # create new parameter list to change model\n", " for event_id, event_dict in param_change_vals.items(): # iterate over events\n", " for key, val in event_dict.items(): # iterate over all properties separately\n", " changes_list = dict()\n", " changes_list[event_id] = dict()\n", " param_list.append(\"event_%d_property_%s\" % (event_id, key))\n", " for i in range(2):\n", " # calculate positive and negative values\n", " his = pynoddy.history.NoddyHistory(history_filename)\n", " if i == 0:\n", " changes_list[event_id][key] = val\n", " # set changes\n", " his.change_event_params(changes_list)\n", " # save and calculate model\n", " his.write_history(new_history)\n", " pynoddy.compute_model(new_history, new_output)\n", " # open output and calculate distance\n", " NO_tmp = pynoddy.output.NoddyOutput(new_output)\n", " dist_pos = determine_strati_diff(NO1, NO_tmp)\n", " NO_tmp.plot_section('y', position=0, colorbar=False, title=\"Dist: %.2f\" % dist_pos,\n", " savefig=True, fig_filename=\"event_%d_property_%s_val_%d.png\" % (event_id, key,val))\n", " if i == 1:\n", " changes_list[event_id][key] = -val\n", " his.change_event_params(changes_list)\n", " # save and calculate model\n", " his.write_history(new_history)\n", " pynoddy.compute_model(new_history, new_output)\n", " # open output and calculate distance\n", " NO_tmp = pynoddy.output.NoddyOutput(new_output)\n", " dist_neg = determine_strati_diff(NO1, NO_tmp)\n", " NO_tmp.plot_section('y', position=0, colorbar=False, title=\"Dist: %.2f\" % dist_neg,\n", " savefig=True, fig_filename=\"event_%d_property_%s_val_%d.png\" % (event_id, key,val))\n", " # calculate central difference\n", " central_diff = (dist_pos + dist_neg) / (2.)\n", " distances.append(central_diff)\n", " return param_list, distances\n", "\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 168 }, { "cell_type": "code", "collapsed": false, "input": [ "changes_fault_1 = {'Dip' : 1.5,\n", " 'Dip Direction' : 10,\n", " 'Slip': 100.0,\n", " 'X': 500.0}\n", "changes_fault_2 = {'Dip' : 1.5,\n", " 'Dip Direction' : 10,\n", " 'Slip': 100.0,\n", " 'X': 500.0}\n", "param_changes = {2 : changes_fault_1,\n", " 3 : changes_fault_2}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 191 }, { "cell_type": "code", "collapsed": false, "input": [ "param_list_1, distances = noddy_sensitivity(history, param_changes)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " STRATIGRAPHY\n", " FAULT\n", " FAULT\n", "{2: {'X': 500.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'X': -500.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Dip': 1.5}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Dip': -1.5}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Dip Direction': 10}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Dip Direction': -10}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Slip': 100.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{2: {'Slip': -100.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'X': 500.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'X': -500.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Dip': 1.5}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Dip': -1.5}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Dip Direction': 10}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Dip Direction': -10}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Slip': 100.0}}\n", " STRATIGRAPHY" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " FAULT\n", " FAULT\n", "{3: {'Slip': -100.0}}\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10e10c8d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10f88a1d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1110a33d0>" ] } ], "prompt_number": 192 }, { "cell_type": "code", "collapsed": false, "input": [ "print param_list_1, distances" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['event_2_property_X', 'event_2_property_Dip', 'event_2_property_Dip Direction', 'event_2_property_Slip', 'event_3_property_X', 'event_3_property_Dip', 'event_3_property_Dip Direction', 'event_3_property_Slip'] [3.2970857238891567, 0.72181984885156414, 1.4980320423809366, 1.5071384454650354, 3.9211592979817329, 0.95008458846621457, 1.8136427432104703, 1.5438550566899871]\n" ] } ], "prompt_number": 171 }, { "cell_type": "code", "collapsed": false, "input": [ "d = np.array([distances])\n", "plt.bar(arange(0.6,len(distances),1.), np.array(distances[:]))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 190, "text": [ "<Container object of 8 artists>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10fd4d610>" ] } ], "prompt_number": 190 }, { "cell_type": "code", "collapsed": false, "input": [ "range(len(distances))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 185, "text": [ "[0, 1, 2, 3, 4, 5, 6, 7]" ] } ], "prompt_number": 185 }, { "cell_type": "code", "collapsed": false, "input": [ "c_zz = d * d.transpose()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 173 }, { "cell_type": "code", "collapsed": false, "input": [ "imshow(c_zz, interpolation='nearest', cmap='gray_r')\n", "colorbar()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 174, "text": [ "<matplotlib.colorbar.Colorbar instance at 0x11102c758>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x110d98910>" ] } ], "prompt_number": 174 }, { "cell_type": "code", "collapsed": false, "input": [ "H_ori.events[2].properties" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 93, "text": [ "{'Amplitude': 2000.0,\n", " 'Blue': 254.0,\n", " 'Color Name': 'Custom Colour 8',\n", " 'Cyl Index': 0.0,\n", " 'Dip': 60.0,\n", " 'Dip Direction': 90.0,\n", " 'Geometry': 'Translation',\n", " 'Green': 0.0,\n", " 'Movement': 'Hanging Wall',\n", " 'Pitch': 90.0,\n", " 'Profile Pitch': 90.0,\n", " 'Radius': 1000.0,\n", " 'Red': 0.0,\n", " 'Rotation': 30.0,\n", " 'Slip': 1000.0,\n", " 'X': 4000.0,\n", " 'XAxis': 2000.0,\n", " 'Y': 3500.0,\n", " 'YAxis': 2000.0,\n", " 'Z': 5000.0,\n", " 'ZAxis': 2000.0}" ] } ], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "N_sensi_out = pynoddy.output.NoddyOutput(new_output)\n", "N_sensi_out.plot_section('y', position=0, colorbar=False, title=\"Changed Model\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10dceab10>" ] } ], "prompt_number": 145 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

ucsc-astro/coffee

15_01_27_sqlalchemy/Examples.ipynb

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{ "metadata": { "name": "", "signature": "sha256:bdd723e0f126edb12e17b34d6123c51711e39d0daf416f1f0f7e431032ab2f2a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Learning SQLAlchemy\n", "\n", "* [Basic SQLAlchemy](Learning SQLAlchemy.ipynb)\n", "* [One to Many Relationships](One to Many.ipynb)\n", "* [Many to Many Relationships](Many to Many.ipynb)\n", "* [Practical Examples](Examples.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Practical Examples\n", "\n", "At this point, you're probably tired of my expounding on how to use databases and would probably like some examples on what they can do. I've put together what is hopefully a nice set of examples for you using the tools I've already presented in addition to a few more I'll throw into the mix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###A Full Database\n", "\n", "I'm defining a full database here for your convenience. It will include all the tools that we've used so far and some hopefully useful examples of their use together as a functioning whole. For your first time through this, don't worry too much about the minutiae of each class. Just execute all the way through to the next section, so you can play around a bit with a full database. Later, you should definitely come back and check out all the new pieces of code and look them up in SQLAlchemy's documentation." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import create_engine, Column, Integer, String, Float\n", "from sqlalchemy.ext.declarative import declarative_base\n", "import math\n", "\n", "engine = create_engine ('sqlite:///:memory:')\n", "Base = declarative_base ()\n", "\n", "# We're going to be using some trig functions in the database to do some great circle math, so we need to create these functions\n", "raw_con = engine.raw_connection()\n", "raw_con.create_function(\"cos\", 1, math.cos)\n", "raw_con.create_function(\"acos\", 1, math.acos)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now define a full Star object. We'll give it a convenient init method, so you can use RA and Dec tuples instead of floats. We'll also implement some great circle math, so that you can query based on proximity of stars to sky coordinates and other stars." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import func\n", "from sqlalchemy.ext.hybrid import hybrid_property, hybrid_method\n", "\n", "import numpy as np\n", "\n", "class Star (Base): \n", " __tablename__ = 'stars'\n", " \n", " id = Column (Integer, primary_key = True)\n", "\n", " name = Column (String, unique = True)\n", " ra = Column (Float)\n", " dec = Column (Float)\n", " cos_dec = Column (Float)\n", " sin_dec = Column (Float)\n", " \n", " def __init__ (self, *args, **kwargs):\n", " try:\n", " kwargs [\"ra\"] = float (kwargs [\"ra\"])\n", " except TypeError:\n", " kwargs [\"ra\"] = self.raToFloat (*(kwargs [\"ra\"]))\n", " \n", " try:\n", " kwargs [\"dec\"] = float (kwargs [\"dec\"])\n", " except TypeError:\n", " kwargs [\"dec\"] = self.decToFloat (*(kwargs [\"dec\"]))\n", " \n", " kwargs [\"cos_dec\"] = math.cos (kwargs [\"dec\"])\n", " kwargs [\"sin_dec\"] = math.sin (kwargs [\"dec\"])\n", " \n", " super (Star, self).__init__ (*args, **kwargs)\n", "\n", " def __repr__ (self):\n", " return \"<Star Object %s at (RA=%f, DEC=%f)>\" % (self.name, self.ra, self.dec)\n", " \n", " @classmethod\n", " def load (cls, session, data, tags = None, convert = False, **kwargs):\n", " filters = ('u', 'g', 'r', 'i', 'z')\n", " if tags is None:\n", " tags= []\n", "\n", " for datum in data:\n", " if convert:\n", " star = Star (name = str (datum ['objid']), ra = np.radians (datum ['ra']), dec = np.radians (datum ['dec']))\n", " else:\n", " star = Star (name = str (datum ['objid']), ra = datum ['ra'], dec = datum ['dec'])\n", " session.add (star)\n", " for tag in tags:\n", " star.tags.append (Tag.get (session, tag))\n", " for filter in filters:\n", " phot = Photometry (filter = filter, mag = datum [filter])\n", " phot.star = star\n", "\n", " session.commit ()\n", " \n", " @staticmethod\n", " def raToFloat (hours, minutes = 0.0, seconds = 0.0):\n", " return np.radians ((hours + minutes / 60.0 + seconds / 3600.0) * 360 / 24)\n", " \n", " @staticmethod\n", " def decToFloat (degrees, arcmins = 0.0, arcsecs = 0.0):\n", " return (np.radians (degrees + arcmins / 60.0 + arcsecs / 3600.0))\n", " \n", " @hybrid_method\n", " def greatCircleDistance (self, other):\n", " return math.acos ((self.cos_dec * other.cos_dec * math.cos (self.ra - other.ra) + self.sin_dec * other.sin_dec) * (1.0 - 2.e-16))\n", "\n", " @greatCircleDistance.expression\n", " def greatCircleDistance (cls, other):\n", " return func.acos ((cls.cos_dec * other.cos_dec * func.cos (cls.ra - other.ra) + cls.sin_dec * other.sin_dec) * (1.0 - 2.e-16))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also implement a SkyLocation object. This is designed to hold an ra and dec and we can use it to calculate distances between fixed sky locations and individual stars. This can be used, for example, to calculate the angular separation between a star and the zenith to filter by airmass." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sidereal import LatLon, SiderealTime, AltAz\n", "from datetime import datetime\n", "import numpy as np\n", "\n", "class SkyLocation (object):\n", " \n", " def __init__ (self, ra, dec):\n", " try:\n", " self.ra = float (ra)\n", " except TypeError:\n", " self.ra = Star.raToFloat (*ra)\n", " \n", " try:\n", " self.dec = float (dec)\n", " except TypeError:\n", " self.dec = Star.decToFloat (*dec)\n", " \n", " self.cos_dec = math.cos (self.dec)\n", " self.sin_dec = math.sin (self.dec)\n", " \n", " super (SkyLocation, self).__init__ ()\n", "\n", " def greatCircleDistance (self, other):\n", " return math.acos (self.cos_dec * other.cos_dec * math.cos(self.ra - other.ra) + self.sin_dec * other.sin_dec)\n", " \n", " @classmethod\n", " def zenith (cls, latitude, longitude, time = None):\n", " \"\"\"\n", " time should be a datetime object or None to use the current time\n", " \"\"\"\n", " if time is None:\n", " time = datetime.now ()\n", " altaz = AltAz (math.pi / 2.0, 0.0)\n", " radec = altaz.raDec (SiderealTime.fromDatetime (time), LatLon (np.radians (latitude), np.radians (longitude)))\n", " return cls (radec.ra, radec.dec)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also implement the Photometry class from the One to Many lesson to show some applications of the photometry class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import ForeignKey\n", "from sqlalchemy.orm import relationship, backref\n", "\n", "class Photometry (Base): \n", " __tablename__ = 'photometry'\n", "\n", " id = Column (Integer, primary_key = True)\n", "\n", " filter = Column (String)\n", " mag = Column (Float)\n", " date = Column (Float)\n", " comments = Column (String)\n", " \n", " star_id = Column (Integer, ForeignKey(\"stars.id\"))\n", " star = relationship (\"Star\", backref = backref (\"photometry_set\", \n", " cascade=\"all, delete-orphan\", lazy = \"dynamic\"))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also implement the Tag class from the Many to Many lesson to show some of its applications." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import Table\n", "from sqlalchemy import ForeignKey\n", "from sqlalchemy.orm import relationship\n", "from sqlalchemy.orm.exc import NoResultFound\n", "\n", "star_tag = Table('star_tag', Base.metadata,\n", " Column('star_id', Integer, ForeignKey('stars.id')),\n", " Column('tag_id', Integer, ForeignKey('tags.id'))\n", ")\n", "\n", "class Tag (Base):\n", " __tablename__ = 'tags'\n", " \n", " id = Column(Integer, primary_key = True)\n", " tag = Column(String(50), nullable=False, unique=True)\n", " \n", " stars = relationship ('Star', secondary = star_tag, backref = 'tags')\n", "\n", " @classmethod\n", " def get (cls, session, tag):\n", " try:\n", " return session.query (cls).filter_by (tag = tag).one ()\n", " except NoResultFound:\n", " return cls (tag = tag)\n", " \n", " def __repr__ (self):\n", " return \"<Tag %s>\" % self.tag" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy.orm import sessionmaker\n", "\n", "Base.metadata.create_all (engine)\n", "Session = sessionmaker (bind = engine)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Populating the Database\n", "\n", "We've now set up a database, and I'd like to show you a few neat things you can do with it, but first, we need to populate it. I have three datasets for you: some M3 photometry, some M13 photometry, and a list of the 10 brightest stars in the night sky. We're going to put all three into the database and look at some applications." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "session = Session ()\n", "\n", "# Load the M3 dataset from a csv file using numpy, tag all the data as \"M3\"\n", "Star.load (session, np.genfromtxt (\"sdss-dr7-m3.csv\", names = True, delimiter = ',', dtype = None), tags = [\"M3\"], convert = True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "# Load the M13 dataset from a csv file using numpy, tag all the data as \"M13\"\n", "Star.load (session, np.genfromtxt (\"sdss-dr7-m13.csv\", names = True, delimiter = ',', dtype = None), tags = [\"M13\"], convert = True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# The 10 brightest stars in the night sky, tagged as \"Bright\"\n", "sirius = Star (name = \"Sirius\", ra = (6, 45, 08.9), dec = (-16, 42, 58))\n", "sirius.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (sirius)\n", "\n", "canopus = Star (name = \"Canopus\", ra = (6, 23, 57.1), dec = (-52, 41, 45))\n", "canopus.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (canopus)\n", "\n", "rigil = Star (name = \"Rigil Kentaurus\", ra = (14, 39, 35.9), dec = (-60, 50, 7))\n", "rigil.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (rigil)\n", "\n", "arcturus = Star (name = \"Arcturus\", ra = (14, 15, 39.7), dec = (19, 10, 57))\n", "arcturus.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (arcturus)\n", "\n", "vega = Star (name = \"Vega\", ra = (18, 36, 56.3), dec = (38, 47, 1))\n", "vega.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (vega)\n", "\n", "capella = Star (name = \"Capella\", ra = (5, 16, 41.4), dec = (45, 59, 53))\n", "capella.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (capella)\n", "\n", "rigel = Star (name = \"Rigel\", ra = (5, 14, 32.3), dec = (-8, 12, 6))\n", "rigel.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (rigel)\n", "\n", "procyon = Star (name = \"Procyon\", ra = (7, 39, 18.1), dec = (5, 13, 30))\n", "procyon.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (procyon)\n", "\n", "achernar = Star (name = \"Achernar\", ra = (1, 37, 42.9), dec = (-57, 14, 12))\n", "achernar.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (achernar)\n", "\n", "betelgeuse = Star (name = \"Betelgeuse\", ra = (5, 55, 10.3), dec = (7, 24, 25))\n", "betelgeuse.tags.append (Tag.get (session, \"Bright\"))\n", "session.add (betelgeuse)\n", "\n", "session.commit ()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Using the Database\n", "\n", "Now that we've loaded the data, let's try to actually use it. For most of this, we'll actually be plotting the data, so let's import matplotlib and use the matplotlib magic to make figures appear inline" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####1. Plot All Targets on Sky\n", "\n", "We can do a very simple query on the database to plot the location of every star on the sky. We'll color them by tag to make the tags more apparent. Here, we use a Mollweide projection, but you could get the same effect by just plotting RA and Dec." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "query = session.query (Star, Tag).join (Tag, Star.tags)\n", "results = query.all ()\n", "\n", "tagToColor = {\"M13\": \"blue\", \"M3\": \"green\", \"Bright\": \"white\"}\n", "\n", "ras = np.array ([star.ra for star, tag in results])\n", "decs = np.array ([star.dec for star, tag in results])\n", "colors = np.array ([tagToColor [tag.tag] for star, tag in results])\n", "\n", "fig = plt.figure ()\n", "ax = plt.subplot (111, projection = \"mollweide\")\n", "\n", "ax.scatter (ras - math.pi, decs, c = colors, s = 50)\n", "\n", "ax.grid (True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/projections/geo.py:489: RuntimeWarning: invalid value encountered in arcsin\n", " theta = np.arcsin(y / np.sqrt(2))\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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KRFGUNR9JKZmZmfDw8JBVR23E39/f6mX6jDFgMIChFbx4BoA/QKL93nfmkJ2d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\ngiDQr7/+Kpk8Obhy5YqkYQWe5yWfI33nzh0KCAi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"text": [ "<matplotlib.figure.Figure at 0x109bc5080>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####2. Filter by Dataset and by Functions of Color and Magnitude\n", "\n", "We can use the tags to filter and color by dataset, and we can even add filters that are functions of both color and magnitude.\n", "\n", "There are a few interesting features we're using here. The big one is <b>aliasing</b>. An alias is a reference to a table in the database, but occasionally, we may want to filter by more than one entry in a database, e.g. when filtering by color. To filter by color, we need two Photometry type objects, and the way we specify this is with aliases." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sqlalchemy.orm import aliased\n", "\n", "# Set up the aliases to the Photometry object\n", "photog = aliased (Photometry)\n", "photor = aliased (Photometry)\n", "\n", "# We'll need to set up a query that returns two photometry objects and a Tag; we join the Tag table to the Star table\n", "query = session.query (photog, photor, Tag).join (Tag, Star.tags)\n", "# We join the photog Photometry table to the Star table and then filter by forcing photog to be a \"g\" filter\n", "query = query.join (photog, Star.photometry_set).filter (photog.filter == \"g\")\n", "# We repeart for photor\n", "query = query.join (photor, Star.photometry_set).filter (photor.filter == \"r\")\n", "# We then construct a filter to filter based on color and magnitude\n", "# Try commenting out this next line\n", "query = query.filter (5 * (photog.mag - photor.mag) + 20 > photog.mag)\n", "\n", "# We separate the query into one for each cluster\n", "m3 = query.filter (Star.tags.contains (Tag.get (session, \"M3\"))).all ()\n", "m13 = query.filter (Star.tags.contains (Tag.get (session, \"M13\"))).all ()\n", "\n", "m3gmags = np.array ([photog.mag for photog, photor, tag in m3])\n", "m3rmags = np.array ([photor.mag for photog, photor, tag in m3])\n", "\n", "m13gmags = np.array ([photog.mag for photog, photor, tag in m13])\n", "m13rmags = np.array ([photor.mag for photog, photor, tag in m13])\n", "\n", "fig = plt.figure ()\n", "ax = plt.subplot (111)\n", "\n", "ax.scatter (m3gmags - m3rmags, m3gmags, s = 5, lw = 0, c = \"green\", label = \"M3\")\n", "ax.scatter (m13gmags - m13rmags, m13gmags, s = 5, lw = 0, c = \"blue\", label = \"M13\")\n", "\n", "ax.set_xlabel (\"g - r\")\n", "ax.set_ylabel (\"g\")\n", "\n", "ax.set_ylim ((25, 15))\n", "ax.set_xlim ((0, 2))\n", "\n", "ax.legend ()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "<matplotlib.legend.Legend at 0x10a5a8ef0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ax+muNLochEOqiGV7qLNZarlYg1OFUgwsme5XogutrU0NApaVPtus1HGNXKzx\nQkt9TPXvxGRi06ZN3HbbbUUdI+sKXyHE30op/yHh929KKf9RCPH/SCm/PcZ+5wLLga3Ah4GfSinP\njr33mvNzwvZlXeGbiJNFkphtk2r9p1uBm7ToK4FAXcBt1+iSsjagac6H2Hv93oznBEYFeYnGBgjH\nnZCllo8jMIXUgsn2gOcqAGlnU0WIR6UKT67XFQ7DokVqoDh0aPQak2LEP/UaymGNj3XMSv3bTDfK\nucL3wym/z4v9nzEOEPP53wnsAO4GfgfcIYT4ZmyT23O9wGJIm7ee8MB1rOvIGPBNJ9KpA6VAEKhL\nqG08XKterx5ktreB/tiKX8f37zSHd1w16ZqiZBKEsYTCsmDePOjpUaWWU0UmF3Kpy5+NdNdYqCBZ\nUUtZzkXEMcpJPtcSis1vncE5/ncv3P1UCW6XSvp7aPInF/E/LoT4OdCNcgEdiln1K4HvpNtBSvk0\n8HTJrrIAMqWp4Yn/mC3Txx72qge2Pv6a09gFVG/fHVfuoNZTy+K7zqdvy8vq9W+eR5/9No3fXK4a\nwI8M0HPUZsndqxA1Njuu2EG432Jpaw1CCDo7DPBYrvgnDhAZP1+etW+KDRqOp5XnDCL2sBdBN2KS\ntpp27llTrJZfel956W5oubKzUov7pWOiazFp8ier+EspNwohPgksBJ6RUv5X7K1PlvXKSkhyVkaO\nTV2u6aZ1oZe2LYZ6oDyAJCmoG46EOfOeM/F5ffQNJawCjtZiH/fSW/MWc7xzoF+5gIIAG+az8N6F\nyOFa5LH9GKKKs1p8hK1+AjevUI3igR1f2OMOPM6D5TSaAZItbNPk4EHlYkh0AZRKAHK1MtMFUlMz\nRvK5LqN60A12TzYr07lntg27doHPl2z153oMyOyiSyf0pV4ElhrYTusyrIDV6Zr8ySnPPyb4/5V1\nwwpirDS1XL6cpsdklO0tlKvHZ/qQtiQ8GEai/qdawoZmZledRN/3XsAAGr+1nFDkTaBWFX9D4Jtx\nCr3DbyuRX9dKg+dUwnf/HFuqNpFhK4QcrmXhGTOAfg4fstyKoE6DmcRBIJHly5XYCFVnLmP6YTnd\nBGmzkvLMDqn0NM9csW3V9WzZMjh4UL2Wq3We6X5VgrtHMzWY0ou8MqWp5bpvUmpbVPX2dXz8oaEQ\nATPAo595lE/+KDYJqo7w3vAf3dW+j33mMS57YjVEHfGHvn/Zjr+uEfmVVnrvfw5qG7G/chJ4LH5x\n1Utc9qPgGc7dAAAgAElEQVTLOG7X0ysFiNgKYilVgq1QriiipRHHXF05E7HYazKLPqj71NWl2l6m\nevAmk2A76xQc0l17uQZrHVAuL9Omnn8xJK6+DVthljywxA3+hiKjslVhuBYhDER1BHuoBjYfhIEm\nMIMgJIGZfp78ZZAV51VhMwIb5mPUDBKoC/DUZ3dwTssspITX9ng4+QSTRds+CsDuK/eyrK0BgTFm\nxo5zvb769NlEE5WnXeqHuZDj5bNPKa63HJlO4yGKEz3DmOjzTyZ0PX/KVPwqJTuofVu7qtuTgCEM\nXr32VS754SX0RnppnD0TgJAVW9ogJNT1qCbvHotgxMf55+1CShvWtxKYPRPETIIDQdb+YC22fBGk\nYO3q2QgBPX81gvBEiESTVxGn+9wqhTV2vdd0J/nLJ/oBcgadUsQjCmrOk8c+pVqlW+zK3lIfU6Nx\nmDLiX3TP2hyzFaQdnyk1Vjfysy/9jA9/6MP4PKfywpdeZeXj5xK2wjTUNLhlH+bctJSjw32q/v9w\nLWx7GTkQgLoeRPUQz1/1EqseXYWUUs0Abl6BPVRD+Hu/BgTi3i6EgNV158MG6FzfhWmaSSUGJDad\n+wbdz20Pe2ld6EVkuB+FuHKKzeiYjNacbcddN+X8XlUaRWeHFfm5J8LVON2YnDl0Jcax7pu3NCe5\neCDuz+ze0I3P9LHnK3swYretb7iP8+67iCXfu5TT542w8IwZBI8eU+mdg2F1gGEzLvwO7kygBf+H\n6tyXm2Y2sevaXYjqCH1V3cgNzTR+9TKE1QQDTcioF6N6MMlt09Kiirv1HOuhZWsLoDIuutZ3ZU2R\nzMeaHesepd3eyi+zJd/jOOKQjyDns4+zbVeXEn5nlW4+15nvPas0Cp3tZPrc+X4npsNK84lkylj+\n5bQUEq0Xp8b/iodXEO4fgC0HCFOFry42I4h6aaz1UVUzBMdN5H37CQ2EkesWgSc2AGxoZrZnDu+J\nPkIRwZn3nAnAH772B3ymDxHLM2qaM4tdVzzD0ofV71037lEPhMd02wsaBux8ZZClj63AiBWAMz0m\nZr1qHqPux/g+QZks/Hz/RtlmCuV2wzji09GhBtm2tsyroyfbjGa80feo8pgy4g+FB8acvH7n50y4\ni49sGyHjKRyNdQ2wrhXbCsHWTsJA4OYV/OJLT3PpZgMxcAJy695Yxo6EjfPoF/+TtGAMiPv0Bfhm\nnMpTn3lB9Rw4lBB0jprgSRVSk0M3dSVdvxW1aHuodLnXxWR0JLoAcp5ppFiJhcZzShE0Nc30xduy\nWbFTJWU133s/VT73VGdKiX8iuVoacb+5mXHbxKmr06AdIFDvQ37rfFXhs9pCRCM4EYHQQIizHzkD\neXkj3NuBIaqQAwHV+CVai6yOMFuexNGocgkJBCsfWcmeq/4bOdBA+J9+zeJvNyJmBvnDGxZLH1xK\n8PYXaZrp5WC3MWpKPB4PWa7dtZLWWHjyXwCUGmyFzLXks9U7KkWufLoZS+oxMs1oJrv4FRr4Tv3c\n2odfeUxZ8c+FXErzhq0wrVtbEUK4vvTWra0A/PqKX7Pw3oUAHL7hMADn1p5PyAohqy3V4tEMw8Z5\nzJlxCkf/ZYfK8PGowG//3XvUmoANzcjqCMH3PmBJSz32SCf2MXUxcgTe6X8nKdCs6t7EC8+lbeeY\nxvpKtVQLCV7mKuSuSEbTHCdPq7sU11MsuQwQpSA1HbdUxy72WLmUp85GKdNdNcUzZcU/H0sjU2le\nK2rRsrXFbfDuiGzn+k5XaJrqVOEWn+lTx/JEoFpt95+f/08u+9FlUB2hlwOIDfNUU4TqCGK4jjne\nOfRGepOuxZaqTEPsN7huCef/8I8IBI3fXM6vr36BlgfbCQ6oMtG7r93N8oeWA2kK1cWqglrEi6Sl\nLtVPvF/u5y7hA5euPWU2qzvd364QqzHTdyCf70ame1EOSzbVyi5V56xi/O3O50wskTJehMPFDzia\nzExZ8YfCpvOpCFQTll9f8eukgcBpx3hwo1q371jgRmz6IBBc9eRVyvqP1f6X1fGWBrJ6gN4vN6hf\nYplAonoQed0i+JdfqxXB1y6B2e/EOuhIQlaQCx66gPDgB0AtQRmkfVs7Iqoyhlq2tmAIQ4lt1GRe\ns60Wo21siV1TN4kJXuk6UY0lFIX6cguxyHMJ7uZyPcXkypcj4FzppBvsHKNoPN02lqVSbINBVam2\nmOPA1PxbFcuUFv9cyPalkFISHAiy6tFVbhYOpPc1mx6TQzceImypBu+rH1uNv9av3EBIt+Qz1REM\nDGT1oHrdOReS3qoDNH5zGUaVgfQMEXZcNbH+ACEpQAoa6hro+/IceoY/IPDwWyqtc10rdl2furYo\n9BzrB8Bn2xg1g2odQAkCv4nkHQwssYhMdp96Iqn3phT3yYpa4FEFDbMdK9fBbrwEVUol/F1d6c+V\nSw8KnWGUmWkv/tkQQrj/VG9dhVPvP9XVYnpMfKaP5i3NCATbr9qu4gJDseYuUsD6FmTdUWZ7ZquO\nXyn02m8hbMEJNSfw3OXP8ef/+8/pG45gCANbAlaAPgtVM8gTITzQq9YB3PksbGymdWsrL3/5Zfw3\nr0bG+gILRCxNNPlcqQKTr0ukkIdrsjyEhQhwscKYanEXQ6mbxTivOXGyzk5VrTSf/fPBqYmUSfi1\nsBeHXuQ1Bo5bp+frPRzaeAif6cNn+pKau7Tc28K8zfPcmUDYChO2lNNeCMEp9adw+MbDzJ4xRwn/\nQBNs7UIOe9MKv4NE8vinf8rHH/o070Xf4/d//TqHD3nw37QGY2YYY2YYUT2EEAYN6/6MRv+I+7BI\nJEsfXKqqgN7fSeiO3yCjtelP5LHcxinu584xo6MSKHYxmZO9lIl87oUjSM3NpVngNt44g50z4Dmf\nJRxO/jxSqsZBLS2ZP2cp7oVTnbZQEj/PZPk+jyfa8s9CuoYvjq/5rf633GwfK2oRtsLMvWsuAPuu\n38cp9adgRS0i0QjvV70L61tga5dbrRNQvQAG+5Ly/QEYNvnsyo8hrf3IdW1E+uvo4x3Cx99CyhH8\ndX5euO4PrFo6h1DQgLog9jXtBGr9bL/iaS587EKkUHWHmmY28fIVXe4aAQcranH6nS2jUkhzvjcT\nlL6XKCbFlvSYynXoC4nROPcwHFYiD6ODrp2d8dfKRbbvVq7fvXJkYU0VtPgXwYWPXYiU0i3zvOSB\nJdixcs6rHlnF7ut2u4OBlBLq+lTaZ00DR6Xy+7+67lVMj0lXTxcf//7H3RhAo9lIrxVWs4Wte1n8\nXT/UGbC+BoSkNxImEh1hRI4oV5C04cFdhA3BBdYnEZ7Z7Pnrp/FtNAiHYenZZlmyJkzTsZ4L97/n\nk4Kabg1ApVCJueyF/E3celFSNaJZvjz5fZ9PtQqFsQPqxd6L8UqvzcZUdTFp8S8CIQQnzDyBl7+s\n2jcaCaaQYRhEohG3JeOvLv8Va76/BqojHJXvuNv1WX2Y9SanN5wePy4CUR2BDc3Kr781HmvAE4F1\nrTTWBjjn8QPY19SobQDu68QeEYRu3wPAudEl7PvmiyxbZhIMgt8fr6jpuDoO3dSFtR5MT+5Wf2oV\n0WL9yk7Hq3RNaFK3TXVPFSMw+VrGuVh/U0UYQFn2Pl/hHcPyuReTtQDeZEaLf4E4wqFKKKvgrxMQ\ndr7Iyx9aTqD6NHZcuYNTfI0EzEBSOWgbm8UPLAagyWzCV+tDCokhDIIDQUS1qgrKxnn4WMB/XP4T\namt/yyVLTwNAXtMA2PjrZ/HM1U+ydvYKgqEo3PUmSIPQ5v+i9ysWEi9guH7art9bbhG4Qzd1jar7\nPxapVlCSG8nCLT1RCjL1HyimxHIquYpNOuuvElwB5egXMO5pnRXufhvrflTCd6BQtPgXQbpYAKhM\nIFvaMGwSuuNFLry7ic59gxiGgSEMGr2NKq0z0uu6eUKRkOsy8nl9scwe9bsQBn1bfs6fPRJg5LpF\nhAaeBaBRfJijW7YjTB8nfzXKnht2cO7dKwldtxh+8AsMA1b+cAnyqjnI218FhFudMnj7bwCU1V9P\nwbiDYGwRGeSf+eO4bxIzjkYNMin7TKT4JpbSdq5vIh7+YtwRY5W+gMoTs4meGUzFjCMt/kUyagVr\n7EsqEPz8i0/xiS0qHmBFVbmHpromtyyEEIKGmgaODh2l0dtIOBLvCdxY08iMqhkgYGSwhr7Y+UT1\nIGxoRmDQa9sgbYIDQc66ZwUAoTtecruG2Ve3E4q+AyJEoMlGUEVXF4AJx2Kuomh+6RTprCDTkxxI\nzodcRTT1vJYFp8c8ZYcOlf/BS13pOlZJkEogVcRzEfVyiVm2c2dzv1X6zGCyosW/BKQu9OpY18FZ\n95zF2Y+cgf+rH0EIwfLvD7lrAgD8plr81TfYR8AMYEe9yGGvu9r36PBR9l2/j5WPrCQ8cgSxoZld\nX/sDprmH7t5uzn/4fEA1jQcIRQcJeE5TcQdhYA8E4MFd+G8+H2PmILu6jsarakZNAgEl+on+/3wz\nQlJfK+nirXSDTEq+eTAY/znXFbvFXF/ifplKgown6WZNkH7WlCrq6QbTcqSn5jqgTEZBr8QAfz5o\n8S8TjhUfjP4BEa2jiaZR20gpCZgBdnxpDwvPmAHSpvFrazBqhqiqGaLRbFTpmcO1NM2ZhWnConsX\nIW0VREZCQ73J0cGj+Gr97LjqKbgqSuT9Xs5eEkUIwfNXP8+qx5ew9NEWiKV+qvLVXgCs6KDb9jGT\nVZXr4FDQA+Cx6NjHqHLPTn2jTIuITBOamnI/b6ms2kTBHWuB03iQadZkWSqAnm1Wkrh9vvEUZ7AY\nj8GvkktET0bRd9DiXySJwuhaTlETv+cjhKJvYg/VIO7uRtb5aRELANh+xXa35IPj1wdACvq+tx0h\nVD8Ay4Lwd17CALb//jhvh/voOfo+VEd49H89ypX/cSW9/RaN3g8DQ5x5z5kIIWiqa+IPb8SKz3ks\nggPKRPabfqSQ9Fq9BC31p++1jmNLW5WlzvD5EnsYJz18scJxhT4AqdN5UAcKh+PC3tOTXmRNEw4e\njP88XozlpiqmImchsxLLUq6nxIVQide3c2f8eLlaqLkK/7x56m8TCIztdiuVdVxpoj8V0OJfBEnC\neE13QtVMEyG68W9oRngE1AUQQmAPewnd+SyrNvvx3XAqweN/oHewl1WPL0FuOAbDNcj7OtUxkLFU\nUfUnWvXApYT++VmQ3bChmSv/40q33k8vKPdPtURKyYgcwVevFqcd6Q+rWQJqphGOhFn1+BL8N5sI\nBKt/ZIGE577w8qhFYInYts2iexe5pSLsgTmI+7pUIblEq3McA3OJ7opcRK2onrRZXCLFVOQsZFaS\nGHtw3E8OUiqX2NKlcWHOlp6ay70p1C002azjSg16lxot/mXCEFV0XL9XfYFurAIgbO1g7p1RQgMh\nfEJZ4tiABH99nUoD3TBfvS4NVj6wFp/ZqSy7NHHZhtpG3otZ7Inrg4MDQd7qf4tGs5H2be1q9iEM\nwlK5onoGenjza29iekwW3dVOz3sfsPCMGfjNEXa/NuRa2s7q5o51HbRsbSE0EFLXFjUJfvdZxIBw\nLXTIPzCXaTrv8ymr0vk5E4U0ZcmFdMFSV9j3KTeVM+vJhG2P+XbJMIzkz2Waua/ATfycme5NptXU\nBw8mD7q5xl0qncmewZMPWvyLIFW8UlMSHSva+QL5PCaBW1oYGRkhfPwt7KhSCEMYvHrtq5yz7Ryo\nGeSZv3qGc1pmYdsSCGEYgn3XPk3kyg9YfP/HoDqisoFmRPF96zyOHz9On4wknfrMe87EN+NU5PBs\n2PIqQhg0fmM5oZE3MYThuqnk5v0YVi+2LenpgbkL3sd/83lUeYdcATc9ppup9PIVnUT6TVZv9kKd\nGGV1gpphZOv2lXgPE3FmDj6fer3c7pOxhD714ZfY7voIsaUbgZEUQE0MvuZTk6aQWclY++SyAjfx\nc+58xcI0GbXeY6zV1InptvPmqdcOHiyNWJbK8p4uFnyhaPEvkqRMn0T/bxor2LKUv3/14+1gqXRQ\nJ8+/wWxwG8M0mA1ArAWWVGK68pGViOoIgTmzCFtDVFWp2UQo+qZ7DDVjUGsG5MAcQvfuVP2ATT+G\nEDz5md/wpz+7AKN6ECtq8ZF/OgeOddA0K8BPn+nhvOU92LGZQVP1rKTP6OTyt5wRWy0csNn9mpUi\n0vFZQtt9bXRc051f397UGEDUzFpiODVjJR+rLedMlJiwW9FBlv94EHtYBcudNRPOuVvUuEBXV3rx\nH0uMChGobK6cXLClzWkL+hECeo6oASBe6C75u51usLGs+CwtHFYDTzFiWyrLu5iKs5M5gycftPiP\nE0fCYeaeHgVmELjZJFAX4KkvPMXiBxa7Qd/O9Z2uOyFwS4ty19zfCUDQCiGOR/DV+tw+wr5aX1I/\nAKTKMmLYRNz338iBJsCmV/YhrQbOazmBQFM3L+89Sl9/H2z9LQBPv/Au809tIHDzWQStHpVuKmeR\nRNR0xyNQPQ7at32cN27ZkyTSHfvULMEe9tK60IuguIdYyrHfT7TYS5GqmO7hjwdRTTr2dWOaEP6C\nQXu7et3J/3dSTyF7z9+8ArtliKM4nzPcP8jcM5LPlTgAZ+sDYJoQCNhICcuWGRnLc+RSe79SmOqi\n76DFv0wkuoQAlm5bii2fxxAGu67dBR5V7dNZybvkgSUYx2fClgMIIdj+ynZqTYl5wwze7n+bxT8c\nQEp48nNPct7D5wFK6Bu9jaoWkBAIQ/0f10s1qEgr1jEMg3AIzr37AsLH38IQ3SDgE0+somvjLvZ8\ndQdLti0hbA0hjITGNQkLqrq6oLc/wsKzIXTns1g3ktQjIHGW0LpFWZaWlb5ukGUpsTFNYoNe6oK5\nuPiPJR6Fln7IZOWNaVF71CC4bBmEQvEuU4mpp3gsVeaiBGJdzgVOpgmnmiY9R2K/e0ysFBVOtwYg\n6f54LOT6JchhL/Lh3ajgVHKwIdvAl+o6ymZ55zIYTicLvlC0+JcRt3F61MKoGeSEW1ay69pdSii+\nq6p9vnrtq6x9fK1Kxxw+hhFLyzzje2cD0PSD/wFOgKvUsT5c28xzlz+nisQBvYO9CAS+Wp/bR6Bx\nVi29Gz4KVgM8uFtdzBUrafjJf9MX6SNkBRF1Fv5vna+eVU/EbUrvtK3c9eVd7rWH+yEYjD9Bp5xo\n0jTTq4Q5amDWpz5oJpgW9ldaCH73WVrbmkaVi7YsmNds03OsH/9NFyHu60JgcPBgspvIMOLNw4Vh\n07lvMGMtIifIms/DXsggYVnKrdPUlJxpc/Cgul9tD40W62xiVE7/dLYUVF99Ym/l+Owmsedyau/n\nxBlN8Luq3Ij/phaMmiHwdJHoMsrl+hzXkWVlCfLnMRhWiuhXauxBi/84kBoYdkQalH/fEKrmj2/2\nTMTN5yOHvAT/+bcgBUERQghorDqZo3dtZ+kjc5Abrkk6vkSlcDbWNtIb6aVvqA+qJVS/AxtjJlV1\nhL4rPwxbO+G+Tl7bO8D8E08GcIvTORVIQc1UJLHfh00CTSrA6VjVL78ySPvZNbS2KUF2xMLB+cIL\noapNq6JvCWsiohZS1qjrH/YS6hHufs5D4gimZUFrm03PsR5atq7g0E1do2ItHR2q56vjhin1g5Zq\nAadbWatmMGMfIx1pi8YlWLfFLHDKlILasc8a9Tdzr9Njjur4lpGoiRhQpUKEEBjVg6OPl2XgU66j\nzO9PZio5e0iL/ziRlMpo+uj5ek88IyYW1BVCsPfG3bwd7mPxPwNCIte1QXWE3hEJ0saWNgJoqmti\nxB4BCcIQ9EZ6XX8/SKiOqD4DtRDs/0CtCfBYCGHgM32cXD8cvzZ8KjgbE523+9/m7AfORiLdxV/7\nXu6j0fSpmUDYYumjS+kZUG6slntXYNQMJgW22xaaGHSx77VBVq8waGm1YWMLRvUgO6/ZybIHl8HG\nWg5/eRem5wVaHoqXm0i6b7HBpnPfIC1bV6QVl7EI98eyh/KoXJoO5yF2Sk87i6tSfdylWI1aCldP\noptGuonAhvt7y1b1t3AC65DZBZausF7iz01N6m/XeeOezANKluB0tuwkd9sx7m9q3KCShLYS0eI/\nQZge07W2Id4r2PSYNNRbbs0eqiNIlNhzywUAGNWD/PwLT3PJDy+hd7AXn9cXqyJai3G3Wvbq/9b5\n7L52O339gyxeVKeOta4VkIStEC33rkB6BiBqYtx9EIHJzo4w7Y8uomdAzcEdFxDDJquXzQGUm0YI\n8H3Thg3NSGGAZxbpOoIKDGo9ZiwQKvAPe5GeCEvuWUXIep8T5sREOWomNelOlzPuqzc5dFOXe+8S\niS94snn51UHM2M7hfoumU1UTeyeTxdm+nKUJ0onfWOUqRrmVxpg9pFtRns6HnrguQW5QaUhmfRfd\n3aYq6fHQoLtt20K1bSbLNJubyllpDSoeUkiRv7xcdelKkGRxT00UlRx70OI/wQghVJYPznTb5NT6\nU3lp3a84/+HzlfCbTey9fi8QKxdtS9cyB2X5N5lNyBm1GLGuYlLA0geXYg95gReds6kURCkYGawm\nPHwYOVzLCVJic5xzNq8gPPI+oloNRH7Tz9NffJq1j/wpoYEgfjPgXvfzVz/PqkdXIRB0Xb8rud1l\nQr0eVYJAXeeOK3dQa8Lc06MI4OVDHjdTSGLz8iuDLD83boWOmgVkqDtkRUHipWegh/ZtF/HGLXsy\nWstO8DoYTPbZZ7U401jAie+NRa7lKtyfM1i36VaUw2gffKoF7MyWLEsF5331CckIWRarpSOtmyoH\n90aps5bSDX6O+FcSlSb6Dlr8J4hMpaBBPSR/9pM/A5TFv/f6vfhMH+F+i+c+/zJ4LM783jkwXEPj\nrDp2XPlLJcQ1Ebp+N4QVtVj6WKyLWHUENswHJIHZs/jFayOcvbiK3u89h/zqPIyaQf6/F/Zy3v0f\nh+8pR/ZrewdoqPfSvq2dc7adowrQ3Xw+O67cwZn3nIkEaj1/cGcZ5o2G6yN2BMoe9tK1vktZsTPf\nR0qblY9cxJ7rXnGDxSYGVtTCljWx1NGLMFCB31xIFMOfP9fBOUsMQnf+ivC14PMpn3bPEdWsIJ3b\nxwkk52olFpNzni9jCaQ97HWPmbrWYHTWU6Z+C85AXXwgOpc023xdWbmkhiY3FbLY+Uq8Xanz+TWZ\n0eI/gSRmAyUXOIs3Xneybpx1ArY0CNz0p/gfeo9wsIqjM3tYVXUeoeMh5HAtb/e/zSeeWIW0pVvF\n8/d//RqrH1uNQNBQ70WIaGxtgSos97/+7SLVHjJ2XoBINELICsViDIId1z5FrSfZpHJE2opaEI0P\nYiNDNYTueJGWzUrkBR8gr11G+J93sfT+Gra/MMjqFSatbTZyQwtygySAwKgZpGPfYF4Lw0CJ4SdW\nN8BALFh9toQbm12fdmpw+NChuLg4AdB0OKmozt+qEDGxLNUDNxBQ/XDHymQZyzXkLqK7ppvWhV6W\nbzHYuVPV73GC3Ikkzmay9VvIJxDtbJ94vlJ3WMt7sV7Uou0+ZXAII56YkOnYzmeY7pRN/IUQlwJn\nA+8Aq4G/A/4W5YNYDGyXUv5nuc4/mUlsEdmytYXgQBC/5yOA6r4lpaQq0ToW4JtxKuE7X+KcLWB/\n9X1EdQQhBIG6AI1mY7xqp8dizk0r6B0Ku70Degd7ef2rr3GxeR69Vi+LHxlwO46FIiECdQFWP7pa\n9QF4+D11nK+9R8c+S13jNlUuumt9Vyxl1KuCzdIg2ANCBHj9K69x6Y/nEBwIsfKHaxBCFbATUrkl\nXv7yy/hM35juGkjvHrEsaNti4PfHgrBG0tK3Ue6GRIEaqz3f6afb9Ay8D0hOmOUdla6a6brS4fTD\nzcRYrqFki950yzyZZnL9nlRRTo2dpBPnbOsnUquGJp4rcX/ntUz3Id9AeGqQOt353c8aG9iM6rGN\nh0rOvJkIymn5dwG/kFJKIcQi4GPA/y2l/B8hRAewCdDiT/oHI/EBkcO1CI9g3++Ps+qRVcjqAZ7a\ndZCTzflY0Q+BZzft29rd7QNmAKotZbnbdvJgcm8LvfJdoJYff/r/sPHZL1NlVFHrqaVv5G0kNTBc\ni10d4bFPPcGJM0+kod7L3LvmYg95CUhJ2Apz2veaCcyeiW3bhCIhRLSOrjf+yLtH30dU93C48yhY\nPk47DaQUNNT6gBGklISiR+Dq2UgpafLMQkrV77hjXceozw7xNQFSQlengc+XYpHXp6ZeGljReNP7\n0++M9yse1XpzDAsxGBRI6Ye6YMZtsolJujUCY503G4nprzBa0BPdP5JYGq4nuUxGtut33DitbXas\namhmSzqfgGbOvn5PPEg91pqB+PlKU+9/us0Kyib+Usp3AIQQJwF1wM+llLZQieRfBP6vcp17MpIp\n37rry4doObMGIQS1Vw6qBjEDQRY/soDfX3eENeeegsSL2CDw33QRT3/xF1z25BC2FG7+vxW1MPFh\nWZbK3Y/WIe7u5ot3g/3VYwRmz2L1o6uxh7wYdx9ULqF1rVy2TK0ReP11qWYWHEFsXECjbdM7EiE4\noI7HcC1y8wHW/L9NUHcA/y3nqwfIY9HUFA8Ch6x+ZdENexHVg7HZyYcAtRK4ZWsLAsHBjQdHBTp7\njr2P/YGfRYts9u4fdBdSdVyjUhXd/PXYINDWpvbf+YpF8HYV8E7tV5yt1k4gIJAIdr9Wj+kpvF9j\noqhmGiyyVTJNLazW0qKC1k49/VQk8XURu67owrbNnFpOxlNaVQkRkOCpJ5MAJ36+XMkl8JtvSm+6\nNSbp0obHmulNt1lBWX3+QohzgeXARuBkIURf7Of7Mu2zYMEC9+dbb72VTZs2lfMSKx5VUVPlly87\nxwR5AK6ud102Dtu/sIsLlzdw2f0gN9RieCKcUHcCCFWOoeWcEYIDUQI31/G7G/aw6mE/PceU2kjl\nmKdpZhPSVCuF53h9qk+AFFy64gQMcZDAhvnYnmOEBoIwXIvP66eqdgh7hpdwQlmJJ//ySVq3tiKE\nYDfuU28AACAASURBVNdr3a51Hrh5Be++9z7GfV346/zs7nzPDcSGrTBz71KrnlMrgpom+DZ8kuA/\n7CYUEu7D7dQPknbcNdHSEs/DNwx1/5pmemP3Mq5+uTzsQqi4hukx09bnLzSNLzFIm0gmX3/i+gIh\nVJOWYDBz2ejEdREAy871IkR8AElMc013/RIbhMR/08cR1YOYZhdjkY/FnEvgt5i1Etn+rlNF1Ddt\n2sRtt91W1DGELFNeVMznfyewA+W4ewv4c+C3wCDQIKX8y4Tt5wP79+/fz/z588tyTZMVZxre1qYe\nzH9/vouGei8LfAs4Eg7Tvq1drROIlRne2RF2awetfGQlof4BxBb1MPm/dT57v7Yby4qt4vUMuLWB\nfn3Fr1n1wCcIWkECs2fy9F88Ty0NrFmuVOm5V97ijAdOdZvIADTdfIF7vPZ7VmHPGACPRe9gLwHP\naW6tooPdBngswv0Wy9pUraHEUg1W1OL0zaqAUNeXD41u69hvsegML0iD3a9bmPWWsvhjGSxxix/3\nd0fg0olTLvVmcmnOkqvwOds51T8TM4zGsoTTiX9HhzqGlLB3b+ZYghW1krJ8nM/hNIBJ3c8JcDtl\nq7s27oKoOWawOxzOnDGV9r6XuRl7UQX0Jqnb58CBA47RvEBKeSDX/com/vmixT874X7lsw9G/0Cg\nLuAGWN899i6BugB7r32DXkt1BgtZoaQFZL4ZpxIcCGLUDOI3/VQZVXSs61BpofdeqNxB1RGCA0F8\nXh9GlSo54awAtaIWFmE+8o9nQtQL96m1CYGbLua/v/YKvnqTI/1HVGxA2gTMAM9/6RXVmxjY9/vj\nnOJrVN3FwmHa71mFqBl0/fAqX1+J1bK2BqRt0NUVFyjLgo9+FEIhCXVB/F9fwxu37HEfWKcUscpl\nHy1W6QQ210qTqQNIYpAza257wsANowcSR2zTZSa5f/dw8nXmGztIvN5M7RcT4wRyg8qUyrSWIPG4\n2Y7nfObEuEQh+f6FfN7JJuKFUqj461TPSYRpovL2E1aAOvV3hBBYhDnzgdPcsgxyuJZAXYA9N+wA\ncN0qUqoUTytqseSeVYS/8xI+04fYqGr7OvV9nG2saGx2cbyOpkf6kVLyi9d7qPV4uXD5XtoeiDU1\niT3MhjDYc90efGYj/psX0XPsXRZuG8Jv+tlx5Q5WbfsEPXe+gCEMrPWAGVsbIG0YNun54EXEwAm0\ntEoOHRwdbLSP+Qh+91nC11ss/75S0o51HbRsXgoQWwmcHDNIZ21mGyCSMosSRG1ec6wEd8fYTvRw\nWNUbUvc83nUrMejautBL8NiLBG5ZMWr/1IEj1eWUjrRZUQk/v/wytLdn7vIlMOhc36X2iZpZF0yl\nK3CXiG3HXXFOs5dcRT/XQTb1M08X0S8WLf6TCNNjcnDjwVhevYmJ6Vr/jmALIZBDtfznZ17mkx/3\nERbAdR589SY9X++h1+pl5cMrsUds2re1Exx4H6RN2Arz6hef4hNPrALUABEcCHLWPWcRjoSVNT/j\nNAwEISukykCvV75glZanfOOHbzyM6THxmcpk33HtU5x5z5nYUjWJWXjvQnwzTsUQBv46P6ZnBhB/\nykV1BP9NHyf8z88RHJBY0XrMmJAbBvj9qqelUdWE6YkHBS2LMQO79rB3zCBiLu4IK2oRtix6jsVG\nX0+9W+8ej1pp7K7dsGDRovhKYifHf5RbRBoE6proWt81alaS6u7JxlguD2cgWb48HjdIHSDi/n/H\nFTf2atnEzKNMwdVwGObOjV9DrsKcOnPIZbvpEqgtFVr8JxmqnrxJ80L1e3e3zy0P4TN9HL6+h/aW\nOXzydgMpbeTMHtofaMeoUbn0qx5dRTASBFRKqFEzhNywABubtf86iz3XqdIIzoAipUQO14K0kbUD\nbH/1j6x+bLXqBkYY+dULQYDFLlq+qyzvruu7CPcrN9GqR1a5ZSiMWM7289c9zWqxIlZOuguiZryw\nXMzvvLRGHcsJNjrWXVUVdHQ41UWTyxQ0zbRj98iIr5iOxQUE3W4OeLbaPpaFqsefxlctpcR/s1fV\nYTK7MD3pBw7LUvX+neDz8uXpy0MocTUyWsOJq1VzFTaJjRUddAfNxLo3tq2ua9myxJo8mY+fS4ZQ\n4swk9Xg+X/EVOwsNrGvGRov/JEfVj1dPX/eGbmVxy3gGj3/jpxDVEd49+gHn3r0SUR0T4phrBuDc\n+88l1H+M4Hsf0L6tnTdufIODGw9yIHyASx7+DHLz6wCENy7gwh8tpWujGmxatrbQM/ABgboAvf0R\n3r39eZCCM++aQZ/Vh73ubERdHwK12ExKiUBQ66mFaiXO8cJiZoI/3KRrX5dbAtpxn2QKVIIShYPd\n8YVsiTVwwHSzdjL5qZMWjI3h5xZCsPva3SoO0m8q71Ka1bOmqSz+bG6TTOJaiOCZpirk1rK1hbaH\nkqusOou1du9Wwi+Euq/Oz4k1epxj5btGIZ2LyjRzr9iZ7+d3rme8B4apElPQ4j8JSfIbJwiPsnYt\ndr9u0n62F4lkz18/A6hiaiFpE/ibi3n96hc42dfgBlpVBzEl8NysKoeGrTDnbDsHe0jV3FdumgBC\nDLlWqhyuRW5+hZAwiPzFiHsd4WO9YAVgayf+W85nzw073NlEz0APSx5Yomq/i/TKpyxXi+UPtWEP\neZGbDxAKVqkVsDH3ijtDeCy2gGvjIeLVPONunkwClnTPYu6a1DII4XDcek3XxxicASTNIj0z2bJO\nV/vGEWv182glKURcTDM5R97NEksYOA8ejGcdOW4pZ9tUF4pzz8LhzLGHTAXvivkc2fbL1d2TSxXU\nfMjlvJNlcNDiP0mJf7FGl4II1AWQG7yErBDLHvsQL1/Rib/OT/BYiPC/PMcnHvCzq/MoLY+djpSS\nHVftYdXdSgH23qCqhy55YImq61M9iO/m86kSVXTduNsVSStqsePKHSy8Qwl4Q72XppvbOX78OL3H\nBmBrF4Yh2HPdHk6tV6Z65/pOFt27iJAVAlRJCstKdmt07LNYdNcS2rdFwDNCz3vvw7EgPp+Pn7/Q\nQ9tDS2O1g2KlLjZ8AKhMqFN9JuF+K9Y7OO7mSRR0R5SVa2d01610vurDhx1/vZmxRs5YNewzBW0z\ndf3KRKbso9TrSHSFhcPJwebUffz+eLA2cYBKDbYmxh4SB8XU41WSeyaXKqglP2dCrCYxW60SKXzZ\noqZiSCqnHENURxDVESRSWccb5vPa6yP4zEaCA0GW3L+EnmM99Az0sPrxdvb+bpg3Ds5wUybDkTAC\nwW+u/g0ebxSjZtA9T7jf4vQ7W1jz42UcPuSh50g9psdECEGVdwhj5nuIG+fz6msjbuA33K+C1Huv\n34vf9GMP1RC8/TfMPbmWlpb4dVtRi9CdzxK64zeMfDAb7u2AgQDhgTBnP3KGamADIAW2LZHRWsSW\nbpa1NXDkfyzO+qcL+J/3e5A2owKo4bC6DrdvsMeKFbhLuZ8p/u+lS9UD7aRcHjqk3EY9PXG3kZOq\nmgv5bJt4/c3N8etwfk47o4jFhebNUwOYbaevcinE6EGhuzuejpp4fMNQg8TOneqYTU3x+5F07hxi\nE07MpRica81XyG27uHOPdV7nc9m2mlG1thb/OcuJtvynCKbH5NDGQ0mrYx2BadnaQij6Jpf+9FxC\n1xxDSpvgcdU8XkrJyFANvVYvSqfVvn7TT8gK8Zkff4Zf/NUvmO9Tay8cy9pJT1QpgfGURfur82ia\n42fHtXu48NyTIFanf+4ZTlOVenZcuYMz/mVxwtWrbCEratG+rR1bqjTQcGS0uuy4cgeNnlNYtFmV\ngXbWMti2ZMniGoLB3er3uvg+iWsEZN37CAGBuhrYuBSkyc5rd6Wt+RMIxH32tq0eZsc/nlhfyIpm\nrx8Ud9MlB4dT3UWZLHpHVHIJwObKWAXbEn9PrQc0FrnEBUplGWcT/dSVwqVq9ZnJ1eO4g3btisdS\nKhkt/lOI1BmA87OTDurU95exMs3KAvcSuuMlzrgDArecxZ4bdqgWi0CDOJlgf4jFDyzm9zf8njXf\nX6Oaw8gDBMwmtn9hl2ouM6xeg3hHslpPLe9+oMpHRKLJvpJGzykE6pqQN5+HsBqxqyNYPI+J6fYf\n8NUFYEYVwfUtcM9e5pgNeEw/a76/ho5ruqkyqgjM9CNnz0LcfD4M1/7/7Z19dJzVfec/9xGjlysb\nxXg04zbBwIJ5MZacGuyxeTGGvJFku03SPd3NtoubYBJEYpsNtA09Z5ukPaekLSRgB5sEk0DOhu62\nZ7dJN2nahFBMTSzZhK40RgbLQsRAYV5kIyw9kj32c/ePO/eZZ2aeeZMl20T3ew6HseZ5uXPnmd/9\n3d/v+/v+yNz/JI5YpBktab1QRSPw6hu6Ab1SgKdQQtc5cKyF7NeeYtWWBex70S2qNnZz4DjSZ8iA\nB6JYxtoY8d239IfSTMPkoN2czkeUfkdQOZ5sEt6mujcarR1eMeEtk9QNM3iG7lnN4AdDV2ZsIyPF\nYZ9a4y9F0DM23P9S+ItMoO/zdFAqE3I6DLLJq5h7nq05AGv85wCiMsrBTfppzLpZEo8kQMDT65/m\nhoc+ARM63p9+6ygrv6Wrg8lJztvxS3CzfktJz/NIHR8h+pllON/cx41rzkNt1InVp3qPcMNj78dp\nP1dTT3MSId4GYGGHJHWoEO5Yvfw8HHWQH//LG1y1ognPU1zJWl74g2foW5/UBWXNkzy9/hmu/8bH\nyEzGOOJCdOJdeM1vFrFanMgxejf0subRNcT/+Dp+/O+TfOj9rWQn06x67Br9ue59FiHa6Iwp6Hkf\nIjKlaabH22A8Tuqox7IHV/LyPZr9ZCidvf0DSKJ+8/jOu95P/+a9EKEobBPUDyLn+PmEoh4NObOw\nAFu05AWbnJotD0sTs40UMhkDXdrdyjCe3nxT725efjl8AaiEYL1Co4ZNSu3xm8WsmvSGrjauXv0c\nRK2xVGMQnaqBrnTtszlBbI3/HIH54SzuWEzyjiRd27u46bs3sedz/ax6WFfzio75ZCcPIYRgYVuU\n0cnDOMJh16d3c37H+T5fP5M7hOOmic+L+4VepmH7wOAUUamZFUWCahGXrvsSWthtXFNFb/7eh1jY\n9v/IpB3S9z3JlWot4lsDZCaeRW1cwrrH1/GDTz7DNV8BcMhu+QfE5iXa8EZARHTPgqiM0n97P6Nj\nk9x4dSuOgM673gcRl/Rb4yg8YjGPfckmZMdz+eYfy/GEoDPmkZ5IIwIMGZUvSEs83sXwpmEGBqFr\n23WI5kncnMuye29ANE/Rt6FfF7R1SA4Olcg4DBbm3tBZFR4nP3U92YkfEZ8XK/+OKhgQx9FGurSK\ntl6ZhDBv1/P0gpDJFGoeqjW7r7YbCDKJqvUTMAgyjippJjWKencdtUI2tQx0tZ3IdMZ/JovUrPGf\ng9BKoTp8Ee2Q7EkeyYu8TfrFtj+45a/5LdaQnczy298/l94Nvf6OICY7ce65joGeJNEOnQBWeDok\nktP/lhIO5qtfpYRDWZf0V3eBp1jQ8wnOaX8b0X4Y7zPL4Ov/DEKRdtM4ExktNdEewxHHePfCBTAv\nDRMxQKGOt5B8bZhP/u9PolQLL9ypvfVl25aROnIUZ/yg7oB2216IaCqo+uL17Py9PchAs5j+27Xh\nZlOTriKWhVj9QM8Ayx5cBTnhj59mF+94G6u6FpBKDeDMy5J48DyEAwODbrnBDBaujUlfgTO79Ud6\nV/FcodF80XcjC4nDMK69QXastiaQgTH+htZKQPrC7AgqNbsvHVsplNKJ766uQPK7DoNWzcgVPrOT\n1/OvvMCdjoTqdHciUH23caZhjf8cRBEdEFj50FrSE2PEY+f4eYBrv/kBRPMksY5O3+OPRS4kff8u\nnPYYyf3HfGbQ8keXoDa20rs+SVd3C+mJMWL3XFck2rbqkVV43i4Yj3P4vp/TGT/JyduWMXryEGy6\nhIUySlNzE5mNSxDtMfYFaKWZ11xGx45w3Y6byD4wwE3bBbALhGL0U5OMRkZJT6ShWdH5R9fS+6l9\nrLlaomjl6ef2cMNjN7C0K0d8nm5u0rVDq4fq2gCKtJIAJFG/P7HbM6W9vZzUgnbgS1MoFOnxNF3b\n859VSl/uwBSrGeaMENDb65BIaI8/2hGeua0kiBZcELJZ6OpuJT1R0ASqSP2UBQG55Y/qVod965MI\noQ88FV1HKbXHb1Q9K6HW2Cq9l39V9brBRWa6Rna2DXR9C50N+1icJhiv5dAbLumv5DV6vtTF3s89\nw8quDtREGrVxCf942z/ykSc+wppH1/D07/eybmvM1xEKwmnWRjI9MYanPErVYkXzJNy2Gh4Y0eGG\n8VHU15/SfRw3XsroiVeJNceIzpd4J7x8jFySHUN7yB3wN7/zN9z0QOCewuH6b1/L6MlXISfpbIuy\n7849SKTfzGTdY+v845VSjLr5hQJ4NTsKOZcbvqdzDMObhv15ETgodIMZb/xdsOMAjgOqp5tOYM8d\nO1n1yCrURBoRObcwrxV+wEKYhGN9dJ1gc/mgEqiRc06nHWLxfNgtJ6t62maBOzn+LtL3Pcnqra38\n+KcuH3p/K01OQSojdUgfH+b1h8EsRqUVvKUMoVPR128kHt6I8WxEDK6Rnch0cKbyANb4z3HoEJBm\nvwTF4RzhEG2P856O9yCEwFMe6x5bh/psK6p5kuWPHvO3vqbyddQdxfu8bsZz0mn3aacyIunb0Mfr\n2SN87K8V2Yk03oaVsGM3ANG2KIe91zg51cLo0QnY/nOu2NJMk9AxaR2XH0OITri9GyITdEq9CGVP\nvKoZSFtfYlQ4uJ/VMWXTzMRpnmL3Lf0c/gTc/L+6ueFbEFWXQfMkSy+P4B1dAO27EJsvLYzX95an\nWPZgK+k/3wPKIR5XiOYpv3K4qeUY8eb5DPQMlLFKjPFzcy5PPaOT3mFsk7DCLbN76F7u+apIweOF\n0DmA5ICjd18hoY8y45GTsD2JmBB47YoV14whxNuMvHiuH36q1+ib69dj1OsJy1Sjt9YSqptO0/iw\n69aTLM6/qv9G08TpygNY4z/HodsHCtzcMWTHTrq3d5P+9NvEZIx9d2qRt9237ubw2BRXdc3X5/zh\nNcXFUTnJ8qWQ886BTwPNkxw+dozu7d2+CukFD1wAQOfGC4giUJEpMnkW0U9v/Tk3f+cTZP7iWTgB\nuDGyExCLaf5/ESIuNE/yk09/n4888RFiLTGe+M/f56atOmm98lsrGfmDF4h2SIbvSmojeoUklVJ4\nbU+CG8MRDud9cQVePhAvhCAmY9qgUDAmEsnO393L0q84IAQ7dwoWLtJyx7W6TZkEauz8MdR4jEWL\nPIYPOmVSE5dcor38vj4t/gb5H3yHi/fZlfrYjr0MDckiqYZksnCNYGiHCGSzsqgmwRyXSeu5/Lu/\nn+S69wtAzXqooRbDZjpJX9eFiy8uMKCCMhr1ItgF7Wxm5MwmrPG3yPO1paYhkq8ObtFtIi/ecrEu\npjrehlIvAZCZzBBvnV90jZPqBJkJLdsg0M1jVK7AiTcYPfEqnvKIN8d58b/9K22RNl0FfKJNFzFN\nLcITHrSn2fuvecJ8xGXVI9eilMJpORc4lw8/8WE/tPRf/v5jxL7YqpvVNJ9btOMoUCkFnTLO6GR+\nd3NCEr/nOv7xPz3DeR2trHqojYsuPkF8nsfBIcc33jeubaWzU/B3/9flxptaEei4PpHaW38356I8\nQMFJT4+11HtNpbQhWrlSK5b6yVkXMvc/qV9vhmhAotrE1/3cwKCbl6rI11w8OEQ67fiaPea+Rl3z\nE/9BEpOt9D0/VZe3H8YqKjXq1ZRSSz+zgWEKGfkJA1M5XE89w3RRugOrlvuotDjM1oJwuvIA1vhb\n+Aj2Cyg1bCoy4fP9VWQSKBh/KeHv/uUFrvn2Gt1bONeO+OaQjm9vcojKKIvmLeKkdxKB0NIRQnB+\nx/laSmLMRTw8QKwdnHlaQKbvFx1EowXOvYgoRL42oS3SxppH13BSnfQ7lu3/3H7aIm0kHkn4Ow7T\ngaz3F5BYIRHC4cmnXW5a28bofbuI/smVfPQHCV2kdf+TqPFOPKl8VkzX9i7S48/ivR3nmnVjOE1H\n6WyPAk11z6fjHMUTkJnIkB07l8WyWGAsFtMLgJFP8BO8WUmn1L13Te/h0lg64OcliLgI8gubozV7\nenuLY9rDw0FP2ykUtVUxYqaYzVMeyZ6kL9cRPN60cgw2lA8zkKXJbGNw+/oKnzubhXhcv5FKiYoK\nruazNBryMWjE+Idh1kMzEbNKzp71t9o+FkUwjViM5zy8aZiRzSMsal9EfMG5xBecy6L2RUXNR7Ju\nluu+twLRPMVTtzzFK3e+QpM4B0FBV79vfRIxEUXk2hm8Y5BkTxI35xK/L86FD1xIeiJNdjJN3/Mu\nwwcdFv9a4aFXx9s4eayFN8ffZOm2pazesZrdt+p8gclV3Pj4jQCk3TSpiRTZMZdLlnjEF4+x8qHr\nAQ+lPN73ES0EpyWOhf+ZF82Pa0+5p5vuHZdw4I3XUEpx3mc/oQfhLsI7oWsCqunyBHV7ZETmFwtg\nvJPEitYiXR7Q4ZtXXtHGzBRPua4ugsqkHZQX/hM1C8HAoM5BOMJhoGeA4buSDPQ7OI4OIxmPPGgo\n+/uLw0HVdILMHKcn0nRv7y777Gas6XS5Aa11beP1JxJ6QXJdPX+e8vwucpUgZfF8NUL5NHMXNNqO\nU1teu5Gw1KlQUM2Cu2TrkoY1oBqB9fwtqkJGJIs7FjO8ebjs71Bs7AAWtC4IpRee+PqLZDNJaE+z\nlms4p/U4T93ylJaMzrUQvfMmmtqOE43qBinmHv23DtF1RQujE2n4/MXQcgzP85jMTdLkNBFvj/uS\nEjIiibXHisYHgvSDPyQ+D3bumuKK3/BAeIj2UfbcsdP3fgcGdZFX944jvHF4jN9Y1o4jnmXh5z8K\nwsMRDl5JWAYIeGgUNT83EtN7nj/G6ymXj9+sm9aPjrl4XivCAdd1QhuhBBGsgoUQyWUKuQdtPA2r\nSB+XzWrjKoTeWdS6Xxlykr71SVY/3h3K8jJjjMf1TqO0W1kQpTsXxymvPo5GIf7l7vzrvTVDK9Mt\n0AoeV0+YpV6a6pks2moU1vhb1IXQxuJuVhu7nOQXv7+fD/6PD/Ib37gG0TzFK59LF3T/j7WSTTuA\no4u1cm3QehzchThbR/DGozhxSL54rOw++t9ayTPWHuf7//UJrvvOdSzdtpSRzSNFYQigaEcyMDjF\naPZcll7aRsYFIi7RL9xE5q+ewst30DIGWyA4uOkgAz0DXPnAStIACETzFPEvd7Pntr1I2aENSb6T\nmin6EZHJvL4RZE7o/Mirb7i0ReCiy98GJYhKRXoiw9IVAG/nK5D3ojA6PwW3U8qA7DTFNM9S6GN0\neMsUaY0MuwwNRclmdb/eVKp8EQneq1pCVhuyqJa6kOXPQS2uful7wWNKFyD9nvRlNmpRWOvxsOst\n0JqOkZ4tw16LTDBTsMbfYlpwc65uznLkKGLr89z8YIzM+G5AoXq6SXQvwBFN9A/qXgMX3Z/FOxol\nFoMX7nwO0E1RmFA4AlAi/EGPuKiNy1ATabInJvn4//x44b2c1L2MZWGrDLp6d/nD+cbutw7RGdP9\niG/43jUI0QqTencw6R4h8VgXb068qVVE3SwyInnhzr2M/h6sS5yH+NY+evsPE+3QhmT11drIG317\nlBZqM/0Fol94H5ktP2PpvQuJdiq8t1tAgJJKH4w+SeTrItTGvJ51pLjpvJT6s+vq6fN0J7KQJKth\nCz2tdeW0uulDa3nhzudIJCSZjOl9XDmBGpaQDf5N4ZHYkSgznPUkPBt5r3A9s6usfG6lYrgwKAWn\nUMvWEGYqWTubRt/AGn+LaUMIQSxyAcgYgiYcdxEAv7j1RT76vUJSNNohGXkJDr99hPcskgFuuofX\nlkIIB+F0EpaC0rx2Qexd8xDMx3EcRjaPMDkmSXRFcRwTYgpv0u66hbCCEIKdn3qGpX9afB+BINoW\nJbEj4TfDSW4YRjg63p14JIFo1t59ZvxZYjKuk9mCQvVzu6476P3McyQebiU9IRBC0Zln2IgNCWLt\nR9hz2548C6kPKHTd0mJwgURpzvWZVrHPX8jAHcmSrm3gutJnC91wrWTXTpdrrs+R/drPcD9T0AQK\nax4fhrCQhal3WP5o8byaJO90egxXuvcll+jXRumzXkNa696mT3Kyp0DTnU2czaGeIKzxt5gWTDy+\ne2krCIfeXli9WseELz0/WhbzZ8uBvOxDF8N3aS9XoRCONpJCFLRngp6lab6eHJzKt3B0kURZvVr6\n7BLNYCk0aY/KkpaLKRAiTt96/eMfPDAJEZd1f9uFEIL9n/klbZE2Vj/eXfiAERf1+S5S42/i5KaI\nNce03MU919G3PpmXj0A3k2meIrn/WD6WLBkehlezWdZ+Zy3Z+5+C8Ths+SGxL16bNz6wZGt+Z3J7\nP+6YpOvyVhAnfdmMIETzZF4ldLlOcKNzHLs/OURnp2kp6fHxD5+H42qxOimP+cVip2KYpQTcgk6R\naYdokrydnZTVFEwHhvYKemExi1U9+YPgggn4CqrBZC7kY/1lbU9Pj5d9NsIaf4tpQ0akJheKcg1z\nAHL5RvKBPbdSBSplekLHqHftGefdC88pakRvNIEUraC0THLiCd2msjNyAYIh4vGmIqVL06TdjI0I\nOlnrCGIxvTCZuHjnH11L5oS+1o1XvxuBw8DgQVyyPtNJNE/itEzRKTv9XEI2m4/JDumEbffyfLn/\nJqfwuSMuN/5tN5mcbhyjdwiaibTswVXsuW1PoUguJ0lc1cqbKQUyzZUPvI+RP37OZ1qZUJSMSLzj\nrfmeDFN4x1tZfbWmgj4/4HLz/1lJ5v4n6YzFoKeb5Y++pVsXLteD8nsFlyQ7i75PWcgrBKmaejdQ\nqG+A4iSvKU47FRjaq1IeidVauqPWYlIUrjKU1OOtiK1DCJyi3Qvg9382z1ZQcnsuLgCW6mkxx9h6\n/AAAHCRJREFUbQQpcER0oVEp9U6hIOLSN3CEkeEITvMUyx9ejptzEUKglOKa776XxONdRVXD/q7h\ns13aIFzVomUcgEzul6iNSxjY7/oeYigVL+LCpiV0fmkZyRddv22jpzwUinh7nD237fEbtACseXSN\nny8Y6BlgZPMI+257GUkUd0xy0Xsk8bjylUsFTtH5ZuwAixacy8jwOYy8Nkk8L8CW+erPWdW1AI7L\nAi9fgDNPVzZlv/azoiTm6h2rNcXSBbF1COcbBxnYcFBr+iiHdBo+/IFWRPMUsXuuY0/yCE3z3ioa\nj8LjygeuJr54jEuWeBWTpKYGwNAuzd9KKZxmvg8ehMWL860f84Vm9aI0WSsl+ju6o4vUeMqvvq50\nfCP3MPmTmaBP1ptkPh1qo6cK6/lbnBLKkq2BRtm7+11Q2uNNPN5FsidZxK3vbG8mNZ4CFOpYG3s+\ns4doR6FBvC7umtJyzxNpYsJh8I5BbnjsBoTQFcjBH3d+REU/bqd5CvJaPBJJ/J5VAOzbvFcni/Ne\nPFASU3f9LmXak9Sdr0DrIK16ZCX77t5J/6D0wwzmvK7tXSil2HfHPqIyiptzSW7uYzQLS+9dSGYc\nosdbcJqN9LVDdmw+iataUErgjjnQoa/15vibqONtjI65CAq7GimDipoO/fm+CkZKmoiLjGijPOqO\ncsVDh1AhgnvVYBYDz4M9ewrftf7eA6tsxGX5w401og9L1koJonlKPyMi/PhKFbZFSrWbnDLZiNKm\nOdNh1NRLK7VUT4s5DcMQIaL7ATvCKfvB7dvv0rVtLYpFiK1DrHmoSXuQ0s1fQ0Gz3jUkdmiRtoVy\noe49fLyN7qWtCPJdvfIyzcmepO+5h/XIffnufXqAOcklV+iXA/td34D0395fdHwQ0SiMvDbJqkdW\nIuRhlm1bhjjRjtiq7zEwaJRN0/452TGXZQ+u1HmBnmQ+/OShEKjx83BdLd2wWEr6euGCC+DCC3U8\nHanHydaXWPedBSQHCgud5vNLX+MHdMP2VApisVbYHKCgAvEF8+GetfTd1gcRCSGVo6VhH9fVhj+T\ngVUJjz3Jw6z5bnFo7lRQpFw6qL+DZE+S7q2tZbspc7xZ7MOMrD+ekO5oYcbe5C9MbUQYZlPTp7T6\nudJ9ZmsM1vhbnDJKf1jFDBGH/p7+sv7CoFlAw3dr8bXl32jypQqc5in6b+8vajijk8QaQgidbFWF\nRLFp5v7a2Gt4yvPPDTNQbs7FHYNUSgKKK//qes45780iimhw4WBT3hBFdJhp3907ufKv1pJ2M8Tf\npauU0hNa1z+5qY94e15UJ6cTuanUAM78DO4Gl+FhyavZwyzt+hne0U66Hha+JPKk3sz4Ri7aIYm3\nx0nnP4uLDg11belCHW/D+cZBP7YdBnW8jcxXNQd0ZDiim9vsKNQ0lM5NmMhaXx9cdJEildIU0qZ5\nqrzYK1ecEC6b75IQiM8iyt8v+L0PbRzi4FBBzsKcu3t3oRK4VoGaEbgbCjQTgvKx1fLSwxhI9RSD\n1cNQCt5793Ouzt8E8hTVCvtmCtb4W8wISkW/gpWn1TxEGZHIDv1gZ93DJB53geJdQil7o//2frq2\ndcHGS+m/QzdRickYaTfNVY9cRUzG6O/pz2v7VKBPRi6kMzZEOiXIbPkhsbvfz+jYpM4HHG8jO+ay\nOC8s46JZRsvzDJ2n/mO/3wNh5ytHaLvtCKseuRan+ZivjxQctyN00juxI0FyUx9rv7MW5T2VX6CE\nX4ULOomK8JAdU7gu2lO/DRKPXMpF215hYetCMpMZOC5ZZBa/vDdsCsOk1LrzrgtdW1vy83yMrkd0\nTYPItec/X21LEo1CLKZITaRxWo758tW+tMeYm9+BydDwijFypi7CsILM9xlGJS1NNitV2IHE44XP\nG9YysjSRS04WQoMNIshAaoQ11ci9FB4rH7qezNEfsmh+HHDKwmKzBWv8LWYNjYQFXLJaQgBRaLFI\nuPcFOo8A5GP9kr7b+nyePgJNn1zhIRx8lc4gRPMkfc9PkVghgThq+wBL708T/cICsl/7GRd9NcfI\ncJZoh/SFzQzFsi3QBrMtIlnz3SU4zYr+24t1/YnoJOayB1aSzv0SIvMZHZskff9PAY+FX1zB05/9\nKYlE1KdN7nxWU1C7tqHbXgKDSW1RvWOtZI5NQDM4LVP0DRxBEi3y1o0QWtZ1Gc2NojZ+WHvqkT6/\nab3a+hKJbxfCSMHqW58VFJiwZNKBSHGrSyjkN1JHd+V3O/XxR0p3GNGO2o6C6WHQ21vIQ5iQV9Az\nLsrbuLp3snmv9Bmo5KUbVVHDQCp9fybCMObe2bEpLrr8h7pGJN/aszQRXm0n4bqFHWOjsMbf4owj\nO+aybNsqMrmUNiIBby3U+wpo+ABcfL+ukk1uKiQ9u67Q9EkxL6179FIQqvPpk0j6+gAcVq/Js0uO\nt+UrQj1WPbKKfZsLtEy/aUtO0tmp/+bTTPPaQqXccSmhaf5bLFLnMtAzADmJI8bw8MhyQNcCpF/A\nZDhvXNsKG4OzI1iXOA8YIoZH1s2gNi6h813z8vOT9SuA/fl0s8Tvi/sLVnxeXDfUWZ/kqq9/gFEc\nUKJc8z/PsIKC11wwrBJy4fHx0gRtEKUeehAnT8Krb7icv7iC0Y+49A+Wv+d5OifS3a2T3kX3C9kx\nNgKtKqpfp1LlXcqmk9CtVk8gI5L4PJ2XMa09Gw0dnThRewxhsMbf4ozCdaF7qa6c7fzitfSu3+Oz\nhcyPSzd1yYczwDfi5vz0vXltg55g9fBJnPkZOu96P1LuLbpnYvuNqOOtiO1JX/e+9xdTTOYi3Lhm\nL7F2hbq9i6aWY/4PVyD8cEfWdclMjuWv1lHUD7mUOx6WaEwd0l3P1j1xLuSOEYsplBI4jqaODuQV\nTyd/N7+zuNrB86BJOMTaY3iyE8EUXdu6SLtpOj9/Afvu2EdYQ3iAvg19up6gS3I4/TyxmKKvTy94\nnqfrFWoZsbB8gOnQltiywN8J+ccHDF7YtXfv1rmEy5e0EP9yNy/fs9dPwAI+NTM4lwbJZKGwLMxQ\nBhO/tbzmizVPoEiGOoh6vfuw3YBRKQ0uqMEdrVlEBvpNK83G73sqsMbf4qxArD1G8o595d5RxIXN\n5fo3BVVRiEmtkml07zW/v4tOpfxG8AaFxULQ2ab/pvBIPK7vIRiiyWmi/87nIKLDGumJNPH2eIDm\niN84PRgKcXOuz64JIizRHe0oLGBscoqMpZuDiy7WwjapQyUMnNwxlj96HKWET9sUzZNFxiIqowze\n9grXf/t6mlqPafG7HICHXsYcZIfLyc9cTfZrT9G9PJYPjZUsVJHi4rBSuDlXs382tdLfk/RDFmE7\nCDN+f04qGONLlmg6at9AZaJ8aEFhCCqFZ8xcu66pji6O6Y+M6P9Ho+Fee6mcRdhuoCAo14raWC47\nEkS9uYSw80x9y3vf2/j51vhbnFkExM20IS321rJ5Bzvsx2O8USEcBvqLf0Dm+DCVULPNHug3i0Uh\n4dg/OOXLNLg5XWkaa4/Ru6G3iAlk2EdF189Jn/bJJieUchgGEzs3bBcCgmauC2uu1q9L4+NAUQVw\n8Jybrj6fc3iZgfzncXFRPStR9/0MiOHmXDK5Qyh1soj7XxrTN0a8/9Yhdu8uePHZLP7nMz2Nwwxe\nWNxd6xi5dP7JShSKfXc/g4xIDmVd3nh7DFCseugD7Plcvx9mK+PpBwxvWDK2Ym1ASU1KPF5YmEp3\nAmHJ46L8Qh0wO7lSJlS9oZ1akBLa2qZ3rjX+FmccpYY9+MM22j7GKIfBbP/NOVDQ9jGGI+gFBqmE\n+b+GJhyL4sclKB1L1s3qKlxCWk+VwPRAMEVkastLZCZ2+buJaIckdSj8PsG/uTmXNY9qbYUw3n1Q\n7gJMAZVCOEBO00i5Zy3JPGPKXLP0vt7xVrquaCWd0klpIUzXLknyxYDmT65wX2PwyEk/h+LmpsDV\nBtRTLaQ//UutW4T+fhJXtcJEC8g06fufJPHweVr0r4JukCED6NqG8tBNrXo2w5Ayr7NZincCHZXP\nLb1OmHR14W/hz+3pCO1UgzX+FmcUpQY2tIWk54Qm70p/dMUUOVmkg28an/f+Ii8JEbIjCCLraspH\nsF9AJUaKSbACDD73Cgvlwoo/+GBbRIFAecrvffz0+qf9awfF3Yo+Y0C8LEzyOMhxL+XXJzf1QY+E\nnOML4fX2Hw73ivOMq/7b+3FdWL2lQkexiIQ8FVZKWWbw3JzW60lNpHQdRI8O3QkhtBJq82Teu9eL\nhiMU0XkxHCEAp6zZSyNMmzDjX5aDqbI7Kz3WzVVWMC12Psr/fjbCGn+LM45KQltS6h9bd3ehuKfS\nj64WlIJUSnHhZWPE//g6hu8KxOpLQgdBY566O+VLNABltQOluOF7K2lymop3DCExb0c47L51N4kd\nCdRG3af4xic6ioqvSg1duXiZpH9wyPews25xmMaIuu3uz7LqsWUIIXSeIWfmRJF4JIHTMlU0Xk95\ndG/XCqcKpVtE7h/yeyf4Y4u4uBTqH4Y2DpUtem7OBdHidxYrLNgORHS1tTHCA/tdv7jN3KO7u3Ad\nd4wiMgARl4H9+OMq/U4qtmWssIM01M7gohGWoK0U8qlU03C2LgCn1fgLIdqBF4D1Sqmdp/PeFu9M\nSFnebLvasaUFROZ1by+sWqXIlHCiw0IHpaimGAl6d5C6O1UUhinXBjLnSHbfks/g5nQVbnwBEOgp\nDDoEEUbFDIrf6WtIbRSXhycXDWU1dTyFQODmXKJS0j/osmzbMjK5XxJvjvvHm97Iax5dU6YDVJTk\nJEv39m4ty52vfyidM50z0AnhkQ19RDuSJd52eX7BO96qG8XnFwAhwFMntZSGEAiG/N7Q1fSEjONg\nXldC0QIbcel7HlZfJSs6G6cbsyk7PSvGXwhxM7ACeB1YB9ytlBoFvgAMzsY9Ld7ZqCS01WhiLIxR\n4rpadthxHEZe7Mj3Ca58MWPMzeusmy03uiHnABVzBAbZMTfP5BH5JvI/Z2T4HE1R9VkoWvMmnS5w\nzs1iAjo8tHCDdmtNwZP2NIuTi4Wq6UmcnENMxorrD1qOEWuO6foD8Kuf4+1xejf0Fnm9QY/bJasX\nDjdDvD1eVPUbzGcopfTOoXnKF+yrBu94K+l7d9G9tZWDecPbP+jSta2LTO6XxNpjfgK7UrgmGHYp\npaaGHWsKCAf2FxYfIYYoLVir5zk0x4TRXKdTGDbbstOz5fkngX9SSikhxDJgjRDideDfgNQs3dPi\nHY5q2/GZgBDkjVAgdo4sKhQyvQaMMTeGTCAY2NSHNLTMiD632mco1QaSErJvgBb4F6ijUZTQ7SRd\n6Ra1nnQcSSyG369Ax84VqSNHWXr5OcTneT5bqbibVmBMEdcXYjP9joOiZsXx7ILVVCgSOxI6J4HS\nLKaAx929o5uMm6FTdjLQM1A0V6XV0AO3F8tBVIKMyDJRN2MwnZYp4s3x/L3MdcqdhVqyCGVhtJIC\nQtDkgwLjK/y8qihROAU540qfMyX0NivGXyn1OoAQ4teBduAnwH1KqU1CiOuqnXvZZZf5r7/0pS/x\n5S9/eTaGaPErhqpVlKWJ4UAYhy0HAEFfry56So2PEbunOCcAhQpe0D9uT3k6PCHL2T1lY8l7qa4L\niRWSzrZWdj47xbrrtHFd90QCIq5vMEPZIxHJQM8Ayx5cRabC56qEYIFa0bwEWEMAw5uG/dfGc9fj\nd3UfYxkl67o6VNUeLzL8QZh8hqFpurn6mDPRDsnBwMbJNJExeY2w8VdT5QwmZyvJSJsCwkoSE7Mt\n0VyxFiHAWguOyYzn7be/zPj4V07p3qIRfe+GLizE1cAaYDtwLbAWOARsAHYBjymlXgocfynw0ksv\nvcSll146K2Oy+NVE1s1qnrxw6tOTzxv/k8dayHz153hHO4nHdWuVtJsqM/7FBVhuUXikVB2z2la9\nVDrALER+kvb2cPXT0rG7bnFI4VTmptJ4/RBUzqV7e7duG3lLv198NjIc8YXvgseb18sfXo53rNXX\nJ0od6ihrT1kNjWjnKzx6n5sqShSXhnyCInFKFWSku7brGpPSxb7ecYRW9oY4IpU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"text": [ "<matplotlib.figure.Figure at 0x109c6d6a0>" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####3. Querying for Visible Stars and Prioritizing by Airmass\n", "\n", "We can use our database to query for any stars in our database that are currently observable, and we can prioritize them by airmass (or by whatever metric you desire). This could be useful in the construction of automated queues that choose the best targets on any given night." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "\n", "# We generate a SkyLocation object at the current zenith and specify our desired maximum airmass\n", "zenith = SkyLocation.zenith (36.974117, -122.030796)\n", "maxAirmass = 2.0\n", "\n", "# We want the query to return a Star object and its distance from the zenith\n", "query = session.query (Star, Star.greatCircleDistance (zenith)).filter (Star.tags.contains (Tag.get (session, \"Bright\")))\n", "# We filter by our maximum airmass, making sure the angle from zenith doesn't exceed the desired airmass\n", "query = query.filter (Star.greatCircleDistance (zenith) < math.acos (1.0 / maxAirmass))\n", "# We order by the distance from the zenith so that the stars appear in order of the best potential observations\n", "for star, dist in query.order_by (Star.greatCircleDistance (zenith)):\n", " print (\"Found visible star, %s, with airmass %f\" % (star.name, 1.0 / math.cos (dist)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Found visible star, Vega, with airmass 1.237511\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####4. Building Statistics on Clustering\n", "\n", "We can use the angular distance math to calculate statistics of clustering in the globular clusters in our database as well." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy.orm import aliased\n", "from sqlalchemy import func\n", "\n", "# We need two star aliases because we're comparing distances\n", "first = aliased (Star)\n", "second = aliased (Star)\n", "\n", "# We want the query to return the star and the distance to the nearest neighbor (not including itself)\n", "query = session.query (first, func.min (first.greatCircleDistance (second))).filter (first.id != second.id)\n", "# Both stars should be members of M3\n", "query = query.filter (first.tags.contains (Tag.get (session, \"M3\"))).filter (second.tags.contains (Tag.get (session, \"M3\")))\n", "# Since we're returning the min of the collection, we need to group by the first star.\n", "# This would return a group of results, except that the min function now takes the minimum of each group\n", "query = query.order_by (first.greatCircleDistance (second)).group_by (first)\n", "\n", "results = query.all ()\n", "\n", "distances = np.array ([distance for star, distance in results])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a numpy array that contains, for every star, the distance to the nearest star in order of increasing separation. We can do any number of things with this information. For example, we can look at the probability density function of this information." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "values, base = np.histogram (distances, bins = np.logspace(np.log10 (min (distances)), np.log10 (max (distances)), 50))\n", "\n", "fig = plt.figure ()\n", "ax = plt.subplot (111)\n", "\n", "ax.plot (base [:-1], values)\n", "\n", "ax.set_xscale (\"log\")\n", "# ax.set_yscale (\"log\")\n", "\n", "ax.set_xlabel (\"Angular Separation to Nearest Neighbor (Radians)\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "<matplotlib.text.Text at 0x109df1a90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109c788d0>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Try It Yourself!\n", "\n", "Now, it's your turn. __Try querying the database and making some interesting plots of your own. Or add your own dataset and try out some of the tools you've seen here.__" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Conclusion\n", "\n", "Hopefully, I've provided you with a series of useful tools that can be applied to your own unique data set, whatever that may be. Databases are extremely powerful ways to query and organize data, but like any tool, their features can be abused.\n", "\n", "First, there are many examples (particularly with homogenous data sets) where a database is the extremely powerful tool you don't need. A small, homogenous data table is almost always easier to deal with in the form of numpy arrays. Relational databases are much more useful when dealing with large, heterogeneous data sets. In addition, excessively building relationships can slow down your database, particularly if your database contains huge numbers of entries.\n", "\n", "Tha\n", "\n", "A final note: all the work here has been done with sqlite databases, which are nice for learning purposes but do not scale well to production-level code. Now that you're familiar with the basics, I definitely recommend looking through some of the SQLAlchemy documentation [here](http://docs.sqlalchemy.org/en/rel_0_9/index.html).\n", "\n", "Good luck!" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

ganguli-lab/single-trial

notebooks/Analysis - GP Manifold Recovery.ipynb

1

143490

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Relevant library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Method definition info(Any...) in module Base at util.jl:334 overwritten in module Logging at /home/prgao/.julia/v0.4/Logging/src/Logging.jl:61.\n", "WARNING: Method definition warn(Any...) in module Base at util.jl:364 overwritten in module Logging at /home/prgao/.julia/v0.4/Logging/src/Logging.jl:61.\n" ] } ], "source": [ "using PyPlot, Optim, Logging\n", "Logging.configure(level=DEBUG, filename=\"GP-recovery.log\")\n", "push!(LOAD_PATH, \"../src\")\n", "using GP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpolating individual neuron's tuning curve from training data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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hZdx+QOjo6EBXVxd3QaG+vj5cXV0xc+ZMzJw5E25ubjAxMVFY+WT6KPjfAz777DOUlZUBABYsWCBz8dJow8PDaGxsRE1NDTcwKBAIAIxt1dNZ2lj9/f3IOXMGPQ0NMJBIIOLxYOHigsglSzSuH9zMzAzLly/H4cOHAQA//fQTvLy8lNo9M/r7Mzw8PKbbzNraGnw+H+7u7nB3d4eNjQ2N/6kRBX8t19vbi7/97W8ARlp8O3fu5H5QAoEAdXV13DTDhoYGSCQSatXfhb6+Ppz997/xRWcn5gJgAEgAXG1txfM1NVjy9NMadwAICgpCbm4uysrK0NvbiwsXLmDZsmUqrcPo71VnZye6u7u5swNjY2Pw+XzudrczrMjdoeCv5Xbv3s111axfvx4WFhY4e/aszJxyatVP3/WzZ/GPzk7EjNrGAxAL4PPOTrx15gzmP/SQmmo3PoZhkJSUhM8++wzDw8O4evUqQkND4eLiorY6jf4eDg4OcmMT0um20usuPDw84OTkRGcGSqSxwX/btm2wtLSU2bZx40Zs3LhRTTXSPB0dHXj//fcBjPzQHRwcsH//fo2ZeXIv6WlokAn8o8X8735NZG1tjYULF+L8+fNgWRYnTpzA008/DT09PXVXDcDY6bajr70wMjKCp6cnvL294eXlBRsbGzXWVDMdOHAABw4ckNnW3d0t12M1Nvjv3LmT8vmPo6urC2VlZSgtLcW//vUv7oMODQ2Fra0tAAr2ymAgkWCiNijvf/drqri4OBQUFKClpQWNjY3IycnR2CnXow8GAoEAxcXFKCoqAgBYWFjA19cXPj4+8PLygqGhobqqqTHGaxBL8/lPRWODPxkhkUhQU1ODkpISFBcXo6OjA8BIFsq0tDQAI63+hQsXqrOa9zwRjwcWGPcAIPnf/ZpKT08PSUlJ2LNnDwDgwoULCA4OhrGxsZprNrXRB4Oenh5cv34d2dnZ4PF4cHd3h7+/P/z8/Ois4C6oJPjv3r0bXV1daPjfqfGPP/6ImpoaAMArr7wCCwsLVVRDa4hEIpSVlXFzqYVC4ZiunLS0NG6mTnh4OH35FUwgEKCzsxN9fX3o6+uDQF8f6QDixtk3A4CFGvvR5cHn8xEcHIwbN25gYGAAFy9eVPngryJIfwMSiQTV1dWoqqpCcnIybGxsEBgYiICAADg7O9NYgRxUEvw//vhjbhk8hmFw7NgxHD16FAzDYPPmzRT88Wt6gZs3b6KkpARisVgm4N8+UHb16lUAI1M0qdWvOCKRCLt37x633/QRAAcwMsjLw0iLPwPAC9bWWLJkCbdff38/Tp8+zeXHcXV11YguisTERBQVFWF4eBjXrl3D7NmzYW9vr+5q3bXRZwUdHR24cuUKLl++DHNzcwQFBSEoKAiurq50IJiASoJ/ZWWlKorROmKxGOXl5dxCHJMF/NGysrIgFAoBjFzNaWVlpbI63+sMDAwmnA1VC2AVACdTU1jOmMHN819y2zz/xsZGFBQUcGsm6+npwdXVlRu8dHNzU8uURisrK8THx+PixYuQSCQ4c+YMHnvsMZXXQ1mkv5fe3l5cu3YNGRkZsLCwQEhICEJDQ7lMA2QE9fmrQVNTE3Jzc5GXl4ehoSG5Ar6UdMqeVFzceB0R5Ha9vb0oLCxEX18fFi1aNOm+bm5u6OnpgZ2dHczNzWFmZgYzMzMYGhqCx+PBzs5uzEy00RobG2X+FovFqKmpQU1NDS5evAhTU1O8+OKLarniNT4+HtevX0dPTw+Xs8fX11fl9VA26e+op6cHV65cQVpaGhwdHREeHo7Q0FC62hgU/FVGIBCgoKAAWVlZaG5uvqOAP1p+fj76+voAAAEBAdwMHzJWb28vbt26hZs3b3JjTHp6eoiLi5t0QZP169dPq9zY2Fj4+PigqamJS+8sHagHRjJlqiv4GBoaIjExEUePHgUAJCcnw8vLS2OmfiqD9EyuubkZycnJOHv2LPz9/REVFQVPT0+d7Rai4K9kLS0tuHbtGvLy8mQWMLmb6ZgSiQRXrlzh/o6Pj1dIHe8lYrEYJSUluH79ukwmytH3V1RUIDAwUGl10NfX5/r7IyIiAIzMva6oqEBxcTFcXV2VVrY8QkJCkJmZidraWrS1tSEzMxMxMRNdxXDvkUgkXL4jKysrzJkzBxERETq3whkFfyVgWRalpaW4cuUKqqurp1ypSl6lpaXc1bx8Ph9ubm7Tfs57TVdXF7777rsx221tbREUFITAwEA4OjqqvF6WlpaIiIjgDgaTGRgYwH/+8x+EhoYiPDxc7nTM8mIYBsuWLcM///lPAEBKSgpCQkI0Lj2FMkkbBV1dXThz5gzOnz+PiIgIxMTE6MzZNAV/BRKLxcjPz8fly5fR0dHBnU4q6qIr6bx+gPr6J2Jraws+n4/q6mpYWFggLCwMwcHBcHBw0JrT+1u3bqGlpQXnzp3DL7/8Aj8/P0RGRsLHx0dhA8Wurq4ICwvjxp1SUlKQlJSkkOfWRmKxGNnZ2cjKyoK/vz/mzZt3zzeuKPgrgEgkQk5ODi5fvsz1xwOKzaFTV1fH9Vvb2dndk4N0ipKQkACRSARvb2+tTBQmPbsDfu2iKCoqgoWFBSIiIhAeHg5ra+tpl5OYmIjCwkIIhUJkZWUhKipKLWdFmkL6ey0tLUVxcTH4fD4WLlwIT09PNddMObTvl6FBpPOld+3ahdOnT8sEfkW7fYaPNga16ejv78eVK1fwj3/8Az09PZPu6+HhAV9fX619j5YtW4aXX34Z8+bNk+ny6enpwcWLF3Hs2DGFlGNubo758+cDGAl8p0+fpqR/+PVMvaamBt9++y327NnDXad0L6GW/12QSCTIz8/HL7/8gt7eXqWX19fXh5s3bwIATExMEBISovQyNYFEIkF5eTlycnJQXFzM/Sjz8vK4oHWvsrW1RWJiIhISElBaWoqcnBxuAFuROa9iYmKQnZ2Nrq4uVFZWoqioSKGLvmgz6YGwtrYW//nPf+Dl5YXExEQ4OzuruWaKQcH/DpWVlSE5OVnm1FzZcnJyuMAXEREBAwMDlZWtDu3t7cjLy0Nubu64rfzW1lY11Eo99PT0uEXYe3p6kJeXp9CZSgYGBli6dCk3SH7mzBn4+vpCX59Cg5T0IFBVVYWvvvoKISEhuP/++ye91kMb0Ccsp7a2Npw+fRrl5eUqHTgUi8XIysri/o6KilJZ2epy9uxZLpOjlKmpKcLCwhAREaHSlAQMw3A/fmNjY1haWkJfXx9DQ0Po6urC8PCwzD7KZGFhIdcZT3p6OhoaGhAREQEPD48pu79mzZoFDw8PVFVVobOzExkZGZg3b56iqn3PkDbAbty4gVu3biE+Ph7z5s3T2sYYBf8pCIVCXLx4Eenp6eOugKVsxcXFXOvX399fIQN9mi48PBxFRUVgGAa+vr6IiIiAn5+fSi9EkgZ0JycnREZGwtfXd0xLTyKRoKGhAUVFRcjJyeEWulcnlmWRmZmJjo4OFBQUwMrKCuHh4QgPD58wDYh06ueXX34JlmVx6dIlhIWFybUOtC5iWRZisRipqanIycnBAw88gICAAK2ZTSZFwX8CLMuiqKgIP/30E/r7+8GyrFoGwzIzM7n/a2oO9jslFosnDeS+vr5YunQpgoODVR6ApEGfz+dj0aJFmDlz5oT78ng8uLm5wc3NDQkJCcjNzUVKSgr3fVGHrq4umYNQV1cXUlJSkJKSAj6fj5CQEAQFBY1J5+zk5ITZs2dzeaPOnz+P1atXq7r6WoVlWfT19eH777+Hl5cXkpKStCq7LgX/cfT09ODkyZMoLS1Vaz1aW1u5pHg2Njbw8vJSa32mo7OzEzdv3kRBQQH4fD6WL18+4b56enqIjY1VYe1GMAwDc3NzJCUlwc/P744eq6enh9mzZyM0NBSXL1/G5cuX1dJgsLa2xmuvvYbi4mJcv34d5eXl3H3V1dWorq6GpaXluFOFExIScOPGDQwNDSE3NxdRUVH3/Fx3RamqqsLnn3+O++67D7GxsVqRLoOC/ygsyyInJwfJyckyqRjU5fZWv7ZNXZTm1ikoKEBdXR23va+vD8uWLdOY1yNt7UdHRyMxMXFafbgGBgZISEhAcHAwjh49iqamJgXWVP46BAcHIzg4GF1dXcjLy0N+fj7a29thYmIyYSPC1NQUCxcuRHJyMoCRdTe2bt1Kg79ykI4HnD9/HgUFBXjooYfg5OSk5lpNjj7V/+nu7sbx48c1Jv20QCBAbm4ugJEfc3h4uJprJL/q6mqcO3cOtbW1495vZWWFvr4+jVjHgWEYzJgxA2vWrIG3t7fCntfe3h5PP/00Ll68iNTUVJUNCt/OysoKCxcuxIIFC9DY2Iiurq5JW6Vz5sxBVlYW2tvb0dLSgrS0NFov4g61trbiq6++woIFCzB//nyNPQvQ+eDPsizy8/Nx6tQpiMVidVeHk5eXx+XsDwkJ0Yol96QMDAzGBH4HBweuNapJ/aLu7u5Yu3atwvPnACNdQYsWLYKHhwe+//57CIVCta2vzDAMXFxc4DLFimMsy8pMr7148SI8PDzA5/OVXcV7hvQgf/HiRRQVFWHNmjUauWiOTgf/gYEBnDx5EoWFhequigzpjA2pOXPmqLE2Y7EsO+nMBmdnZ1hYWMDIyAizZs1CSEiIRi6kER8fj0WLFim9+8nLywvPP/88Dh48iKamJo2+irayshIikYj7WyKRYM+ePXBxcYGfnx/8/Px0fplElmXR2NiIyspK1NbWor29HT09PRCJRODxeDA2NoaNjQ2cnZ3h4+ODxsZGJCUlITo6WqPeN50N/pWVlThy5AgGBgbUXZUxqqqquAuZ3N3d1d53yLIsOjo6UFZWhtLSUujr6+ORRx6ZcH+GYfDcc89p5IIZDMNAT08Pq1evRlBQkMrKtbCwwJYtW3Dq1CmuO08TWVpaIioqCoWFhejv7+e2NzQ0oKGhASkpKXj11Vd1Ysrx7Xp7e5GVlYX8/Hx0dnaOu49EIkFvby96e3tRXV2NjIwMGBkZ4fTp01i/fj2ee+45jcmeqnPBXywWIyUlBZcvX1ZbP+xUNGF6p1AoRGVlJcrKylBWVibzZdfT04NQKJx0XVpNDfxmZmZ49NFH1XJA1dfXx8qVK+Hk5ITTp09r5PfPwcEBDz74IJYvX47MzEz8/PPPMvfb2dnpXODv7e1FSkoKrl+/Pm63nZ6eHiwtLWFoaAiJRIL+/n6ZA6dAIMC1a9eQlZWFw4cP45NPPtGIs3mdCv7d3d34/vvvUV9fD0C1F2vJq6enh+uGMjU1VUuelfLycuzfv3/C/mkTExN0dnZqVQZIaZ/3xo0b1dryYhgGc+fOhZ2dHQ4dOgSRSKSR30Mej4e5c+eivb0d165dAwCYmZlh7ty5Uz42OTmZm5psY2OjUV0dd0IgEODKlSu4cuWKTFcYwzDw9PREQEAA+Hw+7OzsxnQd9vf3o6amBkVFRbh58yaGh4chkUiQkZGBhIQEbN++HX/605/UOhis9OAvEAjw1ltvYe/evejq6kJoaCjeffddJCYmKrtoGcXFxTh27JjMh6iJsrKyuGAwe/ZstUyzc3BwkAn8PB4P7u7u8PHxga+vr1blxpcKCgrCqlWrNGbaore3N55++mns378fPT09GnkAAIDFixejuroazc3N6OvrQ3V1NaKioib8/Pv7+5Gens79bWlpCU9PT27QWFvOGoqLi3HixAmZTL2GhoaYM2cOoqOjp8zrI224BQQEYNmyZbh27RrS0tIgFAoxMDCAd955B6mpqTh06JDaBoOV/kt44okncOTIEWzbtg2+vr7Ys2cPli9fjgsXLqhkGUKxWIxz584hIyND6WVN1/DwMLKzswGMtC4UmcdnYGAA1dXVqKqqgoWFxaTvvbm5OZfOwMfHB56enjAyMlJYXVQtISEB8+fP17gDlr29PbZu3YoDBw6gvr5eIw8ABgYGWLt2Lb766iuIRCLcuHED9vb2E07/vH2qdHd3N3Jzc7lxDktLSzz22GMaOQEAGPmdnD59Gvn5+dw2Ho+HqKgDGJdAAAAgAElEQVQoLFy48K7OGo2NjbFw4UJERkbi1KlTXN6qlJQUzJ07F4cOHVJLzi6GVeI37tq1a4iJicFHH32E1157DcDImYB0ZaXRK1NJ5eTkYPbs2cjOzp526tquri4cOnQIjY2N03oeVSkoKMCRI0cAAIGBgdNaSFzaSquqqkJ1dTVaWlq4+xwcHPDCCy9Mu76ajGEY8Hg8PPTQQyod2L0bw8PDOHHihEzA0TRFRUU4ePAg9/fKlSvH/X1KJBI0NTWhoqIC5eXlqKmpkZlCzePx8Lvf/W7S8SJ1KSsrww8//CDT2vf19cWyZcsUtrQjy7LIzc3FyZMnuffFyckJn3zyCdasWaOQbiB5Y6hSW/6HDx+Gvr4+tm7dym0zMjLCU089hTfeeAP19fVKW8y6qKgIP/zwAzdXXhsoaqD3ypUrOHPmzIT3t7a2Ymho6J5dsJrH48HIyAiPPfaY2hdLl4e+vj5Wr14NOzs7/PLLL+quzrhmzZqF+++/H+fPnwcwcvWvnp4ewsLCZPbj8Xjc9QTz5s2DSCRCfX091whhGGbKwJ+RkQEejwc+nw97e3ulT8WVTgJJTU3lthkZGeGBBx5AWFiYQs8YGYbhMtP+97//xcDAAJqamvDSSy+hsbERTzzxhMpSRSs1+F+/fh1+fn5jLqCRBrbc3FyF/zi1qZtntKamJm6ZRnt7e3h4eNz1c90+EMswDJydnbl+V3d393s28DMMA1tbWzz22GNalW+dYRjMnz8ftra2OHr0KCQSicZ1A82bNw/9/f3cb+vYsWPo7+9HbGzshAHSwMAAHh4e3Pd5qtfEsizS0tK4RZKMjY3h7u4OPp8PDw8PODo6KnSQtKenB0eOHJFZqcvHxwcrV65U6hXobm5u2LJlC7799lv09PSgtbUVH330Ebq6urBx48Y7zi11N5Qa/BsbG8dd9Ua6raGhQaHldXR04Pvvv1dLPpXpks6oADDmYhDpPHtpN46fnx+Cg4MnfK6ZM2fC3d2d+9G4u7trdZ/9nfDz88PDDz+skd0K8ggMDIS1tTX279+PwcFBtV0RPB6GYbB06VKIxWLuLPXMmTNobm7G8uXL5fqOTdWK7ujokFkdb3BwEMXFxSguLgYwMui6evVqhSxoU1ZWhqNHj3LX+jAMg8TERMTGxqok75SdnR2eeOIJfP311+jr60NdXR0OHDgAiUSCuLg43H///UqdoKDU4D84ODjuF0La6lRU/nOWZZGXl6dxKRrkNTg4iIKCAgAjX+7Q0FC0tbWhqqqKO10e/YNgWXbS4G9oaIgtW7Yovd6aZt68eVi0aJHGDezeKWdnZzz77LM4ePAgGhsbNeoMgGEYLF++HKampkhJSQEwkoqkpqYGy5Ytg5+f37TefysrK2zdupVr6NTU1MjECaFQOOG6BPISi8W4ePEiLl26xG2zsLDA2rVr4e7uPq3nvlM2NjbYtGkT9uzZA4FAgOLiYly4cAE8Hg8VFRVYt24d7OzslFK2UoO/sbExBALBmO1DQ0Pc/RPZtm3bmNP2jRs3YuPGjTLbNDVFw53Izc3lpqCGhYXh1KlT3MFgPNLrFMivA7urV6+e9ICobczNzfHkk0/i5MmTyMvLU3d1ZDAMg/vuuw+2trY4ceIEhEIhOjs7ceDAAfD5fMTHx8PHx+euWs96enrcmEFsbCwkEglaW1u5dNRNTU1TXqCXn5+PlpYW8Pl8zJw5U6aLc7xuHl9fX6xevVpt1384OTlh3bp12L9/P1iWRWpqKlxcXMDj8fDFF19gyZIlE6aGOHDgAA4cOCCzrbu7W65ylRr8nZ2dx+3akc6+mSzJ1M6dO6ec7XPr1i2cPHmSO5hoI4lEMiaPT2lpqUzwNzAwwMyZM7k+e20YxFQFaf79jRs3qj0FhjLo6+tj1apVcHZ25tIsa9JZQEhICFxdXXH8+HEumEqDtHSee2BgIPh8/l330/N4PDg6OsLR0VHuq2Lz8/NRVlbGXcXv5OQEPp8PIyMjZGZmynTz3H///YiLi1N7enEfHx8sWbKE+5yPHz8OZ2dnWFlZ4eeff8atW7ewcuXKMUkRx2sQS2f7TEWpwT8iIgIpKSno7e2VWZHp6tWrAHDXaYo7Oztx+vRplJSUKKSeqtDV1YXKykpUVlYiNjaWG/eoqKhAR0cHAMDT0xP29vYQi8Xw8fHhgr2Li4vGpoVVJw8PD6xdu1YjU0koivSKYCcnJ3z33XcYGhrSqAOAjY0NnnjiCdy8eRO//PIL913u7+9HVlYWsrKyYGhoCA8PD3h5ecHLywv29vZK65qTSCTcxAng1yRst0/3Njc3x7p161TezTOZmJgY1NbW4tatWxgaGsKRI0fw5JNPgsfjoba2Fp9//jkWLFiAuLg4hYwFqGSe/4cffojt27cD+HWev729Pa5cuTLmMZPNUR0YGEBaWho320CTBsNuNzw8jOrqai4ZWltbG3dfYmIit0D2f//7X+4gtn79eoUMZN3LpPlwFixYgIULF6q9xaZKvb29OHTokMzCOJpEIpGguLgYeXl5KCsrm3BBJDMzM3h6esLLywt+fn4K727p7e1FTU0NqqurUV5ejvb2dpn7HR0dsXnzZo1JsDba0NAQvvjiC3R1dQEAlixZgri4OJl9LC0tsWjRIgQHB4/7/deIef5z5szBunXr8Pvf/x4tLS3w9vbGN998g5qaGuzZs0fu52lpaUFWVhauX78OsVisUS2f8fzwww+4efPmhKkkpC2T9vZ2LvBbWFjA399fZXXURgzDwMjICGvWrIGPj4+6q6Ny5ubmeOKJJ3D+/HmZFAqagsfjcSkNBAIBSktLUVRUhIqKCpnsuX19fSgoKEBBQQGXJyc4OBgBAQEKWbfC3Nwc/v7+aGtr44KolJmZ2ZT5nZqamlBXVwdvb2+Vp6OYMWMGHn74YXz99dcAgF9++QX+/v4yF5l1d3fj2LFjSElJQUxMzF2v96H09A7ffvst/vjHP2Lv3r3o7OxEWFgYTp48ybV8J1JUVISmpiaUl5ejo6NDIzMgTkQikYxJBOXm5gZvb294enpyffbS7i9g5EBJXTuTc3Nzw9q1azViBTB10dPTw5IlS+Dh4YGjR49CKBRq5O/CyMiIW7xHIpGgpaUFFRUVqKysRFVVFff7YFkWFRUVqKiowKlTp+Dn54ewsDD4+PjcVdcGy7IoLS3F2bNnubToAGBra4tVq1bJ1c1TUFDAZR+ws7ODr68vfH194e7urpLcUO7u7pg7dy6uXr2K4eFh/Pjjj3j88cfHtPI7Ozvx888/4/Tp09yU7jtpQCq12+duSE9Ztm7dCjc3N43r2uno6ICFhcWkX4L8/HwkJydzidC8vLzG9EsPDQ1hx44dEAqF0NfXx2uvvXZP913fLWnf8MKFCzF//nyd6uaZSk9PD44ePSozc0UbDA8Po76+HiUlJbh58+aY1jkwMhMwODgYISEhcHNzm/JzF4lEKC4uRnp6usxsOIZhEB8fj4ULF8q9NvOXX345bkoYQ0NDeHl5ISQkROkpQ4RCIT7//HPuvVm+fPmUA94Mw8DGxgZxcXHq7/aZLk0I/CKRCFVVVVzffUdHBzZt2jRpt0NQUNCE/XFS169f51JPhIeHU+AfB8MwsLCwwJo1azBz5kx1V0fjWFhY4PHHH0d6ejrOnz8PlmU18izgdvr6+uDz+eDz+UhMTER9fT1u3LiBgoICLg/+4OAgMjMzkZmZCRMTE3h4eMDNzQ02NjYwNTUFwzAYGBhAR0cHamtrUVZWNmZauaurK5KSkqZcuvJ2Dz74IMrLy1FaWoq6ujruPRUKhSgqKoKlpaXSg7+hoSFWrlyJb7/9FgBw9uxZ+Pr6TtoNxbLsHV3npNHBX13a29tRWlqKsrIyVFVVjRm4KisrmzT4T9V9I5FIZLp85MmRrkukXXwRERFYunSp1l6tqwoMwyAuLg7e3t44cuSITFeHNpB2ibq5uWHx4sWoqKhAXl4eioqKuN/dwMAAbt26hVu3bsn1nI6OjliwYAECAwPvalaRq6srXF1dsWDBAgwMDKC8vJxr/A0MDEw53jTVMqfy8vLy4lrwIpEIycnJk66gd6co+I/ju+++k8mCKcUwDNzd3aedjrakpIQ7nfP29tbIxZ3VRbra1qpVq+Dt7a3u6mgNR0dHPPvss0hNTcWlS5fAMIxGnDnfCT09Pa5/fWhoCIWFhSguLkZFRcWUCRql60WHhYXB09NTYVNJTUxMEBISgpCQEEgkEjQ2Nk75+7958yYuX77MvRZ5uq0msnjxYhQVFaG/vx9FRUVTNjzvBAX/cfj4+HDB39zcXKbvXhEJ0UYnnYuJiZn2890LpK396OhoLFq0SGdyESmSnp4e7rvvPgQEBOD48eNak8p8PDNmzEBERAQiIiIgFovR2tqKpqYmdHV1YWhoCBKJBMbGxrCwsICzs7PCE76Nh8fjyXWBZWlpKZqamtDU1ITU1FQYGxvD29sbvr6+8PHxuaMppjNmzMDixYvxww8/AAB+/vlnPP/88woZeNaZ4N/X18fNKli0aNGkM0aCg4NhYmICHx8fODo6KvSClLq6OlRVVQEYmYFArdsR9vb2WLlyJV29rACOjo54+umnkZ2djbNnz2J4eFgrxgImoqenBycnJ625inv0+r3AyPjFjRs3cOPGDQAjK/StWLFC7ucLDQ1FVlYW6urq0N7ejqtXrypkIax7NvhL19CsqalBZWWlTKZPDw+PSa8uluYWUYbLly9z/9eEy8rViWEYGBgYIDExEbNnz9bp90LReDweoqOjERAQgLNnzyI/P1+rpktrs02bNqG3t5cbJygvL5cZjL7TxHQ8Hg/Lly/HV199BQC4ePEiQkJCpj3l+Z4M/v/85z8nTX5WWVl516klpqOlpYVbws3c3HzMQhi6QnomNXv2bCQkJNBMJyUyMzPDQw89hOjoaPz0009a3RWkTczNzWW6rWpra7lJJL6+vpM+tqGhAQUFBVwj1NraGi4uLoiKikJWVhaEQiHOnj2LNWvWTKuOWhP8h4eH0d/fj56enimn/Y3XL+/k5ARvb294e3urbdrg7a1+TVlMXFWkLU9vb28sWbKEBrpVyM3NDc888wxu3ryJs2fPoqenR91V0hl6enrcgjaLFy+ecv/KykqZK7iNjIzg6OgIKysrGBgYQCQSoaCgANHR0dPKTaSx0eenf/8bMDBAr5ERBgcHZUb733jjjUmn/3l6ekIgEHALmsycOVPteTw6Ojq4TJ3GxsZyZd27V0iDvqurKxYvXqxRybR0CcMwXBqF7OxspKSkKGxNDaI4t2dCFggEXBf2aKdPn8bTTz99192lGhv8j4nFEIrFeGRoCLdnp+7q6pp0utW8efOmTB+haleuXOH6W2NiYnRi7ro06Ds7O2PRokXw8vLS+oVW7gV6enqYM2cOIiIikJmZicuXL9NBQINI1w5ubGxEQ0MDGhoaZBZzMjIygkAgQENDA/Ly8hAREXFX5Whs8OcBiANwAMB6Q0PoOTrC1NQUlpaWcl+mrSl6enpw/fp1ACNX7smbl1xb8Xg8SCQS8Pl8LFiwAB4eHhT0NZCBgQHi4uIQHR2NnJwcXL58GX19fTQwrGZmZmbw8/OTWcd3aGgIbW1taG9vR09PD86fPw8AOHfuHAICAu5qCrrGBn+pWACulpZIeuopdVflrqWmpnKXXUdHRyskc6Emkgb4gIAAxMXFKW3GFFEsAwMDzJ07F1FRUbhx4wbS0tLQ2tpKBwENMmPGDO5KaGCka6iwsBD9/f1ITU2Vayzhdhof/HkADLTsSsXROjo6kJ2dDWDkRxYbG6vmGimWNEAYGRlh9uzZmDNnzpjlN4l20NPTQ1hYGEJDQ1FVVYWMjAyUlJTQQUADLV68GCUlJRCLxUhPT0dkZKRM2md5aHzwlwAQafH875SUFO4y+9jYWJiZmam5Rooh7dpxdnZGdHQ0goKCtK47joxPmmPf09MT3d3dyM7ORnZ2NgYGBuhAoCGk2TtTU1MhkUhw5syZMcs5TkXjg38GAAst7T5oaGhAfn4+gJEZPrevyKNtpAF/xowZCA8PR0RExLTzHBHNJl01auHChSgrK0NOTg5KS0vpAKAB5s2bh9zcXPT29qK4uBhlZWWIioqS+/EaG/wlAK4AeMHaGkuWLFF3de4Yy7I4ffo09/eCBQsUkhdI1aQtPT09Pfj7+yM0NBQ+Pj608IyOkX7+/v7+6O/vx40bN5CXl4fGxkY6G1ATIyMjJCYm4tixYwBG8v7cycWrGhv8H7eygjOfjyVLlqh9jv7duHnzJjcv19bWFtHR0WqukfykP2YejwcfHx8EBwfDz8+Pkq0RAICpqSnmzp2LuXPnorOzk8tb09LSQgcCFQsJCUFmZiaX9+fChQtISEiQ67EaG/znrV+vtbNFhoaGZFr9S5cu1fireaVdOgYGBvDz88OsWbPg6+tLAZ9MytraGvPnz8f8+fPR0dGBoqIiFBYWcovM08FAuXg8HpKSkvDVV1+BZVmcOXMGzz77rFyP1eyIpKXOnTuHvr4+AOByemua0T9Ka2trbl4xn8+nLh1yV6SDkHFxcejv70dZWRlKSkpQVlYGoVBIBwIlcXZ2xty5c5GRkQGRSIT3339frsdpbPDX1gyP1dXVyMrKAjAytTMpKUkjLnCS1oFlWW4tUmmuo8mWhiPkbpiamiIsLAxhYWGQSCSor69HWVkZysvL0dDQwHUratuCM5oqISEBN2/ehEgkQnx8PLcA/WSUGvybmpqwa9cuXL16FVlZWejv78eFCxewcOHCKR/r5OQEDw8PtLW1ca1oTf+yDA0N4ejRo9zfixYtuuP0rYoyOtgbGBjA3d2dm77n5OSktQdXon14PB5mzpyJmTNnIiEhAQKBANXV1aiqqkJFRQWam5u5/TT5961ppGdSDMPAxcUF27dvR0xMDOzt7fHBBx9M+XilBv+ioiJ88MEH8PPzQ2hoKNLT0+VuBZ8/fx4XL17kFmqura1FVVUVSkpK0NHRoXGnkCzL4tSpU+juHslE5O7urtK1eUf/cExMTMDn8+Hu7g4+nw9HR0cK9kRjGBkZyaQvEAgEqK2tRU1NDaqrq9HQ0MCt30sHhF9J3wsejwd3d3f4+PiAz+fDyclJZkwxJydHrudTavCPiopCR0cHrKyscPjwYZk0pVNJTU3FF198geeffx4mJibcNLOlS5eis7MTxcXFuHXrFmprawGof2ApMzOTy9ppZGSEhx9+WGkBd/QPQrrK0cyZM7nLvy0sLDSiq4kQeRgZGcHHx4dbm1YikaC5uRl1dXWor69HTU0NOjs7Acie0eoC6W/d0NAQ/v7+mDVrFry9vRUyEUOpwX+6V7Nu27YNc+bMGZP+2NraGjExMYiJiUF/fz93IKisrOSOjKpsLVRVVcnM7lmxYoVCunsYhpFZiFtfXx+Ojo5wdXWFs7MznJ2dYW9vT616ck/h8Xjc91s6RVogEKCxsRGNjY1oampCXV0dOjs7uYPAvXSGIG3IGhsbIyAgAAEBAfD09FT4RAyNHfAFRj7wNWvWIDs7e8K8FaampoiMjERkZCQEAgEqKipQUlKCkpIS7nJ0QHkthebmZhw8eJD74sXFxSE4OPiOnuP2IM8wDCwtLeHs7AwHBwc4OjrC0dER1tbW1KInOsnIyIhbEEVKJBJxC7u3tLSgubkZzc3NMumpteGgMLqODg4O8PPzg7+/P1xdXZX6e9fY4B8SEoKCggJUV1dj9erVOHv27JRXyBoZGXFHSpZl0dLSgsrKSlRXV6O6upr7UvB4PLAsO+0DQnt7O/bu3YuhoSEAgLe3NxITE8fdd7wyDQ0NYWNjA3t7e9ja2sLOzg52dnawtbXV+OsCCFE3AwODcdfbHhgYQFtbG3drb29Ha2sruru7ZQ4EiooDd+L2Mm1tbbmDmoeHh0pzf8kdYViWlVmEeDKKSGPw/vvv48knn0RzczMuX76M3/zmNzh48KDcpz4Mw3At5piYGLAsi+7ubjQ0NKC5uRktLS1oaWlBd3c3l2559GOnOuI2Njbi22+/RX9/PwDA1dUV69evl+mCmTFjBiwsLGBpacndrK2tudu9mtqZEHUyMTHhVvEbTSKRoKenB52dnejq6kJXVxf3d3d3N/r7+yESicY8nzzxYLTxDihGRkawtbWFg4MDHBwc4OTkBBcXF7VeRCl38L948SIWLVok175FRUUyCxHcjffeew9+fn5oa2uDWCzG4cOHMX/+fFy6dOmuWsUMw8DKygpWVlYIDAzktrMsi76+PvT29qK3txcDAwMYHBzE0NAQRCIRhoeHIZFIuBYDj8dDZmYmvvnmGwwMDAAAfHx8sGfPHri4uMDExASmpqYwMTGhi6UI0SA8Ho+LARMRCoXo7+9Hf3+/TCwQCoUQCoUyMWH0eIOenh4MDAxgaGiIGTNmwNjYGKampjA3N4elpaXSgvyBAwdw4MABmW3SGYdTkTuKBgQE4D//+Y9c+zo5Ocn7tBPauXMnIiMj8fPPP2PVqlUQiURIT0/H2rVrsW/fPoWdHjEMA3Nzc5ibm0+57+DgIN566y189NFH3LbY2FicOnWKLpQi5B5gaGgIQ0NDrfk9b9y4cUwq55ycHLnWCJc7+Ds6OmLz5s13XrtpeuCBB3DkyBGsXbsWQqEQx48fR3x8PA4fPqyytAksy+Knn37Ca6+9hpKSEm77unXrsGfPHq1MPEcI0W1aMUdwxYoVOHnyJCwsLAAA+fn5CAsLw44dOyAUCpVW7vDwMI4ePYr58+fjwQcf5AK/oaEhPv74Y3z33XcU+AkhWknpU0reffddACMpjgHg22+/xaVLlwAAb775ptzPs3jxYly9ehWrVq1CSUkJBgcHsX37dnz66ad4/fXX8eijjypk+cC6ujqkpaXhwoULOHr0KFpbW2Xuj42Nxb/+9S+ZcQNCCNE2DKvkeU48Hk8mB4W0OIZhxsyyAX7tr8rOzkZkZOSY+/v7+/HGG2/g73//u8z2GTNmYOHChUhISEBoaCjc3NxgbW0NKysrGBsbcwM2Q0ND6OjoQHt7O9ra2lBTU4Py8nJUVFQgPz+fy8F/u8DAQPzlL3/Bww8/THPtCSEaa6oYKqX0lr+iL7AwNTXFJ598gt/85jd48803kZycDGAkqVpycjL3tyIYGxsjKSkJW7duRWJiIgV9Qsg9Q2uvJIqKisLp06eRm5uLf//73zh27Bjq6+un9ZwmJiaYM2cO4uPjER8fj/nz598zC64TQshoWhv8pcLDw/Hpp5/i73//OwoLC5GdnY1bt26htbUVnZ2d6OzsxNDQEIyMjLhpXDY2NtzVtM7OzvD29oaXlxecnZ0pTw4hRCdoffCXYhgGgYGBNBBLCCFyoGYuIYToIAr+hBCigyj4E0KIDqLgTwghOoiCPyGE6CAK/oQQooMo+BNCiA6i4E8IITqIgj8hhOggCv6EEKKDKPgTQogOouBPCCE6iII/IYToIAr+hBCigyj4E0KIDqLgTwghOoiCPyGE6CClBv/z589jy5Yt8PPzg6mpKby9vfHMM8+gqalJmcUSQgiZglKD/29/+1tcunQJa9aswaeffopHHnkEhw4dQkREBJqbm5VZNFGRAwcOqLsKRE70WWkXZX9eSg3+u3btQllZGf7v//4PW7ZswV//+lecPHkSzc3N2L17tzKLJipCAUV70GelXbQ6+M+bN2/Mtvnz58PGxgZFRUXKLJoQQsgkVD7g29fXh97eXtjZ2am6aEIIIf+j8uC/a9cuiEQibNiwQdVFE0II+R99eXdkWRYCgUCufWfMmDHu9kuXLuHtt9/Ghg0bcN999427z+DgIACgsLBQ3qoRNeru7kZOTo66q0HkQJ+Vdrnbz0saO6WxdEKsnC5cuMAyDCPXrbi4eMzjCwsLWRsbGzYyMpLt6+ubsJx9+/axAOhGN7rRjW7TuO3bt2/SmM6wLMtCDs3NzUhOTpZnV6xevRoWFhbc37W1tYiPj4ehoSHS0tLg6Og44WPb2tqQnJwMDw8PGBsby1UeIYSQEYODg6iqqsLSpUsnHVuVO/jfrfb2dsybNw9dXV24fPkyvL29lVkcIYQQOcjd5383+vv7sXz5cjQ2NuLChQsU+AkhREMoteW/evVq/Pjjj9iyZcuYAV5zc3OsWrVKWUUTQgiZhFKDv6enJ2pqajBeER4eHqioqFBW0YQQQiah9D5/QgghmkdjUjoLBAL89re/hYuLC0xMTBATE4Nz586pu1rkNpmZmXjppZcQFBQEMzMz8Pl8bNiwAaWlpequGpHDX//6V/B4PISEhKi7KmQcOTk5WLlyJWxtbWFqaoqQkBB8+umnSilLY1r+GzduxJEjR7Bt2zb4+vpiz549yMzMxIULFxAfH6/u6pH/Wbt2LdLT07Fu3TqEhoaisbERu3fvRl9fHzIyMhAUFKTuKpIJ1NXVwd/fHzweD56ensjPz1d3lcgoZ86cwYoVKzB79mxs2LABZmZmKCsrA8uyeO+99xRenkYE/2vXriEmJgYfffQRXnvtNQAjZwLBwcFwcHBAWlqammtIpNLT0xEdHQ19/V8nipWVlSEkJARr167F3r171Vg7MplHHnkE7e3tGB4eRltbGwoKCtRdJfI/PT098PPzw7x583D48GGVlKkR3T6HDx+Gvr4+tm7dym0zMjLCU089hfT0dNTX16uxdmS02NhYmcAPAD4+PggMDKRMrRrs0qVLOHLkCHbt2gWWZcEwjLqrREb573//i5aWFvz1r38FMDJNXiKRKLVMjQj+169fh5+fH8zMzGS2R0dHAwByc3PVUS0iJ5Zl0dzcTJlaNZRYLMbLL7+MZ555hrrlNNS5c+dgYWGB2tpa+Pv7w9zcHJaWlnjhhRfkzql2pzQi+Dc2NsLZ2XnMdum2hoYGVVeJ3IH9+/ejoaGBMrVqqC+++AI1NTV458lxgg0AACAASURBVJ131F0VMoHS0lIMDw9j9erVeOCBB3D06FFs2bIFX3zxBZ588kmllKnUK3zlNTg4CCMjozHbpdlBp8xOR9SmqKgIL774IuLi4vD444+ruzrkNu3t7Xjrrbfw1ltvwdbWVt3VIRPo6+vDwMAAnn/+eezatQvAyEWyQqEQX375Jf7yl7/Ax8dHoWVqRMvf2Nh43FOboaEh7n6ieZqampCUlARra2scPnyY+pE10Jtvvgk7Ozu8/PLL6q4KmYQ0xm3cuFFmu/TvjIwMhZepES1/Z2fncbt2GhsbAQAuLi6qrhKZQnd3Nx544AH09PQgNTUVTk5O6q4SuU1paSn++c9/YteuXairq+O2Dw0NQSgUorq6GhYWFrC2tlZjLQkwEuNu3bo1JuOxg4MDAKCzs1PhZWpEyz8iIgIlJSXo7e2V2X716lUAQHh4uDqqRSYwNDSEFStWoKysDCdPnsSsWbPUXSUyjvr6ekgkErzyyivw8vLibteuXUNJSQk8PT1pHEBDREVFAYDMQRr4dbzT3t5e4WVq1Dz/Dz/8ENu3bwfw6zx/e3t7XLlyRc01JFJisRgPP/wwTp8+jePHj2PZsmXqrhKZQHt7Oy5fvizTHceyLN5880309fXhk08+gbe3N80A0gC5ubmIjIzEo48+in379nHbH330URw5cgTV1dUKP7vWiG6fOXPmYN26dfj973+PlpYWeHt745tvvkFNTQ327Nmj7uqRUbZv344TJ05gxYoVaGtrk/miAsCmTZvUVDNyO1tb23Ez5+7cuRMAsHLlSlVXiUwgPDwcW7Zswddff43h4WEsWLAAKSkpOHz4MN544w2ldKtqRMsfGGnp//GPf8S+ffvQ2dmJsLAwvPPOO1i8eLG6q0ZGSUhIwKVLl8bN1MowDMRisRpqRe5EQkIC2tvbKb2DhhkeHsbf/vY37NmzBw0NDfDw8MCLL76IV155RSnlaUzwJ4QQojoaMeBLCCFEtSj4E0KIDqLgTwghOoiCPyGE6CAK/oQQooMo+BNCiA6i4E8IITqIgj8hhOggCv6EEKKDKPgTQogOouBPCCE6iII/IYToIAr+hBCigyj4E0KIDqLgTwghOoiCPyGE6CAK/oQQooMo+BNCiA7SiAXcR2tra0NycjI8PDxgbGys7uoQQohWGRwcRFVVFZYuXQo7O7sJ99O44J+cnIxNmzapuxqEEKLV9u3bh8cee2zC+zUu+Ht4eAAYqXhAQIB6K0OmtG3bNuzcuVPd1SByoM9Ku9zt51VYWIhNmzZxsXQiGhf8pV09AQEBiIyMVHNtyFQsLS3pc9IS9Flpl+l+XlN1m9OALyGE6CAK/oQQooMo+BNCiA6i4E+mZePGjequApETfVbaRdmfFwV/Mi0UULQHfVbahYI/IYQQhaPgTwghOoiCPyGE6CAK/oQQooMo+BNCiA6i4E8IITqIgj8hhOggCv6EEKKDKPgTQogOouBPCCE6iII/IYToIKUH/8zMTLz00ksICgqCmZkZ+Hw+NmzYgNLSUmUXTQghZAJKX8nr/fffR3p6OtatW4fQ0FA0NjZi9+7diIyMREZGBoKCgpRdBUIIIbdRevDfvn07oqOjoa//a1EbNmxASEgI3nvvPezdu1fZVSCEEHIbpQf/2NjYMdt8fHwQGBiIoqIiZRdPCCFkHGoZ8GVZFs3NzbCzs1NH8YQQovOU3vIfz/79+9HQ0IB3331XHcUTolYsy6K/vx8DAwMQi8XQ19eHiYkJTExMwDDMlI9vbW3FB6+/jlvXrkFveBhifX0EzpmD1z/4APb29ip4BeReoPLgX1RUhBdffBFxcXF4/PHHVV08ISo3MDCAyspKVFZWoq6uDm1tbRCLxWP2MzQ0hLOzM3x8fDBr1qxxz4xbWlrwSFwc/lZejg8AMAAkAK7duoUNqan4Lj2dDgBELioN/k1NTUhKSoK1tTUOHz4sVyuHEG00MDCAmzdv4saNG6itrQXLsuDxeJBIJBM+RigUorq6GjU1NTh//jzc3Nwwb948+Pn5cb+VD3/7W/ytvBwxox7HAxAD4K/l5fjg9dfx4Z49Sn1t5N6gsuDf3d2NBx54AD09PUhNTYWTk9Ok+2/btg2WlpYy2zZu3EjrkBKNJZFIUFpaipycHJSWloJlWTAMA5ZlufvlId2/vr4eBw8ehKOjIx588EG4ubnh1rVr+GCCx80F8O61awp4JURbHDhwAAcOHJDZ1t3dLddjVRL8h4aGsGLFCpSVleHcuXOYNWvWlI/ZuXMnIiMjVVA7Qqano6MDOTk5uH79OgYGBmQCvvTfuyF9bEtLC/79738jKioKPJEIE50v8wDoDQ/fdXlE+4zXIM7JycHs2bOnfKzSg79YLMaGDRtw9epVHD9+HHPnzlV2kYQo3dDQEG7duoXr16+jrq5OYQF/PNLny87ORltvL1hg3AOABIBYXy1zOIgWUslFXidOnMCKFSvQ1taGffv2ydy/adP/b+/O46K6zsePf2bYVxl2AQUEiSCiKCLuu0bFRE2IS91NNcYsjUmTb9rGpk3T5pu0v69tTBqza2KMxi3EGHeN4ooiiiIKKgyL7PvOzNzfH2RuGFlVEJDzfr14Jdy5M/cM4HPPnPOc58xv6yYIHZQkSdy6dYtr166RkZFBSUkJxsbG2NnZ4evrS9++fbGxsWnvZsoqKipITEwkPj6exMREdDqdPBbf2gG/IZIkYe3qyunMTOqvnoEzQEBoaJu3Q3g4tHnwv3jxIgqFgh9++IEffvjB4DGFQiGCfxeTkpLC/v37OXToEIcPHyYnJ6fRc42MjAgLC+Oxxx5j4cKFzc4Ttbbq6moyMjJITk7mxo0bpKen1xvHfxBBv67giRNZmZLChwUFhFE71KOjNvD/0ceHLe82NiMgCIYU0oP+622Gfrzq/PnzYsz/IZGTk8PWrVv55ptvOHny5D29hrGxMXPmzOHPf/4zvr6+rdq+mpoaCgsLyc/PJycnh5ycHDIyMsjLy6sX7O+FRqMhIyMDtVpNTk4O4eHhmJiYNHp+YWEhBQUFeHp6olTWX4dZVlZGzP79FGdk0M3cHFMbG5HnL8haGkPFAKHQJkpKSvj+++/55ptv2L9/f4N57TY2NowaNYqgoCA8PT2xtbVFo9GQm5tLfHw8UVFRcgkQjUbD119/zebNm1m6dClvvPEGPXr0aPT6kiRRU1NDWVkZZWVllJaWUlpaSllZGSUlJZSUlFBUVERRURGVlZXy8xoaxrmXwF9cXMzVq1e5fv06arWampoa+bHhw4fj7Ozc6HPj4+PZv38/VlZWDBgwgCFDhmBrays/bmVlxciZM+XvJ02a1GAZFUFoigj+QqupqKhgz549fPvtt+zevdsgqOr17duX2bNnM3HiREJCQgwK/kmShFarRaPRyF+JiYls2rSJjRs3UlBQgFar5ZNPPmHjxo0sWLCAJ598Ep1OR0VFBeXl5XKAr6ioaPCGo+9JN5Z2eb8fhIuKitixYwcpKSmNnpOYmMjNmzfJzs6msLCQmpoaFAoF1tbW2Nvbk5ycDNT28E+cOMGpU6cIDQ1l9OjRWFhY1Hu9/fv3Y2pq2qIMD0HQE8FfuC9ZWVkcOHCAvXv3EhkZSUlJSb1zevbsyaxZs5gwYQJOTk4UFRWRnp5OQkICpaWlVFZWUl1djUajaTD4qlQqVqxYwenTpzl58iTV1dVUVVXx6aefsmPHDqZOnUrv3r1bFLhbmmt/r6ysrMjKyjI4ZmNjQ/fu3ampqZF/Xs2p+wlEp9Nx+vRpLl26RHh4OAEBAfXO3717N1ZWVi1KoxYEEGP+wl3Izs4mLi6Oy5cvc/nyZaKjo7l48WKD59rb2zN8+HD69u2LlZWVQS9cqVQiSdI99bLLy8s5duwYZ86cMXh+nz59mDx5MiqV6u7fWCv7/vvvUavVBAYG4uTkRHx8PPHx8ff8enfOOUyePLnBYR4jIyOWLFmCu7v7PV9L6PzEmL9wz0pKSrhy5Yoc6PX/bSozB2p7vf369cPX1xdvb29MTEzQ6XT1hl/utvddd4LTRKdDq1QyzM+PlLIy0tLSgNqaUUlJSYwaNYphw4YZDCe1hvz8fOLi4kg+dw4HMzNMJYkapRJbNzcGTpqElZWVfO6UKVNIS0sjKiqKn3/+2eB1jIyM8PHxwcfHBw8PD1QqFebm5uh0OoqLi8nMzOTGjRvEx8dTUVEBGA5F2draEhQU1GAbdTod33zzDc8880yHSpEVOiYR/AVSUlI4evQoUVFRBpOsLeHt7U3Pnj3x8vKql53SGkMspaWlHPjsMz4qKGAIvxYyO5OTw0qViv7TpnH06FHKysrQaDQcPnyY2NhYpk6del9ZQdXV1ajVapKTk7l+/TrZ2dn0ALYCQ0pKDNuhVjPp6aexsLDg2rVrREVFkZ6ebvB6VlZWDB06lEGDBjU4bq9UKrG3t8fe3p6AgACmTJlCXFwcx48fJz8/Xz5Pp9ORk5NjcLPRkySJiooKeVK8tW+AwsNFDPt0UWVlZWzYsIENGzZwtgX1YFxcXAgICMDd3R1LS0skScLR0RELC4s2HUc/vnMnb1282OCippPAmv79GTxlCkePHq03FNSrVy+GDx9Or169WlxEUJIkvvzyS1JTUw3elwMQCQxr4DkngOd79CC1ooLc3FyDx1QqFcOHD6d///5Npnc2RqvVcvbsWY4cOUJ1dTVQOww0efJkhgwZ0uD7UigUDBo0iGnTpt319YTOTwz7CA2qrKxk7dq1vPvuuxQUFNR73NjYmODgYAYMGEC/fv3w9/fHwsKC9PR0rl+/jlarvadiZfeqOCPDoIJlXWG/PG5ubs6jjz7KgAED2LNnD2q1GoCbN29y8+ZNnJ2dCQgIwNnZGUmSmtw3WqFQUFVVVe99qYyNGdpI3ZyhQH5qKnXDvouLCyNGjCAgIAAjI6OWv+E7GBkZMXToUPz9/dm1axfJyclIksTevXvJzMwkPDy8Xg9fkiTOnTtHz5496dev3z1fW3i4ieDfhRw6dIinn35aTiXUGzBgADNnzmT06NEMHjwYCwsL1Go1ly5dIjo6murqaoNyxG31YVGn05GXl0d5eTnl5eVUVFSgKStrspCZSZ0g7erqypAhQ0hPTzeYZ8jOziY7OxuoDe4VFRX06NEDR0fHBgOzm5sbNTU1eHl54e3tjZeXFye/+AJFXl6j7TD75f979uzJyJEj8fX1NeiVN7R+wMzMDDMzMxQKBVqt1iA99c7yz3Z2dixcuJDDhw8TFRUFQGxsLLm5ucydO7fBYaDIyEjc3d2xt7dv5CcodGUi+HcBGo2G//mf/+Ff//qXfEypVPKb3/yG1atXM2DAAKB2YdK5c+eIiYmhqKjIIADdbw9fv+jK1NS0yXM++OADg2Oe0GQhs5o7VsAqlcoG8/vrXmP37t1A7accV1dXVCoV1tbW2NjYYGZmhpubG+7u7mi1WjIzM7l+/ToVRUVNtkNhZsbTCxbg4eEBGGbomJub07NnT9zc3HB1dcXR0ZFu3bo12GMvKyvj9u3b3Lp1iytXrlBcXCz/HpRKJRMmTMDFxYXvv/8ejUZDWloaX3zxBQsXLqSiogJ7e3t5eEmn0/Hdd9/x9NNP39enD+HhJIL/Q66wsJCnnnrKILd87Nix/Oc//yEwMBBJkrh58yZnz57l2rVrBkGrpKTEIMumseyWO2k0GjIzM0lPTyczM1MumdC7d2+efPLJRp9nZGSEmZkZVVVV8rFS4BQNj7WfBmzd3AyOWVpa4uTkhImJCSYmJhgZGaHRaKioqKC4uNjgtfXBU58x1BSHJtpxCvDo04eePXui0+kwNjbG29tbznpydHRs0ZyDfqFX79696d27NxMnTuTmzZscP36clJQU+XfTr18/HBwc2Lx5MyUlJeTm5vLxxx9TWVmJv78/s2bNQqFQoNPpyMzM5OjRo4wfP77Z6wtdiwj+D7GcnBzGjx9PXFwcACYmJrz33ns8//zzSJLExYsXiYqKIjc3t96wRJNZNr9kt9x5Azh//jwxMTFkZmY22PtuLlUUkCeoLC0tMTc3B+Dpn3/mk9JShvJrIbPTwLMqFZMmTTJ4vqenJ6tWrWrwtSVJIj09neTkZG7fvk1GRkaD8x4NyQPmKRRskqR67VilUjFz5kxCQkLw8/PD09OzVTJtFAoFPj4+9OrVi6SkJH744QdKS0uRJAk3NzeWLFnCxo0bKSwspLS0FIC4uDicnZ0ZOXKk/DpRUVH07t2bnj173nebhIeHCP4PqezsbMaNG8eVK1cAcHBwYOfOnQwbNozY2FiOHj1qsBr3znH8CwcO8N9fKkfqKamd3PywoIA1+/cb1JeB2hvGnSmOet26dUOlUsmF0hozefLkescCAgL4cwOfQCY18wnkTgqFAg8PD3loBqCqqkqu9VNaWmpQg0epVGJtbY2trS22trZotVqDduhMTOgVFMRX//wngYGBbbYtqUKhoHfv3qxatYqffvpJXlhnb28v3wDy6sxHHDp0CGdnZx555BH5+Tt27ODZZ59tcthN6FpE8H8IlZaWMnXqVDnwu7u7c+jQISRJYt26dRQWFjb7Gi3JsrmTPqg6ODjIQbZ79+44OTlhZmZW7/yWurOQWWvST7o2tFl6XfohlxlPP01gYKCcPfQgmZmZ8fjjj+Pu7s5PP/2EJEl069ZNvgHoJ7UBdu7cybPPPoutrS2SJFFcXMzBgweZOnXqA22z0HGJ4P+Q0Wg0zJ49m/PnzwO1gX/Hjh2cPn2a5OTkFvdOTXS6FmfZ6Hl6evLaa681uIips6k79+Hq6krfvn3x9/fHwcGh3ds1ePBgbGxs+O6772o3eLG2ZuHChXz22WfyMFZlZSXbtm1j8eLFcjmN6Oho/P398fb2btf3IHQM9YuFC53aH//4R/bs2QPUDrW89dZb7Nu3T859b0maZlZWFgXl5TR2ZkNZNlCbPdNZA79CoZBvjCYmJvj5+REeHs7q1atZsWIFI0aMaPfAX1efPn2YN2+e3GZra2sWLFhgMAymVqs5fvy4/L1CoeD777+XF4sJXZvo+T9Etm/fzru/7ORkbGzM4sWL5aDfEomJiRw7dozU1NQms1sayrLpLBoq6WxlZYWHhwc9evTA09MTNze3BjdR6Wh8fHyIiIhg69atSJKEvb09CxYs4LPPPpPnLq5cucLo0aMB5OGfI0eONDi3InQtIvg/JNRqNUuXLpW/nzRpEvb29ne1ICs7O5vU1FSgNrtlLvANtCjLpjU0Vu1TqVRiamqKqakpxsbGGBsb1yt5rN8LQP//UNvTVSqVmJiYYGZmhoWFBdbW1lhbW6NSqbC3t8fJyQlLS8tWfy8PSp8+fQgPD5e3SHV1dWX+/Pl8+eWXSJJEdnY2586dIyQkBKj9eZ0+fZq+ffsaTHwLXY8I/g8BnU7HkiVLKC4uBiAwMJDBgwff9UrcgQMHcvToUezs7Bg8eDA+Pj78+dix+86yqUs/vFK3521tbY2DgwMODg6oVCrs7OywtbXFxsYGKysrkaHSjIEDB5Kbm8upU6eA2rmX6dOnExkZCcCePXtwcnLC09MT+HX455lnnhGLv7owEfwfAuvWrePw4cNA7Th/eHj4PaUdWlhYsGLFChwcHOTn32uWzZ1BXqFQYGdnJ69wdXJywtHREQcHBxHcW8GECRPIzMyUa/8MHDiQrKwszpw5g06nY8uWLSxfvhw7OzskSSI3N5cTJ04watSo9m660E5E8O/kEhISePXVV+XvH3/8cXlx1J2ys7ObTU9sLuWxIXXLQCgUCrp160b37t1xdnbGyckJZ2dn7O3tRS+zDSmVSp588kk++ugjeSHYpEmTyMnJ4ebNm5SXl/Pdd9+xZMkSeQHazz//TN++fTvURLbw4Ijg34lpNBoiIiLkkgVDhgyhV69e9c4rKChgz549JCYmsmLFCrp3735f160b7FUqlTxJ2r17d1xcXO6pdLFw/ywtLXnqqaf4/PPPgdpyGREREXz88ccUFBSQnp7OwYMHefTRR+XnREZGsnjx4jZboCZ0XCL4d2KrV6/m8uXLQO3Cqjvrt+h0Os6cOcPhw4fl7I99+/axaNGiu/7Hrs97t7a25pFHHqFXr154eXl16snSh5GHhwdjx46VhwEtLCyIiIjgs88+Q6vVcvr0aby8vOjTpw86nQ61Wk1sbCzBwcHt3HLhQRPBvxOSJIktW7awfv16+djMmTMNxs6zs7PZtWsXGXVW4trY2NzVBjn6Hr5+68CAgABcXV1FL7GDGz58OElJSaSlpaHT6XD7ZZL+p59+AuC7775j5cqV8hDfvn376N27N9bW1u3ZbOEBE8G/k9HpdPz444+8/fbb8mKdQYMGyWl7+pWc+/fvR1Nn85HBgwczfvz4RucD6tJP1vr7+zNo0CC8vLxEwO9ElEolM2fO5MMPP5SH50JDQ7l8+TKpqalotVo2bNjA7373O4yMjKiurmbv3r1NVlwVHj4dfyWLINPpdOzatYvt27fLwz0WFhYGwz1ZWVns2bNHDvyOjo4sXbqUadOmtSjwm5qaMmLECH73u9/x5JNP4u3tLQJ/J2RnZ2cwtq9QKJgwYYL8fUlJCT/++CNQ22G4cuUKiYmJD7ydQvsRwb+T0Gq1bNu2jdjYWLl8A9Sm+NUdd3d1dWXEiBFAbW9vxYoVzZbyVSgUmJmZMX78eFavXs24ceOwsbFpmzciPDDBwcH06tVLXq3s6ekpb9wDtXu93rx5E6j9G4iMjDTY70B4uIng3wnoA//Vq1c5c+aMXBff3d29wYm6sWPHsmjRIqZOndpk5o1CocDY2JhRo0bx0ksvMWLEiPuqvil0LAqFgscee8ygVEV4eLhBZ2Hr1q1UVFTIu4gdOnSoPZoqtAMR/Ds4feBPSEigvLycn3/+WX5s6tSpDdagMTIyarJyo/45wcHBvPjii4wZM0YE/YdUt27dDEpxGBsbM3v2bPn7yspKtm/fLpfViI6OJiUlpT2aKjxgIvh3YFqtlu3bt5OQkADULsrRfywPDg7G3d39nl7Xw8ODZ555hunTp4sMjy4gJCQEDw+PRod/kpKSuHDhAlD7aWHXrl0Gm9oIDycR/DsonU7Hzp07uXr1KgD5+flER0cDtb230NDQu3o9hUKBpaUls2bNYvHixbi4uLR6m4WOSaFQ8PjjjxscmzJlisHwz08//UReXh6SJFFUVCSvExAeXiL4d0CSJPHVV1/x0d/+xo8ffMD+999n3/r1qOqUUNi9e3eL6rLrM3UGDhzI888/T79+/UT2Thfk6OhoUMfHzMyMiIgIeUV4TU0N27dvR6PRyJU/k5OT26m1woMggn8HI0kSmzZt4v9eeom/XbzImZwcTuTlEV9VRSTQg9p/qOnp6c32zvTF1JYsWUJ4eHiLUj2Fh9fw4cOxt7eXb/7e3t7MmTNHru2TkZHBkSNHgF/3/a2srGy39gptSwT/DkSSJPbv38+X//ynvHm6vo+upHZjlc2AA9CjR49GKzLq/3GHhYWxcuXKZlM9ha7B2NiYxx57zKDUt6mpKU888YQ8H3DixAlu3ryJJEmUlpby448/3nVpcKFzECt8O5CjR49y+vTpJjdPHwrYm5gQsWBBg6WQFQoFNjY2zJo1S67fLgh6np6eDBw4kAsXLshB3c3NjfHjx3PgwAGgdvP3lStXYmlpyeXLl/Hx8TGYIO7MJEmipKSEnJwc8vPzKSkpoby8nOrqanQ6nZz+bGZmhrW1Nba2ttjZ2eHg4IClpeVDNWQqgn8HcfLkSY4dOwY0v3m6o41NozXwAwMDmTZtmkjdFBo1YcIErl69SkVFhXxs6NChJCUlcevWLUpKSoiMjGT27NkoFAp+/PFH3Nzcmi0H3hHp9y5ISkoiOTmZ1NRUg/et/8RT99NN3QBfd9MhMzMzXFxc6N69u/zl6OjYKbb8bIgI/h3AuXPn5F4X1G6OLkGDNwAdoLmjLr5CocDIyIjw8HD69+/fpm0VOj8LCwumTJnCjh075GP6ekD//e9/qaioICEhgTNnzhAWFoZWq2Xz5s2sWLGiU8wbSZJERkYGV65c4cqVKxQXFxts+1lX3eBe9/kNqaqqQq1WywXzoHYozdXVFQ8PD9zd3XFzc0OlUnWKTwgi+LezuLg4ucaKnq2bG6dyclq0ebpCoUClUjFnzhycnJzatrHCQyMwMJDY2FiSk5PlQGZra8vjjz/Ot99+C9RW+3RxccHb25uioiK2b9/O3LlzO2xPt7i4mIsXL3LhwgUKCgoM9p1ozXmLujcMjUZDWloaGRkZ8nFTU1Pc3NzkPS466g1BBP92dO3aNXbu3Fnv+MBJk1iWmMin5eXNbp7u5+fHzJkzxTCPcFcUCgXTp0/ngw8+MAhmJiYmGBsbyymfW7duZfny5ahUKpKSkti3bx9Tpkxpx5YbkiSJs2fP8u6rr5J69arBXtMD72Ov6bttQ3l5ORqNBqVSiVarJTk5GbVaXe+G4OHhIX89iLY1RQT/dnLr1i22bt3aYI/EzMyMdCMjHgesAWdbWzAzq7d5+qhRoxgzZkyH61EInYOdnR3jx49n37598rE7e6gVFRV8++23LFu2DFNTU86ePYutrS3Dhw9vjybLqqqqiI2N5cCBA2z7f/+PjwoKGELtUKkOOJOTw0q1mklPP92qQVan05GVlcXNmzdJT08nKyuLoqIig/LpANbW1jg4OODh4YG3tzfe3t71bggqlYpevXrh7e1Nr169sLCwaLV2toQI/u0gPT2db775ptGPojExMZSUlACg8vVl2vz58mP6WvuPP/44QUFBD6S9wsMrNDSUS5cukZmZiSRJ2NvbEx4ebvCJNCsri507dxIREYFSqeTgwYMYGxszZMiQB97egoICzpw5Q0xMDDU1NRzfuVNOi9ZTUpsV92FBAWv272fkzJn3fd38/HxiY2O5ePEiRUVFzZ5fRs4ewgAAIABJREFUWlpKaWkpKSkpnDhxAgsLCwIDAwkNDZWHZwsKCrhw4QLnz59HoVDg4eFBQEAA/v7+dOvW7b7b3Jw2D/5VVVWsWbOGr776isLCQoKCgvjb3/5mUFu8K8nOzuarr75Cq9U2GPyrq6vlrB+AcePGyf+vT0ObO3duk4XbBKGllEolM2bMYP369fLfY//+/bl16xaxsbHyeVevXuXHH38kPDwchULB3r170Wg0DBs2rM0/eUqShFqt5tSpU1y7dk3eUhRoMi067JfHG5OUlERJSQkVFRXodDr5y9jYWB6mKS8v58SJE40WuzM2NkalUmFjY4OJiQk6nY6KigoKCgooKyuTz6uoqCA6Opro6GgCAgIYN24cjo6OBnMSqamppKWlsW/fPnr06MHAgQMJCAhoNLPvfrV58F+8eDHbt2/npZdeonfv3nzxxRdMnTqVI0eOtPtHxwetoKCADRs2UF1dbRD4y8vLyc/Px8PDg+joaEpLSwHw9/fH7ZfJXYVCgbm5OQsXLsTV1bVd2i88nJydnRk9erS8uhdqa/+kpqaSl5cnHzt//jxGRkZMmTIFhULBwYMHKSoqYvLkyRjdkYHWGjQaDZcvX+bUqVNkZ2c3mJbZXFq0SQPZPHo7d+40CNB3sra2lv8t6ikUCnr16oWfnx89evSgsrKS7t27Y2FhQVlZGTH791NcWEhPc3OqzMzAxoYyCwtu3LghF8uLj48nISGBYcOGMXr0aIOy6/r3lpaWRmpqKnv27CE4OJjQ0FB5JXZradPgf/bsWbZs2cI///lPVq9eDcCCBQsIDAzk1Vdf5cSJE215+Q6luLiYL7/8ksrKSoM/3urqajZv3szt27eZMWMGUVFR8mNjx44FantnlpaWLF68uNX/AAQBYMSIEcTHx5OdnY0kSXLtn08++QStViufd/bsWTQaDdOmTcPIyIjo6GjS0tKIiIhApVK1SlsKCgo4f/4858+fp7KyUv5k0VBaZnNp0TVNZCZZW1s3GfzrBn4HBweCg4MJCgrC1tYWgMzMTDZu3AjUZko5VVTwdU2N4dxDfj4rVSqeeeYZEhISOHnyJGVlZeh0OqKiokhISCAiIqJeoUV9jKipqSE6OpqzZ8/i5+fHiBEj6NGjR6NtvhttGvy3bduGsbExy5cvl4+ZmZmxbNky/vCHP5Cenn7PZYk7k/LycjZu3EhpaanBH7BWq+W7774jNTUVgMjISLlYW1BQEM7OziiVSqysrFiyZEmr/eMShDsplUpmzZplMPzj6urKtGnTiIyMxM3Njdu3byNJEjExMRQVFREREYG5uTmZmZl88MEHjB49mrCwsCY3EGpMdXU1V69eldNP6w7tNJWmaevmxumcHIY28NgpDNOi7zR06FCqq6uxtLREo9EQFxfHjRs3DM5xdXVl9OjRPPLII/VSXOt+KjIpLmYjND73cOwYI2fOJCQkhBMnThAVFYVOpyM3N5dPPvmE8PDwRldR699/UlIS169fx9PTk7Fjx973Cv42Df4XLlzAz8+vXs34wYMHAxAbG/vQB//Kyko2btxIfn6+wR+xTqcjMjJS3jfV1NRUflypVMpZPFZWVixduhQ7O7t2ab/QdTg7OzNhwgT2798vHxs4cCA2Njb4+vpy+fJldu7ciU6n48aNG6xfv55Zs2bRo0cPtFothw8f5uTJk4SGhhIYGNjkuhNJkigsLOTWrVtcv36dpKQktFpto4uxGnsN54AAFsTHs6GmxiAt+hSwwsaGqXXSou80YMAAdDod58+f59ChQwZF7JycnJgwYQJ+fn6NzmnY2dkxePBgMjMzMUpNbfAGBIZzD2ZmZowbN47AwEB27NhBZmYmGo2GXbt2kZ+fz9ixYxu9nr7jqFar+fLLL/H29mb8+PH3HEPbNPjfvn2b7t271zuuP5bRxGTMw6C6uppNmzbJH6XrOnjwIBcvXgRqd97y9fUlPj4eqP0Hp68lsnjxYhH4hQcmLCyM69evG6Qk9u7dG4B+/fphY2PDli1b5EnNzz//nNDQUMaMGYOFhQWVlZUcP36cY8eOYW1tjbu7O926dcPMzMxgMjQzM1Mus9DSXn5dBQUFbN26ldu3bwPIadFmgM7YGEtnZ0bPmNFkmqdarWbPnj1kZmbKx0xNTRk7diyhoaHNzmO4u7vLgXfff/6DIj+/wfMamntwdnZm2bJlbNq0SS6dfezYMUpLSwkPD29yIZ3+Z5SSksKnn35Knz59GD9+PI6Ojk22905tGvwrKioaXHykXyJet8bGw6ampobNmzeTnp5e7w/6xIkTnDx5Eqj9w586daq8KbuxsTGjR4/GzMyMRYsWYW9v/8DbLnRdCoWCWbNm8eGHH1JVVVXvb9fLy4vly5ezfft20tLSkCSJM2fOcPHiRUJDQxk8eDA2NjZA7Zj59evX6/Vk7xy7v5fVtzY2NgZj8hWWlvQJDpY7Tk0pLS3lwIEDcudLLygoiIkTJ8rtvxsaI6O7nntQKpXyzUtPn8I6c+bMZldS63+O169f59q1awwcOJAxY8a0uM1tGvwtLCzkbQfr0n+8ampRw0svvVQv13Xu3LnMnTu3dRvZBjQaDd9++y0pKSn1/rBTUlIM6viEh4eTkZEhT6oNGTIElUrFggULRLkGoV3oq8J+8803DT6uUqlYsmQJJ06c4NixY2g0GiorKzl27BjHjx+nV69e9O7dm169euHk5NQmqaDGxsYMGzaMS5cuERYWRkBAQLNzDVqtlujoaI4cOWIQl1xcXJg6dep9jaE3NfdwZ0kWvfT09AbjY1xcHBkZGTz66KP4+Pg0eROIi4sjLi4OgG+++QZzc/MWrxFo0+DfvXv3Bod29Hc7tyYmY5599lmeeuqpTrd6VR/4b9261WCPpmfPnowYMYKoqCjGjRuHl5cXu3fvBmo/co4cOZK5c+c2+bMRhLbWu3dvhg8f3mhGnpGREaNGjaJv3778+OOPco0gSZK4ceOGPHFqbW1Nz5495SGS7t27t1opktDQUMLCwloUI/SlKXJycuRj+vH3kJCQ+05VHThpEivVaj78ZcFZUyVZ9JycnJg1axbXrl0jMTHRYGe+vLw8Nm3axLJly5rM7unXrx/9+vWTv7ezs2PkyJEMGjSo2Ta3afAPDg7m6NGjlJSUGHyUOnPmDECTNcIPHz5MQUEBjz76KF5eXm3ZzFaj0WjYvHlzo4Efaj9WT5gwAR8fH7y8vNixY4d87rBhw5g3b568tZ4gtKdx48aRnp7e4CdYvdOnT3Pz5k0GDBiApaUlV65cMVgBW1paSnx8vDyfpVAocHJykm8G3t7e9YZpCgsLOX36NL6+vvj6+jbavpYE7OzsbPbv309SUpLB8eDgYCZOnCgno9z5/u68oUiS1OTwlJWVFZOefpo1+/dTnJFhUGNoUiM1hiwsLAgKCiIoKIiamhpu3LjBiRMn5Ow/qL0JtFZq550UUhtu03P27FnCwsJ47733ePnll4HaFb/6TAD9uHddMTExDBo0iAA7O9y8vAieOJEBAwYwceLEDj0Mos/Xb+ofyp0yMzP56KOPALC0tGTXrl1MnDixLZspCHelvLycjz/+mOLi4np/12q1ms8//1z+vlu3bkyaNAmVSkVycjK3bt0iOTlZXtzUGJVKha+vLxYWFmRkZHDjxg0kSaJnz54sXbr0ntqdkZHBiRMniI+PN2h3jx49WLBgAcHBwVhYWGBubi4Xs6u7iEyn06HRaKipqaGqqoqqqirKysooLS2VVwXr6UuuNLQO4V6cPHlSzrgyMjJiyZIleHh4tOi5dXv+58+fZ+DAgY2e26Y9/9DQUCIiInj99dfJzs7Gx8eHDRs2oFar+eKLL5p87obCQmpiY1mZkoJy+XISExPlCY17mZBpSxUVFXz99ddyHnRL1V1ROW/evC5b8kLouCwtLZk/fz6ffPIJNTU19QLp1KlT2bdvH1qtlqKiIr777jucnZ0JCQlhxowZmJubk5ubS3p6uvyVlZVl8DoFBQVER0fXu3Z6ejr5+fktTnrQaDQkJSVx9uxZbt68afCYu7s777zzDvPmzWuVktT695ufn09ubi7Z2dlkZGSQk5Mj7wgG9zaZPXToUPLy8jh//jxarZZvv/2W5cuXy4vL9DQaDWfPnpVvZHerTXv+UNvTf+ONN/j6668pKCigf//+vPXWW432cPU9//PAQOAksKZ/f0bOnIlCoUCpVDJ06FCGDx/eITaWKCoqYuPGjRQUFNzVL7pur0mlUpGamtruJV4FoTG3bt3i66+/brB3m5uby+7du+WURT1ra2tefvnlekMo1dXVZGZmcu7cOS5dutTkdW1tbQ3mC7p164a5uTkKhYLKykqKi4vJyspCrVZz8+bNehvOOzs789JLL/Hiiy8+kKqZGo2GzMxM1Go1ycnJ8iefunsLtPR1vvrqK7mmkJubG0uWLDGY1I6JiSEyMhIzMzOGDRtGWFgYLi4uLe75t3nwv1t3Bn8dEObkxLRVq+RzFAqFPDkaGhp6TysKW4O+OmdlZWW9X6w+zbWhXoZOp+Ozzz4jPT0dgA8++IBnn332gbRZEO7V1atX2bp1a4OPSZJEYmIix44dIy0tDYCAgACeeuqpRl+vqqqK//3f/0Wn06FUKrGxsUGj0TRZcqGlfHx8eOWVV1i0aNEDL5Vcl1arJS0tjcTERBISEgxWBTenrKyMTz75hMLCQgBCQkIIDw8Han/eH374ocEEtoWFBTNmzOCVV15p/2Gf1tDQAglJkqiqquLgwYOcPHmS0aNHM3DgQIyNH9zbuXTpEpGRkXKGQ11lZWVs2LABR0dHZs2aVa9dly9flgO/v7+/QfkLQeio/P39mTlzZoMbECkUCvz8/PDz8yMzM5O4uLhmM9bMzMyYOHEi9vb2eHt7y6vcc3JySExMJCkpibS0tGbnDPRUKhVTp07lN7/5DZMmTWqTYnN3y8jICE9PTzw9PZkwYQIFBQUkJCRw9epVeWK37iK3uqysrJgzZw6ffvopGo2Gc+fO4e3tTd++fVEoFMyZM4eoqChiY2ORJImKioq7yo7slD3/hlhbWzNq1CiCg4Pb9CZQU1PD3r17iYmJafDx0tJSNm7cSHZ2NlBbHndmnXri1dXVrFu3juLiYqB2q7yG0sAEoaO6fPmyvP9vW4cPff2b9PR0cnJyKC4uRqfToVKp8PDwwNXVlYCAAIJ/WeDVEQJ+S5WWlpKQkMCVK1fkIbOGbgT64R2ovWGuWLHCYB4kLy+Po0ePkpmZyeuvv87o0aMfjp7/KcDS2bnZ80pLS9mzZw9Hjx5l+PDhDBo0qNW3NlSr1ezcubPRzRxKS0vZsGGD/FHMxsaGUaNGGZxz6tQpOfBPnTpVBH6h0wkMDMTCwoItW7bI2z22FaVSibOzM87OzlhYWDBmzBgGDRrUqYJ8Y6ytrQkJCSEkJISysjISEhK4fPlyvRtBcHAwt27dIi4ujqqqKrZt28bSpUvlTq6DgwNPPPGEPLfQUh225x8NVAFzgUwTE7liYEt79SYmJgQHBxMSEnLfKaJFRUUcPHiQy5cvN/oRraioiK+++orc3FygdqJq0aJFBjnMxcXFvP/++9TU1GBkZERcXBz+/v731TZBaC/x8fH8fskScm7darO9cxUKBSYmJowYMYIhQ4a02cYmHUlpaSlXr17l0qVLpKWlyZPb69evJ/+X+kFDhw5l8uTJ9Z7bYVI978dsc3NKq6vJ1umgpoaDBw9y/vx5wsPD8fHxafb5detgu7u7ExQUhL+/f4vTRCVJ4vbt20RHR3Pp0qUmC09lZmayadMmeetFW1tbFi9eXC9F7eDBg/L45TPPPCMCv9BpZWdn89xjj/H3GzfaZO9chUKBkZERYWFhDBs2rF0nbR80a2trBg8ezODBgyksLOTy5ctcuHCBJ598ks8++wytVsupU6fkMhr3qsMG/wkLF9KtWzcOHz5MTEwMkiRRUFBgsAS6OfpArc8v/umnn3BycsLLyws3NzccHBywsrLC1NQUjUZDeXk5ubm5pKWlkZSUREFBQaM9fT2dTsfOnTvlwG9vb8/8+fPrBf6kpCQ5rc3Ozo4333zzLn8igtBxvPfaa/z9xo022TvXzMxMLt1gaWnZGs3ttOzs7BgxYgTDhw8nPT0dnU7Hp59+CsCuXbtYuXJlvZL5LdVhgz/UznZPnz6dwYMHs3fvXgD69OlzX6+Zk5NDXl5eg4tK9Orm5DY3KqZUKuU7soODA/PmzavX46mpqWHfvn3y9++9995dl18VhI4k/uxZ3m3kMX39+pbmtus7WC4uLoSEhBAUFNQlhnfuhn6D948//ljuyJaVlbFz507mz59/TzXQOnTw13N1dWXRokVUVVW1SqG35v4g73aZtpOTkzzMc+cfrUKhIDo6Wp4EHj16NMuWLbu7BgtCB2Ok0TS5d669lRX9+vUjMTGR8vLyhl/DyAgPDw969eqFv79/hy7f0lEoFAo2bNhAUFAQmZmZ3Lhxg6tXrxIQEHDXr9Upgj/8uoF5UzQajTxW+KA1tKm6QqGgoqKCgwcPArUfZz/++ONOV6lUEO6kNTZusn69kaUlM2bMAGrXveTl5VFVVYVOp8PMzIxu3brRrVu3Vim10NU4OTnx1VdfMWnSJCRJYufOnSxYsIDc3Ny7GibrNMG/JWJiYjh16hQjR46kf//+7ZoOplAosLW1ZceOHfIniTfeeAM/P792a5MgtJaA0FDOxMcbjPnrnfnlcT0rKytRuqSVTZgwgd///ve8++671NTUsHr1amJiYrC2tm50DdKdOuxt18zM7K56yBqNhqioKAoKCoiMjGTt2rUcPnxYXhp9ryorK4mKipIXWbSEUqlEpVKhVqvl3YICAwP5/e9/f19tEYSO4tV33+UPPj6coranD7/unftHHx9efbexGQGhtbz11luEhIQAkJiYyKpVq+5qzUWH7fnPmTOHgoICzp0712zGDdSWnnV0dJQXUJWUlMg7C/n4+PDII48QEBDQoh6ITqcjPT2dS5cucfHiRTnDKDg4uNna2gqFAnt7ezw9PXnxxReB2jUHX375pZjEEh4aTk5ObDl1indffZW/nT2LkUaD1tiYgNBQtrz7rhi/fwBMTU3ZvHkzwcHBcmUBfZWDluiwwd/c3Jxp06YRHBxMZGQkWVlZTZ5va2vLwoULSU1N5cSJE1y7dk3egCEpKYmkpCTc3NyaDP4nT54kJSUFtVrd4P7CarW6yeCvUChwdXVl7NixDB8+XL5h/f3vf2/RzjqC0Jk4OTnxXjOl2YW25evryyeffCJvb7tq1SqDPRaa0mGDv56bmxvLly/nzJkzHD58GJ1O12Q2To8ePZgzZw7FxcXExMQQExNDcXExlpaWdO/evclrXblyRS64pmdsbExQUBBDhw5ttjfj4+PD9OnTmTBhgpzdM3nyZFavXt3CdysIgnB39AXePvjgA6qqqvjjH//Youd12OCv0Wjk/9fX8Pf392f37t3y/qBNsbW1ZcyYMYwaNYrMzEyKi4ubzSzQz5Sbm5vj7e2Nn58f/v7+Ldo3YMiQIUyYMIHf/OY38hoCLy8vNm3aJDIaBEFoU//61784e/YsWVlZ/OUvf2HRokXNPqfD1vZ57LHH2LVrV4N7acbHx7Nnzx4qKipatahUZmYmpqam2NnZtShg6zeXCQ8Pp3///qxevZq1a9cCtTeSEydONLlPsSAIQmtJS0vDwsKClJSUFtX26bBd0sjISP785z/XO65QKOjbty/PP/88ob+kk7VW3ryrqyv29vYtDvz29vYsX76c/v378z//8z9y4DcyMmLr1q0i8AuC8MB4eHgYFJJsTocd9oHaVCYLCwtef/31eo+Zm5vz6KOPEhwczN69e0lOTm5RVlBzysrKiNm/n+KMjAYrFeqXrA8bNowxY8agUCh46aWX+Pe//y2/xvr165k2bdp9tUMQBKEtdejgD/CHP/wBjUbDn/70pwZ7+C4uLixcuJCkpCQOHDhATk7OPd8ESktLOfDZZ3xUUNBopUJ/f3+mTp2Ki4sLZWVlzJ8/n127dsmv8dFHH4nyDYIgdHgddtjnhRdekP9/zZo1PPfccwaTwHUpFAp69+7NypUrmT17tlxq4W4nWi8cOMB/CwoI49dl63UrFRbduMHixYtxcXEhLi6OkJAQOfAbGRnx2WefsWLFirt8p4IgCA9ehw3+ixYt4r333pO///DDD5kwYUKT+f4KhYI+ffqwfPlynn76aYKCguTN3VtyIyjOyGhwuTrUVipMS0igurqaN998k5CQEBISEoDaHbt+/PFHli5d2uL3JwiC0J469LDPK6+8grOzM8uWLUOj0fDzzz8TGBjIunXreOqpp5qc6HV3d8fd3Z1p06aRnJxMUlISarWarKysRtcJmOh0TVYqLM3P55FHHiElJUU+PmDAALZs2SJq9giC0Kl06OAPsHDhQnx9fYmIiCAjI4Pc3FzmzJnDf//7X95++22GDx/e5PONjY3x9fXF19cXqE0VLSkpoaSkRC7bYGJigo2NDWe2bEHKy2u0UmFKZiYpdV73lVde4c9//nOL1gEIgiB0JB122KeuYcOGyduY6f3888+MGDGCwYMH88EHH5Camtqi19JX23R3d8fb2xtvb288PDzo1q0b/iEhnG7keaeA0l/+/9FHHyUmJoZ//OMfIvALgtApdfiev56zszPfffcdO3fu5LXXXiMxMRGAc+fOce7cOZ577jm8vb0ZMGAAHh4euLu70717d0xNTTEyMkKpVFJZWSn3+vVfWVlZpKenk5aWhlqt5jtgM7WTvEp+rVQ4DxgydSqvv/46I0aMaK8fgyAIQqvoNMFfb+bMmYSHh7Np0ybef/99g9rVt27d4tatW/f1+qnA44A1YA4oLSzw7NuXyE8/pX///vf12oIgCB1Fpwv+UDtGv3jxYhYtWsSlS5f4/vvvOXjwIOfOnWuwGmdL2NnZ0aNHD4KCgujfvz/9+/cnNDQUOzu7Vm69IAhC++uUwV9PoVDIgXrNmjVoNBoyMjJIS0sjPT2drKwsNBoNWq0WrVaLubk5NjY2Bl+Ojo64u7uLnYYEQehSOnXwv5OxsTE9e/akZ8+e7d0UQRCEDq1TZPsIgiAIrUsEf0EQhC5IBH9BEIQuSAR/QRCELkgEf0EQhC5IBH9BEIQuSAR/QRCELkgEf0EQhC5IBH9BEIQuSAR/QRCELkgEf0EQhC6oTYP/oUOHWLp0KX5+flhZWeHj48Nvf/tbMjMz2/KygiAIQjPatLDba6+9RmFhIREREfTu3ZsbN26wbt06du/eTWxsLC4uLm15eUEQBKERbdrzX7t2LUlJSfzjH/9g6dKlvP322+zevZusrCzWrVvXlpcWHpDNmze3dxOEFhK/q86lrX9fbRr8G9rucOTIkdjb25OQkNCWlxYeEBFQOg/xu+pcOnXwb0hpaSklJSU4Ojo+6EsLgiAIv3jgwX/t2rXU1NQwe/bsB31pQRAE4RctnvCVJImqqqoWnWtubt7g8WPHjvGXv/yF2bNnM2bMmJZeWhAEQWhlLQ7+P//8M+PGjWvRuQkJCfj5+dU7NnPmTIKCgvj0008bfa5+A/arV6+2tGlCOyoqKiImJqa9myG0gPhddS73+vvSx059LG1Mi4O/v78/X375ZYvOdXV1Nfg+NTWVSZMmoVKp2LNnT5ObpScnJwMwf/78ljZNaGeDBg1q7yYILSR+V53L/fy+kpOTGT58eKOPKyRJku751VsgLy+PESNGUFhYSFRUFD4+Pk2en5uby759+/Dy8sLCwqItmyYIgvDQqaioIDk5mcmTJzeZWNOmwb+srIxx48Zx7do1jhw5QnBwcFtdShAEQbgLbRr8Z8yYQWRkJEuXLq03wWtjY8Pjjz/eVpcWBEEQmtCmwd/b2xu1Wk1Dl/Dy8uLmzZttdWlBEAShCW0+5i8IgiB0PB2mpHNVVRWvvfYabm5uWFpaEhYWxsGDB9u7WcIdoqOjee655+jbty/W1tZ4enoye/ZsEhMT27tpQgu8/fbbKJVK+vXr195NERoQExPDY489hoODA1ZWVvTr14/333+/Ta7VYXr+c+fOZfv27bz00kv07t2bL774gujoaI4cOdJkupLwYD355JOcOnWKiIgIgoKCuH37NuvWraO0tJTTp0/Tt2/f9m6i0Ii0tDQeeeQRlEol3t7eXLp0qb2bJNSxf/9+pk+fzqBBg5g9ezbW1tYkJSUhSRLvvPNOq1+vQwT/s2fPEhYWxj//+U9Wr14N1H4SCAwMxNnZmRMnTrRzCwW9U6dOMXjwYIyNf10ikpSURL9+/XjyySf56quv2rF1QlPmzJlDXl4eGo2G3Nxc4uLi2rtJwi+Ki4vx8/NjxIgRbNu27YFcs0MM+2zbtg1jY2OWL18uHzMzM2PZsmWcOnWK9PT0dmydUNfQoUMNAj+Ar68vAQEBolJrB3bs2DG2b9/O2rVrkSQJhULR3k0S6vjmm2/Izs7m7bffBmrT5HU6XZtes0ME/wsXLuDn54e1tbXB8cGDBwMQGxvbHs0SWkiSJLKyskSl1g5Kq9Xy/PPP89vf/lYMy3VQBw8exNbWltTUVB555BFsbGzo1q0bzz77bItrqt2tDhH8b9++Tffu3esd1x/LyMh40E0S7sKmTZvIyMgQlVo7qI8++gi1Ws1bb73V3k0RGpGYmIhGo2HGjBlMmTKFHTt2sHTpUj766COWLFnSJtds020cW6qiogIzM7N6x/XVQZsrUCS0n4SEBFatWsWwYcNYtGhRezdHuENeXh5r1qxhzZo1ODg4tHdzhEaUlpZSXl7OypUrWbt2LVC7SLa6upr169fz17/+FV9f31a9Zofo+VtYWDT40aayslJ+XOh4MjMzmTZtGiqVim3btolx5A7oT3/6E46Ojjz//PPt3RShCfoYN3fuXIPj+u9Pnz7d6td+PutIAAAGoElEQVTsED3/7t27Nzi0c/v2bQDc3NwedJOEZhQVFTFlyhSKi4s5fvx4vUquQvtLTEzkk08+Ye3ataSlpcnHKysrqa6uJiUlBVtbW1QqVTu2UoDaGBcfH4+Li4vBcWdnZwAKCgpa/ZodoucfHBzM9evXKSkpMTh+5swZAAYMGNAezRIaUVlZyfTp00lKSmL37t306dOnvZskNCA9PR2dTscLL7xAr1695K+zZ89y/fp1vL29xTxABxESEgJgcJOGX+c7nZycWv2aHSrP/7333uPll18Gfs3zd3Jy4uTJk+3cQkFPq9Uya9Ys9u7dy/fff8+jjz7a3k0SGpGXl0dUVJTBcJwkSfzpT3+itLSUf//73/j4+IgMoA4gNjaWgQMHMm/ePL7++mv5+Lx589i+fTspKSmt/um6Qwz7hIaGEhERweuvv052djY+Pj5s2LABtVrNF1980d7NE+p4+eWX+eGHH5g+fTq5ubkGf6ggNuHpSBwcHBqsnPt///d/ADz22GMPuklCIwYMGMDSpUv5/PPP0Wg0jBo1iqNHj7Jt2zb+8Ic/tMmwaofo+UNtT/+NN97g66+/pqCggP79+/PWW28xceLE9m6aUMfYsWM5duxYg5VaFQoFWq22HVol3I2xY8eSl5cnyjt0MBqNhr///e988cUXZGRk4OXlxapVq3jhhRfa5HodJvgLgiAID06HmPAVBEEQHiwR/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELogEfwFQRC6IBH8BUEQuiAR/IUuS6lUik1OhC5LBH/hoXfy5En+8pe/UFRUVO+x1tx9LCMjgzfffJOLFy+22msKQlsRwV946DUV/FtTRkYGf/3rX0XwFzoFEfyFLuNBFbAVhXKFzkAEf+Gh9uabb/Lqq68C4O3tjVKpRKlUkpKSIp+za9cuAgMDMTc3JzAwkH379tV7nfT0dJYuXYqLi4t8Xt2Nho4ePUpoaCgAS5Yska+zceNGAI4fP05ERASenp6Ym5vTs2dPVq9eTWVlZVu+fUFoVIfYyUsQ2soTTzxBYmIimzdvZu3atTg6OgK/7ol6/Phxtm/fzqpVq7C2tuY///kPTzzxBGq1Gnt7ewCysrIICwvDyMiIF154AScnJ/bs2cOyZcsoLi7mxRdfJCAggL/+9a+sWbOGFStWMHLkSACGDRsGwLZt26isrOTZZ5/FwcGBM2fO8P7775OWlsbWrVvb4ScjdHmSIDzk3nvvPUmhUEgpKSkGxxUKhWRubi7dvHlTPnbp0iVJoVBI69atk48tW7ZMcnd3l/Lz8w2eP3fuXMnOzk6qqKiQJEmSoqOjJYVCIW3YsKFeG/Tn1PXOO+9ISqVSUqvV9/X+BOFeiGEfoUubMGEC3t7e8vf9+vXD1taWW7duAbXj99u3b2f69OlotVpyc3Plr0mTJlFUVERMTEyz1zE3N5f/v6ysjNzcXIYOHYokScTGxrb+GxOEZohhH6FL69mzZ71jKpWKgoICAHJycigqKmL9+vWsX7++3rkKhYKcnJxmr6NWq1mzZg2RkZEUFhYaPNbWWUiC0BAR/IUuzcjIqMHj0i8ZOzqdDoAFCxawaNGiBs/t169fk9fQarVMnDiRwsJCXn/9dfr06YOVlRVpaWksXrxYvoYgPEgi+AsPvftZyOXk5ISNjQ0ajYZx48bd03Xi4uJITExk48aNzJ8/Xz5+4MCBe26XINwvMeYvPPSsrKwA5KGcu2FkZMQTTzzB9u3buXLlSr3H6w75NHYd/aeLuj18SZL497//fdftEYTWInr+wkMvJCQEgD/+8Y/Mnj0bU1NTwsPDGz1fumOR1jvvvMORI0cYMmQIv/3tb/H39yc/P5+YmBgOHTpEXl4eAD4+PtjZ2fHRRx9hbW2NlZUVYWFh+Pv74+PjwyuvvEJ6ejo2NjZs37693ti/IDxQ7ZprJAgPyN/+9jfJw8NDMjIykpRKpZScnCwpFArp+eefr3eul5eXtGTJEoNj2dnZ0nPPPSf17NlTMjU1lbp37y5NnDhR+vTTTw3Oi4yMlPr27SuZmJhISqVSTvu8evWqNHHiRMnGxkZycnKSVqxYIaeVNpQaKghtTSFJYi26IAhCVyPG/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELogEfwFQRC6IBH8BUEQuiAR/AVBELqg/w9W75vjl9A6awAAAABJRU5ErkJggg==" ], "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f7762658940>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Δ = 1.0 # smoothness\n", "ker = Kernel(Δ) # kernel object\n", "\n", "res = 199 # resolution of the generated random functions\n", "θs, xs = tc(res, 1, ker) # generate the true tuning curve\n", "\n", "Ps = [2, 10] # number of samples for the trianing data\n", "σn = sqrt(0.1) # observational noise's std\n", "# rs = xs + randn(size(xs)) * σn # noisy training data\n", "\n", "figure(figsize=(4, 4))\n", "for (ixP, P) in enumerate(Ps)\n", " # angles of the training data\n", " θs_data = θs[1:round(Int64, ceil(res / P)):end]\n", " # training data\n", " rs_data = xs[1:round(Int64, ceil(res / P)):end, :] + randn(P, 1) * σn\n", " \n", " # interpolation needs the noise covariance of the training data\n", " Cn = σn^2 * eye(P)\n", " # mean and variance of the interpolated function at full resolution\n", " xs_infer, σs_infer = tc_interp_uniform(rs_data, θs, ker; Cn=Cn)\n", " \n", " # plot the interpolated tuning curve\n", " subplot(2, 1, ixP)\n", " PyPlot.locator_params(nbins=4)\n", " # interpolated mean\n", " plot(θs, xs_infer, \"k--\", linewidth=2)\n", " # uncertainty\n", " PyPlot.fill_between(θs, xs_infer[:, 1] - σs_infer[:, 1], xs_infer[:, 1] + σs_infer[:, 1], color=\"grey\")\n", " # true tuning curve\n", " plot(θs, xs[:, 1], \"k-\", linewidth=2)\n", " # training data\n", " plot(θs_data, rs_data[:, 1], \"ro\")\n", "\n", " xlim([0, 2π]);\n", "end\n", "xlabel(\"theta\")\n", "tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Theoretical recovery performances" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "R2_approx (generic function with 1 method)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# computes the exact R2 by summing over the frequencies\n", "function R2(Δ, P, snr)\n", " s = 0.0\n", " for p in 0:(P-1)\n", " n = tanh(Δ/2)^2 / sinh(Δ * P) * cosh(Δ * (2p - P))\n", " d = tanh(Δ/2) / sinh(Δ * P / 2) * cosh(Δ*p - Δ * P / 2) + 1/(P * snr)\n", " if ~isnan(n / d)\n", " s += n / d\n", " end\n", " end\n", " return s\n", "end\n", "\n", "# approximation when P << NTC\n", "function R2_approx(Δ, P, snr)\n", " return (P / (4/Δ)) / (1 + 1/snr)\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulations versus theory under different SNRs and NTCs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# start workers\n", "if length(workers()) > 1\n", " rmprocs(workers()[2:end])\n", "end\n", "addprocs(11)\n", "@everywhere push!(LOAD_PATH, \"../src/\")\n", "@everywhere using GP" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# simulation parameters\n", "ntrial = 10\n", "M = 100\n", "Ps = round(Int64, logspace(log10(2), log10(500), 10))\n", "Ptest = 100\n", "snrs = [0.5, 1, 2]\n", "Δs = [1/2, 1/4, 1/8];" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# theoretical answers\n", "theory = Float64[R2(Δ, P, snr)\n", " for P in Ps, snr in snrs, Δ in Δs];\n", "theory_approx = Float64[R2_approx(Δ, P, snr)\n", " for P in Ps, snr in snrs, Δ in Δs];" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# running simulations\n", "sim = SharedArray(Float64, length(Ps), length(snrs), length(Δs), ntrial)\n", "for (ix, Δ) in enumerate(Δs)\n", " ker = Kernel(Δ)\n", " for (ixsnr, snr) in enumerate(snrs)\n", " debug(\"ix: $ix, ixsnr: $ixsnr\")\n", " σn = 1 / sqrt(snr)\n", " for (ixP, P) in enumerate(Ps)\n", " @sync @parallel for ixtrial in 1:ntrial\n", " # training stimulus\n", " θtrain = linspace(0, 2π, P + 1)[1:P]\n", " # testing stimulus\n", " θtest = rand(Ptest) * 2π\n", " # noiseless response of the population during both training and testing\n", " x = GP.tc2([θtrain; θtest], M, ker)\n", " xtrain, xtest = x[1:P, :], x[(P+1):end, :]\n", " # noisy responses\n", " rtrain = xtrain + randn(size(xtrain)) * σn\n", " \n", " # inverse covraince matrix of the training data\n", " Cinv = inv_cov_uniform(P, ker; σn=σn)\n", " # cross covariance between training and testing\n", " Pmat = GP.val(θtest .- θtrain', ker)\n", " \n", " # recovered noiseless test responose\n", " xtest_hat = Pmat * Cinv * rtrain\n", " # store the error\n", " sim[ixP, ixsnr, ix, ixtrial] = mean((xtest_hat .- xtest)[:].^2)\n", " end\n", " end\n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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], "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f77392bf8d0>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the performance separately for different SNRs\n", "colors = [\"r\", \"b\", \"g\"]\n", "figure(figsize=(9, 2.5))\n", "for (ixsnr, snr) in enumerate(snrs)\n", " subplot(1, 3, ixsnr)\n", " # plot the simulated data\n", " for (ix, Δ) in enumerate(Δs)\n", " c = colors[ix]\n", " plot(Ps / 4 * Δ, 1 - vec(mean(sim[:, ixsnr, ix, :], 4)), c * \"o\", markersize=8)\n", " end\n", " \n", " # plot the exact\n", " plot(Ps / 4 * Δs[end], vec(theory[:, ixsnr, end]), \"k-\")\n", " # and approximate theory\n", " plot(Ps / 4 * Δs[end], vec(theory_approx[:, ixsnr, end]), \"k--\")\n", " \n", " ylim([1e-2, 1]); yscale(\"log\"); xscale(\"log\")\n", " xlabel(\"P / NTC\"); ylabel(\"R2\")\n", "end\n", "tight_layout()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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" ], "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f77371901d0>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# collapse everythingh onto the same curve with R2 normalized by infinite data limit\n", "# and x axis normalized by the trainsidion number of trials\n", "colors = [\"r\", \"b\", \"g\"]\n", "symbols = [\"o\", \"s\", \"^\"]\n", "\n", "figure(figsize=(3, 2.5))\n", "# low P limit\n", "plot([0.01, 1], [0.01, 1], \"k--\")\n", "for (ixsnr, snr) in enumerate(snrs)\n", " for (ix, Δ) in enumerate(Δs)\n", " # color and symbol\n", " c, s = colors[ix], symbols[ixsnr]\n", " # normalized number of trials\n", " Pnorm = Ps / 4 * Δ / (1 + 1/snr)\n", " # normalized recovery performance\n", " R2norm = 1 - vec(mean(sim[:, ixsnr, ix, :], 4))\n", " \n", " plot(Pnorm, R2norm, c * s, markersize=6)\n", " ylim([1e-2, 1]); xlim([1e-2, 1e2]); yscale(\"log\"); xscale(\"log\")\n", " end\n", "end\n", "\n", "PyPlot.axvline(1, linestyle=\"--\", color=\"k\")\n", "tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulations versus theory in the P vs SNR plane" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GP.Kernel(0.25)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# parameters\n", "Ps = round(Int64, logspace(log10(2), log10(500), 20))\n", "ntrial, M, Ptest, Δ = 10, 100, 100, 1/4\n", "snrs = logspace(-2, 2, 20)\n", "ker = Kernel(Δ)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theory = Float64[R2(Δ, P, snr)\n", " for P in Ps, snr in snrs]\n", "sim = SharedArray(Float64, length(Ps), length(snrs), ntrial)\n", "for (ixP, P) in enumerate(Ps)\n", " debug(\"ixP: $ixP\")\n", " for ixsnr in 1:length(snrs)\n", " snr = snrs[ixsnr]\n", " σn=1 / sqrt(snr)\n", " @sync @parallel for ixtrial in 1:ntrial\n", " θtrain = linspace(0, 2π, P + 1)[1:P]\n", " θtest = rand(Ptest) * 2π\n", " x = GP.tc2([θtrain; θtest], M, ker)\n", " xtrain, xtest = x[1:P, :], x[(P+1):end, :]\n", " rtrain = xtrain + randn(size(xtrain)) * σn\n", " Cinv = inv_cov_uniform(P, ker; σn=σn)\n", " Pmat = GP.val(θtest .- θtrain', ker)\n", "\n", " xtest_hat = Pmat * Cinv * rtrain\n", " sim[ixP, ixsnr, ixtrial] = mean((xtest_hat .- xtest)[:].^2)\n", " end\n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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" ], "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f7735a05080>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Psnorm = Ps/4*Δ\n", "\n", "figure(figsize=(6, 2.5))\n", "\n", "# theoretical recovery performance\n", "subplot(121)\n", "PyPlot.locator_params(nbins=5)\n", "contourf(log10(snrs), log10(Psnorm), theory, nchunk=15, cmap=\"RdYlBu_r\",\n", " levels=linspace(0, 1, 11))\n", "plot(log10(snrs), log10(1+1./snrs), \"k--\", linewidth=2)\n", "xlim(extrema(log10(snrs))); ylim(extrema(log10(Psnorm)))\n", "xlabel(\"log10(SNR)\"); ylabel(\"log10(P/NTC)\")\n", "colorbar()\n", " \n", "# actual recovery performance\n", "subplot(122)\n", "PyPlot.locator_params(nbins=5)\n", "extent = [extrema(log10(snrs))..., extrema(log10(Psnorm))...]\n", "imshow(1 - squeeze(mean(sim, 3), 3), vmin=0, vmax=1,\n", " origin=\"lower\", aspect=\"auto\", cmap=\"RdYlBu_r\",\n", " extent=[extrema(log10(snrs))..., extrema(log10(Psnorm))...], interpolation=\"nearest\")\n", "plot(log10(snrs), log10(1+1./snrs), \"k--\", linewidth=2)\n", "xlim(extrema(log10(snrs))); ylim(extrema(log10(Psnorm)))\n", "xlabel(\"log10(SNR)\"); ylabel(\"log10(P/NTC)\")\n", "colorbar()\n", "\n", "tight_layout()" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.0", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

DavidPowell/OpenModes

test/Test MFIE.ipynb

1

4555

{ "metadata": { "name": "", "signature": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os.path as osp\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import scipy.linalg as la\n", "\n", "import logging\n", "logging.getLogger().setLevel(logging.DEBUG)\n", "\n", "import openmodes\n", "import openmodes.basis\n", "from openmodes.operator import EfieOperator, MfieOperator\n", "from openmodes.operator.mfie import TMfieOperator\n", "from openmodes.sources import PlaneWaveSource\n", "from openmodes.integration import DunavantRule" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "from openmodes.ipython import init_3d\n", "init_3d()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "basis_class = openmodes.basis.DivRwgBasis\n", "#basis_class = openmodes.basis.LoopStarBasis\n", "\n", "\n", "name = 'sphere'\n", "\n", "parameters = {'radius': 5e-3, 'mesh_tol': 2e-3}\n", "\n", "sim_efie = openmodes.Simulation(name=\"EFIE\", operator_class=EfieOperator,\n", " basis_class=basis_class)\n", "sim_mfie = openmodes.Simulation(name=\"MFIE\", operator_class=MfieOperator,\n", " basis_class=basis_class)\n", "\n", "mesh = sim_efie.load_mesh(osp.join(openmodes.geometry_dir, name+'.geo'),\n", " parameters=parameters)\n", "sim_efie.place_part(mesh)\n", "sim_mfie.place_part(mesh)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "#num_freqs = len(freqs)\n", "\n", "#extinction_efie = np.empty(num_freqs, np.complex128)\n", "#extinction_mfie = np.empty(num_freqs, np.complex128)\n", "\n", "e_inc = np.array([1, 0, 0], dtype=np.complex128)#/np.sqrt(2)\n", "k_hat = np.array([0, 0, 1], dtype=np.complex128)\n", "\n", "pw = PlaneWaveSource(e_inc, k_hat)\n", "\n", "\n", "s= 2j*np.pi*1e9\n", "Z_efie = sim_efie.impedance(s)\n", "V_efie = sim_efie.source_vector(pw, s)\n", "I_efie = Z_efie.solve(V_efie)\n", "\n", "Z_mfie = sim_mfie.impedance(s)\n", "V_mfie = sim_mfie.source_vector(pw, s)\n", "I_mfie = Z_mfie.solve(V_mfie)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "#%debug\n", "from numpy import savetxt\n", "savetxt('nodes.txt', mesh.nodes)\n", "print mesh.polygons[0], mesh.polygons[96]\n", "print mesh.surface_normals[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "sim_efie.plot_3d(I_efie*100, output_format='webgl')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#V2 = sim_mfie.empty_vector()\n", "#V2[:] = la.solve(Z_mfie[:].basis_o.gram_matrix, V_mfie)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sim_mfie.plot_3d(I_mfie*100, output_format='webgl')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Confirm that the absolute magnitude of the EFIE and MFIE solutions also agree" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(10, 5))\n", "plt.plot(abs(I_efie), 'x')\n", "plt.plot(abs(I_mfie), '+')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

tiagogiraldo/Machine_Learning_Nanodegree_Projects

student_intervention - 4.ipynb

1

46701

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Supervised Learning\n", "## Project 2: Building a Student Intervention System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n", "\n", "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. \n", "\n", ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1 - Classification vs. Regression\n", "*Your goal for this project is to identify students who might need early intervention before they fail to graduate. Which type of supervised learning problem is this, classification or regression? Why?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: ** \n", "\n", "I think the kind of supervised learning to use here is classification, because I need to find which student(s) are more prone to abandon the school, and this kind of forecast is discrete, the variable isn't continuous. For example, you can use a clustering method to classified the students in order to find which features of the students has the higher probability to abandon the school." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploring the Data\n", "Run the code cell below to load necessary Python libraries and load the student data. Note that the last column from this dataset, `'passed'`, will be our target label (whether the student graduated or didn't graduate). All other columns are features about each student." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Student data read successfully!\n" ] } ], "source": [ "# Import libraries\n", "import numpy as np\n", "import pandas as pd\n", "from time import time\n", "from sklearn.metrics import f1_score\n", "\n", "# Read student data\n", "student_data = pd.read_csv(\"student-data.csv\")\n", "print \"Student data read successfully!\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset used in this project is included as `student-data.csv`. This dataset has the following attributes:\n", "\n", "- `school` : student's school (binary: \"GP\" or \"MS\")\n", "- `sex` : student's sex (binary: \"F\" - female or \"M\" - male)\n", "- `age` : student's age (numeric: from 15 to 22)\n", "- `address` : student's home address type (binary: \"U\" - urban or \"R\" - rural)\n", "- `famsize` : family size (binary: \"LE3\" - less or equal to 3 or \"GT3\" - greater than 3)\n", "- `Pstatus` : parent's cohabitation status (binary: \"T\" - living together or \"A\" - apart)\n", "- `Medu` : mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n", "- `Fedu` : father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n", "- `Mjob` : mother's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n", "- `Fjob` : father's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n", "- `reason` : reason to choose this school (nominal: close to \"home\", school \"reputation\", \"course\" preference or \"other\")\n", "- `guardian` : student's guardian (nominal: \"mother\", \"father\" or \"other\")\n", "- `traveltime` : home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)\n", "- `studytime` : weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n", "- `failures` : number of past class failures (numeric: n if 1<=n<3, else 4)\n", "- `schoolsup` : extra educational support (binary: yes or no)\n", "- `famsup` : family educational support (binary: yes or no)\n", "- `paid` : extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n", "- `activities` : extra-curricular activities (binary: yes or no)\n", "- `nursery` : attended nursery school (binary: yes or no)\n", "- `higher` : wants to take higher education (binary: yes or no)\n", "- `internet` : Internet access at home (binary: yes or no)\n", "- `romantic` : with a romantic relationship (binary: yes or no)\n", "- `famrel` : quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n", "- `freetime` : free time after school (numeric: from 1 - very low to 5 - very high)\n", "- `goout` : going out with friends (numeric: from 1 - very low to 5 - very high)\n", "- `Dalc` : workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n", "- `Walc` : weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n", "- `health` : current health status (numeric: from 1 - very bad to 5 - very good)\n", "- `absences` : number of school absences (numeric: from 0 to 93)\n", "- `passed` : did the student pass the final exam (binary: yes or no)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Data Exploration\n", "Let's begin by investigating the dataset to determine how many students we have information on, and learn about the graduation rate among these students. In the code cell below, you will need to compute the following:\n", "- The total number of students, `n_students`.\n", "- The total number of features for each student, `n_features`.\n", "- The number of those students who passed, `n_passed`.\n", "- The number of those students who failed, `n_failed`.\n", "- The graduation rate of the class, `grad_rate`, in percent (%).\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of students: 395\n", "Number of features: 30\n", "Number of students who passed: 265\n", "Number of students who failed: 130\n", "Graduation rate of the class: 67.09%\n" ] } ], "source": [ "# TODO: Calculate number of students\n", "n_students = len(student_data.index)\n", "\n", "# TODO: Calculate number of features\n", "n_features = len(student_data.columns)-1 # The variable dependent must be subtracted \n", " #in order to find the real features number.\n", "# TODO: Calculate passing students\n", "n_passed = len(student_data[(student_data[\"passed\"])== \"yes\"])\n", "\n", "# TODO: Calculate failing students\n", "n_failed = n_students - n_passed\n", "\n", "# TODO: Calculate graduation rate\n", "grad_rate = float(n_passed) / n_students * 100\n", "\n", "# Print the results\n", "print \"Total number of students: {}\".format(n_students)\n", "print \"Number of features: {}\".format(n_features)\n", "print \"Number of students who passed: {}\".format(n_passed)\n", "print \"Number of students who failed: {}\".format(n_failed)\n", "print \"Graduation rate of the class: {:.2f}%\".format(grad_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing the Data\n", "In this section, we will prepare the data for modeling, training and testing.\n", "\n", "### Identify feature and target columns\n", "It is often the case that the data you obtain contains non-numeric features. This can be a problem, as most machine learning algorithms expect numeric data to perform computations with.\n", "\n", "Run the code cell below to separate the student data into feature and target columns to see if any features are non-numeric." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature columns:\n", "['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu', 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']\n", "\n", "Target column: passed\n", "\n", "Feature values:\n", " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", "0 GP F 18 U GT3 A 4 4 at_home teacher \n", "1 GP F 17 U GT3 T 1 1 at_home other \n", "2 GP F 15 U LE3 T 1 1 at_home other \n", "3 GP F 15 U GT3 T 4 2 health services \n", "4 GP F 16 U GT3 T 3 3 other other \n", "\n", " ... higher internet romantic famrel freetime goout Dalc Walc health \\\n", "0 ... yes no no 4 3 4 1 1 3 \n", "1 ... yes yes no 5 3 3 1 1 3 \n", "2 ... yes yes no 4 3 2 2 3 3 \n", "3 ... yes yes yes 3 2 2 1 1 5 \n", "4 ... yes no no 4 3 2 1 2 5 \n", "\n", " absences \n", "0 6 \n", "1 4 \n", "2 10 \n", "3 2 \n", "4 4 \n", "\n", "[5 rows x 30 columns]\n" ] } ], "source": [ "# Extract feature columns\n", "feature_cols = list(student_data.columns[:-1])\n", "\n", "# Extract target column 'passed'\n", "target_col = student_data.columns[-1] \n", "\n", "# Show the list of columns\n", "print \"Feature columns:\\n{}\".format(feature_cols)\n", "print \"\\nTarget column: {}\".format(target_col)\n", "\n", "# Separate the data into feature data and target data (X_all and y_all, respectively)\n", "X_all = student_data[feature_cols]\n", "y_all = student_data[target_col]\n", "\n", "# Show the feature information by printing the first five rows\n", "print \"\\nFeature values:\"\n", "print X_all.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocess Feature Columns\n", "\n", "As you can see, there are several non-numeric columns that need to be converted! Many of them are simply `yes`/`no`, e.g. `internet`. These can be reasonably converted into `1`/`0` (binary) values.\n", "\n", "Other columns, like `Mjob` and `Fjob`, have more than two values, and are known as _categorical variables_. The recommended way to handle such a column is to create as many columns as possible values (e.g. `Fjob_teacher`, `Fjob_other`, `Fjob_services`, etc.), and assign a `1` to one of them and `0` to all others.\n", "\n", "These generated columns are sometimes called _dummy variables_, and we will use the [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed feature columns (48 total features):\n", "['school_GP', 'school_MS', 'sex_F', 'sex_M', 'age', 'address_R', 'address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T', 'Medu', 'Fedu', 'Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services', 'Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other', 'Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home', 'reason_other', 'reason_reputation', 'guardian_father', 'guardian_mother', 'guardian_other', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']\n" ] } ], "source": [ "def preprocess_features(X):\n", " ''' Preprocesses the student data and converts non-numeric binary variables into\n", " binary (0/1) variables. Converts categorical variables into dummy variables. '''\n", " \n", " # Initialize new output DataFrame\n", " output = pd.DataFrame(index = X.index)\n", "\n", " # Investigate each feature column for the data\n", " for col, col_data in X.iteritems():\n", " \n", " # If data type is non-numeric, replace all yes/no values with 1/0\n", " if col_data.dtype == object:\n", " col_data = col_data.replace(['yes', 'no'], [1, 0])\n", "\n", " # If data type is categorical, convert to dummy variables\n", " if col_data.dtype == object:\n", " # Example: 'school' => 'school_GP' and 'school_MS'\n", " col_data = pd.get_dummies(col_data, prefix = col) \n", " \n", " # Collect the revised columns\n", " output = output.join(col_data)\n", " \n", " return output\n", "\n", "X_all = preprocess_features(X_all)\n", "print \"Processed feature columns ({} total features):\\n{}\".format(len(X_all.columns), list(X_all.columns))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Training and Testing Data Split\n", "So far, we have converted all _categorical_ features into numeric values. For the next step, we split the data (both features and corresponding labels) into training and test sets. In the following code cell below, you will need to implement the following:\n", "- Randomly shuffle and split the data (`X_all`, `y_all`) into training and testing subsets.\n", " - Use 300 training points (approximately 75%) and 95 testing points (approximately 25%).\n", " - Set a `random_state` for the function(s) you use, if provided.\n", " - Store the results in `X_train`, `X_test`, `y_train`, and `y_test`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set has 300 samples.\n", "Testing set has 95 samples.\n" ] } ], "source": [ "# TODO: Import any additional functionality you may need here\n", "import random \n", "# TODO: Set the number of training points\n", "num_train = 300\n", "\n", "# Set the number of testing points\n", "num_test = X_all.shape[0] - num_train\n", "random.seed(123)\n", "rows = random.sample(X_all.index, num_train)\n", "\n", "# TODO: Shuffle and split the dataset into the number of training and testing points above\n", "X_train = X_all.ix[rows]\n", "X_test = X_all.drop(rows)\n", "y_train = y_all.ix[rows]\n", "y_test = y_all.drop(rows)\n", "\n", "# Show the results of the split\n", "print \"Training set has {} samples.\".format(X_train.shape[0])\n", "print \"Testing set has {} samples.\".format(X_test.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training and Evaluating Models\n", "In this section, you will choose 3 supervised learning models that are appropriate for this problem and available in `scikit-learn`. You will first discuss the reasoning behind choosing these three models by considering what you know about the data and each model's strengths and weaknesses. You will then fit the model to varying sizes of training data (100 data points, 200 data points, and 300 data points) and measure the F₁ score. You will need to produce three tables (one for each model) that shows the training set size, training time, prediction time, F₁ score on the training set, and F₁ score on the testing set.\n", "\n", "**The following supervised learning models are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**\n", "- Gaussian Naive Bayes (GaussianNB)\n", "- Decision Trees\n", "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n", "- K-Nearest Neighbors (KNeighbors)\n", "- Stochastic Gradient Descent (SGDC)\n", "- Support Vector Machines (SVM)\n", "- Logistic Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2 - Model Application\n", "*List three supervised learning models that are appropriate for this problem. For each model chosen*\n", "- Describe one real-world application in industry where the model can be applied. *(You may need to do a small bit of research for this — give references!)* \n", "- What are the strengths of the model; when does it perform well? \n", "- What are the weaknesses of the model; when does it perform poorly?\n", "- What makes this model a good candidate for the problem, given what you know about the data?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "Classifier 1: Support Vector Machine\n", "\n", "- Describe one real-world application in industry where the model can be applied.\n", "\n", " Ans:/ Some applications can be found in text categorization, bioinformatics and image recognition. (Moguerza, Javier and Muñoz, Alberto. Support Vector Machines with Applications. arXiv:math/0612817v1 [math.ST] 28 Dec 2006)\n", "\n", "- What are the strengths of the model; when does it perform well?\n", " \n", " Ans/: \n", " - Effective in high dimensional spaces.\n", " - Still effective in cases where number of dimensions is greater than the number of samples.\n", " - Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.\n", " - Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.\n", " source: http://scikit-learn.org/stable/modules/svm.html\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", " \n", " Ans/:\n", " - If the number of features is much greater than the number of samples, the method is likely to give poor performances.\n", " - SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below).\n", " source: http://scikit-learn.org/stable/modules/svm.html\n", "\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", "\n", " Ans/: Is a horse work in machine learning classification problems like this, for the size of the dimensions of the problem are not large enough to destabilize it (as is explained by the disadvantages of the model).\n", "\n", "\n", "Classifier 2: Stochastic Gradient Descent (SDG)\n", "\n", "- Describe one real-world application in industry where the model can be applied.\n", "\n", " Ans:/ \n", " -This algorithm is widely used in the field of neural networks (source: https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Applications)\n", " \n", "\n", "- What are the strengths of the model; when does it perform well? \n", "\n", " Ans:/\n", " -Efficiency.\n", " -Ease of implementation (lots of opportunities for code tuning).\n", " source: http://scikit-learn.org/stable/modules/sgd.html\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", " Ans/:\n", " -Stochastic Gradient Descent requires a number of hyperparameters such as the regularization parameter and the number of iterations.\n", " -Stochastic Gradient Descent is sensitive to feature scaling.\n", " source: http://scikit-learn.org/stable/modules/sgd.html\n", " \n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", " \n", " Ans:/ This method is very versatile and can be used in this kind of problems, . \n", "\n", "Classifier 3: Logistic Regression\n", "\n", "- Describe one real-world application in industry where the model can be applied.\n", " Ans/: There is a lot of examples of applications like credit risk analysis, marketing segmentation and forecasting in medicine (source: https://en.wikipedia.org/wiki/Logistic_regression#Fields_and_example_applications)\n", " \n", "- What are the strengths of the model; when does it perform well? \n", "\n", " -Allows properties of a linear regression model to be exploited\n", " -The logit itself can take values between - ∞ and + ∞\n", " -Probability remains constrained between 0 and 1\n", " -The logit can be directly related to odds of disease \n", " source: https://onlinecourses.science.psu.edu/stat507/node/18\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", " -requires large sample size to achieve stable results\n", " -may have multicollinearity\n", " -may over fit the data\n", " source: http://www.justanswer.com/1byy-statistical-analysis/6n506-advantages-disadvantages-logistic-regression.html\n", "\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", " Ans:/ The logistic regression model is par excellence the first reference in classifications methods, is a probabilistic model that behaves very well and very easy to understand.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup\n", "Run the code cell below to initialize three helper functions which you can use for training and testing the three supervised learning models you've chosen above. The functions are as follows:\n", "- `train_classifier` - takes as input a classifier and training data and fits the classifier to the data.\n", "- `predict_labels` - takes as input a fit classifier, features, and a target labeling and makes predictions using the F₁ score.\n", "- `train_predict` - takes as input a classifier, and the training and testing data, and performs `train_clasifier` and `predict_labels`.\n", " - This function will report the F₁ score for both the training and testing data separately." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def train_classifier(clf, X_train, y_train):\n", " ''' Fits a classifier to the training data. '''\n", " \n", " # Start the clock, train the classifier, then stop the clock\n", " start = time()\n", " clf.fit(X_train, y_train)\n", " end = time()\n", " \n", " # Print the results\n", " print \"Trained model in {:.4f} seconds\".format(end - start)\n", "\n", " \n", "def predict_labels(clf, features, target):\n", " ''' Makes predictions using a fit classifier based on F1 score. '''\n", " \n", " # Start the clock, make predictions, then stop the clock\n", " start = time()\n", " y_pred = clf.predict(features)\n", " end = time()\n", " \n", " # Print and return results\n", " print \"Made predictions in {:.4f} seconds.\".format(end - start)\n", " return f1_score(target.values, y_pred, pos_label='yes')\n", "\n", "\n", "\n", "def train_predict(clf, X_train, y_train, X_test, y_test):\n", " ''' Train and predict using a classifer based on F1 score. '''\n", " \n", " # Indicate the classifier and the training set size\n", " print \"Training a {} using a training set size of {}. . .\".format(clf.__class__.__name__, len(X_train))\n", " \n", " # Train the classifier\n", " train_classifier(clf, X_train, y_train)\n", " \n", " # Print the results of prediction for both training and testing\n", " print \"F1 score for training set: {:.4f}.\".format(predict_labels(clf, X_train, y_train))\n", " print \"F1 score for test set: {:.4f}.\".format(predict_labels(clf, X_test, y_test))\n", " print \"\\n\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Model Performance Metrics\n", "With the predefined functions above, you will now import the three supervised learning models of your choice and run the `train_predict` function for each one. Remember that you will need to train and predict on each classifier for three different training set sizes: 100, 200, and 300. Hence, you should expect to have 9 different outputs below — 3 for each model using the varying training set sizes. In the following code cell, you will need to implement the following:\n", "- Import the three supervised learning models you've discussed in the previous section.\n", "- Initialize the three models and store them in `clf_A`, `clf_B`, and `clf_C`.\n", " - Use a `random_state` for each model you use, if provided.\n", " - **Note:** Use the default settings for each model — you will tune one specific model in a later section.\n", "- Create the different training set sizes to be used to train each model.\n", " - *Do not reshuffle and resplit the data! The new training points should be drawn from `X_train` and `y_train`.*\n", "- Fit each model with each training set size and make predictions on the test set (9 in total). \n", "**Note:** Three tables are provided after the following code cell which can be used to store your results." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training a SVC using a training set size of 100. . .\n", "Trained model in 0.0210 seconds\n", "Made predictions in 0.0060 seconds.\n", "F1 score for training set: 0.8228.\n", "Made predictions in 0.0050 seconds.\n", "F1 score for test set: 0.8625.\n", "\n", "\n", "Training a SGDClassifier using a training set size of 100. . .\n", "Trained model in 0.0630 seconds\n", "Made predictions in 0.0370 seconds.\n", "F1 score for training set: 0.8205.\n", "Made predictions in 0.0010 seconds.\n", "F1 score for test set: 0.8718.\n", "\n", "\n", "Training a LogisticRegression using a training set size of 100. . .\n", "Trained model in 0.0320 seconds\n", "Made predictions in 0.0010 seconds.\n", "F1 score for training set: 0.8310.\n", "Made predictions in 0.0010 seconds.\n", "F1 score for test set: 0.8201.\n", "\n", "\n", "Training a SVC using a training set size of 200. . .\n", "Trained model in 0.0160 seconds\n", "Made predictions in 0.0130 seconds.\n", "F1 score for training set: 0.8182.\n", "Made predictions in 0.0030 seconds.\n", "F1 score for test set: 0.8734.\n", "\n", "\n", "Training a SGDClassifier using a training set size of 200. . .\n", "Trained model in 0.0010 seconds\n", "Made predictions in 0.0000 seconds.\n", "F1 score for training set: 0.7768.\n", "Made predictions in 0.0000 seconds.\n", "F1 score for test set: 0.8415.\n", "\n", "\n", "Training a LogisticRegression using a training set size of 200. . .\n", "Trained model in 0.0050 seconds\n", "Made predictions in 0.0000 seconds.\n", "F1 score for training set: 0.8188.\n", "Made predictions in 0.0000 seconds.\n", "F1 score for test set: 0.8472.\n", "\n", "\n", "Training a SVC using a training set size of 300. . .\n", "Trained model in 0.0160 seconds\n", "Made predictions in 0.0070 seconds.\n", "F1 score for training set: 0.8515.\n", "Made predictions in 0.0020 seconds.\n", "F1 score for test set: 0.8790.\n", "\n", "\n", "Training a SGDClassifier using a training set size of 300. . .\n", "Trained model in 0.0010 seconds\n", "Made predictions in 0.0000 seconds.\n", "F1 score for training set: 0.7959.\n", "Made predictions in 0.0000 seconds.\n", "F1 score for test set: 0.8625.\n", "\n", "\n", "Training a LogisticRegression using a training set size of 300. . .\n", "Trained model in 0.0060 seconds\n", "Made predictions in 0.0010 seconds.\n", "F1 score for training set: 0.8326.\n", "Made predictions in 0.0000 seconds.\n", "F1 score for test set: 0.8630.\n", "\n", "\n" ] } ], "source": [ "# TODO: Import the three supervised learning models from sklearn\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.svm import SVC\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size = num_test, random_state = 123)\n", "\n", "# TODO: Initialize the three models\n", "clf_A = SVC(random_state=123)\n", "clf_B = SGDClassifier(random_state=123)\n", "clf_C = LogisticRegression(random_state=123)\n", "\n", "# TODO: Set up the training set sizes\n", "#rows_100 = random.sample(X_all.index, 100)\n", "#rows_200 = random.sample(X_all.index, 200)\n", "\n", "#X_train_100 = X_all.ix[rows_100]\n", "#y_train_100 = y_all.ix[rows_100]\n", "\n", "#X_train_200 = X_all.ix[rows_200]\n", "#y_train_200 = y_all.ix[rows_200]\n", "\n", "#X_train_300 = X_train\n", "#y_train_300 = y_train\n", "\n", "\n", "# TODO: Execute the 'train_predict' function for each classifier and each training set size\n", "\n", "for size in [100, 200, 300]:\n", " globals()[\"predict_A_{}\".format(size)] = train_predict(clf_A, X_train[:size], y_train[:size], X_test, y_test)\n", " globals()[\"predict_B_{}\".format(size)] = train_predict(clf_B, X_train[:size], y_train[:size], X_test, y_test)\n", " globals()[\"predict_C_{}\".format(size)] = train_predict(clf_C, X_train[:size], y_train[:size], X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tabular Results\n", "Edit the cell below to see how a table can be designed in [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet#tables). You can record your results from above in the tables provided." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Classifer 1- SVC** \n", "\n", "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n", "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n", "| 100 | 0.0210 | 0.0050 | 0.8228 | 0.8625 |\n", "| 200 | 0.0160 | 0.0030 | 0.8182 | 0.8734 |\n", "| 300 | 0.0160 | 0.0020 | 0.8515 | 0.8790 |\n", "\n", "** Classifer 2- SGDClassifier** \n", "\n", "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n", "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n", "| 100 | 0.0630 | 0.0010 | 0.8205 | 0.8718 |\n", "| 200 | 0.0010 | 0.0000 | 0.7768 | 0.8415 |\n", "| 300 | 0.0010 | 0.0000 | 0.7959 | 0.8625 |\n", "\n", "** Classifer 3- LogisticRegression** \n", "\n", "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n", "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n", "| 100 | 0.0030 | 0.0000 | 0.8310 | 0.8201 |\n", "| 200 | 0.0050 | 0.0000 | 0.8188 | 0.8472 |\n", "| 300 | 0.0060 | 0.0000 | 0.8326 | 0.8630 |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choosing the Best Model\n", "In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F₁ score. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3 - Choosing the Best Model\n", "*Based on the experiments you performed earlier, in one to two paragraphs, explain to the board of supervisors what single model you chose as the best model. Which model is generally the most appropriate based on the available data, limited resources, cost, and performance?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "I choose Support Vector Machine as best model by two reason: Because the F1 test score are the best for this problem. Maybe this model is not as fast as the logistic model, but in three settings has better F1 score, and I prefer the score rather than the efficiency over time, at least for a case like the one discussed in this exercise. It would be good to also analyze in a sample of several million items which is better. One would think that in very large samples the efficiency over time should not be significant, but right now I haven't arguments at the time to assert such a claim.\n", "\n", "I think the running time changes depending on what applications are running on par with jupyther, or if all code runs at the same time, and usually the time scales are similar to those presented in this report, being always faster the logistic model , Followed by the stochastic model and being the most \"slow\" model SVM. This also makes I prefer, at least, in this situation the F1 score to time as a selection criterion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4 - Model in Layman's Terms\n", "*In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "Dear board of directors, in this problem I recommend using the Support Vector Machine model, this algorithm separates failed students from those who do not, taking data about previous students like their family group (parents education, parents job status, family size, etc.), school performance (weekly study time, number of past class failures, extra educational support, etc.) and others of a personal nature (with a romantic relationship, free time after school, etc.) with their respective statistics of success / failure; establishing boundaries between those who have succeeded and those who have failed. This allows us to find patterns that are not so obvious and can be predicted by the SVC model.\n", "\n", "In practice, when you are working in plane in order to find an equation which separate and classify two kind of data, when data is nonlinear is very difficult. To address such problems, you can apply the SVM which is an algorithm that capture nonlinearities well. To do that, the method set a margin, between a boundary and the classified points, and this boundary will separate the data in the desired classification. In order to find the best boundary, the algorithm must find the longest distance between the boundary and the adjacent points, this can be done by using the kernel \"trick\" or kernel functions. It has been called a trick, because this functions use a very elegant mathematical solution, which at first glance seems like a trick. In theory, to get the boundary you must project the points in a hyperplane and find the equation from each plane and optimize in order to find the best solution, and it is quite complicated. \n", "\n", "The beauty of kernel trick is in using the inner product of vectors (algebra linear operation) that can bypass the projection of each plane of hyperplane, and get the best solution boundary with the resulting vector (from the inner product) through an optimization process that maximizes the widest gap between classes and the boundary, and the output vectors are the support vectors.\n", "\n", "The resulting boundary separate the classes in the desired classification, which will allow us to predict in the end and in a more sophisticated way, which students will fail and be able to intervene them before they drop out the studies as the board of directors expected to be achieved with this project.\n", "\n", "The SVM algorithm was selected because it is simpler and it has a better predictive power than other alternatives evaluated like Stochastic Gradient Descend and Logistic Regression. This model has a good fit with training data, and has a better capacity of forecast, given its F1 test score, and you could say that this model will be correct approximately 86% of the time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Model Tuning\n", "Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:\n", "- Import [`sklearn.grid_search.gridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n", "- Create a dictionary of parameters you wish to tune for the chosen model.\n", " - Example: `parameters = {'parameter' : [list of values]}`.\n", "- Initialize the classifier you've chosen and store it in `clf`.\n", "- Create the F₁ scoring function using `make_scorer` and store it in `f1_scorer`.\n", " - Set the `pos_label` parameter to the correct value!\n", "- Perform grid search on the classifier `clf` using `f1_scorer` as the scoring method, and store it in `grid_obj`.\n", "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_obj`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Made predictions in 0.0080 seconds.\n", "Tuned model has a training F1 score of 0.9849.\n", "Made predictions in 0.0020 seconds.\n", "Tuned model has a testing F1 score of 0.8679.\n", "Grid Search Cross Validation in 76.1640 seconds\n", "F1 score for predicting all 'yes': 0.8030\n" ] } ], "source": [ "start_2 = time()\n", "# TODO: Import 'GridSearchCV' and 'make_scorer'\n", "# from sklearn.model_selection import GridSearchCV -> Conda doesn´t update\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import f1_score, make_scorer\n", "\n", "# TODO: Create the parameters list you wish to tune\n", "parameters = {'kernel':('linear', 'rbf'), 'C':[1e-3, 1, 1e3],'random_state':[123],'gamma':[1e-1, 1, 1e1]}\n", "\n", "# TODO: Initialize the classifier\n", "clf = SVC()\n", "\n", "# TODO: Make an f1 scoring function using 'make_scorer' \n", "f1_scorer = make_scorer(f1_score, pos_label='yes')\n", "\n", "# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method\n", "grid_obj = GridSearchCV(clf, param_grid = parameters, scoring = f1_scorer)\n", "\n", "# TODO: Fit the grid search object to the training data and find the optimal parameters\n", "grid_obj = grid_obj.fit(X_train,y_train)\n", "\n", "# Get the estimator\n", "clf = grid_obj.best_estimator_\n", "\n", "# Report the final F1 score for training and testing after parameter tuning\n", "print \"Tuned model has a training F1 score of {:.4f}.\".format(predict_labels(clf, X_train, y_train))\n", "print \"Tuned model has a testing F1 score of {:.4f}.\".format(predict_labels(clf, X_test, y_test))\n", "end_2 = time()\n", "print \"Grid Search Cross Validation in {:.4f} seconds\".format(end_2 - start_2)\n", "print \"F1 score for predicting all 'yes': {:.4f}\".format(\n", " f1_score(y_true = ['yes']*n_passed + ['no']*n_failed, y_pred = ['yes']*n_students, pos_label='yes', \n", " average='binary'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 5 - Final F₁ Score\n", "*What is the final model's F₁ score for training and testing? How does that score compare to the untuned model?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n", "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n", "| 100 | 0.0210 | 0.0050 | 0.8228 | 0.8625 |\n", "| 200 | 0.0160 | 0.0030 | 0.8182 | 0.8734 |\n", "| 300 | 0.0160 | 0.0020 | 0.8515 | 0.8790 |\n", "| Grid search | 0.0080 | 0.0020 | 0.9849 | 0.8679 |\n", "\n", "The model obteined by Grid Search Cross Validation didn't obtain better results than those obtained by untuned model, only was better in execution training time, but this measure in this case is a little tricki, because the function only output the time of the selected model, doesn't display the total execution time of the grid search, the real execution time was 76.1640 seconds. This last time would be worst or longer if the gamma parameter changes in a range for example of [1e-5,1e5], I did that and the execution time was approximately 2 hours and get the same F1 score.\n", "\n", "This show me the selection and implementation of ML models goes beyond repeating a code and shows me that, also requires an intuition around the implementation of these codes to produce better results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

dipanjank/ml

text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb

1

244435

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Level and Group Classification on Train and Test Datasets</h1>\n", "\n", "We have two classification tasks:\n", "\n", "* Predict the level, which ranges from 1-16.\n", "* Predict the group of a given text, given this mapping from levels to group:\n", " - Levels 1-3 = Group A1\n", " - Levels 4-6 = Group A2\n", " - Levels 7-9 = Group B1\n", " - Levels 10-12 = Group B2\n", " - Levels 13-15 = Group C1\n", " - Levels 16 = Group C2" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "\n", "import pandas as pd \n", "import numpy as np\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we load the DataFrame for the full training set and repeat the classification approach identified in step 2." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 368 ms, sys: 268 ms, total: 636 ms\n", "Wall time: 636 ms\n" ] } ], "source": [ "%%time\n", "raw_input = pd.read_pickle('train_full.pkl')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>article_id</th>\n", " <th>grade</th>\n", " <th>level</th>\n", " <th>text</th>\n", " <th>topic_id</th>\n", " <th>topic_text</th>\n", " <th>unit</th>\n", " <th>group</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1009674</th>\n", " <td>1034964</td>\n", " <td>95</td>\n", " <td>3</td>\n", " <td>In Rio I recommend the Copacabana Palace Hotel...</td>\n", " <td>19</td>\n", " <td>Renovating your home</td>\n", " <td>3</td>\n", " <td>A1</td>\n", " </tr>\n", " <tr>\n", " <th>98801</th>\n", " <td>100816</td>\n", " <td>88</td>\n", " <td>2</td>\n", " <td>Hi, I'm Arielle from Brazil. I speak portugues...</td>\n", " <td>15</td>\n", " <td>Writing a personal profile</td>\n", " <td>7</td>\n", " <td>A1</td>\n", " </tr>\n", " <tr>\n", " <th>1013567</th>\n", " <td>1038947</td>\n", " <td>70</td>\n", " <td>3</td>\n", " <td>In my country.Living cost is going up. housing...</td>\n", " <td>21</td>\n", " <td>Giving suggestions about clothing</td>\n", " <td>5</td>\n", " <td>A1</td>\n", " </tr>\n", " <tr>\n", " <th>891706</th>\n", " <td>913874</td>\n", " <td>90</td>\n", " <td>3</td>\n", " <td>In my country, the price going up. I live in S...</td>\n", " <td>21</td>\n", " <td>Giving suggestions about clothing</td>\n", " <td>5</td>\n", " <td>A1</td>\n", " </tr>\n", " <tr>\n", " <th>747383</th>\n", " <td>765853</td>\n", " <td>95</td>\n", " <td>2</td>\n", " <td>Dear don't be unhappy.You should play cards wi...</td>\n", " <td>10</td>\n", " <td>Writing a birthday invitation</td>\n", " <td>2</td>\n", " <td>A1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " article_id grade level \\\n", "1009674 1034964 95 3 \n", "98801 100816 88 2 \n", "1013567 1038947 70 3 \n", "891706 913874 90 3 \n", "747383 765853 95 2 \n", "\n", " text topic_id \\\n", "1009674 In Rio I recommend the Copacabana Palace Hotel... 19 \n", "98801 Hi, I'm Arielle from Brazil. I speak portugues... 15 \n", "1013567 In my country.Living cost is going up. housing... 21 \n", "891706 In my country, the price going up. I live in S... 21 \n", "747383 Dear don't be unhappy.You should play cards wi... 10 \n", "\n", " topic_text unit group \n", "1009674 Renovating your home 3 A1 \n", "98801 Writing a personal profile 7 A1 \n", "1013567 Giving suggestions about clothing 5 A1 \n", "891706 Giving suggestions about clothing 5 A1 \n", "747383 Writing a birthday invitation 2 A1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_input.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 944247 entries, 1009674 to 275300\n", "Data columns (total 8 columns):\n", "article_id 944247 non-null int64\n", "grade 944247 non-null int64\n", "level 944247 non-null int64\n", "text 944247 non-null object\n", "topic_id 944247 non-null int64\n", "topic_text 944247 non-null object\n", "unit 944247 non-null int64\n", "group 944247 non-null object\n", "dtypes: int64(5), object(3)\n", "memory usage: 64.8+ MB\n" ] } ], "source": [ "raw_input.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check for Class Imbalance" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "level_counts = raw_input.level.value_counts().sort_index()\n", "group_counts = raw_input.group.value_counts().sort_index()\n", "\n", "_, ax = plt.subplots(1, 2, figsize=(10, 5))\n", "\n", "_ = level_counts.plot(kind='bar', title='Feature Instances per Level', ax=ax[0], rot=0)\n", "_ = group_counts.plot(kind='bar', title='Feature Instances per Group', ax=ax[1], rot=0)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Level Classification Based on Text\n", "\n", "Here we apply the same approach of converting text to bag-of-words features and then using a maximum entropy classifier. The difference is we are now running on the full dataset which is much larger. The optimizer now requires more steps to converge, so we change the `max_iters` attribute of `LogisticRegression` to 1000. We address the label imbalance by setting `class_weight='balanced'`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to\n", "[nltk_data] /home/ec2-user/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n", "[nltk_data] Downloading package punkt to /home/ec2-user/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "{'have', 'those', 'over', 'o', 'ma', 'theirs', 'had', \"needn't\", \"you'd\", 'yourself', 'a', 'such', 'wasn', 'did', 'of', 'whom', 'them', \"won't\", \"don't\", 'which', 'no', 'on', 'doing', 'its', 'haven', 'through', \"doesn't\", \"couldn't\", 'below', 'll', 'mustn', 'that', 'him', 'do', \"should've\", 'above', 'just', 'but', 'themselves', 'here', 'yours', \"mustn't\", \"hasn't\", 'before', 'does', 'y', 'at', 'until', 'hers', 'own', 'further', 'hadn', 'any', 'who', 'i', 'other', 'been', 'has', 'down', 'am', 'than', \"you're\", 've', \"aren't\", 'weren', 'only', 'then', 'same', 'both', 'not', 'too', \"haven't\", 'should', 'these', 'out', 'isn', 'couldn', 'myself', 'between', 'how', 'he', 'ourselves', 'or', 'mightn', 'herself', 'so', 'it', 'were', 'having', 'being', 'doesn', 'be', \"didn't\", 'itself', 'our', 'if', 'an', 'd', 'and', 're', 'now', 's', 'under', 'her', 'because', 'in', \"isn't\", 'will', 'where', 'some', 'after', 'while', 'she', 'for', 'when', 'my', 'their', 'can', 'most', 'about', 'needn', 'by', 'himself', 'is', 'there', 'didn', 'from', \"that'll\", \"shan't\", 'are', 'we', 'during', 'once', 'with', 't', 'hasn', 'all', 'aren', 'again', \"wasn't\", 'won', 'off', 'his', 'they', 'against', \"hadn't\", 'each', 'was', 'ours', 'you', 'yourselves', 'more', 'me', 'very', 'don', 'm', 'your', \"she's\", 'shan', \"weren't\", 'this', \"wouldn't\", 'shouldn', 'as', \"you'll\", 'what', \"mightn't\", 'to', 'into', 'ain', 'the', 'nor', \"you've\", \"shouldn't\", 'few', \"it's\", 'up', 'wouldn', 'why'}\n" ] } ], "source": [ "import nltk\n", "nltk.download('stopwords')\n", "nltk.download('punkt')\n", "from nltk.corpus import stopwords\n", "\n", "en_stopwords = set(stopwords.words('english'))\n", "print(en_stopwords)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "from sklearn.metrics import confusion_matrix\n", "\n", "\n", "def display_results(y, y_pred):\n", " \"\"\"Given some predications y_pred for a target label y, \n", " display the precision/recall/f1 score and the confusion matrix.\"\"\"\n", " \n", " report = classification_report(y_pred, y)\n", " print(report)\n", "\n", " level_values = y.unique()\n", " level_values.sort()\n", " cm = confusion_matrix(y_true=y, y_pred=y_pred.values, labels=level_values)\n", " cm = pd.DataFrame(index=level_values, columns=level_values, data=cm)\n", "\n", " fig, ax = plt.subplots(1, 1, figsize=(12, 10))\n", " ax = sns.heatmap(cm, annot=True, ax=ax, fmt='d')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n", "\n", "def build_pipeline():\n", " \"\"\"Return the combination of a Feature Extractor and a LogisticRegression model in a ``Pipeline``. \"\"\"\n", " \n", " counter = CountVectorizer(\n", " lowercase=True, \n", " stop_words=en_stopwords, \n", " ngram_range=(1, 1),\n", " min_df=5,\n", " max_df=0.4,\n", " binary=True)\n", "\n", " model = LogisticRegression(\n", " # maximize log-likelihood + square norm of parameters\n", " penalty='l2',\n", " # steps required for the L-BFGS optimizer to converge, found by trial and error\n", " max_iter=1000, \n", " # use softmax instead of one-vs-rest style classification\n", " multi_class='multinomial', \n", " # use L-BFGS optimizer \n", " solver='lbfgs',\n", " # This prints out a warning if the optimizer hasn't converged\n", " verbose=True, \n", " # to handle the class imbalance\n", " # automatically adjust weights inversely proportional to \n", " # class frequencies in the input data\n", " class_weight='balanced', \n", " random_state=4321)\n", " \n", " pipeline = make_pipeline(counter, model)\n", " return pipeline\n", " \n", "\n", "def classify(input_df, target_label='level'):\n", " \"\"\"\n", " Build a classifier for the `target_label` column in the DataFrame `input_df` using the `text` column. \n", " Return the (labels, predicted_labels) tuple. \n", " Use a 10-fold Stratified K-fold cross-validator to generate the out-of-sample predictions.\"\"\"\n", " \n", " assert target_label in input_df.columns\n", " \n", " pipeline = build_pipeline() \n", " cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=1234)\n", "\n", " X = input_df.text\n", " y = input_df.loc[:, target_label]\n", " y_pred = cross_val_predict(pipeline, X=X.values, y=y.values, cv=cv, n_jobs=10, verbose=2)\n", " y_pred = pd.Series(index=input_df.index.copy(), data=y_pred)\n", "\n", " return y.copy(), y_pred" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 27.3min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 27.5min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 27.9min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.1min finished\n", "[Parallel(n_jobs=10)]: Done 3 out of 10 | elapsed: 28.8min remaining: 67.2min\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.2min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.4min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.3min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.4min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.5min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 28.4min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9.23 s, sys: 6.13 s, total: 15.4 s\n", "Wall time: 29min 30s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=10)]: Done 10 out of 10 | elapsed: 29.5min finished\n" ] } ], "source": [ "%%time\n", "levels, levels_predicted = classify(raw_input, target_label='level')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 1 0.97 0.98 0.98 279800\n", " 2 0.96 0.96 0.96 131874\n", " 3 0.94 0.93 0.94 88792\n", " 4 0.95 0.97 0.96 133638\n", " 5 0.94 0.94 0.94 68754\n", " 6 0.94 0.93 0.94 42836\n", " 7 0.94 0.95 0.95 77351\n", " 8 0.90 0.87 0.88 34512\n", " 9 0.92 0.90 0.91 23386\n", " 10 0.94 0.94 0.94 29170\n", " 11 0.87 0.84 0.86 12901\n", " 12 0.90 0.87 0.88 7654\n", " 13 0.91 0.89 0.90 7182\n", " 14 0.84 0.81 0.83 2995\n", " 15 0.73 0.72 0.73 1826\n", " 16 0.80 0.79 0.80 1576\n", "\n", "avg / total 0.95 0.95 0.95 944247\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_results(levels, levels_predicted)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Group Classification Based on Text" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 20.7min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 20.7min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 20.9min finished\n", "[Parallel(n_jobs=10)]: Done 3 out of 10 | elapsed: 21.9min remaining: 51.1min\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.3min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.6min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.6min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.7min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.9min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.9min finished\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 22.0min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9.89 s, sys: 5.76 s, total: 15.7 s\n", "Wall time: 23min 9s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=10)]: Done 10 out of 10 | elapsed: 23.1min finished\n" ] } ], "source": [ "%%time\n", "groups, groups_predicted = classify(raw_input, target_label='group')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display_results(groups, groups_predicted)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification on Test Set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we report the performance on our classfier on both the `leve` and `group` classification tasks using the test dataset. For this we re-build the model using the hyperparameters used above, and train it using the entire train dataset." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from functools import lru_cache\n", "\n", "@lru_cache(maxsize=1)\n", "def get_test_dataset():\n", " return pd.read_pickle('test.pkl')\n", "\n", "\n", "def report_test_perf(train_df, target_label='level'):\n", " \"\"\"Produce classification report and confusion matrix on the test Dataset for a given ``target_label``.\"\"\"\n", " test_df = get_test_dataset()\n", " \n", " assert target_label in train_df.columns\n", " assert target_label in test_df.columns\n", " \n", " # Train the model using the entire training dataset\n", " pipeline = build_pipeline() \n", " X_train, y_train = train_df.text, train_df.loc[:, target_label]\n", " pipeline = pipeline.fit(X_train.values, y_train.values)\n", " \n", " # Generate predictions using test data\n", " X_test, y_test = test_df.text, test_df.loc[:, target_label]\n", " predicted = pipeline.predict(X_test.values)\n", " \n", " predicted = pd.Series(index=test_df.index, \n", " data=predicted, \n", " name='{}_pred'.format(target_label))\n", " \n", " display_results(y_test, predicted)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Level Classification on Test Set" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 12.7min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 1 0.97 0.98 0.98 69973\n", " 2 0.96 0.96 0.96 32966\n", " 3 0.94 0.93 0.94 22245\n", " 4 0.95 0.97 0.96 33412\n", " 5 0.95 0.94 0.94 17268\n", " 6 0.95 0.93 0.94 10754\n", " 7 0.94 0.96 0.95 19290\n", " 8 0.89 0.87 0.88 8550\n", " 9 0.91 0.90 0.91 5800\n", " 10 0.95 0.94 0.95 7324\n", " 11 0.88 0.85 0.86 3232\n", " 12 0.89 0.87 0.88 1896\n", " 13 0.89 0.89 0.89 1769\n", " 14 0.83 0.80 0.81 750\n", " 15 0.76 0.77 0.77 442\n", " 16 0.80 0.80 0.80 391\n", "\n", "avg / total 0.95 0.95 0.95 236062\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%% time\n", "\n", "train_df = raw_input\n", "report_test_perf(train_df, 'level')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Group Classification on Test Set" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 9.1min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " A1 0.98 0.99 0.98 124702\n", " A2 0.95 0.95 0.95 61427\n", " B1 0.94 0.94 0.94 33760\n", " B2 0.92 0.90 0.91 12650\n", " C1 0.88 0.82 0.85 3135\n", " C2 0.78 0.78 0.78 388\n", "\n", "avg / total 0.96 0.96 0.96 236062\n", "\n", "CPU times: user 46min 50s, sys: 2min 39s, total: 49min 30s\n", "Wall time: 9min 53s\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x720 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "\n", "report_test_perf(train_df, 'group')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

hypergravity/cham_hates_python

exercise/cham_teaches_python_05_aplpy_healpy.ipynb

2

222287

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# APLpy basics\n", "\n", "[aplpyreaddocs](http://aplpy.readthedocs.io/en/stable/#)\n", "\n", "### - Introduction\n", "\n", " **APLpy : Astronomical Plotting Library in Python.**\n", "\n", " For convenient fits image or cubes plotting with astronomical coordinates\n", "\n", "### - Dependencies\n", "\n", " APLpy is based on **matplotlib**, so any matplotlib originated functions/classes can be easily adopted.\n", "\n", " For image reprojection or mosaicing purpose, APLpy relies on **Montage**, which is developed by IPAC team. To enable functions like making RGB cube, one should first install Montage package.\n", "\n", "[Montage package docs](http://montage.ipac.caltech.edu/docs/)\n", "\n", "### - Tips\n", "\n", " For plotting plenty of similar images, one could define a function to set up a set of standard properties (*i.e. def standard_setup( )*), such as axis and tick labels and use this user-defined function instead of the calling the APLpy functions time to time.\n", "\n", " Of course, a different setting by directly calling APLpy functions will overide the settings after calling the user-defined standard setup function.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# import matplotlib.pyplot as plt\n", "# import numpy as np\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Auto-setting vmin to -7.876e+00\n", "INFO:astropy:Auto-setting vmax to 2.249e+02\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -7.876e+00 [aplpy.core]\n", "INFO: Auto-setting vmax to 2.249e+02 [aplpy.core]\n", "\n", " There are no layers in this figure\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Auto-setting vmin to 1.136e+01\n", "INFO:astropy:Auto-setting vmax to 2.037e+02\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Auto-setting vmin to 1.136e+01 [aplpy.core]\n", "INFO: Auto-setting vmax to 2.037e+02 [aplpy.core]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Auto-setting vmin to 2.521e+02\n", "INFO:astropy:Auto-setting vmax to 2.600e+02\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Auto-setting vmin to 2.521e+02 [aplpy.core]\n", "INFO: Auto-setting vmax to 2.600e+02 [aplpy.core]\n", "\n", " There are no layers in this figure\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Auto-setting vmin to 2.521e+02\n", "INFO:astropy:Auto-setting vmax to 2.600e+02\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Auto-setting vmin to 2.521e+02 [aplpy.core]\n", "INFO: Auto-setting vmax to 2.600e+02 [aplpy.core]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Setting slices=[0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Setting slices=[0] [aplpy.core]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Deleting work directory /tmp/tmpGhiu9F\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Deleting work directory /tmp/tmpGhiu9F [montage_wrapper.wrappers]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Deleting work directory /tmp/tmpi6zAw8\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Deleting work directory /tmp/tmpi6zAw8 [montage_wrapper.wrappers]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Deleting work directory /tmp/tmprpGGoF\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Deleting work directory /tmp/tmprpGGoF [montage_wrapper.wrappers]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:Red:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": Red: [aplpy.rgb]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:vmin = 1.175e+01 (auto)\n", "INFO:astropy:vmax = 2.671e+01 (auto)\n", "INFO:astropy:Green:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": vmin = 1.175e+01 (auto) [aplpy.rgb]\n", "INFO: vmax = 2.671e+01 (auto) [aplpy.rgb]\n", "INFO: Green: [aplpy.rgb]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:vmin = 7.930e+02 (auto)\n", "INFO:astropy:vmax = 8.299e+02 (auto)\n", "INFO:astropy:Blue:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": vmin = 7.930e+02 (auto) [aplpy.rgb]\n", "INFO: vmax = 8.299e+02 (auto) [aplpy.rgb]\n", "INFO: Blue: [aplpy.rgb]\n", "INFO" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:astropy:vmin = 2.530e+02 (auto)\n", "INFO:astropy:vmax = 2.548e+02 (auto)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ": vmin = 2.530e+02 (auto) [aplpy.rgb]\n", "INFO: vmax = 2.548e+02 (auto) [aplpy.rgb]\n" ] }, { "ename": "AttributeError", "evalue": "'WCS' object has no attribute 'naxis1'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-b632221637e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[0mpmax_b\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m98\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[0mstretch_b\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'linear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m vmid_b=None)\n\u001b[0m\u001b[0;32m 90\u001b[0m \u001b[0mfits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutpath\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34mr'wise234_RGB.fits'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'CTYPE3'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferenceDimension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnaxis1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnaxis2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 552\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferenceValue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrval\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpatial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReferencePixel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrpix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'WCS' object has no attribute 'naxis1'" ] }, { "data": { "image/png": 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EhB4gZp63DA8IENEtAC6ZtxwJCQkJLbiVmS/dzgzS9zAhIeE0wcTfw0SgExISEhISEhIS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M5zIi+guX1ne4ewsiut69059R4hwioj909fVj7H5UhwV5X0VEdxLRG9y/i4no7Uq4HyOi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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f7f94cd3fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import aplpy\n", "from astropy.io import fits\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def standard_setup(fig):\n", " fig.frame.set_linewidth(1)\n", " fig.frame.set_color('black')\n", " fig.recenter(084.78475, +30.09747, width=0.12, height=0.12)\n", " fig.show_colorscale(cmap='gist_gray')\n", " fig.add_colorbar()\n", " fig.colorbar.set_location('top')\n", " fig.colorbar.set_font(size='medium', weight='medium', \\\n", " stretch='normal', family='sans-serif', \\\n", " style='normal', variant='normal')\n", " fig.axis_labels.set_xtext('R.A. (J2000)')\n", " fig.axis_labels.set_ytext('Dec. (J2000)')\n", " fig.set_tick_labels_font(size='small')\n", " fig.set_axis_labels_font(size='small')\n", " fig.ticks.set_length(2)\n", " fig.ticks.set_color('black')\n", " fig.ticks.set_linewidth(1.0)\n", " fig.ticks.set_minor_frequency(3)\n", " fig.list_layers()\n", "\n", "\n", "dirpath = r'./data/wise_image/'\n", "outpath = r'./data/wise_image/'\n", "\n", "filepath1 = dirpath + r'w2_cut.fits'\n", "filepath2 = dirpath + r'w3_cut.fits'\n", "filepath3 = dirpath + r'w4_cut.fits'\n", "\n", "hdulist1 = fits.open(filepath1)\n", "hdulist2 = fits.open(filepath2)\n", "hdulist3 = fits.open(filepath3)\n", "\n", "fig1 = plt.figure(figsize=(15, 7))\n", "\n", "f1 = aplpy.FITSFigure(hdulist1[0], hdu=hdulist1[0].header, figure=fig1, subplot=[0.1, 0.1, 0.29, 0.8])\n", "\n", "standard_setup(f1)\n", "f1.colorbar.show(location='top', log_format=False, ticks=np.linspace(100, 300, 3))\n", "f1.show_colorscale(cmap='Blues', stretch='log')\n", "f1.colorbar.set_axis_label_text('WISE W1')\n", "\n", "f1.ticks.show()\n", "f1.axis_labels.show_y()\n", "f1.tick_labels.show_y()\n", "f1.axis_labels.show_x()\n", "f1.tick_labels.show_x()\n", "\n", "f2 = aplpy.FITSFigure(hdulist3[0], hdu=hdulist2[0].header, figure=fig1, subplot=[0.40, 0.1, 0.29, 0.8])\n", "\n", "standard_setup(f2)\n", "f2.colorbar.show(location='top', log_format=False)\n", "f2.show_colorscale(cmap='Reds', stretch='linear')\n", "f2.colorbar.show(location='top', log_format=False)\n", "f2.colorbar.set_axis_label_text('WISE W4')\n", "f2.add_scalebar(0.02, \"5 pc\", color='black', corner='top right')\n", "f2.show_contour(r'./data/image_data/G178_final.850.fits', levels=np.linspace(0.08, 0.23, 5), colors='white')\n", "\n", "f2.ticks.show()\n", "f2.hide_yaxis_label()\n", "f2.hide_ytick_labels()\n", "f2.axis_labels.show_x()\n", "f2.tick_labels.show_x()\n", "\n", "\n", "aplpy.make_rgb_cube([filepath1,filepath2,filepath3], outpath+r'wise234_RGB.fits')\n", "aplpy.make_rgb_image(outpath+r'wise234_RGB.fits',\n", " outpath+r'rgb_image_wise234.png',\n", " stretch_r='linear', \n", " vmid_r=None,\n", " pmin_r=10, \n", " pmax_r=98,\n", " exponent_r=2,\n", " vmin_g=None,\n", " vmax_g=None,\n", " pmin_g=10,\n", " pmax_g=98,\n", " stretch_g='linear',\n", " vmid_g=None,\n", " exponent_g=2,\n", " vmin_b=None,\n", " vmax_b=None,\n", " pmin_b=10,\n", " pmax_b=98,\n", " stretch_b='linear',\n", " vmid_b=None)\n", "fits.setval(outpath+r'wise234_RGB.fits','CTYPE3',value='RGB')\n", "\n", "f3 = aplpy.FITSFigure(outpath+r'wise234_RGB.fits', dimensions=[0,1],slices=[2], figure=fig1, subplot=[0.70, 0.1, 0.29, 0.8])\n", "standard_setup(f3)\n", "f3.show_rgb(outpath+r'rgb_image_wise234.png')\n", "f3.colorbar.hide()\n", "f3.add_label(0.8,0.9, 'WISE 2/3/4\\nRGB', color='black', relative=True, size='large',layer='source')\n", "\n", "f3.ticks.show()\n", "f3.hide_yaxis_label()\n", "f3.hide_ytick_labels()\n", "f3.axis_labels.show_x()\n", "f3.tick_labels.show_x()\n", "plt.show()\n", "\n", "fig1.savefig(outpath + 'WISE_test.pdf')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Healpy \n", "\n", "[healpyreaddocs](https://healpy.readthedocs.io/en/latest/)\n", "\n", "### - Introduction\n", "\n", " To take care of fits files of Healpix pixelization schemes (http://healpix.sourceforge.net/downloads.php), including fitsfile io and transformation to standard coordinate systems. \n", "\n", " This is particularly useful for data from all-sky surveys such as Planck data, or SCUBA2 data release 1.\n", "\n", "### - Example (omitted)\n", "http://reproject.readthedocs.io/en/stable/api/reproject.reproject_from_healpix.html#reproject.reproject_from_healpix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

fabianrost84/Rost-Rodrigo-Albors-et-al-2016

calculations/outgrowth.ipynb

1

265671

{ "cells": [ { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy as sp\n", "from uncertainties import ufloat\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "exec(open('settings.py').read(), globals())" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": true }, "outputs": [], "source": [ "outgrowth = pd.read_csv('../data/outgrowth.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Outgrowth in mm" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"2\" halign=\"left\">length</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>mean</th>\n", " <th>sem</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.06</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.16</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.45</td>\n", " <td>0.04</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1.28</td>\n", " <td>0.06</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2.26</td>\n", " <td>0.07</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " length \n", " mean sem\n", "time \n", "0 0.00 0.00\n", "2 0.06 0.01\n", "3 0.16 0.02\n", "4 0.45 0.04\n", "6 1.28 0.06\n", "8 2.26 0.07" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_outgrowth = sp.around(outgrowth[['time', 'length']].groupby('time').agg(['mean', 'sem']) / 1000.0, 2)\n", "mean_outgrowth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "800 micron zone vs 500 micron zone" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.62+/-0.08\n" ] } ], "source": [ "levan_zone = ufloat(500.0, 0.0)\n", "source_zone = ufloat(800.0, 100.0)\n", "fraction_day0 = levan_zone / source_zone\n", "print fraction_day0" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [], "source": [ "outgrowth_day8 = float(outgrowth[['time', 'length']].groupby('time').agg('mean').loc[8])\n", "outgrowth_day8_delta = float(outgrowth[['time', 'length']].groupby('time').agg('sem').loc[8])\n", "outgrowth_day8 = ufloat(outgrowth_day8, outgrowth_day8_delta)" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.738+/-0.025\n" ] } ], "source": [ "fraction_day8 = outgrowth_day8 / (outgrowth_day8 + source_zone)\n", "print fraction_day8" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Finding the avarage trajectory to use for Fig. 1" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i, row in outgrowth.iterrows():\n", " if row['time'] == 0:\n", " outgrowth.loc[i, 'chi2'] = 0.0\n", " else:\n", " outgrowth.loc[i, 'chi2'] = ((1000 * mean_outgrowth.loc[row['time']]['length', 'mean'] - row['length']) / (1000 * mean_outgrowth.loc[row['time']]['length', 'sem']))**2" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>chi2</th>\n", " </tr>\n", " <tr>\n", " <th>ID</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>t1</th>\n", " <td>51.930013</td>\n", " </tr>\n", " <tr>\n", " <th>t2</th>\n", " <td>35.759247</td>\n", " </tr>\n", " <tr>\n", " <th>t3</th>\n", " <td>14.185924</td>\n", " </tr>\n", " <tr>\n", " <th>t4</th>\n", " <td>35.708305</td>\n", " </tr>\n", " <tr>\n", " <th>t5</th>\n", " <td>32.285244</td>\n", " </tr>\n", " <tr>\n", " <th>t6</th>\n", " <td>25.272910</td>\n", " </tr>\n", " <tr>\n", " <th>t7</th>\n", " <td>29.312819</td>\n", " </tr>\n", " <tr>\n", " <th>t8</th>\n", " <td>37.376990</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " chi2\n", "ID \n", "t1 51.930013\n", "t2 35.759247\n", "t3 14.185924\n", "t4 35.708305\n", "t5 32.285244\n", "t6 25.272910\n", "t7 29.312819\n", "t8 37.376990" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chi2 = outgrowth[['ID', 'chi2']].groupby('ID').sum()\n", "chi2" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'t3'" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "averageID = chi2.idxmin().iloc[0]\n", "averageID" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Animal t3 has the most average behaviour" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>time</th>\n", " <th>length</th>\n", " <th>label</th>\n", " <th>chi2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>12</th>\n", " <td>t3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>t3</td>\n", " <td>2</td>\n", " <td>38</td>\n", " <td>axolotl_sc_outgrowth.tif:t3_2d_0.8x</td>\n", " <td>4.840000</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>t3</td>\n", " <td>3</td>\n", " <td>134</td>\n", " <td>axolotl_sc_outgrowth.tif:t3_3d_0.8x</td>\n", " <td>1.690000</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>t3</td>\n", " <td>4</td>\n", " <td>554</td>\n", " <td>axolotl_sc_outgrowth.tif:t3_4d_0.8x</td>\n", " <td>6.760000</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>t3</td>\n", " <td>6</td>\n", " <td>1285</td>\n", " <td>axolotl_sc_outgrowth.tif:t3_6d_0.8x</td>\n", " <td>0.006944</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>t3</td>\n", " <td>8</td>\n", " <td>2194</td>\n", " <td>axolotl_sc_outgrowth.tif:t3_8d_0.8x</td>\n", " <td>0.888980</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID time length label chi2\n", "12 t3 0 0 NaN 0.000000\n", "13 t3 2 38 axolotl_sc_outgrowth.tif:t3_2d_0.8x 4.840000\n", "14 t3 3 134 axolotl_sc_outgrowth.tif:t3_3d_0.8x 1.690000\n", "15 t3 4 554 axolotl_sc_outgrowth.tif:t3_4d_0.8x 6.760000\n", "16 t3 6 1285 axolotl_sc_outgrowth.tif:t3_6d_0.8x 0.006944\n", "17 t3 8 2194 axolotl_sc_outgrowth.tif:t3_8d_0.8x 0.888980" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outgrowth.query('ID == @averageID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the colors:\n", "Iterate the colors, but then make sure the average ID shown in Fig. 1A gets green, which is the 3rd color in the color cycle." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [], "source": [ "colors = {}\n", "markers = {}\n", "lss = {}\n", "markerlist = ['o', 'v', '^', '<', '>', 's', 'D', '*']\n", "lslist = ['-', '--', '-.', ':', '-', '--', '-.', ':']\n", "colorlist = [1, 2, 3, 4, 5, 6, 7]\n", "for i, ID in enumerate(sp.sort(list(set(outgrowth['ID'].unique()) - set([averageID])))):\n", " colors[ID] = colorcycle[colorlist[i]]\n", " markers[ID] = markerlist[i]\n", " lss[ID] = lslist[i]\n", "colors['t3'] = colorcycle[0]\n", "markers['t3'] = 's'\n", "colors['t4'], colors['t5'] = colors['t5'], colors['t4'] \n", "colors['t6'], colors['t8'] = colors['t6'], colors['t8'] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the outgrowth:" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"193pt\" version=\"1.1\" viewBox=\"0 0 127 193\" width=\"127pt\" xmlns=\"http://www.w3.org/2000/svg\" 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xlink:href=\"#ma4f294b3af\" y=\"14.9582826404\"/>\n", " </g>\n", " </g>\n", " </g>\n", " <g id=\"text_10\">\n", " <text style=\"font-family:Helvetica LT Std;font-size:8.0px;font-style:normal;text-anchor:middle;\" transform=\"rotate(-90.000000, 13.007500, 88.329764)\" x=\"13.0075\" y=\"88.3297637795\">Outgrowth + 0.8 (mm)</text>\n", " </g>\n", " </g>\n", " </g>\n", " </g>\n", " <defs>\n", " <clipPath id=\"pe5bc1d754a\">\n", " <rect height=\"153.779527559\" width=\"87.874015748\" x=\"35.07125\" y=\"11.44\"/>\n", " </clipPath>\n", " </defs>\n", "</svg>\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f2c4edf5850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "L0 = 0.8\n", "fig, ax = plt.subplots(figsize=(40/25.4,70.0/25.4))\n", "fig.patch.set_alpha(1.0)\n", "\n", "for ID, IDdata in outgrowth.groupby('ID'):\n", " pitem = ax.plot(IDdata['time'], IDdata['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[ID], marker = markers[ID], label = ID)[0]\n", "\n", "# plot the average guy last such that you can see him\n", "ax.plot(outgrowth.query('ID == @averageID')['time'], outgrowth.query('ID == @averageID')['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[averageID], marker = 's')\n", " \n", "ax.set_xlabel('Time (days)')\n", "ax.set_ylabel('Outgrowth + 0.8 (mm)'.decode('utf-8'), labelpad=8)\n", "\n", "ax.set_xlim(-0.5, 8.5)\n", "ax.set_xticks([1, 3, 5, 7], minor=True)\n", "ax.set_yscale('log')\n", "\n", "# plt.legend(loc = 'upper left')\n", "plt.savefig('../figure_plots/Fig3_outgrowth_trajectories_semilog.svg', transparent = True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Least square fit with an exponential" ] }, { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import probfit\n", "import iminuit" ] }, { "cell_type": "code", "execution_count": 348, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def growth_model(time, L0, r):\n", " return L0 * (sp.exp(r * time) - 1.0) " ] }, { "cell_type": "code", "execution_count": 349, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>time</th>\n", " <th>length</th>\n", " <th>label</th>\n", " <th>chi2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>t1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>t1</td>\n", " <td>2</td>\n", " <td>81</td>\n", " <td>axolotl_sc_outgrowth.tif:t1_2d_0.8x</td>\n", " <td>4.410000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>t1</td>\n", " <td>3</td>\n", " <td>99</td>\n", " <td>axolotl_sc_outgrowth.tif:t1_3d_0.8x</td>\n", " <td>9.302500</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>t1</td>\n", " <td>4</td>\n", " <td>333</td>\n", " <td>axolotl_sc_outgrowth.tif:t1_4d_0.8x</td>\n", " <td>8.555625</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>t1</td>\n", " <td>6</td>\n", " <td>983</td>\n", " <td>axolotl_sc_outgrowth.tif:t1_6d_0.8x</td>\n", " <td>24.502500</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID time length label chi2\n", "0 t1 0 0 NaN 0.000000\n", "1 t1 2 81 axolotl_sc_outgrowth.tif:t1_2d_0.8x 4.410000\n", "2 t1 3 99 axolotl_sc_outgrowth.tif:t1_3d_0.8x 9.302500\n", "3 t1 4 333 axolotl_sc_outgrowth.tif:t1_4d_0.8x 8.555625\n", "4 t1 6 983 axolotl_sc_outgrowth.tif:t1_6d_0.8x 24.502500" ] }, "execution_count": 349, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outgrowth.head()" ] }, { "cell_type": "code", "execution_count": 373, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<hr>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td title=\"Minimum value of function\">FCN = 61.7813563148</td>\n", " <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 199</td>\n", " <td title=\"Number of call in last migrad\">NCALLS = 199</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Estimated distance to minimum\">EDM = 2.08438064812e-08</td>\n", " <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n", " <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n", " UP = 1.0</td>\n", " </tr>\n", " </table>\n", " \n", " <table>\n", " <tr>\n", " <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n", " <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n", " <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n", " <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n", " <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n", " <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n", " <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><a href=\"#\" onclick=\"$('#GcuYbrACpb').toggle()\">+</a></td>\n", " <td title=\"Variable name\">Name</td>\n", " <td title=\"Value of parameter\">Value</td>\n", " <td title=\"Parabolic error\">Parab Error</td>\n", " <td title=\"Minos lower error\">Minos Error-</td>\n", " <td title=\"Minos upper error\">Minos Error+</td>\n", " <td title=\"Lower limit of the parameter\">Limit-</td>\n", " <td title=\"Upper limit of the parameter\">Limit+</td>\n", " <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n", " </tr>\n", " \n", " <tr>\n", " <td>1</td>\n", " <td>L0</td>\n", " <td>6.702804e-02</td>\n", " <td>7.415289e-03</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>2</td>\n", " <td>r</td>\n", " <td>4.500367e-01</td>\n", " <td>1.475923e-02</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " </table>\n", " \n", " <pre id=\"GcuYbrACpb\" style=\"display:none;\">\n", " <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n", "\\hline\n", " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n", "\\hline\n", "1 & L0 & 6.703e-02 & 7.415e-03 & & & & & \\\\\n", "\\hline\n", "2 & r & 4.500e-01 & 1.476e-02 & & & & & \\\\\n", "\\hline\n", "\\end{tabular}</textarea>\n", " </pre>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<hr>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chi2 = probfit.Chi2Regression(growth_model,sp.array(mean_outgrowth.index)[1:],\n", " sp.array(mean_outgrowth['length', 'mean'])[1:],\n", " sp.array(mean_outgrowth['length', 'sem'])[1:])\n", "\n", "minuit = iminuit.Minuit(chi2, L0 = 0.8, r = 1.0, error_L0 = 0.1, error_r = 0.1)\n", "\n", "minuit.migrad();" ] }, { "cell_type": "code", "execution_count": 367, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", 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id=\"line2d_78\">\n", " <g>\n", " <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"122.945265748\" xlink:href=\"#ma4f294b3af\" y=\"13.7855217602\"/>\n", " </g>\n", " </g>\n", " </g>\n", " <g id=\"text_11\">\n", " <text style=\"font-family:Helvetica LT Std;font-size:8.0px;font-style:normal;text-anchor:middle;\" transform=\"rotate(-90.000000, 13.007500, 88.329764)\" x=\"13.0075\" y=\"88.3297637795\">Outgrowth + L0 (mm)</text>\n", " </g>\n", " </g>\n", " </g>\n", " </g>\n", " <defs>\n", " <clipPath id=\"pe5bc1d754a\">\n", " <rect height=\"153.779527559\" width=\"87.874015748\" x=\"35.07125\" y=\"11.44\"/>\n", " </clipPath>\n", " </defs>\n", "</svg>\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f2c4eb79610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "L0 = minuit.values['L0']\n", "r = minuit.values['r']\n", "fig, ax = plt.subplots(figsize=(40/25.4,70.0/25.4))\n", "fig.patch.set_alpha(1.0)\n", "\n", "# for ID, IDdata in outgrowth.groupby('ID'):\n", "# pitem = ax.plot(IDdata['time'], IDdata['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[ID], marker = markers[ID], label = ID)[0]\n", "\n", "ax.plot(time, growth_model(time, L0, r) + L0)\n", "ax.errorbar(sp.array(mean_outgrowth.index),\n", " sp.array(mean_outgrowth['length', 'mean'] + L0),\n", " sp.array(mean_outgrowth['length', 'sem']))\n", "\n", "ax.set_xlabel('Time (days)')\n", "ax.set_ylabel('Outgrowth + L0 (mm)'.decode('utf-8'), labelpad=8)\n", "\n", "ax.set_xlim(-0.5, 8.5)\n", "ax.set_xticks([1, 3, 5, 7], minor=True)\n", "ax.set_yscale('log')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Least square fit with an exponential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but this time time-depndent rate switch" ] }, { "cell_type": "code", "execution_count": 353, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def growth_model2(time, L0, r1, r2, tswitch):\n", " if tswitch <= 0:\n", " return L0 * (sp.exp(r2 * time) - 1.0) \n", " else:\n", " if time <= tswitch:\n", " return L0 * (sp.exp(r1 * time) - 1.0) \n", " else:\n", " L1 = L0 * (sp.exp(r1 * tswitch) - 1.0) + L0\n", " return L1 * (sp.exp(r2 * (time - tswitch)) - 1.0) + L1 - L0" ] }, { "cell_type": "code", "execution_count": 354, "metadata": { "collapsed": false }, "outputs": [], "source": [ "chi2_2 = probfit.Chi2Regression(growth_model2,\n", " sp.array(mean_outgrowth.index)[1:],\n", " sp.array(mean_outgrowth['length', 'mean'])[1:],\n", " sp.array(mean_outgrowth['length', 'sem'])[1:])" ] }, { "cell_type": "code", "execution_count": 355, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['L0', 'r1', 'r2', 'tswitch']" ] }, "execution_count": 355, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iminuit.describe(chi2_2)" ] }, { "cell_type": "code", "execution_count": 487, "metadata": { "collapsed": false }, "outputs": [], "source": [ "minuit2 = iminuit.Minuit(chi2_2, L0 = 0.8, r1 = 0.10, r2 = 0.1, tswitch = 3.0,\\\n", " error_L0 = 0.1, error_r1 = 0.01, error_r2 = 0.01, error_tswitch = 0.1,\n", " limit_r1 = (0, None), limit_r2 = (0, None))" ] }, { "cell_type": "code", "execution_count": 488, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<hr>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td title=\"Minimum value of function\">FCN = 0.623720538704</td>\n", " <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 330</td>\n", " <td title=\"Number of call in last migrad\">NCALLS = 330</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Estimated distance to minimum\">EDM = 5.88530247454e-07</td>\n", " <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n", " <td title=\"Error def. 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<tr>\n", " <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n", " <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n", " <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><a href=\"#\" onclick=\"$('#XOqXoGDIQb').toggle()\">+</a></td>\n", " <td title=\"Variable name\">Name</td>\n", " <td title=\"Value of parameter\">Value</td>\n", " <td title=\"Parabolic error\">Parab Error</td>\n", " <td title=\"Minos lower error\">Minos Error-</td>\n", " <td title=\"Minos upper error\">Minos Error+</td>\n", " <td title=\"Lower limit of the parameter\">Limit-</td>\n", " <td title=\"Upper limit of the parameter\">Limit+</td>\n", " <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n", " </tr>\n", " \n", " <tr>\n", " <td>1</td>\n", " <td>L0</td>\n", " <td>2.029391e+00</td>\n", " <td>9.705397e-01</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>2</td>\n", " <td>r1</td>\n", " <td>1.456833e-02</td>\n", " <td>7.267077e-03</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>3</td>\n", " <td>r2</td>\n", " <td>1.352757e-01</td>\n", " <td>4.443491e-02</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>4</td>\n", " <td>tswitch</td>\n", " <td>2.746200e+00</td>\n", " <td>1.103757e-01</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " </table>\n", " \n", " <pre id=\"XOqXoGDIQb\" style=\"display:none;\">\n", " <textarea rows=\"14\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n", "\\hline\n", " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n", "\\hline\n", "1 & L0 & 2.029e+00 & 9.705e-01 & & & & & \\\\\n", "\\hline\n", "2 & r1 & 1.457e-02 & 7.267e-03 & & & 0.000e+00 & 0.000e+00 & \\\\\n", "\\hline\n", "3 & r2 & 1.353e-01 & 4.443e-02 & & & 0.000e+00 & 0.000e+00 & \\\\\n", "\\hline\n", "4 & tswitch & 2.746e+00 & 1.104e-01 & & & & & \\\\\n", "\\hline\n", "\\end{tabular}</textarea>\n", " </pre>\n", " " ] }, "metadata": {}, 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</g>\n", " <g id=\"ytick_9\">\n", " <g id=\"line2d_39\">\n", " <g>\n", " <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.19125\" xlink:href=\"#mf0c55a9a47\" y=\"32.729680023\"/>\n", " </g>\n", " </g>\n", " <g id=\"line2d_40\">\n", " <g>\n", " <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"209.939281496\" xlink:href=\"#ma4f294b3af\" y=\"32.729680023\"/>\n", " </g>\n", " </g>\n", " </g>\n", " <g id=\"ytick_10\">\n", " <g id=\"line2d_41\">\n", " <g>\n", " <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.19125\" xlink:href=\"#mf0c55a9a47\" y=\"21.4922361153\"/>\n", " </g>\n", " </g>\n", " <g id=\"line2d_42\">\n", " <g>\n", " <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"209.939281496\" xlink:href=\"#ma4f294b3af\" y=\"21.4922361153\"/>\n", " </g>\n", " </g>\n", " </g>\n", " <g id=\"text_9\">\n", " <text style=\"font-family:Helvetica LT Std;font-size:8.0px;font-style:normal;text-anchor:middle;\" transform=\"rotate(-90.000000, 13.007500, 121.282520)\" x=\"13.0075\" y=\"121.282519685\">Outgrowth + L0 (mm)</text>\n", " </g>\n", " </g>\n", " </g>\n", " </g>\n", " <defs>\n", " <clipPath id=\"p77c63dc558\">\n", " <rect height=\"219.68503937\" width=\"175.748031496\" x=\"34.19125\" y=\"11.44\"/>\n", " </clipPath>\n", " </defs>\n", "</svg>\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f2c4d025e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "L0 = minuit2.values['L0'] \n", "r1 = minuit2.values['r1'] \n", "r2 = minuit2.values['r2'] \n", "tswitch = minuit2.values['tswitch']\n", "\n", "fig, ax = plt.subplots(figsize=(80/25.4,100.0/25.4))\n", "fig.patch.set_alpha(1.0)\n", "\n", "# for ID, IDdata in outgrowth.groupby('ID'):\n", "# pitem = ax.plot(IDdata['time'], IDdata['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[ID], marker = markers[ID], label = ID)[0]\n", "\n", "# plot the average guy last such that you can see him\n", "# ax.plot(outgrowth.query('ID == @averageID')['time'], outgrowth.query('ID == @averageID')['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[averageID], marker = 's')\n", "ax.plot(time, sp.vectorize(growth_model2)(time, L0, r1, r2, tswitch) + L0, lw = 2)\n", "ax.errorbar(sp.array(mean_outgrowth.index),\n", " sp.array(mean_outgrowth['length', 'mean'] + L0),\n", " sp.array(mean_outgrowth['length', 'sem']))\n", " \n", "ax.set_xlabel('Time (days)')\n", "ax.set_ylabel('Outgrowth + L0 (mm)'.decode('utf-8'), labelpad=8)\n", "\n", "ax.set_xlim(-0.5, 8.5)\n", "ax.set_xticks([1, 3, 5, 7], minor=True)\n", "ax.set_yscale('log')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 493, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'L0': 0.06702804326025881, 'r': 0.45003665810065296}" ] }, "execution_count": 493, "metadata": {}, "output_type": "execute_result" } ], "source": [ "minuit.values" ] }, { "cell_type": "code", "execution_count": 494, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <span>Minos status for L0: <span style=\"background-color:#92CCA6\">VALID</span></span>\n", " <table>\n", " <tr>\n", " <td title=\"lower and upper minos error of the parameter\">Error</td>\n", " <td>-0.719447722239</td>\n", " <td>1.42399704559</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Validity of minos error\">Valid</td>\n", " <td style=\"background-color:#92CCA6\">True</td>\n", " <td style=\"background-color:#92CCA6\">True</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Did minos error search hit limit of any paramter?\">At Limit</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"New minimum found when doing minos scan.\">New Min</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "minuit2.minos('L0', sigma = 1);" ] }, { "cell_type": "code", "execution_count": 496, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <span>Minos status for L0: <span style=\"background-color:#92CCA6\">VALID</span></span>\n", " <table>\n", " <tr>\n", " <td title=\"lower and upper minos error of the parameter\">Error</td>\n", " <td>-1.15355020954</td>\n", " <td>5.53562550449</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Validity of minos error\">Valid</td>\n", " <td style=\"background-color:#92CCA6\">True</td>\n", " <td style=\"background-color:#92CCA6\">True</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Did minos error search hit limit of any paramter?\">At Limit</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"New minimum found when doing minos scan.\">New Min</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "minuit2.minos('L0', sigma = 2);" ] }, { "cell_type": "code", "execution_count": 504, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <span>Minos status for L0: <span style=\"background-color:#FF7878\">PROBLEM</span></span>\n", " <table>\n", " <tr>\n", " <td title=\"lower and upper minos error of the parameter\">Error</td>\n", " <td>-1.43601173992</td>\n", " <td>0.970539729502</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Validity of minos error\">Valid</td>\n", " <td style=\"background-color:#92CCA6\">True</td>\n", " <td style=\"background-color:#FF7878\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Did minos error search hit limit of any paramter?\">At Limit</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td title=\"New minimum found when doing minos scan.\">New Min</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " <td style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "minuit2.minos('L0', sigma = 3);" ] }, { "cell_type": "code", "execution_count": 471, "metadata": { "collapsed": false }, "outputs": [], "source": [ "minuit2 = iminuit.Minuit(chi2_2, L0 = 3.0, r1 = 0.10, r2 = 0.1, tswitch = 2.5,\n", " error_L0 = 0.1, error_r1 = 0.01, error_r2 = 0.01, error_tswitch = 0.1,\n", " limit_r1 = (0, None), limit_r2 = (0, None), limit_tswitch = (0.5, None),\n", " fix_L0 = True)" ] }, { "cell_type": "code", "execution_count": 472, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<hr>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td title=\"Minimum value of function\">FCN = 1.20252280967</td>\n", " <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 154</td>\n", " <td title=\"Number of call in last migrad\">NCALLS = 154</td>\n", " </tr>\n", " <tr>\n", " <td title=\"Estimated distance to minimum\">EDM = 9.74611676354e-10</td>\n", " <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n", " <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n", " UP = 1.0</td>\n", " </tr>\n", " </table>\n", " \n", " <table>\n", " <tr>\n", " <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n", " <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n", " <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n", " <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n", " <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n", " <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n", " <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " <td align=\"center\"></td>\n", " <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n", " </tr>\n", " </table>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><a href=\"#\" onclick=\"$('#iQKvmVuWSg').toggle()\">+</a></td>\n", " <td title=\"Variable name\">Name</td>\n", " <td title=\"Value of parameter\">Value</td>\n", " <td title=\"Parabolic error\">Parab Error</td>\n", " <td title=\"Minos lower error\">Minos Error-</td>\n", " <td title=\"Minos upper error\">Minos Error+</td>\n", " <td title=\"Lower limit of the parameter\">Limit-</td>\n", " <td title=\"Upper limit of the parameter\">Limit+</td>\n", " <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n", " </tr>\n", " \n", " <tr>\n", " <td>1</td>\n", " <td>L0</td>\n", " <td>3.000000e+00</td>\n", " <td>1.000000e-01</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td></td>\n", " <td></td>\n", " <td>FIXED</td>\n", " </tr>\n", " \n", " <tr>\n", " <td>2</td>\n", " <td>r1</td>\n", " <td>9.901341e-03</td>\n", " <td>1.633914e-03</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>3</td>\n", " <td>r2</td>\n", " <td>1.018932e-01</td>\n", " <td>2.739342e-03</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " <tr>\n", " <td>4</td>\n", " <td>tswitch</td>\n", " <td>2.787752e+00</td>\n", " <td>8.313742e-02</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.5</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " \n", " </table>\n", " \n", " <pre id=\"iQKvmVuWSg\" style=\"display:none;\">\n", " <textarea rows=\"14\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n", "\\hline\n", " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n", "\\hline\n", "1 & L0 & 3.000e+00 & 1.000e-01 & & & & & FIXED\\\\\n", "\\hline\n", "2 & r1 & 9.901e-03 & 1.634e-03 & & & 0.000e+00 & 0.000e+00 & \\\\\n", "\\hline\n", "3 & r2 & 1.019e-01 & 2.739e-03 & & & 0.000e+00 & 0.000e+00 & \\\\\n", "\\hline\n", "4 & tswitch & 2.788e+00 & 8.314e-02 & & & 5.000e-01 & 0.000e+00 & \\\\\n", "\\hline\n", "\\end{tabular}</textarea>\n", " </pre>\n", " " 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</defs>\n", "</svg>\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f2c4f051d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "time = sp.linspace(1, 8)\n", "\n", "L01 = minuit.values['L0'] \n", "r = minuit.values['r'] \n", "\n", "L02 = minuit2.values['L0'] \n", "r1 = minuit2.values['r1'] \n", "r2 = minuit2.values['r2'] \n", "tswitch = minuit2.values['tswitch']\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(3*80/25.4,100.0/25.4), sharex = True)\n", "fig.patch.set_alpha(1.0)\n", "\n", "# for ID, IDdata in outgrowth.groupby('ID'):\n", "# pitem = ax.plot(IDdata['time'], IDdata['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[ID], marker = markers[ID], label = ID)[0]\n", "\n", "# plot the average guy last such that you can see him\n", "# ax.plot(outgrowth.query('ID == @averageID')['time'], outgrowth.query('ID == @averageID')['length'] / 1000.0 + L0, '-', markeredgewidth = 0, color = colors[averageID], marker = 's')\n", "for L0plot, ax, title in zip([0, L01, L02], axs, ['', ' + L01', ' + L02']):\n", " ax.errorbar(sp.array(mean_outgrowth.index)[1:],\n", " sp.array(mean_outgrowth['length', 'mean'])[1:]+ L0plot,\n", " sp.array(mean_outgrowth['length', 'sem'])[1:]\n", " )\n", " ax.plot(time, sp.vectorize(growth_model)(time, L01, r) + L0plot, lw = 2)\n", " ax.plot(time, sp.vectorize(growth_model2)(time, L02, r1, r2, tswitch)+ L0plot, lw = 2)\n", "\n", " ax.set_xlabel('Time (days)')\n", " ax.set_ylabel('Outgrowth{} (mm)'.format(title), labelpad=8)\n", "\n", " ax.set_xlim(-0.5, 8.5)\n", " ax.set_xticks([1, 3, 5, 7], minor=True)\n", " ax.set_yscale('log')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### AIC" ] }, { "cell_type": "code", "execution_count": 418, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def AIC(minuit):\n", " SSE = minuit.fval\n", " # only works if no fixed paramters are used\n", " k = len(minuit.values)\n", " \n", " \n", " AIC = 2 * k + SSE\n", " \n", " return AIC\n", "\n", "def AICc(minuit):\n", " AICv = AIC(minuit)\n", " k = len(minuit.values)\n", " n = minuit.fcn.data_len\n", " AICc = AICv + 2 * k * (k + 1) / (n - k - 1)\n", " \n", " return AICc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "compute AIC" ] }, { "cell_type": "code", "execution_count": 420, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "65.7813563148\n", "8.62372168808\n" ] } ], "source": [ "print AIC(minuit)\n", "print AIC(minuit2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we cannot compute the corrected AIC because of the low number od data points" ] }, { "cell_type": "code", "execution_count": 419, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# print AICc(minuit)\n", "# print AICc(minuit2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

mauriciogtec/PropedeuticoDataScience2017

Alumnos/Fernando_Briseno/.ipynb_checkpoints/Tarea_2_Fernando_Briseno-checkpoint.ipynb

1

3604695

mit

mtasende/Machine-Learning-Nanodegree-Capstone

notebooks/prod/n08_simple_q_learner_fast_learner_full_training.ipynb

1

271897

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fraction of shares in the total portfolio value). One initial attempt was made to train the Q-learner with multiple processes, but it was unsuccessful." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Basic imports\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import datetime as dt\n", "import scipy.optimize as spo\n", "import sys\n", "from time import time\n", "from sklearn.metrics import r2_score, median_absolute_error\n", "from multiprocessing import Pool\n", "\n", "%matplotlib inline\n", "\n", "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "sys.path.append('../../')\n", "\n", "import recommender.simulator as sim\n", "from utils.analysis import value_eval\n", "from recommender.agent import Agent\n", "from functools import partial" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "NUM_THREADS = 1\n", "LOOKBACK = -1 # 252*4 + 28\n", "STARTING_DAYS_AHEAD = 252\n", "POSSIBLE_FRACTIONS = [0.0, 1.0]\n", "\n", "# Get the data\n", "SYMBOL = 'SPY'\n", "total_data_train_df = pd.read_pickle('../../data/data_train_val_df.pkl').stack(level='feature')\n", "data_train_df = total_data_train_df[SYMBOL].unstack()\n", "total_data_test_df = pd.read_pickle('../../data/data_test_df.pkl').stack(level='feature')\n", "data_test_df = total_data_test_df[SYMBOL].unstack()\n", "if LOOKBACK == -1:\n", " total_data_in_df = total_data_train_df\n", " data_in_df = data_train_df\n", "else:\n", " data_in_df = data_train_df.iloc[-LOOKBACK:]\n", " total_data_in_df = total_data_train_df.loc[data_in_df.index[0]:]\n", "\n", "# Create many agents\n", "index = np.arange(NUM_THREADS).tolist()\n", "env, num_states, num_actions = sim.initialize_env(total_data_in_df, \n", " SYMBOL, \n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS)\n", "agents = [Agent(num_states=num_states, \n", " num_actions=num_actions, \n", " random_actions_rate=0.98, \n", " random_actions_decrease=0.999,\n", " dyna_iterations=0,\n", " name='Agent_{}'.format(i)) for i in index]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_results(results_list, data_in_df, graph=False):\n", " for values in results_list:\n", " total_value = values.sum(axis=1)\n", " print('Sharpe ratio: {}\\nCum. Ret.: {}\\nAVG_DRET: {}\\nSTD_DRET: {}\\nFinal value: {}'.format(*value_eval(pd.DataFrame(total_value))))\n", " print('-'*100)\n", " initial_date = total_value.index[0]\n", " compare_results = data_in_df.loc[initial_date:, 'Close'].copy()\n", " compare_results.name = SYMBOL\n", " compare_results_df = pd.DataFrame(compare_results)\n", " compare_results_df['portfolio'] = total_value\n", " std_comp_df = compare_results_df / compare_results_df.iloc[0]\n", " if graph:\n", " plt.figure()\n", " std_comp_df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's show the symbols data, to see how good the recommender has to be." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sharpe ratio: 0.4566770027925799\n", "Cum. Ret.: 3.304502617801047\n", "AVG_DRET: 0.0003519913231219332\n", "STD_DRET: 0.012235538451970583\n", "Final value: 205.54\n" ] } ], "source": [ "print('Sharpe ratio: {}\\nCum. Ret.: {}\\nAVG_DRET: {}\\nSTD_DRET: {}\\nFinal value: {}'.format(*value_eval(pd.DataFrame(data_in_df['Close'].iloc[STARTING_DAYS_AHEAD:]))))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 5268 days of simulation to go.\n", "Date 2014-12-22 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.35022401809692383s. Value: 17810.80999999997..Epoch: 0\n", "Elapsed time: 202.3375527858734 seconds.\n", "Random Actions Rate: 0.005042577271024555\n", "Sharpe ratio: 0.3408933931947961\n", "Cum. Ret.: 0.7917059999999969\n", "AVG_DRET: 0.0001287523937952762\n", "STD_DRET: 0.005995660018413462\n", "Final value: 17917.05999999997\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 5268 days of simulation to go.\n", "Date 2014-12-22 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.35897254943847656s. Value: 17028.43.9999996...Epoch: 1\n", "Elapsed time: 155.4470453262329 seconds.\n", "Random Actions Rate: 2.5946515851279002e-05\n", "Sharpe ratio: 0.7820881392599728\n", "Cum. Ret.: 0.7028430000000001\n", "AVG_DRET: 0.00010325941270997878\n", "STD_DRET: 0.0020959176812144886\n", "Final value: 17028.43\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 5268 days of simulation to go.\n", "Date 2014-12-22 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.34699511528015137s. Value: 17513.640000000014.Epoch: 2\n", "Elapsed time: 148.03176951408386 seconds.\n", "Random Actions Rate: 1.3350746029993633e-07\n", "Sharpe ratio: 0.9006462250592255\n", "Cum. Ret.: 0.7513640000000015\n", "AVG_DRET: 0.00010822374788170119\n", "STD_DRET: 0.0019075178347247657\n", "Final value: 17513.640000000014\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 5268 days of simulation to go.\n", "Date 2014-12-22 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.380657434463501s. Value: 17844.77000000002.18.Epoch: 3\n", "Elapsed time: 143.5039553642273 seconds.\n", "Random Actions Rate: 6.869609028782328e-10\n", "Sharpe ratio: 1.0444534903132408\n", "Cum. Ret.: 0.7844770000000019\n", "AVG_DRET: 0.0001113870317004017\n", "STD_DRET: 0.0016929564861823407\n", "Final value: 17844.77000000002\n", "----------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "# Simulate (with new envs, each time)\n", "n_epochs = 4\n", "\n", "for i in range(n_epochs):\n", " tic = time()\n", " env.reset(STARTING_DAYS_AHEAD)\n", " results_list = sim.simulate_period(total_data_in_df, \n", " SYMBOL,\n", " agents[0],\n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", " toc = time()\n", " print('Epoch: {}'.format(i))\n", " print('Elapsed time: {} seconds.'.format((toc-tic)))\n", " print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", " show_results([results_list], data_in_df)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "StandardScaler(copy=True, with_mean=True, with_std=True)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env.indicators['rsi'].scaler" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_in_df['Close'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 5268 days of simulation to go.\n", "Date 2014-12-22 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.3939797878265381s. Value: 17844.77000000002.8.Sharpe ratio: 1.0444534903132408\n", "Cum. Ret.: 0.7844770000000019\n", "AVG_DRET: 0.0001113870317004017\n", "STD_DRET: 0.0016929564861823407\n", "Final value: 17844.77000000002\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b1fc828>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/abGvxa6wBYTCXcZiW4SGxRYBbK/cjPjU3wE56frT+zu1\n6IOdKXkNAOgpat0+eXyBSBDjO2ePa/mCVvS0gFBBtkvr91ap5LZXIgGd1liWpc89+G6z5/kFEkjO\nxTOLtWl/jSzL0pC8ttcJC/uo9LAG52VEso2SEV4O6/Fbqmn06rITRijHlo3+wg1ztezW09s9J3RP\nBIQA9DkrS4/ok/3Vkf1Eae5tUdPo09D8TJ04OvhU9cLjh0XO5WSk6U9fnaPnvhFalx1TR2hYQZbs\nXX7tnV/CS8aaK8SZlqJHrq4EGUjhINkdL1D3AUDfsutwsLPYsFAAP/Z9uzVTi/Oj9hO9x3ZnXr+l\nl9YGM0ZfXbc/qWvqWmkv37P+BIDOY1lWVDb5lOJ8NXj9emzp9sgyfmOC45JV3ejV0k/LdcH0Ya0P\ntjHGyOU02rC3Sm5fQCMH5ESdz8t0qbh/dpvuie6PgBCAPif8xfWf35yr+ZOGxLXfbatat0+5GWka\nVZij0nvO0wmjo9PtTxlbpJkj+0uSPKFAy13/HSxGGuwqlvj1I62Fm8kESnOk5i080f3rE3y5f29r\nOXWFAPRaP35pg15eu0/n3b9MkiJP5xMVS27JFSeOjNrPaMMT+u7AFwgoP1Q0trqZLpqx9hypj9qf\nHupaFpaoqQKA4Hc9y5JOGVuoX188TSMHBoMwP3t1U6S+pGUl//DS4wvovx8KZusNT5CV3po0h0Ob\nDwY7KM4YUdDKaPQGBIQA9DkNoS/32elpcjqNvDF1D/Ycqdftz69LKvjhD1iq9/ijUmpb4vYG73nc\n0DzNGFEgjz8gjy/xh3z4qXR6MxlCqXrqnOj+sdn96/dW6bLHP9AvXtuUktcEgO4kELD0xLul+uaf\nm4pJjwg9Hc9uYwH/2IL/owbmNDOye3nvtjM0YkC2vP6mgFC4i+Y7W8r0xLs7El7n9Qc0/76lkf1r\nTx2tJ78yO2pMVZKBJaCvCXf1O+nYgbpoZrFKBibOwEn2gVxlvUfby4JLPdtTI9Pt80cCUc01Q0Hv\nwt8ygD6nMZT9kpXulMth4j5kb352jd7fflgXTD8msgysOYs/OSAp+Y4Lbl/otV1OpTsdcvsCqnUn\n/qIcnle4VlBhbobKa92R86mqyZBoSZoj5t7hINrHuysT3mPTgWptL6vTgilDUzInAOhMh2rcccfu\n/vxkDcrL0NdPHd2me8V25fnM2MKjmltnOaYgS0PzM+X1W5HPhXAXzav+8GHw3yeVyBmzXLm20Re1\nf/HMYhVkp3fCjIGezbKsSPZc+OFcoq5eUvIBIY9tXHtKC9iT5vtl0l6+LyAgBKDPabQFZQbnZWpf\nZaN8/kDkSUqDNzoQ05Lrnl4Vuia5JQXhD9rsdKfS0xyqafSpusGXcGz4wz+cCfTKjZ/R9rI6XfrY\n+5Kki2cVJ/WarUn0c9q/8PsDli5euFxS8wW4w0+HS+85LyVzAoDOFLvk6W9fP0mZLqdunT+hzfey\nNw/Y/rMFRz23zuRyOlTv8UU+f2pigj0NXn9cBlT4M1WSHrtqlsYO7hd33y/NHt4BswV6tlG3vxrZ\nDgdvmqsb6WkhIFTT6FV1o09H6jw6/4FlkePtKYgwuignkmEU+/86eieWjAHoVZ5aXqr3t1fo8aXb\ndd79SxOOcdsCPgNz0+UPWGq01TcIL9Vqy3MVZxuzdbLTgxlCHl9A1Y3NZQgFP8rDtYIG52XqJFvr\n+kH92t99wi7R0jP7FxK37cs+nWIA9EZbDtZG7U8cGh/USNbAnGB2TJbLGekW2VO4nEZevyVv6HMw\nEFPItiFBfbnG0GfqzJH9deaEQZHjV544Ug9cOkP5WS5lpKgrJtBb1Huig63HHZPfzMigw7YutLGu\n+sOHmnvPm3p5bXQR+PDSr7b4w5eblnvGZgOidyLsB6BX+d9/bGh1TDjAkZHmUEZacKmXxxeQQp2B\nPaHzrRXBPOWXb0a22/qlPyuUIeT1B6KertqFnwbFFn1eeedZKSsoLSWuIWRPM7b/PsB3AwC9zf6q\nBn3/hXXKzUhTrTv4S9rRLJUwxuixq2ZpdFHPqB1kl+YMfi41V8DWXmB766FanXXv2xqcF/zw/Nop\no6I+C8PNE37wj/Xt+sUU6M1e+Hhv1P6cUQNaHH/vv7fo0atmJTz38a7gcv69ldH/n7nbWBBfSi47\nHr0Lf+MAeoU6ty8qk6Ulbl9ADhMMeoSfWh6pb3ryEg7EtBYQ2n246YM32C0sednpaUpPc+jTQ7VR\n97ELP6HNiAnYDMzNUH526tZ1J0pPth/z2yJCsbWFpOgvIG1piwoA3cGZv3lbUrDYf6qcfdxgHVuU\nm7L7dRaX08gXsCKfg3Vuv3aU10XO2z9nX98QrKF3sDpYf2nEgMQBsMp6r/6z6VDC7CIgVd7bWq7v\nv7BOgaPsHNsZdh+u1x0vrE9q7KVzgsstE3V/jbWjPDrTcUBO22t5kc3X95AhBKDHK691a9ZP30i6\nG0qGlEAAACAASURBVILbF1BGmlPGmMiTkDN/87Y23TVfmS5nZEnZjrJanTKmsNXsn0tmDdeZEwe3\nac5Oh4nKzPnstGN03pQhGjOoKbAUqSGU1rFpObEZSHNKBsjjD0QCPUs2l0XOJfqzmHtPU6aUP2Ap\nLUXdzwCgM4R/0frmGWNUXuuOdP3pi9IcDvn8gcjnz7q9VTr910si5+1/NrEt6VtrrnDar97S0ltP\nj2TmAqn0i9c3a83uSp06tkjzJw/p6um06JG3tyU1LlyX8dODtSpLUPhekg7VNEa27UGjS+eM0Fc/\nM6rNc8tw8f9nX0MIEECPt7Mi+PQytvillDhjxe31K8MVfPuzt4sPP70MfxH+0UufaPT3X9XT7+9s\n8fVnjCho17ztablTh+Vr/uShGjOo6YlyOGW/uQKDqWKvIZSR5tATX5mtkba2pzf+pakNc2tLxpZv\nr0j5/ACgI+VnuXTliSN16rgiXXh8sb44q+8WQHY5HfL6Ld33xqcJz/tsS8n8MYGz5uqN3HjGGEnB\nTm4HqxL/UgscrXA29XVPr9T9/0n83293URnKSp8+PLnvjyt2HtHmgzVxmfDby2r1iq1uULgYtCRd\nMP2YdrWdzyRDqM/hbxxAj1dR23yhvdgvrFI4Qyj49mdP6Q+nyHtilord+WJyab3JePuWeXrqmjmS\nogNCeVnx2U3HhupPDM1P3II0VeyZShOG5iknI00n24pX2yVaMpbparr+yv/7MPUTBIAO4vb5VdXg\nVf8ULsPtyYJFpZtfLm0/54954NJcdujxI/tHtg/XN/95DRyNKlvG2r2LtzQ77r2t5brk98ub7Zra\n0ZZvq9Cr6w5o+IAsLfqfExKOeeiy4/XcN06O7IfrC+2vbIwad+7vlurHL32S8B7tfZiY5nRo7KBc\nfWPese26Hj0PASEAPd7+qsZmz20tq407Fl4yJkkltkyYcCCotdpBsWK/FLdk5MAcnTquSFJMQChB\nAdMbzxyrZ687STNtX6Y7QtSXhtDP8sVZw3VCggKH4SfAb246qPe2lUtKHCQCgO7OH7A0/s5/SZK2\n2+rk9GVpTqNDzSxNkZoesliWlXSGkP2zLrazEpAqNQk6tu4+XB/XneumZ1brgx2HW/zvvCNd+tj7\nkoJ1KDObWZ513tShUd/9vn3WWEnSvtii0S18X03UQTZZi79zmm6dP6Hd16NnISAEoMc7WN18QGj+\nfU2t56sbvbr172v1wfaKSIZQWkx79W1ltW2uH9HechNnjG9qz5uXFR8QSnM6NLuk5a4TqZCogLQx\nJhK4sgu3nb/myRW67LEP9N2/rYlas95aDQkA6C4O2D47vr9gYhfOpPtoLasgvJT5u39bo6eWRy+n\nbq77pb1ILYWl0VYb91dr04HqVsfVuKODjU+8u0On/PItnX//0qjj4azo8lq3Fr69LapzXkfbeqgm\naj/Ztu7FBcGHl3sqk+/W19HlBtB78F8KgB6vOsFToURueXaNnlmxW/uqGiM1hCTpmrnBonuN3oDO\n+e07Ca9tqXvWqWML2zDbJieMHqjC3GAHiEQZQp3F/hTJnnJ96ZwRrV773Ko9kqQHLp2hgmyXhuZn\npn6CANAB/j979x0fRZ3+AfwzW9N7DwkhdEIH6SAoSrNiQz09UU+94tk9f9bzPMVyerazn3p6ejY8\nz4506VU6AQIJJARI79k+vz+m7Mzs7GY32Z7n/Xrd67ZMdkeymZ15vk8RghMvLRqNvJTAluZGCrWL\nyFF9knHLjGIAzgyhLxUjswFA6yZblJE8/lKY93Yh4WfeS+tki3tqWJZFuyIgJJRSVSuyyIXzvxeW\nH8bTP5TKevAE2mNf7xdvnzOEWxS8dUYxPrllksefy042gmFcM4Q8oYAQ8RZ9UgghEU+tmbSUEMzJ\nTDSKj8XpnT17ZgziAjpmm8MlBV7o4yOsPH267QT2VjWLr3nHuQPRN1191K43hHRftR5CwSI9WX/s\nghLxttrUNpvd4dJjCeAaI84emg2TNTQ1+YQQ4ithkqK7so3eSKeSsZAUqxenNq0qrXH7s1o3JSod\nZmcGBl2kkkCob7d4la1dUdcuNl4WzmV2VTYFctdkhCy6pb+dgneuHw8A+L/5QzGpWL1vo8Co0yIj\nwYjqpk44HCxYloWji//gnpSMkd6Fxs4TQiJeVwGhTqsdb/18DPtOOlOOYyWlTUI/IeX0BoA7yQCA\ns59djV8ePR9/WroXAFD25DwA6ifPvhADQiHMEJKaPSxbvK3232a1OzBfkn49LDcJB061oCAtDjF6\nDerazDBZ7XSBRQgJa0dr2/Drd7km+HS8clKbSnSmxYT0eC6b9d0N5chINKj/rJvvw4nFaVg8tQjv\nbajA2G5O5SS9k7Tn1PaKBqTFG1AsGQZy3gtrcc6QLBxV6RcpkE5N3XG8UbwtZJcfq3P/s/5mstox\noV9at3pDZiYYUd9mwfyX16G+3YLXrx0re744Mx7Hatvx68l9YbGzyKesR+IlCtMTQiKeWiPBNffO\nxAPzuIZ4647U4cUVR2SrQNJeN0L68L6TzS6v09TBvXZjh/w9hIlk7lZEvTW6D3dyrJaNE2qMSvq/\nxc6irMZ58hRn0IoTyWL1WphtDgx55Meg7SMhhHSH8jhGOEaVkdNDc5NkmbDP/nhI9WfdBYT0Wg0e\nu7AEiTE6sQcRId6QTi69/I1NOOf5teL9jUfrcKSmDW/+fAxnWrgG0e/eMN6lGXKeZFKrzeHMYq7l\nm0o3tHvXdsAfOix2xHfzeJOeYEBduwWlp1tR22rG/V/skT3/h1kDkJloxG0z+2PJwhHdGjlPeqfw\nuwIhhBAfSTOEhuQkYtFZBSjKiBcniElXhAQZCc7yMaHB4FPfl3p8H2k5mdBIuacZQm//ejzK69pD\n/sXdLyMeF47K87hNUowOuxWp1XVtZozgg1rSZtwsy6oGlAghpDs6LDY4WCDB6J9TV2m/kUFZiX55\nzWigli31q0l9vfrZrr7HdBrGpSybEE/Uzt8ED365V7yt0TA4e1AmzhmSjbMHZeHtdcfE6WLSXufS\npuZCEMmikh0eKO1mmyxjyRcZCUax3A1wnYyYYNRh20Oze7R/pHei0CEhJCJ1WuzotNjBsixKTzun\nNvTLiMcNfJPo5Fgurb1WZbRolqSfUEGqd1/O647Uyt4fALRupqp4KzlWj9EFoU+hX33vTNx93iCP\n27SolOZV1HeIfZaaJVlUtApMCPGXw2daMezRZRj+2DK/vF5VYwfu/mw3AGDrg+ciOS48SnbDQawk\nIHT0qfnY//gccdrlg/PVx1B/e/s0vHXduC5fW6fVyDI0CPHk0OlW1ce/4huaV9R3iI/trmwSM/20\nGgYf3DhBfM4mOR8xqfRA9HWybHcVPfAdjtW1I97QvaB2glEn9j1TM2VA9wacEEIBIUJIRBrzxE8Y\n+uiPsrR/QL5CmRrPneTXtbkGhKQXAMlxetwwpUi8f56kj47ULyec2THCmNKeZghFgyn9uZOQJsmE\nMjrpJ4T4y4HqrkdOe6uqsQPTnlkt3k+LV++H01vFGpzfoVoNg3hJRpY0s1ZqeH4yzi/J6fK19RqG\nFguI14RR84mKrMDnlx8SB3ukSM7lpL0hs5Kcn1VpwEc4d5O+pi0In8nKBmfwKs7YvZKxGL3ny3Z/\nZU+S3ocCQoSQiCRMs+qwyFN9pVMVUjxkCCnDONILjsvG5uPdG7jpDy9cOUp8PFVy4tEhZghFd0BI\nyKQalpvkdhshw0nawNDXk/69Vc34cV/wRr8SQiKH8jjfXZUNHbiHzwwShLpcN9ykxasHfQDAoNJf\nyBetZhu+2FGF5QfO9Oh1SO8g9HB8X5LtAwAOh/OYcNX4AvFxaXZbhuRzLO0zabI6YNBqxMmxADcs\nI9Dmv+QcxtHdwA01vyeBQqFEQkhE288HcqYPzMC6I3XQSPrWCCtH0oBQerwB44tScdGofNnrbK1o\nEG/rNBqcMyQbFU8vAACU17XjlVVlSJWsJHf2kgyhrZJ69KIHvnN5fvbQLPEi4cH5Q3Gq2YQVB8/4\n3CfiwlfXAwCOPTUfmij/NyWEeMfuYPHBpgo8/s0B2WPdDcRPf3Z11xv1cgOyEtw+Z1AJnv1mej+v\nX1vo9/famjK3mbiECEpPtyIlTo+SPPmCVEO7BT8f5kr4c5NjxMeF8zKA6ylU+sRcPPNjKT7dVik+\nbrLaYdRrxMEgQHBKxqQBqLo2S7degwJCJFBoWYQQEnE2Ha0Xbz/4X66pYL8Mro+NtFFojF4Lo04j\njo4HgNyUGLx53XiPPSOUk8OEkfDS5tXCiUe0Zwh1RS+5QIg1aDFrSCYAwNbNFTcrlZoR0it9uKkC\n932+W7z9zI+leGfdMVkwCAAsKj1AvCGUmAhevnoMNjxwTrdeK5rlSS6wlfSKDKGPfzMRDy0Y5vVr\n/+XiEgBAkWRiGSHu7K9uxuDsRJdAyKCcRPz2o50AAINOiwUjcgHIz/8A7hww0ahDp9Uu/v2brHZZ\nJhEQnAwhaQZ1d0bOA1Atk1t+1wycOyQLc0oowEq6jwJChJCI89LKwy6PiQEhRWmBWXHx4M33vl7R\nKFoYS//X75wXJiZhylgPx85HkneuH+/ymF6xYixkTFm7ueLW3Ys9Qkhke+R/+/H5jio0dVjwyP/2\n4/U1R7Fs/2mX7czdmAh0/t/Xik2kBVP6p8su0gjH03RIo+J4766nkDsXjuQmWY7qk+zy3P7qZhQ9\n8B0On1FvJEx6l30nm7Gnqhk1fIb3WUXOIMruyiYU88Mspg/MwCVjuIxvtfOHWIMOLOs8FzRZ7YjR\na/HFbZPFbYIREGq32HD95L4ofWIuLh/Xp1uvIbRKkJacDcxOxD9vOAtvXud6fkaItyggRAiJOJuP\nNbg8VsQHhDrMrpOwpNy1i3hv8VnibWXST2EaN4VMusC852Qz/3q95zA6Y1Cmy2M1rSbZfaF8zGzt\nXs8PajhKSO82+i/LxdvtZtfjiDLI35UOiw2Hz7Thv/xkIoGQ+Ulc/XTXDPx01wyXx5U9hHwtmRay\nb9VKdD7fXgUAuP3jX7C3qhn7q5t9em0SXYSR6mMKuR6Fn982RSzjB7ggzqzBmShIi8P4vqmI1Wtx\n69n9XV4nnm/gLGR4m6wOxOg1GF+UhtvO7o8BWQkwWR2ycfSB0G62Id6o61HZl9AQm5pHE3/rPVcy\nhJCoJjQQ7Kr5qM5NAGemJNihLCcTToKlX8KvrznKv17vyRBS+29VBueEFWO1Rt7uSFfngrFSRwgJ\nP31SXbN1jtW1oSQvCe8vPgt/vpArTTL5GGw+3WxyeSwt3tDjBsnRbFB2IgZlJ7o8rvw3EzJzvaXl\ns4/Uesw18KXdh8604sJX12PBy+t9em0SPViWxR//8wsA4FeT+qpuU9nQiUQ+qJsab8DBJ+ZiUnG6\ny3Z5ydxxpaqRm/JlstnFoMwD84bg1hnFANSn0fqL2WaH1c4i3tCzHkBCq4J4CggRP6NvQ0JIVBDG\neLobd56RYEBJXhIeuWCo6vPSNPn+mfKmmkJZlLQXkfK53kCt2fNtihU5ocHj6RbXizB3GiT/rqtK\na7q5d4SQcHW62aQayCk93YLLXt+IH/edRptKdqfVzmJwdiJmDs5CZiJ3bPliR5VP731cMu752omF\neGDeEPzv91N9/C8ggGtTW0/lZWqEnntqGUKNHd1rtEuiyy8nGnHRqxvE+8pG5refM0C8/fXu6i5f\nr286l+H90sojWLb/NLYca5B9joWFvnaL5+zynljyfSmAngdyzi/JAQBMG5DR430iRIpCjISQqNAv\nPR6LpxbhmgmFqs+vvnemuJrUFeUJiDQzJj3eIAsM9faMlt8qAkLZSXxASGVV3h1pNtHek8242j+7\nRggJAw4Hi0lLVmLm4Ey8v9g5PvqDTRV49H/7AQA7ju+AVsNgbGEKdp5okv28kJWi58uNXllVhrvP\nG4RVpTWYOTiry8b+Qr+3Zy8biUvH5veqIL6/+dozSEn4LlXLEDrZ1Nmj1ybR4dLXNsruGxVZaXNK\ncvDKqjIAwMR+aV2+XgFf8r/mUC3WHOImk0kDQnFCQEilPNUf2s02vL+xAgCXmdgTZw/KRPmS+Vi2\n/4wf9owQJ/pWJIREnHiDc6qEQKNh8NiFJRiokuYO+DauU5kJI72AUJakOdje3fNGWV6XGKNHglHn\nU4bQ0do28bbJYscl/9jQ6wNthEQL4e97zaFaWGzOXh1CMEhgd7A4vyQHn94yCSvvOVt8XAgIzRyc\nBQDonxmPz3dU4aZ/bcdSL7KFhGP2xOI0Cgb1UEpsz/ouad0EhExWO47VtvfotUl0Uv7NSssWX7hq\ndJc/r3buFyN5jQK+VHXT0Tqf9uvHfadwvL7rz2xVozPQOXd4jk/voYZhGDE4Toi/0DcjISSitJlt\naLfYve4jMXMw1xvIm14/v5/VH3EqNd7SSWKdivdVW+nsDV64chSW3enadBQAcpJjfMoQkjZz/PKX\nk9hV2YQzPgSUCCHh64NNx8Xbk5esxNBHf8S768tVt509NBsTi9NlZbuDc7ggv0GnwSWj89DYYcX9\nX+wBAFQ3u88qaTVZcd0/t2Af35xYOWqa+E6tbNgXQonZSyuPiAMJWk1WDHnkR9XtW0zWHr0fiSzH\nJItDgmRFEFKaMZTZzYw1aZCoODMBQ3ISXTITu3Lbv3di9gtru9xOKEV794bxMOr8cwwSAqvKbHZC\nuos+SYSQiPL59koAwEpJr5nXrx3rdvs3fjUO6/80y6teB/fNGYIDf5nr8rh0hWr6QHntdm9NEFo4\nto94oaaUFKMTJ3p4Q62fxIeSi0hCSGRpaLeIwfLS0y3iiGih3PYv3x5AUXqcy7ScAVnOQFDF0wtQ\nvmQ+rp3obCobo9fKeo5Jg8kmqx0d/MUXy7K49p0tWHekDu9tqAAAxPawoSuRmz00q0c//9+dJ2G2\n2THizz+53WbFASqN6U1aVM4bUhVlVtIMIW8bw0tH1gNAjF6juK/tVlay1c5ib5XnaXgL+RK4BKP/\nphoKAa1BOQldbEmIdyggRAgJK5OeWonF720V79vsDrAsC7PNjn9trBDTbz+7dbK4zTxF+ZhUjF6L\nPqlxPdon6XhioUGhQG0yTm+n02qwvqwO2yoaut4Y3O8YAKQxu12Vvq3WEULCQ7vZhrFPLMeT3x0E\nwPUIK8lLdtmu1WTDxaPzsP/xOW5fSxnIV5Z/SEt45774M4Y9ugw2uwMtnTbsUVyo9WTcM3F64pLh\neOKS4Xjn12f16HUK0+LQ1sXCgbffISTymax23P6fnQCAhWPy3W7XnayYC0bmye4rs450GsanbG/p\nthe+ut5tUIiVrBgOzPJf8OasojQ8d/lIPHPZSL+9JundKCBECAkbNa0mnG4xYTXf+O+F5Ycx4KEf\n8PQPpdhQVofHvt6Pf64vR35KLIbmqmenBEJmohGD+d5E0rH1j19UgvFFXTc17G2E+vYr3tjkcfWs\n1WTFyD8vw9rD3O97XKFzFe+8YdmB3UlCiF+V17WjpsUkZvB8vJXL8qtpNSMzwegSTG812ZAUq/dp\n8o5RsbLfypcUVTd1oqKemyY24KEf0GF1DTRQ/yD/uG5SX1znZhS4N27hx3y/v7ECjR2eS8Istl6a\ngtsLbTpWj8oGbsFv8dR++Ojmifj6D67TAL3NCpIanp8kuy8MvxBoNYxqprI7ymyiunb1kfV1bdyx\n8Hcz+7tkOvWEVsPgivEFqoF2QrqDvh0JIWFDWWb08sojAIA3fz4mfrECXOp/V5Nl/O0cPj1e2sxv\nXN9Ud5v3alpJ0OzCV9eLZRxKN7y3DS0mmxgAHFOYIj4XqIkfhBD/a+6wYtbf1mDCUytx6WvcyGiT\n1YF9J5vRYbEjK8mIvunx4vaDsxNhsTuQGMMFg9beNxNf/m5Kl++zlj9WCIQStIOnWmSPVzdRD7Jw\ndeX4AgDAlvIG/G3ZIY/bLt1Zhc3H6oOxWyTElu07Ld7OTjZi6oAMjOyT4rJdd/rwjOsrX7hTTvvS\nazU+ZQhZFAEhadbSvpPNsNi456saO/j3p3NFEt4oIEQICRtaSXlAs2LlULoiY9Bqgh4QumAkV5Ym\nLU/zZWW7NzErGm+breq1+TuON8ruS/89y1SaSxJCwlMlf+EDQBa8v+CV9QCArEQjpIfsQ2daAQB6\nPnjcNz0eYwu7vmiSvjYA1PP3rXb5xZww5vn5K0Zh9tAsrLhbvQE+CT5pU2BlU/C7zxuED2+agHvO\nGyQ+9um2yqDtGwmdNjO3cPTEJcORlRjjdrvuTthaducMTOAzuqf0l/eC1GoYsXTdG0LAR/C3n7jA\n5pEzrbjglfV4YflhlJ5uwR8+/gUAety2gJBAo6sZQkjYsDmcX7ILX98ge076BWzQacTSrWCN3yzJ\nS0bF0wtk+xFvpJ4UalwmsXnZedskCRztr/bcqJEQEj7q2y0en89MNGJDmetY55K8JJWt3SvOjEdd\nm7M8o7yuHX9fflhsWi0QvhUuHZOPy8b18ek9SGBJp3Yq+zyN6JOM6QMzMX1gJp5ffhgAsF7lc0Oi\nT2OHBSPyk7ssR/RmQIiawTmJ+Oy2yarPCWXr3lKWjP1yogk2uwPbKrhFro1H67B0ZxVqW7ljFfWa\nJOGOMoQIIWFDWsN9tLZdvD2mMEXWfNKg4zKE7p87GN/ePj2o+ygNQCkn5PQGn9wyCT/e6fnfXHmS\nr0zFdjhYbDzqepJv4gNJybF6lNe1uy01I4SEl/o29R4agqzEGIwu4Mo/vuAvynQaBlMGZHj6MRcv\nLxoju99pteOllUfwze5TAIDbzxkAAPh6dzX6pMb2eEw68T9Po8J1Kr8v4aKaRKc2sw0zn1uNDWX1\nsmCvJ89cNgLL7gxd1p9VpbfVmkO1OMpnNu+pakZThzNITtnkJNxRQIgQEjZsdvVMkr5pcWI6MeCs\n1/7dzAFuR58HinR1KrYXTq2ZVJyOITm+reormzW+u6Ec17y9xWU7IQDULyMeLAs0ddFwlBASHkpP\nt4q3h+cnufQDyko04p83nIUtD54rNkLNTXFfFuJOTrLzZ167dqx4W2guLfQkAiBOpCThReehubeX\nyaQkiqw9VCs2hP/tzP5e/cxVZxUG5NyP9fID+M76Yy6P6bQMzrQ4e5f50pOIkFCjgBAhJGwo03AF\nX+2qxps/O7+AuzNlIhC6m7oc7aYPlK/6OxQnRsfq2qGUkWDEvOFcf6ZZg7kG3u1myhAiJBJsOVaP\nsYUpuGxsH7xw5WiMLUxF2ZPzxOdT4vRIitEjOykGsQYtliwcgY9vntSj95ROLdtSzo0nT4rRu9uc\nhJGkGPWMidPN1Ay8t2kzc8Hcpb+dgusnF4VkH87np5p6G8T5YNNx1ce/3XNKvE3xIBJJwuOqihBC\n4JpJ4o4hDMYHSydiEblbZ8hX+ZS/V6MioPfiVaOx/K4ZmDUkCxVPL8CIPlwGUhsFhAgJOLPNjkf/\ntw+L39vqErz9dNsJ2aq3O6eaTRiYlYjnrxyFQdncyr00E0QZPL96QiEK0rrXaHXNvTPxwx3TVRu1\nJsc6A0Jq5UckPDx/5WjVx88vyVZ9nLItotPqQzX409K9AIBB2Qkh249RfDlrTz5mJxo6VB+/c/bA\n7r8oIUFCRY2EkLDQ3GHFXZ/u8mrbUGcI/fLIeYg19L5yMW9NU2QI2R3yzC/p2NhPb5mEicXpsucN\nWu55q52FyWpHbau52xePhBDPlnxfKq54t5psSI7jgioN7Rb8aeleaDUMjj413+NrNHdaxZ+TSonT\n+730sygj3u1zGYnO/jTUtyN8nTfMNfDz4U0TkBJnUNkaONXcSZOaogzLslj83jbxfih7MgrxakcP\nahbVpqnec94g/IHva0ZIOAv9MjshhAB48+ejXvd8MNvsXW8UQKnxBsT0wv5B3aWsBJT2+UhQKR0Q\nptCsKq3BC8sPY/qzq71uNkkI8c3hM87+P098dwCH+H5AQoPUrrIzKhs6YLY5ZNk5gg1/Ogd7/3y+\nH/fWsxTJPqTFqwcXSHgYlivvRddqcp8ROu2Z1YHeHRJk0kDxpWPyQ1qCr+HfuycBIZPV9bw0Jd5A\nrQVIRKCAECEkLLhL779hSpHLY+uO0BjacDdUcrJvU2QISVcC1RpzC5Pc3lh7FG/xvaNqWiggRIi/\nnWruxMaj9eL9L3ZUYc6LPwMA7vzEmbF5vN6175dg/kvrAKiX8sYbdUgMUl+fq8YXyDI3375+fFDe\nl3SPcjFAeTH+yS3yHlNUNhZdavjpcbfOKMYLV44K6b5oxAyh7r9Gh0pAKL8bjfMJCQUKCBFCQs5k\ntePlVWWqz507NMvlsTgq1wp7n/xmEobwU0CUJ/LS2J9a6Z1O4/rV1GmlfkKE+NvkJavcPidt2jz7\nhbU4fKYVnRbXi55WvtdXKEe8f/X7qXjm8pHI5EvGNAwwICt0PUlI1/qkxAIA/njOADxywTBxqIBg\nbGGq7D71lIsuQm+yc4dmhzyLxpcMIXeBSeHYePWEQvExfRj0uyTEG/RJJYSE3M7jjW6fU+sDIe1B\nQ8JTcpweDy0YCgCw2OQZQla784RKLUNIKBmTajc7L0Q7LDZaLSa90r6Tzfjrtwf8cnG8SZIZVKjS\no6vdYkceP+bdamdx/t9/xqK3Nsm2kZZJXCO5EAq20XxTWKNOiw0PnIPv/jg9ZPtCvNMnlQsIsQBu\nmtYPWkVA0aDToFjSL4oCQpHtx32n0NRhAcD1D7rlw+0AgCxJ369QEQJCrPqgWxnl+YxAOBYmGJ3n\nNMrPNCHhigJChJCQS5L0ffjo5omy5/SSbJFnLx+JS0bn4c3rxgVt30j3CSUkZsUJlDSYo9aLSS1D\nqLHDAqvdAZZlMezRZfi/L/f4eW8JCX8PfLkH76wvx/ojtT1+rZUHz4i3P/6N/Lh75ZubsLuyCQVp\ncbhpWj/x8d1VzbLt9ldz95csHBE2jfbzU2JlJaskPAnZXA3tFrfb3DtnsHi71eTf5uQkeE41ub+w\nYgAAIABJREFUd+K2f+8Uy0tPt5hg4pswC4HBUBLiNvYuMoRYlsXQR3+UPfby1WMASANC0kmHdJlN\nIgN9UgkhIScNEAjjPwHguz9OE6c/DMlJxJXjC/DiojEYnp8c7F0k3WDkgz3KJuDSMfTKEfSAeobQ\nHZ/swsCHfsDOE00AgM+2V/lzVwmJCML1yhk/9NQ61cyVbFw+rg/6pMZh96PnY2whd/zdWt4AgCu7\ncjety2S147LXuYyhviGaArjynrNdFhFIZBDKhDxdgkv7UnlqOk3CW3UTd6ypbjbBZLWjjf9dvnrN\nGOjCoKxKKHcVSsYqGzrwwk+HwCoCRDaVzGQhy1n4fEp7Y1GGEIkUof8rJIT0ekLT4X/dOAHxklXm\nkrxkcdWF+gZFHiHYoxzHapOMHVPrHZDmZvQwAFz2+kY/7R0hkUe4wOhUaWDqjdPNJuyubALLsthS\nXo9Lx+Tjb1dwDV2T4/R4/srRsmPt/XOGyEogAGfJRHMnl7ExKDsBk/und2t/eqp/ZgKmDsgIyXuT\nnhEO/cqLbimtZHGAMoT85/Fv9uPKNzd1vaGfSBeFNpTViVnDao3oQ0HZQ+imf23Dy6vKUNXYiR3H\nG8TzUGm52PSBGbh0TD7OHpSJ3OQYlPFTGQ2Sz6y7YSmEhJvw+EskhPRqFhv3JazXMi4BAmF08KzB\nrs2lSXgTAkIWxdx5tVU2qVQaF016mU6LHX/4eCdO1HeoPn+gugUPLN2DPXzJ1s+HfS8ZW3OoBpOW\nrMTF/9iAY3XtqGuzYGK/NNk2/TLiZf2E4o1aKK/XOyzcSrhwgX77OQND3hSWRB6xb4uHrwPpp4oy\nhPzjv79U4b0NFdha3oDTfJZgoEn7BtodrHhOoFfJEA4FMSDEn6qU1XDBnVPNJlz2+ibc+/luAIBV\nci4zKDsRf79qNAw6DTITjajjp6YVpjv7XlGGEIkU4fGXSAjp1YQMIelEhoVj8gEAxZkJWHvfTPx+\n1oCQ7BvpPrFkzOraQ4hhgCNPznP7s/ecNwjFmfF47dqxAd1HQkLJ7mDx2bZKfLXrJL7dcwr3L93t\nss2x2jbMf3kdPtlWKT628Wg9Vh+q8fo92s02fC4psxQCT4P4SYBSmZImrzqtBscbuG0T+dKxdn6a\nTnMnd4GeGKNeUkaIJ8LFsqdYojTQuLYbQVAiZ7E5cNenzmPMhrK6oLyvVZJZY7Y5xEwbY9hkCHH/\nL2QICWtWdW1ckGf5Aa7fmjRDaFxf5xS81DiDeFzUaxnx9dTK3wkJR13+JTIMU8AwzGqGYQ4wDLOf\nYZg7VLaZyTBMM8Mwu/j/PRqY3SWERCNh1UVIry1fMh8vXDVafL5venxIRxqT7hFLxhQ9hKwOB/Ra\njceRrLefOxCr7pmJ84Zlu90mWCezhATKuiO1uH/pHvzfl3sBAOV17S7b/O6jnao/e5Rfxe7KPZ/t\nQsljy/Dd3lMAuAvxE3yQJ97gGsx5/KISAEBqHNcc9dqJhUiO1eOO2QMBAO1meYaQdCgAId66aFQe\nrp1YiPvmDPFq+y93ngzwHkU/IbtPcM/nrgHoQJBm1pisdjGwYgiXDCH+/HLdkVpslJxXCA3PhRI3\noSwMAOaPyBVvC8dKANAyDPJSuEbZDOi8lUQGb5Z1bADuYVl2J8MwiQB2MAyznGXZA4rt1rEse4H/\nd5EQEu0a2rkLi1S+dwyVH0QHISDUokj1t9tZ6L0M8Hmqwd9V2UT9Q0hEO3S6VXa/ptUMs80Oo47L\nrntvQzlKFdsI3DV7Vr7+V7uqxfsZCUbUtZnx2Nf7ATgbokoVZybgq99PRUkeN6mrJC8Zux87H6tL\nuYwkISBU18ZdLKV66PlFiDsxei2evHREqHejV1HrPbbvZHPAB3VIy8bv+8I5ITRsAkL8Oeeflu6V\nPf7wV/tk97dXNKr+vLTMXathMKpPCqoaO10WwwgJV13+JbIse4pl2Z387VYABwHkB3rHCCG9x5kW\nro49K8nYxZYkkggne88tOyR73OZgva6tVwsODuHLXAZnu5a7EBJJyiRZPjdMKQLLAs0dzua5j38j\nX3u7ekIhVt87EwDEST2ezH95nez+NRMLZfdjDOqngaMLUlwy+IQAVKvJhkv+sQH3fr4bcQZtWIyN\nJtGpOCNedt/RRf854pkQzJXafKw+4O8rLbWS8pQlHExp8V1nOTocLHKSYgAAV47vI/95SVBco2Hw\nzOUjsWThCIygibgkQvj0l8gwTBGAMQC2qDw9hWGYPQzD/MAwTImbn7+FYZjtDMNsr62lWmBCequ6\nNjOuenMTNh7lUnNrW81INOoQp1K+QCKXuwkiNoejR6Nm+2clAAD2V7d0+zUICbVjtW2yz7DQzLmm\n1Yy/fHMA7WabbOoiwGXdCSPeD572/PmvazPDLrmAfmnRaOQmx8i28SW7R5g+dv27W7GrsgkAF5wN\nl4s6En0KJA3Oga4HEhDP/rm+wuWxjITAL8QJWcJ5iuNPuGQIefNvYGdZsdT2kQuGyZ5LkWQIJRp1\nSDDqcPWEQsp2JxHD679EhmESACwFcCfLssqzkJ0AClmWHQngFQBfqb0Gy7JvsSw7nmXZ8ZmZmd3d\nZ0JIhFv42kZsKW/ANW9vQXVTJ2paTZQdFIXcnQzZ7N5nCKlJ4XuW/H3FYXy67QRKu7gwJiTcHDrd\ninOeX4sDp7jP7oxBmWLfiUv+sQHvbijHB5uOi41KBW1mm9jvQuipctWbm3DeC2td3uPKN5xjpZ+/\nYhQuHp0vlnECwOyhWT4Fc9T+nJs6aBQ4CR47BYS6zeFg8Z+tJ1wej1EpG/W3pg4LNIxrv7FwGTuv\n03S9H3YHi5pWE7KTjEiMkf93SDOElM8REgm8+ktkGEYPLhj0EcuyXyqfZ1m2hWXZNv729wD0DMNQ\nYwdCoojDwWLfyWa/1EQLqywAMOXpVThe34GsxBgPP0GixfaKBnyyrRK1/IjW7pBexP5p6V7MfXGd\nh60JCQ8OB4ub/7UdRQ98h19OOHtR/P2qUfjgxgnitC4hC0LaAPalRVyT/ZZO1wDMlvIGHKlpQ1mN\nvNeQMOHmyJPzcNk4rsQhnw86Dc5OxNvXj/dp/4flJrk8Zvc0M5wQPxMmkhLfLd3pnDL46S2TxNuB\n/Dc1We3YfKwer6wqQ4pKNmK4ZAgZdF0vUNkdLDosdtVG/HGSTM4EmrpIIpA3U8YYAP8EcJBl2Rfc\nbJPDbweGYSbwrxv4olRCSNB8saMKF7yyHi+tOOL3195f3YJsyhCKavurm7GhrA7f7jnl88/eNK2f\n7L5R5SRSrTcCIeGk1WTDioPc+OIHvnQ2LxVKZZUXRycbOwEA//z1eIwuSAEAnD2Yy66+bGwfpMcb\nUNNqErdf+NpG2c8XpMahJC9JFkCdWJyOvX8+H8vumuFzOQPDMLj3/EGyxxwUECJBZLPT5627dlc1\nibelDemlE8D8bcKTK7Dorc0AnJm9Uj3JFPYnbzKEbA4WnRY7Yg2uGVXS8fJxQci4IsTfvAnNTgVw\nHYBzJGPl5zMMcxvDMLfx21wOYB/DMLsBvAxgEcvSWQIh0eSXSm5Fu9EPJQIzBrmWjA6kBsFRbcHL\n63HtO1vQNz2u640VZg/lRs8LF8W5yTF4f/FZsm2O13e4/Bwh4aTFpH7szEjgVs6VjVcP8xk/BWlx\n6Jsej92Pno9rJnBNoWcOzkR9uwUTnlwpbi+dIFTbakZ9uwXJKhdhPSlpyEqSZ3LmJlFDaRI81EOo\n+yobuADz0t9OwfD8ZNw3ZzAAwBqgIFtju0U2YfRvV47CrCFZAIDrJ/fFO9ePD0r/Im9IAzruCBlC\ncWoBIUlASRMmQS5CfNFlXhvLsusBePx0syz7KoBX/bVThJDw85+tlQD8s5rUqnJhpDb+mEQ+Ycy1\noDsNaCf3T0fZk/PwybZK7KpsQlFGPGYOzpJtY+pGKaPDwYKFc5WyttWMt34+invnDBbHfrtjttnR\nbrYjLZ5GbhPvtKpMBXvswmEYW5gKABhflIpLRueJY+L3nWxBjF6DfvykpeQ4ZyBnqEr5lhA4PVDd\nIk4Xm1uS49f/Bp3iYuf1X4316+sT4gn1EOoeq92B/dUtuGxsH4zryx1vLh/XB88tO4TaVjPMNnuX\n33m+uuotZw+zrQ+ei6ykGAzISsDsoVkY1zfNr+/VU970MnpvQzk6rHYkqZSEeVNyRkg4C4/iTUJI\nxHA3PtQXan0wwqWWnPjXjIHydnJCQPHGqf3UNndLp9Xg2omF+Or3U12CQQBgtvr+ubx/6R5Me2aV\neP8fq8vw9rpy/I+/IHfnvQ3lGPzwjxj7xHKf35P0Tg4Hi999tEO8Pzg7EVsePBeLp/YTS7eMOi1e\nXDQGV08oELcbkpOkGkRVW6UWjqHSUfNqGUI9IfQgAoCf75uF9DBZ4Se9QyDLm6LVlzurMPChH1DX\nZkZVozOTVjiuPLfsEG54d5vf37dD0hBf6B+UFKMPu2AQAK+mnr6yqgyN7ZYuM4QIiUT0CSaEeEUY\nV+yPgJDaSrlaXxgS+c7qJz/5Ez4/9yh6kXiDYRixbEypO83Ov9hRhVPNJggVzil8BsbRmja3P3Oq\nuROPf3PA5/civVtzpxUVfFnjU5eOwNLfTUF2knoj/SULR4qZOMqAqkDZ2HRITiJMVte/gaRY/zY4\nnVicjv/+bgpKn5iLwm6UfxLSE5Qh5JvaVjPu/my3eN8sOX+TlkltOub/tq8zBztbA4T7gp83JWMA\nN+UxTqWptLc/T0i4Cu+/UEJIWFj83lacauaal24/3tjF1l1TCwgdq2vv8euS8KPMbvhoywnoNIzf\nA4CmbmQICYSVTOGktdzDZ/G2D3fI7tMFSviqbOjo0TQ7f+qQBGsuG5ePBKPnQI3w96HWbw3gAj3z\nR3DlYMmxehj1WpisDjQo+galxfs/g2dMYWpQRlUTokRTxnyjPP69d4Oz914gR753Wuz4cd+ZgL2+\nv6n9W5w3LNvlsYZ2CzotroH3QP5bEhIM9AkmhHRp9aFa8bahhyshVrtD1vxUMLFf+KURk55TTiE6\n0dCB/NRYr1K0u/LEJcPF275mCEmzKRo7LDjTYsLSHdxY3p8OuD+RVfZu8UfGHPEPlmXxj9Vl+N1H\nO3C0tg3Tn12N6c+u6voHg6CTHyH/8tVjvOrV8dXvp+LqCYUY0SdZ9XmGYfCPa8bihilF+PCmCYjR\nabDzRCPGPrEczZKS3H4ZlMVDItsHN07A7ecMAMBNJCXea+q0iLffvG4cUiU977rTz89bq0prUNdm\nxsMLhmLnI+cF7H38RdkbDXCfnfnj/tMuj+WlUHN9Etn8m0tMCIk60gveK8b1wc9Haj1s3TUhOygr\n0YgayepVYRpduEQjtXHw/soOipG8jtnLwAzLsrjz012yPkGfbqvEK6vKZNu1mW2qWRzKCxKL3YFY\nULZEKP1rYwXe21COxy8ejueWHQIAfL+XO2nvSeaYPwlZaN6OJB6YnYglC0d43IZhGPz5ohIAQIxe\nq5p5WZyZ4OOeEhJeZgzKFAP4d3yyCxePzg/xHkUOab/GOYoG84Ec+f7Ccu44PK5vakQMXlD7t+B6\nFe736ufjjTo8vXAEivgBAIREGsoQIoR4VCbppxKj1/Z4RKmwer2IH58sCORqFQmdDrX0aj8FhC4a\nnYeLR+cB8D4gdLy+w6VptDIYBAAdKoEsANh7shkAMCCLu9CmDKHQe+zr/aio74C1i98Fy7LYx//+\ngq3dzAeEVBqS+kOMXv439Y9rxqJ/ZjyK0ukChUQ+6tHiO5Zl8cS3BwEA3/9xelDfuz8fiHbX8y/c\nCI39pfqkxuKxC4fhqUvlgfkbphSpvsaiCYWYVJweiN0jJODoCowQ4tGBU1xGxIq7z4Zeq0FDuwX1\nbd3vyyGsWA3Klq9cB3K1ioSOWoYQA//8ro06rVg2ZlYpQ1RTXu9dr6oLX13v8fnrJ/cFAGwOQDNO\n4j1peZTJTdmgkF3wr40VuOCV9fj5sOcsx/o2c7ealHvSaeX+DuK66B3UXcqePgtG5mLlPTPDvpkr\nId6gKU7q3lh7FPd9vhsOlV52rWYbTjZ1YmhuEobmJnp8nUSVUerdZbE5oNMyGJCVoBpoiRQMw2Dx\n1H7ISJBnOCkzrQiJBnSEJYR4tO9kM2L1WvTLiBdX6a55e0u3X6/FxF3AZSfFoOLpBeLjtAIYna6Z\nWOjy2F4/ZmnE8P1YvM0QWvyed+N1z7SoBz1T4/T41aRCsWzhiW8PYIcfGq2T7vluzynxdoWiGbgw\nIr26qRMAsLK0BgBw/btbcaxWfZIcy7IY99cV+P1Hv+Dhr/bixve34VRzp8/7VdNqwr83HxfviyVj\nAcoQot7mJJqp9Xjp7a58YxOe/qEUn++oQp3KIt1pfhDIb2f27zIw469/3bKaVgx6+Ad8v/d01Axc\nUJ6bGnT0WSTRhwJChBCP9lc3Y0BWArQaBif5C6tDZ1q79Vo1LSY0tHNNDpNiuEk4wnmenlYAo1Kf\n1DikB7CHgJ4/WTvBj/QOJLuDRVOnFWnxRnGSU02rGb9+d2vA35uoWyZp8Pnq6jIYdRpcwpcRClNi\nTrdwF0bS8sW/fncQN/9rOxrbLZBq6uAC1isOnsG/N5/AqtIaTF7ie2PqWz/cgYe/2iceM4X3jg3Q\ndK6TjYH//BMSKpRBLNfUYcHWigbxvlqvtM+2VQIA8pJjgrZfQu82wPO0zkiiVZybUnsDEo3oU00I\ncavFZMW2ikYxoyM3yXlioVYK5InN7sCEp1bijk92AeAyLQCIE3e0lCEUtZSTxvzZZFJY+fx0e2WP\nXueJi0tw29n98d5i51jeNYdqsL/amc3U1GEBy8IlwNVmtoFlo2M1NJKYrHaslZR/mawOXDomH0sW\njsRjFw7DBSNzAQBm/mKpscMZ/FlVWoMVB8/g693yflLV3cgGUiMEKIW+Rp0BzhAa1zdVvB2o9yAk\nVDQUEJKpbJAfp9otrudjP+zjgjPD89UnFUq1mGyo4QPnPfHC8sM9fo1wo2WUGUJ06UyiD32qCSFu\nfbjpuOz+n+YNEW/bfEwHFpqqAkCiUYcsPrg0p4RbxacMoeilTFfPSQrMiuUjX+3rchudhsF0lXGy\n+amxeGDeEMwanCU+dsN722QlZo189kgKH8yU2k5lY0G3q7IJADBvuLOng9nmQKxBi8VT+yHOoOMf\n4449TR1WWeAE4IJ5UtVN3b8oeuLbA/jDxzvR2G5BPZ95JLy+8P/CPvnb/XO5Y3NxZjwO/GVuQN6D\nkFChcJDcgVPysmu14Q1mmx3XTCx06S/mjr9Lnw1Rkkmj7CdHGUIkGtGnmpBeantFQ5e9MYQRzgLp\nF6HN7tt0pVazs/nrvBHOC7hnLh+JdffPQiytavcaI/t0vWLZHevL6jw+z7IsbA7WJSgAAHl8vxml\nmlaz2AtBmCgmZLV9fPNEMbh0xRubcNGr66Omb0K4O3iqBYve2gwAKEiLEx+XnrwLk7c2H2tAbasZ\nTR0WpMa5ZndJfbrtRLf2x2yz45/ry/HtnlNioArgmug3tFvw3LJDKM6Id5kG5i96rQZHnpyHn+6c\nEZDXJySUIrk5cSAcPMWV7Qsl02rnYyarQ+yx5412laCSGoeDRX2bGVbFewo9iwDggxsnYMuD53r9\n3uFM+R0RLYEuQqToU01IL8KyLBwOFizL4vI3NuHSf2x0+VJXs+bemS6P+ZohJP1STZFclBl1WtkF\nHYk+ymz/u88f5NfX/9/vpwIA5g73PP2jmj9h1Ws1WHf/LHxw4wTxucwEo9uf21XJrZwKfyvCSfiU\nARm4aVo/cbs9Vc14aeWRbvwXEF/tqXIGXS4cmYeL+b5BsXpnBo6RXxl/f2MFbvlwOxwsMHNwpux1\n/r35OA5Uc5MU311fjhUHucbTi6cWYXJxOsYUpsCo03R5nPy/L/eKtxe/78wqu+adLdhazvX6+M2M\n4oBe2Oq1GujoYoVEIYoHOR2rbcM3u6sxJCcRH908CQBgtbuej5msdp8C0N62Aej/0PcY99cVeGDp\nXtnjs/62RrydEqdHagB7BwaTMvuKSsZINKJPNSG9yKK3NmP8kytQwfe3ON1iwsCHfkC72YYf953C\ndr5JoRA0GpabhNlDs1CUEe/yWhYvpzoJpCcbJi9HhJNoIT+bN/qwaumNUQUpiDNoxX4t7kx9mmsO\nbLLaUZAWhxmDnMGBBMnY3XeuHy/7udpWC2paTLA5hICQ86uzKF3+t/EyBYSCoqHdmXGYEqfHkoUj\ncNX4Ajw431nWapScuP9yggsgDclJxN+vGiU+3mqyYf7L6wAA3+xx9hN67MIS/OeWSbhhShHMNgcO\nnmpxuy9f7qzClztPun2+ie9dJP28EUK8p6GIEABueuE5z69FfbsFiTE6cXFCGbA2We2wOVif+okp\nM2HUsCwLoV3e0p1Vsuc6Jed1vp4fhpupA9LF2/OG58iyiqlkjEQj+lQT0otsKW9AQ7tFtpIDACWP\nLcNt/96Jy9/YhJNNnSh+8Hvc98UefoVJfkJx69nFAFxPQLrSKLmAo/Kw3kWZIRSIEcJ6rcbrrLU9\nVa5j76VBqtnDsjFfUtb48Ff7MOGplWju5D7D0jG0ymCp9ESSBI5Q7jpzcCYK0uIQZ9DhmctHIl2S\n6WVUWcnNTYnF4Owk1dds7rC6PDa2kLsQuOjVDW7LAV9ZVSbeVusvJVwoBWrCGCHRjsJBXJBl09F6\n8T7DMGJwwqI4HxPKVoszE7x6ba2GQYekMfWVb27Cn77Y47KdNFtGWvotPTbeNK2feNyMRDsfOQ8f\n3jhRvJ8SZ8DS304R7+tpAAqJQhQQIoTICI2kv9hRBZPV7nIRM6pPCgD1Maee3PzBdgDA2YMysXhK\nvy62JtHEqEhbD8QIYb1W43JSLCWdAvb7WQPE22/8aqxYbiT12rXjxNt1bWYAXEBVeC+p5y4fKd72\npWcD6b6ymjaMLkjB+4snuN1GGcxmGCAr0eh2yl2LyQathsE95zlLGvukOntLTXxqJbZJRj0LpvTn\ngoC7Hzsft8woFh+fW8IFFd9ce4zfHzrlIqQ7KEEIuOCVdeKUVoCb1CqUL9kUJWNCRuP4Iu8CM/EG\nrWzwx9byBny6vRIORRBcOuo+UZJVKwTolywcgUcuGBaRU+G2PnQudjw8G2nxBo/7TyVjJBrRp5oQ\nIsPCeQJQ3WxyyeYZmsutrm/oooGvO09eOhw5yYGZMkXCk7JELBDp/3ot47FkzMw/d//cwZjQL018\nfO7wXLy0aIxX7yFc2CszSfpKysYaJKPNSWDUtpqx8Wg9BmR5Xv1WZqJlJRqh12qQkxyDrQ85G54O\nyUkEALSZrbhpWj/cfu5A8Tlpz5+6NjOueGOT7DX3nWzGR1u4RtTJsXoxiwwAzuI/Z6f5cc4ULCSk\newKxiBDuVpWeweL3tmLW39bg/Q3lOHymTfb8gKwEcXFCmrHd0G7BuiN1SDTqPPbGkzLotOJ3pHTx\nZM3hGvG21e6QTd2UBqEqG7iAUEFq5PaDzEqMkWWYukMTcUk0ok81IUTGrMj8UWYI9cuIR05SDA6d\nae3W61PZRO8zpiBFdj9QGUKeSsbMiglhPZGWIM8wkfbHamx3BoRWHDiDY7Xyk3jScy+uOAwAssCe\nGmUD56QYZzlXVqIzKB1r0MJqd8BkdSDB6DoWfvldM2TlZzOfW40PNlUAcGZUCo7XdYi3lU1aI3HV\nnJBwMCxXvcwzmt34/nasPlSL8rp2/PmbA+LjGQkGPL1wBO44d5BYviTNjp3w5AqsKq1Bfmpsl03s\nF47Nx5CcRGg13KREi80h+x49Ue88np1slE+llU7ffG0NVzabneRdACqS0XGcRCMKCBHSC9gdrNfN\nbqsaO2T3lWUXANdDxeHgJpZ520sons808mYFhkSXJy4ZjqW/nSzeD8T5lE7LuC0Z+/lwLf7LN8BU\n6yvji9EFKS79EaR9FRokAaGbP9iOc55f26P3I65ONHRgWG4Srhxf0OW23/9xupjVqJzA9dKi0QC4\nfhsVde0AoBoQGpidiF9PKRLvV9R34INNx+FwsNh7Ut6P6sH5Q8XbdN1AiH8wDINbzy7u8fE7GuQk\nx2DRhEIYdBpxBLrF5oDZZhebSQNA6emuF+1euHI0frxzBnQaDb7ceRKDHv4Bl0uyIKXfqRX17eLt\n9HgDrPz7fLzlONYd4TLG6fyOkMhER1ZCeoHNx+rxwvLD4n2DVoOrJ8gvpoTyCmHssiBf0kNDoNUw\nsDlYXP7GRgx95Eev9oFhGCyeWuTjnpNoEKPXYlzfNCTHchkagRi9bdBqcFxywgpwqe8OB4vr390q\nrrCm93AU7lkqPRlmDXFOj2ox2WC1O2SZQd6O8yXeOdVsQt9070oThuUliT1+ZgzMkD138eh8LBiR\nC5YFzvv7zwDUA0IAcMHIXNn9jAQD1hyuwQG+V4fQO6gwPQ7r7p+Fn++bhcVTqVcaIf6i13g/OCDS\nnWzqdPtctiS7USgZ+9fGCgx++EfsPNEoPvf7Wf29fj9pFdRuviE1IM8Yr5JkCKXFG2Dnp25KM2ZS\nYl2b6hNCwh8FhAjpBT7a4ixrmD4wA8vvnoH8FHmg54c7pqv+7EyVUclaDQM7y2LniSavTtBMVjva\nzDZk0OpRr7bi7rOx5t6ZAXnt0tOt2HeyBVuOOaewTH92NYof/F62XW6Ka4DTnY9unujyWEqca0Ap\nzqBDxdML8MTFJQCApg4r7v18t/i8tBEo6Zk1h2pQVtOG3GTvf49CY+hWlcCcMuOgJF+9NGVEfrLs\n/on6Dtz4Ptco/745g2WZQQVpcShMj0O8UYftD8/2ej8JIe4ZdBrYHazbaX+RzGS14+2fj+EQn9Uz\n49nVqttlJBhw52xn03s9f/w6UsMtQKw5VCs+11WPNSmtm0UaIUOood0iLrhcN6kvijLisb+6BatL\na2RtAKicipDIRAEhQnqB7/eeFm/fMqMYfdPjkarIlFDLBOqbHoesJNcG0FqGkU2feGOS5lA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w4H4LzwZSggRIJMGXwItekDM8XbZps9hHviXzuON+LFFUfw8Fd7AQAN7RbY3GRACb+Tn++bhbIn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\n2CuCUUlMcNVDohJCIiIiIiIi0nVaTQgZY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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b1ca0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "env.reset(STARTING_DAYS_AHEAD)\n", "results_list = sim.simulate_period(total_data_in_df, \n", " SYMBOL, agents[0], \n", " learn=False, \n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", "show_results([results_list], data_in_df, graph=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's run the trained agent, with the test set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First a non-learning test: this scenario would be worse than what is possible (in fact, the q-learner can learn from past samples in the test set without compromising the causality)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 484 days of simulation to go.\n", "Date 2016-12-28 00:00:00 (simulating until 2016-12-30 00:00:00). Time: 0.1946086883544922s. Value: 10566.060000000001.Epoch: 3\n", "Elapsed time: 11.412826538085938 seconds.\n", "Random Actions Rate: 6.869609028782328e-10\n", "Sharpe ratio: 1.2605807833904492\n", "Cum. Ret.: 0.056606000000000156\n", "AVG_DRET: 0.0001152856771454073\n", "STD_DRET: 0.0014517938182464988\n", "Final value: 10566.060000000001\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b2d6cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Loc5VU5tPjW1+FWaO0tD1UEhJZquCjrTRuT/6NBKdRZWSpp719ZSO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\niafnj9sXEe8HbiT7zdniliy4os4Xh5TSdET05a/9bwN+J799KaWUImKEr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b134240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TEST_DAYS_AHEAD = 20\n", "\n", "env.set_test_data(total_data_test_df, TEST_DAYS_AHEAD)\n", "tic = time()\n", "results_list = sim.simulate_period(total_data_test_df, \n", " SYMBOL,\n", " agents[0],\n", " learn=False,\n", " starting_days_ahead=TEST_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", "toc = time()\n", "print('Epoch: {}'.format(i))\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))\n", "print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", "show_results([results_list], data_test_df, graph=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And now a \"realistic\" test, in which the learner continues to learn from past samples in the test set (it even makes some random moves, though very few)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 484 days of simulation to go.\n", "Date 2016-12-28 00:00:00 (simulating until 2016-12-30 00:00:00). Time: 0.2298264503479004s. Value: 10566.060000000001.Epoch: 3\n", "Elapsed time: 12.214732885360718 seconds.\n", "Random Actions Rate: 4.23704263256852e-10\n", "Sharpe ratio: 1.2605807833904492\n", "Cum. Ret.: 0.056606000000000156\n", "AVG_DRET: 0.0001152856771454073\n", "STD_DRET: 0.0014517938182464988\n", "Final value: 10566.060000000001\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b14e160>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Loc5VU5tPjW1+FWaO0tD1UEhJZquCjrTRuT/6NBKdRZWSpp719ZSO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\niafnj9sXEe8HbiT7zdniliy4os4Xh5TSdET05a/9bwN+J799KaWUImKEr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb55b2694e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "env.set_test_data(total_data_test_df, TEST_DAYS_AHEAD)\n", "tic = time()\n", "results_list = sim.simulate_period(total_data_test_df, \n", " SYMBOL,\n", " agents[0],\n", " learn=True,\n", " starting_days_ahead=TEST_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", "toc = time()\n", "print('Epoch: {}'.format(i))\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))\n", "print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", "show_results([results_list], data_test_df, graph=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What are the metrics for \"holding the position\"?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sharpe ratio: 0.44271542660031676\n", "Cum. Ret.: 0.1070225832012679\n", "AVG_DRET: 0.00025103195406808796\n", "STD_DRET: 0.009001287260690292\n", "Final value: 223.53\n" ] } ], "source": [ "print('Sharpe ratio: {}\\nCum. Ret.: {}\\nAVG_DRET: {}\\nSTD_DRET: {}\\nFinal value: {}'.format(*value_eval(pd.DataFrame(data_test_df['Close'].iloc[TEST_DAYS_AHEAD:]))))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle\n", "with open('../../data/simple_q_learner_fast_learner_full_training.pkl', 'wb') as best_agent:\n", " pickle.dump(agents[0], best_agent)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "cap_env", "language": "python", "name": "cap_env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

cpatrickalves/simprev

notebooks/CalculoEstoqueMetodoProb.ipynb

1

62576

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sugestão de metodologia para cálculo de Intervalos de Confiança \n", "\n", "Conforme mencionado na LDO de 2018, o modelo oficial do governo se define como determinístico: \n", "\n", "“[...] *ou seja, a partir da fixação de um conjunto de variáveis, o modelo determina de maneira única seus resultados* [...]\n", "\n", "Como se trabalha com probabilidades, não necessariamente todos os eventos previstos podem acontecer. O modelo da LDO é determinístico por trabalhar apenas com médias (ex: média de pessoas que se aposentarão) e não considera diferentes cenários onde isso pode não ocorrer, ou seja, situações diferentes do comportamento médio.\n", "\n", "Este documento busca apresentar uma forma diferente de se projetar estoques considerando diferentes cenários onde nem sempre os segurados irão se aposentar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Método determinístico\n", "\n", "Considere o seguinte cenário:\n", "* Segurados = 1000\n", "* Probabilidade de se aposentar = 0.35\n", "\n", "A forma mais simples e utilizada na LDO de se calcular o número de aposentados é:\n", "\n", "*num_ap = segurados x Probabilidade*\n", "\n", "*num_ap = 1000 x 0.35 = * ***350***\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Método probabilístico\n", "\n", "Como se trata de uma probabilidade, o evento de *se aposentar* pode ou não ocorrer para cada segurado.\n", "Diante disso, uma outra forma de se calcular o estoque de aposentados, seria calcular individualmente a probabilidade \n", "de cada segurado se aposentar.\n", "\n", "Esse cálculo individual seria feito através de números aleatórios, onde para cada segurado gera-se um número aleatório o qual é \n", "comparado com a probabilidade de se aposentar, conforme apresentado abaixo:\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "\n", "n_segurados = 1000\n", "prob = 0.35\n", "\n", "# Lista que salva a quantidade de aposentados para cada cenário\n", "lista_nap = []\n", "\n", "# Lista de seeds -> 50 cenários\n", "seeds = range(0,50)\n", "\n", "# Executa 50 cenários (seeds) diferentes\n", "for seed in seeds:\n", "\n", " # Define o seed para geração de números aleatórios\n", " np.random.seed(seed)\n", " # Gera 1000 números aletórios entre 0 e \n", " lista_na = np.random.rand(n_segurados)\n", "\n", " # Número de aposentados\n", " num_ap = 0\n", "\n", " # Determina quantos irão se aposentar para o cenário\n", " for na in lista_na:\n", " # calcula a probabilidade de cada um dos segurados se aposentar\n", " # Se o número aleatório for menor ou igual a probabilidade o segurado irá se aposentar\n", " if na <= prob: \n", " num_ap += 1\n", "\n", " lista_nap.append(num_ap)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observem que diferente do método simples, para cada cenário (seed) ocorre uma situação diferente, ou seja, o número de segurados que se aposenta é diferente:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[353, 347, 369, 353, 383, 333, 370, 330, 360, 335, 341, 358, 334, 343, 350, 360, 344, 347, 364, 331, 349, 343, 345, 344, 344, 357, 348, 344, 343, 363, 367, 334, 362, 347, 328, 361, 350, 314, 341, 350, 367, 360, 338, 337, 356, 385, 338, 325, 324, 356]\n" ] } ], "source": [ "print(lista_nap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se calcularmos a média, temos um valor bem próximo ao do **Método determinístico**." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Média: 348.5\n" ] } ], "source": [ "media = np.mean(lista_nap)\n", "print('Média: {}'.format(media))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Porém, com diferentes cenários, podemos calcular medidas de dispersão, como o desvio padrão." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Desvio padrão: 14.497241116847025\n" ] } ], "source": [ "std = np.std(lista_nap)\n", "print('Desvio padrão: {}'.format(std))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualizando em um gráfico:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Qp06dDGmvvvpqvv322yxlAhg1ahQtW7akUKFCJCYmUrdu3YCzQKpXr57jc3IT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jssDMMwjLypTYxUV8PSpbrctWthx9QY+c9/1KumogJGjYJ+/WDYMHjuOaiqSnp0tWNixDAMw8ib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\nDCOSJKkrw4gkSerKMCJJkrr6AQ/ri9nx/oxKAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2ae598f1780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "medias = [350] * len(seeds)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(seeds, lista_nap, '--', linewidth=2, label='Método Probabilístico')\n", "ax.plot(seeds, medias,label='Método Determinístico')\n", "ax.set_ylabel('Número de Aposentados')\n", "ax.set_xlabel('Seed')\n", "ax.set_title('Cálculo do estoque usando diferentes métodos')\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aplicando o método probabilístico no cálculo dos estoques (onde as probabilidades são aplicadas), teremos para cada seed, uma projeção/resultado diferente.\n", "\n", "Na média o resultado vai ser o mesmo obtido pelo método original, porém teremos diversas curvas ou pontos para cada ano, o que nos permite calcular medidas de dispersão como desvio padrão e ** Intervalos de Confiança ** para os resultados de receita e despesa.\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "210.16999999999999" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.var(lista_nap)\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

Z0m6ie/Zombie_Code

Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week4/Week+4.ipynb

1

52061

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Distributions in Pandas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "0\n", "1\n", "1\n", "0\n" ] } ], "source": [ "for i in range(5):\n", " coinflip = np.random.binomial(1, 0.5)\n", " print(coinflip)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.503" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.binomial(1000, 0.5)/1000" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chance_of_tornado = 0.01/100\n", "np.random.binomial(100000, chance_of_tornado)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "101 tornadoes back to back in 2739.72602739726 years\n" ] } ], "source": [ "chance_of_tornado = 0.01\n", "\n", "tornado_events = np.random.binomial(1, chance_of_tornado, 1000000)\n", " \n", "two_days_in_a_row = 0\n", "for j in range(1,len(tornado_events)-1):\n", " if tornado_events[j]==1 and tornado_events[j-1]==1:\n", " two_days_in_a_row+=1\n", "\n", "print('{} tornadoes back to back in {} years'.format(two_days_in_a_row, 1000000/365))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8477519368060076" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.uniform(0, 1)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.7418276998824014" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.normal(0.75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Formula for standard deviation\n", "$$\\sqrt{\\frac{1}{N} \\sum_{i=1}^N (x_i - \\overline{x})^2}$$" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.96843810415836251" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distribution = np.random.normal(0.75,size=1000)\n", "\n", "np.sqrt(np.sum((np.mean(distribution)-distribution)**2)/len(distribution))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.96843810415836251" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.std(distribution)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.03293898548813612" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy.stats as stats\n", "stats.kurtosis(distribution)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.1586539371740313" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.skew(distribution)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9433800012480495" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chi_squared_df2 = np.random.chisquare(10, size=10000)\n", "stats.skew(chi_squared_df2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.3149434950712884" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chi_squared_df5 = np.random.chisquare(5, size=10000)\n", "stats.skew(chi_squared_df5)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fd217b186a0>" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sWbMmy+PceOONJCUlsW/fvtROiuB03jx79my6cyxMdevWxVpLSEhItvNz1K1bFwA/P79s\nr11gYCDe3t6Z/u737NmT7n1xuQZFRX0cCtjKn1cyc/tMDscd5u0tb7PjWPEbSiNSlsTEXD3j5c6d\nO/nss8+uuledUfXq1WnevDlz5swhPj4+tfzLL79k9+7d6er269ePK1eupA4bTCsxMZHY2FjAuY/t\n4eHBtGnT0tV55513cnxOLpeLvn37smjRInbt2nXV+rTn3K9fPw4fPsx77713Vb2EhAQuXHAm9sos\nabn55pux1mY52gOge/fuqcNQ03rjjTcwxtCjR48cn1d+9OnTB5fLxfjx4zNdf/r0aQBatmxJ3bp1\nef3111NHqKSVcu1cLhddu3Zl6dKl6YZzRkdHExUVlW6b4nINiopaHArJ5LsmEzY3zN1hiBSY6JPR\nJfLY/fv3x9vbm9tvv52goCB27drFe++9R8WKFZk0adI1t580aRI9e/akXbt2DB06lFOnTvHPf/6T\npk2bcu7cH7cz77jjDoYPH87kyZPZsWMHXbp0wcvLix9//JGFCxcydepU+vTpQ1BQECNHjuTNN9+k\nd+/edOvWjZ07d7J8+XICAwOvau7Oqpl68uTJrF27lrZt2zJs2DAaN27M6dOn2bp1K6tXr079AHzo\noYf4+OOPefzxx1mzZg3t2rUjMTGR6OhoPvnkE6KiomjRogUTJkxg/fr19OjRgxtvvJHjx48zbdo0\ngoODad++fZbXJzQ0lIcffpgZM2Zw5swZOnbsyJYtW5g7dy59+vShY8eOOfk15VudOnX4xz/+wYsv\nvsiBAwe455578PPz4+eff2bp0qUMHz6cZ555BmMM//73v+nevTtNmjRhyJAh1KhRgyNHjrBmzRr8\n/f1ZtmwZAOPHj2fFihW0b9+eESNGcPny5dTf/XfffVfsrkFRUeIgItkK8AnAx8uHgUsGujUOHy8f\nAnyyfrZBVu69914++OADIiMjiYuLIzAwkPvuu48xY8ZQp06da27ftWtXPvnkE1566SVefPFF6tat\ny+zZs1m6dGlqD/wU06ZNo1WrVkyfPp3Ro0fj6elJSEgIgwYNol27dqn1XnvtNXx9fXnvvfdYtWoV\nt956K1988QUdOnS4agbErO6bBwUF8fXXXzNhwgSWLFnCtGnTqFq1Kk2aNOG1115Lt/2yZcuIjIxk\n7ty5LF26FB8fH+rUqUNERAQNGjQAoHfv3hw8eJBZs2YRExNDQEAAnTp1Yty4cfj5+WV7jWbOnJnu\nulSvXp3Ro0czZsyYq+rmty9Eds+JGDVqFA0bNiQyMjK15adWrVp069aNXr16pdbr2LEjmzdvZuLE\nibz77rucO3eO6tWr07ZtW4YPH55ar1mzZkRFRfHMM88wduxYatasyYQJEzh69Gi6xCE31yCr+HNb\n7k6muHW6yIwxpgWwdevWrdd8mlp+zP9+Pg8sfoCVD63krvfv4vvHv6dpUNN0dZr+qyl/qvMn9p/Z\nj8u4WDZgWbr1S/cs5d4F97Jq0CrC5obx9V++pnWN1oUWs0hB2LZtGy1btiSrv7GS/JCrkiI2NpYq\nVarw8ssvX3OYqEha1/r7TVkPtLTWbsvv8dTicA1fH/mavTF7qeBZgb6N+7o7HBG3CPYPLtUf2kUt\ns2crREZGYoyhU6dO7glKJIeUOGTjbMJZwuaGcSnRmWt8Vu+r54AXEcmtBQsWMHv2bLp3707FihXZ\nsGEDH330Ed26deO2225zd3gi2VLikI1Xv3qVS4mXWNRvEQMXDyTuYpy7QxKRUiA0NBQvLy+mTJlC\nXFwc1apVIyIigokTJ7o7NJFrUuKQiaZBTbnnpnv4NfZXutTtwh033lHsOqeISMl1yy23XDWkT6Sk\nUOKQiYrlKrKk/xJ3hyEiIlLsaAKoQlLew5k5rvOczoS8FcL+0/vdHJGIiEj+KXEoJA0DGrLw/oU8\n3/55DsYeZNtv+R4BIyIi4nZKHApR38Z9Gdl2pLvDEBERKTC5ShyMMY8ZY3YaY2KTl03GmG4Z6kww\nxhw1xlwwxnxpjKmXYX15Y8y7xpgYY0y8MWahMebq59CKiIhIsZPbFodfgVFAC6AlsBpYZoxpBGCM\nGQX8FXgUaAOcB74wxpRLs4+3gB5AX+AO4AZgUT7OQURERIpIrkZVWGv/m6HoJWPM48CtQDQwEpho\nrf0PgDFmEHAcuAf42BhTCRgKDLDWrkuuMwSINsa0sdZ+na+zERERkUKV5z4OxhiXMWYA4ANsMsbU\nBqoDq1LqWGvjgC1AylRorXCSlbR19gKH0tQRESn2Bg8eTO3atd0dRokzZcoU6tati6en5zWfPfT+\n++/TqFEjypUrx3XXXVdEEV7buHHjcLnKbhfBXM/jYIxpCmwGKgDxwL3W2r3GmNsAi9PCkNZxnIQC\noBpwKTmhyKpOkVmxfwUjV4zEWss9N93DLdVvKeoQREqEQ4cgxr3PuCIgAILz8LiMb7/9ltmzZ7N2\n7Vp++eUXqlatyq233so//vEP6tevn+d4iuNTC4u7qKgoRo0axaBBgxg/fjwBAVk/7XTv3r0MGTKE\n7t2788ILL+Dj41OEkWavrP/u8zIB1B7gZsAfuA+Ya4y5o0CjKiKLdi8i7mIc9a6rx+wds7mlmxIH\nkYwOHYJGjeDCBffG4eMD0dG5Tx5effVVNm3axP33309oaCjHjh3jnXfeoUWLFmzZsoXGjRsXTsBy\nlTVr1uDh4cHMmTPx8PDItu7atWux1vL222+rZaeYyXXiYK29Avyc/Ha7MaYNTt+G1wCD06qQttWh\nGrA9+fUxoJwxplKGVodqyeuyFRERgb+/f7qy8PBwwsPDc3saqYL9g/lzvT+zN2ZvnvchUprFxDhJ\nw7x5TgLhDtHRMHCgE0tuE4e//e1vzJ8/H0/PP/6769evH82aNWPy5MnMnTu3gKMtXBcuXChW375z\n4/jx43h7e18zaUipC1CpUqVr1s3saaNl1fz585k/f366stjY2AI9RkHcpHEB5a21B3A+/MNSViR3\nhmwLbEou2gpcyVCnIRCMc/sjW5GRkXz66afplvwkDSKSc40aQYsW7lnyk7Dceuut6ZIGgHr16tGk\nSROio6NztI+lS5fStGlTvL29CQ0NZenSpZnWs9by1ltvpdatXr06jz32GGfPnr2q3rhx46hRowa+\nvr6EhYURHR1NSEgIQ4cOTa03Z84cXC4X69evZ8SIEVSrVo1atWqlrj969ChDhw6levXqVKhQgaZN\nmzJr1tVP8b106RJjx46lfv36VKhQgeDgYEaNGsWlS5fS1fvyyy/p0KEDVapUwc/Pj5tuuonRo0df\n8/okJiYyceJE6tWrR4UKFahduzajR49Ot3+Xy8WcOXM4f/48LpcLDw+PLJO22rVrM27cOAACAwNx\nuVxMmDABgJCQEHr16kVUVBStW7fG29ubGTNmpG47b948WrVqhY+PD1WrViU8PJzDhw9fdYwtW7bQ\nrVs3KleujK+vL506dWLTpk1X1du4cWPqcerXr5/uWLm9BmnjX7duHa1bt8bHx4fQ0FDWrVsHwOLF\niwkNDcXb25tWrVqxY8eObK781cLDw6/6nIyMjMzVPq4lVy0OxphXgOU4nRn9gAeBjkCX5Cpv4Yy0\n2A/8AkwEDgPLwOksaYyZCbxpjDmD00diKvCVRlSISFE6fvw4TZs2vWa9qKgo7rvvPpo2bcrkyZM5\ndeoUQ4YMoWbNmlfVffTRR5k7dy5Dhw5l5MiRHDhwgHfeeYcdO3bw1VdfpX7Tfv7555kyZQq9e/em\nS5cu7Ny5k65du3Lx4sVMYxgxYgRBQUGMHTuW8+fPA3DixAnatm2Lh4cHTz31FAEBASxfvpxHHnmE\n+Ph4nnrqKcBJUu6++242bdrE8OHDuemmm/j++++JjIxk3759LF68GIDdu3dz991307x5cyZOnEj5\n8uXZv39/ph+mGT3yyCPMnTuXfv368eyzz7JlyxYmTZrEnj17WLTIGW0/b948pk+fzjfffMPMmTOx\n1nL77bdnur+3336bOXPmsHTpUqZPn46vry+hoaGA079gz549PPDAAwwfPpxHH32Uhg0bAvDyyy8z\nZswYBgwYwLBhwzh58iRTp06lY8eObN++PbX1YvXq1XTv3p1WrVqldnScNWsWd955Jxs3bqRVq1YA\n/PDDD3Tt2pWgoCAmTJjA5cuXGTduHEFBV089lJNrkBL/vn37ePDBBxk+fDgPPfQQU6ZMoVevXkyb\nNo3Ro0fzxBNPYK3llVdeoX///uzdW8xaxK21OV6Af+Pcpvgdp3UhCrgzQ51xwFHgAvAFUC/D+vLA\nO0AMTuLwCRB0jeO2AOzWrVttQfrLsr/YNu+1sS+vf9kGvhZoP/zuQ8s47LmL566q6/Oyj337f2/b\nJu82sU8vf9r2/LCn7TW/11X1lkQvsYzDnjx/0lprbVxCnGUcdsEPCwo0dpGCtHXrVpvV39jWrdaC\n89NdCjqG999/3xpj7OzZs69Zt3nz5rZGjRo2Pj4+tWzlypXWGGNr166dWrZhwwZrjLEfffRRuu2j\noqKsMcbOnz/fWmvt8ePHrZeXl+3bt2+6euPHj7fGGDtkyJDUstmzZ1tjjO3YsaNNSkpKV/+RRx6x\nNWrUsGfOnElXHh4ebqtUqWITEhJSz9XT09Nu2rQpXb3p06dbl8tlN2/ebK219q233rIul8uePn36\nmtckrZ07d1pjjB0+fHi68r///e/W5XLZtWvXppYNHjzY+vn55Wi/48aNsy6Xy546dSpdeUhIiHW5\nXPbLL79MV37w4EHr6elpJ0+enK58165d1svLy06aNCm1rEGDBrZ79+7p6iUkJNg6derYrl27ppbd\nc8891sfHxx4+fDi1bM+ePdbT09O6XK48XYOU+Lds2ZJalvJvxNfXN92xZsyYYV0ul123bl3WF8pm\n//ebdj3QwubiMz+rJVe3Kqy1f7HW1rHWeltrq1tru1hrV2eoM85ae4O11sda29Vauz/D+ovW2iet\ntQHWWj9r7f3W2hO5icNd5uycw69xv6Yr2396Pw8ufpAHFj3A7B2z3ROYiOTYnj17+Otf/0q7du0Y\nNGhQtnWPHTvGzp07GTx4MBUrVkwtDwsLu6pT5cKFC6lcuTJhYWGcOnUqdbnllluoWLEia9asAWDl\nypUkJiby+OOPp9v+ySefzDQGYwzDhg27qhf/4sWLufvuu0lMTEx3vC5dunD27Fm2bduWGlejRo1o\n0KBBunqdO3fGWpsaV+XKlQFYsmRJyhe2HPn8888xxhAREZGu/G9/+xvWWv7734zT/+Rf7dq1ueuu\nu9KVLVq0CGst999/f7rzDAoKon79+qnnuX37dvbt20d4eHi6evHx8YSFhbF+/XoAkpKSiIqK4t57\n76VGjRqpx2nYsCFdu3bN1zVo3Lgxbdq0SX3ftm1bwPl3lfZYbdu2xVrLzz//THGix2rn0Jg7xhD1\ncxRtarRhcPPBvLTmJQCmbpnKsj3LuN7vej778TPev/d9N0cqIlk5fvw4PXr0oEqVKnzyySfXHFJ3\n8OBBwOkTkVHDhg3Zvn176vt9+/Zx9uzZTJuxjTGcOOF8Pzp06FCm+6xSpQpVqlTJNI6QkJB070+e\nPMnZs2eZMWMG06dPz/Z4+/btY8+ePQQGBmZbr3///sycOZNhw4bx/PPPExYWRp8+fbjvvvuyvU4H\nDx7E5XJddT7VqlWjcuXKqdewIGU2ymL//v0kJSVl+rsyxlCuXLnUekCWSaPL5SI2NpaEhAR+//33\nLH/3y5cvT32f22sQnKGHb8otlIy3v1IGA5w5cybTWN1FiUMOjWo/ilHtR2W6rk6VOgxpPoQxa8cU\ncVQiklNxcXF069aNuLg4Nm7cSPXqBTt1TFJSEtWqVePDDz/M9Bt7Zh/cOeXt7X3VsQAGDhzIww8/\nnOk2KX0CkpKSaNasGZGRkZnGldLZskKFCqxfv541a9bw3//+lxUrVrBgwQLCwsKIioq6ZpJVlPMa\nZLwe4Jyny+VixYoVmU7OlNJilHLt3njjDW6++eZM91+xYkUSEhJyHVdOr0FWo0qyKs9NC1BRKJOJ\nw8R1Exm7diwWS+eQzoV6LA+XB+U8ytF/YX8GLRnEioEr6BTSqVCPKSLpXbx4kZ49e7J//35WrVqV\n2pnuWm70n8IvAAAgAElEQVS88UbA+daeUcYOa3Xr1mXVqlXcfvvtlC9f/pr73L9/f+prgNOnT+f4\nm2VgYCB+fn4kJiZy5513Zlu3bt26fPfdd3TunLP/6zp37kznzp15/fXXmTRpEi+99BJr1qzJ8jg3\n3ngjSUlJ7Nu3L911PXHiBGfPnk13joWpbt26WGsJCQnJtJUgbT0APz+/bK9dYGAg3t7emf7u9+zZ\nk+59cbkGRaVMzpm57uA6Wt7Qkhk9ZzDnnjmFeiwfLx/WPryWGT1n4OnyZMvhLYV6PBFJLykpiX79\n+rFlyxYWLlyY7t7ytVSvXp3mzZszZ84c4uPjU8u//PJLdu/ena5uv379uHLlSuqwwbQSExNTx9KH\nhYXh4eHBtGnT0tV55513chyXy+Wib9++LFq0iF27dl21PibNNJ/9+vXj8OHDvPfee1fVS0hI4ELy\nzF6ZJS0333wz1tosR3sAdO/ePXUYalpvvPEGxhh69OiR4/PKjz59+uByuRg/fnym60+fPg1Ay5Yt\nqVu3Lq+//nrqCJW0Uq6dy+Wia9euLF26NN1wzujoaKKiotJtU1yuQVEpky0OAPWuq8ewlsOK5Fi3\n1bqN22rdxgurXiiS44nIH5555hk+++wzevXqRUxMDB988EG69Q8++GC220+aNImePXvSrl07hg4d\nyqlTp/jnP/9J06ZNOXfuXGq9O+64g+HDhzN58mR27NhBly5d8PLy4scff2ThwoVMnTqVPn36EBQU\nxMiRI3nzzTfp3bs33bp1Y+fOnSxfvpzAwMCrmruzaqaePHkya9eupW3btgwbNozGjRtz+vRptm7d\nyurVq1M/AB966CE+/vhjHn/8cdasWUO7du1ITEwkOjqaTz75hKioKFq0aMGECRNYv349PXr04MYb\nb+T48eNMmzaN4OBg2rdvn+X1CQ0N5eGHH2bGjBmcOXOGjh07smXLFubOnUufPn3o2LFjtte3oNSp\nU4d//OMfvPjiixw4cIB77rkHPz8/fv75Z5YuXcrw4cN55plnMMbw73//m+7du9OkSROGDBlCjRo1\nOHLkCGvWrMHf359ly5YBMH78eFasWEH79u0ZMWIEly9fTv3df/fdd8XuGhSVMps4iEju5HCupGJ3\n7J07d2KM4bPPPuOzzz67av21EoeuXbvyySef8NJLL/Hiiy9St25dZs+ezdKlS1N74KeYNm0arVq1\nYvr06YwePRpPT09CQkIYNGgQ7dq1S6332muv4evry3vvvceqVau49dZb+eKLL+jQocNVMyBmdd88\nKCiIr7/+mgkTJrBkyRKmTZtG1apVadKkCa+99lq67ZctW0ZkZCRz585l6dKl+Pj4UKdOHSIiImjQ\noAEAvXv35uDBg8yaNYuYmBgCAgLo1KkT48aNw8/PL9trNHPmzHTXpXr16owePZoxY67u95XfvhDZ\nPSdi1KhRNGzYkMjIyNSWn1q1atGtWzd69eqVWq9jx45s3ryZiRMn8u6773Lu3DmqV69O27ZtGT58\neGq9Zs2aERUVxTPPPMPYsWOpWbMmEyZM4OjRo+kSh9xcg6ziz225WxXEmM7CXijgeRzC5oTZAQsH\npL7PyTwOGaXM4/Dk50/aZv9qZt/c9Kat+ErFq+ZxSKvqq1Xt5A2TM9mbiHtlNw784EFrfXyceRTc\nufj4OLGUVmfPnrXGGPvKK6+4OxQpYYp6Hge1OOTD10e+5ocTP+Dr5evuUEQKTXCw842/pD4dszjK\n7NkKkZGRGGPo1KmTe4ISySElDnkUcWsEr296HYBBNw/it/jf3ByRSOEJDi49H9rFwYIFC5g9ezbd\nu3enYsWKbNiwgY8++ohu3bpx2223uTs8kWwpccijO2vfyZ21/xjKE7m5YB8iIiKlV2hoKF5eXkyZ\nMoW4uDiqVatGREQEEydOdHdoItekxEFEpIjdcsstVw3pEykpyuQ8DiIiIpI3ShxEREQkx5Q4JDt/\n+TxL9ixxdxgiIiLFmhIHnI6O13lfx7qD6+jZoCcVPCtceyMREZEySJ0jgVtr3sqvEb+6OwwRt4t2\n5/SQIpInRf13q8RBRAgICMDHx4eBAwe6OxQRyQMfHx8CAgKK5FhKHESE4OBgoqOj0z1VUURKjoCA\nAIKLaJY2JQ4iAjjJQ1H9xyMiJZc6R4qIiEiOKXEQERGRHFPiICIiIjlWJvo4/L/t/48NhzbgYTwY\n32m8u8MREREpscpE4vCXT/9CnSp1OBx3mCDfIHeHIyIiUmKVmVsVo9qNokalGu4OQ0REpEQrM4lD\nceDt5c3o1aPxedmHBT8scHc4IiIiuabEoQh9Fv4ZkV0jCfAJYPWB1e4OR0REJNfKXOLw46kfOXnh\npFuO3bx6c55s+yTVKlZzy/FFRETyq0x0jkzRvHpzFkUvAuCBpg+4ORoREZGSp0wlDh/1/YizCWcB\nCPApmoeBiIiIlCZlKnHw8vAi0DfQ3WGIiIiUWGWuj4OIiIjknRIHERERyTElDgUoySZx6sIpd4ch\nIiJSaHKVOBhjXjDGfG2MiTPGHDfGLDHGNMhQZ5YxJinD8nmGOuWNMe8aY2KMMfHGmIXGmBI9F3Sg\nbyAXLl/gL5/9Bb9yfpT3KO/ukERERApcbjtHdgDeAb5N3nYSEGWMaWSt/T1NveXAYMAkv7+YYT9v\nAX8G+gJxwLvAouT9l0gPNnuQmpVqknAlgTpV6uBX3s/dIYmIiBS4XCUO1truad8bYwYDJ4CWwMY0\nqy5aazOdZckYUwkYCgyw1q5LLhsCRBtj2lhrv85NTMWFMYZOIZ3cHYaIiEihym8fh8qABU5nKO+U\nfCtjjzHmX8aY69Ksa4mTsKxKKbDW7gUOAbflMx4REREpRHmex8EYY3BuOWy01u5Os2o5zm2HA0Bd\nnNsZnxtjbrPWWqA6cMlaG5dhl8eT14mIiEgxlZ8JoP4FNAbapS201n6c5u0uY8z3wE9AJ2BNPo5H\nREQE/v7+6crCw8MJDw/Pz25FRERKhfnz5zN//vx0ZbGxsQV6jDwlDsaYfwLdgQ7W2t+yq2utPWCM\niQHq4SQOx4ByxphKGVodqiWvy1JkZCQtWrTIS8giIiKlXmZfprdt20bLli0L7Bi57uOQnDT0Bjpb\naw/loH5NoCqQkmBsBa4AYWnqNASCgc25jUdERESKTq5aHIwx/wLCgV7AeWNMyvOhY621CcYYX2As\nTh+HYzitDK8CPwJfAFhr44wxM4E3jTFngHhgKvBVSR1RkVcXr1xk82EnV2oW1IyqPlXdHJGIiEj2\ncnur4jGcURRrM5QPAeYCiUAoMAhnxMVRnIRhjLX2cpr6Ecl1FwLlgRXAE7mMpcR77svnmPr1VAA6\nBHdg/ZD1bo5IREQke7mdxyHbWxvW2gSgWw72cxF4Mnkps3479xvtarWjcWBj1vySr36jIiIiRULP\nqnAzHy8fqlSo4u4wREREckSJg4iIiOSYEgcRERHJsfxMACVlWFISnD/vvK5YEYzJvr6IiJQOanGQ\nPHn0UahUyVkmTHB3NCIiUlTU4iB58s030KULnDrlvBYRkbJBLQ6SZw0aQM2a7o5CRESKkhIHERER\nyTElDiIiIpJjShxEREQkx5Q4iIiISI4pcRAREZEcU+IgIiIiOabEQURERHJMiYOIiIjkmBIHERER\nyTElDiIiIpJjelaFmyzbu4wrSVdocX0Ld4ciIiKSY2pxcINX73qVbvW60bNBTyZ2nujucERERHKs\n1LY4nLt0jrb/bkv0yWgsFt9yvu4OKdWdte/kztp3pr5fHL34qjqnfz/N75d/x8fLhyreVYoyPBER\nkSyV2sQh5kIMu0/u5um2T9O6RmsGNB3g7pBybG/MXlrOaMn5y+epVL4S24dvp06VOu4OS0REpPTf\nqujRoAcPNHsAlyk5p3ow9iDnL59nUtgk4i7GcTjusLtDEhERAcpA4lCStanRxt0hiIiIpKPEQURE\nRHJMiYOIiIjkmBIHERERyTElDsWEtZbLiZdJsknuDkVERCRLShyKAb/yfvx05ifK/aMcnWZ3KvTj\nJSTATz85S5LyFBERyQUlDsXAc+2eY96983j45ofZ9OumQj/e/fdDvXrOMmZMoR9ORERKESUOxUA5\nj3I8GPogt9a8tUiOt3cv9O8PLVtCVBS8/z4cOQKJibBggfN+69YiCUVEREqYUjtzpGQvOBhuugnG\nj4dBg6BzZ6clYsQIZ32FCnDqFPj4uDdOEREpXtTiUIaNHQsXL8Jf/gJxcRAfD1WqwAcfOP0grlxx\nd4QiIlLcKHEow4yBcuXAwyN9mZeX+2ISEZHiLVeJgzHmBWPM18aYOGPMcWPMEmNMg0zqTTDGHDXG\nXDDGfGmMqZdhfXljzLvGmBhjTLwxZqExJii/JyMiIiKFK7ctDh2Ad4C2wF2AFxBljPFOqWCMGQX8\nFXgUaAOcB74wxpRLs5+3gB5AX+AO4AZgUR7PQTJx/Dg0bgyVK0P9+nBYz8kSEZECkKvEwVrb3Vr7\nvrU22lr7PTAYCAZapqk2Ephorf2PtfYHYBBOYnAPgDGmEjAUiLDWrrPWbgeGAO2MMXqqUwH58UeI\njnZGT+zfD7t2uTsiEREpDfLbx6EyYIHTAMaY2kB1YFVKBWttHLAFuC25qBXOaI60dfYCh9LUyZdt\nv21j3S/rCmJXRS7JJvHt0W8LbH/9+xfYrkRERPI+HNMYY3BuOWy01u5OLq6Ok0gcz1D9ePI6gGrA\npeSEIqs6ebbz2E5av9eaJJtExXIVCfYPzu8ui0zDqg1xGRejV4+mhl8N/Mv7X3ObuDhn7gWADh2c\nIZYiIiKFJT/zOPwLaAy0K6BYrikiIgJ///QfpuHh4YSHh6e+P/37aZJsEhuGbKBxYGOu876uqMLL\nt861O3P0b0dJuHyJj2dXZdabF2DPY9iHs97m+edh2jTndYMGzuROIiJSNs2fP5/58+enK4uNjS3Q\nY+QpcTDG/BPoDnSw1v6WZtUxwOC0KqRtdagGbE9Tp5wxplKGVodqyeuyFBkZSYsWLXIU4/UVry9R\nSUOKIN8gVq+Gv0fAdQFeEDONXUN30DEk8/rnz0P79hAWBv/+d5GGKiIixUzGL9MA27Zto2XLllls\nkXu57uOQnDT0Bjpbaw+lXWetPYDz4R+Wpn4lnFEYKQ9h2ApcyVCnIU4ny825jac0Spl4KXKGk0cl\nXjFujEZEROQPuWpxMMb8CwgHegHnjTHVklfFWmsTkl+/BbxkjNkP/AJMBA4Dy8DpLGmMmQm8aYw5\nA8QDU4GvrLVf5/N8yiwPDzh2zLldcfvtMHRo7rb9/nv45Zf0k0GJiIhklNtbFY/hdH5cm6F8CDAX\nwFr7mjHGB5iOM+piA/Bna+2lNPUjgERgIVAeWAE8kdvgy6KhQyHl9tX//d8f5U8+Cb//Djt2wJw5\nzvMnMrp40ZlWOjExfflLL0HFimAt/OlPcPZs4cUvIiIlW64SB2ttjm5tWGvHAeOyWX8ReDJ5kVz4\n8kvo2BH27YNVq5wposGZ6OmVV2DuXFi+PPNt27eHb5NHel6XpvvH9dfDq6/+8f6TTwondhERKfn0\ndMwSqG1buHAh99vt2AHDh0PPnvDnP+du2127nImkKlSALl1yf2wRESkdlDiUYHv2OD/Ll8/5Njff\n7CQOuREf7yQr588772fPzt32IiJSeujpmCXAsr3LeOAfn/Ds3xNT+x+EhcF33zlLWFj22+fXpUtO\n0jBrFvj6wqlThXs8EREpvtTiUIwF+gYCEH1iH6v++RKVrrtIrVre9OwJrVvD2LFFG4+/P7iUaoqI\nlGn6GCjGyns69yBm954NuOg3ciu7dztJg4iIiDsocRAREZEc062KUurkSXdHAKdPO3NL+Po6w0VF\nRKTkU4tDKRPodItgwABn6GTFiu6JY9cuqFnTWWrXhkOHrr2NiIgUf0ocSpCfzv7E4ujFJCYlZlnn\nz3+GjRudSaC+/RZuuKEIA0zjwAGntWHSJGcmyiNH3BOHiIgULCUOJYBJnh5yzYHV9P24L1O3TM22\nfrt20K0bNGlSFNFlr00bd0cgIiIFSYlDCeAyzq9pWo//4wa/GziboIdJiIiIeyhxKEG8vbxTkwgR\nERF30KiKYuSXX+Dhh+G335z3KQ+wKk6MgchIiIlxHrYlIiJli76+FiMrVsD69dC5M7z9tjMaAaBv\nX+enl1fRxnP58tWP4J45E7p3dxKcZ58t2nhERMT91OJQzHh4wPTpf7xfsMB5hLafH/TrB6PeKfwY\n/PycnwEBznDOtI/Zvu8+Z8nO4cN/tJqIiEjposShmOvXr+iP2bUrLFsGW7fChAnOEE9jICjo2tuu\nWOG0SFgLlSqBt3fhxysiIkVHiUMZsXIlJCXlrK4x0KuXs/TvD8ePOzM/3nLLtbfdt8+5pbJiBQQH\nQ0JC/uIWEZHiRYlDKVelCjRoAK+/DuXLQ/Pmudu+cWNnuZazZ2HaNPjqK+d2S+fOTvmuXbmPWURE\nii8lDqWcry/s3u10cjSm8DpYTprkJCcBAX905hQRkdJHiUMZ4OHhLIXp8mW46Sa1MIiIlHalJnH4\n5ewvPLDoAY6dOwagiZJEREQKQalJHFYfWM3mw5t5ss2T3BRwEyGVQ9wdUplx5QpcupT37a2Fc+ec\n1z4+hd86IiIieVfqvpa/1e0tRrQekfpgKClclSvDF1/Au+86r/MiMtIZulmpEjzwQMHGJyIiBavU\ntDiIe7z9tjPPA0D79nnbx7ffOk/yrF8fvvmm4GITEZGCp8RB8sXf35nrIb+qVXOGfe7cmf99iYhI\n4VHiUELtPrmbj374CIABTQfQODAHky242aefOq0LXl7wzDPOUFERESlZlDiUUH/9/K9sObIFgA2H\nNrDm4TVujih7v/8O99zjTEh15gyUKwejRrk7KhERya1S1zmyrLiSdIW+jfpyX+P7uJJ0xd3hXJO1\nzvLOOxAY6IzEEBGRkkeJg4iIiOSYblWUMN6e3ry84WWSbBKNAhqRkKinSImISNFR4lDCLOq3iLW/\nrAVgYOhAnv7iafcGJCIiZYoSh2IgMRGWL8/ZUMRm1ZrRrFqzwg9KREQkE0ocioE33vhjhEHLlu6N\nRUREJDu57hxpjOlgjPnUGHPEGJNkjOmVYf2s5PK0y+cZ6pQ3xrxrjIkxxsQbYxYaY4LyezIl1alT\nEBICJ07AV1+5OxoREZGs5WVUhS+wAxgB2CzqLAeqAdWTl/AM698CegB9gTuAG4BFeYil1PD0dIYp\nli/v7khERESylutbFdbaFcAKAJP1k6QuWmtPZrbCGFMJGAoMsNauSy4bAkQbY9pYa7/ObUwiIiJS\nNAprHodOxpjjxpg9xph/GWOuS7OuJU7CsiqlwFq7FzgE3FZI8YiIiEgBKIzEYTkwCLgTeA7oCHye\npnWiOnDJWhuXYbvjyeskly5euciRuCNcvHLR3aFcxdsbjIG77nLepzyfIjYWjhxxZpMUEZGSo8AT\nB2vtx9ba/1hrd1lrPwV6Am2ATgV9LIFAn0C+OfoNNSNr0vuj3u4O5yp16kBUFMyYAQsWQK9eTl+O\nKVOgZk3nsdwiIlJyFPpwTGvtAWNMDFAPWAMcA8oZYyplaHWolrwuSxEREfj7+6crCw8PJzw8Y9/L\nsuOVsFe4q85dzPtuXurEUMVNSmtDiqgo+O47ePpp+OEH98QkIlIazZ8/n/nz56cri42NLdBjFHri\nYIypCVQFfksu2gpcAcKAJcl1GgLBwObs9hUZGUmLFi0KL9gSqJxHObrV68b/Dv+v2CYOGd1wg7NU\nruzuSERESpfMvkxv27aNlgU4SVCuEwdjjC9O60FKn4U6xpibgdPJy1icoZXHkuu9CvwIfAFgrY0z\nxswE3jTGnAHiganAVxpRISIiUrzlpcWhFc4tB5u8vJFcPgdnbodQnM6RlYGjOAnDGGvt5TT7iAAS\ngYVAeZzhnU/kIRYREREpQnmZx2Ed2Xeq7JaDfVwEnkxeREREpIQorHkcJIcuX3YeclVWJSU5i4iI\nlAxKHNwoMhLKlXMecuXn5+5oip6fH8ya5QzTLIvnLyJSEunpmG60eTM0aQLPPQcdOrg7mqI3Zw6s\nXOm87tZNczqIiJQEShzc7IYbYNCggtuftZZle5cRfzGe2lVq0z64fcHtvIAV9LmLiEjhU+JQisRd\njOO2mbex5cgWADyMBwdGHqCWfy03RyYiIqWF+ji4wcaN8Nhj8O23BbfPu+rchV95P/ae2kvPBj1Z\n+dBKEm0iv1/5veAOIiIiZZ5aHNzgmWfg558hJAQeeaRg9tk+uD1HnjmS+n7DwQ0Fs2MREZE0lDi4\ngbXQty9Mn+7uSERERHKnxN+qWH1gNQGvBfDE509QzqOcu8MpNsp7lgeg5YyW1H+nPofjDrs5opxJ\nSoL4eGf58UeoXRs8PZ2fP/4IFy64O0IRkbKtxCcOaw6s4UrSFSZ2nkjUwChcpsSfUoFoU6MNH/T5\ngJFtR7L/9H52ndjl7pCuqXJlOHgQKlVyloYN4ZdfYMAA52fDhnDddbBli7sjFREpu0rFrQr/Cv48\ne/uz7g7jmo4ccR4jXcBPOM3SA80e4HDcYV7e8HLRHDCfIiKcloW0M2nWqQOtW8PIkfDTTxAe7lzD\ntm3dF6eISFlWKhKH4sRaWLwYTp+GWrWciY1S3HUX7NnjvP7rX90TX3Hm6Qn33Zf5utatoVUrJ3EQ\nERH3UeJQwFauTP/h98MPzuyQACdPwgsvwBNPOJMfiYiIlDTqEFDAzp93fq5a5fzM2JnP3x9q1ABj\nijYuERGRgqDEQURERHJMiUMplzLKZOinQ2n+f8358dSPbo5IRERKMiUOpdwNfjcwu/dsHmz2IN8d\n/45VP69yd0giIlKCqXNkGfBw84cBiPxfpJsjERGRkk4tDiIiIpJjanGQEu3XX2HZMud1z57Og8NE\nRKTwKHGQEu2xx2D5cmd465IlfwyDFRGRwqHEQUq03393ZpOsUAGio90djYhI6ac+DiIiIpJjShyk\nRPHwgOeeg7p14auv3B2NiEjZo8RBSgxj4NNPYcQIiIlx+jbkxNmzzpNJT58u3PhERMoCJQ5F4MgR\n2LwZLl92dyQlX/fuMHEiVKmSs/oHDzojLWrWdJ5WumtXoYYnIlLqKXEoZAkJcMstcPvtEBfnNLFL\n0TlyBGJj4ZVXnAeO/fKLuyMSESnZNKqikF2+7DxO+/XXoVcvqF/f3RGVTW3auDsCEZHSQS0ORaRW\nLSUNIiJS8ilxEBERkRxT4lCGGAyTv5rMrf++la1Ht7o7HBERKYGUOJQhs++ZTbe63YiOiWbBrgXu\nDkdEREogJQ6F7OhRd0fwhweaPcD0u6dTzbeau0MREZESKteJgzGmgzHmU2PMEWNMkjGmVyZ1Jhhj\njhpjLhhjvjTG1Muwvrwx5l1jTIwxJt4Ys9AYE5SbOC4nXmbtL2v5JfaX3J5CkahcGcqXh4cect5X\n02d1gTt4EM6c+eN9bCysXQsnTrgtJBGRUi8vLQ6+wA5gBGAzrjTGjAL+CjwKtAHOA18YY8qlqfYW\n0APoC9wB3AAsyk0QL656kc5zOjPvu3k0rNowD6dRuIKDYccOWL0avv0WOnZ0d0SlS8OGMG+ec40b\nNnSW3buhc2fo1s3d0YmIlF65nsfBWrsCWAFgjDGZVBkJTLTW/ie5ziDgOHAP8LExphIwFBhgrV2X\nXGcIEG2MaWOt/Toncfx27jfa1GjDB30+4Aa/G3J7GkXippucRQre0qXO5E4Adeo401H36QPvvgsf\nfeTe2ERESrMC7eNgjKkNVAdWpZRZa+OALcBtyUWtcBKWtHX2AofS1MkRHy8f6l1XDx8vn3xGLiWN\ntzfUq+csLpeTONSrB1WrujsyEZHSraA7R1bHuX1xPEP58eR1ANWAS8kJRVZ1REREpBjSqAoRERHJ\nsYJ+VsUxwOC0KqRtdagGbE9Tp5wxplKGVodqyeuyFBERgb+/PwDbfttGwpUE5pebT3h4eEHFX2Zc\nSrxE/MV4KparSOZdVUREpKSZP38+8+fPT1cWGxtboMco0MTBWnvAGHMMCAO+A0juDNkWeDe52lbg\nSnKdJcl1GgLBwObs9h8ZGUmLFi0AGLh4IEfijyhpyIPKFSrz9pa3eXvL27x616s81+65LOv+Fv8b\nZxLOULFcRYL9g4swShERya3w8PCrPhe3bdtGy5YtC+wYuU4cjDG+QD2clgWAOsaYm4HT1tpfcYZa\nvmSM2Q/8AkwEDgPLwOksaYyZCbxpjDkDxANTga9yOqJC8mdRv0Vs+nUTL294mW+OfpNlvUOxh2jy\nryacu3QOT5cn/3vkf7S8oeD+8YmISMmTlz4OrXBuO2zF6Qj5BrANGA9grX0NeAeYjjOawhv4s7X2\nUpp9RAD/ARYCa4GjOHM6SBGo5V+L/k37c73f9dnWO3H+BOcunWNqt6lcSbrCodhDRRRh/lkLCxbA\nsmVZ1/npJ3jvPWc5daroYhMRKcnyMo/DOq6RcFhrxwHjsll/EXgyeRE3+uHED4xZM4a+jfpyc/Wb\nM63TJKhJEUeVf8uWwYABzut69eD6THKkAQOcybkA1q+H998vuvhEREoqjaoow/o26sv5S+d5c/Ob\nDP/PcHeHU6DOn3d+XrgA+/ZBlSqZ13nmGeje/Y/6IiKSPSUOZdijLR/lUMQhBoYO5ErSFXeHIyIi\nJYASB8HDePD9ie9p8E4Dno161t3hiIhIMabEQXjpjpd4uu3TXO93PbN2zHJ3OCIiUowpcRCu97ue\nV//0Kj3q93B3KCIiUswpcRAREZEcU+IgZcpnn8HMmZCY6O5IRERKpoJ+VoVIsVS5MrRqBbNnw/Tp\n7o5GRKTkUuIgZYK3N3yTPLt2hQowbRocPuzemERESiLdqpAyZ8wY8PeHtm1h8GB3RyMiUrKoxUHK\nnBdfdBYREck9tThIOolJiRyJO8K5S+fcHYqIiBRDShwkVaBPILEXY6kZWZPm/9eci1cuujskEREp\nZqo6wnMAAB9tSURBVHSrooBY6wz127jR3ZHk3eDmg7mx8o2s/HklkzZO4mJiyUwcfv8d/vc/d0ch\nIlI6KXEoILNnw9ChzuuGDaFiRbeGkyfGGO6sfSenLpxydyh5dsstEB8P//wnNGsGXl45227fPnjj\nDejRA266qXBjFBEpyXSrIp9++w0iImDWLPD1hdjY/9/encdFVe4PHP88LAqJqKCCadfILfdd09xy\nq9TSXDJzKfNetdJEb2WZv/JW95a5ZaUtVpqZWFlmmZZLmrtCuAupuYMLbmiACMz5/fEMCDLCIDNz\nZuD7fr3OS84634dnnPlyzrPA7t26y19R89mOz3hp9UtcSb1idig31b27roPERIiKAh87UuMHHoAT\nJ+Dll6WXhRBC5EcSh0KaPFmPCXDxIjz9NAQGQokSZkflGK+ufRWAMiXLMLjBYI4lHuOdTe/w2Y7P\nTI4sbwEBBauH0aPh0iUYPhxiYqB3b/jkE+fGKIQQnkoeVRSSxQI1a+q7DEVFl2pdGFBvAJeuXuKZ\nZs9QP6Q+8x+ZD0DgW4FYDIvJETrH8OFw5Ajs3w9r1uh1IYQQOUniIHIp61eWhX0W3nR/hiWDtIw0\nfL3tbEDgIRo0gJ9/1m0dXn/d7GiEEMI9yaMKUSClS5bmxdUvUuLNEny1+yuzwxFCCOFikjiIAlk9\neDVf9PqCKoFV2Hqy+PR5jI2Fdetgxw6zIxFCCHNJ4iAKpHaF2gxpOIQg/yCzQ3GZo0ehUSO47z5o\n0gQ2bDA7IiGEMI8kDkLk4+JFSE2FL77Q62fOmBuPEEKYSRIHIexUubL+9+OPYcwYnVAIIURxI4mD\nnd59Vw8u9NBDurueKH5Kl9ZdNC9e1CNTfubew1kIIYRTSOJgp1de0aMLrlsHc+aYHY1whdOnoX17\nePRRve7rq+82REVBmTJ6fhIhhChuJHEogH/+E6pWhWvX9HwI8sVRdBmGHgRq/Xro2FGP7dCoUe7j\nLBb9XpD3gxCiuJDEoYDKloXZs/WQxv/9r9nRmOvC1QvsT9hPuiXd7FAcqmxZnQgMGgT+/jB1Kowb\nB0rlPvaZZ/R7ITAQXnvN9bEKIYSrSeJQQF9/DYsW6VkYIyPNjsY8VQKrsHDPQurOrsvzK583OxyH\nGjoUlizR9bx5s27bcDORkdC5M7RoUbzfD0KI4kOGnC6gypWhf39YsMDsSMwV0SeCfWf3MX71eA5d\nOGR2OA7l5QW9etl/fI0acPYsJCU5LyYhhHAXkjjk49tvYc8e3a7hRjExelrt4iiwZCCt7mhFkH9Q\nkXtUIYQQ4ubkUUUezp/XLepnz4awMD1yYKbeveHqVd3yPrPVvShevv5ajyophBDFiSQOecjI0P/O\nnQsHDkD9+tf3DR0Kx4/rZeJEc+IT5nn+efDzgzp1YMgQs6MRQgjXkcRBFIq3lzdrj66l5vs1eXvj\n22aH4zITJsDGjXreinvuMTsaIYRwHYcnDkqp15RSlhuW/Tcc87pSKl4playUWqWUqu7oOIRrvN3p\nbZ5t/iyBJQP5cveXZocjhBDCyZx1x2EvEAKEWpc2mTuUUuOBUcBwoAWQBPyqlCrhpFiEE9UIrsE7\nXd6hXdV2ZocihBDCBZzVqyLdMIyEm+wbA7xhGMYyAKXUEOAM0Av4Jr8Ln08+z8bjGzlx+QReSp60\nCCGEEK7krG/eGkqpOKXUX0qpBUqpOwCUUmHoOxBrMg80DOMysA1oZc+Fhy4dSq+ve7H+2HoahdgY\nA1gIkxw7pie/io01OxIhhHAeZ9xx2Ao8CfwJVAImAeuVUvXQSYOBvsOQ3RnrvnydTznPY/Ue470H\n3iP4tmBHxSxcYPH+xUTGReLr7cuL975IYMlAs0NymE6dYNkyPd12/fqwc6fZEQkhhHM4PHEwDOPX\nbKt7lVLbgWPAo0Ch/hYbO3YsMYkxnChxgmFfDANgwIABDBgwoDCXFQ5y8vJJHv/ucTqFdWJYk2E5\n9qWkpdDv236ElAohITmBgBIBvNTmJZMidbynn9bLCy/Ajz+aHY0QoriKiIggIiIix7bExESHvobT\nR440DCNRKXUAqA6sAxS64WT2uw4hwI78rjVjxgxG7xxNreBafN7zc2eEK27Rk42eZM/ZPUTFR/FD\n7A+5EgcDPXXk9PunM/bXsWRYMswIUwghijRbf0xHR0fTtGlTh72G01sXKqUC0ElDvGEYR4DTQKds\n+wOBlsBmZ8cinKdBSANWDV7F6BajzQ5FCCGEEzljHIcpSql2SqmqSqnWwBIgDVhkPeRdYKJS6iGl\nVH1gPnASWOroWIQwQ1oaxMVBcrLZkQghhOM541FFFWAhEAwkABuBewzDOA9gGMY7SqnbgI+BssAG\n4EHDMGxMIyWEZ6lQAY4cgSpV9NTr0dFmRySEEI7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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd217a32278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "output = plt.hist([chi_squared_df2,chi_squared_df5], bins=200, histtype='step', \n", " label=['2 degrees of freedom','5 degrees of freedom'])\n", "plt.legend(loc='upper right')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hypothesis Testing" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('grades.csv')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>student_id</th>\n", " <th>assignment1_grade</th>\n", " <th>assignment1_submission</th>\n", " <th>assignment2_grade</th>\n", " <th>assignment2_submission</th>\n", " <th>assignment3_grade</th>\n", " <th>assignment3_submission</th>\n", " <th>assignment4_grade</th>\n", " <th>assignment4_submission</th>\n", " <th>assignment5_grade</th>\n", " <th>assignment5_submission</th>\n", " <th>assignment6_grade</th>\n", " <th>assignment6_submission</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>B73F2C11-70F0-E37D-8B10-1D20AFED50B1</td>\n", " <td>92.733946</td>\n", " <td>2015-11-02 06:55:34.282000000</td>\n", " <td>83.030552</td>\n", " <td>2015-11-09 02:22:58.938000000</td>\n", " <td>67.164441</td>\n", " <td>2015-11-12 08:58:33.998000000</td>\n", " <td>53.011553</td>\n", " <td>2015-11-16 01:21:24.663000000</td>\n", " <td>47.710398</td>\n", " <td>2015-11-20 13:24:59.692000000</td>\n", " <td>38.168318</td>\n", " <td>2015-11-22 18:31:15.934000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1</td>\n", " <td>86.790821</td>\n", " <td>2015-11-29 14:57:44.429000000</td>\n", " <td>86.290821</td>\n", " <td>2015-12-06 17:41:18.449000000</td>\n", " <td>69.772657</td>\n", " <td>2015-12-10 08:54:55.904000000</td>\n", " <td>55.098125</td>\n", " <td>2015-12-13 17:32:30.941000000</td>\n", " <td>49.588313</td>\n", " <td>2015-12-19 23:26:39.285000000</td>\n", " <td>44.629482</td>\n", " <td>2015-12-21 17:07:24.275000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>D0F62040-CEB0-904C-F563-2F8620916C4E</td>\n", " <td>85.512541</td>\n", " <td>2016-01-09 05:36:02.389000000</td>\n", " <td>85.512541</td>\n", " <td>2016-01-09 06:39:44.416000000</td>\n", " <td>68.410033</td>\n", " <td>2016-01-15 20:22:45.882000000</td>\n", " <td>54.728026</td>\n", " <td>2016-01-11 12:41:50.749000000</td>\n", " <td>49.255224</td>\n", " <td>2016-01-11 17:31:12.489000000</td>\n", " <td>44.329701</td>\n", " <td>2016-01-17 16:24:42.765000000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>FFDF2B2C-F514-EF7F-6538-A6A53518E9DC</td>\n", " <td>86.030665</td>\n", " <td>2016-04-30 06:50:39.801000000</td>\n", " <td>68.824532</td>\n", " <td>2016-04-30 17:20:38.727000000</td>\n", " <td>61.942079</td>\n", " <td>2016-05-12 07:47:16.326000000</td>\n", " <td>49.553663</td>\n", " <td>2016-05-07 16:09:20.485000000</td>\n", " <td>49.553663</td>\n", " <td>2016-05-24 12:51:18.016000000</td>\n", " <td>44.598297</td>\n", " <td>2016-05-26 08:09:12.058000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5ECBEEB6-F1CE-80AE-3164-E45E99473FB4</td>\n", " <td>64.813800</td>\n", " <td>2015-12-13 17:06:10.750000000</td>\n", " <td>51.491040</td>\n", " <td>2015-12-14 12:25:12.056000000</td>\n", " <td>41.932832</td>\n", " <td>2015-12-29 14:25:22.594000000</td>\n", " <td>36.929549</td>\n", " <td>2015-12-28 01:29:55.901000000</td>\n", " <td>33.236594</td>\n", " <td>2015-12-29 14:46:06.628000000</td>\n", " <td>33.236594</td>\n", " <td>2016-01-05 01:06:59.546000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " student_id assignment1_grade \\\n", "0 B73F2C11-70F0-E37D-8B10-1D20AFED50B1 92.733946 \n", "1 98A0FAE0-A19A-13D2-4BB5-CFBFD94031D1 86.790821 \n", "2 D0F62040-CEB0-904C-F563-2F8620916C4E 85.512541 \n", "3 FFDF2B2C-F514-EF7F-6538-A6A53518E9DC 86.030665 \n", "4 5ECBEEB6-F1CE-80AE-3164-E45E99473FB4 64.813800 \n", "\n", " assignment1_submission assignment2_grade \\\n", "0 2015-11-02 06:55:34.282000000 83.030552 \n", "1 2015-11-29 14:57:44.429000000 86.290821 \n", "2 2016-01-09 05:36:02.389000000 85.512541 \n", "3 2016-04-30 06:50:39.801000000 68.824532 \n", "4 2015-12-13 17:06:10.750000000 51.491040 \n", "\n", " assignment2_submission assignment3_grade \\\n", "0 2015-11-09 02:22:58.938000000 67.164441 \n", "1 2015-12-06 17:41:18.449000000 69.772657 \n", "2 2016-01-09 06:39:44.416000000 68.410033 \n", "3 2016-04-30 17:20:38.727000000 61.942079 \n", "4 2015-12-14 12:25:12.056000000 41.932832 \n", "\n", " assignment3_submission assignment4_grade \\\n", "0 2015-11-12 08:58:33.998000000 53.011553 \n", "1 2015-12-10 08:54:55.904000000 55.098125 \n", "2 2016-01-15 20:22:45.882000000 54.728026 \n", "3 2016-05-12 07:47:16.326000000 49.553663 \n", "4 2015-12-29 14:25:22.594000000 36.929549 \n", "\n", " assignment4_submission assignment5_grade \\\n", "0 2015-11-16 01:21:24.663000000 47.710398 \n", "1 2015-12-13 17:32:30.941000000 49.588313 \n", "2 2016-01-11 12:41:50.749000000 49.255224 \n", "3 2016-05-07 16:09:20.485000000 49.553663 \n", "4 2015-12-28 01:29:55.901000000 33.236594 \n", "\n", " assignment5_submission assignment6_grade \\\n", "0 2015-11-20 13:24:59.692000000 38.168318 \n", "1 2015-12-19 23:26:39.285000000 44.629482 \n", "2 2016-01-11 17:31:12.489000000 44.329701 \n", "3 2016-05-24 12:51:18.016000000 44.598297 \n", "4 2015-12-29 14:46:06.628000000 33.236594 \n", "\n", " assignment6_submission \n", "0 2015-11-22 18:31:15.934000000 \n", "1 2015-12-21 17:07:24.275000000 \n", "2 2016-01-17 16:24:42.765000000 \n", "3 2016-05-26 08:09:12.058000000 \n", "4 2016-01-05 01:06:59.546000000 " ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2315" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "early = df[df['assignment1_submission'] <= '2015-12-31']\n", "late = df[df['assignment1_submission'] > '2015-12-31']" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "assignment1_grade 74.972741\n", "assignment2_grade 67.252190\n", "assignment3_grade 61.129050\n", "assignment4_grade 54.157620\n", "assignment5_grade 48.634643\n", "assignment6_grade 43.838980\n", "dtype: float64" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "early.mean()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "assignment1_grade 74.017429\n", "assignment2_grade 66.370822\n", "assignment3_grade 60.023244\n", "assignment4_grade 54.058138\n", "assignment5_grade 48.599402\n", "assignment6_grade 43.844384\n", "dtype: float64" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "late.mean()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy import stats\n", "stats.ttest_ind?" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=1.400549944897566, pvalue=0.16148283016060577)" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.ttest_ind(early['assignment1_grade'], late['assignment1_grade'])" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=1.3239868220912567, pvalue=0.18563824610067967)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.ttest_ind(early['assignment2_grade'], late['assignment2_grade'])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=1.7116160037010733, pvalue=0.087101516341556676)" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.ttest_ind(early['assignment3_grade'], late['assignment3_grade'])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

koverholt/notebooks

dask/create-cluster.ipynb

1

2533

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a Dask cluster using Coiled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll create a Dask cluster with Coiled:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating Cluster. This takes about a minute ...Checking environment images\n", "Valid environment image found\n" ] } ], "source": [ "import coiled\n", "cluster = coiled.Cluster(n_workers=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's point the `distributed` client to the Dask cluster on Coiled and output the link to the dashboard:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dashboard: http://ec2-18-220-201-179.us-east-2.compute.amazonaws.com:8787\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/koverholt/miniconda3/lib/python3.7/site-packages/distributed/client.py:1136: VersionMismatchWarning: Mismatched versions found\n", "\n", "+---------+--------+-----------+---------+\n", "| Package | client | scheduler | workers |\n", "+---------+--------+-----------+---------+\n", "| numpy | 1.18.5 | 1.19.5 | 1.19.5 |\n", "+---------+--------+-----------+---------+\n", " warnings.warn(version_module.VersionMismatchWarning(msg[0][\"warning\"]))\n" ] } ], "source": [ "from dask.distributed import Client\n", "client = Client(cluster)\n", "print('Dashboard:', client.dashboard_link)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can connect to this Dask cluster from other notebooks and clients to run distributed computations in the cloud.\n", "\n", "That was easy!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }

bsd-3-clause