Datasets:

codeparrot

/

github-jupyter

like 5

SyrakuShaikh/python

learning/scientific_computation/Cython/cb_RNG1.ipynb

1

2602

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gpl-3.0

ericmjl/Network-Analysis-Made-Simple

archive/1-introduction.ipynb

1

8283

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Before We Start!\n", "\n", "1. Look at the instructions on the whiteboard.\n", "1. Github repository for these notebooks: **github.com/ericmjl/Network-Analysis-Made-Simple/**\n", " 1. Please clone the repository if you'd like to do the hands-on coding activities.\n", "1. Some of the coding activities are going to be hard! Be ready to discuss the problem with your fellow Pythonistas, or even better, **pair code**.\n", "1. If you are using legacy Python, you may wish to pair up." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Quiz!\n", "\n", "In the list comprehension:\n", "\n", " [s for s in my_fav_things if s[‘name’] == ‘raindrops on roses’]\n", " \n", "What are a plausible data structure for `s` and `my_fav_things`?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "my_fav_things = []\n", "my_fav_things.append({'name': 'raindrops on roses', 'line': 1})\n", "my_fav_things.append({'name': 'whiskers on kittens', 'line': 1})\n", "my_fav_things.append({'name': 'bright copper kettles', 'line': 2})\n", "\n", "[s for s in my_fav_things if s['name'] == 'raindrops on roses']" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Prerequisites\n", "\n", "Use the `checkenv.py` script provided in the repository to determine whether you need to install any new dependencies. You may do so while we quickly go through some background information." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Network Basics\n", "\n", "*All your relational problems are belong to networks.*\n", "\n", "Networks, a.k.a. graphs, are an immensely useful modelling tool to model complex relational problems. \n", "\n", "Networks are comprised of two main entities:\n", "\n", "- Nodes: commonly represented as circles. In the academic literature, nodes are also known as \"vertices\".\n", "- Edges: commonly represented as lines between circles" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Edges denote relationships between the nodes. \n", "\n", "> The heart of a graph lies in its edges, not in its nodes.\n", "> (John Quackenbush, Harvard School of Public Health)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In a network, if two nodes are joined together by an edge, then they are neighbors of one another.\n", "\n", "There are generally two types of networks - directed and undirected. In undirected networks, edges do not have a directionality associated with them. In directed networks, they do." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Examples of Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Facebook's network: Individuals are nodes, edges are drawn between individuals who are FB friends with one another. **undirected network**.\n", "2. Air traffic network: Airports are nodes, flights between airports are the edges. **directed network**.\n", "\n", "Can you think of any others?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Take-Homes\n", "\n", "It is my hope that when you leave this tutorial, practically, you will be equipped to:\n", "\n", "- Use NetworkX to construct graphs in the Jupyter environment.\n", "- Visualize network data using node-link diagrams, heat maps, Circos plots and Hive plots.\n", "- Write basic algorithms to find structures and paths in a graph.\n", "- Compute network statistics." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Take-Homes (cont'd)\n", "\n", "From a broader perspective, I hope you will be able to:\n", "\n", "- Think in terms of \"interactions\" between entities, and not just think about the entities themselves.\n", "- Think through statistical problems in network analysis." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tutorial Format\n", "\n", "- Student notebooks for coding exercises.\n", "- Instructor versions for reference.\n", "- Feel free to skip ahead of myself if I'm too slow for you." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Credits\n", "\n", "Much of this work is inspired by Prof. Allen Downey (Olin College of Engineering) and Prof. Jukka-Pekka Onnela (Harvard School of Public Health).\n", "\n", "Statistics methods are inspired by Dr. Jake Vanderplas, UW.\n", "\n", "Hive and Circos Plots' original inventor is Martin Krzywinsky of the BC Genome Sciences Center.\n", "\n", "Circos plots were implemented with help from Justin Zabilansky (MIT).\n", "\n", "Many thanks to the PyCon Rehearsal class for providing feedback on the material prior to PyCon 2015.\n", "\n", "Thank you all who attended actual iterations of this tutorial, at \n", "\n", "- SciPy 2016 & 2017 (Austin, TX)\n", "- PyCon 2016 & 2017 (Portland, OR)\n", "- PyCon 2015 (Montreal)\n", "- Data Science for Social Good (Boston)\n", "- PyData NYC 2015 (New York City)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# The Data\n", "\n", "In this tutorial, we have a number of data sets that have been downloaded from the Konect network analysis repository.\n", "\n", "<!-- Todo: List the datasets here. -->" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "192px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false }, "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 }

mit

M-R-Houghton/euroscipy_2015

matplotlib/mpl_tutorial.ipynb

1

496538

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015 - matplotlib tutorial" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Simple plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using defaults" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tg4oVoVo100nMmrhtIt1rdydnDpsvCU5Drlzw4osyKOttpNBnwJcOCM5IVJRs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\nH2jVyuqff/ll06nMud2a/zn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"text/plain": [ "<matplotlib.figure.Figure at 0x107bcd810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "X = np.linspace(-np.pi, np.pi, 265, endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "plt.plot(X,C)\n", "plt.plot(X,S)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Instantiating defaults" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hTmXmftef1f1XDRpAYiLExKhOYk3BVYOZuX+mnIyVDlKkheXJrG7zXE+8zrpT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\nDShVylg3LVyHFGkLerXuq/ywI+OzRFJ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"text/plain": [ "<matplotlib.figure.Figure at 0x107d31a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "# Create a new subplot from a grid of 1x1\n", "plt.subplot(111)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 1 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n", "\n", "# Plot sine using green color with a continuous line of width 1 (pixels)\n", "plt.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")\n", "\n", "# Set x limits\n", "plt.xlim(-4.0,4.0)\n", "\n", "# Set x ticks\n", "plt.xticks(np.linspace(-4,4,9,endpoint=True))\n", "\n", "# Set y limits\n", "plt.ylim(-1.0,1.0)\n", "\n", "# Set y ticks\n", "plt.yticks(np.linspace(-1,1,5,endpoint=True))\n", "\n", "# Save figure using 72 dots per inch\n", "# savefig(\"../figures/exercice_2.png\",dpi=72)\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing colours and line widths" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rwgSejJUJTNIUeNZQd9WqshOEHLRtG7BqlbS7dw/EaSXt2gF58kh7zBizsQRS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\ndevKL28gEjQAXHstsHy5vbVp/HipULZhg/uvnZI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"text/plain": [ "<matplotlib.figure.Figure at 0x1040ec390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting limits" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NKCGKyZlCVkqK+RksVYoHXLjG5MnAsWMy7t7dbiwB0rGj6T4Xhs2pQlOBArLx\nEABmzTKNb0IUkzOFrJkzTSvF5583/Y/JopQUc8BF6dKykzYMFSoEPP20jGfOBLZssRsPeXiXajjf\nhyGKyZlClrMZOE8eoHVru7GQx6xZ5orpuefC+kgwZ1JA65DPA+GjfHnTym3yZODgQbvx+IDJmULS\n0qXSfAoAnnmGrTpdw1lnyJ077K+YKlQwJ19OmmT2PpBlPXrI+4QEs/chBDE5U0hy1vkyZwa6drUb\nC3ksXy4NzgGgQ4eIuGJy7p7PngVGjbIbC3k8+CBwyy0yHj5cknQIYnKmkLN9O/DNNzL+3//k3GZy\ngcGD5X10dMRcMVWtCtx8s4xDOA+EF6XM3fOBA8Cnn9qNJ4OYnCnkDB8uR/cBYbsZOPTs2mU6MzVu\nDFx9td14gsQ7D+zfD3z2md14yKNxYzk7HAjZpiRMzhRSTp0Cxo+XcbVqwI032o2HPIYPB5KTZRxh\nzc3DIA+En8yZgWeflfGGDcB339mNJwOYnCmkfPSRHDoAmJ89suzUKWDMGBk/8ABw++124wmyLFnM\nLP769SGZB8JTu3ZATIyMQ7ApCZMzhYzUVHOeeqlSpmKCLJs40ZyfGKHrDO3bh3QeCE958wJt2sj4\nu++AdevsxnOFmJwpZHz/vWn606VLWJfQhg7vpiPXXhuxV0x585rKse++AzZtshsPeXTrJqeVAGbD\nYohgcqaQMWyYvM+RI+xLaEPHjBmyfR4I+6Yjl9O1q+nt7nyvkmVlygANGsj4009DqikJkzOFhG3b\ngDlzZPz003KnQi7g3DXnyQO0bGk1FNuuu840JfnoI+DIEbvxkEe3bvL+3DmzNyIEMDlTSBgxwuyC\njZASWvdbs8Y0HfFedI1gTh44cwYYN85uLORRpYopRh85EkhMtBtPOjE5k+udPAlMmCDj6tWB66+3\nGw95OLvzoqKATp3sxuISDz0EVKokY+/qMrJIKXPVtHcvMG2a3XjSicmZXM+7fIp3zS5x+DDwyScy\nrl8fuOYau/G4hFKmxG/XLmD6dLvxkEeTJkDBgjIeMiQkitGZnMnVvMunSpc2a3pk2bhxplclr5jO\n89RTQL58MuZpVS6RLZscwg0Av/8O/Pqr3XjSgcmZXG3BAuCPP2TM8imXSE6WTQAAcMMNQGys1XDc\nJkcOWYIH5OS0FSvsxkMeHTtK5zAAGDrUbizpwORMrsbyKRf69luZswVkDtepH6J/depkLiRDIA9E\nhiJFgCcPA403AAAgAElEQVSflPG0acDu3XbjuQwmZ3KtrVtN+VSLFlKtQy7grDPkzSvHgtF/lCgB\nPP64jKdOBfbtsxsPeTgbw1JSZOe2izE5k2sNH27GXbrYi4O8rF0L/PijjNu2lSkNSpOTB5KSeNaz\na9x+O3DvvTIeO1Zq3lyKyZlc6eRJadkMADVqsHzKNVg+lW533w3ccYeMR4+WHhjkAs5Vk3fFgQsx\nOZMrTZ4sCRrg6VOucfgwMGWKjOvVk9NH6KKUko6mAHDggExvkws0bGjOGx861LVlVUzO5Dre5VNl\nywK1a9uNhzzGj2f51BV6/HGgaFEZh0h5bfjLnNmsk23YACxcaDeei2ByJtf57jtgyxYZd+liDpUh\ni7zLpypVAqpWtRtPiMiSxcz+r14NLFliNx7yaNcOyJ5dxi7dTs9fe+Q6TvlUTAzQqpXdWMhjxgxg\n504Zex+/RJfVoQOQNauMXZoHIk++fEDz5jKeNUtO1nEZJmdylS1bgLlzZdyiBZA7t914yMNZZ8iT\nR1pgUboVLGgqzqZPB3bssBoOOZzNLFqb728XYXImV2H5lAutXQvEx8u4bVuePpUBzgbh1NTzv8fJ\nokqV5CQdQEpDnAb+LsHkTK5x4oQpn3r4YaBiRbvxkIeTTZRi+VQG3XST6XI6fjxw+rTVcMjhXDV5\n1266BJMzucakScCpUzJm+ZRLHDlyfvlU6dJ24wlhzgb3Y8dcXV4bWWrXBq69VsbDh8vUhkswOZMr\nXFg+VauW3XjIY/x44OxZGbN8yif16klbT0C+11lW5QJRUUDnzjLetg2YP99uPF6YnMkV5s83Gya7\ndmX5lCskJ5sp7euvB6pVsxtPiIuONqsC69ebLqhkWatWZh+FizaG8VcguYJTPpUzJ9CypdVQyDFz\nJsun/KxtW1NW5aI8ENly5waeflrGc+fKiTsuwORM1v3xBzBvnoxbtmT5lGs42SN3blMTSj4pUABo\n1kzG06ebax+yzLs0xGm2YxmTM1nH8ikXWrcOWLRIxm3asHzKj5yl+9RUnlblGtdfDzz0kIwnTjQ7\nUy1iciarjh+XXdoA8MgjQPnyVsMhh3f5lLNhhvzilluA++6T8Ycfmv12ZJlz1XTiBPDRR3ZjAZMz\nWcbyKRc6cgT4+GMZ160LlCljN54w5OSBw4d5WpVrPPqoOWlt+HDr2+mZnMka7/Kpa68Fata0Gw95\nTJjA8qkAe+wxoFgxGbOsyiUyZTKzRJs2AT/8YDUcJmeyZu5c4M8/ZczyKZdISTFT2hUrmnU48qvM\nmYFnnpHxqlXAL7/YjYc8Wrc2p1VZ3k7PX4dkDcunXGjmTODvv2XM8qmAat9ejpQErOcBcuTLZw52\nmTkT+Osva6EwOZMVmzfLuc2A9AC46iq78ZAHy6eCpnBhoHFjGX/1FbBnj914yMNZytEaGDnSWhhM\nzmQFy6dcaP16YOFCGbduLVMaFFBOHkhOBkaPthsLedx4I/DggzIePx44c8ZKGEzOFHTe5VO1agHl\nylkNhxwsnwq6O++UNwAYMwY4d85uPOThXDUdPWrtlBImZwq6iRPNkXksn3KJo0dN+dSjj8rpIxQU\nTh44cAD48ku7sZBH/frWTylhcqagSkkxy5rlysm5zeQCEyaY6TuWTwXVE08AhQrJmBvDXCI6GujY\nUcbr1gGLFwc9BCZnCqq5c4Ht22XcpQvLp1zhwvKp6tXtxhNhsmYFOnSQ8W+/yRu5QLt2Vk8p4a9G\nCiqnfCpXLqBFC7uxkMesWcCOHTLu0oXlUxY884zcrAG8e3aNAgWApk1lPH06sGtXUF/e5+SslKqp\nlNqslNqqlHrxIs8Z5vnzNUqpW3x9TQpNGzcCCxbImOVTLuJkg6uuMkfnUVAVKwY0aiTjzz8H9u+3\nGw95OEs8KSlBP6XEp+SslMoEYDiAmgCuB9BUKVXxgufUBnCt1vo6AO0B8ByWCMXyKRfasMG0KWT5\nlFVOHkhKAsaOtRsLedx6K3DvvTL+8EMgISFoL+3rnfOdALZprXdorZMATAVQ/4Ln1AMwGQC01ssA\n5FFKFfbxdS/vwIGAvwSl37Fj5qCX2rWB666zGw95sHzKNe69V06sAqTmOSnJbjzk4Vw1HToU1FNK\nfE3OxQF4T8Tv9nzscs+52sfXvbjFi6WAvFw5V5zJSYLlUy509Oj5V0zXXms3nginlMkDe/YAX39t\nNx7yaNQIKFpUxsOGBa2sKtrHv5/eKC/cYZLm33vttdf+HcfGxiI2NvbKIzp61Gx7//hjsx2erPHe\nDFy+PFCjht14yGPiRFM+xSsmV2jSBHjhBTlK8oMPgCeftB0R/XtKSb9+5pQS50DuKxQfH4/4+Ph0\nPVdpH64ClFJ3A3hNa13T8/glAKla6ziv54wGEK+1nup5vBnAg1rr/Rd8Lu1LLP9KSZE7gB07pCxk\nwwbuPrVs5kygXj0ZDx/O2VNXSEmRtYW//gIqVJDdevw5cYWXXgLefVfGK1eaqW6yaN8+oEoVoG1b\nKbHKl88vn1YpBa11mj94vk5rLwdwnVKqlFIqC4AnAcy44DkzADztCeRuAMcuTMx+5bIzOcmUT3Ez\nsIvMnm1O3GH5lKt07Gjq/1lW5RJFigBbtgAvvui3xHw5PiVnrXUygC4A5gPYCOBzrfUmpVQHpVQH\nz3PmANiulNoGYAyATj7GfHlt2gA5csjYyQxkxcaNwPffy7h1a6lvJhdg+ZRrlSwJNGgg408/lX1I\n5AJBvoD1uc5Zaz1Xa11ea32t1vodz8fGaK3HeD2ni+fPK2utV/r6mpeVN685k3PWLNOSioLOyQHc\nDOwi3ldMrVrxismFnI1h584B48bZjYXsCN8OYd5nco4YYTeWCMXNwC7lXT7FgnNXevBB4IYbZDxy\npBwpSZElfJPzDTcAVavKePx4llVZ4H2WAjcDu8SxY8DkyTKuVYtXTC7lXVa1axcw48KdPBT2wjc5\nAyYjHD8OTJliN5YI410+VaECy6dcg+VTIeN//wPy5JExN4ZFnvBOznXrAtdcI2NLZ3JGqtmzzVkK\nXbtyM7AreF8xlSvHKyaXi4mRva0AEB8vJxdS5Ajv5OxdVrVxI7Bwod14IgjLp1xozhyzObJrV57X\nGQI6dTIXtt696Sn8hf9PZ5s2QPbsMmZZVVB4n6XQpg3PUnANZ26U53WGjDJlgEcflfGUKbLJkiJD\n+CfnfPlMWdXMmabxAgUMy6dcaNOm88/rZPlUyHA2hp05I5ssKTKEf3IGWFYVRN7lU48+CpQtazce\n8uB5nSGrenXZVAnIr6+UFLvxUHBERnK+8UbAOURj/HhzPBL53fjxwNmzMuZmYJc4fvz88ime1xlS\nvMvR//pLtg5Q+IuM5AyYu+djx1hWFSDem4ErVgQeeshuPOTB8zpD3tNPm5UIllVFhshJzvXqSdNa\ngGVVATJzJvD33zJm+ZRLpKSY3+bXXQc8/LDdeChDcuWSrQKAbB3YvNluPBR4kZOco6OlLgGQ7cSL\nFtmNJww5m+Fz5waaN7cbC3nMncvyqTDhvbmSZVXhL7J+Utu2BbJlkzHnhvxq/XpzvcPyKRdxrphy\n5mT5VIgrVw6oWVPGkyfLVgIKX5GVnPPnl554gDSrZVmV37B8yoU2bjTlU61bS0cYCmnO1plTp2Qr\nAYWvyErOgPnuTk2V417IZ0eOAB9/LOO6daVxArmA9xUTy6fCQs2aZrP9Bx+wrCqcRV5yrlxZzmMD\n5KBUllX5jOVTLnTheZ0snwoLUVHm/mL7dpZVhbPIS87A+WVVn3xiN5YQl5xs+rpcfz1QrZrdeMhj\n/Hhz+lS3bnZjIb9q2dKsUAwdajUUCqDITM716wMlSsiYZVU++fZblk+5TnLy+QXn1avbjYf8Klcu\n2UIASA/79evtxkOBEZnJ2busav16OY+NMsS5cs+Th+VTrjFjhrlievZZXjGFoS5dzH8rC0/CU2Qm\nZ4BlVX6wahWwZImM27WT82fJBZzyKV4xha2yZc1pVR9/LJsyKbxEbnIuUABo1kzG3nOzlG7OXXNU\nFMunXGP1auDHH2Xcti2vmMKYs5Xg7Fngww/txkL+F7nJGWBZlQ/27wc++0zGDRsC11xjNx7ycGaB\neMUU9qpVAypVkvGIEbLVgMJHZCfnm28GqlSR8Ycfmt2tdFljxgCJiTLmZmCXOHjQVB/Urw+UKmU1\nHAospUzp4q5dwPTpduMh/4rs5AyY7+6jR1lWlU6JicCoUTK+5Rbg/vvtxkMeY8cC587JmFdMEeGp\np4B8+WTMsqrwwuTcoAFw9dUyZllVunzxBbBvn4y7deNmYFdISjJLMzfdBDzwgN14KChy5JDNmADw\n00/AypV24yH/YXL2Lqtat85spqE0aW2u0AsVApo0sRsPeXz1FbBnj4x5xRRROnUCMmWSMe+ewweT\nMyCXnlmzyphlVZe0dCmwfLmMn3nGfNnIMqd8yrsKgSJCyZKyKRMApk6VzZoU+picgfN/oU2fzrKq\nS3CuzDNnBjp2tBsLefz+u1w1AUD79qZ+nyKGs8UgMVE2a1LoY3J2sKzqsnbtktlTQKazixSxGw95\nOFdMmTKZJRqKKPfdB9x6q4xHjTKVFBS6mJwd3tuOx46VA1PpPCNHmiPquBnYJfbulR16APD440Dx\n4nbjISu8y6r27TPfEhS6mJy9Pf+8vD92DJg82W4sLnPmjFyzAHKVftttduMhj9GjZac2wCumCNek\niWzSBGQyhYUnoY3J2Vv9+kDp0jIeMkSmuAmAlIA7/XuZA1zi3DlJzgBwxx3A3XfbjYesyppVNmkC\nsmnz11/txkO+YXL2limTyTzbtgGzZtmNxyW8y6dKlDA7Q8myzz8HDhyQMU+fIkhyzpxZxiyrCm1M\nzhdq3dqcZD54sN1YXGLhQmDDBhl37iyl4WSZ1qZ8qkgRoHFju/GQKxQtar4Vpk0Ddu+2Gw9lHJPz\nhXLlktN8ADnnefVqq+G4gXMFnj276UZElv30E7BihYyfeQbIksVuPOQazuRfSgoLT0IZk3Nann1W\nTvUBIv7uecsWM7vfvLnp40uWDRok77NmZcE5neeOO4B77pHxmDHA6dN246GMYXJOyzXXAI0ayfiz\nz6RcJUINGWJ2fT73nN1YyGPbNjmDHJArJmeLLpGHU3hy5Ajw0Ud2Y6GMYXK+GOe7OylJDkuNQIcP\nA5Mmybh2baBiRavhkMO7ToZXTJSGhg3NiaGDB7PwJBQxOV/MPfcAd90l49GjgbNn7cZjwZgx5p/d\nvbvdWMjj6FFgwgQZ16wJVKpkNx5ypehos/a8dSswe7bdeOjKMTlfipORDh8GPv7YbixBdu6cOQPk\nppuAatXsxkMeY8dKRxiAV0x0Sd6FJ++/bzcWunJMzpfy2GNy5AsQcU1Jpk41ZzZ3784SWldITDTl\nUzfcAFSvbjcecrWrrpJzUAA5CdfZ3E+hgcn5UqKjzYEYmzYB8+fbjSdItDabgYsWBZo2tRsPeXzx\nhTmzmVdMlA5du5qzniO88CTkMDlfTtu2QEyMjCPku/uHH4C1a2XcpQtLaF3B+4qpcGGe2UzpUrIk\n8MQTMv78czYlCSVMzpeTJw/Qpo2MFywA1q+3G08QODkge3agQwe7sZDHjz8Cq1bJuEsXqW8mSoce\nPeR9crLZR0Lux+ScHt59i4cMsRtLgG3cCMydK+NWrYD8+e3GQx7OFVO2bOZ0A6J0uP12oEoVGY8Z\nw9NwQwWTc3qULSsnVgHAlCnmsIEw5Fx7KMXTp1xjyxZg5kwZt2gBFChgNx4KOc7G/uPHgYkT7cZC\n6cPknF5OU5Jz58K2Ye2BA6abUN26QLlyduMhD+/ZGjYdoQyoWxe49loZDxkifbfJ3Zic06tKFZkf\nAoDhw02taRgZNUquPQCW0LqGd5u2OnWAChWshkOhKVMmc123fbvp/kruxeScXkoBL7wg48OHw25u\nKCHBdCm97TbggQfsxkMeo0axTRv5RcuWQN68MmZTEvdjcr4Sjz0GlCkj40GDZPtjmPjoI+DgQRmz\nhNYlzp41TUduuQWoWtVuPBTSYmLMXsJffgGWLrUbD10ak/OViI42dQnbtwNff203Hj9JSQHee0/G\n3nWRZNmkSeaKqVcvXjGRz7p2NX0L4uLsxkKXxuR8pVq2NLtlBwwwpwOFsOnTpTk+IHfNmTPbjYdw\n/hVT6dLA44/bjYfCQtGiwNNPy/jbb4HNm+3GQxfH5HylcuSQJhCANKtdtMhuPD7S2lxB581r+q2Q\nZV9/LbMzgMzWREfbjYfCRs+eZhJm4EC7sdDFMTlnROfO0j4LCPnv7h9/BH7/XcZdugA5c9qNh3D+\nFVP+/NINhshPypcHGjSQ8ccfA//8YzceShuTc0YUKGBuMefNM42oQ5CTA7JlM2d8kGWLFpkjhLp2\nldkaIj/q1UveJyUBQ4fajYXSxuScUd27A1GeL1+I3j2vXSvXFoCc/VqwoN14yGPAAHmfPbvM0hD5\n2d13m3LJ0aOBY8fsxkP/xeScUaVLm23Nn30G7NxpN54McHJAVJTZhE6WrV5tjiZt25atOilgXnxR\n3p88KQma3IXJ2RdOU5KUlJA7EGPHDmDqVBk3bmzKt8kyZxYmUyY2HaGAqlULuOEGGQ8dKo2IyD2Y\nnH1x221AtWoyHjsWOHrUbjxXYPBg01/XWX8iy3bskEN3AbliKlXKZjQU5pQyP/v79snmMHIPJmdf\nOd/dp09Lq8UQcPgwMG6cjGvUkOZT5AKDBvGKiYKqSROgRAkZDxzIAzHchMnZVw8/DNx0k4yHDg2J\nAzFGjDBhOutOZNmhQ+aK6eGHgZtvthsPRYTMmc3qydatPBDDTZicfaWUyXAHDgDjx9uN5zLOnAE+\n+EDGt95qZuXJshEjzAEXvGumIGrb1hyIERcXFk0PwwKTsz80bgyULSvjAQOAxES78VzChAlykwbI\nNQXbNbvAqVPmgAteMVGQ5cxpKvZ++00aE5F9TM7+EB0NvPSSjHfvliOeXCgx0WwGLlMGaNTIbjzk\nMXo0cOSIjPv04RUTBV3XrtKICADefttuLCSYnP2leXOzs+Ldd115nOSUKaYcu3dvqdYhyxISzOG6\nFSsCDRvajYciUqFCQLt2Ml6wAFi2zG48xOTsP1mymLrnP/80JTEukZwMvPOOjK++2pxMQ5ZNmCB1\nLIDMvkTxR5LseOEFcyJd//52YyEmZ/9q21YuQQGZG0pNtRuPly++ALZtk3GvXkDWrHbjIUhjY6e5\neenSQNOmduOhiFaihJyICwAzZwJr1lgNJ+JlODkrpfIppRYopbYopb5TSuW5yPN2KKXWKqVWKaV+\ny3ioISB7dtMHc+NG19QlpKaadaRCheQaglzgwnUGHgtJlnkvd3Ht2S5f7px7A1igtS4H4AfP47Ro\nALFa61u01nf68HqhoWNHU5fw1luuqEv49ltgwwYZ9+hhTrski1JSzDpD8eJAixZ24yGCbBRt1kzG\nX34JbN5sN55I5ktyrgdgsmc8GUCDSzw3craf5soFdOsm45UrzSEGlmgt1wiAXDN07Gg1HHJ8+aV0\nfQBksY/rDOQSL70kBQNam+tHCj5fknNhrfV+z3g/gMIXeZ4G8L1SarlSqp0Prxc6unaV4kHA+t3z\nvHlyjQDINUOuXNZCIYf3OkPBgmabLJELVKxoyiw/+QTYvt1uPJHqkotcSqkFAIqk8Ucvez/QWmul\n1MUy0H1a671KqYIAFiilNmutl6T1xNdee+3fcWxsLGJjYy8Vnnvlywd06iQNSX7+GVi8GHjwwaCH\n4X3XnCuXXDOQC8yaBaxbJ+Pnnwdy5LAbD9EFXn4ZmDZNVl/i4oAxY2xHFB7i4+MRHx+frucqncG7\nOqXUZsha8j6lVFEAi7TWFS7zd/oBOKW1fj+NP9MZjcWV9u+XU4USEuR0ie++C3oIixaZZlO9e3OK\nyhW0Bm6/XaYzcucG/v5b3hO5TN26ch2ZObNUhzptHMh/lFLQWqe57OvLtPYMAM4ulhYApqfxwjmU\nUrk84xgADwNY58Nrho7Chc+v6v/116C+vNZAv34yzp5dbtDIBWbMMOsM3bszMZNrvfKKvE9K4oW9\nDb7cOecD8AWAkgB2AGistT6mlCoG4EOtdR2lVBkAX3v+SjSAT7TWaf43h92dMwDs2gVce630zXzk\nEVkADpIffgCqV5dxz56mbSdZpLX0zl69GsiTR85vZnImF6tVS35tZc4sfRJKlrQdUXi51J1zhpOz\nv4VlcgaALl3kxCEA+Okn4L77Av6SWgNVqshyd44cwF9/md4oZNH06aY95xtvAK++ajceosv47Tfg\nrrtk3KGDtIEn/2Fytumff+TEqnPngIceAr7/PuAvuWCBHAkMSDcwpwkVWZSaKnfNa9ZITduOHcBV\nV9mOiuiy6tQB5syRu+ctW2QrDflHoNacKT2KF5dLTkDmmhcvDujLaQ307SvjnDlNu2+ybPp00w+x\nRw8mZgoZr78u75OS2HM7mHjnHAx790r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"text/plain": [ "<matplotlib.figure.Figure at 0x107f72090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting ticks" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UnPsZJJh0yRWcXm66dEC7dnZjIY+ffwZWr5Z2p07m0Nkw4r1ubNgwGW0ny5Qy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\nRAHCpEtERBQg/wffPf+3/jLcTQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108971c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi])\n", "plt.yticks([-1,0,1])\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting tick labels" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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V7FlumjRAmzZmYyGPlSuBdetk3KGDPnQ2hHjvGxs2TFbbyTDL0ldDR44A335r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\nAYcOySz30iXgwQeB33+X0l8yrEULfdr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"text/plain": [ "<matplotlib.figure.Figure at 0x108a97ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],\n", " [r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n", "plt.yticks([-1,0,1])\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Moving spines" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LmDBBnRJhzh/5EZtfm32T82rAdevk5BwbYcKkTJs7F/jvP2n36OGzW/zsJS4O\nGD9e2o8+ClStqjceDW6+Nm/dqjceStG9u5yUA9husywTJmWKYciRSgCQP79Pb/Gzl5kzpXYs4NOF\nCtLifG02/05Js4IF5SQTAJg3Dzh2TG886cCESZmyeTOwbZu0O3UCQkP1xkO4sVBBoUKqyoofKlhQ\n/e//8AMLGVhGr17yOSlJjYTYABMmZcrYsfI5KEgSJlnA8uXA3r3S7t5djvLwY+bin4QEmdYlC6hU\nCahWTdqTJ9tmsywTJmXYyZNy1w7IUOy99+qNh1KYR8Vkywa0b683FguoUkU+gBsPbCHNzG1O0dHA\njBl6Y3EREyZl2KRJUhoSUGcRkmaRkcCvv0q7RQu/KVSQFvPafOaM1P8mC2jYEChSRNpjxtji1G8m\nTMoQ560klSurO3jSbPx4deHp3l1vLBbyyitSdx6QxT82uDb7vqAgoFs3ae/dK1MJFseESRnyww/q\ntCj2Li0iJgaYMkXazzwDlC2rNx4LCQ4GunaV9s6dwOrVeuOhFO3aydQBoKpSWRgTJqWb81aS8HDg\n1Vf1xkMppk8HLl6UNu9ibtGhA5Ali7S5xcQicucGWrWS9uLFwN9/640nDUyYlG6bNgHbt0ubW0ks\nwjDUkuWiRYEXXtAajhXlywc0aybtH38E/vlHbzyUwvnmzvwbtigmTEo3cxFmcDDPvLSMFSuAffuk\n3bWrFFOlW5iLfwyDZ2VaRunSQJ060v72WzVKYkFMmJQuUVFSnAPgVhJLcd5K0rat3lgsrHx5md4F\ngK+/Bi5f1hsPpTDvZJzn4S2ICZPSZeJEbiWxnMOHgV9+kXbz5txKkgbz2nzpEvDdd3pjoRR16gD3\n3y/tMWMsex4bEya5LD5e9l4Cso3kscf0xkMpuJUkXerXB0qUkPaYMUByst54CEBAgLoDP3LEsuex\nMWGSy+bMUfW82bu0iJgY4JtvpF2zJlCunN54bCAwUN1XHDwI/Pab3ngoRatWQK5c0rboMmYmTHKJ\nYahpsnvu8et63tYyYwa3kmRA69ZAjhzStui12f9kzy77MgEgIgLYtUtrOKlhwiSXbNx441aSkBC9\n8RBu3Epy333Aiy/qjcdGcuaUpAkAy5apWvWkWbduMjwLWPJOhgmTXMKtJBa0cqW60nMrSbp1766O\nCTX/vkmzokWlxixw45muFsGESWk6cUJtJXn9dRmSJQswr/JZs3IrSQaULKnqO0ybBpw/rzceSmEu\nY3ZeZWivP0TSAAAgAElEQVQRTJiUpokT1SpvTpNZxD//qJWEzZsDefLojcemzGvz1atq7RRpVr06\nULGitL/8Erh2TW88Tpgw6Y7i4tRN3uOPy8kkZAHcSuIWzzwDlCkj7S+/tOz2P//icKg7mX//BRYs\n0BuPEyZMuqPvvwfOnpU2e5cWceWK6g7VqCHlayhDHA51wtSRI6r+A2nWpAmQN6+0LVRflgmTbst5\nK8m99wKNG+uNh1LMmAFcuCBt3sVkWosWavufha7N/i1rVqB9e2lv2AD88YfeeFIwYdJtbdgA7Ngh\n7c6duZXEEpy3khQpwq0kbpA9u9pismIFt5hYRufOaouJRe5kmDDptszeZUiInCVIFrBqFbBnj7S7\ndpVT6ynTunZVW0x4iolFFCkCvPSStGfPtsQWEyZMStWJE8D8+dJu0kQOiiYLMO9ismThVhI3KlkS\nqFdP2tOmWfqEKf9iLmiLj5fjZTRjwqRUTZigVgxyEaZF3LyVxFwUQW5h/p1fuQJMnao3Fkrx9NOq\nPvKECeqoJE2YMOkWV6+qrSRPPAE8+qjeeCjFl1+qozV4F+N2tWsDpUpJe/x4nmJiCQ6H+ls/fhz4\n8Uet4TBh0i2+/x44d07aXIRpEVeuqCGpp58GKlTQG48PCghQW0wOHQKWLNEbD6Vo1kyd8ap58Q8T\nJt3AeStJgQLcSmIZM2dyK4kXvPmmrJoFtF+byRQWpubrV6/WeooJEybdYN064M8/pd25sxRbJ82c\n72IKFwYaNNAbjw/LmVOOZQSkh/n333rjoRRdulhiGTMTJt3AvKvmVhILiYjgVhIvModlAZnLJAso\nVkztOZ4xQ1ulfCZMuu74cVW2sWlTIH9+vfFQCuetJOYBu+QxpUsDtWpJe+pU4PJlvfFQCnPxz9Wr\nwJQpWkJgwqTruJXEgo4cAX76SdrNmnEriZeYf/+XL8u+TLKAZ58FHnxQ2uPHa6mUz4RJAOSm7auv\npP3UU8Ajj+iNh1JwK4kWzz8vo4CATJmZB8OQRjdXyv/1V6+HwIRJAKTyFLeSWExsrNpK4nxGIHlc\nYKBMFwPA/v3A8uV646EULVvKyixAyzJmJky6YRFmwYLAyy/rjYdSzJwJREdLm3cxXtemDZAtm7S5\nxcQinCvlL18O7Nvn1adnwiSsXQvs3CltbiWxEOetJA0b6o3FD+XOLRUIATkn8/BhvfFQCrPrD3h9\ni4lXE2ZERIQ3n45c1L9/BAAgNJRbSbxlyZIlKF26NEqVKoVhw4al/k1//SWfu3ThVhJNzCkzw5Dp\nZF7DLKBUKVUp/7vvEOHFU7+ZMP3csWPAmjURAGQryd13643HHyQlJaFbt25YsmQJ9u7di9mzZ2Pf\n7YaWQkO5lUSj8uWBGjWk/c03wNKlETrDIZNTpfwIL/YyvTske+0aEBPj1aekO5swQbW5CNM7tmzZ\ngpIlS6Jo0aIIDg5GkyZN8KNzUemjR1W7WTMgXz7vB0nXme+LCxeA3bv1xkIp6tRRlfK3bPFapXzv\nJMy4OODtt4HPP2fpDAtx3kpStSrw8MN64/EXUVFRKFy48PXHhQoVQlRUlPqGL79Ubd7FaNeggUwj\nA8DmzdxiYgkBAWouMzraa5XyHcYdXn2Hw8E/DSIi8juGYThu/tode5iGYbjv46uvYADyMW+ee382\nP9L9kZxsoEIFeUUKFTJw7Zr+mPzlY+PGjahTp871x4MHD8bQoUPl8eTJMO9SjblztcfKD/k4c8ZA\naKi8Xxo10h8PP1I+evSA0bgxjIgId//sW5IlkEYPMyW/uUdsLFCokHSfn35aCkqTNqtXq8UMgwcD\n77+vNRy/kpiYiAceeAArVqxAgQIF8Nhjj2H27Nl4sHRpKU6wezccAIyEBK6OtZA2baS2bEAA8M8/\nQJEiuiMiGIY6xcS9Uv2h3lv0ky2bWu2n+UwzUlv8QkOB9u31xuJvgoKCMG7cONSpUwdlypTB66+/\njgcffBBYs+bGVSVMlpZiTicnJ9+4WI408kyyvP3Tea2HCUj9vxIl5C+uXTtg8mS3/nhyzdGjQPHi\n8jK0aSPL5ckCGjeW42JCQ+GIj0ca703SoGpVYP16qYF//DiQNavuiMhDNPcwAaBoUXX47YwZqngp\neRXreVvQsWPAokXSfuMNvbHQbZnvl3PngO+/1xsLeZ/3S+OZf3FxcezaaBAbqzr21aoBDz2kNx5K\nwbsYW2jUCChQQNpjx3KLib/xfsKsWRMoW1ba48cDiYleD8GfuVrPOzExEQcOHPBOUP7u6lV1F1O1\nKj5OKfU1ePBgjUFRaoKDgU6dpL1jB7BhA98r3nb69GlcvXpVy3N7PWFGnTyJN7JlQ2UAjx87hvpV\nqmDSpEneDsMvGYY6daFQIeCll27/vREREQgICEBCQgLGjx+PkSNHon///t4J1N/MmgWcPw8AWP70\n09fnLhMSErB27VqdkVEqOnQAQkKkPXYs3yvetm/fPpw+fVrLc3s9YR49ehSzVq1Cr2zZ0BPArzlz\nomPHjt4Owy+tXq0WYXbteudFmAcOHECpUqUwb948NG3aFH369MH+/fuxefNm7wTrL4wbz1bbEBiI\nh1NKLlWqVAkrV67UGJx/cbXnEh4OvPaatOfPB7Zs4XvFCqKiovDGG2+gcuXKePzxx1G/fn23d8Y8\nsm49NjYW81KKEzgLCwvDK6+8ggMHDiBXnTqIXLhQ9mPu2gVUqOCJUMiJeV3OkiXtet4BAXIvdeDA\nAVy4cAGdO3dG8eLFceLECVSpUsXDkfqRtWvVFqsuXXD65ElkSzmEMSwsDKdOndIYnH/Zt28fihYt\nivvuuy/N7+3eXdYtJiYC69bxvWIFR48exaxZszBr1iw4HA40bdrU7c/hkYSZLVs2tGzZ8rb/PnPm\nTPT68EMsXbgQiQCCxo1TRU3JI44cAcz63i+9FIUePfri4MGDCAwMRN68edGgQYPrPf0tW7agcuXK\nAID33nsPySmLUXbu3IkePMjYvW7aEJs8YAACAwMByKkmZpv0iYqKQt++t75fHnusI7Zs2YJNmyoj\nPp7vFU+KjIzELylz+4cOHUKePHmQJ08eAECzZs2QL18+PPnkk9IZy5ULkZGRHolDy87o48ePI/dD\nDyF/6dKI3L8fD8yYAQwdCqT8Asj9nBdh1q17FK1a3f5ObPv27ejcuTMAIEuWLABknuaZZ55BwYIF\nvRq3T3PeSpJytlp4eDiuXLkCALh06RLu5nlr2t2u5xIWBrRosR3R0Z0xdy7QvDnfK55SokQJ9OzZ\nEwCwevXq244EzJw5E7169cLSpUuRmJiIIDcX/9CSMKdOnQoA6DduHPDcc7JK8JtvgL59dYTjk5yH\nxePj1SExZcqEoVWrV+54J5Z801E5586dw/r169GvXz9vhO4/JkwAkpKknbKVpGrVqti6dSsAYOvW\nrXj22Wd1RecXMtNzefVVoHPnZMTEyOKf5s35XtHt+PHjyJ07N/Lnz4/IyEg88MAD7n2CNArQelZy\nsmGUKWMYgGHcd59hJCZ6/Cn90aRJ8isGDGP+fPla//79jfPnzxs9evQwEhISrn/v/v37jWXLll1/\nnJycbIwZM8ZISkoyEhISjOXLl3s7fN8UG2sYefPKi/LUU9e/nJycbPTu3dsAYLz77rsaA/Q/ERER\nxpEjR1L9t9TeL/v37zeaNVt2/b21aRPfK96wdu1a4/jx455+mlRzovf3YTpzONQm7aNHgZ9/1hqO\nL3JehFm4sCq0dPOdmCkiIgI1zKrsACZOnIj+/fsjPDwc4eHhuOeee7wYvQ+bPVtVunIqVOBwODBy\n5EgAwNChQ3VERqlI7f0SERGBwYNrXF9t3qMH3yveULVqVRQqVEjLc3u3lmxqYmJkU+DFi1LUgMvo\n3WrlSsAc1Rs6FHj33Tt//9ixY9GdlWY8yzCASpWAnTulbMyRI7Ij3onD4WAtWS9bt24dihYt6vLF\n2HyvNGkCzJkjL+Hx47LthGzPArVkU5M9O9C2rbRXrQL++ktvPD7GLFTgylaSkydPcqGCN6xdK8kS\nADp3viVZkh7p6bk4v1fM+8uEBIA1WHyb/oQJyC5685gW8wpPmfbPP8BPP0m7eXM5YeFO1q5dizp1\n6ng+MH83erR8Dg0FWLTDlpzfK08+CaTUmsCECcC1axoDI4+yRsIsXhx44QVpT59+vUwYZU5663m/\n/vrrCAsL82xQ/u7o0Vu2kpD9OL9XHA5Vl/nUKWDePI2BkUdZI2EC6i/u6lVgyhS9sfiAK1eAr7+W\ndo0aLKRkGePHq7uYlH1lZH+vv67ufcwBBPI91kmYzz4LPPigtMePV/vTKEOmTwcuXJA2C45YxJUr\n6lSS6tV5tpoPyZJFnWKyZQuwaZPeeMgzrJMwnbeYHDkCpGwmpvRz3kpSpAjw4ot646EUM2bwLsaH\nde6sDjRgL9M3WSdhAkCLFkCuXNI2r/iUbsuWAfv2SbtbtzufSkJecvNdTMOGeuMht7v3XnWKybx5\nQFSU3njI/ayVMLNnB9q0kfbKlcCePXrjsSnz7jZbtrS3kpCXrFgB7N0r7bTOViPbMqelExNlxSz5\nFmslTIBbTDLp77+BxYul3bIlkDu33ngohXkXkzUr72J82GOPAY8/Lu1Jk4C4OL3xkHtZL2GWKAHU\nry/t6dOB6Gi98diM8z0Gp8ks4tAh4Ndfpd2yJU/l8XFmL/PsWWDWLL2xkHtZL2ECavFPbCy3mKTD\nxYvAt99Ku3ZtteiYNBs3TuYwAdc2xJKtNW4sFQ8BmbZmhUPfYc2EWasWULq0tMeN4xYTF02ZIqV5\nAW7xs4xLl9RN33PPAWXL6o2HPC44GOjSRdo7dwJr1uiNh9zHmgnT4ZDlnYBsMTGHs+i2kpLUcOz9\n9wN16+qNh1J89x1w+bK0OUbuNzp0kMqHALeY+BJrJkxA5npy5pQ2t5ik6ZdfpHYsIKN+AdZ9Zf1H\ncrK6i3Gemyefd/fdQLNm0v7xR7nvJ/uz7mU1Rw6gdWtpOy/Jp1SZd7E5cwKtWumNhVIsWQIcPCht\n3sX4HXNAITlZipeR/Vn7HdytG7eYuGDXLjkZDZAdCzly6I2HUph3MdmzA2++qTUU8r6KFYGnn5b2\n119LZUSyN2snzJIlgXr1pD1tGreY3IY5Yh0QoKZ+SbN9+4ClS6XdurWqYEV+xVx8d+GCXMLI3qyd\nMAE1rhEbqwpX03VnzwIzZ0q7QQOgWDG98VAK5xER3sX4rQYNgPvuk/aYMeqgGrIn6yfM2rWBMmWk\nPXasHGtO1331laomwq0kFhEdLatjAeD552XZMvmlwEB1v7R/P7B8ud54KHOsnzAdDqBXL2mfOMHT\nWZ0kJMgh0YCcd2nOl5BmU6bIiAjAuxhC27ZS1xngFhO7s37CBGR9dr580v7iC5bOSDF/vjoRoWdP\ntT6KNEpKkmIbgBTfqFVLbzykXe7cauX64sVS75nsyR4JM2tWOWwOALZuBTZs0BuPRZh3q/nyAW+8\noTcWSvHTT2rTXY8evIshADdWROSCf/uyR8IEpNZUSIi0v/hCbywW4Hyqe8eOcuI7WcCoUfI5Vy45\n35UIUte5dm1pf/ut1H0m+7FPwrznHtWNWrhQlbXxU59/Lp+DglTnmzTbtk0VDu3YUfZfEqUwp7Nj\nYmRfJtmPfRImALz1lnxOTvbrcnlHj6q1T6+/DhQsqDceSmGOfAQF8VQSukXdusADD0h79Gg5ZJrs\nxV4Js2JF4JlnpP3NN3IShB8aO1Yd4NK7t95YKMXx48APP0j7tdeAQoX0xkOWExCg3q/Hj8uiPbIX\neyVMQG0xuXxZkqafuXRJ1W+oUQN4+GGt4ZBp3DjVZTD/Rolu0qIFkDevtEeO5IJ/u7FfwnTeCD5m\njN+dlencsWbv0iJiYoBJk6RdvTrw6KN64yHLyppVnZW5dSuwfr3eeCh97JcwAwLUXOaRI8CiRVrD\n8abERLWV5P77eVqUZUyZopY98i6G0tC1q1rwby7eI3uwX8IE5KzM3Lml7Ud/cQsXyoIfQEb9eFqU\nBSQlqa0kJUsCL7ygNx6yvPBwoHlzaS9aBERG6o2HXGfPS25YmCzbB6SIwZYteuPxAsOQOQ8AyJNH\n7hnIAn78UW1x6tVLiocSpcGc5jYMdb9F1mfPhAlIReOgIGn7QSGDjRuBzZul3bmzqk1JmpkjHM71\nz4jSUK4cUKeOtKdM4cmFdmHfhFmwoGxCBIC5c2Wdtg8zr8shITwtyjI2b1arNjp1kpEPIheZ092x\nsXLqEFmffRMmoMY1nAte+6DDh2X+EpBiR/fcozceSmGObAQH8y6G0q1WLelpArLg/9o1vfFQ2uyd\nMB95BKhWTdqTJsneTB80erQ6eJZb/CzCudxSkyZAgQJ64yHbcThUL/PkSVX3gqzL3gkTAPr0kc8X\nL/pkgcYLF1R9hueek3MvyQKcyy3xLoYy6I03ZNUsINMuLGRgbfZPmC++qAo0fvGFnKrsQyZPBq5c\nkbZ5b0CaXbyoyi3VrAlUqqQ3HrKt0FDZlwkAO3YAERFaw6E02D9hBgQAb78t7ePHgTlz9MbjRteu\nqUIFZcqoVXWk2aRJqtwS72Iokzp3VsfzjRihNxa6M/snTEB2AZsrYT77zGfGNWbNAqKipN2nD88i\ntoT4eLVxrlw5KdVIlAn58gGtW0t78WJg92698dDt+UbCzJJFTrcH5K/t99/1xuMGycmS+wFZT9Ks\nmd54KMWMGcC//0q7b1/exZBb9OmjKncNH643Fro930iYgOyDMw/sNTONjf3yC7Bvn7TfekvmOkiz\n5GR1NStcGGjaVG885DNKlABeeUXas2cDx47pjYdS5zsJM3duoEMHaa9aBWzbpjeeTDJzfq5cqgog\nafbTT8CBA9Lu1Uv2XxK5yTvvyOfERL8qkW0rvpMwAemKmeXybDyusX69KiDTuTOQM6feeAgyLz5s\nmLTvugto105vPORzHnkEePZZaU+eDJw7pzceupVvJUznYbJ582x7DIDZuwwJUVOzpNn69cCmTdLu\n0gXIkUNvPOST3n1XPsfGAl9+qTcWupVvJUxAFmIAMt9kw3GNvXtl5A+QWt733qs3Hkph9i5DQ3kX\nQx7z3HNqW++YMZI4yTp8L2GWLw/UqyftqVOBM2f0xpNO5kiyw6G2l5Jme/bIKiwAePNNVZqFyM0c\nDjWXefYs8O23WsOhm/hewgRUL/PqVWD8eL2xpMOJE8DMmdJ++WXg/vv1xkMpnO9iWKiAPOyVV4Bi\nxaQ9YoQsAiJr8M2EWaMG8Oij0h43TtWWs7hRo1RlP3MugzRzvotp3BgoVUpvPOTzgoLUfdk//6ga\n/6SfbyZM53GNc+dU9XILu3BBKq4Bku8fe0xrOGQaNUrd4pt/U0Qe1rq1VAACfKp4me35ZsIEgEaN\ngJIlpT18uJQ0s7AJE4CYGGnzumwRN9/FVK6sNRzyH9myqbVlO3YAy5frjYeE7ybMwEDgvfekfeIE\nMG2a3njuIC5OFVmvUAGoW1dvPJRi3DjexZA2XbpI4gTUIm3Sy3cTJgC0aCF7MwFg6FDLzp5PmQL8\n95+0WZ7UImJi5Lg4AHjoId7FkNflzQu0by/tFSuAzZv1xkO+njBDQlTP4PBhSx79de2aunssVgxo\n0kRvPJRi0iTg/Hlp9+vHuxjS4u23VQXGTz/VGwv5esIEgLZt1b65wYOloIGFzJihCi2//76q7Eca\nxcWpgwlLl5b5cCINChWSrb8A8PPPwJ9/ag3H7/l+wsyaVa3R3rsXWLRIbzxOEhOBIUOkXagQ0LKl\n3ngoxZQpwKlT0v7gA3XuEpEG770nSzIAuecnffzjStCpk5xmAsi4hkXWaM+dCxw6JO2+fXmElyUk\nJKhivsWK8Qgv0q54ceCNN6Q9bx6wf7/eePyZfyTMHDmAnj2l/ccfwJIleuOBjAybcxL58/PwC8uY\nORM4elTa777LMXKyhPffl2l0w1CjUuR9DuPOvS1rdMXc4fx5oGhR4PJl4MkngXXrtC7kWLhQTY0N\nG8ZdC5aQlAQ8+CBw8CBQoIAsFNPU7Xc4HEjjvUl+5tVXpYcZGAj8/bf0PMljUk0O/tHDBIA8eWRj\nEwBs2ACsXq0tFMMAPvlE2rlzy5mXZAHz5kmyBDhGTpbTr598Tkrivkxd/CdhAkCvXkCWLNLWuEZ7\nyRIZGQZkpJhHK1pAcrJaUZEvn9oAR2QRDz0EvPCCtKdOlXos5F3+lTDDw4EOHaS9fLmWncDOvcsc\nOYDu3b0eAqVm0SJg1y5p9+oFhIXpjYcoFWYv03ltGnmP/8xhmk6ckMH/hAS5Xfv5Z68+/dKlQJ06\n0n73XSlARJolJ8vt++7dMnT/zz9AzpxaQ+IcJt1OrVpyvx8aCkRGAgUL6o7IJ/n5HKbJeSfwL78A\nW7d67akNAxgwQNrZs/OAaMtYuFCSJSB7djUnS6I7Ma8h8fG84fY2/+thArJtoFQp6WXWqwcsXuyV\np12yRJ4OkGXi3IRsAcnJQMWKwF9/SfHOf/6xxKQye5h0J7VrA8uWSfXPyEjpB5BbsYd53X33qY2P\nv/0GbNzo8ad07l3myKGKD5Fm8+dLsgSky2+BZEmUlo8+ks/XrnFfpjf5Zw8TkLnMEiXkL65WLZlc\n9KBff1Ur3P73P+Djjz36dOSK5GQ5T23PHlkZ+88/MlZuAexhUlrq1ZNRq+BgqRhWpIjuiHwKe5g3\nKFRIrZhdtkwKGXiIYQADB0o7Z06gd2+PPRWlx7x5kiwB2XdpkWRJ5Aqzl5mQwOkdb/HfHiYAnDwp\nK2bj44FnnpFD5zzg55+BBg2k/eGH6g+dNEpKkt7l3r3A3XdL79JCW0nYwyRX1K8vSzCCg6Xmxn33\n6Y7IZ7CHeYsCBVSZnZUrgYgItz+F89xlrlyyxY8sYO5cSZaA1CW0ULIkcpVzL5PnZXqef/cwATnG\nqXhx4OpVoHp1SZpurDG7aBHw8svS/ugj6WGSZomJQPnycuxD/vxSM9ZiCZM9THJVgwYyihUUBBw4\nwBqzbsIeZqruuUfVmF2zxq3DsklJQP/+0r7rLnVgCmk2bZo6I+nddy2XLInSw1wfkZgIDBqkNRSf\nxx4mAJw+LWcfxsYCjz0GbNrkll7m9OnqUOjBg2XvJWkWFyd7cE+ckIVfBw+q+sIWwh4mpUfjxsCC\nBXLZ2rULKFdOd0S2xx7mbeXPryYXt2yRv7xMio9Xw6/33svepWVMmKCqVg8caMlkSZRen34KBATI\nmgmz3iy5H3uYposXZV/muXPAAw/IZvZMHB48dizQo4e0J0wAOnVyU5yUcZcuyQSPm15jT2IPk9Kr\nXTvgm2+kvX69HPtLGcYe5h3lygV88IG0DxwAvv02wz/q8mVVmKBECaBt28yHR27w+eeSLAE5Msai\nyZIoIwYMUEe4vvee9DbJvZgwnXXpAhQuLO2BA2XlbAaMGgWcOSPtTz6RPVKk2enTwMiR0n7kEZn0\nIfIhhQsD3bpJe+1aqQJE7sWE6SxLFrWxKSpKxlXT6cwZYPhwaVeqBLz2mhvjo4wbPBiIiZH2kCFu\n3TpEZBXvv68O23n/fan+SO7DhHmzli2BMmWkPWQIEB2drv98yBAZkjXbAfwN63f0qEwkA1LR6bnn\n9MZD5CF580odDgDYuRP4/nu98fgaXs5vFhioCjNeuJCuY82PHQPGj5d2jRpyBA9ZQP/+UmQfkNeW\nvUvyYT17AuHh0nb+06fMY8JMTYMGaonZ6NEyPOuCfv3UHydH/Sxi2zbZEAtIyaUqVfTGQ+Rh2bOr\ngimHD6vBFco8biu5nbVrpVQeALRqleaq2S1b1LW4cWM5CIM0Mwzp6q9ZIyuv9uyRogU2wG0llBnX\nrknxgoMHgdy55fivPHl0R2Ur3FaSLtWqAS+9JO3vvgO2b7/ttxqGOrIrJCRdo7jkSYsWSbIEZPmg\nTZIlUWaFhKjFh9HRPCHJXdjDvJNDh2QBUEICULWqXHxTGWedO1ethu3blwnTEq5dA8qWVbfWhw7J\nrbZNsIdJmWUYssYtIkK2HP/1l9TrIJewh5luJUuqcj3r1gHz59/yLXFxalVavnwsS2UZX34pSRKQ\nHd02SpZE7uBwAF98IZ8TE+VmnjKHPcy0XLggQ3lnzwJFiwL79t1Qf/Szz+TAC0Cu0ebxmqTR+fNy\nsxMdDdx/v9xa26x6BHuY5C5t2wJTpkh7+XLg2Wf1xmMT7GFmyF13qTNzjhyRVbMpTp+WSj6AjNy2\nb+/98CgVgwap/bPDh9suWRK50yefqBPseveWYwcpY5gwXdG+vcyHAXIswH//AZDTSMwiBSNHsjSp\nJfz9t9oMW7Mm8OKLeuMh0uzee6W2LCBHf02dqjceO+OQrKuWLgXq1JF2+/bY1e0rVKokpafq1gV+\n+01veARZ5VC3rrxWDoesbK5USXdUGcIhWXKn2FhZ8HPihBQ1OHBAzpug2+KQbKbUrg08/zwAwPj6\na4xusQ3JyVIYaMQIzbGRWLBAkiUgowI2TZZE7pYtGzBsmLT/+0+d1Uvpwx5mehw4AJQvDyQkYAsq\n4wlsxFu9A68fgkEaXbkClC4tt9B588prlTev7qgyjD1McjfDkFmK1aulxvX27cBDD+mOyrLYw8y0\nBx7A1e6yNvsxbEXfXJMxcKDekCjFJ59IsgSAoUNtnSyJPMHhkOn9oCCZSurShaeZpBcTZjq9d6kf\n/kFRAMCgxPeRI/Y/vQERsH+/OuvysceANm30xkNkUWXLAm+9Je2NG9Os+Ek34ZBsOmzbJtfj541f\n8AtSVl+2bCml80gPwwBq1QJWrJBb6K1b5YBom+OQLHlKTIzMXkRFSbGVAwdYZzYVHJLNjKQkGcIw\nDGBp8Au4/GxD+Ydp02RSgPSYO1eSJQB06uQTyZLIk7JnlwpAgNRj+eADvfHYCXuYLpo4UVXx+eAD\n4NMOR4EHHwSuXpWqBX/+yQ3y3nbpkvzuzVvlv//2mRJ47GGSJ928A2vTJhk9o+vYw8yoU6fUXdh9\n94p0WB8AAA2ESURBVKXUi73vPrU2e+9edctG3vPBB+qs0mHDfCZZEnmawwGMGyenmhiGDM4kJuqO\nyvqYMF3QvbuqtDZmjOxpAiB1pkqXlvbAgarYN3ne+vVSvBcAnn4aePNNreEQ2U2pUqoO9o4d4PY4\nF3BINg0LFsiB0IAc4TVnzk3fsGaNXLABOax4xQrZ5ESeEx8vG8j27wdCQ4Hdu33urEsOyZI33PxW\n2rVLzisgDsmmW3Q00LWrtPPkkd7lLapXV5ObERHA5MneCs9/ffqpvMMB6dn7WLIk8pbQUOCbb2SI\nNj4eaNeOezPvhD3MO3A+FmfaNKBFi9t846VLQLlywPHjQI4cwJ49QOHCXovTr+zaJSthExPl1njL\nFp9cbMUeJnlTjx7A2LHS5jGFAG7Tw2TCvI0lS4B69aRdty6weLHchbn0H9SrB/z6axr/AaXbtWuy\nlG/nTiniu2UL8PDDuqPyCCZM8qaYGClqcOyYbDvZtQsoVkx3VFpxSNZV58+rYjE5csiWkjRzX926\nUsQAkKNLvv7aozH6pUGDJFkCwPvv+2yyJPK27NnVbFJMDNCqFc/NTA17mKlo0kQt7pkyBWjd2sX/\nMDpairNHRcmJrTt3AiVKeCxOv7J5M/DkkzLB8tBD8jgkRHdUHsMeJunQpQswYYK0R4wA+vTRG49G\nHJJ1xfffA02bSrtBA2DRonSOrC5bJkeBAXKBX7NGhg8p42Jj5aiuv/+WJLltm9yY+DAmTNLhyhWg\nYkUgMlLeatu3y/IMP8Qh2bRERckdFiCFY776KgPTkLVqycZNANiwAfjsM7fG6JfeeUeSJSDDsj6e\nLIl0CQsDpk+XnXHXrsks07VruqOyDvYwUyQlAc8+q8rCLlgAvPxyBn9YbKzMrx04ICs4N2wAHn3U\nbbH6lUWL1AvhRz129jBJp379gMGDpd27t18WNeCQ7J0MGgQMGCDtNm1kb1KmbNsGPPGEbH8oXhz4\n4w8gV65Mx+lXjh+X8aHoaPnd/fknULSo7qi8ggmTdLp2DXjqKbmMAcAvvwD16+uNycs4JHs7a9cC\nH30k7dKlb1OgIL0efRQYMkTahw8DHTpI0UZyTVIS0KyZqkk4ebLfJEsi3UJCgNmzZZcAIJUnzbLN\n/szvE+a5c8Abb8jiy9BQWR0bFuamH967t9qb+cMPrAKUHoMGyZ0MALRvD7z6qt54iPxMyZLApEnS\nPnsWaN6cW038ekg2KQl44QWpOQB4qMLFmTOyDeLkScnImzbJY7q9xYvlhTEMOUJt2zanivf+gUOy\nZBXOFc8++EAqU/oBzmHerH9/4JNPpN24sZxF7JHiPKtXA888I93YokUlAeTN64En8gGHD0vpuwsX\npKu/ZYuceelnmDDJKq5ckQJbe/fK40wtiLQPzmE6+/FHlSwffBCYOtWDleyeflrNZx45IpURePjc\nrWJjgUaNJFkCclvrh8mSyErCwoCFC4GcOeVxq1bq7AN/45cJ88ABVUg9Rw75YzAntz2mb185HwwA\nli9XJ1KTME+xNUvf9e6tfl9EpNX998v+TAC4fFl6mJcu6Y1JB79LmOfOAS++KC86IKeQPPCAF57Y\n4ZAek7npfvhwYNYsLzyxTQwZot6RTz8NDBumNx4iukGDBjKNBUgPs2lT/xso86s5zPh4qVq3Zo08\n/t//gI8/9nIQkZFA5cqyXSIkRHqb1ap5OQiLmTtX9SaLFpU6sfnzaw1JN85hkhUlJQENG8phTICc\nFzx2rE8ezOTfi34MQ8o8zZghj19/XTp4ATr62CtXAnXqyO1ZnjxSCcgr3VwL2rwZqFEDiIuTSZKN\nGzlvCSZMsq7Ll4GqVeUIMAAYNQro2VNvTB7g34t+PvpIJcsnnwS+/VZTsgRkxax5/Nf588Dzz8v2\nE39z8KCM88TFSbm7uXOZLIksLkcO6WEWKCCPe/WSCpb+wC8S5pgxqpJP8eLy4mbJojcmtGqlavEd\nPixJ059m0aOipFD96dPyeOxYdcoLEVlaoULAzz/LClrDkIX/K1fqjsrzfD5hfvedGi64+27ZE3/3\n3Xpjum7AAHXo9LZtUqzxyhW9MXnD2bOSLI8elccffuiBihFE5EkPPywFzIKCZH1IgwYyw+LLfHoO\nc8ECqaiWnCy1u1etkmMVLSUhQYL88Ud5XKuW3LqFhuqNy1MuXpT/x61b5XG3bjIE4IOrBjKDc5hk\nF99/L+VFDQPInVvqtPjACXz+NYc5f74MEyQnS1W1X3+1YLIE5PivOXPUcOSyZZJA4+L0xuUJ588D\nzz2nkmWzZsDo0UyWRDbWpAkwcaK0o6PlmERzO7Wv8cmEOWOG7FJISJCdGwsXylE1lhUaKkGa20t+\n/lk2i/rS8OyZM/JOMs8LevllKa+kbeUVEblLhw7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"text/plain": [ "<matplotlib.figure.Figure at 0x108ff5950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],\n", " [r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n", "plt.yticks([-1,0,1])\n", "\n", "# Move spines\n", "ax = plt.gca()\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0))\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0))\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding a legend" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Axo1y47EyTpjM5mbOFNMd3LvUiJs3gfXrqW2+AlJDnJzEGqTbt8WQPpOse3dx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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108fbfd10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],\n", " [r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n", "plt.yticks([-1,0,1])\n", "\n", "# Move spines\n", "ax = plt.gca()\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0))\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0))\n", "\n", "# Add a legend\n", "plt.legend(loc='upper left', frameon=False)\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Annotate some points" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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J1CMuTnJEvnzJ88acOZmLKaBs3mzM//6XfCigc2djYmKci2n9emNq1Up+d9O6tTGnT/vs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\ngnz+hLBoVRWTF0IIIUQ5pAtXCCGEMIAkUCGEEMIAkkCFEEIIA0gCFUI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"text/plain": [ "<matplotlib.figure.Figure at 0x1090d1fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks - additional yaxis 0 removed\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],\n", " [r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n", "plt.yticks([-1,1])\n", "\n", "# Move spines\n", "ax = plt.gca()\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0))\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0))\n", "\n", "# Add a legend\n", "plt.legend(loc='upper left', frameon=False)\n", "\n", "\n", "# Annotate some points\n", "\n", "t = 2*np.pi/3\n", "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n", "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n", "\n", "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n", " xy=(t, np.sin(t)), xycoords='data',\n", " xytext=(+10, +30), textcoords='offset points', fontsize=16,\n", " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n", "\n", "plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n", "plt.scatter([t,],[np.sin(t),], 50, color ='red')\n", "\n", "plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n", " xy=(t, np.cos(t)), xycoords='data',\n", " xytext=(-90, -50), textcoords='offset points', fontsize=16,\n", " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final details" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2lLnPsDD4+2+oXdvpqPLO7xPonDnwwAMAfEAXnuYDuna1j5RTDoqPl43SmzZB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\nvHZ+vhCdOtFHq3lzIa5dkyUMQ1JfboQQglugNcnLk6pz2Nr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"text/plain": [ "<matplotlib.figure.Figure at 0x1090ab9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure of size 8x6 points, using 100 dots per inch\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n", "C,S = np.cos(X), np.sin(X)\n", "\n", "# Plot cosine using blue color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n", "\n", "# Plot sine using red color with a continuous line of width 2.5 (pixels)\n", "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"sine\")\n", "\n", "# Set x limits\n", "plt.xlim(X.min()*1.1,X.max()*1.1)\n", "\n", "# Set y limits\n", "plt.ylim(C.min()*1.1,C.max()*1.1)\n", "\n", "# Set x,y ticks - additional yaxis 0 removed\n", "plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],\n", " [r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n", "plt.yticks([-1,1],\n", " [r'$-1$',r'$+1$'])\n", "\n", "# Move spines\n", "ax = plt.gca()\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0))\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0))\n", "\n", "# Add a legend\n", "plt.legend(loc='upper left', frameon=False)\n", "\n", "\n", "# Annotate some points\n", "\n", "t = 2*np.pi/3\n", "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n", "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n", "\n", "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n", " xy=(t, np.sin(t)), xycoords='data',\n", " xytext=(+10, +30), textcoords='offset points', fontsize=16,\n", " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n", "\n", "plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n", "plt.scatter([t,],[np.sin(t),], 50, color ='red')\n", "\n", "plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n", " xy=(t, np.cos(t)), xycoords='data',\n", " xytext=(-90, -50), textcoords='offset points', fontsize=16,\n", " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n", "\n", "for label in ax.get_xticklabels() + ax.get_yticklabels():\n", " label.set_fontsize(16)\n", " label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))\n", "\n", "# Show result on screen\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Figures, Subplots, Axes and Ticks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(See webpage for details)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subplots" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ej8c3o3S5XMrJyfFd9u9yubR06VLt3LlTlZWV+vrrrzVs2DC1t7fLtm1Nnz5d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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1097328d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "subplot(2,1,1)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)\n", "\n", "subplot(2,1,2)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)\n", "\n", "show() " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFZdJREFUeJzt3Wts1YUd8PHfaSktLcilghNBvABeNi4qKotGt0RdonhZ\ntsV4gWTZTJbNTV/s1d75ejFxL7ZlL0zccAGdCyJjGV4yL0vQoWsZinhBipSLIFgqlNr29DwveM55\nuLTlVygt4/l8EhPp6fmfP6W/fv/9306hVCoFADCwqpFeAQD4XyCYAJAgmACQIJgAkCCYAJAgmACQ\nMGqgBwuFgmtOIKlUKhVGeh0GYp4hr695HjCY//dJp2dt4CxSKJzRrawwz3Bi/c2zXbIAkCCYAJAg\nmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCY\nAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgA\nkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQ\nIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAg\nmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCY\nAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgA\nkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQ\nIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAg\nmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCY\nAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgA\nkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkCCYAJAgmACQIJgAkDBqpFfgdGhpaYk//vGPMX78+Hj0\n0UeH9bWff/75WL9+fdx8883xrW99a1hf+0hPPvlkbN++PX7+85/HxIkTR2w9zgSrVq2K//znP3Hf\nfffF7NmzR3p1GCTzbJ6PNJLzfFYGs6xQKJwVr93W1hbNzc1RV1cXCxcuPOHnb9y4MVpbW2P+/PnH\nDVepVIqWlpbYsWNH7NixI7Zv3x779++PiIg77rgjFixYcNLrefDgwXj//ffjk08+iZ07d8aXX34Z\nhUIhxo8fH5dcckksXLgwJk2adFLL7urqii1btsT27dsr637o0KGIiHj44YejsbGx3+fedNNN0dzc\nHC+//HLMmjVrRL8vOHnm+eyZ5/3791eW/dlnn8WBAweiuro6Jk6cGLNmzYqFCxfG2LFj+3zuSM7z\nWR3Ms0VbW1u89tprMWHChBMOWG9vb7zyyitRKBTipptuOu7xr776Kv70pz/1+dxT/cZ7/PHHo1Qq\nVf48evToKBaLsXfv3ti7d280NTXF3XffHd/4xjcGvexPPvkknnnmmZNar/Hjx8e8efOiqakpmpub\n46qrrjqp5cBQ+P99nvfv3x9PPPHEUR+rra2N7u7u2L17d+zevTveeeeduPfee+Oiiy467vkjOc+C\neZb54IMPYt++fXHJJZf0u+umpqYmpk6dWvlvzZo1ceDAgVN+7VKpFDNmzIirr746Lr300mhoaIhS\nqRStra3x97//PXbt2hUrVqyIyZMnx3nnnTfo5Tc0NMTUqVPjggsuiHHjxsWqVavSz7366qujqakp\n1q5dK5j8zzgb57kc4dmzZ8f8+fPj4osvjrq6uujt7Y1PPvkkVq9eHW1tbbF8+fJ4+OGH+/xNc6Tm\nWTDPMu+8805ERMydO7fPx+vq6uJXv/rVUR97+eWXh+S1f/jDH8aFF1541McKhUJMnz49Fi9eHL/7\n3e/i4MGD8eabb8bdd989qGVfdtllcfnll1f+3NbWNqjnT5s2LSZOnBh79uyJbdu2xfTp0wf1fBgJ\nZ+M8jxkzJn7yk58cF9mqqqqYOXNmPPDAA/GHP/whvvrqq3jnnXfi5ptvPm4ZIzXPwxrMYrEY69at\ni/feey/27NkTXV1dMWbMmBg7dmzMmDEj5s6dG9OmTYuIiFdffTVee+21mDdvXtxzzz19Li97QP6D\nDz6ItWvXxq5du6K3tzfOO++8uO6662LOnDl9fv5jjz0WERGPPPJIdHV1xeuvvx4tLS3R2dkZEyZM\niLlz58YNN9wQ1dXVg/4a9PT0xLp16+Ldd9+Nzz//PIrFYowfPz5mz54dN9xww3FbU0888UTlmERb\nW1tl3cruvvvumD9/fkREtLe3x+bNm6OqqiquuOKKQa/bqTp2uI5UX18fs2bNiubm5ti5c+eglz0U\nxym+/vWvx7/+9a9oamoSzCFgns3zycxzbW3tgL+RnnvuuTFt2rRoaWkZcNkjMc/DFsze3t5YunRp\nbN26NSIO/wCsra2Nzs7O6OjoiN27d0dHR0d8//vfP+p5mR+U/X1OqVSKN998M9asWVN5vZ6enmht\nbY3W1tbYtm1b3H777f0ud9u2bbFq1aro7u6O2traiIjYu3dv/POf/4yPPvooFi9eHKNHj85+CeLg\nwYPx9NNPx65duyIiYtSoUTFq1KjYt29fvPnmm9Hc3BwPPPBA5YdMxOHdkF1dXXHo0KEoFArR0NBw\n1DJramoq/9/S0hIREZMnTx7Ueg2XMWPGRMTh74WRUP66btmyZURe/2xins3z6ZznzLJHYp6HLZgb\nNmyIrVu3Rk1NTdx5551x5ZVXRnV1dZRKpWhvb48PP/wwvvrqqyF9zY6OjnjppZdi3rx5ceutt0ZD\nQ0N0dnbG66+/HmvXro1169bF9OnT+90yXb16dUyZMiXuuuuumDJlShSLxdiwYUOsXr06WltbY82a\nNXHnnXem12fFihWxa9euGDNmTCxatCiuuOKKKBQKsWPHjli5cmXs3r07li9fHj/96U+jvr4+IiIe\neuiho06rf+SRR/pdfvmH19SpUwfxVRo+5R8AU6ZMGZHXv+CCCyLi8JZ9e3t7nHPOOSOyHmcD82ye\nT9c89/b2xqeffnrCZY/EPA/bjQtaW1sjImLevHkxZ86cyu6P8mnK1157bdx4441D+prd3d1x8cUX\nxz333FPZkqurq4vbbrst5s2bFxGHdxX1Z9SoUfHggw9W/tGqq6tj/vz5cccdd0RERFNTU2X3yols\n3bo1Nm/eHBER3/ve9+LKK6+sbElPnTo1lixZEnV1dXHw4MF46623TurvW959MXny5JN6/um0adOm\nyvqN1Ek3Y8eOjbq6uoiI2L59+4isw9nCPJvn0zXP//73v+PgwYNRKBQqu6f7MhLzPGzBLO8C+fLL\nL4frJSMi+h3a8ina+/btq+xSOdaCBQsq/yBHmjdvXpxzzjlRKpXi/fffT63Hxo0bI+LwMF166aXH\nPd7Q0FC5Zuq9995LLfNYBw8ejIj/tzvjTNHe3l45o/Wyyy7r8+8/XMpfm46OjhFbh7OBeTbPEUM/\nz5999lm88sorERFx3XXXxbnnnjvg5w/3PA9bMGfNmhURhw/YL1u2LN5///3KheenS3V1db8HridN\nmlQ5IN/fgeW+rgGKOLwVXV5uf8N5rPJrXHzxxf1+Tvmxffv2RXd3d2q5Ryp/05xJA9bV1RXLly+P\njo6OmDBhwqDPjh1q5a9N+YcRJ8c8m+ehnucvv/wyli9fHj09PTF16tS49dZbT/ic4Z7nYTuGOWPG\njPj2t78dr732Wnz44Yfx4YcfRkREY2NjzJ49OxYsWHDSd43oT319fVRV9b9NMG7cuDhw4EC/WycD\n7RMfN25cROT/ocqvUX7eQK9XKpWio6Mjxo8fn1r2maqnpyeWLVsWO3fujIaGhnjwwQfPqOHn5Jln\n8zyU83zo0KFYunRptLW1RWNjY9x///0nddby6Tasl5XcdNNNMXfu3Hj33XejpaUlWltbY+/evbF2\n7dp466234q677qocizhb9fT0nLZl19fXR3t7+2nf0s8oFovx7LPPRktLS9TV1cXixYsHvH3dcCl/\nbY49O5HBM8/meSh0dnbG0qVLY8+ePTF+/PhYsmRJej6He56H/cYFEyZMiBtvvDFuvPHGKJVKsXXr\n1nj11Vdj69atsXr16pg5c2Y0NDRUtiQH+obs7Owc8LU6Ojqit7e3363S8vGX/r7Y7e3t/W4ln+i5\nx6qvr4+9e/cOeFJBe3t7RBzeRVQ+q24wGhoaor29fcSPz/X29sZzzz0XH330UYwePToeeOCBk7qz\nz+kgmEPLPJvnU9HV1RV//vOfY+fOnTF27NhYsmTJoM52He55HtG39yoUCnHRRRfF/fffH1VVVdHd\n3V05NlA+OF/+pjtWqVQ64QWzxWIxtm3b1udj+/btq9w+6vzzz+/zc8qndff12uXH+nvuscqnhpdP\nxe5L+XqixsbGo67HKp99d+R9HQd6jd27d6fW6XQolUqxYsWK2LRpU9TU1MR999131HVoI+nAgQOV\nH8pn6qn6/8vM89HM88C6u7tj2bJl0draGvX19bFkyZJB7cYfiXketmAWi8X+V6KqqvJNVN4CLW/B\nbN++vc/7Im7YsKHf4TvSG2+8MeDHGxsb+91aWrduXZ9bvf/9738rd+7P3oHjyiuvjIiIPXv2xKZN\nm457/MCBA/H2229HxOE7WBypfEbiia5rK5+4sGPHjtQ6DbVSqRSrVq2Kd999N6qrq/u9efJIKV8K\nMWHCBNdgniLzbJ5PRbFYjGeeeeaoXbyDvXxmJOZ52IK5YsWKWLlyZWzevPmob5S2trZ4/vnno1gs\nRk1NTcyYMSMiIqZPnx7jxo2LYrEYzz33XOXeod3d3fH222/HqlWr+jxF/Eg1NTWxZcuWWLlyZeVg\nfmdnZ7z00kvR3NwcETHgLbh6enri6aefrmzhFYvFaG5ujr/97W8RcfgGwNl/qAsvvDBmzpwZEREr\nV66MjRs3VrYwd+zYEUuXLo3Ozs4YO3ZsXH/99Uc9t7GxMaqqqqKzs3PA097LZ+V9/vnnAx73KN+N\npfxfeT26urqO+nhfPxSfeuqpeOyxx+Kpp5467rE1a9ZEU1NTVFVVxQ9+8INBn27+xBNPxGOPPRbP\nP/98n48fuW5H/v0OHTrU59/nWOXfTi655JJBrRfHM8/m+UT6m+fe3t7461//Gps3b47a2tp48MEH\n42tf+9qglh0xMvM8bMcwe3p64r333qt8Y9fW1kaxWKxsgVZVVcWiRYsqZ11VVVXF7bffHs8++2xs\n3bo1fvOb38To0aOju7s7SqVSXHXVVdHb2xvr16/v9zXr6+vjm9/8ZvzjH/+ovP/ckVuY11133YBv\nTbNo0aJ44YUX4ve//33l7WfKt2qaNm1afOc73xnU1+C73/1uLF26NHbt2hV/+ctforq6Oqqrq6Or\nqysiDp8ife+99x535llNTU3MmTMn1q9fH88++2zU1tZWfrjcdtttla3dcePGxcyZM+Pjjz+OjRs3\nxjXXXNPneixfvrzP3VMvvvhivPjii5U/H3lfy2Mde/uy/fv3H3WB9qpVqwZ8N5Ff/vKX/T7W363R\nfv3rX/f58SeffPKoPz/66KN9npFYvnbOu5WcOvNsno80mHnetm1bZUOhWCzGsmXL+n3u+PHj46GH\nHurzsZGY52EL5i233BIXXnhhbNmyJfbt21c5yD5p0qSYMWNGLFy48LjbIF1++eWxePHieP3112Pn\nzp1RKpXi/PPPj2uvvTbmz5/f728iZYVCIa6//vqYOHFi5WbNNTU1J7xZc9n06dPjxz/+ceVmzcVi\nMSZNmhRz5sw5qZs119fXx49+9KPKzZr37t0bxWIxGhsbY9asWX3erLls0aJFMW7cuNi0aVO0tbVV\nTjY49vqua665Jj7++OPYsGFDvwOW1Ve4yv9uxx4zOPK3ut7e3kFfF9Xb21s5ueF0HI/Ytm1btLW1\nxZQpU86YY6r/y8yzeR7IQPN85LJ7enoGPBHsyGO/RxqpeS4MdOC5UCiUTnRg+mxUfgeB/n5TOZP1\n9vbGb3/72/jiiy/iZz/72ZBeynHgwIF4/PHHo6amJh599NGTOvOvP62trfHkk0/GOeecE7/4xS+G\n/BqsF154IZqamuKuu+46LVukhUIhSqXS8L31+0kwz+b5SOa5f/3N84ieJcvQq6qqiltuuSVKpVK/\nJ0icrPIZgQsWLBjS4Tpy2Sf7NksDaWtri/Xr18fkyZMHvDclnGnM8/FGcp4F8yx0xRVXxLRp02LD\nhg3xxRdfDNlyP/3006ipqYkbbrhhyJZ55LLHjRt3yrud+vLGG29Eb29v3HLLLUPyvpownMzz0UZy\nnu2S7cP/8i4cRoZdsmcu88xg2SULAKfAb5gwBPyGCWcPv2ECwCkQTABIEEwASBBMAEgQTABIEEwA\nSBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABI\nEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQ\nTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBM\nAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwA\nSBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABI\nEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQ\nTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBM\nAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwA\nSBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABI\nEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQTABIEEwASBBMAEgQ\nTABIEEwASBBMAEgQTABIEEwASBBMAEgYdaJPKBQKw7EewDAwz3DyCqVSaaTXAQDOeHbJAkCCYAJA\ngmACQIJgAkCCYAJAwv8BQDL09iWbSSUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108d0fe90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "subplot(1,2,1)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(1,2,1)',ha='center',va='center',size=24,alpha=.5)\n", "\n", "subplot(1,2,2)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(1,2,2)',ha='center',va='center',size=24,alpha=.5)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8771bt27d0sKFC4MuQ5fuXmq9c+dO1dTUKDk5Wa2trSovL5fNZtMbb7wx4Gty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\nCEwAAAwQmAAAGPg/Jo6nmGFF0v4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109619f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "subplot(2,2,1)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,2,1)',ha='center',va='center',size=20,alpha=.5)\n", "\n", "subplot(2,2,2)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,2,2)',ha='center',va='center',size=20,alpha=.5)\n", "\n", "\n", "subplot(2,2,3)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,2,3)',ha='center',va='center',size=20,alpha=.5)\n", "\n", "subplot(2,2,4)\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'subplot(2,2,4)',ha='center',va='center',size=20,alpha=.5)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YtIZ64sQJjY2NKTc3V5WVlcrMzJQkhcNh9ff3q7m52er/g9Df36/R0VFJUnp6+gNrF8Di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\nEwAAA/8PrtLmgQ2CXUEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10896d1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "import matplotlib.gridspec as gridspec\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "G = gridspec.GridSpec(3, 3)\n", "\n", "axes_1 = subplot(G[0, :])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)\n", "\n", "axes_2 = subplot(G[1,:-1])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)\n", "\n", "axes_3 = subplot(G[1:, -1])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)\n", "\n", "axes_4 = subplot(G[-1,0])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)\n", "\n", "axes_5 = subplot(G[-1,-2])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)\n", "\n", "show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Axes" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fPl3t7e2OF+/w8HDZbLY+TzCKjIyUn5+ffvnlF6flOTk58vX1vexEq8bGRn38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\nD56HHAJmcvkgAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108d0f910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "axes([0.1,0.1,.8,.8])\n", "xticks([]), yticks([])\n", "text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)\n", "\n", "axes([0.2,0.2,.3,.3])\n", "xticks([]), yticks([])\n", "text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IxMXFSeXO1ZTefPNNaULTyy+/jJqaGhQUFEjP+Op0OgQHB7sspmGxWJCfn49Vq1ZJjziN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\nQR5tQZ1rawUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10971ba10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "figure(figsize=(8,6), dpi=64)\n", "\n", "axes([0.1,0.1,.5,.5])\n", "xticks([]), yticks([])\n", "text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5)\n", "\n", "axes([0.2,0.2,.5,.5])\n", "xticks([]), yticks([])\n", "text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n", "\n", "axes([0.3,0.3,.5,.5])\n", "xticks([]), yticks([])\n", "text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n", "\n", "axes([0.4,0.4,.5,.5])\n", "xticks([]), yticks([])\n", "text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n", "\n", "show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ticks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(See webpage for details)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Animation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Drip drop" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " fig.waiting = false;\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.send_message('closing', {});\n", " fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Close figure', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "#%matplotlib nbagg\n", "\n", "# New figure with white background\n", "fig = plt.figure(figsize=(6,6), facecolor='white')\n", "\n", "# New axis over the whole figure, no frame and a 1:1 aspect ratio\n", "ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)\n", "\n", "# Number of ring\n", "n = 50\n", "size_min = 50\n", "size_max = 50*50\n", "\n", "# Ring position\n", "P = np.random.uniform(0,1,(n,2))\n", "\n", "# Ring colours\n", "C = np.ones((n,4)) * (0,0,0,1)\n", "# Alpha colour channel goes from 0 (transparent) to 1 (opaque)\n", "C[:,3] = np.linspace(0,1,n)\n", "\n", "# Ring sizes\n", "S = np.linspace(size_min, size_max, n)\n", "\n", "# Scatter plot\n", "scat = ax.scatter(P[:,0], P[:,1], s=S, lw=0.5, edgecolors=C, facecolors='None')\n", "\n", "# Ensure limits are [0,1] and remove ticks\n", "ax.set_xlim(0,1), ax.set_xticks([])\n", "ax.set_ylim(0,1), ax.set_yticks([])\n", "\n", "\n", "def update(frame):\n", " global P, C, S\n", "\n", " # Every ring is made more transparent\n", " C[:,3] = np.maximum(0, C[:,3] - 1.0/n)\n", "\n", " # Each ring is made larger\n", " S += (size_max - size_min) / n\n", "\n", " # Reset ring specific ring (relative to frame number)\n", " i = frame % 50\n", " P[i] = np.random.uniform(0,1,2)\n", " S[i] = size_min\n", " C[i,3] = 1\n", "\n", " # Update scatter object\n", " scat.set_edgecolors(C)\n", " scat.set_sizes(S)\n", " scat.set_offsets(P)\n", "\n", " # Return the modified object\n", " return scat,\n", "\n", "animation = FuncAnimation(fig, update, interval=10, blit=True, frames=200)\n", "#animation.save('rain.gif', writer='imagemagick', fps=30, dpi=40)\n", "#plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Earthquakes" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'import urllib\\nfrom mpl_toolkits.basemap import Basemap\\n\\n# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php\\nfeed = \"http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/\"\\n\\n# Significant earthquakes in the last 30 days\\n# url = urllib.urlopen(feed + \"significant_month.csv\")\\n\\n# Magnitude > 4.5\\nurl = urllib.urlopen(feed + \"4.5_month.csv\")\\n\\n# Magnitude > 2.5\\n# url = urllib.urlopen(feed + \"2.5_month.csv\")\\n\\n# Magnitude > 1.0\\n# url = urllib.urlopen(feed + \"1.0_month.csv\")\\n\\n# Reading and storage of data\\ndata = url.read().split(\\'\\n\\')[+1:-1]\\nE = np.zeros(len(data), dtype=[(\\'position\\', float, 2),\\n (\\'magnitude\\', float, 1)])\\n\\nfor i in range(len(data)):\\n row = data[i].split(\\',\\')\\n E[\\'position\\'][i] = float(row[2]),float(row[1])\\n E[\\'magnitude\\'][i] = float(row[4])'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''import urllib\n", "from mpl_toolkits.basemap import Basemap\n", "\n", "# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php\n", "feed = \"http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/\"\n", "\n", "# Significant earthquakes in the last 30 days\n", "# url = urllib.urlopen(feed + \"significant_month.csv\")\n", "\n", "# Magnitude > 4.5\n", "url = urllib.urlopen(feed + \"4.5_month.csv\")\n", "\n", "# Magnitude > 2.5\n", "# url = urllib.urlopen(feed + \"2.5_month.csv\")\n", "\n", "# Magnitude > 1.0\n", "# url = urllib.urlopen(feed + \"1.0_month.csv\")\n", "\n", "# Reading and storage of data\n", "data = url.read().split('\\n')[+1:-1]\n", "E = np.zeros(len(data), dtype=[('position', float, 2),\n", " ('magnitude', float, 1)])\n", "\n", "for i in range(len(data)):\n", " row = data[i].split(',')\n", " E['position'][i] = float(row[2]),float(row[1])\n", " E['magnitude'][i] = float(row[4])'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other types of plot" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WaP9dXba2QTsswGFr25TQRO+FNt2rLHuI/fZE25VeECLsaZhKWhWpPNjW1Nvw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\neRm/wsFyrp6LsqDre/GgAJNKqHs2n2X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MxuPteW3gncJ9SFvT13tyuReXVAXqXAMG8CSA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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109efcfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.figure(figsize=(8,6), dpi=80)\n", "\n", "n = 1024\n", "X = np.random.normal(0,1,n)\n", "Y = np.random.normal(0,1,n)\n", "\n", "plt.scatter(X,Y)\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 }

mit

PythonFreeCourse/Notebooks

week07/1_Classes.ipynb

1

85170

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/logo.jpg\" style=\"display: block; margin-left: auto; margin-right: auto;\" alt=\"לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <span style=\"text-align: right; direction: rtl; float: right;\">מחלקות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">הקדמה</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בוקר חדש, השמש הפציעה והחלטתם שצברתם מספיק ידע בקורס כדי לפתוח רשת חברתית משלכם, בשם צ'יקצ'וק.<br>\n", " אתם משליכים את מחברות הפייתון מהחלון ומתחילים לתכנת במרץ את המערכת שתעזור לכם לנהל את המשתמשים.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " לכל משתמש יש את התכונות הבאות:\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<ul style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>שם פרטי</li>\n", " <li>שם משפחה</li>\n", " <li>כינוי</li>\n", " <li>גיל</li>\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בחרו סוג משתנה שיאפשר לכם לאחסן בנוחות את הנתונים הללו.<br>\n", " צרו שני משתמשים לדוגמה, והשתמשו בסוג המשתנה שבחרתם.\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " לפני שנציג את פתרון השאלה, נעמיק מעט ברעיון הכללי שעומד מאחורי הדוגמה הזו.<br>\n", " <mark>כל משתמש שניצור הוא מעין אסופת תכונות</mark> – במקרה שלנו התכונות הן שם פרטי, שם משפחה, כינוי וגיל.<br>\n", " לכל תכונה יהיה ערך המתאים לה, ויחד הערכים הללו יצרו משתמש אחד.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נמצא עוד דוגמאות לאסופות תכונות שכאלו:\n", "</p>\n", "\n", "<ul style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>תכונותיו של שולחן הן גובה, מספר רגליים, צבע, אורך ורוחב.</li>\n", " <li>תכונותיה של נורה הן צבע ומצב (דולקת או כבויה).</li>\n", " <li>תכונותיו של תרגיל בקורס הן השבוע והמחברת שבהן הוא הופיע, כותרת והוראות התרגיל.</li>\n", " <li>תכונותיו של שיר הן מילות השיר, האומנים שהשתתפו ביצירתו ואורכו.</li>\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " חשבו על עוד 3 דוגמאות לעצמים שאפשר לתאר כערכים עם אסופת תכונות.\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ניצור שני משתמשים לדוגמה לפי תכונותיהם שהוצגו לעיל:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user1 = {\n", " 'first_name': 'Christine',\n", " 'last_name': 'Daaé',\n", " 'nickname': 'Little Lotte',\n", " 'age': 20,\n", "}\n", "user2 = {\n", " 'first_name': 'Elphaba',\n", " 'last_name': 'Thropp',\n", " 'nickname': 'Elphie',\n", " 'age': 19,\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " תוכלו ליצור בעצמכם פונקציה שיוצרת משתמש חדש?<br>\n", " זה לא מסובך מדי:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_user(first_name, last_name, nickname, current_age):\n", " return {\n", " 'first_name': first_name,\n", " 'last_name': last_name,\n", " 'nickname': nickname,\n", " 'age': current_age,\n", " }\n", "\n", "\n", "# נקרא לפונקציה כדי לראות שהכל עובד כמצופה\n", "new_user = create_user('Bayta', 'Darell', 'Bay', 24)\n", "print(new_user)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <mark>נוכל גם לממש פונקציות שיעזרו לנו לבצע פעולות על כל אחד מהמשתמשים.</mark><br>\n", " לדוגמה: הפונקציה <var>describe_as_a_string</var> תקבל משתמש ותחזיר לנו מחרוזת שמתארת אותו,<br>\n", " והפונקציה <var>celeberate_birthday</var> תקבל משתמש ותגדיל את גילו ב־1:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def describe_as_a_string(user):\n", " first_name = user['first_name']\n", " last_name = user['last_name']\n", " full_name = f'{first_name} {last_name}'\n", " nickname = user['nickname']\n", " age = user['age']\n", " return f'{nickname} ({full_name}) is {age} years old.'\n", "\n", "\n", "def celebrate_birthday(user):\n", " user['age'] = user['age'] + 1\n", "\n", "\n", "print(describe_as_a_string(new_user))\n", "celebrate_birthday(new_user)\n", "print(\"--- After birthday\")\n", "print(describe_as_a_string(new_user))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl;\">\n", " <div style=\"display: flex; width: 10%; float: right; \">\n", " <img src=\"images/recall.svg\" style=\"height: 50px !important;\" alt=\"תזכורת\" title=\"תזכורת\"> \n", " </div>\n", " <div style=\"width: 90%\">\n", " <p style=\"text-align: right; direction: rtl;\">\n", " הצלחנו לערוך את ערכו של <code>user['age']</code> מבלי להחזיר ערך, כיוון שמילונים הם mutable.<br>\n", " אם זה נראה לכם מוזר, חזרו למחברת על mutability ו־immutability.\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בשלב הזה בתוכניתנו קיימות קבוצת פונקציות שמטרתן היא ניהול של משתמשים ושל תכונותיהם.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נוכל להוסיף למשתמש תכונות נוספות, כמו דוא\"ל ומשקל, לדוגמה,<br>\n", " או להוסיף לו פעולות שיהיה אפשר לבצע עליו, כמו הפעולה <var>eat_bourekas</var>, שמוסיפה לתכונת המשקל של המשתמש חצי קילו.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">חסרונות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " אף על פי שהרעיון נחמד, ככל שנרבה להוסיף פעולות ותכונות, תגבר תחושת האי־סדר שאופפת את הקוד הזה.<br>\n", " קל לראות שהקוד שכתבנו מפוזר על פני פונקציות רבות בצורה לא מאורגנת.<br>\n", " במילים אחרות – אין אף מבנה בקוד שתחתיו מאוגדות כל הפונקציות והתכונות ששייכות לטיפול במשתמש.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הבעיה תצוף כשנרצה להוסיף לתוכנה שלנו עוד מבנים שכאלו.<br>\n", " לדוגמה, כשנרצה להוסיף לצ'יקצ'וק יכולת לניהול סרטונים – שתכונותיהם אורך סרטון ומספר לייקים, והפעולה עליהם היא היכולת לעשות Like לסרטון.<br>\n", " הקוד לניהול המשתמש והקוד לניהול הסרטונים עלולים להתערבב, יווצרו תלויות ביניהם וחוויית ההתמצאות בקוד תהפוך ללא נעימה בעליל.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " החוסר באיגוד התכונות והפונקציות אף מקשה על הקורא להבין לאן שייכות כל אחת מהתכונות והפונקציות, ומה תפקידן בקוד.<br>\n", " מי שמסתכל על הקוד שלנו לא יכול להבין מייד ש־<var>describe_as_a_string</var> מיועדת לפעול רק על מבנים שנוצרו מ־<var>create_user</var>.<br>\n", " הוא עלול לנסות להכניס מבנים אחרים ולהקריס את התוכנית, או גרוע מכך – להיתקל בבאגים בעתיד, בעקבות שימוש לא נכון בפונקציה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">הגדרה</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " במהלך המחברת ראינו דוגמאות למבנים שהגדרנו <mark>כאוספים של תכונות ושל פעולות</mark>.<br>\n", " משתמש באפליקציית צ'יקצ'וק, לדוגמה, מורכב מהתכונות שם פרטי, שם משפחה, כינוי וגיל, ומהפעולות \"חגוג יום הולדת\" ו\"תאר כמחרוזת\".<br>\n", " נורה עשויה להיות מורכבת מהתכונות צבע ומצב (דולקת או לא), ומהפעולות \"הדלק נורה\" ו\"כבה נורה\".<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <dfn>מחלקה</dfn> היא דרך לתאר לפייתון אוסף כזה של תכונות ושל פעולות, ולאגד אותן תחת מבנה אחד.<br>\n", " אחרי שתיארנו בעזרת מחלקה אילו תכונות ופעולות מאפיינות עצם מסוים, נוכל להשתמש בה כדי לייצר כמה עצמים כאלו שנרצה.<br> \n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נדמיין מחלקה כמו שבלונה – <mark>תבנית</mark> שמתארת אילו תכונות ופעולות מאפיינות סוג עצם מסוים.<br>\n", " מחלקה שעוסקת במשתמשים, לדוגמה, תתאר עבור פייתון מאילו תכונות ופעולות מורכב כל משתמש.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<figure>\n", " <img src=\"images/user_class.svg?v=1\" style=\"max-width: 650px; margin-right: auto; margin-left: auto; text-align: center;\" alt=\"במרכז התמונה ניצבת צללית של אדם (משתמש). בצד ימין שלו יש תיבה עם הכותרת 'תכונות', ובתוכה המילים 'שם פרטי', 'שם משפחה', 'כינוי' ו'גיל'. בצד שמאל שלו יש תיבה נוספת הנושאת את הכותרת 'פעולות', ובתוכה המילים 'חגוג יום הולדת' ו'תאר משתמש'.\"/>\n", " <figcaption style=\"margin-top: 2rem; text-align: center; direction: rtl;\">איור המתאר את התכונות ואת הפעולות השייכות למחלקה \"משתמש\".</figcaption>\n", "</figure>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בעזרת אותה מחלקת משתמשים (או שבלונת משתמשים, אם תרצו), נוכל ליצור משתמשים רבים.<br>\n", " כל משתמש שניצור באמצעות השבלונה ייקרא \"<dfn>מופע</dfn>\" (או <dfn>Instance</dfn>) – יחידה אחת, עצמאית, שמכילה את התכונות והפעולות שתיארנו.<br>\n", " אנחנו נשתמש במחלקה שוב ושוב כדי ליצור כמה משתמשים שנרצה, בדיוק כמו שנשתמש בשבלונה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " יש עוד הרבה מה להגיד והרבה מה להגדיר, אבל נשמע שמתחתי אתכם מספיק.<br>\n", " בואו ניגש לקוד!\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">יצירת מחלקות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">מחלקה בסיסית</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ראשית, ניצור את המחלקה הפשוטה ביותר שאנחנו יכולים לבנות, ונקרא לה <var>User</var>.<br>\n", " בהמשך המחברת נרחיב את המחלקה, והיא תהיה זו שמטפלת בכל הקשור במשתמשים של צ'יקצ'וק:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl;\">\n", " <div style=\"display: flex; width: 10%; float: right; \">\n", " <img src=\"images/recall.svg\" style=\"height: 50px !important;\" alt=\"תזכורת\" title=\"תזכורת\"> \n", " </div>\n", " <div style=\"width: 90%\">\n", " <p style=\"text-align: right; direction: rtl;\">\n", " ניסינו ליצור את המבנה הכי קצר שאפשר, אבל <code>class</code> חייב להכיל קוד.<br>\n", " כדי לעקוף את המגבלה הזו, השתמשנו במילת המפתח <code>pass</code>, שאומרת לפייתון \"אל תעשי כלום\".\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בקוד שלמעלה השתמשנו במילת המפתח <code>class</code> כדי להצהיר על מחלקה חדשה.<br>\n", " מייד לאחר מכן ציינו את שם המחלקה שאנחנו רוצים ליצור – <var>User</var> במקרה שלנו.<br>\n", " שם המחלקה נתון לחלוטין לבחירתנו, והמילה <var>User</var> לא אומרת לפייתון שום דבר מיוחד. באותה המידה יכולנו לבחור כל שם אחר.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הדבר שחשוב לזכור הוא שהמחלקה היא <em>לא</em> המשתמש עצמו, אלא רק השבלונה שלפיה פייתון תבנה את המשתמש.<br>\n", " אמנם כרגע המחלקה <var>User</var> ריקה ולא מתארת כלום, אבל פייתון עדיין תדע ליצור משתמש חדש אם נבקש ממנה לעשות זאת.<br>\n", " נבקש מהמחלקה ליצור עבורנו משתמש חדש. נקרא לה בשמה ונוסיף סוגריים, בדומה לקריאה לפונקציה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user1 = User()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כעת יצרנו משתמש, ואנחנו יכולים לשנות את התכונות שלו.<br>\n", " מבחינה מילולית, נהוג להגיד שיצרנו <dfn>מופע</dfn> (<dfn>Instance</dfn>) או <dfn>עצם</dfn> (אובייקט, <dfn>Object</dfn>) מסוג <var>User</var>, ששמו <var>user1</var>.<br>\n", " השתמשנו לשם כך ב<dfn>מחלקה</dfn> בשם <var>User</var>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נשנה את תכונות המשתמש.<br>\n", " כדי להתייחס לתכונה של מופע כלשהו בפייתון, נכתוב את שם המשתנה שמצביע למופע, נקודה, ואז שם התכונה.<br>\n", " אם נרצה לשנות את התכונה – נבצע אליה השמה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user1.first_name = \"Miles\"\n", "user1.last_name = \"Prower\"\n", "user1.age = 8\n", "user1.nickname = \"Tails\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נוכל לאחזר את התכונות הללו בקלות, באותה הצורה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(user1.age)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ואם נבדוק מה הסוג של המשתנה <var>user1</var>, מצפה לנו הפתעה נחמדה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(user1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " איזה יופי! המחלקה גרמה לכך ש־<var>User</var> הוא ממש סוג משתנה בפייתון עכשיו.<br>\n", " קחו לעצמכם רגע להתפעל – יצרנו סוג משתנה חדש בפייתון!<br>\n", " אם כך, המשתנה <var>user1</var> מצביע על מופע של משתמש, שסוגו <var>User</var>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ננסה ליצור מופע נוסף, הפעם של משתמש אחר:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user2 = User()\n", "user2.first_name = \"Harry\"\n", "user2.last_name = \"Potter\"\n", "user2.age = 39\n", "user2.nickname = \"BoyWhoLived1980\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ונשים לב ששני המופעים מתקיימים זה לצד זה, ולא דורסים את הערכים זה של זה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(f\"{user1.first_name} {user1.last_name} is {user1.age} years old.\")\n", "print(f\"{user2.first_name} {user2.last_name} is {user2.age} years old.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " המצב הזה מתקיים כיוון שכל קריאה למחלקה <var>User</var> יוצרת מופע חדש של משתמש.<br>\n", " כל אחד מהמופעים הוא ישות נפרדת שמתקיימת בזכות עצמה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " צרו מחלקה בשם <var>Point</var> שמייצגת נקודה.<br>\n", " צרו 2 מופעים של נקודות: אחת בעלת <var>x</var> שערכו 3 ו־<var>y</var> שערכו 1, והשנייה בעלת <var>x</var> שערכו 4 ו־<var>y</var> שערכו 1.\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " שמות מחלקה ייכתבו באות גדולה בתחילתם, כדי להבדילם מפונקציות וממשתנים רגילים.<br>\n", " אם שם המחלקה מורכב מכמה מילים, האות הראשונה בכל מילה תהא אות גדולה. בשם לא יופיעו קווים תחתונים.<br>\n", " לדוגמה, מחלקת <var>PopSong</var>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">מחלקה עם פעולות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " יצירת מחלקה ריקה זה נחמד, אבל זה לא מרגיש שעשינו צעד מספיק משמעותי כדי לשפר את איכות הקוד מתחילת המחברת.<br>\n", " לדוגמה, אם אנחנו רוצים להדפיס את הפרטים של משתמש מסוים, עדיין נצטרך לכתוב פונקציה כזו:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def describe_as_a_string(user):\n", " full_name = f'{user.first_name} {user.last_name}'\n", " return f'{user.nickname} ({full_name}) is {user.age} years old.'\n", "\n", "\n", "print(describe_as_a_string(user2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הפונקציה עדיין מסתובבת לה חופשייה ולא מאוגדת תחת אף מבנה – וזה בדיוק המצב שניסינו למנוע.<br>\n", " למזלנו הפתרון לבעיית איגוד הקוד הוא פשוט. נוכל להדביק את קוד הפונקציה תחת המחלקה <code>User</code>:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def describe_as_a_string(user):\n", " full_name = f'{user.first_name} {user.last_name}'\n", " return f'{user.nickname} ({full_name}) is {user.age} years old.'\n", "\n", "\n", "user3 = User()\n", "user3.first_name = \"Anthony John\"\n", "user3.last_name = \"Soprano\"\n", "user3.age = 61\n", "user3.nickname = \"Tony\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בתא שלמעלה הגדרנו את הפונקציה <var>describe_as_a_string</var> בתוך המחלקה <var>User</var>.<br>\n", " פונקציה שמוגדרת בתוך מחלקה נקראת <dfn>פעולה</dfn> (<dfn>Method</dfn>), שם שניתן לה כדי לבדל אותה מילולית מפונקציה רגילה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " למעשה, בתא שלמעלה הוספנו את הפעולה <var>describe_as_a_string</var> לשבלונה של המשתמש.<br>\n", " מעכשיו, כל מופע חדש של משתמש יוכל לקרוא לפעולה <var>describe_as_a_string</var> בצורה הבאה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user3.describe_as_a_string()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " חדי העין שמו ודאי לב למשהו מעט משונה בקריאה לפעולה <var>describe_as_a_string</var>.<br>\n", " הפעולה מצפה לקבל פרמטר (קראנו לו <var>user</var>), אבל כשקראנו לה בתא האחרון לא העברנו לה אף ארגומנט!<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " זהו קסם ידוע ונחמד של מחלקות: כשמופע קורא לפעולה כלשהי – אותו מופע עצמו מועבר אוטומטית כארגומנט הראשון לפעולה.<br>\n", " לדוגמה, בקריאה <code dir=\"ltr\">user3.describe_as_a_string()</code>, המופע <var>user3</var> הועבר לתוך הפרמטר <var>user</var> של <var>describe_as_a_string</var>.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " המוסכמה היא לקרוא תמיד לפרמטר הקסום הזה, זה שהולך לקבל את המופע, בשם <var>self</var>.<br>\n", " נשנה את ההגדרה שלנו בהתאם למוסכמה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def describe_as_a_string(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", "\n", "user3 = User()\n", "user3.first_name = \"Anthony John\"\n", "user3.last_name = \"Soprano\"\n", "user3.age = 61\n", "user3.nickname = \"Tony\"\n", "user3.describe_as_a_string()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl;\">\n", " <div style=\"display: flex; width: 10%; float: right; \">\n", " <img src=\"images/warning.png\" style=\"height: 50px !important;\" alt=\"אזהרה!\"> \n", " </div>\n", " <div style=\"width: 90%\">\n", " <p style=\"text-align: right; direction: rtl;\">\n", " טעות נפוצה היא לשכוח לשים <var>self</var> כפרמטר הראשון בפעולות שנגדיר.\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " צרו פעולה בשם <var>describe_as_a_string</var> עבור מחלקת <var>Point</var> שיצרתם.<br>\n", " הפעולה תחזיר מחרוזת בצורת <samp dir=\"ltr\">(x, y)</samp>.\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">יצירת מופע</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הפיסה החסרה בפאזל היא יצירת המופע.<br>\n", " אם נרצה ליצור משתמש חדש, עדיין נצטרך להציב בו תכונות אחת־אחת – וזה לא כזה כיף.<br>\n", " נשדרג את עצמנו ונכתוב פונקציה שקוראת ל־<var>User</var> ויוצרת מופע עם כל התכונות שלו:<br>\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_user(first_name, last_name, nickname, current_age):\n", " user = User()\n", " user.first_name = first_name\n", " user.last_name = last_name\n", " user.nickname = nickname\n", " user.age = current_age\n", " return user\n", "\n", "\n", "user4 = create_user('Daenerys', 'Targaryen', 'Mhysa', 23)\n", "print(f\"{user4.first_name} {user4.last_name} is {user4.age} years old.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " אבל הגדרה שכזו, כמו שכבר אמרנו, סותרת את כל הרעיון של מחלקות.<br>\n", " הרי המטרה של מחלקות היא קיבוץ כל מה שקשור בניהול התכונות והפעולות תחת המחלקה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נעתיק את <var>create_user</var> לתוך מחלקת <var>User</var>, בשינויים קלים: \n", "</p>\n", "<ol style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>לא נשכח לשים את <var>self</var> כפרמטר ראשון בחתימת הפעולה.</li>\n", " <li>כפי שראינו, פעולות במחלקה מקבלות מופע ועובדות ישירות עליו, ולכן נשמיט את השורות <code dir=\"ltr\">user = User()</code> ו־<code dir=\"ltr\">return user</code>.</li>\n", "</ol>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def describe_as_a_string(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", " def create_user(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " עכשיו נוכל ליצור משתמש חדש, בצורה החביבה והמקוצרת הבאה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user4 = User()\n", "user4.create_user('Daenerys', 'Targaryen', 'Mhysa', 23)\n", "user4.describe_as_a_string()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">תרגיל ביניים: מחלקת נקודות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " מינרווה מקגונגל יצאה לבילוי לילי בסמטת דיאגון,<br>\n", " ואחרי לילה עמוס בשתיית שיכר בקלחת הרותחת, היא מעט מתקשה לחזור להוגוורטס.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הוסיפו את הפעולות <var>create_point</var> ו־<var>distance</var> למחלקת הנקודה שיצרתם.<br>\n", " הפעולה <var>create_point</var> תקבל כפרמטרים <var>x</var> ו־<var>y</var>, ותיצוק תוכן למופע שיצרתם.<br>\n", " הפעולה <var>distance</var> תחזיר את המרחק של מקגונגל מהוגוורטס, הממוקם בנקודה <span dir=\"ltr\">(0, 0)</span>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נוסחת המרחק היא חיבור בין הערכים המוחלטים של נקודות ה־<var>x</var> וה־<var>y</var>.<br>\n", " לדוגמה:\n", "</p>\n", "\n", "<ul style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>המרחק מהנקודה <pre dir=\"ltr\" style=\"display: inline; margin: 0 0.5em;\">x = 5, y = 3</pre> הוא <samp>8</samp>.</li>\n", " <li>המרחק מהנקודה <pre dir=\"ltr\" style=\"display: inline; margin: 0 0.5em;\">x = 0, y = 3</pre> הוא <samp>3</samp>.</li>\n", " <li>המרחק מהנקודה <pre dir=\"ltr\" style=\"display: inline; margin: 0 0.5em;\">x = -3, y = 3</pre> הוא <samp>6</samp>.</li>\n", " <li>המרחק מהנקודה <pre dir=\"ltr\" style=\"display: inline; margin: 0 0.5em;\">x = -5, y = 0</pre> הוא <samp>5</samp>.</li>\n", " <li>המרחק מהנקודה <pre dir=\"ltr\" style=\"display: inline; margin: 0 0.5em;\">x = 0, y = 0</pre> הוא <samp>0</samp>.</li>\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ודאו שהתוכנית שלכם מחזירה <samp dir=\"ltr\">Success!</samp> עבור הקוד הבא:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "current_location = Point()\n", "current_location.create_point(5, 3)\n", "if current_location.distance() == 8:\n", " print(\"Success!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">פעולות קסם</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כדי להקל אפילו עוד יותר על המלאכה, בפייתון יש <dfn>פעולות קסם</dfn> (<dfn>Magic Methods</dfn>).<br>\n", " אלו פעולות עם שם מיוחד, שאם נגדיר אותן במחלקה, הן ישנו את ההתנהגות שלה או של המופעים הנוצרים בעזרתה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h4 style=\"text-align: right; direction: rtl; float: right; clear: both;\">הפעולה <code>__str__</code></h4>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נתחיל, לדוגמה, מהיכרות קצרה עם פעולת הקסם <code>__str__</code> (עם קו תחתון כפול, מימין ומשמאל לשם הפעולה).<br>\n", " אם ננסה סתם ככה להמיר למחרוזת את <var>user4</var> שיצרנו קודם לכן, נקבל בהלה והיסטריה:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user4 = User()\n", "user4.create_user('Daenerys', 'Targaryen', 'Mhysa', 23)\n", "str(user4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " פייתון אמנם אומרת דברים נכונים, כמו שמדובר באובייקט (מופע) מהמחלקה <var>User</var> ואת הכתובת שלו בזיכרון, אבל זה לא באמת מועיל.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כיוון שפונקציית ההדפסה <var>print</var>, מאחורי הקלעים, מבקשת את צורת המחרוזת של הארגומנט שמועבר אליה,<br>\n", " גם קריאה ל־<var>print</var> ישירות על <var>user4</var> תיצור את אותה תוצאה לא ססגונית:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(user4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " המחלקה שלנו, כמובן, כבר ערוכה להתמודד עם המצב.<br>\n", " בזכות הפעולה <var>describe_as_a_string</var> שהגדרנו קודם לכן נוכל להדפיס את פרטי המשתמש בקלות יחסית:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(user4.describe_as_a_string())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " אבל יש דרך קלה עוד יותר!<br>\n", " ניחשתם נכון – פעולת הקסם <code>__str__</code>.<br>\n", " נחליף את השם של הפעולה <var>describe_as_a_string</var>, ל־<code>__str__</code>:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", " def create_user(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", "\n", "\n", "user5 = User()\n", "user5.create_user('James', 'McNulty', 'Jimmy', 49)\n", "print(user5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ראו איזה קסם! עכשיו המרה של כל מופע מסוג <var>User</var> למחרוזת היא פעולה ממש פשוטה!<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בתא שלמעלה, הגדרנו את פעולת הקסם <code>__str__</code>.<br>\n", " הפעולה מקבלת כפרמטר את <var>self</var>, המופע שביקשנו להמיר למחרוזת,<br>\n", " ומחזירה לנו מחרוזת שאנחנו הגדרנו כמחרוזת שמתארת את המופע.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הגדרת פעולת הקסם <code>__str__</code> עבור מחלקה מסוימת מאפשרת לנו להמיר מופעים למחרוזות בצורה טבעית.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h4 style=\"text-align: right; direction: rtl; float: right; clear: both;\">הפעולה <code>__init__</code></h4>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " פעולת קסם חשובה אף יותר, ואולי המפורסמת ביותר, נקראת <code>__init__</code>.<br>\n", " היא מאפשרת לנו להגדיר מה יקרה ברגע שניצור מופע חדש:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __init__(self):\n", " print(\"New user has been created!\")\n", "\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", " def create_user(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", "\n", "\n", "user5 = User()\n", "user5.create_user('Lorne', 'Malvo', 'Mick', 23)\n", "print(user5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בדוגמת הקוד שלמעלה הגדרנו את פעולת הקסם <code>__init__</code>, שתרוץ מייד כשנוצר מופע חדש.<br>\n", " החלטנו שברגע שייווצר מופע של משתמש, תודפס ההודעה <samp dir=\"ltr\">New user has been created!</samp>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הכיף הגדול ב־<code>__init__</code> הוא היכולת שלה לקבל פרמטרים.<br>\n", " נוכל להעביר אליה את הארגומנטים בקריאה לשם המחלקה, בעת יצירת המופע 🤯\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __init__(self, message):\n", " self.creation_message = message\n", " print(self.creation_message)\n", "\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", " def create_user(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", "\n", "\n", "user5 = User(\"New user has been created!\") # תראו איזה מגניב\n", "user5.create_user('Lorne', 'Malvo', 'Mick', 58)\n", "print(user5)\n", "print(f\"We still have the message: {user5.creation_message}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בתא שלמעלה הגדרנו שפעולת הקסם <code>__init__</code> תקבל כפרמטר הודעה להדפסה.<br>\n", " ההודעה תישמר בתכונה <var>creation_message</var> השייכת למופע, ותודפס מייד לאחר מכן.<br>\n", " את ההודעה העברנו כארגומנט בעת הקריאה לשם המחלקה, <var>User</var>, שיוצרת את המופע.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ואם כבר יש לנו משהו שרץ כשאנחנו יוצרים את המופע... והוא יודע לקבל פרמטרים...<br>\n", " אתם חושבים על מה שאני חושב?<br>\n", " בואו נשנה את השם של <var>create_user</var> ל־<code>__init__</code>!<br>\n", " בצורה הזו נוכל לצקת את התכונות למופע מייד עם יצירתו, ולוותר על קריאה נפרדת לפעולה שמטרתה למלא את הערכים:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __init__(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", " print(\"Yayy! We have just created a new instance! :D\")\n", "\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", "\n", "user5 = User('Lorne', 'Malvo', 'Mick', 58)\n", "print(user5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " איגדנו את יצירת תכונות המופע תחת פעולה אחת, שרצה כשהוא נוצר.<br>\n", " הרעיון הנפלא הזה נפוץ מאוד בשפות תכנות שתומכות במחלקות, ומוכרת בשם <dfn>פעולת אתחול</dfn> (<dfn>Initialization Method</dfn>).<br>\n", " זו גם הסיבה לשם הפעולה – המילה init נגזרת מהמילה initialization, אתחול. \n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " שפצו את מחלקת הנקודה שיצרתם, כך שתכיל <code>__init__</code> ו־<code>__str__</code>.\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">ייצור מסחרי</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " צ'יקצ'וק שמה את ידה על פרטי המשתמשים של הרשת החברתית המתחרה, סניילצ'אט.<br>\n", " רשימת המשתמשים נראית כך:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "snailchat_users = [\n", " ['Mike', 'Shugarberg', 'Marker', 36],\n", " ['Hammer', 'Doorsoy', 'Tzweetz', 43],\n", " ['Evan', 'Spygirl', 'Odd', 30],\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נניח, לכאורה בלבד, שאנחנו רוצים להעתיק את אותה רשימת משתמשים ולצרף אותה לרשת החברתית שלנו.<br>\n", " קחו דקה וחשבו איך הייתם עושים את זה.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " זכרו שקריאה למחלקה <var>User</var> היא ככל קריאה לפונקציה אחרת,<br>\n", " ושהמופע שחוזר ממנה הוא ערך בדיוק כמו כל ערך אחר.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נוכל ליצור רשימת מופעים של משתמשים. לדוגמה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "our_users = []\n", "for user_details in snailchat_users:\n", " new_user = User(*user_details) # Unpacking – התא הראשון עובר לפרמטר התואם, וכך גם השני, השלישי והרביעי\n", " our_users.append(new_user)\n", " print(new_user)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " בקוד שלמעלה יצרנו רשימה ריקה, שאותה נמלא במשתמשים <strike>שנגנוב</strike> שנשאיל מסניילצ'אט.<br>\n", " נעביר את הפרטים של כל אחד מהמשתמשים המופיעים ב־<var>snailchat_users</var>, ל־<code>__init__</code> של <var>User</var>,<br>\n", " ונצרף את המופע החדש שנוצר לתוך הרשימה החדשה שיצרנו.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " עכשיו הרשימה <var>our_users</var> היא רשימה לכל דבר, שכוללת את כל המשתמשים החדשים שהצטרפו לרשת החברתית שלנו:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(our_users[0])\n", "print(our_users[1])\n", "print(our_users[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"align-center\" style=\"display: flex; text-align: right; direction: rtl; clear: both;\">\n", " <div style=\"display: flex; width: 10%; float: right; clear: both;\">\n", " <img src=\"images/exercise.svg\" style=\"height: 50px !important;\" alt=\"תרגול\"> \n", " </div>\n", " <div style=\"width: 70%\">\n", " <p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " צרו את רשימת כל הנקודות שה־x וה־y שלהן הוא מספר שלם בין 0 ל־6.<br>\n", " לדוגמה, רשימת כל הנקודות שה־x וה־y שלהן הוא בין 0 ל־2 היא:<br>\n", " <samp dir=\"ltr\">[(0, 0), (0, 1), (1, 0), (1, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)]</samp>\n", " </p>\n", " </div>\n", " <div style=\"display: flex; width: 20%; border-right: 0.1rem solid #A5A5A5; padding: 1rem 2rem;\">\n", " <p style=\"text-align: center; direction: rtl; justify-content: center; align-items: center; clear: both;\">\n", " <strong>חשוב!</strong><br>\n", " פתרו לפני שתמשיכו!\n", " </p>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">טעויות נפוצות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">גבולות מרחב הערכים</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נסקור כמה דוגמאות כדי לוודא שבאמת הבנו כיצד מתנהגות מחלקות.<br>\n", " נגדיר את מחלקת <var>User</var> שאנחנו מכירים, ונצרף לה את הפעולה <var>celebrate_birthday</var>, שכזכור, מגדילה את גיל המשתמש ב־1:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __init__(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", " \n", " def celebrate_birthday(self):\n", " age = age + 1\n", "\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ניסיון ליצור מופע של משתמש ולחגוג לו יום הולדת יגרום לשגיאה.<br>\n", " תוכלו לנחש מה תהיה השגיאה עוד לפני שתריצו?\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user6 = User('Winston', 'Smith', 'Jeeves', 39)\n", "user6.celebrate_birthday()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " ניסינו לשנות את המשתנה <var>age</var> – אך הוא אינו מוגדר.<br>\n", " כדי לשנות את הגיל של המשתמש שיצרנו, נהיה חייבים להתייחס ל־<code>self.age</code>.<br>\n", " אם לא נציין במפורש שאנחנו רוצים לשנות את התכונה <var>age</var> ששייכת ל־<var>self</var>, פייתון לא תדע לאיזה מופע אנחנו מתכוונים.<br>\n", " נתקן:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class User:\n", " def __init__(self, first_name, last_name, nickname, current_age):\n", " self.first_name = first_name\n", " self.last_name = last_name\n", " self.nickname = nickname\n", " self.age = current_age\n", " \n", " def celebrate_birthday(self):\n", " self.age = self.age + 1\n", "\n", " def __str__(self):\n", " full_name = f'{self.first_name} {self.last_name}'\n", " return f'{self.nickname} ({full_name}) is {self.age} years old.'\n", "\n", "\n", "user6 = User('Winston', 'Smith', 'Jeeves', 39)\n", "print(f\"User before birthday: {user6}\")\n", "user6.celebrate_birthday()\n", "print(f\"User after birthday: {user6}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " באותה המידה, תכונות שהוגדרו כחלק ממופע לא מוגדרות מחוצה לו.<br>\n", " אפשר להשתמש, לדוגמה, בשם המשתנה <var>age</var> מבלי לחשוש לפגוע בתפקוד המחלקה או בתפקוד המופעים:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user6 = User('Winston', 'Smith', 'Jeeves', 39)\n", "print(user6)\n", "age = 10\n", "print(user6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כדי לשנות את גילו של המשתמש, נצטרך להתייחס אל התכונה שלו בצורת הכתיבה שלמדנו:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user6.age = 10\n", "print(user6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">תכונה או פעולה שלא קיימות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " שגיאה שמתרחשת לא מעט היא פנייה לתכונה או לפעולה שלא קיימות עבור המופע.<br>\n", " לדוגמה:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class Dice:\n", " def __init__(self, number):\n", " if 1 <= number <= 6:\n", " self.is_valid = True\n", "\n", "\n", "dice_bag = [Dice(roll_result) for roll_result in range(7)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " יצרנו רשימת קוביות וביצענו השמה כך ש־<var>dice_bag</var> תצביע עליה.<br>\n", " כעת נדפיס את התכונה <var>is_valid</var> של כל אחת מהקוביות:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for dice in dice_bag:\n", " print(dice.is_valid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " הבעיה היא שהקוביה הראשונה שיצרנו קיבלה את המספר 0.<br>\n", " במקרה כזה, התנאי בפעולת האתחול (<code>__init__</code>) לא יתקיים, והתכונה <var>is_valid</var> לא תוגדר.<br>\n", " כשהלולאה תגיע לקובייה 0 ותנסה לגשת לתכונה <var>is_valid</var>, נגלה שהיא לא קיימת עבור הקובייה 0, ונקבל <var>AttributeError</var>.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נתקן:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class Dice:\n", " def __init__(self, number):\n", " self.is_valid = (1 <= number <= 6) # לא חייבים סוגריים\n", "\n", "\n", "dice_bag = [Dice(roll_result) for roll_result in range(7)]\n", "for dice in dice_bag:\n", " print(dice.is_valid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">סיכום</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " במחברת זו רכשנו כלים לעבודה עם מחלקות ועצמים, ולייצוג אוספים של תכונות ופעולות.<br>\n", " כלים אלו יעזרו לנו לארגן טוב יותר את התוכנית שלנו ולייצג ישויות מהעולם האמיתי בצורה אינטואיטיבית יותר.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " נהוג לכנות את עולם המחלקות בשם \"<dfn>תכנות מונחה עצמים</dfn>\" (<dfn>Object Oriented Programming</dfn>, או <dfn>OOP</dfn>).<br>\n", " זו פרדיגמת תכנות הדוגלת ביצירת מחלקות לצורך חלוקת קוד טובה יותר,<br>\n", " ובתיאור עצמים מהעולם האמיתי בצורה טובה יותר, כאוספים של תכונות ופעולות.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " תכנות מונחה עצמים הוא פיתוח מאוחר יותר של פרדיגמת תכנות אחרת שאתם כבר מכירים, הנקראת \"<dfn>תכנות פרוצדורלי</dfn>\".<br>\n", " פרדיגמה זו דוגלת בחלוקת הקוד לתתי־תוכניות קטנות (מה שאתם מכירים כפונקציות), כדי ליצור קוד שמחולק טוב יותר וקל יותר לתחזוק.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " פייתון תומכת הן בתכנות פרוצדורלי והן בתכנות מונחה עצמים.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">מונחים</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<dl style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <dt>מחלקה (Class)</dt>\n", " <dd>\n", " תבנית, או שבלונה, שמתארת אוסף של תכונות ופעולות שיש ביניהן קשר.<br>\n", " המחלקה מגדירה מבנה שבעזרתו נוכל ליצור בקלות עצם מוגדר, שוב ושוב.<br>\n", " לדוגמה: מחלקה המתארת משתמש ברשת חברתית, מחלקה המתארת כלי רכב, מחלקה המתארת נקודה במישור.\n", " </dd>\n", " <dt>מופע (Instance)</dt>\n", " <dd>\n", " נקרא גם <dfn>עצם</dfn> (<dfn>Object</dfn>).<br>\n", " ערך שנוצר על ידי מחלקה כלשהי. סוג הערך ייקבע לפי המחלקה שיצרה אותו.<br>\n", " הערך נוצר לפי התבנית (\"השבלונה\") של המחלקה שממנה הוא נוצר, ומוצמדות לו הפעולות שהוגדרו במחלקה.<br>\n", " המופע הוא יחידה עצמאית שעומדת בפני עצמה. לרוב מחלקה תשמש אותנו ליצירת מופעים רבים.<br>\n", " לדוגמה: המופע \"נקודה שנמצאת ב־<span dir=\"ltr\">(5, 3)</span>\" יהיה מופע שנוצר מהמחלקה \"נקודה\". \n", " </dd>\n", " <dt>תכונה (Property, Member)</dt>\n", " <dd>\n", " ערך אופייני למופע שנוצר מהמחלקה.<br>\n", " משתנים השייכים למופע שנוצר מהמחלקה, ומכילים ערכים שמתארים אותו.<br>\n", " לדוגמה: לנקודה במישור יש ערך x וערך y. אלו 2 תכונות של הנקודה.<br>\n", " נוכל להחליט שתכונותיה של מחלקת מכונית יהיו צבע, דגם ויצרן.\n", " </dd>\n", " <dt>פעולה (Method)</dt>\n", " <dd>\n", " פונקציה שמוגדרת בגוף המחלקה.<br>\n", " מתארת התנהגויות אפשריות של המופע שייווצר מהמחלקה.<br>\n", " לדוגמה: פעולה על נקודה במישור יכולה להיות מציאת מרחקה מראשית הצירים.<br>\n", " פעולה על שולחן יכולה להיות \"קצץ 5 סנטימטר מגובהו\".\n", " </dd>\n", " <dt>שדה (Field, Attribute)</dt>\n", " <dd>\n", " שם כללי הנועד לתאר תכונה או פעולה.<br>\n", " שדות של מופע מסוים יהיו כלל התכונות והפעולות שאפשר לגשת אליהן מאותו מופע.<br>\n", " לדוגמה: השדות של נקודה יהיו התכונות x ו־y, והפעולה שבודקת את מרחקה מראשית הצירים.\n", " </dd>\n", " <dt>פעולה מיוחדת (Special Method)</dt>\n", " <dd>\n", " ידועה גם כ־<dfn>dunder method</dfn> (double under, קו תחתון כפול) או כ־<dfn>magic method</dfn> (פעולת קסם).<br>\n", " פעולה שהגדרתה במחלקה גורמת למחלקה או למופעים הנוצרים ממנה להתנהגות מיוחדת.<br>\n", " דוגמאות לפעולות שכאלו הן <code>__init__</code> ו־<code>__str__</code>.\n", " </dd>\n", " <dt>פעולת אתחול (Initialization Method)</dt>\n", " <dd>\n", " פעולה שרצה עם יצירת מופע חדש מתוך מחלקה.<br>\n", " לרוב משתמשים בפעולה זו כדי להזין במופע ערכים התחלתיים.\n", " </dd>\n", " <dt>תכנות מונחה עצמים (Object Oriented Programming)</dt>\n", " <dd>\n", " פרדיגמת תכנות שמשתמשת במחלקות בקוד ככלי העיקרי להפשטה של העולם האמיתי.<br>\n", " בפרדיגמה זו נהוג ליצור מחלקות המייצגות תבניות של עצמים, ולאפיין את העצמים באמצעות תכונות ופעולות.<br>\n", " בעזרת המחלקות אפשר ליצור מופעים, שהם ייצוג של פריט בודד (עצם, אובייקט) שנוצר לפי תבנית המחלקה.\n", " </dd>\n", "</dl>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">תרגיל לדוגמה</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כתבו מחלקה המייצגת נתיב תקין במערכת ההפעלה חלונות.<br>\n", " הנתיב מחולק לחלקים באמצעות התו / או התו \\.<br>\n", " החלק הראשון בנתיב הוא תמיד אות הכונן ואחריה נקודתיים.<br>\n", " החלקים שנמצאים אחרי החלק הראשון, ככל שיש כאלו, הם תיקיות וקבצים.<br>\n", " דוגמאות לנתיבים תקינים:\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <ul style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li><span dir=\"ltr\">C:\\Users\\Yam\\python.jpg</span></li>\n", " <li><span dir=\"ltr\">C:/Users/Yam/python.jpg</span></li>\n", " <li><span dir=\"ltr\">C:</span></li>\n", " <li><span dir=\"ltr\">C:\\</span></li>\n", " <li><span dir=\"ltr\">C:/</span></li>\n", " <li><span dir=\"ltr\">C:\\User/</span></li>\n", " <li><span dir=\"ltr\">D:/User/</span></li>\n", " <li><span dir=\"ltr\">C:/User</span></li>\n", " </ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " המחלקה תכלול את הפעולות הבאות:\n", "</p>\n", "<ul style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>אחזר את אות הכונן בעזרת הפעולה <var>get_drive_letter</var>.</li>\n", " <li>אחזר את הנתיב ללא חלקו האחרון בעזרת הפעולה <var>get_dirname</var>.</li>\n", " <li>אחזר את שם החלק האחרון בנתיב, בעזרת הפעולה <var>get_basename</var>.</li>\n", " <li>אחזר את סיומת הקובץ בעזרת הפעולה <var>get_extension</var>.</li>\n", " <li>אחזר אם הנתיב קיים במחשב בעזרת הפעולה <var>is_exists</var>.</li>\n", " <li>אחזר את הנתיב כולו כמחרוזת, כשהתו המפריד הוא <samp>/</samp>, וללא <samp>/</samp> בסוף הנתיב.</li>\n", "</ul>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "\n", "class Path:\n", " def __init__(self, path):\n", " self.fullpath = path\n", " self.parts = list(self.get_parts())\n", "\n", " def get_parts(self):\n", " current_part = \"\"\n", " for char in self.fullpath:\n", " if char in r\"\\/\":\n", " yield current_part\n", " current_part = \"\"\n", " else:\n", " current_part = current_part + char\n", " if current_part != \"\":\n", " yield current_part\n", "\n", " def get_drive_letter(self):\n", " return self.parts[0].rstrip(\":\")\n", "\n", " def get_dirname(self):\n", " path = \"/\".join(self.parts[:-1])\n", " return Path(path)\n", "\n", " def get_basename(self):\n", " return self.parts[-1]\n", "\n", " def get_extension(self):\n", " name = self.get_basename()\n", " i = name.rfind('.')\n", " if 0 < i < len(name) - 1:\n", " return name[i + 1:]\n", " return ''\n", "\n", " def is_exists(self):\n", " return os.path.exists(str(self))\n", "\n", " def normalize_path(self):\n", " normalized = \"\\\\\".join(self.parts)\n", " return normalized.rstrip(\"\\\\\")\n", "\n", " def info_message(self):\n", " return f\"\"\"\n", " Some info about \"{self}\":\n", " Drive letter: {self.get_drive_letter()}\n", " Dirname: {self.get_dirname()}\n", " Last part of path: {self.get_basename()}\n", " File extension: {self.get_extension()}\n", " Is exists?: {self.is_exists()}\n", " \"\"\".strip()\n", "\n", " def __str__(self):\n", " return self.normalize_path()\n", "\n", "\n", "EXAMPLES = (\n", " r\"C:\\Users\\Yam\\python.jpg\",\n", " r\"C:/Users/Yam/python.jpg\",\n", " r\"C:\",\n", " r\"C:\\\\\",\n", " r\"C:/\",\n", " r\"C:\\Users/\",\n", " r\"D:/Users/\",\n", " r\"C:/Users\",\n", ")\n", "for example in EXAMPLES:\n", " path = Path(example)\n", " print(path.info_message())\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">תרגילים</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">סקרנות</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " מחלקת המוצר בצ'יקצ'וק החליטה להוסיף פיצ'ר שמאפשר למשתמשים ליצור סקרים, וכרגיל כל העבודה נופלת עליכם.<br>\n", " כתבו מחלקה בשם <var>Poll</var> שמייצגת סקר.<br>\n", " פעולת האתחול של המחלקה תקבל כפרמטר את שאלת הסקר, וכפרמטר נוסף iterable עם כל אפשרויות ההצבעה לסקר.<br>\n", " כל אפשרות הצבעה בסקר מיוצגת על ידי מחרוזת.<br>\n", " המחלקה תכיל את הפעולות הבאות: \n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<ol style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li><var>vote</var> שמקבלת כפרמטר אפשרות הצבעה לסקר ומגדילה את מספר ההצבעות בו ב־1.</li>\n", " <li><var>add_option</var>, שמקבלת כפרמטר אפשרות הצבעה לסקר ומוסיפה אותה.</li>\n", " <li><var>remove_option</var> שמקבלת כפרמטר אפשרות הצבעה לסקר ומוחקת אותה.</li>\n", " <li><var>get_votes</var> המחזירה את כל האפשרויות כרשימה של tuple, המסודרים לפי כמות ההצבעות.<br>\n", " בכל tuple התא הראשון יהיה שם האפשרות בסקר, והתא השני יהיה מספר ההצבעות.</li>\n", " <li><var>get_winner</var> המחזירה את שם האפשרות שקיבלה את מרב ההצבעות.</li>\n", "</ol>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " במקרה של תיקו, החזירו מ־<var>get_winner</var> את אחת האפשרויות המובילות.<br>\n", " החזירו מהפעולות <var>vote</var>, <var>add_option</var> ו־<var>remove_option</var> את הערך <samp>True</samp> אם הפעולה עבדה כמצופה.<br> \n", " במקרה של הצבעה לאפשרות שאינה קיימת, מחיקת אפשרות שאינה קיימת או הוספת אפשרות שכבר קיימת, החזירו <samp>False</samp>.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", "ודאו שהקוד הבא מדפיס רק <samp>True</samp> עבור התוכנית שכתבתם:\n", "</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def cast_multiple_votes(poll, votes):\n", " for vote in votes:\n", " poll.vote(vote)\n", "\n", "\n", "bridge_question = Poll('What is your favourite colour?', ['Blue', 'Yellow'])\n", "cast_multiple_votes(bridge_question, ['Blue', 'Blue', 'Yellow'])\n", "print(bridge_question.get_winner() == 'Blue')\n", "cast_multiple_votes(bridge_question, ['Yellow', 'Yellow'])\n", "print(bridge_question.get_winner() == 'Yellow')\n", "print(bridge_question.get_votes() == [('Yellow', 3), ('Blue', 2)])\n", "bridge_question.remove_option('Yellow')\n", "print(bridge_question.get_winner() == 'Blue')\n", "print(bridge_question.get_votes() == [('Blue', 2)])\n", "bridge_question.add_option('Yellow')\n", "print(bridge_question.get_votes() == [('Blue', 2), ('Yellow', 0)])\n", "print(not bridge_question.add_option('Blue'))\n", "print(bridge_question.get_votes() == [('Blue', 2), ('Yellow', 0)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <span style=\"text-align: right; direction: rtl; float: right; clear: both;\">משחקי הרעב</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " קטניס אוורדין הלכה לאיבוד באיזו זירה מעצבנת, ועכשיו היא מחפשת את הסניף הקרוב של אבו־חסן למנה משולשת ראויה.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " צורת הזירה היא משולש שקודקודיו <span dir=\"ltr\">(0, 0)</span>, <span dir=\"ltr\">(2, 2)</span> ו־<span dir=\"ltr\">(4, 0)</span>.<br>\n", " קטניס מתחילה מאחד הקודקודים ומחליטה על הצעד הבא שלה כך:<br>\n", " \n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<ol style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " <li>היא בוחרת אקראית באחד מקודקודי הזירה.</li>\n", " <li>היא הולכת מהמקום שבו היא נמצאת את מחצית הדרך עד לקודקוד שבחרה.</li>\n", " <li>היא מסמנת על המפה את הנקודה שהגיעה אליה.</li>\n", "</ol>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " כתבו פעולה בשם <var>plot_walks</var>, שמקבלת כפרמטר את מספר הצעדים של קטניס.<br>\n", " הפעולה תצייר מפת נקודות בגודל 4 על 4, שכל נקודה בה מציינת מקום שקטניס סימנה במפה שלה.<br>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p style=\"text-align: right; direction: rtl; float: right; clear: both;\">\n", " השתמשו במנועי חיפוש כדי לקרוא על פעולות קסם שיכולות לעזור לכם, ועל מודולים לשרטוט גרפים.<br>\n", " שימו לב שנקודות יכולות להיות ממוקמות על x ו־y עשרוניים.\n", "</p>" ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

taku-y/bmlingam

doc/notebook/tests/test_cli.ipynb

2

18432

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosave disabled\n", "Temporary files for test_cli()\n", "data_file: /tmp/tmp_mxm6nlz\n", "model_file: /tmp/tmptoxmvoxd\n", "\n", "Made artificial data and saved as /tmp/tmp_mxm6nlz.\n", "---- Algorithm parameters ----\n", "Number of MC samples: 100\n", "Number of candidate models: 1800\n", "\n", "---- Data ----\n", "Data loaded from /tmp/tmp_mxm6nlz.\n", "Data contains 100 samples.\n", "Variable names: ['x1_dst' 'x2_src']\n", "\n", "---- Inference for variables \"x1_dst\" and \"x2_src\" ----\n", "Inferred : x2_src -> x1_dst (posterior prob: 0.711, loglikelihood: -268.213)\n", "(best_rev): x1_dst -> x2_src (posterior prob: 0.015, loglikelihood: -272.078)\n", "\n", "Hyper parameters of the optimal model:\n", "Causality : var2 -> var1\n", "Standardize : True\n", "subtract_mu_reg: False\n", "fix_mu_zero : True\n", "prior_var_mu : auto\n", "prior_indvdl : t\n", "v_indvdl_1 : 0.000000\n", "v_indvdl_2 : 0.200000\n", "df_indvdl : 8.000000\n", "L_cov12/21 : 0.300000\n", "dist_noise : gg\n", "beta_noise : 0.750000\n", "\n", "Hyper parameters of the reverse optimal model:\n", "Causality : var1 -> var2\n", "Standardize : True\n", "subtract_mu_reg: False\n", "fix_mu_zero : True\n", "prior_var_mu : auto\n", "prior_indvdl : t\n", "v_indvdl_1 : 0.200000\n", "v_indvdl_2 : 0.000000\n", "df_indvdl : 8.000000\n", "L_cov12/21 : -0.500000\n", "dist_noise : gg\n", "beta_noise : 0.750000\n", "\n", "Optimal model was saved as /tmp/tmptoxmvoxd.\n", "/tmp/tmptoxmvoxd\n", "---- Variables x1_dst and x2_src ----\n", "Inferred causality : x2_src -> x1_dst\n", "Posterior mean : 0.707533\n", "95% Credible interval: (0.336794, 1.069183)\n", "\n", "True True\n" ] }, { "data": { "image/png": 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gEXdvX8tjqWjk2XnnwZo1MHhw7CRSqqZMCXN+pk5Va6NQUjUQ7u5rgJ8CzwLv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\nIpLY/wPomw+0Uq2rmwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a4e67c18>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Posterior samples was saved as /tmp/tmptoxmvoxd.\n" ] } ], "source": [ "%autosave 0\n", "%matplotlib inline\n", "import os, sys\n", "sys.path.insert(0, os.path.expanduser('~/work/Dropbox/tmp/20160929-bml-github'))\n", "sys.path.insert(0, os.path.expanduser('~/work/git/github/pymc-devs/pymc3'))\n", "\n", "from bmlingam.tests.test_cli import test_cli\n", "\n", "test_cli(plot=True)" ] }, { "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": "3.5.1" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jamesmarva/maths-with-python

04-basic-plotting.ipynb

3

83290

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many Python plotting libraries depending on your purpose. However, the standard general-purpose library is `matplotlib`. This is often used through its `pyplot` interface." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command `%matplotlib inline` is not a Python command, but an *IPython* command. When using the console, or the notebook, it makes the plots appear inline. You do not want to use this in a plain Python code." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1069d35c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi\n", "\n", "x = []\n", "y = []\n", "for i in range(201):\n", " x.append(0.01*i)\n", " y.append(sin(pi*x[-1])**2)\n", "\n", "pyplot.plot(x, y)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have defined two sequences - in this case lists, but tuples would also work. One contains the $x$-axis coordinates, the other the data points to appear on the $y$-axis. A basic plot is produced using the `plot` command of `pyplot`. However, this plot will not automatically appear on the screen, as after plotting the data you may wish to add additional information. Nothing will actually happen until you either save the figure to a file (using `pyplot.savefig(<filename>)`) or explicitly ask for it to be displayed (with the `show` command). When the plot is displayed the program will typically pause until you dismiss the plot.\n", "\n", "This plotting interface is straightforward, but the results are not particularly nice. The following commands illustrate some of the ways of improving the plot:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pqTjs2iWz+aPnhwQZHTnEECf87e+/JcvllCnyufVWWe+n8LdE4bRZRoa02aRJ\nEjbYoYOs1zaLHU5bDx4sLzCZmbBvH5QrJ+u1rXPjtNnHH8vLXmYmHHOMzO6HYLeZ1pCOI2lp0K2b\n1ikoDFu3wmuvSTqN/v2hUiW3JQo227bBQw9JSmqd/FYwCxdKpFKfPv4zxXkyK6uiiqGw1KgBvXrB\niSeqYkgE1arJhENVDIXjtNOkMFIyoY+uGLN5M1x2maSB2LbNbWn8ibXw3XcFb6cUnZ9/llTUStGx\nFrZsgc6dk8MvpsohxpQtK6OFsWNh7ly3pfEXv/8utXuPO05yUimxZ8gQuOMOGTWceabb0vgHa+HU\nU2Wew9ixEnoddNQhHWOqVoUmTfxnl/QCRx4JN98MrVtLviUl9gwaJJPfbrpJ5uIohcMYqetQs6bc\n2xUrui1R/FHloHiGo46Ce+6R72oLjx/GaG2R4lCzptsSJBZVDjHACXfbs0fS+6any6dBA1kf5HC3\nWJFXJtsdOzSTbSxw2nbJEgnBHDw4+3pt2/yJ7pv79sGLL0rIddAz2WooawzZu1fqEvTuLfbJ995z\nWyL/MXs2PPwwzJgBDzwg4a1KbOjVS/J8zZ8PixZF0qMrhePgQaknXb68+G2eecYfqUc8m7I7VvhB\nOTj4Ob2v2/z5J/z2m2S0ff55t6UJJl26SNtqmHXROXBAlKyf7m+d5+ARYlCAKampVUuyX5YtK8va\nnrHDacvy5VUxFJfodN1B75vaRWLErl3QsKGk99V8SiWndWsJbf3sM7clCQaDB8Mbb0jFvSDaxxOF\nteJLfOcdGYEFGVUOMaJiRcmNX79+wdsq+fPTT3DbbZIN8/ff3ZYmGJQuLQ7pc86RkGGleOzbJyk0\npk+H2rWDPRmuQOVgjBlijOlrjLnWGHN0IoTyI8aIE/rcc6XzKMWncWOZybt2LfzrX25LEwxuv13m\nNmzapDUzSkL58lJXevBg+Oc/gx1yXWAoq7X2TmPMqUAz4DljzDnAZ8DL1tokSV57eJwwt6wsseP2\n6JF9fVDD3OJFXiGt0Wh7Fh5ty9iSbO1ZYLSSMaZZeLup4eUbgXlAK2vtwPiLeEgOz0Yr7d8vaXxP\nP12GmaGQOvxiQUYG/Pe/EtJ63nluS+Nf+vWDnTvFRPfOO5EU3Urx2LdPilM9/7xkav3gA7clyp94\nRitdDLQyxnxqjBkMnA7UBTYV9WBBpWxZmfT21FMaCRIrhg+XSmWhEKxa5bY0/ubMM6Wm+bhxMHOm\n29L4n8xtImppAAAgAElEQVRMmX9TpkxkRn8QKcwM6a+AI6y1hyzpxph7gDVxk8qH/PorXHopTJ3q\ntiTB4MorZc5Dnz7i+FOKzwUXiG28UiWp062UjEqVZOSQliZRdaFQsMxJDgW+41prF1hrZ+T4baC1\n9vv4ieUvNm+O2CKD2EncoFIlyZ+fmhr8ePJEENQHmJs47RnU/qkGkBJirUTUvPaaRIQ0a+a2RMHB\nWqhTR1JqBPUGjDfPPy+mj3nzNNleLNmxQ6rD3XyzlBANIqocSogxsGKF1Dy+9FLxOSix4c03ZbZ0\nenqw48njyfXXS+jqsmUSgqnEhqwseWm54gq4/HK3pYkPmpW1mOQMa3vrLSl16eRcCVpYWyKJDg++\n+2547jmYOFE+oG1bGHL2z0WLYPx4+YC2YUlw2rZ+fVi5UqLBqlaNrA9K22rivRIyf75kt/Rj4XG/\noIkMS462YfxIS4Nu3bwbpaiJ91zif/+TZHGDBkm4oBJbnPj8Z56RUEyl8HTrBhdfLCOvrVvdliZ4\nbNgg9/9774lJOWiocighXbrAxo0SLugUplFix4ABkkrDGFi3zm1p/EWnTuKzyciQjxJbSpeWe/6S\nS0RJBA1VDiUkFJKwy7vuCnaeFbd44gkYOhR69oQ//nBbGn9RtWokUVyHDm5LEzxq1ZIR7R13wJQp\nbksTe1Q5lIDx42HbNomkCYIDyqto2xadAweyL2sbxo/UVHkGBM2srMqhBHzxhfga6tdXk0c8WbUK\n3n1X2nvMGLel8Qe9e8OJJ8LXX8O0aW5LE1yWLYMbboCXX5YcYEFCQ1mLSHSIYK1aUuTnrrvk4WVM\ncMLYvIDT1tOnw/r1sGCB1HqYEZ6vr22dG6fNrBUnaf/+MHAgjB0r67XNYoPTzhkZEnK9e7cU+wpS\nKLuGspYQDRFMHNrWRUfbLDF4uZ2LG8qqI4di8vnnUK2aZGhUFC/x99+SlltTcyeWv/+Gv/6SMqJB\nQH0OxSQ9XepF9+0r5ReV+DJ1qmTCbN4cXnjBbWm8zSefSKTSBRfA0qVuSxN8ZswQs3LdupK2PyjE\nXTkYY9oYY5YYY5YbYzrns10TY8wBY8x18ZYpFjzxhDiiRo0SW6MSXxYsEAfrc8+Jn0c5PB07wqOP\nQufOcOGFbksTfI4/Hrp2hS1b4OST3ZYmdsTVrGSMSQHeQgoGrQNmGmNGWWsX57FdH2As4KvZAm3a\nuC1BcnDvvfIBmDTJXVn8QLly2jcTRa1a8NBDbksRe+Ltc2gKrLDWpgMYY4YD1wA580M+BHwONImz\nPDFh4ECJTNq6VaJCdPJbYrE2UrNbyc7mzbB3r9tSJCdZWdL+v/0m1ff8TryVQ12yV4xbC2SrBmyM\nqYsojAsR5eC9kKQwTvjawoXiZ1iwAPbsgXr1ZH0Qwte8iNPuixbJZ+FCyYZ54omyXts90kZLlsA3\n30ho5ezZcPbZsl7bKD447b58OYwcKc+D9HRo2lTW+7nd4xrKaoy5Hmhjrb03vHwbcJ619qGobUYA\nL1trpxtjhgDfWGu/yGNfngtl7d5dwtd05JAYPv4Y9u+HOXPg9de13Q+HtfDgg2LqOOUUt6VJDrZt\nkzkPAwd6L6TVq6Gs64D6Ucv1kdFDNOcAw43c6TWBy4wxmdbaUTl3lhbV6qmpqaS6rJKN0QdUIrn1\nVvmbnq7tnh/GiB1cFUPiqFZNPl4gFAoRikHpxHiPHEoDS4GLgPXADKBdTod01PaDkZHDl3ms88TI\n4aWXpIbDHXfAhAmSpkBJLGlpcN55ko66TBm3pfEOq1dLqpF9+yQRnNfeYJOBLl2gZUt5gfFKOg1P\n1nOw1h4AHgS+BxYBn1prFxtjOhpjOsbz2PGiRQvJDpqWJvWNlcTy+uvw2Wdw9dXi+FMirFwJTz4J\nV16pub7cYNgwqQr32muSZt7vaPqMYuDlqfJBZ/RoSZH+ww86ajscXbuKz6FuXbclSS4yMmQkW7as\nt54RXvU5KEpMufJK+RsDk2pgKVdOFYMbVKzotgSxRZVDIXDC1X76SXK2L1ggbwlOZ/BzuJpfiM6G\nC9Cjh8wzqVoVUlKS+xqEQvDll+JzaNBATBvRJHPbJIrobLibN8M774j/5/jjZb0fr4GalYrAqlVS\n4KdfP3j/fWjiiyl7weOxx6Rub/Xqcj1OOsltidxnzhx48UV5QDVoIGnOlcTzyitS2rZSJXjzTckF\n5jbFNSupcigGXrInJiOTJ8OIEeKcVrJjLTz9NDz/vNuSJCfOzH0vPSM8Ga0UJKxVO7dXaN5czEmg\n1ySaUEjmOJQt67YkyUt0She/901VDoXkwQfh9tvlrUyzsLpPairs3CkzUg8edFsad1m0SN5ShwyR\nvEp+s20Hjb/+kqilp56C7793W5rio8qhkPTtK/nxS5WC005zWxrltdfgmGMkQ+uWLW5L4y7ly4tS\n+PFHuP9+VQ5uM2gQTJwo18XPhX/U51AEvGRHTHaWLoXjjhMnrF4TIS1N8n1pahFv4JXnhc5ziANO\neFpmJpQuLYVmovFjeJrfySukNZpkuibaFt4iaNdDRw6F4IknpPRi9epi1z3nHFfEUHLgZB9t3Bju\nvjs56zvMny/hk6mpMHeuRnB5haVLpYxwxYpwzz1w2WXuyaIjhzjSt6/kxf/0U6hQwW1pFIfTT5cc\nQrt2if/hiivclijx1K4tLy3ffy/zcBRvMG4c/PmnBLH4tfhSEr5rFR1jYNkyeUNt1MhtaRSHSZOk\nVvLQoTBzptvSuEOtWlC5soxstSyod3joIbjqKmjfHubNc1ua4qHKoQA2bZJUGeAve2EyUL26XpNo\ntC28hd+vhyqHAhg6FI4+WtJlJHs8vRdp3BjGjJHQQZ+4z2LG3LnQurXUFZk61f8PoyDy2GOSTmPM\nGLclKTrqkD4M0ZEHmZmSjuChh+RtFfwXeRA0QiF5KL76KtSoITU2nnlGkvBBsK+P0zcPHJDz/vBD\naNVK5uFAsM/dDzjXZ9YsqeU9YQI8+2wkYCLR10dzK8UZr8QsK9nJzJTZqMl8fZL53P2A29dHcyvF\ngUWLYMmS5DNX+IlkLROalaX90k8cPCgvMn5ClUM+TJ4sNt169SRaSfEe1sq1mTkTLrooefxCU6ZI\npFKHDhJTr3iTr7+WEPhatSQk3k+ocsiHe++F++4TZ+cNN7gtjXI4OnQQpVClSvIoh+bNoV07OP98\nrfrmZYyB666DhQth/363pSkaOgmuAIyRYjJaUMabGAPTpsn3tLTkSVdtjDji77vPbUmU/Lj6avn4\nEVUOh+Hnn2V+g9+0vRJ89u3zn/1akcilHTtkhOsHVDnkwAlDW7hQ7Nh//AErV8Ipp8h6DRP0DtF1\ne3//HT76SOo73HGHOKqDdq2c8129WsJXMzNh9mxJ7QLBO1+/41yvOXOkbOumTZLuxakH4/XrpaGs\nBfDUU1Lgp2LFhB9aKQK33w5r10KvXtCsWWS+Q1DJyICOHcXvkIw5pfzEtGkSXfbdd9CzZ+KPr6Gs\nMcaZAFe2rCoGP/DBBxJZ1ry55FwKOjNnih9MFYP3adZMAgdSUvxVOlSVQx6MGCFhZ8uWaSy5H5kw\nwW0J4seOHdIvg3yOQSUrCz7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"text/plain": [ "<matplotlib.figure.Figure at 0x106a00208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi\n", "\n", "x = []\n", "y = []\n", "for i in range(201):\n", " x.append(0.01*i)\n", " y.append(sin(pi*x[-1])**2)\n", "\n", "pyplot.plot(x, y, marker='+', markersize=8, linestyle=':', \n", " linewidth=3, color='b', label=r'$\\sin^2(\\pi x)$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A basic plot')\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whilst most of the commands are self-explanatory, a note should be made of the strings line `r'$x$'`. These strings are in LaTeX format, which is *the* standard typesetting method for professional-level mathematics. The `$` symbols surround mathematics. The `r` before the definition of the string is Python notation, not LaTeX. It says that the following string will be \"raw\": that backslash characters should be left alone. Then, special LaTeX commands have a backslash in front of them: here we use `\\pi` and `\\sin`. Most basic symbols can be easily guess (eg `\\theta` or `\\int`), but there are [useful lists of symbols](http://www.artofproblemsolving.com/wiki/index.php/LaTeX:Symbols), and a [reverse search site](http://detexify.kirelabs.org/classify.html) available. We can also use `^` to denote superscripts (used here), `_` to denote subscripts, and use `{}` to group terms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By combining these basic commands with other plotting types (`semilogx` and `loglog`, for example), most simple plots can be produced quickly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some more examples:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106d98b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi, exp, log\n", "\n", "x = []\n", "y1 = []\n", "y2 = []\n", "for i in range(201):\n", " x.append(1.0+0.01*i)\n", " y1.append(exp(sin(pi*x[-1])))\n", " y2.append(log(pi+x[-1]*sin(x[-1])))\n", "\n", "pyplot.loglog(x, y1, linestyle='--', linewidth=4, \n", " color='k', label=r'$y_1=e^{\\sin(\\pi x)}$')\n", "pyplot.loglog(x, y2, linestyle='-.', linewidth=4, \n", " color='r', label=r'$y_2=\\log(\\pi+x\\sin(x))$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A basic logarithmic plot')\n", "pyplot.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106c63518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi, exp, log\n", "\n", "x = []\n", "y1 = []\n", "y2 = []\n", "for i in range(201):\n", " x.append(1.0+0.01*i)\n", " y1.append(exp(sin(pi*x[-1])))\n", " y2.append(log(pi+x[-1]*sin(x[-1])))\n", "\n", "pyplot.semilogy(x, y1, linestyle='None', marker='o', \n", " color='g', label=r'$y_1=e^{\\sin(\\pi x)}$')\n", "pyplot.semilogy(x, y2, linestyle='None', marker='^', \n", " color='r', label=r'$y_2=\\log(\\pi+x\\sin(x))$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A different logarithmic plot')\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will look at more complex plots later, but the [matplotlib documentation](http://matplotlib.org/api/pyplot_summary.html) contains a lot of details, and the [gallery](http://matplotlib.org/gallery.html) contains a lot of examples that can be adapted to fit. There is also an [extremely useful document](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb) as part of [Johansson's lectures on scientific Python](https://github.com/jrjohansson/scientific-python-lectures)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Logistic map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logistic map builds a sequence of numbers $\\{ x_n \\}$ using the relation\n", "\n", "\\begin{equation}\n", " x_{n+1} = r x_n \\left( 1 - x_n \\right),\n", "\\end{equation}\n", "\n", "where $0 \\le x_0 \\le 1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1\n", "\n", "Write a program that calculates the first $N$ members of the sequence, given as input $x_0$ and $r$ (and, of course, $N$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2\n", "\n", "Fix $x_0=0.5$. Calculate the first 2,000 members of the sequence for $r=1.5$ and $r=3.5$. Plot the last 100 members of the sequence in both cases.\n", "\n", "What does this suggest about the long-term behaviour of the sequence?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3\n", "\n", "Fix $x_0 = 0.5$. For each value of $r$ between $1$ and $4$, in steps of $0.01$, calculate the first 2,000 members of the sequence. Plot the last 1,000 members of the sequence on a plot where the $x$-axis is the value of $r$ and the $y$-axis is the values in the sequence. Do not plot lines - just plot markers (e.g., use the `'k.'` plotting style)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4\n", "\n", "For iterative maps such as the logistic map, one of three things can occur:\n", "\n", "1. The sequence settles down to a *fixed point*.\n", "2. The sequence rotates through a finite number of values. This is called a *limit cycle*.\n", "3. The sequence generates an infinite number of values. This is called *deterministic chaos*.\n", "\n", "Using just your plot, or new plots from this data, work out approximate values of $r$ for which there is a transition from fixed points to limit cycles, from limit cycles of a given number of values to more values, and the transition to chaos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Mandelbrot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Mandelbrot set is also generated from a sequence, $\\{ z_n \\}$, using the relation\n", "\n", "\\begin{equation}\n", " z_{n+1} = z_n^2 + c, \\qquad z_0 = 0.\n", "\\end{equation}\n", "\n", "The members of the sequence, and the constant $c$, are all complex. The point in the complex plane at $c$ is in the Mandelbrot set only if the $|z_n| < 2$ for all members of the sequence. In reality, checking the first 100 iterations is sufficient.\n", "\n", "**Note**: the Python notation for a complex number $x + \\text{i} y$ is `x + yj`: that is, `j` is used to indicate $\\sqrt{-1}$. If you know the values of `x` and `y` then `x + yj` constructs a complex number; if they are stored in variables you can use `complex(x, y)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1\n", "\n", "Write a function that checks if the point $c$ is in the Mandelbrot set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2\n", "\n", "Check the points $c=0$ and $c=\\pm 2 \\pm 2 \\text{i}$ and ensure they do what you expect. (What *should* you expect?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3\n", "\n", "Write a function that, given $N$\n", "\n", "1. generates an $N \\times N$ grid spanning $c = x + \\text{i} y$, for $-2 \\le x \\le 2$ and $-2 \\le y \\le 2$;\n", "2. returns an $N\\times N$ array containing one if the associated grid point is in the Mandelbrot set, and zero otherwise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4\n", "\n", "Using the function `imshow` from `matplotlib`, plot the resulting array for a $100 \\times 100$ array to make sure you see the expected shape." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 5\n", "\n", "Modify your functions so that, instead of returning whether a point is inside the set or not, it returns the logarithm of the number of iterations it takes. Plot the result using `imshow` again." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 6\n", "\n", "Try some higher resolution plots, and try plotting only a section to see the structure. **Note** this is not a good way to get high accuracy close up images!" ] } ], "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" }, "nbconvert": { "title": "Plotting basics" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ellisonbg/talk-2015

12-JupyterLab.ipynb

1

890612

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building Blocks for Interactive Computing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are the building blocks for interactive computing?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".output_png img {\n", " display: block;\n", " margin-left: auto;\n", " margin-right: auto;\n", " margin-top: 16px;\n", " margin-bottom: 16px;\n", " \n", " box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),\n", " 0 1px 10px 0 rgba(0, 0, 0, 0.12),\n", " 0 2px 4px -1px rgba(0, 0, 0, 0.2);\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext load_style\n", "%load_style images.css\n", "from IPython.display import display, Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## File browser" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0a9/CdG3XLNpOoW+SQshzbVfO29sunX+wob1Ceo3LGjcodJ/Z1g9bUq+xxhrm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\n7LXXXm5sSgVuF154oVGw54u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F3o8EtgS2Geuar2DFCGy1o3xC22KFtdqvP49itLDV9lQy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\nnjZ8+HATtvT0XcfqHHwrTH8++b7mGtjmu12/fCl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C7B/sNjvpd5P++w7yuWKCgaPEbbyppA9MOnLpcjx04G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\nQHoEJkyYIFpVqtsXX3whl112Wdgwq1atstWc27Ztk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Oe9e0Ad2a50m1/Pd1dtid7yU9v0N7tbXmUUV4uuUf//3Gnvpgptu8tVqF1+avR5XFT1x4gDWp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\nDG3EImY8n3iczz33nFWoUCFU8cdYEA7iZ3E9bGNPWQmw0s1ioVw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jszxd5vKrKhemhD8J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FxGjRrlNqzyF198Ie3axedo/vzzz+1z575xbbqjPe0DzmwIttAXkUcXeWVRtMCNc09s9HNG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\nqqqzQHSF89aZuxX7gwsVAqizVK1aVc3XrFkzZ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FFRoQPBcQlhc9myZTbqql7E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\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MHy2eunN+W92vnxC8bt0669REW+3qdH1BKHbCpQ61q/sNJdiib28Y35MnT9rcthCn9RpC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\nL4E7HAWGyAEDBtj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EACJEACJEACJEACJEACJEACJEACJEACJEACJEACqUWAgm1qkWY/JEACJEACJEACJEACJEACJEAC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\nVEZzc+VLmZDLXa4pK8XyZZOl5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EKQNohI3ZuXl7ZXltC3I573Gr1UvvllixWg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\nNPsQc4BhHt1blJNK5gWITSZs+dvTVlkecU9N/mfjeQ4PdG142aF5vxmyc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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": { "image/png": { "width": "80%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/lego-filebrowser.png', width='80%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Terminal" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DK96ip+74BB6eY/GR1hprrNF48sknkzRgztUrBnPemT8+aZeij/Bl88u063rG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\nAdhvLf3nF4Ea7777bsMvqjX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HPLAchN+YDodrJ1scT8/V+hHhJbxSMsD1Pex3+B3mq3/3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L5j9/92222XPYci1oWeeeaZZ0Kf3DucugPthJ54ad3fmt12283ReemllzZvv/22Oe2004y9izQK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\niyZtrF2Yx+ua9OiWx7l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NJZlYN57t8NZ7ISAEhIAQEAJCYPpEoGtDZX/AFhsq6zi/D2dQ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QnPod3i6NITpLg4fr5xz+CBBQsWuHPNQNe8efMKm2++eUXtkGVhXNfhhhtuCJYH2mCtBCyS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\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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": { "image/png": { "width": "80%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/lego-terminal.png', width='80%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Text editor (a place to type code)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ggAACCCCAAAIIIIAAAgikXIBgNZnBqhf90n1Lpd7oej6rrBDUWegOVrUV6Ibe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\nWolpS7Q77rjDdCGs3XXq+/3iiy+KthZ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1K96uXbva1WkouWzZMhMW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7g+wlisus+B+yf+4F0BTOCJIF4e5j6M+3iieaBcqAveA83zAEzAJitCKtskK8qbWedQ8zOI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\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fX+bNmxeS0O/bHufYbxgHyYpdGw5L3TDM/6ZtniYPT3vYFgMDNb8+8/Xy57//tIVE1IUg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PV2bMoY6zvMqE7mdRbkHlwpG7QKlVHPgXFzZVQ6mbQJJ66/AXReIK+2ZzP5cnwBNuPALDA0JbPs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6CDrhKtFW9aSyTUT+K8XC+SvnqAV5iAdjwZ+lXnw7Y4Z9L8DkrnZ6wttd16yzLLBx8YQdddxx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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": { "image/png": { "width": "80%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/lego-texteditor.png', width='80%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Output" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 5, "metadata": { "image/png": { "width": "80%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/lego-output.png', width='80%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other building blocks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Kernels (processe for running code)\n", " - Python\n", " - R\n", " - Julia\n", " - Scala\n", "* Document formats for storing code and results\n", " - Notebook document format\n", " - Text files\n", "* Narrative text\n", "* Debugger\n", "* Profiler\n", "* Variable inspector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notebooks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Jupyter Notebook is one way of assembling these building blocks as a linear sequence of input and output. There are other ways of assembling these building blocks:\n", "\n", "---\n", "\n", "* Text editor hooked up to a kernel and output area\n", "* More traditional REPL\n", "* Dashboard with only output\n", "\n", "---\n", "\n", "Recently, we worked with collaborators from IBM to perform a UX survey of Jupyter users. The executive summary can be read [here](https://github.com/jupyter/design/blob/master/surveys/2015-notebook-ux/analysis/report_dashboard.ipynb).\n", "\n", "This survey, along with many years of talking to users has lead us to the following vision:\n", "\n", "<div class=\"alert bg-primary\"> Jupyter needs to provide flexible building blocks for interactive computing that can be assembled and applied to different workflows </div>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## JupyterLab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "JupyterLab in the next generation user interface for project Jupyter that will provide this frame work for assembling these building blocks in different ways. It will ship alongside the existing notebook in version 5.0.\n", "\n", "JupyterLab is an **IDE = *Interactive* Development Environment**" ] } ], "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" }, "widgets": { "state": {}, "version": "1.1.0" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

NelisW/ComputationalRadiometry

07-Optical-Sources.ipynb

1

1167644

mpl-2.0

nick-youngblut/SIPSim

ipynb/bac_genome/n1147/microBetaDiv_valueRanges.ipynb

1

16797

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal\n", "\n", "* Get values ranges for microBetaDiv simulation run\n", " * values for MS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting paths" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "\n", "# paths\n", "workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1147/'\n", "buildDir = os.path.join(workDir, 'microBetaDiv')\n", "R_dir = '/home/nick/notebook/SIPSim/lib/R/'\n", "\n", "fragFile = '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags_kde.pkl'\n", "genome_index = '/var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/genome_index.txt'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Init" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob\n", "import itertools\n", "import nestly" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext rpy2.ipython\n", "%load_ext pushnote" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: \n", "Attaching package: ‘dplyr’\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: \n", "Attaching package: ‘gridExtra’\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following object is masked from ‘package:dplyr’:\n", "\n", " combine\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n" ] } ], "source": [ "%%R\n", "library(ggplot2)\n", "library(dplyr)\n", "library(tidyr)\n", "library(gridExtra)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting results" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/bac_genome1147/microBetaDiv/DESeq2-cMtx_byClass.txt',\n", " '/home/nick/notebook/SIPSim/dev/bac_genome1147/microBetaDiv/DESeq2_l2fcCut0.5-multi-cMtx_byClass.txt',\n", " '/home/nick/notebook/SIPSim/dev/bac_genome1147/microBetaDiv/qSIP-cMtx_byClass.txt',\n", " '/home/nick/notebook/SIPSim/dev/bac_genome1147/microBetaDiv/heavy-cMtx_byClass.txt',\n", " '/home/nick/notebook/SIPSim/dev/bac_genome1147/microBetaDiv/DESeq2_multi-cMtx_byClass.txt']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "F = os.path.join(buildDir, '*-cMtx_byClass.txt')\n", "files = glob.glob(F)\n", "files" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library variables values shared_perc perm_perc rep\n", "1 2 Sensitivity 0.5940594 80 20 7\n", "2 2 Specificity 0.9168421 80 20 7\n", "3 2 Pos Pred Value 0.4316547 80 20 7\n", " file method\n", "1 DESeq2-cMtx_byClass.txt DESeq2\n", "2 DESeq2-cMtx_byClass.txt DESeq2\n", "3 DESeq2-cMtx_byClass.txt DESeq2\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i files\n", "\n", "df_byClass = list()\n", "for (f in files){\n", " ff = strsplit(f, '/') %>% unlist\n", " fff = ff[length(ff)]\n", " df_byClass[[fff]] = read.delim(f, sep='\\t')\n", "}\n", "\n", "df_byClass = do.call(rbind, df_byClass)\n", "df_byClass$file = gsub('\\\\.[0-9]+$', '', rownames(df_byClass))\n", "df_byClass$method = gsub('-.+', '', df_byClass$file)\n", "rownames(df_byClass) = 1:nrow(df_byClass)\n", "df_byClass = filter(df_byClass, perm_perc <= 20)\n", "\n", "df_byClass %>% head(n=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# min/max" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [5 x 4]\n", "Groups: method [?]\n", "\n", " method variables min_val max_val\n", " <chr> <fctr> <dbl> <dbl>\n", "1 DESeq2 Specificity 0.8761805 1.0000000\n", "2 DESeq2_l2fcCut0.5 Specificity 0.8547190 1.0000000\n", "3 DESeq2_multi Specificity 0.8380567 1.0000000\n", "4 heavy Specificity 0.2867470 0.6331658\n", "5 qSIP Specificity 0.6984318 0.9818731\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Specificity') %>%\n", " group_by(method, variables) %>%\n", " summarize(min_val = min(values, na.rm=TRUE),\n", " max_val = max(values, na.rm=TRUE))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [5 x 4]\n", "Groups: method [?]\n", "\n", " method variables min_val max_val\n", " <chr> <fctr> <dbl> <dbl>\n", "1 DESeq2 Sensitivity 0.4368932 0.9036145\n", "2 DESeq2_l2fcCut0.5 Sensitivity 0.7169811 1.0000000\n", "3 DESeq2_multi Sensitivity 0.7373737 1.0000000\n", "4 heavy Sensitivity 0.7619048 1.0000000\n", "5 qSIP Sensitivity 0.5377358 1.0000000\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Sensitivity') %>%\n", " group_by(method, variables) %>%\n", " summarize(min_val = min(values, na.rm=TRUE),\n", " max_val = max(values, na.rm=TRUE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mean +/- s.d." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [5 x 4]\n", "Groups: method [?]\n", "\n", " method variables mean_val sd_val\n", " <chr> <fctr> <dbl> <dbl>\n", "1 DESeq2 Specificity 0.9436168 0.02409554\n", "2 DESeq2_l2fcCut0.5 Specificity 0.9376568 0.03057763\n", "3 DESeq2_multi Specificity 0.9283780 0.03370445\n", "4 heavy Specificity 0.4829208 0.05901246\n", "5 qSIP Specificity 0.9002039 0.05484518\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Specificity') %>%\n", " group_by(method, variables) %>%\n", " summarize(mean_val = mean(values, na.rm=TRUE),\n", " sd_val = sd(values, na.rm=TRUE))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [5 x 4]\n", "Groups: method [?]\n", "\n", " method variables mean_val sd_val\n", " <chr> <fctr> <dbl> <dbl>\n", "1 DESeq2 Sensitivity 0.6444863 0.08562299\n", "2 DESeq2_l2fcCut0.5 Sensitivity 0.8623038 0.07178682\n", "3 DESeq2_multi Sensitivity 0.8670507 0.06916366\n", "4 heavy Sensitivity 0.8843186 0.06473706\n", "5 qSIP Sensitivity 0.7374046 0.11817601\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Sensitivity') %>%\n", " group_by(method, variables) %>%\n", " summarize(mean_val = mean(values, na.rm=TRUE),\n", " sd_val = sd(values, na.rm=TRUE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mean +/- s.d. (by params)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " method variables shared_perc perm_perc mean_val sd_val\n", "1 qSIP Specificity 100 15 0.8578252 0.05452097\n", "2 qSIP Specificity 100 10 0.8610767 0.07027121\n", "3 qSIP Specificity 100 20 0.8696673 0.04016473\n", "4 qSIP Specificity 95 10 0.8750393 0.05087529\n", "5 qSIP Specificity 90 20 0.8864501 0.04312228\n", "6 qSIP Specificity 100 5 0.8870155 0.07076713\n", "7 qSIP Specificity 95 15 0.8899009 0.04000713\n", "8 qSIP Specificity 85 10 0.8921552 0.05463595\n", "9 qSIP Specificity 80 10 0.8933374 0.06100142\n", "10 qSIP Specificity 90 5 0.8945461 0.05550165\n", "11 qSIP Specificity 95 5 0.8952365 0.07230950\n", "12 qSIP Specificity 85 20 0.8961290 0.05672697\n", "13 qSIP Specificity 90 15 0.8980968 0.06084383\n", "14 qSIP Specificity 80 5 0.9054185 0.04751081\n", "15 qSIP Specificity 85 15 0.9072644 0.04392689\n", "16 qSIP Specificity 95 20 0.9078017 0.06382407\n", "17 qSIP Specificity 80 20 0.9119918 0.04781756\n", "18 qSIP Specificity 95 0 0.9121775 0.03901323\n", "19 qSIP Specificity 100 0 0.9152231 0.04975427\n", "20 qSIP Specificity 90 0 0.9202989 0.04838376\n", "21 qSIP Specificity 80 15 0.9217062 0.04433629\n", "22 qSIP Specificity 90 10 0.9246495 0.04682681\n", "23 qSIP Specificity 85 5 0.9251176 0.03179668\n", "24 qSIP Specificity 80 0 0.9276176 0.03548019\n", "25 qSIP Specificity 85 0 0.9293541 0.04049040\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Specificity',\n", " method=='qSIP') %>%\n", " group_by(method, variables, shared_perc, perm_perc) %>%\n", " summarize(mean_val = mean(values, na.rm=TRUE),\n", " sd_val = sd(values, na.rm=TRUE)) %>%\n", " arrange(mean_val) %>%\n", " as.data.frame" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " method variables shared_perc perm_perc mean_val sd_val\n", "1 qSIP Sensitivity 80 15 0.6179407 0.02374451\n", "2 qSIP Sensitivity 80 20 0.6331728 0.04369966\n", "3 qSIP Sensitivity 80 10 0.6348964 0.03625015\n", "4 qSIP Sensitivity 85 0 0.6457721 0.03598500\n", "5 qSIP Sensitivity 85 10 0.6459374 0.05489723\n", "6 qSIP Sensitivity 80 5 0.6486920 0.04008792\n", "7 qSIP Sensitivity 80 0 0.6503242 0.04281886\n", "8 qSIP Sensitivity 85 5 0.6569599 0.04067362\n", "9 qSIP Sensitivity 85 15 0.6651051 0.02761506\n", "10 qSIP Sensitivity 85 20 0.6666168 0.04769413\n", "11 qSIP Sensitivity 90 5 0.6947537 0.01960167\n", "12 qSIP Sensitivity 90 15 0.7030872 0.02989937\n", "13 qSIP Sensitivity 90 20 0.7080290 0.03330200\n", "14 qSIP Sensitivity 90 0 0.7103585 0.03044823\n", "15 qSIP Sensitivity 90 10 0.7140962 0.05075187\n", "16 qSIP Sensitivity 95 5 0.7173819 0.03749605\n", "17 qSIP Sensitivity 95 15 0.7238655 0.04690307\n", "18 qSIP Sensitivity 95 20 0.7348206 0.02857758\n", "19 qSIP Sensitivity 95 0 0.7556593 0.04552502\n", "20 qSIP Sensitivity 95 10 0.7597457 0.03589931\n", "21 qSIP Sensitivity 100 5 0.9406894 0.02736742\n", "22 qSIP Sensitivity 100 15 0.9442823 0.03504697\n", "23 qSIP Sensitivity 100 10 0.9474809 0.01864351\n", "24 qSIP Sensitivity 100 20 0.9506753 0.02259105\n", "25 qSIP Sensitivity 100 0 0.9647720 0.02194082\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " filter(variables == 'Sensitivity',\n", " method=='qSIP') %>%\n", " group_by(method, variables, shared_perc, perm_perc) %>%\n", " summarize(mean_val = mean(values, na.rm=TRUE),\n", " sd_val = sd(values, na.rm=TRUE)) %>%\n", " arrange(mean_val) %>%\n", " as.data.frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Difference between qSIP and MW-HR-SIP" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " mean_diff\n", "1 0.04341295\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "df_byClass %>%\n", " dplyr::select(-file) %>%\n", " group_by(method, variables, shared_perc, perm_perc) %>%\n", " summarize(mean_val = mean(values, na.rm=TRUE)) %>%\n", " ungroup() %>%\n", " filter(variables %in% c('Specificity'),\n", " method %in% c('qSIP', 'DESeq2')) %>%\n", " spread(method, mean_val) %>% \n", " mutate(diff = DESeq2 - qSIP) %>%\n", " arrange(diff) %>%\n", " summarize(mean_diff = mean(diff)) %>%\n", " as.data.frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": true, "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 }

mit

IST256/learn-python

content/lessons/08-Lists/ETEE-Bad-Password-Checker.ipynb

1

2962

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# End-To-End Example: Bad Password Checker\n", "\n", "- Read in list of bad passwords from file `bad-passwords.txt`\n", "- Main program loop which:\n", " - inputs a password \n", " - checks whether the password is \"good\" or \"bad\" by checking against the list\n", " - repeats this until you enter no password. \n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 97k 100 97k 0 0 350k 0 --:--:-- --:--:-- --:--:-- 350k\n" ] } ], "source": [ "!curl https://raw.githubusercontent.com/mafudge/datasets/master/ist256/08-Lists/bad-passwords.txt -o bad-passwords.txt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your password:········\n", "Don't use the password 12345!\n", "Enter your password:········\n", "Don't use the password qwerty!\n", "Enter your password:········\n", "Don't use the password zxcvb!\n", "Enter your password:········\n", "The Password sdahjfgkjfhagtrew seems ok!\n", "Passwords you tried: ['12345', 'qwerty', 'zxcvb']\n" ] } ], "source": [ "from getpass import getpass\n", "\n", "def read_into_list(filename):\n", " the_list = []\n", " with open(filename,\"r\") as f:\n", " for line in f:\n", " the_item = line.strip()\n", " the_list.append(the_item)\n", " \n", " return the_list\n", "\n", "\n", "passwords = read_into_list(\"bad-passwords.txt\")\n", "tried = []\n", "while True:\n", " candidate = getpass(\"Enter your password:\") \n", " if passwords.count(candidate) > 0:\n", " print(f\"Don't use the password {candidate}!\")\n", " tried.append(candidate)\n", " else:\n", " print(f\"The Password {candidate} seems ok!\")\n", " print(f\"Passwords you tried: {tried}\")\n", " break" ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

tensorflow/docs-l10n

site/ko/tutorials/estimator/premade.ipynb

1

21019

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1Z6Wtb_jisbA" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "QUyRGn9riopB" }, "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": "H1yCdGFW4j_F" }, "source": [ "# 사전 제작 Estimator" ] }, { "cell_type": "markdown", "metadata": { "id": "PS6_yKSoyLAl" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/estimator/premade\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org에서 보기</a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab에서 실행하기</a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub에서소스 보기</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/estimator/premade.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">노트북 다운로드하기</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "R4YZ_ievcY7p" }, "source": [ "이 튜토리얼에서는 Estimator를 사용하여 TensorFlow에서 Iris 분류 문제를 해결하는 방법을 보여줍니다. Estimator는 완전한 모델을 TensorFlow에서 높은 수준으로 표현한 것이며, 간편한 크기 조정과 비동기식 훈련에 목적을 두고 설계되었습니다. 자세한 내용은 [Estimator](https://www.tensorflow.org/guide/estimator)를 참조하세요.\n", "\n", "TensorFlow 2.0에서 [Keras API](https://www.tensorflow.org/guide/keras)는 이러한 작업을 상당 부분 동일하게 수행할 수 있으며 배우기 쉬운 API로 여겨집니다. 새로 시작하는 경우 Keras로 시작하는 것이 좋습니다. TensorFlow 2.0에서 사용 가능한 고급 API에 대한 자세한 정보는 [Keras에 표준화](https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a)를 참조하세요.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8IFct0yedsTy" }, "source": [ "## 시작을 위한 준비\n", "\n", "시작하려면 먼저 TensorFlow와 필요한 여러 라이브러리를 가져옵니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jPo5bQwndr9P" }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "id": "c5w4m5gncnGh" }, "source": [ "## 데이터세트\n", "\n", "이 문서의 샘플 프로그램은 아이리스 꽃을 [꽃받침잎](https://en.wikipedia.org/wiki/Sepal)과 [꽃잎](https://en.wikipedia.org/wiki/Petal)의 크기에 따라 세 가지 종으로 분류하는 모델을 빌드하고 테스트합니다.\n", "\n", "Iris 데이터세트를 사용하여 모델을 훈련합니다. Iris 데이터세트에는 네 가지 특성과 하나의 [레이블](https://developers.google.com/machine-learning/glossary/#label)이 있습니다. 이 네 가지 특성은 개별 아이리스 꽃의 다음과 같은 식물 특성을 식별합니다.\n", "\n", "- 꽃받침잎 길이\n", "- 꽃받침잎 너비\n", "- 꽃잎 길이\n", "- 꽃잎 너비\n", "\n", "이 정보를 바탕으로 데이터를 구문 분석하는 데 도움이 되는 몇 가지 상수를 정의할 수 있습니다.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lSyrXp_He_UE" }, "outputs": [], "source": [ "CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']\n", "SPECIES = ['Setosa', 'Versicolor', 'Virginica']" ] }, { "cell_type": "markdown", "metadata": { "id": "j6mTfIQzfC9w" }, "source": [ "그 다음, Keras 및 Pandas를 사용하여 Iris 데이터세트를 다운로드하고 구문 분석합니다. 훈련 및 테스트를 위해 별도의 데이터세트를 유지합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PumyCN8VdGGc" }, "outputs": [], "source": [ "train_path = tf.keras.utils.get_file(\n", " \"iris_training.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\")\n", "test_path = tf.keras.utils.get_file(\n", " \"iris_test.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\")\n", "\n", "train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)\n", "test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "wHFxNLszhQjz" }, "source": [ "데이터를 검사하여 네 개의 float 특성 열과 하나의 int32 레이블이 있는지 확인할 수 있습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WOJt-ML4hAwI" }, "outputs": [], "source": [ "train.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "jQJEYfVvfznP" }, "source": [ "각 데이터세트에 대해 예측하도록 모델을 훈련할 레이블을 분할합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zM0wz2TueuA6" }, "outputs": [], "source": [ "train_y = train.pop('Species')\n", "test_y = test.pop('Species')\n", "\n", "# The label column has now been removed from the features.\n", "train.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "jZx1L_1Vcmxv" }, "source": [ "## Estimator를 사용한 프로그래밍 개요\n", "\n", "이제 데이터가 설정되었으므로 TensorFlow Estimator를 사용하여 모델을 정의할 수 있습니다. Estimator는 `tf.estimator.Estimator`에서 파생된 임의의 클래스입니다. TensorFlow는 일반적인 ML 알고리즘을 구현하기 위해 `tf.estimator`(예: `LinearRegressor`) 모음을 제공합니다. 그 외에도 고유한 [사용자 정의 Estimator](https://www.tensorflow.org/guide/custom_estimators)를 작성할 수 있습니다. 처음 시작할 때는 사전 제작된 Estimator를 사용하는 것이 좋습니다.\n", "\n", "사전 제작된 Estimator를 기초로 TensorFlow 프로그램을 작성하려면 다음 작업을 수행해야 합니다.\n", "\n", "- 하나 이상의 입력 함수를 작성합니다.\n", "- 모델의 특성 열을 정의합니다.\n", "- 특성 열과 다양한 하이퍼 매개변수를 지정하여 Estimator를 인스턴스화합니다.\n", "- Estimator 객체에서 하나 이상의 메서드를 호출하여 적합한 입력 함수를 데이터 소스로 전달합니다.\n", "\n", "이러한 작업이 Iris 분류를 위해 어떻게 구현되는지 알아보겠습니다." ] }, { "cell_type": "markdown", "metadata": { "id": "2OcguDfBcmmg" }, "source": [ "## 입력 함수 작성하기\n", "\n", "훈련, 평가 및 예측을 위한 데이터를 제공하려면 입력 함수를 작성해야 합니다.\n", "\n", "**입력 함수**는 다음 두 요소 튜플을 출력하는 `tf.data.Dataset` 객체를 반환하는 함수입니다.\n", "\n", "- [`features`](https://developers.google.com/machine-learning/glossary/#feature) -다음과 같은 Python 사전:\n", " - 각 키가 특성의 이름입니다.\n", " - 각 값은 해당 특성 값을 모두 포함하는 배열입니다.\n", "- `label` - 모든 예제의 [레이블](https://developers.google.com/machine-learning/glossary/#label) 값을 포함하는 배열입니다.\n", "\n", "입력 함수의 형식을 보여주기 위해 여기에 간단한 구현을 나타냈습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nzr5vRr5caGF" }, "outputs": [], "source": [ "def input_evaluation_set():\n", " features = {'SepalLength': np.array([6.4, 5.0]),\n", " 'SepalWidth': np.array([2.8, 2.3]),\n", " 'PetalLength': np.array([5.6, 3.3]),\n", " 'PetalWidth': np.array([2.2, 1.0])}\n", " labels = np.array([2, 1])\n", " return features, labels" ] }, { "cell_type": "markdown", "metadata": { "id": "NpXvGjfnjHgY" }, "source": [ "입력 함수에서 원하는 대로 `features` 사전 및 `label` 목록이 생성되도록 할 수 있습니다. 그러나 모든 종류의 데이터를 구문 분석할 수 있는 TensorFlow의 [Dataset API](https://www.tensorflow.org/guide/datasets)를 사용하는 것이 좋습니다.\n", "\n", "Dataset API는 많은 일반적인 경우를 자동으로 처리할 수 있습니다. 예를 들어, Dataset API를 사용하면 대규모 파일 모음에서 레코드를 병렬로 쉽게 읽고 이를 단일 스트림으로 결합할 수 있습니다.\n", "\n", "이 예제에서는 작업을 단순화하기 위해 [pandas](https://pandas.pydata.org/) 데이터를 로드하고 이 인메모리 데이터에서 입력 파이프라인을 빌드합니다.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T20u1anCi8NP" }, "outputs": [], "source": [ "def input_fn(features, labels, training=True, batch_size=256):\n", " \"\"\"An input function for training or evaluating\"\"\"\n", " # Convert the inputs to a Dataset.\n", " dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))\n", "\n", " # Shuffle and repeat if you are in training mode.\n", " if training:\n", " dataset = dataset.shuffle(1000).repeat()\n", " \n", " return dataset.batch(batch_size)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xIwcFT4MlZEi" }, "source": [ "## 특성 열 정의하기\n", "\n", "[**특성 열**](https://developers.google.com/machine-learning/glossary/#feature_columns)은 모델이 특성 사전의 원시 입력 데이터를 사용하는 방식을 설명하는 객체입니다. Estimator 모델을 빌드할 때는 모델에서 사용할 각 특성을 설명하는 특성 열 목록을 전달합니다. `tf.feature_column` 모듈은 모델에 데이터를 나타내기 위한 많은 옵션을 제공합니다.\n", "\n", "Iris의 경우 4개의 원시 특성은 숫자 값이므로, 네 개의 특성 각각을 32-bit 부동 소수점 값으로 나타내도록 Estimator 모델에 알려주는 특성 열 목록을 빌드합니다. 따라서 특성 열을 작성하는 코드는 다음과 같습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZTTriO8FlSML" }, "outputs": [], "source": [ "# Feature columns describe how to use the input.\n", "my_feature_columns = []\n", "for key in train.keys():\n", " my_feature_columns.append(tf.feature_column.numeric_column(key=key))" ] }, { "cell_type": "markdown", "metadata": { "id": "jpKkhMoZljco" }, "source": [ "특성 열은 여기에 표시된 것보다 훨씬 정교할 수 있습니다. [이 가이드](https://www.tensorflow.org/guide/feature_columns)에서 특성 열에 대한 자세한 내용을 읽을 수 있습니다.\n", "\n", "모델이 원시 특성을 나타내도록 할 방식에 대한 설명이 준비되었으므로 Estimator를 빌드할 수 있습니다." ] }, { "cell_type": "markdown", "metadata": { "id": "kuE59XHEl22K" }, "source": [ "## Estimator 인스턴스화하기\n", "\n", "Iris 문제는 고전적인 분류 문제입니다. 다행히도 TensorFlow는 다음을 포함하여 여러 가지 사전 제작된 분류자 Estimator를 제공합니다.\n", "\n", "- 다중 클래스 분류를 수행하는 심층 모델을 위한 `tf.estimator.DNNClassifier`\n", "- 넓고 깊은 모델을 위한 `tf.estimator.DNNLinearCombinedClassifier`\n", "- 선형 모델에 기초한 분류자를 위한 `tf.estimator.LinearClassifier`\n", "\n", "Iris 문제의 경우 `tf.estimator.DNNClassifier`가 최선의 선택인 것으로 여겨집니다. 이 Estimator를 인스턴스화하는 방법은 다음과 같습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qnf4o2V5lcPn" }, "outputs": [], "source": [ "# Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.\n", "classifier = tf.estimator.DNNClassifier(\n", " feature_columns=my_feature_columns,\n", " # Two hidden layers of 30 and 10 nodes respectively.\n", " hidden_units=[30, 10],\n", " # The model must choose between 3 classes.\n", " n_classes=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "tzzt5nUpmEe3" }, "source": [ "## 훈련, 평가 및 예측하기\n", "\n", "이제 Estimator 객체가 준비되었으므로 메서드를 호출하여 다음을 수행할 수 있습니다.\n", "\n", "- 모델을 훈련합니다.\n", "- 훈련한 모델을 평가합니다.\n", "- 훈련한 모델을 사용하여 예측을 수행합니다." ] }, { "cell_type": "markdown", "metadata": { "id": "rnihuLdWmE75" }, "source": [ "### 모델 훈련하기\n", "\n", "다음과 같이 Estimator의 `train` 메서드를 호출하여 모델을 훈련합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4jW08YtPl1iS" }, "outputs": [], "source": [ "# Train the Model.\n", "classifier.train(\n", " input_fn=lambda: input_fn(train, train_y, training=True),\n", " steps=5000)" ] }, { "cell_type": "markdown", "metadata": { "id": "ybiTFDmlmes8" }, "source": [ "Estimator가 예상한 대로 인수를 사용하지 않는 입력 함수를 제공하면서 인수를 포착하기 위해 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html)에서 `input_fn` 호출을 래핑합니다. `steps` 인수는 여러 훈련 단계를 거친 후에 훈련을 중지하도록 메서드에 지시합니다.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "HNvJLH8hmsdf" }, "source": [ "### 훈련한 모델 평가하기\n", "\n", "모델을 훈련했으므로 성능에 대한 통계를 얻을 수 있습니다. 다음 코드 블록은 테스트 데이터에서 훈련한 모델의 정확도를 평가합니다.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A169XuO4mKxF" }, "outputs": [], "source": [ "eval_result = classifier.evaluate(\n", " input_fn=lambda: input_fn(test, test_y, training=False))\n", "\n", "print('\\nTest set accuracy: {accuracy:0.3f}\\n'.format(**eval_result))" ] }, { "cell_type": "markdown", "metadata": { "id": "VnPMP5EHph17" }, "source": [ "`train` 메서드에 대한 호출과 달리 평가할 `steps` 인수를 전달하지 않았습니다. eval에 대한 `input_fn`은 단 하나의 데이터 [epoch](https://developers.google.com/machine-learning/glossary/#epoch)만 생성합니다.\n", "\n", "`eval_result` 사전에는 `average_loss`(샘플당 평균 손실), `loss`(미니 배치당 평균 손실) 및 Estimator의 `global_step` 값(받은 훈련 반복 횟수)도 포함됩니다.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ur624ibpp52X" }, "source": [ "### 훈련한 모델에서 예측(추론)하기\n", "\n", "우수한 평가 결과를 생성하는 훈련한 모델을 만들었습니다. 이제 훈련한 모델을 사용하여 레이블이 지정되지 않은 일부 측정을 바탕으로 아이리스 꽃의 종을 예측할 수 있습니다. 훈련 및 평가와 마찬가지로 단일 함수 호출을 사용하여 예측합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wltc0jpgng38" }, "outputs": [], "source": [ "# Generate predictions from the model\n", "expected = ['Setosa', 'Versicolor', 'Virginica']\n", "predict_x = {\n", " 'SepalLength': [5.1, 5.9, 6.9],\n", " 'SepalWidth': [3.3, 3.0, 3.1],\n", " 'PetalLength': [1.7, 4.2, 5.4],\n", " 'PetalWidth': [0.5, 1.5, 2.1],\n", "}\n", "\n", "def input_fn(features, batch_size=256):\n", " \"\"\"An input function for prediction.\"\"\"\n", " # Convert the inputs to a Dataset without labels.\n", " return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)\n", "\n", "predictions = classifier.predict(\n", " input_fn=lambda: input_fn(predict_x))" ] }, { "cell_type": "markdown", "metadata": { "id": "JsETKQo0rHvi" }, "source": [ "`predict` 메서드는 Python iterable을 반환하여 각 예제에 대한 예측 결과 사전을 생성합니다. 다음 코드는 몇 가지 예측과 해당 확률을 출력합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Efm4mLzkrCxp" }, "outputs": [], "source": [ "for pred_dict, expec in zip(predictions, expected):\n", " class_id = pred_dict['class_ids'][0]\n", " probability = pred_dict['probabilities'][class_id]\n", "\n", " print('Prediction is \"{}\" ({:.1f}%), expected \"{}\"'.format(\n", " SPECIES[class_id], 100 * probability, expec))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "premade.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

HSE-LaMBDA/modern-technologies-for-ml-and-big-data

lecture9/Word2Vec.ipynb

1

5853

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "println(sc.version)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val wreg = \"\"\"\\w+\"\"\".r" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val sentences = sc.textFile(\"file:///home/jovyan/work/data/corpus/*/*.txt\", minPartitions=128).map {\n", " case line =>\n", " wreg.findAllIn(line).filter { _.forall { _.isLetter } }.map { word => word.toLowerCase }.toVector\n", "}.filter{ _.size > 10 }.persist()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4036418\n", "Vector(please, take, a, look, at, the, important, information, in, this, header)\n", "Vector(we, encourage, you, to, keep, this, file, on, your, own, disk, keeping, an)\n", "Vector(electronic, path, open, for, the, next, readers, do, not, remove, this)\n", "Vector(this, etext, was, done, by, a, number, of, anonymous, volunteers, of, the)\n", "Vector(gutenberg, projec, to, whom, we, owe, a, great, deal, of, thanks, and, to)\n", "Vector(if, our, etexts, manage, to, make, it, to, an, average, about, million)\n", "Vector(then, we, will, have, given, about, billion, etexts, away, when, the)\n", "Vector(we, are, now, trying, to, release, all, our, books, one, month, in, advance)\n", "Vector(the, official, release, date, of, all, project, gutenberg, etexts, is, at)\n", "Vector(midnight, central, time, of, the, last, day, of, the, stated, month, a)\n", "Vector(and, editing, by, those, who, wish, to, do, so, to, be, sure, you, have, an)\n", "Vector(in, the, first, week, of, the, next, month, since, our, ftp, program, has)\n", "Vector(a, bug, in, it, that, scrambles, the, date, tried, to, fix, and, failed, a)\n", "Vector(look, at, the, file, size, will, have, to, do, but, we, will, try, to, see, a)\n", "Vector(we, produce, about, two, million, dollars, for, each, hour, we, work, the)\n", "Vector(fifty, hours, is, one, conservative, estimate, for, how, long, it, we, take)\n", "Vector(per, text, is, nominally, estimated, at, one, dollar, then, we, produce)\n", "Vector(million, dollars, per, hour, this, year, we, will, have, to, do, four, text)\n", "Vector(the, goal, of, project, gutenberg, is, to, give, away, one, trillion, etext)\n", "Vector(this, is, ten, thousand, titles, each, to, one, hundred, million, readers)\n" ] } ], "source": [ "println(sentences.count())\n", "println(sentences.take(20).mkString(\"\\n\") )" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val Array(train, test) = sentences.randomSplit(Array(0.25, 0.75), seed=333L)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MapPartitionsRDD[6] at randomSplit at <console>:18" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.persist()\n", "test.persist()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import org.apache.spark.mllib._\n", "import org.apache.spark.mllib.feature._" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val model = new Word2Vec().\n", " setLearningRate(0.1).\n", " setMinCount(10).\n", " setNumPartitions(16).\n", " setVectorSize(100).\n", " fit(sentences)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val mother = model.transform(\"king\").toArray\n", "val woman = model.transform(\"man\").toArray\n", "val man = model.transform(\"woman\").toArray" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val queenLike = mother.zip(woman).\n", " map{ case (a: Double, b: Double) => a - b }.zip(man).\n", " map { case (a, b) => a + b}" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "val queen = model.transform(\"queen\").toArray" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def norm(vec: Array[Double]): Double = {\n", " math.sqrt(vec.map { x => x * x }.sum)\n", "}" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.3718098920732813" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "queen.zip(queenLike).map { case (a, b) => a * b }.sum / norm(queen) / norm(queenLike)" ] } ], "metadata": { "kernelspec": { "display_name": "Scala 2.10.4", "language": "scala", "name": "scala" }, "language_info": { "name": "scala" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

SiggyF/notebooks

esmf regrid.ipynb

1

6029

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "os.environ['PATH'] = os.environ['PATH'] + ':' + '/home/fedor/Checkouts/esmf/DEFAULTINSTALLDIR/bin/binO/Linux.gfortran.64.mpiuni.default'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# This should show a missing source\n", "!ESMF_RegridWeightGen" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " ERROR: The required argument [-s|--source] is missing.\r\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "import netCDF4\n", "src = netCDF4.Dataset('src.nc', mode='w', clobber=True)\n", "src.createDimension('nodes', 10)\n", "src.createDimension('elems', 13)\n", "src.createDimension('three', 3)\n", "mesh = src.createVariable('mesh', 'int32')\n", "mesh.standard_name = 'mesh_topology'\n", "mesh.dimension = 2\n", "mesh.node_coordinates = \"lon lat\"\n", "mesh.face_node_connectivity = \"nv\"\n", "\n", "nv = src.createVariable(\"nv\", \"int32\", dimensions=(\"elems\", \"three\"))\n", "nv.standard_name = \"face_node_connectivity\"\n", "nv.start_index = 1\n", "\n", "lon = src.createVariable(\"lon\", \"float\", dimensions=(\"nodes\",))\n", "lon.standard_name = \"longitude\"\n", "lon.units = \"degrees_east\" \n", "\n", "lat = src.createVariable(\"lat\", \"float\", dimensions=(\"nodes\",))\n", "lat.standard_name = \"latitude\"\n", "lat.units = \"degrees_north\" \n", "src.close()\n", "\n", "dst = netCDF4.Dataset('dst.nc', mode='w', clobber=True)\n", "dst.createDimension('nodes', 10)\n", "dst.createDimension('elems', 13)\n", "dst.createDimension('three', 3)\n", "mesh = dst.createVariable('mesh', 'int32')\n", "mesh.standard_name = 'mesh_topology'\n", "mesh.dimension = 2\n", "mesh.node_coordinates = \"lon lat\"\n", "mesh.face_node_connectivity = \"nv\"\n", "\n", "nv = dst.createVariable(\"nv\", \"int32\", dimensions=(\"elems\", \"three\"))\n", "nv.standard_name = \"face_node_connectivity\"\n", "nv.start_index = 1\n", "\n", "lon = dst.createVariable(\"lon\", \"float\", dimensions=(\"nodes\",))\n", "lon.standard_name = \"longitude\"\n", "lon.units = \"degrees_east\" \n", "\n", "lat = dst.createVariable(\"lat\", \"float\", dimensions=(\"nodes\",))\n", "lat.standard_name = \"latitude\"\n", "lat.units = \"degrees_north\" \n", "dst.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "ncdump -h src.nc\n", "rm PET*.RegridWeightGen.Log\n", "ESMF_RegridWeightGen -s src.nc -d dst.nc --src_type UGRID --src_meshname mesh -m conserve --dst_type UGRID --dst_meshname mesh -w w.nc\n", "cat PET*.RegridWeightGen.Log\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "netcdf src {\n", "dimensions:\n", "\tnodes = 10 ;\n", "\telems = 13 ;\n", "\tthree = 3 ;\n", "variables:\n", "\tint mesh ;\n", "\t\tmesh:standard_name = \"mesh_topology\" ;\n", "\t\tmesh:dimension = 2L ;\n", "\t\tmesh:node_coordinates = \"lon lat\" ;\n", "\t\tmesh:face_node_connectivity = \"nv\" ;\n", "\tint nv(elems, three) ;\n", "\t\tnv:standard_name = \"face_node_connectivity\" ;\n", "\t\tnv:start_index = 1L ;\n", "\tdouble lon(nodes) ;\n", "\t\tlon:standard_name = \"longitude\" ;\n", "\t\tlon:units = \"degrees_east\" ;\n", "\tdouble lat(nodes) ;\n", "\t\tlat:standard_name = \"latitude\" ;\n", "\t\tlat:units = \"degrees_north\" ;\n", "}\n", " Starting weight generation with these inputs: \n", " Source File: src.nc\n", " Destination File: dst.nc\n", " Weight File: w.nc\n", " Source File is in UGRID format, dummy variable: mesh\n", " Source Grid is a global grid\n", " Source Grid is an unstructured grid\n", " Destination File is in UGRID format, dummy variable: mesh\n", " Destination Grid is a global grid\n", " Destination Grid is an unstructured grid\n", " Regrid Method: conserve\n", " Pole option: NONE\n", "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\n", "Program received signal SIGSEGV: Segmentation fault - invalid memory reference.\n", "\n", "Backtrace for this error:\n", "#0 0x7FCD61454117\n", "#1 0x7FCD614546F4\n", "#2 0x7FCD610A90AF\n", "#3 0x7FCD62CA949D\n", "#4 0x7FCD62CADE31\n", "#5 0x7FCD62CBEB9B\n", "#6 0x40766B in esmf_regridweightgenapp at ESMF_RegridWeightGen.F90:623\n", "bash: line 3: 875 Segmentation fault (core dumped) ESMF_RegridWeightGen -s src.nc -d dst.nc --src_type UGRID --src_meshname mesh -m conserve --dst_type UGRID --dst_meshname mesh -w w.nc\n" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

celiacintas/dss_practica

ipynb/fit & predict.ipynb

1

5788145

gpl-2.0

jnw29/AvrPto_Transcriptome

List_generator_for_genes_meeting_specific_cutoffs_vs_Mock.ipynb

1

12226

{ "metadata": { "name": "", "signature": "sha256:2b16da5cdf28b881e19c6358b7fa3f2a4148a48f9faa183aa4cd686f1e98627f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#the point of this notebook is to be able to call out specific sets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#import modules\n", "import numpy as np\n", "import xlrd as xl\n", "import xlwt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Load book, define sheet as X1, print the number of rows, set n to number of rows\n", "#of sheet 1 column 1, numbers is column,row format. List is X1a, creates array z.\n", "#Create new Workbook\n", "X1 = xl.open_workbook('avpfloat.xlsx').sheet_by_name(\"NoFormulas\")\n", "X2 = xl.open_workbook('avpfloat.xlsx').sheet_by_name(\"NoFormulasBECE\")\n", "book=xlwt.Workbook()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#ID test, should be 34730 x 28\n", "m =X1.ncols\n", "n =X1.nrows\n", "print n,\"x\",m\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "34730 x 28\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#ID test, should be 34730 x 10\n", "m =X2.ncols\n", "n =X2.nrows\n", "print n,\"x\",m" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "34730 x 10\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#Test and append up and down regulated genes of each domain\n", "#a-> RPKM A_mean (WT)\n", "#b-> RPKM B_mean (I96A)\n", "#c-> RPKM C_mean (2xA)\n", "#d-> RPKM D_mean (I96A + 2xA)\n", "#e-> RPKM E_mean (Mock)\n", "#pae-> pvalue AD\n", "#pbe-> pvalue BD\n", "#pce-> pvalue CD\n", "#pde-> pvalue DE\n", "#rae-> Ratio AD\n", "#rbe-> Ratio BD\n", "#rce-> Ratio CD\n", "#rde-> Ratio DE\n", "#AvrPtoUp-> List of genes significantly up-regulated in D29E pCPP45::avrPto relative to Mock\n", "#AvrPtoDown-> List of genes significantly down-regulated in D29E pCPP45::avrPto relative to Mock\n", "#CTDUp-> List of genes significantly up-regulated in D29E pCPP45::avrPto I96A relative to D29E Mock\n", "#CTDDown-> List of genes significantly down-regulated in D29E pCPP45::avrPto I96A relative to Mock\n", "#CoreUp-> List of genes significantly up-regulated in D29E pCPP45::avrPto 2xA relative to Mock\n", "#CoreDown-> List of genes significantly down-regulated in D29E pCPP45::avrPto 2xA relative to Mock\n", "#BothUp-> List of genes significantly up-regulated in D29E pCPP45 AvrPto I96A, 2xA relative to Mock\n", "#BothDown-> List of genes significantly down-regulated in D29E pCPP45 AvrPto I96A, 2xA relative to Mock\n", "#Description, Ratio and pValue lists also made - shortened titles.ID = GeneID, D = description\n", "#R = Ratio, P = adjust p\n", "\n", "AvrPtoUpID= []\n", "AvrPtoUpD= []\n", "AvrPtoUpR= []\n", "AvrPtoUpP= []\n", "AvrPtoDownID= []\n", "AvrPtoDownD= []\n", "AvrPtoDownR= []\n", "AvrPtoDownP= []\n", "CTDUpID= []\n", "CTDUpD= []\n", "CTDUpR= []\n", "CTDUpP= []\n", "CTDDownID= []\n", "CTDDownD= []\n", "CTDDownR= []\n", "CTDDownP= []\n", "CoreUpID= []\n", "CoreUpD= []\n", "CoreUpR= []\n", "CoreUpP= []\n", "CoreDownID=[]\n", "CoreDownD= []\n", "CoreDownR= []\n", "CoreDownP= []\n", "BothUpID= []\n", "BothUpD= []\n", "BothUpR= []\n", "BothUpP= []\n", "BothDownID= []\n", "BothDownD= []\n", "BothDownR= []\n", "BothDownP= []\n", "\n", "AvrPtoUpID.append('GeneID')\n", "AvrPtoUpD.append('Description')\n", "AvrPtoUpR.append('Ratio')\n", "AvrPtoUpP.append('Adjust p')\n", "AvrPtoDownID.append('GeneID')\n", "AvrPtoDownD.append('Description')\n", "AvrPtoDownR.append('Ratio')\n", "AvrPtoDownP.append('Adjust p')\n", "CTDUpID.append('GeneID')\n", "CTDUpD.append('Description')\n", "CTDUpR.append('Ratio')\n", "CTDUpP.append('Adjust p')\n", "CTDDownID.append('GeneID')\n", "CTDDownD.append('Description')\n", "CTDDownR.append('Ratio')\n", "CTDDownP.append('Adjust p')\n", "CoreUpID.append('GeneID')\n", "CoreUpD.append('Description')\n", "CoreUpR.append('Ratio')\n", "CoreUpP.append('Adjust p')\n", "CoreDownID.append('GeneID')\n", "CoreDownD.append('Description')\n", "CoreDownR.append('Ratio')\n", "CoreDownP.append('Adjust p')\n", "BothUpID.append('GeneID')\n", "BothUpD.append('Description')\n", "BothUpR.append('Ratio')\n", "BothUpP.append('Adjust p')\n", "BothDownID.append('GeneID')\n", "BothDownD.append('Description')\n", "BothDownR.append('Ratio')\n", "BothDownP.append('Adjust p')\n", "\n", "for i in range (1,n):\n", " a= X1.cell_value(rowx=i, colx=3)\n", " b= X2.cell_value(rowx=i, colx=3)\n", " c= X2.cell_value(rowx=i, colx=7)\n", " d= X1.cell_value(rowx=i, colx=10)\n", " e1= X1.cell_value (rowx=i, colx=14)\n", " e2= X2.cell_value (rowx=i, colx=2)\n", " pae= X1.cell_value(rowx=i, colx=17)\n", " pbe= X2.cell_value(rowx=i, colx=5)\n", " pce= X2.cell_value(rowx=i, colx=9)\n", " pde= X1.cell_value(rowx=i, colx=21)\n", " rae= X1.cell_value(rowx=i, colx=16)\n", " rbe= X2.cell_value(rowx=i, colx=4)\n", " rce= X2.cell_value(rowx=i, colx=8)\n", " rde= X1.cell_value(rowx=i, colx=20)\n", " if (pae<=0.5 and rae>=2 and (a>=3 or e1>=3)):\n", " AvrPtoUpID.append(X1.cell_value(i,0).encode('utf8'))\n", " AvrPtoUpD.append(X1.cell_value(i,1).encode('utf8'))\n", " AvrPtoUpR.append(X1.cell_value(i,16))\n", " AvrPtoUpP.append(X1.cell_value(i,17))\n", " if (pae<=0.5 and rae<=0.5 and (a>=3 or e1>=3)):\n", " AvrPtoDownID.append(X1.cell_value(i,0).encode('utf8'))\n", " AvrPtoDownD.append(X1.cell_value(i,1).encode('utf8'))\n", " AvrPtoDownR.append(X1.cell_value(i,16))\n", " AvrPtoDownP.append(X1.cell_value(i,17))\n", " if (pbe<=0.5 and rbe>=2 and (b>=3 or e2>=3)):\n", " CTDUpID.append(X2.cell_value(i,0).encode('utf8'))\n", " CTDUpD.append(X2.cell_value(i,1).encode('utf8'))\n", " CTDUpR.append(X2.cell_value(i,4))\n", " CTDUpP.append(X2.cell_value(i,5))\n", " if (pbe<=0.5 and rbe<=0.5 and (b>=3 or e2>=3)):\n", " CTDDownID.append(X2.cell_value(i,0).encode('utf8'))\n", " CTDDownD.append(X2.cell_value(i,1).encode('utf8'))\n", " CTDDownR.append(X2.cell_value(i,4))\n", " CTDDownP.append(X2.cell_value(i,5))\n", " if (pce<=0.5 and rce>=2 and (c>=3 or e2>=3)):\n", " CoreUpID.append(X2.cell_value(i,0).encode('utf8'))\n", " CoreUpD.append(X2.cell_value(i,1).encode('utf8'))\n", " CoreUpR.append(X2.cell_value(i,8))\n", " CoreUpP.append(X2.cell_value(i,9))\n", " if (pce<=0.5 and rce<=0.5 and (c>=3 or e2>=3)):\n", " CoreDownID.append(X2.cell_value(i,0).encode('utf8'))\n", " CoreDownD.append(X2.cell_value(i,1).encode('utf8'))\n", " CoreDownR.append(X2.cell_value(i,8))\n", " CoreDownP.append(X2.cell_value(i,9))\n", " if (pde<=0.5 and rde>=2 and (d>=3 or e1>=3)):\n", " BothUpID.append(X1.cell_value(i,0).encode('utf8'))\n", " BothUpD.append(X1.cell_value(i,1).encode('utf8'))\n", " BothUpR.append(X1.cell_value(i,20))\n", " BothUpP.append(X1.cell_value(i,21))\n", " if (pde<=0.5 and rde<=0.5 and (d>=3 or e1>=3)):\n", " BothDownID.append(X1.cell_value(i,0).encode('utf8'))\n", " BothDownD.append(X1.cell_value(i,1).encode('utf8'))\n", " BothDownR.append(X1.cell_value(i,20))\n", " BothDownP.append(X1.cell_value(i,21))\n", "\n", "z=len(AvrPtoUpID)\n", "sheet=book.add_sheet('WT up-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,AvrPtoUpID[i].decode('utf8'))\n", " sheet.write(i,1,AvrPtoUpD[i].decode('utf8'))\n", " sheet.write(i,2,AvrPtoUpR[i])\n", " sheet.write(i,3,AvrPtoUpP[i])\n", "z=len(AvrPtoDownID)\n", "sheet=book.add_sheet('WT down-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,AvrPtoDownID[i].decode('utf8'))\n", " sheet.write(i,1,AvrPtoDownD[i].decode('utf8'))\n", " sheet.write(i,2,AvrPtoDownR[i])\n", " sheet.write(i,3,AvrPtoDownP[i])\n", "z=len(CTDUpID)\n", "sheet=book.add_sheet('CTD up-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,CTDUpID[i].decode('utf8'))\n", " sheet.write(i,1,CTDUpD[i].decode('utf8'))\n", " sheet.write(i,2,CTDUpR[i])\n", " sheet.write(i,3,CTDUpP[i])\n", "z=len(CTDDownID)\n", "sheet=book.add_sheet('CTD down-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,CTDDownID[i].decode('utf8'))\n", " sheet.write(i,1,CTDDownD[i].decode('utf8'))\n", " sheet.write(i,2,CTDDownR[i])\n", " sheet.write(i,3,CTDDownP[i])\n", "z=len(CoreUpID)\n", "sheet=book.add_sheet('Core up-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,CoreUpID[i].decode('utf8'))\n", " sheet.write(i,1,CoreUpD[i].decode('utf8'))\n", " sheet.write(i,2,CoreUpR[i])\n", " sheet.write(i,3,CoreUpP[i])\n", "z=len(CoreDownID)\n", "sheet=book.add_sheet('Core down-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,BothDownID[i].decode('utf8'))\n", " sheet.write(i,1,BothDownD[i].decode('utf8'))\n", " sheet.write(i,2,BothDownR[i])\n", " sheet.write(i,3,BothDownP[i])\n", "z=len(BothUpID)\n", "sheet=book.add_sheet('I96A+2xA up-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,BothUpID[i].decode('utf8'))\n", " sheet.write(i,1,BothUpD[i].decode('utf8'))\n", " sheet.write(i,2,BothUpR[i])\n", " sheet.write(i,3,BothUpP[i])\n", "z=len(BothDownID)\n", "sheet=book.add_sheet('I96A+2xA down-regulated vs Mock')\n", "for i in range (0,z):\n", " sheet.write(i,0,BothDownID[i].decode('utf8'))\n", " sheet.write(i,1,BothDownD[i].decode('utf8'))\n", " sheet.write(i,2,BothDownR[i])\n", " sheet.write(i,3,BothDownP[i])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "book.save('Mock comparisons.xls')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

sdpython/code_beatrix

_doc/notebooks/exemples/image_mary_poppins.ipynb

1

1092368

mit

luwei0917/awsemmd_script

notebook/Optimization/cys_protein_simulation_analysis_nov12.ipynb

1

10407099

mit

carthach/essentia

src/examples/tutorial/example_clickdetector.ipynb

1

462163

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ClickDetector use example\n", "This algorithm detects the locations of impulsive noises (clicks and pops) on\n", "the input audio frame. It relies on LPC coefficients to inverse-filter the\n", "audio in order to attenuate the stationary part and enhance the prediction\n", "error (or excitation noise)[1]. After this, a matched filter is used to\n", "further enhance the impulsive peaks. The detection threshold is obtained from\n", "a robust estimate of the excitation noise power [2] plus a parametric gain\n", "value.\n", "\n", " References:\n", " [1] Vaseghi, S. V., & Rayner, P. J. W. (1990). Detection and suppression of\n", " impulsive noise in speech communication systems. IEE Proceedings I\n", " (Communications, Speech and Vision), 137(1), 38-46.\n", " [2] Vaseghi, S. V. (2008). Advanced digital signal processing and noise\n", " reduction. John Wiley & Sons. Page 355" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import essentia.standard as es\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython.display import Audio \n", "from essentia import array as esarr\n", "plt.rcParams[\"figure.figsize\"] =(12,9)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def compute(x, frame_size=1024, hop_size=512, **kwargs):\n", " clickDetector = es.ClickDetector(frameSize=frame_size,\n", " hopSize=hop_size, \n", " **kwargs)\n", " ends = []\n", " starts = []\n", " for frame in es.FrameGenerator(x, frameSize=frame_size,\n", " hopSize=hop_size, startFromZero=True):\n", " frame_starts, frame_ends = clickDetector(frame)\n", "\n", " for s in frame_starts:\n", " starts.append(s)\n", " for e in frame_ends:\n", " ends.append(e)\n", "\n", " return starts, ends" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating a click example\n", "\n", "Lets start by degradating some audio files with some clicks of different amplitudes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,u'Signal with artificial clicks of different amplitudes')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4ccbff0590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fs = 44100.\n", "\n", "audio_dir = '../../audio/'\n", "audio = es.MonoLoader(filename='{}/{}'.format(audio_dir,\n", " 'recorded/vignesh.wav'),\n", " sampleRate=fs)()\n", "\n", "originalLen = len(audio)\n", "jumpLocation1 = int(originalLen / 4.)\n", "jumpLocation2 = int(originalLen / 2.)\n", "jumpLocation3 = int(originalLen * 3 / 4.)\n", "\n", "audio[jumpLocation1] += .5\n", "audio[jumpLocation2] += .15\n", "audio[jumpLocation3] += .05\n", "\n", "groundTruth = esarr([jumpLocation1, jumpLocation2, jumpLocation3]) / fs\n", "\n", "for point in groundTruth:\n", " l1 = plt.axvline(point, color='g', alpha=.5)\n", "\n", "times = np.linspace(0, len(audio) / fs, len(audio))\n", "plt.plot(times, audio)\n", "\n", "l1.set_label('Click locations')\n", "plt.legend()\n", "plt.title('Signal with artificial clicks of different amplitudes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets listen to the clip to have an idea on how audible the clips are" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <audio controls=\"controls\" >\n", " <source 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/S79//zs/Nr81PzU/LX8v/zB/LH8lvy5/KT8lPyK/I78hvx5/Fb8X/xW/FT8TPxh/Er8VPw8/Fv8OvxM/DL8PPwp/Av8K/wf/Dz8NPwv/ED8Uvxl/F/8cfxz/HH8gvx9/Iz8ffyA/Ij8kPyS/Jj8r/y1/MH8s/y//Nr80vy9/Lf81Pzc/PX86vwN/RX9Kv00/V39Yf1y/YD9pf21/bz9vv3j/ev9Bv4q/lT+if6q/sD+/v4h/zf/RP9n/4n/pP/D/+D//v8YABgAUACDAJkAugDLAN8AGwE+AW8BgwGqAdgBEwJAAowCwgL1Ai4DagOXA8oDDgQlBE4EdQSHBLYE3wQLBScFTgVxBZYFwwUJBiYGKAZZBnwGmwavBrwG4Qb1BvcG1gbnBv0G4wbnBucGvgalBqMGggaABlMGKgY0BhEGEQbsBdQFngWMBXsFWQU4BeQEkQR5BFQEEgTEA4cDPwMHA5sCXQIqAuIBlAE8AQYBiwAvAMf/WP8A/4/+N/7h/Xb9Mv3k/JL8NPzP+3v7J/vj+pH6N/r9+b35fPky+Qv5w/iF+Cv4+PfV96j3YPck9wL30Pal9nT2TfYy9ur1yvWx9aP1bfVn9Tz1OvUA9fL00/TL9L/0svS/9Mv01fT69Cn1V/V89aD1tfXo9f31KvZf9pP2zPb79k33f/e29/j3NfiD+NP49vhN+ZD54Pkk+ov63foj+3/71/tC/Jr83Pw8/Zn9+/1U/rD+6/5I/5T/6v9HAIsA1QA1AX8BxwH0AV8CkgLeAgcDPQN2A6wD2wMKBD8ElAS4BAIFGwVbBX4FqwXQBfcF+wUmBiwGXQZ4BpcGrwbKBukG+QYYB0EHXgeHB50HsAfRB9UHBgg3CEIIWAhQCHkIgQiQCJoInAikCLMIvQjBCMUI3AjaCNwI6AjaCMcIswizCI4IjghtCGkIVghGCCcIEgjwB8sHhwdUBygH9Qa8BoIGLAbeBZQFNAX0BKIEUAQMBMgDgwMqA+0CkAIwAtMBfQEjAcAAWgAEAJ7/SP/X/pP+KP7G/Yj9Hv3L/HH8IfzL+3P7MfvZ+o36O/rt+a35Z/kV+eT4rPhj+Cn4+vey93f3JPf39q32XfYg9ub1t/WQ9V/1FfUN9d70v/Sq9In0cfRe9D30L/QQ9PHz/PPl89/z1fPC89fz0/Pl8+/zFPRM9Hf0pvTL9BH1WfWS9bf12vUg9l32ivbr9iT3VPeN99n3J/hx+MH4E/lr+av5A/pH+oL6vvoS+2D7x/sE/C38XfyI/Mn8//wk/WH9gP2d/bz97f0S/jP+ZP57/rb+zf7y/gj/If9E/3P/ov/R/woAJgBYAK4A7gA8AXEBsQHeASQCSAKOAsIC/wI9A2YDpwMaBGoEuATTBBkFJQVbBYAFswXWBeQF/QUeBj4GbgatBuMG/wYqB1EHkQfPBwIIOQhiCIwIywgPCR0JJQlECUYJUQldCVEJVQlACUwJPglXCVsJRglGCUQJFwn8CPgI7AjXCLAIxwigCG8IRggnCP4HxgeiB40HcAcxBw4HEAfrBroGqwaEBmUGKAYLBtYFjgVIBfYEwQR7BAQEowNTA/ECrwJhAh0CkAFKAdcAhwAKAJj/QP/G/l7+8f2M/Sb9rfxS/BH8rvtq+y374fqy+mr6KPrY+Z35NPny+JT4QPjh94/3GPfS9o32OPYH9q/1gvVG9fb0zfSm9FT0JfT488LzkfNk8zvzIvMH8+ny7/Lc8s7yzvL18uDyEPMm81jzZPN086bz2fMI9CH0b/Sa9Mv0EfVj9bn1B/Zo9sr2Hvdm99H3E/hz+L/4OPmM+f/5ZPqu+hb7aPvJ+w/8bfy1/Pn8Iv1L/YL9l/3C/bz9wP3U/fn9EP4o/mT+e/6b/tP+6/4l/1j/mP/R/+j/MQBNAIcAugD2AC0BQAGOAckBHQJGAoYCuQL/AjsDaAObA7wD6QMfBFIEhwSoBMsE/AQrBTQFFQURBSEFBAUJBR8FNAVfBXcFrwW5Bb8F8AULBjoGPAZuBnQGjAaZBrMGqwanBqUGiAZ8BmEGcgZfBn4GkwazBroG7Qb/BiAHIAcqByIHKgcxBxgHCAfvBv8G8wbhBukGEgcvB30Hmwe+B7QHtge8B8QHsAe4B6IHgQdkB1QHNwc1Bw4HAwf3BuEGoQaCBl8GQwYeBtwFtwVpBTAF+gSyBGQEGATKA34DPwPtAswCggJTAh0C7gGUASUByQCBACIAvf9M//b+jf4q/tb9dP0q/cH8dfwI/Kr7ZPvt+pn6P/rc+Yr5Ovnk+Kr4e/gp+Pr3vveN9yT31vaE9k328/W19XP1RvUl9fr0y/Sk9Hv0VPQj9BD0/vPt8+fz6/Pd8+3z2fPv883z4fP48/HzEvQt9G30pvTZ9A31dfW79Q/2Qfaj9hD3Wve89wb4jvjL+BX5gvna+Sb6UfqH+rz6EPsr+1T7c/t5+4n7eft9+2D7ZvtY+2L7e/uq+9n79PsC/Fn8jPzc/AX9S/2R/cL9A/4o/oX+pP7d/h3/a/+3/wwAYADFADcBmgHwASoCdAKpAu0CBwMkA0cDcgOdA6wD2QPzAykESgRUBG8EqATNBAYFQgVlBYgFrwXBBd4FBwZABmEGiAaRBqsGuAbtBjUHQwdeB2YHnwfLB9cH2we8B8IH1QfyBxsISAgjCD4IVAh/CG8IWghzCIUIpAicCKIIogicCMkI1Qj4CPoIGQk4CToJUwlbCUgJQgk8CSwJGQnkCOwI0wjBCH0Icwg1CDEIEAgQCPQHxgefB3gHSQcDB7oGbAYXBrUFRgXfBJEEKwTjA7oDSQMgA7sCeAIbArUBZQH0AIUAOQDc/3X/CP+2/lj+Dv7Y/Wr9Gf25/Gn87vuY+yP7zPps+hj61Plv+ST5y/iW+Fb4+vem91b3//a89nL2GPa19Vn1G/XZ9Iv0QvQI9PrzxPOB8zvzHvP/8vHy5/Lc8sDyufKz8qPykPKA8o7yjPKC8oLyp/Kr8rny+fJJ80/zWvOb8+XzH/RE9GT0f/TP9A31Z/We9eT1MPaG9uP2FPeJ99H3Ifht+Lf4//gd+Uj5b/mn+cz5/fkm+pn6zvoe+2b7sPsC/Ez8rfwH/Uv9o/39/Vz+m/4K/3P/6P87AKYACgGDAeQBWQKvAjUDaAOjA/wDQwSLBK4EAgUwBXUFwQUXBl8GgAahBvEGIAdcB2YHugffBwoIKQhWCIEIhQiuCNwIFQk6CV0JpQnSCfsJFApPCnQKiQqPCr4KzArMCsoK4wr+CsQK6wrnCgALCAsACxoL8QoCCxALHgsSC/kK/ArtCs4KyAq2CoQKggqTCoIKcApdCk8KBwrkCdIJowlfCQsJ2gjRCJQIbwhpCDEIDgjRB8QHpAdkBwMHvAZRBugFiAUpBcMEdwQcBKMDLgPGAm8COgK7AXMBFwG2AD8Aw/9z/xT/if4Y/qH9Vf0B/a38X/wT/LT7XPsM+8z6dvow+uv5lvlC+Qv52viS+Fr4Gfjl95n3VPcx9xr36fbG9pX2ePYo9gP2yvWI9Vv1D/XL9Jz0d/Rg9DH06fOj83TzOfMg8wzzyvK+8rHyvPK+8q/ywPK58rXyufLK8rzym/Kv8rXywvLG8u3yM/Mz82LzifPT8wD0M/SL9A31UPWM9c71HvZh9pP27fZc98/3DPhz+Mv4LvmW+d75Fvpk+pf6zPrh+uv6EvtB+2r7pvuW+wT8Y/yM/K/8Rf3O/en9FP6b/iP/H/8h/5b/0f/T/wgAhwDlABUBSAGQAc8B7AFRApQClAK3Ag4DaAOlAw4EngQ0BbsFRQbUBksHpAf0Bz4IaQiWCO4IegnoCVMKoQrpCgwLJwtBC3ALogvDCwAMTAx5DHEMdwyYDKIMjgxaDGkMXwxSDAYMDwwfDCEMDwwEDEAMNgxGDGkMdQx/DKQMlgyzDK8M3AwTDf0M/QzcDLkMYwwzDDEMFwyoCzsL9QrCCmwKKAoBCrsJOgnDCHMIHQh7B9IGIgavBUoF/gSPBCcE0wOFA/kCswI8Ag0CpgExAb4ALwB9/93+Uf7a/YD9PP3S/Gf88PuT+x77o/pL+sL5Vfn6+KD4J/jA90X31vZj9iT2wfV59Tj1+PTX9Jr0jfSH9HX0YvQv9AL0yPOs85nzdPOX88bz0/Pb8wb0FvQx9Fb0XPRM9CP0+PPn88rzn/OD8zfz//LC8pXykvLE8s7y/fIi80PzbPOH87TztvPK85/ztPPA88LztPPV8wT0UPSW9Nv0UPXM9Uf2rfYa91L3wPcE+ED4Z/h1+Jr4mvjP+FH5ufkk+qX6Q/uk+/D7K/yW/Lv8w/zi/BP9Nv1j/Zf95f0a/lr+j/7n/lD/rP8vAJsA2wAVAUYBewGeAaYBwwHiARMCOAK3AjsDvAMzBKgEHwV7BdIFFQZTBn4GygYaB1YHhQfLBxQIQAh1CLMIDwlnCbsJEgo7ClUKPwpJCnoKpwrhCiALTguRC98LKQxUDHUMigzgDA0NSw2pDRAOWg5kDmQOcg6fDp0OjQ6oDrYOug7ZDhYPdw+eD8sPoA8zD7IOQw7lDXQNLA0iDSYNywx5DHsMbwwCDG4LKwvjCm4KowmqCAAIcgf9BqEGigaGBkMGCwagBfwEXgTxAwUDUQKoAQIBPQA1/2T+7f2C/ff8rfyK/Gn8CPxi++X6Kvph+aj4M/jZ93/3P/f19rr2dvYe9tr1lPU29cn0tPSB9GL0XvRM9BL03fN08yrzs/I08uTxyfHk8TLymfLg8ibzKvM38z3zHvMB8+PyAfM18zvzaPN282TzU/ND81zzO/P98tjy6/ID8ybzHvNT83DzePNu85Hzn/OP85Pz4fMj9Fb0nvS59M/0xfQA9Tz1ivXk9VH23fZW94H32fdA+Hv4kvjP+BH5V/mG+Rr6tPoj+5r7+vs4/C38Nvxz/Iz8gPyg/Dz94f1u/uf+Wv+s/7v/uf/2/yIAUgCmAEgBwwEHAlcCswK7As4C1AIoA2YDyANYBBEFgAW3BeQFPgZZBnoGxAYcB3YHrAcCCI4I5AgwCVsJhAmfCcYJ7QlRCpcKCguFC90LFwxxDLUMzQzWDPYMVQ2zDfENYA4ED1gPmg/HD/QPTBB3EF0QfhDQEDwReBGfEcQRthGhEYoRIBG/EBsQtw99D4sPpg/PD+4PiQ/+DnQOuQ37DCsMrgtiCwoLjQpLCvcJmAnyCH8IJQiuBy0HzAY4BnsFxQQKBGADoQK/AR8BWgBr/4/++/2Z/Un9C/3W/JL89PsO+1f6fPmY+Nn3g/cM98b2hvZy9kv2t/VM9dn0lvRc9Er0O/Qn9OfzePMS85XyEfKc8Trx8PCq8JzwrPD48C/xVvGE8ZrxbfFK8S3xBvEM8S3xjPHY8SjyR/Kr8sjygPKC8knyNvIq8iryGfIX8gvyC/Le8fDx+/ER8hvyT/Kd8gHzN/OX8wT0XPRx9BT07fPR89/zAvR39Cn1/fWv9j33pvcQ+E74bfh/+LH46vgH+Vv5zPky+pn6wPoM+0j7ovvk+zj8r/z//ED9q/0i/oP+tv7T/sL+8v4K/5z/FAC+AJABUQLiAkEDfAPAA8YDxAO6A+EDSAS4BF0FAQaIBs4G+wZcB7AHCAhzCO4IbwmfCQUKPwpRCnIKiwrdCgILUgu0C1IM9gybDWgOBg9tD30Piw+/D88P1Q8wEKkQDxFbEe8RphLREqYSwhIzE1QTPBO1E2UUdhRIFIAUvRQwFFgTkxICEkkR/RAFEUMRbBGZEbERdhG7EMkP9A4xDlsNzQyGDM0L+QqpCqUKTwqQCTYJ6ghiCHAH6QYwBlQFagSjA/cC4gG6AJD/gf56/bv8VvxI/BX81fum+9n6+/nY+L73vPb99W/1KfXu9LL0a/Qd9LjzR/Op8k3yG/Lc8cXx5vHU8ZDxJ/Gg8Cfwau/U7kDuOO5C7pbu9+6L7/nvP/BW8Fbw4++J71vvP+9b75HvUPAG8Wfx6PEZ8jby+/HJ8ajxbfEj8frw8vAE8SfxQvFS8VbxXfFQ8Xnx4vF88hTzpvMY9LD06PSU9Dn0yPO48+PzXPQL9Rj2Ave89zr4pPj0+BX5OPlZ+YT5kvnS+Qf6JPqd+r760/rb+jn7g/u4+y385vx4/e39ZP7P/sv+wv6d/nL+bP7N/nX/QwBOAXwCTwPMA/MD+gPhA6wDqgPCA0MEvwRZBf8FmQb1BioHaAfPBzcIswhPCe0Jiwr8CmwLywvqCxEMHwwxDEAMzQyrDbYOgQ+aEGYRCBI9ElYSWBJWEikSGhKgEiUThRPgE5kUDhUSFQEVhxXZFfoVIxZ5FqYWvRagFggWAxXRE/oSZhIiEv0RXBLuEisTIRPuEm4SMhEPEGkPtg54DTEMvguLC/kKkQqdClMKmAnyCF4IlQeTBoYFCQVWBIsDrwKoASIAlf5N/W/8vftO+0z7b/s/+7b6IPp4+SP4tvbH9SH1G/RV8x7z6/K58nLyMvLN8Trxy/CT8HnwKfDd75XvV+/x7lvuz+0Z7ZPs++sD7FHsvOxE7Q3uzu4Y7zDvAe/B7jzu3u3i7QHuMO6f7mLvGPB18KDwvvCJ8EXwAPCr71XvC+/o7jvvhO9+73rvfu9L72bvn+9F8Bnx7vHO8tPzO/Qd9M3zk/Mz8wzzXvMK9PD00vXI9tn3XvjR+BP5dPlT+WH5fPmZ+bv52vlN+rj6APsA+0H7ifuy+xf8ovw4/dz9T/72/pb/oP9r/0D/Gf8b/5L/VgB3AXYCegN3BPoELQUnBSMFDQUZBUYF2AVjBvsGdAcKCEwIfQi5CB8JnQkLCrQKaAv4C3sM5gxXDZANpw2+DfMNFg7NDtcPIhESEuESsRMHFAcUFxR4FKUUjBR6FBQV1RUWFnsWMhetF4gXbRfqFz4YDRgHGGoYZRjoFxsXbBaZFb0UFRSzE70TLhSCFMYUwhQoFGUTkxLdEfkQDxAWD/cNywxMDEwM7gtBC8wKbgq5CckIiQeTBqkF2wTpAyQDHQKkAAz/rf3L/NP7Ofv1+vH6ofpD+tb5MvkQ+Of2AfY09SH0WPPI8l3y9vHL8Z7xIfGm8FDwIPDG73rvKu/U7nHuze1v7fjsUex26x7rFesq613rFuzX7FTt3O1l7mnu9u2a7XvtWO0b7Vjty+1n7uvufu/v7xrwEPAa8AjwuO8074buFe787ebtEe5f7m3uSO6E7vvuke9W8FDxZfJH86jzDPQO9K7zLvMS8yjzjfMz9Dz1VfYI98X3UvjH+BH5Rvl0+Vv5T/kw+RH5bfmz+RL6U/q6+hj7Zvv4+5L8Jv2n/WD+6f4b/1T/cf9n/yP/BP9e/xgAFwE8AlkDTATkBDYFTgVCBR8FEwURBToFswUyBq8GMQe2BxAIUAjNCFEJ5Al+ChgLvgthDNwMQA2lDRQONQ43DoUObQ9pEE8RixKcE0sU0BQqFUkVVxVJFXAVyBVUFt8WgBfuFy4YCxgmGLgYKBk3GYsZEhopGt8ZthkiGYgXGhaLFSwVxBS1FBYVYBV6FacVkxUaFUYUrxPXEsgRaRAWDyoOmw32DIEMCAxQC2AKggn+CNcHjAbSBUwFYgRHA0sC2QDV/gP92/sY+4f6Ufp4+lP61PlX+cf4uveP9qf15vTb8wHzbvIg8sfxcfEG8bjw9e+E7y7vIu/r7p/uPO4P7nPtxOw17HrrZ+qs6Ynpw+lG6ibrHOwA7a7tD+707ZztTO0b7c3suOzb7DntpO1t7vfuU++A76XvbO857wXvv+4w7pjtUu1Q7UbtOe1p7VDtRu2i7WPuV++X8PvxN/MQ9Gv0d/Qz9LjzU/Na89PzefRC9Uv2ZvdE+L/4Bfld+a35lvmA+Xb5Tfk6+XL5yPkY+kn6j/rf+vz6bPsj/Nz8kf1g/kT/pP/c/+z/wf9r/1j/pP9eAG0BrwLzAzgF/wVTBjgGGQbiBckFnAXSBS4GrwZkBxAIlAjeCCwJggnKCTkKxgqDCzwM7AzSDWwOsA4EDykPaw+YDwIQ4BBSErUT7xTVFaQW/BbJFqgW6hYLF+oWfRd+GCQZSRmyGQQaCBo+GrcatRqMGrsaExv+GncaaBkVGAYXLRaRFSgVYhWHFbIVLRbDFn8WyhWLFR4VzxMmEgERng9RDrkNow37DEIMrgsnCxQK4gh/B2wGfgXLBBAEXgM0AmAAS/6i/GL7ePr1+Q76C/ql+WX5OvmI+IH3sfb79df0vPPl8k3yy/Fh8VLxKfGH8Pfvne9N7+3ueu4P7tHtd+3h7Jvs6esF6xXqoumU6a7pBuri6vfrxuxS7eTtF+6/7Z7th+077eXs+OxI7ejtn+547+3vL/At8CLwCvC271nvBe+p7jLu9u3R7dPtsO1n7W3tt+0o7h7vc/DY8SzzL/Tb9Eb1afUw9fz0/PT09Eb11PW09qb3efhf+fH5Nfpq+oD6Tfrx+dT5yPmS+bP5/fk/+oL64/oY+1r78PuA/D79Gv4G/4P/wf8QAB4A+v/k/yYAvACOAcgCGARbBV8GDAdkB4MHbAceB7gGtQbxBlwHugdICLMIJQmKCe0JJApwCvcKiwspDPsMvg1wDucOWg/LD/IPNhCvEHIRlxIFFCIVPRYyF68X1hcNGBMYzhfDF9gXmRhgGdUZKRqqGgcbXxtvG4QbtRuQGzIbMhsRG/AZ/Rd5FpMV+RRnFI4UBRWDFZ8VBhZ1FnkWDhaJFZQUQBPKEU4QsA5+DZYM9AtuC/wKggoSCv4IVAfQBdkE6wO3ArUB3wB//w39BvsJ+jz5d/ha+JT4Vvju95H3Fvdq9qv1pvSB84byzfEh8Y/wLfAI8PXvm+857+Lu7+6l7u7tQu0Q7WrsoevY6lvqdemL6NDnmef557Loi+mI6ufr5exn7Yftju2U7WLtDu3C7Lbs6exl7UDuD++470fwh/CF8KDwuvBs8LLv/+5d7vjtf+1c7VTtM+0b7VDt6O3z7kHwkvHO8jH0MvWO9bv14PWc9Xn1jPXU9Vf2FPcr+Db5EPqw+jf7hfuR+4v7avsr+776dPp8+qH67/oS+4P76vsb/LP8Lv3K/ZH+XP/0/1QApADdAPAA9gAnAagBfAKbA+4EVQZ/B14IqAjTCLUImAgxCBcISghpCJ4IAQmKCQMKkQoGC1ALnAsEDG0M8gxlDS4O8Q5/D/YPhBDsEFERqRE3EmUTsRTvFQAXJhgIGWYZQxk9GTMZIBk3GWYZ2RknGpQaHxvgG3IcqxywHI8cYhwgHAUcYxteGtYYjBeNFroVRxUqFZkVxBXIFboV9RUCFpcV/xQ+FDMTBhKtEEwPDA7kDO4L9Qp8CmAKxgmzCB4HtQUpBOQCuwG0AIf/FP4b/Fz6Rvme+NX3pvd99zP3mfYR9mX1t/QS9FjzMPJA8aTw+e9f7//u3u5v7v7twe217aDtXu227Ajsp+sD6yHqien66E3ot+dI5x/nm+du6A7p+unW6pnruOvS6wHsauxe7DvsSeyL7NvsWu0d7vfule/n7xTwg/C48NPwvPBO8I3viu7N7WXtXu2B7ZTtwe0V7tzuyu8b8WPyUfMY9M30PPWA9cz1IPYm9mH2zvZw91D4T/lg+kH7y/s8/G/8vfzm/Lv8gvxA/Oj7nvvB+zz8zvxh/eP9av7h/lL/5P9NAHkAfwCRAOcAZQG/AQcCfgI7AxoEKwVRBmwHZwhRCb0J6AnvCfMJ7QngCf8JHgomCnQK6QqPCzwM0gwoDYAN9Q1HDn8OuA4dD4kP4A+CEPUQoRE5EroSgxNbFHIVjRZCF9wXiBgaGVQZdhl4GcAZIxqzGjAbERx0HHgcnRwxHaodcB0YHSId6RwNHPIamRnaFxoWBxWjFIQU2hReFTsWhRZUFgAW6xVoFW8UeRN/EkERXA/MDT4NrwyJC7wKugoqCgcJfwdVBu4EagMdAh8B1/8i/nn8n/qm+HD35fa+9or2o/aj9iz2NPUQ9CDzNPL48OPvRe/U7lfuAe5X7q/uiO7o7ajtSu3X7CDsmeva6jLqj+kC6RzoUeda5rLlUOWT5X3mlOfT6NPp8uqt6/3rnesu6wfrw+p36orqY+so7Ofs6u0U7xLwkfCq8Kjwh/Ac8LrvbO/U7gPua+3h7Lrs0ewS7WLtwe237tnvVPG38srzh/Rt9dz1Hvbs9S72W/aA9iT3TPhP+Sb6EPvm+1v8ivyx/Lf8u/zF/OD85vzH/Kv81vxH/db9bP7p/j7/tf8EAK4ATgGYAXMBoAETAlkCfgLaAngDRgRrBaEG+gcwCfUJkwrVCvMKugqrCoQKpQrzClQL0wstDJYMNA3IDRYOSQ6VDtsODA9MD8EPThDQEB4RjxFJEroSIxPaE70UthWsFvcX8ximGSsabxqFGlwaXBq9GtUaUxsLHMocMR2NHccdDB4rHuMdfx1gHQQdHBzXGoUZtxfnFdAUkhStFKkUdhVmFmYW/BUGFt0VEhUNFLIShBHuD2AOFQ2QDOML2wo5CmgK+wnLCBQHpwXbAwsCuADV/1b+ufwS+xP5Sfdb9ub1q/Wr9bX1iPUf9Qz0zvL78SXx5e+U7rPtXO3p7NXsKe2m7XPt9uzN7LjsTeyC65zq5Ok56Xbo6+dV52vmvOVQ5R7ldOWk5unn7+j26djqUes46+Lqw+rB6pLqb+qG6k/rMewl7TrudO838KTw0/Cs8GDwCPDE70Pvo+7u7VztFO0Z7Svtde3J7XPume8I8YLyevNK9Pr0fPXO9Tr2k/bW9i/3svdQ+DL5ZvpI+y386vxR/Vf9W/1w/UD9Xf1H/V/9NP0o/Wz9GP7p/of/DACPAAoBdQGKAeoB7AGDAX8BJgK7AvECbANKBFsFWwZ7B8MI7QlgCsAKFAv3CqsKgAqfCr4K8QonC7gLGQyIDEANEg5uDqYO9g57D4cPqA/+D5QQ6BAiEbYRuhKIEyQU1BSTFUwWJxcsGA4ZhRnTGQgaYBqWGqga3BplGxocvhxuHfIdRB6rHjYfDx/DHkgeJx6wHeUcWRs/GQQXORVvFG8UihTMFLoVnha6FocWUhYCFgcVihMOEhgRog/UDaYMLQyFC64KLApPCvEJjgjcBkIFpQPkAZcApv8k/nP8Yvo8+J/23PX79Q329/Wp9Vn14vS681nyJfEA8FHu6+xq7Gzs9esK7JvsMe0U7azsT+wa7LXr1Oq06QrpSeiW5xnnkuao5eHkeORZ5OPkNea55/PoE+oZ62XrIuuQ6kLq9ul36RrpJekl6lPrbuzB7Rjv1O/37yfwBvCL7xLvyO4s7kztuuxu7GbsHuwe7Frs5ezq7Srvx/Bl8ovzPfQC9YD1hPWg9fP1Zvax9gr34/cV+Wr6iftA/LP8v/zY/LX8p/xl/EL8a/x7/Jr8t/ww/b79Zv4d/9P/gQD2AE4BpAELAuABjAF/AfQBPgKAAhQDCAQlBWMGugcjCRoKnwrQCu0KpwqCClEKeAq4CvUKXAsADHsM8AyZDRIOQw6fDh8PbQ9vD3sP5g+SENYQKBHQEaISbxNIFD8VBBbTFqQXpxhyGdsZ9hkpGnMaxxooG3obDxyyHDUd1x2YHhUfkh/3HxAgzB9lH/AeMx5YHdQbWhn2Fj0VXRTsE/gT0hTAFZYW9BYtFxEXfxZJFbsTVhIaEZoPAw4qDZQMlwujClsKowpBCvAIVge5BfoDGwKPAE7/+f0t/PX5mfck9qf1lvWz9er1zPVb9X/0U/MD8svwS++i7U/syuvA66nrr+uN7BLtuuxL7DfsHOy169Dq6uk36Wjon+fs5mHmZuVw5Mrj7OOX5Abmguf46DTqEet46/fqZ+r46bDpEOm+6Bbp1+kT60fsy+0m7/XvCPAM8PHvfO8c77XuPu6i7QDth+xL7EXsSexo7J3sf+3/7nnwAfJL8zX0rvQX9UT1kPX39Xj25/Zu9xn4A/kS+j37APxv/Gv8bfxj/CX8Efxp/KL80Pze/Or88vxX/Q7+5f6J/wAAjwAlAYgByQG3AWsBZQGcAfoBdAIWA+sDKwVnBqoH2gjiCWAKeApuCmwKUwo5ClkK6QpgC8cLUAyvDO4MHQ2ODdoNPQ6mDgwPOQ9QD5oPFxCpECoR2BGPElATKhTrFKcVfxYZF84XjhgUGXAZ0xlaGm0acRoTGzIcBh2mHZoeZx+1H7wfKCBYINQfFx+aHhse8xw+G0sZShePFWsUCRTsE5QUuhWoFt8WkRZIFvUV/xRQE9QR0hCQD8oNuQziDGkM+QpLCtUKkQrFCNIGcwXGA54Bzf/G/or99Pve+dP3X/Zr9VD1PvUp9fr0jfSZ8x3ylfCh747u2+xw6wnrFeun6sPquOvA7IHs8+vI687rAevb6dXoEuhp56rmCuZ95f/kS+T74/njqeS85Ubnh+iF6Urqnuon6j3pFukz6dfoZujR6AbqIOs57KLtIO/t7x7w6e+872Tv1u6I7hPute1l7THtCu3j7LzsvOwf7abtyu4i8JjxvvKy84P0AvVQ9YT1JPbp9oP3BPjF+Lf5kfop+9f7afxz/Ez8hvxf/Db8RvzY/Av9Lv1V/Zv9vP0t/qb+Wv8pAJ4A9ABQAaABkAFKAWMB2AEfAm8CJgMWBBcFIAZLBzMIDwmSCeYJ8wkNChwKPQpiCqkKMwvHCykMbQyUDPsMUw1uDbENTw6qDt0O7w5gD+YPbxD5EK0RZhIOE8kTnxRJFdkVhxYyF8MXTxgGGYkZ5xlGGgMbtRsoHLIcnB13HrUeyR4XH5kfnR9dH24fXR9zHkEdaBw0GwMZ3RZLFXQUyRO9E6EUoRXVFU8VTxWXFVEV+hNmEmgRUBDdDrMNXw2/DCcLDwr3CdQJzwgxB5YF+ANZAuEAcf/v/X381/qq+K32ivX29GT0FPRC9Ar0OfPw8U7wQ++/7n/t7+vs6pTqbepx6hzrzOu+6w/ru+qW6kjqtunj6DLor+d25/Lmc+aX5brkFuTQ47HjbuTh5SHnHOhO6UTqL+pW6QzpO+n16HLolehY6S3qMuuJ7BfuFu9V73zvl+9L7+Lune5G7hvuG+7+7bPtWu3n7InsiezN7I7t7+5/8PDxKvMb9Nf0NPWI9SD2r/Ze9yH4s/gw+cL5ifr++oP7sPu4+837uPuc+9H7b/z3/En9av1w/TT9W/18/f39qP6M/yIAjQD4AFYBVAEdARkBQgFeAaoBZQKDA6gEwQW+BrYHkgglCUAJVwk2CUgJTwmnCT0K+QpzC24LaAuRC/ILAgwGDHkMOA3ODQUOWA74Dm0P/A+AECARqxE5EtESnhN6FDkV6xWaFlsXKhjIGP8YeBkSGoMaPBvBG0sc7Ry0HVIekh6xHvQePB88HzgfKB9MHuUccRu+GYgXQxV5E38SRxKTErkTJhX8FcIV7RUAFiwVnBPxEZQQ+A5yDU4MDAx7C/kJNAmhCYQJQAgNBnUE4AI6AZz/XP6I/G76hfi69h31GPSP8xzzGvNN81HzrfIt8bDvyO7J7RzshOq/6Y3pVOmN6ZTqiOs468Hqq+qn6iHqWulV6Izn8uaK5hDmfeV+5Izj9uLq4lXjkeTu5RfnJuhx6TrqCOot6cDoo+hP6Njn8+fR6DTqUeu47GPudu+2763vm+//7qHuRu747Zjtf+1S7VDtIe3d7Krsieyu7JTtIO+X8L3xxvKf83v04PRO9eT1dPYx9yn4qPhV+Sr65fo9+6z7qvtk+0r7WPs/+8P7gPwB/Tr9eP2Z/Xr9W/2T/QH+ZP7P/nH/9v90ALAAeQB/AOEAGwFIAawBTQJDA5YEqQWbBngHKQiHCHUIYAh3CIMIrgg6CRgKxAoCCy0LPwt/C3sLQwtiC+gLdQz2DGEN4A1/DjkP3g9hEO4QehECEq4SaRNGFOsUhxU3FjgXExiXGOsYVBnVGawaQBuzGzYc5xxeHckdJR6QHvAeDx8kH4QfyB9THwoeIBz0GVIX3hTbEpURWRHWESsTvRTjFQgWNRZQFswVUxSmEvkQLw+EDa0MPAwpC+AJjAnGCbMJYgh2BsMENQO9ARoA6/5H/Qz7g/g69uz07fNi80/zofMQ9OfzLvMq8tHwcu8N7mbs0uro6VrpHOl/6YrqY+s66/DqyeoN65Dq6uk36Zno+edT57/mDObE5ILju+K74kjjp+Q35n7ntuj06XvqNOqc6Tnpvuj354zn6+cK6TTqZ+vV7Hzuoe8E8Bzwvu9D77XuIe6o7VjtVu0n7TXtPu1A7dfs3exn7W/uwO8R8fbx0vKf81j0x/Rl9cP1NvYQ9+z3iPg4+TD6yvoG+z/7TPsn++/64foS+3n7Lfy5/DD9Yf1q/Xb9s/35/TX+eP70/kD/y/9DAI0AXgBUANMAHQECAUoBHwJFA2QEbwV2BkkH4wcSCCkIIQjJB7gH6wesCI4JRwq4CvcKHgtBCyALyArbCj0L3wspDLMMUw0sDu8OgQ8NEIQQ8xB2ERASzRLBE5QUcBU5FuQWhBfUF0kYqxgzGQQa8hrGG48cHB15HeodVh7JHkQfeB94H7Ef4B/IH70evBxxGv0XTxUEE8wRZhHOEekSlhTxFZ4WuhasFucVfhQAE3wRuw/9DSINtQyRC0cK8wkPCugJawh4BgYFmQO/ATsARP8c/vL7H/nG9kL1MfSh88jzFvQd9OfzjfPO8l/xuu8N7kvs2Or46YHpM+mL6aLqk+t06wXrMuts6wPrIeqJ6Svpauip50vny+Zy5R7kU+Ml447j5+R35u/nPemQ6gHrgOrd6XvpBukS6Fnn4ucp6VLqjutU7dDuhO/K7yLwIvCn7yLvdu7s7VDtAO387DntYO1A7T7tWu3e7QHvR/Bz8STyAfPT8yH0ZvTm9L/1NPbz9un3z/jE+Wb6+voU+936fPpy+qn6o/qf+gz7x/tK/Kf82vwe/e78Hv2M/eH9Ev5u/hT/lv+k/57/s//B/xoAdADJAAQBvwELA20EgAUqBvMGWAeZB5MHpgeXB4cHywd7CCMJmAkPCo8K1wrxCukK6wrlCnsLxwtMDK0MOg0YDrwOTg/LD4gQNBG2EUkS+hIBFL0UbBXzFagWPhfoF5cYLxm2GWsaXxsYHKUcKx2+HSMeTB6mHhMffh80H3AfpR9fHzgepxwsGzEZnBaGFKYTeROcEz4UahUYFgAWBhYUFqUVtxQ8E9sRhBBeD50O2g17DP4KeAp6CtwJXAiKBkQFyAMPArYA5P+6/n38Cfov+Hj2MPVm9Hv0mvSc9Ej0jfN68ifx++/m7oXtA+zi6krq/ukr6u7qgusD64Lq0uom64Lqz+mU6TnpaOj75/PnhOc15t/kbuRV5LjkpOUN50voYOmQ6hfry+od6gLqsunp6FPo1+jF6X3qk+sj7XbuOe+C78rvre80787ut+4+7mXt7ezp7DXtKe0n7TPtnO0X7lvv0fDu8cTyi/Ml9IP0kPTB9Ej13vW29tv32Pih+Vf60/r6+sD6avo9+jL6IPpX+vf6uPsp/IL88PxN/Uf9Uf3C/SL+dP7L/o7//P8aAP7/x/+5//T/GAA7AK4AtwEUA38EUgUTBsgGbAdDBx4HIgctBwYHbAcrCOQINgm9CUcKnwqlCs4K3wpeC+4LbQzSDEkNwA0SDokO5Q5gDwsQyhC8EXkSjBOxFI8V0RXpFWIWJxeSF/UXkRiBGUQaJRuLHKIdsh2BHQAe/R5ZHzYfSx/UHwMg+R9wH6IeFh0BGwoZRhePFQUUsROlFIUVThZSFi8WNRb8FVUVPBQGE8IRdRCDDzUP0w6bDegLygp6CgsKxwjcBkYF5wOjAicBRQBQ/w39O/pC+Az3CfY29b30B/Vj9eT0xPNy8rjwT+9t7mXtFuzo6kbqL+px6kDr1usw60rqYer/6pTquOlQ6Urp8ehu6Bbowuec5mTl0uTU5Enlc+bc5wrp+unm6mPrF+sy6uHpwekp6azoGOkR6trq8ete7cbuj++w72zvJO/97nru2u177YXthe1C7UjtWu0x7R/tsO3K7jnwrfG58qPzI/Sy9Lf06PQP9XH17vXU9rz3lPiC+XL6zvrG+tf6p/o7+t75pfnY+a76pPtr/PX8Ov1f/ZH93/37/T/+1/6Y/zEATQBkAD8ABgAIAGwAdABqAPYAQgK+A+4E0gWXBg4HMwf3BsoGcgYyBj4G8wbGB5oISgnmCVkKugrfCv4KEAuXCzMMwQwuDZIN7Q1YDrwOJw/ZD4wQQREaEsASwxPMFLIV2xX6FXMWIRecF/0XeBgeGSMakhvjHJMdwR0VHqIeMB9TH44fECBYIHAgSSCQH9Ud5BvZGbUXOxWvEysTCxQSFQ4WVhYvFpEW9ha0Fk8VnhMUEpAQXA/lDsYOAQ6UDH8LJwvpCrMJqAdDBiMFfAP0AcUAx//K/TH76vh090X2QPXu9Cn1t/WS9bL0O/OO8Qrw+e7i7TPsv+rX6ZbpLeo+6xDswuv36ubqZ+tF63XqAOrZ6YnpIenp6FzoHefb5fXkjeTh5Prla+fG6CfqXesv7AHsV+u76mnqx+ka6S3pwel16m7rOe3M7oTvvO+C7yLvmu547j7u3u2k7XXtXO037SXt8uwI7abtmu7777nxM/M19Pj0iPWc9YD1hPWI9er1p/aL90b4Ovk9+uH6HPsr+wD7rPr9+b35Cfrx+u77pPz//Dz9eP2z/f/9dP6+/mn/MQDbAEIBQAH+AKIAmwDnAOcA1QBjAaECJwRlBV0GPQfnBwwI9geLBx4HxgaxBkcHOQgsCcgJMgrKCk4Lcwt5C8cLXQz/DH4N3A1JDoEO4Q5YD+wPbRADEesRzxKFE4gUuBWqFvYWCBdKF30XyxciGMYYqBmeGvMbQx1AHr0e9h5ZHxggciCmIOEgFiESIdsg6x+BHsYcjBruF2gVeRPREogTsRS0FcwV2RWiFoAXPhfpFRMUaBI/EdAQcRCHDyQO1gzlC6wLZAu9CbIHXwYpBY0D8AHfANH/7f1M+y75jfcu9uz0nPQ29bX1ZfV19Gzzx/El8A/v9u0g7HPqoOl/6Tbqcuv163Dr1OpH6wrsvOvg6jrqz+ms6cPpf+mN6DLnuuX95J/kwuRQ5ZrmFOio6SDr0Ot06yjr1OoX6jXp7+hi6d3pUuqO61Ltv+5f76fvLu9z7hPuT+5C7trtRO1E7Y7th+0p7dXs6+x97bPuYPAB8ibz/vMf9av1hvU49Rv1J/W59Zf2i/dh+HT5o/ol+0z7WPsp+2r6yPmI+RL6LftZ/Mn8NP1f/Yr93P05/rD+3/6//8UAtwETAvABYQH4ADEBTgH0ANcAawG5An0EyQW4BocHSghtCCEIzQdeB/kGGAf0BwUJxAlBCgILdwvVC+4LJQyKDD4N8Q1HDuMOSA+sD/APHRB1ECwR/RHnEpQTSBRqFZEWXxfHF8sX8BcwGHoYFBnbGd4a5hseHU4eVR/3H3wg+iA7ISchqCEHIlciLiJQIcofhR25GrMXeBW7E/gSqBMFFVoW6BavF7gYSxmrGAgX4hQEEzIRBxCSDzMP9w3gDN4MMg35DGILFwk9B5YFMwTnApwBOwBk/pH7C/mB91P2efU29Yb1OvYg9h/18fOj8tHwEu9g7Vvr1ems6MrorukJ6/Xr5+t066PrIuza64DqcelO6TfpGunO6FPo9uYK5crjmuP548TkLebS53/p2uq466XrEetQ6mTpVeil57nnfuia6SDrEu377v7vO/CG77/u3u3+7Frs++sQ7H/s2ewK7QLtsOyq7AztW+6p71Tx3PIA9NX0Z/Wn9X71B/XJ9O70fPVo9pn3DflZ+l77v/vB+1z7WfpX+cn4Lvka+kr7afxH/fn9S/5N/kf+ov4I/6z/dwBMAdMBVQJIAhUCvwHDAY4BCAFQAYwC9QM4BWoGyweiCJwIIwiZB0sH9wb3BqYHGwl0CosLcwzeDAMNyQxzDHUM6AypDXwOUA8pENIQdBH7EWoSohLsEncT7hOhFJMVuBaCFwEYfhj1GBoZGBmDGYkaYxuJHMcdQh/KIIkhsCHNIcshyyH6IU4iYyIuIvYhMyF+H5EcgxmRFkYUUBMEE0gTtRM8FJ0VIxcyGDQYQheZFcUTHhLxEIIQPQ+IDcUM1AzUDHUMBAvTCNwGiAVmBGIDVQLpANf+SPzY+cL3zPUd9EnzrPPv83jz/fKb8hnyBPH770/u2utv6TLoKOiN6FzpCOoh6mHqXesI7J3rZ+qi6WbpGOlU6Y/p5egB50XlSeSU42XjfuNH5HflPOf66Nvp6OkI6iPqz+n66B7ocudf5/fnkem462ftYe617qPuE+5Y7bDs6+uI62frfuuM6/HrHOzv6wrs1+w07tnvkvH78uHzpPRO9W/1J/XL9Fb0OfRx9Ln0dfXb9qD4PfpM+4/7Ofty+tb5ofnS+Rb6zvrJ++78l/0c/pP+If+W//b/ogBxAQUCigLEAlsCBwJlAooC+gGoATYCRwMxBGcF9wZWCBkJZwl2CcUIEAjNBxII/Ag2CjELPgwuDTcOzQ4CD80OAA+LDwsQfhDSEGgR5RF/EhsTeRPJE2MU/xSJFTUWGRc6GAEZjRkfGmAagRqMGgUbsRutHOMdvB/LIakiMCPfI14kWiQUJIYjDyPKIrEiMiJsIIkdJxpMF3YVvRTUFB4V/RSvFO8VDxhcGYMZRxiPFqkUEBPhEegQKw+1DToNcA0aDl4OxQyPCtMILQdSBcYDfgIOAfL+cfxL+hv4IPbB9FT0svTi9PbzxPLi8SXxi/DQ7+rtgut/6Sbo6+fX6DrqgOr06VLqyOuu7BDsp+o96XLoUeiT6HbojufX5V3kfuMd483iweJZ47zkc+bE54nopeiq6J3oU+iv5+DmSOZt5jLnCulN6z7tjO7S7pDuSu4N7v7sV+tn6i/qQurB6lnrl+tq67PrvOz67VPvWPCI8anysPNG9Kr0pvRt9FL0tPTi9OL0ivUU96/49fnd+nf7Fvtw+uf5r/n1+Xr6JfsA/PD85/2Z/j7/VP87/5b/UgAEAagBNgKKAogC2gJ4A2gDowIoAq8CugO2BKsF6waMCNwJbgpJCr0JFQmsCAsJ0gnbCu4LZw2jDrcPgBC1EFUQSBBtEJoQvxA4Ee0R8BLRE9AUjRXpFUgWkRauFoIXkRi2GWUa4BpZG9QbTxyLHMAc+Ry+HQUfGyE4I0EkxyRzJSwmLibPJeMk8yORI5UjJCP2IHcdTho4GO4WSBb4FZUV8xQWFckWyBjJGZEZBRh9FhwVIRQjE5URcw/xDQ4OyA4SD5UOxQyVCqYIIAezBU4EegLwADP/7PxH+jP4gPYb9Uz0RvTV827yMfGB8Bbwtu/k7vzsa+ps6NTnhehY6dHp2end6XPqp+tP7ILr6um86BLoxufr59bne+aK5Hfj6OJE4sbh3+GH4rXj++Ql5r3mqObq5mXniOei5rLlQ+XP5f/mHOk063vsI+1/7bvtmu3X7JXrNupo6RzpZOkA6oDqourW6lXrM+yQ7cjuxO+c8MPx1vKF89nzwvN281rzdvOV8+Hzu/Qy9v737/nV+qP6Q/rg+bX5cvly+b35cvqY+9r87/2o/iP/QP9W/4z/CgBJAKIACgGeAREClgI7Az0DkAI6AtgC1wP8BCwGnQfPCNQJhAqRCsYJ3AiYCAcJ4gnhCkQM2g1xD6sQhhHCEXIRPxFdEZsR3REWEqwS9BMkFS0WuhYpF0IXMhegF6UY6Rn2GuQbfBzzHGgdsh0jHk4eih48H6wgaSL7I0olUyZTJ8InlSfaJgMmSiXNJF4kfiJNHyIc4xnGGAUYmBfqFjMW6RXsFr4Y6RkrGjEZpxchFvcUFRR0EowQyA71DY0ONw83DywOcQyVCssIZge9BdsD9gEOAL79Dvui+Of29fU89fT0ZPQc83/xaPD374Lvn+7w7OLq6+ir58DnaOgM6Wrpj+n46f3q3Ot064TqhenC6AHom+cs5xXmIORk4rDhgeHz4MbgaOGS4qPjcOQU5QzlGuV75ZvlJOVV5PPjLORa5U3nXOk+63vs3ezA7MbsYuxw60TqTOmT6EfocOi+6BzpdekE6hPre+yY7ZDuhu+B8D7xu/Eu8j7yKvJP8rHyP/OT85T0+fXZ92f5mfoS+6X6Sfrp+Zn5fPng+YD6N/sG/AP96/2H/tn+Cv9O/5b/0/9QAKYAvgAAAaABbwLcAsQCrwISAy0EMgV2BtsHKAk5CroKxApTCq8JawmWCQ0K2woGDHoN3Q4JEAERoxHjEcYR2BHfERASJhLLEtYT2hTKFc8WxxdjGI4YrRg9GVgakhu6HHMd3x0dHqQeJB+nH/8fmSDLIUcjpiTNJeIm0CcQKMgnGicmJtMlqCXbJBgjGCDlHL0avBmNGW4ZxBirF1AXJhh/GZgaGxtpGrUY2xa2FUkVLhQGEvwPHQ9ID4kPdw9YDmkMkQrNCDcHywU/BHQCNQC4/VT7GfkU9xr2mvVl9bf0KPPD8ZPwxu8y74ju4+zW6rLoX+cs5+3nwOjK6JPoAukl6rnqJepi6bjo2uc+5wfnxeZk5SfjSeFA4LXfSt9Y39Tf+eAW4vLiiOOU48Pj9+P948PjUOMC4zDjQ+RE5oXoOupT66nrguuK63Trsepx6T3okOdZ53jnjOej56fnFugY6Ybq0uvT7AvuIu/174vwJfFt8THxL/Ft8djxevK882P19/ZW+KP5U/o9+pb5IfkH+RX5U/nO+Vf66/rm+wP9l/3A/fn9WP7L/jv/qP8SAB4AUgAIAd4BUwIRAioC6wLzAxUFjgZKCMwJowo5C2IL8Qp4CjAKKApqCiILMwyjDQ4PExCzEDwRnRG2Ed0R8RFcEqgSTBM+FCQVORaCF3YYIhlYGaIZaxrUGykdKR7UHjofsx8cIHggiyDGILYh5CJsJJglnSahJ8EoZyn+KOUn2CZbJgsmAiXuItwf8xzJGggauhlwGRQZsxiVGBYZLRpMG/cbOhtaGUgXKxayFYgUexKKEFoPRg/RD+IPmQ6UDIAKCwmqB8EFvgMHAhgAvP0r+5D44fba9Sf1E/WN9OnyOvFW8N/vGu/87Zfslups6OrmteY+5+fnKOgO6Kfou+lE6gjqUOlT6DznkuZE5r7lauQ14jLgWN/u3k7e49013kDfdOB84efh/OFm4uLiBeOW4ufhm+EQ4iXjCuUR5wjpd+oJ6yDrCeuz6qrph+iU5+zmbeZc5qrmF+ch52nnbui26b/qZetW7G3tlu5T7+nvUvBO8Gzww/BA8eDx3vJC9Fn2QviW+ST6U/oa+v/5m/lh+Vf5sfkm+g775vuY/An9fP31/Vr+Vv5a/qj+Rv+H/9X/ZgBjAQkCKAJpAvsC5QMNBakGSginCcwKsgsnDO4LgwslCyALbgv0C+AM+Q0jDw0Q7hCGEa0RwhH9EWgSpBK6EmsTxhQWFhUX9xcqGQIaThqJGmsbfhx3HZoerx+fIOUg7SAlIcMhjiJBIzUkPiUoJjQngyiSKeQpYymYKAAolycHJ+wl5SNzIUcfWB2WG5QaVhoGGhgZrxhHGX0alBsyHIgbZxrzGLkXShcaFskT8REgEaUQlBALEHAOsQx7C00KgwhABn8EegOmAaj+3fu3+fD3NvYJ9Zb0zfMN8tXwXPBO8Efvwe2o7DTrYOly57nm3uaI59jnhueU52joXOkE6VXos+cT58fmg+YM5orkqOLp4Kbfst6u3fHc1tyX3X/ecd/k3w/gvODh4VziuuG54NbgXuH24VvjYuVP5/Hoz+k66lDqSOq/6fXozufT5kDmE+Y35pTmZeZn5l3nm+iL6QTqq+rI6wzt3O2I7jLvZu+r7y3wvPAb8dLxm/Mg9vz3tfhP+RD6LPoS+sT5ivmb+dj5fPoY+6z7OPyv/Bf9Sf0k/Rf9Pv3G/aL+H/9Q/9z/MwEsAhMC2AE0AjIDXgTJBSoHswggCmwL+AsEDKYLdQuHC8ULIQyvDMINzQ7gD8wQMhEuETIRnxEKEiISNxIpE5sU7RXfFtwXIhkSGmUa1xqlG6UcwR3gHgwg6yAnIUYhuSFSItYiWSObJDomPScpKDwpTyqhKq0qWyqCKZYo0ydBJ8slRyOmINQeHB3cG44bAxsbGj0Z7Rk6G/sboxzvHEccqhomGT4YSBduFZQTYhKnEcEQhBC9DzEOEwy+CuIJBAjSBQ4EZQLR/+r81frN+KP2NvW99D/0kPIn8XXwK/B07y7uDO2d657pm+fP5tjmEedv523nT+cD6OPo7+h66P/nkOfu5ozmCObE5M/i8+Br3z/eMt2U3BvcG9z73BjeD99v3+7f3uBy4T3hleBZ4HTgFOGJ4mPkSOar5+foqukL6gvq2ek56Ufojufo5iXmuuWk5YflP+Wk5QXnLOjb6Hvpx+pB7FTtLO7c7hzvD++A7xLwg/Al8W7yYvSA9t/30/iC+ef57/kU+tb5qfmd+QX6U/qN+hL7w/tM/I780PwN/Rn9Uf3l/Zn+Dv9v/2oATgFnAVABpgGWAnQDngQ2BsQHEwmdCq4LywuNC3kLfQuLC54LFwzsDL4NlQ6wD6UQExEeEUsR0hHtEfcRYhKKE9YUBBbJFtwX8Ri4GQIazxrQG+McBh58H3YgwiDnIFYh5CE2IrcidCPAJLUlwCbpJz4pIiqxKogq+yk4KYcoMyhwJ4Ml7CLZIA0fBB2YGxMbnhrCGQgZqhkuGxEcvBzxHA8c5RlsGHoYyxfpFdoTcBLEEUERyhDiDxoOMQzSCpAJgwegBSkEYwLR/7P81vnH9971ffTE8xbznPFo8DfwIvBP78Hte+wo62rpW+eK5qzmMOeS53znNueC5yjocuhF6JTnr+Yp5vrle+Us5GbiR+CL3gvdB9xi2+vaOdtj3Nvdbt7b3n/fjODa4FHg4t+e36bfjOBI4knk3+Vh57zoaOmF6Zjpnunx6PPnVef95pjmJ+YK5nTlp+RR5D/loOZd5wHox+mj6zPtc+5y737vPe+N7+PvMfC28F3yZvRs9sv34vin+eT5Mvoy+s75ePl8+TD6zPon+4X7I/xM/CX8J/xt/Kf8NP3t/cL+Qv/V/80AmAFpAeMAugBYAWMC1wN5BSAHjAgmCmQL5QuDC1oLcwtiC1oL6gurDJcNdg5gDwAQDRDuDxUQuRA6EYAR8RH0EjgUWRVWFkAX8hdPGMYYVhknGmkb6xxQHkcfwh8iIAAhuSHyISkiuSK4I/QkbSbGJ40o+CiGKVkqgCrsKWcptijvJ68mbSUPI8ofPR2zG1QahRkkGa0YmRimGS4b1ByRHfEc2BtpGvEYghi1F7IV0ROFEjwRzhBKEMoOKg1WC+IJFQnAB8MFDgTmAZv+c/uQ+Av2I/R+8prxVvG48Bbw3e+d7+Tulu1a7ODqwOi15lLm4uZN503nMOfy5gvn7eeD6D/odOfF5pbmYeaL5erj1eER3+jcXttY2q/ZyNmL2gXc0N3s3qjfQOCb4JvgX+C932/f2N//4Mvi7eS55kfoN+mJ6eTpJ+ru6Yvp4+hV6OvnaecL557moOVy5GHkDOUr5lvnrOhx6lzs3O0s7/7vzO+j7+3vJ/Bw8JLxZvOK9XL34Pjx+cD66/oS++36hfoi+kn6vvon+4v73/tK/Cf8z/ug+9f7jPxf/Vj+Qv/i/24AXgHPAVIBngCsAFABggLnA7EFgQcZCZsK2QsxDLQLgQtSCzMLFAt7CyUMOA0YDtUOVg+UD1QPww9+EP8QHBGTEbAS1hPnFPUVtBY8F8cXPhggGS0aehvxHEweNh8eIJUgjSDfIDMhXCF1IV0i/SMKJb8lHCd9KB0p2CmMKpsq1Cl9KfMqLi7VKxInEyb5I1UfzRpRGDsWexOvERkTaBWrF1ga8xsUHFEbwBktGSQYnxTMECsPiw4iDZwMeA2ZDXcMzwtzDN0L5girBYwCmf1S9wvyZ+5q7HLrxuuy7Kjtgu4o73bvV+6R62LoJear443hLuEp4vHj1+Uy56vn5OcY6D3o1udj5j/lS+Un5o7muOVD5L7hG98g3VDb/9ii1xvYXdkG26PcNd6x3xrhAOI/4tfhHOEq4XPiQeSX5Q3n+Oiz6hnrguuL7MDs/eul67Xrauuc6rbpNemx5zvlD+RF5N3kx+VG56Lps+uW7YvvOPEv8ZXwHfG78VbxefGV82j28vgE+7v84f1N/qb+l/6K/Rf8k/tM/L38ZfxQ/NL8xfzm+1b7Zvtg+yv7tPuW/DL9yP0K/2gAxwBWAEUAPgE4Av0CcQRDBvIHZQleC+gMJg0oDa0Ngg3UDNIM5AxjDfsNbg70DkYPaQ/0Dy4RBBIEEhISPhN+FGQVWhY6F0sYFhlgGiwcQR25HpcgYSLAI/gkwyU8JsQmpycrKMEoIyk6KgAvQzKpMRoz4jQiNdw0RDQYM18wxyyMKdAmDyM/HR0XVhPbD2YL3gnvCrEJGAdnCEwLiwuNCgALRQsaCiwJCAumDEcLqwnkCfEJGwjyBDQCO/80/JX6k/rE+Xn3y/TM8kfwm+tx5uTiDOEF4BfgieGj4yblY+Z+6ATqxOgs58PmquWx5GPmi+l46xTtTe++8LTwWPAe8LHupOzt65Ps0ey+6wvq7eff5ALiROAU3trbEtuj3JbfCuJ643DllOfx6HvpPup76jLq3Oqa7V/xHfQl9dT2efj896v2D/Zi9ITxOe9C7oPspulX54HlROL63lvdvdxE3KDb/9xw4MXjlOa46cDs8+4V8crz+fUp95D4i/um/psAnAHuAaABxwDR/+X+gv2P+qj33fZo9r/0RfNo83/zZfLC8sH04vV49nf4f/uj/UT/6AHoBMYG8AdHCrUMuQ23DZsOqg/JD4EPZg/+DgoOFQ37DJYMfQvgCW8ICAgQCPIH1weHCLsJxQuHDnIR7BMxFoAYCRsVHnAg/CH/I5AmeykCLEQt/S2qLykwyC4kLpkuOy4PLXMsJS2OLZItOC7+LpEu8S0NLlIvPjBnMJ0xkzL8MlgzbDKTLvgoniUJI9MdyxcCE3AOZgs6DQUQNQs7AxECMgN5/1z7mfr6+IL1SfZ1+2z70PWS8kXzaPNF8O/r+OjW53bnlufM6E7pteYA44nhROCw20nWY9Uc1/zWmtft2pPd5tzc2ybde96R3WrdRt9r4Nbg7ONU6brsiexB7JbtfO4Y79fvxO7y7UnwHfRx9Qj0A/KV7zftaOzX7HLrI+nN6cftHfEu8szy1fM+9VP2Tfcn+JT4qvgL+hn9jP/c//7+8P5L/ov7V/mK+Dv3kPS18U7w8+4k7PPo1eVK4mXfPN9T4RDiwOCJ4dvlkuqT7Krt+e868lr0FvdP+TL6xvqG/Nr9y/xk+sv4hff79dH0hfR086TxVvEq8uzxyfDo8CTz9vTe9VT4I/w1/3sBxgOvBVEGVAfiCYULzgqKCT8KAAwRDAkKiwd1BdMDtwL9AXkBmQC8AOcCIQWGBbYE+gQQBxUJ7wo8DTQQ1hPuF2QcaiA2ItoitCRbJr0myCcmKqIr1SsELLUsCS4/Ln8sMCq4KBUp3imEKqUqpCzoLyAxQzIRNRw24zXJN6Q6/TyOPl1AF0P/Q6ZBnj7POoMzoinYImIduRR9DD4G3f5t+Bn58Pyg9yTsJeai5u3n3+gC6sHpJepV79T2yffp8+bxjPHL8AHvxOtK5mzhcuDX4vPjzd+418PRxM8IzWHHqMKowsHFc8lGzXrR1tR01qLYIt214PDiouY860ruI/Fo9gv82f7j/p39PfvD+B34iPih9lHzRfPS9if4h/TI7yntj+wv7ertBe5T7qTwZfX1+pf+z/+6ALkBegI/BE4F6QO7AukDZQWMBdsDcACB+zD2GfJA7gHo+uER4FTfcdxY2jPb8NrQ2X/bMd513ojfB+SJ6HTrJO+59Cr6fP10/nj+Cv83/yT93PnN9372XvR28lHyjPEJ78jubPCH8F/vavC68xr3n/k2/coCgQioC8INPBCIEVcRWRHuEH8P8Q2vDQoOuQzKCdQGxAMmAGf8//lz+J/2FfYh+CX8FABpApYEEghADEIPNhBRESgUCRhTHCogRCIqI0MkcyXmJXkkByI7INsg1iJJI0oinSNVJkUnEifJKH4qnilrKQAt2DFXNIc2bTurP8tBvERQSKhIDUeKRolInknaRohCET83OUUuUiFNGeATgglO/yb60/Tp7IzqyfHF9aztz+Vj5ijoiObI53rukPJi81X5G/9q+8v0gPKt8drqa+BP2fzTOc/2zXXQfNJNzirH88TDxfPDAsHfwafHeM5E1bze9OYB6xvuNvJh9Uf2Rfc8+cL5Evrk/IMA2gEtAJ39sPr/9SPx0O557THs7eyS8QT3gfjl9834gvqi+z78KP33/Yn+XgHABiIKVwqYCRIIQgW/AQD/Vvyk+Or0//Jw8kXw5eyC6nbn3eGU3AbaKtny1wLY5dqe3u/gI+NF5UTmUOYH587oNem86KvqxO5H8vj0x/dX+ZP32/R/87HxHe4B62Hrge0h7rDv5/P896H5tPrD/Eb/6QDIAlkGQwogDWUQbxQ8F64WRBT1EAkNnQkeBu0CcAA7/1IAQAJ7AXv+H/yJ+7z6iPhW9yX4cPoi/lcDNgn9DRcQlRF3E28UtxPUEdwQeBGeEy8W5hc9GUAacRrBGnEbXxuSGlsbdR4TIuMkLSkcLmUxPDTNNuI3DTidOHU62DySPhhB3EPFRZlHnUiXSHxH/kRWQgBBMD/mO5Y4jzasMvkmDxhrD0kNUwlzAaX5H/Ed6pvsl/ZX+pvyMuqZ6Fzpj+gh6unsDuzV7P7zVvef7wbmC+TU5GjetNKLyH/CesCCw//KHdDLzbnJU8qZzlvRitGE0pPWpdwP5G7sf/OT96n52/qj+uj4LfcP9hL0vvLR9Hb5/Psc+975Z/mS+Mv3R/ci9p71b/jN/joF+giGCWsI4AiCCuAJlAV5AND93/4NAtYC8P91/F35k/bA8xnysPBG7ebp6ulu7Bvupe7O7gjt2ejc4xvgcd5u3nPfkOCF4dXh+OJB5dPleuRA4yfikt/b3dzgfeax6oLu5/Ii9p/2q/Zi9+/2AvU99Nz1+PfI+UX9CQKIBWoG3AU2BeQE8ARHBhsIdwjHCMsLOhBqEmsQLQxlCA0GugNKAcn/sv5w/qQAtASxBpQFvAO3AhsBN/57/KT8bP1Y/3gD1wfGCWYKpgtADWENewukCC0HRgjZCw8QnBO9Fq8YYhpaHWwgySFlIrIk2yfpKnQuTjMmOIs6tDrDO5s8wTpeOTc66jqjObY6eEACRZFE0kPkRT9HNka7Q61C9kFfQEY+yzpUMHkeJhKvEV8QKAd+/UT00ehD5ffunfou+jvtReHv3MTc/t0f40vo/eYV5orqTet55vnkJOe74sbVtMjCwKi9k8EzzIjU29NnzjvM2c+l1j7bI9sc2R7Zk93I5yzzIflJ+gn5ofba9b734/fJ9KbzP/af+jX/IQJlAr0BBgG5/6r+XP6r/Yb99gBuBzoMYQ30DIQM3wqsB6EDT/7a+e35e/yR/S38y/h59E3ykvJP8qTw2u2a6ZDmOemO7o3wHvDa7pTqseQa4ZbfxN353Irf0eEi4SHfROAf41HkHeOO4LncatlC2x/ipegv7T7yrPd8+sf7r/1B/p77u/mc+6n9w/wm/b8B7waICakJOwgoBkYEfANcBEkGdgchCGAKXA4uEEUO9wqkCHAGJgPP/zX+1v3+//AEzwhlCHgGbQVXA5f+APuA+i769vjW+fr+NQTQBpoICgshDAYLuQiXBjAGoAjSDEMR2xW2GdAb8B2wIeolFidIJZYlWykRLcUwpTTNNgc5oTwAPlk7ajknOkM6rjoOPbs+/z8VQ35G2EcSST5KL0iSQ+VBuUOeQrg8XzT8J1QX8gtLBwkD2P1P9gjq7t5U3/nqQPV/88zoUt6C2OfWm9qN4vTlkeRE5/LpbuWe4irnfOfa2zzOjsNTuWa20MAozszSe9D7zaXOrNSS3Pvfyd6J3Y/eIORv7kD4Yv7uADX/C/xv+0H7qPiq94b5Kvr4+8MBUwYKBy0HhgbAAx0CZwJ3AfoAsgQyCp0N0Q9tENsOmAxzCTADKfyb+fH5zPl4+SP4lfNk78Tuau/x7krtdekv5Wbloukv7Prtj+9n633i593q3tXd4dnQ2WTdPt9a3/vgGuQC5VHk/OLf3u7Y29fd3cHj4Obt7HP1xvn1+mD/KAP6/7r67fnJ+yr9pP4KAU0DmwZdCqQLYAq5CCYHSgX0BGAHuQkUCrILvQ+UEJwM7giBB2UFCAGV/cf7svvq/zwG7gikB7MGQwalAk399/q++vD4ZPfW+Z7/FAQUByIKhgzwDF4LMQizBYYGIAosDSkQnRTAGcEd0SHWJSMo3SghKXgqXy0HMVU0YjY2OKo6pj13PuQ7xDkNO3o8ZTxcPfU/kEIZRiJLWU1bShZIBUeXRMdCaUKBPrY1Xyk5GvgLPAVmAzn/rfbC68DgoNvF4m7wPPVU7Y3hIdg81PjWnt5F5H7khuRQ5bniTeDu4v7lNuDk0ebCorfXs266DcdE0ELRLM63zXDTpdx342PkneGf4ATlUu1t9bH8pgFvAfn9cfyC/Lr6PPnx+eX6RPyq/24DyQVoBzkHUAUEBP4DuAPlA6kGYAqCDSkQMBHwDw4OQwvNBPL8JPn6+JT4wPfu9Z7x8Ox66+XrVuz96wTpw+Mo4bHjq+ey6U3rdus852PghduF2jXbFdwV3Lnbodx13kXhUOP944rjduE23VXZitnv3Vvj2+ga8KH2P/rO/b8B/wK+AGD+D/23/OX9lv9eAegElgl1DPIL1AlYCPkGcQUCBQUGIge7CJELnw3yDPMKbwiKBbUBxv3O+uL4NfpoAEcHVQkvBy0F4QP0APn8q/ls9zz25fZu+qD/bwSUCGkMfA5rDTwJ1QQEBNQGLgoTDbsQ+BWtGyEhryVBKB8pJir0K0ItDy6xMCk23TrnPLo8bj2BPrI+BT8xPRI66Dt9QUFEqEXPSLNKeEtZSyFKsUa8QYo/Gz/COf4sgBxNDdgF8gRgBOP9k+8p4sLd4+TG7sXxQO2T5NfahtVM1wndYuKK4xHjjeRz41Deodw24HXfhdT8xfC6gLXYuP3DiM7e0b7Pis6v0djZq+P+6G/nKuRr5jvva/iV/nkB0wCz/U78bP3A/Wr74vkM++X9YQHNBOQFuwWMBvgHLwcfBR0FKgeVCuANgQ80EaQSSxGVDjoM8Afq/0D5Lfgo+fT30fQ78BnryOch55vnlOeO5o7jGeCQ4O3kH+n+6V/nE+SP4X/eTNtJ2nHb5ts+223brdyh3ajf2uBi3m/bHNqh2dzYlNvN4SrnT+tv8bf4tP44AmUCGQGeAKwAEwFvApkDvgNABZYIwAqyCuAJiQhdBoUEgwMHAykESQeECvQLcAujCU4IYgfuBKgA0PxM+0b8LQDoBQsJwAcEBeACTQAD/ej4rvQV8obyV/U8+en9uQJ2BvQI5gleCHkF9QOGBSwJ4gz2D4QU0xqPIYwmRCkoKkUrxSxBLpgwWDMyNXM3HTufPTY/GkBMPtY7ujyiPjc+cz8TQxxHp0upTs5OCE+6TyZOYUo/RctB3z2lMmshtBH7Cb0EI/we84jrNOO92+LbOOQv6pvoM+XW3yDZltgY3nvi7uLw4t/kLuTa3+HeGOJy4JjUD8dzvqa3C7X2um/Dn8fEyD3LDNDd1zHhuecW6Xfp4+zG8hv5Hf8JBbYHyQTBAQEDAASgABn9A/6D/yYAfgKQBfEGBAhzCdUIHAczB5QIRwpvDNMO4BC8EqASuRBWD6sM1gUU/rD6sfk09rnxUe6O6ojmR+Qk5PPjnOIJ4B7dFt3536DiiOQr5Xfi4d4g3TfbhtgG1yPXhdYh12TaodxL3RPfjeGu4fDeuds72ivbkd4n4xLoAe7X9Dv72wCRA3wDNwOAA4wCgwFCArkCLANTBhwKPwseCwgLWwl4BsEFyAb5BtAGUggCC5AMtAttCdAGBgQKAIX79vgq+bb70f9eAzEE2QOqA9EBW/0X+fP14/Ls8AfyB/Y3+zcAcQQECEsKAQoICK8G6wYfCNIJBw3pEeQXfR3UIq4nXitiLL8sVy4XMd0zsTVeNtI47T0cQLk+aT6PQP5A4z25O70/JUYCSeBKuEyzTTJRVlSKUWVKUEXnQ75AZTftJ6AWYAjt/hz76/ms8xnmfdtb2b7deeZD6w/nG99Q2L3U0tWm23niGuX55JnlIePM3GLaKN3r2gzQ+sEktq+wGrZwwVXKjs4bzrHNjdIy3KDmF+6+7x/unvAy+ToCvAeXCoQJngWyA5gEowNrARkCzAPMA3sFaQluCqUJjQotCx8JiQdRB3gJEA4gERQSyxOSFPMRUA9wDbwHXP+j+Rj3ivVe9C/x1uqA5LDhl+Bf37Te/t1g2zzZAtuY3h3i9eSY4zfe39oo2oTY79YA12LXnNis2vTaGdt83SnfRd4y3E3ZlNcg2bvcheFT55TtH/QK+5gBbQWvBQ8FXwVKBYIFCAfGB9oGLwicC0ANJA2aDEkK0gbZBJwE5ATyBVAI3wr2C/kKcwlECCIGhgKh/Rn5lfeI+bH9/wEhBMwDTwK3/2H89vhQ9Tzx++6t7xnymfaK/LcBWQUGCPQIXgc6BZEEqQWBCCkMGxDcFOIaIyHHJfgobCtVLZMuMTAtM501EzelOVA+2UHHQRJBUEHAQEg/dkDaQopDqkWQSk1PtVB/UAxT61J2S3dGvEeyRAI6sCzIG4kLrgNsALn53u0m4QbbXdk919LcB+dY5ZPdsNof13PU+dgH4Zfl0uNw4Orfat4n22zdTN760CjAkrj3tcy1A7wwxPrIL8kgyzrUZt5h5DDrUvB/8CX0U/3DBF0Jpw3+DrsMHAq5CG0IswZ+A9kDNAbABg4I8wpzDDIN9AxoC3QKRwtODKoOCRF6EckS8RRAFKsRkw6wCEQBG/x1+Lv1fPPm7gnoLuOV4JPdwdui2znaWtdf1YXWdNoA3kXhUON64NXaudh+2TDZ+Ndg1mHVYNZU2P3ZYdyL3R7dDN613L3Ytdl13pvhbOXW6zbypvhg/6YENQhKCQEJ6wmOCWAIfAkWC/cKfwtdDPwL0wvdClQIbQXSAogCfgXXB/AHXQmJC/kKXwm0B48Ex/8n+8P4OPlY+3v+PgFYATcAsP+M/RX5b/Qt8Szvlu2O7QzxSfc6/YUBOAW0B8kHFAfMBjMIVwrmDPIPQhQfGvYgpyYgKsYriyzeLUIw7zJqNW034zmPPUo/jUA4Q1RFxESiQf4+XEFVR2tKYkucTfRPhlRTWQBXDlC9TAFLw0XzPFkuVxv1CtoBpPzl9u7theLD1wDRftJ726TiX+A92rXVpdK2093a+OI35RTi9uIw40bcsNfg3NfdS9I1xKG5irFTsZW6rcMrxv/DeMTryl7WhOSk8b31vPNq9lgAOQoSD6sR3xJQDyALQgx/DpMLNQh4CRUJxga5CJULygo3C+oMNgwCC7wLAQ4iEUQT1xJeEwsVohMsEGAP6AxrBeX9cvr09yn0ve6C5xffVtrD2FLXttYO10bVu9FD0o/WO9sz3zbgbdxL2avZwNnl2Tvb1tnx1drUi9fE2ufdiN8M3mncZ9yl3EfdSN/44XnlxekP7yb2l/4HBrMJbgvJDPIM7gsdDUQPNQ4gC8YKKQx5DM0Mtwy/CeIFNwP9AfMCsQVcCPwILQhHB6wH1Qc2BQIAPfvo+Fj48/nO/VABQgEI/z/+Jv3Q+aD1mvF37fXqjutl7ijzGflW/r8BtANSBFAFdga8BxQK5gyaD38SWReZH98nvCtrLNQtGzD+Mlg2qTi2OUA7cz6VQQ9DzEP2RKhEcUKNQRVC6UQlSadLY07gUKlR4VMzV+tW51J8TitJnkIJObYoAxRGBV7+N/dA7hviTteg0yvU/NYl3ufgY9xa16fSCs+E0UTYwdxD3eXdLuDB3xnc7tvd3hPZmMlVvM+zBrBJtSq9ucK9xgTIvMtX1pvh++rz8pD1rfXH/BkIYA4pEnsW8xWpEHYNRw47DucKowlMC2gLQgxrD2EQABAqEccP/AvECg4LdwxfEGgS8xGfFLIWuBInD6MNlQfV/tT5rfa38uLuH+kk4dHbZdkw1n/T3dIc0rDP18/l02nYmtsg3VXcAtvC2szYDtb11pLYRNhI2LPZ9dnB2xvf9N4k3T/dlNzx2TXawd6/4/PoGu+E9mD+5ATHCFQLsA7gEDYRsxDgD3gO8w2RDiMPiQ5HDf4KXgfbBF4EjwNbAmQD7gUKBwgIpQkmCiwJygYFAtL8Evpr+fH5pPtF/qr/Rv9i/r79Zvtu9i3x1ezf6d/oAOpe7dby7viM/UIBXgNIBKcFVgcfCAcKrQ3tETUWDxwJI7QomixYL3Qx5zJbNYg3bzgyOeg7zEAoRPVDy0KiQsNCkkPMQ71CEEQKSTBOlFG8U8tUUFiSWwNZIFOrThlKtUJaNjwiogwxANX6yfTE6zrgXdVVz2vNaM/U2O3dW9kR0arJz8ia0T/dX+PM40nhyd+e3r7a7dnV26zURcQ5tzaxNa+rtKa9lcRwx/jFcMjb0xHgNusP9v359flMAWkMkhRWGbcbmxhYEoMOERC4Eq0QoQ0xDl4O/w3eD+AQQhDND9gM6AmpCpYMYA+qEkQT/BJ2FRgWiBHgDY0LAAWz/F73yfQT8i3theXx3HjWzNKy0KzPN89fzi/M1cyA0dnWCttR3a7b39eZ1t/Wd9eR2vTbANjF08vU89g/3Vjfytzc2dzYrdhe2kbftuTd6D3sYPBc98IANAnnDYkP7hAiEnARYxF+FF4WyxO5DzYNpgv3Cp0KqAikBKgAkf5x/zADEgi0CvcJcQgGCHsHngVdAvX9ZPqc+Dj5UvwSAKIBNf+H+0z4BPWy8APsbugG5kvlz+Zq64DyKPkF/WL+FgBCAkwETwbRCDwMExDgE2cXuBwSJBYrYi4HLjIu6i/dMjU3oDscPXY8ej2PQEVErkVFRGlCTkFJQxVHvUkrTGNR/VQ3Vg1Yn1k2WWZXQVM4TsZKd0aAOyQnZxDPABL6ufEb52TdRNTtywTIucm802rdMtzD1PLM78RqyPrWlN/M4CXj9OLc25fWLtmM3PPWCsgPuKisHqeGrUC7asXbyBjIS8eZz0nd3enk9R77LPo3APYMdBVsHB4kvyK6GC8TGxNeEn4QMBBtEGQPPw7yD5sRMRJNErAOKgosCRwK9gw0ETETsxOcFnsX9hJ/D+UNugb++zb11PEW7yTs9uW53DbVo8+Xy8TK58tnyuPHm8jnywnR6NeU3CXcKNnF19nWHNak16PZONif1c7Vw9cj2xrebt1M22zaONkN2fPdhORq6AztofNJ+nwCdQsJENwQHBKqE0YTcRMyFU4WEhXHEhMQUQ4BDqgMSggmA1AALf8UABIDhAYTCZMKpQpfCcAHIAZEAqD8iPj39mj3V/pd/TP+HP0l+8L3BfPm7ubq4OZD5A3kauXd6Ubxk/fG+kn9z/95AMsBQgWFBwkJNg1kEgYXGhwLImAoTC3KLn4uxS8AMwQ3fTpRPYc+1D+zQ8xG00XNQgFEUEayRWZEpEWoSXFQUFdeWtla0FgCV5NW/FPtTtBL9UfrPDcrPRUeA/b7ifeW7prjCdYjyrzHVM0P1Tne+N7800zGIsFwxFnOy9f92KPW2dct2vTaEdx83RHZT8sguRmsxqdSrNW3FcLpx2jLks2T0+XgReyn8tT1A/YZ+VcFsxM/HNkgXyPsHjYYQhdZF/gTIxD4DrIOlBChFVQZCRg9FhEUfA6pCjMLfwt/DGkPChKfFA4ZGxqxFMYOYAiG/BPyIO8G7ZXoB+St3LDT0c9Bz1zMWsnnx/XE2sIkx9jOeNXd2k/dfdoj1xPYQ9nH2MLZn9ks1RTT6dVs2tvel+Bt3AjXKta21/baDuL86H3sGfLJ+/4EDQ2KFLsX6RUgFSUWCxUqFXMXbRcsFfgSAxHTDvQMmAnhA0D/cv2//CL+awIIB+gJ8QqZCsoJMwgtBcP/a/mx9db1zfeu+kX9Af0m+oj2QvIP7hPqoOXn4F7e8N/35LTsLvUl+0n9if7B/9cA/QLHBXkIZgs5D+ITshndIIUoDC9wMfIv/y2+L/k0/jnnPJU9wz7WQrNGB0dIRplHjEkpSBVGoEbbSGlNdlUiWndbHlpAWLtVhFLMUTRS2k2JQUMyjB9uCqr7SvUP7j3iY9XtyvrFJsdMzczY1t+I2QrMNsASufG9M9A72/zWMtIf1ZLYZNrp3Qze3NHUvPKrJaVzpTqulb3yyKfLCs3k0WDacuVO8Nz4T/mK9qn9pQ2lG2QkbymhJy0eMhccFqEVPhMPER0QihAFFKYZ4RxGG44XfxKZDRYL1QrWDK4PpxD2Ev8XnhpCF+AQ9gh2/uHzFO0U6SnmZ+PW3O/Sbc3SyzXJXcfcxsjD78AHxJ/KItKy2q/fm92t2GjWP9el2BPZZ9jp1mfVNdYy2U/dVt+A3XbZl9bC1c3Xnd205UjtovRN/fAF/g6WF5IbxRrcGAsYbRdnF/EYEhkZF9UV/RTxEfENRwqqBDD9qvgJ+ar7If8kA5cGRgmHC9QMfwvnBlgBw/si97T0tfVd+X384vzv+oP30PI67tXpyuQz31DbBNv+3rHm7+/X9yH8+f17/00AkgHBBH0IHwxCDy8TKhkAIdEoIC9yMjkyDzHLMAoyIDU/Ok4+skAiQylFK0YUSJdL4UvQR5NEpkUdSgVPpFMSVrtYrVyPXq5b3FX+Us1TDVEBR/84mSe0EaT+jPUu7rniC9bXy5HFj8R2yGfRKNny17bPasfSwPi+qshh1b3VBNNS2A7bLtb91C3X0NETw+mxv6UposGo8LRVwC3Il8tTzqPWZuL36+T0Wvtl/M0BdRBwHcoixCbgKWMloRz3FwAW0RKIEK8QFRHPEkkYKBuVGXoYeRY4EesONw+uDtIQ0ROAFLoW/hp4GakRfAp8A9H4yu5S6gnnh+Li3OfTEsubyHjHzsOKwm3DoMEvwnXJ/tAU16ndad8z2y7ZA9oH2RXYq9kZ2fbUstIB1ZPZ3d7N33LabtYb2BfcW+BC5o7tdfTR+98EFg53Fhodjh71HJMcpRxZG20b/hyrGxcYVxVYEiAOJgr9BVL/Zfnj94j5mvzJAeUHQQvlCzwMQgyQCUgEBv7u99P0/vTC9vn5kvwl/Dz4AfOO7vTp0uSS3wzbndna3FfjV+s586H5NP2D/s/+MQDZAxAIMQxnEI4U5RlsINMngy8KM9wxMjHYMF8xbjVpO2RAokJDRMRHpkwATUFMJ00BS+dIP0grSYFMTVLzWA1cM1tGWw1dM1vvVoZSIk9VSylB7y7fFqABnfZT7nHjIddyy0PEacL+xGrMk9YF2aXShMbjupy3fMC1zgrUr9E900DYYtqQ27jdp9gXySG3d6laoeyi7rAXvzTH/cpMzeXTSt/56pr1dfto+7T+/AqeGjknlS5oLy4qzyF6HOIaahiSFAITnBOIFFkYcx5LH5Ib4BcCEqAMsguaDFgOmhAxEnAVwBkzGukVaA6uBHL64/CB6VTlZOK93HfU78srxq/Dv8JWwey/275Qv1fEH80s1bfbFd/r3K3Ygddd2GXZJtr32THXJdQQ1O7YnN9i4hvfkdnO1kbY1d5G5zzu+vRE/N8DDw3JFkQevSH4IA8f4x14HN4bnh3FHnYcjhh/E2gOQQumB6wBSvum99f3p/pC/wkFYAreDEgM7QqgCAQEVv7P+Hn0EvM69Rv5PPx9/P/53vUc8EDqZuV44PDaQNga2iHf4uZe8Aj4Pvxo/u3+JABqA7IH1AytEXwVgRojIVooAjCVNSs32zU7M5wzYzd7Owc/PkK5RT9IIUqAS8ZLuUrTS49MZ0q9Sc1M/FNJWkddSl8kYZJfilzcWCxVsFDvShhBYi9jFGH9fPPB6tXddNJ2yEnBIMAlxdXM3NQY2kDY1s2pv1W4Zr3tyiXRrM9ez6vR79FP1cvbf9fMxxO45avuoeaik6/eu6rFyc2G0nPY2+In8BD7GwLxA5oF9Q2OGskk3SspL9oqYSJ5Hbca0BdZFWUUfBR+FUsZTB58IIAfqRzsFjYRXA8KDyEPGxB1EEkS8RUVF/ISnAwjBNb5tO/g52LiuN0Z2GLQIMh1wkvA0L9Vv0C/G79lvwnEXMyO1E7bkd7M3Abaodm+2X7Zqtp52xfZOdbt1sLZW91j4Hvfjtua2LPZ194l5vrt5PX5/S4GgQ85GV4h2ySLJCYj+CGrHykedx7oHZYakxZwEpUNyQg7BH3+JfhO9In0Ovjk/A0CiwciC/oLhAw1C+YFaf+Z+oD2PfQn9cv36/lq+qD4a/RD723p4uMI36jaeddt2CXeweYM8Bv4Jv3q/8kBMAX4CG0MbRAkFG4YVh6kJHQqezDcNL42XDZzNLAzgTYNO6s/l0OgRd9H00g2SqZM3k2TTvtN/EvZS4tPDFRUWwJiXGLxXdVbOFwVWfdSwE7eSfY9lSptEPf2t+ph5yzh+dLRwam72cAWyLXNZNa926fW2snLvsC8TsKKypLOc82VzqfSCNSW1AXWntOmyQS6A6v3oyeoKLE7uinGi89c0wjb/OgT9YP+SwaeCOUL+hV2IM0lmCkALfsqpiSbIEwdhRmYFlMUFRS9F8EbkRxsHA4daxsZF6gTGBIBETARrRCXDtYQShbEFbIPfgkfAhX40u61503hy9ve1D7KRcE9vue9wryGvIu9pr6lwK7FlM3r1b/bct2S3Bnc0Nxg3cLdmd093afcs9k51rrWsdwo4erf99zu21fcKOCN6afyrPiT/q0GBRCyGdshiCbOJ+kn6SfpJA4gzx1BHYYbrhbuDxYKEQb/AQb8kfe19UD1lPX9+Fr+FgT9CZAMOwv+CM4FywC8+hP2NfSL9FP2hfig+Hz2m/Nu707pH+Ne3pXaaNfP1yzdJuXe7cb2hv0vAacDxAbMCucOGRMpF6AaTR/yJNIqIDFFNV439DeBNvU10jg9PalAOkIsRDZHYUr8S7dOuE/WTbtN8051UFhUB1hsWspcEGF6Y1xhDltVVQFSrU60R4U2Uh6DBCLzlOl735nVtcqTwPW/IMiHzHrOpNQP2OfVcM5lwwq+fcKtzXnT3tA5zDjOONGv0R/RTMmmu4awtqglpUuqMLTFu5zGXdJ32Dvh4u4Q+6UDOgxjEV4V1x2ZJ3cs4SvJKQsnmyB4G0MZRBf5FKUUjhSbFTEahx1wHbIclBpoFvcUlhaJFtkVHBbiEzETchUdFEsNYwX5/DXzv+ob42XcYNYDzwLGSb3st/K2F7jzuTS8Ar+gwjXJhtH92PzdM9+C3dLcBt753Q3dadw43Pfc8NvD11zUHNcY3VDey9vI2ebbp+M47tj2S/2cBE0OZRhAH5cjeidsKhwr9ykYJ68ieB+THe4anRUuDkMHuwEF/ez3bPM88tf0oPg0/Nf/dQQ0CrwNNA3aCP8C+/1o+j/3PvUd9YD1P/Yy9s/z8e/B6lfkHN4q2rvYMthV2UrfAOll8Qz4Iv4mA7oGVwrUDRURthXRGvIeEyPKJ1ctmzFxNG42wDbVNkI4uTu1P/pB4USgSP5MvE8FUnFRtk/8UP5T71XeVCRVHVgjXIVdol8oYP9buVjsU3VNAEm4PWUmxAn68wHozd/g1aHLdcJ1v7DFpMzP0HbVqdnx1vjMdcJEvhrBzcmS0SjSENA20XDPbs7F0JrJcbtUsGCoJKPbp3GzLL38yO7Uedte4crrivhtAnAKghCWFyQgiiljMLkw3y6DK2Mj3xx7GZAUlRGoEwUVFReyHBcffR7YH2UeARkEFn0Wxxb0FnsXBha0FccW8hNbDbEFdP3h81vq7+GH20XWRtAQyPi+p7jUtQC0CbX/uCC8kL94x/7PPdeg3qrh5d6H3eHe3d2n3LXc39ry2kXdx9sq2mXc8d1A3O/Z89l23Ifh+Okr9On6FQKnDUkZQiEWJxEqYynhKEopIyi/Jfcj7iGeHakXYxBFCtYFv//b93/wEu1T79v0Ifv2AK0FMAnwCzwMLAlvBFL/qfme9F7zLfR19cX3lvhC9Qbwx+k94jTcPNhp1WfUTdYy3YrnhvFP+ocAzAOKBgUKzwxvEGYVThoVH14kpyk1L+Yz+zUcNgA0hTIjNKc4YD4fQ89FSEoeT29RSlSUVS5VcFMIU/xTWlYlW5NgaWK1YPJhcmE7XS5YklEDSy5DHzSxGxkBZe6m5sHe99Icxxi+Ar+8x9/PWtTV10fZBdVxzMTDm8ASxKXLfNHZ0I7OL9Cn0fPRZNBXx0S43qoOpBGiyqf+sia8UMa50L3YgeHH7c/73wZmD9IUQhvsJQExrDclN2AywSyxJQEf+hmBFmcU+RQIF7UXOxlfGyIcyBw8G4sW4RJrE4sVtxfXGVQZihh+GJkVkQ5ABpP92/Rk7Bvj9dlo0jnLYcKVuTGzTq8orp6vlrPguAK/vcbGz8HXJt2G35zezN2538Lgbd8a3vzdU+D04Y/eUtii1HbVhNg12qvZHtof30rqIvdWAYQKtxM0HFUjOScCKYErzC7UMD0vnikTJYoiwB/aGnQSDghU/x35U/O97qLtJ/E49gT7sP8WBCgJEw1nDc4JSQMN/TT55fa79S71EfUP9Zvzk/Al7fXnIOAR2dzUNdM11ErYmt/M6L/xIvozADsE3ghHDUoQ9hKPFl0brCG4J4csFzFENDQ1gDVXNYQ1Xje4Ohc/VUPYRpFMyFLCVVpX2FjHWAlW31bjWW9cLF3mX0pipWMAZRljcV6jWKlRIUkKPSMl6wb78RXnmd1q03bIX8Cvv5LGjc/e1MjVv9hS2EXPcsNKv+bCIcp80kbViNFE0CzS6NAkz4bHJLnlq8SjPaGXpzCydbo5xCDPWdZW3/fqXvd0AloMARUYHRYnNjBQNzk5EzQJLYYmjh9EGz0ZxxYfFu4XbhnaG6IdlxzWG20aMRYHFGgVhheqGdobwxpqGKsY1xW+DkEHqv6c9dzus+df31bXl85qxBW7jbNurresE62kr8G0Y7urw4vMIdR+2RXcZdtL2krc1d7S4GniEOEZ31vgJ9+b2XfXM9cH1uXWithi2xjiEuwp94EBDQkwEMgYUR8TI+AmPCrNK+It0C4HLf8p1iYcI4kdiRVDC6sCF/yn9X3xX++w7xb0PfpQ/3kELgk8DBcNkQqHBIP+EPrc9Uvz6/LU8T7xVPG47/nryuf24enaSdbn03nTndWW28rkPu6c9ej7GQFCBZoJGg4QEn0WqRu6ICEmiyveMMU0EjZyNr41BTVTNaw2Vjt1QRRIRk19T2xTBFp0XC1b0FmJWhNcxF3cX7djgGOoYnNmMmfRXtJZeFY/TOg+/ix8FAv6c+ZT3EvVA8qBvjS85MJOyWnQCdja28jZI9Q+yhLBKMBUxrnOZ9GVzpbNWdGxzvvKXMhLvUStJqOZnyui76vwt5jCLs4Y1lbeNeki86j+iwsyFBQauCQdMKY37DukOugzPix6I3gc9RiNFtwUZhVxF5Iaxx6vH88dmxzrGXEWeBW6Fj8ZCxyCG/0YBhkXF/IP6ggOAaH2zu5q6F3gxdjs0anHxbtcszSu4ap/qa+q/a4etZm8KcaRz5HWl9oU2//YqdnE3Oje9t8N4IXeQd1122rXYNbu2JjYIde715Pa4eEf7az3cABeCKgOXBVDHXgj7yZ2KqIsqS3OLoIurCxfKt4m1SC+GGQOvwTY/Vz3dPLX76HuK/E391n9wANJCo4NVw2TC/UG4v9t+aD0cPDz7t7uq+7r7hHupevv5yHiZduh1TLSLNGb0h/Xt99Z6kvzQfpmADoFxAkuDrwR5BRiGk0g9CQPKSwtFTEjNGE12zMBMhAydTTROk5C/0YeSzlQA1ZgW2FfCF+7XKlcpl47YehioWS/Zg5pIGtiaUhjz1v9VOlPmUf0M3wYbvoC5SnfAdkZzizDk7oavcbIW9Fq10Hejd2C1fPLIsEHv0PHfs7y0EfP98uwzLfNussgyB7AZ7B5oZGcb54Wp1u1esCBybLSsNp/5fHzlQDvCv8UnxvdI28wVDp9PZc8zzefLpAlSR9vGq0YgBftFYYXOhvSHk4hRCELH0Ec7xjiFwYashzRHS0d1xrLGUcZ8BPICpYCEPoQ8MboUOIt2sLSF8rvvMCxWKw8qSWoPKqOrSqypbmOwrbLPtTm2NrYJ9cO17fYm9oL3AndYdwM28baPtmJ1/TX/tbU1WjXf9uo4cPpxPNu/akFgw5EF/AewyVZKeQp0yuHLzoxCTFuL3YrsyYCIhUbfRIeCiYCCfpH89fvUe/U8uT4/P5MBKoIIwzIDTYNWwpwA5X6WPQK8XTv1O+T8HbvYO1z6vDlCuFC3FHWBdF6z0DRKNWH3SroC/Iu+pT/fAOMCN4N7RGKFP0Xkx1LJBcqiy7oMJMy/TRPNWIz/DL9NVM5wD05RNZKKlGBV/Fcrl1KXnJgwmAiYX5jdmR9ZXtqk2wibEBqSmZwYYxcE1TYRt8yPxVG+UHpUt4n04fJgMBpu7bBKsv+0GvYRd7d3WHYcMtQvj3AsMkt0JnT+dFQzKLNyM+mzDnI97yHq5+fMptJnO+m6rRevgrJKdP72XvlAPRsAKIMTBe0HYInHTQXPOA/Lj99N50umyZ/HrUaWBr/F8kWhRnzG68eliHnIBUfOR2TGQgZFBx5HhggiSBSHT4b+Bn2Et4JoQKW+UfwtOnj4YLZTtPJySS8nbH5qiCn/qfpqgWud7M+u9DDAM131IXXVNfE1enV7Nex2WLbEt7Z3djcnt4L3dnXztV31NDR/NOT2YHez+bE8nT9kwdjEXYZpiHtJ/QooCnbK9gtfzC0Mk8xji1bKdYjFB0FFfEKTQKA+Qzx7u3r7/vy6PhoAPAEzwhfDfYOJg4WC4kDOvzV9+3zGfL18g3yuu9G7TLo6eF23cPYFNN4zwrPd9F21rzeJ+or9In6O/8WBCcICgz9EekVJhl/HrYknil+LU4wwzDoMNww4y9EMV80/jbxPDlFSEygUJBUSltdYMJgWmIQYqRh4ma6bBltKGzrbKBs4WvuZl1feFkXUdhGeTeEGzb8tOhl3y7VW8vPwZG6773GyJnSCNpY35jeOdrdz/jBQL9Hx0bNgdAc0hPOyMsvzavLNsdCvGCs3p6nmCKZxaKJsoe9hcg11DTcROYK9EkAqAvyFmodcSUWMj47qz9PQE85qi9OKI0gCBqfGCgYzxaGGFccdB/gIdQipyJuIEkcNhv0HQEgvyHmIqEfyhumGpsUSwp5ATP4kO7U563goNfZ0DHJeL07swCtj6ipp4CqV65Us9O6ZcO4y0vTpNd71wPVZ9Ri1prYrtpm3Rbe7ttk2hPZqtS00q/SCNB0zwXVAdx75lj08P9FCtoTpBpgIDknWypFKxEt9y3pLl8xXTEYLjQqtSVMHn4UuwnJAEL5Z/Ju7y/whvIp+JkAvAZQC+YPEBLQEK0NjwdFACv7O/cl9Iryw/Co7aDqjOZZ4Lba2tUF0dzNVs1v0CjWut0a6ODx0fiy/nEEewgXDEsRPxa1GpsfNyS4J50rny5mL+EvtC+jLVkttzAKNrE7M0LCSrtQhlTUWUZfqmHwYgFk32WIZsJou22UcZhxbHA8bsxn/WBdXKtVa0q4NpcYtvvA6Pbe5da0zBDBkbqdwD7Kc9EM2k3hbuE92vbNBsHPvs7HmtAk06rPrsujy1zMKMvkxla6Vqj4mjOWYZjQopaw7Lv1xyzS7dn85abz9v4MC14WBB2qJQAzpTx6P5o+iDhyLuQlRyAUHOEYpxdAGGUauB0EIgYkkCJ/IUcf8xtNHAogxyLPJDElcyGWHu8ccBWHCjMBuvZ/7FbmUeBb2L/RUMnAvCiyRKyqqOGnR6pNrlKzqLrWwwTNRNRG15nVatIm0nHUFtfj2eDbbdvP2vjbgdtc14LSuc4CzJ7NCtTy2u3k1vH5/acJmxQoHCEiVCgTKicpQyrXLEwv3DGVMlwvKCtaJYkdqxSgC8ICm/nU8Rvule+99Hv76QJuB0cKTw4FEbEQCg5YCD4BjPyW+Qr3pfXx88bvCOqj42vcrNZq0ozOJcyPzN/PWdZ/3uTnWvFc+J78SQDkBCQKGxCJFYwa3h9IJUop2ir6K6gszSyQLB8swiu2LoA1fjzcQrxHfEsRUjNatl1JXdld+WClaPNu5W8YcOFwD3IJcrNtZGSaXk9aEFP8SNY7GSUxByXwz+HL1/bN5sIFuam5LsQezzrZZuFM4t/eUdURxli+a8Nsy+DOW85dy4bKUcu/yQ3HJ740rROeIpU3k+WcSq22ufLFk9Ix2trjPPGR/cMIUhNpGtchny6qOsY/rUDEPCszRShhHy0Zkxb8FawVyBUkGe0fzSR7JBQk0CMOIMMdbiA0IxclwiY3JPkfFx6pGCwNMAIv+NXseuTZ3djVUc+FyUK/H7TQrTaqLagHqiCu77JBudHBIstU05LYxdhV1WTTqtQJ1j3XDNoX20nZKdhU17/UT9K2z5PLIcol0NDZnuP77vf6XwZDEkkcSCL8J58rPysIKx8sqS0AMPEx+C9HK14loB2fFU0NsgSi/L/04u6n7l7zZfnjANMHLwuvDfEQCBLaEK8N2Aa1/5b7Bvhr9dnzBPA+6mjkZt1Z1jjSkc8dzTTNeM/60yzcXuXu7Vn2FfwA/00Dxwg1DtYTsRgHHIsgxyVMKXIqBSrFKbspAyrUKpAtCjJbOetA+kUUS25S9Ff8WyhgYWAhYk1n9Wrsbf1xRXOhc/Rx8GyGahhovmFVWP9N+T/PLJ0RjfYQ5rrZ9s1ixAm80bpixVfS99ih3e3hmt9k1qjJ/79XvwnGassRzZrMassCzFXKPcW/u2OtgZ4QlT6TvJm1pqu0i8HPz2LafOGn6wH53QMUDpkZ3SFFK8k3HT/MP3Y9QTZqKzchWBrTFu0VPRa/FzobnSDCJEIlTiXJJcQjSiFCIsAkxCbfKI0oziOMH+YaDxCZAy76mvDN5nvfadgX0TzKAMHWtQmuwangpminDKwOsta4YMHwyc3QP9aJ147UItLU0tvT3NTP14Xai9qn2WPYqNTlzjbKzsdNxxHNKNZS3ofpxfh2B1kUIR6MIjYmsyn9Kd8o0CrSLY8vMDHJL+MrBScvIbAZ6g9fBtz8/PSg8MnwB/Xz+SYA5wa8C2QPZRPWFNUSzw5dCY0DCv9g+833DPR872bpt+MW3mjX/9GxzuXLzMtNz2XVXdzI4zPsMfT5+cT99gFYB1oM2hASFYQYgR3xIkYmeicJJz4mGyaEJocohytUMEc2gjypQ5xJr06EVYhchl/QXz1iaWadbK1x4HHrb6lyrXESbTFpeGNaXtdaEFNNR9472ShyDd3zi+Hg1K3Ol8i2wNq/pccM0F7aGOU85/nfcNKNwce6asFxyTvMFswuyqDJmMnpxLLA3LhdqgmcKpTdkhOc+a0CvsrKfNXa21fjie+A/P4IqxRRG+ghvC7hOkFAVUCKOyoyDyYLHKIXABcVF2MX9RjhHOQiyiepJ4wmdCcvJTAiciReKA4rsCzkKGkioh5PGOwL2QCJ9/XrFOKU2yzV4M57yPa9ObLyq9Cpk6j/qXKvbLYivbzEDc131LnY5Njy1APSmdJk01bTs9Xb1xTXeNXj09nQC85UzUzMdcxs0mba8OLb75H+kwooFTkdtiEHJ+MrWivoKYMssC4SL3UvsS0AKVkjNR2qFf8MHwRh/Lj2avNI9F35QP9SBfcK9A4vEjwU3BOCEWUNWQZYAIj9Qfr79cbzsPAN6hnj0tzc1SPRjM5DzBzM38981i/e5ubk7lf1gvoG/qoBOwcbDH4QPRUnGo4fYyODJQ0m+iWWJfMm8SanJuEqTzLcOPg+oEayTFNRwFZYW+tcol59Ya5lP2zrb2Jv9HHFdMxyW3FPa8li713hWTlTYUoeOg8i5QfP8Nfh49ZgzHjDrr7cwojNwNXj2vzh4+X5327S7cCSuK69m8cOzIrKyMcBx2LI0Mahv3SyTaN3lwySFZPMnJisq7txyv7Xr+Aa6Ufz2f4eC8YVkx3+JP4v9zvvQHE+SzmHL1YkCRy/FiwUsxRWFj0Zbx7ZJPooLynyKAYp2ibPJR4oUyoMLMMsLSkwI94e5hdwC+X+SvSR6Tjgddig0G/KV8RkupGvKKrLqMOobqvssIW3ub4Pxz3PpdX72KDXZNP/0WTTbtNh0sbSSNTz1djVvtI4zuLJCMhiyfDNf9Tg28jkyfHi/1YMkRgzIcElmilnLUAsyiqVLv8x9zFdMOUraSbkIdYbaRO6C7ADgvo29Wn0u/U/+7cC5QeFCwAPFBKoE20TERHZC80Eh/+a/Fv5hvVZ8hnuz+ba3/vZVtNRzirLN8nIys/PxtU73jXoEvDe9TH7AP8PAqwHUw2SEI4TTxiJHQYhkiL/IuIhMyEqI9MkzSU7Kw4y/jnHQn9JL093VN5YAVwRX8ZgpWOIZ0RqumyucLlxnnB4b+tr2meBYnFbXFYWUodL40FPLukO4PEL5F7aTc8BxzjAV78GyY3TJtqo4Sbl397L083CDrP9tRPDg8jEyOvI9MX6xf7FQcGUt9Ooy5nnkoOT05kEqYm6bsg41S/eGuRE7hb7BQb3ESIcdSJdLZM6qUAIQfU8GDJ+Js0eeBh4FHQVYRhGGyggySUPKmQrpyoyKocoEyU2JhYrES2sLAcqxSQDIDUahQ6MAZH2v+rc4CHbxdNky7/Fpr10shCrqKjXpy6qga+ptW68mcSOzeLUC9ll2L/U79ET0sLSYNNA1QrXGdWU0YXPY81vyp/KzspZygTQWdmD4lfvuP7KCjkV6B2lI3soqS3yL5wvjDH+Mlkx9i9XLhcqwCOTHaEVVAx/BOn99vg89n72j/pSAGwGqwwiEckTdBVoFWsTXg/yCFsCj/45+8z2tvPh76LpTOIf25fT7MyVyNDGyMfXy9TSRNtB5MHt0vVM++3+JAK4Bn8Lfw9CFKEYMBw6H1ogQiElIsUhVSDEINkhxibTL6Y3vz5qR0VPslPkWE9dnl/TYe9jBWfNbMtwz3C7cSJynHGVb4dpAWA4WE1VOFT9TTFC7y7gFLr6Deph3xLTHMeivhjAUctV1XDaJ98u5Lnj79mZyKK2HbG8uYXE18WUwuXARsLrw2bARrdfqRWbi5I1ko6YUaPxsizEPtRw4Lvn++4Y+qMGCRHkGrEiKipNNSM+LD+CO88zyyhpH98Y5BRyFIMWShogIP8l+SooLrguSy6cLbYrvCpMLGQuFC5iK4wm4yDBGmEQlgK49sbsSuJs2czROMoYxJm9FbULrfCofagOq8WvRbXLu9DDO8yZ063YW9nC1bvRY9Gd0jDSFtJO07bTYtM102fRG83LyOrGTMma0IrZVOKJ7Kj4EgcIFp4hECiHLGcwIjEnL48uDzDYMRMxry3TKNYiYh04GPMR9QllAev6XvcI9+L51f/IBlgMCxD/EeAT6RRIFA0RtArgAtr98/rM9o7yZ+7E6EPhk9ne0cbLosggxwPHB8qS0M7Z/ePJ7ez0YvrA/rUBtAQJCXwNmBAhFF8XxRoVHrwfDx/LHbgdWB4MIc8llix+NDk970ROTEVSJ1idXFReEV81Ycdm92qLbEtv1HGmcAVvmmn0YThfhlwhV/pPpkhkReVEyjlAH/4AsOlO3jPXmM28xDjAVMWPz/7XWdwU4UvhsNY6xwa2Ea03s9G+VcQWxG7BUL+av2O//rnhrvChzZaYlGmaOKZWt7DIZNdy4C3mCe7s95QCaw1xF2cf4CavMIw7kUD3PFE1yCqbIFoa0xbhFTkZRR3VIYcnQizhL/cxeDEULgsqlihTKscs9iw6KjUlzx7QGK0Q4wOR9ibr++Cg2NbRGstmxTXBNbrHsMiq7Km8q+GvA7V/unXBA8rg0bTXndrW2P7T19Ba0NPPwM9v0UHSNNHAz2fOPs10zkTQD9HT0xzaceNj7rD6yAb/EWocjiXtK2wvjDFpMRMwrTCQMQAwMC1dKWAkdh+yGXQSywtXBokAFPtn+cH70wCfB7kNUxGsEtgTnBP7EYEPhAoiA478x/dK9BHyHO/s6a7iBNuP0xXNcMhMxtzGgsqo0OjXXeC36sjzpfly/XsAXAMxB98L1w9pE6AWrBmAHGQd7RzUHAYdUB7sIkgoiy6sNmc/VkhgUEBVh1fDW/FduV8bY0Vl2md5bCJv42+7cRVufmjzZONdb1fwVPRPdkqpR15BuTMrHp4B9er83mXVJ8w+xhfDfsaY0HTWwNoG4vvfP9PWwo6xMqrFs5C/MMCvv1XA/r5pv06+sLZOrBGiV5gilVmbQ6eCuL3KEdgS4fvnSO61+KoE+Q39F3IgGidPMkk8Uz1mOrE0yymTIF0bkBdTGPcccCBQJOwpVS5nMXIyjjAeLsArPCl4KsYtLC2pKUMkiR1fF1MObgDb89/pRN9s1qDQ78pOxlLBPrijroSqJqqmq+GvPrV/uwvDlsp80XXXp9l31yLTrM8ozurNa81Yz37STdIC0DrOoswVzVbQbNKi1HLaSuMB7+X93Ax4GI8hLSnfL9g09TUENCAyQDF8MfQv8itOKDUlCiCpGPUQTQqyBFIA2/sk+vX8TwIVCfEOSRL/E70UihMVEYQN4QeqAbn8BvhP89Lvauw054TftNZ4zqjITsUHxA3GrcoT0qraluPZ7MP0Hvov/kYBygMaBz8LiQ7hEksYhBt8G8MaCRtHHC0eECDEIzIqBzKbOSFCYkv9UY1WNVuFXZ9d9F9qZRJo5mnda+Frn2vha1Jp32TNYTpc0lVkT8lMc01eTNBDgjF+FXH45uaV3RLTW8vixr3CAcfRz8nUA92h5JXdqMxCuwyrDKgStzXCIsGevwK/wb5mwB7Aq7ixrUCif5kVmM+e36uhvWHOP9pu4eDmX+6C+tYG7BCHGmYhvSgyNRs+Iz5sOqEySSf9Hhcbrxi6GWseRCLzJqgslDBBM6IzqzD8LDYqMikGK9krXyo/J0YhnhouFeMLaP6x8k3ofN1Z1kzRGMtdxufALbZQrNyp7arqrEaxtraIvFnEBs2q1MbZFNv014LSo85xzZDN4M6U0dnTK9Mn0afOvMwOz2jSydPE1sLdF+bU8n4DBxGeGlwkuivvLkUy+DPaMQwwbTAZMWAvkiz4KK4kjh/wFz0P3QexAq/9o/lu+mL+5wKOCegPYBKsErISyhC+DQwL6gWD/vb41PU08sPtpOlD5H7cw9QszoPIm8T4xM/IOM5v1VPdReUf7rv1O/qd/n4CkQTJB+IMpxDJE44YqxuWG8Ua7hq7GuYbxR5yJGUsBzSFOslCjUy6U2lYqFu4XdleIWJxZvlnUGpHb8twUm37anxn/WAzXj9dIlX6S9FIDkiaRs89lyc3CIzu9N/11WXNv8Ykw9/FVc+P1v/ZqeCl5LXb5cjctBanIam8tlu/NsCSvza/u75UviW787HBpROct5ekmVej+rNDx+TXJePE6J7tI/VU/8oKQxWiHbIkfC2DN2Q93DtwNd8rLSGwGcMWrBZeGoMgAyZiKycwuTOrNbc0AzGaLCIqkyrRLBktgiqkJX0eExeTDp8CXfVj6pvgqteA0ZrMVMgwxGi9srJlqryo8aoQryy1ubtEwrPJyNG014/aSdlz1JnPv8xTy27Lbs5h0aHSOdOV0q7QstCL0tfTi9aR3b3mm+/j+pYIEhUiILkpcy9iMkA0YjNQMC8vJTAIMIIu+ynlI/QehRoYFSoO6wdGAtj8ofrw/M8BLwdjDasR2BHaENkPGA53CwgHYv9E+FL0iPFx7nTrQOcc4V/ZltEJy/vGhsYSyKXLktFn2VLicuv888z5s/wn/+ABqgRCCHMM4g9QE+oXsBprG2cbihtsHAsfjiIlKP8wrDmcQaJJv1FgVtxYHFosXOpem2FEY/djg2Y/aVlrj2uKa0ZmWVwrV7pWnFCKSgdKPEbIQAQ68iQQBDDrZd+v2GHSqcoMwoLDE81Y1A7aduET34PQV8CYsDCnSa77vFjC6sErwoHCqsGIwFq9D7XdqK+etZrmnl6r47x7zSPbC+QB6H/tWPc6AVILdRY6Htskbi8FOZU8XDq7Mz4qSCHiGrwY8BkEHq0jdSj4LHcwOTJLMnMwES0bKYomRyccKgYrKSiDIUgauxS3DJD/U/Kr57TecdeC0qvN4cgVw2a6JLGHq4KqlqzWsPi2170QxKPKKtKc19TZtdjL06POmsz8zHzOXNNW10bXZ9RO0RrP4c/80ILR6dXX3u3oaPPjALkPEh1tJgIsZC/3McMzZDKuL/4uhjB/MActuihSJKseSxiAERgLvQRQ/0H7jfrj/XwCMwekDDAQZRBMEOgPywyUCW4GMwCF+I3z2+9h6/rmgeLi2x/Uf82HyRTIrsifyuDOXta23rzlXu1M9Xn7x/9+AoMESwcIC5sOHBKbFRQZTBsJG0IamRwmH4IirieILaM0Vj3zRF5LK1MfWFVZnFs5Xtte8mGqZshnEmhpahFrdmfpZMRhQFuvVedPMEqGR0BGVkEcNr0hNAVg7UHhXdkt0FPK6Mbcxi3Mu9Fu2QfjZOJn1RvD4LC2qKyyesDcwmjB8MFOwTfBJ8H3u52yAKnin+Sbi6Coq5i7+c0s3EDjXefH7Un3KALwDFoWDB6vJQowJDmiO5Y4pzE5J0AeFBoeGEcYvBzsIb0mcSwEMCYxlTHqL/ArDSmyJ/4oZiuyKzkopCHJGbASJgps/gvy1uaN3WbXvtLNzTPJisONuvCwlauZqtatSLOfuHG+UMUOzHjScdfi2NfW1tImz6TM+ctrzbHR1NUQ1iPUfNGdz9fQitRm1pbYpeAz7Gr3NQSREb0bHiT9Khouai93MBcwWS4jLSct0Sw9KxQn3iLTHTwXExG+CtsEzwAK/qn8vP6TA6IHKQxvEPkQiw8UDvcK5QaxAp78x/Wr8afux+ok5x3jut1v10rRmMzByrnKCszF0NHXX9/75z/wk/Ze+zv/sQESBC8HTwoJDeIPdxNPGMgbXxzzGxQd8h4+InonvS24NbI+qkXMS25T3ljRW/pd0V6KX/ZhhmTHZfpoA2t/aXFmRGMlXqJa5VUeTlhIzUVJRHdBBTgaIOYBMewj4hDbINM7zODG1cV8yk/SxdsV46HhDNRowFOu8qiQtFLCbsQ5wSnCPsKjwJfBr78UtnCq3aEEniyjubDpwGPRPd7X4pnlnO0n+JICrQ3+Fr4dniUAMPU4djwNObEweCYTHkQaXBnFGhcfwCOXJzEs5S/mMEcxqC/zKi4nHCcdKRgrLSvEJtweYxfOEB0IR/1K8ZvlqdzJ19PT5c5MydzCbrnfr9aqSasWr5azmLgbvjnEHsux0VPWE9jA1UrQP8wgzKnO7NFl1JDVZ9Wx1G7TAtNt1PHVYtc12wPkZO9i+50HuhI4G+YiZSk+LX8v4DC0L3cs4SrlK5otgSwBJ1YhhhwjFrsPnwrLBcIAQP0+/Lr93AEvB3ULyA37DaQMUwqfB2AE6P+A+QrzVe4+62roZuVD4RHc7dUn0MHMusuPzAzPEtPq2ILgfugE8O/2TPzE/nIA4AJVBocKag73EM8TPBhRHPQdBB77HlEgRySGKv4v/DbDP2FHP08DViVXHVeCXAhi02JrYpFhe2KqZu5qPmtCZ4FhnVx3VyRSl09mS1RFKUGOO8guOxn5/YLndd6z2I3P7MkGxiTHS86/1F7acuC43evOm71BrkWqqrfiwxzElMM/xWPDKcE3wYa7W7FtqXWi95/xpw+1IsRd1UriY+Yv6TXwVfmiBIgQphmBIbEpCjOTOpI7LzeDL7AkdhykGfUXgxkaIGUlCCkRLrov4y4OL6MtvynaJiQmIid5KeQp6SRoHIwURA3XA5n3p+tV4fnZV9ZJ0kPMr8a/v0+2Qa6Fq/yrzq7is865lL9jxsfMs9Gt1fPWS9MdzsDLCs3q0MPULddr2E7XitEIzYbOOdNy1drVbNmh443zcgNMEIMZkSAsJ7orUS33LnswoC8PLRArKisrLDIqFSZtIb0apRHbCgEHgwONAIL9H/yf/gEDdAanCgoPOQ+BDIQKjwfXA/D/+/n/8UfsUehz4z7fbdzf1/HRdc1zysrKA87B0czVhNxH5HbrpfJr+Un+ngGSAh8E6wfTC7QOdBL6FhAa0hvvHMEeGyGrI4snvyyWMwQ7DUN2SkhQE1V2WVVdkGAbYyhkl2T0Zkpq0GqYacxn2WQOYgtdUVXpTylMVUrrR+o/4S+XGFD/6Oro39XWls3gyXLHx8b+zMHUEdzt5NXijNF4vTevMK2CufzEh8R5wrLEC8QGwvnALbutsNunZqHDntuk/LIixNbUfOCZ5DDnX+5b+c8EOhBEGisigCqCNEs8dD3kON8vJiQFHIYYWhadGDYfjSQAKL8s/i+1MEkxJS+VKtUniSjhKossNStGJqYerBX5DAkDkfdk7OvhCtoB1vvSCc6iyNHBx7YArd6pnau4rxO19biZvSXFCswL0TrVEtbc0VTMPsmwyXPNztEj1BXVpNTm0XPQOtLg1SrZqNpP3OjjS/JYAV8Npha2HUMj7yf8K4suni8iLmIr/ChzKBcq1Cn9JTgiqxylFNwNKgqjBt4CNwBa/kz/kQORB8YKAQ66DjYMeQjXBG4Af/tm9lLw7esW6cXlDuIC367asdRwz/HL4MrXzFrQLNUR3AHko+tp8oP4Af2O/7gAzwFtBLIH3wvSEIMV+BmyHKId5B6NIUEk3SdxLIsyhTpXQ3RL1FF/VzFba1wmXflfXWP1Y7ljt2ZjaoBrdGtxalJmbmDcW99SSUtGSXFGvz4FNUwh8gRD8JvkwNno0ULQAMykxU/HkM3n1jHizeI61WbBabDYqga2U8TwxTTDfsTIxMXC1sCovQG2+Kvxo8uexKA+rcO/p9EA3yblouZW7P73cgM1DtQYqiC4J7MxKToYPb87WzQTKYUgLhvwF2oZMh8GJHYncisEL30wCzG2L/0qQycgJz4pYisSK2AnASDmF40PqQUt+33wX+Tl2SzVq9JwzkLKNcQuubSuHqpuqpuu17OYt8G7E8ImyPTMv9FA1CTSis2YyZvIXMzK0prXRdm32FvVTNDszRrSu9jh2qjbJOHU6wj7Bgy7Fxgd9iHzJrspWCu9LFEthSxFK/ApEypnKlIoUiUvIVIajxElCyEIKwWBAV7/3P+5Al0GjAkVDNgNkA0eC4UH0wNz/+355fPB7ZfoNeWD4eDcJ9h30wPP68vgyp3LwM5I1Fra5+Dg52vuTPR6+aH9bf+qAJcDhwhLDfsREha4Ga0cPB5yHwQhZiM0JjcrXDJqOe0/W0epTmtVFFoHXLBe+GG9Yw5mSmm6a8ZuVXIsboFoTWdIY5Zb81VmT7tJu0kYR2s7PyjND5r1V+S/2xTTvc3Vy1TGOsPPyZnWpeMH50PZRMJ1sLWqULSGws/FicFfwnTDdMBVv/G8u7Tdq6Gjn5xFndqpSbyMzgPda+My5C7o4/Lq/4oMcxdRH/Em4zE2Oz4/Tj6/Nw4s9CGlHLQZvhmWHqwkYCgKK18uUTGyMvExpy2KKccocitmLngu5SqtIy8aoRA5Byf84fDJ5XnbH9Vo0gPPgspaxcO7l6+UqeqpxazisfC2rrogwETG88qRzz3T3tGmzCLIrsUiyGXOotNn1WvUEdHmzTDOzNE/1wTbm9294jTuXf22CrUUpxy/Irsm3imOLJYtZy3dKycpvidCKRwqdCaGIlMfdhmXEkEOqQp0Bi4D+ACNAN8DjAg2DBgP6g8FDgAMLAlMBFb+4/eV8ADq1+UH40Tg1NyG2IXTbM+RzCjLP8x2z57T+9g04APoTe/x9UH7e/4WAPoA2AKRBj0KDg5QE9wXKRqCG9Edox8yIigm/SlJLzc3eEAbSXVRAFiQXL1f+mGHYmdjGGXiZqZpC2uybIxuW24ia0hmqWBsWmlVfVBUSQxBEzexI1UJBfNe5T7c6tTVz43LqcpzzQXRztbh3pXhpteFxJSx4ahysa/ATMYLwzHCLsOVwQHAvLwGtLepe6J5ntafQKrquu/LP9qs4jnlaulY8ykATw2dGYQivSl8Mjc6Fj0bO/Az7ihLIDscRBrsGvEfVSbQKlMuCTG+MbwxLjFTLsIq/ymLK4ItZC4eKwkjLRr5EOgFIvpR77jk19ud1sjS/81IyeLDVrovsMqqgKoFrhizmreTvJzCAch0yyDOds+5zSvJz8XGxCLHPs2p0oDVhNUA053O/ssXzsjSEtdo2nLguerH+C0HCRQCHeIiHSZFKC8r5iyOLIUrfCpvKZIpTSoAKGgk2yHtHCIVrA4zCwwIrASGAowBjwOUCMcMZA5eD3MPbg09CqMG4wAi+kr09u0B6AvkXeDm2z/X79F5zMHJx8nQyq3NItOA2QPgDect7Z/ytPc9+1/8Rf2O/wwE9wnTDvASABefGWEbTxzsHXIgJiT0KJMvuTePQJJJoFASViZcJmAeYeFgZ2PcZylq92upblNvt26fbwpsImWYXutamVUeTyNKykQdPvktnhK49h3m9dyW1YDRGs9hypnIwcx51C3fKORR2UnEKrFKqEWvYr6VxKLBM8KAw9i/2LwvuvWyJquopG2eqp0tqCC5iMpw2Rbhd+I65mbwPP0SC0UYoiGGKYUy/jm8Pdg8ujY5K3MhlR3MGyocbyG8Jwwr8S3HMCoxSzGAMfUuJiscKvorGi9CMEAsHCSkGuYQ/QWF+qzwI+Yh3BjWp9I4zoPJQ8SYuqiv1qnWqbms+7Got1+8d8E4xv7IBMyrzrbMJsi4xQDFdcb3yt7Q4dNG1BjSxs5WzMfMEtAF1l/cWuJs7L35Zwb/EaUcKCPsJfgo6ytMLOcqQCkjKDkoZSkyKYYmAiLkHvYa1hQdEK8Mwwg9BG8BQAECBH0IdQyqDoMP7w5jDeMLvQgNAuf6OfRE7b3mUOIt3/raT9ZM0fzMV8r6ycTLbs/606vZ+99x5ons1vHI9sj6r/z5/bwA1wQjCYINAhLbFUkZtxqDGpIb1B73IhooiS5ZNbY9uEdEUAtVcFlUXvhhyGNsZaRm3GeQatNtFG9KbjVsX2qJZYxfolqvVdVQkE0ZSZw+HisuEGz2Y+fM4OzYEdIqzwPLKMhAzUPWa9/+4tLYEcMOrwyo5LCZwHTHNsOCwPnAc771vES7DbTKqjGk4p6ansWoh7kXynHXaN4f36TiSO1i+0kKihdsIPcniDGLOXo8BjuINFspuR/gG9oa/xt3IdcnEisLLUkvEzApMHAvMi3MKgErTC1dMBMxuyymJL8bWhL5Bg77tPB35jvdQdcI1NfPB8qOwz26la/qqVOqKK5es7+4xr1WwtDGQMq4zD7On8t5xgXEXMQpxhTLw9AO0x7SI9DgzQTM6curza/RkNgF4Zvs4PlKBVoPtRoqI7EmtChZKioqKym2KAYoEijBKGgoESZzIlcflBulFeAQYQ3GCV0FZQJlAiEFwQm5DUwPbw+8DooNWAzWCesD5Pvu9G3uGuiM4w3gZ9uQ1W3Qccxzyu/KRM2w0DjVT9r737jlZet/8A/10fij+qL7qP7jAygJgA2NEUsVVxj8GfgZzxo2HnIjLylkL/k18zz4RHlN9lM4WIVamFzFX05jomYpaRFq0GtOcIlz6W+Had9l42HGXTxbPFUpTA9HHkBGLaASJPkJ5wDejtiq04DRE84vyGvJc9Tw3wfjF9hrws+vAaqBs5bC48g7xKTB5cFgvde51bdLscOoIKMCnoicwqaOuPzIFtaM3EfdteAU7DP7qQoeGOkgQSjOMUc5TjtVOS0zISjHHm8byRpVG9MgqSf/Kocszi0SLycw2S9xLbArhyz1Lj8y2zNHLywmrRzuEpEHJ/xT8iLoGN5o12DT9853yXfCKrmZrxOqY6qPrtezT7lfv53Em8jtyp7Mw82Ty9zGp8RZxCPFmMn+z7rSKNKy0MPNB8qByaDN8tP82knh0em09nkEXxD8GtchOSQWJ0kq4ClHJxoncCdtJjQm2ySkIUcf9B07GsgUZxAHDQsKCAh+BUgE4wY9CzsOqA9dEJwPag70DA0KoAT3/YP3pvDX6fHk7eBE3HrWsNAZzSnMAM2dztbRvtYq3IHh5ub961rwpvNm9qL4XPqG/VcC4waZCpAPJhRiFuEWbRfbGd8dtSK4J0UuDDZCPvlGjk16UchVyVp6XZNewGAdYyFmX2qYbVVuQm43bVdq+GXXYcJcV1hRVc5RqEx1RWo1oBpUAHPtnuJQ22TWZNNgz3TLLMv90dndeOQo3a3HdrF+p1Kvj8B6yqHHDcOGwwLBnbwquT2z1qoUpNqeP5zho7G0Hcb406rbU9z/3DPmQvXkBF4T8h0HJnUv/DeeOhY5wzM+KbseTBrJGdwaDiBbJxgrtSy8LqovRy9HLhAs+ymZKqktjjErNO0xeyldH98Vvgq4/lz05Opk4WDa9dXU0Y3MJcYDvTWyIKurqUqtlLPCuRG/pcTlyBjLmsxjzePLdMckxBrEsMUzyR3OFdEG0BbMVshHx83JUc6j0iXXst1S6fD49wbJD0UWKR30ITElQyhbKTcoFidtJpUkciPrI7ciih9iHOYXrhLTD6oO1gxzCSQGbQTfBhcMmg8DEf8RVxE7D/QMggkOBL79yPa97iTos+MX4Cbdo9l11MDPtc2ezXjPVtOs18Pb1OCF5q3rvPBW9MH1d/fi+Q38nv+iBFII5QtNEU8VeRY0FzEZzByPIXAnxi1bNFs790PQS9lQEVWbWctbfFz2XrBiRma2aQdrbGxdbvVu8GwoZ7VgBF4wXFhXVVGQSjFCpzT3HGwAEOw24wvd1da60+DQ+c5M0M/UR93e4gHdcMt9txqrK691v6bJ/cfdxF3Du74Muh236rDmqLeiJZ79nLykWbSdxLbSfduw3djfyOe/9J4EpxRyH5YmFi87Nm03MDUfMO4lrRuQF/kXIRqVH4ImJiu1LXMvfy/lLnQuTC3XKzss4y6+MuU1nDORKpIfehXmCe/9GPQD68jh59pN1trRqswAxlW9aLOkrG6r766XtaO8jMLzx0fM+MxGzTzO5ctZxk/DJMM3xMzHcc1x0d7Res8czP3KYMzwzRDPq9Lt2SHmb/W5AikLLRIhG3ojUigOK1wr1yhzJVkjGCOZI5kj0yG2HfQZphb2Eg8R7g9TDj0LWgfqBAYHxwzbEXcT3xIDEdMOJA3jChoH5gH1+oXzy+yE50DjHd+H2mvUKM4ay8TLW86+0oDYyt1G4mvnrOzP8Fr0N/fe+H75Yvua/xkF8QlpDYQQShMJFekVVhYKGeAeXiWmLEgzaDnnQOBJOVDuU49XJlm6Wkddn2DoYo9ol2zKa61r3G3YapdkhWE5XrJaMFlWVLFK8kJNOUkjNwfU79bg8tv+22zaKNVt0D3PzdP13cbkEd+8zLm4xayir9m9xclkywzI1Ma3wh+7/7UesnOsTqUZn6mbXKBcrwTBr87+1m7ZKtp24a7wfgKmEp4eLCeTLxM35Dh6NbwvLCZZGzEWnBYiGeIeCSYoKgYsMC04LeosvSxuKy4qMSuXLrYy2TbnNhAv1CJ9F9wMRALN+NvvN+aL3prYEtP5zUnHBb2+samq7qmArWO0vLxLxAfKZc1jzoTOcM42zT7JhcSmwnrDwMfyzWzS69Lb0G/NMsqvx+zGgclz0CnbQ+VX78r6twW/EGQdpCXEJ4gpkCy+Ky8oECdJJ94moCWaIZ8brBagEygRTg/zDd8K2wdFBoAGogjPDFERUBODElURzhCLD9YNEAukBdX+vfiW8Q3qOOT63uDYpdKFzBTJMMqxzYLRndbK3B/i3ud/7TPxZvTp92H5n/r3/R0CrwX8CN0Lwg6IEa4SORKFEw8X4Bv8IQEqDDJ+OedAqUfPTHlQYVSZWQNdZl5iYahl0Wm9bWZvB28ZbiNpBWPXXgJbAVa5VVtVTk1oRZs88ihVDiT6buuD4hze/ttU2uTX3dNH0u/ZxeMf47fUCr4brPGqrrkTyqnN88drw7TAW7uutiKyGqsdpTSiDKCkoQ+uFsEN0cLabt683e7fQOuS/GMN3xgiIJUnGi8OM0IxTi3zJtIcJxaBFocZPh59JRwrvy0qLtwtGi7vLegsYCxlLWQvIjJ8NQY2qC8UJLoY4w1dAjH4CvAc6MLg69ow1r3RHsw/xBq6UbJiryixK7dpvsrDjcgIzNHM78vAyyrLusikxR7E+cMWxXzKktAo0nDP18uayUjJ98sx0A3V7tsW5M/srPgWBO4LtxM2G8wfUyNiJycpxCaHJPMjqSN1IksgZx7UG7UYLRZRFJESuRCZDk4MSwo6CT0KiQ7dEssT0RLrEeYP1gw4CagERv8H+TbySesf5hvidd4K2nvUu9C6z1rQAdK51cHb8eDU5OXoeOxU8PTznvUD9lr4gP1TAiMFbwgPDRERDBNvE+oTbxZEG+YhCCmuL3c20T15RZJKRE3XUN9WkVrXWmdbbl27YnRoOGv1a0Jq1mZPZHVj4l8uXPhaJVgNUmpL9EE/LycWU/1E7Vrl3uAs3fbbD9wg2RbWj9Yg3WDiGNqqxf+xfqoXtU/IBNODz1TJCsU8vx65TrSjrUmnmaTuod+gR6gwuBfH79H+14DYctnL4sn0aQgZFwcgTSduL4czGTFILGMm2x1FFi4VmRmkHm4keir8LEgsXyrcKeQpbSmGKdwqty3vMYM2JDljNAgpvBx/EloIbv5q9szvN+lI4hnbddSfzvDGXbzbsgeuo66ltLq9+sXWyqDNgM6QzaLMxsucybPGI8a2xdPF7sme0KrTRNGyzALJ08hHzPXO+s8d1CnepumR81n8iwM2Cn8TkxzZITElXigJKvEnESbWJQglXSPrIBwd9xegE7ERGhEcERMQRA0WC9IJpQpUD/YTARV1EyARWA/GDYELSAjpA5H+cfjO8lDtJueD4nXejdc50F/NV86u0LfUk9kK3ifiK+a76r/ukPHp88H12fe9+5gBwAaACjEOGxBmEZMSThOQFPAX3R38JKIsCTUdO2ZAD0eFS4FPrlPNVzVa/FuhYZRnOWmTbCdwz22XaNFlmmIaXStbfVuAWRNVaEwZPvspqxAY+87uJ+Zi3kTbtN4t4s3hMd/Q3bzh5uO42U7FfrHQqvq2EcoQ0wbQ7snMxC2+DLdQr26nF6KooC+hkqIUq4m6iciE0tbYHtp92h/iZvTXCAsYZiFFKBguTjC1LDwl5R32FmoRnhJDGQcfYSUnLEcvAy43K1spZihsKFkqTi2AMQo2mznXOv80Myi/GvgP/QVd/Nf0tO/L6nLkt9wm1hLQJsdhvGizL7CssrK5JMPoyhjP5NBx0OTNOcsQyTjGC8R2xE7G1scEyR7Lds4J0SfQ5Mlmxe7G9cuA0WnYXeDR6EnyNv0jBf8JmRK8HP0ioCW5JvYlXCSBJbcmDSMwH8IcUBqQFxoVxRPwEoUS6hAqDhsNXw07DwUUyRYsFQQTLxODEgEOVwkPBg8Cfv2s+AL0De8x6eri6NyZ1pbQxs6c0LbSdtZ+3K7h2eXB6Urt1+8n8SryifNN9yX8qP/AAtYGwArSDVgPHxDxEGoSpBdnH/UmvS18NDg7UEHoRSFKrU4/UlBUqVXmV/Jb42B8Z3Zu6W5AastmCmVmYUFeh16nXNNWQ1JDS2w8PScxD4/7qPDm6gzmD+Re5crh9NoN2eHdgeXS4+PTAL+nse6zRsO40/LXCNAKyRTE0ryXtUSwfauAp4Kju56vn2Ksnb3VyUvS0tUD1a3Y+ObG+g8MNBgFIHclTSqAKtYm4iL3HGAV+RFkFv0byh/wJR4rvCp+Jy4mVSfAJ7QoXCu+LikzITcYOgg6hzIVJe8YiBB5CHf/x/jA8ojrbeOz2zLVHs+CxjC8YbXUtPW4JMDtyBHOHM+wz9zOM8zsybDIfMcPxvLFhcUrxuDKcs+s0KvNT8jOxOTGXsyg0KXSeNZO33brU/aI/AkCaQkrElwapiBoJHkliCXwJCAjPyHCH1geVxzCGY0WkBPTEk8U7RToE40R2w/iD+ERBRWPFvcUJBL3EGAP4Qt5CKcG2AI0/Xf4svP27VPoFeNN3fHWX9Jx0efTtteo23DgduSU5kHpF+2T79XwA/Iz9F73Bvt7/sMBDQUECPwKQgz/DK4PlBPRGZohRimxMJQ3hT2QQoVE3EbZS21Q7FMqVTJZDF76YblmF2p1aR9npGVfY65eC1nmWDBcmFzQVT9I2zVyH08KFvv68CHqLuc36Qvr++SA3BraHuEc6O3hsMywtriu5rgjzDjZoNjlz2nJLMTpvMO0lq3PqGqn2qWIot+k37LFwkDNkdIx01jTINn16Hr9cA7zGJ0f+CVLKqcnACG3G8UWoRGhET4X7xwZIhQojytFKtYmsCTlJLMlqyZpKbgu7jSxOHI51zaQLWYgFhVRDeAFAf4f+Nry1uuI4/zbsNZ10AnGW7tstpK4x767xrfNcdB30HXQB8/8y8LIB8eYxnvGa8Y8xi7H/8omz8bPkcxbx+zF8smL0KHVWNcj2JLcFedK9OP9DgS1CHINFxQYHXkkESYtJDgi/x8tHZAbShupG+wanRiPFS0T5hMtFrYVnhOREcEQBhLIFBYWqxReEpgQIg6tCaAFkQNdAqL/vvp59WLwhuvZ5aDeJ9dw0gDTRdZR2Qvd1eLv54Pphemn69ruffBA8cDyxfX/+RD+FwL3BRcJ1woIDJ8N6BB+FR8bPiKGKjMyYTdbO4I/zEO4Rz1LV04WT3VR3VdKXp9jq2dDaNhmPmZ6ZJVgeV70Xqld4VrMVu1O3EKDMjob/QIa81jtE+5b7wXvouxy513fRd3I5N7n1dudx9q3wbTZwQHVRd1O2EbQuckWwQK3xa9ErIyq2actpKej56qOuFDGts/10oTS3tQb4GnyiwTXEkwbKiC4JGsljh8xGRgW6RGFDmoSkBq8IPokeSiNKOAlwiNBI4ckICciKsEttjKiN/g6oDp3M6MmRhr3EEsKjwS9/0/6GvMI6trg3NnN1KDN2MMYvY68PcCxxsrOntTO1XfT489szJjJasdnxtjG5MZOxTnEvcaXyzbOe8xLxz7DaMWBzEPTP9eG2BTb1+HI61P2y/4IBN0HywzvFOgdxyIRI0QhBh5EGkAYZBkRG2sbYBoBGOIUoRRMF9IYZRcqFWMTFxMkFTIXjBfxFeESIRATDcsIFwVyAywCz/4i+gf1fO+M6ljlHt6P10TVRtdz26DfpuJQ5v7pU+uR6wDtPO7z7zby2/QC+HP7CAAUBE0H5givCXwKfg0eEnYY3B/IJpgtFTT7OLM7O0F4RM1FHEh6SrZMgFL4WtNhhmQgZHhjEWQ+Zh1j51qjWZNakFtxW8NTa0VPMpAbGAdJ+kT08fM49Zz1Pu014SbdyuTv7rvtxNxbxl+457lgy2jeDuLd15vPqcp3wti4BLSusjqweqv6pEWjhazHupLGsMyCzcvMudCs3l3yQwN8DuEVZhwdIsEhNhxYFnsSJw8JDV8RYhlEHk4iuSXdI1MggSDiIgokZSW6KLcsXjIJOAQ7YjmGMUAlSBqME1UOBwnNBH3/hPYg7Lnj/N1b2WXR2MY/wIPB5sYbzSHU39dq1sXT2tEDz1bMQcs4yjnJFsg0xibEVMVwyMPJvse6wde9XMICzEnSqtQj11Db4OAl6QPz8fq4AHkFcwsUEkMYqxyIH4gfNBsOFq0UUBbgF/cXmhf8FsQV7RWQF+oXwRbTFTsWxRe8GcMaShrSGMsW2BOuD+MKugbXA5wB8v4z+3T2qPHC7ELmXt6G2aXYsNoh32PjjOY06jftSO6S7g/vFvCQ8VHzYfXl97L7Rv9yAp4FBge0BzYJewz9EA0X7h0MJcMsYDNlOFo9BEGmQdpCbEdkS1VOZVGfVRdchmOSZ4NmBWOtX7peWl5TXXtar1U/T/lD3DG5GtQGxv18/Sr9MvxU+4D1j+yS59Lr9vMk8tTgEswkwPLB+s/D39jkU9we0oLLhMOautS1C7RRssCuh6jkpZisLLksxMzKks2zzpfTS+CA8mkCrAulED0V/RjoF1oSkg2PCiEIwQgKD1YWyRoGHpghfiL/H0geMSBQJIUoZyy6LpAxQzYiOYM2dS2JITYYYxOkDycLDwb8/8D34u4J5/TfitkW02jMeckCzLTQgtX22tDdjtv61k7THdBMzbDL7cqdyJHFucMkxGPGV8YYxFbCdMD2vgbB1sY+zdHQvtNR2v3gducr8Bn5vP69AkIJRRLfGBUbwRpHGNgUfxJFER4S+BKXEn8ShRNhFLIVCRiZGb4YIxekF9EZnxxxHoUdIRq9F24V6BA2DQILSAgWBN7/Qft89nby5O1t55Df/dnA2a7d+uJX6FztGfHz8nLzBPQM9NPzsPMA9Hz1Tvjr+tL8L/8ZAqoD3wRLBy8MTRKCGCIf6SSPK8Qx5DeMOz0+MEAvQu9Dq0aYTLVRYFeJXe9hb2PRYtte711mXqBYXlNAUXZKcjwvK7gYlwrKBl0JDQmfBxUFGPuc8B/tofKW+dz1p+MUzwnGOM1H3jjrVurb3gDUocukwja7W7lVuaC25rBsq0mrarLEvOTG/Mz2zVzP1dcq57j3rAT1CcgJ/Ap/DDIK1AavBkkHbAbrBwIPORYGGY4awRvAGW0XSxknHiQjQCmZLv8wrjPuNiQ2jjBuJ90cOxWrEawPeQxFBwQADPfe7dHmxOEm3SXXBdIu0tvXmd3E4QblIuQ53gDXONLlz/3Opc6/zXjLNchSxXzEZsWXxATCwb9ovWG8GMA0x1POTNSw2trgBOU66gfzc/vD/+kC/AdTDXcPhw5rDccMUAssCYwI+ghVCSkLiw8ME6EU7BZkGbgZKBlFGR8aUxxfH74f9xxuGQIXFRRYDxYLyQczBOz/+PsK+OXzpvB47snq7OVu5NjmYeqg7djxT/bM+W76Zfnl9x73lfZF9oD2//bl98n4uPp8/WkB4wPmBVMJYg5xE2IZfCA0J1UuqjOsNgE4SjrvPDBAZUNWRUtHhUvTT09VWFvFW9VXcVTzVbRXPVY6UdNIAzxxLewevRR5D/ALFwwjD64PowbK+VLxd/TJ/Hf7COxK2I7NztJX42rwrvAT5ijcmdW5zSjEbL2MvLS9F7xKt3WzsLZuwIPJIM7tzyPRitVC4tvzHQEkBgwHrAfhB3kEif55/J7/EgNHBqYMzRLWFKcVdRfHFtYToBPDFsEbTiIRKV8uRTJANJcyMi72JbsbsROcD0cOCQ1xCXYDJ/uS8ajpq+RV4fncYdgr2PncTONB6AvrRup25BTd8tde1HzRw9Am0uTR4M03yeTGp8apxoLDC7+Ku7i5nLttwubJ587K1dXey+XH6nnxqPiO/bUBjAYDCvEKMQs5C6UKCwnHBUYEXwYfCAQIsAjfCh0Nww9vE1wVoRUpFlcY+RsiH8QgZx9XHBka9Re6EhkN1AmlBjQCqf3B+IHzcO+47NnpkOdN58npOO7e8mP2Jvkx+337y/uM/PX81v2q/nv+Dv5I//D/xv5H/tP/TQLoBBkIGwwDEaYWxBwHI2kpLy/xMmQ2JTsUPfg9/T8kRFdKTlAEUytTBlN6UidT91FsTw1LpUYMQRU31yhvGuAQlw75ERMTRBM9ElYLCQJW+2H9+AQDA/ny2eGA2evcXukZ9fn2R+wC30fWi88pyWDFHsT1w2LBLbvtuFa+fMfgzgbUP9aq1tXbo+fH9cAA1gUyBUkDlAJa/z36nvjj+pb81/51BQ8MFA80EHgRqxBJDr8P2RW1GyogJSVSKX0sci6nLl4royJhGI0RSQ4ZDI0Kuge3Ac/4BPCe6afkfOEh3+vc192J4lPoUO2o8I3v7ulV43Tdjtgw1XHUA9W+01DQh8sXx7DEgMNhwsbAQLz0t0W608Lhy0/SMddq3WbkKusy8hD3/fnY/B8BnAXsCDQJVAiVBtACWv6S/A7/5AJbBUUGAwfDCFQMMBB/EnkTVRRaFnwYhxoEHbYd6hvJGY8WNBEKDGcI+gRtASj9Ifiw9ETygu8G7V7spuxC7g3ynvcm/dEANQEtADsA+AAfAlQEggUIBNYC/QICBCQGtAc6CQ0KfApoCz8ONxNlF2IciiL8KGYr1C2VMUU2yTtePmU/vkE6RjpKGVD2U91SFk9zSV5E6EKLQJM20Ce+GAwOMwvVD6oVSBdlF/EQdwQL+kP7UQerDXcEVvF849zjqPHa/9kDlfqm7BnjRNxy1XHQSc8iz/zMeMcMwt7CiMqn0krX6NiK2QjeNehI9df/cwRGBDUE/QXtA4L9//ke+s76BvxMAZAIpQrcCNQG+gQCBGwGPg08FJEY4ht+H6UjGiiGKssoayLVGccSZg6MDFAM4AmhAy/7OvLL6mPmVuWv47Hfmd2I4DPmZetu70rxpe4K6XzkrOEX337d2Ny929HXpdGLzHvJ1sf6xUzChrwVt3+3gb2Nxe7MHdSt3IPlbus38Bj2P/uq/oMAswIhBCUEGgTVAx0CDABp/wgAPwCw/vD8Kv2Q/8ACHge2CwoPNhGJElEU6hZiGbca0xm0FhYSKA70C2cJfgXHAFD7SvUg8HLsC+st7A3uuu8B8t71dPrn/lkC1wM5BKIEKgbHCKcKTAsMDAsNKg0LDV0MIAt9DIgQWhOsE8oU1BefG38e0SGQJusqvi+dMugzoDeZPAhBFEUNR5dHl0gYS5VL5EXGPxU7PjQiK5EdZg6ZBtUHWQ2nEb4S/g+4B1H9pvf7/HgGxAYW+7fuF+qR79T8pgdECHb9GPCI553hbtpt1c/UzNW807HNvcpYz8/XFN414fHhruIJ6AT0JwHmCLYKeQg+BncFYwLt/cr9OwD0AFIB3AXCCrAKsAg2BtwB4f7wADsHIA69E6MY9RuMHn8hOiNKIYEa6xF7C48HdgaCBkUDoPsW8zPsDefi4tLgTeCI3wXgoeOq6PDsvO+n70PsluZa4RDeD91q3djcP9q61kzU4NHBzcLHS8NfwOG8PbrBu3bEs86F17be++QI6jTvXfbh/agB7ACFAZEEYAfIBsgDugD4/m790PwU/u7/rgAxAP7/CAEQA2UGcAr1De0OJw+EEdUV8RidGDIVoxACDPMGYQLC/hL7BfYz8Anrm+fR5sTnN+kD67vtefFe9wj+pwJXBR4HLwgHCZUKvguVDRcQyhA0EOAQDBKLEpoTshWJFncXSRgSGY8dIiSwKPosPzKTNkE55DoQPhFCCEXWRqxIXkuSSvxFjUELPMYylyf+Gh8P6ggYBw0JJg6FEkkSFApu/nX0/PNy/q4DVPy+8Bfq8Oyn+bAERAX//PXy0urm4w7e2dqB2n3aS9mv1L7P4c/C1nrghuee6l7sN/Bl+CwD9AuLD8IOJQz9CQAI+wWvBekGUggoCcYKzQzJDNELIgoUB70EcQU/CvkQYhbVGegbKR2qHc8dEB0zGqsUiQ7tCRYHrgRU/5f3vO8q6P3go90C30fhy9+N3knhgeUS6L7o/Oje51/kzuAE3/PdNNzW2VTXedSqz3rK38WkwoLAf74qvFy68bw9xaPP5dZA3D3iBulm7372e/4QA04E0wRZBmsJ+Ql8Bm4DdwFQ/yr+hf/WAvsFdgbDBEYE2AajCt0O8xFhE20UyxYMGVwaABrjFcEPxgqcBbv/bPos9tjxzeyn58Hj8uIJ5Gzl/uXY5vTpW+919UX6nf3w/14BjQMNBuEH1AmzDO0Ovw+OELgRixK1E58VCBfBF/0XEhq3Hs4jJSiULcc0TTzwQqdH9kmWST9LjU86UXROMUjzP4Y4EzDAIzgYDxBuC78M7BCmE6oTTAyeAB726PXZ/msB4vnt70Dreu9x+5IFGQhYATL1qeoj43Xb7dWg1JrUvNPnz1fL1cuo09/dIORV55TqaO/b9uMAfgmfDVUOQA0GDDkL0AlrCf4Llg8gEfsRZxQ3FecSZxCrDRILKwtYD2QV9xtcISkkbyUFJ/Mn4iUGIRcbCxUsECwNQwpKBT79IfQs67bkkOCH3pHert553rPgIOTZ5WrlbuTe4vLeS9rA1uDUBtT008nT0dD7yt3EjL+/uqi36bX2s+myaLZrvzLKrNMP3K3jBumY7bXyz/d7/FQAiALDBZAJbwn9Bl8FIQRhAvIBgANnBpoJWAuTC4YNLRJSFlMYVhroG1ccKR5zIVcjjiIZH2oZpBISC4ADx/yy98Lyge0G6XHmzeXy5srn5+dI6WfrPu3P8B/11ffg+cv7Ev6q/zcBkAG1AXwC6QLdAzIFegZmB0UHygnMDd4PGxPOF+MdYyXOLhU4Tj6EQo5GtUkpTIJNl0vdRy5DbzsuMvAojyO7IXUeCRzlHOEdSRhsDqwEsQGJCswR9Aui/0r4afn/AtkObBL6C5QBNvl88ojrvePX3evZL9eC0q7LvMiYzAXV0dua3iTgrOLc57LviPgA/9YB8wKnA40EZwV+BS4GhwidCn0LCw2NDoINkQtPCjIJrQmSDBATMxqtHtMhmCXOKUQtOy/vLn8sHiiSInEedhsbF40PiwdO/0f35PG/7tfs5OpI6cnpvuuM7S/treo852XjNuCD3q7d/tpk1nHRJ8z8xPi+6LrNtqyytq/frj2vJbOKu/zEDszo0MvUJtq731Pkgenc7tTya/Ur+Bz70vyg/Oj7CPzB+9n6pvv4/4YFxwibCu0NWBNkGSkeAiGhI6cmKSnaKgErRShaJLMfbxpXFK0NCAg4BboDeQFF/uj7Pfq3+C/46ffY9uH2EPjQ+f77dv3A/Rr+tv4h/1r/c/9S/17+Svy4+vb4Cvd795D4rPjt+vYAVQZJC5cRGxeiHqEntyzdLjUvui5aMCYyMy9lKoMlmyBQHjIfhyECJOwkDSMyG0gPMAYwBV0QohlbFDUIuAMJCsUWKyLTJLkfZBYLDToFoP+C+r/0L/B97RToF+A+3OLfeOcw63Hp4ude6APrsvCG9g/5kPiy9334bPvY/c3+9gCnAzsEpQMzBN8DSgHv/fX6A/rt+mz+CwV/DM4R5BRTGE8cFiCYIg8jfyE6H8ocOBvcGsQYrRQfD+oI5AJ1/4f+gv3D+y76r/l0+Wn5qPjS9hv0rvCJ7PzoFeda5oDkJOHM3AXWY832xcPBX8LUw6vA7LrDurfClcy20hXV4NQV1d/W39oF4EfkPOc/6e/r++7r7tXsw+0v8IbvUu2n7of0Ofto/jP/XwJRB38Lcw+zEwYX6xlzHekfMSH6IbAgRx20GXYVWxC3DF4L0AoECx4LkwrGCo0LyAqYCUgJuQkwCq4Lsw03DyIRdhEfEE4PRg+QDfILkQpfBmsB0f5q/rr+Ev8c/vf89gBiBzwJMQvIDvwPQhOQF7saiRy7GpgTZAogBmYHLAr4CysPPBHSDccEbvoY9Ob1t/9aBLr9H/UO9Nf7nwqQGrogZhz3FLkQqg+5DS8L8wlzCIMEif4x+Mz2MvqF/tH+GPte+C75k/vy/30E0AZLB4cHgwgvC7QOXg98DoIQbBL3EcwRDxD1CcoCqf2Z+vX5HvvI/UABtARqB5AJ6AwbEJAQrA9WDx0PXA7bDr0Qpg+lCpEEn/7t+rn5EPit9azzUfLy8IPwlfDO7sjru+lg6GnnluYk5QTlRueR6CnmR+EW3s3fweJD4drbAtdl1SfXody64R/j6+G54NDgd+Ig5UPoQ+uO7qTx7fJ689r1ffft9sH1PfTH9Pb3ivzJ/yEBVQJ8A4UD2AIyAzcDSQOwA7QEpQZlCOYIrgg1CHYHnQafBi8ICQquC+UL8QouCr8JQgkjCaUJigklCZsK+Q2tEZ0URxWxE4MSRhNPFMwUWRTzECcPExA1D3wNrg8qFDIULBR+FVsUkxV9F+kVoRUrFqgSCQ0qBw0CpADnApsGJwt6DY4JDAFz+HjySfDg8pr1xO6Z5Zvkr+sZ+J4EuQh7BZkAgvzp+gj7ufmZ9/L3jvgB9mvxxO+u80f6Bv6K/Nn6BPzp/vECeAcPClMJJwi/CcgNZhHYE2oWeRqQHvMfTR8MHT8ZERSqDv4KcQniCVMN2xFZFMYV7hapFzAYuBjSF70U9RE6EEAQqxE0EVUNKQitAg/95Pgs9vjzjvHk7h7sb+mM5/TlSOPy3trYutON0r3Vk9m722XchNw+3BHca9sc2oDZxtmR2tfaydqi28/fgeXn6FbpQegL6Ejq8u3a8Rb0sfYn+8/+0QDjAAgA5QCQAj8E4wY7CycP3g8fDxYOcQweCzQK7gjlBgAFnAXEB08K8gyjDhoODAwwCg0Jqgj0CJQJTQo9CgMKSwpbChsJOwfWBpsH+gesCO0Jago0CtwJnwqvDF0MPAkWCs0MeQzpDVAQ5hAjE/UVRxWJFdMVoRGJDzYQeww3B8IAsPuX/eEA6wM1COMHCQJb+ZLyq/FS9RT6bfkr8frpZuy09Mb9FwVyBs8Bkvzg+Iz4hvn09yT2fPVy8/Pv6u1X7131Mvzn/b37mPsR/LH8lv/9AmwDywEMAcoCegYJCuYMUg8eEfEROxJ6ERYPiQtlCIIGCQZqBugI4w3bETETtRQVF50YhxkzGqgZBxiFFUwTuBImEoMPPwtwB64Du/+M/Dn6/vfl9iT2XPNN75/smOqA583ivN5K3xvic+Pa4xrlLeZ05YbkFuVN5abiBt9I3Pba+9nn2krekOIx5cDkf+UT6ojutPAN8sDz1vVl+Hb6gPz+/uj/xf9WAWgDoASPB8UL1g17C6IIsgeVB4sHCAf5BUAGtQaZB/EKNQ8uEQ4SYROOE4ESlREmESIR7BBTEPgObA6BD6sQLBFwEf0QtxAWEqwSGBJcEsIRAhCND+oQVhJsERMRShNIFBUUdxObEVoSjRKCEOkOsAp4Aj/6DPPN8LbzoPea++L8evnX87Lvju668GjzBfKi7NrmyONc6OjxMvk+/N/61/eD9FfyzvIG9Evzz/FO8L3uOO6w7xrz4fcd/Ij9R/6mAI0DPAZgCBQKdAoNCTUIpQliC78MJg53D0sRBxS6FXwUfBFaDhwLJQkFCf0JBgyuDo4Q8xFeE+kUBxWQE6ERhQ7+CsMIEghiBxsF5QCw+/32b/SL9IXzRPEY7w7t9+v56u3o5eXE4VjeFdwh3OPdZd8M4bLiyuRn58jnAedR59rmvuS94urizOMQ5e/olu2K8jv3OPk9+hX8dv1k/t7/rgBxAecD8QbgCMQJqwl2CXAKhgwdDzIRUBL1ESEQpg5+DaYLegliCPIHOQdKCEgMXRC2El4SPBAsDlsNEwyTCqUKZgvRCyIN7g+dETQRJBFbEaMQVRHHEi8SwhGpEUIQHxABEToRZRCAEOESiBS6FY0ViRIRERsTnBNED4UHgPyY9KX1Zvqk/1YBjP3T+Fr0QPFU8UvyOvIf7rHmIOGe4qTqLPNv+Ov5GPcd8qvvJfF68jbxBPDr7vbsjOvQ6x3u2PF/9AP2x/dg+qf8d//5AlkFCwZZBaoEPgWiB2oKOgyEDJcNcRBEE1EUwhI9D4AKTwdXBioH5Ag0Cp4LlQ2wDqIPNBHTEkcSzQ8TDKIIFQYXBpsHvgY+Apj7mvX18tbxUPA+7kTqguQR4PzeDeAh4iHilt/i3EjcZN514+vobeq15+Tjm+HG4XPj/eN74qHg4t8B4Q7laer47UPw4vHO8pz01fch+yL+ngBkAwwHUAuJD6ERuBIrE6gSPxHMEEERBRFAEEIPIg5nDSoN9gyADT8P3BAzElUUlBc9GZ4aoxsSGu4XCxfjFVcVkxVKFuEWtReqFp8VjxVhFPoTVhPvEV4SeRNaEr8Tdhj9GHgYNhvEG78buR8UIbodiRrCFMQKaQLr/df6DvoV/CL6kvT57mjsyO7c8hb0oe+l59/hDOFq5HPqRfAT8onwo++R76nuMu5V7knrwOWZ4SrgR97E3a3gYOWO6t7t5++09PX6bf9uA98HOQoUC/4LPA03DucOrA98EGwRhRIyFB0UfhFkDxYOtgu5CTIJGAqVCzoMXQwtD8sTmhbQF2MXzRNsDroLcAsYCnoGkP9N9xfxlezM6FTlW+Al2/PYsdlM2//cCd1G3Pzblttx3NHeDuGj4Sfi3OM95W7lYuVS5b3jHOG03fLaktwq4BjiwOQw6Kfr3e9k86z3Rf36AVAFwwhrDa0RqRTuFiQYFxc3Ey4Q+A+CEGMQCRDmDzMPRQ5aDvAPlhA6EKkQXhI3FVsYAxzyHq4gUyAbHxEeTRyRGMwVRBO9D3IOtA6uDocOpw0SCyMJxgkADAgP+BImFNQUrBljHv0fKyLGIzYi7h0iGFcRzAnZAwUCkQPyBJIBgvox9LDz8/bc+Hv4L/dM9AjxafI0+Zr/jgHwAWMBFgB9/i790vzD+xv42vFs7Kzo3+QC4ubib+ZB6Vnr3O5E9IH4c/s//lwAfQGMAX8B0wE/A9ME1gZECYcKlgnjByQGXAMQAAf90fvN+7H9+gBgBGAHHgqrDKgPbhFPEUYQ9A5hDc0L7wkHBssAh/tP9vbweuu+5abhCeBu4FziyuMw4/vgzd7y3pDg6uLb5frl3eR95WfnOuqJ7LPrxOc84zzgLd9a3mjd+dwc3hDhL+UA6UXs6vC69lj7M/5MAY0EOQefCvwOuhE/EvcQSA/KDXMMmgvRC18MFwy+C04MeA03DkYPIxA2EK0QzRLFFg0bCB71H0wh2SBJH1McGBk1FlgTxxC/D3UQzBD+DgUOug5TDWEN+xCOEx8UXhZWGYIcCCFpIlogeCBSHokVfwwhBdr/ef+CAqUC8vyo9NDvWPA/8y33DvfH8DLrJuv77qH2tv5RAvQA+/2U/D79gv1x/Aj7+Pgw9evv9eue6snqIOw970fyTvSg9578CwJJB1YL+wyZDf0NPw5FDlEO8g8cEqoTTRQZFGASMBDMDZ0KxgepBSkEEQU5CBsMTg+tES8TWxR6FUsV8BL4DtUKAQc1A7r+Mvmv8WHq8OW347Dhpt4U2wbYvNb61//Z0du53ObbMtk11/jXCNts3trg+OEg4APc1tgy2RfbaNpV1tTRa9CI0mXY7eAi6IHsM/Dx81D4xP2xAc8EKgmKDaoPzhCQE1wWkxadFJkSkxEcEWYRrhLNE6cURxUGF14a2BuYGoYbYx5yICIjvSa0KFUnCCXWIgUgEB2+GVQWdxP2DwUN8AsHDeEOug7jDbMNGA94EXcS3RL4E6MU4hQYFQATRAzDAen5Vvhz+0j7z/Rx7fnnO+Xs5jjrL+zX6VXozOdo6OfrlfDQ9gL/uwRJA+39Pfqw+gX9jf6//I33G/Ii7w3vW/KK9lb4gvmI/HkANwMBBhoKLQ9ZFPcYgRoPGAgWGRhbG84bLxowGKIWpxRKE1sRJA5cC9QJ8wlqC4gMBw1JDgcR+BOwFRoWWRUEE0IPZgo5BNr9D/kD9vnyq+4n6d7jLOBB3qDezd5T3UjbkdqW3EHetN6G33Dg7eE65ATmPObY5EjjaeIc4WvfXt7k3EjbNdon2zfelOJl58Tr9+6j8ob2KvoW/g0C7ATIBmkItwmhCqEKiwqACocKkQqbCmwK5QorDFQM2QtQDKkNfQ8QEtcV/hk7HHse4SC9ISsiiCIbIhwgshwrGscWQhR6FOESDxGaD/kNIxDXEisT9BP/E6wTaxSbFUgWtRRFEjANUwaIArr+s/xe/nL6DvC558Xj5OMH6P/n9+P53xbatNbQ3SPq0fPZ96f1UfJ/8XH0dPpc/139H/V47qLs5ez77v3yvfTV83TzHfWy9+T7WAGtBpUKKQzRDo0SDRS+FesZZh1YHqAdpRylHIYckBr8FuoTFRH3DQUNGQ1BDuIQAhO1FE4XEBrcG6wa5xUbEEMLIAcKBEED1AI9/jr2Ju/i6sroHOjY5jrjrt4n20Tbst7O4C7g6t553uzfDuFR4Snj7eTr5FHk4eGw3lDee96v3OjbMN203hbhe+ak7JXwzvKK9ZL5CP74AKkCUgWNB04IKAqFDj0SQRK1EHoQUBDDD9UPdRBCEO4P7hC9EzoY6BsaHd8dWR9cIOsgwSL2JGclWCWNJB8iWx9IHtAcxBnUF5EY4Bo+G00ZyRfMFaEUtxQ5FdMTJw8lCCECXfz1+o7/gwGM/Az0COmV4ADiNuc/6ELj1dsx1wvVp9Xx2YHfz+Kp46fjbuSz5u7pO+2V7y3x9e8N69zmWObl6JTtOvJG9An2Lvlx/BT/3AFQBO4FZgfiCIsKnAxQDyUTiBeWGwge3h6MHikeAh6THPYZ6hfPFrAWOhgvGd8YNhgyGH0Z5BuaG8cXChIkDRIKDAePBAECGP67+cH0NfCf7r/umO6k7DPp2eUi4aHdxt0/3fjaYdl42Uzbbt0V4KjimuLt4Gnfdt1V3Bbe49433svfjuOm5uHpiu5y8jP0+/U6+c78YgDnAhMF7QamCIsKNgz9DWYPnBAUEkQUThZGF5oXsRd9FsoV3RZ/GYwajBouHB8eph6KH7shsSI+Ipkgkh5BHbAc9hrTGlscMBteGrgcLx3WG+4aZxeqFXoY7xWIEIQNtQXL/ND6Hvs4/Gz9OPap683mBOWC55btCe/66RTiHduR2vTf1uMe5Bnmtujc5q/khehA7m7wme8B72XuAexF6Pzo5O5Q9IH4yfs+/X//QwNfBaIHMQtjDIQMeA08DQ8NNQ4WD8oQShOaE3cTlhQSFbkUYxTREqMR1hEgEZwQVRHmECMQ4hDdEbQRsg9qCh8Et/+n/Q39qPs/9wPyWe5+6/rpkOp268vqsOnG50nlGuSX5KPkTOK/3wzeq9yu3nvi+OKd4QfhLuAF4IHhCePS47jkN+XW5nvqJO+q9Hn4ufkh+9X+1AKnBZcH1QjTCDMIygmCDckPkBCPEUYT0BShFc8WjBi+GFwZiRqjG0kdRB46Hiseih4IHtccEB2HHV8c5huVHCUbcBiRFs4VBRUdFDsVBBeNFg0YjhqxGGUXJRfzEN8KEgeR/kD4wPfE9qX2MvVM7mLpwuhe6CXqmesP6t/l8uKo4mjkRuZK6bXrZOx/7ajtnu2S7sLv7PA88X7vsuzp6PjlQ+la8MX1NPm9/EoBqwUBCTYMWRDbEkURKQ+BD9UPfQ9MEH0S5xTrFecUVxSoE4sSfRJYEpkRYBIlE9MTSRVOFmsX1hiuGf0YLxYVEXsLeAeWBd0Du//g+eXzlu5A6+jqaOy57UXsweol6Ybn3Oce6HfmtuQv4sPfyd+b4YjjyOSU4zvhd94D3InaOdpB2pzbq92m3wDjXOgs7mfxEvRG+Kf8K//LAX0EpwVdBbcFMweuCBEJKAkUCh0M6Q0CD44QsBLsE/ATZBWOF7EXhxkOHQEf2yDUIl8iBSNNJMwiUCIRI4gecxoQGoEWaRQIF7MXnRgBGx8bWhpDGSgZARniF0YXOhRcC+YBkvzx+X34UvQ86rrewdij2XLh2ejU51zh+tsc2j/eq+QU5ZjifeLL4gXjkOeg7d3veO4h7efssera5jjjg+K55oTr7O2J8NXzg/i9//IIqRGFFpUY9BqPHegdmRzeG5Qa5xiZGP0X2hffGGAZ1RnVGZ0ZjRkmGX4YExjQF6AX8BdKGhwcDxtyGDETRA+8DmsM4Qbj/nH14O3m6r/pDefS5P7iB+Ff4MrhfuOK5MLkHOR145rit+Jl45TjWeMQ4hbhl+Av30zett6z4Dzj+eM+4zfl6eiL7Jrwa/XO+fH9EANHCg8QQRFyESsSvBKDE84UOxXKFJsUGhVOFu4XPRlYGSoZ5xkDG+ccOh+qIFoh3SFmIXAgkSANIt4i7CGCIlAicB+mHbsbSxldF7EUHhKhEWYP5Q2lDZoMUAxxCHkBZ/zD9QrwefGE8qvu3Ods3kvZOdv13cvf+N5P2pTU/tDt0rjX5dmk2pTb19pW2krbO97742/nfugC6nPqhejo5q7pnPBm9tf3LPmz/BgAmwPXCG0PohMQFTUWaxeEGMIZexqAHBsfPSAUIYEhJyH4IGgg5x9ZH00f3B6cHZ8ciRx4G7IZ0BhEFwEU1Q5MCZsGCQVLAmj+Wfn08+Hwju7x63vpXecx5XvileCG3+zemt5y3QPcpNrK2fHZUdoU2wndld2n3Irc99z/3OfdauFq5B/mC+hP6wvvIfTM+bj+owIyBVoHrQnJDOoPrRHZEkkVsxftGJIaghyTHU4e7R+7IRciNCMgJDAjQyMVJUImtifJKOknTSeFJbEjiCZ/KCwm2STgIdQccRvbHCce6h7qG1YXjxV6FUsVOBSVEpwP0wh0ACT6EPTX74jugu7H7WLort2x2PjbD9+t4MvfJdvA00/OYs9R1XDZCtof2BbWmdZK2K7aEt5N4c/hIuAn3wzfcd8v4ono3u5q87z28fm4/Y4BagbFC9cO/A8VETQRhBAmEToT0xW3F9UZjBtxGxcbCRxQHdUdPB6DHeAbbxpKGjcaxhnAGBcXPxW8EYoNoQkBBsQCh/4J+WD0a/Es727sV+t261DpnuZN5Qfk1+JL4RfgRt8m3d3aOdp92i3a5dn52XzZ7Ng+2YbYQNjy2lbeNeFy5C7nQ+nH7Tv0YPoxAFkDfwTlBmYK6gwUD0ER7BIUFSkXmRj6GvIdDR+ZH78iPiZyJ1knXifCJ1QoYSlqK6osBCwWK00qjCoALD8roii6J3om/SNJI6kibyFaIBUeMBxsHLwcKRr6FpcS3Q55C8wD5/nB9WDzp++W7oHsQOex4LjezOB25CvmT+ET2aXVJNUt1EfWitnW2azWK9R61i3b89zi3Kje5eAx4fbeIt2a34flm+uk8MH0nfl0/hsCXwZEDE8ReBEsEFERHBKAEPsQTxT7F4kacxsAHSwfOSBDIKwgWyKSIgYhJB8+H9cgKiB3HZgaIBkhFwoTQw7PCNMD0/+j+q/1GfJZ7iLrNeiU5hPm1OSQ4org2t/Z3dXajtmC2NrVFNPX0JPPWtBk0M7OHM970K3OQs2Nz8jSl9Ya2s3bZt6M44Xpe/FZ+Sj9pv43AYsEJQiaC6ENvg0IDyYSMBUHGAEbFh0VH+ghviR4JzgpLSmYKW4rhi2iL4wwsTGyMoUyGTGGMDwxHzCLLugt7SvlJ2okuyKuIRQh0SEuHygcfhzTGm4YnxkiGYESIQlqAD36vvee+Fj4CvOn69ni79yJ3Sbh2+GC3RjWac6ryufLAtFP1oXXVtSx0afSjdYd29nd8N9T4M3ep93l3X7gMORT6JXsQ/C39CX42fqU/zgF9wkoDs4Q7RGTEiwUnBc0HIsgVSNIJf0mfSi4KEooXChYKAknsyXlJOEkbiRwI/Yh3h+3H4Qe+BorFwATFA7hCncIrQVPAgP+gPmK9bHyDvBw7FPoJuSE4AHdJtqr2PjWL9RC0THPE84XzlnOeM5uzqLN8csIzODN3tCy06zTKdOj1STaI9/N5T7re+yB7ZzxNvbK+fX9+gCVA3IHtAvHD9oTgBewGZobhB7yIVQlKifQJ1YpZyqTKpYsCC+NL0gwfS+fLW4vIjHPL98vyTAlLekqKSwZLGoqLSlTJ+MkpSMYIewdKhyfGdYUtw0pBV39rPhg9xb3TvT87XDlM98K3vvfr+D+3QDXQM5xynDLtc7U0arTpdLfz1DN787u08TWstZZ1vzWGdgL2LHYl9095YLqk+wK8J71Sfps/UgBbgY7C4ANUQ5pEJ4TFBZhGBQcyCAgJCcltyVpJhAnByfpJhAoLykcKH4mqSasJxgoSybAI78iECCqGv0USBC9DCIK2AbaAi8Apf3a+M30afS+8/vvRuoE5ZDiGOEE34veLt3U2VLX1tVq1oXXGtfq1I/So9Fn0WfRytKb1gfZbtms2rLeXeTD6UruDPHc8lX1CvhX+lH9KwA2AocEngidDSARfRIsFEoXtBlvG4Ed+x7/H9MgPiLCI3gmKylVKtcreyysK4crlStbKtIqTSrvJsElWiXbI/0j2CPgItsjUCIZH+oe4x0zGaEVqxSfDmADZfxf+Rf44ft8+gvyJOur46Pdq+Dw5Yrjidqmz/rJi8uS0CHXydsB2b3ROc/d07zZOtx52+HZK9hm15/WGdnX3m/jaeed607wOvV99/T3svsVAoAG8gjtCWwK/guaDxsUgxkXHrwfXx+zHzMgkyAIITUhDCHtIJIfxh+QIm4jUiLdILcfLB+rHDIYNxNHDgcKvAcoBiMFAwJj/X75rfbe9aX1qvOM7szo1OS64TjgLuGj4F/cUdp72nbaUNsu3cXbVNhm1xzWeNXD1yLattof3NHeEOIh5zvtG/Ke9F/1HPev+cv7IP4hAYcDbQXqCOMNrhJFFbQWbhicGhwcFh0iH+8g5R+3Hnog8yNQKJkqCymgKSIryygjKUQtUSuXJx4ndCQNIyomhiZEJbclkSR/ITcgOCJ/IKsbCxj9FN4PaQhLAu/92fsB/Uv+Dvpr8U7pZ+N04ZfkJeYs4FDXMc9XywfOSdY23FDbldY40mvR7tTS2V/cQtzF2CrWKddy2aXcYuFM5vnqae5s8DPz/fbi+Ur81/8ABaYIkgk0CR0MsRF7FpIauR6YIR8hJh9YHvkebCBYINQeex4PH+sfzyAUIbshPSCTHbsbIBkcFUIPlgnvBvMGrwWKAvL/Sf2I+O70MfRP8wzw2em54+7fDN4a3hrd+trr2XzZlteL14faatr11ZjUa9UG1InTmdXx1kbYs9tc3rThC+hS7Srvd/EC9Kv13fbR+JL8HAANAlIFvgq7D/YSsBVyGK4aWRzEHF4d5R/tILggcyGnI7Ul4yeiKckpVCuSLH4qsykkKywqzic9KOoodCfTJa4ngSg+JsElRCUAIpAfUR/ZGacUtxOUD0MHkwBS/MH4gvkT/d36QPH/5z/hOt9B5LHm7N/U1UnOLcxf0Z3ab9+G3CjWFdEY0tLYEN6d3bjZrtcN2ULca9814nfliel67lzzavcQ+vX6F/xeAP8Frwk3C+EKAgyeDygVfxoRHsYgpiFTIPEfhSBuIL4fMh+KHs0d/h06Hx4gqiD3H4QcgBi9FmUUrA4LCcsETAEn/6b+8/0j/OH3HvM/8ATwEPBc7CnmdOAj3G7a+Nv93OXa+Ncs1ZrU1daK2Una7td01a/UCNT01DrYdNpj3KTfDeOf57/tEvMc9kv3s/jx+QT7Tf1YABADoAWFCJAM+RFkFh4YMBjlGFUbGB19HXMerR62HQAe6R/VJPwnfCYZJdskUiQVJTYmsSVQIjQfQh9yHwQehx3UHAMchR1GHoscjht+GB0UKhRIFFAQTw4YCgECrPvZ9wr4hvz//c/0+uke5Q/gM97943Hmzd+q1unPpNA92sfiDeMw4A3dqNqi27zhyueW52Liq+BD5QDpl+gv6UrtPPG99MX4q/yO/7f/z/4uAykLxg6oDtgN+A8qFUkZMBw/IMoieiDDHRMe5R0eHasbzxk1GdEZchnjGJcZ4xnYF1MVOhONEXYOgQhPA54BsP9P/VL8mfop+JT14vFi75nvMO+h68flduC93OfZT9pI3M7Zi9dY13XX2Nl43FncotqK2PrX3Ngg2fTby94+37rhNOeD7Kbw1/Ql+Pn5AvtG/EX93f4xAf8CTgWoCCwNbhIjFzUZfxnRGTob7RxUHZcdjx2RHOscjB60IKkiFyISIaQhuSGYIUIhQSDDHtkcpRzhHOYaEBr+GnsaeBwJH4YcShrSGPUUJhHsD+MN9gwXDRsJlQAe+6/5FPcm+bf5ivKH6YHhV9wl327kNORp3+vWSdL21MzZB+As45zf3doZ2+fdiuB+5L3mzuSa4z7mWulj61rtzO588vj3Zvv//AP+5/4HAoAGEgvqD+cRExHFEtAXdhzoHq4g1SDCH0Qf3h4DH+YekxwtGhkajhrqGvgamxgvFwUYHxYdE4YROQ94CnMF1gGJ/qX6O/cT9fHyG/FV7yTsjem66A7mvOET31fdmdrN16TXOtjx1oTVGNbp1nbWu9cH2eLXdtao15/ZDNqH28bcU9xW3lnkIutA8Uv2Nvn3+kv9MQFXBcYHvQg0CfMJ6gyoEkgXRxhyGOEZXxsCHdgfTCFDINIffyD6IdQjYiRoJKIkZCTaJdcnOiZgJFIk/iAiH2EfkR3wGksZGRo1GVQXhRmqGsEX6RTPEtcPqwyXCzgMNAqhBuMDcv3A9vv1K/j4+Eb4C/Ln5CzZ/NcT3IzfrN6I2RDTss9f0s/a5eSk5kPh99243tvhWuaq6Lzopul06/Tsp+5A8TXz2/Qm+T7/0QC6/hb+EABvAvUFZgv+D+kRVxRnGBwcMSDlI4skGCM4IqEg1R2/G44bQhubGcgYiBgpF28WwBWQFFQSkA/YDfALrgjVBMMBvv78+sn3HfWh8grxre9B7OjmheL94IXeH9tH2RLWBtNW08jVGteH1onW29cj2ILYXNpD2nfYz9dG2DnaT90x34bfxOCt47ToSu4183/3C/k++Ib5HP67ArsEQAZUCJUKUw7hEkoW1hcjF+MVPBfPGakbXxtiGqAamRwFH5ghxCN5JOsjCyNeJL0lzyQTI+kfGR5UHUsc+h3RIDUh8SD0HgQdxR0MGrsUkxWvFMcQ9g9PDmUJswhqC/EKzweTAzv71/Op9jv7SfbA7MPj89yC2YXeFecO5V7bHtaM2O7ez+al6F3jd98u4ErjZugp7WvtUOqb6yzyCvep9u70mfZq+gj8d/wK/4MAXACFAYwFgwtxEIQRxBFRFUoaBh3tHOUdnyB0H4Ib1xpMHRQd0xp3Gv4ZxxdSFgQXWxe9FtgTzw5iC5oJ1AZoA1gBXAHRAc3+Pvje8rLvVu177FXrVejw4jTdkttL3UreHN1U2izWTNRj1ZLVWtSs1JDVRNVk1k3Z0tjO1ZHXNNyg3wzi/OLI4YvhwuU57VrzbPYU94L28PdP/WQDRgXdBJ4FBgeoCIEMJxALEeAQFRGDEiUWmRnPGs8aBxvqGxodvR6ZH9IewBzVGuAboh75H5Mg3B+8HZ8cWxx3HTEdCRxXHHwcNhtzGzAcZRrfFqYSNw8FDcEMrA+0D1ALjgYwBQcCH/97/oz4XfJm80z18e8b6uzmn+Tu5rPqMOiy4WTdityz4KnnUesZ6gnnz+WZ6OTutfJZ8tPwtfH79Wz6Kfte+uX63/2yAKECCQPrAgAEaQV7CEsN2w/1EOES4hRaFngYYhq5G54dxR5DHSwc5RxRHMEazxn0GSoZKRakE7cThBRaE6sRkA8iDWYKkgXy/jP7+vrX+vD4bfRI7i3qV+gk6O/oF+ej4QvdWtuw2i3bQNzd2sXXdNWc1PHVENey1zTY2dZJ1lnZWts42QnYuNlK3BnfzeIu5ATl8uns8DT1e/h9+2D7Dfyq/wsCCQNtBBkGDggHCjgMSw4MD9EPiBF5E9gUahXbFVsX1RmCHNsd2h45IF4g8x/OH1kfjB+bH+gdOx1VHNcZdBhPGNIYXhl4GVYZphlqGfoWNheVGIAUlw16CdAGWgjdC8gKeweoBPYAPPzE+S72evK18XryYPCL6PvjjuMI5bnqUO3t5wrhcd+h4QHnQu1d78jrKuiy6fjtW/KY9PrzEvOg9Yz5O/uP+077nvzX/2ADzwQtBPQETQb2CGENAxEnE28UMRZ+GOMZ/BrXHK0efR59HeEcrB1nHiIdtxv+Gkwa1BikF9MWgxaZFV4T5BCqDjINPwqaBCX/7vsh+bz2I/Xs8Yvs1uYj4ubfoeAW4WbdVNi01rDXudhe2jfbEdgW0+nS2NUQ1xrXn9Zh1VHWJtoG2+TY7NgU2/Pcgd9p4rbkG+fm6kzxFvf5+dX73f40AuQEUwaDB0YJcwvcDIgNUw5WD6wPPhAOE5UVyBWXFdUWHRr7HCceZR8OIeAh4CFCIh0iNSF6IBwgHiBnHwodZhl4FecSXhLdEqQShhEVEcoRTBDRD/gT5BNNDsMLmwq2BMICnQfWCe4IHgcp/+vz7PAb8gTxm/L6883sH+Oe33zgvuQ869Htp+rB49feNuN27jj1hPX683XxDPAl8dHzo/aK+Mn3OPa09yz6fPp8+T/7QgG/BVIFCgTsBQ4IDwovD9sV1hg2GLcXFhltG/McFh0iHUMd9xwjG3QZyxkQGnEXehTrFGwVARTlEskSgRJQEnARUg9HDfsJ3wQM/yr5TvXn80fzwu+d6N/hMtw02fTaFt222tbV1NKe043X/tuP3TfbFNeQ1SvYydpe2+Xactmg17HY+tsP3TbcptsZ3BbdS+AD5IXmv+ot8bH2jfq4/hsCUgUqCfQLbg2HDmMQNRJgEmERnhAREbYRfxJ7E70TKBQGFuAXXBpJHQsfkh+8H0kgXiDPIDch3SBmICQf4RxMGjEZ+hbjEbwOAw7JDPkKVQprDfYO8AvJDAURPBBID9EPZwy1BhQE6gTEBg8KeAqPA8n3jfCT7zzxW/WX9k7tIuCO293e++Mf6SjshOrb5CfjK+mQ8en2LPlx+Ir2OPbQ9TT1p/WN96j4gvnj+kX7LPqO+bf8mQKGBQkFyQSaBXoGTAlYD1YWKRpbG7Icex3FHdoeBR/hHYQcexplF3wVpxXAFCIS9g/FDy8PqQ2RDpoPAQ0cCkkKggqMCMEFwwHk+yD2R/Mb8p/v9Owp6UnhDdm71B3UBdWe1FzTkNDSzgHSadho3Zreetxy2hzZ0dpI3zjfsdwG33zgL96c34Djd+LS4KrifOQj5r/pWe7W8SL25PvJANwFLQwbEC4QCxGxFIwXWRhZGKQWDhN1EAsRbxM2FLoSDxEVEaoSJBRXFcUXXhk9GT0ZxhnsGk0cWRwUHHochRpTGG0XmRWnEFYL/wlaCFQH9ge8BsAG8waRB/YL+g6wDpsOIg63DIgN6Az0CIwGzgU8Bg0KIgaA+fbwhu8Q9ED8mv8t9GTls9+g3/rmRPIt9LPtEum+65L0g/7FBCcFEwJy/qT7L/sH+nj5fvm3+SH7Kfzb+5P7L/7gATIDAgWrBiEFGASABSkIaQyfEQ4W5BelGNwYgxlOGpAaCBqYFi4REQw0CQwIAAimB8gGwAbpB78J6AsJDScLjAi4BjwF4wNtAfn93Pnc9RnyYvCC7zHtduhT4UvZ6NR900PSdNIS043S7NH41OvaXd/34N/hL+I34dXh0uSx5tHlG+Mp4nLh5+FL5Z7mCOUX5sjnZeoO8G31SffP+BH8HABIBZcLsA/KEWcT9xQVFwcYkRa6EtkOXwxkC04LHgtkCwYLiQqPC0MO5BAeEqYSjhN0FdUWohdmGaIaRRmgF7QWqRSkEpAQwA7VCzsI7wbrBsEFGQY9B/UGPAbtB8cMXg+bDQoP/RT6FpUSNhCgDzcLHAqGDK0JHAR2/b72oPRP+WEC7gFU9wXv6ukY6afvTvjD+Mfxau+b80z82QNLBgcG8gRcA0ABWv9w/pz7tvdB9k74mflm9wX2SPkW/gwBtQGWAs4CUQIHA+UDpwWECT4Nog+xEMgRKRMyFMoU7BPFEOUKugZ6BkYF+gF/ABcCxgMdBdkHMAqTCh4LkQvKCUAILQjaBZkAQvx4+mf43vTm8RzvFevL5U/geNyK2fXWCdWJ1gPZEdm+2TvdIOHj5LDo3+mD6SfqW+rq6irsa+py5R3j2uO+5HvlvOWf5DPlh+jy7fnyWvQd9Mr1k/qwAPIFUwnBC8INog9SEp8UBRQnEEwL8Ac1B/EGmgU7BOEDowOPA9oFvQmqC9ELEwzQDV0QIhJlFCMW1RUqFUQU3hOsE7ERgQ7XC/EKpQlRBv8FqgerBkUHGwkEDCkPlBAIE9gYMBvuGuodaiDdHTsahRUcEiETBRQ4EWALSwO3/Jb/eAllDAgEAPjG71zt/fIy+iX7rfVI8fjzBvyWAlkF/ARoA0wBFP/k+2L3evJP7zzuv+4l7h7sNez+74X07vfY+MP4PfpK++D5cvm5/K8BggUjCLwKnw2YDwoSKhWbFeMRLA5MC8sHlgXXBFYECQXQBngJlgzVDwgSyxOfFLASMhD0D2AP/AvXB6gELgI+/8v7xfhd9anvDeph5j7jSt4m2nPYINoW3sHf7N4E31TiSOfw6pTt4eyn6mbp5+fi5g/kPuD53Wfc1NyN3bvbv9sT4CzkPOfm6rLsGOws7u/zEvop/xAEhQfICWsNehDMEMMPRA1pCAAEUAEM/1H9Jv2z/YX+9P+WApkG3AmPC7MN7A9eEmIVyxd4GQYaFhnSFzwXzxa6FuMVFRQUEg8Q3gydCrQL7AvNDJQPVxDbET4X4xxmIRQnlyobKWYoeCpOKeUk+iEPH48ZABN8CoEBSf2J++77ZwF4Arn47PAj8bny5/Mr9RLzIuwL6Inpi+/v9iX7aPs+/AL8dfhB887uz+lH5Yfi096O26HcM+GM50juPvU++fz6jv2kAHICIgPXAx4G+AgYC6ENaRAxEw4WRxmUGiQZ+haSFHARzA1kC8oJTwktCwgPGxR9GR4cAB18H4MhjB+EHFEZvBKPCrsFNwN5ALf8qvf08J3rTun/5hPjI95w2UvWyNWp2Abb2dqI2eXait9h43/lDef05t/lHuQm4Wnc/NeE1YLSktBO0CLPotBK10bfeuTA5w3r8uzx7lzzuPYX+db9QwPABl0KFA6TDoYNrww9CmoG3wOUAQ7+0ftW/K7+BwKvBpEKDQ2kDzETZRerG9YeuR9mILkhiSHqHlYd5RyJGacXMBgyGPgWSxTaE00VvBK/EMkTmRWuFp0YGxuuINgmYypsK20pdyjpJxojrB0OGiEU6w0sDoANCwI59GrvH/QW+wT8kvTq6iLkiuNS6uPyo/Ia6bTh9uFT5wjtIvD78WbzivJJ71HsROqt53/lbOQY4uDfUeGi5QrszfO3+cf8DABPA4wFFQZsB+MKvQ8DFCkWJRcmGY8c1B+4IMYfQB/4HfsbChoWGWcYPRbkE+IUrxdwGNQYoRuzHvYeiR1DHAccKhnuEiEM0gagAc78hPl19ejuFujb4tbf0N372XXUkNCMzpLNJM+A0ffSrtOU1XnXHdgO2vbbmtwJ3AvZndW202/RmNCw0NbOD80JzuzRYNfZ3Yzjb+cZ6w3v1vI3+B796P9KBDAJKwz0DhkTrhWAFMIS/xG/EHwOigyuCrsItQg7CwwODRHmE4sWwBkcHWIgByMpJLok0SS4JMIj3SFsIBkevRoSGRwY8hZmFTsSdRAxDxgOUxFsEeIMRA06EacU6xhwHcEeuh1CHuAfTh1wGNcSvA1mCroKXgsNBZr40O6U7dLy8/qr/KbxF+bq4s7kY+qR8Ovuh+Vw4MXjlOnk7n/zXfb495L4mfZD86vxNfDz7tzuFe6Q7TDvYfKb91b+pwOIBnoJuQwXDbQL9AusDxwVMRlzGjYbOxziHnwidiNlIuIh6R+pHDMaLBheFS0SGRAAD0IPwRBoEj4TmhNhEy8T1RKiEkwQQwriAs79YPtc+DX0FPDe6rnjs9ws2eTX1tSuz9XLVMkyxqDFrMnqzVTQV9HS0bbTC9j93IzgHuG83TrYdtVD1uvWItb91OHSsdGt1Yrc1+Ii6PPrbO+F8+L4z/7zAq0FUwl9D1gW9hn3G/IdQB53HVod6RwSGTwU8A/YDN8Kkwq7DHwOpA9DEqMUiRaLGfEcnB5lHhUeNR07HFsbwBmeF3cWCRXdEWQPXA7lC8sHWAQuA0ACWAHsAVMDagbgCfILyhCUF2AaAxttG7gZDxgtGpQbkhdAE5gQhglBAHP4f/FI7Xzvy/Rj9ULyj+8u7+Xyvvft+fb4cPPV7Vrtw/G9+Lz+eAMqBtYFQQMrAX0Abv0d+Jz0I/Gm7Qbt4/A393z9nQL7BgIL+A7QEJ4SKhVZF+gXBRhOF7AViRX+FlIXOBcTGA4Z/xdwFUwTxQ+RCmoGsAOFAckArQIsBpoJ4A0JEVMRjBCBDmoLOAa3/yD69fWz8g3veutd58Tgldpw1qDTxNK10fnOa81rzRHNbswsz3rSbtM90yjVMNmN3a7h8uWj5/DlseSc42ThjN9q3TDcAt7r4ZbnHe6T83v38PsOAR8EZQa5CEsKDQ0eEV4VtRi1G4kcGRtUGs0Z+xfpFJoQRgygCJcGeAY9Bx8ItAf1BlAI9wqrDd4PZhIfFAUUsBJjEe4Qog/5DJ0K7gjzBl0F+wV8BnMF4QOhA68CK/9p/5ICCwVMCRMNPQ6+Evoa/h2KHgoh7SC9HoAf6iFrH80a6Rh7GUgXcg4QAYTy8+vn834C/ghWAUX2aPayADoNwRNrDYz83fAW81769P72AGUCoATYBbIDOf8x+w/56vVO8O7q5+dP6L/tXvSM+Qb+QwNwBz0KZwypDfsNRhC5E4gTrw2LB6cG+gc/B10G1wftCVgM7Q7EDbwKaQiIBrYD8gBK/0EAxQTJC4wQdxLYEcEQyhAhD4UHgPw49mX1APQx8WntV+hj45TfVdyR2cbZvNr72R7Zr9no2PDY1Nxr4J7em9ow2dDceuOY6R3tze0O7FfonuW45O3ghN3e3HPc492u5Tvvx/R0+Wz9xf/UAh4GMgakBMkFQgkoDWYSDhZKE+wQUhJyEWAOMwxZCTgFOAJQABf/f/8jAeMA7f5r/4ADvAdcC4cOaxAJEccQDxBwDnMLagf0BOIF7wYiBq8GAQliC+IM5Qs6CRoHcwV+BioNsREaEjIXYhxuIAMm+ijdKzotMilDJ+cn0yRaIc0g3SC8HRoWAAzgAbr+JwS1CZwImAT6ADD9KwAgB1YIMAOr+Vb0zvX69zr5ZvqI/dr/Cv99+7T2+fEg7Ivored55bvjreSE5/XrsfL090368PzNAMcB0QHbAwYHSQoeCxUJIQg2CUgJVAc1B9AJ+AvVC38LvQyIDH4Kgwh6BlcFrQVsB90LpxDYE0EVmRWrFy4YZhKOCf0C7gAKAJ39sPrN91L0pPB17Ynpq+Oj3UHaDNvB3L3bPNm91/LXodnN2iXbXttG25Tcgd/P4rzlteeS56fja+AZ4M/eUd0q3bXft+O26KXu6fJ19Gz25Pl3/Kv84PwQ/qgBgAbOCrwNYA+5Dy0PFA59DB4KsAfqBV4DawFYAW0BDgFYAdMArP8OAGgD1AbmCOUKfQw2DTgNqAwRDFILYAowCo0L5AzcDSsPpxBPEZoQcA6YDA8N6gxdDGsP8hIfFlcbzh8iIL8hmCXsKA4rdSleJ9snESmwKCAn1iIaHRAZ9haUFLgOQgXC/jsDzwyNDtUIrANb/fr3HvtH/nD3u+7f7HHtsPAd9WD3qPgU+n346fN+7/Dq3ufS51/nsuUr5gDqXe5A8c/zBPck+ib9M//4//z+c/+3AuYEJQXOBZsGDAjrCoYN9A48EMcSYxQlE+wPBgtsB0EHPghaCPYI9QqIDfsQSBQmFWIS5A96DjcLYwUsAkoBIgD+/i79mfni9LrwRO3C6Afjq9362xLdNNzU2JvVS9M90k7U8dYt2NjYMNkH3OnhDed46IznEOb+4svfE+AM4Q/f9d2L4ZPlO+jx6xvuae4U75LxffTl9p/6YP7LAKYEVQnBDD8OHA6EDAQLLQtuC7QKCwnQBQsD+gEdAbf//f3c/XL+iwAQBDMHyAmMDCsPWRCWD5UOuA6sD6kQ8xDpEdYTuRTrFBMUqhJFEc8OuQxNDVUOcg50EpoWdBhvGhIdoiH+JKsm2CZFJ7UqMi7+K2YonijbKLcl6SMXIiwbAA93AT/6PfvNAQYIrQIb+D/zWPQ7+xQEegMi99Hs1OuU7iTycfT29Pz3o/qM+CbzlfAU753syer154zjieE842/nxOuT8Dj11vjO/OEANgK5ApwFRQq1DMcLDwr6CM4JjQvJC24LMg0+ECMQ9g60DsULFAgMBx4HzgYgB0IJyQwjEMcSzRMbFCcWFBbwD38HQQNhAkABAgCI/bX5c/Rb71XruedQ46Te09sM22jaMtkA2HPXZNfk1bfU79V12KLajtst3gPh4OLW5BPmfuS84GjeKt2k2iPba9914xvmCOlh62Ts5+939Dr2O/e3+HH7fwBGBS8Hqgc6CQYLnQqWCRcJiQg1CGAHSQZzBAwEnQMNAkEAjP8CANYBrATJB2IKJg2cEFwSUhJjESUQkBAiEokSCRGOEGYS/xN6FbYW8xS2EqMRkxGpERAT+xSlFfMYGRsNGxUfOyH8ISEmWynsKZworifRJVgl9SbmJa8ljiL3G0AaYBrZEuMDMfjZ9Gz7sQWWBFT47vAA8Qv5YQVuBgT4X+t16WPqR+y+75HwivHA9qn2+vBL7Mnqguvs6sTnX+Pl4H7j5eis7YbxofZ1/Lf/igG1AlMDyAb6C/UNLwvLCAwI/gcJCf4IjAiXChsP5RH0D3kM8wrSCmgKgglBB7MFgQhJDroRNxJJElYS/xPxFNEPMgae/0H+wv6q/uj85fdp8t7u5Oqa5jniWN7y2zXby9qj2ULYydcf19zUK9MY02HU9dWE2FLb890k4frij+TG5IXio+Dd3i7dpdw/3unhU+ez63brG+pO7Urx9vM59zf6ifqc/LMCHgdMCDgJkwpYC8MLPAw2DD4MtwwbDEMKQAnwCIUHtgRtAtkA5wAfBPgHiQrBDBIPhhD7EXkS6xEIEhkTJxOiEuwS4hM3Fa8XhhjoFjUVxBSGFAEVwhX3FEsVJxbDF74ZmRmvGyIfriCqIUckFSYmJ1EmViUyJl4owiaYIvQg8h4yHIYXwA2d/hP1Qvn3Ah4Dh/e07ITqdu59+JcAbPcN6vHnLOuv7prw7vGm9An5m/oa9zXzK/HS7hHuAe5B6S7jyuM053To7urK7hj0zfi2+5P+agAMBH4Kow4xDhcNDQ1CDWUNzQzfClwL8Q5VEEQPNA38CAMGcgekB4UD3QCnAmIHDAx4DhsPQg/YEAUUwRNTDbwG+gNtAqQBbADk++j1CvEK7DHmAOKB3uvaudjd1qPV+9WR15TYGteV0xjSC9W12Kra/9wX38jg1eLK5L7lj+T44unged523BjdGOKs5jLowOgh6nLsQfBk9Hz2ffhv+8v+qgMbCO0JEgtnDHwNlw1nDX4NZQ20C3YJggnICeQIrgeMBdoCcQFfApwEGAfKCc8LsQ3DEJMS1RKsEkcShhHdETcSwhHnESUTvxMdFBYVpxS0Ev4Pgw8oEdsRXhMVFBUT4xXYF2cYURsfHjAf+R+EI0ckmyNmJPoljCbtIyIg9RuJFrcTxRN9EpAQZwiM/XX8IgPLBy4GeQE3+EPwQvIZ+G73kPHo7QTtsOyn7y7yJPNU9wn5aPZ08wfvIuvm6sLrCOo+5yroX+t17X/w8PTs9975UPzf/b38Z/wm/tP/rgCZAqwDfwSVB+gJKAqaCzwNmw1jDYwMoQkcBj4FKwXAAxIEjAaqCIcKhA2gD9ANzQz1De4Mbwi8A0IB/P9c/1D/A/7b+yH5yvXW8iLvE+t26JHlXuFj3A3Zutla20DbZdhL1WXUT9aO2Yjc/t343vDfTeDG4N/hJOTV5fnk0eHI4NzjXui87BPuEO2o7Hft8e988mjz5vV2+uX9s/+pAp0G+ggACwIMsgrjClkN/w0dDAAL0gqjClwL9wrZB9wF6AWvBmwH2QcfCbALpQ1mDnAO8Q4yENARUhPNE2kTPhNyFMQV7RUUFu8VsRQpE2YSIBGlEB0TvRQfE0sS9BOlFSUWDhb2FgoaYRuVHNAfXCBNH4ofFSOtIl8enxslG9UacBlhGLYSPAX79p7wxfX8+wv8Ivfu7cHqbvOP/q8CWfz/8i/t8Ozm7lPvVfP295D58/oe+6j3ZvM78zfzPu6n6ELmouZq6PvqvO+J9MH45vve/On97P+AAzcIRwqnCagIrggXCd8HKAa7BD8DugMDByEJggYiA8gD3wOKArUBBAHHAFEC/QZFCt0KHQ1hEB4SBxA5CuMDAgGjAiwDsgCt/DX4fvVw87Dv+Okx5q7lkOPS39DcCt7N4g7lU+T734vas9kJ3fvf3ODN4g7lnubR6JzqC+s260DrFefe4A/g1OO+6DPsgOuu6Ufr9++o81X13PVU9+H6Uv9XAhAEZQWZBqoHIgfYBpAIDwnTB8QHAgjyByEJXQkuBowCRAHCAC8BAQN3BBwHcAtkDh0QMxIfE88S7BLsEggSlRG6EuITQBS5FHAVRxWzEzcSGhLhEWwSQxI5EtMS7BP4FXIVmxVSF5sYTBsUHW8exiCqIbshUiKCIsIgUB6CHP8bPBuxF5sRzAnk/1b8wAAjAZP6HPAs6AXnIu9++g35U+6I64Xt7e+M9WP5Jfhx+IX7hfrw+PL40vZo9iz2PvH86I3lNeiM55vliej96v3rYe5S8RDzC/b/fz4BZQIfBBIHlAgrC7wNbAv0CCcLTAy2CgsJ7gQ3/67+DQI3ATX/VADfAE8CSQcKDAMNnw6OExAWnRTsEh4RUxBbEXASGA/tB1MDlwAX/Ir2PPEC7eTqkeke5Q3gfN2k3tfhveKj4KHdhNw64FTm8+hi6UjqlOqw6S7oaefn5RTluOTx4Rrh/+NA5r7kb+Kr4NDgreMu5x/pH+k17OnzhfrA/voAdwEVAjcDlATwBEUGkQe3BbYE8AVnBRAEGwW0A+b/SQB8AqUDNgVgBw0J/AoYDokPyQ+sEhAVJxZDGLQZ4Rn8GuUcERwUGXMWTxTbEdgQPBH/EG4RvBL0EuYTJhVkFW4V7RXfFtwX8xisHcAgoB0OIDUlbyJIIYIi+h3jGEMWmRTrFAgTiwopAGP5N/jG+h79dv5z+xP2EfVJ94f7W/wi9jrx3O5K6s3ps+5P89z1R/e89jT2tvNo7xfuiexz6WXnruXH5RzoHuvc7qrzuPd/+JL4NfsT/Aj8ZP47AMkAiAI9BDMEpQMnBOQFmgjYCQALAw3BDI0KDwn4CFQIFwjGCaALgQxrDWkP0xIYFiwYJBg5FnkTOw9sC50JHwneCKMGagM7AOb8m/lN9h30hPIl8FvrY+Y14kngleFk4kvgT9ww2RHZft2u4WTi4uI441biSOIm5HTk0+EG37bd390T4F3kPeVx4jXhHuG54irnfugS6InpQO0P8q/2t/nJ+wj/cwGWAV8CGwVuB4kIzAp7DAQMBAzhC/EJ5weZB64I0gkpCwwM0gyaD1oSyRKgEsUTSxVqFY0V0RaaFyMatBzSG28amxl+GLkXrhaZFMISbROFE4YRZhHOEcIRwRAmDnwNfQ8QE3IV0xUVGBAaoxu2HTscWhoxGj8ZNhjDF4UWKhXwE7YLAgFz+zH74vzY9k/vKOwh6XjrjvGe8eTpUOYI7IftNusy76v1X/y6AysFUACx/AT7Wvir9nv0Ou4v6ons6O1w6+3rCvD78abxGfFM8WfxlvQP/NEBEQZcC9sOyhAiEroSLRJ0EhEUDhPgELwOxgp/CB0IuwXsAbYAdgLvAwYFSQdrCYoMKhHMFCEUzw80DVUNGg5HDgwO7gw/CskHPQOR+93zCe+o7LDp5OPB3IvXhda/2Ina7dkp2DPX6teD2/nflOIm5ZTnAOk96VnoUehy6L3mHuQ34mvjPuZm5fTho+A04GfgG+JE4+Tjf+Z260rxGPb7+Zn+rAMUCOAJXQoPDAMO9A6aD3MQExC8DRILjgiEBj4FMgXeBVsFkAVwB1MK1gwBDQ8NRA+wEqsUZhVlF5EZbxsWHRYdSBvzGNcWQBRZEcQO8gwgDV4OzQx2CsoKXww8DV0MPg27D1wS1RUKGV8bdByHHbEepR+MH6Id1RqqGUcZyRkmGVQTbgt7BBz+Fvpy90Ty1exk7DXwwvMV9VT4+/kw9h727faL87rzg/eC+uj7F/19/2AAtP4h/PD37fKF8Lft7OlK6aLpIer+7Hfw0vEf8cDzkPi2+mP9BwIVBr0JxwxkDuMNyQw2DPMKlglvCPQHfwcFBuIF9wXrA+4B1QBFADEAxwBdAoQFHgr5DRsQqRE5EioRPBCyDxoOiwt2CngJgAbnAtv+t/mH9Dfw5+wa6B/jT+DT3p7fYOIJ41HhHeDD33Dgo+H+4nzkN+Us56zpour96/DsFOzo6f3m4+SQ41zi7eBp32nfqOFb5PLlLOdQ6SHt7vHB9f/4vvqg/PwAOQTHBU8GSwdECWQKowpDChEJjgimBzgGIwXzAwsDxgJFA8gDhwQiBzYKKwwSDv0QJhTVFU4XTRmzGuoa0RqgGtEZ+xiTGPkYkxiKF40WahVZFFIT5xEHERUQQg+6DhIPmxJuFY4X/hnLGj8dwx30HrQgUB18G5scxRrYGJEYPBSEDEcGLQEn/2j+nfkh9J/uoOru6qnvQPV38MHpd+oB65DuoPUo+QL8Iv4n/wYAmwBO/1P9Xfzp+Yr1z/Cr7o/v3O6A7mLwO++N7IPsxu4/8LTzD/l1+7j9ZAPGBgQIcAqeCzcLMg1/D7YO6Q4cDuUKTwm7CJcGRQObAM3+Yv6m/zcBFARqBw8KAAz/DNgMyQuaCy4NkA/sEGkPcwz+CmUJYwY8Anz9a/gG9O/vS+vR5jLjc+J14v/fytzJ2rHZLNw+4GLivuQ257DoD+pF7P7tNu4S7TLqGOiR6VvrBOoo523jdOBP4EjiIePC4IbflOIf56fqve5l8rD0UPhv/Jr/1gFxBQEJpQriDLwNTQ0kDVoLZwjJB2YHSgWqBFkFxQWjBqIIGArWCbIKkg2AENcS2BRZF7UaXx7KIGshgSBlH1QeHR6XHWkb+RiiF40WwRMGEtIQDA+QDdQNIg6XDasQSxRKFpwWWRhRG8gblBudHOYbahgnGuAbsxdnFGkQFAgYA8sB1P0C993wQu497K7s4++y8Evsp+qF7A7tXe7f8DH0H/kP/Qb/4wBtAooCZQE/AJn9ovgN9mX1z/Mg8n7yC/JQ8Dbvt+3D7Wjv2/C88rH1p/mM/XQC8AV4BkUHWwqgDDINqQ3/DTwNSAxjDLQLIQmWBf8CMQEMAMf/Hf+B/wcCsgQZBtwGfQeDB7cIQwv0DNYM8gtEDJANQA31ChQHPAJR/nf7k/cZ8hHurerW53vmU+Qd4KvcGdyN3Qjevt4i4fDi9+Sx5+Hp+uy57p/uAe6z7iXwZPBR74XtdOtU6f7oeOg95ajinuKA5Cbo4Ora6qPrj+8E9Ef3YPoh/Gz+swJ7ByAL/AutDO0NDg7rDVkNUguACZQICAhHB18G9QU0BbYEMgWTBqYIiwodDFIP8BPdFrMY1RrgG9QcXR65HsUexx5OHuUd4xxgGiQYpha6EscPFg+BDG8MEwxADTgQghDlEYUTkhS0FoYXURhKGhIaMxkCGmoYLROZDlgM8wrCBnMBpv7Z+kX3lvUA9FvyWO0+6uvoIeYO6B7sJfFv+MD6Xvsb/+cAdwCoALX/7vz1+h774fqK+Rf5CfoB+f317/KC72jsOuur6zHsy+x07wXzufWo+Mz6H/xa/9UD8gXZB/cKjQumC2UNMA3ACkAIQQeeBesDSQM3A5QE/QULBuQESQMDAugB7AHeAssEXQY3CO0KjgwfDP4KrwmPB94FJQTfAJ39yPpA+F/2hfMD73Pp9eSq4gLi0uBa4W3j4eSo5Rfnyugf6azpiupP6yftyO/28d/zzfRY8zryr/Fo72zrsuiJ6P/ns+eN6ArpYOkR6t/rXu0K7dTu9fLf9tP6lf5IAc0EeAflB80IXQn0CNAJ6QpFC9cLkAyUDAwLEgoTCTcHTQYgBzsIEgp/DPEOihH2E9UVJxdnGJEZkBrUGycdOB6BHpIePB4OHaAazhegF3UWtxMVE6MU5BR0Fe4TdBJhFDUTthLBE08UfxOoEp4SPRKaEDINIQxYCw0GnwKeAdP/iv26+xX9Bvsl9NDvhO9i8PzwU/O99N/z+vTy+IX7MPo4+Vv5CPgC+Ij4ZfiU+c76pPu6+8b6A/kc9s30rPPa8abxIPNg9Kb0IvYY91/2nfZ1+BT65fqU/ML+sP+TAFkDdQVSBREFzQQxBPAEUwZqB7UJLwt8Ci4JwwifBzIFtgPMA2gE9gS9BQcGigWUBfUFtARAAjEAy/6p/VP9X/2A/Mv7k/tG+Xn1GPOJ8GHuUu3y7EbtF+2d7Azt2ezY6+/rS+z56k7quOsT7h7wefCU8V/xCvB18ADwu+4H7kbuTe8R8RfyGfJl8U7xxfFO8RXxdvJV87n0Tfc0+dD6Tf2i/s3+/P7H/3cADwKYBKcG1QcoCUMKQwr/CSgKJgqdCg4LVAuGDGoOqg+eEE0RVxHYELMQpxFkEncSthJ1E0AUWxSCFDoULRObEvsRthEiEvYSeRNrE1YT/BLtEdQRJhLAEZMRtRANEJIQ1w5hDPALZApqB88EzgIuAkgCmwLRA2IE/wJHAFT+VP7H/Bj7sPvf+1L7tf2cAQ4EtgRoA9gBJwExAOP+n/5L/tL9Iv4x/x4A6f6I/cP8j/r891z3nve+9+T4NfpD+hz6yPpM+z/6QPmv+Bn4xfi8+gH9AP+1/zH/j/7A/mb+yP3v/YP+Jf/X/3MBFAP1AuIBrAGsAN/+pv5//2n/Tv9UAEoBfwBI/2r+QP0a+3759vgk+SL6cPqf+kf65PiP94D2BPV2897xw/Ee83TzHfSY9fv1zfRB81vy4PFY8dLxmfLr8mbzv/Tz9Qf28PS683/zR/NN88P0HPbj9nT3Qvhv+Ab4x/dN94b2bPaL91P5J/ve/AP+cP5w/lT+sP45/67/SQD0Aa4D5AQZBl4HzQc1B6kGrQaRBh4HTghZCTYKyArMCo8KVQq/CT4J+gjgCPgIrwkEC4ELAAtJCyoKkAjgCJ8JTAk0CsoKwAojDF0NlQ0VDSUMBguRCpsKYgrrCa8JqQklCZkHggVmBHkE7AQhBUIFHQWUBFcFPAZABXgDBwM/A5UDzQQuBoUHuQijCQMKuwm3CFoHVQbaBRcF3QTkBRoHGgeEBr8FIQQZAjUBvABDAG4AUgF0AiQDrAMKBCYD7AFYAD7/0/5W/6gAKgLeAigD8wLvAr0CvQF3ANP/z/+yABsC7QKEAkgC0QHnAFL/EP7q/Ib88vyz/e39mf0y/Xv8gftL+Qb3hPU29aD1Q/b39jn3ZPc393z2B/W489byV/KM8j/zTvQy9av1TvWL9I/zNPIv8RHxIfFn8VHyzfMt9MjzdPPx8hHyefGB8U/ycvOe9OL1GvfA9/b3uPfK9kH2GvZw9h73RPhN+RL6XPpi+gH6nflC+ej4D/mU+Y360fss/ef9xP2p/fn9R/6D/mv/8gAyAr4DZwWKBtYGugbyBSMFrASPBGQEpAQNBbsFXQZ0BtIF7gRUBBQEIQS2BHMFswWTBt8HwwjHCG8Izwh2CZYJvQlLCg4LrgtpDF0MtgoHCeoIjAmKCW8JEgqyCvMKDwxdDa0NMQxkCzoMOg2ADbgOPhC9EO4QDhL8EhgSpxCQEAcRUxBcD2kP1Q9/D24OWQ3LC78JqgjPCA0J1wgDCQsKQwuHCzcLOwrgCDkHMgZ7BQkFhgWRBgwH7QaZBiAGXQVUBBgDkAGBAL4AbQG1AVYBiwCD/y/+E/0P/HP76/rK+in7ffum+/H6zvk3+L/1U/Pg8WHxY/Gx8VnyFvNo86HzYPNT8kTxYvCF8NfwjPFd8m7zLfQC9OnypPFg8Evvmu4+7tPt0+1f7k/veO/m7v7tDu0z7Dnssuyc7e3usvBr8u/zffSJ9Gb0TvTN853zCPTy9Pv1JvcO+B/4vvdg9wL3Q/Z89bn1IPY795b4wPmN+pP6zvox+5P7tvuK/BT+nP8XAS4CWQOdA3wDHgPgAoQCjgIyA90DfwTUBcYGAQenBtAGKgfpBq8GSwcSCBUJVwo/C+gLAAzZC2kMAw0BDT4NKg5/D1UQDxBmD+kO4w7pDtsOnQ5cDoUOaw8REWgSrhIMEqcR0hEUEgoSDBJkEqYSixLpEicTDBP0Eo8SQRKXEacQpRBFEYQR0BBMD8QNcQxmC9sKZgrGCTAJlAldCh4LrAvlCnEJ3QdLBiMFagRYBBAE5wPxAzcEUATzA4MD3gIDAvAAlQD4APAAkwA1ALX/Wv71/Aj8cfv3+oL6Yvrf+mb7tPt7+2z6zfjf9m/1wfR19Fb0gfTm9Hn12vX99cH1RPVG9I3zKvNe85nz2/M/9Kb0pPQU9D3zjPLi8UbxNvEX8Qzxe/HH8XvxifCh7+Dut+6A7kzuX+5L72zwRPHo8RXyMvLN8cnxhPGO8a3xIvIU86HzpvMx9IP0mvRg9N/zsPOu85fz6fNt9LT00fQN9Wn11PUR9q/2VvcC+PT46fnK+tn7oPzS/AP9av0q/uH+pP+NADEB8gHOAqUDbQQrBa0F/QVuBp0GUQcCCOoIgAnSCUsKZAsjDLsMyg2VDqoO6Q7oD8wQnBCWD+APqRDeEFMReRIVE5EStBLTE28UeRPTElITiBPAEtkS0RM4FJoTPBOeE6QTJxM+E5oTLxMxErQRvBHKEUURUBBtD94NkAwrDDwMcQy9DF0MDAzjC6ILpwpdCRQIdAb6BEwEjwRnBS4GRQYNBssFVAWWBEMEnwOdAs8BmgGUAaQBdQEpAS0A0f4T/W38nPyc/DT8Ovxn/ET8/Ptm+yr6vfgt9xX2gPXH9WH28fbC9w74svcC9wn2WfVt9GTz0vLY8nTzM/S79MP0FPTg8sXxHfGX8Cvw6+/577LwL/FW8RfxefBT70Lulu037XntCe4i7wjwaPCw8PbwxfDf7xjv9e4Y76PvlfDB8Xjyr/Kn8pny3PE48bTwkfBz8H/wa/F+8izzvPP089/zuPOb89HzVPRO9ZX24fdf+XT6Zvu0++b7r/xs/TP+If+ZACoCUwMYBDgFwwXoBSAGkQbzBhwH2QeUCRgLmgv4C60Mbg2dDZkNbA4jD5YPZRBHESQRwxD3EMYRUBJDEqYSLxOoE3oUmRXTFTcVuRTMFAsV5BTcFHAVeBUHFRQVhRU9FcwUfhQyFE4TPRIKElgSDhIyEQUQcA7SDPILAAzNC5cLhQsXDF8MZwx1DIcL4AlMCBIH+wX3BZUGBgcBB9YGiAZXBtQFBAX8AwUDDQKvAbsBCwL6AXcBhwD2/sT9qfx3/KD8xfzs/Fv94/03/uX9ovyp+vb4XPdq9hX2Lva49mL3pPeT91L3o/bB9Z70k/Mm803zGPRn9fv1lPXX9OfzxvKO8ZrwHvAO8EzwH/EX8l/ycfFu8Brvju0G7KfrTezd7K7tHO9q8PTwHfEA8RTwfO6q7fztlu5077jwwfGk8Uzx2/A78GTvs+6G7qXuHu878IHxXfKZ8pnyX/Lm8bPxY/I581T0FfZA+N757frD+/z7tvsn+3P7e/zv/a7/dwHSAg4EEQUZBowGlQbxBkUHEgiECWILsQydDZsO+A78DiMPcw+7D1cQHhHAEQ4SEhL9Ee0RoRE/EToRlRHpEWYSkBPEFBQVqxRlFJQUzBSAFF8UoxSSFJIU8xS6FboV0BQ0FL8T6RLEEVcRTRG1EPIPow4mDagL3QrdCrgKYApLCr4KZgv8C9MLsgo6CdkH5wZLBs4F0AVqBt8GGgcaBwYHfAaaBXMEiQOOAvYBCwKdAkgCcwFQADX/Of4s/Y78H/zk+zz8oPwk/Tz9UPwz+4D5g/cg9pT18/W49in3aPcd+KL4bfio92j27vTT87zzUvT69Ij1//UR9l/1G/QK857xhfDj7xbwSfCe8H3xw/E48ePvlO417RLs2Otk7AztAe4c71TwI/EP8Rbwyu7J7SvtRu0D7kHvOfAb8ZjxZfG48Dfwpe/z7mfugu6J78/wIvJB86jzcvMq8w7zHPOF81L0wfWT9z753foj/Ob87Pyr/IT8B/0Y/o7/RgHgAl4EvQXOBn0Hpge2B+0HEgjeCHYKCgySDcoOtA/bD5oPxQ9tEIQQqRA0EfER5xHEEaISGxM3EiARHBF8EasRDBIrEwEUihNYE+4T9hNYE/QS+BKgEjMSwBLeE0YUQhQHFJgTexJRESQRARFXEPQOwA2WDDcLsgpNCooJ6AjDCNoIDwlMCXoJKgk5CDEHCQYLBY0E2QRQBYIFeQWzBRcG6AURBU4EZgNpAq8BmAGxAUABlwAEABv/5/21/CH82/ug+2j7Bvyv/Cz9JP0p/Kn6B/mm98j2l/aj9t32kfc8+LH4g/gG+On2fPWo9Dv0PfT49Oj1cPaC9ir2ZfU/9CjzFfJG8eHwdfFE8rHyEPPW8vDxrPAs797tRO1I7aLta+6Z74/wgfHB8VLxDPAD75buFO+G72rw1vHE8lrzdvPv8kfyZ/F38M7vzO9i8ELxSfJJ89fzrvN081HzT/N/81b0nvUm9/j42/pj/DT9jP2Z/Yb9uv2u/iIAvwGlA+4EUwa0B04ITAgGCP4HJQjiCAkK0wtdDYsOoA9EEDYQExBpEAcRZhE/ESoRWxEQEuUSHxODEtsRkRHIERASMRJaEskSrBLjEk4TLRNgEkkSNxKVEdgQMhE9Eh8TlhM3E7QS+RFdEUkRLBHeD6UNDwxiC6kK6wlnCd4IVggXCNMHrgfGB88HXgcyBqYE7QOjA/gDVAQ1BNMDsgMABGYEUAT+A2YDxAIVAr8BnAFCAVAACP+z/X38k/s/+0H79frC+uv6qvvy+7b7Dvvt+ab40fcp9yD3Hvdw96L3L/gV+I33WPdY9932FfaW9Q/27/Z/98D3WPe09uD1MPVv9HTz2PIB81jzkfPP87rzAfMP8hvx5e/W7m/upe7k7kHvK/Bn8SzyePKC8ubxrvAU8IPwUPH/8afyRfNq8wrz+/Le8kfylvEr8Vjxs/GG8lPzwvPh85fzOfMW8yzzi/OS9Jz1Sfcs+e/6iPxw/c798/3r/WT+Z/+NABEC4wNXBXgGvAcNCdIJHApTCtkKqgvUDE8Ong9EEOgQxhFmEQ0RgBGpEXwRNhFwEbYROxLdEo4TExNYEncSiRKNErwSdRPBE2kTtRNNFKEUVRQ4FLsT+hKVEhsTnBNzE0QTnhKXEYQQRhAnEDkPhA3yC7QK0glfCUYJwQj+B7AHcgcgB58GegYPBvIEzAMSA6kCxgIkA00DKgN0AioCTwLaAiYCmQCB/wL/j/6k/iP/if66/Q39GfwW+5X6XPrr+Wf5IfkT+Wv5tfkH+l/5SPgU9wv2fvWc9SL2LPYi9lv21PaV92b3QfY29T30FvTZ9PH19fab98v3jffA9nP1LfQD86/yn/LY8mrzDPR59CP0VfPD8U7wgO+Z73rvNO+f777w3vHS8p/zyvOO8nPxZfEi8uXyyPPT9F31jPVf9fT0oPS4837yu/Hc8TDyYvPe9Nz1Cfbm9bf1SvUr9Uj1pfUN9lD3NPla+yT9dv4n//b+sP4z/1QAwQE7A7AEbgbrBzAJcAovC4MLOws5CwoMSQ2TDmMQ7RG0ErgS0xIbE88SDhINEXEQmBCfERsTYxSIFBEU4hPLE3kT5xKyEgoTYxPWE58UdhXxFR8WBhYoFcUTWBOYE9oT3hNxE/4S7hJaEqERdxBkDuoLCQriCFQIOQgnCFAINQhsB4IG2gULBQAEAQM2ApwBqgF8Al4D1QOjAwsDHwJWAaQA9v/j/lj+Q/4M/vH9yP0k/f77U/rN+G73k/aP9rj2BPdg9433nveX99T22PWS9DnzwvLz8pXzqvS19Zf2vPZm9qD1v/Td843zofP288307vUp96D3IvdH9hn1ZPPQ8vHy0PLj8rTzpPS/9Er0DPRR89bxpPBQ8GDwh/C38WTzTvRm9K70lPTX887yhvLG8h7zEPSS9dD2WPc39xT3VfaW9HjzxPOy853zafTD9XT2gPah9or26PUn9Sn1zvV+9rT3ivla+578Tf3n/Wj+eP4f/0sAZwHvAhkFQQdOCE8JZgpbCiwKfgqDCykMvQzxDcsPVRHhEcsSdRMCE3ISmxJ9Ep8R2xFWE1cUZRTYFF4VFhXOFDkVZhXIFCEU4hS0FVMVRRUAFhoWLBWrFIYUdhTyEzwUdhTkExsTxxLTEisSnhDADu4M6wq9CWkJwwgtCLoHrgciB3gG2gX6BI8DBwKwALD/QP+O/0UAgwByAEsABgAQAFr/j/7x/Vv91Pyv/Ir8Tvys+6X6Z/mo9372rfV59ZL1cfWI9cP1NPY89pr1ovQz857x0/Cs8FjxG/Jc84f0APVE9dP07fNN8yTzEvMk80j0wfXI9nT35fdq9xX24vQb9HzzAfM38xv01/T29Pz0jfRe8xHy9PBu8ALwTvAz8QXzUPTZ9FX1OPWa9N/zLPMH89fzAPU/9mT3pvgN+db4Ifgr9yj2jPV19RH2mfY59wD40fjB+Cf4kfcM93T2RfYM9334GPqe+978sf0Q/k/+uv41/6b/EwELA0AFWAeWCVwLBgx5DM0MNg2MDY0Oqg+eEJ0R4xLPE1MUARR7E7ASvhENEfMQpREcEuES7hNvFGUUmxQBFc4UbRRGFIAUBRUyFfoVJxfkF1sXjxZgFl4WbBbJFggXLxZDFT0V1BTHEzESHRC+DaQLxAmYCN8H/gdMCAgISQd0Bg8GawV/BOICqgAS/0n+jf5//3cAmQCLAKoAwAAtALf/G/8K/iD9qfx1/M37WPvK+pb51fcu9tX0PfQA9Mjzf/No88jzFvT480Xzx/H376PuiO5i75fwFfKR82b0ufQj9Wv1zfTb8zPz4PKD8430rfWh9hD3tvbg9Wb0GPNN8o7xe/Gv8f3xKvKp8ufylfJp8RDw6O5v7hLvXPC/8RTzkvS/9Sb2JvaY9Y309vNi9F/1kfb090v5+/nk+VX5lvh391H2wfWt9fn1nfZ/9+j4rfl++QH5L/hy90H3uPe9+D/65Pth/cD+9v+VAMIAzwBCARsCbgMbBS0HYQktCy0M1AxXDS4NyQzoDLUNnw7HD5UR0RIxE4MTGRPpEYwQ1Q/FD/gPmhC4EdMS0xOlFAMVPxXiFE0UBxQ0FGUU5xQjFikX0BerF1YXnBb1FboVeBUBFcAUlhSbFFMUexMUEg8Qyg2uCwkKBQlKCEQIzwguCfwIdQiBB1UGgwTnAmMBx/8p/9P/pAAbAfAA1wCNAOj/FP/l/QX9KfzD+837z/ta+7j6BfrL+Cb3t/Xu9Jr0hfSN9Pz0GfX+9MX0TvTY8hnxoe+l7qvuMO9J8L3x5/JR82TzSfOd8rPxHfHw8ETxJPJ287T0JfUf9b/00fMM81fykPFj8erxr/K+8oDyQPLg8RHx2e/v7mHueO5m79Hw8PEc8xT0i/Sy9Cf0QfPe8kPzO/RC9cb2J/gX+Xz5ePkT+WP4hffO9gb3mfcz+P/4AfqL+mj6HPqn+RX5B/mO+Wr6YPuC/Jv98P7g/x4A+P8KAGoA7gA0AgQEFwYpCI4JUwqiC0QMFwwhDAUNeA49D2kQRRJvE4oTuxNYE0EStRDoD8sPCRCCELQRwBJaE8UT0RPaE4wTNRPZEp4S5RIJFPEUoRXrFbAVfBUDFd4UNRVuFUcVdBUAFkgWwBX3FMMTbhGhDjgMfArECRwK2QoOC5UK6AlfCVgIpQZKBE8C7AAiABgAlwAfAToBNQE+AaQAdf+d/mL+Kv7G/c796f3O/Vv9x/xO+wf5JPcY9rP1mPWQ9dj1GvYD9r31DfXt8xnyYPAN7yruI+5878nwjPEJ8lfyNPK18YHxG/Hq8DHx+/Es81T0Y/Wg9Qf1CvTS8q/xTvFa8d7xIvKA8sLyLPP/8svxHvDB7sXta+0P7jLvIvBh8anyj/Of82zzQfM78yzz3fPZ9Cj29Pdf+Wr6hfpy+hT6VfkR+ST5kvlw+vX6sPt3/L/8hvz4+y/7nfqN+j/7SPyK/en+/P+0AF4BzwGmAZIBLAL9AnsEjAZMCBIK4wsRDXYNzA39DVUOIw8ZEAURrRECEyYUdhQuFIoT+xFVENMPJRCEEPEQ1hF5EiYSSxLCEkcSBREZEAIQaxAgEXAS/RO3FJAUaRQ4FA0U6hNCFEIU6hMTFGUUyhTKFLcTEBLFD1MNjQvdCucKnQouCtgJQAl1CL4HjAZkBHoCtABY/1D/9v/2AIUBewF3ATUBZgDJ/zP/i/4B/vn9N/6P/n3+7f0V/Yv7Zfn490H37/af9jD2+fX39Y71ufS28wnyTvBZ7nvtt+2r7pHvnPBd8cvxZfEf8Svx2/Bm8GLwE/Ff8uPzd/Uk9oL1LfRg84byv/GB8Z7xpvHB8fTxU/L/8RXxp+887vjsRezj7B3uVe8x8Drx8PFf8njyrfKf8pXyWPPm9HT2+PfI+WD7oPs3+936ZPog+mT6GPuw+w38LP05/nT+JP5j/ZD87vtz+yf8Q/3v/f7+ZgBcAaABJgLYAgEDIgNWBEUGUAhgClIM4A3rDmIP5A+CEBERZhHlEbwSsxO1FAsVqRTLEy8SOhAbDx0PPQ9kD1kQ4BAFEUURjxE6EeYPoQ5aDnwOwg48EAwSfxK6EikTQhNEEy0TGRPXEnsSbBLbEkwTwhKxEREQEA4CDJcK2gmOCYIJowlGCZ4IFAgoB2EFSwP+ADv/P/5L/kT/OwCkAKYAsACmABYAK/+J/hj+zv3c/Wj+1f7E/mD+Jv1x+9D5ovi492b3HvfO9pv2bvbi9df0IvMX8SDvru1G7bvts+7l777wLfFM8SPxMfEM8XfwWvAR8T7yg/O59Mr1pfXP9A70S/N28nPxMfFx8XPxnvHi8aTxBPEK8Jru6eyx67Pr5+wl7hzvDvC48Hnx7PH58ebxpPH98e/yVPRJ9v73q/mp+vX62fqw+mT6ifrQ+n37RPw8/Uv+CP8G/3T+V/2g/I785PxP/U/+qP8VARsCEAPbA+MD9QNiBM0EKgZtCLYKxwyqDncQXRExEt0SJRNrE7cThBSXFXEWnhY9Fk8VBRSBElcRQhCoD8MPNBCnEAcRhBHIEYIRWxDVDk8O/Q0xDkoPZxAgEZURXBL2EoETwxP8EhwSyhHdERQSahL7EbUQ/g4BDVILIAqSCeAIJwjnB7wHQQerBr8FTgQPAsn/pf3m/Cz9+f3C/vL+TP8n/y3/I/+8/hj+V/1P/av9Of60/hT/h/4k/ar7m/rp+YD5H/m5+Bv44ffL91T3IvZI9O7xn+8+7ujtWe4c7+fvpPDR8HXwyfBI8V/xQPE68f3xLPPg9I/22/Yw9oT18PRI9Ovz9PME9Fjz4PIq8xTzLPJn8YXwju7b7PnrLewd7VvuSe+4793vNfDX8NvwmvDq8JDxV/IQ9E32Ffj4+OL5LPoo+v35Yvo/+8P7H/zS/Nb9kf7y/sL+Sf6G/cH8+fyj/Yv+kv+0ANYBLALGAukDlAS7BFQFnwZrCIAKVQ0FEJER/xEOEyYUjBRwFagW4RZCFzYY4RhLGC0X/hXrFGkTRRLzEXgRMBGNERoS4xEuEbsQGxDZDiwOAw4WDiQOAg+xEAISMxLPEpQTnhMbE3ASYBLlEakR0hE8EZ4P5w2UDBgL4gmgCPgHfwedBooGbAZrBaMDjgGo/639LfzD+yn8gvw8/VT+sP6X/rr+WP68/Rz9QPwj/K38Jv2C/bz9D/36+wj7Lvo4+W/4H/ho9+/2yvZy9nH1RvRp8j3wdu6a7a7tYe427/vvvPAZ8WHx5PHg8ajxpvEo8tDy1fNp9ZX2dvbs9V/1e/SX82jzXPPn8pLyofJm80fzGfIT8Zvv+O2f7EHs0ezm7Q3vsO9M8Ajxb/GE8ZrxQPEt8QnyifOK9X/3Y/l6+hr7TvsM+7r6FPtq+8n7L/z9/PP9xv4Q/7j+EP5F/fD8KP0x/un+4P8QATQCPQOLAzUEDwVpBWcGNQidChkNaQ/QEdYT2BSlFV4WnhauFi0XBRgkGHsXABdSFzwXWBYUFckTRxKnEdQR0hGRES4RgBG2EUkRThAGDzkObg7lDusOEA9OEJkRnhIjE90SLxLCEZMRWxGAECcPrw3sDJoM7AvKCuAJrQk0CUQInQf1BugFWwVqBLECrADR/vf9jv0Z/dL80PwD/S79kf2A/RH9sfyv/Cv8hftQ+3/7TPv3+pv69/nO+fv5nfkX+Tf4Tffx9iT2+PR28w3yYvBF74LuQO5R7rnuC+8i747uD+6/7mLvre8g8LDwpvFl8onztPQE9aj0L/QE9NHzxvOD82TzPfN68tbxY/Hq8EXwzu/i7uDt0e2f7u/vsPD48P7wTPFp8TPxPvGB8e7xq/K88wT11PZS+Pj4Pvmn+bf5sfkq+iX7uvtI/CT9Mf6u/qb+vP66/qb+Rf7R/tH/DAFfAnoDxwSQBQEGvAYxBxcIEQnYCbILGA4LEMQRSBN6FA4VZhUlFr8WyxYRFzgYTxjwFmgWwxZeFsIUMRPxEcsPqA4bD98ORw6nDdQNlw5iDioOZg5uDsYONQ9gD7sP7BAWEmgS/RE2EcwQOhAlDw4O8AxqC/MJJQnVCBAIDgelBvUFMgVmBJUDGgNZAj4BIgAU/x7+r/3Y/ev9eP2t/K/8MP0D/Qn9Gf27/OT7cfs/+w778/qL+lP6Vfpe+mL6JPqK+ar4Vvep9Rv0N/N08rjwq+5M7SXtOe1/7fjtOO6e7RLt9u3k7vXuDe/37xPxdfHs8W7zrPR99Aj01/PE89XzIfQV9QL1KfS4867zm/Oz8nvxKfAL70TuHe7M7pnvNfAQ8GDw6vBf8Qfyn/Iu88bzUvSv9bj3n/kK+7j7xftj/LH8ufxq/RL+Nf7r/Tf+Hf9S/9X+vP6u/pv+ov4z/2IAMgIcA0sDdwSlBqoHiwc+CNgJlwrjC24OahFaEwEUSxVxF84XgxYrF+UYMBihFS4V6RXGFUMVJBUmFHkS9RAdEPEOAw2TC/wKRwqpCSoJgQh3CCgJfAmcCC0IxAm8CwkNFQ3ODZsODg9aD6YPNw9fDVALUwocCugIsAdaBwwHjAV+A1ECJQF//xz+Df3B+5P6lfpm++b75Puu+5j71fsb/Gn8gPzm/Of9mf5p/+j/AgD+/3X/bP5w/cH81vxF/Q39Rvwa+yb6Cfl097v14PLh79rtAO0v7RLtAu1C7WbsNOsi6x7rJOvY6/Lsa+7p71/xgfNM9bX15vXz9Xz1CfbQ9r72tPaX9oD2cPaY9Yn0wPLs8Ofvdu5t7QnusO9z8FDwYPCc8PLwTvEw8sjykfMN9Z73fvlx+9T9Of+J/8P/BACiAIgBkgJgA4sDzAONBOQEHAQUA9wBkAGDAbkBmwISA4kDlATVBCsF1AYpCLkIGgrlCwUNcw/dEicWnheIF7oY1xl2GT8ZXBnyGfEYPxZsFmMYcxa0EpkRhw/HC0IJBQmaCFIFEQK3AlgEdgPAAsACOAJtAVoBVQPuBH4FwwViB3MJAwrWCf0JiwrkCRkIZQbFBdwFqwVEBXkEIgNKATn/Gv7O/QL87PgI92T39vfw97f4Pfrz+az4HflF+1b8PPzQ/Kr+gwB3AXgDtQXUBWoE4QN5BIMEtAN+A7ID6QLFAN/+q/0Z/LX44vRR8nnwGO+77mjveO9C7intVO3H7RLtDu1f7nDvavDM8or1f/dS+PT3kfcp92r2cvYy9gv2PPZQ9cX0YfUb9ErxGu+m7dbrpOrU6sjrvOzV7OHs5u3x7lnvzO8p8RLznvSV9sj5sf1HAA4BHwJyA/4DKwRUBeUGrAfAB+YImwrlCtoJ0AlrCecGkgX1Bn8IRAmECpULXQz3DU4QyBH5EfcRyBQRGKgZWB0DIMAfQh7+HWAgXCAgHDAYYxdSFt4UNRUHFKEOVghfBqcGHAME//v8aPsO+8P7Qvyg+yb6ZfmW+eD5kvnW+T/7t/xh/ZP+UABOAaYB7gGsAeT/Lf4X/aD8JfyQ/Nj8cvpB97H14PXs9Yr1TPWB9GDzFvQE9//5vvq++pb7cv0QALUCigXZB3EJUAsSDuwQ0hFTEUcRxRCSD48ONw67DKcJowYTBRQD4v/D/FX6b/hD9u3z9vGD8O/vvO+F8PLwgO/e7svwqvM89V/2O/c796r3Yfkn+xj6H/hq9//23vVS9cLzTPGp75DuLe2p64fp4Oe55pnl5+Xw5l3np+di6a7s++6+71Py7PVQ+F76Ev5hAUIC9QLOBf4HQAigCNEI0wfdB/AI8gisB58GEgeoB3QGlgW0B9wIXgdaBxoLGw8kDtAN0RM/GVEYORkUIDIjPyAQII0kMyRhHkcdOSEDIIwYHRRhFEkSlw2hCv8Grv8J+ff2HvZV89/v3O2m7S7u3u4n8VzzB/NY8TLyGPZl+c/7rfz++zj82f5jAUgBJgDQ/ZX6a/nE+XP4mPV/80PzzfFJ79ftRu6r77DwV/IH9bL39fnS/CcBTgWvBrQHwAp8DtsRLhUWGYIbHxtbG04dnB06GwkYzhQUEvoP+A4JDbUIJwTPAfr/T/2J+nX4K/d49oj2o/Yw9sr1JPb99hD3fva89h34cvk/+h38sfyA+qL4l/fF9ZjxTO4U7bbpF+cu51/n2ubh5NDjLuTQ4x3j6uP65aDmPeg37G7vOPKB9K/2IfmR+0v+9v9hAbkCJwTaBYoFhQSwA5YCswE+AZQB0QDAAA8C7QN+BdoF+Qb4BwoIrQkZDGkNlxLDFn0WnxgRHxYk0CMJI3AkfCYQJ6AlMSRlH6wZbhjMGKwVcg4MB1AF3QMq/k/5Ffki9mLvL+xU6nvpVOpg7K/u5O4g8AL05viR+2f46PSG9vn5Svsw+bT25PUs9nH1tPSg9MLyq+8N7oPtyezt6xTtGPBz8ZTxG/IC9Fj4Kfxe//UDiQgrC/8MfBAPFHYVlxVSFisXjRYdF7wYphl4GW8XqBa6FsAVUBMbEM4NBAxqC9kLggrbB6EGrwV+A6AB0QD6AHQAm/7T+0v5XPhK+BD42vXB8evu0O5w8PjwS++m7YXs/+s463/pRudA5hXmM+W85SDoSOmE6jXse+y67HbuMfAQ8ATwP/C58Zb0QfcZ+FD4gPoh/Hv7RPy2/rv/lwBEAqwDUgTXA8kEkwazBjgGGgfFCAYLUAyGDdsPOBGdEWgSGxMFFNoU2hQCFtwYiRruGk4dgh++H2sfFx/5H18fKR38GoUWKBHADSoO9A/OCkUDVABJ/nH7OPmj9l7wQupN6NznMucW5e/k6edr6rvqw+oT7hDwz+2X7M/t5O4N7wjwDPH97ivtJe7p75jxqPHz7/nu2+8E8ePwV/JC9Tn3F/hV+fj7Jf8DAoQGsgpjDW8PjxEGEwEUSxVkFuoWOhgmGdEZjhr4GsUaURntGEMZDxhoFjcVExPSENAQ6hCuDv8JswaUBXQDYgAU/mf8FfwC+1L4PPZ884Xw5O547l7tC+uB6Z7qR+wb7Y/siuvk6svpkOc85ufkS+T/5FjmS+fn6Grrpuzn7CjuN/C+8MfxJ/SG9b737fkb/IH+2v/sAM8BZANrBXIGeweHCFMJUwo3CwYLawkRCasJIAqNC18Nfw+cEM4R+hNFFd0VYhZcFgYW6BbRGfsbFh0HH+whIyKiIVUj3SPLHjQbqx87IfMY/RC0DgcNyQhjBg0Fkv/y90HzQvJX8mjvNuoP5/blIeJE32niGec/6E/n/ujF7ZrxLPJ67zfspOoQ7IjuiO5k7P7pH+mk6QrsMO517efsLe1G7SvtXe+T82L3Afry/HX/qAGIBY4JJA0nEMUSCxXnFXYVnxTCFOsVdBVhFC4UkBRjFHQUKxY3FngU+hPEFGEUYBKlEDQQOhCDD5sN1QqkB/IEpwK5/878Zvri+ND2nfNJ8ELta+oQ6VnoD+ee5XTlmOZ46Gzrz+w+61/ri+wk60HprOmb6/HrMOtG7Zfw3/Bm8P3x5fLu8cHx2/Og9Qn2GvdP+S38YP7V/6YBgwO/BOEGAwofDIgNKQ+NETcS7hBfEJwQqRCOEDgRkxKqE70U/BU8F/UXqxenFzgXVhZpFxYZfRo7HYAfSR/PHucgeCNwI3AgASAjIR8eihhlE5kO2AnkBc0EeAOe/JnzIPAF8mzzgu9+6IjkduF53nPfNONB5c7ksOVX65/vQ/CA73Puku0n7evsIe1O7XLrv+ma6a3rce5i7/PvXvDv74/wf/OZ93j6ivy4/q4AZwJlBQsJFQyBD5cSnxSNFUMVKBRYEwgTtBINEdcPqg8HEIwQ/xFUEw4TCBK6Ee8R5hApD1EOAw7CDUQM3AhKBMkAIf/1/Ln5CfUw723qYOmX6xPrVOZB4bXfaeA34W/ir+Of5KTlcOhF66XrF+vm6lLqlOnq6VnrQeyM7fvun+/28K7zZ/Ul9T/0OfOk9CP44vzCAFUClgS8B9MIQAnECtIMtg6DDwkQGBFNEoMTzRPnEtsR2BAwEHoQwRC5EGoRdRO/FowYTxj9F8oYuhkOGUkZSR3PILEerRzCH8UiDyJfH+0fRCFxG6UVTBcvFokLOwODBO8GqQJO+x72rfH/7p3vIO+P7zPtn+R64MrkvOhy6E/o0Ope7fTt8e7H8aXyBvHm7ZnsE+787Wrrc+nD6ZPshO7p75XzC/bJ9JD0VvdK+7P96P+nA7UFswZ+CVIM8Q7zEVQT+hPWE6ISKhEPEAAQnA9HDnAN3gxdDJUNSg97D5IPoRAaETQQaQ9EDyoOXQyCCtMIRQYZAXP7VPjf9vrz+e8W7G/pOOfs45/hBeEP4P7dUdyn3STgkODv4drm2OuT7MXqn+u77fvrEOk86r7rL+q+6J/rBvBW8F/ve/Ex9Dn0e/Qk9375A/qD+zEAzQQ9B/4IzArcDNMOABD7EVEUehSmE5IT3hPwEpkR6hD6D9kPrxDnEUoTuhWrF8gYaBlEGkAbGRt8G4Qc6BvNHWghzSDgHwAi3SFdH44enh3VGkYXCBa3E6wLGAT/AXoCggJq/iH4WvO88FzwbfGd8r7wUesv5grlwOdZ6m/qEepw61jsI+1o8M7ypvE+7qfr6exJ79XtRur86dvsre8m8pH2Cfom+pT5f/uw/pgB8QPHBUcH6AgmCvQLTg/zEbASchJeEvERvxCYD38PUA/RDjUOwA0cDucOmg+7EI8SpBOFE8cSCBLxEBQP4gzdCiUIegP//R/59/Wh89/wlu2P6YPlVeMp4uXgmN8l3s7c1tw93k3gvOET47blvujJ6kXrweoE6vXo8edf51zmbeaQ59DnCOnP7KHvqe/H8LLzjvUc9kb4Cvs2/U0AMgNlBuQJsAtCDAoORhDaENsPPhBPEbEQjQ+5D8sPWA8/DzMPlBCsEpQUYBYEF18YjhrkG7Eb4hrMG64dSR2sHXggCiE/IHMhACJVIKYelxz0Gg4aBBdUEnoOXwm6A4ACIwTPAQH6UvR79Bv1CvMz8bTvmurG5LTkduha6VHnrOY66njucu+a7hTwOPJi76fqwOvM75Du+eqH7Mnw1vJa9ML3pvue/MH74vzRAN8DZgTqBGYHPQqRCzEMNw9oElISFBK/E1kU0RKKETARrxB3D+MO7Q7sD2MRahJQE8oU+hUQFbsT6RL/EDsO3wu5CZ0HSgTg/+f6TfcF9on0Gu8657ng094u4S7j9uJT4MrcVNsD3ALfieLg4kLiE+Tt52nqkeje54nppec45A3ksOVo5VXj9eP86JTtVe9H8PTxZPPc8hv0dPpJAGEBcgK8BocKagoACwEOOw+pDasMfg09DgoOPA0BDWENPg0XDEwLXwwmDoUPTRI9FsYYTBrSG7YdfR4jHtUdlx0CHmMfPB+MH8ch4yBMHQgdFhwLGAoWSRVhEUMLqwV+AuIBuwHJ//38bfka91D1nfPK86vysu997Sjr6eeD5jrn5ega7M3tte2q7STvd/Dt7k/s1OsA7f7s4+vl7Dbvd/HV9PT4t/y7/9UA6QBGAgoEvQVcB4YJ+gu1DNoMdw8tEskTYBUhFsgV/RSQEzkSfBEqEQ8R/RCREe4SyRPCFIUWDxdiFroVSxSAEaYObQx2Ch8IqgRFADX7cvYC9PPyk++x6rnmfuSY47Di/OHd4Yrg1d7+3ePeHuGu4o7j9uWh6JrpJ+lu6PTmzuTs4knhieE849Tj0uTP6Z3u/u+a8YX0nvUd9YD2oflQ/Kr+/QHDBdwIggqkC28MhgwGDAAMjgwVDeQMVgwgDYwN+wyeDMYNPQ8uEIIR/xRFGIMZoBrzG9YbNxq8GBgZhRoUGioZkBr5HDQcHhzTHRYcuhi2FlwWwRamE+8N1wp7BwcD8AAyAu0CbP6P9wf1hvJX77fu6+up5/vjtd8J4Bbk4eRu5cjoIOy77dbunPBi8PDtZ+1f7qXvQvHw8BfxxvME90f6gv3H/yUBDgG0AEACsgMdBWAH9ggnC/sMEA9ME14WOBioGVQZrRhFGIMW5xShFMkTmhM7FewWxRdPGNAYSxlwGIcWQxW4EpIPJA77DBALIQn1BQ4Bh/ss9p/yCvAm61rmXeSe4wbiG+BM38neB93k3EXejd5j3+Hh0ORZ6NXpeel16d/oYed05fnjp+Nr45Hkaecc6Rzr3u0o7wLwpPGw89D1SPhm+9n+TQIkBmkJYgt5DPoLGQxfDR4O0Q6gD8EPzQ+oDwYPwg1QDNsL6AvwC60N8xCCFMcWFRhuGV4aaRptGr4ZMxm9GpUcYB17HZEc3xzoHUccJBmPFssT2xElE0AUIQ9LB7UCbf8W/ob9j/qz+KL3GPMP7wnvZvDu7bLpveZw4WLdSeCR5Bvn1+iP6DTruPD98TDuUupQ6hLsaOwT7ivwYPDc8e71+vqf/n3/9v8ZAXEBSwI1BA4HpQo+DGUM/Q3uEHUTJBXfFusYPRnpGNUZhRlAF+kVOxWdFC4VhRazFx4ZfxqcGrYZ+xhIF/ATUxBeDnkMiQqCCVwHegIc/Xf4TPTv8ljx4ewH5xbhgt193kLiUeSX4bfci9qW22Xf3OPJ5RfmVuZz5v/m0ud/5objH+K94sjj4eVs6MfpDOxu73LyqfXh9nX1x/Th9lb7pABTBmwKIwxJDYcOxg4pDwcQZg+2DiwOsQ1NDmAPFRB3D+ANkg1kDqQPyhDnESoUpha6GAkbdhuyGWoZNRrVGdcZihsrHVscqRufHE0cfBiwFbIVUxS5EDUP4A9NDoAJwwWdAxv/wPkv+Cn44/Y69XPxpuwe6/Ds0ezc5p/gtN603YrfL+VP6HDo8ukK7QDx8PCf6wHo0elu7E/s5ewA8Uz0Hvav+VX9nf78/ur/JQFYAZwBPwTBCHcM4w0/DskPfxJvFLIVsBbmFwEZahn3GDYXBRXDEyMT9hKFEzAUBRXDFnIZFxqRGHMWMBSrEP8MmgsiC10JUQcLBbwAYvsk9pbxce577NvpK+bi4mDh5+B04J/gdOCi3sbc4NyI37LituW+6FLq3urW65PrTOms5oLk++MA5jbqDu0j7v7vwvJv9Db24/dx+L34kPk0/AYB/wXSCWsMmQ39DfkNZA5GDzEPXg7KDvAPiBB3EIkP5w0RDB0Mfg0OD7sQmxKnFGkXChqBGkMZMBiSF1YXIhhaGkobShvQHEEdjBtYGncX/hJbEa0QMBCuDzsPPBDbDo8Hvf+c+2n8ufzV91vyHvCj79zt4O2T77XrJeJb3aHgsuJd4GPg5eWR64nt4u3r7rzvPey151TpxO7j78HupvHS9gn5H/nP+4f+mf2i+7f8OwBeA8MFcwjbC4UOiw6HDioR3RK8Ei0TWhZNGeUYghfoF+EWjhPfEfsR/REiEi0T7RTZFskWaBV6FDMSxg4QC+gIrAhCCCYH/wXuAQb7tPTR8ITu7+sB6BrkxuEx4QriUOI34TjgG9+V3XLdiN/d4ejj8uZf6jXsBuxh607qIOiz5hvnT+jf6RzsNu8x8cLyHfVU9zj5svqW+/D8yf9PA8AH4AyWD7IPuQ+SEHUQbRD7ENsR/xG0EdIRqRF3EMYO5gwYC90KMwzcDZQPRxJFFmEYMBhjF5IX/xddGFkYeBl+G94b7RykHgwd7RjzFW0T0BErEi8SpxAvD/wOjQ7iCboAbfkf+Iz51vin9vv1W/JW7N/sVfJ38O7lTN+O3zDgs+B45DfpR+wL7n7vbvIk8xfuCun/6hjwXfHy8G/06ffC9+L4DfwY/qv99vs0/IH++gAcBIEHuAorDE4L4wqSDUQQcxC8EQYWFhkoGSgYRhc8FOwP+w2RDvgOJw8VEFERFRMZFJIT2BHxDs4KRwc8Bv8FSgXqBaMGSgQA/wr4wfAV6znpWOlp54jjxuAF4MPf9t7j3hXfbt212xrdHeB841Pn4+un7pLuzew+69LqYep36cvpIuzh76fygfNF89/ztfUE94334PlZ/GH9AgEGB6gLoQ7gDwAQAA+ODVgOsg88EAURchHAEagSsRFWD5kNzwtZCpcKdwzMDTUPKxMNFxcY/RfmFxsX2RY8F3oYuxq6HFccbRtFHCEbIBUkEksV4BR+EMgRVRTpEfkQSxFUDPECyPlo9kz41/uQ/+f9zPYt8bXuzu9P82/uSOJh3Bzeu+O56ubuO+8o7x3yTfbS9RfxJe0S7Abw1PUX+P733/fc+Gr62/u8/Z/+7f0B/ST9Rv+GAhkGhQi7CGwH9wagCBUM0w/1EQ0U5hZIFw4WrRQ/ER0NdArMCgUNbA4TEGIS2RKNEXUQ+w0QC+4IMAZcA1sCrQJKBFMGkgUdATj52e8h6YDn7+gr6eHlG+LD33/e7t4+3wDe8dwZ3LfcfOBT5FHn9epC7vXuM+1P6wLqGugX5+Hol+yb79Ty1vUp9X7ytfIb9Pv13PhO+yD9BgDbBCcLSg+ZEU0SpA/UDGsNXA/BEFUR9xE1E0QTXxElD5gMdgrqCG8IYgpdDMkM/A6DE4MVUxSYE7UT6hOCFJ4WmRiaGggdrR4VHocaoxXbEsUTqhOAEAkQyxLHE5wTaxNjEEQJwQFO/M/41ffj+g7+XP7J+4T2afHJ8JXzzfEW6OPgDeMj6V3uT/IC9LD0CfUo9vT36PQi7y7ubPNj+Tn7fPmK+Uv6Ufrv+sH7ovxz/Dj8Vv5OAfED8AWFB54ILQctBS4GrQlZDQsQdxK1FEUVvRPFED4NnQpYCEcHSgjMCuoMZA4tD/QO9gwDCkoINAaEAmAA4wAuAsYDdwWoBDUA3vjj8PLpmeXF5dDnAecp42/fOd7u3urejd0X3DXbhdtz3oLjzuec6fvrte6Y7n7rmun46U7qjusV7prwuPNf9pD1LvON8231DPfs+CP8oP/1AvUG0QtpD5oQXA9XDYwMFQw8DDEOWxCMEGsPeg5kDu4M+AgnBbQDKwROBTsHawkXDFgPpxExEpES5xIxE7UTTxVMF8sZshxYHZQbzxkjFpMRfhGVEuwPlQ5YEnYUghGtEAgTYxBuB5v+4/rZ+/n9eQFfBtMErPqn8m7zKfdV9dftHuhn5nLn5euG8j33VPe09BH2Q/uN+sv0WfIs9p77D/04/W79TPsH+QH5j/vM/a38Nfut/PT+WgCYAYcDjAXiBNQCfANyB9AKuQ3lETkVHhWIE7YS5A8kChUGgAWhBn8ISQrPC1IMygrFCKgHgAYcBC8BkP+o/4UAkgJdBRMGPQP1/fz31fCy6RnmB+jw6o3o/uKk3xXf5d6s3mLeU93g28zcDuH25bLpaOy/7tvvX+6f66nqquxP7/Pv5fA/9Kb3qPcE9XbzVfO79EP39/mK/FD/fgKjBm4KCAyODPILdgm8BsYGAQm2C1cN9AzFCykL/AqgCEAF4AIDAsYCNQQoBqgIVgykD+YQDREiEZERRRKkEtYTSBYNGAgZ+BqWGtYXrxP9EPER8RE4Ed4TKxaPFZUVBhbAFQoTEAtpAYb9iQB1BFsFPwf6BxYA8vgc+z/7KfUe7/fr2Ooa7A/xw/X9+BX8Z/xE/E3+w/wE96T0/Pcz+yn7VfrG+nr6y/iq+Pn5L/ty+nb5kfvK/ZP+Wv9KAbsC1gJpAbECnwafCYYMlBDHEx8UlRLVD5EL1gZDA5YCYATKBtwIvArwDC4OFQ0AC70IWQXaAowC7wIfBAsGXgezBosDCv+0+3H4x/DU5/XkIekD7PPo4uP031XdYN1Q353gw98j3yvisunA71LwP+8O8Env3Osb6v/rZ+007u3vSfIJ9Xr2v/Us86Pvt+3S72n0K/iC+jr9ZQGXBmIKWAtiCjAJDgc9BPIEXgi+CksK5ghnCOsHsQUcA0gBJAAr/7n/6wKfBqgIWQoHDRwOnQ1hDTUOMBBsEnYUaxfsGp0cwxrRFpsUoBM6EVUODxBqFbgVGxPhFmcbrxjuFlkYWBK7BS8BCggxDogMaQhKBCAD/ANNAFD7Offc8dDuf/E19Krz9PDn8wD7bv2F+zD5Pvl++pv5qPhH+tH7O/sF+r/4tPdj9TP0fPbX90n3Xvcy+oz9Gf2s+/L8FP9//7f/dQFdBeQIRQtgD40SSRF+DZcK9ghdBskEMAZnCOQIFAqcDE8NTgyTCp4I1AbFBXkFAwZDBmQHFQmCCe0H2QTy/5H6B/Xe7o3po+gv7BLt+ec54oTgA+Du3gfgy+IH45Ti5uZE7envu+5Z7hTwzu/J7c3sYO0a79Xw7vHb8zX0QfCL7CbrgOvV7LnujvLH9SP4MvzZAJkDagRYBG0EXgPwAUQC8QOIBpoIpAiDB34F0wNRAhQA7/3y/Cj9hf55Ab8EQQdMCDAJMwsPDZ8Oaw8yEQEVzhilG2ocDRy7GxAaLxbLE+QTzBQEFsEXshkQGvwZQBu/G9EZ+BVVELEJjwfRC/IPbQ/yCywGRv/F/Oj//wH4+zPxAO2s8F31+/aM9VD0D/Z1+Hz6xvps9w70UvQf+Gz6dPdC9GLz9/IZ8mXx6vEk89jyNvJ08+b1tfgm+pH7Gf2A/HH8FP/6A98HkAm+C0wPcRBiD3QN0QuHCgkJTAh6CvkMaQ1jDVUO6Q6CDd8LVAvRC5MKYwmZCtMLVgwLDQ8N7wpdBvoAzvy9+Qf2BPEG7FXrN+2f61jmyOHE4Dvh8+B74mDl4+U35tfpze0J7sLru+pT6wrstuzZ7Pbsse7T8NjxvfHE7wDtiuor6SHq/es67mXx1vVV+oD8M/4OAKr/if6q/vz+ZP7l/gIBjAJ6AgsCDwLFAVoA6f49/qr+gf83AO0CBQaJBwEHgwdmCv0MUQ6YEEIUbBacFyEaixxLHGoZnBfPFg8U7BJSFmwZjRlQGq0cix3pHFIeJiBLIJgerRjnDvcJsQ2ZFDUW8g/MBuv+efvN/ioC6vyp8kfs1+2i8UzxyO9z8LDz9/ZF9zH3aPYp9OXzzPYV+Mf0kfD/7pbuvOxd68bs1O717wbw9e+O8Ur0o/bD+Dn6FPsR/Fz+nQJkB7IKXA5aEtAUtRTTEqkQfQ87DrUMRA3GDmQPsg7TDq8QvBGMEPgPsA9JDqENKQ/kECoRaxA+EFAP0QsMBzADIADs/Mv3ZPBq6e/nH+py6CHiA92S28/bbN3v4Cbkr+Qx5TXooOot6WPmPOfP6UTqu+nh6cnpc+qW7Q7wH+4A6VHkEeMN5B3mZukc7Mzul/N0+Tr8Vvw0/cD+0f/hAHcBeQF0AoUE5gU6BawD6wLxAjAChwBK/yf/bAByAg4EngTLBJYFyQd1CzUPMxL5FGUXYBksG0EdxR4rHl8cbBxqHLQZ0Bj0GigbLRrBG6YelB7+HYoehx0pHZkcuRcwEKALKA1uER4R4AzjB4f/D/mQ/DMAE/lp7cDoUOoF6zrrzuuv6zvtNu888Tvzk/CB7ATtgfH/8mLvdOwb7fbt8uxN7H/ts+237SLvpPGK8iDzFfbk+N37efyr/D0A7gU7CpsNAhJzFswYnRjSF40WBRTfET8S8hPYFGUUNhR8FYcWVxWqE5cSQRH0DrYO1BCNEbEQhhADERYPEgsBBj4B7f7d/q388/XR7Xfqiuue6SviO9qF1nXYW9zM3ajeS+A34vPkeOfY5rniq+D15HDo/eYr5iboaeql67zsm+xo6QTl2OOn5FTliubj6PvrU+618f31cvkN/M78if40AsME1wS/BBkG/QZXBrAEnQPxA38E8QMmA14DhQRdBlYIgQirBr8FCgg+DGkPzBFRFF8XuxrSHEYejB8PH8EdaR5gHbkaqhrcG+oaKhlSGsQcsxuSG+Ie+x5DHG0e7iHfHaoTWAv0CKUKgQ5xEAEN4QBE9EnyrPjm+wrz9+TT3pnhM+X95sTo4ens6bzrSvHW8Q/rmefY60ny/fHC7Cjrh+zP7ITrnevy7IHtSO4+8QrzifNd9nH7VAHvA6UC2wNxCSUQTxTwF9Qc8R9jH54e8B7MHCwYPRYnF4gXqBakFtQXDxgSFgEUZRMKE60RXxAHEVASjRIUEoESEhKPDvgIcQR5Aar+CPyl+U70j+xC55PlteAq1qvO8c8Q1D7VpdW92AXcOd5H4bnjruEp3tneROLa413kNeYD6FrpcOw+7qDqNeVe4a3gJeL14wbmDugb6n7ut/QY+kn+RAG9AkYEGQg6DAcNvwy+DUANxgqcCMUIdgnyCIcHWQbYBtUHBgg/B/IFYAQrBPEGsAvoDz4TQBcaHCIfcCDdIaAhbh8THqoduxumGeMZjRmtF9YXPBjTFR4V/haiF2EX1hgeHLAcqhr3GGYSNwipBckL6hCLDpQILwGj9T/s3+xk8PvrpuHW2RvY29pb4E/nFuz36wXrquwb7uHrmOnI6/rwUPQf9Ojx3+/l7+LxyPPT9Er0n/L18gv1Pvig+3v/dwSPB4EIAgsjD9AU+hqhICskrCRSJOgiTyBUHdEarRjmF48ZtRpWGbUXExf1FfYSxw+sDgENiwsfDOcNHQ/rDlUOGw0NClsFUgAZ/WD7HPoC+Bjzr+ob4tDdD9yH1nHNFMhvykLQntSR1xjaTNsl3MLdP96u253Zfty+4NPibuSc5mrpVu147yXtXuh75Y/kE+Qe5bLoHOzG7jbySfdf/YYCwQXLCAILTw06EaITNRMBEbQPqA67DNsKgAoiC4sKBQmmCIkKoAtdCukHuwSMApcDAQerCrwNrxG4FiUb4R32HqIe/B3KHEwaTxh9F34V9hLiE5kVXhMEE8oUpRHkD8oUyRdWFt0VERffGCkXXA+/BNP/bQRMCxsMLQfJ/z33Lu6H6Cvpnuny4qzatNZS11zbZ+MB7r7z3vHI727wE/HM7+TunfLh9qv2hPZQ9zP3vveI+VD7Pfvc+Oz3pfll/Dn/QgLSBnALLA70DwITexdzGygg4CUMKOQm9CTJIbAdRBoRF8QU7xRqFW8U6RLrEf0QWg+mDC4KAgj1BeIEogUQB0oIbQljCRQHNwP2/i/7//gQ93/0ivFU7IrjO9qf1YzV29PCzu3KJ8xM0PDTXNeB2xjeet2M3ELcMNzS3AvgYOUm6Jjph+0E8b/xOvEG8GDtauna5zTq/+t97Z7xCvfx+j7/1QQGCC0IignHDEYPnxE0FPAT3hDrDpEOwA1OC/UJuAqlCtwIwAdACEYIuAasBP0CEwJbApwEiQjoDF0R6xQAF+QXuBibGOUVyRLGEUcR6hDGEV8Rng/XDw8RlREFEXUQ6xEyEUYPphIXFzIYTRg9Ga0XsRDLB04EnwbSCgIMTwf1/UDxTOYf5mTsBetG4lbbh9qr31Pnzu8K9yP4m/aV97r2YfI98IH0EPsD/bn8E/x8+iH7e/ya/GP8Vfmn9tX3mfpX/ZEAngXHC6APrRDdEU0UfhgIHXYgSiKlIt0hgh/1G5QXkBOaEOwPvRCSEFAPBQ7eDMcLLAoxCBEGpQN9AQ0CxQTGBh8IeAkJCnUIxgMo/hr6VPew9NTyJfDQ6pjisNoc1lvVStR9zDvElMULzgHVLdhU2lHc1NxU3hjiZuIB4HfiXepI7grtZu8y9W72NfT882vyqO0s61HunvEP8njzTvg+/cIA0QORBpIJUQ0uEeoT1RXYF/UXsxR0EToQNw7vCsMIzQfSBn4FkQRiBIMEtAM3APz6E/nj+sj9LgJqBysLcg6VEg4W+BalFS8TtRBiDtgNZg+cD0QNMwzuDDAN/gsXDKYMZQ3CDpgQEBI6FAgZ3x00H6gd2hpWFi0PUQk6DI0SBhLNDHAGc/sP7w3qfeq46N/lA+R84QjiKucz8Gj79gFYAKf8tfg49j334fpu/ur/VAGxAjcByf4R/Q/8Mfus+G/1jfTF9cn4y/zlAN0EDgh8CmUMJw/jEugW8BpnH60iICP/Hy8alhS9EEMOogxQDG0MJwzjCzwMEwxjCccFFgNSAKn96f0GAb8EfQhaC0QMzAqjBkABH/xg987yFvAo78ntwelM4uzYudFM0DLS4tCozF3LXM/p1QfcTeHK42biNOAk4T3iCeNC57/tx/HG8g/1Wvgn+Mn0XfJx8RrvpOxs7xf1XPg7+pH+TASjBg4HbQlUC98LpQ0KEhoWxxZ6FR8TeQ++C5AJOwfXA/YAaADAANf/6/4C/9r9LvoC94r2Kfib+uX+vwS5CBILkw6hEZ0RsA+fDuUN8gxyDVgPvQ+aD10Qgw85D18RXxHDD4gRJBVBFo4X5huWHk4drR7gIv0jFx+RFoIQvQ88E4gXShY1DicByfT77lLtWOwL6p3o8OlS6SLoBe4x+IH/7ADU/Tr5FvQs8v32k/0G/w/9Gv65/5r8mvhp+N33zfT98jnzhvKK8h72L/zRAHsEHQg/CuoMNhFZFWoYOxzWHx4gph19GToUCRAODjQNFw1jDQENLQzbC4kLCQlpBR4DEwFO/+X+BAHkBLIHWQrNDFYM1QdbAqX95vjV9Czy0fDC71rspuV23S7VrNB00uXT2s0ex2rIztFx3N3hh+Kh4Azf1ODd5LvnVuqz7Xry0vXU9q/4EPos+QH2W/Ka8NTvPe+K8b72vPpQ/JH99ADUAgcDSQbhCioNjQ4KEzgXXBaHEkgP4QqMBsoDawEj/yL9x/ze/I/7vfkF+Wb3h/Nq8Cby7fZ5+zcBegahCkIPlBQTF1kV7hL7EaERkRECErET8xTdEuwPghD9EKAPzhDdEmUUZhVdGIcdSyASINIieSjWKWwnVCSSHrwVvxMJG2cevBWIDDwF7PgV7gDt2+zG5NrfkeUI6iPmV+co8478Pf64/er7bfVc8ID1g/4X/zD6MfsC//D8Z/i+91D3yvL97qzwufG876rw1PbK/UoB4QOFCDQNPBAHFIoYdhydH1gh9CBoHK8UfQ/0DsAOIA2kC/ALxwt3C0wLEwkUBBIAkP+Z/uD8Jv5RAtgF9ghnDNYNVwolBK7/F/xm983zbvKz8djuweml467b29Oy0NnS19NO0H/Mk8wd0djYYeDq4x3jduDU38PilufV7Tv0lPhT+vr6bPqo+Bz3lfbx9cP0BvTC8+XzvPa++vf8T/3u/OT8q/0IADUEbQi/DOIQixInEO4LOwi/BfwDPAEt/3v+pf1G/Ez8mvzr+jX4Xfb29KjzRPT09+T8FQIDBxALDA/bEdESMRPBEwsUBRTuE/sUQRaHFokVuxRGFKATwBQTFwwZFRsjG7UbdiCII24kKyiZKtQptiusK0wlIBz4FdEVjhjiF8sS3woZ/yjzl+888Qfv3uem4Sjg3uLg5oXsj/NG+MT51/ql+gf1IvCL9Dn7dPok9+P2Wveg99P3g/d79Cbv0ewc77zwCvDo8BL3Cv4yAiUFNwjRC8oOwBLYGGgdkh5hH8UeMxrpFMcQeg0VDCUMvAoyCSoJ4gkkCn8IagTLAPz+kf5Q/wQBsAO8BjIKSQ2+DYYJXQIi/Uv6FPeH8/3xQ/CJ7BDoJ+Pk2xjT8syczYLS1tJqz6rQGNdz3LvfueJZ5Mfi7+EO5bzoVur87bn09Pc89m/18fVR87bvuu/+8FbwSe8P8fz0ovdh+e77if4UAAsCtQV4CYUL/w1xEK8QJw8qDYwJHQUfAtUAQP/u/J78tf3D/P/5S/ce9uD1jPUN9h34SPv6/ikEiwpLDhkQ3xJRFfMVshV+FXIVdhVmFRwWpxdsGN8WtxSXFYwYAhpTGwgeYx4CHeAe0iOVJ3wnnSYXKdotcC+jKpIieBkpEsMTqBqrF7YKuv1H8iDrJOwu7zHsK+UN4P7dl90M4t/sePbf9+3zIfEw70DurPOs+1j7w/Wy9Hn14fOz8qD0PPXu8IDrteqD7DPtX++g9dv7eP7hAK0FJwvrDuMRshXGGZ0cvh11HRkbuhbLEhgRuRDDD+IMagqGCQ0JfQiJCCQH4wOkAWUBwQFPA0kGwQjOCisMLQztCcMFHQFF/Xb57PR08r3xNO8D633mcuB32KHRJ9DZ0vXSSc5Xy7TMDM9w047bBOI34Y/eLOAs5G3nMeyK8hb0IvLY8kD1CvPb7wfyZfWL9DjyMvLX8/n1Ifh0+t78xP61/1ABIwSqB4cL1Q/FEkUSXg9ADJkKhgmHB/oEgwMxAe393vyZ/YL80Pk4+Rr66fkY+rX9yQFWBBQIgg2VEREUtBYIGZAaXxzwHaEctxrDGrkb/BpzF8oU+BUVGF4Z/Bk9GYsZzhz/HxEiqiSBJJEjKiaiKAkqBypbJzwfdxPPDjwTfRbUESgJfv237l7leOd46/Xkl9lt1I3Xst7i5lvuyvKq9Dj2sPfm9NTuWe9n9bj3MvVs81r0KPbR+K35V/Wr7nTrduz87ejuYvCq9D/7SAFIBREJeg0qEWEUbBiDHSwg6R/vHwgeVBllFDQRRg9HDewLJQvnCocLLQysC7cIGgTuAIMAgQHIAtsDggUpCOMKfwweC7EGiwCd+k32ufLO71ztaerD5mTi69yA1snQ3s1lzmrPSszEyBLMg9MX2CLapdyg3gjf++B75R/pwusp8XT27Pen9jT2Ffb48/bxHPOs9AT0LPMd9fL4bPun/F7+mv/o/+QBcQUsCRENxxBuEq8RCxHbD2MNWAt2CZ4FCwIMAWMBGwGU//39nvzH+3X7x/u1/NX+xQEPBj0K4A2hEV4VsRfQGJkZbBliGdMaZRvBF/UUIBh5GvwWAxSNFe4WahYkGEMcthykGV8bDiAKIUYi2CbQKegosCdUKBAn2xxvD98LhBFAE30MowI29WrlyOAW6Gjp5+CV2d/WWNf72bngF+oK8fHzgfOp71nrW+s/8/D8Pv1F9+j1ivbD9en2lPic9d3vde0D73bv7e/B9Fb7OwB2A5QF3gihDfsRBBZSGs0eayHBIagh0B/+GjEWTRSxE0kSFRC3DdUK6AjPCMAHPQTVAJH+k/1q/nkAegKUBNwF/QWMBWYD2/4J+Qf1BfJ67m7shOvO6MPjRd6V2abU586MypbJzcimxarEt8qC0hLWUNiK3MXfN+F75Xvs4e/176fynvcV+cf35/cX+KP1g/Re93b5Nvm7+fr7//0K/oH+YgAkAvgDrQYUClEOMxLCFbMYhBjHFpkULBEmDT0K4QbnAsf/L/5U/gr+PPxH+pT5hPkZ+f/5Df3c/ysBoAQLCZgMrg+mEs4VZRf8FiUXHBkvGd8W5RUaFvEU7hLcECYRBBNwEsgR+RRdGKsXVBlTH5AiDiDSHuwkRClNJ3wmkSTdGeAMcArWEcgUNg3dA3H4EOlO4hrpte1a5UnZP9ZF2srczeHr6xbwge147nTzKfV38fbzjPx4/u/6yPoI+y/3ZfXf9273RvGQ7cPwCPSm84P05vga/ksAXwIWBy8LewyIEDYYex3qHuAffyGMHxIarhVtEzYR1A2VClQIbAYaBMQCwwFv/0j8zvn3+X38if7y/2UB7gFSAeABCwIO/+f55vXd80TyafGN8KTtjehr41Lfgdop1N7Ozc33ztjNJMv2zB7Tadir3GjheOTz5OrmHOx18JzxuvO1+Or73fq7+aD7Lv3d+3f7NP1y/Zj7uvoe/Zz/Yv9a/r7+iQDpAsgG/gsCEAoSdxPYEwwTExHtDdkKHwg7BPj/9/12/eD8e/yi+8b5bfhE+HX44Pho+uL80/5TAvgH7AzTD2MRBRRQF+wX5haGF0kZQxjcFCMTQBOVEs4RAhLPEzwU1BFlE2cYpBkzGc0d+yPzIw4gESOrKYsokibVKA0mfRkJDf8NPBRmEaIL5waT9x3jcOB+67jreOAD2tzZfthz3Abq4vQj9VHzq/U29T3vLu759lT7bven9c/38/ag9fD4GPpq8zfta+2C7unsOO5c9OL5E/0/ABQEPQenClIP4BNEF8YZ1hsiHcQclxncFHcQSQ0ICzgJ8gfcBiYGfgbtBpIFjgJt/7j9Dv4tABcC/QIEBHEFxgYrCCsIGgTK/Tb5U/b+8wPzLvK07x7rB+cl4yXePNhg09jRNtJ30f7PT9LX1zjcX9/Z4rrk+eSg5tzqjO5H8PHzs/lW/F77CvsY+xf5l/bz9gv5W/lW97r25Piq+6f9Jv45/jUA1gKxBZ8JgA1ZEPkQsRBMEBoOPgwOC3UI6QPF/9v+w/8v/3b95PuM+Uf3h/dL+TX6yft0/t0A6wMjCRQPGBJSE0gW+RjAGMsXRxgeGYYX2BSxFHgVuxQKE/UR7xGPEpAUrRfGGPUYqhpfHDgepx/AIMklGCupKXom5CavJSAcmg+HC3cPiBDlCtMBPfMk5b/iMuqA60vht9gG2PXZK9725nPxSPVY9Iv0M/T+8FbwQ/Zi+1r4HPOl8pj0GPak96X2L/GT6zPpa+rl7ODt2++r9fD7DgBcAwYHhQuaD9ESgxW4GPkbCh1DHPQZEhbqEPENFw3RC7sJcQjwBx0IQghaB44FWQOsAS8BFwIIBEUGTAgSCkkL9gu+ClYHlAIk/a/4X/WN87nyh/Bq7DnoVeT730Pa+tMK0E3PB88DzpPPY9QV2ejbiN8H44Dk4+V/6ePsW+6q8FP26/qp+tb4KPmb+br3Sfas98v4qvei90363/ur/Ib9O/4M/+b/uQHoBO0HKQs5DgAQmA++DfYLtwlABj4CZP7++0L8x/yq+3T6Evrk+cT6+PvH/If+swE6BdoI6Az1EHIU0Bd2GVEZsxjKGMYYBxjNFpUV/xSfFG8UMRMbEJ0ObxDAEvkUCBYXF1oZIB17Id0jwCMDJn0pzyjMJ9AmBiSGH7YVywu7CRENjA2nBhf9k++34k3gnuWA53zgtdgQ17rZG+Al6tH0Ifiy9EPzlPRi87nxSPVa+NX0uvD28LDw1u7l72nynvGd7ArpQOp47E/vQPXL+7j9cv6LA9gJAg8KExUYEh3gH80gNSC5Hl0bahYvErkP0A0+DB0M1wt9C7oL1QvSCl4IYwZhBWkF9QYXCRQL+AsrDKILpQqBCNEEnv8S+uz1BPSw89TxG+2I57fisN5u2VXSmswazC7PrNCjz/TQFdWr2C7c/N7234LgAuP756Dq2uuJ8Hn3Wfn19fbzvfT68//xhvJ59AL1RvWP9uH3R/oN/Tn/IABAAcgDBAhNDZER2BOGFDgUrxPhEesOvwz8CgIIvQSRA90EVAVVA/gAvv7s/PX8O/6H/+EAiwNuBm0JAQ0NEA4SuRPIFFUUwRMsFewWZBbtFC4UoBI0EA0QpxDHD9UPMhH1EUES1RJtFNQYGBxbG6Ub+h3AH8Mh3yQ5KDknWiEbGoMPMwjbCvIP+wx6AqP1sOi05JbqBe7G54PerNr+2jPe/+RP7wn21vW79Vn2G/RK8Q31XPtl+STzevLi9Azz6vF/9NLybOxc6DnpZ+oq7N/wofbv+g7+SQMVCY4NOBGxFD4Y+hrwHfsf0h/dHZoawxZwErIPzw6lDZULmglPCacJqAgGBzgG0gXiBEIFQQfuCOIJMwviDF0NpgvRB3oDkf7G+Vv2e/Qm8hTv6uqq5qzipN6R2b7Spsy5yoTK0sfqxqLNAdZw2aTbE9+d4NHiQ+hK7eXszOtU8Rj3ivat9WP4Mvqk9wf1TPWU9Ur1j/Yg92z2DPeK+XX8vv7XAIUE3AmoD30TpRSfFeoXPhi0FY8Rlw7dC4kHYARiBKYEkQPtArEBbf8i/vT+c/94/k/+ZgBTA74G1QoxD6ITWhYtF18XPBdnF4gXNxbFE5sSQxIVEcsPsQ2bDYgQsRBQD9ARbRRTFXkXVhkxGj8caR+aIogjkST8KL0p0yHGFYcOAA/0Dz4NCwnJAIbyMOfh5XDoweZM4s7d+9iW17PcxeZS8e3zQ/Pm9fH1fPLB8a32O/oK+PL06vTo9Hv08fWv9jnzru3U6tTqbOtM7jn0JPqI/bQABwZJC4sPTBP0FiMa3RwgIMEhVCE+HscaDxeZEkIP2g21DNUKhgm/CbkJtwhhBoMELgMhAl8CXgSpBkgIxAkMDPQMHApjBfwAJ/xL9wz0KvKr74fslumg5s3i/t1X2FnSGc2Yyj/Mg89+0RTTlNWy1/zXatqf4P/mTulV6E7pOe0s8yH50Pxm+032hfQL9rP1ivWt+Rz+6vwV+RP5c/zb/s//wwFAAmUCpQYBDbcQTRLQFMsX2RZNEm0PIg35CTUI6wZCBRAElwMaA7gAl/4C/0EAYv+V/v7/5AELAwkGwgrWDRQPfhHsE6kUvRTyFkgahxlDFrEUtxN0EqAS/xA4DWgOiBGREQ4SMBQ1FrAWgBiFGsUaFhyBIe0mByYKJQUmSCLaGIEOEwyND2QOZwhrAXX1gear5EXraOnC4Ova5Nde1sDayORx7mfyPvLt8zn0//HD8eL1yvmm94P0Y/XS9lb3U/pb/I/36vCz7f7tIO8Z8cn0jvln/Q4AHQUeCwAPBhIOFcsX/hpQHjMgAyDbHScakRYjE+QQ6g8cDt0L9QmlCTwJiwfiBVwEFwLD/0cATwK8A5wERwboCA0KtAelA+b/rvs79xj0TfKh79/rxOiF5gvj2d1K2BbSHMxpyaXLdM+u0LfRKNbp2nPeTuJv5jfpOOrl62nuwfDR9If6iPyZ+Vv20vaF+If3cPa5+Lz6Z/lR+c37rP7c/5kAGQGFAOwBXAd6De4QpBLYFBIWIhX6EhUQSgzpB5ED3P/h/Z/+AACg/8L98Pys/pj/J//B/6QBvgM2BaoIZQ1QEBgSDxRHFRQVyBVjF6kXgxVDEjYRgRLjEcQOmgxRDXINPQ6tEXUT8hMXF1sc8xx9GugbliFAJgAlESOMJZ8m8iRaICgUjgnBCP4K1wgkAL/0uOnO4Brhi+WE433eKty42vHZJd/u6pb0mvXV82318vQ88r7yDvcp+A70V/Je8/3ysPO19fz0EvDx65HrH+1x7qLxpfYE+/b/wQUWC2QPtRNCFwAaLBzLHhYhYiBBHckZPxbFEsUPtw25DNkKhwjZB5wIOwirBqQF+gRgBOsDlgXXBxcJ3gn5CpcLGApdBh8Cgv1E+PzzTvGj7uLqRufE5OXhtN2A2RHVT8+WyuLKEs980vXS9tRV2UDcD9+h43bn4+hx6VXrG+5O8Mrzjfcb+IL1JvNO9Bb3Bvgn+O35d/us+k/6dfzl/jkACQLIAx0FNwgwDfgSgxY0F8EWFhbaFI8S7Q7dCgIIlgWyA6MDSgSFBDsD0wB3/23/qP+o/9P/VAC9ASMFOwrCDuQQ/RGQEyIVcBU7FdMVlxXiE1cRrg7KDSoOxQzlCgQL8Ax5D+0RNxPsEp4TYRe3GgMcGx5TIO4hdyQkJh8mZCTtH54WTgwUCPUJaQkuA6z74PF95u3h5ONT5KHgwdzb2lHaV91O5v/ytfgx91/26vV19KPy3vR99zr1BfNi9Db1UPTR9P70ZfFL7Drqyerh6+/uvPPN+Cb+NQM5CM8MQhCIE7IWvBnMHIwfHiBMHowafxbFE3EQYw2VC2wKLgk5CMkInwmQCEcG/gSNBPwDRgRHBqwIMgoeC38MpAwuCfgDlP/++sr1w/GR71DtPupj55/k+eA43PrWLtEUy3nJec3I0XbSGtPr1t/aot704jzmE+ev5pvoNezY7ijymfb492f1mfKz8nv0KvZk9zz5GPq8+tb8Pv91AV0CzAK4A9cE+wYOCzQQehQ4F2sXuBbAFYoTLhDFC7QHhQQdAh8BFwErAQwB3v9N/u3+ZABnAY4CLwQ6BvQH8wrhDi4RyBGuErMTJRM3ElYT2hS8Ep0OYQ3WDZoMsgrgCVEJlgl7DNUPHhLNE2EUiRYjGzMdHB0qIMgj6yMJIxMlHihNIzYX1wv5BXcFTQeABt8AAvSK49bf1udq6BrhP90h3IjYVNo75TzxX/Yg9nf38veb8yfwf/OI+M71J/EZ8ub0APTG8yL2SPQ+7knr7+vC7O/uvPOc+LX9wgJWB6ILBg+eEp0V3heoGh8e2B++HVAaABdpFLkQNg38C8AKKQgSB/oI/gpVCrUIGQgUByMF7gSbB8oJMAqpCkIMiA2oC8wG3QCF+m31dvNL8jnvoupd53flveN44KzbzNVFz6PKBcvvzknSx9OJ1qzadN1V4JzjjOYq5/jmJ+oJ7g7wNfN69mL3Y/X28631Xveo9xP5Tvt9/GH9vv6PAPABPgKdAvwD+wZzCQENgxLVFjgYvxYgFbcUMRP+DrsJRwYlBCYDKgNsA/0BFAC0AFIBPQDF/xsBxAKZA7QE4wfSDHoQthEMElwSSBMqFGMUgxODEsARAhBuDTULWgvsDMsLDwlCCewMkhBcE10UDRT6Fmca1RqEGxUf7CKXJD4mSyb5IlYdtRPSCUQFRQabB/8ClPib66Di/eT46c3mNuDu277ZptrW3/zotfLI9hP2LvZP9jT12fR09zX4G/T28U706PTA84v0QvX98fjs8OqM6xntRfBp9CT5tP7VA38ITAx5D4kSAhYBGYob6B1YHrQc3xlGF44UBxEFDjoMKAofCIMIfgr1CvEJ4ghSCGgH5QbTB2EJXQoWC9cL+QwmDeEKSwYhAdP7UPd79IbyTe8m64Ln3eT84SvefNnA063NSsq6y8vMbcmfyCfRStsP4HzhkuJQ4znl3OrV8CTyw/Cm88T2h/RN89f38vvn+W73Ufkx+677Cf0g/rL+jv9aADoBPAKABdkL7BL1FUgWzheFGV4ZoBd6FI0P1woICCcFUQJhAVsC+gFSAGwApAFfAjICQAEpAeQCHwX0CBIOaRCUEKAS1RVgFgAWGhY+FAcQ5AxaDJULGAqmCM8IKAm7CKMJdwy0D2sQ6hDAFEUYxBh6Gxwg1SGKImgkBSYyJgUj7RnsDGgD+APPCO0H8ACE9WHnTeEo5xHqC+M520PaKtxk3bTiju0W9zX4wve9+bD3YPPj83v4x/dH8qDxPPUh9Y7yh/Pt8znwbuwN68Trqu0G8TT1i/oGAL0ECQl5DM0PCRTOFyEb1R1PH8keZByyGfgWbxPZDq8MxQsNCiUJagqODH8MbAqMCOEHSQenBg4I+wn5CjMLYQwmDucNYAp7Ber/6fmv9ePzDfJA7s3p/uW/49jgStzO1mfRD82dy/TNsNDi0BLQ/tBM1KHZXd/i4xblGOTA5LXqPPJL9mL3aPZZ9fTz1/NN9v/4AfqU+W76xfwa/jv/6gG2A6kCWwJbBR0JlAyNEWYWDRirGBAZNhgpFpYT8A9eCwQIEAcXBgIENgKoATUBiQDAANMBFwJrAUQCSAT/BXMI2QsUDk0OzQ4REdUSmBPPE3ASAhAqDhsNVAuzCC0HuAeDBx8IEgskDRsPsREmFZgWQhdUGfwdSCLZIA0fcCAcJLAkKyEGGqIM/QFoA1MJyAbI/f/1e+xf5MHjaeak5VrfNdqp2avcJ+Oi7KP2W/nL96j3uvdE9cDyqvQP9WXyPvE38yf0f/Nr9GzzBvBU7Z3s5+wK7QrwEfU1+38AFQXtCSwNCRA3E60XpRvJHe4e2B4vHS0avReHFekRfA4fDKQLKQyoDLkN3g2/DOkKKglECOMHcwj6COIJhApWC9gM9Az1CaAEqP7m+MH0JvKV72bsLuhB5GLhCt8I21HVz89wy5LJBcpHzPrPqtO01gfZ6tuc3mjhGuWA5y3p9epr7q/yXfUF9nf1/fXr9oD2hPWd9uD4kfoP/b//DAEBA6kFXAcKCJQJxg0OEhAVJRcMGaYa5hpWGWwWExMjD5oLhwj1BfYEsASbA1QBVAC6AKgAFgAgAKAAoAGVA9gFngh3C34NmQ4rD40Pww9ZEBcQow5xDGQKtwlPCrcJKAfIBpQIwwhrCf8MIhFpEBYPDhOpFzUZWhnHHWogQB9VIBwkmycYI8UathHFBVwByQfRCx8FefjA64zjf+aw7Fnr7OKH20Xa7Nt24STrlPSP9gT1pfbp9zD1ivLu9bH4tPP57yLyqvNd8RPya/T78WntbuvA7IjuOvEf9V764QCUBcoJ3g3hEcoVLRkiHX8goCFkIDYeZBxtGkwXYxPSEJAPJg38CwcNdg4sDrEMvgsQC+sJXQm2CrQL1QsxDPIMzg3FDGsJnAQG/wP5TPRf8n3w5+wc6M7jR+Gw3iXb3tVE0HDLZ8rHzILO8M3uzJzND9Im2eHe3ODR4S7jIuX46aDwJPbE9ub1l/aA9t32yPn1/AH9wfuv/AAAQgL/AvoEjgYgBrEFLQeVCmQOOhOiF1EZMxmDGe0ZcBjjFdESPw5NCpIIMwdKBK0CwgLKAiEBmv/o/64ACAGaAXICeAMyBRQI5Qr8C0YMQQ7KEH4RPBBNDvILeArWCScIIQW8A/YERQdeCK4HigkcDr8P8A9zE2EXZRhNGbgcWR9HHfke7SZAKYsk6x/tGJwLHAPOBrwLDAfN+2jvJ+Np4OfnuOu/5hDez9o23cjhc+mC8q73N/eg90r42PX18uL1lfrn9z3zXPSA9QXz8vH+8yLzbe7H6v7p1OoI7SDynPh3/9MEmAj2DI8RnxVWGbQdTyBFIKMfAR9pHqUcdhnrFVwSMhBaDpYM/guQDFYMHAsPCioJnAi5CGsJjwpkCxcM9gzKDWMNjQpJBkoBj/sm9oLyP/B77UjqHea+4fje4twR2VbTC874yVLIhsq3zbTP8dFo1h/bVt5P4crkLueW5+jpbe7i8dHz6vWs99v2bPZX+WH80/vG+rv87/0m/sX/CwMnBHQDOgWcCHULqg5AFJcYGhkMGVoaeRrBF9wUNxI5DgsK2QejBocENwM1A6UCrAB0/ln9Ff0Z/cr9Ev8YAJQB0wReCGgKGQztDcYO6w2EDBoLggmsCNwIdgknCFIFKAMPBckIPAkwCXcLpQ1jDY8OWBLZFicayhwGIRghgR3XIBEpayx+JrMeKhSNB8IDOQgGDBYH4/0W89zntuQd6t/sxOcV4FLbQ9ob30no2vI4+Xv4RvmJ+gL4wfSf9tj5gvYd8mPyR/M88lHyXvQK86nufeqa6c7q8O0m8z75t/8pBI8HjAzGEZEVtRjvG4YeRyAIIYkguR/JHSsalRWXEQYP+wxeCz8LjgzeDC0MAAwcC4cKLgo2ChALtgtrDDwN+w0yDeEKrwZaAbL7AfaK8cbu/+vr6BTlJuFD3p7bAtcb0TfMSskTyj7Nvs8A0avSedSo1PvVGduj4cDl9OYu6G3qu+5b9Sb6rvry96H2DPeT96T4BPz2/kEAfwGbA9IF0we1CQQLHAszDN0OuhKwFcsX0RkhG/IaDhmuFiQUOBAvDL0JsAcwBU0D6AH4/hf8/PqP+8/75Pt1/Hb9z/5oACIDAQa0B8EIEgq+CoQKDwpRCs4KAQpYBwIEkAFPAogCUQK6A+YEkAVFB90KjQ54EUQUSxX7F48cwCC+JFkmnCVlJX0rNC57JREYxwykCMQJAgxACdH+Be974iLk4evp7JDm9t863JnaUuJo7wX5fvr5+RH8pPus+BX24/cg+qf2GfIn8eTxCvEP8WPxze2X6NvkneSS5njr/fGL9/P9wgPqCFgOnBMXGPQZ4hvJHQogkiGXIKgeTxz3GBsUhhA5D+AMRwphCTkLawwRDAYMYQyGDIULPAxaDqAP1w8bD+8O/w2LCzMHpgH8+vTzre/e7cLrn+d14sHeMNz41wfSx83by7zIgsaDyJrNqdGp1c3apd1F3rrhh+g86x/pIek97Gbv/fH29Nb2KPY49Sr2tPcu+Zz77/1J/pX+XADSAgIFcgeUCSILxg0TE9QX+xj9GAEbtxt9F8USzBBPDioJNwRVApQB+AC8AGgA5/7//Bf8ufzW/UX9bfzO/WQA1gG8A4UH7wq2CxQLeAo9ClMJqAcVBisFrQKs/+D/YQIcAxYEOQi8CuQMCRDLEi0TtxRQGm0ebR5UHcgf5iLII+4lTCkIKyQnVB2VEbEGegKnBUMKSwbT+mjwSOrc6m/uae4R64jk8t493YLj9u3u9U36rPoq+i33QvST8wT0lfME8Z/vKO9X7j/v/fGu887vpOq06EHpA+vi7nn1JfxVArgH8AtKEMsTLxZ0GG8aYhxlHjcg4SAsIBod8xgyFfcRcQ8TDWsM2A03D3cPww/mEHMQMw/eDWMNpQ0yDWEN+w0DDoEMGgoNBqr/QvjH8WfumezT6XHmJ+MN4D3drdms1I3PdsrIxG/CJ8XDyenOBtRK2BLaStyP4armGujv5+7pI+0A8MTz5ff9+aX5o/o8/M37kfuE/T3+X/yt/JkAOQPXA0oFxAeGCSANiRKwFUgWWRfrGcUaLBgHFRAThw8UCgcGWATOAkAB0wBJAAz+Avxe+6n6H/nB+L353/pG/AT/bgPrB/MKjwsgC+0KnwrLCFcGfwRuA9oCVwIDAv8BWQOrBvQI5Qc5Co0OUA9aDwQT3BgJG0sc3B/2JO4lEidCLJos+CiXJ/IkEhmzCLQDwwjKCqAFHv6Q8sXljeWf7/Dxduir31ndut1g4n/smvU196b0W/bL96b0dPOK+C/7lvRF73XxePOM8kvya/Hz64PmWuX/573q2O6/9XX8HQLoBZsKhhDeE0sVlhfuGrwdrCDCIykkzCI/IOobBBd5ExQS9g+EDaYM/wyrDZEOiQ5LDegMyQy5DHoNTQ6TDn8OUw7DDGsJ1wT8/hX5LPN87wDtrerC5wHk5N8y3NPXLtIfzfbJaMcDx67I8slfykTN19NP2q7eh+HK44bk6eT66MHuevJo86D0bPbx9vL3i/oT/e/9EP60/kkAyQE9A1cFKAa2BD8EUQaNCo8OlxGfFGMXjBipF0gWTxTjEdUOZQnVA0oB7ABqADEA/P/p/hr+Bv6T/j/+Xf0R/ZD8SvyX/QQBKwVACDwJ6AmiC1AM/gq/CFwHwAZtBawDxgLpAiUEPwdYCKUG/AgQD9USxRJ7E6QXWRsGHVEf+iHJIZIhUSYDKw0pvCQVIrEY2gjpAnMJpgzpBk3+D/LW5DjkPu647wTmAt7v3Efe0OPq7Xn3gPoB+ef5S/oo9ojxf/Ov9rfyDe/X8GPyf/D48P7ziPGl60vokeg46r/uQvUp/IUDqgixDJMRvBXOGBkb+B1HIHchEyJtIUMgkR2oGSAV/xGxEMYO/QwBDgcQ6hDoEBMRfw96Df4LzwtdDJgMMg1JDfYM8gvFCJcDIP5G+KDxEOy26G/mJuQD4f7d49oh1z/SGc7KyiTHzcVdxxfKPM3j0trZ5d3X3lfgo+MG5gPo1utX72rwL/HI81v2ofaC9hb3tPc1+JL5vfsK/pMACgRwB0QJyAkUC/QMdQ8MEvYTBBb9FyQZURhsFigVUxSjEJUKkAVTAgYAJ/9A/8L+Yf3B/BH9w/xE/Dz9qv6H/mL+ywAPBTEIMgmpCdIJFQnbBxAItwidB5cGfQe4BvwD3QQZCGIItAd9CKUJKQsjD6gTExivGxUetCDYIkEkaCR/JRYnnycHJoAftxQ/CpsHWgvgDAkK5wMX+IHpW+Q16Yvswenm4gXdDtuO33LrF/iV/V77qfrB+6H5I/UE9JX22fQE8GvuwO9g8AzxaPM589DvvOsb6tTrBe8Q9EX6QAFHBnEJyA7tFKoZOByzHmIhziIwI90hSR++HFEZZxRIEE4PmQ5nDbcNIQ8XEDYQFRB5DwMOgQyqC0gMcwwKDP4LLQzDC+AICgT0/vP5+vM67rHqyOev5AHhZt3K2eTVodGKzoXMmMk9yE7JKMsvzRXRi9dN3XTgd+Ic5ZTmdOdZ683w1/PV9MH1VPe+92P4yvmL+vz6SvuM/Gz9Sf7g/1kCvwQmBvEGVAhWC88OzhHzFPoWkhc/FisT9RCUD/kMcQg9BFgBb/8M/6z/if/v/bf86vzU/O77fftn/Jn9ov6JAO8DJwhQC9IMzA1HDQQM0ApPCskIFAcHBoIFYQU1BBEF6AgcC20MYg7uEOQQEBL2FnobVRzlHTIjCiSJIdEhACiBK7wn9SJaGjANUwO0BNIJogiXA8/7SPH65Rzl5+z+7RnnpN6z243eY+Yb8ZH6zvyL+vr6O/pq9t3zTPTW9Rrz1/CO8b/xofKJ82rz4vGr76ztVu3X77T0TPsFArgHbAsbD1oThxYpGuEdDiGgIk8jdiMxIZ4e+RslF48S4w5jDQ0NVQ2LDlgPyw+UD0UOQgwWC34K5glNCg4LMQtmC+MKwwi0BET/uflK9MrudeoO6HPmo+SD4bPcRtck0nzOYstiyIzHXMm8yxPObtIr2F3dLuFj427k5eRN5wHsvvDb8wT3W/ku+i37Avyw+376z/ts/a/8H/yx/RgAQAKJBEMGmwbGBpgJNw7sEPgSxhVtF8AVJhLwD50OIQz8BwQE3wA1/j/+uf/4/1H+iPxB+2j6IPrX+rP8lf7w/w8ClgWOCYoMuA7HD+cOmw3sDEQMGgrCBwcJawl2BkoFPgnfCrsIOAx8EQcRJRCVFQEb5RhBGWogdyQEIYMh2SdbJ2YkvyWlI7oWuQi5BdkHHgfXA6IBuvq87MLksOi87C/p5OMa4ZHdvt056Gv1tPrY+KP53fuI+Vf1P/S19cv0IPJz8RrzQ/Oh8kb08PQ08rPuzu418HvxE/bo/PoD8AgCDCkPNROzF8UahR3vIE8jMCONIQEgwx3+Gg8XGBIhD+MNsw0cDjEP8g/rDjEOQA17C5oJngixCW4KlwqLCoAKegrRCAoEB/3A9gvyA+9y7MHpjuZ/4v7epdxN2VHSzczVybbHLMflyG7MWtBl1XvbeOA848TkUuar51nruPDf8z30JfTO9df3xPnx+uT5lffv9nj5Mfuu+x38rP76AV4DYgSgBagHfQvTDyMTdhXuFv0XHRcoFKsQUQ0JCjwGDwJq/lf9H/8bAWgAff4X/fL7+vq8+kb8Mf4M/9r/EAPrB/4LXg5lEGwRlhCNDy8P0g1FCn0HwQhfCWMGVgTrB+YMswxCDUQPXBIHFFQWEBrNGo8cAyBSIYMg4iGqJQYobif9IiUblA9hBv8GzgliB08Ch/tV7rLhveLT7KztauQz3qzeh97q4kPvLvnr+av2Ovj2+A31a/Kg9bf49PN077PyqPf69xj37vdT9mvymvD78VrzMPWO+Z7/kQbLC6YObBJ1Fh8adhwwH6IhXCGMH04dZxszGq0X4BMgEd4P2Q5oDlAPsxC7DzwNlQsaCv4H2gasB1YIRAhOCM8IyAlnCaIFif8X+UT04e8t7BXq0egV5rLhN94H3PTXc9Enza3KEsjMxnnJcs4o0gnWRNwh4pDja+Nh5hvqDu2V8Db1cvnZ+sn7uP3G/Sv8f/v++jH7zvrv+V766PxAAZcDsgP4A5QF8gd0CpkOwBIQFZMV+BW8FUQTIRAWDskLZQbLAEL/WABuAIX/xv5X/YX6v/jR+GP5h/op/MT+7AEjBREJ6Q1dEc0SHhL7EIoR2RKlEdwNagvLC9ML7gj6CBQO+Q3UCVoMWxBHEjkV8BomHCIZjBsRI68lTyAJIj0o9iiXJBAdLBSmC3kIqwq+C0AGovvC70LjI96F4lPne+Z33uDYtdkz4vvuVvjj+pr43/eo92b2sfV598z6Mvma9S71Qfan9gP2pfUD83jv3+wS7efvcvP0+Cb+KQRlCXcMehG2FlIalx2qISUlByabJDMh9B0RGxcXfRNGELYOtQ1/DNQMwg1EDS0LpQlUBy0FVwXhBmkJHgv+C6IMNA0rDBcJWAS6/aX24fB57QPsrunL5jLjQt/b2tHUU88WyyvIXccNxy/FocTgyq3VNd5O4objAeSA5B/pi/DQ9ez3jPnX+n76APtt/L/80/q7+dL5fPlD+sn8yf66AKkCiwSRA5ACkAV6CpMO3xKcF6wZNxlsGN0WIRTFDxYLDwZGAdr9zvza/UP+F/3B+/H6gPqt+Yf6TPxN/ncAFAOfB60MoRAGE9YUWhZ5Fq8UvhJhEfAP5w0BDQoMpwrDCKoHjwoHDgUOfA5/EusVWRhwHSoj8iTqIWYhJiY7J1wlfST0HW4OHATLBIwJ6wnNBJb/d/Sg5cffzOTJ5YDgc9uh2ffZWN736776p/0L+vz6QvxC+Gv0IvYJ+d32PfMh9Nz1fPWc9R73NPbu8djuxfDK8yr2ePqi/4ME/gdqCl4OfROCF70aHR8CJAMnEyY4Ig8eCBo5FYIQ+wyyC1ILsgo6DA4Pqg/7DQIMKAl3BeQCUQNVBkoI3AjtCUwLIAsJCQYFUP+1+BXyie1m7NbrEukC5ffg5d0q2SfTqM85zBTJWcr1zu/SItad2fLaT9py2rzexeNx5pnoHusD70T1nvzZAIcALf4/++T4k/fJ+Lb6mvtW/Cr9wADLBJsHzwi2BwoHjgieCx4O0w9LEWMTBRSiEpIQGg6NC/gHHAQoArsC0QPUAkMAeP2Z+iT5lPml+fP5afyuAHMEugcPDOoPHhIGE5sRnA86ELAS1RIBEcMQ3hAJEXcQjQ81DzYQ2w9mDmYOEg/hEjQX1xmQGsobsiCeJfQo1SeoJOEjxCAtFjYJAgTgBbUGMgVzAd744+tM41rmWOmx5DHeAdrt1ufWo+Fv8W35n/bF9TD6Ofoa9gX2MvqM+bv0nfMw9RX23PWX9xf4cPMl7rDtY/Kv9oL6jP/VBNkH3AhiC/MQIRalGKgaMh8NI+EkYCQ2ItgeWhktEygOdwwVDNkKhwvgDGkNbwwtC3gK8AfLBEcDNAVDB90HbQj5CV0KLwiNBEkAsPo79HTvfe0O7SDrq+eO44zffdud1RvQwcymydHIasu8z7vRhNK11IXa1OD/5GPnUOa05YPoLu+i97H8U/1O/Bn8cvpz+Hn49fmf+q/4B/kh/C0A4wP/BowIMwisB7AI5QvlDpcRhhTuFuQX1RWVEsEQkQ75CY8EiAEAATEA2f7r/Uv9bPsk+Tb5gvq2+iP76f25AQ0F2ghkDkUSSBMbEyASOxKYE6gTHhENEF0R3BAxD/wOrRF3EP4LcAuhDsoRQhMNF8wcax9nH2shMiZ4Jnsl8iXzItMarRC8CjIJ2ggzCOkHIQV2+sLrTeG94qLmEeMo3LjWxtXp2XTlUfIq+XT5rPi/+Gz2QfMp9G34/fi08/rwn/MP9hD37vdq91b0ffDE7jjxA/bp+kj/pwKaBd8HQwvuEI0WQhqqHVIiUiVtJcAjcyHBHRUY6REkDcUL8gvlC5AMGA7hDvIMtgqWCGsFEgOdAvoDwwXrBn0IYAoUC4UIIQSo/yj6RvSX70ztDuwK6YvlFuJi3hnZENNszxjMash6x9rKds+L03HYmd2p4OHhr+Mp5oDnlunc7dTyMvaE+cP8wv6J/rL7iPh798n4Hvqj+uP6v/vR/iADPgbCBswGEAhVCrMMhQ/ZEu0UtBWEFJURQw68CxoK/wUgABP97f0l/+v+0f5s/qz7e/iI+Br6i/pr/AABzwTSBjUL6RHlFXMWJBW7E3sSzhBcD3MPJxD0D54QAxF3D04Paw93EFISehREFAMVlRkHHAweuSHxJhwnVCTcJdUn5SOrG0kSOwgSA2MGEQl3BRz9AvQG6nrhkuAO4oXh/ttU13vX6drr5Lfy5Ps8+dH0kfbU9SnxRfD79Xv3wPJt8Wrz0/S09KH2Vveh8yDvRO0R8jH3NfsAAZUHSQupCvkM4xK1FyEbqx4YI44lqiUVIx0f7xvgF+MRKA2RC/EKagphDB8PNBA/Dh4LcwgPBfYB5QCbAxwHMwiWCDIKmwrrBkwBlPwa96bwuuvh6fPoEeet5MHikN8s2anS3M5dy5vHhMffyzXQ+tPE2bHfquHn4QPkUuWs5Q7pIu8Q8131T/mZ/YH/wP6e/CL6lPic+I74W/lF+17+RgKeBWAHZAcaB7cIQwtJDVAPIhK3FPEVyBTCEmYRaA6dCQ8FTgF2/vP9a/9yAKT/Bv5x/N75KfhY+Fv5TPvZ/q0CjAaJC18QbxNTFAsU2RL1EGIPDg/8DiEPfBD5EWISvBEIE1oT9xFsEaYSfBSGFBMXuRtTH7MfuSGmJQcn2iYNJtgiVhrMDcMFSwe0C/MK7AWA/U/vHePX4eTneea03rvYStiM2eHd+OiS9Oz3bfSX89f05fLJ8XH1r/lg92Py9vMJ9rT0i/Qc9vL0CPHc7UjuufIx+Oz8dwGbBpYJMgp6DdES5BaSGjofRSNKJQwlACLhHaYanBaIEZUOdg1/C2AKXQxUD3YOmgvkCN4FIgPiAa0CfgU1CN4Iywj9CZ0JawXP/6X6lPR47+nsR+vH6cTnN+VP4eLc69Y60avNQMpPyLLICMzL0G7WH9wm4Ini8ePU5Bzl2ubA69/wI/TW9on6Cf2R/ST9uPsU+iX4K/iU+Zv6Ifxk/ssBkQTwBRkG3waUCA8KDQ3EESoVZBadFSYU6RG3D88MAQknBEUAZP7P/q4ABgFx/2z95vu++qH5jvmh+qf8lP+bA4UIqw0aETMTuhU9Fo4TOxLnEqMR6A9ZESASAhBED68RqhJOE9wTmxK6EokVMhipGD8ccSHTIXEhHiOxJTwmySTcHoUTkgi7BRgLAQ4qCSH/4fNU6QXhMOA8467hZtrR1p7YpdwH5/bzU/nx9rf0APTi8T7xQ/Ov9vr3cvaK9QX2tPar9q32XfUI8SzuZ+5v8Ur1tPpKAUUGRgl9C58OaBKyFmcbJh8PI8clViWKIvkedxrKFRQSLQ8PDb4L1wsHDZkOKw8BDWEJhgXaAfT/bQHNBIUHtwiQCX4KWwoqB8kBAPwe9rzwN+0R6wbq8+g05+/jWt9f2UHTTNBbzvHKqslmzCXRitUM2vHdiuA74p3k7OUt5hrpTO4U8/X2svqi/Jj8E/wS+834avf0+I36U/rv+jP+/QKTBs8HgQfOBrMG8AjiDP4PjxIUFTsWfhS2ESUQOQ66CksGyQGf/mL+YACuAGv/g/7s/P/5RPkp+6L8K/+WAnsFZwivDJwQKxLAEmwSJhH6D4oQARE2EE4PDxCuEo0S3g8bEDkSxxDVDlYSqhVaFuUYPh+MItgf6R+5JW8p1iU4IosdzxLlBtYCSAkHDrwHGf+/+PbtI+NL5IvoTeRP3KDXu9j42h7hwe3W+Pn5UvXF9DD1M/Pq8WX1M/gQ9FLxrfLE8w70E/XK9VjzVPCR73vxX/Xp+s0AsAQwCZMLdQzVD1UU1Bg/HRAh4SNtJSkk2B4GGoMW2xE+DUwLUAuWCV8J0gyYD9EOigxtCYQFJAIAAV4D+QbLB8sHFwlpCdwGGwLe/AL4PPKi7Xbrw+nI58LlYePy3tDZiNTH0LHNAcqwyNLLFdHA1izcLuAl4mfjJuUp5sLlNOc97IXw7fIU96z7MP3o/KL8Sfpb9qj0d/Uc9uP2xPpmAB8FZAd5CJoJ7Qp3DGIOCRG5Ez8V2xXTFbUTSRHXD0cNfQhMBKYBJAAiAI0ADADb/ub8SfqI+M341/ps/ccAkQTwB68MoRHHE4wUbhUDFcISVxAFDqwOBRHkEAURXBJUE/kQow4mEYES8Q6VEYoYQheQF88dvSKQIs0gWCTcJhUifxr4E/ALVQZ7CEQMLwvTASLzTuYK3sne9+P24tDc6tgH2Uvds+es8//4t/Xp82X14vHW7ovzSfr6+i/3C/aN9xr3v/Tu9AL1tPA77GLsAPAK9DL5HAAYBz8KvAr/Df4SiheQGugd4CJeJRIkDCFSHXQZTxQJECYOYw0fDMIK1wo4DO4MGguMCBUGUQMiAIMAwQRiCMwJfgqyC3cLNwfpAC38avcE8Sbsp+pY6XTnrOUn49fegNjj0rzPhczgyc/J28x60cTV6dq334PiKeX65u7mvecP60vvt/IT9W34Zvt5/A77W/n896v20vXQ9p74u/n8+7wA/ASKBoQGdgfUCUELlgxvEPsUcRetF50V8hLVDwsNowpWB94ClP/+/nP/z/+w/4/+1vxF+9P6EvuO/X4CNgZKCAoM4hDDEz0V0xbmFt4UIBIFEJQP1w9WDz8PXRDxELYOyg2/DwQPNQ8VEYcV5Rh2GFMc7CKaJfQkuCSDJWYkGh0mEk0K3AakBeAInQo8Bbf5vOyx49bfkt8q4AbfJNk01W3XS+G48Mj52vkQ9/v18vQh8QDwg/TV9xX2bfSm9Nf04PQf9UH2ovQ777DswO8O9Kj3yP04BSQKOQuyC54P3hT9GC0dEyL/JWUmDyMuH3gbORZKEBUMSQrtCV0JbAqMDAcOpw2sC9oIHwVvAU7/wQEuBsAHVghNCusK6gg4BZMA8/pz9B3uiurH6bDo4OZD5HnfT9kb1JbQq82YzGfN3s79zs/PddPf2R7hruXf5TbjceIO5kXsmvHO9f349/n/+VH5tPeT9sz2HPae9Pv1rPob/3YCTwZTCesJJQkdCUEK/gsUDxUUfBjVGTUZKBgSFjUS2w4lDG0IZARhAUcA0wATAhMCagDC/lH9jvzp/VoBgwSmBxgLxg56EdoTBhYjFmoVMhQCEmkQxBG8EisSQBNtFD4TRg+nDQ8QJRBmDyARVRRVFZwXyhylIjckAiIuIigfMBSaCD4F8gfQCWYKbwlIAiv00egO5vTmM+Vh34jZytYt1yXchOed8q/2WfUM9CX03PI48c/z2/fw91n1rvRD9xv5Ffiw9yn4T/YJ8qLwrvNz+IT89ACGBlkK7QryCzIQORWKGD8czSHNJWwkaiCwHXMaqRTlDlQMMwuQCe4I/go6DVkNjwsFCfkFPAJi/17/mgG0A70EmwZxCHsIGQbiAej8hfcF8jLuZuzF6qfocOVa4dLcftjp00HPYc30zVTQ0NIK1wrbBdxv3HffwOQR57Xni+gn6sjr6e+X91f96/1b/BT71vg69k71MvYA+G34Ufm5/NgBoAV7BwQICgjrB9sHigkmDXoQlxIRFPMUjBPLDw0N6AvyCEoFlgTLBTwFQQPaARsBAAAU/7b+/P7JAAQEBgiiC4cPihNOFhEYHBhAF0wWBRVJFekVNRXcE/wVmhedFc0TGRNIE3IR9xDfEXgUDRimGjcd1x3sHiAfmiHXIBoWOgmLA6IFYgheC/UK7wNQ9wDp3d423VXhKOFq2fPSotOl2TnlLPLd9rnyR+/Z78Xw3O7G7tn0RPkW93n0sfbY+fb4gfcn+Ab37/L58Vj0IPdZ+jP/cwVTCgUNVQ6ZES8XsRvmHn4jrCeGJpYhhR0IGsYV7xExD4ANswwXDFoMNw6TDtsLtAelA9H/oPy9/J7/IQK3AgQExgZmB1AEjv97+/P2H/FH7Mnpi+jJ5qnjJ99+2fDUD9L9zhXNYc3AzzrSsdVF2n3eCOLn5HXmg+Vl5mvqmO6N8B3y//au+0D81/oB+nn3RvTN86P1pPdf+aH9BQLuBFIIMwxZDS0MqgtSDHwOHhG4En0TwxNnEwgSzw+gDHYJ3wZ9BI4C4AFGAlUCWgEgAAj/Ev6q/iUB4wMeBqkJjw4rE8wVdhgfGsIYqhZZFfATDBNtEx4VUxjrGT8ZyxfZFrYVZxTJE1kUfhSME74V2hudH+sfnCLFJO8glBfUDRQHuAPyBKgIYQnpA1X6tu975vHhbuFh4Pzb8dUr1L3YHeL87Er0uPNd7w3vIu/S61nrPPF+9tz1v/Ut+GH43/ZR9hP2YPTk8fLwz/NL98z5R/44BZ0KzQuoDFsQghTFF/0bKSIuJhcmuCP1H+IbaxfuEuoP5Q5FDgcNyA3sD2sQ5w3xChAHGwKX/hj+vf9SAfIBdAORB4IJnAUQAIP7yPau8ITrQ+m5533li+LN3jzZuNM5z/zM38wXzj3QcNNY13ba791f40PoXOgO5t7mgOrq7Q/xJ/U6+fD7Af3H/Mv7pfnp9mv03vJu8272XvueALAE5QedCq0Mswx7C74KnAygD0MRchFsEWgRxw8LDXQKOQgmBrQDtwHLAOkADwLnAksCtgCH//T/ZwJlBWIIwQyoEqQWCximGVsbZRvEGTwYABamE3QSYRMYFlYZaBmnGFkYHRcEFvEVzxbiF1MY4hpNH7kfAx9hIwcnPiItFs4JNQQvBAoH3AiWBfL7UPHG6Nvin+Gm3xfbdddY1DTVrNuY5i/xtPRI8R7vD+907ELqL+y08HbyZ/ES9CL3avYC9fr0VvQo8kXwvPCJ8/n20ProAfAIMQzIDccPeRM4GLkbeB+2JIAmhSReIVoeeRqFFQ0RQw54DXoNaw0vDygRsRD/DYAKAQbsAIf+z/+lAuUDnwPnAyEF1wQuAwAAR/oT8pbqu+bB5l/nuuTX3sXXcdGozMHKv8zKzwnRlNEF1Z3avOAs5+/rwuuU5yvlB+cv6iHtpPAd9aj4/vqB+535i/e89mP1BPR39Ov22vmL/ucDxwiiDFAPaBGMEEMOtA5uEu0U6RSkE60Rbg7bCvgHDQZeBD8DdQFU/0D/MwG5AgsD0AJ5AQoAcQHsBFAIFQwoEWQWohkwG34bwRpUGTgXMhRHEtESQxVKFrwVGxd0GGAZphl7GowaZxqQGpAbWR8hIsIkxiZXJzYjkhoIEksK3AU6BjgJAwkoA2b62fDv50rizuDr4OLcjtVn1OLYLuFw7HT2Vvih8szuU+7d7KDpgOrK7g7wGO+C71Xy6fO88xj09vN78WzvQPHq9Or40v3IAiUIewzCDVAPkhNDGdUd4CHRJH8lTyNYHuMZmhbpEqwOawxzDN4Mcg5VEbkTKxODD6UKswZvAkb/WgD8A7MFDwb6B3MJOQjzA3v+K/g/7/LlkuAs4IHh/d9k2uXSQ8xYyTbLm8860lXSo9IH1rvb+OFi6H3svuuT6Avobeou7qvyR/ZA+Qz7rvtq+7b6qflj9oDyNfBb8un3Lf7fA4oJAA8rEogT9BInEHYNtQ0yEPERthH9EDwQNQ4SCnwG2wRoAwQBbv67/Ob8T/57/yQAov8f/1QACQMNBugJyQ+hFT0Zhxo4G7kbNRqeF/EUxBEwEFYTbxeDGb4ZRxw8HzgejBtYGhYcZh1RHEkckyBhJq0qFS37Ks4iGRdoDv4L/AtNCo0HsgOE/UPzYOgF5MjjeOAG2knWT9aC2F3f8uoN9br2afJT7wztOup05xDolewF79buwu+o8QXyRfDM7r3uI+0V6jTrzfA29tX6gwAQB1gMtA7sDxkTIhguHPsf7SN7JWwjdh92G9MWUhLGDhUNEw39DAoO+RDkE8kTnhDNDLMIQwQNAgcDlgU/B0oI5AmuCjQJJQWm/4X4Q/At6ZfkIuH/3IrY+9V709zOSMkxyJHLKM4U0GrTkdc724DglOdL6yXq3ehA6v3qKOsd7tnzsfiQ+Qv5CPgl+OX34PSE8aTwUfPh91P9vANdCWYOoBKlFAsU1xLFEskTzxNLEpcRchJ8ERsN7Qd7BE0CbACP/qT8K/zh/S//Mf8Q/23/v/+TAKUCGgQaB0kNShONFmwYDBo1GmoYVBa7E/4PfA4eEWMTyBT+FawaDR+gHg8cTxzYHwsiPiIkI/Al5SdRKpQtSix3IlIWlw5aDA8NQw7QDZIIIv4M86LqWuYx5TfijNyY1yHXbdzL5fXvQ/dI+Wn0Q+/C7FDqYedr5vXonuoC6gHrzO7q8EPvw+0d7Y3sYOzV7BbwC/UH+vj/WQZPCrALVQ0aEfwWshwSId8jXiROIRgc8BZQEisPhgwUCsQJagt/DhYSIBW8FdESnw0xCK4EoQNqBOIFNQeyB3cIBQo2CRUFNf4s9mPugOjb5fHjOOBP2vrTGc5nyfrIOcwaz0fPvM7m0abXhN1d45bnX+cW5IDjMeZI6cbrdu+g9Gn4QPmi+Pj36fYr9arzgvK+8lv1GvpQAO0Gfwx8EXYVohbyE3oRaBIqFNEV+hZzFxAVMRLoD7EMjAjTBK8Ccf91/MH73vxu/tv+vv4U/gn9N/4CAcsErggXDQoSgBXhFnkW+xTaEyETyBGsDzkPFhLaFA4WtRf+Go0cpxtAG8YbvRsWHK4gEyWnJ68poiz4LJooeh8BFIMOgw6/D7wNxwiaARz6QfM27oDrwOe44bXbTdov3g3kVuzy9Cj5e/e48ynxr+6f6/roXeq67BDtqu0774fwsO9i7TrrmurP6evogOtK8ev2BvwfAjUH2glsC/ENVxFKFhMbpB6WIRcipB6wGVEVNhHoDLMJywhKCaMKig23EMIS1BHVDgsKTgXaAlMDHQU3B1kJkwviDDYMDwm1Aj36UvC559bjquLB3yjZutLJzRjLd8zGzlPPcc0tzKfOHdQl26PhjOZy6NznUObL5sHpk+yS7tvwr/It9P/1Hfh7+Nr15/JS8f3xd/WB+8UBgwjND0EWXhlUGdcWwBQTE0sSzxMtFugXBBfxFI0Srg/bC/AHAgRE/9v79fqz/LD+ZACxATgC3AKXA+0DQgWsCEkNkBCXEvoTtxTCFFITNBAyDXUM1A2LDi4QtxNeFrcXoxguG+YbaRvrHG4fyiBeIaAk3yibK0gp2yBCF/sR5xFSEmER/w3iBGr6PfRK8eDuduxI5wDi8t4d38jjH+7+98H7Jftr+Dj1c/Gx7tHtVO3L7ALt0e0273/wzu987qrtLewL6tXob+oq7rHyRvh0/hQDYQapCfQM1hBvFiUaghtMHckdgxpCF8IUtRBODNQJFAiNBzQJCgvSDFoOhw7fCy8IMgWRA2gDhQQwBrIHzwiCCa8JZwhIBJ39JfUx7W/mZeD62+rYCNSIzWjISMkIzf7PqdFy0nLTjtW02hng7+OJ5YPlfea5517phewY8J3y+POw9M30d/QY9K3yovCn7jbvFPPo+M3+lAUmDWsToBZuGAYZaBbkE+kSeRMyFGkUwxPPEiwREg9PDfMKRwdRA8f/ZP6m/uT/0QGHA6YEHQVpBTYG4AhCDI0PixIdFKYToxFxEBUQow2dCk0K0Ap0CnQKKA5lE5MVHxaJFvUX1Rk9HDAfNyF9ITYj4imCLh4qmx90FcQRTRLSFAsUfQxIAlr7//bs8H/tLewa6ZHkCuFW4q/nUvBq+r4AWv/G+ff2pPd29m7yDvBa8MvxRfPn80nzf/E776jtM+wy6irotOg37bLzNvnK/S4DNQh/C2kNJxAsFVQZDRzZHdccFhlgFU0Ssg+WDH0ICQZHBnsHqghuCuULcAv+CAkF4gFMAbcCTAXtBr4HuwguCuAJyAYHAin7lfKt6tbkgd9J2sLV2NGLzHLIrMk8zm/RtdHo0S3Tatdc3mHkMuc15oDkuePS5Jvo6exk71Dw7PDy8Kjwd/Hs8Zrwdu6w7czuzvIu+UMAIwgfEGYW9BmdGz4bORrYGAsXLRYfFycXFhV0EjAQsA6GDLgK+gdnAmL+n/7HAK0CHARMBd4FugZECAsKHQ1hEZsVrxdPGH0XuxRQEncQBQ0BCh8JxwgECLsIpQqBD28UjxZeFUgUfRZNGc4bVxx5HhgjLif2JQgeRRXsD/kRlhbPEx0M8QNh/DL2dvJ78CftJ+nh5ATiAuNy5yXwAPuTA64EZADc/YT8p/mr9hX18fNJ89vzcfUv92T3Z/UY9E/y6+5q6wbqGOyt8X/38vxcA50HRgm5DBURGRTWGGodSh4cHP0YyBRfERsPVAt6BokDPwNDBB4GkAlaCwALZQl3BR0Buv6m//4A1gFUBMwGJQiDCIkHfgP3/E32HPAN6uvhzNZezMjHWMmBzE7Nicyhy0rNpdHg1YTZPd6D4rnjluNX4xfjIOV76qfve/EX8SjyRPXb9oD1a/Kj7+btEO0O7YTvOPX//dwG/g7pFJoXcBm9GjEagBipFyEXNRaIFAYSqxAyEUMRIw+0C+AImgXoATsAaAC1ARgD0QPxA/YENwiaDK8QOhRQFjYXCRhHGHkXFhVcEqQP0gwbDGoL6Qp2CtkL1w83E3gV8RSpFO0VghfRGQwarxv+HQEgQB+TGHcS0w/bEgYWlhMWCgz/ovc48cTuou146wnnf+L+4U3l0ur78nn8zwElAUH+b/wv+0b5l/Z09t/2EfY89oH3F/ku+hX5qPcT9vXyvu9l7vvvbfS0+mgAUQZBC+UNAxFiFVgZrRwPHuUcRxndFVYTsxAoDnML/AinBhMGZgeaCOQJVgtzCx8IsAP+/6L+h//XAJwBfAJYBKsFXQWJA0j/vfje8Tvsa+Za3jLV9syvx33F/8YVynXNi8/20OfSItZA253gIOUK5vPjDOKT4b3j0egN7l3xPPLy8ZnyT/MU85LxD++67CLriutR7/n13P0XBk8OsxSxGMEaqBqdGc4XVBa6FYYU7hJQECQOIg7rDgMOVAx+CUkGlwMFAtYCiQTiBX4G6wZaCJsKHg6wEikXgRkIGjEaEBnfFvATYxHRD64OogzKChsMag4wEfITRhfYF3cW3Be8GSwbSBttG8IckR25HugacBK1DEwMwxDCEiAO0wPU+cH1LPN47ofpTeWy4lbiC+Tu5W7oie90+vkCFARQAWD+8vtT+mP4xfUy9Rr29/VX9k33DPgw+T37wvoe97jzFvPX9Ir5rP/vA6QH7gwIEgIWexo8H5Qh7iJjI+sfNxrCFXkSYA7zCssI7AXJBOsGIwnICacKXAvsCAIFGwIkAFr/OwAOAW0B1AJaBJcDbAA+/HD20O/06ZDixdfLzFzFosH8wcTE7MbEx5LJa81h0avVstqc3/rijeQ85ALjNOSI50bq2Ous7dzuyO+K8Sjzv/EL70DuPO7s7RjvTfIx+GcBrgqCEYsWjBsfHuUd/h1aHh4dKBtNGZEV9xBoDsYNfQyCCnIHrAPcAVIBYgBaAOgBoQORBLsFGwggC5YPqxR4GPIZ4hoZGy0aahl7F+4TXxDaDbILqwkbCaEKFw1GD/sQ5xKiF6sbSxyUG+obzR3ZIGYjsCADGKwPNw5SEscW/xSRCrb+rfb174/pS+eQ5sXiFN7f3aHhSOfA7xD67AB0AHv+gv0G/gP+zvr+91r3Gfha+Az3aPYu9nn1rfWp9SLzl/Dn7wDxQvW8+r3/SgWwCxoRmRXkGhIgZiRoJ3on2CIQHTsZLhXWEKoOEw3vCdUHsAggCqMKFwyODMYJ1AX5AggBGwK7BTEHdAYeBxkI/wa2BGsBIfsD8wHsHuWS3ALTG8k/wTC//sHzxNLG1MfZyA3Lus8Z1ZPZAN4d4I/eytxK3unhDecz7CDvKO+17kHw2vGu8Artluki6LjoH+ru7TL1Bv52BrEN5BNDGPwZqBqgGm0aoBo4G8gbOxpSF2kU4xLxEc8PjQtcB1AE+AEAACv/mQBbAuEDdwS5BdoInw1/EuEWhxqCHF8cIRumGVsYVhaYE/cRFRCaDMgJwwy0EQ4TZhKCFJwWEhayFREYyRp2G1oe+R9aHkcY1hHXD4kS6xXkFOAPOgY/+/3x4exe7CvqfuP13a7bj97j5RnucfVZ/Pb+2v3O/O356ffl9qP2S/dc9z/3tfg9+mT7K/z/+Vj3v/V2807wi+8+8mL3gv2LAy0IWgxmEXsXex2MIoglySREIqsfzhxtF4UT+xGDDxUNVgzyDBQOvQ9nECcP7gseB7cC3wD4AIgCQQRZBgoIEwngCaIIgwTC/lj4CPAZ55Xe/dUxzTLGncMQxGrFDcapxjvJBM3o0M/UMNg72inbFdzK3Kvd3t+941nolupV65Xsp+6X8KTwquyz59XlkeVd5/fqD/Fn+WMCwgoFEZcVRRhUGYMZjxnGGG8XUBcXF/oVVRTyE4ETERFuDWkJ/QVBBBIDPgKnAg4E9gSKBbEGLglpDe0R1RWAGPYZARs2G04anBfmE/UQqg/GDf4KyAkTDPoPFxNTFQUV/xMUFvAZThrCGJYaKx5JIP0f0hxMFx4RAhCcEwkV/RHeCa7+xfRT79Drbelo6ITk/t2V3TDkWOwO9Nf6Pf77/Vv90/vY+VL4ove+9/D32vg2+dP4h/pt/FH6+vfD9WPyx/B38S7zofbF+70B1ga8CqYONROIGIEewyIBI8chHCC+HP8YexaqEpsOfA1HDSUMMQx8DjAQERDKDiILQAZ6AzkDLwT7BTkITAnkCYMLDAs5B5gBS/qM8RHrlOak3pXTscrSxsXGbMh/yaTIJMgPykTOWNOg19rZF9sU3d3dQ93+3UnhaORY5mToP+kr6S/qmeui6WLlweOG5Azm3ei57fr0av2MBUAM9xCAFOYWvRa/Fq0XERjoF5IXBhehFbcTmRETELENggmKBasCJwH4AKgBGgRqBvcGtAdnCd0Lww+sE3MW7Ri1GpQb+xuzGvAXDhUQEh0Pbww5C3sLVQ00EEsSlhNfFOYWFhlRGPoWFRh0HCgfwx7eG7cXSBOwDxQSGBXKEQ8MnATV+vvxCOy86N3oV+ch4tndt98V5yLwSvha/nH/kf2C/ej8m/qm93H1b/UC9/r3TPju+EP6/vqJ+vD4v/VT8r7wufFU9Kj4Hv5UBOsJCg59EqsXKBzGIHgjJyIHH7UbsRiwFXAS+A6SDMkLGgs3C9IMAA8NEJgP8Q0JCr0F3QOHBNwF/wZ/CMYJ2wrDCz0LcgekARL71fNJ7BXjx9eazT/IvMdCyVLJ58eIxznMINNv123Ys9kZ2z7cct3B3KTb/dwn4pTnTOp76nXqq+sn7VTsredC45TiAuMy5Jbn0+2j9v7/XAhpDf0Q4BODFqgWXBUWFasUERRtE1AS7hATEQsQ0A0EDK4IUAXbA5kCGwEAAYwC9QMTBRkGvwhPDfkRoBZ/GTAb6xxBHR8bTBdKE9EP3Q4aDk0Nrw1+DesOPxJzFoIYBxg6GKEY3xg4GIUa2x2OH9QfiR3uF90RHBGNEqYTDBNyDR8BL/Re7Xfp1+i+6OXlCuFo3nXjbOwh9UD8VAD0/+/90Pyv+Tj2YPSQ9I320/eg9zX3Wfmu+737EvpD9+/zCPGR8NTyNPbj+ssBNwhEDJ4QYRSZGIgetyLBItcgoh03GUUW4hP4D8MMmguoC0ULbAvxDYIQdBHsEO0NIQgABMwD9gQyBjsHjAhqCoQM5AynClsGyQAN+avxHOth44rZL9DFyUnHVslHzHfNVM33zlvRfdNA2HLdpt8E3onbLdrV2+7fyuSy6IzqX+pG6sjrDu1+6xPn1ONV40bjqeMm5+jtbPYA/3EFjwqQDy0THxReE9USaxNtE9ESoRE8ELAPaQ+qDgUN4QofCMsE5gG6AO4AXgGxAvEDOQRSBVQIUQ0MEkMV2hfjGWIaZRqZGZQXzhRyEZYPiw4YDjoNlw5zELkQHRN1FsIZ1BdmFkAYkhq6HZweFiDyIcUhhBx6FL0PEg5BEW8UBRFsBo33a+3N6WnqveoG6YXicN0O4ULmLe369Jf9ggL9AvgARPyq+L73Sfpo+iH45/ZW9D30pPco+qj4o/ZR9gz0fvKk8ZnzgPmLANYG6AkZDRMRgxYcHfsicSWjI28hFx5UGTkVIhLCDqwLGgp9CHgHOgmWDIUP+g8GD0ULLQfYBWcFlAUQByoJNgnWCdkLTQrMBjcDTf7c9XHtkueC4LrW1cw8xhbFkMnYzVPObc2vzkTRoNT92bzecuDG4G7gFd9S3/7icueQ6p3sPusm6FPn0+io6cDn3eVU5Znldeas6Yfw+/kyAtkH9Au5D+cRBBOEFFMVixWJFa0U5xEID3wNTAxHC1UJigYFA6L/FP6t/cL9Kf+3AQkDbwTIBkQJ4AyhEXAV1Bc3GeEYKRcjFp4WXhb6E7MQIw/ADqMOqA4lD4sSiRXmFgAXABeuGY8csx8aIFogvSLlJJ4lbCAOGSISKQ8aEUkSVg8qBiv71vHJ6uHoc+me6QPnf+IS4RHkbe3V95T/mwDf/br7Ofpp+bz3r/Yu9gf2ZfUH9bD0nvQg9jv3Ovbx8vHvme9C8er1DvtHANIF9wpjEPgTNBcBG7kflyOCI6wg8xtWGQYXDxT/EMINwQsMC8kLzwzWDb0PNBGpEBcN0Qg4BoAFyQc0CnsLrgv6CycMjQrABzIDY/2z9Qfukudm4VHaHdGQyW/GK8giy+nLQ8tPyoPMOtLa2UjfHd/C3Znda9944Q/joOWW53/pIeqn6PLm1ufm6nTrxugr5uHk+OV56jLygPlcAPsGjwt2Dl0R7RT2FnEXgRZTFUYTAhLKEfoPIg3nCvAIigVbAk7/X/0G/vD+x/8bAXwCMwTQBVgIuAtpDyMTshVKFyAYmxiGGG4YwxbyEmIPIA4UDtQNHg4bEB4SKxM4FK4WgRrMGxEbRBszHTcgGiM+JZEkjSB9Gc8TERG3D04QKQ9wCooCDPi/7ZbnMugR6yzoUN/P2+riy+1L+UMAKQFFAJUAqgCj/XP4hPKX8NLyX/Wl9UvzWvS9+MX7JPoY9BTvmu6X8JLyDfZ1+7sBdwh0DtESPRZAGmkfZiP5I+cgEB15GoIYZhWEEPYLIgoyCvwK6AwOD7EQJRNhEw0QnQpFBp4EtAR+BrMI2AndCqsMtw3wCzkINALr+QLx7ejZ4sTd9tdcz4LHasQ4xxjM887Zz8jPrdHq1OzY5Nzf3uHe/t3r3QzfWOLk5pzqqOx27B7riOqV64/sH+p55XPjdOWu6EztqvRz/NMDvgrXD5kRYhL4EwUUpBIgEi8S+RCmD30POQ/TDkkOrAt9BzcEdQHJ/vn9rv5r/xQA2QAZAsIDrAf8C5gPVBIPFDkVPxY4F5oWPxVjEwAQPA0+DckPqRHdEiwU+xSPFbYVwxbLF28XRxgxGmwcYR9MIVkjAyaXJ4QjDhklEMwNcBEAE84NjQQZ+arwyeyb7Czv3u3P5lbij+Q26oXwi/ev/E3+e/y7+L31BvRN887yGvN69kH6kvmq93f3QPgV+IL1F/IN75rt++4n9dz8yALfB98LKw/nErQV7BdXG1QeVx+FHV0bdhkVGHcWyxMREb0PiQ+mD0YQ3REtErMQ8Q7TCxYHjwNHA5IFAwkQC+ULGw3ADYwMMAlyA6D7t/Jz6inlFuEh20PT28sYyNHIO8w6zpzN/MyIzurRsdW12e/chd4h33PfD+CJ4jPmrOk86wXrzenb6JzpHuu56pPo5OY051Lp/O039C37swHLB/kMIRDWERAT9BPqE+4T/xPFE6gSrRDDD7oOxQzOCToGaQIO/8v8Mvw4/SD+BgDsAZQC4wOZB4kLKg5rEI0ShhScFsEXLhhnGA8XihTCEW0Q6A+DD6APgBHAEZURbRSTFscWpRjGHOweWB4xHq4gYiQGKDgpYSP4GScTQg9yDTUPNBBBC0IC8Pic8Y7rEOhU6hjsd+bH38Pf8ubu8Qj8KQE3AGH9svr9+Mj2SPS18j7xWfIA9AHzD/La8pL0EfZx9LbwOe/z7/TxIPfs/IMBSQbsC3MPqxF2FbMasx+aIm4joiH8HawaYReUEyEQkA0MDO4LwQwBDq0QfRPcE18RBw02CTcHWQZwB+gJ6AuBDCYN9gzKChoHTwIj/LD0AO0V56biP91h1VLMqMX1xPTI18wEzRjLysr0zcjS1Ni83XXfHN5T3LXcrd+f5LDp1+zk7SntDOw463Ls2+wj6izn0+Z56SXt4PEr+Kb+xwQeCusNGA9dEPcRKxNIFEUVaBXuE08RrA9IDzMOAgz6CBcFJQEQ/nX8lPyO/dH+nv8mAHIALAIwBsgKMQ8rEqsUHxfzGNkZfBiPFgsV6RIeEaUQWxGTEboSbxQTFOoTyhSRFrMXbBicGngbuh2+IP8j5SdqKq4oXCG+GV4TDA8oDvsNRgyoCA0C7/ae6q7i2+Sj63zuSOmH4XzgF+oV+a4DWASl/Wz32vV89aPzvfF18BPynPU89qrz+vC98YX0pvS27xHrq+rN7eXzf/vpAKgEUwnnDi0SaxOcFvUbUCEtJA0jpB4QGnUWThPcEOEOcQz0C+MN5Q6wD9sR+hP2EiEPVQokBj0ElAVpCewMtA5gD4UOSAzXCH8EUv+S+MTvZ+eH4kvgkts70+rJNcTkww3HvcoSzN3M+c7E0mLXd9sz3ofeTN4j3h7eI9/j4Wvm0upQ7VrsC+oV6gjsn+zg6inp2egw66TwvveJ/t0DeAk/DksRkRJOE2kUiRXhFlsXDRd2FK8QKg4XDNIJ7wasA1ABAv/s/FL8cv0Z/9MAVAFsAI0A5AIeB04MwRA8E4gU0RVbFyoY/BYUFQ4TMBDGDvoOYg/ZDx0QdRBDEY4TJxY4GKEYhRnuHTshuyKpI+QlkyjvKrgr8Sb0HWsUvA4wDZ8OdQ/lC9cECvt077XmOuY261rsj+UP357fPehM9Ir91wCj/bv4bfXu9NX0PfRP8nvw3PEh9DX0TfNo843zdfFn7hjsMuuk7DPwvfUE/K0CCAjDC/EO6RE5Fvcb5SCpIy0kuSH3HFgZGRfmE2kQWg6QDaIM+QxmDzESfxOmEk4PwgpLB84FIgbRCIoMPQ9+EJYQXg6CCpkGfAJz+1Hy9Olw5NvhmN8g2k7QXcZhwoPEtsgwy7PKEMk6yrzPOdcB3NrcpNv420fei+FL5TXpL+2d7yDvlu1A6xPqy+o663Xqc+l26zvw//a7/KQAqgS3CDoMBQ47D18QFhJCFEMWvRfuF14WHxO0D6ENogxHCpoFPwDh+8D5A/pE/Bz+7/3S/ef+gwHSBe0KLw8cEVISkBM5FVYXUxhrF1oWehVnFGEUuxQQFcwVkRWiE5wPcQ+tEdEVuRqDHdofriD3IvglnCgeK+crtymdIw4Zsw1rCfUK/Qz/DcELtgQG/OTxNel46NHpfucQ4R/b5Nsq5AnvgPpaAUEAVPxc+IT1mfIu73HtEe7+7VTtfe0d7qfvSfO289Xwhu6P7MPt9fJP+iMBPAaqCH0Luw+SE4YY0x20IXQkuiREIhEfVxsXGCwVKhHUDKcKygovDMUPEBOzFK0UXBIhD6ILHQh2BkkHFwkSC88LCAssCQsGFQJg/ur4h/BZ6KTi6t923aLYI9GfyLzBG78nwSfGcsuKzvLQotRX2RbeT+Bt3//cU9x53tPhkeVm6X/tXPCV8mv00/N+8uXw/O0M7LDsIPBv9Rz7MQC7BFEKhw9cEnkS3xFwEsUT/xSfFBYSyQ/VDl0NNwv+CIQGQQRhATP+yfye/AP9w/wL/Hv75vuD/lUCDwa3CSINvxDkEyMWrxdrF+EVLxPMEPgPng+vEFQS/hLfEcISpxV8GJEZlxn+GWEbmh0CIVYkOCbgJjEoXivLLDUsVycMHs0SWAxjDHQO7w5wCwYE/fiz7j3omedG6h/p/OLq3lHgW+db8jH7+/0Q+7r2bPOe8Mbs6uo664/sR+9s8DTvUe7o7qzw9vDu7Wfr8euw7YHxQvhE/20ERAkgDZoPCBK+FWMblyBoI5sjGSK7HukZ1xUjE6kQQQ6bDaEOOQ+9ENoT8RVqFVQSig3iCL0FfQS5BfwIogtODC8MBQprBd0ADf0++MrviuZn4JXdA9wo2cDSPMqUwxrBocTmys3NMc2szADQHtbk26rend3h2uHavd/n5WPqlO2X7yPx8PGZ8lzzgfFC7qPrNOta7avxwvbs+0sAcAMoCbcNzQ7EDXIN+g7FELwStRNkEvYObg1pDYEMCAu3CS0HawI5/kD8Uvye/Oz7Zvom+mT7bv5+AsgGPQqQDcEQzxJxE/ITnRSWFF8U9hNZFOITfxLJEp8U+BYXGDwX0hScE4kVjBrYHpIfpiDDIh8lPyhVKk4slC1gLJknfB/vFN0L9wm+CqcKPgmGBlIBH/hY7Mrkl+QX5zPmJOCH2l7beOSv8QX9av4h+FL08vHG7v/r7Ok/6TLq5+us7PDsnu0a8PzzF/W58V3ub+5J8h/4Nf4WA4IGGApyDrgRGxTyF9ccuSH2JDUk5SASHd8YrhVIE0YP5QuuC9IMZg4cER8UiRVyFPkQDAx2B4cEIgN+A8UEVAWkBSIGywSBADz8BfmZ853rT+Tc37jd0dtA2JzRWsnGw3LE18kizzrRCdEp0R7TndbT2jne+t4p3qreUeET5lfrNfDK8970rPR6833xeO/k7jzuDe4M8HLz2ffa/G8B0AVuChwODg/zDeANTQ4tD5IQSRHBD0MOIA7SDdgMHQxwCnYG4gFT/Wn57vdj+Eb5T/pm+9T8zf+dA7oHxwvADogQbhEtEg4TPhQmFYQUaxPaE2EU2hPsE/UV4RYTF/YWOBe8GIQY/BoGHswf7yBGIsYjdidqKx4uETEdMbQuPildHPMNWQagBBsIzgpuB4YCpflT7vHo7ejl6NXlfd8r2zHbnN8o66b35vut+YP3VvTs8MPtduyw7Izrletn7hjvte0h7rrvq+8L7sns+u1Q8Uj1/PunAx0IKQsMD/kQaxDVEnAYIx5zIVUj2yPjIPUbXxjMFVMRbwx4CToJWAsPDX8PAhNtE2YRSQ5mCk8GjQODAxMF/QXvBpAIiQfOAon+APuE9jXwsOkY5Sfitd9S3pDbo9ULzv3HYcbGyNDLt83tzwTTGdhy3U3g3uCk34feGeDv45jmvOiG6krtoPC38rzzFPOy8C7u0+1N703yyPaF+97/CwN0BgUK6wpSC7cMTQ1rDS0PuhGbER0Qcw8WDzwNkwqQCL8FfQEi/v38k/2o/hL+Ov2O/dj9WP8hApoEjgYXCTgMaA7ND1YSwBSPFe0UXRRIFOkUoRV5FuQXpRg5GSsaYhrLGTsZHxoRG3Acyx6FIdIjWCWHJzEsoi85L7stGCvyJBIdMRNvCDUB4gG5CLsMJgrJAGv0Eula5iHqN+kY4iDauNc23KbldfBx+J/5BvdZ9tf0CvAe7J3s7u0z7ZvsJOyI63Tr5O1E8dzx2fDa8fr0x/eV+lYALAaYCNgJ7QrtCvsMuxP5G5IhDCRLJOQijh8OGrkUHBEyDegItAfGCdML3g3dERYVYRR2EdANfAmOBWYENAVHBowGXwbuBQoEKQAD/df60PYb8YnsVukK5kDjMeGf3RbXfM6Xx9vF5snGzyvTAtPU0R/UwtmN3pDfgN3y2kjbCeCZ5fbp2exm7yDziPUn9VPzmPFq8IbvUPAu81v2OPkk/UwBRgTnBhsJNArICTIKLQyiD+kRbBH8D3YOqw1zDKwLBgvoCP8FLgMbAWoASgEzAWf/EP7K/S3/1gG4BEMHdArGDfEQsxMsFUMVOxU9FvgWahbBFlwWqhXRFkIa5hufGUgXfRcBGS0ZvRdYGeIa+RuNIE4lqCUjJVYpACyOKu0npybkIpEYYwyOBcQC3wNCCCEJvANO+HDs1eUm5+HoJujq46DbRdk63xvqUvT9+Nz4d/gr97v0iPLd7+Ttlu2/7frt0e3P7Dvtju617rvu3+/F8EDykvSz+Iv+5AJLB3ALOAxQC4kPVhY4Gxcf9CElIrce/hl9FpsU3BA+DCEJKQiJCBwKTw0cEY0SthGDDz4MfQjOBpkH2wf0B50HxAePB30ENQEU/4X70vWJ8NvsC+ow54jkieGf3ODVs85DyKrFlMY6ytzOWdH90hHVBtgB2R7Zp9mu2hTdy99d41Tm7Oko7zv0yPZj9dryAPAw7oztte7U8Uz05/Yp+1z/3wBNAkkG8wlHC7wLlQ1OD/gP4hAvEkcRwA40DS4NhgyhCpwIcAcaB0MGrgSEArD/R/1f/Qj/twGaBKoHWgtaD9cScBXDF7oYLhhvF3cWMBX3FPEVZBaJFtMW3xUsFHMToxTAFHcSMRNjFKQT6xRIGnUdXx7KIKYkjCmuKx0tzyw0J+oeIhgGD2gEYABNAqwEuwS1/2T3Lu725o/oju2R6c/f8tpI263g+et591D8cvo3+rX8Wvs99H3wb/EU8FLt7+vv67vqEunU6mntruw97P/uSfIT9S75wP5KBMIHyQjICtYMkw5jFPccGyGkIWYhcCByHKIWRBPzEGILLgbcBagHggluDakRVhPAEZcOUQ1tDHYKHAo1C3YKFAgrCOYIUwZhAbH9ePoH9VfviezH6sHmaeL63qLb69bP0JzJ6MIrwSrH/c170GfRFdRp2JrbfNwj3J7b1ty64UjmVeeA6NLrAvCz8jjyxfBw7zHt+uwJ773x4vRx+J77wv5tAbwDAwftClEOCRCeEKISmRXGFWUU9hP4EpgPpAwrC3YKVQkxCGsIPggoBy4GQAUwAx8CoQMmBnYHLQj/CRENjBDmE90VvBWdFBsTqxEeEeQQoA/dDs8O5w6HDosOGg76D/sRKhHEEb0TuhUpFtYXKxo7HFUgpiSfJzcnOSiZKpoppSc3JfkcWg7BAcD+UwNMCPwHgwHW9jvs3+jh7Lzw4O365X7g4d5/4Q/rr/WV+kH6/flx+4z5APRj8aTxaO+F7DTrTOmK5vDlGugD60vs6ezc7Qnvx/EQ9+v9dgN2BtcIVAuQDJQPcxZ9HVUgNyC8HwoeShqoFlwVnBPxDm4KCQmWCIcIugtEEPURcBFBEe4Q9g8fD5wPYRDSDe4IJgaaBWgD6P/J/P35B/UA8Obt6eu/5knhB9112APVDtO30GzMN8gkyMvNtNIE1HnUrtMM02HUktjW3Kjesd9k4tLktOXZ6Ofs9+7f7yvwf/AE8HTvDfKz9Zn3u/gP/KwAKAOWBIsHYAvIDt8RwhRyFf0TdRMsFEMV5xQbFIgTnhJ6ETIQOBDADrgLSAlRB10FcANwAzAFOwdcCAUJ/QkAC3sLaQwmDXgNMg06DHALiQvPCwENLA4mDv0NMA3oC30LKwxjDY4NhAzVCwQMxRCFFSUXLBhEGuYe8yMhKOQpiClZJ7ElCyePJMEb0BDdB14EUwa4C50OnwquAJz0XO117efveO4653zgNt2Z4PLqu/VW/Oz8zftv+6r4ufLN7JPrr+vs6avnKOdc6FTpQOq/7VXvBe4t7SXuzO8a82v4tf23AtAGswj1CtsOFxTLGc8d3h7mHrwdCxvOF7UUlxEDDsIK8AgrCN4IiAwJEeATFhXEFIsSXg9dDdYMzwxeCycIpAXRBJgEnwMLAuP+2vh+8hfuHOv156Pj9t7G2m/XxNWh1YnTwc1FyBbIks3q0T3TiNJkzxvO8dE22dDdld733WHfqOKg5XHq8e4j8c3xVfK+8qXyjfOI9Rj37vfM+YT9yQEtBfoIwwwVEbMUqBYOFoYUOxXJFnsXwReCGOcYRBdNFXwUTRS8Ek4PpgtGCJwFFATlAwoEkQPGA80EHwUPBXcFbgZUCNwJYArvCUwJ4AgpCMEIawncCEkHAQbkBfAHqQqnCmIKowncCMwKkQ7QEI8R5hOQF2sbWx9vJccpeCsOLKYrOyuVKsspnyYYHHANBwZICeoM6AzVC9QFdPdI6lnq0vHQ8hLpvOFE4C/f5+Vx9GD+xP38+lv8Gf3u9YHsiuvV7CXpb+c36CjnReUh5lfol+hj523nhel56uvswvPq+zICzAarCcQKYgtcD/AWzhvzHPseCiCJHLUYXxd4Fc8Pkwq3CFgIogeJChISUhf/F/0XIhiXFbgRMBCTEboRPw4tDA8M1QigBNACawEv+57xX+sI6cvmfuRP5Dni6NzO2A/Z79nO1vPRc81/yabIrc4/1iPX8dG2zBnNgtHv1sba3NvP2rDbsOHO6NLuSfLX9PP1hPWG9l/5VvwM/lYA8QK2BMYGDwqEDWYPRxL1FfsXuxdaFvoVtBZrF8UX6RhFGQsXSBSkEogRVxEiEp8RJA19BycFZwXuBGoD2gGOAU4Bzf8M/5L/7v9wAA0CjgLCALD+av61/xsBrwEuAkUDjwNSBFUGggoFDVoMuguNCqIICQnqCxIPMBEnFnkawx3dIdAn/CzaLZQt3i0YLxIvsC66K+weXRCcDOMRkRZPFaQPVgQ381LpdO/y+FrztuUb39vdpeBt6jv3Gf2W+Z74Rvw3+kHwhOp47KHra+Y+43PjyOG34F3kVeef5DXh8eFT4/HjqugM8UT5lv9iBO0H7QlpDKASchnlHIYe7SC4IIAcWBqqGrEYRBNtD+sNRQu8CsEQ5BcsG7Mbsxs5GoMWrxMuFHwVyxM+EM8O7w2RCuEH+QZFAz36dfD36jLo7uXh5MHjSeBi24rYStii2K7WQ9KMypDCm8BIyQbT/NPVz+bKVMkJzjfXetwl25HZ891u5VDqcu/49FT3gvaG9nr5zvrE+pL8RgI7B9EI5QvKEJsSkxJRFZsYEhnWF3gYABp/Gq0brR6BIfcfdhxuGd0WRBNPEUERWg9yCpwFNQQABKMC0f+n/Yj82fr/+af6WPtC/D79vP2n/QH9qvuZ+n37F/3W/bn8w/ux/QwA/QL5BWEGRgRfArsEBQZ/B1ILtw9VEUUSchgXHrsiViiSLfwvWDDvMhk0gDX/Ncgy+Cs6HnoRsw3QEIoU8xXGEbUCoPE27iD3V/1r9BfnnN+w2+vg4+8y+jP42vUy+in8b/Tu6bfnfuj348Hff99D3vbasNuX4IHir9+q3tjglOKb5X/tvvYY/swDjQeHCgoM1w6zFBIaph3XIO8jNCKoHg8faR7fGRgVnREFDroLABDBF00cLx6RILYghR2WGn8Zqxj4FqoTJBE2ELENDwrtB1gE5PsK8yPtp+hH5IvhX+Dw3u/det2a2yXYadX60x7SQ8+gzCPKOsdiyAHOVtBiz6nNBs18zkHSwdd+3MXegOCF5V/rkO7u8VH2svpx/OD82v8lBQMJsgvkDxISjRFlE8sXQhr/GNYYYRvCHIIc5R2+H50g7yChIEIenhqtF6kUlRFkDisL8AjCBtcDvwFAARQAy/xp+Wr2k/MT8VjwR/CG7/PvnPGM8cvwVfIJ9R72xfXs9JD0w/Ub+Mz6av2Y/3cB4QOMBloI4Qc5CFUOhRPCFJ4XmxywIYolkShULMgxOjSVNsk7xjwGPeo/pz9LNZImCRwMGgAeoxwLFVkJY/bE6FTsZvfq9RHq7+Bq3Z/dMeXV8CD2gfPg8Z707/Ls6RHkZuVZ58Lk4OB/39ndqNsb3JPdNNz21xDXJNr33AjivuxG+Kb/NgYRDNUP7BMGGuoemiIhJasmTyeYJekjOiO4IFMcihgOFgcU2BTIGFEcxR04HkweLx3FGs4YuBi8GRwZDxd0FeQTPxEgDnEJNwEc9tDrAuVI4tTgRt8T3APZMtmK2YXX4NTr0tnQIdDJ0DXQAszlx9jGi8ihy6jM18zHzBDM8M1O1KjbwOB65GrozOvZ7ALwaPeR/VAAGgMpCMANixJvFx8aAhoQGeEZOxwwHNwaKhsPHD8dwR7rINIiyiJcIUQfXxt3FgwTBRBsCxEGEAOdAvQBhwD3/fX6Zved88vwc+176YzmauXj5Njju+MM5Ynll+Ub5w7p9unk6Yjru+508uL1PPlU+6f85v+jA9YF5wfECWYK0A1eEykXLBsTHmshMCYiKnYuBDQaOU4++UPASFJJyUh2SLtF4D8uNWwouSEAItsgph11Fy0HoPRW7H7vkvLH6nvfgNk82WDexeq59eL18PC67+zwjOuH4aDe4+Fo4UXdA9zV2zLZ5dZ/16zXrNMH0SbV29oJ4JnoR/Po/KUD/giJDokS+BWjGxwjJShuKiEsHCtiJ48kqyIpHgMZuBXEFAIWARnDHeYhoSNkJAokQiF/HSgb+RsGHvIdARx7Gp4XOxK5DLEGUf2K8Q/nJuD+2+van9qQ2DDWMdTi0VzQHdGl0VjQJ9Dt0ujUIdTI0WvNJMcdw2nGu8r3yiPKesvH0NDY1uCb52nq1+j86DLuJ/XN+x8C1gk8ETUW+BpGIY0k5CLPIScivCDfHRYdmh2VHNEaYRtHHFgazhcYFgUUihFcD+ANIAsMBw4D8P60+mD3VfVa9Czyyu7f7Grse+qY5nzjzuDU3GPZRtic14fXttqy3uXgUuLN5YDqGe5W8ZD0svcO+hX9ZQEiBuMKoQ4pEpsVChkoHEAeqR81IWEl7imvLcEwjTP6N2E/p0bhS85NQk3UUT1WY1T5TsREQDQ7JAkfWyMZJkkdhAxA+P/msecZ+MD5KueN14DStdUA37boue0F7hXqnei76uvkNt2N3lbiDN6i1CLSctPs0E3PItJH0jjN1s2d1YzbvOA666r3lQANBjgM4RJUF18bBiEOKKYsjC1ZLdotnyv6KBkpiiY9IZ4e6h67HpkgCCUzKNso7yf9JmslDyNtIo8jWSPAIAschxb5EcQNJwhUAJf2BuzS43Peatp71+zULNJC0GnOzstEyiPMR8+Hz/HOts9X0R7Sd9CWyq3CI7+gwr7HH8nVyHzK58561czcv+KE5zrroe959TL6Wv8zB/QP8BZbG4oeZCB9IcoiFiSeJEUjuiAEHq4ZOxUbFLkUDRSCEWQP2A0IDDwMFw0nDLAIqgM//jb5c/W28wHzX/GG7kfraOnz5ynl3+Gc3uXaUtdE1f7TQdMV1YjZI97n4M/icuVi6d3vfPZh+Xz6Nv14AlcGVwmxDTQRkhOYFmsb2h6kIfYlgCpvLd4t2DC8Nts6xjznQKJIqk9zVFJXu1vNXuBcH1vUWcRSWELLL0QhOBsXHjsdtBUZBXjsYt6V5TXwzOeg11vOgMutzrPYhuN35n3iv99M4qfg19ZJ0hDW3dfc0ajMqc2WzdjKLMv/zaTMwcmjzrbXkd5A5zz1JgM2DNkShRlfH8Ijcyg5L001njfIOG879DruNxg2gzNtLWAoTycZJmokoiVxKSEsrit4K38rbyhmIwohRCIlIVcbGhb/EeULVgTA/ZP2Nuvd3vTX0dO1zu3IqsQ+w2vDPsOKwvbBEcI4w37GRsqYymXJ58vo0D/QPsrTxDXC0MAfwynJOcx/zQfRH9fL3uTmNO7x9Zv9WwJ0BkEL5xEbGmAhwyV0JmkmBCinKWUpfCfTJRYhGxq3FLoRZA+GDd4MjAwFCiIHZgc0CUoJRwfNBBsBEPt89fPyF/FW7YPpV+e25I7g7dxW23jZ7dXt0t7Ry9B8z+DQydTD18baYd/I48Lnku1c9L350v3/AbgGFAoVDW8QiRIwFdgYhh6NI2ElhinyMGQ2FjnyOro8bT74Ql5IbEsDTnlQQFW2WhJen2GEY0xjnl/WWIRSHUqTNo8dGw3SCZcRoBI/APTiN8xFzALiiOtn21LIz76EwFvO/dmV2oLZNduA3MvbDdX2z7jSMdfE1WHOzclZysTLWMx/z8XQyc1D0pnd2ObI73z9WAyKF1Ug7ihHLtowXTTiN7Y6hT2OPoM+Ez83Pew3UjQ7MtEsEChwJ7AkwSFdI0cnuigJJ1IlHCMtHg4a4xkmGW0Uqw2nBXT+tflx9FbsOuAn0c7GvcOOwlC/17qztya2QbX2tuC4ZLmCu6i+LcF2w3THFMzF0FbTP9MP0sPRadEl0D3Pa9Eu1kLbeOFA59zqW+9e9z0ArgfNDiQVohmNHKYgryXLKCorWit0KlkqRSojKYom2yEfG0cVghAjDCsIEQWWAv7/N/5g/hn/yf40/X762Pao81/yJ/C665LmROMA4pvhJ9812mjWPtTA0urREdH+z+fPxNKL1gHZStv03gPklOq78U74gP25AmcIYg6sEzoX7RgGGv0bFiAOJDsnQCoaLvoyuzdoPVRBz0JTRAFH1krDTKFOf1CAUqtVMlnrWmRaMVqzWSRaFVhBUppJOT7ZLo4aVQkvAO77JvZq6/Tal8f1wDnQK9tZ1ufOJsiwxOrMCdYg1ojU7NRT1oDZd9er0rLTB9lm2ibW49K80g/Sv9AX1PzWP9Yv283lmu7/9bcCzhF7HbcmAC8bNLw1ADdIOOQ43jiIN3Q1jjQDMnwutS0jLV0p/CTsIpAflBvoGlsb1xqfGVUY0xYVFEcRFxBBDpAJhwOr/Pz0oO1h5ojfp9Zlyj/Al7ytu3a5I7d9tia2wrZDuYC8NL8lwnPGrsnfyxzPGdQa2lDebd+W32zht+MB5P7hdd483IzfG+f27CLwQfPF9w7/gQilEFYWtxqMH7okoSdUKJopryqGKsMo3Cb8JFQhbRsSFdMP7QnJBIgBbP0C+B30QfN28yLz0fO/9N/z6PD/7knvB+8t7aDpVuWm4VbfXN5R3Qjb+NdJ1nzWkdYc1p3WHtlr3K3fOeJQ5abpsO/C9pD8XgEGB2UM5BB2Fd0ZxBxUHtMgEiRVJgQoVSrtLcYxvjUlOo4+PUFpQ7VGQEmwSOBGHEidS+5N1E1fTWNOL1BtUT1TUlTQTbhH+UaSQnU6QjDjH9IMIf8G+Pb0J+1Y2ovFTLp2vbzL4NHAy//D98AMyNTV7NiK0cDPHdTR1qXWbdVL1k3aVN4K3pjYotMC04PUPtT006zUaNdK38ro/fHY/B0JXRSoHokoLTB8NC44tzscPcc7fzqSOF00WDC5LAQotiQ2Iz8h8B6uHfoaERf9E7YS5RJLEncPPwtpCWIKfQsjCbIEov8d+PHvf+k94jLZ0s7OxMW+FbwIu3y5Z7c5t025r7vIvVW/FcPRyVDQ+NMq1iraZd+V5PvnAumH6HPpK+247yDvvOvM5/rmwusz8aXyAvVE+Yf+hwReC7YRIBi7Hk0j4yTLJGclKCYHJlolQCK+HWIZnRT0DtIJlgV3AMb6A/ZO8WDtBuyT7K7sWOw37ULu3O7w8KHzgfRV8zbx6+4d7c7rh+lW5gDj4+Bu4BXgAN8k3cbcPt/L4sjk3uY96bzs+/Lt+v8BwgYMC9sPzhRuGUcdnx8uIOkgLiLAI28lYCeCKeMqViy0LtIxZzR5Nzw8wT6UP/E/jUGfQ0lEB0afR35HoURBREVEOEOeQVlAIT++PF45ODV6LkQlzRmKDM/+t/Kx7qPvLujj0rm72LR9xcbZ5tg3zJ7CksIe0kvkMeJ41hHVWNui3zTdNdYo1iPet+N44V/cfNlj2RncnN6k3p/dtuFP69fzMfsXBbsQaxtLJDErTC9yMbAzDjZgNic0PjFMLUgoBiSjIK4dTBuzGIcWnxXgFFgTCBMSEjIQSA/eDfsJZQZ1Bf0FEwXhAGX88vee8LrobeOP3uLVX8oxwibAyMBFwVHAXL63vt7C0MekyZLJG80Z1VLbKt1s3tvhs+Z+663ugu4h7RHuVfKK9fr0PvL77tPtKu8g86j3t/nU/E8CRweNC2gRGxdpG2Ue7B5PH9gethwfG7kbGhxbGDETuA4pC3sHAAR9AQn9k/dk84/wie1N7D7uXPAt8HXwKvK99A731/c79071hPKX77bsw+nT5lDltuQ45FnjEOI74QziYeTs5QHnYuiW6tXtwPIr+P/8TAGRBIUIQg1REYETExSJFVAXEhnJGVQZShrlHFMgqyKqJFIozyw4Mco1RTnHO80+j0GzQttBV0AyQPtA+UCGP8s+ukAOQVk/bz82P1I+WD7qO9g0/S3YKX8klht1DJP7HvbF+yX7OOvC0sa9lbp1zSjdkNhNy7PC2cSK1P7i1uBR2YDYL9sv207XIdTC1pLf8+Q84NzZP9ca1wXZudz13DPazd666CHxu/j7AhQOMRZfHGsi5CZfKTMrui6sMoEybi6xKbcm8SNVIFgdvhnGFfMUSBYgFWMToBJFEbkPFg8mDR8I6QPtAsUENAb5Atr8mfa48DbrXOZp4NHWaMx+xnfF08VpxvrF0sNvwgLFu8kGzHPMcM/r1u/dA+HV4vTmh+zJ8Hry9fIH8/bzIvb9+NL5ffj4+Cb5s/UJ8u/yqvey+6b/ZgOWBQgIEA71FLMXQheeF4UZURlEF1oWzxZDFakQzQuHB/UDewHw/qz7zvbt8ubxafH37+nv9vAC8dbun+6b8o/2XPcG96v2nvQ+8rzw3O2/6dvlv+NG4q3gQN/L39XhqOIR4wHkXeRs5WvqIfHH9RH5NP0XAQkFjwpMEIkSaBICE9gUJBjiGpAb4hsxHcIfRSOxJkgoHSlZLVozkzY3OeA+2UTdR1RI5Uc0RnxEMEO3QhtCxj9PPYs9Sj66PeI+vkDUP+A70zauMnkwrS3wK3EoFxq1BaP5rPh3+8/3DuUAydq0/bW9xYPMnccRwMm6csFK0JDV1NJ21Z/Z99i21kvSGNDp1ivesN2R2WvYhNhd2OfZXNvx2Rfb1eHj6Dbvbfj6AxwOjxbPHcUkcirLLEku8zEbNPMxWi9eLBooUCK4HQQaLxZvE6ISVBMnExwSDRGUEAQStBGSDTMItgRtBCIG3AWvAg7/N/qH86bt5ei24cHXkM4zyE7F38UcxxHGIcZxyWjMLs9V0bzTptcN3FbfSuL85XPqd+1d7i7vHPBg8E7wvvBE8ZbxPPI585nzp/KZ73br0upK8Sz56vxE/9ACIgcBDgEVTRizGCYYDxdUFmoV6hNgEuwQBw49CvAHFQbmAaj7LvYR8vTtfOu07JDtPey87D/wyvMJ9ir5pPtt/Fn9Zf05+5r4Qfe79GDwIe1H62rpIOgU6JvoE+rw6qnqoOrS60HvZfXr+p39if8aBBwKFA9FEuwTdhUMFicWJRcwGO4XihfGGEoaZRoBHIIfKiNnJUcnHCtWMAc1JDmFPQhBjkJXRHlFtESUQlxBD0AIPtw7YjnWOFk4tzfYNxo5BTjgNEwzITMiMl8wTS4yKsEi6BYnCAP6kPKD83ry7eTpyyW0PK1iuq7Fi8RRvXu0jbPUwK/NnNB40t7U5NQC1BvQb8330a/YV9wH3L7Zwtbx1rXZAdz828PbMODS5DjqvvME/78JjhO7G1Ai9SYDKiYuyjIBNR41djW+MbIroig2JmQhaxsFGMEWxhXaFMIUHxRwEg4SCBIUD7AISwN6AksDmQJoACr+Ivox89rtQ+to5XvbfNH2yDrCsb+vwCTBAL/rvNe9hMC9wvLF1sq0z9/SbdVs2QHdh97+4fvn6+sv7Hntk/DA86D3NPx0/pH9X/we/ab+VPxQ+Nf3MfvaAVYICg6FE8UW9BlaIP4kSSRNI8IjryJ6HzgcFxq7F70U1BG8DjMLQgjPBH0AgvwJ+fX2WfV88qnvd/Aj9Ev3pvje+PP58vun/c78Yfma9dzyd/Dn69rmdeMI4lzhO+El4k7iqOI75dPpxu4z87D3H/yV/k0AOQRSCE8KVgu+DTgQGBKdFBcXJhlxGtUaMBsTGwsb6xywIUYmEymNKwYwYTV1Om8/M0LFQnBDz0SDRU5FZ0NMP/k7vDlHNpwzSzE6LZwpBCltKVYooimBLFgsvynLJc0hOyAPIjUhPBsLEVoAE+5h5FDmkuo85vXSE7j5psaqU71RxGa5I6xHp+iwIsR6ztbNANFx2GzdHN5z26nY/tvv4yPmleEw3TPeruEE5SjoiOZW5QPr9vHv9un9AwcYD0gXSx+MJScpGCtMLIMvwDJ6MjMwQixeKE0kByC9GsAS7gvBCYEIPAVXAm8BiALSBd8HXARo/i/86vwz/nb9w/hX8m3u7+uF6KPkKd/e1ePMqce6xGnDA8N3wlbBUsJfxjbKBM0u0crWq9z24aDl6+ip7in1LvrM/c//RwDNATQFpAhgCyANOw6+DnkPahE8E7ET/RNIE44QYA+FEh4YCB2ZIJ4hpCFnIpsk5CV1JfMjsCATGwkVVRBfDZUKQAaJAG76a/UD8iDwCe9l7TbraOhm5Yjk9Oau6RXr3OuP7G/uZvB78Xjy9fKa8bPu++tH6PvkLOTI5G7l0+Wm5qHnv+oS8MP1U/p2/ab/awJFB0cLSw0AD7EQ0BHWE7IWLBhhGMoYohlGGq4aEBqmGbQZVhrGG2ke2yFrJZIpCS7qMJsyuzRyNmc3dTdaNiM0mTI0MUEvpiw/KK8j3B+sHUkcBxuvGDMWUxRTFHAVSBYIF4gYJhnwF5EWfBVyFEAUaxNcDuMHLP3k7eziueDm3zjYGsWIqjOaLaWBvZTGmcEjuqG46cdw4D/otd+i2yfe/+CD4orgpt5S5bzvx/Gu7HLntOTT5b3pe+kp42rhpefK7rH2+v6iBQ0NOxWyHMojOioDLZcvsjOONJkxnixLJAccOBcyFLIO3gh4B88IMAqTCqMJ1AY2BTgGjAV0AMj6sfkB/mQD9ASEArr+3foU93H0LfEr6QTfLdgX1CzRNtH50WvQxc3Nzf/RTNgz3gXkAetX8mT3wvoE/xkCXwKxAb4ATP/lALUGjwuGDMYNnA/3EA4S4xKdEVwPgQ5vD20P4A0IDA8MTg+IFOQWUhbuFmcXDReWF9oXwhSeEOYMqQbCAGL/y//0/oz9SPw3+sf4cvem80PvvOu156fj6OKt5BPmwOeP6ZDqGuz47SrvFPDJ8LbwhfDj7+7tyuts627rAevU6kvrbuy870b1cvoq/iAAqP9r/9gBxANXA4ICwgIQBBgHyQsHED8RHhGpEVYSEhIyENoNEw0YDtkOmhBWE4sV8Bf+GjMdHB2gHbEfRiL9IzIjpCEfIRIhJSEzId0gTh6QGqwZbRrwGcsXpRXMFK8UWRVoFbUTBREgElsXrhmVGfQZ9xuqHWceNR2KGLoSIQ/sC0kAjvHk58HixeII4rPUrL6OsTi1UsKnyvrI6MIXwvrM7N5b56Dlt+Yp6ZPrEu0K6YDkUefM7pzxVe9+60PoKOfG5+jpg+h75TDoKO4p9Wz+/Ac2EfgZiyHQJ0ItZTA1Mq00CDZGNOMvJSnBItUdexkFFPkN1AkICEwI4AkNCvIH8wbsCNoJEgcuA78BuwICBcAGngXyAST+tPpU91HzxOyp47/betaH0yfTI9Qb1DnTBNQA1/Tamt8I5uzt5/O29z375f6WApIFZAf3BoADpgCrAlYHzApVDdsOrA7ZDm8QkxK2EjYRTRGLErcUnhYrFxgVihMQFUoX0BjQF0UVNRIhEKYPAw4lCYkDxv6h+tj2vvMv8XLvYu8i7zXtpOo+53XjG+C03R3cQ9pN2tXb8dwx3nzhhOOv48TkFedv6VbqYepg6WDpkOoZ66Lq2egY6PTpfezv7jLyhPaQ+f76Tvzt/Tf/mwDgASEC5wI2BfIHEAvlDXEPlBA8EYoQwQ/VDzQQWA9HDpcOLhAmElMUlhb1F3IYVBlGG5ccpB1AHyIgch/5HnYf6B4hHnkd+RuOGi8acxqzGpgaFBnoFukU8RGsDk8O9A/bDuULZAr+CxQPQhQ7GUcZcxcXF8sXnhYkFFMR/xCkE7IS8gwwA7n17umf58npU+NF0o6/QLgZwrLQnNhV2XDWCNcR4zbvtu9j7r3xfvVo98L3X/Va9LH4Wf2p/Cb2Ju806wHr9Oxi7FbqyezK7+vyrPpIAUUGxg7WF38dISKJJ68q1SwuLdoqRSjHIVEYohJXEB8MwAawBGYEOQSkBdAG/gQ0ArIDdgcPCecGXQWyB2YLXw0sDooN7AiGAl7/Af5L+nzzrOw854Pizd+13/vfsN7I3dTf9+NT6DntjfOW+Uf9jP/uAQsD0gIUAxgDQAEm/lr78/ml+ob9Zf8v/6b+nf0B/tEBgQROBM4D2QNeBFUGyQdiBwsGOAUGBY8ERwNtAXcAUP9C/Cv4vfSc8bbvi+/S7tXs3Ooy68LsYe6i8ETyBfOM8vDwvu+y7/XvPu6867DpAejc53npmOqE6yXt6+8F8iXx/u/q8LXxDPEA8B7wHfEU86v1qPef+VT8WP93AJb/tv5Y/j/+hf5y/kX9sfzy/MD9Wv/kAWIELAa4B5AIvwkrDDkOhQ6dDX8M9gtxDNoNsg5MDz8PlBArE6cUjBTgFIcVlxVJFU0VURW6FTQXDhntGf4arRzBHgcg7SDfILMfOh4YHEcZ8BaqFeUVwhVlFBMUthUVF8kZcx2tH+kgZyIoI40hhSGtIuYiJSGPHcsZQxH4BI75BfN28j/w8uI5y+K31bbOxLTQX9GnyvPGmND548jus+2u7ePvUfJN8x3xF+7Q73L24Plc90zxz+x97M/szeyY6Z7mf+l27q31eP4BBkkNFhX3HF4khCqVLiYxvzPQNOgzuzDgKUoiURw4F4QRpAsOCMQHswifCUAJ2QfZB/MJWwqfBpkCUgGvAr8ESQbwBTsD8v7Q+a/1SfLd7Lrkitxq1pXTedPn0+vSt9AC0DbSOdYd2yjgYuVd6truZPPX9077E/0M/iL+Bf0y/CD9Cv5R/k0A4wP/BtMIHwkhCeIJOAxSDzIQlg+pENgTFhZ7Fn4VyRNgEpgQeA2rCm0IYwbZAykA8/qp9jr1qvN78Pbsh+nm5tTkyuTh5XPme+aU5n3m/uVK5lDm6eQs4+vhHeJr4pvhFOEG4pTjYOUV5r7l2eVb5+rpr+uv64brrO1l8Rf1Y/he+wz+YgBrAo8ErQa1BhwG5QbVB64IZwmMCegIlAhLCs0L2wtSDBwOtxCREjwU+BVMF2oYORkoGSgYuBYSFsQVZhXIFLkTuRP0E6ATQRLkEH8PZg7WDSYNpAx2DVYPihDIEcUTPxX8FaQXKBiWF+QXOhisFdIROhBKEJwQIxDODUkLXAvUDdoQABNDFV8YyxrAHDYelSAQJOYlfSXNJSQmZSbyJGIcGQ0HAqYABvzC6/nVZsWLxNHPj9qa3B7ZTdpu5fH15vtg9871oPj3+lX6/fik9zr4efyh/XX4kfA06grmeeJ332XbdNm/22bdEOJn6hjwHPYn/xkIVg/bFqQdPyEiJKYl6yMRHlEUeQwpCCwDD/3e+fH50/pT/eEAtwHi/y0ANQT/BhwG2wQOCCwNThCREu0UOBQwEOgLWwn5BYUA8/mw83XtS+i85QPkGeA63M/bVt7r4cHma+3282/42/tC/4UBHQHD/3D+W/wB+nH4L/cu9vv1dvbs9JLxqe9K8Y30vvbF9+D4CfrU/KECmQfyByQHzwcbCC8ISAjMBsQDCwK6AOD8L/iY9dvzY/GG70jul+xx7Srv3e9E8RT0CvcT+RL76vwk/uv+Dv9D/pH7w/hF9wv1jvJM8c7vUe4d7YPs8eso63LrPezL7Mntie+Y8XbzuvaP+jL9VP4j/3H//v7y/gb/ff6x/dL8tPvM+l76kfrI+j/72fs6/Gr9AgBDA+EG7QpPDlAPpA96ETMTFRPWEVkQew8ED20PXA+fDokONw/4Dw0Q9g/gD78QFxPjFQMYzxnuGoYcBx9MIbAhBiHvH/seuR7oHtEd5BsrGjsZURijFZ8Ryg0XDBsNZA/zEHgR8RAbEygYZhzfHS8e0h5EH0MghSBnHgEbGxvOHBEbKxfRDwsDb/Ub8VDwJut33ozJ77m/ugrIttOq1n3UfdP53BbvRfdO9fj0nvVt9L/xhu8q66nqM/Aj8UTtqedf467ipuIx4q/f8N4U4dTjLOu28yT5FgBTCQMRNhfbHdYjbCjaKnQrOCqzJagdYBXvDlEJpwP+/zX+cP5eAV0F1QcKCHEJLA4aEfYOKQv0C6wP9xEtEyUTBRAaC5MG+QL2/m/5LPLb6cniZ9+T3qfdy9pq19nWJtko3CzgM+bT7A/yl/Z9+53+K/8h/zX+wfvo+H72mPQf9DD1nfYe99v3Wfm0+rz6UPv2/uwE7AjdCvAMihBuFQMbyR3MHOQaghj+FbcTVREPDcgGGwG5/OL5r/hD96rzMfB67y/wDPHc8ov0mPW29s/3nPhR+QH66fll+Cn1PPEx7dHo8eTR4ZzekNya2yPbANu920Pdnt8l4pHkeud76kTu6/Ii9/X5LfxJ/mv/cgAEATsArP+Y/9H//v8xAFwAx//o/+QBPwRnBjcI6AljDYgRWRWZGCcahxpOGusZihieFu0UqhI4EBsP3A0zDHkLLQv1CbkIkQevBr4GGwhKCTsKzwtjDbQOTBCpEcIRzhBkD3QOVQ6/DFMK8AdbBg0G6QbjBkoEfAIHArkCVASzBkIIWQk5CvkJ5QvDEIUVsBYRF08YAxlPG1odQB48HogfbyHOIqMjkSOnI0AlGCMcGSgOOAZrAlL87+s313DLydCr3NTkDuUi4dzj6vCz/aD8rfUR8j3v/u3f7O7pkuf56h7va+7k6rjl0eEr3y7dSNu+1hnUdNUP2bnfI+Z266PyCvuLA1AM3BQBHJ0gPyNjI5sgjBqiEugLdAaKAhkBggIlBT4J7w6cE/MU2BS5F1YaMRlIFnYViBgoHCce7h61HqIdbxrnFS4R8AwHCVUDQPyv9bnxBe8O7J/nJePK4Qnj7OW/6hXxqfbM+Zr8tv50/in8Rvi88/Xv6+yo6eTnlegf6hzrVesZ627rCO167irv8e8g8kj1kPmk/ksDTwbeCF0M1hAoFM0TMhHaDesKGwnaBtwB2vm+83fxd/FP8irz//JX8jvzT/bU+Vn8wP3G/cT9yfzB++36LvpL+b73XfWK8hTwSO2U6k/ok+UQ4snfl90Z24nbI9+H4pnlYeoy78DzU/nU/SH/RP+J/4f+Sf1Q/Pf6MPrG+XL5G/mi+Kb4Jvk3+sb6/vub/nMBbgMiBnMJLQyrDQIPgQ+gD5wPkQ75DFoMVAwCC/gIZQaPA20BVAAG/0v98PzO/WX/RAKKBtkKsQ1AECsSMxOYE4oUixVoFR0UiRLpEXAR0w8/Dj4N6AuCCi0L4QtWC4EMmhD4EhISxRB8EMwRQBSqFnAYSxloGbkaOh+2IygmqybZJOwh0h/RIPIhRiGgIVwkOSc6KTwqUil5IogXAQ4WBKX1vuH00I/HRMagzAfWfdpN2Qrebuvs9F7zKO7X6a/mDOXb5WXmK+at6gDwIvAG7MPm0uP24Sffy9uZ2a/Z99ku3T3lJOtM7jL1z/5nCAgSbBx6JgsukTISM1YvvSjlIIMZiBG9CfMG0wg7CmsM7xELF2kXSxW2FTYXeRaBE6MRmBMGFyYZxBmdGCMWaxNgD7UJQwTP//r6JfTA7LXn1uMK31nYBtPq0T3T/dWH23fjteoS8G/0APjx+e35GPfC8iDvEO3U6+rq1uqn6xDtCu0Q7RzvzfH18rDzzPXR+Hn7Qf6QAZQEGwimDD8RKRbXGn0eeh/6HTAbxxbaEB0JaQFm+i7zN+wk6ODnlOk37Lfto+5i8EL0Uvec+A/5XPj99uj03PK88FPvDe+Q7SbsLOtU6j3ppObi4uHegdsZ2ZXW5tRI1QXZQOCd6KHvS/bY/BkCswUWB1MGbQTpAo0Ax/yr+f/4//j29/v2rPdV+QL7A/1g/sAAgQSYCE4LQgxADcAO0w98EN4QLBE0EZ0RsRE4EV8Q7w14CmwGwgLP/nn7Nfo7+3D9tgD2BLUJjA2fEV4VQhcjF4sWKRZ4Fc4UDxQYEr0QwRBfEDkPtQ3/DKsNCA9/D5IPiBC+EaURyBEeEhYSQBQOFmwW3RUBGWIdux56IBkihyH9HwIhCSK6INAfGyFSISgg3iKIJR8m8SfwK+0tJSxjKYIfYRDpAtX0IOA2y1+/iLv3vC/Fks7W0bnYr+ak8Fzw6upT5D/dH9v13LbeteAG5qTs9e4n7Vnqx+ZD4b/b3ddE1TLV79YZ22rhUef+7Cf0Ff1rCEkVZSK4Lto4fD8bQl1AvzhCLWEi5xgbEE4LJA1FEV8UChqHIYsk+SKUIRQg8xuiFyEWcxfVGV0cTh3GG7IZwRZVET8KIgMD/Tv3uvCg6TbjyN1K13zPzchixTPF68cxzfbUNt3U5PXqfO5i71XuEetK5n3ix99T3VPcTN7l4YnlGOgU6dbq0O4D82H1Yvd2+sz9FQH4BPYIIg33EbEXoRw7IYolHCj8Jx8mCSJTG88S0ArbA6n9b/j78n7uku0n8EvyofNd9Wb3zPm9+/n85Ptn+eH2HPPE7l3qQufF5VLlYuUg5ZHkAuNE3+3aytbK0iLOV8ptyQfL48862FXh6+h+72H1n/o7/uL/b//7/fL86PtH+vL4J/h7+FH5Dvq8+uj7wP7/AkkHKwuHDhgS4hQEFmoWnBacFpoWvxYpF08YKxpPG08bcRrQGGwVcxAiC/kFVQJcADkAngFKBDUIMQyeD9cSuBUGF2IWKBWKEw0R3Q6CDQwMygqlCQgItQWqBBMFKQXZBEIF5gWxBmUI6wk3C+UL4AzeDDQNWxD5FGMYZRo6HnshvyIiJBwksCGvH+MfsR/XHDsckx0iHwcjkydAKRorYi5jMHcwjC32JLUY3Q5xBYj1ht9WzWjFYcaozGLTw9Ta1XjgIu8P9d/ws+tf5w/jrOKh5Mrk1OfZ7y72YPde9Grwn+xb59viOd7l2cXXI9gU3dzj+ecE7b31EgAUC3kWWyKlLXA2RjvyOzg4xS/gJQ4d8hOUDOUK4AwSD/oSRRlaHY0cwxqZGT8WjBDsC3cL6Q2qD0URpxE4EWsQmw05CKwBH/yl9pPw7unq4xnfLdo/02jLYcasxFDF6cg9z+PWDt665LHqp+7n7wPu5um05RXjf+GZ4B7hP+Tr6NPs6+478JLyiPWZ9y75J/vx/dsANQTtB6ALLw9oErgVWBmwHcEhlSPqInQgWh3SGNESAwoq/uDyyOvM53/lFuV75Z7l6+fe7YryxPMp9f/1XPQT8U7tlun/5ljmtuUg5CHjnuO+5F3kuuG83TDZ4tRyz/zJhsf2yELNBtSc22PjiezI9okA2ganCcgKOQs9C0AJVQbTAwcCJwEzARkB8ABLAq8F5gi2C2sP3xKfFeoXXBkpGokaQhv1G48cTB0tHj8gvSJuI6IhIx6VGXgUug7XCFcDAv/B/AX9BP8wAnAGxArIDbIP4BDiECcQTg+jDlENpAuyCmwKcApACaQHNAYhBSkF3AWKBWUFGgfICToMfw7yD/UQVBLnEu4SehUxGugeYSIPI24jFiQmJt0nNCb3IucgxSHUIpAlaCgsKpQtwDI6NMoy+TR9Nxk3WDN0KpIbkgzlAGXxg95c0BTIG8ZVy1DULdjp2r7lzvLr9v/yvuuD4pTfveK/49fh2OMP6t7uX++16/rl9eAb3MzWJNLUzkDOF9EI12jeCOXk6hXyufwyCqYWayGCKiwySTYRNZkugSXtHE0U7wpyAwECMgWpCcoOfhQsGHQYExf+FWUTzw9NDdIMQQ5mEdgUrRRyEpwQhA2dB38AP/qu9ILvL+nb4f7bI9eS0DvJcsQ6w5zFv8rb0EbYkuAe6ELt3+8S8KTtf+k75WTi/eB+4P/gIeOo5hnqvuv07O3ut/ES9L31gfd2+k3+HQJZBcIH1QpcD/ATIBhsHDAfFR/bHEMZoBOgDAsGJf+x9j/vMup15l3k1+Vs6N3pIe1S8RD0YfVN94H4P/fa9Szzre8r7Xrrlupo6Urp9unX6ELmW+O738LaYdUi0g/Rm9Jo103dfuPW65n2AAHnB5QMAA/xDkYPDxDZDt0LWwnNB9QGCQaABR0FigU9B3YJEAttDHEPnhPVFqsY0RmuGv0bxx0RH5kfASB+H7Mehh6kHR8aCxUuEK4KAAX8AA7+afwr/Ev+tQHJBHUI/wySEM4R5RFFEosSiBFrEEgPpQ0dDUINTgyXC7ILbAtBCjsKaAsGDHANFg9QEBAS+RSrFwYZjBp+HB0egh+LIV8jMyVBJ7wnISbNJIMk1CNvIqYhuSHsIg4lXSZsJzopVivfLq8x+TGDLywq4x+7EJ8D2fe15tHTm8cdwsvC8sjXz/nSadgh4zDrgOq45afgadx02jXa+trG3LXgz+ZV6wjseOgO4jHbJNZD00rQQs0RzW/QwNbS3XzjVOrH9HcAwApcFS0hDCvkMEgzhjGfK/Ui3xjsDzgJTgVdBRkIBgx8EXsX/BmrGJgXkRbfEmwOZQx8DeQPKxJjEw8UBxR6ESMM3AWu/6v54/OH7UvnLeLS3C7W588yy2zIV8fGyOzML9R23HXjZOlf7kfw4O6d62nn9eN/4WXf593+3a/greQc6DzqDuxZ7srv7vCh87z2wfhB+7T+IQJXBXYJ5Q1BEowXqRuTHGAaZRfQFBIScA3aBWz9jfY08tPwOfD07XTsI+5C8vX2aPoZ/Ob8T/5W/9D9Bfr59Y7yJfAy7j/s7urS69ftIe7f7H3qu+bH4jzg/N1j3DvdbuE451TtoPS3/D8EpQkZDZUOVQ5PDmQO6gxkCtUIxgdABl8FAgUjBQUGDAj9CVYMcQ+yEsIVhBiDGkQbIRtCG1MbBxt9GjMaQBrmGhUb7RkdFzQU7hB/DM8HlQN5AI/+uP4OAT0Ehwf5CrgOzhFAFLoVUxW7ExgSHhELEMgOig13DC0MsQytDIsLYArACokL/guIDCINXg5fEcoUURWIFHIVUBeVGakbOx3bHSQf5SBVIngjuCM/IaYeSR0KHjEgNCIrIp4iWiWoKLwrezDPMlsuTSNoEkz/YPDB417UkcdDwS7ASsWszwTXFdxa5X/t8Oz750zjEt1W28DdMOB14k3lVOkY7Brs5ejV4VrbwtUu0iHRU8+PzyfTKtnC4LHnmO2/9JP+GgpFFnIjwS2YM2Q2QjRcLFcichhPDogGRgHJ/5ACDAhvDwYW1RkWGawW/BVME4kP/Q1uDpwQYRN+FdUVXxQYESULCATO/CT2xfD76xPmKuAx24DVz8+vyurG5MUkyB3NitTE3RPmn+ye8Vjzu/FI7qzpk+Qx4Zzfpt6a32biXuXg50HpWull6kLuPPI49Sf4HPsc/pYCMwjRCw0NjQ8vE9kWAhpRG4kazBixF0EVIw8/Bx4ADPu294T16/LI78ju1fBm8xX26Pj1+tT8WP60/uP9u/wC+xf4ZPQK8Uzu1+w97L7ruusq6w7pJeaA48TgGN6E3J3cWt8K5QHsZ/KO+I/+hQRVCdcLUgysC+8KeArACpkKVQmbB44GEwaKBfwEywSlBkkKgg0LEQMVsxjOG/QdQh5kHUkcHRvCGU8Z2xlgGsMa1RmrF1EV5xFJDTUI4QPpADn/df9QAaQEgwiuC0EOsxDbEjAURhTkE/YSzhEoEbcQew/zDdcLFAqwCA4I3QfbBx8IcQnsC7kNJQ8REboSMRP6E3IVfRemGS4c3R2VH80hWyO0JFYlRCXaIo0goR+7HsAflCFKIsUipiSdKi0wUzIZMU0nlBfJB1D73O1x3DnPusfqxrvNBNeC3cThhejd7wrwK+pd4wXdVNuS3PzdMOB/4vrmeepZ6g/n7eCM2+jXXtTm0XvQC9H6073XBd3V4UzmQO6o+OEDUA/aGsUkgyv3LpQtzCagHcsTGguuBMv/SP9FA4cI9A7zFMQYhRlDGGUXdBXVElMRRxHAEo4UnxXpFcgUOhHfCyIGZgDz+j/2IvKR7FrmreAK25vVNdAKzO3Kecyo0FTXH98T5tLrSfCO8XLvm+sJ53HiuN4V3Hjag9oB3MnejeE44yzkoOV+6MnsOvHP9MX3n/ov/nYCjAZPCfoLVRCWFP4V7RUAFncWvhW3E8INggUgAFX9uvpo9/D0EvMx8yr20fhe+g39rP8+AXYCygKzAQwAg/6Y+8X35fMp8Qjwwu/z7rXt7ey66xvqO+i45RnjHOGf4LTiLufn7EvynvcX/SoCmwa/CJAI5wcCCAoHHwUGBMQDbgNPAy4DLAKpAtEEVAevCdELog+1FBIZsxs5HTYecB3cG/watBlyGP8XihgkGQEZZxiIF9MVvBI9DlsKFwi8B70IuAq7DHMPPhP6FkUZFBoCGvcYnhdWFp0UphNlE8AS3hCNDhMN/AtkCzkLQwpWCOcH0ApLDuIQLRLsEoMThhRYFk4WQxYvFwga/xswHPId/x6pH7kfAB55GmcXBRhcGoYbwhxdH58jgyjGK5IpIh9MECIAlfBH4NvS98pNyAvL6NHE2vjhWulm8A7zDvAI6ozjz96V3ufg8uLw5Svqa+2A7grs8uam4QXdPNgN1T/TQdOK1RHYFNu13xrk1+nY8Rn8nQZLEu4d0yWxKh4reyUUHGASQgnwAMb6WviZ+sX/UAVWC6kQyxPWEyUTLxKYEHwQVRGyEmEUyBW6FfgTlRFJDc0HfgLa/Df41fS68AXrJOV330XZddOnzgbNBc950+TYMOBH6HruqfKe9EHzJ/Dn6yHneeJW3zbd4Nue21vcid3o3pDgf+II5VPoK+0x8/j34/pH/bn/cgLSBWcJ3Qs1DwsUlha4Fp0VdhT5EYEOtQgzAYf7LPnw9wf2pvQn9Er13Phn/I/+5QAjBLEGHwhWCKkGIwS5AZf+IPpG9V/yufFP8gfyxfDf76/u6eyg6ibnq+NM4gfjg+W46UHvCfWX+rP/VQOGBb0FawVMBfgEKAMtAUkAov9x/4z/k/5s/aj+lAGNBP4HKwyGECoVQRm/G3UdSh6LHbkbJRpmGYIYZRjjGPkY4hf6FWsUExOAEG4NCgvBCVsKewwMDzYRGRTFF0oa9xvUHKccrRsEGlsXhhSXEvEQtA48DBoKWAg/B6UG0gZWByYHAwegCOULcg7RD28QpRBFEvEUhRZ3Fo8VkxZAGOMYchmuGSsaLxnkFtMVaBUeFc8WdxrdHWwgACVMKVkqByf3GzMLyvnD6qXd59IrzOXI7ct41pLf8OVJ7KDxZPMx8Djq/OJ84Nni7uXC6Dzrhu438H7vy+yf52bhotqV1hPVctM10+TUstdz263fKuRt6m7z/f0aCo0WKiCHJwYsnyooI50ZXxCjBs/+avql+eb75v+tBRsM0BDUEccQzBD4D1oPPhAWEqkUcxdyGU0ZsxfaFDQQ1QryBE7/Kfv/9q3xFux55i3fNNjd0ofPks6u0GvVLtyf5Dvs0vFr9aX2+PQx8Qbs6uYy41Pgqd2G3Obcbt0O3uzeKuA74fzib+YK7Lvxo/Wo+Kr76f6lAtwGPgmkCzAQNhSLFX4VXhU0FFkR9gyABtH/WPsp+Hn1j/NB84X0O/d6+v384P/9ApYFnQf8CJYIigaJBLEB+f01+ib2+fJ98b7wcO/M7lXu/Oxw60rpJeal4+zijOPp5YrqFvAR9W76b//SAnsEGwXdBHoDHwED/i/8gftU++36P/pl+Y/6fP2oAIAD9wa0CzoRZhbZGT0cNh67Hp4dRxxRGykajxmyGRIaDhoOGYgXsBX0EkoPPgxTCiMJagpZDa0QsxMlF/YasB1jH5kfZx5uHAIa6BesFbQShw9NDaoLJArsCAAIugeyB/oHhwdFB0oJzQtuDXAOLQ+9D5MRqxTJFk4XOBhMGoobghwmHHEbFxuNGZwWdxMWEiUTvBi3HuIhYCSFJ44poiREF64EBvRO5njaItLBzMXMw9R84J/oC+4x85D1R/OC7jDniODY39niH+e/6urtwO+X7wvubeoi5VrfMdrX1/7Xx9gs2e3a4d1Z4PDit+ak7aj3kgKjDoEa8yK0J6Ip3CUgHUYTIwnJACv8p/of/PT//ARLCkIPxhHUEAIQHRBxD14PDxGqE3UWbBgPGAYWChNyDjoJpgT4/3f7ovhV9XfwD+uK5LfcC9Z60QfP9M+X00PZXOFo6cju0PJj9Y30Z/Ft7cbo2+TZ4uvgoN6n3WzdPd1J3bLdZt4V4EDjqug07w70r/Yd+dH7YP5vAZEEsQb1CZMO0BFSE+YTdRNNEeUNkggLAiD9p/mf9hX10/Qf9Vn20fgO+zz9jwDRA+AFjQdECH4GNQTwAY/+EvoD9gPzOvGN8Hzvp+6/7g3uT+wf6vbm8ePm4pLjrOUa6frt+/IT+C79BgFmA3EEGAS+A+QCHwEz/4H+zv1D/Sr9Hv1F/f7+wQHiBMUIVQ1NEgsXphr7HIoeFx/+HcocUxwBHOgbPRyCHNccRRywGpsYNxbAEqoP6w0yDVcNyg7/EI4TphZ4GUQbyBzoHdcd7RwNHM8aWxiJFT0S1w5fDNsKAwmKBugEdQWoB9IJ3wsYDm0PHw9PDvQO2w+eEKASZxTxFIAV+BZaGnwcpRyvG28b/RuKG8ob4hqsGh8e7CJlJj8nCyf+ISwVJwS09IXp+eAV2WHSM89S0yvefuhj7tDyk/Yu9cDv2eiU4wnjoeTC58fpEOwq7kTuMe0R6szjrdzj1j/TFdGj0dnTNdYe2jHfguPx5/fuovgyA2sNYxfjH6QkzSSuIGgZsxAqB0z/Mvon+EL5efysAUcH3QtKD6QPhQ47Do8OLw88EKQS7RXOGMAZlRhgFncSTgwXBqYAnvuN99Xz+e5B6Tjj7dxS13vT9dHI0kHWjNv04pfrz/Ho9JP2sfUV8h/tNeja4wzhnt9o3vXddd4P33vfM99m3uXetOGI5jnsTvG79JH3Evu+/lECagS4BNgFjAiHC/kNnA/6DwgP7gzVCOQCEP5u+gr3c/Xi9Qb3A/ll/NP/swIyBZUHvwjLCBQIrQaWBCYCnP8R/an5kfbq9IPzgfFo8FTwyO+77mDtXeuD6dvo+Ogj6vLs4fDT9Gv5DP7PAe4EjgYiBh0FkQMVAeH+fP1f/If7Vvu2+9L8c/90AncFQgmpDYsSNhcdG7od9R8AIWogTx+WHhke7B3HHckdrh2VHNcaGhndFhUTMQ9HDe4MXQ1/D8ASHxbbGUkdgh/CIMUhxyFHIDEeIBwfGvIXWRVcEnkPtQyrCdgGXQX+BD4FUwZxCG4Lag4sEIgQaRDOEMIRchI+E00U3xVbGB8bBh1qHPIZKBi7FzUZRRwZHrUexB9+IzQnHSaSGv8FZ/Ls5YDdAdY60ULQQtQx367sf/QM+CP7nfo+9TvtOORY30PhveaE6nvsBe6U7d/rWukJ5AvdBtdw04DSK9O31IvWDdky3GffAOOL6NLxV/3wCKMUvh2aIrYjhSA8GIoMJwFA+CDzLPKB9Ln4Nf+tBakK5Q70DzsOTw3GDXgOMBBlE9EWiRmaGsoYnxXfEVYMDAdRAt/9n/oB+dr1TvCm6UTiNds71pXTvtPf117eKeaJ72r38vsS/kX90fg88tLrF+aL4qngjd7E3Hjcn9yp3MHcnd1T4GjkeOhm7OjwivXS+f38q/1b/ef+CQIwBWoHDQldCkAMIg6ADZMKYwXp/sT5Dvfs9HbzI/Sn9mP56vwEAUIFAQpcDmkQGxChDogMwQnkBUoBdfwl+MP0hvIR8h3yfPLO8lny9vAU71rtzOuA6hnqXetZ7rPyzfc2/IH/VwLyBDYFZgMrAfz+t/zx+lf5Wvc/9hL3a/h4+QT7gv0TAh8IhA0WEu4W6BtrH1AhfSGnHz8d9RuAGxMbuRrLGqUbOxzoG04ayxckFR4SDA9fDWMNTg9vE2MYwhxpH/YhwiQTJkQlyiLvHycdzxpnGIkVvhLPD4YMqQkKB90EHwRxBWIINArjC3QO7BBhE4YUoxSOFBcUwBKHEmcTUxXGGNUapBmyFUQU0xVhF8IZxhwqIL4jryMUGnwJy/gZ7rHmSd3R04jORNAS23nqzPUG+yj+kf7p+bfytuhc4UnhWeQD51Ho8urR7HHtvuzg51HgdtnH1LbT+tOa1IPWiNnB3FfggOMJ57DtBvhqA4UOhBicHn8gfR5MF2sMAAEC9/jwV+/s8L30xfvTBI0L6A9mEnYRHxA2EBcQ+xBrE7QWMxrXHF0csRinFD0P5Aj6A1IAU/24+7X5dfVT767o9OGI3AHZpNfC2Q/g8efy8Jn5WP7o/0L/aPuB9DntIefk4tzged8C3g/dfNyr3MLdO9663ZDfJOTD6avvV/WU+D/6D/x5/KT7I/v4+5f+JgL3BTMIPAl2CZkHSQMO/lP5LPaN9OHzUPSR9n76Gf+hA+UHygqkDFgO9g4xDu4MZAt7CHEECgCc+7j32/Rg8/Py2PLh89H0qvSV80/y6vD57jvtCO2z7v3x+/XI+QP+ngFcA2IDcwF2/iP7K/iS9Zfzn/Kt8k3z8vRw9tn3T/rz/TwCLQcpDE0RMxacGn0dTh6THRYcmBrlGLkXWRcFGMQZxxrVGkQa0RluGBIWrxNiEuMSqhXnGWEecSKqJUMouiqDK+wp7SfPJYoj7SA6HkQbRRjzFWUThw8lDKcKbAoSC2sM4A2aD5US+BXuF4IXKRZXFfASeRIyFDYUwRNTFFUVzhVZF3AZzxmkGnwbAhYlCZr4n+j23p/ZWtT3zqbNRdPb3tPsXPd3/KD/VAGN/mL36u166IDo1+kc63rr4+xA7rPuru1x6S7jJN1q2U7YIdes1nHYt9vU3z/kg+m870j4hQNYDjgYKCDYI3ojDR+LFYIJo/2R85nsYOmS6kPv9/ZM/0sGBgzlDmoOCg6TDloOyg4VEL4SOxbcGOsYbxcJFRkQhAqtBd8Au/xy+av1jfDS6hDl8N+a3ATbTtta3w7m1+3b9v7+owNnBcsEKQF0+q3ygutO5qziOt/13HPcDd0A34/hKeMu5H/mtOkZ7SPxB/Wv9oH3MPmu+n/7qPvH/Df/EQKHBFkFVgTNARD+GPp29ofzQvGR7yzv4+9n8hT3T/38A0wJ5AxtD6cQaxACD7cMfwjOAoT9Cfln9RrzVfLp8lD09PTD9Eb07fMF86jxL/CM7kLuBvAM88j28fmk/Jj/7AFTAnAAIP5C/Pf51ffB9e3z1vKM8oLyEPNa9Ab3jfq2/l4DFAjPDOYQ9hNYFicXmhY7FaQT4xLNEikTmBPQFJwWOBheGcAZkxgjF9kVbBVzFiYYMRopHfEgoCReJ2Ep/yrVK/YrgCovKD4mICRaIRMejho6GAQXpRWMFMkTMRPuEwsVDRdaGj8dfR5oHREc8Rs5GqIX0xU1FbEU+RTcFP0UqxfQG3AfvB1lE44B5+xD3TXTzsqTxPLBuMShzmLeH+0k9zn/YARHA7n8dPPj61/qKOzl7JXsRu1Q7SrsPOuF6KPjGd/V2xfbNNwA3n7gkuPq5hXqI+1h8Q74ewHoC7AWxiC+J44q9ig3IS4VYggj/Pnx2Ouv6p/sqPFP+UkA8wZIDBAODA8oEVIS3RIdFPgVqRddGL0W4RK2DusJyAOd/u/6Y/j19kz1nPGd7MznjeLr3X3be9sS3rHjPOte9JH9SAQUCC4JMwe9AZ363/NI7pbp3eS13+TbFNt+3E7eWN/O4F/kOurd8KP2dvqM/ID9uP2f/Vn8evoq+rL7T/5oABECjAKLADD91Pl69u3ysu9U7ejqlukw60/v4PSn+ur/+AS1CUINWA/mD+kOeQsFBif//Pdw8lfvmu3b7B/tTO6277/xsPPd89zyUvGL713uiu7377zyNPbW+Rf99P8ZAWwAKf8H/Qf6bveQ9R30/fL58Z7xafIt9DL20/ht/AgBTQYCDAMROxVFGIMZuBhWFg4TIxD/DT4MbAvsC/8N+RDsE5sVzhVHFfEU8RTIFScXSxkgHM4fryM/J9IpMyuuKxorVikQJ0Al4SPTIeAfux7rHK4a2BhnF5wW1RYsGH0aMR1mIMAjpiUHJ3InSybgIjIfMx1VHA4d4B4EIdQjniXBISQVSwKK7njggNZMzCfBJLzZwJjME9zX6H/xjvju/Pv8d/iI8cvs6+xT79Tv3u6L7zfwn+8x7RbobuHR24jZm9nO2SLaz9sh3wvjQucO7OTxIvqiBbQR1R0xKCUvPjFJLhsmNRo8DVYBEPjO8hvxSvEA9Zr7xQHlBhgKWAuaDP4OWRElEzsW1xlsHNcdqxzjGLEUtw+nCZkD8P6++rz2qfLR7LTlD9802STVYtPb06zXM9/h6D3zq/z6Ax0IaQmbB2kC1fsk9lTx8OyS57nikOBJ4AHgcd6P3aDeK+Le51/uDPNy9qP5PPzx/QP+xfx9/Bb+HQEiA5QE0wQyA8UA7/3I+V70Nu8i66vnouUC5unoWe4j9FH5Yv5iA/EG2gm8CzEMCAtKCOMDav54+Vf1v/Gp7jvs/+qI6oTr0+zL7Truze0Z7ZPsh+x37XrvivIc9nr5Svyi/hgAZAD6/jr8fPnC9m30KvJo8G7vJO+w71jxxvPQ9sD6rP8fBbQKmhAlFvwZoRu7G5wadBh4FZ4SJBGrEKMQeBFAE2MUfBQbFJQTwBKqEkQToxSkFvoZXR5VI6Mn8So6LR4upy1nLO8qrCh8JlAkeyGcHpAbehhDFo8VdRatF3YZuxvKH5Yl3ilfKtQp6ChPJ+IlaSUUJ30pnysQKxIkjxboBUP2L+qf3e/PoMUlwt3Fg8+u28LlCO3h87X4/fhF9sTywPIw9tP3wPd39xj3QvWO8WbsPeWj3dnX7NRp1cDWb9jy27Hgg+WY6hbw9fZ0AAwMNBeJIYAqmi+sL6wrRSNjGKENlQP0+3/3HPan9gf6R/6WAbsE2AZyB1kJ2gxhEE8UXhmOHs0hhCJYID0ccRdhETIKWAS3/4v7dPfC8pfs3+Uw4Ajbn9bc1PnVKtpg4SHqOfMQ+14A8wKbA6YBJv1W+LL0BPF77MDoTubp5Ojijt9v3PTaHdzW3xzlTupd76704vnk/Kn9jv1d/R7+uf/yAUYEFwWsAyYCNwFK/3X7+/aM8VrsWunI6EzqYO3l8E70Uvg0/Dn//QHuBBoHewdnBr4DugAe/jP71fcn9KDwE+7N7G7sIuwY7Bjsautf6jPptugG6bjpwOsD70/yHfUz+IL6KftH+gH50/ff9iX1vPJU8dHwpPAZ8TTy9fJw84D1s/iC/CMBPAZaCwAQwxPtFRkXwxbpFWgVaBXaFFcU6xSTFk0Yhxm6GSgZExgbFy0X4BdkGaEbVR+PI3ImCiicKesqkSqWKVAoiyenJjElhiNmIRAgYR8JHy0dohnmFm0XRBrxG3wcYx6kIQ8jwCM+JfEnHCviLVMtqiTQFP8CmfZ97arhM9StyqbJ0s5a16/gvOir7vbz2/Zn9T/vD+t37YzxM/RP9i75Zfx9/qX96/nR893sPuai4uXgjd493p/gOuQq6DnsgfG5+H4D3w70GY8kpS1JMoUyUS6mJR8bnhCmB3kBKv50/Tv/uAOJB/8JFgvxCWAI5weDCEwJUgtCD1EUjhh5Gs8Z3hc+FFoOHwhFA5j/NvyW+MTza+0s56jhMtys1zLVu9Uv2pXhjurf87T7qADYAiECyP209yrzy/DU7v7stets6x7sf+yM69HpJuiZ55vos+qb7DnvLPNo9nn3Xvcd+Hr6LP0b/7P/kP/y/x8CVgQEBIEAtvtq94Pz1+8z7S/sNe2L75/yTPUC+GD77f4KAbYAuP6A/NX6jPkn+Ir2W/Xs9Fb0rvMU84DyAfL68J3vhO5t7brrAuon6VLpH+pX67Tsg+2C7l7wn/KN9KP1Y/Ye9zf3hPZf9Xv0XvOn8uvycPOQ9OH2XPpc/tMBoAQkB4IJogtRDYEOBg+mD4QQkxF3E7IV2BebGc8anxtNHK0c8RyjHPcbURtZGygcxBykHSwfJSHcImokiiUwJyMpXSpAKhMq/Sk+KUgoWSeSJkYlBCIxHq0cVh4WIaIhoCGpIv4kdyjfKAohLRKdAj/20uu536jT+Mxpzo7V5d0t5ZDqdu7c8ij2HvPJ6mPjWOLz5Nbml+iV68rvyvMb9fjz9PBS7aTqtumu6YPo9+fq6WzsXe8F85734f0xB14SrB1OKB0xpDY4OAs1qiyCIroYBRDcCbEGjAaxCWYPrxRLGNwaFxuLGTAYMhfVFf0U3RWgF3wYAxhDFuQTExFaDJkGoAFj/Tv67faA8gbsVOVv38LZJNVR0m7SVdbB3AvkeOvO8kD48frQ+jX3LfEg7KDp1Ofu5YLkUeQI5nboD+ob6h/qk+uM7sfxrvMf9S/3Z/kh+2H80P28/i3/JAARAkQF8gi2Cw8NxQtWCBcF9gGN/iP7afiT9n71q/UY92n5RPyy/nsAxQB1/+39q/wK+zz42/Sg8QvvlO347IPsjewp7Wvtd+0p7c7rcelY5tLjXuIr4gzib+IJ5FDmsuhL61rtzu4U8Bvxv/EP8k/yQPIo8izyyfFf8f3x+/J59D33Gvtc/7wDFAgRDDUP8RC0EeMRwhEaEogT8RWTGGkbjB7mIbwkYSZXTvolHCQpIlMgVR/0Hpkf5yCsIXciHiSUJn0o4ydyJl8m6yaAJ08nSCbTJJgiIyHrH1gdoBrnGAoapxvJHfMf5B4tF/gI/fnE7szjINYLyhXDLsN3yUPTrdz15HrrovDw8ejuduju4iHioOIA4tHiruUO6XLsGe5r7R/qI+ax43fj5ONT5Nfllukw7vPyhfhc/0sHXxCqGvol5DCjOWk+Hz8HPBo13ytcIXIYXhL0Dg4OihDZFYwbiSDSIxUl1SSZJFcjrCHlIMIg7yCfIGsfSR38GYkVvw8LCuYEFAAX/Kb3TfKd643kwt2N19DS3c+Vz8zS6ti54NfoPfCM9Uj4ffft82TvqetR6HDkA+Gk30ngK+Kp48DkpuXo5inpZuyr787ytfWK+BD7m/25/4EAmwCoAbIE7wnhDlgS9BM2FIUT+xEvD0EL1Aa1Amv/F/1Z/PD8xP7HAYEEOgb5BusGGgePB6UGVgTuAEX9Xfmg9Y7y1++z7b7sqOzp7Ajtz+za6+Hp2OZT4//f2Ny42jvaEtv/3ITfCeO35pbpOusi7NfsDO2T7OPrA+tS6uTprumC6lbsLu+Z8lf2lfqm/70EeAkBDaYPpxF3Eg4TGxReFcsWpBn2HZYiwiagKVorNywtLEsrLymtJlolTCV9JX4mzicHKm8shC4ZMF8wdS/ALkcvrC9ILfIohCbHJXck6CKJIOgd5xzBG9EWugtR/Rvxnehw3bnNvMBsvJa/VMVLyy3Q5tQj24PhreMo4EXZktSq0/nSo9Hq0dnTN9dL2ljbo9lT1rPU7tRC1WPVO9aZ2XPfa+YH7hH2Rf5yB+MRvhzEJncvaDZMOkI7AznRM7UspCVNIIAciRqoGiEe2yPoKIEshy/EMZcy5zIOMs8wWjDmMIIxfTCNLqYruCdVIvAahRMrDEQFsv48+DbxNupT5FTfDNqr1TPT+tO91xLd4uKV6E7td/CM8SXwKOzF5qPhtdxf2MrVYdWD1lLY0Nn+2tXbtdz23sHic+aF6Tvtq/LB+IT9LwAoAmkF3wqhEMAU1RYLGAQaXxy+HNoaWRcOE80OzgrVB4IGMweICXEMDg+ZEeoTPxaEFyMXVRVgEucOKQtiBw4EbQFG/9782vnW9jn0zfEo777rQ+jM5GThsN7t3LfbrNuf3A7eGeAb4xnmHOhx6YrqZ+tP6w3q9+fu5eDjK+L24VjiQOP75PvnYuzN8Uf3ffwfAUgFPgmgDNMPshJmFVsYjBvaHvwh1SR4JzopRSp+KqkpACjxJu0mgiesJ58nyyifKhUtvi6cMCU0cTdgOY05KTd8Na81JzTnMfsuHivyKLclIh3rDs3/j/NW6cjdFs8Zwpy+88OCyi/QrtME1wPdiuOt4yPelNek093S0tEoz+DNa85ez5nP3s11yUjDdb+evqS+Qr8IwZbFfc1V1kDfsefF8Pz65AUTEZ4azCKCKXMswCvdKDYjxBvpFIAQUQ3gDFIPYRSaG9wisid9KzcvlzE1M0Y0DTX2Nlo6xjz7PNo76TiWNLAu0CbNHYcVhQ54B6gACfrI85ruaep/5r3iPeFU4vLlXeuq8PD0kvip+tD6PPja8lzskuY94QPcUtis1knWttbO1gPWr9RQ1FvV7tdO2ynfvuQ46ynwyvOJ92X8uQKUCIwM0Q5IEIsS3BSLFokWJBQ4EeAN4gkWBxEG5QYbCTMLkA2pEA8Ulhe1GrIcNx3GHLsbbBloFjAUhRL1EF4O8QpiB9cEZwK+/rb6qfbt8jvvcusu6Bnn6edo6drqAu1u7xPyJ/TT9Jz0PfRc8+7xTe/v633pGOgN53nlUeRT5NXlCunZ7OzwhPWs+h4AmgXOCRMN8A8/Em0UPhcAGjIccR4vIdQjjyR5JOsjRSPII8Yj/CHbIHMhZiQnKX4tijDVMlA22Tl+PIE9ejyYOuk5Szm9Nzw1+i9KJbIWbwiR+rbsld14zvHDUMJMxivMwdGO1RHZyd784lfgUdrS1efTBtPa0erQnNEW01LTytE2zn7Hcb9Oupi3jLVttHK167mJwQPK8dJR3JDmH/HL/MsIkBPMHFUjySWBJVUijRzrFNwNpgiEBSMFUQcbDM0SiRljHvYhuiQSJ2Up8SopLHQuSTJ8NR03tTdWNggzfi0mJvQdfBVXDTYFp/3x9WHuO+g64xvfhtwy3CPeb+Ia6OLtS/NB92X5RPnf9pLyde1u6DTjnN5p25PZd9hM11fVdtKD0LDQftI+1XXY2NyD4mbo5u0S9F77JgMkCuwP0xPBFuEZshzyHTMdkhqAF9ET1w+3DGoLUgvjCyANjQ8YEiYV8xidHK8fUCFdItAibSLZIFoexhu8GOQUbRB9CyYHowPT/zH7MPbg8cntYulY5e7iVuL+4sDkPei47KTxsfVa+Gf5W/m5+Jf3o/UF8+7wne+h7r/tWu1K7QXuT+868Vb0Nfh0/UMEOwt7ErMYVB5uI3gmhSgKK0ItvC7DL6gwhjHTMmE0WzTbMqgwUy5pLfYs0Sw2LiswITOoNgQ6IjxhPOY73zznPBk89zkJNXUtliEAFsULqP6Z7DHXZ8aGv/u/Z8LzxPfGDcue0Y3XJNkg1kDR6M1Py2PHNMM6w4rHictszAvLScd1wdC8ELqct860ZLLBsxy5jcCZyCLS3NzY55/yVv6fClkVFR64I8omoSabI10eWRf5ELgL8gjoCKALvxBeFlMbRx8mIzYm/CdkKIkolCm6K4AudTDEMfsxtTCWLdMnBCGHGcwRdgm+AFb4RfAS6WviCdxs1qfSgNEW07TWStuE4FDlUOm16/frJeqF5hLiP9152NzUM9Of0nfREs9ozH7Ky8l4ykzMxM4L0ajTx9fQ3eHl8+5N97z+JwWsC40SwhikHRAhWSObIxghuhyMGPMUEhLBDz0OBw7PDu4QQBT9F10bZx5mIbYjrCQOJaQlbSahJqwlWyMxIFUc/RfsEg0NxgaqABz6IvNL7Ebn6OOJ4Rvg0OB1453neOxz8a/2N/si/mL/0/5D/ZP6i/fb9Mjy/vA579zt4u0c723x3fME96P6DP8RBRUMyxI3GZ8f0yW8K0IwMDQNOOI75j5MQfJCEUK1Pz09tDrgOEQ38zULNQ00pTQfOCo9CEGMQ25HEEwRTThKREV9PTI0Kiv/IlAXFgRt7U7e89gT2EPWStSS0ZbQQtSp2TvbANgQ0ynQkM0myLzBH8JNx4bK48tdy/PGiMA2vHq5mbUKsISq9afYqkSwBrciwETKqtR04MLsEPhHA0kNVxQ1GTYcNBxFGfMUgQ8ECzIJHQkMC8gOUBOAFyAcuiD3I9QmSCgMKGAohCr2LP4usTHqM+Y04DOUMOkrNibDHg4WVQ1KBEH7KvOT66njodzb15nVNNU51q3YUdxd4J7jz+U+5rbkn+Fb3YHXHdFKzKjJBshlxuDDWMGRwAjB0cLExKrEbcP5w9DHQs4D1j3dI+P46fvy3PxGBagLzhC4Ff4ZVRtGGjQYYBVQEn8P9gyECkYJJgqiDHoQzhT1GJMd9CEKJWonGSl7KboolycbJmgkuyJBIN8cNRkeFT4QMAqxAsT6H/SA7uPoQeS+4YvhceLC5HboRu1w8q/23PlG/Iz9+f1s/eb7RPlD9gzzffCZ77TvAPEu8vHzzffe/EsCCgjIDW8TxBiVHaMi9SdnLYUzRTmJPZdAA0MARuFIv0nfSFlGu0OeQglD3EKtQJw+zj+ZQ61HdEqfSwNLFEhhQ8Q9Fze2L6wlghTw/5DxT+xC7THtLugp4kLgMeIc5fjlf+Iu3PXWctIczO7GYcc4ylDM8sz7ymXH7cMtwurBj8AuvDG3aLVUt2S6Hb95xbbLCtPP27jlgu9h+RsC9giRDucRvBIcEZ0NGgqHCMcIuQkyDXISOBcPGywfFSPLJVEnpSbYJQMmICfHKIwqpCziLW4ueC1XKngm8CGrHFgWdQ9tCAsCW/ws9hTvuecE4uHend1s3fnddd8z4TLjcOR45KjiTN+22jLVps/mysLH4sX/w6jBnL9rvrS+wMCJwSTAdb+7wpDJntA/1k/aaN5N5SHuPPbV+6T/SgSaCQkNag6mDtINpgttCfYHqwaOBT4GngiuC4UOOhGAFPkXnBqwHG0eVx9RH6YeJR6DHcIc1RreF5sUAhJcDysLhgWa/0X6NvWL79Dqgue25VLlRual6KzsPvH69ND21/fN+Ob4w/gC+Mr2sfUU9J3zz/Sr9nv4Ovks+pr72v0kAgwHAAxXEZEWYxuIH3QjNSiwLPIvdjIiNsM6Mz6mQSZEyEQ1RMdC0UEKQZdAykD+QMpAlUHYQz5GZkSEP3c7pDaGMAkqCCERFBMF6/na9f32zPYi8qDt/+tK7RXxvvMx8RDsgueo4snbA9Z11JLU4tRH0yvQv83oyhnJL8n4yADGDMKvwGHCpcbXzCvTdtne4JnnC+5G9b/82QPBCHcMVQ6BDwoPUAzWCbAHVQaKBiUJOA3GEckWAxxEIeglQiniKREpAijvJ/AoYSmSKXkpJynzJwIliSH5HMsX6RFYCyEFqv8r+0f38fI27krqfufR5XDlHeZd55/oH+o+61/rX+qZ5ynjct2R1uPPt8q2x8fFQcRrw6nDGcaDyZLKnMk7yT3M6tFW1ynb490I4o3o9e+G9jn7av6vAS0EaQXwBSQGuQXiBJsDkgIdAa7/zf/gAWQEuAYqCQ8N5xF3FhAahByHHQIdDRsDGJ8VQhOqD6QLPghSBRADFgDc/Gv5+fUs8urtc+pk6Lnn+eeb6DrqIe0i8KnynvRD9pn35/fC96L35fZZ9Xjz4PKd84n08vS99dv3avt7ABEGugvCETgYZx6GIyonairxLcMwtjJYM7M0HTdlOIc5rjqrO0s9cT51Po89MjzpPF4+4j4HP0c9BjobN8szxzCrLQQpPCK9FvcFQ/du8BXxIPLH7UTnYuJ44RPnd+1x7Tnp/+TP4fXcCtdL1QLXF9l72AXVQNEKzfrIfch7yeHHncTqwlnEI8nCz5nWIt3o46DqOPHC9y3+VAT+CiYR9RQNF10XZhX4EjIRBRCUD6MQvhLxFdsZGx78IUAmESpiK8wqVSrfKlIsbS0LLkkuZC6GLRArjyc2IvsbkxV5D60JjwRiACf8ife+8m/uVeti6eTnXuai5b7llOY/6F3qF+sM6SvlY+BR2snTPs71yovIx8Vrw1fDTsVFxybIUMnvy/7PNdTU2Dnd+OE96JPvLvYn+y//0QPyBzAKiQvPDK0NngzGCpIJjAg7B4YGegbzBpUHSglODFcQbxQgGLEbwR7xH8gfxR59HXwb9RcXE/ENjAm3BUoBUPyo9+nzi/D+7PTp6+eU5sLlCuVS5cfmpunj7N3vcPIj9Oj0r/V89mr2PvUO83XxYPBW8DrxT/L28132iPlL/QoBfgZ6DZAUmBoeIKYkeyhcLEowxTMcNS41RTabOMk7wz6OPw1AW0A2QF9AfEDoP9U+0Dw9OQk1YzEUL20tDitBJ/0fvhFaAQf2kvLk9Hz1We//5nnil+Tk6sHt5OpZ5CnfT9on04zOfM7bzz7RUNBjzUbJj8SkwZ7CZ8NFwc2+Db+owdzG4M2x1C/bLOFG5pnryfH+94r9tAPwCAoMQw7PDkkN2wtkC0YMkA1zD7ISGRccHGwgUiTRKP4rIy32LK4spS0aLyswlDCSMN0vmi2bKuImniGfG+0VihCeC5kH5wP4/zT8afg49fHy7PC97l7t5exk7DnsA+yp673qaOif5KTfuNmT0yzPH83EylXH0cTCxALGS8cGyczLCNC51EnZR93l4M/lm+wk80X3TfpR/hwDegYbCc8LHA7pDq8NkAw4DPQLfwyODUUO7Q5QELISEhamGUkdQyAPI/IkMyXXJA4kZyKpH2MbvhULEEULzgYyAmP9S/nA9iP1NfMz8TTvmO0g7PLqjurs6vnrDO1p7mrwsfHI8h/06PQh9AnyuPAn8bHxkvHg8RD0zvZr+Qv9pgHCBs0M2hMQGq0ebSJXJ/IsCTEfM6IzMzO4Mh005DdbO6870DtnPnBA1EC6QC9B8kEFQGw6ZzSoLx0s4imyKFIl4xxaD8/+iPID79zxJPLh7Ablbd+t4DDopuxr6orkON+X2YvSzczxyxHOqM9+zgnLbcbjwHi9m70zvZG6B7jXt7+6CMElyX7Rctl44EbmEOxC8if47/1cA5EHqQrcDMsMggpACHoGggWKBscInAuFD94UyRkrHsEigCYpKNcnYCcUKP8pcivPLIYt8izEKionhiJyHD0V4A3SBs0AVPzu+Gf16vGt7qLsqeuO66nrhOvI643squ3c7l/v5u5J7ITnB+GA2S3Tn89hzYjKKsf+xDXEL8VVx2XKXc5q0mLW7dqU37zlSu2U9DD6qP5HA/oHLQvcDIEOQhCNEb8QqA5jDOYJRAgfCC4JvgqcDD8PwBKiFiUb5x+PJM4nMikTKfwniiZBJFAhhR2MGA4Tsw0dCJYC9/38+j74H/UZ8uHvOO5e7brsduxv7Unv+/ET9ZD4rvvQ/Uz/df+y/j78hPnd93r2RPXM9Sv41ftK/94BswXnCtIQRBe5G+geniILJ88rMzDDM6Y2aDkLO/o6ajyrP7pBRkKsQZBCbkTxQ3BB2z3EOJozBy6XKhQn2yFyIEIhjxxQD9H/DvfF9Yb2+POs7DrkSt9+4wjtVPAc7H3mdOFK2+HTPM6py+/LwssbyQXHKsS7vxK+e75uvQa71bnLun2+3cXSzszWPd4+43/mUupT7kDyX/ZS+xAAsgNLBo0HiQfxBg8FTgQCBeoFqgemC9IQCBaYGy8hPSTwJL4kmyR9JbkmKCeAJ6knGCflJCMhxBt0FQoP8Af4AJP7vPdA9YPzF/E07uHraerx6FvnfeY35ojmvefK6G/pJens5vTis9z01JfODcuyyA3GeMMVwrLBMsNexYTH2sknzWrSVNjb3Q3kbOv/8uL4n/1IAnkF5QZpCDsKJQzDDJIMBAx8Cs8IqginCWoKwgpQDMcPtROGF60bQyB7JI0nPilhKY0oCycrJSAjox/DGroV2BBBC4gFNQEv/hb7dfif9rf0ZPOX8knyMPLr8vHzo/Vh+B779/x2/vj/ogBp/1X9Bvye+/r6mfmI+Wj72v2qAFQE5gifDWgSJReOG4AfCiS/KJwtgTDcMXc0SDhOOs06oDvFPgVDiUUrRnNGOEaURfJFmUTrPXM0hSwlKLUmCiUFIzgeJRMfBfL7e/wrAW//xfhS8e/rXu0e88r2hPVW8cTsVuYn3u3Wf9Mf1SDWr9KIzcbI8cS7wtjCL8KMvyi9nbwjv3HFcc0o1iPegOTC6CbsDPBq80H2Rvlv+0n+CAE6AZsA6P/l/hL+wP5QADwCIwVCCZUN8BLAGLId7x8OIMQf5yCaIu0jsiQpJUQlrSOXIPUbdRY0EDoJMgKN+5P2gfMn8TDvqO1w7O3rwuuC67HqrOk36TPpJeqZ66vrVun55BvfMNj/0cXNGcr4xUbC9b/Uv7bBlcTlx2XKtMyfz5vTe9iP3iHmoO1e8/r3ivwvAC4CcANfBWUGUAVRA5ABSwCB/y0ArAH5Aq4DewVKCVsNbBEpFmsbvh+CIv0jriTNJP8jSCLSH5EcIBjJE0wQ8AvIBpYCWv9E/Nr4l/Z89Xn0XPPa8mjzNPUc9yj5VPzw/zoCRQN5BPAEcgOWAawAEACF/l/9dv05/70B0wQoCfMNKxJ3Floa7Rx2HygjVyebKsksHzB3NME3JDk1Orc7hDz1P85DQUSGQ4ZDikI2QGM7XTXpLrgncCBqHB4d/xvkEykIlvwk9kP6wACT/rn0r+sT61Xz9/oa+zf4+/W18QHrgOO93OnZptqE2W3VY9EIzP3H+shKypHHGsX/w5vE48js0KbareSs7ITxr/XC+tb9Mf86ARkC6AFFA0AFigViBAkD/QFvAUgB5AGFBCkIfwt1D+IU6BppH6IhmiEEIaQhXyJGIlUiMCKoIGseNBspFuYPEQk3AZD5ZPND793sIOsl6U/oJekR6uLqgOtb63nqZeqS6u7q7+u+7Djr2Obm333XXdH7zffK0MZAwky/1L8Rw77H48sqz5/SmdaU25vgI+Zn7Xf0p/lF/lUCJQVfBigH9gfRB3sF4gG4/k781/pW+/f8vP2V/j4B+gTPCJ0NuhJlFyIccCAXIuwhECEUIG0e7xttF7gRhAy4B6kCcv6/+z75dvbE89rxZPD3727wBvGY8Xjzk/Yy+j/+fAIwBXIGjwd7B/AFogSVA5IBLf+t/Y/+NQFzBAQI3QvRD+QTvxfAHMchKyUcKBIr7S3QMf81RDhXOL846jpePYM+VD55Pto/AEEYQKk8gTf0MDIpDSJbG40V9hPPE/oO4AWu+rnyevIG+Kf6PvXC66bleuhh8U/2FfWK8YXtLuiR4araT9a31dPW2tTb0N/Mt8msyZ3Ltss4ysTIf8jSytrR09qe4wDtDPT+95D88AAJA9kEpQbGBoIG+AcPCfwINwivBv8FrwYfCMgJOgxcDyUTlBdRHFwgwyJdI5Qi+CEjIjoi+CEnISAgWB5GGxEX8xEdDDIFQP0/9vTw6O3n63vpLOe95nrndOgv6YPpL+n16I/p7OlK6l3r7Oqn6D/kBN6g1xDTTNAVzgfLmcd5xXPG/sl4ziLSetV22crdruI36ILurfUI/BkBLQVACHoKwwuMDCYNVAxZCaIFCQLN/7D+dP7G/uP+PQASA+AFQgnnDR4SyBWiGX4cOR1HHZ8cUxuDGfgWwBJZDfgHlgLr/Wb6Sfcb9Hfxn+8D7n/tA+6p7ijvx/Cw80f3P/tr/9YCPAVeB8UIoAioB6UGCwatBagExgMVBTUIogudDkMR7xQQGUkdvyJXJ6IpsSr2LHAx9jbTOvk7MjvsOno8U0BNQxlDS0ASPgg9OToMN4UzZSzgIrcaYxSEECMP1A1tCSf/7vCU5ljmkO3K7z7qTeHP25vgVOqp7wfvhOuZ53fiJ9s706nOfM8K0GHNqMk5xQrCPMOzxt3HPsd8x2jIpsy91GTdNeaz7iv1Lvqf/h0CnAQqBygJ2gkvCywNUQ1hDEUKrAcsBmoGnwcRCWoLSQ5eErcXbB1AIvwkJSW4IxojYSP/In8h1h9fHuAbwxf0EswNCAiiAOj4EfJE7T7rFeoF6LDlIOXb5fDma+db5wXnPOe75/roSeuZ7C/sb+qc5tzgptra1X7SWM9ozE7J0scKybTMZ9Hi1cDZ/t3F4v3nFe469YT8DwKdBt8KdA7KEPsRshLpEkERhA2YCa8GewQgA9ICZQLYAd4CFwWbBz8K1A3hEdUVSRksHGseUR+9HtUdZBwUGawTZw1yB+oBqfyY+A32i/P28Dbvgu5t7szuZO8i8IzxN/TP95L8RAH0BIMH9wlaC/kK1AnqCHUIJwh0BzMHoggnC6sNPxG8FQEZTxsmHyskdyiwK6ovCTQDONg7Mj/XQIlAAUBQQcpDXkTIQ3JD3kLxQPU/nD6nOc4x4yc4HsMWfQ+VC2kMmQp2AuD0UecX41TpU++h647gM9f11lLfvees6XTnQuMO3vzXddAXylTIpsm6yAzF48GYvgq9J7/6wbPCOMIhwpXEW8uK1KPdmehC8pf2vvovADkDzwRyBmYHvQgMC60MywyDC+AICgfxBl4HNwgkCrMMlA/9E+sZ+x8cJLclbCTaIsUi1iKLIfkffR56HOMYPBS+DqIIYwEH+QTx6OoN56Hk6uJY4bzgOeF74s7jJuSc4yHjfOOZ5Cvmheig6QTpjOZe4eDb0deW1OTQoMzrx2LFpcbaynjPqNOk16TbcuC05cDrCvO++nkBagaeC6EQcxODFYYXWxjQFxQVBxEZDacJdAfIBm4G6gSsA8UE+wbGCacNRRGxFJEYLhx7HsQf/R9nH2MeDRyGFzkSjgxHBnQAtvtj+J71ufLv7wfunu3i7dPt0e3M7gzxYPR7+Ev9uwGEBTwJogyrDXQNuQ2rDbkMfwuNCp8KtgsSDsEQMhThGFQdjSFEJcUpui41Mwo3aDrGPT9B2kNORaNGC0eORnJH30iqSdNJX0qRSFNEzUFmQME79THWJU8b8BNCD4gNcQz5BmX5L+r/44Dn2Ove6jrkXNqI1LXYwuBR5AvjG+Bl3KfWAc9eyOjFWcavxi/FAMIvvae5g7p4vd6+tL5/vtC/l8TYzY7YweKH7B30S/ki/hkCggW8B1wIZwkADOANnw3PDDkL8gieCOQJ/gq0C80Mdw9WEwMYpB1+IiIksyI/IeIh0iLdIUsgeB+eHW4Z4hRjELsJpgGz+Yrxpumz46XguN5+3M3ag9pa26vcct2T3cjdq91F3l3g7OLc4+DjI+Mo4JLb1de51IrR8s3VyafGYcZyyLvKUM2m0G3UuNkJ4PjlOexm8/r6OAK1CKYOKRPlFeIX1xmsGpcZkxagEj0PQAyVCrkJTghfBhMGKwjMCt4M5w7CES4VWRj0Go0cHh33HPkcXxy8GZEVuRAiC+wEI/92+t/2RfOE73bsnurs6eTpLeqc6i/s9e7A8mz3C/ysAPQEVgjOCloM4AxdDMMLfQsrCz8L7gwpD08RvxMdF80aoh6RI/oofC3PMGgzYzdwPQ1DzEatR9tHSUhGSadLk0t6S79N9E9jUZVP90qsSKxIy0XBPu0ybSYRHGIVKRO2EecOIgY1+NDqz+Iz5RPqeuft3RvUINLi10bfz+Ig4evdp9nP0xvNecZVw73Di8TkwoO+Frrhtvq2k7mLugK70ry/v9vFbs9Y2uXlc/AO9yn7OQDPBHQHZgplDPMNWxCqEkATchKhEPQO8Q0uDe4MJg6pEIMTHxeZHAsidyVoJ6wnQyfEJqUmzSWPI/Qhwh/9G/AWIxCsCEkAi/cg71HnsuHC3Urbjtk02F3Y1Nj52LHYV9gB2RTaxNqm20Hdtt763sfeq9zS2ErUf9CEzcTKycaQwnnBY8PFxRbI2soxzzDVDdxf40rqD/Kp+ksDOwprD2ETyRbhGZYbjBt7GgkYTRQREeMO8gwUC94JjAjCBzcIxAlWDO0O2hAME6YWThrSHPYdYx5fHp4dxBvoF0MSAAwTBdT9m/e18kDuK+r05vfkC+Se47vjuuQk50bq0e1h8ZD0WPgX/bUBtASABnsHdgeTB+sHDAgvCDQJSQuQDXcPkRGhFCAY1xzUIr8o5C2sMoI4JT4LQ+hG/kjeSq5LGkzNTJxM00t2S6lNXFBlUZhQt06bS/hJskjxRO08aDJ9KKkf8ximE0URrA8QB+73JOga4cbkAejI40fZeM7Cy5jRSNjw2sPYZdUH0l/NxMf0wV3AjcFUwRm/e7uHtz6147ZUujq8Sb19vlbB7ccN0aLb6uao8NL2svuMAREGygluDfwOTxEsFfIX3BjGGPIX1xYjFsMWARjQGEsZVxtPHzkkvyh1LPktjizcKt8qeiqwJ6Qk8SImIJobiRa5EOQJGQLz+THxeemT5KrhmN5S29TYkNhT2THaytk82AjXbtbC1oTYTdqL2hLaWdnh1rrTEdEmzyfNocq9xgPDK8KjxInI0ssuzvjQ/9Vn3DLjAupp8aT4jv8VBsMLbxCQFLEYQhsNHOgbNxphF28UGBKlED0Pvg1CDCILDgtADIEOwxCiEmMUThZqGG8aghsqHNIcSRxeGdQU+g8nC+gELf5W+F7zpe5K6j7nUuW/457iBOLG4dfheuPH5lvqVO1m8Jz0ofkI/iUBAQOuA8oDDAT0BJAFkQbrB0YJ/AobDQsQtxMRGBwdJSLkJrwr4DGKNyA8Q0BJQ4lEsEXBRtZHF0ncSZhJ0UlvTGRQ2VIaU9FP40sFS0hMBUqLQfU1CSqpHz4YNRNmDyEMWgQX9SDl4Nyy3hDiNuCU19fLisYBy6XS8dag187WBtSqzy/Jx8LjwP7BosGSvhC6T7VXsh+0pLd2uXG6ybs9vjbDTswp2L3jRu7h9lH9jQMjCYINbxBDElkUBBfXGdob8xznHBEcHRsjG/0bfBxZHC0dZx8sI04oDS0gL6culi3YLfst1ysAKYwmcCM+H9Ua2RVgD3kIHwEx+Nzuh+jr5JnhAN5S2/3ZwNlc2jfa99jq1wjXfNYG123YX9n72Xja9dnB2H3XbtaM1IrRl86hy3HJQMkey5jN/s+60pXWotuq4VnoNO8E9VP6PwDUBiANqBLqF6EbWB1QHkIetBwzGnEXBxWmEnMQHQ92DhgOdA0mDfENfw8eEWgSjhP1FEUW0BdeGUwaChrqF2EULw9CCVkDHP7o+KHzzu5l6q/mKuRc4pvgRN+H3v7ep+B141vn6upj7pDy7fbf+sr9xf/uACUB5gH7AisEvQRfBbUGeQiTCiQNvw+PEtMVURkMHnYj8Cg7L+A0mzgbOz0+j0HcQytGKEcJR7FG9UYmSItInEkETOFMP0zfSyhKpUfBRf5B5jpBL5EgjhNKDDQJCgcBAsrzT+FZ1ivXft0q3WnV0spOwmbEb82R03DTAdJq0oPPMclcwhK+Ib6DvsS9E7yqt1SzoLP+tqW4i7kVu6m8jL/HxjnTPOBl6wT13PwiA94IfA5/EuQTGRQrF5EcgB81IPkfZR5LHMgclh4XHiwccBzwHhEiRiZWK0UuJC4HLXks1SuhKgIpaickJrojJCANHE4XGhFtCdEAtPeJ79npYuW34Hzcqdnq2AfZjNjj1ufVt9W11ePWYdkn2zzcxt1o3lHd3NvF25LbAdp716LUkdL10a3RW9Hg0V7TftVZ2ELccuCF5sHumfYs/XwD4gksEM4VmBvbIOEjjyRoI7QhoR/xHKYaMhiZFDwRYA+ZDqUN2g3jDkgPZA8TEAERSRIyFLIWgBhsGbIZZxhcFQUR9gtPBjEA/fnA84ft/uio5Y3i6t/V3T/dVd0M3gngsuIS5QHokey58SH1DviH+j78+/zz/fr+H//G/hv/MwDsAI4B9wL4BGQH6wkZDVUQbRQrGqwhBimBL5A0ITg6PPk/vUL8RKNGB0ZXRDZDQEI4QhFDmEIPQzJDX0IMQjBDE0OSQlhB/j1UN3csdSEnF7EMpQbsBIUAkPVQ5YvWQdLG1QrXgNEyx967FrkGwbrIucpNy7LLDcrBxVvACr14vLy98b82wNC80rgouCu6HrztvAi+1b7wvjTDyc312a7lDPGf+TP/iQQYC5oQLxPOFDAYlRyZIDojiyTxI0YiNyFeIdMg8B4CHigfkyCCI9soRC1mLm8tqCwdLFwrIioyKfgoWSfEIxogVRwnFxsQ1Qdm/rD0SO076GfjD9/420/aBtra2TDYNdZE1U3VhtWb1h3YE9la2lfckNxr217bV9xX3M/aedhq1qvVC9ah1qfWn9ZO10vZStwi4NDks+pn8dH30P3dBPgLVhLEGAEfziPYJpMnmSYlJUsjoSD2HUwbLhhRFQUUPhPlESgRMBG/EKYP2Q7DD+AQ/RFCFL8WZRjIGP0XQxVMEMgKagYhAjT8Afa68HvsfOgg5SPig97i25PaZtr32QjaKtzU3w/kGujt6wfvUvFe8371K/ez+AP6hfvg/EX+h/+DAIUBgAM6BqwIyApADYIQRxVZGy8hyCZELM4xmTY3OvE8lD/XQdpDAkXtRPpE60MgRDtFwkTpQ4NEbEThRLZFtES8QYo/Mj9CPmA5FzDbJPMYCg63BYMBxv7M9sDnjNUHymvJbMzGy+rFW7w/tgW5nr92xKDGZca7xjvEpL5bueS30rluvOC+Pb6MuMm0sbX3uEK7/7xXv9q/AMJvyiHX1+J97AT15vsTAsUIlA/UFEYXRhpnHxclzSgmKnMpAyeSJRcmgifMJ5knCihgKDQqJy/uNLQ2+zQlMxIyCTEIL0gtfyzvKq4niiN5HrUYVhJcC5QCafng8VfroeTw3tnaJ9jp1S3UU9Jsz0zN1cwdzZXOXNBX0Z/SnNRe1sbWMdeY2CjaptqC2fTXEdiv2fraK9sl23/bbN1H4D7j7uYe7P/xcPfO/b8EHgs2EcEXcR4KJPMngCqVKxgreSl0J38lQyNeILAdGRvzGEIX/hXoE+kR1hCGEMMPGw/BD1MRlhMcFtUWLBXOEbUNKgnzA4n+s/gL8unrZefI47HfO9u910LVAtNQ0RHRkNHZ0h7WWtsi4FfjaebX6d/s6+7V8MzylPTH9UX30fgH+mD7p/3e/1QBJgOQBV4I4wt3EEwWixxhIvUnPi1NMiE3BzzbQFxFO0icSfJJF0qdSkRKv0lyR5NEWUMTQulAsEAiQWZBTEH+QKlAxj+LPfY6YTgcNU8unyMlFokItv5n+X72jO3/3F/K0b05vUHAb76AuNmv76pprUizy7frufe8UMEpwsm+CruQu7C9HMD7w1rFZsFgvUu9ScCOw4bGI8nkyUzKVtDu2wnn3/Bs+rcCpQlDEVwZwh/hI2AoXS5xM8U2BTkWOvk4rjYzNtE2YTVBMygyKDEzMEkxtDPrMgYw9y0ALBMpRCWGIlgh+x+oHeMZVxSuDgkJRALa+c/xIOsi5bTeXdgr00bQtc44zWXKscbCxMTE9MUkyErKy8z2z+HT/tbH2AzardwJ4H/iKuR95YjniuoU7Urus+4F73TvbO/S74Hx0/Mx9837MwHtBp4MghErFmsb7yDeJeYoDyr3Ktsrzyt4KkQpjycEJBMfrhrqFskSfA7XCuEGfAKi/yr+wfx1+wD7vfuO/M78bfzl+jX4fPbF9XzzIO8y62zoP+X94Ezc/teY1EfSRNEQ0HTOdM7B0BXUN9c52p/dYOGB5Q/qDe5w8oX3KfwvAJsDlwYBCT8Lnw17D9AQHhIdFG8WiBjPGkwd6x/7Iv0mzCqfLgwzgzf2OtE9YkHDRR1JSkq5SVZIPkaHRLdDw0FQPs06QDhkNhc0aTGFL9IuPS79LQkt2CkAKJQmBiVMIrIdUBeWDM//yvPf6yroO+Wh3ZHMNrxWtqu4aLldtRexOaxerGSzDbsMwWDFl8tT0rvRws6pztHQ99I/1j7c6Nz92GbXR9mb2qbamtxK3kvdw9705mHxufmtAgoMDhMMGT8gZSYLKVorXzAWNlc5tTvYPCU6HzS2L/MtDivIJsYjPSDSG7kaJR06HuobChkhF70UJhKgD08OSw7nDucOLQwpCMMErAAK+7D0ie8y60zm9eDH2+zXYNZm1trU1NFez/nOn8960cvUv9e22hHfweM+5yfpuOt27xrzqfVi99j4tPpx/Cj+uP4M/sv8avv5+Rv4JPeo97X4U/lW+/z+OALaBWIKlQ4IEgsVJxeKGA4Z1BinF2IW2hS4EuQPQgwSCGoDlv8L/KT39fK97u/rLepa6WLpqeq07O/uQPGx8pnyYfIY9A/20vUU9BfymvAW777sgenT5c/iCuET3w/d4tt63B3fPeJq5PrlMOj/6xrwXvRx+ND8BgEXBdoIDwxHDv4PZhFNEhASyBHKEa8RYRG4Ed8SDxT3FKoVCBcBGWMbtx5VIlAlsifUKl0uXTFBM2k03jQaNYg1njTpMnoxazC6LlgsAiktJbUiXSIjIase+xwaHUYe/x6nH/QhxyT+JAoknSPgItMhzx4VGPEN3QPY/U/6C/Un7aHhotBixX3F08jTxZnAor6TwMjHUtC+1gPc3t/W5Nzn0eV65M3mH+kN6iTs1+0r6oLk1eHO4E/dD9lt2CvYGtde2rvi8Okl8Cf4FgD1Bc0L8BJYGaseRyTvKjEwizP1NDMyWCxuJx0lrSIlHgAamBduFVEUchXLFmYVvhLUEHoQ4hCdEcsSBxXQF28axBtpGjIYkRWYED8KhQRQABn8wvbh8LnqtOV94ozfe9uw1mjTW9Ig02XVFdnr3MrgfOSQ51jpc+rT7BLwcvJP85nz2/OU9Af2iPbF9CzyifCA7hnrcOhN51LmheW15hjpHOtn7SvxwPbB/HQC3wYmCuwMoA/aEHUQnhCTEXAR5Q7FC3MJpwZPA1QAhP3C+QP2/vN084PzrvRN9zn6ivwI/g7+nf20/vgAfAJEAoUB0QCO//X8+vhO9CTvderH5mPjQN9r28Lab9wY3j7freDX4o3klOaW6aTtS/Jg96r7rP4MAQUDPwSSBYgG7QZqBgYF3wPhA9cE8gXWBqQH2weqCJEKJg1TEPIT+RcKHZ4h1iXBKe4sCjAdM3g1WjYIN5w3TjeJNpg0mTE4MBEwUS2EKSgmaCR7JL4k9yPiIuohFyIWJIolYyVTJocosyqZKrooxifQJ2slhSC3Gl4ShQgg/nf1aO/U68rn+dxizJi/Tr8ow3jALryKu7u/zMc30FDYM9/141vqY+6O64XoN+no6nDrgu4u89bx9OyR6TznR+Q14S/i2+Lj4IbjwOte9AD8AgQhDI8SuxcMHfwhKCZiKxgzCTkvO3c66jfUMfkpgSR6IFEb3RUgErAO7gv5DEgPlQ3SCZ0HuAbiBV8FeQX3Bu0JpQ0sDnsL9AjSBnICjvx/93bzEu8I6obkh9712YXXBdXu0KrMHsuZy8nMPdDY1AHaAeDf5bHqpu1k8ET0ufgf/Hj9bv5S/wYAOQDy/2j+b/xY+xD69vds9jb2wfXw9Cf1JPa29vL3rvq8/ioD8ActDGkPChLkEz4UEBORERkQBw7ZCtMH6gQVASj9cvmU9dHwgexA6mToPOd85wzpFetg7bTvg/DZ7/HvTPF48mzzOfTZ9KL0FPSC8rDvh+zN6bvmy+Ls3qvcp9xP3fje9uGn5HTnS+sP75Xy0va0+xAACASzCMMMGA/gEBsTkhR8FNoTfxN3EigRehD2Dy0Pwg5GD8cPXg/vDpAPoxAaEscTPxbzGEYbMR2mHgEggSHHIlcjxyLJIY8g1h4YHRwcShvpGb4YwReyFvMV6xSOFA0UkhMrEiwRQxK1E+wTKhSDFikaXRx/HYofiyA0H+Yezh8YIN0gayF4H1McGBlFGJUV5Q2tBS8AZ/2m+2L6WPPY5vDiKOi76oDnHeNk4p7mY+7O9ef5k/rh+zMBpP+G+XD26fJs73vtd/AC8YTrpeff5Cfiq91K2/bbs9hx12PcnOLg57ntcfTF95v5QvwG/6wAlgK+B0oMcg4+ENYQDQ2vBSUBDAAz/hf89frC+qj7xf+tBQYIeAeHBx0IEAjeCFgLTQ7pERkXcxoCGnYYnBfKFbQREQ1+CZ8HwgaEBWsCyf5r/On6uPfS8r3u2+zj67rsKO/U8W/0GPf9+AP5nve29tv29fan9kv2gPUv9Hzz4PIp8KTs0OpQ6WPm6uPa43bkHOWS5uDnsef65v3nsulN63bucPIh9S33JPkv+937K/wG/v7+3P0o/bH9yP35/Hn8YfxI+yb6Yvqu+uX6TvwD/uz/DwJYBNcErQLHAJkA5gGVAxQEtgOFA6UDTwNaATv+lvvp+Uz4lPWp8o3wcu/v79PwPfBZ7lrtJe3j7CzuY/Gu85D06PWB99r4pflo+ij6EfkX+en50vkf+d74Mvmp+eT5wPpD+9n6b/tJ/Yf/XAHXA3YHzAqQDZAQVBPpFdwY7xsvHjof4x/VIMggLCCPIP4gJiCWHqwdtB0KHsMekB8OIEEgLyEHIiIjmSR/JSEmrSZLJ8gnNCfuJdskVSMCIcEeZh2SG+oaaBmiFygYwxZkFbkTXRFEEMgOfg3rDTcOFQy2CvIIrQXLAbv/Vv5B+4j1Z+qZ4Ffgwd/s26LXPdK00z7YuduF3h3gAuLR5djnXOXx5C3lwuRe5mrruO/i7nftj+zp60XrK+lm6bLoXOaw6IHsMO8X8kj1JPeF9+b4sPqQ/IH/TAVjDPMRJxaXGc0ZgxbkE6ITKxNdEUQQtw/VD2ERWxRtFBURlw1SC4oJnQdRBtIFWQZACckLmgtFCscIYAcGBSwCgQC6ADAChAKIAWf/dv3b+3T5Y/Wu8CXtTuoK6SfpL+qz6wrtMO587gnuze2h7s3wbvL080b1R/Yn+Nj5YPql+Xz5evps+kD5ePnk+cr5dPkF+Ub4qfa39MbzrPMp9Gv1zPb098f4t/lg+j36gvrh+qn6T/pw+pf69/lb+UT5E/mv+KL3zPbA9rT2wvZB91L3JPd09iv1h/N/8b7wxfHv8g70KfV09tP3Jvk++cf47vjr+Rj6Rvmm+Lv48/nZ+5j8cfzD+8r6o/lC+YX6M/se+wj8vP3y/kT/jP8iAN8AQgK8A8oDlwN3BCgGCAd2B4kHWgfcBhcGZwWDBHYD9QIgAz0DIANVAzcDAwNZA4kEywWEBsQGogd3CHcIMQjFCIQJQwq6Ci4KDwrZCqgLhAyiDC0MJQySDMEMiAwdDRQPkxFtExQVWBYvF+oX+xjEGfYZ9BplG8YbYh2pHwYhaCHoIcci5CLNITkhmiKlIrMiriSKJQ4kKyLHIXgfuRv9F60UPBHSDdULCAthCZgFOQB29tLrCehB5ZrfWdkm1vHZyd5I4nDl6eTN4svih+I6313c8tvz3FbfYOVa6Znn9eSH4n7gbt0z2mXZdNlG23DgTeVK6ens6+4E8OHwv/Gw8/T3jP36BMIN5BS6GBcbkBpbFzcVFhX/FOQUVBbfGDgbFR4/IK0eZxrHFkYU4xK+ErMT/RSPFngZKhv8GXcW5xLuD1oMUwk1CIcIsQkmCmUIngRcAIL8Dvjz8rXu0usG6mjpO+n46MLoJuj95hLl6uIW4jrjpOUu6Mfql+xt7VHugO7J7XXtU+4W8G3xwvKS9BH2Evdc98j2Y/XT8y7zDPR89WD3//lS/Mr9mf7r/rj9Y/w+/Oz8Dv5p/wQBtwLzAooCBQK0ABL/yv2x/ar+0f/LADwBbwEqAqoBqP7l+gD4QfcW91b3Y/hb+ZH6Dvt2+XT25/MB8zzyM/HT8BnxqPFh8gzzqfJW8WTvsO3t64LqlupX62TsA+6r74vwOfDb79DvIPAC8XDyYPQY9t/3JPrP+5b8Df2R/ZX9of1q/kb/0/9kAOABbgM3BKQE9AQbBaQFiwcHCuELrw2eD08RIBK4EpYT9BOqE70TiBQ5Ff8U+xQkFSoV3BQ2FNETExOHEm8TxBTEFfAWcBg1Gc4YEhndGfwZ0xlOGi4b8xt4HNQcshxpG9cZsxcEFlwVFBWvFPQTlhQnF+EYWRh3FgsV+xTPE24S9xF2Ed8R5xHSEBUQUQ47C2UItQXMAx4DUwIS/2P4lfIg8FLtQem345reZt0V4Ofko+hG6m7r1e2b77PtvOvm6qfqs+tm8Oj1d/cC92b2afVi883wxu5F7CLrs+0C8fTzPfcB+pf6Xvoa+mH5W/lW+5EA3wdyDqYTExc4GMkWchTFEnIRaxAPEKcQLxI+FJ4WsBa/E0YPKQuJB/YEBgQjBNQFFQn+C/YMngvTCOQFMAOXAPz+6f43AH4CPwQjBJsCWgC1/f35Gval8sjvSO6Y7jvvbO9+753uSey96cTnx+aq5jLo9+pz7aPvZ/GM8kHzg/Oh81b0UvW29iP4X/mZ+hP8VPwv+1/5IveK9cn07vSG9Tr2AvdH9y/31PYR9pj17vUC9x/4bflI+wL8gfsJ+p74SfcT9kT16vR+9f/2LPnM+rL6kvnR91D1X/Jw8CrvkO627/vxJ/TT9D30l/N88oTxpvBM8NfwMvLI8yf1sfai93339/WY9LTz6/L78hj0//UT+EX63fvV+3P7BPuy+vz6BvzU/QYAdAJABVwHZwguCQMKAQpfCS4JxgmJCgwLvAtEDK0MngykCxYKqAigCDAJlgkoChAL6gsrDBcMFwwhDAAM4wslDE4MswwXDSgNCw3iDH0M3QtkCwYL4wp/CysMvQwBDewMaQzoC/ALDAzyC8cMiQ4CEK8Q+RG+Ep4S+xF8EdgQDRDqD/gP9xACEs8SQhMIE/ITJhRIE6wTgBRwFfQWeBkVG94bpRxzHV4dKBxlG3EadhkDGaQZSRlRGB4YLRfOFdAU+BIaDvkG7gGmAIn+o/oi9kXz2/M/9hH5//gC9yr2EPfX93D23vUA9Qb0PvV9+L76iPmq97/1JPMU8MTsr+oc6KHngekR6nvqq+rs6ujpm+hT51TlMOSy5YTqEvC09GX4XvqF+v/5r/k4+Ub58/nM+nn8+P5IAiMFWQXkAmv/Qvyb+lX6dPqs+4f+cgKgBfcGXQYrBesD7gEOAEz/qgB8A7MGzQiECX4JrgjfBjsE4AHe/2T+1v0M/jH+Jv5X/Q77hffd85rwjO4R7tbu/u8b8WHyqPMU9J3zUfKM8XvxJvIQ8x30MvV29i/39/bD9f7z/fH28JXwvvAZ8YrxHfIy8vDxDPBv7SLrVOo46tTq5esh7vDwcPMH9R31Y/Vq9rD3PPkY+1f9CgAuA9oFHgefBpwFcwRsA60C4gFpAhYEAwYYB7sF0QP/ASsAdP4J/Q39KP4gAO0CqQUKCFkJswnBCFoHEwZnBQYFwwWHB4UImAibB/AFqgO2AAH+xfvT+s76uPsP/cb+CAAeAKL/5/7Q/b/8hvxn/bb+OwD6ASUEhAV+BZ4EiwO7Ap0CpQLvAtcD8ATDBcMFcQU2BfwEngQ9BKgEewU/B0YJ4wojDNIMPA1jDQEN9gyfDcoOiBBDEpgTPhSGFNoUZxR9E8USZhIOEvkRbhKyEnASjRGAEBsP0A25DEYMRgwLDS4OEA/yDykQqA+TDhcNvAu8CrQKNwuHC0YMNA1+DdgMWgyiC94JCAiZB8QHLQc3B6QHGQjEB4sH7gX6A3ICHwKQAoIClwPkBJYF0wTtAtEA2/59/Dj5MPZz9HH0yvVB97X4qPj897z3hfeo97b2ePbQ9kz4Tvtd/Z/+e/6Z/SP8Lvrw9x72u/U09j/3BPja+On5bvog+oz4v/X18gjxnvBn8THzKfVZ9hD3IvfI9iD2o/VG9Ub14vXG9qz33vhy+tP6OPkk9h7zG/H+72bvwO9O8arzivX99bf14PSP83fxVe8J7rPt6+7B8L7yNfRd9Qf2IvYL9u713PV+9gj4T/q//I3+mv9a/z/+gPyb+kL59PjC+a774f0KABcCbgPzA8ADKgOOAjoCiALGA3UFXAdhCe0KQQuHCnEJogigCDoJJgrvCh8Mkg01DoYNtAtrCTEHHwVqA0ICKgL1AiUE8gTHBLgDgAKmAbQA5P8+/+7/XAFLA7AERAX8BNEDgAJtAY0A3v+Y//L/YACLAGAAcf+f/Qz7ffgg9tX0qPTo9Zf3Lvm++o/74fvH+5b7NfsA+3f7efxD/iAA2gHaAukCBwKZAE7/lf6Z/hn/CAA1AUICRQONA9ACPgGk/27+vP0I/hD/4wCSArID6wOjAxgDcgJ5AY0A9v9DAFABvQIhBJgEcwTRA94C4gHfAIUA9AD/Ae8CSwMHA3QCSAHJ//P9bfwW+5/6GvsV/Cb99/1u/k3+yP3q/Ob7e/vX+5r8iv1//nH/OwCLACYAKf9m/g7+ZP4I/yQA5wD2AfcClwM7A58C3gFYAQQBawFdAqEDUgW4Bp8HBAg3CIcIogiqCFUJOwpBC5gMbg3MDfkN4w1VDToM2wueC3ULTgxXDUEOwA4xD3MP8Q4sDmcNzQzVCzYKhQiRBxwHuAZ8BpoF5QMyAiYDywSIBQMGHgazBlQH/AcSCMgGsgTeAkgBdf9d/c/7b/ug+3n7wPqA+tn6GPvn+qf5UPfP9GLzwPKr8nLyD/L98XryRfO888/zWPTB9dL29/Y397j3mvid+TT5GPeU9Jvyt/Ff8VLxz/Eo8y718fby9zz4afgj+Cv3s/VW9OnzpvTc9Qr3Cvi7+Db5jPle+jn79vss/QD/DAHSArwD+AN0Ay4C8P9V/az7J/vw+0D9i/7B/+kADwJfAkYB0/+V/gH+HP6s/nH/pgA4AnIDwANZAw4DRQNWBMUFXgclCb4KawxpDQUNPwsXCdQG1wReA/UCfgOeBLkF8gVZBSsENQOOAqQBpgAmAKwAMAIUBOIFVwYFBkgFxQQlBLwD+gOLBGsFKgabBlEGigUxBDYCx//U/Wn88Ptn/Cb9dv0y/Yj8Zvv9+a/4rvcU9+P2Qfff98P4rflw+on65PmB+IX3YPc1+Cr54vmJ+ov7H/zk++X6n/lc+Df3tvaP9v32ffc6+JD4c/jF9yv3nfYy9tj1W/aH9wP5n/oG/Bn91v0O/v39zv3//b7+JACeAcICXgOFA5UDPQM4AggBLQC9/+L/mQCaAaMCgAPrA74DKgPKAoICqQIDA+UD+AQXBtQGJAcoB6MG/wVzBTIFJQVIBccFRQaXBoYGAwbsBHoDmQLkAYMBnAFTAiYDPQQfBYgFuQXwBY4FMAVjBegFrwbAB6wIFwn8CLsIPgjdB14HvgalBlwHhQg8CQEKwgolC8gKjAm+BoEEFAS7BJAFgAUnBcUE9wWqBw4I3QdNBwoHUQd1CBgKygqpCmIKZwlNB3MEGQKiAF4ASQCF/0j/QwDoAe0CYQKQ/zL8lPkn+KD3Evct97j3cfgN+QP5afim93T3bvck9y/3CviE+dH7R/2k/C76efeQ9Xv0xPOP8zn0Ovao+JH6VvuW+0j7Bfrd98f1f/TZ9Pv1hffm+Ln5MPou+hL6Bfpo+jn7+fxa/xECcQT9BbEGXQbLBE8CIgDC/s/+z/8QAW8C7QNzBSoGmAUMBGcCSgEOAXkBCQLzAjsEpAVNBuAF+gRgBLYEwwUMB4EICwoCDJkNBw4BDRQLzwjIBvgEnQMFA2YDKwTHBHMEVQO1Aeb/O/53/EX73fqi+xX9lf5n/8//4P/N/73/rP/q/40AywFcA9EEVAXPBGgDbwHt/lT8aPpp+XT5O/pM+1L82vzW/Bv88/qG+R/4Mfc79wb4Ffn/+YX6j/ou+nb5qvhA+ED4ufh6+cb6Mvwc/Qv92fvI+ST3+PR286fyxvJ/85z0rfUD9uz1X/Ws9G7zIvI68fDwHfEb8izzG/RW9In0gfSD9MH0efXr9s344fq9/PX9if5J/lX9Ffy++rn5LPlp+SL6UPuS/LX9eP4A/wT/xP6T/rT+O/8eAB8B7gGtAhYDDgOIAgUCPALeAt8D2wQ+Bo8HzQiECX4J4AgCCD8HdgbyBSIG1gYCCIQJhArjCuMKBgvtCkULzQvdCwYM7gzrDWoOgw6ZDhgOfg1nDZ8NKg43D3oQwhFyEssSzxJHEigRZA5cC/0JZQlXCfkJJgqACV8JWgvmDOQM2gzNDAkN2g3vDssPWA/GDhIOfQweCjEHKQWsBN8EngQOBJ4ExQUgBq8FoQNJANr8S/p5+En3r/ah9sj2YPf298X3HvcS9y/3L/cp9173+Pdf+en67/ok+Vf2+vN48p7xa/Hm8WLz3vUb+Gn5ufmO+aT4+/au9Lny+/HA8in0s/U190j49Phj+cD5Cfqn+iH8Vv7fACYD0wT3BdoFtASIAjEAYP7Y/ar+RQAfAvUDxwUYBygH2gXrA+YBaABp/wj/ff9qAJABdgKQAhcCCgGbANMAzQEJA5QEKgYKCBUJ8gh7BzYF8QLlAD7/T/4z/qr+f/8vABYAIf94/Wz7PPn99q/1dfX59fv2UvhV+R76xvpD+4/79Pur/Mz9If9/ANoBuQK1AqwBt/+z/bb70vme+Fj4wfiZ+Wz6GPsj+6z67/m1+F73MPaQ9cP1XfYM96j3+Pcr+Az4Jfgr+F74pvhf+Wr6ZPv8+/r7jftX+rX4yPY89S303/NM9PT0gvXq9Uv2QfbF9bD0sPPn8oTyU/KZ8kvzFPTw9Hn1rfW19f/10vYO+F35p/rL++z8vP2+/Q392fu0+uf5jvl6+db5p/r4+2X9Uf7d/jf/h/+m/9r/MwDFAJQBnwK+A0gEngTwBFkFxQVPBjMHcwivCfkK6AuYDN4MtQw8DIULxgpyCqMKQwv0C88M9Q0ODx0QwRDQEOAQLBGFEjwTgRPcEzoU1BQ1FTsV0BRtFLcUuRTgFHoVwhU9Fp4WfxabFXoUMROcEMgNBAyLCqkJWQkZCQQIlQe/CNYJ6wkLCs4JYwmQCQsKhwoYClcJOwjABh0F/QItASYAy/99/1r/9v+XANUA7ADH/yD9Nfru96H2w/VA9TT1Z/U09lj3vPeo96b3sveo94P3kfcK+Fn5n/q4+g/5zPb09M3z7fJP8nzyuvO59VD3jPgR+er4x/fc9XLzG/Hl7/PvpPC/8RTz+PNr9Mv0H/VV9bn1l/Yb+Cb6K/zv/df+zf7M/R38EvpI+IX3+vdP+fr6mPw7/m3/kv+L/rX83fqA+SH5Cfnn+Rj7u/xN/if/Mf/f/hD/s//+AI4CcwRyBnMI0gk/Cl0JzQcTBqIEiQMOA5kDvQTUBYwGxAYRBrgEBQPdAMD+ev06/Tz9dv1B/uP+Rv+W/8H/qv+m/6oA+gFNA70EogUgBtQF7AQcA/oA4/7q/Hv7EvtK+8P7UPyg/F/8hfuC+kb5i/ca9jT1pvRK9Eb0qPTk9L/0jfSe9Jb0DfXS9Tn3sfgy+mT7v/uW++H69fnD+F73U/b59ZP2S/c8+AP5nfnY+YT5g/iP9wT3n/aE9p/2+faJ93X4V/nW+f/5kfqD++z8Yv7e/2MBnwKnA/MDkQN6Am8BgwDD/yP/wP4G//r/tgBcAXUBlAGDAWMBSgFWAVYBkAETArsCEgNLA5cD9QNaBNEEvwWfBq4HEQlwCj8LWgtQCxoLkwrcCTwJ5gj2CHwJQQrhCroLuwybDQUO/Q0aDo0OZA8+EJ4QxRA6EeERJhLvERgSBhLpEVwSPBPaE1UUIhWjFVwVlBSQE8wRYA+tDNkKdgmeCGkIBAidBxgHNwfTB0YIQAjwB74HIQhiCMcI2ggpCAYHYwWZA4oBpv8G/7r+0f7+/m//EAArALP/Dv5v+3P4p/Wb82Hyy/Hi8RPyVfIB81rzUfMH8w7zZPPI8z30MvWn9hv4s/jn9+716/N88qTxWPGx8f/y+PTG9gb4VvjZ95/2z/SC8mDwBe8S7wDwX/Ha8g70svQf9WH1w/Vy9pP3cvnX+1z+WAB1AX0BxQAl/yD9Rfs/+lP6cfsi/Rn/ngDJAf8BOgGc/+f9q/wt/E78Sf2d/jEAkAF8Aq8CYwJEAq0CugNbBSYH5giJCvoLswz8C2QKWAhqBssEPQRaBAAF4gVjBmEGhgUpBEACJAAG/lb8y/sV/DD9IP7R/gj/Ev8X//z+If9O/7v/aAB7AXIC8QKxArsB+v90/Rz7DfkX+F74KPkQ+oL6svrt+tf6Rfom+Yv3IvZ+9ZD1R/YM97L3E/gd+Mn3bPds9w74DfkU+kX7nvzj/eX+Hf+N/jb93fu8+i76V/oE+/L7rfww/VX90PwN/AD74Pm3+Mf3ZveT93X4fPky+mr6P/o3+nT65frd+xn9jf4KABABuwHJAWEBngCH/1j+bv0g/Z/9Vv5C/xwAwAACAckAiwDu/5r/ff/e/5cAfQE+AhwDqgMWBCEEfQQRBTAGjwcLCYIK0wvPDJINvg1fDbUMEQyXCycLUgv6C/AMxA1qDpMOYg5eDl4Odg6qDkQPmg88ED8RAhJmEpkSQxLbEecRCBLfEesRiRLXEvoS3RItEtgQmA/CDsANRgwkChcICgeTBuAFMgX8A+QCaQI/AzMEmgQVBX4FtQX3BYoGygY4BoAFogRmA7sBPQAr/+X+tP7Y/Yj89Psf/HP8K/yb+kD4Ovaw9L7zhPLk8eLxNPLG8hLzdvPd86b0RPV19Zr16vVu9nL3x/go+Vr4uvZA9Vj0nfMm88ryVfN/9Jb1KvYk9sz1/vRg83Xxle/M7vvu7e/o8Afy8fKj8yX0kPTw9IT1j/Yl+Cj6J/wK/kL/oP8j/7r9APyn+iL6j/px+2X8wv0E/8n/qv+u/i792fsQ+9n6GPsC/GX9AP9UAP4ACAH0AE4BDwIcA30ETwZpCIsK4QtvDLwLgArgCJUHrwY4BkkGowYxB3AH0gaKBQwEjAKsAN/+jP1F/c79BP8xAJkAlwC+ANMA7AAnAYUBQAJJA48EfgXcBXsFbwTnAgQBOf+v/ff8Af1d/Xr9pf2Z/WH9lvyq+3b6OvlW+DX4nvhR+ef5QfpD+hL6t/mW+eD5cvpc+2f8l/3f/u7/oACHAL//j/6R/a/8WfyA/NT8Uf21/dD9iP0H/WH8bPub+jD6C/pX+gz73/uc/AX9Iv0k/Sr9ev0a/vT+TQCBAZYCjQMOBO0DRQNZAgQB3v9l/2v/nv8CAGAA3QBAAWEBBgFmAO7/wf8CAHcASAEhAqkC4AL5AgcD4ALgAk8DJwQJBRcGMQdMCA0JTwkdCWkIugf9BsIGlQalBjMHEAiYCHsIngjHCLUI3ggfCYYJ1gnhCuwLeQwwDYINGw3HDCYNVw1pDQoO1w4xD6wPExC3D1IPAA8/DgUN/gtWC5cKbQnnBzoG+AQIBCgDGQItAQgBjAG9AgAE5gQ2BVkFigXQBQsGzgUVBW0E+AMLA+IBbAA3/2T+Ov3w+wr7ifqJ+iz6OPlL9xn1SfOv8VTwP+/a7gPvcu838EzxD/KI8sLy//Iq81XzbPM59FD1SfYT9uL0dPOK8rnx8PBa8IvwlPHt8vjzkvSk9DP0RfO58UPwIO9H7wbwefEY85b0yvWC9i33ovct+Oz4ifqE/L7+tAAyAvkCGgNPAq4ADP///dz9Zv5x/6oAFQIwA4kD+QKkAVYAZf8U/2n/OwBtAdACPQQ2BXMFNgUGBSsFtQXKBk4IJgryC4QNZA4HDskMJQufCUgIkwdWB24HJwjTCKgIfQfOBe0D1gEkACH/hf5y/jP/ngCmAXICCwNqA4MDiQPCAzcEPgVjBqoHNwjfB7UGSgWZA8MBHgAv/+X+EP/N/5sARgE6AZsAlv8//rv8g/sK+xj7WvuR+xX8ivzH/JT8dfwI/OT7RPwm/T3+ef9WAM0AXgBx/zf+w/y2++H6evqV+gz7f/sp/Eb87vvn+sD5a/g193r2YfaI9un2hfcO+Gf4SvhA+H34wfgs+Rr6ZPug/LP9bP5//t/9+fz4+xT7LPr7+RL6uvqY+3X8Gf2M/YT9JP2g/IL8lPzH/FH9IP7V/mD/1/9YAH0AywBUAUQCOQNBBFQFnQaZB/wHzQdHB2EGeQUhBdUE2QQEBbEFlQYoB5sHywflBykIWgi3CAkJPgmtCXAKAgsgCxwLRQsaC1oLrAuRC48L0wuSDM8MswxlDLAL3woqCn4JJQnkCPQIWAhsBl4EwAOLA14DuwKiAYcAaADiAVcDQQSmBE4EUAS9BHMFtQVfBfQEgQSTAxUCOwDt/oH+8/2E/ND6ZPrh+qj7rvs3+v73+/XZ9CH0qPOH8+/zc/QV9Yb1/fVT9sL2P/cx9/P2yvZc9zz5DPuW+1P6VvjK9hH2iPXk9OD09fWB96z4Nvlp+aH5Ovkb+F327vS99Nz1jfcs+Yf6SPuu++H7I/xl/Pn8Mf5WAMwCCwXcBv4HYgjPB+4FtgPqAWcB+AHEApEDfwTLBWwGTwYCBVMD+gF5AaoBNgJFA7YEWwZiB1gHWQYyBcMEMgUeBmYHtQibCpAMAw4sDgsNLQtCCZ8HIgYdBegEkAWOBrwGigVoA0IBnP8v/uT8L/w2/A/9pv6TANEBawJhAi4CnAE+AUABEQJsA8kEXQUEBVYEhQMyAmAAS/5O/HH75vsu/R7+e/4m/jz92ftH+t74rvcE9//2VvfJ96D4ivkw+hr6KvnZ9+f2yvZc92f4QPnt+f/5yPna+I33OvYl9Vj05/MS9M/0BfYG94n3Fvfa9Ur0zvLW8W3xYfHH8bzy9PMR9dz1KvYi9v/18fVR9i/3iPga+mL7Ovxl/BX8ffvI+hr6fvlE+d75GvuO/A7+I/9x/0L/pP7Q/TT9Iv14/Rz+If/y/80AmAFGAm0CaQJ8ArkCdAN7BMsF5wbEBzkIHwh2B74GJAaEBW0FpAWGBqQHmghZCe8J/wmMCQ8JBQkRCR0J7QnMCqoLsQxJDYQNeg1rDV8NYw3ADfcNgQ5WD8MPbw89D/wOHg4NDb0M3gzWDAsNqQ3WDckM9wphCScINQcsBvwEMwQ9BE4F0gZcCPwIxQiqCOgI/ggXCSEJLgncCOMHdAbNBP8CJwGg/2b+Z/2//O78DP4M//r+Y/11+6v5lPiy99/2ivYm9+n3x/h6+Tf6ifrQ+pX6S/ou+pH6zftH/TX+yP1G/F766PjF98L2GvZX9mb3//hg+kz7tvuT+7r6PPlF9wv2Cfbd9hP4BfnY+U36i/qT+s76Avt9+8f8y/7hALcC+AO2BHcE/wKbAGz+HP3B/E39Iv6S/+UAFwLCAooCVAECACH/6/4S/3P/cgC9AfMCbgPvAhUCnAGoAToCQQPFBIgGgQguCvEKFgoxCNYF1wMoAtcA3P+B/x4A7gApAX8Ac/83/hX9gvxb/MP8fP2B/pz/QwBUALn/bv4k/QD8Avt6+rr6I/yx/Vb+Fv42/Tr84fpR+Zv3FfZh9VD1UvWO9Zj1jvUZ9Xn0rvPQ8mPyrfJ/82v0FfUw9SP1ufQG9MDykvHR8NPws/EW83f0oPW69nf3Yvef9sf1DfXB9Jr0bfSS9A31x/Uc9g32nvUy9Qv1OvUP9gj3afjC+Qj74fsC/IH7FvvE+sz61/ox+wL8Tf3d/tz/RwBUAFoAPwDw/6j/ef/4/9sA0QFLAooCrwLKAuIC7wIDA1kDUAR1BasGage0B9kHyQeTB/cGfgaGBtYGgwctCPoIiAkmCp8KxgpmCvUJ3gk0CpsKMwu8CzYMvQwmDXgNkA2pDfcNug6iD5AQTRHCEZcSEBPZEoUS1BGCETARLhE8EQkRSxEKEkUSGBJ4ETgRxxCGEGEQRA+dDfwLTwqSCUoJMQjYBu4FNAadBqYHGQmrCTkKqQqfCjsK0gmQCagIHgepBeMDDwLAAM//K/8g/uT8Jfwb/Pv85PzH+wf6RPiK9hv1yvPW8pXyMfOF88LzN/Qr9U/24fbb9pn2cvYC96r3Xvjg+Fz4Hvdv9RT0TfPQ8rPyHPM19GH1dPZU9773tPfz9iv1QfMs8gXy2vLT8+70IPY99/z3g/gL+a/5ofrw+zL9vP5NAM0BaQL4AYMAtP4B/f77tvsG/Bz9bP4MAGMB+gGFAWoAN/8o/kf9x/z//Cr+h/+oAC8BTgElAfYABgEvAcMB0AIjBFQF4AW/BaAEBwMKAbj+ivw9+yv7Ovyt/T7/+v/4/5T/9P73/dT8vfs7+wr7Xvuw+/T7W/xQ/J77nfrC+Xb59fk1+0T8w/z5/Nr8X/xK+9z5N/jQ9iz2JPbO9vD3Ffnz+Vf6Cfo8+SP4WveT9t71VfU29Vn1kvWt9bX1Y/UV9dH09PSC9YL2svfY+Hj5q/mA+Rf5Xvi49xT3+fb/9rL38vhg+p77UPyg/KT8ZfwN/NP71fv2+3f83PxT/cD9Mf5c/lH+YP6f/of/ngDRAbcClwNBBFwEQQThA3IDAwPxAjsD6wPkBBcGGAfPBzEIPgjbB64HgQeJB40HwgfhBysIiQieCKwI0Qj8CCUJnwlFCt0KjQtCDJoMTgz4C5oLLwvhCvkKKwuVC0IMTQ16Dn8PQBClENYQIhFJEWgRYRGhEcIRoRF6EUMRPxFFEV8RjRFJESwRVRHfEVgSVhLMEeAQ1w8zD48OdA3FC8gJ/Ac7B+UGNAbsBQUGMgYuBg4H8gfdB+kH9geJB4gG4AVXBXsERQPuAW4Asv4c/fr7Wvuy+sb5z/iQ+Ln4Hfj19nz16fNA8tPwAPCb78jvh/BM8Sjy5fKD89vzHfTx84Xz+/Lx8lPztvNY9IP02/P38iTyw/Gk8cHxPvJT84v0ivVH9oT2TfZb9f7zdvJl8RfxqPHC8mL0+/V792v4Jvmf+RD6pfpi+1/8rf0M/0MAHQFAAaAAqv9y/or9Nv2d/W7+if/XACgCuwJLAmUBUAAv/yb+gv2R/Xj+BAA6AUQCxAJ+AtoBPAGyAFIAUgDhAL8B6QJ0A1MDtwLkAewAz//4/qz+/P6Y/6AAGQHhABYA8v5q/dv7R/pd+Tb56/nK+s37xfw8/fX8RPxB+yD6evmE+Qn6qfpU++r7VPwV/Jr7xvrt+WP5Pvmb+VH6Evue+/z7z/vh+l359vfI9gn25vVd9iL3H/gR+cr5t/lT+bX4QPj69xP4VPgm+R769fpo+3/7PfvZ+nL6aPqC+jX7Lfyh/cv+f/+W/0T/n/6j/b38Nvw6/Ln8p/3A/qT/TQDbAAYBxwBuAIUA3QCUAZ8CmQNvBBEFgAV7BUIFBgX8BHEFUQZLB2IIvwnSCoELbAsiC2oK1AmxCesJJgq0CpMLdQwDDUkNNA3PDIwMhAytDCoNdA39Dc8OUA87Dx8P9g7CDp8Otg4lD88P8xAQEtcSUBN9Ey0TsBIiEp0RchGIEeURYBLCErwSgRJDEt8RLhFOEHEPlw7gDVcNTAx+CmAIVwaBBC4DRAKOAUQBWgFXAvoDzgVsBlkGBQbWBV8FuwTEA9ACCwJEAUEAvP6T/av86Psc+xb6X/kH+RX58vgC+F/2GPTs8Rjw3u4H7mftXO007o/v/vDw8anyIvOL81Pz6/KI8sDyjfMl9AL0FvOI8m7ycvJw8mXyzPLX8yP1GvaG9rT2QfY49X/zhPH+7zTvW+838EDxePLh82P10vb49/T40Pnh+hf8W/2L/mf/OQB3AAwAQv+f/mT+tv59/2wAogEqA0YE8gSaBI8DHQJ5AOP+Xf0l/Jj7+Ptt/Pv8Nv1j/QH+DP/m/4MAAgEsAqMD6AR3BQsF6QOjAnkBLQDy/k3+bP47/8H/+P/P/33/6f6h/aT7lPnf98r2fPaP9q328/aq9yf4e/jP+Fn5HPon+3H8ev0z/sL+zf4z/gP9YvsD+jb5LPmC+Rb6/Prk+4z8ufxp/IX7SfoT+fz3OfcS90P3/vdr+MX42Pgh+Zn5RfpO+1v8s/01/5sAvwEJAtYBcQHbAB4Ag/95/x4AKQEqAv8CgAPtA+sDjQPCAtwBOgE3AWEBswE4ArcC/wI7A40DowP6A7IErQXfBisIIQkPCuEKUAs/C/MKqwq+CuUKzQuSDHoNZg7xDmkPdQ8KD0sOnw04DeQMlgzUDAENBQ0sDYwNcg2VDWIOFg9cDwIQcBG8EmkTOhTgFK8UVxRXFFEU9BOgE9MTHRRlFJ0UWxRvFEAUyROeE1ATwBLIEQsRXRCBD1wOzQzdClYI/wXbBHUEBgRTA+ICTQM3BF8FigbIBmwG4gWEBfQEAATgAuYBsgB7/9z9a/x1+4v6yPny+Lz3X/Zt9TT12fRu81rxGu9g7WDs0utL69LqBev36+HsnO0u7kPvUPC+8DXwLO+C7qnuNO+J7z/viO4s7oruMO8O8Inw+PCB8U/ykvK38sLyl/L08dfweO9v7urteO5874vwhvHY8rf0hvYr+Jn5i/pF+9/7p/xR/cr9ZP7A/tv+tP5w/pH+zf4j/yv//P7l/tv+vP5i/pv9w/zH+/P6N/oS+n76uvsJ/f39mf51/40AkAEuAtMBNQG8ANMAEAEOARkBTgE+AQwB5QC0ALoAzQDZADcAOf9W/nr9qfzh+5/6Pvkj+ML37Pdl+Bf55Pnn+t/7yfxT/YL9wP3r/dT9ev1R/af9Qf7j/kT/af97/73/CgAxAOb/Xv+y/i3+xv0o/WP80fs9+7T6SfpF+uX6y/vq/O39zf67/7AAhQEHAlsClALkAlkD4QOoBIoFrQZPB8IHKwiSCPQIIQkhCd4IhwgdCNkHjwf5BlEG0AW3BcUF5gVTBjMHMwgZCZQJHgp4CvcKdQucC5cLxQspDMsMmQ1cDrYOLQ+7DzQQRhBKEFMQYxBTEOwPaQ8ID7oOhw5kDkUOGA5HDsoOkA/oD6kQYRG8EeMRChIMEswR/xFeEpMSphLTEnUTLhSdFBYVTRVuFeAUlBPMEacQyw/XDvkM6wnGBqwEagSRBBoEGAMdAjgCIAP8BFMGFwZ5BRcFdQQ/A5oBagCw/6b+y/yh+pb54PmZ+tn6n/mP9//1nvXF9bn0vvJJ8PztSez56gjqQ+nh6AjpKelx6cXpx+rN7Ibuse6D7QbsgusW7HbsGOw46+7qm+vb7A3u8e4G8AbxZ/FE8Z7wYPCJ8F7wfO/27Xvsq+vI64vs4+wv7QPuwu/k8enztfVi97P4hvmz+Z35nfkS+pn61frM+tP6oPvq/N/9N/73/Yz9cP1D/fv8VPyB++n6V/qU+bf4Bvjs97f4Ufly+cz5zPp7/P39BP/r/n/+g/4M/0T/Nf9p//7/ywA+AUwBLQE3ARkBmQBc/+v9x/wI/JH74/rc+fT4s/i5+Jz4ufg4+Q76AvsL/Nz8ev0a/rj+J/9K/wL/Dv+s/8UA0wF0AhQDrANMBMEEvQRBBGgDuQIfAoEBtgD8/8n/w/+M/yH/Ev/T/7wAqAFnAgcD1wO0BKkFNAY0BlcGuAY/B50HQAg+CYsKsAtzDOoMSQ21DRwO+w1JDZgMIQzsC8cLngs3CycLDAsYCz0LiwvBCzwMxwxfDZ0Nvg1RDpsO0w7jDukOHw+cD4wQvBFBEukSqhMyFIoUtRTkFAEV+RSjFAUU0xPRE5ITmBNCE3sS7REWElQSHBJWEqwSuhLhEi0TShNKE0gTVBMnEz8S0hB7DxgP5w7eDfgLNAmTBkgFmgWnBX8EwALaAagBEwLcAv8CqwJEAu4BhQG+ACAAAgDy/wr/x/w7+tr4kviI+JH3hPUm84jxOPHd8LTvH+5R7P/qJ+qk6Xnpc+mu6ajpOelR6MrnS+jB6Q/rD+s46vbpF+vR7MXtSO1B7NTr5+sa7E/sxOyq7Yjube5v7arslewp7frsCuw66+7qResW7Nnsae2u7Q/umu5b73PwBfLr86n1Kfc8+FX5fvqs+wv8y/tB+xD7M/ug+6r7Qfu8+kn6rfmS+JP3qfZL9mH2m/Z+9ln2tvaH91T4PPim9yn3i/fH+Nr5m/pY+3384/38/mf/mP/+/3cAVAC3/yP/FP9Y/zH/L/7H/H37mfrG+QH5Rvj09+f3OvhI+Gn4pPgm+W/5pfmt+QH6vvr++3T90/7X/54APAHwAaMC5wLaAtQCBQNwA8ID4wO6A2ID7QJvArUBMQHPANMA8gD6AD4BlgFRAucCLgNTA7wDeQRxBYIGvgdGCcwK/Au9DGENNQ4vD+gPMhBKEKkQdhFDEosSlxI7EhoS2xFwEfsQxxDmEAMRzBCGEIwQtxD3EBMR7BC3EAERAhIZEygURxU5FhUXnhdZGAYZNxmsGT4alBrNGiEb0BtuHLIcghzSG/IaYhpYGk4aRhqZGagZehtlG+MZaBk1GbgWfRN1EKEOaQ2mDBUMbAqfB8EFygbmCIAJUAjfBt8GIQiUCVkKOwp2CVoIPQfcBQIEFQICAXQA9P76+1/5BPiX9+P2xfRn8Vvu0+yd7JXsU+t16RjogOcZ53nm/uUT5trmgueM56XnbOgZ6vXrVOza6pPoaef/5/Xo6ehL6Cbo/Oj+6YzqjupZ6lnq1elu6KLmqOX65b3mjOay5QblFuWw5c/mHOhY6bvqxOwP7yfx5fJi9Jz1OvYs9v31UfY992P4//g4+XL5yvnY+Yb5XvjI9lv1XPSy89rySfJd8rXyLPNs8xjzIPME9Mr1avcl+Ln4rfke+1v83PyA/Cv8c/zq/F394/17/h//jP9E/0v+MP1b/Jb7UfrD+IP3xvax9p/2p/Z69l/2dPaf9tb2IPfl99j4rfk1+pX6/vqc+4L85vwm/Wr9Hv5O/6gAvQFRAjQC3AGYAToBkQDk/xL/qP5P/gz+Of5s/qz+Bv8j/0L/ov9qAHkBfgKNA6gEwwXWBqQHpAijCcwK5Qv2DPsNRg/5EEcSHxNcE3ETlhPeE/ATvROOE7cTAxRnFLUUzBQYFWYVshWsFfoVixZnF2UY7RiLGVoaQhtVHPccuB2eHoYfYiAfIewhTiJZIq0i8SJQIo0hHSHnIDUglx9wH8EeFx7oHaYdBRwxGlEY0BQSEqsQrg9kDvQLJAquCMEInwoxC3IKLgrlCmcMQA1RDYgNiAwtC9cKMgpvCGwGYARTAxsCwf+E/XP8zfsa+3z5uvam87nxh/BT7xDt3+nn5z7nhufG56XnHOhU6WXqp+px6krqE+tW7HTsFeuY6SnpCOoH66vqmOkp6azpderF6r/qq+oV6+Lq8Olw6PDm0eVB5YDkoeMT43HjmeRC5vfnienq6mTsGe6L76DwivFd8jPzxPN39BP1t/VN9ob2LvYH9uD1ufVj9aj0fPOM8ozxmvBD7/DtCO2f7C3t1+1j7k/vK/GR8z718fWP9iD38vec+JT4Tvh3+Ej5LPrM+j/7oPvL+6L70PqG+UT4f/fY9uj1zfTX82TzHPPG8m7yivIe8yn0a/Wn9pX3Xvg++QX6Ivqv+SH55Pg6+ZT5P/r3+sP7ufzI/a7+vv4q/pn9X/0B/aT8EfyL+2r76vv4+xv8jvxH/RL+G/8UAG8B3AIOBFsFWwYeB+sHlghGCQ0KKwuvDNgNCA9XEEERNxJAE70TkBNKE4wTqhNhE1ITShOOEwcUlBR+FY8WERdFGLwZjBoqG3YcDB6EHvAePSAfIdMhmCIKJFIlDyYgJxYoqigVKbYo8Sc0J8MlyCMCIuMfJR1aGtIYkhdAFIYQWA9YD74OKg7HDBILMAqNCzYNTAyXCsIKxQsLDRQOWg4DDtoNgg0NDUgMdgpQCFsGngT/AtMAF/+d/qj+Lf55/EH6G/jf9hP2/PT78j3wXe4P7gXuTO2R7JXsZ+2/7WLt+uy17T3v1/A48Xnwke+47zPxMvJY8e3v6++g8OHwc/A58P7vaO+v7tftmex+60frfOvO6vjpEerk6mrrEuwZ7ejtqe627wTxJvJu86D09fW69uv2JPfh95T4kPjZ9zv36faC9k/2zPUn9FnygfET8Sfw3O657UbtXO0F7pbujO7m7svwt/Kf853z2fPk9Ef2aPeT9/v2K/cA+Or4LPk0+WP5p/nW+fD4Sffa9dX06/Oz8vzwgO8B7zDvcu857yLv7e8f8UnyJPPG8wD04vTq9YL2LvZ39XX14PXe9cz1Ffal9lz3Rvii+On3zPZ29pn2QfbF9WH1CfU+9ST2/fZc+P35XPvL/Dv+Dv9uAJICngQLBgEHRAi5CSALzQtIDHYNCg98EGoRYhIXE/oTNxWRFdAUTRQhFE0U1BQBFacUehWWF+kY4xkbG2Ycdx2BHsofBiEtIVIiUCT2JfUmwCcpKYoqzCpaK4ksiSz4K7wr5yrkKM8lYyIQIJ4dLBhHEhAOQwuACcsHigUuA6gBowJABVMGewWiBaYH7wkzC9EL5QuJC2ILdQsEC8oJYghuB1EG/AQ7AzwBEAC8/pr86flm9hrz2/C472bvDe847pTtV+7v72XxDfI989P0ufWp9bn1cPbl96P5l/oo+h35F/l4+gT89vt0+q/5AfpH+mf5Ifgr9971I/Tn8sHxlfAg8D3wbvAz8EHwnPF088v0tfXf9jX4ZfnX+i38Lv0S/s3+Z/9//0T/RP+J/y3/7f0p/Lj6Pvkr9xf10vLZ78ntrOyv64jquOm76bTpaerE6/bssO1f7/Tx2fPD9JD1jfab9574qvhM+Pr3y/cO+HP4G/iD99T24vV79Cjyzu8P7iHtHuwy6/fqPOvv6zntzO7C7/jwHvMV9ZX2EPix+fX6bfxw/WX9nPwP/LL7Q/uV+kP6HvoF+or5HfkK+OL1yPNu8ljxEPAw7xjvuu/f8MzyE/XS9m/4YPoI/JH9kv8PAnEEqwYHCSsLDw3ZDncPww9vEJoQExHCERQSJBIpEocS6xEuEb0Qxw8jDyUQpRDQEG4SOhQ5FkUY/BkJHAYdUB6RIAIiviO/JR4nyygHKmMqOioyKjwqcSjhJJQhpx+qHTMa3xU2EXsL8gdHB7YEKwBN/c78ev0w/fD8+fwu/dv+vAA+AW8BSAFYARECOwMzBD8EaAO5AmMCzQHuANH/A/7N+4L5k/ZJ8xfxAPD57h3uEO3/66/rvOxd7kPv/u/W8TH0Lvak90b5h/ts/h8BMgIBAuoBkwP7BSgHAQe+Bj0HwAftB0sHWQWjAkUAj/7W/CX7ZPrz+X75RvmU+bT65vvc/Kn9jf78/30BTQMZBacGzQecCDQJAwl5CAAIfQd0BrQE0AIEAZ3+8PvP+Cn1d/Ey7srraeqi6VbpLekE6cXpMus/7D7txu6q8AnyKvOq9J/2I/jt+d/6m/rS+UT58vjW+PL3DPeV9uD1+vPo8e/vMu667N/riOsX6wPrIOzy7Znv5fCj8qL0QfZL94H40Pka+4T8rf1a/p/+g/4K/or9Mv2K/PL7qPs5+1f6Qvm+94r2UPVk9N3zsPOf8yv0RvVw97n5/PtL/lQADwLAA3sFNwidCikMXg6WEBwSWhMuFDwU6RT9FJcVIxYwFTcVDhZyFRgVVxX5FFEU7RSuFjYX7hb4GfQdwx7KH9gj4iVmJHMlqihOKHQmNCcKKHkl7SC6HWUb2hd+FFURYQycBcv/av4I/sT5+vRy8qLwuO8P7wnvwO8z8TnzNPUg9tv0kfPF9LH1IfWQ9AD05/IH8lDxwfCn7w/u8esO6SHmiOPI4f/gCuGq4eXh5+EG4nfi/OIu5HDl5OeC6wfvS/I49Uj49vuF/yYCEAN6A14EMgYxCL8JMArhCvgLbQzhCy4KHwj1BY0EoAQdBR8FKQXcBXYGdAYQB6oI7QlHCi8LTw2SDzIRdxNTFeIUCRQoFJwTnBCZDUoMBwokByEFKAOW//j7Kvna9cnxre6z7X/tx+2Q7kHvzu/J8MDylPTs9Rz3TPiW+Uf6m/qq+0n9kf5N/qv8XPuG+Vr3bvb39Zj0XPNH8wfzPvEt8A/xsfHk8TnzAvWM9UH2ZfjM+qn8sP59AOgBhgKvAp0ChAIkAr0BYgDT/tz8Xvpn+Hf3xvbm9RH1G/Uj9Yf0UPS59Nz1tvcU+nf8VP5WAOICIAYjCZMLkg2SDwMRFhJWE7MUNRYZF9AX/Rd5FosVrhXGFPQTVRQBFR8WxxYuGFYauRr1G7ce/R/rIP0i4CUyKboqtSw2MAExwzBTMiYxSi3FKY4mGSINHPgWnhPDD4MLwgcRAm35+vTe9THzI+6K693pr+fa5mjpVuzz67LsXe/U7irs6uoZ627rjuqe6fzoe+YL4wfhiN8K3tXbDdhC1JbRtM+3zn/QutII1FzWb9go2tfbvt054eHk3ufj69PwEfVO+Gj60/v9/YkAOgEYABf/Dv+1/+UAbQLUAlMC1gGcAVYAM/6C/Vj+CgCjAsEF7ghuCsMLhg0nD2sQTRHEEakRZhEIEnkTQhQvE/sQhw7XCt4FxwA4/EL4NPVR8/nxzu/s7azs+eo76cjoouls6zvtU+948iv1y/fT+mz9n/57/gb+Ff3b+7L66/kY+j36ivl9+M72w/T78ojyO/PN86T06vUI+DD63PzVAGQEzgZ6CYoMrA51D7MQfxKcEwUU/RPPEkMRRg+WDNYJXgfyBFkCrv8X/TP7X/lI+Pb4T/pa+4r8WP7R/zMBQwMoBlcJngwpD1sRUhM5FTYXDhn4GeMZORmnGI4XWBajFTkWLRf0FkwWjRXCFSsW6BYkGD0ZyxqXHXkh7CQjKNAq3C1RMcoxPTL3NIc24jQTNGY1sDJVLgwsmSc4HxAW4hDrDQUJ3QTRAYX70/QG9Jz13/OJ7yjsOurl6Lnqqe4Z8fPyNPWe9Qzz4+957QnrQejX5Xzj9N/w2xzakNjg1cLTNtGHzOPIpcZIxifJ+szEz1TT0dd324Pez+HQ5NHmNemM7XDyR/YT+Sn7Lfz6++D8Ov3t+mL3APXe9K/1Evfw+Ln51PmN+of7rPsa+6z7gv3c/6oDFwg9C1gMGw0cDrwOaA5hDW4L5ghuBlQFpAStArv/w/wk+Srzd+0I6qLmjeIO4fjhGOK24bni7+MH5KXkLudB6Rfq++ud7s/wdPJc9A/24PUp9JLyu/FO8L3t1+xs7Ajs9Ox27p/umO6j74fwU/KM9W35kvwQANUEAQn8CxYPOBHlEnYUFxRlE+kSPxGaD9kO2A3DCwMKNQi5BV4D+gFvASUBmwDHALcBYwKPAzgGjAnHC5cNng86EVYSLRMLFCYVkxZKF3sXrxcPGAEYPhdEF+wX7Bc6F8MWWReiGbcbTh1HILwjbSVZJmkpkytNKy4tgzKkNvo2kDeiOkU9BzxIO7g8MjvbNqoz4zFhLcIn3SROIW4ZZxMwEXkPOg1RCTIDmf4aAM4C8AG7/9b9Xvr19h34uPuC/UL84/p2+l329vAN7h7sPehM4qHdTdoU1lLTFtOS1NrVd9TD0bnQ5NEC04zV69p33wbisuVE6jHtoe5m8K3xy/F58W7yKfTq9If0+/Lu8VvyX/Lw8IDupOw/7MftefET9lH5WPux/ewAoQJ+AvEC6gTABiUI0Am2CusJhwhiB/IFmwNmACf8qvc59HvxBPC97v7sseq7587jAeCR3h7e0twk3VTf0OAQ4iLkE+Zx5vDl/+Zr52HmmubQ51Xom+fn50jpMek85yTl6OPm4gDiQuLY41LmsOhy68buq/H287j2ZvrQ/QQBNQQgB+YJewy6DtsPvQ9IDzEO7At6CfYHWQZcBKMCNALRARMB7AAlAYUA8P8VAVUCjAILA6YEuQVHBk4IEguiDOoMDw0oDQ0NfwzwC1YM8gwRDeoMvA15DwMRUhJZFOEWfhh7GTYb/B0jIUskpSdFK3wtpy7aMMwyqjI/Mz81DDfoNyg4kzlIO706sDr3Owg7GTdENEkx4CwIKQkmLiK2HcYbxhvmGnsXNBFLClYIiQpFCi8IaATd/qb7qfwl/7YAfwBk/uP6EfZv8czvxu+W7VDq7eeA5JLgWt8b4K/gaOGS4vbhF+BX4PDiWuYI6nftMu+j733wrfJS9LT0TPVy9hz3lfcb+Zf6MPpC+C33APhI+dr5U/mx+OT4+vpE/5cDagZWB3IHUAhbCQsJCwmnCc4J5Aj0CEIIQAV0AscApP6u+2P5hPYB8/Pvlu0y6xfq/Olk6EPkJOCT3rTdCdxW24bcNN2I3Afdx95a39vecd9f32LeXN4H4HjhBeFJ4NjgbeK743zjkOLv4bbh5+F/4tbjxeWX6C/s4e8D8nbzkPVR+cf8Pf7T//0B2wPsBK8FIAdMCNwIywdbBQIEjQN9Aev+Of7a/eb8+fwM/uf97Pzq/A7+cv7z/UX+TP9p/3P/gwE1BGMFuQULBq0FvwSsBJgFowYqBwoIaQkEC2sMQQ6lEJESpRQrFuUVyxYtGpoehyHmIm8mlCmOKS4qei30L44wbzDWMR00jTVVOOk5LTqTOW84CTgMNo8v5CnCJochKBxtGokaChm8FSUQKwgCBK0F3AaxBcIC9/zJ99r4Qf4sA1IFtgMzAPz7DPd28xTz0fOI8cPtnOq/5vfjGOR35XXmN+Vp4uDf6N+a4n3mX+v17z7yNfTj9pD59fqL+578zP01/0wBXgO/BDgFeQSrAi4CkgIfAgQBIAC9/1wAsgPwB/cKJQzZC6oLewxrDAQLAAsRDSoOqg4lD8YN8woVCVQHIQTXACT+lfrW9qzzJ/Eg8PHvn+4f6mTla+Lk36XdjNzQ3IfdL95z3/vgyOGS4h7kq+RZ5KfksuVp52DotujK6HHp7urc63Trx+nQ51fnIOhR6Gjp0uuh7jjxzfOn9bz2Ffmt/Kz+rP8EAU0CkQMZBQUGqwb5BgsG6wMdAssAqP6A/GT7svpg+mD64/qy+wD8qvs9+wr7APuW+1n8SPyQ/Pf9RP9DAJgBUQICAfb/NwCHAIcA/ABlAjEElAWZBocIFgtHDYkPjxFyEX8SHBbCGVUczR7CIAQiaiQeJ6Un8ybpJ+IpjyuvLdkuVC8hMJUxLDJPMiUwjCrPJC0hpxx3FjQUaBVPFKkRYw3BBcT+GP6HAMH/X/xo9j/vve1e8875Tf1P/Uv6b/Xq8A3uGe1l7S/sPekZ52jk/+Ak4U/kUOU841XhNuDi34XhmeWQ6mnuwfFr9PX2Lvk7+u/6wfui/I79lP+nAsUEmgUPBbID0AIkA3IDDgNAAnsBHQLmBBMJ4gzjDmIPsg5qDiIOCw1IDEcNwg2ADesNwA0GCwgIlwYzBIMAW/18+vP2fPNd8dXwf/HB8PjsJ+lz5pTj5eDe3ybgjODn4L/ifeVK5urmBOmu6UzpF+oI7HPt0+3W7grwYfGZ8zL1ivWy9J/zEvQC9aD13fZd+Sv8Yv57ACQCKAMRBcsHXwlVCocLyQwBDhAPVg97D6YOTw0xC0gJsgc6BbMCfwEVAf4A2wApAbMBkgGmAAoAVABDAKj/d/9g//r+9P/oAe8CkQP+A6oDFAMIBAAF0QTJBDAGvgeaCcsLjw6UEOsRpBMQFkAYFBqLHNYeASCkISskVyZeKDgqtSqlKmspyyg7K+ku2S/jL0cx/zAzL9IuXS1DKIYiVB1QFxISYxBTEOIP8As9A7/7zPpv+1P6Zfgi86DqsebD6o3wavNc9AHzA+866urmpOVD5e/jEOLa3+/c/dmp2Rnci93B3F7betnR1xzZCt5E49rmmOod7gbw+/Gc9Kv1cfWp9Yr2CPhP+lT8o/24/jP+//zM/eP+nf1n/E/9Mf72/3UEnghTCiILyQuBC+0K4AlCCOMHQAjnB/0G1gVNAzMAyP0O+4f35/NS8K7sVOlh5xXnYuiq6TznI+JE367eft272yfbc9sx20rc+d+R4SDhE+MS5efkj+SH5fjmdOeq6AvrBO1D7w/xevKy81L0cfU799z4Cfoe/dEB1QS+BvAIzAnzCuMNxQ/JDw0QkxG0EgQTphOqEzUTIBJ/D9QMrgv/CaoHygbCBrgGWgecCO4IIwmCCSwJDQn+CIcIBghcCAIIAAhPCT0KEgoLChwKjAmvCcwKWgvyC7ENkA8yEXkTMRZPGKoaWh2tHooeHiC0JOwpbivAKlAsZC8kMo0zAjSZNSQ2pTQPNNg0dDVCOLM8qD2ROmU3BzVWMF0qoiXnIGwcdhmaFrUTRA8KCPsCwAOkBKwB//xR9k3vx+0o8hr32vgd+DT1dfBV6+Dnz+aZ5ebixuDb3i3blNce2WPcPNz32YLY59av1THXzdvE4cfmIuvc7q3xZPMV9Yb2Jvau9C71i/c6+a35Mvqw+gP6T/ka+qX6/fgE9+f2Z/hs+8f/gwOpBSAHHgc4BooFHQUfBM4DrAMYA4oCGQGI/Xj52PZu84bu6ulS5VHgQNxq2sba7dzd3e3aFteW1EnTkdLQ0c/QYtBx0RfUH9df2PHZndwj3krett7s3zzgC+Da4GDir+Ph5XzoHerw6uTqO+w/7wXyEvRL95z7Gf9TAu4EjAaiCHkLKg1HDVcNNQ6LD7UQ7BCeELcP2g3hCq4I9QYQBA4Bf//j/v7+AAB5AcgC5QOiBLAEuAS9BJ4EkQRSBPMDIQUiBy8Iuwg8CTQJHQmSCRQKLAoACx0MrQ2eD8gRxROgFqYa9B2IHmEeoyCjI2clzim+LkAw2TIZOHw5jjfMOfg9+D55PuQ+4j9sQAJCP0TVRGdDnD6uNyQxwSkDI/wgFiFxHrQZ0RKgC/QIjwrLDNMLzgX3/NX3C/k7/pMDzAb/Be4BAf0Q+E/z1fBX76LsvOg35e/hWeBJ4Ujj5OPr4b7ea9zc3BffMOMl6U/vSPQp+JP7nf4iANH/lf7G/bX9sv5JAD4BbwGkAH3/jv8IAJf+Efzt+rb6Uvub/g4DPgZMCMoJ0glrCWAIhgbqBN0E0wQGBH4DIQKZ/n764/br8iHutugf4/Pd+9lU2AHZatoS2jfXz9Oh0TrRBNApzQnLl8tjzdfP69Ls1FPWhti22tfbRtzE3EHdPd4P3+7fvuFD5KTmXuii6UbqF+vT7J3vqfID9nj5nvwE/+4A2gJ7BLUFtQYiB+MG6QYQCCgJKAn6CEgIggYGBAEC5P/O/OL5dfhp+Oz4tfms+vb75vxV/ZH9OP1Q/MX7j/vx+nL63ful/ZH+VP8mAMUAUAGAAo8DmgRTBikIrwlcCzMOxhF4FB8Wuxf+GX4byBwRH2cieCYUKzsuHTCLMtEzejXWOGQ5pjftOG07LT2UPzNCZ0ItQbxAnj5AN6ktNibNIcQfSB6hG5wXQhB8CbMIRQs7CycICwMe+/H1BvgO/lMDsgQIBC8Bz/tZ9gHz1PHQ76zsCOrW52rl4eTP5sDoheiU5jDk3eG04f/kB+vl8Nz17fqi/8QCQgUaB3QGBgT9AgQE3gUUB9MHdwiDCMYHkwe8B1cGuANpAnwCTwOpBj0L/w3KDvYOwA6fDc0LvQkzCKwHFgfCBuwFzAOBAJ78Yfg78y7uL+lN5PTfXdxi2kjb9d3q3kzcTtdu06fSYtNV0l7Pw82KztLR1NXf14rY6dkN3Mbdi97j3qTfEuHF4pPkYeas6AvrB+578EzxyfF/84L2Ufrj/bAA3AL4BPEGIwgRCa0JMAo2CsEJtQlHCqcKkwpbCqEJNQcxBHsBYP+t/Gf5BPeK9nT3jPhN+QH6Yvp0+mL6pfkX+Hr2gvWq9Az0H/Ut9w35FvrA+if7Ffyl/fT+w/81AYkDYQYTCboLzQ7QETcVCxiIGJsYsxoeHcUeYiDdIZUkrypjMKIwvC42MKIzYjZ+OWE7+DqWO+89pz9sQO8/sj2UN7YuMCZ6IEkcIhmWF50UawxjAmH97f5IAl0CUf019JLtgO6j9c78kP8p/6X94Pkv9OfvV+5i7E7pmOYw4wrfYN3H3wLj6OMb4qjfSt4K353hG+ba68nxx/de/j8DQAaQCCoKTAnnB0oIggmzCasJZgoJCrsIcQiHCGQHDwXeAuQBUQP3BqkKrw3wDwsRkRFoEfwPZw3dCysL9QneCOEHrQUdAiD+wvny9EfvUumh49PeDtvX15TVt9UJ2Z/a6Ndm0y/QLdD90SDTktEI0MvQ59Pm16Ta09sJ3VTfU+HR4SHi7OJd5FDmpehL6znt7+6z8W30zvVe96z6//3wAMkEUAinCscM2w5hEFUR9xH5EToRThD6DwAQyQ/XDucNcQziCUUGLAPwABr+BvvB+Kb3/Pdl+Qj7E/xx/KT8ivw6/A77mfnB+DP4z/dp+HL6AvxI/Db8ovyR/Uz/tAB1AVMC8wMoB7QKHA50EcoUPBjiGnocWB5/IOgi2CUjKLMpwCs+LWktAi/FM1U4kTmIOCY4ZDqaP+pCV0PlQxdD0kDpPeI3KC69JqMj/R8HG8EXvhLLCOf9XPgb+bL7h/vc9XjsTeXh5d7ttPbC+t/6r/hY9B7vKOyn61bqSOfG5HTgJtqn1ozYb9uA3F3c19p72NfXy9rC4Izn6ewB8rv4Hf+RA/MGPAmACd4Jzwt6DfQMSAz8C6cKawn8CKIIgwcJBcsBaf/m/2IDageTCk4MGw14DWEN6gx1DEwLggkGCN8GZwXOAo7/8PuZ9yzyYuwB5zvh9Nr31W7SsM/UzmDQC9Gjz5XMe8lwyC7KBsyhy87KsMsJzpbRpdXq16XYl9pk3TPfQuCj4YzjsOUS6GXqxuxw78nxpvMl9bH2kPi6+9r/WATXB40K8Ax2DvwPPBHnETkSlxI5EuQQMBCnEO4QGRBoDuEL+ghpBSYCh/+C/U77dPk4+d75gvon+yH8ovx1/Nf7+vqI+Vz4Svg6+f/5Kfui/N78H/xf/EX+5wDtAhQEDwVkB9UKxg5DEqMVdhlaHVMgESKlI8QmByrnKuMrBy51MG0zJzeyOao6wTo6PN4+0UKtRktIBkj5R7xHqUfhSI5Jo0ayPqgyaCchInUhhSD7HKYTQAVp+Wj2GPrA/cv7vPOg6Wrk9+de8CL3xvl2+f32F/Jg7P7om+gO6OXlUeFG23/XGdhB2rPbVdzL253Z2dd02UzeMuPQ53jusfZH/pEDMQdsCsULcAtpDJMOXA9cDpcNLg0XDBcMRgzrCsYHewQJAncBRwPIBg8KEwz7DDgNDA5LDtwMWwq4B3kFEgRZA5gB9v6Y+wr3yfDS6sXlw9+l2EvTWNA2zofMcczqzPfKpsiNyHvIxcaexfjF6sUCxl7IcMvezZjQ9dIM0z/T79Vf2arbnd0k4IniReWL6HzrjO0W8DfzxfUg96L45PuyAHEFZQgNCqwLmAzqDMINMw6SDc0MoQ0cDlIMbAqEChwLKAqFB9UE6AE5/i/7hvms+IP3Ave69l32lfao96j3lfYi9sH1+PPJ8djxk/N99N70PPYt9zX3Xvjz+sH8Tf7sAGgDRAVxCDwNxhH5FC4YMhwMIMMiWCQ+JgsqdS0CL5Ax+zSdNf00XTWXNis6MEAMRUZF1kO0RaxLjlB8UYxR9FB+TjBLjUVIOlEuByqXKi8oISJwGUAM//199wf6Cv5K/KzzQek14gLjoet39VX6vvoO+o33kvH87Hft4O707I/oIeOp3OrYxtot3kzfmN4u3Svbydqp3SHjzenp70328/0UBAAI4Qt+ECkS9xGOFKIXAhdTFXIVRRUxE+8RpREzD8YK2AbRBD4FOQg4DBYP0w9zDw8QKhHyD0QNUgu3CcwGogTdAx8Cff4Y+r/0Ye7158/iV90j11HS+s+MzpDNxc2zzQ3LVcecxlLIlchDx2XHFMlzynLLq81Vz5nPkNA20p/S7NPZ11fcyd7x4Ffk2ue/6lrtzu/B8ZXzR/ZZ+Tr8N//pAnIG3ggDCrIKCguXCmYK6wrOClsJmAgqCXUI5AXmBB8FDAQQAYf+PP33+h/4zvaE9ur0AfOn8hjz8/L78iTzV/Iv8VTwHO8t7Tns+uw27nbup+5R7zPwR/Il9Vb3H/m2+/z+qgGcBBUJ8Q1YEv4WxhugHuMfOiIyJq0pYCzcLYgtwy0zL6UxIzTZNjk6lTw3PSE/zkOsSKxLUE3HTLFKjkoATZxNb0kpQbQ22Sx8Jo8jXiFPHP8QAgHd87jvLPOD95H2+O2y4srdLOMO7ajzqfa89/v1rfHN7T7tce0v7MnpgeVg3g3ZRdlz3OXdCt683YzbT9ou3Rbir+Zs67/xJPoBAxgKIw/2EjkV1RbIGNUavRoGGQMY3xZ+FRgVQxWzEw8QFwwTCboHSgkHDawPKRD+D0MRdxNtFCkTdRDADdkKqAiVB9gFigJy/pD5WvPR7Avo9OJG3PHVTdIC0AHPIdAw0enPrswWy7TM089f0VbQAc85zzDRy9N61f/UUNRV1vvYvtnf2lLeyOHQ41jmI+o37bDvXfIZ9U33BfqM/ewAYgN1BXsIfQsyDQ8NjA23DcMMBAwxDGQLWwlECBIIlwZmBBADwAIBApMAQP9w/mX8m/ly9w/2ovRH827yOPJu8lPzifOr8lTxAPBt7ljspOr+6VvqIOvf67rsDe5z8JvzEfZC+Bz7HP6DANMDAQlgDqoS5RXlGeMdnSBVIqAkUScZKX4qkiyeL2cxXzFgMgM19DYmODc6XTzZPQZBB0YrSZpJ/0rmTL1MdkvHSPFDmTxCMQokQBsHGI0W+hLiCLH2w+be40LqZe7Q63/i2dcP1bXcfudr7WTvjvGK8QTtqedh5szn7uZ/5Vfj2txG1QjUDdge2qfY+dUz05vSeNbp3TfmWOyp8sn82gbJC4UO7BLRFWIWeRfNGQUbrhqoGssZiBdDFR8UxRK4DqMJmwbaBZ0HewvRDtEPuw/DEBoSghFYD/0N3gy2CpAI9AdnBjYC9/x396LwaOlN5KLfMNmg08XR0NHe0XbS0dMb1EPSEM8Czd7NHtKn1mnY7tc910zYN9tH3o/esdyA3ILdcd4b4K3j1Ofh617wsPNt9TP33PkN/V4AOwPLBUoI2wpJDi4RVhIQEu0ROhGHD5UNQAyPC+UKeAmLB4YGHgYLBYsDrwLsAaYAF/8B/ib9nvtg+gP60PkJ+W/4+vel9lX1CfUW9HnxBe/g7SHtQ+wa7Ons3u0y7qnuoPDX81D3k/qX/b4AVgRYCKYMbhFsFh0a9xwHIEkj7iWTJ2woySlCLAkuJy8fMCsw4y/1MZU1+jflObo8MD/7P0JCzkaoSXdJLkogS5RKaEhDRw9G+T/RMpUjiBf5EIsO+gsBA7Dw89321/7ea+ao5nbgR9bCz2PUq99254DqRu3Y7q/rTeWL4dniC+RW4tHe7tcczwTNc9F700TRcs9UzWDMEtDR1zLg4uYJ7pj4RgT0C+APtRTUF/AWaBb5F+sY/xcFGDoX9BMYESkQhw/hCzIG1gGmAOIBXQXQCeoLcAtnDEYPkhA/D2kNcwvLCGUGmAWmBG0BRPxd9qfuK+Zw4FncPdaoz5zMDMwOzAfOPNHd0pPSE9KT0oDSPdLT1I/aF9884ILgn+Ad4KvfreBF4YrgcODS4GXgMeFK5oPtbvI49Yv3ePmB+w7/lwNqB7oKRQ6xEeATKhXlFRwWIRZTFKERDA9xDB4K1wiTBwYFJAPRA18FfQTYAuoBZQHVAGsB0ALSAtEA+P4t/qT+Jf9x/6L+nPyf+Y32o/Oc8RfxqPAY7+TtC+4g71rwmfLe9Xr5Ff2BAPwD0QfDDPURlhamGskeryLTJGkl+yZdKmEtcC7ELm4vEzAJMVwyOTOlMjAxLDGhMmo2gjsnP4VB9UMLR+ZKgE76T61NBUpbR+dEpkIQQSg8rTASIOwQBQpPCsQJqgCX6xvUE82E2DbkV+SE3XrV/NDv1mzhweb06T7uve6J6ZDi496p4F3jB+Ga27jTD8o0xnDLEtCjzpXLt8kDyz7Rmdkx4UbnAe4m90AC1wprDwUU0BeQF8EW6xjpGXUXJBVDEqEN+wmICVcKwAd5AZT87PzD/z0DMQfaCLYHlAjPCwMNcwyxDN0LYAhxBEACewHN/sP4I/FQ6cbhB9yH1+rRbMykyY/ILcm+y2XO+s+00FPRstN310vapttN3YjffOMc6Kzp+uZw4ejcmNw6377gA+Bx393eOODw5fzsEfHR88j2Zfms+x3/uwRyCpIPMhRAF/wWehUGFqYWnxQnEI8LOQhbBhUFsANlAtUAFgBqAFYB+gHNAWUCLgOlA78EvAZnCCkImwaYBD8D4AEGAVwA+/1F+u323vQ986/xRPHB8MbvS+918HLytfVJ+r7+7QICCE8NvBF6FbMYLByIH54iLSWEJn4mVyciKhMtPS9aMB8wQDBhMcQx+zBTMR80YDbsN0U65zzzQGtGzUkySolLU07QTvxMR0uoSalHAkW0QRs+iTZ1KEoXkAkZBXIGtAP09EbcM8zQ0QbiJ+lE4yXYUtDm0U/cCOYo6D/pKOvm6ZHlcODN3gHgId/A2mvUxMuqxL/FHMuizNrJR8dQxqPKm9NZ3OTjAOr78Q7+BwowEdwU/xekGfcYrRhWGZwXhRWgExAP5glzCPkJqwk8BTP/6PtJ/TwBlgXcCE8JQAl5DE4QihDkD88Pnw3BCOYECwNyAEX7svMy62LiFNuw1rTStMydx6bFqsXhx8DLcs/70aLTLtUj2IXb/N2v4B/jQOOK49zmlOkQ6GXjZt2z2JTXg9oT3BnZodal2TjfzuQ26rjv5PTz+fL+7QJHBloLuBElF1oZ1hgvF+MVSBR0ETMO7wooB7YDmgFU/0v9Sf0S/9MAEwFpAacCwgOxBfwIFQyzDPoLGwyRC+sJKwgiBq0Cz/6F++73pvPb8Mvwk/D97vzsdOxY7bfuX/Hs9KL48PyUATAG6QqtEDkWJhkdGtAbzx0kH+cgIyI8I1Qlxif5KaIs2S4+MGEwczBHMf8xMTN8Neg35DjdOvw+Q0R2SAVLSE1STVdLtkuiTOVLI0oBR7lCCj6XORM3azH0IQ8QrQYrBeEDpv7r7jnXT8vF1DbjS+RH2rzPBM1m06zeaOVL5NvkBeeV5aTfv9eo1FbXL9eI0X7KrcJIvsnCy8mTy7TIFcaAxxTP/dhN4S/p7++q+OYEuQ+MFFIX7RuWHmocRBr2GS4YlxXdEkgPbAtXCSAKrgqDB2gDAQMMB6YMYxHLEyUT9hLbFYQYvxelFaYToA+dCUAF6QL+/mH40u9K5iLdttaP04nPRspox3rHgciSypnO9dIg1gTXbdiw29XegeGj5PLlE+RT4wzlVOVG4pndztjc1J7TVdVX1q3VSteU27nfKuSz6uDx/Pdq/dwCmwfoC0ERBhcTG7sbQBoMGRcY2xVUEq4PPA2nCVkGFAQmAqgAvgA3AXcBRAJoBOkGfQi5CQ8MmQ4hD2YOPQ72Cx0ICwXKAhL/Pfst+C30q+8x7Y/sQ+tI6enoJelm6BjobusX8S71uPdq+3sAvQWPC9oQGRPuExoWnxhcGkwbvRuAHLseTiFyI6IlSigeK/ktpC8lMLUxcDV4OBY6bTu1PJI+fEDUQi1FOEfOR7JIRkkmSIZHckcCRWBBPT5oOis2VTJdLn4nsBltCT4CHwRgBFT7VOYBzonFotCx3ErbGNAVx8/F6MxZ1r3Yh9dF2SLZGdRXzuDKoMzz0t/TKs+qyFTCS8HOx5TNiM1+yyjKZ83K1iThQemd7w32jP3hBqwPUxUMGdQcYx/iHukfJyFkIIEevRrFFmsULhVMF4YXLhWBEsUSLhVNGCEaaBlQF9kVshXxFO4TMRMyEbUMHAYXAZ/9VflX8ubpQuLc2+LXzdQX0bXNSsz1y/PLW810zyjS8tSR1t/XA9pz3DXeR+B04UDgwd5E3wfhyuA13kDbSdp42h3ba9yu3TPfiONO6d7to/Li+dYBNwgJDSgRXBVgGRwdASDfICQgSR84HtIbchh4FUITxRAiDZoJMQcNBkAGwgahBpsG9QbZB3EJugrsC1sNAQ4bDbYK9gfTBH0B9f3i+ej0KfCN7MboJeZB5dLkiuMH413ju+NT5Njm/+tS8Vf1ivhA/GUBSQdEDDsP1BCZEgcVhxaiFpYXBBrUHF8ewx7eH3EizSX3KUgtHi82MNQxXzVIOtE9LD/sPrA+vT8cQc4/kD6LQdVFCEgaSNZGI0WaQoRAeEBDPQo3ATGrKtkgvBIlBO36w/j/+LP1q+cXzgy6PLtMybTQpswUwQ63PrdrwhjQGdV31J3VxdR2zk7JacmyzCTPIc0UydbCar2av2PHVMyjy4TKestt0XTdc+kD87D6XQLhCyEWLx71IsElMidYKIwpzSuyLFEroCmvJU0gJx0SHVscNxmlFBoSuBJyFcIZrRyEG8sXrhViFlkVAhLvDkMLIAZJAE/9Lfvx9XPtSeT026fVpdFNzujKncf4xZjGj8gUyyfNes9K0XLSMdRg17fbpN+W4szjeOSt53Lrmewd6uzmaOXC52Xqh+l35n3lKeqH8Fn1m/k7/hwEygqGENgUexl+H98kEidbJ8YnmCheKHQniiWZIOwaNhdZFJAQUAy5CIoGIQXfA90D/gOFAzkD3gK2A7UFiQfTB08HHAZGBCQCYP/p+lD1tO+a6mzlgeEg4BPgv99E4Kbh4OL342DlDujz6yfwCPSi93f8qQJcCNcL7Q6bEocVQxalFUUV+xSlFTYYcRpnG2wcCx8jIiUlDCgMK98u+jIDOM47Azw+O409YUItRY5Cujx8OQU5IjlHOZQ4bjYhNFk0rDN9MA0u8ivlJyUiqB3bHK4a0xOzBvn1TOqY6Q3uMuu716u86a4Rtc/BUsWwvX60NbJduB3Cl8cgyO7MI9Rm0ujMX8rKy4DSmtjA2azW5tCpzSvTidvI3VPc/NoT3AXk+e+Z+Tn/KQUoDkUYmx83JN0nRytXLYssQiy1LUEu3CywKGciDB3qGkobohm5FGYP7gzkDOMO6BCIELENwwuEClAIqwWRBNcExAKM/HL2sPMR8jntYuXY3CTVYtAJzhDMwshMxjDHoMmVyyLOw9ED1X3X09qE3y7ka+fo6mbvxPLT9DX3l/cC9P3xFPTl9qz3x/UX8hDwm/Ik+Vj+zf+MAT4GmgsDESkXpxwrIfYkFij4KHUoAijdJ6wnPCV6IC4bjxb0EqwOvgrYBqkCaf92/f/8cP2K/Sj9Cf3J/KP90f+FAJ7/Gf/r/qv9mvvW+a32PvIq7pLqVOYZ42bi0eEM4WDhHeOl5Fjm0enF7b7w+fKR9qT7QQCTA3gGcwl1DLsPaBLLE70TyRLFElkUtBbzGGcavRoyG8sdDSJ3Jecn/ypkL/gyYTRHNQw3jjgvOrU8Oz1/Olo2cTPeMegw1DHpMcUv+y1JLmMtqCvlKrkpZCdaJCkiEiAdGlQPMALB9TruTets6B3jVNqiyca4SLOuuWvCWcR8vBqyWLBGuKXG4NGS1IbYKNw22JHTudQP2TDdMd5/2rHVS9N61rXc3ODn4Prefd754CXpwPO1/EEEtAoAEwYduiR1KOQpoyrLKewo/SmXK68qlSdhI94eIRsJGDkV4hCgCzUIZAfkCMsMpxH4EgAQVw27DMMLWgjiBNoBEv6U+XT2tPRd8enrq+R63OLUk8/fzNDKrMgYyHLL+c7T0CnUS9m/3O3dseD/5LjoReyk8Bz2l/qf/fj/gQDj/qT8Ufma9cr1+fnY/LD6l/bO9eP6kgJKCeQM3g36D5MV9BmTHTYjmCjhK98r3yiQJTwjvSEiH64a1hQgDo8HoQLH/yT9C/ps97f1vfTZ9Nb1dvZU93L5uPuI/D79k/5q/ir9dfth+Qf2JfHE7GDoTuPw3yff3d7N3jjfkN+S3/3gseRM6Xbsuu9y8/f29/oOALUFbgqQDYsOXg56DgoPrg95D1QPmA+OEMQRYRMvFpEZCxy2HWQgCCSLKNws1S+qMlo2zjlXPHY9Pjy4OUg3RzUwNBwy5S6iK04pkSfgJoYmJSWqJNoiER60GbIZHB3SHo8c/xfOFZEVCxT7DO39Ru5L5xToFOXm1Da7nKyNsj2+1cR/vhSyQ7LrvADJqM/pzh/RvNpr3yDd5NzJ3nTkq+sJ7u7pveM14czj0enR6Wjlz+Em4BXmfO+K9rL6c/+7CMUTixwCIqknVy0uMTIxlS+NL+Uuyi0yKiwj8RsNGAcYyxYOE/4OzwvrCYQKUgxYC8IHiwQoAwoBMf9wANwBUAAt/HH4FvdC9XfxDeu04Q3ZAdUS08jPxss2yqHLZc39zdfPENQp2GXbX+Av5Zbp4u6c9Pf5o/0OAAUCGAMVAroATAE3A24GdwirBoQCqADJAdICvwFHAAQBowN4B9QMfxI6GDgeNCMhJcckkyO5IVUfZRtbF0YTIw8UC6EG9wIz//H65/Y98+3vV+4o7sPtjeyj66Tspe4l8GnxT/JX8n7yB/PQ8t3weu7K6+/osuVa4kbfitxO2yzcVd0S3nPfz+L356DtDfI49Wv4SvyqAHAD6gRjBrMIRQt0DXYOmQ4fD6kQrBKWFFwVNxVgFVIWFRisGh8ebyETJVAp4i2fMUE1JjmgOxk82DtKOx467DY9M3gxoDB0LooqDCWTIGkf4x81IRkiNiNFIwkilSD4IE4iGCQ6I3ceLRkpFlwTGQzc/8bzzOuj5zjjA9xjzTi4Jqtwrsa5r7+bvKe0R7IrulbIeNYd3FLe9eTC6Pjlr+Os5nTryO4R7svpD+RB4f3kxOuM7ULqVeeS5lTqFvSG/RYE4AhmD8EaqCVSLMkwtzTTNs025zYtN581IjJkLvAoTiJSHUga7RXVDkwJkQY9BKMDJAYbCPkFVQLXAHQAtv7o/M/7qPjK8xzwgO9T7zvsoOYK3+vWpdGyz/3N/8rxxwjIRMqTzA7QT9ZR3OjfnOI45xntBfOM+AH+vQLqBR0J3wrYCd8HygYBBxIIfwjLBUIBH/9MAY8EbwRUAVz/qAGABs4KxQxcD4gUmRk3HfAeyx6XHYQbIhhzEwoO/Ag1BDP+Qvh88yjuielv5vXjpuFw4MHf7t673+ziXOaZ6KTpIuvx66ztkfD/8bbwq+6R7Gvqduj85Vvjo+FE4M/e/N3N3m7hAuVB6e7t4PJm9wD8DgFOBQkJSgw9D5ER2BMHFa8UARQhFB4VVBYcFsAUZRS8FUgX3hc4GN8Zthy1HtEgKyQMKBgrOC5HMcwyzzPANf42AzW3MIAtuivOKdom/yI2H7UbAxmgFxYWOBSpFJAUghFbELcT2hiRHCQcnhrVGfcYHhj+FlcVnhMlEG0EpvT56qDqHe6W6mPZsb8/r1Gywb5UyFfHXb86v6nKVts96Srubu+S9EP2fPKz8SX0HPrnAC4CCf0B9j7yavP/9oj1Be/M6ATlH+r39b//HgafCWYOqBb5HzImzimILVgweDFHMdgwNy87LL0l3BpIEK8J+QYUA/v9qPvC+lz6gvzP/9H/jPxb+d/2IvaX9+f6hv28/ej75/mb+cn4u/W076DlHN1n2eLXgNWH01/SWNPt1SvXmNcc2k7fOeXY6rjwmfbc/HwCHAcmCpMLVw1iDhEN8wmVB3YHcwlFCyoJUwIO+w746vgl+AT0IvCP7zry+/ap/C0BvQUxCwUQFhJmEtUSKxJ1D7YKJwWeAGv8pPf08RbsLOee4xDhZt5z3J7blNv+237cut7F4+bpKu/x8ub1C/k4/R8BwALeAbX/7/2y+qP2UfPb8NftiOpc6I7maeab6EPsOfDG83T3uvuTAOQFBwoxDM8M5Q1/D6ERixJXEaQP9A41D3kPtA7iDOMKuQkBCbcIUwn8CrEN5BCOE9cWgBuZH3wjwid0KkUrECz8LBstlixmK9ApQyfhI7QhLCAbHiAcLhtxGqQZAxmwGQUc0x0uICsi0SGcITIijiIKJPIlryU7JK0fAxuQG0MdIhi5Cfr3LezG65bujen4153Alba/vxvOGdQWzzvI28sM12HjAu3b7xfynPii+H3xk+xn7RvyjvVi9DDuUOZX45vne+wA6p7jJ95R3IHikO189ib9dANrDGUYzCKLKLUsGTHTMj4xMy9BLuAscymVI6waPBGuC3gJKAa7ARD/Nf7P/vgBMAZxCJsHLQUFA/YBGQJ7BIIGcQVZAvb/z/8I/yH7M/N06MHelNiS1BfRv83ty/bM8c8g0kbUIdil3YHi0ebW62Hx9fbm+40A6QPRBAAFfwT4ATf/1v3c/Pf8Yv7W/XL52vXU9gX5jviU9PLwy/CL9Lb6rAAhBJwIDg9GFBcXHBgDGC8XtxNiDvYI5wOS/1385vgA9Ivvk+wX6uPoyOgg6GPnV+f151vqoe7K8l31OfdY+P/4GvvW/Sr+7PtA+bz2/vMt8WPuW+oT5jjjleDn3XTd8t5G4h3n2Ote8F/1FPqZ/hgDXwb4B44IKAnxCaEKYAo6CYoJZgotC0MLRQqpCfMJHgqACuoLKA42EVkUABdIGuAepSPCJ5crYi6yL1Ex1jFbMUkxkjAeL20tdiowJrMikyBiIGQg/B3tGagWzBRbFGoWxBg+GDUZjBoGGnwcnh6hHy0huCB7HmkbvxY3FeoX1hcJFIUO8gev/XvxROfN4hjiItrbxfuwC61xu4/M4dKLywXHWtBr3yrsPvGB8bv12Pm09ofz1/PQ9uT7HP0d+Z7xSeuR6V7sme/c7o7r+uVc5rvxEf3xAxkIow2MF7EjeyxxMOA0SDjYOFY2SzHnK9ol+h0qFO8K3wOBAMn/dv5N/V77gvmZ+qz+xwAt/6X9UPxO/GH9Hf/FAZACkQB3+xr2jvJW8GDsmOOd2Z7T8tB8zhDMqcr1yhvNDM8B0kPXYN7h5ZXs6fN7+9oBcgb7CdoMJA6tDb8MZAs9CiMJ6wbwBdEHIgqzCSAGewHw/nX/KwBW/8L9VvxA/VABdAaeCzYQSRIhE9kSihHgD+YMiAnDBLT+a/n683zvwOy16svm1+Ho3oTd693L34zg1ODT4YDkcOgS7QbxX/KM8k3zAvT89Ib2Zvex9g/13vJW8HPtR+sj6XnmU+RE44jk/ed27Nrxyffj/fMCVgenClUNaw+UDz8PsA5CD54QDRFEEIMPzQ7vDYYNdA3YDHsLeAq2CjYMgw8ME6MVOBedGD4bSh43IMIgoiHbIQohvB9/Hs8d3xzeGvYW+BJdEDMPjw47DoMO6Q7EDsMPWBIBFS8X7BcVF+cVoRQnE/wSrBZtGvAanxmTGJEZshw4Hu0cmxmfFAcRkxFFEmsQxwyoBOP6aPaK9tr1tfJw6MPXc82GztbYz+JI4qvcRts24FLqbfUe+hn5wfvB+4D2d/T89Hz5p/3P+yD3ne/c6o3s2fCR8Hbsi+hX5G/nlvEU+8sBjwTABvAMqhWDGpscCh6GHjEevBz8Gr4Zqxe/ExkNEwV//6j+qf09+4z5ivjH+HH85AFWBHYDdQFO/yn/1wAWA8YDvQI6Ad7/F//r/S/86fdq8OPoGOSc4tfiqOKy4T/ii+Wj6Afrte088dP0lfef+ov+WQKKBaoInQpYC30LoQotCI4FQwM1AYsATgEKAbv/IP4K/jP/J/+x/Nr4ZvYw9mb23PVh9bH2Tfkv/DX+M/9G/0v+t/y6+mT3CfUK9BXy++807rTsRex47JDt3O3p7LHrveoN6wrtRfBP84j16/Z394X4CvsU/vz/tf9//mf9nf1U/v39TPyR+qb49/XA8o3w5+9D8GHxQPIi8xX2DvoL/Qj/pgDwAfoBkgH2ANcA9ADNANEAEAGQAX4CfgKoAbwAu/8f/y3/N//+/7kBNwN9BEMGdgm9DB8P/xBuEqoT8RSmFugXQxitGAYZeBhhF7YWsRerGF0YiBcHGKgZGhyUHhQgsiAAISsiaiPEI/whih8NH4QfUx9sHdEa+BliHQohUiLtH/UbhxpPG8Mavhn6Ga0YKxe/F2AZHBlXFQIMugCT+wn5zvJH4RTI1Lz2xVbT2tgT0trJL9Ar37bpOe3F6UfoTO4m7xTsFO0R8eT18vic+BrzQ+uc5gXnjemh5zzkhuDK3TflmPG5+Er77PyzAjgNtBaxG98gHCjYLRUwgC5sK9knZiMYHEYTTAywCDsIVggVCZ0Jbwn7CeULOgyWCaUGVgS8AykEtQWsCKILcwwcCtoFGAMbATL9Qfa17e7mD+RK45HhQ9552zfbtduW22fcw95z4j7ms+oU8Ij13Pmk/Jv+s/+o/23/6/6k/kf+MP13/I3+MgJ4A0QBhf7C/jsACgAh/3v+2f7R/wwAdADDAa4DIgawCA8JnAj2B7MGVwWnAygC4QAq/i/7S/kI9yv1PfQe88vwJu8b7qzsVevs6sjrAu0J7g3v9e6z7Vrt0+3V7fzsCOz56yPt6O1t7XjsQ+zv6w3rEuk35sbkqOU854/omOoo7sDy7fY5+g/9+v6eACgCsgPLBJQFFwbSBnUIiwonDP0MWQ0ZDSQNtQxDC7QKXAurDDMOQg8VEGoRexNeFboWUBd3F9wXIBg8GFUYEhlFGV0YshbaFPYTKxOlER0Q2w6bDtkPvhHhEp0UthZ1F1QXvhVfFHgUchRjFEIULRMEEu0REBN8FA0UeBGDDz8P1BAvErEQYA/ZD2kU6RjEGVEZRxi1GGUb4x0vHhEb7hKeCPoBqADV/t7xsNpQyebJAtTT293aoNTu2IPoy/T/+PX2tfVO+638Avde88TzIflB/r7+f/sC9djuVevy6QvnHeJ43BLWddhM4+frk/Dp8qj39ACBC54SeBgBH8UkRSjdJ8cl3SMBIJsY1Q6/Bez/GP52/mAAZQIsAxIELAboCD8KZQmLB70FnAWfBwYMCxHyE4MThw/fCwsKkQeiAaz4K/Ao607pluam4hHf491m3dPbB9xO3s/ieOiu7WzzS/mX/gUC6wMzBLkC9AAx/5X9nPx3+w76hfog/Q7/o/1H+n/4jvgF+iL6EPi090n6Cf3T/tX/iQDHAYUDnATuBBwEQAK+AJb/Yv51/GX5OvZ/8xHyZfLL8RLwGO8w7wDwM/HK8gPzdvID89nzrPT49HX0ePN681j0uvP58TPxbvJA9Un25vRa8//xlPEj8SDvqu0u7jTu4O7j8O3zHfit/ET/+P86AQ4DmwPZA7IEtgSwBAAFvQUTBmoGegY8BooGqQbiBbgEIwQSBLUFfwfEB/sGbAdECWQKpwoTDKMNxA6aDy4QtxCnEd8R9xAvD+oM+QrqCNoG+wU+Bv0GjQfyCFALHA40EDIRQBO2FZgXkBe0Fi8WKxe4GMAZqBq0GV0YQBgtF1cVpRQfEy4R7BBBEcYR9BOHFrAZNBxSHZoeuSE6JZQmUya+Jyko8SdZJjsgGReVDiYH8v7Z7y/a8shCxtvMPtR71HTO2c/P29/psfGV76LqQ+x27jvsFuwh7gvyWvfn+T33Hu+l6ODmuOSq3q3YKNU+0e7Tat1/5UXrRe8M9ID8Vgf+D88WTB6SJoAtPjC6LzIu4yozJH8a9xB/C7kJ9ggRCekKiAxDDscPghA9Dx8MCwn5BmMGQAg6DNQQohN0FOcSpxAUDkcKrASe/BL0qO0V6xjpBuXy32ncPtvC2aLYE9mY2xPgHOXo6iXxM/eF++/9H//P/2IATP+G/fL8sf1N/h//BAEqAh0B+P5e/rz+ov4L/Vj7TvyB/58CbwRxBWwHYApIDNIMLwxMC7QK8ghTBmgDWgHa/z79QPln9QfznvGw8FHuUezS637rOuvY67bsouz87Ezu9+4H7lTswuud60fr4uq26ePoLerA65/sc+1t7dfsAu3j7N3sCO1r7ZDuAPFt9MP4oPw5/8sBbQS7BcsF4AUeBlUG/QXmBHIDVwOiBCoGAQZABQsFbQUNBX0EvwTDBfkGCgdNBskFJgbrBl4HaAfjBpMGcgYkBhcGRQYiBtwFjAVIBVAEzALmAfABqwKlAzEEuAS4BkEKPA1LDpoPTREUEhgS4xE9EpYTaxRnFMQV0hd9GboZKBlyGGcYVhktGd4XYxeIGOoaJx6NIB0iVCTtJgsnVSaGJs4moijeKVApISmTK/Qs+CwEKGodHxR6CpUDkPkN48zLQMKRxfLMxtI+ztDK+dLq3vHoMusz5jDk4eWR5G3jruW46IzuJvPA8w/vpuUB4Lvfjd1I2ATUh8/QzqXYyOPO6uvuGvO2+s0E5g8AGhUjCy0tNxQ9TD7pPYk6dTMmKgcfkRWCEWoRwBIFFaIXTxkZG0UccxuCGL0U3xGgD0UR2xVGGzMgISIsIOYbuReEEfkJpADm9QDt6ecx5UvhLtwv157TB9FdzgbNYc1f0WTXyd4L50ju+POw91/5Tfmg+PX2DvTJ8fjwUPF08oP0R/fF+Bf4ZPdq9773TvgT+YL6s/2BARQEcgYoCpMO8RH6E5wTqREMD/4L1whoBGIAfv0S+tj2k/N38OLupe7k7Vjt5+xo7Mnsc+0+7vnvs/IY9EnzC/KP73TsC+vs6YbnoeQi5OHkCOaW5+3nFees5trm/uVm5APk3eTq5u7p2u3o8Rr2//mK/MD9qP4t/nn8xfs9+zL6Dvr3+mf8ZP4WAAYBsQEOA/gDugMOAzkDkQSMBqwIqAjrB2cIZwmnCUIJ2wfaBmgHpAiKCY4JDQoIDJUN6Q3yDGgLRQqdCZQJ7wkOC+4LVw08EFQTdBV5FjsWFBX9EysSTBAVEK0RGRMHFCAV1xarGKQaqRvRGnQYfxbxFZMWCBnsGigcgx3nHwsjLSSrI48jMSQ3JfYklSQMJaEn/SnaKpsr/SqPKFcnqCX3H9oX+wzPAUr4K+md1krJJcbQyonP6sxWzNnTTN+W6vvuIerw5f/mWuYk5Tzn6Ooi8A70q/by9F3u0ek657DhANvd13bVPNWp3Bfn2e9+9U/50f+vCYoU2B6HJ/4vJzqNQaVDnEL+PmM3oiyPIAYXsBJyEh0UyxbrGbYcQh7BHQ0bKxajECcMbwlDCrwOsRMiGMEa7RnTFWkPAwca/sv0HOvi4kfeV9x42fjUo8+8y67IzsRmweHADsWdyxTTsNt84zLq0u538JPvV+7N7KfqzelV6zLuJ/GL9FL4nfp0+lf5RPmM+fP5p/ov/Hv/yAM3CJYMqRCFEzwUcRPyEl4ShBCODZ8KrQZbAjf/Qvx4+cL2j/MG8NXs1env58jnduho6R3qyekz6eDqAe4H7y3tz+kt5lXjeuFu4ATfAt7H3q/g/uGi4gLjI+Ow4pvhiOAg4ArhAeSh50PrtO+t9ff6k/4+AcYCtwJvAaQAYwEoAoQC4AKfA9EE1gZECLUIYQnmCeIJoQl+CvsMlA8iEe0RHBKDEgwTlxKVEdYQvxD7EMwRMxNNFHIV+BVZFRMU5xHZDjwMsgooCvcKUgzMDYsP6RHKFEwWxBXGFBcTzhC5D90OAw5oDgYP5hAjE1kU/RQgFUQUjhQaFQ0UwxPsE3QVIBijGBoZShr7HAkfcB/kHosgXSNvJTcnIyggKmYrWynvJsslcyVZJpYlniJkIeUgdR4vGisTLApEAU7wn9oKzdfIXMxm0kTRPM6I1X3izO4w9Rny/O2a7Q3r9Onh6/Pu9PP49475Jfhb8pPsh+k/5Y3eANuC2JbXYN6Z6Cfx0vbQ+fH9+AT7DTQXCh5GJfcu3TYSOuQ6ZzhhMSMo0hwpEiILXAhaCBYKKQytDcYOgQ9vD0IMBgeGAqj/UP8hAk8GegpzDJ0Kiga5Ab/7svTn7HbkS90w2TfX99Vo04fP6csAyanG0cRHxEnHoM061Wrd/eRR62jwmfPi9OL06/MF8hfxC/L88wH21/e4+ub8Uvx3+w/8Y/3h/sv/kwC2A/YHlws/Dm8QUBJSEzUTBBKtEFUOFAu3CFMGkgKL/rP5DfVE8dXt2+n45YfifuBD4f7i/eMQ5Qvnh+nA667tq+5n7uHsKepV6N7m/+SM423i0+Er4rniJ+OW413ke+VM5ivmE+fT6BTsR/AG9NP3GfzyALUF8ggIC0kNuA7ZDiAOSQ0yDbENYw20CzELpAszDOYMCg4UDzAQxhH2E3sWsxfeF18XxRZDFqUVhhRrEyQSBRHgEIAREBKjEdEPsw2ODLYLYgq3CBwHOAZJBtAG2QfcCI8K4gw9Dt0O8Q6DD0QQ+g9CDzcO+QygDCoN3g1rDyUQaxAYEuoT3hTdFqoZTBrhGbcaaBx8HysiySHjILkh8yNVJkMnnybWJjsnIShxKVcq/ymiKLIosigPKU0qFyrqKTgppCVsIIQYDA4PBZj4UOIKz0vHE8b5yiTOB8u0zOvV4+Ei65/rd+W742/jUeGe4o/lQOuv8SH1v/W18bnqieUv4qjbpdVF0o7OE9FI2ybljuuG7mPyH/noAYELSBQ4HPglKTB6NUQ39jbIMcUoJR5uEtgJjAb9BWQHagpVDRAPkA8zD1gMnwe3Aon+LP0mAMkEXwmzDUYPGQ0VCfoDgP1H9sftVOWg397c8Nri14PTbs9zzPDIiMbqxYnIGc611EXdNeak7Wjz0vbJ95H32/ZQ9YnzxPKB8+L1F/hN+tr8fP2a/H38rfyO/An9iP1//i8BTgQUB5gJcQxID9YQPBChDl8NHguHB5MD6/6u+o32w/E+7Zzpvear5E7ihN903andUN9i4lDm5OlH7BPuhfCE8unyuPCQ7ebqLem757fm9OYB6LbpfOvr66vrvuuT7LPtu+6t70jxTPVs+q7/BAVkCuMOGBLuE/sUyhVzFhwWYxRDEgkRuxDuD+ANXwzDC6AL6AtzDLMMXQ15DxASaRSqFYUVKBUQFbkTYBLqEEoP6w1wDfQMAQ02DW8MAAtPCXgH/ATuAZr/+v6M/1AA/QGqBAgI+gvrDpYQMxL+EtgT7hNoEnAR3RGbEWoRjxFUEqkUnhZ3F9IXwRe+GQscNR2KHmEfvh/jIJghXyKnIochmiH8IcYjDSY0JmYnVilDKmcqdykuJ2smtSVpIi0hfSFYICggTSAdHoUZNhGXBij+o/Nz3rnJ/b9NwLHGjsmPxbvGltBZ3fLmdud34uXh/OGa30TgAuMq57zsTvBr8V7tw+bj4cXevNp01ifUq9ET1NXdl+ie8BT0hfe+/cMFrA4IF5AeeifUMOE2yDhhOI40qCx1IjgXPQ5hCckHNwg/CqIMHg6sDrYOyQwVCfAE0wAj/xcBDQWzCPYLPA1kC+EGagDC+cryBOpJ4V7bitgx133U088SzF3KBMghxa/DccU2yjHQYteo37vmS+z+75LxovFa8W7wdO+R75rwcvJO9HD20fht+WP4PvgD+Zv5XPpo+0P9fQBvBL4HWQplDGgOOBDoEFAQdw9fDZEKEgg4BQMCmf4q+tD1QvI38KnucuzJ6TvoQega6TLqSexP7hrwD/Il9B31KfXd8wnyP/Dk7VvrvekE6dHpU+tP7M3ssO147nzvsPCQ8W7ykPTw9z37aP7WAi0IcwxeD9gQTRLBE6cUXRRGE2oS4xF+EeoPQQ7MDVMNTQ33DdEOaQ9TELwRRhQvFwMY9xdIF54WOxaZFTQUohIgEdEPGA8/DuwMLQxBCz8KRAmFBxkGvwUoBp8G1gYBBx8IegprDdEPchEME5IUnRXVFSAV2BPCEtgRYRHUETMStBIAE/gT7RVSF+wXzBjyGfoaYhxJHa4drR+zIpckxSTdJLgkXCXwJZ4kHSXoJXImBSd6JtsjXyLjIwwkGCMYIXQf/B0zGecRYggX/xb06OKm0JzFC8QEyB7LB8pqzLvUx94m593oIOSM4BXfsdyL3QHhR+V36ozud/Dq7dLn1+EF3cfXvtIl0APOFtD92OLipOri7iDycvee/zIJrxEYGawhDis4Md0zsjLNLB4khxqwD+sGOQPeAicFYQm1DJEOjQ+JD7UMCggWA1r/1v0kACUFUQpPDjAQiw7aCSoDIfw29SvtYeRc3lLbq9nZ117U1c9qzErJAsaFxLfGCMzE0svaa+Op6u/vDPM79MbyYPAD7j/s5etG7eXvfPI29cf39vh3+Df4efhj+H/4Y/kU+xr+oQLUBmkJvgr0C6cNdQ+cECMQXA4dDCIKnwe9BPQBxv6u+m72mfJi8F3vau9c8HXxq/EP8rzzRfaS+K/50vku+Zf3x/Xl81HyafHV8OXvEu+L70bxg/MZ9SH1N/Sf84Xz4fO/9MT2afkr/HP/bwRqCgIPdhHZElwTJxN/Eh4RdQ8mDmcNEw2oDDYMKA18DrgOUA/eD9MPAhByERsUgxaUFycXbBYaFp8VRxUqFNgRmg81DlgOLw+WD7IPNQ8kDnwNCQ0xC78JZwkLCUQJNwuCDRMQ5hMbFy4YERi/Fx4YpxenFTwUHRO+EbERExPCFHkX9xjBF40WkxZNGOgaqBqZGMkXlRjoGwohPCKmIMYffyGnIhcifyFoIQkjwiR5Jb4jLiODJc4nZybkHi4UNgmw/wr3O+ki1iLHOsNZx3HNgc/czavRpNrc47To1OSQ3w/fbN4g3tTgleRL6N3sUPFg8D/pMeFr3K3ZVtRuzxrMa8rCz6/Y8N9W5R/pjO029a7/3gkoFGseMidXLsQxojAxLD4loRt6EAEHcgKqATkDHAfBCwIPgBA0EKcNlgn5BRADCAE+AZAFWAwQEnYVpRWbEjoNCAfJ/+v25u3W5mbiRt9E3J7YhdPzztfLzchOxoDGucof0ZjYAeG+6MHuFPMR9cjzuPCf7r7szuoG6nrr7O2T8Bjz7vRv9R/1kPU69h72DfY59/n5rf3lALECdwQSB8YKgw5OEOgPrA40DREMugpUCKIF/wKi/3P8ivkM98X13vQr9Krz9fLl8rT0Mfh1++D8Ov29/JD8YPtG+S/38PS18rPxcvKN9BD3+PiE+Wv4VfZS9IbyuvCN7xbv8e8s8iT2BvuXAMUFIQkcC20MOA1+DdINqQ2BDBIL1wr0C0cNZg4ADzcPaw+HD/wPOBD6D/QPKBG0Ek4TWBMRFCwVDhYOFtYUFRR8FIkV/BaKGDMamBv/GzgbqBkGF+ATkxFWD58N7w32DyETaBZCGjYemh4UHJUZ6he4FsUTuRCbEa0UNBdUGTMZtRfmF5kYXBYTE74R9xHtEa0QSRL/GOoeeR6/GkcYCRjwGgQdoh0bHi4gzSXEK38s1SQtFnMFH/hZ6tzYLssDxBzFxc3S1THbt+CT6MzyM/dV71rlDOLh4YDkp+rI7/bxBPQ59474+PML6nTh2tlo0lDQtNCF0MXTA9p/36TitOTE5//uKPnlAyQOGRguIosrADBrLaUmaB15EroG7/3t+if7Sf21AeEGPwvRDpYPvwwCCKAEoQM3BCgGGgsgEbgVhBjbGXUXwBEgC20Fb/+S+HTyWO0l6VjlE+Cl2JnS784OzBXKqctf0SbZUeH86InvcvMf9dj1pvOW7lPrF+rt6E3oI+kX6rnqtevN7K7sA+s26n7rv+227+7wNvGO8bL0lvkD/R3//wHkBTIJ6QpaDNYNNQ4qDcQK/QUrAfH9hftb+Zv3VfZ+9e70D/Xo9Uf33Pi6+nf8zvz5/NL9ZP6n/c37u/mw96/2y/dn+Un6mfrj+lz6SPl39730hvHq7QHr6ulC6v3r+e7c8lD3YfyHAMIDTwYICPwISAkFCcEI5wdaBz0HHQhfCZsKQQtaC3UMwg75EMQRzhGwEgUU6RTrFCwVixWoFpcYVBqOGl8bFR6+IOwhmCLUIjIi7SDxH88eUxydGT4YcBn8GrcbCh1UHl4gCyM7JKMiwB/3HM8a8BfcFHUT2hMNFJkUNxbeF1YaxBvgGtkZuhnTGVEZOhcQFSsWiBiwGYwaUhrJGqQdxiCVH8oUlATG+vzzqeex2e/PbcpZyjzRZ9n534LkA+zC9qz4HfLU7kzuHuyb7NLxH/Qp9JT1yfgm+Sjy5uku4zzcDdhS2L/YZ9gs3Jjizuer6/TtnvE6+IMAKAkGErAahCI9KM8oDiTGHIUTFQm7/1H6Z/hl+cD98QKJBwILnAwOCwYHWQJz/zf/EAH6BHoKFRAyFR4YtBZ8EScLcwRy/Xv3IPN877bs2Ov16uDm1uC53F/ZSNWJ04DWCdwA4onpQPGz9Uv3x/dq9jHxMOvY5yXmuOUH52jplurg6njrHuzU6tTnl+RT4W/fB+E/5DPmEOgD6wXv2PJd9ef3Cvsu/cX/wQG1AVkCBATrA1kCAgAk/Tv6Pfd89RH1qPQC9Kb02PVL92r6+f11/1b/aP6K/Vf9iPzb+0r7wvpq+9j9w/++ANUA4/4Q+6f2rfGy7C7om+Ug5a7lQeif7KbxRfbA+tf+JwEJAqsCRwOwAycECQWIBUwFXQX7BQ8GYwUGBSsFfgVnBlAImQq9DFAPgRJDFUQXNxn2GiAc3R2oIP0jKicgKkQtLy+gL9cvai4WK9MnbiRtIdwfnR8fIWMjGSZnKfkqDSq9KYYpBimTJ3QkwCOiJC8lsCQcJNUkjCbPJSki0B9VG9gYkxhvFxIZ0xr4GkgbpBo8F54PNwEI8czkntdOzH3JNcg+yRPRO9rP4WDptPAy9gj0YepA5iTnDuWn5xDw+fUX+X38Wv6U/F32Ru515ineA9aU1JjXOdtg4ffnx+3C8un2sfw0Bf0NZhXbHeolsit2LqYsliZHHRgSlQbi/AD4afg9+9z/pAW6CgwOeA7RC+sGYQHg/Kj7ff6uA3AKuxCqFZ0Y9Rf0EqILowOR+hvyeOu25VHhG9+A3bzZV9U/08jSqdJY1C3aMeLy6bfxmPjm/Nj9qfyr+bT0hu+L7H3q5eie6SXtne9N74buqOzE6P3kU+ND4bLdidvm3HrgNeW161rz1Pkp/wYEugZVBm0FrgQ8An/+O/p09lHz0fCV7zDuN+xp6h/peOjv6KLqcuyp7urxpfUD+bD7E/1Z/Sj9jP0g/kj/gQAbArYDAAS5AvT/m/pP8/vrM+XY353c4Nts3rPjiOo28cz2SPtG/2MChQP9AmcCWQJnAiIDtASABj8HbAf+B2AILQhiCNwI0QhECaoL9g4mEeESTRWEGBsbpB0QIfYkvyh1LF8wtDNcNq43NzdBNWAyMy9lKlQl8iHpID0hsiGCImQkLieEKUosXy5lLe4p6ydZJxEqFy3lK1oroiy9LEkuxC54K+QpuCfJJa4kIhwHDcb+fPNT54nX88p9xUXEmMbvz0bbTOOz6sz1uP5i+x3yOe2M6gbmhORt6XzuK/DI89j4RvmJ83Ts2Oal4IDZLtZg1wTbkuBw6DzxnvgI/zMHYxEuG5EjaiuTMu42mTY3Mg8qtR6mEgEHsf01+Jv3svqW/wsGpAylEI0Rhw/dC4MHPQPgAQgEjQfbC+4QfhWeFrETiQ4dCD0ADPfM7sroluMt3mrawdfT00jQnc+c0MXRktUS3sToJvLI+dz/agP1AmX/Mvoq81frE+b35NXlDef66F/q9epy7KLtwOvF5hjiXeBX4FPhGuVV6yDyq/mUAWAHggodDQgP5Q1RCscFPAHW/I74IfXU8eDtoulh5tvkFOUt5rvnu+rk7v3yn/Yg+oL8TvxL+q/4O/e39Vn1OPZq93v4A/pK+w76t/WT753oGOHC2o3XTNdl2bjd/+Pg6l/xsfbE+q39vP4I/sX8q/yr/cn+GgCSAeICWQOZAo4B8ADbAE4BzgJQBWMJ7Q0gEicWbBm1GwQeDCHhI9olzSjaLZQzSDd4Ocs6SzkcNYYwiSzbJw8jnyA/ILwfiB+NIakmhSwvL7Mt2SsgK3ssDi+UMLswSjDAMZIzmjRxM2MwMytRHyYN6vyL853oI9u00I3Lzsq/zILSu9i53Hzh6egd7gHswem77XLyXPP29LH4Q/qM+ej4Uvgr9DjrK+IS3Z/aBNiw1vrXe9r63trmle9U9wj/QggxE1IehSfbLkE1GjmlOOo0hC5dJssehhjwEpkOHQ3jDdsPpxHnEWERZxBtD9YNogwBDfAPmRRFGf4czyBXI/Qh+xxFFgwPDAez/W/0Iuzp5BDetdg31PPOocqcyUjKncsoz0LVDdxI4vHn3+zK71/vG+3k6j/pFeda5eTjy+Jh43nlgeZm5dHhitxb2Q3Zqdkp2wTehONy60L0jP1LB5gP5BROF5oXrhYOFUQTRxECD/4LwQgDBjUDcACI/fP6I/g49QfzO/P49DH3gPnN+03+fQDeAW0CmQKcAc3/O/6z/Lr6uPcn9Bbwueo/5Fze7dnz1XDSYdEY0wfWSdkx3izk1+ip6w/uSPHT8/j0Ffa+9yT59fqd/cP/VACJ/7b+bv64/uH+mv+9Ac8EkgjiDMwR/Rf2He4iKickKqosky9RMuI0/jauN+k2xTS+McEvPS++LmAspig8JaQliCmaKToqQS5yMVAzYjOBM6M1oTXsM3QuhyA5Dw4EHP7I843k29rg2MbWetVQ2NrbiNyU3AjfRt+32FzTgNV/2obcpN4Y5Dfpmev27AHuyusN5OXaZtZC1GTQ08yOzrLTodks4Zrqk/Ou+/gE4g+8GZ8gDSbAKxgvuS04Kr0mGCS2IcofnR/fIIIjxiZOKdoqxiolKdAmDSM+Hw4diR1sILIkgikNLnYx1TKYMEUrRiXRHVUUcAoAARf5fPK27BLoX+Po3hXcQtvV2pHaQty/3x3i9uJ14g7hwd913lLbcNYc077TdNba2BraA9rM2GnYetkl2EzUPNE20YfT/Nbk3CblPO7C96oAJQhCDTgQ+RGIE5gT0BF1D0sOzg2ZDYgNFw1dDEMLpwq0CikLLQu6CusK6gsuDcYNfA7dDuANLwzjCmwKowlGCOkGiAXIAgL/g/tw907x4enK4y3fWNsu2YHagd6c4v7lM+n07J/vLfDn75XvHO/g7ertXvB88wH2SPho+8b9FP7G/Vj+DgCmAVECgwSRB2wKEg7dEWQVDxiuGvgdMSGXI4wlQCkLLgkxWC+mLDcsxywmK2Yneid3Kboq+CxfMGszWzQeNb42LzObJnQVmwah/ej01OsG6YLnE+Nm5HjsYPM39N7xgfD76+vh5dkI1wbXVNdF2Qjel+CE3wjfAeGA4NTZ9NCXy8DHrsRwxGPHW8swzr/RF9lg4crog/CA+k4EUAsHEVwWHBj3FCwQCgtPBtQCSAKcBcwK9g++FfsbJyGgIo0h9x/KHMYYTBd4GagdhCKYKEkuqzFIM1wyLS/YKvQkLR5lGGUTxg1gCLQD7f69+Rz2/vPe8irzsvQ991H6Pvxt/CP7ZPcw8hrsQuYo4TTcwtk32krcKd+s4e/jk+UO5e7i0uCw3izdEt1K3yzjLueV7NHzYPpJAG0FmAgsCaoHigVqBBwEjQMkAwAEuwVnBmQHuQnoC94MDw3tDQAPiw8TEUQTrxSEFCkTIBKnEDUOpAyBDHEMKQxrDEcNEw3xCvAHDgRk/rL3NPKV7+/uJu9n8Yr1t/kl/Gj+iQCHAC3+d/sS+jf66fqY/Ab/cQEOBHYG6wcXCFsGZQUFBqUGAwd4CXwNPhBLEaISXxRHFVkVZxcoG1scDx9hI7El8SbkKQYsiStOKRAndCb8JL0h5h4hG7IVsg7uBdb8ufRO8EfwTPDF7Qrt9PDs9ZT4X/ms9xzzze0268PqaOnc5+foReuT6+Pod+Va4ibdq9XCzn7K7MZyxNvFScsC0cHUTNhu3t3kcem/7cjy/fbs+GT77/3//eH7tfkf+Dj2jfTN9LD3uPsU/wUCUAUnCLMJXQqhCv0JZgqvDTESABZYGmsf3iLpI8UikB/BGgsVHw+aCb8E0wA5/sv8Uvv2+H33IvcO9+b17PTD9br2m/a29pv2i/Rq8E3skeh25BTh2t9A4OvgAuJ45HfmRuaZ5dbkB+O64YXhDOJR5EHoWO1H8zr5pv5pAjEEYwWxBSUFDwUtBeQFbgeYCUML5gx2DuIPYRAREeUREBPUFAIXHhkZG08c8RzEHAcc7hrpGPAWChYnFr8WQBfDF/AXyRYJFJgQ7gxMCacFEAP2AckBzgK4BHYHDQrzCiwKEQkMCBQH7AUnBYIFCAczCPcJUAzkDJwM2gzhCwMJcgapBtoJVAwtDAsN1w+JErUURhe9F60XvBjkG1kf/x9KIYwmqCttLNwqgSikJFMfvxqJFv8QDggv/v/4Kfgk+of7avql+QL7T/6pAq8FDgQ5AKz+N//w/lz7I/gw+Wr6lPgR9VTxjO226ObjqN9D2uPTQc/5zv/RitTb1vvZVN8t5XXq6e9p9Gz2iPbh9jn3u/XP8WXtVurb6BXnK+Yh56rppOyW7uHvw/Dw8SDzAvSF9GH1H/iI/GcBcgZmC9kOKhHQEUYQ+QygCN8DUP+q+173tPOW8bDvkewG6cDnnOle7Jjuzu+w7zDvMfB68pfzCfJz7pLqi+h86Obp4+ua7YLuwe7P7aHrVOm95izknOLj4azhF+Nl5l/rkvEv97L6sfwY/hv/sP8gAPj/h/9y/n/+lv8vAIMAlwB0AGIAfQDlANEB4AKLA/gDtgSQBW4GpgcuCYcKywtCDU4PLxLaFJgWIReYFj0V5RJMEK8NXgtCCRQIZQgkCuIMoA95EpUVURh2GTMalBrtGRoZ9RikGYUZihj8GYAcCh4lHXMa/BpHHLsbBxvIG/AajxnGG6ogpCE6H7QgvyV5Kf0pFSqKKRkmhiKtIhggOhi8DYQCifs2+W35SfrF+Fz3w/ii+wYANQOdAvAADv9U/u78XPvh++X9WgD2AQQAs/zc+TH3KfWG8njuu+mm5afkheY36TLrwOyZ7zf0jvnw/o0DygbwCHoKwwtOCwgI2wMrAIT8p/kd+Lj3qPgD+kr7oPvw+138lPzT+8L6N/rQ+t/7dP4PAkIFNQdWCJoILQe2BHkB8vxh9nPu4uci5Arhrt0Z2xfY4NX+1s/axt2y3mnfbuHg4nPjJOTm4z3iO+FR4Y/hIuGh4BjhFuJV43XjBeMr4rHgDN8x34zgo+FC41rmguvY8Vj4bv7aAiYG3AnWDIwNSAzSCfkGBAVYBN0DxgLaAQMCwAIOAzkDsgOmBKIEcQSFBLIEEwUNBuEHHArBCwUNYg4lD5YP9g+ND44NwAqFB5EEnAG2/hf9C/x5+138h/6wAIADnQdkC0ELNAp+CgILsgsMCxYKWgvgDHEM9w2hEXMTIRNvE4YUbROdEfURSxRqFXoU5xQWFWgSPxEpE+cVERegE8INPQreCC8LQw7hC7MFcADP/nEBsASbBsgG8AX3BfUGuQgLCb4HVgcoBy8EZAA3/q/92P12/oP+X/1m+7r61vxi/xwAHf9B/iH/IwEFAxQExwToBDIF/wYVCacJQgmOCGoHRQbyBFwDOAKzAQYB5P+e/4UAAwJkA80ElgXSBeQFJgafBp8GJQVLA90A+f0V/Ef6tPe99Lfxgu8777Tv4u5K7QzsOOsG6inpI+mj6E/oAOlW6o7rzews7ovvUPBZ7z7uI+7g7ens5+tV68rrKuzZ7J/up+9s8J3y2vWs+E/6f/ul/SIATAEZAY0AsgAOAfAByAP4BAAFMgWEBvgH8AezBlcFHARNAjMBVAGCAocDUgT0BL0FNgYoB3EItwjNB6MGAQarBRUFdQTxA44CPAECAJ7/tf+9/7AAPgFtAQ4DoQM1A8gC4gI1AyQC7gFKBJkGdAZJB5cLyg55D9sPzhHhElURwRB/EoESCg/jCh0I1gaIBnAGlQeLB5UD0f9S/wABwAPfBG4Dv/93+3r5qfps/qMCfgOqABb+q/3//ZP9MP2U/I75u/Qz8UPwd/CJ8FbxN/ME9HLz+POt9sb5e/tb/Cr9zv3p/Sr+hf/o/wj/uP6c/5UATgFcAVAAN/+f/pH+k/4o/rH9U/1s/az+xwDQAlYEcQX/BUQFXARQBMYDNAISAMz9pvs0+ZH3pvcm9+L1Z/UE9Zfz+/EP8tLyGfKo8Pvvq+8H7wPvg/D58Rbzi/Ow8x7zo/J68k3yWfLb8HzuIe1Q7cHuTPDo8ZPztPQL9j743vn3+kb86Pz1/D79xP3t/ur/MQFdAokDuwSzBqoIdgn+CM8IggkwCsAKWAt/C0kLsgt+DREQhhEtEvgS4hPYFJkVNxWvFIIUXRRnE/cRPxHzEawS4RJUE1cUCxWvE/oS/RSXFZoTDBJXEbkPQg/MEOkSzRO0EqkRuhI2FEAUYROTES0PlQtFBq8CbQJTAjICZwGe//7+Nf6O/WL+jP9z/+j8Vfq1+Gz3w/Wr9Q74Qfus+4j5f/iO+PT4ufkm+n75xvbl8nvxYfKX8oTyP/Tx9sL5dfua/Pr+FQL1AxcFUgVqBIwCpAH2ATADVgTwBIYFKAZjBpMGwgZ6BjQFXgPDAUMAO/++/sL+/v7Z/s3+Of+H/xn/6f2C/Gj6L/jt9hH2WvSG8UzugeyM68npS+iz5yrnjObX5ezlxeWX5MPjAeSV5LbkEeRj5Oflfufn6EDqdOsi7F3rHOsq7G7sfuuV69/sa+4K8Mvx9vO19Q73r/iH+rT7Dfyk/Df+qv+ZAG8BKgKjApICrwI1AyADpQJfAvYBJALaApcDpwPEAzUEqgTFBGMFjgZ2B44IHgpMCyMMFQzFC8MLlQsSC4IKnwr3ChYLPQslDKcNqg5gD9kPWxAJEPwP5hDCEiYVHxbbFcYV7hZxF9gXYhYeEkMOuwkaBzMHMQexBW4DRAHm/7v/7gAWAwIEfgJtAfoAXAApAUsCUQJKAVIAxf/+/ij+av03/rL+KP3V+iL6a/kx+Oj4ofnI+X76zvxCAcEF8gfBCcsLZw10Dh8PlQ52DfgLDAuPC78MRw2dDcwNmw2EDc4NoQ0XDV8M2QuVCqkJHQngCOQIbQnaCSoK/wmOCS4JlAhuB84FkQNYALX8zPnC9xP2wfTZ853zCPRx9Fz05vR39U71d/Ro82Xys/Ff8ebxsfKE8obxgfFp8kfzjfM389zynfI48g3ysfJu8zX0SvVm9qr3Z/lK+/n8L/72/lj/sP9DAOr/rP5F/b/8Kv0o/cH88PxZ/UD9q/wN/cL+rv+W/67/6P+7/zsAyQEoA3gDdAOnAzAFIAZQBRsFMAXdBDcElwP/ApIBbwLDBNIGSwfTB/MK3AwRDTUORBD8DykPug6YDDYJVAWOAuMA5f4V/Wj7jviY9fHzwfSG9aD1yfSK8njvAu0r7djuwu+z8X71+/W58gPy8/La8uTx2e/n7AbpH+a+5Z/noukB60/s6+4V8WfycPPe9bP4n/o0/GH9jv0G/pb/3wAfAqMD7QMnBG8FjgajBrEGowboBR0FwQSeBNcE6AVACMwK7AwrDzgR+RHUEfUQhQ/cDQQMIgpECHQGOAVIBDIDYQJYAQIAJ/8E/93+XP7W/SL+1/70/nT+9f3j/fn9WP4G/4H/z/8YAFIAXgDR/6z+rf1t/Az72/po+wf9g/8RAvoDKgZpCIIKugvDDIoNLA7lDskP/g/XD5gQ0BGLEjcSRRHSEF8RyBF0EY4Qxw+kD7MQYxEPEBIOyA08EdwUBxUaFXsWHRdPGGAZRxjOFR0TUBJNErERHxD4Di4QbBKREu0RNRJKEJ0Nyws7CG0CWf3b+2X8ifu5+WT3bPYt9/z3WPhK+H71lfBK7lfviPHL8LftHe0b7sHuX+/U7/fvE+607Bvtce1B7C/qqukZ6tnpwug56LDp4evi7v3x0fMP9Sb2lfev+IX4PffO9QT1zfRB9hX4cvkn+0D9O/5c/uH+3f4m/jP++/2A/U3+XP+1/wYAKQBaAKgAcACS/9H+Nf44/Qv8h/vV+kT5effY9Xv0NfM48pDysvNI9FT0ofMB847y3PEr8F/uku0b7RDth+397grwl/D78ZXz1fPA8sPx9vEB8+fzv/Tx9RX4ZvpM/MD98P9/AQ0CVQKFA+IERAUEBccFUQflB+UHEwmpCu8K8gtmDiMQkBAlEP0QrBIOE7oSJROcE78TkBQGFosWnhZ5F/oZlBtpGv0YoBeIF3AY9ReRFrQV5BTuE6QThRKXESoO0AndB6cGBAVTAm4ANf/j/dz8eP17/ij+0vzN+9f6ZflS+NH4Rfpf/CL+Zfxi+l76ZPu5/NL89PtF+p74RPgU+g/8/fyE/RD+If/+/2IAmAGqAwIFPgYiBwgH5QZ/B5cHRQfCBpsGFQatBUcGVAflB4UHJAd7BxgHJgaeBU4F/ASDBMADFgQVBXkECwPNAVAAtv4D/cv72fq/+PX2bvZd9WzzBfKO8dfwvu9b76fuc+3i7XjvVPDC7/nuLO/A793vHvBg8EHwqPDo8VXzCPTN8wj0jPXb9m73l/cV+JT5uPuX/Tv/NwDXAO4BUQOUBN8E2wSpBQgHRQf7BsYHsAh7CBIIKwhHB0kGZQY5BzkISggMCHYJwgrtClcKowlTCjEMwwwzDL4LhAyODRwOMQ/DDxsPFQ3dC+ULBgsBCS4GzgJ3/1n9hvxS+0b5zfeL9zX3S/b79v73wvZr9fD0uvO98QDwwu8V8nH0qvPU8fnxo/Ks8yX0+vOb8j3wm+8p8cDyJ/Rv9d323vi6+nP7t/wK/9UA3wACAbkBdQHDASoCiALpAoACSALtAgIEGwXgBZUGWAesBzcHLQc3B1EH3wedBzMHagcXCGIIIQhqB5sGzgVMBKEC2AECAdP/Nf8K/0v+OP3w/LH8//zg/Bn8Evuu+vP65/qT+tz5tfie93T3Ffj295H3O/dJ9433RPhp+GP4Y/hM+KD4qflg+rr6vfvv/aD/PwDCAKwA7gDuAYICswKFA/MDYgP1A4oFUQaCBroGKAfwBxsIlwdGCCoJYwl6CYgJBQrZCnkLpAutDCoOfA7bDiUQKBGIECEQxBHJEjwRnhAYEiYSMBHSEL0P3gyOCbwHxAcgBhAEWQIzAckA3P8v/+3+rv49/lX9bPv5+QX5rPfO9kz4Vfra+G31ZPTx82rzM/QZ9ar0u/G8757wf/HP8ZfyaPN/8yX05PSG9g/5aPq4+xX9hv3k/BP95/12/sT+7f55/xABzwE8AqoDgwRiBJwENgU4BRMFEwWgBXQGmwa6BuUHWgiyB0EHOQfEB3YHjgYuBsEFTgQUA8gCKALJAFL/mf47/ob9dP2G/fX9ev3u+xb7wvrO+ej4iPhj+Jv3Ave49u/2tPap9tv2r/b99eL05PRX9Rj2Y/b39dv2cfjP+IH4TfmR+uT7KP3f/rX/jv9Y/3kAywEqAnQCNAILA2QEaAQIBIYFuQj5CVkJ1QhNCk4MhgwdDe0NFA6RDlkQ1hGoEtkSPxIaEpgT3BNsEsUPDw2PCm4HtgT7AugBQwBN/sX8FfzN+1j7Evut+eH3Ivau8wfyUvFU8KfvVPCE8XPw7u2D7A7t3O347H7rb+rd6Hrn5+de6U7qhOur617sVe/P8WDzQvWi98z5fvqR+gb7QPz7/GX9Mf8tAV0C+APwBZMHdgmwCkcL0wv6C/ALrwyIDekNdg5sDqEOyQ/+D2IP0w7XDpsO7w3WDFQLcgruCO8GwQUVBQ4E8QJTAqQBtAA7AE0AtgDB/4b9kfuP+jv6YvoJ+vb4a/jq+Mv4tfgb+ST5TflX+Wn5LPl0+TD6HPuY/CD9KP3Z/pcAoAFmA/AE0gW8B4YJ/QlwCgYLhwv0C2kMAQ0oDXAOuQ+/D8cQlxHbEqoTRhMJFAUVihSzE60UJRelGHkX+hZTGFcYlhdAF8sXkxZcEwISww8PDPoIjgXWAloBqP5a+0f6fvnL98v3GPfm9MryZ/JI8TTvJe77697qeuvs6qvqQOpm6OLmy+Z/5pHlvOUS5dDjTONE4xPk2eWb5wTpjuqq7Dbv9vCI8v31Svh/+FL4fPli+qP66vtj/cD+gwCaAesCHQUiBpsGKQgVCVoI8Ae+ByMIGwkUCi4KeAlECVMJgAmvCbsImQe+BjgF0QNTAtsAmv8//hf9/vtq+oz50vka+mX5Wvi695P35/aM9YXzgvJR8rHxiPFz8ZHwXvAn8UDyFvMH81nyLvON9M30UvSo9On2z/gL+n37lvzf/YP/4AGZA/oDvQSxBaEGeAfCB6gIqwo2DOQMcg0DDlIPYREVE2MUCxRnE60UrRdaGewX5xV7F7caRBt3Gj4aKxpDGWEY/hlRGRUUeA4jDPILSwpHB30EegLVAI0AGwGi/2T+Lv0e+4r5nvhs90D1PfQx9OfzM/Or8bjwVPCn7wHvle9t7gPs/evv64LrWeur6tbqyutS7SrvgfGP86f1Ffit+cj6rPtz/H38mPxd/az+sAAfApcD9QXAB4cIpQlBC+gL2wszCxQLUAy/DIgMkg22DsIOxg5MD9kPExCoD5EOkA2aDNUKjAheB2EGagStArcBMQGNADsACgB3/4P+zP17/G76t/gv99D1zvUs9oj1mvRv9Pb01PUm9g32NPWX89DyKvPh8+fzvPP89LT23fex+Pv5JfvH/PD+VAAOAUABjgHRAYQCHwRABYoGTwdFB5YIwArXC28Mow3xDRcNfA01D+YQmxEmEhwScRPZFYkWcBURFLkUwhXUFGERPw5WDNMIngVBBKwDkwDJ/Gz7lvsx+9b5A/mB+FD3r/XP83Ly6vCr77PuD+7o7cnsiuuc6sHpcenz6NrnjOZs5Qvk6ONF5KHjoeNs5ATmm+eH6dbrVO2C7yTyh/MQ9FT0pPQV9ZD1R/aZ9x/5bvop/In+0wAdAmIDdQTbBKYEJwSRBIoF0Aa6B1wI6AkWCzMMjA1/DocOqQ3FDN0LtAoJCRwHtwV7BH4DEANXA2QD5AL/AtUD2QRqBL0CtABY/2r+7f0y/fr7CPul+oP7QP2X/bP8Y/wL/Mj6sfkf+f34OPld+Tv6L/wM/sf/wwHnA3cFtQasB6oHdAcjCIUIqgeRB6oIeAl+CbEJzwufDg8QMBDkEPQStRNeE5oT5hOnFJEVZhaZGOYaHxu2GQUbNx1lG3EXtxR6ETwMbwj8B/AHCwa3AnQCLwRVA7MBBwLyAWL/S/0f/Ef6jPiE9oz1TfYk9vTzwfHP8Jzwc/Cd70/u0+wV66jp+uiL6OLndOjV6RPr2ezv7mvxDPT39bj2vPYq9lv1SvVC9Zz09vTK9pD4yPlM+xf9Lf8ZAUIBSAETArcBDAF7AdoCgwOLAysEbQUzB50HtAfaCJoJtQgIB8EFhQSUArAAd/+8/i3+Pf62/oz/6P8AAI8AugCY/4D9uvuV+gf6Bfrk+QX58vgm+pz7APx3+wj7BPsa+m/4g/dH9yn3K/cA+Hj53fpq+1T8VP+YASsBawHRAXQCWQJbArcCZwKIAo0DOAWeBVsFYgcDChoK3gmHC3cMtgseC+gLAw6jDp0OPhDxEQwSFBKUFJoWlBRjEWMQTA/LCx4HAAXiBDYC+P9kAJEAeP46/CT9bv7F/A76hfjw90P2YPSm83DzO/Ny8//ymvHj8HnwtPDF8J/vKO7C7Enr9OkQ6RTp6OnQ6tDrz+2B8Nzy9PSX91X5Jvl5+Ez4t/iS+Ir49fnm+5f9Av/PAGIDsgRxBSIGeAa/BX0EgwS5BRcGqwVqBvoHhAmlCmYLuwyEDV8N2AyzDBUMIwkYB1UGPgU/A94BmAH0AcICmQNqA2UCtQEQAZb/o/11+036rvoA+4n7qvsV/Nz8k/72/x//wP3y/Oj7sPq2+tD68fkQ+vr78/0t/6T/GQGlA34FBAVdBTUIlgmOCPwHaQkiC7oLfwxLDvYOHg6dDrgR2BOiEjQQaxDlEcYREhKFE0IUxROMFNMWCxg4GMEXbxfBFuAT+A8/Du4MMQjMA3oCOAKBAKr+sf24+7X54vjP+Fb4R/Yh9Hjyb/Fz8LbvOe+57i7uJe387Lrslev36gvrQ+sG6nbnh+UY5dbkEeTT5XLosun96jTu1vGc9Jf2g/ds9yT3SfeB91r42Php+Uz76/3T/1YB/wKmBHUFqQUZBugFAgX8A90D5gSgBWUGrge9CNwJkwrLCyAN+wxkC9IJZAdSBGsCWgHD/z799vuv/Mb92v1+/WH9ovy++h/5wPfM9Sf0jfPl8070rvRC9a31qfZ/9yn3KPbD9BT0P/ML8v7wefHY8m/0tfXh9gX5IfsJ/eD/bwKyA9UDagSxBYYGWgedB7AHNAmjChcMNw5ED7AP+xD0EssTpBIUEhATPhTnFOcUJRaXGMMaahwSHeEc0huSGrQZKhh0FKoO2giMBfgE+gPYAbn/Sf3N+577cfvI+pn5Nfgw9erxxu/c7o7u1u7Q7r/tYOyA68Dr5+tF6+zq0ekY6PzlC+QF4xXjpeS+5V3nz+ke7CjvDvMU9276Yvvv+mr7Vvw8/GP8cv2O/0gBMgNrBcYHTwoCDA0NKg4cDpINaQ2SDbMNKA7RDnMPJRA2EUMShRPAFPcU5hPFEnIR0Q8bDWoKOwinBk4FxgOMAjQC0AKlAzsDswEaAGz+OPzC+ob5nvcq9oT2DPiO+OX3F/jY+P/47veK9p71MfQR8qjxzvJy88jzpPSx9tr4XvrD+7H9df/m/24A9AEoA3QDCwN0A9cELgbABx4KQAx5DG0MHA6DD8UPyQ+WEM8PIw+cELoSXBUEFxcYChlNGVAaVxsPGzcZrBaPFW8UaBKiD1IMCwm6BiUF6wJNANz9lvt0+Zn33PXb84DyRPH37+bt1urt6Jbpc+qw6R7oWOab5Hrjw+L44iHip+CY3x3fat6T3YDdIN5l39zgWuEt4lfkKOfk6bjsQe+P8JbxafLj8nf0bvYI+Of5uvup/Zj/GwKiBHoG3Qe/CG0J2glbCtAKjQuKDBkNkg0oDiEP5BBmEskSUBL/EYQRNhCbDioNSQvqCN8GzgXyBKMD0AILA5QC+ABz/03+E/1F+0v59Pf99uv2YPe898L3LffI9v/2S/db9gT1GPSB8znz3fM69aP18fVH94r5/Pp5++z8nP+4AOMAzgLFBcYGRwabBioJGwyvDRgPJRBVEDIQthGrFN8VbxScE3QVBxijGIMZ+xygHg0fTiEyIk4huCBTIDshOCIOIccd5xwAHeoafBiKFK4P3AzdCnQHBAQfATT9nPuu++n6Bfol+Af1dPOj8yrzH/GZ7xPug+ye6tHoO+gD6Avnz+ZT5/DmL+Zy5efkwOTx457itOJR5GLl3OZx6ubtkPGo9KP2qPgQ+vH5+fkz+3P8r/yA/VL/GQEBA04FjwcwCc4J3Ak0CvcKPQs/C5UK/wkXCTIJfgpqC3EMbg21DYINSw2WDCALwwgVBmwDlQD9/eH71/qP+jD6Lvo1+kv58vef9jj1ofNl8uzwp+8573zvV+827wrwrvBA8bHxy/Hq8KHvke9s8P7wJ/FK8d7x3vLV9Gr3dvlW+8D93P/JAFYBcgIiA9cDbQUGB5sHTAjWCXALdg0ODz8PYg+sD70Pfw+BD94PoRH4E9oUaBVUFlkXxhj6GW0a2Rl+GLUXrxfUF70X9hYsFWMTrBJ0EcUM3wYpBP0C4QAN/Yr52/Yh9HTyFPQo9oP0HfF672Tvwe5E7Sjs8+tl68HpfOgv6eTpMup26+fs2+yM6hDodOfr58znHec+54Doa+oo7Ljv1/QT+Rf8YP5iAFwBcwGYAbMC2wN5BC0F2gYRCQwLfg0JEL4RMRIcEmwRYxDgD+gPdQ9/DvcN3w44EBURaBJVFAMVtxNwEkkR8Q5oC3cIcAZZA2wAtP4c/o79OvzB+7v8xfwi+j/3LPbu9JXyrPAa8AbwIvCk8L3x6/JY85nzmvSg9eL0iPIA8T7xPvHo8K3xkfPe9ab30PmC/Pz+JwGNA1kFrQUfBZQEYQUcB1oI7gi3CZoLqw3ZDr0PoxCdETsSvhHFEEwQmhDEEUgTphMuFUAXxhj+Gi4ckRzcG0YbQBukGlUY4xWuFecVJhWMFKIT3hDWDIAJRQZ0AuP+WvtU+F31VfKD8JXwY/Jq81fyxu/w7U7ti+yt64rqTumC5/zl9eTf5NnlFedk6Ezp7+hR58Dl9+TS5LTkQ+RJ5Azl3ua76rbvt/RX+Zr8h/5l/8f/if83/2v/CADLAMkBmwMkBgcJsAvSDXkP9A/nDmsNBQ1nDUQNJwymC3cLagv4C2cN1Q7DD70P2w4mDZ4Legm1BroDgQAy/XT6X/ll+SH5BPhu93/3rPfO9rDzDPBO7Qrsdus+62HrPOuT633tL/BU8XfxnPHi8SrySPE58IvwwfH78o/zifRw9tH4+vvG/nEBOQMnBFAF0AafB5cGWQXeBckH1gm+CnMLOg2BD2YRrBK5E3sTBhL/EKMQ9xAqEZ0RQBMeFSkWmBbQF74ZnhodGkQbrxsiGVgWVxVOFgAXMhVOEy4UsxSNErUQKQ8MCw0FuQEWAMv7Hvch9Dby2PER8pfzqPSB9MjzNvJ98CzvF+7p7CjsYevm6SHpE+pR7Druoe/H8ADx3e/H7Uvs2uvr6+fsYO157YbvjfOa+MT9hAJ1BXQHjgiBCGAHDgftBmUGkQYnCLEJhAowDRMRgxMyFBcUWhP1EY4Quw9ID6oO2A0ZDcgNXg49DjEPYRD8D1MOQgwBCgoHdgNFAE39tPqo96n1TvVt9aX1ufU+9YXzwfGP8PXuY+un6LTok+jE51nov+mc6gPrYuw278nwRe+u7UDuvu8G8BTwhvGX80j0K/WV95P62vyw/rQAnQJLA2QDlQNGBfkFhAW9BQoHYAjgCZULrQwuDnkPpg7GDY8ORw6zDPALbQw4DYMO6w5kD+gQvBFiEt4TxROSE00UhhQCE7oRKRLdEbUQ1hGgE0cRYA4/DxURXRCaDPoHMAOl/Xn4DfYR9kL1wfGv7ujuP/Dw8QL0v/T98tjur+tb6wHsCuwX6z7r7eto7Hvs9OwP7+bxHPNp8qzwHu+z7Sntb+4r8JrwL/HV89P3mPzLASIGeAnXC6QLoQmHB1EGcQX6BHMFxgaiCI8K9gwbECMTchRhExQS2BCVDogMYww4DSoNcwwZDU0OCg81DuwMcQxeC1YI1wTmAVT+HPqP90P2b/Qc8z/zc/Q89tD2fvYY9lf1RfO27y3s8unK6BzogOiN6YrqT+wL7lvvyu/3707wsvBk8N/vWPBH8qX1G/kK+4b8pP7PAaoENgbMBgoH9Ae1CPIHrQYZBowGagfRB4EIOQoGDKsNKw9IEBsQ9A8TEKAPhw6cDMsLFg56EZcSRRKXEvoT+xRlExwSqBIGElkQzw8pEGQPYxDHEgUU4hMEE2QSHxNLFRgVZhLBEGsQAhBnDBkGrP8G+3T5ZflT+Qz35/Ix8bfyJ/Ui9oj2XfXA8pfwn+7o7lrxa/Sc9f70IfRC8mXyGPRK9LjzJPOM8VHvp+7g7iDvQ/Bw8s/0HPbA90z7iwCaBWsI2AnhCtcKIQjBBaoEMQQaBIkE6gS7BCsF9wZcCNwJNAo0CZkHbAb9BeoFuAbJCFEKtgreCS4JggkYCqkJcgdXA7L+EvvX95j0CfJ67y3tqeub64nsu+1N78fwAvEt8HDvne7y7I7q6ecX5+/or+qx6lDqJOt868rr2ezd7AvrSOnb6H3qgexP7n3wJ/QE+AH6F/zL/7UCSQMABEgFigXNBDEEyAOxAhcC+wJ9BE0GUQeHB1gIaQkmCloLlgxrDEwLHgvdCwkNJw/3EAoSBhNlEwMUchTTE8ASBBO8EnMPGQ0UDkwPwQ8mEbwSyxP9FOQUpRUNF2MY9RfhFtUWphb4FbMUQRWwFd0SGQ2UBR8B2v+w/6b+Uvyq+Kj0RvTy9xH9h//W/Cr6Efnw9xX2wvYF+e353/om+VP2TPXx9Vb3qPgI+Nnzpe/D7czuUvC48JTxWvSu97H5t/xwAKwEngjACqALmAy4C5gJOAmaCdwJKArhCg8MNg12DbUNHw81D4INAAvLCM8HqAc9B/cG7Qa4Bv0GMQhrCRUJeAc/BNUAvv4o/bD6j/f689TvGOwb6trqMewM7NrqSOo66lvqv+px6tHog+Zu5Ibj6ONR5KXkheX/5vropOrH6jLrp+vg6nvpwOgZ6vXrJ+0h7gzw0PKa9Zj4UvsT/ef9Mf8vAU8CFQJSAWcBbQJgA7wDGgRzBcYGPweoB5IIzgn1CkMLBQprCN0HvgeoCP0JxgqpCrALNw6wDxYPmw7XDmYO5QvWCaEJQQoiCp8JswldCuELZg4dEPoO+w0MD4cPExCTERASKBH9EYsVihcDGPEYkhtrHhEexRpzFocPswY2AmAElgWDAXv7ivg0+VL78ABRB08Hs/+/9V7wi/CI9Rz76Pyb+gT3hvbM+UX+ngAI/9r5tPNq71vrSun56lDtmu7M7uTuQfCH9An6xP5lAnkEwwV4BjYGsQXBBbkFxQX/BtsHTAjeCZcLRA2ZDvUNVAyPClgHaANyAgQE9wYaCnsLYQyADXINlAyaDDULGAd/Abr7l/db9Qr01PKI8ZjuKOvb6dLq0eyJ7ZPrMOis5vTmiuen55Lm8uUf52rpxOvm7Z7tCOw57L7sWesu6I/kjeHz4AnjzeZA6ybv2vFe9Mb21Pki/UH+7f35/Xb9Vf0f/54BtwL/AtcDAgXsBSMFtgMuA6sC8AEdAk8CzQExAUABiAJvBNIGmAlGDGEN9AzuDMEMHQyFC9IJqQb8A1kD2wNpBQwHmAiACjkLPwvqC60M1gy3DLcMJwx+DR0P8RBIE2wWGhklGhkbbRsPGxEbHByVHGEbPhpYGrQcZx9eIRIkvyVNJIcdXBXdETwR0BCUDDMEz/tb9pn3AP+mBKgBrPjP8AnvyfEr9XD2BPT58RHyO/Mq9n/4efj/9iH1yPIX7lTqhurf69DrBOoW6Bnn9eeY6fPr+e5X8jz2GPvH/60CbQRCBZYFIAZsBnQGdgZqBtQGBAgeCqYLpgvQCY4GYgR1BBcFZQZgCEEKiAx2Dh0QFhKYE1EUUhMXEAkNWQrfBwUGUwPy/kz4L/G67Lnq0elu6OTmouaI5lDmH+fM58flQuKI4I3hkuKF4i7kt+ZL6BXqiexQ7VzpyeKm3gndVd0Q3nLd7dy03jTj7+gD7j7xyvKW9Mf3Wvv3/QAAdwFTAsAChwNQBJsD0AJTA64DhQNyA5EEjAWYBIQCWACm/9r/pgB0Ao8E9wbACkQPvxOqFqIW+RRAExgR4A2VC9cKVQoJCVAILAnxCrQLOQszC2YLPwuXC1oMBQ2MDQwPTRFhE6YTuhLsEiUTGRNjE5MVZhYOFTUVKRYjFpYUNRZ5FwkVJxPLFwUf2h/SHzIiviD9GFYTwhSQFJoLQQDb+un3t/VO+Ez8fPrP88vx1fTU9t32o/Yp+Bb6BfqG+e/58/o8/K38P/vU+ez3fPUl9Pjz5fIq74Tr9erC67nqbeqj7jX0Avjt/QsGZgvcDCIOZxCvEYAQPQ9YD0UO1QqYCYkLLQzAClEJlAi0ByIG9wVACDgJOQh3CPMJ3QpcC/YMcQ/yD1wOGg49D+sONQtRBpkAzfi88Pnrc+mZ5R3fY9mi2ATbCt584D3hpN9T3Z/dROA34ebfOODd4eHhSuJJ5ZPomeh458bnLufW5NPi/uHq37Ld+d3A4DDjQ+XN6bjvwfVz+4cA6gR0B5oJNwtWC+8KCAvrCuYJeAnaCZ8JqQk5CkUKlQe4BK4DYAO/AdX/nP+RAFQBoQKtBQsJygppDLoOcQ/sDMgJtQhPB/sCS/6J+5/5N/ib9+n33vhb+Sz6Jftf/Dv+/P8fAtcDxwQDBtcHUwr9DZgQ+A/gD64SBxUJFPgTvBXsE90RyhTaGPcYChkMHTkhQyRUKXUwqDOYMw012TPFLEwlmyPuIqMcoxDcBYMAEP9zASADbv7A95P22Pmw+y37pvvs/PL8vfxv/Mf7L/u9/H//xv0y+ZH2ifSs8MLrH+fh4WHc+dhh2N7YaNoP39flIOwy8o75QwBOBEMGZAcfCVkKLArxCSoJMwfqBRgHxQhYCLwG0gUgBm4GtQZ0B4MIvQkrCxcNGA/nEf4VIRrGHFodyhxUHa4d4hoYFZMOiQgUA879rvcx8Cnpt+ab59LnReSK3xLelt+J4YfiQOML40Ph8N8J4G3i4eUW6W3px+Zx45XhluKc48XiA+Dc3JPawNmZ2t7bo9394JHlz+lS7UrxTfb0++IBXwbVB6QIAgvBDGUMxQsVDMcMmgw9C2MJfwfsBYEE4QMuA0oBmP+5/yIAdADcAbAEuAe3CYkKagrZCrgLOAyfCqYHBgVnApT/+fy6+q/4f/c/9+n20PaR9wn5M/t4/Yn/VgGhAicE3wY0CvQMgQ9uERATzhSLFSwVjRWcF8cXiRbZFhsX5BYxGeAfSyMNIs4jfCp9MIcyODSkN3k7MjyKOPQv5iWyIBEfXBmOCVT3uO+c8Ur11/Qn8Wnuq+5p8drycfH/7izuGO/a7hvuw+3r7wD1/veh9j7yJ+156SnlG98X2RHVbNK6z0vPUdJ/19Ldg+Vh7lT3UP/hBiYOExOlFAMV2xUCFjQUXRFiD3kPuxC+ElUUCRTnEuwSvBK7EBYO9AxBDj4QkxEEE6QWVRyJIfsjziIKIPYd0hxiGZ8RHwgKAIL6KfUU7+3oLuMr3rDa/NdK17nY5dnf15XS8c4v0DfUrNYm1pPTCdEn0+naxuFM4h3gFeC9357eqt5p3zfeHt2i34niqePp5ePrsvOI+Vf93QC2BKgIEw2PEekUVha/F9cZlBrTGZ0ZTBqoGkQaYxguFZkSAxEMD68MpQpeCKkFygMWA4AD5ASxBs0HEAi2B5UGjgWNBJkCsP6f+i33wPPD8BTv+e6a7rXtPu0f7SXtLu5g8O3yh/PX8xH2evp2/nEBgwR7B1MKNg0HEDUS+hKTElYSjBP/FGoWghgFG+Qbth05IWkltynaLe0xCzWkNvQ3yTrmPxhFqUZfQrI69zFNKiomTCEaFcUB3vE27kLxf/Of8jfwpe++8KzzD/b89O7xPPEd8i/wRO3i7rLzD/Yw9ffy0O746CHj0d7G2UHS48tSyVbJ+MmNzD/SNdpC4ivquPPh/hcIxg3DELQS7BOoEywRFg7XC7cJzwhyCn4Ndw+NDzMPzQ72DIQKiQjwCCAK2Qp1DCUQXhYtHZwh5iLPISYgpB7oGzsWLg69BZf+2Pj78j3snuU74e7eeN2s22DaO9oe2kPXN9R900LV89W60wXRs9EW1jrcc+Ka5uLmouVh5onoC+ie5bnja+IG4mPju+aY6t7uFPTo+Ir8i/5YASsFQAjiCaoLhw5mEekSBxSAFZUVLBUnFvYWVRW0ESoOcwu9CKsFRAIE/4/7/fjw9xD4hfjI+Vv8S/5k/q/9tf33/QH94/ox+Pz04PFU8MDvRe/S7n7ulO4k74Lvvu9u8CLy/POC9Tf3APuRAMsFSgnNDDwRMxYHGwweYx7iHrAgNiIYIwok6SQBJlYoIivfKwkt5DAvNkk5VDpAPOA+P0EzQltDbEQGRcFF4EMSPf4yJSkwImIc9A9x+w3q7OMR50XoBuVO49/kTOmm7XvwJu8C6lzosump5wXkReWG6rXt8Owm60vo1eKA3R7aAdUdzU/IUsizyQnLxM4s1i3fV+dJ73b5EQVIDwIXpRvNHQ0f3B4mHIoYaBV/EtgQdBLRFfUXHhgsGGIZ6RhcFf8RehEKEo0SgRNKFiEbhyBuJIglXSPEH/McRBobFJMK5wCO+U/zf+y65CDdeNZL0jbRcdDnzvnOd9A60RvQS8/OzqDMhchexPzB5MM+yjLS2dc42ZnZW9xL4ETj8OKm33nbbNn62/3gheWx6jHxVPf8+8X/vgOwB90KtwxZDRUNkg1cD00RmRIVE8MTiRUfF4MW/RSQFPkUnhMNEBILVAcfBEEATPwk+gP6Gvsm/UL/JADP/0b/z/5n/QX6X/Vv8Q/uvutj6z/s/uya7dTupvD58TbyqfIk8zXzhfPR9Fj34fpl/7QDLweVC7MQoRT7F2ca/RtDHQsfvSIjJRslvSYuKmgr2iowLYsyvjZ0Ofk8SD8zQWBEM0j8SHdGKkTCRF1GFkVNQCg4Qy5dJpoh4hcXBSrziezl7K7suept6trq6+x48p73v/VR70LtXe927FXn1+ji7mPxtO8W7xXu5Od64CzdD9lQ0enLVswRzg7PKNJ22U7i3+ja7o33xQGuCo8SdBjiGiocOx2xGz4XyRJGEK4OqQ0QD+0R/BLhEtgTvRR5EvEOYQ2bDcAN7Q3uDw8UyBj1HJIfBh68Ga4WNhTNDjQGBv6e9wrxI+ow5P7eatmm1CLTmdL60IvQztLB1PbTJNNI1MnUudEHzoXMfsvuyc7Kz9Ah2P/cMuDt4dLgs9+y4fHjLuMC4gvk+uVI5wbtvvaD/vIBYAOpBZMHBQnfCicMWAxSC8YKugriCSwJdQsGD7IPxQxgCrgK1wtoC80IQgVKAXr9APuO+FD1tPMd9aT3jPjy97/4uPoK++D4A/Yo87Lwne5Y7bDsCO3W7ojx/vQO+FP5t/lB+sf7OP3l/Sn/oAEaBK0GbgpGD58UjxlkHT0gQyOWJVEnySmqK+cr6Cz2L0MyKjHWML0zXTi/Ogs8MT5wQCVCSUQuR3dGBUNwQXdC8kEQPUI0SSv7IzAfQxhnCFHzE+b65d3pLOjk40zilOPZ6Ovudu+/6qHnG+pv6kPl2ONm6RLv/u8s71Hu2+ih4T/eC9xo1h7PfcwdzmzP8NA71xzh1eiw7aT00/40CbQRWxjZHCAfFiCXH/kcMRkYFlsU/hJCE+AUKRY7FsYVRRVKE/QPhg3oDDQNeA16Dt8RuBazG7keqh2NGaMVYxO3D20IpP+a+EnyhutW5YTf+9hd0mXODc3fy4DLy8ydzpXOCc4az+bQ38+yy4fI18gPyqHKUszP0CbWPttr37Hg296+3Ubf7eHQ47bl/+ff6bXtMPXY/EYChgUfCO8KrwyfDdUOrxAOEiYSYxEAECoO2A1dEKgSbhFmDkcNIg6DDkQNoQqTB74DUACz/QT7TPiD9zr5jfpV+mr65vuk/Ej7sfi79e3ye/Dz7pbtHuzM66DtwfBu8131CfZw9n33+Pgc+gT7Sf1aABIDdwVfCZcOCxQDGO4a8B1PIGkiLSWbJ+sn2yfSKoEvVjBfLrAulDNKOCU76z1KP8M/qkGeRndJnkabQ59Du0L0Peo0QiyCJtgidBzlDd751uvg6rTvIu9F6N7ii+I16LjvKPJK7kTqleyl7t/pY+Zd6lTwTPDV7CTsFukj4uHdmtww2BLQq8tOze3P7tCm1O3cquUD6+jwEPrdA4ELJBGdFUkYGRpKG1gaqBZ/Em0QJRBfEDYRkxHMEAAQNBCWD24NEAuzCa0JSwqdCsMMlxG/FrwZDhmjFWYS8RDxDqAIN/+y97zzk+9x6Vvj/t2p2MPUcNNR0tHQBdHO0rLTpdJm0ozUcNbF1DHQfczezSLSf9T+007Uxddj3CrgYOIh4nTgLuGb5HLoluot7c3xOfdS/M//ZwKIBWkJlgwTDXcLCgsXDYMP6A83DjoMFQxXDeANpAwICzMLhAzFDJ4Lkgk7CJUH0AWzApL/Nv1O+yb6zvlh+Zr4X/mw+p36kvg49sH0M/N/8b7vwe6z7kvvmvBN8lL0vPYu+SL6+fnG+ur8df+CAhUGlghsCp0NkxJ/FqoZbh3AIJoiFCTuJawnmiihKuYtQS+tLk4vajKpNYE3ujpXP1hB6EEMRPJFSkV6RG1FAEbiQkI+WTj1LSYkiyBCHvkQwvo96Qbl7ej36lrpeeVg4W/jnev08IbuUOpD7N7uVOpM5rbpLO+i8GbvU+7x6DLg3NsJ3JTYYNAiywfLqMxCzlfSeNlZ4HDlPeyW9fD+mwclDw0UTBbLF2IZuBh2Ff0R5Q7oCz8LlQ23D2IPYw3eDGEMWQplCLIHhwebBzUIKArKDVASExdRGaAWvhEOD68NeAnYAZn6z/Qi78vpCOV/32fYTtNH0j3SKdFf0ZfSttI60m7TsdWR1hvVw9F2zqbNH9CF0+TVb9fc2evcBeHY4zzkq+Pc46jlV+je6tvshu8Y9Nn6aADcAmAEGge2CsUMawxiC5ULAw33DSANGAtmCrQLKA3eDAgLrwnrCeUK+Qo4CdgGIwXnA7sBGf8T/Gf5Cfls+u36Sfq8+nP8Jv04/Pr6ofkk92D0vPL/8ZbxlvFT87f1Xvfy+J77vv1k/of+xf8qAnEFFwlnDG0PnhKgFskasx4rIkckQiXDJRwnYymPK3otpC8DMSgx0DG2MoI0/DY9Og49/j0lP55CI0U1RLFCTUPARB9DkjuoLzAjjR0mH2kbGgrP843l9eQn6rnqb+af4YjfnuOc6h/uMewR6xLvlfCc6i/mWerX8Pbxl+9v7VHoj+HS3a/cItna0e7Mkcz5zWbQF9UJ3cTkEepU8BH5MAKVCrMQMhQEFlYXDhkkGbIW3xIID4QMzQyNDu8O5w2UDAAMkQodCOMG6QYOB/kGowbAB4cLzhBLFfwVbhJmDjYNJwyGBjj9//W98WDtTeco4Wfb79Yv1GjSF9GH0EDRhtKj0tTR9dHA0/nV3tQz0B/NY8750ejUbNZ72M3bsd8j41nkz+Kk4kPlxuhZ6r/qvuz68Gb2dfwxAWADpARmBy4KzgoBChoKfQuvDHUMTAsSCmAKjAxkDkEOpAzlCusK9gu0C9YJagc6BSwD7ADP/ln8HPr/+UP7LfuX+iH7efzm/CH8vPr2+GH2TvQq887ygPKl8iv0KPYG+Ev6t/xN/vr+9v+oAcoDBwbHCJwL1Q4zEu8VdxpVH5AiJiQ1JaMmQShpKSIq6SqQLIIuvi+iMI8yUDZXOXc7jz2iPpg/1EKpRqtGkUQzRZZGoELWOLwulCaFISAcyA5Q+wPrdOiK7mTwS+v55Erjgudt7snxv+7j64nvzvJy77Tsd/BD9pn2T/Ps8Mrrr+Qs4GjeR9pZ0vLMO8zBzQPPY9GD1xffFOVF65vzC/3GBtMOOhPpFNMV3xbLFkIUjhA+DRIL/AqKDLcNqwy6CqEJrAh0BisEXwL4AQEDpATcBgYLVxBwFWkXHBVREe8O4w1XCrcCvvqI9W3xhez45tjg3dq41ljU+dKA0bLQ79Hs01fVpdVp1UHWH9dG1SrSMNHd0vrU6daz2f3cs99Y4h7k++NO4sLhqeMh5lHnSeiO62PxM/gZ/U7/QAEOBAMHNQiDB8oGvgfWCQQLPwraCG0J3Qp5CxwKTAjvBogG+wZoB1UGgQTYAtgBsgDZ/jb8YfkK+Pb4q/m9+dX60PwG/rH93vys+2/5HPc+9dXz7/Ik80r1ivjh+mX8cP6mAOIBsQLMA7EF+gcgC/oObBJLFSoZMx7VIVkiCSLzI3AnyyhzKCsptiujLowx1zJNMioyWzQEN+Q4gzqUO5E8Uz++QzhG7kXRRJtD6j4VNTgqayLlHasY+wx5+7HrWedI7XPxqOyl5Cniy+ZK7crvt+5Q7Tfw2PH27Zvr2u7N8xf1WPMf8brrQ+VS4uHh3d2C1d7Oq81Hz/rQrtNs2T3hxuf27eb1F/9gCKgPFRPcE4QUJRZiFjAUDRHQDWoL6wrHDGYOaw0KC3EJ0QiDB7kFyAO5Ak8DewQJBpoJ/A76E3QVVBN3EN0Oow2dCTYC3/rH9TjxQ+wX54vhwdv8167WZdWZ05PSUtPw1E3WUNdx1xbX49ZT1Y/SX9GV0tjUq9X11gba0N2j4N3hNeKL4SThEeP05QXnkOfS6jHxKfgZ/WL/wAAuA2UGSAgXCGgHxgdhCYAKNgqaCJAIowr2C40K/gehBloH8AilCaAIgga9BLQD5wJKAbb+RPxW+337SvsS+1D8kf7c/17/rf34+3D6a/kO+Dr2bfQM9PH1Rvm0+1/9jP+cAXQC5ALXA2sF3weXCgUNFA/OEaMVWBpCHjcgdiAYIdIi0yQPJjcnNCkOLLIutC87L4QwJTTZNg43fzbpNkc5NT0UQXFCPUHfQQ9D3D7iM6cnNCIfIe0Zqghb9dbr9O2y88Lzv+0V53nlT+zx80706+5r7VfykvKz7XPtrfKf9q31SvTq8evrfebf5ODj1d0u1ZTQDtAX0aXSJNWk2jfhr+Zp7Xn1Iv5NB1MO2BF/EqYSgRNjExERkA3oCRQICQmLC38MNQu9CR8J/AeiBTcDTgGiAEIBPgJ9BEIJaw9jFEUVixIbEJYPTw4fCX0BO/tD98byoO2j6Irj4d6J263Yl9Zv1U/VfNaB1x/YLNli2gLbn9kB1jnT5dLD1BbXIdh02cTcdOFs5fLlCeRk4rDi+eTP5nHmcebR6Ubxdvkx/i//FgC8Az8HvAcmBtcEqwXyB4oJ8gg1B+0HTgvUDJkKSwfeBf0Gdwg7CG4GVAQwA7cCeQEI/1D84/rV+uH6bPqy+vX83v9WAewAZ//U/YL8svtZ+kb4yvYv99T53Pzj/uMAEgOYBOwENAW9BecGJQluC5ANehCpFJEZ4R0CIWojmySUJS4nXihFKL8oOysULvQvnDCQMbQynjOzNMM3TDsPOxw5DzsWQMFCIkN+Q8hAKzZAKkolniQXHpUOZ/0u8g/vG/J99AbxSulm5dXpZPCI8cPtG+2W8SbzX+7j7A3yHveT9xH2b/Tr7pfoXuYv5hbiatrk1C/TidP/1CXXbds74dXlyerW8Y/64QPyC4QQFBLhEvgTqxQRFFkRYQ31CV8JkwvjDcwNzQtfCVYHIgYdBesCUAC7/+EA+wK1BhEMdBFGE4oRHw8KDqAMrgjkAWD71vbM8lXuu+mr5Izfh9t72NDVSNSb01jTZtNh1GrWptdn2PjXudV80hzSkdMh1NnTjNXQ2RLeQeHH4vLiSuLT4WfjVuWm5fTlaOhC7iX1m/pw/oEBcwT/BjcI8Af1BvkGzQh8CiIK4ghVCUIMag5wDZMKJQhPB90HsAg1CGcGuARGBDMEDgPRAAz/Gv6O/cX8afw6/Vz/YQEhAlwBlP/j/U/9NPx4+rn4vPfB+Fr7Pf7LACIDvwRzBc4FjgYmB28IkwqWDOcOChLhFo8cwCBtIvci5yQ7JxIoBChSKCUpGiu8Lkcy8TK0MnY1hTlFOuA4Tzi6OU88cEA/RPNDrEHgP8M7VjOjKk0mzSGAFNMBEvTD8Nn0Offw9MruF+p663PwL/Qa8xDwoPGs8xvxkO6S8lH5yvoC+F318vDJ6kbntebl5Fre3dc81YLV/NaQ2M3aFd/W48To4O6A9nP/QAiuDp8RORLHEjwTVBKsDwAMTAgDB8UIHgvtCnEIJAYEBdkD9gEX/1v8rPug/Fr+sQHvBn0Mgw+RDtELagoHCk8HOgES+qX17fJH7w/quuT63hLbu9j31R7TQtGj0U7TlNRX1g3YAdlT2SXYGtZp1FbUmdXX1prX69nh3VLiWuWB5dbkReRb5GblPua55u/nCuxD8+v5yP0CAE8DCAcoCRMJEghBBwIItQmNCtYJLAnvClUNSw2yCrYHWQZjBmUG8gVQBSUEUwMuAwcDrAEI/9T9Nf7S/S/85Pse/gIBEwJIAe7/Xv5N/ZL8N/t++Sn4Zfi8+h7+vADCAnEEuQWGBiIH9AdtCWQLGQ2HDooRxxYyHKcfFSLGI3kk5CVZJ2YnRSffKCUsRy4pLz4wjTL3NJM2JzerOGg69DqIO/k8mD9tQjBESUMJPLcwyCdCJRwkmRmpBij2TvG99bH5d/dD8MHqq+vH8VD1EPM/7/TwAfam9M7vPvG69+X6z/ip9qfyqeuW5+zmh+We37HYRtWC1S/X7td42XTdpOKI55XsfvJc+oUDjwqxDWoOBA8fEHMQOw/oC+MHiAVHBrcIawlsB+4E3gIjATUArv7u+2T6FvuI/H3/ZgQ5CgoOpw17C58KRwo3CLUC7PtY95j08PBR7E/nVuJq3o7bMtjD1BDT29M41cTVaNYb2DXaNtwl3GPZ/Nb81oTYVdk22TPayt0w4wXn+uap5Mzjh+XS5w7oaeZv5vLpjfBU92z7q/2RAEAFmAjiCE8HswbNB34JNArwCFgHQghBC10MmAkLBqgEIQUCBfwDnwJtAeMAIwGgAZUAKv7Q/Ff9uv3g/Dj8Rf2D/woBHwEWALT++/2l/Zb8m/rN+JT4JPqi/BT/MQEOAxEFGgfAB80HOAkMDFUOpg/AEWwV0xl/Hm0iPSTNJE8mFykvKSgnpycGK18tNC25LVYwxjInNPU1tDaJNgM4ejwhP4s+wz45QVpCrD1tNGgrGyaEIpwaXAtW+8TygfQB+fb38vFU7E/rXe8t9JT08vBq7wHzRvSc8Qfyd/eT+2z6uvda9NzuiOpF6P7lzOBU2oDWLtZW15rYutmW3GThkOYI7ATxavciABIIuwxNDvQOIxA2ETwQfA0WCqwHmQd2CdYJsgd+BdUDVwL8AO3+xfvz+cr6gPy2/rkCYghADbQObg0ZDD0L+wmKBosAzPqX9izz3e/I6+rmO+JU3kPaNdcU1oDVAdU61XrWYdhY2n7cJt3f2mHZldqa27PbmtyD3irgtOFw5ADmtuSc4/3kD+dh5l/k7eRT6NXtUvQL+QL7efwcABkFrgd9BzgGXwbhB+gIogiLB8YHkglmClYIKwVRA1MDgAOKAgwBgf+y/hv/y/8QADX/7f2d/en9zv0i/Rn94/34/m3/Sv/G/uv9X/0m/Ur8o/pT+Tn6PvwK/kr/CAEeA2sFIgc3CIAJwwsiDqgPYRGAFPkYWB2DIIgiyCN7JacnzyhxKH4nTCjtKpot6y7HL00xlzL2M3s2wjhTODc2xTYyOf873j49QDI8pDOiK3QnsSVJIJsSUgDv8h3xEfZE+Ar0Nu7f62vt7PCz8nHx7e/y8cLzqvAT7ubxSvib+hf5hvW276/qWegq57TkkN9m2oLYWdkc2uvan91T4Q7lTunT7WjzH/xGBS8LvA1eDrQPPBGNEWQP3wslCHoGPgjnCpcK2wf6BDsDKgJWASv/Xfwr+5j7rf1cASYGtAoXDbEMKws7CnwJ+QYdAsf8v/gu9c/xSO5k6STkA+Da2wnYXNYL1mHVc9Qk1QDYidrJ22/cRts+2QXZ79lc2p/a+tt/3lXh5OMI5qzmlObR5gvoeujF5t/ln+cW7PbxpfY2+eP6Mf7AAvUF9wZ0Bl8Gdgd1CKYIxgc5B7sIYAqACZsGSAQpBMkEZATnAvYA4P/P/wAA0/+b/j790PwN/WX80/tt/Bb+e//+//D/0/9x/6T+Ev5f/f77dPoe+lz7Z/32/nsADwIrBNYFDAcrCPUJ0QuODYMPtBJIFukZFh10H38h2CNAJocnxia5JYgmBCmwK6gsTi33LlMxvjKWM7s0QTbVN1M5wTpLPGo9oT2xO/E0xSwWKOwlaR9vEMH/GfWj8/z3V/lx9PTtEuyh78TzQvXf8zLym/MU9ELxHvC88zj5BPs6+cH1wu8e693oKudj5G7eRtgY1oPXdtl42jjc6t9j5FzpsO378u/6xAKeCEgMjg1PDnEP7g/UDT8K9wbDBZsHawlMCK0FgAN0AgUCBAHl/hP8k/q6+uz8JQFTBpkK4gwLDWEMzQsrC/4IDgST/nb6BPfx8pruVOpt5iHjut6K2SLWitUe1sjWNdcw2BHZTdqG3JDcpNo82VnZBtqw2n/b/dzw30/kwudt51LlCOXT5pvnaebr5A7leuhV7pPzNvYd+Av8AAFYBB8FeQS0BHQGHwgrCCQHBgeFCDQK0AlDB+oExQRKBQkFgwNpAQgAmP/D/z7/1v1G/L/79vvJ+337UPzr/bP/iQBSAL3/1/5Y/kH+dP2i+0P6uPr2+3T95/6JANEBhwP4BLsFqQZ5CJcKLwz9DbcQSxTcF3wbEx/gIYQjDiWCJmAn/CdvKesqBivWKgAsXS59Lw8wVTFBM3E03TWeN2o5LTtDPEU9DTswND4sACh+J0UkTRlCCTn7YfYs+mT+ePqO8XLs5eyo8UL1K/To8Rny7fNu8vvuFPDo9Qv6fvkN9jjxxuzS6ujpgufr4TnbANic2AbaSttB3R3gJOTb6Pjs2/DU9sT9gAPhBwcKUgulDUgPEA8BDcYJKgeZB7EJrQkIByEEegIXAl8CCgEU/nn72fr6+5X+YQLABg0KKQvbClsKnQlMCAYFAgCF+2z3ofNH8J3s5+jH5TXiTd2p2CTWfNbt1gfWdtUo1ujXZtpr2yDaOthz1zLYvtnJ2lDb291m4jzm+OYn5pbm4eiz6uHpY+fs5QPos+2283b20PbP+Kf9TQIaBBYDrwKNBO8GmQeCBrkFvAfpCuwLtQlbBkIFbAZRBzAGdgNIAXsAhQAIAGr+jPwr+376HvrO+Rr6K/ue/Ib9r/3W/fv9//0v/s795Pz4+wD8F/3N/hgAAgEfAgQEqwV4BiAHzQj5CrEMnw2oD/YS7BaeGtEdIiDmISIkYic6KZwo6igQK+gscS0YLpAwlzInNI01eTfBN3k3nTgVO/k8DD0ePAw6lzV+Lu4pBingJWUb1Qtl/Y32Qvja/EX70fTz7vLtrfK89s72OvV/9Kj06fK07+nv0/SM+aX5Hvb68N/rIelE527lsOHV2wDYUNjz2YHbzt3I4Dbk+eep61Xv3vVD/fkC6wcIC4EMJg5UD3oOAAySCL0FvQXGB78I+QaDBBoDzAKxAr0BjP+v/Kj71PxO/1UD0QePC/AMeQz5CrkJ0wg2BjUBRPwl+KD0LfFU7T/p8uWL7yneJNlr1bHU0NXd1mTXltfH1z7Z29oC2yja7tjF11/Y2Nkl227dS+E55aTm7OWi5bPmxOgA6W/nsuWI5irrwfC59Hr2f/hM/MIAhQNyA9ICUgR+BoUHEgdjBrgHJAoeC+IJfwceBuwFVwaeBXQDNQFaAHkAOwA1/7P9OPwz++X63for++T77PwO/sT+4f70/vL+Ev+H/gH9b/sG++b71P1z/1IAPgH3AtMEHAbxBgAIQgmdCsULEA6jEYUVpBl9HZ0gSCI1JOkmISl/KGooJCvxLR4v8y4SLxEwiTLDNLg10jWNNoI4sDr2Ogc8Fj6ePsU6NzMnLN8oLCcDIAwSnwL3+QT7wv4D/Wb3U/LU7xfy7vVo9lT0l/NK9DH0hPJl8Tf0JvmN+hv4m/N27mPrVulr5+bjPd482dPXlthU2kbcJd7c4DHlx+l97fXy/fk9AGUFiQilCe0K3gy1DXMMeAlbBv8FHwhGCbIHIwUoA0gCpgF/ANP+Pvzn+rT7sf0OAcUFowlcC3sLBgupChgK2wd+A0T/ZPuZ99Xz1+8M7Nvo4eXA4aHc9Nca1sTWC9gt2KbXb9ec2B7aZNrG2fPYftgJ2STa5drm3CrhSOZc6FfnBOYs56TplOrd6CXm/uXN6fvuo/IG9IT10/i4/dEA9gBiAEgBsgO5BRMG1AXnBnoJbAvnCqgIWAcaBzMH6QZdBakC2QBaAD0AZf/l/RH8tvra+Y758/kG+1383/2u/rL+if4r/yQAaABG/+v9p/2H/jkA5gHCAmIDNAUaB50HYAcICA8KkwstDBwOoxE1FUkYZRv8HU0gwCNmJzQp7ihaKKcqzC55MIsvNS8pMFUy7DRmNkU1BDTSNTk53TolO986zDiWNLIunCmAJ7sl2R1ID2YAJvkp/N8Aff4x+Enz2/D/8qj3n/kr9/T0TvWm83vw5fDo9Ln4GvpI+CTyUexl6ufooeen5KzeR9l52IfaxNwj33rhX+PY5irrPe9M9FH6x/9qBLAHiglqCnALfwyhCqcGDATqBB4H6wc0BqUDOgL2AegBPAFS/yv8dPpS+6v9NwGgBZQJXguwCnYJMAnwCHgGEQJk/uv6bveh85XvZOz+6Wvnv+Mt3+3ab9iH1wXZSNu72xvblNsh3BLbBNv22+zbCttU22Hcrt2j4FDlsOho6CbnZ+cG6fTpK+n358roFOy073TyXvRR9oD5Uf32/2oAz/97AKcCrAQEBfwE/wVQCFEK4gk9B84FVwYIB8IGVAWJA9oBDgHJAFAABP+E/ZT8uPuN+jL6TPvO/E/+CP9x/17/BP/s/ycB3wDH/2//EAA6AdYC8QPFBOQFSQd1CPgIQAmwCscMqQ2HDvsQWRSeFwcbLx49IMkhPyQuJ+cnZifuKF4sqS7dLgQvqi9hMCwywzTbNf80hDSPNnY4nzgLOVM4uTPDLK4oUiiFJRYclQ79AY/7NP3HAK/9Nfdk8wHzefVM+Dj5Z/jO9iL2K/UD8yrztPbg+Uf6ZfgM9E/v4ev+6S3p+OVY35XaWtq923LdnN+y4aHjz+aY6qfudPM1+Gr9DgOtBuUHLgmbCgoLkgl6BtED+gPmBWwGgQRdAi8BEwEZAWAA3/3C+lX5Rfpp/P7//gM/B/4IjAkLCcUIYggZBpICov+C/LH4JfXP8fPum+wE6i/mxOEc3rfbo9n/2Nfa9dwo3c7cs9zu27DbFN0l3ofdT9yl3BPfYOIh5jHpmOmD6MLo0Oq46/jpdOic6fbstO8n8WPySvSo92L7mf39/ef9Av8xASADuAOlA+QEeAfSCc4J8gc4BlMGLwcUB0gFnQOpApQBOwBM/3L+dP3H/Eb83foH+sL6hvy6/dv+6v8IAG3/7v9cAbEBYwFKAZ4BkgLAA5oEZQVsBkcHEghICRYKxgrHC/8MOw7OEE0UbxdWGjcdLh9sINAiMyUhJtYmOSjhKMEpaivrKx0tkS++MYUy6zFVMqwzUjThNRE45jcCNq4yRi1fKcsoTyfvILMTFQVJ/jn/yQAk/jL5VfX483n1Zfgk+nT58Pfd91j3zPWW9TH45frJ+0P7M/jf8wrwCO356q/nPeIE3g3d491G3/LfQOBW4vLlpeif6/nvcfSx+ND9ygJLBkAIXQntCZ4IgAWpAo4CXAQwBZcDdwEmACf/l/5u/tD8Ufn19q32c/j4+0kA3wP/BRAHbAdgB/kGWQWtAr4AEP9f/An52PUB83Dw1e1E6hXmXuJ/39LcHdt/28bdjt/F3+jeo93a3J3dK9/e33/f294N4G3jF+c16RfqL+pO6lnrGOwq61rp9ehn63Pure8K8IHxZPRD9w355/nh+mv8uv1e/6IAQgEyAqIELQf+B+EGhgVGBREGMAbmBGYDdgKzAboAjv9J/lf9e/y4+w77P/pT+pH73vwc/rv/xwD4AJwBrQJTA08DSQOJA38EdwU+Bq0GYgdQCOQIXwlJCk4LzwsjDLkMrg5DEkUVNhcMGQkbKx6NIaUjbCSFJL8lric+KXYrWy0qLagswy2eL44xvjKFMt4xKDIpM700OjXxMv8tbSnTJ6cmYSKwGa4OWQXBAZkDnwLg/Jz4qPdl+Gz6PfuF+qH5XfmC+ab4HPe89/X5Hvs9+0H6hPY+8nzv6u1N647mieGW36Tfot884CjhUOKr5CrncemB7LTwi/Q6+Mn8dADQArQECwZ+BlIFVQJYAFoBKgP5An0Bff+H/kv+e/4e/mz7N/gW95H3r/ks/awArwIlBC0FmgXYBYAFmQNUAQIAJP66+xH5gvZG9Dbyle/564Xo8OVj46/g197l3srgL+Jq4bffut7f3tzfVeD0393e7N7K4YHlY+fn5wjpFeqV6+3sjeyp6oPpueqa7XrvxO/Z72PxVPSv9i/3tPcs+Wz6ZPvQ/NT9Rf6Q/2EC8gQCBZUDPQNiBIQFMgUSBJUDTQP/AjACqgA7/4f+Af75/OH7IfuP+7H80v3+/kUA+gAMAaoB/QKyA8gDxgM3BIIF8QZRB8IHzwhpCZQJlQr6C78M0gyQDRIPGBFYE4kVdRedGdwbwx1TH1whPyPFJO4lnSbzJ2MpqSkxKyAuWi/QLu0unC9nMAEyAjStNLYz1jEYL8srnilBKEYmayF6GF0NIQUxBEcG/QJp/FH52PgB+f76iv0i/gL82PlX+pf6MPm3+YL8jP1G/JT5Dvdv9VHzie+n613nLONF4XbheeK34nzhz+Gf5KfnFerE7MLvZfKp9Xj6af/MAmgEAAU6BYADLQBU/3UBDgPiAX//tf0q/XD9xv2Y/F35WfYV9af14/c/+0v+ngAZAsgCKgMfBA4EHQJiAJz/xv4g/S37H/kK9/L0a/Ii777rNelT52zl/OI74SXikeSf5Ivi+eDt4EPhHeKF4gziKeIi5ILnH+nV6A3q2eyE7rfute3h697qlewF71Dwj+/W7t/vgvK39O70a/QT9dD2+vc1+BP5p/pz/Kj+wABcAWcBxwFLA1IEpwNLAi4C5AIoA4ICFQFa/4/+XP5R/Qj8Hvuj+hL7F/xF/eX+dwB5ARUCqQJJAzEEBgXSBdAGUggqCYYJNgqPC5IMywwwDakNeA2ODS0POBH+EpkUKRYPGI0ZcxvZHcgfWiBCITYjTiXrJoUnAihPKsEtfy+JLkQtOy7aMMIypTKbMV0xrzEkMQIviirkJr0lDyP1Gx4RwAcGBaUGpgSJ/pf6a/ku+t78/P7y/sX8HPtQ/Av99PuU/KL+Sv9R/ob82fra+ED13fAD7rvqwOUp4ufh9OK74lXhreBg4pvkeeYW6cbrCe5z8TP3c/xi/xcBMAL5ArEBIf97/rD/lQDu/1H+Kv3k/L38zfvT+qT4X/VT8xbzovRG+N/7uP1U/jv/zwDgAnIDoAGU/7D/rgBsAO3+7vxS+wf6I/jZ9N3wve1s7Onrb+qp55vlAuaI51XnxuRY4i/iLuMs5LHk6+Se5ZTn7ukq6wvrKuzv7nXwq++Y7Xvsye3+793wle+u7b3t4+/a8TLyzfGx8Vnyo/MM9DH0q/Xh99b5FPsp/OP9OwDNAcACswJTAiYCmwIoA0EDbQIdAS0Au//0/tz9S/2i/OH7vfun/Dn+uf9tAa0CLgOLA30EbwVsBoEHIQgzCBsJ6wp1DDwN5Q2dDg4Pvg6jDqIPcBFeEucSCxQcFXkW1BhvG5Uc+xtBHcAgYyLBIcsh0COMJlAotSldKowpUCnVLHswri8XLdgtLzAbMfov7iwdKTwmGyVXI0cdZRM7CgsGbgc9BzsAdvqn+in8KP1s/hf/pf3w+6/8Bv6v/eL8GP6oAAwB0f6G/N36Lfii9JbxQu4x6R7l++NL5NrjeeJq4T3iuePS5BXmDuhQ6tHsX/GX9r35j/sk/RT+Pf54/VD8ZfzA/eP9q/z0+/D7eft8+h76jvi59P/xLfHw8bL0j/eF+DD5Jfv5/CT+9P6q/tT9if4GAAgA9v4l/8P/RP9f/af6l/ev9b30UfP08GHuWuyz69jrueqb5+nkseSq5bDlcuXT5Sbn1egv6o7rouyW7bjvtfGx8S3wxu+v8Unzs/Lf8Crvmu5y73Pwf/Bo74zu8+7X79/vJu8A8PnxyPMp9a32RPlE/JH+VAC1AWMCyAIgA48DowN2A/0CFwJrAaoBJQHo/7j+4f1s/dT95f7c/8AAdQELAtgCZATaBYIGDgfXB4kI6wkrDBgOVg9lEAMRYxH1Ee8RrxGoEt4T9BM4FMIU0xVvF6gZ1xrFGigb0Bx/HW8erR8mIHch6yM6JnYmtSaSKRks/itOLMwtZC4aLvkuSjC0LjQqRSftJkQmByLgGqoSzgr3BicIfQcZAfz6Lvqi/JH+F//w/uX+M/5V/dT9I/9yACkBvQF2AlQBk/5b/F765/fm9EHw8Oqz507mmeWH5ZnkPeLr4GTh9uJj5HLlQOZs6PTsv/Gk9Kn2jPjv+Vz66/m9+XT6Qfu/+1z7aPoc+v/53vnY+En2r/Lv70/uzu6Q8dvzBPQE9HX1f/cb+VX64/oO++b7V/0E/6gAyQGMAsIC8gHN/6v9BPx8+q/4wvYU9G/xUPAK8NPtLeoO6GnnxeZC5mPmpubH5jnoC+v+7KbtJu958Tvzk/OP81L0b/X39Yz14/M88Qjw+vD28abwxe0U7DPsl+wG7DzrSesU7MXtJfBh8rT0MfcL+jT9VP8gAIUAlgEWAz0EqgRgBD0E2QTwBLwDTQIdAUUADABgAJUAeQBeAK4AcwGlArgDCgQ1BPAEWQZUCOcKdg2mD7ER5xK1E8AUYBX5FAMVQxbmFjcW/hWqFuwX0BhHGaMYzhfFF0AYZBl1Gl4aERsVHnogZiEZIhQkYSZoJw0p1CogK74rHS2+LuEvuy2GKvwoVycCJTUhyBtZFMcM4AklC6gI7gC0+xT7Svyh/cT+Yv7S/PT76f2eAAoBFQH4ARgD+ANRA54BdADy/uj84/pP9nXwouyM6zbrhenZ5VbiFuGb4WDiP+I54a3gkuJ95kTqMe1H73HxOfRR9o326fZA+NL5vPqR+g76GPql+rT6iPmb9u/yfu+W7eLtAe8B7wfuUe7f79zx0fPu9Nf07PTG9oL5+vuT/mUB5wMrBS0FrgQvBEkDSwLjAL7+0ft6+Xn4pveK9Uny5u4o7Mfqw+lk6K3nHOhY6dbq6+uk7ObtefBB83n0SvT29O/2Yfkq+jH4nvUV9T/2Mvb+8/rwZ+6S7bXt4ezQ6h/pQ+mU6nLr9eue7YfwgfPQ9a73KvlL+pD8h/+cAaUCrANSBfMGCAgfCHYH+wZjBqcFrgTZA0MDJAMiA8QCcgKKAmMC/wFXAi4DWgQ+BiwJiwuvDagPahEKE6MUiRW6FW8WvxelGKsYKhkhGgUbGRusGWUYjBggGDQX8hbHF50Ykxg/GeYaCxzUHKIdyx5JIEQhhCOIJb8lZSb+KLMq9SnhJ6kmpyZ7Ja0iAx9AGv0UBxH0DxQO4wcHAkUAGADa/3v/2/4B/hH95f05/6j/Mf8d/2wABwJvAlsCZQIJAj4BLwCl/V/5S/Zp9Y30bvIN77Xrfeqm6fnnquUd4yLho+Cj4VvjeeWj51zpLutK7aPuOe8g8LPxXPMz9OD05vVY94H44Pjd95T1DPMM8VzwpPAW8NTufO4m7+vve/Dj8D/wPe+w79LxH/ST9vb4i/vG/aT/MwE+AmsCMgIVAgcCYwGXANkAcQEAAfj+x/xg+u73B/Yp9Jvy3PH88BrwbO9d7zfwpPC48LLwlfCe8azzKfUi9vH1zvWl9tP38PfS9kP2MPbU9ez1kPW09Ej0DPRR86jxUPAt8Pbwy/Hs8c3xY/I382T0NPao95b4wvnd+2z+HQEuA4MEJgaLBxkI9gcvCKYIHQkyCe4IgwjTCBUJrAjXB+sGrQUJBW8FJAZ2BvMGAgg+CX4KMwsMDP8M2g3vDkwQehHTEswUFRceGDAYERgZGGEYzhgmGMUXCRhnGAEZDhnEGJkYIhjYF3MXxRbPFmEXOhh4GQIaoBpJHCUdlRw0HHwcFhzyGlgZ9xcjFxIWhxV4FKkQ3gwVDBkMNQtPCYkHtQZPBl8Gfgb0BO0C+AHyAXcBVACJ/7v/FgD2/xL/p/0r/JP7Vvtu+nf4hPZG9Zj1tfVr9P/xo+8X7rDsleve6iHqlul96fDpPurX6aTpnOk76TfoQucD57fnAulf6knrCOyT7FztEe6Y7izvSe9y70fwr/Ez80L05vS/9Dv0J/SN9L/0b/Rp9An1BfYp90D42vgJ+R/5C/nw+PT4b/nl+oj8p/3t/Qb+1/7V/5L/xP68/b38NPyQ/M780ftP+tj5nfl1+Kn2c/WB9PrztvNg89jyxPKX85z0vfQQ9KHzXvS79e/23/c3+Jj4fPmH+t/64foe+xr7/PoE+/f6BvuT+yn8PPy0+2T7E/xu/Wz+5/4t//D/FQEoAtwCCwM5AwQE5gRhBQcGGgd/CFsJAwriCZ0JaQmhCeQJSglICNUHTgjFCHkIIwjTB58HqAfXB/AHEggJCXoKrAtQDJoMLg3zDY8ODg/vDtcPFRGJEqoTJBQmFGUU2BRgFZMVDhU+FJ8UmxXjFVAWuhZAF3MXAhemFlgWzBUDFUgUqhPyEjsSORIrEooQgw5PDp8OkQ4kDooNhg1hDWsN8Q2bDfILkQooCkwJxgfOBpEG5gWcBFMDKgJYAWYAa/9q/h79YvsL+q35ePlp+Bb3RPXA827yYfE+8WvxBPE38CLwLfDl75XvNu887p/scOss637r3+tP7MTsQu2S7eLtae7o7kPvfu++7zPw5fDD8afyPfOl8n/x/PBM8dbxKvJL8nLyFPM/9G31JPYq9sP1ZfVK9Vn1ufW29lL4wPlw+lz6evrT+hT7sPqZ+T74P/f99k338fb19fj0kPQx9HLzivL28Qnyn/IH8+nyFPPR84n0oPSL9E70nvSr9Uf3rPiW+YD6z/vq/Av9x/xx/Cv89Pts+y37RfsE/L38l/3E/an91P17/hf/Sv8z/5r/fQAtAZYB4AFRAtoCgwPpA4kEPgVTBnsHTAjFCLcIZQgfCKoH6wYJBqAFuQUVBlMGjgbYBgoHIgdiB1oH4wYiBxsIMAmvCT8KMwucDDoNqQ1NDuUOmg+9EPMRxxI8E80TwBTpFHoUBxSzE8ET9BNGFGsU6RTfFQIXUBe4FgYWNxVNFM0TrBOME2UTzxO7E2wSjxHKEa8RuRCUDwAPzQ6mDicPwQ+0DiQNNgx/C1sKCQmUCFoI1wdJB9QGUwbYBWsFQQRjAvr/T/6M/WH92PzL+z36FfkT+P32gvWF9I3zkPLD8ZbxnvGE8ZzxmvEC8WTvJe4R7qXuHu9m79nvvvCv8cjyQfP18kvyG/JT8hXyA/Jd8gHzT/Px8jbykPEx8TzxSvHF8Dvwc/C18UPzJ/Tn8wzzkvLa8l7z0fNI9CH1P/Yr96738PeR9+P2NPZI9Qz0A/OZ8rny6fKx8l3y1PFW8QDxnPAQ8N3vJfDX8AXy/fIK9H/05PQp9WP18/Xz9lD45PlD+138fv10/rz+sP5o/tD9LP31/Db9n/1F/gT/sP/o/6T/Zf9O/zv/7f4v/9r/mwAbAckBXwKOAqkCygLeAlEDAgToBLMFbgb/BmQHVAfUBhUGdQXRBJgEuAQ0BccFnQYYB0EHOwdLByQHKAeFBysIywifCf4Kcww0DRAOug5iD6IPDRDkELYRaBJWEw8UKhQ2FGMUTRQdFEsUuRTkFEcVRRZZF+YXRRh+GC4YJxcaFtMVrBWnFZ8VpRWrFBcTahKBErYRPhAKD3gOAQ6VDTsO8Q49DgkNbwwPDMgK9wn5CRgKkAlgCIkHXgdLB6UGbwWFAxkBN/+V/qb+Of7O/Fr7JPoT+Xv3NPav9cf0YPOQ8tzyIPNm8wj0M/Rc88vxF/FC8avxv/Eb8unyQ/OB81T0B/XT9OHz+/KA8lXyrfJL88LzPfM+8ifx2/Cc8BDwJu9K7jDuzu6+79nww/EB8rnxffEf8bbwtPC58QXz6/Mx9IP0yfTm9N70h/Rm8wfyMfFf8bXxa/ER8c/wYPDj75XvZu9i75fvN/AP8SryxPNl9Yr2BPfz9rz23/bp92H5cvpm+7n8HP4t/6z/6P/H/0T/kf4U/gz+T/7V/rP/wf8+/+v+F/8h/+P+uP6d/r7+nP+gAGkBoAGaAVYBTAEbATwBrwGMAmgDKwT0BJAF7gU8BvIF+gQcBLwDyANIBP4EiAUTBnAGqQbzBv0GGgdkBwQIogg6CWAKxQvHDDwNqQ2xDfENbg4MD7APZxBfETMS+hKDE8kT4hPwE9MTqhPTE6EU6RVZFzgYihijGJcYvBiXGJIXAhYUFSQVEhXeFJAUYRNbEQcQ6g9QD0EOlQ3aDfkN7w2dDkoPqA5hDY4MngsFCh8JkAnBCRMJ5QdUB+UGUwZdBRYEdgItAKr+Nf5L/hL+Xf30+yr61vhm93b2Ivac9cv0hfT29KP1E/Zo9iL2BPVc80TyMPKK8sLyQ/MS9Gb0lPT09F/1z/S0887yJvKv8Xvx//Fj8uzxqPCb7xbvc+767YntO+0I7VLtSO587znwQ/DG7wPveO5A7rvu0u8X8e7xiPIK84vzyPN88/XyD/In8Yvw3/BI8cnx8PEP8vDxkPE88T7xpvER8oryNfOJ9CT2svdl+Mn4qPi1+DD5Lvo/+0b8Uf2u/tr/bAByALQAugAMABT/qv6s/uH+VP/B/5r/y/5Y/lz+Rf7E/WP9l/3x/Wr+Hf8AAEUAGgDB/5z/e/+i/z0APAE4AtACgwMABFoEXAQtBJMDHAP7Aj0D9QP+BPAFkQbxBv0GwgahBtYGLQdeB7QHUghXCXIKPQuuC+ULAgwdDNgMlw2BDosPsRC6EVoSyxL+EkoTdxODE5ITmBM2FHYVohZdF2EXbxdQF90WnhaDFtMVehQsFMAU6RRvFL8TWhJlED8PJw/XDuUNYQ2nDYQNgg3/DfMNCw3TCwwLOwpTCUQJyAnWCR0JXgiNBwYHxAZXBhkFPwO/AQIB1wB9ABAA+v4c/Wz79fmW+JX3gfcm95X2GvZJ9qn2IPdB99j2nPVG9MDzIfSe9LL01fQE9fD0v/Sw9Fb0rPMO82HytfE88THxb/E48TPwue6H7bLsWuw57Onrfut06zfsSO0h7qvu1O617mHuRu6f7p/vz/D08WfyzPI/8+HzL/Q79OHzcvNF81HzfPPK8+fzvPOJ8xjzqfKj8gXzsvNp9PD0q/UM97n4xPkO+vX5Afp4+hr79Pu3/JH9Yv43/7v/0f+7/6r/sP/w/u/9dP2f/bj96f2p/f38MvwC/Ez8afwf/Nn7afwi/an99f1//r7+lf5H/hb+DP5y/lj/VgAIAZAB5gGbAiIDQwP3AoYCdAKhAhgDiQP+A4kEGQWSBbkFoAW3BUkGwgYkB3gHZwh2CaMKPwu2CyUMfQzsDHgNNQ66DkwPOBC3EPUQGhFDESoRPxGtEfERIhIjE4AUMhXMFaQWWRdAF/gWfRfOFzYXgRZgFhoWZhVsFQMVFRNCEAIPRg8fD+UN4AygDHkMcQxrDXwNUAxMC3MLYgvnCrIKCAu0C64Lhwo2CeAI+gjRCB8I3wbwBI0DOQNVA6cCrADC/e77qfrs+K73wveN9932j/aG9kn2Nval9oj2K/Vw89LyXPMW9Ab0qvPN84fzGPMH89DyNPLN8WfxwfDh75nveO9P71XuVuyS6tXpD+pQ6kTq9OkI6vnqEuwU7cntF+4b7j7ujO4L7/nvhvEW89Pz9PM19JT0SvWg9Z718PS79Oj0NvUr9ez0efQA9LzzifNi85Hze/Sp9V32jfYM91b4yPkY+tT5lvmp+Tn6N/tM/JL83vyC/UH+bv75/Xj9o/2f/dr8RPwt/Cn8SvxG/Ab8Ifus+vX6ovvX+6z75PuU/Db9dP2C/YT9Z/1A/S79X/3K/ZP+oP+bAOkA7gAbAZIBBQLyAZwBvwE6ArMCSwPpA6gEQgX1BWcGswb9BsYHsAihCeIJIgqwCnMLqgusC6ALugsGDKYMMA2QDeUNsg6HD7QPyw8lEK8QahEcEssSLxPJEyIVdxZCF28XtRcwGG4YzBj9GKUYnBfmFqQWGBZJFYIUBhO7ED0PHQ8dD3YO2A1jDQsNSw0FDk0O1g16DXYNNA3FDPYMYQ2QDTANdwxiC3oKSQo/CrcJQAhdBgYFEgRVA34CAgFk/hX8jfoX+ez3bvcK91/22PWI9Xz1qfXe9an1tPRL8zzyOPLC8tzyvvJu8jTyIPIm8ujxH/Ge8ATwOe9h7vTt7u307ULt3+t36tnpsOky6n3qbeqp6r7r9OxT7v/uaO+G75/vxu8S8NXwGfKF81D0w/T29GX18fUu9vP1efUf9fj0TPWI9WX1SvU29fr0sPR79Jj08PSp9YL2FveF92P4ivkS+sz5X/ku+W35Fvqp+iP7qPs4/Or8Uf0o/cP8r/zH/GP84fui++z7efy//Hv86PuR+7T7PPxZ/C38IfyI/Ev9sf2v/ZX9V/02/Qn94Pzc/Hj9Yv5a/wQAZgD4AIoBAQJTAmMCoQImA/EDrgR3BXwGTQcCCEgIdQieCNwIXQngCRoKPwqPCh4LZgtMCzkLPQtcC+gLlAwHDYwN3Q7qD4QQ4hB+EcwRRRJKE94TJBSxFB8WjhdJGLUYMxliGYEZyxnLGcQYihcnFx0XnBbGFfMUaRNdEVAQNBDHDxQP9A7dDqYOEA/+D2EQqA/rDpkOHA6bDasN/w0aDqEN2AzXCy0L4QqHCk8JNQf6BHQDgALWAR0BoP8m/QT7UfnJ99T2cPbg9dn0XPQr9EL0OfRt9D30XPPu8fjw5fAr8RXxHfEx8cPwefCs8LLwCPAs73Pu1+1C7RntWu2o7TPtYOyr61vrXevE6w7s/+v/68vsE+5d71zw3fDq8LDwf/Cw8GHxYfI789PzYPTg9Jb1EfZJ9hH2ufVh9SH1PPV+9XH1hPWj9Xz19vTD9NP0B/Ur9Xf16vVL9kP3Ovix+Hf4NfgI+CX4cfjy+Ib5Q/rd+sX7ZfyM/Fn8nPyn/EL8tvuo+8f7L/yA/JT8RPzy+x/8Ovwr/Aj88Psv/Jz88vwc/WX9Z/0y/eb84vzm/IL9cv5K/ykA8gDFAXoCJgN+A7wDBARMBJoEIQXkBe8G1wdWCJoIsAjBCNEIFQlfCaMJvQlFCusKTgtzC54LuAvyC1IM0gydDWYOZA9+EEcR3xFmEjMT7BNAFHIUBxXtFccWARgWGdkZ6xntGUIaVhrwGRYZUxjLF3EX+BaoFroVnBOrERwR+xA+EEYPLQ85D1wPPBAgEQMRERBaDxYPZA56DSQNiA1lDXEMUAu0Cj8K8wkRCZ0HXQVDA0IC8gFSAfj/3/3D+wH6KfiX9ub1kPXR9Cn08fPv8xL0afSe9J/zxfEY8Lbv0O/j79fvFvA78DXwbvCF8EnwzO9+70Hvve5V7p3uS+9d73HuTO2m7H/suOzN7KbsgewQ7ULum+978Mnw3/Cs8GzwK/Bc8FbxevKD8yH0ffTo9OD1hvaC9tb1gvV19aX17vXx9bv1jvWM9TL1nPQz9FD0xfRQ9aX12PVR9kf3+vfh90f31PbO9nD3Gfi5+EL5JvpK+zz8nPx5/Fn8oPyG/Bf8BPwp/Jb8A/0X/aD8APzw+0L8X/wR/N37E/y5/En9jP2G/Vv9KP3y/ND8y/wc/T3+g/+bAFgBAwLUAnID4QP4A+0DCgSLBDwFBQbCBpUHPgjJCL8Iogh9CM8IRgm7CQEKVwoQC5cLwQvqC/ALIQyQDBcNsw1eDqYPBRHQEZ4SFxOSEx0U4hR4FaoVQxY6F6cYyRlWGrAa8hpEG9oaShpBGVUY7BcNGLUX+hYAFmcU1xJFEqERwxDgD5QPgw9ID/oP1BCIEJ4P1w5aDpANxQzJDPQMYQxHC9AKdAr7CVEJlAgmB9UEyAK/AVgB0QDg/yb+Ffwk+mX4JPd29qf1b/SJ8zvzLvNa88jz9PNF8/Dxf/Ag8C/wK/Ax8H3wsvC28B/xjPFM8ZXwFvCp7znv7e4e74nvwu8S7yjulO2F7UztKe3r7Jnsm+yB7ebuBvCm8K7wZvAn8PfvVvAA8e7x0PJk8/rzpPSK9RX2D/ac9fD0rvSJ9P70SPUr9cn0y/So9BT0zfOd85HzlfPn81j0+PQH9u32FvfS9kn2GvaE9gr3kfcf+Pj4Qfpe+zT8Uvx9/F38H/yi+4v7qvvu+1/8ufyK/C/8Svyc/LP8Y/wE/Av8gPwu/b79FP4i/g7+s/2j/Xz9of0i/iX/HgD8ANMB7wK+A3cEiQSkBIsEoAQdBcMFdgZLBw4IywglCWEJaQmjCe8JIApPCsYKpgtjDOYMHQ10DbkNGA6PDhsPZA8yEGgRkxKsE2kUMhWHFRIWfxaeFhEX2BcGGdsZmho+G+8bZBywHCYcvxpmGT8ZRxnQGCQYPBdkFZ4TaRMrE+kRdxDbD54PHQ+yD8cQwRDFD98OMQ75DAgMOgyQDLQLbgrGCcoJ0gmxCQsJYAfwBNwCvwEdAY0Aw/8g/u77EPpW+AL3gPbk9Z70HPOl8tbydvMj9Fb0n/Pq8V7wwu+f73DvLO+P7wbwRfDf8I7xq/EI8Vbwm+/Q7mfuxO5f75vvlO6z7XXtZ+0Z7c3sZuy+67jrsuxM7qnvavB/8EXwzO9R73bvTPBQ8SjyvvJg82n0jPVh9kv2vfXo9Cn02/Mz9Gv0bfR39IX0OfS+85XzmfNo803zm/MW9Nn0zPUC94X3Cvd49hH2X/bz9oP3RPg0+XD6pvuM/NL89/zQ/Eb8g/sS+zf7mvs+/Kf8zvyc/L/8Bf0g/c78mPyg/AP9r/1a/sD+4/66/mL+9f3c/fH9rv6u/6IAfwF4AoMDTATfBPQEpgRtBJQE+AS5BdQG7QfeCMQJWQqXCq4KAgt/C8MLFwyWDIYNVQ7pDi8PQg95D6gPCRBnECQR9xErE4AUfBUGFsMWhhfYF7sX7heMGEcZNRpxG0scaBzxHMkdxx1bHM8aiRqDGuUZQxmRGN0W3BQoFPATthINETIQ6g93D8MPihDaEDwQiQ8dD9wNnAwhDB8MvAuyCt4Jegl6CeYJYwnnB78FCgTIAr8B7AAUAJn+jvzt+k35h/eI9jD2YfUC9Bbz/fKD8/zzH/R68yLyXPBu70/vA+9j7mvuFO9u78TvZvDV8IvwCvBw78ruYe5A7ozute7T7cDsaOxk7Bzs5+vS64jrU+sI7ETtTO7o7jDvC++I7jLuUe7o7qfvsPCQ8UDyIvM79Cv1GfWq9Dn04/Ni827zm/OV8z3zhfOH8w7ztfIW83Lzg/Os80L00fSv9Xz2yPZb9vP12vUT9nb28/aR95L4ufnv+rT78vsI/CP80/ts+wb7GvtF+9v7dfxv/Ej8e/wB/TL95vy//Nj8NP2Z/Q7+O/4e/vf9k/1w/UX9Vf3h/Qj/AgDdAOYB8QLMA1wErgSYBIkE1QSgBUkG8wbyBy4JLgrQChALbgvHCy0MngwBDVUNEA7hDmAPiQ/TDxkQYRC/EE0R+xG+EtMTIhUlFpoWExerF2UYdhhyGBgZuBllGmcbPxzdHAwdix2XHbocMhtQGkoaBhpgGa0Y1xaSFL8TyxMAE1sRCRB9DxgPkg+CENYQPhBID7IOxg2GDGMMiAwMDO8K9QltCT4JigmaCZAIEwayA3wC5gFGAZ4AM//5/Lz6Ovnf97z25PX89LjzufJL8ojyRfPR82rzA/IC8IzuWe5z7gfunu3D7e7tgu4277TvO++K7gnuhe3t7MnsJ+217TXtFuxV6xXrB+v/6uDqa+r86YLqEuyM7VPuo+587i7uee0+7YztVe5L70fw/vCg8XryXPMC9N3z8/Ig8vTxSfK58vny5fIF8zXzJPMS8wPzDvNP88TzQvRm9FD1ofZW9/P2C/ZX9UD1xfVP9sT2k/ea+BD6ZPvX+7j7y/vF+zv7rvp8+sT6XPtW/Nb8zvyp/Bz9pf2Z/Rz9wfz5/KX9WP6R/p/+Yv41/u/9rf2p/QP+CP9gAIMBiAJ4A5gEsQUVBsMFZQVnBdoFpQafB44IxAnzChsMrQziDP8Mkg0zDn8Onw45DxcQ5hAmESIRGBEkEVMRJBKuEvYSwRM3FbQWbRe5FzYYvhjaGOEYRxnTGZgafBuEHBwdYh2THXsdzBxjGzkathnTGX0ZYRhDFooUGxTNE7gSZhElEBYP8Q7gD5gQNhCgD4MPKw/vDaQMUgx9DOoLygoDCnYJOAmOCa8JWgiIBSIDOALLAdUAoP9B/n38nfrL+GD3OPZZ9Uj0AfML8n/xoPGM8gfzZfKX8Nbu7O2s7Vbty+yJ7KLs1+x77R3uRu697X3t+Owv7IDrletg7KTs9+sF663qbepC6i3q6ulK6Tvp7ulh65fsHe1x7V7tEu1R7DHsm+yO7YTuQe/x777wu/Gt8i7z2PIq8pzxsfH98TTyV/KK8tjy6fLA8qHyevKt8gzzk/Ph8zv0WfWN9v32ePa/9XH1b/XO9UX2yPZ/97v4Fvo1+4f7kfu2+4f7HPul+pv6vPpc+xv8nPx5/I788vxh/Uf99/z9/Ij9T/76/hn/O/8r/+X+tv6d/qr+N/8xAGsBowKZA20EcQVnBpEGTwYkBkUGmQZLBykIFQkoClgLRgzHDAENcA0ODrAO4Q5MDy4Q4BBHEZkR1BHMEasR5RGPEs8SWBNIFLwV4RZZF8MXKhiCGMIYBhk7GYEZIRoPGwccoRwgHT0dghxpG1QaABrPGbgZNxmvF3AVZxSSFGUURhP1EYIQdw+sD/0QqREeEVcQuQ8WD+UNIA0uDdgMmgsuCngJ8AgjCbEJTwlmB4sErwLwAaYB7gCY/5X9dfur+UL4SfeI9nH10/Nw8prxefH58aHyhvJW8VfvmO0X7fzsgezI64brVeuA64HsUO1c7cTsWOzj61Xr7upD6wPs2uvk6iHq2enT6ajpmOkz6Yfop+j+6ZPrbOzr7Abt0+w/7PvrKOy47JrtUe7o7o3vy/AF8vPyN/Oh8uDxq/ED8orys/Kv8sDy8/L98try0PLK8gXzXvPG8+vzufT99d320vZH9rP1Z/Wc9SL2l/Yz9wL4Lvl2+lj7tvvP+8H7b/vh+nr6cvr1+qD7Tvx1/Hv8yfx+/ef93P2G/cb9aP5I/7n/CgAxAOj/rv9z/3v/tf9JAIMBjAKFA4UEqwXKBngHhQckB/kGGAeuB3cIkAmXCpwLqAxnDbkN8Q2JDi0Pcw9rD/QP2hBuEfMRKxJUEisSGBKuEi8TYxMDFJEV/hanFxcYxBgeGUsZgxm8GbYZ9BkDGywcthwOHZMdex2CHDobqBpxGjMaBBrfGJwWLBVFFYsV2hRIE6ERaxBXEIQRPRLKERUR4BBXEAgP2A2CDTQNOAzlCpQJxwjDCJgJ1AknCEQFIgNPAuYBOgH4/7z9k/sc+uD4ffdL9l31I/TG8lrxoPAP8TTyW/KM8bLvpu3N7OfsdOxb67XqiOri6pvrcuyd7GbsQewS7G7rluqx6oTryusF6/rppunJ6fjpv+lS6V7oKOhk6Q3rFOxk7KrsuuxJ7Azs/+tU7O3sy+1R7truhu8b8avyTfO88rvxoPHF8SzyfPJu8l3ysfIk8yzzB/P38hrzhfPx8xD0kPTO9Tv3wPc592z24vUy9pH24/Yt96j33Phc+nX70/vs+xn8+Ps5+6P6bvq4+mL7Qvyk/Ij84Pyx/Vj+Yv4c/hL+nf6M/zkAdACFAH8AVAAtAAIA3P87ADUBXQJeAzUEXwWtBrQH2QdgBxwHAQdUBwwI0witCbYKFQwbDZsNzA2FDkwPyQ/TDxkQ1hDEEVASpBK6EroS7BJSE7cT3BMwFGIVRBdbGK8Y3BhsGQQaKxoOGhQaXhoZGxYcCB2oHQge/B1DHVkcqxsXGw8byxpgGdcWlxUYFkoWZBULFJMSRRH3ECIS3RJeEk0RBRHkEH0PBw6GDV0NVgy6Cp0JwwjaCJgJBQqQCLMFYgN4Aj4CZwGc/3L9aPse+r/4VvdD9jj18fOX8l/xi/Cw8JrxQvJr8U/vTu2i7KrsCOwD61DqIepp6jLrHuxa7BzsEuwK7EXrTOo06uzqQOuI6prpI+lK6VLpVOmj6L3njufV6KTqqesG7EPsSexF7PfruOvU63LsTu347Z3uk+/s8HryT/Pr8ujxUPF98SDyWfIb8gnykPLn8vPywvKf8qPy6/Kh86bzK/R39Rz3pPcp90325vXm9Qn2UfaX9gL3BPje+Rz7k/uc+937svv++lz6MPpi+gL77PtS/G/8wfyx/Vz+Xv4I/tb9bP5//1QAxQAIAd0AyQCwAI0AUgBsAC8BZwKNA1YEZQXUBgQIIwiZB0sHGgdUB/IHrAiUCaEK7gs0DeUNIg6RDkoP7g/2D0YQURFBEskSSBOzE+ITqBP6E1cUWRSSFI0VNBdqGP8YdhkIGm0aohrVGgEbKBu/G6Eclx1MHrseOB5iHcwcaBztG5QbARsWGeQWjRYRF6gWoxWfFGET5RHEEdESVhOLEp8RlREoEaYPsg6ZDgEOeQwECw0KOgkjCd4JpQmbB/AEagOtAuIB1wD6/qT8pfof+c33uPae9Uz0EPPq8bLwNfCR8CvxK/Ez8B/ug+xU7C3sVeuM6i3qC+pd6hnr2uvf65Prkets66TqDept6hnrx+ry6UbpCum06KXotugD6AnnU+f86FnqIOuA68Lrqetb61HrcOv5667slO0s7uTu3+8p8XjysfIg8lbxCPFx8fnx7PHm8STyjPJw8knyafJH8l/y1vJq87jzb/Tx9Tv3QfeG9g324PXq9Qf2U/bn9o330/hN+lL7YPuF++r7gfvO+lP6aPrz+q77b/yz/O78W/39/WD+Tf4O/lr+K/8vAMkAMQFMAUgBHwHPAIUAiwAEAeQBCwMlBAsFBwZYBzUIEAiLBzUHYAeHByUIEwlJCnULiAyVDT0Org5pD2EQ7BAgEacR8BL0E5kU+RRHFUMVXhWVFQQWXhbuFgEYchmWGuYaNBvOG+0bnRtbG6sbKhyGHGwdax4RH8MeFR7FHXUdsBx4HF0cLRp1F90W0hfYF+4WpxUuFIcSPRI3E1wTXhJNEQMR0BDNDw4Ptg5BDgMNiQs7Cg8Jngj0CAkJcAf2BDcDaQJfAskBAgA8/eH6Wfnw94r2cfUY9KvyafGq8CXwTvDB8ATxFvDc7f/rxuvO6zDra+r86ZjpmumU6lvrQOvU6i7riOvo6oLqD+u+66nr9epr6s/pcel96Xvptui759TnHOmk6p3rNexD7OHrzOvA63jrp+tm7GntAe587lPvnvDF8YzyS/J38eHwIfEu8nryKPIq8ojyn/Jb8jzyLPIR8nLyEvN48+/za/X79pP38/Ys9tz1EfaE9p/2vPZ396z4IvoU+2r7i/u0+5P7P/vf+pP61frk+7P8jvxl/I78Wf3K/en92v24/Wj+uf/JADwBWAEnASUB+gByAD0ApABtAWcCmQOwBLMFwAbfBz4ICAibB50HEgjPCJgJbAqLC8MMxA1JDpcONQ9ZECYRlxE3ElQTfBR2FfEVQRYIFisW9BbHF+wXbBjJGXEb5xzTHW0evx40H2kfGx/kHpAebR4HH1sfGR/yHV0cXRvyGjEawhnTGYAY3xUUFd8V6RX/FNYTgRLKEEgQiBF0EiASIBGlEJIQog+TDsQNwQw7C34JEgjjBhMGOAZ8Bl0FIAMrAVYAagByAI7/av0c+4z5Vvib97H26PTg8mfxkfAe8BTwd/C68H/wte6R7PHr7etN64bqNurJ6cfpu+oQ7Fzs7evl6/nrgusT603rBuw37A7sr+s0697q6ur56kLqFum+6MPpcOu+7Mvt/u3u7dztBe657S/tZ+0R7l3u0O7U7/Dwr/GA8qfy2PF18BTw7PBI8T7xd/Hu8U3yhvID8z3z4PLM8nTzM/SW9Oz10ffq+O74oPhA+Ab4G/gI+K73z/fJ+BD6BvuP++j71/u4+z/7j/r1+d75jfpa+7b7svtW/Bz90P3p/f39xP3n/cn+MwAAAU4BawGoAaABUgEpAR0BUgEXAjcDHATiBBwGNwcOCAwI5QedB48H8AeJCEQJSQqiC/QM5Q16DkYPJRDsEIoRTRJWE58U9RX6FpAX3hf5F8AYXBlJGb4ZHxupHCcetx/rIGsh3SHUIuAiMiLZIcshkiGYIZYh7yA0H1IdKBx7Gp8YpBcUFrwSBRD0D2EQGRDFDycPow1hDLwN0w/XD+EOug5cDwgPdg5oDgMOSAyLCh8JKAfuBNcDtgO1Auz/iv3s+zf7M/v6+sb50/c/9jr10fSW9Bv06fLS8UTxH/GG8f3xR/KC8jrx+e537QjtWuzA67HrY+vF6kvruOw17cnskeyq7BbsXetj603s8Owl7R/t0+wz7Mzr4+uO62Pqjek+6rzrSO3i7tnv8+/771bwP/Cj7z/vxO9e8MXwhvGd8kfzi/PG8xbzYfGG7/vuNu8c7zbvwu+477jvIPCu8JrwN/DJ8HfxNPK88/31J/iC+fP5EPr/+QP6T/pm+ir6rPrs+zT9vv0O/kH+1v3y/CX8/vrx+cz5xPo5+xb79fpa+/r7UvxO/ET8Ffxx/BL+0//RAH0BSALUArcClgK1ArsCBwPZA+wE3AXUBvoHxQjyCOYIRAhqBx4HXgchCPIIAQqVC+gMqw1LDkQPNBDUELoRDBNvFBoW1heJGXcauRohGyActhzUHOUdyh9cIa0iJiRlJZ4lcyVtJq0mFSXnI+MjkyMDIygj1iIOIGgdCRwGGhMY+hbNE+EOWAxKDEAMgwufCuQIhAZnBn0IIwkOCDcHsAcfCDcH4wZ0By8HCwaKBeIErQKyAA4BYwEd/w/8wPnb98724fY29gT0LfHm7n3tO+0t7Uvshusy63brl+xE7rzvoPAp8cPwxO+473/wBPHg8cTy5fKt8nzzifTv8z3z8fIm8s/wxu++7xDwVPDz7+Dupu1u7O/rzOsw6wDqxeln6+btJfDJ8U3ytfIO86rz2fMf9D/0pvSj9Sv39veI+M34Wvi49vD0LPPo8K3u8O0T7ontxOyz69LqSOoG6h3qx+nh6Vnq0Out7rvxePPD9An28/bC9/r4Jvpc+un6vfyy/oP/SwBQANP/4f4i/h79Xvvk+bH5PfpP+rX5D/kN+Vn5r/nc+eL5IPpD+/38L//2AP8BwAK2A1wE/gTeBcIGiweQCEEK2QuxDDINjg18DQsNfwwPDKgLgQuoC3cMmQ05DhIOWA5rD2kQQxFSEg8UBBaeF5kZ3htMHX8evh8UIYwi7iIgJKcmJyiMKSEsDy2qLJIszi33LfQrSyvWKtcncSW1JSMluSGeHfQaPBcTE68T8BP2DDgGWwUkBkAFoAT2BGkCFP/JASgGGQXaAUABfAKSAdP//P91/9b93/1//kD82fc+9dn0+fIi7+HrH+nF5trmkeiR6Ezm6uM74j3haOFt4s/ipOJh5D3ogutZ7sXxKfTd8zbyfPJc82bz2fTJ94z5rfkQ+6L8ZPu3+Lz31Pb088vxv/F88mjzB/XO9V70qPHT8GHxrvCX7+vvBfIR9Sz5+fzJ/ov+j/6J/pf9+PuH+937D/wy/Lf8jPzt+rX4oPWE8ZPsYugT5vPjBuIK4uzjWeTF4gDiTuIW4l3j+uVG5+vnSet38Qv2+PeC+Tf6Kvrb+m/8bfxm+6r7Iv21/eb8TvsT+T/3v/X48x3yuvAl8bfyjfRT9hj3gfev+Hz6nvwi/vr/UwLHBHIHgApjDNgM5gyoDKgLVQoSCjsK1gkhCYcI7QdmB/kFXATxAvoB2gGUAgwEvwXdBwoLHA5rEDES4BOmFh8aJx0/IOQiWiVUKDErNC06LdMr9itxLGYrECtyK48ruip4Ki0rOCoXKXUovSZEJswnSChbJ8klaSVPJt4mFSbwIb0bERfJFhoZdBUBDYcH2AUyBtAGxwXHAOn5F/lF/Y78jfcl9I3z8fKc8b/xUvH97kju2e+o8Pzt7OnV6H/p4egw52Ll2uPF44/l1Oex57LlDeQC4wDjiuSU5sjn4+h66wPu5e/a8mv01/OM8ijyQPJa8+L1/fip+mz7JP1S/4X+kftw+on6KvrO+Rr61/pO/Fj+9v++/mb7CfmS+Pz3K/dw9w74r/jd+tr9Xv6k/LT67vi+9tX0XvSc9IX0RvTV8/3ysfFd77zrJOdE4/vg0t+i3vXd39684QvkjeR25I3kEOXY5r3pyOsg7Bft0fCU9R34VvjN9zv3YPcJ+Tv6w/hH9x34MPnH+Pb3bvfO9lf2Jvdx+Ir5xvoq/VgAWQPHBLkFJgfoCD0KzApkC8MLDAzBC0ULRQo8CSkIagZWBNICGQLeAUYBVACW/y3/Ev+7/7AA7gHjAzgGcwluDTQRiBNXFYIYlBs1Hbgd2h5QIaEjeSSRJCcldib/JbAkZiRcJMAj8SOuJOUkMyUeJ78p6yrMKmQrDy5MMHEwATLBNIEzpi97L5IwTS6UKd4lqx8uFagPbBISD44CJvkg95P3XPee90P2AvHQ74T1ffjV9N3w6PHh8zXztvOI9aP1hPVH9mb2R/NO7c7oFeca5Xbh293i2xncj90437vfAN983dLc29574pjmkupZ7tPzKvn0+4X+1QCBAPz+Zv7f/fX8xP56AkMEBwOQAZ4BXv/O+YL24vUQ9G7y7fNy9if4n/qf/Y/+MvyH+nn7UPxi+/T7GP7G/uv+zQAjAZ39Cfks9lzzzO4N65jqsepg6OXlauWh49zf69yF2uLXaNaZ1qDYsNq43Z7icucG6srrqO127x/xBfNx9Cv0/PO39Hz2Lfii93f1GPTt85b09vSS9Ej0O/RM9eP2lvjS+QL7S/1mAGQDvQUvCJEL/g61ECgR4hCQEIQQXA/gDOsJPQfwBG8Cv/8H/Zb77frk+U/5Z/nE+Xz6Jfzc/V7/jQBCAikFlgi0C38OExHeE+gW+xjCGQ4ahxodGuMZpBk1GfEY1BjSGF4ZcRo4GyQc/h2dIL0ilSRUKN8rJC5xMGoyIDUhOKY6YTvmOjw8Cz/3P508WjbjLxMtUSsWJ3QfjxXQCTMB9wIUBKz65e9Y7Kvua/KC9tr4E/Y49bL7qADZ/rL6WflB+vj4cPci9/j0/fEU8LPuRuqZ4djZCNen1VHS7c6rzvLQf9SR2Uren+Hg4wTmMurt77H1aPuTALAE2wdPCcoJlAggBjUDs//T+2H48Pd8+WT6CfpG+Qf58Pea9NDyO/QY9sX3xvov/2YDNwcAC60MogtnCZ0HoAXcAroA5/7B+5T46/ai9BDwt+pp5pDi396I3FXcdN1Q3qje1N9k4bjhU+H/35He3d6j4L/iyONQ5bDo5+x+7/Hv1O4n7UXszexK7U3ryuj66Hrryu5K8ajxJvKF9O731fqz/BT+5v8kAmYELAbABy4J9wmbCn8LtAtDCzcLywvhC6sKhQidBuIFDQW9AuT/L/4c/dD8x/yG/Lf8zf4fAfYBmwJuA0YE0QQjBSkF1QSWBOoE1AUYB38IcwmbCuoLCw05DisPkhBBEloTjBS4FrwZoxzsHRMfaCHbJMgnrii1KaAsny6XLxYyCzTTM3QysjPpNUU2yDUCNwE47DfiOKI6eTd/L2Eq9SlWKEEj6xxjE5cGbwJNB6sFe/uh8pPwGvPn9pn5pvcg88H0Zfkk+W31b/Ee8NDv4u6n7jvtluqn6DXofOfq4kTce9g/12bWC9Wi1LHVsdjp3fziiOZD6QvrO+1I8X728frp/ZsAxgNlBSEFAASAAukALf9s/fr6lPk3+2z+SQAtAHkAawGw/5T8gPwG/hT/dAAYA3EF3waWCFMK1gmtBlMDgwA4/RD64fdt9TryZPC2783tMuof53DkyOGr3zHfleB94n/icOGf4SHjlONc4aXdUNum203d+N7Y37zghuMD6Ffr9etx6lDpZeo97F7tZuz162nu6fKX9+/5n/qP+yD+XAGbAg0CJgISA+8DSAQpBTwGgAavBpEHHwgQCMIHngiTCgILiAnEB5cH0wd0BiEEJAKiALP/pv97/+X+9P6iACQCAQJEAd0A+ABsAPr/ZABcAEUAewEOBN8GoggkCuoMXg+pEHoRPBP/FKwV2xafGPIanh3eHr4fCyKNJFol/yXjJ10qAiwiLhUwcTDUMawzMDWTNk04wDk/OlQ6rztQPqM/2DzWNeMuTytMKXomVSBTFc4FjPwKAMMBwvn37/XrDu3q8L/1m/aC8mfxn/aC+U32v/GD8EbxsvC48H/wPu2o6afnLueQ4xfcFtYM1KjTjdKx0Y/SdtWi2mXgYOXz6P/qge238mP4C/0fAYEEWAfeCKYITQe/BJQCZwH6/xH9AvsV/In+8v9UAJcAHwGz/2X8ZvsR/Z3+lwB4A9oFVgdzCXULfgp/B3cEnAGn/YD58fZO9H3wGe5r7YLru+en5Ozir+Aj3jbdUt4u4E3hkeEg4TPh/uHC4dve+Nsn2/Tb6d2I34TgLeKN5Wbpueoy6gLp3+iG6lzsh+yb67DsFfGn9ov6Rvwi/Uj/nQKeBFYE9QOHBBUFFwWSBewF9wWOBsYHjgimB44G3AZeCIYJ7gg5ByQGPAZjBicF3gJGAZMAtgDCACkAqP+RANYBRAKoAekAGgDX/hL+Nf4Y/tr9pv4VAQoEkQamCJoLqg6QELgRXhOwFe4Wwxf4GVMcRB6qIIQiYyPNJJQm5ycWKNkohCozK+Isni+eMEsxdDIPNOM2xDnTOsk6GTwwP8VCVEKRPQQ3vzBQLIwpKibZHbsPaf+/+FT8gfvj8wPs7+iC6tbu/fLu8ZLuh/D39QX28PFN7yvwJ/EM8aHyvfEt7XPpfueV5TDgotjb03LSodEb0NvPp9Fv1S/bLOEX5hHqsOzl7xX2Gf2pAigGgwjzCsELeApQCBMGNQQuA8sBtv7S/IX+wADbACYAFgDR/2z9FvoD+mf8Lf82AmUFEAjiCcEL5AywC9UIJwVMAS79m/nj9nDzC++N7IzrrOgT5NLgPt8g3Tnb39oy3MTdvN4n30rf2t/94FvgB93d2gXcot5T4BDhy+I/5S7o2up661Lq4+gz6Szr2utH6+7qBO3q8U338/rW/Br+sgAOBJQFFQX6BF8GFgdhBnQGhQcrCLcI1AmECpYJRgjfB7AIfAlcCM4FTgRGBHME+QJ7AO3+3f4z/yX/Iv7z/fj+SwCgAEkA6P+s/67+r/3K/Vr+mf5K/5gBIQWdB4oJhAyUD34RsBLgE6UVpBZMF/kYbRsHH0whZCHDImcmpClLKpIpEyqoLDMv6DA6MWcx2zK3NKo33zkeOrI61jxzPiRAj0HzQBg9WjYiL+opZyYVI4AcuA7F/Kb0qvfJ+HbzT+ws6Cfpve1S8e7wMu+R8Mjzq/JB7wrtL+1C7UDux/Cp7+zqTOYa5PjiPd4f1//RxM8Sz9vPHdGl0pfW69yE46zoVOy77orynviU/1sFUAjzCUwMKA2RC3UIqQX+A4MD/QEr/3v+gQCKAq0CqAGQAbUBFP9O+wT7r/0TAYMEnQexCQ4L5Ay8DQoMFAhFAx//Zvvf90r0IvAm7FTqHOnN5TvhIN6h3CHbhtlZ2XPb9d3X3ljeQd5p38jg7N7l2n7Zqtqj3ZXgL+Ls4tzjLucj6vLqtOka6Kfnbelb637rxOsZ7mbz9vj7/Ar/PQA3A1cGFgfrBlgH+AdPB9oGkwfVB7YHKQhrCSQK9AjpBpEGgQd1CC8HlATOAqMCFAMXAuT/9v6+/sL+2f6s/oP+Sv8mAKAAXAAAADn/uv3s/A39jP1a/pT/wQFYBNAGjgneDO4PeBGNEkAUAhZOF8QY8BrRHZsgwSIzJMsl5yeVKqgrIivEK5AtfzAwMuMxozLBNIg3MzpgOu850DsUPsxA70BVQFU/lDvzNBctMCfPJIsh0BiEBlr0wPIT+Bv1qe5a6bfmpefy7ITx8+8j7WTw+vNS8X/sR+tU7UDtKu5c8K7tMOcO4t/hmeGk24HUa9CIznTOstCB02HVvdhA3x3m2OvU7nvx//ZB/tUEuwgaCmkMRg/8DugLrgivBogFWASpAsAA3QDeAjsEmwOfAlMDlwN3AF/9HP55AfwEQAjxCtkLsQxmDm4Olwu1BuABuP2z+db1q/G+7Jrpmehz5sbhT92926bbT9oD2XjZqtvA3fDePN953+vg2+GM32XbutnN2uHdseAA4ovigONY5k7pe+mz53vmRucn6bXqUeto7JPv4vWc/LgAXQJmAyYGhwghCTAJ+gimCOsHBgiYCK4HxAZDB8cI/gjnBrAEewQJBvsG4gVXAy4CZQLaAugBBgBg/3v/sP/a/5z/s/9wAIkAJABc/3v+Ff1z+9/6WvvX+wP9vv41AfgDugYqClsNvw9XEScTTxXLFvMYGRuJHLcfDSNmJMsl/ieIKqgr0yttLA0t3C1JL6Qw8TH6Mocz4zVmOew7XTzhPKw+LUEBQ+JCQUFQO8wx4CnzJlokfx5hE1QBe/HK78X0dvJr6uPkqeR+50jqGOwQ7GTsfu9z8Gnt/umJ6cDr0+20713v2upF5Wzh++Bp39jYudEszo/MBM1C0JHTbta62tzgT+f47Prwu/RD+kABhwfhCv4LaA6CEY0RMw4mCuUHjQfKBjQFTASPBCEFpgTIA3YDDgSAAz8Aq/0x//ECjAaCCSEMYQ2EDdgNFw3ICZ4E6P/o+5n3TfPc7krqZedr5mHkd98v28LZkdm/2OjXMNjA2ZDbgtzG3KndiN+/3xTdFNqG2S3bVt4H4U7isuKK4zPmI+k96bXnTeeT6CHqqesI7fHu2PI6+RL/fgL4A4AFGQgWCioKwQl+CToJxQj+CH4JKAlUCMcIuwkTCZEGSAROBLMFSwZcBPQBAAHPAYYCzQHe/7T+/v7H/7f/F/8d/8v/HADk/6T+Tf3X+4/6Yvo7+wL8v/yP/pYBcwTEBvcJ2AymDk4QSxJPFHkWxBiWGgwdMSGFJBUm6yYVKUEroiz4LKosWy3fL8YylTKhMXUz4zaUOII5DTyoPYk9iz12QE9Dt0LaP+84BC+zJgwlpCTpHIoNWfqN7yPx+/H27afojOOu4T/lieny6aro9eqB8DbvBOkM5qzoJuym7FfuL+1850biIuCr4E/dYtZE0M/MFMu8zOrQDtQO1xfct+Kn6KLttfGj9mz93QMlCWUMTw5DEcUTfxMpEBEMkgkPCXsIPQdTBmcGswZPBoAFuwTfBPwD5wCB/o7/iwPyB4IKaQyfDTEO/Q0jDJoIugOu/pP6r/ZH8lrtwui/5o3lb+Kj3eHZMtgG13LWmdbf1pzXitnN2hvbjtye3k7eVtuY2MnYCts93obg1+Go4pfkeOfN6UbqTulz6cPqL+z47AnucPBZ9SH8jgEIBDQFJgf3CUEL7wpiCiQKBQq7CZYJ4gnECb0JuAoACygJOAaPBM0E0gW/BdkDsQFKASQCPAJcAab/av47/tv+ff6t/Tv+3/7b/of+yP2a/AT7t/nt+fX6xfuA/FT+TgEABO8GagqxDO8N9g8vEg0UoBZLGIkZRx2cIjol9iUpKC4qRSvDLCAu4C3gLbouCzH2Mx80XzRYNqk4RjubPdM+sj1wPadA+0JOQoY/zjjnLuEnayWgIr0aJQsa9/3rH+7574jrVOWj4EHhSua76afoOOcv6hnuFOx26BXm8ua76mPu1+8i7PjlR+Gc32/f3dpu00LO38tDy/TN/9GW1N3XV93q4kPoZe7j8uX3Zf8QB5gMlg9qEVATIhXEFNYQ/AtPCkMKNAknCH0IwwhzCJsHLgbZBIEENQM/AED/4wDfBG0JzQx4Dt0O/A5yDv4L8Ad8Ahn9Cfm39b3xcuzS51jlqeNT4KzbStga1hXUh9OB1M7Ve9ee2JzYW9nX21ndv9tP2RvYNtjJ2nfeTeEL48jkNudq6RfrbOun6h/qHOv07P7tJO/D8cf3uP64AwUGLQf6CMwKIQxdDK4L7QrMCkwL2wsjDIkLVgttDH0MyglRBpwENgWzBsEFuwKJAL4AeQE+ATMApP58/aP9bv5o/mr+L/+H/zn/e/7K/WX8lfqx+Y36xftt/Oj8Wv+vApwFvQj1CkwMpg6XEdESKhThFoMZuRunH1cjrCRyJvcpFy1+LeosKi3ULnIxAjO7MxozDDM1N4Y7yztXPOw+A0DWP/U/xkA5QRc/bzp3MPokHyJ0I58fFRMv/3Lvnu0p8DbuOekV4zrfquFH6IDq7+ca6W3uQ++e6ffkhORA57Xq2ewo7OjmXuER3+LfGN591/7QBM1oy9PMnNFC1bvXl92f5M3pG+4o8oD2Rf0hBOAIKwxrD4cSDhXzFcsTUg/lC2QL/ApECYkI9Aj8CAEJtQi4ByQHXQYQBF4BkQC1AhIHvAonDMsM0g3SDYsLnQcUA0H+lvlZ9RvxMeyr5zfl+eNy4UXdYdlm15vV2dOT03HUm9V/1+zYMtlD2uzbTNyH2kzY3dYh2Hfb097U4Gni/eQQ6JjqKuwG7LXr+uxo7xnxa/Gv8uP2cv3bA1gH8AfyCCsL6Aw0DVAMiQsrCzELVAsvC8YKUQo3C3UM9wpmB+wEtgQuBkcH9wUWA9YBjgLKAgMC5QAv/6/92P0D/iT9Gf3I/cD9Ff1I/On6Jvms9933a/mj+iH7Bf2XAIUE8AegC88OdxAiEnwU7hYYGVsbBh0wHwMjsyWfJ6spZSr9KiEt3C1vLTIuui+GMUozzjXPNiE33Tk3PZY/8UCiQSNCD0N+RMVFR0GaN+YtzCatI7kfZReuCJ/zI+li7Orth+gl4mPfM99I4lXndugt5rrofu6e7drntuSG54rr1+0V7qvqKeWr4H3fAeDl2uHSrc0vzLDL+Myv0TzVpdht33/mvutK8Zf2NvztAmUJfw5XEbMT+BVWF04XSBTLD4wNiA2UDM4KUwocCh8JxwjtByIGigVIBGcB2v9vAckELQg3C/IMNg1lDWkNWQrJBSUBiPxB94TyzO706Q7lPeIs4PvcMtlm1vLUBtNl0VPRVtOn1cDWTtc+2NjZ/trG2u/Z1Nhv2OHaK94B4BTi/+RL6C/qIusk7FHs7eyj7kfwbfGd8vr0avpAAd4FJQgUChEMQA2zDdQNTw2oDHsMHQyuC2QLHAsICyMMRAyjCUkGmgSHBJ4FlgUJAwYBFwGKAS0BhwBK/yD+7f3f/cn8bfyK/cT9+fxC/Hv70vkr+Iv3ffjS+bL6kfsU/uoBvwWfCbEMuA6GEeQTLBVvF2UaRRz0HS4i5CX3JlgoBitfLSwuRS6RLukuEC+AMUY0FTRKNGM3ijtYPXs+mkHmQkJCy0LMRLRF3kOSPiQ1WSpSJLQjDx+CEEn68ulD6e3rfelT5PPdjtu34NTn1Oee45XlR+wE7T7nnOMU5WDpku1U8A3uaedW4sTg0ODB3HPUcs6Zy53KPcyq0OXT7dXJ25Lj5+jD7VPzlvlrATwJFg/lEjUVmBfjGTcaOhfjEY8OSA8ZEKoOeg3ADSYNqguJClIIQAawBE8CGABeAHIDDAhSCyANNQ7jDmoOyQvdB08Dwv4Y+oj1efHZ7MDnV+QU4i3ek9lX1sPUWNMy0q3Ro9F20sXULdfl1vzWHNmp2YLYjdfZ15XZdtxp4DbjauT95rPq1+xg7eXsLe1h7tvvvfHp8rL0YfjG/poFmghnCfkKMg1yDtQN+QyWDEgMxQwRDXsMugvDC8ANCg9pDIcIRQYPBp8GnQaGBaECewD6AIUBdADy/p39NP0k/d78W/xz/Bf9Pv2e/Lr7Ofr0+A74Bvhb+Xj6Hvvk/B4A/AOuBwYLaQ2yD/sRbxOuFboYHRrEG58fCyMsJvwnxSn2LFovPS9qL8cvUC88MPozOTbONBo2OzlxO8w85j9HRElEgkDxQMJE7kWZQ6k7JS+/JcAjvSLXGc4Fb+6+5d7qnevP5orgfdp63Hbk1+gz5Yrg/+S463HqJuTY4KnkB+up763vE+rI43rgAeA64O/ZntBizFnLgMtyznrS7tMW193eF+Zq67DwIvbG/TkH/Q19Ej0VxRafGB0aYhmsFcMQ1w+RETgR5g/+D/YPKg7eDMAKWAerBRoEMwE5APIBewUDCqIMNA2lDZcOCQ2QCYYFwAC/+1b3f/NB77jp4eQM4gjf2dqL113VK9PD0QPScdGq0FXSF9Wl1TLVXtaU16zW3NWb1tvXldkU3tniLuR75SfpZuzq7Rvuc+077Wnu+PDT80r1m/aP+yQDoAhTCioKagvSDeEOIg7oDEQMbQyvDTkOXQwxC7kMDA8pD3cLewcXBhAHdgfhBu4EewEYAC8BQgFY/7X9Iv1L/Tb9qfw+/GH8Y/xr/Oz7cvq9+J73m/dA+fH6wfvo/ET/uwIWB9sKrwxiDi4RcxMaFf4VXxi3G54dJSFYJZkmOyc0Ktwsci4GL4EvQy8AL5Ax0jXsNic3qTl2PfNATkJhQnBBx0FuRDhGskVIQkg6jy+jJzcl2iKYGsEL0PWI5oLn3uo36SzkvN302+vgZea35rnjZebK63DriObd4ZbiOeh+7rLvJ+oZ4yHfld6G3z7bdNJCzRjM6ctvzUTRFdTZ1hbewOV16gjwJPae/P4E1Ay0ETgUABZyGEYaIxpEFzcSiw8gEUMS+xDeD2kPqQ19DMcLFQnSBkYFdgLpAMMBpgRECLQLWQ2MDSgNFQxVCRwG4gH1/E74i/Pg7t3pO+UU4hffStvL13zVM9P/0WHSBNOZ0gDR9M/C0pnVqtY51+3Wn9YW11nYKNp522TebePb5ajmHuhf60jueu7v7mTwxfGF8w31Nfds+2oA6AX3CQYMEQyeDGAOug6MDR0NWgxSDOwMWAyNC0wMrg6kDxkNLglfBhEGUQeEBhgErwI0AqABRAGuAOr/2/7I/f/8PPyD+/z6dvrt+gb7C/qI+Hf3efdY+PX54fqg+9L9tgAUBC8INQu8DdIQBhMDFYQXkRmFGlgd4CEzJIUkcibeKagsRS73LVkuAS6ZLnoy0DR1M1s0VziGPHw/gEBdQ2BF4EU3RVhF1UTBQvI+pjZ3KdkgWCD0HnIUyP3x6G/mJuuJ6Q3k7dwb2A3cLuQZ5//jJeNt6QfuCesH5FjibeZD6/Xuy+z25aPgOd6o34XeC9aMzdTK98rkygnOm9Ik1cTaceN16lLw4PQS+sUBUQq0D/oSKhVCF1YaMhxvGjsWORIWEjwTdBGmDrIOFg7LCxALZQnaBoIFqgMMAb4AwgImBogJgQvXC2EMZQzvCaEGGANw/lv5efS07/nq4+Xh4ZDf09tG183UsNOE0ZjQ7tD6z4PP2dDK0p7TfdSV1pHX39Zt12fZcNq729neFuJp46blXOl06/rsB+6U7jTvL/E19D/2ffco+c79pASSCUULZQzcDW0P0Q9cD6wOtQ3UDbgOZg5CDHULdg0vD1UOxgrKBm8FxwUFBtUExAK+AH8AHQFFAJ/+gv3e/E78h/s9+nL5yvks+hD6Zfkf+Mb2E/ax9oP4Qfrp+pr88P/vA8sHZAtDDiMQORLcFNMWSRgKGt0cdCCeIjUk8SamKEMqES33Ldgtbi66LnkvHDJQNN43bzsePek8IEBERkdHjEaARkpFLUW4R8dFLztiLKwkgSQ/ISEXiwPv6x3jKugE6jnlh9572Ena4uO1573jWuEm5xzsjupS5TXhyuNj6rLvBPCN6dHhsN3+3jzf9te3zq3KA8pnyjHNgtEk0+fWiOBg6Ert1PIb+XAAVQnsENAU0xZVGPQZTRxvG30XmhOZEo4TVBMCEtAQoA/cDVQM9wkMB2cFsgOwAAIAMgJpBaoIxQsZDQMNjAwcC0YI3QQ1AKP65PXw8IjrzeZ74tHeNdua183UN9NZ0R/QktAH0VDQcs9Yz23QhNLU1RfYOdcI1zzYytkq3JjeU+HD4z/lZefT6Y7rae3s7aXui/De8sX07PV1+LX9DgRdCbgLuwz7DQgPKRCqDwwOIA0gDQMOKg6YDHALpAxmD1QPkQscB20EWARhBaIE0wHH/3X/zf/H/+X+m/3F/Gf8LfwM+7X5G/lT+UT5v/jC99j1vfTH9d33U/n/+RD77/3LAX4FFQmuC8gNDxDsEhQVKxfzGMMa3B61IgQk3yQ3J/8q3C3cLTguBi9bLnAvyzDOMSUzlzZ8PDI/1D9fQkhGRUe2R3JH4EU7RdFC+TtdMQknUCJ3IV4dvA4+9U7jvuS06ermFOGV2V3VEdzI5J7mweIh43fpF+u75h7h1uCB5tfs9+9s7D/lJ99m3ZLfV9zD0WfKXMnmyTTLS88n03DW291M5v/rBvFB9r38VwWIDcASqhVsGGIajRxoHVwa2RVQE0IUiBSLEscQFRDTDjoNlwttCJgFMQRvAXH/MwDUApkGvAp7DCUMBgwvC78IjgW7ATz8k/ak8f7s7edA4+TfUd1V2dzUttKp0RbQi89O0ALQDc4vzXXQCtPH0/fVQdfE1uvW6Nh52xzd8N6945vlBub86Bzsku467k/uGvC58aHzNPWo96r7dACRBmoLRA1+DfMNxQ8NEP0NJA3qDAMNnQ12DZoM2wvUDb8Plw0FCd8EHAQrBQ8F7QLbAOD/UP+g/1z/sf0T/H/7d/s3+975qPgk+ZL5Jvnp95v2G/XV9NT21vgS+t364vxyAAQEeAd4CpAMBg/QEe4ToRX1F4UabB0CIfMi9SNhJh4qHSzoLQwvwi5OL28w9jB3MyE33jgmObU7oUDtQ4FFb0axRl9H9UYLRqhFtz8yNYoq2COLIcYcHROH/8LokOLj6CPq8eNb3LTXM9s74qrlLuQn4qDm8uke6FPjMuBt4yPqWe8h7QbmW+C43eHe/t0e1krNW8q/yiLLiM2W0S7VENs64xPq+e/a9bj7CQM1Cy8SrhXkFlsY2hrUHA8ccBjSFFcUrhXQFK4SkxFxEJcOoAzkCeUGNgXYAqoAIQGZAtkEnAjJC0AMaAvICmEJTwZPAtb9qPgi8yjuVulJ5NbfFt2H2lnWVtMD0oPQKM8bzmvOvM/w0FPRcdGC0nbVYdg82Rba+9nM2YXbh95B4RnjoOWR6E7qrevh7IHtHu/i8Zz0OPZH99z5Uv/aBcAKAQ10DqoPzQ+SD3kPTg+XDusNFA6nDRcMCAsrDCoOkA3GCZgFcgOyA2IE9QM4ApkANwBWAKz/Kv5F/Sb9oPwr+0/5t/gJ+Qn5Vvi298L28vS486b0Nfe9+IT5VPtY/tEB1AUgCtwMrA79EOATPRYsGLIZpRvmHoYiwiSfJtAnzimGLU4voS7eLTEvijCxMQc0lTXrOIM9QD/GQNdElkY8R05J4Eb6RexGt0NjO2wvByfnIzUhghjVCEvzGeNT41Pob+YB4AfZA9Z83OTj5eS34l3jBefC53/ls+Cd4Bnnvuzo7kXrA+RG33veFN452mrSncoQyJbJyssUz7bSzNZ/3uLmiewd8nP4Xv/AB8UPKhTXFnQZMBsCHbod0xpdFyMWAhYnFn4V9BILER8QDA4aC6oHHwWjA7cBzf9YAFcDqwb7CXsM+Au6CvMJFAgdBT4BxfsR9hHxBuzy5m/iI96s2ifYqtQ90i7Rus83z8DOr82rzvzQI9HX0FjTidao16vYudic2HXb2t/J4jLkdOUH6N/r1e2a7szu++4j8XbzbfXK9jT5V/0sA3YJVAz7DMAOQBCYEOQPgw7pDYEOkQ7MDdQMsQwwDa4ONQ+cDB8J9QUnBF4EQwSEAksApv4G/k3+Pf4D/ar7YPu6++v6E/mM+A/5x/iu9772s/WW9Fr00vUC+Pf5e/s8/ZcAngRACIELmQ1cDzsSsxRvFrwYHRuBHmkigyWnJoInMCrTLJUuQS/TL1ov1S8QMvk0BDafOOU8nz97QnhDukSpR+NI90ehR1lH0UW6QEc1SSoYJCsisB2jDm31KeJe4ijndOUV4DLZm9Xg28XjxuR44RLhBef46Efkpt6/37TlMuss73bsUOVX4Lreh94d25XS4so+yYHJb8mwzI7RRNWk2+Djkurl8Pv25f31Bi8PsxQNGIsZSBtJHdkdcxsTGAQWSBZxFy8W4hNvE4cSbw95DEoJwQUGBMkBvf/w/3YCngVTCV0MXQyVCzMLAwl5BU4BSvwU95TxVOw25wTiGN6B2y3Y0dMg0j7Rs87szfbNw8z4zIHP8c/Rzx7SKtVc1vDXFdnN17XY1Ny04Zfk8OVB6JXrOu7176zwFu8z8JPzRPX79Vr4VvyzAhYK8Q2RDikQmxGlEQsR5A+oDoEOKQ95D6YOZQ2xDfgPmBD/DfsJ+wV8A7IDjwO/Adz/cP41/gP+D/0R/MX7tPus++36T/mW+Ff5wPkR+XH40vZM9W/16/bL+J36Kfzl/S8BLQXkCNELzA30D+wSKhW0FpcYtxpQHl0iziOYJeQouCtfLaYvLjEtMA8wBTKgM700zTYsOUs8kj/yQTZG6EleSYpHi0ioSNJGBkXRPc8vLSUoI+AhZBlPCSTyBuUa6Ifpt+P/3MfYltec3h7kveNL4IPh3enk6Yrj1d7a4Pbm9+uB7e/odOSD4ZLf9N5s2ovQVcrcym3Kb8mmza3Su9fk3+vn6u0b9K/5IABXCREQ4BO2FuUY1RrMHLIdDxzSGNUWfRezF+EVnxQvE84QtA7uDM0IfgXXBGcCUACgAa4DIgZXCVwLkQstC+YJ3AYzBBYAYPpZ9fPvIeox5eDgdt2T2tHWctPj0qnR2s5/zXPNNs6Fz9/Psc50z3rSGtY62FfYVtec167bVN9m4m7k9uWy6Fjswe5s74vvAPDv8or1cPaH9y76UP8JBnMLYQ0fD08RChKREYoQvA7zDUsOlQ73DcMM9gvLDMIORg8BDSUJqQWHBCsFIwTPARQAXv8f/3v+3vyy+737YfxI/Pf6Ffll+Fn5yPkV+ab3ivVz9Av1hvbH+H76Rvwr/zsDzQc1C8ANpRB7E+8UNRafGM8a3BtTH+EkDScGKN4pSyv4LF0wODE6MLEw0jFfNMU3ijihObI+XUNMRhFHdEf+SI5KSUssSx1JcULHN0osbSUhIiQcdQ//+EXkvuGU5yro9OHf2nPX4Nut44Plh+IF4LjlqOnC5SzhOOBs5XDsIvBc7RXnIeM54UDgjNyx1JXM9sm3ykDJRsqVzzTVRtxw5J/rkvIu+cX/DggJEAMVghfCGEAa3htOHd8c2RmuFsMWiBjDF8gVaxR7ErQPEw0BCl0GVgRAAlL/Zf+gAXcEWghMC9EL4wo9CqAITgVIAUr8wPZq8NvpxuSj4HLdz9po1zXTZdGM0YHPTs13zCnNDs8M0E3Pnc4U0KrTWtf01wjXNdZX2TneTuLl5Jzmoejc6xrvle/k7oDuQvEU9ID1zvZV+ef9fwRuC6wOiw+zEKsRRRI2EdcONg3/DJIN4A1LDSsMmgvBDAIP4w3ECZAFhwPjA5kDGQHZ/vv9dP18/f38ZPuP+pH79/yA/Ef6qviq+Ir5afkd+E32ufSs9JH28PiN+tv7kf7nAlwHFAtTDZwPiBOjFY0WoxilG+4dtiCwJCgnXidKKewsHi8dMFExADBrME8yPTNUNws76DsfPg9DNEZhR6pICkynS2RIEkjGR+9A/TQrKRojDSLAHCwQsfik4hrhR+i76ZriV9hg0/rbJ+bb5bXj/OFU5gfrO+gz4h3f5OPl67DwlO0M5nzhwOFU4nzdY9VIzWXKj8x3zDbNH9GR1vjeQei57nP0+fn8//oIKhGZFUAYYhlCGkUcLx6wHD0ZphbVFbAWNRa9FAkUixLoD8EMFgp7B3EF5wKB/8v+8AD8A7wHNgrxCZwIUAjSBkcDQv8y+gT1ie+06PriTN/W3GraStcW02vQZtDazkLNfc1jzp/P+NAS0FXPWdEH1jrZ5tjr1rfVTtho3fbhKeWs5rToEOwA8C/x3/Bo8Hzy2PWb9mH2KPmk/rgEqgsyELMQ+xD9EQIT8RHbDvQLQQvDC7gLhwvlCsYJwgqMDRsNxQiDBMYCXgOhA1ABjf6d/VP9Af2A/Bz76fn/+dv6O/sy+qD4vfht+fj4CPjd9ln1vfQY9qj42frD/H//EARlCZcNrRBCE/EVgBeTGJgafhw8H6EgJCPNJXYmeShvLawvvC8SMvUxUzFvMy025jdJOdU6LT6OQqxF6kbcSflK+0rKS7hLRUd4PW0w1SSSIcwfQhfaBubt692142PqF+b23uzYh9aQ3zznGOUh44rkiene6vzlM9/i36Xn3u3z7yjrDOW/4r3jPOPI3JnTGMxTy4HM5spYzRDT99hx4jvs+/KC+YX/AgVcDq4VnhcxGQQazRl+G8gcfRp5F7IVZhUCFlEVhBS5E48RKg7xClQI1gU7BO4B/v7l/lYBwQTDCOMKigkXCIEHrgTZADb9Wvei8S/si+Va3+zbs9mu1xnVV9Gwz8jPsc6tzunOPc+F0GnRNdBDzxPSb9X11ivXMNZD1ofavOCh5IHm2Od96lfuF/Hu8KfvKfBe85X2y/cV+Q/9xwEvCL4OqxCEEHoRvBLCES8P9AxaC4QKAwreCYwJMAlCCWgLAQ2RCrwGJwQWA9ICNAK9/xX9C/zk+9H70Pqz+Rb6P/tz+9361vnu+FP5fPkr+Fv24vQz9K70qfYy+QD8RP8sA1oH7gv6D4cSZBV2GPEYlRk5HcYfoSC8I1Uncyk/K7ctPy8bMAwzOTJcL7ovsDMEN5s5KzvDOmw91kINRiJIFUoFS7VKkkk3SINEJjkALHwmeCO0HJAQAfoO4svewOd66PTh3Nnj07HYU+Rn52rkEOIx5SHpReha4rjeV+RU7DjyifBw6Djk3+Qt5tPiVtrq0MPMy82RzGDMitEU12beiOdj7ob1+fz3ArQKxxOEGEgadhtbG6EbWh2PHKQZoBbgFLQVKhX6Ex0UGRO7D2EMiAmkBR4D8gEr/279j/7uADEEHwghCU0HqwZ1Bb8BDv4m+RLza+0452zh4Nzz2YvXLNXs0QLQTtCtznHNRc8J0bTPPs3Vy4bNftFX1qzWZdUd1NvTetm/34zj+OVa6XfqgewY8JnyYvM183v0/fW69q/2U/obAXYGTgtaDjwQQREeEbMQpA+nDQgL8gj6BwgIQAj0CHYJpwqMDHMLUQedA+ABLgJAAnX/TPw3+/X6tvrf+oX6mfp7+xz7hfr/+Tr5+vg8+Xf4h/cc9on0u/Qc92P5c/sp/wMDfgadCiMPiRJHFasYdRrMGwAdHR9PIPIhJSV+JzIplyrzLXcvBzEzMuYwMjGaMwo3ljdZOVA65DsnQktHUUpNSxpLB0vXS5VLXkT0NpwpzCIdIeUcpxEe+5Tj2t9T6KzoeuHt2WPVftiQ453o5eUy4yzoT+5q7HzkZ9+f5FTsvvDX76bpouWr5BXm9eOk2ybSAM0+zo7NBsy/0ADY8N9z6X/xZvdu/d0D+AvIFPEYZxqhG1McqRyPHRwd/hnmFrMU/xMrE74RyBHcED0OLQvBCLEFNQO7AY7/+f3w/rcBgQSzBp4IUgjMBtMELQGG/ZL4S/Ku7M3mEOHB213YEtao06LQHs+6z0fOG81gz3XRYNCZzsLOCtB+0XTVaNaG1azUEtYG2sHexeKF5evobeqR7Jfvs/Ix88ryifSI9kL4YfhD+38AlgVcC28P/RCEELEQmREVEIEMRAl7B2oGRwYTBpYFcQViBzIK3AnSBfoBywD0AKgAVv7P++P6gPoy+vP5aflR+bD6UPvz+pX6tfld+en5aflW+OH2PPXZ9Nj15ffz+g7/2AKxBswKfQ+FE4MWlxi0GR0bQx0MIB0i6yMBJ88oBis8LWst2DDMMpsxVjCbMfwy6DSoNwE5ETsRPHJAp0amSPhJHEujSg9KsUoJRm84Syp/Ic8gWh5/EgD89OGN3VTlOucE4sfYk9Kh1YniCejn5ZLjNOeo7XbsGeaq4Vnkcutf8gnyAeuK5vXkPudc5QfccNNjzrvNv82IzVDRttcL4KjpmfJ1+Nz9IQSkC4kVURsDHNIccB2WHu0f0h4sG60XSxUmFPgSGhHeECcQ6gxVCmwH6wN+AuEAGv6Y/Mb9eQCAAzoGqgd/B2UGxQRaAej8+PcT8irs4eWo37raC9ib1XrSuM8FzkfOMc0SzCbOYNDP0PjPpc42znXRO9bm2E/ZZtco1vvZWN8w5APnEOmC6grtcPD18ovzgfPu9Bb34Ph9+PX52/7TBPcKOQ/WEMEQDxE2EfQPiA0UCnYHJAbuBWUFpATqBP8GLgl/CKYEsgBQ/8P/YP+//B76s/gL+Zb5ivgt92L3QvmR+mD6ivna+Dj5gvrA+ln5wPeK9pf2svdp+Qb8GgACBJkHHQyyD8USUBe5Gv4aTBvbHNgeeSGPJA0nXykkK+Qs/C9sMtkyMzNXNQM11zKiMxU4kzqKO0c9fD/9Qh5I70trTaJMa0p/SetEQDjHKJUg6R/xHAQSUf2d5F3dEOXn58rj2drU0jzU7N/T5vPkjOPL5rrsCu2U5jHhDOXA6+rwZ/KP7PrmP+UT5wTmUt4m1aHOZ87rzpLNLNGE2EXhA+u48x76sP6iBC4NahZtG7MboRwzHgEflSCZINAc2BhxFu0UZRQmEnEQZxBbDWEJigaZA2UBwf9w/fj71Pwh/0ICZQUOB5kGZwVyAzMAY/ye90jxMuvf5Hfeetmn1rHUGNJez0bNUM0VzUrM4M2uz3DP4M7pzpfOcdDa1EjYHtkV2I3XI9uO3yXjmOZi6bPqEu2q8CTz9PN79A/2F/jp+f/52fqe/+YEDQquDr0QaxCGEJsRHxBaDLcI4wbLBZQEdANeA+MD3wT5BvUGmwPc/xL+6/3A/Tb8EvrH+Nb4F/l5+Gb3S/eQ+Mj5cvnR+Pb3z/cy+Wb6KPkc9xP2w/WX9p74oPvP/x8EdAfhC/MQuxSeF8EaHh1sHWkejh/0IVUmEynUKYkrJi5FLygxOTLuMEsx0TIOM7U0BjeNOd88Jz+XQChEoUcOSSdML0x2SkhKK0kRP3AvBiVqIN0dghQeABzkTNj+3uriQuI32vPRotOQ4PHnV+Qq4Unk1OvT7AXngeKg5ajsvvNy9nXwuOmI5ozn6Oa83mnVLdASz/HOMM4n0aXWbN4B6OzwDvjC/VIEGw1ZFwsf2yBiIFggbiACIkYiHx5uGXcXrBa4FZwT5RE0ESIOnwkeBq8BCv+R/Rj7s/kO+8D9ogApBN8GkwetBrQEwQH5/fL4kvJY7Ajmtd8e2pPWZ9RZ0Z3OLc2CzWbMisosyzbNT86KzhrPrc5m0IrVT9lR2QnZc9iL2kLfHORD6PLq3ewe70Dyv/RX9bT0jfRb9en2Y/jS+WT+kQQ/C+4PVREqEcEQDREbD24LqgipBqwESATmBGoEkwOUBK8GLAZTAnT+q/z2+7r7sPra+En3Ave89zP32vX79dv36vjT+GP4mffl99j5kfr/+Gr3m/ZJ9gj3GfnX+0j/zgPdBwIMAxEkFfMY1xwhHvgdLB8CIeIhHyW/KKMqLyzOLl8wMjEGM9cyMzLFMyczOTIvNjM6TDo4PK0/FkQoR3lJtEzVS09LeEsuSghBZi8IJXkiJx7WERf/K+YO11fdieK24WHZi9IK05PdKebb4mHgHeJk6KvqS+eE41zlEO3X80X3KvKO6i7oAOmf51PgnteU0JvOEs/5zc3PRNX33A3nXPAQ+G7+rwVTDu4XqR8pIf0fZiDCIJIhTiIgILUb5RhfGB4YYhaME74Row4sCcEEgwEt/rD72PnF+H75I/z6/rkC8AVhBm8FlAQmAsL9HflH83vs/uXH3+3aJ9d31MPRM8+szPPLqszvyy/MMc06zSPNPM66z/bQydN1103Zbtpg2+Dbi96/4+foPu1r7gPvZ/GR86j0EPTx86P1Vvg7+lD71f70BOkKExDYEZwQZg+QDzUPdwzBCAMHdga5BbAEHwQMBNUD5ASIBQkCgv3y+zT8pPva+fD3aveP9932ufUb9c716/ZW+C75Y/hU91b3y/i9+eT4R/eb9tT1dfX49yn8BgDrAusGHguHD+ATgBhfHOUdBh6zHqohwiP4JXkpvCvNLK0u2DHzMU0y6zISMsswtzE5M2c09jacOtA8gj8PQ4BHoEy/TIpK00kmSmNJSEbgO1Qrjh/eG4MZQQ7E9jbcgtVA3BLdL9vg1SPRd9M/3mrlRONL4Evk2upP6+fkbOF057Xud/S49nPxdeoD58Lni+Xr3AbTfc1ZznjOkM6Z03/aeOGl67n18PvsAJ0HQRGtG3chcSLBIlUjICQ8JYskrx+MGgYZTRjyFhIV4xJ8EHMMcQg4BVIA5Psy+s34O/dn+HH8CgADA0gF4AUGBecCgf/T+5H2Yu9G6e/jBN6r2K/VcNM/0BHN0MvBzArMvcpWzObNJc2szAPOgdAi08LVsdls2gbbTNzV3hvjAubH6aDtgu9K8TP0HPZh9tb1U/bf9/T4FvrD++wAEAcVDMEQ7xGXEZ0RIBFmD5UL1wfmBSMFRgVIBEkD2QOoBDgGCwXnAED9f/sO+0v6qPjh9sz2CPft9nz1JfQE9b72xfeJ9wj3+fZg9+L4+/ng+Hn37/ab9qL38flf/XMBawWBCKgLqRDrFVQZXRzbHfwdSSAOJEwl4Sf/Kl4sBS72L3oyuDJ+Mhoz8TKoM9UzODXmN9A5aDr5O1s/mUR1STVMxEuWShRLgk21Tu9HXTiAJxAgIR7wFwgIFPCC2G3Vet1s3nrZrdFHz8zVvd/x4TPeK97Y5OHpZuhX48jgNOfh7z/2w/SJ7ZPoNOck6HHjetl70PjMX86OzVLNVdFZ2NvhpOyp9Qv8vwHeCU8U4x1tIk4iWSJ0I4Uk4yR+I8Metxq3GuQaghh8FWMT+RDgDAQIVQMx/s76D/n+90P3mvht/LQASATSBREFEgSQAjH/i/rL9D7up+hC493dr9jW1NjR+86TzGTLS8tWyVLISsp0yy7Lq8sBzo3PadFA1IvWcdfS2ETb892D4svms+ss7/TwtfJE9XD25PQQ9Iz1yffc+Fn6Xf3zAn8Isw0DEf0QRhCrEJgQtQ06CVsGbgapBjYG9ARQBHUEXQWtBugEWgBj/Zz8G/xD+vL3xPbd9kn29vRo8yTzUPRL9rj3k/cI9033nPiM+bf48/ZR9iL2G/Uw9n756f2GAl8GlAncDQsUQxnkG3wcpxwAHoUhmSOyJNcn1iqnLagwEDLAMpQzUjSUMz8ywTDEMl01ejUHOXA9JkARQ1VKrkzWSkRK4UwCTCRLlEqbQKswOiK4HVYdwRYPBanqIdW51Mbajtwe2XPQkM6r1Sbg2+Li38bgXecm60Tn3uCG4Ibnpe8o9kz0DuyD5i3mWuaQ3zLWPc9FzHfNF86Lz0rUQNyW5rrwYfih/WsCegqKFLId8SK0I8AjYSXYJjQmqSPSHuQaIRoUGc8W9hMMEiUQGQ0JCaoD6/7d+6v5Lfhk93L5Mv3nADkE3gVqBJsC8gD9/Vn5aPMO7RHnBeG328nX9NQu0jXP9Myny6fKmcgCyTbL7cupyzXMtc1K0K7TDtcJ2erYFNrs20feuOFo5W3qIe6R8AHzMvVT9h72ePYA+Hj5fPlL+qH95wMZCesNvBGTEt0RkxG1EHQNtQgVBr0FggWNBJsDDAS4BHcFlQbNBHQAKP1W/DL8cvof+En33/eT9471NfQh9Fv1LPbf9tD23PUs9oP3K/i49/v2Hvbc9av1lfbg+Zv+DgNnBsIKxQ/QFAwZihtmHXEeeh89IbojpCXdJ6EqlC0GMBkxMDJ9M2k0qjMvM4MzLzOKNKI3iDs3PXZAIERcSK9KJ01VTktO1E3hTFlKckO5M5sjLx0fGvMUrQYb7SfUetIl3Crd0Nm10RXN/9Hz3WTixd5g3vPj2+li6D3i096i5VHvufSH863rCuXW43DlcOFx19DOScvdzEzNRM2/0c/a1OS97lr3ff5qAxgKXRRWHvMigCN0JCUlWiU6JqAljSGjHLEbZRuxGGYVDBP/EIoNPgleBIH/5PuA+Vj47Pdf+XT9qgF5BLQE6wMuA80Bkf7e+Rj0/uwZ55XhT9w22ELVfNIqz5PL/MkPylLI/cdZyiDLKMreylLN9M+f0m3VL9fV1vjX5dqX3RviQuZz6u7tcPAm88f1Wvef9oL2G/gk+hz65/qy/n0EnwmvDfEQbhEsEeURfBGsDiQKrAfVB0kH1AUjBUoFPAXUBZkHuQUEAZv+Rf6h/aD7e/ho9nT2mfZX9ZXzr/Jk8yH1ZvY49mX1qfUz95L4NfjE9uj1NvZq9lT30PnA/ZwBNgUoCpEO+BJvF6ga2xwbHu4eqCHmJaUmjycmKpQsBjCdMqAzxTPrMokytjM6NKoyqDMnN4o7Pz2/P95DXUbXS15PolCrTVhM1k0eTjFIbDrUKdgeTRxJGB8MsPPq1wTQsdh323HXZtAgzLbP+dka4a7emtur3yznWegI4tLcGOIX7bzzLfTu7YXmIeN+5FriothEzkjJT8vPzP7Lhc8Q1zjgOur686T7+v/5Bf8Q5huEIpck0SVCJhcmdidwJ6sjKx7IHKwdihvBFz0VQhNMDwILjgaBAEz7WPgG+Pj3iPgG/JEAKgPlA84DtANlAkL/TvtP9nzvFOkT5PDeM9o31jHTws/xyzXJz8iXyDjHnchGylLJ0chTy6nOJdET1EvWK9ch18DZgN0s4fDlfeln7d/vsfJz9Rj3L/fj9rj3pPjs+F/5gPwdAuMHPAzZD+0R7RHJEvYSVRAIDCUJ1wi7CEkHfgZuBnwGtQZ0B1MG/QHw/s/+M/7y+0/5Sfet9t/2Ffau8wfytfKS9IT1UPU89QP2Tfdc+Pj3l/ZO9Uj1XfVh9df3tvvy/+UDXgieDOgQhRV/GSocnxxSHdIf4CICJGcm8ig3K5MvrzELMbkx9jMaNa80UjTtMn0zYTfBOjM9HkCIQvlG80rDTbFOaEznS8hOkU+/TJ9DVTQGJYMdthxDFecCP+h+0SvQVdY31x7WptCxzbbSQd2r38/bv9zs4sXlUuIF3PzbqeTN7Uvz5vGM6rXjFOIZ4/fccNISzMrK48uHy9nMktHm2JjiGe1D9pj8qAGYCeQUsx8PJkgoxCdAJm0moSc+Ji0hzR0KHpwdyBsvGWQWWhOqD38L0AUU/yL6ifdW9x/4rvqu/poBbAN5BO4ETgT0ATv+XPrZ9K7t8+c54mfcitiS1e/RkM1xymjI8cfuxtbGeckRymjIaMgvzPzPdtKs1HLWkddD2cnbN9604Zvlc+pD7/bwFvPu9X33yvaV9u/2P/cn+Cb6HP5+A/gIcA2TEaoTihM6E1AS1Q9nDDIKawnmCQcKKgklCUIJDQm5CP8GKAMGAPT++f2q+7f47PeM+FD4NvZg83by4/Lh8/b0r/W99TL2FPfb9933Sfcm9y/3R/YF9ur43P0+AnUFSgl0DcgRUBZ9GaQaDRuNHNYf4iLtI/gk2SfDLOMvvDGJMwk0DTTZM7Q1VTRKM+40RDhFPE89Yz+5RWdKg0zjTsdQlU6sTK5MjUxqSB5BiDSzJTUdMRnbETEBYuVE0WfRs9UL1i/TAc5ezFLUw94o4Pjbptuh4JLiV+A23G3cC+T07Mryg/DZ6OLij+Es4YbZr86ByH/IWcolylDNkdP02hjkRO0u9lD8UgFmCuUVCiD0JZooBCiWJh4o4ClPJyMhsh0jHkQeFhzyGQcYvxN1D/wL6gWq/sb5HfhY+B/5h/vl/v0B0wM4BdoF1wQGARX9+fk59F7txueo4Ujcydhh1QXR28xxydLHZceMxvXGQMkUyerGQceNy1bQUNOI1WDXZdjc2SDdqeBG4yrnfOun7onw7fMt9/L3L/f/9jX3nfZQ94T58/1kA9UIvg49EkYT+BOZFH0TdRBuDUkLugqBC7YL2QukC+kKRwvSCocHCwNDADv/gP2b+nH46fdp+NH3ePai9O3y9/K59Pn10PWW9Xz2qPcp+Pj3effI9hH2ivWA9m/5Nv2RAFAEhQjuDHgR7xWlGEoavRv6HWggPiKpIwol6yfdK+MuwTAoMmQzYjMENMk2sjZlNAA0lTboOvw+E0NQRolIk0yQUIFQf08XTQhMQE0TSodBxjE/I0MdfBsHFDsDJugQ0OHP5dZa11rTDc2dywXSjNzH3/PcO9t33xfjkOBK253Z2uBh6ujwL/Ak6K/gGd924LTaH9CXyBzHTMl4yvLMv9EX2XPjZ+279TL8lQBzCEsUnR/2JYsnnyciJ5gorSpUKIoiax5EHpweEB34GfgWoBM0EHoNBAhsAMb6Qvj/+Bb6ZvsD/roAfAPPBCUFOQSgAcr9jfpb9aztvef44WPcgNh81SXRWMzTyODGJ8ZIxuDG7ciiyTPIpccuynTPN9Qt11LYA9kW2l/cC+Cp48nmb+oF7oHwzPJ+9d/2Ofeg98f3jfZ09rX46vx2AywJ/Q20EbcTZhUYFtwU7xElD7EN4Ay5DFYMXgtgC3cLGQx7C+UHyAMCAaL/bv5Q/Gn56fdQ+N/3OvYC9Hjy6/KY9A32a/We9MH1efcn+Ab4Mffx9Yr1zPUu9mX45PuM/4ADHAe8CqYPxBQNF0sYDRv+HCYfhyF3IrEi+CW0K14v9DCMMRcxpTEDNVY2RTbyM1gzXDYSOsE+TEJBRahId01NToJOsk/STYZN+U6eTXdGOju+KiwgnB2KF6EGkOp60QDNwtKj1a7T6M2jyx/RTNy/31ramNdI3DDged4o2vrXYN5O6RHy+fFD6dPh2N9n4ADb9s/Ix4DG+MjpyErKBc8T1RDeOeie8eb4qP7jBn0Scx4dJgIpoClMKKAoAyqRKFUjXx8OIBghAyAGHjAb3RZYE20QCQoNAoP7Nfi3+Jb57foe/iMBwANlBmYH7gXUAin/Lfx59+/vI+nT4sTcA9ko1iHRusv0yEXHecWFxBzFcMcEydjH1MYryQHO0NKP1pzYw9gJ2LraPt9k4lfnbOuC7gbx2fMG92r3lfY89sz1PfQO9HT2bPsuAqoIHA6bEWUUahZbF2YWIxNIECMPCA8ODwYPfA6XDtUOSQ7uDM4J7ARxAUMA9P6e/Ff6OPmo+ET4Rfem9CzyyfFc82L03fPP81/1h/em+Mn4ivhU95f2fvZf9qL3ofov/gkCogXECUUOcBLKFR4YfRplG8AcoB7xHwIhBCRzKXUtBC8PME8yUTV/Nqk4ijgkNSU0qDezO70+dkAnQkFIrEyjSwBMnkzVS5VLUk1aTI9Eqzi4K9oiXh3/F5oMuvbA2ZvOn9W/1zLWqNB/zaHP6dbK3L3bvdjx2V7e6d3g2PLUcNmU4wztZO9B6ZziPt8V4LjdcdQmy9THgMr5yrvKS85E1dfe5Ocr8G/4/f0qAx0NfxlAIkYmyCd8JzknRikZKs0lBCFqIJgh5SArHpYbSRkWFu4S2Q5uBxL/MPrg+Wj6zPqK/B//oAEUAwYEagQ6Aun9/Pru9wDxmOmc45ndX9kA11TTac5vyiLIHMe1xibH68hHy1PKP8idyITLzs4105bX0dd/18nYfN234hPnB+sn7VHuivFh9sX3Bvcv94j4EPgk94r4jfsjAeEGIQxlENMSPxXQF/0XWxSUENMPvRDqDwUN8Au1DXoO+w1kDtYMeQi4BDwCSv8b/Pn5y/j89yD2NvUb9ZnzYfK082H14vSm8970+/Zc9z/37Pcp+Mv3O/c190v5Mvxc/o4B1gVNCmQOSxI3FeQW8xjHGp0c3h6RIIkhjyQCKEosgTArM3UzSDNbNbc3qDcdN+Q3CTjoO9Q/f0HNQWhHk0xpTXlNAUuxSfZJ/0mMSi1F4zZ+JhUfNR17Fw0KYPTW2BjPZNal2APWvM5Fy8jPhNjW3JXZQdfd2vPd5NxA2QvVFNeB4bjsx+0f5irg195r4ITdqNSFzAnKAcvzy1POetEL1mngC+tn8tj48v5WBGsN/BlTI60msyapJgIoKCoLKqQlEiFNIBQh/CDUHhYcSRnuFqsURg+6BtP/tPs/+rz6+voz+2H99gCjArcCkgJwACX8r/g09cruwOdw4fzb5tjI1qPSzc18yvvHe8ZrxmXHXMkLy5LKM8k7yY3M2dJv1xvYNdpH2i7Zb9tm4bvnT+uD7ZHv5vFV9SH42Pmu+uv5ffiF+HT5lvzpAq0Jiw7YEbkUmhb9FykXnxRSErMQABCVDm4N5gzNDHINvg7cDRQKHAZBA1oAlf6l/Xn7jvkq+WX4Ufaa9I3zzPLe8hDzmfL58czyjPXd9x/4VPfr9oH3Nffl9vT4wfsq/n8BGwX2CPUN3RLfFuEYVBriGyceuiCHIRki9iT2KO4ssTCZMuozZzRQNiE4EzgmNgg28znlPOI+ZEFIRs9JUkzeTkNPJE6ZSwJMv0yZSIFCqDbOJsUeZB1LFEIBH+ej0TjRf9Sg1LHRAMz7ykHS9NtM3FzXg9aZ2tPb1tmL1rfVr9yI56nuWuyD5aPg4t8g4BTavs9ryfrJ/crkynjOL9NA2fLipuyJ9Br79gAlCRsUUx9jJr0oJSlvKOwpSixfKhAl2iKRIzwjOCL0IN8dOxmNFjgUigx2A0v9cvoS+jf6FvtA/Pv9XACKAkcDeQFW/mT7nvgk82rrEuXl3j/av9eD1KvOHcovyJrGosXXxQvH8MjPyEHH5sasySrP7tNT1XXXxdhD2erbXeAg5Q7pxuyE76/xyfTJ+IL6nfrf+mL6nvgd+Ur84wABBzoMMhC/E2oWOhg0GNEWghQaEgMRDRAxDx0Pag7rDU8OZA5zDE4InAR7AQr/uv05+3/4pPes9272UPTG8oHxnPGG8tDyBfJZ8hj0RfZe98D3Uvft9pf3Cvik+CD6yfzZAIMEewcGCzIQhBQZGLMaVxwEHs4f0iLHJBclfieFLN4wtTBXMt40eDWDN/c4xjk6ONM53jtiPQhBaEU4SqRM7E11TexMqkzhTGJMi0jzQ/I2ZyawHQUcGxftB57xiddqz2bTl9NS07rMYMi8zD/Widq/2H3XHtpq3bHc39fj0yXYXOFI6mLs9ObI4Qfg8+DI3e7UPM0YywzM98tAzirSEdi24fXrIfSH+6gAhAWoD4gbdCTFKOgpUinoKXkssiy/KEEk7iL5Ipgi8SDyHYMaIxe9FAIQ5Qc3AJ/6bfiq+Lv5Xvrl+jj9mP9FADv/e/wu+dv2ePOR7EHl9t5L2rTXNNXJ0JHLBMh7xV7EasQ/xdTHYsj1xkXHpMk2zYDR8tTO1jDYp9jb2gzebeJr50Hs2+8P8kL1ivnP+0T8Ov3S/GL7Q/uc/Kj/gQSOCeANRxJkFTwX9RfqFqMUKxJlEMsPhw70DFYMQAwZDP4LoAvoCOIE/QHe/6X97fqg+Pb3z/c29irz5fCw7/fvUPDx75XvRfA08lT0o/Wp9mb3nvf490n3DveI+A77if61Aj4GIgrrDgsUVxixG+EcKx4IIeIiviOoJZgoGCvwLykzejKvNHU39jcwOGU4dDgWOS07tz4vQXRDdkeBTE1PsE9GTWdKZUmbSp1LrkXmOFQo8B77HEwaPw4K+AjbsMwq0l7TU9ECzFXHh8mA0qnZMNiq1pPZ79z62/jWQdL91Abf7eiV7LTolOOI4KjhwuCp2LDPlcvPzO7NEtBu06zXU+Hj693z/PoUAPwDHQyxGIQjeyktK+0q4yo4Lcou8iuEJiAjAiKwIfgg4h6rG2oYxhU2EVMKqwIU+y/3m/cG+Pb30fiZ+jj8Hv7A/rT7z/el9cjyhezZ5ZzfCtpg1lLUftHhzAzI7cRgxIDEYMTwxRXHmsYox3vJZsz0z9PTYNc62cbZwNq53J3gK+Yt7FrwUfME98n7d/9yAOz/+v4q/vf9Bv4G/xADJQhNDdgRjRXkFzAYXxcYFdsRog/pDmkNQwvACnkLjQvLC4ELzQhlBZYCWv+K/G76b/ho92T3s/XG8kPw++737gnvgu4F7jDvoPHC8yv1W/aZ97n4EfkX+HL39Pg/+6j+1AJlBvsJSQ4OE3EXyRq6HLMelyDQIoolpScBKqYsGTGeM440JDW4NhY5bDmuOdg4QTrhPANAD0MYRSBI60qnTZNOTU+ETShLa0pTSt5GizqhKswfERuFFsAKd/R32GHNmtHM0d3PrMlSxdfIHNJp2FLXv9QG19Pb2tv619/TUda532/pTexT50jiguDb4RXg3dfrz+PMBs1rzQrQedNE2GThT+uF82z75gHcBiUPvRo3JT0rTC17LBcs5C0nL/IsTigfJVkjxyIPIkcgoRyZGFEV2w+7CKYBnfrl9jf3uPfC9+T4Ufrn+mH8Zf3p+oD2o/Ok8FPrf+WK31bavNZr1BPR68saxxjEE8MHw2vDfcVMxlDGW8fsyc3MfM9Z0oDVu9eC2Arbat6C5NTqyu6c8Qz0cfhq/cf/CgCu/rj9Fv7f/pD/MQH4BJIJnw1XEfATqhWuFm8W+hO9EC0P6Q6ADWoL6wo3C+8KsgqOCVEGMANrAdH+dfvJ+JP20vUZ9VPzCvHm7vTt4u5B7zTuIe6l7yTytPSK9Uj1VfY8+Oj41fds98H4RPxqACkEAghfDO4QORXhGM4brB3/HmghzCOBJQsn6CjPLFsx9zSBNoE2KTa4NuM5+jnkOGE7Fj1yPbc/h0X+SAZM+lA+UdRNrU4LThRL4EpxRss6FykgHZcZ0hSZB0Tyftg4zbLPXs+ZzuLJC8bXyMnQN9al1TDVN9fx2cTapdhI1Q3YzuBo6ULt3ekT5CvikOO736PWK9DDzXnNv8xJz/rT/dhr4nLsx/RX/YkDeQi3EPEbeyV4Ky4u0i1CLTEv5S/TLNQpgidWJPAhxyHCIEUcrxfgFMoOnwZgACb6a/WU9DT1tfW+9o74KvpU+8P79fld9uPyO+8p6oLkPd7D2H7VUNP0z+LKwcZsxNDDWMK1wmLEncQ9xdbHr8qYzKXOEdGm1HvXRdpI3D3eNOOL6anvsPNo9hr7NQCxAg0CWgD2/wwAUgBvAVcCqgS3CHAN4xGKFNsVHBYqFZsSbw+BDu0NIQxwClMKVQq7CRoK7Qn3BukD/wEn/6L7Qvj39e70OfTE8lvvm+w97AztZ+0A7RftLu7B8GLzu/RG9VH2K/hy+cb5U/kg+v398QKlBo8Kug6VEh8XYxvLHl4gAiFlIh0lAyd5KI0r1C7VL/kxJjZKN5s2/Dc5OfQ6njsGPe0/4UCKQ8BH4UsVTvxQ+VH+UF5POk1/TAFKl0OnNIIjZBmYFtMPeQDz5wPPxss90OXPpswSxcTDkMpa1HfXy9QF1ZXart5T3YbYDtYp22zkn+xY7DXmouJk4ubjKd9v1UTOEc6nzrPNZtCU1JzbkuZe8PL4PgFqBuULexaFIZgpky7ZL8wuHi/9MKQwOi1KKbUmciRmIYIfaR50GcgU9RFMC9QCVPyf9rzzJfSo9Dv0FfaF+Pn5efvV+kn3YPRA8lLtOOeP4dPbi9fc1FXSB84xyNDDqsJ7wlLB/8JoxQLG5ccsyvHKZsvUzlrUtNZ52EHaktzc4PLmhO7f82/1UflC/10CnwPKAnUBQgHJAWkCpAEQA2MGGguDD8ARnhMeFTAVZxPbD2ENSw1RDfsJDgcSCFoIpgfqCDMIlAQFAur/3vxb+Q735vUC9VPzl/DP7RPrLuuO7QDtW+sk7B/uOPE59OL0cfX49yb6XvoB+mL6//zyAfsFvQnxDU0SCBeIGzwfCCGzIksjfSTzJisoXypSLM8v+jJpNBw14TWkN1E52TrLOkE6OTrZPshEe0huSy1MA1E4UkpRXlDsTYRKxUkuR9U6vShgGnUX/hbcDRb37thNyJPMoc+dzgHIbsFvw2vNOdbT1rfUvdhA38Lg6Nzw12HYv98j6oHwbuwQ5ZnhUeRH5Ezcd9OHz3fQRNDB0KLUDdkd4v7sLvZ4/hoEzwcfD3EaGyaALl8xwzAVMRYzqDN4MWMtZSmiJWMifh8KHUMZDhU6E4sOTwYv//b3jPLe8evyAfIs8oP0vPZA+Kj4wvcN9dTyme806tbjtN2U2J/V1dMa0hPNdcazwpbCbsHzwDjDOcQUxXfGbckbzU7QhdM31xbaZ9vI3QrhqOWT66LxSPWR9/D7+gAPBfsFVgQyA/kCnQIXAqUC1wQnCI8Liw7UEQ8UDxT+ErUQGg4GDHsLcgrJB3YGggYvB9kHZAeBBD8AiP2N+9b4cfVP8zvzFPNt8Y7uCOxs67rshexN6y7raOzI7jjyBPUH9nD37/nV+4L8Vvzy/Or/YgQ+CDsLgw5uEoIXTh1mIIUhqyJWJAknYyl+KtcsVi+MMRU0zjVYNok2NDnuO0U9RT3fOpE8f0JvRspG7UfmTElPsk8IUPBNTEw5S0BKq0LwMHwftRhiFoYRMANf633QrMj3zlHP8MzKwwPAX8Yzz0zUUtTI1ZDbQOCU3/Daydct2yHjie3n7xLpdOTq4xDm/eMs3N/TJ9E00sHRi9ND1krcGuiI8r76bwLMBvUK6xTpH0wpZzDcMccwATKHM3UzzjGnLYkoQCXXIQ8eqRsPGNwTfhC5Cb8BRfv684/vNfAY8F/uxO9X8oHzE/Vj9q70LPIS8JnsB+jp4VTbWtck1RLT7tCCy4fEtsFhwhjBM8KyxAzF08XCx3zK0M3g0BPUnNgz2wPcQN+h47ToTe+z9bP4ZvsjAYIF9AeZBzIGEQa9BTkE7wMEBRcGcQhdDHUPWRFwErwRFxBNDjwMOwvVCowInAVzBOgEngUNBvYEmAFe/jz8l/oV+Bn1ZvQd9KfyU+/n683p9Ok46nzoIOiR6Rzrju7e8h/1R/Y++ez7fv0U/g7+lv+bA3kI7gw8EJwT1hciHaghICPvI+olKSivKY4qrisqLSMwVjPjNR41wzTONS03jjuDPS094D60QEhFJkriSuZMJ1DOUvVSxE8lTSlMkkpMRik7/Ce2GSgUhhHABq3xdNbqxkTK5cuCywjFyL8jwmbL09Q61QjUVNiO38Xi9t9l2+jc4uN27Bfy3O2Z59Lkf+Zl5lbfkdY90lPSo9Hh0j/Wl9op4zzue/dv//cFmwpQEggdbicjMBM0uTNGNOM1HjbHNP8wSizrJ18jnR/BHbwZCxRQEMIKTQI/+5D0we5a7UDuw+137VfvB/Li9NL2kvUi8+jwv+1W6ZPk/N7c2cDWfdR80hvO88f2wjXBqcDtwNPC9MK1w+rFGcmezWnRm9MA1z/av9ua3injV+g/76v16Pj6+8sAEwa/CWoKCgjOBcsElATbAwgEQwY3CJEKRA2eDyQROBFfELYOyQupCTQJGwjoBRMFKwWIBS4GgAVtAov+y/ve+Vj4TPW18vDx0fDM7h7sh+lu6B/p+uib53LnFOnl7BXyv/VJ9/D4/Pui/nIAvgAQAf4DswjkDDYQSBMRF18cEiETI9Qj1ySZJvwoxylfKqQsCC9vM1A3UjbSNYE2djhNPDs9iz3zPBE/dkSkSYdLFEz3Tf9Rf1P4T/5MzEofSkxJC0C8LwQeuRSOE4MPav7t4BLIg8XKyqfLNciuwdS/XcaR0ovXvNOQ1NPbqeCx323bPdqJ3qHoBfPX8+3saeb45TXo0OM72zXUK9N5033Tetbh2dLfQupv9K38LgM9B4YNnBf9IokuyjQoNSw1VjaXNsY1bzOeLzkrcibNIfEfjx2tF0gTwg4ZBk/9GvaC7q/qY+sD7LjrJuz27bzwXPMl9KHylfD27WvqBOYa4TDcNNjc1e7UKtISzPLFNsPXwWbBa8MgxBfDQ8RGxizKcs4V0aLUA9kC2wfdzuA35i3t4vSH+p786v/ZBHwJ4Qv3CgEJ0AY4BeQEPwR5BNIGRgmJC5sNdw8/DwEOIg3lCgQIZAckBwYFtAOfA3MENgb5BukDL//u+0v6LPkY92302vI08prwc+2C6mDoZOhe6A/n3+VQ5nvpn+4H88H1h/e1+Sj96P+8ADEBYgPnB5YMKRA1Ew8XRxx3ITMkJyWYJXomESlHK8YrcSyVLj4x1zPsNns3/zUxNlk4mjtyPaA+Fz9OQXRHN0w0TcVNy1DEUttSrVEtTdZKh0i5Q1I3TSa4GI8SFRB1BWbwKdNyxO/Ha8moyPDCL771v4zJL9NX1c/UkNhf3xDhWN7Z2iDdjOPt7G7z0u8f6uzmNeiO53LhHtkh1DPUCtOS1JDYZN3J5ljxZfnhAMQGUQo7ErYdACgsMVI2bjbnNv04JDkCN+gz6S4pKcAjaR8vHska0hRfEGQKUgGC+WPydutg6SfqpOlB6cPqvOzX7z3z+/LT8B7vvus+5yXje97I2QrXwNVY1ETQu8mTxHfCCMKxwqnDY8KDwf3CLsc5zJvPU9Iq1l/Z59kb3Dfhpuby7ST26/o1/qgECgxKEGsQWA6wC44ICgd3BWYESgUUCDMLtw1kD6gPWA9yDs8LtgcRBf4EfANnAUQBQALhA/UGpwbeARX9tPrG+Uz4fPUK8yjyWPH37gjskeie5r3mMeaE5Mzj2+Vd6jfwH/U++J36Ff0mABkCAQKrAnEFIwmlDSwRVRQGGaMfUiSkJakmqSY3JwkqpitFK5kr9y62M3824DexOFs5gjsHP/M/aD0tPQRBB0YZSjJL+0pSTclQgVNPUuBN+0pxSY5Gsz+MMPodTxSXEiYNJP1v4nvIMMQ1yErJ1sZdv6q9P8Qcz4PUoNSG1WDaK97f3ZXaVdmh3KTl6PE39B3uP+kF6G/piuaN3mLWA9VC1DPTRtd/29fhbux89YL8RgTiCFkNDRcHIgstozTKNec1ODgYOXQ4GTe0MiotEymCI9gfzx7XGTYUfw/pBmX9GPbe7e3nO+hc6CbnfudD6crrT+/R8PHvte6+61foJOU24D7b0ti61qjUV9KmzI7GUcRnw3nCyMOnww7C5MKFxH7Hzsudzr3R69VU2LjaSt/h5BzsRfNp+XT9mQL6CIEODRFgD1MNfwyTCt8HbgarBa8GtwjOCiINyA6+DuwMnQrjB4gFTgUOBDgCGwEbAZQChQT1BckEhQAp/Cb6Zfhh9f/yEfIR8TLviexs6PzlouaI5qHkguO14+jm/etk8Fb0vvc7+jL9IgCQAdwCIQXuCAoOchKNFekYnh2QIjwmsCdmJwoogCrwLBst6CyALnIx8zRUN803STbHNuo68TxvPnU/oUCZQ41ISUtUTVRQmlQmVg9VVVKUTaZMWEmCQ6I32COhFEQPkA1bBZjxiNUHwyvGk8h5xgjB+LqbvAvGM8/30gzUbdfZ3rjhZd/T23zcteB56iv0/PDh6dbmcuju6cLkVNoz1L/UQ9Oq1MPYq9xE5vbxKPrWAbkI7QpoEY0cqyYJMec2NTbuNzw8lzxrO+84KzOULSMoUCGtHj8cORZoEagLQgFP+UXz6uoh54TnBObh5B/mt+cu6yzvJu+o7XTsh+n05sPjwt0g2WTXs9XP1N7R2srsxQPEmsLPwtrDlsK6wZTCPsMqx23N/s8i07rW/Nd52+fg4Oed7132h/tHABMG2AxBEjwTdhH6DyQOmQplCL4GLAYICDAJgArwDCIOOA0OC4kI0gWyBA4DZgCM/5j/XAC7AlQF7AUYBEUAS/0n/IT5bfUK8zPxS++Y7fzoaORs5GDlAeRr4uPhDeN85/rspvAl9Gr3MPrv/ZcAlAGLAw4HfwtGEBcUOhcoHCkhViUrKBIo1SddKVUqjioUK8YqrS2SM9A1WzTMNbs35Di4PU4/MT4cPN4/bUWdSAxM5U/LU4ZUFFZDVoNTT08bTkZNc0WeN2ojAxUaDokLOAL87bLQI74OwhzEysOpwOu8yr2hxNbNI9EL0prUqtsb4I3eSNtg203hSezz9YHzHuyo5m3mu+bx4ezYjdOe1PHSINOJ17/cvuTx75n5XgFBB74KChIGHd0opDPrOGU4bDk4PBQ9cjz8OX416jAYK8Aj7yCXHeYWkREtC64AefcA8F7op+Sv5KXkT+ST5JXl9ejJ7JTtAu336xnqCeip4/fdyNm915vWLNYT0jvLVMZTw3/B5cHgwkvB1r+hwObCpcYWy0nPb9Sn2Mna69z54BPnWe9C+Dj9igEvB9gN+BKKE2QSRxFmDggLngjeBb0EhAZPCVwLMA2EDbgL0gmfB2EFRgQwA3cBmv9A/3AA/wJ1BfkFlQMZ/w38p/rp9+/zo/J48vzwqO3I6Brlj+Qk5Q3kuuHB3/PgZ+bI6yLv4/Ia90H7J/86ASQCrAMUB+ELihBlE+cVlBpfH4YjqSb1JzcoTChpKRwrCCvSKiotHDJJNXo1BDZ3NyE6Oz1rPrw9lT1uQXNGEEksSohOZlPuVPFVYlbCUulPak9KTHlJ6j8qLmsawxBkDyMJSfcf207CN77wwfPDRcFju/K6FsCdyD/PB9EX0dzVH9va3Lja1tgX21rm0fO39GfuWeeb5P3m5eQJ3YTVhdOn0gTTh9YS2tTgNur48zz9hQSmB+4M9BboIskw2Dd9N9g4EzysPYc+nTymNwk0ITCJKKUjJSEqHN0WwRBFBqz7Q/Mo67Dl+eOl40Dj7eFU4uXlBupy6/Xr8uq26FPoquWG30jbhNlh2CnYdNX5zrnJh8XdwbTB4sJawpPACb+Wvz7DT8jbzA3RwdQ02MvahNyX4erpevJd+bn/igW0Cz0SeBR0FJ8UwhKZDiUL9AeSBTAG1QcHCkAMKg3wC1UJcgbdBE4EHgMPAo0A0f4IACQDogUMB/8FfQFw/df7MvrO9i30G/Ro8+jwg+yp5y3luOQy5Mbhjd4A3qPhbefr67rvgfRP+Uv9LQDkASQDwQVNCrQOIhK3FCAYFB1lIusmkyiqKPQorSmYKegoeykqKpQsDzDOMcczqjYqOUo7szxuPZY+e0H/Q11GOkoMT2JSylXdV0FZFlpBVxZTSE19SWZFKj2jLWoZoQ4EC5gFzvZ02knAxrw8vzDATL5Ot9a1k7zeyUTRC9FC0bHV69pE3M/bm9kD2c/ijPFk9H3twOXZ4vPk1uNR3LbSXs82zlzQpdZR2h3g7OpK9af9EwVCCHoNkBeVI80w4jexOHk7sEBKQilB9D4eOuI0gS/XJ2YjtCB4G6wWrxCOBWj7gfQe7CblLON34gPhMuBJ4bzkBuky67jsHOwK6WXn4eRb4G3czNnT1+vW5NSfzxrLmsbAwZnBucPqwV2/P75GvjPBu8Wwy4XQz9MF2UrcIN7e48/tm/aR+3cAWQXGCfgPSxR4FWQVHRO3D2EMWAjmBaUGEgiqCNgJRQp2CTkIYQZMBeYEaAM1Adr/5/4MAbgEFgcOCO0GSATaAS8AVf2r+cj2O/Ry8sLvMusz5u/kXOXM4wXh7t4E35fhLebc6hvufPKI+N78Zf9nAuYFMAmxDf8RfBReFt8YjxzPICkk3CUdJkgm3CblJ1YoXynQKW4qNyzQLg0xITT0Nu85uTt4Pf5ALUXBRjRHcUppTjBSFFfkWHNY11c8WQRY0lI+ToxJSUTUOIomJBTYCRUGcgDc7j7REr3UvCzAyMAQvcu3cbcKvi3IE848zgXRmtdT3FHdZNo51+DYquXs8SDwU+fT3infiOPR4ZTY6tDRz/vOyNEH1t/ZUuI77a73QwBHBmsJmRFRHEUnJzOtOBo6TT0/QVZCK0JXQNw7Bjd9L2Yn1iPtH8AZkBTpDT8DHPoK8zLrYOVE4/bi5+EP4NDgbOSN6B7ruOxZ6p/nJueh5HPfSdpv15TXe9i11AHOxsgQxInB4MIkw4K/mby6vPS+08Kpxx/NwdHC1fza490f3znlO+8e95z7XgCMBVgMbxNIFpgWkRUKEyMQBw2QCAMG3AbNB30IDwo1C7wKTAnEBxYH2AYwBZ8DygLqAZkCtAQ7B4EI6QfRBGsB/v7D+1T4F/WC8gbx9+6O6tflmeRJ5aXkYuIb4FDfYuFW5ubqwe5m87n4//yY/xUC9gQZCOwLyA4DEfgSQxVgGUQe0SGTI7gkLyXWJTwmLiYTJo4mrCjtKx0wFDMBNUM5Fj3BP+5BpUOmRbtFg0VySA9O2FGoVHNY31lOWJJXMVeKVK5QRUzwRWY9Qy9WHjEPlwaAA9L8nejJyVa6qLp6vbm/FbsItmq1nrtZxNjK/Mzk0GnV89aN1krU39Kn1jLkjewR6tXiMdvi3LngMd8h16PR+s8uz3LTBtgn3vLmPPHg/LcFigk8DO4TMx1ZKRo1AznkOrY9HkGfQ25EYUJYPhA5ZC/MJwwkzx4rGpUVsw31Ahj6t/I17OjmNuME4k/got6t3zvij+Rd56Dpu+eT5ZDjON/A2rrWLNUS1oLV8tA7zN3IaMRMwk3DhMMxwSe+m7wOvmPCvsiBzx7Tqtad3E/gQOO76VXyvfj5/FwBcAYRDUUS2hRFFm8WEhWoEqoOeApxCAsJSQo3C5MLrAvMCnMJCwm9CRMJSwYhBBADBQKKAuQEkggHCrAHkQNQAMr9EPvj91T04vHb71LtSukM5mHmSOeo5W3iW+DE4BPj3Obc6mrvH/SB+BH8mP8WA2oGaQktDHIOERGvE+UVsxhNHGkfcSHxImwjTSP7I0MkLyU3JwYptSqYLfMx5TVwOUo7yT4TQsRD6EUESApJJEuSUK5TZ1TYVQlYM1rDWPpWJ1S7UKZMt0k3Re85jyc/FXgJVgTZ/ijvhNL1udW2cbuPvUq6Y7QbtK24GsBLxArFusjIzujRJ9Np0RTPDNCq28ToYulC46ba/dj83STgA9qw0KrPb9G91LjXF9tk4tfspPiKAdoGNglGD8IZqiVDMig5vTu+PaFAbER+RuNEFEBbPLY2HC+GKvolKCA0Gy8WCAymAIX3je/M6IjkouJL4C/e690X353hKuT+5U/kEuKu4WXf2dos1nXTwdSD1xbWxM/IyjLHxMRuxHzEmMKOvz++PL/jwanGTM2b0l/Vkdkb3CDdTuNe7T71dvn1/YwC5giGECoV+harF/8XDBaXEmkPBQ4KDlMOqg8fEE4QcQ/tDWMMlwsnC3kI/QVzBJ0DegMpBdcHxQj1Bu0DswEh/337BfnM9hL0p/Jx8R/uhuoE6k7qk+gk5cHiReGm4T/lpejJ6mvuYPRR+ZH7p/0IAE0DnwfVCpwMaA64EecVnRlWHeAf9CBMIfsiWCQ9JBokJyUrKEIs6y6qL48ymDdNPMs+FEA6Qm1D8UQ/SBpLHU1YUFhTtVUJWGdb4Vq+WRpanFduU7xOiEpSSP5AMi7uFxIKPAWR/kjujNEquFS0YrYjuFK0Q65ur6eyj7lMvl+/ysCmxQXLkczwzdnM/suY1/3mAOoL5JrbmNgH3U/grtr/0WLPPdBp1D7Z7Num4cXq5PQo/jsEsAgEDy8XlCLgMMo5yz1HQS5E2EbiSZ9K0EbyQo09hzb5MSctjSeCIsQbqxBeBJH62PEC6XPigd/93BTaS9kq2pzbL9/j4SPfRNy53CPcOtkF1pPTWNQ711jXoNN2z43L48dZxivGKcWPwaq+v76RwNvE3MoHz6HRUdWc2AjaY9+26cby5fcZ/SIDBAsrEzoYNRplG0EdKR07GmgWwRPRE58VExdUFpIUsBKaEN8O/QypCokH6ASjA9ACEQLiAugFHwiFB7QEYQEj/yD98fpj+Cr2ffSb8nLv1usv6orqXumk5W/iZ+Aq4DPimeQh53Pq8+6z8k71nvgf/GAAmgTRB2gKfg06EYcVPxnUHG4fViFbIvkieiM7JJQluyZWKEsqzytuLjAydjWlOWA9pj5CP/BCOkYiRytImUstUEdTvFa8WetZRFvFXtBfqVx3WDJVrlBOTI5GOT5ELeQWQAZU//D4F+7B2Ba9G7CQsV6zzrFsq2KnOacnrcKyTLcTvErC6ceGyu3LX80UzBLQct3U5P3gBNtW12DaX99C4OLYjdLU0U7UEdkr233f7egM9Gv/xgfkDEQQRBe7IoUv2jjAPFNAJUX4STNPJlIxT1lKZUYdP3c3TTFJKv8iVxzYE2sIjP1I9L7rWeR13sjZSNXQ0qXS39IF1d7Yk9qt2djZS9od2DjVJ9Mv0xnV49a/1HPQ+Mx6ylbJ58dSxvHDdsCsvse+68BoxZbKwM6Z00LYgdrb3U7mDPG3+Hj92gKlCV4Sjhp+H1kiECUcJ5wlrCFEHlUcZRtxGnYZlBdtFI0RhQ9TDvQMfgk3BEsAe/9y/rz9Uv+YAWECOAKkAK7+efwS+jH4Ufaa9GfyU+/G67Lp9umR6NPlXuKP3j7cY9yL3knhcuTk54HsX/GS9Wn5//wTAcsFpwldDHUPRhNzF8EbNh8nIQAihiLcIt8jHSUTJaol2SeAKhEtMi40MQ425zn3PBpARkL/QytGjknwTKpQ4FTRV/hXpVmdXeNdGl05XmpdnlcxU/9N3Uf9Q+Q+gzKMG1kFPPkF873qhNmOvDunOKbHqfioQaTgooyig6hhsEOyOrQUuafAasV0yL3KEsimyY3XLeWw5V3d+NZZ2Mrda+CM2VXRgtE31pbcMuBE4lzo7fKO/8sHsw1UEmsXpiEYL5w7BEJ3RmxMBlDGUcNTLVOLT3pLskR8PKk1SS8WKFogMhctC2z+rfJb5zHeLdhe1KbQAc4HznTOwdBj1WDXC9YR1fXWQdeW1N7RQtFw0y3XW9j41OvPfsueyVrIp8Y3xCTAtr2Rvea/FsRayIrNhdMc2RTdo+AH59fwBPyUBEkKsxAOGVUgGyVzKBor+Cx9LA0pYyOnH8EeMx6GHEYa5BcRFCUQ1Ay5CU8HZgSsAGP9jfvf+n37uv2D/8H/pP4J/Z77X/kI91L12fNd8gbwKuzA5wzmnOZQ5e3hVt6S28/aJ9uj3D7g7eSg6XftOvGW9X767v+NBAoI3wpRDrERyhRVGFccOh8tIXUiwiPYI7YjUCQ8JSQmDSfFKHQqlC2PMtM2ojt4QAlE1EZwSBhLzk2HT1tSe1QWVqhW5FghW6xbZ1xwWuRX5FQIT/BI8kJHPX03HC7JHsYJlvgw75rpKOGazeGyi6QmpBanJqdAokufDqCTpyytMa9qsiy5EsGBwoTDzsSOw9/IztbJ37TeS9q21nLZMuDU4wDfaNrP2ivfl+WN6aPuj/YAAcsMXBMfF/watCDFKUo0FD7sQmtGmEpZTgVSXlNRUuNO20jQP5I3eTDmKMshORr3EPIELPoV8Xbn/N7k2FrUR8+azOHL+cquzBTQ2NG/0ZDRktHZz23NR8tDzBTPOtEu0TnPIc20yy3Myst4yo/IFcbVxIPFdMiizMzRe9hQ39vkruhb7q31r/wlBM4K6A+NFT8dXyK9JV0qCi8ZMJ0u9CtrJqwhcCA1IGwddRreF/YTlhD9DYIKbAbxAiX/qfnq9Zj0zfRP9nn4Jvm29s30JfTY8gTxwu947szre+n75wzmXOVK5gjmWeO54Fbfbd+134Hhj+Te52Hrju4q8jj24/q3/4cDzgYoCrMN1hAHFDgYuRshHtQf8SBSIpMjciQrJDkk0SVsJ0woPCrYLeMxVja0Oug+VUM6R/JJEU35ThRPkFChUlpT9FN9VIxUi1OVUk9SdE9xTBlKJkiwQCA50jTcLRkmPBvBCMv06Olq5Wnf+9LLuzGpjqb3p96m+KEvnTKc85/hpDipiayfsb+6mr9MvxjBBMJyw9rOEdzL3wze4NtA38jnMO5N7APo5ubl6ITuD/Jb9YX7EwVtEEwXvBmlG7wf2CUgLgw39DolPmtDvkiSTTVQ3FBpTulI3UBkORAyzikBIxEcjBOCCRgA//aB7WrkvduI1DrNqshZxj/EbsSrxnDIeMjjyN7Jick9yM3F8MVcyRnOb9Ew0ujRHNJj1E/WMNYy1Y/THNJj0SvTFNbE2WHfh+Xm6trte/Gc+L3/GwXZCvYPQBTmGrshoiXwKBstrC+aL8otXypbJsUiYCEsHzobchjIFXgR5gzqCJoEBABz+7f1GfHY7mnuYe7x7kXwc/B879TunO3I6wfr/+tq7N7qHOnK58bnQelU6sboPOaf5HDkBeTq4+7li+kd7e3v1PLe9UL5av3NAQsGLgn0C4cOWRFwFVoZbxsyHLYdJiDoIdgiDSOrI78lTiiAKjsreSw/L6gyhzauObU8IkB0Q55FA0frSA1La0r0SU1KA0ojScdIukd2SKRJcEh5RotEgUITP5I7STZIMHgqESLxFbgE7vVU7armed/y0BG4aabpo8Sjfp+Ym5mZZZh2nPShKKeUrOa0U729vzPB58Q0xhzLLtlU5vnq9OmA6KnqQfNE+Bv1pPBT7truF/H488v3fv0RBvoOQBOkEz0VXBnAH7cpJzPsNyw8CkKZSHhO5FGJUnZPakjqPxc4/zDyKZEj9xw4FGUJrv6a9OTpit8111XPLch2w3e/iryTvS3CTsWPxSPGcMf/x9jHDsm/zDDSIdgF3P/chN3c3+Di4+Tr5Hviid4O2+3ZI9xx3yPiK+Un6qXuqPFx9Zr7WQPqCLIL1w6eE5kZbh9RI9Qm+SqzLTAtsypJJykl0CIRH6sbOhj2Eu0NVwqZBrcC8v5q+vr0wu+V6/XoIelh64/s3+s461zsI+587+fvWe8Z7r3tlO287NLrXOyB7f7tqO1J7HXqrumt6hTsQez07EjuLfBR82j3Rfs9/qgBuQW7CYwMYw2FDhMR2hTBFy4Y/RdUGVMcYR9kIVUi4CIcIxEj+yOBJZImaCeCKV8t+zC7Mzc3EzzQPwpBcUJpQ09EnEU6Rp5G3URJROZF40elR31FLES7QolBej+GO5E2jjD+LGgo7B7SEPD/OPI26lrl0tzWxtmszKPepXukyKAGnsqbG56JpD2oQqywsvq6PcCrwErCYMTFxk7QD9+F6K7pZ+cd5iDsjfQQ9DTve+yz63TskfCY9I75VQJgC2gR3RLsE6AWvRq/ITcrMzKTNU463j+/RUFLUk0ATYlIIkFUO/01lS5iKIckZx76FXEM2gFb9oTrquIz2qnRMss4xi/B+b/Aw2PHdsgAya7JI8oByqzJTcuU0JTXRd0J4IvhgOO15nfqVuxl6xbo6uNl4LPfqOEs5N7mhelw66TtnvDg9DD6rv8/BBsIuAoODvISLBjHHZwivyWxJmMlgCLZIIsgByBiHT0ZchWoEssPKQwMCAAFbQJ6/aH20/D07NDqqepd6wXr2en+6cnqA+zX7fXuRe9V793vOe8p7Ujtz/Bg8zfzW/K48Bjw6PB38Uzxu/Hg8vfyF/Jr8kT1Dvgm+nP8qP5JAD4BQgKZA5IFrAdnCT8KPwv7DfsQyROoFqYZExtfG5IbBRwlHWEfsSL6JTcoDisvL70z0TdtOwc/rkGmQoxCtUN0RGpEAkYeR/hFBEYcR21FWkIPQrRBBz/dOr86eTpRNWMx8S6pKhYkdBktCPb36e8t6nDgdstptKGqq6dnpc6i6p6FmoWacJ2jnzWkd6j9rlm0Q7aRuqy+8sE0yhPZPOPd5XLkR+TM65r0hfce9mzzGfJE9V765vvP/scEbgsbEKkRRxIrE8IVGB2UJqYsWjBfNeY640GgSGJM7kxGSqBGX0MUPlk4ATV/MLEpVyJcGZkOcgP/+FPuAeTR2uXTssz0xYvE3cX7xibH3MaIxq/GxcbfxZ7G1Mq/0cXYLt0d4M3i8OZN667tUO1I6jHmC+N54hvj7+Py5ZnoeerB6l3rte4j9LH5r/2FABQDsQbFDCkTVxj2HbsihyQ3JYMlUCWIJf0l/CTiITEefBtYGUEW+xESDgcKggXk/0D5UfMA8O3usO1N7NLq0+lv6jHsE+7/7p3v0/Cc8ZfwxO9w8CTyHfSQ9Tf0/fE+8TjxWPCt7tXslOqH6Afns+YF5+LnQelT69Ptd/Am8uHzwfVK+Df70v0mAMICKAf8C20QoRRAGCUbuhw8HjgfKiBxIewieSXRKBAs4S4WMnk21zpCPtY/KUGtQ/5FpUfwSAdKPkr5SpNLMEutSrhIfkbjRERDG0JqQKQ9ojp6OOo0uTDVK+ck1ByjEcICv/Fu5ZrfCdmnysuzUqTYn+6dZ5rFlsaUyJSvl4aYI5vBogysELMZtC+2LrneuxPDkNCN3QXjS+Vs5cfqYPRg+nH7xPkD+lz6KP2wAG8E7QrzERsXUxjOGEsZvxoUILgnIi7UMVo3vj3/QypLf1B2UnVRrU6MSiFG60D5O703EDMuLZMkWhqUD7gE7viX7Afh39e+zxrHxsDTvrK+G749vT68GbvFup+5vbgxu43BUMlJz2nUR9nA3WnjAunp69DrBuoZ57rk7eTD5hLpMOvX7V3vp++58Qv21/oG/4oCewSVBmoLsRHSF2wdWSPzJxMqRysvLOos4i3ALuIscipjKU0nlyPuHtMaCBZvEEYJ0wDN+H7ydu4R6+vo9ubG5I3ktuWS5i3mEOYX55voTejn5C7jr+QV5wXofudE5r7l/uVZ5E7iN+FT4H3egNy321vcHN0G3yfipuWJ6dHsgO5Q8a31iPkq/RMB7gRdCQIPmRSLGY4ediOnJi8osigUKMgn1ilYLIgtPy9TMio18DddO/o9EUAtQlVDGES2RX5GJkdkSIBKoUv6SHVG7USrQv0/WTwDON408TLaMBUw2i2+K78pVCVmIeEcaBXCClH+R++L4h/cK9eZzxy9TKlvoVefZJwbmB2U2JBBkgWU65UInVWmWrDBtOu1HLlPvU7CiM0Y3iznHOt17WnxpPu9BaMKHQn6B6sGKAc4CXML3hB/Fvsc3h8yHxcfrCB7JAEr6zGuMwQ2wTvhQR9JgU/+Uo1SsFAjTUtIlELfPXA5fTPmLQUmtxoZELkF5/kA7U3gz9MxycW/nrf+snyxp7GIsYSxiLGlsdyxV7JDta+7tMR5zCTS3dfE3bzkouwb8jvzWfLP8LzvTvAH8t/z5PXP+Ov6zPoC++X9KgK4BlEKSgxHDocSJBglHXkiVCiLLFsuni9tMPYvIzBWMC0v5iweKjYmzSElHU8Y7hIRDL0ERvzE8zfskuYh4lbeGdsr2GbWadVI1afVPdZ/13fYadg313zWz9e62vHcS90w3Z3dQN+54BbhU+Gc4ubjOONi4mXjJOXA5zjrUe5x8UT1VPiw+/r/rgTrBy4JrgtTEMYUNBjgG/kfECSdJ/AoZym/KU8qiioqKk8qQyt1LI4tNy+IMcczMDWTNSI1LDUYNqg3SDhdOMo5ijsiPC87QTn9NcEzZjIgLxIrBijpJngmxCYkJhAlniT9I9MhBB7gGoIXjxJVDSIGZ/mR68jkYOFQ2+PMUrcRqgmnbKTxn2ObppkknO6dFZ6cov+parKxuLy6pr4Pw+nHLNIf4kbu+vRR+Yr8+AMyDboS7BIUEogR5xEvE0ATABaUGn0eKCDlHYka5xhlGggeayIrJIgmLSs6MCs36T3lQWFDQkMEQTc+OjuYN8cz4S/rK5wlrRwfEyoJQ/6x8W7l8dnIz6nGP74LuTW3lbbItT61frXLtlK3w7dUujLASMmb0iraGOH15/XvDvil/RAAhwD8/z7/2/6H/kL/5QD3AvMDjgJEAY4B6QJiBP0FQQcECNYJ1A28ErUXnRxJIB4jZiTMI7EiVSKlIkgiyh+wHH8aXxeeEgwO1glkBBP95vTC7HTlpN/X2pjXX9W+0uHPo84G0BbSUNOo1N/WZdhR2QHarNuW3+zjb+bp53np8esJ7qXuie8j8RfyQPEI8OnvLfF68+j1g/f6+EX7Cf0I/zMBXgOUBe8GUgi6CtQNYxEkFaEYxBv2HQcfoR98H58f0B8iICQgNyAzIZIipyMCJZAmlScOKOcnTSfTJ8coWSlbKjssGi+aMGswNjEgMlcyyDFgL24r1io+KhgoHid4J8sopChIKBQnnyZtJpglpCG5Gz0ZfhVPDgAEa/lk8LPqE+ZQ233JMrnMspStQajhpB2irqDXoTuko6dHr723FL6Wv8PB08UOyVPP29o/6Mfw8fXR+F7+0QcrD6UQyg6aDOYJ/wlmC5gMRhAYFWUYXxgnF2gVVxQnFp4aEB3xHPEfNyV1LLQ1ujxKP3RATEEFQCI9BzlfNbgyQDBtLJolth1qFssPcAaJ+kLuseOs2ujR6MqgxezCacIww1LC+8A/wTfBrMG2xDbKyM/51SDdheLr57jvaPeY++f9j/7u/In7zfsN/Yz9T/4Y/hn8cvrx+tf7UPze/CL9FP4S/8kA8wJ+BVUK/g4FEfEQmxEVE5YUFxd8GOYX2xZ9FpsVohO3ELEMnAh/BO7/nfmb8lTtNuq15yDkHeAq3JHZHNnn2TPaBNvO3UfhuePp5Lvm6+jY6tnsIe3l64PsC+657iXwovED87bzm/P98knyZ/G08BPxL/Eo8g7zh/Mk9or5K/xH/an9Kf8VAo8ERwZ7CIEMTRGrFNUWrxgqG1Adjx0gHSUdfR1iHXAdKx5HHyMhPCM5JCsk3SS7JTgmMicaKAIp2iroLOkuyjH/NEU2jzXsNEQ0/DKgMA0usCxxLFQsJCsnKUAqwyy6Kx0peiZrIgYeHBk+FJcRVgsxAPTzWev16GvmBtv1xsi4nbLRq92ktqA7oNOhM6QWpCCoH7B2ubbAbsHCw13GR8ihzlnckenc8m34c/sFAigKXRB0EYcP0g0gDbUNNQ6IEasXoh6lIrkhUh6hG80aHBxYHkQeax/sIronqi+vOMM+XEEjQhxBbT+5O203ezPNMOUv5ixZJ1ggkhriEwMK+f3W8VPnAN5T1rPORsnuxvbF78TYwnnBML8svCm6NLt6v8vF6Mzr0hnZ4+Bv6uTxjfbz+TX7FPtw+mj6n/ph/LT+qv/a/dX7rPu4+2387vxl/Cf7/vpW/O/9dwA+BZcKDg7MEHARwhFnFDQX5xgSGVoZfRnQGBEXDhZyFbASIQ9wCo0EEv5l+AXzJO8v7BvngeHC3dDcU9xW23/b0t2z3/Dfwd9N4M3ikeXB5hvnkueX6JrpeeqZ6+ztNu/I7pDtPeyI65nrlewX7dHt++5Q8G/xS/NL9sf4BfoQ++j8N/8VAm8FLglPDq8TjBd0GaAagByBHmkfjh9uH/0eMh/KHxQhUiLMI1QlaSWDJMYj6yPVJHgmYCf3J2EpgSsJLV8uezCZMf8wczA7L8UsqSq3KYsoziY8JSskVSISIWghSSCVHf0bwhmDFoIUhxIJELcNQQtlBlr+HfUK8E3spedI3/fOzcFJvDS4+LPnscOwC7G2s4i0E7ghv5fHgs3qzZ3OHdGz1L7as+at8rH5TP8cA+cHFRD2FrwYwxdcFm4VxhQgFdwXBxxTH7Agph33F9QUUBJJEWwRQBB9DzgR5RWjHGgkiCr/LXkvzS9uLzYt8ypMKWYopScmJr0iLR4+GooU3AzrAjz4W+6k5bzdGNaL0CPNIMzGymfJgsczxcTDEcK1wpbG28uh0fbXDN4z5afuxvbF/GoACQL4AdcAOQBSAI8AeQH6AZkAnf4w/aT8//x4/aD8hfr490f28/YO+pH+owJlBeMHbArFC0QMcg4FEVASIhKaEFQPWA+yD7sPQg90DTAJgAPA/UD5pfUV8jDu6um65efhTt/F3iHfAeCO4EngI9933m/fl+Gx5AvnT+fH5q3nPOpw7FPuYPC98b3xKfE68XnxfvIW9B/1zvXn9s/3g/gh+wz+mP+k/2//tgDWAlAFhQdVCocOgxJNFe4W0Bf1GD4ajhqgGp4akRnAGIAY2Bi6GXMavRoMGhIZVRjsF0oXuhZfFzoY0hg/GWsaNBy/HuMg3SEyIlAi4iIaIycihyFrIQIhXiDXIAghNyHqITwi+SIDI3wjeCOQIt0hxSJvInggbiADH20aDhPvCssEqgG3/FnuSd3p0yzPT8rsxcjDv8K/wpjCHsEXw5HI685dzprMZ84E0J7TT9zC5+Pvq/W3+F35o/3bAzEHnQZKBfoDnwLGAsYD+QabCjgM4gkvBDv/1ftS+x79Lf4p//IBfgZ8DdMWMh/nJDkoqynGKmgr6yvZLFEu/C/PMIMvNSwRKfQlUSDFF94NagTc/Cb2zu6P6A/ktOGG367b7Ndv1GHRsc1gyy/Mss9I1bjaSt+Z5Gbs5/M6+d78Af56/U781frt+ZL5Bfp0+lH5MfdM9cbzfPJt8W7v6ezF6v7oJOiy6OfrKfDt8vT0qve3+Zb7vP3J/20COAUxB/YIPQv/DSwQwxClEGEQfw5gCnsFogG4/k78DfnH9RTzxfBb75btCOz76+vrqepW6W7oV+iD6X7rz+118K3ygfSI9aX2a/hB+hr7WPvj+jv6dvro+139Bv5a/iL+iP1v/Nf6gPmg+MX3Fvfr9tD2oPd2+Zb7q/0d/+z/FAAGABoAfwATAvoDtwV4ByEJlQtNDpoQABOUFDsVYBb7Fz8Zpho2HI0dbh/NIAAigiIRI6wk1iUbJnom2CYMKCAqoSrEKgQsNC2zLbstYy1rLT0uWS2MLYsuyi5SL4EwIDGiMDYwAi+jLQQsYyrEJkAeJBWMDNwFaABW923mmdaizbbFqL5Rufq2ILbOtNGyf7Nxt6O9DsL3wOfBfMQLxqPK5tRj33jnEO0k7w3ywPdd/HL9Wf3a/Bn8I/us+uL8XAFABbcFIQJb/RL69vcd+Jv5DPuf/TwByweeEOsZriF+J9Urui7HMOkxFzQUNrE4JztDPIw7IDmsNlUyOysXIusYmhBfCQUCj/rw9KLw8uzC6Ibj991+2J/SrsyqyZDJQ8yQ0FDUUtij3b/jbelM7hfxZ/J/8bDvHO+J717wf/Gg8Vjw9e4n7ZXr8Oni54Hlw+JZ4LzeBN44397iqObq6UHsF+7w8KD0SPiT+nz9EQIzB7gLjhAYFmsbuR8nIs8h+R8/HT0ZfhUQE68QcA0iCroHBQbAA5cAmf3x+l74ZPRu7zfs9eri6ozrdOxC7YbuHu8m7/fvwfDD8GjvjO5P7qvule848XDzfvVy9vz0PPLK78ntvut16WPns+Yb54Do8Opx7gvyp/Ve+I756fkM+1H9YAAvBK4H5wqdDkoTKhhVHL4fiyF1InojWiTBJRonoChqKgIsKSzRK0srpSrLKTEoVSYtJGkieSKvIvsiHiQXJfol+ybvJmcmZybDJVolSiXvI0EjEiS/JXAn3yeuKNopDynBKOEn8iSxIv4gEh2fGJEStgrKAzP/Dflf633ats8GyIbAE7sOtwK0aLUlt/i2troYwTXFRcRZw3zEh8W+x2vOy9cb33fmduu97ZXzkvmo+4v6w/i091737vfv+eP9ywGHBAAEQQAR/bT6yPkW+kH6SPtB/hAEWAwhFg8fjCUiKkAtky+3MQQ0xDXlNlk57judPKA7njryNwAzOSwHI9UZFhI1C2wDTPwE98zyhe3551ji6NvC1UXP7snax43I78pJzpzRaNa93H3ifOdD63ntRu7w7e7t3O1K7sTvffBd73Pu1+3L7DfsPusf6c3mcOVZ5Gnjt+P65SPpO+wk73HxwvOp9hD6xfy5/7IDQAhwDbgSXRjZHWsipCWAJzInbyXZIdEdkBo4F4cSQg2/CF0F/wLP/w38g/hb9V/xz+zl6AbmauQF42bi2+LQ44XlOOdZ6D7qhOuZ69Drduyo7W7vnPFg9Gr3jPmP+iL6G/mq95z16/L37wvuye2a7grw//Kh9pb5X/yN/hwAkAEHA0YESwa/CYQNAxFRFGUYmxzIHwIi9yL7It4iHiMyI3Aj5SNwJNMk5yRyJJ8jXyKRIF0e9Ru+Gf0XKxcZF60XyBiDGgMcyBygHaIeMB8iH00fNh/kHrUfkiGnIw8mgyg0KhwrQSvZK+8q8iibJ5YmHCTjH/QaGRT2DNsHPALb9oHmINnnzjvFcb4YuoO257Xftla2Lbc8vP7BU8PnwIPBqMK5wmPHcdBh2T/itunp7NvwP/dY+9X7/voJ+pb4GPeB95H6eP6sAZ0CI//G+s33WfVG9If0YfXr9jD6xwCHCrUU7xxTI3soOSw3LyIymjR9Nqw55Ty3Pkg/ij/XPq87gzbbLtglKR5pFz0PGAfjAN/7tvbc8Y3s8OYk4bjaCtTpz8DO5c/o0dnTndZU25LgTeUE6s3tm+/171fvPO4F7mLvvPD28H/wAvDo7qHuyu4V7uPsWetI6Tjn+OUh5i7n4+h666Dt6O6w8PPyq/Vl+cv8PQCDBBkJRA0EEo4X2xwzIdsjuiRNJG0idiCBHvcbxBihFHUQ7Ay5CKMDIf9k+1L3Z/Iz7YvoZuS+4QngrN6H3i3frd9N4Nfh5OO05dbnHer16zbuZ/Gi9FD4+PuQ/68BpwL2ATMAYP6x/DH7KPrO+bP52PnO+gb8PP1a/jH/+P71/Xj9wP2S/7sCJAYqCagM/g97EzIXHxr1G54dGR8zIM8gKyFfIu8jySRwJIIiByBAHgMcDhkMFm0TlRF1EGkPaA4xDuEOCRDmEDwRGhKYE34UmRULF84Y6hpeHaEfESIxJJsmQCmNK10tVy6tLsMtFy2OLIIqkSecIkIbShO7DFsFLPnZ6Q7eQtTQyvfDmL4+u+i6mrqyuR27xb5/wrjBir85wcPCT8S1yczRXdkW4sLowOuJ8K31Hfiq+Pr3VPcY9/f2wfjq/IsA9QIsAlT+lfo/92v0t/Jj8tDyf/Rh+C3/TAjQES8Zxx7WIhcm0imcLSYxDTVZOQg9xj/CQJVBl0ETP1w6NTPpKi0kcx7LF5cRTgztBmMBBPsl9H/tGeex3+bY3dOh0dLRj9Pe1UnZyN1g4uTm/epb7mDwhPGS8l7zRvQN9t33sfjg+HX4l/dH9w32ZPPw8K3u/etB6VPnt+a55vDmVedZ5+fn2eiU6WvqBO1871PyCPdU/KIBDgd9DBUR7xS3FzMZXhkvGakY8BY3FWcT5BB8DrkMpwl7BZIByv3p+aD1L/HE7HvpeOfu5RDlMeUt5irnXOh16ebqYuw87rLwz/MQ9zD6vP3mASoGVwklC9ULVAtkCpIIKAY3BCgDBQLpABIAg/8C/wz+Ef1U+9H4n/Y49Wv0kPSr9Wr39/kN/fL/lgI2BXgH4AlGDIUOIhHeE8EWphkaHIcdnh4RH54ekRy0GRkX6xQKE5URAhDlDrgOlQ6dDsYOmw5cDjcPehCEEBERDBOyFf0Ycx3XIIYjvCcKLHMvajJuNXU3Szm/OmQ60Dg4OG03ozF5KX0hthm7ELIDxvPh5dXa7tBlx1/ABb2auvC3m7Z1tz+5tbszun64K7svvSW/tMQ4zT3Wtd/y5ajpUe+u9Cv4nvgd+ED4CPiB+Fz64Pza/2sC8AAr/Bv4h/Rd8aPv1O6Q7l7wnPT1+qoDtQxTFA4aUB51IuQmFispL3szwjgjPsFBZUM/RTJGI0WVQbY6iTMkLrIo5iHiG7oWzhGzDB4GAv8l+Hnx8ume4u/ck9kj2ADYvdii2ufdsuGZ5S/pDOy17eDu8e/h8DLyyPMR9f313/bz9tT2R/Zx9J3yyfDy7R3q6uYs5Pzhp+D030bfnN4W3tjcHdx83dXeV+AD5I3oJe2G8vL4Kf/gBTYMtRCQE90V8he8GLwZ2hoGGkUYvRa5FOURFA/fC2sI6gQ1APH6zPWt8n/wPu5J7NjqQuoA6s/p8uns6p3s+O2T75DxbfQn+C38XABmBKQHXwnUCegJ2AmmCNgGWQWLA7sB6v+l/cX7Ofpe+PX1evJB7yPtwOsm64TrKuy57T3wvvK99QP5Yfxv/68CFQb5CQMO6REKFpYaTh4IIeIi+yOJJHQkXyN3Ia0fxx18G1QZ6BcAF/wV4hRNFOQTcxOiE7kT3BP5FCsXgxmtG6QeRiKaJW0pniz1LtYx3jRBNow38TjMOVI6SjgfNAst8iSiHtUVQwbK9rTpn9yE0rvJksKmvuy707dotfO0N7b1tSyyELJdtK+0t7d1v3/IENNv3K7hvebL7AfykvV595b5gvz//XsAcwS1CLMMMw6hCsMEov87+jr2/fKX8PPv0fCm80D5bABsBm8M9RDDE6QXRRz+IBsmniz4MmU4jjwyQNhC6UNpQ84/pjqPNlQzwC4RKs0lnCHdHAgXlg+dB7v/Fvez7d3kSt4G2pjXSdYo1mjXMtmw25HeqOHI5LnmIuiq6fXrpe5j8dnz/fXf9xn54vkg+kj5nPhw99n0v/Fz7lvrBOlR557lzuPj4TzfZdx72s7ZBdmn2Yrc6t+a44vo8+5P9qX9sgNlCIEMUxCoEyUWURhiGqMbDRwBHP4aFBnFFg0UKRDtCuIE/v6v+QT19vAv7YPpKud95TzjteB731bfkN/k3/PgueMc6AbtIvJQ9wX94AGJBCoGzQfeCLUIpAgdCVUJCQlpCF4HJgbRBGMC0f5I+5P3i/RP8qbxY/HP8Qfz1/SN9pD4CPsg/df/4gJhBlMKtg5xE44YtB3sIUQlgif8KF8pEylcKLcmISWrI5YhvB9IHmgccRpdGOcVwRPxEQ0QUQ5PDX4NYA4pEJUSkhTNFgwaUh0iIFIkuifJKSMtri98MR00oTWPM7stCigKJD0cEg9TApH2Fes34vPYd9DjzMnJ3cSNwJq+xL0ju4u2FbXOtX61Vrd0vMzDw82+1oDczeHx58PtIvIA9Wn4efw7/2UCcgahCh0PxhH2D0wLbAYjAbP8D/kF9hL09fKw8xb3ovtQAAkFHQgDChENtxByFKsYlh7uJJkqPS9aMy03jTp4PC07BTgmNkw0UTEPLjkrXii6JLkfHBnnEfkKJANF+m3xAOpo5KPgFt6d3G/cFN0t3rffuuH347LlUecI6ejqg+1/8EPzGvaY+LD6UPx2/Q/9QPz4++P6QPgp9UDyk+/d7FvqEuhU5abhLd4b27vYsNdY18fX49k53TbgiuR56sHwwvaN++D/6QNuB+MKXA4BEVgTKhUxFmQW4RXYFKATWxHxDZ8JiQS5/6j7oPeH8/XvzexS6t7nKeUn4/zhZOEQ4RThquII5s3p4O2S8pn3JfzL/50C/gRFB4MImgkIC6AMnw0QDkMOBQ4BDf4KgwiaBYYCif8q/X/7rvoq+sr50Pli+gz7G/xA/cn+zwDvA5cHjQt9DxMUPxndHcEhqiTCJoMonClUKeYouCgOKAUn+CVSJFIiPyD8HR8buxeOFBgSiw9uDegMvwzmDCwOUA9BEfoT1RVpF7gZLhytHrogUSN4J7spcSjhJMshWx+bGBcNFQKc+Crv6Oa/3zvaj9eq1BLQicsryfDGfcEgvFi6C7l3tzC5173bxJLN9tR42Q7et+Od6HbrQu4i8p/25/qa/5wECwqFD9QRCxCMDDAJQgV1ARD+avtN+jn6ifv5/RkBhwTfBkwIkglDC+AN3BBEFF4ZDR9hI0UnTyupLkAx5zLSMTYwKzCFL04tXisqKl4oYSUGIXgbyhVGEDwJPAHM+evzOe9063zoNeY75TXlSeWe5ULmE+cH6NPoXun66ZHrYO0a74jxtvOK9Zn32vgy+b355/no+P32ZvQu8srvs+0e7ObpIecu5Cbh0d4i3Q3cmtvV2yDdGd9J4aHkUOkZ7nDygPY7+pH9AAHoBBsIuAoJDaYOFRBoEQgSOxJBEkcRmQ5SC6YHVgTuAEf9Lvne9Ajxku156q/ng+Wl48ThROBj33DgzeLw5WTpAO2J8GL0G/hO+2L+HQFmA44FrgdFCpwMpg7+D28QDRAtD2sNcwvrCaAI9wYpBeEDUQMLA+cClgIdAiwCfgL/Am0EvAaxCQ0NhhAmFOgXcRvJHoUhrSMKJbslyCaNJ9kn7SdMKNsneiZoJF0i7R/ZHN0ZCxfeEyARrg9DDv8Mtw1uDkMOCg+lEGgR7xFxE3AVoBfVGT0crxsiGSYYlha3EDMIYACy91fvk+iw4p7eMNwq2jHXutPk0RbQo8q2xMXCOMBYve29kcCsxWbM8dIz1w7bs9//40TmeOiR7BrwzfMy+aj+SgTICYYNow0nDDkKlQcxBGEBsgDD/+f+JACrAmUFfwiXCkcLNgz7DaYPeBE4FJUY7Rx8IPsjlSeXKuIsGC6KLZwszSy/LC8r2il5KY0oDya/In0ewBnWFEgPlgjiAaT8SPgS9M3wse7C7DzrHepx6RbpGOkj6fro7+hM6fbpb+pl6+XskO4W8A/yG/SS9cr2/vdt+AT3afSZ8qDxp+937Tzrj+jn5dbjFOL74LngOODd3oXebuBO4uTjVeej67fujvHw9GP45vw6ArUFzwcaCmcM/Q2gD5URDhPsE5QTPxJ8EKoOXQwZCTQFOwDX+gn2vfFM7pnrh+h75Q3jZuFp4MLgtOEj4/XkXecA6pPsle8385n2T/n++5/+uwEZBckHsQlaC0IMjgyaDJQMkgwPDFYLnwouCm0JswikB50GngWmBEMEuwTiBX8HbQnLC98O5REqFTIYuxqNHX4fFiFNIwglZSbCJ7IoHSlUKQYp7yetJpglLSQIIRwdzRrLGRMY4RWOFPISTxEcEUAQBA8dD2IP+A5ODycQJhEQEpITkhTdESYO0wu/Be78hvbI7zfoVeM63/rbDNsc2obYeNbe1HbStMzJxpXE3MLbwB/CGMVGydLOwtOJ1/baUN5H4aDihOPW5vDqIu959ej7LwE+BmMJIAqrCbAI3wYPBcIDnwM1BEYFnwe6CkcNjQ8FESIS9BLwE64VWReBGfMciyBPI0AmACm+KvAr7Cx/LOUrOSwfLBQrUyoyKmUpNycgJPcfVRu2FqkR2wubBpABC/3F+PT0A/LQ78ftNewZ6/Tp7eg16KPn8uZ95mfmJ+Zr5g/nZedD6JLqauyS7cjuVPB18XnxxfBJ8F/vTO6f7OTpb+cK5tDkdeMn4wnj4OLT4g3k8OWW53HpYetS7VzwMfRU9z37cABmBAgHRAkzCyANBA9CEK8QdBH5ETMSHBLdEeQQpg55C2QHegKV/Sb5pPT28FLtw+kX5z/lKuTM42njYeOa42Hkg+XR5qXoBetx7Y/w9PNJ98j6Qf7fAOcCoAS1BY4GDgeRBzUI3AivCW4KEgtOC/cKtQkZCHoG+AQEBL4DAgQpBfkGcwmBDJgPrhJHFYQXnRlbG74cjB5uIF0igSQPJmYnMSgAKR8pQyhFJ8clfCM1IZIf1R0/HIUakRmTGPQWmxVIFFYSLhEVEDsOlQ3pDT0O9g4XEDESrBOOFBUT0w/lCnIDUPv+80rtPObK4Hbc89md2eXZKtq/2KXWldIGzGXGx8IkwL+/ecJYxR/JS85300jYhtwl39zf1N8N4CHisebZ7FT0f/s3AdYFhQjiCTsKeAnpB8EF+gPdAwsF3Qe0CzsPthGzE08UpxQ9FWoW5BdYGUob3R38IBQkRSdfKQ4r6yvdK30rAyuJK58rbCv/KnIqVimVJzEl5iH2HVoZmRSUD+UKUwb4AcL9evmv9ZLyUPB67rbsMusN6jHpfuiG547mvuWA5HPjB+MR4/fjGeZR6JrpZeu07FTtue3i7eHst+og6Ejm8eQ85ILkoeSp5BblEuXS5MvlC+dG5zznfud66Fvqde3k8f/2xfuS/7ECMgXCB7EJsAqyC6sMSw1PDvIPSRJhFCIVZRRDEs8OhwoJBs0B9/0B+o320fMJ8v7wAvDS7uLtieyt6ufoq+dL52XnP+ii6aPrZ+6g8a70rPce+t/7cv2B/kr/EAAtAXgCNwRPBn0Iegq0CzEMqgtgCnUIiAY6BYMEewQEBVUGYAiVChcNLw+OEJURQRK0EnMTyBSgFu8YWRvoHUsguSKZJL0l1iUXJTck1CJeIbQgKCB2Hz4f7h4rHm4dOxx1Gi4YMxYPFM4RWRCFD2QPYg9AEL8QpxB8EW0TJhT0Ew4SngzYBdv+oPgk8uTq/eSG4Obc6tuS3Cjdnd1A3BnYT9L5ywPHhsMhwqfDAMaPyMXMxNJl2AvdHeD/4H7gT+A54YLk2+nR8Cv4mf3iAYwFKQj3CZEK8AhuBjkEZANDBGMGxAnsDAgPwRDCEVASHRMBFAEV8xW/FjgYZxpeHfggXSMlJZ8mtidiKH8oqCjqKA8p/ChiKHInJibpJMoiAyB8HAMZQxV+EVUN2ghYBPz/1ftG+Ij1GPNp8QLwyu7c7fjs9etK6ufnZuXT4lXhzuBT4friDuVT50zpX+oZ63jrXeqs6I7mQeRe4oPh+uHV4orjmeSP5fDlN+bA5brkMOTx497jUeRC5nvpv+1h8vf2yvqX/e7/WgHgATwCvQLzAzwGTAnmDFsQTBM5FWQV6BMaEUkNAQnuBG8Bz/4g/WH87Pvx+oz52/en9fnyO/C77dbrx+qa6hfrcuyY7ljxrvPB9ZH3+Pgg+i/7Ovw8/ab+NQAoAo8EKAelCYULhgyGDPYLhwrXCGYHZQatBZYFIAZBB88IagqNCycMkgy3DJAMoAxlDWAO1Q8gEm8UhRbGGMEaMBweHVQdFh24HGYc7xsUHOIbvxvoG84bkhvFGosZ7BdWFmUUohLEEUER4BDQEAERfhEWEqwS/BLlEg4TJRODE+kUUhahFIUOWge9ATr8l/YC8aXrqecg5W7kvOT65dzm4eQA30DYutIDzpvLUMzjzkjRENOZ1ZPZmt4d41jl6+T94+rjGOWT6JjuivUc+5H+JABtASwD9AS/Bf4ECwM8AYUAcwEWBM4GVQnXChwLFAtYC3UM1g0KD/gPgBBFEX0SrxRSF58Z4hrWG9Ic8h0XH1ogiSHKIpsjciM6Ii0hnSCMH2YdwRo4GPgVihO7EAkNCwk6BccB3f6W/On6Y/l997v1f/QB8xfxwe7I66zo7OU/5IbkAuad52Dpzuqh6yLrPOoV6vTpeOjX5bfineCn4Evhc+I25HDlSeVm5NbjmuOM44rjteMD5GjkvuW26K7tGPPO9uj4O/r6+on7WfzW/VIA/wJ+BfwHvgqpDTwQkxE4EQYPsAsxCHUFlwNAAuUA7v8x/wH+ufxm+//5iPij9r/0B/Pm8X/xoPEm8jvzO/RM9aP27vdR+aX68vs6/Z/+IACiASYDqAQVBrQH6AjzCWoKMgqQCaQI1QdNBzcHeAftB38IFQmvCWIKZgtaDO4MLA0VDQcN3g3GDvIPOhF7ErkTVRUrFgsXTRgQGQwZuhjDF4EWshU3FdAUGxQHFOwTBBPvEVERRBDGDjoNGww7C10KBQpFCgQL/AuoDDoNWg6XDqYOtA+5D3cQmxIwFJEVPhiiGm0ahxYPEaQMZQUX/Zv2yu+u6dzmm+W25drnd+lt6X/meeJF3o7Y9NMM01DTqNOR1kXax96P5Fjp/uyn7kXv9+/o8CrytfUS+l7+PgKoBI4GbwggCmgKSAncBj8EQgHz/VT8nPug+xX8//wq/qD/oAEgAxoEhwQ7BBoDZQK3AhwEZQZhCQsN8RCGFEAXWBkPG3Ac6RxkHKsbhBuzG+QbYRv4Gn8aIBmgFpQTgQ9HC5kH+gMtATX/vv2C/O77w/u2+0z7MPpY+JD1C/LG7o3sFevW6njrHuzb7CXuZO9e8H3wDvCC7njruOiY5i/l8eSV5dflYeYb57fnLOgg6PvnZedQ5hrl1OSy5QXoOut87kbxvvNS9Rz2hvYz97T33fdp+PH5L/wG/8MBGASvBSwGTgXIAwkCXADn/oz9jPyg+/f6pfq0+r76j/oD+jT5lvgO+IX3P/c395X3Rvh2+fH6kPyL/nQABQJXAzkE2QSKBUMG4wbJB8cI/wmNC3sMNA2xDakNMg2BDJELCAubCgEK2AlFCv4KzwuKDFcNFA6oDg4PNQ8jD/QOnQ5eDnQOqA7GDikPiQ/HD4EP4w7EDWUMOwvBCYEIsgevBrsFPgXRBFAEwgObAscBRAGRAEUAXgDXAGsCegPpA/UF4weUCFMKjQsnDKMNGA4gDjkPsRAxEpgT/xRCF/0YFBoiHDUdKx2JHIkZsxTfEagOwAqRB8gD7P9P/dH7aPpr+Zz4hffi9JDxwu8N7o3sXOyH7PPrRezZ7KTtYu9Q8ZnyGPMg8/bzNvWX9pT4gPpc++r77vvx+rr68/pJ+pb42PZS9UT0g/Pl8uny4PLA8mfyjvJN82v0RvUa9v32ifdC+P/4O/oX/Br+AgDsATUEgAaqCHIK+AuIDZEOaw9IEDQR9RGeEqoSbhKFEmQSGBKrEewQ+g+SD28PyA52DgEO8AzfCz0LZAqWCdMI6QfuBe0DFQKo/0P99vv++q/5BfmB+MX3YveZ9oL1OfTm8ZnvKe0068fqiuqi6UHpK+mn6CToeOfN5rHmbeYf5hXmEOZ/5kTn1Ode6IvoIOgU6Dvoh+ht6W/qdOvb7Prt6O5u8EnyofPF9Or1kfbC9pH3WPjw+DD6e/tn/J391f7k/x8BYQI3A8gDJwR7BPYEtQUmBsIGXgeqB5sH2QcrCIUISgnOCSAKiwoxC7ALWAycDNoMOg2EDTcONw/VD1MQqRCYEEIQ2Q95DykPJQ8UDwoP2w7ZDoEOrw2QDPcKawnLBy4G5gTEA9QC+AHVAJb/bv5h/R388frC+aD41/dH9/X2BPc597T31/dA+An5jPlF+kP7Kfzs/KP9if7c/4gB+wK7BPkF/QazCPkJkQrwC8QN4w4nEIYR0xLuE60UIRZdF9YX1hhtGgsbfhy1HvEfPyGIInIjCCRkJBgkRyOkIVMfYh30GogY6xUjE2MQ+Q2iC3EJHgfOA5UAEf0m+TL1B/JB793sw+oK6YDnWubu5Z7lIuWT5NjjROP64kzjA+Qg5Q7mveaG5xjo1+i76avqFese6yLrt+rU6lfrA+zd7OrtzO7x7zHxn/Il9H71cPaJ9w35o/pr/Cf/9gG4BIsHIAoGDP0N6A+hEQATbxTtFYIX/RgUGgMbtxv3G+gblhvoGhkaZBljGFsXYBZcFSgUQBNNEtoQtA5IDBoKBghxBaECCgCI/fH6G/l/9yT27vSJ89jxie+i7SLsPurI6LXndebd5Avk1OOp40Tj+OK34ini0eGR4Xrhz+El4jPiG+JK4qjiOOMJ5FjlVua75oDnbOhQ6Vvqp+vr7JTuffBR8n305/Zb+XP78vxY/qL/xwDyAUUD7gSzBm0IWQrwC3ANBg9lEDAR1BEpEkcScBKyEvQSEBMKEycTRhN5E28TIxOLEsYR6BALEPYO/w30DEgMbAu2CqUJhQggB9YFWAStAicB/v9G/4f+Af6r/R79wfxK/N37L/tm+lf5f/i69yn32/aP9nD2gvZ89m72JPYq9gP2//UB9vf1sfWQ9V32JPey98H4vfly+sn77Pwk/uL/3gGVA2cFkQcYCuoL4A3OEOcSlhSuFkkYXBkXG8gc6B0VHyAgjSDfIGAh8CETIici8iGuIRQhSyDWHzwfWB5OHV8ccRuJGrAZYhkMGQkYgxY0FGgRZg9fDZMKHQi5BWADdQGz/9L9z/tR+XT2Q/Mv8LntfOtv6TLoMOcE5u3kUeQi5LvjG+Py4m3i6+FY4iXjEeRo5ermJuiF6X7rku1070jxT/PB9OT1Rfex+AX6d/vL/C3+qv8QAXwCtAPsBEkGDgdgB/wH3AivCakKkwt/DCYNkA0oDosOeA6BDmwOQw5yDtcOBA8EDzEPIQ/RDocOTQ75DXoNyQy+CwkK5wdwBvgEGANIAV7/LP1o+9r54/ez9Y/zF/FK7tjrZeqL6Y/oHOgB6EjnkOYA5lLlWuV05VDlUOWs5WXmiue66Lvp0OqK617sTO0J7truzO9U8OXw/vBh8RPy5fLI84H0TvVN9on3f/hl+Un6Yvtf/Ev9Wv7k/3MBzAI/BIoFmQaZB44INAnYCUcKVQqHCu0KKQtcC1QLGAt0Cu8JRAm1CCMIIgcFBtsEAAQDA2sCvwE8AYcAkP+8/vP9V/3F/Db80/tz+zv7JftM+4n7mvuN+xb7ofpL+hT68fnt+SL6Sfp4+sj6Nfue++b7/vv8+/D7Rvyr/Cr9uP05/nb+5/6M/xYAXgDTAGMB+gGvAlwDKQTfBKIFUwYkB0YIEQnvCR4LEwyeDE8NLg60DmkPHRBzELkQRRHhESkSyxJQE8UT/RNIFAEUyxOqEzcTyxItEnoRLBE8ES4R4BDiEGMQlA8lD8IO1g3sDN4MUAxOC50KmwoPCmcJIwmeCP4Hvge0B2IHJAc/B4wGlgXsBCsEBwMhApwBpADR/6T/Wv+2/nv+P/4+/U78nPvQ+tT5ofk++eT41viK+LP40/gm+W355Pk3+pP6BPuc+y38dfyg/N78x/wH/Yr9zv1c/jX/mP/R/zsAQwAOALn/pP+H/1T/O/9Y/wL/rv5s/uX9Q/37/LX8dfxE/E78Uvwp/P77wfus+6j7xfuy+6z7xfsP/Cf8Dfzf+1b7m/r1+YD5B/lz+N33WPeh9sP17PTr8/HyHfJt8brwLfCt7zLv++4J7wHvGO9q7xDwkfA48ejx9/Lv89P0//VF91L4afmb+sP7w/yE/Rz+kf4M/1b/a/99/7n/vf+H/1r/Jf+i/hr+Pv2G/Kb7BvuF+gn6mfk6+db4RvjF95X3WPdD90P3ZPem99X3KfiF+DD5xPli+t/6Nfug+xP8c/yi/Lv85vzu/OL82Pyr/Jj8r/zQ/HX8NvwX/Pz72fvD+7T73fs+/M78Y/3z/YP+Of+5/4MAXgEkAvsC7QPXBMMFjgZUB04ILgniCU8KrgrrCh4LUgsiC6EKCwqQCe4IgwjbBzkHoQYBBn4F+ASWBCME4QOyA64DwAO4A/oDMQSFBNkEGwWEBeAFcAYeB9MHbwgXCToJZwm7CdYJ3AkUChIKCwrYCbUJfgkNCZwIOwiXBxQH/QYIB9AG7QbUBq0GswZyBoYGwAYYB3sH+Ac+CMUImAkaCnYKugoYC40LvgthDOYMRA1+DeUNBQ5BDokOrg6mDrIOqA6TDkUO3g1lDcUMRAxOC24KyAk6CbcIIQhqB7EGFwYbBVAElQPQAv0B3QAQAED/lf4B/pH9Af1d/Nf7ffvr+qf6Sfrc+V35B/no+Nj49Pj6+Dr5RPkT+ej48Pjk+KL4PPg6+AL4qPeN94X3jfd791L3GPen9jL29/W/9Sv10/SU9Aj01/PG87bztvPN8070rvTL9PD0SvWU9bX1C/ZB9rb2M/ew9zr4u/gR+Zv52vka+nj6sPqp+tP6/PoC+yH7PftM+2b7Svsn+/P6nfpq+vn5m/k4+eT43Pjw+PL4/fgJ+Qv52viz+Mn4t/jF+J74s/ia+Kz43PgF+Sb5+vjN+H/4GfjZ99X3l/cr99v2pfZ+9ln2JPbO9Xz1TvUw9S71a/XH9R72pfY59/z32vjE+ZP6dftt/EX9KP4X/wgAvgCFAU0CAQO8A4MEIQWeBQsGagaZBq0G2AbnBgYH9Qb3Bu0G0Ab7BvMGKAc9BxwH3AazBroG4Qa8BpcGpwaXBo4Gugb3BhwHAQfhBrwGgAaABmoGPAbwBa8FnAVnBQkFvwR7BD0EyAM5A7kCcgIVAqYBQAH4AMcAywDRAM0AxQDLAMcA4QAxAUwBmgEVApQCOwPrA6gENgXSBXQGHAe8BzkItwg4Cb8JOQqVCsoKGgtqC4MLxwvPC54LlwuHCz8LLwsvC0cLKQsOCycL7wrVCtIKzgqNCk0KFgrxCbcJawnwCIwIKQi0BxoHpQYgBoIFxwQKBDIDmwL4AW0B2QA1ALP/7f5a/uH9PP2U/FT8PvzT+0j7CPu8+qf61/oC++X69fo3+z/7GvsU+/X6zPqn+q76rPpy+j/6Sfou+jL6CfoF+uT5zPmp+bP5hvl6+Xj5evmr+dz52Pnr+TL6JPos+v35EPou+mL6gPrM+vf6Kft7+7b75vvw+9v7y/v0+x/8Ifzw+8P7qvue+6L7gfuB+2D7Vvst++n6wvpe+g762vnA+Z/5mfmZ+W35QPkN+Q/57vjB+K/4a/hc+FT4WPhC+Bv4CPgC+Pr34/fT96z3lfeH93T3wPfu99X3vvfL9/j3H/gz+G/4t/jk+Bf5lPnt+U/6wvov+7/7F/y3/AX9k/3t/Rz+nf45/8P/OwCoAAoBZwHWASoCaQLMAkkDsAMnBJwEFwVfBacF8AUuBpsG8wZBB48H4wc3CGIImAjLCPYI/gjkCL8IjAiSCGkIXggECKwHXgcSB6EGMAarBUYF6gR1BBIE2wNNAwEDuwJpAk0CHwIqAv8B6gG7AZYBmgGeAb8B2gH6ARMCKgIJAvoBBwIVAhsCJAImAjoCTQJbAmUCWwJIAlcCaQKbAs4C5wIwA3QDvAPhAxoEbQSeBNcEKwVvBZgFqQXsBQUGPgZlBoYGqwbMBtoG7wbpBu0G1AatBpEGNAbuBakFewUwBegEgQQWBO0DvANcAw4DtQJVAs8BsQFYAS8B2wCDABgAz/+J/xn/vP50/iT+wP2C/Tz93PyA/Dr8Efzd+6j7WPsp++36pfpV+gH6xPl4+W/5U/lb+U/5Uflb+VH5RPkh+QX56via+Jj4qPjm+P34LPmC+av5yvnk+R76WfqV+pf6p/q0+sT6Fvsx+337w/vh+yX8bfyG/Hf8e/xp/HH8a/x3/F38QPwh/Ej8bfx5/Er8Lfzy+6D7nPt/+3f7lvuR+4X7lvtv+yf7FvvK+tn6l/p4+kv6Nfom+jf6N/oe+kP6TfqV+sT68fol+3X7f/vL+9H7Avw2/F38ivzH/Nz80Pzu/Af9KP1T/Zn94/0o/kn+cv54/pH+rv68/tP+vv70/h3/d/+1/+L/CgBSAJkABAGaAToCtQJiA9sDZgTJBDYFsQX7BWwGvAYBB3AHyQc3CIcIrgjaCOgICQn0COgIEwlhCWcJCQn2CKgIogiiCD4IAAh/B0UH+wbxBucGwAahBpEGcAZXBh4G0gVzBRsF2wSkBHEEPQQpBNsDqgNkA1kDKAMQA9gCmQJhAjoC+gHWAbkBjgGSAYUBnAGoAX0BmAG3Ae4BRAJlApICuwLnAi4DgwPEAx8EbQTBBPoEQgWIBbsF2AUDBioGYQZZBnAGYQZwBkAGKAYTBhkG8AW5BYoFWQUrBdcEjwRBBOkDsAMYA60CQALcAYEBKQHHAGwA8v+k/0L/tv4//vv9mf06/QX9tfx3/Cf83fuq+4X7Zvs7+x77/vrC+ov6Wfoc+tL5fvlE+S75H/n9+P/48Pgh+V35Wflf+W35kvmb+cL55PkF+hT6NfpD+ln6mfrV+vz6Fvtg+2b7hfuW+9H79Pvq+9X74fv+++T73fu9+3/7b/t5+3X7UvsU+9/67/rt+uv62frQ+qz6dPpg+mz6YPow+hj6HPoq+iz6+/kF+gv6Dvrz+RL6B/oe+i76R/p4+o36nfql+qX62fr++lT7dfu9++z7Rvxx/Iz8lvy7/P/8Vf12/YD9q/31/Sr+Sf5Y/kH+S/5B/i/+Rf5g/nL+m/7C/uP+AP8d/2v/tf/6/zMAiwDuAF4BuwEfAowC8QJeA8ADKwTJBDoFpwXuBUAGhAbnBvEG7wYOByYHVgeDB5MHpgeHB4MHZAc7B0EHVAdJBzUH/wbvBtAGnQZ8Bl8GRwZFBl8GfgZfBjgGMgYoBioGBQbqBcUFsQXOBaQFjgV5BVAFDwUABbAEngSFBFQEHAT4A9UDlQNiAzcDEAPWAsACygLUAswCuwLCAvcCIAM7A14DmQPfAzMEbQS2BAYFVwWnBccFFQYwBnwGnwa1BsIG5Qb/BvcGBgfaBqkGTwYkBgkG3gWSBXMFQgURBdsEpASWBFIE+gOjA2ADCQO7AngChAI+AgkCswGUAWEBHQHFAGwADADD/2v/+P6w/i/+wv1D/dj8PvzJ+x771fpu+iz62PmO+R35AfnL+NH41vj2+Nb4ufjw+B/5afmv+ef5Sfqp+vH6M/tY+8v7D/xh/G38kPzB/Oz89fzm/Pv84vy1/Jz8nPx5/Jr8ffxO/Ej8NPxd/ET8OPwV/Av8+vvb+9f72/vX+9H7v/us+6j7i/uB+3X7Xvtc+z/7BPvt+u367frM+rD6j/pi+iD65Pmp+YL5cvl0+VP5TflR+VH5cvmO+bf5u/nG+cb5Dvok+kn6avqC+pH60/o5+3X7sPvJ+w38Kfxp/ID8oPzH/PL8Mv1T/W79lf3a/ST+lf7N/vL+Kf9e/6D/xf/i/xAAhwDnAFoBswHkAfoBQgKWAtoCDgNPA48DxgMGBCMEfQTVBE4FigWrBdAF9QUiBkUGbAZ0BnQGpwa4BrUGoQalBowGgAaEBpUGkQZwBpMGZQZuBk0GMAb/BdwFlAVEBQ8FDQUABdEEgQRmBC0ECATnA+0D1QONA4ADgwNyA3oDWQNJAyADEAPtAtgCxgL9AtYC9wIiAyADPQOAA9UDGgQ9BGIESgRKBG8EdwTDBMkEwwTiBAsFMgU6BVQFawWQBaQFngWKBXsFfgVxBVkFRgUlBQsF+gTsBPYE3QTbBMEEiwRoBBYEEAS+A1kDEAO1An4CQgIHAtoBpgFCAbgAYADo/5j/K/+s/iT+5f2j/Yb9OP35/JT8RvwT/M/7qPuJ+4f7YPto+3/7kfuT+5z7mPuu+8H7w/vw+/77E/wj/Db8Xfxz/Ij8kPyS/Nb8Kv1Z/XT9vv0I/jv+Zv5m/nL+e/5u/nv+f/6i/p3+n/6q/tf+AP8E/wL/7f7l/qz+kf5i/gj+rf18/XT9hP1q/Wz9Sf0L/dT8lPxb/CX8Avzs+6b7efua+5j7Xvsj+zf7Gvv++t36wvqX+oX6YPpT+j36JPoY+j/6Rfpg+m76XPpm+nD6fPp++qH6h/qX+pP6rPrI+gT7TvuD+8f7+Psn/Dr8lPy//Pn8Mv1A/Yr9xP0W/kf+lf7L/gD/Nf9n/7D/EgByAKgA5QAjAUwBkgG1Ae4BNgJnAqsC6wIHAyQDXgOlA7oD5QPjAwAEFAQ3BEgEVgRYBFIEWgRIBFYEZARSBHMEOQQpBAYE8QMKBOUD/AMnBDsEPwQzBCUEEgTbA7YDnwO2A6oDrAO4A74DpQN8A5MDlQN2A2ADQQMqA/cCKgMeAyQDMAMUAw4DCwMUAxAD+wIaAwkDCwMkA0MDbAONA64DtgOqA6oDxgPIA+0DAAQpBEgEWgRcBIkEeQRSBFwELQRMBBAE0wOwA7ID0wPKA5UDqgNuAzcD1gKKAjoCAQKxAXMBTAEdAdsArACHAEkAIgDc/6T/ff9G/x3/9v6w/pf+eP5k/kv+Jv4q/ib+7/3I/aX9eP1H/Rn9Ov0y/RX9Gf0Z/f/8A/0V/Uv9Ov0Z/f/8Gf1V/XT9bv2A/cD94f3W/ev9Ev4//m7+f/6B/oH+dv6T/sT+yf7p/hL/K/85/zf/Qv8l/xn/Hf8O/wz/+v7d/tP+4f7f/vT+8v7V/tP+sv6J/oX+ff5i/of+h/5i/kv+N/4O/u397/3I/af9p/2h/aH9Wf1P/RH97vy3/EL8AvwP/AL8yfu4+8P7nvuR+3H7bPto+1T7ZvuB+4/7cfuR+5b7mvvJ+/77FfxO/I78nvzg/CT9dP2V/av92P0S/kn+e/64/t/+J/95/8v/BgBLAGYAgwC8ABABPgGSAbMB8gEbAlMCYQKOAqkCxALSAqsCuwLMAt4C/QIOAwkD9QLOAt4C8QLUAqcCvQK1AqMCkgKjAqsCrwKGAnQCfAKMAnoCgAJtAlUCGQIhAjQCOAIdAiECTQJNAkYCJAI0AjoCYQJRAlkCOAI0AjoCFwIHAgcCFQIbAi4CJgJVAmECdAJZAlkCZwJlAnwCowKSAowCkAK9AsICzgLIAoYCZwJbAksCRgImAiQCGwL/AQUC8AHkAeIBtwGQAWMBTAEXAR8BAgH2AAgB5QDZAN0AyQCyAKQAYAA3ACYAIAAYAA4A5P/L/57/f/9S/zP/Gf/h/t3+sv6V/mL+Jv4U/t/9zP2t/X79Vf1V/Uf9av1N/Rn9NP0R/RX9+fwF/ST9C/33/Or8/fzi/MP8lPyQ/J78sfyt/K/80Py9/JT8gPxz/HP8ivyY/JL8nPyG/Jr8oPzH/M788Pzg/Nr85Pz3/A/9Kv02/U/9ev2A/Y79nf12/Xb9nf2f/bz9iP2E/Z39Zf1P/TT9Uf1V/Wr9Yf10/Vn9Mv0w/ST9Af3q/MP8p/zJ/P/89/wu/R79HP0k/Qv9D/0V/Sb9Pv1P/WP9W/2R/cr91P3A/bz94f3j/Yz9ev2C/Zn9sf2+/bX9xv3M/QH+Kv4o/hD+Jv5w/sL+4/4Z/0j/kv/k/zMAcgDLAOkA7gAlAXsBnAHNAQUCUQJvAn4ClALGAucCGAM3A0sDNwMaA/0COQN6A3IDdgN2A6EDtAPGA8gD3wPtA/UD+AMaBBAEDAQfBCUEGgQSBBYEBgTfA8YDqgNkAx4D0gKSAmECcgJpAjACDQLWAf0BEQIXAugB+AEBAiECTQJ0AmMCjAJ8ApIC7wIkA14DVQNuA2YDbgNXA0UDFgMsAxwDKAMHA/cCtQKWAmUCQgIfAtgBwQGmAYgBbQE+ARMBzwDlAOcA4QDJAJkAiwCFAFAAIAAeABoAQwBiAJMAiQBiADcASQAtABgADADy/+z/5v8CAPT/0f+g/2n/UP8r//z+3f6L/nj+Wv50/nv+ZP4m/gP++f3j/cD9o/1s/V/9PP0m/RX9Mv06/Tz9Jv0i/ff83vzw/Dj9Pv0u/QP9Cf37/AX95vyx/G/8Pvwb/Dz8Tvwr/Av8E/wt/Dj8RPxd/Fn8RvxO/Gn8iPyz/L/82Pza/A39Jv0s/TT9Iv0N/ff8/fwc/Tr9Vf1T/TD9//zs/ND8vfyS/Gv8J/wn/Cf85vvq+5b7efuL+5b7h/ue+4f7e/uR+5P7tvvN++j7+vsv/Hn8wfzs/EX9Tf1j/Wr9gv2n/eH9A/5D/l7+j/64/u3+DP8n/xD/Of91/5L/s//N/97/4v/L/+L/3P/c/w4AIgA5AHkAlwDTAPAADAEnAUwBdQGKAbUB4AENAjwCUwKSArkC2gLtAu0CEgM1A3gDsgPGA7IDugO+A98D4QPVA9ED7QPhA9ED2QPnA/oD+APfA98D1wO4A7YDiQNqA3gDZgNiA3IDgwN0A2wDOQMuAxoD4gK9ApICrQKjAqkCygLKAu0C7wLgAuQC9QLcAtoC3ALzAgED8QL3AtYCBQMUAyID9QLzAucC9wLvAusC2gK5ApYCYwJdAjICRgJCAj4CPAIZAugB4gHLAcEBtQGSAW0BYQFYAT4BJQEGAdsA5QDpAPYAyQCmAI0AjQB3AFwAJAAWACQACgDe/wAA6P/s//D/4P/c/97/sP97/2f/I//J/rL+l/6k/o3+cv5W/ij+CP4M/v390v2n/V/9PP0w/SL9Ef3m/KL8gPyU/IT8iPyi/JT8mvx7/Hn8d/xh/GP8dfxp/Fn8dfxx/HP8gPyY/Ib8v/y1/Mv8y/zY/Mn82vzB/NL84PzO/Mv8tfyk/KL8lPxt/FT8YfxZ/Gf8afxZ/G38X/xl/F38ffxh/FL8K/wr/Eb8Qvw4/Dz8Jfwn/A/8APzw+9n75Pv2++r74fvq+yf8PvxU/Ez8IfwN/Bv8SPxO/Hv8nPyS/OD8F/08/VP9dv2n/b79A/5D/lH+Xv50/ov+qP6s/rb+zf7l/uX+7f4b/yf/af+Y/8v//P8IABYAYACiAM0A6QDuAAwBVgGIAZoB3AEoAnICowLOAukCCwNPA4UDfgObA6MDsAO+A7oD7QPfA98D7QPTA7ADyAPhA+kD+gMCBAQEGgQxBBIEAAQIBOcD+AMGBBgEHAQlBCcEHAQYBO0D0QPMA8QDnQNuA3IDZANiAyoDAQP/AtwCqwKjAoYCdgJVAi4C8AG9Ac8BvwGoAaoBjgGIAZIBpgHDAaYBtQGqAZwB4gH0AfQBAQIFAhUCHQITAvoBBwILAg8CFwIbAjYCOgIbAiECTwI8AiYC+AHkAeQB5AHWAegB6gENAgUC+gETAioCLgIdAk0CaQJhAmkCYwJbAmcCTQIHAqoBXgH+AKoAkQBSAP7/1f9v/yP/2f6o/mb+M/75/bH9m/2M/WP9Nv0e/f38Bf37/P384Pza/LX8c/xU/FT8Vvw+/FT8TPw6/Cv8K/wZ/AT8C/zX+6D7k/vN+/L7Hfxr/J781vzw/BH9E/0c/S79Mv0i/T79Ov0u/WP9Ov0w/Sb9Nv04/fv84vyr/Iz8ivyC/Hv8pPy5/Nz8A/0k/Wr9s/3t/Rr+N/5i/nj+i/6Z/sT+CP8I/wb/DP8G/x//AP/L/sL+n/6P/mj+Xv50/kX+Q/4S/t/95f3Q/bj9q/2h/bz9xv3f/ff9Ev7v/fP9Dv5B/ij+9/0G/hD+7/3j/cb9yv3Y/dD96f3Q/ff9Iv4t/lT+Xv6T/tv+Nf+M/xgAiwAGAV4B8gFvAtwCSQOVA8ADFgSHBLQEqAS2BLsExwSuBLgE7gTkBKgEbwQ9BAQE3wOyA4sDeANoA14DRwNPA2IDWQNuA2YDgAO0A7gDCATzA+EDBAQlBBwEBAQKBPMDnQNwAxID3gKIAk8COAIFAtgBiAE8AQYB3wCwAI0AUgBsAGYAYgBQAF4AbAByAJkAvgDHAOEA7AD2APoA7AD4AAIBEAEOAQQBGwElASkBGQEQAfYA4wC2AK4AmQC8AMsA8gDsAOcA8gAVAVABOgFQAWcBrwHRAeAB3gEuAmMCigKtArkCkgKAAksCLgL6AdoBywHuAaoBbQF5AXEBOgETAdMAjwA3APb/ov83/+n+ov6m/oH+ov7C/sv+uv6k/rr+sv6Z/mj+N/4G/v/9Af7l/dr93/3j/bH9hP2A/Yb9W/0+/ff8q/yC/Gf8NvwN/Nf7zfvF+9X76vv6+1D8jPyS/MH8Bf1b/Yj9wP3t/RL+Gv5D/mD+k/6f/uP+/v4K/wT/Mf9K/xn/y/62/pX+R/4g/vX9+/3h/e/9Mf5c/mr+lf6d/p/+tP7l/vz+H/9I/z7/bf+F/7P/1//4/wAA8v/6/9X/wf+S/3X/Nf8A/7L+gf45/gr+0P2r/XL9fv2V/af9p/28/Qj+KP5g/qz+2/4b/0z/jv/k/xIAWAC2AAoBFQFaAZABkAGIAYMBVgFAAfYAsgBqAHIAwADHALwA+gAvAVoBdQHPAQUCOgJrAp0CyAIgA2wDwgMABGIEqgQGBT4FVAUjBQAF4gSqBFQEEASjAyQDtwJbAgcC2AHNAaYBewEnAd0AzQCgANkA9AAQARsBQAFrAYoB+AFRAnoCjgKbAqkCpQLWAsACrwJyAhECwwFhAT4BHQHdAGwACgCY/1j/a/8+/xD/0f7j/rb+qP7J/i3/af+U/5j/w/8QAFoAmwDHAPgAVgHBAcsBzQH4ASECEQIVAvoB+gHmAb8BhQFKAfgAsgCXAFQA8v/g/8P/mP9c/0L/RP8h/+P+xP7X/iP/QP9I/3v/pP+g/+D/IAB5AIsAjQCbAIkAewB9AJsAjQBeADsAFgDR/6L/0f+u/3P/Tv8j/+3+uP6H/oP+L/7O/dr9yP3I/dj99f3M/YL9av1j/UD9T/1P/TL90vx9/HP8XfxC/FL8EfwA/Pz7H/wt/B/8/vvm+8P7qPvD+y38w/xZ/ZP90P1U/tn+VP+//0sAXABSAHcAsgCiAIUAogBsAPL/pv+Q/57/a/8z/wz/+v6L/k/+S/49/lr+S/49/nT+sP7p/hv/Sv+c/xIAJgBmAIUArAC6AN0AHwEXAfoA7gDnAKgAjQCVALgAhQBkAFwAXABYAG4A0QAIAR0BNQE6AVABdQHNAQkCKAJdAp8ClgKhAp8CpQJ6AgkCywHNAZwBhQGIAXUBWgFUAWEBNQElATMBKQEXASkBMwE8AVwBHwEhAWsBYwFKAWUBjAGiAZoBngGoAc0B+AE6AkACTwJ8At4C5ALUAtgCDgNFA1EDpwMWBBQE2wPjA98D6wOyA2YDDgNEAtMB3AGaAQQBmQBUAAgAvf+3/+L/1//B/5D/d/9O/0L/4v9NAKgAGwF3AbcBNgLaAoUDnwN6A1wDdgN0A3YDIgOtAiwCgwEEAckAvAB3AAoAmP81/wz/sv6J/pH+tv6f/pH+nf7P/h//df9O/zn/Ev8I/1D/lv+w/4f/af83/8D+Tf4a/gj+r/2G/X79bv1T/WH9l/2f/bj9Cv4//nv+5f5x/9P/RQCNAKgA8gCMAa8B3gEDAiEC7gGeAQ4BRQC9/zP/jf7z/W793PyA/FD88vuR+4f7b/tM+1D7gfum+7j7Efxj/KT89fxd/cD9KP54/sT+4/7Z/sL+nf5a/jH++/2x/Xb9bv1+/WP9Iv35/Nr86vz1/Bf9KP1Z/dz9N/5s/o3+/v5c/3H/c/+k/6z/lv+B/57/kP8x/7z+R/7W/Z39T/0c/dL8lPyC/Ij8mPzF/A/9fv0i/oH+3f41/9P/dADwAEQB3AERAiwCigLnAiIDDgP7AsoCSALPAcEB1gGsAZYBmAF3AWEBZwFYAV4BLwECAZsAZgCoANMAJwFKAW0BewF5AZABZQEMAdcA5wCHAPD/vf+s/7n/vf+3/+z/KwBLAH0A8gBEAX0BmgHcAQ8ChALIAhADLANgA3QDpwO2A4kDNQMOA8YCYQL2AbkBxwF1AQYB0wD4AN8A4QDFANkA9gAnAVQBewGOAYoBmgG1AZ4BgwG3AdYB/QERAlMChAKOAjwCAQIFAvIBxwHRAa8BfwFrAcEB7gHmAdYB9AH0AT4CRgJLAjACFwL4AeYByQGsAaYBbwExAQIB1QCbAEMAAgBx//L+tP5c/vP99f37/fP93/3j/RL+Sf57/uf+Tv+u/wwAQwB5AKQAsgC6AKgAZAA/ABYAFgAOAPD/v/9W/+v+Lf6r/VH9D/3o/KD8s/zL/Nb89fxF/bX9EP5g/pf+sv6f/qr+F/9K/1j/Rv9v/2L/I//X/rj+hf5a/hj+wv1b/SD9Gf0w/Tz9iv2v/aX9hv2V/dr9xv3M/cj91v0D/n3+1f4E/xf/H/+4/nb+//21/Yz9Sf0B/a38PPzV+9H7v/ui+2L7Nfv3+hL7hfvF+xH8a/yI/Mf8F/2l/U3+y/53/zcAeQBDAHcAHQExAe4A3QC8AIUAKwAEAOT/qP9t/zH/BP/y/i3/UP9O/5j/3P/+//r/MwC4ABcBqAHoAdMB2gHYAaoBXgEVAeUAwgBaAD0AXgAKAHX/Bv/R/o/+f/5q/nL+tP5//xoAcAC0AMUAwADHAO4ADAE1AUYBigGzAdoBBwIRAv0BCQL6AeQBqAFrARcBwAB9APr/mP9r/4P/CgCDAOcALQG5ARECNgJyAooCEQILAlUCUQITAhMCZQJ+AjQC9AH2Af8BBQIuAu4BeQFKAT4B7gCkACcBkgGDAW8BvwEsAogCAQNDAzIDBwMLAwMD+QIeA1cDbgMWA/ECrwJlAmUCdAImAqIB2wBJAAwA8v/4/8H/g/8p//7+FP8S/yX/ef+z/6j/ef9i/2n/jP/c/xgADAC3/4H/XP9x/7D/8v/q/4z/c/9p/3//XP8Z//7+0/6f/r7+tv6+/hT/bf9E/0r/Z/+B/5D/w//4/8X/xf/4/xQAGAAKAPb/3P/s/wAAz/+H/xn/7f7P/lj+DP73/Sb+eP6R/pf+dP57/oH+R/7t/dj9Fv7V/jn/gf/P/zcAdACBAJUASwAAAOL/YP/r/rr+nf4I/oD9Jv0+/W79ev1u/Yz9vP2E/Sr9y/xl/Gn8yfyr/Iz8q/zm/CT9fv3Q/T/+WP49/nD+n/6F/hb+0v2l/Sz9A/0F/SD9o/1u/hL/Yv9t/zH/y/7E/o3+bP5a/qz+yf68/vD+Rv+B/5j/xf9HAGQAWgBQAHcAfwB0ACAAtf95/2D/ov81AJEAgwCXAI0AMQAMABQACgC3/3v/VP8U//D+J/+B/+7/JgArAFQAuAD8AP4AJQH0AKAAgQDHANcACgFAARUB1wDsAOEAtgCHAG4AXgBLAE0AGgD2/yAAOQAgAAAA7P8IAC0AVADbABMBKwFIAX0BzwH/AR0CFQLeAeABwwFvAUABSAE3AekAywCRADUABgA1AC8A7P+//7f/5v8UAEEAZABNAFgAYABHAEkAlwDTAC8BmAHqARECawJVAjQCFwIbAu4BjgFjAW8BIwHRAH8AVgA3ACkACACa/23/df9a/zn/O/8z/yH/1/6X/rT+H/+D/8v//P87AJUACAGBAQ8CWQJGAgMCrwGcAdYBBwIFAnsB8gDRAP4ALQH2AI8AEACs/1z/Mf9M/xf/rv4z/hL+Pf6i/gb/Sv9c/y3/Qv9E/0z/cf9z/3X/df95/3n/wf8kAE0AGADo/wQAEAA/AGIAWgAOAMn/g/9W/+X+j/59/qr+kf5s/lH+f/6B/mT+WP5i/oX+tP70/gD/tP5c/vX9Dv7v/cT97/0a/jf+bv7C/v7+yf5F/vv94f2p/cL9xv3K/cD9Bv45/lb+pP7p/kz/qv8KAAoAPQBNACYAJAAiAAIAGgAvAHkAdACXAHQAdABNAC0A4P+q/4X/SP/4/rD+ff5Y/i/+FP4g/mL+uP7t/vT+FP8X/xf//v4X/zf/af+k/wIARwByANUALwE6AQYBQAGMAWEBLQHfAJEAYgCDAKAASQD0/7f/f/9t/2f/UP8z//r+rP6k/tn+Jf9Y/4X/h/+1/8f/0/8EAJEA8AAKAdsAyQDFAKIAugDLAIkAOQDs/+D/AgAtABAA7v/B/7v/BABNAHsAlQDZAMkA2QDTAPgAJQFGARkBDAEjATcBUAEvAQQB2wC4AFYAIAAaACkA5v+O/zX//v4h/5L/CAA/AIEAlwCkANsAFwErASEB6QCiAJMA7AApATcBZQF/AVoBPgFcAXUBlgGOAWUBIwETASUBFQHPAKgAPwDu/7D/sP+o/67/nP9v/0T/Zf+a/+j/EAAKAOL/1f+g/2D/f//m/yQADABLADcABgAkAE0AFACu/xf/pP45/h7+S/6d/uP+Av8X/1j/t/+o/6b/sP91/zP/G/9M/2//lP+9/+j//v/u/9r/z/+z/5L/kv+B/zH/7f7L/sT+rP6Z/sv+Gf9A/1r/ff+s/7//7P8eAB4ANwApAAIAoP9x/yv/9v7N/sn+z/7b/hD/e/+a/23/DP/N/mT+EP5D/nD+l/7b/kr/v//q//z/EgA9AFgAhwDAAOMAugCDAGIAFAD4//j/5P/H/9H/6P8GAAAA0f/L/8v/Zf83/y//Qv93/1z/K//p/s/+BP8d/1j/ov8kAFgAQwA/ADcAOQA5AC0ARwB3AMkAGQFSAUYBMQEMAQYBAgFAAToBCgHFAMsAvgCmAHQAcgBJAD0AUACFAKoAsgDLAKgAdwBoALwA/AAXARkBHwEIAckAjwCFAJEAxQCyAJEAmwCqALAAagAxABoAAAC3/6j/zf/u//z/FgAiADMARQCVAKoAkwBcAKIAtACBAG4AhQCeAH8AfQC0ANEAzQCeAF4AUgBWAC8ALQBUAEkAMwAmAAAA2v+z/7P/nv+S/7P/vf/X/8f/z//w/yIARQBLAHkAwAAhAToBSgFQASMB3wC+AL4AugCbAKAAwgC6AIEAXgBQAGYASwA/AEEAEAACAAoAIAAQAPT/AgAkABAAAAD+//T/vf+H/0b/If8t/zn/Lf87/07/SP9K/zf/O/9E/0T/O/83/2X/ff+a/6r/mv9r/1D/b/9t/1T/VP9e/1j/L/8Q/yP/Pv87/1T/cf9n/yn/Uv97/5T/rP+5/6L/if+5/8v/AgAKAAoAx/+c/6z/pP97/1T/Rv9C/wD/3f64/rT+vv7J/tH+0/7y/jf/d/+w/+D/DgAYAAwAOwBYAIcAjQCbALIAxQDFAL4ArgCJAH8AUAACALn/wf/X/83/sP99/2D/cf+Q/4z/ov+M/57/pP+i/7v/3v8AAPD/1//y/woA5v/R//j/AgAOAAYAMQA5AD8APQBSAFgAQwBHAGIALQAQABYAJgAeABIADAAaAB4ALQAvAEMAQQAxACAAPQBiAIMAlwCbAJMAaABBABQACAD8//j/+P8IABAADAAGABgAIAD8//7/9v/N/8f//P8vADUAGAApAFYAjwCuANcAAgETAQgBBAHbAKYAngB5AJUAngC0AKwApACVAGwAWgBYACsACgDw//L/AgAAAO7/BAAKAC0ALQBHAEMANQApADEAIAAKAOL/5v/V/+D/3P/2/+T/1f/D/7//t/+//8n/9P8cAEUARQBWAFgATQBFAGoAagBmAIkAsACRAIkAeQBuAFIAPwAYAAwAFADo/+D/w//D/7X/rP+w/8f/3v/w/wAA8P/s/+L/0//c/+z/AgAkACYAGgAkADUAKwAgACAABgD8//7/EgAQAPr/+P/u/+L/1//J/9P/z//T/8//y//T/9X/6P/i/8//zf/J/73/rP+z/73/qv+g/6L/qv+o/9r/4v/T/8H/pP+1/7n/4P/V//D/3v/a/83/4P/m/+D/1f+3/7P/oP+1/6j/vf+3/8P/t//D/8P/2v/s//b//v/2/wIA/v/+/wgAHgAeACYAGAAOAPr//v/4//L//v/u/+z/1//g/+T/3P/g/9f/3v/e/9P/2v/c//L/8P/6//L/BAAWACsANQAzACkAKwAtAEcAOwAxAC8AIgAgAAoAEgAKAPz/6P/o/97/5v/c/9H/zf/B/8//z//T/9z/6v/6/xIACgAcAB4AQQBHAE0AUABBAEUAMwApACQAFgAaAAoADgD+/w4ABAAIAPr//P/u//r/8P/w//L//P8MAAgAHgAYACkAKwAtAC0AJgApABwAHgAUABoAIgArAC0AHAAaAAgADgD6/wQA+v/0//L/8v/s/wgA/P8QAAgADgAQABQAHAAeABgAHAAaABoAGAAWABoAFgAaAAwADAAOAAYACAAAAP7//P/6//7/AAD8/wYAAAAGAAgAAAAMAAAABAAEAAIAAAAEAPj/CAD6/wYAAAAAAAQA/P8CAPz/BAD4/wYA+P8GAPz/AgD+/w==\" type=\"audio/wav\" />\n", " Your browser does not support the audio element.\n", " </audio>\n", " " ], "text/plain": [ "<IPython.lib.display.Audio object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Audio(audio, rate=fs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The algorithm\n", "This algorithm outputs the starts and ends timestapms of the clicks. The following plots show how the algorithm performs in the previous examples\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4cca594a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "starts, ends = compute(audio)\n", "\n", "fig, ax = plt.subplots(len(groundTruth))\n", "plt.subplots_adjust(hspace=.4)\n", "for idx, point in enumerate(groundTruth):\n", " l1 = ax[idx].axvline(starts[idx], color='r', alpha=.5)\n", " ax[idx].axvline(ends[idx], color='r', alpha=.5)\n", " l2 = ax[idx].axvline(point, color='g', alpha=.5)\n", " ax[idx].plot(times, audio)\n", " ax[idx].set_xlim([point-.001, point+.001])\n", " ax[idx].set_title('Click located at {:.2f}s'.format(point))\n", " \n", " \n", " fig.legend((l1, l2), ('Detected click', 'Ground truth'), 'upper right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The parameters\n", "this is an explanation of the most relevant parameters of the algorithm\n", "\n", "- **detectionThreshold.** This algorithm features an adaptative threshold obtained from the instant power of each frame. This parameter is a gain factor to adjust the algorithm to different kinds of signals. Typically it should be increased for very \"noisy\" music as hard rock or electric music. The default value was empirically found to perform well in most of the cases. \n", "\n", "- **powerEstimationThreshold.** After removing the auto-regressive part of the input frames through the LPC filter, the residual is used to compute the detection threshold. This signal is clipped to 'powerEstimationThreshold' times its\n", " median as a way to prevent the clicks to have a huge impact in the estimated threshold. This parameter controls how much the residual is clipped. \n", "\n", "- **order.** The order for the LPC. As a rule of thumb, use 2 coefficients for each format on the input signal. However, it was empirically found that modelling more than 5 formats did not improve the clip detection on music.\n", "\n", "- **silenceThreshold.** Very low energy frames can have an unexpected shape. This frame can contain very small clicks that are detected by the algorithm but are impossible to hear. Thus, it is better to skip them with a silence threshold. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "2.7.15rc1" } }, "nbformat": 4, "nbformat_minor": 2 }

agpl-3.0

brianjpetersen/when

develop/re.ipynb

1

5762

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re\n", "import collections\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5, 9)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = re.compile(r'(?P<word>\\b\\w+\\b)')\n", "m = p.search( '(((( Lots of punctuation )))' )\n", "#dir(m)#.groups(1)\n", "m.span()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "substitutions = {'year': '1776'}\n", "_regex = re.compile('|'.join(map(re.escape, substitutions)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "time data '01' does not match format '%Y'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-df44fdaa06ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m//anaconda/lib/python2.7/_strptime.pyc\u001b[0m in \u001b[0;36m_strptime\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m raise ValueError(\"time data %r does not match format %r\" %\n\u001b[0;32m--> 325\u001b[0;31m (data_string, format))\n\u001b[0m\u001b[1;32m 326\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_string\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\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 327\u001b[0m raise ValueError(\"unconverted data remains: %s\" %\n", "\u001b[0;31mValueError\u001b[0m: time data '01' does not match format '%Y'" ] } ], "source": [ "datetime.datetime.strptime('01', '%Y')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<map at 0x7f512c62a400>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "specifier = '1776-07/04'\n", "'1996-03/02'\n", "\n", "\n", "substitutions = collections.OrderedDict((\n", " ('year', '1776'),\n", " #('two digit year', '76'),\n", " #('two digit month', '07'),\n", " ('month', '7'),\n", " ('day', '4'),\n", " #('two digit day', '04'),\n", " ))\n", "\n", "#iterator = p.finditer('12 drummers drumming, 11 ... 10 ...')\n", "map(re.escape, substitutions)\n", "#re.escape(\n", "#(?P<word>)\n", "#_regex = re.compile('|'.join())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'year|month|day'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "'|'.join(map(re.escape, substitutions))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 4)\n", "(5, 6)\n", "(8, 12)\n" ] } ], "source": [ "for match in re.finditer(r'July|1776|76|7|4', 'July 4, 1776'):\n", " print match.span()\n", "\n", "\n", "def parse(string, specifiers, year=None, month=None, day=None, hour=None, minute=None, second=None,\n", " millisecond=None, microsecond=None, meridian=None, timezone=None):\n", " pass\n", "\n", "\n", "# replace July in with regex options = 'January|February|March|April...'\n", "# then, use" ] }, { "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

GoogleCloudPlatform/asl-ml-immersion

notebooks/kubeflow_pipelines/pipelines/solutions/kfp_pipeline_vertex_automl_batch_predictions.ipynb

1

18089

{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Continuous Training with AutoML Vertex Pipelines with Batch Predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Learning Objectives:**\n", "1. Learn how to use Vertex AutoML pre-built components\n", "1. Learn how to build a Vertex AutoML pipeline with these components using BigQuery as a data source\n", "1. Learn how to compile, upload, and run the Vertex AutoML pipeline\n", "1. Serve batch predictions with BigQuery source from the AutoML pipeline\n", "\n", "\n", "In this lab, you will build, deploy, and run a Vertex AutoML pipeline that orchestrates the **Vertex AutoML AI** services to train, tune, and serve batch predictions to BigQuery with a model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "from google.cloud import aiplatform" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "REGION = \"us-central1\"\n", "PROJECT = !(gcloud config get-value project)\n", "PROJECT = PROJECT[0]\n", "\n", "os.environ[\"PROJECT\"] = PROJECT" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: PATH=/home/jupyter/.local/bin:/usr/local/cuda/bin:/opt/conda/bin:/opt/conda/condabin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n" ] } ], "source": [ "# Set `PATH` to include the directory containing KFP CLI\n", "PATH = %env PATH\n", "%env PATH=/home/jupyter/.local/bin:{PATH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BigQuery Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have not gone through the KFP Walkthrough lab, you will need to run the following cell to create a BigQuery dataset and table containing the data required for this lab.\n", "\n", "**NOTE** If you already have the covertype data in a bigquery table at `<PROJECT_ID>.covertype_dataset.covertype` you may skip to **Understanding the pipeline design**." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BigQuery error in mk operation: Dataset 'kylesteckler-demo:covertype_dataset'\n", "already exists.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Waiting on bqjob_r21048939c4d82a5f_000001809057d82b_1 ... (3s) Current status: DONE \n" ] } ], "source": [ "%%bash\n", "\n", "DATASET_LOCATION=US\n", "DATASET_ID=covertype_dataset\n", "TABLE_ID=covertype\n", "DATA_SOURCE=gs://workshop-datasets/covertype/small/dataset.csv\n", "SCHEMA=Elevation:INTEGER,\\\n", "Aspect:INTEGER,\\\n", "Slope:INTEGER,\\\n", "Horizontal_Distance_To_Hydrology:INTEGER,\\\n", "Vertical_Distance_To_Hydrology:INTEGER,\\\n", "Horizontal_Distance_To_Roadways:INTEGER,\\\n", "Hillshade_9am:INTEGER,\\\n", "Hillshade_Noon:INTEGER,\\\n", "Hillshade_3pm:INTEGER,\\\n", "Horizontal_Distance_To_Fire_Points:INTEGER,\\\n", "Wilderness_Area:STRING,\\\n", "Soil_Type:STRING,\\\n", "Cover_Type:INTEGER\n", "\n", "bq --location=$DATASET_LOCATION --project_id=$PROJECT mk --dataset $DATASET_ID\n", "\n", "bq --project_id=$PROJECT --dataset_id=$DATASET_ID load \\\n", "--source_format=CSV \\\n", "--skip_leading_rows=1 \\\n", "--replace \\\n", "$TABLE_ID \\\n", "$DATA_SOURCE \\\n", "$SCHEMA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding the pipeline design\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The workflow implemented by the pipeline is defined using a Python based Domain Specific Language (DSL). The pipeline's DSL is in the `pipeline_vertex/pipeline_vertex_automl_batch_preds.py` file that we will generate below.\n", "\n", "The pipeline's DSL has been designed to avoid hardcoding any environment specific settings like file paths or connection strings. These settings are provided to the pipeline code through a set of environment variables.\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Building and deploying the pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us write the pipeline to disk:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting ./pipeline_vertex/pipeline_vertex_automl_batch_preds.py\n" ] } ], "source": [ "%%writefile ./pipeline_vertex/pipeline_vertex_automl_batch_preds.py\n", "\"\"\"Kubeflow Covertype Pipeline.\"\"\"\n", "\n", "import os\n", "\n", "from google_cloud_pipeline_components.aiplatform import (\n", " AutoMLTabularTrainingJobRunOp,\n", " TabularDatasetCreateOp,\n", " ModelBatchPredictOp\n", ")\n", "from kfp.v2 import dsl\n", "\n", "PIPELINE_ROOT = os.getenv(\"PIPELINE_ROOT\")\n", "PROJECT = os.getenv(\"PROJECT\")\n", "DATASET_SOURCE = os.getenv(\"DATASET_SOURCE\")\n", "PIPELINE_NAME = os.getenv(\"PIPELINE_NAME\", \"covertype\")\n", "DISPLAY_NAME = os.getenv(\"MODEL_DISPLAY_NAME\", PIPELINE_NAME)\n", "TARGET_COLUMN = os.getenv(\"TARGET_COLUMN\", \"Cover_Type\")\n", "BATCH_PREDS_SOURCE_URI = os.getenv(\"BATCH_PREDS_SOURCE_URI\")\n", "\n", "@dsl.pipeline(\n", " name=f\"{PIPELINE_NAME}-vertex-automl-pipeline-batch-preds\",\n", " description=f\"AutoML Vertex Pipeline for {PIPELINE_NAME}\",\n", " pipeline_root=PIPELINE_ROOT,\n", ")\n", "def create_pipeline():\n", "\n", " dataset_create_task = TabularDatasetCreateOp(\n", " display_name=DISPLAY_NAME,\n", " bq_source=DATASET_SOURCE,\n", " project=PROJECT,\n", " )\n", "\n", " automl_training_task = AutoMLTabularTrainingJobRunOp(\n", " project=PROJECT,\n", " display_name=DISPLAY_NAME,\n", " optimization_prediction_type=\"classification\",\n", " dataset=dataset_create_task.outputs[\"dataset\"],\n", " target_column=TARGET_COLUMN,\n", " )\n", "\n", " batch_predict_op = ModelBatchPredictOp(\n", " project=PROJECT,\n", " job_display_name=\"batch_predict_job\",\n", " model=automl_training_task.outputs[\"model\"],\n", " bigquery_source_input_uri=BATCH_PREDS_SOURCE_URI,\n", " instances_format=\"bigquery\",\n", " predictions_format=\"bigquery\",\n", " bigquery_destination_output_uri=f'bq://{PROJECT}',\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding the ModelBatchPredictOp\n", "When working with an AutoML Tabular model, the ModelBatchPredictOp can take the following inputs:\n", "* `model`: The model resource to serve batch predictions with\n", "* `bigquery_source_uri`: A URI to a BigQuery table containing examples to serve batch predictions on in the format `bq://PROJECT.DATASET.TABLE`\n", "* `instances_format`: \"bigquery\" to serve batch predictions on BigQuery data.\n", "* `predictions_format`: \"bigquery\" to store the results of the batch prediction in BigQuery.\n", "* `bigquery_destination_output_uri`: In the format `bq://PROJECT_ID`. This is the project that the results of the batch prediction will be stored. The ModelBatchPredictOp will create a dataset in this project.\n", "\n", "Upon completion of the `ModelBatchPredictOp` you will see a new BigQuery dataset with name `prediction_<model-display-name>_<job-create-time>`. Inside this dataset you will see a `predictions` table, containing the batch prediction examples and predicted labels. If there were any errors in the batch prediction, you will also see an `errors` table. The errors table contains rows for which the prediction has failed.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create BigQuery table with data for batch predictions\n", "Before we compile and run the pipeline, let's create a BigQuery table with data we want to serve batch predictions on. To simulate \"new\" data we will simply query the existing table for all columns except the label and create a table called `newdata`. The URI to this table will be the `bigquery_source_input_uri` input to the `ModelBatchPredictOp`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Query complete after 0.01s: 100%|██████████| 3/3 [00:00<00:00, 1720.86query/s] \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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "CREATE OR REPLACE TABLE covertype_dataset.newdata AS \n", "SELECT * EXCEPT(Cover_Type)\n", "FROM covertype_dataset.covertype\n", "LIMIT 10000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compile the pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by defining the environment variables that will be passed to the pipeline compiler:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: PIPELINE_ROOT=gs://kylesteckler-demo-kfp-artifact-store/pipeline\n", "env: PROJECT=kylesteckler-demo\n", "env: REGION=us-central1\n", "env: DATASET_SOURCE=bq://kylesteckler-demo.covertype_dataset.covertype\n", "env: BATCH_PREDS_SOURCE_URI=bq://kylesteckler-demo.covertype_dataset.newdata\n" ] } ], "source": [ "ARTIFACT_STORE = f\"gs://{PROJECT}-kfp-artifact-store\"\n", "PIPELINE_ROOT = f\"{ARTIFACT_STORE}/pipeline\"\n", "DATASET_SOURCE = f\"bq://{PROJECT}.covertype_dataset.covertype\"\n", "BATCH_PREDS_SOURCE_URI = f\"bq://{PROJECT}.covertype_dataset.newdata\"\n", "\n", "%env PIPELINE_ROOT={PIPELINE_ROOT}\n", "%env PROJECT={PROJECT}\n", "%env REGION={REGION}\n", "%env DATASET_SOURCE={DATASET_SOURCE}\n", "%env BATCH_PREDS_SOURCE_URI={BATCH_PREDS_SOURCE_URI}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us make sure that the `ARTIFACT_STORE` has been created, and let us create it if not:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gs://kylesteckler-demo-kfp-artifact-store/\n" ] } ], "source": [ "!gsutil ls | grep ^{ARTIFACT_STORE}/$ || gsutil mb -l {REGION} {ARTIFACT_STORE}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use the CLI compiler to compile the pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We compile the pipeline from the Python file we generated into a JSON description using the following command:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "PIPELINE_JSON = \"covertype_automl_vertex_pipeline_batch_preds.json\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py:1266: FutureWarning: APIs imported from the v1 namespace (e.g. kfp.dsl, kfp.components, etc) will not be supported by the v2 compiler since v2.0.0\n", " category=FutureWarning,\n" ] } ], "source": [ "!dsl-compile-v2 --py pipeline_vertex/pipeline_vertex_automl_batch_preds.py --output $PIPELINE_JSON" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** You can also use the Python SDK to compile the pipeline:\n", "\n", "```python\n", "from kfp.v2 import compiler\n", "\n", "compiler.Compiler().compile(\n", " pipeline_func=create_pipeline, \n", " package_path=PIPELINE_JSON,\n", ")\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is the pipeline file. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"pipelineSpec\": {\n", " \"components\": {\n", " \"comp-automltabulartrainingjob-run\": {\n", " \"executorLabel\": \"exec-automltabulartrainingjob-run\",\n", " \"inputDefinitions\": {\n", " \"artifacts\": {\n", " \"dataset\": {\n", " \"artifactType\": {\n", " \"schemaTitle\": \"google.VertexDataset\",\n" ] } ], "source": [ "!head {PIPELINE_JSON}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deploy the pipeline package" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating PipelineJob\n", "PipelineJob created. Resource name: projects/693210680039/locations/us-central1/pipelineJobs/covertype-vertex-automl-pipeline-batch-preds-20220504183214\n", "To use this PipelineJob in another session:\n", "pipeline_job = aiplatform.PipelineJob.get('projects/693210680039/locations/us-central1/pipelineJobs/covertype-vertex-automl-pipeline-batch-preds-20220504183214')\n", "View Pipeline Job:\n", "https://console.cloud.google.com/vertex-ai/locations/us-central1/pipelines/runs/covertype-vertex-automl-pipeline-batch-preds-20220504183214?project=693210680039\n", "PipelineJob run completed. Resource name: projects/693210680039/locations/us-central1/pipelineJobs/covertype-vertex-automl-pipeline-batch-preds-20220504183214\n" ] } ], "source": [ "aiplatform.init(project=PROJECT, location=REGION)\n", "\n", "pipeline = aiplatform.PipelineJob(\n", " display_name=\"automl_covertype_kfp_pipeline_batch_predictions\",\n", " template_path=PIPELINE_JSON,\n", " enable_caching=True,\n", ")\n", "\n", "pipeline.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding the resources created by BatchPredictOp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the pipeline has finished running you will see a new BigQuery dataset with name `prediction_<model-display-name>_<job-create-time>`. Inside this dataset you will see a `predictions` table, containing the batch prediction examples and predicted labels. If there were any errors in the batch prediction, you will also see an `errors` table. The errors table contains rows for which the prediction has failed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2021 Google LLC\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", " 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." ] } ], "metadata": { "environment": { "kernel": "python3", "name": "tf2-gpu.2-8.m91", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:m91" }, "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

bdestombe/SWItest

SWI1D/3cell1.ipynb

1

36884

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SWI - single layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import sys\n", "import numpy as np\n", "import flopy.modflow as mf\n", "import flopy.utils as fu\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paths" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "os.chdir('C:\\\\Users\\\\Bas\\\\Google Drive\\\\USGS\\\\FloPy\\\\slope1D')\n", "sys.path.append('C:\\\\Users\\\\Bas\\\\Google Drive\\\\USGS\\\\FloPy\\\\basScript') # location of gridObj\n", "\n", "modelname \t= 'run1swi2'\n", "exe_name \t= 'mf2005'\n", "workspace \t= 'data'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model input" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ml = mf.Modflow(modelname, exe_name=exe_name, model_ws=workspace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test variables" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tscale = 365.*2000\n", "nstp \t= tscale/100. \t\t\t#[]\n", "perlen \t= tscale*1.\t#[d]\n", "ssz \t= 0.2 \t\t\t#[]\n", "Q \t\t= 0.01 \t \t#[m3/d]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discretization data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nlay = 1\n", "nrow = 1\n", "ncol = 5\n", "delr = 1.\n", "delc = 1.\n", "dell = 1.\n", "\n", "top = np.array([[-1.,-1.,-1., -0.7, -0.4]], dtype = np.float32)\n", "bot = np.array(top-dell, dtype = np.float32).reshape((nlay,nrow,ncol))\n", "initWL = 0. # inital water level" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BCN: WEL" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lrcQ1 = np.recarray(1, dtype = mf.ModflowWel.get_default_dtype())\n", "lrcQ1[0] = (0, 0, 4, Q) #LRCQ, Q[m**3/d]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BCN: GHB" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lrchc = np.recarray(1, dtype = mf.ModflowGhb.get_default_dtype())\n", "lrchc[0]=(0, 0, 0, -top[0,0]*0.025, 0.8 / 2.0 * delc)\n", "lrchc[0]=(0, 0, 1, -top[0,0]*0.025, 0.8 / 2.0 * delc)\n", "lrchc[0]=(0, 0, 2, -top[0,0]*0.025, 0.8 / 2.0 * delc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SWI2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1L, 5L) (1L, 5L)\n" ] } ], "source": [ "#zini \t= bot[0,:,:]*-1.5\n", "zini = -2.*np.ones((nrow,ncol))\n", "#zini=bot\n", "isource = np.array([[-2,2,-2, 0, 0]])\n", "print zini.shape, isource.shape\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model objects" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ml = mf.Modflow(modelname, version='mf2005', exe_name=exe_name)\n", "discret = mf.ModflowDis(ml, nrow=nrow, ncol=ncol, nlay=nlay, delr=delr, delc=delc,\n", " laycbd=[0], top=top, botm=bot,\n", " nper=1, perlen=perlen, nstp=nstp)\n", "bas = mf.ModflowBas(ml, ibound=1, strt=1.0*0.025)\n", "bcf = mf.ModflowBcf(ml, laycon=[0], tran=[4.0])\n", "wel = mf.ModflowWel(ml, stress_period_data={0:lrcQ1})\n", "ghb = mf.ModflowGhb(ml, stress_period_data={0:lrchc})\n", "swi = mf.ModflowSwi2(ml, nsrf=1, istrat=1, toeslope=0.02, tipslope=0.04, nu=[0, 0.025],\n", " zeta=[zini], ssz=ssz, isource=isource, nsolver=1)\n", "oc = mf.ModflowOc(ml, save_head_every=nstp)\n", "pcg = mf.ModflowPcg(ml)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ml.write_input() #--write the model files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = ml.run_model(silent=True, report=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read the output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "only one head entry and one zeta entry in binary files" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "headfile = modelname + '.hds'\n", "hdobj = fu.HeadFile(headfile)\n", "head = hdobj.get_data(idx=0)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zetafile = modelname + '.zta'\n", "zobj = fu.CellBudgetFile(zetafile)\n", "zeta = zobj.get_data(idx=0, text=' ZETASRF 1')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-2. -2. -2. -2. -2.]]\n", "head: [ 0.05 0.05 0.05 0.0525 0.055 ]\n", "BGH head: [-2. -2. -2. -2.10000014 -2.20000005]\n" ] } ], "source": [ "import gridobj as grd\n", "gr = grd.gridobj(discret)\n", "print zini\n", "print 'head: ', head[0, 0, :]\n", "print 'BGH head: ', - 40. * (head[0, 0, :])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.5\n", "-0.200000047684\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABIcAAAHaCAYAAACaWSkVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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)\n", "ax.plot(gr.cm,bot.squeeze(),drawstyle='steps-mid', linestyle='--', linewidth=3. )\n", "ax.plot(gr.cm,zini[0,:], drawstyle='steps-mid',label='Initial')\n", "ax.plot(gr.cm,zeta[0,0,:],drawstyle='steps-mid', label='SWI2')\n", "ax.plot(gr.cm,head[0, 0, :], label='feshw head')\n", "ax.plot(gr.cm,top[0.0]- 40. * (head[0, 0, :]), label='Ghyben-Herzberg')\n", "\n", "\n", "ax.axis(gr.limLC([0,0,-0.2,0.2]))\n", "leg = ax.legend(loc='lower left', numpoints=1)\n", "leg._drawFrame = False\n", "\n", "print np.sum(zeta[0,0,:3])+4.5\n", "print np.sum(zeta[0,0,3:4])+1.5" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0xa1da390>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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}, "nbformat": 4, "nbformat_minor": 0 }

mit

ldhagen/docker-jupyter

PandasandJupyter.ipynb

1

28268

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Below is from https://dev.socrata.com/blog/2016/02/01/pandas-and-jupyter-notebook.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0.3\n" ] } ], "source": [ "import pandas as pd\n", "print(pd.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pd.read_json?" ] }, { "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>geography</th>\n", " <th>geography_type</th>\n", " <th>year</th>\n", " <th>less_than_high_school_graduate</th>\n", " <th>high_school_graduate</th>\n", " <th>some_college_or_associate_s_degree</th>\n", " <th>bachelor_s_degree_or_higher</th>\n", " <th>location_1</th>\n", " <th>:@computed_region_uph5_8hpn</th>\n", " <th>:@computed_region_i2t2_cryp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Atherton</td>\n", " <td>Town</td>\n", " <td>2014-01-01T00:00:00.000</td>\n", " <td>13.6</td>\n", " <td>12.3</td>\n", " <td>2.7</td>\n", " <td>3.5</td>\n", " <td>{'type': 'Point', 'coordinates': [-122.2, 37.4...</td>\n", " <td>2.0</td>\n", " <td>28596</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Colma</td>\n", " <td>Town</td>\n", " <td>2014-01-01T00:00:00.000</td>\n", " <td>6.3</td>\n", " <td>6.4</td>\n", " <td>10.4</td>\n", " <td>2.4</td>\n", " <td>{'type': 'Point', 'coordinates': [-122.455556,...</td>\n", " <td>4.0</td>\n", " <td>28588</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Foster City</td>\n", " <td>City</td>\n", " <td>2014-01-01T00:00:00.000</td>\n", " <td>11.9</td>\n", " <td>9.7</td>\n", " <td>2.0</td>\n", " <td>2.9</td>\n", " <td>{'type': 'Point', 'coordinates': [-122.266389,...</td>\n", " <td>6.0</td>\n", " <td>319</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Portola Valley</td>\n", " <td>Town</td>\n", " <td>2014-01-01T00:00:00.000</td>\n", " <td>48.1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.8</td>\n", " <td>{'type': 'Point', 'coordinates': [-122.218611,...</td>\n", " <td>14.0</td>\n", " <td>28597</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Redwood City</td>\n", " <td>City</td>\n", " <td>2014-01-01T00:00:00.000</td>\n", " <td>16.4</td>\n", " <td>10.6</td>\n", " <td>6.6</td>\n", " <td>3.0</td>\n", " <td>{'type': 'Point', 'coordinates': [-122.236111,...</td>\n", " <td>21.0</td>\n", " <td>28607</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " geography geography_type year \\\n", "0 Atherton Town 2014-01-01T00:00:00.000 \n", "1 Colma Town 2014-01-01T00:00:00.000 \n", "2 Foster City City 2014-01-01T00:00:00.000 \n", "3 Portola Valley Town 2014-01-01T00:00:00.000 \n", "4 Redwood City City 2014-01-01T00:00:00.000 \n", "\n", " less_than_high_school_graduate high_school_graduate \\\n", "0 13.6 12.3 \n", "1 6.3 6.4 \n", "2 11.9 9.7 \n", "3 48.1 0.0 \n", "4 16.4 10.6 \n", "\n", " some_college_or_associate_s_degree bachelor_s_degree_or_higher \\\n", "0 2.7 3.5 \n", "1 10.4 2.4 \n", "2 2.0 2.9 \n", "3 0.0 1.8 \n", "4 6.6 3.0 \n", "\n", " location_1 \\\n", "0 {'type': 'Point', 'coordinates': [-122.2, 37.4... \n", "1 {'type': 'Point', 'coordinates': [-122.455556,... \n", "2 {'type': 'Point', 'coordinates': [-122.266389,... \n", "3 {'type': 'Point', 'coordinates': [-122.218611,... \n", "4 {'type': 'Point', 'coordinates': [-122.236111,... \n", "\n", " :@computed_region_uph5_8hpn :@computed_region_i2t2_cryp \n", "0 2.0 28596 \n", "1 4.0 28588 \n", "2 6.0 319 \n", "3 14.0 28597 \n", "4 21.0 28607 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_json(\"https://data.smcgov.org/resource/mb6a-xn89.json\")\n", "\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['In',\n", " 'Out',\n", " '_',\n", " '_3',\n", " '__',\n", " '___',\n", " '__builtin__',\n", " '__builtins__',\n", " '__doc__',\n", " '__loader__',\n", " '__name__',\n", " '__package__',\n", " '__spec__',\n", " '_dh',\n", " '_i',\n", " '_i1',\n", " '_i2',\n", " '_i3',\n", " '_i4',\n", " '_ih',\n", " '_ii',\n", " '_iii',\n", " '_oh',\n", " 'df',\n", " 'exit',\n", " 'get_ipython',\n", " 'pd',\n", " 'quit']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(32, 10)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "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>less_than_high_school_graduate</th>\n", " <th>high_school_graduate</th>\n", " <th>some_college_or_associate_s_degree</th>\n", " <th>bachelor_s_degree_or_higher</th>\n", " <th>:@computed_region_uph5_8hpn</th>\n", " <th>:@computed_region_i2t2_cryp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>32.00000</td>\n", " <td>32.000000</td>\n", " <td>32.000000</td>\n", " <td>32.000000</td>\n", " <td>30.000000</td>\n", " <td>32.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>17.80000</td>\n", " <td>6.462500</td>\n", " <td>5.946875</td>\n", " <td>2.856250</td>\n", " <td>17.733333</td>\n", " <td>25062.093750</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>19.29944</td>\n", " <td>4.693905</td>\n", " <td>4.728430</td>\n", " <td>1.873919</td>\n", " <td>9.762466</td>\n", " <td>9502.711577</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>312.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>6.82500</td>\n", " <td>1.925000</td>\n", " <td>2.525000</td>\n", " <td>2.100000</td>\n", " <td>9.500000</td>\n", " <td>28587.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>13.90000</td>\n", " <td>7.750000</td>\n", " <td>5.500000</td>\n", " <td>3.000000</td>\n", " <td>18.500000</td>\n", " <td>28595.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>20.97500</td>\n", " <td>9.450000</td>\n", " <td>8.800000</td>\n", " <td>3.600000</td>\n", " <td>25.750000</td>\n", " <td>28604.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>100.00000</td>\n", " <td>16.400000</td>\n", " <td>18.500000</td>\n", " <td>9.100000</td>\n", " <td>34.000000</td>\n", " <td>28613.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " less_than_high_school_graduate high_school_graduate \\\n", "count 32.00000 32.000000 \n", "mean 17.80000 6.462500 \n", "std 19.29944 4.693905 \n", "min 0.00000 0.000000 \n", "25% 6.82500 1.925000 \n", "50% 13.90000 7.750000 \n", "75% 20.97500 9.450000 \n", "max 100.00000 16.400000 \n", "\n", " some_college_or_associate_s_degree bachelor_s_degree_or_higher \\\n", "count 32.000000 32.000000 \n", "mean 5.946875 2.856250 \n", "std 4.728430 1.873919 \n", "min 0.000000 0.000000 \n", "25% 2.525000 2.100000 \n", "50% 5.500000 3.000000 \n", "75% 8.800000 3.600000 \n", "max 18.500000 9.100000 \n", "\n", " :@computed_region_uph5_8hpn :@computed_region_i2t2_cryp \n", "count 30.000000 32.000000 \n", "mean 17.733333 25062.093750 \n", "std 9.762466 9502.711577 \n", "min 1.000000 312.000000 \n", "25% 9.500000 28587.750000 \n", "50% 18.500000 28595.000000 \n", "75% 25.750000 28604.250000 \n", "max 34.000000 28613.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 7, "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>geography</th>\n", " <th>geography_type</th>\n", " <th>year</th>\n", " <th>less_than_high_school_graduate</th>\n", " <th>high_school_graduate</th>\n", " <th>some_college_or_associate_s_degree</th>\n", " <th>bachelor_s_degree_or_higher</th>\n", " <th>:@computed_region_uph5_8hpn</th>\n", " <th>:@computed_region_i2t2_cryp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>32</td>\n", " <td>32</td>\n", " <td>32</td>\n", " <td>32.00000</td>\n", " <td>32.000000</td>\n", " <td>32.000000</td>\n", " <td>32.000000</td>\n", " <td>30.000000</td>\n", " <td>32.000000</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>32</td>\n", " <td>3</td>\n", " <td>1</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", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>San Carlos</td>\n", " <td>City</td>\n", " <td>2014-01-01T00:00:00.000</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", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>1</td>\n", " <td>15</td>\n", " <td>32</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", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>17.80000</td>\n", " <td>6.462500</td>\n", " <td>5.946875</td>\n", " <td>2.856250</td>\n", " <td>17.733333</td>\n", " <td>25062.093750</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>19.29944</td>\n", " <td>4.693905</td>\n", " <td>4.728430</td>\n", " <td>1.873919</td>\n", " <td>9.762466</td>\n", " <td>9502.711577</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>312.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6.82500</td>\n", " <td>1.925000</td>\n", " <td>2.525000</td>\n", " <td>2.100000</td>\n", " <td>9.500000</td>\n", " <td>28587.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>13.90000</td>\n", " <td>7.750000</td>\n", " <td>5.500000</td>\n", " <td>3.000000</td>\n", " <td>18.500000</td>\n", " <td>28595.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>20.97500</td>\n", " <td>9.450000</td>\n", " <td>8.800000</td>\n", " <td>3.600000</td>\n", " <td>25.750000</td>\n", " <td>28604.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>100.00000</td>\n", " <td>16.400000</td>\n", " <td>18.500000</td>\n", " <td>9.100000</td>\n", " <td>34.000000</td>\n", " <td>28613.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " geography geography_type year \\\n", "count 32 32 32 \n", "unique 32 3 1 \n", "top San Carlos City 2014-01-01T00:00:00.000 \n", "freq 1 15 32 \n", "mean NaN NaN NaN \n", "std NaN NaN NaN \n", "min NaN NaN NaN \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max NaN NaN NaN \n", "\n", " less_than_high_school_graduate high_school_graduate \\\n", "count 32.00000 32.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 17.80000 6.462500 \n", "std 19.29944 4.693905 \n", "min 0.00000 0.000000 \n", "25% 6.82500 1.925000 \n", "50% 13.90000 7.750000 \n", "75% 20.97500 9.450000 \n", "max 100.00000 16.400000 \n", "\n", " some_college_or_associate_s_degree bachelor_s_degree_or_higher \\\n", "count 32.000000 32.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 5.946875 2.856250 \n", "std 4.728430 1.873919 \n", "min 0.000000 0.000000 \n", "25% 2.525000 2.100000 \n", "50% 5.500000 3.000000 \n", "75% 8.800000 3.600000 \n", "max 18.500000 9.100000 \n", "\n", " :@computed_region_uph5_8hpn :@computed_region_i2t2_cryp \n", "count 30.000000 32.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 17.733333 25062.093750 \n", "std 9.762466 9502.711577 \n", "min 1.000000 312.000000 \n", "25% 9.500000 28587.750000 \n", "50% 18.500000 28595.000000 \n", "75% 25.750000 28604.250000 \n", "max 34.000000 28613.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop(\"location_1\", axis=1).describe(include=\"all\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "geography object\n", "geography_type object\n", "year object\n", "less_than_high_school_graduate float64\n", "high_school_graduate float64\n", "some_college_or_associate_s_degree float64\n", "bachelor_s_degree_or_higher float64\n", "location_1 object\n", ":@computed_region_uph5_8hpn float64\n", ":@computed_region_i2t2_cryp int64\n", "dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.8562500000000006" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.bachelor_s_degree_or_higher.mean()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geography.count()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Town', 'City', 'CDP'], dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geography_type.unique()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0 4\n", "14.2 1\n", "8.5 1\n", "7.0 1\n", "100.0 1\n", "9.5 1\n", "11.9 1\n", "4.8 1\n", "31.1 1\n", "26.7 1\n", "6.2 1\n", "15.7 1\n", "22.1 1\n", "16.4 1\n", "6.3 1\n", "44.4 1\n", "20.9 1\n", "7.7 1\n", "9.2 1\n", "37.8 1\n", "3.3 1\n", "15.1 1\n", "48.1 1\n", "18.3 1\n", "21.2 1\n", "16.1 1\n", "13.6 1\n", "13.4 1\n", "20.1 1\n", "Name: less_than_high_school_graduate, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.less_than_high_school_graduate.value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def mapGeography(x):\n", " if x == \"City\":\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "df['geography_mapped_value'] = df.geography_type.apply(mapGeography)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 17\n", "1 15\n", "Name: geography_mapped_value, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geography_mapped_value.value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "df['geography_mapped_value_lambda'] = df.geography_type.apply(lambda y: 1 if y == \"City\" else 0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 17\n", "1 15\n", "Name: geography_mapped_value_lambda, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geography_mapped_value_lambda.value_counts()" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

huilyu2/DataVisualization

project-spring2017/part1/Final-Part1-Trips.ipynb

1

330301

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LIS590DV Final Project - Group Athena\n", "## Part1 - Routes with Different Numbers of Trips\n", "## Part1 - Shapes of Routes with Most/Least Number of Trips\n", "### Author: Hui Lyu" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Ellipse\n", "import matplotlib.patches as mpatches\n", "import csv\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly \n", "plotly.tools.set_credentials_file(username='huilyu2', api_key='LYEkqxDQFmZzZIBXn9rn')\n", "import plotly.plotly as py\n", "from plotly.graph_objs import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trips_df = pd.read_csv(\"GTFS Dataset/trips.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['route_id', 'service_id', 'trip_id', 'trip_headsign', 'direction_id',\n", " 'block_id', 'shape_id'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips_df.columns" ] }, { "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>route_id</th>\n", " <th>service_id</th>\n", " <th>trip_id</th>\n", " <th>trip_headsign</th>\n", " <th>direction_id</th>\n", " <th>block_id</th>\n", " <th>shape_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>TEAL</td>\n", " <td>T4 UIMF</td>\n", " <td>[@14.0.51708725@][4][1277756770140]/0__T4_UIMF</td>\n", " <td>WEST - ILLINOIS TERMINAL</td>\n", " <td>1</td>\n", " <td>T4 UIMF</td>\n", " <td>TEAL 26</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>TEAL</td>\n", " <td>T4 UIMF</td>\n", " <td>[@14.0.51708725@][4][1275505811421]/0__T4_UIMF</td>\n", " <td>WEST - ILLINOIS TERMINAL</td>\n", " <td>1</td>\n", " <td>T4 UIMF</td>\n", " <td>TEAL 23</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>TEAL</td>\n", " <td>T4 UIMF</td>\n", " <td>[@7.0.41893871@][3][1243541396687]/72__T4_UIMF</td>\n", " <td>EAST - ORCHARD DOWNS</td>\n", " <td>0</td>\n", " <td>T4 UIMF</td>\n", " <td>12E TEAL 13</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>TEAL</td>\n", " <td>T4 UIMF</td>\n", " <td>[@7.0.41893871@][4][1243540851671]/4__T4_UIMF</td>\n", " <td>WEST - ILLINOIS TERMINAL</td>\n", " <td>1</td>\n", " <td>T4 UIMF</td>\n", " <td>12W TEAL 12</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>TEAL</td>\n", " <td>T4 UIMF</td>\n", " <td>[@7.0.41893871@][3][1243541396687]/74__T4_UIMF</td>\n", " <td>EAST - ORCHARD DOWNS</td>\n", " <td>0</td>\n", " <td>T4 UIMF</td>\n", " <td>12E TEAL 13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " route_id service_id trip_id \\\n", "0 TEAL T4 UIMF [@14.0.51708725@][4][1277756770140]/0__T4_UIMF \n", "1 TEAL T4 UIMF [@14.0.51708725@][4][1275505811421]/0__T4_UIMF \n", "2 TEAL T4 UIMF [@7.0.41893871@][3][1243541396687]/72__T4_UIMF \n", "3 TEAL T4 UIMF [@7.0.41893871@][4][1243540851671]/4__T4_UIMF \n", "4 TEAL T4 UIMF [@7.0.41893871@][3][1243541396687]/74__T4_UIMF \n", "\n", " trip_headsign direction_id block_id shape_id \n", "0 WEST - ILLINOIS TERMINAL 1 T4 UIMF TEAL 26 \n", "1 WEST - ILLINOIS TERMINAL 1 T4 UIMF TEAL 23 \n", "2 EAST - ORCHARD DOWNS 0 T4 UIMF 12E TEAL 13 \n", "3 WEST - ILLINOIS TERMINAL 1 T4 UIMF 12W TEAL 12 \n", "4 EAST - ORCHARD DOWNS 0 T4 UIMF 12E TEAL 13 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many trips for each route?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(np.unique(trips_df[\"route_id\"])) # How many routes in all" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_values([53, 33, 1, 129, 76, 250, 48, 52, 20, 29, 9, 6, 1, 4, 23, 112, 3, 61, 19, 89, 38, 135, 224, 33, 67, 71, 32, 333, 3, 138, 111, 36, 21, 21, 141, 25, 23, 6, 84, 16, 76, 49, 49, 2, 88, 68, 94, 4, 12, 38, 222, 39, 76, 45, 16, 27, 122, 21, 1, 69, 4, 48, 47, 32, 50, 1, 59, 29, 27, 4, 30, 92, 5, 9, 116, 56, 238, 40, 46, 8, 60, 37, 36, 34, 63, 69, 34, 1, 25, 54, 108, 12, 51, 5, 22, 72, 127, 21, 24, 8])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips_count = Counter(trips_df[\"route_id\"])\n", "trips_count.values()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XgT9Xl9yfGhGvq1rGxnMWo3XwiIjYgtGWmvdX74+ugq/W5et3UoKrTv4S9GTm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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x20c204dfc18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (8, 8)\n", "fig, ax = plt.subplots()\n", "\n", "n, bins, patches = plt.hist(list(trips_count.values()),bins=np.arange(0,400,50),edgecolor = 'k')\n", "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n", "cm = plt.cm.get_cmap('Purples')\n", "\n", "# scale values to interval [0,1]\n", "col = bin_centers - min(bin_centers)\n", "col /= max(col)\n", "for c, p in zip(col, patches):\n", " plt.setp(p, 'facecolor', cm(c))\n", " \n", "plt.xlabel(\"Number of Trips for Each Route\",fontsize=16)\n", "plt.ylabel(\"Count of Routes\",fontsize=16)\n", "plt.title(\"Distribution of Routes with Different Numbers of Trips\\n(Total: 100 Routes which Exist Trip)\",fontweight=\"bold\",fontsize=18)\n", "plt.xticks(bins)\n", "ax.set_axisbelow(True)\n", "ax.yaxis.grid(color='gray', linestyle='dashed')\n", "plt.savefig('Distribution of Routes with Trips.svg', bbox_inches='tight')\n", "plt.savefig('Distribution of Routes with Trips.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('SILVER', 333),\n", " ('ILLINI', 250),\n", " ('TEAL', 238),\n", " ('ILLINI EVENING', 224),\n", " ('TEAL SATURDAY', 222),\n", " ('YELLOWHOPPER', 141),\n", " ('ILLINI LIMITED SATURDAY', 138),\n", " ('ILLINI EVENING SATURDAY', 135),\n", " ('GREEN SATURDAY', 129),\n", " ('YELLOW SATURDAY', 127)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips_count.most_common(10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "shape_id_dict = {}\n", "trip_count_dict = {}\n", "for word, count in trips_count.most_common(10):\n", " trip_count_dict[word]=count # A route corresponds to its number of trips\n", " shape_id_dict[word]=np.unique(trips_df[trips_df.route_id == word][[\"shape_id\"]])\n", " # A route correponds to its several shapes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import operator\n", "trip_count_list = sorted(trip_count_dict.items(), key=operator.itemgetter(1), reverse=True)\n", "trip_count_df = pd.DataFrame(trip_count_list,columns = ['route_id', 'number of trips'])" ] }, { "cell_type": "code", "execution_count": 13, "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>route_id</th>\n", " <th>number of trips</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>SILVER</td>\n", " <td>333</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ILLINI</td>\n", " <td>250</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>TEAL</td>\n", " <td>238</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ILLINI EVENING</td>\n", " <td>224</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>TEAL SATURDAY</td>\n", " <td>222</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>YELLOWHOPPER</td>\n", " <td>141</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>ILLINI LIMITED SATURDAY</td>\n", " <td>138</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>ILLINI EVENING SATURDAY</td>\n", " <td>135</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>GREEN SATURDAY</td>\n", " <td>129</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>YELLOW SATURDAY</td>\n", " <td>127</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " route_id number of trips\n", "0 SILVER 333\n", "1 ILLINI 250\n", "2 TEAL 238\n", "3 ILLINI EVENING 224\n", "4 TEAL SATURDAY 222\n", "5 YELLOWHOPPER 141\n", "6 ILLINI LIMITED SATURDAY 138\n", "7 ILLINI EVENING SATURDAY 135\n", "8 GREEN SATURDAY 129\n", "9 YELLOW SATURDAY 127" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trip_count_df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "routes_df = pd.read_csv(\"GTFS Dataset/routes.csv\")" ] }, { "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>route_id</th>\n", " <th>agency_id</th>\n", " <th>route_short_name</th>\n", " <th>route_long_name</th>\n", " <th>route_desc</th>\n", " <th>route_type</th>\n", " <th>route_url</th>\n", " <th>route_color</th>\n", " <th>route_text_color</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>GOLD ALT</td>\n", " <td>CUMTD</td>\n", " <td>10</td>\n", " <td>Gold 1 Alternate</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>c7994a</td>\n", " <td>000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RUBY SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>110</td>\n", " <td>Ruby Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>eb008b</td>\n", " <td>000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>SILVER LIMITED SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>130</td>\n", " <td>Silver Limited Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>d1d3d4</td>\n", " <td>000000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>BROWN ALT PM</td>\n", " <td>CUMTD</td>\n", " <td>9</td>\n", " <td>Brown Alternate PM</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>823822</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>YELLOW LATE NIGHT SUNDAY</td>\n", " <td>CUMTD</td>\n", " <td>100</td>\n", " <td>Yellow Late Night Sunday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>fcee1f</td>\n", " <td>000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " route_id agency_id route_short_name \\\n", "0 GOLD ALT CUMTD 10 \n", "1 RUBY SATURDAY CUMTD 110 \n", "2 SILVER LIMITED SATURDAY CUMTD 130 \n", "3 BROWN ALT PM CUMTD 9 \n", "4 YELLOW LATE NIGHT SUNDAY CUMTD 100 \n", "\n", " route_long_name route_desc route_type route_url route_color \\\n", "0 Gold 1 Alternate NaN 3 NaN c7994a \n", "1 Ruby Saturday NaN 3 NaN eb008b \n", "2 Silver Limited Saturday NaN 3 NaN d1d3d4 \n", "3 Brown Alternate PM NaN 3 NaN 823822 \n", "4 Yellow Late Night Sunday NaN 3 NaN fcee1f \n", "\n", " route_text_color \n", "0 000000 \n", "1 000000 \n", "2 000000 \n", "3 ffffff \n", "4 000000 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "routes_df.head()" ] }, { "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>route_id</th>\n", " <th>agency_id</th>\n", " <th>route_short_name</th>\n", " <th>route_long_name</th>\n", " <th>route_desc</th>\n", " <th>route_type</th>\n", " <th>route_url</th>\n", " <th>route_color</th>\n", " <th>route_text_color</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>40</th>\n", " <td>SILVER</td>\n", " <td>CUMTD</td>\n", " <td>13</td>\n", " <td>Silver</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>cccccc</td>\n", " <td>000000</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>ILLINI</td>\n", " <td>CUMTD</td>\n", " <td>22</td>\n", " <td>Illini</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>5a1d5a</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>TEAL</td>\n", " <td>CUMTD</td>\n", " <td>12</td>\n", " <td>Teal</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>006991</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>ILLINI EVENING</td>\n", " <td>CUMTD</td>\n", " <td>220</td>\n", " <td>Illini Evening</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>5a1d5a</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>TEAL SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>120</td>\n", " <td>Teal Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>006991</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>YELLOWHOPPER</td>\n", " <td>CUMTD</td>\n", " <td>1</td>\n", " <td>Yellowhopper</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>fcee1f</td>\n", " <td>000000</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>ILLINI LIMITED SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>220</td>\n", " <td>Illini Limited Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>5a1d5a</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>ILLINI EVENING SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>220</td>\n", " <td>Illini Evening Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>5a1d5a</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>GREEN SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>50</td>\n", " <td>Green Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>008063</td>\n", " <td>ffffff</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>YELLOW SATURDAY</td>\n", " <td>CUMTD</td>\n", " <td>100</td>\n", " <td>Yellow Saturday</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>fcee1f</td>\n", " <td>000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " route_id agency_id route_short_name \\\n", "40 SILVER CUMTD 13 \n", "45 ILLINI CUMTD 22 \n", "93 TEAL CUMTD 12 \n", "91 ILLINI EVENING CUMTD 220 \n", "18 TEAL SATURDAY CUMTD 120 \n", "97 YELLOWHOPPER CUMTD 1 \n", "100 ILLINI LIMITED SATURDAY CUMTD 220 \n", "67 ILLINI EVENING SATURDAY CUMTD 220 \n", "10 GREEN SATURDAY CUMTD 50 \n", "63 YELLOW SATURDAY CUMTD 100 \n", "\n", " route_long_name route_desc route_type route_url route_color \\\n", "40 Silver NaN 3 NaN cccccc \n", "45 Illini NaN 3 NaN 5a1d5a \n", "93 Teal NaN 3 NaN 006991 \n", "91 Illini Evening NaN 3 NaN 5a1d5a \n", "18 Teal Saturday NaN 3 NaN 006991 \n", "97 Yellowhopper NaN 3 NaN fcee1f \n", "100 Illini Limited Saturday NaN 3 NaN 5a1d5a \n", "67 Illini Evening Saturday NaN 3 NaN 5a1d5a \n", "10 Green Saturday NaN 3 NaN 008063 \n", "63 Yellow Saturday NaN 3 NaN fcee1f \n", "\n", " route_text_color \n", "40 000000 \n", "45 ffffff \n", "93 ffffff \n", "91 ffffff \n", "18 ffffff \n", "97 000000 \n", "100 ffffff \n", "67 ffffff \n", "10 ffffff \n", "63 000000 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_routes = pd.DataFrame()\n", "for k in np.arange(10):\n", " df_routes = df_routes.append(routes_df[routes_df.route_id == trip_count_df.route_id[k]])\n", "df_routes\n", "#df_shapes = df_shapes.append(shapes_df[shapes_df.shape_id == shape_id])" ] }, { "cell_type": "code", "execution_count": 17, "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>route_color</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>40</th>\n", " <td>cccccc</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>5a1d5a</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>006991</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>5a1d5a</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>006991</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>fcee1f</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>5a1d5a</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>5a1d5a</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>008063</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>fcee1f</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " route_color\n", "40 cccccc\n", "45 5a1d5a\n", "93 006991\n", "91 5a1d5a\n", "18 006991\n", "97 fcee1f\n", "100 5a1d5a\n", "67 5a1d5a\n", "10 008063\n", "63 fcee1f" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_routes[[\"route_color\"]]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6TNKnJR0i6X7n3GTv/aYM7uqG3DY/LukK59wghfmbVkh6rMg6f/be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\nQQe13X7PPfe0zf/U0NDQbtuDBg0qej1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NnDlTNTU1eulLX6qLLrpIW7duHdy3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PSPLAycAOSKU2vjqq+v944AR/QfG/3HRfex0X9s9I8sLBoOIFcNDQ2pra3NOwYAAAAAYAKwaDgA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\nCapmTwJBtXbRUL0L+Bvgr6maTJtSNcHmAx/IzMvq4/6BamHsb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"text/plain": [ "<matplotlib.figure.Figure at 0x20c20963128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (16, 16)\n", "fig, ax = plt.subplots()\n", "colors = ['#cccccc','#5a1d5a','#006991','#5a1d5a','#006991','#fcee1f','#5a1d5a','#5a1d5a','#008063','#fcee1f']\n", "plt.barh(bottom=np.arange(10)[::-1], width=trip_count_df['number of trips'], height=0.5,alpha=0.9,color=colors)\n", "plt.xlabel(\"Number of Trips\",fontsize=20)\n", "plt.ylabel(\"Route ID\",fontsize=20)\n", "ax.set_yticks(np.arange(10)[::-1])\n", "ax.set_yticklabels(trip_count_df['route_id'], fontsize=18)\n", "plt.title(\"Top 10 Routes with Most Number of Trips for Each Route\",fontweight=\"bold\",fontsize=24)\n", "ax.xaxis.grid(color='gray', linestyle='dotted') \n", "for i, v in enumerate(trip_count_df['number of trips'][::-1]):\n", " ax.text(v + 2, i-0.05, str(v), color='k', fontsize=18,fontweight=\"bold\")\n", "\n", "plt.savefig('Top 10 Routes with Most Trips.svg', bbox_inches='tight')\n", "plt.savefig('Top 10 Routes with Most Trips.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'GREEN SATURDAY': array(['50E->MNBDY', '50E->PC', '50E-WKND-HP->CF', 'GN1SATPO', 'GN1SUNPO',\n", " 'GREEN WEEKEND 23', 'GREEN WEEKEND 44', '[@15.0.63188916@]15',\n", " '[@15.0.68513015@]219', '[@15.0.68513015@]220',\n", " '[@15.0.73006437@]18', '[@15.0.73006437@]37', '[@15.0.73006437@]38'], dtype=object),\n", " 'ILLINI': array(['22N ILLINI 10', '22S ILLINI 20', '22S ILLINI 21', 'ILLINI 34',\n", " 'ILLINI 46', 'ILLINI 47', '[@15.0.63192528@]43',\n", " '[@15.0.66063553@]12', '[@15.0.68513188@]4'], dtype=object),\n", " 'ILLINI EVENING': array(['220N ILLINI 10', '220S ILLINI 20', 'ILLINI EV 25',\n", " '[@14.0.56288498@]24', '[@2.0.85634827@]37'], dtype=object),\n", " 'ILLINI EVENING SATURDAY': array(['[@124.0.92260187@]220N ILLINI 10',\n", " '[@124.0.92260187@]220S ILLINI 20', '[@124.0.92260187@]24',\n", " '[@124.0.92260187@]ILLINI EV 25'], dtype=object),\n", " 'ILLINI LIMITED SATURDAY': array(['22N ILLINI LIMITED WEEKEND', 'GR2PO', 'ILLINI LIMITED WEEKEND 845'], dtype=object),\n", " 'SILVER': array(['SILVER 120', 'SILVER 2', 'SILVER 42', 'SILVER 43',\n", " '[@15.0.73007178@]121'], dtype=object),\n", " 'TEAL': array(['12E TEAL 13', '12W TEAL 12', 'TEAL 23', 'TEAL 24', 'TEAL 25',\n", " 'TEAL 26', 'TEAL 27', 'TEAL 34', 'TEAL 35', 'TEAL 45', 'TEAL 98',\n", " 'TEAL 99', '[@15.0.79613563@]47'], dtype=object),\n", " 'TEAL SATURDAY': array(['12E TEAL WEEKEND 13', '12W TEAL WEEKEND 12', 'TEAL WEEKEND 16',\n", " 'TEAL WEEKEND 45'], dtype=object),\n", " 'YELLOW SATURDAY': array(['[@14.0.57766396@]100N', '[@14.0.57766396@]100NGRNWRT->',\n", " '[@14.0.57766396@]100NY1', '[@14.0.57766396@]100S',\n", " '[@14.0.57766396@]100S->BRWSWDFLD', '[@14.0.57766396@]Y1SATPO',\n", " '[@14.0.57766396@]Y3SATPO', '[@15.0.63192124@]23'], dtype=object),\n", " 'YELLOWHOPPER': array(['YELLOWHOPPER 17', 'YELLOWHOPPER 23', 'YELLOWHOPPER 25',\n", " 'YELLOWHOPPER 26', '[@15.0.63189099@]29'], dtype=object)}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shape_id_dict" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('10W GOLD ALT', 1),\n", " ('7W GREY ALT', 1),\n", " ('1N YELLOW ALT', 1),\n", " ('BROWN ALT1', 1),\n", " ('5W GREEN ALT 2', 1),\n", " ('1N YELLOW ALT PM', 2),\n", " ('5E GREEN EXPRESS 1 ALT', 3),\n", " ('GREEN EXPRESS ALT', 3),\n", " ('5W GREEN EXPRESS 2', 4),\n", " ('1S YELLOW ALT', 4)]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips_count.most_common()[:-11:-1]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "shapes_df = pd.read_csv(\"GTFS Dataset/shapes.csv\")" ] }, { "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>shape_id</th>\n", " <th>shape_pt_lat</th>\n", " <th>shape_pt_lon</th>\n", " <th>shape_pt_sequence</th>\n", " <th>shape_dist_traveled</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[@2.0.86175868@]34</td>\n", " <td>40.114158</td>\n", " <td>-88.173105</td>\n", " <td>0</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[@2.0.86175868@]34</td>\n", " <td>40.114158</td>\n", " <td>-88.173106</td>\n", " <td>1</td>\n", " <td>0.134184</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>[@2.0.86175868@]34</td>\n", " <td>40.114171</td>\n", " <td>-88.173107</td>\n", " <td>2</td>\n", " <td>1.560577</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>[@2.0.86175868@]34</td>\n", " <td>40.114186</td>\n", " <td>-88.173108</td>\n", " <td>3</td>\n", " <td>3.228456</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>[@2.0.86175868@]34</td>\n", " <td>40.114200</td>\n", " <td>-88.173109</td>\n", " <td>4</td>\n", " <td>4.787531</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " shape_id shape_pt_lat shape_pt_lon shape_pt_sequence \\\n", "0 [@2.0.86175868@]34 40.114158 -88.173105 0 \n", "1 [@2.0.86175868@]34 40.114158 -88.173106 1 \n", "2 [@2.0.86175868@]34 40.114171 -88.173107 2 \n", "3 [@2.0.86175868@]34 40.114186 -88.173108 3 \n", "4 [@2.0.86175868@]34 40.114200 -88.173109 4 \n", "\n", " shape_dist_traveled \n", "0 0.000000 \n", "1 0.134184 \n", "2 1.560577 \n", "3 3.228456 \n", "4 4.787531 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shapes_df.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['ILLINI LIMITED SATURDAY',\n", " 'TEAL',\n", " 'GREEN SATURDAY',\n", " 'SILVER',\n", " 'ILLINI',\n", " 'YELLOWHOPPER',\n", " 'ILLINI EVENING SATURDAY',\n", " 'TEAL SATURDAY',\n", " 'YELLOW SATURDAY',\n", " 'ILLINI EVENING']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_ten_shapes = list(shape_id_dict.keys())\n", "top_ten_shapes" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_shapes = pd.DataFrame()\n", "\n", "for key in top_ten_shapes:\n", " for shape_id in shape_id_dict[key]:\n", " df_shapes = df_shapes.append(shapes_df[shapes_df.shape_id == shape_id])\n", "\n", "df_shapes_group = df_shapes.groupby(\"shape_id\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_shapes_least = pd.DataFrame()\n", "shape_id_dict_least = {}\n", "once_routes = (\"BROWN ALT1\",\"10W GOLD ALT\",\"5W GREEN ALT 2\",\"7W GREY ALT\",\"1N YELLOW ALT\")\n", "\n", "for word, count in trips_count.most_common()[:-11:-1]:\n", " shape_id_dict_least[word]=np.unique(trips_df[trips_df.route_id == word][[\"shape_id\"]])\n", " \n", "for key in once_routes:\n", " for shape_id in shape_id_dict_least[key]:\n", " df_shapes_least = df_shapes_least.append(shapes_df[shapes_df.shape_id == shape_id])\n", "\n", "df_shapes_least_group = df_shapes_least.groupby(\"shape_id\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2fl+1R0REBGTotV3J0jl79my27gVxiKCR9PT0YEvwm/zUnhf3cdGiRQGPM7+w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rHZbBw+fLjHxywWC2lpaaqrINobFRUV1NfXA67Fx8zMTOGdAMFi37598u0xY8YoqMT3ZsRizLoh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\nphLmqNTW1irT96kKX6TSr1/XzKmo0vepcKp+r9dLVdVMAYdf/vKXc/bZtm0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8BNwcNuArbmePqTwBrP81Z5nlcePP+h4HMHgNcHt98AvDyWJXzOgWKqGz5o20Hbr2wHbb+S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\nXjrJqqEOHTrExz72MQBmz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cp2ML/LlO2g7Xdlt3OyguycLMuyLOujH/0ohw8fBuDzn/88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\nbgff/Z7bwXd/Xto3btxIV1cXAA0NDRx++OH9W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"text/plain": [ "<matplotlib.figure.Figure at 0x20c20a672b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (14, 14)\n", "\n", "for name, group in df_shapes_group:\n", " plt.plot(group['shape_pt_lon'],group['shape_pt_lat'],linestyle=\"solid\",linewidth=3,alpha=0.3,c=\"k\")\n", "for name, group in df_shapes_least_group:\n", " plt.plot(group['shape_pt_lon'],group['shape_pt_lat'],linestyle=\"solid\",linewidth=3,alpha=0.3,c=\"gray\")\n", "\n", "plt.xlabel(\"Longitude\",fontsize=20)\n", "plt.ylabel(\"Latitude\",fontsize=20)\n", "plt.title(\"Shapes of Routes with Most/Least Number of Trips\",fontweight=\"bold\",fontsize=22)\n", "plt.grid(color='gray', linestyle='dotted')\n", "\n", "black_patch = mpatches.Patch(color='k')\n", "gray_patch = mpatches.Patch(color='lightgray')\n", "first_legend = plt.legend(title=\"Routes\",handles=[black_patch,gray_patch],labels=[\"Top 10 Routes with Most Number of Trips\",\"5 Routes with Only One Trip per Route\"],prop={'size':14},loc=1)\n", "\n", "rectangle1=plt.Rectangle((-88.26,40.1266),width=0.01,height=0.0067,alpha=0.6,facecolor=\"yellow\",edgecolor=\"None\")\n", "rectangle2=plt.Rectangle((-88.20,40.0866),width=0.01,height=0.0067,alpha=0.6,facecolor=\"yellow\",edgecolor=\"None\")\n", "rectangle3=plt.Rectangle((-88.21,40.1066),width=0.01,height=0.0067,alpha=0.6,facecolor=\"#adff2f\",edgecolor=\"None\")\n", "rectangle4=plt.Rectangle((-88.20,40.0934),width=0.01,height=0.0067,alpha=0.6,facecolor=\"#adff2f\",edgecolor=\"None\")\n", "rectangle5=plt.Rectangle((-88.25,40.1134),width=0.01,height=0.0134,alpha=0.6,facecolor=\"#7fff00\",edgecolor=\"None\")\n", "rectangle6=plt.Rectangle((-88.23,40.1134),width=0.01,height=0.0067,alpha=0.6,facecolor=\"#7fff00\",edgecolor=\"None\")\n", "\n", "ax=plt.gca()\n", "# Add the legend manually to the current Axes.\n", "ax.add_artist(first_legend)\n", "\n", "ax.set_yticks(np.arange(40.05,40.16,0.01))\n", "ax.set_xticks(np.arange(-88.32,-88.15,0.01))\n", "ellipse1 = Ellipse(xy=(-88.242,40.123),width=0.044,height=0.021,alpha=0.9,facecolor=\"None\",edgecolor=\"hotpink\",lw=2)\n", "ellipse2 = Ellipse(xy=(-88.201,40.101),width=0.023,height=0.028,alpha=0.9,facecolor=\"None\",edgecolor=\"hotpink\",lw=2)\n", "ax.add_patch(ellipse1)\n", "ax.add_patch(ellipse2)\n", "ax.add_patch(rectangle1)\n", "ax.add_patch(rectangle2)\n", "ax.add_patch(rectangle3)\n", "ax.add_patch(rectangle4)\n", "ax.add_patch(rectangle5)\n", "ax.add_patch(rectangle6)\n", "\n", "second_legend = plt.legend(title=\"Density of Bus Stops\",prop={'size':14},handles=[rectangle1,rectangle3,rectangle5],labels=[\"highest\",\"very high\",\"high\"],loc=2)\n", "ax.add_artist(second_legend)\n", "\n", "plt.savefig('Shapes of Routes with Trips.svg', bbox_inches='tight')\n", "plt.savefig('Shapes of Routes with Trips.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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 }

mit

msschwartz21/craniumPy

experiments/templates/TEMP-landmarks.ipynb

1

6850

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Landmarks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import deltascope as ds\n", "import deltascope.alignment as ut\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.preprocessing import normalize\n", "from scipy.optimize import minimize\n", "\n", "import os\n", "import tqdm\n", "import json\n", "import time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import raw data\n", "The user needs to specify the directories containing the data of interest. Each sample type should have a key which corresponds to the directory path. Additionally, each object should have a list that includes the channels of interest." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# --------------------------------\n", "# -------- User input ------------\n", "# --------------------------------\n", "\n", "data = {\n", " # Specify sample type key\n", " 'wt': {\n", " # Specify path to data directory\n", " 'path': 'path\\to\\data\\directory\\sample1',\n", " # Specify which channels are in the directory and are of interest\n", " 'channels': ['AT','ZRF']\n", " },\n", " 'stype2': {\n", " 'path': 'path\\to\\data\\directory\\sample2',\n", " 'channels': ['AT','ZRF']\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll generate a list of pairs of stypes and channels for ease of use." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-651a40b3f0ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdata_pairs\u001b[0m \u001b[0;34m=\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[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'channels'\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 4\u001b[0m \u001b[0mdata_pairs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\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[0;31mNameError\u001b[0m: name 'data' is not defined" ] } ], "source": [ "data_pairs = []\n", "for s in data.keys():\n", " for c in data[s]['channels']:\n", " data_pairs.append((s,c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now read in all datafiles specified by the `data` dictionary above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "D = {}\n", "for s in data.keys():\n", " D[s] = {}\n", " for c in data[s]['channels']:\n", " D[s][c] = ds.read_psi_to_dict(data[s]['path'],c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate landmark bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# --------------------------------\n", "# -------- User input ------------\n", "# --------------------------------\n", "\n", "# Pick an integer value for bin number based on results above\n", "anum = 25\n", "\n", "# Specify the percentiles which will be used to calculate landmarks\n", "percbins = [50]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate landmark bins based on user input parameters and the previously specified control sample." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lm = ds.landmarks(percbins=percbins, rnull=np.nan)\n", "lm.calc_bins(D[s_ctrl][c_ctrl], anum, theta_step)\n", "\n", "print('Alpha bins')\n", "print(lm.acbins)\n", "print('Theta bins')\n", "print(lm.tbins)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate landmarks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lmdf = pd.DataFrame()\n", "\n", "# Loop through each pair of stype and channels\n", "for s,c in tqdm.tqdm(data_pairs):\n", " print(s,c)\n", " # Calculate landmarks for each sample with this data pair\n", " for k,df in tqdm.tqdm(D[s][c].items()):\n", " lmdf = lm.calc_perc(df, k, '-'.join([s,c]), lmdf)\n", " \n", "# Set timestamp for saving data\n", "tstamp = time.strftime(\"%m-%d-%H-%M\",time.localtime())\n", " \n", "# Save completed landmarks to a csv file\n", "lmdf.to_csv(tstamp+'_landmarks.csv')\n", "\n", "# Save landmark bins to json file\n", "bins = {\n", " 'acbins':list(lm.acbins),\n", " 'tbins':list(lm.tbins)\n", "}\n", "with open(tstamp+'_landmarks_bins.json', 'w') as outfile:\n", " json.dump(bins, outfile)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:test]", "language": "python", "name": "conda-env-test-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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

mne-tools/mne-tools.github.io

dev/_downloads/1242d47b65d952f9f80cf19fb9e5d76e/35_eeg_no_mri.ipynb

1

7576

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# EEG forward operator with a template MRI\n\nThis tutorial explains how to compute the forward operator from EEG data\nusing the standard template MRI subject ``fsaverage``.\n\n.. caution:: Source reconstruction without an individual T1 MRI from the\n subject will be less accurate. Do not over interpret\n activity locations which can be off by multiple centimeters.\n\n## Adult template MRI (fsaverage)\nFirst we show how ``fsaverage`` can be used as a surrogate subject.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Joan Massich <mailsik@gmail.com>\n# Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n\nimport os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.datasets import eegbci\nfrom mne.datasets import fetch_fsaverage\n\n# Download fsaverage files\nfs_dir = fetch_fsaverage(verbose=True)\nsubjects_dir = op.dirname(fs_dir)\n\n# The files live in:\nsubject = 'fsaverage'\ntrans = 'fsaverage' # MNE has a built-in fsaverage transformation\nsrc = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')\nbem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the data\n\nWe use here EEG data from the BCI dataset.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>See `plot_montage` to view all the standard EEG montages\n available in MNE-Python.</p></div>\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_fname, = eegbci.load_data(subject=1, runs=[6])\nraw = mne.io.read_raw_edf(raw_fname, preload=True)\n\n# Clean channel names to be able to use a standard 1005 montage\nnew_names = dict(\n (ch_name,\n ch_name.rstrip('.').upper().replace('Z', 'z').replace('FP', 'Fp'))\n for ch_name in raw.ch_names)\nraw.rename_channels(new_names)\n\n# Read and set the EEG electrode locations, which are already in fsaverage's\n# space (MNI space) for standard_1020:\nmontage = mne.channels.make_standard_montage('standard_1005')\nraw.set_montage(montage)\nraw.set_eeg_reference(projection=True) # needed for inverse modeling\n\n# Check that the locations of EEG electrodes is correct with respect to MRI\nmne.viz.plot_alignment(\n raw.info, src=src, eeg=['original', 'projected'], trans=trans,\n show_axes=True, mri_fiducials=True, dig='fiducials')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup source space and compute forward\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fwd = mne.make_forward_solution(raw.info, trans=trans, src=src,\n bem=bem, eeg=True, mindist=5.0, n_jobs=None)\nprint(fwd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here on, standard inverse imaging methods can be used!\n\n## Infant MRI surrogates\nWe don't have a sample infant dataset for MNE, so let's fake a 10-20 one:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ch_names = \\\n 'Fz Cz Pz Oz Fp1 Fp2 F3 F4 F7 F8 C3 C4 T7 T8 P3 P4 P7 P8 O1 O2'.split()\ndata = np.random.RandomState(0).randn(len(ch_names), 1000)\ninfo = mne.create_info(ch_names, 1000., 'eeg')\nraw = mne.io.RawArray(data, info)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get an infant MRI template\nTo use an infant head model for M/EEG data, you can use\n:func:`mne.datasets.fetch_infant_template` to download an infant template:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "subject = mne.datasets.fetch_infant_template('6mo', subjects_dir, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It comes with several helpful built-in files, including a 10-20 montage\nin the MRI coordinate frame, which can be used to compute the\nMRI<->head transform ``trans``:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fname_1020 = op.join(subjects_dir, subject, 'montages', '10-20-montage.fif')\nmon = mne.channels.read_dig_fif(fname_1020)\nmon.rename_channels(\n {f'EEG{ii:03d}': ch_name for ii, ch_name in enumerate(ch_names, 1)})\ntrans = mne.channels.compute_native_head_t(mon)\nraw.set_montage(mon)\nprint(trans)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also BEM and source spaces:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bem_dir = op.join(subjects_dir, subject, 'bem')\nfname_src = op.join(bem_dir, f'{subject}-oct-6-src.fif')\nsrc = mne.read_source_spaces(fname_src)\nprint(src)\nfname_bem = op.join(bem_dir, f'{subject}-5120-5120-5120-bem-sol.fif')\nbem = mne.read_bem_solution(fname_bem)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can ensure everything is as expected by plotting the result:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.plot_alignment(\n raw.info, subject=subject, subjects_dir=subjects_dir, trans=trans,\n src=src, bem=bem, coord_frame='mri', mri_fiducials=True, show_axes=True,\n surfaces=('white', 'outer_skin', 'inner_skull', 'outer_skull'))\nmne.viz.set_3d_view(fig, 25, 70, focalpoint=[0, -0.005, 0.01])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here, standard forward and inverse operators can be computed\n\nIf you have digitized head positions or MEG data, consider using\n`mne coreg` to warp a suitable infant template MRI to your\ndigitization information.\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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

Kadenze/siamese_net

siamese_net_example.ipynb

1

10825704

apache-2.0

Centre-Alt-Rendiment-Esportiu/att

notebooks/Serial Ports.ipynb

2

11484

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Serial Ports</h1>\n", "<hr style=\"border: 1px solid #000;\">\n", "<span>\n", "<h2>Serial Port abstraction for ATT.</h2>\n", "</span>\n", "<br>\n", "<span>\n", "This notebook shows the ATT Serial Port abstraction module.<br>\n", "This module was created for enabling testing on ATT framework.\n", "The Serial Port abstraction provides an Abstract base class so it can be extended and implement whatever kind of serial port we need.\n", "We have used this class hierarchy to build some Mocks, in order to test the ATT framework.\n", "</span>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set modules path first:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "#sys.path.insert(0, '/home/asanso/workspace/att-spyder/att/src/python/')\n", "sys.path.insert(0, 'i:/dev/workspaces/python/att-workspace/att/src/python/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The main abstract base class is the following one:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "class SerialPort:\n", "\t__metaclass__ = abc.ABCMeta\n", "\t\n", "\t@abc.abstractmethod\n", "\tdef isOpen(self):\n", "\t\tpass\n", "\t\t\n", "\t@abc.abstractmethod\n", "\tdef readline(self):\n", "\t\tpass\n", "\t\t\n", "\t@abc.abstractmethod\n", "\tdef close(self):\n", "\t\tpass\n", "\t\n", "\t@abc.abstractmethod\n", "\tdef get_port(self):\n", "\t\treturn \"\"\n", "\t\t\n", "\t@abc.abstractmethod\n", "\tdef get_baudrate(self):\n", "\t\treturn 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, we can see a dummy implementation:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "class DummySerialPort (SerialPort):\n", "\tdef __init__(self, port = None, baud = None):\n", "\t\tpass\n", "\t\n", "\tdef isOpen(self):\n", "\t\treturn True\n", "\t\t\n", "\tdef close(self):\n", "\t\tpass\n", "\t\t\n", "\tdef get_port(self):\n", "\t\treturn \"\"\n", "\t\t\n", "\tdef get_baudrate(self):\n", "\t\treturn 0\n", "\t\t\n", "\tdef readline(self):\n", "\t\ttime_delay = int(3*random.random())+1\n", "\t\ttime.sleep(time_delay)\n", "\t\treturn self.gen_random_line()\n", "\t\t\n", "\tdef gen_random_line(self):\n", "\t\treturn \"Hee\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Building Serial Ports</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "In order to build an instance of a SerialPort class, we have 2 options:\n", "<ul>\n", "<li>Call the constructor directly</li>\n", "<li>Use a Builder</li>\n", "</ul>\n", "</span>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Calling the constructor</h3>" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import hit.serial.serial_port\n", "\n", "port=\"\"\n", "baud=0\n", "dummySerialPort = hit.serial.serial_port.DummySerialPort(port, baud)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "The DummSerialPort is very simple. It just says \"Hee\" (after a few seconds) when its method \"readline()\" is called.<br>\n", "Port and Baud are useless here.\n", "</span>" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hee\n" ] } ], "source": [ "print dummySerialPort.readline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "Let's create a more interesting Serialport instance,\n", "</span>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import hit.serial.serial_port\n", "\n", "port=\"\"\n", "baud=0\n", "emulatedSerialPort = hit.serial.serial_port.ATTEmulatedSerialPort(port, baud)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "The ATTEmulatedSerialPort will emulate a real ATT serial port reading.<br>\n", "Port and Baud are useless here.\n", "</span>" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hit: {0 1048 3204 576 196 592 212 1 r}\n" ] } ], "source": [ "print emulatedSerialPort.readline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Using a Builder</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "Let's use a builder now.\n", "</span>\n", "<span>\n", "We can choose the builder we want and build as many SerialPorts we want.\n", "</span>" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import hit.serial.serial_port_builder\n", "\n", "builder = hit.serial.serial_port_builder.ATTEmulatedSerialPortBuilder()\n", "\n", "port=\"\"\n", "baud=0\n", "\n", "emulatedSerialPort1 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort2 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort3 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort4 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort5 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort6 = builder.build_serial_port(port, baud)\n", "emulatedSerialPort7 = builder.build_serial_port(port, baud)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "And call \"readline()\"\n", "</span>" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hit: {0 2080 8744 1076 884 1904 1184 1 r}\n" ] } ], "source": [ "print emulatedSerialPort5.readline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "There is a special Serial port abstraction that is fed from a file.<br>\n", "This is useful when we want to \"mock\" the serial port and give it previously stored readings.\n", "</span>\n", "<span>\n", "This is interesting, for example, in order to reproduce, or visualize the repetition of an interesting set of hits in a game. Because Serial line is Real-Time, there are situations where it is needed to provide the ATT framework with a set of know hits, previously stored.\n", "</span>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span>\n", "We can use the data use in \"Train points importer\".\n", "</span>" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6,6)\n", "hit: {0:25 1549:4 2757:4 1392:4 2264:7 1764:7 1942:5 2984:5 r}\n", "hit: {0:33 1521:6 2712:4 1364:4 2226:10 1894:10 1905:8 2932:5 r}\n", "hit: {0:34 1554:7 2766:5 1233:4 2273:10 1766:4 1951:12 2993:4 r}\n", "hit: {0:31 1667:6 2878:4 1345:4 2209:4 1880:8 2056:14 2935:4 r}\n", "hit: {0:28 1529:6 2737:5 1211:5 2244:9 1735:4 1920:6 2967:4 r}\n", "hit: {0:35 1525:8 2720:5 1207:6 2237:10 1744:5 1922:5 2939:5 r}\n", "hit: {0:9 1521:10 2746:5 1218:8 2251:9 1744:5 1929:4 2971:4 r}\n", "hit: {0:16 1694:6 2910:5 1372:4 2415:8 1913:6 2098:10 2965:4 r}\n", "hit: {0:5 1703:6 2911:5 1561:4 2416:9 1402:4 2094:6 3136:5 r}\n" ] } ], "source": [ "!head -10 train_points_import_data/arduino_raw_data.txt" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import hit.serial.serial_port_builder\n", "\n", "builder = hit.serial.serial_port_builder.ATTHitsFromFilePortBuilder()\n", "\n", "port=\"train_points_import_data/arduino_raw_data.txt\"\n", "baud=0\n", "\n", "fileSerialPort = builder.build_serial_port(port, baud)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<span>\n", "And now we will read some lines:\n", "</span>" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hit: {975:5 1515:4 3011:5 827:7 2027:5 0:8 859:14 3408:5 r}\n", "hit: {955:5 1502:4 3067:8 807:9 2058:5 0:9 839:16 2780:4 r}\n", "hit: {818:4 1336:4 3040:6 666:5 1873:6 0:8 700:19 2747:4 r}\n", "hit: {958:6 1483:5 3019:7 824:10 2024:5 0:8 690:4 2733:4 r}\n", "hit: {988:5 1526:6 3217:6 838:6 2057:6 0:4 872:19 2093:4 r}\n", "hit: {954:5 965:5 2541:7 314:10 1522:5 0:8 171:12 2241:4 r}\n", "hit: {962:5 1473:5 3182:5 829:6 2023:6 0:9 861:18 2057:4 r}\n", "hit: {779:6 1301:4 3019:7 644:5 1860:6 0:6 678:19 2728:4 r}\n", "hit: {1493:4 1848:4 3411:8 1343:5 2215:7 0:13 521:14 1735:4 r}\n", "hit: {1325:4 1841:5 3233:6 1175:5 2048:6 0:34 344:16 1581:4 r}\n", "hit: {1490:5 1838:5 3357:8 1340:4 2204:6 0:25 514:15 1737:4 r}\n", "hit: {1463:6 1824:6 3202:5 1328:4 2022:4 0:32 510:12 1710:4 r}\n", "hit: {1491:9 1855:8 3252:7 1167:6 2052:6 0:37 525:11 2265:9 r}\n", "hit: {1455:4 1967:5 3325:6 1314:6 2174:7 0:7 505:20 2379:8 r}\n", "hit: {1466:5 1829:6 3218:5 1496:5 2030:4 0:30 513:12 1713:4 r}\n", "hit: {1488:6 1841:6 2544:4 1170:6 2045:6 0:39 527:13 2250:5 r}\n", "hit: {1319:4 1823:7 3209:5 1173:6 2040:5 0:35 357:20 2251:9 r}\n", "hit: {1318:5 1865:4 3256:9 1170:7 2060:7 0:41 357:14 1580:4 r}\n", "hit: {7103:4 1833:5 3212:7 1168:5 2037:7 0:36 342:21 2249:9 r}\n", "hit: {1969:7 2309:7 3731:8 1665:5 2520:6 0:5 854:23 2734:9 r}\n" ] } ], "source": [ "for i in range(20):\n", " print fileSerialPort.readline()" ] }, { "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

pryvkin10x/tsne

examples/iris.ipynb

1

24187

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from tsne import bh_sne" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "iris = load_iris()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "X = iris.data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "y = iris.target" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "X_2d = bh_sne(X)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(X_2d[:, 0], X_2d[:, 1], c=y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<matplotlib.collections.PathCollection at 0x1075e4950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jSE5Pk5zeo6oz+Kzt293/bysrozIvj9DuTfdMqT0aZEv4Kb4MrxqODRvBrhAC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\noPT0adKmTcNaUgJAVVERY19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"text": [ "<matplotlib.figure.Figure at 0x1075bde50>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

mdda/fossasia-2016_deep-learning

notebooks/2-CNN/5-TransferLearning/5-ImageClassifier-keras.ipynb

2

14691

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Re-Purposing a Pretrained Network\n", "\n", "Since a large CNN is very time-consuming to train (even on a GPU), and requires huge amounts of data, is there any way to use a pre-calculated one instead of retraining the whole thing from scratch?\n", "\n", "This notebook shows how this can be done. And it works surprisingly well.\n", "\n", "\n", "## How do we classify images with untrained classes?\n", "\n", "This notebook extracts a vector representation of a set of images using a CNN created by Google and pretrained on ImageNet. It then builds a 'simple SVM classifier', allowing new images can be classified directly. No retraining of the original CNN is required." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import os\n", "\n", "from tensorflow import keras # Works with TF 1.12\n", "#import keras\n", "\n", "import numpy as np\n", "import scipy\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import time\n", "\n", "CLASS_DIR='./images/cars'\n", "#CLASS_DIR='./images/seefood' # for HotDog vs NotHotDog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use Keras Model Zoo" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# https://www.tensorflow.org/api_docs/python/tf/keras/applications/\n", "#from tensorflow.keras.preprocessing import image as keras_preprocessing_image\n", "from tensorflow.keras.preprocessing import image as keras_preprocessing_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Architecture Choices\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NASNet cell structure\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ensure we have the model loaded\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#from tensorflow.python.keras.applications.nasnet import NASNetLarge, preprocess_input\n", "#model = NASNetLarge(weights='imagenet', include_top=False) # 343,608,736\n", "\n", "from tensorflow.keras.applications.nasnet import NASNetMobile, preprocess_input, decode_predictions\n", "\n", "model_imagenet = NASNetMobile(weights='imagenet', include_top=True) # 24,226,656 bytes\n", "print(\"Model Loaded\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the model and select layers we need - the features are taken from the final network layer, before the softmax nonlinearity." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def image_to_input(model, img_path):\n", " target_size=model.input_shape[1:]\n", " img = keras_preprocessing_image.load_img(img_path, target_size=target_size)\n", " \n", " x = keras_preprocessing_image.img_to_array(img)\n", " x = np.expand_dims(x, axis=0)\n", " x = preprocess_input(x)\n", "\n", " return x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_single_prediction(img_path, top=5):\n", " x = image_to_input(model_imagenet, img_path)\n", " preds = model_imagenet.predict(x)\n", " predictions = decode_predictions(preds, top=top)\n", " return predictions[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_path = './images/cat-with-tongue_224x224.jpg'\n", "im = plt.imread(img_path)\n", "plt.imshow(im)\n", "plt.show()\n", "for t in get_single_prediction(img_path):\n", " print(\"%6.2f %s\" % (t[2],t[1],))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "image_dir = './images/'\n", "\n", "image_files = [ os.path.join(image_dir, f) for f in os.listdir(image_dir) \n", " if (f.lower().endswith('png') or f.lower().endswith('jpg')) and f!='logo.png' ]\n", "\n", "t0 = time.time()\n", "for i, f in enumerate(image_files):\n", " im = plt.imread(f)\n", " if not (im.shape[0]==224 and im.shape[1]==224):\n", " continue\n", " \n", " plt.figure()\n", " plt.imshow(im.astype('uint8'))\n", " \n", " top5 = get_single_prediction(f)\n", " for n, (id,label,prob) in enumerate(top5):\n", " plt.text(350, 50 + n * 25, '{}. {}'.format(n+1, label), fontsize=14)\n", " plt.axis('off')\n", " \n", "print(\"DONE : %6.2f seconds each\" %(float(time.time() - t0)/len(image_files),))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#model_imagenet=None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_imagenet.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "# Transfer Learning\n", "\n", "Now, we'll work with the layer 'just before' the final (ImageNet) classification layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#model_logits = NASNetMobile(weights='imagenet', include_top=False, pooling=None) # 19,993,200 bytes\n", "#logits_layer = model_imagenet.get_layer('global_average_pooling2d_1')\n", "logits_layer = model_imagenet.get_layer('predictions')\n", "model_logits = keras.Model(inputs=model_imagenet.input, \n", " outputs=logits_layer.output)\n", "print(\"Model Loaded\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------\n", "## Use the Network to create 'features' for the training images\n", "\n", "Now go through the input images and feature-ize them at the 'logit level' according to the pretrained network.\n", "\n", "<!-- [Logits vs the softmax probabilities](images/presentation/softmax-layer-generic_676x327.png) !-->\n", "\n", "\n", "\n", "NB: The pretraining was done on ImageNet - there wasn't anything specific to the recognition task we're doing here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the network layout graph on TensorBoard\n", "\n", "This isn't very informative, since the CNN graph is pretty complex..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#writer = tf.summary.FileWriter(logdir='../tensorflow.logdir/', graph=tf.get_default_graph())\n", "#writer.flush()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Handy cropping function" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def crop_middle_square_area(np_image):\n", " h, w, _ = np_image.shape\n", " h = int(h/2)\n", " w = int(w/2)\n", " if h>w:\n", " return np_image[ h-w:h+w, : ]\n", " return np_image[ :, w-h:w+h ] \n", "im_sq = crop_middle_square_area(im)\n", "im_sq.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_logits_from_non_top(np_logits):\n", " # ~ average pooling\n", " #return np_logits[0].sum(axis=0).sum(axis=0)\n", " \n", " # ~ max-pooling\n", " return np_logits[0].max(axis=0).max(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use folder names to imply classes for Training Set" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "classes = sorted( [ d for d in os.listdir(CLASS_DIR) if os.path.isdir(os.path.join(CLASS_DIR, d)) ] )\n", "classes # Sorted for for consistency" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "train = dict(filepath=[], features=[], target=[])\n", "\n", "t0 = time.time()\n", "\n", "for class_i, directory in enumerate(classes):\n", " for filename in os.listdir(os.path.join(CLASS_DIR, directory)):\n", " filepath = os.path.join(CLASS_DIR, directory, filename)\n", " if os.path.isdir(filepath): continue\n", "\n", " im = plt.imread(filepath)\n", " im_sq = crop_middle_square_area(im)\n", "\n", " x = image_to_input(model_logits, filepath)\n", " #np_logits = model_logits.predict(x) # Shape = 1x7x7x1056 if pooling=None\n", " #print(np_logits.shape)\n", " #np_logits_pooled = get_logits_from_non_top( np_logits )\n", " \n", " np_logits_pooled = model_logits.predict(x)[0] # Shape = 1x1056 if pooling=avg\n", " \n", " train['filepath'].append(filepath)\n", " train['features'].append(np_logits_pooled)\n", " train['target'].append( class_i )\n", "\n", " plt.figure()\n", " plt.imshow(im_sq.astype('uint8'))\n", " plt.axis('off')\n", "\n", " plt.text(2*320, 50, '{}'.format(filename), fontsize=14)\n", " plt.text(2*320, 80, 'Train as class \"{}\"'.format(directory), fontsize=12)\n", "\n", "print(\"DONE : %6.2f seconds each\" %(float(time.time() - t0)/len(train),))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build an SVM model over the features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from sklearn import svm\n", "classifier = svm.LinearSVC()\n", "classifier.fit(train['features'], train['target']) # learn from the data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use the SVM model to classify the test set" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "test_image_files = [f for f in os.listdir(CLASS_DIR) if not os.path.isdir(os.path.join(CLASS_DIR, f))]\n", "\n", "t0 = time.time()\n", "for filename in sorted(test_image_files):\n", " filepath = os.path.join(CLASS_DIR, filename)\n", " im = plt.imread(filepath)\n", " im_sq = crop_middle_square_area(im)\n", "\n", " # This is two ops : one merely loads the image from numpy, \n", " # the other runs the network to get the class probabilities\n", " x = image_to_input(model_logits, filepath)\n", " #np_logits = model_logits.predict(x) # Shape = 1x7x7x1056\n", " #np_logits_pooled = get_logits_from_non_top( np_logits )\n", " \n", " np_logits_pooled = model_logits.predict(x)[0] # Shape = 1x1056\n", "\n", " prediction_i = classifier.predict([ np_logits_pooled ])\n", " decision = classifier.decision_function([ np_logits_pooled ])\n", "\n", " plt.figure()\n", " plt.imshow(im_sq.astype('uint8'))\n", " plt.axis('off')\n", "\n", " prediction = classes[ prediction_i[0] ]\n", "\n", " plt.text(2*320, 50, '{} : Distance from boundary = {:5.2f}'.format(prediction, decision[0]), fontsize=20)\n", " plt.text(2*320, 75, '{}'.format(filename), fontsize=14)\n", "\n", "print(\"DONE : %6.2f seconds each\" %(float(time.time() - t0)/len(test_image_files),))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------------\n", "## Exercise : Try your own ideas\n", "\n", "The whole training regime here is based on the way the image directories are structured. So building your own example shouldn't be very difficult.\n", "\n", "Suppose you wanted to classify pianos into Upright and Grand : \n", "\n", "* Create a ```pianos``` directory and point the ```CLASS_DIR``` variable at it\n", "* Within the ```pianos``` directory, create subdirectories for each of the classes (i.e. ```Upright``` and ```Grand```). The directory names will be used as the class labels\n", "* Inside the class directories, put a 'bunch' of positive examples of the respective classes - these can be images in any reasonable format, of any size (no smaller than 224x224).\n", " + The images will be automatically resized so that their smallest dimension is 224, and then a square 'crop' area taken from their centers (since ImageNet networks are typically tuned to answering on 224x224 images)\n", "* Test images should be put in the ```pianos``` directory itelf (which is logical, since we don't *know* their classes yet)\n", "\n", "Finally, re-run everything - checking that the training images are read in correctly, that there are no errors along the way, and that (finally) the class predictions on the test set come out as expected.\n", "\n", "If/when it works - please let everyone know : We can add that as an example for next time..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": 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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

Joshuaalbert/IonoTomo

src/ionotomo/notebooks/SpectralIndexCalc.ipynb

1

469576

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beams: [ 927.28689232 188.37621839 39.97541645] (arcsec^2)\n", "px/beam: [ 33.64306185 47.0940546 25.58426653] (pixels)\n", "Uncertainty per px: [ 0.24136833 0.01748631 0.01779328] mJy\n", "Working on source C1+2\n", "Initial L: 9.345961954855219e-245\n", "Converged in 79776 steps\n", "Acceptance: 2500, rate : 0.031337745687926195\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bd5b1400>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf514f98>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0d283c8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source C1+2\n", "Max Likelihood: 0.005908494371457167\n", "alpha: -1.9273435186182524 +- 0.37507072470338765\n", "MAP alpha: -1.9745340770665338\n", "S14: 0.5527914786364434 + 0.41571841514436314 - 0.23727749079253185 mJy\n", "MAP S14: 0.5382750708731132 mJy\n", "(lognormal) S147MHz: 42.19649876185541 + 15.714049476622819 - 11.450036123876174 mJy\n", "(lognormal) S322MHz: 9.354035071060595 + 1.1429322367698678 - 1.018487331918795 mJy\n", "(lognormal) S608MHz: 2.7582204377088226 + 0.8284834663050393 - 0.637114211326204 mJy\n", "(lognormal) P14: 0.5786707983221794 + 0.29275821835997784 - 0.19440554387178233 mJy\n" ] }, { "data": { "image/png": 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KAy5QSvVqrZu11kPAL4FfKqVCmqHTWr+hlCo77enzgSatdQuAUupJ4HpAAoZY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\nJP8fvE2l6sP/ygMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0edcbe0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source NW1\n", "Initial L: 0.03639598753467122\n", "Converged in 128530 steps\n", "Acceptance: 2500, rate : 0.019450711896055396\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0d530f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0c950b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0e69400>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source NW1\n", "Max Likelihood: 0.3712008177274933\n", "alpha: -0.8779117325460012 +- 0.18868195170593835\n", "MAP alpha: -0.8727344638393251\n", "S14: 19.249191290300875 + 6.626122047537148 - 4.92931193298468 mJy\n", "MAP S14: 19.951426951812888 mJy\n", "(lognormal) S147MHz: 138.67424477641853 + 25.550039117950035 - 21.5749601378604 mJy\n", "(lognormal) S322MHz: 69.81844120718404 + 6.629943636032053 - 6.054965463411264 mJy\n", "(lognormal) S608MHz: 40.02992194732944 + 6.653943248968737 - 5.705543612941867 mJy\n", "(lognormal) P14: 13.02148417001432 + 3.2455938049440025 - 2.5980356409696963 mJy\n" ] }, { "data": { "image/png": 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ZvOsoz76aefV/Jr/2OXoj0gGI7S7DFZnldVO5UQNVbb1UtfUSF2lnfko0uUlR\n2kpb+czJnF57E3gd+BZQaowZBRCRBOCzwH+IyJ+MMY/5b5pKzRwpMQ5SYhz0DXoSD5q76Rv0Xb0d\nExJKZeZlVGZeBrhvOF299R7sI70+bSrX0TvElsp2dlR3kJ8cRWGK028rOTV3nEzKtN0YMyQiTmAJ\n0GCMKZ9sHz/O0zKavaZ8bXTUUNPex8HGbpr80eV0rKlc+UNktLzPYGg0h3Ku5UDuF+kPTwI+mb3m\njbTYcOanOMmM08QD9Uk+y14bF0zeBHYCC0WkGbjNGNM2YR+l1AQhIUJOYiQ5iZF09A5yqMnF4eYe\n33U5FaE5YQVvJawgvnMfxRUbOKXit3RF5XM463Lm1b5EfNtO7Axx+ZsXsqPoqxzJXDutoRo6B2jo\nHCAq3EZhSjQFydFaZFRNybTu0xGRa4B/Aq4yxtT5fFYBpCsdZYXB4VEOt7iLjXb1+b7LqbPnCK6I\nDHLqX2Pl7u9gMx+PMRziYPOi+6YdeMYLEchJjKQo1UlSdLjXx1Mzl19uDvUkFaQDGcBa4IbZ0thN\nWxuoQGnwFBut7ejD1/dqX/7mhUT1139qe48jnec/+5pPx0qIsjM/1cm8hEhCNfFgzvH5zaEi0gBE\nAA1AHVAPbJz2DIOMMWYjsHHFihV3Bnouam5Ji3WQFuugx9PltMyHXU4j+xuOsf3TgchbbT1DbK5o\nY3uVO/Fgfko0Tu1sqiaYys2hRcaYLr/NRKk5Lio8lKXZcSzKjKWqrZeDjd1edzntdaRNutIZCIsH\n3B1O01o/8GlTucHhUfbXd7O/vpuMOAdFqU7SYx1abFQBJ3efjhi3YwacsX18OzWl5iZbiJCXFEVe\nUhRtPYMcbOzmSOv0upzuKPoqZ5beR+joxzd7Doc42LrwXwDIq32B0/b/iK7IeezLv5XDGet81lQO\noK6jn7qOfqIdocxPiSY/OYrwUE08mMtOJmX6LeCPwPPGmKpx28OAs4GbgTeNMb/13zSto4kEKhgN\nDI91OXXh6p9a4sG82pc4fdf/xc4QvY70T2SviRkhq+ENSioeIqFrH73hKezPu4n9uV/0usTOZEI9\nmXxFqU4SorTY6Gziy9YGDuA23BUJ8oAO3Nd2QoDXgF8YY7Z7PeMgoUFHBTNjDPWexIO6KXQ5LX71\nBgD2XvTEsQ5MWssHlFQ8hJgRXl/5KAAhI4M+XfmMlxQdRlGqk+yESO1wOgv48j6dfuAXwC9ExA4k\nAX3GmA7hs0VFAAAbjElEQVTvp6mUmgoRISMuggxPl9OyJhflzT0Mept4IEJD8ioaklcROtwLgGOg\nhUvfvZLKzHXsy73JZ03lxrS4BmlxteKoaqcgOZrClGiiwrUG8Wx3wvWziLwhIiVw9CbQ04G7ReQM\nf09OKXVsToed5TnxXLEsgzPzE0iI8k2m2HBoJODuaFqffA5FRx5n/duXcMbue3H2VPpkjPH6h0bZ\nU9fFCzvreOdgMw2dWmx0NjuZk7ZZxpg9ACKyCvgfIAf4rYhc6c/JKaVOLNQWQkFyNBcvSufCklRy\nkyLxxdmqvog0Plh6PxtXv0R59jXk1r3E2ncuJ6q31vuDT8IYqGnv46/7m3hxVx0HG7u9X8GpoHMy\na9nxWWs3AQ8aY/5FRFKAF4A/+WVmSqkpS4oOJyk6nFNzRihvdt/z0zPgXZfTnshMPir5NqWFXyaj\n6V16IjMBWHj4UdpiimlKWOFVU7nJdPUN85Gn2GheUhRFKU5iI/Wen9ngZIJOmafszTvAFcBVAMaY\nJhHRuhdKBSGH3UZJxsddTltfw+tqB/3hSVRku09u2Eb6WXj4d0QONNESt4Q9+XdQm7LG5xlvwyOG\nQ40uDjVql9PZ4mT+hvwj8GWgFthmjNkE7srSQLQf56aU8tKv3qmgqq2XUJsQahMy4hyUN7t452Cz\nV8cdsTnYuOYlthR/G8dAK2u2fYVL37uapPYdPpr5pzV1D/BeWQvP76yltLaTvkHvVnAqME4me60B\nuEBEQsZ66Xh8FnflaaVUkFqSFcvdj28nxXk/sRF2vhoawh+31XDH2XleH3vE5uDQvOspy76GefWv\ncMrhRxj09PFxDLQwFBrtdVO5yfQNjrKrppPS2k6yEyKZnxpNitP34yj/mFaV6dlM79NRs82m8hZu\n2vAhqTHh9A2N8sCNy1lVkERdRx+7ajpo6/FRZxJjjl7bOXvb10hu38qB3C96mso5fTPGMcRF2ilK\njSY3MUqLjQbIyd6nMyf+74hIvohsEJFnAj0Xpay2qiCJ1Jhwajv6+cKZOawqcDd2y4iL4KKSNM4u\nTCImwgf3x4xLJjiQeyPtMaew7OBPufytC1l64L9wDLR4P8YxdPQO8eHhdv60vZatR9ro6tcWX8HK\n0qAjInEi8oyI7BeRfSLymWke52ERaRKR0kmeu1hEDohImYh8E8AYU2GMud3b+Ss1E20qb6Gxa4DM\nOAePba5iU/nHX/4i7rI0ly5K58z8BKLCfVMXrTlhBW+d/iCvrHqK+qRVFFc8TEn5b3xy7OMZGjEc\naHDx4s563tzfRE17L3o2J7hYenpNRB4F3jXGPOSp3RY5vrKBJw27zxjTPW5boTGmbMJxVgMu4HfG\nmEXjttuAg8AFQA2wBXfPn72e558xxlxzvDnq6TU1m2wqb/Fc0wl3X9P53Hzufnz70VNsE42MGsqa\nXJTWdvqsvQKAs6eSYVskfY4Uktu2Ulj9DHvzb6fTWeizMY5Fu5xaI+hOr4lILLAa2ABgjBmcpJTO\nGuC5sVRsEbkT+O+JxzLGvAO0TTLMGUCZZ2UzCDwJXO67d6HUzLKrppMHblxObIT7HpdVBUk8cONy\ndtV0Trq/LURYkOZk/bIMlmTFYrf5JjW5OyqXPkcKAM6eKrIa32Dte1eyeus9JLbv9MkYx9IzMMLO\n6k6e31HLB+WttLgG/DqeOj4rT6/lAc3AIyKyXUQeEpGo8TsYY/4AvAo8JSKfx11o9G+mMEYmUD3u\ncQ2QKSKJIvIgsFxEvjXZC0VknYj8urNz8n+MSs1Ed60p+NSKZlVBEnetKTju6+y2EBZlxrJuaQan\npDvx5bX5iuwref7c19hV+Hckt2/nov/9Aqu33uP9jUQnMDIKh1t6eG1PI38ubaCi2cXwdPpFKK9Y\nGXRCgVOBXxpjlgM9wDcn7mSM+RHQD/wSWG+McXk7sDGm1RhzlzGmwBjzw2Pss9EY86XY2Fhvh1Nq\n1nDYbSzPiWfd0gzmp0b7pLwOwGBYHKXz/5bnz32NrQu/QXP8qe5EBGPIaHoHMf69B6etZ5D/rWjj\n+R11bK9qxzUwtXYRavqsLOlaA9QYYzZ7Hj/DJEFHRM4BFuEur3MvcPcUxqgFssc9zvJsU0p5ITIs\nlNNzE1iY5mR3TSeVrb0+Oe5waCQH8m46+ji1dTPnbv17uiLnsTf/Nioz1zEa4r/yNwPDo+yr72af\ndjm1jGUrHc9NptUissCz6Xxg7/h9RGQ58Gvc12FuBRJF5AdTGGYLMF9E8jyJCtfjrg+nlPIBp8PO\nqsIkLl2cRmZ8hM+P35R4Ou8u+wnDoZGsLL2X9W9dzILDv8M24v/K03Ud/bx1oJmNu+rZ39DFwLBW\nPPAHq+/TuQf4vYjsApYB9094PhK41hhT7ql+cBNwZOJBROQJ4ANggYjUiMjtAMaYYdwro1eBfcDT\nYxWylVK+ExcZxpqiZC4oTiU1xnclGI3YqE6/iD+veoo3VzxId1QOi8p/jYwVQzH+vwbj6h9m25EO\nnt9ex+aKVtp7Bv0+5lyiFQkm0JRpNRtd96sPAHjqy9O6Ne6E6jv72Fntw+oG4zj6m+l3JCNmhAs3\n3UhTwgr2591EnyPV52MdS7IznPkp0eQkRGqx0WPwWedQpZQ6kfTYCNJjI6hu62VnTQddfb67MN/v\nSAYgdLiXruh8Fhz5PUVHHudw5nr25d9Kd1Suz8Y6lubuAZq7B9hW1U5hirvLaWSYfn1Ox5wog6OU\nskZ2gru6wUofVjcYM2R38sHSH7Jx9YtHm8pd9s560po3+XSc4+kfGqW0tovnd9Tx7qFmGru0y+lU\naahWSvlUSIiQnxzNvMQoyppc7KnrpH/Id9dieiKzjjaVK6z6g7uJHJDR9DbDtki/NJWbyBiobuuj\nuq2P2Ag781OjyUuKwq7FRk9Ig45Syi/GqhvkJ0dxoKGbffVdDI347hpyf3gSpfP/9ujjRWW/Iqlz\nN81xS9mbfwe1Kat93lRuMp19Q0e7nOYnRTFfu5wel4ZlpZRfjVU3WL8sg+KMGEL9dCH+jTMfZkvx\nt4kYaGHNtnu49L2ryWh6xy9jTWZ4xHCw0cVLu+t5Y18j1W29jI5qotZEutJRSlkiPNTGsuw4FqQ6\nKa3rpLzJhS+/kz9uKnc18+r/THHFw4QNucta2Ub6APFLU7nJNHYN0Ng1QGSY7WjigRYbddOgo5Sy\nVESY7ePqBrWdVLb4prrBGBNipzJzHZUZaxHcUa2o8nEWVv4PB3K/YElTuTG9gyNHu5zmJERSqF1O\n9fSaUiownA47qwrc1Q2y/FDdAAnBiHt10ZxwGu0xCy1rKjfRqIHK1l5e39vEK7vrKWuau8VGdaWj\nlAqouMgwVhcl0+IaYGd1B41dvm890BK/jLdOf5D4zj2UVGyguOJhktp38MbK3/p8rBNp7x3iw8Nt\nbK9qJz85mqLUaJyOuZN4oBUJJtCKBEoFVkNnPztrOmh1+a/8jNN1mNCRXtpjSwgfbGfZ/v9kf97N\nljSVm0x6rIOiNCcZASo26ouKFVqRQCk1I6XFOkiLTaO6rZddNZ109vm+tE53dN7RPyd07mFew6sU\n1D5HTcq57Mm/g9b4pT4f83j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Ai4A/AfdOcYhaIHvc4yzPtjnDcwrkTeDiic/Nws/2LGC9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\n8Gn2jWwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0d132e8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source NW2\n", "Initial L: 0.9811460174097606\n", "Converged in 132720 steps\n", "Acceptance: 2500, rate : 0.018836648583484026\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0db0f98>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0ebf080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf8ea128>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source NW2\n", "Max Likelihood: 0.994767794216708\n", "alpha: -0.8036772959617937 +- 0.16044196354220117\n", "MAP alpha: -0.818985643166825\n", "S14: 23.27965403095864 + 6.73302240378402 - 5.222540964768495 mJy\n", "MAP S14: 23.453460238328425 mJy\n", "(lognormal) S147MHz: 141.92023953359595 + 24.05262232834494 - 20.566940185012214 mJy\n", "(lognormal) S322MHz: 75.72148461734498 + 7.48709389745639 - 6.813406448038464 mJy\n", "(lognormal) S608MHz: 45.505269143301106 + 6.784319910542699 - 5.904087392341204 mJy\n", "(lognormal) P14: 15.269475105526329 + 3.2650869288410114 - 2.68990243658698 mJy\n" ] }, { "data": { "image/png": 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si2ZFlp28pDiZ8xFBE4oFP7fMIR4h5o2U+Bg2FMWwZsTHoQ43dZ0DId9crj11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\nEEIYRpKOEEIIw0TaJm5ikVJKLQH+G+g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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0c48470>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source H\n", "Initial L: 8.33919603937688e-09\n", "Converged in 107659 steps\n", "Acceptance: 2500, rate : 0.023221467782535598\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0c70d30>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c2205390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bfbd9208>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source H\n", "Max Likelihood: 0.999544658950585\n", "alpha: -1.3171699208545307 +- 0.1955169953447675\n", "MAP alpha: -1.3134082039367216\n", "S14: 0.9621399370089945 + 0.3768054682419295 - 0.2707650275038609 mJy\n", "MAP S14: 1.0017847942561613 mJy\n", "(lognormal) S147MHz: 18.616950546815545 + 3.34649515114155 - 2.836601123099511 mJy\n", "(lognormal) S322MHz: 6.649169804849519 + 0.7674519462936091 - 0.6880380959406418 mJy\n", "(lognormal) S608MHz: 2.886065098279546 + 0.5818697655566574 - 0.4842403586149038 mJy\n", "(lognormal) P14: 0.7813977761984569 + 0.22686862245917527 - 0.17582122870981676 mJy\n" ] }, { "data": { "image/png": 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/y5RSHxpxjBBzlTXUXTi2FKezJCUaXz+bS5Z8g/qUy1h27E9s2bGRguqHptzA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\njPRb4ZCCIYSYlwZHHZTU91D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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0e9aba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source E\n", "Initial L: 3.686085763760527e-21\n", "Converged in 128825 steps\n", "Acceptance: 2500, rate : 0.019406171162429653\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c220e8d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bde719e8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf58f828>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source E\n", "Max Likelihood: 0.6964178058303124\n", "alpha: -1.419668073316915 +- 0.1683053403347885\n", "MAP alpha: -1.4303067491278878\n", "S14: 2.4103013612784183 + 0.6868904368663444 - 0.5345529314715995 mJy\n", "MAP S14: 2.4557334995803015 mJy\n", "(lognormal) S147MHz: 58.730867302795694 + 11.429092891896723 - 9.567287897008995 mJy\n", "(lognormal) S322MHz: 19.36110466244512 + 2.034455981077734 - 1.841004114688058 mJy\n", "(lognormal) S608MHz: 7.87522909931089 + 1.1256601596078406 - 0.9848839808905199 mJy\n", "(lognormal) P14: 2.042701964421124 + 0.42396044256219145 - 0.3510917531344593 mJy\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0eda400>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source X1\n", "Initial L: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\josh\\Anaconda3\\envs\\compute\\lib\\site-packages\\ipykernel_launcher.py:87: RuntimeWarning: divide by zero encountered in double_scalars\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Converged in 86819 steps\n", "Acceptance: 2500, rate : 0.02879554014674207\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bfb8fef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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wIvC9iBjfi/YOA+4BTixdU/kqcG9ELO1k/c0Ug2T1q+FvLS+Hilki6ZsUvYbD\ngaUR8U8NrlLVJI0EfgY8W82zKul26QspRii8XdJbKE6n/bTyGpCkIyjCpwU4KSJ+nb0B1m84VMzM\nLBtfqDczs2wcKmZmlo1DxczMsnGomJlZNg4VMzPLxqFiZmbZOFTMzCyb/w+GMAbEoROC7wAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf93db38>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source X1\n", "Max Likelihood: 0.530155691818247\n", "alpha: -3.108053717197623 +- 0.24529914523349275\n", "MAP alpha: -3.117082024829818\n", "S14: 0.0392165791998012 + 0.017186854456080584 - 0.011949798004931466 mJy\n", "MAP S14: 0.04075554273041413 mJy\n", "(lognormal) S147MHz: 42.61427930720599 + 12.57538925908564 - 9.709990369661718 mJy\n", "(lognormal) S322MHz: 3.7537101588559905 + 0.5778747385352077 - 0.5007807368366013 mJy\n", "(lognormal) S608MHz: 0.5238182874806313 + 0.11090286748389033 - 0.09152515189973465 mJy\n", "(lognormal) P14: 0.06709975051048614 + 0.02101326137710699 - 0.016002002038180786 mJy\n" ] }, { "data": { "image/png": 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1lATXmkv3fJ6OqHwOFH6W/nDbiMVNChakx7I4y4bFwGyqj//6HQAev/uCcX0O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\nmDlnt0uff/rPXLzvy1yx8w4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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bfc1e7f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source X2\n", "Initial L: 0.12866953192029082\n", "Converged in 102922 steps\n", "Acceptance: 2500, rate : 0.024290239210275743\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf522278>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf907400>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf90d128>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source X2\n", "Max Likelihood: 0.8324632818886533\n", "alpha: -0.5058801596048667 +- 0.20705310424104348\n", "MAP alpha: -0.5260722140641654\n", "S14: 2.716081572390427 + 0.9542865326290784 - 0.7061744195383426 mJy\n", "MAP S14: 2.713408230216085 mJy\n", "(lognormal) S147MHz: 8.474402804438478 + 1.955938434452321 - 1.5891531997465576 mJy\n", "(lognormal) S322MHz: 5.706599697753342 + 0.614698956120896 - 0.5549240859010665 mJy\n", "(lognormal) S608MHz: 4.14160192046123 + 0.6692183613611231 - 0.576125460577638 mJy\n", "(lognormal) P14: 1.5739260920034261 + 0.3909128514454976 - 0.3131391194382247 mJy\n" ] }, { "data": { "image/png": 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QpXzEKcuNbLriTXbP/x5OYxQXlP6c6/+xBoOrP9Cl+VVdex+vH2lgZ2kjjfa+QJcjhBgH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\nWWv9xHn2UwA0AX9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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf90e5c0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "Working on source S\n", "Initial L: 0.5024940003807835\n", "Converged in 92221 steps\n", "Acceptance: 2500, rate : 0.027108793008100106\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c21d5f98>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0e71630>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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L+tkDHQN8r7Z/LLuDxHG1dD0fquDxEare2YRcrCjalWGrmJBs3yrpEnavSPdp\n298FkLSK6od0K9W62T8f4jSbqALIbez+Q+vNVGuePEQ1d/Ah2zcM05yTgScDs4GHJX2Fau7hRbav\nBt4t6fJS9sL6sJWqddjfASwc4tzH9F9jGcKabPun5Vg9YJwA1Nc4+X5p/0eHaXvEoLKeR+xzJB1k\n+6GyGt83gUUl2DwNOA94DdVyvBdQ9Qh+DXyrNucx2DkfVdf230l6CfBC2xeWMv+N3XMa/XMek4Cd\nwBbgAQbMeVAFgK3Ag8ClwMPAl2wf3eB6pwI3AC+ozXl8ArjZ9rJByt8L9I7lRZmi+xI8Yp8j6bNU\nvYADgWW2/67LTeqYpCOA71CtZT7ssx7lNuS3AB+0/RVJz6YaBvv2wDkaSZOpgkwPcIztn4z4BcSE\nkeARERGNZcI8IiIaS/CIiIjGEjwiIqKxBI+IiGgswSMiIhpL8IiIiMYSPCIiorH/DyYFn2RGX39q\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a6bf910470>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "---------\n", "Results for source S\n", "Max Likelihood: 0.9650440666817541\n", "alpha: -0.8270578077500623 +- 0.23805352810709424\n", "MAP alpha: -0.8612729573555803\n", "S14: 4.501115233720177 + 1.719738591265279 - 1.2443214048963318 mJy\n", "MAP S14: 4.455932626238705 mJy\n", "(lognormal) S147MHz: 28.921916739293174 + 8.348468132365305 - 6.478433240127888 mJy\n", "(lognormal) S322MHz: 15.151821601407077 + 1.855104102738684 - 1.6527505855971967 mJy\n", "(lognormal) S608MHz: 8.971674777783155 + 1.4637172861167365 - 1.2584093992105432 mJy\n", "(lognormal) P14: 2.9812096533838526 + 0.7805167770320858 - 0.6185681477264735 mJy\n" ] }, { "data": { "image/png": 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IxhZNgrcFT1xWWOb7OOOCZdaFaVpmrSJfKJLOPUAZsJf3b68ZY8zNM44yAmnS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\nm7ipiCAihcD/A7qA/caYH4Y5pAVjsV7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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0cb8e48>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--------\n", "C1+2 & $66 \\pm 12$ & $7.7 \\pm 1.2$ & $4.2 \\pm 0.71$ & $-1.9 \\pm 0.38$ & $0.55^{0.42}_{0.24}$ & $0.58^{0.29}_{0.19}$\\\\\n", "NW1 & $1.6e+02 \\pm 25$ & $62 \\pm 9.3$ & $46 \\pm 6.9$ & $-0.88 \\pm 0.19$ & $19^{6.6}_{4.9}$ & $13^{3.2}_{2.6}$\\\\\n", "NW2 & $1.5e+02 \\pm 24$ & $77 \\pm 12$ & $47 \\pm 7$ & $-0.8 \\pm 0.16$ & $23^{6.7}_{5.2}$ & $15^{3.3}_{2.7}$\\\\\n", "H & $19 \\pm 3.2$ & $6.9 \\pm 1.1$ & $3 \\pm 0.62$ & $-1.3 \\pm 0.2$ & $0.96^{0.38}_{0.27}$ & $0.78^{0.23}_{0.18}$\\\\\n", "E & $57 \\pm 11$ & $22 \\pm 3.4$ & $7.7 \\pm 1.2$ & $-1.4 \\pm 0.17$ & $2.4^{0.69}_{0.53}$ & $2^{0.42}_{0.35}$\\\\\n", "X1 & $41 \\pm 9.9$ & $4.6 \\pm 0.71$ & $0.48 \\pm 0.11$ & $-3.1 \\pm 0.25$ & $0.039^{0.017}_{0.012}$ & $0.067^{0.021}_{0.016}$\\\\\n", "X2 & $9.5 \\pm 2$ & $5.5 \\pm 0.86$ & $4.4 \\pm 0.7$ & $-0.51 \\pm 0.21$ & $2.7^{0.95}_{0.71}$ & $1.6^{0.39}_{0.31}$\\\\\n", "S & $32 \\pm 9.4$ & $15 \\pm 2.3$ & $9.3 \\pm 1.4$ & $-0.83 \\pm 0.24$ & $4.5^{1.7}_{1.2}$ & $3^{0.78}_{0.62}$\\\\\n" ] } ], "source": [ "'''Compute the posterior estimates of spectral index, S1.4GHz, and P1.4GHz\n", "as well as the posterior estimates of measured fluxes (S_i) using the Metropolis Hastings algorithm.\n", "We assume priors: Gaussian measurments fluxes, uniform spectral index, uniform S1.4, and uniform P1.4.\n", "\n", "Detection is defined as 5*sigma_rms. \n", "The detection mask can be defined to include nondetection measurements (a valid assumption for point sources).\n", "\n", "The posterior density is then: prior x Likelihood (with priors described above).\n", "The likelihood is an L2 on spectral index and S1.4 due to the Gaussian prior on observables.\n", "\n", "Likelihood = exp(-1/2 * Sum (S_obs - g(alpha_i,S1.4))**2 / (Cd_i + Ct_i))\n", "\n", "where S_obs are the measured fluxes\n", "g(alpha_i,S1.4) gives model S_i\n", "Cd_i is the measurement variance S_i\n", "Ct_i is a systematic for g(...) taken to be (0.15*S_obs)**2\n", "\n", "assuming z ~ 0.516 +- 0.002 we use the sampling of alpha and S14 to monte carlo compute the mean and variances of \n", "posterior S_i and P14 in lognormal as suggested by their posterior plots.\n", "\n", "We find that the posterior distributions for:\n", "alpha is Gaussian\n", "S1.4 is lognormal\n", "P1.4 is lognormal\n", "S_i is lognormal\n", "'''\n", "\n", "import numpy as np\n", "import pylab as plt\n", "from matplotlib.patches import Polygon\n", "from matplotlib.collections import PatchCollection\n", "\n", "def g(alpha,S14,nu):\n", " '''Forward equation, evaluate model at given nu array'''\n", " out = S14*(nu/1400e6)**alpha\n", " return out\n", "\n", "def L(Sobs,alpha,S14,nu,CdCt):\n", " '''Likeihood for alpha and S14'''\n", " #only as nu_obs\n", " d = g(alpha,S14,nu)\n", " L2 = np.sum((Sobs - d)**2/CdCt)\n", " #L1 = np.sum(np.abs(Sobs - d)/np.sqrt(CdCt))\n", " return np.exp(-L2/2.)\n", "\n", "def P(nu,z,alpha,S14):\n", " c = 3e8\n", " h0 = 0.7\n", " ch = 1.32151838\n", " q0 = 0.5\n", " D = ch*z*(1+z*(1-q0)/(np.sqrt(1+2*q0*z) + 1 + q0*z))\n", " S = S14*(nu/1400e6)\n", " out = 4*np.pi*S*D**2 / (1+z)**(1+alpha) * 1e26\n", " return out/1e24\n", "\n", "def MHSolveSpectealIndex(nu,S,Cd,Ct,name,z,dz,nuModel=None,plot=False,plotDir=None):\n", " '''Assumes S in mJy'''\n", " if nuModel is None:\n", " nuModel = nu\n", " if plotDir is not None:\n", " import os\n", " try:\n", " os.makedirs(plotDir)\n", " except:\n", " pass\n", " N = int(1e6)\n", " alpha_ = np.zeros(N,dtype=np.double)\n", " S14_ = np.zeros(N,dtype=np.double)\n", " alpha_[0] = -0.8\n", " S14_[0] = S[0]*(1400e6/nu[0])**-0.8\n", " print(\"Working on source {}\".format(name))\n", " mask = detectionMask[idx,:]\n", " CdCt = Cd + Ct\n", " Li = L(S,alpha_[0],S14_[0],nu,CdCt)\n", " print(\"Initial L: {}\".format(Li))\n", " maxL = Li\n", " alphaMAP = alpha_[0]\n", " S14MAP = S14_[0]\n", " accepted = 0\n", " binning = 50\n", " i = 1\n", " while accepted < binning*binning and i < N:\n", " #sample priors in uniform steps\n", " alpha_j = np.random.uniform(low=alpha_[i-1] - 0.5,high=alpha_[i-1] + 0.5)\n", " S14_j = 10**(np.random.uniform(low = np.log10(S14_[i-1]/100),high=np.log10(S14_[i-1]*100)))\n", " Lj = L(S,alpha_j,S14_j,nu,CdCt)\n", " if np.random.uniform() < Lj/Li:\n", " alpha_[i] = alpha_j\n", " S14_[i] = S14_j\n", " Li = Lj\n", " accepted += 1\n", " else:\n", " alpha_[i] = alpha_[i-1]\n", " S14_[i] = S14_[i-1]\n", " if Lj > maxL:\n", " maxL = Lj\n", " alphaMAP = alpha_j\n", " S14MAP = S14_j\n", " i += 1\n", " if accepted == binning**2:\n", " print(\"Converged in {} steps\".format(i))\n", " print(\"Acceptance: {}, rate : {}\".format(accepted,float(accepted)/i))\n", " else:\n", " print(\"Acceptance: {}, rate : {}\".format(accepted,float(accepted)/i))\n", " alpha_ = alpha_[:i]\n", " S14_ = S14_[:i] \n", " #integrate out uncertainty unsing MC integration\n", " logS_int = np.zeros([len(alpha_),len(nuModel)],dtype=np.double)\n", " logP14_int = np.zeros(len(alpha_),dtype=np.double)\n", " i = 0 \n", " while i < len(alpha_):\n", " logS_int[i,:] = np.log(g(alpha_[i],S14_[i],nuModel))\n", " logP14_int[i] = np.log(P(1400e6,np.random.normal(loc=z,scale=dz),alpha_[i],S14_[i]/1e3))\n", " i += 1\n", " logS_mu = np.mean(logS_int,axis=0)\n", " logS_std = np.sqrt(np.mean(logS_int**2,axis=0) - logS_mu**2)\n", " logP14_mu = np.mean(logP14_int)\n", " logP14_std = np.sqrt(np.mean(logP14_int**2) - logP14_mu**2)\n", " S_post_mu = np.exp(logS_mu)\n", " S_post_up = np.exp(logS_mu + logS_std) - S_post_mu\n", " S_post_low = S_post_mu - np.exp(logS_mu - logS_std)\n", " P14_post_mu = np.exp(logP14_mu)\n", " P14_post_up = np.exp(logP14_mu + logP14_std) - P14_post_mu\n", " P14_post_low = P14_post_mu - np.exp(logP14_mu- logP14_std)\n", " P14 = P14_post_mu\n", " P14u = P14_post_up\n", " P14l = P14_post_low\n", " alpha = np.mean(alpha_)\n", " std_alpha = np.std(alpha_)\n", " mu = np.exp(np.mean(np.log(S14_)))\n", " S14 = mu\n", " S14u = np.exp(np.mean(np.log(S14_)) + np.std(np.log(S14_))) - mu\n", " S14l = mu - np.exp(np.mean(np.log(S14_)) - np.std(np.log(S14_)))\n", " if plot:\n", " plt.hist(alpha_,bins=binning)\n", " plt.xlabel(r\"$\\alpha$\")\n", " plt.ylabel(r\"Count\")\n", " plt.title(\"alpha\")\n", " if plotDir is not None:\n", " plt.savefig(\"{}/{}-alpha-posterior.png\".format(plotDir,name),format='png')\n", " plt.clf()\n", " else:\n", " plt.show()\n", " plt.hist(S14_,bins=binning)\n", " plt.xlabel(r\"$S_{\\rm 1.4GHz}[mJy]$\")\n", " plt.ylabel(r\"Count\")\n", " plt.title(\"S14\")\n", " if plotDir is not None:\n", " plt.savefig(\"{}/{}-S14-posterior.png\".format(plotDir,name),format='png')\n", " plt.clf()\n", " else:\n", " plt.show() \n", " plt.hist(np.log10(S14_),bins=binning)\n", " plt.xlabel(r\"$\\log_{10}{S_{\\rm 1.4GHz}[mJy]}$\")\n", " plt.ylabel(r\"Count\")\n", " plt.title(\"log(S14)\")\n", " if plotDir is not None:\n", " plt.savefig(\"{}/{}-logS14-posterior.png\".format(plotDir,name),format='png')\n", " plt.clf()\n", " else:\n", " plt.show()\n", " print(\"---------\")\n", " print(\"Results for source {}\".format(name))\n", " print(\"Max Likelihood: {}\".format(maxL))\n", " print(\"alpha: {} +- {}\".format(alpha,std_alpha))\n", " print(\"MAP alpha: {}\".format(alphaMAP))\n", " print(\"S14: {} + {} - {} mJy\".format(S14,S14u,S14l)) \n", " print(\"MAP S14: {} mJy\".format(S14MAP))\n", " for fi in range(len(nuModel)):\n", " mu = S_post_mu[fi]\n", " up = S_post_up[fi]\n", " low = S_post_low[fi]\n", " print(\"(lognormal) S{}MHz: {} + {} - {} mJy\".format(int(nuModel[fi]/1e6),mu,up,low)) \n", " print(\"(lognormal) P14: {} + {} - {} mJy\".format(P14_post_mu,\n", " P14_post_up, \n", " P14_post_low))\n", " #plot the Gassuan model and data\n", " if plot:\n", " plt.errorbar(nu, S, yerr=np.sqrt(CdCt), fmt='x',label='data')\n", " plt.errorbar(nuModel, S_post_mu, yerr=[S_post_up,S_post_low], fmt='--o',label='model')\n", " plt.xlabel(r\"$\\nu$ [Hz]\")\n", " plt.ylabel(r\"$S(\\nu)$ [mJy]\")\n", " #plt.plot(nu,S_map,label='map')\n", " #plt.errorbar(nu, S_model, yerr=CdCt[idx,mask], fmt='--o')\n", " plt.legend()\n", " points = []\n", " for j in range(len(nuModel)):\n", " points.append((nuModel[j],S_post_mu[j] + S_post_up[j]))\n", " #points.append((nuModel[j],S_post_mu[j] - S_post_low[j]))\n", " for j in range(len(nuModel)):\n", " #points.append((nuModel[j],S_post_mu[j] + S_post_up[j]))\n", " points.append((nuModel[-j-1],S_post_mu[-j-1] - S_post_low[-j-1]))\n", " \n", " plt.gca().add_collection(PatchCollection([Polygon(points,True)],alpha=0.4))\n", " plt.yscale('log')\n", " plt.xscale('log')\n", " if plotDir is not None:\n", " plt.savefig(\"{}/{}-fluxes-posterior.png\".format(plotDir,name),format='png')\n", " plt.clf()\n", " else:\n", " plt.show()\n", " print(\"--------\")\n", " return alpha,std_alpha,S14,S14u,S14l,S_post_mu,S_post_up,S_post_low,P14_post_mu,P14_post_up,P14_post_low\n", " \n", "if __name__ == '__main__':\n", " names = ['C1+2','NW1','NW2','H','E','X1','X2','S']\n", " nu = np.array([147.667e6,322.667e6,608.046e6])\n", " rms = np.array([1.4e-3,120e-6,90e-6])*1e3\n", " beams = np.array([43.3*18.9,17.5*9.5,7.2*4.9])*np.pi/4./np.log(2.)\n", " print(\"Beams: {} (arcsec^2)\".format(beams))\n", " pixels = np.array([5.25**2,2**2,1.25**2])\n", " print(\"px/beam: {} (pixels)\".format(beams/pixels))\n", " print(\"Uncertainty per px: {} mJy\".format(rms*np.sqrt(pixels/beams)))\n", " #measurement mask\n", " detectionMask = np.bitwise_not(np.array([[0,0,0],\n", " [0,0,0],\n", " [0,0,0],\n", " [0,0,0],\n", " [0,0,0],\n", " [0,0,0],\n", " [0,0,0],\n", " [0,0,0]],dtype=np.bool))\n", " #measurements\n", " S = np.array([[ 66.034 , 7.653 , 4.241 ],\n", " [ 159.14 , 62.206 , 45.998 ],\n", " [ 147.575 , 77.056 , 46.834 ],\n", " [ 19.28183596, 6.89683661, 2.9826925 ],\n", " [ 57.346 , 22.343 , 7.6797 ],\n", " [ 40.672 , 4.556 , 0.48076422],\n", " [ 9.45811655, 5.508 , 4.426 ],\n", " [ 32.342 , 15.314 , 9.277 ]],dtype=np.double)\n", " std_d = np.array([[ 6.58200000e+00, 2.94200000e-01, 3.12511200e-01],\n", " [ 7.85100000e+00, 3.86200000e-01, 1.05200000e-01],\n", " [ 8.11100000e+00, 3.54800000e-01, 3.58600000e-01],\n", " [ 1.40738833e+00, 2.66343558e-01, 4.32929199e-01],\n", " [ 7.16500000e+00, 3.11300000e-01, 2.90741019e-04],\n", " [ 7.82100000e+00, 2.09200000e-01, 8.90090959e-02],\n", " [ 1.40738833e+00, 2.27200000e-01, 2.34200000e-01],\n", " [ 8.07500000e+00, 2.77200000e-01, 3.67694221e-01]],dtype=np.double)\n", " S = np.array([[66.034,7.653,2.357 + 1.884],#c12\n", " [159.140,62.206,45.998],#nw1\n", " [147.575,77.056,46.834],#nw2\n", " [648.7*pixels[0]/beams[0],324.8*pixels[1]/beams[1],76.31*pixels[2]/beams[2]],#h\n", " [57.346,22.343,(7.619+6.07E-2)],#e\n", " [40.672,4.556,12.3*pixels[2]/beams[2]],#x1\n", " [318.2*pixels[0]/beams[0],5.508,4.426],#x2\n", " [32.342,15.314,3.744+5.533]],dtype=np.double)#s\n", " std_d = np.array([[6.582,2.942E-1,np.sqrt(2.086E-1**2 + 2.327E-1**2)],#c12\n", " [7.851,3.862E-1,1.052E-1],#nw1\n", " [8.111,3.548E-1,3.586E-1],#nw2\n", " [rms[0]*np.sqrt(937.1/beams[0]),rms[1]*np.sqrt(928./beams[1]),rms[2]*np.sqrt(925./beams[2])],#h\n", " [7.165,3.113E-1,np.sqrt(1.845E-4**2 + 2.247E-4**2)],#e\n", " [7.821,2.092E-1,rms[2]*np.sqrt(39.1/beams[2])],#x1\n", " [rms[0]*np.sqrt(937.1/beams[0]),2.272E-1,2.342E-1],#x2\n", " [8.075,2.772E-1,np.sqrt(1.900E-1**2 + 3.148E-1**2)]],dtype=np.double)#s\n", " Cd = std_d**2\n", " Ct = (S*0.15)**2\n", " CdCt = Cd + Ct\n", " #previous estimates\n", " #alpha0 = np.array([-2.5501,-0.8804,-0.8458, -1.4624, -0.1102, -0.8988, -0.3312, -0.7236],dtype=np.double)\n", " #S140 = np.array([0.4034,20.5293, 23.2775, 0.113, 13.2874, 1.1842, 3.0169, 6.2674],dtype=np.double)\n", " #P0 = np.array([0.4,13,15,2.1,5.7,0.9,2.3,3.7],dtype=np.double)\n", " \n", " #samples \n", " m = S.shape[0]\n", " #posterior moments\n", " alpha = np.zeros(m,dtype=np.double)\n", " std_alpha = np.zeros(m,dtype=np.double)\n", " S14 = np.zeros(m,dtype=np.double)\n", " S14u = np.zeros(m,dtype=np.double)\n", " S14l = np.zeros(m,dtype=np.double)\n", " P14 = np.zeros(m,dtype=np.double)\n", " P14u = np.zeros(m,dtype=np.double)\n", " P14l = np.zeros(m,dtype=np.double)\n", " S_post_mu = np.zeros([m,3],dtype=np.double)\n", " S_post_up = np.zeros([m,3],dtype=np.double)\n", " S_post_low = np.zeros([m,3],dtype=np.double)\n", " idx = 0\n", " while idx < m:\n", " mask = detectionMask[idx,:]\n", " alpha_,std_alpha_,S14_,S14u_,S14l_, S_post_mu_,S_post_up_,S_post_low_,P14_post_mu_,P14_post_up_,P14_post_low_ = MHSolveSpectealIndex(nu[mask],S[idx,mask],\n", " Cd[idx,mask],Ct[idx,mask],\n", " names[idx],0.516,0.002,nuModel=nu,plot=True,\n", " plotDir=None) \n", " alpha[idx] = alpha_\n", " std_alpha[idx] = std_alpha_\n", " S14[idx] = S14_\n", " S14u[idx] = S14u_\n", " S14l[idx] = S14l_\n", " S_post_mu[idx,:] = S_post_mu_\n", " S_post_up[idx,:] = S_post_up_\n", " S_post_low[idx,:] = S_post_low_\n", " P14[idx] = P14_post_mu_\n", " P14u[idx] = P14_post_up_\n", " P14l[idx] = P14_post_low_\n", " idx += 1\n", " \n", " i = 0\n", " while i < len(alpha):\n", " print(r\"{} & ${:.2g} \\pm {:.2g}$ & ${:.2g} \\pm {:.2g}$ & ${:.2g} \\pm {:.2g}$ & ${:.2g} \\pm {:.2g}$ & ${:.2g}^{{{:.2g}}}_{{{:.2g}}}$ & ${:.2g}^{{{:.2g}}}_{{{:.2g}}}$\\\\\".format(names[i],\n", " S[i,0],np.sqrt(CdCt[i,0]),\n", " S[i,1],np.sqrt(CdCt[i,1]),\n", " S[i,2],np.sqrt(CdCt[i,2]),\n", " alpha[i],std_alpha[i],\n", " S14[i],S14u[i],S14l[i],\n", " P14[i],P14u[i],P14l[i]))\n", " i += 1\n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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WSCm/EEL8V0p5w/nmV8Lu/xwpsbMlp5yaOs9p14I8VvpWLKBv5b/Qe6zkWGay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"text/plain": [ "<matplotlib.figure.Figure at 0x1a6c0e71b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.ticker import MaxNLocator\n", "\n", "def plotSpectrum(nu,S,CdCt,S_post_mu,S_post_up,S_post_low, mask, ax, name):\n", " ax.errorbar(nu[mask], S[mask], yerr=np.sqrt(CdCt[mask]), c='black', lw=1.,fmt='--.',label='data')\n", " #ax.errorbar(nu, S_post_mu, yerr=[S_post_up,S_post_low], fmt='--o',label='model')\n", " ax.plot(nu, S_post_mu, c='orange',ls='-',label='model')\n", " points = []\n", " for j in range(len(nu)):\n", " points.append((nu[j],S_post_mu[j] + S_post_up[j]))\n", " #points.append((nuModel[j],S_post_mu[j] - S_post_low[j]))\n", " for j in range(len(nu)):\n", " #points.append((nuModel[j],S_post_mu[j] + S_post_up[j]))\n", " points.append((nu[-j-1],S_post_mu[-j-1] - S_post_low[-j-1]))\n", "\n", " ax.add_collection(PatchCollection([Polygon(points,True)],alpha=0.4))\n", "\n", " #ax.set_ylim([])\n", " ax.set_yscale('log')\n", " ax.set_xscale('log')\n", " ylims = list(ax.get_ylim())\n", " ylims[0] = 10**(np.floor(np.log10(np.min(S_post_mu - S_post_low))))\n", " ylims[1] = 10**(np.ceil(np.log10(np.max(S_post_mu+S_post_up))))\n", " ax.set_ybound(lower=ylims[0],upper=ylims[1])\n", " ax.set_ylim(ylims[0],ylims[1])\n", " ax.set_yticks([10**e for e in range(int(np.log10(ylims[0])),int(np.log10(ylims[1])+1))])\n", " ylabs = [r\"$10^{{{:d}}}$\".format(int(np.log10(tick))) for tick in ax.get_yticks()]\n", " #ylabs[0] = \" \"\n", " #ylabs[1] = \" \"\n", " #print(name,ylabs)\n", " ax.set_yticklabels(ylabs)\n", " ax.set_xbound(lower=100e6,upper=1500e6)\n", " ax.annotate(s=name,xy=(0.05,0.05),xycoords='axes fraction',weight='bold')\n", " #ax.set_xticks([])#('right')\n", " ax.set_xticklabels([])\n", " \n", " return ax\n", " \n", "f, axs = plt.subplots(4,2,figsize=(5,7))\n", "cols= 2\n", "rows = 4\n", "i=0\n", "while i < len(alpha):\n", " #i = row*2 + col\n", " col = i%cols\n", " row = (i - col)//cols\n", " if rows == 1:\n", " if cols == 1:\n", " ax = axs\n", " else:\n", " ax = axs[col]\n", " else:\n", " ax = axs[row][col]\n", " #plt.figure()\n", " #ax = plt.subplot(111)\n", " mask = detectionMask[i,:]\n", " mask[:] = True\n", " plotSpectrum(np.append(nu,1400e6),np.append(S[i,:],0),np.append(CdCt[i,:],0),np.append(S_post_mu[i,:],S14[i]),\n", " np.append(S_post_up[i,:],S14u[i]),np.append(S_post_low[i,:],S14l[i]),np.append(mask,False), ax,names[i])\n", " if col==1:\n", " ax.yaxis.set_label_position('right')\n", " ax.yaxis.tick_right()\n", " if row != 0:\n", " ax.get_yticklabels()[-1].set_visible(False)\n", " #ax.yaxis.set_ticks(ax.yaxis.get_ticklocs()[0:-1])\n", " if row == rows - 1:\n", " ax.xaxis.set_ticks((list(nu) + [1400e6]))\n", " ax.xaxis.set_ticklabels([\"{:3d}\".format(int(f/1e6)) for f in (list(nu) + [1400e6])])\n", " ax.set_xlabel(r\"$\\nu$ [MHz]\")\n", " if i == 0:\n", " ax.legend(frameon=False,loc='upper right')\n", " if col == 0:\n", " ax.set_ylabel(r\"$S(\\nu)$ [mJy]\")\n", " i += 1\n", "#axs[-1][0].set_xlabel(r'$\\nu$ [Hz]')\n", "#axs[-1][1].set_xlabel(r'$\\nu$ [Hz]')\n", "#axs[4>>1][0].set_ylabel(r'$S(\\nu)$ [mJy]')\n", "f.subplots_adjust(hspace=0,wspace=0)\n", "plt.savefig(\"spix_v2.pdf\")\n", "\n", "\n", "#plt.setp([ax.get_xticklabels() for ax in f.axes],visible=False)\n", "#plt.setp([ax.get_yticklabels() for ax in f.axes],visible=False)\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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

molgor/spystats

notebooks/.ipynb_checkpoints/model_by_chunks-checkpoint.ipynb

1

10212

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Here I'm process by chunks the entire region." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load Biospytial modules and etc.\n", "%matplotlib inline\n", "import sys\n", "sys.path.append('/apps')\n", "import django\n", "django.setup()\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "## Use the ggplot style\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/biospytial/lib/python2.7/site-packages/IPython/core/pylabtools.py:168: DtypeWarning: Columns (24) have mixed types. Specify dtype option on import or set low_memory=False.\n", " safe_execfile(fname,*where,**kw)\n", "/opt/conda/envs/biospytial/lib/python2.7/site-packages/IPython/core/pylabtools.py:168: DtypeWarning: Columns (24) have mixed types. Specify dtype option on import or set low_memory=False.\n", " safe_execfile(fname,*where,**kw)\n" ] } ], "source": [ "from external_plugins.spystats import tools\n", "%run ../testvariogram.py" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1841, 46)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "(1841, 46)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "section.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm for processing Chunks\n", "1. Make a partition given the extent\n", "2. Produce a tuple (minx ,maxx,miny,maxy) for each element on the partition\n", "3. Calculate the semivariogram for each chunk and save it in a dataframe\n", "4. Plot Everything\n", "5. Do the same with a mMatern Kernel" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "minx,maxx,miny,maxy = getExtent(new_data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1556957.5046647713" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "1556957.5046647713" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "maxy" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "46465.4895488\n", "28670.5754781\n", "46465.4895488\n", "28670.5754781\n" ] } ], "source": [ "## If prefered a fixed number of chunks\n", "N = 100\n", "xp,dx = np.linspace(minx,maxx,N,retstep=True)\n", "yp,dy = np.linspace(miny,maxy,N,retstep=True)\n", "### Distance interval\n", "print(dx)\n", "print(dy)\n", "\n", "## Let's build the partition \n", "## If prefered a fixed size of chunk\n", "ds = 300000 #step size (meters)\n", "xp = np.arange(minx,maxx,step=ds)\n", "yp = np.arange(miny,maxy,step=ds)\n", "dx = ds\n", "dy = ds\n", "N = len(xp)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xx,yy = np.meshgrid(xp,yp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Nx = xp.size\n", "Ny = yp.size" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#coordinates_list = [ (xx[i][j],yy[i][j]) for i in range(N) for j in range(N)]\n", "\n", "coordinates_list = [ (xx[i][j],yy[i][j]) for i in range(Ny) for j in range(Nx)]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from functools import partial\n", "tuples = map(lambda (x,y) : partial(getExtentFromPoint,x,y,step_sizex=dx,step_sizey=dy)(),coordinates_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chunks = map(lambda (mx,Mx,my,My) : subselectDataFrameByCoordinates(new_data,'newLon','newLat',mx,Mx,my,My),tuples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Here we can filter based on a threshold\n", "threshold = 20\n", "chunks_non_empty = filter(lambda df : df.shape[0] > threshold ,chunks)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "len(chunks_non_empty)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lengths = pd.Series(map(lambda ch : ch.shape[0],chunks_non_empty))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lengths.plot.hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For efficiency purposes we restrict to 10 variograms" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "smaller_list = chunks_non_empty[:10]\n", "variograms =map(lambda chunk : tools.Variogram(chunk,'residuals1',using_distance_threshold=200000),smaller_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vars = map(lambda v : v.calculateEmpirical(),variograms)\n", "vars = map(lambda v : v.calculateEnvelope(num_iterations=50),variograms)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Take an average of the empirical variograms also with the envelope. \n", "### We will use the group by directive on the field lags\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "envslow = pd.concat(map(lambda df : df[['envlow']],vars),axis=1)\n", "envhigh = pd.concat(map(lambda df : df[['envhigh']],vars),axis=1)\n", "variogram = pd.concat(map(lambda df : df[['variogram']],vars),axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lags = vars[0][['lags']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanlow = list(envslow.apply(lambda row : np.mean(row),axis=1))\n", "meanhigh = list(envhigh.apply(np.mean,axis=1))\n", "meanvariogram = list(variogram.apply(np.mean,axis=1))\n", "results = pd.DataFrame({'meanvariogram':meanvariogram,'meanlow':meanlow,'meanhigh':meanhigh})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result_envelope = pd.concat([lags,results],axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanvg = tools.Variogram(section,'residuals1')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanvg.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanvg.envelope.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result_envelope.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result_envelope.columns = ['lags','envhigh','envlow','variogram']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "meanvg.envelope = result_envelope" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanvg.plot(refresh=False)" ] }, { "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": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "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-2-clause

mforets/polyhedron_tools

examples/asphericity.ipynb

1

62886

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{jX}{\\mathcal{X}}$\n", "$\\newcommand{jE}{\\mathcal{E}}$\n", "$\\newcommand{R}{\\mathbb{R}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definitions\n", "\n", "- a *convex body* is a compact convex set with non-empty interior.\n", "- a *convex polyhedron* is the intersection of a finite set of closed half-spaces. \n", "- a *convex polytope* is a bounded convex polyhedron. \n", "- we embed $\\R^n$ with a $p$-norm $||\\cdot||$ for some $1 \\leq p \\leq \\infty$. In `polyhedron_tools.asphericity`, the infinity norm is used.\n", "\n", "*Remark.* Although the following notions and algorithms also apply to the more general context of convex bodies, we only work with convex polytopes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asphericity at an interior point\n", "\n", "Let $\\mathcal{X} \\subset \\mathbb{R}^n$ be a convex polytope.\n", "\n", "The **asphericity at $x$** is defined as the ratio of the circumradius to the inradius with common center $x$; more precisely:\n", "\n", "\n", "**Def.** The **asphericity at point** $x \\in \\textrm{int }\\mathcal{X}$ is:\n", "$$\n", "p_{\\mathcal{X}}(x) := \\dfrac{R(x)}{r(x)},\n", "$$\n", "where:\n", "\n", "- circumradius: $$R(x) := \\max_{y \\in \\mathcal{X}} ||x-y||$$ is the radius of the ball of center $x$ that contains $\\mathcal{X}$ of minimal volume.\n", "- inradius: $$r(x) := \\min_{y \\in \\overline{\\mathcal{X}^C}} ||x-y||$$\n", "is the radius of the ball of center $x$ that is contained in $\\mathcal{X}$ and is of maximal volume. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asphericity of a convex polytope\n", "\n", "The **asphericity of a polytope** $\\mathcal{X}$ is defined as:\n", "$$\n", "p_{\\mathcal{X}} := \\min_{x \\in \\mathcal{X}} p_{\\mathcal{X}}(x).\n", "$$\n", "\n", "Remarks: \n", "\n", "- intuition: $p_{\\mathcal{X}}-1$ somehow measures the difference of $\\mathcal{X}$ from a ball (in the given norm).\n", "- complications:\n", " - $R(x)$ is convex, and $r(x)$ is concave.\n", " - $p_{\\mathcal{X}}$ can be non-smooth, neither convex, nor concave." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computation: sequence of LP problems\n", "\n", "**Theorem.** The asphericity can be approximated with arbitrary accuracy by the sequence of convex problems: \n", "$$\n", "p_{\\mathcal{X}}^{(k)}(x) := \\min_{x \\in \\mathcal{X}} R(x) - \\alpha_k r(x)\n", "$$\n", "where $\\alpha_k := p^{(k)}_{\\mathcal{X}}(x_k)$, and $x_{k+1} \\in \\mathcal{X}$ is the solution of the problem above. In the sup-norm, these reduces to a sequence of LP problems.\n", "\n", "\n", "\n", "See: [Method for Finding an Approximate Solution of the Asphericity \n", "Problem for a Convex Body, S. I. Dudov and E. A. Meshcheryakova](http://sci-hub.cc/10.1134/s0965542513100059)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Algorithm\n", "\n", "We implement the computation of the asphericity of a convex polytope in the infinity norm. See [Dudov and Meshcheryakova 2013](http://link.springer.com/article/10.1134/S0965542513100059) for further details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "We created a program to compute the asphericity (in the sup-norm), given an input polytope $P$. The functions are on ``polyhedron_tools``.\n", "\n", "To illustrate it, let's consider a random polygon with $10$ vertices, and find its asphericity. Then, we plot the original set and its bounding boxes at the aspericity center that was found." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%display typeset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from polyhedron_tools.polygons import random_polygon_2d\n", "\n", "from polyhedron_tools.misc import BoxInfty\n", "\n", "from polyhedron_tools.asphericity import asphericity_polytope, circumradius, inradius" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create a random polygon with 10 vertices\n", "P = random_polygon_2d(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The algorithm requires an initial interior point $x_0$. We use as starting point the [Chebyshev center](https://en.wikipedia.org/wiki/Chebyshev_center) of the polytope." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from polyhedron_tools.misc import chebyshev_center\n", "\n", "x0 = chebyshev_center(P)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Z82RqKFBBpmSUFKIxEgDgYbFwjLqCGgpWkLFK7FgCAHgadQW1FawgYxJkAADeRl1BbQUm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\n8sal0AcAAAAASUVORK5CYII=\n", "text/plain": [ "Graphics object consisting of 13 graphics primitives" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P.plot(alpha=0.2, color='green') + point(x0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we compute the asphericity and the corresponding center:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The asphericity of P is : 1.41264318049 , with center of asphericity (0.02206986652707576, -0.06248306721074004)\n" ] } ], "source": [ "# compute asphericity\n", "[asph, x_asph] = asphericity_polytope(P)\n", "print 'The asphericity of P is : ', asph, ', with center of asphericity ', x_asph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the inner and outer boxes with respect to the asphericity center." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Xn/7Rn+r4+4/rymuv1Oc+8Tm97XVv0wN/94Dy/flVvyaTy+iBv3tg8WfPxe5O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\n6YbYDXp08lH9bPZnGkwNKh3npAMAdLq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"text/plain": [ "Graphics object consisting of 23 graphics primitives" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add the initial set P together with the optimal point\n", "examplePlot = P.plot(alpha=0.2,color='green') + point(x_asph, size=100,color='black',marker='x')\n", "\n", "# add smallest box with center x_asph that contains P\n", "examplePlot += BoxInfty(center=x_asph, radius=circumradius(P, x_asph)).plot(wireframe='red',fill=False)\n", "\n", "# add biggest box with center x_asph that is contained in P\n", "examplePlot += BoxInfty(center=x_asph, radius=inradius(P, x_asph)).plot(wireframe='red',fill=False)\n", "\n", "examplePlot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Multi-dimensional case" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The multidimensional case can be handled similarly, here are some examples." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "P = polytopes.hypercube(6)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[16.3214285714, \\left(0.0,\\,0.0,\\,0.0,\\,0.0,\\,0.0,\\,0.0\\right)\\right]</script></html>" ], "text/plain": [ "[16.321428571428573, (0.0, 0.0, 0.0, 0.0, 0.0, 0.0)]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asphericity_polytope(random_matrix(QQ, 6) * P)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "P = polytopes.octahedron()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[3.0, \\left(0.0,\\,0.0,\\,0.0\\right)\\right]</script></html>" ], "text/plain": [ "[3.0, (0.0, 0.0, 0.0)]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asphericity_polytope(P)" ] } ], "metadata": { "kernelspec": { "display_name": "SageMath 7.5.1", "language": "", "name": "sagemath" }, "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 }

mit

steinam/teacher

jup_notebooks/datenbanken/Sommer_2015.ipynb

1

19667

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Subselects" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "%load_ext sql\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Connected: steinam@sommer_2015'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql mysql://steinam:steinam@localhost/sommer_2015" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sommer 2015\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Datenmodell\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aufgabe\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Erstellen Sie eine Abfrage, mit der Sie die Daten aller Kunden, die Anzahl deren Aufträge, die Anzahl der Fahrten und die Summe der Streckenkilometer erhalten. Die Ausgabe soll nach Kunden-PLZ absteigend sortiert sein.\n", "\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "%%sql \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "%sql select count(*) as AnzahlFahrten from fahrten" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Warum geht kein Join ??\n", "\n", "```mysql\n", "\n", "```\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>kd_id</th>\n", " <th>kd_firma</th>\n", " <th>kd_plz</th>\n", " <th>AnzAuftrag</th>\n", " <th>AnzFahrt</th>\n", " <th>SumStrecke</th>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>Öhlandi</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>3199</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>Müller</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>None</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(2, 'Öhlandi', None, 0, 0, None),\n", " (1, 'Trapo', None, 2, 7, Decimal('3199')),\n", " (3, 'Müller', None, 1, 0, None)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql \n", "\n", "select k.kd_id, k.`kd_firma`, k.`kd_plz`, \n", " count(distinct a.Au_ID) as AnzAuftrag, \n", " count(distinct f.f_id) as AnzFahrt, \n", " sum(distinct ts.ts_strecke) as SumStrecke \n", "from kunde k left join auftrag a on k.`kd_id` = a.`au_kd_id` \n", " left join fahrten f on a.`au_id` = f.`f_au_id` \n", " left join teilstrecke ts on ts.`ts_f_id` = f.`f_id` \n", " group by k.kd_id order by k.`kd_plz`\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Der Ansatz mit Join funktioniert in dieser Form nicht, da spätestens beim 2. Join die Firma Trappo mit 2 Datensätzen aus dem 1. Join verknüpft wird. Deshalb wird auch die Anzahl der Fahren verdoppelt. Dies wiederholt sich beim 3. Join.\n", "\n", "Die folgende Abfrage zeigt ohne die Aggregatfunktionen das jeweilige Ausgangsergebnis\n", "\n", "\n", "```mysql\n", "select k.kd_id, k.`kd_firma`, k.`kd_plz`, a.`au_id`\n", "from kunde k left join auftrag a\n", "\ton k.`kd_id` = a.`au_kd_id`\n", "left join fahrten f\n", "\ton a.`au_id` = f.`f_au_id`\n", "left join teilstrecke ts\n", "\ton ts.`ts_f_id` = f.`f_id`\n", "order by k.`kd_plz`\n", "```" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>kd_id</th>\n", " <th>kd_firma</th>\n", " <th>kd_plz</th>\n", " <th>au_id</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Trapo</td>\n", " <td>None</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>Öhlandi</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>Müller</td>\n", " <td>None</td>\n", " <td>3</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 1),\n", " (1, 'Trapo', None, 2),\n", " (1, 'Trapo', None, 2),\n", " (2, 'Öhlandi', None, None),\n", " (3, 'Müller', None, 3)]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql select k.kd_id, k.`kd_firma`, k.`kd_plz`, a.`au_id` from kunde k left join auftrag a on k.`kd_id` = a.`au_kd_id` left join fahrten f on a.`au_id` = f.`f_au_id` left join teilstrecke ts on ts.`ts_f_id` = f.`f_id` order by k.`kd_plz`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Winter 2015" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Datenmodell" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "\n", "\n", "Hinweis: In Rechnung gibt es zusätzlich ein Feld Rechnung.Kd_ID" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aufgabe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Erstellen Sie eine SQL-Abfrage, mit der alle Kunden wie folgt aufgelistet werden, bei denen eine Zahlungsbedingung mit einem Skontosatz größer 3 % ist, mit Ausgabe der Anzahl der hinterlegten Rechnungen aus dem Jahr 2015.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Connected: steinam@winter_2015'" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql mysql://steinam:steinam@localhost/winter_2015" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```mysql\n", "select count(rechnung.`Rg_ID`), kunde.`Kd_Name` \n", "\tfrom rechnung inner join kunde\n", "\ton `rechnung`.`Rg_KD_ID` = kunde.`Kd_ID`\n", " inner join `zahlungsbedingung` \n", " on kunde.`Kd_Zb_ID` = `zahlungsbedingung`.`Zb_ID`\n", " where `zahlungsbedingung`.`Zb_SkontoProzent` > 3.0\n", " \tand year(`rechnung`.`Rg_Datum`) = 2015\n", "group by Kunde.`Kd_Name`\n", "\n", "```\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>count(rechnung.`Rg_ID`)</th>\n", " <th>Kd_Name</th>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>Mustermann</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>Peters</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(4, 'Mustermann'), (2, 'Peters')]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql \n", "select count(rechnung.`Rg_ID`), kunde.`Kd_Name` from rechnung \n", " inner join kunde on `rechnung`.`Rg_KD_ID` = kunde.`Kd_ID` \n", " inner join `zahlungsbedingung` on kunde.`Kd_Zb_ID` = `zahlungsbedingung`.`Zb_ID` \n", " where `zahlungsbedingung`.`Zb_SkontoProzent` > 3.0 \n", " and year(`rechnung`.`Rg_Datum`) = 2015 group by Kunde.`Kd_Name`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es geht auch mit einem Subselect\n", "\n", "```mysql\n", " select kd.`Kd_Name`, \n", " (select COUNT(*) from Rechnung as R\n", " where R.`Rg_KD_ID` = KD.`Kd_ID` and year(R.`Rg_Datum`) = 2015)\n", " \n", " from Kunde kd inner join `zahlungsbedingung` \n", " on kd.`Kd_Zb_ID` = `zahlungsbedingung`.`Zb_ID`\n", " and `zahlungsbedingung`.`Zb_SkontoProzent` > 3.0\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>Kd_Name</th>\n", " <th>Anzahl</th>\n", " </tr>\n", " <tr>\n", " <td>Peters</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <td>Mustermann</td>\n", " <td>4</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[('Peters', 2), ('Mustermann', 4)]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql \n", "select kd.`Kd_Name`, \n", "(select COUNT(*) from Rechnung as R \n", " where R.`Rg_KD_ID` = KD.`Kd_ID` and year(R.`Rg_Datum`) = 2015) as Anzahl\n", "from Kunde kd inner join `zahlungsbedingung` \n", " on kd.`Kd_Zb_ID` = `zahlungsbedingung`.`Zb_ID` \n", " and `zahlungsbedingung`.`Zb_SkontoProzent` > 3.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versicherung" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zeigen Sie zu jedem Mitarbeiter der Abteilung „Vertrieb“ den ersten Vertrag (mit einigen Angaben) an, den er abgeschlossen hat. Der Mitarbeiter soll mit ID und Name/Vorname angezeigt werden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Datenmodell Versicherung" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 rows affected.\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql -- your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Connected: steinam@versicherung_complete'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql mysql://steinam:steinam@localhost/versicherung_complete" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>Erster Abschluss</th>\n", " <th>Mitarbeiter_ID</th>\n", " </tr>\n", " <tr>\n", " <td>1974-05-03</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <td>1974-08-07</td>\n", " <td>10</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(datetime.date(1974, 5, 3), 9), (datetime.date(1974, 8, 7), 10)]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql \n", "select min(`vv`.`Abschlussdatum`) as 'Erster Abschluss', `vv`.`Mitarbeiter_ID`\n", "from `versicherungsvertrag` vv inner join mitarbeiter m \n", " on vv.`Mitarbeiter_ID` = m.`ID`\n", "where vv.`Mitarbeiter_ID` in ( select m.`ID` from mitarbeiter m \n", " inner join Abteilung a\n", " on m.`Abteilung_ID` = a.`ID`) \n", "group by vv.`Mitarbeiter_ID`" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result = _" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>Erster Abschluss</th>\n", " <th>Mitarbeiter_ID</th>\n", " </tr>\n", " <tr>\n", " <td>1974-05-03</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <td>1974-08-07</td>\n", " <td>10</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(datetime.date(1974, 5, 3), 9), (datetime.date(1974, 8, 7), 10)]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] } ], "metadata": { "hide_input": false, "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" }, "nbTranslate": { "displayLangs": [ "en", "de" ], "hotkey": "alt-t", "langInMainMenu": true, "sourceLang": "de", "targetLang": "en", "useGoogleTranslate": true }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

caganze/wisps

notebooks/One direction.ipynb

1

28373

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding 145 sources from /Users/caganze/research/splat//resources/Spectra/Public/LRIS-RED/ to spectral database\n", "Adding 89 sources from /Users/caganze/research/splat//resources/Spectra/Public/MAGE/ to spectral database\n", "Adding 2404 sources from /Users/caganze/research/splat//resources/Spectra/Public/SPEX-PRISM/ to spectral database\n" ] } ], "source": [ "import popsims\n", "import matplotlib.pyplot as plt\n", "#import wisps\n", "import pandas as pd\n", "from astropy.coordinates import SkyCoord\n", "%matplotlib inline\n", "import astropy.units as u\n", "import numpy as np\n", "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "Rsun=8300.\n", "Zsun=27." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_rz(ds, l, b):\n", " rd=np.sqrt( (ds * np.cos( b ) )**2 + Rsun * (Rsun - 2 * ds * np.cos( b ) * np.cos( l ) ) )\n", " zd=Zsun+ ds * np.sin( b - np.arctan( Zsun / Rsun) )\n", " return rd, zd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "NSAMPLES=int(1e4)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50/50 [05:02<00:00, 6.05s/it]\n" ] } ], "source": [ "coord=SkyCoord(l=np.random.uniform(-180, 180, 50 )*u.degree,\\\n", " b=np.random.uniform(-90, 90, 50)*u.degree, frame='galactic')\n", "rs=[[], [], []]\n", "zs=[[], [], []]\n", "for c in tqdm(coord):\n", " p= popsims.Pointing(coord=c)\n", " dists0=p.draw_distances(0.1, 5000, \\\n", " 350, 2600, nsample=int(NSAMPLES))\n", " dists1=p.draw_distances(0.1, 5000, \\\n", " 500, 3000, nsample=int(NSAMPLES))\n", " \n", " dists2=p.draw_distances(0.1, 5000, \\\n", " 900, 3600, nsample=int(NSAMPLES))\n", " \n", " r0, z0=get_rz( dists0, c.l.radian, c.b.radian)\n", " r1, z1=get_rz( dists1, c.l.radian, c.b.radian)\n", " r2, z2=get_rz( dists2, c.l.radian, c.b.radian)\n", " \n", " rs[0].append(r0)\n", " rs[1].append(r1)\n", " rs[2].append(r2)\n", " \n", " zs[0].append(z0)\n", " zs[1].append(z1)\n", " zs[2].append(z2)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-1973.14253025, -932.33209022, -858.50927921, ...,\n", " -1805.29535666, -2353.79459428, -1708.79439348])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "#thick_dists=[p.draw_distances(0.1, 5000, \\\n", "# 350, 2600, nsample=int(NSAMPLES))\n", "#thin_dists=p.draw_distances(0.1, 5000, \\\n", "# 900, 3600, nsample=int(NSAMPLES))\n", "#\n", "#thin_dists_incorrect=p.draw_distances(0.1, 5000, \\\n", "# 900, 2600, nsample=int(NSAMPLES))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'exp'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.dens_profile" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "zs=np.array(zs)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "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": [ "fig, ax=plt.subplots()\n", "_= ax.hist(zs[0].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='b', linewidth=3)\n", "_= ax.hist(zs[1].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='r', linewidth=3)\n", "_= ax.hist(zs[-1].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='k', linewidth=3)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "rs=np.array(rs)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax=plt.subplots()\n", "_= ax.hist(rs[0].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='b', linewidth=3)\n", "_= ax.hist(rs[1].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='r', linewidth=3)\n", "_= ax.hist(rs[-1].flatten(), bins='auto', log=True, \\\n", " histtype='step', color='k', linewidth=3)" ] }, { "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

ES-DOC/esdoc-jupyterhub

notebooks/cams/cmip6/models/sandbox-3/ocnbgchem.ipynb

1

79368

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocnbgchem \n", "**MIP Era**: CMIP6 \n", "**Institute**: CAMS \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Ocnbgchem \n", "**Sub-Topics**: Tracers. \n", "**Properties**: 65 (37 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?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 16:53:43" ], "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', 'cams', 'sandbox-3', 'ocnbgchem')" ], "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](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Time Stepping Framework --&gt; Passive Tracers Transport](#2.-Key-Properties---&gt;-Time-Stepping-Framework---&gt;-Passive-Tracers-Transport) \n", "[3. Key Properties --&gt; Time Stepping Framework --&gt; Biology Sources Sinks](#3.-Key-Properties---&gt;-Time-Stepping-Framework---&gt;-Biology-Sources-Sinks) \n", "[4. Key Properties --&gt; Transport Scheme](#4.-Key-Properties---&gt;-Transport-Scheme) \n", "[5. Key Properties --&gt; Boundary Forcing](#5.-Key-Properties---&gt;-Boundary-Forcing) \n", "[6. Key Properties --&gt; Gas Exchange](#6.-Key-Properties---&gt;-Gas-Exchange) \n", "[7. Key Properties --&gt; Carbon Chemistry](#7.-Key-Properties---&gt;-Carbon-Chemistry) \n", "[8. Tracers](#8.-Tracers) \n", "[9. Tracers --&gt; Ecosystem](#9.-Tracers---&gt;-Ecosystem) \n", "[10. Tracers --&gt; Ecosystem --&gt; Phytoplankton](#10.-Tracers---&gt;-Ecosystem---&gt;-Phytoplankton) \n", "[11. Tracers --&gt; Ecosystem --&gt; Zooplankton](#11.-Tracers---&gt;-Ecosystem---&gt;-Zooplankton) \n", "[12. Tracers --&gt; Disolved Organic Matter](#12.-Tracers---&gt;-Disolved-Organic-Matter) \n", "[13. Tracers --&gt; Particules](#13.-Tracers---&gt;-Particules) \n", "[14. Tracers --&gt; Dic Alkalinity](#14.-Tracers---&gt;-Dic-Alkalinity) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Ocean Biogeochemistry 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 ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.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 ocean biogeochemistry model code (PISCES 2.0,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.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 Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Geochemical\" \n", "# \"NPZD\" \n", "# \"PFT\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Elemental Stoichiometry\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe elemental stoichiometry (fixed, variable, mix of the two)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Fixed\" \n", "# \"Variable\" \n", "# \"Mix of both\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Elemental Stoichiometry Details\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe which elements have fixed/variable stoichiometry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_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": [ "### 1.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of all prognostic tracer variables in the ocean biogeochemistry component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_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": [ "### 1.7. Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of all diagnotic tracer variables in the ocean biogeochemistry component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.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": [ "### 1.8. Damping\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \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; Time Stepping Framework --&gt; Passive Tracers Transport \n", "*Time stepping method for passive tracers transport in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time stepping framework for passive tracers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"use ocean model transport time step\" \n", "# \"use specific time step\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Timestep If Not From Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Time step for passive tracers (if different from ocean)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \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. Key Properties --&gt; Time Stepping Framework --&gt; Biology Sources Sinks \n", "*Time stepping framework for biology sources and sinks in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time stepping framework for biology sources and sinks*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"use ocean model transport time step\" \n", "# \"use specific time step\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Timestep If Not From Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Time step for biology sources and sinks (if different from ocean)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \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; Transport Scheme \n", "*Transport scheme in ocean biogeochemistry*" ], "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", "### *Type of transport scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Offline\" \n", "# \"Online\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Transport scheme used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Use that of ocean model\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Use Different Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Decribe transport scheme if different than that of ocean model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_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": [ "# 5. Key Properties --&gt; Boundary Forcing \n", "*Properties of biogeochemistry boundary forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Atmospheric Deposition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how atmospheric deposition is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"from file (climatology)\" \n", "# \"from file (interannual variations)\" \n", "# \"from Atmospheric Chemistry model\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. River Input\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how river input is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"from file (climatology)\" \n", "# \"from file (interannual variations)\" \n", "# \"from Land Surface model\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Sediments From Boundary Conditions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List which sediments are speficied from boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \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. Sediments From Explicit Model\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List which sediments are speficied from explicit sediment model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \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; Gas Exchange \n", "*Properties of gas exchange in ocean biogeochemistry *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \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": [ "### 6.2. CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe CO2 gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. O2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is O2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \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": [ "### 6.4. O2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe O2 gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. DMS Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is DMS gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \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": [ "### 6.6. DMS Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify DMS gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. N2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is N2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \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": [ "### 6.8. N2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify N2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.9. N2O Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is N2O gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \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": [ "### 6.10. N2O Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify N2O gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.11. CFC11 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CFC11 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \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": [ "### 6.12. CFC11 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify CFC11 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.13. CFC12 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is CFC12 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \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": [ "### 6.14. CFC12 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify CFC12 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.15. SF6 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is SF6 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \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": [ "### 6.16. SF6 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify SF6 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.17. 13CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is 13CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \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": [ "### 6.18. 13CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify 13CO2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.19. 14CO2 Exchange Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is 14CO2 gas exchange modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \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": [ "### 6.20. 14CO2 Exchange Type\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify 14CO2 gas exchange scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.21. Other Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any other gas exchange*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Carbon Chemistry \n", "*Properties of carbon chemistry biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how carbon chemistry is modeled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OMIP protocol\" \n", "# \"Other protocol\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. PH Scale\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If NOT OMIP protocol, describe pH scale.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea water\" \n", "# \"Free\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Constants If Not OMIP\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If NOT OMIP protocol, list carbon chemistry constants.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \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. Tracers \n", "*Ocean biogeochemistry tracers*" ], "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 of tracers in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.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. Sulfur Cycle Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is sulfur cycle modeled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \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": [ "### 8.3. Nutrients Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List nutrient species present in ocean biogeochemistry model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Nitrogen (N)\" \n", "# \"Phosphorous (P)\" \n", "# \"Silicium (S)\" \n", "# \"Iron (Fe)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Nitrous Species If N\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If nitrogen present, list nitrous species.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Nitrates (NO3)\" \n", "# \"Amonium (NH4)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Nitrous Processes If N\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If nitrogen present, list nitrous processes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dentrification\" \n", "# \"N fixation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Tracers --&gt; Ecosystem \n", "*Ecosystem properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Upper Trophic Levels Definition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Definition of upper trophic level (e.g. based on size) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Upper Trophic Levels Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Define how upper trophic level are treated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \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. Tracers --&gt; Ecosystem --&gt; Phytoplankton \n", "*Phytoplankton properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of phytoplankton*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Generic\" \n", "# \"PFT including size based (specify both below)\" \n", "# \"Size based only (specify below)\" \n", "# \"PFT only (specify below)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Pft\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Phytoplankton functional types (PFT) (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diatoms\" \n", "# \"Nfixers\" \n", "# \"Calcifiers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Size Classes\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Phytoplankton size classes (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Microphytoplankton\" \n", "# \"Nanophytoplankton\" \n", "# \"Picophytoplankton\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Tracers --&gt; Ecosystem --&gt; Zooplankton \n", "*Zooplankton properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of zooplankton*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Generic\" \n", "# \"Size based (specify below)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Size Classes\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Zooplankton size classes (if applicable)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Microzooplankton\" \n", "# \"Mesozooplankton\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Tracers --&gt; Disolved Organic Matter \n", "*Disolved organic matter properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Bacteria Present\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there bacteria representation ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \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. Lability\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe treatment of lability in dissolved organic matter*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Labile\" \n", "# \"Semi-labile\" \n", "# \"Refractory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Tracers --&gt; Particules \n", "*Particulate carbon properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is particulate carbon represented in ocean biogeochemistry?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diagnostic\" \n", "# \"Diagnostic (Martin profile)\" \n", "# \"Diagnostic (Balast)\" \n", "# \"Prognostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, type(s) of particulate matter taken into account*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"POC\" \n", "# \"PIC (calcite)\" \n", "# \"PIC (aragonite\" \n", "# \"BSi\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Size If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No size spectrum used\" \n", "# \"Full size spectrum\" \n", "# \"Discrete size classes (specify which below)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Size If Discrete\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic and discrete size, describe which size classes are used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Sinking Speed If Prognostic\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, method for calculation of sinking speed of particules*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Function of particule size\" \n", "# \"Function of particule type (balast)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Tracers --&gt; Dic Alkalinity \n", "*DIC and alkalinity properties in ocean biogeochemistry*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Carbon Isotopes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which carbon isotopes are modelled (C13, C14)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"C13\" \n", "# \"C14)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Abiotic Carbon\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is abiotic carbon modelled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \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": [ "### 14.3. Alkalinity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is alkalinity modelled ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \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": [ "### \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

dhimmel/SIDER2

compounds/index.ipynb

1

16384

{ "metadata": { "name": "", "signature": "sha256:fa8c70f84ad6b970d91ffdd31b3b00ec6e4cabeb0ceeb79b2938411ab8c0073f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieving PubChem Compound Information\n", "### By Daniel Himmelstein\n", "### January 30, 2015\n", "\n", "Here, we take the pubchem compound identifiers for sterio drugs in SIDER 2 and find the corresponding parent compound and canonical smiles. This code is the third notebook in a project to [parse](../parse) and [analyze](../similarity) SIDER 2 data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import csv\n", "import collections" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the *pubchempy* package to query PubChem's API within python. Information on *pubchempy* is available at the:\n", "\n", "+ [online documentation](http://pubchempy.readthedocs.org/en/latest/)\n", "+ [pypi -- package download](https://pypi.python.org/pypi/PubChemPy)\n", "+ [github](https://github.com/mcs07/PubChemPy)\n", "+ [pubchem PUG](https://pubchem.ncbi.nlm.nih.gov/pug/pughelp.html)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pubchempy" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_pubchem_parent(cid, orphans_as_self=True):\n", " \"\"\"\n", " From a pubchem_cid, retreive the parent compound's cid.\n", " If function is unsuccesful in retrieving a single parent,\n", " `orphans_as_self = True` returns `cid` rather than None.\n", " \n", " According to pubmed:\n", " \n", " > A parent is conceptually the \"important\" part of the molecule\n", " > when the molecule has more than one covalent component.\n", " > Specifically, a parent component must have at least one carbon\n", " > and contain at least 70% of the heavy (non-hydrogen) atoms of\n", " > all the unique covalent units (ignoring stoichiometry).\n", " > Note that this is a very empirical definition and is subject to change.\n", "\n", " A parallel query can be executed using the REST PUG API:\n", " http://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/11477084/cids/XML?cids_type=parent\n", " \"\"\"\n", " assert cid\n", " \n", " try:\n", " parent_cids = pubchempy.get_cids(identifier=cid, namespace='cid', domain='compound', cids_type='parent')\n", " except pubchempy.BadRequestError as e:\n", " print 'Error getting parent of {}. {}'.format(cid, e)\n", " return cid if orphans_as_self else None\n", " try:\n", " parent_cid, = parent_cids\n", " return parent_cid\n", " except ValueError:\n", " print 'Error getting parent of {}. Parents retreived: {}'.format(cid, parent_cids)\n", " return cid if orphans_as_self else None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "path = os.path.join('..', 'data', 'sider_compounds_pubchem.txt')\n", "with open(path) as read_file:\n", " reader = csv.DictReader(read_file, fieldnames=['pubchem_cid'])\n", " rows = list(reader)\n", "rows[:3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "[{'pubchem_cid': '119'}, {'pubchem_cid': '137'}, {'pubchem_cid': '143'}]" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "for row in rows:\n", " cid = row['pubchem_cid']\n", " parent_cid = get_pubchem_parent(cid)\n", " cid_props, cid_parent_props = pubchempy.get_properties(\n", " properties=['canonical_smiles'], identifier=[cid, parent_cid], namespace='cid')\n", " row['canonical_smiles'] = cid_props['CanonicalSMILES']\n", " row['pubchem_cid_parent'] = parent_cid\n", " row['canonical_smiles_parent'] = cid_parent_props['CanonicalSMILES']\n", "rows[:3]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Error getting parent of 271. Parents retreived: []\n", "Error getting parent of 312. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 402. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 784. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 807. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 888. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 947. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 948. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 977. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 2770. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 3161. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5238. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5785. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5825. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 6691. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 9052. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 14791. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 14888. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 20585. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 23954. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 23987. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 24393. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 24843. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 25959. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 26924. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 28486. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 43805. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 65027. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 66376. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 71368. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 145068. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 160051. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 4517618. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5280452. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5280962. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5280972. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5281008. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5281011. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5281021. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5281106. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5282044. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 5360126. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 6326970. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 6474909. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 11598201. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 11979316. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 16132418. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 16132438. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 25077648. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 44387541. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Error getting parent of 45267081. Parents retreived: []" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "[{'canonical_smiles': u'C(CC(=O)O)CN',\n", " 'canonical_smiles_parent': u'C(CC(=O)O)CN',\n", " 'pubchem_cid': '119',\n", " 'pubchem_cid_parent': 119},\n", " {'canonical_smiles': u'C(CC(=O)O)C(=O)CN',\n", " 'canonical_smiles_parent': u'C(CC(=O)O)C(=O)CN',\n", " 'pubchem_cid': '137',\n", " 'pubchem_cid_parent': 137},\n", " {'canonical_smiles': u'C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O',\n", " 'canonical_smiles_parent': u'C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O',\n", " 'pubchem_cid': '143',\n", " 'pubchem_cid_parent': 143}]" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "collections.Counter(str(row['pubchem_cid']) == str(row['pubchem_cid_parent']) for row in rows)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "Counter({True: 990, False: 140})" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "path = os.path.join('..', 'data', 'compounds.txt')\n", "with open(path, 'w') as write_file:\n", " fieldnames = ['pubchem_cid', 'pubchem_cid_parent', 'canonical_smiles', 'canonical_smiles_parent']\n", " writer = csv.DictWriter(write_file, fieldnames=fieldnames, delimiter='\\t')\n", " writer.writeheader()\n", " writer.writerows(rows)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download\n", "For constructing compound networks, [compounds.txt](../data/compounds.txt) can be used as a node attributes table and [similarities.txt](../data/similarities.txt) can be used like a .sif file for edges. To exclude similarities for compound pairs where less than all three methods produce a score, use [similarities-complete.txt](../data/similarities-complete.txt)." ] } ], "metadata": {} } ] }

cc0-1.0

orbitse/data-512-a2

hcds-a2-bias.ipynb

1

195184

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## A2: Bias in data\n", "\n", "### Project Overview\n", "\n", "The goal of this project is to explore the concept of 'bias' in data by analyzing Wikipedia articles on political figures from different countries. \n", "\n", "The data will include a dataset of political articles on Wikipedia, the predicted article quality scores for those articles, and a dataset of country populations. \n", "\n", "The analysis will quantify the number of Wikipedia articles devoted to politicians in each country, label the quality of those articles, and consider how those measurements vary between countries. \n", "\n", "The data visualization will include a series of plots that show: \n", " 1. The countries with the greatest and the least coverage of politicians on Wikipedia compared to their population sizes. \n", " 2. The countries with the highest and the lowest proportion of high quality articles about politicians. \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "The code in this notebook cell is optional, you do not need to run this\n", " cell in order to run the code in subsequent cells, but if this cell\n", " isn't run, some intermediate values won't be displayed.\n", "To run a cell, position the cursor inside the cell, so the cell border\n", " turns green, and simultaneously press the keys: control and return (or enter).\n", "\"\"\"\n", "\n", "# This code displays all results created within a jupyter notebook cell.\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"\n", "\n", "\n", "# This code displays Matplotlib objects inline.\n", "from IPython import get_ipython\n", "get_ipython().run_line_magic('matplotlib', 'inline')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1. Getting the Data\n", "\n", "We will be combining three sources of data: \n", " - the Wikipedia dataset,\n", " - the population dataset, and\n", " - the article quality prediction dataset.\n", "\n", "### Wikipedia Dataset\n", "The Wikipedia dataset about political articles (also called pages) can be found on [Figshare](https://figshare.com/articles/Untitled_Item/5513449). The English language article data was extracted using the Wikimedia API, saved as a CSV file named page_data.csv, and uploaded to Figshare. For more information, see the `README.md` file in this `data-512-a2` repository. \n", "\n", "A copy of the `page_data.csv` file is also available in this `data-512-a2` repository. \n", " \n", "The columns in the page_data.csv file are:\n", " 1. country: the country name, extracted from the category name\n", " 2. page: the Wikipedia page (aka article) title\n", " 3. rev_id: the revision_id for the last edit to the page" ] }, { "cell_type": "code", "execution_count": 1, "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>page</th>\n", " <th>country</th>\n", " <th>rev_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Template:ZambiaProvincialMinisters</td>\n", " <td>Zambia</td>\n", " <td>235107991</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bir I of Kanem</td>\n", " <td>Chad</td>\n", " <td>355319463</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Template:Zimbabwe-politician-stub</td>\n", " <td>Zimbabwe</td>\n", " <td>391862046</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Template:Uganda-politician-stub</td>\n", " <td>Uganda</td>\n", " <td>391862070</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Template:Namibia-politician-stub</td>\n", " <td>Namibia</td>\n", " <td>391862409</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " page country rev_id\n", "0 Template:ZambiaProvincialMinisters Zambia 235107991\n", "1 Bir I of Kanem Chad 355319463\n", "2 Template:Zimbabwe-politician-stub Zimbabwe 391862046\n", "3 Template:Uganda-politician-stub Uganda 391862070\n", "4 Template:Namibia-politician-stub Namibia 391862409" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "The code in this cell reads in the Wikipedia article data from the\n", " file page_data.csv to get this data:\n", " RangeIndex: 47197 entries, 0 to 47196\n", " Data columns (total 3 columns):\n", " page 47197 non-null object\n", " country 47197 non-null object\n", " rev_id 47197 non-null int64\n", " \n", "The data is then stored in a pandas DataFrame (DF) object.\n", "\"\"\"\n", "\n", "import csv\n", "import pandas as pd\n", "\n", "page_data = pd.read_csv(\"page_data.csv\")\n", "page_data.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of rows and columns in original page_data DataFrame (47197, 3)\n" ] } ], "source": [ "print(\"Number of rows and columns in original page_data DataFrame\", page_data.shape)" ] }, { "cell_type": "code", "execution_count": 3, "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>page</th>\n", " <th>country</th>\n", " <th>rev_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Template:ZambiaProvincialMinisters</td>\n", " <td>Zambia</td>\n", " <td>235107991</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bir I of Kanem</td>\n", " <td>Chad</td>\n", " <td>355319463</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Template:Zimbabwe-politician-stub</td>\n", " <td>Zimbabwe</td>\n", " <td>391862046</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Template:Uganda-politician-stub</td>\n", " <td>Uganda</td>\n", " <td>391862070</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Template:Namibia-politician-stub</td>\n", " <td>Namibia</td>\n", " <td>391862409</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Template:Nigeria-politician-stub</td>\n", " <td>Nigeria</td>\n", " <td>391862819</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Template:Colombia-politician-stub</td>\n", " <td>Colombia</td>\n", " <td>391863340</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Template:Chile-politician-stub</td>\n", " <td>Chile</td>\n", " <td>391863361</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Template:Fiji-politician-stub</td>\n", " <td>Fiji</td>\n", " <td>391863617</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Template:Solomons-politician-stub</td>\n", " <td>Solomon Islands</td>\n", " <td>391863809</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " page country rev_id\n", "0 Template:ZambiaProvincialMinisters Zambia 235107991\n", "1 Bir I of Kanem Chad 355319463\n", "2 Template:Zimbabwe-politician-stub Zimbabwe 391862046\n", "3 Template:Uganda-politician-stub Uganda 391862070\n", "4 Template:Namibia-politician-stub Namibia 391862409\n", "5 Template:Nigeria-politician-stub Nigeria 391862819\n", "6 Template:Colombia-politician-stub Colombia 391863340\n", "7 Template:Chile-politician-stub Chile 391863361\n", "8 Template:Fiji-politician-stub Fiji 391863617\n", "9 Template:Solomons-politician-stub Solomon Islands 391863809" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "The code in this cell:\n", " (1) standardizes some of the country names in page_data for merging page_data\n", " with the population data; and \n", " (2) removes two rows with revision ID values that don't exist in the dataset\n", " being used by the ORES API, and therefore return errors [807367030, 807367166].\n", "\"\"\"\n", "\n", "# Part 1: standardize country names\n", "# COUNTRY_MAP from Gary\n", "COUNTRY_MAP = {\n", " \"East Timorese\" : \"Timor-Leste\",\n", " \"Hondura\" : \"Honduras\",\n", " \"Rhodesian\" : \"Zimbabwe\",\n", " \"Salvadoran\" : \"El Salvador\",\n", " \"Samoan\" : \"Samoa\",\n", " \"São Tomé and Príncipe\" : \"Sao Tome and Principe\",\n", " \"South African Republic\" : \"South Africa\",\n", " \"South Korean\" : \"Korea, South\"\n", "}\n", "\n", "# A sample of original rows with values we want to replace\n", "page_data.loc[page_data.index.isin([272, 443, 448, 541, 602])]\n", "\n", "# use isin to filter for valid rows, and then use replace to replace the values\n", "\n", "if page_data[\"country\"].isin(COUNTRY_MAP.keys()).any():\n", " page_data[\"country\"].replace(COUNTRY_MAP, inplace=True)\n", "\n", "# Verify that the values were replaced in those sample rows\n", "page_data.loc[page_data.index.isin([272, 443, 448, 541, 602])]\n", "\n", "\n", "# Part 2: removal of revision ID values\n", "\n", "# check if rev_id values in page_data\n", "page_data.loc[page_data[\"rev_id\"].isin([807367030, 807367166])]\n", "\n", "# remove rows with rev_id in [807367030, 807367166]\n", "page_data = page_data.loc[~page_data[\"rev_id\"].isin([807367030, 807367166])]\n", "page_data.head(10)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ending number of rows and columns in page_data (47195, 3)\n" ] } ], "source": [ "print(\"Ending number of rows and columns in page_data\", page_data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Population Dataset\n", "The population data is on the Population Research Bureau website, download the CSV file. In this notebook, the population data CSV file is named, `Population Mid-2015.csv`." ] }, { "cell_type": "code", "execution_count": 5, "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>country</th>\n", " <th>population</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Afghanistan</td>\n", " <td>32247000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Albania</td>\n", " <td>2892000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Algeria</td>\n", " <td>39948000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Andorra</td>\n", " <td>78000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Angola</td>\n", " <td>25000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country population\n", "0 Afghanistan 32247000\n", "1 Albania 2892000\n", "2 Algeria 39948000\n", "3 Andorra 78000\n", "4 Angola 25000000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "The code in this cell reads in the country population data from\n", " the file population_mid-2015.csv and does some processing.\n", "\n", "When you read population_mid-2015.csv without any parameters\n", " like the page_data.csv file above, the title becomes a\n", " single column. So, to get 6 columns instead of 1, set the\n", " second row (index 1) as the header with parameter: header=1.\n", " \n", " The original data in population_mid-2015.csv looks like this:\n", " RangeIndex: 210 entries, 0 to 209\n", " Data columns (total 6 columns):\n", " Location 210 non-null object\n", " Location Type 210 non-null object\n", " TimeFrame 210 non-null object\n", " Data Type 210 non-null object\n", " Data 210 non-null int64\n", " Footnotes 0 non-null float64\n", "\"\"\"\n", "\n", "pop_data = pd.read_csv(\"population_mid-2015.csv\", #skiprows=0,\n", " header=1, sep=\",\", thousands=\",\")\n", "\n", "# Only drop unneeded columns if they are in pop_data DF\n", "if len(pop_data.columns) > 2:\n", " pop_data.drop([\"Location Type\", \"TimeFrame\",\n", " \"Data Type\", \"Footnotes\"], axis=1, inplace=True)\n", "else:\n", " pass\n", "\n", "# Rename columns to standardize names for future merging\n", "pop_data.rename(columns={\"Location\" : \"country\",\n", " \"Data\" : \"population\"}, inplace=True)\n", "\n", "pop_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Article Quality Prediction Dataset\n", "\n", "The predicted quality scores for each article in the Wikipedia dataset comes from a Wikimedia API endpoint for a machine learning system called __ORES__ (\"Objective Revision Evaluation Service\"). ORES estimates the quality of an article at a particular point in time, and assigns a series of probabilities that the article is best described by one of the categories listed below. \n", "\n", "The range of quality scores are, from best to worst: \n", " 1.\tFA - Featured article\n", " 2.\tGA - Good article\n", " 3.\tB - B-class article\n", " 4.\tC - C-class article\n", " 5.\tStart - Start-class article\n", " 6.\tStub - Stub-class article \n", "\n", "These quality scores are a sub-set of quality assessment categories developed by Wikipedia editors. For more information about the scores, see [Project Assessment](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_assessment#Grades). \n", "\n", "The ORES API documentation can be found [here](https://www.mediawiki.org/wiki/ORES) and the web API is [here](https://ores.wikimedia.org/v3/). The API requires: a revision ID, which is the third column in `page_data.csv` (originally titled \"last_edit\"), and the machine learning model, which is \"wp10.\" \n", "\n", "When you query the API, the ORES returns a JSON object that includes a predicted quality score, as well as the probability values for each of the six possible quality scores. But, for the analysis in this project, you only need the predicted quality score value, not the probabilities. \n", "\n", "The cell below is an example of a response in the JSON format from the ORES API: " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Example of the JSON format of ORES API response\n", "{\"enwiki\": {\n", " \"models\": {\n", " \"wp10\": {\n", " \"version\": \"0.5.0\"\n", " }\n", " },\n", " \"scores\": {\n", " \"774499188\": {\n", " \"wp10\": {\n", " \"score\": {\n", " \"prediction\": \"Stub\",\n", " \"probability\": {\n", " \"B\": 0.03488477079112925,\n", " \"C\": 0.06953258948284814,\n", " \"FA\": 0.0025762575670963965,\n", " \"GA\": 0.007911851615317388,\n", " \"Start\": 0.4106575723489943,\n", " \"Stub\": 0.4744369581946146\n", " }\n", " }\n", " }\n", " }\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New page_data DataFrame rows and columns: (47195, 4)\n", " page country rev_id \\\n", "0 Template:ZambiaProvincialMinisters Zambia 235107991 \n", "1 Bir I of Kanem Chad 355319463 \n", "2 Template:Zimbabwe-politician-stub Zimbabwe 391862046 \n", "3 Template:Uganda-politician-stub Uganda 391862070 \n", "4 Template:Namibia-politician-stub Namibia 391862409 \n", "5 Template:Nigeria-politician-stub Nigeria 391862819 \n", "6 Template:Colombia-politician-stub Colombia 391863340 \n", "7 Template:Chile-politician-stub Chile 391863361 \n", "8 Template:Fiji-politician-stub Fiji 391863617 \n", "9 Template:Solomons-politician-stub Solomon Islands 391863809 \n", "\n", " article_quality \n", "0 Stub \n", "1 Stub \n", "2 Stub \n", "3 Stub \n", "4 Stub \n", "5 Stub \n", "6 Stub \n", "7 Stub \n", "8 Stub \n", "9 Stub \n" ] } ], "source": [ "import requests\n", "import json\n", "import time\n", "\n", "def get_page_quality_scores(rev_ids):\n", " \"\"\"\n", " Function takes revision id values, calls the ORES API, and returns the\n", " revision id values recognized by the ORES API, the article quality scores, and\n", " the time for each API call.\n", " \"\"\"\n", " # start is the time in seconds as a floating point number\n", " start = time.time() \n", " \n", " # Local variables\n", " local_scores = []\n", " local_rev_ids = []\n", " endpoint = \"https://ores.wikimedia.org/v3/scores/{project}/?models={model}&revids={revids}\" \n", " params = {\"project\" : \"enwiki\",\n", " \"model\" : \"wp10\",\n", " \"revids\" : \"|\".join(str(x) for x in rev_ids)\n", " }\n", "\n", " api_call = requests.get(endpoint.format(**params))\n", " \n", " response = api_call.json()\n", " #print(json.dumps(response, indent=4, sort_keys=True))\n", " \n", " # Strip out the quality score from the JSON object and save in list (scores)\n", " for rev_id in rev_ids:\n", " try:\n", " local_rev_ids.append(rev_id)\n", " local_scores.append(response[\"enwiki\"][\"scores\"][str(rev_id)][\"wp10\"][\"score\"][\"prediction\"])\n", " except:\n", " print(\"exception with rev_id:\", str(rev_id))\n", " pass\n", " \n", " # Measure time to run batch of Get requests in minutes\n", " end = (time.time() - start)/60\n", "\n", " return local_rev_ids, local_scores, end\n", "\n", "\n", "try:\n", " # This is a shortcut to redefine page_data as a DF with the article_quality data\n", " # since getting the API data takes a about 3.52 minutes\n", " page_data = pd.read_csv(\"page_quality_data.csv\")\n", " \n", " if page_data[\"Unnamed: 0\"].any():\n", " page_data.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n", " \n", " print(\"New page_data DataFrame rows and columns:\", page_data.shape)\n", " print(page_data.head(10))\n", " \n", "except: \n", " # If no page_quality_data.csv file, then get the data from the ORES API\n", " lst_rev_ids = []\n", " lst_article_scores = []\n", " run_time = 0\n", "\n", " for i in range(0, len(page_data[\"rev_id\"]), 100):\n", " # batches of 100 rev_id values to send to get_page_quality_scores()\n", " revision_ids = page_data[\"rev_id\"][i:(i + 100)] \n", " \n", " rev_id_vals, quality_scores, batch_time = get_page_quality_scores(revision_ids) \n", " \n", " for id_value in rev_id_vals:\n", " # Keep a running list of rev_id values as an index for quality scores\n", " lst_rev_ids.append(id_value)\n", " \n", " for score in quality_scores:\n", " # Keep a running list of quality scores to add to page_data\n", " lst_article_scores.append(score)\n", "\n", " run_time = run_time + batch_time\n", " \n", " # Test print statement for tracking time\n", " # print(\"i:\", str(i) + \", batch_time: %f, run_time: %f\" % (batch_time, run_time))\n", "\n", " # Add a column with the predicted article quality scores to the page_data\n", " df_scores = pd.DataFrame({\"rev_id\" : lst_rev_ids, \"article_quality\" : lst_article_scores})\n", " page_score_data = page_data.merge(df_scores, how='outer',\n", " on=[\"rev_id\"]).dropna(axis=0, how='any')\n", " page_score_data.to_csv(\"page_quality_data.csv\")\n", "\n", " print(\"\\nTotal Run Time: {0:5.2f} minutes\\n\".format(run_time))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(47195, 4)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "page_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2. Combining the Page and Population Datasets\n", "\n", "After retrieving and including the ORES data for each article, we merge the wikipedia article data and the population data together. Both DataFrames have columns named \"country\" and we will merge the page_data and pop_data on the country values. \n", " \n", "While merging the DataFrames, some entries won't be merged because a country name isn't in the other DataFrame, _e.g._ the population dataset does not have an entry for the equivalent Wikipedia country. So, we will __remove the rows that do not have matching data__. We had 47,195 rows in page_data initially and the final combine_df has 46,408 rows. \n", "\n", "After consolidating the data, we save the DataFrame as a single CSV file with these columns:\n", " > country \n", " > article_name \n", " > revision_id \n", " > article_quality \n", " > population \n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 46408 entries, 0 to 46407\n", "Data columns (total 5 columns):\n", "article_name 46408 non-null object\n", "country 46408 non-null object\n", "revision_id 46408 non-null int64\n", "article_quality 46408 non-null object\n", "population 46408 non-null int64\n", "dtypes: int64(2), object(3)\n", "memory usage: 1.8+ MB\n" ] } ], "source": [ "try:\n", " # Rename page_data columns\n", " page_data.rename(columns={\"page\" : \"article_name\",\n", " \"rev_id\" : \"revision_id\"}, inplace=True)\n", "except:\n", " pass\n", "\n", "# Combine DataFrames on country names and remove any rows with empty values\n", "combine_df = page_data.merge(pop_data,\n", " how='outer', on=[\"country\"]).dropna(axis=0,\n", " how='any').reset_index()\n", "# Remove the old index which is now a column named index\n", "# because I reset the index\n", "combine_df.drop([\"index\"], axis=1, inplace=True)\n", "\n", "# Change revision_id & population values from float to int\n", "combine_df[[\"revision_id\",\n", " \"population\"]] = combine_df[[\"revision_id\",\n", " \"population\"]].astype(int)\n", "combine_df.info()" ] }, { "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>article_name</th>\n", " <th>country</th>\n", " <th>revision_id</th>\n", " <th>article_quality</th>\n", " <th>population</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Template:ZambiaProvincialMinisters</td>\n", " <td>Zambia</td>\n", " <td>235107991</td>\n", " <td>Stub</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Gladys Lundwe</td>\n", " <td>Zambia</td>\n", " <td>757566606</td>\n", " <td>Stub</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Mwamba Luchembe</td>\n", " <td>Zambia</td>\n", " <td>764848643</td>\n", " <td>Stub</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Thandiwe Banda</td>\n", " <td>Zambia</td>\n", " <td>768166426</td>\n", " <td>Start</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Sylvester Chisembele</td>\n", " <td>Zambia</td>\n", " <td>776082926</td>\n", " <td>C</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Victoria Kalima</td>\n", " <td>Zambia</td>\n", " <td>776530837</td>\n", " <td>Start</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Margaret Mwanakatwe</td>\n", " <td>Zambia</td>\n", " <td>779747587</td>\n", " <td>Start</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Nkandu Luo</td>\n", " <td>Zambia</td>\n", " <td>779747961</td>\n", " <td>C</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Susan Nakazwe</td>\n", " <td>Zambia</td>\n", " <td>779748181</td>\n", " <td>Start</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Catherine Namugala</td>\n", " <td>Zambia</td>\n", " <td>779748285</td>\n", " <td>Start</td>\n", " <td>15473900</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " article_name country revision_id article_quality \\\n", "0 Template:ZambiaProvincialMinisters Zambia 235107991 Stub \n", "1 Gladys Lundwe Zambia 757566606 Stub \n", "2 Mwamba Luchembe Zambia 764848643 Stub \n", "3 Thandiwe Banda Zambia 768166426 Start \n", "4 Sylvester Chisembele Zambia 776082926 C \n", "5 Victoria Kalima Zambia 776530837 Start \n", "6 Margaret Mwanakatwe Zambia 779747587 Start \n", "7 Nkandu Luo Zambia 779747961 C \n", "8 Susan Nakazwe Zambia 779748181 Start \n", "9 Catherine Namugala Zambia 779748285 Start \n", "\n", " population \n", "0 15473900 \n", "1 15473900 \n", "2 15473900 \n", "3 15473900 \n", "4 15473900 \n", "5 15473900 \n", "6 15473900 \n", "7 15473900 \n", "8 15473900 \n", "9 15473900 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save final combined DataFrame to CSV file\n", "combine_df.to_csv(\"page_pop_data_final.csv\")\n", "combine_df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3. Analysis\n", "\n", "The analysis calculates the proportion (as a percentage) of articles-per-population and high-quality articles for each country. \"High quality\" articles are defined as articles about politicians that ORES predicted would be in either the \"FA\" (featured article) or \"GA\" (good article) classes. \n", "\n", "Examples: \n", " - if a country has a population of 10,000 people, and you found 10 articles about politicians from that country, then the percentage of articles-per-population would be .1%. \n", " - if a country has 10 articles about politicians, and 2 of them are FA or GA class articles, then the percentage of high-quality articles would be 20%. \n", " " ] }, { "cell_type": "code", "execution_count": 10, "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>country</th>\n", " <th>total_articles</th>\n", " <th>population</th>\n", " <th>percent_articles_per_person</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>122</th>\n", " <td>Nauru</td>\n", " <td>53</td>\n", " <td>10860</td>\n", " <td>0.4880</td>\n", " </tr>\n", " <tr>\n", " <th>177</th>\n", " <td>Tuvalu</td>\n", " <td>55</td>\n", " <td>11800</td>\n", " <td>0.4661</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <td>San Marino</td>\n", " <td>82</td>\n", " <td>33000</td>\n", " <td>0.2485</td>\n", " </tr>\n", " <tr>\n", " <th>115</th>\n", " <td>Monaco</td>\n", " <td>40</td>\n", " <td>38088</td>\n", " <td>0.1050</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td>Liechtenstein</td>\n", " <td>29</td>\n", " <td>37570</td>\n", " <td>0.0772</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>Marshall Islands</td>\n", " <td>37</td>\n", " <td>55000</td>\n", " <td>0.0673</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>Iceland</td>\n", " <td>206</td>\n", " <td>330828</td>\n", " <td>0.0623</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>Tonga</td>\n", " <td>63</td>\n", " <td>103300</td>\n", " <td>0.0610</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Andorra</td>\n", " <td>34</td>\n", " <td>78000</td>\n", " <td>0.0436</td>\n", " </tr>\n", " <tr>\n", " <th>143</th>\n", " <td>Samoa</td>\n", " <td>77</td>\n", " <td>194210</td>\n", " <td>0.0396</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country total_articles population percent_articles_per_person\n", "122 Nauru 53 10860 0.4880\n", "177 Tuvalu 55 11800 0.4661\n", "144 San Marino 82 33000 0.2485\n", "115 Monaco 40 38088 0.1050\n", "99 Liechtenstein 29 37570 0.0772\n", "109 Marshall Islands 37 55000 0.0673\n", "74 Iceland 206 330828 0.0623\n", "172 Tonga 63 103300 0.0610\n", "3 Andorra 34 78000 0.0436\n", "143 Samoa 77 194210 0.0396" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the total number of articles for each country\n", "df_test = combine_df[[\"country\",\n", " \"article_name\"]].groupby(\"country\").count().astype(int).reset_index()\n", "\n", "# Merge total number of articles for each country with population for each country\n", "df_article_pop = df_test.merge(pop_data, on=[\"country\"])\n", "\n", "df_article_pop.rename(columns={\"article_name\" : \"total_articles\"},\n", " inplace=True)\n", "\n", "# Divide the total number of articles by the country's population\n", "art_per_pop = df_article_pop[\"total_articles\"].div(df_article_pop[\"population\"],\n", " axis='index')\n", "\n", "# Get a percentage value by multiplying by 100\n", "df_article_pop[\"percent_articles_per_person\"] = round(art_per_pop*100, 4)\n", "\n", "prop_df = df_article_pop.sort_values([\"percent_articles_per_person\"],\n", " axis=0,\n", " ascending=False,\n", " inplace=False,\n", " kind='quicksort')\n", "# The 10 countries with the highest percentage of articles per person\n", "prop_df.head(10)" ] }, { "cell_type": "code", "execution_count": 11, "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>country</th>\n", " <th>total_articles</th>\n", " <th>population</th>\n", " <th>percent_articles_per_person</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>13</th>\n", " <td>Bangladesh</td>\n", " <td>324</td>\n", " <td>160411000</td>\n", " <td>0.0002</td>\n", " </tr>\n", " <tr>\n", " <th>169</th>\n", " <td>Thailand</td>\n", " <td>112</td>\n", " <td>65121250</td>\n", " <td>0.0002</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>Korea, North</td>\n", " <td>39</td>\n", " <td>24983000</td>\n", " <td>0.0002</td>\n", " </tr>\n", " <tr>\n", " <th>160</th>\n", " <td>Sudan</td>\n", " <td>98</td>\n", " <td>40883900</td>\n", " <td>0.0002</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td>Mozambique</td>\n", " <td>60</td>\n", " <td>25736000</td>\n", " <td>0.0002</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>Indonesia</td>\n", " <td>215</td>\n", " <td>255741973</td>\n", " <td>0.0001</td>\n", " </tr>\n", " <tr>\n", " <th>184</th>\n", " <td>Uzbekistan</td>\n", " <td>29</td>\n", " <td>31290791</td>\n", " <td>0.0001</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>Ethiopia</td>\n", " <td>105</td>\n", " <td>98148000</td>\n", " <td>0.0001</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>China</td>\n", " <td>1138</td>\n", " <td>1371920000</td>\n", " <td>0.0001</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>India</td>\n", " <td>990</td>\n", " <td>1314097616</td>\n", " <td>0.0001</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country total_articles population percent_articles_per_person\n", "13 Bangladesh 324 160411000 0.0002\n", "169 Thailand 112 65121250 0.0002\n", "88 Korea, North 39 24983000 0.0002\n", "160 Sudan 98 40883900 0.0002\n", "119 Mozambique 60 25736000 0.0002\n", "76 Indonesia 215 255741973 0.0001\n", "184 Uzbekistan 29 31290791 0.0001\n", "54 Ethiopia 105 98148000 0.0001\n", "34 China 1138 1371920000 0.0001\n", "75 India 990 1314097616 0.0001" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The 10 countries with the lowest percentage of articles per person\n", "prop_df.tail(10)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "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>country</th>\n", " <th>total_FA_GA</th>\n", " <th>total_pages</th>\n", " <th>percent_FA_GA</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>68</th>\n", " <td>Korea, North</td>\n", " <td>9</td>\n", " <td>39</td>\n", " <td>23.08</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>Romania</td>\n", " <td>45</td>\n", " <td>348</td>\n", " <td>12.93</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>Saudi Arabia</td>\n", " <td>15</td>\n", " <td>119</td>\n", " <td>12.61</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Central African Republic</td>\n", " <td>8</td>\n", " <td>68</td>\n", " <td>11.76</td>\n", " </tr>\n", " <tr>\n", " <th>111</th>\n", " <td>Qatar</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>9.80</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>Guinea-Bissau</td>\n", " <td>2</td>\n", " <td>21</td>\n", " <td>9.52</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td>Vietnam</td>\n", " <td>18</td>\n", " <td>191</td>\n", " <td>9.42</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Bhutan</td>\n", " <td>3</td>\n", " <td>33</td>\n", " <td>9.09</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>Ireland</td>\n", " <td>31</td>\n", " <td>381</td>\n", " <td>8.14</td>\n", " </tr>\n", " <tr>\n", " <th>142</th>\n", " <td>United States</td>\n", " <td>86</td>\n", " <td>1098</td>\n", " <td>7.83</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country total_FA_GA total_pages percent_FA_GA\n", "68 Korea, North 9 39 23.08\n", "112 Romania 45 348 12.93\n", "116 Saudi Arabia 15 119 12.61\n", "22 Central African Republic 8 68 11.76\n", "111 Qatar 5 51 9.80\n", "53 Guinea-Bissau 2 21 9.52\n", "147 Vietnam 18 191 9.42\n", "12 Bhutan 3 33 9.09\n", "61 Ireland 31 381 8.14\n", "142 United States 86 1098 7.83" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# number of GA and FA-quality articles as a proportion of all articles from country\n", "\n", "# Gets all the articles rated \"FA\" or \"GA\"\n", "fa_ga = combine_df[(combine_df[\"article_quality\"]==\"FA\") | (combine_df[\"article_quality\"]==\"GA\")]\n", "\n", "# Gets all the articles not rated \"FA\" snd not rated \"GA\"\n", "no_fa_ga = combine_df[(combine_df[\"article_quality\"]!=\"FA\") & (combine_df[\"article_quality\"]!=\"GA\")]\n", "\n", "\n", "# Adds up all the FA & GA articles for each country\n", "total_fa_ga = fa_ga[[\"country\",\n", " \"article_quality\"]].groupby(\"country\",\n", " as_index=False).count()\n", "\n", "# Adds up all the articles for each country regardless of quality rating\n", "total_articles = combine_df[[\"country\",\n", " \"article_quality\"]].groupby(\"country\",\n", " as_index=False).count()\n", "\n", "# Merge total_fa_ga and total_articles on country values\n", "art_type_df = total_fa_ga.merge(total_articles, on=\"country\")\n", "\n", "art_type_df.rename(columns={\"article_quality_x\" : \"total_FA_GA\",\n", " \"article_quality_y\" : \"total_pages\"},\n", " inplace=True)\n", "\n", "art_type_df[\"percent_FA_GA\"] = round(art_type_df[\"total_FA_GA\"]/art_type_df[\"total_pages\"].astype(float)*100, 2)\n", "\n", "sort_prop_type_df = art_type_df.sort_values([\"percent_FA_GA\"],\n", " axis=0,\n", " ascending=False,\n", " inplace=False,\n", " kind='quicksort')\n", "# The 10 countries with the highest percentage of high quality articles\n", "sort_prop_type_df.head(10)" ] }, { "cell_type": "code", "execution_count": 13, "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>country</th>\n", " <th>total_FA_GA</th>\n", " <th>total_pages</th>\n", " <th>percent_FA_GA</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100</th>\n", " <td>Nigeria</td>\n", " <td>4</td>\n", " <td>684</td>\n", " <td>0.58</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>Luxembourg</td>\n", " <td>1</td>\n", " <td>180</td>\n", " <td>0.56</td>\n", " </tr>\n", " <tr>\n", " <th>138</th>\n", " <td>Uganda</td>\n", " <td>1</td>\n", " <td>188</td>\n", " <td>0.53</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>Fiji</td>\n", " <td>1</td>\n", " <td>199</td>\n", " <td>0.50</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>Moldova</td>\n", " <td>2</td>\n", " <td>426</td>\n", " <td>0.47</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>Lithuania</td>\n", " <td>1</td>\n", " <td>248</td>\n", " <td>0.40</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>Czech Republic</td>\n", " <td>1</td>\n", " <td>254</td>\n", " <td>0.39</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>Peru</td>\n", " <td>1</td>\n", " <td>354</td>\n", " <td>0.28</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>Tanzania</td>\n", " <td>1</td>\n", " <td>408</td>\n", " <td>0.25</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>Finland</td>\n", " <td>1</td>\n", " <td>572</td>\n", " <td>0.17</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country total_FA_GA total_pages percent_FA_GA\n", "100 Nigeria 4 684 0.58\n", "79 Luxembourg 1 180 0.56\n", "138 Uganda 1 188 0.53\n", "42 Fiji 1 199 0.50\n", "90 Moldova 2 426 0.47\n", "78 Lithuania 1 248 0.40\n", "33 Czech Republic 1 254 0.39\n", "107 Peru 1 354 0.28\n", "132 Tanzania 1 408 0.25\n", "43 Finland 1 572 0.17" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The 10 countries with the lowest percentage of high quality articles\n", "sort_prop_type_df.tail(10)" ] }, { "cell_type": "code", "execution_count": 14, "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>B</th>\n", " <th>C</th>\n", " <th>FA</th>\n", " <th>GA</th>\n", " <th>Start</th>\n", " <th>Stub</th>\n", " <th>All</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Dominica</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>Barbados</th>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Eritrea</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>12</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>Belize</th>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>Guyana</th>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>Bahamas</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>7</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>Guinea-Bissau</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>Seychelles</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Sao Tome and Principe</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9</td>\n", " <td>12</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Antigua and Barbuda</th>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>8</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>Zambia</th>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>French Guiana</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>21</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>Trinidad and Tobago</th>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>15</td>\n", " <td>7</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>Liechtenstein</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>15</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Uzbekistan</th>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>12</td>\n", " <td>9</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Lesotho</th>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>9</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>Swaziland</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>23</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>Equatorial Guinea</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>15</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>Kiribati</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>23</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>Bhutan</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>20</td>\n", " <td>33</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " B C FA GA Start Stub All\n", "Dominica 0 2 0 0 4 6 12\n", "Barbados 0 5 0 0 3 6 14\n", "Eritrea 0 2 0 0 2 12 16\n", "Belize 0 5 0 0 7 4 16\n", "Guyana 0 6 0 0 8 6 20\n", "Bahamas 0 3 0 0 10 7 20\n", "Guinea-Bissau 1 2 0 2 7 9 21\n", "Seychelles 0 1 0 0 3 18 22\n", "Sao Tome and Principe 0 1 0 0 9 12 22\n", "Antigua and Barbuda 0 7 0 0 10 8 25\n", "Zambia 0 6 0 0 14 6 26\n", "French Guiana 0 2 0 0 5 21 28\n", "Trinidad and Tobago 0 5 0 1 15 7 28\n", "Liechtenstein 0 1 0 0 13 15 29\n", "Uzbekistan 0 6 1 1 12 9 29\n", "Lesotho 0 7 0 0 14 9 30\n", "Swaziland 1 4 0 0 4 23 32\n", "Equatorial Guinea 1 1 0 1 14 15 32\n", "Kiribati 0 3 0 0 6 23 32\n", "Bhutan 0 3 0 3 7 20 33" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This code produces a crosstab of countries with their quality articles\n", "# This code is partially based on the code from StackOverflow\n", "# https://stackoverflow.com/questions/46290726/how-to-make-dummy-variables-with-comma-separated-valued-columns\n", "\n", "df_articles = combine_df.set_index(\"country\")[\"article_quality\"].apply(pd.Series).stack()\n", "\n", "# margins=True adds the All column & All row\n", "cross_tab = pd.crosstab(df_articles.index.get_level_values(0),\n", " df_articles, margins=True).rename_axis(None).rename_axis(None, 1)\n", "cross_tab.sort_values([\"All\"]).head(20)" ] }, { "cell_type": "code", "execution_count": 15, "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>B</th>\n", " <th>C</th>\n", " <th>FA</th>\n", " <th>GA</th>\n", " <th>Start</th>\n", " <th>Stub</th>\n", " <th>All</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Norway</th>\n", " <td>0</td>\n", " <td>33</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>177</td>\n", " <td>440</td>\n", " <td>658</td>\n", " </tr>\n", " <tr>\n", " <th>Nigeria</th>\n", " <td>1</td>\n", " <td>64</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>349</td>\n", " <td>266</td>\n", " <td>684</td>\n", " </tr>\n", " <tr>\n", " <th>Netherlands</th>\n", " <td>3</td>\n", " <td>54</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>234</td>\n", " <td>401</td>\n", " <td>702</td>\n", " </tr>\n", " <tr>\n", " <th>Germany</th>\n", " <td>8</td>\n", " <td>87</td>\n", " <td>7</td>\n", " <td>12</td>\n", " <td>303</td>\n", " <td>286</td>\n", " <td>703</td>\n", " </tr>\n", " <tr>\n", " <th>New Zealand</th>\n", " <td>2</td>\n", " <td>89</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>245</td>\n", " <td>444</td>\n", " <td>791</td>\n", " </tr>\n", " <tr>\n", " <th>Poland</th>\n", " <td>11</td>\n", " <td>56</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>221</td>\n", " <td>508</td>\n", " <td>809</td>\n", " </tr>\n", " <tr>\n", " <th>Italy</th>\n", " <td>15</td>\n", " <td>56</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>273</td>\n", " <td>476</td>\n", " <td>828</td>\n", " </tr>\n", " <tr>\n", " <th>Iran</th>\n", " <td>10</td>\n", " <td>102</td>\n", " <td>0</td>\n", " <td>17</td>\n", " <td>229</td>\n", " <td>472</td>\n", " <td>830</td>\n", " </tr>\n", " <tr>\n", " <th>Canada</th>\n", " <td>19</td>\n", " <td>170</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>376</td>\n", " <td>262</td>\n", " <td>852</td>\n", " </tr>\n", " <tr>\n", " <th>United Kingdom</th>\n", " <td>32</td>\n", " <td>249</td>\n", " <td>12</td>\n", " <td>42</td>\n", " <td>324</td>\n", " <td>208</td>\n", " <td>867</td>\n", " </tr>\n", " <tr>\n", " <th>Spain</th>\n", " <td>13</td>\n", " <td>95</td>\n", " <td>30</td>\n", " <td>11</td>\n", " <td>314</td>\n", " <td>418</td>\n", " <td>881</td>\n", " </tr>\n", " <tr>\n", " <th>Russia</th>\n", " <td>26</td>\n", " <td>141</td>\n", " <td>9</td>\n", " <td>26</td>\n", " <td>348</td>\n", " <td>332</td>\n", " <td>882</td>\n", " </tr>\n", " <tr>\n", " <th>India</th>\n", " <td>8</td>\n", " <td>59</td>\n", " <td>3</td>\n", " <td>12</td>\n", " <td>236</td>\n", " <td>672</td>\n", " <td>990</td>\n", " </tr>\n", " <tr>\n", " <th>Pakistan</th>\n", " <td>4</td>\n", " <td>68</td>\n", " <td>3</td>\n", " <td>11</td>\n", " <td>324</td>\n", " <td>635</td>\n", " <td>1045</td>\n", " </tr>\n", " <tr>\n", " <th>Mexico</th>\n", " <td>4</td>\n", " <td>41</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>132</td>\n", " <td>897</td>\n", " <td>1081</td>\n", " </tr>\n", " <tr>\n", " <th>United States</th>\n", " <td>64</td>\n", " <td>262</td>\n", " <td>19</td>\n", " <td>67</td>\n", " <td>414</td>\n", " <td>272</td>\n", " <td>1098</td>\n", " </tr>\n", " <tr>\n", " <th>China</th>\n", " <td>66</td>\n", " <td>208</td>\n", " <td>7</td>\n", " <td>35</td>\n", " <td>402</td>\n", " <td>420</td>\n", " <td>1138</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>14</td>\n", " <td>183</td>\n", " <td>11</td>\n", " <td>33</td>\n", " <td>470</td>\n", " <td>855</td>\n", " <td>1566</td>\n", " </tr>\n", " <tr>\n", " <th>France</th>\n", " <td>26</td>\n", " <td>140</td>\n", " <td>7</td>\n", " <td>23</td>\n", " <td>372</td>\n", " <td>1121</td>\n", " <td>1689</td>\n", " </tr>\n", " <tr>\n", " <th>All</th>\n", " <td>722</td>\n", " <td>5637</td>\n", " <td>278</td>\n", " <td>858</td>\n", " <td>15016</td>\n", " <td>23897</td>\n", " <td>46408</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " B C FA GA Start Stub All\n", "Norway 0 33 3 5 177 440 658\n", "Nigeria 1 64 0 4 349 266 684\n", "Netherlands 3 54 3 7 234 401 702\n", "Germany 8 87 7 12 303 286 703\n", "New Zealand 2 89 1 10 245 444 791\n", "Poland 11 56 4 9 221 508 809\n", "Italy 15 56 2 6 273 476 828\n", "Iran 10 102 0 17 229 472 830\n", "Canada 19 170 8 17 376 262 852\n", "United Kingdom 32 249 12 42 324 208 867\n", "Spain 13 95 30 11 314 418 881\n", "Russia 26 141 9 26 348 332 882\n", "India 8 59 3 12 236 672 990\n", "Pakistan 4 68 3 11 324 635 1045\n", "Mexico 4 41 2 5 132 897 1081\n", "United States 64 262 19 67 414 272 1098\n", "China 66 208 7 35 402 420 1138\n", "Australia 14 183 11 33 470 855 1566\n", "France 26 140 7 23 372 1121 1689\n", "All 722 5637 278 858 15016 23897 46408" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_tab.sort_values([\"All\"]).tail(20)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Andorra', 'Antigua and Barbuda', 'Bahamas', 'Bahrain', 'Barbados',\n", " 'Belgium', 'Belize', 'Burundi', 'Cape Verde', 'Comoros', 'Djibouti',\n", " 'Dominica', 'Eritrea', 'Federated States of Micronesia',\n", " 'French Guiana', 'Guadeloupe', 'Guyana', 'Honduras', 'Kazakhstan',\n", " 'Kiribati', 'Lesotho', 'Liechtenstein', 'Macedonia', 'Marshall Islands',\n", " 'Monaco', 'Mozambique', 'Nauru', 'Nepal', 'San Marino',\n", " 'Sao Tome and Principe', 'Seychelles', 'Solomon Islands', 'Suriname',\n", " 'Swaziland', 'Switzerland', 'Tajikistan', 'Timor-Leste', 'Tonga',\n", " 'Tunisia', 'Turkmenistan', 'Zambia'],\n", " dtype='object')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This code produces a list of countries with no quality articles\n", "countries_noQA = cross_tab.sort_values([\"FA\", \"GA\"]).head(41)\n", "countries_noQA.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4. Visualization \n", "\n", "Produce four visualizations that show: \n", " 1.\t10 highest-ranked countries in terms of number of politician articles as a proportion of country population \n", " 2.\t10 lowest-ranked countries in terms of number of politician articles as a proportion of country population \n", " 3.\t10 highest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country \n", " 4.\t10 lowest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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GUeMo/ykiLomIXfo+0r9NndZ+vzAi7tDOTW+nEtjvXY35T2bQubDfWVQ9zgfe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\niFg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"text/plain": [ "<matplotlib.figure.Figure at 0x11278e4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn\n", "from textwrap import wrap\n", "\n", "# based on example from http://matplotlib.org/examples/lines_bars_and_markers/barh_demo.html\n", "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(20,15))\n", "\n", "top_country = prop_df[\"country\"].head(10)\n", "y_pos0 = np.arange(len(top_country))\n", "top_percent = prop_df[\"percent_articles_per_person\"].head(10)\n", "\n", "ax0.barh(y_pos0, top_percent, align='center',\n", " color=\"cadetblue\", alpha=0.8)\n", "ax0.set_yticks(y_pos0)\n", "ax0.set_yticklabels(top_country, fontsize=14)\n", "ax0.invert_yaxis() # labels read top-to-bottom\n", "ax0.set_xticklabels(labels=[0, 0.1, 0.2, 0.3, 0.4, 0.5], fontsize=12)\n", "ax0.set_xlabel(\"Percent of Articles per Person\", fontsize=14)\n", "ax0.set_title(\"Countries with Highest Percent of Wikipedia Articles per Capita\",\n", " fontsize=16, fontvariant=\"small-caps\", fontweight=\"semibold\")\n", "\n", "bottom_country = prop_df[\"country\"].tail(10)[::-1]\n", "y_pos1 = np.arange(len(bottom_country))\n", "bottom_percent = round(prop_df[\"percent_articles_per_person\"].tail(10)[::-1], 5)\n", "\n", "ax1.barh(y_pos1, bottom_percent, align='center',\n", " color=\"darkcyan\", alpha=0.8)\n", "ax1.set_yticks(y_pos1)\n", "ax1.set_yticklabels(bottom_country, fontsize=14)\n", "ax1.invert_yaxis()\n", "ax1.set_xlabel(\"Percent of Articles per Person\",\n", " fontsize=14, fontstretch=\"semi-condensed\")\n", "ax1.set_title(\"Countries with Lowest Percent of Wikipedia Articles per Capita\",\n", " fontsize=16, fontweight=\"semibold\")\n", "\n", "top_FA_GA_country = sort_prop_type_df[\"country\"].head(10)\n", "y_pos2 = np.arange(len(top_FA_GA_country))\n", "top_FA_GA_prop = sort_prop_type_df[\"percent_FA_GA\"].head(10)\n", "\n", "ax2.barh(y_pos2, top_FA_GA_prop, align='center',\n", " color=\"firebrick\", alpha=0.8)\n", "ax2.set_yticks(y_pos2)\n", "ax2.set_yticklabels(top_FA_GA_country, fontsize=14)\n", "ax2.invert_yaxis() # labels read top-to-bottom\n", "ax2.set_xticklabels(labels=[0, 5.0, 10.0, 15.0, 20.0, 25.0], fontsize=12)\n", "ax2.set_xlabel(\"Percent of Wikipedia Articles Rated FA or GA\",\n", " fontsize=14, fontstretch=\"semi-condensed\")\n", "ax2.set_title(\"Countries with Highest Proportion of FA and GA Rated Wikipedia Articles\",\n", " fontsize=16, fontweight=\"semibold\")\n", "\n", "bottom_FA_GA_country = sort_prop_type_df[\"country\"].tail(10)[::-1]\n", "y_pos3 = np.arange(len(bottom_FA_GA_country))\n", "bottom_FA_GA_prop = sort_prop_type_df[\"percent_FA_GA\"].tail(10)[::-1]\n", "\n", "ax3.barh(y_pos3, bottom_FA_GA_prop, align='center',\n", " color=\"darkred\", alpha=0.8)\n", "ax3.set_yticks(y_pos3)\n", "ax3.set_yticklabels(bottom_FA_GA_country, fontsize=14)\n", "ax3.invert_yaxis() # labels read top-to-bottom\n", "ax3.set_xticklabels(labels=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], fontsize=12)\n", "ax3.set_xlabel(\"Percent of Wikipedia Articles Rated FA or GA\",\n", " fontsize=14, fontstretch=\"semi-condensed\")\n", "ax3.set_title(\"Countries with Lowest Proportion of FA and GA Rated Wikipedia Articles\",\n", " fontsize=16, fontweight=\"semibold\",\n", " fontstretch=\"semi-condensed\")\n", "\n", "plt.tight_layout()\n", "\n", "plt.show()\n", "\n", "# Save plot to file\n", "fig.savefig(\"WikipediaBiasDataPlot.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writeup\n", "\n", "You are also expected to write a short reflection on the project, that describes how this assignment helps you understand the causes and consequences of bias on Wikipedia. \n", "\n", "Write a few paragraphs, either in the README or in the notebook, reflecting on what you have learned, what you found, what (if anything) surprised you about your findings, and/or what theories you have about why any biases might exist (if you find they exist). \n", "\n", "You can also include any questions this assignment raised for you about bias, Wikipedia, or machine learning. \n" ] }, { "cell_type": "code", "execution_count": 27, "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>article_name</th>\n", " <th>country</th>\n", " <th>revision_id</th>\n", " <th>article_quality</th>\n", " <th>population</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>13641</th>\n", " <td>Mahatma Gandhi</td>\n", " <td>India</td>\n", " <td>806203768</td>\n", " <td>FA</td>\n", " <td>1314097616</td>\n", " </tr>\n", " <tr>\n", " <th>28858</th>\n", " <td>Theodore Roosevelt</td>\n", " <td>United States</td>\n", " <td>806771467</td>\n", " <td>FA</td>\n", " <td>321234172</td>\n", " </tr>\n", " <tr>\n", " <th>28864</th>\n", " <td>Woodrow Wilson</td>\n", " <td>United States</td>\n", " <td>806921107</td>\n", " <td>FA</td>\n", " <td>321234172</td>\n", " </tr>\n", " <tr>\n", " <th>28906</th>\n", " <td>Franklin D. Roosevelt</td>\n", " <td>United States</td>\n", " <td>807395895</td>\n", " <td>FA</td>\n", " <td>321234172</td>\n", " </tr>\n", " <tr>\n", " <th>28909</th>\n", " <td>John McCain</td>\n", " <td>United States</td>\n", " <td>807428251</td>\n", " <td>GA</td>\n", " <td>321234172</td>\n", " </tr>\n", " <tr>\n", " <th>37175</th>\n", " <td>John F. Kennedy</td>\n", " <td>Ireland</td>\n", " <td>807423724</td>\n", " <td>GA</td>\n", " <td>4630308</td>\n", " </tr>\n", " <tr>\n", " <th>41792</th>\n", " <td>Margaret Thatcher</td>\n", " <td>United Kingdom</td>\n", " <td>807039360</td>\n", " <td>FA</td>\n", " <td>65092000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " article_name country revision_id article_quality \\\n", "13641 Mahatma Gandhi India 806203768 FA \n", "28858 Theodore Roosevelt United States 806771467 FA \n", "28864 Woodrow Wilson United States 806921107 FA \n", "28906 Franklin D. Roosevelt United States 807395895 FA \n", "28909 John McCain United States 807428251 GA \n", "37175 John F. Kennedy Ireland 807423724 GA \n", "41792 Margaret Thatcher United Kingdom 807039360 FA \n", "\n", " population \n", "13641 1314097616 \n", "28858 321234172 \n", "28864 321234172 \n", "28906 321234172 \n", "28909 321234172 \n", "37175 4630308 \n", "41792 65092000 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combine_df.loc[combine_df[\"article_name\"].isin([\"George Washington\",\n", " \"John Adams\",\n", " \"Thomas Jefferson\"\n", " \"James Madison\",\n", " \"James Monroe\",\n", " \"John Quincy Adams\",\n", " \"Andrew Jackson\",\n", " \"Abraham Lincoln\",\n", " \"Ulysses S. Grant\",\n", " \"Rutherford B. Hayes\",\n", " \"James A. Garfield\",\n", " \"Theodore Roosevelt\",\n", " \"William Howard Taft\",\n", " \"Woodrow Wilson\",\n", " \"Warren G. Harding\",\n", " \"Benjamin Harrison\",\n", " \"Grover Cleveland\",\n", " \"Calvin Coolidge\",\n", " \"Herbert Hoover\",\n", " \"Franklin D. Roosevelt\",\n", " \"Harry S. Truman\",\n", " \"Dwight D. Eisenhower\",\n", " \"John F. Kennedy\",\n", " \"Lyndon B. Johnson\",\n", " \"Richard Nixon\",\n", " \"Richard M. Nixon\"\n", " \"Gerald Ford\",\n", " \"Jimmy Carter\",\n", " \"Ronald Reagan\",\n", " \"George H. W. Bush\",\n", " \"Bill Clinton\",\n", " \"Hillary Clinton\",\n", " \"Hillary Rodham Clinton\",\n", " \"William J. Clinton\",\n", " \"George W. Bush\",\n", " \"George Bush\",\n", " \"Barack Obama\",\n", " \"Donald Trump\",\n", " \"Donald J. Trump\",\n", " \"John McCain\",\n", " \"Bernie Sanders\",\n", " \"Sarah Palin\",\n", " \"Mahatma Gandhi\",\n", " \"Margaret Thatcher\",\n", " \"Vladimir Putin\",\n", " \"Vicente Fox\",\n", " \"Enrique Peña Nieto\",\n", " \"Enrique Nieto\"])]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reflection\n", "\n", "### About the Data\n", "\n", "First and foremost, this English-language Wikipedia article data was generated from the \"Category: Politicians by nationality\" and one subcategory. So, the editors who created these articles had to categorize the subjects of these articles as \"politicians\" and decide on the subjects' \"nationality.\" \n", "\n", "The [Merriam-Webster Dictionary](https://www.merriam-webster.com/dictionary/politician) defines a politician as \"a person experienced in the art or science of government,\" or \"a person engaged in party politics as a profession,\" or \"often disparaging: a person primarily interested in political office for selfish or other narrow usually short-sighted reasons.\" \n", "\n", "Especially, since the term \"politician\" can be considered disparaging, it's possible that editors would be reluctant to label their articles as politicians. For example, out of the 33 most well-known U.S. presidents, only four have articles in our dataset, and one of those is John F. Kennedy, who's country is listed as \"Ireland.\"\n", "\n", "Politicians by nationality‎ (243 C) \n", "► Assassinated politicians by nationality‎ (129 C) \n", "► Political candidates by nationality‎ (15 C) \n", "► Politicians by ethnic or national descent‎ (12 C) \n", "► Politicians by former country‎ (18 C) \n", "► Politicians by nationality and city‎ (15 C) \n", "► Politicians by nationality and party‎ (232 C) \n", "► Politicians convicted of crimes by nationality‎ (72 C) \n", "► Politicians by nationality and century‎ (45 C) \n", "► Leaders of political parties by country‎ (39 C) \n", "► Politicians by century and nationality‎ (4 C) \n", "► Politicians from dependent territories‎ (9 C) \n", "► Women in politics by nationality‎ (234 C) \n", "► LGBT politicians by nationality‎ (41 C) \n", "► Sportsperson-politicians by nationality‎ (62 C) \n", "► Politicians of African nations‎ (65 C) \n", "► Politicians of Asian nations‎ (49 C, 1 P) \n", "► Politicians of Caribbean nations‎ (28 C) \n", "► Politicians of European nations‎ (62 C) \n", "► Politicians of North American nations‎ (10 C) \n", "► Politicians of Oceanian nations‎ (31 C) \n", "► Politicians of South American nations‎ (16 C) \n", "\n", "### Findings\n", "\n", "__Country with the Highest Proportion of Per Capita Articles__: Nauru (0.4880 %) \n", "\n", "__Country with the Lowest Proportion of Per Capita Articles ( > 0 )__: Bangladesh (0.0002 %)\n", "\n", "__Country with the Highest Proportion of Quality Articles__: North Korea (23.08 %) \n", "\n", "__Country with the Lowest Proportion of Quality Articles ( > 0 )__: Finland (0.17 %) \n", "\n", "__Countries with No High Quality Articles__: 'Andorra', 'Antigua and Barbuda', 'Bahamas', 'Bahrain', 'Barbados', 'Belgium', 'Belize', 'Burundi', 'Cape Verde', 'Comoros', 'Djibouti', 'Dominica', 'Eritrea', 'Federated States of Micronesia', 'French Guiana', 'Guadeloupe', 'Guyana', 'Honduras', 'Kazakhstan', 'Kiribati', 'Lesotho', 'Liechtenstein', 'Macedonia', 'Marshall Islands', 'Monaco', 'Mozambique', 'Nauru', 'Nepal', 'San Marino', 'Sao Tome and Principe', 'Seychelles', 'Solomon Islands', 'Suriname', 'Swaziland', 'Switzerland', 'Tajikistan', 'Timor-Leste', 'Tonga', 'Tunisia', 'Turkmenistan', 'Zambia'\n", "\n", "__5 Countries with Highest Number of Articles__:\n", " > France\t 1689 (population 64,346,720); \n", " > Australia 1566; \n", " > China\t 1138 (population 1,371,920,000); \n", " > United States 1098; \n", " > Mexico\t 1081 \n", "\n", "Spain population: 46,368,000\n", "United Kingdom population: 65,092,000 \n", "\n", "### Possible Biases\n", "\n", "Initially, one might think countries with higher populations, would have more politicians, and therefore more articles about those politicians on Wikipedia. That hypothesis is anecdotally supported by China being one of the most populated countries on Earth and having the third most articles in this dataset, and even India has the seventh most articles with 990 articles. However, France has the highest number of articles, yet has a population 21 times smaller than China. " ] } ], "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

obulpathi/datascience

scikit/Chapter 6/Encoding Dictionaries.ipynb

2

2104

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Encoding Features from Dictionaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Users = age, location\n", "\n", "age is float\n", "likes puppies in ['yes', 'no']\n", "location in ['Paris', 'Tokyo', 'New York']\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = [{'age': 15.9, 'likes puppies': 'yes', 'location': 'Tokyo'},\n", " {'age': 21.5, 'likes puppies': 'no', 'location': 'New York'},\n", " {'age': 31.3, 'likes puppies': 'no', 'location': 'Paris'},\n", " {'age': 25.1, 'likes puppies': 'yes', 'location': 'New York'},\n", " {'age': 63.6, 'likes puppies': 'no', 'location': 'Tokyo'},\n", " {'age': 14.4, 'likes puppies': 'yes', 'location': 'Tokyo'}]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_extraction import DictVectorizer\n", "vect = DictVectorizer(sparse=False).fit(X)\n", "vect.transform(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vect.get_feature_names()" ] } ], "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

rsignell-usgs/notebook

wms_sample.ipynb

1

944200

{ "metadata": { "name": "wms_sample" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Exploring Web Map Service (WMS) \n", "\n", "1. WMS and OWSLib\n", "2. Getting some information about the service \n", "3. Getting the basic information we need to perform a GetMap request\n", "4. More on GetMap request\n", "5. TDS-ncWMS styles and extensions\n", "6. WMS and basemap\n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. WMS and OWSLib\n", "- WMS is the Open Geospatial Consortium (OGC) standard interface for requesting georeferenced __images__ through HTTP.\n", "- OWSLib is part of [geopython](http://geopython.github.io/), a GitHub organization comprised of Python projects related to geospatial. \n", "- OWSLib is a Python package for client programming with OGC Web Services (OWS) developed by [Tom Kralidis](http://www.kralidis.ca/).\n", "- OWSLib supports several OGC standards: WFS, WCS, SOS...and of course WMS 1.1.1. [More](http://geopython.github.io/OWSLib/). \n", "- Does not come installed with canopy but is available in the community packages.\n", "- Installation with enpkg: \n", " * enpkg OWSLib\n", " * current version (07/09/2013) --> 0.4.0-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Getting some information about the service \n", " \n", "* We will use OWSLib package and in particular the owslib.wms module.\n", "* Within the TDS context, if WMS is enabled and set up in the catalogs, each dataset has a WMS url.\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib.wms import WebMapService\n", "#We just need a WMS url from one TDS dataset...\n", "serverurl ='http://thredds.ucar.edu/thredds/wms/grib/NCEP/NAM/CONUS_12km/best'\n", "wms = WebMapService( serverurl, version='1.1.1')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The WebMapService object gets all the information available about the service through a GetCapabilities request:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#This is general information, common to all datasets in a TDS server\n", "operations =[ op.name for op in wms.operations ]\n", "print 'Available operations: ' \n", "print operations \n", "\n", "print 'General information (common to all datasets):'\n", "print wms.identification.type\n", "print wms.identification.abstract\n", "print wms.identification.keywords\n", "print wms.identification.version\n", "print wms.identification.title" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Available operations: \n", "['GetCapabilities', 'GetMap', 'GetFeatureInfo']\n", "General information (common to all datasets):\n", "OGC:WMS\n", "Scientific Data\n", "['meteorology', 'atmosphere', 'climate', 'ocean', 'earth science']\n", "1.1.1\n", "THREDDS Data Server\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Bounding boxes, styles and dimensions are specific to each layer.\n", "- Each variable in a dataset translates into a layer in the WMS service. \n", "- Besides, the server creates virtual layers if it founds vector components in CF-1 or Grib conventions. " ] }, { "cell_type": "code", "collapsed": true, "input": [ "#Listing all available layers...\n", "layers = list(wms.contents)\n", "for l in layers:\n", " print 'Layer title: '+wms[l].title +', name:'+wms[l].name" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Layer title: Vertical velocity (pressure) @ Isobaric surface, name:Vertical_velocity_pressure_isobaric\n", "Layer title: Thunderstorm probability (3_Hour Accumulation) @ Ground or water surface, name:Thunderstorm_probability_surface_3_Hour_Accumulation\n", "Layer title: Convective available potential energy @ Level at specified pressure difference from ground to level layer, name:Convective_available_potential_energy_pressure_difference_layer\n", "Layer title: Geopotential height @ Level of 0C isotherm, name:Geopotential_height_zeroDegC_isotherm\n", "Layer title: Temperature @ Tropopause, name:Temperature_tropopause\n", "Layer title: Categorical Snow @ Ground or water surface, name:Categorical_Snow_surface\n", "Layer title: Convective precipitation (3_Hour Accumulation) @ Ground or water surface, name:Convective_precipitation_surface_3_Hour_Accumulation\n", "Layer title: Relative humidity @ Specified height level above ground, name:Relative_humidity_height_above_ground\n", "Layer title: Pressure @ Tropopause, name:Pressure_tropopause\n", "Layer title: Snow depth @ Ground or water surface, name:Snow_depth_surface\n", "Layer title: u-component of wind @ Specified height level above ground, name:u-component_of_wind_height_above_ground\n", "Layer title: Minimum temperature @ Specified height level above ground, name:Minimum_temperature_height_above_ground\n", "Layer title: Temperature @ Level at specified pressure difference from ground to level layer, name:Temperature_pressure_difference_layer\n", "Layer title: wind @ Tropopause, name:wind @ Tropopause\n", "Layer title: Best (4 layer) Lifted Index @ Level at specified pressure difference from ground to level layer, name:Best_4_layer_Lifted_Index_pressure_difference_layer\n", "Layer title: Convective available potential energy @ Ground or water surface, name:Convective_available_potential_energy_surface\n", "Layer title: Maximum temperature @ Specified height level above ground, name:Maximum_temperature_height_above_ground\n", "Layer title: Pressure reduced to MSL @ Mean sea level, name:Pressure_reduced_to_MSL_msl\n", "Layer title: u-component of wind @ Maximum wind level, name:u-component_of_wind_maximum_wind\n", "Layer title: Convective inhibition @ Level at specified pressure difference from ground to level layer, name:Convective_inhibition_pressure_difference_layer\n", "Layer title: MSLP (Eta model reduction) @ Mean sea level, name:MSLP_Eta_model_reduction_msl\n", "Layer title: Convective inhibition @ Ground or water surface, name:Convective_inhibition_surface\n", "Layer title: Relative humidity @ Isobaric surface, name:Relative_humidity_isobaric\n", "Layer title: Geopotential height @ Isobaric surface, name:Geopotential_height_isobaric\n", "Layer title: Composite reflectivity @ Entire atmosphere layer, name:Composite_reflectivity_entire_atmosphere\n", "Layer title: v-component of wind @ Tropopause, name:v-component_of_wind_tropopause\n", "Layer title: Parcel lifted index (to 500 hPa) @ Level at specified pressure difference from ground to level layer, name:Parcel_lifted_index_to_500_hPa_pressure_difference_layer\n", "Layer title: Wind speed @ Specified height level above ground, name:Wind_speed_height_above_ground\n", "Layer title: wind @ Specified height level above ground, name:wind @ Specified height level above ground\n", "Layer title: Water equivalent of accumulated snow depth @ Ground or water surface, name:Water_equivalent_of_accumulated_snow_depth_surface\n", "Layer title: v-component of wind @ Specified height level above ground, name:v-component_of_wind_height_above_ground\n", "Layer title: u-component of wind @ Level at specified pressure difference from ground to level layer, name:u-component_of_wind_pressure_difference_layer\n", "Layer title: Probability of Freezing Precipitation (3_Hour Accumulation) @ Ground or water surface, name:Probability_of_Freezing_Precipitation_surface_3_Hour_Accumulation\n", "Layer title: v-component of wind @ Maximum wind level, name:v-component_of_wind_maximum_wind\n", "Layer title: Relative humidity @ Sigma level layer, name:Relative_humidity_sigma_layer\n", "Layer title: Probability of 0.01 inch of precipitation (POP) (3_Hour Accumulation) @ Ground or water surface, name:Probability_of_0p01_inch_of_precipitation_POP_surface_3_Hour_Accumulation\n", "Layer title: Relative humidity @ Level of 0C isotherm, name:Relative_humidity_zeroDegC_isotherm\n", "Layer title: Categorical Ice Pellets @ Ground or water surface, name:Categorical_Ice_Pellets_surface\n", "Layer title: Total precipitation (3_Hour Accumulation) @ Ground or water surface, name:Total_precipitation_surface_3_Hour_Accumulation\n", "Layer title: Dewpoint temperature @ Specified height level above ground, name:Dewpoint_temperature_height_above_ground\n", "Layer title: v-component of wind @ Level at specified pressure difference from ground to level layer, name:v-component_of_wind_pressure_difference_layer\n", "Layer title: u-component of wind @ Tropopause, name:u-component_of_wind_tropopause\n", "Layer title: Categorical Freezing Rain @ Ground or water surface, name:Categorical_Freezing_Rain_surface\n", "Layer title: v-component of wind @ Isobaric surface, name:v-component_of_wind_isobaric\n", "Layer title: Pressure @ Ground or water surface, name:Pressure_surface\n", "Layer title: wind @ Level at specified pressure difference from ground to level layer, name:wind @ Level at specified pressure difference from ground to level layer\n", "Layer title: u-component of wind @ Isobaric surface, name:u-component_of_wind_isobaric\n", "Layer title: Total cloud cover @ Entire atmosphere layer, name:Total_cloud_cover_entire_atmosphere\n", "Layer title: Reflectivity @ Hybrid level, name:Reflectivity_hybrid\n", "Layer title: wind @ Isobaric surface, name:wind @ Isobaric surface\n", "Layer title: Wind direction (from which blowing) @ Specified height level above ground, name:Wind_direction_from_which_blowing_height_above_ground\n", "Layer title: Reflectivity @ Specified height level above ground, name:Reflectivity_height_above_ground\n", "Layer title: Pressure @ Maximum wind level, name:Pressure_maximum_wind\n", "Layer title: Absolute vorticity @ Isobaric surface, name:Absolute_vorticity_isobaric\n", "Layer title: Surface Lifted Index @ Isobaric surface layer, name:Surface_Lifted_Index_isobaric_layer\n", "Layer title: Precipitable water @ Entire atmosphere layer, name:Precipitable_water_entire_atmosphere\n", "Layer title: Storm relative helicity @ Specified height level above ground layer, name:Storm_relative_helicity_height_above_ground_layer\n", "Layer title: Probability of Frozen Precipitation (3_Hour Accumulation) @ Ground or water surface, name:Probability_of_Frozen_Precipitation_surface_3_Hour_Accumulation\n", "Layer title: Visibility @ Ground or water surface, name:Visibility_surface\n", "Layer title: Temperature @ Specified height level above ground, name:Temperature_height_above_ground\n", "Layer title: Categorical Rain @ Ground or water surface, name:Categorical_Rain_surface\n", "Layer title: Relative humidity @ Level at specified pressure difference from ground to level layer, name:Relative_humidity_pressure_difference_layer\n", "Layer title: wind @ Maximum wind level, name:wind @ Maximum wind level\n", "Layer title: Temperature @ Isobaric surface, name:Temperature_isobaric\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Getting the basic information we need to perform a GetMap request\n", "\n", "- All the information clients need is available in the capabilities document, which is stored in the WebMapService object.\n", "- TDS-WMS only supports GetMap requests on one layer (variable).\n", "- We need to choose our layer, bounding box, spatial reference system (SRS), size and format of the image.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Values common to all GetMap requests: formats and http methods:\n", "print wms.getOperationByName('GetMap').formatOptions\n", "print wms.getOperationByName('GetMap').methods\n", "\n", "#Let's choose: 'wind @ Isobaric surface' (the value in the parameter must be name of the layer)\n", "wind = wms['wind @ Isobaric surface']\n", "\n", "#What is its bounding box?\n", "print wind.boundingBox\n", "\n", "#available CRS\n", "print wind.crsOptions\n", "# --> NOT ALL THE AVAILABLE CRS OPTIONS ARE LISTED" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['image/png', 'image/png;mode=32bit', 'image/gif', 'image/jpeg', 'application/vnd.google-earth.kmz']\n", "{'Get': {'url': 'http://thredds.ucar.edu/thredds/wms/grib/NCEP/NAM/CONUS_12km/best'}}\n", "(-152.9859023956905, 12.123672938563633, -49.308990314247126, 57.35625318852111, 'EPSG:4326')\n", "['EPSG:3857', 'EPSG:32761', 'EPSG:4326', 'EPSG:32661', 'EPSG:41001', 'CRS:84', 'EPSG:3409', 'EPSG:3408', 'EPSG:27700']\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#Function that saves the layer as an image\n", "def saveLayerAsImage(layer, inname):\n", " out = open(inname, 'wb')\n", " out.write(layer.read())\n", " out.close()\n", "\n", "#let's get the image... \n", "img_wind = wms.getmap( layers=[wind.name], #only takes one layer\n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(512, 512),\n", " format='image/png'\n", ")\n", "\n", "#Save it..\n", "saveLayerAsImage(img_wind, 'test_wind.png')\n", "\n", "#Display the image we've just saved...\n", "from IPython.core.display import Image\n", "Image(filename='test_wind.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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uGC9sxDNhn92Nscf6bPP6H+/QpDyLmqux34oGTj1yyAIxz9V8lJCWiu7chQIO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AflQIadd/XmFr9wQ/+m1VZaBJXQ0CtZL122vZ5fR6fiKB5qTbwjw6BteukKVyW5wvGUAqqtJVEK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"prompt_number": 5, "text": [ "<IPython.core.display.Image at 0x3812850>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. More on GetMap requests\n", "\n", "* Handling time and vertical dimensions\n", "* Changing styles\n", "* Changing the spatial reference system (SRS)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling time and vertical dimensions\n", "* Getting available times for a layer:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Times are available in the timepositions property of the layer\n", "times= [time.strip() for time in wind.timepositions]\n", "print times" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['2013-11-27T00:00:00.000Z', '2013-11-27T03:00:00.000Z', '2013-11-27T06:00:00.000Z', '2013-11-27T09:00:00.000Z', '2013-11-27T12:00:00.000Z', '2013-11-27T15:00:00.000Z', '2013-11-27T18:00:00.000Z', '2013-11-27T21:00:00.000Z', '2013-11-28T00:00:00.000Z', '2013-11-28T03:00:00.000Z', '2013-11-28T06:00:00.000Z', '2013-11-28T09:00:00.000Z', '2013-11-28T12:00:00.000Z', '2013-11-28T15:00:00.000Z', '2013-11-28T18:00:00.000Z', '2013-11-28T21:00:00.000Z', '2013-11-29T00:00:00.000Z', '2013-11-29T03:00:00.000Z', '2013-11-29T06:00:00.000Z', '2013-11-29T09:00:00.000Z', '2013-11-29T12:00:00.000Z', '2013-11-29T15:00:00.000Z', '2013-11-29T18:00:00.000Z', '2013-11-29T21:00:00.000Z', '2013-11-30T00:00:00.000Z', '2013-11-30T03:00:00.000Z', '2013-11-30T06:00:00.000Z', '2013-11-30T09:00:00.000Z', '2013-11-30T12:00:00.000Z', '2013-11-30T15:00:00.000Z', '2013-11-30T18:00:00.000Z', '2013-11-30T21:00:00.000Z', '2013-12-01T00:00:00.000Z', '2013-12-01T03:00:00.000Z', '2013-12-01T06:00:00.000Z', '2013-12-01T09:00:00.000Z', '2013-12-01T12:00:00.000Z', '2013-12-01T15:00:00.000Z', '2013-12-01T18:00:00.000Z', '2013-12-01T21:00:00.000Z', '2013-12-02T00:00:00.000Z', '2013-12-02T03:00:00.000Z', '2013-12-02T06:00:00.000Z', '2013-12-02T09:00:00.000Z', '2013-12-02T12:00:00.000Z', '2013-12-02T15:00:00.000Z', '2013-12-02T18:00:00.000Z', '2013-12-02T21:00:00.000Z', '2013-12-03T00:00:00.000Z', '2013-12-03T03:00:00.000Z', '2013-12-03T06:00:00.000Z', '2013-12-03T09:00:00.000Z', '2013-12-03T12:00:00.000Z', '2013-12-03T15:00:00.000Z', '2013-12-03T18:00:00.000Z', '2013-12-03T21:00:00.000Z', '2013-12-04T00:00:00.000Z', '2013-12-04T03:00:00.000Z', '2013-12-04T06:00:00.000Z', '2013-12-04T09:00:00.000Z', '2013-12-04T12:00:00.000Z', '2013-12-04T15:00:00.000Z', '2013-12-04T18:00:00.000Z', '2013-12-04T21:00:00.000Z', '2013-12-05T00:00:00.000Z', '2013-12-05T03:00:00.000Z', '2013-12-05T06:00:00.000Z', '2013-12-05T09:00:00.000Z', '2013-12-05T12:00:00.000Z', '2013-12-05T15:00:00.000Z', '2013-12-05T18:00:00.000Z', '2013-12-05T21:00:00.000Z', '2013-12-06T00:00:00.000Z', '2013-12-06T03:00:00.000Z', '2013-12-06T06:00:00.000Z', '2013-12-06T09:00:00.000Z', '2013-12-06T12:00:00.000Z', '2013-12-06T15:00:00.000Z', '2013-12-06T18:00:00.000Z', '2013-12-06T21:00:00.000Z', '2013-12-07T00:00:00.000Z', '2013-12-07T03:00:00.000Z', '2013-12-07T06:00:00.000Z', '2013-12-07T09:00:00.000Z', '2013-12-07T12:00:00.000Z', '2013-12-07T15:00:00.000Z', '2013-12-07T18:00:00.000Z', '2013-12-07T21:00:00.000Z', '2013-12-08T00:00:00.000Z', '2013-12-08T03:00:00.000Z', '2013-12-08T06:00:00.000Z', '2013-12-08T09:00:00.000Z', '2013-12-08T12:00:00.000Z', '2013-12-08T15:00:00.000Z', '2013-12-08T18:00:00.000Z', '2013-12-08T21:00:00.000Z', '2013-12-09T00:00:00.000Z', '2013-12-09T03:00:00.000Z', '2013-12-09T06:00:00.000Z', '2013-12-09T09:00:00.000Z', '2013-12-09T12:00:00.000Z', '2013-12-09T15:00:00.000Z', '2013-12-09T18:00:00.000Z', '2013-12-09T21:00:00.000Z', '2013-12-10T00:00:00.000Z', '2013-12-10T03:00:00.000Z', '2013-12-10T06:00:00.000Z', '2013-12-10T09:00:00.000Z', '2013-12-10T12:00:00.000Z', '2013-12-10T15:00:00.000Z', '2013-12-10T18:00:00.000Z', '2013-12-10T21:00:00.000Z', '2013-12-11T00:00:00.000Z', '2013-12-11T03:00:00.000Z', '2013-12-11T06:00:00.000Z', '2013-12-11T09:00:00.000Z', '2013-12-11T12:00:00.000Z', '2013-12-11T15:00:00.000Z', '2013-12-11T18:00:00.000Z', '2013-12-11T21:00:00.000Z', '2013-12-12T00:00:00.000Z', '2013-12-12T03:00:00.000Z', '2013-12-12T06:00:00.000Z', '2013-12-12T09:00:00.000Z', '2013-12-12T12:00:00.000Z', '2013-12-12T15:00:00.000Z', '2013-12-12T18:00:00.000Z', '2013-12-12T21:00:00.000Z', '2013-12-13T00:00:00.000Z', '2013-12-13T03:00:00.000Z', '2013-12-13T06:00:00.000Z', '2013-12-13T09:00:00.000Z', '2013-12-13T12:00:00.000Z', '2013-12-13T15:00:00.000Z', '2013-12-13T18:00:00.000Z', '2013-12-13T21:00:00.000Z', '2013-12-14T00:00:00.000Z', '2013-12-14T03:00:00.000Z', '2013-12-14T06:00:00.000Z', '2013-12-14T09:00:00.000Z', '2013-12-14T12:00:00.000Z', '2013-12-14T15:00:00.000Z', '2013-12-14T18:00:00.000Z']\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": true, "input": [ "#We can choose any of the available times and make a request for it with the parameter time\n", "#If no time is provided the default in TDS is the closest available time to the current time\n", "img_wind = wms.getmap( layers=[wind.name], \n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[len(times)-1]\n", ")\n", "\n", "saveLayerAsImage(img_wind, 'test_wind.png')\n", "Image(filename='test_wind.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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3sp4JznH94AfFun8Cm7dEQdyxY2e+spy2vZHfEWP/0VXz5Rf2W++MbBRkXnD1\ng5u4pP9OLYqsyUErzMMmt9xT0+yWK76ybbW3QyRJfHqJoYT2BaYw2UmYmclUb8zPAw5FzOTyNKTl\nPhoBGqqwstCFCuGS0vpUcu85QFw5flwoA2zbto37y+28116zVrvi3K5zkvTqK7xCbdP81H3kqjYi\nCRxqf+ADIedQuFB4z77yTIj2Jn3FqSr5/N693pzKlb39KgptgW8sw2t7nPw2/TVca1r+wB3YsQNz\noOzWgK3V/fHx5rBKZtDH+rxY3e/A55iX2L4Hn5Xuxw/g1+HXZ+TvLcIZAdv5r49o522MELEbUNxO\np6tqNn29r4A+2gFYNynOQGmtoQXnbKRiCZh4+3yPnXYXTwSYu5Vksj3Di3T8jLQBqTXTLmZxXU6d\n9FW7U0OiJ4x9revavOHwBXyMvcPBWCFlAaZ99kyEg58+4dxD/u14fPPrzjOqPGWIwFp+wS1zL7HC\nMhowcjJsXyAockdyrrOVOuPl/eJW8dfvK8rOoklCVTv3mF69Bjb5dVbRO9g3VFtU67bjo3Sxd7dt\nvVfusM1ajTabu4L1eXApvxte+c7ncpzvhMff+618x/fiF/7gN7wQz8THWz7233GHgOUqnOVNXstb\n2e528VzvEVC98SnoB1iC4eYl928V85HsSrHozIoKIslrxQ6plRPxsC2GHiEXSp89qlUx31ZJVEA0\nTzFAAik3hjhVNKFybn0xRtf873jIl/rLmei4652osAvHOglvZvB5iN3XUkjKO6ndllgpRfSik6s8\nGcSt54YNoSVzpW0CCQtc8g62e9pKZ/hlwqwarTK176oGl5j4s/a8hbEO6cT4mqaJoiDvuSaR6+4M\nF8MnzIqDSUK23tMkzvZ1UDR1pq06W/Y7n1N3z7o/TnHR9+XxZwEESKq0Hkh+927alnIrbnl1ELJ5\nd7/g3Fmv4tKEs4pvP2Q1xSlTK+VGOKPdD3Y6p3Tx32aNuE0szyxZC5Q1X2i0hWdT9UM81PnYFCBM\nDadyNA9hxguARb7tiSCrx2Sjv8mbBeePXvVKXfSMuDKHGfUghb2r6OnT3gbpx3xbjX9rfbPEJckF\nXApZnvayrv9l9CkCVVT74jVflXU5ABGpFRXkzzoSwAoUm3fNRmxqhvvdjSUVyYFuvYslGHfz/fzT\ndW4F+Z7+Vn5nvfVbed/n3jLC5xCyHfiZs4wqV2npPyFIb9j+UWf9bsaFbJ+uHvVrGEWhr2Jvj81y\nZXzh2cW41Lg1C5QJNic14CJOSJGdoeNFBwIm0AhmieQsM/pmwnngIIaYIDZH4tdAvNMQ1316ThUW\n5oRJXck6JRc5nReSaIFFaynRcLjPnVOiHJ/OJOFPqyyEjnvC2vtYEQsUW5X5/l/u1qavpItSSpaL\nvtdZSG/Ma+GS6FrLrhwTuRk5Wjv6SMzuXb9f77qv1OT6NtEhcqelPjigsX47uEO/K8+mott3VL7i\n6+xxUer+oWc+jRRWYgLvhLp3N7TiRahHLWwc6fe/eKo1MOo2X4W+mM6raCqBY4ktDIenBJ8krjNz\nboNwZVKLclr3apm7LePJEy8kL5wjUScHicPz4xNLML6cLch4p3Y4uQCPKddk3z9zah1lRTOdbNeE\nNMIFbBHCkXOh3VQTeHskKWXBoyDZ7ZTmCmMawZR0s3DYOeQ+j1T2mUbaf5FEHYbyw2uiECWVaDhi\nOAjnDfcX8psf2Wrk+fefCvEOiYvFf4WcKYkKuyevZ8D1T4fERN/1YhX++u5vkTLaCfjeZ/mKZpwR\nqUWJcnfb65uw+9pVp6q+3NjrsVj2V/+wRraWwqd7T9VzL7fchTsuuUhPkMDl99BdSNpo755orAJw\nBaMmLY9edhWEaj1kkoo+mFTgjQx9k4tZecE5VeOwnOH63OOl7HqdkzW5oO92/05mIjLdgT7PCP8W\nDUBWcWa2ZOdhUUXMaj2Qg6swCoUJGov7IkQtSKKZhc9AZBmCnF2KBncOp9Stz1upg/s640ct2VZa\nVVt76BwDtxSGZLXGv6EyI7GhcpsLEzE1r8FOxZUWgmXycDn0RH72W4LUZUVOzGLALJf/2bqYy8GE\nD2b8FmEtOebd0DMa4nEq2Ag8mXzzZ4+DRlhc4NsWWvhCKst3WCnpQ8aGFclr4zstDT9HDE69FAop\norBEaEJDdRAqqTCNx8uZiNgaLT9PZliZwRZYr7DeeCM03PgTp1M0PJcfYH568hgrmxgrEFlLoiTi\nyVfgWZ1ZekJOFsNyNX3i6lBg+4Fr1/yBmmbNti2dl5SUfpoTFC7OfvQIBFEhZ3PnDmQFYoDbt2Gg\nII3tb+KNJxiEu1AZlBdUZkkjhbIrZfoq0TDlcER+omHPs5I8pzvnR1+ssa6CPimcoaYWNT/ly0ki\nRDX6urvSzBjdn20/lCC0gVYPorlGXxTH4oSixZVvIA+WWojVjwlWV0mwfOlyKV6vs1kTEKlnfHwx\nFw2cPozL4yPAN3OnLOfgK3j4IjQRY0UDIssQ5Rwh3JhJymnFqRWLFdOLEdUhaEk2jgaSI4JIRivM\nDN6EKK2abViVGY9LreoAu2FPw8XxGyvECpO345sQY3VYqYw2FyUdoqIpN6G7Uho/KYROm2eX1xOh\nK4nd2rU7vx+9QFLJINi2Q7NQRLIfWAe1VJnaeAdbyE2nZ9T+1zXDO8cUJyPXxEyH6ngEK0Apcxc3\ne76qHqjqychsidtjERovbsXyADRrIlMuORAiqIvZ2a7eiGSUb5S6hDqZ0+RPUF5WK1SBR035UJh9\ngYCAIR/gF+A/GFY/QCc03ELvgbWV0E+3TAirMPCMrI1/dS4K9jZsVLNYFI9yU6GqYHPWcpL0TOwx\nDjwBBfozv28Z404iAI3gHXwMiktSqDvlaM5rZ+Ha3ajsJ7q+tj106Fhdm1BGmvA0i4LQn1LLFLmL\nmA/QaDVhYiym5zGShCXPnbKu+pNveLv6Ztd0LC6VSL/lzoYvmgz177kc3j6bCwrbwlOnkky/8UYR\nxQ5J2cdWeswhPulSMCMj48R8LIc0hJlDqyCWFnKyhxzjIVg1LqtVXKGVda+8ZQwkala1/hrXdqqR\nnk9S25zCLU8SDoTINfzg0xjjBqMW/vEoxxicRXak0kjS0IpZAS7PxCrsPM6Pc3Tx8jtSkRKGM7H7\nx8JCiI0bV/Gvioq6h5KELos3PEjhOBd1fCKLoeWLEEic8vM6mZ/jm2/ALGJmNGhxotRZY0m4+GCL\nRYbUv5m6yIkoxZ08/xssyoTdyhDFvsoYXmhQiJAAuXGzSk7zIoc6FEooLSGkRjkxctbWNsYNNoNK\nBktSYSPFL8ueCP/vJEnYNqs+e28tupDTmhapwf6fMIOniJaJ5DPknWKKKQAs0w3x/ClJCVIWFGbM\nPYXcf908YYDBkxDwavKNu6g9adwrVxr23o2VXXGsALp6yzS9BKqxxU+eWNQYuxVpHC90yIlQeTa+\n5rNApey/0XFsT3V1Ky6VzyyPdazhRAnnmoN1Q5lCrDAc/82SHQInI3QnEtfw/VWIFkaaoqt6X/ZE\nW2ME8Bi4cuFiZEnzRUAmnc4KlKbTmGZgTW1qxQ4XrjKBljFMCuKT1dwqYDj10zYbvqrMUyb5iaGR\nJTQmQN77lwTLe2OpqyMGW4hU5KVjHoiKBHbQTDRcrHZ7lB//3AGzVbeYu+qd8PEWeH9t3wVOyrnF\ntDVnBumIfilJg7s409ATOm47NYvTLtoXZ9Z6A7NixyH6SysUginvM1FOZ8UX427ryQR/R8B1YJ6Z\nZHWAUYsJiWYDuPnx8Vj2+5tD0J3alLEQkFpekM9gF/knb36LPCDGCQEyRuQi0KHciyUWtYtcxPD9\nqbRfqoltrLN4CgPcIMArDpcM2rpP1YjTStQq3ZUWLCzUDR6CS5Y2WpNo/FK/aNY0q4MDMud9U+LK\nGUhH52mGHLT9eLzR1q3FF91kiMOunflza83a6ho5MQN4xFlh9PgzJn0/oow84istL6uJwZo1tVVj\nszhuPMbDcNTsr8yyb3QC8uKgewJDHij5Hlq+y+INTEj5AWecCjzNOhCHIb5y8Z1dUFvpTM93oIbL\nK6J0j1kPNrhydYjYX1SqY+dv+yzDeRuNxyWAg4cOHwxAFydW+w8eci5XAzb3jUxvRD2xikNBqmpB\nextunpLFZY/KjlCFV+DAvtcePVzLW51C14tETrsBsAsAMYMgI2ToQga3IcnE55JF34thpK7oD8tl\n/5hFK4tPbuYrvpXrV5bZkx9eZ8qopm89kaINurbyJlr4bNw5LoSSDgWPmqJKNbrDuaLLVm8sBBNy\n9PHxIiTtZNkUB25K2EzqNc1RpDEZl8UDWKJpijSW2KKTKejDoK7eGCKeU1kuiz9yQrbHmfhJ0VBx\n1IR7eFnMFmsgTjIdOpxVmcTsCB/WOYqB3GUnaocPc4nRL8PrnKZuWhip5JfmpVWvj/wYl0N84k0f\nYQRqd41X3QFf/Sm0kZNzp+dYCppj9+6Bq5IchsGs1sR/fv2DMir+7Lsl5vKz75Xb85NN+XptYZW4\nbZtv12A0qQf+eevMdSsuCNZy/76l7JfEap1dgf3wFl4FU/tJKrzI9RlVUNoDljeYuE+c0NDNBoRp\niJRX3NZ3ojXg/bH//CYkivSEYUQcZDTKideCW4zrgkIjUt89f3JmEKDz5hB4+tRU+eHxsfLx/n2l\nGt2jmFGj3ZJVzQDvC7k3e+Cgd7sMSmRI+5VmLVOr115nXbXm77z71GuWI0V23NQgHXKr2INqtHYW\nBPbtFeTgahMJwm/tgqzpnOgm7NpFg4L1Q0jd3GHRSslyOuu75XJ87/uKpw2Zz02bfKSwecuW6E6J\ncDPq74bIlXg5/Kq9zB0dI5U8VdfSjmIk1CVxVXuYOfCwXGtiufWpLIN5gZkjQVxO5eixBBwUIrYx\nz4eeJ5NwUOfkIpUxUeW8D55b08T3hw05FGx6clDiTnWCB1GL4l4tJOs77pMcp32+RHAIb6WNtEdx\nfc4ravqcF2eC0UsWzjLwiL9AhOjCeZ6wwv2jTBKpHz4PoXNVdNYedx2rq30XQzl5uQZ7haBFpvuR\n8Ek4h3605x6+tsd//mq72m+jOLEgBY/j2Wdl/BdtHYDN5O2N3w+S9Wu9PVHCtEAIUVd9XwEz4f2w\n6X70YmW+3Z24M2+Wwnxj9Isq5xJMVJ7MAy0KzV4R8pl0z62C+aCnqbfWBseKvXff3M3jbpbGS8je\nygmQqQVY+s/4Zvjq6Zg88F7+eKdfdUy451Ml4gQsWVUHgsuLcC5/hIDHOhll61Ty9SW7MRBHSx/2\nQvbsclb+Qo8/SJaQgTD4FrYSv/Btvmok2cyjh7QgfQHh8DnJvxv9wkvOob+A0i2MUf/tuewWtSbG\nNVxrGX7FNwClM+OCYCBG4lVda9uDF8DFhvifUMRn7b4hbesZXosG8Vg56CTh+PLsQpKs9IZfEw7a\ngo/9+tz1seJtvvZn4WY4ws2e4NiB+hyNwD3OoWhYKjqLLvSo1n6unnivZ4gr/JLXZb8CzzG3NbZt\nSIasyF0/Xfl29KUkracFt0UUqQo0cRWHMVGXFDLD1AzmdzsN3tKYTpRXTbi0PDNoH7Hw797s4mjm\nMBXH+V1L+tbTwUO5jFkbuXvpqupiTohi47TxFalfnQ30gPac8c6Any96lT1xPMhC5sSILIuppix6\n7oW+bBJBnb7+rm2NjtThKPPAVZ359w62zugeZO9+td34ZS+d/LM3+K7dkLNm8bo7qLG+/1naVb9R\ndRSfEDY//H4E9n0W9dqv4+tEPwxwHJS2rC1wt6wc3kgsVqKNW0u9iruNmt5IK12CRtMuPA2skffU\nkrYDzx8uE04rNB4Xw5z+WXcbSX5PYqRIWFDF0G9oI06GWqfEul5YNL9vxpdjmrwQruEMrrMqJgwr\nPBUcGxoCZy4RYfMQkHr65OmEfqCgsPJPtHGqm+LijTKMaZ2Q7JHzxobBQu47K+siRc5W+cNf4Rax\nq5Vb14Y4MaE+2cvXuAZ5pSURLNh7RUyGuxuN89TOC/D5Ud1clxNjQbqe9bQBT1gdGhWWDoKTm88+\ny94ojyFVDb8vrKQpKv8hfPIgZ12De2Wp+oYv6+uwE9NDbbPX1zcazrlze4khP/OO10f4D0PZ1HbE\nQyr7iGGejGaAfZ+LOn7QpmqIH2jRDQtPQqeKWJSDFTyhKvBEbEYw4gVNDk0EK8Wa/6pTpwbcLKKB\nZjEqO8WjtFPWgp5i1DDcxrtKpFMnYoRrfKulU+v+OWo1537wss5I8Z+z++HyRZ5pVMlQSGLTd8U5\ncrzOvGEvnJdDNwrach87fN5r3X69xuGAk6vr130FkJf82Zs3u4sxlBTk+wEe6vfaD1iPJc4Z9/qv\nU87RXQfj/AJlkVzavXyuT7lyVWs2Up+CE8xlBOdx9Vra8JD8TvTOvDTWUgCLqNYgjCUxed6a9Tsc\nPDtPbNKmgaTDyVMzsIAwUPQZW8Y8voOxTObOHQlQYoQTK4+rODjJOgUDsOfMe0hlGOw4IzoDwrOc\nSd6Fyhx6yH22AgXMJFd5itB3NHOBg7/zio684a+ylYsbLzjd30JrfO/+QbjItFAMOhE5ZY11sanY\nh2gaZz/hnDKvuNe+qnob4XnWVmGkLa/vDb4t24ZorO95hjKvsn7zmzIv+uvfBCQR+deLKj19WlbU\ni24FKnS4fokLN4wfQMYvbqjNqNNXoxXVvh4tB+rzhXKF2VfzNRK25FMPfnCYjxIzXNaAlA3EbkTJ\nrbYwMRHvuYRkmBo1cHwCl0Su9BPHKADV5QOnTwQ8lgqgTj6kMgx8EhCehj2TDx+w+RhLogSq8Wq8\nVUyjTJqV0rF3rziH08YZBYlei+iiQJB4jBxWLMAXL/BHWfmJg3EIzp8713LaqpV8IyWKNwYq9dsP\nP4zE3BRYw8l21l37QH47mJrJqasHMeKBC2uAgpE8DqZATGSsSmDdLnv4yLgQpterauYn+MqZ+WrN\nPOJh9+71HoiDIORfqqY8VlmhSTqq10YGH8NoWgh3wMRUu88gBJyGtZQ703I6HkC1FMeOUOgoCyVb\nHRzly9DEWbVzR8QlquQdxwMeS8Uqd7K6dSJYv32smKedMrExT2Iw25eigdSn6Iuytehpav3YKFUt\nBkK3RSXZQWsDMF55sdjvQs7j+LEGqQk6eMYWdRwmaet9xFfUvVHMeALUGWRMxM71fax1txINaT2g\nbLYkZfDEYnBHTAjHF5hFpt/wga1CP9reT376p38CvTVlVCHqc4KGh6bF3u1x5WaBE8eDyMTpxpmK\nMlyasINKC+MMLn9anQLNAsTRCIkKK0jhynJKaDmJXgk8afrx0wE2E5dndnao0/rYn+moajs8S5rp\nwjQShXLlL26Atb6F2aOVw7v8xGw/qVa3lSfjbHVQuoyAbmg4JQDKRNLIGhwcF8GD1awYrU33d+g0\nRO9jffcz6PRBUZcuJdES4dByWQmlKSqKEQLiv5728gCOMN55CzgrCF9Sq1VvGQRdURyKyTXp8SWN\n8phrWIVU+VfU2WT+ohW3fiNk17ITH+OMQ0KQlkAD8Wa4g/UU5pxPUvM+XcSHqYVRshvcK7tYrEKK\nFGezWjV2ER/r9pLNQA4dwS6ixgSXP4GrpDmzpWzbeEFDD5QJ6QZGKFRgNFR0cqU9mG0YQudxqDaM\nasvas5GKqiv6+ddsFniyr1XvlSJoxtDu4c67hygU1ZJuDD6gkiJ54UAlOrrXqSAc5kOZ55JClIHC\n4tI/XZl/3ypkAdWT8sX0IwGPSLKBjoUaGsiS2cDmWJnGQG2pX7jzxzlujyQ9SJ2bjDK8EIu9sTyW\nGDl12ZZhjNKIXThzTD/fcpbXSd18V6xgqCUcLlmELFYzcwNFoYQ0KdpY9IeTmGeD4bTSAWwhscgI\nTi1Mf0eGJwVqzIrpxkJcLZqLB/whFsRLafZQup2Vrfz9auvWmVHEGA3pylpcJyq0mIfC9BgAX6HE\nslmJHfxiDnjHJKr2eX2915dzIaVOeeYv3a81O5OWm6FbbFBDITwc7Jz/5jPHjp2JHc6GqhiwcRqZ\nq9d9WYJ4O46ruQrmM3COeRT5vABH0OQV6NMFZ4MgVRezlqUKzjQROE9OUw61I74+9WSdNVMk1hAg\ndGy4DeTDxYOBT0aPdABySFScZQ6aa1ex0iEYxhOJNzpZldHr1no8IRo/YtAKOLlAVwXSSq9WWD2J\n/6Y6rbWVxZLniYYMagyC9cwnmBYdQw8kYUfZPwnehwOtKonxQ7OM18kLE1T9Hr4I15/z0GG3lY46\nI1OxyWulV5B19UXgcXtSw1Le/dN47HSaUIuLVsF/3ObDOsQI73I8jTMhsZnpXsLMbIcBqb4RitbR\nNv5lJo0KeWDzJD+KhklQJ1DkEa8nuxjKdMDgGLKKuuxf5iFx8RNup57xRGN+kmwOCMqo8eQBnz1W\nvhfKMEZU/H3r5xiiTfByokRo4XZ64wMnHrZWe8c5okeHioKB3NXOZmXnKOxY1SbsBsVZt3Hs9go0\nlFGFPfdcfsgfBf/BidAHv7TaZAvwk32HuPLK244bK4TIwVTQO8BLqXOiFR16OtwA1vh1LxhGGf2h\n8cqk3/QrGR41g33HCoqcvDEZirEYsQs5k6O/xU6pGkx9GLi+e27zuQH+11PdBISkUWdDA5kPUpdK\n3//SpQTlIEjZA5npIUROZ9U0kp/ADcePByOpvICHJUysoFJNKh2FMr7PI90DICibH29SKCropUBc\nDdUowMsQqbLlzwvu+efXEyygwBHr3vuDTmj4DBTt7PCDOP5Rn9tUMhHf27Y97B2Rrevw7LOQOYXv\nSwxXgS+WN06yvuNc+AvnFLdAtaYo0M/6MRf9Ep8e7BGRbvWlEHGS6fG+m+fcFEqSII3JzToScnZU\n2W8yppJKaDl6OrcoXCiehPW6ip4U8eHMzGxe6RnQbil+7Xhn47opIv7rgFdUvrv+4EE4r5OeJL1s\n4OjRqFKV2HzFidZxSGH4pYMHwGoflHu5MnAQmQYCAj+UtdTJA2RdmXw/DjLCV0kS+TDvvvRSTLPx\npz748Y9pXY21ibKfT54ptfKnz3QSEc+EMEW73u5vKtqi6S5s35HEUBQWi5bEwE60qw9g1CnmNf6m\nH/NyfyrFz0NHZJtx5cGYygmJcZ/Yw1pCioaMO3a7MRsFCMoLONuCn2vvrrtX0zRDUcbHBDZzfLiV\nwiw1pDF+lqeImwajfPucEg/jbikO3S70YNd5mhnizTM/V6fD/yT/ledVJ8bTveFjHzyQgWxYrgQB\nExidjD12NPYSaYoBjksce+xYAEKRvQT7D5DH6clAzgO+r7CVLhX3ndIjEUCodChDY7OTBgeTHm9a\nZsx76adZXofehx/jY0s6FLu5n4FPizjwmU8jutn99+kzz3wKhVnZBA/0YWwt2ynjouC5js/h5OpX\n/NuGhvo9OdUbvGt+9z/KYnAyElu9Xw3Ufey7vdrSuUDEsDyWRcQSxkqMtijlB9EnJzpOUiAHonxM\nBhZIXyNN7h38HuZlkjwXw+EpQT5El7K5EIFAbiaaPy9eszOS3TgZHpOCzXyTWJoFNgF+BLH3XbhA\nfFE7JuSQh1muskLZCcnwyghQ3pWUdtIF7clFlJoyHD6rfq5z5XlM3dURTYE1cpyDVyR8i/J0+Hzw\nmzi5ShcOHT7vd1CIpuAlJ17uLT9ZJzSpS1eV0jxC584HK/gJfPJDlqz4mImLPlFL8sOEx8LBqVj6\n8EN08hXp1z50/38gAXJDtQnY1d9+F37VvuiXT6phFayJQm5kK1nIJ0f6SoX4EfOSw5HKfi4cy6xr\nODeQ2Gf45r2h/nOECGX8WKQ36kyxIsfdPct5Rcb91GAn/kZf9/PMcXp2CN1HB/kwU6rNLj2//CVd\nQivJgF+SeTtpBsK56MW1yIWTJY3qdW6MvOmsPc7iJ6FgFAQ4Kh/kxxcZ7+yCAVthxRCai5cO7Lva\ntBJFHuJPnj/MkhUFybkoLGrs6mfJnPce7wlEaq8H+k/WzPppEKTsYfb4gdydH6b3309VZ+e+34Y0\nsxa9XMXObGL2VCQzynMHenWvquh/ggf/Ye2rPmP4GNhgeG70mlJhOOMeWeYx9064S5+h/9IT3ZJh\nJAi1DRuYRWYvj0Wl1IL7RhxNp5wZM8xqI3Rq2mebBEuPdDz0k7HZR+t0yVkj82GFlpCcxlxSelrG\nY80kDTUz4DrFuYUZQr3kOU9DRcZWlPMyDPE+dCFF9RSoIFpINJgYkT1StTnC5lK2Zyu+Cu+vs/L1\nSdAutcxwGtrJrrZiFFS9nT+s5jbXUApBBRip3Zm9HAhkvVP64oBKyQYIPMgC3c2ipySstkF4kvH7\nYRCkoM5J5tdlHuA2+MjGSgQL1o1YW5LGLpnN0DTVU1wSq6p6tEL87+Hu337VF/4UlhDpJFTGatLB\nMeh5IkVQPByvDvvUcI3ICHa+JTo5J+PISsHS83vDTw8M2TUWhsUJKeagNl+lNgYXCEgT7IkV6Qll\nG8iv2xOLDPiqUAQL5kWw1NRl0jQzC8NKErlgpdk3qcE8cjZACcEPk48o8Jr49ELwdLVfSyjxzVEn\n/YKjaa3qRc4rK30MA9YOeVspnhX6xNilUGo+HOUoHNeXsTRgqHo14MvwbkZZwIJF60aFeRHQydWD\n+wrbZO3CIvfgk1AgUpGDTx6kwpaTq48/TmEjbbv7UR5EQpCrWDthb3hUWXZlQbmV61/AxqegpzxF\n0l9RYYjnMct8QJyhJi4CYQLBc8VeNvzJUzTYyxbFZSa7wYhz8zqI2ovecvD1/M/pNLQ+PLY+68PS\nTqd5DadjSiEPBGeG5cOg6M7Aor+QqNtrQdFn1wpgAOGTjh0nSgNLlFBa4v/WnSUTGESOm2NnzkBK\nDVtWfxcvXODN4t594YL0YhwIZ3H+/PlSVanxvXiphlCdffndd4uLefF9egIei/OMBFtlzGEEKTjP\n/D7EwXfy+EFI+ou8bHbv92lJ/fOR56sPye7rQ5LOld3YQGqVcX/9i3rDU1+Bb1jgflwP/g1dG6mn\n2iQghzLic8KZUZQo0g/zQqdlsONYSiB36qRIACY2UiHUm1xMOd3jWeOuJmolGWOoLM9brHTwyHSQ\nntmOS1VkSh9L9FcAyWKFU6YjR5RfbCv23EWeACKx0xn087VXuB6Ixgav6wx42hhthz4E5w1keZhD\nor6Sg3q4VFhOY50TpIMWI108+Q6WOdH3X3iMj+VsGaZCDGI+UoryWVEpvPJTVTGM0WGzKWN4Pe9n\nKxeDeRTm8026SH1i3ExlqrpXSwdh+8X/86gV8KZh5uiGt5TxDGtaCZUGFf6wDdlaXitFbklqqVVW\no5ZNbB3ROx40JPnBwI/mx4wEVkqxheMakAk02BPGi/o6zfCBivUsjfJtWPTjF4/CaTaGc54ka51y\nYaIBWQdAk420ClgMSFxsqWxoQ9hOFAcC2Zhy0871kDM3GJEcma1NrIEpCXtA28di+js5WJD1KAnn\nFCutKpXtYxfOoGDVJb4kzp8sJiwNB8344c1SfrdephIai6iIdXIERKzw1rJ+tkoraP4UV633WFl0\nakbykY2AEo0YMaNoUq4DCQeQIZETy23FPBahWhZhTANdWcNa2JkCZAxCfdHdqKMZvJlPU7MZS2HH\nLY8d79RtZtcNk/LG9GE6izJYRGo+zaEOETGiCOKB0NKj8ONbKQ82wyAbyjApMv7cXMgADIV3VZxi\nW1mqMNBQUtFINMTY190GA/QTgTJ83zo0BIHHRW6ahWJoIuX8bEHAB+frYOgM6EXRxkrqMlaxtRUT\nnS8zLi0QP+YTez2146IQP7j4x5nDcRhbrE0rEyucSTtLWN7NWZyGjhJeyHGMy2MlB8i5I2UfTwTR\n6KhEiiObZh8XesMTu1yD5Al1JXmKWk9EYY6e7VL7xBm0JuTaEvTd52wtDmYyAi0M+TYBnXCle9TQ\nUGAPhVytsyGtEDAaWkeW1nXef2hkKHBeJsIS1ks0DMVGRXqpiw9G7LAnRToE5cOSHnfmC4B/9s8M\nd+JUPNiDNZDzPwFf5bKDe06oDa21ftov+ZFf0tfAIJsqYv6wYUPqx7eXM+7537n5DlI2XSP/trxc\nvnxO00bGJ99hOctGcEp2cQX8iB8YDvHD9Rz4kyc7cGRPDzKZjTHkX48fhaOxWyTX08HJ8hZJ2BY4\n62m6UwsoG4FYVZjqc0YoMTBUXtPZV4NyQuQJD9gFToCQ9X7qLj48jSd9zNSFfABxqotSQvMQUomD\nT1yUpGXoX6WBtNyvDf+cReW/5UYnBG9P0Zn0VzimWa4MNwG0/iQ8WYP8sFxNzEtrJqtyLoeMn4Ve\na7yxPJrynSHaoHlKRV/t2sfc5q+cKK/6LBw9G6vN7n1nm7HQeij29vTxzp3E9RAS3ezDyYh2GLLW\nXmUdT9A37yXqXMaFwHyV2ndsTjkzCCTHgstU/mltFVRWB8ph8sIhUn9UJiE5qe0Bhhl/fkqs+//H\nTxAsyHiyngiqTd3BGfq97ANNrCEZnKZTBn++I7Z7BL2AgnkH31pZ2R43uMrSTWVfjqkPS7Bu/sW6\nol6rtoLxW6czyfLRxlxxwd0rW0k0IF6UMI5iZFxcNvyJTZI9Q+v57LgOjia5BSdLII1IvNaX1I85\n3k09nChcNsouLNHgVpmxJwzzHClOWdUV41yhxG6FDCV4XubMGNuIojPI1H6krXc29iA8UWN9/zfC\nk4dPmmiVEfEjZfAYyo0jxkzSs3AzK71eV9EK3/Chs3MZDP+f//3/Cwmveeg8kzMdOHbOeVxOVrjZ\nYsFo9V+21CQrn8WJqYWApnIuz0p95BJCzzxUqTszMEqkk7GkgckU5bBCr8GNjRExKygLX2NwxUyH\nqzu6YnGhTpX9YDM8gkBGc4Z3rHgFHVHlS5BoUr1rJdldG4JYad/B2I3LVAAx3mMrab1p53SMhNY5\nGCq0nFFOduyeqqVoJiwtWL0A75OPzkV5/XiYvJgBUCjhAMwIh4bHsSWkAGPH3uMIycYbsFsJZ/wt\n/DDTefwNLFefR/De6IiRqXDsbp2WG3iRqyecTe2lzJR2oitd36I/FzMzJ9byovQIjBJHd8dCiEdD\nXSDKyWBzOiuEgq8oY50RbLmpNEqUa2/t0BaJ6UG5mu2ktpzCKuymvl9YvqO3OpZwLKl+oMkR/z2M\nEprIb9RY1mFivFel5976Xld36jUGsDsWLl1Qb1VSi2S4l4yT7r3KORy/gBd/8iL7nEarYB8Og6UN\nCBYphqZ81+aOjG1KrRbynds76n53MTQYbwr1Wjj2c89LXdqES9rNYzO4cQf+CuB/gBHD9NK8M/wY\n0daz9LhP9HrMmyxSlU+zXDI6yXFes2Ti+RupQw/qWspp47thBQ3uPezGIJL/lj5p32BCAW8+U4gV\nwFxWj54b8qUB9ofYcWoX0mRDGbO3IlvVp+IkDE5Cxn8xo8jkZLoDS+NjcYsD2oZOhHoFtkHmKpOS\nLdRZIIXg1CkAPyjVIGR0r4XV9v0PAF54QSMvn+0fANKWgvWMJyN+sHnTOpLlD7sZ8x5UurMj3SL+\nV/iT4lp5feUfPidd/wkpxxDAu744/Vec193Q8wh3DHMYwshIZDr7CfBhmAD6MTUHS8J+Fpp+v+VW\nMIOniuoMDYZtiHkaJt92MQOYYSr9sKnOlBodV4lZbRBz4RrEY2VGFoayHQXp1vrNGHhGujD2nstP\nC1Ho3NpMTYlMLaTA161PzJrI2yQeSRNc6TBY325fmbJRQQPII8Jq7f0AJzsX9/d6I8SRd9tfa19+\n8QVvV40MKn9+mJvVqRUyO6AK+6ZctB7koAd5vCkDK4BObC2+fXd4ScmTII6Jfpbl6sO0uM8FSjaC\n39k+wJb/FJj8GbgtQr+jtZHz2en1yZD2pnIuTto1NoJrWw4Jc7LH4hQNJgp/LFiFh8V1mOV2hX4k\nV9JG9RV1NdT0dBSuOX0uiBYGF2tmPYcjTn5YJFpSdcVu3oQGxDbIlfC2zSt4yO+PcSEyVG5D6z1d\nZgnMFPIhgXInQMShzs47CtLJqj6bcXZiz+swupEvc89ej4f4lToDzz337NCN2+VuoERQuumB1gUV\n9xDG0W1WWAO/Kv7YZlE4d7bv+EgFksXoJt7atftGuqFOcnbd8vOc/FzMzIe54QzZx7Xu0c+/A9Cz\nPWqoMdCvtEOOkwniYhpbefzRUqhVDnLTcqeP9NBxYGkg44EGIQKZTTEVyki3xbzrzYX3Z4rZvIfh\nvF8ybTwYX0b0tL+k+dmF4EQVwL65aZhTzyr5+HMFuP2U+47Z9eSqnLNEsdaGwopJc5QFtHP5bIQ0\nAGGMM3KhdODiy7PyHUcYXsV4K6tV2EPqaBy6oLoM5fWzR3l9JUCJDbqvfqCzId3PL8n86EMFQmMC\nUa5fKxSR2dKB0WzKXivdLSFay16/LZf/LGSAvugJ7XKvZsMXYuzrieRNr64rY9vGOS7Nwz/0m8YK\nttxJSMtZvB5vPKZ3Yni2zpnx5UhtwwpNXLZFX+N3z/VJ2A/DREz9w4KVjTeQ8QSLUiP0T5zgQBKP\nng3CHwWLsz7uaONL0uciIzRY2FmwTLWW+B5nfa7Vj29K3ub0XNedQSrHoJR2uMwVUtEA5TMyPhPo\njjWPOncElJlvDDxOIyOigHOa2NN6QmCXPOQRqhe0QKeC6ATrnK9rH8pKzf3VnoUNlQsnhRYEQxi5\nCwYTCR3Bgo91WTZn8KwoXPchzmmNeCxJgW8NguUXSo3fdVl+XaNdt6PiSHwQ2SyLymyoDSM9Kmge\nPlxbbQW+pzrCiVzN/XC2ZniRIP99owaFZnE/co2rtFyaUDPFs1OMVmU9K+Mc+dbn3NqJZE0sx3t6\nPAgWeo3lJOuIpN+lG2FCBUumg3DS2mmIRTQNuzmzlLViGDvFkoVdD6+TFMehOVXMcxJInS8oOlek\n9ixjXnWAti7qGCzFYgKLJIvXOW6gwNiqeSGoxcMBG+M3/NHssXOhqhH3Jdw88UGz5szjqKl6CE5h\npTLhzmGZl3poEMQTfrNrfeAk60FW/buf5E6k495Wmdcbv/+WHwQds2e7bhfHKHEkqrwaiX+lN5Cp\n54QFUTNqjQm/UadNN2bP44pXbUluhKFlDTNO/zJxuTgB5SwKOHYmcJgpmDI6ZdS5x9FBO7HIMpuz\n2VKLzhgOcWrLGY60Tj4ng8tmk0WG0QVIu8cCRtCfWsGlFOSJh+X8d+Obixgyf0SMnz/seTzUqTSI\nbOk39ZjvnOXqfXAecAO4WlcWX/gQCn9mSEZvKCnIlvvltMxN8OBB7qtuTo9FcrbC3TvZRB2nGm/d\nVC4JD6+9nXMypzFiVDRyu73R9sFWdQ+UnzcQKff7falUwepak9PNahaMPDyJtMYVq2442J07XRoT\nn7kSIpCxKCqng2OCA+COE0qNRXllnm/huM9RdUK8ueGF1cF9jMNqZzQsUoysvnnnp/KzGrQRl7NU\nZlhOMIE0Z0V8DvXIsErdEYrYgaPef+dGKZWrX/yCaaXaHkehzpj8kgoveViCtGsKmUPUw2eQhs0N\nyOimFHyt3GD5dCGPPxMS9miHcxeLioKYgH2quu4Z23/YPGX6f2i/Wm0VvCoFQMPzwGylGD6MddhA\nTMGQYc5IcB6wsmva5itdS7auPXWZHHZSFVaaIqVX4amL4oUxwCp1LOht0MaY9sTSOCzh2GlFmUj2\nUiZnrLQt5XgjHMIr/5jxHQiPKTUSpklIxXikDGpl0lTVgWgmMFC6TVQL7NQcFkMY23QPR03ljc4x\nVVtcR3W3xa8W0mpT42prqpGepBiTIOwcWtHqCtY9yHuBS3qnchpYmn9ECSwb+dZILXQiuEhvhW5z\nCg+eqJ3/btcePoKR0dUv6y8f9o2ya0o0LyzLCkzOSFGs1KpkI5JQ4LL+snZqXlMJTONQYyZYNOXD\nNi9Ycq5BVS37lTqeoXEhbO5z3rx5tiyNDqRNUj9+umnTFKz1MSX0WMHCLmskgx3mS+tLAyC56EnB\nAJwlQZ98p6QJk7cBI4wrzpgooCv+HhsX/lRqeEhYyWyfRp7Skn+80J1PhM2khCeUwKohynkICU2g\nGsjKbZTHN9RxT3NW0Lb1DRFrX37RmG/Bn35rtDh+03rv1EQyNcqGAntaPyZu8oVo9q9HIAxkzZd7\nNp6vmOnlpeXlzBPGM6dLPFZ+/6uVLmqdltaXoCEC9HXoskp/ZYoGTGQ5JovygsIwkEVekKIiiEhs\nfwmRQ/ktN7UfECKGgfsSKhjdGOBOjwdlDXCQbr07cFrDLzlVoUMqz1NgpXacEKdg6HSK1EbYNRPQ\nUO+/Y2dstY/09LcebnDuVugEVZp3iqP0dLx4LRkJnmGlsHTUvsIJwayx+35ChKhTpZmFRCkVt/xK\n4YgGlg293HM51ZpRo5mj9nh05hmPtKNvLjhFTXogaJ9inTVEZjNMRBz4F2lxsYNHhuymYIepKnko\nmNd6PW1gXUmPm77Qf7TByRaPsTVIoZxKX1+wfByR0TLhY7YhJYwDxqF7npM78R6kLrrhsNTnJMm6\nJmr1YvPF03/7rdHqIWKav2cVxyZ8+FUjUdwKsWQ5iaptYhhGOykJI9+EATMJ4SJrONe5kwPjdP3f\nZ7FQWsnZlQbYEJf7eaTsZRn7BIjRuqjk+BbJoCN1OL9VsvwB9b6fXEdJYu765sORYm6V8ruRfalS\n1MYZlf5tVYXGpP3Wl16xXg9NcLq+PtBvfaXetYI5lC+Qn0jfA2bKFwvgSdTcPoV6PZOq7OcgXPj7\n0b99amTk6S8CYRqlSZxtbXyapcK2thQ9LpVc70UJwtT4QG4lneb8UBcZh6Lz8hzlueRJnz6+Yiwp\nuj9YtuVqSAEbI/b8CRKVF5GmJI/eiZrCRES90JODii63i35z4zraEwuOjYzuM+DkRbbkPuJIlfoG\ndSqdlNsNgilLJ19XsIompFysUr9XGrQaTs9zQFM7WC/n4ZcflZM285/rKlr+6c//v39XmUfmD1wv\nPB6AonoqAmFoqnNWR7Aa04K04UwGSID0yxunzqbn0feHzRmzLmE3dSbCJYAjDSk0hzU4HV6zXIG1\n6S6lmbShFBkoW+LdnIL5QsCG8IbMJXoyFDmbp+npuew+nOqoLMIumMmDGdeRpyhXGPrledAHl8vc\n/w3/ypxsQL2qEofWyxb7wNL/5pTYboBbCby58+sK1va7AAXhGuUWkTo540ikLqMstarXFdE727lG\nzcks0mkCzz5GN258+iH2GqEagwC4gghQ4fiv5+0RarCibYFK1qlv5wYHwUcsTE4vdGoh+Q2hDIuV\n+SWYgRYHVQ7mNiWZmY5GIul/mVzIQIOTYZv4t3baW2PqKz9bxvx5+51c80FTKJKXJ0iBClDQwIRP\nrLBPzolAacc0TW2wqRDbnjMDwljEMx1ClR7hxQ+Yo6ZywdHTjKW5uVvQ5B8N8wofnyBNnlxAyeTG\nzP2zBbKxme4itkJsjVAUu2+L84/vJEeDgi28HiMlUVi3mKz1d38F34HRXq/qVWJ9lhYDlyYq74cc\n9OAxAYZ69H/mA8o+nXC2b1b6fWHZFDMkBmkJKQc6ZOB87MR+pdFSbvOcKtr/vFEu4sT4IkRHczLI\nFXr8zIzC/hJrnwjddBH3zxEndalATvrqAeRgqrkBgA5mmL3Bm89D1ExvA9QjozjSG4GRwzX06h6P\nP6xrHnF/iNujUpf/By+8IPX8NaNrfmsIAvJrCBYOctWyJKXKgpeszJeArYl6QMqGu/yKCb/fdsEB\nfuSzxs+W+oqe9XgHUP7tnjA2KVR0fGIym0fdxMzSStGfqOjJ+SjpqdRtltd1HAkysnDMuPyGiBOu\nk4yiIJEaq58slmxq0qlSk2H3+LmpkAuZVekqBXNhPkH68+R9WUE4GSUrAYLmYHo67xAbL7sHpVQY\nGL6d8zRyAHpPjcKI8zdGnCC5f/aOjIwwjJLlqtfrXTlQJ4o/98sv4eW9vcrqKMzduwVt99Hj8ydD\nBGvroCfEc+ICiozvyj3YQvkF34OtHVxcBIzqPMw77GVRwPVdv15EhDdYstxHP2n6/ws8I+QyDZ3V\nWsvUFHrOPX7aKGkHLofJvYBjXESO40kV8R3hUwoj8bZnEruRNiWQqwnKBQG+RidJxtwYUE0zOtmL\nYqIAJsezVVxQncSixccZwGsFrTX9hJyYk6s3O0/pl0yH7mgMIzYCH76iHcI2qth72uss3UiP9ZRT\nUlX1DrzG8jXS642g+9vt7teciImv8ZMXX3jhx9yF8/oey0Hi86IXbotc8SF2fKOoMFGOC+/M/cyL\n3wY6gin4GZvCY3+gOFFl1+0gZ3cEuxxBO9f53+vJV74RQwweKgSj/VXpmnAPF9kxH12tbPQd3K6B\n0wK3luFDgq1YZsduIU2+DuWoBec46cj5jLo+TCUJ070M0xZh1qR2JI8Boew3J0GkriS+EUF4LkeX\nSYzhLCh/pUdOxHmI7hsX1DKHtHcJzxK5m596sjy/mYwh4IAgTrJjujQ+vuSpgAQGtHJCwFkcWziF\nfw3eecUw8pg75pk47ecsBT31sV733/Py+4SlV/AOw3vZbFw3uOvJ6bp28Ll7eX4nGr37us2j4NzT\nw24qADeUj9n8KLUsbL9DNhZR3f187nrIr8aUt2HEflWT/bs/PGq4lfnEonOr6o12ba0VMnKFqSsJ\nTYVKNcYwU2eO7NiCbzcJsbZQIbuorTUeUuxu8rzokdmgqcVQV8c5a5WGyKEw5jHNmFfQUbB8Gti9\nf8nPcBFugTGZUE0TGX6PPzpduFT+li/4VCWfPE3nRWpcv6DRDefK7BvG8vq8/i6iu8S4eB3tJDWn\nlRPMQyciLTm5d1+GX6DwatjIyPdLftwmhj54X8ncBdvvVNa71JgNq+0I4Cji7tsh0Nn+DQUrcR95\nlKiTnFhA3u4lK8B1t2RwLeUa4hj0dvIwtkW5Sii1mJ1C3RpGJctdXv/fftFyrshaqjf0eqtfNgSe\nkVOHFTJ+ixRZ07b80DLvqrT8cf69IltZDiCZZNNznBOOR384OlHCo3RMMqB+EJv7XyTrcJqyfJhx\nJefjqdsTnGxfihgNHklNEbf5ZqRPk9xCGfJPLvhsgIr6tBJrdQOmdcxgURYbIliJ69LHK4vBqnuo\n34qa/Lry0OIP/KQGqpg2DaSFWCaG8SK+wJKWslWrL8G1mtpevea2/Oho9fxNQ36c9PZvYAq35qaO\nJeC+ilakW7qjXxeqB3TPQ/3iQuzsDEa8A1kfdE48FqIwVBSVFN+p+i82/jsrk6io+eofwQiTsYX8\ngCGrJCJxIF/IIIe8v/YHB5Meb8G4tzaYtw3qnVEYX7hNDMA6XKBmIE620CfGlzJF5JSBk6sFyxjC\nN0vLNrVQCslCio746ufLtCnB4/s5h/RCxt+m5yEMAJXRZKw/J5Y8PPe0VtZPrPhlv4bVy35vMVFU\n28K7PRakSkaR8k60L7ACYy57Xle3gV+6ZqtH1KNHDYoFvSGkQoa+oSmEe53c4Rbx35O7ul1KMHGy\nu7x+D0OVwNIO59zF/oQ4zKyANpQNMR7pItMwee7no79d+0oZAbH3nzfNw1U/wZ7Bm8Lbh5WgVsDz\nG8k+kyyXNMExYqeNgq2K0/flUxb3+cD1qE+uR9Ca4EguRA0i7FEU52IJfe5ydAudQnCGZ6GVpPib\nwXkumfuzpJfPUdihlg/pMX57DHYxZnGxaPVIBDWTASnmAWuK2FgRjvcR50u8GjSU+2/N2pbnQn8o\nPAFCFGWfl9esCTbFPmoq22cLURNuqCqf4OLa6fZvVNKhlELmf7awf5UBqbZzSICx7Olev5cAR/zc\nRxmiL1apsZxFSWWVDj3rryBgwGxcG3koWwrs3/0JjMKq9ZDOAMHysyF1FLSNoVlkkaCckBZTUTfi\n0FGHofnXzuaSfuFQfqtxHbSnUt+uwPJYsIOn6HHxdzawfN36zvqFn8SLQZQmISBQGcXKlUnIkBHa\nHQc/egGlas88K86BkkmQSD1rX4ZL8DSaERSeNydQN9jOUYX+SFxOcQ/WnKvRStOwJsVbeJzKGipY\n2+4Wm+XjAh+r9Rl5YHSN7gF1lwtDojHGgpHPqovGxYzXFZwm/g7PTuz1mpFWO5RsM+rEqx/4BccX\nhHUwFOtFGn0RNYJhKCsHhMMsJ0LUgPXToU8Foq5gSOSTPdRZmmPlw9Nai1xcX5iSIcPBCl9ZV3yM\nhwXYGY6V4xiyCpV8zaLORguazJyxSejYuL3N/qz4rNwhX3/1bkP4kMcnyzR3mc6thSUMvgX7rB6c\n1LcbnVtmtYeHvlEeK+F+ShDW4G85QHYQ84OJq8lnggdglakHOHpD34Hv8uTwpzbooELG67U44pvr\nqR1zep7JelrpDT4mCCwqhu5lf4zM4DYdUFI4qmjUM2c6N/K8p+T0p3rxYikNHq4VE6in4URxOWjM\nAG9T5qb5B9Z23fZhqg7XFbKixTTagGyOX0p1e4S437pVHPRSyfhClrANGzZI/+rDvsohx1A6FyWY\nHQX5ySXYPvSbthGKRGhb+mYaqxDFYi/5sfAZidfQkXIhQ4kQpiOhHwFUtAnEAZ0Ys6vf06erfm2Y\nDXVi0bnn/R5u7P2dDecxsSDkpDx8BMbmO0fVFox4X43VOYf52LQQl/nQ/0zsvYvSdyE3XhfpUGGK\nvC8cWTtUskz0mvCxPrjna+uWjodbQqQh7ns5NdJTRKY6J3ZBGgS181rDIFSlWGYBY25NIx563Wxc\nIw6s+6y0knoNAya5LbpeY/vhoiiz2oz03A1om9F+O9p8M+ddo7gO0DVtCGG99CGHjgoKmYCAwZDm\nd5M+F+eDxt7pXGV6J1pKCDLF51MLj75YfWiVCKq30Y5gv3nUgM9Lz/GApxptX1vLIYI+fDERjSY3\nsKqodRIq8ONlCqotOLom0J8UxNJIecwI2Yhm280mhcsb42ZlG8el4TrNgtmsPusjirLDa1AgaT34\nUjYuI/MjSn/Qg9UJ6zBgVcq5EiNV6LWSFfrn/uqXTofVxmzIZsF45j+eEfzIiZYkc9gE2m8Z7PEA\n5rbCn3xDjbX9I6+NCDtro32BmLkrBUMkxnF0ptxzHnheANn0l5DE5S5F0vEELgYcGVntrWpaZp42\nrPacKPVWvVaYntXJi8a/PqhlDSS3YjLJewKTaT9ixClCtumf5FFjkRggkdmvARwdmFhbWudwYut8\nhkrxJOggFXMLg52gQ0c/YluDh6pX3vt0EkcyBKGiP/AAFLJmrRfJ/NL5cY+nFX1hZXbhw3pD05o1\nChipbw70KyQLs7kSKllhAm427TX1ZVFiXNsmKpDK8mD022+HepN79MNP4Rl4sGnTx2ifGv23IxI0\n49Sskyy3Sdqnv5rg4Q+zM+JP6taaM5NTc4mXFYTJn7ODOt7W7c438eQQdRCHd1CW/yfEQehfCBSo\nwKUNGe/i82ZE64hoibHrolgHDeg05MOA6TGynt+nQYS0YHkbGFE+beYncs553z7VQjtqNCEKf9p+\noW2//ZE0qzXc8aqpGuvBlU6yvONwlX369WWnemPo039TmsLSIlK2xxBTE4iPU4JnbML9+Zs/gz/7\nsz//838TPOb/8x/DP3Y//yZanz/39Uj8vwH+S/ib/+u/+hs2rxtfgflvw7/Cf2Xa1hk+nl34L4H+\nAv7X35/8vThk47D033z7r//69xE5qRmNf/n7v4B/+u1vf/v3qKrf/gUf6Pc5eiOVQiMZVvD1up1V\nmVmk3MkueMn8iyd/z+bGGP/5yX/610PdcYKMjiQ7ztS3/zqKx8y3vx3kqhw1gIAFeVdBXZxaN9Mb\n/bX0hHHI8ERnfOVfr672Hj768inrwetV3R8Zqa0wjJLpQozwx/9HS8pixEme//1/+6/5lX/yr91N\n+iffMCqETs067wxJDZMURwTsIErBGUnNJ0eh3M2ruu550Vc3w8rs3JGQCeqL3eHxjk5Q3gqJzfar\nvgQyTz1Vcf111rttp+MnYsUIMIBN5rxyMCe1lEP5NDkaMqOYhuYkI/96uFuIw6df4smTs+LBKA86\nTk2WJRs2ygRZ524Scy3yTWlhIBO2ubwMTF7z5uDv5AZSJEkviwtCXuFc7lGjcsVVC3vp1T3w5eoa\n/Ps/fKkTY7bAxg0byLle7P45q7czB4C18MuX9+qpV2SjVF9x79kH31SwIM6896Nxyqt4tiNmThAD\nr5/+uz3zJ7cqcuZ2iNF37342d1h2RskjbeplP1i8zeuj34qr02+BRwrUTwXAoOX5C+jvVpbVN3hy\nZjrXKuNSagmCn8ZdZrjFNFcze8c6YD/KqlD5z8xMziY6JcC+qQJKNblQJlhxKmOR4PcvdIFY09PT\nWfQ4M5smvXRJB/Mt4t41kfINRlMIoEMX2b1q+u1F2H+QEW5fwWqfawYfb60MbRgdgcoIBPjWTsoY\nbvqrl+HAQazVc6VDh6GThP76USGE5Lk/7W0+ty6Lb0mHxMVhQu7JnTzlJMtM7IB7RjfOZl9c/Mi3\ngu9WbbX7pjehzne/g7j148BKvBnuWF/ss+3aF4dhGZUL6k94s7W0f06S7YY5w4xtJ2CBzyguspny\n4OQFP2tgnGEkdmY2FEJUa1nVXhhq2/lsiI4LgLG2HpgVKfFhgp8J64H3c7EeMQkFxM+DGyLSVYh5\nF1M/zURHpMTFmptOWL8AkB9wrgoL6TUFMTeTV64yJbw+8FZqM171SV/O0FUjvaf5M1t5b1M/2Gl3\nl257dHMGkHkLm1bqXRfwIFxkq//aH+m8a/3G3fi727beS/Xl6ykfhYpjYFG6Q153ySidXB/fE5QW\nxRbxWyRyFSZW3MEsc0yxNYEXhK+6bmQYIzz6FuviBurW0063AV+PnW6VOczjJ1iGnpX6Kj+chlnE\nPEIbg5L79nBnMMMBuBwabiQVfaR8nU7kn5+OR2cgmMKvKASnHo21EEyf/0uy9osTw9a+BGzN6dCe\n2SJITA0ZihbhjbQMS+MTi96pd4Jw1gnbnmtyFjaTBV6FPr0iBuPe1m33gEcmt6Fpauetrmvwlh+a\neV7JDZ/QprOexlKVhSoocFdVFt9sefzh80oi6lZ6V/6J3Du7H/azoLgUQOoHKnvBClNjsrbalv/o\nhBKZQ2TbR/2vZMVsNTrqLqhZXSWyPlld8SQmz3zlwXOotGhTizIhWklYnDz2NavMBO+zJtBLot6I\nFZ++0rM4LPnRCBjE/c6bANp/iY/KhEmHheDn6NlgM09otYgbz06loqd745hoKE/PlmmwRRIXmCaX\nFMTlB1pPhM4RSlI6lwWBUcRmy/udmOS8HvTPL3l1LDWoSwecupHLtn15H4+7lAWr92fQA046p/t5\nS+5Cs2b7lucFX2utUi7zkh644C6xNq/+EYJ1O57q9ojSInF+Yl9g7JjYmUkWQegIuh9KXARb73hi\nQ+/S7boZ7Yz0zUPGeBXpRdTFW/tytRGYVdXbYAw2XzZkw+hwkgK7jGSggLTgIR4iTWwgZTqXwZpY\nsBj/NO3uPlNa8DGCYOlEplg0P8L4qwsYBIv7ha54wZJdypLFghX4h487wRp39phmSsGyE0Lyr6sc\nWtTSoAyvok7HsagwsQBlPVXbvrQtaDrayU4u9SQjCzPzbTzwTICtkuvz5c23ObPQt+oYHYILnqaI\n52M9HwVL1175zW7KTWgeMofGQbiKre/C9ww/brvuhT/CeU/K9s7duwqF9+75hx9+GEuFevdvl3Ll\nPtHBStwNJVC/ILeSP4M0wAOAmQcjvF+UMvhF85BQFwa1nCVykPKBIKT1QoRweyifxyMK41gqfsY6\ndLCzV9xf+zOvm+s9R4s0w5hezamToZFQuD9FrELwubDgVOjiov8K5x2KXF28eKGXbLkfy4MRRjaX\nZ2T5sadhxgEU/IyPjeXvpaVljxNS1Xz2wiXQOhL77fKRQxc8ORatGmob9m3u3k3jHjhqv3FDOUyZ\n/AAOXrxsFCBnDOIw9pKvr7G8ysKEY7+rPFRuyZ73KisESLvChEJ/X3eovvJtiZ7cl/JAgjAM4UCM\n6BVSJeCpp0P1Y+2rr0zLsDJnCo1pvuxb5S420nnPjFgtQsCSkB86RxWFgXdoTG2aR6Ru/SkOjnQs\njbbYsC08IYmLIOqHQ7Uw8CpLUH2RIn3uIQXaqAXWWRGLmpoOJSKEvAMrRMjqUlfCQ6wO9EWybRLG\nThGnnGolm2JuSFH2pMwxT0kx9BMWPCOFcz58RvMa47itDy9CMbqu3fK8CNfrMBGFL3N3GODNN7xd\n5QHKRvq/if8hSb+7X8x++KOc99iZsC1PCJBOKflVHu3eKvkhdsBdo/RVGJufsRDynPEHu2FznAqv\n7okZsWtVo52wCPXT/yFO/hJ0TFsVPDbkS0q8CBhG3zFtyqopQb2qzSeWtN9g2DjWUCzZV9Q43Cc9\nXmsd8JSyHxAOBuPaZjjaMh//VXf2++HghazVFTtVnQ6tAc9EnJ5LK8PvnZn19LYZLQOK63aaAf/q\n47JcXebPtFRpvcJkRQSnzdtf4IhliQ/khzcpS+bbRvEYxtPG2gAY+YdwN6gvczdbR8aCXXerZgYG\nxSQZ+ggDKi+V5wJiqwSmkcECBBcGcGfVuBc+aHqwVvcVF23snjMPM5pOd7nj8wX5ayAoVYxDKBlW\nvX7sP06QYDLcdTCRNncku0vsCfs7aIGDGcAqG20T7dLsuugXqcxjXVlfD+Xx9AUZziDzcOeJOexM\naZ2VmHCmVAW0ANIVqEqc8AzDiUERt4aKMqOMp3UrVbfI/qjxu5JSBQtZiti9wlYZYUUulQ6lhT/a\nx8KysK6DRYBnzwWMD3R4/rxJoeg12TijasjsJx3aggEtEfyNHSmL/jy80OPMqLu6Pu/AV+BYlU8M\ndRZiqlsFdJGRSQlzdpoOmNGABp7XFgfPmeKCp4n8o3TuXEceLruf/PGlSx6ipWmi5ZViud7wd1mI\n6LJUc5qELYuu7CmC5xhY7li76XRfQRdowq/O+uMhZkg4QuGs86aAW+U45dyGvHN2T01dqRZqnR5q\ntZ0VMfnPYVghn7Ak/NxF8bBuHa186I/TWDtvZejq7HIqBhBWViUmjgQtKmc4iNBCGtyCGXY0/8y2\nQnUB/PSdRtdtrSf/Ts557cS6fjrLhlNMJmEYiKWuDf/7p38nhR1/u4wNR2R+qyKHfI69rNKOXe5A\nfS7kTEwrjJvJEAUzc5Dx6VKnSIwMecIsWy48KqERPoBDnrjLMWhmGew517EyYvA0j8xMyc4tqKxn\n5jF6IowRBTbJpPU/GXBN5H/HABNiGW3aCnkEpHNbWyXC4fk8Bp8Q9tVPgnpgae04480uXGWZInsA\nLOuT750RmZ6Mfqi2B9+a+Il/cUsBUNJBYbWV7AE8svsLI+GtivM0kmjPxDcokdGkN3q9atZQkmId\n5iWfWxrPrsGTreWPcahVy3XMSmCVm8lhC0M5IXFUWtd8+UnurrStUe5jdZGVJR4GSgTG3PQwCWS7\nxmrbOtkBnZStpsNISqJ1SqcxPcP8mvo8Ct2MTQOc+RMkkNGWmRy4v9B9V9MKxXkQ0H9A5j1ekuyA\nipuEalthRoJdOiWpEo8JUEQx141ULA7i1qIIlw3GSJiily+rjDbVSMzveGqV6RzaAtLpHOErjOrA\nZOie/nsKL6UpFjLiahm6koVD0XYJR0NxUuOQ1ZqZG1w9TYhID21rm17S1xybNs0glnmdMbrl8Pey\nBlSgfTR8YOJDtXExPGWh6DUbe7Bam5qb5m0qzBur3IjBOabW9BurVkmmYPqRel9jcu8T81ihsmyt\nkoQ6Lci8JJ5/K41Zj+XnHLXmJWPbNul5oFA63bkr8x8+vv9Jviz3792TDKt69c99eJ0nXDAiVjot\n9v/sfWm9CmNsZ2bn2BmfSXdllp9hNcUKmx3ZhYADxNEOaae1cVoMFAyeQ2SLCMthvBjbMZJZR6lD\nz87FynHgAAn8YSeO6ytNwEh4pwsHeZCHG5Bhc7own4iXAP3+A5WgZbAz32zDhh6McM3Z+ViBQYwr\nC8JfG1xpOdOHa5rZCZN92dnft9eCpScMtXyMYO2ykVM8lP7Zced7XFW1SSxFARTgJ0Wk61I76KLK\n7du320DjxdC+nTt3drB1nwb0Cm7V1IYkCnSY03sH3RtH3PXTCpdJUQgO5OqlLsfN5b6/gDsApj0L\nVVgwDAkZSKJAOpuWBO6HMeVEw7FC6y4hZigpyeMrw+mcl6qp0PYu5zo+xmXFyvYbkkYFxD17xAuq\nFUVdld47Zi0fJYx3He7P1EeXPmAto0J0QDYl74SjROb+qGttpzBh8qKnRhaoOb9AX/Bv1b4IdhGt\ndQH2uVOn1/8BGos8I7ZGExLVSBfKM7Ii/z9vb9ok53Wlid1z38wC1R3+KZ6YdoS/OFrT/gMdnhbF\nReIiriABYikUUPsCofa9UIWd+yqRlMjusf0D2t0d0x8cHjti/oijHSMRlfne43u2u7xZXAAQLImo\nrKzKzHc599yzPOd58vn+Cl0WJbMTQyXCCcyP9eyvdFgTpGkdTUtiBvyPJ8O8rUfzCcJ1ajGyFuNx\ncHdAYAJpBYFbq/EkJGvPlsWV/jknstAYFIus+0iPWL7zwEuij++irWDkwchtViQBg/HVeMlZrRXk\nDbMxkNvebU71B9gb42rAmTPxVHisZ+zUz5gjeqYzrDNXQQoTIKycxOvsgTZYU66NFmXuMvUEXPun\n+0zUS7QzMqrzis6Vq3nBr2RmAQcSVNw8d85wGVydPoyxydmzjxpjIacZvD97EQSOj/6Xf1D2Ddn5\nP3tOcXtacn7qj8lboNFGfG7Bg3UWJUgjOhsCJEvtTUiUvny6ik/GwPLRthdznP5xgZwsWx5yRxU5\nwv9IerGts1FLq2BYP3S5PWmFYByN0sGN0C6nCAi/t0g472rG4yl32wdP40fOEcjgrnV4AtGCjbst\nqMBic+uQx6E6yYMbJVEdIb2U70Nf4LYlmfJ49l4jfoHJHx1++PKHLk8c+/Dp858xJplxgTEBuHH+\nvLshLtATTewP+Pouw/r176QKGHfdX/+OC1hOx3H+t7/9j3/f2fl/TaZl4+Vf/fJri10sOP8CCmzq\nF88QtwOXRqJd/QNUPPJ/cPBlcT/Rtf/dfS3GMQAC+wO7aj7dfZ/xoxsMTeG4HLX9uxejfj/bAdk0\nRFLXBijTDyxuEnbaBCfujJo2TG9ZxUH134WPaD7F1tIIPMvoE9ZKcmJc3BymgviFLuR5rjYZJDGB\nbwG81yVcV7CRUmzkQ6FOp/H7G7efIDqQROfkXvrQGX0Ej9k/9ym5jb4LqdEcTcvHWPdCQtGceRSP\nhdQH/B01vD+HeBwxG/zb+Oz/TkKZX0tu9Otsh9Rg8uRsvnLuF18VnKVfGkJCbsMXNZruHzjEqit3\nEFLbmR79xZ8kK239kEOAlHMmXhGm6k4SC5AIaakGSjQXTVxnG669SnMWSqXECcCCW6mjgcuk9gGZ\nsP6COyR6yiOlEIwGcFQZ16RoThP/VMkOTZYltEl6HXYm3c2G7gXvL+5mRvVi2wJ7AVG52LSmzboY\n1zqURY+OF4T5olUtMK+NlBcGYnacoa4mXNm1I7tMjdDDi2ffCY4aZfAiwQrwo9+89JEc+G/0nYlo\nyj9x6t+Kal68/57X9J1jHL8VHr3cID1JoWHmg+ap0l987cmNkM8UcNhnyRk8qU6WLs8fxaV9aavr\nS3FbX9gcz9e2NamL+iqVJcSJfSjZLQpp78AfumHQroMCNPNIIEWC61bw0PnGzZCoSSR7pNlq74X4\nIfhaclhMaw/w0nXjh3XU0KtqvRcOnRsv0H7RtNIYGa5X7mStutAQbiqNUTyuaFl3bCuEcGHkos+V\n9KliUoAVLRtVXeZH6va5P0Zaj/TD5R2oCS3GCTw68DGsANEh52rhbz7qkO9/QjDmn338TdPcb15z\n7t2fHfve8C/e+1O0xifecMML3wOL+a6Gz+9YCUAxhX9owOjk/1ae+SpwVE9HQ0LPglMA4/T7WpIt\n/t/TX1TBAD7zJSpcBmxoxub+QBXlGEkhwWTAb745ZtieG+sHHA6lFYmG6svtJCF1wMBK4Or7W+pA\neevEr1BMKnVnpqdZIKbQcnCGxqL34dKBs2bTBRaKPErzXhf55+sy/0YB76Qh6zqtv+xcaIMcay65\nQ43z5TDP3ZYh5pY7MONcw0/WO1vjmlMov56LDmRYC+kzzHfPbmq3Tyb0CHAYferkrkqCu6RHeJdY\nxoARHnqHP86hHPsxvhwtzY75F9xHcExSPFTIHzRw+hDOP4JhORKHiw6DLfqP0GTYFJnW14gigE04\nmk9sfsUnGsC/Vw1KyRehIru2HN0aWlhM2wUF+0mpiix3OIiGFeLC7vV7A/ZYTEtTKcw7SzK5iOC9\n6pRTsNUD3/D62mqHLOWUtJ4wGtaGNPvzINhlosjadwl4QZZ10d1I3U5+q4OS4GJSgHXYURGYX7OO\neHy23/OXjmQ8juWdehDO3dbmsOT5l5mEUk1xVoJFTOHerNlZmlidp6L7WhXqcQNrK48ncE2ILGvS\n1LGFPJHD79skMiHDq8+zHWXl1hcJvlKg5J77lEMRaT4Ngn/9pgvnH6VAigqR4ig8xw9/G+3qP/2n\nHC6+YNA/dRxkV3//NZbDCCewxhboPEyRN9RoI66dBFl+bbwbcfdl9HoSBuOIK1mhznZws0TYOawV\nfIPwetNAqkNPPHGqrwD4Lnc9/7NHSL+JbCBHpGzr0sqQGOtS7hYk8LzaeCLNXTPNDvKRfQF5kY8g\nvB2BtG/bW/Cght/ZnsrXZyNaldBBQw2mKStVMFpzVy4wFPZf/rPtHXOqLjvdeEG81EOR7OrTTz8p\nQEwvuo8/LgBlAJ9J4ajhGxav4T3qwDxSjEXIMA3nvvLaKYju6n/N8BSGwNfZ7pMiH10lUpBm3kaT\nZ3cS16axlQZOCIE8ayCwf4ChSWQEi1FTRmdodl9wGHHkHuOzw0GYmt3jTzjVggykLPCCn9nCovXi\n4OBSfREuFi0n8WDV12R57Gh19pEKPnsP7ga4tqVyKHZk0XzYLlv5bFkbNY3YXDUiJputqZ6TBc+y\nGNn0dh02TlbAJoLtXydlL1LRZsIi2m5KnBd5q5J8JNMZSds1ngA03ws9/s6t8BMmAPE83utLKWUr\npYWQkJPsY6EkDcLMCFK1EjMJdvIwWIgQBYH2Sr1uKN2kb4bH1FU51QzYgxJnLYdjnpWbQMl6KOZq\nmZVNUKJxE0XCeBADBm3bHKyS0bVIWq1BmsZus2iGgKGU92zIQ+3oSJ83szrIU662j3Nfe261RC1z\nGEim3vSdBDQsat02vSZZXFB3i6GaafxWntvEJJ/CsAxyM1FPesOWr4LRo+5qOUzQLwh9pkXk21o5\nRawFnpK/SAzm8WsQA683HsljvfCRy8ODFQI0RRw+KAbCOz8CIMpWhQWgD7IUaRfthZnPRqki4wZ4\n9ib2hj0iH/UcnogGhufBVsg1sNComg2DRqQvIOxCgdk2LVElfzbIm6TbqI55N/7hpaIXBATHu5iv\nvP14EkcCEdg6Rnlmzlkt3TaJdJ1PDwC6NSjtcnwbYAZPBgCuVUPRYEAeh0ZuEQO3xG/rM32SdwEy\nF14Jp0i+C5IuD+buu1bo/fc2ob9X/cuXjIoCpCkGuqXr5hK9dUnagh3yVigrw6XECJaymfEtn6Fp\nC+EYoSiAS3T9IVMxYTNUpBC5nmFCuuetu6iJq+AEi9HuSZRKbbMh2SWVGjxDTtZFvq4sau9jBx97\nCNUeflgxZ5ZMc5afJB8yt5m5QmMGQAc4DM4XVFcVTuIkwAycaF1Q1FryF0OAuJkQ1FoY0r1tV1wr\neIEXG8YYgccDOjBqjwVsvxOvSCbSeHxUw6JyOxNFQOFZsBJRdCDW5ysVYqz7uF1SvbKTz3isr3Qp\nawdIpT5bJJd77oaU0NeppdBCSDhkn3GIvDFKE56RbtHP6x49Wa5136on9L2h67cpoS84OoyQoohL\nOvKRJ6Bq1AvTVrSwpiVYP8cNwI38hzGTOgzcswzedafUWawOOrChWg6qOpr1EvJtT29NMxNYCC0T\nipbqkAAFfZy+KF4o3/pK3xAyY7irNiltLSDFIegf0bC8113Z5Y5aaVZK9Yne5dJBWU0shiK6xD16\nNf6uRhP80jwPvTgawRvlfkk3akUjOg7Zmf42cwtqDM7zrozGAkzy9AJ+kxxT+j8kHrreeuh2R5KS\nSfZjcAIw+KSvmdGnNmdmNxOvIbstIZsMUPh+Dik81rmNA/f9uCfoNsq3tOGesWddJ5p2NWLaBqYH\nNkSnxCmo+gtWpzZaYkzTa6F51K3whd9B4YkU4aaXI2TJWCibwgWcPce3JRfbU9QOtL/+e7UtNcUv\nMSHeEX172t3Sgon8fqNNAza662nzJ+GmbAaAt734/02AKd2qmzb4ukTtQy6wY6JgwlH2Y3BuZFS0\nqFAk+9hg/Ys1s4m4M+GmIxUw69cdME4krhj1o0feOAUa4dLEk8SSoVyj+Zgyt23tcVwaVUsN0Dz6\nlNkDaVAi/kedytQaZTpkDAMYM2nM1HE38uD4msZKBQ8P9IOS/Uom/zBNzCbqVGBy54qxoVS/fvpp\nyNSRNPXz1FO/7HzOH10GfxkGLLTuzs1AfXVLxTZbgwtCqWmKGVSFeXJTVm0YTqmDIJjMkIkAL15k\nuwoy1NfhYwwleVEK/wox7hOyNn08s5HqV3TCc4oEWy+cXCu9Cjk2d+PItS2pcLTDkNauSzAscCOs\nSSX90chadibu53icsiA5YTBdnpsydGYweJ/NoMtc50vxaIYyQirdnefTdhhdXK955eXv3Qm/37Cg\n6uXJ9An+ohDPoivwjO2D2i7/RR1efemefrpcVX/4Y9zznnwyhVhfmS3RmTzrfveZjkq2UgvBIwH5\nr7iZBdArl98/687NByxSAWb6lNPbyfVWIrSLljpOBdBpDG3hh5RoCzNT12jWDyObT8ksvZH9IRN+\nboRM2kAONh7HlYrNL5pZGA4Hxzy6x8oEOYiHrh0VSL6RLbLggxPGDqF/QyyjEiyIWexO0nRO614K\nRUUI3XuvnibliURI/SmbVh6j+sS9+OjQZKf0DZi51uLlejLUSqpP1Vfi619goZvDeoVPP5WVdcRf\nfS2v+TvWTOG7GcoEAVuur5+XHN8jg5DdIhhuE6tZWPALiytGqoSuiNRIx22XHVhgWhLfNIQtmpqZ\n3nFGOu0K/xfQ1RrpJw1hfet1LQuYm9KVkXSCVknYyguCi2fj48Mhp7vRusLENnYTwdpbzdbPFtX3\nTCgnkxQd0LRT/z+qRBkk6HzvZTEzXX3hHff6Swn3F6/IJ+S1MNd8P/lOqcIflBU+yzxJ7ukvaavm\nehB91h9/+fSX5fL6w1O//MrupWzoT7o/ysb0VOdc0h74lQT+f2eABtalIB62z1z4RAEzXNo5YlsZ\nI5qU1QUpExWk3qgoiXkuZEGHQjYQcdWud9lkCpRag6HyPWWCD5WUTldhuZpvo6s/s9V1bpCcg7q/\nIEMbgtziP930U+5KjLaHSkbybQm0jvBO15DkXIUugkYwpfKEwizfhwlT+MHEgTQtGuE/APfS+55n\nU+lFotn00gcctOdg6gVusDx/UjXtYWAzv2LVw2eUyxYF4Vn4/2eczN/bBFYynF9mFcM/QHi6FDX8\nIzzpXCLGpV98ye4KXEn/EQoKa6SaS/y+vORyhZin+JiPCpiYQehUV4piMbvAnWhv+3lDz18Tun8V\n7OkIxJdVBtAXmaDswpGZ0UV3vVopk1yPTAGyhSKVNebsbCslqUgT2Dtx+0sKD7tOeMPtpVM7rNSz\nnWoZW+y0NrFMJ2ZLVYxoOVeo2wyaLyPrn2za6TBsZJIuiHOX9nmMJ3gD8r3yPinloPvNt6IIP3Hf\nrmnwcHisZ6NpMUG3rVXe2r7IH0te6ZdfdT/pl+Sz3B/IIsMz0bjMqORlX6kIztfGjydSS8QbIO5K\nqE7wSA2tJYIC5655MGk2LTAQQUQ6/1VxS8nOPcH8lFqd4adhq9943xRUdK0vGoUdVm42iIuH9aUc\nPwTDaCniON7rmc28F3J3TY14vaPEsFUgo4gPYSNXKq5URT7xQpNWrOHq+UxZ4YLpTdY0K0nFOT4P\n2anyvzN0eOxRFUs6ydgNSfEzXO+VewT8az6OcfswnCW1HT6Uj/jNX0x29ZnBpPARgX74+a+Y8OoZ\nZSki1DDDi5/5nN/8C1UDc0/+kc/jj1w7e5L9VKrTeLarXKB7ksTEZESet3LIlEPWkeKWC+ZUl2cp\n8YQKtWjIE/R3A4c8UlsQOEMvZAMir9hC+Bn9wTuvxxg+hPvREIe+2PeQPcWV3cxaNJ4tiz7/8GL6\nmW/bzuSJ5auT4PNQdfcJE9imxpbGT1tYcYtOcuLB3esJwU9s5kw9w8B0RWielYp6VgDJFXXvAkMd\nJkhLjy+uv8kfd1aOqeGwlHWL3veVhJnMS0Fi5MBHD97D7373e41XuNgQv33xRe0Qv/wyhxv8T7Sr\nr76CrOrw1B+K3D2e/ldfafAnhXKhtA2WbxfTZGmXCs5G8HJN/2qS/I6/2tyK2wIJadK65WQHC5l5\nDEO2tSGvanzznrt7M+YFENdV31cg261uqhV908WLVPDQpODQ8A7qare3RhNplzEoWm6FiugmEWJW\n1re1vU1zYN6QA2B2ZaWara3N8u9niJDcjaaSOiOSRgBm7OZI+WBnbz/futAOBYt/6450BAgp7d66\n/Q49n3KXFz9JjL4y7/n9kgnfjW4QsB8Irv33afkZ76idjsZZsow8wpOqbliEj/7k0FRLT7qppT4O\nmzCVfGSQjYVUW2o/t7ZZBY6prsX/oyjJ9+IWMLvmGu6RDXWZNw0ecwM/mhmNtg8JBtXvE5+bG2I7\nfp34qYZEIZ/mdmKcQ7A4KAammWHmCGsYzX4quGFJ+WjbUlkFgxMYLTBLDyq7Kfmjbd8jCUH2DZed\nzK3xX/kJs3pM27U5sKwFMrU/4fYYfmnZMnBffHOGpnwF/khXQN5MJBqxITrMO0wkxkTi7n68/j0S\nRoEkcGzImTSl8dz3GtYPw7yLIjjTTsV/ns1WpfH7l5lxiod0SqbuonIMI1sCFLPTqRYJQldazVji\nwBuZR8rTrl1lQ15aY2ts4uYCJV5e1IW498O+K4ZsVGTmCWnOON116hmilt/tpm9NV7A4KMedeIu5\n2MmJZkYD3RPZG7D7NFRVcyIXmeIJyCeQya1sTyF33u4wZRKllGWDcTOB3Xg2T9KRyezG+N3X52ay\nNfvQaPq5r22TFvq3vMwocMknehrfIk+ueLRuCUjcCPBD1F1+oMdim0pUACeMgZu+ieFqfNHpYAZ/\nQ9f6WttB2npJ+8hK5uwDglQqZTuj4nA75OIM08qJYKZjkkTHbLU0ymRGNRfNKwyFI8hDSxpFbNWN\nlASh16NWQb/lvRKpRtmGXnpLiUh2igKSov4O9ewSHqu1fHVaSqOApc4npPqGGxGhKJARxq8nkLJo\nTgSjpl2/DecMxpNmaTtYW8XCbMpQF9KkoBzrdptdpRVo2WNR2bYsmcoa9Ay2Y1JEmuBtg/IJeB1y\nyLPYiYvg1+7H8FiBadYcV7BAqOqwm3iagBwUld1CucnV0Cv4ln4u5qJqkFFSYay3fSeH7b6gg15c\nS6Q28Sjn3dyGUXYExoy0jYW14ggICi7iHiF16RI/+0aqQto5HoCSQ8rPR/HvLlZuZxNNGFSendMK\nAI60/rpAhTSS3SMCGn+fOaLiXoWn4uffoE4eYfQxXTM0802ufjOZMqTJ9J3qKq8ZsS7XryrZJ5VE\nEuppcInPFxsBbrHQaepKZuKVH+S1foBhcUkpeNP8Mj4ssL0dO6wamVsvDYgijKr1ZohPLZAcf3op\nZm067C2wOuINK9qQFsevKemGD2B+ej5n7J778VzEmHJ7MvIaBPgQnRa9hj4rmloDmIUpN3Vrq67e\ndZNqkoO4ntAz4Dq6lOwisCDegxMvaDIv8KeoiYkQreoJ5henu3zMVBPxLMeQosBOdVZJobfqPdXs\nareknqVru06UO1agEdeghu/FI2AzbMhZBaVFaLx4EF90wCv8DP44hiUOKeQgwxUYlYIaqwIBmPdG\nISe0gHMUdBmqJnBIE5Pqs1Q7FwuD1W+BkicGti2sAMtd2g2e3XBKtslohllty5E6n5d+Pl2dBhkp\nEW8q0ptspMNgacuuTpsrxPTwO+qDIguwfiLF0QmIibg79WRlPUFBScwwqAwfjwzbHgN7KG9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vPEkvzxi859ECTOQnyLYgXJXtbTZinwgd+Wne74URsM1F5PekbBdSKFaF9LpPWOgHncWsImgAlq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2VC8R77Dk69pUz5BOcv2Hf9DQPaf1N92/wkxLhkF4qgHp0sOHauef63vSTnhnSYHsRi7ltpgV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\n7q+lReHiShRcfZmd+UUrx0GsiitEhYDeL6s1XXUxjS3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Mx4Zm1RfTevKWeSxK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\n0K6GhtUf0xre3vrb/werfTFEokDGUQAAAABJRU5ErkJggg==\n", "prompt_number": 7, "text": [ "<IPython.core.display.Image at 0x3812f90>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": true, "input": [ "#We can also specify a time interval to get an animated gif\n", "#Format must be image/gif\n", "img_wind = wms.getmap( layers=[wind.name], \n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/gif',\n", " time= times[len(times)-4]+'/'+times[len(times)-1]\n", ")\n", "\n", "#Image(url='http://python.org/images/python-logo.gif')\n", "#saveLayerAsImage(img_wind, 'test_anim_wind.gif')\n", "Image(url=img_wind.url)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://thredds.ucar.edu/thredds/wms/grib/NCEP/NAM/CONUS_12km/best?layers=wind+%40+Isobaric+surface&styles=&srs=EPSG%3A4326&format=image%2Fgif&request=GetMap&bgcolor=0xFFFFFF&height=600&width=600&version=1.1.1&bbox=-152.9859023956905%2C12.123672938563633%2C-49.308990314247126%2C57.35625318852111&time=2013-12-14T09%3A00%3A00.000Z%2F2013-12-14T18%3A00%3A00.000Z&exceptions=application%2Fvnd.ogc.se_xml&transparent=FALSE\"/>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "<IPython.core.display.Image at 0x37d17d0>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Getting the available vertical levels: \n", "OWSLib does not support vertical levels, meaning the layer objects do not have a property \"elevations\" with the vertical levels. So, we need a little extra work to get the available vertical levels for a layer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Next version of OWSLib will support this...\n", "#elevations = [el.strip() for el in wind.elevations]\n", "#print elevations\n", "\n", "#In the meantime...\n", "def find_elevations_for_layer(wms, layer_name):\n", " \"\"\"\n", " parses the wms capabilities document searching\n", " the elevation dimension for the layer\n", " \"\"\"\n", " #Get all the layers\n", " levels =None;\n", " layers = wms._capabilities.findall(\".//Layer\")\n", " layer_tag = None\n", " for el in layers:\n", " name = el.find(\"Name\")\n", " if name is not None and name.text.strip() == layer_name:\n", " layer_tag = el\n", " break \n", "\n", " if layer_tag is not None:\n", " elevation_tag = layer_tag.find(\"Extent[@name='elevation']\")\n", " if elevation_tag is not None:\n", " levels = elevation_tag.text.strip().split(',')\n", "\n", " return levels;\n", "\n", "elevations = find_elevations_for_layer(wms, wind.name)\n", "print elevations" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['100000.0', '97500.0', '95000.0', '92500.0', '90000.0', '87500.0', '85000.0', '82500.0', '80000.0', '77500.0', '75000.0', '72500.0', '70000.0', '67500.0', '65000.0', '62500.0', '60000.0', '57500.0', '55000.0', '52500.0', '50000.0', '45000.0', '40000.0', '35000.0', '30000.0', '25000.0', '20000.0', '15000.0', '10000.0']\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#now we can change our vertical level with the parameter elevation\n", "#If no elevation parameter is provided the default is the first vertical level in the dimension.\n", "img_wind = wms.getmap( layers=['wind @ Isobaric surface'], #only takes one layer\n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0],\n", " elevation=elevations[len(elevations)-1 ]\n", ")\n", "\n", "saveLayerAsImage(img_wind, 'test_wind.png')\n", "Image(filename='test_wind.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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NpmRyY32dIYpsZIJkaUYz7KDYf02PJpAlASnK2A1UL5DjetIB7bT5SFgoDW2W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"prompt_number": 10, "text": [ "<IPython.core.display.Image at 0x37d1910>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing styles\n", "* We can specify the style (any from the available styles for a layer) in the param styles" ] }, { "cell_type": "code", "collapsed": true, "input": [ "#available styles: \n", "#print wind.styles\n", "#Change the style of our layer\n", "img_wind = wms.getmap( layers=[wind.name], #only takes one layer\n", " styles=['barb/rainbow'], #one style per layer \n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0]\n", ")\n", "\n", "saveLayerAsImage(img_wind, 'test_wind_barb.png')\n", "Image(filename='test_wind_barb.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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KtmiK+5Bvqy+w2kcZoXFfzzLW0Sc0kZFdQcSkCsTtDZhvwWBxJIsnWKqrQWaB82MFuNoB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\n8E+Jplccyh1l3kwhJFks0ZI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"prompt_number": 11, "text": [ "<IPython.core.display.Image at 0x37d1650>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing the spatial reference system (SRS)\n", "* We can reproject to any of the available SRS." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Reproject the bounding box to a global mercator (EPSG:3875, projection used by Google Maps, OSM...) using pyproj\n", "from mpl_toolkits.basemap import pyproj\n", "epsg = '3857'\n", "psproj = pyproj.Proj(init=\"epsg:%s\" % epsg)\n", "xmin, ymin = psproj(wind.boundingBox[0], wind.boundingBox[1])\n", "xmax, ymax = psproj(wind.boundingBox[2], wind.boundingBox[3])\n", "img_wind = wms.getmap( layers=[wind.name],\n", " srs='EPSG:'+ epsg,\n", " bbox=(xmin, ymin, xmax, ymax),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0]\n", ")\n", "\n", "saveLayerAsImage(img_wind, 'test_wind_3857.png')\n", "Image(filename='test_wind_3857.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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J7NmCPwGSNEipwFHC+NIPnHn/wJmRF82xLpGPsci6tngloZzQn6SKND+dAW5y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wds1FUUbFqt09aCzaTCUqcYyzaF7F06q90bliGdlfJ/v0Gkv/ks47H4HE6cFJcl4TwkYDKmWB36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nP4Rj5+Zo7xW6DMt7bXfieHjAJ675KpTSLzC2oKlXuGsXHwQu8/fwl73OTu1SY9YudBnqwKbG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\nGrQRR9BisWC9B4f9OV0hes5UDExoh8MK+ypKY0USaFw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"prompt_number": 12, "text": [ "<IPython.core.display.Image at 0x37d1a50>" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool, we already know how to make get map requests. Let's change our layer..." ] }, { "cell_type": "code", "collapsed": true, "input": [ "temp =wms['Temperature_isobaric']\n", "img_temp = wms.getmap( layers=[temp.name], \n", " styles=['boxfill/rainbow'], \n", " srs='EPSG:4326',\n", " bbox=(temp.boundingBox[0],temp.boundingBox[1], temp.boundingBox[2], temp.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0]\n", ")\n", "\n", "saveLayerAsImage(img_temp, 'test_temp.png')\n", "Image(filename='test_temp.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJYCAMAAACJuGjuAAADAFBMVEUAAI8AAJMAAJcAAJsAAJ8A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\nRU5ErkJggg==\n", "prompt_number": 13, "text": [ "<IPython.core.display.Image at 0x37d1bd0>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "...well not that cool." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. TDS-ncWMS styles and extensions \n", "\n", "* ncWMS/THREDDS provides some __[non-standard WMS parameters](http://www.resc.rdg.ac.uk/trac/ncWMS/wiki/WmsExtensions)__ that allow clients some control on the styling.\n", "\n", "- Change the scale range: \n", " - Default is -50,50. Parameter colorscalerange allows us to use a different scale \n" ] }, { "cell_type": "code", "collapsed": true, "input": [ "img_temp = wms.getmap( layers=[temp.name], \n", " styles=['boxfill/rainbow'], \n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0],\n", " colorscalerange='250,320'\n", ")\n", "\n", "saveLayerAsImage(img_temp, 'test_temp.png')\n", "Image(filename='test_temp.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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iYLNej8ZpjkYqbCX1VZauW+ED5fZNc5wBrURgDgYUPcMl2fCvwO4JQQtD4fP/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\n3xyzvgN5N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13Wg4r+g0j4lgy6X4W5c7xzeiYuRRYylgRode5OKlq0AOp83xbORJp+s+K9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"prompt_number": 14, "text": [ "<IPython.core.display.Image at 0x37d1c90>" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* abovemaxcolor, belowmincolor params give us control on how we want the values out of range to be displayed.\n", "* valid values for those params are: extend (will use the highest/lowest value of the palette for values larger/smaller than the maximun/minimun), transparent and a color in 0xRRGGBB format" ] }, { "cell_type": "code", "collapsed": false, "input": [ "colorscalerange='290,310'\n", "\n", "img_temp = wms.getmap( layers=[temp.name], \n", " styles=['boxfill/rainbow'], \n", " srs='EPSG:4326',\n", " bbox=(wind.boundingBox[0],wind.boundingBox[1], wind.boundingBox[2], wind.boundingBox[3]),\n", " size=(600, 600),\n", " format='image/png',\n", " time= times[0],\n", " colorscalerange=colorscalerange,\n", " abovemaxcolor='transparent',\n", " belowmincolor='transparent'\n", ")\n", "\n", "saveLayerAsImage(img_temp, 'test_temp.png')\n", "Image(filename='test_temp.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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LOn8v/c+PDeL0DNo//XOxq/OkGyB73NkA/M84NEiHM7nXoKC6j0k/iiY4s00BV8dmG45h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"prompt_number": 15, "text": [ "<IPython.core.display.Image at 0x37d1c50>" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The GetLegendGraphic request gives us a legend for the map, but the request is not supported by OWSLib." ] }, { "cell_type": "code", "collapsed": false, "input": [ "params ={'request': 'GetLegendGraphic',\n", " 'colorbaronly':'False', #want the text in the legend\n", " 'layer':temp.name,\n", " 'colorscalerange':colorscalerange}\n", "\n", "legendUrl=serverurl+'?REQUEST={request:s}&COLORBARONLY={colorbaronly:s}&LAYER={layer:s}&COLORSCALERANGE={colorscalerange:s}'.format(**params)\n", "Image(url=legendUrl)\n" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://thredds.ucar.edu/thredds/wms/grib/NCEP/NAM/CONUS_12km/best?REQUEST=GetLegendGraphic&COLORBARONLY=False&LAYER=Temperature_isobaric&COLORSCALERANGE=290,310\"/>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<IPython.core.display.Image at 0x3812510>" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. WMS and basemap \n", "We can use basemap to overlay the layer with a coastline..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import urllib2\n", "from mpl_toolkits.basemap import Basemap\n", "import matplotlib.pyplot as plt\n", "from matplotlib.offsetbox import AnnotationBbox, OffsetImage\n", "from matplotlib._png import read_png\n", "\n", "m = Basemap(llcrnrlon=temp.boundingBox[0], llcrnrlat=temp.boundingBox[1],\n", " urcrnrlon=temp.boundingBox[2], urcrnrlat=temp.boundingBox[3]+5.0,\n", " resolution='l',epsg=4326)\n", "\n", "plt.figure(1, figsize=(16,12))\n", "plt.title(temp.title +' '+times[0] )\n", "\n", "m.wmsimage(serverurl,xpixels=600, ypixels=600, verbose=False,\n", " layers=[temp.name], \n", " styles=['boxfill/rainbow'], \n", " time= times[0],\n", " colorscalerange=colorscalerange,\n", " abovemaxcolor='extend',\n", " belowmincolor='transparent'\n", ")\n", "\n", "m.drawcoastlines(linewidth=0.25)\n", "\n", "#Annotating the map with the legend\n", "#Save the legend as image\n", "cwd = os.getcwd()\n", "legend = urllib2.urlopen(legendUrl)\n", "saveLayerAsImage(legend, 'legend_temp.png')\n", "\n", "#read the image as an array\n", "arr = read_png('legend_temp.png')\n", "imagebox = OffsetImage(arr, zoom=0.7)\n", "xy =[ temp.boundingBox[2], temp.boundingBox[1] ]\n", "\n", "#Gets the current axis\n", "ax = plt.gca()\n", "\n", "#Creates the annotation\n", "ab = AnnotationBbox(imagebox, xy,\n", " xybox=(-46.,100.),\n", " xycoords='data',\n", " boxcoords=\"offset points\",\n", " pad=0.)\n", "\n", "#Adds the legend image as an AnnotationBbox to the map\n", "ax.add_artist(ab)\n", "\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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rqwt6enq9EmiMJlNT02H/m6NuUygUkEqlQ25Fa2lpwc2bN/HNN9/g4MGDsLe3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FqwYioyGnwAj5tGdDa7YhsVQ7FyQe9cy48canuPXWVlzjbM/ioYcu3OPH9OHDx4GDeDxObm4u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HYdnHiybSuZJj3wvTqZTBKTXCXHIO0tPYRmxgyCFc80EtwIdVXXXBVQWBtD205LphNhCPFs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91o3whuvgjb8B9z5IL9Pnqynte+tg2lw01pm1GCQicXAsnqnTSzzpmdsXj1hKJ20DfR95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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x3c87950>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise:\n", "- Get the vertical levels for the layer temp.\n", "- Change the request for getting the highest level.\n", "- Change the color scale range to appropriate values." ] } ], "metadata": {} } ] }

mit

mvaz/osqf2015

notebooks/DataPreparation.ipynb

2

2793

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction\n", "Simply the first step to prepare the data for the following notebooks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import Quandl\n", "import pandas as pd\n", "import numpy as np\n", "import blaze as bz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data source is http://www.quandl.com.\n", "We use blaze to store data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('../.quandl_api_key.txt', 'r') as f:\n", " api_key = f.read()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "db = Quandl.get(\"EOD/DB\", authtoken=api_key)\n", "bz.odo(db['Rate'].reset_index(), '../data/db.bcolz')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fx = Quandl.get(\"CURRFX/EURUSD\", authtoken=api_key)\n", "bz.odo(fx['Rate'].reset_index(), '../data/eurusd.bcolz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Can also migrate it to a sqlite database" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bz.odo('../data/db.bcolz', 'sqlite:///osqf.db::db')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext sql" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%sql sqlite:///osqf.db\n", "select * from db" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Can perform queries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = bz.Data('../data/db.bcolz')\n", "d.Close.max()" ] } ], "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

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/migration/UJ14 legacy AutoML Vision Video Classification.ipynb

1

38802

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\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", "# 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.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "title:migration,new" }, "source": [ "# AutoML SDK: AutoML image classification model\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip" }, "source": [ "## Installation\n", "\n", "Install the latest (preview) version of AutoML SDK.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1pHEB-ZsMda_", "outputId": "babb3ee0-14e0-43fc-afc7-7e6bd608bmigration" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-automl --user\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the Google *cloud-storage* library as well.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QnnZhATdMdbB", "outputId": "5c8e0911-d97b-4bec-bb6d-02f56441ac0d" }, "outputs": [], "source": [ "! pip3 install google-cloud-storage\n" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the Kernel\n", "\n", "Once you've installed the AutoML SDK and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4pKvX2SwMdbD" }, "outputs": [], "source": [ "import os\n", "\n", "\n", "if not os.getenv(\"AUTORUN\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin" }, "source": [ "## Before you begin\n", "\n", "### GPU run-time\n", "\n", "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n", "\n", "### Set up your GCP project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the AutoML APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n", "\n", "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in AutoML Notebooks.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" #@param {type:\"string\"}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id", "outputId": "1433ef90-4d91-4070-9201-7c9071cb920f" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID:\", PROJECT_ID)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id", "outputId": "66d8ef94-2134-4f39-95a9-917619534207" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID\n" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for AutoML. We recommend when possible, to choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You cannot use a Multi-Regional Storage bucket for training with AutoML. Not all regions provide support for all AutoML services. For the latest support per region, see [Region support for AutoML services]()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dZBY6GRCMdbI" }, "outputs": [], "source": [ "REGION = 'us-central1' #@param {type: \"string\"}\n" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wiCt8N48MdbJ" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your GCP account\n", "\n", "**If you are using AutoML Notebooks**, your environment is already\n", "authenticated. Skip this step.\n", "\n", "*Note: If you are on an AutoML notebook and run the cell, the cell knows to skip executing the authentication steps.*\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_nBq2y6zMdbK" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your Google Cloud account. This provides access\n", "# to your Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# If on AutoML, then don't execute this code\n", "if not os.path.exists('/opt/deeplearning/metadata/env_version'):\n", " if 'google.colab' in sys.modules:\n", " from google.colab import auth as google_auth\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this tutorial in a notebook locally, replace the string\n", " # below with the path to your service account key and run this cell to\n", " # authenticate your Google Cloud account.\n", " else:\n", " %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json\n", "\n", " # Log in to your account on Google Cloud\n", " ! gcloud auth login\n" ] }, { "cell_type": "markdown", "metadata": { "id": "bucket:batch_prediction" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_NAME = \"[your-bucket-name]\" #@param {type:\"string\"}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n", " BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y5_NZRmyMdbP", "outputId": "e5a3655d-8cf0-4cbd-9c18-f64bffb4ca00" }, "outputs": [], "source": [ "! gsutil mb -l $REGION gs://$BUCKET_NAME\n" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aRnzBOTfMdbQ" }, "outputs": [], "source": [ "! gsutil ls -al gs://$BUCKET_NAME\n" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "import_aip" }, "source": [ "#### Import AutoML SDK\n", "\n", "Import the AutoML SDK into our Python environment.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z_yz9CUZMdbR" }, "outputs": [], "source": [ "import json\n", "import os\n", "import sys\n", "import time\n", "\n", "\n", "from google.cloud import automl_v1beta1 as automl\n", "\n", "\n", "from google.protobuf.json_format import MessageToJson\n", "from google.protobuf.json_format import ParseDict\n" ] }, { "cell_type": "markdown", "metadata": { "id": "aip_constants" }, "source": [ "#### AutoML constants\n", "\n", "Setup up the following constants for AutoML:\n", "\n", "- `PARENT`: The AutoML location root path for dataset, model and endpoint resources.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_tmHDZHGMdbS" }, "outputs": [], "source": [ "# AutoML location root path for your dataset, model and endpoint resources\n", "PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION\n" ] }, { "cell_type": "markdown", "metadata": { "id": "clients" }, "source": [ "## Clients\n", "\n", "The AutoML SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (AutoML).\n", "\n", "You will use several clients in this tutorial, so set them all up upfront.\n", "\n", "(?)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OiFdpU4mMdbT", "outputId": "d29dc331-d1f3-494d-ec73-e36176953a10" }, "outputs": [], "source": [ "def automl_client():\n", " return automl.AutoMlClient()\n", "\n", "def prediction_client():\n", " return automl.PredictionServiceClient()\n", "\n", "def operations_client():\n", " return automl.AutoMlClient()._transport.operations_client\n", "\n", "clients = {}\n", "clients[\"automl\"] = automl_client()\n", "clients[\"prediction\"] = prediction_client()\n", "clients[\"operations\"] = operations_client()\n", "\n", "for client in clients.items():\n", " print(client)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_file:flowers,csv,icn" }, "outputs": [], "source": [ "IMPORT_FILE = 'gs://automl-video-demo-data/hmdb_split1.csv'\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KHJnNxI5xXiS", "outputId": "7714d066-2301-4b47-d155-d3c5d454def7" }, "outputs": [], "source": [ "! gsutil cat $IMPORT_FILE | head -n 10 \n" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_create:migration,new,request" }, "source": [ "*Example output*:\n", "```\n", "TRAIN,gs://automl-video-demo-data/hmdb_split1_5classes_train_inf.csv\n", "TEST,gs://automl-video-demo-data/hmdb_split1_5classes_test_inf.csv\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "create_a_dataset:migration" }, "source": [ "## Create a dataset\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MuX78NX0MdbZ" }, "source": [ "### [projects.locations.datasets.create](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.datasets/create)" ] }, { "cell_type": "markdown", "metadata": { "id": "request:migration" }, "source": [ "#### Request\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nDWXlqoKcDnf", "outputId": "bf41d872-b199-4272-b47b-2353b8c3b6cc" }, "outputs": [], "source": [ "dataset = {\n", " \"display_name\": \"hmdb_\" + TIMESTAMP,\n", " \"video_classification_dataset_metadata\": {}\n", "}\n", "\n", "print(MessageToJson(\n", " automl.CreateDatasetRequest(\n", " parent=PARENT,\n", " dataset=dataset\n", " ).__dict__[\"_pb\"])\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "pSSBWJ49Mdba" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"dataset\": {\n", " \"displayName\": \"hmdb_20210228225744\",\n", " \"videoClassificationDatasetMetadata\": {}\n", " }\n", "}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "call:migration" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"automl\"].create_dataset(\n", " parent=PARENT,\n", " dataset=dataset\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "response:migration" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,response", "outputId": "99f328e4-877e-4d4c-d9a6-6f44a7f03a4c" }, "outputs": [], "source": [ "result = request\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_create:migration,new,response" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/datasets/VCN6574174086275006464\",\n", " \"displayName\": \"hmdb_20210228225744\",\n", " \"createTime\": \"2021-02-28T23:06:43.197904Z\",\n", " \"etag\": \"AB3BwFrtf0Yl4fgnXW4leoEEANTAGQdOngyIqdQSJBT9pKEChgeXom-0OyH7dKtfvA4=\",\n", " \"videoClassificationDatasetMetadata\": {}\n", "}\n", "```\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dataset_id:migration,new,response", "outputId": "4af2c8c9-84c6-434b-a5c1-0bf199be69f5" }, "outputs": [], "source": [ "# The full unique ID for the dataset\n", "dataset_id = result.name\n", "# The short numeric ID for the dataset\n", "dataset_short_id = dataset_id.split('/')[-1]\n", "\n", "print(dataset_id)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cV7PMAk0Mdbi" }, "source": [ "### [projects.locations.datasets.importData](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.datasets/importData)" ] }, { "cell_type": "markdown", "metadata": { "id": "BFtdHDpfMdbi" }, "source": [ "#### Request\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_import:migration,new,request", "outputId": "8166381f-48f3-41e6-a86f-f101da50ceaa" }, "outputs": [], "source": [ "input_config = {\n", " \"gcs_source\": {\n", " \"input_uris\": [IMPORT_FILE]\n", " }\n", "}\n", "\n", "print(MessageToJson(\n", " automl.ImportDataRequest(\n", " name=dataset_short_id,\n", " input_config=input_config\n", " ).__dict__[\"_pb\"])\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LzHxgr_6Mdbj" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"VCN6574174086275006464\",\n", " \"inputConfig\": {\n", " \"gcsSource\": {\n", " \"inputUris\": [\n", " \"gs://automl-video-demo-data/hmdb_split1.csv\"\n", " ]\n", " }\n", " }\n", "}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LWRK-vjJMdbk" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_import:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"automl\"].import_data(\n", " name=dataset_id,\n", " input_config=input_config\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "bUrXTi34Mdbl" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fETkBwGaMdbl", "outputId": "431361a6-8a4a-408b-b6f8-d631d2b6fe69" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_import:migration,new,response" }, "source": [ "*Example output*:\n", "```\n", "{}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "train_a_model:migration" }, "source": [ "## Train a model\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "text_models_create:migration,old" }, "source": [ "### [projects.locations.models.create](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/create)" ] }, { "cell_type": "markdown", "metadata": { "id": "XhbhTx6DMdbo" }, "source": [ "#### Request\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_create:migration,new,request,icn", "outputId": "aaa36418-7aa8-4b6b-8b06-5343fa1b35e0" }, "outputs": [], "source": [ "model = {\n", " \"display_name\": \"hmdb_\" + TIMESTAMP,\n", " \"dataset_id\": dataset_short_id,\n", " \"video_classification_model_metadata\": {}\n", "}\n", "\n", "print(MessageToJson(\n", " automl.CreateModelRequest(\n", " parent=PARENT,\n", " model=model\n", " ).__dict__[\"_pb\"])\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "1__HJXz0Mdbq" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"model\": {\n", " \"displayName\": \"hmdb_20210228225744\",\n", " \"datasetId\": \"VCN6574174086275006464\",\n", " \"videoClassificationModelMetadata\": {}\n", " }\n", "}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8_Z5loUyMdbq" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"automl\"].create_model(\n", " parent=PARENT,\n", " model=model\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fZLpwN_dMdbr" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,request", "outputId": "7e7f19e1-771d-4e4e-8edc-87eab3672b9e" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "trainingpipelines_create:migration,new,response,icn" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/models/VCN6188818900239515648\"\n", "}\n", "```\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "training_pipeline_id:migration,new,response", "outputId": "0de12c6d-ffac-470e-d39e-0d0b1a92f4c0" }, "outputs": [], "source": [ "# The full unique ID for the training pipeline\n", "model_id = result.name\n", "# The short numeric ID for the training pipeline\n", "model_short_id = model_id.split('/')[-1]\n", "\n", "print(model_short_id)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "evaluate_the_model:migration" }, "source": [ "## Evaluate the model\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "yfufMwAEJjkX" }, "source": [ "### [projects.locations.models.modelEvaluations.list](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models.modelEvaluations/list)" ] }, { "cell_type": "markdown", "metadata": { "id": "x6tyDgg2Mdbt" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "models_evaluations_list:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"automl\"].list_model_evaluations(\n", " parent=model_id\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Pz4WmA2SMdbu" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AxEY7WFHj2YC", "outputId": "e284ae12-dbb8-4b8f-c576-03d23cc822af" }, "outputs": [], "source": [ "import json\n", "\n", "\n", "model_evaluations = [\n", " json.loads(MessageToJson(me.__dict__[\"_pb\"])) for me in request \n", "]\n", "# The evaluation slice\n", "evaluation_slice = request.model_evaluation[0].name\n", "\n", "print(json.dumps(model_evaluations, indent=2))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "models_evaluations_list:migration,new,response,icn" }, "source": [ "*Example output*\n", "```\n", "[\n", " {\n", " \"name\": \"projects/116273516712/locations/us-central1/models/VCN6188818900239515648/modelEvaluations/1998146574672720266\",\n", " \"createTime\": \"2021-03-01T01:02:02.452298Z\",\n", " \"evaluatedExampleCount\": 150,\n", " \"classificationEvaluationMetrics\": {\n", " \"auPrc\": 1.0,\n", " \"confidenceMetricsEntry\": [\n", " {\n", " \"confidenceThreshold\": 0.016075565,\n", " \"recall\": 1.0,\n", " \"precision\": 0.2,\n", " \"f1Score\": 0.33333334\n", " },\n", " {\n", " \"confidenceThreshold\": 0.017114623,\n", " \"recall\": 1.0,\n", " \"precision\": 0.202977,\n", " \"f1Score\": 0.3374578\n", " },\n", " \n", " # REMOVED FOR BREVITY\n", " \n", " {\n", " \"confidenceThreshold\": 0.9299338,\n", " \"recall\": 0.033333335,\n", " \"precision\": 1.0,\n", " \"f1Score\": 0.06451613\n", " }\n", " ]\n", " },\n", " \"displayName\": \"golf\"\n", " }\n", "]\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "i6T0bzuNJjkY" }, "source": [ "### [projects.locations.models.modelEvaluations.get](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models.modelEvaluations/get)" ] }, { "cell_type": "markdown", "metadata": { "id": "0FoWkw05Mdb0" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "models_evaluations_get:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"automl\"].get_model_evaluation(\n", " name=evaluation_slice\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "u2H0pOs9Mdb1" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IlGAsJM2Mdb1", "outputId": "14f4d517-f948-455b-8cfe-0658e4320c94", "scrolled": true }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "models_evaluations_get:migration,new,response,icn" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/models/VCN6188818900239515648/modelEvaluations/1998146574672720266\",\n", " \"createTime\": \"2021-03-01T01:02:02.452298Z\",\n", " \"evaluatedExampleCount\": 150,\n", " \"classificationEvaluationMetrics\": {\n", " \"auPrc\": 1.0,\n", " \"confidenceMetricsEntry\": [\n", " {\n", " \"confidenceThreshold\": 0.016075565,\n", " \"recall\": 1.0,\n", " \"precision\": 0.2,\n", " \"f1Score\": 0.33333334\n", " },\n", " {\n", " \"confidenceThreshold\": 0.017114623,\n", " \"recall\": 1.0,\n", " \"precision\": 0.202977,\n", " \"f1Score\": 0.3374578\n", " },\n", " \n", " # REMOVED FOR BREVITY\n", " \n", " {\n", " \"confidenceThreshold\": 0.9299338,\n", " \"recall\": 0.006666667,\n", " \"precision\": 1.0,\n", " \"f1Score\": 0.013245033\n", " }\n", " ],\n", " \"confusionMatrix\": {\n", " \"annotationSpecId\": [\n", " \"175274248095399936\",\n", " \"2048771693081526272\",\n", " \"4354614702295220224\",\n", " \"6660457711508914176\",\n", " \"8966300720722608128\"\n", " ],\n", " \"row\": [\n", " {\n", " \"exampleCount\": [\n", " 30,\n", " 0,\n", " 0,\n", " 0,\n", " 0\n", " ]\n", " },\n", " {\n", " \"exampleCount\": [\n", " 0,\n", " 30,\n", " 0,\n", " 0,\n", " 0\n", " ]\n", " },\n", " {\n", " \"exampleCount\": [\n", " 0,\n", " 0,\n", " 30,\n", " 0,\n", " 0\n", " ]\n", " },\n", " {\n", " \"exampleCount\": [\n", " 0,\n", " 0,\n", " 0,\n", " 30,\n", " 0\n", " ]\n", " },\n", " {\n", " \"exampleCount\": [\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 30\n", " ]\n", " }\n", " ],\n", " \"displayName\": [\n", " \"ride_horse\",\n", " \"golf\",\n", " \"cartwheel\",\n", " \"pullup\",\n", " \"kick_ball\"\n", " ]\n", " }\n", " }\n", "}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_predictions:migration" }, "source": [ "## Make batch predictions\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_file:automl,image" }, "source": [ "### Make the batch input file\n", "\n", "To request a batch of predictions from AutoML Video, create a CSV file that lists the Cloud Storage paths to the videos that you want to annotate. You can also specify a start and end time to tell AutoML Video to only annotate a segment (segment-level) of the video. The start time must be zero or greater and must be before the end time. The end time must be greater than the start time and less than or equal to the duration of the video. You can also use inf to indicate the end of a video.\n", "\n", "example: \n", " `gs://my-videos-vcm/short_video_1.avi,0.0,5.566667` \n", " `gs://my-videos-vcm/car_chase.avi,0.0,3.933333` \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "get_test_items:automl,icn,csv", "outputId": "5eed7f11-5c61-4fdc-9f65-96a31e4f1313" }, "outputs": [], "source": [ "TRAIN_FILES = \"gs://automl-video-demo-data/hmdb_split1_5classes_train_inf.csv\"\n", "\n", "test_items = ! gsutil cat $TRAIN_FILES | head -n2\n", "\n", "cols = str(test_items[0]).split(',')\n", "test_item_1, test_label_1, test_start_1, test_end_1 = str(cols[0]), str(cols[1]), str(cols[2]), str(cols[3])\n", "print(test_item_1, test_label_1)\n", "\n", "cols = str(test_items[1]).split(',')\n", "test_item_2, test_label_2, test_start_2, test_end_2 = str(cols[0]), str(cols[1]), str(cols[2]), str(cols[3])\n", "print(test_item_2, test_label_2)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "aZYEuzIccDnq" }, "source": [ "*Example output*:\n", "```\n", "gs://automl-video-demo-data/hmdb51/_Rad_Schlag_die_Bank__cartwheel_f_cm_np1_le_med_0.avi cartwheel\n", "gs://automl-video-demo-data/hmdb51/Acrobacias_de_un_fenomeno_cartwheel_f_cm_np1_ba_bad_8.avi cartwheel\n", "```\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tK_A0CKQMdcF", "outputId": "9effe058-4f8c-4312-9cd3-15869bd206c8" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import json\n", "\n", "gcs_input_uri = \"gs://\" + BUCKET_NAME + '/test.csv'\n", "with tf.io.gfile.GFile(gcs_input_uri, 'w') as f:\n", " data = f\"{test_item_1}, {test_start_1}, {test_end_1}\"\n", " f.write(data + '\\n')\n", " data = f\"{test_item_2}, {test_start_2}, {test_end_2}\"\n", " f.write(data + '\\n')\n", " \n", "print(gcs_input_uri)\n", "! gsutil cat $gcs_input_uri\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JjkG44EScDnq" }, "source": [ "*Example output*:\n", "```\n", "gs://migration-ucaip-trainingaip-20210228225744/test.csv\n", "gs://automl-video-demo-data/hmdb51/_Rad_Schlag_die_Bank__cartwheel_f_cm_np1_le_med_0.avi, 0.0, inf\n", "gs://automl-video-demo-data/hmdb51/Acrobacias_de_un_fenomeno_cartwheel_f_cm_np1_ba_bad_8.avi, 0.0, inf\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "text_models_batchpredict:migration,old" }, "source": [ "### [projects.locations.models.batchPredict](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/batchPredict)" ] }, { "cell_type": "markdown", "metadata": { "id": "YvQJiFamMdcG" }, "source": [ "#### Request\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,request,icn", "outputId": "6ffce530-ea59-4b6b-c990-5cd684cc4c22" }, "outputs": [], "source": [ "input_config = {\n", " \"gcs_source\": {\n", " \"input_uris\": [gcs_input_uri]\n", " }\n", "}\n", "\n", "output_config = {\n", " \"gcs_destination\": {\n", " \"output_uri_prefix\": \"gs://\" + f\"{BUCKET_NAME}/batch_output/\"\n", " }\n", "}\n", "\n", "batch_prediction = automl.BatchPredictRequest(\n", " name=model_id,\n", " input_config=input_config,\n", " output_config=output_config\n", ")\n", " \n", "print(MessageToJson(\n", " batch_prediction.__dict__[\"_pb\"])\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "zlU4Mbd5MdcJ" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/models/VCN6188818900239515648\",\n", " \"inputConfig\": {\n", " \"gcsSource\": {\n", " \"inputUris\": [\n", " \"gs://migration-ucaip-trainingaip-20210228225744/test.csv\"\n", " ]\n", " }\n", " },\n", " \"outputConfig\": {\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210228225744/batch_output/\"\n", " }\n", " }\n", "}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qjugtUM7MdcJ" }, "source": [ "#### Call\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"prediction\"].batch_predict(\n", " request=batch_prediction\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "u1lEkLr8MdcK" }, "source": [ "#### Response\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q6HGGVlaMdcK", "outputId": "160c9c08-f46a-4e07-cb7e-b558ddd23a39" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_create:migration,new,response,icn" }, "source": [ "*Example output*:\n", "\n", "```\n", "{}\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup:migration,new" }, "source": [ "# Cleaning up\n", "\n", "To clean up all GCP resources used in this project, you can [delete the GCP\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wuJTfRWNMdcL", "outputId": "4e7f019b-c477-42db-ae3f-4a3a78e1df88" }, "outputs": [], "source": [ "delete_dataset = True\n", "delete_model = True\n", "delete_bucket = True\n", "\n", "# Delete the dataset using the AutoML fully qualified identifier for the dataset\n", "try:\n", " if delete_dataset:\n", " clients['automl'].delete_dataset(name=dataset_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the model using the AutoML fully qualified identifier for the model\n", "try:\n", " if delete_model:\n", " clients['automl'].delete_model(name=model_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and 'BUCKET_NAME' in globals():\n", " ! gsutil rm -r gs://$BUCKET_NAME\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z5i6Q68BcDns" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "name": "[UJ.14 OLD] AutoML Vision Video Classification.ipynb", "provenance": [], "toc_visible": true }, "environment": { "name": "tf2-2-3-gpu.2-3.m55", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m55" }, "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": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

QuantumTechDevStudio/RUDNEVGAUSS

rms/.ipynb_checkpoints/parser_test-Copy1-checkpoint.ipynb

1

303068

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas.io.json import json_normalize #package for flattening json in pandas df\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import json\n", "import os\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"./runs/lagaris/1d_trapz_preparation/\")\n", "origin_path = os.getcwd() \n", "runs_id = os.listdir(\"./\")\n", "runs_id = [int(item) for item in runs_id]\n", "runs_id = sorted(runs_id)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_list = []\n", "for run_id in runs_id:\n", " os.chdir(\"./\"+str(run_id))\n", " f_in = open('out.json', 'r')\n", " run_info = json.load(f_in)\n", " f_in.close()\n", " a = json_normalize(run_info)\n", " #a.set_index(pd.Index([run_id]))\n", " df_list.append(a)\n", " #a = pd.concat(a,b)\n", " os.chdir(origin_path)\n", "res1 = pd.concat(df_list,ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_correct = res1[res1['Model info.n_sig'] < 14]\n", "#res_correct" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56]\n", "[0.011780256085941876, 0.011736751563021893, 1.277780155043645e-12, 7.488822090133562e-14, 4.289031869856569e-12, 3.420371447596467e-14, 2.5082068052931187e-12, 5.099499618561339e-13, 3.829922387972831e-14, 1.9179481553955045e-13, 6.490171044496423e-14, 9.305164742611212e-14]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_n_sig5 = res_correct[res_correct['Model info.n_sig'] == 5]\n", "\n", "m_train_all = np.array(res_n_sig5['Model info.m_train'].tolist())\n", "\n", "mse_all = np.array(res_n_sig5['Out info.MSE'].tolist())\n", "\n", "mse_means = []\n", "std_errs = []\n", "h = 8\n", "\n", "for i in range(12):\n", " ds = res_n_sig5[0+h*i:8+h*i]\n", " mse_l = np.array(ds['Out info.MSE'].tolist())\n", " mse_means.append(np.mean(mse_l))\n", " std_errs.append(np.std(mse_l))\n", "\n", "my_set = set(m_train_all)\n", "m_trains = sorted( list(my_set) ) \n", "print(m_trains)\n", "\n", "figure = plt.figure(figsize=(12,8))\n", "axes = figure.add_subplot (1, 1, 1)\n", "plt.grid(True)\n", "plt.title('MSE vs number of train points. 5 sigmoids', fontsize=15)\n", "plt.xlabel('Number of train points', fontsize=15)\n", "plt.ylabel('MSE', fontsize=15)\n", "plt.scatter(m_train_all, mse_all, label = 'MSE for all points', marker = \"D\",s=40)\n", "\n", "plt.plot(m_trains, mse_means, color='black', marker='x', linestyle='dashed', linewidth=3, markersize=16, label = 'Mean MSE')\n", "plt.errorbar(m_trains, mse_means, yerr=std_errs, ecolor='r', lw=2, capsize=15, mew = 3, zorder=3, label = 'Std.error of MSE', linestyle='None')\n", "axes.set_yscale ('log')#, nonposy='clip')\n", "\n", "plt.legend(loc=1, prop={'size': 25})\n", "print(std_errs)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_trains" ] }, { "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": [] }, { "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": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2c6d80e6c50>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#%matplotlib notebook\n", "n_sig_all = np.array(res_correct['Model info.n_sig'].tolist())\n", "n_sig_all = n_sig_all.reshape(n_sig_all.size,1)\n", "\n", "m_train_all = np.array(res_correct['Model info.m_train'].tolist())\n", "m_train_all = m_train_all.reshape(m_train_all.size,1)\n", "\n", "mse_all = np.array(res_correct['Out info.MSE'].tolist())\n", "mse_all = mse_all.reshape(mse_all.size,1)\n", "\n", "\n", "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", "fig = plt.figure(figsize=(15,10))\n", "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", "ax.scatter(n_sig_all, m_train_all, np.log10(mse_all))" ] }, { "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": [] }, { "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": [] }, { "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": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x2c6d8558a20>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,7))\n", "plt.grid(True)\n", "plt.title('MSE vs m_trapz', fontsize=15)\n", "plt.xlabel('Number of integration points', fontsize=15)\n", "plt.ylabel('MSE', fontsize=15)\n", "ax = plt.gca()\n", "res_correct.plot(\n", " x='Model info.n_sig',\n", " y='Out info.MSE',\n", " logy=True,\n", " #logx=True,\n", " ax = plt.gca(),\n", " style = 'ro',\n", ")\n", "\n", "ax.legend(\n", " loc='best',\n", " fontsize=15\n", ")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2c6d85b2b70>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 720x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res2 = res_correct[res_correct['Model info.m_train'] > 35]\n", "plt.figure(figsize=(10,7))\n", "plt.title('MSE vs m_trapz', fontsize=26)\n", "res2.plot.scatter(\n", " x='Model info.n_sig',\n", " y='Out info.MSE',\n", " logy=True,\n", " ax = plt.gca(),\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#1 6 11 16 21 26 31 36 41 46 51 56 " ] }, { "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>Model info.a</th>\n", " <th>Model info.b</th>\n", " <th>Model info.m_train</th>\n", " <th>Model info.m_trapz</th>\n", " <th>Model info.n_sig</th>\n", " <th>Out info.MSE</th>\n", " <th>Out info.Std</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>9.171818e-03</td>\n", " <td>9.581753e-02</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.920493e-02</td>\n", " <td>1.386512e-01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>7.728864e-03</td>\n", " <td>8.795795e-02</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.717892e-02</td>\n", " <td>1.311339e-01</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.315710e-02</td>\n", " <td>1.147618e-01</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>5.025866e-03</td>\n", " <td>7.092881e-02</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>6.931093e-03</td>\n", " <td>8.329484e-02</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>2.586761e-03</td>\n", " <td>5.088566e-02</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.879685e-14</td>\n", " <td>1.371702e-07</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " 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<td>1</td>\n", " <td>1.589623e-14</td>\n", " <td>1.261433e-07</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.791141e-14</td>\n", " <td>1.339005e-07</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>8.637847e-15</td>\n", " <td>9.298652e-08</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>6.686226e-15</td>\n", " <td>8.181026e-08</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.373421e-14</td>\n", " <td>1.172517e-07</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.251171e-14</td>\n", " <td>1.119117e-07</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.690202e-14</td>\n", " <td>1.300728e-07</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>8.332502e-15</td>\n", " <td>9.132821e-08</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.503682e-14</td>\n", " <td>1.226861e-07</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.748505e-14</td>\n", " <td>1.322972e-07</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>9.911629e-15</td>\n", " <td>9.960698e-08</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.120490e-14</td>\n", " <td>1.059062e-07</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>1.581202e-14</td>\n", " <td>1.258088e-07</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>2.134972e-14</td>\n", " <td>1.461885e-07</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>6.126785e-15</td>\n", " <td>7.831295e-08</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>16</td>\n", " <td>150</td>\n", " <td>1</td>\n", " <td>9.613702e-15</td>\n", " <td>9.809855e-08</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", " </tr>\n", " <tr>\n", " <th>1218</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>7.984492e-12</td>\n", " <td>2.827098e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1219</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>2.268066e-12</td>\n", " <td>1.506764e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1220</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.864015e-12</td>\n", " <td>1.365972e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1221</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.768474e-13</td>\n", " <td>4.207427e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1222</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.750102e-13</td>\n", " <td>6.126872e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1223</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>8.059370e-13</td>\n", " <td>8.981892e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1224</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>8.397497e-13</td>\n", " <td>9.168371e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1225</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.298917e-12</td>\n", " <td>1.817201e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1226</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>9.814563e-12</td>\n", " <td>3.134388e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1227</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.678945e-11</td>\n", " <td>6.068466e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1228</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>2.501470e-12</td>\n", " <td>1.582395e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1229</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>4.791286e-12</td>\n", " <td>2.189996e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1230</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.824327e-12</td>\n", " <td>1.351352e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1231</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>46</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.132218e-11</td>\n", " <td>5.599422e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1232</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.660446e-12</td>\n", " <td>1.289228e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1233</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.064679e-12</td>\n", " <td>1.032349e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1234</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.299135e-12</td>\n", " <td>1.817261e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1235</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>4.037451e-02</td>\n", " <td>2.010346e-01</td>\n", " </tr>\n", " <tr>\n", " <th>1236</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.280692e-12</td>\n", " <td>1.132243e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1237</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.538897e-11</td>\n", " <td>3.924840e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1238</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.362255e-12</td>\n", " <td>1.834563e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1239</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>51</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>4.222644e-11</td>\n", " <td>6.501439e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1240</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.393860e-13</td>\n", " <td>3.735311e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1241</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>5.967793e-13</td>\n", " <td>7.729015e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1242</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.199917e-14</td>\n", " <td>1.095956e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1243</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.319197e-11</td>\n", " <td>5.764130e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1244</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.705098e-12</td>\n", " <td>1.306448e-06</td>\n", " </tr>\n", " <tr>\n", " <th>1245</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>1.755027e-13</td>\n", " <td>4.191401e-07</td>\n", " </tr>\n", " <tr>\n", " <th>1246</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>4.038075e-02</td>\n", " <td>2.010502e-01</td>\n", " </tr>\n", " <tr>\n", " <th>1247</th>\n", " <td>-5</td>\n", " <td>5</td>\n", " <td>56</td>\n", " <td>150</td>\n", " <td>13</td>\n", " <td>3.399462e-11</td>\n", " <td>5.833408e-06</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1248 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " Model info.a Model info.b Model info.m_train Model info.m_trapz \\\n", "0 -5 5 1 150 \n", "1 -5 5 1 150 \n", "2 -5 5 1 150 \n", "3 -5 5 1 150 \n", "4 -5 5 1 150 \n", "5 -5 5 1 150 \n", "6 -5 5 1 150 \n", "7 -5 5 1 150 \n", "8 -5 5 6 150 \n", "9 -5 5 6 150 \n", "10 -5 5 6 150 \n", "11 -5 5 6 150 \n", "12 -5 5 6 150 \n", "13 -5 5 6 150 \n", "14 -5 5 6 150 \n", "15 -5 5 6 150 \n", "16 -5 5 11 150 \n", "17 -5 5 11 150 \n", "18 -5 5 11 150 \n", "19 -5 5 11 150 \n", "20 -5 5 11 150 \n", "21 -5 5 11 150 \n", "22 -5 5 11 150 \n", "23 -5 5 11 150 \n", "24 -5 5 16 150 \n", "25 -5 5 16 150 \n", "26 -5 5 16 150 \n", "27 -5 5 16 150 \n", "28 -5 5 16 150 \n", "29 -5 5 16 150 \n", "... ... ... ... ... \n", "1218 -5 5 41 150 \n", "1219 -5 5 41 150 \n", "1220 -5 5 41 150 \n", "1221 -5 5 41 150 \n", "1222 -5 5 41 150 \n", "1223 -5 5 41 150 \n", "1224 -5 5 46 150 \n", "1225 -5 5 46 150 \n", "1226 -5 5 46 150 \n", "1227 -5 5 46 150 \n", "1228 -5 5 46 150 \n", "1229 -5 5 46 150 \n", "1230 -5 5 46 150 \n", "1231 -5 5 46 150 \n", "1232 -5 5 51 150 \n", "1233 -5 5 51 150 \n", "1234 -5 5 51 150 \n", "1235 -5 5 51 150 \n", "1236 -5 5 51 150 \n", "1237 -5 5 51 150 \n", "1238 -5 5 51 150 \n", "1239 -5 5 51 150 \n", "1240 -5 5 56 150 \n", "1241 -5 5 56 150 \n", "1242 -5 5 56 150 \n", "1243 -5 5 56 150 \n", "1244 -5 5 56 150 \n", "1245 -5 5 56 150 \n", "1246 -5 5 56 150 \n", "1247 -5 5 56 150 \n", "\n", " Model info.n_sig Out info.MSE Out info.Std \n", "0 1 9.171818e-03 9.581753e-02 \n", "1 1 1.920493e-02 1.386512e-01 \n", "2 1 7.728864e-03 8.795795e-02 \n", "3 1 1.717892e-02 1.311339e-01 \n", "4 1 1.315710e-02 1.147618e-01 \n", "5 1 5.025866e-03 7.092881e-02 \n", "6 1 6.931093e-03 8.329484e-02 \n", "7 1 2.586761e-03 5.088566e-02 \n", "8 1 1.879685e-14 1.371702e-07 \n", "9 1 1.978691e-14 1.407363e-07 \n", "10 1 9.352529e-15 9.675687e-08 \n", "11 1 2.907727e-14 1.706059e-07 \n", "12 1 2.471366e-14 1.572844e-07 \n", "13 1 6.527128e-15 8.083107e-08 \n", "14 1 1.589623e-14 1.261433e-07 \n", "15 1 1.791141e-14 1.339005e-07 \n", "16 1 8.637847e-15 9.298652e-08 \n", "17 1 6.686226e-15 8.181026e-08 \n", "18 1 1.373421e-14 1.172517e-07 \n", "19 1 1.251171e-14 1.119117e-07 \n", "20 1 1.690202e-14 1.300728e-07 \n", "21 1 8.332502e-15 9.132821e-08 \n", "22 1 1.503682e-14 1.226861e-07 \n", "23 1 1.748505e-14 1.322972e-07 \n", "24 1 9.911629e-15 9.960698e-08 \n", "25 1 1.120490e-14 1.059062e-07 \n", "26 1 1.581202e-14 1.258088e-07 \n", "27 1 2.134972e-14 1.461885e-07 \n", "28 1 6.126785e-15 7.831295e-08 \n", "29 1 9.613702e-15 9.809855e-08 \n", "... ... ... ... \n", "1218 13 7.984492e-12 2.827098e-06 \n", "1219 13 2.268066e-12 1.506764e-06 \n", "1220 13 1.864015e-12 1.365972e-06 \n", "1221 13 1.768474e-13 4.207427e-07 \n", "1222 13 3.750102e-13 6.126872e-07 \n", "1223 13 8.059370e-13 8.981892e-07 \n", "1224 13 8.397497e-13 9.168371e-07 \n", "1225 13 3.298917e-12 1.817201e-06 \n", "1226 13 9.814563e-12 3.134388e-06 \n", "1227 13 3.678945e-11 6.068466e-06 \n", "1228 13 2.501470e-12 1.582395e-06 \n", "1229 13 4.791286e-12 2.189996e-06 \n", "1230 13 1.824327e-12 1.351352e-06 \n", "1231 13 3.132218e-11 5.599422e-06 \n", "1232 13 1.660446e-12 1.289228e-06 \n", "1233 13 1.064679e-12 1.032349e-06 \n", "1234 13 3.299135e-12 1.817261e-06 \n", "1235 13 4.037451e-02 2.010346e-01 \n", "1236 13 1.280692e-12 1.132243e-06 \n", "1237 13 1.538897e-11 3.924840e-06 \n", "1238 13 3.362255e-12 1.834563e-06 \n", "1239 13 4.222644e-11 6.501439e-06 \n", "1240 13 1.393860e-13 3.735311e-07 \n", "1241 13 5.967793e-13 7.729015e-07 \n", "1242 13 1.199917e-14 1.095956e-07 \n", "1243 13 3.319197e-11 5.764130e-06 \n", "1244 13 1.705098e-12 1.306448e-06 \n", "1245 13 1.755027e-13 4.191401e-07 \n", "1246 13 4.038075e-02 2.010502e-01 \n", "1247 13 3.399462e-11 5.833408e-06 \n", "\n", "[1248 rows x 7 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_correct" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": 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gpl-3.0

Boussau/Notebooks

Notebooks/UniversiFood/Analysis Ademe data.ipynb

1

16485

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "with open(\"Base_Carbone_V16.0_export_util_recoded.csv\", \"r\") as fin:\n", " first_line = fin.readline().strip()\n", "col_names = first_line.split(\";\")\n", "d = pd.read_csv(\"Base_Carbone_V16.0_export_util_recoded.csv\", sep=\";\", names=col_names, low_memory=False, skiprows=[0])" ] }, { "cell_type": "code", "execution_count": 3, "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>Identifiant de l'élément</th>\n", " <th>Nom frontière espagnol</th>\n", " <th>Unité espagnol</th>\n", " <th>Incertitude</th>\n", " <th>Qualité TeR</th>\n", " <th>Qualité GR</th>\n", " <th>Qualité TiR</th>\n", " <th>Qualité C</th>\n", " <th>Qualité P</th>\n", " <th>Qualité M</th>\n", " <th>Commentaire espagnol</th>\n", " <th>Code gaz supplémentaire 3</th>\n", " <th>Valeur gaz supplémentaire 3</th>\n", " <th>Code gaz supplémentaire 4</th>\n", " <th>Valeur gaz supplémentaire 4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>14367.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12288.000000</td>\n", " <td>3352.000000</td>\n", " <td>3343.000000</td>\n", " <td>3352.000000</td>\n", " <td>3352.000000</td>\n", " <td>3343.000000</td>\n", " <td>3343.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1635.0</td>\n", " <td>0.0</td>\n", " <td>1635.0</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>22015.437878</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.844482</td>\n", " <td>2.227029</td>\n", " <td>2.554293</td>\n", " <td>2.570107</td>\n", " <td>2.056683</td>\n", " <td>1.959617</td>\n", " <td>2.037691</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>3934.601689</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>19.562892</td>\n", " <td>1.381576</td>\n", " <td>1.974394</td>\n", " <td>1.935357</td>\n", " <td>1.271456</td>\n", " <td>1.220897</td>\n", " <td>1.369197</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>9356.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>21018.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>20.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>22276.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.000000</td>\n", " <td>3.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>24532.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.000000</td>\n", " <td>3.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>27089.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>400.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Identifiant de l'élément Nom frontière espagnol Unité espagnol \\\n", "count 14367.000000 0.0 0.0 \n", "mean 22015.437878 NaN NaN \n", "std 3934.601689 NaN NaN \n", "min 9356.000000 NaN NaN \n", "25% 21018.000000 NaN NaN \n", "50% 22276.000000 NaN NaN \n", "75% 24532.000000 NaN NaN \n", "max 27089.000000 NaN NaN \n", "\n", " Incertitude Qualité TeR Qualité GR Qualité TiR Qualité C \\\n", "count 12288.000000 3352.000000 3343.000000 3352.000000 3352.000000 \n", "mean 30.844482 2.227029 2.554293 2.570107 2.056683 \n", "std 19.562892 1.381576 1.974394 1.935357 1.271456 \n", "min 5.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 20.000000 1.000000 1.000000 1.000000 1.000000 \n", "50% 30.000000 3.000000 2.000000 2.000000 2.000000 \n", "75% 40.000000 3.000000 5.000000 5.000000 3.000000 \n", "max 400.000000 5.000000 5.000000 5.000000 5.000000 \n", "\n", " Qualité P Qualité M Commentaire espagnol \\\n", "count 3343.000000 3343.000000 0.0 \n", "mean 1.959617 2.037691 NaN \n", "std 1.220897 1.369197 NaN \n", "min 0.000000 0.000000 NaN \n", "25% 2.000000 1.000000 NaN \n", "50% 2.000000 2.000000 NaN \n", "75% 3.000000 3.000000 NaN \n", "max 5.000000 5.000000 NaN \n", "\n", " Code gaz supplémentaire 3 Valeur gaz supplémentaire 3 \\\n", "count 0.0 1635.0 \n", "mean NaN 0.0 \n", "std NaN 0.0 \n", "min NaN 0.0 \n", "25% NaN 0.0 \n", "50% NaN 0.0 \n", "75% NaN 0.0 \n", "max NaN 0.0 \n", "\n", " Code gaz supplémentaire 4 Valeur gaz supplémentaire 4 \n", "count 0.0 1635.0 \n", "mean NaN 0.0 \n", "std NaN 0.0 \n", "min NaN 0.0 \n", "25% NaN 0.0 \n", "50% NaN 0.0 \n", "75% NaN 0.0 \n", "max NaN 0.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(14367, 67)\n" ] } ], "source": [ "print(d.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Type Ligne', 'Identifiant de l'élément', 'Structure',\n", " 'Type de l'élément', 'Statut de l'élément', 'Nom base français',\n", " 'Nom base anglais', 'Nom base espagnol', 'Nom attribut français',\n", " 'Nom attribut anglais', 'Nom attribut espagnol',\n", " 'Nom frontière français', 'Nom frontière anglais',\n", " 'Nom frontière espagnol', 'Code de la catégorie', 'Tags français',\n", " 'Tags anglais', 'Tags espagnol', 'Unité français', 'Unité anglais',\n", " 'Unité espagnol', 'Contributeur', 'Autres Contributeurs', 'Programme',\n", " 'Url du programme', 'Source', 'Localisation géographique',\n", " 'Sous-localisation géographique français',\n", " 'Sous-localisation géographique anglais',\n", " 'Sous-localisation géographique espagnol', 'Date de création',\n", " 'Date de modification', 'Période de validité', 'Incertitude',\n", " 'Réglementations', 'Transparence', 'Qualité', 'Qualité TeR',\n", " 'Qualité GR', 'Qualité TiR', 'Qualité C', 'Qualité P', 'Qualité M',\n", " 'Commentaire français', 'Commentaire anglais', 'Commentaire espagnol',\n", " 'Type poste', 'Nom poste français', 'Nom poste anglais',\n", " 'Nom poste espagnol', 'Total poste non décomposé', 'CO2f', 'CH4f',\n", " 'CH4b', 'N2O', 'Code gaz supplémentaire 1',\n", " 'Valeur gaz supplémentaire 1', 'Code gaz supplémentaire 2',\n", " 'Valeur gaz supplémentaire 2', 'Code gaz supplémentaire 3',\n", " 'Valeur gaz supplémentaire 3', 'Code gaz supplémentaire 4',\n", " 'Valeur gaz supplémentaire 4', 'Code gaz supplémentaire 5',\n", " 'Valeur gaz supplémentaire 5', 'Autres GES', 'CO2b'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 14367\n", "unique 4680\n", "top 0\n", "freq 327\n", "Name: Total poste non décomposé, dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"Total poste non décomposé\"].describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 7,55E-03\n", "1 0,267\n", "2 0,391\n", "3 0,267\n", "4 4,00E-03\n", "5 0,035\n", "6 1,01E-03\n", "7 6,65E-03\n", "8 3,00E-05\n", "9 1,35E-03\n", "10 2,00E-05\n", "11 1,06\n", "12 0,53\n", "13 0,624\n", "14 0,312\n", "15 1199\n", "16 3300\n", "17 3300\n", "18 3180\n", "19 1424\n", "20 807\n", "21 148\n", "22 3190\n", "23 2211\n", "24 1100\n", "25 938\n", "26 6,9\n", "Name: Total poste non décomposé, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"Total poste non décomposé\"].head( 27)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def lower_case_e (x):\n", " return (x.replace(\"E\", \"e\").replace(\",\", \".\"))\n", "d['Total poste non décomposé'] = d['Total poste non décomposé'].apply(lower_case_e)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "d[\"Total poste non décomposé\"] = d[\"Total poste non décomposé\"].astype(float)\n", "\n", "#pd.to_numeric(d[\"Total poste non décomposé\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 14367.000000\n", "mean 635.295204\n", "std 9035.972487\n", "min -2090.000000\n", "25% 0.053300\n", "50% 0.515000\n", "75% 35.000000\n", "max 710000.000000\n", "Name: Total poste non décomposé, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"Total poste non décomposé\"].describe()" ] }, { "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" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-2.0

mssalvador/notebooks

notebooks/cvr/.ipynb_checkpoints/ViewMetaData-checkpoint.ipynb

1

107654

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import sys\n", "\n", "from pyspark import SQLContext\n", "from pyspark import SparkContext\n", "from pyspark.sql.types import StringType, IntegerType\n", "from pyspark.sql.functions import udf, mean\n", "from re import findall, sub" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('hive.metastore.warehouse.dir', 'file:/home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/spark-warehouse'), ('spark.app.name', 'ImportMetaCVR'), ('spark.sql.catalogImplementation', 'hive'), ('spark.rdd.compress', 'True'), ('spark.driver.host', '10.52.1.5'), ('spark.serializer.objectStreamReset', '100'), ('spark.driver.port', '33269'), ('spark.master', 'local[*]'), ('spark.executor.id', 'driver'), ('spark.submit.deployMode', 'client'), ('spark.driver.memory', '6G'), ('spark.app.id', 'local-1483957370811')]\n" ] } ], "source": [ "#spark = SparkSession(sc).builder.master(\"local[*]\").appName(\"TestingCvr\").getOrCreate()\n", "#conf = sc.getConf()\n", "#conf.setAppName(\"ImportMetaCVR\")\n", "#print(conf.getAll())\n", "#sqlContext = SQLContext(sc)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<pyspark.sql.context.SQLContext at 0x7f351d611f60>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sqlContext" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "ename": "Py4JJavaError", "evalue": "An error occurred while calling o28.json.\n: java.lang.RuntimeException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1523)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\t... 33 more\nCaused by: java.lang.reflect.InvocationTargetException\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\t... 39 more\nCaused by: javax.jdo.JDOFatalDataStoreException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\nNestedThrowables:\njava.sql.SQLException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\n\tat org.datanucleus.api.jdo.NucleusJDOHelper.getJDOExceptionForNucleusException(NucleusJDOHelper.java:436)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:788)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\t... 44 more\nCaused by: java.sql.SQLException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat com.jolbox.bonecp.PoolUtil.generateSQLException(PoolUtil.java:192)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:422)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\t... 73 more\nCaused by: java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\t... 85 more\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mPy4JJavaError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-a7fd7d3f6ba8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mmetaDataLink\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/virksomhedersMetadata.json\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmetaDataDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqlContext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetaDataLink\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/share/spark/python/pyspark/sql/readwriter.py\u001b[0m in \u001b[0;36mjson\u001b[0;34m(self, path, schema, primitivesAsString, prefersDecimal, allowComments, allowUnquotedFieldNames, allowSingleQuotes, allowNumericLeadingZero, allowBackslashEscapingAnyCharacter, mode, columnNameOfCorruptRecord)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_spark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPythonUtils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoSeq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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[1;32m 221\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRDD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/share/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 932\u001b[0m return_value = get_return_value(\n\u001b[0;32m--> 933\u001b[0;31m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m 934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 935\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/share/spark/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/share/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m 310\u001b[0m raise Py4JJavaError(\n\u001b[1;32m 311\u001b[0m \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m raise Py4JError(\n", "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o28.json.\n: java.lang.RuntimeException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1523)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\t... 33 more\nCaused by: java.lang.reflect.InvocationTargetException\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\t... 39 more\nCaused by: javax.jdo.JDOFatalDataStoreException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\nNestedThrowables:\njava.sql.SQLException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\n\tat org.datanucleus.api.jdo.NucleusJDOHelper.getJDOExceptionForNucleusException(NucleusJDOHelper.java:436)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:788)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\t... 44 more\nCaused by: java.sql.SQLException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------\r\njava.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.createPersistenceManagerFactory(JDOPersistenceManagerFactory.java:333)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.getPersistenceManagerFactory(JDOPersistenceManagerFactory.java:202)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat javax.jdo.JDOHelper$16.run(JDOHelper.java:1965)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat javax.jdo.JDOHelper.invoke(JDOHelper.java:1960)\n\tat javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1166)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)\n\tat javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)\n\tat org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)\n\tat org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76)\n\tat org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)\n\tat org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)\n\tat org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)\n\tat org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)\n\tat org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)\n\tat org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)\n\tat org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)\n\tat org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)\n\tat org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)\n\tat org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)\n\tat org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)\n\tat org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog$lzycompute(HiveSessionState.scala:50)\n\tat org.apache.spark.sql.hive.HiveSessionState.catalog(HiveSessionState.scala:48)\n\tat org.apache.spark.sql.hive.HiveSessionState$$anon$1.<init>(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer$lzycompute(HiveSessionState.scala:63)\n\tat org.apache.spark.sql.hive.HiveSessionState.analyzer(HiveSessionState.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)\n\tat org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:382)\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:143)\n\tat org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:287)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:211)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n------\r\n\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat com.jolbox.bonecp.PoolUtil.generateSQLException(PoolUtil.java:192)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:422)\n\tat com.jolbox.bonecp.BoneCPDataSource.getConnection(BoneCPDataSource.java:120)\n\tat org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl.getConnection(ConnectionFactoryImpl.java:501)\n\tat org.datanucleus.store.rdbms.RDBMSStoreManager.<init>(RDBMSStoreManager.java:298)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:423)\n\tat org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)\n\tat org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:301)\n\tat org.datanucleus.NucleusContext.createStoreManagerForProperties(NucleusContext.java:1187)\n\tat org.datanucleus.NucleusContext.initialise(NucleusContext.java:356)\n\tat org.datanucleus.api.jdo.JDOPersistenceManagerFactory.freezeConfiguration(JDOPersistenceManagerFactory.java:775)\n\t... 73 more\nCaused by: java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)\n\tat org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.getNewEmbedConnection(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.InternalDriver.connect(Unknown Source)\n\tat org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:664)\n\tat java.sql.DriverManager.getConnection(DriverManager.java:208)\n\tat com.jolbox.bonecp.BoneCP.obtainRawInternalConnection(BoneCP.java:361)\n\tat com.jolbox.bonecp.BoneCP.<init>(BoneCP.java:416)\n\t... 85 more\nCaused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1@1f29a244, see the next exception for details.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)\n\t... 98 more\nCaused by: ERROR XSDB6: Another instance of Derby may have already booted the database /home/svanhmic/workspace/Python/Erhvervs/src/notebooks/cvr/metastore_db.\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.iapi.error.StandardException.newException(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)\n\tat java.security.AccessController.doPrivileged(Native Method)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)\n\tat org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.FileMonitor.startModule(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)\n\tat org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)\n\tat org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)\n\tat org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)\n\t... 95 more\n" ] } ], "source": [ "\n", "metaDataLink = \"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/virksomhedersMetadata.json\"\n", "metaDataDf = sqlContext.read.json(metaDataLink)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "metaDataDf.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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

vincentadam87/cosyne-visualization

data/data_extraction/.ipynb_checkpoints/poster_counts_only-checkpoint.ipynb

1

7918

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from numpy import ndarray\n", "\n", "# making authors list\n", "def make_authors_list_from_csv(filename):\n", " authors_list = []\n", " with open(filename+'.csv') as f:\n", " next(f)\n", " for line in f:\n", " split = line.split('|')\n", " authors = split[2].split(',') \n", " authors_clean = []\n", " for author in authors:\n", " authors_clean.append(author.lstrip())\n", " # authors list\n", " for author in authors_clean:\n", " author= author.lstrip()\n", " if not(author in authors_list):\n", " authors_list.append(author.lstrip()) \n", " authors_list = sorted(authors_list)\n", " authors_dict = {}\n", " i = 0\n", " for author in authors_list:\n", " authors_dict[author] = i\n", " i+=1\n", " return authors_list,authors_dict\n", "\n", "\n", "\n", "# making dictionnary for indexing author \n", "# construction poster dictionary \n", "# for each poster: the list of collaborators\n", "def make_poster_dict_from_csv(filename):\n", " poster_count = {}\n", " poster_dict = {}\n", " with open(filename+'.csv') as f:\n", " next(f)\n", " for line in f:\n", " split = line.split('|')\n", " posterid =int(split[0])\n", " authors = split[2].split(',') \n", " authors_id = []\n", " for author in authors:\n", " author = author.lstrip()\n", " authors_id.append(authors_dict[author.lstrip()])\n", " if author in poster_count:\n", " poster_count[author]+=1\n", " else:\n", " poster_count[author]=1\n", " poster_dict[posterid] = authors_id\n", " \n", "# for author in poster_count: \n", "# poster_count_list.append([author,poster_count[author]])\n", " \n", " return poster_dict,poster_count \n", "\n", "\n", "import os\n", "# poster count when multiple poster\n", "def make_json_poster_count(poster_count,filename,number_min=1):\n", " poster_count_list = [[author,poster_count[author]] for author in poster_count]\n", " poster_count_list = sorted(poster_count_list,key=lambda x:x[1], reverse=True)\n", " authors = [x[0] for x in poster_count_list][0:100]\n", " counts = [x[1] for x in poster_count_list][0:100]\n", " with open(filename+\".json\", \"w\") as outfile:\n", " outfile.write(\"{\\\"name\\\":\\\"\" + str(authors) +\"\\\",\\n\\\"count\\\":\"+str(counts)+\"}\\n\") \n", " return \n", "\n", "# poster count when multiple poster\n", "def make_csv_poster_count(poster_count,filename,number_min=1):\n", " poster_count_list = [[author,poster_count[author]] for author in poster_count]\n", " poster_count_list = sorted(poster_count_list,key=lambda x:x[1], reverse=True)\n", " authors = [x[0] for x in poster_count_list][0:100]\n", " counts = [x[1] for x in poster_count_list][0:100]\n", " with open(filename+\".csv\", \"w\") as outfile:\n", " outfile.write(\"Authors, Counts\\n\")\n", " for x in poster_count:\n", " outfile.write(x[0] +', '+ str(x[1])+\"\\n\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "filename = 'AbstractsCosyne2012'\n", "authors_list,authors_dict = make_authors_list_from_csv(filename)\n", "poster_dict,poster_count = make_poster_dict_from_csv(filename)\n", "make_json_poster_count(poster_count,'poster_count12',2);\n", "make_csv_poster_count(poster_count,'poster_count12',2);\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "filename = 'AbstractsCosyne2013'\n", "authors_list,authors_dict = make_authors_list_from_csv(filename)\n", "poster_dict,poster_count = make_poster_dict_from_csv(filename)\n", "make_json_poster_count(poster_count,'poster_count13',2);\n", "make_csv_poster_count(poster_count,'poster_count13',2);\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "import csv\n", "with open('cosyne2011.csv', 'rb') as csvfile:\n", " spamreader = csv.reader(csvfile, delimiter=\"|\", quotechar=\"\")\n", " for row in spamreader:\n", " print ', '.join(row)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "\"delimiter\" must be an 1-character string", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-25-2c6c7a86dd01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcsv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cosyne2011.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcsvfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mspamreader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcsvfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\u00ca\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquotechar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"|\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mspamreader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m', '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: \"delimiter\" must be an 1-character string" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "lt[0:5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[0, 1, 2, 3, 4]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

jabooth/menpo-archive

examples/Transforms/Piecewise Affine Transform.ipynb

1

3397

{ "metadata": { "name": "", "signature": "sha256:f8f59a265a6bd4ea04fe9dbea7eedeb48b7c823311bd239190d899da59ee73bd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from menpo.transform.piecewiseaffine import (\n", " DiscreteAffinePWATransform, CachedPWATransform, \n", " TriangleContainmentError, PiecewiseAffine)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We build a `PiecewiseAffine` by supplying two sets of points and a shared triangle list" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpo.shape import TriMesh, PointCloud\n", "a = np.array([[0, 0], [1, 0], [0, 1], [1, 1],\n", " [-0.5, -0.7], [0.8, -0.4], [0.9, -2.1]])\n", "b = np.array([[0,0], [2, 0], [-1, 3], [2, 6],\n", " [-1.0, -0.01], [1.0, -0.4], [0.8, -1.6]])\n", "tl = np.array([[0,2,1], [1,3,2]])\n", "\n", "src = TriMesh(a, tl)\n", "src_points = PointCloud(a)\n", "tgt = PointCloud(b)\n", "\n", "# broken until Affine is updated\n", "#slow_pwa = DiscreteAffinePWATransform(src, tgt)\n", "fast_pwa = CachedPWATransform(src, tgt)\n", "# pwa is just a CachedPWATransform alias\n", "# a PointCloud source results in Delaunay being used.\n", "pwa = PiecewiseAffine(src_points, tgt)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets make a random 5000 point `PointCloud` in the unit square and view it " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "points_s = PointCloud(np.random.rand(10000).reshape([-1,2]))\n", "points_f = PointCloud(np.random.rand(10000).reshape([-1,2]))\n", "points_f.view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets see the effect having warped" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#slow_pwa.apply_inplace(points_s);\n", "fast_pwa.apply_inplace(points_f);\n", "points_f.view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "test = np.array([[0.1,0.1], [0.7, 0.9], \n", " [0.2,0.3], [0.5, 0.6]])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#slow_pwa.index_alpha_beta(test)\n", "fast_pwa.index_alpha_beta(test)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#slow_pwa.apply_inplace(test)\n", "fast_pwa.apply_inplace(test)\n", "print test" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

texib/deeplearning_homework

muki.ipynb

1

268926

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traing & Testing Data Array" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "img_data = np.random.randn(250000,1)\n", "img_hat = np.random.randn(250000,1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show Training Picture && Get Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:18: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:19: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:22: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:23: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:24: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "250000\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbcf9708210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "muki = np.zeros((500,500,3))\n", "muki.fill(1)\n", "plt.imshow(muki)\n", "\n", "import random\n", "\n", "f = open('./muki.txt','rb')\n", "count = 0\n", "for line in f:\n", " y,x,c = line.split()\n", " x = (float(x) )*100. + 250\n", " y = (float(y) )*100. + 250\n", " c = float(c)\n", " \n", " img_data[count] = c\n", " \n", " if c == 0 :\n", " muki[x][y][0] = 1#random.random()\n", " muki[x][y][1] = 1#random.random()\n", " muki[x][y][2] = 1#random.random() \n", " else:\n", " muki[x][y][0] = 255#random.random()\n", " muki[x][y][1] = 0#random.random()\n", " muki[x][y][2] = 0#random.random()\n", " count += 1\n", "# print x ,y \n", "# if count > 10000 : break\n", "plt.imshow(muki)\n", "print count\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR (theano.sandbox.cuda): nvcc compiler not found on $PATH. Check your nvcc installation and try again.\n" ] } ], "source": [ "import numpy as np\n", "import theano \n", "import theano.tensor as T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Muki NN\n", "<img width=500px src='./muki_nn.png' />" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = T.matrix(name='x',dtype='float32')\n", "y = T.matrix(name='x',dtype='float32')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "w1 = theano.shared(np.random.randn(128,250000))\n", "b1 = theano.shared(np.random.randn(128))\n", "w2 = theano.shared(np.random.randn(250000,128))\n", "b2 = theano.shared(np.random.randn(250000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 需採用 dimshuffle\n", "* 請參考 ?b1.dimshuffle()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z1 = T.dot(w1,x) + b1.dimshuffle(0,'x')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a1 = 1/(1+T.exp(-z1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 輸出第一層" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[ 1.56017964e-181],\n", " [ 1.19529311e-048],\n", " [ 1.48989046e-162],\n", " [ 8.27906975e-082],\n", " [ 3.03025839e-053],\n", " [ 1.00000000e+000],\n", " [ 1.10863970e-110],\n", " [ 9.14591620e-212],\n", " [ 1.00000000e+000],\n", " [ 1.04986434e-158],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.95860728e-010],\n", " [ 1.00000000e+000],\n", " [ 1.06546212e-166],\n", " [ 4.20434214e-173],\n", " [ 6.35468722e-001],\n", " [ 0.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 9.99999909e-001],\n", " [ 1.00000000e+000],\n", " [ 1.39900748e-056],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 4.34008440e-231],\n", " [ 9.99878454e-001],\n", " [ 1.32599896e-121],\n", " [ 8.20985075e-015],\n", " [ 1.00000000e+000],\n", " [ 1.70963787e-077],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 5.55436633e-273],\n", " [ 8.26438265e-288],\n", " [ 8.78962130e-091],\n", " [ 0.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 5.96785337e-100],\n", " [ 1.00000000e+000],\n", " [ 6.61774245e-184],\n", " [ 1.00000000e+000],\n", " [ 1.58452192e-161],\n", " [ 1.00000000e+000],\n", " [ 0.00000000e+000],\n", " [ 9.10799260e-050],\n", " [ 9.80768289e-042],\n", " [ 1.00000000e+000],\n", " [ 7.95363146e-251],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 0.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 4.51735444e-113],\n", " [ 1.00000000e+000],\n", " [ 4.20453471e-209],\n", " [ 0.00000000e+000],\n", " [ 4.70925241e-032],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 7.16390494e-008],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 9.99999928e-001],\n", " [ 4.79379738e-173],\n", " [ 9.44764520e-006],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 9.45281261e-166],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 6.05078416e-262],\n", " [ 5.72168109e-027],\n", " [ 1.00000000e+000],\n", " [ 9.22230099e-194],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 3.57417567e-295],\n", " [ 0.00000000e+000],\n", " [ 0.00000000e+000],\n", " [ 0.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.03537112e-213],\n", " [ 1.00000000e+000],\n", " [ 2.00967576e-165],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.02901520e-147],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 0.00000000e+000],\n", " [ 2.25184067e-047],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 5.72460933e-145],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 9.57577400e-165],\n", " [ 2.65091877e-180],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 1.00000000e+000],\n", " [ 6.33921065e-258],\n", " [ 1.00000000e+000],\n", " [ 2.23193499e-046],\n", " [ 0.00000000e+000],\n", " [ 1.00000000e+000]])]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fa1 = theano.function(inputs=[x],outputs=[a1],allow_input_downcast=True)\n", "fa1(np.random.randn(250000,1))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z2 = T.dot(w2,a1) + b2.dimshuffle(0,'x')\n", "a2 = 1/(1+T.exp(-z2))\n", "fa2 = theano.function(inputs=[x],outputs=[a2],allow_input_downcast=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 輸出第二層" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 5.19197895e-04],\n", " [ 9.92725141e-01],\n", " [ 9.99336571e-01],\n", " ..., \n", " [ 1.38755491e-03],\n", " [ 9.99998723e-01],\n", " [ 2.98336350e-02]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fa2(np.random.randn(250000,1))[0]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_hat = T.matrix('reference',dtype='float32')\n", "cost = T.sum((a2-y_hat)**2)/250000" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dw1,db1,dw2,db2 = theano.grad(cost,[w1,b1,w2,b2])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "def Myupdates(ps,gs):\n", " from itertools import izip\n", " \n", " r = 1\n", " pu = [ (p,p-r*g) for p,g in izip(ps,gs) ]\n", " return pu" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train = theano.function(inputs=[x,y_hat],\n", " outputs=[a2,cost],\n", " updates=Myupdates([w1,b1,w2,b2],[dw1,db1,dw2,db2]),\n", " allow_input_downcast=True,)\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.45419874991\n" ] } ], "source": [ "img_predict,cost_predict = train(img_data,img_hat)\n", "print cost_predict" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.453660017596\n", "0.453632815155\n", "0.453643801177\n", "0.45365839559\n", "0.453626019807\n" ] } ], "source": [ "\n", "img_hat = img_data\n", "for i in range(10):\n", " img_predict,cost_predict = train(img_data,img_hat)\n", " if i%2 == 0:\n", " print cost_predict" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbce4e14510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def showimg(img):\n", " muki_pr = np.zeros((500,500,3))\n", " l =img.tolist()\n", " count = 0\n", " for x in range(500):\n", " for y in range(500):\n", " muki_pr[y][x] = l[count]\n", " count += 1\n", " plt.imshow(muki_pr)\n", " \n", "showimg(img_data)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 將結果輸出成圖片" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lJZubmxkUFMT169dz6NCh/PLlC1+8eMGAgAAqKSmxpqaGR44c4YkTJ7hx40bK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\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gJSWFx48fY+vWrTA3N0ffvn2hp6eHXbt2YcCAAQgPD4e9vT10dXUxffp0iMVi/PPPP5CV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\nFa34l9EaOFrRilb8y/g/Jsn8c72yYFAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbcf83ae9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "showimg(img_predict)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 結論: 學習出來的結果蠻糟的，且速度非常慢\n", "* 以一張圖作為一個 instance 的話 input array 過於長會產生下列的問題\n", "* Weight 的數量過多 : input_size x l1_size + l1_size x hd_size \n", "* 所以 Training 需要耗費大量的時間" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "64000000" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "500**2*128 + 128*500**2" ] }, { "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" }, "widgets": { "state": { "40D2D6877A564AB1820FB8E4230DA497": { "views": [] } }, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ryan-leung/PHYS4650_Python_Tutorial

notebooks/05-Python-Functions-Class.ipynb

2

11767

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Functions and Classes\n", "\n", "Sometimes you need to define your own functions to work with custom data or solve some problems. A function can be defined with a prefix ``def``. A class is like an umbrella that can contains many data types and functions, it is defined by ``class`` prefix.\n", "\n", "<a href=\"https://colab.research.google.com/github/ryan-leung/PHYS4650_Python_Tutorial/blob/master/notebooks/05-Python-Functions-Class.ipynb\"><img align=\"right\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\">\n", "</a>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def hello(a,b):\n", " return a+b" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Lazy definition of function\n", "hello(1,1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'ab'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hello('a','b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Class is a blueprint defining the charactaristics and behaviors of an object. \n", "```python\n", "class MyClass:\n", " ...\n", " ...\n", "```\n", "For a simple class, one shall define an instance\n", "```python\n", "__init__()\n", "```\n", "to handle variable when it created. Let's try the following example:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class Person:\n", " def __init__(self,age,salary):\n", " self.age = age\n", " self.salary = salary\n", " def out(self):\n", " print(self.age)\n", " print(self.salary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a basic class definition, the `age` and `salary` are needed when creating this object. The new class can be invoked like this:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n", "10000\n" ] } ], "source": [ "a = Person(30,10000)\n", "a.out()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``__init__`` initilaze the variables stored in the class. When they are called inside the class, we should add a ``self.`` in front of the variable. The ``out(Self)`` method are arbitary functions that can be used by calling Yourclass.yourfunction(). The input to the functions can be added after the self input." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Python Conditionals And Loops" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The for statement\n", "\n", "The for statement reads\n", "```\n", "for xxx in yyyy:\n", "```\n", "yyyy shall be an iteratable, i.e. `tuple` or `list` or sth that can be iterate. After this line, user should add an indentation at the start of next line, either by space or tab." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conditionals\n", "A conditional statement is a programming concept that describes whether a region of code runs based on if a condition is true or false. The keywords involved in conditional statements are if, and optionally elif and else." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "boys: 7\n", "girls: 5\n" ] } ], "source": [ "# make a list\n", "students = ['boy', 'boy', 'girl', 'boy', 'girl', 'girl', 'boy', 'boy', 'girl', 'girl', 'boy', 'boy']\n", "\n", "boys = 0; girls = 0\n", "\n", "for s in students:\n", " if s == 'boy':\n", " boys = boys +1\n", " else:\n", " girls+=1\n", " \n", "print(\"boys:\", boys)\n", "print(\"girls:\", girls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The While statement\n", "\n", "The While statement reads\n", "```\n", "while CONDITIONAL:\n", "```\n", "CONDITIONAL is a conditional statement, like ``i < 100`` or a boolean variable. After this line, user should add an indentation at the start of next line, either by space or tab. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "332833500" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def int_sum(n):\n", " s=0; i=1\n", " while i < n:\n", " s += i*i\n", " i += 1\n", " return s\n", "int_sum(1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Performance" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11 ms ± 195 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit int_sum(100000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<img src=\"images/numba-blue-horizontal-rgb.svg\" alt=\"numba\" style=\"width: 600px;\"/>\n", "<img src=\"images/numba_features.png\" alt=\"numba\" style=\"width: 600px;\"/>\n", "\n", "Numba translates Python functions to optimized machine code at runtime using the LLVM compiler library. Your functions will be translated to c-code during declarations. To install numba, \n", "```python\n", "pip install numba\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import numba " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "332833500" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@numba.njit\n", "def int_sum_nb(n):\n", " s=0; i=1\n", " while i < n:\n", " s += i*i\n", " i += 1\n", " return s\n", "int_sum_nb(1000)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "180 ns ± 4.04 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%timeit int_sum_nb(100000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import random\n", "def monte_carlo_pi(n):\n", " acc = 0\n", " for i in range(n):\n", " x = random.random()\n", " y = random.random()\n", " if (x**2 + y**2) < 1.0:\n", " acc += 1\n", " return 4.0 * acc / n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.144108" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monte_carlo_pi(1000000)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "324 ms ± 4.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit monte_carlo_pi(1000000)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "@numba.njit\n", "def monte_carlo_pi_nb(n):\n", " acc = 0\n", " for i in range(n):\n", " x = random.random()\n", " y = random.random()\n", " if (x**2 + y**2) < 1.0:\n", " acc += 1\n", " return 4.0 * acc / n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.139924" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monte_carlo_pi_nb(1000000)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9.06 ms ± 309 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit monte_carlo_pi_nb(1000000)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "@numba.njit\n", "def monte_carlo_pi_nbmt(n):\n", " acc = 0\n", " for i in numba.prange(n):\n", " x = random.random()\n", " y = random.random()\n", " if (x**2 + y**2) < 1.0:\n", " acc += 1\n", " return 4.0 * acc / n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.139348" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monte_carlo_pi_nbmt(1000000)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.82 ms ± 101 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit monte_carlo_pi_nbmt(1000000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary\n", "\n", "Python loops and recursive are not recommended because it needs a lot of system overhead to produce a function calls and check typing. But new tools are avaliable to convert these codes into high performance code. " ] }, { "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

gprakhar/janCC

Janacare_Habits_dataset_upto-7May2016.ipynb

1

35209

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hello World!\n", "This notebook describes the effort filter out users to resurrect with Digital Marketing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clean up data\n", "\n", "de-duplicate : based on email i'd\n", " \n", "Partitioning the Data:\n", "two methods - \n", "\n", "A) cluster the data and see how many clusters are there: used **MeanShift method**\n", "\n", "\n", "B) Bin the data based on *age_on_platform*\n", "\n", "email capaign to Ressurrect users\n", "April 30th will be the cuttoff for the first_login value, for Binning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Looking around the data set" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n" ] } ], "source": [ "%reset" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import the required modules\n", "import pandas as pd\n", "import numpy as np\n", "import scipy as sp" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# simple function to read in the user data file.\n", "# the argument parse_dates takes in a list of colums, which are to be parsed as date format\n", "user_data_raw_csv = pd.read_csv(\"/home/eyebell/local_bin/janacare/janCC/datasets/Habits-Data_upto-7th-May.csv\",\\\n", " parse_dates = [-3, -2, -1])" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import the pyexcel module\n", "#import pyexcel as pe\n", "#from pyexcel.ext import xls\n", "\n", "# load the file\n", "#records = pe.get_records(file_name=\"/home/eyebell/local_bin/janacare/datasets/Habits-Data_upto-7th-May.xls\")\n", "#len(records)\n", "#for record in records:\n", " #print record" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(53239, 9)" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data metrics\n", "user_data_raw_csv.shape # Rows , colums" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "user_id int64\n", "first_name object\n", "last_name object\n", "username object\n", "email object\n", "phone_number object\n", "date_joined datetime64[ns]\n", "first_login datetime64[ns]\n", "last_activity datetime64[ns]\n", "dtype: object" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data metrics\n", "user_data_raw_csv.dtypes # data type of colums" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": true }, "outputs": [], "source": [ "user_data_to_clean = user_data_raw_csv.copy()" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Some basic statistical information on the data\n", "#user_data_to_clean.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Clean up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the last section of looking around, I saw that a lot of rows do not have any values or have garbage values(see first row of the table above).\n", "This can cause errors when computing anything using the values in these rows, hence a clean up is required." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a the coulums *last_activity* and *first_login* are empty then drop the corresponding row !" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 53239 entries, 0 to 53238\n", "Data columns (total 9 columns):\n", "user_id 53239 non-null int64\n", "first_name 53108 non-null object\n", "last_name 5360 non-null object\n", "username 53239 non-null object\n", "email 53089 non-null object\n", "phone_number 53094 non-null object\n", "date_joined 53239 non-null datetime64[ns]\n", "first_login 50773 non-null datetime64[ns]\n", "last_activity 44903 non-null datetime64[ns]\n", "dtypes: datetime64[ns](3), int64(1), object(5)\n", "memory usage: 3.7+ MB\n" ] } ], "source": [ "# Lets check the health of the data set\n", "user_data_to_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As is visible from the last column (*age_on_platform*) data type, Pandas is not recognising it as date type format. \n", "This will make things difficult, so I delete this particular column and add a new one.\n", "Since the data in *age_on_platform* can be recreated by doing *age_on_platform* = *last_activity* - *first_login* " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But on eyeballing I noticed some, cells of column *first_login* have greater value than corresponding cell of *last_activity*. These cells need to be swapped, since its not possible to have *first_login* > *last_activity*\n", "Finally the columns *first_login*, *last_activity* have missing values, as evident from above table. Since this is time data, that in my opinion should not be imputed, we will drop/delete the columns." ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "last_activity_count=8336\tswapped_count=922\tfirst_login_count=170\temail_count=2\tuserid_count=0\n" ] } ], "source": [ "# Run a loop through the data frame and check each row for this anamoly, if found drop,\n", "# this is being done ONLY for selected columns\n", "\n", "import datetime\n", "\n", "swapped_count = 0\n", "first_login_count = 0\n", "last_activity_count = 0\n", "email_count = 0\n", "userid_count = 0\n", "\n", "for index, row in user_data_to_clean.iterrows(): \n", " if row.last_activity == pd.NaT or row.last_activity != row.last_activity:\n", " last_activity_count = last_activity_count + 1\n", " #print row.last_activity\n", " user_data_to_clean.drop(index, inplace=True)\n", "\n", " elif row.first_login > row.last_activity:\n", " user_data_to_clean.drop(index, inplace=True)\n", " swapped_count = swapped_count + 1\n", "\n", " elif row.first_login != row.first_login or row.first_login == pd.NaT:\n", " user_data_to_clean.drop(index, inplace=True)\n", " first_login_count = first_login_count + 1\n", "\n", " elif row.email != row.email: #or row.email == '' or row.email == ' ':\n", " user_data_to_clean.drop(index, inplace=True)\n", " email_count = email_count + 1\n", "\n", " elif row.user_id != row.user_id:\n", " user_data_to_clean.drop(index, inplace=True)\n", " userid_count = userid_count + 1\n", "\n", "print \"last_activity_count=%d\\tswapped_count=%d\\tfirst_login_count=%d\\temail_count=%d\\tuserid_count=%d\" \\\n", "% (last_activity_count, swapped_count, first_login_count, email_count, userid_count)" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(43809, 9)" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_data_to_clean.shape" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create new column 'age_on_platform' which has the corresponding value in date type format\n", "user_data_to_clean[\"age_on_platform\"] = user_data_to_clean[\"last_activity\"] - user_data_to_clean[\"first_login\"]" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 43809 entries, 0 to 53238\n", "Data columns (total 10 columns):\n", "user_id 43809 non-null int64\n", "first_name 43806 non-null object\n", "last_name 2385 non-null object\n", "username 43809 non-null object\n", "email 43809 non-null object\n", "phone_number 43801 non-null object\n", "date_joined 43809 non-null datetime64[ns]\n", "first_login 43809 non-null datetime64[ns]\n", "last_activity 43809 non-null datetime64[ns]\n", "age_on_platform 43809 non-null timedelta64[ns]\n", "dtypes: datetime64[ns](3), int64(1), object(5), timedelta64[ns](1)\n", "memory usage: 3.7+ MB\n" ] } ], "source": [ "user_data_to_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Validate if email i'd is correctly formatted and the email i'd really exists" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of email-id invalid: 49\n" ] } ], "source": [ "from validate_email import validate_email\n", "\n", "email_count_invalid = 0\n", "for index, row in user_data_to_clean.iterrows(): \n", " if not validate_email(row.email): # , verify=True) for checking if email i'd actually exits\n", " user_data_to_clean.drop(index, inplace=True)\n", " email_count_invalid = email_count_invalid + 1\n", " \n", "print \"Number of email-id invalid: %d\" % (email_count_invalid)\n" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 43760 entries, 0 to 53238\n", "Data columns (total 10 columns):\n", "user_id 43760 non-null int64\n", "first_name 43757 non-null object\n", "last_name 2381 non-null object\n", "username 43760 non-null object\n", "email 43760 non-null object\n", "phone_number 43752 non-null object\n", "date_joined 43760 non-null datetime64[ns]\n", "first_login 43760 non-null datetime64[ns]\n", "last_activity 43760 non-null datetime64[ns]\n", "age_on_platform 43760 non-null timedelta64[ns]\n", "dtypes: datetime64[ns](3), int64(1), object(5), timedelta64[ns](1)\n", "memory usage: 3.7+ MB\n" ] } ], "source": [ "# Check the result of last operation \n", "user_data_to_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove duplicates" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "user_data_to_deDuplicate = user_data_to_clean.copy()" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "40495" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_data_deDuplicateD = user_data_to_deDuplicate.loc[~user_data_to_deDuplicate.email.str.strip().duplicated()]\n", "len(user_data_deDuplicateD)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 40495 entries, 0 to 53238\n", "Data columns (total 10 columns):\n", "user_id 40495 non-null int64\n", "first_name 40492 non-null object\n", "last_name 2111 non-null object\n", "username 40495 non-null object\n", "email 40495 non-null object\n", "phone_number 40487 non-null object\n", "date_joined 40495 non-null datetime64[ns]\n", "first_login 40495 non-null datetime64[ns]\n", "last_activity 40495 non-null datetime64[ns]\n", "age_on_platform 40495 non-null timedelta64[ns]\n", "dtypes: datetime64[ns](3), int64(1), object(5), timedelta64[ns](1)\n", "memory usage: 3.4+ MB\n" ] } ], "source": [ "user_data_deDuplicateD.info()" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Now its time to convert the timedelta64 data type column named age_on_platform to seconds\n", "def convert_timedelta64_to_sec(td64):\n", " ts = (td64 / np.timedelta64(1, 's'))\n", " return ts\n", "\n", "user_data_deDuplicateD_timedelta64_converted = user_data_deDuplicateD.copy()\n", "temp_copy = user_data_deDuplicateD.copy()\n", "user_data_deDuplicateD_timedelta64_converted.drop(\"age_on_platform\", 1)\n", "user_data_deDuplicateD_timedelta64_converted['age_on_platform'] = temp_copy['age_on_platform'].apply(convert_timedelta64_to_sec)\n" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 40495 entries, 0 to 53238\n", "Data columns (total 10 columns):\n", "user_id 40495 non-null int64\n", "first_name 40492 non-null object\n", "last_name 2111 non-null object\n", "username 40495 non-null object\n", "email 40495 non-null object\n", "phone_number 40487 non-null object\n", "date_joined 40495 non-null datetime64[ns]\n", "first_login 40495 non-null datetime64[ns]\n", "last_activity 40495 non-null datetime64[ns]\n", "age_on_platform 40495 non-null float64\n", "dtypes: datetime64[ns](3), float64(1), int64(1), object(5)\n", "memory usage: 3.4+ MB\n" ] } ], "source": [ "user_data_deDuplicateD_timedelta64_converted.info()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Clustering using Mean shift\n", "\n", "from sklearn.cluster import MeanShift, estimate_bandwidth\n", "\n", "#x = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]\n", "x = pd.Series(user_data_deDuplicateD_timedelta64_converted['age_on_platform'])\n", "\n", "X = np.array(zip(x,np.zeros(len(x))), dtype=np.int)\n", "'''--\n", "bandwidth = estimate_bandwidth(X, quantile=0.2)\n", "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", "ms.fit(X)\n", "labels = ms.labels_\n", "cluster_centers = ms.cluster_centers_\n", "\n", "labels_unique = np.unique(labels)\n", "n_clusters_ = len(labels_unique)\n", "\n", "for k in range(n_clusters_):\n", " my_members = labels == k\n", " print \"cluster {0} : lenght = {1}\".format(k, len(X[my_members, 0]))\n", " #print \"cluster {0}: {1}\".format(k, X[my_members, 0])\n", " cluster_sorted = sorted(X[my_members, 0])\n", " print \"cluster {0} : Max = {2} days & Min {1} days\".format(k, cluster_sorted[0]*1.15741e-5, cluster_sorted[-1]*1.15741e-5)\n", "'''\n", "# The following bandwidth can be automatically detected using\n", "bandwidth = estimate_bandwidth(X, quantile=0.7)\n", "\n", "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", "ms.fit(X)\n", "labels = ms.labels_\n", "cluster_centers = ms.cluster_centers_\n", "\n", "labels_unique = np.unique(labels)\n", "n_clusters_ = len(labels_unique)\n", "\n", "print(\"number of estimated clusters : %d\" % n_clusters_)\n", "for k in range(n_clusters_):\n", " my_members = labels == k\n", " print \"cluster {0} : lenght = {1}\".format(k, len(X[my_members, 0]))\n", " cluster_sorted = sorted(X[my_members, 0])\n", " print \"cluster {0} : Min = {1} days & Max {2} days\".format(k, cluster_sorted[0]*1.15741e-5, cluster_sorted[-1]*1.15741e-5)\n", "\n", "# Plot result\n", "import matplotlib.pyplot as plt\n", "from itertools import cycle\n", "\n", "%matplotlib inline\n", "\n", "plt.figure(1)\n", "plt.clf()\n", "\n", "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n", "for k, col in zip(range(n_clusters_), colors):\n", " my_members = labels == k\n", " cluster_center = cluster_centers[k]\n", " plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n", " plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=14)\n", "plt.title('Estimated number of clusters: %d' % n_clusters_)\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\ny = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]\\ny_float = map(float, y)\\nx = range(len(y))\\nx_float = map(float, x)\\n\\nm = np.matrix([x_float, y_float]).transpose()\\n\\n\\nfrom scipy.cluster.vq import kmeans\\nkclust = kmeans(m, 5)\\n\\nkclust[0][:, 0]\\n\\nassigned_clusters = [abs(cluster_indices - e).argmin() for e in x]\\n'" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Clustering using Kmeans, not working\n", "'''\n", "y = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]\n", "y_float = map(float, y)\n", "x = range(len(y))\n", "x_float = map(float, x)\n", "\n", "m = np.matrix([x_float, y_float]).transpose()\n", "\n", "\n", "from scipy.cluster.vq import kmeans\n", "kclust = kmeans(m, 5)\n", "\n", "kclust[0][:, 0]\n", "\n", "assigned_clusters = [abs(cluster_indices - e).argmin() for e in x]\n", "'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Binning based on **age_on_platform** \n", "day 1; day 2; week 1; week 2; week 3; week 4; week 6; week 8; week 12; 3 months; 6 months; 1 year; " ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [], "source": [ "user_data_binned = user_data_deDuplicateD_timedelta64_converted.copy()\n", " \n", "# function to convert age_on_platform in seconds to hours\n", "convert_sec_to_hr = lambda x: x/3600\n", "user_data_binned[\"age_on_platform\"] = user_data_binned['age_on_platform'].map(convert_sec_to_hr).copy()\n", "\n", "# filter rows based on first_login value after 30th April\n", "user_data_binned_post30thApril = user_data_binned[user_data_binned.first_login < datetime.datetime(2016, 4, 30)]\n", "\n", "for index, row in user_data_binned_post30thApril.iterrows():\n", " if row[\"age_on_platform\"] < 25:\n", " user_data_binned_post30thApril.set_value(index, 'bin', 1)\n", " \n", " elif row[\"age_on_platform\"] >= 25 and row[\"age_on_platform\"] < 49:\n", " user_data_binned_post30thApril.set_value(index, 'bin', 2) \n", " \n", " elif row[\"age_on_platform\"] >= 49 and row[\"age_on_platform\"] < 169: #168 hrs = 1 week\n", " user_data_binned_post30thApril.set_value(index, 'bin', 3)\n", " \n", " elif row[\"age_on_platform\"] >=169 and row[\"age_on_platform\"] < 337: # 336 hrs = 2 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 4)\n", " \n", " elif row[\"age_on_platform\"] >=337 and row[\"age_on_platform\"] < 505: # 504 hrs = 3 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 5)\n", " \n", " elif row[\"age_on_platform\"] >=505 and row[\"age_on_platform\"] < 673: # 672 hrs = 4 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 6)\n", " \n", " elif row[\"age_on_platform\"] >=673 and row[\"age_on_platform\"] < 1009: # 1008 hrs = 6 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 7)\n", " \n", " elif row[\"age_on_platform\"] >=1009 and row[\"age_on_platform\"] < 1345: # 1344 hrs = 8 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 8)\n", " \n", " elif row[\"age_on_platform\"] >=1345 and row[\"age_on_platform\"] < 2017: # 2016 hrs = 12 weeks\n", " user_data_binned_post30thApril.set_value(index, 'bin', 9)\n", " \n", " elif row[\"age_on_platform\"] >=2017 and row[\"age_on_platform\"] < 4381: # 4380 hrs = 6 months\n", " user_data_binned_post30thApril.set_value(index, 'bin', 10)\n", " \n", " elif row[\"age_on_platform\"] >=4381 and row[\"age_on_platform\"] < 8761: # 8760 hrs = 12 months\n", " user_data_binned_post30thApril.set_value(index, 'bin', 11)\n", " \n", " elif row[\"age_on_platform\"] > 8761: # Rest, ie. beyond 1 year\n", " user_data_binned_post30thApril.set_value(index, 'bin', 12)\n", " \n", " else:\n", " user_data_binned_post30thApril.set_value(index, 'bin', 0)\n", " " ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 39303 entries, 0 to 51465\n", "Data columns (total 11 columns):\n", "user_id 39303 non-null int64\n", "first_name 39300 non-null object\n", "last_name 2064 non-null object\n", "username 39303 non-null object\n", "email 39303 non-null object\n", "phone_number 39295 non-null object\n", "date_joined 39303 non-null datetime64[ns]\n", "first_login 39303 non-null datetime64[ns]\n", "last_activity 39303 non-null datetime64[ns]\n", "age_on_platform 39303 non-null float64\n", "bin 39303 non-null float64\n", "dtypes: datetime64[ns](3), float64(2), int64(1), object(5)\n", "memory usage: 3.6+ MB\n" ] } ], "source": [ "user_data_binned_post30thApril.info()" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform equal to 1 day or less, aka 0th day = 10855\n" ] } ], "source": [ "print \"Number of users with age_on_platform equal to 1 day or less, aka 0th day = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 1])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 1].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_0day.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform between 1st and 2nd days = 1552\n" ] } ], "source": [ "print \"Number of users with age_on_platform between 1st and 2nd days = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 2])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 2].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_1st-day.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 2 complete days and less than 1 week = 4537\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 2 complete days and less than 1 week = %d\" % \\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 3])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 3].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_1st-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform between 2nd week = 3433\n" ] } ], "source": [ "print \"Number of users with age_on_platform between 2nd week = %d\" % \\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 4])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 4].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_2nd-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform between 3rd weeks = 2271\n" ] } ], "source": [ "print \"Number of users with age_on_platform between 3rd weeks = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 5])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 5].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_3rd-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform between 4th weeks = 1797\n" ] } ], "source": [ "print \"Number of users with age_on_platform between 4th weeks = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 6])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 6].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_4th-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 4 weeks and less than 6 weeks = 3337\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 4 weeks and less than 6 weeks = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 7])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 7].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_4th-to-6th-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 6 weeks and less than 8 weeks = 2293\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 6 weeks and less than 8 weeks = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 8])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 8].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_6th-to-8th-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 8 weeks and less than 12 weeks = 2706\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 8 weeks and less than 12 weeks = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 9])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 9].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_8th-to-12th-week.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 12 weeks and less than 6 months = 5463\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 12 weeks and less than 6 months = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 10])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 10].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_12thweek-to-6thmonth.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than or equal to 6 months and less than 1 year = 927\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than or equal to 6 months and less than 1 year = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 11])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 11].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_6thmonth-to-1year.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform greater than 1 year = 132\n" ] } ], "source": [ "print \"Number of users with age_on_platform greater than 1 year = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 12])\n", "user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 12].to_csv\\\n", "(\"/home/eyebell/local_bin/janacare/janCC/datasets/user_retention_email-campaign/user_data_binned_post30thApril_beyond-1year.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of users with age_on_platform is wierd = 0\n" ] } ], "source": [ "print \"Number of users with age_on_platform is wierd = %d\" %\\\n", "len(user_data_binned_post30thApril[user_data_binned_post30thApril.bin == 0])" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Save dataframe with binned values as CSV\n", "#user_data_binned_post30thApril.to_csv('user_data_binned_post30thApril.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] } ], "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

gibiansky/IHaskell

ihaskell-display/ihaskell-juicypixels/test.ipynb

1

1281

{ "cells": [ { "cell_type": "markdown", "metadata": { "hidden": false }, "source": [ "# Notebook test\n", "\n", "This IHaskell noteook should just test, whether IHaskell and JuicyPixels are properly installed and working.\n", "\n", "Just click in the box below and click on the \"Run\" command in the above menu. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hidden": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-}\n", "\n", "import IHaskell.Display.Juicypixels\n", "import Codec.Picture\n", " \n", "myImage = generateImage pixelRenderer 250 300\n", " where pixelRenderer x y = PixelRGB8 (fromIntegral x) (fromIntegral y) 128\n", " \n", "myImage " ] } ], "metadata": { "kernelspec": { "display_name": "Haskell", "language": "haskell", "name": "haskell" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "8.10.4" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

james-prior/cohpy

20171027-dojo-collatz-sequence.ipynb

1

2738

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Inspired by [Joe Knapp](mailto:jmknapp at gmail.com)'s \n", "[Collatz conjecture](https://en.wikipedia.org/wiki/Collatz_conjecture)\n", "[email](https://mail.python.org/pipermail/centraloh/2017-October/003198.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def collatz_sequence(n):\n", " while True:\n", " yield n\n", " if n % 2 == 0:\n", " n //= 2\n", " else:\n", " n = 3 * n + 1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 9\n", "1 28\n", "2 14\n", "3 7\n", "4 22\n", "5 11\n", "6 34\n", "7 17\n", "8 52\n", "9 26\n", "10 13\n", "11 40\n", "12 20\n", "13 10\n", "14 5\n", "15 16\n", "16 8\n", "17 4\n", "18 2\n", "19 1\n" ] } ], "source": [ "for i, x in enumerate(collatz_sequence(9)):\n", " print(i, x)\n", " if x == 1:\n", " break" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def stopping_power(n):\n", " for i, n in enumerate(collatz_sequence(n)):\n", " if n == 1:\n", " return i" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "known_stopping_power = {\n", " 27: 111,\n", " 9: 19,\n", " 97: 118,\n", " 871: 178,\n", " 6171: 261,\n", " 77031: 350,\n", "}\n", "\n", "for n, s in known_stopping_power.items():\n", " assert stopping_power(n) == s, f'{n}, {s}, {stopping_power(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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

cod3licious/simec

09_interpret_similarities_zappos50k.ipynb

1

1338823

mit

wcmckee/wcmckee-notebook

wcmgit.ipynb

1

19628

{ "metadata": { "name": "", "signature": "sha256:d46e0179296d90442605354f3dd79bc0b026ce59cf8210e13a48ba9d885890c9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "WCMCKEE GIT\n", "\n", "register form.\n", "script runs. asks for users name and password. \n", "saves this as a hash to login. \n", "\n", "login: enter username and password. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import getpass\n", "import hashlib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "m = hashlib.md5()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "m.update('testing123')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "m.digest()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "'\\x7f*\\xba\\xbaB0a\\xc5\\t\\xf4\\x92=\\xd0Kl\\xf1'" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "paset = raw_input('set password: ')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print hashlib.md5(paset).hexdigest()\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "pa = getpass.getpass()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# -*- coding: utf-8 -*-\n", "# <nbformat>3.0</nbformat>\n", "\n", "# <markdowncell>\n", "\n", "\n", "# <h1>pywgit</h1>\n", "\n", "# pywgit\n", "# ======\n", "# \n", "# + Give user, or lookup via user. ty \n", "# + Downloads repos from github\n", "# \n", "# This is a python script to download repos from github\n", "# It takes the login name (wcmckee in my case) and downloads the repos of github user (the system login name). It downloads it to the home dir. \n", "# The program checks if you have local folders that are also on github. It will skip them from downloading from github. \n", "\n", "# <markdowncell>\n", "\n", "# This notebook also contains a project using new zealand transport data - cameras from around the country. It is a xml file that is opened and converted to a python dict. It is also converted to a json object, for giggles. \n", "# I am always working with for loops and lists in python and rarely with dict. This project was perfect to getting me back to using dict. There is no python module for nz transport data so i had to work from xml (json doesn't seem to be an option). I had never converted xml over to python dict so it was a great chance to give it a go.\n", "# During the process I felt I lost lots of work. Maybe it's because I'm working on a enourmous notebook.\n", "# I have problems with getting the xml file and reading it. NZ Transport requires a login to access the data. I couldn't figure out how to get this to work with requests, but managed to run bash and curl then just save the output to file (linkz). \n", "\n", "# <codecell>\n", "\n", "from github import Github\n", "import os\n", "import getpass\n", "import git\n", "\n", "# <codecell>\n", "\n", "theuser = getpass.getuser()\n", "\n", "# <markdowncell>\n", "\n", "# muliti support for user names - cycle through a list, user input, and get username from login. \n", "\n", "# <codecell>\n", "\n", "usergen = raw_input('Github Username: ')\n", "\n", "# <codecell>\n", "\n", "for us in usergen:\n", " print us\n", "\n", "# <codecell>\n", "\n", "#opaz = open('gpaz', 'r')\n", "\n", "# <codecell>\n", "\n", "#opac = opaz.read()\n", "\n", "# <codecell>\n", "pausr = raw_input('Github Password: ')\n", "\n", "g = Github(usergen, pausr )\n", "\n", "# <codecell>\n", "\n", "gitlist = []\n", "\n", "# <codecell>\n", "\n", "searchpy = g.search_repositories(theuser)\n", "\n", "# <codecell>\n", "\n", "typy = g.search_users(theuser)\n", "\n", "# <codecell>\n", "\n", "blehgit = g.search_repositories('reddit')\n", "\n", "# <markdowncell>\n", "\n", "# oh man, what have i got happening here. This started with a way of downloading repos in bulk from a user and ive started to bring in more github module. Here I am searching repositories on github for reddit. \n", "# What things could i get it to search for?\n", "# - list\n", "# - search your repos on global to find similar named ones.\n", "# - \n", "\n", "# <codecell>\n", "\n", "repolis = []\n", "\n", "# <codecell>\n", "\n", "print repolis\n", "\n", "# <codecell>\n", "\n", "for bleh in blehgit:\n", " repolis.append(bleh)\n", " #print bleh.full_name\n", "\n", "# <markdowncell>\n", "\n", "# I'm having a problem with auth! Need to get myself loged in here. \n", "# Needs better security for logging in. - SSH Key? - hash the password \n", "\n", "# <markdowncell>\n", "\n", "# What to do with all the output i am geting from searching. appending it into a list. maybe turn to dict? Make a rest feed? \n", "\n", "# <codecell>\n", "\n", "for blzgit in blehgit:\n", " print blzgit.name\n", "\n", "# <codecell>\n", "\n", "print blehgit.totalCount\n", "\n", "# <codecell>\n", "\n", "print typy.totalCount\n", "\n", "# <codecell>\n", "\n", "print typy\n", "\n", "# <codecell>\n", "orgtolookup = raw_input('Github Organization: ')\n", "gepy = g.get_organization(orgtolookup)\n", "\n", "# <codecell>\n", "\n", "gepy.email\n", "\n", "# <codecell>\n", "\n", "gepy.blog\n", "\n", "# <codecell>\n", "\n", "gepy.url\n", "\n", "# <codecell>\n", "\n", "gepy.created_at\n", "\n", "# <codecell>\n", "\n", "brorepo.totalCount()\n", "\n", "# <codecell>\n", "\n", "gepy.public_repos\n", "\n", "# <codecell>\n", "\n", "gepic = gepy.avatar_url\n", "\n", "# <codecell>\n", "\n", "gepic\n", "\n", "# <codecell>\n", "\n", "with open(gepic, 'wb') as handle:\n", " response = requests.get(jpgcam, stream=True)\n", "\n", " for block in response.iter_content(1024):\n", " if not block:\n", " break\n", " handle.write(block)\n", "\n", "# <codecell>\n", "\n", "gepy.type\n", "\n", "# <codecell>\n", "\n", "gepy.raw_data\n", "\n", "# <codecell>\n", "\n", "print alrepo\n", "\n", "# <codecell>\n", "\n", "brorepo = gepy.get_repo('linux')\n", "\n", "# <codecell>\n", "\n", "brorepo.size\n", "\n", "# <codecell>\n", "\n", "gepy.get_public_members()\n", "\n", "# <codecell>\n", "\n", "\n", "# <codecell>\n", "\n", "gepy.location\n", "\n", "# <codecell>\n", "\n", "searchbleh = g.get_api_status()\n", "\n", "# <codecell>\n", "\n", "print searchbleh.status\n", "print searchbleh.last_modified\n", "\n", "# <codecell>\n", "\n", "searchpy.totalCount\n", "\n", "# <codecell>\n", "\n", "import geopy\n", "\n", "# <codecell>\n", "\n", "koapi = ('d2b321e45a2041f19551a3f3b223fce0')\n", "\n", "# <codecell>\n", "\n", "geoloc = geopy.geocoders.GoogleV3()\n", "\n", "# <codecell>\n", "\n", "address = geoloc.geocode('8 Margaret Street Levin')\n", "\n", "# <codecell>\n", "\n", "print address\n", "\n", "# <codecell>\n", "\n", "address.point\n", "\n", "# <codecell>\n", "\n", "for se in searchpy:\n", " print se.url\n", "\n", "# <codecell>\n", "\n", "for repo in g.get_user('wcmckee').get_repos():\n", " gitlist.append(repo.name)\n", "\n", "# <codecell>\n", "\n", "os.mkdir('/home/will/github')\n", "\n", "# <codecell>\n", "\n", "os.chdir('/home/' + 'wcmckee')\n", "\n", "# <codecell>\n", "\n", "lisdir = os.listdir('/home/wcmckee')\n", "curlist = []\n", "for ls in lisdir:\n", " #print ls\n", " curlist.append(ls)\n", "\n", "# <codecell>\n", "\n", "dlrepo = list(set(gitlist) - set(curlist))\n", "\n", "# <codecell>\n", "\n", "print dlrepo\n", "\n", "# <codecell>\n", "\n", "\n", "# <codecell>\n", "\n", "'''\n", "for gi in gitlist:\n", " #print gi\n", " #git.Git().clone(\"https://github.com/\" + theuser + \"/\" + dlrepo)\n", " print (\"Downloading: \" + theuser + \"/\" + dlrepo)\n", "\n", "\n", "\n", "'''\n", "\n", "# <codecell>\n", "\n", "from clint.textui import colored\n", "\n", "# <codecell>\n", "\n", "for gitbl in dlrepo:\n", " #print ('Downloading - ' + theuser + \" - \" + gitbl)\n", " print (colored.red('Downloading - ' + 'wcmckee' + \" - \" + gitbl))\n", " #git.Git().clone(\"https://github.com/\" + 'wcmckee' + \"/\" + gitbl)\n", "\n", "# <codecell>\n", "\n", "def printme( str ):\n", " \"This prints a passed string into this function\"\n", " print str;\n", " return;\n", "\n", "# Now you can call printme function\n", "printme(\"I'm first call to user defined function!\");\n", "printme(\"Again second call to the same function\");\n", "\n", "# <codecell>\n", "\n", "import requests\n", "import json\n", "\n", "# <codecell>\n", "\n", "jamft = requests.get('https://raw.githubusercontent.com/leafo/compohub/master/jams/2014.json')\n", "\n", "# <codecell>\n", "\n", "jamft.text\n", "\n", "# <codecell>\n", "\n", "compdict = json.loads(jamft.text)\n", "\n", "# <codecell>\n", "\n", "compjam = compdict[u'jams']\n", "\n", "# <codecell>\n", "\n", "lencomp = len(compjam)\n", "\n", "# <codecell>\n", "\n", "import random\n", "\n", "# <codecell>\n", "\n", "genlin = random.randint(0, lencomp)\n", "\n", "# <codecell>\n", "\n", "ggj = compjam[genlin]\n", "\n", "# <codecell>\n", "\n", "jamlis = []\n", "\n", "# <codecell>\n", "\n", "#for gj in ggj.keys():\n", " #print gj\n", " #print ggj[gj]\n", "\n", "for jec in range(lencomp):\n", " print compjam[jec][u'name']\n", " jamlis.append(compjam[jec][u'name'])\n", "\n", "# <codecell>\n", "\n", "len(jamlis)\n", "\n", "# <codecell>\n", "\n", "fujamft = list(set(jamlis))\n", "\n", "# <codecell>\n", "\n", "lencv = len(fujamft)\n", "\n", "# <codecell>\n", "\n", "chran = random.randint(0, lencv)\n", "\n", "# <codecell>\n", "\n", "lencv\n", "\n", "# <codecell>\n", "\n", "import re\n", "\n", "a = 'a b .c???d;;'\n", "chars = [' ']\n", "\n", "print re.sub('[%s]' % ''.join(chars), '-', a)\n", "\n", "# <codecell>\n", "\n", "for fuj in fujamft:\n", " a = 'a b .c???d;;'\n", " chars = [' ']\n", "\n", " print re.sub('[%s]' % ''.join(chars), '-', a)\n", " \n", "\n", "# <codecell>\n", "\n", "fujamft[chran]\n", "\n", "# <codecell>\n", "\n", "jamlis\n", "\n", "# <codecell>\n", "\n", "for mehc in ggj.keys():\n", " print mehc\n", "\n", "# <codecell>\n", "\n", "compft = compjam[0:lencomp]\n", "\n", "# <codecell>\n", "\n", "compft.sort\n", "\n", "# <codecell>\n", "\n", "dictv = compdict.values()\n", "\n", "# <codecell>\n", "\n", "dictv.count('id')\n", "\n", "# <markdowncell>\n", "\n", "# <h1>This is header one</h1>\n", "# \n", "# This is going to be a \n", "# \n", "# new line.\n", "# \n", "\n", "# <codecell>\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "g = Github('wcmckee', 'bill123now!')\n", "\n", "# <codecell>\n", "\n", "gitlist = []\n", "\n", "# <codecell>\n", "\n", "searchpy = g.search_repositories(theuser)\n", "\n", "# <codecell>\n", "\n", "typy = g.search_users(theuser)\n", "\n", "# <codecell>\n", "\n", "blehgit = g.search_repositories('reddit')\n", "\n", "# <markdowncell>\n", "\n", "# oh man, what have i got happening here. This started with a way of downloading repos in bulk from a user and ive started to bring in more github module. Here I am searching repositories on github for reddit. \n", "# What things could i get it to search for?\n", "# - list\n", "# - search your repos on global to find similar named ones.\n", "# - \n", "\n", "# <codecell>\n", "\n", "repolis = []\n", "\n", "# <codecell>\n", "\n", "print repolis\n", "\n", "# <codecell>\n", "\n", "for bleh in blehgit:\n", " repolis.append(bleh)\n", " #print bleh.full_name\n", "\n", "# <markdowncell>\n", "\n", "# I'm having a problem with auth! Need to get myself loged in here. \n", "# Needs better security for logging in. - SSH Key? - hash the password \n", "\n", "# <markdowncell>\n", "\n", "# What to do with all the output i am geting from searching. appending it into a list. maybe turn to dict? Make a rest feed? \n", "\n", "# <codecell>\n", "\n", "for blzgit in blehgit:\n", " print blzgit.name\n", "\n", "# <codecell>\n", "\n", "print blehgit.totalCount\n", "\n", "# <codecell>\n", "\n", "print typy.totalCount\n", "\n", "# <codecell>\n", "\n", "print typy" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "gepy = g.get_organization('brobeur')\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "brorepo = gepy.get_repo('linux')\n", "\n", "# <codecell>\n", "\n", "brorepo.size\n", "\n", "# <codecell>\n", "\n", "gepy.get_public_members()\n", "\n", "# <codecell>\n", "\n", "\n", "# <codecell>\n", "\n", "gepy.location\n", "\n", "# <codecell>\n", "\n", "searchbleh = g.get_api_status()\n", "\n", "# <codecell>\n", "\n", "print searchbleh.status\n", "print searchbleh.last_modified\n", "\n", "# <codecell>\n", "\n", "searchpy.totalCount\n", "address.point\n", "\n", "# <codecell>\n", "\n", "for se in searchpy:\n", " print se.url\n", "\n", "# <codecell>\n", "\n", "for repo in g.get_user('wcmckee').get_repos():\n", " gitlist.append(repo.name)\n", "\n", "# <codecell>\n", "\n", "os.mkdir('/home/will/github')\n", "\n", "# <codecell>\n", "\n", "os.chdir('/home/' + 'wcmckee')\n", "\n", "# <codecell>\n", "\n", "lisdir = os.listdir('/home/wcmckee')\n", "curlist = []\n", "for ls in lisdir:\n", " #print ls\n", " curlist.append(ls)\n", "\n", "# <codecell>\n", "\n", "dlrepo = list(set(gitlist) - set(curlist))\n", "\n", "# <codecell>\n", "\n", "print dlrepo\n", "\n", "# <codecell>\n", "\n", "\n", "# <codecell>\n", "\n", "'''\n", "for gi in gitlist:\n", " #print gi\n", " #git.Git().clone(\"https://github.com/\" + theuser + \"/\" + dlrepo)\n", " print (\"Downloading: \" + theuser + \"/\" + dlrepo)\n", "\n", "\n", "\n", "'''\n", "\n", "# <codecell>\n", "\n", "from clint.textui import colored\n", "\n", "# <codecell>\n", "\n", "for gitbl in dlrepo:\n", " #print ('Downloading - ' + theuser + \" - \" + gitbl)\n", " print (colored.red('Downloading - ' + 'wcmckee' + \" - \" + gitbl))\n", " #git.Git().clone(\"https://github.com/\" + 'wcmckee' + \"/\" + gitbl)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import geopy\n", "# <codecell>\n", "\n", "geoloc = geopy.geocoders.GoogleV3()\n", "\n", "# <codecell>\n", "\n", "address = geoloc.geocode(raw_input('What address: '))\n", "\n", "# <codecell>\n", "\n", "print address" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

saudijack/unfpyboot

Day_01/03_Homework/HW2_Survival.ipynb

1

1744

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2: Survival Driven Development\n", "===========================\n", "\n", "Survival Driven Development (SDD) is the newest software development fad. In this development framework, you specify what the software is supposed to do, then randomly generate source code to fulfill these requirements. Most of these attempts will fail, but hopefully one will succeed.\n", "\n", "Your task is to use SDD to make a function to approximate `x**2 + x`.\n", "\n", "Hint 1: Randomly generate lambda functions using a restricted vocabulary of source fragments.<br>\n", "`vocab = ['x', 'x', ' ', '+', '-', '*', '/', '1', '2', '3']`\n", "\n", "Hint 2: Only evaluate `x` at a small-ish number of values and save the difference between those answers and the true value of `x**2 + x`.\n", "\n", "Hint 3: SDD is error prone. Be sure to catch your errors!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy\n", "import random" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

sshh12/StockMarketML

backtest/ZiplineSimulator.ipynb

1

97006

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Imports\n", "\n", "from contextlib import contextmanager\n", "from datetime import datetime, timedelta\n", "import sqlite3\n", "import os\n", "\n", "from zipline.api import order, order_target, record, symbol\n", "from zipline.finance import commission, slippage\n", "import zipline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Access Database\n", "\n", "@contextmanager\n", "def db(db_filename='stock.db'):\n", " \n", " conn = sqlite3.connect(os.path.join('..', 'data', db_filename), detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)\n", "\n", " cur = conn.cursor()\n", " \n", " yield conn, cur\n", " \n", " conn.close()\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Custom Slippage Model\n", "\n", "class TradeNearTheOpenSlippageModel(slippage.SlippageModel):\n", "\n", " def __init__(self, deviation=0.001):\n", " \n", " self.deviation = deviation\n", "\n", " def process_order(self, data, order):\n", " \n", " rand = min(np.abs(np.random.normal(0, self.deviation)), 1) # Generate a random value thats likely zero (zero=openprice)\n", " \n", " open_price = data.current(symbol(stock), 'open') \n", " close_price = data.current(symbol(stock), 'close') \n", " \n", " new_price = (close_price - open_price) * rand + open_price \n", " \n", " return (new_price, order.amount) \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Perfect Algo (aka already knows the future)\n", "\n", "def predict_perfect(stock, date): # ~Perfect~ Predictor\n", " \n", " with db() as (conn, cur):\n", " \n", " cur.execute(\"SELECT date, adjclose FROM ticks WHERE stock=? AND date BETWEEN ? AND ? ORDER BY date DESC LIMIT 1\", \n", " [stock, (date + timedelta(days=-5)).strftime('%Y-%m-%d'), (date + timedelta(days=0)).strftime('%Y-%m-%d')])\n", " before = cur.fetchall()[0]\n", " \n", " cur.execute(\"SELECT date, adjclose FROM ticks WHERE stock=? AND date BETWEEN ? AND ? ORDER BY date ASC LIMIT 1\", \n", " [stock, (date + timedelta(days=1)).strftime('%Y-%m-%d'), (date + timedelta(days=5)).strftime('%Y-%m-%d')])\n", " after = cur.fetchall()[0]\n", " \n", " if after[1] > before[1]:\n", " return 1., 0.\n", " else:\n", " return 0., 1.\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "H:\\Users\\Shriv\\Anaconda3\\envs\\tf-cpu\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n", "H:\\Users\\Shriv\\Anaconda3\\envs\\tf-cpu\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n", " warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n" ] } ], "source": [ "# Load Stuff\n", "\n", "# import pickle, os\n", "# with open(os.path.join('..', 'models', 'toke-tick.pkl'), 'rb') as toke_file:\n", "# toke = pickle.load(toke_file)\n", "\n", "from keras.models import load_model\n", "from gensim.models.doc2vec import Doc2Vec\n", "model_type = 'multiheadlineclf'\n", "vec_model = Doc2Vec.load(os.path.join('..', 'models', 'doc2vec-' + model_type + '.doc2vec'))\n", "model = load_model(os.path.join('..', 'models', 'media-headlines-ticks-' + model_type + '.h5'))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Load Stuff Part 2\n", "\n", "# from algoA import predict, correct_sign_acc, model_type\n", "# from keras.models import load_model\n", "# import keras.metrics\n", "# keras.metrics.correct_sign_acc = correct_sign_acc\n", "# model = load_model(os.path.join('..', 'models', 'media-headlines-ticks-' + model_type + '.h5'))\n", "\n", "from algoB import predict\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Actual Algo\n", " \n", "def predict_deep_nn(stock, date):\n", " \n", " # preds, prices = predict(stock, model, toke, current_date=date)\n", " \n", " preds = predict(stock, model, vec_model, current_date=date)\n", " \n", " return np.mean(preds[:, 0]), np.mean(preds[:, 1])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "stock = 'INTC'\n", "\n", "def initialize(context):\n", " context.set_commission(commission.PerShare(cost=0, min_trade_cost=1.0))\n", " context.set_slippage(TradeNearTheOpenSlippageModel())\n", " \n", "def trade_logic(pred_func, context, data):\n", " \n", " date = data.current(symbol(stock), 'last_traded').to_datetime()\n", " \n", " up, down = pred_func(stock, date + timedelta(days=0))\n", " \n", " shares = context.portfolio.positions[symbol(stock)].amount\n", " \n", " if up > .6:\n", " \n", " max_shares = context.portfolio.cash // data.current(symbol(stock), 'price')\n", " if max_shares > 0:\n", " order(symbol(stock), max_shares)\n", " \n", " elif down > .6:\n", " \n", " if shares > 0:\n", " order_target(symbol(stock), 0)\n", " \n", " record(stock=data.current(symbol(stock), 'price'))\n", " record(shares=context.portfolio.positions[symbol(stock)].amount)\n", " \n", "def handle_data_perfect(context, data):\n", " \n", " trade_logic(predict_perfect, context, data)\n", " \n", "def handle_data_algo(context, data):\n", "\n", " trade_logic(predict_deep_nn, context, data)\n", "\n", "start = pd.to_datetime('2018-01-01').tz_localize('US/Eastern')\n", "end = pd.to_datetime('2018-05-8').tz_localize('US/Eastern')\n", "\n", "perf_perfect = zipline.run_algorithm(start, end, initialize, 100, handle_data=handle_data_perfect)\n", "perf_algo = zipline.run_algorithm(start, end, initialize, 100, handle_data=handle_data_algo)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x188c692b828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(20, 20), sharex=True)\n", "\n", "perf_perfect.portfolio_value.plot(ax=ax1)\n", "perf_algo.portfolio_value.plot(ax=ax1)\n", "ax1.set_ylabel('Portfolio Value')\n", "perf_perfect.stock.plot(ax=ax2)\n", "ax2.set_ylabel('Stock Price')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tf-cpu]", "language": "python", "name": "conda-env-tf-cpu-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": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

stephenl6705/fluentPy

13. Operator Overloading - Doing It Right.ipynb

1

34429

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Unary Operators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WHEN X AND +X ARE NOT EQUAL" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import decimal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ctx = decimal.getcontext()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ctx.prec = 40" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "one_third = decimal.Decimal('1') / decimal.Decimal('3')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Decimal('0.3333333333333333333333333333333333333333')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_third" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_third == +one_third" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ctx.prec = 28" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_third == +one_third" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Decimal('0.3333333333333333333333333333')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "+one_third" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ct = Counter('abracadabra')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({'a': 5, 'b': 2, 'c': 1, 'd': 1, 'r': 2})" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ct" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ct['r'] = -3" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ct['d'] = 0" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({'a': 5, 'b': 2, 'c': 1, 'd': 0, 'r': -3})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ct" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({'a': 5, 'b': 2, 'c': 1})" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "+ct" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Overloading + for Vector Addition" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from array import array\n", "import reprlib\n", "import math\n", "import numbers\n", "import functools\n", "import operator\n", "import itertools\n", "\n", "class Vector:\n", " typecode = 'd'\n", " \n", " def __init__(self, components):\n", " self._components = array(self.typecode, components)\n", " \n", " def __iter__(self):\n", " return iter(self._components)\n", " \n", " def __repr__(self):\n", " components = reprlib.repr(self._components)\n", " components = components[components.find('['):-1]\n", " return 'Vector({})'.format(components)\n", " \n", " def __str__(self):\n", " return str(tuple(self))\n", " \n", " def __bytes__(self):\n", " return (bytes([ord(self.typecode)]) +\n", " bytes(self._components))\n", " \n", " def __eq__(self, other):\n", " if isinstance(other, Vector):\n", " return (len(self) == len(other) and\n", " all(a == b for a, b in zip(self, other)))\n", " else:\n", " return NotImplemented\n", " \n", " def __hash__(self):\n", " hashes = (hash(x) for x in self._components)\n", " return functools.reduce(operator.xor, hashes, 0)\n", " \n", " def __abs__(self):\n", " return math.sqrt(sum(x * x) for x in self)\n", " \n", " def __neg__(self):\n", " return Vector(-x for x in self)\n", " \n", " def __pos__(self):\n", " return Vector(self)\n", " \n", " def __add__(self, other):\n", " try:\n", " pairs = itertools.zip_longest(self, other, fillvalue=0.0)\n", " return Vector(a + b for a, b in pairs)\n", " except TypeError:\n", " return NotImplemented\n", "\n", " def __radd__(self, other):\n", " return self + other\n", "\n", " def __mul__(self, scalar):\n", " if isinstance(scalar, numbers.Real):\n", " return Vector(n * scalar for n in self)\n", " else:\n", " return NotImplemented\n", " \n", " def __rmul__(self, scalar):\n", " return self * scalar\n", " \n", " def __matmul__(self, other):\n", " try:\n", " return sum(a * b for a, b in zip(self, other))\n", " except TypeError:\n", " return NotImplemented\n", " \n", " def __rmatmul__(self, other):\n", " return self @ other\n", " \n", " def __bool__(self):\n", " return bool(abs(self))\n", " \n", " def __len__(self):\n", " return len(self._components)\n", " \n", " def __getitem__(self, index):\n", " cls = type(self)\n", " if isinstance(index, slice):\n", " return cls(self._components[index])\n", " elif isinstance(index, numbers.Integral):\n", " return self._components[index]\n", " else:\n", " msg = '{.__name__} indices must be integers'\n", " raise TypeError(msg.format(cls))\n", " \n", " shortcut_names = 'xyzt'\n", " \n", " def __getattr__(self, name):\n", " cls = type(self)\n", " if len(name) == 1:\n", " pos = cls.shortcut_names.find(name)\n", " if 0 <= pos < len(self._components):\n", " return self._components[pos]\n", " msg = '{.__name__!r} object has no atttribute {!r}'\n", " raise AttributeError(msg.format(cls, name))\n", " \n", " def __setattr__(self, name, value):\n", " cls = type(self)\n", " if len(name) == 1:\n", " if name in cls.shortcut_names:\n", " error = 'readonly attribute {attr_name!r}'\n", " elif name.islower():\n", " error = \"can't set attributes 'a' to 'z' in {cls_name!r}\"\n", " else:\n", " error = ''\n", " if error:\n", " msg = error.format(cls_name=cls.__name__, attr_name=name)\n", " raise AttributeError(msg)\n", " super().__setattr__(name, value)\n", " \n", " def angle(self, n):\n", " r = math.sqrt(sum(x * x for x in self[n:]))\n", " a = math.atan2(r, self[n-1])\n", " if (n == len(self) - 1) and (self[-1] < 0):\n", " return math.pi * 2 - a\n", " else:\n", " return a\n", " \n", " def angles(self):\n", " return (self.angle(n) for n in range(1, len(self)))\n", " \n", " def __format__(self, fmt_spec=''):\n", " if fmt_spec.endswith('h'): # hyperspherical coordinates\n", " fmt_spec = fmt_spec[:-1]\n", " coords = itertools.chain([abs(self)], self.angles())\n", " outer_fmt = '<{}>'\n", " else:\n", " coords = self\n", " outer_fmt = '({})'\n", " components = (format(c, fmt_spec) for c in coords)\n", " return outer_fmt.format(', '.join(components))\n", " \n", " @classmethod\n", " def frombytes(cls, octets):\n", " typecode = chr(octets[0])\n", " memv = memoryview(octets[1:]).cast(typecode)\n", " return cls(memv)\n", " " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([9.0, 11.0, 13.0])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 = Vector([3, 4, 5])\n", "v2 = Vector([6, 7, 8])\n", "v1 + v2" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 + v2 == Vector([3+6, 4+7, 5+8])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([13.0, 24.0, 35.0])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 + (10, 20, 30)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from vector2d_v3 import Vector2d" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v2d = Vector2d(1, 2)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([4.0, 6.0, 5.0])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 + v2d" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([13.0, 24.0, 35.0])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(10, 20, 30) + v1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([4.0, 6.0, 5.0])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v2d + v1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'Vector' and 'int'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-21-76c8c91eaf2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Vector' and 'int'" ] } ], "source": [ "v1 + 1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'Vector' and 'str'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-30e5276cebc2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'ABC'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Vector' and 'str'" ] } ], "source": [ "v1 + 'ABC'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Overloading * for Scalar Multiplication" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 = Vector([1, 2, 3])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([10.0, 20.0, 30.0])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 * 10" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 = Vector([1.0, 2.0, 3.0])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([14.0, 28.0, 42.0])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "14 * v1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([1.0, 2.0, 3.0])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 * True" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from fractions import Fraction" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([0.3333333333333333, 0.6666666666666666, 1.0])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1 * Fraction(1, 3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## THE NEW @ INFIX OPERATOR IN PYTHON 3.5" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "va = Vector([1, 2, 3])\n", "vz = Vector([5, 6, 7])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "va @ vz == 38.0" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "380.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[10, 20, 30] @ vz" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for @: 'Vector' and 'int'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-9dd52ac6de9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mva\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for @: 'Vector' and 'int'" ] } ], "source": [ "va @ 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Rich Comparison Operators" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "va = Vector([1.0, 2.0, 3.0])\n", "vb = Vector(range(1, 4))\n", "va == vb" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vc = Vector([1, 2])\n", "from vector2d_v3 import Vector2d\n", "v2d = Vector2d(1, 2)\n", "vc == v2d" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t3 = (1, 2, 3)\n", "va == t3" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "va != vb" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vc != v2d" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "va != (1, 2, 3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Augmented Assignment Operators" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 = Vector([1, 2, 3])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1_alias = v1" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4371508360" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id(v1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 += Vector([4, 5, 6])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([5.0, 7.0, 9.0])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4371477840" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id(v1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([1.0, 2.0, 3.0])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1_alias" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 *= 11" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Vector([55.0, 77.0, 99.0])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4371478680" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id(v1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import abc\n", "\n", "class Tombola(abc.ABC):\n", " \n", " @abc.abstractmethod\n", " def load(self, iterable):\n", " \"\"\"Add items from an iterable\"\"\"\n", " \n", " @abc.abstractmethod\n", " def pick(self):\n", " \"\"\"Remove item at random, returning it\n", " \n", " This method should raise 'LookupError' when the instance is empty.\n", " \"\"\"\n", " \n", " def loaded(self):\n", " \"\"\"Return 'True' if there's at least 1 item, 'False' otherwise.\"\"\"\n", " return bool(self.inspect())\n", " \n", " def inspect(self):\n", " \"\"\"Return a sorted tuple with the items currently inside.\"\"\"\n", " items = []\n", " while True:\n", " try:\n", " items.append(self.pick())\n", " except LookupError:\n", " break\n", " self.load(items)\n", " return tuple(sorted(items))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random\n", "\n", "class BingoCage(Tombola):\n", " \n", " def __init__(self, items):\n", " self._randomizer = random.SystemRandom()\n", " self._items = []\n", " self.load(items)\n", " \n", " def load(self, items):\n", " self._items.extend(items)\n", " self._randomizer.shuffle(self._items)\n", " \n", " def pick(self):\n", " try:\n", " return self._items.pop()\n", " except IndexError:\n", " raise LookupError('pick from empty BingoCage')\n", " \n", " def __call__(self):\n", " self.pick()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "\n", "class AddableBingoCage(BingoCage):\n", " def __add__(self, other):\n", " if isinstance(other, Tombola):\n", " return AddableBingoCage(self.inspect() + other.inspect())\n", " else:\n", " return NotImplemented\n", " \n", " def __iadd__(self, other):\n", " if isinstance(other, Tombola):\n", " other_iterable = other.inspect()\n", " else:\n", " try:\n", " other_iterable = iter(other)\n", " except TypeError:\n", " self_cls = type(self).__name__\n", " msg = \"right operand in += must be {!r} or an iterable\"\n", " raise TypeError(msg.format(self_cls))\n", " self.load(other_iterable)\n", " return self" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('A', 'E', 'I', 'O', 'U')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vowels = 'AEIOU'\n", "globe = AddableBingoCage(vowels)\n", "globe.inspect()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globe.pick() in vowels" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(globe.inspect())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "globe2 = AddableBingoCage('XYZ')\n", "globe3 = globe + globe2" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(globe3.inspect())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'AddableBingoCage' and 'list'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-af411a756e83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvoid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglobe\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'AddableBingoCage' and 'list'" ] } ], "source": [ "void = globe + [10, 20]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globe_orig = globe\n", "len(globe.inspect())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globe += globe2\n", "len(globe.inspect())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globe += ['M','N']\n", "len(globe.inspect())" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globe is globe_orig" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "right operand in += must be 'AddableBingoCage' or an iterable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-16-c6ec4d97e758>\u001b[0m in \u001b[0;36m__iadd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mother_iterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-27-f0e1c411856a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mglobe\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-16-c6ec4d97e758>\u001b[0m in \u001b[0;36m__iadd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mself_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"right operand in += must be {!r} or an iterable\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself_cls\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 20\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother_iterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: right operand in += must be 'AddableBingoCage' or an iterable" ] } ], "source": [ "globe += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [fluentPy]", "language": "python", "name": "Python [fluentPy]" }, "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

hetland/python4geosciences

materials/8_beyond_notebook.ipynb

1

46477

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coding outside of Jupyter notebooks\n", "\n", "To be able to run Python on your own computer, I recommend installing [Anaconda](https://www.continuum.io/downloads) which contains basic packages for you to be up and running.\n", "\n", "While you are downloading things, also try the text editor [Atom](https://atom.io/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have used Jupyter notebooks in this class as a useful tool for integrating text and interactive, usable code. However, many people when real-life coding would not use Jupyter notebooks but would instead type code in text files and run them via a terminal window or in iPython. Many of you have done this before, perhaps analogously in Matlab by writing your code in a .m file and then running it in the Matlab GUI. Writing code in a separate file allows more heavy computations as well as allowing that code to be reused more easily than when it is written in a Jupyter notebook.\n", "\n", "Later, we will demonstrate typing Python code in a .py file and then running it in an iPython window. First, a few of the other options..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Google's Colaboratory\n", "\n", "Google recently announced a partnership with Jupyter and have put out the Jupyter Notebook in their [own environment](https://colab.research.google.com/notebook). You can use it just like our in-class notebooks, share them through Google Drive, and even install packages. This may be the way of the future of teaching Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter itself\n", "\n", "The Jupyter project is becoming more and more sophisticated. The next project coming out is called [JupyterLab](https://towardsdatascience.com/jupyterlab-you-should-try-this-data-science-ui-for-jupyter-right-now-a799f8914bb3) and aims to more cleanly integrate modules that are already available in the server setup we have been using: notebooks, terminals, text editor, file hierarchy, etc. You can see a pre-alpha release image of it below:\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MATLAB-like GUIs\n", "\n", "Two options for using Python in a clickable, graphical user interface are [Spyder](https://pythonhosted.org/spyder/) and [Canopy](https://www.enthought.com/products/canopy/). Spyder is open source and Canopy is not, though the company that puts Canopy together (Enthought) does make a version available for free.\n", "\n", "Both are shown below. They are generally similar from the perspective of what we've been using so far. They have a console for getting information when you run your code (equivalent to running a cell in your notebook), typing in your file, getting more information about code, having nice code syntax coloring, maybe being able to examine Python objects. Note that many of these features are being integrated in less formal GUI tools like this — you'll see even in the terminal window in iPython you have access to many nice features.\n", "\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using iPython in a terminal window\n", "\n", "Here we have code in our notebook:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# just for jupyter notebooks\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.linspace(0, 10)\n", "y = x**2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fed1c38f240>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fed1c411588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(x, y, 'k', lw=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's switch to a terminal window and a text file..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Open iPython\n", "\n", "Get Anaconda downloaded and opened up on your machine if you want. Open a terminal window, or use the one that Anaconda opens, and type:\n", "\n", "> ipython\n", "\n", "Or, you can use redfish. On `redfish`: \n", "\n", "Go to the home menu on redfish, on the right-hand-side under \"New\", choose \"Terminal\" to open a terminal window that is running on `redfish`. To run Python 3 in this terminal window, you'll need to use the command `ipython3` instead of `ipython`, due to the way the alias to the program is set up:\n", "\n", "> ipython3\n", "\n", "\n", "Note that we will use this syntax to mean that it is something to be typed in your terminal window/command prompt (or it is part of an exercise). To open ipython with some niceties added so that you don't have to import them by hand each time you open the program (numpy and matplotlib in particular), open it with\n", "\n", "> ipython --pylab\n", "\n", "Once you have done this, you'll see a series of text lines indicating that you are now in the program ipython. You can now type code as if you were in a code window in a Jupyter notebook (but without an ability to integrate text easily)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> Copy in the code to define `x` and `y` from above, then make the figure. If you haven't opened `ipython` with the option flag `--pylab`, you will need to still do the import statements, but not `%matplotlib inline` since that is only for notebooks. \n", "\n", "> Notice how the figure appears as a separate window. Play with the figure window — you can change the size and properties of the plot using the GUI buttons, and you can zoom.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Text editor\n", "\n", "A typical coding set up is to have a terminal window with `ipython` running alongside a text window where you type your code. You can then go back and forth, trying things out in iPython, and keeping what works in the text window so that you finish with a working script, which can be run independently in the future. This is, of course, what you've been doing when you use Jupyter notebooks, except everything is combined into one place in that sort of setup. If you are familiar with Matlab, this is what you are used to when you have your Matlab window with your `*.m` text window alongside a \"terminal window\" where you can type things. (There is also a variable viewer included in Matlab.) This is also what you can do in a single program with Jupyterlab.\n", "\n", "A good text editor should be able to highlight syntax – that is, use color and font style to differentiate between special key words and types for a given programming language. This is what has been happening in our Jupyter notebooks, when strings are colored red, for example. The editors will also key off typical behaviors in the language to try to be helpful, such as when in a Jupyter notebook if you write an `if` statement with a colon at the end and push `enter`, the next line will be automatically indented so that you can just start typing. These behaviors can be adjusted by changing user settings.\n", "\n", "Some options are [TextMate](https://macromates.com/) for Mac, which costs money, and [Sublime Text](https://www.sublimetext.com/) which works on all operating systems and also costs money (after a free trial). For this class, we recommend using [Atom](https://atom.io/), which is free, works across operating systems, and integrates with GitHub since they wrote it.\n", "\n", "So, go download Atom and start using it, unless you have a preferred alternative you want to use." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*: run a script\n", "\n", "> If you are running python locally on your machine with Anaconda, copy and paste the code from above into a new text file in your text editor. Save it, then run the file in ipython with\n", "\n", "> run [filename]\n", "\n", "> If you are sticking with `redfish`, you can type out text in a text file from the home window (under New), and you can get most but not all functionality this way. Or you can try one of the GUIs. \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Package managing\n", "\n", "The advantage of Anaconda is being able to really easily add packages, with\n", "\n", "> conda install [packagename]\n", "\n", "This will look for the Python package you want in the places known to conda. You may also tell it to look in another channel, which other people and groups can maintain. For example, `cartopy` is available through the `scitools channel`:\n", "\n", "> conda install -c scitools cartopy\n", "\n", "Sometimes, it is better or necessary to use `pip` to install packages, which links to the PyPI collections of packages that anyone can place there for other people to use. For example, you can get the `cmocean` colormaps package from [PyPI](https://pypi.python.org/pypi/cmocean) with\n", "\n", "> pip install cmocean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running Jupyter notebooks on your own server\n", "\n", "We've been running our notebooks on a TAMU server all semester. You can do this on your own machine pretty easily once you have Anaconda. There should be a place for you to double-click for it to open, or you can open a terminal window and type:\n", "\n", "> jupyter notebook\n", "\n", "This opens a window that should look familiar in your browser window. The difference is that instead of connecting to a remote server (on redfish), you are connecting to a local server on your own machine that you started running with the `jupyter notebook` command." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run a script\n", "\n", "When you use the command `run` in iPython, parts of the code in that file are implemented as if they were written in the iPython window itself. Code that is outside of a function call will run: at 0 indentation level (import statements and maybe some variables definitions are common), but not any functions, though it will read the functions into your local variables so that they can be used. Code inside the line `if __name__ == '__main__':` will also be run. This syntax is available so that default run commands can be built into your script. This is often used to provide example or test code that can be easily run.\n", "\n", "Note anytime you are accessing a saved file from iPython, you need to have at least one of the following be true:\n", "\n", "* is in the same directory in your terminal window as the file;\n", "* are referencing the file with either its full path or a relative path;\n", "* have the path to your file be appended to the environmental variable PYTHONPATH." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> There is example code available to try at https://github.com/kthyng/python4geosciences/blob/master/data/optimal_interpolation.py. Copy this code into your text editor and save it to the same location on your computer as your iPython window is open.\n", "\n", "> Within your iPython window, run optimal_interpolation.py with `run optimal_interpolation` (if you saved it in the same directory as your iPython window). Which part of the code actually runs? Why?\n", "\n", "> Add some `print` statements into the script at 0 indentation as well as below the line `if __name__ == '__main__':` and see what comes through when you run the code. Can you access the class `oi_1d`?\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Importing your own code\n", "\n", "The point of importing code is to be able to then use the functions and classes that you've written in other files. Importing your own code is just like importing `numpy` or any other package. You use the same syntax and you have the same ability to query the built-in methods.\n", "\n", "> import numpy\n", "\n", "or:\n", "\n", "> import [your_code]\n", "\n", "When you import a package, any code at the 0 indentation level will run; however, the code within `if __name__ == '__main__':` will not run.\n", "\n", "When you are using a single script for analysis, you may just use `run` to use your code. However, as you build up complexity in your work, you'll probably want to make separate, independent code bases that can be called from subsequent code by importing them (again, just like us using the capabilities in `numpy`).\n", "\n", "When you import a package, a `*.pyc` file is created which holds compiled code that is subsequently read when the package is again imported, in order to save time. When you are in a single session in iPython, that `*.pyc` will be used and not updated. If you have changed the code and want it to be updated, you either need to exit iPython and reopen it, or you need to `reload` the package. These is different syntax for this depending on the version of Python you are using, but we are using Python 3 (>3.4) so we will do the following:\n", "\n", "**For >= Python3.4:**\n", "\n", " import importlib\n", " importlib.reload([code to reload])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> Import `optimal_interpolation.py`. Add a print statement with 0 indentation level in the code. Import the package again. Does the print statement run? Reload the package. How about now?\n", "\n", "> What about if you run it instead of importing it?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### *Exercise*\n", "\n", "> Write your own simple script with a function in it — your function should take at least one input (maybe several numbers) and return something (maybe a number that is the result of some calculation). \n", "\n", "> Now, use your code in several ways. Run the code in ipython with\n", "\n", "> run [filename]\n", "\n", "> Make sure you have a __name__ definition for this (`if __name__ == '__main__':`, etc). Now import the code and use it:\n", "\n", "> import [filename]\n", "\n", "> Add a docstring to the top of the file and reload your package, then query the code. Do you see the docstring? Add a docstring to the function in the file, reload, and query. Do you see the docstring?\n", "\n", "> You should have been able to run your code both ways: running it directly, and importing it, then using a function that is within the code.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit testing\n", "\n", "The idea of unit testing is to develop tests for your code as you develop it, so that you automatically know if it is working properly as you make changes. In fact, some coders prefer to write the unit tests first to drive the proper development of their code and to know when it is working. Of course, the quality of the testing is made up of the tests you include and aspects of your code that you test. \n", "\n", "Here are some unit test [guidelines](http://docs.python-guide.org/en/latest/writing/tests/):\n", "\n", "* Generally, you want to write unit tests that test one small aspect of your code functionality, as separately as possible from other parts of it. Then write many of these tests to cover all aspects of functionality.\n", "* Make sure your unit tests run very quickly since you may end up with many of them\n", "* Always run the full test suite before a coding session, and run it again after. This will give you more confidence that you did not break anything in the rest of the code.\n", "* You can now run a program like [Travis CI](https://travis-ci.com/) through GitHub which runs your test suite before you push your code to your repository or merge a pull request.\n", "* Use long and descriptive names for testing functions. The style guide here is slightly different than that of running code, where short names are often preferred. The reason is testing functions are never called explicitly. square() or even sqr() is ok in running code, but in testing code you would have names such as test_square_of_number_2(), test_square_negative_number(). These function names are displayed when a test fails, and should be as descriptive as possible.\n", "* Include detailed docstrings and comments throughout your testing files since these may be read more than the original code.\n", "\n", "How to set up a suite of unit tests:\n", "\n", "1. make a `tests` directory in your code (or for simple code, just have your test file in the same directory);\n", "1. make a new file to hold your tests, called `tests*.py` — it must start with \"test\" for it to be noticed by testing programs;\n", "1. inside `tests*.py`, write a test function called `test_*()` — the testing programs look for functions with these names in particular and ignore other functions;\n", "1. use functions like `assert` and `np.allclose` for numeric comparisons of function outputs and checking for output types.\n", "1. run testing programs on your code. I recommend [nosetests](http://nose.readthedocs.org/en/latest/usage.html) or [pytest](http://pytest.org/latest/). You use these by running `nosetests` or `py.test` from the terminal window in the directory with your test code in it (or pointing to the directory). Next version will be `nose2`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can load files into Jupyter notebooks using the magic command `%load`. You can then run the code inside the notebook if you want (though the `import` statement will be an issue here), or just look at it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load ../data/package.py\n", "def add(x, y):\n", " \"\"\"doc \"\"\"\n", "\n", " return x+y\n", "\n", "print(add(1, 2))\n", "\n", "if __name__ == '__main__':\n", " print(add(1, 1))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load ../data/test.py\n", "\"\"\"Test package.py\"\"\"\n", "\n", "import package\n", "import numpy as np\n", "\n", "\n", "def test_add_12():\n", " \"\"\"Test package with inputs 1, 2\"\"\"\n", "\n", " assert package.add(1, 2) == np.sum([1, 2])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, run the test. We can do this by escaping to the terminal, or we can go to our terminal window and run it there.\n", "\n", "Note: starting a line of code with \"!\" makes it from the terminal window. Some commands are so common that you don't need to use the \"!\" (like `ls`), but in general you need it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!nosetests ../data/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> Start another file called `test*.py`. In it, write a test function, `test_*()`, that checks the accuracy of your original function in some way. Then run it with `nosetests`.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PEP 0008\n", "\n", "A [PEP](https://www.python.org/dev/peps/pep-0001/) is a Python Enhancement Proposal, describing ideas for design or processes to the Python community. The list of the PEPs is [available online](https://www.python.org/dev/peps/).\n", "\n", "[PEP 0008](https://www.python.org/dev/peps/pep-0008/) is a set of style guidelines for writing good, clear, readable code, written with the assumption that code is read more often than it is written. These address questions such as, when a line of code is longer than one line, how should it be indented? And speaking of one line of code, how long should it be? Note than even in this document, they emphasize that these are guidelines and sometimes should be trumped by what has already been happening in a project or other considerations. But, generally, follow what they say here.\n", "\n", "Here is a list of some style guidelines to follow, but check out the full guide for a wealth of good ideas:\n", "\n", "* indent with spaces, not tabs\n", "* indent with 4 spaces\n", "* limit all lines to a maximum of 79 characters\n", "* put a space after a comma\n", "* avoid trailing whitespace anywhere\n", "\n", "Note that you can tell your text editor to enforce pep8 style guidelines to help you learn. This is called a linter. I do this with a plug-in in Sublime Text. You can [get one](https://github.com/AtomLinter/linter-pep8) for Atom." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> Go back and clean up your code you've been writing so that it follows pep8 standards.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Docstrings\n", "\n", "Docstrings should be provided at the top of a code file for the whole package, and then for each function/class within the package.\n", "\n", "## Overall style\n", "\n", "Overall style for docstrings is given in [PEP 0257](https://www.python.org/dev/peps/pep-0257/), and includes the following guidelines:\n", "\n", "* one liners: for really obvious functions. Keep the docstring completely on one line:\n", "> def simple_function():\n", "> \"\"\"Simple functionality.\"\"\"\n", "\n", "* multiliners: Multi-line docstrings consist of a summary line just like a one-line docstring, followed by a blank line, followed by a more elaborate description. The summary line may be used by automatic indexing tools; it is important that it fits on one line and is separated from the rest of the docstring by a blank line. The summary line may be on the same line as the opening quotes or on the next line.\n", "> def complex_function():\n", "> \"\"\"\n", "> One liner describing overall.\n", ">\n", "> Now more involved description of inputs and outputs.\n", "> Possibly usage example(s) too.\n", "> \"\"\"\n", "\n", "## Styles for inputs/outputs\n", "\n", "For the more involved description in the multi line docstring, there are several standards used. (These are summarized nicely in a [post](http://stackoverflow.com/questions/3898572/what-is-the-standard-python-docstring-format) on Stack Overflow; this list is copied from there.)\n", "\n", "1. [reST](https://www.python.org/dev/peps/pep-0287/)\n", "> def complex_function(param1, param2):\n", "> \"\"\"\n", "> This is a reST style.\n", "> \n", "> :param param1: this is a first param\n", "> :param param2: this is a second param\n", "> :returns: this is a description of what is returned\n", "> :raises keyError: raises an exception\n", "> \"\"\"\n", "\n", "1. [Google](http://google.github.io/styleguide/pyguide.html#Python_Language_Rules)\n", "> def complex_function(param1, param2):\n", "> \"\"\"\n", "> This is an example of Google style.\n", "> \n", "> Args:\n", "> param1: This is the first param.\n", "> param2: This is a second param.\n", "> \n", "> Returns:\n", "> This is a description of what is returned.\n", "> \n", "> Raises:\n", "> KeyError: Raises an exception.\n", "> \"\"\"\n", "\n", "1. [Numpydoc](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt)\n", "> def complex_function(first, second, third='value'):\n", "> \"\"\"\n", "> Numpydoc format docstring.\n", "> \n", "> Parameters\n", "> ----------\n", "> first : array_like\n", "> the 1st param name `first`\n", "> second :\n", "> the 2nd param\n", "> third : {'value', 'other'}, optional\n", "> the 3rd param, by default 'value'\n", "> \n", "> Returns\n", "> -------\n", "> string\n", "> a value in a string\n", "> \n", "> Raises\n", "> ------\n", "> KeyError\n", "> when a key error\n", "> OtherError\n", "> when an other error\n", "> \"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Documentation generation\n", "\n", "[Sphinx](http://www.sphinx-doc.org/en/stable/index.html) is a program that can be run to generate documentation for your project from your docstrings. You basically run the program and if you use the proper formatting in your docstrings, they will all be properly pulled out and presented nicely in a coherent way. There are various additions you can use with Sphinx in order to be able to write your docstrings in different formats (as shown above) and still have Sphinx be able to interpret them. For example, you can use [Napoleon](http://sphinxcontrib-napoleon.readthedocs.org/en/latest/) with Sphinx to be able to write using the Google style instead of reST, meaning that you can have much more readable docstrings and still get nicely-generated documentation out. Once you have generated this documentation, you can publish it using [Read the docs](https://readthedocs.org/). Here is documentation on readthedocs for a package that converts between colorspaces, [Colorspacious](https://colorspacious.readthedocs.org/en/latest/).\n", "\n", "Another approach is to use Sphinx, but link it with [GitHub Pages](https://pages.github.com/), which is hosted directly from your GitHub repo page. Separately from documentation, I use GitHub Pages for [my own website](http://kristenthyng.com). I also use one for documentation for a package of mine, [cmocean](http://matplotlib.org/cmocean/) that provides colormaps for oceanography. To get this running, I followed instructions [online](http://gisellezeno.com/tutorials/sphinx-for-python-documentation.html). Note that GitHub pages is built using Jekyll but in this case we tell it not to use Jekyll and instead use Sphinx.\n", "\n", "We can see that the docstrings in the code are nicely interpreted into documentation for the functions by comparing the [module docs](http://matplotlib.org/cmocean/cmocean.html#module-cmocean.tools) with the code below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load https://raw.githubusercontent.com/matplotlib/cmocean/master/cmocean/tools.py\n", "'''\n", "Plot up stuff with colormaps.\n", "'''\n", "\n", "import numpy as np\n", "import matplotlib as mpl\n", "\n", "\n", "def print_colormaps(cmaps, N=256, returnrgb=True, savefiles=False):\n", " '''Print colormaps in 256 RGB colors to text files.\n", "\n", " :param returnrgb=False: Whether or not to return the rgb array. Only makes sense to do if print one colormaps' rgb.\n", "\n", " '''\n", "\n", " rgb = []\n", "\n", " for cmap in cmaps:\n", "\n", " rgbtemp = cmap(np.linspace(0, 1, N))[np.newaxis, :, :3][0]\n", " if savefiles:\n", " np.savetxt(cmap.name + '-rgb.txt', rgbtemp)\n", " rgb.append(rgbtemp)\n", "\n", " if returnrgb:\n", " return rgb\n", "\n", "\n", "def get_dict(cmap, N=256):\n", " '''Change from rgb to dictionary that LinearSegmentedColormap expects.\n", " Code from https://mycarta.wordpress.com/2014/04/25/convert-color-palettes-to-python-matplotlib-colormaps/\n", " and http://nbviewer.ipython.org/github/kwinkunks/notebooks/blob/master/Matteo_colourmaps.ipynb\n", " '''\n", "\n", " x = np.linspace(0, 1, N) # position of sample n - ranges from 0 to 1\n", "\n", " rgb = cmap(x)\n", "\n", " # flip colormap to follow matplotlib standard\n", " if rgb[0, :].sum() < rgb[-1, :].sum():\n", " rgb = np.flipud(rgb)\n", "\n", " b3 = rgb[:, 2] # value of blue at sample n\n", " b2 = rgb[:, 2] # value of blue at sample n\n", "\n", " # Setting up columns for tuples\n", " g3 = rgb[:, 1]\n", " g2 = rgb[:, 1]\n", "\n", " r3 = rgb[:, 0]\n", " r2 = rgb[:, 0]\n", "\n", " # Creating tuples\n", " R = list(zip(x, r2, r3))\n", " G = list(zip(x, g2, g3))\n", " B = list(zip(x, b2, b3))\n", "\n", " # Creating dictionary\n", " k = ['red', 'green', 'blue']\n", " LinearL = dict(zip(k, [R, G, B]))\n", "\n", " return LinearL\n", "\n", "\n", "def cmap(rgbin, N=256):\n", " '''Input an array of rgb values to generate a colormap.\n", "\n", " :param rgbin: An [mx3] array, where m is the number of input color triplets which\n", " are interpolated between to make the colormap that is returned. hex values\n", " can be input instead, as [mx1] in single quotes with a #.\n", " :param N=10: The number of levels to be interpolated to.\n", "\n", " '''\n", "\n", " # rgb inputs here\n", " if not mpl.cbook.is_string_like(rgbin[0]):\n", " # normalize to be out of 1 if out of 256 instead\n", " if rgbin.max() > 1:\n", " rgbin = rgbin/256.\n", "\n", " cmap = mpl.colors.LinearSegmentedColormap.from_list('mycmap', rgbin, N=N)\n", "\n", " return cmap\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Debugging\n", "\n", "You can use the package [`pdb`](https://docs.python.org/3/library/pdb.html) while running your code to pause it intermittently and poke around to check variable values and understand what is going on.\n", "\n", "A few key commands to get you started are:\n", "\n", "* `pdb.set_trace()` pauses the code run at this location, then allows you to type in the iPython window. You can print statements or check variables shapes, etc. This is how you can dig into code.\n", "* Once you have stopped your code at a trace, use:\n", " * `n` to move to the next line;\n", " * `s` to step into a function if that is the next line and you want to move into that function as opposed to just running the function;\n", " * `c` to continue until there is another trace, the code ends, it reaches the end of a function, or an error occurs;\n", " * `q` to quit out of the debugger, which will also quit out of the code being run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### *Exercise*\n", "\n", "> Use `pdb` to investigate variables after starting your code running.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Make a package\n", "\n", "To make a Python package that you want to be a bit more official because you plan to use it long-term, and/or you want to share it with other people and make it easy for them to use, you are going to want to get it on GitHub, provide documentation, and get it on PyPI (this is how you are able to then easily install it with `pip install [package_name]`). There are also a number of technical steps you'll need to do. More information about this sort of process is [available online](http://python-packaging.readthedocs.org/en/latest/minimal.html)." ] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

TomTranter/OpenPNM

examples/tutorials/Heat Transfer (1D).ipynb

1

542541

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D Heat Equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Heat transfer in 1D is governed by the following PDE (See [here](https://ocw.mit.edu/courses/mathematics/18-303-linear-partial-differential-equations-fall-2006/lecture-notes/heateqni.pdf) for more info):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\frac{\\partial u}{\\partial t} = \\kappa \\frac{\\partial^2 u}{\\partial x^2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\kappa = K_0/c\\rho$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\hat{x}=x/L_*$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\hat{t}=t/T_*$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\hat{u}(\\hat{x},\\hat{t})=u(x,t)/U_*$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\frac{\\partial \\hat{u}}{\\partial \\hat{t}} = \\frac{T_* \\kappa}{L^2_*} \\frac{\\partial^2 \\hat{u}}{\\partial \\hat{x}^2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$T_* = L^2_*/ \\kappa$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\frac{\\partial \\hat{u}}{\\partial \\hat{t}} = \\frac{\\partial^2 \\hat{u}}{\\partial \\hat{x}^2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose the initial temperature distibution in a 1D rod is constant i.e. $f(x) = u_0$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$u(x,t) = \\sum ^\\inf _{n=1} B_n sin(n\\pi x)e^{-n^2 \\pi^2 t}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$B_n = 2u_0 \\int_0^1 sin(n \\pi x) dx$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$u(x,t) = \\frac{4u_0}{\\pi} \\sum_{n=1}^{\\inf} \\frac{sin((2n-1)\\pi x)}{(2n-1)} e^{-(2n-1)^2\\pi^2 t}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$u(x,t) = \\frac{4u_0}{\\pi}\\left( sin(\\pi x)e^{-\\pi^2 t} + \\frac{sin(3\\pi x)}{3}e^{-9\\pi ^2 t} + ... \\right)$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(10)\n", "np.set_printoptions(5)\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def approx_sol(u0, x, t, N):\n", " out = 0\n", " for i in range(N):\n", " n = i+1\n", " out += np.sin((2*n-1)*np.pi*x) * np.exp(-(2*n-1)**2 * np.pi**2 * t) / (2*n-1)\n", " return (4*u0/np.pi)*out" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution maximum: 0.46835\n" ] }, { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "u0 = 1.0\n", "x = np.linspace(0, 1, 101)\n", "plt.figure()\n", "for t in [1/np.pi**2]:\n", " sol = approx_sol(u0, x, t, N=10000)\n", " plt.plot(x, sol)\n", " print(f\"Solution maximum: {sol.max():.5f}\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "t = np.linspace(0, 1, 101)\n", "plt.figure()\n", "for x in [0.01, 0.25, 0.5, 0.75]:\n", " plt.plot(t, approx_sol(u0, x, t, N=10000))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thermal diffusivity (kappa): 1.15955e-04\n" ] } ], "source": [ "# Copper\n", "K0 = 400\n", "rho = 8960\n", "Cp = 385\n", "kappa = K0/(rho*Cp)\n", "print(f\"Thermal diffusivity (kappa): {kappa:.5e}\")\n", "l = 1.0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def approx_sol_dim(u0, x, t, N, kappa, l):\n", " out = 0\n", " for i in range(N):\n", " n = i+1\n", " out += np.sin((2*n-1)*np.pi*x/l) * np.exp(-(2*n-1)**2 * np.pi**2 * t * kappa/l**2) / (2*n-1)\n", " return (4*u0/np.pi)*out" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diffusion time: 14.56323 mins\n", "Solution maximum: 0.76754\n", "Solution maximum: 0.46835\n", "Solution maximum: 0.28410\n" ] }, { "data": { "image/png": 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Lu7yCQobO3k9MQgYbX2intpBTinXuRjr9pu+laaAnP41vic5G9fUxFyFEpJQyrKjnVDlDOfTFtiiOxabyyeCGKpkrBqlVyY33+9Vn34UkZoRHmzsc5R5UQi9nwqMSmLM7htEtA+ndUC0aUQw3NCyA/o39+Hr7OQ7GqBlVS6QSejlyIy2Hl5cfo05lN97uE2rucBQrI4Tgo4ENCPRy5sWlR0nOzDN3SMpdVEIvJwoLJVOWHyU7T8/0UU1xtNOZOyTFCrk62DJ9VFOSMnOZuuq42r7OwqiEXk7M2xvD79FJvNM3lGBfV3OHo1ix+v4VeK1HHX49fYMlh2KLf4FSalRCLwdOxt/i861R9KhXiRHNVb25UnLj29WgfYg37288RXRChrnDUW5TCb2My8or4IWlR6jo4sAngxqqbeQUo7CxEXwxtBFOdjpeXHqE3AK9uUNSUAm9zPt48xku3sxk2rBGqk+LYlSV3B35bEgjTl1N46tfz5s7HAWV0Mu0XecS+fnAFca3rUGbYG9zh6OUQd1CtWm82bsvcPhSsrnDKfdUQi+jUrPyeHXFMUJ8XXmlR21zh6OUYW/1CSXA04mXlx8jI7fA3OGUayqhl1FvrztFcmYeXw1vrEoUFZNydbDly6GNiU3J4qNNZ8wdTrmmEnoZtOHYVTYcu8pLXUOo769a4iqm16KGFxM7BLHk0BXCz6oNy8xFJfQyJjE9l3fWnaRRVQ8mdaxp7nCUcmRKt1rUruTG66uPq12OzEQl9DJESslba0+Qmafny6EN1VZySqlysNXxxdBG3MzI4/2Np4t/gWJ06je+DFl/7CpbT93g5W61CPZ1M3c4SjnUIKACz3aqyao/4th++oa5wyl3VEIvIxLSc3h3/SmaBHowoX2QucNRyrHnu4RQp7Ib/15zgtQs1cCrNBmU0IUQPYUQUUKIaCHE60U8/4QQIlEIcfT21wTjh6rcz7vrTpGVp+fzIY3U5gOKWdnb2vDF0EYkZebxoap6KVXFJnQhhA6YATwKhAIjhRBF9V5dJqVsfPtrnpHjVO5jy4lrbDl5nX91raUabykWob5/BSZ1DGJlZBy7zyWaO5xyw5ARegsgWkoZI6XMA5YC/U0blmKo1Kw83l53ivr+7jzVvoa5w1GUvzzfJYQgHxfeWH2CTLXgqFQYktD9gTt7ZMbdfuxug4UQx4UQK4UQqqVfKflw0xlSsvL4dLCqalEsi6Odjs8GN+TqrWw+3xpl7nDKBUMyQFETsnd3td8AVJdSNgS2AwuLPJEQE4UQEUKIiMRE9TGspHafS2RlZByTOgZRz08tIFIsT1h1Lx5vXZ2F+y8ReVn1ejE1QxJ6HHDniDsAuHrnAVLKJCll7u1v5wLNijqRlHKOlDJMShnm4+PzMPEqt2Xn6Xlz7QmCvF14vkuIucNRlHt6tUdt/Co48cbqE+QVFJo7nDLNkIR+GAgRQtQQQtgDI4D1dx4ghLhzt+F+gLq1bWJf7zhHbHI2Hw9qoHq1KBbNxcGWDwbU49yNDObsvmDucMq0YhO6lLIAmAxsRUvUy6WUp4QQ7wsh+t0+7AUhxCkhxDHgBeAJUwWswOmraczbc5FhYQG0Cqpo7nAUpVhd6lSid4MqfPNbNBdvZpo7nDJLmGuT17CwMBkREWGWa1szfaFk0Mx9xCVnsePljng4q00rFOuQkJbDI9N2Ud+vAoufaql2z3pIQohIKWVYUc+psggr8/OByxyLTeWdvqEqmStWxdfdkdcfrcP+mCRW/RFv7nDKJJXQrUhCWg5fbI2ifYg3/Rr5mTscRXlgI5sH0qyaJx9vPkNKpmoLYGy25g5AMdwHm86Qqy/kg/711cfVksjPhvTrkJkIObcgNw1yM0CfB/p8kHoQOtDZgc4eHFzBoQI4VgBXH3CtDHaO5v5bWCUbG8GHA+rT59u9fLb1LP8d1NDcIZUpKqFbiT3nE//atKK6t4u5w7F8+dmQeBZunNb+m3IRki9B6hXIvVXy8zt6gEcgeNUAzxrgUwcqhYJ3bZXsi1G3ijtPtq3O3D0XGdIsgGbVvMwdUpmhbopagZx8PT2/3g3ALy91UGWKd5MSEqPgyn6Ij4D4P7QkLm/XPOvswaPa7eRbHdwqa6NsV19t1O3gro3CdfZgY6t9FRZoXwW5kJcBOWnaaD7jBmRc10b4KZchOUZ7kyi8vaGD0IFvXfBvBgFhENgGKtYE9YnqbzJzC+g6bRcVnOzY+Hw7tcr5AdzvpqgaoVuBWbsucCkpi5/Gt1DJ/E9p1+D8NogJh0t7tekTACcvLZnW6QOV6mlfXkFgY8J/N30BJF+AG6e0r6t/wOm18MftBdNuVaB6OwjqDCHdtDeScs7FwZZ3+9Zj0s+R/LDvkmr5bCQqoVu4K0lZfLfzAn0aVqF9SDleXSslJJyB0+sgahNcP6E97lZFS5Q12kO1tlryLu3RsM4WfGprX/UHaY8VFkJSNFz+HS7tgZhdcGKF9pxfE6jdG0L7g0+t0o3VgvSoV4nOtX34evt5+jXyw9ddTVWVlJpysXATFh5m34UkdrzckSoVnMwdTulLugDHl8HJ1ZB0HhAQ2Apq9YCQ7uAbah3TGVLC9ePap4pzWyHusPa4T12oPxgaDgPPauaN0Qwu3cyk+1e76dWgMl+PaGLucKyCmnKxUjvO3GD7mQRef7RO+UrmuelwYiUcXQxxhwChjcBbPQN1+1rnlIUQUKWR9tXhVUi7Cmc2wqk1EP6h9lWtLTQeBfUGgb2zuSMuFdW9XXi6YxDf/hbNyBaBtFQrn0tEjdAtVE6+nu5f7cZOJ9jyYgfsbcvBTaPrJyFiPhxfrt2I9KkDjUZCg6FQoaiOzWVEymU4sRyOLdWmaRwqQKMR0Hy8No1TxmXn6ek6bReuDrZsfKEdduoG6X2pEboVmr0rhivJWSya0LJsJ/PCQji/FfbP0OaabR21EWrYOAhobh3TKSXlWU0btbd/RZtzj1gAkQvg0GwI7qp9Mqn5SJn9t3Cy1/FO31Ce/imSH/dfZnw7tVHLw1IJ3QLFp2Yzc1c0vRpUpm2wt7nDMY2CPG1UuvdrbW7cPQC6vQ9Nx4KTp7mjMw8htGqY6u0g8+btpD4Pfh6szbW3n6K92enK3q9t99BKdKzlw9e/nqN/Yz+8XR3MHZJVKsNDP+v18Wat+/C/e9U1cyQmkJ8DB+fAN01g3XNg5wSD58OLR6Hti+U3md/NxVsbtb90AgbM0h5b/RRMbwaRP2grWssQIQRv9wklO1/PF2p3o4emErqF2X8hiU3Hr/FMx2ACPMvQjbGCPDg8H75tClteBY+qMHoVPL0bGgzRltkr/2RrD41HwjP7YMRicK4IG16Eb5vBkUVaDXwZEezryri21VkWEcvxuFRzh2OVVEK3IAX6Qv6z4RT+Hk483bGMLLQoLNQqVqaHwaYp4O4PY9fBuC0Q0rXMzgsbnY0N1OkNE3bAqBXg5AHrnoXvWsKZDVpZZBnwwiMhVHRx4L31pygsLBt/p9KkEroFWXzoCmevp/N2n7plY0Xopb0w7xFYNR4c3LRENH4bBHVSifxhCQG1usPEXTD8ZxA2sGwMfN8TYg+bO7oSc3O0Y2rP2vxxJZW1R1WL3QelErqFSM3KY9qv52hTsyI96lU2dzglk3oFlo+FH3prvU8GzNSmVmp1V4ncWITQavKf2Q99vtZ6yszvCque0mrcrdjgpgE0qurBp7+cJTO37EwplQaDEroQoqcQIkoIES2EeP0+xw0RQkghRJE1ksq9fb39PGnZ+bzdJ9R6W+PmZ0P4f2F6czi3DTq/Cc9HaotlTNlLpTzT2Wolni8cgfYvaz1kvg2DPV9q9y2skI2N4J0+odxIy2XWLrUH6YMoNqELIXTADOBRIBQYKYQILeI4N7T9RA8aO8iyLjohnZ8OXGZEi0DqVnE3dzgPJ3o7fNcadn0CtXvB5MPQ8TWtikUxPQdXeOQdeO4Q1OwMO96HWe20aS8r1KyaJ/0a+TFndwxxKVnmDsdqGDJCbwFESyljpJR5wFKgfxHHfQB8BuQYMb5y4cNNZ3C20/FyNyts1JSRACvGabXSNjrthufQBVoVi1L6vGrAiEUwajkUZGvTXmuegaxkc0f2wF5/tA5CwCdbzpo7FKthSEL3B2Lv+D7u9mN/EUI0AapKKTcaMbZyITwqgZ1RidrdfWtaTCGltlR9Rgs4uxE6/VsrrQvqZO7IFNCalz17ENr9S1vANaMFnFpr7qgeiJ+HExM71GTj8WtEXLK+NyRzMCShFzWh+1c9kRDCBvgKeLnYEwkxUQgRIYSISExMNDzKMqpAX8hHm85QvaIzj7epbu5wDJd2FRYNhTVPQ8UQmLQXOk0FWyt6QyoP7J2h63vwVDi4+8GKx7WKmAzr+d2b1DGIyu6OvL/xtCpjNIAhCT0OuPPzcwBw5210N6A+sFMIcQloBawv6saolHKOlDJMShnm41OOe3vftuRwLNEJGbzRq6519GuRUmuc9V0rredIz0/hyV/KRQMpq1alIUz4TUvu57Zq///ObDB3VAZxtrfl1R61OR53i/XHrLt6pzQYkkUOAyFCiBpCCHtgBLD+zyellLeklN5SyupSyurAAaCflFK1UryPtJx8vvr1HC1reNE9tJK5wyleVrI2wlv9lLZv5qS90GqSql6xFjpbbfrl6d1a58plY2D109q2ehZuYBN/6vu789kvZ8nJ15s7HItWbEKXUhYAk4GtwBlguZTylBDifSFEP1MHWFZ9F36B5Mw83uptBWWKMbtgZls4uxkeeVcblVesae6olIfhW1dbbdpxqraD0qx2cMWyC9NsbARv9Q7l6q0c5u+9aO5wLJpBn/OllJullLWklDWllB/dfuwdKeX6Io7tpEbn9xebnMX3v19kUFN/GgRUMHc496bPh1/fhR/7g70LTNiudfxTo3LrprODzv/W3pgRsOBR2PkpFFru6LdVUEW6h1biu/BoEtNzzR2OxbKCiduy57OtUdgIeLWHBc89p17RftF//1prafv0LvBrbO6oFGOq2kKbOmswBHZ+rL1xp10zd1T39EavuuQWFDLt13PmDsViqYReyo7FprLh2FUmtAuy3G3lzm6GWe0h4SwMWQD9vtFG6ErZ4+gOg+Zo7RniI7UpmOgd5o6qSDW8XXisdTWWHb7C+Rvp5g7HIqmEXoqklHy0+QzervZM6mSBc9D6Atj2Niwdqe2iM2n3/3axV8q2xqO08kYXH22RWPh/tU6ZFuaFLiG4ONiqxUb3oBJ6Kdp+JoFDF5N5sWstXB0sbNeZjAT4aQDs+wbCnoTxv4JXGWnhqxjGtw489Rs0HK61cFg8zOJWmHq62PNsp2B2nE1g/4Ukc4djcVRCLyUF+kI+2XKGIB8XRjS3sGXxcREwuyPEHdZ2x+nzlVokVF7ZO8PAWdD7S4jZCXM6wrXj5o7qb8a1rY5fBUf+u+WMWmx0F5XQS8myiFguJGYytWcdy9rV/Mgi7eanzk4blTceae6IFHMTAppP0Kpg9AUwvzucXGXuqP7iaKfjlduLjTYcV4uN7mRBmaXsyswt4Ktfz9O8uqflLCLSF8CWqdquN4GtYeJObUWhovwpIOz2z0UjWPkkbH/PYkobBzT2p56fO59vjSK3wDJisgQqoZeC+XsvcjMjlzd61bWMRUTZKbBoMBycBa2egzGrwdnL3FEplsitEjy+AZqNg71faStMczPMHRU2NoLXH61DXEo2iw5cMXc4FkMldBNLyshl9q4L9KxXmaaBFrCjfdIFmNcNLv0O/WdAz4+1ZeGKci+29tD3a+j1hdYL5vsekBpb/OtMrH2ID+2Cvfn2t/Ok5eSbOxyLoBK6iX37WzQ5BYW82tMCFhFd2gtzu0BWEjy+HpqMMXdEijVp8RSMXqEl87mdIS7S3BExtWcdUrLymbMrxtyhWASV0E3oclImiw5eZlhYVWr6uJo3mGPL4McB4OqrlaZVa2PeeBTrFPwITPhVW2j2Q2+zd21sEFCBfo38mLc3hoQ0tbeOSugm9OW2c+hsBC91DTFfEFJqfTrWTITAVjB+m7arjaI8LJ/aMH47VKoHyx6DfdO1nzMzeaV7bfSFkq+2nzdbDJZCJXQTORmv9W9+sm0NKo9mFmMAACAASURBVLk7micIfQGsn6z16Wg4Qrv56WQB8/iK9XP1gSc2Qt2+sO1N+OV1s60sDazozOiW1VgeEUtMovlv2JqTSugm8vnWKCo42fF0RzMt8c/LhKWj4MjP0OFVbbGIrb15YlHKJjsnGLoQWj2rVUytHAf55pn2eK5zMA62NnxZzht3qYRuAvsvJLHrXCLPdqpJBSe70g8gMwkW9oXoX7VVn13e0haLKIqx2dhAz/9C9w/h9FqtD0x2aqmH4ePmwIR2Ndh0/Bon4ix/0w5TUQndyKSUfLb1LJXdHc2zT2hqrFZWduMUDPtJ68uiKKbW5nkYNA9iD8IPfSD9RqmHMKFDEJ7Odny2tfw27lIJ3ch+PX2DI1dSealrCI52pbwRRGKUlswzEuCxNVC3T+leXynfGg6FUcsg+YL2c5hcursLuTva8VznYPacv8m+6Julem1LYVBCF0L0FEJECSGihRCvF/H8JCHECSHEUSHEXiFEqPFDtXz6QsnnW6MI8nZhSLOA0r14fCR831PbZWjcJlWWqJhH8CMwdj3kpP7vk2IpGtOqGlUqOPLp1iikGStvzKXYhC6E0AEzgEeBUGBkEQl7sZSygZSyMfAZMM3okVqBdUfjOZ+QwZTutbAtzQZcl/bCwn7g4Abjt0LlBqV3bUW5W9XmMO4XEDawoFepLkBytNPxUtcQjsWm8uvp0p/2MTdDsk4LIFpKGSOlzAOWAv3vPEBKmXbHty5AuXtrzCso5Kvt56jn506v+lVK78Lnf9VuRLn7ad3xVA9zxRL41oFxW8CxAvzYTxt0lJLBTQOo4e3Cl9vOoS9n7XUNSej+wJ2NG+JuP/Y3QojnhBAX0EboLxgnPOuxPCKW2ORsXulRGxubUqooOb0elowE71raL4+7X+lcV1EM4VVDG2S4+2uDjvPbS+WytjobpnSrRdSNdDaWs/a6hiT0orLTP972pJQzpJQ1ganAW0WeSIiJQogIIUREYmLig0VqwXLy9XyzQ2uP26mWT+lc9MRKWPEE+DXRuuG5eJfOdRXlQbj7wbjN4B2ibW14dnOpXLZ3gyrUreLOtF/Pka+3vK30TMWQhB4H3LnFTgBwv7e9pcCAop6QUs6RUoZJKcN8fEop8ZWCH/dfIiE9l1d71Cmd9rhHF8OqCVof88dWg5OH6a+pKA/LxVsbdFSqD8sfg1NrTX5JGxvBqz1qcTkpixURcSa/nqUwJKEfBkKEEDWEEPbACGD9nQcIIe5sVtIbKDdNFdJz8pm58wIdavnQokYp9BSPXAhrn4WgTlrnOwc3019TUUrKyRPGrgP/MG2zjBMrTX7JzrV9aVbNk292nCcnv3xsglFsI2wpZYEQYjKwFdAB30spTwkh3gcipJTrgclCiK5APpACPG7KoC3J93svkZKVz6vdS6E9bsQC2PgSBHeD4T+DnZl6xFi4fH0+17Ouk5CVQEJWAolZiSTnJJOSm0JKTgoZ+Rmk56WTkZdBrj6XnIIc8grz0Bfq0Us9EolAoBM6dDY67HX2OOoccbR1xNXOFVd7V9zs3PB09MTT0RMvRy98nHzwdfalkkslfJ19sbMxwwphS+foDmNWweLhsPopraFXw6Emu5wQgle612bk3AMsOniF8e3KflM6Ya5azbCwMBkREWGWaxtLalYe7T8Np01wRWY/Fmbaix2eD5umQEh3LZmX802cC2Uh8RnxXEi9wKVbl7iUdonLaZeJz4jnRtYNCuXf501thS2ejp54OHrgZueGm70bLnYuONk64aBzwEHngM5Gh42wwUbYUCgLKZSF6Av15OpzydXnklWQRWZ+Jhl5GaTlpZGam0pqTioFsuBv19IJHZWcKxHgFkCgeyDV3atTo0INanrUxM/FzzJ2rTKnvEwtqV/+XduUvNFwk15u9LwDRF1PZ9ernXFxsP7NXIQQkVLKIhOO9f/tzGjO7hgy8gr4V7dapr1QxPe3k3kPGP5TuUvm+fp8zqee53TSaU4nnSYqOYrzqefJLsj+6xgvRy8C3QIJqxSGv5s/fi5+VHLWRss+zj6427ubJJFKKUnLS/vr08CNrBvEZ8QTnxFPXHoc2y9vJzX3f71NXOxcCPYIpo5XHUIrhhJaMZSaHjXL14je3kVbUbp4OKydpD1mwqQ+pVttBs/cx8L9l3i2U7DJrmMJ1Aj9Id3MyKX9p+F0C63ENyObmO5CkQthwwvlKpmn5qQSmRDJ0YSjHEs8xumk0+TqcwFws3OjTsU6hHiEEOIZQrBHMDUq1KCCQwUzR31vKTkpXEq7xPmU89pX6nnOJp8lMz8TAEedI/W869HIpxFNfJvQxLeJRf99jCYvCxYP00bqg+ZCgyEmu9STPxwm8nIKe6Z2xt3Rut887zdCVwn9IX2w8TQLfr/I9ikdCTLVbkRHF2s3QIMfgeGLyuyceXpeOhHXIzhw7QCHrh8iOjUaADsbO+pWrEsjn0Y09G5IvYr1CHALKBNTFoWykCtpVziVdIoTN09wLOEYZ5PPUiALEAhqedaieeXmtPZrTVilMJztnM0dsmnkZcLPQ7SmXkPmQ72BJrnMyfhb9Pl2Ly8+EmL6T9QmphK6kV2/lUOHz8Pp18iPL4Y2Ms1Fji+H1RMhqCOMXKr1ni4jpJREpUSxJ24Pe+L3cDzxOHqpx8nWiSa+TQirFEazSs2o710fe1356eGeU5DDiZsniLwRScSNCI4mHCVXn4utsKWxb2PaB7SnvX97gj2Cy8Sb2l9y07WFR/GRWn91EzWVe+bnSPacv8me1zrj6WK9P1cqoRvZ22tPsuTQFcJf6URVLxOMnE6vgxXjtAZbo5aDvfWPzvL1+Ry+fpjfYn8jPDachKwEAEIrhtLWry2t/VrTyKdRuUrgxckpyOFo4lH2X93P7/G/E5USBYCfix+dAzvTuWpnmlZqWjbm33PS4KeBcO2YNoAJ6Wr0S0RdT6fn/+1mUseaTO1Zx+jnLy0qoRtRfGo2nT4PZ0izqvx3kAmaYJ3bCktHg39Tbcs4BzNvLl0C+fp89l/bz9ZLWwm/Ek56fjpOtk609WtLh4AOtA9oj7eTWuFqqBuZN9gbv5edcTvZf3U/ufpcKjhU4JHAR+herTstqrSw7uSenaptzHLzHIxeCTXaG/0Szy85wo4zN9j9Wme8Xa3zfpRK6Eb0xurjrIqMJ/zVTvh7GHkaJGYnLBoGvnXh8fVaYyMroy/UE3kjks0XN7Pt8jbS89Jxs3ejS9UudK3WlVZVWuFoWzbvBZSmrPws9l/dz69XfmVn7E4y8zPxcPCge7Xu9A7qTWPfxtgIK9zuIDMJfuilbdQydi1UbWHU00cnZND9q12Mb1eDN3tbZ5dvldCN5EpSFl2+3MmoloG837++cU8eewh+7A+eNbTNd51LYdWpEcXcimF99Ho2xGwgISsBZ1tnulbrSo/qPWhdpTV2OiseOVq4XH0u++L3seXSFsKvhJOjz8HPxY++NfvSv2Z/qrpXLf4kliT9Bix4FDJvar8LVRoa9fRTlh1l04lr7HmtM77m2sC9BFRCN5JXVhxj/bGr7HmtM5WM+YNw/QT80BucK2p9pN0qGe/cJpSVn8XWS1tZeX4lxxOPoxM62vm3o09QHzpW7YiTbdm5kWstsvKz2HFlBxtjNrL/6n4kkmaVmjE4ZDDdqnWznk9HqbHahi0FOVrHRu+Q4l9joEs3M3lk2i4ea1WN9/rVM9p5S4tK6EZw8WYmXaft4vHW1XmnrxE/qt2MhgU9QecAT24Bj0DjndtEopKjWB61nE0XN5GZn0mNCjUYGDyQPkF98HEuO03XrN31zOtsjNnImvNruJJ+BTd7N/oG9WVY7WHU9Khp7vCK99fvhr3WHtqzmtFO/drKY6w9epXdr3amcgUreZO7TSV0I5iy7CibT15jz2td8HEz0s2UW3Ewvwfoc7WRubflrmLL1+ez7fI2lkUt40jCERx0DvSo3oPBIYNp4tukbJXRlTFSSg5fP8zK8yvZfnk7+YX5NK/cnOG1h/NI4CPY2ljwgvE7P70+uRVcfY1y2tjkLDp/YaLpUxNTCb2EYhIz6DrNyDdSMpO00Uf69dvzhCaqZy+hpOwkVpxbwbKoZdzMvkmgWyDDaw+nf3D/8rGasYxJzklmzfk1LI9aztXMq1R2qcyI2iMYUmuI5f7//PP+UsWa8MQmoxUL/FngsOu1TlSpYD3Tgyqhl9C/lh1lizFH57npWnlWwhl4bI1FbugccyuGH0/9yIYLG8grzKOtX1tG1x1NW/+21lk9ofyNvlDP7rjdLDqziIPXD+Koc2RA8ADGho61zJuo57fDkhEQ0FzbA8AIC+3+HKWPbBHIBwOsZ5SumnOVwIXEDNYdjWdC+yDjJPOCXFg6Cq4dhxGLLS6ZH0k4wvwT89kVtwsHnQP9gvvxWOhjBFVQe5WWJTobnbY4KbAzUclR/HT6J1aeX8myqGV0rdaVJ+s/SX1vC0pyIV1h4CxtY5cV47SOo7qSpa+qXs4MDavKssOxPNOpJn7GLkM2AzVCL8aLS4+w7dQN9kw1wkKEQj2sHKetBB04GxqNME6QJSSlZG/8XuadmMcfCX/g4eDByDojGV57OBWdKpo7PKWUJGYlsvjsYpZFLSM9L52WVVoyocEEWlZuaTn3SA7Nhc2vQOMx0H86lDCuPxcKDgurykcDTbBQ0ATUCP0hRSdksP7YVSZ2CCp5MpcStkzVknn3Dy0imUspCY8NZ9axWZxJPkNll8q83uJ1BgYPLLvNoJR78nH24cWmLzK+/nhWnFvBj6d/5KltT9HIpxGTGk2irV9b8yf2Fk9BZiLs+lS7Qdr13RKdzt/DiWFhVVkeEcuznYONv1iwlKnJ0Pv49rfzONnpmNjeCNMNuz+Hw3OhzQvQ5vmSn68EpJTsuLyDoRuG8mL4i2TkZ/B+m/fZPHAzo+uOVsm8nHO1d2Vc/XH8MvgX3mr5FglZCTyz/RlGbRrF7rjdmOtT/V86vQHNxsHeaXBgZolP91xnrbrsu/DoEp/L3AxK6EKInkKIKCFEtBDi9SKenyKEOC2EOC6E2CGEMF7BqJn8OTp/rHU1KpZ0dB65EMI/gkYjoet/jBPgQ5BSsjN2J8M3DuelnS+Rq8/l43Yfs37AegaGDFSrOZW/cdA5MLzOcDYN3MR/2vyHlNwUntvxHGM2j2Ff/D7zJXYhoPeXULcv/PI6nFxVotP53TFKj0/NLv4FFqzYhC6E0AEzgEeBUGCkEOLu2r0jQJiUsiGwEvjM2IGWtum/ncfR1gij86hfYOO/ILgr9PsWbMzzoejgtYOM2TyG5397noz8DD5u9zFr+q+hb82+ll2HrJidnc6OQSGD2DBwA++1fo/E7ESe3v4047aO40jCEfMEZaODQfMgsA2smQQXd5fodM/eHqXP3Gndo3RDsksLIFpKGSOlzAOWAv3vPEBKGS6lzLr97QEgwLhhlq4/R+dj25RwdB57GFY8ofWiGLoQzDACPnXzFE9te4oJ2yZwI+sG77V+j3UD1qlErjwwOxs7BtcazKaBm3iz5ZtcTrvM2C1jeW7Hc0QlR5khIEcYuRi8amodSq+feOhT/TmXvuxwLFeteJRuSEL3B2Lv+D7u9mP3Mh7YUtQTQoiJQogIIUREYmKi4VGWsum/ncehpKPzpAva9lpulWHUilJvgxubFsuru15lxKYRRCVH8Vrz19g0aBODaw227haritnZ6ewYUWcEmwZu4qWmL3Ek4QhDNwzlzb1vci3jWukG4+QJY1aCvSssGqqtvn5If47Sv7PiUbohCb2o29pFTp4JIcYAYcDnRT0vpZwjpQyTUob5+Fhmz4+YRCPMnWckajuwCAFjVoFr6f1dU3NS+fTQp/Rb149dcbuY2HAimwdt5rHQx3DQWWf/Z8UyOds5M77BeLYM2sLj9R7nl4u/0GdNH6ZFTiM9L730AqkQoCX1P7ezy04t/jVF8Pdw+qsu3VpH6YYk9DjgzqVjAcDVuw8SQnQF3gT6SSlzjRNe6Zv+WzT2tjZM7PCQo/O8TG1knn5d222oYuk0QcrT57Hw1EJ6renF4rOL6V+zP5sGbuL5Js/jam+9m2Qolq+CQwVeDnuZjQM30qN6DxacXEDv1b1ZcnYJ+YX5pRNEpXraYqOkaFg2RlvA9xCe7VQTKWHWrgtGDrB0GJLQDwMhQogaQgh7YASw/s4DhBBNgNloyTzB+GGWjks3M1l7NJ4xLas9XN15oR5WjodrR7UNbwOKrP03Kiklv135jQHrBvBFxBc09G7Iyr4rea/Ne6rzoVKqqrhW4eP2H7OszzKCPYP5+ODHDFk/hN/jfy+dAII6woDv4NIebXP1wsIHPkWApzNDmgWw9FAs12/lmCBI0yo2oUspC4DJwFbgDLBcSnlKCPG+EKLf7cM+B1yBFUKIo0KI9fc4nUWbER6Nnc6GiR0fYnT+58Khc1vg0c+gTm/jB3iX6JRonvr1KV4MfxE7Gztmdp3JrG6zCPE0Xu9oRXlQoRVDmd99Pv/X+f/IL8xn0vZJPLfjOS7dumT6izccBo+8AydXQviHD3WK5zoHo5eS2butb5RuUJmDlHIzsPmux96548/G39G1lF1JymL1kXjGtq6Gr9tD9Ec+8J22cKj1ZG01mwml5aUx8+hMlpxdgoudC6+3eJ1htYepm52KxRBC0CWwC+3827H4zGJmHZ/FwPUDGRs6lqcbPm3axWvtpkDKZdjzJXhUg2aPP9DLq3o5M6iJP4sPXuGZjjWtalcjtVL0tu92RqOzEUzq+BBz3qfXwdY3IbQ/dPvA+MHdVigLWXN+DX3X9GXRmUUMDtFKyEbXHa2SuWKR7HX2PFH/CTYO3EjvGr35/uT39F3Tl80xm023MEkI6D1NW/ux8V8Qvf2BTzG5SzAFhZI5u2NMEKDpqIQOxKVksTIyjhHNqz741nJxEbB6otbWc+Bsky0cOpt8lse3PM47+94h0C2QZX2W8Xbrt/Fw9DDJ9RTFmLydvPmw3Yf83OtnvJ29mbpnKhO2TeBCqommNXS2MPQHqBQKyx+H6ycf6OXVKrrQv7EfPx+8TGK69dR4qIQOzNx5ASF48NF5yiWtR7NbZRi5xCg9mu+WkZfBp4c+ZfjG4VxJv8IHbT9g4aMLqVuxrtGvpSim1sinEYt7LebtVm9zNvksQ9YP4avIr8jKzyr+xQ/KwU2rNHNw1yrP0h6sRn5y52DyCgqZt9d6RunlPqFfu5XNiog4hoZVfbB+yNmpsGgY6PO0hUMu3kaNS0rJtkvb6L+2P4vOLGJIyBDWD1jPgOABaoMJxarpbHQMqz2MDQM30KdmH74/+T0D1w1kd1zJlu8Xyd0PRi3Tfl+XDNfKig0U5ONK30Z+/LT/MsmZecaPzQTKfWaYvSuGQil55kFG5/p8WD4WkmNg+CLwqWXUmK5mXGXyb5N5edfLeDl5sajXIt5u/bblbhGmKA/By9GLD9p+wIIeC3CydeK5Hc8xZecUErKMXPlcpSEM+V5rDbBqglZebKDJnYPJztfz/d6Lxo3JRMp1Qk9Iz2HJoSsMbOJPVS8D77pLCZumwMVd0Pf/oEZ7o8VTUFjAwlMLGbBuAIevH+bVsFdZ0nsJDXyso/G+ojyMsMphrOi7ghebvsjuuN30X9ufZWeXUSgfvI78nmr3hJ6fQNRm+PWd4o+/LaSSG4/Wr8wP+y5xK6uUFkmVQLlO6HN3x5CvL/yrH7JB9n0Lf/wI7V+GJqONFsvZ5LOM3jyaLyK+oEXlFqzrv46x9caqBlpKuWCns2NCgwms7reaet71+PDghzy+5XHj3jRt+TS0mAj7p0PEAoNfNrlzCBm5BSzYZ/mj9HKb0JMycvn5wBX6N/anureLYS86s1F7dw/tD53fMkocufpc/u+P/2PExhFcz7zOFx2/4Nsu31LFtYpRzq8o1iTQPZC53ebyUbuPuJh2kaEbhjLr2Czy9UYaHff4LwR3g00vw4Vwg14S6udOt9BKfL/3Imk5lj1KL7cJfd7ei+QU6A0fnV87BqufAv+mRitPPJJwhCHrhzDvxDz6BPVh/YD19Kjew/zbfCmKGQkh6FezH+v6r6NrYFdmHJ3B8E3DOXXzVMlPrrPV5tN9amvljInnDHrZC11CSMsp4Kf9l0segwmVy4SempXHj/su0atBFYJ9DWhclXYNFo8AJy8YUfLyxKz8LD459AmPb3mcPH0es7vN5sN2H6qbnopyh4pOFfms42d82+VbbuXcYtTmUXwV+RW5+hLWhTu6a5UvtvZaOWNWcrEvaRBQgU61fZi/9yJZeQUlu74JlcuEvuD3S2Tm6Xm+iwGj87wsWDoScm7BqKXgVqlE1z58/TCD1w9m0ZlFjKgzgjX919DGr02JzqkoZVmnqp1YM2ANA4IH8P3J7xmyfgjHEo+V7KQegTBiMaRdvd2dsfiyxOe7hJCcmcfig1dKdm0TKncJPT0nnwW/X6R7aCXqVHa//8GFhbD2Gbh6FAbPg8oPX22SlZ/Fxwc/5smtTyKEYEGPBfy75b/VhsyKYgB3e3f+0+Y/zO42mxx9DmO3jGVaxLSSjdartoD+0+Hy77DpX1oF2300q+ZJ2+CKzN4dQ06+4aWPpancJfQf918mLaeA57sY0JFw16dwei10fQ/q9Hroa0beiGTw+sEsPbuUMXXHsKrfKsIqm761rqKUNW382rCm3xoGhQxiwakFDN0wlJM3H2xZ/980HAYdXoUjP2vVL8WY3DmExPRclkfEFnusOZSrhJ6VV8D8vRfpVNuHBgHFzFefXA27PoFGo6Dtiw91vZyCHD4//DnjfhkHwPc9vmdqi6k42Rq/RYCilBeu9q682/pdZnedTVZ+FmM2j+GbP755+EqYTv+Guv1g29twbut9D20V5EXz6p7M2nmBvAIj1skbSblK6IsPXiE5M6/4ufP4P7SplqqtoO/XWve2B3Tq5imGbRzGj6d/ZFjtYWpUrihG1sa/Dav7r6ZPUB/mnpjLyE0jOZdiWNXK39jYwMBZ2pTqyvGQcOaehwohmNwlhKu3clj9x8PvX2oq5Sah5+Trmb07hjY1K9Ksmte9D0y7BktHgYuvtqWV7YPtXJRfmM/MozMZvXk0mfmZzO46m7davaXmyhXFBNzt3fmw3Yd80/kbErMTGbFxBAtOLkD/AMv7AbB3gZFLwd4ZFg+HzKR7HtohxJtGARX4bucFCvSWNUo3KKELIXoKIaKEENFCiNeLeL6DEOIPIUSBEGKI8cMsuRWRcSSm5zL5fnXn+dlaMs9N1ypaHnBz54u3LjJ281i+O/YdPWv0ZHW/1bTxVxUsimJqnQM7s6b/GjoEdGBa5DSe3PokcekPOIKu4K+VJadf13o13aPyRQjBc52DuZKcxcbjD9bB0dSKTehCCB0wA3gUCAVGCiFC7zrsCvAEsNjYARpDvr6QWTsv0DTQg9Y1KxZ9kJSwbjJcPQKD5mibzhpISsnSs0sZtmEYsRmxfNHxCz5p/4mqK1eUUuTl6MVXnb7io3YfcS7lHEM2DGFt9NoH20gjoNntype9sOW1e1a+dK1biTqV3ZgeHk1hoYk26ngIhozQWwDRUsoYKWUesBTof+cBUspLUsrjgGV9/rhtzZF44lOzeb5LyL1XYe6dpu1D2OWtB9oP9Gb2TZ7d8SwfHfyIZpWasbrfanpU72GkyBVFeRB/rjJd1W8Vdbzq8PbvbzNl5xRSc1INP0nDYdDuXxC5AA7PK/IQGxttlB6dkMHWU9eNFH3JGZLQ/YE7a3Tibj9mFfSFku/Co6nn506n2veYQjm7GXZ8APWHaE23DBR+JZxB6wZx+Pph3mjxBjO7zsTX2ddIkSuK8rD8XP2Y330+U5pNYWfcTgatH8S+q/sMP0GXd6DWo9rG7zE7izykV4MqBHm78O1v0abbTu8BGZLQixrSPlT0QoiJQogIIUREYmLiw5zigW08fpVLSVlM7hxc9Oj8xmmtR4tfY+2jlgEVLVn5Wfxn/394IfwFKrlUYlmfZYyqO0r1YFEUC6Kz0TGu/jiW9F6Cm70bT//6NJ8e+tSwxUg2NjB4LnjX0nq+JP9z1yKdjeDZzsGcvpZGeJSRe7g/JEMSehxQ9Y7vA4CrD3MxKeUcKWWYlDLMx+fBbjg+jMJCyXfhFwjxdaVHvcr/PCArWdtCzt5FWwZsQI+WM0lnGL5xOKvOrWJc/XEs7rWYmh4PsbG0oiiloo5XHZb1WcbIOiP5+czPjNo0yrC2vA5uMHKxNshbMhJy0v5xSP/GfgR4OlnMKN2QhH4YCBFC1BBC2AMjgPWmDcs4fj1zg6gb6TzbuSY2NneNnv/cdSj9mrbrkLvffc9VKAv54eQPjNo8iqyCLOZ1n8eUZlOw09mZ8G+gKIoxONo68u+W/2bGIzO4mX2T4RuHs/Ts0uKTsFcQDF0IN89rn+TvKoe009nwdMeaHLmSyv4L9y51LC3FJnQpZQEwGdgKnAGWSylPCSHeF0L0AxBCNBdCxAFDgdlCCCP0uSwZKSUzwqMJ9HKmb8MikvXWf8OlPdquQ1Wb3/dciVmJTPp1El9GfkmngE6s6ruKFlVamChyRVFMpUNAB1b1W0Xzys356OBHvBD+Aik5Kfd/UVBHbbejc79A+Ef/eHposwB83RyYHh5toqgNZ1AdupRys5SylpSyppTyo9uPvSOlXH/7z4ellAFSShcpZUUppeE1fyay5/xNjsfd4plONbHV3fXXjFwIh+ZA68nQeNR9z7MrdheD1w/mSMIR3m39LtM6TcPD0cOEkSuKYkreTt5898h3TG0+ld/jf2fw+sEcuHbg/i9q8RQ0HQt7voSTq/72lKOdjokdgth3IYnIy8W8OZhYmV0pOj08msrujgxqeldBzpUD2m4lNbtA1//c8/W5+lw+OfQJk3+brN347LuMIbWGqBufilIGCCEYEzqGJb2X4GrvysRtE/k68mvyC+/R/4X22QAADJFJREFUD0YI6PWl1g5k7XPahjd3GNUyEE9nO2aYeZReJhP6oYvJHLqYzMQOQTjY6v73xK04rfexR6C2a4mu6P06Y27FMGbzGBadWcSYumP4udfPBFUIKqXoFUUpLbW9arOszzIGhQxi/sn5PLHliXuvMLW1h+E/gXNFWDIKMv5Xqedsb8uTbWvw29kETsbfKqXo/6lMJvTp4dFUdLFnZIvA/z3457L+/BwYuQScPP/xOikla86v+Wt/z+ldpjO1xVQcdA/Wz0VRFOvhZOvEe23e4/OOn3PxlraP6S8Xfyn6YFdfGLEIspL+0R5gbJvquDnYMnOnETe2fkBlLqEfj0tl97lEnmxXAyf726NzKWH9C3DtuFZb6lP7H6/LzM/k9T2v886+d6jvXZ+VfVfSsWrHUo5eURRz6Vm9Jyv6rSDII4hXd7/Ke/veI7sg+58H/rlm5co++OV/ra0qONnxWOtqbD55jeiEjFKM/H/KXEKfER6Nm6Mtj7Wu9r8H930LJ5ZDlzeh9qP/eM2ppFMM2zCMXy79wuTGk5nbbS6VXEq21ZyiKNbH39WfH3r+wPj641l9fjUjN44kOqWIefEGQ7R9Ev6/vXuPjvFOAzj+fSZBUpewiaisW622pGwoSlXdq9hi95SQFauqWvRGtdWtY+t0u62WqnLq0INoEyuIVqPVsq1L1AqCtK7tutOqO6mSiMlv/3hnbRphhsy8k5k8n3Nyzjvz/uZ9nyfvzDPv/N7LL2s2ZM258vSQtrdRIdTht730oCro3x/7meU7jvFIm3pUCXOdH77nS/jyFYjtDfc//6v2xhirn3xZInnOPOY8OIcn4p4gxBFSzNKVUmVBOUc5RjYfyYwHZnA27ywJnyWw+PvFV5+z3vkVaNAFlr0AB9cDEFmpAgn31GFJ9g8cPn3B9tiDqqBPX7WH8HIhDL7vNuuJU3sh7VGo3gh6T//VZf3n8s4xctVIJmycQNuYtqT1TKN5jeZ+ilwpVdq0iWlDWq80mkY3Zfz68YxZO4bzlwp1pThC4OHZULUuLBxonXQBPN6uPg6BmRn276UHTUE/eOoX0r/5kQGt6vCbiuWte5qn/hnEYV2+W6HSlbbZx7Ppu7QvGT9k8GLLF5naaaqeW66UukpUeBQzH5jJM82eYcWBFfT7tB+7ThUa0Si8qnXbkPyLkDoA8i9SMyKcPs1rsTDrCMdzcm2NN2gK+ow1+wh1OBjarj4UFMDHw6zLdfvOhWr1AOvy/aTtSQz+YjAOcZDcPZmBsQP13HKl1DU5xMHQ3w9l9oOzyXXmMmDZAObvnv//LpjohtYYCkezYelIMIZh7X/HZWcBs77eb2+stq7NR346l8vizUfo26IWNaqEQcZE2P0pdH0N6ncA4EzuGZ786kkmb55MxzodWdRzEY2jGvs1bqVU4GheozlpPdO4N+ZeXt/wOqPXjCbnkuuGXQ3/YA02/W0qZE6nbmRFesXFkJJ5kDO/FD/ykS8ERUF/P2MfTte3IruXwerXIS4BWg8HYMuxLfRZ2ocNRzcwttVY3m7/NpXLV/Zz1EqpQFMtrBrTOk3j+RbPs+rQKuKXxrP95HZrZrsXoOFDsGIc7FvN8A4NuHDJSdK/D9gWX8AX9FPn85i/8RC942Ko7TwMHz0OMc3goXcowDBr2yweXf4oYSFhzOsxj/4N+2sXi1LqpjnEwaC7BpHULQmncTLw84Gk7EzBiMCfZlj3UF80mDsrnKJrbA3mrtvP+bzL9sRmy1p8KGndAXIvO3mqTZR1ELRcGPSbx2nnRUZ8NYJ3t7xLl7pdWPDQAhpFNvJ3uEqpINE0uilpPdO4L+Y+3tz0JqNWjyJHjHUlqXFC6gCebhtDTu5lUjIP2hJTQBf0nNx8Plh/gO6x1amfMQrOHID4ZLbkHqPv0r5sOrqJca3HMbHdRCqVr+RucUopdUMiKkRc6YJZc3gN8Uvj2WFyrXtFHd9Jk81jub9BJLPW7ic33+l+gSUU0AU9ef1Bfs69zPjK6fCfFRR0m8DsnJ1XulhSeqQQf2e8drEopXxGRBh01yDmdp+L0zhJ/DyRefnHMJ3GwY6PeC16JSfP57Ew67D7hZVQwBb0i5eczPl6P6Nr7SY6expnmybwVM5WpmyZQuc6nbWLRSllq7jqcVe6YCZsnMBo5xF+ju1FnS1vMeTWvcxcs498Z4FPYwjYgp666RCRF/Yy4uwksmvH0Sd/D5lHM3m51ctMaj9Ju1iUUraLqBDB1E5Tea75c6w8tJJ+ISfZdWtDXvplIqHn9rNk6w8+Xb9HBV1EuonIdyKyR0ReKmZ+BRFZ4Jq/QUTqeTvQwi5dLiB1zTd8cMsUUqpVZXC5HEIdoST3SCahYYJ2sSil/MYhDgY3HkxStyTyCi6RWDGPxZXDmBs+hbmrt+Es8N1g0m4LuoiEAO8B3YFYIEFEYos0GwKcMcY0AN4B3vR2oIUt2XKQUXkTeSPKyaRKoXSo3ZGFPRdyV6TfR75TSikAmkU3I61nGvfUbMU/IsKYHnmBx3Im8cW2oz5bpyd76PcAe4wx+4wxl4BUoHeRNr2BD1zTaUBn8dFusrPAcGj1i0ypfZq1FW9hTMsxTO4wmSrlq/hidUopddOqhVVjeufpPHv3s6yoVJE5tX9k179evvrOjV7iSUH/LVD48OwR13PFtjHGXAbOAZHeCLCoaR+PZl70LvLLVeTD7ikkxiZqF4tSqtRyiIPHmjzGrK6zySkXTmrkJmZ9Nt436/KgTXHVsujXiydtEJHHRSRLRLJOnDhRzEvci6lUj7i8cNIeXkaT6k1uahlKKWW3ljVbsuCPnxB7KZwaFeu6f8FNEHe7/iJyLzDeGPOg6/FfAYwxbxRqs9zVZr2IhAI/AdXNdRbeokULk5WV5YUUlFKq7BCRzcaYFsXN82QPfRNwu4jcJiLlgf5AepE26cAg13QfYOX1irlSSinvC3XXwBhzWUSeApYDIcAcY8wOEXkVyDLGpAOzgWQR2QOcxir6SimlbOS2oAMYY5YBy4o897dC07lAX++GppRS6kYE7JWiSimlfk0LulJKBQkt6EopFSS0oCulVJDQgq6UUkHC7YVFPluxyAngZsdligJOejGcQKA5lw2ac9lQkpzrGmOqFzfDbwW9JEQk61pXSgUrzbls0JzLBl/lrF0uSikVJLSgK6VUkAjUgv6+vwPwA825bNCcywaf5ByQfehKKaWuFqh76EoppYoo1QW9tA1ObQcPcn5ORHaKyLci8pWI+OZO+TZyl3Ohdn1ExIhIwJ8R4UnOIhLv2tY7ROSfdsfobR68t+uIyCoR2ep6f/fwR5zeIiJzROS4iGy/xnwRkamu/8e3InJ3iVdqjCmVf1i36t0L1AfKA98AsUXajABmuKb7Awv8HbcNOXcEbnFNDy8LObvaVQYygEyghb/jtmE73w5sBaq5Hkf7O24bcn4fGO6ajgUO+DvuEubcDrgb2H6N+T2Az7FGfGsNbCjpOkvzHnqpGpzaJm5zNsasMsZccD3MBGrZHKO3ebKdAf4OvAXk2hmcj3iS81DgPWPMGQBjzHGbY/Q2T3I2wP9Ge48AfrQxPq8zxmRgjQ9xLb2BD40lE6gqIjVLss7SXNBL1eDUNvEk58KGYH3DBzK3OYtIM6C2MeZTOwPzIU+28x3AHSKyTkQyRaSbbdH5hic5jwcSReQI1vgLT9sTmt/c6OfdLY8GuPATrw1OHUA8zkdEEoEWQHufRuR7181ZRBzAO8AjdgVkA0+2cyhWt0sHrF9ha0WksTHmrI9j8xVPck4A5hpj3naNZZzsyrnA9+H5hdfrV2neQz8C1C70uBZX/wS70sY1OHUE1/+JU9p5kjMi0gUYC/QyxuTZFJuvuMu5MtAYWC0iB7D6GtMD/MCop+/tT4wx+caY/cB3WAU+UHmS8xBgIYAxZj0QhnXPk2Dl0ef9RpTmgl4WB6d2m7Or+2EmVjEP9H5VcJOzMeacMSbKGFPPGFMP67hBL2NMln/C9QpP3ttLsA6AIyJRWF0w+2yN0rs8yfkQ0BlARBphFfQTtkZpr3TgL66zXVoD54wxR0u0RH8fCXZzlLgH8D3W0fGxrudexfpAg7XBFwF7gI1AfX/HbEPOXwLHgGzXX7q/Y/Z1zkXaribAz3LxcDsLMBnYCWwD+vs7ZhtyjgXWYZ0Bkw109XfMJcx3PnAUyMfaGx8CDAOGFdrG77n+H9u88b7WK0WVUipIlOYuF6WUUjdAC7pSSgUJLehKKRUktKArpVSQ0IKulFJBQgu6UkoFCS3oSikVJLSgK6VUkPgvuBYBytmi14QAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "x = np.linspace(0, l, 101)\n", "plt.figure()\n", "t_diffusion = (1/np.pi**2)/(kappa/l**2)\n", "print(f\"Diffusion time: {t_diffusion/60:.5f} mins\")\n", "for t in np.linspace(0.5, 1.5, 3)*t_diffusion:\n", " sol = approx_sol_dim(u0, x, t, 10000, kappa, l)\n", " plt.plot(x, sol)\n", " print(f\"Solution maximum: {sol.max():.5f}\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import openpnm as op\n", "ws = op.Workspace()\n", "spacing = 1e-2\n", "net = op.network.Cubic(shape=[101, 1, 1], spacing=spacing)\n", "# translate to origin\n", "net['pore.coords'] -= np.array([spacing, spacing, spacing])/2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Length: 1.00000\n" ] } ], "source": [ "l = net['pore.coords'][:, 0].max() - net['pore.coords'][:, 0].min()\n", "print(f\"Length: {l:.5f}\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "geo = op.geometry.GenericGeometry(network=net, pores=net.Ps, throats=net.Ts)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "geo['pore.diameter'] = spacing\n", "geo['throat.diameter'] = spacing\n", "geo['throat.length'] = spacing\n", "geo['throat.area'] = spacing**2\n", "geo['pore.area'] = spacing**2\n", "geo['pore.volume'] = spacing**3\n", "geo['throat.volume'] = 0.0" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "phase = op.phases.GenericPhase(network=net)\n", "phase['pore.conductivity'] = kappa\n", "phys = op.physics.GenericPhysics(network=net, geometry=geo, phase=phase)\n", "c = 1.0 # mol/m^3\n", "phys['throat.conductance'] = c*kappa*geo['throat.area']/geo['throat.length']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Effective conductivity: 1.15955e-04, kappa: 1.15955e-04, do they match? True\n" ] } ], "source": [ "alg = op.algorithms.FourierConduction(network=net)\n", "alg.setup(phase=phase, conductance='throat.conductance')\n", "alg.set_value_BC(pores=[0], values=1.0)\n", "alg.set_value_BC(pores=[-1], values=0.0)\n", "alg.run()\n", "K_eff = alg.calc_effective_conductivity(domain_length=l, domain_area=spacing**2)[0]\n", "print(f\"Effective conductivity: {K_eff:.5e}, kappa: {kappa:.5e}, do they match? {np.allclose(K_eff, kappa)}\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "alg = op.algorithms.TransientReactiveTransport(network=net)\n", "alg.setup(phase=phase, conductance='throat.conductance', quantity='pore.temperature',\n", " t_initial=0, t_final=880, t_step=87, t_output=10,\n", " t_tolerance=1e-12, t_precision=12, rxn_tolerance=1e-12, t_scheme='implicit')\n", "alg.set_IC(values=u0)\n", "alg.set_value_BC(pores=[0], values=0.0)\n", "alg.set_value_BC(pores=[-1], values=0.0)\n", "alg.run()\n", "res = alg.results()\n", "times = list(res.keys())\n", "times.sort()\n", "plt.figure()\n", "for time in times:\n", " plt.plot(alg[time])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diffusion time: 873.79389 sec\n" ] } ], "source": [ "print(f\"Diffusion time: {t_diffusion:.5f} sec\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'pore.temperature@870' in times" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum error: 0.02174\n" ] } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "plt.figure()\n", "x = net['pore.coords'][:, 0]\n", "a = alg['pore.temperature@870']\n", "b = approx_sol_dim(u0, x, 870.0, 10000, kappa, l)\n", "plt.plot(a)\n", "plt.plot(b, '--')\n", "plt.show()\n", "print(f\"Maximum error: {np.max(a-b):.5f}\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def approx_sol_inhom(u1, x, t, N):\n", " out = 0\n", " for i in range(N):\n", " n = i+1\n", " out += ((-1)**n)/n * np.sin(n*np.pi*x) * np.exp(-n**2 * np.pi**2 * t)\n", " return u1*x + (2*u1/np.pi)*out" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "u1 = 1.0\n", "x = np.linspace(0, 1, 101)\n", "plt.figure()\n", "for t in np.array([0.01, 0.1, 0.5, 1.0, 1.5, 3])/np.pi**2:\n", " sol = approx_sol_inhom(u1, x, t, N=10000)\n", " plt.plot(x, sol)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def approx_sol_inhom_dim(u1, x, t, N, kappa, l):\n", " out = 0\n", " for i in range(N):\n", " n = i+1\n", " out += ((-1)**n)/n * np.sin(n*np.pi*x/l) * np.exp(-n**2 * np.pi**2 * t * kappa/l**2)\n", " return u1*x/l + (2*u1/np.pi)*out" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "alg = op.algorithms.TransientReactiveTransport(network=net)\n", "alg.setup(phase=phase, conductance='throat.conductance', quantity='pore.temperature',\n", " t_initial=0, t_final=870, t_step=87, t_output=10,\n", " t_tolerance=1e-12, t_precision=12, rxn_tolerance=1e-12, t_scheme='implicit')\n", "alg.set_IC(values=0.0)\n", "alg.set_value_BC(pores=[0], values=0.0)\n", "alg.set_value_BC(pores=[-1], values=u1)\n", "alg.run()\n", "res = alg.results()\n", "times = list(res.keys())\n", "times.sort()\n", "plt.figure()\n", "for time in times:\n", " plt.plot(alg[time])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "plt.figure()\n", "x = net['pore.coords'][:, 0]\n", "a = alg['pore.temperature@870']\n", "b = approx_sol_inhom_dim(u0, x, 870.0, 10000, kappa, l)\n", "plt.plot(a)\n", "plt.plot(b, '--')\n", "plt.show()\n", "print(np.max(a-b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now consider a source term $sin(\\pi x)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$u_{t} = u_{xx} + sin(\\pi x)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "subject to B.Cs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$u(0) = u(1) = 0$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def approx_sol_source(x, t):\n", " return (np.sin(np.pi*x)/(np.pi**2))*(1-np.exp(-np.pi**2 * t))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def approx_sol_source_dim(x, t, kappa, l):\n", " return (np.sin(np.pi*x/l)/(np.pi**2))*(1-np.exp(-np.pi**2 * t * kappa/l**2))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "x = np.linspace(0, 1, 101)\n", "plt.figure()\n", "for t in np.array([0.01, 0.1, 0.5, 1.0, 1.5, 3, 100])/np.pi**2:\n", " sol = approx_sol_source(x, t)\n", " plt.plot(x, sol)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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UzWknP0pcwuPZlTjL3IwWGpDsOprMSdYIL1Mf/yMWJYReX4Bet4BwpIThYQujI2Gy2SwwE9Aulwu3243VYsWsN2ES9WhFLRpERARkZLIoSLJEIpsilkkQjUbx+/0EAgH+6/+NVqulpMRKaWkMq22YdLqFdNpLRMjhtPladikraYvr0YUyFI+nCA75uEq3l1uN29hviHG33cOSgxlWnzQxWD6LyqIL6c8bZ3LOGbb6L+a64Z+S27OM7pUOLCcHibpKUQSBIqOTm7/82bPZlKqzRA101VknSRI//vxXiNsMaJIpMuIE1eZGgqPb6C6Zz/j8Es4fP0VN//lMTx7D0r2fkbwIT6zVcn3ax4UJMz9ObGYrazBXzgS53arlImMnS+P3kC/3YDCUAAsZHS2gqzNJNisjiiLFxcUUeQrJ0znJyVixRLXI0ymygSRyXPqH90E0a9G4jQg5BuKWDD5tDK8UYHRqnNHRURRFQavVUFunp6hoHFk+Sjo9zrimjoOmD/BMYhbxiETxeIpYv4/rNC/xMcOTbLOkeVR0s+WlLLkBO8nq1Tjz5tM963m2exZRc/IMVYN9GKvWM+pvBU0JskGPNSrxuR99+81rNNXbkhroqrMqHI7yi9vvIG23oA/H8BZEOWe4jhHLvTxddAvLKg9T2V5O+UQeY31Pkhcc4b51EnWeBDcHE9yf2sxv5YvQV+QyUWqi2AoXa3ayJHEvZhEM+hWMjFZw5nQGRQGn00lNeTUl2lw8ARPKUAI5mpnZGAE0LiPaXBNatxGNTY9g1pAVJbJCduYRdMgIaBAFEY2iQZPVosQlspE0kj+J5EuQDab4r6l40aZHKDMx5YwznJmia6CHcDiMKArMmauhqKiPZPIAMUXLIfOHeUpaxURYpnAkQWbAyy3ap9ii384vXVaGxg289yUd454yiisuYaBwiJ55Xlq757F+/LeUpD/AvryTuP0OMlYzhlCcW7/zDcwm49lrYNVbSg101VkzOjTOvf/2UySrCbMvyFi9htVHrYyWvcgj1R/hKvEZqk+vJz7egaVzF21VEY6tEPlycJKh9AruSFxFuqyMyQozxXa4QtjK4tSfMOo8pJIraW21EY3K2O125lbUUSnnYetXyE4nAdA4DBhmOdAUGIkIQXyREQJTowQmxghNTRIPBUlGI393P4w2OxaHE0dePq7CIlx5xeRaS7HKdrJjCZK9IeRIeuY7PSbCFQp9wiSn+jqIRqPY7RoWLAii0+8lkQly0HA9W5WLmQwp5PfHMI8McIf5T7j0R/iuI5+Ve7LUDttJ165DW1RF79wdPJzcyDX9P6doZDN7Fngp7BFJuu3oInFuvu0LePLcb2pbqt4e1EBXnRVth9t44tEHyBoNWL1ThOe5mX1gku6aKO0NS9gw0k19/zKG+5/EM9XNvRdmWWsNcUHMzudj76cjp5mpWVZycjVcLT7O0tRDmPWlTPuW0tamQ1FE6qprqDeUk9OnRfGnQBRmArzKzHR2jMH+NkY7TzM9MoyizBz41BmMOAsKceYXYna6sDicGG02dHoDGp0OUaNFzkpImTRSOk0iHCIeChEL+glOThCcGEdKpwAQRJHckjKK6+ZRXt5AjliI1BMl1R8CBcRcA1MVGU4nBujq7UajUWhsTON07SeWnmC/4T08nN1EyJvB0xtlQfAQ3zH9J4/bEhwK2HnfC1omi+opqbyIM1V7eLpgAUvbd1DVXUL7KhvutggxTy6aeIJr3/9hZtdXn80mV70F1EBXveVefm4PO3c/i6zVYpseRa6pwHLqAMcqG7HNS7LwpIv8YQuxjicYz/Hy8nkCXw162ZG4mH8XLida44ZiM5fp97E+dRd2fT6TE0s4eVKPwWCisWoedeF8dH0zUx+GKgdKlZ7B4Cm6W/Yz3tMFioLOaKKkbg4F1TXkVVZjzy1FypgI+5KEfQnikQyJSJpkNIOUkclKMnJWQdQIaLQiWp2IyarDZNNjsuux5xpx5BrRaOOEvENM9fUw3tPFaOdppFQKBIGimnpqGpdTZp+D0p0kPRgGAVLVBs5YxmjrO006nWRBY4rcnIMEM36eM36CJ1KL0Q3HsXZ7+aTwCEuMz/MtRx4XvKjgiubjqL2ckRIvh+emoT3Fgt5+fM1NGE+NEvEUIKYybLp4CwtXLTzbza96E6mBrnpLPffodg4e34ciipgDQ9hLG0iPPcHzldeyovwIc08sJTPUg7NzN1tXJqgqjXJZ2M6t8ZvpL57PdLWVFbYxrkl9iwKtjH96Na2tVkwmC4vKGpg9loPoTSNadegXuBhJd9F++EWmBnoByK+qZtaipRTXzUeRPUwMRPEORpgaipAIp/97QwUwWXUYrXqMFi06vQZRK6LRCGSzCllJRkpnSUYzJCIZkvEMf3YKOxaHHk+5HU+ZjfwKCyiTjJxpo/foQbyDM6dLFlTX0LBoPUWaKtItfuS4hJyvp6PAx7GBNtLpBI2NIRzOlxnLmnjQ8BWOhPPI6Y5QN36cH1p+wwP2JL4+CxcdNhOtWwdlxbQ3ttLSO4e1/U8iV24g09dBNKcEUcqyds0GVl1wzlvZ5Kq3kBroqrfME/c/QkvHiZmHUESGKXYuIBB/kCdqbmGzaRdz29fh7XsBm7ed310s84nsFD2xtfxEvJ7p2lxySzTcKN9Fs3yAdGoNR4/motFYWFK+gOpBJ5pQFl2hhcwsgZbuF+g+sg85myW/qpr6VefhqWzEO6QweHKayf4wclZBEMBVaCGvzEZuqQ1nvhlHnglbjhFBhHAqTCgdIiklycgZMnIGnahDJ+owao04DU5sehtKFiLTSYJTcYKTcbzDEbyDEQKTcVBAoxUpqLJTPi+XnOIsk30nOL3nJXxDA2i0WmqXrmZ+1To03VmkyThZt4bO4mmODLQBCRYvnkTU7uaoZh338wFCIxlyO6f4AveTa93H3bKHm7ZrCBY14q5cw6n5O3g2sJqNPfdgdV+Nf6KVmKsMQc6yYuE5rL9iw9nuDqo3gRroqrfE1vsepK3n1CsPah6m0rgQn/B7nqj/KFcm91F/ZgXezkeI6AY5sD7Ll4Jxvhm7mYN5KwnU2bnQ2spV6R9hFWdx4kQ9kYiFpoq5zB3LRx9U0JVaCRWEOHRkK1P9vRgsFuatOZ+yhtV4h/X0HJsiOBkHwFNmo6TORUmti4IqO9NZH6enT9MV6GIoMsRQZIix6BiBZICskv27+6YVtLiMLoqtxZTZyyi1lVLrqqU+px6XkMN4b4jRzgDDHQGmR6LAzA+R6oUecouT9J/Yw+k9O0gnEhTOqmXposuwjljIjMVIuwXaCsZp7T+NyxVn/oJ2wtkR/qj/PC9F6nGdCbHGt4vPWu/hWzYb657VoBOryKu5nNPz9vAoq7mi81c4DTcwFmwhaSsFRWFp4zI2XHXJm9rmqreeGuiqN90Tv/sTLV1toCiIqRFqaGLC8BDPLngvV0ycprK7lkT7Qxyp8WOZG2VdsIBbUx9nqLYSZ4WOD8o/YYHQydjoKrq73VQWVrAsUY1lArQFZgJFAfbve4jgxDju4lIaL7gYjXEO3YenmegLIwhQXOtiVpOHsvk5jCmDHJk4wuGJw7RMtRBMBQEQECiwFFBmK6PYVkyOMQe30Y3D4MCoNaJ/5QIjSZZIy2mSUpJgKkggGcCb8DIaHWUoPMRkfPLVfXcb3TTnNbOoYBFLCpaQny1hoN1H3wkvo91BUKBotpPZi5ykoidpfeEpAuNjuItLWbniGuxDVqSpBJFi2K/rYHhihLr6afLz93CUZu4RPka8L0VFdzf/Zvg5j7kC6E+YmDvowdZwHT01bTzqbmRz+29xK9cxkDxOxlIGisKyppVcuGXj2eoWqjeBGuiqN9UT9z9MS2frK2E+Sm22gVHrE7y84HI290/h6bWhObmVh9fE2ewMEAit5oeGm5huyGG5u5+bMt/GrMzi8KFaDLpcVjnmU9RnROs0EqtMsGvf/YQmJ8irnEXjBVcSDhRwZv8EqZiEq9BC3fICqhbm0J44wc7hnewa3oU34QWg1FbKwvyFzM2Zy5ycOcx2zcakNc1seFaChB9iXkgEQEqClAY5A6IOtHrQGsHkBosHzG4QNQDEM3G6Al2cnj7NqelTHJ04ylhsDIACSwFrStZwXtl51Osb6D0yTceBcUJTCUw2HXNWFmKyDtHy7KP4hgdxF5Vy7rIbMPboyEZSDM2Ksy/QhixPs3jJGUIMc4/uaxzzFZPb7uXL0m/AcZRdXieb99oQG7YwNnuSx0rLWd/yB/LTV9GXaSVjnRmpr1x8LudfdsFZ6RuqN54a6Ko3zTMPP8nh1oMAaBIjzJYbGLY/w7HG89nUkcHSFcfU8zy/35Tis4qf+yPvZVveBtLz7Nyo/QNrlecZGzmH3l4PDcV1NI8UoZM0KHP0vNz+R8b7u/CUVdB40bVMDbvpPjSJAlQtyGXemmK8zkGe6nuKZweeJZwOY9aaWVm8knNLzmVp4VIKLAWQisDESZhog8lT4O+DwACERvgfRzn/HkEEewm4K8FdBflzoXDBzJ96C6PRUQ6NH2L38G72j+0nmU3iNrrZULGBiysvxuEron3XKAPtPkSNQN3yAtz5kxx/+o/4x0YomT2PlfVb4FSSlD7L0aIRzox1UVMzSV7hyzwnXsqDqSsxtQe52v8Um21/5JdSDjc8ayBZu5FQrcLj1W5Wn3iCgvSlDCRbSdlKERSF8865gHMuPPfN6AKqt5ga6Ko3xd7n97Bj93MgCGjjI1Qr8xiwPU9H03I2nDKj6RhBN7qHRzenuS2W4ouxT9FS3URONXwi+3XKETl6pBGdppBztA0UTJjQVlhoi75M+5EXsOV6aL7oGvyTxfQc9SJqReasLKJmTQ47As/yUOdD9If6MWqMrC1by6aqTSwtXIohk4T+PTCwDwb2wuRJXg1ukxtyqmdC2VkO1jww58yMvrWmmVG5qJsZpUtpkBIQn4bYNEQnITgI/n7w986M6mEm6AsaoGI1VKyCilUkNTr2j+3n6b6n2TW8i7ScZrZrNtfWXss5tvM5s3OKjgPjKDLULvFgc/dzfPvDxAJ+mpdvol63BGk4zmhxnJcTbSjCOAsXHadHMfBzzdeJdEks6jvC7dZ/4/sGC1c/qSddsY5krYun6kUWHn+eovRmBpOtJO1liFKWSy69iqZlTWetv6jeGGqgq95wbYdbeXzrQ8haDcbwEBXaeQxaXqS7aQkb29xIHafJ+g+yb2OSDwaNfFL6PL0Ns2kuGOJm6VtIoQba26qozathyWgpBq2eQGmAl3bfiyLLNG64HEVo4sx+L6Io0LCmhIIVeh4a+gNbe7aSkBI05DZwde3VrC9fjyUegDNPQud2GNwPsjQT0KVLoHwFFDbOjKZtBX/1YRZSViadlUlLMpmsgk4joNeK6DUiWo34vytAUSA8CuNtMHYChg7A8GHIpkCjnwn22o1QfwkRg4XnBp7joc6H6PB3YNVZuXL2lVxVfB1De2Oc3DOKgMCcVblkU4dpeW4bWr2e88/5IPZBG0klxYGCQXq9vTQ1dSNZO/m19nZOjuZRe7KDnxl/wC9tWdY/pUcpWA21FTwxL03j8R0UpS9mJNZO3F2GmM5w7Xs/QM2c2W9+B1G9adRAV72h+rsGuP+eXyPr9Zj9QxRb5jFieJGepkVsavWQPnWUWOoEPevirPWX8jnNp5loKuYK27NcrjxMb9cKpn1lrLbOp2LcjqbCxN6BRxnuP0lV8xIKZl/MyT0RMmmZOSsLyTtH5A+D/8n2vu0AbKzayPV11zPXWgKnn4C2P82MxFHAUwc1G6DmQiheNDPiBpKZLB0TEbomIgz6YwxOxxkNJpiOpgnE0kRSr32TLptRi9uiJ8eip8RlpjzHTJnbTF2BnZoCKwbtzLw6mSSMHIGuZ2de0z0zo/fKc2D+NSh1l9Aa7uWBMw/w3OBzaAQNm2dt5rri9zKyM0nHoQkMZi0Nq80MnXycofYWKmsWsqzwErJDcXqLguwNt1FQOEh51QH+JL6PpwLnUtQyzM/4IVudUzQ9b0ZvW4xm3hyemJtgwYmd5Cqb8AZOE80tRpNI8y+f+ix5RZ43u5uo3iRqoKveMMFAmF9859tIZiNW3wge9xy8bKjWFiYAACAASURBVKereTmbWj2k2g8xpWklviJKmW8BX7d+jGSTm5t1v2AZfRw9shirvpK14XrskglfkZeXXr4Xo9VG44Xvoa/NQXAyQdlcNzWbHPx+/B629W7DoDFw5ewred/c91EQnoQjv4X2RyATm5lCmX8NzLsScmYBMBpMcLh/mkN9fo4PBej1xsjKM31dIwoUO02Uuk3kWAy4LXqcZh1GnQa9RkSnEchkFdJZmWQmSzCeYTqWxhdJMRyIMxZM8Mqq0IoC1XlWmstdLK10s7QyhwLHKzfK8nbObGP7n2bm7PU2WHAtLP4gwyYr9528j8d7HkdWZK6YfQXXet7HqW0+RruCuIstlNVN0fLMH8ikU6xf9SEcIw6ChgQvmU+TkvtZuPAQu4T53JP8MI4TU3w78TPacrso2GXBqVuIZt4Cts2L0nhsBxbDZsITHUQ9RehiCT51+x1YrKaz0INUr5ca6Ko3RCaV4Qdf/BJpuwXr1ASOgllEU0/TvvBcLm3NId1+gCFjG45FYZL+c/mR6yYMTRY+zR2UJESOH2+ixlnL0tEy9HkmDvqepr/3ODXLz0VvPY+eo2HsuUaaLy9he/YhHux8EAGBa+qu4YNz3k/OyFHY97OZ6Q2tCRquhIUfgOJmJFnhyECAlzom2dExRZ83BoDdqGVhuYuGYgdziuzUFtgpcZnQ/bVplH9QWpIZDsTpGI9wejzEydEwxwcDr47ya/KtrK3LZ119Hs1lLjQCMHwIjt4Lp7bOTMtUngMrb2WqsIHftP8Hj3Y9ilbUcmP9jazLXs7RrUNEAylql9qJ+p6j9+gBamqXsdC+nqQvzoHiAfpDXSxcdJxevZ6fKV9GPh7hK6F/J5xzGO1+Ox6a0c5r5NF5SRYffRqd/VISI4NEPR70oRif/8Gd6iPt3oHUQFe9Ib77qX8l6bRg9vnRlhUgBrZzdPEGrmy3kG05SJ/1JMWNQXr9l/PbwivxzM3yOeUrCL5yOjvqWWVpZLbXTao0y7OHfo2gFWg4/0Z6Whwkoxka15cwWtPKL07+nEAywGXVl/HR+f9CQf8+ePlH4O0ARyksuwUar0c2ODk+FGBb6xhPt40zHUuj14gsrXKzpjaP5VU51BbY0Ij/e848LGUZTaaZzkj40hJBKUtalknJCpKioBUEDKKAQRRx6bS4dRpydFpKjXqs/zXF8meyssLpsTAH+6bZ2TnF4X4/kqyQZzOwaX4hlzYWs6DEgRD3w4n74dCvITI+czB19ecYLmnml22/4um+p/GYPNza8Gnc7bW07RzB4tBT2TBN2wu/R0TLRYtvQTcMHfleDkTamDv3JEnXJN8XvkWoNcvHvQ9iz3mOxCE7hVITwoL5PFwvsPLoVlIFG1D6AiRyHJiCMW776Q/eiq6jegOpga563X7wmS8QsxsxBiKky0y4Jnbx8pLNXH1GQGk5Tpe9jdnzghwLXMcfyjczqybAZ+Tbmeprwu+tYZ00n8KMgwFrFwePPUpJfQPW/IsZaE3hKbNRsdnAjwa+zenp0zTnNfOFxbdRP9UNO78zE+R5c2Dlp2DeFfiTCo8cG+aBQ0MMTMcxaEXOn5PPJfMLWT3bg8Xw36POqJSlNRLnVDTBqWiSjliCoUSagPT3rw59LW6dhnKjgTqrkblWE3OtJubbTFg0/x304WSGPV1enmwdY2eHl3RWpjrPyvVLyriyuQSHXp6Z+9/3M5jungn2tV+l1ZnPdw9/j5PTJ2nKa+KTRbfR/UQU/1iMWc0G/EOPM959htVLrqcoUMaYMcgOsZ2Cog4cZaf4oXgHw2csfHjkEcrdj+M75qAsswi5sZY/VZtYc+hxfKXLMQxJpB1WHDGJT//gW6+rb6jeWmqgq16Xu26/k0kliS6WIFaSpai/hR1LN3FdXxqh5RQ9luPUzguyO/BeHpm9kebKPj4q/4Suk8vQpOtZF5iD3Wpln/dxhsdOM++8KxjvryYellhwYTEHCrbx+87f4zQ4+fziz3ORxo3w3Bdh7DjkzIbzvgRzLuPMZJT/eLmPp1rHSWdlFle4uGZxGRvmFWB9JcQTWZl9wSgv+yMcCEU5FU2QfaWLe/Ra6i1GKkwGyk0GSow6PDodbr0Gl1aLURTQiyJaQSCjKGRkmaSs4M9Ir47kh5NphpJp+uMpTscS+DMzPxi0Asy3mVnqsHCu28ZypxWDODOtE0pkePbkOA8cHqZ1OIhBK3J5UzEfWl1Fda4J2h+GXXfOzLOXLUe+4Fs8kRjmx8d+TDQT5QP1H6RxcD1tL45iyzHgKTrJ6d1PUlnRxFL7RgKJCC86TqE1n6ai/ij/Jn6BU92FvHfgcea5/sTUMQel8lIyjRU8UOxi45GnGKyoxzphQDIZKLPm8IHP33o2upbqn6AGuuqf9tg9f6CtvwMxIxF3hqkaGuappedzzYQfw7FB+vWHqVoQ5MXAB9lWdyEry1r4kHwvrcdWkCPOYY23Fk2elu2tvwa9QNXiG+g5YcDhMVF6qYbvDnydkegIW2q28KlZW3Ds/iGcegxshbD2KzD/Wg4MhLhrdy97uryY9RquWljCDcvKqcm3ARDISGz3htjuDbEvGCEpKxhFgSa7mWUOK4scFhqsJvIMuje0bhRFYTIt0R6JcyQU41AoxolwnLSiYBJFznFb2Zjr5CKPA/sr0zSnxkL8/uAQjx0fISXJnF+fxy1rqllYYp2Zinnp2xD3wYLr8a/6JD/suJ8n+56k0lHJ50tup29rklggxazmOF3770ePkQ1zPkzal+IlTwcRTjG36SB3iR/laF8N1/Q8RVPOHwgcdlIkriC+yMPDrgI2H9pDf7ELYyIXRSOyvGkpF1yp3vflnUANdNU/5cSBY2x76lFAIKtMUuVT2Lp4PpdHJrAdCTDIXooXBtntfz+Pz9nAuuIDvCf7MMePrqJCO48VvipinijPHv41nspqTK5LmeyXmb3Uw8m6HdzXdQ9F1iK+tfx2FvUfgl3fBUWGlbfCyls5PJriJy90caBvGo/NwE0rKrhhaRlOs56ULPOMN8QjkwF2+cNICpQb9azPtXN+jp1lDivGv3HgU5KiJJNjZDKBmZcUQpZTKHIGWZEQBS2CqEMUDei0TnQ6Fzq9C6OhCK3W8prrjWdl9gej7JgO88J0iJFkBr0gsDbHxlUFbi7IcaATBaajKe4/OMjvDgzij6U5p8bDp8+fTVOeOHO84OBdoDHAuq+yv3gOXztwB76Ejw/X/gtVrSvpO+6jqFokPPEo/pEhNi75KIYpAy/n9jCWPUXjor38VnMT+wYXcG3nk8zLeYDEQQeFhnX4lmjZrivm8hO9DDgTyPoixKzMNTe8j9p5tW98R1K9oV53oAuCsAH4GaAB7lYU5buvUW4L8DCwWFGUv5nWaqC/vQX8IX7x3TvJGvUYwkOUJwt4fI6NdaKPvMMC4/EXcC4LsN9/E4/N3cjGoh1syTzDsaOrmK9ppDFYwpC5h4MnH6Oy+RwCviVISai71MXP4nfQHehmS80WPle4Fsv222Dq1MyFOBd9j86ki+9sP8PuLi+5VgMfO28W1y0pw6jT0BtP8rvRaR6e9OPPZCk26Lg0z8Vl+U4arCaEv7hoSJIihMNtRKOdRKNniMV6SCSHyWQC/3Td6HRuTKZSLJbZWK11WK112G0NaLXW/1FOURROROI8MRnkiakgE+kMuTot1xS6eW9RDuUmA/G0xP0HBvn17l4C8Qzn1+fzxY11zBInYfvnoPclKGoivOE7fHfwKZ7se5K57rl8VPdlTj3lw2QBq30fg20HWT3/OgojpRxxD9KZbaVp0V7+oN3CS0PLuKHrcWrcDyHscZLrvpjhpTEOxRxs7BUY0gyTcJeijaX4zDfvUJ9P+jb3ugJdEAQN0AWsB0aAI8B1iqKc/otyNuBpQA98XA30dy5Jkvj+v36RtMOCxTtOvqmc/Z5J6nIlqva78E9tQzo3QLv/PTw852IuLnqey1IvcfzYKpYITcxJFtGW2UvHwH5qVmxh6EwJ9lwT+o1T/KD3W5h1Zr659Guc070b9v98Znrlou/jLVnPj1/o4qEjQ1gNWj6+tpobl1Vg1InsD0b592Evz0+H0QkCG3Id3FDk5hyXDfHPQlySIvj9+/EH9hMKHSUa7eS/LvvX6z1YLTWYTGUYTaUYjUXodTkzo2+dA1E0IIp6BEGLokjIchpZTpHJBMlkAqTT0ySToySSwyQSQ0SjnWQy/le+WcRmrcfhXIjbtQKXa/n/CPisovDSdJg/jE/zwnQYRYGLPA4+UprHYoeFaErivn39/Hp3H8lMlvcsK+fWtdW4+p+EZ784cxOx1Z/jhfJGvn7om8iKzBcqbyf0pI14KE1pbR+d+7ayoHodtSyizTJCi9JC86KXeVB/KTuGVvK+rsfIz3mEnBddOEuupGPlBCMjGRoj1fjCg0Tz8jAGY3xBPfPlbe31Bvpy4HZFUS58ZfmLAIqi3PkX5X4KvAh8DvicGujvXD/49G3EHCZM00Es+XlMZFsxVbtp3FdOqO9BptYHmfBfwf31W9hU9DybEztpObGaVXIz1XIB+3yPMxnup3T+9Yz1OCmZ4+TAnEd5enQbSwuW8p3Z15K3/Uvg64SFN5E9/xv8/kSAHz7XSSKT5cbl5Xxy7WycZh0vTIf58cAkLZE4bp2G9xfnclNxLh79f8+HJxKjTHmfwed9kVD4OIqSRaOx4LA34XAuwuFowmatQ6/PfcPrKpXyEo2eJhQ6QTB0lFCoBVlOIAg6nI6FeDzr8eRtwGgoePUz46k09474+N3YNEEpyxKHhc9WFHCOy8p0LM1PXujiwcND2E06vrChjqvn2hCfuw3aHoKCBsYu/Ca3nbmHFm8LW0qvpaFlA+NdIUpm++g/8SBluXNYYt9Ih36Ug5ygedEe/mi4hB2Dq7il+/do3M8y61knttobObS6D83pYSyWJaRH/SRdduyxNJ/5wXfe8LpSvTFeb6BvATYoivKhV5ZvBJYqivLxPyvTBHxFUZQrBUHYxWsEuiAINwM3A5SVlS0cHBz8J3dJ9Wa5+84fM5IMoYslyBQKWL3HmGqex9p9VURP3UfnRQHwX8CvZt/IxrIX2JzYSVvLuaxJL6RMk8uLg78jpU3iLL6G6TETs9e6uEv/DXpDvXx0wS18OBxDs+tOsOTBpT+n1bCIL21t59RYmNWzc7lj81wqcy3s8Ef4fv84bZEEpUY9nyzPY0u+G9Mr8+KZTICJiW1MTG4jHG4BwGqtJzdnDe6cc3HYGxHFN/Yg6D9CltMEQ8fwT+/BN72TWKwbAIdjEQUFl1KQfwla7czB3Fg2yx/H/fxyaIqxVIZFdjO3VRay2m2jazLCVx8/yaF+P01lTr59WQNzQnvgqU9BMoR0/h38Up/m7pO/Za5rHu+P3EbPy348pTF8/X/EJro4r/haeplkr/YYjc27ech4CTv6VvKv/f/BtHMvTdtzMDW+j+eWD1F55CjevGaMkxokk4HZniJu+MRH3vL6U/19rzfQrwIu/ItAX6IoyideWRaBl4CbFEUZ+FuB/ufUEfrbz/4dL/PCzmcRsgpJa5CSqV6OrlzOVUc8pI7dz6ENXorCy/lu5b+wvnIPV8afp631XNYmmyk25PBM12/QuixozZcTj+gp2iTwvcCXEEWR7y+8jRX7fzNzF8Q5l5Hc8GN+vHeKu1/uw2Mz8LWL57KxoYDWSIJv9I6xPxilzKjnUxX5XJXvRicKKIpCIHiQ0dEH8XpfQFHSWK315OddTF7eRZjN5a+5b9lsFp/Ph8/nIxgMEgwGCYVCxONx4vE4yWQSSZLIZrPIsowoimg0GrRaLSaTCZPJhNlsxuFw4HQ6cblc5ObmkpOTg0bzvy80+i+xWC9TU9uZnHqaWKwbUTSQ57mI4uLrcDgWIggCKVnmwXE/Px+cZDSV4Ty3ja/OKqLeYmTriVG+/fQZQokMH10zi48vdaF/+hMz94qZfSEvLbmeLx/5HjpRx79av8Pw9gw2d5Jk8BGEmMKFVR9gIDPFbsNRmpp3c7/xSvZ2NnPHyC84bWvhnOcL0S29mgfnR1i5dx9DnhIM6ZnfZC6/7BrmL57/hvcz1evzpk65CILgAHqB6CsfKQD8wOa/FepqoL+9BPwhfvG9O8kadOhiIxTFZJ5eMY/39GoQ9j/FzvNGWJio5YvFn2V17TGujz9O24nzWJdcSIHRwdOn78JaXERK2oBGY0a+cIifj32POncdP5l1DcVPfR4ycbjo+xxzb+RfH2mnzxfjuiVlfGljHTEBvtE7xmOTAXJ0Wj5bkc+NRbnoRIFsNsXk5DaGR+4jGu1Aq3VSUHApRYVbsNnm/K99URQFn8/H8PAww8PDjI2N4fV6kWX51TJGoxGHw4HFYnk1sLVaLRqNBlEUkWWZbDaLJEkkEolXgz8UCpFMJl9dj0ajwePxUFxcTGlpKaWlpbjd7v91cFZRFCKRdsbGH2ZiYhvZbBSbbR6lpe8nP28joqgnmZW5d9THTwcnCUtZri1086WqQnSSwjeePM1jJ0apzbfxwy3zaRj7Ezz/FTC7Gdj0XT7VcR8D4QE+U/BVUs94EMQEoryNlC/AxpqPMJz2sdt4jAXNO7nb8B6Ona7jJ1N3skc3xLqXK5BWreWBMh2bD3QwZpJI2UvQJlJ85hvqQdK3m9cb6FpmDoquA0aZOSh6vaIop16j/C7UEfo7zp2f+hwppxXL1DgFWg9P1Fu4PJzC/HIrO5vPsE7I45M5X6N5XicfSD5A+4m1rE0sIt9o46lTv8JVWUM0fB4Wl4WeFTt5bOqPXFh+Ad/Eg2n39yC3hsyV9/FvbSK/3NlDocPE97fMZ0lVDv8x4uVHAxNIisJHSvP4eFkeNq0GSYoxOvYgQ0N3k057sVrrKC25ifz8S9Bo/mfIxONxenp66O3tpbe3l2h0ZnxhNBopKSkhPz+f/Px8PB4PLpcLo/GfD6lEIkEgEMDr9TI5OcnExASjo6OkUikA7HY7s2bNevVlMv3Pm2Bls3HGJx5nePg+4vFeDIYCystupqjoGjQaI4GMxE8HJ/ntiBeLRsNtlQW8rziXXR1TfGlrO9PRNJ9eX8NHauNoHn4vhIaJnf81bkt0sXtkD9fnv5+i3UtJxmKYTC8QHhrk4jkfZTQdYLfpCA3NO7lL/yE6W0v4eehrPJ8Oc+6pBkLn1vCsTs/GfoWp1ARRT756kPRt6I04bXEj8FNmTlu8R1GUbwuC8A3gqKIo2/6i7C7UQH9H+cnnv0zIrMPoD2PLyeGkYYz5NgOFu0LsK93PeqeJT5i/SUWTl4+nf0P78TWcF19MntHG06d+Rc6sBoL+1bhLrLw4914Oh/Zzy9wPcEvXIYSuZ2DeFgZX3Mknt3bTOhxky8ISbt88l65kik93DtMZS3J+jp3/x957h1dZZf/b9+k1J733nhCSEBIITXoHUUEQEcuMin1U7HVsWHCso4N1BlFRAUWlSyf0lgDpvfeck5zen/eP+MVhwDKj47w/zX1dua4E9tk8e/Hks9dee+21n0mOJE6lwOOx0dT0AY1N7+FyGQjwH01s7M34+488x/O1WCyUlpZSVlZGXV0dgiCgUqlITEwkISGB6OhoAgMDEYv/80JcPxWv10t3dzeNjY3U1tZSW1uL3W5HLBaTkJBAeno66enpqNXqs58RBC89+n3U16+gr+84cnkQsTE3ERl5FRKJgkqLnUeqmikwmMnSqng5LZpoiYyHvzzDptNtDI8P4JVL4ojcvRQqNuPJmMurMamsLP+Ysf6TGFV4BX0dZnS+uzHUVzF70K00O/QUqI8yOHcfL0vvpPu4kledj7G7w0tez1iax8up6zQSI03A1tGLw8+HQJeYO5Y9/l+34QA/jYGDRQN8L1998BmFNcVI7U7cQV7kfWV40+IZvEtFGZvJTXbxgGgZ6mFwv+dlSk+MY6x1GKFyHzaVriAoKQdD10hC07Ssjn6RBlsdy4b8iRn7VkB3FUx7lo2qi3ng8zNIxCKem5vF+IwQXqht593mLsIVMp5LiWJakC9er4vWtrXU1f0Vp7OTwMDxxMfdjq/vd7fsuN1uqqqqKCoqoqqqCq/XS0BAAIMGDSItLY2IiIhfRcB/DI/HQ0tLC+Xl5ZSWltLb24tEIiE1NZUhQ4aQmJh4TuzdYDhKXf1fMRgOolREEJ9wJ+FhlwFivu7q5dGqFvQuN7dFh3B3bCibT7Xx+FfFSMQiXp6fxWT9J7DzKQjP4osR1/LUqb+Srs3g0uo76Ko14RdQgKGulFnpt9Lo0nNIfYT03AMs4yFERy08IXmUsmIVyapLOT7BiO50Ob2BaSj0KjwyKRcNG8OkOdP/dwYd4CwDgj7ABWlrauPdN19HkErw0Em4qZnSUdlML/CjpWU1wSP6eN3+Z/pGhPKI+BmqToxkpHkkkTJfNpetIDBpGIau4YRmqlgR9Bh2wcbradeT983TADjn/oNlpcF8cKiBoTF+/HXRUFrwcEdZIw12J9dFBvFIQjg+UgndPXuoqlqG1VqLr28eSYn34ef33TtrMpk4fvw4J06cwGw2o9FoyM7OJisri9DQ0PNi1v+K1+nB3WHF1WXFY3Dg6XXg7nPgtbrwWt0IdjeC24vgFsArgESESCJCJBUjUkkRq6RINDIkvgokfgqk/kqkIWpkISpEsu/fFIX++HlbWxunTp3izJkzWK1WdDodeXl55ObmotF8d/JUrz9ITc2LGE2n0WpSSU55jAD/kRhcbp6sbuXTdj3JagV/TY/F3yVw68cnKWk1ctO4BO6Lr0f6xY0gU3FgykPcXfIWAdIg/tj6GJ3lFvwCD2CoK2Z22i3Uebo5rj1I/NDjPOV+gujD1Vyrfg5rgT8hCVfz1ahecgtO0aEJwiuPROJ0cfv9D+If6Pcz3rgBfgkGBH2AC/Ls3ffh1KlR97QQ6pWxbWQsi0qUWE5+hHFKFztNf6Jo+FCeUP2ZppNDyDGOJl4ayKbyFQTE59HbM5zgHBmv+jyAVqFlReRMkrY/A4FJdM5eyY0b9Zxq6uXGi+K5a2oKrzV28kZjJ1FKOa+lxzDST4vVWkdl1TJ6enajUsWRnPwwQYETzwp0R0cHBw4coLi4GK/XS3JyMnl5eSQlJX1vdong9uJsMeNsNOJsNOFsMePR289pI9b2i7NYI0OiliJSSRHJxIgkYhABXhA8XgS3F6/Njdfqxmtx4el14LW4vutIBNIAJbIoH+TRPshjfJBHavv7uQD/t8I4duwYtbW1SCQSsrKyGD16NEFB/dklgiDQ2bWV6urnsdubCQ6eTnLSw6hUkezVm7irvJEup4t74sJYEh7Es5vL+PhII8PjAnhruoaAr64BYyslUx/ntvp1eNxebtc/S1exHV///fQ1lDIr7RbKPW2U+h4gdEgJT1ie4qJjBeTo3iH8m3AUwxfwUYqb2Ufa6BRZMAdHoOg189Crf/n5L94AP4sBQR/gPF6+9yGMWgWqnl4CfALZE+7mUqMC2b4dnJlYj960gPVDZ/OY71P0nU4k1TCGdHEYmypW4Bs7BKNhJIHDRPxFfj/Ruije1gwm9MBfIWEChSNe5cY11dicbl5aMISUBH9uLqnntNnGovAAnkqKRC320NDwDvUNbyISyYmPv53oqGsRi/uvjGtubmbfvn1UVlYik8kYOnQow4cPJzAw8ILjcXVasVfosVf14qzrQ3D1Z7RI/BTIo32QhWmQhaqRhqiR+it+1Kv+IbxODx6DHVenDXeHBVe7BWeTGU9f/6aoSC5BkeCLIskPVVoA0qAL3wzU1dXFkSNHKCoqwu12k56eztixYwkPDwfA47HT2Pgu9Q1vASISEu4kOuoPGD3wUGUz6zt7ydOpWZERx4myLh784jT+ajnvz09k0L6bofEQTePuYYn+EHqbnrvNy+kudKHzK8DUVM6s5JspEjXS4L8fxeBWlukf5pqiNYg0Gxm2LxnLhJF8I7czoldLb28Pdn8d/k6489kn/mPbDfDzGRD0Ac5h06dfcKykEKnDiTdIjMXeQGxQBBHfNLE/u5AY7zCWp9/K3WGvoCjVEtE9nhwhhs1V7+ATlYGxbxS+wzy8JLuf9MA0Vji0+J1ZC0MW82nYUh7bUEGUv5p3rs6lGDf3VjQhE4l4JS2aGcF+9PadoLz8ESyWKkJCZpKS/BgKRQgAbW1t7N69m8rKSlQqFfn5+QwfPvyczUTo92JdrRZsp7uwlfbg7rIBIA1WoUjyQ5nohzxGh0Qn/9Xs6ulz4Ggw4qjpxVHdi7unf1UgDVWjyghEnRWMLOz8wl5ms5kjR45w9OhRHA4H6enpTJgwgZCQfpvY7a1UVD5Bd/dOfLQZpKUtQ6fLZH2HgfsqmpCKRLyaFkOUE2768ATdZgcvXprKnLqnoGQ9XUMXs0RopaGvkaXW5fQWCvj47sPWUsP0xBs5Iq1BH7wbQ6rAmy238GTly5R7SxhTlk/D9CD0TfWgjUdqkOGRSpk4ZgJjp0/61ew6wLkMCPoAZzH09PLG8ufxyKUIng4CHM105aQzbLuIo75bGBIQxN0RT3Ft0qfE1ehRtU1mjDuFrTXvoQhPwGIahzbPycuyBxgWOpTXe0xoqnfgHfcQy8wX8/6BesalBPPigmxebOnkw9Yehuk0rMiIJVwmUFv7Eo1Nf0epjCA15UmCgiYA0Nvby44dOyguLkapVDJq1Cjy8/NRKBTnPL+nz4HlZCfWwk7cnVYQi1Ak+KLKCESZHojUT3GhYf9PcOvt2Ep7sJf24KjrAwFk4RrUOSGoc0KQ+Jw72djtdg4dOsShQ4dwOp1kZ2czadIkdDodgiDQ1bWNisoncbl6iI29hfi422iwC9z07epnSVQwt4YGcucnhRyp0/OnCQncLVqN6ODr9KXP4laNQElPOUutyzEWidBoduLubGNK/B/YIy/FG/UNxbGxfFFxMa91PszJNjO5rjkUTHYTd7iEHmU4+d720gAAIABJREFUXkUEEruTe54cyE//XzEg6AOc5fm77sXup0XT1UoQsG90JPMOqajt+oj4TA/3qZYzIvsEk9qOYK+bzhRHJjvqVyIODsNqnYQ618kr8gcZHT6cV5ubUDYdxTHjJW4rz2ZHWQfXjYrjj5OTWFLaQJHJyu0xITwQH47NfIaS0vuwWquJjLyKpMQHkEo1OBwOCgoKOHToECKRiJEjRzJq1KhzcrcFr4Cjuhfz4Tbs5T3gBXmsDvXQENSZQYjV//4Rf4fVgqmnG7vZhN1sxmG14HG78LhceD1exFIJEqkUiVSGQqNFqdWi0urQBgSi+JfVwk/BY3ZiO92NpbATV5MJxCJUgwPR5IejSPA9Lx3zwIEDHDlyBLFYzOjRoxk1ahRyuRyXy0hV1TO0tX+OVjuIQYNeRK5O4anqVt5v6WaEr4Y3U2N4bUs5a443Myc7gpei9iHb+TjW+LHcGuxHYdcZllqXYyoClWobEr2JcdGL2Koswjd5C18FT6b0ZBLPuB6k7biSqPhr+GyIjclHOukUXFiCQ1EZzDzw2kA8/X/BgKAPAMDf/vwMnSI3il4T/j5+7Av3ML9djf3YOtzju3jb/RTq4S5uMH5IR9ksZlpzOdCyFpefCpt9GsohTl5TPcRF4fm8UlOCoqeavpkrWHwwjJLWPv58cQZJ6YEsKWnA4fXyenoMM4J0NDS8Q23dK8jlQaSnv0BgwBgEQaCkpIStW7diNpvJyspi0qRJ+Pr6nn1eweXFUtiBuaAFd5cNsUaGJi8UzfAwpIE/fmO94PViaG+lo64GfXMjPc1NGNpaMHZ34bRZ/2M7KtQafIKCCYiIIiAymsCoaEITkvALDf/RbBvoj/dbjrZjOdGBYHMjDVXjMzYKdXYwIul3m6l6vZ4dO3ZQWlqKr68vM2bMIC0tDYCurh2UVzyCy2UkOekBoqKu5YsOA/dWNOErlfJuRizHitpZvrWCvFh/PsitRrPlLqwR2dwRGc3xrtPcZXwBSzEo5ZvQWMTkhV7MJu1xIjM284b6JmSHbVypeBrdrggko+bwpb+ZXL0MS58Jp05DtErH9Q/e8x/bcYD/jAFBH4BTh0/y5cbPEXm8eHQO7I4Okv1jCdxWRMXYMkqtN3Fm5GAe9i6n8dQUZhtHcLpnOwa5Baf7YlSDvbzq8yDjw/N5qfIkcmMr7TP/zoLtSrpMDt68KocmHwkPVzWToFLw98HxxEj7KCm9B4PhECEhM0lLXYZMpqOnp4dNmzZRW1tLeHg4s2bNIioq6uyzeh1uzAfbMB9owWt2IYvU4nNRJKrBQecI3r/icbtoq66kqfg0zeUldNRU4bBaABCJxPiFheMfEYlvcCg+QcFoAwJR+ehQaX1QaLRIZTLEUilisQSv19PvsTtdOKwWbCYjNpMRU083pp4ujF2d6Fub6e1oh29/h5QaLWFJKUSmZRAzOIvQhGQkUun3Pq/g8mA91Y15fzOuditinRyfiyLR5Icjln+3aVtfX8/mzZvp7OwkJSWFmTNn4ufnh9Opp6z8Qbq7dxIYOIFB6S9Q5VTzxzN1tDlcvJQWjbrTwd2fFRETqOazcQYCN9+ILSiFP8Ulc7SjiLsMy7GUuZFJviJUCCXFfwybdAeIHbKLp7yPMvrwASKVH5F1KJOWGal0ttfgVUUiNfeXBl58zfUkpiX+rHdzgH+PAUH/neN2u3nhgYdxaZXITS34ejtpG5pC3mYLRxJ3oROP5b2cxTypfYrWwmFM7R1Hi/Ekje46PMJc1OkSXvW/n4vC8ni14jgyi56aqStZsEXAKwi8e+0w1jssvN/SzeRAHSsGxeIyHqa45C48HhupKY8THj4fQRA4dOgQu3fvRiKRMHHiRIYNG3b2INBZIS9oxmt1o0jxx2dc1HkhiX/GrO+h9uQxak4cobHkNG6HA0QigmPjCU9KISwphdD4JAIio5HKfvnqiy6nA31LMx21VbRXV9JWXUl3Yz0AMqWK2MxsEnKHk5AzDI2f/wX7EAQBR1Uvpr1NOGr6EGtl+IyLOkfYPR4Phw8fZs+ePQBMmTKFvLw8RCIRzc2rqKp+HrnMn8GDX0fQDOHG4nr295r5U0wIE5CzZNUJNAop66baidp6PXa/aG5PSONEZwl/6lqOpcqKWPicZFUW/poUdvvvITjnNE/0Psbdp9+izXmCkS3TOTBDTuLhGgxif+x+UciNVh5+ZaA0wK/JgKD/zvnL0gcw61Sou/QES2XszdMx97CUYsdq4qNDuC/2Se6LfgV3UQQjeibhsbVzxngAQXI56iQtrwffR35oFq9XnEThMHJmwkoWbnLip5az4tpcnuvsZrfexM3RwTyaEEZT41vU1r6CRpNE5uA30GiS6Ozs5KuvvqKlpYW0tDRmzZqFj09/GVnB48VytB3jjka8FhfKVH90k2ORR/tccDyWXgMVh/ZTfnAvbZXlAPiGhBKfM4yYzGyi0zNRarUX/OyvgdXYR3PpGRqLT1F78jimni4QiYhKyyBt9FiS80ej1vle8LOO+j6MOxpxVPci1snxnRKLOjcUkbh/Quvt7WXDhg3U1NQQGxvLnDlzCAwMxGQq5UzxbdjtLSQlPkhY5HU8Ut3Ch609zAzy5XZ/f27+4Dh2l4d1M7yk7LwBqzaYWxLSKO6u5rbWF7A19OJ1rCUvaAoOuY6ikB04Mo28UXcTr7Q+RF2NiTS/xawd6mbCGRudDgu2QD98rS7uXr7s1zTx75oBQf8ds3XNlxw+cwKp3YnUT0ql1s3sHn9MhWvRjLTyiPYlZmdsJbHSQHzLdEKdYva3r0eivhxVZCBvRD1ATkgqb1aeQuk0c3TsP7h6k52YADUvXz2Uu+tbKbfYeCElmitC5BSX3E1Pz25CQ+eQlvoMYrGKo0ePsn37duRyOTNnzmTw4MGIRP3lcO2lPfRtqcfdbUMer8N3RjyKGN154/C43dSePMqZnduoP1WIIHgJjokjddRYEnOHExgd+5Pi1782giDQ1VBH9bHDVBwqQN/ShFgiIT4nj8yJ04gfkov4AgekHLV99G2tw9loQhqqxm9mPMrUgLN9FhUVsXXrVrxeL9OmTSM3NxePx0xp6X10dW8nJHgGaWnPs7LdxuPVLeTo1DwfFc4dq47TZXLw6UwxWbv+iFkbzE1xSVT1NHNz/bPYO9txmT5nQtRC6qVW2mM2UZQYT0HRCB513o97vz+K/EvZrm4nyq0CA3jkUiaNmchF0yb+2ub9XTIg6L9TrDY7L/35z3gUcvB2IPN24R+VStimItrGVrDe/Si6fDOXtH2DvOYShrjC+ab+A2S6i1EEx/Bu/KMkBkXwbk05aoeRglF/5w9bHaSH63hsYRa3VDXR6/bwXkYc+aoeTp1egs3WSEryY0RGXoXJZOLLL7+ktraWlJQU5syZg/Zbz9nVZaX36xocVb1IQ1T4To9HmX5+2VmzvodT2zdzeuc2rH29aP0DyBg/mbTR4wiK/v765z8FQRBwOTw4rG6cNjcetxevR8DrERBLRIglIiRSMXKVFKVGhlQu/lmTxv+Je9n+PZTu29U/noBAsiZNJ3vKDNS+fue1txX3YNxah7vHjnJQIH6zE5AG9KcLGo3Gs/ZNTU1lzpw5qNVqGhvfpbrmRbTaFLIy32a3WcutpQ2EK2T8NSGKhz4qpFFv5ePpkLfvBow+YfwxOopOg4lrqx7HaajB1buZ6Qk3cEhWhzjlK1YHzUN62MVE2V+IO5BJw8VpuGsqsRGIRxmB1OrgweefRfoDewYD/DIMCPrvlOV33oPV3wdNVwcB2CkZFcq4TUaOJ27HJJrH/vx87nW8juHMHKY4B7Ot7j0kfuOQ+gzio9Rn8PdXsbKxHl9bH7vz3+P6b1zkxvpz22UZ3FTZiEIs4uOsBCKcxyguuRORSEbm4Dfx9x9OZWUl69evx+VyMX36dHJz+y9z8Do9mHY1YipoQSQVo5sai3ZEBCLJuULZUVfDiU1fUnGwAK/XQ0JOHlmTpxM/JO+CHu334XZ5MLRZMbRbMLRb6eu0YjY4MBscWPoceD0//f0XS0VofBVo/RVo/ZX4hajwD9PgF6YmIFyD5Ac2bP8Vj9tN7YmjnN65lfpTJ5HIZKSPGU/urEvPm6gEtxfzgRaMOxsRvAK68dH4jI9GJO2v237kyBF27NiBSqVi3rx5xMfH09Oz79v/EwmZg9+gRpzJNWdqESHi7eQoXlhTTFmbkY+mehlxYAndfpFcGxqIyyhhful9eKynEPqOMDnuj2xTFhKctYlnxfcx6+g2RJ6tDG+ZxfYpKnKPGOj2SrAEB6Hps3DfQDz9v86AoP8OWfveSkoaa5FZ7Gg1Ko7FillwRkaxeTXBkfE8l3Evf9Y9Q8/JMcwyj+ZA0ye4fJJBns9Xma8j+FpY1d5DsLGD3fnvcv0OL/nxgVxzcSq3VjYSrpDzaXYCop7PqKx8Gq02lazMt5HLw9i1axcHDhwgNDSU+fPnn61RYq82YFhfjafHjjo3FN/pcecdrmmtLOPwF59RV3gcmVLF4AmTGTp9Dn5h4T86ZkEQMHbbaa0y0F7TR2ejCX2LBa+3/x0XicAnUIlPgBKNvwKtnwKFRoZSLUOukiKRiZF8W5RL8Ah4PAIelxenzY3d4sJucWHp7Z8MTHo7Jr39/+6fRiwVERihJSTWh/BEX8KT/dD9hNRKgJ6WJgq3fE3J3l24nQ6Sho1kxNwrCE1IOqedp89B7+Y6bKe6kIao8J+bjCKuPxbf3t7OunXr6OnpYdy4cYwdOxa7vYFTp2/CZmsgLW0ZNt/ZXHGqhj6Xh7dSYnjjixLONPfx8RQ3+QeW0BKcwDX+SnR9oUw7vQSRdz9KUzPDI+exxXcfvjnHeLr7YZ6qfpqO2g5Sgq9ibbKNIR1e7CYnbpWcEYNzmb7g0p807gH+MwYE/XdGX6+R155/FkEqRZD2YBXbyJEl4jq4EcVICw8HvsrNSe8iKQxnYs8UKjp2Y1DI8IqmUDDkY9p8q/nQ4CK6u5Y9w9/mD7uljEwI5NIZSSytbiZNo+KTrDgMjS/S1PQPgoImMzjjFaxWD2vXrqWxsZHc3FymT5+OTCbDa3fTu6EW64kOpIFK/OYmo0w8N7zQXl1JwaeraDxThNJHR+7MSxgybRZKzQ9vbjpsbppK9TSU9NBcpsds6K+nolBLCYn1IThWR3C0D/7havyC1Uhkv1xpXbfTQ1+XDX2bha5GE12NJjobTDhtbgB8ApREDwogdnAgUWn+yJU/HI6wmYyc3LKBwq1f47BYSBg6jNFXXE1IXMK57Sr09K6vxtPrQDMiHN8Z8YgVEhwOB5s2beL06dPEx8dz+eWXo1B4OXPmdvSG/cTF3YYy/DYWnq6lye7ktaQoPviqnFPNfXw6wcywQ7dSE5HJtRonyaah5BVegkTYQohbQUzwSAqCt2DOtvBh2UKeMC6FAwG4x0+mpq8CkaADcSgSx8Ap0v82A4L+O+OFO+/B5u+DursNX3EfnUOiyVxfQ9PIQtaKniB9eCVDKhtIa5qDu6+BKlcNXtFllOTs4qTPHlbaVKS3FnMg/w0W7/FhdGIQk6fG81BNK/m+Gv6REU5jxT10d+8gOvoPJCc9RFNTC2vWrMHhcDBnzhwyMzOBb73ytVV4jA58xkWhmxRzTmGsnpYmDnz6IVVHD6Ly0TH8ksvJnjIT2Q/cKGQzO6kt7KL6RCetlb14vQIKtZSotAAiU/yISPYjIFxzNjPk10TwCvS0mmmt6qWlopfmcj1OuwexRERUWgBJucHEZwej1Hx/CqXDaqVo20aObfgch8VC2uhxjF6w+JxVitfhwfhNPeaDrUgClATMT0ER54sgCBQWFrJp0ya0Wi1XXHEFYWHBVFQ8TmvbGkJDLyY0aRmLzzRTbLbxUkIkazdVUdTUy7px3eQcvpPCuGHcKNFzkfFiEk8NR+z5nEHqbJw+gdTHfkVBQjadR4KYxvMknBjO8UtiiDvejsGrxRIcgtpg5v6BU6T/NQYE/XfE6jffobKzBbnJSqBSxeFUEZcVODgR/AUG2VxOjcjgesNqlOXzSLAqOaTfiki2gNaMarb4f8ibolBG1R7hRP6rzN8bRH58IBOnJfBYbSvj/X14J82fyuIl9BmLSEl+jOjoazl27BhbtmzBz8+PK664gtDQUASXh74t/YIjDVbhPz/lnOwVm8nIoXWfUPTNJmQKBXmz55I76xLkqgsfq3e7PNSd6qb8UDtNZXoEr4BvsIrEocHEZgYRFq9D/D0la/+XeDxe2qv7qD/TTU1hF6YeO2KxiJjBgaSPDCc2M/B7Y+92i5njG77gxOav8Lo9DJ05hxFzr0Ch/q7Al6O2F/26KjwGO9qxUfhOiUUkFdPS0sJnn32G1Wpl9uzZZGdn09D4DjU1y/H3H0VC+htcV9rJ0T4LLyRG8sXGSopb+vjqoiYGHb6fnSljWepuZG7PzQSUhiE413BRyCWUqnrxDvqCN7U3MuHIfuTWbeSaF/D5CBFDawXMVjsujZLclAwuXnzFr2Xm3xUDgv474btQiwRB2kuXystEcwyNdZ+gTYpiecZSHlc+h/XkTMbaUtnZ/CGC8lJsyR4+DH2B51RJzC7bRWneM8w5mMiQaD8mTE3g6cZ2pgTq+GuSgrLTf8Rma2RwxisEBk5h69atHDt2jOTkZObOnYtKpcLVYaFndTnuDiva0RHopsWdPSDj9Xgo+mYzh9Z+jMNqJWvKDEbNX/S9edmGdgvFe1uoONKOw+pG668gJT+MpNwQgqK0/79MVfw+BEGgq9FE1fFOKo+2Y+1zotTKSBsZzuCxkfgGXzjmbjboOfDZhxTv2YHKR8eYhVeTOWEqon86kNW3qQ7L0XZkkVoCrkxDFqTCYrGwdu1a6uvrGTVqFJMnT6aj4yvKyh/svzwj8z1uLDdyoNfMM3HhrN9YSVWHma35Z4g/sYw1g6fytLmCP7Y8hrTOjsi2kckx17FDfRrt0J08ZXyQx6uexlJiIChjPkfF1fg4FEAQEoeLB556Grny16t2+XthQNB/J/xzqEUr0eNMTyRy43Hso1p4OuAVbk16F46nMs04joLGj3EqRyGKiuKd2Ee5XZfMjWe+oSF7KVOODyct3IdJ0xN4rqmTWcG+/CXWQ8npa3G7zWRnvYNSmcXatWupra1l9OjRTJo0CZFIhOVoO70bahErJQTMTzmbOw3QVlXBjvf+Rmd9DTGZQ5hwzQ0ExcSdNw7BK9BQ3MPp3U00lRkQS0Uk5oSQPjKcyDR/xL9AKEXwePD09uLpM+I1GfGYzAguJ4LbDR4PSCSIpFJEcjkSHx/EPj5IfH2R+PmdFdKfg9fjpbFUT/nBNupOdeMVBGIzAsmaGEX0BdI3ATpqq9n9wbu0lJcQnpzK5BtuOye+bivuRv95FXgE/C5NRDM0FI/Hc3bSTU1NZe7cuZjMhzhz5nYU8mDSsz/g1moXu/Umno4JY93XlTTqrezO3kVY8du8kj2dVYZqltQtw9Nehcp+khFRV/BNwA6cOa18UL6I+0xL0R1MomrOYPzPNGP0+PWHXnrN3D9wIcYvzoCg/w74dMV7lLc3IjfZCFQoKEoXMX1rDxUp29gtv5+44XUMKutiROslVLftRC8LRhIwgveTH2V6YBRPndpO96DrGFc8nWh/DbMvTmZZUwezgn15MdpK8enrEInEDMleicsVyscff4zBYODiiy8mJycHr8ODYX0VtqIuFMl+BCxIPZvB4rBaKVi9klM7tqD182f8tUtIGTH6PNHyuL1UHeugcHsj+lYLWn8FGRdFMmhMBOr/oK654PHgrKvDUVmJo74eZ309rsYmXB0duLu6wO3+9w0tkyENDkIWGoY8JgZ5fBzyuDgUKanI42L/I7G39DooKWihpKAVq9FJULSWnKkxJA0NOS+MJAgCZQW72fPh+9hNJobOnMPoKxYjU/TvObh7Heg/K8dZZ0QzLAy/OYmIZGKOHj3Kli1bCAkJYdGiRUAdRaf+iESiYlDWB9xeK2K33sST0aGsXl+O3uJgX+o6dBVruTd7Mge7OvlD5RN4jHsJdtuIDBtFYdR6jqUk0XfIlzzXXxlcP4cNk7QMrhYw2xy41Ury0jKZvWj+v2/nAb6XAUH/jWOx2nj5ySfxyqQIUj2dWglTO0IpNX2CPeQiduWP5fqeTwipWIBM30aVqxWRcjbrBr9EQoicFaf3Yk+Ywdi6a9Gq5My/NI1nWzqZGeTL8uheSk7/EYlEw9CcDzEY5KxevRqv18vChQuJjY3F1Wml56NS3F02dFNi+3Okv/Wi64pOsP2dNzDpu8mZPpvRC64+r/ysx+2l/FAbx7fUY9Y7CIz8VtDyQpD8G3FxV2cntpOFWE+ewH7qNPaKCgT7d1fPSSPCkcfEIgsNRRoaijQ4GImfLxKdDrFWi0guRySTIZJIEDweBJcLweHAYzbjNZnw9Pbh7uzE3dmBq60dZ2Mj7vb2s/2L1GqUqamosrNR5Q5FPXQo0u+5YelCeFxeKo+1U/hNI4Z2K7pgFcNmxpEyPPQ8YbeZTexf/QGnd27FLzScqTfdQXRGFgCCR8C4vQHTniZkERoCr0pHGqiiurqaNWvWoFAoWLx4MRpNL4VF1yAIXgZlreT2OiV7DSb+HBHCys/LwOtid8RbiJr3cX3GSPSdCmYX3wKOLxmkSKHXX4cxfQ1v6v7IvOMboPkkyaELKdA24GsVgygEqd3JA88+g+y/UEfn98qAoP/G+ecDREpZD+KkZHTb9yDkuXkycRmPal/Ee2ImOeZgDui/QSRbwP7M9VjC6vmo4jTK4Eymdi/Fipxr5qazrL2LaUE6lkd0U1Z8PXJ5MDlDPqSx0cy6devQaDQsXryYoKCg/mX+mkpEMjEBV6aiTOovQOWwWtn9wTuU7NlBQGQ0026+k4iUtHOe2+sV+oV8Uz0mvZ3QeB15M+OIHRz4k2LjXpsN69GjmPcfwLJ/P866OgBESiXKwRkoBw3q/0pLQx4bi1j10/LC/x28FguO+noc5RXYy8qwl5ZiLy5GcDoBUCQnoRk9Bs3o0aiHD0Os+PELOASvQN3pbo5tqqO7yYxviIphs+JJGRZ6XuZOU8lptr39On0d7WRPncW4q/5wNkPIVtaD/rNKAAIXpaFM8ae9vZ2PP/4Yp9P5bQaMiJOFV+PxmEnLWsnt9VoOGMw8HhrMW+tKCFW42ahdhtFYz6KEFMLbs8grmYzgWMPYkEs4om1AnLuD5/T38ETjfUiO+NI2cwSasgaMnkCsAweOfnEGBP03zJY16zlSfBKZ1YFOIaUsRcy0TS00ZxXwvs8LXJHxBarjsUzuHc3u5o/xKi+hLr2GI+EbWN3aTpTMj/mup6gyy7nl8gye7upmfIAPr0Z1UXrmBpTKSIbmfEhJSTMbNmwgPDycRYsWoVFrMO5sxLSzEVm0D4GL05H69otVc1kxW958GVN3N8MumcfIeVcilX8XMhGE/hj5ofU16FsthMT6MPziBGIyLhw7/mc8RiPm3bsx7diBuWA/gt2OSKFAPWwYmpEjUeflokxPRyT/323GeZ1O7MUlWE8cx3LwILbjJxBcLsRqNZqxY/GZPBnt+PFItOdfR/fPCIJA3alujm6so6fZTFC0llHzkohOCzinnctuZ/9nH3Jyy9f4h0Uw4/alhCelAv23JvWsKsXVYcF3ejzasZEYjUY+/vhjuru7mTt3LomJ/hQWLsbp0pOWtZKb67QcN1p4PDCIV9YUMyzIzkrPw1SJPFwdrGNS6yIiKoOR2rcwLnox2wN2Y8jpZtupKVxheZT48ilsnBbCoBoPFrsbt0LOlIsmMnrqhP+azX9PDAj6bxS3283zDz2CWylH7Omkw1/K5LYQSryfUu9/BfaRCkZUVjKiZS7FLRswyrKwxPnyWczLvGWBfFMvd6hf5Jt2DXdcnsELfXrydBrejOmksngJKlU0Q3M+4ujRcnbs2EFiYiILFixAhhT9ZxXYS3tQDw3B/7JkRDIxHrebg2s+4ujXn+MXEsaM25cSkZJ+zjP3tJgpWFNFS4UB32AVIy9LJCEn+AeF3Ot0Ytm3j76vN2DeswfB6UQaEoLP5EloJ05CPSzvJ3m+/yv+byVh2rkL065deLq7ESmV+EyciO7i2WjHjEH0AyEJwStQeayDI1/VYtLbickI5KIFyfiFnhu6aiw+zda/vYLZ0MPIy68k/7IF/bXdnR4MayuxnelGNSSYgHkp2N0OPvnkExobG5k5cyZZWVGcLLwKp1NPUubfub5WS7nFzn0aP175ooT5MSaeM9zDnsAI7pI7uLr+EeRNLfi7q0mOnEJhzFq2xw8j6nAroZ0bSddeyX5dA/5GER5FODKLjUdeGvDSfwkGBP03yotL78Oi06Dp7kYj6UGUmIh073bc2QG8k3kzf7K+Q0jplXg7q2nweiF4BH9Pfpz7JRquaCrlxdDlrKgL4Y45g3jNaSRNo+Td2B5qSm5ArYpjyJBV7N17koMHD5KRkcFll10GFg89K0twtVvwnZWAdnQEIpGIvs4ONr2+nLaqCjInTmX8tTciV34X4rBbXBzdWEfx3hbkKgnDZyeQMTbiB2Pkjro6eteuo2/9ejwGA5KAAHSzZuE7aybKrKxfJNvk10bweLAVFdG3cSOmzVvw9PUhCQrCb+5c/BbMR/5PF338K26XhzO7Wzi+uQ63y0v2pGjyZsadcwLVbjGz8/0VlB/YS/SgTGbecS/agEAEQcC0pwnjtgbkMT4EXjMIr0LE2rVrqaysZPz48YwYkcrJwsU4nV3EDV7JtTVqmuxObhGpeXNTBQ8lt7Ck+QHeTxjKWw4z15U/CYY9JEl9sQeH0Zf5GcvFd3B32XJkJ93op4xHU9FCr9cXa5A/vlYndy9/9tcw82+aAUH/DXK84DAbt21E4nSjUYqoTpQwdWMDndlHeSX4Fe6MWoHs2HgyewM50ncIsXYen2a0JYFjAAAgAElEQVS8wGRfCX+uOML6uMe4uzydG6cksVLmIEwhY1Wihfri61CpohmS/SE7dhzm+PHj5OXlMXPmTNytFrpXlSI4PAQsSkP1bUpi1dGDbHvrNQSvwNSb/kTqyDFnn1MQBKqOdbB/bRV2s4uMiyLJn5OAUnthj1TwejHv24dh1SosBw+BVIrPhAn4XT4PzejRiH5D1fwEpxNzQQG96z7HvHcveL1oxl5EwDXXohk96ntXLVajk0Nf1lB+sA2Nr5yLFqaQMOTcVU7J3p3seP9vyOQKZty2lPic/t9/65luDGsqEGtlBF2XgThIyddff82pU6cYNWoUY8dmcbJwEU5nD1GDV7GoUobZ42GBWcrKPbW8n17IxLoXeSB9JMd6RFxechde61pG+U/kuF8TzmGHeafuOm7V30tE8Tg2TQ8lqcGD3SZCkIi5ctE1pGSk/ir2/a0yIOi/QZ5deh9OHxUyaytdASLGdUVSJv6E4/63EDe8nvgiKRO6x7G3dQ2Cah57Mr5CFdbM+2VHKYu/gUvKJjJ/RAxbg0XIRCJWJ7toL70WhSKEIdkfsXXrQU6dOsWYMWOYNGkS9goD+o/LEGv6hUAWpsHr8VDwyQcc3/AFYYnJzLrzAfxCw84+Y1+Xjb2fVNBUqickTsf4RakEx1z40gqvw0Hf+vXoV36As74eaWgo/ldeie/cy5CFhPwyRvN6wNTe/2VuB3MH2PvAbgSnGdwO8Lr724klIJGBRA4KH1DoQOkL2lDwCQOf8P7vf6FVgqu9nd616zB89hme7m7kSYkE/uEP+F588ffuB7TX9bHn4wp6ms3EZQUxdmEKPgHflUzoaW5i42sv0N1Yz4h5VzLy8oWIxRKcTSa6V5UgOL0EXjMIebyOLVu2cOzYMYYNG8aECUMoLFqE220iMP0jFlZ4kYtgdLOLDYWtbE/fRFT9aq5Jy0XemsqI0klI7F9yUeRC9oRsoyRTg/mQjoyeVaSqFnJUWYdvnwyHLgJFn4WHBnLTfxYDgv4b483Hn6JL7EXV04u/xIw9KRTl3u30DY5lY+7FLGrbSHbdIsqbN2OUDacuuYvTsZv4tKYMIXQCI2uvZUxqKOWpGoxuL6tTxRjLr0Im82dI9kds2nSQkpISJkyYwNixY7Ee78CwvgpZuJag6zKQ+Mix9BrY+NoLNJcWM2TaLMZfcwMSab/XLXgFTu9p5vD6GkQSESMuSWTwuMgLHgjyWiwYPluD/h//wN3VhTIzk4DrrkU3deoPxpV/EI8buiuhowQ6iqGrHPS1YKgHj/P89mJpv2hL5CCW9Yu51wNeV7/IO839Qv+vSBQQEA8BCRCcBqEZEJYJgUn9ffwHeJ1OjJs3o1/5AY7ycqTh4QRefz1+l89DfIH6Nh6Pl1M7mzi2oQ6RRMToeUkMGhNx1lt3OR3sfG8FJXt3EJc9lJl33IvKR4e7107330tw99gIWJCKKiuI7du3c/DgQYYMGcKUKTkUFi3C63WiSP2Yq8rtRMhlhJ/u5VRDN4dj3sLSfZyF8QmMrruCiBoFAZ5iYiMnUJ68mvcCFrK46AO0p/ronjoW34pe9F4ldj8fotQ+3PDAvf+RfQYYEPTfFB0tHbz95msASBVOGmPkTN/cRFf2CV6Keol7dX/F/+Q8lJ2t1LgEbJEprEl5kb93GUiVBTOm+yFCgwJxDg+i2uHkwzQV3sqrkIiVDBmymk2bDlFaWsqUKVMYNWoUpl1NGLc3oEjxJ/CqNMQKKW3VFXz9l2XYLRamLLmdQRd9l73Q12Vl16pyWqt6iR0cyPirUtH6ny9EXrsdwyef0vPOO3gMBtQjRhB0802o8/P//eP8DhM0HILGg9B8HFpOgqv/cmjEMghKgcCEfuH1iwVdRL+XrQ0FpR/IVP21db8PQQCXFWy9/V69qR1Mrf0ThL4Oemqgp+o70Zf7QORQiBoGsSMhZiTIfzij5fx/UsBSUED3W29jO3kSaXAwgTffhP/8+Rf02I3dNnZ9WE5LhYGYQQGMX5x21lsXBIEzO7ex6x9vofEP4JJ7HyUkLgGv1UX3qlKc9cb+/ZAxEezZs4e9e/eSnZ3NlCnZFBZdiUgkxZH4EX8oN5KlVGA/2IHN2M1u36coFNm52VfD4qpHkbWdJE3hgz7YF1Pe17zadRt3tC8lrHgsmyYHE91qRnD4IPJ4ufPBh/D1O/9mqgF+nAFB/w3x/J33YPf3QdXTgtnfQY4tnko+YW/gUkbmHCH4eDzDuhM4aNiP2O9SPsx4mnu8Ni436Lnc+yytolBiJ0ZRYLXxTqovfnVX4/U6yRmymi1bTlJaWsq0adMYkT+Cvo21mA+29meyzEtGJBFTWrCbb95+He23whAcGw98e4LxQBsFa6sQi2DMghTSRoadJ86C203v51/Q/eabuDs70YwaRdAdt6POyfnpRhAEaD8NFVuhZie0nOgXU7EUwrIgejhEDO33loOS+0Mn/23czv5VQfuZ/udpPgrtxSB4+ieV6OGQOBFSZ0DIoB+eQP4Fy9GjdL/+V6zHjyOLiCDojjvwvWTOeZvCglegeF8LB9fXIBbBuEWppAz/LgTWVl3B1y89i8NiYcZtS0nOH4Xg8qJfU4HtTDc+46PRTYtl79697Nmzh+zsbCZNSqWwqH/11hyzkj9V6pmqVlOyvYHBsjZWeh5iZUgk73pULCp5EKHvC0YHTeZQUDHNeV1UHUslr/NtEjULKfdWojD5YA0KR2Uw8cBrL/1i5v898bMFXSQSTQdeAyTAe4IgPP8vf38zcBvgAczAEkEQSn+ozwFB//dZ+/4HlDTWIDdaCZC46E3UErBrF12DEzg0bBQza08ysuUSjjR/gVt1KRsGryLPv5una4v5s+5p1ukTGTMjgQ0OK8sS/BjUdgNOZzfZ2R/yzbay78R8eD6GdVVYCzvRjo3Ed0Y8guClYHV/vDx6UCaz737wbEEtm9nJ7g/LqTvVTWSqP5OuTT8nlgvfepz79tHx4os4q2tQDR1K8F13ohk+/KcN3uuBxsNQsh7KN/V7yIj6PeH4cZAwDqKGg/zC1Rr/JzjM0HQY6vZB7R5oO9X/574xkDYLMi7r9+J/QhxeEAQsBw7S9eqr2IuLUaSnE/rA/WhGjDivbV+XjZ0rS2mr6SN5WCjjrkxBoe6f1MwGPV//ZRlt1RWMmn8VI+YtBAF6v6rGcqQdTX4YfpcksXffd6I+fnwMRaeuRa2O5XDwCp6r1zNPrmb75hqWhFawVP8E9yRnU90VwdSSy5HbN5AXNZejMWtZFz2RmSc2EHCqi8aZ4wiucmBwi3BpFIzMzmPavEt+SYv/LvhZgi4SiSRAJTAFaAaOAVf+s2CLRCKdIAjGb7+fA9wqCML0H+p3QND/Pb7LOZchEhloifBh5p4eugYf4pXYF1gqe4vYM4voajlEt3gwxSn16GP382HlabYG38rdjaOZMy2RNdi5Ncqf6b13YrFUkZ31D3btaqS4uLhfzPPy6Vldhr1Mj25aHD7jo3A7HGx+4y9UHztM9pSZTLhuCZJvs01aKgx88/cS7GYXIy5NZMik6PNOMzpqa+lY9iyWAweQxcYQet99aL8t5vWjtJ+BotVQ/EX/RqZUBUmTIHUmJE8FbfB/w9z/HUztULkNKrZAzS7wOEAXCYPnwZCrICTtR7sQBAHj5s10vfQyrtZWtBMmEPrwQ8ijo89p5/V4ObG1gWOb6tH6KZh6QwZhCf0TsNvpZPu7b1C6bxdpo8cx7Za7kEilGLf1lwtQZQURcEUqewv2sWfPHoYOHcqo0X6cPr0EnS6HNZrnWNXWy0KPgi931PJ+3E7yO/7OlSmZxNdNI6nKj3BRA5rwLFqHfMZLplv4U/O9RFRNYlO+kqhWE15pCDKLg0deWv5fMfVvmZ8r6COBJwRBmPbtzw8BCILw3Pe0vxK4RhCEGT/U74Cg/3v85Z77MfuoUXd1gbqXOGUy9ZZP2RF+D5MydxNyJIfYdilnbHpMUclsTn2DzxpqsAVMYWrjYi4eHctarYeLg3Xc7HqKvt6DDM54k4MHLRQVFTF58mRGDR9Jz6pSHDW9+F2SiHZEBGaDni+XP0VnXS3jr72BoTPmAP3H9k9sqefYxjp8Q9RMvT7jvAwWj9lC94q/of9gFWKViuA7bsd/4cIfP8Vp74NTn0Hhqn5BF8sgZRoMngvJ00Dxw7cY/T+B3QiVW6H4c6ja3h+aiRgKQ6+BzPk/Okavw4F+1Sp6VryF4HYTeMMNBC658byN0/a6Pr55rwSLwcGIy76bcAVB4OiXa9n/6Soi0wYx555HUOt8Me1tom9LPcr0AAKvSmfX3t0UFBSQn59PTo6bktK7CAiazkvCUnYbTMzRi9h6tJF9Me/h6DvMVRExzK24B2VrITnaGCpC+2gbXsnJk7mMbFlBVOgVdBsqcTgDsQYF4Gd3cdfzy/6blv7N8XMF/XJguiAIN3z789VAviAIt/9Lu9uApYAcmCgIQtUF+loCLAGIiYnJbWho+A+G8/ujrKiENetWI3Z5UCsEDHESEjbvpy0risJhuUyoqGVEywQOdmxDCLiEjzKe5TlzB/miIIZ1PEBuWjT7Y2Vk+Kh5WvEuhs4vSEt7nqJCNceOHWPcuHGMG3UR3StLcNYb8b88BU1uKN1NDXzx/BPYTSZm3Xkfibn5QH8e9Pa/l9BcbiAlP5RxV6aed72aadcu2p96Gnd7O75z5xJyz9IfL1TVUQpH34HTa/o3NcOyIOdqyLwc1AE//Nn/lzF3wpn/j73zDm+q7vvwndGkaZqONN27tJRCS8umgOwlspEh4gAHLkTArQwVFUEBt4gLEJU9lCmClL0ppUAXdO8mbZq9zvtHeEAUnLw+6MN9Xbn4o4eTc35JPuf7+85VcOJLqD7jTpFMvQPaP+COAfwK9qoqqufOQ79pEx4REYTMmoV3l85XHGM12dm57BznT9QQkxJAr3ubX5qYdG5/Bls/WIAqQMOI517GLyQUw4Fy6jcUuAPh45qxfecODh48SOfOnUlMLCUvfzb+oXfzZONois1WWuebyS0o5YBmNjs8zLwmD2Zs1tOIG9bRKfQ2DoRv5bsmreh5dCshmUYyB3VAU2TDapUiiETcP/FhwqMjr3Z7N7kKf1XQRwL9fibo7QVBmHSN48dePP6eXzvvTQv99/P6E09i9VUiayynLFxGn1MSKuJ/4P3YN3icT0k6cyd5pdswyvvwXco39FIWM626moHW2dh9o6lq7Y9cJuED9fc0lr1Lk7hp5Oc3Ze/evXTq1Ilet/Sg7vNsbKWNqEcn4pUaROm5bNbPfRmph4xhz8y8NLC48kID2z4+jdlgp+uYpiR1Cr3CdWKvrqZq9qs0bt+OvGlTQl6a9esBT0Fw+5f3v+sOcEo9Ifl2aDcBwtv8P6/sTy9DwO4UcLhcOF0CErEIqViMh0T09w3REAQoOQxHPoEz690plk1vhU6TILrTrwZSjQcPUTlrFrbCQnwGDyL4ueeQ+vv/5NQCWT+WsW91Ht7+cm59KAVNhHtHVZZzlvXzXkEkEjH82VmENEnAeLgS3bo85HG+qO9uzpbvt3L06FF69uxJWNhBiooX4R31HA9UdUTqFPA+VEuw5QIrxC/wUlgUx+rj6Jc9CB/7HiLDu5Of8jXvWiYwsfhpIktvY3esgeAqF2b/cDx1jTx7M0D6u/m7XS5iQCcIwtVH0FzkpqD/PpYsfJcLulrk9Qb8xQ3Ig8OoLVnNrpjH6d58NxGHOuNToaXA7s3ZBB2N0T/wRX42sxTT2WRtiV/XMErFLj6JyMNZ+DTh4WOprenHjh07aNOmDQN696fus2xsZQYCxjZDkawh7/B+Nr0zDx9NECOefxnfoGAATmeUsWdlLkpfObdOTLnCxSIIAg0bNlD12usIFguaRx8lYML4a+eSu1xw7jvImOfOWFEGQYeJ0HbCdbXGzTYnF2qNFGtNlOpMlOrM1Bis1DRaqTNYMVgdNFocmGzOa55DKZPg7SlF5elBgFJGoEpOoEpOuJ+CKLUXUQFexAQo8fT4c7nnV8VQ4xb2I4vBVAfhbaHrU27X0zWE3WW1UrdoEbWLP0Hi7U3IzBn49L8ylFV5voGti7Kwmhx0H9eMxA7uLBhteSlrXpuBWa9n8NTniElrg/F4FbpVuchjfVHf05z1320gKyuLAQMGoFR+Q2XVBoh9j/uLw0gQpJTtKuEe1TEeN85jbHxzIs8PJK5ATKJcoDrUk+KOJzl5IpX0C5/h1WwEorJatC4FNh8vUmLiGTH+7uu3fv9i/qqgS3EHRXsBZbiDomMFQcj+yTEJ/3GxiESiQcDMa73hf7gp6L+NzWLjjZkzcHlIEUv1VEcpSf/uJEVpSo6360jPM2W0LmnFAe0xTKHt2dziHVYW55HpPZrHqgeR1jOK/RIH70c34ls4gYCAbjgdE/n2203u3iy3DUH7xRlspQYC7myGooWGUz9sY8fi9wlpksDQZ2bg5eOL0+Fiz8o8sjPKiGqhps+EFlcMObZXV1M5cxaGXbtQtGlD2KuzkcXEXP2mXC639bl7LtScBXUT6DIFWo4C6V9rsFXdaOFUSQOnyho4U95AbpWBEp2Jn37FveVSglRyNCo5Gm8ZPp4eeMulKOVSZFIxUrEIiViEwyXgdAlYHS6MVgcGiwO9xU6dwUaNwUqV3nLFQ0Asgii1FwnBKlqE+ZAa4UdKhC8a77/YNMxudgeF9y2E+mK3G6rbM+4smWsIuzUvj/Lnnsdy+jSqW/sTMmPGFda6SW9j2+LTlOfVk9Y7kvTh8YjFIgw6LWtfn0ldaTH9H51KUudumE5Uo12ZgzzOF79xzVi5dhV5eXmMGDEEm30eDQ3HqYxeytQiGd0dUg7tLGJZyGrCDRsZGxHP8NPTkFftJj2wC0ei9rC5SQt6HNtMxCkPfhyQRGiRHpegRmqx8ezrryL9F7V2+P/ieqQtDgAW4k5b/EwQhFdFItHLwFFBEDaKRKK3gd6AHdABj/1U8K/GTUH/beZNeRKjrzdetdWY/A2kakMp02xkcdwcHhEtJeXsHZwq24JdNYivWy5gduN5EqVN6Vo5mfT0aHb4CDwdIaZ1+Z14ecWi9JrF6tXf0qRJE0YPH4nui7NuMb9omR/5di0ZX35GTFobBk95Dg9PT8wGG1sXuX/8rftF0WFIkysqPvXbtlM5YwYui4WgqVPwHzcOkeQqlqoguIN/O192Bzo1idDtaXfq3p+sqizRmtiXX8vhQi2HL2gp1ZkBt7jGBXqTGKwiIdib+CBvotVKItUKfBUe18WFIggCOpOdEq2JIq2J/GoDeVWN5FQ1cqHWeOkhEh3gRfsYNe1i1XSO1xDu9yd7sjvt7tjCnjfdVa/hbaDXDIjrfvXrczio++RTat5/H4mPD2GvvYp3t26XT+d0sW91Plm7SolqoabvfS2Qe3lgNZlYP+9lSs9m0/u+h0ntMwDjiWp0K3OQN/HDd2wCX37zFaWlpYweMwit9llstmr2BH/Je+V2emsFDh0pZn/wPA5SzlzPOEafehS5cROJEf041+IrPrDezYQLzxJuuJ0ceRFSgz8mTQA+RgtT58256v3c5DI3C4v+gWSfOMXqNSsR2xyopE4cYXIkxzeyv+k9tEk5TtyhW5CUFVJGPDubZ9BBlck0nYku9S8RkRDH/hg5w4MU3KGbgEgsJjTkHb76ahOhoaGMG3Mn+uW52Ir0qO9IQpEcwL4VX3Jo3Qqapt/CgMemIpF6oC038t37mZgabPS46/L2HNwl+5Wvv07D6jV4JicTNncu8rjYq99M2THY9qK7ktM/Bnq84E7V+4NCbnO4OHC+jp1nq8jIq+VCrbsaNEApo12MmrYx/qRG+tEizAcv2X/P0jNYHWSXNZBZWs+RQh1HCrXUm+wAxAd50zUhkF5JQbSPVePxByYyAe62Bplfw49zQF/qFvS+s91FVFfBkpND+VNPY83Nxf/OOwl66skrMmGy95SR8U0uPhoFtz3aEr8gL+w2K98tmMP540foMuZuOgwbhfFYFbrVuXg29Uc5Mo4vli1Bq9Uy9s5+lJY+CiIvPvX+iO11Jtrmm6kvymen93RmBgWQX9eOzufSiJFUYAjXkNfhBIXHY2mdswF9p/7Iy22YbFIEiZj7HphIREzUn1z5/w1uCvo/kNefmIbV1xuZoYLSGCU9Myooaaljd+sB9D9bRHJREkcbLlAaq6EofgXLLuTyGDPJ9UqjpLUfzXw8ecH1PA5zPrGxH/H1VxkolUom3DMe06oLWPPrUY9JRNEykF1LPubElm9p2as/ve5/GLFYQslZLVsXZSGVSRjwcEuCYy+XaZuzsymfOg1bcTEBEx8k8NFHr+4rbyiFHS9B1kpQBkL3Z6HV3SD9/cMnLHYnO89Vs+V0JT+eq6bR6kDhIaFjnJquTQO5JUFDk0Dvvy9w+SdwuQTyqg3syathd24Nhy9osTpc+HhK6ZUUzK3JIXRLDEQu/QMPOLsFjn4GGXPdLQlajYOeL7pbGvz8/a1WauYvQLtkCfKEeMLnz0eecDl7pjyvni0fZQEw4OEUQuP9cDocbP1gAef27abDsNF0Hj0O45FK6tfmo2gRgMegCD79/FMcDgdjxnQgv2AiUu82zHC9QHmjFfURLemOI8x2vs7o2ERS8u4hqOg8HdUpnIw+wromLRl4cjnhZ2PZ1U5JeJEdi38Ynjo9z749/y+v+b+Zm4L+D2P5ux+QV1uFvN6ASqpD7RlGjWsVS+Jf50GPr2l5ZjSZ5dswqfuxNvUtlpflsl8+hlcNg1B2CcXiJWGh8gsE7Qbi4xewZnUuLpeL+8ZPwLmpEsuZOvxvT8CrdRA7Pv2AUzu20ua2IXS7635EIhFn9pWze3kO/qFe3PZo6hU9QXTLv6L6jTeQBAQQPm8uXu3a/fIG7BZ31sqetwAB0h91+8nlV++0+HNcLoF9BbWsO1HG9uwqDFYHAUoZfZoH07dFMJ2aaK5vAPJvxmxzsievhu1nqvjhbBU6kx0fTyn9k0MY1iqCjnG/Pbnp8sl0kPEmHFrkjkF0fxY6PHTVdgeGvfsof/ZZXAYDIdOn4zt82KX3qa828d17mTRqLfS6O4mm7UMQXC6+/+R9sn7YRpuBw+g2bgKG/eU0fHseRVogrp4BfPb5Z3h6ejJ0aDj5Bc/g0oxjUsPtqM0u6jLKectvNQm2DYwPbsbIzKko6rfQIrwvp1t+w+f6MdyR/yJq1WhM+mp0Tm9sKi9aJTZnyJ1jrueS/6u4Kej/IC5VhMo9kIr16KOUNNmZwdHkXkS2KibpQEdcpflUS9LY2HIljzgySXUl0LtmMgmdI8lWwbtBR/CpnENszDNs3eqgvr6e8feOR57RgOlkDX5DmuDVIZjtH71D9u4faD90JF3GuDMMDn97gaObC4lqrqbfA8nIFG7XhbOxkYoXXqRx+3a8u3UjdM7rVwTaLpG3A7Y85fbzNh/idgf4/b4tdFm9mZVHSlh9rJSyejMqTym3JocwJC2cjnEBSK7SrfGfjt3pYl9+LRszy9l2uhKjzUl0gBcj20Qwsm0kwT6/bGx2VeoKYNvz7mKlwGYwYB7Edv3FYY6aGsqeehrTwYP4DhlMyMyZiC8O7bYY7GxZlEV5Xj3pw5rQqm8UCAI7v/iYk9u+I63fbfS8dyKNu8vQbytE2SEEQ1tPlixZQnBwMD16GCkqfpfakNlMqUqilV4g50AxGYHz2CmtYDmtuPXUQMI5hy0ijNMdz9B4TEFa1gky+6SjKazHKdbcrCD9DW4K+j+I+dOeRq/ywqumFoPGQOtiFaUxR/ku7R6GFpyj+flEjjfWkpVgxD9kK7NrGunS8AqhyfEcCZfxfIiWFhUPEBoykqNHkygsLGLs2LFozogw7C/Hp1803t3C2frBQs7u2XWpn4fLJbB7eQ5n91fQvHMo3cYmXpo0b8nNpWzS49hKSwmaOhX1+Ht/OS2osQq2PgvZa93tYwfMczej+g0EQWBffh1LDxSy42wVAtAlXsOotpH0aR78j7bE/yhmm5Ot2RWsOFLCwfNapGIR/VqEcHd6NO1jf6fVnrMFtjwD9UXudgJ9Z/8iDVRwOqn96CNq33sfeXw8Ee++cykryWl38cPSs+QdqSKlewRdRiUgEkHG8s85+u1aUvsMoNd9D6PfWkjj7lJUPSIpjzKzYsUKmjVLpEXyAaqrN3MsdCnzK71oW2RFl3OOXarpTA7RICodQtN8B+38YzgVd4xvIjswKusdwsq7cUZdi0ejHyaNGrXNyeOvvfL/sMr/fH5N0G/mCN1AlBYW0+jpgdhiReovR+Or5rxqFWsCpzPOsI6W1aM4od+JMbgTJZHvMr+okMedL6AKjeBIqAcjAkQkVz6Gj29b8vLacf58FoMHDybwggeN+4vxviUcZdcwtl0U886jxtFxxBjsVifbFp+m6HQd7W6Lod3A2Evi0fDtd1TMmIHYW0n0ki/wavuz75EgwPGl8P10d4pdjxeg8+TfTEG0OpysP1HG4j0XyK82oFbKeKhbE+5oH0Wk+gZqsPU3opBJGNYqgmGtIiisNfLV4WJWHClhU1YFzUJUPNg1jkGpYb8eSE281R0ozZgH+952W+z957jbCVz8TEUSCYGPPooiNY3yadO4cPtIwua8jqp3byQeYvqMb47SV8bJHSWYGqz0ntCcrneORyQScWTjGgB6TngIl9lB464SIgbE0r9/f7Zu3YqfX2/UAUW0q3qYW9Vfss3pQZO6aJ41PMArpfMZGb2F6PrJnKr5kWSvXowKWcUq/8cZUjkXWfhwqDEhttmoF0Bbq0Otucou8CbX5KaFfgMxZ/JULP4+KHTlFDdR0HnvBTJTgqGtnDYHk3AWF1Aja8vqVp+xQHeKAgYx2zYaS3ogkX4ynrc9iqfIidP5LDt3HqFr1660VzSjfkMBXm2C8R0ex/aP3uFMxk46jbqT9BF3YHgPyO8AACAASURBVDHa2fR+JlUX9HQbm0iLW8IBd9pb9Vvz0X7+OYo2bQhfMP+Xk4PqS2DjJDi/C6K7wKCFv1mq3mixs+xgEZ/tLaTWYCUp1If7u8RyW8vQv8UaNzic1Nod1Nkc1NkdGJwuDA4nJqcLuyDgEAScAkhEIBW5pzl5ScSopBK8JWICPKQEyKRoZFKUV0vPvM6YbU42ZpbxyZ4L5FUbCPX15L4usYztEPXbmTxV2fDtZCg94q44HbTwF0FTe1kZpZOfwHL6NJpHHkHz2KOXdl8ndxSzb3U+4Yn+DHg4BQ+55LKl3tftftF942696z+yKRnVxzl06BD9+rXH4XwJm9ifmeK30NbbYH8V85VL8eIHZqhaMfz4WMJc2diiQzjYrgz/YxW0yLKzr30Q4YV2d4tdrZ5n3rkZIP05N10u/wC2rFnHocwTyAxmfMQNaKSB1Mg2siLlGcaV76NZfgqZej0Hk4pJV33PcKOG7tpnCUyPRKeW8qbnuyiNB1D7z2HduqMkJydza9Ou6L7OwbOZGvXYZnz/6fuc3rX9kpgbG6x8+85JdFUm+t7Xgiat3ILtbGigbNqTGPfuxX/sWIKfe/bKLBZBgONL3KmIggv6vuKu8PwVl0CD2c4X+wr5bN8FGsx2bknQMLFrEzrHB1z3DBWt3cE5g4V8k4UCk5XzZiulFhtlVht6h+u6vY+vVEKEpwcRnjJiFXLivTyJ95LTTOmJn8f13fy6XAK7c2v4aHcBhy5oUStl3H9LLHenx+At/5X3cjnh4Iew8xV3W4UB866w1sGdBVM5cxYN69ej6tObsDlzECvdAzlyDlbww9JzBEZ6M2hSGnKl9JKot751MN3uvI+6JWewnq/H/64kNpzYfrHwKI3qmmdp9OnPVON4Quvs1BwqZq//Kyz2MZOjvZVWub608Q0ju+lBPlf3YVz2S/hJRqC31KNzeeFQyOnbsxedenS/rmv5T+emoP8DeHXqU9i9FUjtNehj/Yjev4PNLSfQP3IXqcdGkFWWQWVYKjnNFrGkrIJ+xtnImrcgO0LOHPU+IurmExY6kzVrigkJCWFM16HULzmHLEKF5r4W7PpyMSe3baLj8NF0Hn0X+lozGxaewNRoZ8BDKUQmuf2s1vMXKH34YWzl5YRMfxH/UaOuvFBDtdsqz93qDroNfg/8o695X0argy/2F/LR7gIaLQ56JwUzqWc8qZF+12Xd9A4nJ/QmjjYYyWw0kW0wU2a1X/q7QiwiRiEn0lNGhKeMMLkHgTIPNDIpag8pKqkYb4kEL4kYj4sWuUQEDgEcgoBdEDA6nRgcLhqdTrR2J7U2OzU2B2VWO6UWGyUWG4VmK1bX5d9ShKcHyd4K0lRetPVVkqbywvuPpCX+CkcLtbyzM5+M3Br8vTx4pHs8d6VH//oOpzYP1j/iHrzRfCgMXHCFb10QBHRLl1L1xlzkTZoQ8eGHyCLcu7ULp2rZ9vFpfDSeDJ7cCqWfjB+XLOb4lo20HzqSTkPHUrs4C0eNGd/xzVi2bSX19fUMGxZIWfnrFGhmMKMuleQLZsT5p1jrNZ07I6Jpm/0omsrDJEbcwql269lb0J7emWs51+0WNHk6HIoQ5Hojzy24OYP0p9wU9BucD155hWqnE0VdPTafBpLL5OTGlVDQJo3uxzRICsuplLdibeuP+bw6i8X2h9jj24f8lr7cH6ilR+2DBAffzdYtSiQSCfcOuRPTknwkvjICJ7Zk77ovOfrt2kupZw3VbjG3W50MnJRKSKy77Y7x4CFKJ09GJJEQ8e47eLX5WXOsnC2w4TH3yLc+L0H7idcczmBzuPjqUBHv7cqn1mCjd1IQU/o0pUXYr7b4+U0aHU4O1BvYpzOwt76RMwYLAiAC4r3kpKi8aOGtoLnSk3ilJ+FyD8R/Q466UxAotdjIN1k5YzCTbTCT1WimwGwFQAwkqxR09vOms7+KdF8lyr8o8CdL6pn/fS4ZuTUE+8iZ3Kspo9pGIL2Wj93ldPvVd73qrgsY+iE06XHFIYZ9+yibMhWRhweR77+HIi0NgLJcHZs+OIVCJWPIE2mo1J7s+OR9Tu3YSudR42jXdzjVH2YiWB3Ix8Xx+ZplSKVSevWupLp6FZsDlrC8xovYEw0MNm9isPQLHlGnMfz4vUQ4z2CM9eH7NDtpx3cSW5JCvlqPy6LG4qcixk/NvVMm/6W1+jdxU9BvYKxmK3NnzcQllSCXGvH080FUuoqvU2Zyr2k7Sec6cFKvI6NFLsMl3xNpbcVDlocxdQwiNVDCpMax+Pu24eiRTtTV6Rg/+m5YUYYABD2SypEd69i/ajmpfW+j14SH0JYb2fD2SRAEBk9Ou9Rxr37NGipmzkIWE03kRx8hi4i4fJF2izvoefhjCE6BEYshKOmq9yMIAtuyK5mz5RyFdSbS4wJ4qn8iraP+fHArz2hhe52enXV6DjUYcAggF4to66Mk3c+bdr5KWvl44XOdLODric7u4ITexJEGIwfqDRzXm7AJAjKRiHQ/b3oGqOgb4Eus15/v+XLwfB3ztuVwrEhHQpA3zw9Ionti4LVdWeUnYe0D7pF5nSdDz+lX5K1bz5+nZOJDOKqqCJvzOj4DBgDuTpvfvZuJh1zCkCmt8NV4svXDhZzJ2En3ux+gZYe+1Hx4EpGnFMfQQJas+JKwsGBSWm6jvjGPN5VLKahxIDlQxdfeCzmiyONH8610OBtCG78Islv8yFLxrYw69QrmpiNw1TTgcPogttl57rWbfV7+w01Bv4GZ98RUjH4+eNVWUx4tou2xIjJaphDfvIQ2hwaQU7qf4vCmaJssZm6Nja76V5C2i8UeLOc1nkEtNlFRPp7TpwsZPWIU6p0WHHUWAh9qyenMnez64mOad+1J/4efoLbUyMa3TyKRihj8RCvUoUoEQaDm7bep+2gRys6dCV+4AInqJwVAtXmwery7/0rHR6H3zGtmsJwua+Clb7M5UnhRWG5LonvTXxGWayAIAmeMFr6trmdTTT15JreVm6T0pFeAD93VKtr6KPH8o2XzNwAmp4sjDUZ2afXsrGsk12QB3Pc2INCXQUF+NFP+8X4vgiCw/UwVc7ac40KtkS7xGmYMak7T4GsUc9lM7rz1Y5+7Ozne/qm7LcNFHDodpY9NwnzsGIFTpxLwgLvorKakkY1vn0QsFjFkSiv8gj35buEb5B3aT7+HJpMQ14GaxVnIwpRUdRKxZv062rRpgp//B9SJw3jGOROfCguu4/n8qHqBR0J8icl5mIjKU8RGdORw+g/UZqrokF3BiWQ1mgoZJo0Gn0YTU2/mpgM3Bf2G5fy5XJZ9uQSx3Ym3yESQ2Js6j23sbDWOYWcMeBWYKJO1YGPrRSyvPMMT5ucoi0vnfBMlrytXEmVah1j0HLt2nadXz540y1djzdehuTeZgorjbP1gAfHtOjJoynPUlZnYsPAEHnIJQ6e2wjfQC8Fmo2L6dBo2bMRv5EhCZky/MviZtRo2Pu4W8GEfuVu3XgWd0cab23P46nAxai8Z0/om/vrW/xoUm62srdKxtqqeXJMFiQjSfb0ZEOhLP40v4Z6/v2XAP4Vis5WttQ1srmngUIMRAWiu9GR4sD/Dg/0J+4P3bHO4WH6oiIU78jBYHdyTHsPk3gn4Kq7Rxjh7HWy86M4Y+gEkDbz0J5fNRsVzz6PftAm/O8YQ8sILiKRS6soNbFh40v1fnmiFT6CM9XNfpjgrk0FTniVC2ZS65WdRtAjgREg5e/fupW/fWCzW18hX3cMM/a3E5xppWpLBLMVb3BuYyuBj9xLjykeb4GJV03AGHPsQb49BaK0mjC5PBLGIhydNJig0+M8s87+Km4J+gzLn8alY1D7IG8qpTvQn4fBO1rR5mBFeP5Cc2YsTuip+TMlhAtvQmvrzrmwcpW38eSDgAt3rn8bf7wk2bqwjJSWFHqRgOlKF//AEKiVFbHjzVSKbJzPsmVloKyxsfPskMk8pQ6e2wkejwGkwUDppEqYDBwmc/DgBDz102ZJ2WN3W25FPICodbv8MfMJ+cf0ul8CqYyW8vuUcjRYHd3WMZkqfptcWj6tgcbrYUtvAVxV17NEZAOjgq2R4sD8DA/0I+C822fq7qbba2VhTz9oqHcf1JsRAN7WKsaEB9NP4IPsdw6T/g9ZoY962HL45UkyAUsb0gc0ZnBp29d2SrhBW3QvlJ6DT49BrJkjc6y64XNQsWEDd4k/w7tGD8PlvIVYo0FUaWT//BIIgMHRKa1RqCatefZHq8/kMe3YW6oZAGr47j7JLGFv0h8jLy2PwYCVa3Yds9nuX5bUhhB7RMl34FKsig/WOQaRnh9BWE0dm2lY26W5h8LFlnO3QibCcRszqUBTaBp55Z8F1Wet/MjcF/QZky5r1HMo8jsxgRuahJbbWk+yYKkytgul6oC3FxccpiIjDHv0xU7QK+htmYe4YSmqIwGOGO9H4D2TLlkACAgIYmdgP09YSVN0jMMSYWT37RTRR0Yyc/ioNNU42LDxxhZg7amspfvBBrLl5hL78Mn7Dh12+sIZSWHEXlB93T8rpNfOqfUHyqhp5fl0WRwp1tIvxZ/bQFBJDfl+vFoAis5UlZXV8XVGHzuEk0lPGmBA1I0P8iVL8xR7i/wIumKysrNTyTaWWCqsdjYeUcWEB3BUW8Id2KlmlDby4PovM0ga6xGuYPTSZGI3ylwc6rLD1OTj6KUR1gpFfgOqyNaxdvpyq2a+iSEsj8qMPkfj6ukV9wQkEl8CQKa1Q+sCKl55FX1PFqBmvI8sC44EKvAZH8fWJ7zAYGunTp5Ca+h+Zr1xOfoUL+aESflDNZIZGICj/EWIrzxIUncrOjmcJPnqWxKqmlHibMLj8sCvk9O/Vh47df9nS4H+Jm4J+A/La1KeweSuQ2msRRahRFaxlTdtnuLsii8Bz3hRIYtna5hO+rMplrPFlalqkYo9R8JrzcYI9fTh6pCcmk517eo7CvqYERbIG1y0KVsx6Bi8fP8a8PBeTXsz6BSeQyS+Lua20lOL77sNRVU3EO2/j3fUnP47CvbDyHvePe+gH0HzwL67b5nDxwY/5vL8rH6VcynO3NmNkm8greqRfC0EQ2FdvYFFJDTvq9IhF0F/jyz1hGrr4e/8t2Sj/NJyCwI/aRpaW1/J9rR6AWwN9eSgyiHa+VxHmq53DJbD8UBHztuZgc7qY0qcp93eJvbpL7NRKt5tN4Qejv4SIy7qh37qN8qeeQhYTQ+Qni/EIDr4s6gIMm9oKD5mFr2c8hcNmY8ysuTi2arHm1yO+PYwl21agVitJafkd5XYJzzMH1QUj4eeO8pHyFe4JTOa2Y3eRIKqgPKmSper2jDoyh+qkIXheqMPuFYK8wchzC/+30xhvCvoNxsevz6HcakGhbcCo1tMsV8f21BRSo/NpfaQvJ2oKyWhZyCOuLRw1jGRtwO2UpPgwS7GMZradVFbeT06OljsGjMRroxZpkBeeI0L55uVnEASBO16eh8OhZP38E0g9xAyb1hofjQJLTi7F99+HYLMT+dGHl2d9CoK7W9+250EdB2O+gsCmv7juU6X1PL36FOcqGxmcGsbMQc0J+B0TeRwugW9r6vmwuJpTBjMBHlLuvmht/lEf8f8yxWYry8rrWFZeR73DSRsfLx6ODGJAoO/vehhW6S3M2HCabdlVtIzw5Y0RLUkK9fnlgZVZ8M1YaKyE296C1pdHwxkPHqT00ceQ+PoS9dmnyGJi0FYYWT//OGKxiKHTWuNyaPl6xtPIvbwY88IcDF8V4Wywor9NxYpNa0hNDcLP/wOyFCN4zTCUyMx67tV/Rbj3Jr5xDiE9O4C04EQOd9jGhdwgumVXkR8tR2L0x+LnTZOgIO569NHrubT/KG4K+g2E3W5nzgvTcckkyERGAuTemKzbOdpuMMOO+1J/IZ/c4DhcMe9zly6YO2zPU9cxiDs1BQxofA6x6FF279bTr0cfYva7XSG+4xNYOe8FDNo6Rs+ag1QexLr5JxCJYNjU1vgFe2E+dYriBx5ELJcT9dmnyOPdQ59x2GDTFPfE+cQBMGwReF75I7c5XLy7M48PfixA4y3j1aEp9G7+28Epm8vFqkod7xZXUWi2keAlZ2JkECOC/VH8TRkqLpcDq7UKq7UCq7USq60Gu02L3a7D7mjA6TTidJhwukwIgvPSSySSXHxJkUi8kEgUSCRKPDz88PDwx8PDH7k8GLk8GE95KHJ5MCLR35M2aXQ6WVGh5ePSmkvrOik6mGFB/nj8xk5JEAQ2Z1UyY8Np9BY7T/RuysSucb+01k1aWD3B3dah/UTo99olv7o5O5uSBx4EsZioTz/BMzGRujKD24CQuQ0Io66Yla88T0B4JCMmz0L3yTnEcgk5rQ3s2rObXr2U2OwfsVq1kPXVofgfrGKD5ywWBhhR508ivjYXRVwkK1q66Hr4S0R+AzHoG7CJ/JCarbz45v9uxstNQb+BeGvaUzSqlHjV1lIRIyIp6wQrO45nJAeIPtWUc041W9ssZUltASMMr1LZKomYSBFPmcfh7z2EzZuVtExpSefKWByVJtT3t2DjF3MozznHiOdfxi8kgXVvHcfpcDFsWmv8Q5QYDx2m9OGHkQQEEPX5Z5dzzI21bn958X73AOLuz/+iUOhcpZ6pKzI5U6Hn9jYRTB/Y/DeDnjaXi28qtLxdVEWZ1U6qSsET0cH00/w+S/LP4HAYMRpzMBjzMBrzMBkLMJmLsFjKEATHFceKRBKkUl88PPyQSJSXBFss8kAkkoJIhCC4EAQHgmDH6bTgdJpwOg3Y7Q3Y7fWA62fn9EChiEChiEbp1QSlMgGlMgFv70Qkkj85du43cAoC31bX805RFWeMFqI8ZUyNCeb2YDXS3xB2rdHG9A2n2XSqgrRIP+aPSiUu0Ptnb+CAHTPhwHsQ1wNGfg4Kdz2B9fx5isdPwGWxEPXxIhSpqdSUNLJhwQnkXlKGP9mGqvOnWD/3FWLSWjNg1GRqP81GFuPDTu8z5OTmcOuAWuoad/Ca55dUF9mJOHWKZd4zuDegBQOOjaKFzEx+q5N8a27PiEOryUlORFPqgUmjxt9mY/Jrr/2/rOuNzk1Bv0HQ1mh5b8GbIAgoMRFulnJOXYgrVUO3fZ04U5nLnuQaJrKR7xvGsS1yCI1JKmYLTxHtqSRjd3t8fPwZ7t8V+0kt6rGJ7Nz1BTn7Mxgw6UmiUtJZ++ZxrEY7Q6e2QhOhwpCRQemkx/GIjCDq08/wCL7YYKv6LHw1yl3KP+R9SLn9imt1uQQ+23eBuVtz8FFIeW1YCn1b/HIazk9xuARWVWmZX1hFicVGWx8vnowNoZu/6rr2a3G5HBiM52ioP4Zen4m+8TQm03nA/V0WixUolXEoFNF4KaLxVEResqLl8mCkUh9Eoj+/QxAEFw5HA1ZrNRZrBVZLJWZLCWZTESZzESZTAS6X9eLRYpTKJqhUyfj6pOHr2wZv76bX1ZoXBIHv6/S8WVjJqUYzcQo502KCGRbs/5sP0I2Z5Uxffxqrw8n0gc0Z2z7ql5/V8WXw3RR3i4exKyGgCQC20jKKx4/HUVdH5IcfouzQnsoLDWxYeBKV2pNh01qRd3An3y9+j5Re/ejUeiT1q/OQtg9gVdkPWK0GOnXezXm7jBecLxFwppERlatI9l3DSvvtpJ/xIjGiGd+nH8fveB5R5pZUSi0YBSUil8C0GTNQev3/PCxvZG4K+g3C3MenYFL74qWtoKaZH9Fnt7Ch0yTuLSqHHANnA2Kwxi5kuDaKB3mGmo4apirX0sGxmbzc0dTWCtyZNhjRj3X49I4is3oXhzes5pax99Ky1xDWzT+Ovs7CkCfSCIn1pfGHHyh9YgqeCQlEfvrJ5YEUBbtg5d3goYA7vnYPHP4J1XoL01Zlsievlt5JwbwxIuVXfeWCILC9Ts+rBRXkmiykqhQ8ExtKD/X1EXJBcNLYmI1Wux+d7gAN+uM4nSYA5LJgVD4pqFQtUHk3x9u7KZ6eEX9JsK/H9ZrNJRiNuTQ2nkHfeJrGxixstloAJBJv/Pza4O+fjr9/Oirv5tflegVBYFutnrkXKjhjtNDC25MX48Lo/hufQ5XewpMXP+8+zYN5Y0RL1MqfxTaK9sOKce54yx1fQ1RHAOzV1RSPn4C9rIzID95H2akTpTk6vns3E3WYkqFTWnF4w9ccWreCLmPuJlHWFsOeMhy91Sw/tJ6ISA+iopaxRz6OD/S90RyoZrnkFT4O1BGc8wRNGy5gTRKzJCSeEXvfpSC5JyE5ZkyaIJQ6PU/9D46ruynoNwBZR46zZsNapFY7ntQTWWNle1IILcPLST7ckSyjwPft1rK4Lpehja9T1i6BXuE13GOajNl0L0ePOrm922D8thtRJGsoCy7m+8Xv0rJ3f7qOm8jGtzOpKzUwcFIqEYn+6Lduo+zJJ/Fs3pyoTxYj8bnoFz+2BDZNBU0ijF0BfpFXXOeunGqmrczEZHNc22L7CSf1Jmbml3GowUgThZznm4QyQOP7l4XcZtNSp82gru5H6ur24HDUA6BUJuDv1xFf39b4+bXF0/OX+fE3IoIgYLGUUt9wjIaGY+h0hzCZCgDw8FATENCVgIDuBKi74uHx1/rduASB9dX1vH6+ghKLjVv8vXkpPpzm3te2Zn+6I/Pz8mDhmDQ6NdFceVBdgXtXV1/s7gNzcVfnqKujePwEbIWFRLz3Lt5du1KYVcuWD7MIaeLLwEkt+X7RQs7u/ZHbHn+agBw11oJ6anrJ2JCxmfR0ERKPpSzy+ohDJSqijp/lK+8XmeifTP+jA2jpo+BYh70UFvrS9byNUj8pRrEKl1TChAkPENkk5i+t1z+Nm4J+AzBn8lQsfipkhgqEqEC8KrdwosNQRh4LpqSogAOJFu6UrWKzbjw74gagaKZglu1e/GXd2LFDQ5d26SQd9UHqK8N8i4i1b84iumUrBk95kS2LTlOaU8+tE5OJTQ1Ev3kzZU89jSI1lciPFyHx9nZbVjtnw543oUkvd57xT4KfdqeLN7flsCjjPM1CVLw3thXxQdfOK6+02nn1fDmrKnVoPKQ8FRvC2NCA3wzK/RoWSwU1NduprtlGff0RwIWHRwCagG6o1bfgr+6EXKb5zfP8U7Baq9DqDqCty6BOm4HdrkMkkuLv15HAwL4EBvZFLg/88+d3uVhaVsf8wkoaHE7uDAvg6dgQAmXXjoFklzcw6esTFNYaebxXApN6Jlw5+s+kdVvqRfvcNQpdpoBIhEOno+S++7Hm5RH+9tuoevYg93Al3392hthUDb3HJ7L29RlUFuRy+9OzkW4z4bI6OZlSy+ETR+nbt5wqy1Gme3wCZ4wMKV5LK/UKNhvH0SHfRXBsDF+lmemybxl1MQNR5NVi8Q/7n+yZflPQ/8ts/Holx8+eRtZowuWlI7K4kpUdBzBEOEtgZgBZXqHUJS5gaE0Ek+XPUdsugJnSOSR7NPLjrg6Eh0XTV5cMBjvS4UGsePM5fAKDGDXzDXZ/dYGC49X0uieJZumhbjF/8im8WrcmctFH7r7WTrs7tzjzK3cK2m0LLmUsgHuW56SvjnO8uJ47O0QxfWDza7ZitblcLCqpYUFRFQ6XwMTIQB6PDkb1Jxtj2Wxaqqu3UFX93UURF1AqE9yCpumNSpX8X3Wf/F0IghO9/hQ1tTuoqdmGyXQBEOPv34Hg4EEEBfb/05Z7vd3B/MIqPiurQSEW83RsKOPDNdcMnBqtDqavP83aE2WkxwXw9h1pBKl+MtvUYXW34j29Gto9ALe+AWIJTr2e4vvux3LuHBHvvI2qRw9O7Sphz4o8kjqF0nFoGN/MeAqLwcCYKa9jXlGGKFDOZs/j1NaV0+WWDE44o3nV+hiaQ7V84XqJRYH1xJ2ZTLK1mvLWxWyxJTHyyD5Kw/1w2NXYveT07taTLr17XPVe/o3cFPT/Mv8pIvKw1aDxVlPnOIopNY4ee9uRpa3l+7a7+KA+m5HGOZR0iGd04FGG2D4g59xIDAYlozU9EeUYUY2JZeXimTjtNsbOfosTO3Sc3l1GpxHxtOoThX7LFsqefApFqzSiFi1yi7nV4PaXF/zgzmLp9vQVww0ycmuY/M0J7E6BOSNSGNjy2i6M3dpGXsgrJd9kpb/Gh5fiw4n+E1WdLpedurofqahYQ23dLgTBgZdXPCHBAwkKug2lMu5PrfO/BUEQMBrzqKreRFXVt5jNRYhEMgIDexMaOgK1fxfE4j/eEiHfZGF6Xhm7tI0kKT15vWkEHf28r3n8qqMlTN9wGpWnB++PbU372J/MJnW53Bkw+9+BZgNhxCfgoXCL+oT7sObkEP7uO6i6d+fQt+c5uqmQ1v2iaNZRwVcvPomXjy/D73qRxpUXcKZ4803pdjSBFmJjv2GF/Bk2VibR5MhZlqqe53FVe/oebU+LoDC2dDlJ8LETeHh3xlijxe4VjLzBwHML3/ozS/2P5Kag/xf5dN6blBga8dTpaQgxE11wknU9xjO+uA5TjpYDsTIGeH/BLu29bI4fQFSiwDPW+zA3DifzpIJRaQNQHbCg6hfF5l3vU30+n9Gz5lCaK+PQxvO06hNFpxHxbp/5tGko0tKI+viimBvrYPntUJHpHj/2kwIRl0vgvV35LNiRS9MgFR+Oa/3LtLWL1NjszMgrY111PTEKGbMTIugdcJWClN/AbC6hrHwFFRWrsNlqkck0hAQPISRkGN7eza775KJ/A4Ig0NiYRUXleqqqNmK365DLggkLG0VY2Kg/HEMQBIEttQ1MzyujzGrnjlA105uEob7GhKVzlXoe/vI4xVoTz/Zvxv23xF75OR38yD0cPCrdHSxV+OFsaHCLem4uEe+/h/KWW9j9dS7ZGWV0GZWAOqSe1bNfJLJFS3q3m4BhZym6zjLWHNtC+/YGRJ7fMVv+OTVnHIwsXEtSwCoOaB+gQ2kj4iQvlgZFMHL3ckpiElHoVFj9vGkWEcmYxqGaxwAAIABJREFUB+7/K0v9j+GmoP8Xmf3UMzjkHsjRE2mWclRjICrCTtKBZDJFSs63eJ8x1UFMUjyHsZ2aV0VTiJTEsHNnDN3TuhB/yBNFsobDui2c2bOTgU88g9PVhF3LzpHYIYRe9yRh+HEXpY9PRpGSQuTixUi8le55n8uGQUOJ21+eeOula9Jb7ExdcZIdZ6sZ1iqcV4clX3U+pSAIfFWh5eWCcsxOF5Oig5gUFfyH2tYKggutdg8lpUuoq8sARGg0PQkLG0WAuiti8e9v5PW/jstlo7Z2F+UVK65Yy8iIu/H37/SHHogmp4v5hZV8WFKNr1TCy/HhjAj2v+o59BY7T686xdbsSga2DGXu7S2v/L5krYZ1D0FgMxi3BlTBOBsaKBo/Hlt+AZGLPkLRoSNbF2Vx4VQtfe9rgUWfyfZF75DWdyCp4i5Y8us506aR/acO0qt3Dvl2HTNcc/A5VMtn9lm8F2QmNfNBWnrYONnpBLoCO4mmUMoxYZOq8TBaeOGteddhlW98bgr6f4mFzz5Hvaccrzot1XFiYgp/ZG+30dx1LIALpeV83+oM80wHuNs4h6KOTbnfbx192MPePd2JDE2gZ3E8EpWckphCMlZ8TqeRdxKa2IfNH2YR2cyfAY+2xLx/H6WPPIq8WTOiPvvU3cu8Jsct5lYDjP0Gojtduqb8agMPLjtKcZ2J6QObc3d69FV/xIVmK9POlbCv3kC6n5K5TSNJUHr+4rhr4XSaKK9YQ2npEkymC8hkgYSH3UFY2Mh/TGbKjcx/djvl5Suw27UolQlERtxLSMgwJJLf7wY7YzDzZE4Jx/UmeqpVzEuMvGrzL0EQ+Gj3eeZuO0disIqP72pLVIDX5QPyf3AHS72D4K71oI7FodNRfM+92IqLiVr8MbLU1mx85yRVhXoGT0oj7/Bajn23jt53P0JQdiBOu4PtQWeo0RbSMf17vhMN5su63iQcyuZD1Qu86NmTPpkxhEeHsiTFSq+dX1IY1wlNsRRzgB9BYjGPzJhxPZb3hubXBP3fH236L2E1W9EDIpsNkUpE27ON7I0dTa+KMgyVxRSEyRju2s9i4xgq46Npq66im301Oee6IJf7cUtjU3CBqY2DjJVfkJh+C3FtB7Bt8Wk0Ed70ezAZy5EjlD42CVl8vDs1UaVyT6P5/FZ3IHT8pivE/IezVQx9fx8NJjvL7+/APZ1ifiHmTkHg45Jqehw+x6lGE28mRrI2Lf53i7nNVsf58wvZt78rubmzkEp9aNF8AZ07ZRAXN/mmmF8nFIpI4ps8SedOe0lKegORyINzOS+w/0BXCgs/wG5v+F3nae6t4NvWCcxOCOdAvZFuh8+xtKyWnxt6IpGIh7s34Yvx7SmvNzPovb3szau9fEB8L7jnW7A0uL9/1eeQ+vsT9dmneISFUTLxIexnshjwcEt8A73Y/FEWLXqMJK51O3YuX4SjkxSRxUV3ewtEIhUXznejr/1zkgP1lDaJZ7VxOL1FGRQG++Ior6GPNocjTQbQutAJvi5EDjt1BiMOh4P/ZW5a6P9P/HQSkT7BG/+K3ZR36ET/PUmc0ps40Xo5E6s9eVAxE2dHP15zTsTT1I5jx0IZEdcbv2wXsgFBrPhsOn4hodz2+EusX3gaqVTMiGfaIL5wlqIJ9+ERFkr00qVI1WooPgjLR4KnL9y94VJFnyAILMo4zxtbz9EizIeP72pLmN8vc5KLzFYmny3mYIOR3gE+zG0a8bubZ1mslRQXf0JZ2de4XBY0mt5ERz2An99VDYn/dwSXgLPRhlNnwdlgw9low2Ww4TTYcZkcuMwOBIsDwebEZXch2FzgEkAQEFwCIrEILr5EHmLEMon7X4UUkUKKWCFF4i1DrPJAopIh8ZUj9Zcj9pa5/+/ffb+CgK7+IMVFH1OnzUAiURIRPo6oqAnIfmeqZ5HZypM5JezRGejmr2J+s6tb60V1Rh5YepSCGiMzBzXn7vSYy3+sOgNLh4DghHFrISwNe1U1RXfdhbO+nuhlS7FpoljzxjHEUhGDJiWxYe7zmBr1jL5rFpYtVdS2FLE+dwcdO5aik2XzvOg9vA7UssT+IvM0MjoeH0Gyvw/rO+eTsO97KiO7oc6zY9IEoDIYmfbmv9v1ctPl8jdTW1XL+2/PR+QS8BLqCaqqZGWX3oyr0dJ4zsiO5nU879rCY4ZXyU9vzuPeH5MuKuPHXW25pVlHEk+qUKQHsX7nm9jMZm6fPo/vPy3CpLcx/Kk2eOlLKbrrbiR+fkR/uQyPoCA4/yN8fQeoQt1ifrFgyGJ38vy6LNYeL+O2lqG8eXsqCtmVKYaCILCsvI5ZBeVIgNkJEYwKubo/9edYLBUUFn1IefkqwElI8BCioyeiVMZf/4W9Ci6rE3uVEUeVCXuVCUet2f3SWcD5s++2RIRE6YHY66Ioe0oRySSIZRKQihBJxG4BF7nT9nEJCE4Xgv3iy+bEZXZcfhns7ofAz95DqvZEqlEgDVTgEeSFR7ASabCX+33+BhoN5ygq/JCq6k2IxXLCw+8gOmri78ppFwSBpeV1vFRQjlTk/i6MvIpvvdFi54lvTvLDuWrGdYxi5qAWePwntlJX4BZ1SwPcuRqiOmArLaNo3DgEu53oL5ehl2hY+9Zx/IIU9LgzhJUvP4UqIJCBHR7DfLSGrFQdh3OP0L3HAXYKKXxcfwctDmXyus9MFrpG0CtHibyFlJWqIEYePkBlaAAmfEAQmPbCiyi9f19r4X8if1nQRSJRf+BtQAJ8IgjCnJ/9fSpwP+AAaoAJgiAU/do5/82CPvfxJzCp/fDSViKK8sdkOI2saSgt98RzSC7DL2Yh9TWD+SJ+FB3ji3jY+QbHjv4fe+cdJVWxdfFf5+k03T0558gMGYYcBEQUA6gYEAygYAYTYHrmnHNEBQkq+hATgiKK5AyTc84z3TOd8/3+aB7KAxVh9Kkfe61ei1k3cG/dql2nTp2zz1mE6TIYX5eBIk7LpvZPqC86yIX3PMLutS5aqrs5b14/wmTd1MyYiUgiIXH5cuRxsVC2PuC/DE2Dyz8N+DGBTquLue/vYXetiVtPz+CmcWlHDcx2t4dbSur5ttPMaIOG57ISjquAgsvVfojIVyIIAtHRF5CUeC1KZfxvXnuiEDw+3A1W3PUW3I1WPA0WvJ3Ow8dFMjHS0ENkGqpEYghCYlAg1SkQa+UBIu/BSBrBLwSI3eLG2+XCZ3LiNbnwdh6aVDod4D00vkQgDVMii9Ugj9UiT9Aij9Egkv1xXk+brYra2tdoaV2DSCQjLm4miQlzkMtDfvPamkOrtR3dNs4O1/FUZjyG/4qE8fkFnlxXwhs/VDEyLYxXLhvwk3BbdwMsOTcgwXvZR5A0EldVNbUzZiBSKEhavowmo4IvXz1IQq8QckZ6Wf34A6QPGkZe0CTc7TbWRhVic1bSp98XPC99hPzSUK6vXoYr8lsov4VBgpedo0qRFVbgMwxCUdWNPTQStambO17451Y2OilCFwVUhMqA04EGYBdwqSAIRT875zRghyAIdpFIdB0wVhCEi3/tvv9UQi86cJBVqz5C4vIglnQS0VbGV6efz1VFMmoautg0YCO3Gbu5MugRxEN1POqbg7V5NE2N8VzAcJQeGdWxpWxf+xGnz7mRtvoESre3MOGqXqQkiai59FIEp4vEZe+jSEmBki8DRSkiewU2o1SBwVrRZmXWe7toNTt55qK+x4wv/6ajm1tK6rH4fNybGsOs2LDfFHPyei3U1r5BXf27CIKH6KgLSEq6AaUyrsfb0u/04qruxlXVjavGjKfRetgilugUyOI0yGM0yKJUyKLUSAxB/xN3xy9B8At4jU68LTY8LbbAJNRoxWd2B06QiJDHapAn61Ck6FAkBSNW9HzJPbu9murql2lpXYNEoiIh4RoS4mchlf66FesTBF6ta+OJ6mbC5TJeyk5gpOHo7OFVu+u5a3U+iaFq3r1yMPEhhzZLLS0BS91UC5eugNRxOIuLqb38CqShoSSuWE5poYPvl5fSe2wcKk0hP7y/mNFTriS2PBab2svH7s2kpNYhCdvFnaLXkG/rYJl3IQ/r4xm3ZyhxycEsThEx9bsv6IhMxYkOv1TCNddeR0x8z/fJvwJOltCHAfcLgnDGob/vBBAE4bFfOL8/8LIgCCN+7b7/VEL/eZ1QvV5PocJGrxAI3RHCt/EeZiiX8oj5AfKH9uN2zTP09njZujWHc2LHEFklwz5c4PNlT9L39DPRx5zFjs+qGXx2MgNHGqiZMQNvcwsJS5egzMmBwk/hk9kQ3S8QLqbUA7CtspO57+9GLhXz5uWDGJBgOOIZXX4/D1U28XZDB73UQbzSK5HsX9H5gEDIXEPjcmpqXsHjMREZeQ4pyfNRqZJ6rO0En4C73oyzvAtXmQl3gyUgoCgVIY/TokgKRp4QjDxBi0Tz9y2M4bO4cdeZcdVacNeaA+/pE0AM8vhggtL1KDIMyOO0PTpBWW3lVFU9R3v7OuTyMJKTbiIm5pLfTFI6YLFzQ1EtFXYXNyVEsCA5+iiJh22VnVy7bA9SsYi3rvhZn7N1BEi9ozxQASljIvY9e6ibfTWK1FQSlrzH9q+b2f9tPSOmpdFU9BHFW37g/Jl3I/vRQ2O6i7X1PzJy1H52iMN4uXsug3fu4Ab9Y6wxX824FjFNw5upb3IQK4nF1WzCERKFsrOLhS8932Nt91fCyRL6hcAkQRCuPvT3TGCIIAg3/sL5LwMtgiA8fIxjc4A5AAkJCQNra3/VK/O3ww/r17Nx84/IbE48WjMRbXvZM/5MLtoaw36bE1PO64S3jOC5lCsZk17C1f632LJ5PH1jB9G/NALJkGA+Wv0gEUmpDDhrHt++V0rGkEjGTUukbvZsXMUlxL/1FuoheVDwb/jk6kCJsMs+PqzLsmZ/I7evOnC0tXQIlXYn1xbWkm91cHVcGPekxPxqXLkgCLS3r6ei8nEcjjpCDCNITVtAsDa3R9rM7/TiLDPhLOrEUWpCcHhBBPJ4LYo0PYpUPYqE4D/UNfG/ht/tw11rxlXVjbPcFFiJCCBWSwnKDEHZKxRFugGxomd88N3d+6iofJKurp2oVGmkpy0iNHTsr7qjbD4f/ypvZHmzkYHBKl7rlXhU7dfKditXvbuLNouTFy/p/5Pcst0I708JSDZfvAwyzsD6ww/U33AjqoEDiX39dda/V0bNwQ4mXp3Jto+epLuthQsn34N3dxc7M5oobtnDsGHf8Ir0NnYXJ3Bn3WsUR+YTf3AWuXo1y/I6GPXNF7SH9waXHm+QnAumXkDvgf17pM3+SjhZQp8GnPFfhJ4nCMJNxzh3BnAjMEYQBNd/H/85/okW+uPzbsOp1yC3txGDlPVx4ZzlceEsELOuTzkLrYVcLnsC8TA9j/nn0lA6FrzZnNWSS1Cyjs8PvIjP42Hi9Q+x7s1qIpK0nHt9Lk3zbsK2ZQtxL72Idvz4n8g8Pg8uWwUKLYIg8NoPlTz5dSlDkkN4c+YgdKojk3b+3Wri9tJ6FCIRz2cncEbYr2uDWCxFlJU/RFfXTtTqdNLT7iI09OQL9PrtHhzFRhz5HTjLTeATEKukBGWFBH5pesSq/78JRz6bB1e5CWeJEUeJCcHpBamYoEwDqt5hBGWFIA46OdeMIAh0dGygvOIxHI4aQkJGkZ5+Nxp1+q9e92mriTtK6xGJ4PmsBM4K1x9xvMPqYvaS3Rxs6OKBc3N+ioBxmAKW+s9Ivfvzz2m6YwHa008n4omn+fT5A3S12pk4O4GvXrwbtc7ApLSrcTRZ+Dx0P0pNKaEpW1nE6yi2tvEet/GEYhiTCuMQ9XOzySdlmNFLp9OCUxtFkLGbRS/+83zpf4rLRSQSTQBeIkDmbb/1UP80Ql+9dCkHKitQdNswRzqJ6NhN65BhjPw+lk1qH4OjXmJF5x1sHzKCBdrHSLEoKCjI5XzJcPSCil3+b6gs3M05tz7Ipo/MyORiLlgwgK5H7qN7zWdEP/wQ+gsvhMLV8PHsI8jc5xd44PNClm6r5Zy+MTw9rQ+Kn4llOX1+/lXRyNKmTobo1LzWK/FXwxE9HhOVVc/R2LgSmUxPSsotxERfdEL6If+B4PHhKDZi39+Os9QIPgGJXoEyNwxlbijyhOC/lP/7rwLB58dVY8ZZ2Im9oAO/2Q1SMcrsEFT9wgnKDEEkPfHVy39cadXVL+Lz2YmLu5yU5JuRSn9ZabPW4WJOYQ0HLA7mxodzT0rMES4Yu9vLzSv38W1xG9eOSWXhpMyA9e8wwdIp0FZ0mNSNS5fS+uhjGKZfivqmBXzy+G5EEhHDzgvii+ceIHvQWPo6h2NU2Pm3YzMDBuaTr1TwYvdNjNu1kdNDX6aiYT7DvRLWjW4kbfMm2g0DCDJrcWuCGDNkOKdNPvMX3+XviJMldCmBTdHxQCOBTdHpgiAU/uyc/sDHBFwz5cfzUP80Qn/0lttxa1UoXB3EWZx8OiCXGY0CDbUeDvRfzYjWeP6VcDNjMg8yy/8Bm38cy9iwoaQ06GhJa+GHdUsYe8W1lO+NwNLh4IIFg/CueI3OtxcTPn8eYddeC8WfBzZA4/MCbhaFBpfXxy0f7uer/BauGZXMnWdmI/7Z4Kp1uLi6oIZ8q4MbEyJYlBz9iyp7guCnqXkVFRVP4vNZiI2dQUryvBNW+RMEAXedBfueVuwH2hFcPsRaOaq+4Sj7hCGP79lKRv90CH4Bd50Z+4F2HAc78Ns8iJRSVP3CUQ+MRBarOeH2dLs7qax6lqamD5HJQkhPv4uoyPN+8X4uv58HK5pY3NjBoGAVb+YkHWEk+PwC964pYMWOOi4cGMdj5/cOhDX+nNQvXQlpE2h7+unD/dx/5nRWP72XsHgNsenVbPlgCaefPZeQIj0liUa2te9gxMjveFU8l51FyTzd9BRfRFgZsud0ItMkfBAm5dyiKhqCBNzKf6ZwV0+ELZ4FPE8gbPEdQRAeEYlEDwK7BUH4TCQSfQv0BpoPXVInCMK5v3bPfxKhv/vMM9SazQSZLHQneNGYSpBlpJL0YyRfJZq4TvwFc4Vn8A0P5VFupHz/GCKVAxldm4yvt5RPPnuUnDHj8TGO2vxOJt/YF+2eLw9bLpH33ouo7OtA/c+Y/jDz36DQYnF6mLN0D9uqOrn7rGyuGX2kSuG3nWZuKArsU7yUncDEX3GxWCzFlJbeS7d5H3p9HpkZ96PRZJ5Qe/jtHmx72rDtbMbb7kAkE6PsHYZqQASKFP0pS7wHIPgEXBUmbHvbcBR2gFdAFqVCnReNakDECbtkzOZ8Ssvuw2w+gEE/lMzMB1GrU3/x/M/aurilpI4gsZg3chKPiIIRBIEXNpTz/LfljMuK4JXpAwI5EHYjLD03sFE6/UOE5DE0L7qT7jVriH74ITqSR7PurQLS8yJwmj6jYtd2pp19DxQ42RBfjtm3l4TcLSwSXkO7uYEX5LfzjvsizmwMpnhMC7L8fLr1/dC2yHDp1OQkJTHtqlkn1B5/RZxKLPqD8chtC/CoFAT5TES3NvPFuCFcdVDNPrMTVebLFLddweeDzmSh/nGi2lU01PZninkA6jgdn+x4El1kFKl517FvfRMjp6WT7Cmi8ZZb0E6YQOzzzyGq2ggfXAqRuYE48yAdHVYXV7yzk9IWC09N68PU/j+FaPkFgWdrWnmmpoUcjZLFuUm/KHPr8zmorn6RuvrFSKU60tPuJCpq6glZeu56C9atTdjz28ErIE/Qoh4chbJP2B8SjncKAfgdXuwH2rHtasHTaA1MoH3D0QyLQR77y/K4vwRB8NPY9AGVlU/h8zlISryWpKTrEIuP3YfKbU5mFVRTaXdxd2oM18eHH9F/lu+o5d5PC+ifYOCdKwYH9nZsnbDkbDBWw4xPEGLzqL/uemzbthH/2qsUW+LZ+Xk1eWfHUbDhWVw2G+f1mYelzcxq1S7S0vdSbhDzYuc8Ltm3mpDI1ciL5tArVMF7mU7O3bqfhlA9bmkocquDu57952SPniL0PxAv338fHYhQdppwJElx2JtJC41EvCuY7/oc4GKTmRui/sWoXge5yvtvtm4ZzVlBw4j1GvjB/DHG9gZGTL+HLR+3kT08miG9bNTPmk1QTg4J776DuHlXQAI3LAOu+AyUBhq7HMx8ewdN3Q5eu2wgp2VFHH4es9fHDUW1fNNpZlqUgScy4lH9QhSL0biFkpJ7cDjriIm+iLS0hchk+mOe+0sQvH7s+R1YtzTiabAiUkhQ9Y9APSQaefQ/N1vvrwp3gwXbzhbs+9oQPH7kicFohsegzA1DJPl9k7Tb3UF5+aO0tK5BpUolO+vRX5RysHp9zC+p44v2bs4J1/N8djxqyU/7OGvzm5n3wX5SwtUsnZ0XKJhhbYf3JoO5ES5fg0+fTe3lM3HX1JKwdAmbtkLlvjZGXRTG9+8+SGxiL4bKzqROaeQbxzaGj/ieV6VXsWd/Mm+Z7uFdTTyTCjOxDjPR2liLT5uCtk6E0xBMvFbL7NtvP6m2/avglDjXHwSv10uX3YnI60Wk9ZJQkk9HZgYxxfEUhUuZ6dnDE+KrCc6UcKn/Hfbt7UffkGyiuzRUqopoqilh+MXXsWNNB1EpOoYOldNww43IYmKIe/UVxO0HYcXFYEgKJA0pDVS0Wbnwta20W128P3vIEWReYXdy1p4yNhrNPJIey4tZCcckc4/HTFHxIvbtvxxEYgb0X0529mO/i8z9dg/m7+tpfnIXpg9LEVw+9OelEn1XHoYpaafI/H8EeZwWw/npRN81BN3kFHxWN8aVJbQ8tQvLjw34nccvXiWXh5GT8yz9+r6L3+9kz96LKS29H6/XdtS5GqmEt3KSuCclmi/auzhnTzm1jp8C3c7sHc07Vw6mzmhn2uvbqDfaQRMekKlQh8Gy85FYKol//XUkeh0N117HqAnBhMVp2LHGxIhL5lBTtpfG0GriO4PJNmSxf18/ZvpeRZEdxOPCHC7yb6XEoCSpKI3q7CGkl1QgaD3g99PS/JtxGv8InLLQTwLPL1xIl1KJqsOILz6IGqmbsT49ncVyKvt9jK5tBG/2vYzbwp4mvEGOpWMok9v64E0RWLPhGQacdSF1JYEwsalzU2ibPRO/00nSByuRS43w3jmgDoWr1oI2isKmbmYu3olYJGLprDx6xfxUZOLbTjPXFdYgE4t4OyeZ4YZjL7U7Or6jpOQe3J4OEhKuITnp5t8lt+rrdmH5sRHbzmYEtx9Fmh7tqFgUGcen/fJnwuf1YusyYe8y4bCYsZu7cVotuOw23A47LrsDn8eN1+3G5/Xg9/nw+/0g+EEkRiwWI5ZIkEhlSOVypHI5cqUSuVKNQqVGqdWiDNah1AajNhhQ6wyIJX+OXsvxQvALOEuMWH5swF1tRqSQoBkWjWZELBLt8Sdn+Xx2Kiufob5hCUFBsWRnPUpIyLFzB7/rNHNdUS1i4K3cpCP86vvqTFz57i6UMgnLrh5CWoQmkEn67lngdcBVa3GZpdRcOh1pWBihr77Hv18pQa6UEhm/n4Pffsn54xdClZfPwvYTHrudqkg5r7Zcxy2Fi6mKKKLP/rNR9bGz19lKmFSPvM6LI1RPOHDD/fefXIP+BXDK5fIHwOPx8Pg99+KXSggSutCbDlI2eigjvtfzWXwTl/s3Myv4afL6lTHL9TG7dozlPN9QQg16Ptn9JFEZWUhVU2mrtTL1phzs99yIq6yMxPeXooxSwLuTQKYKkLk+nr11Jq58ZycahZTl1wwlOSxgAQuCwGv17TxU2USORsm7vZOJP0ZIotdroazsIZpbPkGtzqBX9hMEB/c57vf1djqwfN+AbW8rCAKqvhFoRsUij/n9PtqeguD3YzF2YmpupKulia7WFsxtrXS3t2HpbMdu7j6ksnU0ZEFK5EplgKhlciQyGWKJBJFYjEgkRhD8CH4/fp8Pn8eD1+PG63LhdjjwuJzHvCciEapgHcHhEQSHR6ILj0AfGY0+KgZDdAyakND/6aTnrrdg2dSAo6ADJGLUgyLRjo1Dqj9+nfuurt0UFS/E4aghNvYy0tMWIZGojjqvyu7iivwqqh0uHkmP44rYnxQfS1rMzHh7Z0AIbHYeOTG6gKDXO5NALIVZX2Mrb6Vu9tWoBgxAuugpPnupgLisYCxtKzA1NDK19y20Wo18LtrGkGHf8bJ0NiV7onnLeSvLvZOY3BrBt2O76bdhBx2hObgkOiQuD/c89USPtOX/EqcI/Q/AM7fcikUXjKqjA3mEkp0haqa1ayhp9RGW8Tqfme6gfGQfnpDNp2TXaPqJhpFrj+WHrlWYnZ2kD51HyfZuJlyRhXrFY1i+3RBIHBqUEejYPg/M+hpCU9lW2cnsJbsI1ypYfvUQ4gyBAeTy+1lQ2sCHLUYmh+t4MTvhCL/lf2A0bqGoeCEuVytJideSnHzjL25w/Tc8HQ4s39Vh398GYhHqQVFoR8chDTl+EugJOG1W2muqaKupor22hs6GWjob6o8gV4lMdphItWHhaAyhaEJCUOsNKLU6lMHBKDXByFVKxOITt6T9Ph8uhx2nxXzI8jdjMxmxmoxYjZ1YOtsxt7di7mjH5/Ecvk6uVBIam0BofALhiclEJKYQnpSCQnU0If6R8LTbsfzQgH1fwA2hHhiJdmz8cX9Tn89JVdWz1NW/g1IZT6/sp47pW7d4fVxbWMsGo5mrYsN4MC32cLx6VbuVGW/vwOrysmRWHv0TDNBaGLDUlQaY9TXdG3fStGAhuvPOo/PMG9n0QRm5o7QUfPcskaHJDAuazL7geoqFrSQP3M5C1/MM2rKNEaGv4iqeQ0q8n8+CzeTYvNDkwh4Wgt7hYP4Tf29SP0XoPQyb1cYzjzwMiJCJulDa8rEPGEDCDyF8m7ObESYl92fN48aYV4msEfB3jeaMtl7U6MvYtf8zhlxwK/s3+Ok3IZ60ytUY332XyDsXEXLBmfDOGWDvhCu/hKjebCpr55qGCC/eAAAgAElEQVSlu0kIUbH86iFEBAcGXafby6yCanZ027gtKZLbkqKOEtby+ZxUVj5FfcN7qFQp9Mp+Cp2u33G9o9foxLyhDvveVpCI0QyJQjsmHknwH6+h4vN6aKuuoqmshJbKMloqyuhqbT58XKXTExafQGhcIiGx8YTExGKIjkVjCEEk/utsCwVWEB2YmpowNjdgbKyns76OjoY6HOafClAYomOJSssgKjWDmIwsIpJS/hTXjbfLGVh17WoBAdSDI9GOS0CqO77J3mTaQVHxApzORhIT55KSPA+x+Mj+4RMEHqps4vX6dsYatLyZm0TwoaS3BpOdy97eQYfFxXuz8hicFAL1uwIZpYZEuPJL2t9dScdLLxN6w40UGsZTtLmJfuP8bP/keUbmXUp0exxfhuejjthERWwwi+uu5InKJ/gmWMkZpTmUnNZF1NY9WHS9cYr0iL0+Fj38EDLZ3zcT+RSh9zCemjcfm0GPqqONYK2M7zOiuKwoiE2YGR+ylFvFz5E6uI1rncs4uO90plgHI4uSsGbrc/SbdAlle2KJSdMzMrKctgfuwzB9OpF33IjovXPAWBnYAE0YwnclrVz7/l5SIzQsm51HqCYw0CrsTmYcrKLZ5eGFrASmRBqOekaLpZjColuw2cqJi5tJWupCJJJfF+AC8JldmDfUYdvVCmLQDI1BOybud/lbfy+8bjdNZSXUFx2ksbiQ5ooyvO7AhpomNIzo1AwiU9KISE4lIikFtf7o9/07QRAEbCYjbbVVtFVV0lJVQUtFKbYuEwAyRRDR6ZnEZuWQkNuH6PRMJNI/joB83S7MG+sDxC4CzZBotKfFH5cAmtdrpbz8EZqaP0KrySEn59ljauGvaO5kQWk9KcoglvVJPqwD09LtZPrb22nucrL4ykEMTw2Dyo2w4iKI7osw81Oa//Uw3WvWEPnE02wsjaSj0UZyThn5G9Zw/vA7sLU6WaPawYBB63lWcT3t2xQ8ILmdnS1XM0ShYmWylWHNXTg7XNjDwtCaLdz27N832egUofcgTJ1GXnzmaUR+AbHYhMRXiS6pD5JdBmr6rKa7/WzWDZ/IE8rbqdg5jGGekaRKo1hd+CxRWb2wWSciFouZPEGg7aa5qEcMJ/6FZxCtvAgadgVqgKZNYH1hCzes2EtWVDDvz85DrwoMri0mC7MKapCKRCzpncwg3ZHRJILgp77+XSoqn0Ym09Er+wlCQ8f85nv5HV4sPzRg3dKI4BdQD44i+LR4JMdprf0eCIJAe201NQf2UntwH42lRfg8HkQiMRHJKcRm5RCbmU1MRjaakNAe////ihAEAUtnB83lJTSWFNFQUkh7bTUIAlK5gtisXiT1HUBSn/6Exh+7DuzJwmv6aVUmkkrQjo5FMyr2uHII2trXUVJyNz6fnfT0e4iNufSoZ9xssjC7oAbZob478FDfbbe4uOzt7dR22nnr8kGMzgg/lBV9OaSOw3/BEuquuRbnwXzCXnmHzz93IJWLkIrXYKpt4LyMGykSNbJPtpmEgbtZZH2KC3f8G1/4VnodPBPrcBP+or14grJxYwC/wMJ/3YdC9ee6DXsKpwi9B/HkzfOwhxhQdbYSIvPzw4AELtgm58vIOs727+P62EeYlbqMxCoLctN4TjNlsc32BSZ3C2FJV9Pe4OOcSyOxzrsCWVQUicuXIvnyOihdCxcuhtwL+LqghRtX7CU3VseSWXmHiwZ83GLklpJ6kpRylvVJOSpZyOXuoLjoDjqNmwgLm0B21mO/WcxA8Pqxbm/G8l0dfrsXZb9wdKcnIg39bWv+98DjdFKbv5+qvTup2rcbm8kIQFhCEom9+xKf05e47BwUqlPhjv+B02qlobiAusID1B7cj7GxHgisWlIHDCZlQB7xuX2QyXt20vW02zGvq8FR0IlYIyN4QgLqwdG/GcfucrVRVLwAo/FHwsMnkp11dChsuS2wumx1e3i1V+Jhca9Oq4sZi3dS2W7lrcsHMSYjHPYsgc9vhtwL8Y57kppLpuO3Wgl6+j2+XNZAdJqEpqLXidVnMEA2jvUhhUgiNlAYH8MHpVN5vf0OvvCOYVJ3NB8NcDO6uIluqx97WATqri7ueP7vKa97itB7CM31Dbz5+muIvT4EeRduURuZwbm0linRZ7zNUse/kA2XMd/5BsUHJjHVnIdR28SWglVkj7mBqv0yTpuWiPyJ6/HbbCR/+AGy3Y/B/mVw1tOQdw1r85u5aeU++sQFyFwbJEMQBJ6vbeWJ6hZG6DW8k5uE7r+qx3QaN1NUdBter4X0tLuIjb3sV604QRBwFHTS/XU1vk4ninQ9ujOTezRqxWExU7lnJxW7tlF7YB9ejxu5UkVSv4Ek9xtIUp/+/28s8J6AuaONmgP7qNm/h5qD+/A4HUgVCpL7DiQtbxgpAwYTpO657+eut9D1VRXuajPSCCW6s1IIyvz18FRB8FNXv5jKymeQy0PJyXkeg37wEed0uL1ckV/FXrOdB9JimBMfyKUw2dxc9vYOKtqtvDlzIGMzI+DHZ2HDAzDkWlwZc6m59FJkkRFYbnyezatrSe1vp/C71xnX/3KUXQZWa3fQe8BaHpPehmZLB+cGP4W3aBbaXDMNrQeQihPw+g2AiPkLFhJsODGdov8lThF6D+HJm+ZjD9WjNLUQ7rOyc3gK438IYl3aLjK6DLw88Goe199J/a6BjLCOIlaj5997nyF71PlUFyTRe3Q0iWsfw3HgAAlL3kPV+RlsfhbGLILT7uSrQ2TeL17Pkll5aBRSvH6BhWX1LG82cmGkgWez4pH/bOPP7/dSXf0CNbWvoVankZvzwm9qsLibrHR9XhkYqJEq9GclE5T522XJjgdOq5XyXVsp27aZ2vz9CH4/2tBw0vKGkjpwCHHZOX+oP/j/C7weDw2FB6k4NGHaTEbEEilJffuTOWwUqYOG9MhqRxAEnEWddK+twdvhQJGuR392CrLIX7+32ZxPQeE8nM4GUpLnk5h4LSLRT/3W4fNzQ1EtX3V0MycunPvTYhCLRHTZA6Re3mrljcsHclpGOKy7G7a/AuPvwyYbHghnHDmC8qE3U7qjlcTsUsq2ruWC/rdT5Wxjt3oTYQMLubfjIRblv8Q+jYizqnvz5UgXw/c2YvTIsIdGoDKaWPDiCyfdRn82ThF6D6C8uIQVy99H4vLiUXbTHeRmpCedvV0ehoavZL7mBc7L/ZqcqgZU7RMZ7cziy5o3CE6Mp7trImFxGoaZP8f88UfEPPUkuogmWLsABl4JZz/P14Ut3LBiH/3j9bx3iMxtvkDY1zedZuYnRrIwOeoI68jpaqGwYD5d3buIjp5GZsZ9v7rx6bO6Ma+vxbarBbFKSvDEJNSDon53Svh/w+t2U7V3J8Wbv6dq7278Pi+6iEgyho0iY8gIIlOOrmV6Cj0Hwe+nuaKMsh1bKNu2GUtnOxKZjNQBeWSPOo3k/gNPehIVfH6s25oxf1uH4PaiGRpD8ISEX9Wt93otlJTcQ2vbF4SEjCKn19PI5T/Fo/sEgfsrGnmroYNzI/S8lJ2AQiymy+5mxuIdlLUecr+khcLqOZC/Cs57FVOphJb7H0B3xSx+9I/B3GFHGfQ5rtZuJsZewXfqIoTodWyPz2bjgeE86ppHQeOVZEXBXk8+Sn8IEpcOv0TM9TfNIzw68qTa5s/GKULvATxx03wc/7HOPW2UDM8gZ6OW0pzPqe+aStmILO7yPk35gclMNedR4tlGnaUETcSVeFxyJmbUYnnmEULnzCHijCRYdRVkTYaLlrK+uJ3rl++lT5yOpbOHoFFI6XR7mZlfxX6znccyjkzMgEBseUHhfPx+J5mZDxEdNeUXn13wC9h2NNO9rhbB7UMzPIbg8QmIlSehby4ItFSUUfD9N5Ru/RGX3YZabyBrxGiyho8hMjX9FIn/DyD4/TSVl1K6dRMlWzfhMHcTpNGSNWIMuWMnEJGcelLfxWfzYP6mFtuOZsQqGbpJSagGRv6igqYgCDQ1fUBZ+UPIpHpyc188Imb9P4lxD1Y2MVyv4d1D7sQuu5vpbwXcL+9cMZiRycGwYhpU/wiXrqTlw12Yli9Hc9cjrNsfhlLrwlS/mMyIISTSm9XB28nuv5Z/ie5h6JYdqAwbGVA8kZ1jHfTdXkWHSIcjJBJVp4kFL/29rPRThH6SKNi/n08+/hiJ04072EabRsYZbTFskLYyXHqABen3c1/Uw5j2ZDGqaywaFawvWExiv9m01emYOE6K51/Xohl3GnG3Xoxo+fkQMwAu/5TvKs3MfX8POTE63p8d8JnXO91csr+SRpeb13olcubPqsIIgp+amleoqn4BtTqN3rkvHzNM7D9w1Znp+rQCT5MNRaoO/bmpv7lc/jXYzd0UbfqO/O/WY2ysRypXkD5kODmjxxOf2/ukEnZOoWfh83qpy99P4abvqNi1DZ/HQ3hCErnjzqDXqNMI0py4v93dZKVrTSXuWjPyBC3689J+VdnRYikiv+AGnM5G0lIXEh8/64iJ5ZMWI/NL6klXKVjZN5VIhQyTzc2lb22nusPGu1cOZni8IiDm1VGOMHMN9fe/gW3XLsQPvcW3X9uITumkes8SzupzHe0OJzv1G1EOqOXRugU8X7uIzY6RjJUY2BBcTZhdgsQRjF8m4eqr5xCbnHjCbfFn4xShnySeuOkWHKE6grpbCHHU0ZGXg26rHknW+yz23k/vwaWMqD2ApulshvnTWF30PHH9xtFS04u800IIfmYusqgokl66D/HKKaCJhFnr2NTg4+olu8mK1vL+7CHolDKKrQ4uPVCFw+9nae9khuh/GiQeTxeFRbfS2fkDUZHnkZX18DHTriEgntW9rgbbzhYkWjm6s1NQ9g47IetMEAQaiwvZ/81XVOzcis/rJTo9k9zTJpI5bNSfnul4Cr8fTquVkq2bKNi4ntaqCqQyORlDR9B34mSi0zNPuF/Y97bRvbYav80TWPlNTPzFMEev10JR8QLa29cTHj6JXtlPIJX+1L9/MFq4qqCaMJmUD/qmkqJS0Gl1Mf2tHdQZ7SydncfgUA8sngBuO76L/03NnIX4rFY6r3+FvT92Epmwn+b8rUzJnMcPsmJ88V/wdexIGrZHcmHQkwQVT6dhVDdJO8rokEbhCIn621nppwj9JLBzy1a++vorZHY3Xr2DmlAN55Ya+Doyn2i7mlVDz+ch0UPUHJjMFMsQtnesxmuQYrVMJrGXgcy19+I3mUh672XkX84Avxdmf8M2o4Yr391JSriGldcMQa+Ss7PLysz8apRiMSv7ppCt+ckfbrEUcTD/elyuFjLS7yU2dvoxB6EgCDgOtNP1RRV+uwfN8FiCT084IT1yt9NB0aaNHFj/JR31tSjUanqNHkefcWcQlpB0Ms16Cv9DtFZXkr9hHcWbN+J2OIhISqXfGZPJGjnmhEIg/XYP3esPuWG0cgznpqLMDTvmuYIgUFf/NpWVT6FUJtGn96tHrDD3me1cdrASESJW9E2hr1ZFu8XFxW9uo83s4v3ZefRXdcDi00FpwDXhLWouvw5pfAIFo+6iqdKEQr4arUNNf/0kVgdvI2XAWu50Pcic3e9RoZBzhjGdLxNbiTU5kdm0+BRSLptxBWlZGSfcpn8mThH6SeDxm2/BGaIjyNxCsLMGcvvRXaAhOW4li0Jf4qa0VxH2RTPaOAGvv509rd+gCrkcudLA8PYVuLf8QMIbL6I+eA+YauCqtex2xXH5OzuJ1Sv5YM5QQjUKNnSaubqgmmiFnA/6phxRUb255VNKSu5CJjPQO/dldLpjVzL3Gp2YPq3AVWZCFq/FMDXthMIQu9ta2bfuCwq+W4/LbiMi+dCAHz4ameLvmYxxCkfD7bBTvPl79q//io66GoK0wfQZfwb9Jk5GG3psQv41uOrMdK2uwNNsI6hXKIbzUn8xMc1k2k5+wU34/S56ZT9FRMQZh49V2J1ccqCSLo+Ppb1TGG7Q0NLt5KI3tmGyu1l5zVByfSWBqkeRuVjTFlJ//XzkE8/hR+W5CH4j5ub3GJ44Fbdfxc7wb3D0tfBOyUzuN8+ntWYGogFmNPsKMMnjcRiiUHZ2sfClv0dc+ilCP0Fs2vAN332/CZndiUfvpDZaz5m71OxOW0+N/Wxcw+VMbVqPquY8BgqJfFb8IhEZ07B0xTMuuhjf0heJuu8eDJ5VUL0JLvuI/KDBTH9rO2FaBR/OHUqENohPW03cWFxLtlrJir4phMsDkQN+v5eKysepr38XvX4IubkvopAfPdAEv4B1SyPm9bUgEqE7IxH1sJjfXeqtqayEPV+spnznNhBBxpARDDjrXKLTs/4WG5xupxd7txtbtwu72Y3T6sFh9eC0enA5PLjtXlwOL163H4/Lh9ftw+vx4/P68XsFBEFAEAJWpEgkQiQCkViERCJCIhMjkYqRyiXIFIGfXClFoZQiV0lRamQEqWUotXJUwYd+OjnyEywF92dCEAQaigvY+9VnVO7egUgsImPoSAadPZXIlF/enznmvXx+rJubMH9bC2IRujOTUOdFH7MvOp3N5BfciNm8n6TE60lJmY9IFNiDaXK6ufhAJXVON2/lJDExTEeDyc7Fb2zH7vby4dxhZHRuDGSTZk2m0zqWtqefRXTNIjZWJhAcVkN72RrOy5rHVnE5/tTVvB9+IbFbmtCptzOqdjRf9DaT2NqN1KLGFyRj6gUX0Kf/sY2lvxJOEfoJ4vF5t+LUa1FaW5F7qwmNG0pBm5sszR4ezr6LR7X307zvDM6zDmVD/RJUCRl0dQ5lWD8Pyufno7/4IqIHdMC+ZXDuS5TFTuXiN7ahkktZde0wYvRK3m/qYEFpA0N0apb2STksXOR2GykouAlT13bi464kLW0RYvHRIWKeVhumj8tx11sIygpBPyUNqf74l82C30/Vvl3sXPMJTaVFKNRq+kw4k/5nnH1CVtofCa/bR3e7g+42B90dDswdDiydTixGJ1ajE7fTd8zrFCopCpX0MAHLFBKkCglSuQSpVIxYKkIiESOSHCJxkegncvcJ+Hx+fF4Bn8cXmAzcPjxOH26nF5c98HM7jl04QqGSojEEoQlREByqJDgsiOAwJboIJfpwFRLZX0dMDA6tzr7+jPzv1uN2OIjP6UPeuReQ2HfA75rUvZ0OTKsrcFV0IU/WEXJBOtKwo0Nq/X4XpaX309T8EaGhY8np9RwyWUDnv9Pt5bKDVeRb7byQlcCFUSHUdNiY9sY2RMCqa4eRWLYE1t2JMOQ6mr71Yv7qK8zzX2HXXj+6kE14m5oYFXMpn2o3Ez3we+423s9DFXdTYhxLcqIfV/kerPIkHLq/j5V+itBPAN988Tlbdu5EbnUiGJzUJBgYtUVJZ8aHLBPdzZn9vyH0gIQR7ZMw2Uqp81bg8l1ARq9gEt65nqCcHBJn5yDa9DiMup2avrce2RFD1bxS18ZDlU1MCA3mrZwklIeqC1msJRw8OBe3u42szEeIjj7/qOcTfAKWH+oxb6hDrJCgPzcVZd/w4x50Pq+X0q2b2LnmYzob6ggOj2Dg5CnknnY68qCeTfv/vfC4fRgbbXQ2WTE22zA12TC12LGYnPCz7qpQSdGGBqENCUITEoRGr0Ctk6PSKVAFy1Fq5QSppYh/oQRfT8Ln8+M8tBqwd7uxmV3YulzYTC4sJhcWoxNLh+OISUckAm1oEPpINSExakKi1YTGBv4tlf1vo4VcdhsHN6xj71drsBo7CU9KIe+8C8kYOuK4I5kEQcC+u5WuL6sQvAK6M5LQjDh65SgIAo2NKygrfxClMp4+vd9ErQ4UPLd6fVyRX83WLiuPHwrfLWu1HGkYbbsfdryOf/zD1Dy3EXddPZXTX6KmzISEj8hU9keujGN31FfU5mjYvnsw54qfI6HiAr7Ns5HYYEZqCcKjlHP22ZMZNGRYTzdnj+IUoZ8A/mOdK2xtIK4jWT2ULf4mQgSBb4eOZa5pCUFlF5LrC2dd9Tuow2ai1UczYMuDiD1OUh69HMk3t0Dvi2ga9wLT3tiOw+PjwzlDSYvQ8GR1C8/VtnJehJ6XsxMP60S3ta+jqOh2pBItffq8fswiFJ5WG8ZVZXgarCj7hKE/N/W4lPEgkGFY9MMGdq5ZRXdbK2EJSeSddyGZw0b9T6rtuJ1e2usstNVaaK+z0FFvoavVfrguhUQmxhClwhClxhClQh+hQhehJDhMSZD675VxKggCLrv30CrDTldr4GdssdPVYsfn9QMBN48+UkV4vIaIxGDCE7WEx2uRKf787+Pzeij+8Xt2fvYJpqYGDNGx5E2ZRvbIsUikx+dO8pldmFZX4Cw2Ik8MxjAtA9kxrHVT1y7y869HEDzk5rxwWFTO6fNzTWEN33SauTc1hhsSIiho7ObSN7cTrlXw0Zw8wr66Bkq+xDvxZapufw2/Ppxd/e/AaWvA2rqSyWlz2SmpQ5T9Mc+qr2fazk9p9uoZrAmhrXUrbkkyzuAolMZuFr74XI+2YU/jFKH/Tny56mN25R9EbrEj0TlpTNaTtUVJUMpHPBTxLPfEPoVlzwjOsY1gfc3bBEUPx+3OZaR1NZLd35P8wh0oNt8CcYPpnPoB0xbvpd3sYuWcoeTEBHNfRRNvNrQzPTqEpzLjkRxa4tfUvExV9fMEB/ejT+/XUCgijnguwS9g2dSA+ZtaxEES9FPSUPUOP6538rrd5G9cz85PV2E1dhKVlsHQ8y8mZUDen+YfFwSB7jYHTRVdtFZ101JtxthsO2x1awwKwhO0hMZpCI/TEhKrJjhMifh37gX8HeH3+TF3OOlosNLZaKWjwUp7nQVbV0BGWCQWERKjJio5mKgUHdFpOoLDlH/et/P7Kd+1jR3//oi2mkqCwyMZMnUaOWMmHBexC4KAfV8bXZ9Vgc9P8KQkNMfY53E4GjmYPxertZT0tEWH49U9foEbi2tZ09bFLYmRLEiOYk+tiRmLd5ASpmHllb3RfTgF2ktx5D1NzfzH8I6ZwhbxeIKU+3A372dcwhV8FrwJ1cC9PF19EwtNtyOquIQ9Ix3E1nYh6lbiUSk44/QJDBv92wql/yucIvTficfn3YZTryHI3oZD3kaudyDb9Hto8AxDN9xMn+IG8prPo7ljF50qFw7HJIZEVqP56Bli75tHcN0ToArBPGMt05eVUt5q5f3ZQxiUZGBRWQNLmzq5Ji5QwUUkEuHzOSkuXkhr2xdERU4hK+vRo+p8ejscGFeV4a41o8wNRT8l7fj0qj0e8r9bd5jIY7N6MfSCS0ns3e8PJwPBL2BsttFYZqKxrIvmii4clkAFH4VKSmRyMJHJOiIStUQkBqP6E4pn/N1g63bRVmuhrcZMa3U3rdXmw24bVbCc6DQ9sRl6YjMMGKJVf/w3FQSq9+1m2ycraakoO0TsF5EzZvxxEbvP7ML0STnOUhOKVB2GaRlHlcDz+ewUFt1Be/vXxERfRGbmA4jFcnyCwB2l9axoNnJtfDj3pcbww6ECMH3j9Lx/cRLKJRPB56Er5Aaan3yN7un3sLsxkiDF50R7w9DpczgYt5ot6b1QbPGilZYy0plFhXsHIm8szuAogozdLPoLW+mnCP13YPWK9zlQUo7CbEemcdCRqkO3R05IxCZeyF3And5nkRVMI9OlZUvbGpBNJz1JQcLSeYRcOpXIkHVgN+K6cj0zV3ewt87EW5cPYlRGOLeU1rGqxcTNCRHcmRKNSCTC5WrjYP61mM0HSU25ncTEuUcMSkEQsO1sofuLKpCIMZyXirLfb/vK/T4fhT9sYNsnK7F0tBOT2Yvh06aTkNv3Dx305g4HDSUm6kuMNJSYcFoDBB4cFkR0mp7oVB3RaXoMkarfHYVzCj9Nks2V3TRXdNFU3oXVFLDilcFy4jINxGcbiM8OQWP440JMBUGgZv8etq5aTktlOfrIaIZNm07WiNG/6WMXBAHbrha6v6gGEejPS0XVP+K/+r2fqurnqal5Bb0+j965ryCXh+AXBO4ub+Tdxg6ujA3j0fRY1ua3cNPKvYxMD2fxJBWyJWci6ONpqRmKac1aqme+QnWdBb9zOafFXMJ+eQv0Xc2DngUsKr+f7sZJ1A9xEllrwWdW4FEFMXbMKMZOmPiHtd/J4BSh/w48Nv82XDoNckcbZpWZgaZcSmK+4jPFzVyc+wnS3RlMtoxiXdVbSEPOItiQQt+vbkednUbihG5EDTvxzviUazfJ2VDSxguX9OfM3tGHl4sLkqO4JTESkUiExVLMgYPX4PF0kZvzLOHhR3Ygn8UdsGZKjCjS9RguzPjN8mCC30/pth/Z8tEyulqaiUrLYMTFM/8wi9zn8dNYZqKu0EhtYSddrXYAVDo58dkhxGYYiM3UE9zD+uqnEIAgCJg7nDSWmWgoMdFQYjy8CgqJUZPQK4SE3FBi0vRIpD2/OSwIAlV7d7Hlo2W011QRGpfAiItnkDZ42G/2N6/RifGjUtw1ZpS9wzBMTTtK7Kul5TOKSxaiUETRt89i1OoUBEHgwcomXqtv59LoEJ7OjOfj3fUs/CSfc/rG8MIgI+IV0xBSxlHzqYC9vpW94x/FbinFb9rI+MSr+FL3HbaBTazbN4HT/Evo0zaOgqC9BLnCcKojCTJZWPTisz3eXj2BU4R+nPh4ybsUVFajMNsJUtlwJBqwlHtQqjvYMXQw59Rtpm/tRdQ1/4hZo8MvGs2QyjdQmxtIvTETcfEq/FPe4PbSLP69r5GHpuRycV481xXV8mV79+ENHYCOjo0UFM5DKtXSt8+baLU5RzyLo7gT08fl+F1edGcmH9Pf+HMIgkDtwX38uGIJbTWVhCUkMeLimaQO7HkfucPipvpgB7X5ndQVG/G6fEikYmIz9MT3CiGhV+ifsvw/haMhCAKdjTbqi4zUFXXSVNGF3ysgD5IQ3yuUpD6hJOWGEaTp2Q1lwe+nbMdWtny0DFNTA9HpmYyafiXxvXr/xnU/2xdSywiZlkFQ+pElBrO9V38AACAASURBVLu793Lg4FwEwUvv3q8SYhiGIAiHAwsujDTwQnYCb22q4vG1JVw+LJEHorch+up2fLlXUPn0LmwxvdgePQOpeAOhDj9hEQMoSfmQD6InM2nXOmxdKQi9QVvXhceiwq0OYuSIoUyYNLlH26kncIrQjxOPzb8dl05NkKONlhA3eXXpdCZ+wkuRD3On/lnke6aSZlOwx7IZv/giBrAHw9YPSL33TORFryOMup2HHReweHM1t52ewZzTUplbWMPXHWYeSovlmvjABmZ9w1LKyh5Cq8mmT983CVJEHX4GweOj66tqbNuakUWrCbkk8zfFtFqrK9m07B3qCg4QHB7JiItnHNfS9/fA3Omgal87VfvbaansRhACm5hJvcNI7B1KbKYBmfyUMNdfDW6nl4YSE7UFndTkd2DvdiMSi4hJ15HcN5zU/uE96pr5j6tv66rlWI2dJPcfxOjLriIs/tfFr9yNVowflOBtd6AZHYduYiKin60oHI56Dhy8Bru9mqzMh4mJmQbAszUtPFndwtQIPS9lJ/LU1yW8samKm8enc6t3Mex4HWf2jVQ/8Ckd4+dwwJ2ByLuSoYYzOKg24hv0FU+33Mxc4/3ENExhX1gRerMapyaSoC4Li17461nppwj9OPDRO29SVNuAotuGKqgbcUw01e0tdCiSiRjaTPxeJRO7TmND3XsIyqmkhMpJ+fRu4m49F23TG5AzhVfD7uLJdeVcNSKJBWdmcU1RLd92mnksI46rYsMQBB/lFY9RX/8uYWETyM157ghxLU+Ljc6VJXhb7WhGxqKblHREp/5vmDva2PzB+xT/uJEgbTDDzr+YPqefhbSHKpqbOxxU7Gmjcm8bbbUWAELjNKT0DSO5bzhh8ZpTVvjfCIJfoK3OQvWB9v9j77zjpKrO//++08vOzO7M9t6XLbALC0tHuqCogGAvWL6WRGNi8tPkm3wTUzWaZqyxxxqVIiooUqRJLwtsY3vvu1N2eru/PwZXiaAo2GDer9d53Z2dM3PPuXPv5z73Oec8D43l/Zi7HADEZejJGhNLdmksOuOZEXef18PB995hz1tv4nW5KJw+m8mXXf25GaqC3gDWNY04dncjT4oIGTMxn1wffv8QR47cwaB5+7GVpXcjCAKPtPTwx8YuLoqJ5LH8VH616ghv7GvnDxeP4Jqmn0P9RqzGm+h47F3qFj1EW78ZiXMN09OWsd60jrpRcgI75WhdVuJSopB09uFx6vBGqJg0bixzL7rkjByTM0VY0E+BkO9ci9LVR0eclJK6JDwp7/F8/l3cZn2FvLqraevcgkWZit4whpI1dxN70VhidGsRYvJYMeopfvpWLZeUJHL/paO4uaqZTYNDPJSXzLWJ0QQCLiorf0Jf/3pSkpeRk/O/w8ucRTEUr9zybiMSlQzjZXmock+e2d7rcrJn9XL2v/sWAGMuuJiyhUvPSIYah8VD3b4e6vb10ttsAyA2XU/W6BiyxsRgiAlHVjxbMHc7aDjYR8OBXvrb7AAkZBnIHhtHdmnsGZl15BqysXvV6xx8fw1SmYxxl1zK2AWLPjcmkKuyH/OKOkR/kMhLstGM+WTANBj0cfTor+nseoO4uIspyH8AiUTJE629/LahkwtjDDyal8qPXjvIhuoenliSw7xd1yEOddLTNYPeD6s4OPdBnEM7MfnMRMePo2nEK/xDdRO319+Ptu0SytPriO9V4IqI/U5a6WFB/wJeffoJatu7UFodRCjMaEwpNPiOsC3iUi7LXYF+13SyLAoOuyoRFAspq/gn0bF+Uic2IYgiW6e/zrLlbUzJieHRq8dwa00LHw4O8de8FK5ONOH19nPo8C3YbIfJzfkVKSnLhvcddPoYXFGHu3IAZW4UxstyTzodMRgMUPHhBj56/SWcVgv5U6Yz5crr0EfHnrD+qeJ1+Wk42Evtnh7aj5pBhJhUHdmlIatNf4JFIGHOLiy9Tur391K/r4eBDgeCRCAlP4rcsngyS2JOe1GTpbuLra8+T93uHUQYTUy9ahn5k89DkJz4CTRg9TDwn6N4m6xoSmKIXJQ9HDFUFEVaWp6gofGvREaWMWrkk8jlBv7V1stv6kOi/o+cFG56fi8H28y8uiSBcesvRVTqaf4ghn6viT3ZN4FvOWN1E6iIHMQ6YTdrj5xPqX0Dafps3P09uN0GvBEqykaXcMGiS0+r/2eSsKB/Aff/+Kd49FqU7l7aUlXkVEfhiq3i8PgxTKlu57yeuWzt/A+iailF9v0kNr5P9rVqJOZaqua/waJVDkYk6HnuhnHcUdfG5sEh/joihasSTDidTZSX34jH20tR4T+IiZkzvF9Pq43BV2sI2LwY5qUTMSXppAOf7dUVfPjC0/Q2N5CYm8/0628mIfvzc4d+HsGgSHvNIEd3ddN4sA+/L4g+Rk1uWRx5ZfFExoUt8XOVgU47tXt6qNvTw9CgG7lSStaYGPImJJCUE3la003bqyvY/OIz9DTWk5CTx4xlt5z0PBaDIkObWrFtbEVmVGG8Kv+4JBrd3W9TVX0PanUaJcXPoVYn8XRbH/9X38EF0QYeykzi6qd30Tbo5J1LpGSuvYpgdDG1Tw/QMeoyquQ5SNwrmZZ8NVsTVvNB9kim7luPuquMmuxOkrpE3NrvnpV+2oIuCMI84GFACjwjiuID//X+NOAfwCjgClEUl3/Rd35XBP3Vpx6jtqMnZJ3L+ojUZtOu2sxr8XfxI+VzZFVcTUfnVszSAlK0UeR+8Fty7spD3rOZrrlPMm+9CaNWwSu3jOenTZ3HiXlodP4WQKB41NMYDCXAseiI2zqwrmtGalBguiofRYruhO0bGuhny8vPcXTHViJM0Zx39Q3kTZr2lX3X1j4XNTu7qNnZhd3sQamRkT02jhET4onL0Id94mGGEYMiXQ0WanZ1U7+/F587gD5axYiJCYyYmPCV/e1iMEjl1k1se/UFnFYLhefNYupVy9BGntjN6GmyMvhaDQGHj8gFmWgnJAyfp2bzLg4fuQ2JRE1J8TPodIU8097Hr+o6uDDGwG+T47n8yZ14/EHWTW/HtOEuPLFzaPhnFVXz/kDXUCsxwVaiEsfSWvwfHrPcyOL+J0gSJzE01IrTY8SrU1M2ppgLFn43rPTTEnQh5OitBeYA7cBe4EpRFKs+VScd0AM/A97+Pgn6sHXu6aMjXUtsrZwuk4hp3AAZu3LI6lVT4W4jQjeHMRvuJfPaTCJc6xmaeA9zD0zAHxR57dYJ/Lqjh02DQ/wtL4WrEk309W2govJHKJXxlBQ/h0aTDoRyMprfrMVdM4i60ETUktwT5vb0+3zsf3cVu1a9jhgMMu7iJZRdculXikce8AVpPNRH1fZO2mvMIEBqgZH8SYlkjIr+zkX8C/Pdw+cN0FTeR/WOruPOocIpSaSNMiH9CgHQPE4nu1e9zv41q5EpFExaehUl5y844YrTgN0bum6OmkNz1i/NQXIsNLHdXkv5oRvx+4cYNfJxjMbJPNXWy6/rO1kQY+Bn0dFc8dROItVy1uZ/gGbf49hkc2l6vZl9M+7H5XiX0doCKmN7qSvrIbgbYvoiac8eIrnV852z0k9X0CcC94mieP6x178AEEXx/hPUfQF49/si6C8/8Sj13X0orXbU0i5i5QV06T9gZd6tXNO9gQltl7CjaxWoLmfskSfJKPERE7sT34hLWNB5A51WDy//z3j+MjjIhgEbf8lL4ZpEEx0dr1Fz9NfodUUUFz+DQhEa2R92sQx5ibwwE+3EhBNaxM3l+9n4/JNYurvIHjeB6dfdjCE2/jP1vghrn5PKbZ1U7+jCbfehM6oomJJA3oSvbl19Hwi63QSsNoIOO8GhIYIOB0Gnk6DLRdDlQvR4Eb0eRK8X0edD9AcQA34IihAMEgouI4BEAhIBQSpDkMkQ5DIEhRJBqURQKpCo1Eg0aiRqNZKIiOEiNRiQKL985p/vC7Z+F9U7uqje0YXD4kGjV5A/KYGCqYlfaQHZYGcHm//9FE3l+zElpzLrpttPOH899GTbjnVdM7IoFcar84cTuLg93RwqvxGHs4GC/AeJj79k2Kd+cWwkN2v1XPfsbkbEalge+QjSxk30tI6lscHIgdyrkXqWMyFxMbvS3+S5yEu4ovFRYu2z6ffV4/LF4tWpGVtSxILFl5328TtdTlfQlwDzRFG8+djra4HxoijecYK6L/A5gi4Iwi3ALQCpqamlLS0tX6YfZ5xPW+e96Xq0LUPsiy1hQfo6svcvpr99B4OysYywNpLnXE/qlDZEYzbXcx+72lw8s2wcz7mHWNdv48HcZK5NNNHU9DBNzY9gMk1nZNEjSKUaRFHEvqMT69ompPqTu1hs/X1sfvFp6nbvICohkZnLbiW9pPRL9SkYFGk50k/Flg5aqwYRJAIZo6IpnJpISr7xe7ncXhRFglYrvp4e/D09+Pv68Pf14x8YIDDQj3/QTGBwkIDZTMBmQ/R4vtwOZDIEqRSk0tANVhBADCW8IBBADATAf+J45ydDUCqRGgxIo6KQGqOQRRmRRpuQmaKRRUcji41BFheHPC4Oif776eoKBoK0Vg5Sua2D5ooBBCCtyETRecmkFny5c00URRr27+HDF57C1tdD/pTpTLvmRiKijJ+p62m2MvBqDUGnj6iLs9GMC6289vlsHD5yGxbLbrKz7iUt7RYeb+3ldw2dLI6LYiFKbntpP3OzNTzh+jkMddO8IYaauEupUxmJFWvQJpbQMH4NW2smM7K/GUu6lPhGB57vkJV+uoK+FDj/vwS9TBTFO09Q9wW+Jxb6y48/Qn1PP0qrHZW0nejAKDpidlI9powpB4Nkt0dxxDVAnDKf4j1/JPfKIILg51fRj/BKjZ+/XV7CGrmXNX1W/pSTxLLEKI7W/obOzv+QkLCEEXl/QCKRE/T4Ma+ow3W4H1W+EePS3M8sbw74/Rx47212vvkqoigyYfHllC5Y9KXmk7vtPqo+6qRiSwdDg260kUoKpiRSMDmRiKjvvrUYsFrxtrbibW3F196Br70dX2cnvq4ufF1diC7XZz4j0emQGY1IPy5RkSER1RuQGvRIInRIIrRItVoEjQaJWoNErUJQqZAoFAgKRUjMT0FMRVEEvx/R6yXo9SK63QRdboIuJ6LTScBuJ2h3ELQPEbDaCNhsBKwWAmYLgcFB/OZBAgODBO32z3y3oNEgT0gIlcRE5CnJKJKTkaekokhNQarXn5Fj/HViG3BRtb2Tqo+6cNm8GGLUFJ2XRP6kBJSaUz+PfR43e956k71vr0AqVzDlimspnnvBZxbJBexeBl8/iqfOgqY0jshLspAopASDHiqrfkZv71pSU24iO/vnPNLax58au1gaH8V4G/xqVQW3jRS4t+12RJmBoy8FODj5d/Q79zE6Io3KlBY2FpooO/QBiZZp9FCL2x+HV6dmTFEBF192xZk+fF+KsMvlBHzaOrdmRKHobGJd6kKWSVZRenQJe7vXoYhYzLjt91F4lYjc28Sz2Y/x+0Nafj5/BAejpazutfC77ERuSjRQWXkXff3rSUu7nazMn4Yshh4HAy9X4+93hWaxTEv+jHh0HK1mwzOP0d/aTGZpGTOX3YohNu6U+zHQYefwh+3U7u7G7wuSlBvJyOnJZBRHfyOJHb4Mos+Ht60NT0MD3oZGvE1NeJqb8DW3ELBaj6srNRqRJyWFBC4hAVl8yJqVxcUhi41FFh2NRPX9cxsFXS78/f34e3vx9/Tg6+7B39ONr7MrdAPr7CRgNh/3GWlUFIq0NBQZGSgyMlBmZaLMykKekhJ6svgOEfAHaTjYS8XmDroarMiUUkZMiGfUjGSi4k99nYS5q4ONzz1Jy+GDxGVmM/vmHxKflXNcHTEoYtvYytCmVuRxWkzX5COLViOKQWrrfkd7+0vEx11Cfv4D/KN1kAeburkqwUhSq4tHNtXzUKmZpVU/wqMYQeXrAnsn/i9B50rGJFzAgfzXedm1iJnt63AmxhDTaP/O+NJPV9BlhAZFZwEdhAZFrxJFsfIEdV/geyDoIeu8D6XVgVLWgd5TxNG4VrSjA4zZORlb+wHMssmU1Kxi9NQ2IpRVbCn4LdcfyGHZpHT6srSs6LXwf1mJ3JKg4vCRW7FY9h43x9x5qBfz8joEpRTjlSNQZUUe1wa33c62117g8Ib30ZlimHnDrWSPm3BK7ReDIq1Vg5RvaKW9xoxULiFvfOiiMX1qWte3hSiK+Ht7cVdX4zlai6f2WGluBp9vuJ4sLi4kUulpKNLSUaSlIk9ORpGUhER7+oukvq8E7A58HR342lrxtrTibWnB29yMt6kJf1/fcD1BoQgJfG4uqrxclHl5qEaMQBZzajHyv276Woc4vKmN2n09BP0iaUUmimenkJwXdcpPRUd3bGXzi8/gtFoZPW8Bky+/BoX6+Cm17qODDL5+FDEoYrwsD3WB6bi56kbjVEaNfJy/tFj5e0sP1yUYocLMG/vaWTHmMKVVD2AdKuDAgTwqMmcSEzyILGUE+8cfQnpwiKz+PCzyalyBBLwRasaOLGLB0su/rsP2hZyJaYsXEJqWKAWeE0Xxj4Ig/A7YJ4ri24IgjANWAVGAG+gWRbHw5N/47Qr68KpQdy/eFBOywUNszF/MFa0VZDekccThIMMBEzQriM2spyn7emZWns/cgji0pTG82mPm5xnx3J4gpfzQDTgc9RQUPER83EWIgSDWtU3YP+pEkabHdPUIpPpPXB6iKHJ05zY+fOEpXDYbYy64mEmXXX1Kad/8vgBHd3VzaGMb5m4nWoOCkTOSKZySdMaDLZ0qoijia2vDXVmJu6oqtK2uOc7KlCUmoMrJRZmbgyIrC2VWNoqMDKQR565of1UCdjvexkY89Q146uvx1Nfhqa3D3909XEdqMqHKz0dVUBAqRUXIkxK/NT+90+alclsHRza34xryYUqKoGR2Cjnj4k4pAqTH6WDbq//m0Ib3iDCamHXj7WSPHX9cHb/ZzcDL1fg67OhmpKCfk4YgEejsfIPqml+i14+ieNTT/LnNzWOtvdycGE3r9g4+auhnW/5bJDa8QVdVBrul19OssDBGF091dgWvxE1gdu0K3LEF6BvMISv9W47EGF5Y9Ck+bZ1LlR2onLnsSFMzMfkQYw8u4mDXFvTK6Uxt+C3p07uxxU9gQuvtFCQZyTovmRd7BvlJWhx3xnspL1+G19fPyKLHMZmmEhjyMvBKNd5mGxGTEjFcmIHwKbeHrb+Xjc8+QeOBvcRlZjPnf+44pazqbruPiq3tHP4wdEHEpOoonpVCdmns1xIS9fPwm824Dx/GdegQrsNHcB858om7RC5HlZODsiA/JCgjRqDMzUWqO/Ec+zBnjoDViqe2Fnd1De6aGtxVVXjq64cHc6VRUahGjUQ9chTq4mLUo0YiNRi+0Tb6fQHq9vZQvqGNwU4HWoOCUTNTKJyWhPIEU3f/m87aatY/HXJP5o6fzIwbbj1u0FT0BTGvrse5rwdlTiTGK0Yg1crp6/uAisq7UKlSKSl+nj+2B3mmvZ9b403sW99Me7+FHQl/R9NfSf0HsWzPvw+Xez0jE2ewr/QtKhozKe1Q4FLV4vEn4o1QMW5UMRcuWfJ1Hq6TEhb0T/HpmC2ShBgCzv3Ujirjwv1ReFsasconMbniYUZe0EZAE8WsoV8j1ZqYOC+T5/pCmVLujrNx6NAygkE/JcXPYjCU4Gm1MfByNaLLT9SlOWhKPlmOLwaDlH+whm2vvYgoBply+bWMnn/RF0ZDHBp0U76hlartnfi9QVILTYyem0pSbuQ3Ym2Jooi3oQHngQO4DpbjOnAA78czkyQSlDk5qEYWoS4aiaqoCGVuDhJFOOvQd4WgxxMS+YoKXEcqcB85jKe+gY8TtioyM1GPGY1m9GjUo8egyEj/xs6rtqpBDq4PuQwVKimF05IonpWC9gvi/Qf8fva9s5KdK15DJlcw7ZobGTlz7nHttu/pwrK6ITSj7NoCFIkRmM27OHT4VuQyPcXFL/C7DgUvdg7ww1gT779Th84/wLuq/0Nweyhfk86uoluIDuwnmBHPW6MCFB/ZQtAwGlnzIO6IWFRm27eW1Sgs6Md45cnHqOvsQWlzgLIDiTeF9XkjWCLuIrdyJEfsPka2VjJl6nZkCjvXS/9ElS+JCy7O5elBM8uSork3ppvDR25GKtVSUvICEdoc7Lu7sLzdgNSgxHTNJ3NjAQba2/jgX/+ks7aa9OIxzL75h1846DnY5eDguhZq9/QAkDMujtFzU792/7gYCOA5ehTHnj049+3Dtf/AsOtEGhWFevRo1CUlqEuKURcWntN+7u8rAbs9JPDl5bgOluMsLyd47AlLajKhKS1FM3YsmvFlKHNyThpr5UzR1zrEgQ9aaNjfiyAVGDEhgdFzU4mM/fzQE4OdHax/+hHaqypILRrFnP+5k8j4hOH3Pa02Bl+uJujyE7U4B83oWIaGKjlYvgyAUcXP8at2Pct7zNwRFcXrb9UwTdvGo+7/xeOJYsveOdTEJ1Osj6R65C7e8ZYxp64dn7IF/zErfXxJMfMXf/NWeljQj/FxNiKlsxeNKZYBfzVCUSTn7TqPqo49xHozmRf7OHpTN3/S/4oXzUVcenEez9ptXBFv5JcxTVRU/AClMp7RJS+ikidgebsBx55ulLlRmK7IG56SGPD72bt6ObtW/ge5Ss2MZbeQP2X651pAvS02DrzfQkN5HzKZhIKpiZTMTv3aFgGJoointg7nrp04dodEPGgLRViUp6SELuyxpajHjEGR/s1Yb2G+WcRgEG9TE879+3Ht349z7z58nZ0ASA0GNGXj0IyfgHbCeBRZWV/bOWDtc3JwfRs1O7oIBoJkj42jdF7a5xoxYjDIkQ8/YMtLzxEMBJh82dWMufCS4SffwJCXgVer8TbZQuGo52fg8jRz8OB1+Pw2Ckc+zf92xvJOn4VbNTpeffsoP47Zzw8tf8HSFc9a+91YJBXkJJexceJuVEfa0SiLCHYNfKtWeljQgVefepza9m6UNicBdSeCaGD7qGKuaelDUmfFLhnDhdbfk1jYyTuR1/GjnnlcdkEO//Y7WRgbya9NldRU/xStNofRJc8jdesZeLkKb+sQuunJ6OemDy+k6GlqYN2TD9PX3EjuxKnMuuFWNIbIk7atq8HKvrXNtFYOoNTIGDk9mVEzklHrzrz7wtfdjeOjHTh27MCxaxeBgQEA5KmpaMePD13A48Yhj//yK1PDnB34Ojpw7N2Lc89enLt34+voAEAWE4Nm4gS0kyahnTQJeezpRfk8EQ6rh0Mb26jY0oHPEyCjOJqxF6QTm3byufhDg/1seOZxGvfvISE7j/NvvwtTcipAaJLCmibsOzpRZhkwXpWPT9rPwfLrcbvbGVH4GPd2pfHBgI1lqPnPunpeSFzF9ME3aS1PZm30LzGKh3HlynglPoUZFRWI0kHwJuLVqphYWsL533CMl7CgAw/8+Ke4j1nnem08lepB8hJsFO0tpdIuMrPnTQrLjlBrmMy8nlu56LxM3lD5mBet5z7jXhqO/hKDoZSS4mcIdIqf+MuX5qIZFZom5vf52L3yP+xZvRy1Ts+sm24np2zSSdvUUWtm75pmOo6aUUXIKZmdwsjzklGcwgDRqRJ0u3Hu3Ytj+3bs2z/C29AAgDQmGu2EiWgnTkQ7YTzyxMQzts8wZxfe9nYcO3fi3LkLx86dw244ZW4u2ilTiJgyGfXYsWd0/MTt8HH4w3YOb2rD4/STWmhk3IUZxGeeeCBXFEVqdmxl0/P/wudyMnHJVYy7+FIkx+bpO/b1YH6rDqku5Fcn2kv5oRuw22vIynuIn/UW8JHZzuIhKe/taOLDuIdJsZVTvquUrXHzGGnQcrBsJx2tCtJIwN3Xi0sfh2rQ+o1b6ee8oL/2zL842tqBYsiJV9uFFIH6MblcsT+JxrYacm0OZpWsxKaNZ8rALxk7OosPYiVMiYrgD5FbaGv8EybjNEaOfBx3uQ3zyrrQgMt1hSgSQn7knsZ63n/87/S3tVAwbSbTr/8f1BEnnt3RcdTMnneb6KyzoNErGD03lcKpSacdc/pjvG1t2Ldsxb51C87dexA9HgSlEs3YsWinTEE7aRLK3JywCyXMl0YMBkPjLB99hH37R7j270f0+RA0GrTjxxNx3jQipk07YwaC1+XnyJZ2yte34Xb4SCkICXtC1omF3Wm1sPG5J6ndtZ24zGzm3f5jolPTQ9/VNsTAS1Uhv/rSXBQFKg4dvgWLZS+pOb/n7t6xlNscnNfpp/JIHdujfo/KYWZdxTW0aAOkpOfxcomZkfuOIoo+BF8CPo2KKWVjmf0NZjU65wX9gbvuxh2pQ+XswaBI4MM4JfPEPjSH5fi8KVyWeD8Sncg852/RZRaxP0tFiV7DA4b36W59mNjYCyjI+wtD77dj/6gTZaYB49X5SLVyAn4fu1b8h91vvYnWEMmcW+4kc8y4E7ajo9bMnneOCblBwZi5aRROTUR2mrk4Rb8fV3k59s2bGdq8GW99yAqXp6USMe08IqZNRTNu3PdyZWWY7zZBpxPH7t04tm3DvmXrsHtGmZdHxPTp6GZMRzVq1GkPrnrdfiq2dFC+oRXXUEjYyxac3GKv3bWdDc8+gdfpOM5aDwx5GXjpmKt0RgramXFUVN3BwMBm4jJ+yU/6J1Nnd1FU44D2w6xW3YfHouT1vl+iV7QxUGJlm89ImVXAYevGrY//xq30c1rQ33j+GaqaWlEMOfHo+5DgxlsUw/TtBRy1BblS/g+Mif3cHriHWtMUGkfqyNFr+LN+FeaOp0lIWEpe6n0M/qcOT53luPnlvc2NvP/Y3+hrbaZg2kxmXH8LqojPDuJ01VvY/U4THUfNISE/P43CKacn5EGHA/tHH2HfuAn7li0ELBaQy9GMLUU3fToR552HIj39NI5cmDBfDlEU8TY2Yt+8BfvmzTgPHIBAAKnJRMT00c8f4gAAIABJREFU89DNmoV20qTTMix8ngBHtrRz8INW3HYfqYVGyi7KJC79sz52p83KxmefoHbXduKzcpj3g7sxJacg+oOY3wrNV1cVmIhcmk51w7309q4lMuUn/HhwFh12D0kHLUywb+IhHqanNYHXxdspiFTx/pQaDIe6EP1ypJ54fBoVkyeMY86FF5/O4TtlzmlB/8Q670UvNbEhz8h1rRI6G/uZFdhKXvYhnpRcwbOypQyOM5GoV/FgxOs4e14iJXkZ6YafMPhiNX6Lh6iF2WjHxRMMBNjz1pvsXPEaqggdc2658zMr1wB6mm3sebuR1qpB1HoFpeefnkXuN5uxb9rE0PoNOHbsQPR6kRgMRJw3Dd3MmWinTEF6ghtKmDDfBgGrFfu27dg3bcK+bRvBoSEEtRrt5EnoZs9GN2PGV17c9LHFfvCDVtwOH+mjoim7KIOYE0QxPbpzGxuefQKf28WUy69lzIWXIAgSHDs6saxpRBajwXRtHvV9f6Sz6w1UCbfxE8uFWG0eNHv6+X/CS1zlX82Rugl8JBtNdI6RV2MlzGodwu7swaWPRz1o4d5//uN0D9kpcc4K+vKXnqeirhHFkAuPvh+PLEhCQgSJO6Mx+vqZmfkqO+TjucV7F/7xcRiiVDyoeZnAwBukp/+QBO/1mF87iiCXYLomH2W6gYH2Nt5//G90N9SRN2kas268DbXueOugv93OnncaaTrUjypCzpi5aRRNT0L+FYTc19PL0Ib1DH2wHufevRAMIktMCF0Qs2ajKR2DcIKEAGHCfJcQvV4ce/di37iJoY0b8ff0gEyGtqwM3dy56GbPQhYd/aW/1+v2c3hTG+UbQoOn2aWxlF2U8ZlAYA6LmfVPP0bDvl0kjShk3u0/JjI+AXedmYFXaxAEiLpqBG3i47S1PQcxN/BT20J8Fg/izi5eVT9Esfcw7zXfiEupo3FyN5YWG3KvHLkrDp9GwfRpk5k+Z/6ZOmQn5ZwV9Ad+dDfuKB1KRy9aiZ79pRFcucdE3+AAVyb9k15VHPMd9+Ebn4Y8Rs0Dmn+jMK8kK/MeTG0XYl3bhDxei+n6AqR6BQfee5ttr/0buUrN7Jt+QN7EKcftz9LjZM87jdTt70WhkjF6TgqjZqagUH05wfV1dzO0bh22dR/gOngQRBFFVha6ObPRzZmDqqAgPKAZ5nuLGAzirqhgaH3IUPG2tIBEgqa0FN2889HPnfulA4x5nD7KN7RRvrGNgDdA3vh4xi3IOC7BuSiKVG3dxKbn/4UYDHLetTcyavZ8AgNu+l+sxN/vxnBxJr2xr9Pc/Chu0zX8zLYEea8b6b4G1mt/g8Fp5qWuu0iIjuKlEgsTj9hxeAdx6+NRD1i495Gv30o/JwV91SsvcehoLQq7G49hgF6NjqliFM7qAJcb/41c7+MC9+/oGzUSX7KG+9UvoLetJjf7PrS7x+Pc34O6yETUZXnYrQO89/jfaK+qILO0jLm33Hlc/kO72c3eNc1U7+hCKhMYNTOF0XNSUWm/RBzo3l6G3l+H7f33cR04AISmhX18giuzvzjmS5hPCIpBfEEfnoAHb8CLP+jHF/DhE334g34CwQABMVREURzefpqPb5pSIZT4QipIkQpSZBIZUokUuUSOXCJHJpGhlCpRSBUoJIrwzfZL8PHitpABsy40rVYQ0Iwdi27+PPTnn4/MZDrl73MNedm/roWKzR2IokjhtCTGzk9Ho/9kSqWtv491Tz5M65Fy0ktKOf/WH6HRGBh8rQb3UTPaiQlYR22goekhBiOv5Bf2y4hodZJYuZ/Vqt/iMEew3HIl8mKRA95Bouxu5K5YfCoFs2dOZcrM87+OQzXMOSnoD/zoJ7ij9KjsvchlcnpGKSjbomecfA1pMTXc7P0pe7Jm4ciJ4PeqF4izv0texh9QrBuBt9mGblYqupkpVG3bxIcv/AtRhBnL/oei6XOGL1i3w8eB91s4vLkdMRg6eUrnpX1hPIqP8ZvNoRN5zVqc+/aBKKLMzUU/fx668+ehzMz42o7Pd5FAMIDNawsVT2g75B3C5rVh99mxe+04fA7sPjsuvwuHz4HT58Tldw0Xd8CNx+/BG/R+a/1QSBQoZUrUUjUqmQq1TI1GrkEj0wxvtXItEYoIIuQRRCgi0Cl06OV69Eo9ekWo6BQ6pF8Q7+dsw1NXh+2YYeNtaACJBO2E8ejmz0c/d+4p+9yPM7LkEkpmpVAyJ3U4CJgYDFK+fi1bX34emULB7Jt/QO74KVjfa8K+rQNlTiSumQeoa/od7bor+I1jKYbaIaY3r+VhxePU9pZQGTyfzefZSD/kw+dx4jTEfSNW+jkn6KvffIWDh2tQONy49VZaYuNY0hZFlGUPU+Le45/+RTwdtwxbUSS/Vr9IhmsNIxLvR3grmcCQD+PSXMhQsP6pR6nfu5Pk/CLm/eDHw3k9fZ4Ahza1cfCDVrxuP3nj4yn7r8e7kxGwO7Bv3IB1zRocO3aC348iMxP9BRegv2A+yszMr+WYfBsEggEsHgv9rn4GXAMMuAcYdA8y4B7A7DZ/UjxmLB4LQ96hz/0+iSAhQh6BVq5FK9eikWlQy9WoZZ8UlVSFUqZEKQ0VhUSBQqoIWdNSOTJBhkwSKlJBikSQDFvggiAgELpZi4SuC/FYKrqAGCAoBvGLIeveH/TjF0NWvzfoHd56Ah48fk/oxhLw4PK5cAVcoe2xm5DD7whtfQ6CYvCk/RUQ0Cl0RCojiVRFYlQaiVJFEaWKwqgyYlKbMKlMmNQmotXRRCojkQjfraQmX5WPLXfbe2uxvfcevpZWkMuJmDIF/YIL0c2YgUTz+fFeIOQG3f1OI/X7elFqZZTOS2fk9CRk8tCNcrCznfce+xvd9bXkT5nOzBtvI1Btx7yqHplRhe+io9S2/4Za7ZX80bGYqMMW7h54khuk77Op+xKGYkaxOrqf1IFeZM5oAioF8+bOYfy08762Y3POCfoDP/oJbqMBta2bgEokIs1A2gEbF8W/zjaK+YHml1hKTdyreY0iz7vkRd0Pq2IRFFKiryugvb+GdU88jMdhZ/Ll11K6YCESiZRgIEj1ji72vNuE0+olfVQ0Ey7J/MKgWaLXi337dmzvvsvQpg8R3W7kiYnoL7wA/YUXoszL+949ptu9dnqcPfQ4ekJbZw+9zl76nH30unrpd/Yz4B4gIAY+81m5RD4sSlHKqGHBilRGYlAa0Cv0GJSGkNV6zFKNkEeglqm/d8fp8xBFEaffid1rH34S+Xhr9Vixeq1Y3BYsnlD5+AY46BnEH/xsjlOZIMOoNhKjjiFGE0OsOpZYTajEaeOI18YTr4lHI/9iIfwuIYoi7opKbGvXYlu7Fn9PD4JGg27WLAwXLUA7adIXTgzoax1i51sNtFUNEhGlZPzFmeSOj0ciEQgGAuxe9QY7V7yGNsrI/B/8hDhtOgMvVSGKEFzUQt3Ar9mvupK/DV1CzL5envX+ltHBo7zTfSX1ZWrEBieiS8QVGYd6wMy9jzz8tR2Pc0rQ31+1gl0HypE73fgj7LSkx3LpPj8zDM8xoNCxWLiftvHp3KVbwQTfanKEP8D70cgTtBiuyGb72y9zaP1aolPTueCOnxKTloEoijSV97PzrQYsPU7iMw1MXJxFYvbJ47OIoojrYDnWd95maO17BKxWpFFR6OfPQ7/gItSjS76z4iSKIoPuQTrsHXTaO+mwd9Dl6KLT3kmXo4seRw9Dvs9a01HKqJCQaGKJUccQrY4mRhNznBVpVBmJkEd8Z/v+fUAURYZ8Qwy6BkNPP+4BBlwD9Lv66XP1Dd9U+5x9WDyWz3xep9CRoE0YLokRiSREJJAckUxiRCJRylPLKPRtIAaDOPftw/bOu9jWrSNosyE1mdBfcAGGiy9CVVT0uW1vqxlk58oG+lqHMCZqmbgoi7QiE4Ig0F1fy9pH/4q5q4PSBYuYMPcyLK/U4u93EVzQQb3nN2xRXMNT1nmk76lnNb9A4/Gx2X4TK4qDpLR2oXCZCCjlLLhwAaUTTy0D2ZflnBL0P//ox7iMkaisPTj1QQrlCZxne5NITR+L/b/nYNkYboh6l7mBlWRa70OyMxZ1oQnvOCnvPfm34R9zyuXXIlMo6Kq3sGNlA92NVqLiNUxYmEVGcfRJTxpvayvW1W9jfecdfK2tCCoVulmz0F+0gIjJkxG+ROLnrxNvwEu7vZ02WxttQ22029tpHwqVTkcnLv/xSZn1Cn1IACJCIvCxtRenjSNOE0esJhaFNBwL/buGJ+Ch19FLj7OHbmc33Y5PSpejiy5712duzmqZmqSIJJJ1ySRHJJOsSyZFl0KqLpWkiCTk0u/GORz0enFs24b17Xewf/ghoteLIiMDwyUXY7joIuRJSSf8nBgUqT/Qy67Vjdj6XCTlRjLp0mxi0/T4PG62vPw8hz5YQ3RqOvNvuRthmxNPrZnAtA4a1b/lHfkNvNY3nQn7d/GG7Ld021PYGn0J9c5uVFYJrqg4NP1m7nn067HSzxlBX//uaj7avRe500tA48CWZeCGwzsp1B/gTt8drCi+iEvjN3Op+BrpLb9EVpVAxHlJHHXtY8fyV9BERjH/Bz8htagYc7eDXW810ljeh8agoGxBBvmTEk6YeDlgs2F7/32sb60OzVARBDTjx2O45BJ0c+Z8a6nW/EE/HfYOWmwtNFubaR1qpcXWQqutlS5H17CfGEAj0wxfwEm6pNA2IomEiASSIpLQysOxz89WhrxDdNo7Q8XRSftQOx32juGb/Kdv7hJBQoI2gTR9Gqm6VNL0aaTp00jXp5MYkfitDeIGbDZs69ZhXb0a1779AGjKyjAsXIhu7twTXoMBf5DKbZ3sXdOE2+4jZ2wsExZmoY9W03hgL+ueDLldp165jMxAEY6dXfiK22iK/xOvSW9hTftYrqtcwQPyZzhkncizxWMx1Heh9EQTUMhYtGQxo0aXnvG+njOC/uc7f4zLFInG2k13rJwrBgcp077LM/75/Cb7DuZm7ON68d+kVf0ceUcSqjlxbPjwaTpqqsibOJXZN/+QYFDB3jVNVG7rRCaXMOb8VIpnpX4mcJYYCODYsRPrqlUMbdgQsg4yMzEsXIjhogXIExJO0sozj91rp9HaSJO16ZNia6LN1oZf/MTXqlPoSNOlkaoPXYgpupThYlQZv7OP2WG+PURRZMA9QPtQO61DrbTaWoe3LbYW7D77cF25RE6qLpUMQ8ZnyjdpEHjb27G+/TbW1avxtbQiqNXo587BsGgRmrKyz8SV8br8HPighUMb2giKIiOnJzN2fjrBgJMP/vVPGvbtJm3UaGZMuA7Xxm686a005zzI05IfsrVuBH9t/itXyDazw7WY1RFqTH0iTmMcmv5B7nn0n2e8f+eEoG9at5Zt23YgdXsRVXYyjCKXWd7kIFlcHvtHSgsbuYOnSDvwM1SONOzFbtategKAWTfdTnbZVA5vaufAuhb83iCFUxIZtyDjuPmrAJ6mJqyr3sK6ejX+nh4kBgOGCy/EsGjhF/rvTherx0q9pZ4GSwON1sbQ1tJIr6t3uI5MIiNVl0q6Pp0MQwZp+rThbaTym0ldF+bc4OOxlhZbCy22FppsIWOi2dpM+1D7ccZEnCaOrMgsMg2ZZEVmDRe94uRxzs9E+1wHD2J9azW2tWsJ2u3IExNDRtfiRSiSk4+rbzd72PNOIzU7u1CoZZTOT2fkeUlUbV3Phy8+jUyuYN7CO1DtE3BHNdJc/Ff+LtxNTXkMKwZ/QR4dvBhzGW2dbhSBaIJyKUuuWErhyJIz2q9zQtD/fOdduExRaMw99KRL+eXAu3ilcIH2L8SOdXGP8BhpO3+MRp5JhXQXh3d/QGJeAfN/eDfdTSK7VzdiN3vIKI5m4qKs45YOB+wOhta9j2XFypBLRSIhYupUDIsWETFzxhnPo+n0OWmwNFBvqafOUkeduY56Sz39rv7hOmqZmixDFpmRmWQYMsg0ZJJpyCRZl4xMEg4FEObbxRf00TbURpOliUZr43BpsjYd58KJVceSHZVNTmTO8DYzMhO17IunAH8Zgm43Qxs2Yl21CseOHSCKaMrKiLx0Mbq5c5GoP9nfQIedHSvraa0cRB+tYsLCLEwJPtY++hd6GuspnXIxuY5inEIdTWX/5E/iz3Dv9rPGfQ8Bv4oHo+YR0xrEaYz9Wqz0s17QP9q8ng0btiD1+AgqrFwr3UOS0Mzlst/SPz6eX0sfJuujH6EwpLC+7t+YBzqZtOQqEvNns3NVI/1tdmLTdExekk1iTmgFqCiKuA4cwLJ8BbZ16xCdThSZmUQuXoT+4ovPSLaWoBikfaidWnPtcaV9qH3Yv62SqoatmezI7OFtvDb+rJlzHObcISgG6bB30GhppN5SP/zE2WBpGF4MJhEkpOhSyI3KJScqh9yoXHKjckmOSD4jT5i+ri6sq1djWbkKX2srEq0W/QUXELnk0lCo32P7aK0aYMeKBgY67MRl6JmwMIOmA++yZ/VyYuPTmZF6NW5nI3WTHuc3vnvI3nmUl8Q/0iDk8bK7ELkYS1Am4epl15Odk3fa7f6Ys17QH7zjLpzRUajNPYxKqGO+ey+/Ct7Ie+Nn8Af538nbeQcejZr39z+FPjaGKVf9kPoDEpoP9xNhVDJxYRY5Y+MQJAK+3l6sq1djXbESb3Pz8I9tWLwIdclXn2ro9Dmps9RxdPBoqJiPUmeuw+l3AqGTOFWXSk5UTugkjgydzEkRSefcasEw5x6BYIDWodbQU6m5jlpzLXXmOtqG2oaNG61cS25ULnlReeQZ8xhhHEF2ZDYq2VcLxyuKIq59+7CsWBky2lwuFNlZRF66BMMlFyMzGgkGRWp2drH77UacVi/ZpbGkF7nZ8tJjuCxW5o++Dal7kMqJ/+JXrnu5cs9b/EL6GivUk2joyDhmpQ9wz6OPnLFjdVYL+q6dm1n37nqkXj+p8mquU2xheWAavy75Afdr/8GoXbfR5u1mb8MaRkyeicY4m5odA0gVEkrnpVE8MwWpRMS+dRuW5cuxb9kCgQDqsaVEXroE/flzT2lF2qcZdA9SM1BD9WA1RwePUj1YTYutZfjE1Ml15Bo/OTFzo3LJisw644+ZYcJ83/nYEKozh4yhWnMtR81HcfgcQMgQytBnkGfMI9+YzwjTCPKN+RiUXy4sb8Bux/bee1iXr8B16BDI5ehmzCBy6RK0kybh84mUr2/l4PpWgkGRwskmzJ1rqN/zERNzFhGv0HNg/LP80no3fz/yEOdL9vGCdyqdwQKCUgnX33Qj6VlnJh7TWS3oD97xI5zRRuIt9VwTuYHmYByX5/yJ+6KfonTPDRzo3EmXt5ncCZfTdjQar8tPwdQkyhZkILP2YFm+AuvKlfj7+pBGRxO5aCGGxYtRZnxxHBVRFOl19lI9WE31QDVVg1VUD1TT4+wZrpOoTWSEcQQjjCPINeaSb8wnQZsQHpwME+Yr8rHb5uMn3Y+Np/++7vJN+eQb88k35VNgKiBafWrheT11dSFdWL2agMWCLDGByMWXErl4ER61id1vN1CzqxulRkZyTje1O94gTVtAUWIxu8a9xP1dN/NW0/8jWmLjVfMceqPS0fT3c8+jj56R/p+1gr5/z07eXf0uCp+bK1RvEyk4uSjuIW5NW82EvZexreE9JLE6BPn52C0KUguMTLwkDXnFDizLl+PcueuTAc4ll6KbPv2kC39EUaTH2UPlQCVVA1VUDYTEe8A9AITibqQb0kMn0LGTaIRxxJe2FMKECfPVMLvNVA9WUzNYMyzyzbbm4fdj1DHD4l5oKqTAVECs5uRjYUGvF/vGjViWrwgNpALaKVOIXLoEd/Y4PloVSiepM7rxu95DOuBlUvZstoxbyWt181g9cA+dopE3vRcREGTcdPttJKWmnnY/z1pBf/DOO3GajMzzraNMVsN12l8ytaCSSTtns73lPZRxpTjtozAm6iibrEW3bw3Wt94iYLGEpi8tuZTIxYuRx8d/5rt7nb1U9lcOC3jlQCWD7kEgFE41MzKTAmPBsBUwwjjiexcjI0yYsx27185R89FhA6xqoIomW9NwULQYdcywwBdGF57Ukve2d2BduQLLipX4e3qQRkdjWLgQa/F89my1Yum1o404iLfnCFMyz2f9hM1UH07jX+6/sNUzik3KWWgG+rnnkdO30s9KQT90YB9vLV9JgVDDUvlGHpJeDsUKpm0r4ED/PvzSmWgMqYxKs2Pa8xqefftAJkM3axaRS5einTRxeIGB2W2mcqCSiv6KYRHvc/UBIR9dVmQWBcaC0A8fXUhuVG7Y3x0mzPcUp885LPJVA1VU9lfSaG0cHuOK08RRaCqkKLpoWOg/ftIW/X7s27djeePN4fE21YRJ9JRexpFGFW57MzLfFsbFjWPtlFpiD/Rzm/g2r/rmUR/M5JY77yA+8cQhCU6Vs1LQH7zjDjSmALfyOpspYXPxRGZt1VFhHkSmnkG2boDE7U8jNfciT0slaulSDAsX4tarqBqoomKgYljAOx2dwCduk0//mHnGvLB4hwlzluP0OakZrPnEsBuopMXWMvx+ckQyRdFFw7pQYCpAPmjHumolljeX4+voIBCdRPvEm2iw6Qm4N1JiSmDVZDtLD69nDLU8yZV4B4Pc88hjp9XWs07QDx/ez9rXXuZG5Uocoop/jLiG2ds8NLniSUBFZvmLaP0WtLNnYZ0/niPJQSoGQz9Uk7Vp+E6cFJE0LN5F0UXkG/OJUISTLIcJEwZsXlvI+Dtm+FUMVNDt6AZCT+6ZhkyKoosYaSykqClIxPs7cWzajENhonH0MrqCFnJ0g7wzXsn/1T5DQJDyivtibvzxPcSewM17qpy2oAuCMA94GJACz4ii+MB/va8EXgRKgQHgclEUmz/vO09H0B+64wfMjN1LQaCZX6TcwcR9HhyOFEbUvYc2wkLNlBTW5Xsp9zbgC/oAMKqMjIweSWF0IUWmIgqjCzGqjF9p/2HChDk36Xf1UzVQxZH+I8NCb/aYgVCmqlJFFnMr5eRua8HmjqUmey4GXSvbi+U81PkIe6X5bO8r5t5HHv/Kbfg8Qf/CNeKCIEiBx4A5QDuwVxCEt0VRrPpUtZsAsyiK2YIgXAH8Gbj8K7f4cziwdztpiR2Uemv5fdQySndLiGrswm5cwz8uDFCdAhq5h8LIQq6JvoYiU8j6Dk8VDBMmzOkSrY5mWvI0piVPA0Kz3zrsHSEXbl8FR/qP8GBeNe5MJ0XNZuYeaSSiq4yxwST+nnMF9w69QmNCIm3NzaSkp5/x9n2hhS4IwkTgPlEUzz/2+hfHOnL/p+qsO1ZnpyAIMqAbiBE/58u/qoX+jwdv48fO13hVMZtAVRo1us10joIstZIiUcFIlKQjQ0pYvMOECfPN40ekER8VeKkQvDQ53KQckZLjno4uu5qF/u38VXMNP73nq/nST8tCB5KAtk+9bgfGn6yOKIp+QRCsgAno/3QlQRBuAW4BSP2K8zEDfgkbpWPo6/MwY+YhLhPiUSLAtzMUECZMmDDHIUMgFwW5KFgsAhpwjw9SIx5kY3Uim3XFiL6vx+A8FUE/0Z7/Wz5PpQ6iKD4FPAUhC/0U9v0Zfvq/Id/TrK/y4TBhwoT5FlABJccKwPSvaT+nEq6vHUj51OtkoPNkdY65XAzA4JloYJgwYcKEOTVORdD3AjmCIGQIgqAArgDe/q86bwPXH/t7CbDp8/znYcKECRPmzPOFLpdjPvE7gHWEpi0+J4pipSAIvwP2iaL4NvAs8JIgCPWELPMrvs5GhwkTJkyYz3JKqW1EUVwLrP2v//36U3+7gaVntmlhwoQJE+bLEE55EyZMmDBnCWFBDxMmTJizhLCghwkTJsxZQljQw4QJE+Ys4VuLtigIQh/Q8oUVT0w0/7UK9Rwg3Odzg3Cfzw1Op89poijGnOiNb03QTwdBEPadLJbB2Uq4z+cG4T6fG3xdfQ67XMKECRPmLCEs6GHChAlzlvB9FfSnvu0GfAuE+3xuEO7zucHX0ufvpQ89TJgwYcJ8lu+rhR4mTJgwYf6LsKCHCRMmzFnC907QBUGYJwjCUUEQ6gVB+Pm33Z6vA0EQUgRB+FAQhGpBECoFQbjr2P+NgiCsFwSh7tg26ttu65lEEASpIAgHBUF499jrDEEQdh/r7+vHwjefNQiCECkIwnJBEGqO/dYTz4Hf+CfHzukKQRBeEwRBdbb9zoIgPCcIQq8gCBWf+t8Jf1chxD+P6dlhQRDGnM6+v1eC/qmE1fOBAuBKQRAKvt1WfS34gZ+KopgPTAB+eKyfPwc2iqKY8//bu4NQK+oojuOfPxiSRlhBUc+FCVGLoIwIyYiwFmXRax/UQmhZrYJo1TKQtEW4MdIiCjKpaNHGglYZGVGRUVphxitdpEUbjU6L///Cxd6lx+s+h/vnfGGY+f/vwJzDb+6PmTNz78HBNu6JJ3BkbPwcdrZ8f1ObkffEC3g/Im7ATWru3WpcSpnD47g1Im5U/4571FS+J5334t7z5ibpeh+ua8tj2P1/DjxTho7bcDQivo+Is3gD8wPHNHUiYiEiPmvbf6hf9Dk1131tt314aJgIp08pZT3ux542LtiK/W2X3vK9FHeqvQRExNmIOK1jjRurcHHrbLYGCzrTOSI+8u+ObZN0nccrUfkY60opVy/32LNm6Is1rJ4bKJYLQillAzbhEK6KiAWq6ePK4SKbOrvwFP5u4ytwOiL+auPetN6IU3i5lZn2lFLW6ljjiPgZO3BcNfIzOKxvnUdM0nWqnjZrhr6kZtS9UEq5BG/hyYj4feh4VopSygM4GRGHx6cX2bUnrVfhFuyOiE34U0fllcVodeN5XItrsFYtOZxPTzr/F1M9z2fN0JfSsLoLSikXqWb+WkQcaNO/jm7H2vrkUPFNmS14sJTyo1pG26pesa9rt+b0p/UJnIiIQ228XzX4XjWGe/BDRJyKiHM4gNv1rfNfW7YSAAABB0lEQVSISbpO1dNmzdCX0rB65mn145dwJCKeH/tovBn3o3jnQse2EkTE0xGxPiI2qJp+EBEP40O16Tgd5QsR8Qt+KqVc36buxtc61bhxHJtLKWvaOT7KuVudx5ik67t4pL3tshlnRqWZZRERM7VgG77FMTwzdDwrlOMd6m3XF/i8LdvUuvJBfNfWlw8d6wrkfhfea9sb8QmO4k2sHjq+Ked6Mz5tOr+Ny3rXGM/iG3yFV7G6N53xuvqM4Jx6Bb59kq5qyeXF5mdfqm8ALfvY+dP/JEmSTpi1kkuSJEkygTT0JEmSTkhDT5Ik6YQ09CRJkk5IQ0+SJOmENPQkSZJOSENPkiTphH8AufxoFz83BjEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "Qf = 2000*net['pore.volume']/(Cp*rho)\n", "Q = Qf*np.sin(np.pi*x)\n", "phys['pore.source.S1'] = 0.0\n", "phys['pore.source.S2'] = Q\n", "phys['pore.source.rate'] = Q\n", "\n", "alg = op.algorithms.TransientReactiveTransport(network=net)\n", "alg.setup(phase=phase, conductance='throat.conductance', quantity='pore.temperature',\n", " t_initial=0, t_final=10000, t_step=100, t_output=100,\n", " t_tolerance=1e-7, t_precision=12, rxn_tolerance=1e-7, t_scheme='implicit')\n", "alg.set_IC(values=0.0)\n", "alg.set_value_BC(pores=[0], values=0.0)\n", "alg.set_value_BC(pores=[-1], values=0.0)\n", "alg.set_source(propname='pore.source', pores=net.pores()[1:-1])\n", "alg.run()\n", "res = alg.results()\n", "times = list(res.keys())\n", "times.sort()\n", "plt.figure()\n", "for time in times:\n", " plt.plot(alg[time])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum temperature: 0.50278\n" ] } ], "source": [ "print(f\"Maximum temperature: {alg['pore.temperature@4500'].max():.5f}\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum error: 0.40205\n" ] } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "plt.figure()\n", "x = net['pore.coords'][:, 0]\n", "a = alg['pore.temperature@4500']\n", "b = approx_sol_source_dim(x, 4500.0, kappa, l)\n", "plt.plot(a)\n", "plt.plot(b, '--')\n", "plt.show()\n", "print(f\"Maximum error: {np.max(a-b):.5f}\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def crazy_source(x, t, N):\n", " # https://www.math.upenn.edu/~deturck/m241/inhomogeneous.pdf\n", " out = 0\n", " for n in range(N):\n", " a = 4*((2*n+1)**2 * np.pi**2 - 2)*np.exp(-(2*n+1)**2 * np.pi**2 * t)/((2*n+1)**3 * np.pi**3 * ((2*n+1)**2 *np.pi**2 - 1))\n", " b = 4*np.exp(-t)/((2*n+1)*np.pi*((2*n+1)**2*np.pi**2-1))\n", " c = np.sin((2*n+1)*np.pi*x)\n", " out += (a+b)*c\n", " return out" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "x = np.linspace(0, 1, 101)\n", "plt.figure()\n", "times = np.array([0.01, 0.1, 0.5, 1.0, 1.5, 3])/np.pi**2\n", "for t in times:\n", " sol = crazy_source(x, t, 10000)\n", " plt.plot(x, sol)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "plt.figure()\n", "x = np.linspace(0, 2, 101)\n", "plt.plot(1-np.abs(x-1))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "plt.figure()\n", "plt.plot(2*x-x**2)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "def qn(n, x):\n", " return (8/(n**2 * np.pi**2))*np.sin(n*np.pi*x/2)\n", "def an(n, x):\n", " return (4/(n**2 * np.pi**2))*qn(n, x)\n", "def cn(n):\n", " return (16/(n**3 * np.pi**3))*(1-np.cos(n*np.pi))\n", "def bn(n, x):\n", " return cn(n) - an(n, x)\n", "def approx_sol_sawtooth(x, t, N):\n", " #https://faculty.uca.edu/darrigo/Students/M4315/Fall%202005/sep-var.pdf\n", " out = 0\n", " for i in range(N):\n", " n = i+1\n", " np2 = (n*np.pi/2)\n", " e = np.exp(-((np2**2) * t))\n", " out += ( an(n, x) + bn(n, x) * e ) * np.sin(np2*x)\n", " \n", " return out" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "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": [ "#NBVAL_IGNORE_OUTPUT\n", "for t in [0.0, 0.01, 0.1, 0.5, 1.0, 2.0, 3.0, 100.0]:\n", " sol = approx_sol_sawtooth(x, t, 10000)\n", " plt.plot(x, sol)\n", "plt.show()" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

hide-tono/python-training

tensorflow-cookbook-keras/ch02/2.9_test_model.ipynb

1

82329

{ "cells": [ { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #25 A = [[ 6.20395088]]\n", "Loss = 15.9325\n", "Step #50 A = [[ 8.57587433]]\n", "Loss = 1.88605\n", "Step #75 A = [[ 9.45653248]]\n", "Loss = 1.25016\n", "Step #100 A = [[ 9.77085876]]\n", "Loss = 0.994985\n", "MSE on test: 1.27\n", "MSE on train: 0.86\n" ] }, { "data": { "text/html": [ "\n", " <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n", " <script>\n", " function load() {\n", " document.getElementById(&quot;graph0.5446017603692408&quot;).pbtxt = 'node {\\n name: &quot;x/Placeholder&quot;\\n op: &quot;Placeholder&quot;\\n attr {\\n key: &quot;dtype&quot;\\n value {\\n type: DT_FLOAT\\n }\\n }\\n attr {\\n key: &quot;shape&quot;\\n value {\\n shape {\\n }\\n }\\n }\\n}\\nnode {\\n name: &quot;y/Placeholder&quot;\\n op: &quot;Placeholder&quot;\\n attr {\\n key: &quot;dtype&quot;\\n value {\\n type: 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{\\n name: &quot;init&quot;\\n op: &quot;NoOp&quot;\\n input: &quot;^A/Variable/Assign&quot;\\n}\\n';\n", " }\n", " </script>\n", " <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n", " <div style=&quot;height:600px&quot;>\n", " <tf-graph-basic id=&quot;graph0.5446017603692408&quot;></tf-graph-basic>\n", " </div>\n", " \"></iframe>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "import tensorboard_jupyter as tb\n", "import matplotlib.pyplot as plt\n", "from tensorflow.python.framework import ops\n", "from sklearn import datasets\n", "\n", "ops.reset_default_graph()\n", "sess = tf.Session()\n", "\n", "#2.6の回帰の例\n", "batch_size = 25\n", "\n", "# 平均1、標準偏差0.1の正規分布を作成\n", "x_vals = np.random.normal(1, 0.1, 100)\n", "with tf.name_scope('x'):\n", " x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n", "# 10を100個\n", "y_vals = np.repeat(10., 100)\n", "\n", "with tf.name_scope('y'):\n", " y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n", "\n", "# データをトレーニングセットとテストセットに8:2で分割\n", "train_indices = np.random.choice(len(x_vals), round(len(x_vals) * 0.8), replace= False)\n", "test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))\n", "\n", "x_vals_train = x_vals[train_indices]\n", "x_vals_test = x_vals[test_indices]\n", "\n", "y_vals_train = y_vals[train_indices]\n", "y_vals_test = y_vals[test_indices]\n", "\n", "# 予測結果の変数は最初はランダムで初期化\n", "with tf.name_scope('A'):\n", " A = tf.Variable(tf.random_normal(shape=[1,1]))\n", "\n", "# 計算グラフに乗算を追加する。\n", "with tf.name_scope('model'):\n", " my_output = tf.matmul(x_data, A)\n", "# L2損失関数(最小二乗法)\n", "# 平均１のx_dataとAの乗算と10との差分のため、Aは10に収束するはず\n", "with tf.name_scope('loss'):\n", " loss = tf.reduce_mean(tf.square(my_output - y_target))\n", "\n", "# 変数最適化方法\n", "my_opt = tf.train.GradientDescentOptimizer(learning_rate=0.02)\n", "# L2損失を最小化するようにする\n", "train_step = my_opt.minimize(loss)\n", "\n", "# 変数初期化\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "# トレーニング\n", "for i in range(100):\n", " rand_index = np.random.choice(len(x_vals_train), size=batch_size)\n", " # 1行25列から25行1列へ変換\n", " rand_x = np.transpose([x_vals[rand_index]])\n", " rand_y = np.transpose([y_vals[rand_index]])\n", " sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n", " if (i+1)%25 == 0:\n", " print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))\n", " print('Loss = ' + str(sess.run(loss, feed_dict={x_data:rand_x, y_target: rand_y})))\n", "\n", "mse_test = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_test]), y_target: np.transpose([y_vals_test])})\n", "mse_train = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_train]), y_target: np.transpose([y_vals_train])})\n", "print('MSE on test: ' + str(np.round(mse_test, 2)))\n", "print('MSE on train: ' + str(np.round(mse_train, 2)))\n", " \n", "tf.summary.FileWriter('./log/', sess.graph)\n", "tb.show_graph(tf.get_default_graph().as_graph_def())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #200 A = [-0.69047976]\n", "Loss = [[ 0.16616148 0.65559411 0.89609718 0.05554588 0.08533639 0.46616307\n", " 0.07652663 0.10405916 0.02700546 0.11825523 0.95115638 0.03222379\n", " 0.08100253 0.4198207 0.11449566 0.11449566 0.10343304 0.08100253\n", " 0.10612128 0.42004126 0.4198207 0.19641592 0.07536426 0.07536426\n", " 0.10612128]]\n", "Step #400 A = [-0.56424481]\n", "Loss = [[ 0.24569601 0.1195773 0.06775134 0.52938318 0.1195773 0.12276029\n", " 0.08553281 0.05267632 0.29887959 0.43578029 0.15737741 0.4063105\n", " 0.09197678 0.05463177 0.21694562 0.12276029 0.46515566 0.07558858\n", " 0.1195773 0.10910847 0.05534232 0.38110635 0.33341065 0.06278943\n", " 0.06079619]]\n", "Step #600 A = [-0.6002931]\n", "Loss = [[ 0.06295894 0.05455804 0.11334316 0.08955632 0.26493016 1.11108971\n", " 0.13227196 0.11636318 0.43350312 0.07015296 0.05731658 0.05455804\n", " 0.05584619 0.41891679 0.10110906 0.11266427 0.15220828 0.43464652\n", " 0.17198761 0.46298206 0.10512213 1.11108971 0.12293167 0.11266427\n", " 0.11636318]]\n", "Step #800 A = [-0.6427415]\n", "Loss = [[ 0.21864513 0.11751396 0.13938749 0.05818432 0.03074447 0.22911347\n", " 0.31312305 0.4474602 0.31975704 0.26314947 0.13333322 0.07197441\n", " 0.11103227 0.46421763 0.26314947 0.11103227 0.13938749 0.09220471\n", " 0.08532066 0.14856726 0.10799613 0.13214977 0.30573937 0.92213017\n", " 0.14354172]]\n", "Step #1000 A = [-0.53698671]\n", "Loss = [[ 0.8597796 0.16384301 0.1226868 0.23965625 0.05172425 0.25046194\n", " 0.37254855 0.1054479 0.29190826 0.1226868 0.08428752 0.18035971\n", " 0.24031614 0.63877755 0.14716148 0.09382833 0.23212351 0.44505167\n", " 0.06498085 0.08332601 0.06598832 0.05389354 0.13658904 0.04105734\n", " 0.21168759]]\n", "Step #1200 A = [-0.66117775]\n", "Loss = [[ 0.4301883 0.37497833 0.03309396 0.47110409 0.13445054 0.05836788\n", " 0.13107556 0.18185802 0.40434626 0.4299635 0.18185802 0.93327552\n", " 0.04739198 0.07326578 0.30819991 0.93327552 0.11607544 0.05263273\n", " 0.14109612 0.02779742 0.58206779 0.93327552 0.11607544 0.30091578\n", " 0.23660219]]\n", "Step #1400 A = [-0.6949929]\n", "Loss = [[ 0.10361405 0.4184956 0.46784705 0.28768548 0.46784705 0.16685373\n", " 0.02241759 0.03201102 0.03201102 0.20859651 0.05530255 0.39112297\n", " 0.15594549 0.05981818 0.09071283 0.16685373 0.06203062 0.07569257\n", " 0.10299046 0.12695804 0.89342856 0.13671103 0.48394245 0.16685373\n", " 0.29934195]]\n", "Step #1600 A = [-0.75468701]\n", "Loss = [[ 0.25704432 0.20718922 0.29018718 0.06390999 0.12164412 0.61209434\n", " 0.04804517 0.12002506 0.1025841 0.07309439 0.03018377 0.27744466\n", " 0.10375654 0.68698418 0.08140018 0.12164412 0.2842378 0.09074595\n", " 0.13056147 0.37218708 0.03432439 0.08725557 0.3030504 0.3218323\n", " 0.12004825]]\n", "Step #1800 A = [-0.71812469]\n", "Loss = [[ 0.11752354 0.02627871 0.31190363 0.07858957 1.03360128 0.07739706\n", " 0.31190363 0.29361987 0.07739706 0.21260592 0.41042888 0.19154167\n", " 0.24887322 0.12422799 0.37949657 0.21260592 0.40222523 0.09907759\n", " 0.4928804 0.34095863 0.28642654 0.27243623 0.10337221 0.40222523\n", " 0.12425194]]\n", "Accuracy on train set: 0.9625\n", "Accuracy on test set: 0.95\n" ] }, { "data": { "text/html": [ "\n", " <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n", " <script>\n", " function load() {\n", " document.getElementById(&quot;graph0.3495609400446765&quot;).pbtxt = 'node {\\n name: &quot;x_data/Placeholder&quot;\\n op: &quot;Placeholder&quot;\\n attr {\\n key: &quot;dtype&quot;\\n value {\\n type: DT_FLOAT\\n }\\n }\\n attr {\\n key: &quot;shape&quot;\\n value {\\n shape {\\n }\\n }\\n }\\n}\\nnode {\\n name: 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"source": [ "## 分類の例 ##\n", "from tensorflow.python.framework import ops\n", "ops.reset_default_graph()\n", "\n", "sess = tf.Session()\n", "\n", "# 平均-1と3のデータを50こずつ\n", "x_vals = np.concatenate((np.random.normal(-1, 1, 50),\n", " np.random.normal(2, 1, 50)))\n", "\n", "with tf.name_scope('x_data') as scope:\n", " x_data = tf.placeholder(shape=[1, None], dtype=tf.float32) \n", " \n", "y_vals = np.concatenate([np.repeat(0., 50), np.repeat(1., 50)])\n", "with tf.name_scope('y_target') as scope:\n", " y_target = tf.placeholder(shape=[1, None], dtype=tf.float32)\n", "\n", "# データをトレーニングセットとテストセットに8:2で分割\n", "train_indices = np.random.choice(len(x_vals), round(len(x_vals) * 0.8), replace= False)\n", "test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))\n", "\n", "x_vals_train = x_vals[train_indices]\n", "x_vals_test = x_vals[test_indices]\n", "\n", "y_vals_train = y_vals[train_indices]\n", "y_vals_test = y_vals[test_indices]\n", " \n", "# 予測値は本来と離れた10で初期化\n", "with tf.name_scope('A') as scope:\n", " A = tf.Variable(tf.random_normal(mean=10, shape=[1]))\n", " \n", "# モデルは平均移動計算(sigmoid(x + A)だがsigmoidは損失関数が行う)\n", "with tf.name_scope('Model') as scope:\n", " my_output = tf.add(x_data, A)\n", "\n", "# シグモイド誤差エントロピー損失関数\n", "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)\n", "# 最適化関数\n", "my_opt = tf.train.GradientDescentOptimizer(0.05)\n", "train_step = my_opt.minimize(xentropy)\n", "\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "for i in range(1800):\n", " rand_index = np.random.choice(len(x_vals_train), size=batch_size)\n", " rand_x = [x_vals_train[rand_index]]\n", " rand_y = [y_vals_train[rand_index]]\n", " sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n", " if (i+1)%200==0:\n", " print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))\n", " print('Loss = ' + str(sess.run(xentropy, feed_dict={x_data: rand_x, y_target: rand_y})))\n", "\n", "\n", "with tf.name_scope('prediction') as scope:\n", " y_prediction = tf.squeeze(tf.round(tf.nn.sigmoid(tf.add(x_data, A))))\n", " correct_prediction = tf.equal(y_prediction, y_target)\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "acc_value_test = sess.run(accuracy, feed_dict={x_data: [x_vals_test], y_target: [y_vals_test]})\n", "acc_value_train = sess.run(accuracy, feed_dict={x_data: [x_vals_train], y_target: [y_vals_train]})\n", "print('Accuracy on train set: ' + str(acc_value_train))\n", "print('Accuracy on test set: ' + str(acc_value_test))\n", " \n", "tf.summary.FileWriter('./log/', sess.graph)\n", "tb.show_graph(tf.get_default_graph().as_graph_def())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a4bc0b8048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 可視化\n", "A_result = -sess.run(A)\n", "bins = np.linspace(-5, 5, 50)\n", "plt.hist(x_vals[0:50], bins, alpha = 0.5, label='N(-1,1)', color='gray')\n", "plt.hist(x_vals[50:100], bins[0:50], alpha = 0.5, label='N(2,1)', color='red')\n", "plt.plot((A_result, A_result), (0,8), 'k--', linewidth=3, label='A = ' + str(np.round(A_result, 2)))\n", "plt.legend(loc='upper right')\n", "plt.title('Binary Classifier Accuracy = ' + str(np.round(acc_value_test, 2)))\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

siberianisaev/NeutronBarrel

Python/Neutrons preprocessing/Preprocessing Runner (main).ipynb

1

10722

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from neutron_preprocessing import ExpProcessing\n", "# pandas and numpy libraries are used in neutron_preprocessing " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Экспериментальные распределения множественностей задаются в виде массива (например list() или np.array()) начиная с нулевой кратности. \n", "Вносить можно сколько угодно точек, внутри алгоритма автоматически данные будут расширены вплоть до 9й кратности.\n", "Часто используемые данные приведены в комментариях. \n", "\n", "Алгоритм расчитает из измеренных точек ошибку измерений каждой кратности, вероятность испуская каждой кратности с ошибкой, среднее измеренное число нейтронов на распад с ошибкой, дисперсию распределения. Результат расчетов приводятся в виде pandas.DataFrame() и могут быть сохранены в виде .csv файла. Для подробностей можно вызвать help(ExpProcessing)\n", "\n", "Experimental multiplicity distributions are specified as an array (e.g. list() or np.array()) starting from zero multiplicity. You can enter as many points as you want, inside the algorithm the data will automatically be expanded up to the 9th multiple. Frequently used data are given in the comments.\n", "\n", "The algorithm will calculate error of each multiplicity of measurement, emission probability per multiplicity with error, mean amount of neutrons per decay with error, variance of distribution from the measured points. The calculation result is given as pandas.DataFrame() and can be saved as a .csv file. For additional informations help(ExpProcessing) could be called." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# [12375, 25450, 20365, 8130, 1740, 215, 14, 1, 0] - Cm248\n", "# [226, 548, 543, 280, 76, 21, 2] - No252\n", "# [40, 84, 107, 75, 31, 1, 0] - No250 (5 us)\n", "# [30, 56, 55, 31, 6, 2, 1] - No250 (36 us)\n", "# [9, 36, 38, 30, 14, 3] - No254 (december only)\n", "# [183, 319, 319, 271, 119, 29, 3, 1] - Rf256\n", "# [24, 43, 34, 25, 12, 2] - Rf254\n", "# [40, 81, 49, 34, 7, 1] - Fm244\n", "# [224, 671, 986, 799, 408, 134, 34, 4] - No252 Sfinx 2021\n", "# help(ExpProcessing)" ] }, { "cell_type": "code", "execution_count": 4, "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>bin</th>\n", " <th>count</th>\n", " <th>count_error</th>\n", " <th>probability</th>\n", " <th>probability_error</th>\n", " <th>relative_error</th>\n", " <th>mean</th>\n", " <th>mean_error</th>\n", " <th>variance</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>224</td>\n", " <td>14.966630</td>\n", " <td>0.068712</td>\n", " <td>0.004591</td>\n", " <td>0.066815</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>671</td>\n", " <td>25.903668</td>\n", " <td>0.205828</td>\n", " <td>0.007946</td>\n", " <td>0.038605</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>986</td>\n", " <td>22.203603</td>\n", " <td>0.302454</td>\n", " <td>0.006811</td>\n", " <td>0.022519</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>799</td>\n", " <td>16.319722</td>\n", " <td>0.245092</td>\n", " <td>0.005006</td>\n", " <td>0.020425</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>408</td>\n", " <td>10.099505</td>\n", " <td>0.125153</td>\n", " <td>0.003098</td>\n", " <td>0.024754</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5</td>\n", " <td>134</td>\n", " <td>5.176872</td>\n", " <td>0.041104</td>\n", " <td>0.001588</td>\n", " <td>0.038633</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6</td>\n", " <td>34</td>\n", " <td>2.380476</td>\n", " <td>0.010429</td>\n", " <td>0.000730</td>\n", " <td>0.070014</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>0.755929</td>\n", " <td>0.001227</td>\n", " <td>0.000232</td>\n", " <td>0.188982</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>0.353553</td>\n", " <td>0.000000</td>\n", " <td>0.000108</td>\n", " <td>inf</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>0.333333</td>\n", " <td>0.000000</td>\n", " <td>0.000102</td>\n", " <td>inf</td>\n", " <td>2.323313</td>\n", " <td>0.048667</td>\n", " <td>1.689334</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " bin count count_error probability probability_error relative_error \\\n", "0 0 224 14.966630 0.068712 0.004591 0.066815 \n", "1 1 671 25.903668 0.205828 0.007946 0.038605 \n", "2 2 986 22.203603 0.302454 0.006811 0.022519 \n", "3 3 799 16.319722 0.245092 0.005006 0.020425 \n", "4 4 408 10.099505 0.125153 0.003098 0.024754 \n", "5 5 134 5.176872 0.041104 0.001588 0.038633 \n", "6 6 34 2.380476 0.010429 0.000730 0.070014 \n", "7 7 4 0.755929 0.001227 0.000232 0.188982 \n", "8 8 0 0.353553 0.000000 0.000108 inf \n", "9 9 0 0.333333 0.000000 0.000102 inf \n", "\n", " mean mean_error variance \n", "0 2.323313 0.048667 1.689334 \n", "1 2.323313 0.048667 1.689334 \n", "2 2.323313 0.048667 1.689334 \n", "3 2.323313 0.048667 1.689334 \n", "4 2.323313 0.048667 1.689334 \n", "5 2.323313 0.048667 1.689334 \n", "6 2.323313 0.048667 1.689334 \n", "7 2.323313 0.048667 1.689334 \n", "8 2.323313 0.048667 1.689334 \n", "9 2.323313 0.048667 1.689334 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "experimental_spectra = [224, 671, 986, 799, 408, 134, 34, 4]\n", "preprocessor = ExpProcessing(experimental_spectra)\n", "preprocessor.get_data()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "csv_data/No252_2021_June.csv was saved successfully\n" ] } ], "source": [ "# saving to the .csv file\n", "folder_name = \"csv_data/\"\n", "file_name = \"No252_2021_June_\"\n", "file_name += \".csv\"\n", "preprocessor.to_csv(folder_name + file_name)" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

cwharland/data-science-from-scratch

Probability.ipynb

2

6232

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "def random_kid():\n", " return random.choice(['boy','girl'])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "both_girls = 0\n", "older_girl = 0\n", "either_girl = 0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random.seed(0)\n", "\n", "for _ in xrange(10000):\n", " younger = random_kid()\n", " older = random_kid()\n", " \n", " if older == 'girl':\n", " older_girl += 1\n", " if older == 'girl' and younger == 'girl':\n", " both_girls += 1\n", " if older == 'girl' or younger == 'girl':\n", " either_girl += 1\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P(both | older): 0.514228456914\n", "P(both | either): 0.341541328364\n" ] } ], "source": [ "print 'P(both | older):', both_girls / older_girl\n", "print 'P(both | either):', both_girls / either_girl" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def uniform_pdf(x):\n", " return 1 if x >= 0 and x < 1 else 0\n", "\n", "def uniform_cdf(x):\n", " if x < 0:\n", " return 0\n", " elif x < 1:\n", " return x\n", " else:\n", " return 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normal Distribution" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "def normal_pdf(x, mu = 0, sigma = 1):\n", " sqrt_two_pi = math.sqrt(2 * math.pi)\n", " return (math.exp(-(x-mu)**2/ 2 / sigma**2) / (sqrt_two_pi * sigma))\n", "\n", "def normal_cdf(x, mu = 0, sigma = 1):\n", " return (1 + math.erf((x - mu) / math.sqrt(2) / sigma)) / 2\n", "\n", "# binary search to make inverse normal\n", "def inverse_normal_cdf(p, mu = 0, sigma = 1, tolerance = 0.0001):\n", " # make sure it's standard normal\n", " if mu != 0 or sigma != 1:\n", " return mu + sigma * inverse_normal_cdf(p, tolerance = tolerance)\n", " \n", " low_z, low_p = -10.0, 0\n", " hi_z, hi_p = 10.0, 1\n", " \n", " while hi_z - low_z > tolerance:\n", " mid_z = (low_z + hi_z) / 2\n", " mid_p = normal_cdf(mid_z)\n", " \n", " if mid_p < p:\n", " low_z, low_p = mid_z, mid_p\n", " elif mid_p > p:\n", " hi_z, hi_p = mid_z, mid_p\n", " else:\n", " break\n", " \n", " return mid_z" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-1.6448211669921875" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inverse_normal_cdf(0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Probability Distributions" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bernoulli_trial(p):\n", " return 1 if random.random() < p else 0\n", "\n", "def binomial(n, p):\n", " return sum(bernoulli_trial(p) for _ in xrange(n))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Investigate it\n", "from collections import Counter\n", "\n", "def make_hist(p, n, num_points):\n", " \n", " data = [binomial(n, p) for _ in xrange(num_points)]\n", " \n", " histogram = Counter(data)\n", " \n", " return histogram" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({2992: 4, 2990: 3, 3029: 3, 2957: 2, 2971: 2, 2973: 2, 2974: 2, 2978: 2, 2980: 2, 2982: 2, 2987: 2, 2997: 2, 2998: 2, 2999: 2, 3001: 2, 3013: 2, 3022: 2, 3027: 2, 3034: 2, 3069: 2, 2946: 1, 2947: 1, 3056: 1, 2953: 1, 2954: 1, 2956: 1, 2959: 1, 2960: 1, 2961: 1, 2962: 1, 2964: 1, 2965: 1, 2966: 1, 2967: 1, 2968: 1, 3097: 1, 3098: 1, 2975: 1, 2976: 1, 2977: 1, 2985: 1, 2986: 1, 2989: 1, 2994: 1, 3000: 1, 3002: 1, 3004: 1, 3005: 1, 3006: 1, 3061: 1, 3016: 1, 3017: 1, 3018: 1, 3023: 1, 3070: 1, 2900: 1, 3031: 1, 3032: 1, 3108: 1, 3037: 1, 3038: 1, 3039: 1, 3040: 1, 3041: 1, 3042: 1, 3044: 1, 3047: 1, 3052: 1, 3054: 1, 2928: 1, 2933: 1, 3062: 1, 3064: 1, 3065: 1, 2939: 1, 2942: 1})" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "make_hist(0.6, 5000, 100)" ] }, { "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

tensorflow/docs-l10n

site/en-snapshot/addons/tutorials/optimizers_lazyadam.ipynb

2

7623

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0\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": "MfBg1C5NB3X0" }, "source": [ "# TensorFlow Addons Optimizers: LazyAdam\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/addons/tutorials/optimizers_lazyadam\"><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/addons/blob/master/docs/tutorials/optimizers_lazyadam.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/addons/blob/master/docs/tutorials/optimizers_lazyadam.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/optimizers_lazyadam.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", "This notebook will demonstrate how to use the lazy adam optimizer from the Addons package.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "bQwBbFVAyHJ_" }, "source": [ "## LazyAdam\n", "\n", "> LazyAdam is a variant of the Adam optimizer that handles sparse updates more efficiently.\n", " The original Adam algorithm maintains two moving-average accumulators for\n", " each trainable variable; the accumulators are updated at every step.\n", " This class provides lazier handling of gradient updates for sparse\n", " variables. It only updates moving-average accumulators for sparse variable\n", " indices that appear in the current batch, rather than updating the\n", " accumulators for all indices. Compared with the original Adam optimizer,\n", " it can provide large improvements in model training throughput for some\n", " applications. However, it provides slightly different semantics than the\n", " original Adam algorithm, and may lead to different empirical results." ] }, { "cell_type": "markdown", "metadata": { "id": "MUXex9ctTuDB" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cHAOyeOVx-k3" }, "outputs": [], "source": [ "!pip install -U tensorflow-addons" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "42ztALK4ZdyZ" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import tensorflow_addons as tfa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ys65MwOLKnXq" }, "outputs": [], "source": [ "# Hyperparameters\n", "batch_size=64\n", "epochs=10" ] }, { "cell_type": "markdown", "metadata": { "id": "KR01t9v_fxbT" }, "source": [ "## Build the Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "djpoAvfWNyL5" }, "outputs": [], "source": [ "model = tf.keras.Sequential([\n", " tf.keras.layers.Dense(64, input_shape=(784,), activation='relu', name='dense_1'),\n", " tf.keras.layers.Dense(64, activation='relu', name='dense_2'),\n", " tf.keras.layers.Dense(10, activation='softmax', name='predictions'),\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "0_D7CZqkv_Hj" }, "source": [ "## Prepare the Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "U0bS3SyowBoB" }, "outputs": [], "source": [ "# Load MNIST dataset as NumPy arrays\n", "dataset = {}\n", "num_validation = 10000\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n", "\n", "# Preprocess the data\n", "x_train = x_train.reshape(-1, 784).astype('float32') / 255\n", "x_test = x_test.reshape(-1, 784).astype('float32') / 255" ] }, { "cell_type": "markdown", "metadata": { "id": "HYE-BxhOzFQp" }, "source": [ "## Train and Evaluate\n", "\n", "Simply replace typical keras optimizers with the new tfa optimizer " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NxfYhtiSzHf-" }, "outputs": [], "source": [ "# Compile the model\n", "model.compile(\n", " optimizer=tfa.optimizers.LazyAdam(0.001), # Utilize TFA optimizer\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n", " metrics=['accuracy'])\n", "\n", "# Train the network\n", "history = model.fit(\n", " x_train,\n", " y_train,\n", " batch_size=batch_size,\n", " epochs=epochs)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1Y--0tK69SXf" }, "outputs": [], "source": [ "# Evaluate the network\n", "print('Evaluate on test data:')\n", "results = model.evaluate(x_test, y_test, batch_size=128, verbose = 2)\n", "print('Test loss = {0}, Test acc: {1}'.format(results[0], results[1]))" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "optimizers_lazyadam.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

ewulczyn/readers

remote_notebooks/traces/Hash Trace IPs.ipynb

1

6214

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import dateutil\n", "import json\n", "from pyspark.sql import SQLContext, Row\n", "sqlContext = SQLContext(sc)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import inspect\n", "\n", "currentdir = os.path.dirname(\n", " os.path.abspath(inspect.getfile(inspect.currentframe()))\n", ")\n", "parentdir = os.path.dirname(currentdir)\n", "gpparentdir = os.path.dirname(parentdir)\n", "sys.path.insert(0, gpparentdir)\n", "\n", "from src.traces.trace_utils import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_hash_function():\n", " key = name = input(\"Key \")\n", " clear_output()\n", " def hash_function(s):\n", " code = hmac.new(key.encode('utf-8'), s.encode('utf-8'), hashlib.sha1)\n", " s = code.hexdigest()\n", " return s\n", " return hash_function\n", "\n", "\n", "def parse_row(line):\n", " row = line.strip().split('\\t')\n", " if len(row) !=5:\n", " return None\n", " \n", " d = {'ip': row[0],\n", " 'ua': row[1],\n", " 'requests' : parse_requests(row[3]),\n", " 'geo_data' : row[4]\n", " }\n", " return d\n", "\n", "def parse_requests(requests):\n", " ret = []\n", " for r in requests.split('||'):\n", " t = r.split('|')\n", " if len(t) != 3:\n", " continue\n", " ret.append({'t': t[0], 'r': t[1], 'p': t[2]})\n", " ret.sort(key = lambda x: x['t']) # sort by time\n", " return ret" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "input_dir = '/user/hive/warehouse/traces.db/test2'\n", "output_dir = '/user/ellery/readers/data/hashed_traces/test2'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hash_function = get_hash_function()\n", "\n", "def hash_ip(x):\n", " x['ip'] = hash_function(x['ip'])\n", " return x" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "day = '2016-02-29'\n", "host = 'en.wikipedia.org'\n", "partition = get_partition_name(day, host)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "input_partition = os.path.join(input_dir, partition)\n", "output_partition = os.path.join(output_dir, partition )\n", "trace_rdd = sc.textFile(input_partition) \\\n", " .map(parse_row) \\\n", " .filter(lambda x: x is not None) \\\n", " .map(hash_ip) \\\n", " .map(lambda x: json.dumps(x)) \n", "os.system('hadoop fs -rm -r ' + output_partition)\n", "trace_rdd.saveAsTextFile(output_partition)\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['{\"requests\": [{\"r\": \"-\", \"t\": \"2016-02-29 00:17:28\", \"p\": \"/wiki/Bad_Company\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:17:31\", \"p\": \"/wiki/Bad_Company\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:22:42\", \"p\": \"/wiki/Foreigner\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:22:43\", \"p\": \"/wiki/Foreigner\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:27:40\", \"p\": \"/wiki/Thunder\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:27:43\", \"p\": \"/wiki/Thunder\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:33:18\", \"p\": \"/wiki/Simple_Minds\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:33:22\", \"p\": \"/wiki/Simple_Minds\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:39:09\", \"p\": \"/wiki/Marillion\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:39:12\", \"p\": \"/wiki/Marillion\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:43:31\", \"p\": \"/wiki/The_Eagles\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:43:36\", \"p\": \"/wiki/The_Eagles\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:47:27\", \"p\": \"/wiki/Dream_Theater\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:47:30\", \"p\": \"/wiki/Dream_Theater\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:51:19\", \"p\": \"/wiki/Ugly_Kid_Joe\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:51:24\", \"p\": \"/wiki/Ugly_Kid_Joe\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:55:22\", \"p\": \"/wiki/Queen\"}, {\"r\": \"-\", \"t\": \"2016-02-29 00:55:25\", \"p\": \"/wiki/Queen\"}], \"ip\": \"24cbe39b2ddcae901bf755f28ace0aa841b65b46\", \"geo_data\": \"city\\\\u0003Bucheon-si\\\\u0002country_code\\\\u0003KR\\\\u0002longitude\\\\u0003126.7831\\\\u0002subdivision\\\\u0003Gyeonggi-do\\\\u0002timezone\\\\u0003Asia/Seoul\\\\u0002postal_code\\\\u0003Unknown\\\\u0002continent\\\\u0003Asia\\\\u0002latitude\\\\u000337.4989\\\\u0002country\\\\u0003Republic of Korea\", \"ua\": \"foobar2000/1.3.6\"}']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sc.textFile(output_partition).map(lambda xtake(1)" ] }, { "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

peterwittek/qml-rg

Archiv_Session_Spring_2017/Exercises/05_APS Captcha.ipynb

1

547257

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import keras\n", "import itertools as it\n", "import matplotlib.pyplot as pl\n", "from tempfile import TemporaryDirectory\n", "\n", "TMPDIR = TemporaryDirectory()\n", "keras.backend.set_image_data_format('channels_first')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preprocessing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os \n", "from skimage import io\n", "from skimage.color import rgb2gray\n", "from skimage import transform\n", "from math import ceil\n", "\n", "\n", "IMGSIZE = (100, 100)\n", "\n", "def load_images(folder, scalefactor=(2, 2), labeldict=None):\n", " images = []\n", " labels = []\n", " files = os.listdir(folder)\n", " \n", " for file in (fname for fname in files if fname.endswith('.png')):\n", " \n", " img = io.imread(folder + file).astype(float)\n", " img = rgb2gray(img)\n", " # Crop since some of the real world pictures are other shape\n", " img = img[:IMGSIZE[0], :IMGSIZE[1]]\n", " # Possibly downscale to speed up processing\n", " img = transform.downscale_local_mean(img, scalefactor)\n", " # normalize image range\n", " img -= np.min(img)\n", " img /= np.max(img)\n", " images.append(img)\n", " \n", " if labeldict is not None:\n", " # lookup label for real world data in dict generated from labels.txt\n", " key, _ = os.path.splitext(file)\n", " labels.append(labeldict[key])\n", " else:\n", " # infere label from filename\n", " if file.find(\"einstein\") > -1 or file.find(\"curie\") > -1:\n", " labels.append(1)\n", " else:\n", " labels.append(0)\n", " \n", " return np.asarray(images)[:, None], np.asarray(labels)\n", "\n", "x_train, y_train = load_images('data/aps/train/')\n", "# Artifically pad Einstein's and Curie't to have balanced training set\n", "# ok, since we use data augmentation later anyway\n", "sel = y_train == 1\n", "repeats = len(sel) // sum(sel) - 1\n", "x_train = np.concatenate((x_train[~sel], np.repeat(x_train[sel], repeats, axis=0)),\n", " axis=0)\n", "y_train = np.concatenate((y_train[~sel], np.repeat(y_train[sel], repeats, axis=0)),\n", " axis=0)\n", "\n", "x_test, y_test = load_images('data/aps/test/')\n", "\n", "rw_labels = {str(key): 0 if label == 0 else 1\n", " for key, label in np.loadtxt('data/aps/real_world/labels.txt', dtype=int)}\n", "x_rw, y_rw = load_images('data/aps/real_world/', labeldict=rw_labels)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x123c57c88>" ] }, "metadata": { "image/png": { "height": 195, "width": 910 } }, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid import ImageGrid\n", "from math import ceil\n", "\n", "def imsshow(images, grid=(5, -1)):\n", " assert any(g > 0 for g in grid)\n", " \n", " grid_x = grid[0] if grid[0] > 0 else ceil(len(images) / grid[1])\n", " grid_y = grid[1] if grid[1] > 0 else ceil(len(images) / grid[0])\n", " \n", " axes = ImageGrid(pl.gcf(), \"111\", (grid_y, grid_x), share_all=True)\n", " for ax, img in zip(axes, images):\n", " ax.get_xaxis().set_ticks([])\n", " ax.get_yaxis().set_ticks([])\n", " ax.imshow(img[0], cmap='gray')\n", " \n", "pl.figure(0, figsize=(16, 10))\n", "imsshow(x_train, grid=(5, 1))\n", "pl.show()\n", "\n", "pl.figure(0, figsize=(16, 10))\n", "imsshow(x_train[::-4], grid=(5, 1))\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x123f52400>" ] }, "metadata": { "image/png": { "height": 195, "width": 910 } }, "output_type": "display_data" } ], "source": [ "from keras.preprocessing.image import ImageDataGenerator\n", "\n", "imggen = ImageDataGenerator(rotation_range=20, \n", " width_shift_range=0.15,\n", " height_shift_range=0.15,\n", " shear_range=0.4,\n", " fill_mode='constant',\n", " cval=1.,\n", " zoom_range=0.3,\n", " channel_shift_range=0.1)\n", "imggen.fit(x_train)\n", "\n", "for batch in it.islice(imggen.flow(x_train, batch_size=5), 2):\n", " pl.figure(0, figsize=(16, 5))\n", " imsshow(batch, grid=(5, 1))\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training LeNet\n", "\n", "First, we will train a simple CNN with a single hidden fully connected layer as a classifier." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D\n", "from keras.models import Sequential\n", "from keras.backend import image_data_format\n", "\n", "\n", "def generate(figsize, nr_classes, cunits=[20, 50], fcunits=[500]):\n", " model = Sequential()\n", " cunits = list(cunits)\n", " input_shape = figsize + (1,) if image_data_format == 'channels_last' \\\n", " else (1,) + figsize\n", "\n", " model.add(Conv2D(cunits[0], (5, 5), padding='same',\n", " activation='relu', input_shape=input_shape))\n", " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n", "\n", " # Convolutional layers\n", " for nr_units in cunits[1:]:\n", " model.add(Conv2D(nr_units, (5, 5), padding='same',\n", " activation='relu'))\n", " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n", "\n", " # Fully connected layers\n", " model.add(Flatten())\n", " for nr_units in fcunits:\n", " model.add(Dense(nr_units, activation='relu'))\n", "\n", " # Output layer\n", " activation = 'softmax' if nr_classes > 1 else 'sigmoid'\n", " model.add(Dense(nr_classes, activation=activation))\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved model not found, traing...\n", "Epoch 1/5\n", "100/100 [==============================] - 92s - loss: 0.2592 - acc: 0.8818 - val_loss: 0.0899 - val_acc: 0.9687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 2/5\n", "100/100 [==============================] - 84s - loss: 0.0666 - acc: 0.9793 - val_loss: 0.0372 - val_acc: 0.9870\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 3/5\n", "100/100 [==============================] - 87s - loss: 0.0204 - acc: 0.9947 - val_loss: 0.0433 - val_acc: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 4/5\n", "100/100 [==============================] - 87s - loss: 0.0344 - acc: 0.9902 - val_loss: 0.0585 - val_acc: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 5/5\n", "100/100 [==============================] - 80s - loss: 0.0188 - acc: 0.9929 - val_loss: 0.0304 - val_acc: 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] } ], "source": [ "from keras.optimizers import Adam\n", "from keras.models import load_model\n", "\n", "try:\n", " model = load_model('aps_lenet.h5')\n", " print(\"Model succesfully loaded...\")\n", "except OSError:\n", " print(\"Saved model not found, traing...\")\n", " model = generate(figsize=x_train.shape[-2:], nr_classes=1,\n", " cunits=[24, 48], fcunits=[100])\n", " optimizer = Adam()\n", " model.compile(loss='binary_crossentropy', optimizer=optimizer,\n", " metrics=['accuracy'])\n", "\n", " model.fit_generator(imggen.flow(x_train, y_train, batch_size=len(x_train)), \n", " validation_data=imggen.flow(x_test, y_test),\n", " steps_per_epoch=100, epochs=5,\n", " verbose=1, validation_steps=256)\n", " model.save('aps_lenet.h5')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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0LV++XLm5uerXr598fHwkFSVPZ86ccVSw7Dp27ChJ2rJli9LT00u0HTx4UP/5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"text/plain": [ "<matplotlib.figure.Figure at 0x123fa2080>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "def plot_cm(cm, classes, normalize=False, \n", " title='Confusion matrix', cmap=pl.cm.viridis):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " \n", " pl.imshow(cm, interpolation='nearest', cmap=cmap)\n", " pl.title(title)\n", " pl.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " pl.xticks(tick_marks, classes, rotation=45)\n", " pl.yticks(tick_marks, classes)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in it.product(range(cm.shape[0]), range(cm.shape[1])):\n", " pl.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " pl.tight_layout()\n", " pl.ylabel('True label')\n", " pl.xlabel('Predicted label')\n", "\n", "y_pred_rw = model.predict_classes(x_rw, verbose=0).ravel()\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Random Forests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preprocessing to a fixed size training set since sklearn doesn't suppport streaming training sets?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Same size training set as LeNet\n", "TRAININGSET_SIZE = len(x_train) * 5 * 100\n", "\n", "batch_size = len(x_train)\n", "nr_batches = TRAININGSET_SIZE // batch_size + 1\n", "imgit = imggen.flow(x_train, y=y_train, batch_size=batch_size)\n", "x_train_sampled = np.empty((TRAININGSET_SIZE, 1,) + x_train.shape[-2:])\n", "y_train_sampled = np.empty(TRAININGSET_SIZE)\n", "\n", "for batch, (x_batch, y_batch) in enumerate(it.islice(imgit, nr_batches)):\n", " buflen = len(x_train_sampled[batch * batch_size:(batch + 1) * batch_size])\n", " x_train_sampled[batch * batch_size:(batch + 1) * batch_size] = x_batch[:buflen]\n", " y_train_sampled[batch * batch_size:(batch + 1) * batch_size] = y_batch[:buflen]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 27.2s\n", "[Parallel(n_jobs=-1)]: Done 64 out of 64 | elapsed: 42.6s finished\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rfe = RandomForestClassifier(n_estimators=64, criterion='entropy', n_jobs=-1,\n", " verbose=True)\n", "rfe = rfe.fit(x_train_sampled.reshape((TRAININGSET_SIZE, -1)), y_train_sampled)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=4)]: Done 64 out of 64 | elapsed: 0.0s finished\n" ] }, { "data": { "image/png": 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KlSvtz/55/vnn9dZbb9mft1QWq9WqpUuXqnv37vr+++9LtL344ouSir6IjhkzpswvpAcO\nHNBbb70lqWgnt7Fjx5r0bqrvzjvvtD/b6f3331dOTk6pPlu3bq0w6fzpp5+0ffv2CufZt2+fvRJW\nPJGrinHjxtlvAZsxY0aZX6hzcnI0ZswY++/vpZdeuq45GjrbdusJCQklqnU2BQUFGj9+vJKSkioc\np2XLlvafjx8/bm6QVbBjxw7NnDlTktSnTx/NmDFDUtGaveXLl8vLy0t5eXl67LHHTNm5EwCul1td\nBwAA9U1kZKRWrVqlxx9/XNeuXdNf/vIXzZ8/X4899pj69u1rr3QkJSUpOjpaa9euLfeL5rhx47R8\n+XJt3bpV27dvV48ePfTyyy/r1ltvVVZWlrZs2aJ3333XvuPaRx99JH9//1p7r+UJDAzUyJEjtXjx\nYh09elQDBw7UlClTdPPNNyslJUXr1q3Tv/71L/Xu3VvffvttmWOcPXtWgwYN0i233KIHHnhAvXr1\nUps2beTl5aWLFy9q165d+uijjyQV3V727LPPXleMTZo00dy5c/Xkk08qIyND/fv310svvaR7771X\nPj4+iouL0+zZs+0PQR4yZIhGjx7t2IVpYMaOHav169fLarVq+PDhmjx5svr16ydvb2/98MMPev/9\n9xUbG6t+/frpm2++KXec7t27y8fHR5mZmXr77bcVHBys8PBwe+Lt7+9f5sOJzZCamqpRo0bJarXK\n399fS5cutW8oIxXd2jh79mw999xzSkhI0LPPPltp5Q0ATGcAAMoUFxdnDBw40JBU6ctisRiPPfaY\ncfr06VLjZGRkGMOHD6/wfE9PT2P+/PnlxjJgwABDktG2bdsKY96xY4d9zIULF5bZx9Y+ZsyYCsdK\nTU01unTpUm7MPXr0MC5evFjueMVjqey9f/TRR9WO9aOPPjI8PDwqnOP+++83MjMzyzx/4cKF9n47\nduyo8Jq0bdvWkGQMGDCgwn4Vqer1r+pclX02Jk6cWOG1GTVqlPH1119X+rmZPn16uWP8+r1U9T1W\npf99991nb1++fHm5YzzwwAP2fp9++mmV5gUAs1CpAoBydO3aVTt27FB0dLTWrVunXbt26ezZs7p0\n6ZLc3NzUrFkzdevWTXfddZdGjhypNm3alDmOr6+vvvrqK3355ZdavHix9u7dq5SUFHl6eqpt27b6\n7W9/qxdeeEFt27at5XdYsWbNmum7777T7NmztXr1ap06dUpubm4KCwvT448/rueee67EDnK/1r9/\nf+3Zs0f//ve/9f333+vs2bO6cOGCMjIy5Ovrqw4dOmjQoEF6+umnSzx36HpNnDhRgwcP1vvvv6+t\nW7fqzJkzysvLU3BwsPr06aOxY8dq2LBh1R6/ofvoo490991365///KcOHTqkzMxMBQcHq0ePHho3\nbpwefPBB7dy5s9Jxpk2bprCwMH3yySeKjY1VWlpahbfFmuG9997TV199JUkaP368Hn300XL7fvzx\nx4qIiFBiYqL+9Kc/qU+fPrrllltqND4AsLEYBnuQAgAAAEB1sVEFAAAAADiApAoAAAAAHEBSBQAA\nAAAOIKkCAAAAAAeQVAEAAACAA9hSHQAAAEC9snfvXh09elSnT5/WmTNnlJ2drX79+umFF1647rEu\nXbqkFStWKDY2VhkZGWratKkiIyM1YsQI+fr6mhIvSRUAAACAeuXzzz/XmTNn5OXlpWbNmuncuXPV\nGic5OVlTp05Venq6evbsqVatWunEiRPauHGjYmJiNGPGDPn5+TkcL0kVAAAAgHplzJgxatasmUJC\nQnT06FG98cYb1RpnwYIFSk9P17hx4zR06FD78UWLFmnDhg1atmyZnn76aYfjZU0VAAAAgHqla9eu\natGihSwWS7XHSE5OVmxsrIKCgjR48OASbVFRUfL09NSePXuUk5PjaLgkVQAAAABuPPHx8ZKkiIgI\nubiUTHsaNWqkTp06KTc3VwkJCQ7Pxe1/AAAAAEz36quvlnl81qxZtTL/+fPnJUktWrQosz0kJESx\nsbFKSkpSt27dHJqLShUAAACAG05WVpYkydvbu8x22/HMzEyH56JShRuWNblDXYeAesDS7AtJknHp\noTqOBPXJ4Ja31XUIqAfm7i/61/JnI8v+13Q4n63WVXUdQq2wJofV6PguIccl1V5Fqj6gUgUAAADg\nhmOrRNkqVr9mO+7j4+PwXFSqAAAAACdilbVGx68vVZuWLVtKkpKSkspsT05OllT+mqvrUV/eMwAA\nAACYpkuXLpKk2NhYWa0lE8ns7GwdO3ZMnp6e6tDB8SUjJFUAAACAEyk0rDX6qm0FBQU6d+6cvfJk\nExISooiICKWkpGjLli0l2lauXKnc3Fz1799fXl5eDsfA7X8AAAAA6pV9+/Zp//79kqQrV65IkhIS\nEjR37lxJkp+fn0aPHi1JSktL06RJkxQUFGRvt3nqqac0depULVy4UHFxcWrdurUSEhIUHx+vFi1a\naOTIkabES1IFAAAAOBGrjLoOoVKnT5/Wrl27Shy7cOGCLly4IEkKCgqyJ1UVCQkJ0cyZM7Vy5UrF\nxMTo8OHDatq0qYYNG6YRI0bI19fXlHhJqgAAAADUK1FRUYqKiqpS3+DgYK1cubLc9sDAQD3zzDNm\nhVYmkioAAADAidT07n/OiI0qAAAAAMABVKoAAAAAJ1Jo1P81VQ0NlSoAAAAAcACVKgAAAMCJNITd\n/xoaKlUAAAAA4AAqVQAAAIATKaRSZToqVQAAAADgACpVAAAAgBNhTZX5qFQBAAAAgAOoVAEAAABO\nhOdUmY9KFQAAAAA4gEoVAAAA4ESsdR3ADYhKFQAAAAA4gKQKAAAAABzA7X8AAACAE+Hhv+ajUgUA\nAAAADqBSBQAAADiRQgpVpqNSBQAAAAAOoFIFAAAAOBG2VDcflSoAAAAAcACVKgAAAMCJFMpS1yHc\ncKhUAQAAAIADqFQBAAAATsTK7n+mo1IFAAAAAA6gUgUAAAA4EdZUmY9KFQAAAAA4gEoVAAAA4ESo\nVJmPShUAAAAAOIBKFQAAAOBErAaVKrNRqQIAAAAAB1CpAgAAAJwIa6rMR6UKAAAAABxApQoAAABw\nIoXUVUzHFQUAAAAAB1CpAgAAAJwIu/+Zj0oVAAAAADiAShUAAADgRNj9z3xUqgAAAADAAVSqAAAA\nACdSaFBXMRtXFAAAAAAcQKUKAAAAcCJW6iqm44oCAAAAgAOoVAEAAABOhN3/zEelCgAAAAAcQFIF\nAAAAAA7g9j8AAADAibCluvm4ogAAAADgACpVAAAAgBOxslGF6ahUAQAAAIADqFQBAAAATqSQuorp\nuKIAAAAA4AAqVQAAAIATYfc/83FFAQAAAMABVKoAAAAAJ2KlrmI6rigAAAAAOIBKFQAAAOBECg2e\nU2U2KlUAAAAA4AAqVQAAAIAT4TlV5uOKAgAAAIADqFQBAAAATsTKc6pMxxUFAAAAAAdQqQIAAACc\nCGuqzMcVBQAAAAAHUKkCAAAAnAjPqTIflSoAAAAAcACVKgAAAMCJWKmrmI4rCgAAAAAOoFIFAAAA\nOJFCnlNlOq4oAAAAADiAShUAAADgRKxi9z+zUakCAAAAAAdQqQIAAACcCGuqzMcVBQAAAAAHUKkC\nAAAAnEghdRXTcUUBAAAAwAEkVQAAAADgAG7/AwAAAJyI1WBLdbNRqQIAAAAAB1CpAgAAAJwIG1WY\njysKAAAAAA6gUgUAAAA4ESsP/zUdVxQAAAAAHEClCgAAAHAihWL3P7NRqQIAAAAAB1CpAgAAAJwI\na6rMxxUFAAAAAAdQqQIAAACcCGuqzEelCgAAAAAcQKUKAAAAcCKsqTIfVxQAAAAAHEClCgAAAHAi\nhQ2oUnXp0iWtWLFCsbGxysjIUNOmTRUZGakRI0bI19e3yuMcOnRIGzduVGJion2cdu3a6b777lNY\nWJjDcZJUAQAAAKh3kpOTNXXqVKWnp6tnz55q1aqVTpw4oY0bNyomJkYzZsyQn59fpeN8+umnWr9+\nvfz8/BQZGSk/Pz8lJydr//79+v777/Xss8/qrrvucihWkioAAADAiVgbyO5/CxYsUHp6usaNG6eh\nQ4fajy9atEgbNmzQsmXL9PTTT1c4xpUrV/Tll1/K399fs2fPlr+/v73tyJEjevPNN7Vy5UqHk6qG\nU/sDAAAA4BSSk5MVGxuroKAgDR48uERbVFSUPD09tWfPHuXk5FQ4TkpKigzDUIcOHUokVJLUtWtX\nNWrUSFevXnU4XpIqAAAAwIkUGi41+jJDfHy8JCkiIkIuLiXHbNSokTp16qTc3FwlJCRUOE6LFi3k\n5uamEydOlEqejh49quzsbHXr1s3heLn9DwAAAIDpXn311TKPz5o1q9Jzz58/L6koKSpLSEiIYmNj\nlZSUVGFS5OvrqyeeeEKLFy/Wyy+/XGJN1cGDB3XrrbdWegthVZBUAQAAAE7EatT/NVVZWVmSJG9v\n7zLbbcczMzMrHWv48OEKCgrSRx99pK+//tp+PCQkRAMHDix1W2B1kFQBAAAAMF1VKlK1Yd26dVq2\nbJmGDh2qIUOGqEmTJjp37pyWLVumf/zjHzp9+rRGjRrl0BysqQIAAACcSKFcavRlBlslylax+jXb\ncR8fnwrHiY+P12effaaePXtqzJgxat68uTw9PdWuXTtNnjxZAQEB+vLLL3XhwgWH4iWpAgAAAFCv\ntGzZUpKUlJRUZntycrKk8tdc2Rw8eFCS1KVLl1Jtnp6eat++vQzD0KlTpxwJl9v/AAAAAGfSENZU\n2ZKg2NhYWa3WEjsAZmdn69ixY/L09FSHDh0qHKegoECSyt023Xbczc2xtIhKFQAAAIB6JSQkRBER\nEUpJSdGWLVtKtK1cuVK5ubnq37+/vLy8JBUlT+fOnbNXsGw6deokSdq2bZvS0tJKtB0+fFj/+c9/\n5O7uro4dOzoUL5UqAAAAwIlYG0hd5amnntLUqVO1cOFCxcXFqXXr1kpISFB8fLxatGihkSNH2vum\npaVp0qRJCgoK0ty5c+3H+/Tpo27duikuLk6TJk1SZGSkfaOKQ4cOyTAMPfHEE/Lz83MoVpIqAAAA\nAPVOSEiIZs6cqZUrVyomJkaHDx9W06ZNNWzYMI0YMUK+vr6VjuHi4qLXX39dW7ZsUXR0tPbv36/c\n3Fz5+vqqe/fuGjp0qCIiIhyOlaQKAAAAQL0UGBioZ555ptJ+wcHBWrlyZZltbm5uGj58uIYPH252\neP+do8ZGBgAAAFDvFDaAjSoamoZxQyUAAAAA1FNUqgAAAAAn0hC2VG9oqFQBAAAAgAOoVAFoODyH\nyOIRKbl3ltw6y+LiKyN7nYz0ydc/lkuILL4vSp79JZemkvWilLNNxrX3JaPsBwQCqP9yjCyd1FFd\nUrLylSdPeSlILdVO4XK3eNT6OEB9ZDWoq5iNpApAg2HxfUYW984yrNck6wXJpfKtVMvk2kaWgBWy\nuAbKyNkqFfwkud8qi89YybO/jEuPScYVU2MHUPOyjGs6oB3KU66C1FLe8tNVpelnndAlXVBPY6A8\nLJ61Ng4A50FSBaDBMDLeklGYLBWekTx6yRLwWbXGsTSeLotroKxX35Sylvy3we91WXzGS34vy7j6\nvyZFDaC2HNNh5SlXYbpNbSzt7cePG7E6qwSdVLw66/ZaGweorwrFmiqzUfsD0HDkfV+UUDnCtY0s\nnv1lFPwsZX1aosm49g8Z1kzJ6wHJ0sixeQDUqizjmtJ0QV7y1k26pURbO4XLVa5K0hkVGgW1Mg4A\n50JSBcC5ePQu+jPvW0lGyTYjU8o/JIuLt+R+W62HBqD6LitFktRMzWWxlPxXeDeLu/wVKKsKla5L\ntTIOUJ9ZDUuNvpwRSRUAp2JxaydJMgpOld2h4HTRn643105AAEyRpQxJkrf8ymz3lu8v/a7VyjgA\nnAtJVT0VFRWl6dOn13UY1Xbx4kVFRUVp7ty5dR0KUJLll80tjIyy223HXcr+QgWgfipQviTJTe5l\nttuO5//Sr6bHAeozq+FSoy9n1OA3qoiKipIkBQYGas6cOfLwKL3N6bPPPquUlBQtW7ZMrq6u1Z7r\n2WeflaTrThRWrlyp1atXV9gnPDy8TpOonTt36sMPP9QzzzyjgQMH1lkcAAAAQEPT4JMqm9TUVG3c\nuFEPPvhZYZUoAAAgAElEQVRgXYdSrvDwcIWHh5fZFhwcXOLv7777rjw9G+52rQEBAXr33Xfl7e1d\n16EAJRm/3LJjKacSZTtuLaeSBaBeslWQCsqpINmOu5dTgTJ7HKA+s7L7n+luiKTKx8dHFotFa9eu\n1d13363GjRvXdUhlCg8Pt1fWKtOqVasajqZmubm5Nfj3gBuTUfCTLJIsbjf/epuKIm6hRX8WlrPm\nCkC9ZFsDZVsT9Wu2NVC2NVE1PQ4A53JDJFWenp763e9+p0WLFmn16tUaP358lc+Njo7Wli1bdPr0\naRUUFCgkJET9+vXTfffdJ3f3on+Fio+P1xtvvGE/p3hiNGDAAPttgWaKiooqdUug7TbCadOmKSMj\nQ+vWrdPPP/8sd3d3RUREaPTo0QoICCgxzoULF7R27VodOXJEaWlp8vDwUEBAgDp27KiRI0fKz89P\n06dP19GjRyVJH374oT788EP7+R988IG9ilZYWKht27Zp9+7dSkxMVGFhoVq2bKm7775b9957r1xc\n/nsP7cWLF/Xcc8+Vuj5z587Vrl279MEHHyg2NlabN29WcnKyvL291bNnTz355JNUt1Cz8r4v+tOj\nrySLSuwAaPGR3G+XYc2S8mPqIjoA1dRUQZKkS7ogwzBK7NxXYOQrXalykav81axWxgHqs0In3aGv\nJt0QSZUkDR48WJs3b9bWrVs1dOhQtWjRotJzli5dqrVr18rPz0/9+vWTl5eXYmJitGzZMsXGxuov\nf/mL3NzcFBQUpBEjRmjjxo2SpGHDhtnHCA0Nram3VK4tW7bo4MGD6tGjh8LDw3XixAlFR0frzJkz\nevvtt+3J4OXLl/X6668rOztb3bt3V+/evZWfn6+LFy9qz549GjJkiPz8/DRw4EB5e3vrwIED6tmz\nZ4n35OPjI0kqKCjQrFmzFBsbq5YtW6pv377y8PBQfHy8Pv74YyUkJOj555+v8nv49NNPFRsbqx49\neigiIkLx8fH6+uuvlZycrGnTppl6veCs3CTXNpI8JOX993DhWRm5e4qeVeU9qsTDfy2+L8ji4iMj\na5lkZNd6xACqz9viqwCjudJ0QT/rpNrovw/t/UlHVahCtVI7uVqKvvpYDatOnj4pdzc3h8YBAOkG\nSqrc3Nz0xBNP6O9//7s+++wzTZ48ucL+x48f19q1a9WsWTPNnDlTTZo0kSQ9/vjjeuedd3To0CGt\nX79eDz/8sIKDgxUVFaVdu3ZJUpVv4fu1o0ePauXKlWW23XbbbQoLC6vSOLGxsZo5c6batGljP/be\ne+/p22+/1f79+3XnnXdKkvbu3atr165p7NixJRJBScrJybFXlmwbUxw4cEC9evUqc6OKNWvWKDY2\nVkOGDNHYsWPt51qtVs2bN087duxQnz59FBkZWaX3kJCQoL/97W8KDAyUVFQFe/PNNxUfH68TJ06o\nffv2lYwAp+R5jyxevy362aXosyP37rL4zyr62ZomI+OXn12byyVoiwwjTyo4XmIY4+p0KWCFXBr/\nrwyPO6SCk5J7hCyed8go+ElGxt9r5/0AMFUnddcB7dBxxeiycVE+8lO60nRZKfKWr25RF3vfXGVr\n2Ih71apFK3XWHdUeB2iInHWHvpp0wyRVktSnTx+FhYVp3759OnbsmDp16lRu3+3bt0uSHnnkEXtC\nJUmurq4aPXq0Dh8+rO3bt+vhhx82Lb6jR4/ab7P7NR8fnyonVUOHDi2RUEnSoEGD9O233+rEiRP2\npMqmrB0Rvby8qhh1UeK0efNmNWnSRGPGjClxm5+Li4tGjx6tnTt3as+ePVVOqkaMGGFPqKSi6z5w\n4ED9+OOPVU6qXn311TKPz5pV9KXa0uyLKsWCBsQlWBbXkpu6WNzaSG5F/z0YRp4sHn1+abEtIneT\n3G4p/XmwXpJhcZU8B0qegyQVyChMlYwcWQIW1uS7QD0wdz+3Gd+okpLP6x/z5mhP9G6dS/9JQYFB\n+t1vxuq5P74g/8b+9n6J5xM16P5NcnV31dz9s6o9DgBIN1hSJUmjR4/WX/7yFy1ZskRvvfVWuf1O\nnSpahN61a9dSbS1btlSzZs108eJFZWVlmbbGZ8SIEdWuchXXrl27UsdsCUpmZqb9WM+ePbVs2TIt\nWLBAMTExuu2229SxY0e1bt261FPiK5KUlKRr166pRYsW+vzzz8vs4+HhoXPnzlV5zFtuuaXUsWbN\niu5Pv3aNByqiHNaLMqwXq9g5X0b+Ecmt9GfN1q7Cqn9mATQMLUJaaua0tyvt17pla2Vfy5EknT1W\n+v8FVR0HaIisrKky3Q2XVIWFhalPnz7au3evoqOjS1VtbLKysiSpRJWquKZNmyo1NVWZmZn1buME\n2zqn4orfjmcTFBSk//u//9OqVasUExOjffv2SSpKXn73u9+VuiWwPBkZRTsgJSUlVfi8rZycnCq/\nh7Kuqe0ZYsXfQ0VsFanyGJceqnI8uHHZKlR8HlDcs5G31XUIqAdsFapnI8u+8wHOZ6t1VV2HgAbq\nhkuqpKJ1Ufv379fSpUvVq1evMvvYvtRfuXJFISEhpdovX75col9D1bp1a02aNEmFhYU6c+aMfvjh\nB23evFmffPKJvLy8dPfdd1c6hu0a9OrVq9K1agAAAKjfeE6V+W7IVWohISEaPHiwLl68qE2bNpXZ\n5+abb5akMtc4JScn69KlSwoODi5RFXJxcalyFaW+cXV1Vbt27fTggw/qxRdflCR75Uoqu9Jl06pV\nK/n4+CghIUEFBQW1EzAAAADQQNyQSZVUtH7Jx8dHa9asKfO2tN/85jeSpM8//1xXr161H7darVq8\neLEMwyhVxfH19dXVq1eVl5enhuCnn36y3+ZYXHp6uqSi53vZ+PoWPcQwNTW1VH9XV1cNGTJEly9f\n1sKFC8t8/5cvX1ZiYqJZoQMAAKCGWA1Ljb6c0Q15+59UlCQ89NBD+vTTT8ts79ixo+6//36tX79e\nr7zyinr37i0vLy8dPnxYP//8szp16qT777+/xDndunXTyZMn9dZbb6lz585yd3dX27Zt1bNnzyrF\nVNGW6j4+Pho+fPj1vclK7N69W1u3blWnTp3UvHlz+fr6Kjk5WQcPHpS7u3uJ+cLCwuTp6akNGzYo\nIyPDvtZs6NCh8vb21iOPPKIzZ85o69atOnjwoLp27aqAgAClp6crOTlZx44d08iRI9W6dWtT3wMA\nAABQ392wSZVUlBBs2bJFKSkpZbaPGjVKN998szZv3qzdu3ersLBQzZs312OPPab77rtPbr96IODD\nDz+szMxMHTx4UP/5z39ktVo1YMCA60qqyttSPSgoyPSkqm/fvsrPz9fx48f1008/KS8vTwEBAerb\nt6/uu+++Etuy+/r66pVXXtGqVau0c+dO5ebmSpL69+8vb29vubm56c9//rP27NmjnTt36uDBg8rJ\nyVHjxo0VHBysRx99VP369TM1fgAAAJiP51SZz2IYhlHXQQA1wZrcoa5DQD3A7n8oy+CW7P4Hdv9D\nac6y+9+j302s0fFX3PHPGh2/PrqhK1UAAAAASnLWdU81idofAAAAADiAShUAAADgRHhOlfmoVAEA\nAACAA0iqAAAAAMAB3P4HAAAAOBE2qjAflSoAAAAAcACVKgAAAMCJUKkyH5UqAAAAAHAAlSoAAADA\niVCpMh+VKgAAAABwAJUqAAAAwIlQqTIflSoAAAAAcACVKgAAAMCJWEWlymxUqgAAAADAAVSqAAAA\nACfCmirzUakCAAAAAAdQqQIAAACcCJUq81GpAgAAAAAHUKkCAAAAnAiVKvNRqQIAAAAAB1CpAgAA\nAJwIlSrzUakCAAAAAAdQqQIAAACciEGlynRUqgAAAADAAVSqAAAAACdiFZUqs1GpAgAAAAAHUKkC\nAAAAnAi7/5mPShUAAAAAOIBKFQAAAOBE2P3PfFSqAAAAAMABVKoAAAAAJ8KaKvNRqQIAAAAAB1Cp\nAgAAAJwIa6rMR6UKAAAAABxAUgUAAAAADuD2PwAAAMCJsFGF+ahUAQAAAIADqFQBAAAATsQw6jqC\nGw+VKgAAAABwAJUqAAAAwIlYxZoqs1GpAgAAAAAHUKkCAAAAnAgP/zUflSoAAAAAcACVKgAAAMCJ\n8Jwq81GpAgAAAAAHUKkCAAAAnAjPqTIflSoAAAAAcACVKgAAAMCJsPuf+ahUAQAAAIADqFQBAAAA\nToRKlfmoVAEAAACAA6hUAQAAAE6E51SZj0oVAAAAADiAShUAAADgRHhOlfmoVAEAAACAA6hUAQAA\nAE6E3f/MR6UKAAAAABxApQoAAABwIlSqzEelCgAAAAAcQKUKAAAAcCJs/mc+KlUAAAAA4AAqVQAA\nAIATYU2V+ahUAQAAAIADqFQBAAAAzoRFVaYjqQIAAABQL126dEkrVqxQbGysMjIy1LRpU0VGRmrE\niBHy9fW9rrHi4uK0efNmHT9+XJmZmfLz81ObNm00dOhQ3X777Q7FSVIFAAAAoN5JTk7W1KlTlZ6e\nrp49e6pVq1Y6ceKENm7cqJiYGM2YMUN+fn5VGuvTTz/V+vXr1axZM/Xs2VN+fn66evWqTp06paNH\nj9ZNUjV58mSHJrWxWCx65513TBkLAAAAQOUaykYVCxYsUHp6usaNG6ehQ4fajy9atEgbNmzQsmXL\n9PTTT1c6zrZt27R+/XoNGDBAEyZMkJtbyRSooKDA4VirlVT9/PPPDk8MAAAAAGVJTk5WbGysgoKC\nNHjw4BJtUVFR2rZtm/bs2aPRo0fLy8ur3HHy8/O1fPlyBQYGlplQSSrz2PWq1givvvqqwxMDAAAA\nqH1GA9ioIj4+XpIUEREhF5eSG5Y3atRInTp1UmxsrBISEtStW7dyx/nhhx909epVDRs2TBaLRYcO\nHdLZs2fl4eGh9u3bKywszJR4q5VUOXrPIQAAAIAbW3mFmFmzZlV67vnz5yVJLVq0KLM9JCREsbGx\nSkpKqjCpOnnypCTJw8NDU6ZMKXXHXefOnfXKK6+ocePGlcZUEZ5TBQAAADgRw7DU6MsMWVlZkiRv\nb+8y223HMzMzKxwnPT1dkrR+/XpZLBa9+eabWrx4sWbPnq2IiAj9+OOP+vvf/+5wvDW2+192drbS\n0tKUm5urdu3a1dQ0AAAAAOqhqlSkaprxy72Orq6umjJlioKDgyVJbdq00eTJk/XSSy/p6NGjOn78\nuEO3ApqeVB06dEhr1qzRyZMnZbVaZbFYtHz5cnt7Zmam5s6dK0l67rnnys0+AQAAANSABrD7ny1H\nsFWsfs123MfHp0rjhIaG2hMqG09PT0VERGj79u06ceJE/UmqVq9erVWrVkkq2i5d+m92aOPj4yNX\nV1ft27dP3333nQYNGmRmCAAAAAAauJYtW0qSkpKSymxPTk6WVP6aq1+PU17yZTuel5dXrThtTFtT\nFR8fr1WrVsnDw0MTJkzQokWL5O/vX2bfAQMGSJJiYmLMmh4AAABAFRhGzb7M0KVLF0lSbGysrFZr\nibbs7GwdO3ZMnp6e6tChQ4XjdOvWTRaLRYmJiaXGkf77qKhfV7Gul2lJ1aZNmyRJI0eO1N133y1P\nT89y+4aHh0uSTp8+bdb0AAAAAG4QISEhioiIUEpKirZs2VKibeXKlcrNzVX//v3tz6gqKCjQuXPn\n7BUsm6CgIPXo0UOpqanauHFjibbY2FjFxsbKx8dHt912m0Pxmnb73/HjxyVJd999d6V9vb291ahR\nI12+fNms6QEAAABURQN4TpUkPfXUU5o6daoWLlyouLg4tW7dWgkJCYqPj1eLFi00cuRIe9+0tDRN\nmjRJQUFB9v0bio9z6tQpLV68WIcPH1ZoaKguXryo/fv3y8XFRRMmTHB4nwfTkqpr167J29u7wica\nF2dbcwUAAAAAvxYSEqKZM2dq5cqViomJ0eHDh9W0aVMNGzZMI0aMkK+vb5XGadasmWbNmqXVq1fr\nwIEDOnr0qLy9vdWjRw899NBDat++vcOxmpZU+fj46OrVq8rLy5OHh0eFfa9cuaKsrCwFBgaaNT0A\nAACAKjDrWVK1ITAwUM8880yl/YKDg7Vy5cpy2xs3bqzx48dr/PjxZoZnZ9qaKtuzqOLi4irtu3Xr\nVklSx44dzZoeAAAAAOqEaUmVbS3V0qVLdfXq1XL7ffPNN1qzZo0ksZ06AAAAUNuMGn45IdNu/+vd\nu7ciIyO1f/9+vfbaa+rfv7/y8/MlSdu3b1dqaqpiYmJ08uRJSUXbqtu2SgQAAACAhsrUh/+++OKL\nWrBggXbs2KG1a9faj8+bN69Ev0GDBtXY/YwAAAAAyteQ1lQ1FKYmVe7u7po4caKGDBminTt3KiEh\nQZcvX5ZhGPL391dYWJgGDhxoX38FAAAAAA2dqUmVTWhoqMaOHVsTQwMAAABwhJOue6pJpm1UAQAA\nAADOqEYqVZKUn5+vs2fP2ncCbNy4sdq0aSN3d/eamhIAAABApVhTZTbTk6rTp09r9erVOnTokAoL\nC0u0ubq6qkePHnrkkUcUGhpq9tQAAAAAUOtMTaq2bt2qjz/+WFar1X7MVpnKz89XYWGh9u3bpwMH\nDuipp57SPffcY+b0AAAAACrDmirTmZZUxcfHa/78+ZKk9u3b64EHHlB4eLh8fX0lSZmZmYqPj9f6\n9euVkJCg+fPnq2XLlgoPDzcrBAAAAACodaYlVbbnUvXu3VsvvfSSXFxK7oHh4+OjXr16qWfPnpoz\nZ46+//57rV27lqQKAAAAqE1Uqkxn2u5/J06ckCSNHTu2VEJVYkIXF/t26wkJCWZNDwAAAAB1wrRK\nldVqlY+PjwICAirtGxAQIB8fn1IbWQAAAACoYQa7/5nNtEpVixYtlJ2drZycnEr75uTkKDs7Wy1b\ntjRregAAAACoE6YlVffcc4+sVqs2bNhQad8NGzbIarWy+x8AAABQywyjZl/OyLTb/+655x6dPHlS\nK1euVGZmph544AH5+/uX6HP16lWtW7dOGzZs0KBBgzRo0CCzpgcAAACAOlGtpGrWrFnltnl7e2vD\nhg3atGmTWrZsaV9jdfnyZZ07d05Wq1Xe3t66fPmy3n77bU2ZMqV6kQMAAABAPVCtpOrQoUOV9rFa\nrUpMTFRiYmKptqysrCqNAQAAAMBkTnqLXk2qVlI1btw4s+MAAAAAgAapWknVkCFDzI4DAAAAQG1g\nS3XTmbb7HwAAAAA4I9N2/wMAAABQ/1lYU2W6GkmqbJtUXL58Wbm5uTIq2LC+d+/eNRECAAAAANQK\nU5Oq/Px8rVq1Stu2bVNmZmaVzlmxYoWZIQAAAACoCJUq05mWVBUUFOivf/2rjh07Jklq3ry5Lly4\nIBcXF7Vp00ZXrlzRlStXJBU9yyokJMSsqQEAAACgzpiWVG3btk3Hjh1TcHCwXnvtNbVq1UqPPvqo\nGjdubH9YcGJiopYuXarDhw+rX79+Gj58uFnTAwAAAKgKdv8znWm7/3377beSpCeffFKtWrUqs0/r\n1q01ZcoURUZGasmSJTpy5IhZ0wMAAABAnTAtqUpMTJQkde/evcTxgoKCUn1HjRolwzC0YcMGs6YH\nAAAAUBVGDb+ckGlJVV5ennx9feXu7m4/5uHhoZycnFJ9g4OD5e3trRMnTpg1PQAAAADUCdOSqiZN\nmigvL6/EMX9/fxUUFCg1NbXEcavVqpycHGVlZZk1PQAAAICqoFJlOtOSqqCgIOXl5SktLc1+rF27\ndpKk6OjoEn2jo6NltVrVtGlTs6YHAAAAgDph2u5/nTt31o8//qgjR47orrvukiQNHDhQ33//vVas\nWKGrV68qNDRUZ8+e1caNGyXx4F8AAACg1jlpNakmmZZU9e3bV3v37tWPP/5oT6puv/12DRgwQLt2\n7dKXX35Zon9oaKh+//vfmzU9AAAAANQJ05Kq1q1b69133y11/JlnnlH37t21d+9epaWlydvbW926\nddO9994rDw8Ps6YHAAAAUBU8p8p0piVVFbnjjjt0xx131MZUAAAAAFCraiWpAgAAAFA/WFhTZTrT\ndv8DAAAAAGdUrUrVV199ZVoA9913n2ljAQAAAKgElSrTVSupWrJkiWkBkFQBAAAAaMiqlVT16tVL\nFgu7hgAAAABAtZKqV155xew4AAAAAKBBYvc/3LCGDXykrkNAPfD+500kSc8/wucB/zXphHlrg9Fw\nNW+VLUmadOLHOo4EqF3s/mc+dv8DAAAAAAdQqQIAAACcicHeCGajUgUAAAAADqBSBQAAADgT1lSZ\njkoVAAAAADiApAoAAAAAHMDtfwAAAIAz4fY/01GpAgAAAAAH1FilKjs7W2lpacrNzVW7du1qahoA\nAAAA14GH/5rP9KTq0KFDWrNmjU6ePCmr1SqLxaLly5fb2zMzMzV37lxJ0nPPPSdvb2+zQwAAAACA\nWmNqUrV69WqtWrVKkmSxFD1UzDBKpsI+Pj5ydXXVvn379N1332nQoEFmhgAAAACgIlSqTGfamqr4\n+HitWrVKHh4emjBhghYtWiR/f/8y+w4YMECSFBMTY9b0AAAAAFAnTKtUbdq0SZI0cuRI3X333RX2\nDQ8PlySdPn3arOkBAAAAVAWVKtOZVqk6fvy4JFWaUEmSt7e3GjVqpMuXL5s1PQAAAADUCdMqVdeu\nXZO3t7e8vLyq1N+25goAAABA7WH3P/OZVqny8fFRVlaW8vLyKu175coVZWVllbvmCgAAAAAaCtOS\nKtuzqOLi4irtu3XrVklSx44dzZoeAAAAQFUYlpp9OSHTkirbWqqlS5fq6tWr5fb75ptvtGbNGkli\nO3UAAAAADZ5pa6p69+6tyMhI7d+/X6+99pr69++v/Px8SdL27duVmpqqmJgYnTx5UlLRtupdunQx\na3oAAAAAVcGaKtOZ+vDfF198UQsWLNCOHTu0du1a+/F58+aV6Ddo0CCNHz/ezKkBAAAAoE6YmlS5\nu7tr4sSJGjJkiHbu3KmEhARdvnxZhmHI399fYWFhGjhwoH39FQAAAIDaxe5/5jM1qbIJDQ3V2LFj\na2JoAAAAAKhXaiSpAgAAAFBPUakynWm7/wEAAACAMzKtUvXxxx9f9zkWi0Xjxo0zKwQAAAAAlWBN\nlflMS6q2bNlSrfNIqgAAAAA0ZKYlVcOHD5fFUv4TlLOysnTy5EmdOXNGvr6+GjBgQIX9AQAAANQA\nKlWmMy2pGj16dJX6xcTEaM6cOUpJSdErr7xi1vQAAAAAUCdqfaOK2267TePHj9e+ffu0efPm2p4e\nAAAAcG5GDb+cUJ3s/nfnnXfK1dVV27dvr4vpAQAAAMA0dfKcKjc3N7m7uyspKakupgcAAACcFrv/\nma9OKlXnz59XTk6O3Nx49jAAAACAhq3Wk6rz58/r/ffflySFhYXV9vQAAAAAYCrTSkWzZs2qsD0/\nP1+XLl1SUlKSDMOQm5ubfv/735s1PQAAAADUCdOSqkOHDlW5b+vWrfXUU0+pffv2Zk0PAAAAoCpY\nU2U605KqcePGVdju6uoqHx8ftWnTRq1btzZrWgAAAACoU6YlVUOGDDFrKAAAAABoMExLqlasWCFJ\nGjRokAIDA80aFgAAAICJ2FLdfKYlVV988YVcXFzYfAIAAACAUzEtqWrcuLEKCgrk4lInj74CAAAA\nUBVUqkxnWgbUvn17ZWZmKi0tzawhAQAAAKDeMy2puu+++2SxWLRs2TKzhgQAAABgNqOGX07ItKQq\nPDxcEydO1HfffaeZM2cqLi5Oubm5Zg0PAAAAAPWSaWuqxowZI0kqLCxUTEyMYmJiJEkeHh4VrrNa\ntGiRWSEAAAAAqAS7/5nPtKQqJyenzON5eXlmTQEAAAAA9Y5pSdXs2bPNGgoAAABATaFSZTrTkqqb\nbrrJrKEAAAAAoMGodlL1xhtvyM/PTy+//LKZ8QAAAACoQQ1pTdWlS5e0YsUKxcbGKiMjQ02bNlVk\nZKRGjBghX1/fao25e/duffDBB5KkCRMmaNCgQQ7HWe2k6ujRo2rSpInDAQAAAADAryUnJ2vq1KlK\nT09Xz5491apVK504cUIbN25UTEyMZsyYIT8/v+saMzU1VR9//LG8vLzK3ROiOky7/Q8AAABAA9BA\nKlULFixQenq6xo0bp6FDh9qPL1q0SBs2bNCyZcv09NNPV3k8wzD00Ucfyc/PT7169dKXX35pWqym\nPacKAAAAAMyQnJys2NhYBQUFafDgwSXaoqKi5OnpqT179lxXtWnTpk06cuSI/vSnP8nT09PUeEmq\nAAAAAGdi1PDLBPHx8ZKkiIiIUs+8bdSokTp16qTc3FwlJCRUabzExER99tlnGjp0qMLDw80JshiS\nKgAAAAD1yvnz5yVJLVq0KLM9JCREkpSUlFTpWIWFhfrggw8UGBioxx9/3Lwgi3FoTVVWVpY+/PDD\nap9vsVj0pz/9yZEQAAAAAFyH2tr979VXXy3z+KxZsyo9NysrS5Lk7e1dZrvteGZmZqVjrV69WqdO\nndKMGTPk4eFRaf/qcCipysv7/+zdeXjU5b3//9csySSTyb4vBEiIIBAWZUcqQkGFQ2sV43bqUenx\nV6u2lXN6rAtKF7VW62n7k9PqdVrltECJUHFDcQXZUUgCCYQdA4SQjezrLN8/MEOGhM0Zss3zcV1c\nTT/b3LeTyfV5z+u+70+L1q1b51UDKKoAAAAAXA779+/Xm2++qTlz5uiKK664bK/jVVFlNpsva+MA\nAAAA+FgXJVUXk0idS1sS1ZZYna1te0hIyDmv0TbsLzExUbfddts3bsvF8Kqostlsevrpp33VFgAA\nAABQUlKSpHPPmSopKZF07jlXktTU1OQ+/6677ur0mFdeeUWvvPKKZs2apXvuuecbt5fnVAEAAAD+\npBc8p2rYsGGSpLy8PDmdTo8VABsbG1VYWCiLxaKMjIxzXiMgIEDTpk3rdN/hw4d1+PBhDRkyRElJ\nSV6PvqOoAgAAANCjJCQkaOTIkcrLy9OaNWs8Hv6bnZ2t5uZmffvb31ZQUJAkyW636+TJkzKZTO6V\nAfitTCYAACAASURBVAMDA/XDH/6w0+tnZ2fr8OHDuvbaazV9+nSv20tRBQAAAPiRrlr9z1vz5s3T\nggUL9Nprr2nXrl1KSUnR/v37VVBQoMTERN1xxx3uYysrK/XII48oNjZWixYt6vK2UlQBAAAA6HES\nEhL03HPPKTs7W7m5ucrJyVFkZKRmzZqluXPnymazdXcT3SiqAAAAAH/SS5IqSYqJidGPfvSjCx4X\nFxen7Ozsi75uVlaWsrKyvGmah29cVC1fvtxnjQAAAACA3oqkCgAAAPAjvWVOVW9ivPAhAAAAAIBz\nIakCAAAA/AlJlc+RVAEAAACAFyiqAAAAAMALDP8DAAAA/AnD/3yOpAoAAAAAvEBSBQAAAPgRQ3c3\noA8iqQIAAAAAL5BUAQAAAP6EOVU+R1IFAAAAAF4gqQIAAAD8iIGkyudIqgAAAADACyRVAAAAgD8h\nqfI5kioAAAAA8AJJFQAAAOBPSKp8jqQKAAAAALxAUgUAAAD4EVb/8z2SKgAAAADwAkkVAAAA4E9I\nqnyOpAoAAAAAvEBSBQAAAPgR5lT5HkkVAAAAAHiBpAoAAADwJyRVPkdSBQAAAABeIKkCAAAA/Ahz\nqnyPpAoAAAAAvEBSBQAAAPgTkiqfI6kCAAAAAC+QVAEAAAD+hKTK50iqAAAAAMALJFUAAACAH2H1\nP98jqQIAAAAAL5BUAQAAAP6EpMrnSKoAAAAAwAsUVQAAAADgBYb/AQAAAH7E4GL8n6+RVAEAAACA\nF0iqAAAAAH9CUOVzJFUAAAAA4AWSKgAAAMCP8PBf3yOpAgAAAAAvkFQBAAAA/oSkyudIqgAAAADA\nCyRVAAAAgB9hTpXvkVQBAAAAgBdIqgAAAAB/QlLlcyRVAAAAAOAFkioAAADAjzCnyvdIqgAAAADA\nCyRVAAAAgD8hqfI5kioAAAAA8AJJFQAAAOBHmFPleyRVAAAAAOAFkioAAADAn7iIqnyNpAoAAAAA\nvEBSBfRQTa01OlC+QeX1h9XibJTFFKK40AwNir5GAaagi7pGSW2hTjUcVU1zqWqbS+VwtigxdKhG\nJM3p9Hiny6GjVTmqaTqp2uZS1TWXyyWnhsXfoJSIkeds5/GafNU2laqm+aQaW6skSdcMvF8hgZGd\nnlPVWKzSuv2qbT6pmqZStTjqZTHbNDX9QZ/35edPztfuPQXau7ew1/elJ74v6D5BpnhlRD2k2ODJ\nCjBFqNleppMNn2r/qT/J7qy56OvEBn9LA8L/VbbANAUaI9TsKFN1824drv4/VTXndTjeqAD1C7tF\nybbvyhqQIqPBoiZ7icobN+tQ9etqsp/ocE6gMUoDI+5RnHWKgs1Jcrpa1Wg/ruK6D1RUs1wOV0OH\nc0IDMpQWMU8RQSMUZIpTq7Na9a1fqagmWyfq16iz5csutS+SQYHGSE1KWtbr+9JV70tU0FilRdyr\nCMsImYxWNdlLVFL/oQ6cerXT49EzMafK9yiqgB6ooeWUthb9XS2OBsXZMhQSGKXqphMqOrVdFfWH\nNS71XxVoCr7gdQ5VbFZtc6lMhkAFBdhU31J53uMdzlYVln4iSQo0hchiDlGTvfa851Q3lehA+XpJ\nUnBAhMxGi+zO5vOec6J2t4pObZdBRtksMWpx1F+2vhSukmKiY/tEX6Se976ge1jN/TQx6e+ymKNV\nUv+J6lsPK9ySqYHh31ds8GRtLv6+Wp3VF7zO4KhHlB4xTy2OUzpZ/6lanKdkNacqPmSaEkJmKK/s\ncRXXves+3iCTxiX9RVFBV6mu5ZCK61bL6WpVuGWYBoTfpWTbHG0u/lfVtR5ynxNsTtKkpGWymKNV\n0bhNZQ0bZDQEKiZ4kq6M/g8l2/5Fm4rvlNN15vczznqtror/vVxyqbT+M5XYP1SgMVLxIdM1Ov5F\nRddMUH75Qq/7EhLQX2ZjiByuxl7fl654X1JDszQs5km55FBJ/cdqsp9UuGWo0iN+oNjgb2lL8d2y\nu+ou+HsH9EUUVUAPtPvkh2pxNGhI3LfVP/Jq9/bC0k/01akvtb/scw1LuP6C1xkcN01B5lBZAyJ1\nqvGovji67LzHm4wBuip5rsKC4mUx23SgfIMOVmw87znhQQka1+9OhVriZDZZtK1oqU41Hj3vOclh\nmUoOy5TNEiOjwaQ1e5+/bH35499/rrjYeN147e29vi898X1B9xgW86Qs5mgVlD+rr2qWurdfGfUz\nDYz4Nw2O+onyy3953msEmqKVFn6Pmu3lWn/sZrU4zxT3UUFjNSHpNV0R+aDHzXt8yHRFBV2l8obN\n2lZyv9onLBmRDyoj8gENjLhXu8oWuLenhd8rizla+yoX6UDVn9q1wKhxia8qJniCEkOu1/G6t917\nBkc9IqMhQFuK71Fl05fu7XtP/VFTkv+p1LC5OnDqz2pylHjVF7MxRHZnnT4/9t1e35fL/b5YTDG6\nMvq/5JJDm4u/r+rmfPcZ6RE/0OCon+qKqIe1u+I5oRcgqfI55lQBPUxDyylVNBxRcEC4UiOu8tg3\nKOYamQwBOlFTILuz5YLXirb2V0hglAwGw0W9ttFgUqwtXRaz7aLbGxQQpkhrP5lNlos+JywoXmFB\n8TIaTBd9zjftS1xs/EW/Rk/vS098X9D1rOZ+irVOVkPrMX1V41mQ7zu1SHZng5Js/yKT4fxpdrA5\nSQaDSVXNOz1u3CWpsukLtTrrFGiKOuu1UyRJpY2f6+y7spP1n0qSAo2ew0uDA74+p+Gzs1rgVFnD\n56fPMXmeYzWnqNVZ61GESFKLo0JVzTu/PudM27zpS6uzrs/05XK+L7HWKTIZg3Sy/lOPgkqSDlb9\nVS2OKqWEfk9Gw8UNTwf6GooqoIepbCiSJEVbB3S46TYbLYoITpbD1arqxuLuaB6AbhYVPE6SVN64\nSWffQDtcDTrVlCOz0aoIy4jzXqeh9Ss5XC0Kt2QqwBjhsS8y6GoFGG0qb9zisb2u9aAkKTZ4iiTP\nv09x1mslSRVnn9Py9TnWb53VAoNig6fI5XKoonFrh9cJMIYq0jLaY3ugMUoRlkw12UvdbfG2L2aj\nrc/05XK+LxZTzOn22Y+pI6ca7cVf/95ldrIfPY3BeXn/+SOG/wE9TNv8mpDAqE73WwOjVNFwRPUt\npxQdMqALWwagJ7AFDJAk1bd+1en++tavFKvJCgkYoIqmrZ0eI0mtzhrtrXhJV0b/l77V7y2drP9U\nrY5qWQNSFGe9TmUNm5Rf9guPc0ob1qmk/iMlhMzQlJQ3Vd64RS5Xq8IsQxUVdJWOVC/pkJ4dqvqr\n4qzf0uCoHys6eJxqmvfIYAhQbPAkWUzR2lX2tGpaCj3O2V3xvMYkLNK4xP/VyYbP1Nh6TAGmCMWH\nTJPdWavc0kc95vp80760OmoUYArrE3253O9Li+OUJMlqTu74yySDgs1JkiRbwEBVNn3RyTFA30ZR\nBfQwbYsJmI2dD9sK+Hq73dnUZW0C0HOcTlbahq11ZP96e4Ap9ILXOlLzdzXai5UZ+yulht3q3l7f\n+pWO167qMPxMknacfEQZkT9SesT9Cg0c5N5e3rBZxXXvySWHx/EtzkptKr5LI2J/pYSQbysmeIIk\nyeVy6mjtig6piySdatqhzcfv0uj43ynJdoN7e6uzTsdqV6m2ZZ9P+tJgPyqLK1YhAQN6fV8u9/tS\n1rhRTler4kOmKzxwmKpbCtz70sLvUaDpdKoWYArr0Db0QMyp8jmKqh4qOztbK1as0NNPP61hw4Z1\nd3O+kUWLFmndunV6+eWXFRcX193NAQCcJS38Xl0R9RN9Vb1ER2qWqdlRLlvAQA2O+qlGxf9WoVVD\ntLfyJffxRkOgRsY+q1jrFBWUP6OTDZ/K6WxSZNBoDY15TBOSFmvHyfke83SCzUm6Ov5lmYwWfXHi\nhzrVlCOjMUjx1mm6MvpnirdO06biu9RoP+4+JyZ4okbFvaDq5gLllT6uutbDsphiNCDsDg2O+oli\nrd/S1uJ7PAqFb9KXYHOKAow25Zf/utf35XK/L032E9p/6k8aHPVjTUj+m07Wf6Qme6nCLFcqOmic\napr3KswyWC6Xn479gt+jqOoiWVlZFzymuwuoBx88/SyaRYsWdVsbcCahOtfy163uJIvJwIA/cidR\nxs4XLnEnWY7zL7sfFTRWQ6L/QyX1H2tP5Qvu7TUte7T95E90bb93lRb+byqqyVbj1/No0iN+oETb\nDSoof05Ha99wn1PWuEE7Ts7XlJSVGhr9c4+b9xGxzyjMcoXWH7v5TCrjqNfR2jdkMlg0NObnyoh8\nQDvLnvy6X2EaFfeiHK5GbT/5Ezldp1P5Rvsx7al8QcEBKUoIma4k27/oeN1bXvUl0BSuRvuJPtGX\ny/2+SNLBqldV33pIA8L+VXHWqTLIqJqWvfqy5EHFWqcozDJYLY7zPyICPQPPqfI9iqouNnfu3HPu\ni42Ndf98ww03aPLkyYqJiemKZl0Wd955p2666SZFRXU+Nwida5tLda5nFzW451x1/gBXAH1bXesR\nSVJIQP9O97dtr//6uHM5s4DBtg77nK4mVTfnKzjk2wq3DHHfvLedU9nJObUte9XiqJY1IFkBxnC1\nOqtlMlgVHTxWLY6qToe5VTSdvk64Zah7W2TQaAWawlVSv81dhHic07hNCSHTFW4Z6i5EvOmL3dnx\neWy9tS+X831pU1L/sUrqP+6wPT1iniSp6qyVAQF/QVHVxS4msZKksLAwhYX17nHJkZGRiozkxv9S\nRVlTJUkVDUfkcrk8VgC0O5tV1XhcJkOAwoOTuquJALpR241zTPAknV7p7cxXziaDVZFBo2V3NriX\n6z4XoyFAUsclwNu0LcHtdLV2fk6r5/FGBchstHqc03a82Rgig8xyyX7xr2E8R7tMF2jXJfbF0Mmt\nUG/ty+V8X87Hau6nyKDRqmnep7rWAxd1DtDXsKR6D5Wdna2srCwVFBR4bM/KytLChQtVU1OjV155\nRffff7/uvPNOzZ8/X599dvazJiSXy6W1a9fqySef1Lx583TXXXfpgQce0DPPPKNNmzZJkgoKCpSV\nlaWysjKVlZUpKyvL/e/soYDHjx/XokWL9MADD+iOO+7Qv//7v+sPf/iDios7Lu+9aNEiZWVlqbS0\n1L2ttLTUfd3S0lL9/ve/d7fr5z//ubZv3+6L/3y9mjUwUtHWAWpsrVZR1Q6PfQfKN8jhalVi2DCZ\njYGSJKfLobrmCjW0nOqO5gLoYg32oypr2ChrQIr6h93hse+KyAdlNlpVXPeuHK5GSaeLhpCAgbKa\n+3kcW9l0+u9Lauitspg8573GBl+jyKDRcjibdKop98w5jafPSY/4dxkV4HFORuSDMhoCVNW0Sw5X\ngySp1Vmt2paDMhoCNCjyhx7HGw2BGhT5/0mSytst3X2qKU9OV6sig0Z/XTieEWRKcC/c0H65b2/6\nEmSO6TN9uZzviySZDSE6W4AxXCPjfiODweQxzws9nMt1ef/5IZKqXqi+vl4LFiyQ2WzWhAkT1Nra\nqi1btuhPf/qTDAaDpk6d6j522bJlWrVqleLi4jRx4kRZrVZVVVXp4MGD2rx5syZNmqTY2FjNnTtX\nq1evliTNmjXLff6AAQPcP+fm5urFF1+Uw+HQ1VdfrYSEBFVUVGjbtm3asWOHnn76aaWlpV1UH8rL\ny/X4448rPj5eU6ZMUV1dnTZv3qzf/va3WrBggYYPH+6T/1a91dD4mdpa9HcVln6syoavFBIYreqm\nYlU2FMkaEKWM2DPPFWm212njkf9VkDlM16Y/4HGdk7X7VFq3/+vjTg9xqWoq1q4T70mSAk3BGhw3\nzeOcQxVbVN9SIUmqbT5dEB+v3qVTjaeHmUQGpyglYqTHOW3Xk84MW9xXttZd+KWEj1SkNcV9TF1z\nhQ5Xeq4sZXc0e1xncOx1CjRbve7Lo0+cfhZL27m9uS898X1B9ygo/7UmJv1dw2IeV3TweNW3HFZ4\nUKZigserruWw9lb+wX1skDlO1/Z7Rw2tx7X26PXu7SX1H6q8YbNirBP1rX5v62T9J18viJCmOOu1\nMhiM2lvxe7U6q93nHKh6VXEhU78+553TK8J9vSBCRNAIOZyN2l3xG4+27q54TmMS/kcZkT9UTPBE\nVTXlymgMUmzwNbIGJKu+9SsdqvqL+/hmR5kOnHpFV0Q9pLEJf1Jpwzr34g4JId+W2RiikvqPVda4\n3uu+9Au7RWajrU/05XK/L5I0KPIBxVon61RTnloclQoyxynOep0CjKHaU/FblTVuOMdvLND3GVwu\nPy0nu1jbsL9zzakKDAzUTTfd5P7/51r9r+0606ZN0/333y+j8XTYeOzYMf3nf/6nEhMT9d///d/u\n4++77z4FBgbqD3/4gywWzyW6a2pqPIYYnm+hirq6Oj388MMyGo36xS9+oZSUMzdiRUVFeuKJJ5SU\nlKTnn3/evb2z1f9KS0v10EMPSZJuvfVW3XrrmaVic3Nz9eyzz2r06NF67LHHOv3v1N6jjz7a6fa2\nNuwv6OwBhb3HiRPF+sOiF7V+wzpVVZ1SbGycZky/QQ898FOFh595IOSx40c17fpJSk5K0Wcfbva4\nxh8XvaSX//TfZ1/arbNz/vWeW7Xty47LArf53nfn6vlnPK95xfB+5zj6tN/8+ne6+aYzQ1+3btus\n7993/qGwn67ZpJTkM9elLz2zL71V3KDqCx/UwxlkVpA5TmajTQaZ5JJdrY5aNTlKJTnbHRegMMsV\ncrpaVNuyv8N1Ak1RCjCGy2SwSDLKJYcczka1OCpkd3Wcb2SQSRZTjMzGUPcwMpfssjvr1ewol9PV\n0uEco8Hy9TlW93A7p6tFrc5aNTvKPdrbxmwMVaApUiZDsAwySXLK4WpWq6NKLc7Ok/lL7YstIE0G\ng1kul7PX96Ur3hez0SaLKVpGQ5AM7jY1qNlR4U5Ge7twS+9ccflSXXPLi5f1+htW/udlvX5PRFLV\nxVasWNHpdqvV6lFUnY/FYtHdd9/tLqgkKSUlRYMHD9aePXvU1NSkoKAzK8OZTCaPY9tcypytzz//\nXPX19brvvvs8CipJSk1N1fTp07V69WodO3asw/7OxMbG6pZbbvHYNmrUKMXExOjAAcZjS1JiYpJ+\n8+sLD6VISe6nfflHO9334wfn68cPzr+k1/37629c+KCznOv1z2X8uImXfM437Uu/tNMF/dFDpRc4\n+rSe3JdL1RV9Qfdxya5Ge8eh1x2Pa1V1c8E597c4Ki9pxTaXHGpynJQcJy/6HKer2WOZ8Ythd9bK\n7jz/CoZnu9S+SJLLZVdd66GLPr6n9qUr3he7s869+iQATxRVXSw7O9vrayQkJMhq7Tj8Jjo6WtLp\nVKmtqLrmmmv0wQcfaP78+Zo4caKGDh2qK664otPzz2ffvtOrA3311Ved9uHEiROSdNFFVf/+/Tst\n9KKjo92vdSHtU7HOPHzLyxd1HfRt///K08kovw9o78er3+3uJqAHmJy8XJK08fht3dwS9BSz0vxk\n9ULGqfkcRVUvFBLScaKodDqRkiSn80xcf8899yg+Pl5r167VqlWrtGrVKplMJo0ePVp33323EhIS\nLuo1a2tPf8P2ySefnPe4pqaOS8Z25nx9YEQqAAAAehOKqj7OaDRq9uzZmj17tqqrq1VYWKiNGzdq\ny5YtOnr0qF566SUFBARc8DptydYLL7yg/v07fzYKAAAAej4e/ut7LKnuR8LDwzV+/HjNnz9fw4cP\n18mTJ3X06Jn5E0aj0SPlai8jI0OStGfPni5pKwAAANBbUFT1Ya2trSosLOyw3W63q67u9ETTwMBA\n93abzaaamhq1tHRcIei6665TSEiIVqxY0elCEk6ns8MztQAAANAD8Zwqn2P4Xxc730IV48aN83gu\nlLdaWlr01FNPKSEhQWlpaYqJiVFra6t27typ48ePa8yYMR6LSmRmZurgwYN65plndOWVVyogIED9\n+/fXmDFjFBoaqvnz5+vFF1/UE088oeHDh6tfv9PLKldUVGjfvn2qq6vTkiVLfNZ+AAAAoDegqOpi\n51pSXZLi4uJ8WlRZLBbdddddKigo0N69e/XFF18oKChICQkJ+sEPfqBp0zwfLnrzzTervr5e27dv\n1969e+V0OnXttddqzJgxkk4XXS+88ILeeecd5eXlqbCwUGazWZGRkRo+fLjGjx/vs7YDAADg8mBO\nle/x8F/0WTcM+Xl3NwE9AEuqozMsqQ6JJdXRkb8sqf6t775wWa//+Vs/u6zX74lIqgAAAAB/QqTi\ncyxUAQAAAABeIKkCAAAA/AhzqnyPpAoAAAAAvEBSBQAAAPgTJ1GVr5FUAQAAAIAXSKoAAAAAf0JQ\n5XMkVQAAAADgBZIqAAAAwI+w+p/vkVQBAAAAgBdIqgAAAAB/4iKq8jWKKgAAAAA9UkVFhZYvX668\nvDzV1tYqMjJSY8eO1dy5c2Wz2S54fm1trbZt26YdO3aoqKhIlZWVMpvNSk1N1XXXXaepU6fKaPR+\n8B5FFQAAAOBHesucqpKSEi1YsEDV1dUaM2aMkpOTdeDAAa1evVq5ubn61a9+pdDQ0PNeY/Pmzfrf\n//1fRUZGatiwYYqJiVFVVZW2bdumP//5z8rJydH8+fNlMBi8aitFFQAAAIAe5y9/+Yuqq6t17733\n6sYbb3RvX7x4sd577z0tW7ZM999//3mvkZSUpP/6r//SVVdd5ZFI3XnnnXrssce0detWbd26VRMm\nTPCqrSxUAQAAAPgT12X+5wMlJSXKy8tTbGysrr/+eo99WVlZslgsWr9+vZqams57neHDh2vMmDEd\nhvhFRERoxowZkqTdu3d73V6KKgAAAAA9SkFBgSRp5MiRHQqi4OBgDRkyRM3Nzdq/f/83fg2z+fSg\nPV/MqaKoAgAAAPyIweW6rP98obi4WJKUmJjY6f6EhARJ0okTJ77R9R0Oh9atWydJGjVq1De6RnvM\nqQIAAADgc48++min259//vkLntvQ0CBJslqtne5v215fX/+N2rZkyRIdPXpUo0eP9klRRVIFAAAA\nwG+sXr1a7777rpKTk/Xwww/75JokVQAAAIA/cXbNy1xMInUubUlUW2J1trbtISEhl3TdDz74QK+/\n/rpSUlL01FNPXdSzri4GRRUAAACAHiUpKUnSuedMlZSUSDr3nKvOvPfee1q8eLH69eunp556SuHh\n4d439GsUVQAAAIAf8dViEpfTsGHDJEl5eXlyOp0eK/Q1NjaqsLBQFotFGRkZF3W9VatWaenSpRow\nYICefPJJhYWF+bS9zKkCAAAA0KMkJCRo5MiRKisr05o1azz2ZWdnq7m5WVOmTFFQUJAkyW636/jx\n4+4Eq70VK1Zo6dKlSktL01NPPeXzgkoiqQIAAAD8S88PqiRJ8+bN04IFC/Taa69p165dSklJ0f79\n+1VQUKDExETdcccd7mMrKyv1yCOPKDY2VosWLXJvX7t2rbKzs2U0GjVkyBCtXr26w+vExcVp6tSp\nXrWVogoAAABAj5OQkKDnnntO2dnZys3NVU5OjiIjIzVr1izNnTv3ohaZKC0tlSQ5nc5OCypJGjp0\nKEUVAAAAgEvQC+ZUtYmJidGPfvSjCx4XFxen7OzsDtuzsrKUlZV1OZrmgTlVAAAAAOAFkioAAADA\njxh6T1DVa5BUAQAAAIAXSKoAAAAAf9KL5lT1FiRVAAAAAOAFkioAAADAjxic3d2CvoekCgAAAAC8\nQFIFAAAA+BPmVPkcSRUAAAAAeIGkCgAAAPAnBFU+R1IFAAAAAF4gqQIAAAD8iIE5VT5HUgUAAAAA\nXiCpAgAAAPwJSZXPkVQBAAAAgBdIqgAAAAB/4uzuBvQ9JFUAAAAA4AWSKgAAAMCPsPqf75FUAQAA\nAIAXSKoAAAAAf0JS5XMkVQAAAADgBZIqAAAAwJ+QVPkcSRUAAAAAeIGiCgAAAAC8wPA/AAAAwJ/w\n8F+fI6kCAAAAAC+QVAEAAAB+hIf/+h5JFQAAAAB4gaQKAAAA8CckVT5HUgUAAAAAXiCpAgAAAPwJ\nSZXPkVQBAAAAgBdIqgAAAAB/QlLlcyRVAAAAAOAFkioAAADAnzi7uwF9D0kVAAAAAHiBpAoAAADw\nIwbmVPkcSRUAAAAAeIGkCgAAAPAnJFU+R1IFAAAAAF4gqQIAAAD8iZOkytdIqgAAAADACyRVAAAA\ngD9hTpXPkVQBAAAAgBdIqgAAAAB/QlLlcyRVAAAAAOAFkioAAADAn5BU+RxJFQAAAAB4gaQKAAAA\n8Cc8p8rnSKoAAAAAwAskVQAAAIA/cTm7uwV9DkkVAAAAAHiBpAoAAADwJ6z+53MkVQAAAADgBYoq\nAAAAAPACw/8AAAAAf8KS6j5HUgUAAAAAXiCpAgAAAPwJC1X4HEkVAAAAAHiBpAoAAADwJyRVPkdS\nBQAAAABeIKkCAAAA/AlJlc+RVAEAAACAF0iqAAAAAH/idHZ3C/ockioAAAAA8AJJFQAAAOBPmFPl\ncyRVAAAAAOAFkioAAADAn5BU+RxJFQAAAAB4gaQKAAAA8CdOkipfI6kCAAAAAC+QVAEAAAB+xOXi\nOVW+RlIFAAAAAF4gqQIAAAD8CXOqfI6kCgAAAAC8QFIFAAAA+BOeU+VzJFUAAAAA4AWSKgAAAMCf\nOFn9z9dIqgAAAADACyRVAAAAgD9hTpXPkVQBAAAAgBdIqgAAAAA/4mJOlc+RVAEAAACAF0iqAAAA\nAH/CnCqfI6kCAAAAAC9QVAEAAACAFxj+BwAAAPgTJ8P/fI2kCgAAAAC8QFIFAAAA+BMXS6r7GkkV\nAAAAAHiBpAoAAADwIy7mVPkcRRUAAACAHqmiokLLly9XXl6eamtrFRkZqbFjx2ru3Lmy2Wxdfp1z\noagCAAAA/EkvmVNVUlKiBQsWqLq6WmPGjFFycrIOHDig1atXKzc3V7/61a8UGhraZdc5H4oqAAAA\nAD3OX/7yF1VXV+vee+/VjTfe6N6+ePFivffee1q2bJnuv//+LrvO+bBQBQAAAOBHXE7XZf3nAKFm\nKQAAIABJREFUCyUlJcrLy1NsbKyuv/56j31ZWVmyWCxav369mpqauuQ6F0JRBQAAAKBHKSgokCSN\nHDlSRqNnyRIcHKwhQ4aoublZ+/fv75LrXAjD/9BnfVD4m+5uAnoQfh/gid8HnDErLb+7mwB0qY8c\ny7vkdR599NFOtz///PMXPLe4uFiSlJiY2On+hIQE5eXl6cSJE8rMzLzs17kQkioAAAAAPUpDQ4Mk\nyWq1drq/bXt9fX2XXOdCSKoA9Glt35JdzLdiAPwLfx+Ay8ufPlskVQAAAAB6lLYEqS1pOlvb9pCQ\nkC65zoVQVAEAAADoUZKSkiRJJ06c6HR/SUmJpHPPlfL1dS6EogoAAABAjzJs2DBJUl5enpxOz4cV\nNzY2qrCwUBaLRRkZGV1ynQuhqAIAAADQoyQkJGjkyJEqKyvTmjVrPPZlZ2erublZU6ZMUVBQkCTJ\nbrfr+PHj7uTpm17nm2KhCgAAAAA9zrx587RgwQK99tpr2rVrl1JSUrR//34VFBQoMTFRd9xxh/vY\nyspKPfLII4qNjdWiRYu+8XW+KYPL5fLNY48BAAAAwIfKy8uVnZ2t3Nxc1dbWKjIyUuPGjdPcuXNl\ns9ncx5WWluqhhx7qtKi6lOt8UxRVAAAAAOAF5lQBAAAAgBcoqgAAAADACxRVAAAAAOAFiioAAAAA\n8AJFFQAAAAB4gaIKAAAAALxAUQUAAAAAXqCoAgAAAAAvUFQBAAAAgBcoqgAAAADACxRVANAFnE5n\ndzcBAABcJububgAA9HVOp1NG4+nvsPLy8lRVVaXq6mpdc801CgsLk9nMn2KgL2j/Wb+UfQB6P4PL\n5XJ1dyMAoK9yuVwyGAySpH/+859asWKFHA6HJCk+Pl6zZs3SpEmTFBYW1p3NBOCl9kXTpk2btG/f\nPjU2NmrgwIG65pprZLPZKKyAPoyiCgC6wIcffqi//vWvGjFihCZNmqTi4mJt375dZWVl+s53vqOZ\nM2dSWAF9wIoVK/TGG294bEtLS9Ojjz6qiIgICiugj6KoAoDLoP2Nk8Ph0AsvvCCTyaS77rpLSUlJ\nstvtKioq0uuvv65Dhw7ppptuorACeqH2afSGDRv06quvaty4cfrWt76lmJgYLV++XFu2bFFsbKx+\n/etfU1gBfZRp4cKFC7u7EQDQVzidThkMBvdN1ieffKLW1lZt3LhR3/72tzVs2DA5nU6ZTCZFRkYq\nPT1dR44c0ebNmxUQEKCUlBRZLJZu7gWAi3F2cbRjxw7V1dXp+9//vjIyMhQaGqrx48erublZubm5\n2rp1qyZNmqTg4GD33woAfQNFFQB4qaioSF988YXS0tI8bpL27t2rF198UVu3blVra6tmzJihmJgY\nSXIfFx4e7lFYWSwWJSYmKigoqFv6AuDc2qdSkjzmS65du1a5ubkaNWqUJk+eLEmy2+0ymUzKzMxU\nS0uLcnJyKKyAPoqiCgC80NTUpCeffFIbN25Uenq6EhMT3fvCwsJkMplUUlKiyspKDRgwQIMGDepw\nE9VWWBUVFWnt2rUKDw9XRkYGN1tAD3Lo0CEtXbpUI0aM8Fixs6mpSX/961+Vn58vk8mkkSNHKiMj\nQ3a7XWaz2Z1mDR8+3F1Yffnllxo/frysVms39giAL1FUAYAXzGazUlJSZLfbNW3aNI+EyWQyKSMj\nQ83Nzfrqq6906NAhDR06VJGRkR2uEx4ergEDBqi8vFw33nijwsPDu7IbAM7DbrcrOztbn3/+uYKD\ngzVkyBD3PrPZrHHjxunw4cMqKipSbW2txo0bp+DgYLlcLhmNRo/Cym63a/v27crPz9f06dMliS9Q\ngD6AogoAvJSQkKCxY8fKarXqvffe0969ezV48GBJpwurQYMGSTr9jKq8vDwNHz6806IpIiJCEydO\nVERERJe2H8D5GY1GJSYmKjY2VlOmTFFwcLAcDoeMRqNcLpesVqsyMzN15MgR7du3Ty0tLRo0aJAs\nFkuHwmro0KGSpFtuuUUREREUVEAfQVEFAD5gNBpVXl6u5557TgcOHJDValV6erqk04VVenq6TCaT\ncnJylJOTc87CihXBgJ4pLCxMGRkZslqtWrFihd555x2NHTtWAQEBHoXV/v37tW3bNjkcDqWlpXUo\nrEwmk4YNG0YaDfQxFFUA4CNWq1VDhw7VF198ofz8fAUFBZ23sMrMzGQJdaAXMRgMqq+v14cffqic\nnByVlZVp1KhRMpvN7sJq1KhR2r9/v7Zs2eJRWLGMOtC3UVQBgJfaVvByOp2Kj49Xenq6Nm3apN27\nd5+zsNq5c6fWrVunMWPGUFgBvUhgYKDS09PV0NCgDRs2qKSkRKNHjz5nYeVyuTRgwABW9AT6OIoq\nALhEZy+D3PZz2//GxsYqIyPjvIVVS0uLioqKNG3aNNlstq7vBIBL1rakus1mU2pqqurr67Vx40ad\nOHFCo0eP9hgKOHr0aB08eFAbN26U2WzW0KFDmT8F9GEUVQBwCdoP4cnPz9fWrVv1ySefqKysTC6X\nS9HR0ZIuXFgNHjxY06dPdx8PoGc5+8uTlpYWmUwm97b2hdWmTZvciVVbYRUcHKzMzEwdO3ZMc+bM\nYQ4V0McZXC6Xq7sbAQC9QfuC6p///Kfeeusttba2KiAgQE1NTbLZbJoxY4Zuv/129zm7d+/W7373\nOxkMBt12222aMWNGdzUfwEVq/1nfuHGjdu3apX379ikyMlJDhw7V9ddfL6vVKqPRqJKSEq1YsULr\n16/XhAkT9MADDygoKMidajGXCvAPJFUAcJHavqF+9913tXTpUo0bN0733Xef7rrrLl199dXatm2b\ncnJy1NraqszMTLlcLsXFxSkjI0Pbtm3Tpk2bFB0drYEDB3ZzTwCcS9tKfZL0xhtv6G9/+5vKy8sV\nExOj8vJybdu2TUePHlV4eLhiYmIUFham/v37q66uTps2bVJZWZlGjhypgIAASTyDCvAX5gsfAgBo\nc/jwYb3//vsaPny4vve97yk1NVVOp9M9VCgqKkozZ86UdOZmaujQoXr44Yf16quvejw0FEDP0/a5\n/fDDD7VixQpNnTpVM2fOVHp6ukcqZbVaNWTIEJlMJsXHx2vu3LkyGo1at26dzGazHnjgAQoqwI9Q\nVAHAJSgpKVFFRYXuuusupaamSpK+/PJLLVmyRHa7Xc8884xiYmLkcDhUVVXlnjM1fPhwvfjiiwoM\nDOzO5gO4CDU1Nfr000+Vlpam2bNnKzU1VS6XSydOnNC+ffsUFham22+/XYGBge5hfgkJCfre974n\ns9msG2+8kYIK8DMM8gWAc3A6nR1+PnjwoFwul7ug2rJli5YuXaqGhgY988wziouLkyQ1NTXpjTfe\n0MGDB93XoKACeoeqqiodPnxYEydOdKfRX3zxhRYvXqzGxkb3Z93pdOrkyZPu8xITE3XfffepX79+\n3dh6AN2BogoA5FlASZ7zKhoaGtw/txVT+/bt0549e/SPf/xD9fX1HgWVJC1fvlybNm2SyWTqoh4A\n+CbO/uxLp78UkeR+ttT27du1dOnSDp91o9GoX/7yl/rwww/d55rNDAIC/BGffACQ3EXT2rVrNXjw\nYCUmJkqSXn/9de3fv1+PP/64QkJClJCQIElasmSJgoKC1NLSomeffVaxsbHua61fv145OTm66qqr\nFB8f3/WdAdCps1ficzgc7i8+8vPzNWjQIAUFBSkkJETS6S9PbDabli9f3iGNlqSVK1eqrq6O5dIB\nkFQBQJt3331Xf/rTn/Txxx+rtbVV2dnZev/995WYmKiWlhZJ0hVXXKFbb71VdXV1Ki8v1z333ONR\nUG3atElvvfWWJOn2229XcHBwt/QFQEdtBdX//M//aOvWre6CaunSpfrjH/+o3bt3y+l0Kjk5WRMn\nTtSGDRv02muveQz5a7NlyxatX79egwcP1pVXXtkt/QHQc5BUAcDXhg4dqsmTJ2v16tUqLCzUgQMH\ndMMNN2jOnDmKjIx0f8t9ww03qLa2Vh988IGWLl2qY8eOKTo6WgUFBdq5c6eMRqMWLFjgTrUA9Bw7\nduzQunXrlJOTo+joaOXn5+utt97S9OnT1b9/f3fhNWnSJB0+fFglJSXKysryKKjWrl2rt99+W42N\njbr33nsVFhbWXd0B0EPw8F8AaKe6uloLFy5UcXGx+vfvr/vuu8+9DHr7oUONjY1as2aNVq1apZaW\nFjkcDkVGRmrIkCG67bbb3MMHAfQ8H3zwgRYvXiyz2ayWlhbNmTNHM2fOVFxcnHs1P0n66KOPtGrV\nKlVUVCgjI0PJyck6ceKEioqKZLVa9eijj7rnWQLwbyRVACC5b6Ty8vJUXFysuLg4ffXVV9q+fbti\nYmIUExMjo9HoPi44OFg33XSTxowZo6amJlVWViotLU02m809uR1Az9I+bd68ebP27dungIAApaSk\nuJMol8vlXqhmxowZio2NdadbRUVFiomJ0dSpU3XjjTd6pFcA/BtJFQC/dvbE9ZKSEu3cuVPx8fFa\nt26dNm7cqFmzZmn27NmKiYnp9BwAvYfT6VRJSYmefvppRUdH6/DhwwoLC9ODDz6oUaNGSfIsrNpU\nV1fL5XK5h/rxNwBAe6aFCxcu7O5GAEB3aF8cHTx4UKdOnVJqaqrS09OVkJCgpKQk1dbWau3atZKk\n5ORkWa1W99CggwcPymQykUwBPZzT6XR/btsKo5EjR7q/LNm8ebPy8vKUmpqqhIQEGQwGj2GATqdT\nwcHBslgs7r8ZPNwXQHsUVQD8UvuCqm1+xSeffKJx48bJarXKaDQqPDxcycnJqq2t1aeffipJSklJ\nkdVqVX5+vl5++WXt2bNHkydP5ltroIdq/1nfuXOnvvjiC1mtVqWmpiogIEDp6ekKCQnRtm3bOhRW\nklRYWKjc3FylpKS4n0FFQQXgbMypAuB32g/rWbFihd58802NGjVK11xzjZKSkiSduRHr16+fbr75\nZknS+++/r4qKCkVFRamgoED19fW64447eMAv0EO1L6jeffddvfPOOzIajYqNjVW/fv3c+2fNmiVJ\nWrx4sV5++WX9+Mc/1ogRI5Sbm6ulS5eqtbVVY8eOlcVi6c7uAOjBmFMFwG99/PHH+stf/qKpU6dq\n9uzZSklJOeexx44d0wcffKCPPvpIRqNRCQkJmj9/vvr169eFLQZwsdoP31u5cqWys7M1YcIEzZw5\nU8OGDXMf1/4BwKtXr9bf/vY3OZ1OjRgxQkePHlVTU5MWLlyoAQMGdEc3APQSFFUA/FJNTY2ef/55\nSdKPfvQjJScnu/ft2rVLR44ckdlsVlpamgYPHuzeV1hYKLvdrpSUFEVERHR5uwFcmg0bNuiVV17R\nlClTNGfOnA6PO7Db7e5hfdLpZ1CtWbNGdXV1ioqK0v333+/x9wEAOsPwPwB+qaamRgcOHNCMGTPc\nN0xFRUX6+OOPtWbNGvdxCQkJmjdvnkaMGCFJ7mdWAejZXC6X7Ha7tm/frqCgIM2YMcOjoFq/fr3y\n8vJUUlKi6dOna/z48bJarZo6daoyMzNlNBoVEBAgm83Wjb0A0FtQVAHwSzabTWFhYTp69Ki2b9+u\n/fv3a+vWrSorK9PMmTM1ePBgFRcXa+XKldq7d6+7qALQOxgMBjkcDh07dkxRUVEaOHCgJGnPnj36\n5JNPtH79elmtVjU0NGj//v1qaWnR9ddfL0mKjo7uzqYD6IUoqgD0ae3nVbT97HA4ZLVaNWPGDK1e\nvVq//e1vZTQalZycrAULFmjgwIEKDAxUSUmJVq5cqbKysm7uBYBvwuVyKTQ0VAUFBXr11VfV1NSk\nXbt2qaWlRVlZWRo9erQqKir0+9//Xu+9954mTZokm83G6n4ALhlFFYA+q/3KXw6HQy0tLQoODpbJ\nZJLJZNL111+vkSNHqrCwUP3799egQYM8hvrk5OQoKChIQ4cO7a4uALgI7b88aS84OFjz5s3Tb3/7\nW3366acKCQlRenq67r77bvfCNGlpaYqNjVVUVJRCQ0O7uukA+giKKgB9UvuC6uOPP9bWrVt1/Phx\njRgxQuPHj1dmZqbCw8MVHh7usRBFm23btunTTz9VQkICQ/+AHqz9Z720tFR2u10mk0nx8fGSTj+0\ne+HChSouLlZoaKji4uI8Htj9+eefq7KyUuPGjXM/JJikCsClYvU/AH1a21LKoaGhCggIUFVVlSIi\nIjR79mzdcMMNMpvNHjdlkvTOO+/o448/VkNDg5566imWTQd6qLOfQ7VmzRpVVlbKZrNp/Pjxuu++\n+857/pYtW/Tmm2+qqalJTzzxhOLi4rqi2QD6IIoqAH1K+2FAe/bs0YsvvqgxY8Zozpw5CgsLU2Fh\noV5//XXV19frlltu0axZs2Q2m2W323Xs2DEtWbJE+fn5SktL67DUOoCeadWqVVq2bJmSkpKUnp6u\n3bt3q6KiQqNGjdLDDz/cYQW/2tpavffee1q/fr1aW1u1YMECvjwB4BWG/wHo9dq+rW5fUDU3N+vo\n0aMKCgrSrFmz3PMnxo0bp5iYGP3ud7/TypUrJcldWAUFBWnQoEEaPXq0Jk6cqMjIyG7rE4BzO3vI\n3yeffKJrr71W3/nOd5SSkqKTJ09q5cqVWrdunf7whz/oJz/5ibuwqq+v13PPPaeDBw9q1KhR+rd/\n+zclJSV1Z3cA9AGmhQsXLuzuRgDAN1FfX6/AwEAZDAaPgmrVqlVaunSpWltblZSUpOnTp7vnSkhS\nZGSkhg4dqi+//FJ5eXkym80aNGiQwsLCNGjQIF1xxRWyWq3d2TUA59H2WT5w4IBaWlq0ZcsW3Xnn\nnRo4cKCcTqdCQ0OVnp6ulpYWbd68WUeOHNHo0aNlsVgUGBiotLQ0DRkyRHPmzFFMTEw39wZAX0BR\nBaBXOnTokH71q1/JYrEoLS3NfZPV1NSk7du3a8eOHTp8+LCCg4M1adIkmc2ewXz7wio/P1+tra0a\nPHiwAgMDPeZXAehelZWVcjgcCgwM9Ni+evVqvfTSSzp+/Lgk6fbbb/f48sRqtWrAgAEehdVVV12l\nwMBARUZGqn///rJYLF3eHwB9E3cOAHqlkydPqqyszF0QtQkKCtKcOXP03e9+VxERESorK9OBAwfU\n2fTRgQMH6mc/+5kcDoc+++wzNTY2dmUXAFxAaWmpfvKTn+i1117r8PkcOXKkrFarCgsL1djYqObm\nZo9hwC6XS1FRUbr55ps1Y8YM7dy5U88995zq6uq6qTcA+jKSKgC9Ur9+/TRs2DBNnjxZNptNx44d\nU1hYmKTTz6ZJTEyUy+XSrl27dOLECWVkZLj3txcREaGrr75a06dPV3R0dFd3A8B5lJWV6eDBg3K5\nXJo4caICAgIknZ5TFR4ergkTJmjLli2qqKhQQ0ODRo8eLYPB4DHP0mq1auDAgaqqqlJhYaGmT5/O\n8F4APsfqfwB6hbOXPW9vxYoVWrlypR566CFNnjzZvb2qqkpr1qzR22+/rcGDB+u+++5zL1gBoGc6\n+0G+JSUlCg0NVUhIiHbt2qX+/fsrLCzM/Tfh5MmTWrBggaqrqzV37lzdeuutkjouYFNVVSXp9Bcp\nAOBrJFUAerz2BdWf//xnSfJYrau0tFRffPGF9u3bp6ioKPfSyEFBQUpJSZHZbNamTZt09OhRpaen\nd5pYAeh+7T/rDQ0NCggIkM1mU2BgoLZu3arnn39edrtdgwYNksVicS9KMXbsWG3atEk5OTmSpGHD\nhnVIrIKDgz0e+gsAvkRRBaBHa3+T9cwzz2jHjh0aPny4+vXr594+YMAAJScna+3atdqzZ49iYmLO\nWVgVFxdrwIABCg8P77Y+Aejo7M96XV2d0tPTZTKZJJ1ehKampkYbNmyQ3W5XWlqagoKCLrqwAoDL\niaIKQI/V/mbo2Wef1Z49e3T77bfrmmuuca/a1Ta0p1+/fkpOTtbnn3+uwsJCRUdHdyisAgMD9dln\nn6myslLjx4/nRgvoIdp/1n/zm98oLy9Po0aNUkZGhnt7VFSUkpKSVF1drbVr18rhcHgUVmFhYRo3\nbpw2bdqk3NxcNTU1acSIER5DCQHgcqGoAtAjnV1QFRQU6I477tC0adM8Jpnb7Xb3N9lthdW6des6\nLawSExNls9l04403Mq8C6CHO/qzn5+fr+9//vq677roOX55ERkYqMTFRNTU1nRZWoaGhGjdunD74\n4AMVFRVp2rRpLJsOoEtQVAHocS62oNq3b59yc3MVEhIim80m6fyFVXBwsAYPHszQP6CHONdn/brr\nrvP4rDc2NrpX/ruYwmrKlCmaNm0aD/YF0GUoqgD0OG3DddqGAd19992aPn26goOD3ccUFhZq8eLF\n2rhxo2bMmCGbzdZhKOC6deu0f/9+hYaGasCAAR7XBtC9zp5DtXv3bt1+++0dljzfvXu3Fi9erOjo\naMXGxkrqvLBKT0/3WLwiNDS0W/oFwD8xoQBAj/TPf/5TOTk56tevn1JTUz1W7SosLNSyZct05MgR\n/cd//IcSEhIkyf3AT0maNGmSfvrTn6qyslJvvvkmD/YFepi2guqFF17Qzp07NW/evA4FVWFhod54\n4w3l5uZ2OH/gwIG6+eabNW7cOL3//vtaunSp6urqmCsJoFuYu7sBANCZ1NRUjRw5Urt27dJHH32k\n4OBgpaWlae/evVq2bJn27dunJ554QsOHD+/wPJq2/504caJMJpMSExM9Ui4APcPRo0f15ZdfSpJM\nJlOHgqrts75gwQINHTq0w2e9rbBqbGzU5s2bddttt3VXVwD4OR7+C6DHys/P15tvvqn8/HxNnDhR\nmZmZ2rBhgwoLC/X4448rMzOzw02WJFVWVioqKqqbWw/gYuzevVu/+MUvJEmPPPKIJkyY4FFQnevL\nk/aOHDmi0NBQRUdHd0cXAICiCkDP0/6madeuXVq1apXy8/NltVrV3Nysp556SkOGDJHD4ZDJZPI4\nPjc3V2+88YZmzpypa6+9tju7AeAi7dmzR21TvLOysrRnzx4VFBToscce04gRIzotqI4fPy6TyeQe\n/gsA3YmBxwB6nPZzozIzM3XTTTdp1KhRamhoUEZGhntFL5PJJKfT6b7JysvL07Jly1RUVKSBAwd2\nW/sBXJorr7zSXVRlZ2drz549+s1vfqMRI0bIbrd3KKhyc3P1+9//Xh9++KFaW1u7seUAcBpFFYAe\n6ezCavbs2crMzFRhYaH+7//+TwcOHJB0ZrJ7Xl6elixZopKSEj3zzDNKTU3ttrYDuHRXXnmlnn76\naUmnnz9XW1sr6fSXJw6Hw+PLk3/84x86duyYrr32WvdS6wDQnVhSHUCP1X7Rifj4eEVGRqqyslI5\nOTmqr69XYmKiIiIitHPnTi1ZskQnT57UL3/5S/Xv37+7mw7gG4iNjdWwYcO0bt06ff7550pOTlZq\namqnX548++yzfNYB9BgUVQC6VWeTzts7u7CKiopyF1Z1dXWqrq7W22+/TUEF9BGxsbEaPny41q5d\nq61bt6p///5KTk5Wbm6uli5dymcdQI/EQhUAukz7h31Kp4f4mM3mDj93prPFK/bs2SOHwyGr1aqF\nCxdykwX0Ie0Xr7jllluUl5enY8eOUVAB6JEoqgB0ifZF0aFDh5SWlube9/bbb6u2tlZZWVnnnR/R\n/hr5+flavny5jh49ql/+8pfMoQL6oPaFlc1m01NPPUVBBaBHYqEKAF2irRh66aWX9NhjjyknJ0eS\n9I9//ENLliyRyWRSS0vLBa/R9j3Q8OHDddttt+nFF1+koAL6qCuvvFJPPPGEJOkXv/gFBRWAHouk\nCkCXWrVqlbKzsxUSEqJhw4Zp8+bNmjlzpmbPnn3Rz5u50DwsAH1Lc3OzLBZLdzcDAM6JogpAl2hf\nCG3cuFEvv/yynE6nRo8erZ/+9KcKCgqiWAIAAL0Sw/8AdAmDwSCn0ylJqqurcy9asXfvXu3bt899\nHN/zAACA3ub/tXfvQVFedxjHv3sDlhVvIBCDXIwxFhNCI80YFTXaGGu9JNoyk5mkMRPTsdHJNFZN\nO6aOTaa12jaSRtNYjYzVpA0aS1SUaNQaMBpxJCp4nRgB8QLRouIuyF76h7NvIdzExSjh+cw4A+/7\nnrPnZRlmH3/nnFeVKhH5Vnk8Hvbv309paSkej4d169Zht9t58cUXSUlJAf4frOpWrVTFEhERkTuV\nQpWI3FL+MFQ3FF27dg2z2YzVamXLli1kZGRgt9uZNm0aAwYMqLf1+vnz5+natavWU4iIiMgdS9P/\nROSW8Xq9RpDyeDw4nU7jmP+ZVKNGjeK5557D5XKxZMkS9u3bZwSqgwcPsmLFCjZs2KBpgSIiInLH\nUqVKRG6JutWmvLw89u7dy4kTJ+jUqRO9evXihz/8IYmJicb1/oqVzWZjypQpeDweNm/eTFlZGQsW\nLCAmJuZ23YqIiIhIsxSqRKTN1Z3qt2bNGj788EPCwsLo1q0bLpeL8vJyAKZOncqjjz5qtNu2bRvv\nv/8+VVVVmM1munfvziuvvKLnUImIiMgdTaFKRG6Z7du3s2zZMkaOHMno0aOJiYnB5XKRk5NDZmYm\nXq+XadOmMXToUKPNwYMHKSkp4dq1awwZMoTIyMjbeAciIiIiLVOoEpFboqqqikWLFnHu3LlGq03b\nt29n6dKlmM1mXn/9dfr06XObRioiIiISGG1UISI3xf/MKT+Px1Pv+5qaGoqLi7nnnnuMQOXz+Yx2\nI0aMYOLEiXi9XvLz8xvtU0RERKQ9UKgSkZvi34Ri9erVOJ1OLBZLvVBUXV1NTU0NZ8+epbKyErj+\n3Cmz2Wxcl5qaSnBwMF9++WW9PkVERETaE32CEZGb9uGHH7JhwwZef/11XC4XZrPZqFjdddddJCYm\nUlFRQWlpKdCwEtW9e3esVqueQSUiIiLtmkKViNy0xx9/nOTkZE6ePMlrr72Gy+XCYrHg8Xgwm80k\nJSXhcrn4+9//TkVFhVGJMpvN+Hw+9uzZg8vlIiEhAUDPohIREZF2yTJv3rx5t3sQItJ26O4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"text/plain": [ "<matplotlib.figure.Figure at 0x124f1de48>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Rightly classified Einsteins:\n" ] }, { "data": { "image/png": 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Vj92j+tZbbzX76r1w6TBY3vNqaeq71adHTT013HPhOVTTTq+N6sKpk1d9O3Xh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\nzz7b7NND5M6pqEeIvjfmkdNz0COq3h/WRX2n6jusetijqt4o5+1hXel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"text/plain": [ "<matplotlib.figure.Figure at 0x1247b6d68>" ] }, "metadata": { "image/png": { "height": 197, "width": 469 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Wrongly classified images:\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x128422c88>" ] }, "metadata": { "image/png": { "height": 107, "width": 469 } }, "output_type": "display_data" } ], "source": [ "y_pred_rw = rfe.predict(x_rw.reshape((len(x_rw), -1)))\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])\n", "pl.show()\n", "\n", "print(\"Rightly classified Einsteins:\")\n", "imsshow(x_rw[((y_rw - y_pred_rw) == 0) * (y_rw == 1)])\n", "pl.show()\n", "\n", "print(\"Wrongly classified images:\")\n", "imsshow(x_rw[(y_rw - y_pred_rw) != 0])\n", "pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So training on raw pixel values might not be a good idea. Let's build a feature extractor based on the trained LeNet (or any other pretrained image classifier)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22496/22500 [============================>.] - ETA: 0s 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] } ], "source": [ "model = load_model('aps_lenet.h5')\n", "enc_layers = it.takewhile(lambda l: not isinstance(l, keras.layers.Flatten), \n", " model.layers)\n", "encoder_model = keras.models.Sequential(enc_layers)\n", "encoder_model.add(keras.layers.Flatten())\n", "x_train_sampled_enc = encoder_model.predict(x_train_sampled, verbose=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 11.4s\n", "[Parallel(n_jobs=-1)]: Done 64 out of 64 | elapsed: 16.8s finished\n", "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=4)]: Done 64 out of 64 | elapsed: 0.0s finished\n" ] }, { "data": { "image/png": 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0LV++XLm5uerXr598fHwkFSVPZ86ccVSw7Dp27ChJ2rJli9LT00u0HTx4UP/5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"text/plain": [ "<matplotlib.figure.Figure at 0x12d5d49e8>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" } ], "source": [ "rfe = RandomForestClassifier(n_estimators=64, criterion='entropy', n_jobs=-1,\n", " verbose=True)\n", "rfe = rfe.fit(x_train_sampled_enc, y_train_sampled)\n", "\n", "y_pred_rw = rfe.predict(encoder_model.predict(x_rw, verbose=False))\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])\n", "pl.show()" ] } ], "metadata": { "hide_input": false, "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" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

NYUDataBootcamp/Projects

UG_F16/Srikanth-Bailoor-TV Ratings-Final Project.ipynb

1

1285203

mit

robotcator/gensim

gensim Quick Start.ipynb

1

17648

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Getting Started with `gensim`\n", " \n", " The goal of this tutorial is to get a new user up-and-running with `gensim`. This notebook covers the following objectives.\n", " \n", " ## Objectives\n", " \n", " * Installing `gensim`.\n", " * Accessing the `gensim` Jupyter notebook tutorials.\n", " * Presenting the core concepts behind the library.\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installing `gensim`\n", "\n", "Before we can start using `gensim` for [natural language processing (NLP)](https://en.wikipedia.org/wiki/Natural_language_processing), you will need to install Python along with `gensim` and its dependences. It is suggested that a new user install a prepackaged python distribution and a number of popular distributions are listed below.\n", "\n", "* [Anaconda ](https://www.continuum.io/downloads)\n", "* [EPD ](https://store.enthought.com/downloads)\n", "* [WinPython ](https://winpython.github.io)\n", "\n", "Once Python is installed, we will use `pip` to install the `gensim` library. First, we will make sure that Python is installed and accessible from the command line. From the command line, execute the following command:\n", "\n", " which python\n", " \n", "The resulting address should correspond to the Python distribution that you installed above. Now that we have verified that we are using the correct version of Python, we can install `gensim` from the command line as follows:\n", "\n", " pip install -U gensim\n", " \n", "To verify that `gensim` was installed correctly, you can activate Python from the command line and execute `import gensim`\n", "\n", " $ python\n", " Python 3.5.1 |Anaconda custom (x86_64)| (default, Jun 15 2016, 16:14:02)\n", " [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)] on darwin\n", " Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n", " >>> import gensim\n", " >>> # No error is a good thing\n", " >>> exit()\n", "\n", "**Note:** Windows users that are following long should either use [Windows subsystem for Linux](https://channel9.msdn.com/events/Windows/Windows-Developer-Day-Creators-Update/Developer-tools-and-updates) or another bash implementation for Windows, such as [Git bash](https://git-for-windows.github.io/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing the `gensim` Jupyter notebooks\n", "\n", "All of the `gensim` tutorials (including this document) are stored in [Jupyter notebooks](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html). These notebooks allow the user to run the code locally while working through the material. If you would like to run a tutorial locally, first clone the GitHub repository for the project.\n", "``` bash\n", " $ git clone https://github.com/RaRe-Technologies/gensim.git\n", "``` \n", "Next, start a Jupyter notebook server. This is accomplished using the following bash commands (or starting the notebook server from the GUI application).\n", "\n", "``` bash\n", " $ cd gensim\n", " $ pwd\n", " /Users/user1/home/gensim\n", " $ cd docs/notebooks\n", " $ jupyter notebook\n", "``` \n", "After a few moments, Jupyter will open a web page in your browser and you can access each tutorial by clicking on the corresponding link. \n", "\n", "<img src=\"jupyter_home.png\">\n", "\n", "This will open the corresponding notebook in a separate tab. The Python code in the notebook can be executed by selecting/clicking on a cell and pressing SHIFT + ENTER.\n", "\n", "<img src=\"jupyter_execute_cell.png\">\n", "\n", "**Note:** The order of cell execution matters. Be sure to run all of the code cells in order from top to bottom, you you might encounter errors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Core Concepts and Simple Example\n", "\n", "This section introduces the basic concepts and terms needed to understand and use `gensim` and provides a simple usage example. In particular, we will build a model that measures the importance of a particular word.\n", "\n", "At a very high-level, `gensim` is a tool for discovering the semantic structure of documents by examining the patterns of words (or higher-level structures such as entire sentences or documents). `gensim` accomplishes this by taking a *corpus*, a collection of text documents, and producing a *vector* representation of the text in the corpus. The vector representation can then be used to train a *model*, which is an algorithms to create different representations of the data, which are usually more semantic. These three concepts are key to understanding how `gensim` works so let's take a moment to explain what each of them means. At the same time, we'll work through a simple example that illustrates each of them.\n", "\n", "### Corpus\n", "\n", "A *corpus* is a collection of digital documents. This collection is the input to `gensim` from which it will infer the structure of the documents, their topics, etc. The latent structure inferred from the corpus can later be used to assign topics to new documents which were not present in the training corpus. For this reason, we also refer to this collection as the *training corpus*. No human intervention (such as tagging the documents by hand) is required - the topic classification is [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning.html).\n", "\n", "For our corpus, we'll use a list of 9 strings, each consisting of only a single sentence." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "raw_corpus = [\"Human machine interface for lab abc computer applications\",\n", " \"A survey of user opinion of computer system response time\",\n", " \"The EPS user interface management system\",\n", " \"System and human system engineering testing of EPS\", \n", " \"Relation of user perceived response time to error measurement\",\n", " \"The generation of random binary unordered trees\",\n", " \"The intersection graph of paths in trees\",\n", " \"Graph minors IV Widths of trees and well quasi ordering\",\n", " \"Graph minors A survey\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a particularly small example of a corpus for illustration purposes. Another example could be a list of all the plays written by Shakespeare, list of all wikipedia articles, or all tweets by a particular person of interest.\n", "\n", "After collecting our corpus, there are typically a number of preprocessing steps we want to undertake. We'll keep it simple and just remove some commonly used English words (such as 'the') and words that occur only once in the corpus. In the process of doing so, we'll [tokenize](https://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) our data. Tokenization breaks up the documents into words (in this case using space as a delimiter)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[['human', 'interface', 'computer'],\n", " ['survey', 'user', 'computer', 'system', 'response', 'time'],\n", " ['eps', 'user', 'interface', 'system'],\n", " ['system', 'human', 'system', 'eps'],\n", " ['user', 'response', 'time'],\n", " ['trees'],\n", " ['graph', 'trees'],\n", " ['graph', 'minors', 'trees'],\n", " ['graph', 'minors', 'survey']]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a set of frequent words\n", "stoplist = set('for a of the and to in'.split(' '))\n", "# Lowercase each document, split it by white space and filter out stopwords\n", "texts = [[word for word in document.lower().split() if word not in stoplist]\n", " for document in raw_corpus]\n", "\n", "# Count word frequencies\n", "from collections import defaultdict\n", "frequency = defaultdict(int)\n", "for text in texts:\n", " for token in text:\n", " frequency[token] += 1\n", "\n", "# Only keep words that appear more than once\n", "processed_corpus = [[token for token in text if frequency[token] > 1] for text in texts]\n", "processed_corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before proceeding, we want to associate each word in the corpus with a unique integer ID. We can do this using the `gensim.corpora.Dictionary` class. This dictionary defines the vocabulary of all words that our processing knows about." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dictionary(12 unique tokens: [u'minors', u'graph', u'system', u'trees', u'eps']...)\n" ] } ], "source": [ "from gensim import corpora\n", "\n", "dictionary = corpora.Dictionary(processed_corpus)\n", "print(dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because our corpus is small, there is only 12 different tokens in this `Dictionary`. For larger corpuses, dictionaries that contains hundreds of thousands of tokens are quite common." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vector\n", "\n", "To infer the latent structure in our corpus we need a way to represent documents that we can manipulate mathematically. One approach is to represent each document as a vector. There are various approaches for creating a vector representation of a document but a simple example is the *bag-of-words model*. Under the bag-of-words model each document is represented by a vector containing the frequency counts of each word in the dictionary. For example, given a dictionary containing the words `['coffee', 'milk', 'sugar', 'spoon']` a document consisting of the string `\"coffee milk coffee\"` could be represented by the vector `[2, 1, 0, 0]` where the entries of the vector are (in order) the occurrences of \"coffee\", \"milk\", \"sugar\" and \"spoon\" in the document. The length of the vector is the number of entries in the dictionary. One of the main properties of the bag-of-words model is that it completely ignores the order of the tokens in the document that is encoded, which is where the name bag-of-words comes from.\n", "\n", "Our processed corpus has 12 unique words in it, which means that each document will be represented by a 12-dimensional vector under the bag-of-words model. We can use the dictionary to turn tokenized documents into these 12-dimensional vectors. We can see what these IDs correspond to:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'minors': 11, u'graph': 10, u'system': 6, u'trees': 9, u'eps': 8, u'computer': 1, u'survey': 5, u'user': 7, u'human': 2, u'time': 4, u'interface': 0, u'response': 3}\n" ] } ], "source": [ "print(dictionary.token2id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, suppose we wanted to vectorize the phrase \"Human computer interaction\" (note that this phrase was not in our original corpus). We can create the bag-of-word representation for a document using the `doc2bow` method of the dictionary, which returns a sparse representation of the word counts:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(1, 1), (2, 1)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_doc = \"Human computer interaction\"\n", "new_vec = dictionary.doc2bow(new_doc.lower().split())\n", "new_vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first entry in each tuple corresponds to the ID of the token in the dictionary, the second corresponds to the count of this token." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that \"interaction\" did not occur in the original corpus and so it was not included in the vectorization. Also note that this vector only contains entries for words that actually appeared in the document. Because any given document will only contain a few words out of the many words in the dictionary, words that do not appear in the vectorization are represented as implicitly zero as a space saving measure.\n", "\n", "We can convert our entire original corpus to a list of vectors:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[(0, 1), (1, 1), (2, 1)],\n", " [(1, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)],\n", " [(0, 1), (6, 1), (7, 1), (8, 1)],\n", " [(2, 1), (6, 2), (8, 1)],\n", " [(3, 1), (4, 1), (7, 1)],\n", " [(9, 1)],\n", " [(9, 1), (10, 1)],\n", " [(9, 1), (10, 1), (11, 1)],\n", " [(5, 1), (10, 1), (11, 1)]]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bow_corpus = [dictionary.doc2bow(text) for text in processed_corpus]\n", "bow_corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that while this list lives entirely in memory, in most applications you will want a more scalable solution. Luckily, `gensim` allows you to use any iterator that returns a single document vector at a time. See the documentation for more details.\n", "\n", "### Model\n", "\n", "Now that we have vectorized our corpus we can begin to transform it using *models*. We use model as an abstract term referring to a transformation from one document representation to another. In `gensim`, documents are represented as vectors so a model can be thought of as a transformation between two [vector spaces](https://en.wikipedia.org/wiki/Vector_space). The details of this transformation are learned from the training corpus.\n", "\n", "One simple example of a model is [tf-idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf). The tf-idf model transforms vectors from the bag-of-words representation to a vector space, where the frequency counts are weighted according to the relative rarity of each word in the corpus.\n", "\n", "Here's a simple example. Let's initialize the tf-idf model, training it on our corpus and transforming the string \"system minors\":" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(6, 0.5898341626740045), (11, 0.8075244024440723)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from gensim import models\n", "# train the model\n", "tfidf = models.TfidfModel(bow_corpus)\n", "# transform the \"system minors\" string\n", "tfidf[dictionary.doc2bow(\"system minors\".lower().split())]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `tfidf` model again returns a list of tuples, where the first entry is the token ID and the second entry is the tf-idf weighting. Note that the ID corresponding to \"system\" (which occurred 4 times in the original corpus) has been weighted lower than the ID corresponding to \"minors\" (which only occurred twice).\n", "\n", "`gensim` offers a number of different models/transformations. See [Transformations and Topics](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Topics_and_Transformations.ipynb) for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next Steps\n", "\n", "Interested in learning more about `gensim`? Please read through the following notebooks.\n", "\n", "1. [Corpora_and_Vector_Spaces.ipynb](docs/notebooks/Corpora_and_Vector_Spaces.ipynb)\n", "2. [word2vec.ipynb](docs/notebooks/word2vec.ipynb)" ] } ], "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 }

lgpl-2.1

guilgautier/DPPy

notebooks/fast_sampling_of_beta_ensembles.ipynb

1

341630

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Companion notebook of the paper _Fast sampling of $\\beta$-ensembles_\n", "by [Guillaume Gautier](http://guilgautier.github.io/), [Rémi Bardenet](https://rbardenet.github.io/), and [Michal Valko](http://researchers.lille.inria.fr/~valko/hp/)\n", "\n", "See also the [arXiv preprint: 2003.02344](http://arxiv.org/abs/2003.02344) " ] }, { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Companion-notebook-of-the-paper-Fast-sampling-of-$\\beta$-ensembles\" data-toc-modified-id=\"Companion-notebook-of-the-paper-Fast-sampling-of-$\\beta$-ensembles-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Companion notebook of the paper <em>Fast sampling of $\\beta$-ensembles</em></a></span><ul class=\"toc-item\"><li><span><a href=\"#Summary\" data-toc-modified-id=\"Summary-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Summary</a></span></li></ul></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Imports</a></span><ul class=\"toc-item\"><li><span><a href=\"#$V(x)-=-g_{2m}-x^{2m}$\" data-toc-modified-id=\"$V(x)-=-g_{2m}-x^{2m}$-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>$V(x) = g_{2m} x^{2m}$</a></span><ul class=\"toc-item\"><li><span><a href=\"#$V(x)-=-\\frac{1}{2}-x^2$-(Hermite-ensemble)\" data-toc-modified-id=\"$V(x)-=-\\frac{1}{2}-x^2$-(Hermite-ensemble)-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>$V(x) = \\frac{1}{2} x^2$ (Hermite ensemble)</a></span></li><li><span><a href=\"#$V(x)-=-\\frac{1}{4}-x^4$\" data-toc-modified-id=\"$V(x)-=-\\frac{1}{4}-x^4$-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>$V(x) = \\frac{1}{4} x^4$</a></span></li><li><span><a href=\"#$V(x)-=-\\frac{1}{6}-x^6$\" data-toc-modified-id=\"$V(x)-=-\\frac{1}{6}-x^6$-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>$V(x) = \\frac{1}{6} x^6$</a></span></li></ul></li><li><span><a href=\"#$V(x)-=-g_2-x^2-+-g_4-x^4$\" data-toc-modified-id=\"$V(x)-=-g_2-x^2-+-g_4-x^4$-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>$V(x) = g_2 x^2 + g_4 x^4$</a></span><ul class=\"toc-item\"><li><span><a href=\"#$V(x)=-\\frac{1}{4}-x^4-+-\\frac{1}{2}-x^2$\" data-toc-modified-id=\"$V(x)=-\\frac{1}{4}-x^4-+-\\frac{1}{2}-x^2$-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>$V(x)= \\frac{1}{4} x^4 + \\frac{1}{2} x^2$</a></span></li><li><span><a href=\"#$V(x)=-\\frac{1}{4}-x^4---x^2$-(onset-of-two-cut-solution)\" data-toc-modified-id=\"$V(x)=-\\frac{1}{4}-x^4---x^2$-(onset-of-two-cut-solution)-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>$V(x)= \\frac{1}{4} x^4 - x^2$ (onset of two-cut solution)</a></span></li><li><span><a href=\"#$V(x)=-\\frac{1}{4}-x^4---\\frac{5}{4}-x^2$-(Two-cut-eigenvalue-distribution)\" data-toc-modified-id=\"$V(x)=-\\frac{1}{4}-x^4---\\frac{5}{4}-x^2$-(Two-cut-eigenvalue-distribution)-2.2.3\"><span class=\"toc-item-num\">2.2.3&nbsp;&nbsp;</span>$V(x)= \\frac{1}{4} x^4 - \\frac{5}{4} x^2$ (Two-cut eigenvalue distribution)</a></span></li></ul></li><li><span><a href=\"#$V(x)-=-\\frac{1}{20}-x^4---\\frac{4}{15}x^3-+-\\frac{1}{5}x^2-+-\\frac{8}{5}x$\" data-toc-modified-id=\"$V(x)-=-\\frac{1}{20}-x^4---\\frac{4}{15}x^3-+-\\frac{1}{5}x^2-+-\\frac{8}{5}x$-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>$V(x) = \\frac{1}{20} x^4 - \\frac{4}{15}x^3 + \\frac{1}{5}x^2 + \\frac{8}{5}x$</a></span></li></ul></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary \n", "We focus on sampling $\\beta$-ensembles with $N$ points associated to polynomial potentials $V$ with even degree which take the form\n", "\n", "$$\n", "\\begin{equation}\n", "\\label{eq:potential_V}\n", "V(x) = \\frac{g_6}{6} x^6 \n", " + \\frac{g_4}{4} x^4 \n", " + \\frac{g_3}{3} x^3 \n", " + \\frac{g_2}{2} x^2\n", " + g_1 x\n", "\\end{equation}\n", "$$\n", "\n", "We derive a **fast but approximate procedure to generate samples from the targeted $\\beta$-ensemble**. In other words, we wish to sample from $(x_{1},\\dots, x_N)$ with joint distribution proportional to \n", "\n", "$$\n", "\\begin{equation}\n", "\\label{eq:joint_x}\n", "\\left|\\Delta(x_1,\\dots,x_N)\\right|^{\\beta}\n", " ~ \\exp^{-\\sum_{i=1}^N V(x_i)}\n", " \\prod_{i=1}^N d x_i\n", "\\end{equation}\n", "$$\n", "\n", "where $\\Delta(x_1,\\dots,x_N) = \\prod_{1\\leq i < j \\leq N} (x_j-x_i)$.\n", "\n", "To do this we view $x_1, \\dots, x_N$ as the eigenvalues of a random *Jacobi* matrix, i.e., a real-symmetric, tridiagonal matrix with positive subdiagonal\n", "\n", "$$\n", "\\begin{equation}\n", "\\label{eq:jacobi_matrix_J_N_a_b}\n", "J_{ab}=\n", "\\begin{bmatrix}\n", " a_1 \t\t& \\sqrt{b_1}& 0 & 0 \\\\\n", " \\sqrt{b_1} & a_2 \t\t& \\ddots & 0 \\\\\n", " 0 \t\t& \\ddots \t& \\ddots \t & \\sqrt{b_{N-1}} \\\\\n", " 0 \t\t\t& 0 \t\t& \\sqrt{b_{N-1}} & a_{N} \n", "\\end{bmatrix}\n", "\\end{equation}.\n", "$$\n", "\n", "---\n", "\n", "To draw the correspondence with Jacobi matrices, we first augment the distribution of the points $x_1,\\dots,x_N$ with auxilary weights $w_{1}, \\dots, w_{N}$, distributed independently from the points as a Dirichlet $\\operatorname{Dir}\\left(\\frac{\\beta}{2}\\right)$, so that\n", "\n", "$$\n", "\\begin{equation}\n", "\\label{eq:joint_x_w}\n", " (x_{1:N}, w_{1:N-1})\n", " \\sim\n", " \\left|\\Delta(x_1,\\dots,x_N)\\right|^{\\beta}\n", " ~ \\exp^{- \\sum_{i=1}^N V(x_i)}\n", " \\prod_{i=1}^N d x_i\n", " \\prod_{i=1}^{N-1} w_i^{\\frac{\\beta}{2}-1} d w_i\n", "\\end{equation}\n", "$$\n", "\n", "This allows to consider the random measure $\\mu = \\sum_{n=1}^N w_n \\delta_{x_n}$ supported on the targeted $\\beta$-ensemble.\n", "Taking for \n", "The corresponding Jacobi matrix $J_{ab}$ characterizes the three-term recurrence relation between orthonormal polynomials w.r.t. $\\mu$ and Favard's theorem give that the mapping $\\mu \\mapsto J_{ab}$ is a diffeomorphism.\n", "We can thus convert the distribution \\eqref{eq:joint_x_w} of the nodes and weights of $\\mu$ to the distribution of the entries of $J_{ab}$, namely\n", "\n", "$$\n", "\\begin{equation}\n", "\\label{eq:joint_a_b}\n", " (a_{1:N}, b_{1:N-1})\n", " \\sim\n", " \\prod_{i=1}^{N-1}\n", " b_{i}^{\\frac{\\beta}{2}(N-i)-1}\n", " \\exp^{-\\operatorname{Tr}[V(J_N(a, b)]}\n", " d a_{1:N}, b_{1:N-1}\n", "\\end{equation}\n", "$$\n", " \n", "see, e.g., [[KrRiVi13, Proposition 2]](http://de.arxiv.org/pdf/1306.4832.pdf).\n", "\n", "---\n", "\n", "Hence, computing the eigenvalues of the Jacobi matrix $J_{ab}$ \\eqref{eq:jacobi_matrix_J_N_a_b} with entries sampled from \\eqref{eq:joint_a_b} provides a way to sample the corresponding $\\beta$-ensemble \\eqref{eq:joint_x} with $\\mathcal{O}(N^2)$ complexity.\n", "The question remains as to sample efficiently from \\eqref{eq:joint_a_b}.\n", "\n", "For specific potentials, namely $V(x)=x^2, x - \\log(x), - \\log(1-x) + \\log(1+x)$ that are respectively associated to the Hermite, Laguerre and Jacobi ensembles, the stars align perfectly to make the entries of $J_{ab}$ independent with easy-to-sample distributions: Gaussian, Gamma, Beta.\n", "\n", "But for more general potentials $V$, the Jacobi coefficients are no longer indenpendent and sampling from the distribution \\eqref{eq:joint_a_b} remains a challenge.\n", "Yet, when considering polynomials potentials, the interaction between the different parameters $a_n, b_n$ has short range, driven by the degree of $V$.\n", "We exploit this short range of interaction using a Gibbs sampler on Jacobi matrices to generate fast but approximate samples from the targeted $\\beta$ ensemble.\n", "\n", "**After rescaling the polynomial potentials \\eqref{eq:potential_V} as $V\\leftarrow\\frac{\\beta N}{2} V$**,\n", "\n", "for the chain of Jacobi matrices, we compare the empirical distribution of\n", "- the eigenvalues to the expected equilibrium distribution\n", "- the largest eigenvalue to the Tracy-Widom distribution, as expected for some potentials $V$ when $\\beta=2$.\n", "\n", "**Our empirical study seems to confirm the fast $\\log(N)$ mixing time sugested by [[KrRiVi13, p.6]](http://de.arxiv.org/pdf/1306.4832.pdf).**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:**\n", "If you are interested in sampling exactly from the classical (Hermite, Laguerre and Jacobi) $\\beta$-ensembles, please refer to the corresponding section in the [tutorial notebook of the DPPy toolbox](https://github.com/guilgautier/DPPy/tree/master/notebooks)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports\n", "\n", "**Here are the detailed [INSTALLATION INSTRUCTIONS](https://github.com/guilgautier/DPPy#installation) of DPPy**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To install the last version available on PyPI you can use [](https://badge.fury.io/py/dppy) " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# !pip install dppy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💣 **Note: The version available on PyPI might not be the latest version of DPPy.\n", "Please consider forking or cloning DPPy using**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# !rm -r DPPy\n", "# !git clone https://github.com/guilgautier/DPPy.git\n", "# !pip install scipy --upgrade\n", "\n", "# Then\n", "# !pip install DPPy/. \n", "# OR\n", "# !pip install DPPy/.['zonotope','trees','docs'] to perform a full installation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💣 If you have chosen to clone the repo and now wish to interact with the source code while running this notebook.\n", "You can uncomment the following cell." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import os\n", "import sys\n", "sys.path.insert(0, os.path.abspath('..'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "from dppy.beta_ensemble_polynomial_potential import BetaEnsemblePolynomialPotential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💻**You can play with the various parameters, e.g.,** $N, \\beta, V$ `nb_gibbs_passes` 💻" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $V(x) = g_{2m} x^{2m}$\n", "\n", "We first consider even monomial potentials whose equilibrium distribution can be derived from [[Dei00, Proposition 6.156]](https://bookstore.ams.org/cln-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x) = \\frac{1}{2} x^2$ (Hermite ensemble)\n", "\n", "This this the potential associated to the Hermite ensemble.\n", "\n", "In this case, the Jacobi parameters are all independent and sampling is exact [[DuEd02, II C]](https://arxiv.org/abs/math-ph/0206043).\n", "\n", "In our setting, this corresponds to a single pass of the Gibbs sampler over each variable." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([0.5, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sampl_x2 = be.sample_mcmc(N=1000, nb_gibbs_passes=1,\n", " sample_exact_cond=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 423 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x) = \\frac{1}{4} x^4$\n", "\n", "To depart from the classical quadratic potential we consider the quartic potential, which has been sampled by\n", "[[LiMe13]](https://arxiv.org/abs/1306.1179)\n", "[[OlNaTr15]](https://arxiv.org/abs/1404.0071)\n", "[[ChFe19, Section 3.1]](https://arxiv.org/abs/1806.05985)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/4, 0, 0, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sampl_x4 = be.sample_mcmc(N=200, nb_gibbs_passes=10,\n", " sample_exact_cond=True)\n", "# sample_exact_cond=False,\n", "# nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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yGZt39TTSvn0Nly5d0ig7ovJhEUVkBSkpKYiKisK5c+dUY4KCghAXF4d58+bB29tbw+yUeXl5YezYsTh37hyWLl2KsLCwEu9z6dIlDBw4EI8//jj0mebt4kRERID+bjqur3kbN9bPgiHrVonx9erVw5dffonExES88MILqFKligZZmla1alXMnz8fmzZtQo0aNVTj9LcuIzo62qzNlYishUUUkcYuXy54kTC16DYyMhKHDh1CTEyMhpmVjouLC4YPH47ExETMmzcPAQEBJd5n48aNuLr4H8g8spWjUkREpSClERkHN+DqF/9A9vmEEuODg4OxePFiJCYmYuTIkXBxcdEgS/M8+uij+O233xAeHq4ac/78eURHRyM1NVXDzIjMxyKKSEO3bt1CTEwMkpKSVGOGDx+On3/+GcHBwRpmZj43NzeMHj0a58+fx1tvvQV3d3eT8TLvPm5t+xTpKycj/06aRlkSEdmepKQkpK/4N25v/xwyP9dkbJUqVfDmm2/izJkz+Pvf/14pi6eiateujV27dsGjqfpOrmfPnkWvXr1w9y43c6bKi0UUkUZycnLwxBNPIDExUTXmtddew5IlS+Dq6qphZuXj7e2NqVOn4tSpU+jfv3+J8bkXjyH1q9HIOvYjR6WIiIqQUmLBggV46KGHkHup5DOfnn32WZw+fRrTpk2Dl5eXBhlahru7O/wfmwDvdo+pxhw/fhxPPvlkmQ+GJ6poLKKINCClxMiRI7F7927VmLfffhuzZ8+22bMywsLCsHbtWmzduhWNGjUyGSvzsnFzy8e4vm4mrl+/rlGGRESV17Vr1xAbG4uXX365xJ1NGzVqhO3bt2PZsmUICQnRKEPLEkIHvx6j4BMxSDVm+/btGDVqFD9wo0qJRRSRBt5//32sXLlStf+NN97AG2+8oWFGFad37944duwYpkyZAmdn0+d5Z5/Zi4ceeghbt27VKDsiosrnxx9/RMuWLbFlyxbTgU7OeOutt3D06FF0795dm+QqkBACvl2GwafDANWYpUuX4tNPP9UwK6LSYRFFVMG2b9+OSZMmqfaPHj0a06ZN0zCjiufm5obp06fj4MGDaNeuncnY9PR0PPLII5g0aRLy8/M1ypCIyPry8/MxceJE9OrVC+np6SZjXWs2Rs2Rn2Dq1KklrkG1JUII+EaNhFfrPqox48aNMzmTg8gaWEQRVaCrV6/imWeegdFoVOzv378/PvzwQ5udwleSVq1aYd++fZg9ezbgZHpUavbs2YiOjuYZIUTkEC5cuIDOnTtjzpw5pgOdnOEb9TcEDZkDl+q2OXWvJEIIVIt5GVXqtVfs1+v1GDhwYImFJpGWWEQRVRCj0YgRI0bgxg3l85Hat2+Pb7/9Fk5OThpnpi1nZ2e89tprCB7xEVwCwkzG7tmzB23atMHmzZu1SY6IyAq+++47tGnTBgcOHDAZ5xrUAMEjPkbVjk9B6Oz7tULonOD/+ES0atVKsT89PR3PPfcc10dRpcEiiqiCfPTRR/jpp58U+/z9/bFmzRp4eHhonJX1uAaEIXj4h4Vz39VH3m7evInY2FhMnDiR0/uIyK7k5eVhzJgxeOaZZ5CRkaEaJ4SAT8QgBA19H64BoRpmaF061ypYu3YtfH19Ffs3b96M//znPxpnRaSMRRRRBci7nozXX39dsU+n0+G7775DnTp1NM7K+oSzC/yin0PgMzPg5FXNZOycOXMQFRWFq1evapQdEVHFuXr1Krp3745PPvnEZFxQUBB+/PFH+HUdDlHCNGh7VK9ePaxYsUJ1mvuECRNMHhVCpBUWUUQWJo0G3NzyserZFm+88YZd7KpUHu6hLRE88hO4121rMi4+Ph7t2rVDfHy8RpkREVne7t270a5dO+zZs8dk3KOPPopjx46hR48eGmVWOfXp0wcTJ05U7MvJycHzzz+vutaYSCssoogsLPPgBuSlnlXsi4iIwJQpUzTOqHJy8qiKGgOn4t133zW5LiwtLQ1RUVFYuHAh58ITkU2RUuKjjz5CdHQ00tLSVONcXFzwwQcfYOPGjQgICNAww8pr2rRpaN9eeaOJPXv2YP78+RpnRPS/WEQRWVD+nTTc2f2tYp+Xlxe+/fbbEs9OciRC6DBp0iT88ssvqFWrlmpcfn4+XnrppYJDF/VcJ0VEld+9e/cwZMgQ/Otf/4LBYFCNq1evHvbu3Yt//etf0On4tuwBV1dXLFu2THXt8KRJk3Dx4kWNsyL6E/+1ElnQ7Z8WQupzFfvee+891KtXT+OMbEPnzp1x5MgRPPLIIybjFi9ejLTlk6DPVN7xkIioMrh48SI6d+6MFStWmIyLjY0t1Xl6jqpRo0aYOXOmYl9WVhbGjx+vcUZEf2IRRWQh2ecTkH0+QbGva9euGDVqlMYZ2RZ/f3/88MMPePvtt02em5WXehqpS/8PuVdOaZgdEVHpxMfHIzw8HEeOHFGNEUJg2rRp2LBhA/z8/DTMzva88sor6Nixo2Lf6tWrsX37do0zIirAIorIAqQ+H7e2L1Lsc3Nzw6JFizhNoxR0Oh3eeOMNbNy4ET4+Pqpxxnt3kLbidWSd+FnD7IiITFuyZAmio6Nx7do11Rg/Pz9s2rQJb775Jl8XSsHJyQlffPEFXFxcFPvHjh3L4zDIKvivl8gCMn7bAP1t5a24J02ahEaNGmmckW2LjY1FQkICmjVrph5k0OPmD3NxZ9c3kJK7NBGR9RgMBowfPx4jR45U3ZkVAFq3bo2DBw+WOHWZ/lfz5s0xbtw4xb4TJ05gwYIFGmdExCKKqNwMOVnI2LtKsS80NFR1m1YyrVGjRti3bx8GDBhgMu7u3u9wY/1s3L9/X6PMiIj+dPfuXfTt2xcffPCBybihQ4ciPj6ea2PLaMqUKahZs6Zi39tvv23y8GKiisAiiqicMvZ9D2PuPcW+uXPnokqVKhpnZD+8vb2xevVqzJgxw+Q6qfun96Bbt248mJeINHXx4kVERkZi69atqjFCCMyZMwdff/01Xw/KwcvLC++9955i340bNzB37lyNMyJHxyKKqBwuX76MzN82KvZFRUWVOIpCJRNC4N///jc2bNgALy8v1biDBw+iQ4cOOHz4sIbZEZGjOnLkCDp16oSTJ0+qxnh7e2PDhg149dVXTX4QRKUzePBgREZGKvbNnTsX6enpGmdEjoxFFFE5vP3225B65fnvc+bM4YumBfXt2xd79uyBk4/6QZRXrlxB586dsW7dOg0zIyJHs23bNnTp0gWpqamqMQ/Of+rbt6+Gmdk3IQRmz56t2Hfv3j3MmDFD44zIkbGIIiqjlJQULFmyRLHPo/HDCA8P1zYhB9CyZUsED/8ArjUbq8bcv38fAwYMwJw5cyCl1DA7InIEX331FWJjY5GVlaUaEx0djQMHDqB58+YaZuYYOnfujMcee0yx7/PPPzdZ2BJZEosoojKaPXu28raqQgffrsO1T8hBOHn6IWjwu/Bo2k01RkqJiRMn4h//+Af0er2G2RGRvZJSYurUqXjuuedgMBhU41588UVs27YN1atX1zA7xzJz5kzFmR65ubmq66aILI1FFFEZXL58GV988YVin9dDPeFSrZbGGTkW4ewK/8cmoGrnISbjFixYgCeeeMLkJ8ZERCXJz8/H3//+d0ybNs1k3KxZszB//nzVM43IMlq0aIEhQ5Sf/xcsWMC1UaQJFlFEZTBnzhzls0CEDj4Rg7RPyAEJIeD78GD495sI4eyqGrdp0yZ069YNaWlpGmZHRPYiIyMDffv2xVdffaUa4+rqiuXLl2PixIlcC6uRyZMnK/6us7Oz8f7771shI3I0LKKIzHTz5k0sXrxYsc+zeTRcfIM0zsixeTbtgsDB7yIoSP33fujQIXTq1AmnTp3SMDMisnVXr15F165dERcXpxrj6+uLuLg4DB48WMPMqEmTJnj66acV+xYuXIi7d+9qnBE5GhZRRGZasGABsrOzi3cIHapyFMoq3Go2xv79+00u4k5JSUFkZCR27typYWZEZKvyrqegU6dOOHr0qGpMnTp18Ouvv6JbN/U1mlRxpkyZotiemZmJRYsWaZwNORoWUURmyMnJwSeffKLY59H4Ya6FsqIHb2a6d++uGnPnzh306tULy5cv1zAzIrI1OSnHkLbsNVy6dEk1pnXr1ti7dy934LOi5s2bo3///op9H3/8sfLmT0QWwiKKyAzLly9XXbDq04EH61qbr68vtmzZguHD1XdHzMvLw5AhQzBr1ixugU5Exdw7+QvSV70JmXtPNaZ3797YtWsXatasqWFmpGT8+PGK7ZcvX8aqVas0zoYcibO1EyAyR9ikTRX6+MmzYlX7pJT44IMPFPvcQlrALbhhRaVlMRX9+7Omoj+bDBqIqpF5uBu/UjX+9ddfx4zvdqJazMsQOqcKycnU35Maa/6NU/lo8e/Lmv//7P1vU0qJjH3f486ur03GPffcc1iwYIHqDnz2/DyrBXN/f1JKuNZsjLyrp4v1PTf+Lfz7WNVyb/Zh7b9Nqpw4EkVUSrt27cKJEycU+3w6KE8nIOsQQsC3y1BU6zMGEOpPc1lHtuL6mukw5imscSMihyGNBtyK+0+JBdS0adOwePFibmFeiQgh4BOu/Bqcl34eealnNM6IHAWLKKJS+s9//qPY7lytFqrUD9c4GyoN71a9UOOptyBcq6jGZF84iPQVr8Nw77aGmRFRZXHv3j1cX/sOso5sUY1xdnbGkiVL8Oabb3IL80rIo1EEnKsGKvZlHubIIFUMFlFEpZCamoq1a9cq9nm37QthYrSDrKtKvXYIenY2nLyqqcbkpZ1D2jcTkH/zsoaZEZG1paenIyoqCtnnE1RjvL29sXnzZowYMULDzMgcQucE77bKU+7undoNw31ud06Wx3d+RKWwePFi6PX6Yu2enp7waqG+GxxVDq6B9RA0bC5c/ENVY/R305H27avIuXxSw8yIyFpOnz6NiIgIHDx4UDWmZs2a2L17N2JiYjTMjMrC86GeygevG/KR9ftP2idEdo9FFFEJDAYDPv/8c8W+oUOHQufmqXFGVBbOPgEIGjIb7qEtVWOMOZlIXzkZ907v0TAzItLanj17EBkZiaSkJNUYF/9Q7Nu3D61atdIwMyorpyo+8GjSRbEv68gWSGnUOCOyd9ydj+yCuTvnmLP7T1xcHC5fVp7m9fLLL2Priso3BcyedxIq78+WN6c/nn/+eXzzzTfKAYZ83Fw/C2989BHGjBlTqsesjLuycYewyqMy7tJoSbb2t7l69WoMHToUubm5qjFudVqiRv9/IyQkxGLXtefn5bKoiN/HgQEB6NixY7F2/Z00LOnjhejoaJP3t/bfJtkWjkQRleCrr75SbI+MjOQnlDbI1dUVS5cuxeTJk1VjpJQYO3YsJkyYAKORn14S2YsPP/wQgwYNMllAeTaLQuCgadC5e2mYGVlCeHg42rVrp9in9lpOVFYsoohMuHXrFtavX6/Y98ILL2icDVmKEALvvPMOFixYAJ1O/Wlw7ty5GDx4MHJycjTMjogszWAw4P/+7/8wbtw4k4ds+0QMQvW+4yGcuIW5LRJC4Pnnn1fsW716NTIyMjTOiOwZiygiE5YvX468vLxi7Z6ennjqqaeskBFZ0osvvoj169fDw8NDNWbVqlXo3bs3bt/mFuhEtig7OxuDBg3Cxx9/rB4kdKjW+xX4dR3OLcxt3DPPPAN3d/di7dnZ2Vi1apUVMiJ7xSKKyAS14f9BgwbBy4tTPexB37598csvvyAgIEA1ZteuXXj44YeRkpKiYWZEVF43btxAz549VY+oAAAPDw8EPPkGvFv30TAzqii+vr7o31/58F1O6SNLYhFFpOL48eM4dOiQYt/IkSM1zoYqUnh4OPbu3YuGDRuqxpw6dQoRERE4cuSIhpkRUVmdP38ekZGRiI+PV42pUaMGdu7cCQ8emG5X1F6j4+PjcfbsWY2zIXvFIopIxfLlyxXbGzRogM6dO2ucDVW0+vXrIz4+Hp06dVKNSU1NRZcuXRAXF6dhZkRkrgMHDiAiIsLkG+bGjRtj3759aN++vYaZkRa6d++OOnXqKPatWLFC42zIXrGIIlIgpVR9oh02bBjnzNspf39/bN++HU888YRqTFbiiX5IAAAgAElEQVRWFmJjY7FkyRLtEiOiUrt/dj+ioqJw/fp11ZjOnTtjz549qFu3roaZkVacnJwwbNgwxb7ly5eb3FyEqLRYRBEp2LdvH5KTkxX7nn32WW2TIU15eHhg9erV+Oc//6kao9frMXLkSEyfPp0vxkSVSOahTbj+3xnIzs5WjRk4cCB+/PFHVK9eXcPMSGtqr9WnT5/mtGyyCBZRRArURqHCw8PRoEEDjbMhrTk5OeGTTz7BnDlzTMa9+eabGDVqFKTRoFFmRKRESiNu/7IEt36cD0j1s93GjRuHlStXKu7eRvalWbNmaNmypWIfp/SRJbCIIvoLvV6vug3q4MGDNc6GrEUIgVdffRUrVqyAq6uratzixYtxfc10GPPUP/kmoooj9fm4sXEuMvavVo0RQuDjjz/G3LlzTZ4NR/ZF7TV75cqVPEidyo3PJER/sXPnTqSnpxdrF0Lg6aeftkJGZE3PPPMMtm3bhqpVq6rGZF84iPQVr8OQxbOkiLRkyM5E+ndTcP/UTtUYd3d3rFmzBmPGjNEwM6oMnnnmGcX2S5cuYe/evRpnQ/aGRRTRX6idJxIVFYWaNWtqnA1VBlFRUdizZw9CQkJUY/LSziH12wnIv3lZw8yIHNeFCxeQ9u2ryL18QjWmevXq2LFjh+q5QWTfwsLCEBkZqdhn6uwwotJgEUVUhNFoxH//+1/FPo5CObbmzZtj7969qnPsAcBwNx1p376KnMsnNcyMyPHs27cPnTp1gv6W+ocW9evXx969exEREaFhZlTZqL12r127lhsDUbmwiCIqYv/+/UhNTS3WLoQwue01OYZatWph9+7d6Nmzp2qMMScT6Ssn497pPRpmRuQ41q5di+joaJNbmHfo0AHx8fEmD9Amx6A2CpmcnMxd+qhcWEQRFaE2vN+5c2cEBgZqnA1VRj4+Pti0aROGDx+uHmTIx411szBv3jztEiOyc1JKfPjhh3jqqaeQk5OjGlelYSf8/PPPqFGjhobZUWUVEhKCDh06KPZxSh+VB4sookJSStUn1AEDBmicDVVmrq6uWLJkCaZMmWIiSmLs2LGYMGECd4EiKie9Xo/Ro0dj3LhxJqdgebd/HAFPvA4PDw8Ns6PKTu01nEUUlQeLKKJC+deTcOHCBcU+LkqmvxJCYPr06Vi4cCEg1J9K586di8GDB5v85JyI1GVlZaF///747LPPTEQJ+PUYhWo9XoDQOWmWG9kGtdfwkydPIjExUeNsyF6wiCIqdP/sfsX2tm3bIjQ0VONsyFaMGjUKAQOmQLi4qcasWrUKPXr0wLVr1zTMjMj2paamolu3bvjhhx9UY4SzGwIGTIZP+34aZka2pFGjRmjevLli34YNGzTOhuwFiyiiQtnnExTbOQpFJfFo0AGBg9+FzsNXNSY+Ph4dO3bEiRPq2zET0Z+OHz+OTp064dChQ6oxOk9fBD77LjwadtIwM7JFalP6Nm3apHEmZC9YRBEBMNy7jbzUM4p9/frx000qmVtwIwQNex/OfupniSUnJyMiIgJbt27VMDMi27Np0yZERkbi4sWLqjFNmzZF8LC5cAtupGFmZKsee+wxxfY9e/bg9m0elE7mYxFFBCD7wm+K7SEhIXjooYc0zoZslYtvEIKGvge3mk1UYzIzMxEbG4tPP/1Uw8yIbIOUEh988AEee+wxZGZmqsY9OADbuSp3TaXSadeuneIuuwaDAdu2bbNCRmTrWEQRQX0qX2xsLIQQGmdDtszJoypqPDMDHo0iVWOMRiNGjx6NV155BXq9XsPsiCqvvLw8vPDCCxg/frzJHfiGDRuGbdu2wc/PT8PsyNbpdDo8+uijin2m1twRqWERRQ5PGvKRnaQ8575v374aZ0P2QOfiBv8nJuH11183GffZZ58hNjYWd+/e1SgzosrJcP8uYmJi8MUXX5iMe/PNN7F06VK4urpqlBnZk9jYWMX2LVu2wGAwaJwN2ToWUeTwci6fhMzLLtbu7u6O6OhoK2RE9kAIHWbOnImlS5fCxcVFNS4uLg4RERGq2+sT2bu8GxeR9s147Nq1SzXmwdls06ZN4+wAKrOYmBjF5+Nbt25h3759VsiIbBmLKHJ4OSrroXr06MEDG6nchg8fju3bt6N69eqqMadOnUKHDh2wY8cODTMjsr7sC78h7ZsJ0N9JU40JCAjAjh07MGLECA0zI3vk4+ODrl27KvZt2bJF42zI1rGIIoenNpVPbdifyFxdunTB/v370aSJ+oYTN2/eRK9evZCRsN7kehAieyClRMbB9bi2ehpk3n3VuBYtWuDAgQN4+OGHNcyO7Jnaa/uPP/6ocSZk61hEkUMz3LuN/OvJin29e/fWNhmya/Xr18fevXsRExOjGmMwGHB7xyLc3PwhjPm5GmZHpB1jfi5ubvoAt7cvAqRRNa5v376Ij49HWFiYdsmR3VN7bU9ISIAhW31HSKK/YhFFDi07+Yhie7169VCvXj2NsyF75+vri02bNuHll182GXfv+A6kL58EfcYNjTIj0kZKSgrSl72Geyd+Nhn36quvYt26dfD29tYoM3IUTZs2Rc2axc/zk1IiJ+WoFTIiW8UiihxajkoRZWq0gKg8XFxc8Nlnn2HevHlwcnJSjctLO4vUr/8POZdPapgdUcXZsWMH2rVrh7z086oxLi4u+PLLLzFnzhyT/z6IykoIofoar/aegEgJiyhyWFJK5CQfVuzr1auXxtmQIxFCYPTo0di2bRuqVaumGme8dwfpK/6NzCNc8Ey268EBujExMbh586ZqXPXq1bF9+3aMHDlSw+zIEam9xqu9JyBSwiKKHFb+jYswZN0q1q7T6dC9e3crZESOpkePHjh48CBatmypHmTU49a2z/DSSy8hN5frpMi23L9/H0OGDMH48eNhNKqvf3IJCMOBAwfQpUsXDbMjR9WzZ0/Fdv3ddOTfTtU4G7JVLKLIYakN24eHh8PX11fjbMhR1a1bF/Hx8Rg0aJDJuIULF6JLly5ISUnRKDOi8rlw4QIiIiKwYsUKk3EeTbshaOj7XIdKmqlRowZatWql2MfRKCotFlHksHIuKi8g5VQ+0pqnpydWrlyJd999F4D6QaIJCQlo06YNNm/erF1yRGWwefNmtG/fHseOHVMPEjr4Rf8d/o9NgM7VXbvkiGBiSl+Kib9ZoiJYRJFDkkYDci6dUOzr0aOHxtkQFayTmjRpEmo89SaEm6dq3O3btxEbG4spU6bAYDBomCFRyfR6PSZNmoTY2Fjcvn1bNU5XxQc1np4Onw79IYT6BwdEFUVtSl/OpeM8q49KhUUUOaT868mQufeKtbu7u6Njx45WyIioQJX64Qge/gFcqoeYjJsxYwZ69+6Na9euaZQZkWlXrlxBdHQ0Zs+ebTLONbA+gkd8hCqhytOpiLTw8MMPw9nZuVi78f4d6G9etkJGZGtYRJFDyrn4u2J7REQE3N05rYSsy6VaLQQNm4sqjSJMxm3fvh1t2rTBrl27NMqMSFlcXBxat26NX3/91WTcsGHDEDhkDpyr1tAoMyJlnp6eCA8PV+zLuaT8HoGoKBZR5JByLh1XbO/WrZvGmRAp07l5IOCJf8Mv+jlAqD9VX716FdHR0Zg6dSr0er2GGRIVTI1+88030adPH9y4oX44tJOTEz7++GMsXboUOhc3DTMkUqf2mq/2QStRUSyiyOEYjUbkqhRRUVFR2iZDZIIQAj4dBiDw2XcRHBysGmc0GjFt2jR0794dFy9e1DBDcmT6u9eQvuLfmD59usk1JLVq1cIvv/yCMWPGcP0TVSpqr/k5l37nuigqEYsocjjHjh2DMSereIeTC9dDUaXkXrs5Dh8+XOL5Zbt370br1q2xdu1ajTIjR3Xv5E5c/Wo0ci8rb9DzQO/evXH48GF07txZo8yISu/hhx+Gk5NTsXbjvTvQ3+K6KDKNRRQ5nJ07dyq2u9VszPVQVGkFBgYiLi4OkydPNhl3+/ZtPPnkk7i57TMY83k4L1mWMfc+bmz6ADc2vqe4Oc8DOp0O77zzDjZv3oyAgAANMyQqPS8vL7Rv316xT23aP9EDFi2iRIGnhRA/CCEuCyFyhRCpQojtQojnhRDFt0Ep23U8hBB9hBBvCCE2CCGOCyHShBB5QohMIcRZIcR3QoiBQojiHzGQQ1MrotxDHtI4EyLzODk5lfqNadaRLUhdMha5qWc1yo7sXe6VRKQuGYN7x3eYjAsKCsL27dsxefJk6HT8rJYqN9UpfRdZRJFpFnt2E0L4AfgJwEoAsQBqAXAFEASgO4BFAPYLIepY4HJdAWwB8DaAxwA0BxAIwAWAF4AGAAYBWAXgkBCiiQWuSXZASon4+HjFPrc6LTTOhqhsHnnkERw9erTEM830ty4j7ZvxuPPrMkgDN52gssnPz8e0adOQtuw16O+kmYzt0aMHjhw5wvWlZDPU/lZzr5zSNhGyORYpooQQrgDWo6BYAoBLAN4AMBjAqwAe/CW2BbBFCOFjiesCOAlgCYBJAIYCeArA8wD+A+BWYUxLADuFEEEWuibZsKSkJKSnpxfvEDq41WysfUJEZRQcHIy4uDjMmjVL8ayTP0gj7u5ZgbRvJyDvBjedIPMcO3YMHTt2xNSpUwFpVI1zdnbGrFmzsG3bNgQGBmqXIFE5RUREKG54Ysi4Bn2m+o6TRJYaiXoZQJfC24cAtJJSviOlXCmlfB8FxdO2wv5mKCiwyiMBQE0pZXMp5Ugp5Wwp5TIp5Rop5RdSyn8CaAhgf2F8DQBvlfOaZAf27Nmj2O4aWB86F66HItui0+kwceJE7N69G3Xr1jUZm5d2DqlLxuKDDz6A0aj+ZpgIAPR6PWbMmIH27dvj8OHDJmMbNWqEffv2YeLEiYqL9Ikqs6pVq6JFC+WZKLlXEjXOhmxJudcoFa5zerDSWQIYLqW8XTRGSpkjhBgO4AIATwCjhRCzpJQ3y3LN0txPSnlLCPEyCoo6oGCKITk41al8tTjjkywvbNImTa7TqVMnHD58GC+99BJWrlypHmjIx/jx47Fu3TosWrQIjRuXbvRVq5+jskmeZfsvG2X5f5d3PRk3N3+EvLRzJcZ6teyFQ/Fr4enpWZb0/kdl/DurjDmR5UVGRuL334ufDZV75RQ8m3BnSVJmiZGo7gAerHDeLqVU3O9USnkNBeulAMANwOMWuHZJTha5zel8ZKKIaqpxJkSWVbVqVSxfvhzLly+Hr6+vydjdu3ejZcuWmDFjBvLy8jTKkCo7adDj7t5VSF36fyUWUDp3L/g/8TqqPzLGIgUUkTVFRkYqtudeOanYTgRYpojqVeT21hJii/b3scC1S1K/yG3Tq2HJ7mVkZCh+0gSwiCL7IITA4MGDcfz4cbjXbWsyNi8vD1OmTEG7du2wf/9+k7Fk/3Iun0TqkjG4s+troIRNSNzD2iB45KfwbPywRtkRVSy1Iiov/QKM+TkaZ0O2whJFVNGJpL+VEHtQ5X4WJ4TwBDCvSBNPn3Rw+/fvVzyB3Mk7AM4+PMeE7EetWrVQY+A0VOv1DwgXN5Oxx48fR0REBMaOHYvMzEyNMqTKwpCdiZtbP0H6steQX8LGI8K1Cqr1fgU1Br0NZx9/jTIkqnj169dHjRo1incYDaWa1kqOyRJFVKMit5NLiL0MwFB4u6FQ2g7FTEIILyHEE4Vf/YUQI4QQcwGcBfBg/98jAKaW91pk27geihyJEALebR5F8MhPShxplVJi3rx5aN68Ob7//nvFDxvIvkgpkXXiZ1xd/BKyjm4rMd49tDVqPvcZvFv3UdzJjMiWCSFMTOnjVuekzBKH3xadfG9yL0gppV4IkQHAr/DangCyynn92gD+q9J3C8BSAFOklPdL+4BCCLURNb7btmFcD0WOyMWvJgKfnYXMgxtw59dvIfNzVWMvXbqEQYMGITo6GvPmzVPdsYpsW971FNzevgg5KUdKjBWuVeAX/Ry8WrF4IvsWGRmJdevWFWvPvcx1UaTMEkWUV5HbpZk4mo2CIgoAvFH+IsqUeAA7C69JDkxKiQMHDij2cSSKyquy7+ImdE7w6dAfVRpFoEXSKsTFxZmM//nnn9G6dWv885//xLRp01TjKvvPXVqOsgOb4f5d3N2zHJmHt5g88+mBnj17YtGiRQgLC7NoHpXx76Yy5kTaUh2JSj0LKSU/RKBiLHVOlNVIKROllEJKKVBQFAYCeAwF51L1BbAOwKrCNVKlfcx2Sl8AeGCAjTp37hzu3LlTvMPJBa416mmfEJEVuPgGYevWrfjmm29QvXp1k7EGgwHz5s1Dw4YNkXl0G6TRYDKeKi9pyEdGwnpc/XwUMg9tKrGAqlGjBr799lvExcVZvIAiqqzatm0L6Iqfc2a8fweXLl2yQkZU2VmiiCo6klSa00qrFLlt0VXMUkqDlPKalPIHKWUfAO8Udj0F4CtLXotsS0JCgmK7a2A9CCdLDMgS2QYhBIYOHYpTp05h6NChJcbfuHEDt7Z+gtSvxuD+OeXNWahyklLi/vkEXP3yFdzesQjG3Hsl3mfUqFFITEzEkCFD+Mk7OZQqVarANSBMsU/tPQQ5NksUUUU/3je5XU/hwbw+hd/mAyj5Gb183gJwpvD2QCFEswq+HlVSqlP5ghspthPZu4CAAHzzzTeIi4sr1aG7+TdScH3NdKQvm4gcrhGo9Hbv3o1u3brh+upp0N+6UmJ8ixYt8Ouvv2LhwoXw8/MrMZ7IHrkGNVRsV3sPQY7NEkXUmSK3w0qIrQ3gwVjpOVnBH2lKKY0AfizS1K0ir0eVl+pIlMoTJpGjiImJwbFjx/Dee+/By8urxPjcKyeRvuw1XFvzNo4fP65BhmSOhIQE9OnTB127dsXu3btLjPf09MSsWbNw6NAhPPwwz30ix+YarPyegCNRpMQSRVTRV9F2JcS2V7lfRSo6ZdBXNYrsll6vx+HDhxX7OBJFBLi6umLChAk4c+YMhg0bVqr7ZJ87gJYtW2Lw4MGqh1iTdn7//Xc88cQT6NChA7ZtK3nLciEERo4cibNnz2LixIlwcXHRIEuiys1NpYj67bffYDSWvBkLORZLFFFFn617lxDbp8jtrRa4dmk0KHLb5BbsZJ9OnDiB7OziGzQKVw84V6tphYyIKqfg4GB8/fXX2LNnT8Ei6xJIKbFy5Uq0bNkSjz/+OKe8WMH+/fvx1FNPoVWrVli/fn2p7uNWuxkSEhLw5ZdfIjg4uIIzJLIdLv6hEM7FDyjPyMjAmTNnFO5BjswSRdTPAK4X3u4phGiuFCSEqAHgmcJvcwCU7tm+HIQQtQE8WqRJ+aAgsmvq66EaQAib36CSyOIiIyNx4MABfP311wgNDS3VfTZs2ICOHTsiJiYGP//8MzegqEBSSmzevBlRUVHo1KkT1qxZU6rft5NPDfg/PgmBz85Gu3YlTRwhcjxC5wTXwPqKffyQiP6q3O8gpZR6ADMKvxUAvhZC/M+qVCGEOwoOvX2wzfinUsqbSo8nhFgihJCFX1NVYmYLIUy+sgsh6gP4AYBHYdNOKeWJ0vxMZF9U10NxKh+RKicnJwwbNgynT5+GX/cXoKviU/KdAPz000/o3r07OnbsiGXLliEvL6+CM3Uc0pCPr7/+Gi1btkRsbCx27txZqvvpqvjAN+o51HphATybdOaue0QmcF0UlZal9naeD+BJAF0AtAVwVAixEMA5FGwm8XcATQtjT+LPrcfL6kUArwoh9qFgdOk0CnYJdAZQE0BnFIxAuRbGpwF4vpzXJBul9sTnFsQiiqgkbm5u8Al/HF4tY5BxYC0yEtZB5pd8rnpCQgKGDh2KCRMm4OWXX8aLL76IwMBADTK2P/qMG8g6uhVZx+IwIutWqe9XtWpVoOVj8GnXDzo3j5LvQERwC26oeP4Oiyj6K4sUUVLKPCHE4wBWA+gOIATKhdIhAP2llHctcFkBIKLwy5RfADwvpTxvgWuSjcnNzVXdQUzt0yYiKk7n5gHfLkPh3TYWd/euQtbROEh9bon3S0tLw1tvvYXp06ejX79+eOGFFxATEwMnp+KHWtKfpNGAnOQjyDyyBdnnDpR4QG5Rnp6eGDt2LMaPH4+2c/ZWYJZE9kdt196jR49Cr9fD2ZlnS1IBi/0lSClvCyF6AhgEYBiANig4N+o2gBMAVgL4qnD6X3m1RsEmFpEAHgJQB0BVAAYAdwGcB5AAYJWUco8Frkc26uTJk9Dri//J6Tyqwsnb5LFmRKTAydMP1Xq+iKoRT2Oo13F89tlnyMws+dx0vV6PtWvXYu3atQgJCcHf/vY3DBkypFRnVDmSU6dOYenSpbgyfxEMZow6AYCXlxdGjRqFiRMnokaNGhWUIZF9c/YLhnD1gMy7/z/tOTk5OHPmDJo145GjVMCiq+plge+klH2llLWklG5SyiApZQ8p5aLSFFBSyr9JKUXh11SVmGQp5UIp5QgpZVsppb+U0kVK6S6lDJRSRkopx7KAIrWtzV1r1OO6AKJycPL0xbvvvouUlBS8/fbbqFatWqnve+nSJUyfPh1NmjRBu3btMHfuXFy+fLkCs63ckpKS8N577yE8PBzNmjXD7NmzzSqgAgMDMXPmTFy8eBFz585lAUVUDkLo4BpYT7FP7T0FOSZuTUZ27ciRI4rtak+QRGQePz8/vPHGG0hJScHcuXNLvZvfA4cOHcKECRMQEhKCDh06YObMmThx4oRd7+4npcTx48cxa9YstG/fHvXq1cNrr72GgwcPmvU4DRs2xOeff47k5GS8/vrr8PPzK/lORFQi1xp1FdvV3lOQY+LETrJrpkaiiMhyvLy8MG7cOIwZMwYbNmzAxx9/jF27dpn1GAkJCUhISMDkyZNRv3599OrVCzExMYiOjoavr22flX7nzh3s2rULmzdvxubNm3Hp0qUyP1a3bt0wZswYPP7441xbRlQB1LY550gUFcUiiuyW0WjkSBSRxpydnTFgwAAMGDAAR44cwfz587F8+XJkZWWZ9Tjnz5/H/PnzMX/+fOh0OoSHh6NLly6IjIxEZGRkpd/pz5B1G7mpp5Fz8Xe0a/cmDh8+XK7RNeHqAa+HemDf17O4JoOogql90Prg3zGXAxDAIors2IULFxTfuHl4eMDZr6YVMiJyLK1bt8bChQsxd+5cfP/991i0aBH27jV/tzij0Yj9+/dj//79f7TVrVsXbdu2RevWrdGqVSu0atUKtWvXhk6n7Sx1g8GApKQknDp1Cnf3rkZu6hnkpZ2DIfPGHzGHyvH4biEt4NWiOzyadIHOtQoLKCINuPiHADpnwPi/S/lv3bqFy5cvIyQkxEqZUWXCIorsltqwe8uWLZGq4xQYIq14eXlh5MiRGDlyJBITE7FixQosW7YM58+X/eSJpKQkJCUlYc2aNX+0ValSBfXr10fDhg3RoEED1K5dG8HBwX98Va9eHd7e3qWeAmc0GnH79m3cvHkTN27cQFpaGi5evIiUlBSkpKTgwoULSExMRG5uyVu9m6Nu3bq4VbMTPFv0gItvkEUfm4hKJpxc4OJfB/nXLhTrO3z4MIsoAsAiiuyY2lS+Nm3aIFXjXIioQJMmTTBt2jRMnToVCQkJWL58OdatW4eUlJRyP3Z2djaOHz+uejbcA56envD29oa7uzucnJxw5XYOhNBBSiOkPu+PL5f3cmE0lv58pvKoV68eBg4ciKeeegrt2rVD3dc3a3JdIlLmGlhPsYg6cuQI+vXrZ4WMqLJhEUV2S20kqnXr1thc/HmRiDQkhECHDh3QoUMHfPjhhzh69CjWrVuHdevW4ejRoxV67Xv37uHevXslxlXk/oBCCISHh+PRRx/FY489hjZt2nCdBVEl4lqjHpSeJbi5BD3AIorslqmRKFy4pnE2RKRGCIHWrVujdevWmDp1KlJTU/HTTz/hxx9/xI8//oi0tDRrp2gRtWvXRrdu3dCrVy/06dOH5zkRVWI8K4pKwiKK7NL169eRmlp80p5Op0OLFi2ANTuskBURlUZwcDCGDRuGYcOGQUqJ06dPIz4+HvHx8dizZw8SExOtnWIpCLj4h8A1qBHm/WswoqKiULduXY42EdkItR36UlJSkJGRAR8fH40zosqGRRTZpRMnTii2N27cGFWqVNE4GyIqKyEEmjRpgiZNmuC5554DANy+fRtHjx79n6/Tp0+XaopeRfDx8UGzZs3w+z0vuPqHwjW4IVwD60PnWvBc89xzsVbJi4jKTufmAeeqgdDfTS/Wd+LECURERFghK6pMWESRXVJbWN6iRQuNMyEiS/Pz80NUVBSioqL+aJNSIj09HefOncPZs2eRnJyM1NTUP77S0tKQkZGBzMxMs67l7e2N6tWrw9/fH9WrV0dISAjq1KmD0NBQhIaGokGDBqhZsyaEEAibtMnCPykRWZNLQKhiEXX8+HEWUcQiiuwTiygixyKEQFBQEIKCgtC5c2fVOKPRiKysLGRkZCAvLw8GgwFRc7YXHKAJAeHiCuFc8HVu9hNwdXXV8KcgosrExT8U2ecOFGsvaQdQcgwsosgusYgiIiU6nQ4+Pj7/s57BpfoZxVgWUESOzTUgVLGdRRQBgLZHuxNpQErJIoqIiIjKxcWfRRSpYxFFdufKlSu4e/dusXY3NzfUr1/fChkRERGRrXGpVhsQxd8qX7t2Ddeu8agUR8ciiuzO77//rtjerFkzODk5aZwNERER2SLh7AKXarUU+9R2ASbHwSKK7A6n8hEREZElcEofqWERRXaHRRQRERFZggs3lyAVLKLI7rCIIiIiIktw5UgUqWARRXbFYDDg5MmTin0sooiIiMgcpkaipJQaZ0OVCYsosivJycnIyckp1u7t7Y2QkBArZERERES2ytk3CG5ubsXaMzIycOXKFStkRJUFiyiyK4mJiYrtzZo1gxBC42yIiIjIlgmdE5o2barYd/r0aY2zocqERRTZFbUiqkmTJhpnQkRERPagcePGiu1q7znIMbCIIvR9OzYAACAASURBVLvCIoqIiIgsSe09BIsox8YiiuwKiygiIiKyJBZRpIRFFNkVtfnJLKKIiIioLNTeQ3BNlGNjEUV24+bNm7h+/XqxdicnJ9SrV88KGREREZGta9SokWL7pUuXkJWVpXE2VFmwiCK7ofaJUP369eHq6qpxNkRERGQPPDw8EBqqfF7UmTNnNM6GKgsWUWQ3uB6KiIiIKgJ36KO/YhFFdoProYiIiKgicF0U/RWLKLIbap8GqX16RERERFQa3KGP/opFFNkNTucjIiKiisAiiv7K2doJEFlCXl4ezp8/r9hnzkhU2KRNlkqJqFLi37ht4/8/Iu2FTdoEfdYtxb7fTyYi9LUNEDqnEh8neVaspVMjK+JIFNmF8+fPw2AwFGsPCAhA9erVrZARERER2QsnTz8I1yrF2qU+D/qM4serkP1jEUV24dy5c4rtamc7EBEREZWWEAIu1Wor9ulvp2qcDVUGLKLILqgVUQ0bNtQ4EyIiIrJHzn41Fdv1d1hEOSIWUWQX1NZDNWjQQONMiIiIyB65+AUrtnMkyjGxiCK7oDYSVb9+fY0zISIiInvk7KtcROVzJMohcXc+sgtqRZTaSBR3yCF7x79x28b/f0TaK+nf3Z49vui8+cNi7fVcM3G8yH25i6Zj4EgU2Txp0CMlJUWxjyNRREREZAlq7ykuXLgAo9GocTZkbSyiyObpM65Dr9cXa69evTr8/PyskBERERHZm8DAQHh6ehZrz87ORmoqp/Q5GhZRZPP0t68qtnMUioiIiCxFCKG6TEBtWQHZLxZRZPP0d9IU27kzHxEREVmS2ge0arsEk/1iEUU2L19lJIpFFBEREVkSR6LoARZRZPPUDrnjdD4iIiKyJBZR9ACLKLJ5+tuczkdEREQVj9P56AEWUWTTpDSqHnLHIoqIiIgsydRIlJRS42zImlhEkU0zZN4EDPnF2r28vBAQEGCFjIiIiMhe1a5dG25ubsXaMzIycOPGDStkRNbCIopsmtp6qAYNGkAIoXE2REREZM90Oh3q1q2r2Md1UY6FRRTZNP2ddMV2bipBREREFUHtPUZycrK2iZBVsYgim6a/e02xXe1TIiIiIqLyUHuPwSLKsbCIIpumz1AuokJDQzXOhIiIiByB2nuMlJQUjTMha2IRRTZNf1d5Ol9YWJi2iRAREZFDUHuPwZEox8IiimyaPuO6YjuLKCIiIqoILKIIYBFFNkwaDTCoFFGczkdEREQVwdR0Pp4V5ThYRJHNMmTeBKSxWHu1atXg7e1thYyIiIjI3vn7+8PDw6NYe05ODq5dU16rTfaHRRTZLLVNJTiVj/6/vXsPj6q69z/++SaQhKsgEBHlpg2tVUSgilqpFFEUKRftETxaRBFbe7zU/vRYa7XUaqVear1StCoq1rsVWytSFSyKYhFQOIqICIpSLpFrQoAk6/fH7AwTMjsXsmdmz8779Tx5umfvPbMWENP9yVrruwAASBUzY0ofCFHIXn5FJZjKBwAAUsnvWYMQ1XQQopC1/PaIYiQKAACkkt+zBmXOmw5CFLIWIQoAAGQC0/lAiELWqtjKdD4AAJB+TOcDIQpZi5EoAACQCUznAyEKWclVVqh868ak1xiJAgAAqVTbdD72imoaCFHIShXbN0mV5TXO77fffmrXrl0GegQAAJqKwsJCFRQU1DhfWlqqyh1bM9AjpBshClmJPaIAAECmmJnvzBe/5QaIFkIUspLfHlGEKAAAkA5+zxx+zyiIFkIUslLFtuTrobp165bmngAAgKbIbyTK7xkF0UKIQlaq2Fac9HzXrl3T3BMAANAUHXzwwUnP+z2jIFoIUchK5T6/5fH7gQYAABAkv2cOv2cURAshClnJ77c8Bx10UJp7AgAAmiK/Zw5GopoGQhSykt98Y0aiAABAOjAS1bQRopB1XEW5Kko2J73WpUuXNPcGAAA0Rb5rorYXy7nKNPcG6UaIQtapKPlaUs3dwDt16pR04zsAAICgtW3bVm3atKl5obJClaVb0t8hpBUhClmnfCvroQAAQOb5PXuUsy4q8ghRyDqshwIAAGHgX+acdVFRR4hC1iFEAQCAMCBENV2EKGSd8u3Jh8gJUQAAIJ38K/QxnS/qCFHIOuwRBQAAwsB/ryhGoqIu0BBlMWPM7O9mtsbMdprZWjN7zcwuNLNmAbbVzcx+bGaPm9lSM9tqZrvMbIOZvWlmN5hZt6DaQ3gwnQ8AAIQBI1FNV5Chpr2kZyUN3utSZ+9rsKSLzWy0c+7zRrb1gqQRkizJ5Y7e13cl/a+Z/co5d1tj2kO4+G1iR4gCAADpxJqopiuQEGVmeZJmSBronfpC0v2SVkg6WNIFkg6T1E/Sy2Z2nHNuayOaPEJ7AtS/Jc2WtFzSVq+9MxULUfmSbjWzfOfcTY1oDyHhXKUqtn+d9BrT+QAAQDr5h6hiOedkluz3/YiCoEaiLtaeALVQ0hDn3Kaqi2Z2j6QXJA2V9G1J10m6qhHtlUm6V9LdzrmPk1y/w8yukPQH7/UkM3vGObe8EW0iBCpLtkiVFTXO+254BwAAkCIdOnRQfn6+du7cWe28K9+pyp0lyi1onaGeIdUavSbKW+d0rffSSRqXGKAkyTlXJmmcpBLv1KVm1qERzQ50zl3iE6Cq2rxD0nPey2aSzmlEewgJpvIBAICwMDOKSzRRQYxEDZbUyTt+zTn3f8lucs6tN7MnJU1QbJrdSEkP7UuDe4e0Wjyj2NQ+Seq9L20hXCgqASBdevzipUx3AUAWOPjgg7Vy5coa5yu2bpQ69Uh/h5AWQVTnOyXheGYd9yZePzWAtuuyLeG4RRraQ4oxEgUAAMLEv0IfI1FRFkSIOiLh+L067l3g875USWxjdRraQ4pRVAIAAISJ73S+7ZQ5j7IgQlSvhONVddy7RlJVVYAiS2HJEm+t1vkJp5iXEQEVJclnch544IFp7gkAAID/M0hFyeY09wTpFMSaqHYJx7WOWzrnys1sq6T2XtutJG0PoA/JXCnpW97xB2pAiDIzvxG1b/mcR5pU+vxA6ty5c5p7AgAA4P8MUlFKiIqyIEaiEms3ltXj/h0JxympSW1m35f0W+9luaSfOOcqU9EW0stvJIoQBQAAMsHvGaRye33roCEbBbVPVGiY2bcUq8pX9Wf7pXPu7YZ8hnOuv89nv6fYhsHIEL8QdcABB6S5JwCiYtXk0zPdBQBZzHckyueZBdEQxEhU4nS8gnrcn1glb5vvXfvAzHpKelVS1R5Uf3TO3RpkG8icyspK3/nFhCgAAJAJfs8gFSWb5ZxLc2+QLkGEqMSn2o613egVe2jrvdytPZvvNpqZdZX0uqSqEil/cs5dEdTnI/OKi4ulJLMyLa+FWrVqlYEeAQCApq59+/Zq3rx5jfOufKfcrh1J3oEoCCJELU847lHHvQdLyvWOV7iA4rmZdVEsQFW1/5Cknwbx2QiPdevWJT2f26p9mnsCAAAQY2ZM6WuCgghRSxOOk64lSvAdn/ftMzPrrFiA+oZ3arqkiUEFNITHf/7zn6TnCVEAACCTCFFNTxAh6pWE46F13HtqwvHMxjZsZp0kvSbpm96ppySNpxJfNPmHqHZJzwMAAKSDf4iizHlUBRGiZkva4B0PMbPDk91kZoWSxnovyyTNaEyjZra/YkUkvu2del7Suc65Cv93IZsxEgUAAMKIkaimp9EhyjlXLukm76VJetTMqj3VmlmBpEcU21xXku5xzhUn+zwzm2Zmzvua5HPPfpJmSTrSOzVD0livL4go1kQBAIAwYiSq6Qlqn6gpks6UNFCxfZTeN7OpklYoVkxigqTDvHs/lHRjI9t7WXvWX30l6S+STjez2t5T6pyb1ch2kUF+I1E5hCgAAJBBfmXOKxmJiqxAQpRzbpeZjZT0rKTBkroqeVBaKGm0c25LI5s8LuG4i2JroeqyWnVXD0SI+U7na02IAgAAmcN0vqYniDVRkiTn3CZJQxRb9/SSYiNEuyStU6x63kWSBjjnPg+qTTQtrIkCAABhRIhqeoKazidJ8sqKP6X6jQz5fcZ4SePruKfWeXuIJt8Q1ZLqfAAAIHN8Q9R2QlRUBTYSBaTS7t27VVyctBaJclvtl+beAAAA7OG3JqqidIvYeSeaCFHIChs2bFCy/ZNzWrSV5TbPQI8AAABiWrdurdatW9e8UFmuyrLt6e8QUo4QhazARrsAACDMmNLXtBCikBUoKgEAAMLMd0ofxSUiiRCFrOC/RxQjUQAAIPOo0Ne0EKKQFdatW5f0PCNRAAAgDPxCVGXJ5jT3BOlAiEJWYDofAAAIM0aimhZCFLLChg0bkp5njygAABAGhYWFSc9XlG5Nc0+QDoQoZAW/PaJyWrZNc08AAABq6tixY9LzlWWEqCgiRCErbNy4Men53BaEKAAAkHm+IYqRqEgiRCEr+IWoHEIUAAAIAb8QVbGDEBVFhChkBf+RqDZp7gkAAEBNHTp0SHq+khAVSYQohN6OHTtUWlpa84LlyPJbpb9DAAAAe9l///2Tnq8sK5GrrEhzb5BqhCiEXm1FJcwszb0BAACoqXnz5mrXLlnVYKfKsu1p7w9SixCF0POdylfAeigAABAevlP6KC4ROYQohJ5vUQnKmwMAgBDxLy6xJc09QaoRohB6lDcHAADZwLfM+Y5tae4JUo0QhdDzXRNFiAIAACFCmfOmgxCF0PPfI4ry5gAAIDwoc950EKIQekznAwAA2YDpfE0HIQqhR2EJAACQDXyn81GdL3IIUQg9vzVRjEQBAIAw8R+Jojpf1BCiEHr+a6IIUQAAIDz81kRRWCJ6CFEIPUIUAADIBqyJajoIUQg938ISrIkCAAAh4h+iGImKGkIUQq20tFQ7duyoeSEnV5bXMv0dAgAA8LH//vsnPV9Ztl3l5eVp7g1SiRCFUPPfaLeNzCzNvQEAAPDXrFkz5eS3Snrt66+/TnNvkEqEKIQae0QBAIBs4rcFi98vhpGdCFEINYpKAACAbOL3jOL3TIPsRIhCqDESBQAAsonfMwohKloIUQg1/zVRhCgAABA+jEQ1DYQohJrvdD7KmwMAgBDyG4liTVS0EKIQakznAwAA2cTvF72MREULIQqh5l9Yok2aewIAAFC3nILkzyiEqGghRCHUNm3alPS83w8oAACATPKbLeP3TIPsRIhCqG3ZsiXp+ZyC5BvZAQAAZJLlt0x63u+ZBtmJEIVQ27x5c9LzfruBAwAAZFJOQeuk5/2eaZCdCFEINd+RKEIUAAAIoRxGopoEQhRCjRAFAACyid8zCiEqWghRCK1du3Zpx44dNS9YjiyvIP0dAgAAqENtI1HOuTT3BqlCiEJo+Y5C5bWQGd+6AAAgfCy3uaxZfo3zlZWV2r59ewZ6hFTgSRSh5ReijKl8AAAgxFgXFX2EKISWb2U+ypsDAIAQ8/uFLxX6ooMQhdCiqAQAAMhGFJeIPkIUQosQBQAAspHfdD5GoqKDEIXQ8t9oN/kPJgAAgDDw23CXkajoIEQhtBiJAgAA2YjCEtFHiEJoEaIAAEA28ntWYTpfdBCiEFp+P2gocQ4AAMKMwhLRR4hCaPmPRLEmCgAAhJcxnS/yCFEILd8Q5bNYEwAAIAyYzhd9hCiEFtX5AABANsopYDpf1BGiEFoUlgAAANmINVHRR4hCaBGiAABANmKz3egjRCG0/KfzEaIAAEB45eSz2W7UEaIQSs453x80fhVvAAAAwoCRqOgjRCGUduzYofLy8poXcpvJmuWlv0MAAAD1ZHktkp4vKSlJ/nyDrEOIQijVNpXPzNLcGwAAgPqznFzfILV169Y09wapQIhCKFFUAgAAZDO/dVFM6YsGQhRCiRAFAACymd+6KIpLRAMhCqHERrsAACCbsVdUtBGiEEqMRAEAgGyWU5D8mYXpfNFAiEIo+Zc3J0QBAIDw89uShZGoaCBEIZSYzgcAALIZ0/mijRCFUPKdzleQvNINAABAmPiFKKbzRQMhCqHkvyaKkSgAABB+jERFGyEKoVTbZrsAAABh5/eLX0aiooEQhVCiOh8AAMhmjERFGyEKoUSIAgAA2Yw1UdFGiEIobd++Pel5y2uR5p4AAAA0nF+J85KSkjT3BKlAiEIolZaWJj2f07wgzT0BAABouJzm+UnP+z3jILsQohBKfj9gzOcHEgAAQJhYM0JUlBGiEEo7duxIet6a5aW5JwAAAA1nzZM/s/g94yC7EKIQSoxEAQCAbMZIVLQRohA6zjn/EMVIFAAAyAJ+v/glREUDIQqhU1ZWlvR8fn6+LCc3zb0BAABoOL9f/O7cuVMVFRVp7g2CRohC6Pj9hqZly+SlQgEAAMLGLMd3Sh/rorJfoCHKYsaY2d/NbI2Z7TSztWb2mpldaGbNAmyrpZkdZ2aXmtk0M1tqZuVm5ryvQUG1hfQiRAEAgChgSl90BRlq2kt6VtLgvS519r4GS7rYzEY75z4PoMkvJO0fwOcgZGoLUbvS3BcAAIB9xUhUdAUyEmVmeZJmaE+A+kLSdZLOlnSVpI+88/0kvWxmbQNodu/FMZ9L+k8An4sM8/vB0qJFizT3BAAAYN/5lTlnJCr7BTWd72JJA73jhZL6OOdudM496Zy7TbHw9Ip3/duKBazGmiHpWkmnSurknOue0AayGNP5AABAFFDmPLoaPZ3PW+d0rffSSRrnnNuUeI9zrszMxklaKamVpEvNbLJzrnhf23XOnbev70W4EaIAAEAU5LAmKrKCGIkaLKmTd/yac+7/kt3knFsv6UnvZb6kkQG0jQgiRAEAgChgJCq6gghRpyQcz6zj3sTrpwbQNiKIEAUAAKKA6nzRFUSIOiLh+L067l3g8z4gjhAFAACigBAVXUGEqF4Jx6vquHeNpKotmovMzAJoHxFDiAIAAFFAifPoCiJEtUs43ljbjc65cklbvZfNFCsyAVTjF6IocQ4AALJJDiXOIyuIzXZbJxyX1eP+HZLae8dtJG0PoA+BMjO/aYnfSmtHmii/3860bNlS7LYLAACyBYUloiuofaKAwDCdDwAARAFroqIriJGo7dozslSgukeWEudkbQug/cA55/onO++NUPVLc3eanFpD1OY0dwYAAGAfEaKiK4iRqMTH2o613ehtzNvWe7lbUkkA7SNiGIkCAABRwHS+6AoiRC1POO5Rx70HS8r1jlc451wA7SNiCFEAACAKcnxGoqjOl/2CCFFLE46TToNL8B2f9wFxhCgAABAFjERFVxAh6pWE46F13HtqwvHMANpGBFHiHIiG2bNny8xkZnr66adrvXfMmDEyMxUWFqapdwCQeqyJiq4gQtRsSRu84yFmdniym8ysUNJY72WZpBkBtI0IqrXEOYCssWjRovhxv3611+R5773YzhJ9+/ZNaZ8AIJ0YiYquRocobwPdm7yXJulRM2ufeI+ZFUh6RHs2173HOVec7PPMbJqZOe9rUmP7h+zDdD4gGqpC1H777adDDz3U974tW7Zo5cqVkuoOWwCQTRiJiq4gSpxL0hRJZ0oaqFgJ8PfNbKqkFYoVk5gg6TDv3g8l3djYBs1ssKTBe51O/BXmBDMbstf125xzFMkOOUIUoszMMt2FuFTX9qkKUX379q31z71o0aJ4XxiJAhAlfoUlCFHZL5AQ5ZzbZWYjJT2rWLDpquRBaaGk0c65LQE0+z1J19Zy/dwk5/4sdhoKPUIUkP127NihZcuWSap7dGnhwoXxY0aiAEQJ0/miK4g1UZIk59wmSUMUW/f0kqSvJO2StE7S65IukjTAOfd5UG0imghRQPZbsmSJKioqJNV/PVTbtm1rnfYHANnGbzofJc6zX1DT+SRJ3r5PT3lf+/oZ4yWNr8d9kyRN2td2EF6EKCD7JRaV6N+/9t0vqkaijjrqqFBNdwSAxmJNVHQFNhIFBKGystL3tzOUOAeyR1WIat26tXr16uV7X0lJiZYvj+3ZznooAFHDdL7oIkQhVMrKypKez8/PV04O365AtqgKUX369Kn1v93FixersrJSEuuhAESPNctLen7nzp3xKc/ITjyVIlTYIwpR55wLzVeqVFRUaMmSJZLqDkZvv/12/Li2kagXXnhBp59+ujp16qS8vDwdcsghuu6667Rr1y7fPtx3333q16+fWrRoocLCQk2cOFHbtm1T9+7d2dQXQFqYme9oFOuisluga6KAxmI9FJD9li1bFn84+MY3vlHrvX/7298kSQUFBTrssMNqXK+oqND48eM1ffp09ezZU2eccYZatmypf/7zn7rxxhv1wQcfaMaM6nu37969WyNGjNDMmTN11FFH6ZJLLtGmTZv05JNP6ssvv9Tnn3+uoUOHBvSnBYDaWfN8ufKdNc6XlpaqdevWGegRgkCIQqgQooDsl1iy3G+KriS99dZb+te//iVJOvLII9WsWc3/S7riiis0ffp0XXHFFfr973+v5s2bS4qFq+HDh+vFF1/UvHnzdPzxx8ffc9lll2nmzJm68cYb9ctf/jJerOKnP/2pjj76aElMHQSQPoxERRPT+RAqhCgg+yVW5nvuueeSzvv/5JNPdPbZZ8dfH3744TXueffdd3XPPfdo2LBh+sMf/hAPUJKUm5ur888/X5I0f/78+PkFCxZo6tSpGjFihK699tpq1f769euno446Kn4MAOlAhb5oYiQKoUKIArJfVYjKycnRu+++q+9973uaOHGiunXrpi1btmj27Nl66KGHqq1nev/99/X888/rG9/4ho488khJ0l133SXnnNq0aaNJkybVaGfFihWSFC9MIUl33323nHO6+uqrk/atQ4cOkghRANInhxAVSYQohAohCsh+ixcvliSdc845mj9/vubNm6d58+ZVu6djx4569NFHdeaZZ0qKTQE888wztWzZsvg9s2bNkiQ99VTtWw927949fvzKK6+oXbt2Ou6445Leu2bNGrVr106HHHJIw/9gALAPKHMeTUznQ6j4/UBhjyggO3z22WfavHmzJOmYY47R3LlzNWHCBHXu3Fn5+fnq2bOnLrnkEi1ZskRnnHGGrrzySrVu3Vpdu3bVZZddpqKiIkmxtVQbNmzQgAED6qwy+MMf/jD+nnXr1qlr165JN+1dvXq1li1bxn5UANLKr8w5ISq7MRKFUKHEOZDdEtdDHXXUUSosLNSf//xn3/tvvfVW3XrrrTXOV5Vg37BhQ73bzs3NVW5uroqLi5Nev+GGG+ScU//+/ev9mQDQWKyJiiZGohAqTOcDsltViDKz+NqmfdGiRQv16dNHK1eu1BNPPJH0nvnz51f7xUvz5s1VVFSkr776Sv/85z+r3XvbbbfpoYceksR6KADpRYiKJkaiECqEKCC7VYWonj17qm3bto36rNtuu03Dhg3Tf//3f+uBBx6IV9b78ssvtWjRIq1fv16bNm2q9p5rrrlG5513nkaMGKGzzz5bHTt21OzZs7V+/Xr17dtXixYtIkQBSCtKnEcTIQqhQogCsltViOrTp0+jP2vIkCF6++239fvf/15z587V3Llz1bZtW3Xu3FkDBw7UGWecUWPt07hx47Rp0ybdeeedmj59urp06aLRo0fruuuuU79+/dS6dWv16tWr0X0DgPqiOl80EaIQKoQoIHutX79eX331lSTFR40aq3///nr66acb9J7LL79cl19+ebVzmzZt0urVq3XCCSckLToBAKnCdL5oYk0UQoUQBWSvxKISQYxEBamqb0zlA5BulDiPJkaiECqUOAey19ChQ+NV9cKGEAUgU6w5Jc6jiJEohAolzgGkAiEKQKYwEhVNhCiECtP5AKTC9OnT5ZxT7969M90VAE2MX2EJqvNlN0IUQoUQBQAAooSRqGgiRCFUCFEAACBKrHlB0vOEqOxGiEKoEKIAAECUUOI8mghRCBVCFAAAiBKm80UTIQqhQolzAAAQJTmUOI8kQhRCxa9SDSEKAABkI0aiookQhVApKytLep7pfAAAIBv5bba7c+fONPcEQSJEIVT8RqIKCpJXtgEAAAgzy00eotgnKrsRohAqfiNRhCgAAJCNrFnyEOX3zIPsQIhCaJSXl6u8vLzGeTNT8+bNM9AjAACAxrFmyZ9hCFHZjRCF0PCbG1xQUCAzS3NvAAAAApDTTFLN5xi/Xx4jOxCiEBpU5gMAAFFjZr7FJRiNyl6EKIQG66EAAEAU+RWXIERlL0IUQoMQBQAAooh1UdFDiEJoEKIAAEAUUaEveghRCA2/HySsiQIAANnML0SxV1T2apbpDgBV2GgXTUGPX7yU6S7ErZp8eqa7AABNAiNR0cNIFEKD6XxANEybNi1WjcpMc+bM2efPqfqM8ePHB9Y3AMgECktEDyEKoUGIAgAAUURhieghRCE0CFEA0oVRLgDpxJqo6GFNFEKDzXYBJHLOZboLABAI1kRFDyNRCA1GogAAQBQRoqKHkSiEBiEKTVkqK+WFqSIgADRFlsuaqKhhJAqhQYgComvGjBk67bTT1LlzZ+Xn56tHjx6aOHGiVq5c6fueutYtOef01FNPaeTIkerWrZsKCgrUokULde3aVX379tVPfvITPf/88yovL4+/p0ePHjKz+OtHHnkk3k7iVzK7du3SlClTNGTIEB1wwAHKy8tTp06dNHDgQN16663avn17vf4u3njjDf3whz/UgQceqPz8fHXt2lVnnXWW3nzzTUnSpEmT4v1YtWpVjfePHz++Wj9LSkp0yy23aMCAAerYsaPMTD/72c+q/T3NnTtXV199tQYOHKjOnTsrLy9PrVu3VlFRkcaNG6e5c+fW2udVq1bF25w0aZIkad68eRo7dqy6du2qgoICHXroofrpT3+qNWvWVHvvsmXL9JOf/ERFRUVq0aKFCgsLddZZZ2np0qX1+vsCooCRqOhhJAqhwWa7QPRUVlbqggsu0MMPP1zt/OrVq/XnP/9ZzzzzjGbNmqVjjjmmQZ9bWlqqkSNH6tVXX61xbc2aNVqzZo0WWCO6CgAAGYVJREFUL16sqVOn6osvvtDBBx/cqD/H8uXLNXz4cH3yySfVzm/cuFFvvvmm3nzzTd1xxx2aMWOGjj76aN/PueaaazR58uQa/X3mmWf03HPP6Xe/+12D+vXZZ59p6NChNfqV6M4779QVV1xR4/zu3bu1YsUKrVixQo899pguvvhi3XPPPcrJqfv3q3/4wx901VVXqbKyMn5u5cqVmjJlil544QXNmTNHvXr10tNPP63x48dXW/NaVlamZ555Rn//+9/1yiuvaODAgQ36MwPZiMIS0UOIQmiw2S4QPddff73eeustnX766Tr//PPVs2dPFRcXa9q0afrLX/6iLVu26JxzztFHH32kZs3q/39Jv/nNb+IBasCAAZowYYKKiorUrl07bd26VR9//LHmzJmjv/3tb9XeN2vWLO3atUu9e/eWJI0cOVI33nhjrW1t2LBBgwYN0tq1ayVJp512mi688EL16NFDa9eu1eOPP64nnnhCa9eu1UknnaT33ntPRUVFNT7nrrvuigeo/fbbT1deeaUGDRqkvLw8LVq0SLfccouuueaaBgXK0aNH67PPPtNFF12k0aNHq7CwUGvWrFFFRUX8nvLycnXs2FEjRozQCSecoKKiIrVq1Urr1q3TkiVLdPfdd+uLL77QlClT1LVrV11zzTW1tjlz5ky9++676tevn372s5/psMMO0+bNm/Xggw/G/x4uuugi3XLLLTrnnHPUo0cPXXnllerbt6927dqlZ555RnfddZd27Nih8847Tx9//LGaN08+1QmICkaioocQhdBgOh8QPW+99ZZ+/etfx6eAVTn55JOVn5+vhx9+WCtWrNA//vEPjRgxot6f+8QTT0iSjj76aM2dO7fGQ/j3vvc9TZw4Udu2bVN+fn78fK9evard165dOx1xxBG1tvXzn/88HqCuvvrqGiNJp59+uo4//nhdeuml2rZtmyZOnFhjk+H169fHw8n++++vefPm6Zvf/Gb8+jHHHKOzzz5bJ554oubPn1+Pv4GYJUuW6MUXX9Tpp+9ZU9evX79q95x11lm69NJLq/09VDn11FN12WWXafjw4Xr11Vc1efJkXXLJJWrTpo1vm/Pnz9cPfvADPffcc9X+3k866STt3LlTzz//vN544w0NHz5cffv21WuvvVbt80444QTl5ubqjjvu0GeffaZ//OMfGjlyZL3/zEA2IkRFD2uiEBqEKCB6+vbtq1//+tdJr1111VXx4zfeeKNBn/uf//xHUuyBvLZRjDZt2igvL/nDS32sW7dOTz31lCTpiCOO0E033ZT0vksuuURDhgyRFPuzLF68uNr1Rx55RKWlpZKkG264oVqAqtK2bVs98MADDerfuHHjqgWoZLp165Y0QFXJz8/X7bffLknaunVr0imSiQoKCvTggw8m/Xv/n//5n/jxhg0b9NBDDyUNZJdcckn8uKH/9kA2orBE9BCiEBqEKCB6zjnnHN9CDYcddphat24tSbUWmEimao3Tiy++qPXr1zeuk7WYPXu2du/eLUmaMGGCcnNzfe+9+OKL48ezZs2qdq0qmDRr1kw/+tGPfD/jO9/5Tp0jY4nOPffcet9bpaSkRKtXr9aHH36opUuXaunSpdXWNi1atKjW95988snq1KlT0mtHHXVU/Lh3796+f5ZDDjkkHq4+++yzhv4RgKzDmqjoIUQhNCgsAUTPt771rVqvt2/fXlJsBKQhLrzwQknSp59+qkMPPVTjxo3TY489puXLlwe6Se+SJUvix8cdd1yt9x5//PHx4w8++CDp53zzm99U27Zta/2c2gpT7K1Pnz71uu+rr77SVVddpaKiIrVp00Y9evTQ4Ycfrt69e6t3797q27dv/N6NGzfW+lm1/Zu2a9euXvcl3tvQf3sgGzGdL3pYE4XQoLAEED2tWrWq9XpVJbjEQgj18Ytf/ELFxcW66667tH37dj322GN67LHHJEmdOnXS0KFDdeGFF+rEE0/ct457iouL48edO3eu9d7CwkKZmZxz1d4nSV9//XX8nroccMAB9e5fVQitzeuvv65Ro0Zp27Zt9frMun4z3rJlS99riZX9arsv8d6G/tsD2YgQFT2MRCE0mM4HoL5ycnJ0++23a8WKFZo8ebJOOeWU+AjPhg0bNH36dA0aNEhjx46NT8eLotqmF0qx8HbWWWdp27ZtatasmS6//HLNnj1bX375pcrKyuSck3OuWpAJciQPQIw1Y01U1BCiEBqEKAAN1b17d1199dV65ZVXtGnTJi1cuFCTJk2Kj+Y89dRTuuGGG/b58zt06BA/ripm4Wf9+vXxAJL4PilWka/qnroEucbr2WefjY+K3XvvvfrjH/+oQYMGqUuXLtWKTWzatCmwNgHUZLmsiYoaQhRCgzVRABojJycnXg3wnXfeiU8ne/LJJ/f5M4888sj48TvvvFPrvfPmzYsf771WqWpfqo8//rjOaXX//ve/G9pNX4lrusaOHet734IFCwJrE0BN1pzpfFFDiEJosCYKQFB69OgRLyO+YcOGGterfjmzc+fOWj/n+9//fryU94MPPlitit3epk6dGj8+5ZRTql2rKn9eXl6u6dOn+37GwoULqwWfxiovL48fV5VYT+a+++4LrE0ANfmNRBGishchCqHBdD4A9fH111/rhRdeqDXQrF69Wh999JGkWDntvXXp0kWStHz58lrbKiwsjI/gLFmyRNdff33S+6ZMmRIvaz5o0KAaI1HnnXdefGTsuuuu0yeffFLjM6o26g1S4ubC06ZNS3rPXXfdpRdffDHQdgFUR2GJ6KE6H0KDEIWmrMcvXsp0F7LG1q1bNXr0aHXp0kWjRo3Sscceq0MOOUStWrXSxo0b9e677+ree++N/0y59NJLa3zGwIED9emnn2rhwoW6/vrrNXz48GqlxxPLc99+++169dVXtXbtWt10001avHixJkyYoO7du2vdunV6/PHH9fjjj0uKbe57//3312ivsLBQN998sy6//HIVFxfrmGOO0ZVXXqlBgwYpLy9PixYt0q233qpPP/1UAwYM0Pz58yXJd4+t+hozZox++ctfqqysTL/61a+0atUqjRo1SoWFhVq1apUeffRRzZgxQyeccILefPPNRrUFwJ9fYQnWRGUvQhRCgxAFoCG++uor3Xfffb5T0XJycnTttdfq/PPPr3Htqquu0tNPP63S0lL99re/1W9/+9tq1xMr1HXq1Elz5szR8OHD9cknn+ill17SSy/VDL0HHnigZsyYoaKioqT9ueyyy7R27VpNnjxZmzdv1q9+9asa/b355ptVUlISD1GN/fnXpUsXPfDAAzr//PNVXl6uqVOnVpt2KEl9+/bVs88+W2cJdwD7jpGo6GE6H0KDwhIA6qN79+5asGCBbrrpJg0fPlyHH364OnbsqNzcXLVt21Z9+vTRpZdeqsWLF/tW5vv2t7+tBQsWaMKECSoqKqpzT6NevXpp6dKluvfee3XSSSepU6dOat68uTp06KDvfve7uuWWW7R8+fI6N8q9+eabNWfOHJ1xxhnq3Lmz8vLydNBBB+m//uu/9MYbb+jqq6/W5s2b4/fvt99+Df8L2su5556rt99+W2PGjNGBBx6o5s2bq1OnTvrud7+rO++8U++8806D9qYC0HCEqOgx9oOoPzN7r1+/fv3ee++9THclkvLy8pLu51JWVhYvxes35WnV5NNT2jcgKGGatsd/N+E0ePBgzZ49W926ddPq1asz3R0ADZTs53zlzhJ98ccxNc63adNGW7duTUe3mpz+/ftr4cKFC51z/VPx+YxEIRQqKip8N8TMy0v+2xsAiJrVq1dr7ty5kqTjjz8+w70BEBSq80UPIQqh4FdmuKCgoNELqwEgDHbv3q1Vq1b5Xt+2bZt+9KMfxcuSX3DBBWnqGYCUy22W9Hlm9+7dqqioyECH0FgUlkAosB4KTQVT6JqukpISFRUVadiwYRo2bJgOP/xwtW7dWps2bdI777yjP/3pT/r8888lSaNGjdLJJ5+c4R4DCIqZqaCgIGk1vrKyMrVq1SoDvUJjEKIQCmy0C6ApKC8v14svvljrvkzDhg3To48+msZeAUgHQlS0EKIQCpQ3BxB1bdu21V//+lfNmjVL77zzjtatW6fi4mLl5ubqgAMO0LHHHqtzzz1Xw4YNy3RXAaSA3zMN66KyEyEKoUCIAhB1OTk5GjVqlEaNGpXprgDIAL9nGjbczU4UlkAoEKIAAECUMRIVLYQohAKFJQAAQJT5PdMQorITIQqhQGEJAAAQZYxERQshCqHAdD4AABBlrImKFkIUQoEQBQAAooyRqGghRCEUWBMFAACijDVR0UKIQiiwJgoAAEQZI1HRQohCKDCdDwAARBlroqKFEIVQIEQBAIAoYyQqWghRCAVCFAAAiDJCVLQQohAKFJYAAABRRmGJaCFEIRQoLAEAAKKMkahoIUQhFJjOBwAAoozCEtFCiEIoEKIAAECUMRIVLYGGKIsZY2Z/N7M1ZrbTzNaa2WtmdqGZNQuyPa/NVmb2czN7y8zWm1mZma02s6fMbGjQ7SE1WBMFAACijDVR0RJYqDGz9pKelTR4r0udva/Bki42s9HOuc8DarOv1+Yhe13q5n2dZWaPS7rAObcriDaRGoxEAQCAKGMkKloCCVFmlidphqSB3qkvJN0vaYWkgyVdIOkwSf0kvWxmxznntjayze6SXpZ0gHfqXUnTJW2U1FvSRZI6SDpHkpP0o8a0h9SisAQAAIgy1kRFS1AjURdrT4BaKGmIc25T1UUzu0fSC5KGSvq2pOskXdXINv+oPQHqIUkTnXOV3usnzGyqpH8pNiJ1rpk96Zx7qZFtIkUYiQIAAFHGSFS0NHpNlLfO6VrvpZM0LjFASZJzrkzSOEkl3qlLzaxDI9rsI2mU9/JzSf+TEKCq2lytWLirMmlf20PqEaIAAECUEaKiJYjCEoMldfKOX3PO/V+ym5xz6yU96b3MlzSyEW2OSTi+3wtpybys2JRCSfqOme29dgohQWEJAAAQZRSWiJYgQtQpCccz67g38fqpqW7TOeckvRJQm0gh1kQBAIAoY01UtASxJuqIhOP36rh3gc/76s3MchRbVyVJ5ZLeT3WbTdGGDRv017/+NW3tbdy4Mel5QhQAAIgCv2eadevW6f77709bP84880x16LDPq2rgCSJE9Uo4XlXHvWskVUjKlVRkZuaNFjXEwZKqxkO/dM6V13H/6oTjXr53oZrVq1frxz/+caa7QYgCAACRUFuISucz17HHHkuICkAQ0/naJRwnH07weIGnqrR5M0mtUtmep9jnvcgCrIkCAABRwDNNtAQxEtU64bg+K+N2SGrvHbeRtD0N7VVpU58GzMxvWmKfjz76SP3796/Px2S10tLSTHdBkjR48GCZWfz12i+3JL2v/z+vT1eXAAAAfPk9q5w6s3XS8+k2duzYJhHoPvroI0nqkarPD2qfqKYiZ8eOHRULFy6sax0WArJo0aJ63bdwXYo70jR9y/vfZRntBZB6fK+jqeB7PYPeD8mzihcumoI+qj74EqggQtR27RlZKlDdI0uJ0XfbPrZXpT4LZhrcnnMu6VBT1QiV33UgSvh+R1PB9zqaCr7X0ZTUMrMsEEGsidqccNyxthu9jXnbei93a8/muylpz5O4cm6z710AAAAAUA9BhKjlCcc96rj3YMUq80nSin2ozCfFKvxVrXM6yAtmtemecLzc9y4AAAAAqIcgQtTShOO6hoe/4/O+enPOVUr60HvZTLH5jiltEwAAAACqBBGiXkk4HlrHvacmHM9MdZsWK+uWeL0xbQIAAABAICFqtqQN3vEQMzs82U1mVihprPeyTNKMRrT5dMLxj83Mr8DEaZK+4R0vcM6tbESbAAAAAND4EOVtoHuT99IkPWpm7RPv8ULOI9qzue49zrnETXAT751mZs77muTT5vuSXvBedpN0j5lV+7OYWTdJUxJOJf2shnDO9aeiDZoKvt/RVPC9jqaC73U0Jan+fg9qn6gpks6UNFBSP0nvm9lUSSsUKyYxQdJh3r0fSroxgDZ/Juk4SQd4n3+EmT0mqVhSb0k/1p7KfI87514KoE0AAAAATVwgIco5t8vMRkp6VtJgSV2VPCgtlDTaOZd8K+eGtbnazE7z2jxE0gDva29/kXRBY9sDAAAAACmYNVGSJOfcJklDFFv39JKkryTtkrRO0uuSLpI0wDn3eYBtLpJ0pKT/J+ltSRsl7ZT0haRnJJ3mnDvHObcrqDYBAAAANG22b1s1AQAAAEDTFNhIFAAAAAA0BYQoAAAAAGgAQhQAAAAANAAhKgBm1sLMfmBmd5jZm2a23sx2mdlWM/vIzB42s5My3U8gCBbTy8z+28xuN7M53vd61f5u0zLdR6A23vfwGDP7u5mtMbOdZrbWzF4zswvNLKjtP4CMMbNcMzvCzMab2d1m9raZlda1FyeQbcxsPzM7y8ymmNl8Mys2s91mtsnM3jez+8zs6MDbpbBE45jZOZL+JKl1PW6fKWmcc25DansFpI6Z3S7p57Xc8ohzbnyaugM0iLcZfNV2HH6qtuMIrJoskG5m9pykM2q55TfOuUlp6g6QEmb2v5JukJRfj9unS/qxc640iLb5bVvj9dSeALVW0j8l/VvSekmtFNuA+GxJBZJOlfSqmR0X1D8gkAG5e73epti2At/OQF+AejOzPEkzFPu5LMW+b+/Xno3hL1BsY/h+kl72flZvzURfgQDs/bP6a0nFkooy0BcgVXppT4BaKelVSYsV2/aovaSTJJ2p2H8P50oqNLPTnHOVjW2YEBWMtyRNlvSyc65ir2sPm9ltiv2jHqjYvlZXS/p1ersIBOZDSXdIWiDpPUnLJZ0oaXYmOwXUw8XaE6AWShri7XEoSTKzeyS9IGmoYr8UuE7SVenuJBCQdyV9pNjP6fecc5+Z2XhJD2e0V0CwnGL7097qnHsjyfX7zWygpH8oNuhxiqTzFMB/B0znayQza5/4f8K13Ddc0t+8l58757qntmdA+pjZIO0JUUznQ+h465y+ktRJsf/T7e2c+78k9xUq9tvMVopt3n6Qc644nX0FUmWvEMV0PmS9BjyHXyLpbu/lv5xzJza2bQpLNFJ9/uE8L0sq8Y67mVnbFHUJAFDTYMUClCS9lixASZJzbr2kJ72X+ZJGpqFvAIB90IDn8GcSjnsH0TYhKk28aX6J66BaZKovANAEnZJwPLOOexOvn5qCvgAA0mtbwnEgz+CEqDTxpohU/Ra0VBIV+gAgfY5IOH6vjnsX+LwPAJCdEn+Wrw7iAwlR6XNRwvHMIKqCAADqrVfC8ao67l0jqapIUJGZWUp6BABIl8Tn8JeC+EBCVBqY2SGSrvFeOsUq+QEA0qddwvHG2m50zpVLqipt3kyxIhMAgCxkZsdLOt97WaZYheFGI0SlmJm1kvRXSS29U/c55/6dwS4BQFOUuCF6WT3u35Fw3CbgvgAA0sDMOkt6Wnsyz3XOuTVBfHaT2CfKzC5UbCPFRmtIOVAzy5X0F8X2hpJi+5JcGUQ/AD+Z+n4HAAAIC28gY4akg7xTL0m6PajPbxIhStKFkgYE9FmT6nOTmeVImiZphHfqY0mnOefq8xtQoDHS/v0OZIHtiu1eL0kF3uvaJFZv2uZ7FwAgdMysQNKLko7xTr0laYwLcINcpvOlgLcIeaqkc71Tn0o6ydt/BACQfpsTjjvWdqO3MW/VXn67tWePPwBAyJlZnqTnFdsfUJLelTTMORfoz/ImMRLlnDs2zU3eo9hogBQrozjYOfdlmvuAJioD3+9ANlguqad33EO1V+g7WFKud7wiyN9cAgBSx8yaK7ax7mneqUWSTnXObfV/175hJCpgZvZHST/1Xq5RLEB9nsEuAQCkpQnH/eu49zs+7wMAhJQ3i+AJ7VlKs0TSyc65TalojxAVIDO7VdLl3su1igWolRnsEgAg5pWE46F13HtqwvHMFPQFABAgr5jbdElneqc+lDTEOVecqjYJUQExsxu1p/LeOsUC1CcZ7BIAYI/ZkjZ4x0PM7PBkN5lZoaSx3ssyxSo7AQBCyivm9pCkMd6pj5WGWgSEqACY2a8kXeu93KDYP9yyDHYJAJDA20D3Ju+lSXrUzNon3uNVc3pEezbXvSeVv8UEADROQjG3cd6pFYoNZPwn5W2zXrZxzOwixf7xqvxa0gf1eOubzrmNqekVkDpm1k419zvrrj3VKD+Q9Le9rr/unHs91X0DauNVbHpV0kDv1BeK/fxeoVgxiQmSDvOufSjpeOfclnT3EwiCmfVU7Hs60ZGSfuAdz5X0r72uP+ecW5TqvgFBMbPfSbrGe7lb0s8Vq0lQl1nOudJGtU2IahwzmybpvH146/edc3OC7Q2QembWQ9JnDXzbb9i4F2HgjT49qz2lb5NZKGk0RYGQzcxskGLTWBvifOfctOB7A6SGmc2RdOI+vLWnc25VY9pmOh8AoMnwqjQNUWzd00uSvpK0S7G1rK9LukjSAAIUAKA2jEQBAAAAQAMwEgUAAAAADUCIAgAAAIAGIEQBAAAAQAMQogAAAACgAQhRAAAAANAAhCgAAAAAaABCFAAAAAA0ACEKAAAAABqAEAUAAAAADUCIAgAAAIAGIEQBAAAAQAMQogAAAACgAQhRAAAAANAAhCgAAAAAaABCFAAAAAA0ACEKAAAAABqAEAUAAAAADUCIAgAAAIAGIEQBAAAAQAP8fz937PuOGxZfAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 424 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x) = \\frac{1}{6} x^6$\n", "\n", "This is the first time the sextic ensemble is (approximately) sampled to the best of our knowledge.\n", "\n", "In this case, the conditionals associated to the $a_n$ parameters are not $\\log$-concave and we do not support exact sampling but perform a few steps (100 by defaults) of MALA.\n", "For this reason, we set `sample_exact_cond=False`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/6, 0, 0, 0, 0, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sampl_x6 = be.sample_mcmc(N=200, nb_gibbs_passes=10,\n", " sample_exact_cond=False,\n", " nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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RkZozuu3OO+/EQw89pIxxSB95MhZRRJqkpKRgzZo1yli/fv00Z/N3ISEhWLZsGSwW9UfC0KFDERcXpzkrIiIqqNjYWIwYMUIZ8/X1xfLlyxEcHKw5q78zegauWrUK6enpmrMhcgyLKCJNtm7dihs3buQ67+/vjyeffNINGf1d48aNMXr0aGXsypUrhg9hIiLyTLdW44uPj1fGx44di/vuu09zVrk99dRTygUt4uPjsWPHDjdkRJQ3FlFEmnz77bfK8x07dkR4eLjmbNQmT56MWrVqKWPLly/HN998ozkjIiLKr9WrV2Pt2rXK2D333INx48ZpzkgtIiIC7du3V8aMnp1E7sYiikgDm82G9evXK2OPP/645myMBQUFYdGiRRBCKONDhw7lJrxERIXA9evXDff7s1gsWLRoEfz9/TVnZax79+7K899++y0XNyKPxCKKSIOoqChcvHgx13khBDp16uSGjIw1a9bM8MF7/vx5TJ48WW9CRETktPHjxxtuUTF69Gg88MADmjOyr3Pnzsrz0dHRyIhTbw5M5E4soog0MBqO0KRJE5QpU0ZzNnl76623UL16dWVs/vz5OHTokOaMiIjIUb/88gveffddZaxWrVoe+TKsQoUKaNiwoTKWcnKf5myI8sYiikgDoyKqS5cumjNxTLFixfDBBx8oY1arFYMHD+YmiEREHshqtWLQoEGGQ+A+/PBDBAUFac7KMUbPxJsnf9acCVHeWEQRuVhsbCx+/fVXZcxdmxs6ol27dujdu7cytnfvXnz88ceaMyIiorwsXLgQUVFRytizzz6Lhx9+WHNGjjN6JqZfPA5rinqjYCJ3YRFF5GLbtm1Tnq9evTrq1KmjORvnzJ07F2FhYcrYmDFjcPnyZc0ZERGRkYsXLxquuBceHo7Zs2drzsg59913HypWrKiISKSd/UN7PkT2sIgicjGjIqp9+/aGq+B5ivLly+Ptt99WxuLj4/HGG29ozoiIiIy89tpryv0IAWDWrFkeOQc3JyEE2rVrp4ylnv1NczZE9rGIInKxH3/8UXm+devWmjPJn8GDB6NRo0bK2KJFi7B//37NGRER0T/t3r0by5YtU8aaNGmCgQMHas4of4yejalnWESRZ2ERReRCZ86cwalTp5SxRx55RG8y+eTj44P3338fFov642L48OFcZIKIyI2sVqvh1hR5fYZ7mlatWinPZ1w9C2tSvOZsiIwVjn9RRIWU0VC+e+65BxEREZqzyb+GDRti0KBBytjevXuxdOlSzRkREdEtixYtMlxM4qWXXkL9+vU1Z5R/lSpVQs2aNZUxDukjT8IiisiFjIqowjKUL6epU6ciPDxcGXv99dcNx+ETEZHrxMfHY+zYscpYmTJlPHJPqLwY9UaxiCJPwiKKyIWKUhFVqlQpTJ06VRmLjY3FtGnTNGdERESTJ09GXFycMjZ9+nSUKFFCc0YFx3lRVBiwiCJykfPnz+PcuXO5zgsh0LJlSzdkVHCDBg1CvXr1lLF58+bh+PHjmjMiIvJehw8fxrvvvquMNWrUCAMGDNCckTmMeqIyE2JgTU7QnA2RGosoIhfZs2eP8nz9+vUNh8V5Ol9fXyxYsEAZy8jIwCuvvKI5IyIi7ySlxIgRI2C1WpXxBQsWFJrFJP4pIiICtWvXVsbSLh7TnA2RWuH810VUCOzdu1d5/qGHHtKcibkeeeQR9OzZUxnbsGEDvvvuO80ZERF5n7Vr12Lr1q3KWP/+/dGkSRPNGZnL6FmZdvGo5kyI1FhEEblIUS2iAGD27NkICgpSxkaOHIn09HTNGREReY/U1FSMGjVKGQsJCcGMGTM0Z2Q+oyKQPVHkKVhEEblAeno6Dhw4oIwV9reDAFC1alWMGTNGGTtx4gQWLlyoOSMiIu8xf/58REdHK2MTJkxAhQoV9CbkAkYvHNNjjkPa1EMYiXRiEUXkAgcPHkRaWlqu8+Hh4Yb7XxQ2r776KqpWraqMTZkyBdeuXdOcERFR0Xf58mW8/fbbyliNGjUwYsQIzRm5Rt26dVGsWLFc52VGKjLizrghI6K/YxFF5AJGQ/kefPDBQjvR95+CgoIQGRmpjMXHxxsuh05ERPk3efJkw3355s2bh4CAAM0ZuYavry8aN26sjHFIH3mCovHbHJGHMVqZrygM5cvpiSeeQIsWLZSx//znPzhx4oTmjIiIiq70uLP48MMPlbG2bdviscce05yRaxkuLnGBi0uQ+7GIInKBffv2Kc8XhUUlchJCYM6cOcpYZmYmXn/9dc0ZEREVXQnbFimXNBdCIDIyEkIIN2TlOkYvHtNjuCchuR+LKCKTXb9+HadPn1bGHnjgAc3ZuN4DDzyAp59+Whlbu3YtduzYoTkjIqKiJyX6IFJO/6KMDRgwAPfee6/mjFzvwQcfVJ7PuHYBtoxUzdkQ/Z2vuxMgKkqqjdmA1HN/KGO+xcvi/pn/1ZzR31Ubs8El180MbQPhuxIyM/fS5u17/Qvlnp0HIcx7ZxM9o2gNWSEiskfarIj/8WNlTPgFYHPgw059vheWz9CIiAhUqFABFy9e/HtA2pBxhYtLkHuxJ4rIZOmXTinP+5e9U3Mm+viGlUHoA48rY+mXTiH58Ha9CRERFSHJf2xFxpVoZSys8RPwDS2lNyGN7r//fuV5o2ctkS4soohMln7pL+V5v4jqmjPRq/iDT8BSrIQylrBjMYdeEBHlgy09BQk7lypjPiElEda4h+aM9LrvvvuU59Mvq4fNE+nCIorIZOmXva8nCgAsAcEo0Vw9N8qadBWJ+9dpzoiIqPBL/HkNrEnqffdKtHwGFv9AzRnpZdwTxSKK3ItFFJGJZGYGMuLOKmP+EXdozka/kPrt4FemmjKWuHc1Mg1+ESAiotwyb8Qhcd8aZcwv4g4Uu6e15oz0M+qJyrgSjczMTM3ZEN3GIorIRBlXzwK23MvPWoLC4FOEx6zfIiw+CG81UBmTGam4bjAkhYiIckv4aSlkZpoyFt76X6Yu2OOpqlevDuEfnOu8zEzH8eNc6pzch6vzEZnIaKJrm+YP4vuZnbXl4d6Vlx5Dpxt7sHHjxlyR5N9/wK7PZzm8FK+rVhMkIvJ063pXRINZW5Wxzp0749svxuZ5jaLwGWqxWOBf9g6kKVa+/fXXX3H33Xe7ISsi9kQRmSr9snpRCaPhCEXV7NmzYbHk/niRUmL06NGQUrohKyKiwkFKiVGjRik/K318fDB79mw3ZOU+/gYLMx08eFBzJkS3sYgiMpFREWU0Mbaoqlu3Ll544QVlbMuWLdi0aZPmjIiICo8NGzbgxx9/VMYGDRqEu+66S3NG7mW0MNOhQ4c0Z0J0G4soIhMZLSpRv359zZm435tvvonQ0FBlbPTo0ZwQTESkkJGRgdGjRytjYWFhmDx5st6EPIDRgkVHjhzRmwhRDiyiiExy5coV2FIScwcsPqhZs6b+hNwsIiICY8eqx+wfOXIEn376qeaMiIg830cffYRjx44pY+PGjUOZMmU0Z+R+fqUqARC5zl+4cAGJiYrnLpEGLKKITPLnn38qz/uFV4C/v7/mbDzDiBEjULlyZWVswoQJSEpK0pwREZHnun79OiZNmqSMVa1aFcOHD9eckWew+AXCt0RZZczo2UvkaiyiiExiNKzAr5S6iPAGQUFBePvtt5WxS5cuYdasWZozIiLyXDNmzEBcXJxhLDCwaG+sa4/Rs5RD+shdWEQRmYRFlFrfvn3RsGFDZSwyMhIXLlzQnBERkec5e/Ys5s2bp4w9+OCD6NWrl+aMPAuLKPI0LKKITGJYRJWuojkTz2KxWBAZGamMpaSkYMKECZozIiLyPOPGjUNamnpj3Tlz5kCI3HOCvInRs5RFFLkLiygikxgXUd7dEwUAjzzyCLp27aqMffbZZ9zrg4i82oEDB7B06VJl7IknnkCzZs00Z+R5jHqiOCeK3IVFFJEJ4uPjERMTkzsgLPANr6g/IQ80c+ZM+Pj45DrPDXiJyJvd+gxU8fX1xYwZMzRn5JmMiqjo6GgkJydrzoaIRRSRKYzehPmWKAuLX4DmbDzTXXfdhcGDBytjW7du5Qa8ROSV1q9fj+3btytjQ4cORY0aNfQm5KEsAcHwCc29vLuU0nBJeCJXYhFFZALjRSW8ez7UP02aNIkb8BIRZcvIyMCrr76qjBUvXpxzRv/BaHg850WRO7CIIjIB50M5pkyZMnY34F20aJHmjIiI3Ofjjz827EUZP348SpUqpTkjz8YV+siTsIgiMsGJEyeU5719eXOVESNGoEoVdQ/dxIkTcePGDc0ZERHpl5iYaLixbrVq1fDSSy9pzsjzGT1Tjx8/rjkTIhZRRKYwLKK4qEQu3ICXiChrsZ0rV64oY9OnT/fqjXWN+JVUP1NPnjypORMiFlFEBWa1WnH69GllzDe8vOZsCoc+ffqgUaNGyticOXNw/vx5zRkREelz7tw5zJ07Vxlr3Lix12+sa8TomXry5Emu8ErasYgiKqBz584hIyMj13kRUAyWoDA3ZOT5uAEvEXmz8ePHIzU1VRnjxrrGfEJKQvjmXvE2OTkZly5dckNG5M1YRBEVkNEwAr/w8nwQ2vHwww+jW7duytjixYu5AS8RFUlRUVFYsmSJMtajRw80b95cc0aFhxAW+JYop4xxSB/pxiKKqICMPrh9S3AoX15mzpwJX1/fXOe5AS8RFUX2Ptu4sa5jjIb0Gc1NJnIVFlFEBWTcE1VBcyaFT+3ate1uwJt6+hfNGRERuc6GDRuwbds2ZezFF19EzZo1NWdU+Bg9W9kTRbqxiCIqIMOeKBZRDpk4cSLCwtRzx+K3fQpps2rOiIjIfNJm5ca6JjB6trKIIt1YRBEVkNEQAq7M5xh7G/BmXD2LpN++15wREZH5kg5txtGjR5WxcePGoXTp0pozKpzsrdBHpBOLKKICsNlsOHXqlDLmxyLKYfY24E3YuQy2tJuaMyIiMo8t7SYSdi1XxqpWrYphw4ZpzqjwMnq2njhxgvNoSSsWUUQFcOHCBaSlpeU6L/yDYAku4YaMCqfAwEBMnz5dGbPdTEDiz19pzoiIyDzXf/4KtpsJyhg31nWOT2hpwMcv1/kbN24Ybl5M5AosoogKwN6iElze3Dm9e/c23IA3cf86ZCbGac6IiKjgMq9fxo39a5WxBx54gBvrOkkIC/wMVr/lkD7SiUUUUQFweXPzWCwWzJkzRxmTmWlI2KneV4WIyJPFb/8UMjNdGYuMjITFwl/FnMV5UeQJ+C+XqACM5kP5hqs3AyT7WrZsie7duytjyX/8iPRL6v/fRESeKPX8Edw8ulMZ6969O1q2bKk5o6LBqIgyeiYTuYKpRZTI0ksIsV4IcV4IkSaEiBFCbBVCPC+EyL2rpsmEEOFCiFghhMzxVc3V9yXvdObMGeV53+IsovLLaANeQOLajx9z4jARFQpS2hC/9UNlzM/PD7Nnz9acUdHhW7ys8rzRM5nIFUwrooQQ4QC2AFgB4DEAFQH4AygHoDWAjwD8LIRQL8FlnrkA1P+6iExmXERFaM6k6KhVqxaGDBmijKWd/R03j/9Xc0ZERM5L/mMb0mPVw8tGjBiBGjVqaM6o6DB6xrKIIp1MKaKEEP4AvkZWsQQA5wBMANAHwKsA/sw+3wDARiGEemfNgufRDsBzAGwAUl1xD6KcDIuoMBZRBWF/A95FSElJ0ZwREZHjbOkpSPhpsTJWpkwZjB8/XnNGRYvRM5ZFFOlkVk/UEAAtso+jANwrpZwmpVwhpYxEVvG0OTt+N7IKLFMJIYIBfJD97bsALpl9D6Kc0tPTERMTo4z5hJXRnE3RUrp0aUycOFEZs16/hLlz52rOiIjIcdf3roY16ZoyNm3aNBQvXlxzRkWLUU/UuXPnYLVaNWdD3qrARVT2PKdx2d9KAM9IKeNztpFSpgJ4BkBy9qlhQohSBb33P0wDUB3AhRz5ELnMuXPnlPNzLMElYPELcENGRcuwYcPgG15BGZs+fTouXLigOSMiorxFR0cjcd8aZax+/fr417/+pTmjoscSUAwioFiu85mZmYYvN4nMZkZPVGsAt167b5VSHlY1klJeRtZ8KQAIANDNhHsDAIQQjQGMyP52mJTyhlnXJjISHR2tPM/5UOaIWA/AAAAgAElEQVTw9/dHeJt/K2PJycl44403NGdERJS3119/HbBmKGPz58+Hj4+P5oyKJqNnrdGzmchsZhRR7XMcb8qjbc74oybcG0IIPwAfI+vP8rWUUr2jHZHJOB/K9YLuaITA6g2VsSVLlmDv3r2aMyIiMrZz506sXLlSGQuq1QStWrXSnFHRxXlR5G5mFFH35Dg+kEfbXwx+riDGAKgH4AaAl0y6JlGeuDKf6wkhULL184BF/eZ2xIgRsNlsmrMiIsrNZrNh5MiR6qDFF+GPDNSbUBHHFfrI3cwoomrlOI7Oo+15ALdm/NUUQoiC3FgIUQe35z+Nl1KeL8j1iJxh9EHNRSXM5Ve6MkLvf0wZ27dvH5YuXao5IyKi3BYvXoyoqChlLKxRV/gZbBBL+eNr8KxlEUW6mFFElchxHGevoZQyE0Bi9re+AHLPCnSQEMKCrGF8Acjq4fpPfq+luPYB1ReAu8y6BxV+7InSp3jzvrAEqZc8HzNmDG7c4DRIInKfGzduYOzYscqYJbgEijftrTmjos+Hw/nIzcwookJyHDuyN1PODV5CC3DfFwE0RVbP1gtSSo7pIa04J0ofn8AQlGjxtDIWExOD6dOna86IiOi2adOmITY2Vhkr0eJpWAKCNWdU9HE4H7mbWftEaSWEqALg1m9N86WUv5p5fSllQ9UXgKNm3ocKL6vVinPnzilj7IlyjZB7O8CvTDVlbM6cOTh9+rTehIiIABw9ehTz5s1TxvwiqiOkfjvNGXkH3+JllefPnDmj3H6EyGxmFFFJOY4DHWgflOM4v2Nw3kNWD9gZAJPyeQ2ifLt48SIyMzNznbcEFINFsXcFFZyw+KBkmxeUsfT0dIwePVpzRkTk7aSUGD58ODIy1Eual2z9bwiDhXGoYCxBYQgKCsp1PiUlBVeuXHFDRuRtzCiiEnIcl7bXMHtj3lsTGzJwe/Ndhwkh+gHolP3ti1JKp69BVFCGi0qwF8qlAqvWR3CtpsrY2rVr8cMPP2jOiIi82bp16ww/d4JrN0dg1fqaM/IeQghUrVpVGeOQPtLB14RrHAdQPfu4Guyv0FcJwK1XMidl/vpbn8/+byyABkKIBgbtiuc4fkkIcavY+0hKeSkf9yX6n7NnzyrPcz6U65VoNRDWMweQlpaWK/bSSy/ht99+Q0BAgBsyIyJvcvPmTbz88svKmPALQHhrLmnualWrVsXRo7lnWpw5cwYPPPCAGzIib2JGEfUHgA7Zxw0BbLfTttE/fi4/bi2LXg7AVAd/ZlSO4/UAWERRgZw/r15N32jJVTKPX4lyGD16NN56661csePHj2Pu3Ll444033JAZEXmTmTNnGvZ4FG/Siy/VNKhSpYry/IULFzRnQt7IjOF8m3McdzBsleXRHMebTLg3kVsYfUD7hJbSnIl3GjNmDCpVqqSMTZ061bCnkIjIDKdPn8bMmTOVsRo1aiDsgcc1Z+SdKlasqDzPIop0MKMnahuAKwDKAGgrhKgrpTz8z0ZCiAgAtzZKSAXwdX5uJqV8xJF2QohoALcGy1aXUkbn537kPaqN2eBw2yubflGe9wlhEaXDPdN2ILXh08D5GbliKSkpqNOuD8o8rt6zxdNEz1BvJOwKzvwdd5bOPweRPa76e57z7/jLL7+sHFIMAAsWLMCQHZ6964orPwt0WrD3qvL8wg37sdLi2J+Rn12UXwXuicreQPfWuBoB4HMhRHjONkKIQACLcXtz3f9IKZV/84UQnwkhZPbX5ILmR+QK1qRryvPsidInuHYzBFa7Xxm7efy/SDl9QHNGROQNvvvuO3zzzTfKWNeuXdGxY0fNGXkv35CSyvOZSeriishMZu0T9R6AndnHDQAcEkKME0L0EkKMAhCF20P5jgCYZtJ9idzC6APa6AOdzCeEQMm2gwCLukP92pb3ITPVyw4TEeVHamoqRowYoYwFBAQY7hdFrmH04tJ6g0UUuZ4pRZSUMh1ANwA/Zp+qjKxCaQWASAB1ss9HAegopbxuxn2J3EFKm3FPFIfzaeVXqhLCGqvnHmTGx+D6vq80Z0RERdnbb7+NkydPKmOvv/467rjjDs0ZeTejZ6416So33CWXM6snClLKeABtkTXvaQOAiwDSkbUS3o8AXgDwoJSSM76pULPdvA7YrLnOC/8gWAKC3ZCRdyvepBd8DFZFTNyzEpnXuRgnERXc0aNHMWNG7nmYQNZS26+//rrmjMgSFAb45B6NIDPSINNvuiEj8iamFVEAILN8KaXsLKWsKKUMkFKWk1K2kVJ+lD1/Kq9rPCelFNlfkwuQS7Uc14nO73WI/inTYJgAe6Hcw+IfiJJt/q2Mycx0XNvyAd9IElGBSCkxZMgQZGSohwjPnz8fwcF8iaabEMLw2Wv0rCYyixmr8xEVaf9cuWf9+vXosjh3u5b31cJWrvJjKkdXTZKyEzp1isKmTbl3Tkg5uQ/zHkxHjx49zE7PKZ68GpYzq1N58p+DyB5H/56r/o4nH/4R27dvV7bv0qULunXrVpDUXKaorDxn78/RfFcN7N6de8TB4l410K5du/99z88uMpupPVFE3sBo/wmj/SrI9YQQWLBgAfz9/ZXxYcOG4fp1TsUkIudZUxIR/+MnylhwcDDeeecdCCE0Z0W3cK8ochcWUUROYhHlmWrWrInXXntNGbt48SLGjRunOSMiKgoStn8GW0qiMjZ58mRUrVpVGSM9KlSooDx/8eJFzZmQt2ERReQkFlGea9y4cahZs6YytnDhQuzZs0dzRkRUmKWeP4yk375XxurVq4eRI0dqzoj+iT1R5C4sooicxCLKcwUGBuL9999XxqSUeOGFFwwnhhMR5SStGbi2+V3D+AcffAA/Pz+NGZEKiyhyFxZRRE4yGiJgNKSA9GrdujWeffZZZeyPP/7AnDlzNGdERIVR4r61yIhT78rywgsvoEmTJpozIhUWUeQuLKKInMSeKM8XGRmJUqXUy96++eabOHXqlOaMiKgwybh6Hgm7v1DGIiIiDPeLIv04J4rchUUUkRNSUlJw7dq1XOctFgvKlSvnhoxIpXTp0pg7d64ylpqaiiFDhnDvKCJSktKGqxsXAFb10N+5c+ciPDxcc1ZkxOgFZmxsLDIz89yelCjfWEQROcHozVbZsmXh68tt1zxJ//790aZNG2Xshx9+wNKlSzVnRESFwY2oDUi7cEQZa9OmDfr27as5I7InKChIWdTabDZcupR7/ygis7CIInIC50MVHkIIvP/++wgMDFTGR4wYgZiYGM1ZEZEni46ORsIOxW7qyNoT6sMPP+SeUB7I6BnMeVHkSiyiiJwQGxurPM8iyjPVqFEDEyZMUMbi4+MxePBgDusjIgBZK3gOGjQIMiNVGX/rrbdwxx13aM6KHGH0DGZPFLkSiygiJxh9IJctW1ZzJuSo0aNHo169esrYN998gy++UE8eJyLvsnjxYnz/vXpPqIAKd2HYsGGaMyJHGT2DWUSRK7GIInLC5cuXledZRHkuf39/LFq0CD4+Psr4sGHD+KAl8nKxsbF4+eWX1UEfX5TsONzwM4Tcj0UUuQOLKCInGH0gR0REaM6EnNGoUSO89tpryti1a9e4Wh+RF5NSYujQoUhISFDGizftDf/SVTRnRc4wegYbvfgkMgOLKCIncDhf4TVp0iTcfffdytjatWuxcuVKzRkRkSdYsWIF1qxZo4z5RVRH8Qef1JwROYs9UeQOLKKInMAiqvAKCAjAp59+CotF/bH30ksv8a0lkZe5cOECXnzxRXVQWFCq4wgIH25f4elYRJE7sIgicgKLqMKtcePGGD16tDIWFxeHoUOHclgfkZeQUmLgwIGGw/jCGvdAQLkamrOi/GARRe7AIorICVxYovB78803cddddyljq1evxrJlyzRnRETu8P777xuuxudbshKKN+ujOSPKLxZR5A4soogclJycjOTk5FznfX19lbulk2cKDAzEokWLDDfMHDp0KM6cOaM5KyLS6cSJE4a90hAWlO48Cha/AL1JUb6VKVNGef7atWvIyMjQnA15CxZRRA6ytzIfd7AvXJo0aYJXXnlFGUtMTMQzzzwDq9WqOSsi0iEzMxPPPvssbt68qYwXb9obAeVras6KCsLPzw8lS5ZUxq5cuaI5G/IWLKKIHMT5UEXL1KlTUadOHWXsp59+wpw5czRnREQ6zJ49G3v27FHGGjVqhOJNntKcEZmBQ/pINxZRRA7ifKiiJSgoCEuXLoWfn58yPn78ePz666+asyIiVzp48CAmTZqkjAUGBuLzzz/nanyFFIso0o1FFJGD2BNV9DRo0ABTp05VxjIyMtCvXz+kpKRozoqIXOHmzZvo16+f4RyZGTNmGPZOk+djEUW6sYgichCLqKJp9OjRaNGihTL2559/YsyYMZozIiJXGDlyJI4cOaKMtWrVCsOGDdOcEZmJRRTpxiKKyEH2FpagwsvHxweff/45wsLClPEFCxZg06ZNmrMiIjMl//kTPvroI2UsLCwMn332meFG3FQ4GD2LuYk6uQo/MYgcxJ6ooqtatWp49913DePPPPMMLl68qDEjIjJLRkIsrm76j2H8nXfeQZUqVTRmRK7AnijSjUUUkYO4sETR1q9fP/Tq1UsZu3LlCvr27YvMzEzNWRFRQUhrJuK+mQ2Zrl7OvF+/fujfv7/mrMgVWESRbiyiiBzEnqiiTQiB9957DxUrVlTGd+zYgSlTpmjOiogKImHnUqTHHFPG7rzzTrz33nvc56+IYBFFurGIInIQi6iiLzw8HEuXLjWcGzFt2jRs2bJFc1ZElB8pf0Uh8efVypifnx9WrFiB0NBQzVmRq7CIIt1YRBE5IC0tDQkJCbnOCyFQunRpN2RErvLII48Y7iMjpUS/fv0QExOjOSsicoY1KR5xG+YaxmfMmIFGjRppzIhczWhhiStXrsBqtWrOhrwBiygiB1y5ckV5vnTp0vDx8dGcDbnauHHj0Lp1a2Xs8uXL6NevHx/KRB5KWjNx5ZuZsCXnfvEFAJ06dcLIkSM1Z0WuFhQUpOxZtNlsuHr1qhsyoqKORRSRA+Li4pTnubx50eTj44Nly5YZDg/Ztm2b4Sa9RORe8Ts+Q9q5P5Sx8uXLcznzIszomcwiilyBnyJEDjAqokqVKqU5E9KlXLlyWLZsmeGk8ylTpnD/KCIPs3LlStzYv04ZE0Jg6dKlKFOmjOasSBejZ7LRM5yoIFhEETnA6C0W50MVbW3atMGECROUMSkl+vTpg5MnT2rOiohUjhw5goEDBxrGJ0yYYDhMl4oGo2cye6LIFVhEETnA6AOYPVFF38SJE/HII48oYwkJCejevTuSkpL0JkVEf5OYmIgePXogOTlZGQ+s3hATJ07UnBXpZvRMZhFFrsAiisgBLKK8l4+PD5YvX2441v7w4cMYMGAApJSaMyMiIKtXeMCAATh2TL0flE/xsijdZTQXAfICLKJIJxZRRA7gnCjvVr58eaxatQq+vr7K+OrVqzFjxgzNWRERAMyePRtr1qxRxoSvPyIeHwufIO4H5Q04J4p0YhFF5ADOiaKWLVti3rx5hvFx48Zh48aNGjMiovXr12PMmDGG8ZLtX4R/2Ts1ZkTuxDlRpBOLKCIHcDgfAcDQoUMxYMAAZezWQhMnTpzQnBWRd/rtt9/Qp08fw6G0Ifd1REi9tpqzInficD7SiUUUkQNYRBGQtUTywoUL0bhxY2X8+vXr6Nq1K+Lj4zVnRuRdYmNj0aVLF8NFXfzL10LJNi9ozorcjUUU6cQiisgBnBNFtwQGBuKrr74yXGji6NGj6NGjB9LT0zVnRuQdUlJS0L17d5w9e1YZtwSXQJnub0D4+mnOjNyNc6JIJxZRRA5gTxTlVKlSJaxevdpwoYnt27fj+eef54p9RCaTUmLgwIH4+eeflfGAgABE9BgH3zBuqOuN2BNFOrGIIspDRkYGEhMTc50XQiA8PNwNGZEnaNGiBRYsWGAYX7JkCaZMmaIxI6Kib8qUKVixYoVhfNGiRQioWEdjRuRJjIqoa9eu8aUWmY5FFFEerl27pjwfHh7OfUe83ODBg/HSSy8ZxidPnowlS5ZozIio6Fq+fDkmT55sGJ8wYQL69u2rLyHyOEFBQQgODs513mq1QqapN2Imyi8WUUR54HwoMiKEwPz589GlSxfDNv/617+wfft2fUkRFUFbtmzBc889Zxh/6qmn7BZY5D2Mns3WlNwjSogKQj2gn4j+h/OhyB4fHx8sX74cDz/8MKKionLFMzIy8PjjjyPoibfhX7qKGzL0PNXGbHDZtaNnPFbo8vAErvx/UVDpl04hdvkYyIwMZdy/fC3sqdQLd4x17T5tnvz/iG4rVaoUzp07l+u8LeUGwBH4ZCL2RBHlgRvtUl5CQkKwfv16VK5cWRlPSEjA5S8nIPP6Zc2ZERVuGQmxuLRqEmR6ijLuE1oaZXqMh8UvQHNm5KmMns029kSRyVhEEeWBPVHkiPLly2PDhg0IDQ1Vxq1JV3Fp5QRYkxM0Z0ZUOFmT43F55QTYDP7NCP8gRDw5Eb4hJTVnRp7MeDjfDc2ZUFHHIoooD5wTRY6qV68eVq9ebbjgSOa1C7i8ahJsaTc1Z0ZUuFhTbuDSlxOQGR+jbmDxRZke4+EfcYfexMjjGT2b2RNFZmMRRZQH9kSRM9q3b48PP/zQMJ5+6RQur5kKW0aaxqyICg9b2k1cXjUJGVeiDduU7vwKgqreqy8pKjTYE0W6sIgiygOLKHLWwIEDMWPGDMN42tnfcWXNNMjMdI1ZEXk+W0YaLq+ZivSY44Ztwlv/G8XqtNSYFRUm7IkiXbg6H1EeuLAE5cdrr72GuLg4REZGKuOp0b/iyrrpSH/7Mfj7+2vOzjMVxpX1ivqKbTpXGUxLS0OPHj3w3dnfDdu8+uqrmDVrlkvzKGwrK9LfGS8swZ4oMhd7oojywDlRlB9CCMyaNcvu3jYpp/ajV69eyDBYupnIW6SmpmYVUN99Z9hm0KBBmDlzpsasqDDiPlGkC4soojxwOB/llxACH330Ebp3727YZt26dejTpw8LKfJajhRQ/fr1w8KFCyGE0JgZFUYczke6sIgiygOLKCoIX19frFixAoF3NDRs89VXX+HJJ59EWhoXmyDvcquA2rjReKPcbt264dNPP4XFwl9ZKG/GRRSH85G5+IlEZIeUNly7dk0ZYxFFjgoICEDE4+MQWO1+wzbffPMNunXrhps3ufw5eYfk5GR07drVbgHVvn17fPnll/Dz89OYGRVmhnOiUllEkblYRBHZIdNTYLPZcp0PDg5GQECAGzKiwkr4+qNMj3EIqFLPsM3mzZvx2GOPISkpSWNmRPolJCSgffv2+OGHHwzbtG/fHuvWreNnLTklNDRUuVefzEzniqhkKhZRRHbYUpOV58PDwzVnQkWBxS8QEU9MRECluoZttm/fjjZt2hguaEJU2F2+fBmtWrXCf//7X8M2HTp0wLp16xAUFKQxMyoKhBAoUaKEMmb0TCfKDxZRRHbY0tQ9AkYf0ER5sfgHIaLnmwi0s1Hovn370Lx5c5w9e1ZjZkSud+bMGbRo0QIHDx40bMMCigrKsIgyeKYT5QeLKCI7jN5asYiigrD4B6LMExPtLjZx7NgxNG3aFIcPH9aYGZHrHDx4EE2aNMHx48Yb6Xbq1Anr1q1DYGCgxsyoqDEuojjnlMzDIorIDlsaiyhyDYtfACIeH49u3boZtrlw4QJatGiBHTt2aMyMyHzff/89WrRogZiYGMM2vXr1wtq1a1lAUYEZD+djTxSZh0UUkR0sosiVhK8fVq1ahb59+xq2iY+PR7t27bBkyRKNmRGZZ/HixXkumPLvf/8by5Ytg7+/v8bMqKgy7oninCgyD4soIjs4nI9czc/PD0uWLMHIkSMN22RkZOCZZ57BpEmTIKXUmB1R/tlsNowdOxbPPfccMjMzDduNHj0aH3zwgXJFNaL8YBFFOrCIIrLD6AO3ePHimjOhosxisWDu3LmYPn263XZTpkxBv379uJcUebwbN26gR48eef6dnjlzJmbNmgUhhKbMyBtwdT7SgUUUkR1G46fZE0VmE0JgzJgx+OSTT+y+kf/iiy/QvHlznDlzRmN2RI6Ljo5Gs2bN8PXXXxu28fPzw7Jly/Daa6+xgCLTcXU+0oFFFJEdnBNFug0cOBAbNmxAaGioYZtff/0VjRo1wrZt2zRmRpS377//Ho0aNcLvv/9u2CYsLAybN2+2OxeQqCDYE0U6sIgisoNFFLlDhw4dsHv3blSpUsWwTVxcHNq1a4e5c+dynhS5nc1mw7Rp0/Doo4/i6tWrhu2qVq2KXbt2oVWrVhqzI2/DOVGkA4soIju4sAS5S7169fDzzz+jUaNGhm2sVitGjRqF7t27Iz4+XmN2RLfFx8ejW7dumDBhgt2Cvnnz5ti3bx/q1aunMTvyRkbzltkTRWYytYgSWXoJIdYLIc4LIdKEEDFCiK1CiOeFEL4m3SdYCPGoEGKCEOIbIcQfQohYIUS6EOKGEOKEEOJLIURPIQSX+6F8k+yJIjcqV64cduzYkeewp2+++Qb3338/fv75Z02ZEWXZuXMn7r33Xqxfv95uuwEDBmDLli2IiIjQlBl5M86JIh1MK6KEEOEAtgBYAeAxABUB+AMoB6A1gI8A/CyEMB6f4riWADYCmAKgC4C6AMoC8AMQAqAGgKcArAQQJYS4y4R7khfiwhLkbsHBwVi6dCkiIyNhsRh/ZJ85cwbNmzfHjBkzYLVaNWZI3igzMxNvvvkmHnnkEZw7d86wnY+PD+bMmYNPPvkEAQEBGjMkb2b0jJZpXNmUzGNKESWE8AfwNbKKJQA4B2ACgD4AXgXwZ/b5BgA2CiHCzLgvgCMAPgMwBsDTAJ4E8DyAhQCuZbepD2CHEKKcSfckL8I5UeQJhBAYNWoUNm3ahJIlSxq2y8zMxBtvvIFWrVohOjpaX4LkVU6fPo3WrVtj8uTJsNlshu0iIiKwdetWvPLKK1yBj7TinCjSwayeqCEAWmQfRwG4V0o5TUq5QkoZiaziaXN2/G5kFVgFsR9ABSllXSnlACnlTCnlMinlV1LKT6SUQwHUBHBrbEsEgEkFvCd5GSltsBm8teI+UeQO7dq1w4EDB9C4cWO77Xbu3In69etj8eLFXHSCTCOlxPvvv4/69etj586ddts2bdoUv/76Kx5++GFN2RHdxtX5SIcCF1HZ85zGZX8rATwjpfzbDGcpZSqAZwDc+ts7TAhRKr/3lFJelVLG5NHmGrKKu1sey+/9yDvJ9FRA5n7LGhwcDH9/fzdkRARUq1YNO3fuxMiRI+22u3HjBp577jl07NiRvVJUYOfOnUOHDh0wZMgQJCfb/0V05MiR2LZtGypUqKApO6K/CwkJUQ5/lplpkJkZbsiIiiIzeqJaAyiTfbxVSnlY1UhKeRlZ86UAIABANxPunZcjOY45nI+cYjQBlb1Q5G7+/v6YN28e1q5dm+fQ0s2bN6Nu3bqYP38+pI1zpcg5VqsV77zzDurWrYsffvjBbtvSpUvj22+/xbx58/iiidxKCMEhfeRyZhRR7XMcb8qjbc74oybcOy935jiO1XA/KkKMhvJxPhR5iu7du+PQoUN5Dpm6efMmXn75ZcQuHY30y39pyo4Ku/TLp9G0aVMMHz4cN27csNu2TZs2OHToEDp37qwpOyL7WESRq5lRRN2T4/hAHm1/Mfg50wkhigFYkOPUGlfej4oersxHhUGVKlWwdetWzJo1C35+fnbbpsecQMzikYj/8RP+IkGGrKlJuLb1I8R8NhL79u2z29bf3x8zZ87E999/z+F75FGM50VxmXMyhxn7NtXKcRydR9vzAKwAfADUFEIIWcBZz0KIEABtb30LIAxZK/L1AVA++/xBAJMLch/yPlyZjwoLHx8fvPrqq2jXrh369++PP/74w7ixzYrE/WuRdPhHlGjRHyH12+lLlDyatFmRdHATEnYtgy0lMc/2DRs2xOLFi1G3bl0N2RE5hz1R5GpmFFE5/5bG2WsopcwUQiQCCM++dzEABX0lUAnAWoPYNQCLAYyXUnJzAHKK0So+LKLIU9133304cOAAZs2ahalTpyI9Pd2wre3mdVzb/B/ciFqPHx8NQevWrQ3bUtGX8lcU4rd+jIyrZ/Ns6+vriwkTJuCNN97Is/eTyF24Qh+5mhnD+UJyHKc60D4lx3GoCfe3578AdvzjnnkSQhxQfQHgpr1ehD1RVBj5+/tj/PjxOHToEJo3b55n+4wr0WjTpg26deuG3377TUOG5El+/fVXdO7cGZdXTnSogGrSpAmioqIwceJEFlDk0dgTRa5m1j5RbiOlPCqlFFJKgazerbIAuiBrX6rOANYBWJk9R4rIYZwTRYXZXXfdhR07duC9995DaGje76u++eYb3HvvvXjqqadw+LBykVUqQn7//Xc88cQTaNCgATZs2JBn+7CwMCxcuBC7du1CvXr1NGRIVDAsosjVzCiicv6mGehA+6Acx/aX+3GSlNIqpbwspVwvpXwUwLTs0JMAPnXiOg1VXwCOmpkveTb2RFFhZ7FYMHjwYBw5cgS9evVy6GdWrVqFevXqoW/fvjh27JiLMyTdbv1dqF+/PtascWy9pX79+uHPP//EkCFDlHvvEHkiFlHkamZ8GibkOC5tr2H2xrxh2d9m4Pbmu64yCcDx7OOeQoi7XXw/KkI4J4qKikqVKmHFihUo228W/MvVyLO9lBJffPEF7r77bvTu3TvPFdrI8+3duxc9e/bEPffcg5UrVzr0M/7la2PPnj1YunQpV96jQodzosjVzCiijuc4rpZH20rIWpkPAE4WdGW+vEgpbQBy7g5ofzMVohwke6KoiAmsdDfKPTMXpTq9DJ+Qknm2t9ls+PLLL/Hggw+iRYsWWLt2LTfsLUQyMzOxevVqNG3aFE2aNMHq1avhyGPXJ6QUSrY59f4AACAASURBVHUehXL9Z+Ohhx7SkCmR+YoXL648b0vjEudkDjNW5/sDQIfs44YAtttp2+gfP6dDziGD/O2XHGb0QWv0wUxUGAhhQUi9Ngiu3RTX967GjV/WQWak5flzu3btwq5du+BbojxCG3VFsbqt4BMYkufPkX7Xrl3D559/jv/7v/9DdHS0wz8n/IMQ9kB3hDV+AhZ/R0bnE3kuoxeekj1RZBIziqjNAEZlH3cAMMdO20dzHG8y4d6OyDl2xe4S7EQ5cTgfFWUW/yCEt+yPsIZd0CvgV7z77rtITc17gdXMhBjEb/kACds/RXDtZgip3w4BlbnQgLvZbDZs374dH3/8MdasWYO0tLwL41uCg4PhW68Twho/Dp9gviSiooFzosjVzBjOtw3AlezjtkII5a57QogIAL2zv00F8LUJ97ZLCFEJQKccp/7r6ntS0cGFJcgb+BQrgcjISJw+fRojRoxAQECAQz8nM9ORfHgbLn0xFhc/egHTp0/H+fPnXZwt/dPZs2fx1ltvoUaNGmjTpg2++OILhwuowMBAjBo1Cn/99RfCH3mOBRQVKZwTRa5W4CJKSpkJ4K3sbwWAz4UQ4TnbCCECkbXp7a1lxv8jpbyqup4Q4jMhhMz+mmzQZqYQoqq9vIQQdwJYDyA4+9QOKSXX7SWH2dLU+zNzOB8VReXLl8f8+fNx6tQpDBs2DMHBwXn/ULbM+BiMHTsWlStXRvPmzbFgwQJcvHjRhdl6t/Pnz2P+/Plo2rQpqlativHjx+Ovv/5y+OeLFSuGkSNH4vTp04iMjERERIQLsyVyD+M5USyiyBxmDOcDgPcAPAGgBYAGAA4JIT4AcBJZi0n8C0Cd7LZHcHvp8fwaBOBVIcReZPUuHUPWKoG+ACoAaI6sHij/7PaxAJ4v4D3Jy9jS1Xs0O7LnDlFhVbFiRSxYsACTJ0/Ghx9+iHfeecepgmj37t3YvXs3Ro4ciebNm6Nnz57o3Lkzqlev7sKsi75Tp05h/fr1WLVqFXbv3p2va1SqVAnDhw/Hv//9b/aoU5Fn9Ky2ZeQ9bJnIEaYUUVLKdCFENwCrAbQGUBnqQikKwONSyusm3FYAaJL9Zc92AM9LKU+ZcE/yEtKaCVgzcp23WCxOvaEnKqxKliyJMWPG4JVXXsGXX36JuXPn4uDBgw7/vJQSO3fuxM6dOzF8+HDUrl0bHTt2RMeOHSEz0yF8/fO+iBdLSUnBjh07sHHjRmzcuBEnTpzI97UaNmyIUaNG4cknn4Sfn5+JWRJ5LqMiSqanQEoJIYTmjKioMasnClLKeCFEWwBPAegP4H5k7RsVD+AwgBUAPs0e/ldQ9yFrEYumAOoBqAKgOAArgOsATgHYD2CllDJ/r+zIqxn1QoWEhPCDl7yKv78/+vfvj6effho7d+5Epxcn4+ax3ZCZ6U5d59ixYzh27Bjmz58P4ReAgEp1EVj5HgRUuhsB5Wt5fVGVkpKCffv2/a/w3LlzJ1JS1J9DjggKCkKvXr3w/PPPo2nTpvzcIq/j5+eHgICA3HMEpQ0yMw3CjytQUsGYVkQBQPa+T19mf+X3Gs8BeC6PNtEAPsj+IjKdzDAuooi8kRACLVu2ROnOo2BrOwjJR3Yg6bfvkX7J+U5+mZGG1L+ikPpXVNYJHz8ElK+FsZbdeOCBB9CgQQNUqVKlyP7iL6XEmTNnEBUVhf3792Pnzp3Yv38/0tOdK0xVGjVqhOeffx69e/fm/E3yeiEhIcqFVmR6CsAiigrI1CKKqKiwpXE+FJERS2AIQhs8htAGjyEt9iSS//gRYbEHcOHChfxd0JqBtPOHMX367bV/SpYsiQYNGqBBgwaoX78+ateujdq1axe6f4OJiYn/64X77bffEBUVhaioKMTHx5t2j8qVK+PJJ5/Es88+i3vvvde06xIVdqGhobh6Nfc6Zrb0VPgUU/wAkRNYRBEpSC4qQeSQgHI1EFCuBk6/3RF79uzBypUrsWrVKsTExBTouteuXcOWLVuwZcuWv52vUKECateujavxAfANi4BPWBn4hpbO/m+pAt0zP2RmBjKTrsKaeAWZN+Ky/pt4Ga1+jsTRo0cRGxvrkvtWrFgRPXv2xFNPPYUHH3wQFosZO5YQFS325kURFRSLKCIFrsxH5ByLxYJmzZqhWbNmmDdvHnbt2oVvv/0WGzduxOHD5u0ucfHiRburBZb5rDRKlSqF0qWz/nvrKyQkBEFBQQgODkZQUBCCgoKQfOwPCPx9yKCExPLl15GSkoKbN2/+77/Jycm4evUq4uLicPXq1f99xcWp93Dfbtqf+LZ69eqhY8eO6Nq1K5o0acLCiSgPhiv0pau3MCFyBosoIgXOiSLKP4vFgpYtW6Jly5aYPXs2zp49+79V5rZu3YqkpCSX3TsuLg5xcXE4duxYvq/Rb52JCRVAaGgo2rZti06dOuHRRx9FpUqV3J0SUaFi9MxmTxSZgUUUkQJ7ov6/vTuPj6q6/z/+/iQkrCIUiIiyqcFaRRYXqpVKEUWRsmi/Ll8torjUfl1qv/qz1mqp1Updat2L1h3rgvoVWypSFSyKYiGgUEVEBUUpmyjIGpLz+2NuwiTMnWQyZ7Lc+3o+Hnl4594z95xgMpnPfM75HMCfbt266YILLtAFF1yg0tJSdRt3p7Z+tkjbVvxb21a8p/KtuQuqmpIOHTroqKOO0sCBAzVw4ED169ePkuRAFsIzUewVhewRRAEpsCYKjVGPX0xt6CFkraCgQM277K/mXfaXBpws58pVuvZTbVvxnk7uXqqSkhItXLjQS6W6Ri2/QIVFPVS4x74q3GM/Nd/rABV07KoFlqcFa6Q7n1sjPTe9oUcJNGnhQRSZKGSPIApIgUwUUD/M8lTYqYcKO/XQfRNOlCRt375d7733nkpKSvTuu+/qgw8+0OLFi7V8+XIldtJoSkzNdi9Ssw57q+Bbe6uwqKcK99hXBR26yvL5EwzkEoUlkEu8ggMphKX6WRMF5F5hYaH69u2rvn37Vjm/ZcsWLV26VIsXL9b597yoso1rtWPDmsrKeOWbv26Q8ea1aqdmbTsqf7eOata2U+K/u++hgg57q6B9l9hvJAw0lLC/2RSWgA8EUUAKLuQFlkwU0HBatmyp3r17q3fv3rpiXqtdrrsdpZp92WFVquetW7dOX3755S7V9rZs2aIX5n+asp8R/bpVqeLXqlUrtWrVSu3bt6+s9ldR/e+IP7wta8a6JaAxCs1ElbImCtkjiAJSYDof0PRYswLttdde2muvvWrVPmyN2ZPBtMLa9gmgcWJNFHKJTSaAFCgsAQBA08aaKOQSmSggBdZEoSlZlkHmBH7xbw80XuFrogiikD0yUUAKZKIAAGjawjNRFJZA9giigBTCKvcQRAEA0DSw2S5yiSAKSIFMFAAATRtropBLBFFACuUh5U9ZEwUAQNPAmijkEkEUkAKZKAAAmjZKnCOXCKKAalx5mdyO7bucNzO1arXrBp8AAKDxSTedzzlXz6NB1BBEAdWEZaFat26tvDx+ZQAAaAoKCwtVUJBiQ2xXnvLDUiATvCMEqglL8zOVDwCApoXiEsgVgiigGoIoAACigXVRyBWCKKAaikoAABANoZmoUoIoZIcgCqgm7NMpypsDANC0UOYcuUIQBVRDJgoAgGgIzURtI4hCdgiigGpYEwUAQDSwJgq5QhAFVEMmCgCAaGBNFHKFIAqopjzkhZU1UQAANC2siUKuEEQB1YTNkyYTBQBA08J0PuQKQRRQDWuiAACIBjbbRa4QRAHVEEQBABANZKKQKwRRQDWudGvK86yJAgCgaQn7200mCtkiiAKqKd++OeV5MlEAADQtZKKQKwRRQDWUOAcAIBpYE4VcIYgCqnGl21Keb926dT2PBAAAZCPsb3fY33qgtgiigGrKQ15YW7VqVc8jAQAA2Qj7212+gyAK2SGIAqpxO7anPE8QBQBA09KyZcuU58P+1gO1RRAFVONCPp0KeyEGAACNU9gHoEznQ7YIooBqwl5YyUQBANC0EEQhVwiigCTOudAUP5koAACaFqbzIVcIooBkZTskV77L6WbNmqmgoKABBgQAAOoqPIjaJudcPY8GUUIQBSRhPRQAANGRn5+vwsLClNfIRiEbBFFAEsqbAwAQLaHroihzjiwQRAFJKG8OAEC0hE7pKyUThbojiAKSMJ0PAIBoIROFXCCIApJQ3hwAgGihzDlygSAKSEImCgCAaElXoQ+oK4IoIAmZKAAAoiXsb3hYMSmgNgiigCTlFJYAACBS2HAXuUAQBSQJy0QxnQ8AgKaJNVHIBYIoIAklzgEAiBYyUcgFgiggCZkoAACihRLnyAWCKCBJ2AsqmSgAAJqm8Ol8W+t5JIgSgiggCdX5AACIlrDZJGHFpIDaIIgCkpSzTxQAAJFCYQnkAkEUkITCEgAARAuFJZALBFFAEgpLAAAQLWSikAvNGnoAQGNCJgr1rccvpjb0ELxoLN9HYxkHgMYjPBNFEIW6IxMFJAmr1EMmCgCApolMFHKBIApIQolzAACiJexveFgxKaA2CKKAJK6U6XwAAEQJhSWQCwRRQBJKnAMAEC1M50MuEEQBSSgsAQBAtJCJQi5QnQ9IQolz5MqyCSc29BC8aCzfR2MZB4DGj0wUcoFMFJCETBQAANFCiXPkAkEUkCTsUymCKAAAmiYyUcgFgigg4JwL/VSK6XwAADRNlDhHLhBEAYGwqXzNmzdXXh6/KgAANEUtWrRIfaFsh8rKyup3MIgM3hkCAbJQAABEj5nJmjVPeW3Lli31PBpEBUEUEGCjXQAAosmaFaY8TxCFuvIaRFnCqWb2NzNbYWbbzGylmb1iZueambeS6mbWzcwuMLPHzWyRmW0ws+1mtsbMXjez68ysm6/+EH1hmSiCKAAAmjYrSJ2J2rx5cz2PBFHhM6hpL+kZSYOrXeocfA2WdKGZjXbOfZplX89LGiHJUlzuGHx9T9L/M7NfOeduyaY/xAPT+QAAiCYyUfDNSxBlZoWSpkgaGJz6TNJ9kpZK2lvSOZIOkNRf0otmdoRzbkMWXR6knQHUvyTNkLRE0oagv5OVCKKaS7rZzJo7527Ioj/EAOXNAQCIJjJR8M1XJupC7QygSiQNcc6tr7hoZndJel7SUEnfkXSNpCuy6G+rpLsl3emc+yDF9dvM7DJJfwgejzezyc65JVn0iYgrDwmiyEQBANC0hWWiCKJQV1mviQrWOV0dPHSSxiQHUJLknNsqaYykTcGpi82sQxbdDnTOXRQSQFX0eZukZ4OHzSSdkUV/iIGwEudkogAAaNryQjJRTOdDXfkoLDFYUqfg+BXn3L9TNXLOrZb0ZPCwuaSRde2wepCWxuSk49517Q/xEDadj0wUAABNW1iJczJRqCsfQdRxScfTamibfP14D33XZGPSMe+EkRaZKAAAool9ouCbjyDqoKTjeTW0nRvyvFxJ7mN5PfSHJowS5wAARBOFJeCbjyCqV9LxshrarpBUFhwXm1mqEuVeBGu1zk46NTVXfSEamM4HAEA0UeIcvvmoztcu6XhtuobOuR1mtkFS+6Dv1pK+8TCGVC6X9O3g+F1lEESZWVhG7dsh5xEB5WSiAACIJDJR8M1HJqpN0vHWWrRPDvl389D/LszsB5J+GzzcIeknzrnyXPSF6CATBQBANFFYAr752ieq0TCzbytRla/ie/ulc+7NTO7hnDsk5N7zlNgwGBFEYQkAAKKJEufwzUcmKnk6XotatE/+WH9jaKs6MLOekl6WVLEH1R+dczf77APRFZaJIogCAKBpIxMF33wEUV8lHXdM1zAo9tA2eFiqnZvvZs3Mukp6VdJewak/Oecu83V/RF9YdT6m8wEA0LRZAYUl4JePIGpJ0nGPGtruLSk/OF7qnHMe+peZdVEigKro/0FJP/Vxb8SH21Ga8nyLFrVJsAIAgMaK6nzwzUcQtSjpOOVaoiSHhjyvzsyssxIB1H7BqUmSzvMVoCE+XPmOlOebN089BQAAADQNlpe6DEBpaeoPUIGa+AiiXko6HlpD2+OTjqdl27GZdZL0iqT9g1NPSRpLJT7UhStL/UJaWJj60ysAANA0WH5ByvPbt6cuKgXUxEcQNUPSmuB4iJkdmKqRmRVJOi14uFXSlGw6NbNvKVFE4jvBqecknemcKwt/FpBGWepMFEEUAABNXH7qTBRBFOoq6yDKObdD0g3BQ5P0qJm1T25jZi0kPaLE5rqSdJdzbl2q+5nZw2bmgq/xIW12lzRd0sHBqSmSTgvGAtSJI4gCACCSjCAKnvnaJ+peSSdLGqjEPkrvmNlESUuVKCYxTtIBQdv3JF2fZX8vauf6qy8k/UXSiWaW7jmbnXPTs+wXEUYQBQBANBFEwTcvQZRzbruZjZT0jKTBkroqdaBUImm0c+7rLLs8Ium4ixJroWqyXDVXD0SchRSWIIgCAKCJy2NNFPzysSZKkuScWy9piBLrnqYqkSHaLmmVEtXzzpc0wDn3qa8+AZ8oLAEAQDSRiYJvvqbzSZKCsuJPqXaZobB7jJU0toY2aeftAXXBdD4AAKKJIAq+ectEAU0dQRQAANFEEAXfCKKACqyJAgAgmtgnCp4RRAEBMlEAAEST5ZGJgl8EUUCAwhIAAEQT0/ngG0EUECATBQBANBFEwTeCKECSc04KCaIKClLPowYAAE0EQRQ8I4gCJJWVlUlyu16wPOXn59f7eAAAgD9GYQl4RhAFKPxFNCz9DwAAmhBL/Za3vLw8+CAVyAxBFCCptDR1UQmFVPMBAABNh5mFTukLfQ8ApEEQBYhMFAAAUceUPvhEEAWIIAoAgKhjryj4RBAFKM0LaMinVgAAoGmhzDl8IogCRCYKAIDII4iCRwRRgNIEURSWAAAgEshEwSeCKEBkogAAiDrLo7AE/CGIApRuTRRBFAAAkUAmCh4RRAFKl4misAQAAFHAdD74RBAFiDVRAABEHUEUfCKIAsSaKAAAoo4gCj4RRAFiTRQAAJFHYQl4RBAFiDVRAABEHZko+EQQBYjpfAAARB1BFHwiiAKU5gWUwhIAAEQDQRQ8IogCRCYKAICoY7Nd+EQQBYggCgCAqGM6H3wiiAJEYQkAAKKOIAo+EUQBosQ5AACRRxAFjwiiAKXJRFFYAgCASCATBZ8IogCxJgoAgKijsAR8IogCRBAFAEDUkYmCTwRRgNKtiaKwBAAAkUAQBY8IogCRiQIAIOrIRMEngihAFJYAACDqwrYtIYhCXRBEAaLEOQAAkRfywShBFOqCIAqQVFpamvI8m+0CABANYdP5wt4DAOkQRAFKN50vv55HAgAAciHsbzqZKNQFQRQgCksAABB1rImCTwRRgChxDgBA5FGdDx4RRAEiEwUAQNRR4hw+EUQBosQ5AABRRxAFnwiiAFHiHACAyMtjTRT8IYgCxHQ+AACijkwUfCKIApQuiKKwBAAAUUAQBZ8IogCRiQIAIOoIouATQRSgNC+gFJYAACAa2CcKHhFEASITBQBA1IVV3CWIQl0QRAFiTRQAAFHHdD74RBAFiEwUAABRRxAFnwiiEHvOuTRrovLrdzAAACA3CKLgEUEUYq+srEzOuV0vWJ6MIAoAgEgIm6JPEIW6IIhC7LEeCgCAGLA8SbbL6fLycpWVldX/eNCkEUQh9kI/gWI9FAAAkWFmTOmDNwRRiD2KSgAAEA8Ul4AvBFGIvdAgio12AQCIFNZFwReCKMQemSgAAOKBTBR8IYhC7IWviaKwBAAAkRIyy4QgCpkiiELskYkCACAeyETBF4IoxB5BFAAA8UAQBV8IohB7FJYAACAeKCwBXwiiEHvsEwUAQEywJgqeEEQh9kpLS1OeD/u0CgAANE1h0/nC3gsAYQiiEHusiQIAIB5YEwVfCKIQe0znAwAgJgii4AlBFGKPwhIAAMQDhSXgC0EUYo/pfAAAxIPl5ac8TxCFTBFEIfZCXzjJRAEAEC1M54MnBFGIPTJRAADEg+UxnQ9+EEQh9giiAACIB6rzwReCKMReeBDFPlEAAEQJQRR88RpEWcKpZvY3M1thZtvMbKWZvWJm55qZt4/2zayVmR1hZheb2cNmtsjMdpiZC74G+eoL0UaJcwAAYoIgCp74DGraS3pG0uBqlzoHX4MlXWhmo51zn3ro8jNJ3/JwH8QcJc4BAIgHMlHwxcu7RDMrlDRF0sDg1GeS7pO0VNLeks6RdICk/pJeNLMjnHMbsuy2eo3KTyUVKhGwAbXGmigAAOIhrLDEtm3b6nkkaOp8vUu8UDsDqBJJQ5xz6ysumtldkp6XNFTSdyRdI+mKLPucIukDSfMkzXPOrTWzhyWdleV9ETOUOAcAICbyU+8TVVpaWs8DQVOX9bvEYJ3T1cFDJ2lMcgAlSc65rWY2RtLHklpLutjMJjjn1tW1X+ccwRK82LFjR8rzFvJCCwAAmqawqfph7wWAMD4KSwyW1Ck4fsU59+9UjZxzqyU9GTxsLmmkh76BrIV9+sSaKAAAosXyyETBDx9B1HFJx9NqaJt8/XgPfQNZC33hDHmhBVA7M2bMkJnJzPT000+nbXvqqafKzFRUVFRPowMQSyHrnQmikCkfQdRBScfzamg7N+R5QIMJzURRWALIyvz58yuP+/fvn7btvHmJPx/9+vXL6ZgAxBuZKPjiI4jqlXS8rIa2KySVBcfFZmYe+geyQiYKyI2KIGr33XfXvvvuG9ru66+/1scffyyp5mALALJCEAVPfHzU3i7peG26hs65HWa2QVL7oO/Wkr7xMAavzCwso/bteh0I6kX4miiCKPjXmD47cs7l9P4VQVS/fv3Sft/z58+vHAuZKAC5FLbemSAKmfKRiWqTdLy1Fu23JB3v5qF/ICsUlgD827JlixYvXiyp5uxSSUlJ5TGZKAC5FFZ5lyAKmeJdYgrOuUNSnQ8yVPyFj5jQF05KnAN1tnDhQpWVJWZv13Y9VNu2bdNO+wOArFHiHJ74yEQlT8drUYv2LZOON3roH8hK6D5RZKKAOksuKnHIISk/l6pUkYnq27dvo5ruCCB6KCwBX3wEUV8lHXdM1zDYmLdt8LBU0iYP/QNZobAE4F9FENWmTRv16tUrtN2mTZu0ZMkSSayHAlAPKHEOT3wEUUuSjnvU0HZvSRXvTJe6XK9qBmqBEueAfxVBVJ8+fZSXF/6nZsGCBSovL5fEeigAuUcmCr74CKIWJR2nn7MhHRryPKDBhL5wGpko+OecazRfuVJWVqaFCxdKqjkwevPNNyuP02Winn/+eZ144onq1KmTCgsLtc8+++iaa67R9u3bQ8dwzz33qH///mrZsqWKiop03nnnaePGjerevTub+gIxRXU++OLjo/aXJP1vcDxU0q1p2h6fdDzNQ99A1sIzUQRRQF0sXrxYW7YkCrHut99+adv+9a9/lSS1aNFCBxxwwC7Xy8rKNHbsWE2aNEk9e/bUSSedpFatWukf//iHrr/+er377ruaMmVKleeUlpZqxIgRmjZtmvr27auLLrpI69ev15NPPqnPP/9cn376qYYOHerpuwXQpIRkxgmikCkfQdQMSWskdZI0xMwOdM79u3ojMyuSdFrwcKukKdXbAA2BEueAX8kly7duDd/54o033tA///lPSdLBBx+sZs12/Z277LLLNGnSJF122WX6/e9/r4KCAkmJ4Gr48OF64YUXNHv2bB155JGVz7nkkks0bdo0XX/99frlL39ZWazipz/9qQ477DBJTB0E4opMFHzJejqfc26HpBuChybpUTNrn9zGzFpIekSJzXUl6S7n3LpU9zOzh83MBV/jsx0fUBMKSwB+JVfme/bZZytLnSf78MMPdfrpp1c+PvDAA3dp8/bbb+uuu+7SsGHD9Ic//KEygJKk/Px8nX322ZKkOXPmVJ6fO3euJk6cqBEjRujqq6+uUu2vf//+6tu3b+UxgBiisAQ88fVR+72STpY0UIl9lN4xs4mSlipRTGKcpIp5Gu9Juj7bDs1ssKTB1U4nT6gfZ2ZDql2/xTn3lYAkFJYA/KoIovLy8vT222/r+9//vs477zx169ZNX3/9tWbMmKEHH3ywynqmd955R88995z2228/HXzwwZKkO+64Q8457bbbbho/fvwu/SxdulSSKgtTSNKdd94p55yuvPLKlGPr0KGDJIIoIK7CCkuwTxQy5eVdonNuu5mNlPSMEoFNV6UOlEokjXbOfe2h2+9LujrN9TNTnPuzqpZkB8JfOMlEAXWyYMECSdIZZ5yhOXPmaPbs2Zo9e3aVNh07dtSjjz6qk08+WVJiCuDJJ5+sxYsXV7aZPn26JOmpp55K21/37t0rj1966SW1a9dORxxxRMq2K1asULt27bTPPvtk/o0BaPKYzgdffFTnkyQ559ZLGqLEuqepkr6QtF3SKkmvSjpf0gDn3Ke++gR8YE0U4M8nn3yir75KfFZ1+OGHa9asWRo3bpw6d+6s5s2bq2fPnrrooou0cOFCnXTSSbr88svVpk0bde3aVZdccomKi4slJdZSrVmzRgMGDKixyuCPfvSjyuesWrVKXbt2Tblp7/Lly7V48WL2owLiLKRoFEEUMuX1XWKw79NTwVdd7zFW0thatBsvaXxd+wEqhAdRZKKATCWvh+rbt6+Kior05z//ObT9zTffrJtvvnmX8xUl2NesWVPrvvPz85Wfn69161IuudV1110n55wOOaSm3TgARBWZKPjiLRMFNFWhL5yUOAcyVhFEmVnl2qa6aNmypfr06aOPP/5YTzzxRMo2c+bMqSylLkkFBQUqLi7WF198oX/84x9V2t5yyy168MEHJbEeCog1SpzDE+YrIfaYzgf4UxFE9ezZU23bts3qXrfccouGDRum//7v/9b9999fWVnv888/1/z587V69WqtX7++ynOuuuoqnXXWWRoxYoROP/10dezYUTNmzNDq1avVr18/zZ8/nyAKiDEyUfCFd4mIeehWUgAAHkRJREFUPUqcA/5UBFF9+vTJ+l5DhgzRm2++qd///veaNWuWZs2apbZt26pz584aOHCgTjrppF3WPo0ZM0br16/X7bffrkmTJqlLly4aPXq0rrnmGvXv319t2rRRr169sh4bgCaKEufwhCAKsUeJc8CP1atX64svvpCkyqxRtg455BA9/fTTGT3n0ksv1aWXXlrl3Pr167V8+XIdddRRKYtOAIgHSpzDF9ZEIdbKy8ur7DFThfHrAWQiuaiEj0yUTxVjYyofEG9M54MvfNSOWAvfI6oZn1YDGRo6dGhlVb3GhiAKgKS0Jc6dc/ztR63xUTtijfLmQDwQRAGQJLO80JkmZWVl9TwaNGUEUYg1ikoA8TBp0iQ559S7d++GHgqAhhbyN54pfcgEQRRijaISAADES9hsE4IoZIIgCrHGdD4AAOKFIAo+EEQh1sKn85GJAgAgkkJmm1DmHJkgiEKshU/nIxMFAEAUkYmCDwRRiDUyUQAAxAx7RcEDgijEWljq3vL41QAAIIrCZpsQRCETvFNErJGJAgAgXoxMFDwgiEKssSYKAICYYU0UPCCIQqyFlzgnEwUAQBRRWAI+EEQh1sKn85GJAgAgkkI+KKXEOTJBEIVYIxMFAEC8UFgCPhBEIdZCXzBZEwUAQDRRWAIeEEQh1sJLnBNEAQAQRayJgg8EUYg1pvMBABAvlk8mCtkjiEKsUVgCAICYIRMFDwiiEGvh+0SRiQIAIIqYzgcfeKeIWCMThfrW4xdTG3oIlZZNOLGhhwAA9Y/CEvCATBRijTVRgH8PP/ywzExmppkzZ9b5PhX3GDt2rLexAUBYiXP2iUImCKIQa+HT+chEAQAQRWEflJKJQiYIohBroS+YRhAFRBlZLiDGWBMFD5izhFgL3SeKwhJAg3PONfQQAEQQJc7hA5koxBqFJQAAiBkyUfCAj9sRaxSWQGORy0p5jakiIAA0NNZEwQcyUYi10BdMCksAXk2ZMkUnnHCCOnfurObNm6tHjx4677zz9PHHH4c+p6Z1S845PfXUUxo5cqS6deumFi1aqGXLluratav69eunn/zkJ3ruueeqTNvt0aOHzKzy8SOPPFLZT/JXKtu3b9e9996rIUOGaI899lBhYaE6deqkgQMH6uabb9Y333xTq3+L1157TT/60Y+05557qnnz5uratatOOeUUvf7665Kk8ePHV45j2bJluzx/7NixVca5adMm3XTTTRowYIA6duwoM9PPfvazKv9Os2bN0pVXXqmBAweqc+fOKiwsVJs2bVRcXKwxY8Zo1qxZace8bNmyyj7Hjx8vSZo9e7ZOO+00de3aVS1atNC+++6rn/70p1qxYkWV5y5evFg/+clPVFxcrJYtW6qoqEinnHKKFi1aVKt/L8A7MlHwgI/bEWtkooDcKi8v1znnnKOHHnqoyvnly5frz3/+syZPnqzp06fr8MMPz+i+mzdv1siRI/Xyyy/vcm3FihVasWKFFixYoIkTJ+qzzz7T3nvvndX3sWTJEg0fPlwffvhhlfNr167V66+/rtdff1233XabpkyZosMOOyz0PldddZUmTJiwy3gnT56sZ599Vr/73e8yGtcnn3yioUOH7jKuZLfffrsuu+yyXc6XlpZq6dKlWrp0qR577DFdeOGFuuuuu5SXV/Pnq3/4wx90xRVXqLy8vPLcxx9/rHvvvVfPP/+8Zs6cqV69eunpp5/W2LFjtWXLlsp2W7du1eTJk/W3v/1NL730kgYOHJjR9wxkK2yzXUqcIxO8U0SshQdRZKIAH6699lq98cYbOvHEE3X22WerZ8+eWrdunR5++GH95S9/0ddff60zzjhD77//vpo1q/2fpN/85jeVAdSAAQM0btw4FRcXq127dtqwYYM++OADzZw5U3/961+rPG/69Onavn27evfuLUkaOXKkrr/++rR9rVmzRoMGDdLKlSslSSeccILOPfdc9ejRQytXrtTjjz+uJ554QitXrtQxxxyjefPmqbi4eJf73HHHHZUB1O67767LL79cgwYNUmFhoebPn6+bbrpJV111VUYB5ejRo/XJJ5/o/PPP1+jRo1VUVKQVK1aorKysss2OHTvUsWNHjRgxQkcddZSKi4vVunVrrVq1SgsXLtSdd96pzz77TPfee6+6du2qq666Km2f06ZN09tvv63+/fvrZz/7mQ444AB99dVXeuCBByr/Hc4//3zddNNNOuOMM9SjRw9dfvnl6tevn7Zv367Jkyfrjjvu0JYtW3TWWWfpgw8+UEFBQa2/ZyBbFJaADwRRiLXw6Xz8agA+vPHGG/r1r39dOQWswrHHHqvmzZvroYce0tKlS/X3v/9dI0aMqPV9n3jiCUnSYYcdplmzZu3yJvz73/++zjvvPG3cuFHNmzevPN+rV68q7dq1a6eDDjoobV8///nPKwOoK6+8cpdM0oknnqgjjzxSF198sTZu3Kjzzjtvl02GV69eXRmcfOtb39Ls2bO1//77V14//PDDdfrpp+voo4/WnDlzavEvkLBw4UK98MILOvHEnWvq+vfvX6XNKaecoosvvrjKv0OF448/XpdccomGDx+ul19+WRMmTNBFF12k3XbbLbTPOXPm6Ic//KGeffbZKv/uxxxzjLZt26bnnntOr732moYPH65+/frplVdeqXK/o446Svn5+brtttv0ySef6O9//7tGjhxZ6+8ZyBrT+eABa6IQa2SigNzq16+ffv3rX6e8dsUVV1Qev/baaxnd9z//+Y+kxBvydFmM3XbbTYWFhRndO9mqVav01FNPSZIOOugg3XDDDSnbXXTRRRoyZIikxPeyYMGCKtcfeeQRbd68WZJ03XXXVQmgKrRt21b3339/RuMbM2ZMlQAqlW7duqUMoCo0b95ct956qyRpw4YNKadIJmvRooUeeOCBlP/u//M//1N5vGbNGj344IMpA7KLLrqo8jjT//dAtigsAR8IohBrofOfCaIAL84444zQQg0HHHCA2rRpI0lpC0ykUrHG6YUXXtDq1auzG2QaM2bMqHxjNW7cOOWnKTpz4YUXVh5Pnz69yrWKwKRZs2b68Y9/HHqPQw89tMbMWLIzzzyz1m0rbNq0ScuXL9d7772nRYsWadGiRVXWNs2fPz/t84899lh16tQp5bW+fftWHvfu3Tv0e9lnn30qg6tPPvkk028ByIqF/B4TRCETBFGINQpLALn17W9/O+319u3bS0pkQDJx7rnnSpI++ugj7bvvvhozZowee+wxLVmyxOsmvQsXLqw8PuKII9K2PfLIIyuP33333ZT32X///dW2bdu090lXmKK6Pn361KrdF198oSuuuELFxcXabbfd1KNHDx144IHq3bu3evfurX79+lW2Xbt2bdp7pft/2q5du1q1S26b6f97IGtkouAB7xQRa6FBFCXOAS9at26d9npFJbjkQgi18Ytf/ELr1q3THXfcoW+++UaPPfaYHnvsMUlSp06dNHToUJ177rk6+uij6zbwwLp16yqPO3funLZtUVGRzEzOuSrPk6Qvv/yysk1N9thjj1qPryIITefVV1/VqFGjtHHjxlrdM7mSXiqtWrUKvZZc2S9du+S2mf6/B7IVNmWfIAqZIBOFWAt9wSQTBTRqeXl5uvXWW7V06VJNmDBBxx13XGWGZ82aNZo0aZIGDRqk0047LdJvjNJNL5QSwdspp5yijRs3qlmzZrr00ks1Y8YMff7559q6daucc3LOVQlkfGbygEaJEufwgCAKsUZhCaBp6969u6688kq99NJLWr9+vUpKSjR+/PjKbM5TTz2l6667rs7379ChQ+VxRTGLMKtXr64MQJKfJyUq8lW0qYnPNV7PPPNMZVbs7rvv1h//+EcNGjRIXbp0qVJsYv369d76BBo7MlHwgSAKsUaJcyA68vLyKqsBvvXWW5XTyZ588sk63/Pggw+uPH7rrbfStp09e3blcfW1ShX7Un3wwQc1Tqv717/+lekwQyWv6TrttNNC282dO9dbn0Cjxz5R8IAgCrEWmokyfjWApqxHjx6VZcTXrFmzy/WWLVtKkrZt25b2Pj/4wQ8qS3k/8MADVarYVTdx4sTK4+OOO67KtYry5zt27NCkSZNC71FSUlIl8MlW8vSkihLrqdxzzz3e+gQaO0qcwwfeKSLWyEQBTc+XX36p559/Pm1As3z5cr3//vuSEuW0q+vSpYskacmSJWn7KioqqszgLFy4UNdee23Kdvfee29lWfNBgwbtkok666yzKjNj11xzjT788MNd7lGxUa9PyZsLP/zwwynb3HHHHXrhhRe89gs0Zkzngw+8U0SshS0iZU0U6luPX0xt6CE0GRs2bNDo0aPVpUsXjRo1St/97ne1zz77qHXr1lq7dq3efvtt3X333dq6dask6eKLL97lHgMHDtRHH32kkpISXXvttRo+fHiV0uPJ5blvvfVWvfzyy1q5cqVuuOEGLViwQOPGjVP37t21atUqPf7443r88cclJTb3ve+++3bpr6ioSDfeeKMuvfRSrVu3Tocffrguv/xyDRo0SIWFhZo/f75uvvlmffTRRxowYIDmzJkjSaF7bNXWqaeeql/+8pfaunWrfvWrX2nZsmUaNWqUioqKtGzZMj366KOaMmWKjjrqKL3++utZ9QU0GUzngwcEUYg19okCmq4vvvhC99xzT+hUtLy8PF199dU6++yzd7l2xRVX6Omnn9bmzZv129/+Vr/97W+rXE+uUNepUyfNnDlTw4cP14cffqipU6dq6tRdg94999xTU6ZMUXFxccrxXHLJJVq5cqUmTJigr776Sr/61a92Ge+NN96oTZs2VQZRLVq0SP+PUIMuXbro/vvv19lnn60dO3Zo4sSJVaYdSlK/fv30zDPP1FjCHYgKMlHwgel8iDWm8wFNT/fu3TV37lzdcMMNGj58uA488EB17NhR+fn5atu2rfr06aOLL75YCxYsCK3M953vfEdz587VuHHjVFxcXOOeRr169dKiRYt0991365hjjlGnTp1UUFCgDh066Hvf+55uuukmLVmypMaNcm+88UbNnDlTJ510kjp37qzCwkLttdde+q//+i+99tpruvLKK/XVV19Vtt99990z/weq5swzz9Sbb76pU089VXvuuacKCgrUqVMnfe9739Ptt9+ut956K6O9qYAmjxLn8MDYD6L2zGxe//79+8+bN6+hhwJPevfurUWLFu1yfs+z71RhUU9J0rIJJ9b3sBBhjWnaHj/bjdPgwYM1Y8YMdevWTcuXL2/o4QCRkPzau+2LD/Sfx/53lzaHHnqo1+qYaFiHHHKISkpKSpxzh+Ti/mSiEGtM5wPQmCxfvlyzZs2SJB155JENPBogmow1UfCAIAqxFvqCmcevBgC/SktLtWzZstDrGzdu1I9//OPKKUXnnHNOPY0MiBnWRMEDPm5HrIVmolgThRxhCl18bdq0ScXFxRo2bJiGDRumAw88UG3atNH69ev11ltv6U9/+pM+/fRTSdKoUaN07LHHNvCIgWhinyj4wDtFxFp4JooS5wD827Fjh1544YW0+zINGzZMjz76aD2OCogZpvPBA4IoxFr4PlH8agDwq23btvq///s/TZ8+XW+99ZZWrVqldevWKT8/X3vssYe++93v6swzz9SwYcMaeqhApFHiHD7wThGxRolzAPUlLy9Po0aN0qhRoxp6KECshX1QSolzZILV84i18Op8TOcDACCSQopHkYlCJgiiEGuUOAcAIF4ocQ4fCKIQW845SpwDABA3rImCB7xTRGyVlZWFXDGm8wEAEFGUOIcPBFGIrfCiEgRQAABEVsgHpWVlZXLO1fNg0FQRRCG2KG8OAED8mBlT+pA1gijEFpX5AACIJ6b0IVsEUYgt9ogCACCmQj4wZa8o1BZBFGKLTBQAAPEU9reeTBRqiyAKsRVe3pwgCgCASAspIkUQhdoiiEJssdEuAADxxJooZIsgCrFFJgoAgHhiOh+yRRCF2ArNRFFYAgCAaAv5W08QhdoiiEJshe8TRSYKAIAoIxOFbHkNoizhVDP7m5mtMLNtZrbSzF4xs3PNzPtH/GbW2sx+bmZvmNlqM9tqZsvN7CkzG+q7P0RH+HQ+MlEAAERayN96Spyjtry9WzSz9pKekTS42qXOwddgSRea2Wjn3Kee+uwX9LlPtUvdgq9TzOxxSec457b76BPRET6dj0wUAABRRiYK2fISRJlZoaQpkgYGpz6TdJ+kpZL2lnSOpAMk9Zf0opkd4ZzbkGWf3SW9KGmP4NTbkiZJWiupt6TzJXWQdIYkJ+nH2fSH6KGwBAAA8UQQhWz5ykRdqJ0BVImkIc659RUXzewuSc9LGirpO5KukXRFln3+UTsDqAclneecKw8eP2FmEyX9U4mM1Jlm9qRzbmqWfSJCKHEOAEBMUVgCWcp6TVSwzunq4KGTNCY5gJIk59xWSWMkbQpOXWxmHbLos4+kUcHDTyX9T1IAVdHnciWCuwrj69ofoik8iCITBQBAlJGJQrZ8FJYYLKlTcPyKc+7fqRo551ZLejJ42FzSyCz6PDXp+L4gSEvlRSWmFErSoWZWfe0UYiz0hZIS5wAARBub7SJLPoKo45KOp9XQNvn68bnu0znnJL3kqU9EDJkoAADiKayIFEEUasvHR+4HJR3Pq6Ht3JDn1ZqZ5SmxrkqSdkh6J9d9omFMnjxZ69evr7lhHc2ZMyf1BdZEAQAQaWHrn1988UWtWrUqZ/126NBBJ598cs7uj/rj491ir6TjZTW0XSGpTFK+pGIzsyBblIm9JbUMjj93ztVU0H950nGv0FZodH7zm9/o3/9OOTs0pyhxDgBAxIXMOpk4cWJOu+3Xrx9BVET4mM7XLul4bbqGQcBTUdq8maTWuewvsC7kuUBqRhAFAECUMXUf2bLME0HVbmC2XVJB8LCgpsyQmX0uqUvwsItzbmWG/R0p6Y3g4RvOuaNqaF8saUnwcIlzbv9a9BE2LbFPy5Yt8w844IBajxd1995772nr1rCaIbmT17KtmrXtVPn4oL12r/cxAAAAfxZ9/nWVxzs2rFb5lo31Po6WLVuK95H14/3339eWLVu+dM7VuSJ4Oiz+yEzeli1bykpKSmpah4UmrHzLBm3fsnMv6JLcTY1u7L4d/Hdxg44CyD1+1hEX/Kw3sC1btqikpKShhxEXfSS1ydXNfQRR30hqHxy3CB6n0zLpuC4fASTfv0Ut2mfcn3PukFTnKzJUYdeBKOHnHXHBzzrigp91xEmamWVe+FgT9VXSccd0DYONedsGD0u1c/PdnPQXSE7hfRXaCgAAAABqwUcQtSTpuEcNbfdWojKfJC2tQ2U+KVHhb0twvFcQmKXTPel4SWgrAAAAAKgFH0HUoqTjmtLDh4Y8r9acc+WS3gseNlNivmNO+wQAAACACj6CqJeSjofW0Pb4pONpue7TzKza9Wz6BAAAAAAvQdQMSWuC4yFmdmCqRmZWJOm04OFWSVOy6PPppOMLzCyswMQJkvYLjuc65z7Ook8AAAAAyD6ICvaFuiF4aJIeNbP2yW2CIOcR7dxc9y7nXPImuMltHzYzF3yND+nzHUnPBw+7SbrLzKp8L2bWTdK9SadS3isTzrlDqGiDuODnHXHBzzrigp91xEmuf9597RN1r6STJQ2U1F/SO2Y2UdJSJYpJjJNUsbPYe5Ku99DnzyQdIWmP4P4HmdljktZJ6i3pAu2szPe4c26qhz4BAAAAxJyXIMo5t93MRkp6RtJgSV2VOlAqkTTaOfd1imuZ9rnczE4I+txH0oDgq7q/SDon2/4AAAAAQPKzJkqS5JxbL2mIEuuepkr6QtJ2SaskvSrpfEkDnHOfeuxzvqSDJf2vpDclrZW0TdJnkiZLOsE5d4ZzbruvPgEAAADEm9VtqyYAAAAAiCdvmSgAAAAAiAOCKAAAAADIAEEUAAAAAGSAIMoDM2tpZj80s9vM7HUzW21m281sg5m9b2YPmdkxDT1OwAdL6GVm/21mt5rZzOBnvWJ/t4cbeoxAOsHP8Klm9jczW2Fm28xspZm9Ymbnmpmv7T+ABmNm+WZ2kJmNNbM7zexNM9tc016cQFNjZrub2Slmdq+ZzTGzdWZWambrzewdM7vHzA7z3i+FJbJjZmdI+pOkNrVoPk3SGOfcmtyOCsgdM7tV0s/TNHnEOTe2noYDZCTYDL5iO44wFdtxeKsmC9Q3M3tW0klpmvzGOTe+noYD5ISZ/T9J10lqXovmkyRd4Jzb7KNvPm3LXk/tDKBWSvqHpH9JWi2ptRIbEJ8uqYWk4yW9bGZH+PofCDSA/GqPNyqxrcB3GmAsQK2ZWaGkKUq8LkuJn9v7tHNj+HOU2Bi+v6QXg9fqDQ0xVsCD6q/VX0paJ6m4AcYC5Eov7QygPpb0sqQFSmx71F7SMZJOVuL34UxJRWZ2gnOuPNuOCaL8eEPSBEkvOufKql17yMxuUeJ/6p5K7Gt1paRf1+8QAW/ek3SbpLmS5klaIuloSTMaclBALVyonQFUiaQhwR6HkiQzu0vS85KGKvGhwDWSrqjvQQKevC3pfSVep+c55z4xs7GSHmrQUQF+OSX2p73ZOfdaiuv3mdlASX9XIulxnKSz5OH3gOl8WTKz9sl/hNO0Gy7pr8HDT51z3XM7MqD+mNkg7QyimM6HRidY5/SFpE5K/NHt7Zz7d4p2RUp8mtlaic3b93LOravPsQK5Ui2IYjofmrwM3odfJOnO4OE/nXNHZ9s3hSWyVJv/cYEXJW0KjruZWdscDQkAsKvBSgRQkvRKqgBKkpxzqyU9GTxsLmlkPYwNAFAHGbwPn5x03NtH3wRR9SSY5pe8DqplQ40FAGLouKTjaTW0Tb5+fA7GAgCoXxuTjr28ByeIqifBFJGKT0E3S6JCHwDUn4OSjufV0HZuyPMAAE1T8mv5ch83JIiqP+cnHU/zURUEAFBrvZKOl9XQdoWkiiJBxWZmORkRAKC+JL8Pn+rjhgRR9cDM9pF0VfDQKVHJDwBQf9olHa9N19A5t0NSRWnzZkoUmQAANEFmdqSks4OHW5WoMJw1gqgcM7PWkv5PUqvg1D3OuX814JAAII6SN0TfWov2W5KOd/M8FgBAPTCzzpKe1s6Y5xrn3Aof947FPlFmdq4SGylmLZNyoGaWL+kvSuwNJSX2JbncxziAMA318w4AANBYBImMKZL2Ck5NlXSrr/vHIoiSdK6kAZ7uNb42jcwsT9LDkkYEpz6QdIJzrjafgALZqPefd6AJ+EaJ3eslqUXwOJ3k6k0bQ1sBABodM2sh6QVJhwen3pB0qvO4QS7T+XIgWIQ8UdKZwamPJB0T7D8CAKh/XyUdd0zXMNiYt2Ivv1Lt3OMPANDImVmhpOeU2B9Qkt6WNMw55/W1PBaZKOfcd+u5y7uUyAZIiTKKg51zn9fzGBBTDfDzDjQFSyT1DI57KH2Fvr0l5QfHS31+cgkAyB0zK1BiY90TglPzJR3vnNsQ/qy6IRPlmZn9UdJPg4crlAigPm3AIQEApEVJx4fU0PbQkOcBABqpYBbBE9q5lGahpGOdc+tz0R9BlEdmdrOkS4OHK5UIoD5uwCEBABJeSjoeWkPb45OOp+VgLAAAj4JibpMknRycek/SEOfculz1SRDliZldr52V91YpEUB92IBDAgDsNEPSmuB4iJkdmKqRmRVJOi14uFWJyk4AgEYqKOb2oKRTg1MfqB5qERBEeWBmv5J0dfBwjRL/4xY34JAAAEmCDXRvCB6apEfNrH1ym6Ca0yPaubnuXbn8FBMAkJ2kYm5jglNLlUhk/CfnfbNeNjtmdr4S//Mq/FrSu7V46uvOubW5GRWQO2bWTrvud9ZdO6tRvivpr9Wuv+qcezXXYwPSCSo2vSxpYHDqMyVev5cqUUxinKQDgmvvSTrSOfd1fY8T8MHMeirxM53sYEk/DI5nSfpntevPOufm53psgC9m9jtJVwUPSyX9XImaBDWZ7pzbnFXfBFHZMbOHJZ1Vh6f+wDk30+9ogNwzsx6SPsnwab9h4140BkH26RntLH2bSomk0RQFQlNmZoOUmMaaibOdcw/7Hw2QG2Y2U9LRdXhqT+fcsmz6ZjofACA2gipNQ5RY9zRV0heStiuxlvVVSedLGkAABQBIh0wUAAAAAGSATBQAAAAAZIAgCgAAAAAyQBAFAAAAABkgiAIAAACADBBEAQAAAEAGCKIAAAAAIAMEUQAAAACQAYIoAAAAAMgAQRQAAAAAZIAgCgAAAAAyQBAFAAAAABkgiAIAAACADBBEAQAAAEAGCKIAAAAAIAMEUQAAAACQAYIoAAAAAMgAQRQAAAAAZIAgCgAAAAAyQBAFAAAAABn4/6KVY5u0oTxUAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 424 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $V(x) = g_2 x^2 + g_4 x^4$\n", "\n", "We consider quartic potentials where $g_2$ varies to reveal equilibrium distributions with support which are connected, about to be disconnect and fully disconnected.\n", "\n", "We refer to\n", "[[DuKu06, p.2-3]](https://arxiv.org/pdf/math/0605201.pdf),\n", "[[Molinari, Example 3.3]](http://pcteserver.mi.infn.it/~molinari/RMT/RM2.pdf) and\n", "[[LiMe13, Section 2]](https://arxiv.org/pdf/1306.1179.pdf)\n", "for the exact shape of the corresponding equilibrium densities." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x)= \\frac{1}{4} x^4 + \\frac{1}{2} x^2$\n", "\n", "This case reveals an equilibrium density with a connected support." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/4, 0, 1/2, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "sampl_x4_x2 = be.sample_mcmc(N=1000, nb_gibbs_passes=10,\n", " sample_exact_cond=True)\n", "# sample_exact_cond=False,\n", "# nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 424 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x4_x2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x)= \\frac{1}{4} x^4 - x^2$ (onset of two-cut solution)\n", "\n", "This case reveal an equilibrium density with support which is about to be disconnected.\n", "\n", "The conditionals associated to the $a_n$ parameters are not $\\log$-concave and we do not support exact sampling but perform a few steps (100 by defaults) of MALA.\n", "For this reason, we set `sample_exact_cond=False`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/4, 0, -1, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "sampl_x4_x2_onset_2cut = be.sample_mcmc(N=1000, nb_gibbs_passes=10, \n", " sample_exact_cond=False,\n", " nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 423 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x4_x2_onset_2cut)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $V(x)= \\frac{1}{4} x^4 - \\frac{5}{4} x^2$ (Two-cut eigenvalue distribution)\n", "\n", "This case reveals an equilibrium density with support having two connected components.\n", "\n", "The conditionals associated to the $a_n$ parameters are not $\\log$-concave and we do not support exact sampling but perform a few steps (100 by defaults) of MALA.\n", "For this reason, we set `sample_exact_cond=False`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/4, 0, -1.25, 0, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "sampl_x4_x2_2cut = be.sample_mcmc(N=200, nb_gibbs_passes=10,\n", " sample_exact_cond=False,\n", " nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 423 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x4_x2_2cut)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $V(x) = \\frac{1}{20} x^4 - \\frac{4}{15}x^3 + \\frac{1}{5}x^2 + \\frac{8}{5}x$\n", " \n", "This case reveals a singular behavior at the right edge of the support of the equilibrium density\n", "\n", "The conditionals associated to the $a_n$ parameters are not $\\log$-concave and we do not support exact sampling but perform a few steps (100 by defaults) of MALA.\n", "For this reason, we set `sample_exact_cond=False`.\n", "\n", "We refer to [[ClItsKr10, Example 1.2]](https://arxiv.org/abs/0901.2473)\n", "[[OlNaTr14, Section 3.2]](https://arxiv.org/pdf/1404.0071.pdf) for the expression of the corresponding equilibrium density." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "beta, V = 2, np.poly1d([1/20, -4/15, 1/5, 8/5, 0])\n", "be = BetaEnsemblePolynomialPotential(beta, V)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "sampl_x4_x3_x2_x = be.sample_mcmc(N=200, nb_gibbs_passes=10,\n", " sample_exact_cond=False,\n", " nb_mala_steps=100)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 423 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "be.hist(sampl_x4_x3_x2_x)" ] } ], "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": "3.7.2" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "192px", "width": "252px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "292px", "left": "1518.72px", "right": "20px", "top": "75px", "width": "382.25px" }, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

daphnei/nn_chatbot

homeworks/XOR/HW1_report.ipynb

1

13841

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2\n", "=====\n", "Daphne Ippolito" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import xor_network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What issues did you have?\n", "-----\n", "\n", "The first issue that I has was that I was trying to output a single scalar whose value could be thresholded to determine whether the network should return TRUE or FALSE. It turns out loss functions for this are much more complicated than if I had instead treated the XOR problem as a classification task with one output per possible label ('TRUE', 'FALSE'). This is the approach I have implemented here.\n", "\n", "Another issue I encountered at first was that I was using too few hidden nodes. I originally thought that such a simple problem would only need a couple nodes in a single hidden layer to implement. However, such small networks were extremely slow to converge. This is exemplified in the Architectures section.\n", "\n", "Lastly, when I was using small batch sizes (<= 5 examples), and randomly populating the batches, the network would sometimes fail to converge, probably because the batches didn't contain all the possible examples. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which activation functions did you try? Which loss functions?\n", "-----\n", "I tried ReLU, sigmoid, and tanh activation functions. I only successfully uses a softmax cross-entropy loss function.\n", "\n", "The results for the different activation functions can be seen by running the block below. The sigmoid function consistently takes the longest to converge. I'm unsure why tanh does significantly better than sigmoid." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 7 nodes\n", "Adding hidden layer with 7 nodes\n", "Step 50: loss = 0.61, batch acc = 0.67, test acc = 0.67 (0.004 sec)\n", "Step 100: loss = 0.52, batch acc = 0.72, test acc = 0.72 (0.004 sec)\n", "Step 150: loss = 0.45, batch acc = 0.73, test acc = 0.73 (0.003 sec)\n", "Step 200: loss = 0.37, batch acc = 0.71, test acc = 0.71 (0.004 sec)\n", "Step 250: loss = 0.28, batch acc = 1.00, test acc = 1.00 (0.004 sec)\n", "Test accuracy is perfect after 250 iterations. Quitting.\n" ] } ], "source": [ "batch_size = 100\n", "num_steps = 10000\n", "num_hidden = 7\n", "num_hidden_layers = 2\n", "learning_rate = 0.2\n", "\n", "xor_network.run_network(batch_size, num_steps, num_hidden, num_hidden_layers, learning_rate, False, 'sigmoid')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 7 nodes\n", "Adding hidden layer with 7 nodes\n", "Step 50: loss = 0.07, batch acc = 1.00, test acc = 1.00 (0.003 sec)\n", "Test accuracy is perfect after 50 iterations. Quitting.\n" ] } ], "source": [ "xor_network.run_network(batch_size, num_steps, num_hidden, num_hidden_layers, learning_rate, False, 'tanh')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 7 nodes\n", "Adding hidden layer with 7 nodes\n", "Step 50: loss = 0.10, batch acc = 1.00, test acc = 1.00 (0.004 sec)\n", "Test accuracy is perfect after 50 iterations. Quitting.\n" ] } ], "source": [ "xor_network.run_network(batch_size, num_steps, num_hidden, num_hidden_layers, learning_rate, False, 'relu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What architectures did you try? What were the different results? How long did it take?\n", "-----\n", "The results for several different architectures can be seen by running the code below. Since there is no reading from disk, each iteration takes almost exactly the same amount of time. Therefore, I will report \"how long it takes\" in number of iterations rather than in time." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 5 nodes\n", "Adding hidden layer with 5 nodes\n", "Step 50: loss = 0.44, batch acc = 0.75, test acc = 0.75 (0.003 sec)\n", "Step 100: loss = 0.19, batch acc = 1.00, test acc = 1.00 (0.003 sec)\n", "Test accuracy is perfect after 100 iterations. Quitting.\n" ] } ], "source": [ "# Network with 2 hidden layers of 5 nodes\n", "xor_network.run_network(batch_size, num_steps, 5, 2, learning_rate, False, 'relu')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 2 nodes\n", "Adding hidden layer with 2 nodes\n", "Adding hidden layer with 2 nodes\n", "Adding hidden layer with 2 nodes\n", "Adding hidden layer with 2 nodes\n", "Step 50: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.005 sec)\n", "Step 100: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.005 sec)\n", "Step 150: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.005 sec)\n", "Step 200: loss = 0.59, batch acc = 0.72, test acc = 0.72 (0.004 sec)\n", "Step 250: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.006 sec)\n", "Step 300: loss = 0.57, batch acc = 0.74, test acc = 0.74 (0.005 sec)\n", "Step 350: loss = 0.52, batch acc = 0.79, test acc = 0.79 (0.005 sec)\n", "Step 400: loss = 0.60, batch acc = 0.72, test acc = 0.72 (0.006 sec)\n", "Step 450: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.005 sec)\n", "Step 500: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.006 sec)\n", "Step 550: loss = 0.58, batch acc = 0.73, test acc = 0.73 (0.005 sec)\n", "Step 600: loss = 0.58, batch acc = 0.73, test acc = 0.73 (0.004 sec)\n", "Step 650: loss = 0.59, batch acc = 0.72, test acc = 0.72 (0.005 sec)\n", "Step 700: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.005 sec)\n", "Step 750: loss = 0.50, batch acc = 0.81, test acc = 0.81 (0.005 sec)\n", "Step 800: loss = 0.60, batch acc = 0.72, test acc = 0.72 (0.005 sec)\n", "Step 850: loss = 0.68, batch acc = 0.64, test acc = 0.64 (0.005 sec)\n", "Step 900: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.004 sec)\n", "Step 950: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.005 sec)\n", "Step 1000: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.005 sec)\n", "Step 1050: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.006 sec)\n", "Step 1100: loss = 0.60, batch acc = 0.72, test acc = 0.72 (0.004 sec)\n", "Step 1150: loss = 0.58, batch acc = 0.73, test acc = 0.73 (0.005 sec)\n", "Step 1200: loss = 0.62, batch acc = 0.70, test acc = 0.70 (0.005 sec)\n", "Step 1250: loss = 0.57, batch acc = 0.74, test acc = 0.74 (0.005 sec)\n", "Step 1300: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.006 sec)\n", "Step 1350: loss = 0.58, batch acc = 0.73, test acc = 0.73 (0.005 sec)\n", "Step 1400: loss = 0.59, batch acc = 0.72, test acc = 0.72 (0.005 sec)\n", "Step 1450: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.006 sec)\n", "Step 1500: loss = 0.50, batch acc = 0.81, test acc = 0.81 (0.003 sec)\n", "Step 1550: loss = 0.59, batch acc = 0.73, test acc = 0.73 (0.004 sec)\n", "Step 1600: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.006 sec)\n", "Step 1650: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.005 sec)\n", "Step 1700: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.004 sec)\n", "Step 1750: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.006 sec)\n", "Step 1800: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.006 sec)\n", "Step 1850: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.006 sec)\n", "Step 1900: loss = 0.50, batch acc = 0.81, test acc = 0.81 (0.006 sec)\n", "Step 1950: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.005 sec)\n", "Step 2000: loss = 0.60, batch acc = 0.71, test acc = 0.71 (0.004 sec)\n", "Step 2050: loss = 0.51, batch acc = 0.80, test acc = 0.80 (0.004 sec)\n", "Step 2100: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.006 sec)\n", "Step 2150: loss = 0.53, batch acc = 0.78, test acc = 0.78 (0.005 sec)\n", "Step 2200: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.004 sec)\n", "Step 2250: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.003 sec)\n", "Step 2300: loss = 0.62, batch acc = 0.70, test acc = 0.70 (0.006 sec)\n", "Step 2350: loss = 0.60, batch acc = 0.72, test acc = 0.72 (0.006 sec)\n", "Step 2400: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.006 sec)\n", "Step 2450: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.006 sec)\n", "Step 2500: loss = 0.59, batch acc = 0.73, test acc = 0.73 (0.005 sec)\n", "Step 2550: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.007 sec)\n", "Step 2600: loss = 0.64, batch acc = 0.68, test acc = 0.68 (0.009 sec)\n", "Step 2650: loss = 0.64, batch acc = 0.68, test acc = 0.68 (0.005 sec)\n", "Step 2700: loss = 0.49, batch acc = 0.82, test acc = 0.82 (0.005 sec)\n", "Step 2750: loss = 0.56, batch acc = 0.75, test acc = 0.75 (0.006 sec)\n", "Step 2800: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.006 sec)\n", "Step 2850: loss = 0.55, batch acc = 0.76, test acc = 0.76 (0.004 sec)\n", "Step 2900: loss = 0.54, batch acc = 0.77, test acc = 0.77 (0.004 sec)\n", "Step 2950: loss = 0.65, batch acc = 0.67, test acc = 0.67 (0.005 sec)\n", "After 3000 iterations, the network has still not converged. Something must be very wrong.\n" ] } ], "source": [ "# Network with 5 hidden layers of 2 nodes each\n", "num_steps = 3000 # (so it doesn't go on forever)\n", "xor_network.run_network(batch_size, num_steps, 2, 5, learning_rate, False, 'relu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Conclusion from the above:** With the number of parameters held constant, a deeper network does not necessarily perform better than a shallower one. I am guessing this is because fewer nodes in a layer means that the network can keep around less information from layer to layer." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 3 nodes\n", "Adding hidden layer with 3 nodes\n", "Adding hidden layer with 3 nodes\n", "Adding hidden layer with 3 nodes\n", "Adding hidden layer with 3 nodes\n", "Step 50: loss = 0.46, batch acc = 0.76, test acc = 0.76 (0.003 sec)\n", "Step 100: loss = 0.07, batch acc = 1.00, test acc = 1.00 (0.005 sec)\n", "Test accuracy is perfect after 100 iterations. Quitting.\n" ] } ], "source": [ "xor_network.run_network(batch_size, num_steps, 3, 5, learning_rate, False, 'relu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Conclusion from the above:** Indeed, the problem is not the number of layers, but the number of nodes in each layer." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 5 nodes\n", "Step 50: loss = 0.20, batch acc = 1.00, test acc = 1.00 (0.003 sec)\n", "Test accuracy is perfect after 50 iterations. Quitting.\n" ] } ], "source": [ "# This is the minimum number of nodes I can use to consistently get convergence with Gradient Descent.\n", "xor_network.run_network(batch_size, num_steps, 5, 1, learning_rate, False, 'relu')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding hidden layer with 2 nodes\n", "Step 50: loss = 0.00, batch acc = 1.00, test acc = 1.00 (0.003 sec)\n", "Test accuracy is perfect after 50 iterations. Quitting.\n" ] } ], "source": [ "# If I switch to using Adam Optimizer, I can get down to 2 hidden nodes and consistently have convergence.\n", "xor_network.run_network(batch_size, num_steps, 2, 1, learning_rate, True, 'relu')" ] } ], "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

beibeiyang/ipynbdemo

rAssociationRules.ipynb

2

395428

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo on Association Rules with R and Jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will install packages arules and arulesViz. \n", "Note the installation may take a while." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "The downloaded source packages are in\n", "\t‘/private/var/folders/zm/79bb4c_j6n9_kg23gyhb89hnyhx5dc/T/RtmpxwLbIi/downloaded_packages’\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "also installing the dependencies ‘gdata’, ‘pkgmaker’, ‘rngtools’, ‘gridBase’, ‘doParallel’, ‘lmtest’, ‘TSP’, ‘gclus’, ‘gplots’, ‘registry’, ‘NMF’, ‘irlba’, ‘scatterplot3d’, ‘vcd’, ‘seriation’, ‘igraph’\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "The downloaded source packages are in\n", "\t‘/private/var/folders/zm/79bb4c_j6n9_kg23gyhb89hnyhx5dc/T/RtmpxwLbIi/downloaded_packages’\n" ] } ], "source": [ "install.packages(\"arules\", rep=\"http://lib.stat.cmu.edu/R/CRAN/\")\n", "install.packages(\"arulesViz\", rep=\"http://lib.stat.cmu.edu/R/CRAN/\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library('arules')\n", "library('arulesViz')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "transactions in sparse format with\n", " 9835 transactions (rows) and\n", " 169 items (columns)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data(Groceries)\n", "Groceries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Groceries dataset contains 9835 transactions and 169 grocery items.\n", "Display a summary below." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "transactions as itemMatrix in sparse format with\n", " 9835 rows (elements/itemsets/transactions) and\n", " 169 columns (items) and a density of 0.02609146 \n", "\n", "most frequent items:\n", " whole milk other vegetables rolls/buns soda \n", " 2513 1903 1809 1715 \n", " yogurt (Other) \n", " 1372 34055 \n", "\n", "element (itemset/transaction) length distribution:\n", "sizes\n", " 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \n", "2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55 46 \n", " 17 18 19 20 21 22 23 24 26 27 28 29 32 \n", " 29 14 14 9 11 4 6 1 1 1 1 3 1 \n", "\n", " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 1.000 2.000 3.000 4.409 6.000 32.000 \n", "\n", "includes extended item information - examples:\n", " labels level2 level1\n", "1 frankfurter sausage meet and sausage\n", "2 sausage sausage meet and sausage\n", "3 liver loaf sausage meet and sausage" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(Groceries)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "'transactions'" ], "text/latex": [ "'transactions'" ], "text/markdown": [ "'transactions'" ], "text/plain": [ "[1] \"transactions\"\n", "attr(,\"package\")\n", "[1] \"arules\"" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class(Groceries)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>labels</th><th scope=col>level2</th><th scope=col>level1</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>frankfurter</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>2</th><td>sausage</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>3</th><td>liver loaf</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>4</th><td>ham</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>5</th><td>meat</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>6</th><td>finished products</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>7</th><td>organic sausage</td><td>sausage</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>8</th><td>chicken</td><td>poultry</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>9</th><td>turkey</td><td>poultry</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>10</th><td>pork</td><td>pork</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>11</th><td>beef</td><td>beef</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>12</th><td>hamburger meat</td><td>beef</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>13</th><td>fish</td><td>fish</td><td>meet and sausage</td></tr>\n", "\t<tr><th scope=row>14</th><td>citrus fruit</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>15</th><td>tropical fruit</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>16</th><td>pip fruit</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>17</th><td>grapes</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>18</th><td>berries</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>19</th><td>nuts/prunes</td><td>fruit</td><td>fruit and vegetables</td></tr>\n", "\t<tr><th scope=row>20</th><td>root vegetables</td><td>vegetables</td><td>fruit and vegetables</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " & labels & level2 & level1\\\\\n", "\\hline\n", "\t1 & frankfurter & sausage & meet and sausage\\\\\n", "\t2 & sausage & sausage & meet and sausage\\\\\n", "\t3 & liver loaf & sausage & meet and sausage\\\\\n", "\t4 & ham & sausage & meet and sausage\\\\\n", "\t5 & meat & sausage & meet and sausage\\\\\n", "\t6 & finished products & sausage & meet and sausage\\\\\n", "\t7 & organic sausage & sausage & meet and sausage\\\\\n", "\t8 & chicken & poultry & meet and sausage\\\\\n", "\t9 & turkey & poultry & meet and sausage\\\\\n", "\t10 & pork & pork & meet and sausage\\\\\n", "\t11 & beef & beef & meet and sausage\\\\\n", "\t12 & hamburger meat & beef & meet and sausage\\\\\n", "\t13 & fish & fish & meet and sausage\\\\\n", "\t14 & citrus fruit & fruit & fruit and vegetables\\\\\n", "\t15 & tropical fruit & fruit & fruit and vegetables\\\\\n", "\t16 & pip fruit & fruit & fruit and vegetables\\\\\n", "\t17 & grapes & fruit & fruit and vegetables\\\\\n", "\t18 & berries & fruit & fruit and vegetables\\\\\n", "\t19 & nuts/prunes & fruit & fruit and vegetables\\\\\n", "\t20 & root vegetables & vegetables & fruit and vegetables\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " labels level2 level1\n", "1 frankfurter sausage meet and sausage\n", "2 sausage sausage meet and sausage\n", "3 liver loaf sausage meet and sausage\n", "4 ham sausage meet and sausage\n", "5 meat sausage meet and sausage\n", "6 finished products sausage meet and sausage\n", "7 organic sausage sausage meet and sausage\n", "8 chicken poultry meet and sausage\n", "9 turkey poultry meet and sausage\n", "10 pork pork meet and sausage\n", "11 beef beef meet and sausage\n", "12 hamburger meat beef meet and sausage\n", "13 fish fish meet and sausage\n", "14 citrus fruit fruit fruit and vegetables\n", "15 tropical fruit fruit fruit and vegetables\n", "16 pip fruit fruit fruit and vegetables\n", "17 grapes fruit fruit and vegetables\n", "18 berries fruit fruit and vegetables\n", "19 nuts/prunes fruit fruit and vegetables\n", "20 root vegetables vegetables fruit and vegetables" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# display the first 20 grocery labels\n", "Groceries@itemInfo[1:20,]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'whole milk, cereals'</li>\n", "\t<li>'tropical fruit, other vegetables, white bread, bottled water, chocolate'</li>\n", "\t<li>'citrus fruit, tropical fruit, whole milk, butter, curd, yogurt, flour, bottled water, dishes'</li>\n", "\t<li>'beef'</li>\n", "\t<li>'frankfurter, rolls/buns, soda'</li>\n", "\t<li>'chicken, tropical fruit'</li>\n", "\t<li>'butter, sugar, fruit/vegetable juice, newspapers'</li>\n", "\t<li>'fruit/vegetable juice'</li>\n", "\t<li>'packaged fruit/vegetables'</li>\n", "\t<li>'chocolate'</li>\n", "\t<li>'specialty bar'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'whole milk, cereals'\n", "\\item 'tropical fruit, other vegetables, white bread, bottled water, chocolate'\n", "\\item 'citrus fruit, tropical fruit, whole milk, butter, curd, yogurt, flour, bottled water, dishes'\n", "\\item 'beef'\n", "\\item 'frankfurter, rolls/buns, soda'\n", "\\item 'chicken, tropical fruit'\n", "\\item 'butter, sugar, fruit/vegetable juice, newspapers'\n", "\\item 'fruit/vegetable juice'\n", "\\item 'packaged fruit/vegetables'\n", "\\item 'chocolate'\n", "\\item 'specialty bar'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'whole milk, cereals'\n", "2. 'tropical fruit, other vegetables, white bread, bottled water, chocolate'\n", "3. 'citrus fruit, tropical fruit, whole milk, butter, curd, yogurt, flour, bottled water, dishes'\n", "4. 'beef'\n", "5. 'frankfurter, rolls/buns, soda'\n", "6. 'chicken, tropical fruit'\n", "7. 'butter, sugar, fruit/vegetable juice, newspapers'\n", "8. 'fruit/vegetable juice'\n", "9. 'packaged fruit/vegetables'\n", "10. 'chocolate'\n", "11. 'specialty bar'\n", "\n", "\n" ], "text/plain": [ " [1] \"whole milk, cereals\" \n", " [2] \"tropical fruit, other vegetables, white bread, bottled water, chocolate\" \n", " [3] \"citrus fruit, tropical fruit, whole milk, butter, curd, yogurt, flour, bottled water, dishes\"\n", " [4] \"beef\" \n", " [5] \"frankfurter, rolls/buns, soda\" \n", " [6] \"chicken, tropical fruit\" \n", " [7] \"butter, sugar, fruit/vegetable juice, newspapers\" \n", " [8] \"fruit/vegetable juice\" \n", " [9] \"packaged fruit/vegetables\" \n", "[10] \"chocolate\" \n", "[11] \"specialty bar\" " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# display the 10th to 20th transactions\n", "apply(Groceries@data[,10:20], 2, \n", " function(r) paste(Groceries@itemInfo[r,\"labels\"], collapse=\", \")\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's generate some rules from the grocery dataset." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Parameter specification:\n", " confidence minval smax arem aval originalSupport support minlen maxlen target\n", " 0.6 0.1 1 none FALSE TRUE 0.001 1 10 rules\n", " ext\n", " FALSE\n", "\n", "Algorithmic control:\n", " filter tree heap memopt load sort verbose\n", " 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n", "\n", "apriori - find association rules with the apriori algorithm\n", "version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt\n", "set item appearances ...[0 item(s)] done [0.00s].\n", "set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].\n", "sorting and recoding items ... [157 item(s)] done [0.00s].\n", "creating transaction tree ... done [0.01s].\n", "checking subsets of size 1 2 3 4 5 6 done [0.02s].\n", "writing ... [2918 rule(s)] done [0.00s].\n", "creating S4 object ... done [0.01s].\n" ] } ], "source": [ "rules <- apriori(Groceries, parameter=list(support=0.001,\n", " confidence=0.6, target = \"rules\"))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set of 2918 rules\n", "\n", "rule length distribution (lhs + rhs):sizes\n", " 2 3 4 5 6 \n", " 3 490 1765 626 34 \n", "\n", " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 2.000 4.000 4.000 4.068 4.000 6.000 \n", "\n", "summary of quality measures:\n", " support confidence lift \n", " Min. :0.001017 Min. :0.6000 Min. : 2.348 \n", " 1st Qu.:0.001118 1st Qu.:0.6316 1st Qu.: 2.668 \n", " Median :0.001220 Median :0.6818 Median : 3.168 \n", " Mean :0.001480 Mean :0.7028 Mean : 3.450 \n", " 3rd Qu.:0.001525 3rd Qu.:0.7500 3rd Qu.: 3.692 \n", " Max. :0.009354 Max. :1.0000 Max. :18.996 \n", "\n", "mining info:\n", " data ntransactions support confidence\n", " Groceries 9835 0.001 0.6" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(rules)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ECXb88ce744iUrltFLT9KupBPUQa7008/3dTqpD8VBU5qQar05FT51J9tEEAAAQQQQAABBBBAoPgCsQyQxKB5kJ599lm3BenTTz+1bt26mTLcZRaNMdI4mMyi/R988EE32cMXX3zhJn7o3r07LUeZUHxGAAEEEEAAAQQQQKCKBGIbIHn3qE2bNrbddtt5Hwt+1cSy3uSyBe/MDggggAACCCCAAAIIIJAoATpPJup2cjEIIIAAAggggAACCCBQHwECpProsW9eAkFdHLVj2PK8DspGCCCAAAIIIIAAAgiUQCD2XexKYMIhiyjQrl07a9u2bWAwpEyBuSb0LWJVOBQCCCCAAAIIIFAUAT3DkPG4KJSRPAgBUiRvS/wqpUl6ly1bFljxHXfc0Vq3bh24joUIIIAAAggggAACCERJgAApSncjpnX55ptv7Omnn85a+5NOOinrelYigAACCCCAAAIIIBAFAcYgReEuxLwOy5cvj/kVUH0EEEAAAQQQQAABBP4jQIDENwEBBBBAAAEEEEAAAQQQWCtAgMRXAQEEEEAAAQQQQAABBBBYK8AYJL4KCCRQQN0elyxZEnhljRo1IntgoAwLEUAAAQQQQAABMwIkvgUIJFDglVdesTlz5oRe2ZFHHklmwVAdViCAAAIIIJBboGFDOmLlVornFtzZeN43ao1AVoFVq1ZlXb969eqs61mJAAIIIIAAAghUqwABUrXe+SJed4cOHbIerXFjGiqzArESAQQQQAABBBBAIDICPLlG5lbEtyJt27Y15jmK7/2j5ggggAACCCCAAAL/FSBA+q8F7/IQ+OmnnwK3atasWeByFiKAAAIIIIAAAgggECcBAqQ43a0K13X06NFZazBo0CDr1atX1m1YiQACCCCAAAIIIIBAlAUIkKJ8d2JWt4ULF8asxlQXAQQQQAABBBAoXKBBgwamP0oyBQiQknlfuaoqF9h4442tSZMmgQpa3rJly8B1LEQAAQQQQAABBKpdgACp2r8BXH8iBfr27Wv6oyCAAAIIIIAAAggUJkCa78K82BoBBBBAAAEEEEAAAQQSLECAlOCby6UhgAACCCCAAAIIIIBAYQIESIV5sTUCCCCAAAIIIIAAAggkWIAAKcE3t9yX1rVr13KfkvMhgAACCCCAAAIIIFBUAZI0FJUz2Qc76aSTkn2BXB0CCCCAAAIIIJCnAGm+84SK4Wa0IMXwplFlBBBAAAEEEEAAAQQQKI0AAVJpXDkqAggggAACCCCAAAIIxFCALnYxvGmlrPJjjz1m8+fPDz1Fly5d7IADDghdzwoEEEAAAQQQQAABBOIsQAtSnO9eCeq+YMGCrEf97rvvsq5nJQIIIIAAAggggAACcRYgQIrz3aPuCCCAAAIIIIAAAgggUFQButgVlZODIYAAAggggAACCCRdQBnsGjaMZzsD2fdyfzvjeWdzXxdbIIAAAggggAACCCCAAAIFCxAgFUzGDggggAACCCCAAAIIIJBUAQKkpN5ZrgsBBBBAAAEEEEAAAQQKFiBAKpgs2Tvk6peaa32ydbg6BBBAAAEEEEAAgaQLkKQh6Xe4wOsbMmSIvfnmm4F7KTgaNGhQ4DoWIoAAAggggAACCCCQBAECpCTcxSJew/rrr2+HHXZYEY/IoRBAAAEEEEAAAQQQiI8AAVJ87hU1RQABBBBAAAEEEIiIAMMOInIjSlANxiCVAJVDIoAAAggggAACCCCAQDwFCJDied+oNQIIIIAAAggggAACCJRAgC52JUCN+iEnTZpkjzzyiKVSqcCqtmzZ0kaOHBm4joUIIIAAAggggAACCCRZgBakJN/dkGubNm1aaHCkXZYvXx6yJ4sRQAABBBBAAAEEEEi2AAFSsu8vV4cAAggggAACCCCAAAIFCNDFrgAsNkUAAQQQQAABBBBAQBnsGjaknSGp3wTubFLvLNeFAAIIIIAAAggggAACBQsQIBVMxg4IIIAAAggggAACCCCQVAECpKTeWa4LAQQQQAABBBBAAAEEChYgQCqYLP47tG/fPutFMDN0Vh5WIoAAAggggAACCCRYgCQNCb65YZd24IEH2rrrrmuLFy8O3KRv376By1mIAAIIIIAAAggggEDSBQiQkn6HQ65vwIABIWtYjAACCCCAAAIIIIBA9QoQIFXvvefKEUAAAQQQQAABBOogQJrvOqDFaBfGIMXoZlFVBBBAAAEEEEAAAQQQKK0AAVJpfTk6AggggAACCCCAAAIIxEiAAClGN4uqIoAAAggggAACCCCAQGkFGINUWt+KHv3RRx+1BQsWBNahWbNmduyxxwauK9fCVatW2U8//RR4uoYNG1qLFi0C17EQAQQQQAABBBBAAIFSCRAglUo2Asf97LPPstbi1VdftV133TXrNqVcOWHCBJs5c2boKQ477DBr27Zt6HpWIIAAAggggAACCCBQbAG62BVbNEbHW7hwYUVru2LFiqznVwsTBQEEEEAAAQQQQACBcgoQIJVTm3MhgAACCCCAAAIIIIBApAUIkCJ9e6gcAggggAACCCCAAAIIlFOAAKmc2pwLAQQQQAABBBBAAAEEIi1AgBTp20PlEEAAAQQQQAABBBBAoJwCBEjl1OZcCCCAAAIIIIAAAgggEGkB0nxH+vbUr3KaS2jNmjWBB2nQoIH17ds3cF25Fq633nq2fPnywNM1adLEWrZsGbiOhQgggAACCCCAAAIIlEqAAKlUshE47jnnnBOBWoRXoX///qY/CgIIIIAAAgggEDcB/RBNSaYAdzaZ95WrQgABBBBAAAEEEEAAgToI0IJUBzR2KUxgypQplkqlau2kbn6bbrqpNW3atNY6FiCAAAIIIIAAAgggUAkBAqRKqFfROceOHWs//fRT6BW/8847dvzxx4euZwUCCCCAAAIIIIAAAuUUoItdObWr8FyrV6/OetVBLUtZd2AlAggggAACCCCAAAIlFCBAKiEuh0YAAQQQQAABBBBAAIF4CdDFLl73i9oigAACCCCAAAIIVFhA46jjmsVOdadkF6AFKbsPaxFAAAEEEEAAAQQQQKCKBGhBivnNfvzxx2369OmhV7HXXnvZVlttFbq+WlZoQtqVK1cGXq4mpW3evHngOhYigAACCCCAAAIIVJcAAVLM7/fkyZNtyZIloVfx1FNPESA5Oi+99FLWAOmAAw4INWQFAggggAACCCCAQPUI0MUu5vc6Vxa4XOtLffnt2rXLeooWLVpkXV+sldmy6WVbV6zzcxwEEEAAAQQQQACBeAjQysobcAAAQABJREFUghSP+xTbWg4bNiy2dafiCCCAAAIIIIAAAtUnQAtS9d1zrhgBBBBAAAEEEEAAAQRCBGhBCoFhMQIIIIAAAggggAACQQJKlU267CCZZCyjBSkZ95GrQAABBBBAAAEEEEAAgSII0IJUBMRqP8TChQtN6cbDEkI0bdrUjjnmmGpn4voRQAABBBBAAAEEYiBAgBSDm5Stii1btgxNX639tL7U5ccffwwNjnTuVatWlboKOY/frVu30HTorVu3zrk/GyCAAAIIIIAAAghUhwABUszv8/nnnx/zKyhP9bfbbrvynIizIIAAAggggAACCMRagDFIsb59VB4BBBBAAAEEEEAAAQSKKUALUjE1ORYCCCCAAAIIIIBAVQg0bEg7Q1JvNHc2qXeW60IAAQQQQAABBBBAAIGCBQiQCiZjBwQQQAABBBBAAAEEEEiqAAFSUu9sGa+rVatWWc9GE3RWHlYigAACCCCAAAIIREiAMUgRuhlxrUrHjh3txBNPjGv1qTcCCCCAAAIIIIAAAmkBWpDSFLxBAAEEEEAAAQQQQACBahcgQKr2bwDXjwACCCCAAAIIIIAAAmkButilKXiDAAIIIIAAAggggEBugQYNGlhcx1ir7pTsArQgZfdhLQIIIIAAAggggAACCFSRAC1IMbjZDz/8sH300UeWSqUCa9u6dWs799xzA9cVc+HEiRNt3rx5gYds3LixHXDAAYHrirlw1apVNnv27FCLzp07W66sesWsD8dCAAEEEEAAAQQQSJYAAVIM7ucnn3xiS5cuDa3pihUrQtcVc8WMGTNszZo1oYecNm2a9ezZM3R9MVZ88803Nnny5MBmbdVt0003ta222qoYp+IYCCCAAAIIIIAAAlUoQIBUhTe9VJcc1sJV7POp72xQoEaf2mJLczwEEEAAAQQQQKD6BBiDVH33nCtGAAEEEEAAAQQQQACBEAFakEJgWIwAAggggAACCCCAQJBAA3Oy2DWgnSHIJgnLuLNJuItcAwIIIIAAAggggAACCBRFgACpKIwcBAEEEEAAAQQQQAABBJIgQBe7JNzFKrsGJYMImpwtKHFDldFwuQgggAACCCCAAAL1FCg4QPr666/ts88+s759+1qbNm1MmcOaNGlSz2qwezaBjh07Zk3z3bx582y7F21dhw4dbPHixYHHU8DSo0ePwHXFXNipUyfbfPPNQ+dBWn/99Yt5Oo6FAAIIIIAAAgggUGUCeQVI+sX+3nvvtQsuuMDmzJnjEo0bN87atWtnJ598so0aNcp22GGHKqMr3+WOGDGifCfLcqahQ4dmWVueVQoGt9xyy/KcjLMggAACCCCAAAIIVJ1AXmOQLr74YjvxxBNt4cKFtttuu6WRVq9ebVOnTrWBAwfaX//61/Ry3iCAAAIIIIAAAggggAACcRTI2YL04Ycf2lVXXWXHHHOM3X777fbFF19Y//793WvdcccdbfLkyXbwwQfbRRddZMcdd5w1atQojg7UOWICc+fODe1WqJZLdbWjIIAAAggggAACFRFo4JxVf5RECuQMkMaPH++OM7rtttusdevWtRD69Oljp556qv3ud7+zr776yjbaaKNa27AAgUIEVq1aZS+++GLoLhr3pmCcggACCCCAAAIIIIBAsQVydrGbMWOGrbfeem5ChrCTb7PNNu6qBQsWhG3CcgTyFsiVjU5j4igIIIAAAggggAACCJRCIGeApIxhylw3e/bs0PO//vrrbtrlXr16hW7DCgQQQAABBBBAAAEEEEAg6gI5u9gNHjzYTeN99NFH23XXXVcrpfczzzzjjlHaZZddrGXLllG/XuqXQ+CTTz6xDz74IDCNtrq2KVvhxhtvnOMorC5UYMmSJaFjrpRGv3379oUeku0RQAABBBBAAAEE6iCQM0Dq2bOnXXHFFfbb3/7Wtt9+e9NnlWuuucYdd/Tuu+9aixYt7I477qjD6dklagJvv/12YHDk1VOthQRInkbxXidNmmSLFi0KPaBSrDdt2jR0PSsQQAABBBBAAAEEiiOQM0DSac455xxT97lzzz3X1MKg8vzzz7vJG/bdd1+78cYb3ck73RX8DwIIFCyglPnZCuOusumwDgEEEEAAgfIKqFdNw4Y5R6qUt1KcrWgCeQVIOtuwYcPcP82FNG3aNNOEnZtttpnbelS02nAgBCogkC0pBP/xq8AN4ZQIIIAAAggggEAFBfIOkH744Qd77rnn7KijjnLHoajO6manlMwjRoywDh06VPAyOHWSBNSVTCnlV6xYEXhZbdu2DVxel4WzZs0ydRMNKxr7s8cee4StZjkCCCCAAAIIIIBAwgTyCpDuvvtuO++889wgSAGSV9TN7qWXXrJRo0bZCy+8YJtuuqm3ilcE6iVw6KGH1mv/fHdetmxZ1k3DgrSsO7ESAQQQQAABBBBAILYCOTtPTpkyxYYPH26dO3d2s9j5r3Ts2LE2cuRI++abb+z000/3r+I9AggggAACCCCAAAIIIBA7gZwtSE888YRpjMbDDz9svXv3rnGB66yzjl166aVugHTrrbfa/PnzrWPHjjW24QMCCCCAAAIIIIAAAgggEBeBnAGSWoe6du1aKzjyX+BBBx1kCpBmzpxJgOSHieF7pWxfunRpaM1btWoVuo4VdRfYZJNN3B8Ygo6geZD0R0EAAQQQQAABBBAovUDOAEkTg95www02d+5cN1AKqpKX+lsPeZR4C/zsZz8r2wVo3Fq2YGzXXXe1Tp06la0+lTyRxu8xhq+Sd4BzI4AAAgggUJgAmW4L84rT1jnHIO25557uxKHHHnusGyRlXtzEiRPdcUgDBgywYmYXyzwPn5MnsHLlyqwXtXz58qzrWYkAAggggAACCCCAQLEFcrYgqXvdHXfc4SZhUAvRwIEDrXv37u4v/59//rlNnjzZnRNp9OjRxa4bx0Og5AK5gnqlG6cggAACCCCAAAIIVI9AzgBJFKeccorbve6qq66ySZMm2csvv+wKab6agw8+2J0PqWfPntWjxpUmRkA/ABxyyCGJuR4uBAEEEEAAAQQQQKB+AnkFSDrFsGHD3D9ltFPLkQaNb7jhhkb/y/rdAPZGAAEEEEAAAQQQQACB6AjkHSB5VVZARGuRp8FrXATefPNNW7x4cWB127RpYzvuuGPgOhYigAACCCCAAAIIVJdAXgGS5jfSOKS33nrLFi1aZKtXrw5UeuWVVwKXsxCBSgt8++237nxeQfXIlkkvaHuWIYAAAggggEB1CzRo0MD0F8cS13qX0zpngPTTTz/Z3nvvbe+9914568W5qkCgZcuW9sMPP4ReKXMuhdKwAgEEEEAAAQQQQKBEAjkDpEceecQNjg444AC7+uqrrUePHtasWbMSVYfDlktgzJgxoafS/T3yyCND1xdrhVLIUxBAAAEEEEAAAQQQiJJAzgDp/fffd+s7atSo0Ilio3RB1CW3wP333591I7UaUhBAAAEEEEAAAQQQqEaBnBPFdunSxRo3bkyrUYK+HalUKkFXw6UggAACCCCAAAIIIFA8gZwB0h577OEmZXjggQeKd1aOhAACCCCAAAIIIIAAAghEUCBnF7ttt93Wrr32WrvkkkvcbB0777yz29UuaP6jTp06RfASqRIC5raCrlixIpBCc3pREEAAAQQQQAABBBCQQM4A6cEHH3QDpHnz5tmIESOyqtF1KytPVa5844037Msvvwy99nXXXdcGDx4cur5YK4YMGWKrVq0KPJy6kFIQQAABBBBAAIFCBIIaCwrZn22jK5DzyXCdddaxnXbaKbpXQM0iLTBnzhxbuXJlaB01x1Y5ioIgAqFySHMOBBBAAAEEEEAg3gI5AySNQdIfBQEEEEAAAQQQQAABBBBIukDDQi/w66+/tn/+85/2/fffm8Z0ZGsdKPTYbF8eAbUKUhBAAAEEEEAAAQQQQKC2QM4WJO2isUX33nuvXXDBBaYuUyrjxo2zdu3a2cknn2yaI2mHHXZwl/M/0RfYd999o19JaogAAggggAACCCCAQAUE8gqQLr74YrviiiusRYsWtttuu9mECRPcqq5evdqmTp1qAwcOtLvuustOOOGEClwCp0Sg/AJKPvHDDz8EnrhZs2a25557Bq5jIQIIIIAAAggggEC0BXJ2sfvwww/tqquusmOOOca+/fZbu/nmm9NXtOOOO9rkyZOtR48edtFFF7nzJaVX8gaBBAssWrTI7V6qLqaZf4sXL3ZbXRN8+VwaAggggAACVS3QoEEDUxa7OP5V9Y3L8+JzBkjjx4935z+67bbbrHXr1rUO26dPHzv11FNNY5O++uqrWutZUN0CpMCs7vvP1SOAAAIIIIAAAnETyNnFbsaMGbbeeutZmzZtQq9tm222cdctWLDANtpoo9DtWFF9AuqS+e6774Ze+FZbbRW6jhUIIIAAAggggAACCJRbIGeAtPnmm7utQ7Nnz7b1118/sH6vv/6628TYq1evwPUsrF6Bjh072j777FO9AFw5AggggAACCCCAQKwEcnaxGzx4sDVp0sSOPvrowJaAZ555xh2jtMsuu1jLli1jdfFUFgEEEEAAAQQQQAABBBDwC+QMkHr27OlmsFPmuu23394OP/xwd/9rrrnG/XzAAQe4Y5TuuOMO/3F5jwACCCCAAAIIIIAAAgjETiBnFztd0TnnnGPqPnfuuefaJ5984l7k888/7wZGmlPnxhtvNHXFK3dRmvFJkya5czP169fPFMwVWmbOnGkfffSRu5vGw2y44YaFHoLtq1CgefPm7kTJQZeuFlcKAggggAACCCCAQDwF8gqQdGnDhg1z/xYuXGjTpk0zPSButtlm7txIlbh01eGggw5y52Hyzt+7d2977rnnrHv37t6i0NeffvrJzjjjDBs9enQ6JbNSNg4fPtxuuukm9/pCd2ZF1QvsvvvuVW8AAAIIIIAAAtUq4KX5rtbrT/p15+xilwnQvn1722GHHUytLZo4thIllUrZySef7CaPGDNmjBuwqYvf9OnTbdCgQbZkyZKc1Tr//PPdyW33228/e+GFF2zcuHG2//7725133mnnnXdezv3ZAAEEEEAAAQQQQAABBJInkHcLUpQuXXMyvfrqq6bX4447zq2aWrNUNCfTfffdZ6eddpr7Oeh/FGDddddd7rxOY8eOtXbt2rmbDRgwwLp16+YGSdddd501bhxLnvQlq+ujrjWs7L333ta3b9+w1XkvV6uiknWEnUuORx11VN7Hy7ahuni+8847oZtssMEGVqzWnc8++yzdpTTzhPrlaKeddjJl6aMggAACCCCAAAIIJEegVgTw1ltv2Z/+9Kc6XeHDDz9cp/0K3emee+6xZs2a1Xro1kP4WWedZaNGjcoaIKmFaenSpaZxS15wpDporqett97aDb7mz59v6667bqFVi9T2YQGLV0lN7FuMAGnx4sWhwZHOtWbNGu+U9X7VubKVfFoPs+3vX7ds2bLQuitAUjdNCgIIIIAAAggggECyBGoFSJrv6JFHHsl6lQpO9NC7cuVKd7tGjRqVrbudzvn++++7SSHU3c9f2rZta1tssYV98MEHbt3CBsu3bt3aBg4caK+99pp9+OGHbqCk43z++ec2ceJE69+/f+yDI78L7xFAAAEEEEAAAQQQQCA/gVoBktJ2q/XEX5S97tFHH7WRI0fakUce6QYPap348ssv3YQGGv/z1FNP+Xcp2fsFCxa42cM6deoUeA51eVIQ9d1334VObKsdb7nlFjvmmGPcblKHHXaY2yL1j3/8wzbeeGO3i13gwXMslMFDDz2UY6v/rv7222+tVatW/13AOwQQQAABBBBAAAEEEKioQK0ASa0uHTp0SFdK8x9pvI6SGOy1117p5epipGDi+uuvdwMWdW+bO3duen2p3ixatMg9dOfOnQNP4Y0JydXVqk+fPnbCCSe4qcvvv//+9LGU0ny77bZLf+YNAggggAACCCCAAAKZAnoWpiRToFaAlHmZL7/8sikY8QdHmdscfPDBbouMUm/XZS6izONl+6z04iph41o0N5KKuv2FlRUrVtgee+xh7777rikZw7HHHutu+re//c2U3W78+PH29NNPF9y6c+CBB5r+8i3q4kdBAAEEEEAAAQQQQACB6AjkDJA0zueHH35wkxq0bNkysObK9qWWp3IkNejatas7QW1mN0CvYt5yf/IFb533+sorr9gbb7zhdhn89a9/7S22X/3qV273PKX5VurvQw89NL2ONwgggAACCCCAAAIIIJB8gZzzIA0dOtQNGpQ2W1m9MsvkyZPt97//ve266641MsJlblesz0oZrUDMC4Qyj6vlCuQyEzj4t/PGSx1yyCH+xe77ww8/3H198skna62L24JcTb9KiV2MoqQX2c7VsGHOr1ne1dC5spVijunSPF+qe9ifkpVQEEAAAQQQQAABBJIlkLMFqVevXvbLX/7S7UL34osv2pAhQ9y5gjTGZ+rUqe7YpC5dutQ5NXhdOLfccks3A928efPc7n/eMZSYYcqUKW6Gumxd7LwHdiVJyCzqfqfiddXLXB+nz2effXZZqqtgVAkvylE233xzN4NhOc6lubW8+bXKcT7OgQACCCCAAAIIIFB5gbx+2v/LX/7iZnZTN7oxY8bYVVddZTfffLMbpGhskuZO2n777ct2NWeeeaatWrXKRo8eXeOcSiah5ZoLKVsZNGiQu/r222+vNZbpzjvvdNd522Q7DusQQAABBBBAAAEEEEAgWQI5W5C8yx0+fLjpT13YNHeQ0mz37t07azIEb99iv6prnFqRLrjgAvvxxx9t9913dxMrXHnlle64Ia+bnM6rumpeI00Kq/mRVLReLWGa2FbHUpIGjVlSim4FWZoj6aSTTnK35X+SLaCAWvNfhSX90Jg3f1bHZGtwdQgggAACCCCQj4CGFng9kvLZPkrbZBsWEaV6VrIutQIkdTFbuHChOy+Qgobly5ebl1rbq6gCI5Xvv//eW+S+liNJg06kL6TSjx9//PF2+eWX22WXXeaeX0GP5jfKVfTF0GS4mtfppptuMm+8kVrI1J3wiiuuqEjgl6verC++gOby+vTTT0MPPGfOHBs8eHDoelYggAACCCCAAAIIJEugVoD0zDPPuK0wmjBWyQw0earmC8qnaPLYchWlHn/22WfdFiQ94Hbr1s30a39mUctRUL00mP+aa64xtTopC59aEJSiXEESpfICmuxXrTtBRfdIyTqKUYK+G/7jhrUs+bfhPQIIIIAAAggggEByBGo9Za6//vpuF7Rtt93WvcqNNtrI/RzVS27Tpk29JnbVg/YWW2wR1curynopKFF3x7DgpG3btjZs2LCqtOGiEUAAAQQQQAABBEorUCtA6tGjh9vtbL311nPPrPE9+qMgUC4BteqEBUeqg5dpsFz14TwIIIAAAggggAAC1SNQK4vd3XffbWpFmj17tqvw/vvv26WXXlo9IlwpAggggAACCCCAAAIIVK1ArRYkzW+kojmFFCgpC9wf//hH+8Mf/lC1SFz4fwSUACNsXJASX+yzzz50Vyzjl2XBggXuXGRhp9QcZso2SUEAAQQQQAABBBDIX6BWgDRgwAB376OPPtqUqOGLL75wP3uZ4rId+uKLL862mnUxFwgLjnRZ6hb3ySefJC5AinIqTAVIyiQZlGhC9dZ6AqSY/6Oj+ggggAACkRTQ/8/GNc13JEEjVqlaAZImfj344IPt8ccftylTpqSr+/vf/z79PuwNAVKYDMujKtClSxf76quvAoMM1VnZEaNc9B/osAApyvWmbggggAACCCCAQFQFagVILVq0sEcffdRtOfr666/t6aeftquvvtrGjRsX1WugXgjUWUBZEPfYY48678+OCCCAAAIIIIAAAskSqBUg6fL0q/Smm27q/v3www/2wgsvmFqWKAiUQ0BN1pp0WJMUBxW6jQWpsAwBBBBAAAEEEECgGAKBAZL/wJpvhjln/CK8L7WAAnQlfKAggAACCCCAAAIIIFBugZwBkio0f/58u+OOO+ytt96yRYsW2erVqwPr+corrwQuZ2HpBJSCPVvyhFatWtmFF15YugpwZASqTOD11193x60FXXaTJk3c5DZNmzYNWs0yBBBAAAEEEIiBQM4A6aeffrK9997b3nvvvRhcTvVVMduEqtJYuXJl2VCaNWtWtnNxInOz54Tdfy0nu05pviXKDhj270rL9UeAVBp7jooAAghERSDOWexUd0p2gZwB0iOPPOIGR0r5rWQNPXr0MB6Es6Mmde0WW2xh33zzTeDl6YFwv/32C1zHwtIIbLjhhtahQ4fQgysBBQUBBBBAAAEEEECgMIGcAdL777/vHnHUqFHWtWvXwo7O1okSGDJkSKKuJ+4Xoxaidu3axf0yqD8CCCCAAAIIIBApgZwBkuaJady4Ma1GkbptyajMzJkz7csvvwy8GLVSepMWB27AQgQQQAABBBBAAIFYCTz22GPWs2dP69OnT2C9ledg0qRJNmfOHOvXr5+7beCGJV6YM0DSHDGq7AMPPGAjRowocXU4fDUJvPrqq1kv98cffyS9fFYhViKAAAIIIIAAAvEQuPPOO+3UU0+1a6+9NjBAmjZtmh100EE2derU9AX17t3bnnvuOevevXt6WTne5AyQtt12W/dCLrnkEnd+pJ133tntahc0AJz5acpxy6rnHEoQEtWSSqVMf0FFgx8ZABkkwzIEEEAAAQQQqEaBxx9/3M4444zQS9cz1cknn2xff/21jRkzxnbaaSdTduyzzz7bBg0aZB9//LEpM3O5Ss4A6cEHH3QDpHnz5uVsQQp7YCzXxVTjeXI9iKt7JKX4Ak8//bRpEuWgolTPRxxxBEFSEE4Cluk/0AsXLgy8kkaNGrldkgNXshABBBBAAIEqE/j+++/dIOf+++/POlzntttuM/Us0utxxx3nKm222Wbuq1qd7rvvPjvttNPKppfz6XmdddZxo7iy1YgTFSTwxz/+saDt2bg4AsuXLw89UFgK6NAdWBErAXU7piCAAAIIIJDrR2qEzIYOHerOo6ofjg888EA74YQTAlnuueceN4A66qijaqzX57POOsuULC5SAZIeBnggqHGv+IAAAggggAACCCCAAAI5BLbbbju7/PLL3TlVn3jiicCt9cOysmZvvvnm1r59+xrbtG3b1jTNzAcffODOM6heOuUoOVuQ/JVYsmSJO3Dqs88+s+bNm9tGG21k/fv3pyuRH4n3CCCAAAIIIIAAAghEVEBDYpYtW2YTJ07Mu4ZbbrmldezYMe/tvQ1vueUW723oqyZgX7FihYXlMtB5FUR99913tv7664cep5gr8gqQVq1aZTfeeKONHDnSFi9eXOP8G2+8sT366KNuoFRjBR8QQAABBBBAAAEEEEAgUgIKRmbPnu0mP8i3YhdeeKHbEpTv9oVst2jRInfzzp07B+7mBWZqqClXyStAOu+88+z66693s9cdf/zxbsuRUjAro4SaywYPHmwvvviiKeMdJd4Ct99+u82fPz/0IjQmbfjw4aHri7mi0ImJ1TwbNq+S+gnvvvvuTKxazBvEsRBAAAEEEEAgdgKaa3KDDTaokU4710WUsmubeqWprFmzJrAamm5IRYmQylVyBkh66Lzhhhts2LBhNnbs2Fop9v71r3+5D54KosaNG1euenOeEgkoOs+WXjuzBbE+1Tj22GPrs3utfTXxbLb6ffPNNwRItdRYgAACCCCAAALVJqAfjksZ9BTiqR/EVZ+wH+i95e3atSvksPXaNmeANGHCBPcEo0ePrhUcaUXfvn3t0ksvtfPPP9/tP9i0adN6VYidEYiDgPrJev9gM+vbokULxuVlovAZAQQQQACBBAnogT5oTtAEXWLZLkVT0qy77rqhz1V63mrZsmWtBA6lrGDOAGn69Olu17qwfoGqXJ8+fdzBXpr5tl+/fqWsL8dGIBICe+65ZyTqQSUQQAABBBBAAIG4CygJxGuvvWaad9Ufcygxw5QpU2zgwIFl7WLXMBeoMtXNnTvX1D0prKgbnkqPHj3cV/4HAQQqK6CJTGfNmhX4p1mqmdS5sveHsyOAAAIIIIDAfwXOPPNMU1I49Vjzl7vuustdrrmQyllytiDts88+bhOiJnZ64IEHao3heOedd0yTlSrPuXKVUxBIioAmg1WQERRMqFndm+E5itf773//25Q2U10AMosGQWqegVatWmWu4jMCCCCAAAIIIFB2gUMOOcTUinTBBReYEsEpsdb48ePtyiuvtEMPPdQOP/zwstYpZ4Ck7nOK2pTFbpNNNrEDDjjAzWKnwfzKYvfCCy+Y+g7eeeedZa04J0Og1AJPPfWUm3c/7DxfffVVZCdRVhCkwC4ouNP1hC0Pu1aWI4AAAggggAACpRLQD8/Ke6Bs2ZpY9rLLLnNPNWTIEMtnLqVi1ytngKQTXnfddW4yhnPOOcfGjBlTow477bST/fnPf7ZtttmmxnI+xFMgqMXBfyW51vu3Lff7XIMlC00PmSuIUFMwBQEEEEAAAQQQQCC3wEEHHZT1B1qNPXr22WfdFqRPP/3UunXr5uZByH3k4m+RV4Ck05500kn2i1/8wp1n5pNPPnG75/Ts2dO6dOlS/FpxxIoJ7LzzzuaNKQuqxI477hi0OHSZ5sdSV7WwoqBl6NChYasLWr7bbru5sywH7aTgSePpKAgggAACCCCAAALRFWjTpo07dKeSNcw7QPrhhx/sueees6OOOiqdjOGaa65xB06NGDHCOnToUMnr4NxFElAAVGgQlO3U2eZU0n7e5F/ZjpHvOo2BYxxcvlpshwACCCCAAAL1EcjVc6U+x2bfygrkFSDdfffdpolgFQQpQPLK888/by+99JKNGjXKHYu06aabeqt4RQCBDIGPPvrIZsyYkbH0vx979+5t/Bv6rwfvEEAAAQQQQACBSgjkTPOt3OPDhw93c5JrLJK/jB071kaOHOmmAD/99NP9q3iPAAIZAosWLcpYUvOjWmmLVbyxYnrN/CvWOTgOAggggAACCCCQRIGcLUhPPPGEKSPWww8/bPqF21/WWWcdu/TSS90A6dZbb3VnwO3YsaN/E94jgEAFBDQ+UDNPBxWN+9KM1BQEEEAAAQQQQACB2gI5AyRNENu1a9dawZH/UMpKoQBp5syZRoDklynOe2UJ/Pbbb0MPpjTrl1xySej6JK1QbnwlkQjLMKdU9BtssEFRLlnjmcKCDJ1A/y6iWpQ8hQQqUb071AsBBBBAAAEEoiyQM0DaYYcd7IYbbrC5c+eGPhAqq52KHk4pxRfQQ3pYQKCzrVy5svgnjegRv/jiC9P8Q2FFSSGKFSDtu+++YadhOQIIIIAAAggggEBCBXKOQdpzzz3dh/Njjz3WDZIyHSZOnOiOQxowYAAZxDJx+Gy55h7yxspAhQACCCCAAAIIxEVAzy/KYhfHv7gYV7KeOVuQ1I3ojjvuMCVhUAvRwIEDrXv37rZ06VL7/PPPbfLkyda8eXMbPXp0Ja+Dc0dUYP/997dsyQlIyx3RG0e1EEAAAQQQQACBKhXIGSDJ5ZRTTnG711111VU2adIke/nll12upk2b2sEHH2yaD0mDwikIBAlEOQiaN2+eqRU0rKy33nqm1tFiFP17yVb0QwMFAQQQQAABBBBAoLICeQVIquKwYcPcP2W0U8tRkyZNbMMNN3SbFit7CZwdgboLfPrpp25raNgRlHikWAGSxvOtWLEi8FRqoleyDQoCCCCAAAIIIIBAZQUKfiLTgxytRZW9aZw9vgK5WpHie2XUHAEEEEAAAQQQSIZAwQFSMi67uq9iwoQJ9vbbb4dmxmvRooWdccYZ1Y1Ux6tfvXq1TZ8+3Z07LOgQSoPfuXPnoFUsQwABBBBAAAEEEIiAAAFSBG5Crip06tTJnQcpLNW3ujsWUqZOnWqaTyisLF++PGxVxZdvttlmtmzZstB69OjRI3RdOVYsXrzY5KuW1syi7qmaXJkAKVOGzwgggAACCCCAQHQECJCicy9Ca0Jrzn9pWrVqZTvttNN/F0T0nYKhoBIW5AZt6y3TuCW1TAUVddnLlUo9aD+WIYAAAggggEDdBZTmO65TlcS13nW/W4XvSYBUuBl7IFA2AU18++KLL4aeT62LSr1PQQABBBBAAAEEECiOAAFScRyr/ijjx4+3sK55mktr6623rnqjugCEtRx5x1q5cqX3llcEEEAAAQQQQACBIggQIBUBsdoP8dFHH9mcOXNCGRYsWBDZAKlfv362ZMmS0IQVSmVPQQABBBBAAAEEEKgeAQKk6rnXJbvSpUuXluzYpT5w69atbZ999in1aTg+AggggAACCCCAQEwECJBicqOKWc04DM5TGnK17AQVjbvZaqutglZFZllQFjslaIiDfWQQqQgCCCCAAAIIIFABAQKkCqBX+pS77767PfPMM6HdyjQPUiWLWqQ+++yz0CqoO19UA6Q2bdpY//79Q+dBat++feh1sQIBBBBAAAEE4iMQ9GNofGpPTbMJECBl00noul69epn+qqkomcGqVasCL7lx48ZW6FxSgQdyFuo/lt27dw9bzXIEEEAAAQQQQACBiAsQIEX8BlG94gi8/PLLFpbxTXMJDRkypDgnKvJRVLcOHTqEBneaeJaCAAIIIIAAAgggUDwBAqTiWXKkCAtkS5cd1rKU7XK+/vrr0DFSzZo1s4022ijb7nmvU+vWLrvskvf2bIgAAggggAACCCBQPwECpPr5sbcj0KNHD/viiy+qyuLf//632yKVmXRBiRj0p/TgmeuqCoiLRQABBBBAAAEEYipAgBTTGxelanfp0sWOPvroKFWpLHXxgqGynIyTIIAAAggggAACCJRFoGFZzsJJEEAAAQQQQAABBBBAAIEYCNCCFIObVG1VbNmyZdZLbtSoUdb1rEQAAQQQQAABBEopoG70pPkupXBlj02AVFl/zh4iUI1d9kIoWIwAAggggAACCCBQRgECpDJic6rKCay77rqhWedatWpVuYpxZgQQQAABBBBAAIFICRAgRep2UJlSCWy//fZFPbSa1r0//4FJ3ODX4D0CCCCAAAIIIBA/AQKk+N0zahwBga233tqWLl0aWBNN7kqK70AaFiKAAAIIIIAAApEXIECK/C2iglEUWGeddaJYLeqEAAIIIIAAAgggUE8BAqR6ArI7AggggAACCCCAQHUJkMUu2febeZCSfX+5OgQQQAABBBBAAAEEEChAgBakArDituk111xjK1asCKy2fvk48MADrV+/foHrgxaOHTs2aHF6WZ8+fQo63gMPPGBr1qxJ75/5RnXTMfMt06dPtxkzZgRurrmTlKgh1xxL/p1nzZpln3/+uSnxQmbR3AfbbLONtW3bNnNV2T7PmzfP/vWvfwXWT/e3d+/epux9SSnLli2zt99+21b///bOBF6OotzbpSH7vm8kgZCEEEIgAZIQSEICBASBoCgqgnBduICIXpEP3BW5Ki7odUNBREQUdxEBUSAQAoQlgSRkI0ASsu/7Sphv/i19nDOnqmd6pmemu+ep32/OTNfy1ltP9Zypt6vqrQMHrE3q37+/Oeyww6xpREIAAhCAAAQgAIFiCWAgFUsqgfn27t0baIBo8B/GQCqEYOXKlaHkBRlHqmvNmjWhDKTNmzc7XXlLnpwqhDGQtm7danbs2KGi1rBz586aGkjbtm3z2msz4GQgbd++PXUGktrsCps2bcJAcsEhHgIQgAAEIACBogmwxK5oVGSEwH8IyACJQ3Dp4YqPg87oAAEIQAACEIAABOJMAAMpzr2DbhCAAAQgAAEIQAACEIBAVQlgIFUVN5VBAAIQgAAEIAABCEAAAnEmwB6kOPcOukEAAhCAAAQgAAEIxJKAHDYR0kmAnk1nv9IqCEAAAhCAAAQgAAEIQKAEAhhIJUBLSpFCG/U7duwYaVPCeIhTxYX0CyvvoIOCJ0QLpefDCMovz3FB6fmyKnGt+l2eABVfa/2ibnOh9jRv3jzqKpEHAQhAAAIQgEAdEggeUdYhkDQ1efjw4Uaur22hRYsWZvLkybYkZ5zOEXKdM6TzgMaMGeMsa0s444wzzPLly21JpnXr1mbw4MHWNFek2nv44Ydbk2WMqc1hgmQdeuih1iKlyLMKKiOyX79+pmfPnk4JYdvrFBSTBN1jU6ZMsZ77JBUxkGLSUagBAQhAAAIQSDgBDKSEd2CQ+ueee25Qcug0GSxhjZagSjp16mT0iipoLXDLli2jEufNcEUpLzLF3hIkIy3O+kXdXslLm9FXCUbIhAAEIAABCECgPAIssSuPH6UhAAEIQAACEIAABCAAgRQRYAYpRZ1JUyAAAQhAAAIQgAAEKk9Aqzj0SmJIqt7VZI2BVE3a1BU7Art37zbPPvus09nBgAEDnPuQYtcYFIIABCAAAQhAAAIQKJsABlLZCBGQZALz5883q1evdjZh06ZNGEhOOiRAAAIQgAAEIACB9BHAQEpfn6a6RatWrTIvvviis419+/Y1I0aMcKaHTZA7bwIEIAABCEAAAhCAQP0QwElD/fR1Klq6ZMkSc+DAAedrxYoVqWgnjYAABCAAAQhAAAIQqA0BZpBqw72uatXZSXv37m3SZh38edhhhzWJJwICEIAABCAAAQhAAAK1IoCBVCvydVLvPffcE9jS5557zlxwwQWBeUiEAAQgAAEIQAACEIBAtQhgIFWLNPVAAAIQgAAEIAABCKSCgFxl64B6QjoJ0LPp7FdaFREBzgqICCRiIAABCEAAAhCAQEIIMIOUkI6qlpp33nmn2bx5s7O6bt26mQsvvNCZnrSEgQMHGnnGc3mr69evX9KahL4QgAAEIAABCEAAAmUQwEAqA14ai27ZssVs377d2bRmzZo505KY0LlzZ3P22WcnUXV0hgAEIAABCEAAAhCoAAEMpApARWTlCOiMo9mzZzsrGDBggDMtyoTdu3ebPXv2WEW2aNHCtG3b1ppGJAQgAAEIQAACEIBAvAlgIMW7f9Auj0CnTp3MpEmT8mKrf/nII484l+VJmzPPPJPNm9XvFmqEAAQgAAEIQAACZRPAQCobIQKCCMjJgWt/T1C5uKelsU1xZ55W/XRO2Nq1a63N05JWzZpqVpIAAQhAAALxIoAXu3j1R5TaYCBFSbMGsn7/+9+b9evXW2uWcXL55Zdb06oV+d73vrdaVQXWo+VwtsNqVahly5amVatWgeXrJXHbtm1Og7Z9+/bMilXgRlizZo3ZsGGDU/LgwYMxkJx0SIAABCAAAQhETwADKXqmVZW4aNEi8+abbzrr/MY3vmGuu+46Z3q9JNx7773Ogb8MybgYcrXsj3nz5pmXXnrJqUKfPn3M+PHjnekkQAACEIAABCAAgTQQ4BykhPdioaVehdIT3vyi1Q/iEJRWdAUpyLhv377AVuzfvz8wnUQIQAACEIAABCCQBgLMIKWhFyNsQ8+ePU2QK+/evXtHWBuiIAABCEAAAhCAAAQgEC8CGEjx6o+aa3PBBRfUXAcpoLOYXHuG2rRpY/QiQAACEIAABCAAAQhAIGoCGEhRE0Ve2QS0p+qxxx5zytGeobPOOsuZXo0EGWiuc5A0A4dnm2r0AnVAAAIQgAAEIACB6AlgIEXPFIllEghyOiHRcdgzNHny5NCtfP31183ChQut5WT0HX300aZ79+7WdCLTS6CQMV0oPb1kaBkEIACB+BLQ73ZS/z9Ld0IwAQykYD6kQiAyAosXLzabNm1yynv11VcxkJx00ptwzDHHmN27d1sbqB9fuVcnQAACEIAABCBQPQIYSNVjXZGaDjroIBPkXYwDJv+NvXXr1sblpU3nIMUh1HpmrGvXrua1115zztApnRA9AX1H+Z5GzxWJEIAABCAAgVIJYCCVSi4m5a6//vqYaBJvNc4555x4KxgD7QYMGGD0IkAAAhCAAAQgAIF6JoCBVM+9H1Hb165da6ZNmxYoLS7e8QKVzEt89NFHzYYNG/Ji/3N51FFHmaFDh/4ngk8QgAAEIAABCEAAAoknwEGxie/C2jdAe2fSGLZu3WrkMML1WrNmTRqbTZsgAAEIQAACEIBAXRNgBqmuuz+ejdfGdO3JOHDggFVB7SciQAACEIAABCAAgVoRSLIXu1oxS1K9GEhJ6q060VUG0pQpU1LX2kJuNQulpw4IDYIABCAAAQhAAAIxJICBFMNOQaV0Ehg5cqRZsmSJtXEyjtjPZEVDJAQgAAEIQAACEKgqAQykquKmskoSuO+++8zOnTudVRx//PFm4MCBzvRKJ3Tu3NlIBwIEIAABCEAAAhCAQHwJ4KQhvn2DZiEJ7N27N7DEli1bAtNJhAAEIAABCEAAAhCAAAYS90DZBNq1a1e2jDgKKLQnSIf0EiAAAQhAAAIQgAAE0kWAEV66+rOo1tx5551m5cqVJpPJWPO3bNnSfPrTn7am2SJ1HtDmzZuNawanV69etmKxjzvllFM8TjZFZTwNGTKkSZJcf8+aNatJvB8hoyqNDij89vEOAQhAAAIQgAAEkk4AAynpPViC/m+88YbTOJI4l3vtoKomTJgQlJzINM2MHX744aF03759e2D+UtgGCiQRAhCAAAQgAIGqE9CDUnndJaSTAD2bzn6lVRCAAAQgAAEIQAACEIBACQSYQSoBGkWiITB9+nSzY8cOqzA9mRk3bpzp0KGDNZ1ICEAAAhCAAAQgAAEIVIIAM0iVoIrMoghoOZr2Qdleb775pnn11VeLkuNnKuQ0Ia3OJPz28w4BCEAAAhCAAAQgUD4BZpDKZ4iEmBA499xzQ2uiGazZs2c792T16dPHDBo0KLRcCkAAAhCAAAQgAAEIJJMABlIy+w2tIyKwdu1aE+RY4fXXX8dAiog1YiAAAQhAAAIQgEASCGAgJaGXItZRS9G0x8fl5rtZs2YR11g/4tq3bx/YWNgG4iERAhCAAAQgkBgCGksR0kkAAymd/RrYqosvvjgwncTSCejMpzPPPLN0AZSEAAQgAAEIQAACEKgpAQykmuKncghAAAJ2Aprh/de//mX2799vzSAPjyeddJI1jUgIQAACEIAABEongBe70tlRssIEmLquMGDEx5qADCSXcSTF9+zZE2v9UQ4CEIAABCCQVALMICW151Kgd+/evc3GjRutLdHp1MOGDbOmEQkBCEAAAhCAAAQgAIFKEcBAqhTZCOXeeOONZuXKlU6nCnIMcNNNN0VYo13ULbfcYk94K3bgwIFmypQpgXlyE0eOHJl7WdTnGTNmmK1btzrzatbpHe94hzM9P6HQLJUr/YUXXjBbtmzJF+ddt2rVyowdO9aalsbIadOmOWc6xGL8+PFpbDZtggAEIAABCEAgpQQwkBLQsbt27TJ6uYJmW+IQVq9eXXE1tm3bFliHyzOfq1D//v3NG2+84TQ+e/ToYS26dOlSs3fvXmuaIseMGeN5CnRmSFFC0L0ZtEQsRQhoCgQgAAEIQAACKSKAgZSizqQp4QnI5fmQIUPCF6QEBCAAAQhAAAJ1S0ArTOLygDpsJ7hWx4SVk+b88Zh6SDNh2gYBCEAAAhCAAAQgAAEIJIYAM0iJ6SoUhQAE6omAnvC1bdvWuZRTbr4JEIAABCAAAQhETwADKXqmSIRAbAm8+eabZubMmWbfvn1WHbt06WKOOuooaxqR1SUgA2nixInVrZTaIAABCEAAAhAwGEjcBBCoEoEVK1aYBQsWWB1CaDAsr37dunWrqDYHDhwwmzdvdtYhA4oAAQhAAAIQgAAE6pkABlICer/QZrpC6dVqohwexDVMnz7d6QmwTZs2oV1RN2vWzNlUV3/I25vLY57KBHmDc1ZGAgQgAAEIQAACEIBApATiO6KNtJnJFva1r33NfOELX3A2olp7EXTe0u7du616aIB/4YUXWtOijDz55JO9JWKumY7Bgwdbq1u7dq01XpHbt293prkSTjnlFOfekObNmztdfIuTzRW5y6hy1R+neLk037Nnj1UlGZ8ECEAAAhCAQNoI6Hc76GFp2tpbb+3BQEpIj99www0117QUA+ihhx4yQecjac/L2WefXXTbWrdubWQk1Tpo4M/g/9+90LVr11p3B/VDAAIQgAAEIACByAhgIEWGEkE2Apq5cc06KX/QfhibPOIgAAEIQAACEIAABCBQSQKcg1RJusiGAAQgAAEIQAACEIAABBJFAAMpUd2FskknYNt/pDa54qNur079DtrvFGdHG1GzQB4EIAABCEAAAhCwEWCJnY1KSuK+/e1ve17TbM3RQFlnrGiDPcFOQG65X375ZWuijIxjjz3WyHFFsaFly5ZOQ0gGktIrHbSh9NRTTzUuJxcYSJXuAeRDAAIQgAAEIBB3AhhIce+hMvTTYaCugbDELlu2DAMpgO/69evNxo0bnTm2bt0aykAaMGCA0avWQV72CMEE5I5d/esKnTp1wnuRCw7xEIAABCAAgYQTwEBKeAeifnEEjjvuOLNjxw5r5nbt2lnjgyKDlqkFlatW2tKlS82GDRus1WkW6aijjjLMFlnxeJHLly83CxcudGY48sgjY2HsOhUkAQIQgAAEKkpA4wCtxiGkkwAGUjr7NTatKtWQkHtwl/c7uQYfP358qDYecsghofInPfOqVavMli1bnM3QeVGlGIZOgREmyLBznaukZYjdu3ePsDa7KC151A+fbQZW8dXaM2bXjlgIQAACEIAABCpJAAOpknSRbYYOHRr4JL5fv35NKGlw/OqrrzaJ9yPkOjysgeSX5T3+BGbPnu3tncs3rn2j5B3veEf8G4GGEIAABCAAAQgklgAGUmK7LhmKa2mbXmGCPxB2lSmU7ipHfDIIqH/9VzI0RksIQAACEIAABNJEINEG0oEDB8zMmTPN6tWrzYgRI4yWDRUTNm3aFLgBWzLatGljevbsWYw48kAgFQS0tO355593Lh874ogj2HeTip6mERCAAAQgAAEIBBFIrIEk98vnnHNOo+Vbw4YNMw8++KCxLdvKhXDjjTea7373u7lRTT5rGc/999/fJD5JEflLlPJ179ChQ34U1zkE5MzAFTTDEZTuKhfn+L1793p7bmwzdLqXXHvC4twmdIMABCAAAQhAAAJhCSTSQNIA7sMf/rBZuXKl+dWvfmXGjh1rHn30UXP11Vebk046ycyfP9+0bdvWyWLChAnOp+T33nuveeWVVzw5TgEJSejdu7fRoNcW5MHsjDPOsCUR9xYBeSpzOXeQwRDmDKSkQFW7bAZSUvSPUk+bgwbJd8VHWTeyIAABCEAg/gTwYhf/PipVw0QaSLfccouZPn260fsHP/hBr+2DBg3y3j/2sY+Zu+66y1x22WVOJueee67RKz/MmjXL/OAHP/Bmpq6//vr85MRdX3LJJaF1/ulPf2o2b97sLNe1a1fz0Y9+1Jley4SnnnrKrFu3zqqCBv4nnHBCKA9omiFK6ixboX/ahdKtEB2RL730ktPrnDzlHX744Y6S8Y3Wfa4HDK4gT4qE0ghoifNrr71mLazvqf6XJ/V7Z20UkRCAAAQgkDgCiTSQ7rjjDiN3vxdccEEj4Lr+xCc+YW677bZAA6lRobcudKiqDIqOHTua22+/3eiHuh6DzgoKekLuOksoDqx0sOv+/fudqmivWjVcRDsVqGLCyJEjza5du6w1yvDTHruogg4cdgUdtFuKgeT6/lVrdksHwYohIXoCuifkidIVunXrhoHkgkM8BCAAAQhUhUDiDCQNgF944QVv0KVBTG7QU0e5lX7xxRe9gXLz5s1zkwM/f/nLXzZz5871jCM9PSbUjkDr1q2N+sBl7KRxaVvUtPUAQa8kBjmDcBl3SW1TEvuhkjprBtP2ICbKmc1K6o9sCEAAAhBIN4HEGUha/qXZHpcRo6UvGlhrNqFPnz5F9d6SJUvMTTfdZOTkQbNIpQYtG3n22WeLLq7ZGAZ8dlzvec977AnENiKwc+dO654hzcAE7cNrJCTnolozNDlVNvlYyMlKkwJEQAACEIAABCAAgQgJJM5A2rZtm9d8LcOwBX9vgAaOxYZbb73VyGX4FVdcUdbSunvuucfcfPPNxVZr5FY5fxas6MJkrHsCmimVoxJX0AOCY445xpXcJF7Gup7gu4wkzewRIAABCEAAAhCAQNoJJM5AatWqldcntuUZSpCho1CsC2bNNmlPk5ZtXXzxxV7ZUv9cd911Rq9ig+9Yotj85IuewMMPP+x5LXRJ1v1W7n3hkl1u/BtvvBEowrVE0VVIDx1OP/10VzLxEIAABCAAAQhAoC4IJM5A6tWrlzfLI09ItuDHy9lCMUFuveX5TLNH7G0phli68mzfvj2wQS5DPLAQiRCAAAQgAAEIpJqAlrIndd+kyxFSqjssZOMSZyDp/J4ePXoY3xDKb6/i5aGr2KVrWl6nIAOJYAouMbR9qWbMmOG5XXctzZKzjGuuuabmeG3/yGztqbmiCVNADF19b2OesOahbsQEdL+4HjwonnsmYuCIgwAEIACB0AQSZyCphfJy9cQTT3h7eHL3Iskxw4IFC7zzbopZYqfleNOmTfPO3dChoARjxowZY+bNm+dEYXN9rBk41wBZgvxlj06hESXokOBVq1ZZpel+GDJkiDWtlEi1ybXETXXJkK+XMH78eM9xiq29OCGxUanvuEMOOcT4e0XzSch4Knb2P78s1xCAAAQgAIGoCCRyFHfVVVd5ho3OK7r22msbWPz85z/3Bq06C6mYIK9ze/fuNcOHDy8me13kOfHEE41eSQwadLkGXlG3RwcVu1xR6wm49vLUy+yUDoMlQKBYAnp4UK3vabE6kQ8CEIAABCCQSyCRBtLUqVO9WaTrr7/eaA/JxIkTPYPp61//ujnvvPPM+eef39DGOXPmmKOPPtqMGDHCOx+pISH7Yf78+d4lBlIuFT4XQ8A1e6SyruVDxcglDwQgAAEIQAACEIBAbQkk0kDSE/rHH3/cXHTRRebGG280X/va1zyKU6ZMMT/+8Y+LJoqBVDSq1GYMWhoY90ZrKZKWN7oCS5VcZIiHAAQgAAEIQAACbgKJNJDUHO09euCBB7wZpMWLF5u+ffsaebjLD5o5cg2Cw7rlzpfNdXkEfvrTnxodlmsLWp527LHHerODtvSo4jp37uwdKuySF+e9RHITj6t4V88RDwEIQAACEKgsgXpZSl9ZivGUnlgDyccp19waSBOSR2D37t3Ozf1qTdDsSFSt1fJMvQgQgAAEIAABCEAAAhAQgbeDAQLlEpDb9aCnKMV4FCxXB8pDAAIQgAAEIAABCEAgCgKJn0GKAgIyyiOQZM935bW88qX37dvnOSBxuUrv2bOnGTVqVOUVoQYIQAACEIAABCBQJwQwkOqko9PSzDVr1pjnnnvO2ZyBAweaYcOGOdOjStAeuK1bt1rFtWrVKnBGzVrIESlveUEe81yuxh3iiIYABCAAAQhAAAIQKEAAA6kAIJLjRUCHAe/Zs8eplAyoahhIxxxzjFMHEiAAAQhAAAIQgAAEkksAAym5fYfmEIAABCAAAQhAAAI1IKC91zp2hpBOAhhICe/Xb37zm2bnzp3OVsiBwsc//nFnei0TCv1jadmyZWTqLVmyxLiWo7Vt29YcdthhkdWFIAhAAAIQgAAEIACB5BLAQEpu33mab9u2zchdtisEeZdzlalW/NSpU82iRYus1bVp08bI+UNUQWdlBQUMpCA6pEEAAhCAAAQgAIH6IYCBlPK+dh2SG4dm9+/f3+hFgAAEIAABCEAAAhCAQFwIYCDFpSfQI3YEXnnlFfP666879Tr66KNN586dnelRJDRv3txoqaHLk127du2iqAYZEIAABCAAAQhAAAJvEcBA4lZIFIFCh84W2tcUprGrVq1y7luSHHnMq4aBdMopp4RRm7wQgAAEIAABCEAAAmUQwEAqA16Si95yyy3mzTfftDahdevW5tJLL7Wm1Tpy6NChpmPHjk7du3fvXnw1NHIAAEAASURBVFMVNdPz7LPPGtfBrn369DE6q4kQnoCWi27YsMG4lo22b9/e6N4lQAACEIAABCAAgXIIYCCVQy+hZX/xi184DzlVk3bs2BHblmmGqG/fvrHVb+HChWblypVO/XSOEwaSE09ggg7mff75562H8Mpo6tmzpxk5cmSgDBIhAAEIQAACURBIspvvODvwiqJvopCBgRQFxRjLsH0JXDNHfjNcT+j9dN7dBAqxdZckpRAB/7703/Pzu+Lz83ENAQhAAAIQgAAEgghgIAXRSUBaoSVFHTp0SEArKq9i7969ne7Q5VKcAIFcAhs3bjRy0mEzuvTQYfDgwRXff5arD58hAAEIQAACEKgeAQyk6rGuSE2f//znKyLXJvTHP/6x2bt3ry3JW/Z0xhlnGO0RimNg6VUceyW+Omk53+bNm50Gks4fq7SDjvjSQTMIQAACEIBAuglgIKW7fyNtnfYmuZwPqKI5c+bE1kAqBYRteWKunCg95uXKrfRnGbmufpRbcb0IxjP6XTNI8IEABCAAAQhAIL0EMJDS27e0rEwCOudo7dq1VikyngYMGGBNi3OkDKP77rvPOjMiveUh8LTTTotzE9ANAhCAAAQgAAEIVJQABlJF8SL89ttvD3TNrGVKH/vYx2IJSm6j9UpTkBMJ26yI38b9+/f7Hxu9v/rqq07vfJpJO/bYY02rVq0aleECAhCAAAQgkFYCelCa1JUkae2TKNuFgRQlzYTIksOCLVu2OAfKUQ509+zZ46xHuJSeptC/f3+zbNkyZ5s7deqUyOZqP87OnTuduqsfo7xvbBW1a9fOiK/LwOvWrZutGHEQgAAEIAABCEAgFAEMpFC40pH5zDPPNHrFOfzpT39yqqcnNlOnTnWm1zJBBtDZZ59dSxVSW7f2Rg0bNiy17aNhEIAABCAAAQjEg8Db46EGWkDgPwQKHVTLWUP/YcWnyhCQEe66zxTPsorKcEcqBCAAAQhAIA4EmEGKQy+kRIdmzZrVtCV/+ctfnINaKSanCtorQ4BAIQJayte1a1dntrZt2zrTSIAABCAAAQhAINkEMJCS3X9V1b5v377OfSgyjt797ndXVZ/8ylxP/P18OvwzP+zevdvMnDnTua+lX79+ZtCgQfnFuE45Ac0Qpc1BR8q7jOZBAAIQgAAEIiOAgRQZyvQLuvDCC1PXyLlz55pNmzY527V9+3argbRmzRrP0YWtoAbWMibjGGTI9ujRw3ngL44O4thr6AQBCEAAAhCAQDUJYCBVk3Yd1pXWw1afeuop56yTulmOGvIPXNUMl8sTnDjJS1ulg2ZGJkyYELqa/LbkC6j18sp8fbiuDgGdq/XGG29YK9M9cdBB/MRY4RAJAQgknoB+t/ntS3w3OhvAr5cTDQlRELj88svN/fff7xQ1duxYZ1qcE1yupoN0nj17tlm/fr0zi/ZIHXHEEc70WiYMHz7cHH744VYV9CPRokULaxqR6SYwY8YMs2vXLmsjZYzr0OFCD0mshYmEAAQgAAEI1JAABlIN4ddL1XF3KV6tfnA9affrdx3S6qeHedc5V9u2bbMW0cBVSwDDDFxVpmXLllZ5RNYvgaB7WjOmepAQ5j6rX5K0HAIQgAAE4kQAAylOvYEuHgEtNevSpYuRAwVbSOphq7a2VCpu4cKFZuvWrdbBqQaunTt3NlF5YtOywX379lmbIqOqTZs21jQiIQABCEAAAhCAQBwJYCDFsVfQyZx88smRU6inGRA9ufdfkYPMEzh9+vS8mMaXZ5xxRuOI7NW6devMqlWrmsQrQjMOWs7XqlUrazqREIAABCAAAQhAoJIEMJAqSRfZVSVw1llnmZUrV1rr1B6Zgw8+2JpGZPUJrFixwixdutQ6wyXDrmfPnqZXr17VV4waIQABCEAAAhCoewIYSHV/C6QHgGaIBg4cGKpByr9hwwanRzoN1AmVIyBjKD+wZyWfCNcQgAAEIBA3Avqt0v7cJAZ+Zwv3GgZSYUbkSDEBnftTihMJuS+Wi2Nb0D+eQm6xbeWIgwAEIAABCEAAAhCoPQEMpNr3ARokkMA555wTWms5l5B3OVuQUYXzCRsZ4uJMQA8Y5AzEFjSjy1NKGxniIAABCEAg7gQwkOLeQ+iXGgJyPOA6SyjqRmpg6r/yZcuLHQECURAYMWJEFGKQAQEIQAACEIgVAQykWHUHykAgGgKDBg1yPtnXmunWrVtHUxFSIAABCEAAAhCAQMoIYCClrENpDgREQEuf9KpGkIdA14GhLtfq/tIr/z1XTzlusMXn5uEzBCAAAQhAAAIQqBQBDKRKkUUuBOqEwOTJk0O3dOjQoZ4rb1tBGUfdu3e3JREHAQhAAAIQgAAEKk4AA6niiMuv4Nvf/rZZvny5ce0d6dKli/nqV7/apKJbbrnF7N27t0m8IuSF7corr2ySdu+995rFixc3ifcjhg8fbmwHf/7zn/90ziJ07NjRjBs3zhfR8P7kk0+a1atXN1znfmjWrJmZOnVqbpT3efPmzWbmzJlOt9xaWjZ48OAm5XTmzs6dO5vEK6Jdu3ZmwIABTdJ27drluQBvkvBWhM5Vsrn4XLRokZOF3IZHNbOzb98+57lPUrFPnz7GNYPjalO14rXEj2V+1aJNPRCAAAQgUAkCtjFAJepBZvUJYCBVn3noGmUcvfLKK85ya9eutaZt377dGq9IDa7vuusu88EPfrBRnmeffdasW7euUVzuxY4dO5oYSHPnzjVBde3ZsydXRMPnl19+2ezfv7/hOv/D/fff38QF9xNPPBFYZt68eVYDadasWfniG13bDKTZs2cHtkvGZ74xpn566aWXGsnOvVB6KR7wcmX4n+fMmeMZzv51/vv69evN6NGj86O5hgAEIAABCEAAAhAIIJDME64CGpTGJNfMkd9W22GbflrQu01u0P4PLX2ylXHNUgXVrbRCervOGSokN6r0UvQrpHMhmWF0t/VFbvlCuuTm5TMEIAABCEAAAhCAwL8JYCBxJ0AAAhCAAAQgAAEIQAACEHiLAAYStwIEIAABCEAAAhCAAAQgAIG3CGAgcStAAAIQgAAEIAABCEAAAhB4iwBOGrgVIAABCEAAAhCAAAQgEIKA9mXjxS4EsIRlZQYpYR1WDXVdjgRc8dXQiTogAAEIQAACEIAABCBQDQLMIFWDcpl16JwjufJ2GSidOnWy1qCnG64yKmA7I6dz585G5/+4QteuXZsk9e/fP/A8HtcTFtUvHW1B8TrTKD+orXJf7QrNmze3JrVp08bpHtxVplevXub111+3ylOk7TBTnXOkc6Rc3NWXUYXevXubTZs2OcXpHCQCBCAAAQhAAAIQgEA4Am/LDuQy4YqQOyoCvgGwZMmSqEQiBwIQgAAEIAABCMSawCWXXOLpd8cdd8RaT5dy0n/hwoXm4osvdmWJdfyXvvQl07FjR8P4091NzCC52ZACAQgUQWDp0qXGdRaWZu769etXhBSyQAACEIAABCAAgXgQwECKRz+gBQQSS0BP0VxLJZs1a4aBlNieRfFKE3jllVeMXrag79To0aO9p7y2dOIgAAEIQKByBDCQKscWyRAom8Abb7xhnn76aXPgwAGrLO0JGz58uDWtmpGs1K0mbepKC4Hdu3ebN99809kc18ysswAJEIAABCAQCQEMpEgwIgQClSGwZcsWs27dOqfwrVu3xsJAcipIAgQgAAEIQCCFBDTL63JClcLm1l2TMJDqrstpcBQEHnvsMbNjxw6rqBYtWpjTTjvNmkYkBCAAAQhAAAIQgEC8CWAgxbt/0K7CBFavXm2eeeYZZy1yyz1+/Pgm6XKv7VpWpmUxWjbDk6Um2IiAAAQgAAEIQAACsSeAgRT7LkLBYgloudmqVaus2Q866CAzePDgJmkvv/yyc3+PMm/YsKFJGSIgAAEIQAACEIAABAoT2LZtm9m8ebM1Y9u2bU23bt2sabWOxECqdQ9Qf2QEHn300cANz9u3bzejRo2KrD4E/ZuA78HOf/e5aIYtP85P4x0CEIAABCAAgfQTuO6668xPfvITa0Pf//73m7vvvtuaVutIDKRa9wD1Wwncc889Zt++fda0Dh06mHPPPbdJmmvJm5/RJc9P5700Ascee6zzHKTWrVuXJpRSEKgDAvp+uJbi6uFCy5Yt64ACTYQABNJM4IUXXjDt2rUzH/nIR5o0U+OHuAYMpLj2TB3rtXPnTrNixQqPQP4MhIwg7f+pl+AaPPntz+fjx1fz3TU9LoNUh8iuX7/eqk6PHj1M586drWlEQqAeCBx22GFGLwIEIJA8Avr9LfQbHddWVWvsoP3Yc+bMMccff7y5+eab44rDqhcGkhULkVERmDlzpvMgRNXRu3dvM2nSJGd1hWaFnAVTkiAnEUOHDjU6D8kWdA5SNYLcjbvWEOsfbb9+/YwOhc0NKvPqq69al9mpX/fv34+BlAuMzxCAAAQgAIEUEdA+bz30Pu644xLXKgykxHVZshR+7bXXzK5du5xKu5wqOAvEJEFGQbWMt2HDhkXaahkuLoNLS360aTI/yNDRTJDtqZOeEHXq1Ml75ZfTdbU42eomDgIQgAAEIACB2hDQ8joFLaV78sknzfPPP2+0TWLs2LHm8MMPr41SRdaKgVQkKLKVRiCtg+OJEyc6z0EK2ldQGsXoSskweuSRR5wCtU54ypQpTdLVj/6rSSIREIAABCAAAQgkhoCOI9Eh9B/96EeL1vld73qXecc73lF0fmX0DaQvfvGLRrNJftDSxKuvvtrcdNNNRl6G4xjiqVUcSaGT+da3vhXoJU5rTCdPnpwoUpr2feKJJ5yzHAcffLC1Pdo7U+v9MwsXLjQLFiyw6qdIPZ3Jn30qZLBqNogAAQhAAAIQgEB6Cehh6IEDB8zu3buLbmQpjq5mz57tye/Vq5f5/ve/b4466igzd+5c85nPfMbbk6RtBJ///OeL1qGaGTGQqkk74XUVGjyvXbu2pi3UF16uvF1B+53yQ5s2bawzJvn5Kn0tvV1LEfV0xbbXSGcLBBk8OheKAAEIQAACEIAABHIJDBgwwAwZMsTccccdudGRf/7c5z5n3vve95oPfOADplWrVp58PXgeOXKkt7/6a1/7mvnUpz5lXdofuTIhBWIghQRG9uoRsO13CTIITjvttOopF3FNmobWRkZXm0855RTTvHnziGutD3GaadO+K1sQU52NZeNuy08cBCAAAQhAAALFERg/frzRKz9oRkljtj/84Q9m/vz5npe7/Dy1vsZAqnUP1KD+Bx54wCxevNg5+6BB45VXXlkDzf5dpZwE6MmG1sjaQseOHW3RiY7zDT//Pb8xrvj8fHG69o0OmxtUzUb66cXqrP1TchvumsnULJttpm3NmjVmz549zmq0zCCua6CdSpMAAQhAAAI1J2D7fau5UglRoHv37p6mWg0Tx4CBFMdeqbBOy5cvd3oxU9UaMEYVwg6C/XrPPvts/yPvMSDg96Ptx8BlsGht8YgRI5yGeNg9XFqGuGTJEqthJQNyx44dVgMpBvhQAQIQgAAEIFBXBPSbffLJJ3sHXmuvd/74Qas7FOLqzQ4Dqa5u1+o3Vocg5nouydegT58++VGxudZshV62IINBa2jlrrIewsCBA40MHlsQi/bt2zdJ0rlIlejfJM6mNYFDBAQgAAEIQCDFBDQukGOHWbNmeUvptBfJDzNmzDDTpk3zHHu5nGH5eWv1joFUK/J1Uq882+mVxCAPcUEeXhYtWpS4tmkp2aGHHur907L1ic4zsoWgc460flgnZduCv1zSllZPcZqVde2DEgctG2WZX2l3hJZQvvjii9aZShnvw4cPN3379i1NOKUgAAEIQKBkAt/73vc8R1hXXHGFmTlzpucmXAbTDTfc4HkCvvnmm0uWXemCGEiVJpwi+RpsBD29t80ipKj5TZoSxKJJ5gpE+B5hXKJt6f7Ml6tMKfFasukKGvRrP1m9h9WrV5t58+Y5MQwePNhotpUQnoBrr6IvqVC6n493CEAAAhCIloAcTP3tb38zn/zkJ813v/td76XVJSeccIK58847vQe20dYYnTQMpOhY1kTSNddc43k/c1XerVs3z1J3pYeJv/jii82mTZusRfQEPM5Paf/61786jTsZDeeee661XXGO1P6e/HOOcvVlRiKXRm0/y5jW+mvbfi3F19rYri2d8mt3PbxRPAECEIAABGpH4MwzzzR66UGhXkOHDjU6YiXuAQMp7j1UQL9CDhX2799fQELxyXLLqFcSQ9AANCitWm3VHh/XuUUydFwuvl1GkDy+uc6E0tObJPxzioq92hsUGEQH0SENAhCAAARsBPTbke94wJYvjnG1+N3TWZS28yjjyEc6YSDFtWcqqFctvhhRNmfFihXGZfjJgGvdunWU1VVFljYpRrlR8bHHHnMyUoM0vZ00d+n+fWv7QQpyGz5mzBjnnisZmIUMqKrcAFQCAQhAAAIQgEBsCGAgxaYrqqfI2LFjzeOPP+5c1uOaraiehu6a7rvvvkAX5dqsPXXqVLeAOkmxLeXKbbo8yyQtyGOglhW62uZyMNGiRQujFwECEIAABCAAAQgUQwADqRhKKcujQaZeSQyuwXEt2uLPaFSy7l27dpmHHnrIWYWWyk2ZMsWZnqYEzRxVwm14mhjVa1tcy2Rd8fXKiXZDAAIQgEBxBDCQiuNErjokIO9ry5Yts7ZcxlE1vLMFuRmXYnHw0CUWroGobTmcFWgdRLqMe1d8HSCJpIny1qhlkrZ7UPdmEpfcRgIGIRCAAAQgUDIBDKSS0VGwXAJy/bhz506nmNNPP93zk+/MUOEEuV3G9XJhyCeeeKJ1v5OcROisqIcfftgqRHuu4nqCtlXhMiI7d+7szX7ZBvES27Vr1zKk13fRnj17Gr0IEIAABCAAgagIYCBFRbJGcrRfaM+ePc7aW7Zs6UyrdcLmzZsD9xPNnj3bO2U5Cj2Z5YiCol1Gu3btrAmvvfZaoAH86quv1o2BJEZJXdZq7VwiIQABCEAAAikmgIGU8M696aabEt6C6qifxHOOqkOGWiAAAQhAAAIQCEtAD16TuoxcuhOCCWAgBfMhFQJWAjNmzDA7duywpslj2qRJk6xp1Yps376981wl/WNs27ZttVShHghAAAIQgAAEIJAoAhhIiequ+Cr7z3/+07gcCuhsotGjR0eivDZky7NbrcOGDRusm8Kllzho430tnyzJlbsr6AwpHTBsW5qpJZucC+QiRzwEIAABCEAAAvVAAAOpHnq5wm38xz/+YebNm+es5fXXX4/MQKoXl9Y+zEIeuMLuMZNhdP/99zuNOx0eW+vZL7/ttnff+LSliYUOfiVAAAIQgAAEIACBcggwmiiHHmU9AraZiFw0GpTHNWg2ZeHChc7DR3v37m169OhRM/V1zlGUB99qZsvlSU2NjPMBsuvWrTM6CNgVZEyedNJJrmTiIQABCEAgAgJaXr5t2zarJC3hllfJWq6gsCpGJARCEsBACgmM7NUjUI1NhMuXLzevvPKKs1FaSnfKKac402uZsGbNmkDd5aJcyxtrFaLuv0JnPsXZEK9VH1AvBCAAgagJ6DdTD6xsRpAewulhVadOnaKuFnkQqCoBDKSq4qayXAKandmyZUtuVMNnDa6D9tE0ZCzzQ9BsikQXSi+z+rKKy0120H4s/YjV0kDSQbrSz3YQqmaqZNA8+uijTRjoR3fUqFFGjibiGDQwcD091f6tQw45xERtHMaRAzpBIG0E9BBmwYIF1v9Zaqt+s/Qi/JuA7X97PbHR//mk7tnlN6rwnYqBVJgROSpE4LTTTquQZMTGgYA85ekQWVuYP3++WblypfMcLC3bjKuBJMNUh+DafmBkUMsoLbR3zMaEOAhAoLYEdHD5+vXrnUpojyMGkhMPCRBIFQEMpFR1Z/mNueOOO7zBn0uSps0vuugiV3JN47U/RYNXW9CsxOTJkyMbdGtwHOXs0pIlS5xuufWjfNRRR1mXM9jaWu24ZcuWWT3iSQ8ZSQcffHC1VapofX6/++8VrQzhEIAABCAAAQhUnQAGUtWRx7tCPRkP2suxdevW2DbghRdeMJs3b3Y+2V+0aJE57rjjItFfzgDEyhY0e2Bbm23L68ctXbo00OCS97a4nl00e/Zsj3n+jIoMCBl3aTOQ/D7jHQIQgAAEIACBdBLAQEpnv1a1VfLyJlfervXI1V5uVI0n+126dDF6JS1ovXTXrl2Ny+FBt27dSmqSmNu42+JKqoBCEIAABCAAAQhAoEoEMJCqBDrN1ZxwwglGrzSG/FmRYtroOyCw5dVZPWFnl2xySo1T3ePHj7cWl5Gr2SB59rOFQw891FvqZ0urRlyHDh08di6jK657lqrBhjogAAEIVJuA7ffR9f+52rpRHwTKJYCBVC5ByieaQJ8+fTx3pa5/6n379g3Vvo0bN5pZs2Y5y8hAmjBhgjM9TILtxylM+fy8cmnumgVU3k2bNuUXqeq1DrGNq8v1qoKgslgSkEdO1zli+t7r/iVAIA0E+vXr51zyrd+ldu3apaGZtKHOCWAg1fkNEMfma5Dx3ve+1+gwOlvQnhY5k4gi6CDWcePGRSHKk+EaIPkVBO3v8vMU+z5o0CAj5w6uoHOQ4hpatGgRqJpc7S5evLhJHi3XlAvwWgYNAPxXrh4ysvWK2nDNrSPtn+VoxeVFTPeMvCLG1a3uc889Z+1/3RPNmzc3J598ctq7L/HtKzS7Xyg98QCKbEBSl5gX2byis+l/fVLvCX6nCnczBlJhRuSoMgE5JFiadVrgCnIBTTCme/fu3svG4oEHHjB//etfbUme44QLLrjAyDisVZBxp5ctPPLII849UkHnPtlkVSLuiCOOcBrvGry3atWqEtXWhUwZR64HIwLwxhtvxNZA8meh/ffcDguamc3Nx+faEpCXVp2/5+qvWv7PrC0ZaodA/RHAQEpxn99yyy1m1apV1hZqP8c111xjTUt6pO3JiG3QUqidmg1yGWryzjZw4MBCImqWrjOGdKaHK8jbX5Q/9mKez13MFedyCKGn6kl8+qalUiyXct1ZxEMg2QTYy5js/kN7CERFAAMpKpIxlHPDDTeY/fv3OzXr37+/t5QtN4OmzjV4doWePXu6kmoeP2bMmMBzkIYNGxZKx/vvvz8wv85cOvXUUwPzFJs4ePBg5zlIMiTi6uJb7Rs9erT1HCTN9sjxw+OPP27FMGDAADNkyBBrGpEQgAAEIAABCECgVgQwkGpFvgr1FtrvYtt0f+GFF1ZBs8pUMXToUKNXtYKW+0QVZCy4wty5cz3vcrZ0GbRBZW1loo6Towtb0HKp1atX25K8uCj5OSvJJoify+jXvhYZeEmcyQpqM2kQgAAEIAABCJROAAOpdHaULJLAn//8Z+sZOVp+pfXevXv3LlJS/WVbs2aN0csV5HkuKgNJM1VBQcsKkxhkHLmW+Sle+w0wkJLYs+gMAQhAAAIQqAyBZI54KsMCqRUg8LOf/cwEbazX4P/yyy+vQM21EamlcDL8XHue4uz+dPjw4UZuzW26q01J3Xcj3QkQqCUB7sFa0qduCFSOAA/XKse21pIxkGrdAymv3+UNyG+2bTCuM0M0W+FaglWKl7B//vOfgWeURLWXSM4vopLlM6rme+fOnatZnbUu9b1reWhSZ7GsDSWyCYGgWUwZGXEejMitvmumMkqHKE2gEQEBCEAAApETwECKHCkCyyUgA0jL8mx7pCS7V69eoasI8ugW5MgidEUBBTSTpiVxrqB2FTofyFU2P76QISEjNK5BZ924jOO4noETV5ZJ0+ukk05yOpZR3wcZULVua5y9WtaaDfVDAAIQSBoBDKSk9Vid6KuTuvVKU5DDAnm+sz0F10ybBoBa4hZF0EG7W7dubSJKM3bLli0zr732mvfKz6BzQORRrxpBbtRtOqpuzcSxLKkavRCvOvQdwAiOV5+gDQQgAIF6JICBlOJel4cz1wBUMwyugzpTjCRU0/S0Omh2qZTlaDKObMsOFW9bbhhK4ZzMWtJjW9ajmZmXXnopJ2fjjzqENyoDSfVrz5WtXVqKJC93etnCqFGjTNeuXW1JoeNs9YcWUsECmlXcsmWLtQbdF3LCgdFgxUMkBCAAAQhAoCIEMJAqgjUeQufPnx8PRRKqxVlnnZVQzeOhthxWnHDCCVZldG/qMFtXsBmRrryF4rVs0bU3RAaIXrUMmtHTclLbjJmMu+7duxsOr6xlD1E3BCAAAQjUGwEMpHrrcdqbKAKaBdR+IZfBoKVohGACOufIxS8OBpKvfdxnunw9eYcABCAAAQiknQAGUgJ6+Kqrrgp82q6lYPfcc0/FW/KjH/3IueRMT7+1NOvMM89spIcG91q25QphnRLMmDHDPPfccy5xRoeWvuc973GmR5Vw9913O73iaTnURRddFElV4jNhwoRIZFVKyF/+8pdA0VOnTg1Mr3RitYwged6bPn260wOfjN2RI0dWurlmyZIl3j4zW0X6nspgjLO7eZvexEEAAhCIGwH9P631CoRSmdhWLJQqK63lMJAS0LNB5whJfZdL5KibFlSPnn7v2LGjSZWXXnppk7hyIrZt22bd0+LL3L17t/+xou8uL2uqVJz0Yt9IRbsgdsLV50F71gp9j6NqkL4Drhkz1SHnGAQIQAACEIAABNwEMJDcbEhJEQHt4XDtQ6mmy2sNXG1PnBTPE50U3XARNmXVqlXGdvaXnGBof1KlgxxprFixwlqN7tkhQ4ZYHYJYCxAJAQhAAAIQSAABDKQEdBIqlk/glFNOKV9ImRJ0zlHQrFK3bt3KrKFw8UJGWJB+haVHk0NLMmfPnu0dFpwvUUbB2LFj86NTfb18+fImRrVmbLW0duLEiRVv+8aNGwPP7+rdu3ciDaSgPV+FvicVh04FEIAABCBQUwIYSDXFT+X1REBe3fSqZZABNH78eOdBrGH3hJXaFrnwds3oySBwLUfbvn17qVXGtpw/GM+fWdQA3h/EBy2ZC9swMfTrzC2rvg97f9rk5MqM62fxnDZtmnN5spyfjBkzJq7qoxcEIAABCFSYAAZShQEjPt4EXn755cBzgTRjMWXKlHg3IqR2tuVaIUWUnb1nz55GL1uQ+++gPV62MkmOk3MTzS7mBw3iFyxYkB9d9vXixYutMmQ8T5482ZqWtkixDdpTyT6ttPU47YEABCAQjgAGUjhe5I6QwMMPP2x27tzplDhp0qSKn/+yZs0aZ/1KqJbTh0AlSEw1Ae2Ps51zVCkDyQXTn61ypcc1XgftypB06T906FDTo0ePuKqPXhCAQEIJaAY9DsvSS8GX1Nn/UtpaahkMpFLJVbGcZjE2b97srLFaX1DVo0GbLejLFtZ1cKHlUk8++aQ5/fTTG1WnpS9BX+zWrVs3yl+pi4MOOsjpDUycqtUnrvZpU70219uC9q4MHz7clkRcGQTU52LrmpnQ97gaQd+B/OV6fr2u76+fnsR3LcfUck2bgaT/FUEPYZLYXnSGAAQgAIHKE8BAqjzjsmv4wQ9+ULaMKARceeWVUYgpWoZtwHPiiScavWodPvCBD9RahcD6Z82a5TRmVVDG7CGHHBIoo9jEWp9zVKyelc4nA+nkk0+udDUF5Q8aNMjoZQuatU2jkSRDyPb/Iuhhio0PcZUloIc2L774orWvVPMRRxxhDj744MoqgXQIQAACRRDAQCoCUtqy3HrrrUbnCQWFT3/600HJpDkIaO+CBgCuWQTtNYnKMHGoUFR0Uvf4aBD89NNPW9uoQ4L79+9vTUtypAb5+QN9mzFQqTa6ZqNUn/TI161SeiA3+QRcjlnUMt1HQenJbz0tgAAEkkQAAylJvRWRruyriQikRYxcVG/ZssWS8u8oLcGKg4HkVNCRoCV7rv1aGkAfddRR3vIyR/FQ0Vq+GGTAuZZmbtq0KVUGkrgeeeSRzqWc1VqyN3DgQKdDDXWslr0SIFAsARlCNgMfQ7tYguSDAASqQQADqRqUqaMqBP74xz8GPoHU8o2RI0dWRZe0VfLSSy8FOqzQGU5Rzd7ozCqbgbR161YzZ86ctKENbI9mxcIGDT6jXEYng7Vjx45h1Yh1fhmfcmnu8laH0Rfr7kM5CEAAAhUngIFUccRUUC0Cmr3Zv3+/s7qlS5c2MZA0+AsKtX6qqYHuwoULnUv2OnfuHIs1+7YnwkFc1S7X4FTMbc42tPym1v0R1Ka4pC1atMi8/vrrTnW6dOline2zMXcKiVmC6/5zxctAGjduXMxagToQgAAEIBAXAsGjw7hoiR4QqBCBE044wcyfP986Y6Eqo5oVKVV9GQU6F8gV5KEriZuaNSP1yiuvuJrlHWarw2QJ4QkEPSSQtEMPPdTISEpL0LJVlyGkeKUTIAABCERNQA/s9LAliYGHjYV7DQOpMCNyFEFA3rFcjh+058a2tE1fUNfARlUedthhRdRcfpZhw4ZZhch98OrVq43OWckP8limgWZSgwbRtr1oGkwWmlWLos22JXS5cgsN8nPz+p81K6VZRFuIw+G4Nr2IK59A7969A885qrXL/fJbmC4Jrv/5rvh0tZ7WQAACSSGAgZSUnoqxno8//rh54IEHnBo+++yzVgPp3HPPdZaJQ8IjjzwSaMBpGdOECRPioGoTHbS/wmYAaRAiD3svv/yyWbJkSZNy2nsxceLEJvHVjCj1ydbGjRvN9OnTraoeffTRgYNoayEiE0MAIygZXaVlnOormzGk732Sl3kmowfQEgIQKJYABlKxpFKUr9QBqAvBjh07XElefJQbxgMrqnKibRak0ECtGrMzwiBHB7agmZkHH3zQluTFlTJz4xQWcUIhtq7qdL+73K67yhAPAQhET0DLZidNmhS9YCRCAAIQiJgABlLEQJMg7qqrrjI/+tGPnJ6uOnXqlIRmxFJHzdyMHz/e+oR08eLFnsMFbaK3hbFjx9Z8z5NNr2Lioja6bXW2b9/eyfbVV181q1atshUjroIE1q1bZ7QPzhZatmxpSvHCZ5NFHAQgAAEIQKCaBDCQqkk7RnVdeeWVMdKmqSozZ8507ifRMjDbnqamUhrHVGMQrxpd+13mzp1rdFaPK2gpYq2dQrh0Gzp0aOA5SDoAN6qg5Tfaz2abbWvRooX13B3tnVL/2pbuRKVXOXLWr18f6KJcfPv27VtOFTUp+8ILL3jeCG3fLc0ca3+QLa0mylIpBCAAAQhAoEgCGEhFgiJb9QjIOYKeTLuCBs82A2nAgAFm7dq11mLyNHPqqada0+ISaRvcFxpcVsuDjtjqVa0gD3fLli1rUp14xL0fmyidjZA3QvWvq49dziVsshSnWZsFCxZY5en7o/vC5r1N8ZrZiSr4bbK1S3UovtA9HJUuyIEABCBQTQL631at3+Co28X/5cJEMZAKMyJHlQmUumfppJNOqrKmla9Os1GjR4927qFJ6qZmnd8kpwq24A/wbWmugbgtb9zi9IMUlf46NFceFl1BHhaHDx/uSiYeAhCAAAQgAIEAAhhIAXCSkHTjjTc69wBI/+7du5tPfepTSWhKRXXUIbE2r26qtE2bNlWdHQnb0I4dO4YtUnJ+zXQ8+eST1vIadGvJVBThkKzrd71sQd4DS3Gq4DI+XPG2uouJ0+ymy6W9HEmoXdV4OucyuKpRdzGcyAMBCEAAAhBIKgEMpKT23Ft6y4Oca+CvLFu2bKl4CwcOHGhmzJjhHNS2a9eu4joUqkAHk7oGypoity0f01KkoKVPSTzIVAN46b1v374myNRWebFzzeyIX1gDSfJc3P19Q00UKSFCRqTrPpPBIOcZUQU5hNi+fbvVCFJbtR8rqTN7UTFCDgQgAAEIQCDJBDCQktx7MdFdG8w1kxX34Bqou+JPO+00Z5O06V6G6fLly5vkkSOBKJ0WNKmgjAgZg+PGjbNKkBMJzbRFFWRouWajVIcOAnYd0htWh549exq9qhH8+8V/r0ad1AEBCEAAAhCAQPUIYCBVjzU1pYjA/PnzvdmW/OVMGjTrpcF6flqKml9UU2znROUWDHvmkj+7deedd+aKafismbGzzjqr4TpuH9JoUOke91+5vP3vQW4cnyEAAQhAAAJJIYCBlJSeqpKev/zlL402gLuCNtdfdNFFruS6ifcHgGEGvZotkecxW5BjCi1/+/vf/94kWbM+OvhVM1NJCxo8h2Gk9rkMS+1LCtqbpGVvcQ1armkzJHx9Xa7h/fS4vssRhOugaLU5qR6e4sobvSAAAQhAoDoEMJCqwzkxtWiTfpAXOaVXOmgviYwFVwhKc5WJQ/yoUaOMXrZw3333eYaEjb3ixD2MgaTlbS5DV/z69esX28GrnBzIk11+kAt31/6o/Lxxu5azFBm5UQUZHi7DU/EuIzOq+n05HATrk+AdAhCoNwL6P5vU8Ui1fiOSfE9gICW59xKg+4MPPhi4r0VL0c4777wmLTnzzDObxBFRPAGdIyRPa7Z/gjK4unXrFqnjguI1K5xT3vJsQbNHtj1ftrxpj9P35sQTT3Q2s3379tY07Z1zPeTQjI8MOUK8Cej7u2bNGueDLDkscfV/vFuGdhCAAATiQwADKT59kUpNlmY3/Qd5glu1alXV2i1jId9gqObT9qo19K2K1DbXLINNl3w2tjzE/XsJoOteqtb9pPq7dOkSujvmzJnj3RP5fe3rHeUsV2jlKFAUAS0llVdO2/JFGU8yco855piiZCUlk/YzupbXHnTQQYl9ip8U/ugJgXokgIGU8F7XTIBrKZUGQWHdMkeNI8wAvZi6p02b5rlYduXVdLdt9un44493PjlP6v4PF4NS4+WuXf3l6rP+/fuHFu2SFVpQEQU0OHz22Web5NT34PDDD4/sqfoRRxzhPHtM91/c7ydXH+cbTU1AEhELAv53Sve7LfjptrQkxqk9jz32mHPGTC7103hIeBL7Cp0hkCYCGEgJ781Pf/rTCW9BOPV37twZWMD1lLFHjx7WcsuWLTPPPPOMNU2ROkR20qRJTdI1mPRfuYmuwWdunrh+VluPOuqoyNSTPJ0/5BqwVeIAXNe5X3IkENWyI+ldCd0jA48gCKSMgMsYVDMLectMGQqaAwEIVIkABlKVQFNNPAm8/PLLzieT0tjloUtnP9kcCaiMnCnwNN54BsnkyZOFJJIggysouJi74oNkkVY+AX0/XA8sNMumg4IJEIAABCAAgTgSwECKY6/UUCfbuvZcdRhs/ptG1IeSirtrMKkan3rqKeueA82KjBkzJreLUvt50KBBRl7TbE+Mtddt9erVqW170hqmBwuPPvqoU219f0aPHu1MJwECEIBAEggwJkpCL5WmIwZSadxSW+rkk082s2fPdrZv5MiRzjQSSiegzfG7d+9uIkCe6HR2kv4J25aquWa4JEhlbP+8g5arNFGgiIgXX3zRqruKyoA78sgji5BSXBbXLJJmJLT/x8aoOMnkipJAkLGvegqlR6kLsiAAAQhAAAJhCWAghSWW8vx6Sq8XoboE5GJZr/ygAX+hWb38MroeMmSI05mF5LkMDZusQnFBrrc3b94cqYFUSBfSCxOwGc2FS5EjbgRs/aj/F7b4uOmOPhCAAATiTiDRBpKeQs6cOdNbWjNixAgzePDg0Lz3799v5s6da1599VWj81c0Q1LKgDR0xXVSQM4RdGaHK3Tu3NmVRHwZBDp16mT0IkAgl4D2zrnc7ru8773++utG52rZgv5XHn300aGdVsjz5tNPP91EpAb30hEnGE3QNERoVvawww5zzpbyP7UBFR8gAAEIlEwgsQaSNtefc845ZuHChQ2NHzZsmNHBpP369WuIC/pw3333mYsuusjker469thjzW9+85uSjK2guuo17d3vfnekTefpaKQ4UyMsaHldqbNwqYGT05C+ffvmXBX3UZ4j9SDJFWRwhTVoJE+H1uYHfb979eoVWl6+nDRf616XS/56CbondOaXy7APe+/VCzfaCQEIlEcgkQaSBjwf/vCHzcqVK82vfvUrM3bsWG9D8NVXX+2dhzB//nzPvXAQmr/97W/m3HPP9Zb/3H777eaQQw4xP/vZz8ytt95q3vWud5lZs2bhZSkIYI3StEcqyC23lpYR6o+Avr9BjjPkbpwQPYGoH1hELS/6FiOxFgT04JIAAQhAoJoEEmkg3XLLLWb69OlG7x/84Ac9Xv6+mY997GPmrrvuMpdddlkgx69+9aumXbt25k9/+lPDbNFPfvITs2nTJvO73/3OzJgxw2gwnuRwww03OJ+66fTxr3zlK6GaJz6uJ8laavPxj388lLy///3vRk4IbEEDpXe+851eH+Wma++Mq180q+i/csvos/TTYbH5y4h07s/zzz+fn927VlvlMe1f//pXk3TpJ8M8zMBbT8zlbc0VdJhphw4dXMnWeD0sWLVqlTWta9eu1n1N1swVitQy2Dlz5lilyyOdDjouNsi5xNq1a51Li8RO3+laBunnckCge5dlj7XsHeqGAAQgAAEIFEcgkQbSHXfc4Q38Lrjggkat1PUnPvEJc9tttwUaSDqV+7nnnjNf//rXG4wjX9C3v/1tIyNLy/WSHjZu3BjYhDvvvNNcfPHFgXlyE/fu3Zt72eizBq/ql0suuaRRfNCF3DK7lk2o3OOPP27OPPPMIBGN0mQcuQanyihvcPle+DTr4KpDhvK6deuse6hkmMipQpgT3FV/EEMtF813fVzorBjpIbn5Qf2hPXmaWallkH46jNcW5MBh4sSJtiRrnIzpefPmWfcIqr3a76b9MLUK+/bt84xBGc/5MyHiIANp3LhxtVKvqvW2bt3aM35t30d5awz63ldVUSqDAAQgUCIB/Z9P6p71/N+oEhGkuljiDCQ91X/hhReMnrbnP43VE2Rt8JXbYeVzDS79GYMpU6Z4nasNwxp4aXmW9i8Vu4cp6XeGbfBSzTZp0BgUCqUHlbWlaRAdJvj1++/5ZV3x+fmKvbbJ04zIiSeeaDX8NHMkD3K2dlXzn7bqsulQbLvD5nPVZeMXVnYU+aWHTRdbXBT1xVGGDks+4YQTrKotWrTILFmypKr3jFURIiEAAQhAAAIOAokzkPTUWU9qtXzIFrSZU8aRljNpCY8trFixwouWt5+zzz7b3H///Q0/1tp/pL1ILvk2eX6cBkBB59L4+fx3DfSqOZD16+U9WQRcLrm1XLDaxomN3KRJk7zvZH6ajDfX7FF+Xq4hAAEIQAACEIBAXAgkzkDy96y49i7IQFKQ5yVXkHMHBXlY0yyKDCI9qZf3Ou1JklvqJ554oskyGZc8P/7666833/zmN/3Lot5xyVoUJjLFmIAMOJsRp+WJhOQT0GyQK+ihkGum3lUmyKjnoZGLGvEQgAAEIFBNAokzkPxN9q5lNv6yMblCdQXfyNI6eHmr82VqD9OECRM8BxDaf5K/x8klz4//7Gc/a9773vf6lwXflV9nWhAgAAEIxJWAXErrjDhXCLuWXfK6d+/uEhfaUYlTEAkQgAAEIACBEgkkzkDSGRn6QZa3OVvw44PORujdu7dX9Morr2wwjnxZ73vf+zwD6amnngptIGkP1KhRo3xRBd/VFgIEIACBuBMIawQFtUcPr/L3jwblJw0CEIAABCBQbQKJM5DknlreqnxDKB+Y4rXcJ+gH+OCDD/aK2c5NOfXUU7002yGG+XVxXR6BKAdd5WlSWumo9S9FnmZSbeVcM6yltTRepWztjZMDBJt+8SKINhCAAAQgUC4B/a9P6j5yfqcK937iDCQ16YgjjvD2CG3YsKHROSoyauTyWN6TgpbYqbyCltedf/753mf/j1xPK+jMnKQH7atyuZUWn0svvTRUE2Wc6lwgW9CXbfz48bYkZ5yM2F27dlnTJS/fJbc1Y06kZgaDDNvDDjssJ3fhj0ceeaSRxy1bkH7ypBgmDBgwINBpQf/+/cOI85YpySGJKwQtY3KViTJeTlLkat5lvPgPKoqtU/ezlme55Pn7D4uVF3U+7cXReWz+Mt98+SynzSfCNQQgAAEIQCCeBBJpIF111VVm2rRp5vbbbzfXXnttA9mf//zn3gBeZyEFBTlnkCvvX/7yl0bL7Pr27duQ/Yc//KH3Oexgv0FAjD587Wtfi1SbsAfBFqo83zgtlL9QeliDqpC84cOHG72iCjKQ9IoqaKbUPyA5KplRypFB43L1XEo9MtDDGrml1FNqGRnNQXt1SpVLOQhAAAIQgAAEqksgkQbS1KlTvVkkeY3bvn27d9ikDCYd/Hreeec1mhWaM2eOd3jkiBEjvPORhFdemW644QZvBuW0007zDpfVk2kZTH/4wx/MNddcY4499tjq9gS1QQACEIAABCAAAQhAAAI1J5BIA0lrPh9//HFz0UUXmRtvvNH4MyU6+PXHP/5xUVA/9KEPeUuULr/8cqOXgpZofeYznwntqruoCskEAQhAAAIQgAAEIAABCMSeQCINJFHVOUgPPPCAN4O0ePFib5mczSucZo5cexbOPPNMb0+Izj3asmWLGTp0aOw7DAUhAAEIQAACEIAABCAAgcoRSKyB5CPRxudyl8PJsLIZV34dvEMAAhCAAAQgAAEIQAAC9UEg8QZSfXQTrYQABCAAAQhAAAIQiAsBOeYJ8pgcFz1tekh3QjCBtwcnkwoBCEAAAhCAAAQgAAEIQKB+CGAg1U9f01IIQAACEIAABCAAAQhAoAABDKQCgEiGAAQgAAEIQAACEIAABOqHAAZS/fQ1LYUABCAAAQhAAAIQgAAEChDASUMBQJVOfuKJJ8wll1xS6WpSJV+HAz/66KOmS5cuqWoXjSmNwM6dO72Cbdu2LU0ApVJFYOPGjaZz585G5+UR6pvA3r17TfPmzc2ECRPqG0QMW6+xz0knnRRDzYpXad68eeZb3/pW8QVilFO69+nTJ0YaxU8VDKQa9sn5559fw9qTW/W2bdvM5s2bvUEQnliS249RaY6BFBXJ5Mt58803vTPtZCy3atUq+Q2iBWUR2L17t9m0aVNZMihcGQIyjpI8Bkqy7upRGUdJb0Nl7sz/SH1b9hDVzH8u+QSB+BN4+umnzQknnGD27NljWrZsGX+F0bCiBD70oQ95rlZvv/32itaD8PgT0IC4TZs2ZubMmWb06NHxVxgNK0rgu9/9rvn1r39tnn/++YrWg3AIQCB9BFiDkL4+pUUQgAAEIAABCEAAAhCAQIkEMJBKBEcxCEAAAhCAAAQgAAEIQCB9BDCQ0tentAgCEIAABCAAAQhAAAIQKJEABlKJ4CgGAQhAAAIQgAAEIAABCKSPAAZS+vqUFkEAAhCAAAQgAAEIQAACJRLAQCoRHMUgAAEIQAACEIAABCAAgfQRwEBKX5/SIghAAAIQgAAEIAABCECgRAIYSCWCoxgEIAABCEAAAhCAAAQgkD4CGEjp61NaBAEIQAACEIAABCAAAQiUSAADqURwFIMABCAAAQhAAAIQgAAE0kfgoPQ1iRalncDhhx9urr/+etOyZcu0N5X2FUHg/PPPN29/O896ikCV+iytW7c21113nRkyZEjq20oDCxOYNGmSadu2beGM5IAABCCQR+BtmWzIi+MSAhCAAAQgAAEIQAACEIBAXRLgsWtddjuNhgAEIAABCEAAAhCAAARsBDCQbFSIgwAEIAABCEAAAhCAAATqkgAGUl12O42GAAQgAAEIQAACEIAABGwEcNJgo0IcBCAAAQhAAAIQgAAEKkDgySefNGvXrjXnnHOOadasmXnmmWfMypUrzVlnnWVatGjRUOOiRYvMww8/bFatWmVGjx7t5W9I5ENFCeCkoaJ4EQ4BCEAAAhCAAAQgAIH/EDjjjDPMP/7xD7Nr1y4j75vvec97zB/+8Aezbt060717dy/j008/bSZOnGj27dvnXX/sYx8zP/zhD833vvc9079/f3PBBRf8RyCfIifADFLkSBEIAQhAAAIQgAAEIACB4giMGjXK7N69u9Hs0Y9+9CPPOPrGN75hPvShD5lWrVqZ3/3ud+baa681t912W3GCyVUyAQykktFREAIQgAAEIAABCEAAAuUR0NmO+UFL7t72treZK6+80rRr1y4/mesKE8BAqjBgxEMAAhCAAAQgAAEIQMBF4LHHHjNLly71ls2tX7/ePPLII96eJBlIf/zjH71iAwcONNOnT/c+aw/TQQcdZM477zzToUMHl1jiyyDAHqQy4FEUAhCAAAQgAAEIQAACYQgE7UGaMWOGede73mUymYwnUkaSwvve9z7zm9/8xvusP4pfsGCBOfzwwxvi+BAdAdx8R8cSSRCAAAQgAAEIQAACECiZwNSpU82bb75pxo8f73m402e97r77bnPXXXd5crUHSXEYRyVjLlgQA6kgIjJAAAIQgAAEIAABCEAAAvVCAAOpXnqadkIAAhCAAAQgAAEIQAACBQlgIBVERAYIQAACEIAABCAAAQhAoF4IYCDVS0/TTghAAAIQgAAEIAABCECgIAEMpIKIyAABCEAAAhCAAAQgAAEI1AsBzkGql56uUTsPHDhgZs6caVavXm1GjBhhBg8eHFqTMDLC5N21a5eZO3euWbZsmenbt68ZPny46dixY2j9KFA8gRUrVpjZs2ebtm3bmjFjxnjvxZf+d84wMsLkzdVDZ1Do1PJx48blRvM5YgKl9o+vRpjve5i8kr9mzRrz/PPPe2eNjBw50vTo0cOvlvcKEAjbPzYVwtxPYfLu2bPHvPjii2b58uVmwIAB5uijjzYtW7a0qUAcBCpKoHnz5p78nTt3VrQehGcJZP2sEyBQEQKLFy/ODB06VI78G17Dhg3LZH9kiq4vjIwweX/5y19msgOeBr2kY/v27TPf//73i9aNjOEIfPGLX8xkD7ZrYN6sWbPMN7/5zVBCwsgIkzdXib///e+ejlOmTMmN5nPEBErtH1+NMN/3MHm3bt2ayR6+2HCf6n9D1ljO/O///q9fNe8REwjTP66qw9xPYfI+/PDDmaxR1Oh+OOSQQzKKJ0CgVAKnn366d09lH9R6Is4//3zvet26dQ0is26+M/qdzA2PPvqoly/7sDlz7bXXhhpP5crhc2ECGEiFGZGjBAJZ//wZfblldPzqV7/KvPzyy5mf/exnmdatW2f69++f2bFjR0GpYWSEyfvQQw9lsgesZfQjp0FPdhbJM4yy5wl4/3juvPPOgrqRIRwBMddAUwPPWbNmZbKzihn/B+L//u//ihIWRkaYvLmV68epZ8+enq4YSLlkov1cav/4WoT5vofJK/nHH3+81//XX399Zs6cOZlf/OIXGT3Y0f2bPaTRV4H3iAiE7R9btWHupzB5s6sLMtlVBZlOnTp5D3PmzZuXuemmmzJdunTJdOjQIfPaa6/Z1CEOAgUJ+L9/YQ2k/fv3Zy644IKGh42///3vC9ZFhtIIYCCVxo1SBQj8+Mc/9gYUt9xyS6OcMpI00MiPb5TprYswMsLkPfnkkz0d/vGPfzSq9plnnvHiNRgiREcguxTAM0azyxgzb7zxRoPgvXv3evEHH3xwo/iGDDkfwsgIkzenCu/jOeeck+nevbt3H2Ag5dOJ5rqc/vE1CPN9D5P3vvvu8/r+sssu86vy3l966SUvfuLEiY3iuSifQJj+sdUW5n4Kk1d1fetb3/L6/Qtf+EKjqr/0pS958TfccEOjeC4gUC0CMqzWrl1brerqsh4MpLrs9so3evTo0ZnsGu3M5s2bG1Wm5StarnLcccc1irddhJFRbN7sOnfvCbGMoNzBul+/ZpE0pW1L8/PwHo7A/fff7w0m/t//+39NCn72s5/10jQwDQphZITJm1vnT3/6U0+XP//5z967nvARoidQav/kalLs911lwuTVwxPNFuzevTu3Ou+zllTpIQohWgJh+sdWc5j7KUxe1aX/WXqg99e//rVR1boXFH/FFVc0iucCAhBIDwG82GX/yxGiJZCdAjYvvPCCGTJkiMkONhoJzy4YiJZfAAAR3ElEQVRLMNl9Sd6GV+VzhTAywuR9+9vfbrKDHJN9ImyyhlCj6rURV84kskvvmqQ1yshFKALirZAdCDUp58c999xzTdJyI8LICJPXryO7BNT8z//8j7nyyivNGWec4UfzXgECpfRPrhphvu9h8qoOOWXILg32HHRkf+a9/xPZZXYm+8DETJ482WSX3+WqwucyCYTtH1t1Ye6nMHlV12mnneZVeccdd3jv/p/sHlbvo5/ux/MOAQikhwAGUnr6MjYtyc4amX379pmuXbtadcqu3zb6YVy/fr01XZFhZITJ66wwm5B1GGC2bdtm3v3udwdlIy0kgewyAK+E7X7QvaCwcuVK7931J4yMMHlVnwa/F154ocku9TPZ/QUuFYiPiEDY/smvNsz3PUxeffe3b99usnskTXYW0fNaJ8+W8liW3Zdm/vjHP+arwnWZBML0j6uqMPdTmLyqLzujaLIOHcy9997reTm97rrrzKhRo0x2X633QOWss85yqUU8BCCQcAK4+U54B8ZRfQ00FLp162ZVzx8UZ9eDW9MVGUZG1uGCJ6ec+n73u9+Zr371q54b8i9/+cuePP5EQyCoL4u5F6RFGBlh8kr2V77yFc/1+JNPPmnatGljNJNIqByBsP2Tr0lQeeXNvafC/G/ILv/1qpo+fbq57bbbvNnEk046ybzyyivm61//usl6mTIPPvigyS69zFeJ6xIJhOlLVxVBMnLvBZUPk1f5tcrg4osv9gxmHQmhlQcKhx12mPnv//5v47tc9iL5AwEIpIoAM0ip6s54NEbnxyhkvRNZFdJ5Fwr5S9xyM4eRESZvbh3+Zy2f+OAHP2iym/NNdq25yXra85N4j4BAUP8Ucy9IhTAywuSVUaTB7+c//3mWT0XQ18WICNM/NnlB5ZU/954Kk9cfPGtJXdaZjPnOd75jsl4XzTXXXGP0AEXhk5/8pPfOn2gIhOkfV41BMnLvBZUPk1f51e9HHXWUd16bludlva96S7R79epljjnmmIb7QnkJEIBAughgIKWrP2PRGv146Mntpk2brPr48UGHsoaRESZvvkKaNbr00ku95VWPP/64OeKII/KzcF0mgT59+ngS/H7PFefHBd0Lyh9GRrF5tZxKhrEOMP7Upz5ldHCw/1KdGlzpWstFCdERKLZ/XDWG+b6Hydu7d2+vSj0o0axBbpg0aZKRrIULF5otW7bkJvG5DAJh+sdVTZj7KUxe1XfzzTd7s8rZs9G8Byg64Fr70HSt/1k33nijSy3iIQCBhBPAQEp4B8ZR/exhoN76fX/wm6+j4rWUKd+BQ26+MDLC5PXr0Absq6++2mTdtXo/eE899ZTnVMJP5z06AsUMSrIuwAMrDCOj2LyzZ8822XNMvOV1Guxo8KOXv1fqX//6l3f9oQ99KFA3EsMRKLZ/XFLDfN/D5JVecuKSPUC6SdWKl5GkELR3sklBIgIJhOkfl6Aw91OYvOpnzRrJaYe/VM/XQf8v5KBBs43Zg8/9aN4hAIEUEcBASlFnxqkpmomZP3++2bBhQyO19KOzYMECc+yxxwYusVOhMDLC5NXSv//6r/8y2QNKzdSpU820adO8TdiNFOUiMgL+rNxjjz3WRKYf53uza5LhrYgwMorNq8HSVVdd1eR1+eWXe7Vqs77S2XPi6pXS4ovtnyDpklHs/5di82qwPmjQILNo0SJv5jC/fnm47Ny5s5cnP43r0gkU2z+uGsLcT2Hyagm4fiuyh0dbq/Znlv1lfNZMREIAAsklkB6P5bQkTgSyHp+8cyKynuEaqZXd7+HFF3P6cxgZYfL6BxNm9xdw3lGj3qncRXYdfya7nCajc7D8kF2qlMl6B8tk1/Jnsl4N/WjnexgZYfLmV6gzcLL/0TOcg5RPJrrrcvpHWoT5vofJ+5Of/MTrex0EmhtefPFF73y0d77znbnRfI6AQJj+cVUX5n4Kk1fn5WUdMWSeffbZRlWvWLEik51FyujwawIEIJBOAhwUm85+rXmrsk/VMtmndZns0pRMdgN85p///Gfmc5/7nHctwyQ/KE6D0j/96U8NSWFkFJs3O6PlHQSpurLnmmTOPfdc6yu7P6VBDz6UT+Duu+/2+jfrIjcj4zi7+TkzcuRIb9CZPXumUQUajKp/snuDGsWHkREmb6NKshcYSPlEor8O0z/V+t+gVmY9GHr/t3T/6RDQBx54IJN12JDJLrvzjPmsR7voYdS5xGL/dwtTtf83ZPeler9Z2ZnDzDe+8Y3MI488krn11lsz2dll739Udi9SnfcezYdAeglgIKW3b2vesuxyukz20M1M1mGD92OiQceUKVMy2aUqTXSzDYKUKYyMYvL+5S9/adBF+rhe2X1STXQkojwCd911V0YDDZ+5PmfdKTcR6hoEKWOxMsLmzVUCAymXRuU+F9uX1frf4Lc0680u84EPfCDTokUL717NLr3LjBs3LpNvyPv5eS+fQDH/u1VLLf43yEjKnofV8H9L/7+yh6BnHnroofIbjgQIQCC2BN4mzbJfeAIEKkZA3sIWL15stBFfXotKCWFkhMlbii6UKZ2A/t3oXJm9e/d6ezlatmwZWlgYGWHyhlaEAmUTiKJ/wnzfw+TVHhN5rTv00ENN+/bty24rAgoTCNM/Nmlh7qcweVXXxo0bzdKlS72DhOXpkAABCKSbAAZSuvuX1kEAAhCAAAQgAAEIQAACIQjgxS4ELLJCAAIQgAAEIAABCEAAAukmgIGU7v6ldRCAAAQgAAEIQAACEIBACAIYSCFgkRUCEIAABCAAAQhAAAIQSDcBDKR09y+tgwAEIAABCEAAAhCAAARCEMBACgGLrBCAAAQgAAEIQAACEIBAuglgIKW7f2kdBCAAAQhAAAIQgAAEIBCCAAZSCFhkhQAEIAABCEAAAhCAAATSTQADKd39S+sgAAEIQAACEIAABCAAgRAEMJBCwCIrBCAAAQhAAAIQgAAEIJBuAhhI6e5fWgcBCEAAAhCAAAQgAAEIhCCAgRQCFlkhAAEIQAACEIAABCAAgXQTwEBKd//SOghAAAIQgAAEIAABCEAgBAEMpBCwyAoBCEAAAhCAAAQgAAEIpJsABlK6+5fWQQACEIAABCAAAQhAAAIhCGAghYBFVghAAAIQgAAEIAABCEAg3QQwkNLdv7QOAhCAAAQgAAEIQAACEAhBAAMpBCyyQgACEIAABCAAAQhAAALpJoCBlO7+pXUQgAAEIAABCEAAAhCAQAgCGEghYJEVAhCAAAQgAAEIQAACEEg3AQykdPcvrYMABCAAAQhAAAIQgAAEQhDAQAoBi6wQgAAEIFAegXnz5pkLL7ywPCGUhgAEIAABCFSQwNsy2VBB+YiGAAQgAAEINBAYPHiw2bNnj3n99dcb4vgAAQhAAAIQiBMBZpDi1BvoAgEIQAACEIAABCAAAQjUlECzL2dDTTWgcghAAAJ1SkAT+A8++KC56667zH333WeWL19u2rdvb7p27dpAZMOGDeb3v/+9OXDggOnTp09DvD4888wz5l//+pcZMGCAad26tdm8ebP53e9+531+4403zG9/+1tzzz33mP3795uDDz7YNG/evKF8mLwNhbIfXnrpJXP33Xd7chcvXmxatmxpevXqlZvFPPLII2bWrFlm0KBB5tZbbzX/+Mc/TIsWLczDDz/stVMzSCqzbds2T/dGhbmAAAQgAAEI1JqAltgRIAABCECgugT27duXOeOMM7TEOdOsWbNMjx49vM9ZQyLzgx/8oEGZp556you/9tprG+L8D5/4xCe8tBdeeMGLevHFF73ryy+/PJM1pjJZYyszdOjQzNve9rZMx44dMwsWLPCLZsLk9Qt96lOf8nSVvN69e3ty3/72t2cUnzXC/GyZd77znZn+/ftnrrzySk8ftXHcuHFefn3WSzLe9773NZThAwQgAAEIQCAuBFhil/2lJkAAAhCoNoHf/OY33uzRZz7zGW/mZ+3atd7sjGaPFLdly5aSVfrJT35iJk+ebDT7lDWKvJkmzeBMnTrVm7XJFVxs3ttvv93cfPPN5tRTTzWrV682q1atMuvWrTNnnXWWF//9738/V6xZuXKl+fWvf+3NIGnG6etf/7p58803vVklzWbpsxgQIAABCEAAAnEjgIEUtx5BHwhAoC4ILFq0yGvnO97xDm9ZnS6GDRtm7rjjDvPtb3/b7N27t2QO2dkdzzCRUaRw3HHHmS9+8YtGdWrJXW4oJm/2iZ759Kc/bbp06eKV79mzpyeiW7dunpGj5XJarb1z584G0VoSeMMNN5iPfOQj5v3vf7+ZMGFCQxofIAABCEAAAnEmgIEU595BNwhAILUETjnlFK9t55xzjrniiivM3/72N8/AmDJliskuTTO+EVIKgHe9612mVatWjYqqHoXZs2c3ii8m77Jly7wZLc0WZZfqNSrftm1bc95555kdO3aYhQsXNkobM2ZMo2suIAABCEAAAkkggIGUhF5CRwhAIHUEtATutttuM9n9R0bL3GTAaHlddv+OmTlzZlntldOG/NC3b1+T3fdj5s6d2yipmLz+bJctr4T58UuWLGkk+9BDD210zQUEIAABCEAgCQQwkJLQS+gIAQikksCHP/xhs2bNGm/26OMf/7jnae7vf/+7txxt2rRpjdqsZW75QbM2tpB1ANEkevv27UYy8j3OFZNXs0QKuUvociuQbIX8WSt/iV9uXj5DAAIQgAAE4k4AAynuPYR+EIBAKglotuX+++/3jArNGmU91xnFfeMb3zAyWnwHBr7RIdfY+SF/xsZPf/nll/2PDe/+8rdjjz22IU4fismrw10V5s+f773n//Hj/Zmk/HSuIQABCEAAAkkigIGUpN5CVwhAIDUE5KlOe3o0Y5QbRo0a5V22adPGe5cjBIXHH3/cOwvJu8j+0VK5559/3rvMn13SWUjyMpcb5PhBS+wmTpyYG+2dm1Qor/ZDjR071jz00EPmueeea1R+3rx55t577zVaTnf00Uc3SrNd6Cwm10yULT9xEIAABCAAgWoTwECqNnHqgwAEIJAlkD3DyGTPEDKXXXaZ+dznPue5/JYr7OwZRt7hqx/4wAc8TnKJLeMke26Refe73+0dKvulL33JnH766Ub7imxh9+7d5qSTTjJ33nmnN0t1wQUXmD/+8Y/mq1/9qjnhhBMaFSk27w9/+ENz0EEHeW6+v/Od73iHvsq1twwuOW7485//7BlgjYRbLrLnPXluzS+99FIj1+EECEAAAhCAQOwIZJ88EiAAAQhAoAYEfvvb32ayy9K8g1OzPw7e4alZV9+Zp59+upE2S5cuzYwfP74hX4cOHTJf+cpXMj//+c+9uPyDYi+55JJM1ijK6BBXyVUdX/jCFxrJ9A+KLSavX3DWrFmZ7BK9Bj1at26dyXrjy2Rnsvws3rsOilW9W7dubRSvi+zeqoZDcY888sgm6URAAAIQgAAEak3gbVIgdlYbCkEAAhCoEwI6MNU/dHXQoEEma/w4W75582Yv79ChQz3vd/kZ58yZ4y1zk8MH7Wnatm2b5wRiyJAh+VlNmLz5hSU3a7QZ6VGqIwYdjKuZJ3+PVX4dXEMAAhCAAARqReCgWlVMvRCAAAQgYLxldlpGp1eh0LlzZ6NXsUHGVpDBlSsnbN4RI0bkFg/9uZxznkJXRgEIQAACEIBACALsQQoBi6wQgAAEIAABCEAAAhCAQLoJYCClu39pHQQgUEcE5KVOS97kTKFQCJO3kCzSIQABCEAAAmkiwB6kNPUmbYEABCAAAQhAAAIQgAAEyiLADFJZ+CgMAQhAAAIQgAAEIAABCKSJAAZSmnqTtkAAAhCAAAQgAAEIQAACZRHAQCoLH4UhAAEIQAACEIAABCAAgTQRwEBKU2/SFghAAAIQgAAEIAABCECgLAIYSGXhozAEIAABCEAAAhCAAAQgkCYCGEhp6k3aAgEIQAACEIAABCAAAQiURQADqSx8FIYABCAAAQhAAAIQgAAE0kQAAylNvUlbIAABCEAAAhCAAAQgAIGyCGAglYWPwhCAAAQgAAEIQAACEIBAmghgIKWpN2kLBCAAAQhAAAIQgAAEIFAWAQyksvBRGAIQgAAEIAABCEAAAhBIE4H/D1trR8BeeHShAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "plot(rules)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "plot without title" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "plot(rules@quality)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set of 127 rules " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confidentRules <- rules[quality(rules)$confidence > 0.9]\n", "confidentRules" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Itemsets in Antecedent (LHS)\n", " [1] \"{citrus fruit,other vegetables,soda,fruit/vegetable juice}\" \n", " [2] \"{tropical fruit,other vegetables,whole milk,yogurt,oil}\" \n", " [3] \"{tropical fruit,whipped/sour cream,fruit/vegetable juice}\" \n", " [4] \"{tropical fruit,whole milk,whipped/sour cream,fruit/vegetable juice}\"\n", " [5] \"{whole milk,butter,whipped/sour cream,soda}\" \n", " [6] \"{root vegetables,onions,napkins}\" \n", " [7] \"{hamburger meat,tropical fruit,whipped/sour cream}\" \n", " [8] \"{root vegetables,whole milk,butter,white bread}\" \n", " [9] \"{tropical fruit,butter,yogurt,white bread}\" \n", " [10] \"{yogurt,oil,coffee}\" \n", " [11] \"{citrus fruit,root vegetables,whole milk,yogurt,whipped/sour cream}\" \n", " [12] \"{pork,whole milk,butter milk}\" \n", " [13] \"{tropical fruit,whipped/sour cream,hard cheese}\" \n", " [14] \"{herbs,whole milk,fruit/vegetable juice}\" \n", " [15] \"{tropical fruit,root vegetables,whole milk,yogurt,oil}\" \n", " [16] \"{pip fruit,butter milk,fruit/vegetable juice}\" \n", " [17] \"{citrus fruit,whole milk,whipped/sour cream,cream cheese }\" \n", " [18] \"{grapes,onions}\" \n", " [19] \"{hard cheese,oil}\" \n", " [20] \"{tropical fruit,yogurt,whipped/sour cream,fruit/vegetable juice}\" \n", " [21] \"{tropical fruit,dessert,whipped/sour cream}\" \n", " [22] \"{tropical fruit,whipped/sour cream,soft cheese}\" \n", " [23] \"{citrus fruit,whole milk,whipped/sour cream,domestic eggs}\" \n", " [24] \"{citrus fruit,root vegetables,cream cheese }\" \n", " [25] \"{pip fruit,butter,pastry}\" \n", " [26] \"{butter,whipped/sour cream,soda}\" \n", " [27] \"{root vegetables,whole milk,yogurt,rice}\" \n", " [28] \"{citrus fruit,tropical fruit,root vegetables,whole milk,yogurt}\" \n", " [29] \"{root vegetables,whole milk,yogurt,oil}\" \n", " [30] \"{ham,tropical fruit,pip fruit,whole milk}\" \n", " [31] \"{whole milk,rolls/buns,soda,newspapers}\" \n", " [32] \"{tropical fruit,butter,whipped/sour cream,fruit/vegetable juice}\" \n", " [33] \"{tropical fruit,grapes,whole milk,yogurt}\" \n", " [34] \"{citrus fruit,tropical fruit,root vegetables,whipped/sour cream}\" \n", " [35] \"{ham,tropical fruit,pip fruit,yogurt}\" \n", " [36] \"{citrus fruit,root vegetables,soft cheese}\" \n", " [37] \"{pip fruit,whipped/sour cream,brown bread}\" \n", " [38] \"{frankfurter,tropical fruit,frozen meals}\" \n", " [39] \"{tropical fruit,root vegetables,yogurt,oil}\" \n", " [40] \"{cream cheese ,domestic eggs,napkins}\" \n", " [41] \"{root vegetables,butter,rice}\" \n", " [42] \"{tropical fruit,root vegetables,other vegetables,yogurt,oil}\" \n", " [43] \"{other vegetables,butter,whipped/sour cream,domestic eggs}\" \n", " [44] \"{root vegetables,other vegetables,yogurt,oil}\" \n", " [45] \"{pip fruit,root vegetables,hygiene articles}\" \n", " [46] \"{pip fruit,butter,hygiene articles}\" \n", " [47] \"{rice,sugar}\" \n", " [48] \"{root vegetables,whipped/sour cream,flour}\" \n", " [49] \"{citrus fruit,whipped/sour cream,rolls/buns,pastry}\" \n", " [50] \"{sausage,tropical fruit,root vegetables,rolls/buns}\" \n", " [51] \"{butter,soft cheese,domestic eggs}\" \n", " [52] \"{pip fruit,root vegetables,other vegetables,bottled water}\" \n", " [53] \"{canned fish,hygiene articles}\" \n", " [54] \"{root vegetables,other vegetables,butter,white bread}\" \n", " [55] \"{curd,domestic eggs,sugar}\" \n", " [56] \"{cream cheese ,domestic eggs,sugar}\" \n", " [57] \"{pork,other vegetables,butter,whipped/sour cream}\" \n", " [58] \"{root vegetables,whipped/sour cream,hygiene articles}\" \n", " [59] \"{other vegetables,cream cheese ,sugar}\" \n", " [60] \"{sausage,tropical fruit,root vegetables,yogurt}\" \n", " [61] \"{yogurt,domestic eggs,sugar}\" \n", " [62] \"{citrus fruit,domestic eggs,sugar}\" \n", " [63] \"{beef,tropical fruit,yogurt,rolls/buns}\" \n", " [64] \"{pip fruit,whipped/sour cream,cream cheese }\" \n", " [65] \"{root vegetables,other vegetables,yogurt,rice}\" \n", " [66] \"{whipped/sour cream,house keeping products}\" \n", " [67] \"{pip fruit,root vegetables,other vegetables,brown bread}\" \n", " [68] \"{root vegetables,whipped/sour cream,sugar}\" \n", " [69] \"{tropical fruit,butter,yogurt,domestic eggs}\" \n", " [70] \"{rice,bottled water}\" \n", " [71] \"{butter,whipped/sour cream,coffee}\" \n", " [72] \"{tropical fruit,domestic eggs,hygiene articles}\" \n", " [73] \"{tropical fruit,long life bakery product,napkins}\" \n", " [74] \"{tropical fruit,butter,hygiene articles}\" \n", " [75] \"{pip fruit,other vegetables,whipped/sour cream,domestic eggs}\" \n", " [76] \"{butter,whipped/sour cream,sliced cheese}\" \n", " [77] \"{root vegetables,whipped/sour cream,soft cheese}\" \n", " [78] \"{frankfurter,tropical fruit,root vegetables,yogurt}\" \n", " [79] \"{root vegetables,other vegetables,yogurt,hard cheese}\" \n", " [80] \"{tropical fruit,curd,yogurt,domestic eggs}\" \n", " [81] \"{domestic eggs,margarine,fruit/vegetable juice}\" \n", " [82] \"{citrus fruit,butter,curd}\" \n", " [83] \"{butter,curd,domestic eggs}\" \n", " [84] \"{root vegetables,butter,white bread}\" \n", " [85] \"{soups,bottled beer}\" \n", " [86] \"{tropical fruit,domestic eggs,sugar}\" \n", " [87] \"{tropical fruit,other vegetables,whipped/sour cream,domestic eggs}\" \n", " [88] \"{citrus fruit,tropical fruit,herbs}\" \n", " [89] \"{tropical fruit,yogurt,whipped/sour cream,domestic eggs}\" \n", " [90] \"{pip fruit,other vegetables,yogurt,cream cheese }\" \n", " [91] \"{tropical fruit,root vegetables,rolls/buns,bottled water}\" \n", " [92] \"{pip fruit,root vegetables,yogurt,fruit/vegetable juice}\" \n", " [93] \"{root vegetables,butter,yogurt,domestic eggs}\" \n", " [94] \"{tropical fruit,root vegetables,yogurt,pastry}\" \n", " [95] \"{tropical fruit,butter,yogurt,sliced cheese}\" \n", " [96] \"{tropical fruit,root vegetables,herbs,other vegetables}\" \n", " [97] \"{butter,hygiene articles,napkins}\" \n", " [98] \"{sausage,pip fruit,cream cheese }\" \n", " [99] \"{sausage,butter,long life bakery product}\" \n", "[100] \"{root vegetables,other vegetables,yogurt,waffles}\" \n", "[101] \"{citrus fruit,other vegetables,yogurt,frozen vegetables}\" \n", "[102] \"{curd,cereals}\" \n", "[103] \"{root vegetables,other vegetables,rolls/buns,brown bread}\" \n", "[104] \"{frankfurter,root vegetables,sliced cheese}\" \n", "[105] \"{pork,rolls/buns,waffles}\" \n", "[106] \"{tropical fruit,butter,frozen meals}\" \n", "[107] \"{citrus fruit,other vegetables,butter,bottled water}\" \n", "[108] \"{pork,root vegetables,other vegetables,butter}\" \n", "[109] \"{sausage,berries,butter}\" \n", "[110] \"{tropical fruit,other vegetables,butter,yogurt,domestic eggs}\" \n", "[111] \"{tropical fruit,pip fruit,yogurt,frozen meals}\" \n", "[112] \"{pip fruit,root vegetables,other vegetables,cream cheese }\" \n", "[113] \"{other vegetables,butter,whipped/sour cream,napkins}\" \n", "[114] \"{pastry,sweet spreads}\" \n", "[115] \"{root vegetables,domestic eggs,coffee}\" \n", "[116] \"{citrus fruit,tropical fruit,other vegetables,domestic eggs}\" \n", "[117] \"{citrus fruit,tropical fruit,curd,yogurt}\" \n", "[118] \"{domestic eggs,margarine,bottled beer}\" \n", "[119] \"{whipped/sour cream,long life bakery product,napkins}\" \n", "[120] \"{root vegetables,butter,cream cheese }\" \n", "[121] \"{tropical fruit,other vegetables,butter,white bread}\" \n", "[122] \"{tropical fruit,whole milk,butter,sliced cheese}\" \n", "[123] \"{other vegetables,curd,whipped/sour cream,cream cheese }\" \n", "[124] \"{liquor,red/blush wine}\" \n", "Itemsets in Consequent (RHS)\n", "[1] \"{other vegetables}\" \"{root vegetables}\" \"{bottled beer}\" \n", "[4] \"{yogurt}\" \"{whole milk}\" \n" ] }, { "data": { "image/png": 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", "text/plain": [ "plot without title" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "plot(confidentRules, method=\"matrix\", measure=c(\"lift\", \"confidence\"),\n", " control=list(reorder=TRUE))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# select the 5 rules with the highest lift\n", "highLiftRules <- head(sort(rules, by=\"lift\"), 5)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set of 5 rules " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "highLiftRules" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S+rPh6NrdUrmEQQACEICACOAg8TGAAAQg8B8CDyu7UgqLyArl703qlHzCMi1+P9FIFWsyVarucik4R/+j/LeidvH2p8qoPp3NFHmKIw52rtIRmnQfTVmeGXXeWfndpXejOmcdzQhmZ2BqKERpbQyjJp/IehvdXpKjKo9Jv5Fii6NbjjQ1xOoaT+zQtE916oiRt/Q1xOL756jYMVJdTnPctz873nL5N8n/z7fD9FnJznlvyWbHyQ6it99hEIAABFo8AX9DikEAAhCAwAYCjipcFcHwAvolKd6i5MN2oC6T7nQhstq2S3lRmzZHfvpGlXbOYhsSFer6MsvRgRAF8Sn+d/1iZxJ7U2ldi/nQrqlSOyaros7/W/nYseyu8qjo+JPKr4vKIZuJYThWW2qGZvFF6RqprRTMdb4HNkdxnq7JZf+nrvHEDt6nUl1+SeVtorraPjNRk6KJUWFn5e3QBLODPUl6VnI0coRks+P8oPSW5GjXAMljdtsK6TQpttZxgTwEIAABCEAAAhCAAAQCATsYYyUvmmPNVdmL/SmSv5WPjzm/WuonBfNWqNDmrFAZpV7YeuEa2jyj/GGJvNAN9U7jBbeKNRGh+Pgy1f2XdJ70Lyk+drLKwbwlMD4W6kM6NHU8XsiHNpnSc6Lz5mVocEl03Ne30zlK8hZCcw1jWql8bynYhcqEYzNCZQPS9HwdJbJje51kZzj07ShhNpbteK5WZ6FvpzdLx0uer+cYH/uRysE+q0x8zJ8Rm52o16RwbJbyZ0pHSnbSQ73T4Fj73n0QHZus/FelvaWR0hNSOO9t5TEIQAACEIAABCAAAQjUSsCLyz9IYQFZX+oFfzpS8H50/lnKZzL/GGttfc+Ojtn5ctQq2HRlwnnxwjnUhfShcEKSph2G1OGayEQ416k5ZGPnqFE4L5OD5D6+JVVF7UL7kNpxOEWK7UIVwvEZ8YEG5B2xCn1kSsfoeJcs+8t2PHZCFkuZrmcn7dHoWDYOkprXRAnjz0Smvu2IxeateP7sZGob6tbouB1zDAIQgAAEIAABCEAAAvUSsEPhZzfWSmFBGdIPVXe3dLSUybJxkNroRDtJ3lIW+nVUyQvddtJMKdSfoXyw2EH6piodJYqjBXY2rpJKpNia00HyOL4k/UPyixvCvLyA/6fUR0pbtg5J+ry47OjLWdIcKVzT6ULJz321lrK1hozHToe3N4Zrepuht7x5S6GvG+ovUz5YbRGkcNzn3i/F/NzPTMljy2SHqNLXzRT1fFj18fbMTOdTBwEIQAACEIAABCAAgU8QsCOzuzRUOiLJlyptLNtWHR0s7SeVZdFp2kHyKXYE9pIOkBpzbOqu0c2Om8fZX8pmvo0xAPPZRRoi7dYYHWbZxw5qZyclfu4qy1PrbOY5DJacZnO/HYHcRyqX/DmxA45BAAIQgAAEIAABCECgIAhkcpAKYmJMAgIQgAAEINDcBNLbL5p7PFwfAhCAAAQgAAEIQAACEIBAsxHAQWo29FwYAhCAAAQgAAEI5BwBPwvntyRim0egbRan+cUwt0je+hvM214/L50n9Q2VWaTewvttyVvAa1vXZ9PGl/LvoV3uTCNbttePL9tDhW9IX5A6xweifH1tzMPP3n5P+rq0hxTbTSrsFFeQhwAEIACB/CXwBw39iUQn5u80GDkEIJCDBG7TmPxmTqxhBOzs/Fk6O4vT/HKe70Tt/GyrX65j7s9K66SzpLrMDtWr0jzpccnn+/rx847ZtNEpNebnQZ+S/KKgxrKGXD++5nUqeBxPSrOkmdKeUmz1temoxi9IfpGNmb4v+YVLF0jBzlHmN6FACgEIQAACEIAABCAAgUwE/I26X/6BNYyAHRMvxs+u57Q+Oj5D8st/bE79xsv/cSGxS5X6Ffzbh4oM6U9VN1XqlBzzS29WSecmZSfZtHE7R6zGSx5/YzpI2V5fl91on1bOb90cmtQ4Iuex+UvBYNm0+W81tkNkLjY7gL+WXNdLspVKZrivCxgEIAABCEAAAhCAQMsl4K1G50nflLwQjbd67afyIMnmNz8eX4vCItPt/JbEz0kXSyOk1lJsR6pwaFyRIe8+TpW+Kzkyvp0UzG+D9DiCUxHqj1Nm16TQXanbeNH7WelCaR8ptv1VOEjyVrLTpNOlblImG6ZKb137srRzqsEBKvsV+Y5qfEPaS/KY7WDcLAV+yn7C7lWNI0jBTlDG59lxCubIy3LpB6Eilfp+fSzZkYrtHhVeSCqyaeOmZuK+npN+KTWWg5Tt9XXJTexOlV7cpGbD1sMq1e2W1GfT5mG1NevYfN/M2lsZg12lzD9CgRQCEIAABCAAAQhAoOURsGOwXpoi+Zv5tdJjUolku016qSZXVHSS0jcjTVZ+vuRFph0HmxfY70qOeHiL1kLp35Kdq2BzlKlrEdpTx+dKPvdJ6UPpPSl8+x+cjx1VF5sjDXaEbEdLHtd9ksc8LinfoTTYb5R5XpohOSIxU3IfdlKCbaPMWMl9vSatkOys2HkLdocyHucCye0elMzMeff9eymT2al0m4HRwW8r/35UDtlJyvwuFFLpTiq7n/JU/SUqr0zqsmnjpvtKZ0h2LH2+o1CNYdleP32tCar4n1RlD5U93xFJfTZtUl3UFL+hv+5nQHRwv6QufNaiQ2QhAAEIQAACEIAABFoCATtG8QJ0uMofSZ9OJh87SEnVxsQL/HmSv5l3hMBOlZ0hRy26S7ZOkh2Lv7iQ2FFKQ/+hLk7/SwU7SCHy1E75WdJ1kq0hDpKdtFY1Z/3HafpqUraD5AXyxUnZ7f5XWibtnNTdotQO2pCk3FnpXZIdB8/fdofkfr4pdZTsDLgv150t1WYn6ECVFObpdjdLdujS5gjIo+nKpHy4Ul/Lzk1sZ6ngeo8pmzbxuc43poO0Odf3GPw58OchNjutntfXksps2sTnO+/P5VvSBMmf29g+VuG0uCLdID5GHgIQgAAEIAABCECgsAgs1XSGScdIfr7DDoUXoM9JdZkXmI4C2XEJC3F/E/8p6WfSIslmZ+N26WSpvWRztKau/j2mbtK50g6SF6y7SpdLDbU/6oR1yUmPKZ0u2TEJ5ojRnUnB7X4tbSsNSeq+rPT/pHFJeYlSz8+MvpjUOXFUyY6mI0yOqmVj/dVoobQ2aux5r4zKIWun1Y5OJvM5tvR5duJsHaRs2tQ0bqI/m3t9O9phHmFo/jzYQfK8bNm02dByw9/tlDwsdZHOlqqk2Bzh9L3ZaDhIG1GQgQAEIAABCEAAAgVP4ALNsI30qLRY8sLxeKkuc3Tkb5IdnhOl1ZJtzw1JzbY8OwxB1yb1uydpfcltavCI9EvJzsZL0mWSx9lQs1MUm7fC7R9VPK+8nZ5gLytjDm7TVfLC3lG22NzG2+n2iCrteDXU9tEJXozH9r4KXsCnzXXptqGNz7Glz3PZTp+dsGzaqFmDrY/OmJpSpvu8udf32DulRrWtysVS4JFNm9CFt286atRb8hcD06S0ud994sqyuEAeAhCAAAQgAAEIQKCgCbyq2dmxOUw6WjpJ+rvk51D+KGUyb00bIB0uhYWv24VIyPnKv+OKlGXrRDhicLK0m3RsomuUeoweX7B43WpnLdMX/StD4yR15OGDqC4dnfBxm9sEx8+RtdjcxpEnO2/BQttQzjb1Yj82b1m0U5Y2R0leTFcm5feS1G1i216F2ZIjJNm0ic/NNm++j6Qap5n78OZe3zw8j9jCPGcmldm0cdO+kh1kO8D+LIUxKbuJ+Z7Yud9omT5YGw+SgQAEIAABCEAAAhAoGAKOyNwqHSlNkn4sHSTNkuyYZLIKVZ4mjZTSkZU3VGfbS3o+kr+19zmOZmRjX1ejK6QZ0q8kb4m7RbID57XqKsnWeUNS89fXzGSHpirt1DkiFWx/ZWIHyP10kdzG1zGLIVJsjpDsLL0SV6bywdGqa21tB8t9tYrOnaC8Iz8Dozpfy+MMfKNDNVkv9GdK6Xt2jOrCOdm0UfMGmx3kS1JyRCdtm3t98/B9L4469DztkL6b1GXTprfajpPelgZJHk9t1k8HfG8wCEAAAhCAAAQgAIEWSOAuzdmLaC/IO0iO0KyXvinZbpOCQ/EV5b3wv16yI2XnI8gLfdv90iLpfMkL/aOkpdJNUjCff1EoZEhPVJ0jNKdL/jb/QMnb2sZIth0lO1t/k3pLHoOPr5UulGxeVHus70ieUx/pd5KfidpHsjkS5jbuZ2/ps5L7GSu1kmznSeZxg2RHz2N7RZosdZJsd0jPOJMyL+LvlA5I1YfiF5Tx9fcKFUnqBf9zUi/J0aSHpBekUinYT5S5OBSUfltaLh0jtZO+Jfn6Ya7KZtXG7YLZ8bGT2FiWzRi/qIv5M9c2uagZ+F57vo4S+l7Pk34kBcumjaOifnbJ9/O0lPzZCNZDGd+Ts0IFKQQgAAEIQAACEIBAyyLgxeVfpSWSF4ZeENsZKJFssYN0v8puk0n3uLFsO+n3khe1bufFrPvYRgo2R5l/hEIt6U9Vb+fGfdhZelraWQrmha63cvm4HYOvS29KF0q2oyUfu1kKD/XbEfyMFMwOks95UKqS1kqPSl2kYI5cfFfyNdyf+3pM2kEKdocyz4RClPra5vB+VBdn7ZDaiflSXKm874n78/U8pselfaTYZqnwz6iiTPmfS76e5zJFOkuKLZs2cfvGdpCyuf6NGoDn3TEayOeU/0DyvD6U/p/kvmKrq81Oaug+a9M5UUd2MP156x7VkYUABCAAAQhAAAIQaIEEHJ3YTQqO0ZYiaJ30ZwdjS2wXnezIQSYLY3aatuAg9daBNlKPdAOV7SA5MmNzpCZelNdURn/MxXzcV0PM0Zzaxu9+fiE95UwG8yI9dtYyNPlElSMvdrDqsmza1HX+lh7bnOv7c2T+accoHks2beL2mfJ2ln+b6QB1EIAABCAAAQhAAAIQyGcCsYNU2zxiB6m2Nk1d31kXcHTEW8ew5iXgKN0yacf0MBrrW4N0v5QhAAEIQAACEIAABCAAgU0JeGvjj6UfblpNqRkIXKpr/kRa0AzX5pIQgAAEIAABCEAAAhBoUgJ+vmc/KbxsIdPFvH1vj0wHtnKdt42dJnmLGNZ8BE7VpRu6hbL5RsuVIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBnCGQ6T3yOTM4BgIBCEAAAhCAAAQgUBAEdtcszpFelfzjpk1pI9T5AGmytKt0ruQfjV0j2Q6QLpZGSu9I/jFS/1bPeqk+8xvo5ks+J7auKlwijY8rU/ls2oRT/KbpY6QTpf6Sf7h2sWTzbyUdJfWrRf6xXv/grM3X/II0SPIb9KymsPPVqV+ZHcYYruHfmjJnz8W/8zRN8g+41mVh7ieokV9o8Z6UPiebNr6GGZ0k9ZVmSvFn7yaVX5f8A8QYBCAAAQhAAAIQgAAEtioBL/S9yP3Eb840wSjuUp/BUbEj4ev2Sa5jh8HOzRzpr5J/jPTP0tlSfXa4GkzI0MiLeP/460cZjoWqbNqEtnYq/IO2Hvez0vuSHZ4LJNtgycdqk50Cm8e7ULJT4H6qpN9JjW0XqUOP5bhUx3uobOfGDshYyQ6o5+U3DtZmPua27u8VyU7XdGl7KVg2bdz2O5Ln/pI0U1olnSIFO0eZ34QCKQQgAAEIQAACEIAABLYmgeZykNprkntLrZLJOpLixbdTm+tdPluqzyapwfBUI0cmxkvuozYHKZs2cbf/rYIdov5JpZ2rXyd1vZQ6euJ5xdpZ5XnSXyRba2ma9Jq0q2TbS7LD8V0XGsG2UR8ea6Xk+R8nxfY3FRZInZLKfZS6bUVSzpS4PztyxyQHt1X6iGTGpUldNm18Lffz7eQcn/tL6R0p9ON0qrSvhEEAAhCAAAQgAAEIQKDBBPwt/pekL0vbZTjbjsA50vnSgNTxTA6SF7/efuVv+kdIXtTHdqwKO0hHS6dLvqZ1vLSLVJvdpQPjk4PdlLq9F/OOrFwpeTH/NckRljCum5UPTpOyn7CTVTNbin+7aH+VP5aek7z4zuQgZdNGp25iD6t07yY1G7YFetyfT9WH4m+UcbTGETLbpyS3v8iFyO5Ufm5U3pLsqzp5sXSxlMlBel31d0ux2WFLzy0+bqcu3LtQf4Iy7t/3y5ZNm9vV7pma1v/54+jlBVJg5CNXSf9wBoMABCAAAQhAAAIQgEBDCPhHNf2N/PvSy9Ja6ZtSsMuVWS8tlLyA9YLWi9QSyRYckbDFbqDqHF3wFqgXJLf34rmPFMxbov4o+Zhlx+ywJP9VpbVZ7CAdpUY+1/16cTw1Kftat0kvJeUZSn8v1WbP6sDPUgcdeThDciTiEsnjTVs2bdLnZCp/Q5Wex4AMB49Vne/NZ6Njdjgztb80qbfjuaV2mTqwA7qL5GulI0i3qG6p1Euy+d55nBe6UIuZ4Q2pY7up7P6Ds5dNmylq7+u3l+xg+7PTQ0rbfqpw3/3TByhDAAIQgAAEIAABCECgNgKOgqyRKqS2ku0Hkhe/3aWhkhe+P5J83E7RmZIXnnZKbLGD5GjOu9LjUk/JZkdiuvSUC4l5Ifyh5MWrF+FtpM7SCVI4T9lPWG0OkhvaifC4dnVBls0WO8/HY/EiuzarzUGK22fTJm4f8p2UeUuaIHksaZusipibj/eVPM+rXYjMfbh+76huS7O1OUi+X7dLlZLH7+v+WIqjcCpuYq+q5LaxfVsFnzs6qcymzTK1/YU0U3KUa73kexg/g6RijTkKeFqSJ4EABCAAAQhAAAIQgEC9BL6rFo4cOVISzItfOxtevP9KeltKm50gR15ssYNUrrIXvAdIsV2vguuD8+MF7W/iBlnmG9tBcvTJ4xpUx/Uv0TGPty7Lpk36fG8p9JYz898jfVDlwyWP7eQMx7x1zE5sheSx/5+0UHL7faTGsl3UkftMR5BOVd0iaYxUIb0h+XNyiFSbOTLovu6RyqXvSf4cmW2FZKuvTTu1cR9VkqNOdtr9mfq7tEJKR5Kmqu5aCYMABCAAAQhAAAIQgEBWBO5Uq2fqaOnoxQMZjnuL0wdJfewgna86L17tZMU2WAUvbIcllV4UOyrVULtLJ9ipsMVb7FzenAiSI1Ye167uoBbLxvnJpk3cvRf1jg7NkWrbAvY7HZsrlUlp66qK+6S1kqMkvkd2WjyXtJOgqpptiHYWYu3uA/VYJgfJTonvnz8Dsfmz8npckSH/PdUtkTzO1yQ7d8uk70jB6muzWg3fDI2T9Ail7vMrqfp/qXx/XJcJZnycPAQgAAEIQAACEIBAyyZQqel7wZs2b5XygtOLb39rnzafNz9dqbIXrz7XDtIaKdj6JBOf47a5Yp224kC8Re5JydvDDpPek9LWQRVfkm6QAru4jbcnjpTM2bzN8pvSCmmBlLaVqngkVem6zbHBOslbKX+fOvl3Ktvh3kWyY5fJ/luVP5M6Ssul7aVtpXekYPW1cd/PhsZJ+rJSO+bbperdt6+z0Uo25shAAAIQgAAEIAABCEDgkwT8TfyeUuwg7KqyIwSO0EyRDpLaS7ENUsGL0rS5vc2L6NiGqPCR5AjG1jI7eLa61sTBYeu3oWmT/+2tK4yTvB3NDDM5R6qu2aJoJ+RfLqTMQRBHj06S7IQGR/NU5R+X7Lymzdv4HOWK5S15m2N2zmw7bUg2/t1fOTN3RCiTOUp0s+Q2wWk5JclPVGrLpo3b2rGM76s/qy6nHSff13CPlcUgAAEIQAACEIAABCBQN4EuOrxYekz6tORtV3+V/i05MuFogCMNXnh7Qb+3dKfkRXm5ZDtR8qJ3RxdkT0izpK9JjhBcKi2VfiYFswPmxXpsfVS4TTo8rkzl71I52y12PtXj9HgPcCGDOXrmsVyZ4Vio8jjdpi6rrc1PdNLF0Yl/V95RufOk01Ly/IOdq4yZbhcqUunPVbYzup/UWbpG8pbHnaXGNN9/j+O4qNNS5SdJvsd2VDpKZ0l2nP4gBfuiMr6fZmzzNjhHeUZKdv5GSMukM6Rg2bTZS43XS/8rdZeGSv68Pi/ZeQzWQxmP3WPDIAABCEAAAhCAAAQgkDWBQ9RysuTFpDVVcl0wL1rflHzMC1y39eI2WNpB6qYDjnA4kuFzHKm4XrLDFWyVMnYqYvNi2+2/Glem8g11kG7W+euk91P9xMUHVPB4a7PanJ+4fW1t7ET8M2noiIvnV5vOSdo58bjnROV0dkdV3COtlDy/8dKRUmNbJgfJ17Aj9pDkz4Nlh8XOkJ2lYDcq47nGdaNVnin5nHelK6S0ZdNmuE4yW/fj+T8l2VmK7RgV/BlM18dtyEMAAhCAAAQgAAEIQKBWAl5I9pRiRyZu7OMNWWy2U/vd4g6aKe9xtK/j2oN0bLXkRX++2TYasB3S5jI/K9VPClGibMfhz1l9lk0bO3Db1tLRg6r/bS3HqIYABCAAAQhAAAIQgAAE6iBwr4454oEVBoF9NI1lkiNtGAQgAAEIQAACEIAABCDQQAK7qr23Atb2zE8Du6N5MxO4Q9e/rJnHwOUhAAEIQAACEIAABCCQ1wSGafR2lLD8J3CqptAm/6fBDCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDIUwKleTpuhg0BCBQ+ge6a4ulSK+m9wp8uM4QABCAAAQhAAAIQgAAEIFA7gUN0qFq6sfYmHIEABCAAAQhAAAKNS6CkcbujNwhAAAIQgAAEIAABCEAAAvlLAAcpf+8dI4cABCAAAQhAAAIQgAAEGplAWSP3R3cQgAAEmoJAV3V6vDRMWiqNle6X0uZ2R0qfkT6WJkgTpXlSsB2VOVN6VnpVOk7yOc4/IM2RekinSAdK06S/Sm9IGAQgAAEIQAACEIAABCAAgWYhEJ5B+rOuPl3y80gfJKnzo6XYTlJhvbRGmiwtlNyuSjpbChb6vUMVb0lu43OcLpNOlOYm5VC/ROUDJAwCEIAABCAAAQhAAAIQgECzEAiOjB2Xv0gu2/pJdn5WS3EUfL7KVh8p2OHK2EGaFSqUxv3eq/J+0vbSHZKvZTlidJDUVrpKct2tEgYBCEAAAhCAAAQgAAEIQKBZCARHxs6NHZXY7lbBTkv/pHI7pU9KlyTlOBmngtu2SSpDvzNVbp3UOfF2Orf7UGonBeuiTKXkfjAIQAACEIAABAqcQPzta4FPlelBAAJ5SuA5jdvRotjeSQq7K31DWip9JqkrVurnjHpLh0mfkmyuj839ro0qvK3ONkXy80vBFivjrXY7hwpSCEAAAhCAAAQKlwAOUuHeW2YGgUIhkOlHYv2ska1kQ1Lzdzf9/YZ0jtStpqaoaIXS2AlKqmuS+MUNrnD0yLZ8Q8JfCEAAAhCAAARaIoF4cdES519Ic95TkzlX2rWRJtU96e/TjdQf3UBgcwkEZ6iu8/fVwTelb0l/k74sDZD8OX5RsgUHaENpwwsdQp4UAhCAAAQgAAEI1BAgglQ4HwQ/jH6b9HlpViNMq7f6cH83Sd6KhEEglwn4ywE/Y/RV6Z7UQHdJlSlCAAIQgAAEIACBWgkQQaoVDQcgAIE8IuDfP7L5ddyxOQK6T1KRfgYpbkceAhCAAAQgAAEI1BDAQeKDAAEIFAKBMckkrlfq30MaLl0l+c124eULRynfXsIgAAEIQAACEIBArQS25ha7PTQKL1C8YFklnSANkbwdzD8E+YoUzN8Gf02aJD0vfUE6UjpPCuYfc/QiaE9phvS49KCUyXqp8mzJv2uyTvq39L+S33wVm1/5e4p0sOTX/I6XHpU+kGLzN9GnSr6+n3GYKnleHkNs2bbzOdle2217SyMkv+L4NekxqaGWLRP36/txvDRMMrOx0v1S2hoyh2zbNhXD9Ngp5zeB32r4h0tflcJnc57yp0l+A92fpIck/xuAQQACEIAABCAAgZwgYIfCD0nfIvm1uWulRZLrvIDxMwTB9lbG9TdILyR5/zaJzc8Z/EHy8Y+kyZL7cvkBycdjO1oFn+vjC6TQ1osnvwo4WG9l7Iy5nZ0Ab9Vx/j0pXlSVqPxwcmyZ0lelFUn5J0qDZdvO7XtL2Vzbbcslj89js6Nph8/p7yXXnSTVZ9kwOUSduL8/S9OTvB1F11mjpdh6q5DtHLJt21QM43GTLywCfTUdf/niL2Ri21aF/aXSuJI8BCAAAQhAAAIQaE4CwUHy4vrnUifJ0YEjJDsaq6Xeki04SHY83pBOlhwtsV0muY/fSa0km6M9d0mur5CC+Rpe1H8sDU8qHQ3xNhy3vUMK9owy66UzJI/LOlLy2JZLXmDZvij53P+RwmKrg/L/lHz+9pIt23Zum+21t1Nbj8VjsiPk628j3Sh5TFZ9DlK2TIKD5D7/Irls6yctlHy/yqRg2c7B7bNt2xQMw3hJIQABCEAAAhCAAAQg0KwETtXVvdh+XYoX1h6Uo0c+drMLsuAgua5PTc2GP3ZElkp2eLxFKzb36UiRF+7h2I+Vdx+XSbH5+DxpblIZFuIPxY2S/HeVug+P0fZfksuuj+0AFW6S/C21Ldt2Dbn2KPXra1/sC6TsWZV9rD4H6cdJu/qYBAdpltq3lWK7WwVfKzitDZlDQ9o2BcN4HuQhAAEIQAACEIAABCDQbASCg/TLDCPYXXVecPt5H1twkCZuKG78e6hybvfUxppNM48nx32+7R+S2ztKlTZHXkJU6Fbl3e5MabuUfK6P/UmyefuOyyuk6yQ7EiGSpOxGy7ZdQ679tHr3tbtsvMp/Mt9PjtXnIGXLJDhI3mKXtuC4fC450JA5NKRtUzBMz4UyBCAAAQhAAAIQgAAEmoVAcJAyRT/8rImfQ3o9GVlwkP4vNdLTVLaD8ONUfSj+Ijl+bFLxdlIOjlBol04fS9q579pk5yvYt5V5TwptlyhvR2KoFFs27Rpy7dnqfEV8gSgf+NbnIGXLJDhIN0fXCNkrlfHcw7UaMoeGtPX1Gpuh+8QgAAEIQAACEIAABCCQkUB6q1vGRo1cuSZDf61UZ83JcCxTlbfIZbI9k8qFSWrHq0rKFOFJmtQkbmP7geQtZZns/ajyFuX/RzpSOko6XhopnSx9XnpIsmXTriHXtlPiyJejXEul2NrHhTry2TIJXfi5qvqsIXNoSFtft7EZ1jcXjkMAAhCAAAQgAAEIQGCrEAgRjkxb7A7QCLz4D9GK2iJIIarxXIYRF6vOzyC5n07J8UeS8ueScpw4OuVnaWwh8uRoRdq2VcUx0l7JAW+5G5rk4+RrKvjaf0sqs23XkGs/kVzjxOQacRJe1BCiOvGxOJ8tk8Da/aYtHUFqyBwa0rYpGKbnQhkCEIAABCAAAQhAAAIbCTiasLXNz5V0SV30u0l5Uqo+XZysivnSACm8LS60GaiM+/27tCypdN5mBye2YSp8Q3I0xuZtXzaPLW2jVeFno/ZNDvxQ6VPSjkk5JH4zm61yQ1KUbbuGXPufSd8jkjQkHZU5OxTqSbNlUk83mxxuyBwa0rYpGG4y8BZU8AtO7PT6ObtrpF9L9xYXFz8qPan8X6XfST+TLpQcHd1ZaharGDmydUVRUXP8+9Qs8+WiEIAABCAAAQi0TAIhgrRO07cz8RVpuPRbyZGXh6VgtUWQfPxrkttPkU6WDpTOkhZIa6QBUjBvIXQ7b+tyJMTb4fyCgXmSx3G4FCxEVm5TxVBpsORoh8+dIIXtiF9S3td3nR2t3SRHdOzcuf6Lki3bdm6b7bXbqO27kq/jhazHaafu39JcyfX1RZA8j2yYNCSCpC6znkND2jYFQ1+/JVixJunP8LVygBxx9ee9urS0dEnHjh3f6d69+ys77bTTC7169ZrQu3fvcT169Hhu++23f7lz585TWrduPV9tvbWyWufOUGpn6hQp222calq/ff/7328/evToYysqKsJ/WzUnXXHFFT0rrrzy+xVXXHFw/b3QAgIQgAAEIAABCDQugU0WJo3bda29XaUjjuj8PmoxRvmvR+W6srfroMf9c+kvUcP3lLfD8FJU50XeEMlb6b6fSEnNs0522OKIlReAdojOTaSkZlH5K6UVUngW58/K7yeF6IayNfax/l4uhTFl284nZ3ttO4B2yvxt/yWJlNQ4l6OVhuiQ62qzjExKSkoW7L///jeecMIJq9atW9f5+uuvr+382uqznYPPz7ZtUzCsbfyFUm+H/WtybM6qrq7euX379rPlDM3p2bPnH+UMzVN5bTYTXb9+fcm8efO6zpo1a7eFCxcOXLp06WlVVf6uoGYL6e+UPpVNP3W16dCmzQCN8Svq19Gt+9y24vLLDyoqKRlRVVz8ckmrVnb8MQhAAAIQgAAEILBVCfhb5q1ldkjukS6Q/k/qIX1KmilNlhpqHXXCAdIOks9/R6pZwSnNZNursr+0UHLbdVIm20mVB0ne3vOKNEfKZG7n/pw6IvWatEhKW7btfF621/bYfO0+kheRtY1Rh+q0jUwuuOCCyu5duhwoR6lnUXFxB4FcUVJdPaeoqmp2UatWs4sqKhZU1M03XCjbObh9tm2bgmEYb6GkjrpeIZ3atm3bBbvuuusb++233+tdunRZ2RgTXLNmTenkyZP7Tp8+fd8lS5b0kwP2upwbf9nxgOTIZYOsQv99VY0aNVof5L30WavSZ+1X+txtq/wAha3+cfXVV8dfdDSobxpDAAIQgAAEIACBLSHQnA7Sloybc5uQgLY8dSlau7ZXVVlZT12ml5ykbiXFxev0rb6jdLNL1q6d8+GqVXNuvfXWNU04DLrOjkA3NbtROktb56YrCjixf//+3hbXZLZgwYJOL7zwwuHz58//lBylN+UofUMXe7YhF7zyyisPKK2u/qGiRdP9+aouKdm7uKrqmaKysl/p8ze7IX3RFgIQgAAEIAABCDQmARykxqRZoH1dcskl7bQ1q5f2NTq61EvT3LmqurpUTtNCOU1ztECevXbt2tk33HDDsgJFkKvT8nbQnypi9NEhhxzy+N577z1raw70gw8+6DB+/PgjFy1atL+u621335eW1jeGkSNHlvbv2/eHcor6qe370r76h6iTPksvanvnf/3kJz/5sL4+OA4BCEAAAhCAAASaigAOUlORLeB+vcDt27fvztqO10vf+vdKtuVto+1Ry0qqquYUyWHytjxFArydscHbrwoYXWNNbTt1dLuiNyN0H8YOGjToWb18odk4v/nmm7s+//zzx8hJXiNneaTG9kJdE9XLFwbJ0f5WZXX1ktKSkj018A913tuaz55KX1mybNnPiU7WRZBjEIAABCAAAQg0JYGt6SD5t4l2kfy8zpKmnBR9b30Ccoa6xdvy9GxJVz18v0bO0lxFCmaXrls3p+jdd+dW3HdfVi8J2PozyJsrHiBH4sE2bdq0Li8vv18vXsj03NtWn4yeUSp79NFHj33//ff308W/I/nlJp8wf06qKyuv1IHe+seno5yjGZrPh9pmt50iSNvpcyOXuvrOimuuefgTJ1MBAQhAAAIQgAAEtgKBrekgbYXpcIlcIeBXOGvr18ZtedqSt5O25PnztsARpkptzdOievZPf/rTFbky5jwYR7nG+Pdu3bpNP+644/4uJym8WTFnhv7MM88c+Prrrx+nAfm5KL/VcaNVXHTRtkWdOn1fDvORihStkzO0WA6So04fKwo5e31V1WR9RObrDXpTtc0uJxy/jYMnAwEIQAACEIBAiyGAg9RibnXzTlSRgzI9X9IjbMsrKi3dRQvkdtqWtyS1Lc8L42bbLta8lOq8un9r68+77LLLi8ccc8wT4lhn4+Y8qLfd9ZGjNFKOzx81Dj8nVe3n2Dpss80P5CR/Xi9mWK9/eGYowvic3ljn7YFv6PNR77NLzTknrg0BCEAAAhCAQMshgIPUcu51rs20WIvi7kXr1vXyix+q9HpxLfk7K7q0WgvnORu35bVtO1ftci5SspVhlut6j+2+++4TjjzyyPFb+dqbdblp06bt/PTTT58uJ+m2884779Idunf/kZ43Ok0O0YrqqqqHSsrK7tF9nbZZnXMSBCAAAQhAAAIQaEICOEhNCJeuG0bg0ksv7ahtY730+ufwtrwdtTWvWlGH+d6WJy9pzurVq2ffdNNNqxrWc163PlCjH6/I0WsjRox4Ip9m4pc36C13p++/337jPn/SSTvpHk6tqqz82VvTpk267777KvNpLowVAhCAAAQgAIGWQwAHqeXc67ybacXIka3X9+vXQ8+l1LwtT5GmXbS1rI225X1Ysy3PP2LburXflvdB3k0uuwF31txf1TNHi0888cQHcnlbXW3TeeXllw964cUXjx8+bNhvB/fv/72KW29dXltb6iEAAQhAAAIQgEAuEMBByoW7wBiyJeBteTuktuV1UmTio7AtT+nsqVOnziuQCMUDetHFoaeccsrtufhChmxv2mOPPfaZ2bNn761goH8vyT82jEEAAhCAAAQgAIGcJYCDlLO3hoFlQ+CHP/xhp9atW2/clqcteTtoS563b80L2/JWrVo1++abb/44m/5yqM35ih79/Oijj/6NXuWd1xGyysrK4j/96U9f+eijj2bKSRqWQ4wZCgQgAAEIQAACEPgEARykTyChIp8JXHTRRW26tm/fs6p1655FlZX+Edse2prXWtvyFqW25S3O4Xlur7G9069fv0lDhw59LofHmfXQ5s+f3/nhhx8+Xw7S13SS326HQQACEIAABCAAgZwkgIOUk7eFQTUWgYqiopKiiooda7bllZT00o+R+m15HRVdWhlvy2vVqtV8bd+TH5UTdqe21g0//fTTf6tXYBfMK8+ffPLJITNmzOgvJ6mvKC/LCdIMAgIQgAAEIAABCKQI4CClgFAsfAKXXXZZZzlEvfzyhxK9MU/b8rpr1uu1Ne+9sC2vbO7cORV33LG6GWjsq2u+psjR7xRBmtMM12+yS+qHgUvvvvvu8/V7WL/SRX7cZBeiYwhAAAIQgAAEILAFBHCQtgAepxYGAf+Iaae2bXtWtWrVs6iqqpd+yHRnpWVK39cMZ5esXz8neVteg37MdPTo0SeUVFYurLj22ucbQOqebbfddsCpp556dwPOyZumEydOPGjKlClDFEXqqUGvyJuBM1AIQAACEIAABFoMARykFnOrmWi2BEaOHFnav3//nVLb8torurRcW/TmaHE/Wy8emN36uusWVhQVZdyWV9NHv34364dRu1ZWV//+mmuu8W8Y1bddbg+1eWvw4MF/2HvvvWdlO958ard27dqSu+6661vr16+/QeO2MAhAAAIQgAAEIJBTBHCQcup2MJhcJfCjH/2oa1lZ2cZteXrxQzc5TGs13rnelldUVjbbeT3H5Do99lSxS9H69dfLQVomh6qrqh5bsWrVn+p5m95P2rVrd/qZZ555u/soVHv66aePmDZt2h7isluhzpF5QQACEIAABCCQvwRwkPL33jHyZiQgB2ibojVretVsyysq8rNMO2k4JYowLVTqbXldq0tLT9RzTm/IEWin/9B2U/joneLS0j/q3CkZhq5HoIrn9u3b99VCeXNdhjnWVC1atKjj/fff/x0VhkgTa2tHPQQgAAEIQAACEGgOAjhIzUGdaxYcgYry8rKi8vKdN27Lq67+nKJM++g/MP8w6nK9MW95aXFxR+3HW6/yWD2bNLGoTZu35CyFLXrDVP+Etub9rHPnzh8VHKDUhPS7SKctX778X6q+IHWIIgQgAAEIQAACEGhWAjhIzYqfixcqgYrRo6/Xc0o76HeY1iqC1EnhoW0VQWonp6mkuLq6lbyij/Qf39t6Punv2rr3vBylyzp27Pj5L3/5yy3iN4L0soaD9bKGA8TGL2vAIAABCEAAAhCAQM4Q0E/CYBCAQGMS8PNKihjtKOfoAzlDH+s/so/kGC2VQ7RIDoHf3ObXh2+n8rFlxcW3VFVWPtSpU6ezd9xxx/mNOY5c7qt3794zxGIXjbFPLo+TsUEAAhCAAAQg0PIIlLW8KTNjCDQtgZqXOShaJAeghxyjZYoWTSmtqppeVVKyRFvr7CC1VkRpG+2121b53VatWLHHsmXLDh8wYECLcZB69OjxoX4Ed4mibMPEYHrT3hF6hwAEIAABCEAAAtkTwEHKnhUtIZAVAUWPPtDLGG4vWbfu3XmLF8+67bbb1tVz4iAd/0bPnj3fqaddQR3W7z3NX7Jkyf4FNSkmAwEIQAACEIBA3hPAQcr7W8gEco3AtddeO0djsrK1foo6fdimTRu/wKHFmJ65WrJ06dL+irS1mDkzUQhAAAIQgAAEcp8AzyDl/j1ihIVPoN8222zzYeFPc9MZ6rkrz7nfprWUIAABCEAAAhCAQPMSwEFqXv5cHQImsKMcpIJ/tXf6Vrdv3365okfbp+spQwACEIAABCAAgeYkgIPUnPS5NgQ2EOikLXZrWhqMtm3bes6tpVYtbe7MFwIQgAAEIACB3CWAg5S794aRtRACxfqNJDlIa1vIdDdOU89chTl33FhJBgIQgAAEIAABCDQzARykZr4BXB4CIqCfQyrW28BblkVz5mUxLevWM1sIQAACEIBAThPAQcrp28PgWgiBFfo9IG81a1G2du3aMOeVLWriTBYCEIAABCAAgZwmgIOU07eHwbUEAnpRwfJ169YFZ6ElTLlmjmvWrPGcK6UW94KKFnOTmSgEIAABCEAgDwngIOXhTWPIBUdgsaIpbQtuVvVMaPXq1dtom93yeppxGAIQgAAEIAABCGxVAjhIWxU3F4NARgLTVq1atV3GIwVcqR+J7arpTS/gKTI1CEAAAhCAAATykAAOUh7eNIZccATe1naz7lVVLes9DStWrOii7YVvFNzdZEIQgAAEIAABCOQ1ARykvL59DL5ACLwpR6HNggULOhfIfLKahhwkR5DezKoxjSAAAQhAAAIQgMBWIoCDtJVAcxkI1EFgup7FWThr1qzedbQpqENyjtp8/PHHPTSp8QU1MSYDAQhAAAIQgEDeE8BByvtbyAQKgYAiSP9SBGnXQphLNnOYNm1ab7VbLT2fTXvaQAACEIAABCAAga1FAAdpa5HmOhCom8CTixcv3k2/h1Rcd7PCODp37tzdNRNHj9YVxoyYBQQgAAEIQAAChUIAB6lQ7iTzyHcCD8g5avvGG2/YcSho0yvNSxYuXLi3ZvDdkwAAQABJREFUJnlPQU+UyUEAAhCAAAQgkJcEcJDy8rYx6AIksERzelhbz/YrwLltMqXJkyf31Rv7SlX5100OUIAABCAAAQhAAAI5QAAHKQduAkOAQELgdx988MHe2mrXvpCJTJ069VOan52jlYU8T+YGAQhAAAIQgEB+EsBBys/7xqgLk8Ajepvd9Oeee25gYU6vqEgRsp2XL1++p+Z3Y6HOkXlBAAIQgAAEIJDfBHCQ8vv+MfrCIqCX2VVfrRcYDFi2bFm7wprahtm8/PLLRyj3iPRaIc6POUEAAhCAAAQgkP8EcJDy/x4yg8Ii8CdNZ864ceOGFda0ior0AoreS5Ys2Uvzqii0uTEfCEAAAhCAAAQKhwAOUuHcS2ZSGAQqFUU6f/78+QO8Ha0wplRU5DfXvfjii0drPr+WXiqUeTEPCEAAAhCAAAQKjwAOUuHdU2aU/wSe0hT+/Mwzzxy7Zs0av+0t723MmDGD5SS10kR+lPeTYQIQgAAEIAABCBQ0ARykgr69TC6PCXx79erVZU888cRReTyHmqG/+eabu86cOXOwImPnqsKvM8cgAAEIQAACEIBAzhIoiG+nc5YuA4PA5hP4SKe+uGLFCkdcPth5550XbX5XzXemX1kuJ+80OUe/0Shubr6RcGUIQAACEIAABCCQHQEiSNlxohUEmoPAWF30in//+9+fe/vtt3s2xwC25JqrVq1q/cgjj3zZry4///zz/ewRBgEIQAACEIAABHKeABGknL9FDLCFE5io+e84Z86cM7p16/ZOp06dHFnKefOzUw888MApH3300doTTjjh63133/34IUOHtho7duyMnB88A4QABCAAAQhAoEUTwEFq0befyecJgX9qi9r+06dPP3nbbbed1aVLlxW5PG5HjuwcrVy5sp3GPVTRrymDBg9+T//YHDVk8OAe/fbee+pLL71UlctzYGwQgAAEIAABCLRcAjhILffeM/P8IVCtod4vZ2N3vezgjFatWi3YYYcdcvJlB37m6KGHHjrdkSONt1zjnmPM+l2nJQOPOOLtVqWlh7Vv337/Tx922NSJEyeu9TEMAhCAAAQgAAEI5BIBHKRcuhuMBQK1E7CT9JDUbu7cuRfoB1ere/fuPVvP99R+xlY+4rfV+YUM69atmynnaLguvyAewoQJEz4q32uv16vbt9+7rLR00LDDDps1ZuLEnI6GxeMnDwEIQAACEIBAyyCAg9Qy7jOzLBwC/o2kN+QgXagfku2tSNIsRWSaNRKzfv36kqeeemrwq6+++jk5RrdrfKdKKzMhH/PSS+u232GH17tvv3236pKSo4cNH/6hfiMpL9/Ql2l+1EEAAhCAAAQgkP8Ecufr5/xnyQwgsDUJ9FH06B5dcP++ffuOGzRo0LOlpaWOMm1Vc9TohRdeOFq/2dRWF/bvHP012wFceeWVR+gbmiOLqqrGVFx77bhsz6MdBCAAAQhAAAIQaEoCOEhNSZe+IdC0BPya/q9LN7Rp02b1XnvtNenggw+evDUcpRkzZuyoFy0M0jNHe+v6v5N+KH0oNchGjRrVr6y4+OSq4uK3S8aNe7BizJj1DeqAxhCAAAQgAAEIQKCRCeAgNTJQuoNAMxDopmvaQblQL3BYtccee7y4//77v65Xgn/cmGPxVrrJkyfvobfSHbRs2bK+6vsxabT0wpZc5/LLL9+hrKTktKKSkuV6ucOfbrrpplVb0h/nQgACEIAABCAAgS0hgIO0JfQ4FwK5RaCrhvNtbb07T88CdevcufPUXXbZZepuu+02c8cdd1y2OUP1K7vffffdXvodpj0WLFiwd2VlZRv1c7/0U+nlzekz0zkVF17YoahrVz+71GFtZeU911133cJM7aiDAAQgAAEIQAACTU0AB6mpCdM/BLY+AW+9O1I6U87SsXaWFFlapN9QWiAt3m677T7o0KHDitatW69t27btGm3Jq9IzRK31465tFMHZZunSpV1WrFjRbfny5V1U7ql+1qufierHzzzdJ22Ws6Xz6rSK8vKyqiFDTlSjvlVVVX+75ppr3q7zBA5CAAIQgAAEIACBJiCAg9QEUOkSAjlGoL/GUy4dIEdnH6X95Ow42pTpv/9VajNdx6fouDVemiStkbaK6eUNQ/XyhqF6ecOTenmDr41BAAIQgAAEIACBrUYg0wJpq12cC0EAAs1GwP/tt5c6SmWSX8vt3yTKiZckyEnqX1xdfZJCYa8XlZU9XFFRUaWxYRCAAAQgAAEIQKDJCeiLWgwCEGihBPz7SXaMlkurpZxxQsaNG7do0ODB0xXNGipHqe+nBgx4+9lnn80J502cMAhAAAIQgAAECpgADlIB31ymBoF8JjB+/PgVww444I2qdu0ObNe69aHlw4e/qx+V/Sif58TYIQABCEAAAhDIfQI4SLl/jxghBFosgTHPP7+mX79+r7Xv2LFXcVXVkYOGDJmv6NKSFguEiUMAAhCAAAQg0OQEeAapyRFzAQhAoDEIVIwa9ZnikpKB1ZWVj+nlDc83Rp/0AQEIQAACEIAABNIEiCCliVCGAARyksCYceOmDx4yZFlJcfGI8iFDOpSPG/fumKKi6pwcLIOCAAQgAAEIQCBvCeAg5e2tY+AQaHkEtL1uYfnAgTOrWrUaVj1oUJ9hu+8+dcwrr/Dyhpb3UWDGEIAABCAAgSYjgIPUZGjpGAIQaAoCYyZOXDZs2LAp2h98cNV22x00cODAaRMmTPBb+DbayJEjS/v3718yZcoUIkwbqZCBAAQgAAEIQCAbAjhI2VCiDQQgkFME9Da71fsdcMBrertdn1ZlZeVHDB48V2+9W+ZBVpSXl3UdMOCCbt267aqI0xs5NXAGAwEIQAACEIBAzhPAQcr5W8QAIQCBTASef/75yrHjx0/W80gd9XtJI4YMHbpCDtGCIaefPlLPKX1O6lI+bNhTcqbYgpcJIHUQgAAEIAABCGQkgIOUEQuVEIBAvhDQyxumyTlaVVZcfJycpUOLiouHy2GaVV1VtVNxZeXsMePHz8mXuTBOCEAAAhCAAASanwAOUvPfA0YAAQhsIQFFjuYPOeKIdnoN+MVV1dVFJdXVM+UotVV5h+7bbz+OZ5G2EDCnQwACEIAABFoQgZIWNFemCgEIFCiBK664okdxaenxco7eUvSosqq4+KDq6uoPi6qq+vbv2/fTBTptpgUBCEAAAhCAQBMQIILUBFDpEgIQ2HoELr300o5tWrW6WG+1201Ro7eVLtA3P12V76lX2K2Uw7TrwYceOmHSpEnrtt6ouBIEIAABCEAAAvlKgAhSvt45xg0BCNQQaNWqVRdFiyoVPVqqir7SHnKMPlTdEjlHO1UXF/ffpm3bUysqKvj3js8MBCAAAQhAAAL1EiCCVC8iGkAAArlMwK/3Hjtu3Jhhw4c/qWeOXqlev36uokedS0pKtlU0aRupk8r7VFdWrtBb797M5bkwNghAAAIQgAAEmp+A1g4YBCAAgYIjUDxq1Kg+erPdEVVFRV/QP3R7FlVXL68uKRl91VVXPVFws2VCEIAABCAAAQg0GgEcpEZDSUcQgEAuEpCj1K+0uPhriiKN0La7VZVVVZdee+2143JxrIwJAhCAAAQgAIHmJ8AWu+a/B4wAAhBoQgJ6BfiH/fbaa2yHDh1m6BuhQ7X17ughhx320riJExc24WXpGgIQgAAEIACBPCWAg5SnN45hQwAC2RN46aWXqsaOHfvOEYMG/VPRpO7ri4re1LNLC7LvgZYQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAtBPgdpFrAUA0BCOQcge4a0elSK+m9ekbXRcdPlFpL8+tpu6WHR6qDfaU3trSjZjj/VF2zr/RmM1ybS0IAAhCAAAQgAAEIQAACW0DgEJ1bLd2YRR+h7a1R2z7Kf1XaK6prjOxb6mRaY3TUDH3M0DVfaYbrckkIQAACEIBAzhIoydmRMTAIQAACm0/gQ516t/Ri1MVA5W+Xhkd1ZCEAAQhAAAIQgMAmBMo2KVGAAAQgUBgEpmsa3o6HQQACEIAABCAAgQYRwEFqEC4aQwACW0jAz7z0lH4rLY76ulD59tId0iIp2CnK7CLdHCqStKvS46Vh0lJprHS/FGx7ZU6WXpcmSHaW3N52lORr/Y/0kWTzs0q+1sFSO2m89Kj0gZStOSJ/ljRE8vOdL0i/llZLwU5Qxlv8bpT2kM6TnpL+KQXbT5kR0r7S29LT0kQpbb7epyTPx+f8W/Jcna6TYitW4TjpcMn5MdK/JAwCEIAABCAAAQhAAAIQaEYCdgz8HNHIaAx2ZlxnnRnV2wHwVjk7GrbwXNGflXeEyO3twIRzRysfLLQNzyDZgVovua3TNVJ3ydZbel7yMTtbS5L8e0oPkuozP4Pkto9I7sMvhXD/zr8m9ZCC3auM678kVSX5i5QG+44yayUfm5ukbv9LKX6pTluV7Qz52ALJjqCdIpdfljpJwdoo8yfJx6xlSfqgUo+bZ5AEAYMABCAAAQhAAAIQgEBzEPDzP16kB8fFYxiZ1LnekaVgdk5cV5FUBKfHdX+RXLb1kxZKjtSUSbbQNr7O6ar3uY5WxfaMCnaazpAcXbGOlOxILJe2leoyO0ju19GeA5OGdkr+V3L9nUmdk+Ague/rJUd0ukm2YyS3tzO3o2Szo3OP5PoKKdg3lHHdDZLHa9tG+pvk+q9Kwa5SxnX3S3ZGbeYzU3I9DpIgYBCAAAQgAAEIQAACEGgOAq10UTsdjqwEc3RkkeSIyLuhUukPJC/gD03qgtMzS+W2SV1I/EIGt+2fVIS29TlIX0zOeyg5L06+mxw7N67MkA8O0sDUMTtJH0mVUnCCgoMUO03htMnKeA4HhYoktdO3WJotlSR131b6hNQ1KYdkuDLu4ydJRUelS6R5UprZsapzWxwkQcAgAAEIQAACgUD4n20ok0IAAhBoSgLeBvak5OdruiQXKlfqqInr+0g9JZujOHacXnQhsueUd7QotneSwu5xZRb5oUmb+5Rul5KvY/M46rP31eCZVCNvs5sk+d/ZPVLH/pAq29Gxc/eG5O2D8Vg6qDxRMpe+ku0W6SjJWxAdOdpbGiH9SLKFqJKdLffliFuamZ+xWiVhEIAABCAAAQhEBHCQIhhkIQCBrULgH7qKF/CDJUdW9pGeluwg2colR5oGSV7E+3mc2N6LC0l+fZI29N+04HDcqfMdaYnliJYtOHIbSpn/zslcvfHlCj1Sx19PlcM47CTFYwj545P2YSyOKp0s/UtaKU2RHpHSjlhwGB11y2RzM1VSBwEIQAACEGjJBPw/WQwCEIDA1iRgp8c2RCqV7CzZQXIUyBGNcmmm1F6yM5W24Ayl6zenHJyvH+jk2pwIR4fqs/DMULrdbknFwvSBVDmM4wXV35g6FhdDpOwOVZ4uPS/9UPI2OTtJvSRHrbx1zhbStIO24egGxunIUjhGCgEIQAACEGiRBHCQWuRtZ9IQaFYCjgC9Jg2VHCmy8+DFvW2cVC45IlMpPS41pU1V5345gt8cd1/qQn45g1+iUJ9z49PsgPh5nxUuROYomW36hqTWvx6HzQ7VX6XgMLnO5q1y20uOKHWW7ByZ2RFS7DB6vLGFfofFlUne89tJ8jY9DAIQgAAEIACBhEAJJCAAAQg0A4F/6JoHSn5uxtGjYE8q00c6Q3IkxA5BY5ujVsEeSzInhIooHa28o137RnV1ZS9KHdxL5d7SU9I8qS7zPB098pbDgamGjk7ZcbxJsjPk55VsdsZi58hfeH3FB2TFG5KayJIdvP2ldBTpm6qLWSSnkEAAAhCAAAQgAAEIQAACW5uAIyve/mWdF138gKj+sqje2UOSY5m2oF2ZHDvJDWWh7a0bijV/HYXx9V6WviqFt7o9ktTfpnSo5LH9QnIUZ4JUX6T9LbWxs7JOulny80LnSrMl1w2Sgt2rjMdgpydtn1aFrzlXsoPo8X5Zmia5/ljJ5i+23Lf7uVo6VDpbelqaJbn+71JfyeaxuM7RJPd3mPRjaZW0SPL2PAwCEIAABCAAAQhAAAIQaEYCdjocNfHCfc9oHI58+Jkf1zvqEVtwejbXQXLf90t2Wty/t6zZOki/lVwXtFb5X0qO6NRndpAmShdKYU7uZ7H0WSm2uhwkt/uM9J4UxuH0DSkd4To81c5zct/eNhccvgeVD/ZtZcK83adf7OCxTZBwkAQBgwAEIAABCAQCXjBgEIAABFoSga6arH+jaF5q0jup7KiNIzR2GuZIDTVvWXMkyNGZyVKl1FBrrRP2k/pIC6RJUqZ+2qveTqP/HffLGnzNYHsr86FkZzNYF2U8Nv9I7UvSGgmDAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEC+Eqi44oqDKyoq/BpuDAIQgAAEIACBZibg3/vAIAABCECgGQlUlZbuWrV+/bmjRo3arRmHwaUhAAEIQAACEBAB/6ghBgEIQAACzUhg7Nixbw0bMmQbDWHEkKFDV40bN25+Mw6HS0MAAhCAAARaNAEcpBZ9+5k8BCCQKwTGjBv37rBBg1YWFRcfN7S8vJ2cpum5MjbGAQEIQAACEGhJBIpb0mSZKwQgAIFcJ6BnkXoXVVZ+SeOcW1Ra+heV1+b6mBkfBCAAAQhAoJAIEEEqpLvJXCAAgbwnMGbMmKXlw4e/WVRdfWhVUdGBhx122DsTJ05ck/cTYwIQgAAEIACBPCGAg5QnN4phQgACLYeAnKSP5SS9VlxV1bdVaekQ5WerbnnLIcBMIQABCEAAAs1HgC12zceeK0MAAhCok0BFUVFJ0ahRI/Rc0oGVRUUPXn311a/XeQIHIQABCEAAAhDYYgJEkLYYIR1AAAIQaBoCY4qKqvXyhqnlgwevLSkuPn5IeXmJXt4ws2muRq8QgAAEIAABCJgAESQ+BxCAAATygIB+I2lP/XDdF4tKSqaVlJTcr5c3rM+DYTNECEAAAhCAQN4RIIKUd7eMAUMAAi2RgH4bafGwQw+dWt2q1eHV1dX7Dhs2bKqeS+INdy3xw8CcIQABCECgSQngIDUpXjqHAAQg0HgExkyatOqQQw6Z3KasbB+94e5wOUkz5SStbLwr0BMEIAABCEAAAjhIfAYgAAEI5BGBSZMmreu+ww6vde/efQe95e6zesPdIjlJH+TRFBgqBCAAAQhAIKcJ8AxSTt8eBgcBCECgdgJXXnnlYH3LNayoquqpimuvnVB7S45AAAIQgAAEIJAtASJI2ZKiHQQgAIEcI6DnkmaXH3HEoqLS0qPLhwzpqsjSO1OmTKnOsWEyHAhAAAIQgEBeEcBByqvbxWAhAAEIbEpgzIQJiw4fNGhaaUnJkG7du/cb1q/f1DEvvbRu01aUIAABCEAAAhDIlgAOUrakaAcBCEAgRwmMHz9+5aGHHTa5dWnpAUUdOhw68Igjpk+YMOGjHB0uw4IABCAAAQjkNAEcpJy+PQwOAhCAQHYEJk6cuLbvXnu91rFDh55lJSVHDhoyZL624C3J7mxaQQACEIAABCAQCPCShkCCFAIQgECBEBg9evSR+vbr8OrKysf08obnC2RaTAMCEIAABCCwVQgQQdoqmLkIBCAAga1HYOzYsTMGDxmyrKS4eIRe3tChfNy4d8cUFfHyhq13C7gSBCAAAQjkMQEiSHl88xg6BCAAgboIVPzwh72q2rQ5paiycsGS5cv/fOutt66pqz3HIAABCEAAAhAoKiKCxKcAAhCAQIESGDNx4rJhw4ZN0TdhB7dt127AwIEDp+nlDasLdLpMKzcIHK1hDJdek6pyY0gtYhS7a5ZflJZLi+uZ8bY6/nmprTSvnrYchkCLJICD1CJvO5OGAARaCoExY8as3u+AA15r165dn1YlJeVHDB48V2+9W9ZS5s88m5TAyer9QGlydJVblP+u9FOJ181HYOrJ9tHxkyR/gfFBPW0zHbZj+nvpBcnOaV3maz0tedvtw3U15BgEWiqBkpY6ceYNAQhAoKUQ8Na6q6666u6q4uLX5SSdWXH55V7UYhDYUgLXq4NfbGknnF9DYKD+3i4NhwcEIND8BHCQmv8eMAIIQAACW4NAtZykR4uqqv5ZXFZ2gt50d5QuynOoW4M814AABCAAgbwiwBa7vLpdDBYCEIDAlhEYM378vPKBA+dWl5Z+ZtiQIbvot5Pefumll3hWZMuwFtrZO2lC35Iukk6V+klvS6skW2/pXOkzUjvJW+naS9Ol06U9peukQ6WvS+dJ3tY1R/IzMmnbTxVflc6X+kuVktvGdoIKJ0qTpD2kyySvYaZJafPxc6Qe0pT0QZX9rM5x0rtSmFNr5U+TfN4XpO2keVKmH1x2v2dLX5E6S/Ol7aXjpfellVKwbPo1M5+7r7RO6iW9kuSVFHWVPP+LJTPvKC2VVkjBfK7ndb/kPr4tXSQdJJn5e1Kw7sp8U3pJSm+x20919d0LNakZ46VK3c8IyczflHjGURAwCEAAAhCAAATykEDF977XreLKK7+tSNI3Kioq/NA2BgETGCYtlPx8ygzJC2vnP5S8ELZ5ke43Irrecv4WyfaI5LobktQOhp0Q182UvPiP7TsqrJWqpLlJ6ra/lOwABbtXGdd/SXJb5+0AZDI7LV6o24mwgxJbGxVcv0AqSw70Vvq85D59zD+w7LznbgcjtiNV+EDycV/DY7ED8ivJdT4erLcy2fQ7Vu3WSz7fqXnaibGdJIW6ycqHe+Prni0FO1UZn3+3tFLy2DzH0OclygfbWxnX/1+oSNLvKM3mXgxRu48l9/GWNCPJz1RqZxqDAAQgAAEIQAAC+UngkksuaScn6Ww5Sd+74oor/K041rIJdNT035fsDH06QnGE8nYCFkndovp3lHf72IKD5D7Olex829H5b8kL6lFSsGOUcZ0dhB2Tyk5K75FcXyEFCw7SMlVcLx0uxWMJ7UJ6nzLuY0SoSNITk/obo/pnlLcTcoZUnMiOjq/leYcvEDw2O0d2+E6Q2kquC86Rr2fnMVi2/br96ZLPv9CFyOYrbzkCF8xzt4M0K1QoPVXy+dY1UnAMD1HeTtU6qa9ky+QgNeRe2FGz47u7O0vss0p97VtDBSkEIAABCEAAAhDISwKKHpVUjBr1udGjRl1x5ZVX9s/LSTDoxiJwpTryItdp2hyB8LHR0YG6HKQLonbOenHu8+24BPNC23XpKI0jO4ul2VJ4Vjo4SHeqLhuzA+O+b081viepD5/1Lyblh1LtXPxucsyOnu3HkvtMz81O1avJseAgNaRfnZrRQdpO9U9KZp+2carwWNokB4KD9Fi6ocq+Z257S3Isk4OU7b3wvfEWyHlSh6S/kFyqzA9CgRQC+Uwg/MOTz3Ng7BCAAAQgsJkE5CBVVVxzzUMl1dVP62v+k+UkDd3Mrjgt/wkMTKbgiE7anksqgmORPp4up/uYpgZepIeog5+rcV9vSNMlOwNBXnhPlHpKIeqhbI39IWTqSR/VcUe3HDHyot62jWTH6QXJ17WFz/t9yofrhzTM+cialhueqXL28aQcEs/r/lBI0ob0mzp1Y3Gpcna4bpbshO0k+R7ZYfqUZHN9bA/EhST/VJKmWYamDbkXjrS9Inks/5C+LG0v2X4q3ViT4w8E8pwADlKe30CGDwEIQKAxCFRce+2k9dXV9xYXFx+uLXcnV5SXh0VlY3RPH/lBYA8Nc7UUHIN41H4A39a75m/9f/z8TmxVKtiRCOuOsFi3k7Qkg45Xna3LhmTj39c35urOeCHvaJHPH540dZ/tpTuSspMwjjuVT49jQtIujMHOnZ/RmZnUx8ncuKB8Q/pNnbpJcTeVvKXQzt48aZL0X5LHkcnmZKj0/TSPHhmOuSqMNdt78Xmd85h0hHS3tFB6WbpC6iRhEMh7AvwPMO9vIROAAAQg0DgErrnmmrcvv/zy28tKSk6rGjLkrO8ffPCfbrrpplWN0zu95AmBUo3Ta4M1qfHumZS9GM7GvCCvy+ww2V6QbqzJZf7jbXyba7/XiRdL3u7mqI+3oXledpyChXF4a9isUJlK7ZzYPCc7Jt5ilrZdUhUN6Td16sbivsq9KPm6f5SelqZKjn49JH1WstMZ245xIck7Eud7Wtu9C2PN9l7MVl9+ZskO14nSZ5LyNUodobPjlImRqjEI5AeB8E1OfoyWUUIAAhCAQJMSuO666xaWLF78a225K+7Qrt25cph2aNIL0nkuEbAz0koakmFQhyV172Y4tjlVXujbHCH5q3RfSr7OCmmJtLn2sk70szUnSZ2lEZK3oMV9hnHY8UmPwVESjyE4FtOV7yBl+m8ibHnT4RprSL/hnHR6rir8jJHTb0h/kv4t2clLO2SqqrG9QiZKByf52u5dGGs298JzD87Re8r/SvqC1F1ylPHTUn8Jg0BeE8BByuvbx+AhAAEIND6Bil/9amXR+PF3FFVXzy0rK/uanlMKW3Aa/2L0mEsEHk8GY0citmIVjksq7ooPKO+I0+aYnZQXpG7SwFQHjoKMk26SHD3ZErtTJ3vxfrNkZ+MOKTY7QTZHPtI2WhWPSo7k2Dwm27c2JBv/DlLOTkNsDek3Pi/m6WeDbLFD57KdkH2ckfnexOYomecb27FJIX3vQpuG3AtHjczkinBykq5SGrY/VqaOUYRA3hFI/4eVdxNgwBCAAAQg0HQE9Ia78qLi4iFa8Txx9dVXP9N0V6LnHCDQWmOYIvWRbpAelLaRviKdJf1NOlkK5m1ediyukvzA/nPSI5IdLEdavGiOzQtnL6IPTCq90Pdnap50meRr95OuljwGO2VejNvulb4k7SQt8NsX16xZ01UeT/fKsrLupdXV3atKSuwYzL3qqqv+rjTYzsrMlux4OOKxq5RewIcx/1rH/ihVSd6Wd5E0SSqX7KiZj8e/p3Sb5GOOHJ0prZbsPBwlPSnZsu3XbQ+SXpL8AoRbpHukMySP6VWpQloulUuX/H/2zgTOqrL+/3ebjX2ZAURZZccVF7ZZEU0ztfKH2U8r7PcTy1+YlikCg1c2lywt+5vRpmZqUVmWlpk6rCqKO+AuAiIwgOzMdu/9f74z9+Dlepm5s9/l/X31mXPOc57znOd5n0nOZ57lSLukY6Tzpaclew52jeV5T/qV9KH0Zembkhk2x8SN1L6x/oX0Lcki3mdhHK2OQ6TZkpXrkz4n3SwZH2NiDAkIJC0BDFLSPjoqDgEIQKBtCPhnzjwu6PNdoLu95vF4HrOV79rmztylHQjYS7eZhMKoe9+j4+9KVRHpX9D+zyQzHT+XrpQcUxCPQVL22vkr92lrRsYJe3k3w/T3KVOmeIcOHZp755133nvgwIGzp06dOq3/Mcdkej2eHpp849Uv4kENBy0Put3lnkCgvDIYfO/mm28udwoKb/+lrb3A3yLdEE6L3FhdfyKZkXCiWjtmgvzSdskJM2F3Sl+UzDyaubtXMmNi5RdIyyWLxpRr72NmQL8gmeHoLVk7fi1dJjlh9zNTUyk9LHWXxkjDpYekS6VvSxMk5x3vT9o3g3tAsohlkCx9snTEZ2EZwnGctvY7coKTEN6u0tbM4ttR6RxCIOkIOP/nSbqKU2EIQAACEGg7AvYhWa/Xe7HuuH3v3r1/vOOOOw623d25UxsTsHeDwZK9AO+UXpWsxyJWOHk/0smKWBniSMvMyMg4qWvXrif27t276rxzzvkos0OHnjI+ZkZ6qAfTIyO0P9II1cgQafhnucz6vjjKjzfLUcpoZsMjWS/JRulIYXm6ScbH4lbpOmmE9JYUGY0pt6cuzJLMCDkxTDtmgNZJ7zqJ2naRBkprpIAUGXbP0ZK1Y3vkiQb2M3X+eMme/xZppRRdtpJqe+RO0tbyyavW9lrZvWyfgEDSE8AgJf0jpAEJQqCH6nGWZH85e6kV62R/Fewv/VVq6GXE/rI4RRok3S3V94+9TscdbdXWuCtExrYhoJfRLq5A4L/11/qM6urqB/WX+h1tc2fukioE/FOmZLoGDcoNZGUdGhZnRigYCnVX76Rb8972BYPBco8MUEDyVleX76upKU+Q1RQv0nOwHpI7pKcjnon1+LwgWe9bHymWoVAyAQEIJAsBDFKyPCnqeSQC9terIulZ6c0jZWqD9NN0DxteYMNNprfi/f6gsu0fafvroP1170hh/982szYknMGMlTFqiYjV1kR5Di3RPsqoh4C94AaHDbvQ5fX214vsH+fPn/9BPdk5laYEpk+fntUzJyfvkBGSCZIBMiPUNWyE9tQOi5MZqjVCVVXlu6UE75kcp8dpPSrWQ3SL9KQ0QLpasv8uf1/6sURAAAJJTgCDlOQPkOq7LhGDB6T/k6yXpL3CDMI86d/Sfa1YiXgN0ijVwYZdLJcuk9ZLNVJLRKy2JspzaIn2UUbDBNz6mOxkzdYep4/LPq7FG1Y3fAk5UpGAehWz1ZuYl2HD4TyeXjJAtUZIw+K6yECH1BO0+zAj5PWW79ixo/yuu+6qTFIe31a9r5fMGDlh/231SwucBLYQgEByE/Ald/WpPQQShsD7qomZhESJo8MVWapt5Jj1lqhforW1JdpEGY0jENJKYU+WlpZud4dCX9BKd3n++fOfUBGhxhVD7mQhICPUodYIadW4oNebVzsszu3OcwWDnb1ud0gmaJcNi5NJ2uKqqXndlZm5zbNu3Xb/4sVVydLGOOv5c+UzDZGsR+k9yebeHJQICEAgRQjQg5QiDzJNm2GGxFb8uViyOTk29OH/STnSN8PHNuzty9IZ0jTJCRui9g3pZEl/CK+dhLxI262SE/YP4JnSf6T90nlSofSh9EfJ/lF0opd2LpRsidPlTmJ4a0MvJkkDpQ3SQ9IzUmR4dGB1sfsdL9k8JivHttWSE/H0IBkTK+cq6R/SY9Ijkv2V80hc+umccbRx9GVSZFhZJ0m/kXZI0W090nM4oLxEihPQ4g0DMjyer6i34KNP9uz5U3TPgK1CtnjxYuZkJMnvgf/KKzvV9OiR57N5QRFGSP+B6qj5QeoTCn1iRqi2V0hzg+R+yrcrFi1aFPnfqSRpLdWEAAQgEJsABik2F1KTg8ASVXOiZAbHXsBMx0i50lrpNmmSdKq0U7LVgSxKpIcle9FfL2VKfSXL8zXpccnCDIOZmbukS6VO0m7Jyre/in5H+qVkcZpkZuxn0nTJwoza/dJ/SWZOtkt9JAu/dJPtKLKl/0jWFjNo5dIIySeZCSuW7L4W8RgkM0RnSXa9w6VI+1bGkbjY+TLpdukHUmTcrYNvS6Mluz66rUd6DtYOIg0I+K+5poerU6f/VlODrr17H/Tfeecua7a9bAdzc6dr+eVn/AsWPJcGKJKmif7p07vUdO16uBGqGx6X49Z/N/Qfjp2HVo0LG6HMlSt3+MvK7L9lBAQgAIGUJmAvUAQEkpWAvdRfIj0gWW+JvchbmIGxuFLaIJlBeVOy6CyZyTBTNU56XrIwc/JP6T5ppGRmxgkzPD+RbpT2SBOkxyUzTk9K66VY8T0l2r3/JP2v5Fz7O+3PkWxrw9W+Idn9zdDNkEJSB8na9SXJesB+K8Ub5yqj9fr8W5ol3SpZWLssYnGpO9O0n0W67BIp+jk0rTSuSjoC/jvu2OmfOvVXwf79p8goXa5epYczVqz42NWz51S9ZBeoQfY7jUFqhyd7/fXXd82xxRICgcNWjdMcoWz1EtXo46o7PHULJbzn9fme08dXy999992d9Pq1w8PilhCAQMIQ8CVMTagIBFqeQCcVeZ5kJsSJq7WTJ5lBccyRnVsh3Sj9WDIDMVdy4g3tXCs5fzm1vHa8SLIPJ14jRYcZseulHdJl0j7Jwq61e5i5miRZ3bIk60Eyg2QvkhYHpJ9JZpCGSS0Zsbi0ZPmUlYYE/PfeW+HXxyODc+acown73wjm51d61NuoX+h1mpcyWnNYBkrr0xBNWzTZPWPGjG7Z2dmxjFCmhj9W6y9CO2SG7GOq7+gbQit9Bw6Uu370o536D12wLSrIPSAAAQgkEwEMUjI9LeraWAIrdUGkObLrx4cLsWFh0eEYJhtKFhmW1zFHTvrT4R0bChcrrLfGTJIZH8ccOfmsp+t+yVnF6afaN1lYz9EAaZDkGC+39lsyYnFpyfIpK00J+O1le+7cx+bMnt1fv7RXas7KGrfHs1cT+Y8K1tSUCMtv0xRNSzXbfcMNN/TQX1TyAj5fXY9Q3R98crV0doYGxlXJCG0PG6F1MkJLffv2lc+9445PVAHnjy8tVRfKgQAEIJCyBDBIKftoaZgIvB6DwhClVUiOGYrMsi58MDAyUfvvRB3b4QdSlXSMHcSIY8Npr8U4Z3+x3RORbv8/vECynit7iXQM0Xrtt0bE4lLffdQRQEAgPgL+mTOP0y/4WL2Nv6xhXEdplbtRoVBoo8frLfLPmPGM/5Zb1sdXUvrmUk+b/X+uh6uyslekEdLiCD3F1KceoUoZofKwEXpdRqhcBqlc19XO/UpfcrQcAhCAQMsQwCC1DEdKSS4CereoXcDA6cFxaj80vLPVSQhvo/NZckZYG8N5ojeOqXDMTvT5yON7dWBzeFZJNizPFmZYK/WXrLenRf7yO378+GOfffZZTRHp1O3aa68drCV7K/WiVZG1e3fl/EWLvDU10Z1kunNdWD0s4mlLXU5+piUBzT06WqubfcvtdmvlZ/d7+v3aIhDHqxdpqEzSQXdGxpd1fIfUIr/TyQ7ZVvgbPXp0z+j5Qept6ynD4w1mZBx0B4M2H7I85Ha/4qquLndlZ2/TEuuRf2BJdgzUHwIQgEDCEcAgJdwjoUKtTMB6g8wIFUq2KENk2KINFu/VbQ79PO7Q3qc7NrTODMNbnyYdtvd2+Kj4sNS6A7u3rRRnc5iWS2aOzBBNlCJdygQdt1j06dWrdjhg9+7dB+gF7JIMr9fr0v9cubmuz3/+8/0effRR16ABA8Z/4+tfv0pL+VZq3kiFJthXLrz11pOqqqpckydPPj1/7Niuj/3734NfeOEFV8eOHbv+4Ac/OEaTuivvuOOO7IoK65gj0pmAXur76v8UlTJDtnricJmkoLbrpaM03K6HXFHRnJkzX5+7cOGTSkubUM+Oz7V/f25Ac4S8tny28zFV6yUKBj36f+EBgSoPL5awWn+tKD+ghRNuu+22vWkDiYZCAAIQSCACGKQEehhUpVkErFconvi3Mn0+rEiDZGbHVn+zeKBuc+inLfQwR7JlwJ34XnjHenhixWtK3CCdKB0lfSw5MU87ZpJKpZ7hRHsRijRH9v/Nr4fPtUjPzSN/+9tjKu+HGzdufHXu/Pnzal/atm3LruzaNUsep4/OffOjzZuz9Wa7zFa48gaDWf/4178myBzZOVeXTp366LsoPhmtYXbcKy9vqF7u/jcrI8N16imnHL98xQrXKSeffK6is2OulK1CPQqVevGzXrgKDQmyl+cKlV2pF+aKmvBxhiqgrq2KHTt2VEZ/R8fuRSQHgXnz5r2gmr6geTI9s9zuATLgw2SKxtuzVnpXGabeIa93un/WrE+07PeLMVplC4gMl2whFRsuZn/QsIVOkiKmTZuW0bdbt7xAVtZhRkh/kOjuyclxyxztqx0WZ6vGaX6k/kZRvk9zhG6//fb9SdFAKgkBCEAgTQi0yItXmrCimYlJYIyqtVqyYWk/lR6SBknWI/ML6VtSZGTqwM4Nlm6V/ibZwghmRr4h/UW6ULK4WLLyzLjYy9zPpU2S9fh8UzLD8QXJ4jTJhsjZynPTJYv/ln4v2f0WSGYSLpPMiDnX2lC89VI/ab70d2mUZHWxOvaX/iF9X3pb+oN0kWSmy4YvHSnO1AkzgzMka6fFSOlIXOz8W5KZn3ukp6STJeNn5m2AdJy0RjrUVg0RunrAgAHZDz/88GmbNm16xufzrc3Ly3vgvy644KlueXn235dsvRRm2VYvhlmaRJ6tXoa6Y7c7SwbLjjN0/lBoWFZIcy0qI3uxdLLWaNm21mzphTsgc+UNBCpdmZkVNlzQjFfmgQOVrm7dKmT+jLXezYn2JqBnkemqqholszRZdTlbD+Vobd8NhELXzp8/337nLMbKPJXqGdrvbab2q7Rv/1+VJ/c+ryFozv9XLW+jQvOehmje03u6qMV+H6ZPn57VpUuXXK3WlxfUYgnqac3THwLy9PvcTb/PGhUX3Ks/EmwL9wiVmxHavXt3uXpaDzaq8mSGAAQgAIF2IYBBahfs3LQFCdjvsJkaMyrW69Jb6inVZwSO0fnfS9aLExlmDL4rVYUTHYM0R8dnSxPC6bYpk74qOSblkGlQmmOQtFtrpqzcTnagsJc0M13fkT6RLKzcxVJfO1CYIbM2XS5ZXuvxelS6QGpNg2TG7F+SmTUL4/Bj6YA0V/qMQVKa09ZYz2GbzjcYeoH26OUxS6FVirOzXBUV2TU+nzog6nqxVEC23pKz9LL5qdEyY2UGy85pK5dp+4dFUIbUhggeZrRkoqwXSxkrtNxx7daMlpkrX01NZZX2bV5W9s6dla6+fc1oqRiiJQjYc5bRGa/JSVfomYzTM3t7S3n5d+65556rVf5VQ4YMeePss89+9YwzztjYo0ePKvWseJcvX37UE088cdzLL79sfwh5Us/J/j+3J576mInp3qXLRZr/NM7t9d6s+9sfNxoVuiZb5jsv2gip7l2tIP2u7NbvYbl+B8s9GhZXrW3GRx+V25LnjboRmSEAAQhAIKEIYJAS6nFQmWYQMFNkL8mb4yzDfvcHSydIO6VXJRvSExmOQfq2Es3k2F++rVdlvfSGFG+YcRstdZHekmIZh45KN5Nl9Vol7ZecsJ6fHVKs65w8Lbk1LkdJ6yRj05ho7HNoTNn15XXrZTbr4MGDWfprfXZmZmaWVgDL1gpgtSbKhgvasMHaXiyZKhmn2nS9qGcdMlq2r7/+R95EL8DVhw0XtKGBXm9tL5YZLTNWkcMFdW2Fr6qq0tWhQ4Ve8Cs//PDDinT44Kb/6qu7VebkeG+++Wb7Pa03aj9cmpn5NS068I0Hfv/7vhs2bOhy1VVX/en888//8EgXvvTSSz1uvPHGKWK6Rc9kovKZaT9iaLGIAfo9+Lqe8/H6i0TIrR4o/8KFLx3pAv+0aR2q1fWprsw8mfG6HqG6eUKddb+Qfgd2qazDjVBGhq0aZ39EICAAAQhAIMUIHPYykGJtozkQaC6BaIPU3PK4PsEJWK9Dz549s1z79mVXq0vLerG0pnJWQD1YqnrDwwWtV8vtVgdDRIRCNYf1YtlwwQijpZwVAQ0ptK0m51e6srJqhwxmVFRUujp3rti8eXPlokWLqiNKTLjdOXPmXKAevnNkGv8gk7lMxiFyPl3M+vbp0+eHmnN29Vy//4EJ+flHNEfOxR999FHO5ZdffplM8OPiN9VJj9xed911nTt4vWe6MjK+IPOaoef3gbZDZcbu1cpvT2j1xo5axfHQx1T1rHrp2eSpzh3DRmhnpBGS+ynfrkh0/pEM2IcABCAAgeYTwCA1nyElpC4BDFLqPttWa5l/ypTMA4MGZXXo0CHLdeBAdo16s2qHCwYCdb1YYbNlQwZre7Riz8s6bNER/YdaU3a0sqCZq4gVBm0BDEtTWbWLXthwQTv2ak6WloOusBUGNaytosMHH1T6Fy9utd6O0tLSS1Xhi9Rbc1C9LW+6q6v/tuadd16tp/esu5is/+IXv7js6quvfjHeh/HPf/6z3y233DJV+U+VXg5fZ72HA4JVVaM1lO4ssTpKjLbJ9NSIWUfV6VgZofUyPm9q2yHM8hMZonIbHqdxctvMCGWuXLnDX1bWoLEL35MNBCAAAQikMAEMUgo/XJrWbAIYpGYjpICmENALv88VXmFQL/rZGRkZh4YMHnG4oLPoRXhulsyADe08FOEektql25X4qdEyc2XHEUMGD83LMqOVk1OhXpvKAwcOVGiFQTNZNo/usPCXln5L9xunxA0yJTaHLdt6bzSB60l9X2vtwoULbXho5HXT1EF362OPPfZTtS0y/bByYx38t0K9SU/Onj37Tt3jNHXXFSnfQP1jlqOCAto3k+PTfkjnbPnsTJmjLeqlu9V78GD5mg8/3FGPcYt1S9IgAAEIQCDNCGCQ0uyB09xGEbCJ2MdINq/pk0ZdSWYItDOB8EdIs1y7d2dX5eTY/KpsrTKYpRXlsiOHDNb2YkXOy4o0Wm63rSR3KMxkqdflsIUvlFaphRDOUqY+0hYZJDMpQeXrJpOSo/0Kpe1Qz9Jb6ul6qyoYXK/v+9wyatSo3jJcj+l8o+LOO+8sfvrpp0/77lVXvaMLO9l4Rg2h263NAd3HzN4+LSNn99smA1WpHqIuMkmdPD7fd5gzJDoEBCAAAQg0SOCwvzA2mJsMEEgvAvbSZSIgkHQErJdEssUM6l3QoL6G+V0uz+5rrjm0wqCG62XJbNTOy9K3jbLV05SlYW3Z2rceG7f+4tZJ8smYeJXPp/SOOu4jAzXKHQpNVo5ghsdzUKvUDRo4cKAtAtLo6H/MMVv27NmTFQgG79FCDJ/UaEltmbFaU2a9Zloqb0DI4zlWBfeVYcq0e6oHKWSr0Snto0bfkAsgAAEIQCDtCGCQ0u6R02AIQAAC8RHwqyfIVfftnnq/33NjaelkGaFdMkblMkU9ZJZ66tgjc7JRd9qpVeT2ar7U3pDMjExN1f59+25Rvib1ymbl5FhPkXvBggW/VNlmjGLGzJkze+teQ2TMTlc9Bmf4fNYjjEGKSYtECEAAAhCIJIBBiqTBPgQgAAEINIVAB12UK9ky7ztljh6Tc1kjg/KOhrV9pgdL6VO1PJxd0+jQ/KOuGi64Q0P7jmiOrFDNe9qqjWnFjBkzumd7vfQGGxgCAhCAAAQaJIBBahARGSAAAQhA4EgEZIA8mudjy5C/5woEHt9XWfny7bffHvkdr89cqqF6ZevWrbviMyfiSNBHY49VD9LSOLIeyqKV75rUW3WoAHYgAAEIQCCtCGhlVgICEIAABCDQNAJlZWWhifn5a70+39/98+atX7lyZTzfbNpSUVFxg745tWH48OHRH2g+YkXeeeedzvfdd98FnzvrrE8uvvjivQUFBXuXLVu294gXcAICEIAABCDQBAIaCUFAAAIQgAAE2pzAb7p06XLObxW5ubkNfqNJQ+pcU6dO/Up5eXnm97/3vfWa79RP84s+1lyml7SE91Naoe9Nma4d77333h6W8W7zZ8kNIQABCKQUAQxSSj1OGgMBCEAgaQh00VyiF3r16uX7yU9+srhPnz4VR6q5vsPkufbaa89ds2aNDa87TcP6drtqaq7SanXnyCBl6x8yG0K3TR+I3VO7tHfdkt/yTbawnitTy4D/de7cuU8dqXzSIQABCEAAApEEGGIXSYN9CEAAAhBoKwKVMjt/0wdop/zjH/8o1tLdVSNGjNiuNcW1QnhdaGlut3qDht50001f2rRpU1fl/7zOvK5hffvzevde0qNnz/e1xPcxWqXOzmXZx2HVq7RXx1pCz5WhbWfl7+iprn6kbPly5iGFubKBAAQgAIH6CdCDVD8fzkIAAhCAQOsSsNXsfqDOnmulDA23+6hTp07qNDqYqeF0R9XU1GTr/G+kUmmbdFjMmjVrQIbbfbnM0fHqKQrIIHVXOeU6/lAZ++ofuRWaG3X3YRdxAAEIQAACEKiHAAapHjicggAEIACBNiOQoztNlk7MyMg4Jicnp0q9Ss/q+F9Svb0/N9xwQ16mz/c95euvnqStMkgDNPSul45dwUDg2nk33/y87RMQgAAEIACBeAhgkOKhRB4IQAACEGgzAqWlpeNlco7TvCH7GGxcoXlJfbTc+Aypi8vj2SCDdEJAw+30j5wNw3urKhBYom8jfRxXYWSCAAQgAIG0JqBh2gQEIAABCEAgcQh4A4FK1caG1sUdMkhbZIh+qWF2Nre2jxZsOKClx+d5fD4zWR6fxzNtzpw5X1W+vnEXSkYIQAACEEhLAizSkJaPnUZDAAIQSFwCxfn53UNe76glS5asbEwtly5dWl5SWFij+UdFum7ZTXPn/ksLOuwtW7r0dX2r6W2fht5pufCzSoqKjp5YULCTbyg1hi55IQABCKQPAXqQ0udZ01IIQAACyUEgFKrQX+8a1YPkNGzztm1PaFjd37UU3j+cNNva8Dot1vCwepR+EdRiDl63+3/9paWXqkfpmMh87EMAAhCAAASYg8TvAAQgAAEIJBSB2mFwweC0zVu2LFi0aFF1a1Ru5syZvTO93iINxRup+UrvB1yusgULFmxsjXtRJgQgAAEIJBcBhtgl1/OithCAAARSnkBxcXGGTMtYzSF6YcWKFVWt0WANr9uvoXdrSiZNWqd5S8doOIUNvRugoXe7dG53a9yTMiEAAQhAIDkIYJCS4zlRSwhAAAJpQ0AGyS2DNFG9Oy8vX778QGs23D46q7lOaydMnLhWc5SO1up5ZxUVFw8sGT9+tz4uu6s1703ZEIAABCCQmAQwSIn5XKgVBCAAgbQlIIMUkEEq0mILr6s3Z09bgDAjph6ltepRWuMOBI4KejzWozQov7BwtxZ/wCi1xUPgHhCAAAQShABzkBLkQVANCEAAAhD4lIAWUJhZEwotnj9//jufprbdnj4+21MfrC30hELHa1GHjR6PZ4nmRr3fdjXgThCAAAQg0F4E6EFqL/LcFwIQgAAEjkigsLDwdJfb/aF6b7YdMVMrnlCP0kENvXuzeNKk19Wj1Fs9WmeqTkMKCgv3qE6ftOKtKRoCEIAABNqZAAapnR8At4cABCAAgc8SKCosHCODtFVmZPNnz7ZdiuYoHdTQuzfHTZjwmhaNyNM8pcnFhYXDZJz26NzOtqsJd4IABCAAgbYigEFqK9LcBwIQgAAE4iYgg3SCDJLN/9kQ90WtmFE9ShXqUXpr3Pjxr/s8nlx9cHay6ji8ZOLEfVrMYUcr3pqiIQABCECgjQlgkNoYOLeDAAQgAIGGCRQVFIxyezyVMiUJNe/HjJJ6lN4qGTPmtVB2dk8tET5ZizmM0GIO+2TmMEoNP1pyQAACEEh4AhikhH9EVBACEIBA+hEoKikZpla7ZZDeTsTWlz33nPUova0epVc19K67/jE9Q3OURqpHab96lLYnYp2pEwQgAAEIxEcAgxQfJ3JBAAIQgEAbEigqKhrsCQazbentNrxto2+lD9laL9c7mpP0ijsY7Obyes8oLioapR6l/epRwig1migXQAACEGh/Ahik9n8G1AACEIAABKIIyCD113coussgvRZ1KiEPtWBDper6zunjxr3szcjoqg/OTtIwweP0XaUDOleekJWmUhCAAAQgEJMABikmFhIhAAEIQKA9CWi4Wl+Py3WUTMdL7VmPxt5bPUpV6lF6d+zYsa9ker2dgy7XpJLCwuMLiooO0KPUWJrkhwAEINA+BPhQbPtw564QgAAEIFAPgdLS0tP0F7yCypqa+/SR1mx9tDXrwIED5bfddtveei5LuFP+K6/sFMzNnegKBk9V5XZpUYel8+bNe0P7oYSrLBWCAAQgAIFaAhgkfhEgAAEIQCARCLj9paXf1tLex4RCoY5yD0e5Q6EB+kdqnSrnk7EI6Ph3/vnz/5MIlW1sHa699tqOnbKzJ6p9ZpT2uAKBpf6FC1/XPkapsTDJDwEIQKCVCTDErpUBUzwEIAABCMRHoFBLe8tAlCj3bhmjkExRVy31vUYOIijT5FJv0m5M98oAAEAASURBVP22zHZ8pSVWrpUrV1ZruOB7WsxhtXqTclwej0beFZ5UWFRUqaF32xKrttQGAhCAQHoToAcpvZ8/rYcABCCQMAT8fn8XmYe5UrbM0X5VbIy0VFZpmAzSo3MXLHgoYSrbzIr4p03rEOzTZ7yKOd0TCu23HiXXwoWv+WUGm1k0l0MAAhCAQDMJYJCaCZDLIQABCECg5Qho7lGRFme4UiVukE6VMXpBx31dNTVz/LfcklAfjW2JVl9zzTU5XTt2HC9XNNYjUxhwuZZ5vd5XZRYxSi0BmDIgAAEINIEAQ+yaAI1LIAABCECgdQiMGDFic5fOnU+WMcrTUtnddZcaDbvbeNPChX9unTu2b6nPPfdcjYbefaDlwF+UI8rUP8pF2o7RB2eri884Y6uWCGeOUvs+Iu4OAQikIQEMUho+dJoMAQhAIFEJrF69OlhcUBCUOZqgOnZUr4pbizM8LhPxZqLWuSXqJSNUo+XB1xcPHvyiu3PnjKDXW6gPz54qFjV5ffpsXbt2LUapJUBTBgQgAIE4CGCQ4oBEFghAAAIQaDsC+tjqdp/PV6ieo6N010q5pT9qIYO0+Nhq2SuvWI/S+hNOOOHFrOxsn9pfmJebe6oWcwj07t0bo9R2v4bcCQIQSGMCGKQ0fvg0HQIQgEAiErCPrRYVFg5X39GJmii7PxAM/mnZsmVJ9f2j5nJdtWpVQKbwwxNOPPHF7Jwct/6xLuiVm3u6epQCxV7v1rL165mj1FzIXA8BCEDgCATsr1MEBCAAAQhAIKEI6O3/LW8wmKlxZaGM/fttRbu0jLvuuqtSDV+mRRued1VXn66hd0WegoL80oKCFVrMYbXSa9ISDI2GAAQg0IoE6EFqRbgUDQEIQAACTSMwKT+/g76BdL6G2e3+eNeu39vcpKaVlBpXaY5SoGzZsg0lvXq9GMjNDWleVr7M49iS/PyQFnPYovNpzSc1njKtgAAEEoUABilRngT1gAAEIACBQwTG5+cHvG73l2WS9vzoxz9++NCJNN8pW7vWht5tHD5ixAtdcnKC+uDsRH0napyG3rmGjRy5Jd2NZJr/etB8CECghQjwHaQWAkkxEIAABCDQogTcN5aWPqoS99w0b94lLVpyChU2bdq0DC3ecGrtqn/BoEcf2F25devWVYsWLapOoWbSFAhAAAJtSoAepDbFzc0gAAEIQCBeAsVFRacrb/WSpUsfj/eadMtnPUbqUdpUUlLyQjAUqtbQuwmdOncer6F3nuI+fbZYj1O6MaG9EIAABJpLgB6k5hLkeghAAAIQaBUC11xzTY4WIvDcfvvtabtIQ2PB+ouLfa6JE8do6F2+rvXJHT27a9euVeHFHhpbHPkhAAEIpCUBDFJaPnYaDQEIQAACqUxgypQp3hEjRozR0Lt8l1YD9IRCz+7Ys+d5jFIqP3XaBgEItBQBDFJLkaQcCEAAAhCAQIIRMKM0etiwk61HSR/czQqFQs95N2583n/vvRUJVlWqAwEIQCBhCGCQEuZRUBEIQAACEIBA6xAI9yidZD1K6k3KkVl63uPxPKvvKGGUWgc5pUIAAklMAIOUxA+PqkMAAhCAAAQaQ0CGyOOqqjop6PEUeNzuHC0Rvmr3/v3P3nHHHQcbUw55IQABCKQyAQxSKj9d2gYBCEAAAhCIQcDvcnlcM2ee4PJ6C7X6XUcNwVu1bt26ssWLF7PqXQxeJEEAAulFAIOUXs+b1kIAAhCAAAQOEfDLKAVKS4/XNz/Gyiz9VT1M2w6dZAcCEIAABCAAAQhAAAIQgAAEIAABCEAgvQnwodj0fv60HgIQgAAEIJDMBAar8l+UbLGJ7cncEOoOAQhAAAIQgAAEIAABCECguQQuUQEh6crmFsT1EIAABBwCHmeHLQQgAAEIQAACEIAABCAAgXQnwBC7dP8NoP0QgAAEIJBuBC5Ug78gbZZ2RTW+n46vkHpJb0acu0D71ktzlTRBypDekmLFuUq8XLJ8NvTtY+l8aYD0nmRxsXSO9IIUlJzooJ2rpVzJKf887dv9V0pDpBmSvb+Mlawdx0nVUn/plfC+NgQEIAABCEAAAhCAAAQgAIGGCXxfWWxY2rwYWW8Mn/u/8LksbX8XTjug7RtSVfj4r9raeSeytfOQZGWbLL9t/yB9IC2TnHhGO3auo5MQ3poxs/Q/RqTb9ZZ2kWRmyvanS0ukmvCxbSulPImAAAQgAAEIQAACEIAABCAQN4G+ymnfO1oX44rXlWYGqGf4nPXWmCH5rWS9RhY50gOSpfslJ2Zrx9IelgZJ9imRIukjydKXS048ox1La4xB2q38t0jWM2U9TBbMQarjwE8IQAACEIAABCAAAQhAoBkEntS1ZlBGR5QxIpz2aDitk7Y2BO+glBlOczY+7eyQbAidness7ZHel2yYXGTYkL6WMEj3RxYa3scgxYBCEgQg0DwCLNLQPH5cDQEIQCDdCRwrADbfZEi6g0iy9j8Qru+UiHo7+78Pp43Stqv0rGS9SpFhQ9pWSzbEzn4HzFyZSVoh2dC6yLCheNZj1dywoX4EBCAAgVYngEFqdcTcAAIQgEBKEzhNrVskjWuFVg5WmZdJ9vIdGdYj8dXIBPYbTeAvusJ6hv4r4kozSHslpwfJMb021ydWOEP0BuqkmSSLt+s2h/00c7TlsJQjH9T3XvL6kS/jDAQgAIGWI1Dff4ha7i6UBAEIQAACEGg8gfG65DfSpKhLb9HxT6LSOGwcATNCf5NsiJ0Z0OHS8dKfJTNOkRE9vM45NzS8s1Vb61GyiO49sjS73hZfiCdsJToLm79EQAACEGgXAhikdsHOTSEAAQhAAALtTsAZZme9SE5PkpNmlXsnXMPJ4W3kxgzM2HCCLd1tc48snF6nuqO6n2bCMiITtO8MubMhepExLPKAfQhAAALtQQCD1B7UuScEIACB1CQwUs2aK9nwrVul06XouFgJP5CiX5htYr+l2/dyLGzyvX3jxuJMyc5ZT4dtu0tOfjsXGdYLcr1k81VsVbWJUnScpwQrx8Je6G+TzraDNIsn1N5yaUpYG7W11eWceEM7H0unSNE9QNa710P6u2Sry70pWVn2fHtKTth7hv1ORIczPO+kiBM+7V8ecdyYXW9jMpMXAhCAAAQgAAEIQAACrUXAXohthbIHpX1ShbRFsjQbdnWNFBn2Am7nOkYmat9ewC39j+F0m/di1zvlVGr/Qsm2lmay/Z9KTlytHVtMIChtCm8t38+kyBfoP+jY0i+SLK/tT5fSMe5So639pvkxAHwzfG6ttsbfDM03JHvGxv8UyYmp2rFybB7SdyRj+h/JjJP9XiyXnPiqdiyv9Tz9T1hPaGsmzZ77YskJ53n1cRIitmO0b+W8LF0mZUsEBCAAAQhAAAIQgAAE2o2AY5DsJdVesJ35Kqdpf6tULUUOm3pGx5a3IYOkLEf8xo0N/dpmGSLCeoCsXDNWzou0rcD2UDjdr60Tzgu39XzcIk2Qcp2Taba1YXLGzYa8DZRixTQlHpAsnyMzoOOk6LDeKHs+ls8M1HPSKMmuN7PkhFs7d0h2X6fM17V/gtQYg2TlPCLZ75mV00siIAABCEAAAhCAAAQg0G4EHINkf/2PjjlKsJfWyF6eZ8JpLW2QbDiY3ct6FCLDp4Od0gbJhntZOAbp/rrDtP7ZRa03Q/LPBih01vl8yXqRbEEHh6V2Y4aZU2cYZQftW0+dmdXosG8tjZfMRDVUZvS1kcc9ddA3MoF9CEAAAk0lYP9wEBCAAAQgAIHmErBv3UTH00q4SYrsQYrO0xLH9nI8Wloj2ZCtblJkrNCBzWeyerwZceJ3Efvpuvt1NdyGH97TAIC9Or+8njxWxh8lM6M2j8h655w4RzvW02O9SdFhwzKfjU5swvGOJlzDJRCAAARiEsAgxcRCIgQgAAEINJKAzR2JjueVYL0TR0efiHHcnN4Dx4CZSfokRtlOUg9nJ7x9Peo4nQ7NMFrvzSzJODwqNSdsqJz1FH1ZsuF0D0pbJOt1ulsy42ppBAQgAIGEJ4BBSvhHRAUhAAEIJAWBPjFq2U9p9u/M1hjnopP6hxOsp6GxEQxf8IK2P6znYpsbQ9QRuF0bGypnPT3fkWx4YnNjqgqwXrkrpKskJ97SzllSuZPAFgIQgEAiE8AgJfLToW4QgAAEkofAiBhVLQinvRdxznoaLLKk/bV7dT+cXqCIpLh3bdU0i0HSnyXHMFmahc1Lssn79fUuWb50iq+qsdbjY3O3IofDNYeBGWEzQjmSPXub37RK2iAREIAABJKGQHOGNCRNI6koBCAAAQi0OgFbrCEv6i7nhI8fiEhvyvdvbH5LdESmmfGx3iNbic4m/EeG9WwtlazHxIb7EXUEXtbG5ma1lDmK5HpQB/+W/iRhjiLJsA8BCEAAAhCAAAQgkPIEzBjZ8Cx70X5JulI6V/q1ZOn/kiLDei4s3eak/E9YT2i7UTIDs1hywnp+LK+9zF8mZUsWNl/G0m+SbJlqC9taz9Em6VLJrrV7vStZumPWtHtoFTszTwQEIAABCEAAAhCAAAQgAIEWI+AYpEtU4nLJzIiZF5OZHRvGFRk2x+gOyYbaOflskYATpGiDZHkfkaoly+t84+YL2l8fTrMFAJyYrJ2PJKdc29rKdudJkfEHHdg5DFIkFfYhAAEIQAACEIAABCAAgRYncJRKNKNiw93qi046OV4aJTU03Lun8kR/48bM07GS06uk3drI1M9TpCmSzYOJHIqnQwICEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0hoANUSAgAAEIQAACaUNg9uzZwz1u9x2hYHCly+P529atW99ctGiRzXMiIAABCEAAAozN5ncAAhCAAATSi8DgwYP3d+/SZaTb7b5AfyX8YqeOHfOLCgoyS/Lzg8NGjdq1evVqW2iCgAAEIACBNCVAD1KaPniaDQEIQCDdCfhnzy52ud3fkxsaoVUi9mlZu4/FZJOM0w6lfewJhfa6AoGqoBI8Ho+3sqbmnzfffHN5unOj/RCAAARSnQCr+6T6E6Z9EIAABCAQk0DZ0qXrJ+Tnl2m4nU8ZjpE6yDB1CoVCmfrrYS/5omNDbrcNxztJ5qlHTSDw7+XLl1fELIxECEAAAhBIGQIYpJR5lDQEAhCAAAQaS2DZsmX7ho8Y8WynDh12erzeAa5QKCBj1FOGyEzTx9r/SPvd3aHQI/MXLnylseWTHwIQgAAEko8ABin5nhk1hgAEIACBFiRgc46WLFv2ZnFBwX71HB0nQ7RexfvUc3Sstv1kjvYcrK7+fytWrKhswdtSFAQgAAEIJCgBDFKCPhiqBQEIQAACbUtAQ+7eKSwudsskna6eo/dljD7W/KPBqsXuDJ/PVzxp0o6ysrI9bVsr7gYBCEAAAm1NAIPU1sS5HwQgAAEIJCyBErf77VD//kergsfLKAU1J2mzu6ZmftDn6xUKBD5XUlR0tOYtbbeheQnbCCoGAQhAAALNIoBBahY+LoYABCAAgVQiULZ+fbDklFPedmVlnaoFGgYEQ6EH5y5c+OKSJUvWlowd+1bQ4xmoFe3OklHqXXz66eVlK1fuT6X2t2Rb/LNm5RdPnOgrW778k5Ysl7IgAAEItDYBDFJrE6Z8CEAAAhBIKgJlzz13sKSgYKd6j7oerKh4cOXKlbUfkZUZ2qe5Sm/kFxS8q+F3Q0MZGWcVFRfnTpgwYZtWtzuYVI1sg8oWFxefFfB6K5YuXbqxDW7HLSAAAQi0GAEMUouhpCAIQAACEEgVAmXLln1UMnjwswvvvvszxkfD6/bKKL0mo7Tep2XAfR7PmZq71K3k5JO3ylyxDHj4l0BMxnqDwU3GMlV+L2gHBCCQHgQ0xJqAAAQgAAEIQKCpBPx+/2B9UHaShuMd5fJ4XqqoqFh622237W1qealynb+09KqAy7Vs3rx5L6dKm2gHBCCQHgToQUqP50wrIQABCECglQhoZbtPtALeSyWTJm3RR2ZP1IJ3mqJU1OG0007b4gzPa6VbJ3Sx6kHK9wYCb6sHaVtCV5TKQQACEIgigEGKAsIhBCAAAQhAoCkEZJR2aDGH1SUTJuxwezxjMjMyimUSMseMGbPlueeeq2lKmcl8TUlhYVGN271Gc5B2JHM7qDsEIJB+BDBI6ffMaTEEIAABCLQiAa3aVq4epReL8/N3hzye03IyMwuKCwt9x5944serVq3SqLP0iMLCwhKvz/eK9bClR4tpJQQgkCoEMEip8iRpBwQgAAEIJBQBDS3bWrJkyYuBoqJ9Hrd7XIfs7InFBQXuYSNHfrx69epgQlW2hSvjd7k87uLikuqamhe1qAUf121hvhQHAQi0LgEMUuvypXQIQAACEEhjAmUuV0hDzLbk9e79Qs/c3EoZpQmdO3UaL6MULPZ6t9h3l1IRz/HTp2d2yMkpUA/SKvUg8VHdVHzItAkCKUwAg5TCD5emQQACEIBAYhBYu3atGaXNxZMmvRAIhWyYXYF7wIDTZZSq8/r02WrnE6OmLVOLz596ao6rQ4cJlVVVz/KNqJZhSikQgEDbEcAgtR1r7gQBCEAAAmlOQL0pQRmlTSW9e78YyM21T20U5fXqNUbzdaqUvjVV8Iw744yOPq93nIbYrVyxYkVlqrSLdkAAAulBAIOUHs+ZVkIAAhCAQAIRKFu7NiBDtOGEE098sWNWVoaqVlJUWHhSYVHRQaWXJ1BVm1SVyePGdXFlZJyWsX37srLVq6ubVAgXQQACEGgnAhikdgLPbSEAAQhAAAK2qp1WvFs/5pRTVmu1uxzNUZpUXFR0fH5h4T4Zpe3JSqj4rLO6ukKhUzbv2yd/lNoLUiTrM6LeEIDAkQlgkI7MhjMQgAAEIACBNiFg30mSUXr/1NNPfynT5+siozRZw+5GFhQW7pFR2tkmlWjBm0ycOLGH1+M5+Uc//vHTLVgsRUEAAhBoEwIYpDbBzE0gAAEIQAACDRNYuXJltYzSu8UnnviKOzu7h644s6i4eGjJ+PG79X2lpPme0BlnnJEXDARGLlm6dFnDrSYHBCAAgcQigEFKrOdBbSAAAQhAAAKuslWrKmWU3i6ZNOk1dyDQ2+X1nqmhd4OKx4/fWbZixe5ER5Sfn9/b63YPURtWJHpdqR8EIACBaAIYpGgiHEMAAhCAAAQShIBWvauQyXhz/MSJb3i83r4hj+dzJUVF/bRc+Had25sg1fxMNSbl5/cNer0DlixZ8txnTpIAAQhAIMEJ2BKjBAQgAAEIQAACSUDA7/f3CgaDJa5gcITmKb1VFQg8s3DhwnZfHlz18gQCgYneYNAd8Hiq3cHgELfHM8rt9f7OVVlZU+P1Vu/evXvzXXfdxZLfSfB7RhUhkO4EMEjp/htA+yEAAQhAIOkIzJw58yifVrxzaRibx+Va4zp4sMz/ox+126p3/ilTMl0jR87XynXD9MXb/QKaq213Tyj0bsjttneNA0GX61fz5s17NulgU2EIQCDtCDDELu0eOQ2GAAQgAIFkJ7Bs2bJ9S5Ytez2/oOB9uY/hoYyMs7SYQ4/x48dvXb58eUVbt8++61RcUFAtM3Sy7v12KBRyq4erxuXxrNFxJ3cotPmT3bv/YMuat3XduB8EIACBxhKgB6mxxMgPAQhAAAIQSDACs2fPHqQepRJ11hwtB/KKx+NZomFve9qymtdcc01O186db9U9PRoC2CnodnfRS8Y61WmQq6bmNv/Cha+0ZX24FwQgAIGmEsAgNZUc10EAAhCAAAQSjIBM0RBXIDBJ1eolg7Las337Mv/dd+9rq2rKqH1Rq9d9Rfer0RC7HA25O6D9NXPnz79NWyUREIAABBKfAEPsEv8ZUUMIQAACEIBAXAS0st1OrXq3WqvcbQsFAieHcnJKNPQuu2T48C1lq1dXx1VIMzIVFBQc1ItFiZxQhuZH6X9utycYvK9s2bItzSiWSyEAAQi0KQF6kNoUNzeDAAQgAAEItBkBt3/mzNH6hlKxepM6667PeTZseNZ/772tOUfJ7Z8z5+ZgKHSSXjBCmnu0xuXzzVDPVk2btZobQQACEGgmAXqQmgmQyyEAAQhAAAKJSkA9N9vUo/RCycSJe9WrMzbUrVt+SX6+t7hPn49tYYXWqHdxYWEP3atAq+t5ZZQeumnevDdb4z6UCQEIQKC1COi/XwQEIAABCEAAAilMIGQLJHh8vru0utx/9AHXU4IjR37XP2vWBPXs+Fq83aHQThtap3KD+rDtphYvnwIhAAEItDIBepBaGTDFQwACEIAABBKBgOYnhZYuXfpxr169XsjNza3WMtz5+jbRWPUoBfL69Nmydu3aFllEQct958ofnac2H6gJBB6wJckTof3UAQIQgEC8BDBI8ZIiHwQgAAEIQCAFCJgRklH6aNiIES907NgxJKNUmNuz52kySlXFy5ZtLWvmanPF+fmdgx7PhXJb5W+9/fa9LWW8UgA9TYAABJKEAAYpSR4U1YQABCAAAQi0JIHVq1cHZZQ2asW7FzRXyKdvJxW7iopOLigqqlD6tqbeq3jChEwtDHGRepHevPvnP3+sqeVwHQQgAIH2IsAqdu1FnvtCAAIQgAAEEoiA5iNlB4PBiZ5QaKxWvdut+Upl8+bNW9PIKg7Nyck5qaigYGFVVVXZ02Vlc3X9xkaWQXYIQAAC7UoAg9Su+Lk5BCAAAQhAILEIyCh1cNXU5GuY3GkySztqQqFn5s+f/1Y9tbTRKN9QD9QMGayhPp/vgIbuefbt2+cNBAIZOn6lpqbmJuX5az1lcAoCEIBAwhDAICXMo6AiEIAABCAAgcQhcN1113XukJlZoK+9jtEQvK1a0OFpGaX3omqY5/V6H1HamDPPPHPVpZde+trYsWN3OnmefPLJ3g899NCJy5cvP1Vpf1ev1Ne0PeCcZwsBCEAgEQlgkBLxqVAnCEAAAhCAQIIQuP7667vmZGQUuT2eE/XhpE3qFXp6wYIFH6p6XdRr9HzPnj07/upXv1o8atSovUeqsuY05V599dUXHThwYK2un6x8fDj2SLBIhwAE2p0ABqndHwEVgAAEIAABCCQ+Af811/RwdexYrJ6k47Xy3TtzFyw4v3PnzkWPPfbYb3v37l3VUAtef/31Ll/96le/WVlZebfyzmwoP+chAAEItBcBVrFrL/LcFwIQgAAEIJBEBMqee+5g2dKl6yZMnLj26aeeGrlh48aZt95668Mnn3zyrniaIRNVqd6jPatWrZqq/L+VjtjjFF2efdBW33GSNyMgAAEItD4BepBanzF3gAAEIAABCKQagZ8NHDjwTM0xeqixDRszZsy39u7de7uuM9Ubs2bN6qe/5E7S8L4RH2/dOmfRokXV9V7ASQhAAAItQMDXAmVQBAQgAAEIQAACaURACzOcrcUY3m5Kk9Xj9P6KFSs+p96kmAapdihfhw7DXB5PvisUOiHkdnd2a5GIvn37agoUAQEIQKD1CWCQWp8xd4AABCAAAQikFAEt59138ODBzzWlUf369duppb9Hz7z22uO9Pl9O0OvtqHI6eWpqumtp8X7aP1amKEfbKg1z2axtR5mlgIbZMcROMAgIQKD1CWCQWp8xd4AABCAAAQikGgF1InlDTWlURkZGbscOHfp4MjIelBHy6VtLNty/IuTzHVSPUWUgFNqtHqP9HpfL0nvpRxdXMLhr9uzZw7VMeEVGVVWlq7q6wtWtW4VMU6XyNKkeTak710AAAulBAIOUHs+ZVkIAAhCAAARajICW9/74ww8/7NaUAjds2FBTWVW1zu12367vKw2VuxmqVfG6yRxlaStvFKqRKfLo+0sd1GXkU3qujnN8bveXdJzlyslxSy6ZJpd/zhxtgpXqYaqQ0TKzVFErGSn1RtUem6nyWh5tXZmZFdXV1ZW6pkJD/Co6fPBBpX/x4gZX4GtKO7kGAhBIXgL21xkCAhCAAAQgAAEINIbAr4cOHTru8ccfX9yYiyyv5i79786dO+/R7vzwte6ZM2f2ynS5jg54vSep52iiTE1HeaWt0ifK01+G5tW58+f/RPtu/9SpWQd7985SD1a2zmerRyrLVVWVLUOVrfPZGrKXZVtPMJh1KM3trk2rTXe5MrT9NEIhG7pXIbNWZ7Acs2WGytJltMxk2b63pqbOaGVn1x7v27evUkaxYvHixcyP+pQoexBIegIYpKR/hDQAAhCAAAQg0OYEinXH//z2t7+9Jz8/f3u8d7/vvvsGz58//6vKP1J6N9Z1N9xwQ88MW6DB7T5XZqmLeofkdYJPyCD9Olb+xqZNmTLFO3r06CzX7t3ZroyM7JrMzFrzZGZLPU2fmiqPp85UBQLZqsIhg6V8Wer1OuwzKXJY1ZE9WKpvpcvrrTVRqnydqZLBCoTNlhmuFBoqmKdn8EXpNen5Bp5HD50/S7IFPl5qIG+inL5YFbFexr/EqJC9R18kvS6tjXGepCQlgEFK0gdHtSEAAQhAAALtSUA9OE/k5eUNVy/S7/TB2AZ7UDZt2pR9/vnn/4+W+P6z6v2thuqu+UW5wZqaC2RIJoeCwb/qw7R/aOiatjo/bdq0jG7dumVbL1aWwlVRkR2wniz1XJnMbNX2YNmx9oNOD1YwmK1hhFnqrcqS6TrsHazWVDm9VzJTKqdC1x0aNpjAQwVPU11XSbYq4Q+k+sLJ+zNlml5fxgQ694Hqsls6KUadxirNFis5U/pPjPMkJSmBw/7PmaRtoNoQgAAEIAABCLQ9gd56yV+t7yHtu//++/+iD8EecS7Pm2++2emb3/zmRTt27NglIzBOVT0Qb3VLS0uPlxHZL8P0frzXJEG+eocKqqcpy0yW2nHYUEE7rjVbZrQ8niMPFZTRUt66eVkxhgqa2fLZcEENFaxUaF5WRTOGCjqmJx6DNFj1mif9W7pPSoaozyDdqgZcLvWSapKhMdQxPgIYpPg4kQsCEIAABCAAgc8SOFYv6o9lZ2fnaejaCpmgtfpekb2c18b777/f4Z577jnxH//4xwQZo1e1MMKXdKI8fJpNMwgkylDB399338j1mzb9U78Hd+gZf19N0gjElIr6DJINFXxW+kZKtZjG1C6hCQYIQAACEIAABCDQVAL2HaPv6dtGM/SCnN2pU6etHTp0OLh///4OGk7XSy/O25S+QHl+IVU39SZc1/IEnKGCv/jFLy5Wr9KASQUFj5x04omVWiyjdrjgAw88cJGGN3aaVFKyrH+/flVO79WTTz01TgtU9LzgvPNeXLduXd8/P/LI5cOGDFl59tlnL1/14ouD1Bs1MDMjY5/M8prPnXnms7aCoC12IcOcvWzZsnE9evR484Jzznm+Rmm691lVVVU1F1544YO/v//+CZU1NZPV0jzJhq7ZvLPI3xmbv2Z/3H9ImiSdLR0pr07VxvH6+XnpOOkt6RlphRQdVu650gTJ9sukp6R3pFhD7EYr/Q3JTP9fJYv+0hXSCGmvZOetDbbYSHRcoARrw1DJTJj1qv1NioyLddBPulOK5NBBx/8nWXselSzOk+y+P5SGSNOkp6V/SfWF1XmqNEaye9jcsJ9LuySLntI3pZWSDaX8snSGZOU7ES9jjy44WTpTsmvsXsvD28j29VHa1yT7HXhVsudi97R9Y71ROlr6inSSZPMZ/yytkQgIQAACEIAABCDQ/gTsRVsr0f1tyoUXLlFtvi3NkuwFapxkL5tEYhOwl+qQNCWimjZszNJM9rLqhL3k7pBekNydc3ImaqvPWPn+pmGBG2xfWzMEtdceffTR986ZM+c8aco5n/tcqaXrY8FlOv7+nNmzZ8lMl+dkZX1yzDHHvGzntCqhGYuA7Xfs2PED/W59x19aerF/9uwvZmZmbtR9Pu7Spctjdl73sQVCbGib7a8eNGjQAOtZ07ETV2unSgpKm8Jbq5fNgYrMZ4bwYclp7+7w/t+0/Uh6RYqO2UrYL+WETxRqe1CyMt6UPgjvr9d2uOSE3et3kuWzoaZmoqyOdmwv/3beiWe0Y+kdnYTw1nk2f4xItzl6lvciydpr+9Ol+uJzOmnP0vJukZx6bNa+mRSLkZKdv1WyZ277do0T8TLO1gVmhpx7va59M0V2bM++q+TEadqx9HslY2n7leGtPZsLJHuekemf6PhEiYAABCAAAQhAAALtT2DWrFnj5pSWvqEX3oXtXxtq0AQCk3SNvWzeFXGtmSVLM/06It16GizNH05zXmYt7U+SHVuYKdgqVUg+ycLJG3kf5wV4+5CBAz+nDwIPUm/SWC388Yjyh7p37/6ozNHZMlQXaD0MMyshn9e790sXXPBDGafrvjt9+oL+/ftbT0RowIABL+rbWH6lzz5j0qRfWlqXzp3fuuzrX/+OGbQrrrjiK926dn3G0nvl5i7S/LYT7QPE6vX8iaVJdk8zHxZW1/WSpccySKuVHrmynRkdMzzHSk6cpR27PrK9M8Jpv9U2Q7LIkR6QLK9fcuIZ7VhaYwySGYhbpAlSrnSk6KoTZjDN1Nnzt+gp2bV2z3slC8cg7dX+GulCabRkcbZkee0PI30kCyv3IcnS/ZITV2jH0sxoOX80sZ4wY2jpl0lOOL8nlv4H6XjJnsu9kqWZ/izZ76IZr7mSpUVy1iEBAQhAAAIQgAAE2oGAXx91vbG09Ed6KX1fL5wXt0MVuGXzCdiL+h7ptYiirJelXFouvReR/gPt28vo6eE052X2Qx3by2pkPKgDy+u8UDt5I19kHYMU/buTpevsvtZD1EOyiJlXC4SYgXhfqjn33HOP88+YMUQLe7yj45CWob9M5ugMmazPS1+eMWPGJTq3Tz1VO2bdcMP3rvnud+fo+ICGBO4ZPnx4Z10TGefowOofbZAGhNO/Fs7s0zYgWc9Lp3Cas7lOO8bMws7tksyUZEqRYWXskCok59wz2rf7W/siw8yCpcfqQbo/MmM9+zeGy5gRlcfube3YFE53DJLdb3A4zdmYKbR0MyqRYW3ZKW2QPOETV2n7pNQzfOxsJmnHyrjZSdDW+T1Zr32HhZ0+SbK8xilHcsJ+P4z/UieBLQQgAAEIQAACEGg3Av6ZM4+TQXpKPUgvyiAVtVtFuHFzCdhf8m1olmNG7OXXeoT8kr2U9pMs/iVtk5wXX+dlNvJl3fJZ3CTZtefbgcLJG8sgxertcIaija27/JBBaiivvYTbfa0N3WLo7+HzI7S131nLG1knHR6KfdqLNkhXK82Go1nZTqzWjpVjL+k2V8pMTHSYqbQ8T0efCB//O3zeTInFM5Llb4xBOtMujCMeVx4re2KMvNaz0yWc7hikFVH5GsM46lKXlW/lfl4y02T1uEVywvk9+YOTEN7ac7e8y6LS7fCAZHORWiScX+4WKYxCIAABCEAAAhBILwKafH+mxsvYC9wubyDwSXq1PqVaay/MepSuAsleREdJz0j/kSyKJetpypf+KZmZioyPIg/C+9b7Y9HQ++Yu5bHhXtFhPUYWx9Rtan/Gk3dYOP9obe13MlpfCJ83M+gMiVsfToveOD0pkelf0kGZZHVxwtKekMxwPChtlV6WZkldJYshdZvaIWnh3cM268JHAw9L/exBfTxf/2z2mClOu2PlN7OxJ+qq6HyNYWxFWa/ShdJTkpnOtdJjksNEu5+JzVEpZo4soutWl9qCP62yBAQgAAEIQAACEGg0Af+sWfmaHT9BK6BVa5L8Xv2w+Q9EchIw02NRKHklM0tmkN6R9kvF0nrJzLCZqehwzFB0ejzHNgE/Vjg9JxsjTsaT1+pu8YL0w9q92D+sbcPDp46OnaW2vTbszYk87ZhJ/I6TEN5u0PZsycq5QJocPp6v7XnSRMmJTGcnajs0fGzmqr7oHz7ptLO+vEc6ZybLTK4966aEY5DjYWzl3ytdIq2SrpdekcwkWVtWSo750e6haM7v1KFCmrKDQWoKNa6BAAQgAAEIpDkBLcxwdMjj+ZowBF1ut/3FuSIjEGjqy1aa00yI5lsP0GtSkZQh2Uu6vcBaLJWKJTMqAcmGgrVk9FZh1msV3YtkQ+As3q7b1P6MJ69jHAbpij9Lzsu8U8wY7fSSrGfJKbvEORmxtWFmR0k258UJGy5o5f/VSdDW6nSyZL0sxvHusMzgmYEYK42W3pEszDzNqt379IeVafks3qvb1LK2XZuPZSbVCaf3xjluytbaPUQqkB6NKuAeHVvb/zsqPfLQ4RYP4+660MyR/T5NlCKNzwQdJ1yYeyQgAAEIQAACEIBA3ASuv/76rj6P51vuUKij/uxrL3Y7tR9y6YuxcRdCxkQk8LgqdZJkc0Os98iJ/2hnsHSpZH/tN2PR0jE1qsChOraeF3upjhzKZtmm2o+IiM5r9TNjYqZrfEQ+2+0jmeG7XbIXdevJMDN4gnS0FBn/p4No029D6Z6XPo7IaNf9U5oVkWa7ZmqcoWlmLN+Q7LpTpF5SZFg9e0h/l5ye2HXhDPZMnLDOjcudg2Zs7T4W1usVGSU6uELqEJkYY78xjHuGr9+rbaQ5srZ8PXzO/juSMIFBSphHQUUgAAEIQAACiU/g2muv7ZiTmXmFhtQN1fC69/RWY38d3qn9UCAQ6Jz4LaCG9RAwg2SG4Fgp0iA9Fb7G0i1PS4eZh4XSPMle0L8hlUn20vw/UmTEm3e6LpJ/r10m2oyd9Rp9VVou2cv/DyQLMzGlkr0TW5stzzjpRmm2FNmrZb/fk6W/SJHxqg7M/FwmfV86TjJTc700RXpNMrNzULIyvVKZdKFk+ay9VmaVdJPkhJlRi19JxsH0mGRm1Tg0J6xMq9O3pB9KZ0p2799LNdJtUkMRL+P3VdBGaaw0Tzpdmio9KZkxtbAetmG1ewnww5wbAQEIQAACEIAABBok4Pf7s12BgP1V3eYdrdG2i95gvXoL3an9Ht5QKK/BQsiQyASeVeV2Sd0kMwtO2At+uWTPtzUMkpmQOdKPJTMQFnukK6Xn7CAi4s1rvTxnSfdJv4u4fq32L5D+GZH2S+3nSD+SHgyn79fWeousXp3CaZ/XNkt6JHzsbMysmLEyc3F7WNrUxir9/JoUrDt0/UZbe/++U/pTOM02H0lF0mo7CMfD2pqZuEoyQ2NhRuxc6SU7aEaYCSqUrL3XhqVNrZG5WNuVdtBAxMvY2m5lLpbs+Zrs/n+R7Fk8JH1Bsnx23O6h/64REIAABCAAAQhAoGEC1113XecOWVmXBEOhk9SD1FXGqLeG1rndHs+zrmCwb8DtfmnevHk/bbgkckDgEIE3tWeGrI+UIVkvQ6ZkBsDMWmQ0Jq9znZV1vGS9Llske/E/Uu9LD52z+9sQNzMqlVJjwqvMJ0l2L/3fo3Yu0SvhfW0Oi846OlHqLZnpeUcygxArzKBZG6xexuBI+XSqSdFLV42WtkpWj2qpMREv444q9DTJ/IcZRzOhTozUzg5pm5PQnlsMUnvS594QgAAEIACBJCRgPUkaTnesTNFVMkodJXsZtJe4ze5ly77rLyuzvw4TEIiHQKTpaSh/Y/I2VBbnIXBEAjbekoAABCAAAQhAAAJxE5BBqvDu2rXR6/V+7KmsvNZdWXm9/qR9p7TClZfHu0XcJMkIAQgkIgHmICXiU6FOEIAABCAAgQQnEOjWbag7GNw794c/tGFLFpvrNvyEAAQgkNwEMEjJ/fyoPQQgAAEIQKBdCGhY3RBPKGTzFQgINIeATc63uTvxRGPyxlMeeSAQkwAGKSYWEiEAAQhAAAIQOBKBKVOm2AvtYE00+uuR8pAOgTgJvBtnPsvWmLyNKJasEDicAOOED+fBEQQgAAEIQAACDRAYPnx4fy3Q4Nu9e/f7DWTlNAQgAIGkI4BBSrpHRoUhAAEIQAAC7UtAw0+GujyeDXfddVdjl0Fu34pzdwhAAAJxEMAgxQGJLBCAAAQgAAEIfEog6PEM9QQCzD/6FAl7EIBAChHAIKXQw6QpEIAABCAAgdYmoCW+u+nlIc9VWcl8kNaGTfkQgEC7EMAgtQt2bgoBCEAAAhBITgL6QOxQVyi0y3/77QnxxfvkpEitIQCBRCaAQUrkp0PdIAABCEAAAglGQMvXmUGi9yjBngvVgQAEWo4ABqnlWFISBCAAAQhAIKUJ+IuL7fMgA2tcLuYfpfSTpnEQSG8CGKT0fv60HgIQgAAEIBA/gXHjBiqzZ9u2bSzvHT81ckIAAklGAIOUZA+M6kIAAhCAAATai0AwM3NoMBj8cNGiRdXtVQfuCwEIQKC1CWCQWpsw5UMAAhCAAARSh8AQvTgwvC51nictgQAEYhDAIMWAQhIEIAABCEAAAocT0PLePfTS0LMyGMQgHY6GIwhAIMUIYJBS7IHSHAhAAAIQgEBrEAgv773z5ptv3tEa5VMmBCAAgUQhgEFKlCdBPSAAAQhAAAIJTMCW9w663fQeJfAzomoQgEDLEMAgtQxHSoEABCAAAQikLIFp06ZlqHEDPR4PBillnzINgwAEHAIYJIcEWwhAAAIQgAAEYhLo27fvoGAoFNLJ9TEzkAgBCEAghQhgkFLoYdIUCEAAAhCAQGsQ0NLeQ12h0Hot1KBvxBIQgAAEUpsABim1ny+tgwAEIAABCDSbgCcUGqoXBobXNZskBUAAAslAAIOUDE+JOkIAAhCAAATaicANN9yQ53K7u1UEAhikdnoG3BYCEGhbAhiktuXN3SAAAQhAAAJJRSDL47HhddtvueWWT5Kq4lQWAhCAQBMJ+Jp4HZdBAAIQgAAEIJAGBIJa3ls9SPQepcGzpokQgEAdAXqQ+E2AAAQgAAEIQCAmAS3KkClz1J/lvWPiIRECEEhRAhikFH2wNAsCEIAABCDQAgQGe9zuwJo1az5sgbIoAgIQgEBSEMAgJcVjopIQgAAEIACBtidQu7y3y/X+4sWLA21/d+4IAQhAoH0IYJDahzt3hQAEIAABCCQ8AVveW86I+UcJ/6SoIAQg0JIEMEgtSZOyIAABCEAAAilCYObMmb01/6hLVVXVuynSJJoBAQhAIC4CGKS4MJEJAhCAAAQgkF4EMt3uoVrBbuutt966O71aTmshAIF0J4BBSvffANoPAQhAAAIQiEEgKIOkZHqPYrAhCQIQSG0CGKTUfr60DgIQgAAEINBoAv6pU7O1el0/T1UV848aTY8LIACBZCeAQUr2J0j9IQABCEAAAi1MINCv37EqssqVnb2hhYumOAhAAAIJTwCDlPCPiApCAAIQgAAE2paANxQapCF27+lDsZqGREAAAhBILwK+9GourYUABCAAAQhAoCECB6url4VCIcxRQ6A4DwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACjSPgbVx2ckMAAhCAAASShkCeanqJlCF9lGC1Plb1+S9pj7QzwepGdSAAAQhAAAIQgAAEIACBFCRwmtoUkn6YgG27OFy3SxOwbulepQsF4KvpDoH2QyCdCfjSufG0HQIQgAAEIAABCEQRuEXHXaWHotI5hAAE0oSAJ03aSTMhAAEIQAACEIAABCAAAQg0SIAepAYRkQECEIAABFKAQE+14QtSibRLWiI9IkWH5TtDmiwdlJZLK6TNUmTYMKzB0o+lE6QLJLv279LTUo10njRJ6iA9J90vBaToGKkEG9J1nPSO9GdpleREP+3YkLwXpDIpMs7UwUnSb6QdktXhm9JKycr4smTtmSY5cbR2LH249Kz0hNRJypf+I22RnMjUzlekU6UcaZn0T2m75EQ893TyOtsLtdMS/OKpn3NPq2d9z3agzk+RukvZ0g+kV6QnJQICEIAABCAAAQhAAAJJT8CZg/RHteR9yeYj2Yu9bU1zpMj4og7M2FRKb0hbJcsXlKZKkfGYDuzcjPC2Iry1tP8n3RE+trIszfRbyQlnDtKDStgn2fVmTCyf1eEayYki7Vh6rLlUd4fPjQpnNrNleW+VzFDZvhknJ8wgOAzsntY2WyjCKcfOOzFQO2ayrAwzlZ+E923BizGSEw3d08kXuW0uPytroBRP/SxvPM/WTHHk87L9n9rFBAQgAAEIQAACEIAABFKBgGOQ7AX/T5IdW1jPiZkfMwiRIyk+1rHJejacmKAdMxEfOgnhrfOCv03H/yPlSOdKdmz3MzNh6Z2kIdJmydKtF8PCMUiWNl+ynhALq6PVrVoaJlkUSZavMQZpr/KvkaynZrRk0VUyc7Rfst4t6yWxNMcc2T3MJDhhvUtm1i6V3GGZgdotmanqIlk4BinWPetyfPZnc/lZifHWz/I25tlaL549RwICEIAABCAAAQhAAAIpRcDMhr30m7kxMxAZ1nNj5xzz0E37/5Eie250WBtL9dPyZtUd1v50XvCviEiz3b9Klvc6O4iIO7Vv6ZPCaY5BeiIij7NrPVuW1+m9KAofN8Yg2fWRRk+HrhvD5XzbDiLCzM+rkl3jGCRbgtyOH5Wi43tKsHOXh084BsnSou8ZzvKZTXP5NaZ+jX22GKTPPC4SIJBeBHzp1VxaCwEIQAACaUjgebXZeosiw16CLY6V1ki7JMccmGHoIw2UxkknSxaWHh1mniJjU/hgdWSi9q0HyaJv3ebQTzNU0fG0Em6SnB6k6PPxHK9UpvejMp4ePv53VLoZm0ckm0vlRFF4Z7G2ZjAiw3haWG/SL2v36n7EumfE6Zi7TeXXmPo19dnGrDCJEIBA6hPAIKX+M6aFEIAABNKdgM2ZiQ4bOmbhqdvU/hykn9YjZEPjcmtTXC4bNlYV3o+1cYyPc87MhoUNQYsnNsbIZAbE6nd0jHPRSZH1jzz3euRBeN/MoLVlffg4cuMYOyfNMWf3Owkxtj2i0mLdMyrLZw6byq+x9WvKs/1MZUmAAATSgwAGKT2eM62EAAQgkM4EHDNUH4PjdPJFyfL+XnpGeluy3iUbZnaW5Jgf7R6KeMo+lDnGjvVURYetWmf/Pm+NPhHjuH84LVbvVnR2q6sZpFgr6R0TlTkYPv6Bth9GnXMOW2KeTlP5NaZ+TX22TjvZQgACaUYAg5RmD5zmQgACEIBATAI2n8bmGF0mPRSVI9o8RJ1u1uGIGFcXhNPeC28dQ2P1iw6nJyU6PdaxDbmzOVe9pWjz5QwjdK4zc3i2ZIbKhtlFhi3OMEGKLiMyT2vvN6Z+7fVsW5sB5UMAAq1E4Ehd8610O4qFAAQgAAEIJCSBnuFa2epzkTFWB6PCCfH00kReG8/+xcqUF5XxnPDxA+HtuvD2pKh8n9Px4Ki0+g6Xhk9+JypTvo7NDEWGs3iErXYXHXOUYN9Csp6Z9orG1K8pz9bbXg3jvhCAQPsToAep/Z8BNYAABCAAgfYnUKYqXCLdImVLNoeoWLJV7TZJ1ot0pvS0tF9qqeisguxl/1eSDWX7svQVydKWSRY7JOsxsZ6ln0tPSdbj8y1pgzRAiid+qkzWmzJLMlO2UrJyviaVS5Fznh7Xsenz0iLp95INa7PV46ZLK6RYC0wouU2iMfUrU40a82zNkJoxvEmy+zwvERCAAAQgAAEIQAACEEh6AqepBTZv6IcxWlIaPvfF8DnrHfpNOM2uMX0k2YuyzT/aKVmaMxTtsfBxR20j4y4dWD67d2RcpwNLvzScaD1Hdmwv7sslMx92bLIhbR2kyLBeLDNDTp5K7d8sOe0YrX2LkZLluccOYoQZIzM7+yXLZ21cIF0fPrbeJCc6aefXknNP21ZJP5OcRSy02+A9LU90NJeflRdv/Rr7bL+gstdL1t67JQICEIAABCAAAQhAAAJpS8Dm9JgpGhJFwObd2DLYrTX06iiVPVmKNB46/EwMVspEqcdnzjQuwRNVxq06NkMwPEYxVrdzJePSL8b59k6Kt36NebZmqmzVP+tNJCAAAQhAAAIQgAAEIACBFCRwkdr0d2lSVNt8On5ZKpdaywBG3ZJDCEAAAhCAAAQgAAEIQAAC7UtgnG5vQ/lsjs03pL7SeOkPkvUefU8iIAABCEAAAhCAAAQgAAEIpA2Bb6ul6yUzRI6qtT9LIiAAAQhAQARsjC0BAQhAAAIQgEB6EbA5Vtaj9J70inRQIiAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMRNoLS09JQ5c+aUxH0BGSEAAQikOQFfmref5kMAAhCAAARSloDf5fK43O7rQ6HQJ2rkMynbUBoGAQhAoAUJeFqwLIqCAAQgAAEIQCCBCARKS0e6QqFhQZfrvQSqFlWBAAQgkNAEMEgJ/XioHAQgAAEIQKDpBNyh0Pm6upM7GHyr6aVwJQQgAIH0IoBBSq/nTWshAAEIQCBNCMycOfMot8tVqOF1+0Mez7Y0aTbNhAAEINBsAsxBajZCCoAABCAAAQgkHoHMUGii5h91D7lcO1W7PYlXQ2oEAQhAIDEJYJAS87lQKwhAAAIQgECTCdxwww09XV7vZM09stjr9Xp31+3yEwIQgAAEGiKAQWqIEOchAAEIQAACSUTAr5Xrgl7vf6v36BhVu0KLNFjvUVUSNYGqQgACEGhXAhikdsXPzSEAAQhAAAItTGD27GKPxzNRc492aw5StYySt6ampmML34XiIAABCKQsARZpSNlHS8MgAAEIQCDdCOijsMerzV+XtskcddYqdp9IIbfb3SndWNBeCEAAAk0lgEFqKjmugwAEIAABCCQQAb/fP0T/qF+pKtUEg8E98kWd5YzsA7HqRHLnJlBVqQoEIACBhCaAQUrox0PlIAABCEAAAg0TuP7667uGAoErlHOgTFGlhtjlyhXVyBjt1XHIFQjYfCQCAhCAAATiIIBBigMSWSAAAQhAAAKJTCAnJ2evVqz7YygYfFD1rFDv0XAt723zjgZbvd0ez3G2JSAAAQhAoGECGqJMQAACEIAABCCQKgT8tord7Nmlas8WTyjk1UdiR2u/xu313qRhePZNJAICEIAABOohwCp29cDhFAQgAAEIQCDZCFTMmNE12+Nxu/bv/7P/Rz/aPm3atIy8vLw+C+bNwxwl28OkvhCAQLsQwCC1C3ZuCgEIQAACEGgdAhkZGYPt20dmjuwOixYtqtZmY+vcjVIhAAEIpB4B5iCl3jOlRRCAAAQgkMYEtDCDGaT30xgBTYcABCDQLAIYpGbh42IIQAACEIBAQhFwa97RIFcwiEFKqMdCZSAAgWQigEFKpqdFXSEAAQhAAAL1EJg5c2YfLe/dwbVrFwapHk6cggAEIFAfAeYg1UeHcxCAAAQgAIEkIuD1egdrue+tc+++e18SVZuqQgACEEgoAvQgJdTjoDIQgAAEIACBphOonX/kctF71HSEXAkBCEDAhUHilwACEIAABCCQAgT8xcU2KqR/kPlHKfA0aQIEINCeBDBI7Umfe0MAAhCAAARaisC4cf21OIPH99ZbH7ZUkZQDAQhAIB0JYJDS8anTZghAAAIQSD0CPt9gNWqTf/HiqtRrHC2CAAQg0HYEMEhtx5o7QQACEIDjuILsAABAAElEQVQABFqPgL5/FHK7mX/UeoQpGQIQSBMCGKQ0edA0EwIQgAAEUpfANddckxMMhY5i/lHqPmNaBgEItB0BDFLbseZOEIAABCAAgVYh0LVr10Eet7syY8GCj1rlBhQKAQhAII0IYJDS6GHTVAhAAAIQSFECNTWD1bL1fpdLn0EiIAABCECgOQQwSM2hx7UQgAAEIACBRCCg+UdawY75R4nwLKgDBCCQ9AQwSEn/CGkABCAAAQikMwG/39/N5Xb3cGVkYJDS+ReBtkMAAi1GAIPUYigpCAIQgAAEINAOBKqqBrtCoT0yStvb4e7cEgIQgEDKEcAgpdwjpUEQgAAEIJBOBII+37EySPQepdNDp60QgECrEsAgtSpeCocABCAAAQi0LgFPKDSI+Uety5jSIQCB9CKAQUqv501rIQABCEAghQjMnDnzKM0/6uDKzKQHKYWeK02BAATalwAGqX35c3cIQAACEIBAkwl4vV6bf7RN84/2NbkQLoQABCAAgcMIYJAOw8EBBCAAAQhAIHkIuG15b+YfJc8Do6YQgEBSEMAgJcVjopIQgAAEIACBwwn4/397dwInR1mve7y6Z80+ISH7xpaAAcSLiKwGDAjIpmGU5Sro9cRzVbxygASy2SaZBAIKh5wj9+BFcV8ioGzKogFkERRFFpOwJTAhCUvIZJtktu77PJMqTtHMJD17z8zv/fBPVb21vfWtSej/vFVvT5lSqJpxQUMDj9e9l4YlBBBAoE0CJEht4mNnBBBAAAEEukag/thjx2pwhmTw0ktruqYFnBUBBBDomQL+7RMFAQQQQAABBPJXwL/MHKcYrShVbFe8qsfrxmhFZWrZslotUxBAAAEEEEAAAQQQQACBHitQrCs7T3GnYqsiE0Z9bP5tDdLwSy2fokgoKAgggAAC7SDAP6jtgMghEEAAAQQQaCcB/3/5C4qFikGFhYUri4qKXlKs1/yWZDKZVimor68fXFdXN7qmpmZ/LU/Sti8rLlH8XkFBAAEEEGiDAAlSG/DYFQEEEEAAgXYUGKZj/VxxTElJyaN9+/Z9Qj1Ee3x8TslS/23bth2r6Ye17y2Kryl2KigIIIAAAq0QIEFqBRq7IIAAAggg0M4C++h4f9R7RaUDBgz4VXFx8aaWHn/Hjh1jt2/ffo72e15xqmJzS4/B9ggggAACPLPMzwACCCCAAAJdLbC3GvCEHp9rGDRo0LJceo2aa3Btbe2grVu3npfJZFZpm5MUe+yBau5Y1COAAAK9VaCgt144140AAggggEAeCPhJjtvVczS6rKzsp21Jjnwt2r9G7yq9rHeTTtPiEMW9rqcggAACCOQuwPcg5W7FlggggAACCLS3wIU64PF6rK5NPUfxRvnxvH79+v1GdV9XfCS+jnkEEEAAgT0L8A7Sno3YAgEEEEAAgY4QKNJB12hAhlVKkB5u7xNUVVV9WgM3vK3jntjex+Z4CCCAQE8WoAepJ99drg0BBBBAIJ8FPqXG7eXR6jqikTruIzruCYrJHXF8jokAAgj0VAESpJ56Z7kuBBBAAIF8Fzhf7wut8HtDHdFQPWr3pt5tek3H/mxHHJ9jIoAAAj1VgASpp95ZrgsBBBBAIJ8F/Ij7FH0BrL/gtcOKErA1OrhHs6MggAACCOQoQIKUIxSbIYAAAggg0I4Co3WsQUqQ1rfjMd93KCVIPj6P2L1PhgoEEECgeYHC5lexBgEEEEAAAQQ6SGCUj6vH67Z00PEbDxsef4AW+iqq93Su2bNnj9c+hya3b3889e1ve4AHCgIIINDrBEiQet0t54IRQAABBPJAoNRt0DtC9R3ZFn35bHR8n+99CVKqvLy4ftKk0dpu/0xDw1GaHhBkMslgwIAV2p4EqSNvDsdGAIG8FSBByttbQ8MQQAABBHqwwHZfWyaTKdaktqOuUwmPe4+CK2bM+LoGbdiRzGRq6hOJOj1f70fs+2cSiXGqG5TIZBLJgoLtas+bqh8abN1a5f0oCCCAQG8UIEHqjXeda0YAAQQQ6GqBV92AhoaGwXqk7Y2OaowSniFKjGpKSktP1fygTDJZUpDJ7MwEwVZlSNWablYvVpUSpO3K1lxfouWdwVNPkSB11E3huAggkPcCJEh5f4toIAIIIIBADxTw42vr6+rqRiuB6bAEafv27YP1ZbEPJZLJz9XX1U0sSiY/rCTocA2hNzQdBEVKmtJKiAYphmu+RM/89dO66uC44/513vHHb0qm05uUOFWp12lTOp2uKl63blPqllt29sD7wSUhgAAC7wp4mFEKAggggAACCHS+wPf1CNxH99prr191xKmV0ATvvPPOV3TsqxTfic5x8cUXlwwZMmSfoLb22CCZPF4JU4ESqFeVINUpDlCy9KKSp7s0LdPjd4PTCm1Xph6nPj6G1u0IlCwlnTQptE+VesE2NT6Wp56n1IMPRu89RadkigACCHQrARKkbnW7aCwCCCCAQA8SOFHXct/AgQNvUC9Su49mt2PHjn3Vg3SezjFesa4pt9QVV0xIFxefoUfsjtb6LUqKBqQbGm6dX1Fxa/b2qYsuKg0mTChTj9TgQidNSqCcPCnJG6xEaZASqCIlZRklTlu1b5WTp8YeKCVTdZrXkOabUqmU1yknoyCAAAL5K0CClL/3hpYhgAACCPR8gaf1XUX1ZWVld7Xnpbr3aNOmTReqd+cRHfdzezh2IjV79pF6P+kCbTdBAzd8Z/78+cv3sM/7Vs+YMWOAkqAyPbc3uKGgYPB7eqCCYKASqYTa1aDeqCq9fNXYA9WgxEkHauyB2rx586brrrtux/sOTAUCCCDQyQIkSJ0MzukQQAABBBCIBIYPHXrmG2+//ZuhQ4feobqno/q2Trdu3fqRmpqaKTrOgYrXcjmeeneGadCIzyipunfhwoUv5rJPrtuUl5cXTB4zZlB9v36NiZMGihisfcvS6n3yY3zqjfL3NOnJvXSNH93T7K4eqIaGTX6cryYINm3cuLHqpptuqvN2FAQQQKAjBUiQOlKXYyOAAAIIINCEQOob3yhrGDDgdCUD/3Lb7bdPfO655wYNGDDge3rUzslBm4oerZugR+v+pw7yNcX/bdPBOmlnJWfFtbW1g4udLOl9p7R6oHTqxnegPFUC5eHQPS76tncf3VMS5R4ovwNVuG3bptT112/2Fo3b8QcCCCDQBgESpDbgsSsCCCCAAAItEfBjaH2Liz+mx9j82Nth+uC/YcvWrfP0aNk39UjawUqSfqUkqdVf0BomR+fo2D9W/O+WtC2ft73sssv69e/ff7CHRVc7y9wD1Th4REGBk6hBckzqA01DQzq95T2DR7gHqr6+aptG47v22msbv3sqn6+TtiGAQH4IkCDlx32gFQgggAACPVMgkbr00iFBaem+6hU5SIMWHKV3fQ5Rr8dgfZB/SR/e56QWLXpGl95PcbvimD59+tzRr1+/VS3h8DtH6jXyY3Una7/vKb6q0IBzPb+k/KW3qdTAxsEj0uldPVDRo3u7ep/6NypkMv5C3ipZbWp8jC8cvlzvgG0Knn++KrVsWYd9YW/PvwtcIQI9S4AEqWfdT64GAQQQQKCLBebOnTtZoxG4d2iEBiYYq3doytRjVKJnv0o00tte6vkoURNfVt3iBQsWPB9rrr+bcL5ihvZb1bdv30dLS0ubHH0u2seJkZKi/aqrq49T0rW36v9N8V/ReqZBMH369CINa15WUlLS2AP1nsEjdg1f7vvhbHJ74/Dlet9J70a9O3z5zp07N+k+bNZjgL0i4eRnBgEEgoAEiZ8CBBBAAAEE2lFACdJx+p/rJeqlqFbS4neKtimGqW4ffcKuVvL0lt6dWaKBEFY0c9rDVL9YcYo+zK9TD8dqxXolTZsV9UqKihWD9SWzo9Rrsp+2G6T4qWKuolLRW0uBLrxcoTEdGnvjcnJITZ/et27vvT1ceVmBe6A8fHnYA6VkdpDqVd04fLnfcfrvwSPUAxUbvtz3OJ/Kx9SYYxT+EuKbu6hhB+i8UxT3KV5VdJdyrhrq3sTbukuDaWf7C5Agtb8pR0QAAQQQ6N0CiXlz5vyr/gc7Rb1GL2t6kD50+zGvVxRlDZnM95QcPZAD0f7axh/WTlIcrNhLEZUNmvm74vcKf9Gsl3t78RfZVis2Koa2E0Zi5syZA/skErse3SssbHwH6r57752qBHjg1KlTX1IClVDi69H1qvQ+1CYNJPHfg0fU1GzaqB6opUuXOmlrS5mmnT1Qxc9zOIgH6PA7aC5OTj7RONf5f1yoU96i+JTiN4ruUlaroU6G/YuKjir76sBOYh9XrOyok3Dc1gu4O5+CAAIIIIAAAu0nkKmpq/tZSVHREfoQPVWHXadE6a/qDRqrD89PrFq1anmOp3pJ2y0Mw7sUKUoVTgIaFJSOF8hcffXV/rDsiJclWhh0cmnpF4IpU8rS+vJcP7qn+9s4eERCj1aqO2tw0KdP6ZA+fQIlzDv83pNH4NN+VUkPHqHvgqrR8ksvvVS1bNmyPd3Pq3w+RS4J0pnazuVCxe2Nc/yRbwJHqUHfV/hdQRKkfLs7ag8JUh7eFJqEAAIIINB9BfSuSqEeyTpW7xtVuzdB8Yo+MDu5qa9Lp2/N4cNwcxfvXgoHJU8EUg8+WB88+KBHHXS8r+hnoTQ+fHmg4cvVyzRcQ5lP8nDmJYlE4UGTJmVSc+duzR6+vKCgYFOg735KLV269X0H3n3F6HC1kyl+XnZvxVoEmhQgQWqShUoEEEAAAQRaLjBr1qzh6hmYpuSoWB+Gl2gEhQ8HhYXn6l2kTCadvquioqI7vYvRcoD822OEmvQ5xYMKD4hxvuJIxXbFI4pfK+LFrx6cqzhR4UEvXlD4cUg/quYyQeH3nAYr3Jt3ucJf8Hu/wkW3PviQwo9FHqIE6W+a+jyexpOVxnYNLSt75LQzz1z3wAMPnK8h2k8oKS7OjB83rvITp576knqlBq7dvr3s0EMPPeifzz8/UkOYF4wfP/7msrKyZ88+++z7lHht0j5VGiJ+h47tMlzxaYWP7fIlhd9Ju8sLYTlLU1/bAYrVCl/XbxVNlZGqdC+Ur8fvd/1DcZPC7zVllwmqOE0xWeFRGe9V5FrO04Z2d0Lntp2isP2fFX5/Ku52hpYPVFyj2F8xXfFHxe8VUcn1Gn3OTyqOVnj+QcUfFNnFPw9jFdcr4m3xlxu7B2iV4g5FvHxGC76WCYrXFL62qOf4As2frnDxz0k/xX8q3DPsMk7xZYWv08nxcwo7bFJQEEAAAQQQQACB7iOgwRmOVE/AnHnz5pWnLrqo1C1PlZcX6/GqKxXf14flUd3narplS/0OUkYR7805Iqz7d02dpOh1oeCdsM7bLlJEJakZJxOu36xwQuAPqF5erHCZqqhRuM7h+RsULr7nToZcv0HxrMIfqL38d8UgRVT22K7y8vICjb73Ke0QP1/dmDFj/qCfsxmpefNSjTF37hVa/tepJ544W9vWKnyNUdvu1rxLicLvJbneH8T9odvbetnvB3l9vJygBSdCXr9a8Xo47/e7nAjFyxQtVCm87XaFr9nTHypcd7Zid2WlVvoctyi8vc9bH84/qeneiqj8UjPexglIdJ0Xhytbco3e9hcKH8vh++3pbxW+1qcVUVmuGa9zIhMvw7Tg+l/FKv0zuCyst8P6cN7bfVPh8pAiuj5PfX+jazxe8054vX3k4vk1ikkKCgIIIIAAAgggkP8C/hJTfUA9XzFLSdKHslucuvzyEUqa/IGT0rEC/nDqD5NNJUiuv0cxUuFymMKJkpOEIoXLOQpv958K95i49Fe4d8IfZP2BOCovaubNaCGcfllT73+1IhHW9dX0NoXrvxDWeXKEwnWOPbVLmwTvO9/FF19c4p8tJd4H6ufuKP2Mneqfw4EDBrys96FqtXyedwzLFZr6XD9QRNdrr5+E9SlNozJAM742J0PubYvKMZrZonhLMTSsLAvrnGA4EbKbr/kaRXR9rt9dcSLgbX3cw8MNB2vqXhPX/1dY58kvFa7z+a5SHK2I2tKSa5yv/Xyc2xXRffU9WaNw/dOKqCzXjOv6RRXh1Pu5/lex+tlh3TJNnRD758BurygaFPsqXC5QeN+veCFWnLhWK/aL1Z2seW+7NFbHLAIIIIAAAgggkJ8Cc+bM2U8fSi9TTL/yyiuH5Gcre02rdpcg+TEn9/DES9SjcnBY+S1N/UH03+Ibaf6DimsVh8bqX9S8k4h4+boW7ldk/xycqDofd7EiKlGClEu7vE9T54uOlT19VBXuhYiSNCd5VWFdsabxUqgFJ0I7FdG6uZp3ez3NLpeowuvmhSvmhMv/J1yOT/4crss1QTo3vrPmSxQvK5yc7qVwiRKkH+1afPfPllyjE8BNinWK7J+JU1Xn62tNguTjOoF8W+H2xMvXtODjfimsbCpB8r1wEuV2Ze8/Q3WXKyidJOCbQUEAAQQQQACBFgj48afJkyZ9XM/4HKVBGB5/ftWqP7Rh8IUWnJlNWymwXPs5CYiXVeHCvpr6N/d/DZedKLlX4nbF3xT/CEOT3ZYbtNbh4l6U8Yp9FE4qXKKEZdfSrj9zaVd8+5bM+wO5ywcUgxQ+l3vM4sXJx1OKkxTutVihOErh8tCuyXv+fCJcmhxOPx5OnWxml1+rIt4Dlb0+e/mBrIoaLT+m8P05QBGdW7PvDmPueZeWXKN7fsoUbnP2z8TvVLdd0ZpykHZykuTr2JZ1gO9q2Umdr6m54nvhxOx/KO5R3Kj4g8KJ+BIFpRMFkp14Lk6FAAIIIIBAtxdwT5FGHvuSvkz0ECVIP0ktXHgfyVHe39aNTbTQH0hdos9Cd2rePSHuBbhS8aTibcWvFB9T7Kn4l87TFP5Q6w/I/1Tcrdhf0VzJpV3N7ZtrfXT+phIeH8NJkcuExj93tdeJQzwhCVe9b1snVb7Wd6INYtO1sfk9zVZpA1tnl5VhxZisFc9mLbfkGt1ml1d3Td73Z67tjn5uogNEx30mqohN9U9F48/V7hIkb/4pxb2KYxQ/U7yh+LtitsJJLqWTBLJvbiedltMggAACCCDQ/QRSs2b9j5LCwi/rO2226gtCb9QXvvoRIEr+C/jRpVyKe4DGKT6huFaxQVGu+KPiTMXuyi1a6V6T/oqZipMV/mB/vsIl6tHZtbTrz1zbFd+ntfPFzezo3hkXfxiPSoFmCqOF2DR7W19TX4V7ZLJL9js72evjy80lDtExKuMb72Y+l2uM7sPoZo4TnbOZ1e9W++fEJbFr8m6iHS2H1S2avKatT1H42F9V3K6YpFiocOLk+0LpBIGmfvg74bScAgEEEEAAge4joBfhS/XdRmekg2CivuTz/lRFhXsXKD1LwL+19+eihxT3heH3Pr6ouFlxkeIORVNlsCovULjXyMeJeqc02ziQgKddVV4MTzxV09lZjfCH+SPDuijZ9/ZOhI5X/C5cF00+Gs5E276gZX+Ydw/bb8N10eTAaCaH6XBt48cas3uRomP4PLsrLbnGPuGBTmjigANVN1IR79mLkli/ExV//G5i1v5RG6dk1XvRlv5Zuknhnsqmig0+pHDv2OsKP5bncML2F4Xv02RFUz1Uqqa0pwA9SO2pybEQQAABBHqcwOzZs8frO2n+t77gc2/1Gn2P5KjH3eLogmZq5o+KEVFFOH08nEYflKPV8d/mDwkrt2oaT46ccH0+XNeWngUfIn6+8JA5TZ7TVusVhyuGZe1xlJb3Utyp2Byuuy+cnhZOo4nb/8lw4Sfh9PfhNHtbv4tzUbgu10n29k7SzlA46fQjeLsrLblGv+fj3rJDFaOzDvpVLWc7rwi3OSy2re/rv8SWPevExT1AH1Q4yYqXBVo4XVEZr9R8/Fxuy+8Us7O2cVL2bFiX/TOYtSmL7SVAgtRekhwHAQQQQKBHCaT0boqH5y5KJi8MkskX9MWvN6kn6c0edZFcTFzAH/r9uciPybl3YR/FWYqbFS6/3DVp/NMfmp1YfEtxpOIVhT/8et4fhj+iuEhxv8IfxF382//sXofGFTn8kX2+HHZ5d5Mdmpuj8IfxBxXTFP6wf6HiNkWtwtcRlRs187LCycJihXuNTlT8QHGywvs8pXD5D4WvfbriOwr3JDmpeUhRo8i1+IP/IoXtTlC4bQ8qEor/pdhTack1OuGYq/C9Xq44T+Fr/KbCTtm9WI+pzuX/KdwWx92KfRVud1R8vVcqfNwHFOcrbH2X4niF93Fy5uL76fJFxRcUpYp/KJzoeflSxcEK36eZinKFE7BoP81SEEAAAQQQQACBThRQIlSmL3f9Xxq+e4aG8p7UiafmVK0X8KNTGUX8A+4RYd01TRz2inDd2bF1/oDuhMHHiaJa8/7gGy+na2GNwtt8V+FytMKPRkX71WneSdVAhT8cuz56DK2l7WrqfDpck+VR1TphyC5OYnwtUfs8XatwcpBdxqjiIUV8W8/fqChWxMs4LfxFEd/2Ti27za6L+2rxfWWlajYo3L5tiug47tFyXbzY0+tHxCtj8y25xq9rP9+j6Hw+98mKRxRRIqPZxiTtOk2dDEXbPqv5QxX1imWKeLlAC1sV0bZpzf9UMVgRFSd+fr8oOv+wcMXBmjpRivaNpk+obmK4DZNOEPANoiCAAAIIIIBAKKAv3TykYNejROuqa2puX7JkiT/sUHqPwEhd6mSFp+sU/s39W4rs4s9Q7kVwUrQzXNlPUyc/Xuf31LYronKQZjYqWtsL2dT5omPnOh2gDf0I2HCFeyteVPgDfFMlOp8TgXcU/uBe1dSGqnOvic3s8TdFpSLX4gSpTOGkp0hxpKJY4eM0dz6tara05BrdC+jzORl7SlGjaK7014pDFN7WbW7OTasa32Wzx0DFKkVz93yI1pUo/HMWFf3z09hzZEsnSC8rnLB5ntJJAv7hpyCAAAIIINDrBdRrVBzU15+mTz2H6H2j5XrXyL+J50NJr//JAKCDBeIJUgefisMjkJuAXzKjIIAAAggg0KsFNBDD6KChYZq+2yijgRhuVrIU/41ur7bh4hFAAIHeJkCC1NvuONeLAAIIIBAXSKRmzz4mnUicEGQyzyZXrrwntWyZ30GhIIAAAgj0UgEeseulN57LRgABBHq7wIwZMwaUFhV9Wj1GI9V7dFdq0SK/k0FBAIHOFdhfp/N7N35Xh4JAXgiQIOXFbaARCCCAAAKdKeCR6QoTibPSmczG5LZtt6auv741L4N3ZpM5FwIIIIBAJwnwiF0nQXMaBBBAAIGuF9C7RYXpdPoTGoThcD1W96f5CxY8qFYxEEPX3xpagAACCOSNAD1IeXMraAgCCCCAQEcKzJo1a3hxQcE09RqVJAsLb1Wy9FpHno9jI4AAAgh0TwESpO5532g1AggggEALBPTdRkfqJYeT1Gu0Su8c3ankaGcLdmdTBBBAAIFeJECC1ItuNpeKAAII9DYBJUJ9NQDD2bruCQ1B8LsFCxb8vbcZcL0IIIAAAi0TIEFqmRdbI4AAAgh0EwENxLCfeovODtLpbXUNDb9evHjxxm7SdJqJAAIIINCFAiRIXYjPqRFAAAEE2l+gvLy8YPKkSR9PB8FRySB4/PlVq/6wbNkydSBREEAAAQQQ2LMACdKejdgCAQQQQKCbCFx55ZVDigoKzgmSyf4are43CxcufLmbNJ1mIoAAAgjkiQAJUp7cCJqBAAIIINA2AQ3E8CENxHCqjrImKCj4jd4/qm7bEdkbAQQQQKA3CpAg9ca7zjUjgAACPUggddFFpelx487QdxtN0nN092sghid60OVxKQgggAACnSxAgtTJ4JwOAQQQQKD9BNRLNC5dXz8tmUjU1DY03Lpo0aI32u/oHAkBBBBAoDcKFPbGi+aaEUAAAQS6vUBi3rx5UzSE93F63+gpxb2LFiyo7/ZXxQUggAACCHS5AD1IXX4LaAACCCCAQEsEUt/4Rlm6f3/3Gg0J6uvvSC1evLIl+7MtAggggAACuxMgQdqdDusQQAABBPJKIDVr1sEagOF0jVC3fmdd3W1LlizZmlcNpDEIIIAAAt1egASp299CLgABBBDo+QKp8vLiYNKk0/TdRodoMIblqYqKR3XVmZ5/5VwhAggggEBnC5AgdbY450MAAQQQaJGABmIYpR6jaUqMEnXp9K0VFRWvt+gAbIwAAggggEALBBikoQVYbIoAAggg0KkCCX230dEape7EZBA8GxQW3lOxYEFtp7aAkyGAAAII9DoBepB63S3nghFAAIH8F5gxY8aAviUln1JLR+m7je7Wdxs9m/+tpoUIIIAAAj1BgASpJ9xFrgEBBBDoQQJz5syZVJhInJXOZDYmCwtv1SN2VT3o8rgUBBBAAIE8F+ARuzy/QTQPAQQQ6E0CYXL02XQi8afkggUPpYJA4zJQEEAAAQQQ6DwBEqTOs+ZMCCCAAAJ7EChcu/bVYOjQ/zv/2mvf3MOmrEYAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEuligoIvPz+kRQAABBHq2wAG6vGmKjYrNPftSuToEEEAAAQQQQAABBBBAYPcCF2p1RnH27jdjLQIIIIAAAvkhkMyPZtAKBBBAAAEEEEAAAQQQQKDrBUiQuv4e0AIEEEAAAQQQQAABBBDIE4HCPGkHzUAAAQQQ6DyBcTrVlxUHKrYqnlPcrNikyC5nqeJEhd8lWq24T/FbRVNlgipPU0xWPKO4V9FcGaIVH1dMVexQPKJ4VLFOQUEAAQQQQAABBBBAAAEEOkXgeJ3FCYnfC1qpcNLj+TWKSYqolGjmxwqvq1Y4iaoNl3+jqdfHyxQtVCm8/XZFXTj9YVgXfwfJ8/WKGoWP+4bC+6UVFykoCCCAAAIIIIAAAggggECnCDghccKzX+xsJ2veCcrSWN0VYd0PNC0K6/to+pOwPhXWeVKm2KLwKHVOfgoUfRXXKHxcRzxBWq9lx76KqBytGSdIr0YVTBFAAAEEEEAAAQQQQACBjhTwY9UNCj/G1j/rRDO0fHlY53VVCvc0FYd10cTH8JDdOxXRujmadxL0fxTZ5c+qiCdITqYeUFySvaGWH1Z42+zeqSY2pQoBBBBAAAEEEEAAAQQQaLvAUzqEkxAnI+cphimyy0dU4W3+mL0iXPZ7SF5/ULi8PFzeK1yOTy4L18V7kKL1Cc2MVBylcMLk96F83FIFBQEEEEAAAQQQQAABBBDocIFxOsPvFe5JcjLi+LtitmKQwuV8heu/6YUmyr+rzutPDde9pqmTm6bKuar0tvEEaR8tX6V4K1zn9X5Ezz1TnidBEgIFAQQQQKBrBJJdc1rOigACCCDQRQJOZk5ROFH6quJ2xSTFQsW9Cr8/FJXoEbpoOZp6RDsXD67g4qTG7xz58bns0i+r4mAtr1B8TXGbwr1Yhyv2VvxV4eLjURBAAAEEEEAAAQQQQACBDhUYrqM7ORqddRYnMf9UODE5VHFEOP+EptnFj8VFPT1Rj9P9qvO+Z2VvrOVrwnVRD1LU++TEKLs8rwofh3eQsmVYRgABBBDoNAF6kDqNmhMhgAACXS7gxOh3Cj9OFy/btfBsWOFH7zzSnUeZc89O9jtKfl/I7xrdqdiscPEjey7+DqR4GaCFi+IVmh8SLm/Kqj9Syx8I65yEURBAAAEEEEAAAQQQQACBDhXw43NOhHYoLlX4cbfDFDMVHmL7H4roF2df1Lx7c9yzNE3h7S5UbFDUKA5XRMU9Pi8rvP13FB9TnKH4m2KtwvVRD9KXwuWnw7oTNZ2v2KqoVHhb75v9aJ6qKAgggAACCCCAAAIIIIBA+wo4KXIi5EQkHn6cbqIiXqZroVoR384Jz0fjG4XzfqfpL4r4tndq+fSwLkqQ3Dv0/aztXteyk6KTFe+E6z6kKQUBBBBAAIFOF+Axhk4n54QIIIBAlwu4J8k9QvsqnNC498c9Op7PLn5M7oOK4Qo/eveiwr1NTZWkKicrfFz3HrlHqLniZMyDQ6xQvBTbaKDmJyj8PpIf96MggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAnkqwDDfeXpjaBYCCCDQEQLTp08vGjVs2CeDTGZdqqLiyY44B8dEAAEEEECgOwsUdufG03YEEEAAgdwFZs6cOahPcfF52qM42LHjkdz3ZEsEEEAAAQR6jwA9SL3nXnOlCCDQiwVmz549viCR+EwykXhj87Zty6677rodvZiDS0cAAQQQQKBZAXqQmqVhBQIIINAzBFKzZ384nUicGiSTfw3mz7/3uiBI94wr4yoQQAABBBBofwF6kNrflCMigAACeSGQSqWSQX39aUEicVhDENy9YMGCv+dFw2gEAggggAACeSxAgpTHN4emIYAAAq0VUHLUN11f/1k9UjekLp3+ZUVFRWVrj8V+CCCAAAII9CYBEqTedLe5VgQQ6BUCqcsvHxGUlp6ri60Oqqp+kVq6dEuvuHAuEgEEEEAAgXYQ4B2kdkDkEAgggEC+CMydO3ey2nKWhvFeGRQW3qHkqD5f2kY7EEAAAQQQ6A4CJEjd4S7RRgQQQCAHgdScOSdqs2P1vtEfFixc+GgOu7AJAggggAACCGQJkCBlgbCIAAIIdDcBvW9UnK6rm6bBGMYHBQU/W5BKvdTdroH2IoAAAgggkC8CvIOUL3eCdiCAAAKtEEhdcsleQf/+ft8oWVNf//PFixdvbMVh2AUBBBBAAAEEQgESJH4UEEAAgW4qoJ6jfTVSXbneN1q7acuWXy9durSmm14KzUYAAQQQQCBvBEiQ8uZW0BAEEEAgdwF9+etH9cWvJ+sLYB+fP3/+A9ozk/vebIkAAggggAACzQmQIDUnQz0CCCCQhwLl5eUFkydNOiOdTE7OZDJ36Mtfn83DZtIkBBBAAAEEuq0ACVK3vXU0HAEEepuAHqnrHzQ0+H2jgUFNzS9SS5as620GXC8CCCCAAAIdLUCC1NHCHB8BBBBoB4HZs2ePLkomP5vOZDYn33nnl6nvfndbOxyWQyCAAAIIIIBAlgDDfGeBsIgAAgjkm4C+/PWDBUFwhgZjeGbFqlV3L1u2TF91REEAAQQQQACBjhAgQeoIVY6JAAIItI9AQl/+elIikTiyPpO5T1/++kT7HJajIIAAAggggEBzAjxi15wM9QgggEAXCqQuuqg0GDv2HDVhlJKjZQsXLlzdhc3h1AgggAACCPQaARKkXnOruVAEEOguAhqMYai+3+g8DeNdX1tb+4urrrpqU3dpO+1EAAEEEECguwvwiF13v4O0HwEEepRA6sorJ2qkuk8rOVqdXLHi9quWLavtURfIxSCAAAIIIJDnAvQg5fkNonkIINB7BPTlr8cqMTpRX/76J3356/Lec+VcKQIIIIAAAvkjQIKUP/eCliCAQC8V0CN1hel0+qxkJjNJ7xvdrveNVvRSCi4bAQQQQACBLhcgQeryW0ADEECgNwsoORoYvm9UWl9f/4tFixa90Zs9uHYEEEAAAQS6WoB3kLr6DnB+BBDotQKpmTPH6X2jz+ixureSGzb8eNFNN1X3WgwuHAEEEEAAgTwRoAcpT24EzUAAgd4loC9/PTyRyZyWDIKngoULf58KgnTvEuBqEUAAAQQQyE8BEqT8vC+0CgEEeqhAKgiSwZw5pygbOjyTSNyzYMGCp3ropXJZCCCAAAIIdEsBEqRuedtoNAII5LvAvHnzTkju2LEidc01G6K2pqZP75seMaJcgzHsHdTW/ip19dWvReuYIoAAAggggEB+CBTkRzNoBQIIINBzBK688sq9ixKJS4Li4okfPuKIvzz22GN1s2bNGp4YOPDCIJ0OdtbV/aji6qvf7DlXzJUggAACCCDQcwRIkHrOveRKEEAgTwSmqCQSiY+oi35YcUFB2XFTplQXJZPn6Z2jNevffPMXN9xwA4Mx5Mm9ohkIIIAAAghkC/CIXbYIywgggEAbBDRsd1/1El2VyWQKlCRtzqTTRwWJxIZEOv2fqYqKR9pwaHZFAAEEEEAAgU4Q0ABKFAQQQACB9hLQdxkdr+RouHqLNmp6gAZiKMgEQT8dv7a9zsFxEEAAAQQQQKDjBHjEruNsOTICCPQygcZ3jwoKvqrEqEaJ0URdfom66Z/SoAx16UTi8OOOP/65hx9+uKqXsXC5CCCAAAIIdCsBepC61e2isQgg0AEC7t05X/EjPRK3QtNtCn8nUY2iUnGP4jLFeEWzpby8vKCooOCcIJMZlUwkJigxqtH8M9qhv74Itr+OPbIwk/mclnm0uVlFViCAAAIIIND1AoVd3wRagAACCHSJQJnOeoXia0pegn79+r3cv3//NX379v17QUFBjXqBCnfs2DGwurp6+JYtWy5raGhYom3vUsxROPGJl8TkAw8sV1Y1TUlRiVZs1v51Ou5ozb+p+if1HtJrDYnEWi3riTsKAggggAACCOSrAL/JzNc7Q7sQQKAjBT6pg/9AiVBm1KhRj40fP/65wsLCht2dcP369WNee+21Y5Q0HaDtnCzNU9R7n3mzZ5+hZGiGe4o0QMObiWTyqfp0+hElSc9WVFSQFBmJggACCCCAQDcRIEHqJjeKZiKAQLsJuNdo0V577fXYBz7wgeV7Soyyz6okadLq1avPVPLzZ607e96sWUcnCgvna36gRqx7VO8e/XDlypWPLVu2bLcJV/ZxWUYAAQQQQACB/BDgEbv8uA+0AgEEOkfAydH8sWPH3r7ffvs925pTjhs3btWAAQO+/9xzz32mpKTk8bqGhp1696hWz81dXbB5822ppUu3tOa47IMAAggggAAC+SFAD1J+3AdagQACHS/gx+rubEtyFG+i3ksapCTpq2PHjFlz3rRppy685pqX4+uZRwABBBBAAIHuKUCC1D3vG61GAIGWCQzS5i/qsboXDj300AdatmvzW69bt278Cy+84JHpzlLc3fyWrEEAAQQQQACB7iLAMN/d5U7RTgQQaIvAlR6Qwe8cteUg2ftqgIdXBw8e/KTqr1fw72k2EMsIIIAAAgh0QwH+h94NbxpNRgCBFgn4e46+6tHqWjogQy5nOeCAAx7RdmMVZ+SyPdsggAACCCCAQH4LkCDl9/2hdQgg0HaBszQEd8JDebf9UO8/gr43qVrfn7RSay58/1pqEEAAAQQQQKC7CZAgdbc7RnsRQKClAqf4S2A7ovcoasjQoUNf0PxURUFUxxQBBBBAAAEEuqcACVL3vG+0GgEEchRQ59ER6uFZl+PmrdpMgz/4y2AHKPZt1QHYCQEEEEAAAQTyRoAEKW9uBQ1BAIGOENAXuo7VY3CbOuLY0TGVgPm7j+oVE6I6pggggAACCCDQPQVIkLrnfaPVCCCQu0BfjWBXk/vmLd8ymUwG6qnaqT37t3xv9kAAAQQQQACBfBIgQcqnu0FbEECgIwRqGxoaCjviwPFjqqeqSMsdmojFz8c8AggggAACCHSMAAlSx7hyVAQQyB+BN2tqagZ2ZHOqq6tLdfwSxfqOPA/HRgABBBBAAIGOF+jw36p2/CVwBgQQQOC/BWbOnDmouLh4TIHePUoHwejFV1/94vbt20f89xbtP7dp0yYf3+8grWr/o3NEBBBAAAEEEOhMARKkztTmXAgg0K4C5eXlBRMnThyhZ9vGpgsKxiaVFOlloIHpdHpHOpNZqy7yF/Xo231bt279t3Y9cdbBNm7cuL+qnlJUZ61iEQEEEEAAAQS6mQAJUje7YTQXgd4scNlll/XTiHFjlQA1JkPpRGJkkE4Xqqfo7WQ6XdmQSCwvSCYr58+f/3bMaY3mF69fv37syJEjK2P17TKr95uSVVVVH9DBvtUuB+QgCCCAAAIIINClAiRIXcrPyRFAYDcCidTllw9vKC0d68fl1DM0RrGXkqMa9RS9ruRoteb/pC+ArUylUh5Brrnymlbc+dprrx2jBOkXzW3U2vpXXnnlg2qHB2j4YWuPwX4IIIAAAgggkD8CJEj5cy9oCQK9WkBJTml9ff1YDZk9xo/KKQEaLZASJUfvaLq2IQgeL9ixo3L+Nde8oeVMC7Hm7Nix429r1qyZNGHChHZ7T0jH7P/Wm2+eomPeetGUKTtTt9zSwmaxOQIIIIAAAgjkm0Ai3xpEexBAoHcIKCEaqsfTGnuH9IjcWF310CCZrFdytF7JUWWyrq5ym+Laa6/d3k4iFfquom8cfPDB3x8yZMjGth7Tj9b95S9/uUADQpRd/JWvrC8sKlqVyGS+V11b++iSJUu2tvX47I8AAggggAACXSNAgtQ17pwVgV4lkCovL66fNGl04a6R5Rofl1NPUZ8gk9nSmAw1NFTWBUHlCy+8sGHZsmXqLOqQ4h7z3+tLYw9R+VlZWdnm1p7FydHTTz99pgZ/GKd3oo697JJLzlaSd76/N0FdW09l0unbkkVFTygJbE1vV2ubxX4IIIAAAggg0A4CJEjtgMghEEDgvQJXXHHF4NJkcmy6sHDXyHJBMEyjymXUQ7QhHEyhsra2du3VV1/d6iTlvWfMeam/tvydepIO1eh3t+qdJL+f1KKi7zzq88wzz5yzc+fOwdrxZMVfPZreQRMnnq/jTleCNED/sG7W+1I+9oZEQ8Ozuu61GkzinfpkcqveV9q2aNEiJ04UBBBAAAEEEMhDARKkPLwpNAmB7iSQmjKlMDjyyFENxcVjlSA0vj+k5KC/EoHtST0qp+6gSs2vLSoqel09Kv6uoK4u/lLXpYovDB48+MkDDjjgkb59++5xeG73GnlABo2GN0XX4+Tn04oXFVFJzJs37zT1in1ZFWXKB3eoR2mDEiYNrJdUXpgWS0JP4WXW19TVfasLksOonUwRQAABBBBAYDcCJEi7wWEVAgi8XyB18cUDG8rK3k2G1DM0UlsllQy9oeRgrYbarizYtq0ydd11Hlwhn8tpatz1ivF6TG6F3kt6UVGp+S3KZxrbrd6iUn8JrL/nyEN5K8nxaHVXKa5R1DZulPVHas6cqcqEpjdkMgkdZajm1yopWt14zEzmQB3j5/MrKm7P2o1FBBBAAAEEEMgTARKkPLkRNAOBfBRQj48zhRHxwRT0QX+QEiEPq+2R5Sr1Tk+lEojXly5dWpOP17CHNvn6TldcqJiqGKioV0fPTiU1ToZKvKzwl8D+TPEjRZVidyUxb86cL+kYPt4GxUT1Ium/oFr/4D7/TlXVom5qtbtrZh0CCCCAAAI9RoBhvnvMreRCEGi7gL+ItbS0dIz+YRjrkeXS9fWjlBAVFeiLWNP6AtZkff1DNRpQYfHixW+1/Wx5cQRdZnBHGE6W9lVMUHI0QFMngU5wPCz4Hh/B0zZRySQLC38e1NcfkEkkhqjyr4pDlDDtp+fr7qmpqfE5KQgggAACCCCQpwL0IOXpjaFZCHSCQGLWrFnDihOJsemCAg+zPUYZwhD1DtVqZLnXPZiCuk4qt2/fvva6667b0Qnt6VGnmDt37uH6DqdL9fDha3oMcV+5LtdjiO6RStel07+tqKh4tUddMBeDAAIIIIBADxEgQeohN5LLQGBPAqmLLioNRowYk9ZgCtp2jEZVG6PeoRJ1Z2xyMqQP8GtrM5nKYo2wltKH+D0dj/V7FEgoSbpEvW8n6/m6JxKVlYuDCRPS6lk6WbiHK3F6csOGDQ/cdNNNGuGcggACCCCAAAL5IkCClC93gnYg0M4CqUsvHRqUlIyJhtpWL8be6sHQa0PBOidD7h0qLCys1HtG29r51BwuFJgzZ84k9cp9WQnRjQsXLnx3xLvUFVfsGxQVnanN6E3ipwUBBBBAAIE8EyBByrMbQnMQaI3A9OnTi0aNGjW6cTAFvTukY4zR6Gl9NWLa1miobQ+m8Pzzz6/vwC9ibU3Te/w+TlRT3/7229kXqsS0WPfnJPXkfVhJ1F/Wvfnm/fQmZSuxjAACCCCAQOcLkCB1vjlnRKDNAvpwXfaekeUSieG7vmIn84beH/JgCpU7NJgC37XTZuoOP4B6mfbRo45nKlEKdN9+m7rqqjUdflJOgAACCCCAAALNCpAgNUvDCgTyQ6C8vLxg4sSJozTmdOPIckFBgQdTGKDH5KobkyElQkFDw9p177zzOj0Q+XHPWtqKVHl5cfqgg97tTQpWrXogtWxZk9+z1NJjsz0CCCCAAAIItEyABKllXmyNQIcLzJgxY0BfjyqnREhDa49N6otY9f5QgV7qf7NxMIV0em2NBlPQUNsbO7wxnKBTBRp7k4LA7yYFGir8t+opXNOpDeBkCCCAAAIIIBCQIPFDgEA7CGi47OGFekwquXnzL1NLl27J9ZApfQ4OZswYoRf2x3iobSVDY/XuUJl6h3bq/ZTX/f6QB1PYvHnzWr5cNFfV7r2de5OCSZOmamCHI5QU/zW5YsX99CZ173tK6xFAAAEEupcACVL3ul+0Ng8F9Fv+ffWFql/RO0DjEw0NFalFi55urpmp6dP7BqNGjVHysysZCoJRSoj8sv5GPTZX2aCEyIMp6Jj+IlaNDk3prQIa6W6CRiA8S0mSXk9K36FR8Fb3VguuGwEEEEAAgc4UIEHqTG3O1dME/D03xyYymQuVHOkVoaA4k07/bH5FxZ3hhSaU6Oz9nsEUksnGL2LV+nWN7w8lk5XBunVrUzfdVN3TcLietgs0jk44bNhJ7/YmJZP362eKd5PaTssREEAAAQQQaFaABKlZGla0UeBo7T9O8RvFzjYeK+92T1122bB0aWm5GnackqONircymcz4jKZ6T+ge9QqN0ToPtV2qx+WqHli+fNBbGzbsPO/Tn/5xsGTJhhRfxJp39zSfG+TepPB7k5JBXd0dGunulXxuL21DAAEEEECgOwuQIHXnu5ffbf+lmvcZxUjFhrCp0zQtVvw8XO52E/32fkK6ru6IZEHBxzVwwjAlP+8oOeqjZ+EGaX4v/YUqyiST/08XtiZZV1dZrRHmlixZslXLyxUfUpQpKAi0WMC9SSNGjJiq5+0+oscxnwoKC++jN6nFjOyAAAIIIIDAHgVIkPZIxAatFGgqQXpRxxqkGNbKY3bJbh5VrrSw8MP6rppPqofoECVDA/QBNa1pnRpUq8Roi/4ibW7IZLZqmxGaztH7Ii9nNZYEKQuExdYJzJ49e3xRMnmW9k5qpMM7lCTRm9Q6SvZCAAEEEECgSQF9zqMggEBzAnNnz57Xt6TkbvUY3ajk6FNKivbWO0c1mm7WPm83Pl4XBDV6kb62IJGo8XEK9X1FzR2PegTaKlBRUfHqujfeuFGJ+UoNDvK5efPmnXHxxReXtPW47I8AAggggAACuwToQeInIRLw+0JfVhyo8CNhzyluVmxSxIuTaj8qdpLiEMXfFI+EU/eoRCXeg1SqSr+vM1Ph+W8pPNLbQMW+imWKNYp4cZJxrsK9Tn6Pqbnin2Fvd6Jib8ULigcU9ymiMkIzn1M8qHhecb7iSMV2hdv+a0V2abzOgydPXlS1efPogQMHrhw+fPhjhx9++NN9+vSxz86CdLqPh+TWdxWN/NPDD3/q9ddfP3Db9u2D9S7SyvXr16e0jXuNotJcD9IF2mCU4iHFk9HGTBHIRUC9R+M0wt1ZGh6+oD6TubOJnstcDsM2CCCAAAIIIIAAAlkCx2t5h0IdI8FKxepwfo2mkxRRcXLjhMLbbVA8q3BS5OW/K/z4XFScILneyclUhXtXvOzw/A2KS8PlBZpml2+qwtt+NXtFbNlJzF0Kb+cenX8onLx4ebEiKkdoxnX/rnBCl1a8o3CdY5EiXlpynX20oxM8H6dOj9i9Hc57+ZuKqCzXTFW0EE4rNPV2TgD9bhYFgRYL+N2k1Jw5p8ybM+dKJUyFLT4AOyCAAAIIIIAAAgi8T8C9RR5mer/YmpM17w/vS2N17mFy3dWKqPexr+ZvC+u/oGlU4glSVOfeoDejBU3dc9KgWBGri2adfNUqhkQVTUzPUZ3b85+KgnB9f01/r9D3q777rlOUIHnbexQeOMLlMIUTJZ/Hw3RHpSXXOVs7+bhOkpwg2uUYhd8L8bW5h8xluSKeINnQ+7n3Kn5uLVIQaLlAeXl59Heg5TuzBwIIIIAAAggggMC7Av6Nsz/Ir1M4uYiXGVq4PFbxdc3fr8hOWvx4mz/sx3ttckmQtEvj8bzvZC+E5UBNXXdHVNHM9Fvhdv+Wtf6DWr5WcWhYHyVIr2nZvUPx8mMt+FwHxypzvc4B2meL4m1Ftt3XVOfjfknhslwRJUjf1rzX/VzBb/yFQEEAAQQQQAABBBBAIJ8EnlJj/IH9YcV5ilxGmXPP0UGK0xROmrz/VYqo5JogXagdvG8q2lHTuQrXfTZW19TsGeF2WzX1Y3JOhJr6LXqUIP1Q67PLHFX4XGdmrwiXd3edHwn39fVnl6QqBipKwhXLNfVjgNcrfL5XFU21VdUUBBBAAAEEEEAAAQQQ6EoBD9Dgx9Lck+QP7w6/UzRbEX+vyL0d0xR/UPg9nmjb1eF8axIk98JUK/yYX1Se0Yx7ZvpEFbuZurfndUXUlk2a/5XiY4qoRAnSd6KK2PQKzXvfs2N1uV6nk0nv6x6hPZXl2sDbOtxGTz+voCCAAAIIIIAAAgjkkYB/y01BwI+enaJwouRBEW5XTFIsVNyriHo6btH8rxV+nGymwu8pjVGcr3Dxh/6WFvf+/FbhR+wOVPi8hyhuVXjgiD2VG7SB2/0JxbWKDYpyxR8V2b1CTgBzKbdoo1yuM/r74/eOci3uQXLP006FE7Zceuu0GQUBBBBAAAEEEEAAAQQ6Q2C4TuLkaHTWyfpp+Z8KJz2HKgaH889r6h6WeHFC4u0WxypzfcTOu3xS4f39uJt7rTz/ccWeyjHa4GNNbPRF1fkYt4Xroh6ka5rYNrsHqSXXGR33b00c93jV3ak4I1y3XFM/YheV6DHCX0QVTBFAAAEEEEAAAQS6XiD7g27Xt4gWdLaAE6PfKW5UfCV28u2af1bh94zc8zJE4eIeH48QFxX/DH0+XMilJyXqjYr299S9VG8pokSrUvNOKPZUZmoDJ1e+BvccReXxcCbXHqNoP09bcp1+FNC9bx9UjFSsV0RlgWacJDkRioqTtqhcrZkLFJ9V/Exxh4KCwP4iOEnxgMJ/B51g++fI76z50dGnFU2Vs1R5ouIAxWrFfQr3zMaLHwn139GfK7ytfzGyt+LPipsVdYrs8hlVeNsJCv+se9+m/m7mcn5fy4EK/6LC1zld8UeFH++dphiriHpYT9P8fgr/HVumWKOgIIAAAggggAACnSLghOVZxQ7FpYqDFYcpnHykFf9QJMPwByR/yPeHfz8mdpHCH5b84c31dyomKlya6kFyEuDtvqU4UhEvS7XgdY6F8RW7mfeHN2//iOIExT4Kf1B7TOF6DwPucoTCy/5gll2ye5B8rS25Tj9e6GM/r/D8NMVdCtd5GpXlmqmKFsKp2+zt1ioGhXVMerfAubp8/0zcoHhHUat4S+G6GsW/KOKlRAs/Vnh9tcLv8nkfL/9G4fVRWamZ1YpbFF7/hsK/7PD8kwonS1HpoxknJl7nxGl9OO/lbyqi0pLzR/8m+O+t/23xsS4OD3S3ptvDZbdpm2Knwtv474f/blMQQAABBBBAAIFOE3BS5ETIH0bi8YSWo4RHs8HRitcV0Tb+4OQPPQMV/oDj+ui31tGHoRGqi8rpmlmj8HbfVcSLEybXu9dngiLX4mQt+kAYtcsfFK+MHeAIzXtdLgmSd2vJdXp79wRtVUTn94e/nyoGK6KyXDPZCZLX/Ujh/f7LC5ReLxAlSP6ZuF7hxDmhOEaxWeGkYYIi2TzV+gAAB7NJREFUKldoxtv+QFEUVvbR9CcK16cUUVmpGdc54To8rPTPqHuPXB//GZwd1jlJirfhFS377+i+CpeWnD/6N8HXcZXCf8+GKlyifz+87vONNbu+PPlGzbtt3wzrmCCAAAIIIIAAAp0mUKAz+UNTueIcxYcU/mCWXfqpYoriBIXn4+UgLQyLVzQx72PupyjNWucky785/l1WfS6LI7XRVMXnFB9X7K1oa2npdRbqhB9UHKfYk0Fb28b+PVfgXF2aE4JnFf6Zipd/0YLXXRdW9tfUSfcORXFYF02870bFTkW0LkqQfI54cS/Qywr//dtLMUCxRfG2wueIl69pwW34kqKl548SJP9SILtECVL8MV9v438rfL5fe4GCAAIIIIAAAgj0JoHog9dZvemiuVYEsgSiBOk/suq9GCUL0S8RPqI6Jw9/9Momyn2q83r/4sIlSpCiXptdtbv+/LEm3vZIRXTc+3etes+fSS35lxlOqqLtcj1/lCCd9J4j7lqIEqR9stb5Fyp1ir9l1bOIAAIIIIBAhwn4f3YUBLpS4HSd3B8K/UiPf2vu95QoCPR2gRebAFitOj9OOiZct384fSicZk9WhBUTYivc4/R2bDmadfLk4mM7EXN5ZtfkPX+mteTepRpFa87vg/nveXPFvV7x4qTNj/Tx/6q4CvMIIIAAAh0q4McwKAh0pcC1Ovkkhd89iHqRurI9nBuBfBBwApJd/I6RozJrRfQIXVZ144h2rnsjtqKp43p19Lisj31AuL17b3IpLTn/no7nZIiCAAIIIIBAlwrwW7ku5efkEjhPcaxivOJhBQUBBHaNJpnt4CGynbSsCldEvUxTszcMt/Pjci5+vygqwzXT1CN2PrbLC2F4for/yCrHa/lOxRmK1pw/63AsIoAAAggggAACCCCAAALNC0TvIL2qTfbK2uyHWvYjZx5IxaWPYp3CgytkDwziEeK8bfyR1egdpMtUHy/uMapTPB9W+v0in9+9OSPDumjykGZ83MMULT1/9A7SiOhgsWn0DlLUkxVb1TjQxNPxCuYRQAABBBBAAAEEEECgdwhECZITlscVn1ecqLhZ4cTkLkW8fFELrv+nYprCicuFig0KP053uCIqTpCcTPk9pgWKExTe9nWF6z+qiMr5mvFxnTR53sf2uV0Xb0NLzk+CJDwKAggggAACCCCAAAII5C4QJUhztcujCickUSzX/AhFdpmuimpFtJ2naxXxhEeLjaPYOXHy9tsU0fZ+/8912eUCVWxVRNt5gIafKgYr4iXX85MgxdWYRwABBBBAAAEEEEAAgT0KRAnSv4ZbjtbUIz0evIc9/d1FfpfPPT0e9CSpyC4rVeEEyaVI4e3dO1WmaK54IJ9cvt8rl/M3dw7qEUAAAQQQQAABBBBAAIEmBbITpCY3amVlPEFq5SHYDQEEEEAAgZ4v0NRvGXv+VXOFCCCAAAIIIIAAAggggEATAiRITaBQhQACCCCAAAIIIIAAAggggAACCCDQtQKDdPrJiuyBENqjVfvrIH4/iYIAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQBcJ/H+YMraVdHrGlAAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title “Graph for 5 rules”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "plot(highLiftRules, method=\"graph\", control=list(type=\"items\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

luwei0917/awsemmd_script

notebook/Optimization/read_topology_prediction.ipynb

1

40391

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import datetime\n", "import imp\n", "import numpy as np\n", "import fileinput\n", "from itertools import product\n", "import pandas as pd\n", "from scipy.interpolate import griddata\n", "from scipy.interpolate import interp2d\n", "import seaborn as sns\n", "from os import listdir\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.interpolate import griddata\n", "import matplotlib as mpl\n", "# sys.path.insert(0,'..')\n", "# from notebookFunctions import *\n", "# from .. import notebookFunctions\n", "from Bio.PDB.Polypeptide import one_to_three\n", "from Bio.PDB.Polypeptide import three_to_one\n", "from Bio.PDB.PDBParser import PDBParser\n", "from pyCodeLib import *\n", "from small_script.myFunctions import *\n", "from collections import defaultdict\n", "%matplotlib inline\n", "# plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio\n", "# %matplotlib notebook\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = np.array([16.18033, 10]) #golden ratio\n", "plt.rcParams['figure.facecolor'] = 'w'\n", "plt.rcParams['figure.dpi'] = 100\n", "plt.rcParams.update({'font.size': 22})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_seq_fromPredictTopo(topo):\n", " loc = topo\n", " with open(loc) as f:\n", " a = f.readlines()\n", " assert len(a) % 3 == 0\n", " chain_count = len(a) // 3\n", " seq = \"\"\n", " for i in range(chain_count):\n", " seq_i = (a[i*3+2]).strip()\n", " seq += seq_i\n", " assert np.alltrue([i in [\"0\", \"1\"] for i in seq])\n", "\n", " return seq" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "seq = get_seq_fromPredictTopo(\"/Users/weilu/Research/server/sep_2019/1su4_nice_movie/1su4_topo\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "994" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(seq)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start 60\n", "End 75\n", "Start 88\n", "End 106\n", "Start 262\n", "End 279\n", "Start 293\n", "End 320\n", "Start 760\n", "End 781\n", "Start 832\n", "End 856\n", "Start 897\n", "End 911\n", "Start 929\n", "End 949\n", "Start 963\n", "End 985\n" ] } ], "source": [ "pre_res = \"0\"\n", "end_list = []\n", "start_list = []\n", "for i, res in enumerate(seq):\n", " if res == \"0\":\n", " outMem = True\n", " if res == \"1\" and outMem:\n", " outMem = False\n", " print(\"Start\", i)\n", " start_list.append(i)\n", " if res == \"0\" and pre_res == \"1\":\n", " print(\"End\", i)\n", " end_list.append(i)\n", " pre_res = res" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60 75\n", "88 106\n", "262 279\n", "293 320\n", "760 781\n", "832 856\n", "897 911\n", "929 949\n", "963 985\n" ] } ], "source": [ "lines = []\n", "for start, end in zip(start_list, end_list):\n", " print(start, end)\n", " start_res = start + 1\n", " end_res = end\n", " lines.append(f\"resid {start_res} to {end_res} \")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'resid 61 to 75 or resid 89 to 106 or resid 263 to 279 or resid 294 to 320 or resid 761 to 781 or resid 833 to 856 or resid 898 to 911 or resid 930 to 949 or resid 964 to 985 '" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"or \".join(lines)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "IOPub data rate exceeded.\n", "The notebook server will temporarily stop sending output\n", "to the client in order to avoid crashing it.\n", "To change this limit, set the config variable\n", "`--NotebookApp.iopub_data_rate_limit`.\n", "\n", "Current values:\n", "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", "NotebookApp.rate_limit_window=3.0 (secs)\n", "\n" ] } ], "source": [ "import pickle\n", "import gzip\n", "import numpy\n", "\n", "with open('/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl', 'rb') as f:\n", " u = pickle._Unpickler(f)\n", " u.encoding = 'latin1'\n", " p = u.load()\n", " print(p)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ACC': array([[2.14100e-03, 3.12040e-02, 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0.209, 0.38 ],\n", " [0.051, 0.024, 0. , 0.133, 0.065, 0.134, 0.2 , 0.393],\n", " [0.006, 0.019, 0. , 0.144, 0.013, 0.016, 0.286, 0.516],\n", " [0.012, 0.04 , 0. , 0.177, 0.023, 0.065, 0.098, 0.585],\n", " [0.024, 0.077, 0. , 0.241, 0.037, 0.131, 0.091, 0.399],\n", " [0.027, 0.121, 0. , 0.302, 0.025, 0.149, 0.084, 0.292],\n", " [0.014, 0.116, 0. , 0.342, 0.008, 0.153, 0.129, 0.237],\n", " [0.011, 0.105, 0. , 0.339, 0.026, 0.113, 0.107, 0.298],\n", " [0.005, 0.022, 0. , 0.227, 0.08 , 0.035, 0.003, 0.627],\n", " [0. , 0. , 0. , 0.001, 0. , 0. , 0. , 0.999]],\n", " dtype=float32)}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p[0]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'ascii' codec can't decode byte 0x88 in position 4: ordinal not in range(128)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-24-4e48a4656a12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0x88 in position 4: ordinal not in range(128)" ] } ], "source": [ "import pickle\n", "\n", "\n", "with open(, 'rb') as f:\n", " data = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "OSError", "evalue": "Failed to interpret file '/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl' as a pickle", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_kwargs\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 441\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0x88 in position 4: ordinal not in range(128)", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-8dbae0f71845>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl\"\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~/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m raise IOError(\n\u001b[0;32m--> 443\u001b[0;31m \"Failed to interpret file %s as a pickle\" % repr(file))\n\u001b[0m\u001b[1;32m 444\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mown_fid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mOSError\u001b[0m: Failed to interpret file '/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl' as a pickle" ] } ], "source": [ "np.load(\"/Users/weilu/Research/Build/RaptorX-Contact/raptorx.uchicago.edu/pdb25-test-500.release.contactFeatures.pkl\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seq[60]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'01111111111111110'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seq[59:76]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seq[75]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seq[74]" ] }, { "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

reworkhow/XSim.cpp

ReadMe/data.ipynb

2

2320

{ "metadata": { "name": "", "signature": "sha256:5a762ce3a95a8a5b5037f4726d2736803a87ec93b10b00b269562ea472dc65e5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "> genomoe information including number of chromosomes, chromosome length, number of loci on each chromosome and mutation rate" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%file genomeInfo.txt\n", "nChrm 3\n", "chrLength 1.0 1.1 0.9\n", "nLoci 5 4 2\n", "mutRate 0.00000001" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> map postion for each loci on different chromosomes (1 line each chromosome)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%file mapPos.txt\n", "0.1 0.2 0.3 0.4 0.5\n", "0.2 0.7 0.8 0.9\n", "0.5 0.6" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> haplotypes for all individuals (2 lines each individual)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%file haplotype.txt\n", "1 1 0 0 1 1 1 0 1 0 \n", "0 1 1 1 0 1 0 0 0 1 \n", "1 0 0 1 1 1 1 0 1 1 \n", "0 1 1 1 1 0 1 1 1 0 \n", "1 1 1 0 0 1 0 0 0 1 \n", "0 0 1 0 1 1 1 0 0 1 \n", "1 1 0 1 0 0 1 1 0 1 \n", "0 1 0 0 1 1 1 1 1 1 \n", "1 0 1 0 1 1 1 1 1 1 \n", "0 1 0 1 0 1 1 1 1 1 \n", "1 0 0 0 1 1 1 0 1 1 \n", "0 1 1 0 0 1 1 0 1 1 \n", "1 1 0 1 1 0 1 0 1 1 \n", "0 1 0 0 1 1 1 0 1 1 \n", "1 1 0 0 0 1 1 0 1 1 \n", "0 1 1 1 1 0 1 0 1 1 \n", "1 0 0 0 1 1 1 0 1 0 \n", "0 1 0 1 0 1 0 0 1 0 \n", "1 1 1 0 1 0 1 0 0 1 \n", "1 0 0 0 1 0 1 0 1 1 \n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing haplotype1.txt\n" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }

gpl-2.0

jason-neal/companion_simulations

Notebooks/Combined_interactive_spectra.ipynb

1

66959

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Interactive Spectra combination!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "import os\n", "from astropy.io import fits\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jneal/Phd/Codes/companion_simulations/simulators/__init__.py:7: UserWarning: \n", "This call to matplotlib.use() has no effect because the backend has already\n", "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n", " \"__main__\", mod_spec)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/runpy.py\", line 85, in _run_code\n", " exec(code, run_globals)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/__main__.py\", line 3, in <module>\n", " app.launch_new_instance()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 478, in start\n", " self.io_loop.start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n", " super(ZMQIOLoop, self).start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\n", " handler_func(fd_obj, events)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n", " self._handle_recv()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n", " self._run_callback(callback, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n", " callback(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 281, in dispatcher\n", " return self.dispatch_shell(stream, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 232, in dispatch_shell\n", " handler(stream, idents, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 397, in execute_request\n", " user_expressions, allow_stdin)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n", " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2739, in run_cell\n", " self.events.trigger('post_run_cell')\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/events.py\", line 73, in trigger\n", " func(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 160, in configure_once\n", " activate_matplotlib(backend)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 308, in activate_matplotlib\n", " matplotlib.pyplot.switch_backend(backend)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 232, in switch_backend\n", " matplotlib.use(newbackend, warn=False, force=True)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1305, in use\n", " reload(sys.modules['matplotlib.backends'])\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/importlib/__init__.py\", line 166, in reload\n", " _bootstrap._exec(spec, module)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 14, in <module>\n", " line for line in traceback.format_stack()\n", "\n", "\n", " matplotlib.use('Agg')\n", "/home/jneal/Phd/Codes/companion_simulations/mingle/utilities/__init__.py:3: UserWarning: \n", "This call to matplotlib.use() has no effect because the backend has already\n", "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n", " \"__main__\", mod_spec)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/runpy.py\", line 85, in _run_code\n", " exec(code, run_globals)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/__main__.py\", line 3, in <module>\n", " app.launch_new_instance()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 478, in start\n", " self.io_loop.start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n", " super(ZMQIOLoop, self).start()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\n", " handler_func(fd_obj, events)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n", " self._handle_recv()\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n", " self._run_callback(callback, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n", " callback(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 281, in dispatcher\n", " return self.dispatch_shell(stream, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 232, in dispatch_shell\n", " handler(stream, idents, msg)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 397, in execute_request\n", " user_expressions, allow_stdin)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n", " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2739, in run_cell\n", " self.events.trigger('post_run_cell')\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/events.py\", line 73, in trigger\n", " func(*args, **kwargs)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 160, in configure_once\n", " activate_matplotlib(backend)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 308, in activate_matplotlib\n", " matplotlib.pyplot.switch_backend(backend)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 232, in switch_backend\n", " matplotlib.use(newbackend, warn=False, force=True)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1305, in use\n", " reload(sys.modules['matplotlib.backends'])\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/importlib/__init__.py\", line 166, in reload\n", " _bootstrap._exec(spec, module)\n", " File \"/home/jneal/anaconda3/envs/sims/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 14, in <module>\n", " line for line in traceback.format_stack()\n", "\n", "\n", " matplotlib.use('Agg')\n" ] } ], "source": [ "import simulators\n", "from mingle.utilities.phoenix_utils import load_starfish_spectrum\n", "from simulators.iam_module import prepare_iam_model_spectra\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f36a6afbba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Agerage flux ratio 0.020965876730824468\n" ] } ], "source": [ "def load(teff, logg, feh, xmin=21500, xmax=22000):\n", " # Replace with path to model files\n", " name = os.path.join(\"PHOENIX\", \"Z{0:+1.10}\".format(float(feh)),\n", " \"lte{0:05d}-{1:1.02f}{2:+1.10}.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits\".format(teff, float(logg), float(feh)))\n", " \n", " if feh == 0:\n", " name = name.replace(\"Z+0.0\", \"Z-0.0\")\n", " name = name.replace(\"+0.0.PHOENIX\", \"-0.0.PHOENIX\")\n", " flux = fits.getdata(name)\n", " wav = fits.getdata(os.path.join(\"PHOENIX\", \"WAVE_PHOENIX-ACES-AGSS-COND-2011.fits\"))\n", " mask = (wav < xmax) * (wav > xmin)\n", "\n", " return wav[mask], flux[mask]\n", "\n", " \n", "@interact(teff_1=widgets.IntSlider(min=2300, max=8000, step=100, value=5600),\n", " logg_1=widgets.FloatSlider(min=3, max=5.5, step=0.5, value=4.5),\n", " feh_1=widgets.FloatSlider(min=-2, max=1, step=0.5, value=0),\n", " teff_2=widgets.IntSlider(min=2300, max=7000, step=100, value=2300),\n", " logg_2=widgets.FloatSlider(min=3, max=5.5, step=0.5, value=4.5),\n", " feh_2=widgets.FloatSlider(min=-2, max=1, step=0.5, value=0),\n", " xmin=widgets.FloatSlider(min=900, max=4900, step=500, value=2200),\n", " window=widgets.FloatSlider(min=10, max=100, step=10, value=50),\n", " combined=widgets.Checkbox(False),\n", " SNR=widgets.FloatSlider(min=1, max=100, step=1, value=50),\n", " rv=widgets.FloatSlider(min=-20, max=20, step=1, value=-5),\n", " gamma=widgets.FloatSlider(min=-21, max=20, step=1, value=10))\n", "def plt_spectrum(teff_1, logg_1, feh_1, teff_2, logg_2, feh_2, xmin=2100, window=100, combined=False, noise=100, rv=0, gamma=0):\n", " host, comp = prepare_iam_model_spectra([teff_1, logg_1, feh_1], [teff_2, logg_2, feh_2], limits=[1000, 3000])\n", " #wav, flux = load(teff, logg, feh, xmin, xmin+window)\n", " \n", " host.plot( label=\"host\")\n", " comp.plot(label=\"companion\")\n", " plt.ylabel(\"Flux\")\n", " plt.xlabel(\"Wavelength (nm)\")\n", " plt.title(\"Phoenix-ACES Model Spectra\")\n", " plt.legend()\n", " plt.show()\n", " \n", " print(\"Agerage flux ratio {}\".format(np.median(comp.flux/host.flux)))\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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WDte3smRWKY+vtZa0/vQf3yQ/K8DciiIeWLmXC2aO5pWt1ZxWNoz7V+zhcH0rv/nUGSf4YJ7acJBbHlrH7PJCHrt+wQnX+b7le/j1y9uZPDKfH3741BM63n+sO8gN97/FFxdN5OuLp1HX3M5n73mTlvYw2440MquskAdX7SM/M8DKXcd45Wvn8MrWKn7x/FYAsoI+Pv/eiVx/72qW76xhRG4GAb9Q1dBK2EBLRwifCFsPN/LJ+RVUDM+htSPM5Xe9TnVjW5f7XMi3LprOGRWdpslb/76RP78RnW7m/FNG8turo5esfmLdAfYda+aDc8ZyrKmN21/YxpiCLL56wdTIc+rci+1VjUwsyWN/bTN3vLCNprYQhTlByoqyuepdFRGzY21TG8u2H8XnE8YWZqswUmImrYSC88Icbw0xIs/aVtfcDsCEEst88uc3dhMKG648s7zH44gIP7z8NDYfauCC/32Z460dHKpvYfSwrBM6vimj8sjJ8LN8Zw0AT60/yNp9tXz8TMvE4/g3/rnB0jw+8a5x3PaPt2kLWWaiLy6aGBkF94XfJ9x43mQ+e89K/m/pNgA+dHoZd764nT01TeyvbSYUNpw6tnPJ5Kmj8/n0gvHc9dJ29h9rpr6lg1++sJXvf/BUjjS08KnfrWDToYZI+XHDc8jPCvD42gME/cLPPjqbnz+3hf+8eDrGGK7940oeXb0fgLs/OZe/r9nPD57exLIdRzl7YjFPrjvIi5uP8O2LTuGFdyzNaM3eWlraQ6zbV0d9czvnTx+FMYafP7cFYwwvb6ni1r9v5M6rTo9q7zO2tvbEugN8ffE0nnv7MC3tYT50+lgeWb2fLYcb+cgZZZxRUcQ3HlnPnpomNh6oByzB/tCqfXx0bjnLd9bw7+dP4cbzLRNbOGw472cv8dT6g7yxo4ZQ2PD713ZyywVTmT6mgOrGNn7zqbnMLi9k65EG1uyt5fev7uLyu17ngc/PZ17lcI4db+O+FXu46LQxXD2/ktaOEH97cy9PrDtIU1sHORnWs7jtSCNfvu8tAP7f09YyyDkZfpraQuRkBrjpfVMA2Fl9nJsfWMPqPbV888JprNhZw0tbqhhblM2x423Ut3Sws7qJH3zoVA7WNXPZna9xuL41cq1GF2TxtcVTOVzfytkTRzCrvLDXZ6mtI8zynUcpK8rp1rS4++hxNh1qYOWuGg7UttDQ2sHemiYCPqEoJ4P3TC7mmgWV+H3CI6v3M6uskOU7j1Lb1M644TkE/MLx1g7217bgExgzLIuS/Cx2VDfy3iklzCiNXta7qqGVopwgAX+nYOsIhdlW1UhuRoCyomwO1LUwMj+ToN9Ha0eIoM+nQR4nQVoJhbxMa9TV2NoR2dbUFgKIOGpf21bN6IIsKkb0bWPPtY93vLWD6sZWirsxIQT8PmaVFbJsx1EAnrY7/9MrrJeysjgHEXhlq+XPmDOuiIe+MJ+cjABBvzBpZF6/2vjuycVkBnz8a+NhivMymFCcS15mgPrmDg7bWtGogqyo33z1/VN43/RRzCgt4N/uf4tl2626fvcfb7Oz+jifffd4zpowgvdMKSbo83Hvij3852MbeNf4EVwyq5RLZpUCVscF8Pr2ajIDPkYVZHL12ZX88vmtPLnuIHMrhvPVB9fS3B6itDA7Iiid337018sA2PS9xdS3tFPV0MqtF0+nqrGV/1u6ndsaWinJ77zGe49ZvqC9Nc0cb+1g/f468uyO9BFbMF02ZyxBuyPZXdNEdWMr+ZkBLptTyvef2sTK3ccAmDm2IHJcn0+YW1HEg6v2AfCJs8bxlzf28M8NhyIC+qwJw8nPClKSn8nZE4u5Yt44Tv/es7y0uYp5lcN59u3DhMKGLyycyKllVgdXc7yNJ9YdZP+xZrZXHeeVrVVMtu/v76+Zy67qJtpDYT58RhlffXAtD7y5lxvPm4zfJ/zmlR2s3lMLwFMbDrGjqpGPzC3nBx86FYBvPrKOh1fvIzPg48n1B2luC/Hnz5xJYXYGu44e5/YXtnLTA2sByPD7+OO18yIBDl15ZPU+fvD0JqoaWsnN8LP0lnOirvuOqkYuuf1VjreFCPiEihE5ZAX9TC8tIBw2HKpv4afPbuHOpdvIDPgjAy+wBi6hcOfCXkG/EDZEbbt/xR5e+dq5ADz61j4eWrWPN3bUsGRWKT//2GzAeuc++KvX2HK4EbCsADXH2zhlTAE/+vBpXHH3MsqH5/Dolxaw4UAdd7ywjbf2HCMUNuRnBVk8czS3Xjydvccss+6O6uP84rkt+H3C9DEFfHJ+BW0dhpwMP0+uP0hOhp/LzyjjgZX7ONLQQnbQz4i8TMqKsjmjoogCO2oxFUgroRDRFNo6hUJzuy0UiiyhUN3Yxpnjh8d0PMdO3tgaoq0jTFageyfrvMoilu04yifPquDh1fvIDvqZP2EEAJkBP2VF2ZERbFlRdrf26VjJCvqZVzmcV7dVc/q4IkSEYdlB6prbOWqbPEbkZUT9JuD3Rcwe00bn88KmI1Q3tvL0hkNcu6CSb100Par8R84o40h9C5fNGRu1fbQtbKob2ygfno2IkBX0M3/iCF7dVs3K3TWR6/1L24Tz8XeN477le9h0qD5yHLdmUjEihzMqivi/pdt5fXs1l87uPOfhuhYyAj7aOsIcrGtm19HjVIzIoawohznjCjlS38pZE0aw3xYeh+tbqDnexvC8DE4rs4Tyk+sORM7j5pQxnULimrPHk5sR4A+v7eKUMfmMyM2IhC47DM/NoHRYFgfqrHP9Y90Byoqyo4SNc30O17fyhb+sAuC8aSMpyApwztSRyLTOUe0H54zlxs1reGvPMeZWDmdn1XFOH1fI7PIifv/aTsAyWTl8dG4596/Yyx9f38XEklz+92OzWWBHvJ1aNoz3TR/F8p01lBdl8/k/r+KG+9/igc/Pp2JEbpQm+vc1+7npgbWcUVHEF947ke898TbPv3OYK84cFynzg6c3YYC7PnEGp1cUMjI/epABsH5fHbc8tJZNhxq4dsF4xhZlM7t8GKeVFXKwtoWwMeRmBijMCeIXoaqxlUN1LTy94RB3vbSdxtYO8jID/PzZreypaQIsLfsnH5mF3yf8fc0Bthxu5JsXTqOtI8yqPccoL8rhz2/s5pI7Xo08R/+3dBt3vLiNkvxMlswuJTPgZ2f1cf74+i5mlQ/jW49uiAwMRxVkUjEil3uX7+FPy07MMv2df7wNWELV0eTB6gcmjcxj4eRi/v19U3oNVQfYd6yJvMwA+VlBHl61j4yAj0VTSyjMyej1d4NFWgkFxz7b0h7iSH0L33/qHQpzMvD7JOrBLomxU+40R3XQHjI92myvP3cScyuH8+5JxXzrolMQsYSBQ+WIXPbWNJOT4Y/4PQbCmeMtoXD2REvw5GcFqGtujwjD/G6cvg6jCrIIhQ2PvbWfUNhw0WmlJ5TJCvq52Q7DdZOd4Y8IIPf1PHVsIc+9c4Q3d1qj8u8smcF/PW4tlX3tgvHct3wPL9mRXwBbDjVQUmDdg6LcDGaOHUZOhp81e2ujhEJ9SzunjM5n7b46Dta1sPtoE9Ptzvz+z50FWCPTYdlWB97Q0sGxpnYKs4PMKLXKPbXe0ty6OtmnjcmPfB5fnMuEklzaQmFe3370BAHiMLIgiyP1rbyytYpXtlZzywVTozqIvCxnENE5KHl+0xFmlxee0JGcO20kAJfftYynb3wPh+pbmFFawLTRnfU63eW/mF1eyNyKIrIz/Pzp02eeYDbJCvp57xRrfZXbPz6HJbe/xrk/fYm5FUX87fPz8fuEprYObv37Rs6oKOJv152FT4SfPrM5IqTvW76H4rwMnn37MDecO4nFM0fTE6eWDePBL8xn/f465k8YEdW+cd1cv1EFWYwqyGK3LQAO1DYzeWQeB2qb+eKiiZQX5fAfj67nSEMLowuyuHf5bqaNzue6hRMixw6HDa9tq2ZH9XE+v3ACf3ljN798YRsBn/D0jQsj71ZbR5g5332Grz+8njY7mk8E7v3sWUwamcfKXTW8srWaprYODtW38s0Lp/HkuoP84Ol3+MaF07hu4URCYUN1Yyvbqxr564q9rN9fxy9f2MbEkXlcOnssr22r5pmNh7j5gqlRWkRjawfv/fFSThmTz6fmV/K1h9cBMKE4lydueDdZAT8PrNzLqt3HqCzOZeHkkoimOViklVAI2C9KR9hw10s7eGzNAUQgLyPAsOwgPoGwgeK82DrmoN+H3ye0dYRp6whHzBRdyQz4WWi/kFm+E7WJCcW5vLK1mrKi7D5HGbFw3cIJVBbnstiOgsrLDNDc3kFTqzUiyulDKIBlRhOBU1ydYyyMLsiyhUKnYB1v+2te3lrF8NwMPjavnOc3HWH6mAImjcyjOC+TV7dVR8pvOtRAwG9dhyJbaI8vzo2YpwDaQ2Ga2kJMHmUJhX3Hmtlb08SFdkfldtA6nXF9czstbSFyMqxRWsWIHHYfbWJ0QVZUeSAiXMASLBNKLDPPvmPNzKvsXpPMzwrQ0NLBm7uO4RP4zLvHR+/PtDoHt1AAmFhyookwPyvINWdX8sfXd/HPDYdoaOkgPysYJawmuGz9IsIDn58fkw192ugC/nTtmTy4ai+PrN7Pk+sPsmRWKU+tP0RdczvfuHBaxHY/eVQ+Ww430NIe4j8eXR85xuKZY/o8T35WsEcTVU/k2iHNzW0hjreF6AgbinKCjCm0nsu9Nc18+9ENbDxQz/cumxn1vvh8wvc/dCp/XrabLy6ayPaqRp575whzK4uiBlsZAR8LJhXzzNuHGVWQyb2fPYuwMRFT7dzK4cztco8/t3ACnzirIhJy7fdJRJCdPbGYcNhw/s9f4qFV+1gyq5RbHlzLgboWmttD/PDDp0XquXq3ZcLasL+epZuPUJQT5LYlM7jxr2t4ePV+Hl29j9V7asnLDNDY2sGP/7WZ808ZxacXVHLPsl185fwpUVpsIkircISAz2puR8h60ACMsUa4fp9EnH/d+QZ6PqbQHgrTHgqTETi5Dt1x5GUH4xPjnxX0s2RWaURzyQr6aWkPRzSF3s7jmDiW76yhdFh2lEYTC07UUpRQsP0zq3YfY9xwy/58z7Vn8o0LpwGWycyJ5pk0Mo/lO49yrMmyQzv3acywrIjj1BhDY4vVFscmv2r3MTrChspufEF+n5CfaWlLze0hsoLWdXFG3d2NXAtzMrjlgqk88qWzrTa4OuDyou4nCuZmBKygg7pmSvIzTxA0bh9UhmsAMXFk9/6r25bMYFRBJgdqm2lu6yAnw8+M0mFUjsjh6vkVJwwg+uNUnT9xBD+5fBZjC7N5cOVeAB5YuZcJxbnMdWkgk0ry2FF1nF1HOwXyyQwWYiXbpc0fO249E4XZGRTnWs/TvzYe4vlNRyjMCfKhLuZLgLMmjODOq06nMCeDc2xta0E3guk99iBt8sh8Jo3MY8qovtvT2xwcn084e+II1uypZW9NMwfsUPcHVu7jx//aHCnnHti8tLmKKaPyWTKrlCmj8vjPxzawek8tN543mfW3vZ81t76PWy6Yyhs7jnLVb5fzwqYjbK9q7LOeAyWtNAXHdhoKh6NGyzn2zQ4by9nldqr1RYbfR3vI0B7qWVPoiwq7wznZ3/dFZsBHbXMbTW0hsoP+XqOZRg2z2t7UFmJ2ef/nDjh+ltHDOjtOR1MAqOymAx5blM2avdbo6MOnl/HDf25iZmkDPiGiehfnZbJ2Xx37a5tZ8P9e4NaLLT9HUW4GxXkZLN9pOce76+ABcjL9NLeFaGnvnGA3bXQB/9p4mDHDTrSJA1x/zqTI5xGukWZPcyrysiyhcLwtFDEtdt0PcLSxNcomPaG452CCkvxMqhtbaWrvvHfP37yIeATV+HzCh08fyx0vbuOlLVWs2FnD1xZHm7xKC7M40tBCjSsE9+YY7OYnS6YtFJrbQxFbf25mgIJs69qtt7MMPHnDe7q9xm4+OrecwuwMzp8+8oR9H5g5mle2VHHDeZPjVvdJJXk0tHbw4mYrqu7hL87nw/+3jPX7OzMjOPOiAI63hagckYuIcNGppWw5vIXSYVnccN5kRITCnAyuP2cSV545jhU7jzJtdAGVcZhk2hdpJRSC/k7zkZtsW0Not1/U/giFgN/RFMz3jzNcAAAgAElEQVRJd+rzKodzyaxS/v38+D2gbjKDPktTaO3oc8ZxcW4mAZ/QETaMO4kJZc7L6/5tXmYgEh3SXVSXU3b0sCzm2M7TZTuOUujKG1Wcl0nN8bZIZNSflu0CrJHl6GFZbNhvOaq70xTAMhm0hcKWpmBrP7Ptc83pIzwTiOoEexQKmQEaWjtoau0gN+PEVyvD70MEapqi5zhMHd3zKDUnGKC2uT2i0QIxhyjHwuVnlPPLF7Zx9e9XkBnwceW8cVH7RxZkETawwx7hPv7lBREnfSJwtLiW9nDkfcwI+CKO/c2HrcHCqBje0aDfx0WndW/mGpGXyd2fmhunWls4fqkVdlTd5FH5LJ4xmm2u0X11Y2vEzAhEOvmrzhrHhgN1XD2/8oT7Ozw3IyZzXbxIK6HgXOyOkKG1IxTZ7mgK7SFLWPSnMwz6fbSHwrQNQFPIywxw+5VzTuq3sZAV8NPaEbJMXH3U0Wc7Zo8ebzupfEpfPncyowqyOGdaSbf7J5Sc2Gk79vu8zEDETLOnpinKZl6cl0EobNhtmzFa7Cim7KCf0QXZbNhfT2bAF2W2cpPht6KUWtrDZNn3e9GUEp7994Uxh/1OH1PA2wfrIyarrmRn+Glpt2zhOd0IXxEhM+CLmL4+/q5xzC4r7DXFSHaGn4PVVkRTd8ccKONG5LBwSgkvb6niU/MrKOoS6DDcjojZazuAEx166ZjcWjtCEW0q4BfybS2rrrn9hPkKyULZcOt9Wb6zhvzMAAVZQfJt7dHhaGMrFSNyIoOY8cVWX1Ocl8lv4iykTpa0EgpOp90RNrS2d6rvzsv2h2vm8aul22Kao+A+ZpvjU/An50QZR1PoCBmCMfg9HDPa2B5s570xtjCbr5w/5YTtV8wr5+6Xd3TrpD3/lFFcMquUa86uYERuBiKWr6cwp7MDKrY7+y2HrUiYZtu0kBX0R8w/44bn9GhXz7QFY4tLUxARJsdgS3b49SfPYO+xph5DhrMCftpDhoaWjh5NUpkBf8TRPKe8kI/M7XmSJFh+CCeUOBFCAeBnH53Fa9uqu40mcu7BPjus1zGBJQrHp9DcFqLdjgzK8PsI+n1kB/00t4coyE7OOQGOplDd2MpU+7lyQqYdjh5vi/JZxuLLGGwSdodFJAt4Gci0z/OQMea/uin3UeA2rJxza40xH09Undw+hRaXpuA8iOdMGxlxTsVK0C+0tocxJnE+gYGSGfDT2m6NvIK+vutoOZfbY8qFFCu3XDCV68+Z1K0dODvDH6UpDc/J4Ojxtqi4bSd6ZKs9WaneHm1nBX0R53ZvGl5GwEerHSWWGTy5+1Q+PKfXHE2O6aPmeCsTu9GInHo4poNY0k5kBwMR23p2NyapeFCclxkV6uvGCec9ZE98jFcwRE8416QtFI6YeZ33qiA7QHN7KKI1JBt59ryL2qZ2Su1oqWCXOQ3VDa1MHpnPPdeeyXPvHI5LIsp4k8herBU41xgzC5gNLBaRs9wFRGQy8E1ggTFmBvCVBNYnEpLaHjK0d3T6FQYyAgv4fTTZUT3BJM0tkxnw0dJhawoxCK4bz5/M5JF5vdq6+4uI9OkYdHB8OsNcI8LCbEso7HRFwYClKVw4czSTR+Zx3cIJPR4zIhQGYObri8yAIxTauk0A6JSJCIUY6uF+NnMS3CF3h2MuOtJgCYWuEVXxJhAZuJlIZ+r4Ap26JPPs4TJbuy61Ta+ZAV/EN2KMofp4G8V5GSycUsJ3L52ZMIf9QEiYyDXGGMDxsATtP9Ol2OeAO40xx+zfHElUfYCIHTIUNhETCQxsBBb0+zhuj+SSVVMI+n102CauQAwmrivPHMeVZ47rs1yisNTrhmih4AohdpMV9DGhJI9nb3pvr8fMDPg4Zjt4gwnKh+N0mO0hEwlv7q4ejvkoluclJ9MlFIYgLbnj3K493k7QL3F1cneHzyUUHPORc50cDSGZhUKRrd06QiHo7zQftbRbmuqwnOStPyR4noKI+EVkDXAEeNYYs7xLkSnAFBF5TUTeEJHFiayPP6IphDEmPppC0C8R+3ay+hQCdn6Z1l4m2CUTjqmoO6HQlVhHrpkBH8ftyXuJ0ujcdcnN7L5eGS6fQizmo5xgp3AZirUqnHejobWjxzQu8cQvLqEQ6mo+sjWF7OQ0HwERk6czjyMj4IvkdnJyQDlab7KS0KtrjAkBs0WkEHhURGYaYzZ0Of9kYBFQBrxil6l1H0dErgOuAxg37uRHsG7VNBQ3oeAagSZph+vUq6mtIyaTxVDjdJZuoZAd9J+Qcwb6IxT8/RqhnwxZLl9Fb5pCQ4vVOcQkFNzmowT5FHrD7UM4WV9Mf4j4/YyhIxxtPsr3gPnotkumc8qY/EjeKedZa+sIU9ts9RPDktRR7jAoPYTdyS8FumoC+4C/G2PajTE7gc1YQqLr7+82xsw1xswtKek+1DEWAq55Cu6pCgMZgQV8nZpC8goFq91NbaGYzEdDjdMRuUf0ItLtCDFWoZAR8EVCAxOl0WUG+zb1WDbm6BFwbwy1+cjnk4iw6+/s9pMhIhRCJmJ2ca5TdjBaY0hGRuRl8qVFkyLXynn32kJh6uxZ+j1pvclCwnoxESmxNQREJBs4H9jUpdhjwDl2mWIsc9KORNXJneYiynw0AOeZu7NJVkez81I1t4eSVnC5ueLMcs4/ZSRLZnVNxuc4HDuFQ1aM1zzD74tE8STa0Qw9R+m4BUdmPzWFoVrq1NFQsgZDU5BOTaGr8Gxp7//k0qHGuceWpmAJhXTWFMYAL4rIOuBNLJ/CEyLyXRFZYpf5F3BURN4GXgRuMcYcTVSF3CGpYZcVouuEnf4Q8EkkHXTy+hQc81EoMnJJZmaUDuO3V8874eVxggMmuBLIxTqJyW36SNTEp6wYTC1u811MmoLLZDQUmgJ0CrjB0BQcR3PYZeJ1oqjHuMI8vYJT1/ZQOOJTSHahkMjoo3XACdN0jTG3uj4b4Cb7b1BwMqG6o49iGbH1RNDfP3PAUOAIq+Y2b2gKPVE+PIea421MLMljzd7avn/gIrozTlD0kavT7Mms5RYW/fUpDIajt7c6DIamAETSrDjavKM9fOW8KeRnBri4h9QVyYg7mqreFgrJbP6CNMuSCuATwRDtU+gudXGsBPs58hsKHLNZU1tH0tYxFr52wVSuetc45tvrRPQHdwecKGe7u9PsUSgE+isUOsdtQ7W0ZKdQGByh5PMJIWMI2y+pzxYK2Rl+vnzu5EGrRzxwBJqxo/8g8RMAB0ryxnYlCJ9Y4ZnGGMqKsvnHl989IPORe9SZrB2u4+sIGzzhaO6JBZOKrTz49trM/cFt+khc9JHL/h+DUIhFYxkqk5Ebx5cxEI26P/hFCLuCQXxJOMErVhzTV9i4HefJ3Z7k7MUSiIh1g8LG4PfJgAQCRNunT3Y9hUTjnqzlhZDUvoh1ZrQb96h8MOYp9GRqcQunTH/fHX532VYHm05H8+AIKMd85Jh4xcOPrC/KcR4m4JOknMXsxsOX++Rwkq2FTXxGIO5RZ0YML/lQ4AUTV384mdFzlFBIkBnGPZKOxXwUS3LCRCegi4XsITAfhcMmMnvd05pCxHw0sDVXBpPkr2Gc8YlgbE0hHs9alPkoSTUFt8nIy+Yjh5OZxJU52JpCD07h/vo2ipIgpj0nOMjmI8en4GgKg3LWxBDRFMLYa64kf2uGfhgyyDg+hbAxcdcUknUUkOHvX0eU7DiaQn9C+6I0hQRdA3deoKyMnsxHndtjySMkInxt8VTK45ixtr8413uw5kn4fUIo3JkozduagvU/bGsKsQQXDDVpJxQiPoVwZ2TAQHCPvJO1w3X7PVJBUxhbmM2XFk1k0dTY05z318E7UHo2H3Vuj9W2/KVFk/oulECcRYl6yvwab/wi1lwiR1Pw8CMbmXfhIfNR+gkFHJ9CfMxH/Z2MNBR4IUKqP/h8wtcWT+vXbzIHISTVTSzmI68gtgFnsHwKEU0hhXwKYdt85IVBmfee0AHi8zk+hfg8bAHf4I5ATwYvmLgSzWCEpLrp6VkYLLt8PIlMIhukeRJ+n9javDNPYVBOmxCcRy1sTELX8ognyV/DOOOepxDDImR9EohyNCfn5QwOwmzeZMc9Qh+M0VpPpqHByDQab5zlIwfNfBQJSbW+e1lTcJ6DsDF0xLBGejKQduYjn2ueQnwczV7wKaSW+ehkGCzz0b+dOylqTd6uJGvYcm9cs6CSnEw/l5yQoDAxuN9R8LhPQdw+BW+Yj9JOKEgk+ih2R19vRJuPkrPDzYhyNCdnHRPNYEQfAdz8/qm97vei+Sjo93HVuyoG7Xxih42biFBI/o60J/wRoWAJhngEtyQa7z2hA8RyNDuawsCP59YUBsvm2l+iI6SSs46JZrDNRz3hRfPRYOOzJ5gavO1PAFdIqm0O84KAS7sn1Jq8Zj10cXE0e2DkrY7maEfzUCZUS1YTYzJh+f3iZ+IdShwhELI1Hy80J+2eUMdeGQrHR1MIeGAoE/Sp+ShKUxjCe5aZ5BkykwG3idfrQsGxHsRzIJpo0q6H6HzgTFxUOS+MvN3pN9LVfOS25Q+lCu9Fn8JgY5mP4peKZihxz2iOl8k60aTdEyr2A2dM/Gc0JytuZ3ggHnG4HiRZJo0lSz2SGbEXwvLKyLo3IuajsInbQDTRpN0Tai2yY+c+isc8BQ90skEPzKVINMkyQk+WeiQzkaSVYe9rCm7zUdh4I7lf2j2hiZynkKy4RydeqG8iSBYHr+Pw9npnl0hSyafgNh/hkfYkx5syiPgkzvMUkqSziRUv+EASQbKo7Y75SDWGnnEGbgbvawq+LuYjDxgW0k8oOFlSTQLmKXiBdBUKyYKzTOe1C8YPcU2Sl3iHjQ8lnTOa7eAWDxiQ0nJGczwT4iX7Itxd8UIIbaK46xOnM3lU/pDWISPgY9v/XJi0Ex2TAcFb0Tq94WgGxhgM3jAbpp1QcGZLxuuBG6yFR+LFUE7cGmoWzxwz1FUAvGdyHGwcTSEVJq+512j2io8k7Z7OztmS8bEz95Q3P1npaUF5RUkWIgtheSQtRG+4zUfxMlknmrTrISKRDXGa0ayagqLEl06fQio4mq3/nZPxkr9B6ScUcCfEi4Om4LFO1ms+ECX98PmcYJBUSIjXGX3klfaknVDw+Vw+hTjcIaeTPWdqyYCPNRh4TYgp6YeQSgnxrP8mjmHwiSYNHc3iGoUM/AZlBHw88+8LKS/KiUPtEo9GvSjJjgh21gFvOGZ7w6m/wTaHDW11YiLtNAV3Qrx49Y9TRuUnvW9hsFbNUpSB4hN30sqhrk18iOdANNGknVDwuSIbvHCD4sUvPjabrf9z4VBXQ1H6xMmS6pVOtDciJmoTv3xriSbtzEeWozm1RiGx4PMJPk8or0q6E73IzlDXZmA41Q9r9FHyYmVJTY1RiKKkIiIQDnvHMdsb0T4FzZKalPhE7AfO+6MQRUlFJBIM4n1t3ql/2E5z4YWBaNoJhc7Zkt4Pd1OUVKRzwpc3OtHecGofz9Q6iSYthYIxEAp7XzVVlFQkpXwKLvORVwaiCRMKIpIlIitEZK2IbBSR7/RS9nIRMSIyN1H1cej0KXj/gVOUVMQXFTbu7Ze0c/KaIRzGE06FREYftQLnGmMaRSQIvCoiTxtj3nAXEpF84AZgeQLrEsH9wOlELkVJQlIwIZ4x0d+TmYRpCsai0f4atP9MN0W/B/wIaElUXdy4MzB64QYpSrrhTojn9XFb15BUL7QnoT4FEfGLyBrgCPCsMWZ5l/1zgHJjzBN9HOc6EVkpIiurqqoGWqeUmy2pKKmEM3nN0hSGujYDw60peGXltYQKBWNMyBgzGygDzhSRmc4+EfEBPwdujuE4dxtj5hpj5paUDCzxXCrNllSUVMQx8ZoU8Ck4MiCS5sIDoT2DUkVjTC2wFFjs2pwPzASWisgu4Czg8UQ7m6NXdUrkmRRFORlSaZEdd/W90p5ERh+ViEih/TkbOB/Y5Ow3xtQZY4qNMZXGmErgDWCJMWZlouoE7txHKTAKUZQUJJUGbm7zkVd8JInUFMYAL4rIOuBNLJ/CEyLyXRFZksDz9kqnT8EbUltR0g3BW1lFe+OE3Ece8CkkLCTVGLMOmNPN9lt7KL8oUXVx46y85hWprSjpRkpqCvafF9rjAbdHfHEeuFBYzUeKkow4y3F6ZWTdG+7cR+GwZklNSjofOOKyHKeiKPFFItFH3g9JdS/H6ZX2pJ1QEBFC9vRClQmKknykUti4o+kYzZKavAiW6Qi8cYMUJd0QXAnxPN5DuTO+esVH4vFL3n98Ii6hMMSVURTlBHzirayiveH4ELyU4C8NhUKnpuAFp4+ipBsiQjicIpPX7P8Gqz1e8JunoVAQOtR8pChJS0olxHOZj/CIjyTthIK4zEceuD+KknZ0Zh3wxMC6VyKL7GiW1ORFBDpCYQD8KhUUJemwch9ZJhcvjKz7wu0j8cK8i7QTCj77gQPVFBQlGXFWRwynyJK5Elle1BvBLWkoFISOcDjyWVGU5MK95okXOtG+iB6IJn+D0k4oqE9BUZKbVJq8Bva8Cw8Ft6ShUNDJa4qSzLjXUff65DUgsuY0eGMgmgqXvF+4VblUUE0VJdXoXGTHGwnk+sKaG9X5OdlJQ6HQeVdS4YFTlFRDIvMUvB+SCpb5KGT7Mb3Q56S5UBjCiiiK0i3OaNoraSH6wie4knAmf3vSTii474kXbpCipBvOe9kRTo3oIyu4xfk8tHWJhfQTCi6FNBUeOEVJNZz3MlUWwhLBFX00xJWJgbQTCu6b4gX7nqKkG+LSFFLhHRXwVL619BMKPremkPw3SFHSjcgSliliPvL5JBKS6gXSTii45UAKPG+KknL4ojSFIa5MHPDawl7pJxTcPoW0a72iJD9un4IXEsj1hdeWAE67btGn0UeKktQ472UobKLMvV7F53Y0e6A9aSgUdPKaoniBUIr4FMCVb22IaxILMQkFEZnezbZFca/NIBCtKQxdPRRF6Z6IpmBSw6fgLBoE3hiIxqopPCAiXxeLbBG5HfhBIiuWKNw3JRXslYqSaqTiPIVUDEl9F1AOvA68CRwAFiSqUonEfVNUU1CU5CPVwsZ9HkvXH6tQaAeagWwgC9hpjAknrFYJRCevKUpyk2rvpdBpPvLCQDRWofAmllCYB7wbuFJEHkpYrRJI9ChkCCuiKEq3uF/LVNAUohf2Sv72BGIs9xljzEr78yHgUhH5ZILqlFA0IZ6iJDepZuK1ch9Zn73Q58QqFI6IyLgu216Kd2UGA02drSjJTbSJd+jqES/ElTrbC82JVSg8CRisNmUB44HNwIwE1Sth6OQ1RUluojUF77+jbkezF7IoxCQUjDGnur+LyOnA5xNSowSjmoKiJDeSYsEgaZH7yBizGsvp7DkkxUYhipJqSIr5FHyp6GgWkZtcX33A6UBVQmqUYNR8pCjJTar5FHDPaB7iqsRCrJpCvusvE8vHcGlvPxCRLBFZISJrRWSjiHynmzI3icjbIrJORJ4XkYr+NqC/+H1qPlKUZCalfQoeaE+sPoUTOvQYaAXONcY0ikgQeFVEnjbGvOEq8xYw1xjTJCJfBH4EfOwkzhUzoj4FRUlqUtunMLR1iYVehYKI/AMr6qhbjDFLetlngEb7a9D+M13KvOj6+gbwiT7qO2DUfKQoyU1UMMgQ1iNeSFRCvCGuTAz0pSn8ZCAHFxE/sAqYBNxpjFneS/HPAE/3cJzrgOsAxo3rOl2if6SaaqooqUaqTTD1uRbZ8YKY60so7DTG7DnZgxtjQsBsESkEHhWRmcaYDV3LicgngLnAe3s4zt3A3QBz584d0GKnmjpbUZKbVJvRDBAKWd2W3wMN6svR/JjzQUQePtmTGGNqgaXA4q77ROR84FvAEmNM68meI1bUp6AoyU2qRR+5NQUvNKcvoeBuw4T+HFhESmwNARHJBs4HNnUpMwf4NZZAONKf458suvKaoiQ3kmLvqAiEnNxHKTCj2fTwORbGAH+y/Qo+4AFjzBMi8l1gpTHmceDHQB7woH3z9/TmvI4H6mhWlOQm1fx+PhFPrbzWl1CYJSL1WBpDtv0Z+7sxxhT09ENjzDpgTjfbb3V9Pr//VR4YqWivVJRUIjp19pBVI25YmoJ3zEe9CgVjjH+wKjJYpFpkg6KkGm4TSyq8ogKEPTR5zQMWrvjihZuiKOlMquUnE7ej2QPNSWuhkAoPnKKkGqkWDOI2H3mhz0lDoeD6nHatV5TkJyokdeiqETeiHc1DXJkYSLtuMXqNZg/cIUVJM4TUCgZx5z4SD4i59BMKGn2kKElNtDbv/ZfU0hScz0Nbl1hIQ6Hg/uaBO6QoaUbU5LUhrEfc8FjW1zQUCqopKEoy4/NYJ9oXXsu3lnZCQecpKEpyk2p+P7cfwQtCLu2EgoakKkpyk2oJ8cRj7UlroeCFG6Qo6UdqmXi9NhBNQ6HQ+dkD90dR0o5U8ylEaQpDV42YSTuhkGpT6BUl1fDayLovvNbnpJ1Q0NTZipLcpNwaze7PHmhQ+gkFX2rZKxUl1YiKEEyBHsprJusUuOT9I0oQeOAGKUq6kWph42o+SnK8doMUJd1ItfdSzUdJjl+FgqIkNepoHlrSTihomgtFSW5SLRgk2hw2dPWIlbQTCtGzCz1whxQlzYhKiJcCr6jXknCmnVDQGc2Kktx4bWTdF15bHyL9hIKrxamgmipKqpFqy3G6+xwvtCf9hIL6FBQlqUk5n4JqCslNqj1wipJqpNqMZjzW56SdUEg1J5aipBqpN6PZWx1NClzy/hE9CvHWzVKUdEBSzKfgboEX1pxOQ6HQ/WdFUZKDqFxBQ1eNuOG1PicNhYK3ZhcqSrqRau+oeMw6kXZCwWtL4ylKupF6CfE6P6umkISkWgy0oqQaqTbBNEo78EB70looKIqSfHgtq2hfeC0MPv2EQtq1WFG8Rer5FDo/e6E9addFeuGmKEo6k2qT19zmIy+0R4WCoihJhbjzk3nBM9sHXsu3ljChICJZIrJCRNaKyEYR+U43ZTJF5G8isk1ElotIZaLq45ACz5iipDSpl59MHc0OrcC5xphZwGxgsYic1aXMZ4BjxphJwM+BHyawPoBGHClKshPtaPb++6qT12yMRaP9NWj/mS7FLgX+ZH9+CDhPEvwUeOGmKEo6k3I+BXU0dyIifhFZAxwBnjXGLO9SZCywF8AY0wHUASO6Oc51IrJSRFZWVVUNqE5euCmKks54rRPtC6/Nu0ioUDDGhIwxs4Ey4EwRmdmlSHeXqKs2gTHmbmPMXGPM3JKSkgHVKRUeMkVJZVIuJNX12QvtGZToI2NMLbAUWNxl1z6gHEBEAsAwoCaRdfHAPVGUtCYqIV4KvK9e84skMvqoREQK7c/ZwPnApi7FHgeutj9fDrxgjDlBU4gnfnUqKEpSk2prnnjNHBZI4LHHAH8SET+W8HnAGPOEiHwXWGmMeRz4HfBnEdmGpSFckcD6ABBQoaAoSY3X0kL0hdeW40yYUDDGrAPmdLP9VtfnFuAjiapDd3hNlVOUdCPVNIVoc1jyNyjtZjQriuIdUkJT0HkKiqIo8cELnWhfeC1dvwoFRVGSFi90on1iN8ErTUmkozlp+fv1Cwj6VR4qSrLjkX60VxxNwSumsLQUCrPKC4e6CoqixIBXOtLekC7/kx0dLiuKkrSkhFCwm+CVtqhQUBQlafFIP9orEWHgkbaoUFAURUkgjizwSiSVCgVFUZKWVAgIcSKoxCOqgvevuKIoKUvA742OtDfEYyGpKhQURUlaUiFXmVcczA4qFBRFSVoCqWA+GuoK9BPvX3FFUVKWVNAUPKYoqFBQFCV5SQ2h4K02qFBQFCVpSYVFsTw2TUGFgqIoyYvXRtndoY5mRVEUJYK3RIIKBUVRlISimoKiKIoSwWMyQYWCoihKIvGaX0SFgqIoSgLxlkhI00V2FEVJbr5x4TTW7Kkd6mrEhc7cR94QDyoUFEVJOr7w3olDXYW4oY5mRVEUJYLHZIIKBUVRlETimI2MMUNck9hQoaAoipJAnPxNYW/IBBUKiqIoicTvaAp4QyqoUFAURUkgftUUFEVRFAdHKKhPQVEURVFNQVEURenEEQpeiUxVoaAoipJAIkLBI1JBhYKiKEoCcaKPxCO6ggoFRVGUBOL3e2s9ThUKiqIoCaRTU/AGCRMKIlIuIi+KyDsislFEbuymzDAR+YeIrLXLfDpR9VEURRkKAh7zKSQyS2oHcLMxZrWI5AOrRORZY8zbrjLXA28bYy4RkRJgs4jca4xpS2C9FEVRBg2fzyPSwCZhmoIx5qAxZrX9uQF4BxjbtRiQL1bGqDygBkuYKIqipAQRTcEjBqRB8SmISCUwB1jeZdcdwCnAAWA9cKMxJtzN768TkZUisrKqqirBtVUURYkfPo+ZjxIuFEQkD3gY+Ioxpr7L7guANUApMBu4Q0QKuh7DGHO3MWauMWZuSUlJoqusKIoSNwI6ea0TEQliCYR7jTGPdFPk08AjxmIbsBOYlsg6KYqiDCbOymteWY4zkdFHAvwOeMcY87Meiu0BzrPLjwKmAjsSVSdFUZTBJuD3lqaQyOijBcAngfUissbe9h/AOABjzF3A94A/ish6rGv2dWNMdQLrpCiKMqjkBK1u9nibN2JoEiYUjDGv0odwNMYcAN6fqDooiqIMNaWFWYB3sqQmUlNQFEVJewJ+H9+9dAanjh021FWJCRUKiqIoCeZT8yuHugoxo7mPFEVRlAgqFBRFUZQIKhQURVGUCCoUFEVRlAgqFBRFUZQIKhQURVGUCCoUFEVRlAgqFBRFUZQIYoxH5l7biEgVsPskf14MpFtuJW1zeqBtTg8G0uYKY0yfaw94TigMBBFZaYyZO9T1GEy0zemBtjk9GIw2q/lIURRFiaBCQVEURYmQbkLh7qGuwBCgbU4PtM3pQcLbnFY+BUVRFKV30k1TUBRFUW5TGhUAAAd8SURBVHpBhYKiKIoSwXNCQUTKReRFEXlHRDaKyI329h+LyCYRWScij4pIoes33xSRbSKyWUQucG1fbG/bJiLfcG0fLyLLRWSriPxNRDIGt5XRxLnNvxeRIyKyocs5hovIs3abnxWRosFr4YnEq809Hcfel6ptzhKRFSKy1j7Od1zlU/bZtvf5ReQtEXnCtS1l2ywiu0RkvYisEZGVru0n/2wbYzz1B4wBTrc/5wNbgOlYaz0H7O0/BH5of54OrAUygfHAdsBv/20HJgAZdpnp9m8eAK6wP98FfDEV2mzvWwicDmzoco4fAd+wP3/DOZbX29zTcVK8zQLk2WWCwHLgrFR/tu39NwH3AU+4tqVsm4FdQHE35zjpZ9tzmoIx5qAxZrX9uQF4BxhrjHnGGNNhF3sDKLM/Xwr81RjTaozZCWwDzrT/thljdhhj2oC/ApeKiADnAg/Zv/8TcNlgtK0n4thmjDEvAzXdnOZSrLZCCrW5p+O4fpOKbTbGmEa7TND+M6n+bItIGXAR8Fvn+Kne5l446Wfbc0LBjYhUAnOwRkJurgWetj+PBfa69u2zt/W0fQRQ67o5zvakYIBt7o1RxpiDYD20wMiB1jVexKvN3RwnZdtsm1HWAEeAZ40xy0n9Z/t/ga8BYdf+VG+zAZ4RkVUicp2rzEk/254VCiKSBzwMfMUYU+/a/i2gA7jX2dTNz81JbB9y4tBmzxGvNvd0nGQkHm02xoSMMbOxRptnisjM3soPNQNts4hcDBwxxqzqeujuysehygMmTs/2AmPM6cCFwPUisnCg9fKkUBCRINbFvNcY84hr+9XAxcBVxjamYUnVctfPy4ADvWyvBgpFJNBl+5ASpzb3xmERGWMfcwzWCHNIiVebezoOKdxmB2NMLbAUWExqP9sLgCUisgvLFHyuiPyF1G4zxhjn/xHgUTrNSif/bJ+Mo2Qo/7Ck5j3A/3bZvhh4Gyjpsn0G0U6aHVjOuID9eTydjuYZ9m8eJNox9aVUaLNrfyUnOpp/TLRj6kep0OaejpPibS4BCu0y2cArwMXp8GzbZRYR7WhOyTYDuUC+XSYXeB1YPNBne8guzAAu6LuxVKd1wBr77wNYzpe9rm13uX7zLSyP/WbgQtf2D2B5/rcD33JtnwCssI/5IJCZQm2+HzgItGONQD5jbx8BPA9stf8PT4U293ScFG/zacBb9nE2ALemw7Pt2r+IaKGQkm2227XW/ttIdB920s+2prlQFEVRInjSp6AoiqIkBhUKiqIoSgQVCoqiKEoEFQqKoihKBBUKiqIoSgQVCkpSIyI/F5GvuL7/S0TcuW1+KiI3xfmcjX2X6vcxZ4vIB1zfbxORr8bwOxGRF0SkIA51yBCRl10TuRTlBFQoKMnO68DZACLiA4qxJvM4nA28NgT16i+zsWLR+8sHgLUmDqk5jJX48XngYwM9lpK6qFBQkp3XsIUCljDYADSISJGIZAKnAG+JSJ6IPC8iq+388pcCiMgPReRLzsHsEfrN9udbRORNO3/9d+iG7sqISKWdC/83dj78Z0Qk2943zy67zM6Pv0Gs/P3fBT5m5713OuXpIrJURHaIyA09tP8q4O8xnHeprVW9bJeZJyKPiJVP/79dx3vMPqaidIsKBSWpMVZulw4RGYclHJZhZZScD8wF1tkj4Bbgg8ZKDnYO8FM7bfJfiR4ZfxR4UETeD0zGyhUzGzijazKxPspMBu40xswAaoEP29v/AHzBGDMfCNltaANuBf5mjJltjPmbXXYacIF9/P+y8+F0ZQHgTvLW03kB2owxC7FSOfwduB6YCVwjIiPsMhuAed2cR1EAFQqKN3C0BUcoLHN9f90uI8D3RWQd8BxWauFRxpi3gJEiUiois4Bjxpg9WAuavB8rHcRqrA56cpfz9lZmpzFmjf15FVAp1kpZ+cYYp0739dGuJ42VI78aK2HZqG7KDDdWzn2HE87r2ve4/X89sNFYeftbsXLllIOVPRVoE5H8PuqmpCnqcFK8gONXOBVrpLsXuBmoB35vl7kKKxHcGcaYdjtbZpa97yHgcmA0luYAlhD5gTHm172ct9syYuXAb3VtCmElnusuxXFvdD1Gd+9jh4j4jDHhHn6T3c3xwl3KhbscOxNLs1KUE1BNQfECr2GlE64x1joBNUAhlglpmV1mGFY+/XYROQeocP3+r8AVWILBWYHrX8C1dk57RGSsiHRdiCSWMhGMMcew/B1n2ZuucO1uwFp6sb9sxkp8FhdsM1KVMaY9XsdUUgsVCooXWI8VdfRGl211tukFrAVJ5oq1ePlVwCanoDFmI1aHvN90rkb1DJZ5Z5mIrMcSFlGddixluuEzwN0isgxLc6izt7+I5Vh2O5pj4UmsrJ/x4hzgqTgeT0kxNEuqosQREckz9vrIIvINYIwx5sYBHG8McI8x5n1xqt8jwDeNMZvjcTwl9VCfgqLEl4tE5JtY79Zu4JqBHMwYc9AOQS0Y6FwFOzT2MRUISm+opqAoiqJEUJ+CoiiKEkGFgqIoihJBhYKiKIoSQYWCoiiKEkGFgqIoihLh/wOL/0NEj+HwsAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f36bd75b438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def load(teff, logg, feh, xmin=21500, xmax=22000):\n", " # Replace with path to model files\n", " name = os.path.join(\"/home/jneal/Phd/data/PHOENIX-ALL\", \"PHOENIX\", \"Z{0:+1.10}\".format(float(feh)),\n", " \"lte{0:05d}-{1:1.02f}{2:+1.10}.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits\".format(teff, float(logg), float(feh)))\n", " \n", " if feh == 0:\n", " name = name.replace(\"Z+0.0\", \"Z-0.0\")\n", " name = name.replace(\"+0.0.PHOENIX\", \"-0.0.PHOENIX\")\n", " flux = fits.getdata(name)\n", " wav = fits.getdata(os.path.join(\"/home/jneal/Phd/data/PHOENIX-ALL\",\"PHOENIX\", \"WAVE_PHOENIX-ACES-AGSS-COND-2011.fits\"))\n", " mask = (wav < xmax) * (wav > xmin)\n", "\n", " return wav[mask], flux[mask]\n", "\n", " \n", "@interact(teff=widgets.IntSlider(min=2300, max=8000, step=100, value=5600),\n", " logg=widgets.FloatSlider(min=3, max=5.5, step=0.5, value=4.5),\n", " feh=widgets.FloatSlider(min=-2, max=1, step=0.5, value=0),\n", " xmin=widgets.FloatSlider(min=900, max=49000, step=500, value=22000),\n", " window=widgets.FloatSlider(min=10, max=1000, step=10, value=50),\n", " )\n", "def plt_spectrum(teff, logg, feh, xmin=2100, window=100,):\n", " wav, flux = load(teff, logg, feh, xmin, xmin+window)\n", " \n", " plt.plot(wav, flux)\n", " plt.ylabel(\"Flux\")\n", " plt.xlabel(\"Wavelength (nm)\")\n", " plt.title(\"Phoenix-ACES Model Spectra\")\n", " #plt.legend()\n", " plt.show()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f8bb7cadd96947728bbea6adab9f7b59", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>IntSlider</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. 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mit

wikistat/Apprentissage

Diag-coro/Apprent-R-DiagCoro.ipynb

1

48969

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<center>\n", "<a href=\"http://www.insa-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg\" style=\"float:left; max-width: 120px; display: inline\" alt=\"INSA\"/></a> \n", "\n", "<a href=\"http://wikistat.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/wikistat.jpg\" style=\"float:right; max-width: 250px; display: inline\" alt=\"Wikistat\"/></a>\n", "\n", "</center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [Scénarios d'Apprentissage Statistique](https://github.com/wikistat/Exploration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modélisation de données cliniques en <a href=\"https://cran.r-project.org/\"><img src=\"https://cran.r-project.org/Rlogo.svg\" style=\"max-width: 40px; display: inline\" alt=\"R\"/></a>: Diagnostic coronarien " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Résumé\n", "Modélisation de la variable binaire de présence d'une pathologie coronarienne. Comparaison de la plupart des méthodes et algorithmes; intérêt, ou non, d'ajouter des variables ou caractéristiques (*features*) obtenues par analyse de correspondances de toutes les variables rendues qualitatives. Utilisation de [`caret`](http://topepo.github.io/caret/index.html) pour l'optimisation des valeurs des paramètres et introducton à l'implémentation de [`xgboost`](https://xgboost.readthedocs.io/en/latest/) en R.\n", "## Introduction\n", "Des données publiques disponibles sur le site [UCI repository](http://archive.ics.uci.edu/ml/) décrivent des facteurs de risque et résultats cliniques: 13 parmi 75 de l’étude originale de [Detrano et al. (1989)](http://www.ajconline.org/article/0002-9149%2889%2990524-9/abstract), liés à une maladie coronarienne (athérosclérose). Celle-ci est jugée présente lorsque tous les vaisseaux coronariens sont obstrués à plus de 50% par des athéromes. Les variables étudiées sont observées sur un échantillon de 270 patients admis dans une clinique de Cleveland (Ohio) à la suite de douleurs thoraciques pouvant être dues à une angine de poitrine. Elles sont décrites dans le tableau ci-dessous:\n", "\n", "Num| Code | Libellé | Valeurs\n", "-|--|--|--\n", "1|`Age` | \t\t\n", "2|`Sexe`| |sxF, sxM\n", "3|`Douleur`|\tThoracique|\tdlA (angine typique), dlb(atypique) dlc(différent) dlD(asymptom.)\n", "4|`Tension`|\tSystolique|\tmmHg à l’admission et au repos\n", "5|`Cholest`|\tTaux|\tmg/dl (préférable<200, limite entre 200 et 240, risqué au-delà)\n", "6|`Sucre`|\tTaux à jeun|\tscN (<120mg/dl), scO (>120mg/dl)\n", "7|`Cardio`|\tECG au repos|\tcdA (Normal) cdB (ST/T anormal) cdC (hypertrophie ventr. gauche)\n", "8|`FreqM`|\tFréquence|\tcardiaque maximum lors du test d’effort\n", "9|`AngInd`|\tAngine induite|\tpar l’effort : tmA (oui), tmB (non)\n", "10|`PicInd`|\tDépression ST|\tInduite par effort / repos\n", "11|`PentInd`|\tSegment ST| \tInduit à l’effort piA(ascendante), piB(plate), piC(descendante) \n", "12|`Nvais`|\tNombre de|\tvaisseaux fl0, fl1, fl2, fl3 majeurs colorés par fluoroscopie\n", "13|`Thal`|\tScintigraphie|\tthN(normal) thF(défaut fixé) thR(défaut révers.) avec effort\n", "14|`Coro`|\tCoronaropathie|\tCoA(Absence), CoP(Présence)\n", "\n", "Certaines sont associées à des risques potentiels et d’autres sont résultats d’examens cliniques au repos ou à la suite d’un test d’effort. Les variables 1, 4, 5, 8, 10 sont quantitatives, les autres sont qualitatives dont certaines binaires : 2, 6, 9, 14. Le diagnostic (variable `Coro`) a été établi par une *angiographie* permettant de mesurer l’obstruction des artères coronariennes. \n", "\n", "Après la [phase exploratoire](https://github.com/wikistat/Exploration/blob/master/Diag-coro/Explo-R-DiagCoro.ipynb) l’objectif sur ces données est de construire un modèle de prévision de la variable `Coro` à partir de l’observation des autres, pas ou peu invasives, car l’angiographie est un examen invasif comportant des risques. C'est en fait plus qu'un examen puisqu'il permet simultanément de poser par exemple des *stents* pour faciliter le débit sanguin. \n", "\n", "**Répondre aux questions en s'aidant des résultats des exécutions**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Préparation des données\n", "### 1.1 Variables disponibles\n", "Cette étape résume rapidement la suite des commandes qui découlent de la phase [phase exploratoire](https://github.com/wikistat/Exploration/blob/master/Diag-coro/Explo-R-DiagCoro.ipynb) des données qu'il est vivement conseillé d'avoir pratiquée pour se familiariser avec celles-ci.\n", "\n", "Lecture des données et préparation pour construire le *dataFrame*. Les données sont lues et les codes des modalités (niveaux des facteurs) sont renommés de façon explicite." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/data/\"\n", "#path=\"\"\n", "heart=read.table(paste(path,\"heart.dat\",sep=\"\"))\n", "# recodage des classes et nom des variables\n", "heart=data.frame(\n", " Age=heart[,1],\n", " Sexe=factor(as.factor(heart[,2]),labels=c(\"sxF\",\"sxM\")),\n", " Douleur=factor(as.factor(heart[,3]),labels=c(\"dlA\",\"dlB\",\"dlC\",\"dlD\")),\n", " Tension=heart[,4],\n", " Cholest=heart[,5],\n", " Sucre=factor(as.factor(heart[,6]),labels=c(\"scN\",\"scO\")),\n", " Cardio=factor(as.factor(heart[,7]),labels=c(\"cdA\",\"cdB\",\"cdC\")),\n", " FreqM=heart[,8],\n", " AngInd=factor(as.factor(heart[,9]),labels=c(\"tmA\",\"tmB\")),\n", " PicInd=heart[,10],\n", " PenteInd=factor(as.factor(heart[,11]),labels=c(\"piA\",\"piB\",\"piC\")),\n", " Nvais=factor(as.factor(heart[,12]),labels=c(\"fl0\",\"fl1\",\"fl2\",\"fl3\")),\n", " Thal=factor(as.factor(heart[,13]),labels=c(\"thN\",\"thF\",\"thR\")),\n", " Coro=factor(as.factor(heart[,14]),labels=c(\"CoA\",\"CoP\")))\n", "summary(heart)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Découpage des variables quantitatives en trois classes pour permettre une analyse factorielle multiple des correspondances." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "AgeQ=cut(heart$Age,breaks=quantile(heart$Age,c(0,.33,.66,1)),labels=c(\"AgeA\",\"AgeB\",\"AgeC\"),include.lowest = TRUE)\n", "TensQ=cut(heart$Tension,breaks=quantile(heart$Tension,c(0,.33,.66,1)),labels=c(\"TensA\",\"TensB\",\"TensC\"),include.lowest = TRUE)\n", "CholQ=cut(heart$Cholest,breaks=quantile(heart$Cholest,c(0,.33,.66,1)),labels=c(\"CholA\",\"CholB\",\"CholC\"),include.lowest = TRUE)\n", "FreQ=cut(heart$FreqM,breaks=quantile(heart$FreqM,c(0,.33,.66,1)),labels=c(\"FreqA\",\"FreqB\",\"FreqC\"),include.lowest = TRUE)\n", "PicQ=cut(heart$PicInd,breaks=quantile(heart$PicInd,c(0,.33,.66,1)),labels=c(\"PicA\",\"PicB\",\"PIcC\"),include.lowest = TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppression d'une modalité trop rare." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "heart[heart$Cardio==\"cdB\",\"Cardio\"]=\"cdA\"\n", "heart$Cardio=factor(heart$Cardio,exclude=NULL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Création finale du *dataFrame*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "heartT=data.frame(heart,AgeQ,TensQ,CholQ,FreQ,PicQ)\n", "summary(heartT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Gestion, analyse des variables rendues qualitatives\n", "L'étude exploratoire montre l'importance des variables qualitatives. en particulier, l'âge n'intervient pas comme risque de la même façon en fonction du genre des patients. Aussi, une nouvelle variable croisant sexe et âge est construite et une version simplifiée, avec 3 modalités au lieu de 6, est ajoutée à la base." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Sélection des seules variables qualitatives\n", "heartQ=heartT[,-c(1,4,5,8,10)]\n", "summary(heartQ)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Nouvelle variable pour intégrer l'interaction Sexe x Age\n", "SexAge=heartQ$Sexe:heartQ$AgeQ\n", "heartQ=data.frame(heartQ,\"SexAge\"=SexAge)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Simplifier la variable en regroupant les jeunes hommes et femmes dans la même classe\n", "SxAg=as.factor(rep(c(\"sHFageA\",\"sFageBC\",\"sHageBC\"),90))\n", "SxAg[heartQ$AgeQ==\"AgeA\"]=\"sHFageA\"\n", "SxAg[heartQ$SexAge==\"sxF:AgeB\" | heartQ$SexAge==\"sxF:AgeC\"]=\"sFageBC\"\n", "SxAg[heartQ$SexAge==\"sxM:AgeB\" | heartQ$SexAge==\"sxM:AgeC\"]=\"sHageBC\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Complétion du data frame\n", "heartQ2=data.frame(heartQ[,-15],SxAg)\n", "summary(heartQ2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Ajout de caractéristiques (*features*)\n", "Cette étude teste la stratégie suivante: ajouter des nouvelles variables ou *features* sous la forme des composantes principales issues de l'AFCM du tableau disjoinctif complet. Si toutes les variables étaient quantitatives, cela reviendrait à ajouter les composantes principales d'une analyse en composantes principales mais en prenant toutes celles qualitatives plus les quantitatives découpées en classes, c'est une AFCM qui convient.\n", "\n", "Cette approche a un autre avantage, elle permet de considérer des méthodes de classification supervisée qui n'autorisent que des variables quantitatives comme variables prédictives tout en conservant l'information apportée par les variables qualitatives. \n", "\n", "**Q** Quelles sont les principales méthodes de classifications supervisée concernées?\n", "\n", "**Q** Quelle autre approche est mise en oeuvre pour contourner ce problème?\n", "\n", "**R** :L'autre approche, souvent utilisée, consiste à remplacer toutes les variables qualitatives par les paquets de leurs indicatrices (*dummy variables*)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Construction des [composantes principales d'une AFCM](http://wikistat.fr/pdf/st-m-explo-afcm.pdf)\n", "Les nouvelles variables (composantes principales) sont construites à l'aide d'une [AFCM](http://wikistat.fr/pdf/st-m-explo-afcm.pdf) disponible dans la librairie *FactoMineR*. Elles sont encore les composantes principales de l'ACP, avec la métrique dite du Chi2, du tableau disjonctif complet.\n", "\n", "Une première exécution fournit la représentation graphique de toutes les modalités dans le premier plan factoriel." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(FactoMineR)\n", "afc=MCA(heartQ2,quali.sup=c(1,9,10),graph=F)\n", "options(repr.plot.width=5, repr.plot.height=5)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot.MCA(afc,invisible=c(\"ind\"),cex=0.8,habillage=\"quali\",palette=palette(rainbow(14)),title=\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'interprétation de ce graphique était un des objectifs de la [phase exploratoire](https://github.com/wikistat/Exploration/blob/master/Diag-coro/Explo-R-DiagCoro.ipynb). Remarquer simplement la position des modalités liées à la présence ou non de la pathologie: `CoP, CoA`, et celles représentant les facteurs de risque, dont `sHageBC` (hommes plus âgés), alors que le cholestérol (`CholA, B, C`) augmente avec l'âge (tous genres confondus) sans être nécessairement lié avec l'aggravation du risque." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Regrouper toiutes les variables dans un même dataFrame\n", "heartT=data.frame(heart,heartQ2[,c(10:15)])\n", "summary(heartT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Séparation des échantillons apprentissage et test\n", "**Q** Qu'est ce qui nécessite cette démarche?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# séparation apprentissage / test\n", "set.seed(11)\n", "npop=nrow(heartT)\n", "\n", "ntest=100\n", "testi=sample(1:npop,ntest)\n", "appri=setdiff(1:npop,testi)\n", "\n", "datapp=heartT[appri,]\n", "datest=heartT[testi,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les étapes suivantes calculent les composantes principales de l'AFCM ou coordonnées des individus dans la base des \n", "vecteurs propres. \n", "\n", "**Q** Les individus, patients, de l'étude appartenant à l'échantillon teste sont considérés comme *individus supplémentaires*. Pourquoi?\n", "\n", "**R**: Il faut calculer également les nouvelles coordonnées des individus du test dans cette base mais ils ne doivent pas participer au calcul des vecteurs et valeurs propres et donc à la définition des axes factoriels. Pour ce faire, ils sont déclarés comme individus en supplémentaires." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(FactoMineR)\n", "summary(heartQ) \n", "afc=MCA(heartQ2[,-9],ind.sup=testi,ncp=9,graph=F)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(afc$ind$coord)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(afc$ind.sup$coord)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Construction des des bases de données échantillon et test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ajout des nouvelles variables\n", "datapp=data.frame(datapp,afc$ind$coord)\n", "datest=data.frame(datest,afc$ind.sup$coord)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(datapp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 Panoplie des méthodes de classification supervisée\n", "Le problème à résoudre est la prévision de la présence d'une coronopathie. C'est donc un problème de discriminaiton binaire que beaucoup de méthodes peuvent aborder.\n", "\n", "L'objectif est de détrerminer si les composantes principales ont un intérêt ou non pour l'atteinte de l'objectif.\n", "\n", "### 2.1 [Régression logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf)\n", "La sélection de variables et donc la complexité du modèle sont optimisées par minimisation du critère AIC avant de calculer l'erreur de prévision sur l'échantillon test.\n", "\n", "**Q** Quels sont principaux les algorithmes de sélection de variable utilisables pour cet objectif? \n", "#### Sur les seules variables initiales quantitatives et qualitatives" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res.log=glm(Coro~1,data=datapp,family=\"binomial\")\n", "res.log.step=step(res.log,scope=list(lower=~1,upper=~(Age+Sexe+Douleur+Tension+Cholest+\n", " AngInd+PicInd+PenteInd+Nvais+Thal+Sucre+Cardio+FreqM+SxAg),\n", " direction=\"both\"),trace=0)\n", "anova(res.log.step, test=\"Chisq\")\n", "table(predict(res.log.step,datest,type=\"response\")>0.5,datest[,14]==\"CoP\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sur l'ensemble des variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res.log=glm(Coro~1,data=datapp,family=\"binomial\")\n", "res.log.step=step(res.log,scope=list(lower=~1,upper=~(Age+Sexe+Douleur+Tension+Cholest+\n", " AngInd+PicInd+PenteInd+Nvais+Thal+Sucre+Cardio+FreqM+SxAg+Dim.1+Dim.2+Dim.3+\n", " Dim.4+Dim.5+Dim.6+Dim.8+Dim.9)),direction=\"both\",trace=0)\n", "anova(res.log.step, test=\"Chisq\")\n", "table(predict(res.log.step,datest,type=\"response\")>0.5,datest[,14]==\"CoP\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sur les seules composantes de l'AFCM" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res2.log=glm(Coro~1,data=datapp,family=\"binomial\")\n", "res2.log.step=step(res2.log,scope=list(lower=~1,upper=~(Dim.1+Dim.2+Dim.3+Dim.4+\n", " Dim.5+Dim.6+Dim.8+Dim.9)),direction=\"both\",trace=0)\n", "anova(res2.log.step, test=\"Chisq\")\n", "table(predict(res2.log.step,datest,type=\"response\")>0.5,datest[,14]==\"CoP\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Que pensez de la capacité des composantes pricipales de l'AFCM à résumer l'information pertinente apportée par les variables? Justifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 [Arbre de décision binaire](http://wikistat.fr/pdf/st-m-app-cart.pdf)\n", "Comme précédemment la méthode est comparée sur les deux ensembles de variables.\n", "#### Sur les données initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(rpart)\n", "library(partykit)\n", "res.tree=rpart(Coro~.,data=datapp[,1:20],cp=0)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(as.party(res.tree),gp = gpar(fontsize = 11))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table(predict(res.tree,datest,type=\"class\"),datest[,14])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Que dire des facteurs de risque identifiés par ce modèle? de la qualité de la prévision? Que signifie `Cp`? Que dire du choix de la valeur?\n", "\n", "\n", "#### Sur toutes les données" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res.tree=rpart(Coro~.,data=datapp,cp=0)\n", "options(repr.plot.width=6, repr.plot.height=5)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "library(partykit) # si java est bien installé\n", "plot(as.party(res.tree),gp = gpar(fontsize = 10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table(predict(res.tree,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sur les composantes de l'AFCM" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res2.tree=rpart(Coro~.,data=datapp[,c(14,21:29)],cp=0)\n", "table(predict(res2.tree,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 [Réseaux de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf)\n", "Même démarche avec cet algorithme.\n", "#### Variables initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### nnet\n", "library(MASS)\n", "library(nnet)\n", "res.net=nnet(Coro~.,data=datapp[,1:20],size=5,decay=1,maxit=500)\n", "table(predict(res.net,datest,type=\"class\"),datest[,14]) #21" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ensemble des variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res2.net=nnet(Coro~.,data=datapp,size=5,decay=1,maxit=500)\n", "table(predict(res.net,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Composantes principales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res2.net=nnet(Coro~.,data=datapp[,c(14,21:29)],size=5,decay=1,maxit=500)\n", "table(predict(res.net,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Quelle stratégie vous semble raisonnable? Quel est le rôle du paramètre `decay`? Que manque-t-il à cette étape?\n", "### 2.4 [Séparateur à vaste marge](http://wikistat.fr/pdf/st-m-app-svm.pdf)\n", "#### Variables initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(e1071)\n", "res.svm=svm(Coro~.,data=datapp[,1:20],cost=0.5,kernel=\"radial\") \n", "par(mar=c(2.1, 2.1, .1, .1))\n", "options(repr.plot.width=2, repr.plot.height=2)\n", "plot(tune.svm(Coro~.,data=datapp,cost=c(.5,1,1.25,1.5,1.75,2)),main=\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table(predict(res.svm,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Toutes les variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "res.svm=svm(Coro~.,data=datapp,cost=0.5,kernel=\"radial\") \n", "table(predict(res.svm,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Composantes principales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res2.svm=svm(Coro~.,data=datapp[,c(14,21:29)],cost=0.5,kernel=\"radial\") \n", "table(predict(res2.svm,datest,type=\"class\"),datest[,14]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Quel est le paramètre `cost`? Commenter les résultats.\n", "### 2.5 [*K* plus proches voisins](http://wikistat.fr/pdf/st-m-app-add.pdf)\n", "**Q** Quelle contrainte pour cette méthode sur le choix des variables? Commenter les résultats (choix de *k*, qualité)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(class)\n", "options(repr.plot.width=2.5, repr.plot.height=2.5)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(tune.knn(as.matrix(datapp[,c(1,4,5,8,10,21:28)]),as.factor(datapp[,c(14)]),k=2:20),main=\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred.knn=knn(datapp[,c(1,4,5,8,10,21:28)],datest[,c(1,4,5,8,10,21:28)],datapp[,c(14)],k=12,prob=T)\n", "table(pred.knn,datest[,14]) #15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6 [Forêts aléatoires](http://wikistat.fr/pdf/st-m-app-agreg.pdf)\n", "\n", "#### Variables initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "library(randomForest)\n", "xapp=datapp[,-c(14,21:29)];yapp=datapp[,14];xtest=datest[,-c(14,21:29)];ytest=datest[,14]\n", "res.rf=randomForest(x=xapp, y=yapp, xtest=xtest, ytest=ytest, mtry=2,do.trace=50,\n", " importance=TRUE)\n", "sort(res.rf$importance[,3],decreasing=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Quel paramètres faudrait-il optimiser? A quoi sert `importance=TRUE` dans la fonction de cette méthode? Comment en interpréter le résultat?\n", "#### Toutes les variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "xapp=datapp[,-14];yapp=datapp[,14];xtest=datest[,-14];ytest=datest[,14]\n", "res.rf=randomForest(x=xapp, y=yapp, xtest=xtest, ytest=ytest, mtry=2,do.trace=50,importance=TRUE)\n", "sort(res.rf$importance[,3],decreasing=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Composantes principales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xapp=datapp[,c(21:29)];yapp=datapp[,14];xtest=datest[,c(21:29)];ytest=datest[,14]\n", "res2.rf=randomForest(x=xapp, y=yapp, xtest=xtest, ytest=ytest, mtry=2,do.trace=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Que signifie OOB ? Comparer les qualité de prévision.\n", "### 2.7 [Gradient boosting](http://wikistat.fr/pdf/st-m-app-agreg.pdf)\n", "Cette implémentation inclut une optimisation par validation croisée." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Variables initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(gbm)\n", "boost=gbm(as.numeric(datapp$Coro)-1~.,\n", " data=datapp[,-c(21:28)],distribution=\"adaboost\",\n", " n.trees=500, cv.folds=10,n.minobsinnode = 5,\n", " shrinkage=0.01,verbose=FALSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=3, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "best.iter=gbm.perf(boost,method=\"cv\")\n", "best.iter # 381\n", "pred.boost=predict(boost,newdata=datest,n.trees=best.iter)\n", "table(as.factor(sign(pred.boost)),datest[,14]) #19\n", "mean(sign(pred.boost) != (2*as.numeric(datest[,14])-3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Comment ce graphe est-il obtenu? Quelle interprétation en tirer?\n", "#### Toutes les variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "boost=gbm(as.numeric(datapp$Coro)-1~.,data=datapp,distribution=\"adaboost\",\n", " n.trees=500, cv.folds=10,n.minobsinnode = 5,shrinkage=0.01,verbose=FALSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=3, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "best.iter=gbm.perf(boost,method=\"cv\")\n", "best.iter # 381\n", "pred.boost=predict(boost,newdata=datest,n.trees=best.iter)\n", "table(as.factor(sign(pred.boost)),datest[,14]) #19\n", "mean(sign(pred.boost) != (2*as.numeric(datest[,14])-3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.8 Comparaison des [courbes ROC](http://wikistat.fr/pdf/st-m-app-risque.pdf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "library(ROCR)\n", "roclogit=predict(res.log.step,newdata=datest,type=\"response\")\n", "predlogit=prediction(roclogit,datest[,14])\n", "perflogit=performance(predlogit,\"tpr\",\"fpr\")\n", "\n", "prob=attr(pred.knn,\"prob\")\n", "prob <- ifelse(pred.knn == \"CoA\", 1-prob, prob)\n", "\n", "predknn=prediction(prob,datest[,14])\n", "perfknn=performance(predknn,\"tpr\",\"fpr\")\n", "\n", "roctree=predict(res.tree,newdata=datest,type=\"prob\")[,2]\n", "predtree=prediction(roctree,datest[,14])\n", "perftree=performance(predtree,\"tpr\",\"fpr\")\n", "\n", "rocrn=predict(res.net,datest)\n", "predrn=prediction(rocrn,datest[,14])\n", "perfrn=performance(predrn,\"tpr\",\"fpr\")\n", "\n", "res.svm=svm(Coro~.,data=datapp[,c(14,20:28)],cost=3,kernel=\"radial\",probability=T)\n", "\n", "rocsvm=predict(res.svm,datest,probability=T)\n", "predsvm=prediction(attr(rocsvm,\"probabilities\")[,2],datest[,14])\n", "perfsvm=performance(predsvm,\"tpr\",\"fpr\")\n", "\n", "rocrf=res.rf$test$vote[,2]\n", "predrf=prediction(rocrf,datest[,14])\n", "perfrf=performance(predrf,\"tpr\",\"fpr\")\n", "\n", "predboost=prediction(pred.boost,datest[,14])\n", "perfboost=performance(predboost,\"tpr\",\"fpr\")\n", "\n", "\n", "options(repr.plot.width=4, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "palette(\"default\")\n", "plot(perflogit,col=1,lwd=2)\n", "plot(perfknn,col=2,lwd=3,add=T)\n", "plot(perftree,col=3,lwd=2,add=T)\n", "plot(perfrn,col=4,lwd=2,add=T)\n", "plot(perfsvm,col=5,lwd=2,add=T)\n", "plot(perfboost,col=6,lwd=2,add=TRUE)\n", "plot(perfrf,col=7,lwd=2,add=T,)\n", "\n", "legend(\"bottomright\",legend=c(\"logit\",\"knn\",\"rpart\",\"nnet\",\"svm\",\"gbm\",\"rf\"),\n", " y.intersp=2,col=c(1:7),lty=1,lwd=2,text.width=0.15,ncol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** A la vue de ce graphique, quelles méthodes abandonner à ce niveau? Pourquoi? Que dire de façon générale sur le choix de la meilleure stratégie pour déterminer le paquet de variables à utiliser? Distinguer selon l'objectif de prévision ou d'interprétation. \n", "\n", "La suite dépend des choix qui sont opérés en privilégiant la pévision \"brute\".\n", "## 3 Automatisation des optimisations\n", "La librairie `caret` permet d'automatiser les valeurs des paramètres de complexité des modèles pour chaque méthodes. Les échantillons apprentissage et test sont normalisés. \n", "\n", "**Q** Que signifie le paramètre `tunelength` de la fonction `train` de cette librairie?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(caret)\n", "# Extraction des échantillons\n", "trainDescr=datapp[,-14]\n", "testDescr=datest[,-14]\n", "trainY=datapp[,14]\n", "testY=datest[,14]\n", "\n", "# Normalisation\n", "xTrans=preProcess(trainDescr)\n", "trainDescr=predict(xTrans,trainDescr)\n", "testDescr=predict(xTrans,testDescr)\n", "# Choix de la validation croisée\n", "cvControl=trainControl(method=\"cv\",number=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Régression logistique\n", "L'option `tunelength` est inutile. L'algorithme opère une sélection de variables par algorithme `forward` minimisant l'AIC.\n", "\n", "**Q** POurqupoi ré-exécuter la commande `set.seed` avant l'optimisation du ou des paramètres pour chaque méthode?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set.seed(2)\n", "rlogFit = train(trainDescr, trainY,method = \"glmStepAIC\", tuneLength = 8, trControl = cvControl)\n", "rlogFit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Forêts aléatoires" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set.seed(2)\n", "rfFit = train(trainDescr, trainY,method = \"rf\", tuneLength = 8,trControl = cvControl)\n", "rfFit\n", "options(repr.plot.width=3, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(rfFit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 *Gradient boosting*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set.seed(2)\n", "gbmFit = train(trainDescr, trainY,method = \"gbm\", tuneLength = 8, \n", " trControl = cvControl,verbose=FALSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# gbmFit commande trop bavarde" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=4, repr.plot.height=4)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(gbmFit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Comment interpréter ce graphique?\n", "### 3.3 Séparateur à vaste marge" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set.seed(2)\n", "svmgFit = train(trainDescr[,c(1,4,5,8,10,20:28)], trainY,method = \"svmRadial\", tuneLength = 8,trControl = cvControl)\n", "svmgFit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=3, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(svmgFit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Réseau de neurones\n", "**Q** Quel autre paramètre permettrait de faire varier et prendre en compte l'implémentation du perceptron dans la librairie *Scikit-learn* de Python?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set.seed(2)\n", "nnetFit = train(trainDescr, trainY,method = \"nnet\", tuneLength = 6,trControl = cvControl)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# nnetFit # commande trop bavarde " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot(nnetFit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 [Validation croisée](http://wikistat.fr/pdf/st-m-app-risque.pdf) *Monte Carlo*\n", "**Q** Que met en oeuvre cette procédure de validation croisée *Monte Carlo* (fonction `pred.autom`) ? Pourquoi?\n", "\n", "Attention, exécuter au préalable la fonction `pred.autom` définie en annexe à la fin de ce calepin.\n", "### 4.1 Base avec toutes les variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "models=c(\"gbm\",\"rf\",\"nnet\")\n", "noptim=c(6,6,6)\n", "Niter=30 ; Init=3\n", "predH=pred.autom(trainDescr, trainY,methodes=models,N=Niter,xinit=Init,size=noptim,type=\"prob\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calcul des taux de bien classés\n", "obs=predH$obs\n", "prevH=predH$pred\n", "resH=lapply(prevH,function(x)apply((x>0.5)==(obs==1),2,mean))\n", "# Moyennes des taux de bien classés par méthode\n", "lapply(resH,mean)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# distributions des taux de bien classés\n", "options(repr.plot.width=2.2, repr.plot.height=2.2)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "boxplot(data.frame(resH))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predrocH=lapply(prevH,function(x)prediction(x,obs==1))\n", "perfrocH=lapply(predrocH,function(x)performance(x,\"tpr\",\"fpr\"))\n", "options(repr.plot.width=4, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(perfrocH$gbm,col=1,lwd=3,avg=\"threshold\")\n", "plot(perfrocH$nnet,col=3,add=TRUE,lwd=3,avg=\"threshold\")\n", "plot(perfrocH$rf,col=2,add=TRUE,lwd=3,avg=\"threshold\",\n", " print.cutoffs.at=c(0.1, 0.25, 0.5, 0.75, 0.9))\n", "legend(\"bottomright\",legend=c(\"gbm\",\"nnet\",\"rf\"),y.intersp=3,\n", " col=c(1,3,2),lty=1,lwd=2,text.width=0.15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q** Que signifient les valeurs présentes sur les courbes ROC? Quelle conclusion tirer de ce graphique en fonction du taux de faux positif acceptable?\n", "### Variables initiales" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "models=c(\"gbm\",\"rf\")\n", "noptim=c(10,10)\n", "Niter=30 ; Init=3 \n", "predH2=pred.autom(trainDescr[,-c(20:28)], trainY,methodes=models,N=Niter,xinit=Init,size=noptim,type=\"prob\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calcul des taux de bien classés\n", "obs=predH2$obs\n", "prevH2=predH2$pred\n", "resH2=lapply(prevH2,function(x)apply((x>0.5)==(obs==1),2,mean))\n", "# Moyennes des taux de bien classés par méthode\n", "lapply(resH2,mean)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# distributions des taux de bien classés\n", "options(repr.plot.width=2.2, repr.plot.height=2.2)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "boxplot(data.frame(resH2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predrocH2=lapply(prevH2,function(x)prediction(x,obs==1))\n", "perfrocH2=lapply(predrocH2,function(x)performance(x,\"tpr\",\"fpr\"))\n", "options(repr.plot.width=4, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(perfrocH2$gbm,col=1,lwd=3,avg=\"threshold\")\n", "plot(perfrocH2$rf,col=2,add=TRUE,lwd=3,avg=\"threshold\",\n", " print.cutoffs.at=c(0.1, 0.25, 0.5, 0.75, 0.9))\n", "legend(\"bottomright\",legend=c(\"gbm\",\"rf\"),y.intersp=3,\n", " col=c(1,2),lty=1,lwd=2,text.width=0.15)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**Q** Quelle méthode ? Quelle stratégie de choix de base de données finalement choisir? Les différences vous semble-t-il significatives?\n", "## 4 [*XGBoost*](http://wikistat.fr/pdf/st-m-app-agreg.pdf) \n", "Comme le boosting conduit à des résultats encourageants, la librairie xgboost, utilisée dans la plupart des concours de prévision (kaggle), est testée dans l'espoir de les améliorer encore.\n", "\n", "### 4.1 Utilisation élémentaire" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TrainD=as.matrix(data.frame(tab.disjonctif(trainDescr[,-c(1,4,5,8,10,20:28)]),trainDescr[,c(1,4,5,8,10,20:28)]))\n", "TestD=as.matrix(data.frame(tab.disjonctif(testDescr[,-c(1,4,5,8,10,20:28)]),testDescr[,c(1,4,5,8,10,20:28)]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(xgboost)\n", "xbst = xgboost(data = TrainD, label = as.numeric(trainY)-1, max.depth = 2,\n", " eta = 1, nround = 30, objective = \"binary:logistic\")\n", "pred = predict(xbst, as.matrix(TestD))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prediction = as.numeric(pred > 0.5)\n", "err = mean(as.numeric(pred > 0.5) == as.numeric(testY))\n", "print(paste(\"test-error=\", err))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xbst = xgboost(data = as.matrix(trainDescr[,20:28]), label = as.numeric(trainY)-1, max.depth = 2,\n", " eta = 1, nround = 30, objective = \"binary:logistic\")\n", "pred = predict(xbst, as.matrix(testDescr[,20:28]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "err = mean(as.numeric(pred > 0.5) == as.numeric(testY))\n", "print(paste(\"test-error=\", err))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Optimisation des paramètres\n", "Le principal souci de cette librairie est lié au nombre de paramètres qui peuvent être optimisés. La librairie `caret` est utilisée pour facilité cette optimisation en première approche. Le calcul est long et d'autres moyens (*e.g.* GPU) seraient nécessaires pour espérer optimiser les valeurs de tous les paramètres." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(xgboost)\n", "set.seed(2)\n", "xgbmFit = train(as.matrix(TrainD), trainY,method = \"xgbTree\",tuneLength = 4,trControl = cvControl,verbose=FALSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xgbmFit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Tuning parameter 'gamma' was held constant at a value of 0\n", " Tuning parameter 'min_child_weight' was held constant at a value of 1\n", " Accuracy was used to select the optimal model using the largest value.\n", " The final values used for the model were nrounds = 50, max_depth = 1, \n", " eta = 0.3, gamma = 0, colsample_bytree = 0.8, min_child_weight = 1 and \n", " subsample = 0.5. \n", "**Q** Ci-dessus la synthèse de résultats fournis par l'exécution de la commande `train` de `caret`. Que retenir de cette implémentation du boosting?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=5, repr.plot.height=5)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(xgbmFit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Automatisation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "models=c(\"xgbTree\")\n", "noptim=c(4)\n", "Niter=30 ; Init=3 \n", "predH3=pred.autom(TrainD,trainY,methodes=models,N=Niter,xinit=Init,size=noptim,type=\"prob\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calcul des taux de bien classés\n", "obs=predH3$obs\n", "prevH3=predH3$pred\n", "resH3=lapply(prevH3,function(x)apply((x>0.5)==(obs==1),2,mean))\n", "# Moyennes des taux de bien classés par méthode\n", "lapply(data.frame(resH2,resH3),mean)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# distributions des taux de bien classés\n", "options(repr.plot.width=2.2, repr.plot.height=2.2)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "boxplot(data.frame(resH2,resH3))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predrocH3=lapply(prevH3,function(x)prediction(x,obs==1))\n", "perfrocH3=lapply(predrocH3,function(x)performance(x,\"tpr\",\"fpr\"))\n", "options(repr.plot.width=4, repr.plot.height=3)\n", "par(mar=c(2.1, 2.1, .1, .1))\n", "plot(perfrocH3$xgbTree,col=1,lwd=3,avg=\"threshold\")\n", "plot(perfrocH2$gbm,col=3,add=TRUE,lwd=3,avg=\"threshold\")\n", "plot(perfrocH2$rf,col=2,add=TRUE,lwd=3,avg=\"threshold\",\n", " print.cutoffs.at=c(0.1, 0.25, 0.5, 0.75, 0.9))\n", "legend(\"bottomright\",legend=c(\"xgboost\",\"gbm\",\"rf\"),y.intersp=3,\n", " col=c(1,3,2),lty=1,lwd=2,text.width=0.15)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**Q** Commenter ces derniers résultats\n", "## Annexe: fonction de validation croisée *Monte Carlo*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred.autom=function(X,Y,p=1/2,methodes=c(\"knn\",\n", " \"rf\"),size=c(10,2),xinit=11,N=10,typerr=\"cv\",\n", " number=4,type=\"raw\")\n", "# Fonction de prévision de N échantillons tests\n", "# par une liste de méthodes de régression \n", "# ou classification (uniquement 2 classes)\n", "# Optimisation des paramètres par validation \n", "# croisée (défaut) ou bootstrap ou... (cf. caret)\n", "# X : matrice ou frame des variables explicatives\n", "# Y : variable cible quantitative ou qualitative \n", "# p : proportion entre apprentissage et test\n", "# methodes : liste des méthodes de rdiscrimination\n", "# size : e grille des paramètres à optimiser\n", "# xinit : générateur de nombres aléatoires\n", "# N : nombre de réplications apprentissage / test\n", "# typerr : \"cv\" ou \"boo\" ou \"oob\"\n", "# number : nombre de répétitions CV ou bootstrap\n", "# pred : liste des matrices de prévision\n", "{\n", "# type d'erreur\n", "Control=trainControl(method=typerr,number=number)\n", "# initialisation du générateur \n", "set.seed(xinit)\n", "# liste de matrices stockant les prévisions\n", "# une par méthode, une ligne par observation, \n", "# une colonne par échantillon test\n", "inTrain=createDataPartition(Y,p=p,list=FALSE)\n", "ntest=length(Y[-inTrain])\n", "pred=vector(\"list\",length(methodes))\n", "names(pred)=methodes\n", "pred=lapply(pred,function(x)x=matrix(0,\n", " nrow=ntest,ncol=N))\n", "# variable cible des échantillons test\n", "obs=matrix(0,ntest,N)\n", "\n", "set.seed(xinit)\n", "# N itérations, une par échantillon test\n", "for(i in 1:N) \n", "{\n", "# indices de l'échantillon d'apprentissage \n", "inTrain=createDataPartition(Y,p=p,list=FALSE)\n", "# Extraction des échantillons\n", "trainDescr=X[inTrain,]\n", "testDescr=X[-inTrain,]\n", "trainY=Y[inTrain]\n", "testY=Y[-inTrain]\n", "# stockage des observés de testY\n", "obs[,i]=testY\n", "# centrage et réduction des variables\n", "xTrans=preProcess(trainDescr)\n", "trainDescr=predict(xTrans,trainDescr)\n", "testDescr=predict(xTrans,testDescr)\n", "# estimation et optimisation des modèles \n", "# pour chaque méthode de la liste\n", "for(j in 1:length(methodes))\n", "{\n", "# modélisation \n", "modFit = train(trainDescr, trainY,\n", " method = methodes[j], tuneLength = size[j],\n", " trControl = Control)\n", "# prévisions de l'échantillon test\n", "if (type==\"prob\") pred[[j]][,i]=predict(modFit,\n", " newdata = testDescr,type=type)[,1]\n", "else pred[[j]][,i]=predict(modFit, \n", " newdata = testDescr)\n", "}}\n", "list(pred=pred,obs=obs) # résultats\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

Upward-Spiral-Science/the-fat-boys

code/Individual_Reports/Edric Tam Updated Report 1.ipynb

1

480408

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Edric Tam Update Fatboy Report:**\n", "\n", "1. Focus on spatial information. Take features 3 and 4, the distance and moment of inertia and get hierarchical clustering result. \n", "2. Obtained results that are what we would expect according to literature. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/saltpisces/Desktop/School/Genomics/venv/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "# load all necessary dependencies and load in the log transformed data \n", "# for both integrated and local intensity\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import csv\n", "import pickle\n", "\n", "from sklearn import cross_validation\n", "from sklearn.cross_validation import LeaveOneOut\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", "\n", "\n", "N = 1000 # number of samples at each iteration\n", "\n", "dataFile = open('local_processed.p')\n", "localData = pickle.load(dataFile)\n", "dataFile = open('integrated_processed.p')\n", "integratedData = pickle.load(dataFile)\n", "dataFile = open('distCenter_processed.p')\n", "distData = pickle.load(dataFile)\n", "dataFile = open('momentInertia_processed.p')\n", "momData = pickle.load(dataFile)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "markers = ['Synap','Synap','VGlut1','VGlut1','VGlut2','Vglut3',\n", " 'psd','glur2','nmdar1','nr2b','gad','VGAT',\n", " 'PV','Gephyr','GABAR1','GABABR','CR1','5HT1A',\n", " 'NOS','TH','VACht','Synapo','tubuli','DAPI']\n", "\n", "synapType = ['synap','synap','ex.pre','ex.pre','ex.pre','in.pre',\n", " 'ex.post','ex.post','ex.post','ex.post','in.pre','in.pre',\n", " 'in.pre','in.post','in.post','in.post','other','other',\n", " 'other','other','other','other','none','none']\n", "\n", "\n", "# filter synapses such that only the ones with high synapsin expression will be accepted.\n", "# add additional synapse filter so we include only those that have a high moment of inertia and large synapsin area \n", "avglocSynap1 = np.mean(localData[1])\n", "avgintSynap1 = np.mean(integratedData[1])\n", "avgdistSynap1 = np.mean(distData[1])\n", "avgmomSynap1 = np.mean(momData[1])\n", "\n", "intfilteredSynapse1 = [synapse for synapse, value in enumerate(integratedData[1]) if value > avgintSynap1]\n", "locfilteredSynapse1 = [synapse for synapse, value in enumerate(localData[1]) if value > avglocSynap1] \n", "distfilteredSynapse1 = [synapse for synapse, value in enumerate(distData[1]) if value > avgdistSynap1]\n", "momfilteredSynapse1 = [synapse for synapse, value in enumerate(momData[1]) if value > avgmomSynap1] \n", "\n", "highSynapsin1 = set(intfilteredSynapse1).intersection(locfilteredSynapse1)\n", "highArea1 = set(distfilteredSynapse1).intersection(momfilteredSynapse1)\n", "\n", "filteredSynapses1 = list(highSynapsin1.intersection(highArea1))\n", "\n", "avglocSynap2 = np.mean(localData[2])\n", "avgintSynap2 = np.mean(integratedData[2])\n", "avgdistSynap2 = np.mean(distData[2])\n", "avgmomSynap2 = np.mean(momData[2])\n", "\n", "intfilteredSynapse2 = [synapse for synapse, value in enumerate(integratedData[2]) if value > avgintSynap2]\n", "locfilteredSynapse2 = [synapse for synapse, value in enumerate(localData[2]) if value > avglocSynap2] \n", "distfilteredSynapse2 = [synapse for synapse, value in enumerate(distData[2]) if value > avgdistSynap2]\n", "momfilteredSynapse2 = [synapse for synapse, value in enumerate(momData[2]) if value > avgmomSynap2] \n", "\n", "highSynapsin2 = set(intfilteredSynapse2).intersection(locfilteredSynapse2)\n", "highArea2 = set(distfilteredSynapse2).intersection(momfilteredSynapse2)\n", "\n", "filteredSynapses2 = list(set(intfilteredSynapse2).intersection(locfilteredSynapse2))\n", "\n", "filteredSynapses = list(set(filteredSynapses1).intersection(filteredSynapses2))\n", "\n", "# filter away synapses such that those with high tubulin expression will not be accepted\n", "\n", "avgloctub = np.mean(localData[24])\n", "avginttub = np.mean(integratedData[24])\n", "avgdisttub = np.mean(distData[24])\n", "avgmomtub = np.mean(momData[24])\n", "\n", "intfilteredtub = [synapse for synapse, value in enumerate(integratedData[24]) if value < avginttub]\n", "locfilteredtub = [synapse for synapse, value in enumerate(localData[24]) if value < avgloctub]\n", "distfilteredtub = [synapse for synapse, value in enumerate(distData[24]) if value < avgdisttub]\n", "momfilteredtub = [synapse for synapse, value in enumerate(momData[24]) if value < avgmomtub] \n", "\n", "areafilteredtub = list(set(distfilteredtub).intersection(momfilteredtub))\n", "\n", "intensityfilteredtub = list(set(intfilteredtub).intersection(locfilteredtub))\n", "\n", "finaltubFiltered = list(set(intensityfilteredtub).intersection(areafilteredtub))\n", "\n", "\n", "finalFilteredInd = list(set(finaltubFiltered).intersection(filteredSynapses))\n", "\n", "# now that we have the valid synapses, we only want certain features...\n", "# i.e. the inhibitory and the excitatory ones\n", "\n", "exAndInMarkerInd = [i for i,j in enumerate(synapType) if j[0:2] == 'in' or j[0:2]=='ex']\n", "otherMarkerInd = [i for i,j in enumerate(synapType) if j[0:5] == 'other' or j[0:5 == 'Synap']]\n", "\n", "# finalized filtered data\n", "exAndInIntData = np.asarray([integratedData[i][q] for i in exAndInMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(exAndInMarkerInd))\n", "exAndInLocData = np.asarray([localData[i][q] for i in exAndInMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(exAndInMarkerInd))\n", "exAndInDistData = np.asarray([distData[i][q] for i in exAndInMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(exAndInMarkerInd))\n", "exAndInMomData = np.asarray([momData[i][q] for i in exAndInMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(exAndInMarkerInd))\n", "\n", "otherIntData = np.asarray([integratedData[i][q] for i in otherMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(otherMarkerInd))\n", "otherLocData = np.asarray([localData[i][q] for i in otherMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(otherMarkerInd))\n", "otherDistData = np.asarray([distData[i][q] for i in otherMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(otherMarkerInd))\n", "otherMomData = np.asarray([momData[i][q] for i in otherMarkerInd \\\n", " for q in finalFilteredInd]).reshape(len(finalFilteredInd),len(otherMarkerInd))\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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O8wcp7zONfV+0JZ8+0ZICKgqqBFSC7vzzCzoAF5f6SjVVS8CwHbcyxDftW3Tb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59uLtEHkLO5N7JwidwGX+/OMkIOgsYIdUpFTkwYyHEwQQXOBoIBijPYYYEfItxKgxEqsCQHCDXn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lSEmlFy9nbrAoyI2K4gLThmx4U27LRjpx2DLg62SJS3+dbWa/+W+yOP0IqbR7S8I1ADhrMC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\n3F2jt2dThUCE4UdvMc91stD+6dw6XtsuNuuKU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"text/plain": [ "<matplotlib.figure.Figure at 0x104d50390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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FTaJgAoXbqEOMIZhRpQfhgmIQLApG9GkvHM3CkhLJWt+pAZFZXeJYFILaDpjy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SsZcPdMNQV8jO50VuaHALstWykiBYP9LoxKseaM0J95gS67ow9Kro3nxYI1LBXgWAIelqzblgI+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xofYLl4rRYQok3zZkKtpRKcMGwnaHqnbSpKMPIWA4bflp9p+Sp9hIrZOWDZKlsVjvNAobzm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\nHv8WzCepf5cuCRgwZA+QSP6gUxA0yJUV3NQ8m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"text/plain": [ "<matplotlib.figure.Figure at 0x10466a810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(1) # for reproducibility, set random seed\n", "randPermute = np.random.permutation(range(0,len(finalFilteredInd)))\n", "randSample = randPermute[0:N]\n", "\n", "exAndInLocFeatures = exAndInLocData[randSample,:]\n", "exAndInIntFeatures = exAndInIntData[randSample,:]\n", "exAndInDistFeatures = exAndInDistData[randSample,:]\n", "exAndInMomFeatures = exAndInMomData[randSample,:]\n", "\n", "otherLocFeatures = otherLocData[randSample,:]\n", "otherIntFeatures = otherIntData[randSample,:]\n", "otherDistFeatures = otherDistData[randSample,:]\n", "otherMomFeatures = otherMomData[randSample,:]\n", "\n", "rExAndIn = len(exAndInMarkerInd)\n", "rOther = len(otherMarkerInd)\n", "\n", "%matplotlib inline\n", "\n", "\n", "exAndInCorrDist = np.empty([rExAndIn,rExAndIn])\n", "for i in range(0,rExAndIn):\n", " for j in range(0,rExAndIn):\n", " results = np.corrcoef(exAndInDistFeatures[i,:],exAndInDistFeatures[j,:])\n", " exAndInCorrDist[i,j] = results[1,0]\n", "\n", "plt.figure(figsize=(7,7))\n", "im = plt.imshow(exAndInCorrDist)\n", "plt.title('Correlation of Normalized Center of Mass Distance for Ex vs In')\n", "plt.colorbar(im,fraction=0.046, pad=0.04)\n", "plt.show()\n", "\n", "exAndInCorrMom = np.empty([rExAndIn,rExAndIn])\n", "for i in range(0,rExAndIn):\n", " for j in range(0,rExAndIn):\n", " results = np.corrcoef(exAndInMomFeatures[i,:],exAndInMomFeatures[j,:])\n", " exAndInCorrMom[i,j] = results[1,0]\n", "\n", "plt.figure(figsize=(7,7))\n", "im = plt.imshow(exAndInCorrMom)\n", "plt.title('Correlation of Normalized Moment of Inertia for Ex vs In')\n", "plt.colorbar(im,fraction=0.046, pad=0.04)\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104d50f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import hcluster\n", "import scipy.cluster.hierarchy as sch\n", "markerlabels = [markers[i] for i in exAndInMarkerInd]\n", "typelabels = [synapType[i] for i in exAndInMarkerInd]\n", "Z=sch.linkage(exAndInCorrMom, 'single', 'correlation')\n", "localDendro = sch.dendrogram(Z, color_threshold=0, labels=typelabels,leaf_font_size=6.5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104d53090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z=sch.linkage(exAndInCorrMom, 'single', 'correlation')\n", "localDendro = sch.dendrogram(Z, color_threshold=0, labels=markerlabels,leaf_font_size=6.5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x154863c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z=sch.linkage(exAndInCorrDist, 'single', 'correlation')\n", "localDendro = sch.dendrogram(Z, color_threshold=0, labels=typelabels,leaf_font_size=6.5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GTRXiSTYm2ZlkV5ItC5R5XZLdSa5M8vOzbaYkaZIlQzzJOuAi4JHA6cA5Se4zVuZRwD2r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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10400ec10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z=sch.linkage(exAndInCorrDist, 'single', 'correlation')\n", "localDendro = sch.dendrogram(Z, color_threshold=0, labels=markerlabels,leaf_font_size=7)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UcUcQQpwF/k5Kudy0VaFQ3MesOUMQmuPzs2ghkw3Ae4UQ9Uuy9QGPSSl3oY0GPq8fW4mm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"text/plain": [ "<matplotlib.figure.Figure at 0x155465890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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0HUVRaGrSMzIyxejoIHb7QfbseYJkMs7y8nk8niyplIrbXU06HaG+PkpHRyk2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\njnD69GX273dz7twI8/MZTKYRIEdRkQ6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+TLVaIZUyoygWAgGVYLCF2dkYra3HuXXrLi7X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"text/plain": [ "<matplotlib.figure.Figure at 0x153820f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "firstVGlutDist = exAndInDistData[randSample,0]\n", "firstVGlutMom = exAndInMomData[randSample,0]\n", "\n", "secondVGlutDist = exAndInDistData[randSample,1]\n", "secondVGlutMom = exAndInMomData[randSample,1]\n", "\n", "\n", "plt.scatter(firstVGlutDist, secondVGlutDist,alpha=0.2)\n", "plt.title(\"Scatter of the 2 Vglut1 markers' Distance to Center of Mass\")\n", "plt.show()\n", "plt.scatter(firstVGlutMom, secondVGlutMom,alpha=0.2)\n", "plt.title(\"Scatter of the 2 Vglut1 markers' Moment of Inertia\")\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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

neuro-data-science/neuro_data_science

python/setup.ipynb

1

2269

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is used to set up important files for running the notebooks. It will create a \"data\" folder in the root of the repository, and download approximately 60MB of data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "sys.path.append('./src/')\n", "import opencourse as oc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved to: /Users/choldgraf/Dropbox/github/publicRepos/neuro_datasci_open_course/data/matrices_connectivity.mat\n", "Saved to: /Users/choldgraf/Dropbox/github/publicRepos/neuro_datasci_open_course/data/StevensonV2.mat\n", "Saved to: /Users/choldgraf/Dropbox/github/publicRepos/neuro_datasci_open_course/data/StevensonV4.mat\n" ] } ], "source": [ "# Download all data\n", "oc.download_all_files()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Ensure that you have the right dependencies\n", "All of the below packages should import:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mne # <-- Package for electrophysiology analysis\n", "import pandas # <-- Package for representing data as a DataFrame\n", "import bct # <-- Brain Connectivity Toolbox" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "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": 1 }

gpl-3.0

KIPAC/StatisticalMethods

notes/missingdata.ipynb

1

25838

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Notes: Missing Information, Missing Data, and Selection Effects\n", "\n", "In which we will\n", "* incorporate models for data selection into our toolkit\n", "* understand when selection effects are ignorable, and when they must be accounted for" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Overview\n", "\n", "It's worth breaking down what each of the things mentioned in the title of these notes means.\n", "\n", "**Missing information** just means something that we don't know. This might seem like a very silly thing to bring up so late in the course, since, from the very beginning, our models have been full of things we don't know. The simplest example of this is the true value of some quantity that we make a measurement of - we know the value of the measurement, but, unless the sampling distribution is a delta function, we'll never know the true value perfectly. These not-perfectly-known parameters tend to proliferate in hierarchical models, especially.\n", "\n", "**Missing data** is just what it sounds like: data that could have or should have been measured (in some sense) but haven't been. In other words, some of those true quantities in our model aren't going to be connected to measured (and therefore constant) data values. Usually, we mean that there exist measurements of these quantities for some sources in our data set, and not others. This too may seem like a strange thing to even mention, since it doesn't obviously pose a problem for inference. Nothing prevents us from constructing a posterior distribution and/or marginalizing over those parameters in the usual way. In the \"worst case\", they would simply be unconstrained, and their posteriors would be identical to whatever the corresponding priors were.\n", "\n", "However, there can be issues if data are missing in such a way that the existing data set is somehow biased or not representative, yet we would like to draw unbiased inferences about a representative population. Now we have to deal with **selection effects** - in other words the _selection_ process by which potentially measurable quantities are or are not measured has an _effect_ on the data available to us. _This too_ many seem like it doesn't pose a new problem, if we have taken the lessons from the very beginning of the course to heart, namely that the model must account for everything that determines the data. And this is true. Yet this can be tricky enough that it's worth having some notes especially on the subject.\n", "\n", "Here are the key messages for this lesson, of which 2-3 should not come as a surprise by now:\n", "1. In astronomy, it's very unlikley that our data set is perfectly complete.\n", "2. It's our job to know whether the incompleteness can be ignored for the purpose of our inference.\n", "3. If not, we need to model it appropriately and marginalize over our ignorance." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Context\n", "\n", "In astronomical surveys, you might hear the (historical) terms **Malmquist bias** and **Eddington bias**. These have specific meanings linked to the context where they were coined, though they are often misused to refer to selection effects more generally.\n", "\n", "Malmquist bias refers to the fact that flux-limited surveys have an effective *luminosity* limit for source detection that increases with distance (redshift). Thus, the sample of measured luminosities is not representative of the whole population (where population is defined as everything above some constant luminosity). The nifty graphic below illustrates that the failure to detect sources in the shaded region leads to the mean luminosity of the available sources being larger than the mean of the complete, luminosity-limited sample.\n", "\n", "<table>\n", " <tr>\n", " <td><img src=\"graphics/missing_malquist.png\" width=50%></td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Eddington bias refers to the impact of noise or other scatter on a luminosity function, the number of sources in some population as a function of $L$. (Here we need to understand the estimation procedure - measured counts would be deterministically converted to a luminosity and then a histogram of luminosities, without invoking the Bayesian machinery that you are now expert with.)\n", "\n", "If the true $n(L)$ is steeply decreasing, the number of apparently luminous sources will be boosted relative to the truth. This happens even if the scatter itself is symmetric, simply because there are more faint sources that might be scattered up than bright sources that might scatter down.\n", "<table>\n", " <tr>\n", " <td><a href=\"graphics/missing.R\"><img src=\"graphics/missing_eddington_0.png\" width=70%></a></td>\n", " <td><a href=\"graphics/missing.R\"><img src=\"graphics/missing_eddington_1.png\" width=70%></a></td>\n", " </tr>\n", "</table>\n", "\n", "All of the little shifts shown above-right lead to the bias in the estimated luminosity function below.\n", "\n", "<table>\n", " <tr>\n", " <td><a href=\"graphics/missing.R\"><img src=\"graphics/missing_eddington.png\" width=60%></a></td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "These are two particular manifestations of selection effects, which we can treat in a more general framework.\n", "\n", "In addition to these astronomical terms, there is some related terminology that you might come across in relevant statistics literature: **censoring** and **truncation**. (These are not one-to-one with Malmquist and Eddington bias.)\n", "\n", "Censoring means that a given data point (source) is known to exist, but a relevant measurement for it is not available. This could refer to the complete absence of a measurement, but usually this term shows up in the context of an \"upper limit\" placed on, e.g., the flux of a source that is independely known to exist. (Of course, the notion of an upper limit on a measurement doesn't really exist in Bayes' world, just as the \"error bar\" does not exist. A measurement _was_ made, so there is a fixed constant that we can call data, and all we need to properly interpret it is the form of the sampling distribution. This naturally doesn't stop people from reporting upper limits when they fail to detect something.)\n", "\n", "Truncation means that not only are measurements missing, but the total number of sources that *should* be in the data set is unknown. In other words, the lack of a measurement means that we don't even know about a particular source's existence. Truncation is a natural feature of surveys that rely on remote sensing, which is to say all astronomical surveys, hence the fact that Malmquist and Eddington were worrying about it 100 years ago." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## A concrete (albeit simplified) example\n", "\n", "It will help to have a specific scenario in mind as we wade through the formalities. So:\n", "\n", "> LSST will include a galaxy cluster survey, finding clusters as overdensities of red galaxies (richness). The underlying cosmological signal that we care about is their number as a function of mass. Log-richness ($y$) and log-mass ($x$) are presumed to have a linear relationship with some scatter.\n", "\n", "### Complete data set\n", "\n", "Let's start by considering a complete generative model (that is, without selection effects). It needs\n", "* a mass function (number of clusters as a function of $x$), depending on cosmological parameters, $\\Omega$;\n", "* a total number of clusters in the survey volume, $N$ (also a function of $\\Omega$);\n", "* a richness-mass scaling relation (mean scaling and scatter), parametrized by $\\theta$;\n", "* true values of mass ($x$) for each of the $N$ clusters;\n", "* true values of richness ($y$) for each cluster; and\n", "* measured values of $x$ and $y$, $\\hat{x}$ and $\\hat{y}$ (we'll assume independent and known sampling distributions to keep things relatively simple).\n", "\n", "Here is a PGM:\n", "\n", "<img src=\"graphics/missing_complete_pgm.png\" width=40%>\n", "\n", "Here I'm anticipating that $N$ will be some Poisson variable, whose mean is somehow computable from $\\Omega$. In the absence of selection effects, i.e. with a complete data set, we know what it is, so it gets a double-circle for \"data\".\n", "\n", "With some qualitatively reasonable parameter choices, here's a possible mock data set:\n", "\n", "<img src=\"graphics/missing_complete_xy.png\" width=35%>\n", "\n", "This is an inference you already know how to solve, given the modeling details. The likelihood is something like\n", "\n", "$p(\\hat{x},\\hat{y},N|\\Omega,\\theta) = P(N|\\Omega) \\prod_{k=1}^{N} p(x_k|\\Omega)\\,p(y_k|x_k,\\theta)\\,p(\\hat{y}_k|y_k)\\,p(\\hat{x}_k|x_k)$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Truncated data set\n", "\n", "In practice, our sample will only include sources whose measured $\\hat{y}$ exceeds some threshold for \"detection\". The total number of clusters (down to some low limiting mass) will not be known, only the number of detected clusters. So, our incomplete, truncated data set might include only the blue points below.\n", "\n", "<table>\n", " <tr>\n", " <td><img src=\"graphics/missing_truncy_xy.png\" width=70%><td>\n", " <rd><img src=\"graphics/missing_truncy_xy_obs.png\" width=70%></td>\n", " </tr>\n", "</table>\n", "\n", "Needless to say, it is not safe to \"simply fit a line\" to the detected data, even if we only cared about the $y$-$x$ relation and not the mass function, or vice versa." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "So, how do we modify the generative model to deal with the horribly inconsiderate fact that the Universe doesn't come with a White Pages? [See historical endnotes]\n", "\n", "The answer is: we continue thinking about _generating_ a complete data set to start with, but then apply the selection to produce a mock _truncated_ data set.\n", "\n", "To deal with the formalities, let\n", "* $N_\\mathrm{det}$ be the number of detected clusters. It is measured, while $N$ is not.\n", "* $\\phi$ be any additional model parameters that the detection probability might depend on, beyond $\\hat{y}_k$.\n", "* $I_k$ be an indicator variable (0 or 1) telling us whether cluster $k$ is detected (_included_ in the observed data set).\n", "\n", "Here's an expanded PGM including these new features (right).\n", "\n", "<table>\n", "<tr>\n", "<td><img src=\"graphics/missing_complete_pgm.png\" width=75%></td>\n", "<td></td>\n", "<td><img src=\"graphics/missing_truncy_pgm.png\" width=75%></td>\n", "</tr>\n", "</table>\n", "\n", "What's changed?\n", "* $N$ is no longer observed. It's now an unknown parameter!\n", "* $\\phi$ has been added.\n", "* $\\hat{y}_k$ and $\\phi$ feed (possibly stochastically) into whether cluster $k$ is in the observed data ($I_k=1$) or not ($I_k=0$). $I_k$ is \"measured\" in the sense of being fixed by observation (it's definitely 1 for anthing in the data set and 0 for anything not in the data set), as strange as that statement may sound.\n", "* $N_{det}$ is there for completeness; it's the number of $I_k$'s that are 1." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The new likelihood can be written\n", "\n", "$P(\\hat{x},\\hat{y},I,N_\\mathrm{det}|x,y,\\theta,\\Omega,\\phi,N)= {N \\choose N_\\mathrm{det}} \\,P(\\mathrm{detected}~\\mathrm{clusters}) \\,P(\\mathrm{missing}~\\mathrm{clusters})$.\n", "\n", "Let's break this down.\n", "\n", "$P(\\mathrm{detected}~\\mathrm{clusters})$ is the kind of thing we've worked with before, with the addition of the sampling distribution for $I_k=1$. Specifically,\n", "\n", "$P(\\mathrm{detected}~\\mathrm{clusters}) = \\prod_{k=1}^{N_\\mathrm{det}} p(x_k|\\Omega)\\,p(y_k|x_k,\\theta)\\,p(\\hat{y}_k|y_k)\\,p(\\hat{x}_k|x_k)\\,p(I_k=1|\\hat{y}_k,\\phi)$.\n", "\n", "$P(\\mathrm{missing}~\\mathrm{clusters})$ is almost the same (with $I_k=0$), but since these $\\hat{x}_k$ and $\\hat{y}_k$ are actually unobserved, they need to be marginalized over:\n", "\n", "$P(\\mathrm{missing}~\\mathrm{clusters}) = \\prod_{k=1}^{N-N_\\mathrm{det}} \\int d\\hat{x}_k\\,d\\hat{y}_k\\, p(x_k|\\Omega)\\,p(y_k|x_k,\\theta)\\,p(\\hat{y}_k|y_k)\\,p(\\hat{x}_k|x_k)\\,P(I_k=0|\\hat{y}_k,\\phi)$.\n", "\n", "What about this binomial term, ${N \\choose N_\\mathrm{det}} = \\frac{N!}{N_{det}!(N-N_{det})!}$? It's sneaky, and has to do with the statistical concept of *exchangeability* (a priori equivalence of data points) that we mentioned when introducing hierarchical models. We normally don't think about the fact that swapping modeled sources (within a hierarchical class) is meaningless to us when mapping generative models to inferences. Yet, for a model without selection effects, there straightforwardly are $N!$ data sets that could be generated which are perfectly equivalent apart from exchanging data points. Of course, in the case of a complete data set, this $N!$ term would be a constant, and we would normally be justified in ignoring it anyway. With our truncated data set, however, we now have to worry about the fact that our complete model containts two non-exchangeable classes (detected and non-detected), and the resulting binomial term includes the unknown parameter $N$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "It might seem like things have gotten hopeless at this point. We have a potentially _enormous_ number of model parameters, if $N$ is large. Not only that, but the _number_ of parameters is a variable, which means that directly sampling the posterior distribution would involve proposals to parameter spaces of different sizes, which is a level of complexity we haven't even considered before (though it can be done).\n", "\n", "However all is not lost. First, we can note that, with no observed values to distinguish them, the factors in the \"missing\" probability are all equal, so that\n", "\n", "$P(\\mathrm{missing}~\\mathrm{clusters}) = P_\\mathrm{mis}^{N-N_\\mathrm{det}}$,\n", "\n", "where $P_\\mathrm{mis}$ is the lovely integral above (which now needs to be done only once to marginalize over arbitrarily many of those pesky new parameters!).\n", "\n", "Second, if $P(N|\\Omega)$ is Poisson, it can also be marginalized out to produce\n", "\n", "$P(\\hat{x},\\hat{y},I,N_\\mathrm{det}|x,y,\\theta,\\Omega,\\phi)= e^{-\\langle N_\\mathrm{det} \\rangle} \\, \\langle N \\rangle^{N_\\mathrm{det}} \\,P(\\mathrm{detected}~\\mathrm{clusters})$.\n", "\n", "with\n", "$\\langle N \\rangle$ being the mean of $P(N|\\Omega)$, and $\\langle N_\\mathrm{det} \\rangle$ being the expectation value of $N_\\mathrm{det}$.\n", "\n", "While computing these expectation values can be a pain, this leaves us with a likelihood that is a _relatively_ simple change from where we started." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Don't believe me?\n", "\n", "There's a more intuitive, but less generally applicable, way to get to the same place. As an exercise,\n", "* Consider modeling _only_ the detected data. We're back to the original, simple PGM, except that $N \\rightarrow N_\\mathrm{det}$.\n", "* Define a grid covering the $\\hat{x}$-$\\hat{y}$ plane, with cells of area $\\Delta\\hat{x}\\Delta\\hat{y}$. All our data will fall into one of these cells.\n", "* A completely equivalent likelihood (i.e. with the same assumptions) as what we worked with above would be an independent Poisson sampling distribution for the number of detected clusters in each cell. The Poisson mean for each cell will depend on which cell it is, as well as $\\Omega$, $\\theta$, $\\phi$ and $N$.\n", "* Take the limit $\\Delta\\hat{x}\\Delta\\hat{y} \\rightarrow 0$. (Hint: in this limit the occupation of each cell is either 0 or 1.)\n", "\n", "Up to a constant factor of $N_\\mathrm{det}!$, you'll arrive at the expression above, but explicitly marginalized over $x$ and $y$. You'll then remember the comments above about how we never used to bother including a factor of $N_\\mathrm{det}!$, but totally should have, and even that will magically go away." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Commentary\n", "\n", "Keep in mind that this example is not fully general. In particular, we assumed that data were independent, so that the likelihood would factor into a product over clusters, and that the prior for $N$ was Poisson. These assumptions don't always apply, but they're not uncommon either.\n", "\n", "One assumption that we made in the description and figures, but _never_ invoked in the derivation was that of a constant selection threshold in $\\hat{y}$. In fact, we didn't really have to assume anything about $P(I_k|\\ldots)$ except that it's independent of clusters _other than_ the $k$th one. It's slightly remarkable that the final likelihood is modified only to contain the expectation values of $N$ and $N_\\mathrm{det}$, and not to more directly reflect any details of the selection method.\n", "\n", "Even so, I find it helpful to think about what information is provided by the leftmost detected point in a scatterplot like this.\n", "\n", "<img src=\"graphics/missing_truncy_xy.png\" width=35%>\n", "\n", "Specifically, the following:\n", "* For models that have the $x$-$y$ scaling approximately right, any detected points at the left end of the plot are going to be just above the detection limit. So their exact $\\hat{y}$ values don't actually tell us much at all.\n", "* _But_, the fact that there are just 3 around $x\\approx0$ (say). Does tell us something. It tells us that the scatter about the line, combined with the total number of sources with $x\\approx0$, must be such that detecting 3, and not 0 or 3000, is reasonable.\n", "* So, as long as we see enough detected sources to have some chance at estimating the scatter, and as long as our prior $p(x)$ is reasonably accurate and not too unconstrained, we should be ok.\n", "\n", "And indeed we are. Granted, the need to model a hidden population places additional demands on our data, so that the size/quality of data set required to get a data-dominated (rather than prior-dominated) result can be non-intuitive. The need to model a hidden population also, naturally, demands that we have decent prior information about it, in the form of $p(x)$. This example is not one where we can try to marginalize over some \"uninformative\" or super-flexible function for $p(x)$ and expect reasonable results. Such is the cost of doing business in a cruel and uncaring Universe.\n", "\n", "In contrast, people have sometimes attempted to deal with selection effects simply by modifying the sampling distribution to be, e.g., a truncated Gaussian (truncated below the detection limit). This does reflect the first point, that the lowest-$x$ sources will be barely detected. Yet the real information they provide, that they were detected at all, and in what numbers, isn't used. Consequently, the best fit for such a model (even if provided with exactly the right $p(x)$ and $N$) will still go through those left-most detected points, rather than passing well below them." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Ignorability (or, when we need to care)\n", "\n", "Unsurprisingly, fitting the data in this example without accounting for selection will wildly bias constraints on the mass function and scaling relation.\n", "\n", "For a general problem, selection effects are **ignorable** if both of the following are true:\n", "\n", "1. Priors for the interesting ($\\Omega,\\theta$) and selection-related ($\\phi$) parameters are independent.\n", "2. Selection is independent of (potentially) unobserved data.\n", "\n", "Here, read \"independent\" in the probability sense: selection probability may not correlate with potentially unobserved data. This second point is often a non-starter in survey science, since whatever we're interested in usually correlates with our detection signal. Ultimately, these conditions boil down to whether the posterior *for the parameters of interest* depends on selection.\n", "\n", "In our example, selection depended explicitly on one of our observables, so evidently the selection is not ignorable. On the other hand, clusters that are missing because they live in a part of the sky outside of our survey are clearly not missing for reasons related to either of our observables; that particular selection effect is ignorable.\n", "\n", "A less strict version ignorability might be expressedas, \"Does ignoring selection effects bias me at a level I care about?\" Generating mock data and analyzing it lazily is the best way of answering this." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Exercise: other truncation mechanisms\n", "\n", "Consider the following variants of the galaxy cluster example:\n", "1. Selection is at random (not related to $\\hat{x}$ or $\\hat{y}$)\n", "2. Selection is on the observed mass ($\\hat{x}$)\n", "3. Selection is on $\\hat{y}\\rightarrow\\hat{y}_1$ as before, and for detected clusters we have an additional measured observable $y_2$ whose scaling with $x$ is interesting\n", "\n", "In each case, sketch the PGM and decide whether selection effects are ignorable for inference about\n", "1. The distribution of $x$ (parametrized by $\\Omega$)\n", "2. The scaling relation parameters $\\theta$ (for both $y_1$ and $y_2$, or $y_2$ alone in case 2)\n", "\n", "If not, can you identify special cases where selection becomes ignorable?\n", "\n", "<table>\n", " <tr>\n", " <td><a href=\"graphics/missing_truncx_xy.source\"><img src=\"graphics/missing_atrandom_xy.png\" width=90%></a></td>\n", " <td></td>\n", " <td><a href=\"graphics/missing_truncx_xy.source\"><img src=\"graphics/missing_truncx_xy.png\" width=90%></a></td>\n", " <td></td>\n", " <td><a href=\"graphics/missing_truncy2_xy.source\"><img src=\"graphics/missing_truncy_xz.png\" width=90%></a></td>\n", " </tr>\n", "</table>\n", "\n", "Answers are not included in this notebook, but can be found [here](https://arxiv.org/abs/1901.10522)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Historical endnotes\n", "\n", "The \"White Pages\" was a book provided by the phone company, listing the address and phone number of every customer in a given geographic area.\n", "\n", "A book is a stack of sheets of paper, with information printed on them, generally tied or glued together along one edge.\n", "\n", "Paper is processed trees, made into thin, uniform sheets, and once represented the primary medium by which information was transmitted and recorded.\n", "\n", "Trees are perennial plants, generally featuring tall, structural trunks and leaf-bearing branches, that periodically grow between wildfires.\n", "\n", "_The_ phone company (singular) was once an enterprise dedicated to providing phone service exclusively, rather than bundles of internet access and pay-per-view video.\n", "\n", "The word \"phone\" once refered to a device that transmitted audio-only signals between two points via copper wire, rather than a tiny computer.\n", "\n", "We could go on." ] } ], "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": "3.6.10" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

JuliaPackageMirrors/TypeCheck.jl

doc/TypeCheck.ipynb

3

77069

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia is a dynamically-typed language. As with all dynamically-typed languages, users sometimes miss the option for static checking. As my master's project, I wrote TypeCheck.jl, which is a package for type-based static analysis of Julia code. The checks it implements work on any named function. The package has checks for stability of function return types, stability of variable types inside loops, and for potential `NoMethodError`s. TypeCheck.jl is available in the Julia package manager, and was announced on the julia-users mailing list." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "A Summary of Julia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia is a new language designed for technical computing.\n", "It is as easy to use and general-purpose as Python,\n", "but designed for fast computation, low-level control, and easy to express math.\n", "Julia is high-performance, dynamically-typed, and JIT-compiled.\n", "It is not focused on new ideas, but on executing existing ideas well, with a focus on being practical and approachable.\n", "As a language, some of Julia's distinctive features include multiple dispatch, first-class types, and Lisp-style macros." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The Julia Type System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every value in Julia has a type; variables contain values, but do not themselves have types.\n", "Types are arranged into a hierarchy of abstract and concrete types.\n", "Abstract types can have subtypes, but cannot be instantiated and do not have properties.\n", "Concrete types can be instantiated and have zero or more properties, but cannot have subtypes.\n", "Every type has a super type.\n", "At top of the hierarchy is the `Any` type; the super type of `Any` is `Any`.\n", "\n", "In Julia, types are first-class; types are of type DataType, which is itself of type DataType.\n", "Types are used for inference, optimization, dispatch, and documentation, but not for type checking.\n", "Because Julia still works (but more slowly) without any type inference, all of the type inference is implemented in the language.\n", "\n", "Types can also take parameters; these can be types or `Int`s. For example, `Array{T,N}` is parameterized by the element type and the number of dimensions. Instances of the same type with different type parameters (say `Array{Int,6}` and `Array{Number,4}`) are never subtypes of one another.\n", "\n", "To define a type, you use the `type` keyword:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type Point{T <: Number}\n", " x::T\n", " y::T\n", "end" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Point{T} type will have two properties, `x` and `y`. For any `Point`, the two properties will share a type, and their type will be a subtype of `Number`. From the definition, we also know that when a `Point` is represented in memory, `x` will precede `y`. Julia types are laid out in memory in a way compatible with C structs, which made implementing Julia's C calling functionality easier (and the result more efficient)." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Introspection in Julia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia has admirable introspection and reflection abilities, which are very useful for writing static analysis.\n", "For any named function, you can get a type-inferred AST with a simple function call:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "function foo(x::Int)\n", " z = x + 5\n", " return 2 * z\n", "end\n", "\n", "code_typed(foo,(Int,))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "1-element Array{Any,1}:\n", " :($(Expr(:lambda, {:x}, {{:z},{{:x,Int64,0},{:z,Int64,18}},{}}, quote # In[2], line 2:\n", " z = top(box)(Int64,top(add_int)(x::Int64,5))::Int64 # line 3:\n", " return top(box)(Int64,top(mul_int)(2,z::Int64))::Int64\n", " end)))" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `code_typed` function takes a function and a method signature.\n", "Every named function is a generic function: it has one or more methods, each with their own type signature.\n", "Julia uses multiple dispatch, which means that it considers the type, number, and order of all the arguments to pick the best match to a call.\n", "\n", "`code_typed` returns an untyped `Array` of `Expr`s, the type that represents a node of the Julia AST.\n", "For many invocations, this `Array` will only have one element. When the provided signature could match more than one existing method, all possible matches are returned. \"Possible\" matches occur when you pass an abstract type as part of the signature and some methods of the function accept subtypes of that type. The type of an actual value is always concrete, so the method that would actually get called would vary." ] }, { "cell_type": "code", "collapsed": false, "input": [ "e = code_typed(foo,(Int,))[1] #Julia indexes from 1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ ":($(Expr(:lambda, {:x}, {{:z},{{:x,Int64,0},{:z,Int64,18}},{}}, quote # In[2], line 2:\n", " z = top(box)(Int64,top(add_int)(x::Int64,5))::Int64 # line 3:\n", " return top(box)(Int64,top(mul_int)(2,z::Int64))::Int64\n", " end)))" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "An `Expr` has three fields: `head`, `args`, and `typ`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "names(e)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "3-element Array{Symbol,1}:\n", " :head\n", " :args\n", " :typ " ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `head` is a symbol indicating the type of expression. For `Expr`s returned by `code_typed`, this will be `:lambda`. (In Julia, `:foo` is a way to write \"the symbol `foo`\".)\n", "* `typ` is the return type of the method.\n", "* `args` is an `Array` of `Array`s. It contains information about the variables (local, arguments, captured) and body of the function. I'll explain more about the structure of `args` as needed in the rest of this document." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The Base Library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia's standard library is carefully written: changes happen in pull requests that get code review, good API design is an important goal, and there are knowledgeable contributors who pay attention to the details of numerical methods. Despite this care, my static analysis functions still found a few actual problems." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "New Type Annotation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `toq` function is used with `tic` for timing code execuition. `tic()` saves a timestamp, and `toq()` retrieves it and finds the difference to the current time. Because the storage used for the timestamp is untyped, `toq()` has a return type of `Any`. It actually always returned a value of type `Float64`. I added a type annotation to the value retrieve from storage, which is always `UInt64`, which fixed the type inference. This change was merged in to Julia's base library; it was [pull request #4148](https://github.com/JuliaLang/julia/pull/4148)." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Symbol Clean Up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard library lives in the `Base` module. Part of running a check on every function in `Base` is reflecting on the module to discover the functions. In order to make a function easily publicly available, a module puts the relevant function names in an `export` statement. Julia does not warn or throw an error when an undefined symbol is exported. I found an instance of this error and fixed it in [pull request #5542](https://github.com/JuliaLang/julia/pull/5542)." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Calling `QR` with Incorrect Array Dimension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the course of testing `NoMethodError` detection, I found that `qrfact` had a method that failed: `qrfact(x::Number)`. Ignoring any semantic meaning, the implementation would clearly always throw an error." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qrfact(x::Number) = QR(fill(one(x), 1, 1), fill(x, 1, 1))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "qrfact (generic function with 1 method)" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`QR`, the function being called here, only has one method:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "methods(QR)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "1 method for generic function <b>QR</b>:<ul><li> QR<i>{T}</i>(factors::<b>Array{T,2}</b>,\u03c4::<b>Array{T,1}</b>)</ul>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "# 1 method for generic function \"QR\":\n", "QR{T}(factors::Array{T,2},\u03c4::Array{T,1})" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It takes a matrix and a vector. Unfortunately, the second argument that `qrfact` passes in is a matrix and not a vector." ] }, { "cell_type": "code", "collapsed": false, "input": [ "typeof(fill(2.5,1,1))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "Array{Float64,2}" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This results in the following error:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "qrfact(2)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "no method QR{T}(Array{Int64,2}, Array{Int64,2})\nwhile loading In[8], in expression starting on line 1", "output_type": "pyerr", "traceback": [ "no method QR{T}(Array{Int64,2}, Array{Int64,2})\nwhile loading In[8], in expression starting on line 1", " in qrfact at In[5]:1" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I created [issue #5923](https://github.com/JuliaLang/julia/issues/5923) to report the problem, and it is being fixed." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Missing `+`/`-` for `Triangular` type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`check_method_calls` also found a pair of missing arithmetic functions on a special type of matrix. Julia has a set of special matrix types whose speicifc strutures allow doing some operations much more efficiently than a normal matrix. The `+`/`-` methods that take two different types from among these special types are generated by a for-loop; this probably made it easier to miss the lack of `(Triangle,Triangle)` methods to call.\n", "\n", "The following methods always throw `NoMethodError`s:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "-(Diagonal{T},Triangular{T<:Number})\n", "-(Triangular{T<:Number},Diagonal{T})\n", "-(Bidiagonal{T},Triangular{T<:Number})\n", "-(Triangular{T<:Number},Bidiagonal{T})\n", "-(Tridiagonal{T},Triangular{T<:Number})\n", "-(Triangular{T<:Number},Tridiagonal{T})\n", "-(SymTridiagonal{T},Triangular{T<:Number})\n", "-(Triangular{T<:Number},SymTridiagonal{T})\n", "+(Diagonal{T},Triangular{T<:Number})\n", "+(Triangular{T<:Number},Diagonal{T})\n", "+(Bidiagonal{T},Triangular{T<:Number})\n", "+(Triangular{T<:Number},Bidiagonal{T})\n", "+(Tridiagonal{T},Triangular{T<:Number})\n", "+(Triangular{T<:Number},Tridiagonal{T})\n", "+(SymTridiagonal{T},Triangular{T<:Number})\n", "+(Triangular{T<:Number},SymTridiagonal{T})" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The missing methods were:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "-(Triangular{T<:Number},Triangular{T<:Number})\n", "+(Triangular{T<:Number},Triangular{T<:Number})" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I filed this as [issue #5927](https://github.com/JuliaLang/julia/issues/5927), and it is being fixed." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Helper Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While introspecting on functions is surprisingly easy, there is a lot of ugly code created by the unfortunate structure of the `Expr` type. I wrote a number of helper functions to make my code clear. `code_typed` is a function from Julia's standard library; all functions from `TypeCheck` and all functions with implementations shown are ones I wrote." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Function to Retrieve Return Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`code_typed` returns `Expr`s that have lots of type annotations, including the return type of the function. There is a consistent structure to the `Expr`s that are returned.\n", "If we call the `Expr` from `code_typed` `e`, then `e.head` will be `:lambda` and `e.typ` will be `Any`.\n", "The third element of `e.args` will be another `Expr`; let's call this one `e2`.\n", "`e2.head` will be `:body`; `e2.typ` will be set to the inferred return type of the function. (There is currently no syntax in Julia to annotate function return types.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "code_typed(foo,(Int,))[1].args[3].typ" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "Int64" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because this is not especially readable, I wrote a helper function to pull out the return type:" ] }, { "cell_type": "code", "collapsed": true, "input": [ "returntype(e::Expr) = e.args[3].typ" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "returntype (generic function with 1 method)" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Usage examples:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a call to `code_typed` that we know will have one method returned:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "returntype(code_typed(foo,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "Int64" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For calls that might have more than one result:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Type[returntype(t) for t in code_typed(+,(Number,))]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "2-element Array{Type{T<:Top},1}:\n", " Int64 \n", " Number" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "map(returntype,code_typed(+,(Any,Any,Any)))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "3-element Array{Any,1}:\n", " BigInt \n", " BigFloat\n", " Any " ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Function to Retrieve All Expression in the Function Body" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To continue with the names `e` and `e2` from above, `e2.args` will be an `Array` of `Expr`s representing the body of the function. This is frequently useful when analyzing functions, but also cryptic. `body(e)` is a function to wrap this structural access in a readable name." ] }, { "cell_type": "code", "collapsed": false, "input": [ "body(e::Expr) = e.args[3].args" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "body (generic function with 1 method)" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is used analogously to `returntype` above." ] }, { "cell_type": "code", "collapsed": false, "input": [ "body(code_typed(foo,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "4-element Array{Any,1}:\n", " :( # In[2], line 2:) \n", " :(z = top(box)(Int64,top(add_int)(x::Int64,5))::Int64) \n", " :( # line 3:) \n", " :(return top(box)(Int64,top(mul_int)(2,z::Int64))::Int64)" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Function to Retrieve All Return Statements From a Function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also common to want to grab only a particular subset of `Expr`s from the function body. One possibility is the return statements.\n", "`Expr`s that represent return statements have `head` set to `:return`, so the below function pulls them out of the body." ] }, { "cell_type": "code", "collapsed": false, "input": [ "returns(e::Expr) =\n", " filter(x-> typeof(x) == Expr && x.head==:return,body(e))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "returns (generic function with 1 method)" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "returns(code_typed(foo,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "1-element Array{Any,1}:\n", " :(return top(box)(Int64,top(mul_int)(2,z::Int64))::Int64)" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "function barr(x::Int)\n", " x + 2\n", "end\n", "\n", "returns(code_typed(barr,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "1-element Array{Any,1}:\n", " :(return top(box)(Int64,top(add_int)(x::Int64,2))::Int64)" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that we still get a `:return`, even if we don't use the keyword `return`. The last expression in a function becomes the return value if there is no `return`, and this is expressed in the AST by desugaring to a normal `:return`." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Other Helper Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above functions demonstrate the general structure of the helper functions I wrote. There are other helper functions that I wrote used in the rest of this writeup; they will all be namespaced with `TypeCheck`. In Julia, `using TypeCheck` imports this module, making the functions available." ] }, { "cell_type": "code", "collapsed": false, "input": [ "using TypeCheck" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Checking for Stable Return Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is good style in Julia for the return type of a method to only depend on the types of the arguments and not on their values.\n", "This stability makes behavior more predictable for programmers.\n", "It also allows type inference to work better -- stable types on called methods allows stable types on the variables you put the return values into." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following method is a simple example of an unstable return type.\n", "Sometimes it returns an `Int` and sometimes a `Bool`.\n", "The return type of this method would be inferred as `Union(Int64,Bool)`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function unstable(x::Int)\n", " if x > 5\n", " return x\n", " else\n", " return false\n", " end\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "unstable (generic function with 1 method)" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "unstable(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "false" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "unstable(1337)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "1337" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Until now, there has been no way to automatically check that methods do not behave in this way.\n", "Julia's base library is mostly free of this, through the use of code review.\n", "While there are instances of instability, they tend to be less obvious -- they stem especially from retrieving data from untyped storage, from some interfaces to other environments, or from places where it is necessary (higher-level functions)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have written a static checker to detect that this invariant may be violated. It's not perfect." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this check works well for many methods with type annotations on their arguments, it assumes all methods without type annotations are fine. It also passes some methods that have arguments annotated with abstract types that do depend on argument values." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "True Positive:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`check_return_types` lists the methods that failed, and their return types. Methods whose arguments are annotated with concrete types work the best with this check." ] }, { "cell_type": "code", "collapsed": false, "input": [ "TypeCheck.check_return_types(unstable)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "(Int64)::Union(Int64,Bool)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "unstable" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "foo1(x::Int) = isprime(x) ? x: false\n", "TypeCheck.check_return_types(foo1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "(Int64)::Union(Int64,Bool)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "foo1" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "False Negative:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any method that does not annotated its argument types will pass. Some, including the example below, should definitely not pass:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "foo2(x) = isprime(x) ? x : false\n", "check_return_types(foo2) # no printing means it passed" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Deciding Whether the Return Type is Probably Stable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are three kinds of types in Julia: concrete, abstract, and union. Every value in Julia has a concrete type. Therefore, a method that returns a concrete type must be stable. Abstract and union types have zero or more concrete types as subtypes; a return type that is not concrete could be unstable. For example, `foo`, above, has a return type of `Union(Int64,Bool)`.\n", "\n", "The types of the arguments to a method are of the same three kinds. If any of the arguments are not concrete, then the return type might need to change for different subtypes. For example, `+(x::Number,y::Number)` could reasonably return `Number`: it makes sense for `+(5,10)` to return an `Int64` and `+(4.2,5.4)` to return a `Float64`. However, if all the arguments have concrete types (or there are no arguments), then the type should be concrete: there is no excuse for it to change." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Concrete, Abstract, and Union Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I've already mentioned concrete and abstract types, which are the leaves and internal nodes of the type hierarchy, respectively. Union types are collection of types. They are similar to abstract types in that they have subtypes, but they do not have names and do not alter the type hierarchy. Union types provide a way to say \"Any of these types or their subtypes\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "[Int, String, Union(Float64,UTF8String)]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "3-element Array{Type{T<:Top},1}:\n", " Int64 \n", " String \n", " Union(UTF8String,Float64)" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike concrete and abstract types, union types are not represented by `DataType`; they have their own type, `UnionType`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "[typeof(x) for x in ans]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "3-element Array{Type{_},1}:\n", " DataType \n", " DataType \n", " UnionType" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the standard library, there is a convenient function for differetiating between concrete types and all other types: `isleaftype`.\n", "It returns true for concrete types (the leaves of the type hierarchy) and false for abstract types and union types." ] }, { "cell_type": "code", "collapsed": false, "input": [ "isleaftype(Uint128)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "true" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "isleaftype(String)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "false" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "isleaftype(Union(Int,Float32,ASCIIString))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "false" ] } ], "prompt_number": 30 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "The Basic Check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check that the return type of a method is based on the types of its arguments rather than their values by examining the types of the arguments and the return type.\n", "If the return type is concrete, then everything is fine: a concrete type can't be unstable.\n", "If the return type is not concrete and at least one argument is not concrete, then the method gets the benefit of the doubt that the return type is varying based on the argument type.\n", "If the return type is not concrete, but all the argument are concrete, then the method will fail the check." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function isreturnbasedonvalues(e::Expr)\n", " rt = returntype(e)\n", " ts = TypeCheck.argtypes(e) #type of each argument in e's type signature\n", "\n", " if isleaftype(rt) || rt == None\n", " return false\n", " end\n", "\n", " for t in ts\n", " if !isleaftype(t)\n", " return false\n", " end\n", " end\n", "\n", " return true # return is not concrete type; all args are concrete types\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "isreturnbasedonvalues (generic function with 1 method)" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "isreturnbasedonvalues(code_typed(unstable,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "true" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "isreturnbasedonvalues(code_typed(foo,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "false" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this check works on simple examples, it tends to have many false-positives when running on large modules, such as the standard library." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Preventing One Unstable Function from Spawning Many More Warnings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simple function above gives many warnings about functions in the Base library. This is caused by a propagation of return types. If a method of `f2` does some work and then ends with a call to `f1`, the return type of `f2` is determined by `f1`. If `f1` has an unstable return type, then `f2` will also -- despite `f2` behaving well.\n", "\n", "While `f2` does literal have an unstable return type, any change to fix things should really happen in `f1`. This makes warning about `f2` unhelpful. Wading through hundreds of warnings to find the actual method to change is frustrating. This sea of warnings can be drained by adding a second chance for otherwise-failing functions. Methods like `f2` can be filtered out by looking at the `:return`s in the function body and letting them pass if their return type is determined by `:call`s to other functions (which are unstable). While this simple change would catch `return f1(2)` but not `x = f1(2); return x`, in practice this change has be sufficient." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Example of Return Type Propagation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To give a full example, here is an unstable `f1` getting called by `f2`, where `f2` is doing nothing wrong beyond calling `f1`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "f1(x::Int) = x == 5 ? 42 : pi\n", "returntype(code_typed(f1,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "Union(Int64,MathConst{:\u03c0})" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "f2(y::Int) = f1(y + 2)\n", "returntype(code_typed(f2,(Int,))[1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "Union(Int64,MathConst{:\u03c0})" ] } ], "prompt_number": 35 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Preventing Propagation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To give methods a second chance, we can add another section between checking the argument types and returning `true`. Only methods that would have previously failed will encounter this new code." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function isreturnbasedonvalues(e::Expr)\n", " rt = returntype(e)\n", " ts = TypeCheck.argtypes(e)\n", "\n", " if isleaftype(rt) || rt == None\n", " return false\n", " end\n", " for t in ts\n", " if !isleaftype(t)\n", " return false\n", " end\n", " end\n", "\n", " #a second chance\n", " #cs is a list of return types for calls in :return exprs in e's body\n", " cs = [TypeCheck.returntype(c,e) \n", " for c in TypeCheck.extract_calls_from_returns(e)]\n", " for c in cs\n", " if rt == c # e's returntype == the call's returntype\n", " return false\n", " end\n", " end\n", "\n", " return true #e fails the test\n", " end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "isreturnbasedonvalues (generic function with 1 method)" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two helper functions from `TypeCheck` used above.\n", "\n", "* `extract_calls_from_returns(e)` examines the `:return` statements in `e` and pulls out any `:call`s they contain. It returns the `:call` `Expr`s in an `Array`.\n", "* `find_returntype(call,context)` tries to determine the return type of the `call`, given the `context` (the method) it's being called in.\n", "\n", "The implementation of `find_returntype` is explained in detail below." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "isreturnbasedonvalues(code_typed(foo,(Int,))[1])\n", "#passes, correctly foo(Int)::Int" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "false" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "isreturnbasedonvalues(code_typed(f1,(Int,))[1])\n", "#fails, correctly f1(Int)::Union(Int64,MathConst{:\u03c0})" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "true" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "isreturnbasedonvalues(code_typed(f2,(Int,))[1])\n", "#passes, to prevent propagation from f1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "false" ] } ], "prompt_number": 39 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Determining the Return Type of a `:call`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The job of `find_returntype` is to take an `Expr` with head `:call` and determine the return type of that function call. Sometimes that type has already been annotated, which makes it easy. Occasionally, it is necessary to examine the possible methods than could be called and make a `Union` of their return types." ] }, { "cell_type": "code", "collapsed": false, "input": [ "call = TypeCheck.extract_calls_from_returns(code_typed(f2,(Int,))[1])[1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ ":(f1(top(box)(Int64,top(add_int)(y::Int64,2))::Int64)::Union(Int64,MathConst{:\u03c0}))" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`call` is an `Expr`; `call.head` is `:call`. This indicates that `call.args` will have a specific structure.\n", "`call.args[1]` will be the function being called; the remainder of `call.args` (in this case, just `call.args[2]`) will be the arguments." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(call.args[1])\n", "println(call.args[2])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "f1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ":(" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "top(box)(Int64,top(add_int)(y::Int64,2))::Int64)\n" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is another helper function, whose implementation will be discussed later, that I wrote to determine the types of `Expr`s used as arguments to `:call`s. This can be used here to determine the type of things like `call.args[2]`. In this case, the type is already inferred:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "call.args[2].args[2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ ":Int64" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, for `call`, we can just use the return type that type inference has already provided: `Union(Int64,MathConst{:\u03c0})`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "call.typ" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "Union(Int64,MathConst{:\u03c0})" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`call` is an example of the most common kind of expression that `find_returntype` handles. However, there are several other similar types of `Expr`s; they are handeled analogously, but the code has to be slightly different to handle their different structures.\n", "\n", "The code below starting with `if isdefined(Base,callee)` handles looking for matching methods if the return type has not already been annotated. Because `:call` only has a `Symbol` of the function name, we need to `eval` the `Symbol` to get an actual `Function` value. This `Function` value is then used to search through the methods for matches to this `:call`'s argument types, using the filtering of the built-in `code_typed` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function returntype(e::Expr,context::Expr) #must be :call,:new,:call1\n", " if Base.is_expr(e,:new); return e.typ; end\n", " if Base.is_expr(e,:call1) && isa(e.args[1], TopNode); return e.typ; end\n", " if !Base.is_expr(e,:call); error(\"Expected :call Expr\"); end\n", "\n", " if is_top(e)\n", " return e.typ\n", " end\n", "\n", " callee = e.args[1]\n", " if is_top(callee)\n", " return returntype(callee,context)\n", " elseif isa(callee,SymbolNode)\n", " # (func::F), so an anonymous function\n", " return Any\n", " elseif isa(callee,Symbol)\n", " if e.typ != Any ||\n", " any([isa(x,LambdaStaticData) for x in e.args[2:end]])\n", " return e.typ\n", " end\n", "\n", " if isdefined(Base,callee)\n", " f = eval(Base,callee)\n", " if !isa(f,Function) || !isgeneric(f)\n", " return e.typ\n", " end\n", " fargtypes = tuple([argtype(ea,context) for ea in e.args[2:end]])\n", " return Union([returntype(ef) for ef in code_typed(f,fargtypes)]...)\n", " else\n", " return @show e.typ\n", " end\n", " end\n", "\n", " return e.typ\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "returntype (generic function with 2 methods)" ] } ], "prompt_number": 44 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Stable Types Inside Loops" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Julia, for-loops are generally the fastest way to write code. (Faster than vectorized code; faster than maps or folds.)\n", "One easy way to accidentally decrease their performance is to change the type of a variable during the loop.\n", "If all the variables in a loop have stable types, then the code Julia outputs will be the same fast code as a statically typed, compiled language.\n", "If a variable has a type that changes, slower dynamic code will be produced to handle that.\n", "It can be easy to write code that has this problem, if you're not aware of it, even in simple programs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 5 # x is an Int\n", "for i=1:1000\n", " x += 100\n", " x /= 2 # x is a Float64\n", "end \n", "x #x is a Float64" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "100.0" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this code example, `x` begins life as an `Int`. In the first iteration of the loop, `x += 100` takes `x` as an `Int` and returns an `Int`; `x /= 2` takes this new `Int` and returns a `Float64`. After this, `x` will be a `Float64` for all the remaining iterations of the loop. For each of the two function calls (`+` and `/`) code will be produced to check if the type of `x` and proceed accordingly. The extra code slows down every iteration, despite only being needed for the first one. This slow down can be removed by making `x` a `Float64` from the start: `x = 5.0`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 5.0 # x is a Float64\n", "for i=1:1000\n", " x += 100\n", " x /= 2 # x is a Float64\n", "end \n", "x #x is a Float64" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "100.0" ] } ], "prompt_number": 46 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before presenting the implementation details, here are some examples of this check's performance." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "True Positives:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the same example from above; `x` is an `Int64` sometimes and a `Float64` at others, all within the loop." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function barr1()\n", " x=4\n", " for i in 1:10\n", " x *= 2.5\n", " end\n", " x\n", "end\n", "TypeCheck.check_loop_types(barr1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "()::Union(Int64,Float64)\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "\tx::Union(Int64,Float64)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "barr1" ] } ], "prompt_number": 47 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "True Negatives:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `x` becomes a `Float64` before entering the loop, then it does not trigger this warning." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function barr2()\n", " x = 4\n", " x = 2.5\n", " for i=1:10\n", " x *= 2.5\n", " end\n", "end\n", "TypeCheck.check_loop_types(barr2) #no printing means it passed" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `x` stays the same type (`Int`) during the loop, but becomes a `Float64` afterwards, then it passes this check." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function barr3()\n", " x = 4\n", " for i = 1:10\n", " x += i\n", " end\n", " x /= 2\n", "end\n", "TypeCheck.check_loop_types(barr3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the variable `x` is annotated with type `Int` in a local function, then Julia will throw an error if a non-`Int` value would be assigned to `x`. Therefore, the function `barr4` below will throw an error rather than change `x`'s type. This means that no extra code will be generated to handle dynamic typing, so there is no instability here. This function also passes this check." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function barr4()\n", " x::Int = 4\n", " for i=1:10\n", " x *= 2.5\n", " end \n", "end\n", "TypeCheck.check_loop_types(barr4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [] } ], "prompt_number": 50 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Why There Are No False Positives or False Negatives Listed Here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It would be better to have false positive and/or negatives here, but I have yet to come up with an example that does not work. This check is based entirely on the type inference already being done by Julia; this code just looks at the inferred types of the variables. These inferred types are the same ones that the code generator is using to optimize. If the type of a variable is a concrete type, then the code generator will generate fast code. If the type of a variable is not concrete, then the generated code will handle multiple possible types. This check has the same information as the code generator, which allows it to accurately anticipate what code will be generated." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Implementation Details:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables with unstable types can be detected in generic functions by looking at the output of `code_typed`.\n", "Since loops are lowered to gotos, we need to first find the loops and then check the types of the variables involved.\n", "Finding loops can be as simple as looking for gotos that jump backwards in the function: gotos whose labels precede them.\n", "Each instruction between the goto and its label is part of the loop body.\n", "For each instruction in the loop body, we can look at the inferred type of any variables involved.\n", "If the inferred type is a UnionType (or not a leaf type), then the variable's type is unstable.\n", "\n", "The high-level version looks like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "check_loop_types(e::Expr;kwargs...) =\n", " TypeCheck.loosetypes(e,TypeCheck.loopcontents(e))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ "check_loop_types (generic function with 1 method)" ] } ], "prompt_number": 51 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two new helper functions here:\n", "\n", "* `loopcontents(e)` returns all of the `Expr`s from `body(e)` that are inside loops. The implementation is straight-forward, and thus not discussed further here.\n", "* `loosetypes(context,exprs)` finds any variables that occur in `exprs` that have unstable (`Union`) types. The implementation of this function is detailed below." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Finding Loose Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two parts to `loosetypes`: finding all the variables in output of `loopcontents` and deciding if those variables has loose types.\n", "Most of the code in `loosetypes` is a series of loops for digging into each `Expr` to find any variables.\n", "The \"unstable type\" test comes in the line `elseif typeof(e1) == SymbolNode && !isleaftype(e1.typ) && typeof(e1.typ) == UnionType`.\n", "\n", "Variables are `Symbol`s; when they are annotated with a type, the `Symbol` and type are packaged together into `SymbolNode`. All variables in type inferred code are become `SymbolNode`s. If `e1` is a `SymbolNode`, then it's type is `e1.typ`. The rest of that conditional checks that the type is not a concrete type and is a `UnionType`." ] }, { "cell_type": "code", "collapsed": false, "input": [ " function loose_types(method::Expr,lr::Vector)\n", " lines = (Symbol,Type)[]\n", " for (i,e) in lr\n", " if typeof(e) == Expr\n", " es = copy(e.args)\n", " while !isempty(es)\n", " e1 = pop!(es)\n", " if typeof(e1) == Expr\n", " append!(es,e1.args)\n", " elseif typeof(e1) == SymbolNode && !isleaftype(e1.typ)\n", " && typeof(e1.typ) == UnionType\n", " push!(lines,(e1.name,e1.typ))\n", " end \n", " end \n", " end\n", " end\n", " return LoopResult(MethodSignature(method),lines)\n", " end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "loose_types (generic function with 1 method)" ] } ], "prompt_number": 52 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Statically Detecting `NoMethodError`s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia's closest equivalent to compile-time type errors is the `NoMethodError`. This occurs when you try to call a function, but no method exists to handle that combination of arguments. `\"stop\" + 2` would throw a `NoMethodError`, for example, because there is no method that works for `+(String,Int)`.\n", "\n", "These runtime errors can be especially annoying in rarely run code, or code that is only run after a long computation. It is an obviously useful application of static analysis.\n", "\n", "This detection cannot be done exactly because methods can be added at any time. There is always a possibility that the necessary method will, in fact, get added before the call occurs. (This means that zero false positives would not be possible.)\n", "\n", "A high-level view of the implementation is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "check_method_calls(e::Expr;kwargs...) =\n", " TypeCheck.no_method_errors(e,TypeCheck.method_calls(e);kwargs...)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "check_method_calls (generic function with 1 method)" ] } ], "prompt_number": 53 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It combines:\n", "\n", "* `method_calls(e)`, which filters `:call` `Expr`s out of the body of `e` and transforms them into a function name and argument types. While `method_calls` is not interesting enough to show in more detail, it uses a helper function `argtype` to identify the types of arguments to the function being called; `argtype`'s implementation is explained below.\n", "* `no_method_errors`, which takes the output of `method_calls`, calls `hasmatches` on each one, and collects any failures. This is just glue code, so it's implementation will not be detailed here. However, `hasmatches` is interesting. It takes one call's signature and tries to find any matching methods. This is where the actual analysis occurs, so the implementation is described below." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "False Positives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Near the beginning of this writeup, I listed issues and pull requests made to the Julia standard library; several are from the check and can serve as true positives. There are a few known remaining problems with this check, which I will explain with the false positive examples below." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Namespace Issues" ] }, { "cell_type": "code", "collapsed": false, "input": [ "TypeCheck.check_method_calls(transpose)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "(SparseMatrixCSC{Tv,Ti})::SparseMatrixCSC{Tv,Ti<:Integer}\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "transpose!(SparseMatrixCSC{Tv,Ti},SparseMatrixCSC{Tv,Ti<:Integer})\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "\t" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "transpose" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is claiming that there is no `transpose!(SparseMatrixCSC{Tv,Ti},SparseMatrixCSC{Tv,Ti<:Integer})`; however, it only checks in the `Base` module because `transpose` is exported from `Base`. The relevant `transpose!` is in `Base.SparseMatrix`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "methods(Base.SparseMatrix.transpose!)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "1 method for generic function <b>transpose!</b>:<ul><li> transpose!<i>{Tv,Ti}</i>(S::<b>SparseMatrixCSC{Tv,Ti}</b>,T::<b>SparseMatrixCSC{Tv,Ti}</b>) at <a href=\"https://github.com/JuliaLang/julia/tree/840c1fbe418c4741836aec48b462b9149e579d85/base/sparse/csparse.jl#L126\" target=\"_blank\">sparse/csparse.jl:126</a></ul>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "# 1 method for generic function \"transpose!\":\n", "transpose!{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti},T::SparseMatrixCSC{Tv,Ti}) at sparse/csparse.jl:126" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This could be fixed by somehow finding what module a method is implemented in (regardless of where the method is exported) and looking there for implementations of the methods being called. I don't know how to find out what module a method is implemented in.\n", "\n", "The otherside of this problem, a false negative, can be demonstracted by a self-contained example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "module FalseNegative\n", " export problem\n", " problem() = functionthatdoesntexist()\n", "end\n", "TypeCheck.check_method_calls(FalseNegative)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [] }, { "output_type": "stream", "stream": "stdout", "text": [ "The total number of failed methods in FalseNegative is 0\n" ] } ], "prompt_number": 56 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "`MIME{mime}`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a set of functions in Julia that form a system for displaying types in custom ways. They center around a `MIME{mime}` type and `writemime` that takes a `MIME` type and a value to display. `check_method_calls` fails the versions of these functions that take strings and convert them to `MIME` types. For example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "TypeCheck.check_method_calls(mimewritable)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "(String,Any)::Bool\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "mimewritable(Type{_<:MIME{mime}},Any)\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "\t" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "mimewritable" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "methods(mimewritable)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "2 methods for generic function <b>mimewritable</b>:<ul><li> mimewritable<i>{mime}</i>(::<b>MIME{mime}</b>,x) at <a href=\"https://github.com/JuliaLang/julia/tree/840c1fbe418c4741836aec48b462b9149e579d85/base/multimedia.jl#L37\" target=\"_blank\">multimedia.jl:37</a><li> mimewritable(m::<b>String</b>,x) at <a href=\"https://github.com/JuliaLang/julia/tree/840c1fbe418c4741836aec48b462b9149e579d85/base/multimedia.jl#L42\" target=\"_blank\">multimedia.jl:42</a></ul>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "# 2 methods for generic function \"mimewritable\":\n", "mimewritable{mime}(::MIME{mime},x) at multimedia.jl:37\n", "mimewritable(m::String,x) at multimedia.jl:42" ] } ], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example is clearly incorrect because the `MIME` version will accept any `MIME` type you could construct. The relevant definitions from the Base library are shown below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "MIME(s) = MIME{symbol(s)}()\n", "mimewritable(m::String, x) = mimewritable(MIME(m), x)\n", "mimewritable{mime}(::MIME{mime}, x) =\n", " method_exists(writemime, (IO, MIME{mime}, typeof(x)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`mimewritable(String,Any)` usings `MIME(Any)` to convert the `String` into a `Symbol` and then calls `mimewritable(MIME, Any)`. Unfortunately, my code currently infers `MIME(Any)` to produce `Type{_<:MIME{mime}}`. This type is not the same as `MIME{mime}`, which would actually work." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Identifying Argument Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`argtype` expects to get an AST node that is in the `args` of a `:call`. This could be another function call, a variable name, or a literal value.\n", "\n", "Literal values are pretty straight forward to type:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "argtype(n::Number,e::Expr) = typeof(n)\n", "argtype(c::Char,e::Expr) = typeof(c)\n", "argtype(s::String,e::Expr) = typeof(s)\n", "argtype(l::LambdaStaticData,e::Expr) = Function\n", "argtype(i,e::Expr) = typeof(i)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "argtype (generic function with 5 methods)" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For variables, there are two ways they can show up. If they are just a symbol, then I try looking them up in the surrounding `Expr`, which knows about function arguments and local and captured variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function argtype(s::Symbol,e::Expr)\n", " vartypes = [x[1] => x[2] for x in e.args[2][2]]\n", " s in vartypes ? (vartypes[s]) : Any\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "argtype (generic function with 6 methods)" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other case is easier. As mentioned in the section on checking variable types in loop above, some variables come packaged with their inferred types as `SymbolNode`s." ] }, { "cell_type": "code", "collapsed": false, "input": [ "argtype(s::SymbolNode,e::Expr) = s.typ" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "argtype (generic function with 7 methods)" ] } ], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`QuoteNode`s wrap some other `Expr`(s), so their type is the type of their value. `TopNode`s are a representation of intrinsic functions; these occur as funtion names in `:call`s, so they should usually get handled there. There's nothing to do with just the name, all we can do is return `Any` for these." ] }, { "cell_type": "code", "collapsed": false, "input": [ "argtype(q::QuoteNode,e::Expr) = find_argtype(q.value,e)\n", "argtype(t::TopNode,e::Expr) = Any" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "argtype (generic function with 9 methods)" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last case is when I get an `Expr`. Usually, this is a `:call`. This represents something like `foo(2+2)`, where `find_method_calls` is trying to type `foo`'s argument, `+(2,2)`. Here, we want the return type of `+(2,2)`, so I call `find_returntype`, which I described earlier when talking about checking the stability of function return types." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function argtype(e::Expr,context::Expr)\n", " if Base.is_expr(e,:call) || Base.is_expr(e,:new) || Base.is_expr(e,:call1)\n", " return returntype(e,context)\n", " end\n", " return Any\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "argtype (generic function with 10 methods)" ] } ], "prompt_number": 63 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the return type of `+(2,2)` is not already annotated, `find_returntype` would, in turn, call `find_argtype` on `2` and `2`, in order to examine the correct method of `+`." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Checking Method Calls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`hasmatches` takes three things:\n", "\n", "* the `Module` containing the function that is making the call\n", "* the `Symbol` of the function name\n", "* an `Array` containing the type of each argument used\n", "\n", "`hasmatches` either returns `true` if there are methods that could match this call or `false` if none exist." ] }, { "cell_type": "code", "collapsed": false, "input": [ "function hasmatches(mod::Module,cs::TypeCheck.CallSignature)\n", " if isdefined(mod,cs.name)\n", " f = eval(mod,cs.name)\n", " if isgeneric(f)\n", " opts = methods(f,tuple(cs.argtypes...))\n", " if isempty(opts)\n", " return false\n", " end\n", " end\n", " return true\n", " end\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "hasmatches (generic function with 1 method)" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It does this check by taking the `Symbol` of the function name and `eval`ing it to get an actual `Function`. (This doesn't always work because some functions are not top-level and cannot be found by `eval`ing; warning about these causes too many false-positives.) Then, as long as `f` is a generic, named function, `f` is checked for matching methods using built-in `methods` function. `methods` takes a `Function` and a tuple of argument types and returns an `Array` of matching methods. If the result is empty, then the call fails the check." ] } ], "metadata": {} } ] }

mit

neurodata/synaptome-stats

collman15v2/201710/runPy.ipynb

1

20693

{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<module 'annoStats' from '/Users/JLP/neurodata-dev/synaptome-stats/collman15v2/201710/annoStats.py'>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import importlib \n", "import numpy as np\n", "import toolbox\n", "import annoStats\n", "importlib.reload(toolbox)\n", "importlib.reload(annoStats)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Synapsin647\n", "VGluT1_647\n" ] } ], "source": [ "#cubes, loc, F0, nonzeros, ids = annoTightAll.testMain()\n", "cubes, loc, Fmax = annoStats.testMain()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 3207, 3565],\n", " [ 1, 3356, 2999],\n", " [ 0, 3820, 2691],\n", " [ 1, 3803, 2137],\n", " [ 0, 4014, 2079],\n", " [ 2, 3384, 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106,\n", " 72,\n", " 63,\n", " 111,\n", " 64,\n", " 131,\n", " 131,\n", " 101,\n", " 116,\n", " 86,\n", " 49,\n", " 74,\n", " 78,\n", " 72,\n", " 139,\n", " 115,\n", " 49,\n", " 129,\n", " 57,\n", " 122,\n", " 67,\n", " 82,\n", " 55,\n", " 39,\n", " 64,\n", " 49,\n", " 45,\n", " 70,\n", " 139,\n", " 69,\n", " 44,\n", " 146,\n", " 153,\n", " 101,\n", " 44,\n", " 84,\n", " 91,\n", " 129]]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Fmax" ] }, { "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 }

apache-2.0

astro4dev/OAD-Data-Science-Toolkit

Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/03. Numeros y jerarquía de operaciones.ipynb

1

7640

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Números y jerarquía de operaciones\n", "\n", "- Números enteros y flotantes\n", "- Jerarquía de operaciones\n", "- Asignación de variables\n", "\n", "## Números enteros y flotantes\n", "\n", "\n", "\n", "Con los números se pueden realizar los siguientes tipos de operaciones:\n", "\n", "| Operación | Resultado |\n", "| --------- | --------------- |\n", "| + | Suma |\n", "| - | Resta |\n", "| * | Multiplicación |\n", "| / | División |\n", "| // | División entera |\n", "| \\*\\* | Potencia |\n", "| % | Residuo (modulo) |\n", "\n", "Algunos sencillos ejemplos son:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2.**5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2**5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "3/2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "3//2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2//3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "21/3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "21//3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "21%3" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "¿Cuál es el resultado de cada una de las siguientes operaciones?\n", "\n", "- `18/4`\n", "- `18//4`\n", "- `18%4`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "18%4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jerarquía de operaciones\n", "\n", "- Paréntesis\n", "- Exponenciación\n", "- Multiplicación y División\n", "- Sumas y Restas (izquierda a derecha)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2 * (3-1) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(1+1)**(5-2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2**1+1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "3*1**3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "2*3-1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "5-2*2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "6-3+2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "6-(3+2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "100/100/2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "100/100*2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "100/(100*2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Cuál es el valor de la siguiente expresión?\n", "\n", "`16 - 2 * 5 // 3 + 1` \n", "\n", "- (a) 14\n", "- (b) 24\n", "- (c) 3\n", "- (d) 13.667 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Asignación de variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = 15\n", "y = x\n", "x == y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = 22\n", "x==y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = x+1\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x+=1\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x-=20\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Qué aparece cuando se ejecuta la siguiente secuencia?\n", "```\n", "x = 12\n", "x = x - 1\n", "print(x)\n", "```\n", "- (a) 12\n", "- (b) -1\n", "- (c) 11\n", "- (d) Aparece un error porque x no puede ser igual a x - 1.\n", "\n", "¿Qué aparece cuando se ejecuta la siguiente secuencia?\n", "```\n", "x = 12\n", "x = x - 3\n", "x = x + 5\n", "x = x + 1\n", "print(x)\n", "```\n", "- (a) 12\n", "- (b) 9\n", "- (c) 15\n", "- (d) Aparece un error porque no es posible cambiar el valor de x tantas veces.\n", "\n", "¿En qué orden hay que poner estas instrucciones para que se muestre el número 2000?\n", "\n", "- 1) `miplata=1500`\n", "- 2) `print(miplata)`\n", "- 3) `miplata+=500`\n", "\n", "- (a) 123\n", "- (b) 321\n", "- (c) 231\n", "- (d) 132\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"img/rock.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El material de este notebook fue recopilado para Clubes de Ciencia Colombia 2017 por Luis Henry Quiroga (GitHub: [lhquirogan](https://github.com/lhquirogan)) - Germán Chaparro (GitHub: [saint-germain](https://github.com/saint-germain)), y fue adaptado de https://github.com/PythonBootcampUniandes" ] } ], "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": 1 }

gpl-3.0

PerfectoVidal/panda_learning

analisis.ipynb

1

11132

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#importamos numpy y pandas\n", "import numpy as np\n", "import pandas as pd\n", "#cargamos para manejar fechas\n", "import datetime\n", "from datetime import datetime, date\n", "\n", "#configuramos el pandas para que nos muestre por pantalla como queremos\n", "pd.set_option('display.notebook_repr_html', False)\n", "pd.set_option('display.max_columns', 8)\n", "pd.set_option('display.max_rows', 10)\n", "pd.set_option('display.width', 60)\n", "\n", "#usaremos matplotlib para los gráficos\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "#cargamos los datos y solamente leemos las columnas de la posicion 0 2 3 y 7\n", "\n", "sp500 = pd.read_csv(\"tratamiento_datos/data/sp500.csv\", index_col='Symbol', usecols=[0,2,3,7])\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#creamos un dataframe aleatorio para practicar\n", "np.random.seed(123456)\n", "df=pd.DataFrame({'foo':np.random.random(1000000), 'key':range(100,1000100)})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " foo key\n", "9999 0.272283 10099" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#obtenemos un dato solamente\n", "df[df.key==10099]\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.3 ms ± 57.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "#Cuanto tiempo tardamos al hacer la selección por llave\n", "%timeit df[df.key==10099]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " foo\n", "key \n", "100 0.126970\n", "101 0.966718\n", "102 0.260476\n", "103 0.897237\n", "104 0.376750" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#pasamos las keys a que sean indices\n", "df_with_index=df.set_index(['key'])\n", "df_with_index[:5]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "foo 0.272283\n", "Name: 10099, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_with_index.loc[10099]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "89.7 µs ± 1.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "#ahora con loc vemos a ver lo rapido que es\n", "%timeit df_with_index.loc[10099]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Temperature city\n", "0 70 Missoula\n", "1 80 Philadelphia\n", "Index(['Temperature', 'city'], dtype='object')\n" ] } ], "source": [ "#indices básicos\n", "temps = pd.DataFrame({'city':[\"Missoula\", \"Philadelphia\"],\"Temperature\": [70, 80]})\n", "print(temps)\n", "print(temps.columns)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0\n", "0 10\n", "1 11\n", "2 12\n", "3 13\n", "4 14\n", "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')\n" ] } ], "source": [ "#vamos a trabajar con indices\n", "df_i64 = pd.DataFrame(np.arange(10,20), index=np.arange(0,10))\n", "print(df_i64[:5])\n", "print(df_i64.index)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " 0\n", "0.0 0\n", "0.5 5\n", "1.0 10\n", "1.5 15\n", "2.0 20" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_f64 = pd.DataFrame(np.arange(0,1000,5), np.arange(0.0, 100.0, 0.5))\n", "df_f64.iloc[:5]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Float64Index([ 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0,\n", " 3.5, 4.0, 4.5,\n", " ...\n", " 95.0, 95.5, 96.0, 96.5, 97.0, 97.5, 98.0,\n", " 98.5, 99.0, 99.5],\n", " dtype='float64', length=200)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_f64.index" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " A\n", "(0.0, 0.5] 1\n", "(0.5, 1.0] 2\n", "(1.0, 1.5] 3\n", "(1.5, 2.0] 4" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#extraemos un intervalo\n", "df_interval = pd.DataFrame({\"A\":[1,2,3,4]}, index= pd.IntervalIndex.from_breaks([0, 0.5, 1.0, 1.5, 2.0]))\n", "df_interval" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "IntervalIndex([(0.0, 0.5], (0.5, 1.0], (1.0, 1.5], (1.5, 2.0]]\n", " closed='right',\n", " dtype='interval[float64]')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_interval.index" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B\n", "0 0 a\n", "1 1 a\n", "2 2 b\n", "3 3 b\n", "4 4 c\n", "5 5 a\n" ] } ], "source": [ "df_categorical = pd.DataFrame({'A': np.arange(6), 'B':list('aabbca')})\n", "print(df_categorical)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Sector Price Book Value\n", "Symbol \n", "MMM Industrials 141.14 26.668\n", "ABT Health Care 39.60 15.573\n", "ABBV Health Care 53.95 2.954\n", "ACN Information Technology 79.79 8.326\n", "ACE Financials 102.91 86.897" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp500[:5]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Symbol Sector Price Book Value\n", "0 MMM Industrials 141.14 26.668\n", "1 ABT Health Care 39.60 15.573\n", "2 ABBV Health Care 53.95 2.954\n", "3 ACN Information Technology 79.79 8.326\n", "4 ACE Financials 102.91 86.897" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_mov_to_column = sp500.reset_index()\n", "index_mov_to_column[:5]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Symbol Price Book Value\n", "Sector \n", "Industrials MMM 141.14 26.668\n", "Health Care ABT 39.60 15.573\n", "Health Care ABBV 53.95 2.954\n", "Information Technology ACN 79.79 8.326\n", "Financials ACE 102.91 86.897" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_mov_to_column.set_index('Sector')[:5]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Sector Price Book Value\n", "Symbol \n", "MMM Industrials 141.14 26.668\n", "ABBV Health Care 53.95 2.954\n", "FOO NaN NaN NaN" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reindexed = sp500.reindex(index=['MMM','ABBV','FOO'])\n", "reindexed" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Price Book Value NewCol\n", "Symbol \n", "MMM 141.14 26.668 NaN\n", "ABT 39.60 15.573 NaN\n", "ABBV 53.95 2.954 NaN\n", "ACN 79.79 8.326 NaN\n", "ACE 102.91 86.897 NaN" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp500.reindex(columns=['Price','Book Value','NewCol'])[:5]" ] }, { "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

uwkejia/Clean-Energy-Outlook

examples/Extra/Jupyter Notebooks/ZNDX.ipynb

1

82570

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(\"climate_annual.txt\", delim_whitespace=True)\n", "df2 = df.set_index(\"State\")\n", "df3 = df2.loc[\"California\":\"California\", [\"Year\", \"PCP\", \"ZNDX\"]]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ -4.36 -6.81 -0.68 9.9 2.69 4.63 -9.29 7.1 -4.32 9.41\n", " 4.12 2.83 -6.13 4.81 -1.41 0.79 -18.29 -17.11 9.3 2.51\n", " 2.6 0.96 20. 30.01 -3.28 -11.92 -0.72 -11.87 -5.79 -9.01\n", " -16.03 -5.36 -7.91 7.27 -8.86 20.59 12.48 -6.67 29.06 -5.38\n", " -4.56 -7.58 -10.15 -0.9 -6.82 16.07 -0.67 -18.64 -14.24 -10.55\n", " 13.93 2.91 -3.95 -22.27 -20.26 -16.66 1.29]\n", "(57, 1)\n", "[[-10.55 13.93 2.91]\n", " [ 13.93 2.91 -3.95]\n", " [ 2.91 -3.95 -22.27]\n", " [ -3.95 -22.27 -20.26]\n", " [-22.27 -20.26 -16.66]\n", " [-20.26 -16.66 1.29]]\n", "[[ -3.95]\n", " [-22.27]\n", " [-20.26]\n", " [-16.66]\n", " [ 1.29]]\n", "(6, 3)\n", "(5, 1)\n" ] } ], "source": [ "print(df3.ZNDX.values)\n", "\n", "ZNDX_array = df3.ZNDX.values\n", "print(ZNDX_array.reshape(-1,1).shape)\n", "\n", "result = []\n", "for i in range(len(ZNDX_array) - 2):\n", " result.append([ZNDX_array[i], ZNDX_array[i+1], ZNDX_array[i+2]])\n", "ZNDX_newx = np.array(result)\n", "ZNDX_newy = df3.ZNDX.values[3:].reshape(-1,1)\n", "\n", "ZNDX_newx_train = ZNDX_newx[:-6]\n", "ZNDX_newy_train = ZNDX_newy[:-5]\n", "ZNDX_newx_test = ZNDX_newx[-6:]\n", "ZNDX_newy_test = ZNDX_newy[-5:]\n", "\n", "print(ZNDX_newx_test)\n", "print(ZNDX_newy_test)\n", "print(ZNDX_newx_test.shape)\n", "print(ZNDX_newy_test.shape)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(49,)\n", "(6,)\n", "0.971047068009\n", "[-16.66 1.29 0.97104707]\n", "[ 0.81690743]\n", "[ 1.29 0.97104707 0.81690743]\n", "[-0.14109654]\n", "60\n", "55\n", "(54, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/kejiawu/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", "/Users/kejiawu/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", "/Users/kejiawu/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:34: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", "/Users/kejiawu/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:37: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n" ] }, { "data": { "image/png": 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lwP+kSa+WjCS5vn37j38cvhXf//6Wz/X2hql9e++d/uK/aVOoYQ6nH7u3NywV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\n8iaDcr4Y/zf1vvZPAWPj/y+Hcib7DGG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"text/plain": [ "<matplotlib.figure.Figure at 0x11350dd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Lasso regression\n", "regr = linear_model.Lasso()\n", "regr.fit(ZNDX_newx_train, ZNDX_newy_train)\n", "\n", "ZNDX_lassoy_train = regr.predict(ZNDX_newx_train)\n", "ZNDX_lassoy_test = regr.predict(ZNDX_newx_test)\n", "\n", "print(ZNDX_lassoy_train.shape)\n", "print(ZNDX_lassoy_test.shape)\n", "\n", "year_all = np.append(df3.Year.values, [2017, 2018, 2019])\n", "y_lasso = np.append(ZNDX_lassoy_train, ZNDX_lassoy_test)\n", "\n", "ZNDX_17_y = y_lasso[-1]\n", "print(ZNDX_17_y)\n", "ZNDX_18_x = np.append(ZNDX_array[-2:], ZNDX_17_y)\n", "print(ZNDX_18_x)\n", "ZNDX_18_y = regr.predict(ZNDX_18_x)\n", "print(ZNDX_18_y)\n", "\n", "ZNDX_18 = ZNDX_18_y.item(0)\n", "ZNDX_19_x = np.append(ZNDX_18_x[-2:], ZNDX_18)\n", "print(ZNDX_19_x)\n", "ZNDX_19_y = regr.predict(ZNDX_19_x)\n", "print(ZNDX_19_y)\n", "ZNDX_19 = ZNDX_19_y.item(0)\n", "\n", "y_lasso_all = np.append(y_lasso, [ZNDX_18, ZNDX_19])\n", "\n", "print(year_all.shape[0])\n", "print(y_lasso.shape[0])\n", "print(df3.Year[3:].reshape(-1,1).shape)\n", "\n", "plt.figure()\n", "plt.scatter(df3.Year.reshape(-1,1), df3.ZNDX)\n", "#plt.plot(df.Year[2:-1].reshape(-1,1), y_lasso[:-1])\n", "plt.scatter(year_all[-2:].reshape(-1,1), y_lasso_all[-2:], color='red')\n", "plt.plot(year_all[2:-1].reshape(-1,1), y_lasso_all)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8/FDz3/+GwBhf0wghOI4bB1OmhMeXXVY7MAKMGgVPPgnf/GYIjjNnwsUXwxVX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btZjcSodwU+IcD3ngLcC+wDnO52Q5JAW+lrzP00xnQBXkDGl9wYp3YpipJcMv0371tgDDJG+z7g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\nJEmSRjJolCRJkiSN9DnkUlWtOHhCBAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x113327748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,4.5))\n", "fig.suptitle('Lasso Regression', fontsize=14, fontweight='bold')\n", "\n", "#Plot for training data\n", "fig.add_subplot(121)\n", "plt.plot(df3.Year[3:-5],regr.predict(ZNDX_newx_train),label='Predict',c='r')\n", "plt.plot(df3.Year[3:-5],ZNDX_newy_train,label='Actual')\n", "plt.legend(loc=0,fontsize=10)\n", "plt.title('Training Data')\n", "plt.xlabel('Year')\n", "plt.ylabel('Z Indx')\n", " \n", "#Plot for testing data\n", "fig.add_subplot(122)\n", "plt.scatter(df3.Year[-5:],regr.predict(ZNDX_newx_test)[:-1],marker='x',c='r',label='Predict')\n", "plt.scatter(df3.Year[-5:],ZNDX_newy_test, marker='*',label='Actual')\n", "plt.legend(loc=2,fontsize=10)\n", "plt.title('Testing Data')\n", "plt.xlabel('Year')\n", "plt.ylabel('Z Index')\n", "\n", "plt.tight_layout(pad=4, w_pad=4)\n", "plt.show()" ] }, { "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": 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 }

mit

maxalbert/tohu

notebooks/v4/Primitive_generators.ipynb

1

23339

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Primitive generators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains tests for tohu's primitive generators." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import tohu\n", "from tohu.v4.primitive_generators import *\n", "from tohu.v4.dispatch_generators import *\n", "from tohu.v4.utils import print_generated_sequence" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tohu version: v0.5.1+5.g734d94f.dirty\n" ] } ], "source": [ "print(f'Tohu version: {tohu.__version__}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Constant` simply returns the same, constant value every time." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "g = Constant('quux')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: quux, quux, quux, quux, quux, quux, quux, quux, quux, quux\n" ] } ], "source": [ "print_generated_sequence(g, num=10, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boolean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Boolean` returns either `True` or `False`, optionally with different probabilities." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "g1 = Boolean()\n", "g2 = Boolean(p=0.8)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: True, True, False, True, True, True, False, True, True, True, False, True, False, True, False, True, False, True, False, True\n", "Generated sequence: True, True, False, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, False, True\n" ] } ], "source": [ "print_generated_sequence(g1, num=20, seed=12345)\n", "print_generated_sequence(g2, num=20, seed=99999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Integer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Integer` returns a random integer between `low` and `high` (both inclusive)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "g = Integer(low=100, high=200)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: 153, 193, 101, 138, 147, 124, 134, 172, 155, 120\n" ] } ], "source": [ "print_generated_sequence(g, num=10, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Float" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Float` returns a random float between `low` and `high` (both inclusive)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "g = Float(low=2.3, high=4.2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "3.091577757836\n", "2.319321421968\n", "3.867892367582\n", "2.867415724879\n", "2.999982210028\n", "2.667956563186\n", "3.375415520585\n", "2.607206865466\n", "2.536107080139\n", "3.122578909219\n" ] } ], "source": [ "print_generated_sequence(g, num=10, sep='\\n', fmt='.12f', seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HashDigest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`HashDigest` returns hex strings representing hash digest values (or alternatively raw bytes)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HashDigest hex strings (uppercase)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "g = HashDigest(length=6)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: E251FB, E52DE1, 1DFDFD, 810876, A44D15, A9AD2D, FE0F5E, 7E5191, 656D56, 224236\n" ] } ], "source": [ "print_generated_sequence(g, num=10, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HashDigest hex strings (lowercase)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "g = HashDigest(length=6, uppercase=False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: e251fb, e52de1, 1dfdfd, 810876, a44d15, a9ad2d, fe0f5e, 7e5191, 656d56, 224236\n" ] } ], "source": [ "print_generated_sequence(g, num=10, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HashDigest byte strings" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "g = HashDigest(length=10, as_bytes=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "b'\\xe2Q\\xfb\\xed\\xe5-\\xe1\\xe3\\x1d\\xfd'\n", "b'\\x81\\x08v!\\xa4M\\x15/\\xa9\\xad'\n", "b'\\xfe\\x0f^4~Q\\x91\\xd3em'\n", "b'\"B6\\x88\\x1d\\x9eu\\x98\\x01\\xbb'\n", "b'vl\\xea\\xf6q\\xcd@v;\\x9d'\n" ] } ], "source": [ "print_generated_sequence(g, num=5, seed=12345, sep='\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NumpyRandomGenerator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This generator can produce random numbers using any of the random number generators [supported](https://docs.scipy.org/doc/numpy/reference/routines.random.html) by numpy." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "g1 = NumpyRandomGenerator(method=\"normal\", loc=3.0, scale=5.0)\n", "g2 = NumpyRandomGenerator(method=\"poisson\", lam=30)\n", "g3 = NumpyRandomGenerator(method=\"exponential\", scale=0.3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: 1.9764617025764353, 5.394716690287741, 0.40280642471630923, 0.22134847826254989\n", "Generated sequence: 40, 24, 31, 34, 27, 32, 29, 29, 35, 38, 30, 32, 38, 36, 36\n", "Generated sequence: 0.7961371899305246, 0.11410397056571128, 0.060972430042086474, 0.06865806254932436\n" ] } ], "source": [ "g1.reset(seed=12345); print_generated_sequence(g1, num=4)\n", "g2.reset(seed=12345); print_generated_sequence(g2, num=15)\n", "g3.reset(seed=12345); print_generated_sequence(g3, num=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FakerGenerator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`FakerGenerator` gives access to any of the methods supported by the [faker](https://faker.readthedocs.io/) module. Here are a couple of examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example: random names" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "g = FakerGenerator(method='name')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: Adam Bryan, Jacob Lee, Candice Martinez, Justin Thompson, Heather Rubio, William Jenkins, Brittany Ball, Glenn Johnson\n" ] } ], "source": [ "print_generated_sequence(g, num=8, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example: random addresses" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "g = FakerGenerator(method='address')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "453 Ryan Islands\n", "Greenstad, FL 97251\n", "---\n", "USS Irwin\n", "FPO AA 66552\n", "---\n", "55075 William Rest\n", "North Elizabeth, NH 38062\n", "---\n", "926 Alexandra Road\n", "Romanberg, HI 99597\n", "---\n", "8202 Michelle Branch\n", "Baileyborough, AL 08481\n", "---\n", "205 William Coves\n", "Alexanderport, WI 72565\n", "---\n", "821 Patricia Hill Apt. 242\n", "Apriltown, MO 24730\n", "---\n", "486 Karen Lodge Apt. 205\n", "West Gregory, MT 33130\n" ] } ], "source": [ "print_generated_sequence(g, num=8, seed=12345, sep='\\n---\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IterateOver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`IterateOver` is a generator which simply iterates over a given sequence. Note that once the generator has been exhausted (by iterating over all its elements), it needs to be reset before it can produce elements again." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "seq = ['a', 'b', 'c', 'd', 'e']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "g = IterateOver(seq)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['a', 'b', 'c', 'd', 'e']\n", "[]\n", "['a', 'b', 'c', 'd', 'e']\n" ] } ], "source": [ "g.reset()\n", "print([x for x in g])\n", "print([x for x in g])\n", "g.reset()\n", "print([x for x in g])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SelectOne" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "some_items = ['aa', 'bb', 'cc', 'dd', 'ee']" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "g = SelectOne(some_items)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: dd, aa, cc, cc, bb, cc, ee, dd, bb, cc, aa, dd, cc, ee, bb, ee, ee, bb, cc, aa, ee, dd, ee, ee, bb, bb, bb, aa, bb, cc\n" ] } ], "source": [ "print_generated_sequence(g, num=30, seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, all possible values are chosen with equal probability, but this can be changed by passing a distribution as the parameter `p`." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "g = SelectOne(some_items, p=[0.1, 0.05, 0.7, 0.03, 0.12])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: cc, ee, cc, aa, cc, cc, cc, cc, cc, aa, cc, cc, cc, cc, aa, cc, cc, cc, ee, cc, cc, cc, cc, cc, cc, ee, cc, ee, cc, cc\n" ] } ], "source": [ "print_generated_sequence(g, num=30, seed=99999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the item `'cc'` has the highest chance of being selected (70%), followed by `'ee'` and `'aa'` (12% and 10%, respectively)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Timestamp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Timestamp` produces random timestamps between a start and end time (both inclusive)." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "g = Timestamp(start='1998-03-01 00:02:00', end='1998-03-01 00:02:15')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "1998-03-01 00:02:03\n", "1998-03-01 00:02:09\n", "1998-03-01 00:02:07\n", "1998-03-01 00:02:11\n", "1998-03-01 00:02:13\n", "1998-03-01 00:02:06\n", "1998-03-01 00:02:08\n", "1998-03-01 00:02:12\n", "1998-03-01 00:02:06\n", "1998-03-01 00:02:01\n" ] } ], "source": [ "print_generated_sequence(g, num=10, sep='\\n', seed=99999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `start` or `end` are dates of the form `YYYY-MM-DD` (without the exact `HH:MM:SS` timestamp), they are interpreted as `start='YYYY-MM-DD 00:00:00` and `end='YYYY-MM-DD 23:59:59'`, respectively - i.e., as the beginning and the end of the day." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "g = Timestamp(start='2018-02-14', end='2018-02-18')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "2018-02-16 12:40:28\n", "2018-02-18 10:42:18\n", "2018-02-14 01:28:51\n", "2018-02-18 23:26:47\n", "2018-02-18 20:55:23\n" ] } ], "source": [ "print_generated_sequence(g, num=5, sep='\\n', seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience, one can also pass a single date, which will produce timestamps during this particular date." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "g = Timestamp(date='2018-01-01')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "2018-01-01 15:10:07\n", "2018-01-01 00:22:12\n", "2018-01-01 10:52:23\n", "2018-01-01 13:24:48\n", "2018-01-01 07:03:03\n" ] } ], "source": [ "print_generated_sequence(g, num=5, sep='\\n', seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the generated items are `datetime` objects (even though they appear as strings when printed above)." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.datetime(2018, 1, 1, 15, 10, 7),\n", " datetime.datetime(2018, 1, 1, 0, 22, 12),\n", " datetime.datetime(2018, 1, 1, 10, 52, 23)]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.reset(seed=12345)\n", "[next(g), next(g), next(g)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `.strftime()` method to create another generator which returns timestamps as strings instead of datetime objects." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "h = Timestamp(date='2018-01-01').strftime('%-d %b %Y, %H:%M (%a)')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1 Jan 2018, 15:10 (Mon)',\n", " '1 Jan 2018, 00:22 (Mon)',\n", " '1 Jan 2018, 10:52 (Mon)']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h.reset(seed=12345)\n", "[next(h), next(h), next(h)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CharString" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: bFj7lCDM5eUVwz8, QG5ThX0t5TMklKn, Qule67xq5QaV597, SA4TteJc6OZuDxy, HxzQkefvT0jmCgC\n", "Generated sequence: Ylx3SYjPqrPO0vC, udVUmJ5f2xi6RRv, 8ZYmUYrEgjY5INZ, B9cgzt0nNwfbstm, h84ObqDckapVKgd\n" ] } ], "source": [ "g = CharString(length=15)\n", "print_generated_sequence(g, num=5, seed=12345)\n", "print_generated_sequence(g, num=5, seed=99999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to explicitly specify the character set." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "ADBGBDDEGAFF\n", "CCGEDGFAFFCG\n", "FEBBEBECBAGG\n", "CBGEAFGGGFDG\n", "FCAEAGEFCDCC\n" ] } ], "source": [ "g = CharString(length=12, charset=\"ABCDEFG\")\n", "print_generated_sequence(g, num=5, sep='\\n', seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also a few pre-defined character sets." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence:\n", "\n", "andyelmqybtt\n", "jkzrnytduvhy\n", "tqeepfrifbyz\n", "jgyratyzzslx\n", "sibpayqvimjk\n", "\n", "Generated sequence:\n", "\n", "ASF8GQRW7C11\n", "NO9YS70E24L7\n", "0WGGVHYMGC78\n", "NJ7YA1798ZP6\n", "0LCUB8X4MRNN\n" ] } ], "source": [ "g1 = CharString(length=12, charset=\"<lowercase>\")\n", "g2 = CharString(length=12, charset=\"<alphanumeric_uppercase>\")\n", "print_generated_sequence(g1, num=5, sep='\\n', seed=12345); print()\n", "print_generated_sequence(g2, num=5, sep='\\n', seed=12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DigitString" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`DigitString` is the same as `CharString` with `charset='0123456789'`." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: 051914469077349, 659717839761152, 631099329607999, 749730509683433, 534610037812414\n", "Generated sequence: 813878162266834, 307715908319673, 988278241189568, 490143826300232, 199602401027500\n" ] } ], "source": [ "g = DigitString(length=15)\n", "print_generated_sequence(g, num=5, seed=12345)\n", "print_generated_sequence(g, num=5, seed=99999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generates a sequence of sequentially numbered strings with a given prefix." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "g = Sequential(prefix='Foo_', digits=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling `reset()` on the generator makes the numbering start from 1 again." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n", "Generated sequence: Foo_006, Foo_007, Foo_008, Foo_009, Foo_010\n", "\n", "Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n" ] } ], "source": [ "g.reset()\n", "print_generated_sequence(g, num=5)\n", "print_generated_sequence(g, num=5)\n", "print()\n", "g.reset()\n", "print_generated_sequence(g, num=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the method `Sequential.reset()` supports the `seed` argument for consistency with other generators, but its value is ignored - the generator is simply reset to its initial value. This is illustrated here:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n", "Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n" ] } ], "source": [ "g.reset(seed=12345); print_generated_sequence(g, num=5)\n", "g.reset(seed=99999); print_generated_sequence(g, num=5)" ] }, { "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": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

bmcfee/librosa

examples/LibROSA audio effects and playback.ipynb

2

43224

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Audio effects and playback with Librosa and IPython Notebook\n", "\n", "This notebook will demonstrate how to do audio effects processing with librosa and IPython notebook. You will need IPython 2.0 or later.\n", "\n", "By the end of this notebook, you'll know how to do the following:\n", "\n", " - Play audio in the browser\n", " - Effect transformations such as harmonic/percussive source separation, time stretching, and pitch shifting\n", " - Decompose and reconstruct audio signals with non-negative matrix factorization\n", " - Visualize spectrogram data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import librosa\n", "import librosa.display\n", "import IPython.display\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Load the example track\n", "y, sr = librosa.load(librosa.util.example_audio_file())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Play it back!\n", "IPython.display.Audio(data=y, rate=sr)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# How about separating harmonic and percussive components?\n", "y_h, y_p = librosa.effects.hpss(y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Play the harmonic component\n", "IPython.display.Audio(data=y_h, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Play the percussive component\n", "IPython.display.Audio(data=y_p, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Pitch shifting? Let's gear-shift by a major third (4 semitones)\n", "y_shift = librosa.effects.pitch_shift(y, sr, 7)\n", "\n", "IPython.display.Audio(data=y_shift, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Or time-stretching? Let's slow it down\n", "y_slow = librosa.effects.time_stretch(y, 0.5)\n", "\n", "IPython.display.Audio(data=y_slow, rate=sr)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# How about something more advanced? Let's decompose a spectrogram with NMF, and then resynthesize an individual component\n", "D = librosa.stft(y)\n", "\n", "# Separate the magnitude and phase\n", "S, phase = librosa.magphase(D)\n", "\n", "# Decompose by nmf\n", "components, activations = librosa.decompose.decompose(S, n_components=8, sort=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Visualize the components and activations, just for fun\n", "\n", "plt.figure(figsize=(12,4))\n", "\n", "plt.subplot(1,2,1)\n", "librosa.display.specshow(librosa.amplitude_to_db(np.abs(components), ref=np.max), y_axis='log')\n", "plt.xlabel('Component')\n", "plt.ylabel('Frequency')\n", "plt.title('Components')\n", "\n", "plt.subplot(1,2,2)\n", "librosa.display.specshow(activations, x_axis='time')\n", "plt.xlabel('Time')\n", "plt.ylabel('Component')\n", "plt.title('Activations')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1025, 8) (8, 2647)\n" ] } ], "source": [ "print(components.shape, activations.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Play back the reconstruction\n", "# Reconstruct a spectrogram by the outer product of component k and its activation\n", "D_k = components.dot(activations)\n", "\n", "# invert the stft after putting the phase back in\n", "y_k = librosa.istft(D_k * phase)\n", "\n", "# And playback\n", "print('Full reconstruction')\n", "\n", "IPython.display.Audio(data=y_k, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Resynthesize. How about we isolate just first (lowest) component?\n", "k = 0\n", "\n", "# Reconstruct a spectrogram by the outer product of component k and its activation\n", "D_k = np.multiply.outer(components[:, k], activations[k])\n", "\n", "# invert the stft after putting the phase back in\n", "y_k = librosa.istft(D_k * phase)\n", "\n", "# And playback\n", "print('Component #{}'.format(k))\n", "\n", "IPython.display.Audio(data=y_k, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Resynthesize. How about we isolate a middle-frequency component?\n", "k = len(activations) // 2\n", "\n", "# Reconstruct a spectrogram by the outer product of component k and its activation\n", "D_k = np.multiply.outer(components[:, k], activations[k])\n", "\n", "# invert the stft after putting the phase back in\n", "y_k = librosa.istft(D_k * phase)\n", "\n", "# And playback\n", "print('Component #{}'.format(k))\n", "\n", "IPython.display.Audio(data=y_k, rate=sr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Resynthesize. How about we isolate just last (highest) component?\n", "k = -1\n", "\n", "# Reconstruct a spectrogram by the outer product of component k and its activation\n", "D_k = np.multiply.outer(components[:, k], activations[k])\n", "\n", "# invert the stft after putting the phase back in\n", "y_k = librosa.istft(D_k * phase)\n", "\n", "# And playback\n", "print('Component #{}'.format(k))\n", "\n", "IPython.display.Audio(data=y_k, rate=sr)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7", "language": "python", "name": "py37" }, "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": 2 }

isc

apryor6/apryor6.github.io

visualizations/seaborn/heatmap.ipynb

1

2364555

mit

jasemi/Computerphysik-ss17-Uebungen

Uebung-10/Aufgabe1-merlin.ipynb

1

4313

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aufgabe 1\n", "### Teil a\n", "$$\n", "M =\n", "\\begin{pmatrix}\n", "1 & -1 & 0 & \\cdots & 0 \\\\\n", "-1 & 2 & \\ddots & \\ddots & \\vdots \\\\\n", "0 & \\ddots & \\ddots & & 0 \\\\\n", "\\vdots & \\ddots & & 2 & -1 \\\\\n", "0 & \\cdots & 0 & -1 & 1 \\\\\n", "\\end{pmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Teil b " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "M (generic function with 1 method)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function M(N)\n", " side = vcat(zeros(1,N), eye(N-1, N))\n", " diag = -2 * eye(N,N)\n", " diag[1,1] = -1\n", " diag[N,N] = -1\n", " return diag + side + side'\n", "end" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 1.97538 \n", " 1.90211 \n", " 1.78201 \n", " 1.61803 \n", " 1.41421 \n", " 1.17557 \n", " 0.907981 \n", " 0.618034 \n", " 0.312869 \n", " 3.33811e-9" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N = 10\n", "a = M(N)\n", "a_ew = eigvals(a)\n", "a_ev = eigvecs(a)\n", "k = m = 1\n", "omega = sqrt(k/m*abs(a_ew))\n", "omega" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Teil c\n", "$$ \n", "\\xi(0) = \\sum_{n=1}^N \\vec a_n \\alpha_n = \\begin{pmatrix} 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{pmatrix} = A\\vec\\alpha \\\\\n", "mit\\ A = (\\vec a_1, \\vec a_2, \\cdots, \\vec a_N)\\\\\n", "\\dot \\xi(0) = \\sum_{n=1}^N \\vec a_n \\beta_n \\omega_n = \\begin{pmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{pmatrix} = B\\vec\\beta \\\\\n", "mit\\ B = (\\omega_1 \\vec a_1, \\omega_2 \\vec a_2, \\cdots, \\omega_N \\vec a_N) \\\\\n", "\\\\\n", "\\Leftrightarrow \\vec \\alpha = A^{-1} \\begin{pmatrix} 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{pmatrix}\\ und\\ \n", "\\vec \\beta = B^{-1} \\begin{pmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{pmatrix}\n", "$$\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " -0.0699596\n", " 0.138197 \n", " 0.203031 \n", " -0.262866 \n", " 0.316228 \n", " -0.361803 \n", " -0.39847 \n", " -0.425325 \n", " -0.441708 \n", " 0.316228 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = copy(a_ev)\n", "B = copy(a_ev)\n", "for i in 1:N\n", " B[i,:] = B[i,:] .* a_ew[i]\n", "end\n", "xi_0 = zeros(N)\n", "xi_0[1] = 1\n", "dot_xi_0 = zeros(N)\n", "alpha = inv(A)*xi_0" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta = inv(B)*dot_xi_0" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.5.2", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

unlicense

mmautner/email_classifier

gmail_importance.ipynb

1

33078

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "# The Task:\n", "\n", "Train a classifier that can predict whether an email is \"important\" or not.\n", "\n", "We'll approach this using our own Gmail data--from building and evaluating our classifier's performance to deploying it using a combination of Rackspace's [Mailgun](http://www.mailgun.com/) and Amazon's [EC2](https://aws.amazon.com/ec2/) services.\n", "\n", "### Document classification\n", "\n", "Common classification task--we can take a peak at the [cheatsheet](http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html) when it comes to what model we should use:\n", "\n", "<img style=\"border: 1px solid black; display: block; margin-left: auto; margin-right: auto;\" src=\"files/sklearn_cheatsheet.png\">\n", "\n", "### The data\n", "\n", "But what's the tradeoff between the size of our dataset and classifier performance?\n", "\n", "How much data do we need to make a \"decent\" classifier?\n", "\n", "<img style=\"border: 1px solid black; display: block; margin-left: auto; margin-right: auto;\" src=\"files/corpus_size.png\">\n", "\n", "[Source]()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's obtain our data\n", "\n", "First, grab your gmail data: [https://www.google.com/settings/takeout](https://www.google.com/settings/takeout)\n", "\n", "You can be conservative, and only fetch your inbox data as that's all we really need:\n", "\n", "<img style=\"border: 1px solid black; display: block; margin-left: auto; margin-right: auto;\" src=\"files/google_archive.png\">\n", "\n", "This'll take a bit to wait for--we'll continue on, using my pre-fetched personal Gmail data.\n", "\n", "We'll get a zip file, containing [*.mbox*](http://en.wikipedia.org/wiki/Mbox) files, one for each folder in your Gmail account. *mbox* is a file format for storing emails--it's simply a plain-text file of all your emails concatenated together, we can take a peak at one:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# unzip 'em\n", "!unzip /Users/max/Downloads/max.mautner@gmail.com-20131218T185235Z-Mail.zip -d ./data/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Archive: /Users/max/Downloads/max.mautner@gmail.com-20131218T185235Z-Mail.zip\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/meetup.com.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Chat.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Important.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Sent Messages.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Inbox.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Unread.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Archived.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Spam.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/chipy.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/[Imap]/Sent.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/comcast.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Trash.mbox \r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Notes.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/OS-Dev/Django-Dev.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/OS-Dev/SciKit learn.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Drafts.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/[Imap]/Trash.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/amazon.mbox " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Receipts.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Tracked Email.mbox \r\n", " inflating: ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Starred.mbox \r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "!ls -l ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "total 4024984\r\n", "-rw-r--r--@ 1 max staff 303432822 Dec 18 13:10 Archived.mbox\r\n", "-rw-r--r--@ 1 max staff 33416651 Dec 18 13:07 Chat.mbox\r\n", "-rw-r--r--@ 1 max staff 2632 Dec 18 13:10 Drafts.mbox\r\n", "-rw-r--r--@ 1 max staff 545667670 Dec 18 13:07 Important.mbox\r\n", "-rw-r--r--@ 1 max staff 833349412 Dec 18 13:08 Inbox.mbox\r\n", "-rw-r--r--@ 1 max staff 5953 Dec 18 13:10 Notes.mbox\r\n", "drwxr-xr-x@ 4 max staff 136 Feb 13 04:28 \u001b[34mOS-Dev\u001b[m\u001b[m\r\n", "-rw-r--r--@ 1 max staff 193226 Dec 18 13:10 Receipts.mbox\r\n", "-rw-r--r--@ 1 max staff 929 Dec 18 13:08 Sent Messages.mbox\r\n", "-rw-r--r--@ 1 max staff 1639936 Dec 18 13:10 Spam.mbox\r\n", "-rw-r--r--@ 1 max staff 29869 Dec 18 13:10 Starred.mbox\r\n", "-rw-r--r--@ 1 max staff 10119 Dec 18 13:10 Tracked Email.mbox\r\n", "-rw-r--r--@ 1 max staff 780533 Dec 18 13:10 Trash.mbox\r\n", "-rw-r--r--@ 1 max staff 196938301 Dec 18 13:09 Unread.mbox\r\n", "drwxr-xr-x@ 4 max staff 136 Feb 13 04:28 \u001b[34m[Imap]\u001b[m\u001b[m\r\n", "-rw-r--r--@ 1 max staff 29579153 Dec 18 13:10 amazon.mbox\r\n", "-rw-r--r--@ 1 max staff 48258540 Dec 18 13:10 chipy.mbox\r\n", "-rw-r--r--@ 1 max staff 455999 Dec 18 13:10 comcast.mbox\r\n", "-rw-r--r--@ 1 max staff 66992914 Dec 18 13:07 meetup.com.mbox\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from glob import glob\n", "mailboxes = glob('./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/*')\n", "mailboxes" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "['./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/[Imap]',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/amazon.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Archived.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Chat.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/chipy.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/comcast.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Drafts.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Important.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Inbox.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/meetup.com.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Notes.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/OS-Dev',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Receipts.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Sent Messages.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Spam.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Starred.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Tracked Email.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Trash.mbox',\n", " './data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Unread.mbox']" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "!head ./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Inbox.mbox" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "From 1454597329098191988@xxx Mon Dec 16 04:41:53 2013\r", "\r\n", "X-GM-THRID: 1454597329098191988\r", "\r\n", "X-Gmail-Labels: Sent,Inbox,Important,amazon\r", "\r\n", "Delivered-To: max.mautner@gmail.com\r", "\r\n", "Received: by 10.224.38.8 with SMTP id z8csp90515qad;\r", "\r\n", " Mon, 16 Dec 2013 08:41:54 -0800 (PST)\r", "\r\n", "Return-Path: <sophia.kostelanetz@gmail.com>\r", "\r\n", "Received-SPF: pass (google.com: domain of sophia.kostelanetz@gmail.com designates 10.42.46.80 as permitted sender) client-ip=10.42.46.80\r", "\r\n", "Authentication-Results: mr.google.com;\r", "\r\n", " spf=pass (google.com: domain of sophia.kostelanetz@gmail.com designates 10.42.46.80 as permitted sender) smtp.mail=sophia.kostelanetz@gmail.com;\r", "\r\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The pipeline\n", "\n", "Let's enumerate our tasks for building our \"email importance\" classifier:\n", "\n", "1. define, extract our predictive features\n", "2. fit our model to the training data\n", "3. score our model against held-back test data\n", "\n", "If it's good enough, we can go ahead and apply the model in production.\n", "\n", "It sucks? Either:\n", "\n", "- Go back to step 1 and consider more features\n", "- Get more data\n", "\n", "For now, we'll just assume we've got enough data, and that bag-of-words feature extraction will be adequate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature extraction\n", "\n", "Let's start w/ feature extraction--and let's only consider a subset of our folders:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interesting_mboxes = ['./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Inbox.mbox']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import some things to do some basic pre-processing:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import mailbox\n", "import email\n", "from nltk import clean_html\n", "import sys" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "%time len(mailbox.mbox(interesting_mboxes[0]).items())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 1min 23s, sys: 2.38 s, total: 1min 25s\n", "Wall time: 1min 26s\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "9687" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "corpus = []\n", "labels = []\n", "\n", "def create_corpus():\n", " for fname in interesting_mboxes:\n", " print fname\n", " sys.stdout.flush() # make sure to flush to output\n", " category = fname.split('/')[-1].split('.')[0].lower()\n", " mbox = mailbox.mbox(fname)\n", " for msg_id, email_obj in mbox.items():\n", " if 'Sent' not in email_obj['X-Gmail-Labels']:\n", " category = 1 if 'Important' in email_obj['X-Gmail-Labels'].split(',') else 0\n", " else:\n", " continue\n", "\n", " body = ''\n", " for part in email_obj.walk():\n", " if part.get_content_type() == 'text/html':\n", " body = clean_html(part.get_payload())\n", " break\n", " elif part.get_content_type() == 'text/plain':\n", " body = part.get_payload()\n", " else:\n", " continue\n", " body += ' ' + ' '.join(email_obj.keys())\n", " \n", " corpus.append(body)\n", " labels.append(category)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "%time create_corpus()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "./data/max.mautner@gmail.com-20131218T185235Z-Mail/Mail/Inbox.mbox\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 1min 32s, sys: 2.79 s, total: 1min 35s\n", "Wall time: 1min 38s\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do these look like?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print labels[0]\n", "print corpus[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "Hello Max Mautner, \r\n", " \r\n", "The expiration date of your Hubway membership is 2013-10-28. To easily rene=\r\n", "w your membership, please visit our website and log into your Member Profile. \r\n", " \r\n", "Don=E2=80=99t miss a day on Hubway! Sign up for auto-renew and forget about=\r\n", " having to remember to manually renew your membership next year. More infor=\r\n", "mation about Hubway=E2=80=99s auto-renewing feature is available on your Membe=\r\n", "r Profile page.\r\n", "Thank you from the Hubway Team! \r\n", " \r\n", "---------------------------------------------------------------------------=\r\n", "-------------- \r\n", "Check us out online at www.thehubway.com or at Facebook.com/Hubway . \r\n", " \r\n", "Contact us directly: \r\n", "Phone: 855-4HUBWAY (448-2929) \r\n", "Email: customerservice@theh=\r\n", "ubway.com X-GM-THRID X-Gmail-Labels Delivered-To Received X-Received Return-Path Received Received-SPF Authentication-Results Received Received Date From To Message-ID Subject MIME-Version Content-Type Precedence X-Spam-Score X-Spam-Level X-Spam-Report\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How large is our corpus (in # of documents)?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "len(corpus), len(labels)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "(4721, 4721)" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many emails do we have of each label?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "d = pd.DataFrame(labels, columns=['labels'])\n", "print d.labels.value_counts()/float(d.shape[0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0 0.679729\n", "1 0.320271\n", "dtype: float64\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py:29: UserWarning: Module argparse was already imported from /usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/argparse.pyc, but /usr/local/lib/python2.7/site-packages is being added to sys.path\n", " from pkg_resources import resource_stream\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract those features\n", "\n", "Bag of words" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nltk\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfTransformer\n", "\n", "vectorizer = CountVectorizer(tokenizer=nltk.word_tokenize,\n", " stop_words='english',\n", " max_features=6000,\n", " ngram_range=(1,1))\n", "#vectorizer = CountVectorizer(ngram_range=(1, 2), token_pattern=r'\\b\\w+\\b', min_df=1) # bigrams\n", "#vectorizer = TfidfTransformer() # tf-idf" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "vectorizer" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "CountVectorizer(analyzer=u'word', binary=False, charset=None,\n", " charset_error=None, decode_error=u'strict',\n", " dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n", " lowercase=True, max_df=1.0, max_features=6000, min_df=1,\n", " ngram_range=(1, 1), preprocessor=None, stop_words='english',\n", " strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n", " tokenizer=<function word_tokenize at 0x10c4b8488>, vocabulary=None)" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "CountVectorizer?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "%time vectors = vectorizer.fit_transform(corpus)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 19.7 s, sys: 387 ms, total: 20 s\n", "Wall time: 19.8 s\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's feed our bag-of-words model our extracted features and cross-validate its performance:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sklearn.cross_validation import ShuffleSplit\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.svm import LinearSVC\n", "from collections import defaultdict\n", "\n", "X = vectors\n", "y = np.array(labels)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "label_train_scores = defaultdict(list)\n", "label_test_scores = defaultdict(list)\n", "train_scores = []\n", "test_scores = []\n", "\n", "from sklearn import metrics\n", "\n", "cv = ShuffleSplit(len(corpus), n_iter=10, test_size=0.1, random_state=0)\n", "\n", "for cv_index, (train, test) in enumerate(cv):\n", " print cv_index\n", " sys.stdout.flush()\n", " \n", " gnb = MultinomialNB().fit(X[train], y[train])\n", " \n", " for label in d.labels.unique():\n", " train_special = [a for a in d.index[d.labels == label] if a in train]\n", " test_special = [a for a in d.index[d.labels == label] if a in test]\n", " \n", " label_train_scores[label].append(gnb.score(X[train_special], y[train_special]))\n", " label_test_scores[label].append(gnb.score(X[test_special], y[test_special]))\n", " \n", " train_scores.append(gnb.score(X[train], y[train]))\n", " test_scores.append(gnb.score(X[test], y[test]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "3\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "5\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "6\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "7\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "8\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "9\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "from pprint import pprint\n", "for l in d.labels.unique():\n", " print l\n", " print \"Training:\\t %.1f%%\" % (np.multiply(np.average(label_train_scores[l]), 100))\n", " print \"Test:\\t\\t*%.1f%%*\" % (np.multiply(np.average(label_test_scores[l]), 100))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "Training:\t 95.8%\n", "Test:\t\t*93.4%*\n", "0\n", "Training:\t 76.0%\n", "Test:\t\t*74.0%*\n" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are we done?\n", "\n", "There are lots of improvements to be made to this model besides gathering more data.\n", "\n", "One common technique is stemming or [lemmatizing](https://en.wikipedia.org/wiki/Lemmatisation) the words after tokenization:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from nltk import word_tokenize\n", "from nltk.stem.wordnet import WordNetLemmatizer\n", "from nltk.corpus import stopwords\n", "\n", "LEMMATIZER = WordNetLemmatizer()\n", "STOP_SET = set(stopwords.words('english'))\n", "words = 'run runs running ran'\n", "for word in words.split(' '):\n", " print LEMMATIZER.lemmatize(word.lower())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "run\n", "run\n", "running\n", "ran\n" ] } ], "prompt_number": 80 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deploying\n", "\n", "Now that we have a model that's \"adequately\" fit to our data, we can go ahead and place it on a server for it to make predictions on our inbound emails. For this, we'll handle emails forwarded from our gmail account.\n", "\n", "We must first setup an EC2 instance for handling inbound emails from Mailgun--we'll be relaying emails from Gmail to Mailgun (SMTP) and from Mailgun to EC2 (HTTP). Here's an EC2 AMI (Amazon Machine Image) that is an ubuntu server w/ all of the requisite python libraries (namely scikit-learn) that we need to run our model:\n", "\n", "NOTE: Handling our emails via HTTP is a lot easier for a multitude of reasons (namely that setting up an email server is arduous and requires more domain knowledge than is worth your or my time). \n", "\n", "W/ the server up, we have to [serialize our model](https://stackoverflow.com/questions/17511968/python-scikit-learn-exporting-trained-classifier) so that it can be loaded on the server:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# train on entire dataset:\n", "gnb = MultinomialNB().fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "vectorizer.transform?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.externals import joblib\n", "joblib.dump(gnb, 'email_importance.pkl', compress=9)\n", "joblib.dump(vectorizer, 'vectorizer.pkl', compress=9)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "['vectorizer.pkl']" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "!du -hc *.pkl" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 40K\temail_importance.pkl\r\n", "1.6M\tvectorizer.pkl\r\n", "1.7M\ttotal\r\n" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "model_clone = joblib.load('email_importance.pkl')\n", "vectorizer_clone = joblib.load('vectorizer.pkl')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "type(model_clone), type(vectorizer_clone)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "(sklearn.naive_bayes.MultinomialNB,\n", " sklearn.feature_extraction.text.CountVectorizer)" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "model_clone.predict(X[0]), y[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "(array([1]), 1)" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "!scp -i /Users/max/.ssh/keys/ivendorz.pem email_importance.pkl ubuntu@ec2-54-202-114-193.us-west-2.compute.amazonaws.com:~/\n", "!scp -i /Users/max/.ssh/keys/ivendorz.pem vectorizer.pkl ubuntu@ec2-54-202-114-193.us-west-2.compute.amazonaws.com:~/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", "email_importance.pkl 0% 0 0.0KB/s --:-- ETA\r", "email_importance.pkl 100% 39KB 39.1KB/s 00:00 \r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "vectorizer.pkl 0% 0 0.0KB/s --:-- ETA" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "vectorizer.pkl 100% 1674KB 1.6MB/s 00:00 \r\n" ] } ], "prompt_number": 137 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're set to receive our emails and classify them =)\n", "\n", "This requires having a server w/ a simple web app to handle HTTP POSTs of emails from Mailgun (remember, we wanted to avoid setting up an SMTP server). I'll demo this live, but you can take a peak at the [git repo for this talk]() for an example webapp. \n", "\n", "Pending your email-receiving plumbing working, you can go to your Gmail account's \"Settings\" => \"Forwarding and POP/IMAP\" where you can add an email address to forward to (\"whatever@sandbox12345.mailgun.org\").\n", "\n", "This will trigger a confirmation email to your Mailgun address containing a URL you must navigate to in order to \"opt-in\" to receiving all forwarded emails from your Gmail account.\n", "\n", "Setting up email-forwarding can be done w/ any of your other accounts (e.g. with Yahoo, Hotmail), allowing you to use your now generalizable \"importance\" model to screen all your inbound emails." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Takeaways:\n", "\n", "1. This stuff is not scary.\n", "\n", "2. There's a lot of no-brainer optimizations.\n", "\n", "3. Data is really valuable--particularly behavioral data.\n", "\n", "If you've got feedback or want to chat about this sort of thing, feel free to drop me an email: max.mautner[at]gmail.com\n", "\n", "## Questions?" ] } ], "metadata": {} } ] }

mit

rafburzy/Python_EE

Visualizations/Glyphs.ipynb

1

1815896

bsd-3-clause

fsilva/deputado-histogramado

notebooks/Deputado-Histogramado-3.ipynb

1

359941

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Deputado Histogramado\n", "============\n", "\n", "[expressao.xyz/deputado/](http://expressao.xyz/deputado/)\n", "\n", "Como processar as sessões do parlamento Português" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Índice\n", "-----\n", "\n", "1. [Reunír o dataset](Deputado-Histogramado-1.ipynb)\n", "2. [Contando as palavras mais comuns](Deputado-Histogramado-2.ipynb)\n", "3. [Fazendo histogramas](Deputado-Histogramado-3.ipynb)\n", "4. [Representações geograficas](Deputado-Histogramado-4.ipynb)\n", "5. [Simplificar o dataset e exportar para o expressa.xyz/deputado/](Deputado-Histogramado-5.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O que se passou nas mais de 4000 sessões de discussão do parlamento Português que ocorreram desde 1976? \n", "Neste notebook vamos tentar visualizar o que se passou da maneira mais simples - contando palavras, e fazendo gráficos.\n", "\n", "Para obter os textos de todas as sessões usaremos o [demo.cratica.org](demo.cratica.org), onde podemos aceder facilmente a todas as sessões do parlamento de 1976 a 2015. Depois com um pouco de python, pandas e matplotlib vamos analisar o que se passou.\n", "\n", "Para executar estes notebook será necessário descarregar e abrir com o Jupiter Notebooks (a distribuição Anaconda faz com que instalar todas as ferramentas necessárias seja fácil - https://www.continuum.io/downloads)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parte 3 - Fazendo Histogramas\n", "\n", "Código para carregar os dados do notebook anterior: \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pylab\n", "import matplotlib\n", "import pandas\n", "import numpy\n", "\n", "\n", "dateparse = lambda x: pandas.datetime.strptime(x, '%Y-%m-%d')\n", "sessoes = pandas.read_csv('sessoes_democratica_org.csv',index_col=0,parse_dates=['data'], date_parser=dateparse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Na parte 2 já ficamos a saber que 'Orçamento de/do Estado' não se usava antes de 1984, e se falava mais de decretos-lei antes de 1983. \n", "\n", "\n", "Mas sinceramente não encontramos nada de interessante. Vamos acelerar o processo, e olhar para mais palavras:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0117321014404297 s\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114759080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# retorna o número de ocorrências de palavra em texto\n", "def conta_palavra(texto,palavra):\n", " return texto.count(palavra)\n", "\n", "# retorna um vector com um item por sessao, e valor verdadeiro se o ano é =i, falso se nao é\n", "def selecciona_ano(data,i):\n", " return data.map(lambda d: d.year == i)\n", "\n", "# faz o histograma do número de ocorrencias de 'palavra' por ano\n", "def histograma_palavra(palavra):\n", " # cria uma coluna de tabela contendo as contagens de palavra por cada sessão\n", " dados = sessoes['sessao'].map(lambda texto: conta_palavra(texto,palavra.lower()))\n", " \n", " ocorrencias_por_ano = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " # agrupa contagens por ano\n", " ocorrencias_por_ano[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", "\n", " f = pylab.figure(figsize=(10,6)) \n", " ax = pylab.bar(range(1976,2016),ocorrencias_por_ano)\n", " pylab.xlabel('Ano')\n", " pylab.ylabel('Ocorrencias de '+str(palavra))\n", "\n", " \n", "import time\n", "start = time.time()\n", "histograma_palavra('Paulo Portas') #já vimos que Paulo e Portas foram anormalmente frequentes em 2000, vamos ver se há mais eventos destes\n", "print(str(time.time()-start)+' s') # mede o tempo que o código 'histograma_palavra('Paulo Portas')' demora a executar, para nossa referencia\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tal como tinhamos visto antes, o ano 2000 foi um ano bastante presente para o Paulo Portas. Parece que as suas contribuições vêm em ondas.\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1144af9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histograma_palavra('Crise')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sempre se esteve em crise, mas em 2010 foi uma super-crise." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1152a2048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histograma_palavra('aborto')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Os debates sobre o aborto parecem estar bem localizados, a 1982, 1984, 1997/8 e 2005." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11d8aaba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histograma_palavra('médicos')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "e se quisermos acumular varias palavras no mesmo histograma?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11a3e6668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def conta_palavras(texto,palavras):\n", " l = [texto.count(palavra.lower()) for palavra in palavras]\n", " return sum(l)\n", "\n", "def selecciona_ano(data,i):\n", " return data.map(lambda d: d.year == i)\n", "\n", "def histograma_palavras(palavras):\n", " dados = sessoes['sessao'].map(lambda texto: conta_palavras(texto,palavras))\n", "\n", " ocorrencias_por_ano = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " ocorrencias_por_ano[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", "\n", " f = pylab.figure(figsize=(10,6)) \n", " ax = pylab.bar(range(1976,2016),ocorrencias_por_ano)\n", " pylab.xlabel('Ano')\n", " pylab.ylabel('Ocorrencias de '+str(palavras))\n", "\n", "histograma_palavras(['escudos','contos','escudo'])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1144af9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histograma_palavras(['CEE','Comunidade Económica Europeia'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A União Europeia foi fundada em ~93 e a CEE integrada nesta (segundo a wikipedia), logo o gráfico faz sentido.\n", "Vamos criar uma função para integrar os 2 graficos, para nos permitir comparar a evolução:\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1197af0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def conta_palavras(texto,palavras):\n", " l = [texto.count(palavra) for palavra in palavras]\n", " return sum(l)\n", "\n", "def selecciona_ano(data,i):\n", " return data.map(lambda d: d.year == i)\n", "\n", "# calcula os dados para os 2 histogramas, e representa-os no mesmo gráfico\n", "def grafico_palavras_vs_palavras(palavras1, palavras2):\n", " palavras1 = [p.lower() for p in palavras1]\n", " palavras2 = [p.lower() for p in palavras2]\n", " dados = sessoes['sessao'].map(lambda texto: conta_palavras(texto,palavras1))\n", " ocorrencias_por_ano1 = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " ocorrencias_por_ano1[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", " \n", " dados = sessoes['sessao'].map(lambda texto: conta_palavras(texto,palavras2))\n", " ocorrencias_por_ano2 = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " ocorrencias_por_ano2[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", "\n", " anos = range(1976,2016)\n", " f = pylab.figure(figsize=(10,6)) \n", " p1 = pylab.bar(anos, ocorrencias_por_ano1)\n", " p2 = pylab.bar(anos, ocorrencias_por_ano2,bottom=ocorrencias_por_ano1)\n", " \n", " pylab.legend([palavras1[0], palavras2[0]])\n", " \n", " pylab.xlabel('Ano')\n", " pylab.ylabel('Ocorrencias totais')\n", "\n", "grafico_palavras_vs_palavras(['CEE','Comunidade Económica Europeia'],['União Europeia'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boa, uma substitui a outra, basicamente." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115b87ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grafico_palavras_vs_palavras(['contos','escudo'],['euro.','euro ','euros'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Novamente, uma substitui a outra." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11bdd2a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "histograma_palavra('Troika')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok isto parece um mistério. Falava-se bastante mais da troika em 1989 do que 2011. Vamos investigar isto procurando e mostrando as frases onde as palavras aparecem.\n", "\n", "\n", "\n", "Queremos saber o que foi dito quando se mencionou 'Troika' no parlamento. Vamos tentar encontrar e imprimir as frases onde se dão as >70 ocorrencias de troika de 1989 e as 25 de 2011." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frases com troika em 1989: 73\n", "Frases com troika em 2011: 26\n" ] } ], "source": [ "sessoes_1989 = sessoes[selecciona_ano(sessoes['data'],1989)]\n", "sessoes_2011 = sessoes[selecciona_ano(sessoes['data'],2011)]\n", "\n", "def divide_em_frases(texto):\n", " return texto.replace('!','.').replace('?','.').split('.')\n", "\n", "def acumula_lista_de_lista(l):\n", " return [j for x in l for j in x ]\n", " \n", "def selecciona_frases_com_palavra(sessoes, palavra):\n", " frases_ = sessoes['sessao'].map(divide_em_frases)\n", " frases = acumula_lista_de_lista(frases_)\n", " return list(filter(lambda frase: frase.find(palavra) != -1, frases))\n", "\n", "\n", "frases_com_troika1989 = selecciona_frases_com_palavra(sessoes_1989, 'troika')\n", "print('Frases com troika em 1989: ' + str(len(frases_com_troika1989)))\n", "frases_com_troika2011 = selecciona_frases_com_palavra(sessoes_2011, 'troika')\n", "print('Frases com troika em 2011: ' + str(len(frases_com_troika2011)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989:\n", "====\n", "1: narana coissoró (cds): - ainda há a peres**troika**, que é uma revolução\n", "2: isto foi uma graça, não me levem a mal - nós estamos a fazer uma «peres**troika**», com certeza\n", "3: implacável nos propósitos políticos com o mesmo a vontade com que cambalescamente vai silenciando as vozes criticas que no interior do seu partido clamam pela peres**troika** o líder comunista explicou aos portugueses em conferência de imprensa a seguinte e idêntica do diário popular de 30 de agosto de 1989\n", "4: que o pcp nada mudou dizem-no os seus próprios dirigentes, adversários declarados de quaisquer reformas, de qualquer tímida peres**troika**, mas di-lo também a sua continuada prática partidária\n", "5: deputado, que talvez indicie uma peres**troika** no partido comunista\n", "6: primeiro do que tudo, a peres**troika** está longe de ser irreversível\n", "7: ministro referiu as consequências que poderá ter a peres**troika** e também aquilo que será a reunificação alemã\n", "8: esta evolução, para que contribuem, sem dúvida, a consciência de que uma nova guerra mundial significaria o holocausto da humanidade e as aspirações pacíficas dos povos, permanentemente manifestadas, recebeu, contudo, um impulso decisivo da parte dos países socialistas e do movimento comunista, especialmente com as iniciativas de paz da união soviética, tomadas no quadro da peres**troika**, muitas das quais protagonizadas por mikhail gorbachev\n", "9:» tal é, de forma absolutamente inequívoca, a posição do pcp em relação à peres**troika** definida não agora, depois da abertura do muro de berlim, mas há um ano atrás, como já sublinhámos\n", "10: há muitas maneiras de definir a peres**troika** e poderíamos tentar alguma delas\n", "11: e não se trata de um milagre; antes de mais deve reconhecer-se, em homenagem à verdade, que a grande evolução e democratização verificada na urss de mikhail gorbatchov com a glasnost e a peres**troika** desempenhou e continua a desempenhar um papel decisivo na verdadeira libertação que está a ocorrer nos restantes países da europa do leste com a flagrante e dolorosa excepção da roménia - ficando de fora, claro, o ghetto albanês\n", "12: enquanto assim acontecer, enquanto gorbachov apontar como objectivo político para a peres**troika** não o pluralismo político, mas a instauração de um estado socialista de direito, como ele diz, inspirado em lenine, liderado pelo partido único e apoiado numa polícia política que continua a existir, a união soviética não poderá ser considerada uma democracia no sentido ocidental\n", "13: como é do mesmo modo obrigação da europa solidarizar-se activamente com mikhail gorbachev na sua luta pela vitória da peres**troika**\n", "14: não é esta, certamente, a peres**troika** que o sr\n", "15: por isso quem, com o mesmo espírito céptico e descrente na capacidade dos homens para alterar profundamente o curso imaginário da história, tivesse lido a peres**troika**, teria a mesma reacção que o próprio gorbatchev denuncia no seu livro ao dizer: «se em abril de 1975 alguém nos tivesse dito que dentro de 10 anos veríamos o que está agora a acontecer, muito provavelmente não acreditaríamos ou recusar-nos-íamos mesmo a aceitar\n", "2011:\n", "====\n", "1: chamado pelo psd, para quem a vinda do fmi só peca por tardia, já que, se o pec 4 era injusto na versão portuguesa, era insuficiente na língua mais entendida pela **troika**\n", "2: antónio filipe (pcp): — ninguém tenha ilusões: a **troika** não está cá para negociar nada com ninguém; está cá para impor uma ditadura financeira, à custa de sacrifícios injustos impostos ao povo português, e para consumar uma humilhação nacional maior e com muito mais graves consequências do que a do ultimato inglês de 1890\n", "3: o pcp rejeita esta ingerência e não tem nada a conversar com essa **troika**\n", "4: a **troika**, que assentou «armas e bagagens» em lisboa, está a desenhar o programa de governo para o país, para os próximos anos\n", "5: ou seja, o que a **troika** prepara para o país é o aprofundamento da herança do fmi a prestações, que foram os programas de estabilidade e crescimento\n", "6: a **troika** nacional é um eco obediente e cordato da **troika** de bruxelas e de nova iorque\n", "7: ministro dos assuntos parlamentares diria que a solução para o país é a **troika**, a aceitação das imposições ilegítimas do fmi e da união europeia, que o ps, o psd e o cds constituem neste momento do país\n", "8: o cds está numa fase de interrogação: será governo com o psd, será governo com o ps ou a «**troika**» será completa\n", "9: o país, neste momento, está a negociar com a **troika** os termos do resgate\n", "10: pedro mota soares (cds-pp): — o pcp não gosta da tróica, não gosta da peres**troika**\n" ] } ], "source": [ "from IPython.display import Markdown, display\n", "\n", "#print markdown permite-nos escrever a negrito ou como título\n", "def print_markdown(string):\n", " display(Markdown(string))\n", "\n", "def imprime_frases(lista_de_frases, palavra_negrito):\n", " for i in range(len(lista_de_frases)):\n", " string = lista_de_frases[i].replace(palavra_negrito,'**' + palavra_negrito + '**')\n", " #print_markdown(str(i+1) + ':' + string) \n", " print(str(i+1) + ':' + string) \n", " # no Jupyter notebooks 4.3.1 não se pode gravar output em markdown, tem de ser texto normal\n", " # se estiverem a executar o notebook e não a ler no github, podem descomentar a linha anterior para ver o texto com formatação\n", "#print_markdown('1989:\\n====')\n", "print('1989:\\n====')\n", "imprime_frases(frases_com_troika1989[1:73:5],'troika')\n", "#print_markdown('2011:\\n====')\n", "print('2011:\\n====')\n", "imprime_frases(frases_com_troika2011[1:20:2],'troika')\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "\n", "Como vemos na última frase, a verdade é que no parlmento se usa mais o termo 'Troica' do que 'Troika'! Na comunicação social usa-se muito 'Troika'.\n", "E para quem não sabe o que foi a perestroika: https://pt.wikipedia.org/wiki/Perestroika\n", "\n", "\n", "\n", "Ok, assim já faz sentido:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x111c171d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def conta_palavras(texto,palavras):\n", " l = [texto.count(palavra) for palavra in palavras]\n", " return sum(l)\n", "\n", "def selecciona_ano(data,i):\n", " return data.map(lambda d: d.year == i)\n", "\n", "# calcula os dados para os 2 histogramas, e representa-os no mesmo gráfico\n", "def grafico_palavras_vs_palavras(palavras1, palavras2):\n", " palavras1 = [p.lower() for p in palavras1]\n", " palavras2 = [p.lower() for p in palavras2]\n", " dados = sessoes['sessao'].map(lambda texto: conta_palavras(texto,palavras1))\n", " ocorrencias_por_ano1 = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " ocorrencias_por_ano1[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", " \n", " dados = sessoes['sessao'].map(lambda texto: conta_palavras(texto,palavras2))\n", " ocorrencias_por_ano2 = numpy.zeros(2016-1976)\n", " for i in range(0,2016-1976):\n", " ocorrencias_por_ano2[i] = numpy.sum(dados[selecciona_ano(sessoes['data'],i+1976)])\n", "\n", " anos = range(1976,2016)\n", " f = pylab.figure(figsize=(10,6)) \n", " p1 = pylab.bar(anos, ocorrencias_por_ano1)\n", " p2 = pylab.bar(anos, ocorrencias_por_ano2,bottom=ocorrencias_por_ano1)\n", " \n", " pylab.legend([palavras1[0], palavras2[0]])\n", " \n", " pylab.xlabel('Ano')\n", " pylab.ylabel('Ocorrencias totais')\n", "\n", "grafico_palavras_vs_palavras(['troica'],['troika'])" ] }, { "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": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

qrsforever/workspace

python/learn/pandas/apply_map_applymap.ipynb

1

2056

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <div align=\"center\">apply,applymap,map</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 生成数据\n", "data = pd.DataFrame(np.random.randint(0, 10, (4,3)), columns=list('abc'), index=range(4))\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. apply: 作用于一维向量(Serie)， 可行 可列" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.apply(lambda x: x.max() - x.min(), axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.apply(lambda x: x.max() - x.min(), axis=0) # default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. applymap: 作用与DataFrame每个元素" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.applymap(lambda x: x*10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. map: 作用与Series的每一个元素" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['a'].map(lambda x: x*10)" ] } ], "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": 2 }

mit

dborgesr/Euplotid

pipelines/countsFPKM2DiffExp.ipynb

1

24668

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Take RNA-Seq counts and get differentially expressed genes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install packages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "88b24b88-d238-4072-bd07-3f4c2d345e52" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Bioconductor version 3.4 (BiocInstaller 1.24.0), ?biocLite for help\n", "A new version of Bioconductor is available after installing the most recent\n", " version of R; see http://bioconductor.org/install\n", "BioC_mirror: https://bioconductor.org\n", "Using Bioconductor 3.4 (BiocInstaller 1.24.0), R 3.3.2 (2016-10-31).\n", "Installing package(s) 'edgeR'\n", "Updating HTML index of packages in '.Library'\n", "Making 'packages.html' ... done\n", "Old packages: 'XML'\n", "BioC_mirror: https://bioconductor.org\n", "Using Bioconductor 3.4 (BiocInstaller 1.24.0), R 3.3.2 (2016-10-31).\n", "Installing package(s) 'compcodeR'\n", "Warning message in install.packages(pkgs = doing, lib = lib, ...):\n", "\"installation of package 'compcodeR' had non-zero exit status\"Updating HTML index of packages in '.Library'\n", "Making 'packages.html' ... done\n", "Old packages: 'XML'\n", "BioC_mirror: https://bioconductor.org\n", "Using Bioconductor 3.4 (BiocInstaller 1.24.0), R 3.3.2 (2016-10-31).\n", "Installing package(s) 'EBSeq'\n", "Updating HTML index of packages in '.Library'\n", "Making 'packages.html' ... done\n", "Old packages: 'XML'\n" ] } ], "source": [ "#Install needed packages\n", "source(\"https://bioconductor.org/biocLite.R\")\n", "biocLite(\"edgeR\")\n", "biocLite(\"compcodeR\")\n", "biocLite(\"EBSeq\")\n", "biocLite(\"DESeq2\")\n", "install.packages(\"heatmaply\")\n", "install.packages(\"manhattanly\")\n", "#devtools::install_github('hadley/ggplot2')\n", "install.packages(\"RColorBrewer\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load required libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpresent": { "id": "fcaa72c2-d4fb-4c89-a424-b5e3e621fdc7" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: limma\n", "Loading required package: blockmodeling\n", "Loading required package: gplots\n", "\n", "Attaching package: 'gplots'\n", "\n", "The following object is masked from 'package:stats':\n", "\n", " lowess\n", "\n", "Loading required package: testthat\n", "Loading required package: S4Vectors\n", "Loading required package: stats4\n", "Loading required package: BiocGenerics\n", "Loading required package: parallel\n", "\n", "Attaching package: 'BiocGenerics'\n", "\n", "The following objects are masked from 'package:parallel':\n", "\n", " clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n", " clusterExport, clusterMap, parApply, parCapply, parLapply,\n", " parLapplyLB, parRapply, parSapply, parSapplyLB\n", "\n", "The following object is masked from 'package:limma':\n", "\n", " plotMA\n", "\n", "The following objects are masked from 'package:stats':\n", "\n", " IQR, mad, xtabs\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " Filter, Find, Map, Position, Reduce, anyDuplicated, append,\n", " as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,\n", " get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,\n", " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,\n", " rbind, rownames, sapply, setdiff, sort, table, tapply, union,\n", " unique, unsplit, which, which.max, which.min\n", "\n", "\n", "Attaching package: 'S4Vectors'\n", "\n", "The following object is masked from 'package:testthat':\n", "\n", " compare\n", "\n", "The following object is masked from 'package:gplots':\n", "\n", " space\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " colMeans, colSums, expand.grid, rowMeans, rowSums\n", "\n", "Loading required package: IRanges\n", "Loading required package: GenomicRanges\n", "Loading required package: GenomeInfoDb\n", "Loading required package: SummarizedExperiment\n", "Loading required package: Biobase\n", "Welcome to Bioconductor\n", "\n", " Vignettes contain introductory material; view with\n", " 'browseVignettes()'. To cite Bioconductor, see\n", " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", "\n", "Loading required package: ggplot2\n", "\n", "Attaching package: 'plotly'\n", "\n", "The following object is masked from 'package:ggplot2':\n", "\n", " last_plot\n", "\n", "The following object is masked from 'package:IRanges':\n", "\n", " slice\n", "\n", "The following object is masked from 'package:S4Vectors':\n", "\n", " rename\n", "\n", "The following object is masked from 'package:stats':\n", "\n", " filter\n", "\n", "The following object is masked from 'package:graphics':\n", "\n", " layout\n", "\n" ] }, { "ename": "ERROR", "evalue": "Error in library(\"compcodeR\"): there is no package called 'compcodeR'\n", "output_type": "error", "traceback": [ "Error in library(\"compcodeR\"): there is no package called 'compcodeR'\nTraceback:\n", "1. library(\"compcodeR\")", "2. stop(txt, domain = NA)" ] } ], "source": [ "#load packages\n", "library(\"edgeR\")\n", "library(\"EBSeq\")\n", "library(\"DESeq2\")\n", "library(\"plotly\")\n", "library(\"compcodeR\")\n", "library(\"heatmaply\")\n", "library(\"manhattanly\")\n", "library(\"reshape2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load count matrix from RSEM output" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "nbpresent": { "id": "f85c3937-9145-4f4a-82fc-08258b871839" } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>wt_mesc.genes.results</th><th scope=col>prdm14_mesc.genes.results</th><th scope=col>mir290_mesc.genes.results</th><th scope=col>syk1_mesc.genes.results</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>0610005C13Rik</th><td> 9.00</td><td> 4.00</td><td> 6.00</td><td> 16.00</td></tr>\n", "\t<tr><th scope=row>0610007P14Rik</th><td>336.00</td><td>314.00</td><td>266.00</td><td>292.00</td></tr>\n", "\t<tr><th scope=row>0610009B22Rik</th><td>149.95</td><td>113.59</td><td>165.36</td><td>141.44</td></tr>\n", "\t<tr><th scope=row>0610009L18Rik</th><td> 7.00</td><td> 4.00</td><td> 9.00</td><td> 4.00</td></tr>\n", "\t<tr><th scope=row>0610009O20Rik</th><td>666.96</td><td>516.00</td><td>635.99</td><td>578.99</td></tr>\n", "\t<tr><th scope=row>0610010B08Rik</th><td>181.08</td><td>185.48</td><td>314.89</td><td>147.58</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & wt\\_mesc.genes.results & prdm14\\_mesc.genes.results & mir290\\_mesc.genes.results & syk1\\_mesc.genes.results\\\\\n", "\\hline\n", "\t0610005C13Rik & 9.00 & 4.00 & 6.00 & 16.00\\\\\n", "\t0610007P14Rik & 336.00 & 314.00 & 266.00 & 292.00\\\\\n", "\t0610009B22Rik & 149.95 & 113.59 & 165.36 & 141.44\\\\\n", "\t0610009L18Rik & 7.00 & 4.00 & 9.00 & 4.00\\\\\n", "\t0610009O20Rik & 666.96 & 516.00 & 635.99 & 578.99\\\\\n", "\t0610010B08Rik & 181.08 & 185.48 & 314.89 & 147.58\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | wt_mesc.genes.results | prdm14_mesc.genes.results | mir290_mesc.genes.results | syk1_mesc.genes.results | \n", "|---|---|---|---|---|---|\n", "| 0610005C13Rik | 9.00 | 4.00 | 6.00 | 16.00 | \n", "| 0610007P14Rik | 336.00 | 314.00 | 266.00 | 292.00 | \n", "| 0610009B22Rik | 149.95 | 113.59 | 165.36 | 141.44 | \n", "| 0610009L18Rik | 7.00 | 4.00 | 9.00 | 4.00 | \n", "| 0610009O20Rik | 666.96 | 516.00 | 635.99 | 578.99 | \n", "| 0610010B08Rik | 181.08 | 185.48 | 314.89 | 147.58 | \n", "\n", "\n" ], "text/plain": [ " wt_mesc.genes.results prdm14_mesc.genes.results\n", "0610005C13Rik 9.00 4.00 \n", "0610007P14Rik 336.00 314.00 \n", "0610009B22Rik 149.95 113.59 \n", "0610009L18Rik 7.00 4.00 \n", "0610009O20Rik 666.96 516.00 \n", "0610010B08Rik 181.08 185.48 \n", " mir290_mesc.genes.results syk1_mesc.genes.results\n", "0610005C13Rik 6.00 16.00 \n", "0610007P14Rik 266.00 292.00 \n", "0610009B22Rik 165.36 141.44 \n", "0610009L18Rik 9.00 4.00 \n", "0610009O20Rik 635.99 578.99 \n", "0610010B08Rik 314.89 147.58 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "count.matrix = read.table(\"wt_treat_counts_genes.matrix\")\n", "sample.annot = data.frame(condition = c(1, 2))\n", "rownames(sample.annot) = colnames(count.matrix[,1:2])\n", "info.parameters = list(dataset = \"DE_test\", uID = \"11111\")\n", "#Save read count data for a condition to get it in the right\n", "# format for differential expression R package\n", "cpd = compData(count.matrix = count.matrix[,1:2],\n", "sample.annotations = sample.annot,\n", "info.parameters = info.parameters)\n", "saveRDS(cpd, \"wt_treat.rds\")\n", "head(count.matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run differential expression analysis, showing EBSeq w/ default parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "nbpresent": { "id": "3051000c-5621-46da-a325-8b31f5c3f491" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "processing file: tempcode.Rmd\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " | \r", " | | 0%\r", " | \r", " |................................ | 50%\n", " ordinary text without R code\n", "\n", "\r", " | \r", " |.................................................................| 100%\n", "label: unnamed-chunk-1 (with options) \n", "List of 6\n", " $ echo : logi TRUE\n", " $ eval : logi TRUE\n", " $ include: logi TRUE\n", " $ message: logi TRUE\n", " $ error : logi TRUE\n", " $ warning: logi TRUE\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "output file: tempcode.md\n", "\n" ] }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runDiffExp(data.file = \"wt_treat.rds\", result.extent = \"EBSeq\",\n", " Rmdfunction = \"EBSeq.createRmd\",output.directory = \".\", \n", " norm.method = \"median\", norm.path=\"True\")\n", "#look up all different diff exp R possible runs w/ help(runDiffExp)\n", "#runDiffExp(data.file = \"wt_prdm14.rds\", result.extent = \"DESeq2\",Rmdfunction = \"DESeq2.createRmd\",output.directory = \".\", fit.type=\"mean\", test=\"Wald\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Suck in diff exp results and FPKM RSEM output" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "nbpresent": { "id": "4d3d5c2a-8958-4cf3-8f14-bd90eae373bf" } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>wt</th><th scope=col>prdm14</th><th scope=col>mir290</th><th scope=col>syk1</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>0610005C13Rik</th><td> 0.63</td><td> 0.31</td><td> 0.45</td><td> 1.15</td></tr>\n", "\t<tr><th scope=row>0610007P14Rik</th><td>22.21</td><td>23.05</td><td>18.14</td><td>19.69</td></tr>\n", "\t<tr><th scope=row>0610009B22Rik</th><td>15.03</td><td>12.64</td><td>17.10</td><td>14.46</td></tr>\n", "\t<tr><th scope=row>0610009L18Rik</th><td> 0.91</td><td> 0.58</td><td> 1.21</td><td> 0.53</td></tr>\n", "\t<tr><th scope=row>0610009O20Rik</th><td>21.36</td><td>18.36</td><td>21.02</td><td>18.92</td></tr>\n", "\t<tr><th scope=row>0610010B08Rik</th><td> 3.05</td><td> 3.47</td><td> 5.47</td><td> 2.54</td></tr>\n", "\t<tr><th scope=row>0610010F05Rik</th><td> 8.83</td><td> 7.68</td><td>12.47</td><td> 6.22</td></tr>\n", "\t<tr><th scope=row>0610010K14Rik</th><td>73.21</td><td>63.44</td><td>66.81</td><td>63.51</td></tr>\n", "\t<tr><th scope=row>0610012G03Rik</th><td>12.38</td><td>12.05</td><td>12.45</td><td> 9.93</td></tr>\n", "\t<tr><th scope=row>0610030E20Rik</th><td> 3.46</td><td> 1.45</td><td> 3.83</td><td> 2.30</td></tr>\n", "\t<tr><th scope=row>0610031O16Rik</th><td> 0.00</td><td> 0.00</td><td> 0.00</td><td> 0.00</td></tr>\n", "\t<tr><th scope=row>0610037L13Rik</th><td>13.11</td><td> 9.32</td><td>14.40</td><td>10.66</td></tr>\n", "\t<tr><th scope=row>0610038B21Rik</th><td> 0.33</td><td> 0.12</td><td> 0.36</td><td> 0.40</td></tr>\n", "\t<tr><th scope=row>0610039H22Rik</th><td> 0.99</td><td> 0.49</td><td> 0.45</td><td> 0.56</td></tr>\n", "\t<tr><th scope=row>0610039K10Rik</th><td> 0.66</td><td> 0.65</td><td> 1.72</td><td> 0.70</td></tr>\n", "\t<tr><th scope=row>0610040B10Rik</th><td> 1.13</td><td> 0.42</td><td> 0.97</td><td> 0.58</td></tr>\n", "\t<tr><th scope=row>0610040F04Rik</th><td> 2.63</td><td> 1.24</td><td> 2.57</td><td> 2.07</td></tr>\n", "\t<tr><th scope=row>0610040J01Rik</th><td> 0.41</td><td> 0.46</td><td> 0.26</td><td> 0.46</td></tr>\n", "\t<tr><th scope=row>0610043K17Rik</th><td> 0.13</td><td> 0.44</td><td> 0.14</td><td> 0.00</td></tr>\n", "\t<tr><th scope=row>1010001N08Rik</th><td> 0.36</td><td> 0.00</td><td> 0.00</td><td> 0.12</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & wt & prdm14 & mir290 & syk1\\\\\n", "\\hline\n", "\t0610005C13Rik & 0.63 & 0.31 & 0.45 & 1.15\\\\\n", "\t0610007P14Rik & 22.21 & 23.05 & 18.14 & 19.69\\\\\n", "\t0610009B22Rik & 15.03 & 12.64 & 17.10 & 14.46\\\\\n", "\t0610009L18Rik & 0.91 & 0.58 & 1.21 & 0.53\\\\\n", "\t0610009O20Rik & 21.36 & 18.36 & 21.02 & 18.92\\\\\n", "\t0610010B08Rik & 3.05 & 3.47 & 5.47 & 2.54\\\\\n", "\t0610010F05Rik & 8.83 & 7.68 & 12.47 & 6.22\\\\\n", "\t0610010K14Rik & 73.21 & 63.44 & 66.81 & 63.51\\\\\n", "\t0610012G03Rik & 12.38 & 12.05 & 12.45 & 9.93\\\\\n", "\t0610030E20Rik & 3.46 & 1.45 & 3.83 & 2.30\\\\\n", "\t0610031O16Rik & 0.00 & 0.00 & 0.00 & 0.00\\\\\n", "\t0610037L13Rik & 13.11 & 9.32 & 14.40 & 10.66\\\\\n", "\t0610038B21Rik & 0.33 & 0.12 & 0.36 & 0.40\\\\\n", "\t0610039H22Rik & 0.99 & 0.49 & 0.45 & 0.56\\\\\n", "\t0610039K10Rik & 0.66 & 0.65 & 1.72 & 0.70\\\\\n", "\t0610040B10Rik & 1.13 & 0.42 & 0.97 & 0.58\\\\\n", "\t0610040F04Rik & 2.63 & 1.24 & 2.57 & 2.07\\\\\n", "\t0610040J01Rik & 0.41 & 0.46 & 0.26 & 0.46\\\\\n", "\t0610043K17Rik & 0.13 & 0.44 & 0.14 & 0.00\\\\\n", "\t1010001N08Rik & 0.36 & 0.00 & 0.00 & 0.12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | wt | prdm14 | mir290 | syk1 | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 0610005C13Rik | 0.63 | 0.31 | 0.45 | 1.15 | \n", "| 0610007P14Rik | 22.21 | 23.05 | 18.14 | 19.69 | \n", "| 0610009B22Rik | 15.03 | 12.64 | 17.10 | 14.46 | \n", "| 0610009L18Rik | 0.91 | 0.58 | 1.21 | 0.53 | \n", "| 0610009O20Rik | 21.36 | 18.36 | 21.02 | 18.92 | \n", "| 0610010B08Rik | 3.05 | 3.47 | 5.47 | 2.54 | \n", "| 0610010F05Rik | 8.83 | 7.68 | 12.47 | 6.22 | \n", "| 0610010K14Rik | 73.21 | 63.44 | 66.81 | 63.51 | \n", "| 0610012G03Rik | 12.38 | 12.05 | 12.45 | 9.93 | \n", "| 0610030E20Rik | 3.46 | 1.45 | 3.83 | 2.30 | \n", "| 0610031O16Rik | 0.00 | 0.00 | 0.00 | 0.00 | \n", "| 0610037L13Rik | 13.11 | 9.32 | 14.40 | 10.66 | \n", "| 0610038B21Rik | 0.33 | 0.12 | 0.36 | 0.40 | \n", "| 0610039H22Rik | 0.99 | 0.49 | 0.45 | 0.56 | \n", "| 0610039K10Rik | 0.66 | 0.65 | 1.72 | 0.70 | \n", "| 0610040B10Rik | 1.13 | 0.42 | 0.97 | 0.58 | \n", "| 0610040F04Rik | 2.63 | 1.24 | 2.57 | 2.07 | \n", "| 0610040J01Rik | 0.41 | 0.46 | 0.26 | 0.46 | \n", "| 0610043K17Rik | 0.13 | 0.44 | 0.14 | 0.00 | \n", "| 1010001N08Rik | 0.36 | 0.00 | 0.00 | 0.12 | \n", "\n", "\n" ], "text/plain": [ " wt prdm14 mir290 syk1 \n", "0610005C13Rik 0.63 0.31 0.45 1.15\n", "0610007P14Rik 22.21 23.05 18.14 19.69\n", "0610009B22Rik 15.03 12.64 17.10 14.46\n", "0610009L18Rik 0.91 0.58 1.21 0.53\n", "0610009O20Rik 21.36 18.36 21.02 18.92\n", "0610010B08Rik 3.05 3.47 5.47 2.54\n", "0610010F05Rik 8.83 7.68 12.47 6.22\n", "0610010K14Rik 73.21 63.44 66.81 63.51\n", "0610012G03Rik 12.38 12.05 12.45 9.93\n", "0610030E20Rik 3.46 1.45 3.83 2.30\n", "0610031O16Rik 0.00 0.00 0.00 0.00\n", "0610037L13Rik 13.11 9.32 14.40 10.66\n", "0610038B21Rik 0.33 0.12 0.36 0.40\n", "0610039H22Rik 0.99 0.49 0.45 0.56\n", "0610039K10Rik 0.66 0.65 1.72 0.70\n", "0610040B10Rik 1.13 0.42 0.97 0.58\n", "0610040F04Rik 2.63 1.24 2.57 2.07\n", "0610040J01Rik 0.41 0.46 0.26 0.46\n", "0610043K17Rik 0.13 0.44 0.14 0.00\n", "1010001N08Rik 0.36 0.00 0.00 0.12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "out_EB = readRDS(\"wt_treat_EBSeq.rds\")\n", "rsem_fpkm_matrix=data.matrix(read.table(\"diff_exp_fpkm.txt\",header=TRUE,row.names=1,sep=\"\\t\"))\n", "head(rsem_fpkm_matrix,20)\n", "eb_results = 1-unlist(out_EB@result.table[\"posterior.DE\"])\n", "#adding .000001 pseudocount\n", "fold_change = log2(rsem_fpkm_matrix[,2]/(rsem_fpkm_matrix[,1]))\n", "eb_de_out = data.frame(P=eb_results[is.finite(eb_results)],fold_change=fold_change[is.finite(eb_results)], gene=rownames(out_EB@result.table)[is.finite(eb_results)])\n", "colnames(eb_de_out) = c(\"P\",\"EFFECTSIZE\",\"SNP\")\n", "#out_DESeq2 = readRDS(\"wt_prdm14_DESeq2.rds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make volcano plot of results\n", "Note this is very heavy so it might get laggy!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "e1899931-aa49-4cd6-927d-44e44a27b364" } }, "outputs": [], "source": [ "embed_notebook(volcanoly(eb_de_out,highlight=\"Gapdh\",snp=\"SNP\",annotation1 = \"EFFECTSIZE\",annotation2=\"P\",\n", " title=\"EBSeq DE Analysis of \\n WT vs Treatment\",\n", " ylab=\"log10 of 1-Posterior Probability of DE\", xlab=\"Log2 fold change\"))" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.2" }, "nbpresent": { "slides": { "36aac809-454f-4a60-b40f-e0e3ec700f75": { "id": "36aac809-454f-4a60-b40f-e0e3ec700f75", "prev": "b8bc937b-2ac2-44f8-a31e-bc566bdaa952", "regions": { 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gpl-3.0

TESScience/FPE_Test_Procedures

Evaluating Parameter Interdependence.ipynb

1

9632

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluating Parameter Interdependence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Test run on 10/29/15 by Ed Bokhour. \n", "\n", "Using SD PCB Interface Board serial number 002, SD PCB Driver Board serial number 002, and SD PCB Video Board serial number 001. Running with new wrapper (FPE_Wrapper_6.1.2, for San Diego PCBs, dated 10/19/15." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up the FPE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember that whenever you power-cycle the Observatory Simulator, you should set preload=True below.\n", "\n", "When you are running this notbook and it has not been power cycled, you should set preload=False." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from tessfpe.dhu.fpe import FPE\n", "from tessfpe.dhu.unit_tests import check_house_keeping_voltages\n", "import time\n", "fpe1 = FPE(1, debug=False, preload=False, FPE_Wrapper_version='6.1.2')\n", "print fpe1.version\n", "time.sleep(.01)\n", "if check_house_keeping_voltages(fpe1):\n", " print \"Wrapper load complete. Interface voltages OK.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the operating parameters to the default values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def set_fpe_defaults(fpe):\n", " \"Set the FPE to the default operating parameters, and outputs a table of the default values\"\n", " defaults = {}\n", " for k in range(len(fpe.ops.address)):\n", " if fpe.ops.address[k] is None:\n", " continue\n", " fpe.ops.address[k].value = fpe.ops.address[k].default\n", " defaults[fpe.ops.address[k].name] = fpe.ops.address[k].default\n", " return defaults" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get, sort, and print the default operating parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from tessfpe.data.operating_parameters import operating_parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "for k in sorted(operating_parameters.keys()):\n", " v = operating_parameters[k]\n", " print k, \":\", v[\"default\"], v[\"unit\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a number of sets of housekeeping data, with one operating parameter varying across it's control range, then repeat for every operating parameter:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_base_name(name):\n", " import re\n", " if '_offset' not in name:\n", " return None\n", " offset_name = name\n", " derived_parameter_name = name.replace('_offset', '')\n", " base_name = None\n", " if 'low' in derived_parameter_name:\n", " base_name = derived_parameter_name.replace('low', 'high') \n", " if 'high' in derived_parameter_name:\n", " base_name = derived_parameter_name.replace('high', 'low')\n", " if 'output_drain' in derived_parameter_name:\n", " base_name = re.sub(r'output_drain_._offset$', 'reset_drain', offset_name)\n", " return base_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_derived_parameter_name(name):\n", " if '_offset' not in name:\n", " return None\n", " offset_name = name\n", " return name.replace('_offset', '')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = {}\n", "\n", "base_steps = 15\n", "\n", "offset_steps = 5\n", "\n", "set_fpe_defaults(fpe1)\n", "for i in range(base_steps,0,-1):\n", " for j in range(offset_steps, 0, -1):\n", " for k in range(len(fpe1.ops.address)):\n", " # If there's no operating parameter to set, go on to the next one\n", " if fpe1.ops.address[k] is None:\n", " continue\n", " name = fpe1.ops.address[k].name\n", " base_name = get_base_name(name)\n", " derived_parameter_name = get_derived_parameter_name(name)\n", " # If there's no derived parameter reflecting this parameter, go on to the next one\n", " if derived_parameter_name is None:\n", " continue\n", " offset_name = name\n", " base_low = fpe1.ops[base_name].low\n", " base_high = fpe1.ops[base_name].high\n", " offset_low = fpe1.ops[offset_name].low\n", " offset_high = fpe1.ops[offset_name].high\n", " base_value = base_low + i / float(base_steps) * (base_high - base_low)\n", " fpe1.ops[base_name].value = base_value\n", " fpe1.ops[offset_name].value = offset_low + j / float(offset_steps) * (offset_high - offset_low)\n", " fpe1.ops.send()\n", " analogue_house_keeping = fpe1.house_keeping[\"analogue\"]\n", " for k in range(len(fpe1.ops.address)):\n", " # If there's no operating parameter to set, go on to the next one\n", " if fpe1.ops.address[k] is None:\n", " continue\n", " name = fpe1.ops.address[k].name\n", " base_name = get_base_name(name)\n", " derived_parameter_name = get_derived_parameter_name(name)\n", " if derived_parameter_name is None:\n", " continue\n", " if derived_parameter_name not in data:\n", " data[derived_parameter_name] = {}\n", " offset_name = name\n", " base_low = fpe1.ops[base_name].low\n", " base_high = fpe1.ops[base_name].high\n", " offset_low = fpe1.ops[offset_name].low\n", " offset_high = fpe1.ops[offset_name].high\n", " base_value = base_low + i / float(base_steps) * (base_high - base_low)\n", " if base_value not in data[derived_parameter_name]:\n", " data[derived_parameter_name][base_value] = {\"X\": [], \"Y\": []}\n", " data[derived_parameter_name][base_value][\"X\"].append(fpe1.ops[base_name].value + \n", " fpe1.ops[offset_name].value)\n", " data[derived_parameter_name][base_value][\"Y\"].append(analogue_house_keeping[derived_parameter_name])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up to plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pylab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot selected data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_range_square(X,Y):\n", " return [min(X + Y)-1, max(X + Y)+1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot the set vs. measured values of selected channels:\n", "for nom in sorted(data.keys()):\n", " print nom\n", " for base_value in sorted(data[nom].keys()):\n", " print base_value\n", " X = data[nom][base_value][\"X\"]\n", " Y = data[nom][base_value][\"Y\"]\n", " ran = get_range_square(X,Y)\n", " pylab.ylim(ran)\n", " pylab.xlim(ran)\n", " pylab.grid(True)\n", " plt.axes().set_aspect(1)\n", " plt.title(\"{derived_param} with base {base}\".format(\n", " derived_param=nom,\n", " base=base_value\n", " ))\n", " plt.scatter(X,Y,color='red')\n", " plt.plot(X,Y,color='blue')\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 }

mit

mne-tools/mne-tools.github.io

stable/_downloads/82d9c13e00105df6fd0ebed67b862464/ssp_projs_sensitivity_map.ipynb

1

2602

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Sensitivity map of SSP projections\n\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n\nfrom mne import read_forward_solution, read_proj, sensitivity_map\n\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nsubjects_dir = data_path / 'subjects'\nmeg_path = data_path / 'MEG' / 'sample'\nfname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif'\necg_fname = meg_path / 'sample_audvis_ecg-proj.fif'\n\nfwd = read_forward_solution(fname)\n\nprojs = read_proj(ecg_fname)\n# take only one projection per channel type\nprojs = projs[::2]\n\n# Compute sensitivity map\nssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show sensitivity map\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(ssp_ecg_map.data.ravel())\nplt.show()\n\nargs = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,\n hemi='rh', subjects_dir=subjects_dir)\nssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)" ] } ], "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

sourabhrohilla/ds-masterclass-hands-on

session-2/python/Topic_Model_Recommender.ipynb

1

33316

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Based Recommender" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Topic Based Recommender\n", "1. Represent articles in terms of Topic Vector\n", "2. Represent user in terms of Topic Vector of read articles\n", "3. Calculate cosine similarity between read and unread articles \n", "4. Get the recommended articles " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Describing parameters**:\n", "\n", "*1. PATH_ARTICLE_TOPIC_DISTRIBUTION: specify the path where 'ARTICLE_TOPIC_DISTRIBUTION.csv' is present.* <br/>\n", "*2. PATH_NEWS_ARTICLES: specify the path where news_article.csv is present* <br/>\n", "*3. NO_OF_TOPIC: Number of topics specified when training your topic model. This would refer to the dimension of each vector representing an article* <br/>\n", "*4. ARTICLES_READ: List of Article_Ids read by the user* <br/>\n", "*5. NO_RECOMMENDED_ARTICLES: Refers to the number of recommended articles as a result*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "PATH_ARTICLE_TOPIC_DISTRIBUTION = \"/home/phoenix/Documents/HandsOn/Final/python/Topic Model/model/Article_Topic_Distribution.csv\"\n", "PATH_NEWS_ARTICLES = \"/home/phoenix/Documents/HandsOn/Final/news_articles.csv\"\n", "NO_OF_TOPICS=150\n", "ARTICLES_READ=[7,6,76,61,761]\n", "NUM_RECOMMENDED_ARTICLES=5" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.metrics.pairwise import cosine_similarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Represent Read Article in terms of Topic Vector" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(22186, 3)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "article_topic_distribution = pd.read_csv(PATH_ARTICLE_TOPIC_DISTRIBUTION)\n", "article_topic_distribution.shape" ] }, { "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>Article_Id</th>\n", " <th>Topic_Id</th>\n", " <th>Topic_Weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>25</td>\n", " <td>0.324485</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>0.131476</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>127</td>\n", " <td>0.535940</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>0.306691</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>47</td>\n", " <td>0.277037</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Article_Id Topic_Id Topic_Weight\n", "0 0 25 0.324485\n", "1 0 27 0.131476\n", "2 0 127 0.535940\n", "3 1 5 0.306691\n", "4 1 47 0.277037" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "article_topic_distribution.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Generate Article-Topic Distribution matrix ***" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4831, 150)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Pivot the dataframe\n", "article_topic_pivot = article_topic_distribution.pivot(index='Article_Id', columns='Topic_Id', values='Topic_Weight')\n", "#Fill NaN with 0\n", "article_topic_pivot.fillna(value=0, inplace=True)\n", "#Get the values in dataframe as matrix\n", "articles_topic_matrix = article_topic_pivot.values\n", "articles_topic_matrix.shape" ] }, { "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>Topic_Id</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>140</th>\n", " <th>141</th>\n", " <th>142</th>\n", " <th>143</th>\n", " <th>144</th>\n", " <th>145</th>\n", " <th>146</th>\n", " <th>147</th>\n", " <th>148</th>\n", " <th>149</th>\n", " </tr>\n", " <tr>\n", " <th>Article_Id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.306691</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.015589</td>\n", " <td>0.0</td>\n", " <td>0.077002</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.396528</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 150 columns</p>\n", "</div>" ], "text/plain": [ "Topic_Id 0 1 2 3 4 5 6 7 8 9 ... \\\n", "Article_Id ... \n", "0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 ... \n", "1 0.0 0.0 0.0 0.000000 0.0 0.306691 0.0 0.0 0.0 0.0 ... \n", "2 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 ... \n", "3 0.0 0.0 0.0 0.015589 0.0 0.077002 0.0 0.0 0.0 0.0 ... \n", "4 0.0 0.0 0.0 0.000000 0.0 0.396528 0.0 0.0 0.0 0.0 ... \n", "\n", "Topic_Id 140 141 142 143 144 145 146 147 148 149 \n", "Article_Id \n", "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 150 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "article_topic_pivot.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Represent user in terms of Topic Vector of read articles\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***A user vector is represented in terms of average of read articles topic vector***" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1, 150)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Select user in terms of read article topic distribution\n", "row_idx = np.array(ARTICLES_READ)\n", "read_articles_topic_matrix=articles_topic_matrix[row_idx[:, None]]\n", "#Calculate the average of read articles topic vector \n", "user_vector = np.mean(read_articles_topic_matrix, axis=0)\n", "user_vector.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 0. , 0.02488209, 0.06438433,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.02753025,\n", " 0. , 0.18989699, 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.04683422,\n", " 0. , 0.06889868, 0. , 0.00411056, 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.00662661,\n", " 0. , 0. , 0.09912603, 0. , 0. ,\n", " 0.01028336, 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.00661727, 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.05856521,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.04954107, 0.01280254,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.00393764, 0. ,\n", " 0. , 0.03582032, 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.07245383,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0.08082968, 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0.12301701, 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Calculate cosine similarity between read and unread articles " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculate_cosine_similarity(articles_topic_matrix, user_vector):\n", " articles_similarity_score=cosine_similarity(articles_topic_matrix, user_vector)\n", " recommended_articles_id = articles_similarity_score.flatten().argsort()[::-1]\n", " #Remove read articles from recommendations\n", " final_recommended_articles_id = [article_id for article_id in recommended_articles_id \n", " if article_id not in ARTICLES_READ ][:NUM_RECOMMENDED_ARTICLES]\n", " return final_recommended_articles_id" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[864, 2150, 2450, 629, 3643]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recommended_articles_id = calculate_cosine_similarity(articles_topic_matrix, user_vector)\n", "recommended_articles_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Recommendation Using Topic Model:-" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Articles Read\n", "6 Infosys shares likely to fall on Tuesday after...\n", "7 Dialogue crucial in finding permanent solution...\n", "61 Revathy to direct Queen s Tamil Telugu remake...\n", "76 When cricketer R Ashwin started fans club for ...\n", "761 Baahubali to have world television premiere ...\n", "Name: Title, dtype: object\n", "\n", "\n", "Recommender \n", "629 Dilwale review roundup What critics have to...\n", "864 Shah Rukh Khan-Kajol appear on Vijay TV show ...\n", "2150 Year 2014 for Aamir Khan Shah Rukh Khan and S...\n", "2450 Times Celebex Akshay Katrina top the list S...\n", "3643 Will Aditya Chopra Bring Shah Rukh Khan and Ra...\n", "Name: Title, dtype: object\n" ] } ], "source": [ "#Recommended Articles and their title\n", "news_articles = pd.read_csv(PATH_NEWS_ARTICLES)\n", "print 'Articles Read'\n", "print news_articles.loc[news_articles['Article_Id'].isin(ARTICLES_READ)]['Title']\n", "print '\\n'\n", "print 'Recommender '\n", "print news_articles.loc[news_articles['Article_Id'].isin(recommended_articles_id)]['Title']" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Topics + NER Recommender" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic + NER Based Recommender\n", "\n", "1. Represent user in terms of - <br/>\n", " (Alpha) <Topic Vector> + (1-Alpha) <NER Vector> <br/>\n", " where <br/>\n", " Alpha => [0,1] <br/>\n", " [Topic Vector] => Topic vector representation of concatenated read articles <br/>\n", " [NER Vector] => Topic vector representation of NERs associated with concatenated read articles <br/>\n", "2. Calculate cosine similarity between user vector and articles Topic matrix\n", "3. Get the recommended articles " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ALPHA = 0.5\n", "DICTIONARY_PATH = \"/home/phoenix/Documents/HandsOn/Final/python/Topic Model/model/dictionary_of_words.p\"\n", "LDA_MODEL_PATH = \"/home/phoenix/Documents/HandsOn/Final/python/Topic Model/model/lda.model\"" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk import word_tokenize, pos_tag, ne_chunk\n", "from nltk.chunk import tree2conlltags\n", "import re\n", "from nltk.corpus import stopwords\n", "from nltk.tokenize import TweetTokenizer\n", "from nltk.stem.snowball import SnowballStemmer\n", "import pickle\n", "import gensim\n", "from gensim import corpora, models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Represent User in terms of Topic Distribution and NER\n", "\n", "1. Represent user in terms of read article topic distribution\n", "2. Represent user in terms of NERs associated with read articles\n", " 2.1 Get NERs of read articles\n", " 2.2 Load LDA model\n", " 2.3 Get topic distribution for the concated NERs\n", "3. Generate user vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.1. Represent user in terms of read article topic distribution" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1, 150)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "row_idx = np.array(ARTICLES_READ)\n", "read_articles_topic_matrix=articles_topic_matrix[row_idx[:, None]]\n", "#Calculate the average of read articles topic vector \n", "user_topic_vector = np.mean(read_articles_topic_matrix, axis=0)\n", "user_topic_vector.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2. Represent user in terms of NERs associated with read articles" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get NERs of read articles\n", "def get_ner(article):\n", " ne_tree = ne_chunk(pos_tag(word_tokenize(article)))\n", " iob_tagged = tree2conlltags(ne_tree)\n", " ner_token = ' '.join([token for token,pos,ner_tag in iob_tagged if not ner_tag==u'O']) #Discarding tokens with 'Other' tag\n", " return ner_token" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NERs of Read Article => Narendra Modi Kashmir Modi Jammu Kashmir Modi Burhan Wani Omar Abdullah Abdullah National Conference Congress PCC CPI Tarigami Valley Modi Kashmir Jammu Kashmir Infosys Royal Bank Scotland RBS Williams Glyn Infosys IBM Infosys Application Delivery India Royal Bank Scotland Williams Glyn RBS Infosys Infosys Infosys Bombay Stock Infosys FY2017 Infosys YoY Cricketer Ravichandran Trisha Krishnan Ashwin Tamil Trisha Tamil Lesa Lesa Veteran Revathy Bollywood Queen Actress Suhasini Mani Ratnam Vikas Bahl Queen Paris Kangana Queen Telugu Tamil Filmmaker Thiagarajan Queen Telugu Tamil Revathy Suhasini Mani Ratnam Revathy Suhasini Mani Ratnam Suhasini Revathy Suhasini Mani Ratnam South Indian Telugu Suhasini Rajamouli Baahubali Malayalam Mazhavil Manorama Malayalam Prabhas Rana Daggubati Anushka Shetty Tamannaah Bhatia Baahubali Anushka Tamannaah Rana Prabhas Rajamouli Mazhavil Manorma Manorama Music Baahubali Malayalam VCD DVD Telugu MAA Baahubali Dussehra\n" ] } ], "source": [ "articles = news_articles['Content'].tolist()\n", "user_articles_ner = ' '.join([get_ner(articles[i]) for i in ARTICLES_READ])\n", "print \"NERs of Read Article =>\", user_articles_ner" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stop_words = set(stopwords.words('english'))\n", "tknzr = TweetTokenizer()\n", "stemmer = SnowballStemmer(\"english\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def clean_text(text):\n", " cleaned_text=re.sub('[^\\w_\\s-]', ' ', text) #remove punctuation marks \n", " return cleaned_text #and other symbols \n", "\n", "def tokenize(text):\n", " word = tknzr.tokenize(text) #tokenization\n", " filtered_sentence = [w for w in word if not w.lower() in stop_words] #removing stop words\n", " stemmed_filtered_tokens = [stemmer.stem(plural) for plural in filtered_sentence] #stemming\n", " tokens = [i for i in stemmed_filtered_tokens if i.isalpha() and len(i) not in [0, 1]]\n", " return tokens" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Cleaning the article\n", "cleaned_text = clean_text(user_articles_ner)\n", "article_vocabulary = tokenize(cleaned_text)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Load model dictionary\n", "model_dictionary = pickle.load(open(DICTIONARY_PATH,\"rb\"))\n", "#Generate article maping using IDs associated with vocab\n", "corpus = [model_dictionary.doc2bow(text) for text in [article_vocabulary]]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Load LDA Model\n", "lda = models.LdaModel.load(LDA_MODEL_PATH)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(9, 0.016833535075786221),\n", " (16, 0.13360130412473772),\n", " (21, 0.011354918964910036),\n", " (29, 0.048363063836432151),\n", " (31, 0.18383978754651545),\n", " (44, 0.016568883655345965),\n", " (84, 0.017429078934066415),\n", " (93, 0.041131451368969535),\n", " (106, 0.12909919386972013),\n", " (119, 0.20018375535066402),\n", " (127, 0.10765513761665123),\n", " (128, 0.036829724632851543),\n", " (145, 0.041718476547869268)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get topic distribution for the concated NERs\n", "article_topic_distribution=lda.get_document_topics(corpus[0])\n", "article_topic_distribution" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ner_vector =[0]*NO_OF_TOPICS\n", "for topic_id, topic_weight in article_topic_distribution:\n", " ner_vector[topic_id]=topic_weight\n", "user_ner_vector = np.asarray(ner_vector).reshape(1,150)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.3. Generate user vector" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 0. , 0.01244104, 0.03219216,\n", " 0. , 0. , 0. , 0. , 0.00841677,\n", " 0. , 0. , 0. , 0. , 0.01376513,\n", " 0. , 0.16174915, 0. , 0. , 0. ,\n", " 0. , 0.00567746, 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.04759864,\n", " 0. , 0.12636923, 0. , 0.00205528, 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.01159775,\n", " 0. , 0. , 0.04956302, 0. , 0. ,\n", " 0.00514168, 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.00330864, 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.03799714,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.04533626, 0.00640127,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0.00196882, 0. ,\n", " 0. , 0.08245976, 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0.13631879,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0.09424241, 0.01841486, 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0.08236774, 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alpha_topic_vector = ALPHA*user_topic_vector\n", "alpha_ner_vector = (1-ALPHA) * user_ner_vector\n", "user_vector = np.add(alpha_topic_vector,alpha_ner_vector)\n", "user_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Calculate cosine similarity between user vector and articles Topic matrix" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1913, 2003, 1995, 1997, 864]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recommended_articles_id = calculate_cosine_similarity(articles_topic_matrix, user_vector)\n", "recommended_articles_id\n", "# [array([ 0.75807146]), array([ 0.74644157]), array([ 0.74440326]), array([ 0.7420562]), array([ 0.73966259])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Get recommended articles" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Articles Read\n", "6 Infosys shares likely to fall on Tuesday after...\n", "7 Dialogue crucial in finding permanent solution...\n", "61 Revathy to direct Queen s Tamil Telugu remake...\n", "76 When cricketer R Ashwin started fans club for ...\n", "761 Baahubali to have world television premiere ...\n", "Name: Title, dtype: object\n", "\n", "\n", "Recommender \n", "864 Shah Rukh Khan-Kajol appear on Vijay TV show ...\n", "1913 AIB Roast Controversy and Stringent Censor Boa...\n", "1995 Bajirao Mastani Director Bhansali found AIB ...\n", "1997 Twinkle Khanna s Blog on AIB Roast Goes Viral ...\n", "2003 Deepika Padukone Sonakshi Sinha Alia Bhatt C...\n", "Name: Title, dtype: object\n" ] } ], "source": [ "#Recommended Articles and their title\n", "news_articles = pd.read_csv(PATH_NEWS_ARTICLES)\n", "print 'Articles Read'\n", "print news_articles.loc[news_articles['Article_Id'].isin(ARTICLES_READ)]['Title']\n", "print '\\n'\n", "print 'Recommender '\n", "print news_articles.loc[news_articles['Article_Id'].isin(recommended_articles_id)]['Title']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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

DSSG2017/florence

dev/notebooks/Distributions_MM.ipynb

1

329078

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting distributions\n", "First, import relevant libraries:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, load the data (takes a few moments):" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Load data\n", "uda = pd.read_csv(\"./aws-data/user_dist.txt\", sep=\"\\t\") # User distribution, all\n", "udf = pd.read_csv(\"./aws-data/user_dist_fl.txt\", sep=\"\\t\") # User distribution, Florence\n", "\n", "dra = pd.read_csv(\"./aws-data/user_duration.txt\", sep=\"\\t\") # Duration, all\n", "drf = pd.read_csv(\"./aws-data/user_duration_fl.txt\", sep=\"\\t\") # Duration, Florence\n", "\n", "dra['min'] = pd.to_datetime(dra['min'], format='%Y-%m-%d%H:%M:%S')\n", "dra['max'] = pd.to_datetime(dra['max'], format='%Y-%m-%d%H:%M:%S')\n", "drf['min'] = pd.to_datetime(drf['min'], format='%Y-%m-%d%H:%M:%S')\n", "drf['max'] = pd.to_datetime(drf['max'], format='%Y-%m-%d%H:%M:%S')\n", "\n", "dra['duration'] = dra['max'] - dra['min']\n", "drf['duration'] = drf['max'] - drf['min']\n", "\n", "dra['days'] = dra['duration'].dt.days\n", "drf['days'] = drf['duration'].dt.days" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "cda = pd.read_csv(\"./aws-data/calls_per_day.txt\", sep=\"\\t\") # Calls per day, all\n", "cdf = pd.read_csv(\"./aws-data/calls_per_day_fl.txt\", sep=\"\\t\") # Calls per day, Florence\n", "cda['day_'] = pd.to_datetime(cda['day_'], format='%Y-%m-%d%H:%M:%S').dt.date\n", "cdf['day_'] = pd.to_datetime(cdf['day_'], format='%Y-%m-%d%H:%M:%S').dt.date" ] }, { "cell_type": "code", "execution_count": 67, "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>cust_id</th>\n", " <th>day_</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>11</td>\n", " <td>2016-06-07</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>11</td>\n", " <td>2016-06-08</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>11</td>\n", " <td>2016-06-09</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11</td>\n", " <td>2016-06-10</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11</td>\n", " <td>2016-06-11</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cust_id day_ count\n", "0 11 2016-06-07 19\n", "1 11 2016-06-08 16\n", "2 11 2016-06-09 39\n", "3 11 2016-06-10 2\n", "4 11 2016-06-11 2" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cda.head()" ] }, { "cell_type": "code", "execution_count": 108, "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>cust_id</th>\n", " <th>mean_calls_per_day</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>22672249</td>\n", " <td>999.333333</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>17781619</td>\n", " <td>620.500000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20662741</td>\n", " <td>605.888889</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>27959832</td>\n", " <td>587.333333</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>12754963</td>\n", " <td>570.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cust_id mean_calls_per_day\n", "0 22672249 999.333333\n", "1 17781619 620.500000\n", "2 20662741 605.888889\n", "3 27959832 587.333333\n", "4 12754963 570.500000" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mcpdf = cdf.groupby('cust_id')['count'].mean().to_frame() # Mean calls per day, Florence\n", "mcpdf.columns = ['mean_calls_per_day']\n", "mcpdf = mcpdf.sort_values('mean_calls_per_day',ascending=False)\n", "mcpdf.index.name = 'cust_id'\n", "mcpdf.reset_index(inplace=True)\n", "mcpdf.head()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7ff8f88930d0>" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zz9aUJEmj1aqdLmAkmjjvvAHbFx81ayVXIkmSRpsR2XMmSZI0WhnOJEmSGsRw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\nlplnroxCJEmSVK3nTCvBxHn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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff91b95c710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# mcpdf.plot(y='mean_calls_per_day', style='.', logy=True, figsize=(10,10))\n", "mcpdf.plot.hist(y='mean_calls_per_day', logy=True, figsize=(10,10), bins=100)\n", "plt.ylabel('Number of customers with x average calls per day')\n", "# plt.xlabel('Customer rank')\n", "plt.title('Mean number of calls per day during days in Florence by foreign SIM cards')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cvd = udf.merge(drf, left_on='cust_id', right_on='cust_id', how='outer') # Count versus days\n", "cvd.plot.scatter(x='days', y='count', s=.1, figsize = (10, 10))\n", "plt.ylabel('Number of calls')\n", "plt.xlabel('Duration between first and last days active')\n", "plt.title('Calls versus duration of records of foreign SIMs in Florence')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "fr = drf['days'].value_counts().to_frame() # NOTE: FIGURE OUT HOW TO ROUND, NOT TRUNCATE\n", "fr.columns = ['frequency']\n", "fr.index.name = 'days'\n", "fr.reset_index(inplace=True)\n", "fr = fr.sort_values('days')\n", "fr['cumulative'] = fr['frequency'].cumsum()/fr['frequency'].sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below creates a calls-per-person frequency distribution, which is the first thing we want to see. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7ff921d55550>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hys7OPqDzRHqdtMrKSpfOEwcuuP1FSYxJAwDsv/fee0+FhYXq37+/vP3pcSCcc9q8ebO2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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff921f5f990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr.plot(x='days', y='frequency', style='o-', logy=True, figsize = (10, 10))\n", "plt.ylabel('Number of people')\n", "plt.axvline(14,ls='dotted')\n", "plt.title('Foreign SIM days between first and last instances in Florence')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7ff91cae7850>" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AQxC4AQAANASBGwAAQEMQuAEAADQEgRsAAEBDELgBAAA0BIEbAABAQxC4AQAANASBGwAA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\nAreIxHiAoP84LgD0UhVBWxM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3U5fftLnQOmtRtE21SX/9mMfNn2B35rX58OePCc3rVqRf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\n3EKxJzDvs3P7xpbdkdWd++veG5LbBWu3RzaF9sboGbXrt+2JrF5UGYq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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff921886c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cvd = udf.merge(drf, left_on='cust_id', right_on='cust_id', how='outer') # Count versus days\n", "cvd.plot.scatter(x='days', y='count', s=.1, figsize = (10, 10))\n", "plt.ylabel('Number of calls')\n", "plt.xlabel('Duration between first and last days active')\n", "plt.title('Calls versus duration of records of foreign SIMs in Florence')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot this distribution. This shows that 19344 people made 1 call over the 4 months, 36466 people made 2 calls over the 4 months, 41900 people made 3 calls over the 4 months, etc. " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "scrolled": false }, "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>calls</th>\n", " <th>frequency</th>\n", " <th>cumulative</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>220340</td>\n", " <td>0.185261</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>157332</td>\n", " <td>0.317544</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>115733</td>\n", " <td>0.414852</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>88833</td>\n", " <td>0.489542</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>70910</td>\n", " <td>0.549163</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " calls frequency cumulative\n", "0 1 220340 0.185261\n", "1 2 157332 0.317544\n", "2 3 115733 0.414852\n", "3 4 88833 0.489542\n", "4 5 70910 0.549163" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fr = udf['count'].value_counts().to_frame()\n", "fr.columns = ['frequency']\n", "fr.index.name = 'calls'\n", "fr.reset_index(inplace=True)\n", "fr = fr.sort_values('calls')\n", "fr['cumulative'] = fr['frequency'].cumsum()/fr['frequency'].sum()\n", "fr.head()" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7ff8d2d32ad0>" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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r5Y37XCfvIJ2v+u3/I+9ggb2SXpY35jESg+X9M1Evb69unbwDKTrS3XfRY/K+a3Y5595s\na4zCNmtwzu1qu8kbx3rAObc/0mV0sew6SfPkBe46eQfHzIvkM+B3y35b0nK/W3p6N7OkSfp7edtv\nn7zP2990OQdOsO6HdgB9j5ldJ2/Q8owoL3e0vD/mmb3cW9XRsjfLq7knYTZuzOwVeUcT/izoWhKd\nmf2NvAM1Lup2YgDoAnv0AMSEmV1kZqf53TrXSpqoyPeW9ClmNszMKv1hAGXy9pL8Iei6ACQ/zh4P\nIFbK5I2rGiCvK/mj/lgpfFCWvCO3x8jr6n5Q3nnXAKBX6LoFAABIUXTdAgAApCi6bn0FBQVu9OjR\nQZcBAADQrddee22vc66wu+kIer7Ro0dr5cqVQZcBAADQLTPr6io8J9B1CwAAkKIIegAAACmKoAcA\nAJCiGKMHAAB6rampSdu3b9fRo0eDLiWlZGdna8SIEcrMzDyl+Ql6AACg17Zv365BgwZp9OjRMrOg\ny0kJzjnV1dVp+/btGjNmzCktg65bAADQa0ePHlV+fj4hL4rMTPn5+b3aS0rQAwAAUUHIi77eblOC\nHgAAQIoi6AEAgJRx1113qaKiQp/5zGeCLiUhcDAGAACIu0dX1WjhomrtaGjU8NwcLZhbpvmTi3u9\n3B/84Ad69tlnNWLEiBNtzc3Nysjom5GHPXoAACCuHl1Vo9seWaOahkY5STUNjbrtkTV6dFVNr5b7\nhS98QZs2bdJll12mIUOG6Oqrr1ZlZaWuvvpqtbS0aMGCBTr33HM1ceJE/ehHP5LkHdn6pS99SWVl\nZZozZ44uv/xyPfzww5K8y6Pu3btXkrRy5UrNnDlTknT48GHdcMMNmjZtmiZPnqw//vGPkqT7779f\nV111lS699FKNHz9eX/va107U9vTTT2vKlCmaNGmSqqqq1NraqvHjx6u2tlaS1NraqtLS0hOPo6Vv\nxlsAABAz//r423pnx4FOn1+1tUHHW1pPamtsatHXHl6t37y6tcN5JgwfrDs+fEaX6/3hD3+op59+\nWkuXLtXdd9+txx9/XMuWLVNOTo7uvfdeDRkyRCtWrNCxY8dUWVmpSy65RKtWrVJ1dbXeeecd7d69\nWxMmTNANN9zQ5Xq+/e1va/bs2brvvvvU0NCgadOmac6cOZKkN954Q6tWrVK/fv1UVlamL3/5y8rO\nztbNN9+sF154QWPGjNG+ffuUlpamz372s/r1r3+tr371q3r22Wc1adIkFRYWdrnuniLoAQCAuGof\n8rprP1VXXHGFcnJyJEl//vOftXr16hN76/bv36/169frhRde0Kc+9Smlp6dr+PDhmj17drfL/fOf\n/6zHHntM3/3udyV5p5bZutULqFVVVRoyZIgkacKECdqyZYvq6+t14YUXnjgXXl5eniTphhtu0JVX\nXqmvfvWruu+++3T99ddH9fVLBD0AABBl3e15q7xziWoaGj/QXpybo4c+f37U6hgwYMCJ+845ff/7\n39fcuXNPmuapp57qdP6MjAy1tnrhM/xcds45/f73v1dZWdlJ07/yyivq16/ficfp6elqbm7udPkl\nJSUqKirSkiVL9Oqrr+rXv/51ZC+sBxijBwAA4mrB3DLlZKaf1JaTma4Fc8s6maP35s6dq3vuuUdN\nTU2SpHfffVeHDx/WhRdeqIceekgtLS3auXOnli5demKe0aNH67XXXpMk/f73vz9pWd///vflnJMk\nrVq1qst1T58+XS+88ILee+89SdK+fftOPHfTTTfps5/9rD72sY8pPT29s0WcMoIeAACIq/mTi/Wd\nq85ScW6OTN6evO9cdVZUjrrtzE033aQJEyZoypQpOvPMM/X5z39ezc3N+shHPqLx48drwoQJuuaa\na3T++e/vUbzjjjv0la98RVOnTj0phN1+++1qamrSxIkTdcYZZ+j222/vct2FhYW69957ddVVV2nS\npEn6xCc+ceK5K664QocOHYpJt60kWVsa7eumTp3qVq5cGXQZAAAkpbVr16qioiLoMnrtuuuu07x5\n8/TRj340LutbuXKl/u7v/k4vvvhip9N0tG3N7DXn3NTuls8YPQAAgADceeeduueee2IyNq8Ne/R8\n7NEDAODUpcoevUTUmz16jNEDAABRwc6j6OvtNiXo+dbU7FflnUt6fVZuAAD6ouzsbNXV1RH2osg5\np7q6OmVnZ5/yMhijF6btEiySYnrkDwAAqWbEiBHavn171C/h1ddlZ2efdN3eniLotdPY1KKFi6oJ\negAA9EBmZuaJKz8gcdB124EdHZytGwAAINkQ9DowPDcn6BIAAAB6jaDXTqwvwQIAABAvBL0wWRlp\nMb8ECwAAQLwQ9HyFg/qppdVpVnko6FIAAACigqDnG5ydqZZWp+ff5bBwAACQGgh6vv5Z6cobkKXF\na3cHXQoAAEBUEPTCzCoL6bnqWjW3tAZdCgAAQK8R9MJUVYS0v7FJr22pD7oUAACAXiPohfmr8QXK\nTDctWbcn6FIAAAB6jaAXZlB2ps4bk69nGacHAABSAEGvnaqKkDbWHtbmvYeDLgUAAKBXCHrtVJUX\nSZIW030LAACSHEGvnZH5/TU+NFBL1tF9CwAAkhtBrwNVFUV6ZdM+HTjaFHQpAAAAp4yg14GqipCa\nW51e4CoZAAAgiRH0OjBl5FDl9s/UkrWM0wMAAMmLoNeB9DTTrLKQllbvUUurC7ocAACAU0LQ60RV\nRUj1R5q0aitXyQAAAMmJoNeJC08vVEaa6Vm6bwEAQJIi6HVicHampo3J4zQrAAAgaRH0ulBVUaR3\ndx/Stn1Hgi4FAACgxwh6XagqD0mSFnPtWwAAkIQIel0YXTBA4woHcDk0AACQlAh63aiqKNLLm+p0\nkKtkAACAJEPQ60ZVeUhNLU7L1u8NuhQAAIAeIeh145xRQzUkJ5PTrAAAgKRD0OtGRnqaZpYV6jmu\nkgEAAJIMQS8Cs8tDqjt8XG9sawi6FAAAgIgR9CIw8/SQ0tOMkycDAICkQtCLwJD+mTp39FAtZpwe\nAABIIgS9CFWVF2ndroPaXs9VMgAAQHIg6EWoqsK7SsYSTp4MAACSBEEvQmMLB2pMwQBOswIAAJIG\nQa8HqspDenljnQ4faw66FAAAgG4R9HpgdkVIx1ta9SJXyQAAAEmAoNcD547O06DsDE6zAgAAkgJB\nrwcy09N00emFWrKuVq1cJQMAACQ4gl4Pzako0t5Dx7S6Zn/QpQAAAHSJoNdDM8sKlWbS4rV03wIA\ngMRG0Ouh3P5Zmjoqj9OsAACAhEfQOwVVFSGt3XlAOxoagy4FAACgUwS9U9B2lYzFXCUDAAAksJgF\nPTMrMbOlZvaOmb1tZl/x2/PM7BkzW+//HOq3m5ndZWYbzGy1mU0JW9a1/vTrzezasPZzzGyNP89d\nZmZdrSNaxhUO1Kj8/lrCOD0AAJDAYrlHr1nSPzjnJkiaLukWM5sg6VZJi51z4yUt9h9L0mWSxvu3\nz0m6R/JCm6Q7JJ0naZqkO8KC2z2Sbg6b71K/vbN1RIWZaXZ5SMs31unIca6SAQAAElPMgp5zbqdz\n7nX//kFJayUVS7pS0s/9yX4uab5//0pJv3CelyXlmtkwSXMlPeOc2+ecq5f0jKRL/ecGO+deds45\nSb9ot6yO1hE1cyqKdLy5Vcs31EV70QAAAFERlzF6ZjZa0mRJr0gqcs7t9J/aJanIv18saVvYbNv9\ntq7at3fQri7W0b6uz5nZSjNbWVtb26PXdO7oPA3ql8FpVgAAQMKKedAzs4GSfi/pq865A+HP+Xvi\nYnqJia7W4Zy71zk31Tk3tbCwsEfLzcpI04WnF2rJuj1cJQMAACSkmAY9M8uUF/J+7Zx7xG/e7Xe7\nyv/ZduhqjaSSsNlH+G1dtY/ooL2rdURVVUVIew4e01s7uEoGAABIPLE86tYk/VTSWufcf4U99Zik\ntiNnr5X0x7D2a/yjb6dL2u93vy6SdImZDfUPwrhE0iL/uQNmNt1f1zXtltXROqJqZllIaSZOngwA\nABJSLPfoVUq6WtJsM3vDv10u6U5JF5vZeklz/MeS9JSkTZI2SPqxpC9KknNun6RvSlrh3/7Nb5M/\nzU/8eTZK+pPf3tk6oipvQJamjByqJesYpwcAABJPRqwW7JxbJsk6ebqqg+mdpFs6WdZ9ku7roH2l\npDM7aK/raB2xMLsipP98ulq79h/VaUOy47FKAACAiHBljF6aU+Ed0LuEq2QAAIAEQ9DrpfGhgRox\nNIfTrAAAgIRD0OslM9OciiIt27BXjcdbgi4HAADgBIJeFFRVhHSsuVUvbdwbdCkAAAAnEPSiYNqY\nPA3IStdixukBAIAEQtCLgn4Z6d5VMtbukXfwMAAAQPAIelEyuzykXQeO6u0dB7qfGAAAIA4IelEy\nqzwkM2kxV8kAAAAJgqAXJQUD++nsklwt5ioZAAAgQRD0omhORZFWb9+vPQeOBl0KAAAAQS+aZpeH\nJHGVDAAAkBgIelFUftogFefmcJoVAACQEAh6UWRmqqoIadn6vTraxFUyAABAsAh6UTa7PKTGphb9\nZWNd0KUAAIA+jqAXZdPH5qt/VjpH3wIAgMAR9KIsOzNdM0oLuEoGAAAIHEEvBuZUFGnH/qNau/Ng\n0KUAAIA+jKAXAzPLCyVJi9fSfQsAAIJD0IuB0KBsTSrJ5TQrAAAgUAS9GKkqD+nN7Q2qPXgs6FIA\nAEAfRdCLkaqKkJyTllazVw8AAASDoBcjE4YN1rAh2YzTAwAAgSHoxYiZaXZ5SC9ylQwAABAQgl4M\nzako0pHjLXrlvX1BlwIAAPoggl4MnT8uX9mZaXTfAgCAQBD0Ysi7SkahFnOVDAAAEACCXoxVVYRU\n09Co6t1cJQMAAMQXQS/GZpeHJEmL13KaFQAAEF8EvRgrGpyts4qHME4PAADEHUEvDqoqQlq1rUF1\nh7hKBgAAiB+CXhzMqSjyr5JRG3QpAACgDyHoxcEZwweraHA/um8BAEBcEfTiwLtKRpFeeLdWx5tb\ngy4HAAD0EQS9OKkqD+nw8Ra98l5d0KUAAIA+gqAXJ5WlBeqXkcZpVgAAQNwQ9OIkJytdlaUFWrxu\nN1fJAAAAcUHQi6OqipC27WvUhj2Hgi4FAAD0AQS9OGq7SsazdN8CAIA4IOjF0bAhOTpj+GAtWcdp\nVgAAQOwR9OKsqqJIr22pV/3h40GXAgAAUhxBL86qykNqddLSarpvAQBAbBH04uys4iEqHNRPi9cR\n9AAAQGwR9OIsLc00uyykF6q5SgYAAIgtgl4AqipCOnisWSs37wu6FAAAkMIIegGYMb5AWRlpnGYF\nAADEFEEvAP2zMnTBuHyukgEAAGKKoBeQqvKQttQd0cbaw0GXAgAAUhRBLyCzK4okSYvXcvJkAAAQ\nGwS9gBTn5qhi2GBOswIAAGKGoBegqvKQXttSr4YjXCUDAABEH0EvQFUVIbW0Oj3/bm3QpQAAgBRE\n0AvQpBG5KhiYxWlWAABATBD0ApSWZppVFtLz1XvU1MJVMgAAQHQR9AJWVRHSgaPNWrm5PuhSAABA\niiHoBWzG+EJlpadpyTpOswIAAKKLoBewgf0ydN7YPC1mnB4AAIgygl4CmFNRpE17D2tT7aGgSwEA\nACmEoJcAZpeHJElLOHkyAACIIoJeAijJ66+yokF6lsuhAQCAKCLoJYiqipBWbK7X/samoEsBAAAp\ngqCXILhKBgAAiDaCXoI4u2So8gZkaQndtwAAIEoIegkiPc00s6xQS6tr1cxVMgAAQBQQ9BLInIoi\n7W9s0utbG4IuBQAApACCXgL5q/EFykw3Lab7FgAARAFBL4EMys7UeWPyOc0KAACICoJegqmqCGlj\n7WFt3ns46FIAAECSI+glmKryIknSYq6SAQAAeomgl2BG5vfX+NBALVlH9y0AAOgdgl4Cml0R0iub\n9unAUa6SAQAATh1BLwHNqShSc6vTi+/uDboUAACQxAh6CWhySa5y+2dymhUAANArBL0ElJGeplll\nIS2t3qOWVhd0OQAAIEkR9BLU7PKQ6o80adXW+qBLAQAASYqgl6AuKitURprp2bWcZgUAAJwagl6C\nGpydqWlj8jjNCgAAOGUEvQQ2uzykd3cf0rZ9R4IuBQAAJCGCXgKbU+FfJYOjbwEAwCkg6CWw0QUD\nNLZwAJcHx1+qAAAgAElEQVRDAwAAp4Sgl+DmVBTp5U11OnSsOehSAABAkiHoJbjZ5SE1tTi9+G5t\n0KUAAIAkQ9BLcFNHDdXg7AxOswIAAHqMoJfgMtLTNLMspOe4SgYAAOghgl4SqKoIqe7wcb2xrSHo\nUgAAQBIh6CWBmaeHlJ5mnDwZAAD0CEEvCQzpn6mpo4ZqMeP0AABADxD0ksRpg7O1btdBjbn1SVXe\nuUSPrqoJuiQAAJDgCHpJ4NFVNXr67V2SJCeppqFRtz2yhrAHAAC6RNBLAgsXVetYc+tJbY1NLVq4\nqDqgigAAQDIg6CWBHQ2NPWoHAACQCHpJYXhuTo/aAQAAJIJeUlgwt0w5meknteVkpmvB3LKAKgIA\nAMkgI+gC0L35k4sleWP1ahoalZlu+s5VZ51oBwAA6Ah79JLE/MnFWn7rbP39xaerudXpwtMLgy4J\nAAAkOIJekqksLZBz0l821gVdCgAASHAEvSQzacQQDeyXoeUb9wZdCgAASHAEvSSTkZ6m6WPztXwD\nQQ8AAHSNoJeEKkvztaXuiLbtOxJ0KQAAIIER9JLQjNICSWKvHgAA6BJBLwmVhgYqNKiflhH0AABA\nFwh6ScjMNKO0QC9trFNrqwu6HAAAkKAIekmqsrRA+w4f17pdB4MuBQAAJCiCXpKqZJweAADoRsyC\nnpndZ2Z7zOytsLZvmFmNmb3h3y4Pe+42M9tgZtVmNjes/VK/bYOZ3RrWPsbMXvHbHzKzLL+9n/94\ng//86Fi9xiCdNiRb4woHME4PAAB0KpZ79O6XdGkH7f/tnDvbvz0lSWY2QdInJZ3hz/MDM0s3s3RJ\n/yfpMkkTJH3Kn1aS/sNfVqmkekk3+u03Sqr32//bny4lzSgt0Kvv7dOx5pagSwEAAAkoZkHPOfeC\npH0RTn6lpAedc8ecc+9J2iBpmn/b4Jzb5Jw7LulBSVeamUmaLelhf/6fS5oftqyf+/cfllTlT59y\nKksL1NjUolVbG4IuBQAAJKAgxuh9ycxW+127Q/22YknbwqbZ7rd11p4vqcE519yu/aRl+c/v96f/\nADP7nJmtNLOVtbW1vX9lcTZ9XL7SjHF6AACgY/EOevdIGifpbEk7JX0vzus/iXPuXufcVOfc1MLC\nwiBLOSWDszM1qSSXoAcAADoU16DnnNvtnGtxzrVK+rG8rllJqpFUEjbpCL+ts/Y6SblmltGu/aRl\n+c8P8adPSTNKC/Tm9v06cLQp6FIAAECCiWvQM7NhYQ8/IqntiNzHJH3SP2J2jKTxkl6VtELSeP8I\n2yx5B2w85pxzkpZK+qg//7WS/hi2rGv9+x+VtMSfPiVdMK5ALa1Or2yKdDgkAADoKzK6n+TUmNlv\nJM2UVGBm2yXdIWmmmZ0tyUnaLOnzkuSce9vMfivpHUnNkm5xzrX4y/mSpEWS0iXd55x721/FP0p6\n0My+JWmVpJ/67T+V9Esz2yDvYJBPxuo1JoIpo3KVnZmm5Rv26uIJRUGXAwAAEoil8M6uHpk6dapb\nuXJl0GWckmvue1U7Ghr17N9fFHQpAAAgDszsNefc1O6m48oYKWBGab427DmkXfuPBl0KAABIIAS9\nFNB2ObSXNnL0LQAAeB9BLwVUnDZYeQOyuBwaAAA4CUEvBaSlmc4fl6/lG/aKMZcAAKANQS9FzCgt\n0O4Dx7Sx9lDQpQAAgARB0EsRM/xxesvW030LAAA8BL0UUZLXXyPz+mvZhpS9CAgAAOghgl4KqSwt\n0Cub6tTc0hp0KQAAIAEQ9FLIjNICHTzWrNU1+4MuBQAAJACCXgo5f1y+JGk54/QAAIAIeiklb0CW\nzhg+mPPpAQAASQS9lDOjtECvb63XkePNQZcCAAACRtBLMZWlBWpqcXr1vX1BlwIAAAJG0Esx547O\nU1Z6ml7ayGlWAADo6wh6KSYnK13njBrKiZMBAABBLxVVlubrnZ0HVHfoWNClAACAABH0UlClfzk0\num8BAOjbCHop6KziIRqUnaHlnGYFAIA+jaCXgjLS03T+2HzOpwcAQB9H0EtRM8YXaHt9o7bWHQm6\nFAAAEBCCXopqG6fHXj0AAPougl6KGlswQMOGZDNODwCAPoygl6LMTBeMK9DyjXvV2uqCLgcAAASA\noJfCZozPV8ORJr2z80DQpQAAgAAQ9FJY5TjG6QEA0JcR9FJYaHC2Ti8ayDg9AAD6KIJeiqssLdCK\nzft0tKkl6FIAAECcEfRS3IzSAh1tatXrW+uDLgUAAMQZQS/FTRuTp/Q0o/sWAIA+iKCX4gZlZ+rs\nklwt21AXdCkAACDOCHp9QGVpgdZsb9D+xqagSwEAAHFE0OsDZpQWqNVJL29irx4AAH0JQa8POLsk\nV/2z0hmnBwBAH0PQ6wOyMtJ03pg8TpwMAEAfQ9DrIypLC7Sp9rB2NDQGXQoAAIgTgl4fUVnqXQ6N\n7lsAAPoOgl4fUVY0SAUDswh6AAD0IQS9PiItzXTBuAIt31gn51zQ5QAAgDgg6PUhM0oLVHvwmNbv\nORR0KQAAIA4Ien1I5XhvnN6y9XTfAgDQFxD0+pDi3ByNzu/POD0AAPqIboOemZ1uZovN7C3/8UQz\n+3rsS0MsVJYW6OVNdWpqaQ26FAAAEGOR7NH7saTbJDVJknNutaRPxrIoxM6M0gIdPt6iN7c1BF0K\nAACIsUiCXn/n3Kvt2ppjUQxi7/xx+TKTlm/gurcAAKS6SILeXjMbJ8lJkpl9VNLOmFaFmMntn6Wz\niocwTg8AgD4gkqB3i6QfSSo3sxpJX5X0NzGtCjFVWVqg17fW6/AxdswCAJDKug16zrlNzrk5kgol\nlTvnZjjnNse8MsRM5bgCNbc6vfrevqBLAQAAMZTR2RNm9vedtEuSnHP/FaOaEGNTRw9VVkaalm3Y\nq1nloaDLAQAAMdJp0JM0KG5VIK6yM9N17uihjNMDACDFdRr0nHP/Gs9CEF+VpQX6z6erVXvwmAoH\n9Qu6HAAAEAORnDB5rJk9bma1ZrbHzP5oZmPjURxiZ0apdzm0lzayVw8AgFQVyVG3D0j6raRhkoZL\n+p2k38SyKMTeGcOHaEhOJt23AACksEhPmPxL51yzf/uVpOxYF4bYSk8znT82X8vW75VzLuhyAABA\nDEQS9P5kZrea2WgzG2VmX5P0lJnlmVlerAtE7FSOL9CO/Ue1ue5I0KUAAIAY6Oqo2zYf939+vl37\nJ+VdLYPxekmqbZzesg17NaZgQMDVAACAaOs26DnnxsSjEMTf6Pz+Ks7N0Usb9urq6aOCLgcAAERZ\nt0HPzDLlXfLsQr/pOUk/cs41xbAuxIGZqbI0X4ve3q2WVqf0NAu6JAAAEEWRjNG7R9I5kn7g387x\n25ACKksLtL+xSW/v2B90KQAAIMoiGaN3rnNuUtjjJWb2ZqwKQnxdMO79cXoTR+QGXA0AAIimSPbo\ntZjZuLYH/smSW2JXEuKpcFA/lZ82iPPpAQCQgiLZo7dA0lIz2yTJJI2SdH1Mq0JcVZYW6Jcvb9HR\nphZlZ6YHXQ4AAIiSbvfoOecWSxov6W8lfVlSmXNuaawLQ/zMKC3Q8eZWvbalPuhSAABAFEVyrdv+\n8vbqfdk5t1rSSDObF/PKEDfTxuQpI820jO5bAABSSiRj9H4m6bik8/3HNZK+FbOKEHcD+mVoysih\njNMDACDFRBL0xjnn/lNSkyQ5547IG6uHFFJZWqA1NfvVcOR40KUAAIAoiSToHTezHHmXO5N/BO6x\nmFaFuKsszZdz0l821gVdCgAAiJJIgt4dkp6WVGJmv5a0WNLXYloV4m5SSa4GZKUzTg8AgBQSybVu\nnzGz1yVNl9dl+xXnHGkgxWSmp2n62Hy9xB49AABSRiTn0ZOkiyTNkNd9mynpDzGrCIEZnJ2h9/Ye\n1phbn9Tw3BwtmFum+ZOLgy4LAACcokhOr/IDSV+QtEbSW5I+b2b/F+vCEF+PrqrRU2/tkuSl+ZqG\nRt32yBo9uqom2MIAAMApi2SP3mxJFc65toMxfi7p7ZhWhbhbuKhax5pbT2prbGrRwkXV7NUDACBJ\nRXIwxgZJI8Mel/htSCE7Ghp71A4AABJfJEFvkKS1ZvacmS2V9I6kwWb2mJk9FtvyEC/Dc3N61A4A\nABJfJF23/xLzKhC4BXPLdNsja9TY1HKiLSczXQvmlgVYFQAA6I1ITq/yfDwKQbDaxuEtXFStmoZG\nZaSZvnPVWYzPAwAgiUXSdYs+Yv7kYi2/dbZuvaxcza1O08fmB10SAADoBYIePmBWWUiS9Fz1noAr\nAQAAvdFp0DOzxf7P/4hfOUgEpxcNVHFujpasI+gBAJDMuhqjN8zMLpB0hZk9KO/yZyc4516PaWUI\njJlpZlmhHl1Vo2PNLeqXkR50SQAA4BR0FfT+RdLtkkZI+q92zzl5J1JGippVFtKvX9mqFe/Va8b4\ngqDLAQAAp6DToOece1jSw2Z2u3Pum3GsCQnggtJ8ZWWkaWn1HoIeAABJqtuDMZxz3zSzK8zsu/5t\nXjwKQ7D6Z2Vo+th8LWWcHgAASavboGdm35H0FXlXxHhH0lfM7N9jXRiCN7usUJv2HtbmvYeDLgUA\nAJyCSE6v8iFJFzvn7nPO3SfpUkns1esDZpVzmhUAAJJZpOfRyw27PyQWhSDxjMofoLGFA7Skujbo\nUgAAwCmI5Fq335G0ysyWyjvFyoWSbo1pVUgYs8pC+uXLW3TkeLP6Z0XydgEAAIkikoMxfiNpuqRH\nJP1e0vnOuYdiXRgSw6yykI43t+qlDXVBlwIAAHoooq5b59xO59xj/m1XrItC4jh3zFANyErXUsbp\nAQCQdLjWLbrULyNdlaUFeq66Vs65oMsBAAA9QNBDt2aXh1TT0Kh3dx8KuhQAANADXQY9M0s3s3Xx\nKgaJaWaZd5oVum8BAEguXQY951yLpGozGxmnepCAThuSrQnDBmsJV8kAACCpRHK+jKGS3jazVyWd\nuESCc+6KmFWFhDOrvFA/fH6T9jc2aUhOZtDlAACACEQS9G6PeRVIeLPKQvq/pRv14vpazZs4POhy\nAABABCI5j97zkjZLyvTvr5D0eozrQoKZPHKocvtnauk6rpIBAECy6DbomdnNkh6W9CO/qVjSo7Es\nCoknPc104fhCPf/uHrW2cpoVAACSQSSnV7lFUqWkA5LknFsvKRTLopCYZpeHtPfQca2p2R90KQAA\nIAKRBL1jzrnjbQ/MLEMSu3T6oAtPL5QZp1kBACBZRBL0njezf5KUY2YXS/qdpMdjWxYSUd6ALE0u\nydVSTrMCAEBSiCTo3SqpVtIaSZ+X9JSkr8eyKCSuWWUhvbl9v2oPHgu6FAAA0I1IjrptlfRzSd+U\n9K+Sfu646GmfNavcG575wrscfQsAQKKL5KjbD0naKOkuSXdL2mBml8W6MCSmM4YPVmhQPy1hnB4A\nAAkvkhMmf0/SLOfcBkkys3GSnpT0p1gWhsRkZppZVqg/vbVLzS2tykiPpPcfAAAEIZK/0gfbQp5v\nk6SD3c1kZveZ2R4zeyusLc/MnjGz9f7PoX67mdldZrbBzFab2ZSwea71p19vZteGtZ9jZmv8ee4y\nM+tqHYie2eUhHTzarNe21AddCgAA6EKnQc/MrjKzqyStNLOnzOw6P2g9Lu/qGN25X9Kl7dpulbTY\nOTde0mL/sSRdJmm8f/ucpHv8GvIk3SHpPEnTJN0RFtzukXRz2HyXdrMOREllaYEy0kxLqxmnBwBA\nIutqj96H/Vu2pN2SLpI0U94RuDndLdg594Kkfe2ar5R3YIf8n/PD2n/hPC9LyjWzYZLmSnrGObfP\nOVcv6RlJl/rPDXbOvewfGPKLdsvqaB2IkkHZmTp3dB6nWQEAIMF1OkbPOXd9DNZX5Jzb6d/fJanI\nv18saVvYdNv9tq7at3fQ3tU6PsDMPidvD6JGjhzZ09fSp80uD+nbT61VTUOjinO7zf0AACAAkRx1\nO8bM/svMHjGzx9puvV2xvycupqdp6W4dzrl7nXNTnXNTCwsLY1lKyplV7m2v5zj6FgCAhBXJUbeP\nSvqpvLF5rb1c324zG+ac2+l3v7alhBpJJWHTjfDbauR1F4e3P+e3j+hg+q7WgSgaVzhQJXk5Wrpu\njz5z3qigywEAAB2I5Kjbo865u5xzS51zz7fdTnF9j0lqO3L2Wkl/DGu/xj/6drqk/X736yJJl5jZ\nUP8gjEskLfKfO2Bm0/2jba9pt6yO1oEoMjPNKgtp+YY6HW1qCbocAADQgUiC3v+a2R1mdr6ZTWm7\ndTeTmf1G0l8klZnZdjO7UdKdki42s/WS5viPJe+yapskbZD0Y0lflCTn3D55V+RY4d/+zW+TP81P\n/Hk26v3z+nW2DkTZrPKQGpta9Mp77Y+5AQAAiSCSrtuzJF0tabbe77p1/uNOOec+1clTVR1M6yTd\n0sly7pN0XwftKyWd2UF7XUfrQPSdPzZf/TLStHTdHl10OmMcAQBINJEEvY9JGuucOx7rYpBcsjPT\ndcG4fP+AjDOCLgcAALQTSdftW5JyY10IktPs8pA21x3RptpDQZcCAADaiWSPXq6kdWa2QtKxtkbn\n3BUxqwpJY2ZZSNLbWlpdq7GFA4MuBwAAhIkk6N0R8yqQtEry+mt8aKCWrtujG2eMCbocAAAQptug\n14tTqaCPmFUe0s+Wv6fDx5o1oF8k/zsAAIB4iOTKGAfN7IB/O2pmLWZ2IB7FITnMKgupqcVp2Ya9\nQZcCAADCdBv0nHODnHODnXODJeVI+mtJP4h5ZUgaU0cP1cB+GVwODQCABBPJUbcnOM+jkubGqB4k\nocz0NP3V+AItXVcr75SIAAAgEXQ7oMrMrgp7mCZpqqSjMasISWlWeUh/emuX1u48qAnDBwddDgAA\nUGRH3X447H6zpM2SroxJNUhaM8u8K2Msrd5D0AMAIEFEctTt9fEoBMktNChbZxUP0dJ1e3TLrNKg\nywEAAOoi6JnZv3Qxn3POfTMG9SCJzSor1N1LN6jhyHHl9s8KuhwAAPq8rg7GONzBTZJulPSPMa4L\nSWhWeUitTnphPadZAQAgEXQa9Jxz32u7SbpX3qlVrpf0oKSxcaoPSWTiiFzlDcjS0nWcZgUAgETQ\n5elVzCzPzL4labW8bt4pzrl/dM7xlxwfkJ5muuj0Qj3/bq1aWjnNCgAAQes06JnZQkkrJB2UdJZz\n7hvOufq4VYakNKs8pH2Hj+vN7Q1BlwIAQJ/X1R69f5A0XNLXJe0IuwzaQS6Bhs4cOtokSbrqBy+p\n8s4lenRVTcAVAQDQd3V61K1zrkdXzQAeXVWjbz6x9sTjmoZG3fbIGknS/MnFQZUFAECfRZhD1Cxc\nVK3GppaT2hqbWrRwUXVAFQEA0LcR9BA1Oxoae9QOAABii6CHqBmem9OjdgAAEFsEPUTNgrllyslM\nP6ktOyNNC+aWBVQRAAB9W7fXugUi1XbAxcJF1drR0Cgn6fKzTuNADAAAAkLQQ1TNn1x8Ithd+j8v\naOs+xucBABAUum4RM/MmDtPKLfUcjAEAQEAIeoiZeROHS5KeWrMz4EoAAOibCHqImdEFA3Rm8WA9\nsZqgBwBAEAh6iKkPnTVcb2xr0LZ9R4IuBQCAPoegh5iaN3GYJLpvAQAIAkEPMVWS11+TRgyh+xYA\ngAAQ9BBz8yYO15qa/dq893DQpQAA0KcQ9BBzl/vdt0/SfQsAQFwR9BBzxbk5mjIyl+5bAADijKCH\nuJg3cbjW7jygjbWHgi4FAIA+g6CHuLj8rGEyk55krx4AAHFD0ENcnDYkW+eOytMTq3cEXQoAAH0G\nQQ9xM2/SML27+5De3X0w6FIAAOgTCHqIm0vPPE1pJg7KAAAgTgh6iJvQoGydNyZfT6zeIedc0OUA\nAJDyCHqIq3mThmlT7WGt20X3LQAAsUbQQ1xdesZpSk8zDsoAACAOCHqIq/yB/XTBuHw9sXon3bcA\nAMQYQQ9xN2/iMG2pO6K3dxwIuhQAAFIaQQ9xN/eM05SRZnqc7lsAAGKKoIe4y+2fpRnjC/Qk3bcA\nAMQUQQ+B+NBZw7S9vlFvbt8fdCkAAKQsgh4CcckZpykrPU1PvEn3LQAAsULQQyCG5GTqwtML9OSa\nnWptpfsWAIBYIOghMB+aOEw79x/Vqm31QZcCAEBKIughMHMqipSVkabH3+TatwAAxAJBD4EZlJ2p\nWWWFeoruWwAAYoKgh0B9aOJw7Tl4TCs27wu6FAAAUg5BD4GqKg8pOzNNT6ym+xYAgGgj6CFQA/pl\naHZ5SH96a6da6L4FACCqCHoI3LyJw7X30HG9sqku6FIAAEgpBD0EblZZSP2z0vU43bcAAERVRtAF\nADlZ6SorGqSHVmzVg69u1fDcHC2YW6b5k4uDLg0AgKRG0EPgHl1Vo7d37FfbEL2ahkbd9sgaSSLs\nAQDQC3TdInALF1XreMvJB2I0NrVo4aLqgCoCACA1EPQQuB0NjT1qBwAAkSHoIXDDc3N61A4AACJD\n0EPgFswtU05m+kltOZnpWjC3LKCKAABIDRyMgcC1HXCxcFG1avzu2r+/+HQOxAAAoJcIekgI8ycX\na/7kYu0+cFTnf2ex6o8cD7okAACSHl23SChFg7M1qyykh1/bruaW1qDLAQAgqRH0kHA+fm6J9hw8\npuffrQ26FAAAkhpBDwlndnlIBQP76cEV24IuBQCApEbQQ8LJTE/TX08p1pJ1e7Tn4NGgywEAIGkR\n9JCQPja1RC2tTo+8XhN0KQAAJC2CHhJSaWigpo4aqt+u2CbnXPczAACADyDoIWF94twSbdp7WCu3\n1AddCgAASYmgh4T1oYnDNLBfhh7ioAwAAE4JQQ8Jq39Whj48aZieXL1TB482BV0OAABJh6CHhPbx\nqSVqbGrR42/uDLoUAACSDkEPCe3sklydXjRQD62k+xYAgJ4i6CGhmZk+PrVEb25rUPWug0GXAwBA\nUiHoIeFdNWWEMtONgzIAAOghgh4SXt6ALF0y4TT9YdV2HWtuCbocAACSBkEPSeHj55ao/kiTnn1n\nT9ClAACQNAh6SAozSgs0fEi2HlyxNehSAABIGgQ9JIX0NNNHp5Zo2Ya92l5/JOhyAABICgQ9JI2P\nnTNCkvTwa9sDrgQAgORA0EPSKMnrr8pxBfrdyu1qbXVBlwMAQMIj6CGpfOLcEtU0NGr5xr1BlwIA\nQMIj6CGpXHJGkXL7Z3JOPQAAIkDQQ1Lpl5Gus4qH6InVOzXm1idVeecSPbqqJuiyAABISAQ9JJVH\nV9Xo1ff2SZKcpJqGRt32yBrCHgAAHSDoIaksXFStY82tJ7U1NrVo4aLqgCoCACBxEfSQVHY0NPao\nHQCAvoygh6QyPDenR+0AAPRlBD0klQVzy5STmX5SW3ZmmhbMLQuoIgAAEldG0AUAPTF/crEkb6ze\njoZGOUmXnzXsRDsAAHgfQQ9JZ/7k4hPB7sPfX6a3aw7IOSczC7gyAAASC123SGqfPm+kqncf1Otb\n64MuBQCAhEPQQ1K7YtJwDeyXoQde4UoZAAC0R9BDUhvQL0NXnj1cT6zeof1HmoIuBwCAhELQQ9L7\n1LSROtbcqkdWbQ+6FAAAEgpBD0nvzOIhmjRiiB54Zaucc0GXAwBAwiDoISV8+ryRWr/nkF7bwkEZ\nAAC0IeghJcyb2HZQxtagSwEAIGEQ9JASBvTL0PzJw/XEmp1qOHI86HIAAEgIBD2kjE9PG6Xjza36\n/es1QZcCAEBCIOghZUwYPlhnl+TqgVe2cFAGAAAKKOiZ2WYzW2Nmb5jZSr8tz8yeMbP1/s+hfruZ\n2V1mtsHMVpvZlLDlXOtPv97Mrg1rP8df/gZ/Xq6N1Ud8etpIbaw9rBWbOSgDAIAg9+jNcs6d7Zyb\n6j++VdJi59x4SYv9x5J0maTx/u1zku6RvGAo6Q5J50maJumOtnDoT3Nz2HyXxv7lIBHMmzRMg/pl\n6IFXtgRdCgAAgUukrtsrJf3cv/9zSfPD2n/hPC9LyjWzYZLmSnrGObfPOVcv6RlJl/rPDXbOvey8\n/rtfhC0LKa5/VoY+MqVYT721S/WHOSgDANC3BRX0nKQ/m9lrZvY5v63IObfTv79LUpF/v1hS+IVM\nt/ttXbVv76D9A8zsc2a20sxW1tbW9ub1IIF8+ryR/kEZXCkDANC3BRX0Zjjnpsjrlr3FzC4Mf9Lf\nExfz0fTOuXudc1Odc1MLCwtjvTrESflpgzV5ZK4eeJUrZQAA+rZAgp5zrsb/uUfSH+SNsdvtd7vK\n/7nHn7xGUknY7CP8tq7aR3TQjj7k09NGalPtYZ377Wc15tYnVXnnEj26ircBAKBviXvQM7MBZjao\n7b6kSyS9JekxSW1Hzl4r6Y/+/cckXeMffTtd0n6/i3eRpEvMbKh/EMYlkhb5zx0ws+n+0bbXhC0L\nfUTbjry9h47LSappaNRtj6wh7AEA+pSMANZZJOkP/hlPMiQ94Jx72sxWSPqtmd0oaYukj/vTPyXp\nckkbJB2RdL0kOef2mdk3Ja3wp/s359w+//4XJd0vKUfSn/wb+pD/Xbz+A22NTS1auKha8yd3OGQT\nAICUE/eg55zbJGlSB+11kqo6aHeSbulkWfdJuq+D9pWSzux1sUhaOxoae9QOAEAqSqTTqwBRMzw3\np0ftAACkIoIeUtKCuWXKyUw/qS0nM10L5pYFVBEAAPEXxBg9IObaxuEtXFStmoZGmaTb51UwPg8A\n0KcQ9JCy5k8u1vzJxVq784Au+98XtXP/0aBLAgAgrui6RcqrGDZYl515mn62fLMajnBZNABA30HQ\nQ5/wlTnj/397dx9bV33fcfzz9c0jD40pS4E4SZuOLKFpBumswsgmVcCapIgQsW4t27Ru6tYyaVAk\nFhoktmbVOrJF1SAtHQ1dFbqh8hBlLlNgqZYQsrW0iql5Bq9JKGCHQQKYYNdxnOvv/vC9xL72vfcc\n34fz9H5JkZJ7zz3+Jvw4/vj3O9/zU//QSd3934eiLgUAgKYh6CETlp77Pl25/Dxt+9Ev9NYAs3oA\ngNaGojkAAA7kSURBVGwg6CEzvnTFYv1yOM+sHgAgMwh6yIxfO+dMXbn8PN3z41/ozf6hqMsBAKDh\nCHrIlBuvWKzB4by2MqsHAMgAgh4y5fwPnKm1F87T9378MrN6AIDUI+ghc264fLGGTua1dR+zegCA\ndCPoIXN+de4ZuvqiNn3v8Zd1lFk9AECKsTMGMun6y87Xv3f16hObH9XAUF7zWmdr/aolbJEGAEgV\ngh4y6emed5QzU/9QXpLU2zeoW3Y8I0mEPQBAarB0i0zavKtbefdxrw0O57V5V3dEFQEAUH8EPWTS\n4b7BUK8DAJBEBD1k0rzW2aFeBwAgiQh6yKT1q5Zo9vTcuNdmTGvR+lVLIqoIAID6oxkDmVRsuNi8\nq1uH+wbVYqa5Z8zQ2gvnRVwZAAD1Q9BDZq1b0fZe4PvBk7360n1PakdXrz79G/MjrgwAgPpg6RaQ\ntPbCebpwQas273pRvzxxMupyAACoC4IeIMnM9NdXXqDXjw3p7n0vRV0OAAB1QdADCto/9H6t+ei5\nuuuxg3r92PGoywEAoGYEPWCMDWuW6uTIiL7+Qx6cDABIPpoxgDE+ePbp+txvfkjf+Z+XtLf7iI68\nO8Q+uACAxCLoASU+PPd0SdIb7w5JYh9cAEBysXQLlLjz0YMTXmMfXABAEhH0gBLsgwsASAuCHlCC\nfXABAGlB0ANKTLYP7iz2wQUAJBDNGECJ0n1wXdKvz59DIwYAIHEIesAkxu6De9sjL+jbjx1S1ytv\na8XCsyKuDACA4Fi6Baq4/rLFmnvmTG186DmNjHjU5QAAEBhBD6jijJnTdMuapXqq5x1t/1lP1OUA\nABAYQQ8IYN1FbfrYwlb943++qGPHh6MuBwCAQAh6QAAtLaaNa5fpaP8JXXrbbi3asFMrN+1RR1dv\n1KUBAFAWzRhAQIeODCjXYuofyktiazQAQPwxowcEtHlXt/IlzRhsjQYAiDOCHhAQW6MBAJKGoAcE\nxNZoAICkIegBAU22NVquxdgaDQAQWzRjAAGVbo122sycBobymn8WM3oAgHgyd570L0nt7e3e2dkZ\ndRlIkIGhk/rkP+3TaTNy2nnDb2vGNCbIAQDNYWZPuHt7teP4zgRM0ekzp+mrVy/Tz9/o19Z9B6Mu\nBwCACQh6QA0uv+AcfWr5udqy54BeOjoQdTkAAIzDPXpAjb5y1TLtfv51rb59n06cHNG81tlav2oJ\nD1EGAESOoAfU6PGDb2pE0vDJEUnsmAEAiA+WboEabd7VreE8O2YAAOKHoAfUiB0zAABxRdADasSO\nGQCAuCLoATWabMcMSfqt88+OoBoAAE6hGQOoUemOGee1ztKZM6dp+xM92vPiER3tH6ITFwAQCYIe\nUAfrVrSNC3H3/uRl3drxrI70D0miExcAEA2WboEG+NbegyrdXJBOXABAsxH0gAagExcAEAcEPaAB\n6MQFAMQBQQ9ogHKduJfSiQsAaCKaMYAGmKwTd86s6Xqws0e7X3hDbw+coBMXANBwBD2gQUo7cR/Y\n/4q+vOMZvTVwQhKduACAxmPpFmiSO3YfkJe04tKJCwBoJIIe0CR04gIAmo2gBzRJuY7bD7xvZpMr\nAQBkBUEPaJJynbj9x4d1yd/v1qINO7Vy0x51dPVGUB0AII0IekCTrFvRptuuWa621tkySW2ts7X6\no+do4MSI/u/YcblONWgQ9gAA9UDXLdBEpZ24KzftmXBMsUGDTlwAQK2Y0QMiRIMGAKCRCHpAhMo1\naJiJe/YAADUj6AERKtegMeLinj0AQM0IekCEShs0cmYTjuGhygCAqaIZA4jY2AaNRRt2TnoM9+wB\nAKaCGT0gRrhnDwBQTwQ9IEa4Zw8AUE8EPSBGuGcPAFBP5u5R1xAL7e3t3tnZGXUZwDiLNuxUuf9D\nTaNLvetXLeHhygCQMWb2hLu3VzuOGT0gxsrdsyexlAsAqI6gB8RYuXv2xmIpFwBQDkEPiLHSe/bK\n6e0bpCMXADABz9EDYm7sc/ZWbtqj3jLP1Csu4xY/AwAAM3pAglRbyh0czuvG+59kdg8AIIkZPSBR\nijN1m3d1l53Zk5jdAwCM4vEqBTxeBUlTaRm3KGemEXcewwIAKcPjVYCUC9KRm3fnMSwAkGEEPSCh\nxnbkBsH9ewCQPQQ9IMHWrWjTjzZcpts/c1HV2b0iZvcAIDtoxgBSYGyTxuG+QbWYKV/h/tvB4bxu\neuCpcZ8FAKQPzRgFNGMgTTq6enXLjmc0OJyveJxpdCu1Npo1ACBRgjZjEPQKCHpIm46u3qqPYRmL\n0AcAyUHXLZBxYe/fK/7Ixz18AJAeBD0g5YrduTmrtFvueMV7+Ah7AJBsLN0WsHSLtAt6395YLOcC\nQDxxj15IBD1kwdj79oohLowWk0ac4AcAUSPohUT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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff916a3ba90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr.plot(x='calls', y='frequency', style='o-', logx=True, figsize = (10, 10))\n", "# plt.axvline(5,ls='dotted')\n", "plt.ylabel('Number of people')\n", "plt.title('Number of people placing or receiving x number of calls over 4 months')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It might be more helpful to look at a cumulative distribution curve, from which we can read off quantiles (e.g., this percentage of the people in the data set had x or more calls, x or fewer calls). Specifically, 10% of people have 3 or fewer calls over the entire period, 25% have 7 of fewer, 33% have 10 or fewer, 50% have 17 of fewer calls, etc., all the way up to 90% of people having 76 or fewer calls. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7faebeecb790>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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l2089kvNPnJPtkEREJIeUkIkUoIbqikOmXfLd1RkvVarWmIhIcVJCJlKANnUd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\n/ucAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faecc1c2b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr.plot(x='calls', y='cumulative', style='o-', logx=True, figsize = (10, 10))\n", "plt.axhline(1.0,ls='dotted',lw=.5)\n", "plt.axhline(.90,ls='dotted',lw=.5)\n", "plt.axhline(.75,ls='dotted',lw=.5)\n", "plt.axhline(.67,ls='dotted',lw=.5)\n", "plt.axhline(.50,ls='dotted',lw=.5)\n", "plt.axhline(.33,ls='dotted',lw=.5)\n", "plt.axhline(.25,ls='dotted',lw=.5)\n", "plt.axhline(.10,ls='dotted',lw=.5)\n", "plt.axhline(0.0,ls='dotted',lw=.5)\n", "plt.axvline(max(fr['calls'][fr['cumulative']<.90]),ls='dotted',lw=.5)\n", "plt.ylabel('Cumulative fraction of people')\n", "plt.title('Cumulative fraction of people placing or receiving x number of calls over 4 months')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also want to look at the number of unique lat-long addresses, which will (roughly) correspond to either where cell phone towers are, and/or the level of truncation. This takes too long in pandas, so we use postgres, piping the results of the query,\n", "```\n", "\\o towers_with_counts.txt\n", "select lat, lon, count(*) as calls, count(distinct cust_id) as users, count(distinct date_trunc('day', date_time_m) ) as days from optourism.cdr_foreigners group by lat, lon order by calls desc;\n", "\\q\n", "```\n", "into the file towers_with_counts.txt. This is followed by the bash command\n", "```\n", "cat towers_with_counts.txt | sed s/\\ \\|\\ /'\\t'/g | sed s/\\ //g | sed 2d > towers_with_counts2.txt\n", "```\n", "to clean up the postgres output format. " ] }, { "cell_type": "code", "execution_count": 13, "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>lat</th>\n", " <th>lon</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>43.771</td>\n", " <td>11.254</td>\n", " <td>839141.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>43.775</td>\n", " <td>11.252</td>\n", " <td>670489.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>43.772</td>\n", " <td>11.264</td>\n", " <td>513066.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>43.777</td>\n", " <td>11.248</td>\n", " <td>484600.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>43.77</td>\n", " <td>11.247</td>\n", " <td>446210.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lat lon count\n", "0 43.771 11.254 839141.0\n", "1 43.775 11.252 670489.0\n", "2 43.772 11.264 513066.0\n", "3 43.777 11.248 484600.0\n", "4 43.77 11.247 446210.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.read_table(\"./aws-data/towers_with_counts2.txt\")\n", "df2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the same thing as above. " ] }, { "cell_type": "code", "execution_count": 14, "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>count</th>\n", " <th>frequency</th>\n", " <th>cumulative</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0</td>\n", " <td>66</td>\n", " <td>0.003344</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2.0</td>\n", " <td>54</td>\n", " <td>0.006080</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>3.0</td>\n", " <td>51</td>\n", " <td>0.008663</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>4.0</td>\n", " <td>42</td>\n", " <td>0.010791</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>5.0</td>\n", " <td>46</td>\n", " <td>0.013122</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count frequency cumulative\n", "0 1.0 66 0.003344\n", "15 2.0 54 0.006080\n", "19 3.0 51 0.008663\n", "56 4.0 42 0.010791\n", "36 5.0 46 0.013122" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fr2 = df2['count'].value_counts().to_frame()\n", "fr2.columns = ['frequency']\n", "fr2.index.name = 'count'\n", "fr2.reset_index(inplace=True)\n", "fr2 = fr2.sort_values('count')\n", "fr2['cumulative'] = fr2['frequency'].cumsum()/fr2['frequency'].sum()\n", "fr2.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7faebe6e0790>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4AuDfYV6D3SWO66swv2y3whQ5/wWzwHgLzBTXHpgRjCGUlm4zYD4P+2G+F94Et2DychfM\nFIwufl6vlEr7bPe3MCMovTAFyE/0Cus9djnM63AAZn2QnmX8NZgF7FtFpBemwLvQ2m8ngL+E+d59\nzRpD2PP8CZivhRdh1lndBuC7IduXQtj4XoU5YeNvYF7LJ2FOnADMWsLnATxqpWfvgfnDK4jbYNbJ\n3uZZ/n6YhfTPwnz8tyOfCv8PmK+Dp2B+ntwZdPASX4c/hvl62O6Jhgdeg1JQSnUrpQ7oP5ipzGNW\nWciIKPZZW2TfAQBfBvCw9Zl2UZFdyn3vTHlEqbKjjYQQQmBaNsCciHF1pcdCCKk+GAkjhBBCCKkA\nFGGEEEIIIRWA6UhCCCGEkArASBghhBBCSAWoiuarM2fOVAsWLKj0MAghhBBCivLEE08cUUoV9Was\nChG2YMECPP7445UeBiGEEEJIUUQkrDuDDdORhBBCCCEVgCKMEEIIIaQCUIQRQgghhFSAqqgJI4QQ\nQsjwSafT2LdvH4aGhio9lElFbW0t5s2bh1gsNqz9KcIIIYSQSc6+ffvQ2NiIBQsWQEQqPZxJgVIK\n3d3d2LdvHxYuXDisYzAdSQghhExyhoaGMGPGDAqwUUREMGPGjBFFFynCCCGEkCkABdjoM9JrShFG\nCCGEEFIBKMIIIYQQMi58/etfx5IlS/C+972v0kOZELAwnxBCCCEuNnd0YdOWTuzvGcTclgQ2rG3H\nuhVtIz7uv//7v+Oee+7BvHnz7GWZTAbR6NSUI4yEEUIIIcRmc0cXbrzzaXT1DEIB6OoZxI13Po3N\nHV0jOu7HPvYxvPjii7jiiivQ3NyMa665BitXrsQ111yDbDaLDRs24HWvex3OOussfOc73wFgzkD8\n+Mc/jvb2dlx22WV461vfittvvx2A2dLwyJEjAIDHH38cl156KQCgv78f1113HS644AKsWLECd911\nFwDg+9//PtavX4+3vOUtWLRoET796U/bY7v77rtx7rnn4uyzz8bq1auRy+WwaNEiHD58GACQy+Vw\n+umn2/dHi6kpPQkhhJApyj/+ciee3X8icH3Hqz1IZXOuZYPpLD59+x/w48de9d1n6dwmfP4dy0LP\n++1vfxt333037rvvPnzzm9/EL3/5Szz00ENIJBK45ZZb0NzcjN/97ndIJpNYuXIl1qxZg46ODnR2\nduLZZ5/FwYMHsXTpUlx33XWh5/nyl7+MVatW4bvf/S56enpwwQUX4LLLLgMAPPnkk+jo6EBNTQ3a\n29vxiU98ArW1tfjIRz6CHTt2YOHChTh69CgMw8DVV1+NW2+9Fddffz3uuecenH322Zg1q2hP7rKg\nCCOEEEKIjVeAFVs+XN75zncikUgAALZu3Yo//OEPdpTr+PHjeO6557Bjxw5cddVViEQimDt3Llat\nWlX0uFu3bsUvfvELfOUrXwFg2nO8+qopHlevXo3m5mYAwNKlS/HKK6/g2LFjuOSSS2yvr+nTpwMA\nrrvuOlx55ZW4/vrr8d3vfhcf/OAHR/XxAxRhhBBCyJSiWMRq5cbt6OoZLFje1pLATz568aiNo76+\n3r6tlMI3vvENrF271rXNr3/968D9o9EocjlTGDq9upRSuOOOO9De3u7a/re//S1qamrs+5FIBJlM\nJvD48+fPR2trK7Zv347HHnsMt956a2kPrAxYE0YIIYQQmw1r25GIRVzLErEINqxtD9hj5Kxduxbf\n+ta3kE6nAQB79uxBf38/LrnkEvzkJz9BNpvFa6+9hvvuu8/eZ8GCBXjiiScAAHfccYfrWN/4xjeg\nlAIAdHR0hJ77oosuwo4dO/DSSy8BAI4ePWqv+/CHP4yrr74a7373uxGJRIIOMWwowgghhBBis25F\nG25evxxtLQkIzAjYzeuXj8rsyCA+/OEPY+nSpTj33HNx5pln4qMf/SgymQz+6I/+CIsWLcLSpUvx\n/ve/HxdfnI/Eff7zn8cnP/lJnH/++S6B9LnPfQ7pdBpnnXUWli1bhs997nOh5541axZuueUWrF+/\nHmeffTbe+9732uve+c53oq+vb0xSkQAgWilOZM4//3z1+OOPV3oYhBBCSFWya9cuLFmypNLDGDHX\nXnst3v72t+Nd73rXuJzv8ccfx6c+9Sk8+OCDgdv4XVsReUIpdX6x47MmjBBCCCHEw8aNG/Gtb31r\nTGrBNIyEEUIIIZOcyRIJm4iMJBLGmjBCCCFkClANQZdqY6TXtCpE2NNdx7Fy4/YRu/USQgghU5Ha\n2lp0d3dTiI0iSil0d3ejtrZ22Meompow3TYBwJjO0CCEEEImG/PmzcO+fftGve3OVKe2ttbVB7Nc\nqkaEAWbbhE1bOinCCCGEkDKIxWK2IzyZOFRFOtLJfh8XX0IIIYSQaqPqRNjclkSlh0AIIYQQMmKq\nSoSNddsEQgghhJDxompEWCwiY942gRBCCCFkvKgKEdbaVIt0VuGiU2dUeiiEEEIIIaNCVYiw5toY\nAGDbroMVHgkhhBBCyOhQFSKsJmZg4cx6bN15oNJDIYQQQggZFapChAHA5Utb8eiL3TgxlK70UAgh\nhBBCRkzViLA1S1uRzirc30m3X0IIIYRUP1UjwlacPA0zG+LY9izrwgghhBBS/VSNCIsYgtVntOK+\n3YeQzGQrPRxCCCGEkBFRNSIMANYsa0VfMoNHXzxa6aEQQgghhIyIqhJhK0+fiUQsgm3PcpYkIYQQ\nQqqbqhJhtbEI3rR4FrY9exC5nKr0cAghhBBChk1ViTDAtKo4eCKJp7uOV3oohBBCCCHDpupE2Koz\nZiNiCLYyJUkIIYSQKqbqRNi0+jguWDCdVhWEEEIIqWqqToQBZkpyz8E+vHykv9JDIYQQQggZFlUr\nwgAwGkYIIYSQqqUqRdj86XVYclIT68IIIYQQUrVEKz2A4bJmaSu+vv05HOlLYmZDTaWHQ4bB5o4u\nbNrSif09g5jbksCGte1Yt6Kt0sMihBBCxoWqjIQBZkpSKWD7rkOVHgoZBps7unDjnU+jq2cQCkBX\nzyBuvPNpbO7oqvTQCCGEkHGhakXYsrlNaGtJMCVZpWza0onBtLsH6GA6i01bOis0IkIIIWR8qVoR\nJiK4fGkrHnzuCAZSmUoPh5TJ/p7BspYTQgghk42qFWGAWReWzOSwY8+RSg+FlMnclkRZywkhhJDJ\nRlWLsNctnI7mRIxWFVXIhrXtqIm6X36JWAQb1rZXaESEEELI+FLVIiwWMbDqjNm4d/dBZLK5Sg+H\nlMG6FW1421kn2fdnNdTg5vXLOTuSEELIlKGqRRhgpiR7BtL43cvHKj0UUiZH+1P27W9fcx4FGCGE\nkClF1YuwSxbPQjxqMCVZRWzu6MLrb74X93ceRtQQAEA2pyo8KkIIIWR8qXoRVl8TxRtOn4mtzx6A\nUvwin+hof7D9x4cAABlLfD2wh35vhBBCphZVL8IA07h137FB7D7QW+mhkCL4+YMBwP88trcCoyGE\nEEIqx6QQYauXzIYIsHUnU5ITnSAfsG5HfRghhBAyFZgUImx2Yy3OPXkatu2ie/5EJ8gHbEZ9fJxH\nQgghhFSWSSHCADMl+UzXCXTRcX1Cs2FtOxKxSMHyK8+ZW4HREEIIIZVj0oiwNUtbAQD3cJbkhGbd\nijbcvH65LcRmN9YAAM5fML2SwyKEEELGnUkjwk6d1YDTZtWzoXcVsG5FGy5b2opTZ9bjto9cBCA/\nS3KysLmjCys3bsfCG36FlRu3Y3NHV6WHRAghZIIxaUQYAKxZNgePvngUxwfSlR4KKUImm0PEEIdP\n2OTpeKBtOLp6BqEAdPUM4sY7n6YQI4QQ4mJSibDLl7Yim1O4r5OeUxOdTE4hGjEQsURYJjt5ImF+\nNhyD6Sw2bems0IgIIYRMRCaVCDtnXgtmNdYwJVkFZLI5xCKCaGTyOeYH2XAELSeEEDI1mVQizDAE\nly9txQOdhzHkYwhKJg6ZnELEkHwkbBKJsCAbjqDlhBBCpiaTSoQBZkqyP5XFIy90V3ooJIRMViFm\nGIga5ktwMkXC/Gw4ErEINqxtr9CICCGETEQmnQh7/WkzUB+PYCutKiY0mVwO0cjkjIRpG476GlOI\nNSeiuHn9cqxb0VbhkRFCCJlITDoRVhON4NL22dj27EHkJtEX+2RDpyMn4+xIwBRi7z5vPgDgk6sX\nU4ARQggpYExFmIi0iMjtIrJbRHaJyMUiMl1EtonIc9b/aaN93jXLWnGkL4mOvT2jfWgySmSyCjHH\n7MhsiAards8tkUqPgBBCyERkrCNhXwNwt1LqDABnA9gF4AYA9yqlFgG417o/qlzaPhtRQ7CNKckJ\nS9ryCYsUiYTRc4sQQshkZcxEmIg0A7gEwH8BgFIqpZTqAXAlgB9Ym/0AwLrRPndzIoaLTp1Bq4oJ\nTDanEIsIIhJeE0bPLUIIIZOVsYyELQRwGMD3RKRDRP5TROoBtCqlXrO2OQCg1W9nEfkzEXlcRB4/\nfPhw2Sdfs6wVLx7ux/OH+oY7fjKGZHIKUcOAYQgMCZ4dWc2eW0qxJpEQQkgwYynCogDOBfAtpdQK\nAP3wpB6V+S3l+02llLpFKXW+Uur8WbNmlX3yy5aY2o4pyYlJOpuzi/KjhhEYCaPnFiGEkMnKWIqw\nfQD2KaV+a92/HaYoOygiJwGA9X9MegzNbUlgeVsztjElOSHJ5pTtlh8xJDASRs8tQgghk5UxE2FK\nqQMA9oqI/rZcDeBZAL8A8AFr2QcA3DVWY7h8aSs69vbgUO/QWJ2CDJN01uwdCQBRQwJ7R65b0YYv\nr1tm329rSVSd5xYnRxJCCPEjOsbH/wSAW0UkDuBFAB+EKfx+KiIfAvAKgPeM1cnXLGvFV7ftwb27\nDuGqC04eq9NUlM0dXdi0pRP7ewYxtyWBDWvbx1ygBJ2znLFkc/l0ZCQioT5hq5fMAfAHLG9rxi8/\n8YaxeEiEEELIuDOmIkwp9SSA831WrR7L82raWxsxf3oCW3cemJQiTNs36NmD2r4BwJgJsaBzPv7K\nUdzxRFfJY8lkld2yKGpIqGP+iaE0ALOOjBBCCJksTDrHfCcigjVL5+Dh57vRl8xUejijTiXsG4LO\n+ePf7i1rLGmrbREQXhMGAL1D5nNXba2Nqmu0hBBCxptJLcIAYM3SVqSyOezYU77NxUSnEvYNQcfO\nBtgxBG6fUyXNjgRgC+jJ1OSbEEIImfQi7LxTpmFaXQxbd06+WZKVsG8IOnYkoDeP3/ZKKVdhfvFI\nWHWmI1mQTwghJIxJL8KiEQOrl7Ri++5DVfclXoxK2DdsWNuOeNT9sknEIrjqwvklj0ULrnwkLLwm\nTEfCgmZQTlSqa7SEEELGm0kvwgDTquLEUAaPvXS00kMZVdataMPN65fbomhWY82Y2zesW9GGa19/\nin1fW0Z8ad1y3Lx+OSxdFWoloQWXuyYsWCCfqNKaMEIIISSMKSHCLlk0C7UxY1K6569b0YZTZ9YD\nAL5x1Ypx8c86Z/40AMANV5yBh29YZZ9z3Yo21MejWDS7wbXcixZTMaO8dGQmRKgRQggh1cZY+4RN\nCBLxCN5w+ixs3XkAn3/HUkhA/VK1crQ/BSA/i3CsGUiZsyDTmUJRlMrm7PVBZK20YsTwnx3p9Rtb\nclIjgOpLR2om2+uNEELI6DAlImGAady6//gQdu4/UemhjCpKKfQMmJEiHTEaawZTpthL+dTYpbM5\nDKXDRVjaimjFIoU1YdqHrKtnEAqm39h9nebMVkbCCCGETCamjAhbfcZsGAJsnWQpyYFU1hZD4xUJ\n035gXhGWzSnkFIpGwjJ2JKwwHennQ6bXVWskjBBCCPFjyoiwGQ01OP+U6ZPOqkKnIgHgxOD4RMK0\nyEp50pF69ulgOgsV4BsG5CNa0YjDJ8wSWGEeZ5mcCj3uRKOKhkoIIaQCTBkRBpgpyd0HerH36ECl\nhzJq6FQkAPSOU1eAQV0T5omEOSNjQ+ng1KEWXDEfx/xiHmc0bCWEEDJZmFIi7PKlrQAmV0ry2EA+\nEjZeNWGBkTDHfW9K0YmOhOl0ZDQi9jI/7zNx7UsRRgghZHIwpUTYKTPq0d7aiG3PTp6UpBZhEUNs\nP62xxp4d6anRct4fSAWPJW9RURgJ095nTbXmxN36eATT6mOOc1RfcT4nRxJCCPFjSokwwIyGPfbS\nURxz1FIPp5cSAAAgAElEQVRVM/pxzG2pHcfCfGt2ZEBNGJBPWfqh05G6bZHXMX/dijb82SWnAgAu\nPHUGnMEvpiMJIYRMFqacCFuzrBU5BWzffajSQxkVjlk1YfOn1Y1/OjKkJiwsHanFWjTAJwzIC7yu\nY4PoG8rYkTFv9I0QQgipVqacCFve1ow5TbXYOklSkscGUmiqjWJaXXzCzI50buNH1tO2KGoYBbVe\nSetYL3X3I5NTmF4fB1BdXmGK3SMJIYSEMOVEmIjg8qWt2LHnSFFT0Wrg2EAa0+vjaKyNjl86MmB2\nZDqTFx3hkTDdwDu4bZEWePp/S50lwqowEsaSMEIIIX5MOREGmCnJwXQWDz13pNJDGTE9Aym01I2z\nCEv7R8JSJdaEeSNhEUMKIlzeY0+rM4vzq3F2ZPWNmBBCyHgwJUXYhQtnoLEmOilSkkf7U5hWF0Nj\nbQyD6ey4zB4MjISVKMJ02yJXTVjWPxKmmabTkVU4O5IQQgjxY0qKsHjUwJvPmI17dx2q+tl2PQNp\nTLPSkQDQNw7RMG0/kQyrCQvzCfOkI72zIwEzqtZQk+8vP81KR1ZjYT7TkYQQQvyYkiIMMK0quvtT\n+P2rxyo9lBFxbCCFaXVxNNWa6brxSEkOlBAJGwpNR7rbFkUMQU4VRsJOaq5FPGq+RHU6stpFMyGE\nEKKJFt9kcnJp+yzEIoJtzx7E6xZMr/RwhsVQOouBVNZKR5pP5Ylh2FRs7ujCpi2d2N8ziLktCbz5\njFm4b/dh+/6Gte1Yt6INgCmCdASswKIi4zRrLV6YH7NnRxZGwpKZHAZTGVt0/eeDL5n7TqDZkd7r\n5rxOhBBCSDGmbCSssTaGi0+biS07D1RVU2gnum+kmY40I0XlirDNHV248c6n0dUzCAWgq2cQP3r0\nVdf9G+98Gps7ugC4Zz06Z0MC3nRkmGO+u21RxDAKasL2HR1A1/EhW4T1WPYb900Qfze/6+a8TgAb\neBNCCAlnyoowAFiztBWvdA/guUN9lR7KsNAti6bV5WvCyk1HbtrSGWonAZjCa9OWTgD5ejCRwkhY\nqenIfE2YFQmLFEbCXu4e8BUxP37s1dCxjhd+1815nQghhJBiTGkRpht6b6vSht66ZVFLXWzYNWH7\newbL2k7PemxOxFwNu4HSzVrt3pGREJ+wgFmQR/omRrupoOtW6vUkhBBCprQIa22qxdnzW7B1Z3Va\nVeiWRdPrnZGw8tKRc1sSZW034BBhyYK2RaaQaqyNhkbXtAiLGM6aMPexdJTMywzLqqLSBF033+Xs\n4E0IIcSHKS3CADMl+dS+4zhwfKjSQykbZzqyYZjpyA1r21EbC38ZJGIRbFjbDsAtwtLZnKueTkfG\nmmpjRRp4m9vFXLMjgZwjGja9PoaIj3ZZd87cEh7V2LNhbTsSsYhrmfM6EUIIIcWgCNMpyV3Vl5J0\npiNjEQN18UjZkbB1K9rwmSvOsO+3tSRw9UUnoyVhpjdbm2pw8/rl9qw/3eqpORGDUm4He52ObE7E\nwiNh2cJIGABkHYKuJhbBipNb0NaSgACY3VgDADhvgsxkXbeiDTevX27fb2tJuK4TIYQQUowpa1Gh\nOX12AxbOrMe2Zw/imotOqfRwyuLYQBr18QhqomZEprE2ihOD5fuEvWHRLADAv733HFtEvP60mfiL\nW3+PH1x3Ac6Y02Rv64yEAabw0rVdWoQ1JaJl1oSZ/7M5BR1cSmVyOH12I27/85UAgOcP9eGyrz4w\nLh0BSmXdijZc/5MnAQAP37CqYD0nRxJCCAljyosw3dD7ew+/hBNDabvAvRrQfSM1jbUx9CbL9wnr\nS5rCzelQr6/D8QH38fTsSC3CUpkc9BB0TVhTbQw9AwOB59PpyKgnEuaMqiUzOduoFcinLp0F/MV8\nuujjRQghZCIz5dORgJmSTGcVHug8XOmhlMXRgRSm1edF43CbeGsRVu8UYQlt/uo+nq71arEc7J2z\nGM2omKAuHgk3a/UU5hs6HenwCktlcohH8i9Pva1OZRbz6SrFx4sQQgipJBRhAFacPA0z6uPYWmVW\nFccG0nZPRcCMhHlFUynofpN6hiWQj3SdGPRGwtzpSGej7XTGTE0m4uHpyGwuh6ghEPFGwvLHShVE\nwqyUp7VNMZ+uieTjxbmRhBBC/KAIgxlluWxJK+7ffcglKiY6PVbfSI0ZCSs/Hdmf8omE6XSkR4Rp\nYdOSKGyorevDErGIXcDvRyar7L6RQD7KpVONuZxCJqdcIizq2aaYT9dE8vFibRghhBA/KMIs1ixr\nRW8yg0df7K70UErmWH/KbmwNmMJpWOnIocKasKBelAOpDCKGoK7GrKB3itZUVtmzNAdSmcB2UOms\nQtQoFFi6JkynON0izLD3BYr7dJXl40UIIYRUAIowi5Wnz0QiFsHWZ6vDuDWTzeHEUMZVmN9UGy1I\nH5ZCX9KMWjlFWDRioKEmWhAJG0hlUReL2PVaaU9NWDwiSMQjyKlg1/tsLhcaCdMNwp01YXp7XdRf\nzKdrIvl4MR1JCCHED4owi9pYBG9aPAv3PHvIZRo6UdENrafXu9ORyUyu7JRqXzKNiCEFpq3NiViB\n5cVgKotEPGJHqZIZT2F+1LDFT5BhazqnXI74Uc/MRz3+GoeIskWYtY326aqLm9t4/cz0+npr/dzm\nWvp4EUIImVBQhDmYXh/DgRNDOO0zv8bKjdsn9Ey6noG8Uaum0e4fWV40rD+ZRX08YhfK548XLUhH\nDqYtERYQCdPpSL2tH1lPOlL7hHnTkTWRwnRkxlGDtm5Fm93/80cfurBAYK1b0Yb1584DAPzf9ZdQ\ngBFCCJlQUIRZbO7owp2W6KoGS4Oj/aY48hbmA+W3LuodytgCzklTIuabjkzEIohFC0VYKqOs2ZER\ne1s/0p50pLfoXkfCnDVhEUMggoIek8m0eT8ZEP3LWXVplTB5DSiJI4QQQgBQhNls2tKJobT7i7pS\nlgaloPtGutOROhJWngjrS6ZRXxMpWG6mIz2RsFQWdY5IWCrjUxNWJB2ZybrTkRGPRUUyY+7nFGEA\nEDMM12xM57b6vxedWc5kK6eI2L+bEEKIHxRhFhPJ0qAU/NKRTXYkrPx0pLMoP3+8QhE2kMqgLh4t\naFWkbzsjYYHpyJxCNBJsP5HyKcwHTLGW9UTCtHBOpv0jXaqCkTBCCCEkDIowi2qzNPBPR1oGq2WK\nsN5kxuURpmlOFJq/DhQrzHfUhAWmI7O5gEhYcDoSMIvzgyNhQTMx3XVmhBBCyESBIszCz9JABPjr\nyxdVaETh9AykEI/mBQ/g9PYqLx3Zn8y43PI1TYko+pIZ2xYCMKNbda7CfEeroaxCLGqgtlg6Mqc8\nNWH5Bt5AsAiLRYzCmrCMO4XpRacjKxMJY1EYIYSQYCjCLLSlQVtLAgJgWl0MSgHPH+6v9NB8OTZg\nGrU6ZzQ2DbcmbCiD+rh/OtJ7vAFdExb1qQnLmDVhddaxBtP+48jkvLMj3X0hkz5mrXq7rMc+RDvz\ne+v5NDodWcmaMEIIIcSPwm/eKcy6FW0uG4Mb7/wDvv3AC1h52ky8YdHMCo6skKP97r6RANAw7Jqw\njL2vE7t/5FAa06wJAIOpLBKxKGJWJCvUoiLlL4wyVqNvjdes1fYJKyjM90tHFouEMR1JCCFkYsJI\nWAj/8PZlOG1WAz710yfR3Zes9HBcePtGAqaYqY9HyoqEKaXQl8r4F+Yn3P0jlVJWYX5AJMwSYTod\nOZAKjoRFQmZHBomwaMRwpUYBpwgLsqiwxlbBnqACweaOLqzcuB0Lb/jVhPegI4QQMj5QhIWQiEfw\njatW4PhgGn/7s6cmlJP+sYEUptUXens11sbKioQNpLJQCr4izI6EWa75yUwOOWVeFz07MuWKhClX\nJCyoiXfGEmua4NmR7hq9qCF28b5GnyNodqSOhHn3G0869h7DjXc+ja6ewarwoCOEEDI+UIQVYclJ\nTfj7ty3BfZ2H8b3fvFzp4dgcG0i7+kZqmhLRglZDYfQlrebdAYX5QD4SpgVPIubvE5bK5hCPCmIR\nA1FDAmdHBkfCPL0jfWZHemu7iqUjtWFqJdOR23YeLLDrmMgedIQQQsYHirASuOaiU3DZklZs/L9d\neKbreKWHg1xOoWcghek+IqyxNobeZOmRMFuEhUXCrMiaFlV18QgMQxA1xLcmDDCjZUE+YRlP26LC\n3pH+Zq1Rwz07MpdTtggs6phfwXRkT0BT9YnqQUcIIWR8oAgrARHBpnedhRn1NfirH3egP1ne7MPR\npncog5xyG7VqGmujZdWE9Q0FizA9O1IbtmoRps1Y41GjYHakLcJikRCLCndhfkE6MmB2ZDTiTkc6\no1vF2xZVLh3Zkih8noCJ60FHCCFkfKAIK5Fp9XH863vPwUvd/bjpFzsrOpajllu+tzAf0DVhpYsw\nLSj9zFrr4hFEDLHTkYN2JMzcNhYxPJEwZYuwungkOB2Z9aYjA3zCIt5ImDsd6awDSwZE3ey2Rbmx\njYSpkEaRly9rRa1HUCZiEWxY2z6mYyKEEDKxoQgrg4tPm4GPv/l0/OyJfbjrycoVVfv1jdSYkbDS\n05G9IelIEbFc83UkzNy2zhkJs0SYUsqsCbMiXLWxkHRkTvkW5nsd853RMsCcHekUfUOOOrCgSJgW\nR6kxTkf6aTC9bMX8afjMW8+wl7e1JHDz+uUuOxRCCCFTD4qwMvnk6kU475Rp+OzPn8Gr3QMVGYNf\n30hNY220LMf8/hARBpj9KI9bhf4DaU86MmIglXHPPtQpxLp4SDoyoG2R7guZzOZQEzVcRrSAKdac\nZq2uSFgxi4oxTkcWO/pblp8EAJjZEMfDN6yiACOEEEKz1nKJRgx87U/OwRVfexCf+J8O3P6xi11R\nnfHAr2+kpqk2hlQmh6F01vbrCiNsdiRg9Y8sSEcWRsJ0hCqfjowGRsLSBW2LCiNh3nowwLz2/Sln\n9Mv/tpO8RYVbpG3u6MKmLZ3Y3zOIuS0JOzXoXVaqWDIjbhK4XkLWEUIImZowEjYM5k2rw8b1Z+Gp\nvT346rY9437+npCasCbbNb+0aFjY7EjANGwtmB0Z0zVhYs86TFsRMS3CamPBNWHZgLZFzpowr1Er\nYDrmO81ah1w1YeGRMGc6cnNHV4Fv14afPYUNtz81bC+vsEiYiPkH+KctCSGETE0owobJ2846CVdd\nMB/ffuAFPPTckXE997GBFCKG+DbdbrT7PZZWF9Y3lEHUEF/RA5iRtXxhvinYEj6RMP0/5khHBpm1\nprM5V2G+FmS66D6VyRUU5QOFvSOTZdSEOdORm7Z0FkTp0jlVkLIsx8srV0Rd6UdLDUYIIURDETYC\nKtXW6Gh/Gi2JGAyjMMXVWGYkrD+ZQX1NtKD+StOUiNnmr1q46HSkc3ak/q8L8xOxSGDbomxOuYru\ndVAs6zBr9UtHemdjauEVNaRoOtK5Xzn+XKVuW2qEK2wWJSGEkKkFRdgI8LY1Gq8v2J6BlG9RPuCM\nhJUmwnqT/n0jNaYDf9rqG2kKHV1rFo8YthDy1oQlQgvzFaKu2ZFWJKxoTZjbJ0xH2poTseDC/Jw+\nZ359Of5cI/Hyot4ihBASBkXYCHG2Nfruwy+PyzmPDaR87SkAZySstHRkfxER1pyIIZXNIZnJYTCV\nRU3UsFOJ8WhhJKwUx/x0Lnx2pNn+yEeEGYbbJ8wSXk2JWGDqU0fCUo79NqxtR8IzaSFmSIElRjle\nXsXSkRrqMkIIIRqKsFFgvNsa9QT0jQRMQQLkWw0Voy+ZCZwZCeRd848PpjGQytqpSEBbVFjCyVOY\nXxeLIJ1VrjQgYLYaUgrutkV+syN9asLMBt7OdGTWGmO0BMf8/Pp1K9pw8/rl9v22lgQ2vftsfHrt\n8L28imkwvZrRMUIIIRqKsFHA2dbog997DBfffC8W3vArrNy4veTZdeVwtD+FaYHpyDJnRw5lfN3y\nNXb/SFuE5bf1rQmLWjVhllgrLIC36rhcNWECEe/syEJ7DW8Dbz07sikRKzo7MuMRg05xpX27Lm2f\nBQD4i0tPK9vLK3R2JCi+CCGEFEIRNkpMq4/jj89rw+G+FF47PjQsm4NSUEqhZyCNaQHpyIZ4FCIo\n2bC1L5lBY2hNWD6yNpjO2OIKcPeO9EtHAiioC9MiKmoUGrHqSFgyIB1ZUJhvCbymRKxoYX4qxKxV\ni7+M5385+KUjlY80Y2E+IYQQDUXYKLK5Y3/BsnJsDkphIJVFKpvz9QgDzKhSQ7z01kV9yQzqa4JN\nXbXvmF860hRF7qbbzt6RgI8IswRO1JNudNpPBBXmey0qhiwBGFqYbzvmB7ct0tdKHzts2yCKpyOV\n9Z8QQggxoQgbRYLsDMqxRCjG0X5t1OqfjgR0/8hSLSqyaKgJPlY+HZnBQCrrKmiPR52zI901YXo7\nr2FrxhZr3kiY4fAJywbOjkz7tC1qqg0WYcqnJsyL9kHT22SG0+LIZxeXS74K3o4QQsjUhCJsFAmy\nMxiJzYGXnoHglkWaxtpYSZGwXE6ZhflhkbBEvjB/sKAwX/I1YRntE6bTkWYEzVsTpqNNEU86MmKI\nndJLZnKo8SnMjxmGq7Yrmckiagjq4xFkc6qg7gtwtC0KEVZahOXTkuVHwoqlI6m9CCGEeKEIG0X8\nrA/KsTkohWO6ZVFATRigvb2KR8J0Q+5SZkeeGExjMO0uzPetCYvmzVqBwnSkjmTFDPdLzznzMSwd\nmVOmeATMwvyaqIGamLmtXzRM66lUSZGwQnf9Uil1D4oxQgghGoqwUURbH8ywBNLMhnhZNgelYIuw\n0HRkDL3J4pGwPitlGTY7Mh41kIhFzML8VNZVmO8slA+sCUv7pyP9ImF2TVhgYb7byiKZMZuU65mU\nviKsjHSkHQkbVk1YuLzSq1mYTwghREMRNsqsW9GGuz6+EgDwqcsXj6oAA4Bj/cHNuzWl1oQVa96t\naUpErcL8TEFNWCankHP0XYw7GngDKGhdlC/M95kdWaR3pC7m1xGzpNXoW/e99JshqTVPKelIbZ+R\nHtbsyOB1IizMJ4QQUsiYijAReVlEnhaRJ0XkcWvZdBHZJiLPWf+njeUYKkFbSwL18Qj2HOgd9WMf\ns2rCdMG8H6Mtwpqt/pF+syMBM3Lltah48LnDAIBP/s+TLr+0jKeAXxOJuGdH6hSjE6+p61DaioTp\ndKSPV1jeoiI4uvXZnz+DlRu342GrEfuwImElyisGwgghhGjGIxL2ZqXUOUqp8637NwC4Vym1CMC9\n1v1JhYhg8ZxGdB4cCxGWQlNttMDiwYkuzC+W+uovNRJWG8OxgRSSmZwrHakjUG4RJtjc0YV/vnu3\nvZ3TLy0dlI4UsaNqmZxCPOJj1qpFWNbd6Hs46Uivd1tXzyB++MjLruOXRcguSlF8EUIIKaQS6cgr\nAfzAuv0DAOsqMIYxp721EZ0Heke9BujYQDqwb6SmsTaKdFYF2jZoekuoCQPMSNjBE0MA4BsJS2dy\ndoF+LGpg05ZO281eo/3SdLTLa1Gha8JStvN+SDoy60hHxiIlpSO9ImzTlt0F22pD19FKRzqf+rxD\nBdUYIYQQk7EWYQrAPSLyhIj8mbWsVSn1mnX7AIBWvx1F5M9E5HERefzw4cNjPMzRZ3FrI44NpHGk\nLzWqx+0ZSAX2jdQ0OmY0hqEjYY0hsyMB06bigCXCEp7ZkYCOhOVrwsL80nQ9V7RgdqSBTC5nC0f/\nBt4+6cgSI2He6Nb+nqHAxzva6UjnGkbECCGEaMZahL1BKXUOgCsA/KWIXOJcqcwwke/XklLqFqXU\n+Uqp82fNmjXGwxx92uc0AgD2jHJKMqxvpEa73BdrXVROTZiObNXF/CJhylUTFuaXlg5oW6QjYTqa\nFR4Jc/iJFa0JM/97a8JOaqkNerhj45ivWJhPCCHEzZiKMKVUl/X/EICfA7gAwEEROQkArP+HxnIM\nlWJxqynCOke5OD+sb6RGe3sVM2zVIqxYOrLJESlzpyNNIZXKZpHO5mCIKabC/NKy9uxITyQsYtaE\n6bSmr1mrdT49izGZzrpmRw6lC9ORQTVhf33Z4uDjj7JPGGvCCCGE+DFmIkxE6kWkUd8GsAbAMwB+\nAeAD1mYfAHDXWI2hksxsiGN6fXzUI2HHBlKh9hRAPr1YbIZkXzKDWERsERNEk2Mmpm9hfsas5dKR\nMe2XVmutb2tJ2H5pgYX5uiYsJB2p98nm8pGwYj5hQRYVbz97rut+W0vCthMZlmN+qXVkFGOEEEIs\nwkMgI6MVwM9FRJ/nNqXU3SLyOwA/FZEPAXgFwHvGcAwVQ0SwuLVhVGdIDqWzGEhli6YjG+1IWLgI\n609mUF8ThfUcBeIUYU7HfDsdmc0hnVEub691K9rwyAvduH/PITx8wyp7eVBhftQQvNYziD+55VEA\nwBd+udM+Tn6b/Pk2d3Th1e5+vHSkHw90HgQA/OVtv8c//TqBDWvb7f2CImHOyNRlS1rxnx84Hz/4\nzcv4GfYNb3ZkCO72RYXH3tzRhU1bOrG/ZxBzW9zjJ4QQMnkZMxGmlHoRwNk+y7sBrB6r804k2lsb\ncfsT+6CUKip0SsHuG1nC7EighHTkUKZoPRiQT28C7nRk3GNREfNEr2Y0xNHdl3I9/nxNmHvbo/0p\nvHSk367hOjqQxo13Pg0gL8S0cLt310F86/4XobXSiaF8GlLbYej9bJ8wT5TM2esxa0W+dMH/aNWE\n+RXke7fb3NGFG+982u4s4B0/IYSQyQsd88eQxXMa0Z/KoitgtmC55FsWlSbCTpRQE1aKCHMaw9b6\nFuZbIswT3ZrRUINMTtmO9EA+1ed1zN93bLDA5kHbWmh0OvKHj7xa0A4paD99TG+dl1OEZTztijLD\nsqgImR2pgmdPbtrSWfBYvI+bEELI5IQibAxpbx3dGZK6ZVFLkXRkfTwKQ0qrCSspEpbwL8zXkbBk\nNueqCdPMbDDFotOmwy7M99SEBXmaOe0u9PG7+5JFx6z307Va3jovp87S6ce8GBudwnzxWefdLszO\ngxBCyOSGImwMWWTPkOwblePplkXFImGGIWioKd66SNeEFSMwHemKhKmCfo8zG2oAuEVTOqBtUW3A\n5ACn3YUWbjMawh+/c798TZhyG+c6bmY94mt46chCGeafjnRvF2bnQQghZHJDETaGNCdiOKm5dvQi\nYVY6sphjPmAW5xdLR/YmM2goYtQKAM11/rMjXTVhmcJImBZL3f35SFgmYHbk6bMb4K2a07YWGp3C\n/OPz5oXO6HTu54x4OVOS7nSkOSZvbVg5hO6igq1cw+w8CCGETG4owsaYxVb7otGg1HQkUFoT776h\nDBrixUVYQzwKsTzAnNEu1+zIbA6xqKcmrN6MhB1xRMIytk+Ye9v50+swu7HG9iQ7qbnWtrXQ6GL+\nCxZMx8dXnW4vb3HUrDntMAC32HJGuNyF+e505HAiYcW8J4LMWrWdh8Y7fkIIIZMXirAxpn1OI54/\n3DesVjhejg2kURfP+2KF0WQ18Q6jv8RImGEImmpjqItFXLM87UhYxr8mbFpdDCLumjB9HbyzIyOG\noL4mivdddApiEcFvblhVIESiDjPV806eBgD48UcuwpOfX4PGmig+uHIBHvbspxSgh5xxRcLyx814\nRNiwasLCGngX2c45Xu/4CSGETF4owsaYxa2NSGVyeOXowIiP1VOCUaumsTaKE4PBkbBcTqE/lS2p\nJmxzRxf6khn0JjNYuXE7Nnd0AXA65itrdqTXBd/A9Lq4qyYsKBIWNcSeSdmciPlaeuzYY/YQ/diP\nnsDHb/s9gHxUMBY17AjW5o4urNy4HQtv+BX6khlb+Kz9tx322JVfJMwu0B+GWatDXOnz3/7EPgDA\nU3t76NFKCCGkgLE0ayVwzJA80IvTZjWM6FhHB1KYVl88FQmYBqt7DgWnQftTVvPuIiJM+1hpoeL0\nsXpz+2wAZiQsnVUFtU1A3itMo0VYrCASZiBriTCnOaxzHP92z3P2/aPWJIVHX+zGkpOaEI8YSGVy\nBb5bTg6cGLLHfvFpM+zladuaImfdV2V7u+mqL7/z/+Kp/XYbK0IIIUTDSNgYc/rsBohgVJzzjw2k\ny4qEhdWEldo3MszHSteA2TVhkULRMqO+xl0TFlCYb0bCcjhhRcL8xuFnY3HLjhcBmKnRVCbnO16/\nsYfVhDmXlYo+nN/501mF/3ropbKORwghZPJDETbGJOIRnDK9blRmSJabjuwdyvhaJwBmPRiAojVh\nYT5Wukg/lckh5TM7ErAiYf0+kTCPYItEzN6RJwbTLkuMYuM4cHzIPl46q0ry19rfM+hbE5Z11IKV\nO0NSi7qg8x/uLe5tRgghZGpBETYOjNYMyWP9qaJ9IzWNtTFkcyowKqSjZA014UX+YT5WEUMg4pwd\nWfhymtngjYQpa7/wmrByxgEA8WgEyUyuJH+tuS0J28TVkHzUK+2oBSt3hqTWukHnn9VYU9bxCCGE\nTH4owsaB9jmNeLl7AEMhabJiZLI5nBjKoKWMSBgQ7JrfnzTH0lATLurCfKxExK7F8jNrBUzX/N6h\nDJIZ83zpXK4gFQmY6clsNliE+Y1DrOWAlY7M5rBhbXug8atz7Fo0xSJGPhLmiH4Nt4n3hrXtqI25\nzx+LCK5buWBYxyOEEDJ5oQgbBxa3NiKbU3jxcP+wj9Fj9V8sxagVMCNhAHBi0N+moi9pLq8vEgnT\nPlZtLQkICn2s4hEj38DbrybMds03U5LZrELMR4RFDUE6ZwpNPxGmx6GFXk3UwMnTE45xCNKZHNat\naMOGt7iNTrUoq40a9th1+jAeMQpmRwLuqFgp6OOtW9GGL115pmvdO846CauWtJZ1PEIIIZMfirBx\noH3OyHtI9gyUbtQKOJt4+0fC+qxIWGORSBhgCouHb1iFlza+rcDHShfE+1lUAMAMSzRqEZbJKUR9\ntosYBobSOWRzytWr0juOt5w5BydPr8Oi1gac6phtqiNhAHCpNWvzI29cCAD42KWnYe2yVsydlhdt\nWhp8mRQAACAASURBVG7FooajcXdeeJUbCXOW3r397LmudWe2tYT6iBFCCJma0KJiHFgwox6xiIxo\nhuTR/tL6Rmp0cXuQYWvfUGmRsGLEIqY/V3BhvuWa32/WhaWzuYLm3YC7obdfJEwzp7kWB3YOQaEG\npztFWMSwfdFSmbzVBABExKw3e/FwPxbe8CvMbUng/a8/xT7vULowEpbJKmzu6MKmLZ3Y3zOIuS0J\nbFjbjnUr2lzLNU6NlfPtI1maCvM7JwDfcRBCCKluKMLGgXjUwKkzG7BnBMX55fSNBGC3/wmsCUtZ\nNWElOOaHkY+EKdtB38nMBnckLJtTBUatgNuyIkyEtTbVIpXJYX/PEFa157fTYhCAbWWhI1u7D5yw\njV4VTK+zr27dY+/ndcwHgP/b+Rr+bdtz9sQG7Y/2+CtHcccTXQUTHu7vPIRz5reY5/DorXKCYE6P\nsa6eQWz42VOA5AWl06eNQowQQqobpiPHifY5jSOKhJWfjtSRMH8R1juUQSwiJbVACkNbQwTVhM20\na8J0JEwVtCwC3JEwP7NWzZymWgCmmGt2RAW1GATykTAd2XrwuSOu5t1AXqjVRPM1Yc7C/P968CVf\nf7Qf/3av74zTHz36qn3bGwlTSpWcjizwGMupgrFrrzNCCCHVDUXYONE+pxH7jg3aJqnlUm46Mj87\n0j8d2Z/MoKGElkXFMK0hssjklG86si4eQW3MsG0qsrmcbyTMKDkdWeO7XTxq2MLKFmNWZCyoLg4w\nI2HZnOmQ77SlCPL1ygaoKWdrJj+LsdGuCSvFD40QQsjEhiJsnNBta54bZjSsZyCFeNRAXby0yFVd\nPIKIITgRVBNWYvPuYsQjYttd+IkwEcGM+ho7HZnOqaI1YX5mrZpWKxIGeESYKx1pjkdHwppCHqd2\n/c/mlCsSpiN4XiIBrYxmNOTFcZBB7mhSih8aIYSQiQ1F2Dhh95Acpgg7NmAatZbaz1BE0FAT3Lqo\nL5lBfXw0ImGG3YfSzycMMOvCjliu+Zlszjcd6aoJC0m5zm7Mi7AWTyRMR75SnpqwVWfMRo2nXk3f\n18Ixk1NI5xT0MK66cL6vP5rfcgC46oKT7du+kbASK8MKPMYMKUjzaq8zQggh1Q1F2Dgxb1oCiVgE\nnQf6hrX/0f7S+0ZqmhIhImwoY6csR0IsYtgtkPxqwgAzqtRtpyP9C/N1JMwQoCFEHMajhl3s7xRr\n2jQWgEOMmcJn+bwWfPZtS+xt21oS+Piq080xW4LQjITlbIF10akzcPP65fa4tD/al9Ytx83rlxeM\n6w2nz7RveyNhSpWejnR6jLW1JLDp3Wdj07vOdi1z+rQRQgipXijCxgnDECxubRhWJGxzRxd27DmM\n3Qd6sXLjdmzu6Cppv8aaWHBNWCpTtHl3KcSjRj4d6TM7cnNHFx55sRs795/Ayo3bse/YoL9PmLWs\nKRFz1Yf5He+4ZUD7l7f+3r4WsagjHZl2R8IMAa48xxQtf/+2JXj4hlV40+JZ1n7muTJZ5bKo+NP/\n+C02benEzIY4ZjbEXf5ofgLIbVEROPyiaI8xQ2Cf03k+r08bIYSQ6oUWFePI4tZG3G9ZJZTK5o4u\n3Hjn03Z0pxyLgsbaaLBZ61AGJ0+vK2ssfsQi+XSktyZMj91puWAIfM+rI05hRfn6eHq24KHepH0t\nzJowhVxOIZnVPmFahImdftTF+1oo5dORORztT2EglZ+d2NUzCIFZ91YM54zIgkhYGSYVetdS086E\nEEKqF0bCxpH2OY043JvEUas+qhQ2ben0tUooxaKgsTYWWhM2OrMjDVu4eGvC/MaeU8D+nqGC4+ia\nsLCi/LBroT3KUpZxLJD31jIkP7aULcLybYsAMx3Z3Z8qkEsKQLIU93zHJiOZHVmOYCOEEFLdUISN\nI4uHUZwfZEVQikVBU200pHfkKIkwR+9FbyQsaIypbGFfxlIiYWHXQoupdDbnmB1pnkdEYBhms3Ed\nCdPRKp1CzXhmR3rJ+IzZSZhjvlLh4soZOdNDYByMEEImPxRh48hwekgGWRGUYlHQWBv1rQnL5hQG\nUtnRqQlzCC9vYX7QGL0zFYF8JCxMhIVdCzsSlsk5ZkfqSJh5bKehq9Y9zkhYSCkahjLhIsyVjgzd\nMpzxsLcghBAyMaAIG0dmN9agORFDZxntizasbS8QB6VaFDQlYuhLZgq+2HUN16jMjoyK47b75bRh\nbXuBnYMAOH12fcFxtG1FmFu+3/H0tfBLR+r/+vrVRA07SqYjTjoCl8kp1McjgUJsyMcl34nzEudy\n3pqw8HSk8kllsiSMEEImPxRh44iIoL21sSwRtnbZHAjMRtuC8iwKGmujyKl8n0hNn1UnNjqRsIjj\ntvvltG5FG25evxxtjgiWAjBvWmFhfimRMOfxvNdCp0LTGVVSJCznSUeaTv4Gzj25xT5fW0sCjVaD\n86IizHnb1yes1H3Ne8KEJCGETHo4O3KcWTynAXc9uR9KqZJmwD38/BFkFfDtq8/DGxfNKutc+f6R\naVf9l/b1Go2aMFckzMd6Qlss9CczOO9L2zCUzvlaVOiIVFMifExeywZNPhKWzTfwtmvCzG1qHK2N\nvIX56axCJqewdG4zHn+lB5+6bDE+edkinPfFbUAyi6F06elIv5qwUmE2khBCpg6MhI0z7a2N6B3K\n4MCJwhmCfty7+xDq4xFcuHBG2efa/doJAMDrb97u8hfrHUURVhNSE+akviaKRbMbAAC/+sNrBX5n\nj73cDQD4/+7uLMsLTaPFVDLjNzvSHFdNNFJQE6bHfM+ug+gbyuCHj7wCAPhDVw+AfK/IoXQWmzu6\nsHLjdiy84VeFA3ClFAuVVFitl/ITcAyEEULIpIeRsHFGz5DsPNCLk5rDi+uVUti++yAuWTzLjvSU\nyuaOLvz4sb3mceD2F9N9Dkejd2TMJcKCx7i5owu7HWlY53gA4PsPv+K7rlRj0rgVkUtnlT370vYJ\nM/Q2+ZowuzDfuq7f3P68Ky14f+dhbO7osmdMbnv2AG7Z8VKBRYbGOfvRO8lSlWE8obejBiOEkMkP\nI2HjTDk2Fc90ncDBE0msXtJa9nk2bekssILQnlp2Tdgo9I50FuOHCcVNWzrtyJR3PGFjLRVdm5bK\nOCwqct5IWGE6MuaIoDnJ5hQ2bem0i+xv/e2rgQIM8KYRy2tb5GdvwcJ8QgiZ/DASNs5Mq49jdmNN\nST0k79l1ECLAm9vLqwUDwj21+pKjNzsyXmIkbDh+Z6V4odnj8LGocDrm620KCvOLjLnGaqh9pC/c\nYDen/G+XgkugsSaMEEKmDIyEVYD2OY0lRcLu3X0Q5548DTMaaso+R5inlhZhozE70hkJC6sJCxvP\nSLzQvOc2zVp1YX5wJMxbExZ0fqv9JKbVBc/aNI8XXJhvbVH0MZj7mv85O5IQQiY/FGEVYHFrI547\n1Bvq0H7g+BCe6TqB1UtmD+scfp5asYhgw9p2e3ZkfU3Eb9eycBbmey0qio1He3yFrSuVuKM3pC3C\ncm6fsLBImHfshphj1oX5V5w5p2CMTlwpRZ+JlOHpSOV7mxBCyOSGIqwCtLc2Yiidw96jA4HbbN99\nCABw2TDqwYBCT63aqIFcTmFRawN6kxnEIwZqoiMXYcUsKoLG4/T4CltXKjUhvSPFMTvSa9aqx/y+\ni+bbx4pHDZwyow7rVrTZQnlZWzNuXr888PxhkbBiLvg0ayWEkKkJa8IqwGKrfVHnwV4smFnoHg8A\n9+46iHnTEratw3Bwemod7U/hLf+2Ax/83mPoHcoilc1h5cbt2LC2vSyx48Vp1up1zA8bTznrSiFv\n1poXYRpnJKxnIIWVG7ejy6o3e6brOADgnPnTALyCG644A4+9dBSHeodczvef/fkzLtNZL8X8vUqN\nb63/94cBAIMp0xJjJNeEEELIxIaRsAqghdWeAOf8wVQWDz1/BJctaS3J0LUUptfH8a7z5uFQb8qe\n5aetIMr15HLirKkKq68aa5xti3S0S6Nrwvb3DKBnMGMLMAC44/f7zP0cLY4SsQgGU1ncaa3TdIVM\nFCjawLtEFXbwRNI+3kifG0IIIRMbirAKUF8TxfzpCXQGFOc//PwRJDO5YdeDBXHXk/sLlpVrBeHF\naUsRMyr3ctI1XalMrsDuQg/rqX3HC/bTKUunsWttLIKhdA5f3ban5PO7HfPLGnqgQBvpc0MIIWRi\nw3RkhWhvDZ4hee/ug2ioiQ7LJT+M4dhEFEOLn6ghMIK6X48DOhWazhamI3U0sT8Z7POVsqJnhggS\ncQND6SyO9ofbUjhx13X5NfAeXsH9SJ4bQgghExtGwirE4tZGvHi4v0Aw5HIK9+46hEsWzyzbJb8Y\no2EF4UWPMawofzzwa1uk0enIMF80HQmLGGKmI9NZzGmuLfn8fk24g9YXrgteO5LnhhBCyMSGIqxC\ntM9pRCan8NKRftfyZ/Yfx6HeJFafMbxZkWGMhhWEFy2+KlkPBrjTkV73ex2gu2TRzOD9sp6asHQW\nn1h1esnndwovrwYr6pgfsG6kzw0hhJCJDUVYhbB7SHpSkvfuOgQR4NJhuOQXYzSsILzoSNhoR+3K\nxTAEUUN805E6Erbi5GkF+/3ZJQsB5AvzRQS18QiUAi5td9fkhUXG/GwmRoIAI35uCCGETGxYE1Yh\nTp1Vj4gh5gzJs/PLR+KSXwojtYLwko+EVV7Px6MGhtI5u2ekRk8wrfERiquWtOKb971gR8J0OhKA\nbWoLAGuWtuKL687Ehf90r++53Q28vTVh4S28/dY01EYpwAghZJJDEVYhaqIRLJxZ74qEaZf8T7+l\nelJQNROkJgwwRVhfMl2w3HCYtRbs40hjAsCTe49hyzMHAQB/+h+/tbcbyuTsXpR+OF3yfdsWlRkd\nC0rurty4Hft7BjG3JTFijzdCCCGVhSKsgrS3NuKZ/XnbhHt3m1/+w3XJrwQTpSbMHINh98UUyacI\nbREWKxSKtsmrJbDu/H2XXaR/uC9pbzeUztq9KP1wF+Z71hUzci1j5qT2KtMebwAoxAghpEqpfPhi\nCrO4tRGvHh3AQMoUDvfuOoT500fmkj/eTJTZkYAZ1eodMq+lcwKC7ZjvGKNepsWjt9WRl2Q6a/ei\n9CO0MB/FZkcWUopJL33ECCGkuqn8N+cUpn1OA5QCnj/Uh8FUFg8/fwSrzxg9l/zxQIuYShfmA2Zq\nVEfCnCJMfCJh3t6RXoNXL0PpXKBAA8J9wrzrRxP6iBFCSPXCdGQFsWdIHujFoRNJJDO5qkpFAhMr\nEhaLGOjTkbB4BLDcP/KRsMKasHw6MlwlDWWKpSODC/OhihTm+6wqVYfTR4wQQqoXirAKcsqMesSj\nBvYc7EVfMoOGmiguWDi90sMqC92qaCLUhMWjBo5YdVzOSFjECK4JMwxzvXbMj0WkQJDVRARD6SzS\noelI/9slMcwoGX3ECCGkuql8+GIKEzEEi2Y3YPeB3jFzyR9rDEMQi8iEiITFo/lIWF3cWRNmpUx9\nxigQS4SZAut9F56MNiu6pIVlS33ctL4osTDfv21ReY8lSNLWW4+rORGljxghhFQ5lf/mnOK0tzbi\nkRe6x8wlfzyIRQxfgTPexCMGeq2asFpXTZj53zcSJkDMELsm7KJTZ+DhG1bhrHnNaE7EAAD18ag1\nOzLEoiKkMB8YftsiL+vPnQcA+Js1tKcghJBqp/LfnFOcdDZvLvqVrZ3Y3NFV4RGVx+aOLgyms7h3\n9yGs3Li9YuPf3NGFx17qtu8/8cox+7aOhO3Yc7hgv7ufOYCBVBYPP2/u+7uXjmJzRxf2HOzFkT6z\ngXcqkzV7UoaIMKfweuSFI651u187ETp2/5ow/1iYFnvVNHmDEEKIP6wJqyCbO7qwZedB+/5rx4eq\nyvtpc0cXbrzzaVtEVMq7anNHFzb87Ck4s4VO13xDBJs7uvAvW/cU7HvTL3e64lDf/83LMAx3Xdj+\n40MAgIFUNnAMeuvNHV3470dfca27r/MwFs6qL/0BhaAfFiUYIYRUP4yEVZBNWzoLoivV5P20aUsn\nBtNuYVKJ8W/a0ol0SMNGQ8xtvI29gcJZkVlVuEwfuneo0I1fo33CzOfUvX8mp3DHE8ERQl+fsCJb\nG4yEEUJI1UMRVkGCPJ6qxftpooy/2PlEZFTGpI1g/dDRwKDzdPenRnx+IN8eiRqMEEKqH4qwChLk\n8VQt3k8TZfzFzmfI6IypLxkmwlToWGbUx4vu6yRIZCk7EhZ4OEIIIVUCRVgF2bC23eVnBVSX99NE\nGf+Gte2IhagSQ8R3rEChv1lEfJZZx+4LiYTplOWGte0F+0cNwfpzg2vk/BOpQYX51lqGwgghpOqh\nCKsg/3979x4fR1nvD/zz3c0m2V7TQint0lJaIOUSSiRIoV6gCAEqEMtFOOJBvKA/70eNtB4UUbTh\n5KBHz9EjePRQFREKPQEFLUoLaIVCIJRSIBZKadm09Jrecttsnt8fu7OZnZ3Znb3Mzs7u5/165dXd\nuT47NMu3zzzPZ1oaQ1i2uAGhuiAEQKgu6Knsp1Jpf0tjCO1XzUNdPFICSM0J07dV7/sfakgUWQDw\n6XPnoP3KeZg8ZvRYC+unAMjQE6ZryzVnzkha9/76KZg/+4isP5eZxOzIghyNiIjcxNmRLmtpDHmm\n6DJTKu03tuP+57bh6w++BGD01p62zT/9/Bn8/Y1YJMXlp4fwsyffwN7DQ9jXF8H7T5iCs2YfgXE1\nVfjkrzoBjD6a6XfPbbM8v/6W4ruOnYRfP7M18f7EqePx9ObR+Iw5Sx9FVCn4RRBVCkdPqE05nuXt\nyPhpODCfiMj7WIRRWdL3bvl85rcXgdjYKr8ukkLbVv/kgsdeeQeZJD3A2zAJ8x87DuKvm0azw6Lx\njbU/dxwYyHj80fNo7bS9CxERlSh+lVNZMhZaevpeJJ8I/D5fIiok8YgjXRGW6eHeQHLqvXHrdW/u\nTRv0asaqn2uEPWFERGWDRRiVJZ8vudDS0xdoIoDfF3tyQWzb2PJsn+GZ1BNmmO2YbiyZFasai4n5\nRETlg0UYlSW/JBdaSeuSijCBXyRRRGnrsn0Wpj4r1hg5Ma4m+7v+YtEXph2aJRgRkfexCKOy5E/X\nExZ/r21i1mtWo+sJs1OQ6W9HGsP73z1rEqr9hSmbtPOwI4yIyPtYhFFZSluE+SRpeZXJtvrbkZ9+\n/+yM50t3O/L4qePxkfnHWu571PialGWcHUlEVP4yFmEiMlZEfPHXJ4rIZSISyLQfkZv0nVd+Q8Hi\nMxRhSQP14/vpi7ALTp4KILlYM9LfgjQJwMdZxyXnhE2oHb1F+ZOPvMvyuEZagcfEfCIi77PTE/YU\ngFoRCQF4DMBHAdxt9wQi4heRLhH5Q/z9ZBH5s4hsiv85KZeGE6WjL6zE8LdcK6a0Tcx6zWqqRsNe\ntdfDaR4S/krPgcRr45iw2PvkZfqB9Vf97OmU42WaHclRYURE3menCBOlVB+AxQB+qpS6CsApWZzj\nSwBe1b1fAuBxpdQJAB6PvycqqCpdkJbx1p32Pl0Rpu8Je+ofuzKe77FX3kFHVxhA6pgwILV3bDCS\n/YzJ2HG0A2WOzSAiotJmqwgTkbMBfATAI/FlqQ/hM9/xGACLAPyPbvHlAJbHXy8H0GKvqUT26cNM\njbfutFuVWsFllimmH4z/i79tzni+4RGF9lXdAFLHhJndnhwcTl9EWUVQaAWe2TGJiMhb7BRhXwKw\nFMD/KaU2ishsAGtsHv8/AHwdgD6pcqpSanv89Q4AU812FJEbRaRTRDp37crcE0Gkpx8Hlmlgvn5b\nbZ3+IdzvHBi0dc6e3n4AFj1hGd7bpRV4rMGIiLwvbREmIn4AlymlLlNK3Q4ASqnNSqkvZjqwiHwQ\nwE6l1PNW2yizwTKj6+5SSjUppZqmTJmS6XRESap0RZRVTpi23CyiQt8TdfTE1Gc7mpkefzh4ypgw\npPZc5TqiS7EnjIiobKQtwpRSUQDvyfHYCwBcJiJbAPwOwEIR+Q2Ad0RkGgDE/9yZ4/GJLBkfTaTn\nF+ueMONzJgHgi+cfn/F8VT5Ba3M9AHsFUk2VVRxrTKbEfMW+MCIiz7NzO7JLRB4WkY+KyGLtJ9NO\nSqmlSqljlFKzAFwDYLVS6joADwO4Pr7Z9QAeyrXxRFbS5YSNRlTEt/WnjgnTu2xeCMFA+l+V8+qn\noKUxBMB8TJixaKoJVOHoCan5YHalmahJREQeYed5KrUA9gBYqFumAKzM8ZxtAO4XkU8AeAvA1Tke\nh8hSck9Y8jq/4ZZjuvFjQKygG1tThf7IkOX5/vzqTsxa8gh8Ym92JABMGluD7QcGceP7ZuOup5IH\n//cNDWNB22r09PYnbnMCwLNv7gUA3PLQyxgZUYnCj4iIvCdjEaaUuiHfkyilngDwRPz1HgDn53tM\nonSSx4QZbkf6DT1haXrNtGX6yIt0zAflm9881M47NDySsm7f4Qj2IgIACMcH/APAYHzbfX0RLF25\nAQBYiBEReZSdxPwTReRxEXk5/v40EbnZ+aYR5c74fEizdWIYG2a5vU+SirpsmfWCKaUSRVgkmlqE\n2bnb2B+JJmIxiIjIe+z88/7niEVURABAKfUSYmO8iEqW8dFEen7DmDD944j8JlWYT+w9xDsd44xJ\n/XnNijC7enS9ZERE5C12/s8yRin1rGFZbnHfREVinAGpZ3xmpH5GpFlIqkh+PWFmRCRx3kg091H2\n+vFiRETkLXaKsN0iMgfxOyQiciWA7el3IXKXMQtMr8oY1qp/2LfFk7HtjgnLSrz2MusJs1PyBQP+\nRCwGERF5j53/s3wOwJ0A5opIGMCXAXzG0VYR5cmYiq9nzALzZxgTBiQn6Gum2oyYUEqZjgvToizM\nirAjxgYSr0O63i7ttmjdmACWLW7goHwiIg/LWITFE/I/AGAKgLlKqfcopd5yvmlEuTOO+zJd59Pe\nWz/sWxMwGRO24tPnYEvbIkwMBkz2SGY2P3K0CEtdN6529Jhrl4ymwzTOrAMAfOOSk1iAERF5nJ3Z\nkW+IyD0APgpgpvNNIsqfcdyXXkpivi91v46ucGLZgrbV2NeXmhF21Z1/R0dX2PIWpsbssUUHBiLY\ncyj2TEqznrBt+zIMuGdYKxGR59kJaz0ZwFkA3gugXUTqAbyklPqQoy0jykNVmjFhxluVyc+OjBVg\nWgYXEMvpMquz3jkwiKUrN8DOxMnn39qb9F4pYGu80DIrwqIWkfja5+Fji4iIvM9OERZFLJ4iCmAE\nsWc98nmPVNK0wsqsl8o4aF8/JszvE7Sv6kZ/JJq0j9VjgvojUctxZBqlgEc37DBdDuQ2O5IP8CYi\n8j47RdgBABsA/ADAz+OJ90Qlzc7AfG1dlSGiItvsLTvPcdzXF7Fcl0tOGGswIiLvszM78loATwH4\nLIDficitIsLHDlFJG70daT0mTFtj7DXLNnvLbzGYX6OgUDfGevC+2WOLMmFPGBGR99mZHfmQUqoV\nwKcBPArgYwD+4HC7iPIyOjA/dV1KTphh29bmegQDfsPxYn8aDxcM+DEhmLlD+eJTj05Zph1r2E5X\nmgHHhBEReZ+d2ZEPisjrAH4EYCyAfwYwyemGEeXDzu1IbZXx9mRLYwjLFjcgVBeEIJbTVT91PIDk\nbLDpE2uxbHEDJmSIqFAKaJyZ/CsjAhwdP5bZ7ciqTDMuWYMREXmenTFhywB0KaWiGbckKhFaDWOe\nE6atSx4Tpi/YWhpDSTlc1//yWby64yCmTqjFjgOxaIm/3rQQfp/gR49vyrp9E2oDGB8MYPuBQURM\nbkfWHz0eG3sOWO7PGoyIyPvsFGHrAXxORN4Xf/8kgJ8ppaxHGhO5TETg94npmLDRaIrY+3TBrhqt\n6KmpGr1Nme7RSHqbdx/CIy8lP+lrf38E+/tjv0I9+wdS9jnQb/7r9czmWNTFi1v34aPzj01/YsTi\nNtpXdaOntx/T64Joba7PGPKayz5ERJQ9OwPz/xvAGQB+Gv95V3wZUUnzi8DskY/acyDTPcjbSMXv\n/9UEUg+YaWD+M2/sRa9FUWXlbd0MTX1wrObh9T2my/W0vLNwbz8UYnlnS1duSLtfLvsQEVFu7BRh\nZyqlrldKrY7/3ADgTKcbRpQvn88iMT/+t14S763HjxnVGgbs29kvl1uH+jFf7au6U9ZHosp0uZ5Z\n3ll/JJp2v1z2ISKi3NgpwqIiMkd7IyKzEQtuJSppfhHzgflaREXisUXWwa4arSiqqUr9lUnXg1YI\nVrllmfLMctkv13MREVH27BRhrQDWiMgTIvIkgNUAvuZss4jyFxsTZr4cGB0DZmdMmPawbf2YMI3D\nNZhlblmmPLNc9sv1XERElD07RdjfAJwA4IsAvgCgHsBaJxtFVAh+n3lPmPH2o/GB3ma0njCz50Rm\neoB3LvRNaW2uT1kf8Ivpcj2zvLNgwJ92v9bm+pTevkz7EBFRbuwUYU8rpQaVUi/FfwYBPO10w4jy\nFSvCzJcDqQPy0xZh8ZFdZgWXcQam8TB1GXLEzIQm1iZem81MXNQwLeOMRS3vLHHMuiCWLW5Iu19L\nYwhLLp6b1T5ERJQbyyJMRI4WkTMABEWkUUTeFf85F8CYorWQKEc+izFhiccWxVdVZRgT1tEVxgtv\n9QIAHnqxx+R4ye9vX3wa1t9yYeL9gYHRmZG3XnYymk8+KmPb9YP55yx9NGX9qaGJGY8BJBdwa5cs\ntFVMNZ8SS/efNrHW9j5ERJS9dDlhzYg9ougYAHdgdDLZAQDfcLZZRPmrssoJs0jMN+sI0yIbhuKp\n9n1DqXNSjIVelT+5B07/VKJvP/yKrdmSPb2j2WFRk3j8EUbmExF5nmURppRaDmC5iFyhlHqwiG0i\nKgifxe1I62dHpm5sFtlgdh49q7FogP24ikzbsQYjIvI+Ow/wZgFGnmRVDBnHgPkNyfl6dqIZjLtV\n+Xy2MsfywRqMiMj77AzMJ/Ikv09MM7yMY8L88QR9s7rJTjSDseCKnTfLxmaJPWFERN7HIozK189a\nrwAAIABJREFUViys1WS5VU+YSRVmFvMQ2zb1eJqqNLcjC9U/ptgXRkTkeZZjwkRkcbodlVIrC98c\nosLJnBOG+J/WY8K0mYHaA60nj63GnsNDqNEVZsbB/36/dRH23ZZT8NqOg7jnma1py6ix1X4cNpkE\noGFPGBGR96WbHXlpmnUKAIswKmm+DD1hxscWWT1+qKUxlCjGnujeiY/973NJRZYxoqLKYkIAAFx8\n6jRcN38WJo2pxn+uft2y7U4EwBIRUWlJNzvyhmI2hKiQOrrC2LTzICJRhQVtq9HaXJ8opEZ7vmLb\n2nlskca4r36Zxm8RjQGkFn5WhkfSd3UpdoUREXleutuRX0m3o1LqB4VvDlH+tGyvSDRWqIR7+7F0\n5QYAsV6tlJ4wG48t0vhMiihjD1rA7NlGcdqWVRmKsEg8l8wKazAiIu9LNzB/fIYfopJklu3VH4mi\nfVU3AH1OWGxdptuRej6TOAvjbnZuJfozTJ/UCkgrGTrKiIjIA9Ldjry1mA0hKhSrbC9tuTEnzJfT\n7UjdmDCT2ZFWRmMxMp8rnWLMjmRvGxGRszL+r0BEThSRx0Xk5fj700TkZuebRpQbq2wvbbnx9mPi\n2ZFZ3I7UF2EpsyPTFWHQbmfmV4WxQCIi8j47/yf4OYClACIAoJR6CcA1TjaKKB9m2V7BgB+tzfUA\nRm8pagO00kVUGBlvYQKpxVtVugLL8NDwXBWjBnM49J+IqOKli6jQjFFKPWv41/6wQ+0hypsx22t6\nXTBpdqRWJBkH2dvpnBJJ3Ta3MWF5VjjsCiMi8jw7RdhuEZmD+D++ReRKANsdbRVRnvTZXkZ+y4iK\nLHrCdNsa9/vI/zyDpRefZLr/oxt6cO27jy14T1hHV9iy6CQiotJkpwj7HIC7AMwVkTCANwFc52ir\niByk9WIZe8Ls9E6Z3brcuvdw0jbvHBhMRGIY3fr7VxAMVOXdE6bvCNMiObQZocZIDiIiKk0Zb8Ao\npTYrpT4AYAqAuUqp9yiltjjeMiKHjOaExd8nHuidRRGmK6I29hxM2c4YkaEZiIygfVU3qowx+1nS\nz47MFMlBRESlyc7syO+LSJ1S6rBS6qCITBKR24rROCInpD7A235EhbFwA6wLLis9vf22bn2mo88J\nyxTJQUREpcnO7MiLlVK92hul1D4AlzjXJCJnJXq+4u99WURUaPQ9YcaZmJlMrwvmXYTpb0dmiuQg\nIqLSZKcI84tIjfZGRIIAatJsT1TSjD1hVT77tyNH4tWPPmz11OkTUrazKsxqAz60NtfnX4Tpbke2\nNtejpir5V1kfyUFERKXJThF2D4DHReQTIvIJAH8GsNzZZhE5R0QgYjZAP/O+0fh9QH2v2cwjxiZt\nE6oLYtniBtP9v3PZKfHnV1q0LXMTYnQ9YS2NIXz9otGCSzt/voPymYJBROQsOwPzbwdwG4CT4j/f\nVUr9m9MNI3JSlU9GH+CdRUSF1hOW7jmTa5cstCyALp0XW27V63bpvOkZ2wCkRlRcePLRAGIFWLrz\nExFR6bATUQGl1J8A/MnhthAVRUdXGJGowm/XbcWT3bvwufPmALD3AO8nuncBALq29mJB22rTW34L\n2lbjvLlTTPfXai+rgu/FbfvsfATc9dRm3PXUZgDApDEBfH7h8bb2S0efNQYA/RFmMhMROSnPxwgT\neYuWqaUJ9/bjO394BUDmnrCOrjDujBc+2r5LV27AC2/tTdou3NuP3zyzNe2xrOq9rXuzn9G4ry+C\ntj++lvV+etp1Cff2J3rZ9vcPo6MrnNdxiYjIGoswqihmmVoDkREAmSMq2ld1Y2h4JGlZfySKLXv6\nsm6HnV63bESi+Q3gMrsu2nIiInKGZREmIo/H/7y9eM0hcla67KxMERVW+2ZT/mS6HekWZo0RERVf\nup6waSJyDoDLRKRRRN6l/ylWA4kKKV12VqaICqt9cymnCtwRlqBynNLIrDEiouJLV4R9C8A3ARwD\n4AcA7tD9/LvzTSMqvNbm+pQMr2Ag9muQKaLCfF8/Zh0xxvb5BfZnYmYj3weCm2WNacuJiMgZlv/b\nUUo9oJS6GMC/KaXOM/wsLGIbiQqmpTGEZYsbEKoLQqBlap0Gn2QujMz3bcC7jp0MAKgbE0gsv27+\nTIRMepEy3Y48ekINJtRml8A/vrYKrc0nxo+fWzHW0hhKmWE5MVjFqAsiIgdljKhQSn1XRC4D8L74\noieUUn9wtllEzmlpDKUUF60PrLc1WN5s379u2g0A+NdLTsJVTTOS1s1a8kjS+8SjkixOdfxR47Fs\ncQPe+29rMrZF89tPzkdNwIdlf+zO+XYkAJxXfxTueOwfmDQmgH19EYyptpVgQ0REObLzAO9lAL4E\n4JX4z5dE5PtON4yoWPS5YQvaVhcllmHtG7tNl0dHVNYzJ5/atAt/efUdAEDP/oGifQYiIsqPnX/q\nLgJwulJqBABEZDmALgDfcLJhRMVglhumvc/2VpydPigRQUdXGD9Z/brp+p0HBrIetP+Dx7qTbkPm\n8xmIiKh47OaE1eleT3SiIURuMMvH6o9EHcvHkvg5rWK9tu3rTwzetyuqgOGR5AM6+RmIiKgw7PSE\nLQPQJSJrEPt/yPsALHG0VURF4kY+VrpjD0VHChZfketn0HrV+ABvIiJn2XmA970A5gNYCeBBAGcr\npe5zumFExVDsfCyR9McO+CXnGY5GzPgiIipttm5HKqW2K6Uejv/scLpRRMVilf3lVD6WiKC1uR4B\ni+6uoyfUItsazC+pOWH5fAZthmWJhfoTEZUdzkGniqYNXG9f1Y2e3n5Mrwuitbne0QHt2rG//fBG\n9PZHktZNCAayDnL9yoX1GBqO4kePxwb7T5tYi5sumpvzZ+DtSCKi4mARRhXPLPurmOfUZ4lt7DmA\nRT/+a1bHuvPJN/CxBbMS7x/54nsxeWy16bYdXeGk4m/SmABuufQUzqIkInJB2iJMRPwANiql5hap\nPUQVb/v+gay2PzAwjJ+ueSPxfnhkxHS7jq4wWlesR0Q3k3JfXwStD6wHABx/1Lik7Xk7kojIWWnH\nhCmlogC6RWRmkdpDRDnQR1RER8zvI7av6k4qwDSRqDKNs+DtSCIiZ9m5HTkJwEYReRbAYW2hUuoy\nx1pFRDkbtgghSxdZ4WQkBxERmbNThH3T8VYQUcFY9YRNrwsibFFsmcVZ8HYkEZGz7OSEPQlgC4BA\n/PVzAF5wuF1EFcNfgGJHH1ERtbiP2Npcb3qugF8ci+QgIiJrdh7g/SkADwC4M74oBKDDyUYReUm+\nPUZV/tiv4ZRxNRAA0yfWZrX/+NoqfPw9sxLvrXrCWhpD+OT7ZictG1dThfYr56GlMZQyBoxjwoiI\nnGUnrPVzABYAOAAASqlNAI5yslFElUTLBfvFx5rwZtsirGk9N6v9f/Xxd+Os445IvLcaEwYA58w5\nMun9nR89g/EUREQusTMmbFApNaQFOIpIFYCM/0YWkVoATwGoiZ/nAaXULSIyGcB9AGYhdpvzaqXU\nvpxaT+Syjq4wHt2wHQDw/UdfRbXfl3VRY+xJyzasdUQBf39jT+L9h+/8O6r8PvT2RZLGenV0hfHd\nP7yS8XjpEvM7usJFDbYlIipndnrCnhSRbwAIisgFAFYA+L2N/QYBLFRKzQNwOoCLRGQ+Yg//flwp\ndQKAx8GHgZNHdXSFsXTlBvQNRQEAvX0RLF25AR1d4ayOI4k/Y6+yLcKe+sdO/PrpLYn3Bwej2NcX\ngQKSBuIvXbkBew4PJe279vXdlsc13o7UPm+4tz9x7Fw+LxERxdgpwpYA2AVgA4BPA3gUwM2ZdlIx\nh+JvA/EfBeByAMvjy5cDaMmyzUQloX1VN/oj0aRl/ZGoaeZWOr5EL3PsfbZDzO5ZtxVDaW5B6ttm\ndH/nNtvnKdTnJSKimIy3I5VSIyKyHMA6xIqobqXsDdmNJ+4/D+B4AD9RSq0TkalKqe3xTXYAmGqx\n740AbgSAmTOZFUulxypbK+vMLUPVle1A/z2HhjJvZGG3yb7aL7exHQX7vEREBMDe7MhFAN4A8GMA\n/wXgdRG52M7BlVJRpdTpAI4B8G4ROdWwXsFifJlS6i6lVJNSqmnKlCl2TkdUVGbZWumWWzHefpQs\nq7Ajxpk/J9KOI9Psa/ynVqE+LxERxdi5HXkHgPOUUucqpd4P4DwAP8zmJEqpXgBrAFwE4B0RmQYA\n8T93ZtdkotLQ2lyPYMCftCwY8GedueXLM+LimjNnIGAjbMzYVgC4ummG7fO0Ntcn5ZFpx2TGGBFR\nbuwUYQeVUq/r3m8GcDDTTiIyRUTq4q+DAC4A8BqAhwFcH9/segAPZdViohLR0hjCssUNCNUFIQBC\ndUEsW9yQw+zI5DFh2XrPCVPw4TNHi6mx1aPFVkjXS7VscUPKvguOPzJl2Wi7kt+3NIZwScPRScfO\n5fMSEVGM5ZgwEVkcf9kpIo8CuB+xW4dXIZaan8k0AMvj48J8AO5XSv1BRJ4GcL+IfALAWwCuzucD\nELmppTGUdxHiSwzIz60K++Ty53BocHTA/OEh3evB4cRrswH0H/mfdQjFoybmTBmXtM5s5Odpx9Th\n4fXb8Yn3HIdvfvDknNpLREQx6QbmX6p7/Q6A98df7wKQcRCIUuolAI0my/cAOD+LNhKVufzuR+oL\nMKPe/kjitdVzI7Woic+eO8f2OUcYp09ElDfLIkwpdUMxG0JUqRLRFC4+MLs/EsWvnnkLQPrHFWm3\nTlmDERHlL2NEhYgcB+ALiCXcJ7ZXSl3mXLOIKoevBIowANh9cDDpvVl78p1EQEREo+w8tqgDwC8Q\nS8kfcbY5RJUn17FghXbk+Brs0hViZr1dWpwGb0cSEeXPThE2oJT6seMtIapQ+Q7ML4RgwI+Pzp+J\nH/x5U9rttN4x1mBERPmzE1HxIxG5RUTOFpF3aT+Ot4yoQuQbUZFOXXD031khi1BVLWri3PqjAKR/\ngLe2SJlnLBMRURbsFGENAD4FoA2x4NY7APy7k40iqiR2iq8tbYvwT2fFHt91ddMx+N2N8y23XXrx\n3MTrp25amHi9dslCs82xdslC05gNs94uSdyOzNxmIiJKz87tyKsAzFZK5f6AOiICACxoW43W5vqk\noifxAO80+zV+5zGcPG1C4n26bSPR0aGbHV1vJ53bTEdXGO2ruhPPgBwato68GL0dySqMiChfdoqw\nlwHUgY8XIsqblskFIFGIGXvCOrrCKfvt64vg6c17YttD0j5fcig6WiB975HXks5tZunKl9AfGS3c\nBobT3Y5kRAURUaHYKcLqALwmIs8BSEydYkQFUW76I1G0r+oeLcLiy7WixyzZHhi9BSiS/hamvids\ncDjzhGZ9AaZnPjvSeh0REWXHThF2i+OtIKowPbpeKV+iopKUdWZEMtyOtFF45UprKiMqiIjyl7EI\nU0o9WYyGEFWS6fqZiomKSiXWWd061Haw2xNWaInEfMfOQERUOTLOjhSRgyJyIP4zICJRETlQjMYR\nlaNgwI/W5vrEe5/hUUD6dXrJyfrpxoSNFmE1VZknQAcDdiZJx88d/5M9YURE+cv47auUGq+UmqCU\nmoDYg7uvAPBTx1tGVEZCdUEIRjO5kmdHxv7UxnyZxUVMGhPA+XNjOV6ZEi2GhkcLpM8vPF7XhlrT\n7b/XcmrS+5oq6zMkJgSwBiMiypv9fwIDUDEdAJodag9RWVq7ZCHebFtkmsmlzTg0613a0rYIW9oW\noetbF+Kk6RNH97Gok/w+SbodeV48gBUA/naTeU7Ypacnt6fa77f8HD7WYEREBWPnAd6LdW99AJoA\nDDjWIqIKY/dRQH7deCyrvqroiMLD63sS7y/9z78lXne8kBp9AQDv/t5f7DYVL2zdBwD4v64wnn1z\nb0rmGRER2WdnduSlutfDALYAuNyR1hBVoNHB7umrMH+831ophac27bJ1bP0Rv77yJdNt9vVFkt5b\nhbV2dIVx33PbEu/NMs+IiMg+O7MjbyhGQ4gqVeJ5jBl6wnzxe4EjI8Bvnt6a9XkiUXs3EYcstmtf\n1Z1yDGPmGRER2WdZhInIt9Lsp5RS33WgPUQVJ9vbkSNKYfehwfQb50FrhnHcmVV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EAAAa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\n+P+8q8r1+FutHgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faebe7386d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr2.plot(x='count', y='frequency', style='o-', logx=True, figsize = (10, 10))\n", "# plt.axvline(5,ls='dotted')\n", "plt.ylabel('Number of cell towers')\n", "plt.title('Number of towers with x number of calls placed or received over 4 months')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike the previous plot, this is not very clean at all, making the cumulative distribution plot critical. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7faebe1744d0>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MnZLNs8xqkfKdPWm2SSXmEtP4MKk1KshERCQV+138ayoxp6sOTUotqvMdgEgW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\nAAA4I5ABAAA4I5ABAAA4I5ABAAA4+3/sgEOaAGhZJAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faebf17d650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr2.plot(x='count', y='cumulative', style='o-', logx=True, figsize = (10, 10))\n", "plt.axhline(0.1,ls='dotted',lw=.5)\n", "plt.axvline(max(fr2['count'][fr2['cumulative']<.10]),ls='dotted',lw=.5)\n", "plt.axhline(0.5,ls='dotted',lw=.5)\n", "plt.axvline(max(fr2['count'][fr2['cumulative']<.50]),ls='dotted',lw=.5)\n", "plt.axhline(0.9,ls='dotted',lw=.5)\n", "plt.axvline(max(fr2['count'][fr2['cumulative']<.90]),ls='dotted',lw=.5)\n", "plt.ylabel('Cumulative fraction of cell towers')\n", "plt.title('Cumulative fraction of towers with x number of calls placed or received over 4 months')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we want to look at temporal data. First, convert the categorical `date_time_m` to a datetime object; then, extract the date component. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['datetime'] = pd.to_datetime(df['date_time_m'], format='%Y-%m-%d %H:%M:%S')\n", "df['date'] = df['datetime'].dt.floor('d') # Faster than df['datetime'].dt.date" ] }, { "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>cust_id</th>\n", " <th>date</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>11</td>\n", " <td>2016-06-07</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>11</td>\n", " <td>2016-06-08</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>11</td>\n", " <td>2016-06-09</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11</td>\n", " <td>2016-06-10</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11</td>\n", " <td>2016-06-11</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>11</td>\n", " <td>2016-06-12</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>11</td>\n", " <td>2016-09-09</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>11</td>\n", " <td>2016-09-12</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>123</td>\n", " <td>2016-07-28</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>123</td>\n", " <td>2016-07-29</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>123</td>\n", " <td>2016-07-30</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>123</td>\n", " <td>2016-07-31</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>123</td>\n", " <td>2016-08-01</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>123</td>\n", " <td>2016-08-02</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>158</td>\n", " <td>2016-06-05</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>158</td>\n", " <td>2016-07-03</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>158</td>\n", " <td>2016-07-04</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>158</td>\n", " <td>2016-07-05</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>158</td>\n", " <td>2016-07-15</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>158</td>\n", " <td>2016-07-16</td>\n", " <td>19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cust_id date count\n", "0 11 2016-06-07 19\n", "1 11 2016-06-08 16\n", "2 11 2016-06-09 39\n", "3 11 2016-06-10 2\n", "4 11 2016-06-11 2\n", "5 11 2016-06-12 6\n", "6 11 2016-09-09 5\n", "7 11 2016-09-12 10\n", "8 123 2016-07-28 3\n", "9 123 2016-07-29 33\n", "10 123 2016-07-30 4\n", "11 123 2016-07-31 24\n", "12 123 2016-08-01 52\n", "13 123 2016-08-02 41\n", "14 158 2016-06-05 18\n", "15 158 2016-07-03 12\n", "16 158 2016-07-04 41\n", "17 158 2016-07-05 13\n", "18 158 2016-07-15 27\n", "19 158 2016-07-16 19" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.groupby(['cust_id','date']).size().to_frame()\n", "df2.columns = ['count']\n", "df2.index.name = 'date'\n", "df2.reset_index(inplace=True)\n", "df2.head(20)" ] }, { "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>days</th>\n", " <th>calls</th>\n", " </tr>\n", " <tr>\n", " <th>cust_id</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11</th>\n", " <td>97</td>\n", " <td>99</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>5</td>\n", " <td>157</td>\n", " </tr>\n", " <tr>\n", " <th>158</th>\n", " <td>98</td>\n", " <td>391</td>\n", " </tr>\n", " <tr>\n", " <th>193</th>\n", " <td>6</td>\n", " <td>83</td>\n", " </tr>\n", " <tr>\n", " <th>244</th>\n", " <td>35</td>\n", " <td>31</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " days calls\n", "cust_id \n", "11 97 99\n", "123 5 157\n", "158 98 391\n", "193 6 83\n", "244 35 31" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = (df2.groupby('cust_id')['date'].max() - df2.groupby('cust_id')['date'].min()).to_frame()\n", "df3['calls'] = df2.groupby('cust_id')['count'].sum()\n", "df3.columns = ['days','calls']\n", "df3['days'] = df3['days'].dt.days\n", "df3.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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SulS10vz/aQDfxXTTs6dOCPQnALxNRFaIyHwAHwXwQJvLRClodgx+HcAzqvrl\ndpcnTSLSLyJ9zZ9LmB4U8Gx7S5UOVd2sqstUdQDTf++PquqGNhcrNSKyqDkIACKyCMD7APga5Zb5\nQFfV8wD+FsA+THeK7VHVo+0tVXpE5F4AjwNYKSInReTT7S5TitYA+Dima2gTzf/e3+5CpeQyAPtF\n5ClMV2oeUdWuG77XpS4F8BMROQzgpwAeUtUf+Llj5octEhGRP5mvoRMRkT8MdCKinGCgExHlBAOd\niCgnGOhERDnBQCciygkGOhFRTjDQiYhy4v8BZbEO5zh5x20AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fba21e34950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr = df['cust_id'].value_counts().to_frame()['cust_id'].value_counts().to_frame()\n", "\n", "# plt.scatter(np.log(df3['days']), np.log(df3['calls']))\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 41, "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>cust_id</th>\n", " <th>date</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>11</td>\n", " <td>2016-06-07</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>11</td>\n", " <td>2016-06-08</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>11</td>\n", " <td>2016-06-09</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11</td>\n", " <td>2016-06-10</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11</td>\n", " <td>2016-06-11</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cust_id date count\n", "0 11 2016-06-07 19\n", "1 11 2016-06-08 16\n", "2 11 2016-06-09 39\n", "3 11 2016-06-10 2\n", "4 11 2016-06-11 2" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fcec73c7d90>" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcf26892890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fr.plot(x='calls', y='freq', style='o', logx=True, logy=True)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fcec7098390>]" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RCmBmY4BuQH0zWw78HrgRGGdmFxA6+/aLV+Hu00pbEZECkQ9DOiIisgsU+CIi\nBUKBLyJSIBT4IiIFQoEvIlIgFPgiIgVCgS8iUiAU+CIiBeL/AQnAiASaAK2eAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fcec6bffd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x=np.log(fr['calls'])\n", "y=np.log(1-fr['freq'].cumsum()/fr['freq'].sum())\n", "plt.plot(x, y, 'r-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many home_Regions\n", "np.count_nonzero(data['home_region'].unique())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many customers\n", "np.count_nonzero(data['cust_id'].unique())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many Nulls are there in the customer ID column?\n", "df['cust_id'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many missing data are there in the customer ID?\n", "len(df['cust_id']) - df['cust_id'].count()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 11, 123, 158, ..., 30719015, 30719030, 30719039])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['cust_id'].unique()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['home_region', 'Puglia', 'Emilia-Romagna', 'Toscana', 'Lombardia',\n", " 'Campania', 'Umbria', 'Lazio', 'Piemonte', 'Liguria', 'Calabria',\n", " 'Veneto', 'Basilicata', 'Friuli Venezia Giulia', 'Molise',\n", " 'Trentino-Alto Adige', 'Marche', 'Sicilia', 'Abruzzo',\n", " \"Valle D'Aosta\", 'Sardegna'], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_italians = pd.read_csv(\"./aws-data/firence_italians_3days_past_future_sample_1K_custs.csv\", header=None)\n", "data_italians.columns = ['lat', 'lon', 'date_time_m', 'home_region', 'cust_id', 'in_florence']\n", "regions = np.array(data_italians['home_region'].unique())\n", "regions" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'Sardegna' in data['home_region']" ] } ], "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

jgarciab/wwd2017

class4/class4_timeseries.ipynb

1

174695

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Working with data 2017. Class 4\n", "## Contact\n", "Javier Garcia-Bernardo\n", "garcia@uva.nl\n", "\n", "## 0. Structure\n", "1. Stats\n", " - Definitions\n", " - What's a p-value?\n", " - One-tailed test vs two-tailed test\n", " - Count vs expected count (binomial test)\n", " - Independence between factors: ($\\chi^2$ test) \n", "2. In-class exercises to melt, pivot, concat, merge, groupby and plot.\n", "3. Read data from websited\n", "4. Time series " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rpy2.ipython extension is already loaded. To reload it, use:\n", " %reload_ext rpy2.ipython\n" ] }, { "data": { "text/html": [ "<style>.container { width:90% !important; }</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import pylab as plt\n", "import seaborn as sns\n", "from scipy.stats import chi2_contingency,ttest_ind\n", "\n", "#This allows us to use R\n", "%load_ext rpy2.ipython\n", "\n", "#Visualize in line\n", "%matplotlib inline\n", "\n", "\n", "#Be able to plot images saved in the hard drive\n", "from IPython.display import Image,display\n", "\n", "#Make the notebook wider\n", "from IPython.core.display import display, HTML \n", "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Read tables from websites\n", "pandas is cool\n", "- Use pd.read_html(url)\n", "- It returns a list of all tables in the website\n", "- It tries to guess the encoding of the website, but with no much success." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'list'> 1\n" ] }, { "data": { "text/plain": [ "[ Year\\n\\t\\t\\timposed Year\\n\\t\\t\\tended \\\n", " 0 1914 1918 \n", " 1 1917 1918 \n", " 2 1918 1920 \n", " 3 1921 1921 \n", " 4 1925 1925 \n", " 5 1932 1935 \n", " 6 1932 1935 \n", " 7 1933 1933 \n", " 8 1935 1936 \n", " 9 1938 1947 \n", " 10 1939 1945 \n", " 11 1939 1945 \n", " 12 1940 1941 \n", " 13 1941 1945 \n", " 14 1941 1945 \n", " 15 1944 1947 \n", " 16 1946 Ongoing \n", " 17 1948 1994 \n", " 18 1948 1955 \n", " 19 1948 1949 \n", " 20 1948 1949 \n", " 21 1948 1949 \n", " 22 1948 1949 \n", " 23 1948 1948 \n", " 24 1949 1970 \n", " 25 1950 Ongoing \n", " 26 1950 Ongoing \n", " 27 1950 1953 \n", " 28 1951 1953 \n", " 29 1954 1984 \n", " .. ... ... \n", " 174 1993 1996 \n", " 175 1993 1994 \n", " 176 1993 1993 \n", " 177 1994 1998 \n", " 178 1994 1995 \n", " 179 1994 1995 \n", " 180 1994 1995 \n", " 181 1995 1998 \n", " 182 1995 1998 \n", " 183 1995 1996 \n", " 184 1995 1995 \n", " 185 1996 2000 \n", " 186 1996 1999 \n", " 187 1996 1998 \n", " 188 1996 1998 \n", " 189 1996 1996 \n", " 190 1997 Ongoing \n", " 191 1997 2003 \n", " 192 1998 2001 \n", " 193 1998 2001 \n", " 194 1998 2001 \n", " 195 1998 1999 \n", " 196 1998 1999 \n", " 197 1999 2002 \n", " 198 1999 2002 \n", " 199 1999 2002 \n", " 200 1991 2001 \n", " 201 2000 2006 \n", " 202 2000 2000 \n", " 203 2002 2006 \n", " \n", " Principal \\n\\t\\t\\tsendera Target \\n\\t\\t\\tcountry \\\n", " 0 United Kingdom Germany \n", " 1 United States Japan \n", " 2 United Kingdom Russia \n", " 3 League of Nations Yugoslavia \n", " 4 League of Nations Greece \n", " 5 League of Nations Paraguay \n", " 6 League of Nations Bolivia \n", " 7 United Kingdom USSR \n", " 8 League of Nations Italy \n", " 9 United States Mexico \n", " 10 United States Germany \n", " 11 United States Germany \n", " 12 United States Japan \n", " 13 United States Japan \n", " 14 United States Japan \n", " 15 United States Argentina \n", " 16 Arab League Israel \n", " 17 United States USSR, Comecom \n", " 18 USSR Yugoslavia \n", " 19 United States Netherlands \n", " 20 USSR France \n", " 21 USSR United Kingdom \n", " 22 USSR United States \n", " 23 India Hyderabad \n", " 24 United States China \n", " 25 United States North Korea \n", " 26 United States North Korea \n", " 27 United States China \n", " 28 United States Iran \n", " 29 Spain United Kingdom \n", " .. ... ... \n", " 174 USSR/Russia Kazakhstan \n", " 175 United States North Korea \n", " 176 United States Guatemala \n", " 177 United States Gambia \n", " 178 Greece Macedonia \n", " 179 Greece Albania \n", " 180 United Nations Rwanda \n", " 181 United States Peru \n", " 182 United States Ecuador \n", " 183 Australia France \n", " 184 European Union Turkey \n", " 185 United States Nigeria \n", " 186 East African members of Organization of Africa... Burundi \n", " 187 United States Zambia \n", " 188 United States Colombia \n", " 189 United States, Mercosur members Paraguay \n", " 190 United Nations Cambodia, Khmer Rouge \n", " 191 United Nations Sierra Leone \n", " 192 United States Pakistan \n", " 193 United States India \n", " 194 United States Yugoslavia, Serbia \n", " 195 United States Yugoslavia, Serbia \n", " 196 Turkey Italy \n", " 197 United States Indonesia \n", " 198 United Nations Afghanistan \n", " 199 United States Ivory Coast \n", " 200 United States Pakistan \n", " 201 Economic Community of West African States Liberia \n", " 202 United States Ecuador \n", " 203 United States North Korea \n", " \n", " Policy \\n\\t\\t\\tgoal \\\n", " 0 Military victory \n", " 1 Shipping for Allies \n", " 2 Destabilize Bolsheviks \n", " 3 Military disruption vs. Albania \n", " 4 Withdraw from Bulgaria \n", " 5 Settle the Chaco War \n", " 6 Settle the Chaco War \n", " 7 Release British citizens \n", " 8 Withdraw from Abyssinia \n", " 9 Expropriation dispute \n", " 10 Regime change \n", " 11 Military victory \n", " 12 Withdraw from Southeast Asia \n", " 13 Regime change \n", " 14 Military victory \n", " 15 Destabilize Perón \n", " 16 Create Palestinian homeland \n", " 17 Impair military potential \n", " 18 Destabilize Tito government \n", " 19 Recognize Indonesia \n", " 20 Berlin blockade \n", " 21 Berlin blockade \n", " 22 Berlin blockade \n", " 23 Assimilate Hyderabad \n", " 24 Impair military potential \n", " 25 Regime change \n", " 26 Military impairment \n", " 27 Military disruption, Korea \n", " 28 Destabilize Mossadegh \n", " 29 Sovereignty over Gibraltar \n", " .. ... \n", " 174 Independence issues, energy resources \n", " 175 Nuclear proliferation \n", " 176 Coup \n", " 177 Democracy \n", " 178 National identity \n", " 179 Release of jailed ethnic Greek leaders \n", " 180 Civil violence \n", " 181 Border conflict \n", " 182 Border conflict \n", " 183 Nuclear testing \n", " 184 Human rights \n", " 185 Democracy \n", " 186 Democracy \n", " 187 Human rights, constitutional reform \n", " 188 Narcotics, human rights \n", " 189 Possible coup attempt \n", " 190 Democracy \n", " 191 Democracy \n", " 192 Nuclear policy \n", " 193 Nuclear proliferation \n", " 194 Destabilize Milošević \n", " 195 Kosovo \n", " 196 Kurdish leader \n", " 197 Independence for East Timor \n", " 198 Extradite Osama bin Laden \n", " 199 Coup, democracy \n", " 200 Coup, democracy \n", " 201 Support for RUF \n", " 202 Coup \n", " 203 Nuclear proliferation \n", " \n", " \\nSuccess\\n\\t\\t\\tscoreb\\n\\t\\t\\t(scale \\n\\t\\t\\t1 to 16)\\n \\\n", " 0 12 \n", " 1 4 \n", " 2 2 \n", " 3 16 \n", " 4 16 \n", " 5 6 \n", " 6 6 \n", " 7 12 \n", " 8 2 \n", " 9 9 \n", " 10 8 \n", " 11 12 \n", " 12 1 \n", " 13 8 \n", " 14 12 \n", " 15 4 \n", " 16 4 \n", " 17 6 \n", " 18 1 \n", " 19 16 \n", " 20 2 \n", " 21 2 \n", " 22 2 \n", " 23 8 \n", " 24 2 \n", " 25 1 \n", " 26 4 \n", " 27 2 \n", " 28 12 \n", " 29 6 \n", " .. ... \n", " 174 9 \n", " 175 9 \n", " 176 16 \n", " 177 6 \n", " 178 9 \n", " 179 16 \n", " 180 2 \n", " 181 8 \n", " 182 8 \n", " 183 4 \n", " 184 9 \n", " 185 9 \n", " 186 6 \n", " 187 4 \n", " 188 6 \n", " 189 12 \n", " 190 4 \n", " 191 8 \n", " 192 4 \n", " 193 2 \n", " 194 12 \n", " 195 6 \n", " 196 9 \n", " 197 12 \n", " 198 2 \n", " 199 6 \n", " 200 2 \n", " 201 8 \n", " 202 12 \n", " 203 1 \n", " \n", " \\nCost\\n\\t\\t\\tto\\n\\t\\t\\ttarget\\n\\t\\t\\t(percent\\n\\t\\t\\tof GNP)\\n \n", " 0 7.1 \n", " 1 0.8 \n", " 2 4.1 \n", " 3 – \n", " 4 – \n", " 5 negligible \n", " 6 2.6 \n", " 7 0.02 \n", " 8 1.7 \n", " 9 0.2 \n", " 10 1.4 \n", " 11 1.4 \n", " 12 0.9 \n", " 13 1.9 \n", " 14 1.9 \n", " 15 0.8 \n", " 16 1.7 \n", " 17 0.2 \n", " 18 –2.5 \n", " 19 0.2 \n", " 20 negligible \n", " 21 0.1 \n", " 22 0.1 \n", " 23 2 \n", " 24 0.5 \n", " 25 1.2 \n", " 26 1.2 \n", " 27 0.5 \n", " 28 14.3 \n", " 29 0.009 \n", " .. ... \n", " 174 4.6 \n", " 175 – \n", " 176 1.3 \n", " 177 –0.02 \n", " 178 3.2 \n", " 179 2.9 \n", " 180 –5.6 \n", " 181 negligible \n", " 182 negligible \n", " 183 negligible \n", " 184 negligible \n", " 185 4.9 \n", " 186 10.4 \n", " 187 2.9 \n", " 188 0.2 \n", " 189 – \n", " 190 2.6 \n", " 191 5.8 \n", " 192 1.0 \n", " 193 0.2 \n", " 194 8.3 \n", " 195 8.3 \n", " 196 – \n", " 197 0.2 \n", " 198 1.1 \n", " 199 0.3 \n", " 200 negligible \n", " 201 18.8 \n", " 202 – \n", " 203 0.6 \n", " \n", " [204 rows x 7 columns]]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_html(\"https://piie.com/summary-economic-sanctions-episodes-1914-2006\",encoding=\"UTF-8\")\n", "print(type(df),len(df))\n", "df" ] }, { "cell_type": "code", "execution_count": 32, "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>Year\n", "\t\t\timposed</th>\n", " <th>Year\n", "\t\t\tended</th>\n", " <th>Principal \n", "\t\t\tsendera</th>\n", " <th>Target \n", "\t\t\tcountry</th>\n", " <th>Policy \n", "\t\t\tgoal</th>\n", " <th>Success\n", "\t\t\tscoreb\n", "\t\t\t(scale \n", "\t\t\t1 to 16)</th>\n", " <th>Cost\n", "\t\t\tto\n", "\t\t\ttarget\n", "\t\t\t(percent\n", "\t\t\tof GNP)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1914</td>\n", " <td>1918</td>\n", " <td>United Kingdom</td>\n", " <td>Germany</td>\n", " <td>Military victory</td>\n", " <td>12</td>\n", " <td>7.1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1917</td>\n", " <td>1918</td>\n", " <td>United States</td>\n", " <td>Japan</td>\n", " <td>Shipping for Allies</td>\n", " <td>4</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1918</td>\n", " <td>1920</td>\n", " <td>United Kingdom</td>\n", " <td>Russia</td>\n", " <td>Destabilize Bolsheviks</td>\n", " <td>2</td>\n", " <td>4.1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1921</td>\n", " <td>1921</td>\n", " <td>League of Nations</td>\n", " <td>Yugoslavia</td>\n", " <td>Military disruption vs. Albania</td>\n", " <td>16</td>\n", " <td>–</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1925</td>\n", " <td>1925</td>\n", " <td>League of Nations</td>\n", " <td>Greece</td>\n", " <td>Withdraw from Bulgaria</td>\n", " <td>16</td>\n", " <td>–</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1932</td>\n", " <td>1935</td>\n", " <td>League of Nations</td>\n", " <td>Paraguay</td>\n", " <td>Settle the Chaco War</td>\n", " <td>6</td>\n", " <td>negligible</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1932</td>\n", " <td>1935</td>\n", " <td>League of Nations</td>\n", " <td>Bolivia</td>\n", " <td>Settle the Chaco War</td>\n", " <td>6</td>\n", " <td>2.6</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1933</td>\n", " <td>1933</td>\n", " <td>United Kingdom</td>\n", " <td>USSR</td>\n", " <td>Release British citizens</td>\n", " <td>12</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1935</td>\n", " <td>1936</td>\n", " <td>League of Nations</td>\n", " <td>Italy</td>\n", " <td>Withdraw from Abyssinia</td>\n", " <td>2</td>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1938</td>\n", " <td>1947</td>\n", " <td>United States</td>\n", " <td>Mexico</td>\n", " <td>Expropriation dispute</td>\n", " <td>9</td>\n", " <td>0.2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year\\n\\t\\t\\timposed Year\\n\\t\\t\\tended Principal \\n\\t\\t\\tsendera \\\n", "0 1914 1918 United Kingdom \n", "1 1917 1918 United States \n", "2 1918 1920 United Kingdom \n", "3 1921 1921 League of Nations \n", "4 1925 1925 League of Nations \n", "5 1932 1935 League of Nations \n", "6 1932 1935 League of Nations \n", "7 1933 1933 United Kingdom \n", "8 1935 1936 League of Nations \n", "9 1938 1947 United States \n", "\n", " Target \\n\\t\\t\\tcountry Policy \\n\\t\\t\\tgoal \\\n", "0 Germany Military victory \n", "1 Japan Shipping for Allies \n", "2 Russia Destabilize Bolsheviks \n", "3 Yugoslavia Military disruption vs. Albania \n", "4 Greece Withdraw from Bulgaria \n", "5 Paraguay Settle the Chaco War \n", "6 Bolivia Settle the Chaco War \n", "7 USSR Release British citizens \n", "8 Italy Withdraw from Abyssinia \n", "9 Mexico Expropriation dispute \n", "\n", " \\nSuccess\\n\\t\\t\\tscoreb\\n\\t\\t\\t(scale \\n\\t\\t\\t1 to 16)\\n \\\n", "0 12 \n", "1 4 \n", "2 2 \n", "3 16 \n", "4 16 \n", "5 6 \n", "6 6 \n", "7 12 \n", "8 2 \n", "9 9 \n", "\n", " \\nCost\\n\\t\\t\\tto\\n\\t\\t\\ttarget\\n\\t\\t\\t(percent\\n\\t\\t\\tof GNP)\\n \n", "0 7.1 \n", "1 0.8 \n", "2 4.1 \n", "3 – \n", "4 – \n", "5 negligible \n", "6 2.6 \n", "7 0.02 \n", "8 1.7 \n", "9 0.2 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0].head(10)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Year\\n\\t\\t\\timposed', 'Year\\n\\t\\t\\tended', 'Principal \\n\\t\\t\\tsendera',\n", " 'Target \\n\\t\\t\\tcountry', 'Policy \\n\\t\\t\\tgoal',\n", " '\\nSuccess\\n\\t\\t\\tscoreb\\n\\t\\t\\t(scale \\n\\t\\t\\t1 to 16)\\n',\n", " '\\nCost\\n\\t\\t\\tto\\n\\t\\t\\ttarget\\n\\t\\t\\t(percent\\n\\t\\t\\tof GNP)\\n'],\n", " dtype='object')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0].columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Year\\n\\t\\t\\timposed', 'Year\\n\\t\\t\\tended', 'Principal \\n\\t\\t\\tsendera',\n", " 'Target \\n\\t\\t\\tcountry', 'Policy \\n\\t\\t\\tgoal',\n", " '\\nSuccess\\n\\t\\t\\tscoreb\\n\\t\\t\\t(scale \\n\\t\\t\\t1 to 16)\\n',\n", " '\\nCost\\n\\t\\t\\tto\\n\\t\\t\\ttarget\\n\\t\\t\\t(percent\\n\\t\\t\\tof GNP)\\n'],\n", " dtype='object')\n" ] } ], "source": [ "df = pd.read_html(\"https://piie.com/summary-economic-sanctions-episodes-1914-2006\",encoding=\"UTF-8\")\n", "df = df[0]\n", "print(df.columns)\n", "df.columns = ['Year imposed', 'Year ended', 'Principal sender',\n", " 'Target country', 'Policy goal',\n", " 'Success score (scale 1 to 16)',\n", " 'Cost to target (percent of GNP)']\n", "\n", "df = df.replace('negligible', 0) \n", "df = df.replace(\"–\",\"-\",regex=True) #the file uses long dashes\n", "df.to_csv(\"data/economic_sanctions.csv\",index=None,sep=\"\\t\")" ] }, { "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>Year imposed</th>\n", " <th>Year ended</th>\n", " <th>Principal sender</th>\n", " <th>Target country</th>\n", " <th>Policy goal</th>\n", " <th>Success score (scale 1 to 16)</th>\n", " <th>Cost to target (percent of GNP)</th>\n", " <th>Duration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1914</td>\n", " <td>1918.0</td>\n", " <td>United Kingdom</td>\n", " <td>Germany</td>\n", " <td>Military victory</td>\n", " <td>12</td>\n", " <td>7.1</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1917</td>\n", " <td>1918.0</td>\n", " <td>United States</td>\n", " <td>Japan</td>\n", " <td>Shipping for Allies</td>\n", " <td>4</td>\n", " <td>0.8</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1918</td>\n", " <td>1920.0</td>\n", " <td>United Kingdom</td>\n", " <td>Russia</td>\n", " <td>Destabilize Bolsheviks</td>\n", " <td>2</td>\n", " <td>4.1</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1921</td>\n", " <td>1921.0</td>\n", " <td>League of Nations</td>\n", " <td>Yugoslavia</td>\n", " <td>Military disruption vs. Albania</td>\n", " <td>16</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1925</td>\n", " <td>1925.0</td>\n", " <td>League of Nations</td>\n", " <td>Greece</td>\n", " <td>Withdraw from Bulgaria</td>\n", " <td>16</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year imposed Year ended Principal sender Target country \\\n", "0 1914 1918.0 United Kingdom Germany \n", "1 1917 1918.0 United States Japan \n", "2 1918 1920.0 United Kingdom Russia \n", "3 1921 1921.0 League of Nations Yugoslavia \n", "4 1925 1925.0 League of Nations Greece \n", "\n", " Policy goal Success score (scale 1 to 16) \\\n", "0 Military victory 12 \n", "1 Shipping for Allies 4 \n", "2 Destabilize Bolsheviks 2 \n", "3 Military disruption vs. Albania 16 \n", "4 Withdraw from Bulgaria 16 \n", "\n", " Cost to target (percent of GNP) Duration \n", "0 7.1 4.0 \n", "1 0.8 1.0 \n", "2 4.1 2.0 \n", "3 NaN 0.0 \n", "4 NaN 0.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"data/economic_sanctions.csv\",sep=\"\\t\",na_values=[\"-\",\"Ongoing\"])\n", "df[\"Duration\"] = df[\"Year ended\"] - df[\"Year imposed\"]\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fda70222240>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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C9QbmtjXDJR9cES4Cm7p7qqXqq6jZ+3Uz654qkDVZtGgpTU3LC5bJclFZWUG3bp1VHqgs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\nNs1sfQ35pgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fda705b9b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x=\"Duration\",y=\"Cost to target (percent of GNP)\",data=df,fit_reg=False,hue=\"Year imposed\",legend=False,palette=\"YlOrBr\")\n", "plt.ylim((-2,10))\n", "plt.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5),ncol=4)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Parse dates\n", "pandas is cool\n", "- Use parse_dates=[columns] when reading the file\n", "- It parses the date" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"Month\"\t\"Exchange Rate TWI. May 1970 ? Aug 1995.\"\r", "\r\n", "\"1970-05\"\t100.0\r", "\r\n", "\"1970-06\"\t99.6\r", "\r\n", "\"1970-07\"\t99.4\r", "\r\n", "\"1970-08\"\t99.1\r", "\r\n", "\"1970-09\"\t99.2\r", "\r\n", "\"1970-10\"\t99.2\r", "\r\n", "\"1970-11\"\t99.2\r", "\r\n", "\"1970-12\"\t99.3\r", "\r\n", "\"1971-01\"\t100.0\r", "\r\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1. Use parse_dates when reading the file" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Month</th>\n", " <th>Exchange Rate TWI. May 1970 ? Aug 1995.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1970-05-01</td>\n", " <td>100.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1970-06-01</td>\n", " <td>99.6</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1970-07-01</td>\n", " <td>99.4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1970-08-01</td>\n", " <td>99.1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1970-09-01</td>\n", " <td>99.2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Month Exchange Rate TWI. May 1970 ? Aug 1995.\n", "0 1970-05-01 100.0\n", "1 1970-06-01 99.6\n", "2 1970-07-01 99.4\n", "3 1970-08-01 99.1\n", "4 1970-09-01 99.2" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"data/exchange-rate-twi-may-1970-aug-1.tsv\",sep=\"\\t\",parse_dates=[\"Month\"],skipfooter=2)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2. You can now filter by date" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date</th>\n", " <th>Rate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>121</th>\n", " <td>1980-06-01</td>\n", " <td>85.0</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>1980-07-01</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>1980-08-01</td>\n", " <td>85.8</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>1980-09-01</td>\n", " <td>85.8</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>1980-10-01</td>\n", " <td>86.2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Rate\n", "121 1980-06-01 85.0\n", "122 1980-07-01 85.5\n", "123 1980-08-01 85.8\n", "124 1980-09-01 85.8\n", "125 1980-10-01 86.2" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#filter by time\n", "df_after1980 = df.loc[df[\"Month\"] > \"1980-05-02\"] #year-month-date\n", "df_after1980.columns = [\"Date\",\"Rate\"]\n", "df_after1980.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3. And still extract columns of year and month" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date</th>\n", " <th>Rate</th>\n", " <th>Year</th>\n", " <th>Month</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1970-05-01</td>\n", " <td>100.0</td>\n", " <td>1970</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1970-06-01</td>\n", " <td>99.6</td>\n", " <td>1970</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1970-07-01</td>\n", " <td>99.4</td>\n", " <td>1970</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1970-08-01</td>\n", " <td>99.1</td>\n", " <td>1970</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1970-09-01</td>\n", " <td>99.2</td>\n", " <td>1970</td>\n", " <td>9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Rate Year Month\n", "0 1970-05-01 100.0 1970 5\n", "1 1970-06-01 99.6 1970 6\n", "2 1970-07-01 99.4 1970 7\n", "3 1970-08-01 99.1 1970 8\n", "4 1970-09-01 99.2 1970 9" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make columns with year and month (useful for models)\n", "df_after1980[\"Year\"] = df_after1980[\"Date\"].apply(lambda x: x.year)\n", "df_after1980[\"Month\"] = df_after1980[\"Date\"].apply(lambda x: x.month)\n", "df_after1980.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4. You can resample the data with a specific frequency\n", "- Very similar to groupby.\n", "- Groups the data with a specific frequency\n", " - \"A\" = End of year\n", " - \"B\" = Business day\n", " - others: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases\n", "- Then you tell pandas to apply a function to the group (mean/max/median...)\n" ] }, { "cell_type": "code", "execution_count": 38, "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>Rate</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1980-12-31</th>\n", " <td>86.028571</td>\n", " </tr>\n", " <tr>\n", " <th>1981-12-31</th>\n", " <td>91.233333</td>\n", " </tr>\n", " <tr>\n", " <th>1982-12-31</th>\n", " <td>86.441667</td>\n", " </tr>\n", " <tr>\n", " <th>1983-12-31</th>\n", " <td>79.766667</td>\n", " </tr>\n", " <tr>\n", " <th>1984-12-31</th>\n", " <td>81.658333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Rate\n", "Date \n", "1980-12-31 86.028571\n", "1981-12-31 91.233333\n", "1982-12-31 86.441667\n", "1983-12-31 79.766667\n", "1984-12-31 81.658333" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#resample\n", "df_after1980_resampled = df_after1980.resample(\"A\",on=\"Date\").mean()\n", "display(df_after1980_resampled.head())" ] }, { "cell_type": "code", "execution_count": 23, "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>Date</th>\n", " <th>Rate</th>\n", " <th>Year</th>\n", " <th>Month</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1970-12-31</td>\n", " <td>99.375000</td>\n", " <td>1970.0</td>\n", " <td>8.5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1971-12-31</td>\n", " <td>99.741667</td>\n", " <td>1971.0</td>\n", " <td>6.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1972-12-31</td>\n", " <td>99.225000</td>\n", " <td>1972.0</td>\n", " <td>6.5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1973-12-31</td>\n", " <td>111.208333</td>\n", " <td>1973.0</td>\n", " <td>6.5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1974-12-31</td>\n", " <td>113.883333</td>\n", " <td>1974.0</td>\n", " <td>6.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Rate Year Month\n", "0 1970-12-31 99.375000 1970.0 8.5\n", "1 1971-12-31 99.741667 1971.0 6.5\n", "2 1972-12-31 99.225000 1972.0 6.5\n", "3 1973-12-31 111.208333 1973.0 6.5\n", "4 1974-12-31 113.883333 1974.0 6.5" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_after1980_resampled = df_after1980_resampled.reset_index()\n", "df_after1980_resampled.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.5 And of course plot it with a line plot" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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N9se+nnXLMhjNxWxL3MkvcVvIteahKVlMtKt/DJNiJtDFv5P93H7BvegV1J3j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\nIiKimiQaERERUU0SjYiIiKjmvxRorxzGzV66AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fda69b65160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let's visualize it\n", "plt.figure(figsize=(6,4))\n", "plt.plot(df_after1980[\"Date\"],df_after1980[\"Rate\"],label=\"Before resampling\")\n", "plt.plot(df_after1980_resampled[\"Date\"],df_after1980_resampled[\"Rate\"],label=\"After resampling\")\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"Rate\")\n", "plt.legend()\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

MartyWeissman/Python-for-number-theory

P3wNT Notebook 3.ipynb

2

64678

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 3: Lists and the sieve of Eratosthenes in Python 3.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python provides a powerful set of tools to create and manipulate lists of data. In this part, we take a deep dive into the Python list type. We use Python lists to implement and optimize the Sieve of Eratosthenes, which will produce a list of all prime numbers up to a big number (like 10 million) in a snap. Along the way, we introduce some Python techniques for mathematical functions and data analysis. This programming lesson is meant to complement Chapter 2 of [An Illustrated Theory of Numbers](http://bookstore.ams.org/mbk-105), and mathematical background can be found there." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "- [Primality test](#primetest)\n", "- [List manipulation](#lists)\n", "- [The sieve](#sieve)\n", "- [Data analysis](#analysis)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='primetest'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Primality testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before diving into lists, we recall the **brute force** primality test that we created in the last lesson. To test whether a number `n` is prime, we can simply check for factors. This yields the following primality test." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_prime(n):\n", " '''\n", " Checks whether the argument n is a prime number.\n", " Uses a brute force search for factors between 1 and n.\n", " '''\n", " for j in range(2,n): # the range of numbers 2,3,...,n-1.\n", " if n%j == 0: # is n divisible by j?\n", " print(\"{} is a factor of {}.\".format(j,n))\n", " return False\n", " return True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also implement this test with a **while loop** instead of a for loop. This doesn't make much of a difference, in Python 3.x. (In Python 2.x, this would save memory)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_prime(n):\n", " '''\n", " Checks whether the argument n is a prime number.\n", " Uses a brute force search for factors between 1 and n.\n", " '''\n", " j = 2\n", " while j < n: # j will proceed through the list of numbers 2,3,...,n-1.\n", " if n%j == 0: # is n divisible by j?\n", " print(\"{} is a factor of {}.\".format(j,n))\n", " return False\n", " j = j + 1 # There's a Python abbreviation for this: j += 1.\n", " return True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime(10001)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime(101)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $n$ is a prime number, then the `is_prime(n)` function will iterate through all the numbers between $2$ and $n-1$. But this is overkill! Indeed, if $n$ is not prime, it will have a factor between $2$ and the square root of $n$. This is because factors come in pairs: if $ab = n$, then one of the factors, $a$ or $b$, must be less than or equal to the square root of $n$. So it suffices to search for factors up to (and including) the square root of $n$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We haven't worked with square roots in Python yet. But Python comes with a [standard math package](https://docs.python.org/2/library/math.html) which enables square roots, trig functions, logs, and more. Click the previous link for documentation. This package doesn't load automatically when you start Python, so you have to load it with a little Python code." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from math import sqrt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This command **imports** the square root function (`sqrt`) from the **package** called `math`. Now you can find square roots." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sqrt(1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a few different ways to import functions from packages. The above syntax is a good starting point, but sometimes problems can arise if different packages have functions with the same name. Here are a few methods of importing the `sqrt` function and how they differ.\n", "\n", "`from math import sqrt`: After this command, `sqrt` will refer to the function from the `math` package (overriding any previous definition).\n", "\n", "`import math`: After this command, all the functions from the `math` package will be imported. But to call `sqrt`, you would type a command like `math.sqrt(1000)`. This is convenient if there are potential conflicts with other packages.\n", "\n", "`from math import *`: After this command, all the functions from the `math` package will be imported. To call them, you can access them directly with a command like `sqrt(1000)`. This can easily cause conflicts with other packages, since packages can have hundreds of functions in them!\n", "\n", "`import math as mth`: Some people like abbreviations. This imports all the functions from the `math` package. To call one, you type a command like `mth.sqrt(1000)`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "math.sqrt(1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "factorial(10) # This will cause an error!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "math.factorial(10) # This is ok, since the math package comes with a function called factorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's improve our `is_prime(n)` function by searching for factors only up to the square root of the number `n`. We consider two options." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_prime_slow(n):\n", " '''\n", " Checks whether the argument n is a prime number.\n", " Uses a brute force search for factors between 1 and n.\n", " '''\n", " j = 2\n", " while j <= sqrt(n): # j will proceed through the list of numbers 2,3,... up to sqrt(n).\n", " if n%j == 0: # is n divisible by j?\n", " print(\"{} is a factor of {}.\".format(j,n))\n", " return False\n", " j = j + 1 # There's a Python abbreviation for this: j += 1.\n", " return True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_prime_fast(n):\n", " '''\n", " Checks whether the argument n is a prime number.\n", " Uses a brute force search for factors between 1 and n.\n", " '''\n", " j = 2\n", " root_n = sqrt(n)\n", " while j <= root_n: # j will proceed through the list of numbers 2,3,... up to sqrt(n).\n", " if n%j == 0: # is n divisible by j?\n", " print(\"{} is a factor of {}.\".format(j,n))\n", " return False\n", " j = j + 1 # There's a Python abbreviation for this: j += 1.\n", " return True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime_fast(1000003)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime_slow(1000003)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I've chosen function names with \"fast\" and \"slow\" in them. But what makes them faster or slower? Are they faster than the original? And how can we tell?\n", "\n", "Python comes with a great set of tools for these questions. The simplest (for the user) are the time utilities. By placing the **magic** `%timeit` before a command, Python does something like the following:\n", "\n", "1. Python makes a little container in your computer devoted to the computations, to avoid interference from other running programs if possible.\n", "2. Python executes the command lots and lots of times.\n", "3. Python averages the amount of time taken for each execution. \n", "\n", "Give it a try below, to compare the speed of the functions `is_prime` (the original) with the new `is_prime_fast` and `is_prime_slow`. Note that the `%timeit` commands might take a little while." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit is_prime_fast(1000003)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit is_prime_slow(1000003)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit is_prime(1000003)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time is measured in seconds, milliseconds (1 ms = 1/1000 second), microseconds (1 µs = 1/1,000,000 second), and nanoseconds (1 ns = 1/1,000,000,000 second). So it might appear at first that `is_prime` is the fastest, or about the same speed. But check the units! The other two approaches are about a thousand times faster! How much faster were they on your computer?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime_fast(10000000000037) # Don't try this with `is_prime` unless you want to wait for a long time!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, the `is_prime_fast(n)` function will go through a loop of length about `sqrt(n)` when `n` is prime. But `is_prime(n)` will go through a loop of length about `n`. Since `sqrt(n)` is much less than `n`, especially when `n` is large, the `is_prime_fast(n)` function is much faster.\n", "\n", "Between `is_prime_fast` and `is_prime_slow`, the difference is that the `fast` version **precomputes** the square root `sqrt(n)` before going through the loop, where the `slow` version repeats the `sqrt(n)` every time the loop is repeated. Indeed, writing `while j <= sqrt(n):` suggests that Python might execute `sqrt(n)` every time to check. This *might* lead to Python computing the same square root a million times... unnecessarily! \n", "\n", "A basic principle of programming is to **avoid repetition**. If you have the memory space, just compute once and store the result. It will probably be faster to pull the result out of memory than to compute it again.\n", "\n", "Python does tend to be pretty smart, however. It's possible that Python **is precomputing** `sqrt(n)` even in the slow loop, just because it's clever enough to tell in advance that the same thing is being computed over and over again. This depends on your Python version and takes place behind the scenes. If you want to figure it out, there's a whole set of tools (for advanced programmers) like the [disassembler](https://docs.python.org/2.7/library/dis.html) to figure out what Python is doing." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "is_prime_fast(10**14 + 37) # This might get a bit of delay." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a function `is_prime_fast(n)` that is speedy for numbers `n` in the trillions! You'll probably start to hit a delay around $10^{15}$ or so, and the delays will become intolerable if you add too many more digits. In a future lesson, we will see a different primality test that will be essentially instant even for numbers around $10^{1000}$! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. To check whether a number `n` is prime, you can first check whether `n` is even, and then check whether `n` has any odd factors. Change the `is_prime_fast` function by implementing this improvement. How much of a speedup did you get?\n", "\n", "2. Use the `%timeit` tool to study the speed of `is_prime_fast` for various sizes of `n`. Using 10-20 data points, make a graph relating the size of `n` to the time taken by the `is_prime_fast` function.\n", "\n", "3. Write a function `is_square(n)` to test whether a given integer `n` is a perfect square (like 0, 1, 4, 9, 16, etc.). How fast can you make it run? Describe the different approaches you try and which are fastest." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='lists'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List manipulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have already (briefly) encountered the `list` type in Python. Recall that the `range` command produces a range, which can be used to produce a list. For example, `list(range(10))` produces the list `[0,1,2,3,4,5,6,7,8,9]`. You can also create your own list by a writing out its terms, e.g. `L = [4,7,10]`.\n", "\n", "Here we work with lists, and a very Pythonic approach to list manipulation. With practice, this can be a powerful tool to write fast algorithms, exploiting the hard-wired capability of your computer to shift and slice large chunks of data. Our application will be to implement the Sieve of Eratosthenes, producing a long list of prime numbers (without using any `is_prime` test along the way)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by creating two lists to play with." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L = [0,'one',2,'three',4,'five',6,'seven',8,'nine',10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List terms and indices\n", "\n", "Notice that the entries in a list can be of any type. The above list `L` has some integer entries and some string entries. Lists are **ordered** in Python, **starting at zero**. One can access the $n^{th}$ entry in a list with a command like `L[n]`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[3]) # Note that Python has slightly different approaches to the print-function, and the output above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[4]) # We will use the print function, because it makes our printing intentions clear." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The location of an entry is called its **index**. So *at* the index 3, the list `L` stores the entry `three`. Note that the same entry can occur in many places in a list. E.g. `[7,7,7]` is a list with 7 at the zeroth, first, and second index." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[-1])\n", "print(L[-2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last bit of code demonstrates a cool Python trick. The \"-1st\" entry in a list refers to the last entry. The \"-2nd entry\" refers to the second-to-last entry, and so on. It gives a convenient way to access both sides of the list, even if you don't know how long it is.\n", "\n", "Of course, you can use Python to find out how long a list is." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use Python to find the sum of a list of numbers." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum([1,2,3,4,5])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(range(100)) # Be careful. This is the sum of which numbers? # The sum function can take lists or ranges." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List slicing\n", "\n", "**Slicing** lists allows us to create new lists (or ranges) from old lists (or ranges), by chopping off one end or the other, or even slicing out entries at a fixed interval. The simplest syntax has the form `L[a:b]` where `a` denotes the index of the starting entry and index of the final entry is one less than `b`. It is best to try a few examples to get a feel for it.\n", "\n", "Slicing a list with a command like `L[a:b]` doesn't actually *change* the original list `L`. It just extracts some terms from the list and outputs those terms. Soon enough, we will change the list `L` using a list assignment." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[0:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[5:11] # Notice that L[0:5] and L[5:11] together recover the whole list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[3:7]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This continues the strange (for beginners) Python convention of starting at the first number and ending just before the last number. Compare to `range(3,7)`, for example. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command `L[0:5]` can be replaced by `L[:5]` to abbreviate. The empty opening index tells Python to start at the beginning. Similarly, the command `L[5:11]` can be replaced by `L[5:]`. The empty closing index tells Python to end the slice and the end. This is helpful if one doesn't know where the list ends." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[3:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like the `range` command, list slicing can take an optional third argument to give a step size. To understand this, try the command below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[2:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[2:10:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If, in this three-argument syntax, the first or second argument is absent, then the slice starts at the beginning of the list or ends at the end of the list accordingly." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L # Just a reminder. We haven't modified the original list!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[:9:3] # Start at zero, go up to (but not including) 9, by steps of 3." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[2: :3] # Start at two, go up through the end of the list, by steps of 3." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[::3] # Start at zero, go up through the end of the list, by steps of 3." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing list slices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not only can we extract and study terms or slices of a list, we can change them by assignment. The simplest case would be changing a single term of a list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L) # Start with the list L." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[5] = 'Bacon!'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L) # What do you think L is now?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[2::3]) # What do you think this will do?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can change an entire slice of a list with a single assignment. Let's change the first two terms of `L` in one line." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[:2] = ['Pancakes', 'Ham'] # What was L[:2] before?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L) # Oh... what have we done!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can change a slice of a list with a single assignment, even when that slice does not consist of consecutive terms. Try to predict what the following commands will do." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L) # Let's see what the list looks like before." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[::2] = ['A','B','C','D','E','F'] # What was L[::2] before this assignment? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L) # What do you predict?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. Create a list L with L = [1,2,3,...,100] (all the numbers from 1 to 100). What is L[50]?\n", "\n", "2. Take the same list L, and extract a slice of the form [5,10,15,...,95] with a command of the form L[a:b:c].\n", "\n", "3. Take the same list L, and change all the even numbers to zeros, so that L looks like [1,0,3,0,5,0,...,99,0]. Hint: You might wish to use the list [0]*50.\n", "\n", "4. Try the command `L[-1::-1]` on a list. What does it do? Can you guess before executing it? Can you understand why? In fact, strings are lists too. Try setting `L = 'Hello'` and the previous command." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='sieve'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sieve of Eratosthenes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **Sieve of Eratosthenes** (hereafter called \"the sieve\") is a very fast way of producing long lists of primes, without doing repeated primality checking. It is described in more detail in Chapter 2 of [An Illustrated Theory of Numbers](http://bookstore.ams.org/mbk-105). The basic idea is to start with all of the natural numbers, and successively filter out, or [**sieve**](https://en.wikipedia.org/wiki/Sieve), the multiples of 2, then the multiples of 3, then the multiples of 5, etc., until only primes are left.\n", "\n", "Using list slicing, we can carry out this sieving process efficiently. And with a few more tricks we encounter here, we can carry out the Sieve **very** efficiently. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The basic sieve\n", "\n", "The first approach we introduce is a bit naive, but is a good starting place. We will begin with a list of numbers up to 100, and sieve out the appropriate multiples of 2,3,5,7." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes = list(range(100)) # Let's start with the numbers 0...99." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, to \"filter\", i.e., to say that a number is *not* prime, let's just change the number to the value `None`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[0] = None # Zero is not prime.\n", "primes[1] = None # One is not prime.\n", "print(primes) # What have we done?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's filter out the multiples of 2, starting at 4. This is the slice `primes[4::2]`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[4::2] = [None] * len(primes[4::2]) # The right side is a list of Nones, of the necessary length.\n", "print(primes) # What have we done?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we filter out the multiples of 3, starting at 9." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[9::3] = [None] * len(primes[9::3]) # The right side is a list of Nones, of the necessary length.\n", "print(primes) # What have we done?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next the multiples of 5, starting at 25 (the first multiple of 5 greater than 5 that's left!)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[25::5] = [None] * len(primes[25::5]) # The right side is a list of Nones, of the necessary length.\n", "print(primes) # What have we done?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the multiples of 7, starting at 49 (the first multiple of 7 greater than 7 that's left!)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[49::7] = [None] * len(primes[49::7]) # The right side is a list of Nones, of the necessary length.\n", "print(primes) # What have we done?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's left? A lot of `None`s and the prime numbers up to 100. We have successfully sieved out all the nonprime numbers in the list, using just four sieving steps (and setting 0 and 1 to `None` manually). \n", "\n", "But there's a lot of room for improvement, from beginning to end!\n", "\n", "1. The format of the end result is not so nice.\n", "2. We had to sieve each step manually. It would be much better to have a function `prime_list(n)` which would output a list of primes up to `n` without so much supervision.\n", "3. The memory usage will be large, if we need to store all the numbers up to a large `n` at the beginning.\n", "\n", "We solve these problems in the following way.\n", "\n", "1. We will use a list of **booleans** rather than a list of numbers. The ending list will have a `True` value at prime indices and a `False` value at composite indices. This reduces the memory usage and increases the speed. \n", "2. A `which` function (explained soon) will make the desired list of primes after everything else is done.\n", "3. We will proceed through the sieving steps algorithmically rather than entering each step manually.\n", "\n", "Here is a somewhat efficient implementation of the Sieve in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def isprime_list(n):\n", " ''' \n", " Return a list of length n+1\n", " with Trues at prime indices and Falses at composite indices.\n", " '''\n", " flags = [True] * (n+1) # A list [True, True, True,...] to start.\n", " flags[0] = False # Zero is not prime. So its flag is set to False.\n", " flags[1] = False # One is not prime. So its flag is set to False.\n", " p = 2 # The first prime is 2. And we start sieving by multiples of 2.\n", " \n", " while p <= sqrt(n): # We only need to sieve by p is p <= sqrt(n).\n", " if flags[p]: # We sieve the multiples of p if flags[p]=True.\n", " flags[p*p::p] = [False] * len(flags[p*p::p]) # Sieves out multiples of p, starting at p*p.\n", " p = p + 1 # Try the next value of p.\n", " \n", " return flags" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(isprime_list(100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you look carefully at the list of booleans, you will notice a `True` value at the 2nd index, the 3rd index, the 5th index, the 7th index, etc.. The indices where the values are `True` are precisely the **prime** indices. Since booleans take the smallest amount of memory of any data type (one **bit** of memory per boolean), your computer can carry out the `isprime_list(n)` function even when `n` is very large.\n", "\n", "To be more precise, there are 8 bits in a **byte**. There are 1024 bytes (about 1000) in a kilobyte. There are 1024 kilobytes in a megabyte. There are 1024 megabytes in a gigabyte. Therefore, a gigabyte of memory is enough to store about 8 billion bits. That's enough to store the result of `isprime_list(n)` when `n` is about 8 billion. Not bad! And your computer probably has 4 or 8 or 12 or 16 gigabytes of memory to use.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To transform the list of booleans into a list of prime numbers, we create a function called `where`. This function uses another Python technique called **list comprehension**. We discuss this technique later in this lesson, so just use the `where` function as a tool for now, or [read about list comprehension](https://docs.python.org/2/tutorial/datastructures.html#list-comprehensions) if you're curious." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def where(L):\n", " '''\n", " Take a list of booleans as input and\n", " outputs the list of indices where True occurs.\n", " '''\n", " return [n for n in range(len(L)) if L[n]]\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Combined with the `isprime_list` function, we can produce long lists of primes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "print(where(isprime_list(100)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's push it a bit further. How many primes are there between 1 and 1 million? We can figure this out in three steps:\n", "\n", "1. Create the isprime_list.\n", "2. Use where to get the list of primes.\n", "3. Find the length of the list of primes.\n", "\n", "But it's better to do it in two steps.\n", "\n", "1. Create the isprime_list.\n", "2. Sum the list! (Note that `True` is 1, for the purpose of summation!)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(isprime_list(1000000)) # The number of primes up to a million!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit isprime_list(10**6) # 1000 ms = 1 second." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit sum(isprime_list(10**6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This isn't too bad! It takes a fraction of a second to identify the primes up to a million, and a smaller fraction of a second to count them! But we can do a little better. \n", "\n", "The first improvement is to take care of the even numbers first. If we count carefully, then the sequence 4,6,8,...,n (ending at n-1 if n is odd) has the floor of (n-2)/2 terms. Thus the line `flags[4::2] = [False] * ((n-2)//2)` will set all the flags to False in the sequence 4,6,8,10,... From there, we can begin sieving by *odd* primes starting with 3.\n", "\n", "The next improvement is that, since we've already sieved out all the even numbers (except 2), we don't have to sieve out again by *even multiples*. So when sieving by multiples of 3, we don't have to sieve out 9,12,15,18,21,etc.. We can just sieve out 9,15,21,etc.. When `p` is an odd prime, this can be taken care of with the code `flags[p*p::2*p] = [False] * len(flags[p*p::2*p])`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def isprime_list(n):\n", " ''' \n", " Return a list of length n+1\n", " with Trues at prime indices and Falses at composite indices.\n", " '''\n", " flags = [True] * (n+1) # A list [True, True, True,...] to start.\n", " flags[0] = False # Zero is not prime. So its flag is set to False.\n", " flags[1] = False # One is not prime. So its flag is set to False.\n", " flags[4::2] = [False] * ((n-2)//2)\n", " p = 3\n", " while p <= sqrt(n): # We only need to sieve by p is p <= sqrt(n).\n", " if flags[p]: # We sieve the multiples of p if flags[p]=True.\n", " flags[p*p::2*p] = [False] * len(flags[p*p::2*p]) # Sieves out multiples of p, starting at p*p.\n", " p = p + 2 # Try the next value of p. Note that we can proceed only through odd p!\n", " \n", " return flags" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit sum(isprime_list(10**6)) # How much did this speed it up?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another modest improvement is the following. In the code above, the program *counts* the terms in sequences like 9,15,21,27,..., in order to set them to `False`. This is accomplished with the length command `len(flags[p*p::2*p])`. But that length computation is a bit too intensive. A bit of algebraic work shows that the length is given formulaically in terms of `p` and `n` by the formula: \n", "\n", "$$len = \\lfloor \\frac{n - p^2 - 1}{2p} \\rfloor + 1$$\n", "\n", "(Here $\\lfloor x \\rfloor$ denotes the floor function, i.e., the result of rounding down.) Putting this into the code yields the following." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def isprime_list(n):\n", " ''' \n", " Return a list of length n+1\n", " with Trues at prime indices and Falses at composite indices.\n", " '''\n", " flags = [True] * (n+1) # A list [True, True, True,...] to start.\n", " flags[0] = False # Zero is not prime. So its flag is set to False.\n", " flags[1] = False # One is not prime. So its flag is set to False.\n", " flags[4::2] = [False] * ((n-2)//2)\n", " p = 3\n", " while p <= sqrt(n): # We only need to sieve by p is p <= sqrt(n).\n", " if flags[p]: # We sieve the multiples of p if flags[p]=True.\n", " flags[p*p::2*p] = [False] * ((n-p*p-1)//(2*p)+1) # Sieves out multiples of p, starting at p*p.\n", " p = p + 2 # Try the next value of p.\n", " \n", " return flags" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit sum(isprime_list(10**6)) # How much did this speed it up?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should be pretty fast! It should be under 100 ms (one tenth of one second!) to determine the primes up to a million, and on a newer computer it should be under 50ms. We have gotten pretty close to the fastest algorithms that you can find in Python, without using external packages (like SAGE or sympy). See the related [discussion on StackOverflow](https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n)... the code in this lesson was influenced by the code presented there." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. Prove that the length of `range(p*p, n, 2*p)` equals $\\lfloor \\frac{n - p^2 - 1}{2p} \\rfloor + 1$.\n", "\n", "2. A natural number $n$ is called squarefree if it has no perfect square divides $n$ except for 1. Write a function `squarefree_list(n)` which outputs a list of booleans: `True` if the index is squarefree and `False` if the index is not squarefree. For example, if you execute `squarefree_list(12)`, the output should be `[False, True, True, True, False, True, True, True, False, False, True, True, False]`. Note that the `False` entries are located the indices 0, 4, 8, 9, 12. These natural numbers have perfect square divisors besides 1. \n", "\n", "3. Your DNA contains about 3 billion base pairs. Each \"base pair\" can be thought of as a letter, A, T, G, or C. How many bits would be required to store a single base pair? In other words, how might you convert a sequence of booleans into a letter A,T,G, or C? Given this, how many megabytes or gigabytes are required to store your DNA? How many people's DNA would fit on a thumb-drive?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='analysis'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we can produce a list of prime numbers quickly, we can do some data analysis: some experimental number theory to look for trends or patterns in the sequence of prime numbers. Since Euclid (about 300 BCE), we have known that there are infinitely many prime numbers. But how are they distributed? What proportion of numbers are prime, and how does this proportion change over different ranges? As theoretical questions, these belong the the field of analytic number theory. But it is hard to know what to prove without doing a bit of experimentation. And so, at least since Gauss [(read Tschinkel's article about Gauss's tables)](http://www.ams.org/journals/bull/2006-43-01/S0273-0979-05-01096-7/S0273-0979-05-01096-7.pdf) started examining his extensive tables of prime numbers, mathematicians have been carrying out experimental number theory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing the list of primes\n", "\n", "Let's begin by creating our data set: the prime numbers up to 1 million." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes = where(isprime_list(1000000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(primes) # Our population size. A statistician might call it N." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes[-1] # The last prime in our list, just before one million." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "type(primes) # What type is this data?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(primes[:100]) # The first hundred prime numbers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To carry out serious analysis, we will use the method of **list comprehension** to place our population into \"bins\" for statistical analysis. Our first type of list comprehension has the form `[x for x in LIST if CONDITION]`. This produces the list of all elements of LIST satisfying CONDITION. It is similar to list slicing, except we pull out terms from the list according to whether a condition is true or false.\n", "\n", "For example, let's divide the (odd) primes into two classes. Red primes will be those of the form 4n+1. Blue primes will be those of the form 4n+3. In other words, a prime `p` is red if `p%4 == 1` and blue if `p%4 == 3`. And the prime 2 is neither red nor blue." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "redprimes = [p for p in primes if p%4 == 1] # Note the [x for x in LIST if CONDITION] syntax.\n", "blueprimes = [p for p in primes if p%4 == 3]\n", "\n", "print('Red primes:',redprimes[:20]) # The first 20 red primes.\n", "print('Blue primes:',blueprimes[:20]) # The first 20 blue primes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"There are {} red primes and {} blue primes, up to 1 million.\".format(len(redprimes), len(blueprimes)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is pretty close! It seems like prime numbers are about evenly distributed between red and blue. Their remainder after division by 4 is about as likely to be 1 as it is to be 3. In fact, it is proven that *asymptotically* the ratio between the number of red primes and the number of blue primes approaches 1. However, Chebyshev noticed a persistent slight bias towards blue primes along the way." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the deepest conjectures in mathematics relate to the [prime counting function](https://en.wikipedia.org/wiki/Prime-counting_function) $\\pi(x)$. Here $\\pi(x)$ is the **number of primes** between 1 and $x$ (inclusive). So $\\pi(2) = 1$ and $\\pi(3) = 2$ and $\\pi(4) = 2$ and $\\pi(5) = 3$. One can compute a value of $\\pi(x)$ pretty easily using a list comprehension.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def primes_upto(x):\n", " return len([p for p in primes if p <= x]) # List comprehension recovers the primes up to x." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes_upto(1000) # There are 168 primes between 1 and 1000." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we graph the prime counting function. To do this, we use a list comprehension, and the visualization library called matplotlib. For graphing a function, the basic idea is to create a list of x-values, a list of corresponding y-values (so the lists have to be the same length!), and then we feed the two lists into matplotlib to make the graph.\n", "\n", "We begin by loading the necessary packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib # A powerful graphics package.\n", "import numpy # A math package\n", "import matplotlib.pyplot as plt # A plotting subpackage in matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's graph the function $y = x^2$ over the domain $-2 \\leq x \\leq 2$ for practice. As a first step, we use numpy's `linspace` function to create an evenly spaced set of 11 x-values between -2 and 2." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_values = numpy.linspace(-2,2,11) # The argument 11 is the *number* of terms, not the step size!\n", "print(x_values)\n", "type(x_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might notice that the format looks a bit different from a list. Indeed, if you check `type(x_values)`, it's not a list but something else called a numpy array. Numpy is a package that excels with computations on large arrays of data. On the surface, it's not so different from a list. The `numpy.linspace` command is a convenient way of producing an evenly spaced list of inputs.\n", "\n", "The big difference is that operations on numpy arrays are interpreted differently than operations on ordinary Python lists. Try the two commands for comparison." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[1,2,3] + [1,2,3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_values + x_values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_values = x_values * x_values # How is multiplication interpreted on numpy arrays?\n", "print(y_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we use matplotlib to create a simple line graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values, y_values)\n", "plt.title('The graph of $y = x^2$') # The dollar signs surround the formula, in LaTeX format.\n", "plt.ylabel('y')\n", "plt.xlabel('x')\n", "plt.grid(True)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's analyze the graphing code a bit more. See the [official pyplot tutorial](https://matplotlib.org/users/pyplot_tutorial.html) for more details. \n", "```python\n", "%matplotlib inline\n", "plt.plot(x_values, y_values)\n", "plt.title('The graph of $y = x^2$') # The dollar signs surround the formula, in LaTeX format.\n", "plt.ylabel('y')\n", "plt.xlabel('x')\n", "plt.grid(True)\n", "plt.show()\n", "```\n", "The first line contains the **magic** `%matplotlib inline`. We have seen a magic word before, in `%timeit`. [Magic words](http://ipython.readthedocs.io/en/stable/interactive/magics.html) can call another program to assist. So here, the magic `%matplotlib inline` calls matplotlib for help, and places the resulting figure within the notebook.\n", "\n", "The next line `plt.plot(x_values, y_values)` creates a `plot object` based on the data of the x-values and y-values. It is an abstract sort of object, behind the scenes, in a format that matplotlib understands. The following lines set the title of the plot, the axis labels, and turns a grid on. The last line `plt.show` renders the plot as an image in your notebook. There's an infinite variety of graphs that matplotlib can produce -- see [the gallery](https://matplotlib.org/gallery.html) for more! Other graphics packages include [bokeh](http://bokeh.pydata.org/en/latest/) and [seaborn](http://seaborn.pydata.org/), which extends matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis of the prime counting function\n", "\n", "Now, to analyze the prime counting function, let's graph it. To make a graph, we will first need a list of many values of x and many corresponding values of $\\pi(x)$. We do this with two commands. The first might take a minute to compute." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_values = numpy.linspace(0,1000000,1001) # The numpy array [0,1000,2000,3000,...,1000000]\n", "pix_values = numpy.array([primes_upto(x) for x in x_values]) # [FUNCTION(x) for x in LIST] syntax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We created an array of x-values as before. But the creation of an array of y-values (here, called `pix_values` to stand for $\\pi(x)$) probably looks strange. We have done two new things!\n", "\n", "1. We have used a list comprehension `[primes_upto(x) for x in x_values]` to create a **list** of y-values.\n", "2. We have used numpy.array(LIST) syntax to convert a Python list into a numpy array.\n", "\n", "First, we explain the list comprehension. Instead of pulling out values of a list according to a condition, with `[x for x in LIST if CONDITION]`, we have created a new list based on performing a function each element of a list. The syntax, used above, is `[FUNCTION(x) for x in LIST]`. These two methods of list comprehension can be combined, in fact. The most general syntax for list comprehension is `[FUNCTION(x) for x in LIST if CONDITION]`.\n", "\n", "Second, a list comprehension can be carried out on a numpy array, but the result is a plain Python list. It will be better to have a numpy array instead for what follows, so we use the `numpy.array()` function to convert the list into a numpy array." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(numpy.array([1,2,3])) # For example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have two numpy arrays: the array of x-values and the array of y-values. We can make a plot with matplotlib." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(x_values) == len(pix_values) # These better be the same, or else matplotlib will be unhappy." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values, pix_values)\n", "plt.title('The prime counting function')\n", "plt.ylabel('$\\pi(x)$')\n", "plt.xlabel('x')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this range, the prime counting function might look nearly linear. But if you look closely, there's a subtle downward bend. This is more pronounced in smaller ranges. For example, let's look at the first 10 x-values and y-values only." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values[:10], pix_values[:10]) # Look closer to 0.\n", "plt.title('The prime counting function')\n", "plt.ylabel('$\\pi(x)$')\n", "plt.xlabel('x')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It still looks almost linear, but there's a visible downward bend here. How can we see this bend more clearly? If the graph were linear, its equation would have the form $\\pi(x) = mx$ for some fixed slope $m$ (since the graph *does* pass through the origin). Therefore, the quantity $\\pi(x)/x$ would be *constant* if the graph were linear. \n", "\n", "Hence, if we graph $\\pi(x) / x$ on the y-axis and $x$ on the x-axis, and the result is nonconstant, then the function $\\pi(x)$ is nonlinear." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_values = pix_values[1:] / x_values[1:] # We start at 1, to avoid a division by zero error." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values[1:], m_values)\n", "plt.title('The ratio $\\pi(x) / x$ as $x$ varies.')\n", "plt.xlabel('x')\n", "plt.ylabel('$\\pi(x) / x$')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is certainly not constant! The decay of $\\pi(x) / x$ is not so different from $1 / \\log(x)$, in fact. To see this, let's overlay the graphs. We use the `numpy.log` function, which computes the natural logarithm of its input (and allows an entire array as input)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values[1:], m_values, label='$\\pi(x)/x$') # The same as the plot above.\n", "plt.plot(x_values[1:], 1 / numpy.log(x_values[1:]), label='$1 / \\log(x)$') # Overlay the graph of 1 / log(x)\n", "plt.title('The ratio of $\\pi(x) / x$ as $x$ varies.')\n", "plt.xlabel('x')\n", "plt.ylabel('$\\pi(x) / x$')\n", "plt.grid(True)\n", "plt.legend() # Turn on the legend.\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The shape of the decay of $\\pi(x) / x$ is very close to $1 / \\log(x)$, but it looks like there is an offset. In fact, there is, and it is pretty close to $1 / \\log(x)^2$. And that is close, but again there's another little offset, this time proportional to $2 / \\log(x)^3$. This goes on forever, if one wishes to approximate $\\pi(x) / x$ by an \"asymptotic expansion\" (not a good idea, it turns out).\n", "\n", "The closeness of $\\pi(x) / x$ to $1 / \\log(x)$ is expressed in the **prime number theorem**:\n", "$$\\lim_{x \\rightarrow \\infty} \\frac{\\pi(x)}{x / \\log(x)} = 1.$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(x_values[1:], m_values * numpy.log(x_values[1:]) ) # Should get closer to 1.\n", "plt.title('The ratio $\\pi(x) / (x / \\log(x))$ approaches 1... slowly')\n", "plt.xlabel('x')\n", "plt.ylabel('$\\pi(x) / (x / \\log(x)) $')\n", "plt.ylim(0.8,1.2)\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing the graph to the theoretical result, we find that the ratio $\\pi(x) / (x / \\log(x))$ approaches $1$ (the theoretical result) but very slowly (see the graph above!)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A much stronger result relates $\\pi(x)$ to the \"logarithmic integral\" $li(x)$. The [Riemann hypothesis](http://www.claymath.org/millennium-problems/riemann-hypothesis) is equivalent to the statement\n", "$$\\left\\vert \\pi(x) - li(x) \\right\\vert = O(\\sqrt{x} \\log(x)).$$\n", "In other words, the error if one approximates $\\pi(x)$ by $li(x)$ is bounded by a constant times $\\sqrt{x} \\log(x)$. The logarithmic integral function isn't part of Python or numpy, but it is in the mpmath package. If you have this package installed, then you can try the following." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mpmath import li" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(primes_upto(1000000)) # The number of primes up to 1 million.\n", "print(li(1000000)) # The logarithmic integral of 1 million." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not too shabby!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prime gaps\n", "\n", "As a last bit of data analysis, we consider the **prime gaps**. These are the numbers that occur as differences between consecutive primes. Since all primes except 2 are odd, all prime gaps are even except for the 1-unit gap between 2 and 3. There are many unsolved problems about prime gaps; the most famous might be that a gap of 2 occurs infinitely often (as in the gaps between 3,5 and between 11,13 and between 41,43, etc.).\n", "\n", "Once we have our data set of prime numbers, it is not hard to create a data set of prime gaps. Recall that `primes` is our list of prime numbers up to 1 million." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(primes) # The number of primes up to 1 million." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes_allbutlast = primes[:-1] # This excludes the last prime in the list.\n", "primes_allbutfirst = primes[1:] # This excludes the first (i.e., with index 0) prime in the list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primegaps = numpy.array(primes_allbutfirst) - numpy.array(primes_allbutlast) # Numpy is fast!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(primegaps[:100]) # The first hundred prime gaps!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What have we done? It is useful to try out this method on a short list. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L = [1,3,7,20] # A nice short list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(L[:-1])\n", "print(L[1:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have two lists of the same length. The gaps in the original list `L` are the differences between terms of the *same* index in the two new lists. One might be tempted to just subtract, e.g., with the command `L[1:] - L[:-1]`, but subtraction is not defined for lists.\n", "\n", "Fortunately, by converting the lists to numpy arrays, we can use numpy's term-by-term subtraction operation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L[1:] - L[:-1] # This will give a TypeError. You can't subtract lists!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numpy.array(L[1:]) - numpy.array(L[:-1]) # That's better. See the gaps in the list [1,3,7,20] in the output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's return to our primegaps data set. It contains all the gap-sizes for primes up to 1 million. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(len(primes))\n", "print(len(primegaps)) # This should be one less than the number of primes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a last example of data visualization, we use matplotlib to produce a histogram of the prime gaps." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max(primegaps) # The largest prime gap that appears!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.figure(figsize=(12, 5)) # Makes the resulting figure 12in by 5in.\n", "plt.hist(primegaps, bins=range(1,115)) # Makes a histogram with one bin for each possible gap from 1 to 114.\n", "plt.ylabel('Frequency')\n", "plt.xlabel('Gap size')\n", "plt.grid(True)\n", "plt.title('The frequency of prime gaps, for primes up to 1 million')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe that gaps of 2 (twin primes) are pretty frequent. There are over 8000 of them, and about the same number of 4-unit gaps! But gaps of 6 are most frequent in the population, and there are some interesting peaks at 6, 12, 18, 24, 30. What else do you observe?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. Create functions `redprimes_upto(x)` and `blueprimes_upto(x)` which count the number of red/blue primes up to a given number `x`. Recall that we defined red/blue primes to be those of the form 4n+1 or 4n+3, respectively. Graph the relative proportion of red/blue primes as `x` varies from 1 to 1 million. E.g., are the proportions 50%/50% or 70%/30%, and how do these proportions change? Note: this is also visualized in [An Illustrated Theory of Numbers](http://bookstore.ams.org/mbk-105) and you can read [an article by Rubinstein and Sarnak](https://projecteuclid.org/euclid.em/1048515870) for more.\n", "\n", "2. Does there seem to be a bias in the last digits of primes? Note that, except for 2 and 5, every prime ends in 1,3,7, or 9. Note: the last digit of a number `n` is obtained from `n % 10`. \n", "\n", "3. Read about the [\"Prime Conspiracy\"](https://www.quantamagazine.org/mathematicians-discover-prime-conspiracy-20160313), recently discovered by Lemke Oliver and Soundararajan. Can you detect their conspiracy in our data set of primes?" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

KatiRG/MethaneEmissions

.ipynb_checkpoints/make_2levelSankey_BUmethaneFile-checkpoint.ipynb

1

44395

{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to reformat methane data Top-Down file for two-level Sankey\n", "#In BU approach, Sources = methane sources, Targets = regions\n", "\n", "#Output: json file formatted for Sankey diagram\n", "\n", "#Created: 31.05.2016" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import collections\n", "import os\n", "import xlrd" ] }, { "cell_type": "code", "execution_count": 3, "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>proc</th>\n", " <th>Bor_NAme</th>\n", " <th>contUSA</th>\n", " <th>Cent_NAme</th>\n", " <th>Trop_SAme</th>\n", " <th>Temp_SAme</th>\n", " <th>NAfr</th>\n", " <th>SAfr</th>\n", " <th>Russia</th>\n", " <th>Oceania</th>\n", " <th>Europe</th>\n", " <th>China</th>\n", " <th>India</th>\n", " <th>SE_Asia</th>\n", " <th>Temp_Eurasia_Japan</th>\n", " <th>GLO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>mean</td>\n", " <td>32</td>\n", " <td>13</td>\n", " <td>2</td>\n", " <td>42</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>14</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>29</td>\n", " <td>3</td>\n", " <td>185</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>min</td>\n", " <td>15</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>18</td>\n", " <td>1</td>\n", " <td>153</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>max</td>\n", " <td>61</td>\n", " <td>23</td>\n", " <td>4</td>\n", " <td>59</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>13</td>\n", " <td>35</td>\n", " <td>6</td>\n", " <td>227</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>mean</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>199</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>min</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>max</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>297</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>mean</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>5</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>30</td>\n", " <td>21</td>\n", " <td>22</td>\n", " <td>20</td>\n", " <td>195</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>min</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>2</td>\n", " <td>18</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>23</td>\n", " <td>16</td>\n", " <td>18</td>\n", " <td>17</td>\n", " <td>178</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>max</td>\n", " <td>3</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>18</td>\n", " <td>36</td>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>22</td>\n", " <td>206</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>mean</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>20</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>22</td>\n", " <td>121</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>min</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>20</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>max</td>\n", " <td>3</td>\n", " <td>16</td>\n", " <td>3</td>\n", " <td>19</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>5</td>\n", " <td>26</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>31</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>26</td>\n", " <td>133</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>mean</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>min</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>max</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " proc Bor_NAme contUSA Cent_NAme Trop_SAme Temp_SAme NAfr \\\n", "Wetlands mean 32 13 2 42 4 8 \n", "Wetlands min 15 6 1 19 1 3 \n", "Wetlands max 61 23 4 59 7 16 \n", "OtherNat mean -99 -99 -99 -99 -99 -99 \n", "OtherNat min -99 -99 -99 -99 -99 -99 \n", "OtherNat max -99 -99 -99 -99 -99 -99 \n", "Agriwast mean 3 17 5 21 6 14 \n", "Agriwast min 2 15 2 18 5 12 \n", "Agriwast max 3 23 6 23 6 18 \n", "Fossil mean 2 11 2 8 1 9 \n", "Fossil min 1 9 0 3 1 7 \n", "Fossil max 3 16 3 19 1 14 \n", "BioBurBiof mean 1 1 1 3 0 4 \n", "BioBurBiof min 0 0 0 1 0 3 \n", "BioBurBiof max 1 2 1 6 1 5 \n", "\n", " SAfr Russia Oceania Europe China India SE_Asia \\\n", "Wetlands 19 14 3 4 5 6 29 \n", "Wetlands 15 5 1 1 1 1 18 \n", "Wetlands 22 26 6 7 9 13 35 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "Agriwast 6 5 5 17 30 21 22 \n", "Agriwast 5 5 5 15 23 16 18 \n", "Agriwast 7 5 5 18 36 24 25 \n", "Fossil 4 20 2 6 24 3 6 \n", "Fossil 3 18 1 3 15 2 5 \n", "Fossil 5 26 2 8 31 4 7 \n", "BioBurBiof 6 2 1 1 3 2 5 \n", "BioBurBiof 4 0 0 0 2 2 4 \n", "BioBurBiof 10 4 1 1 4 3 5 \n", "\n", " Temp_Eurasia_Japan GLO \n", "Wetlands 3 185 \n", "Wetlands 1 153 \n", "Wetlands 6 227 \n", "OtherNat -99 199 \n", "OtherNat -99 104 \n", "OtherNat -99 297 \n", "Agriwast 20 195 \n", "Agriwast 17 178 \n", "Agriwast 22 206 \n", "Fossil 22 121 \n", "Fossil 20 114 \n", "Fossil 26 133 \n", "BioBurBiof 1 30 \n", "BioBurBiof 1 27 \n", "BioBurBiof 2 35 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU = pd.read_csv(\"Sankey_BU_2003-2012_25MAy2016.txt\", header=1, delim_whitespace=True)\n", "df_BU" ] }, { "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>stats</th>\n", " <th>Bor_NAme</th>\n", " <th>contUSA</th>\n", " <th>Cent_NAme</th>\n", " <th>Trop_SAme</th>\n", " <th>Temp_SAme</th>\n", " <th>NAfr</th>\n", " <th>SAfr</th>\n", " <th>Russia</th>\n", " <th>Oceania</th>\n", " <th>Europe</th>\n", " <th>China</th>\n", " <th>India</th>\n", " <th>SE_Asia</th>\n", " <th>Temp_Eurasia_Japan</th>\n", " <th>GLO</th>\n", " </tr>\n", " <tr>\n", " <th>proc</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>mean</td>\n", " <td>32</td>\n", " <td>13</td>\n", " <td>2</td>\n", " <td>42</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>19</td>\n", " <td>14</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>29</td>\n", " <td>3</td>\n", " <td>185</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>min</td>\n", " <td>15</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>18</td>\n", " <td>1</td>\n", " <td>153</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>max</td>\n", " <td>61</td>\n", " <td>23</td>\n", " <td>4</td>\n", " <td>59</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>22</td>\n", " <td>26</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>13</td>\n", " <td>35</td>\n", " <td>6</td>\n", " <td>227</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>mean</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>199</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>min</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>max</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>297</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>mean</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>5</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>17</td>\n", " <td>30</td>\n", " <td>21</td>\n", " <td>22</td>\n", " <td>20</td>\n", " <td>195</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>min</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>2</td>\n", " <td>18</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>23</td>\n", " <td>16</td>\n", " <td>18</td>\n", " <td>17</td>\n", " <td>178</td>\n", " </tr>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>max</td>\n", " <td>3</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>23</td>\n", " <td>6</td>\n", " <td>18</td>\n", " <td>7</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>18</td>\n", " <td>36</td>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>22</td>\n", " <td>206</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>mean</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>20</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>24</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>22</td>\n", " <td>121</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>min</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>18</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>15</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>20</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>max</td>\n", " <td>3</td>\n", " <td>16</td>\n", " <td>3</td>\n", " <td>19</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>5</td>\n", " <td>26</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>31</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>26</td>\n", " <td>133</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>mean</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>min</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>max</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stats Bor_NAme contUSA Cent_NAme Trop_SAme Temp_SAme NAfr \\\n", "proc \n", "Wetlands mean 32 13 2 42 4 8 \n", "Wetlands min 15 6 1 19 1 3 \n", "Wetlands max 61 23 4 59 7 16 \n", "OtherNat mean -99 -99 -99 -99 -99 -99 \n", "OtherNat min -99 -99 -99 -99 -99 -99 \n", "OtherNat max -99 -99 -99 -99 -99 -99 \n", "Agriwast mean 3 17 5 21 6 14 \n", "Agriwast min 2 15 2 18 5 12 \n", "Agriwast max 3 23 6 23 6 18 \n", "Fossil mean 2 11 2 8 1 9 \n", "Fossil min 1 9 0 3 1 7 \n", "Fossil max 3 16 3 19 1 14 \n", "BioBurBiof mean 1 1 1 3 0 4 \n", "BioBurBiof min 0 0 0 1 0 3 \n", "BioBurBiof max 1 2 1 6 1 5 \n", "\n", " SAfr Russia Oceania Europe China India SE_Asia \\\n", "proc \n", "Wetlands 19 14 3 4 5 6 29 \n", "Wetlands 15 5 1 1 1 1 18 \n", "Wetlands 22 26 6 7 9 13 35 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 \n", "Agriwast 6 5 5 17 30 21 22 \n", "Agriwast 5 5 5 15 23 16 18 \n", "Agriwast 7 5 5 18 36 24 25 \n", "Fossil 4 20 2 6 24 3 6 \n", "Fossil 3 18 1 3 15 2 5 \n", "Fossil 5 26 2 8 31 4 7 \n", "BioBurBiof 6 2 1 1 3 2 5 \n", "BioBurBiof 4 0 0 0 2 2 4 \n", "BioBurBiof 10 4 1 1 4 3 5 \n", "\n", " Temp_Eurasia_Japan GLO \n", "proc \n", "Wetlands 3 185 \n", "Wetlands 1 153 \n", "Wetlands 6 227 \n", "OtherNat -99 199 \n", "OtherNat -99 104 \n", "OtherNat -99 297 \n", "Agriwast 20 195 \n", "Agriwast 17 178 \n", "Agriwast 22 206 \n", "Fossil 22 121 \n", "Fossil 20 114 \n", "Fossil 26 133 \n", "BioBurBiof 1 30 \n", "BioBurBiof 1 27 \n", "BioBurBiof 2 35 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU.rename(columns = {'proc':'stats'}, inplace = True)\n", "df_BU.index.name = 'proc'\n", "df_BU" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"3\" halign=\"left\">Bor_NAme</th>\n", " <th colspan=\"3\" halign=\"left\">contUSA</th>\n", " <th colspan=\"3\" halign=\"left\">Cent_NAme</th>\n", " <th>Trop_SAme</th>\n", " <th>...</th>\n", " <th>India</th>\n", " <th colspan=\"3\" halign=\"left\">SE_Asia</th>\n", " <th colspan=\"3\" halign=\"left\">Temp_Eurasia_Japan</th>\n", " <th colspan=\"3\" halign=\"left\">GLO</th>\n", " </tr>\n", " <tr>\n", " <th>stats</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>...</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " <th>max</th>\n", " <th>mean</th>\n", " <th>min</th>\n", " </tr>\n", " <tr>\n", " <th>proc</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Agriwast</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>23</td>\n", " <td>17</td>\n", " <td>15</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>23</td>\n", " <td>...</td>\n", " <td>16</td>\n", " <td>25</td>\n", " <td>22</td>\n", " <td>18</td>\n", " <td>22</td>\n", " <td>20</td>\n", " <td>17</td>\n", " <td>206</td>\n", " <td>195</td>\n", " <td>178</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>35</td>\n", " <td>30</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>19</td>\n", " <td>...</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>26</td>\n", " <td>22</td>\n", " <td>20</td>\n", " <td>133</td>\n", " <td>121</td>\n", " <td>114</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNat</th>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>...</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>297</td>\n", " <td>199</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td>61</td>\n", " <td>32</td>\n", " <td>15</td>\n", " <td>23</td>\n", " <td>13</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>59</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>35</td>\n", " <td>29</td>\n", " <td>18</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>227</td>\n", " <td>185</td>\n", " <td>153</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 45 columns</p>\n", "</div>" ], "text/plain": [ " Bor_NAme contUSA Cent_NAme Trop_SAme \\\n", "stats max mean min max mean min max mean min max \n", "proc \n", "Agriwast 3 3 2 23 17 15 6 5 2 23 \n", "BioBurBiof 1 1 0 2 1 0 1 1 0 6 \n", "Fossil 3 2 1 16 11 9 3 2 0 19 \n", "OtherNat -99 -99 -99 -99 -99 -99 -99 -99 -99 -99 \n", "Wetlands 61 32 15 23 13 6 4 2 1 59 \n", "\n", " ... India SE_Asia Temp_Eurasia_Japan GLO \\\n", "stats ... min max mean min max mean min max mean \n", "proc ... \n", "Agriwast ... 16 25 22 18 22 20 17 206 195 \n", "BioBurBiof ... 2 5 5 4 2 1 1 35 30 \n", "Fossil ... 2 7 6 5 26 22 20 133 121 \n", "OtherNat ... -99 -99 -99 -99 -99 -99 -99 297 199 \n", "Wetlands ... 1 35 29 18 6 3 1 227 185 \n", "\n", " \n", "stats min \n", "proc \n", "Agriwast 178 \n", "BioBurBiof 27 \n", "Fossil 114 \n", "OtherNat 104 \n", "Wetlands 153 \n", "\n", "[5 rows x 45 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Pivot table so that \"stats\" Mean, Min, Max become three columns under each region column\n", "#and \"proc\" becomes index\n", "df_BU_piv = df_BU.pivot(columns='stats', index=df_BU.index)\n", "df_BU_piv" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Trop_SAme'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU_piv.columns[0][0] #Bor_NAme\n", "df_BU_piv.columns[3][0] #contUSA\n", "df_BU_piv.columns[6][0] #CentName\n", "df_BU_piv.columns[9][0] #Trop_SAme" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "proc\n", "Agriwast 3\n", "BioBurBiof 1\n", "Fossil 2\n", "OtherNat -99\n", "Wetlands 32\n", "Name: mean, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU_piv['Bor_NAme']['mean']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU_piv['Bor_NAme'].loc['Agriwast']['mean']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_BU_piv['Bor_NAme'].loc['BioBurBiof']['mean']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Bor_NAme',\n", " 'contUSA',\n", " 'Cent_NAme',\n", " 'Trop_SAme',\n", " 'Temp_SAme',\n", " 'NAfr',\n", " 'SAfr',\n", " 'Russia',\n", " 'Oceania',\n", " 'Europe',\n", " 'China',\n", " 'India',\n", " 'SE_Asia',\n", " 'Temp_Eurasia_Japan',\n", " 'GLO']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Store region names in list\n", "numRegions = df_BU_piv.shape[1] / 3\n", "\n", "idx = 0\n", "targets = []\n", "for num in range(0,numRegions):\n", " targets.append(df_BU_piv.columns[idx][0])\n", " idx = idx + 3\n", " \n", "targets\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Agriwast', 'BioBurBiof', 'Fossil', 'OtherNat', 'Wetlands']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get target list\n", "sources = df_BU_piv.index.tolist()\n", "sources" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Agriwast',\n", " 'BioBurBiof',\n", " 'Fossil',\n", " 'OtherNat',\n", " 'Wetlands',\n", " 'Bor_NAme',\n", " 'contUSA',\n", " 'Cent_NAme',\n", " 'Trop_SAme',\n", " 'Temp_SAme',\n", " 'NAfr',\n", " 'SAfr',\n", " 'Russia',\n", " 'Oceania',\n", " 'Europe',\n", " 'China',\n", " 'India',\n", " 'SE_Asia',\n", " 'Temp_Eurasia_Japan',\n", " 'GLO']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nodes = sources + targets\n", "nodes" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bor_NAme\n", "Agriwast\n", "3\n", "BioBurBiof\n", "1\n", "Fossil\n", "2\n", "OtherNat\n", "2\n", "Wetlands\n", "13\n", "contUSA\n", "Agriwast\n", "17\n", "BioBurBiof\n", "0\n", "Fossil\n", "11\n", "OtherNat\n", "2\n", "Wetlands\n", "8\n", "Cent_NAme\n", "Agriwast\n", "5\n", "BioBurBiof\n", "1\n", "Fossil\n", "2\n", "OtherNat\n", "1\n", "Wetlands\n", "2\n", "Trop_SAme\n", "Agriwast\n", "21\n", "BioBurBiof\n", "6\n", "Fossil\n", "5\n", "OtherNat\n", "7\n", "Wetlands\n", "47\n", "Temp_SAme\n", "Agriwast\n", "6\n", "BioBurBiof\n", "0\n", "Fossil\n", "1\n", "OtherNat\n", "1\n", "Wetlands\n", "6\n", "NAfr\n", "Agriwast\n", "12\n", "BioBurBiof\n", "4\n", "Fossil\n", "7\n", "OtherNat\n", "6\n", "Wetlands\n", "11\n", "SAfr\n", "Agriwast\n", "7\n", "BioBurBiof\n", "6\n", "Fossil\n", "4\n", "OtherNat\n", "5\n", "Wetlands\n", "21\n", "Russia\n", "Agriwast\n", "5\n", "BioBurBiof\n", "1\n", "Fossil\n", "18\n", "OtherNat\n", "2\n", "Wetlands\n", "13\n", "Oceania\n", "Agriwast\n", "4\n", "BioBurBiof\n", "1\n", "Fossil\n", "2\n", "OtherNat\n", "2\n", "Wetlands\n", "2\n", "Europe\n", "Agriwast\n", "16\n", "BioBurBiof\n", "1\n", "Fossil\n", "7\n", "OtherNat\n", "2\n", "Wetlands\n", "2\n", "China\n", "Agriwast\n", "30\n", "BioBurBiof\n", "3\n", "Fossil\n", "23\n", "OtherNat\n", "4\n", "Wetlands\n", "4\n", "India\n", "Agriwast\n", "25\n", "BioBurBiof\n", "2\n", "Fossil\n", "3\n", "OtherNat\n", "2\n", "Wetlands\n", "6\n", "SE_Asia\n", "Agriwast\n", "25\n", "BioBurBiof\n", "6\n", "Fossil\n", "7\n", "OtherNat\n", "6\n", "Wetlands\n", "31\n", "Temp_Eurasia_Japan\n", "Agriwast\n", "20\n", "BioBurBiof\n", "1\n", "Fossil\n", "21\n", "OtherNat\n", "3\n", "Wetlands\n", "2\n", "GLO\n", "Agriwast\n", "96\n", "BioBurBiof\n", "32\n", "Fossil\n", "11\n", "OtherNat\n", "51\n", "Wetlands\n", "69\n" ] } ], "source": [ "file = open('Sankey_BU_2003-2012_25MAy2016.json', 'w')\n", "\n", "file.write('{\\n')\n", "file.write('\"nodes\": [\\n')\n", "for node in nodes:\n", " file.write('{\"name\": \"%s\"},\\n' %(node))\n", "# remove last comma\n", "file.seek(-2, os.SEEK_END)\n", "file.truncate()\n", "file.write('\\n],\\n')\n", "\n", "file.write('\"links\": [\\n')\n", "\n", "for source in sources:\n", " print source\n", " for target in targets:\n", " print target\n", " print df_BU_piv[source].loc[target]['mean']\n", " value = df_BU_piv[source].loc[target]['mean']\n", " file.write('{\"source\": \"%s\", \"target\": \"%s\", \"value\": \"%.2f\"},\\n' %(source, target, float(value)))\n", "\n", "# remove last comma\n", "file.seek(-2, os.SEEK_END)\n", "file.truncate()\n", "file.write('\\n]\\n')\n", "file.write('}\\n')\n", "\n", "file.close()" ] }, { "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

sarvex/PythonMachineLearning

Chapter 3/Pipelines - motivation.ipynb

1

4225

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Why pipelines for preprocessing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import make_regression\n", "\n", "X, y = make_regression(random_state=42, noise=100)\n", "print(X.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, train_size=.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_selection import SelectFpr, f_regression\n", "from sklearn.linear_model import Ridge\n", "\n", "fpr = SelectFpr(score_func=f_regression, alpha=.05)\n", "fpr.fit(X_train, y_train)\n", "X_train_fpr = fpr.transform(X_train)\n", "X_test_fpr = fpr.transform(X_test)\n", "\n", "print(X_train_fpr.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ridge = Ridge()\n", "ridge.fit(X_train_fpr, y_train)\n", "ridge.score(X_test_fpr, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How not to do grid-searches\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# DON'T DO THIS:\n", "from sklearn.grid_search import GridSearchCV\n", "param_grid = {'alpha': 10. ** np.arange(-3, 5)}\n", "grid = GridSearchCV(ridge, param_grid, cv=5)\n", "grid.fit(X_train_fpr, y_train)\n", "print(\"test set accuracy: %.2f\" % grid.score(X_test_fpr, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A more extreme example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rng = np.random.RandomState(0)\n", "y = rng.rand(X.shape[0])\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=.6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_selection import SelectKBest\n", "\n", "fpr = SelectFpr(score_func=f_regression)\n", "fpr.fit(X_train, y_train)\n", "X_train_fpr = fpr.transform(X_train)\n", "X_test_fpr = fpr.transform(X_test)\n", "\n", "X_train_fpr.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# DON'T DO THIS:\n", "from sklearn.grid_search import GridSearchCV\n", "param_grid = {'alpha': 10. ** np.arange(-3, 3)}\n", "grid = GridSearchCV(ridge, param_grid, cv=5)\n", "grid.fit(X_train_fpr, y_train)\n", "print(\"best cross-validation score: %.2f\" % grid.best_score_)\n", "print(\"test set accuracy: %.2f\" % grid.score(X_test_fpr, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"figures/pipeline_cross_validation.svg\" width=40%>" ] } ], "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

isc

rasilab/ferrin_elife_2017

scripts/plot_simulation_results_figs_3_to_7.ipynb

1

856557

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simulation Data Analysis\n", "\n", "<div id=\"toc-wrapper\"><h3> Table of Contents </h3><div id=\"toc\" style=\"max-height: 787px;\"><ol class=\"toc-item\"><li><a href=\"#Globals\">Globals</a></li><li><a href=\"#Plot-Fig.-3A\">Plot Fig. 3A</a></li><li><a href=\"#Plot-Fig.-3B\">Plot Fig. 3B</a></li><li><a href=\"#Plot-Fig.-3C\">Plot Fig. 3C</a></li><li><a href=\"#Plot-Fig.-4\">Plot Fig. 4</a></li><li><a href=\"#Plot-Fig.-4--Figure-supplement-1\">Plot Fig. 4--Figure supplement 1</a></li><li><a href=\"#Plot-Fig.-4--Figure-supplement-1H\">Plot Fig. 4--Figure supplement 1H</a></li><li><a href=\"#Plot-Fig.-5\">Plot Fig. 5</a></li><li><a href=\"#Plot-Fig.-5--Figure-supplement-1\">Plot Fig. 5--Figure supplement 1</a></li><li><a href=\"#Plot-Fig.-5--Figure-supplement-1C\">Plot Fig. 5--Figure supplement 1C</a></li><li><a href=\"#Plot-Fig.-6\">Plot Fig. 6</a></li><li><a href=\"#Plot-Fig.-6--Figure-supplement-1\">Plot Fig. 6--Figure supplement 1</a></li><li><a href=\"#Plot-Fig.-7A-top-panel\">Plot Fig. 7A top panel</a></li><li><a href=\"#Plot-Fig.-7A-bottom-panel\">Plot Fig. 7A bottom panel</a></li><li><a href=\"#Plot-Fig.-7B\">Plot Fig. 7B</a></li></ol></div></div>" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Globals\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import simulation_utils\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.metrics import mean_squared_error\n", "from customize_matplotlib import clean_axis\n", "from matplotlib.ticker import MaxNLocator, LogLocator\n", "import matplotlib as mpl\n", "import Bio.SeqIO\n", "\n", "# default matplotlib customizations\n", "mpl.rcParams['lines.linewidth'] = 2\n", "mpl.rcParams['axes.labelsize'] = 8\n", "mpl.rcParams['lines.markersize'] = 4\n", "mpl.rcParams['axes.titlesize'] = 8\n", "mpl.rcParams['axes.titleweight'] = 'bold'\n", "mpl.rcParams['figure.subplot.hspace'] = 0.5\n", "mpl.rcParams['figure.subplot.wspace'] = 0.5\n", "mpl.rcParams['font.size'] = 8\n", "mpl.rcParams['font.family'] = 'Arial'\n", "mpl.rcParams['font.sans-serif'] = 'Arial'\n", "mpl.rcParams['xtick.labelsize'] = 6\n", "mpl.rcParams['ytick.labelsize'] = 6\n", "mpl.rcParams['legend.fontsize'] = 6\n", "\n", "modelmarkers = {\n", " 'trafficjam': 'o',\n", " '5primepreterm': 'd',\n", " 'selpreterm': 's',\n", "}\n", "\n", "modelcolors = {\n", " '5primepreterm': 'red',\n", " 'selpreterm': 'blue',\n", " 'bkgdpreterm': 'violet',\n", " 'trafficjam': 'green',\n", "}\n", "\n", "modellabels = {\n", " '5primepreterm': 'CSAT',\n", " 'selpreterm': 'SAT',\n", " 'bkgdpreterm': 'SAT',\n", " 'trafficjam': 'TJ',\n", "}\n", "\n", "# starting yfp sequence for leucine starvation expts\n", "yfp0 = Bio.SeqIO.read('../annotations/simulations/yfp0.fa', 'fasta')\n", "yfp0 = str(yfp0.seq)\n", "\n", "leucodonlocation = list()\n", "for position in range(0, len(yfp0), 3):\n", " currentcodon = yfp0[position:position + 3]\n", " if currentcodon in ['CTG']:\n", " leucodonlocation.append(position)\n", "\n", "# convert pausecodon + pause location into a string\n", "\n", "\n", "def get_mutant(row):\n", " mutant = '_'.join(\n", " [row['pausecodon'].lower() + pos for pos in row['pauselocation']])\n", " return mutant\n", "\n", "\n", "# order codons by their location 5' to 3' in the string created above\n", "def return_mutant_for_ordering(mutant):\n", " positions = sorted([int(string[3:]) for string in mutant.split('_')])\n", " return '_'.join([mutant[:3] + str(pos) for pos in positions])\n", "\n", "\n", "# get number of pauses followed by their location for ordering the double,\n", "# triple and quad mutants\n", "def return_pos_for_ordering(pausepositions):\n", " positions = [int(loc) for loc in pausepositions]\n", " return tuple([len(positions)] + positions)\n", "\n", "\n", "# get interpause distance, followed by the first pause location for\n", "# ordering distance mutants\n", "def return_interpausedistance_for_ordering(pauselocation):\n", " positions = sorted([int(pos) for pos in pauselocation])\n", " if len(positions) == 1:\n", " sortorder = (-1, positions[0])\n", " else:\n", " sortorder = (leucodonlocation[positions[1] - 1] -\n", " leucodonlocation[positions[0] - 1], positions[0])\n", " return sortorder" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 3A" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WRlVZmriUD7dXv3QnoWMCs4+bTVhwWBO2zGpTVI8kVLBq4rfJ5AEQkV5ATrmc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\nwBeA1+PmIv0tcLWI/AHYA3xVVZPfrTbGmFFoWfUyxn7hxzTc8Gla2qN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"text/plain": [ "<matplotlib.figure.Figure at 0x7f03511fa090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=6)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run6_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run6_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([2, 1.23])\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.sort_values(by=['initiationrate'])\n", " ax.semilogx(\n", " subset['initiationrate'],\n", " subset['ps_ratio'],\n", " '-',\n", " label=modellabels[model],\n", " marker=modelmarkers[model],\n", " color=modelcolors[model],\n", " basex=2,\n", " alpha=0.6,\n", " markersize=4)\n", "\n", "clean_axis(ax)\n", "ax.xaxis.set(major_locator=LogLocator(4))\n", "ax.yaxis.set(major_locator=MaxNLocator(3))\n", "ax.set(ylim=(0, 1.1))\n", "ax.legend(loc=1)\n", "ax.set(\n", " xlabel=u'Initiation rate (s-1)',\n", " ylabel='Protein synthesis rate\\n(Relative to no stall site)', )\n", "fig.savefig('../figures/fig3a.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 3B" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/fh/fast/subramaniam_a/user/rasi/virtualenv/default2/lib/python2.7/site-packages/ipykernel/__main__.py:51: 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" ] }, { "data": { "image/png": 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Yf68hpMpbh46lNdzKk5uf5KmKpzhqwlEsmbWECcUT+uehHccZtPRVP9ATgLei/SlF5Gjg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\n0jUvBK4xs3/LSz8EuA4Yb2Yzm1W+epG0KvAa8GMzOykvfSo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f03430ba4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=7)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run7_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run7_data.tsv', index_col=0)\n", "\n", "def reorder_codons(mutant):\n", " positions = [int(string[3:]) for string in mutant.split('_')]\n", " codons = [string[:3] for string in mutant.split('_')]\n", " positions = [positions[x] for x in np.argsort(positions)]\n", " codons = [codons[x] for x in np.argsort(positions)]\n", " return '_'.join(codon + str(position)\n", " for codon, position in zip(codons, positions))\n", "\n", "\n", "def return_mutant_for_ordering(mutant):\n", " positions = [int(string[3:]) for string in mutant.split('_')]\n", " return tuple([len(mutant.split('_'))] + positions)\n", "\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([2, 1.23])\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset['mutant'] = subset['mutant'].apply(\n", " lambda mutant: reorder_codons(mutant))\n", " subset = subset[subset['mutant'].apply(\n", " lambda x: x.find('cta2') != -1)] # get rid of no-stall control\n", " subset['numberofstalls'] = subset['mutant'].apply(\n", " lambda x: len(x.split('_')))\n", " subset = subset.set_index('mutant')\n", " sortedindices = sorted(subset.index, key=return_mutant_for_ordering)\n", " subset = subset.ix[sortedindices]\n", " ax.plot(\n", " subset['numberofstalls'],\n", " subset['ps_ratio'],\n", " '-',\n", " label=modellabels[model],\n", " marker=modelmarkers[model],\n", " color=modelcolors[model],\n", " alpha=0.6,\n", " markersize=5)\n", "\n", "clean_axis(ax)\n", "ax.xaxis.set(major_locator=MaxNLocator(5))\n", "ax.yaxis.set(major_locator=MaxNLocator(3))\n", "ax.set(xlim=(0.5, subset['numberofstalls'].max() + 0.5), ylim=(-0.01, 0.25))\n", "ax.set(\n", " xlabel='Number of stall sites',\n", " ylabel='Protein synthesis rate\\n(Relative to no stall site)', )\n", "fig.savefig('../figures/fig3b.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 3C" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/fh/fast/subramaniam_a/user/rasi/virtualenv/default2/lib/python2.7/site-packages/ipykernel/__main__.py:36: 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" ] }, { "data": { "image/png": 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Q99/n7nl3c8OoG+ic2plOKZ1qnbM5fzN/WfQXKiorAPh8++fsObyHH0/8MTO/nMmsjbOo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\nJ7LzJ06Ea6+trmV89ZVrZt2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uqv4S+D1wh4i0DZOPJBH5MW7vAqvKBisNsba/jeg3xhhP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\ntsJXFXy/TnG2ZwxZynahI9eUY6t23HsAmwGz0xEEQRAEa1B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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034084b110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=8)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run8_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run8_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([2, 1.23])\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset['stalllocation'] = subset['mutant'].apply(lambda x: int(x[3:]))\n", " subset = subset[subset['stalllocation'] > 59]\n", " subset = subset[subset['stalllocation'] < 159]\n", " sortedindices = sorted(\n", " subset.index, key=lambda x: subset.ix[x]['stalllocation'])\n", " subset = subset.ix[sortedindices]\n", " subset = subset[2:-2]\n", " ax.plot(\n", " subset['stalllocation'] - 59,\n", " subset['ps_ratio'],\n", " '-',\n", " label=modellabels[model],\n", " marker=None,\n", " color=modelcolors[model],\n", " alpha=0.6)\n", "\n", "clean_axis(ax)\n", "ax.set(xlim=(0, 100), ylim=(0, 0.25))\n", "ax.xaxis.set(major_locator=MaxNLocator(4))\n", "ax.yaxis.set(major_locator=MaxNLocator(3))\n", "ax.set(\n", " xlabel='Distance between two stall sites (codons)',\n", " ylabel='Protein synthesis rate\\n(Relative to no stall site)', )\n", "fig.savefig('../figures/fig3c.svg')" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Plot Fig. 4" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ERH6GG1BWX0v247gG5ouBD1uaf6+d8U7cANP7gUN3rqVmmapu9p02EfdA/ljE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\nfiMYM3gMA7IHtHf2jGk2Kwm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034094d7d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['cta18' '', '', '', '', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=3)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run3_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run3_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([3 * 2, 2 * (len(mutants) + 2) / 2])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['A', 'A', 'B', 'C', 'D', 'E', 'F', 'G'])\n", "axcount = 0\n", "for mutant in mutants[:1]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(2 * (len(mutants) + 1) / 3, 3, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_initiation_mutants.tsv')\n", " summarydata = summarydata.sort_values(by='richrate_mean')\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate\n", " ) & (simulationdata['mutant'] == mutant)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.sort_values(by=['initiationrate'])\n", " # divide by the second highest initiation rate, this is the wt rbs\n", " subset['initiationrate'] /= np.unique(\n", " simulationdata['initiationrate'])[-2]\n", "\n", " if mutant == 'cta6':\n", " # the 'ATC' start codon variant was not cloned for the 'CTA6' variant\n", " subset = subset.reset_index().ix[[0, 2, 3, 4]]\n", "\n", " # exclude the fitted wt RBS for calculating error\n", " predicted = np.delete(np.array(subset['ps_ratio']), -2)\n", " measured = np.delete(np.array(summarydata['starverate_mean']), -2)\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " subset['initiationrate'],\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=subset['initiationrate'],\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata['starverate_err'],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8)\n", " if axcount == 7:\n", " ax.set(xlabel='YFP synthesis rate\\n(Leu-Rich)',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.xaxis.set(major_locator=MaxNLocator(5))\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1.4, 1.1))\n", " for loop, x in enumerate(subset['initiationrate']):\n", " if mutant == 'cta06' and loop: # teh variant 2 'ATC' is not there\n", " ax.text(\n", " x,\n", " ax.get_ylim()[0],\n", " str(loop + 2),\n", " ha='center',\n", " color='purple')\n", " else:\n", " ax.text(\n", " x,\n", " ax.get_ylim()[0],\n", " str(loop + 1),\n", " ha='center',\n", " color='purple')\n", " ax.set_title(mutant.replace('06', '6').upper(), y=1.1)\n", " ax.text(\n", " -0.5,\n", " 1.1,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig4.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 4--Figure supplement 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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b2QUt+PoiIp1Ge9+fJ/KnjffRvhcbiPfDYnkK5v5cPYREpNO56KKLOOmkk1Z9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\nE7t378ZmOxxnUVVVhdfrZdmyZVx55ZUDMmalukojhLpJREq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vWa/bixpSSnWXP/acl7Td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72m1xD+BYSjH8dzBnpt3Aumcs3YmP81/gFkp9ovZMp8zKHB4RAzNzVs21xn1TJUmSJPVpTd1x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\nREcA/wtMdfe5SY63QQniBbgRGG9mRQTxjwDOBn7UNlGKiIiIiIi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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0340975d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = [\n", " 'ctt6',\n", " 'ctt10',\n", " 'ctc10',\n", " 'ctc14',\n", " 'cta6',\n", " 'cta10',\n", " 'cta14',\n", "]\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=3)\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run3_data.tsv', index_col=0)\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([3 * 2, 2 * (len(mutants) + 2) / 2])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])\n", "axcount = 0\n", "for mutant in mutants:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(2 * (len(mutants) + 1) / 3, 3, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_initiation_mutants.tsv')\n", " summarydata = summarydata.sort_values(by='richrate_mean')\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate\n", " ) & (simulationdata['mutant'] == mutant)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.sort_values(by=['initiationrate'])\n", " # divide by the second highest initiation rate, this is the wt rbs\n", " subset['initiationrate'] /= np.unique(\n", " simulationdata['initiationrate'])[-2]\n", "\n", " if mutant == 'cta6':\n", " # the 'ATC' start codon variant was not cloned for the 'CTA6' variant\n", " subset = subset.reset_index().ix[[0, 2, 3, 4]]\n", "\n", " # exclude the fitted wt RBS for calculating error\n", " predicted = np.delete(np.array(subset['ps_ratio']), -2)\n", " measured = np.delete(np.array(summarydata['starverate_mean']), -2)\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " subset['initiationrate'],\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=subset['initiationrate'],\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata['starverate_err'],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8)\n", " if axcount == 7:\n", " ax.set(xlabel='YFP synthesis rate\\n(Leu-Rich)',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.xaxis.set(major_locator=MaxNLocator(5))\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1.4, 1.1))\n", " for loop, x in enumerate(subset['initiationrate']):\n", " if mutant == 'cta06' and loop: # teh variant 2 'ATC' is not there\n", " ax.text(\n", " x,\n", " ax.get_ylim()[0],\n", " str(loop + 2),\n", " ha='center',\n", " color='purple')\n", " else:\n", " ax.text(\n", " x,\n", " ax.get_ylim()[0],\n", " str(loop + 1),\n", " ha='center',\n", " color='purple')\n", " ax.set_title(mutant.replace('06', '6').upper(), y=1.1)\n", " ax.text(\n", " -0.5,\n", " 1.1,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig4_s1ag.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Fig. 4--Figure supplement 1H" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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j4r68Y8Cb1d7nG5mZmVl51dpIDNkZD18iJabrxdqLu7qRVlxvStq3XnOf38zM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\nz3kXmNnkuNcUM3sKWAX8kJA4nO2haakhu0XbFe7usUJ3XwKcS0hyNkV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034094d410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['tcg5', '', '', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=14)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run14_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run14_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([3 * 3, 5 * (len(mutants) + 1) / 3])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['H', '', ''])\n", "axcount = 0\n", "for mutant in mutants[:-3]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(2 * (len(mutants) + 1) / 3, 3, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_initiation_mutants.tsv')\n", " summarydata = summarydata.set_index('initiation_variant')\n", " # order by increasing initiation rate\n", " summarydata = summarydata.ix[['TTG', 'ATC', 'RBS2', 'wt', 'RBS4']]\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[pretermtype] == pretermrate\n", " ) & (simulationdata['mutant'] == mutant)]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.sort_values(by=['initiationrate'])\n", " # divide by the second highest initiation rate, this is the wt rbs\n", " subset['initiationrate'] /= np.unique(\n", " simulationdata['initiationrate'])[-2]\n", "\n", " predicted = np.array(subset['ps_ratio'])\n", " measured = np.array(summarydata['starverate_mean'])\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " subset['initiationrate'],\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=subset['initiationrate'],\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata['starverate_err'],\n", " capsize=2,\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8)\n", "\n", " ax.set(xlabel='YFP synthesis rate\\n(Ser-Rich)',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.xaxis.set(major_locator=MaxNLocator(5))\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1.4, 1.1))\n", " for loop, x in enumerate(subset['initiationrate']):\n", " ax.text(\n", " x, ax.get_ylim()[0], str(loop + 1), ha='center', color='purple')\n", " ax.set_title(mutant.upper(), y=1.1)\n", " ax.text(\n", " -0.5,\n", " 1.1,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig4_s1h.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Fig. 5" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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Yd/PNvGnpUtTcXOpozMzMrAwN+jkTko4CFgBfBpYA3wLeC2wXEVtExHYRsQ1p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\n8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f03407fb450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['cta', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=16)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run16_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run16_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "\n", "def return_pos_for_ordering(pausepositions):\n", " positions = [int(loc) for loc in pausepositions]\n", " return tuple([len(positions)] + positions)\n", "\n", "\n", "def return_mutant_for_ordering(mutant):\n", " positions = sorted([int(string[3:]) for string in mutant.split('_')])\n", " return '_'.join([mutant[:3] + str(pos) for pos in positions])\n", "\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([4, 3 * len(mutants)])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['A', 'B'])\n", "axcount = 0\n", "for mutant in mutants[:-1]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(len(mutants), 1, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_multiple_mutants.tsv')\n", " summarydata['pauselocation'] = summarydata['pauselocation'].apply(\n", " lambda x: x.split(','))\n", " summarydata['sortcolumn'] = summarydata['pauselocation'].apply(\n", " return_pos_for_ordering)\n", " summarydata['mutant'] = summarydata.apply(get_mutant, axis=1)\n", " summarydata = summarydata.sort_values(by=['sortcolumn'])\n", " summarydata = summarydata.set_index('mutant')\n", "\n", " # make xtick labels nice\n", " xticklabels = []\n", " for location in summarydata['pauselocation']:\n", " xticklabels.append(' + '.join(sorted(location, key=int)))\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[\n", " pretermtype] == pretermrate) & (simulationdata['mutant'].apply(\n", " lambda string: string.find(mutant.lower()) != -1))]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.set_index('mutant')\n", " subset.index = map(return_mutant_for_ordering, subset.index)\n", " subset = subset.ix[summarydata.index]\n", " predicted = np.array(subset['ps_ratio'])[5:]\n", " measured = np.array(summarydata['starverate_mean'])[5:]\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " np.arange(\n", " len(subset)), # no simulation data for No Stall control\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=np.arange(len(summarydata)),\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata[('starverate_err')],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8,\n", " capsize=1.0, )\n", " ax.set(xlabel='Location of stall sites',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.set_xlim(left=-0.5, right=len(summarydata) - 0.5)\n", " ax.set_xticks(np.arange(len(summarydata)))\n", " ax.set_xticklabels(\n", " xticklabels,\n", " rotation=45,\n", " ha='right', )\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1, 1.3))\n", " ax.set_title(mutant, y=1.1, weight='bold')\n", " ax.text(\n", " -0.2,\n", " 1.2,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig5.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 5--Figure supplement 1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "data": { "image/png": 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A4qVxJRIiIrLbUzIhInlTUTaUsk3LQ69EcjODrIISG8SgWAWplgbSqQTxTcup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\nwszMzMzMS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inM/hvwOUk7Az8H3gaaMlZGJ3BDNDMzm+lFxE8k3QHsR6rLPA+pWsKdwAm5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\nQGMsRnk4zIZHH0Urj0Ym28nE64DfdiUSZnYM8Eag2d23QbDg2syeAN6R4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KSgSlGYODje\nemPAlf08t7+ebpcC78567MCx8dYfA+4ZYBsRkbKVGc+/r2FXy2U8/7TkBDZd4b/zzvD4sMPCsnJS\n7hnT0hxLRAqq2IWJ+yje0KH/BdzBplqNacAG4LU+tnfCyFKLAE2hLCKpduAOBzJl/BTuaryLBcsX\nbJxZ+sApB3L4LoeXxwk66ckJhJPdRx/d9HM5KveMao4lstUp6jwTpRTnmbja3XtrUiV90DwTIiND\nUqM2DVXZ53z88XA/Y0ayOfpT7hkXL96yOda555ZXLYoMi+aZSKWizTMxkjpgHwa8knQIEZEklPUJ\nepayz1muJ+jZyj1juTfHkq2CCjylU9ReW2b2DTM7rpivkeHu97r7s4PZ1szeXOw8IiIiW61M/4hM\nPwoRGbGKXTPxLcJwrTfnrjCz9wNL3f2xQr2Yme0HnAHsAoxi8yqdCsKM3K8Dtmdk1cqIiIiUj1Gj\n4MQTN/0sIiNWkifUNwNXMfC8EINiZgcA9xOGfc0UInJn2c48fqoQrykiIiJ9KPfmWJI3NQmSbEkP\nTl3IziDnEGojbgWOA37BpuFijwcui4//CSQ7k5OIiIiIyAgwkpr6vB1YAXzE3TvMbBVwJuDufjNw\ns5k9AfwM+E/gB8lFFRGRpKmDpojI8CVdM1FIDcAj7t4RH2eaMh2Q2cDdf0EYBvXEEmcTERERERlx\nRlJhohXIFCRw99XAKmCvnO0eBXYvYS4RkeJ6/PFNcw+IiIiU0EgqTCwC9s1ZthDYP2fZaArcvMvM\njjSzBWa2wcwazexLZtZnfxAzG21mF5jZ4vicv5nZu4e7XxHZCrW3w29/G27t7UmnERGRrcxIKkzc\nBuxiZpeY2YS47EFgVzM7BsDM9gAOBRoL9aJm9lbg/4BnCR295wHfJ3QI78uvgM8C3wPeDzwP3GZm\n7xjmfkVka3PLLbBqVbjdckvSaUREZCtTig7Yx5nZi70s937WQeg4vdsQXucS4GTg84RmTO9lU2fr\n35nZU8CehBGfrh3CfgfybeAxdz8lPr7DzKqBr5rZj929NXtjM9sZmAV8zt1/HpfdRehA/hnC8LZD\n3q+IbIUWL4a77970+O674S1v0WzDIiJSMqWomRgH7NzLzfpZl7kNWuwjcTChAPGPuKyRMI9FG/Bm\nYCwwnwKN5GRmowg1HTflrLoRGA/M7OVp/yYMTXt1VvYeoIvQBCvf/faVcWp/N2C7we5LRMpITw9c\nfXW472+ZiIhIERW7ZuKwIu9/M+7+KqEmInvZtWZ2K7AP8Kq791UTko9dCZPkLcxZ/ny83xP4c06e\nduBhADOrAHYAvgjsRqhVyWu//Vg6yO1EJE3uvhuWLNly+ZIlYd0731n6TAPJdBLXZGYiUmQayrl0\nilqYcPd7i7n/wXL39cBDRdh1pm9GS87ytfG+boDnnwNcEH/+JfCXAu1XREaygfpH3HIL7Lcf1NeX\nLtNAMh3FAd7wBhg1Ktk8IiJSEEUtTJjZx939iiLt+7ThPN/df1OAGAM1ExuorcF8QifxmcA3gDHA\nKQXYb7YdB1i/HbBgCPsTkaRde23/Ize1t4dtPvOZ0mUaSKajeObnD3842TwpoYn1RKTcFbuZ06/N\n7P3A6bEJUiH9itCJO1+FKEysiffjc5bX5azvlbs/HX+8z8yqgG+b2deGu9+c11jW33qNNCsiRaeO\n4iIiI1axCxMthKFPDzaz09391gLuey7DK0wUwgtAN/D6nOWZx//KfYKZ7QQcAcxz97asVY/G+ynA\n40Pdr4hsRU46CZ59tu/aiVGjwjbloL+O4uedBxUjaYRyEZGtT7ELE3sDlwPvAW4ysznAF9x9bf9P\nG5i7zx7uPgqQoc3M7gOON7MfuHumcHMCofbgH708bSdCrcoGNh+i9kjCDN7P5blfEdla1NfDscfC\n9df3vv7YY8unv0QZdxRXkyARkeEr6iUhd1/u7u8jDM+6Kt4/YWb/UczXBbCgwcwmFfmlzgfeAlxv\nZkeb2f8AXwYucPcNZlZnZm81s23j9g8QOlr/1MzOMLMjzOwSwiR2/+Puqwaz3yL/TiJS7g47rPdm\nQjvtFNaVg8F0FM/0oxARkVQqSf2yu18FTAduJswfcZeZ/cDMagr9WmZ2uJndTmhitRL4UVx+Q3zN\n0YV8PXe/i1BjsCfh95sFfNndvx832Q/4G2ESvcycEscDVwLnEmbuPoLQr+T8IexXRLZmFRUwa9bm\nzYR6W5akwXYUlxFl3rx5vOc979niNm/evKSjiUgRlGIGbADc/RXgBDM7HvgpcBbwbjP7EWHCtt6e\nM3cor2Fm3wC+SZgQryfeZ3oYzyCcxB9oZkfG+R4Kwt1vYssJ5jLr7snKkFm2FvhSvOW1XxGRjbUQ\nd94ZHvdVWyFSQuvXr2flypV0dXWxcuVKJk+eTFVVFevXr086mogUQckvX7n77wl9KZ6J978Erujj\nNmhm9j7gW8ASQqFhYs4mJwFPE4Zh/VTev4CISDnJ9I/I9KMoJyed1P98EuXUUVwKpra2lsmTJzNm\nzBhWrlzJmDFjmDx5MrW1tUlHE5EiKFnNRIaZvRX4GaEgAfBX+qiZGKKzgHbgCHd/Ib7WxpXu/rCZ\nvYswAtOpwKUFeE0RkWSNGgUnnrjp53KSpo7iUjCzZs1i1qxZnHvuuTz55JMccsghXHjhhUnHEpEi\nKVnNhJmNN7PLCR2Q30w4qT/E3d/h7of1dhviS+wP3JcpSPTG3VcC9wG75ft7iIiUnRkzwq0cpaGj\nuBRcU1MTf/zjH3F3/vSnP9HU1JR0JBEpkpIUJszsvYRmTZ+Ii34M7OvuDxTwZaoJNRMDxgHK7PKd\niMgIlYaO4lJwc+bMob29nY6ODtra2pg7d0hdIEUkRYr6SW5mk8zsauBWYAc21Uac5e6tBX65RcBB\nZjamnzzjgAOB5wv82iIiiVq4cGHSEfqWWwuhjuIjWlNTEzfeeCNr1qzB3VmzZg033nijaidERqhi\n95l4BtiWMFP1j4Gv5sz6XEjXABcCl5vZp3JfJw4JezkwiThcrIjISNDU1MSZZ57JDTfcQENDQ9Jx\nenfssfDoo5t+lkFJ48R6c+bMoa2tjdWrVwOwevVqWltbmTt3LmeddVbC6USk0IpdxzyZUAtwiLuf\nXcSCBITCygLCfAwvmFlmSNU3m9lc4DngREIB55Ii5hARKak5c+bQ0tJS3k1JMh3FTzyx/DqKS8Fk\naiWamppwd2pqanD3zZaLyMhS7MLEJYS+EQ8W+XWI80YcAcwlFGIyl76mAycDOwK3AIdrBmkRGSky\nJ2mdnZ3lf7JWzh3FpSAytRLNzc3U1dVRUVFBXV0dzc3NG2snRGRkKWphogS1Ebmvt9bdZwPTCPNK\nnAN8Ffg4sJu7f8DdXy1VHhGRYpo3bx4zZ87k6aef5plnnuGpp55i5syZmmlYEpFbKzFxYpjuaeLE\niaqdEBnBRuRQGu7+b3e/zt0vcvcL3X2Ouzdm1ptZ7oR2IiKp88orr7B06VLa2tro7u6mra2NpUuX\n8sorryQdTbZC2bUS9fX1VFWFbplVVVXU19erdkJkhBqRhYn+mNls4Nmkc4iIDNcjjzxCRUUFPT09\nuDs9PT1UVFTwaKajs0iJ5NZK5A4E0NDQoNoJkREq1YUJM5tsZj83s6VmtsHMHjKzY/rYdm8zuxf4\nNWGEKRGR1GpqaqKxsZH6+npqamoYPXo0NTU11NfX09jYqJM1Kam+aiUyVDshMnKltjBhZtsC/wDO\nIMxhMZowh8TNZnZy1nbVZvZd4DFgZlx8ZWnTiogUljq6SrkYqFYiQ7UTIiNTagsTwNcIHa3/BbwP\n2IfQ4boTuNjMasxsB0KB4yuEGbL/BRzq7p/ofZciIuVPHV2lnGQXbAEaGxtZtGgRixcvpq2tjcWL\nF7No0SIaG0PXRRV4RUaWNBcm3gW0A+9x99vd/Rl3vwj4JtAAHAfcA+wbtzsPmOHu9yeUV0SkINTR\nVcpFpuDa0dHBpEmTaGhoYOLEiUycOJG6ujqqqqqoq6vbuKyhoYFJkybR0dGhAq/ICJHmwsSOwMPu\nviRn+Q2AAZcCuxFqJt7k7t9z964SZxQRKSh1dJVyMn/+fCZOnMi0adOYPn0606dPp76+ntbWVlpb\nW+np6dn4c319/cZtpk2bxoQJE5g/f37Sv4KIDJO5e9IZ8mJm3cB17v7RnOU1QBvgwDzgNBUi+mZm\nU4Gl8eGO7r5siLtI5x+QSEpdfPHFXH311SxatIiamhq6urro6elhw4YNjB07loqKCqqqqujo6GD3\n3XfnlFNO4ayzzko6tmxFLr/8ci6//HK6urpYtWrVxtqz008/ndNPPz3peCJbKyvWjtNcM2HAFoUE\nd++IP64EPqmChIiMFLm1EmPGjKGrq4vu7m6qqqro7u6mq6uLMWPGqHZCElNbW8vkyZOZMmUK06dP\nZ8qUKUyePJna2tqko4lIEVQNvElq3ZdVsBARSb3cjq7Nzc10dnZuXN/T07NxeUVFBc3NzTQ0NDB3\n7tyyqZ1YuHAhe+yxR9IxpMDOOCP70Sx23HHWFtvcd1+4AVx2WUliiUgJjOTCRHvSAURECiW3o+tg\nZTq6nnrqqX0O2VkqTU1NnHnmmdxwww2JZxERkcIYyYUJEZERI9PRNTMMbD7Pnz17dmFDDdK8efOY\nN28ejY2NLF++nJkzZ7LLLrswa9YsZs3a8gq2iIikR9oLE7ua2al5rMPdNWaiiKTG7NmzEysMDNf6\n9etZvnw5S5YsobOzkyVLljBq1CjWr1+fdDQRERmmtBcmDo63oa4DUGFCRKQEamtraW9vx8zo7Oyk\npqaG9vZ2dcgVERkB0lyYuA8NSyoiUvaOOuoofvazn9HS0kJraysTJkxg0qRJHHXUUUlHExGRYUpt\nYcLdD006g4iIDCwzCtXq1asBWL169cYZustllCkREclPmueZEBGRMpc7N0ZNTY3mwBARGUFSW5gw\nsw8WcF8nFWpfIiKySfbcGHV1dVRUVFBXV0dzc/PG2gkREUmv1BYmgDlm9mcze1O+OzCzt5vZg8Av\nC5hLRETYslYiM6ztxIkTVTshIjJCpLbPBHAgcC3wqJn9GfgVcIe79zvWoJk1ACcAZwAzgMeB/Yuc\nVUSkIDafaXhgSc40nF0rUV9fT1VV+Mqpqqqivr6+LGfoFhGRoUltYcLdnzGz/YCzgfOAI4FOM3sM\neAJ4CVgDVALbADsQhop9Q9xFE3AO8GN37yxtehGRkS23VqKhoYGurq6N6xsaGli1atXG7cphhm4R\nERm61BYmANy9G7jIzH4JfBqYDbwl3nKHjbV4v4hQi/HzgWoxREQkP73VSmQXJlQ7MbIkWQMmIslK\ndWEiw91XA98FvmtmOwGHAdOAyUA10AwsBP7q7s8lFlREZCuQWytRUVHBokWL6Onpoa2tjcWLF1NR\nUbFF3wnVToiIpM+IKExkc/fFwJVJ5xAR2Vpl10oArFy5ko6ODgDcnQ0bNgDQ0dFBZWWlaidERFIs\nzaM5iYhImcnUMnR0dDBp0iQaGhqora2lpqaGmpoaRo0atfHn2tpaGhoamDRpEh0dHRrZSUQkhUZc\nzYSIiCRn/vz5TJw4ceMwsPk8f/bs2YUNJSIiRWPuuf2UZWtiZlOBpfHhju6+bIi70B+QSAmlaWhY\nEREpGzbwJvlRMycREREREcmLChMiIiIiIpIXFSZERERERCQvKkyIiIiIiEhe1AF7K6cO2CIiIiIj\nnjpgi4iIiIhIeVFhQkRERERE8qLChIiIiIiI5EUzYIuISMFpcj0Rka2DaiZERERERCQvKkyIiIiI\niEheVJgQEREREZG8qDAhIiIiIiJ5UWFCRERERETyosJEAZjZkWa2wMw2mFmjmX3JzAY106CZvdnM\nOs1s517WLTMz7+W2TaF/BxERERGRodLQsMNkZm8F/g+4DvhvYCbwfcKxvXCA5+4D3EYv70MsMOwA\nfBl4IGf16mEHFxEREREZJhUmhu/bwGPufkp8fIeZVQNfNbMfu3tr7hPMrAb4PPAdoK2P/c6I9ze5\n+wuFDi0iIiIiMlxq5jQMZjYKOBS4KWfVjcB4Qi1Fb94DfBO4ADinj21mAGuBF4eZcWp/N2C74exf\nRERERLZeqpkYnl2BGmBhzvLn4/2ewJ97ed4CYGd3bzaz2X3sewbQDNxoZkcAlYQmUf/l7v8eQsal\nQ9hWRERERGTQVDMxPBPifUvO8rXxvq63J7n7y+7ePMC+ZxD6TDwCvA84GzgEuNfMavOLKyIiIiJS\nOKqZGJ6BCmM9w9j3p4Aud18QH99vZv8kdMY+Ffh/g9zPjgOs345QUyIiUjCXXZZ0AhERKQUVJoZn\nTbwfn7O8Lmf9kLn733pZ9qCZrQH2HcJ+lvW3fpAj2IqIiIiIbEHNnIbnBaAbeH3O8szjf+WzUzOb\nYGanxaFjs5dXEPpovJrPfkVERERECkmFiWFw9zbgPuD4nEnqTiDUSvwjz123A5cC5+Usfz8wBrg7\nz/2KiIiIiBSMmjkN3/nAX4Drzew3wNsIE82d6+4bzKwO2Bt4wd0HVaPg7m1mdiHwbTN7BbgdeCPw\nLeAWd7+rCL+HiIiIiMiQqGZimOKJ/QmEYWBvBmYBX3b378dN9gP+Brx3iLs+H/gMcCQwH/gi8Avg\npALEFhEREREZNnP3pDNIguLEdZm5KHYcqMN2L/QHJCIiIlLeijbijpo5yXBpOCgRERGRrZRqJrZy\nZlZFmGsCYIW7dyWZR0RERETSQ4UJERERERHJizpgi4iIiIhIXlSYEBERERGRvKgwISIiIiIieVFh\nQkRERERE8qLChIiIiIiI5EWFCRERERERyYsKEyIiIiIikhcVJkREREREJC8qTIiIiIiISF6qkg4g\n6WRmVcB2SecQERERkUFZ4e5dhd6pChOSr+2ApUmHEBEREZFB2RFYVuidqpmTiIiIiIjkxdw96QyS\nQkVq5rQdsCD+fCCwosD7LwRlLJw05FTGwklDTmUsnDTkVMbCSUNOZVQzJykn8Y+xoFVlZpb9cIW7\nF7wqbriUsXDSkFMZCycNOZWxcNKQUxkLJw05lbF41MxJRERERETyosKEiIiIiIjkRYUJERERERHJ\niwoTIiIiIiKSFxUmREREREQkLypMiIiIiIhIXlSYEBERERGRvGjSOhERERERyYtqJkREREREJC8q\nTIiIiIiISF5UmBARERERkbyoMCEiIiIiInlRYUJERERERPKiwoSIiIiIiORFhQkREREREcmLChMi\nIiIiIpIXFSZERERERCQvKkyIiIiIiEheVJgQEZG8WZR0jrRLyzHU+10YaTmGackpyVJhQiRl9OFe\nODqW+TOzzPdHrbt7omH6YGZjzWx6Ob/PWcdxXKJBBqD3uzBS9H5Xmpm5u5fz8ZTyYGX6mSAjgJmN\nBT4H7AZUAj8Bnnb3nkSDZTGzWuDrwI5AFXCuu7+UaKiUMrNqd+9MOkfaxf+bM4EdgFeAOe7+SrKp\nNmdm44DLgN2B1wFXA39w9wcSDZbDzG4A3gqcADxcTp89sPE4/gTYg3Acfwpc7+4rEg2WQ+93YaTo\n/R4DXAXMB65y955MwSLhaBvFjO8EdgYeAV4st89JSE/O4apKOoCMTGY2Hrgf6IqLJgDHAacBtyaV\nK1v8YF8AtAIbgDpgH+ClBGNtIRZ4PgtMATqA3wBL3H1DosGymFk18A8zu9Hd/zfpPH2JJ+pnEI5l\nO/ADd1+dbKpN4v/N34BqwIHJwN8JhYqyEI/h34AWQrZWwjE9xswud/dLk8yXYynhxPJHwFnAP5KN\ns0k8jg8CrwFPALXAJcBo4PsJRtuM3u/CSMv7HTUAxxMKPG1mdkM5FSji5+Q9QD3hM7IG+K2Znevu\ny5PMli0tOQvC3XXTraA3QiH1FuBu4PXAWGAb4AHgn8DoMshowK8IX5LTCFX3iR+7XnKOA56Jt6cJ\nBZ3XCF8+OyadLyvnZGAF0AN8Luk8/RzLJ4BHgX8BTfGY7pB0tpivErgh/t/sBown1h6X0w34BPBc\n/N/O1G7PBP5AKPSck3TGrKzHAf8GGoHngYOSzpSV7Svx73DnrGXXAAuByqTz6f3eat/vSsLJ77L4\nef4k8MGs9z7RzyTChZZbgTuAN8fH5wHdwKFJH7+05SzUTX0mpBimEL545rr78+6+wd1fI1SN7wXs\nl2i6oIKQ8a/uvsTd15vZ0WZ2m5k9ZWZ3mtn74xX3RMR2qhcBa4D3AgcRTjJvAU4GLjGzXZLKl83d\nVxJOgruBn5jZeQlH2kxsp3wZsI5wwvE24N3AVEKtTzmoAnYlfPm85O5rgSPM7Ndmdq+Z/cjMDk82\nIhCuVlYQasfczCo9NHf5CnAvcJaZfT7RhJusI/xNnhZ/nmtmBycbaaNdCTWNS7La0TcSrq5/zMzO\nMLN3JZZuE73fhZGK99vdu919FfB/hCZZ44EfAsebWU38G0iyD8U2wN7Abe7+mIemtZcAK4EJZrat\nmTUkmC8jLTkLQoUJKYZqQoGiBjbr5Pp3Qo3AhIRyZcvkGAtgZkcDtwGdhCsx4whXic82s8okAnq4\nvLEjoZ9JI9AaP+g/AVwOHABcZGY7JpEvI+uLcTWh9ukbwP+a2VeSS7WF0WxeeFxFuNq6CHjNzPY2\nsylmluTfZh2hLfVad+82s2MJBYu9CE2yTgV+bWanJ5gRYDmwE7BZQdbdnwK+Q2gX/CkzO7T00baQ\naSLWCswmNB37jZntZWZXmVmSBcllwJ6E99xjM5jjCRdbzgMuBK4ysy8mFxHQ+10oaXm/M8YAo4AZ\nhO/Li4Cj4rpDE8oEoQnWFDY/f92G8Pn5dcJn+sNm9skEsmVLS87CSLpqRLeRdyP8E71IuBI8MWv5\n/oRq03eWQcYKYA6hz8ROhILDRWQ1wQLmEq5u7ZdgznsJVzYyj6uzfv4W4QvqImBcGRzTY2Pe3YBL\n43t9dlz3ZWCnBLONJTQnuClr2SjCidJrhCuGTYTas50TyGfxdiuh5mlPQkHiW8D4uM3OhGaCzwBv\nSCBjRbzfhdBU7A5gu7isMmu7txGamlyS+d1KeRxzHlcRmpacFR+/kdDUbTWwCjg4weP4JuBhwpX0\nv8VMfwNmZK2/Pf7dTtP7rfe7VMcxLjsGeCD+vDOwhNB07KGYvT6p95pQa/IKcC7hIsuT8X0+k9Cf\n50bC988JCbzfqchZ8N876QC6jcwb4WrLSTnLZsR/nHdlLRtLGH2jKoGM+xNqIi6OX5THxuWZD/8x\nhKvXVyR4HD9LOOE9JWtZdoHicsJJ8Jvj48TasxKuVq0hjEI0JR7XHuBxQqGn5CfAWdkM+Fp8vzMn\n6S/GL/MPEQpAP4wf/D8gFDZLfiyBzxAGA/g0cB9wSFxeGe93B9YTC2klyDMaODj7by4u/wqhecZP\ngW0yGbP+d86Nf5f1CWbMHLOfAZdmLf8DofC4GNg/4eO4Zzyp+E9Cf6hj2fxE/aj4P1SStv96v7e6\n97sq6+eKnHUHEgoNb4yPxxAurrUC/521XVE/J3MyVsf7acDvCYWbRsJn+W5Z272eUAC+DqhJ4FiW\nbc5i3dTMSYrC3R9192shjFcdF28T79fF5eOBXwC/JrTLLHXGRwhXzD8NHEm40oJvGk6wndC+sbYU\necxstJldZmbTsxb/iXDV79OZZgTu3pnpy+HupxOurJ8RH3upM2Y1Y/sb4QNzVw8jVXybUBh7I3Cr\nu/+rmNn6ypk1AslVwDmEL8WDCDUTnwZ+7+4vuPsXCSOQHU34Yi3qsczJawDu/nPC1aqfEQrZrXGT\nTDvlRsJJyG4livZd4ArgMDPbOPqfu3+fcBX1Q8A3zOx17t5NaFZCvF8NtCWYsTv++CLh6jlmdhXh\nIsIZQDPwBzMrRR+uvjI+5+6XEa5WTiP0lenO+swcR7gi/HIJMvaXU+93YTKWzfsdv0N+nunv4mG0\npoq4LvNZkz3i0M8I7+8a4GQzO8nMivo52UvGzvh5vsTdjyeMvng78Jy7v5A51u7+PGH0sdHu3lGs\nfGnLWUwqTEjRZX3Ibx/vm+M/30WENqMf89CGPQm/BC4gFBxOzDmRn0j4klwMJZngrBL4FPBNM3tD\n/KBeBPwX4YT8W2Z2CGz8sMp8Ab1MLAiVQHbGvWIWj/fthOM1M257KWGUpxuBT5rZN0uUcbOcwF5Z\nH+wXu/shhKv+7cCT8cs809F+KdDm7l2977Y43N2z+p58kdAErwo408x2cfeeeJy3JQy3/BKU5G/y\nKUIb768Bh2f3H3L3MwjV+CcAl5nZbvH32A7Yl/B/U4r+Rr1mzMr6eHz8B0Kn+2Pc/QrCCebzhJOj\npDJm3vMuQo3TcWY2Jv5NNgDvIdTqrS9Bxj5zgt7vAmUsp/d7FGH+g9PN7DTYVKDw4DXCCH3vNLM5\nwPsJhbQ3EWqfv0LxL7T1ltGzPq87CB3bXxfXdZlZlZlNjesXQEk+J9OSs3iSrhrRbeu5AR8ntBfd\nn3CiuZ7YPCfhXJkOUR2Eccm/TmjTeB3hatYeJchQGXOsJVRz/4VQJZ5pRnAEofnLg8BHsp43iXBi\nXPT2yn1k3D37dQmjVVxAuCq3klBVPoUwDG8z0JDQsdwja30FYWjdl4lV+FnHcn7MXlnMYzmI32Ea\noSalB7iJMALVCcCVwKtkVZUXOcfp8e9uBaEt97vIGcaS0OH+KUKN4yPx5+bsY5tkxvi3sJJQUDw4\n57klGaZ6kMfx9ngMf0doTvQnQjv/fUr4d6f3eyt4v9nUDOcJwvfe48DHe1n/q/gZ9CKh2Vbmc/51\nxM/+BDNmmrV9kdDE7qeEFg4zCf01lxc7Y5pyFv04JB1At5F/y/oAOoFQmHiE0HwjsY7NvWSsIlwV\nynwxNhKGOn1TCTPsQGgq8D1CVfe9bF6gOITQ+fYlVvsjmwAAGINJREFUYB7wv4ST35IUePrJuHvW\n+tPil8/LwNtynve6hI9lds6ZMefvCWOoH0fofL0S2DPpv8esnN+J73cP4Yrlo8C+JXrtzFwsvyeM\nKraI0Ln1CLY8MdqX0GTwSkLztpJ8OQ4mI2FUueNLddyGeRwvJTS1eywey73LNKfe7/S/3+MJtUk/\nJJwIPwPMztnmYMIIfTOzlpWsf+MgM25LqMldFT8nlxKa15bs/U9LzqIeg6QD6Lb13AjVvj2EgkRJ\nrmLlkXF8/ALYnhKPkAQcTrhysQ+hev4VtixQ7E24MvjP+AV1B6W9ctlXxj3i+j0JV17ekvD72GfO\nrGP5UUJ/kw2EAseCYn6ws2UHx9zOmb3WhBDmwngzoZ9E0Tu4ZuclDLV5Q3y8B+Eqa68nRgm9z/1l\nfFfmxCfJrEM9joTOu2Ny/z7KLWcZHku930PLuXf8/NsTmM6mkeJm52w3PsFjOdiMdfGz/mPA24Ht\nlbPE71XSAXTbem6Eq//fpIyu/JbTLX7RPJD1hXgkoZo8U6DIHnLO4hdQSWcT7yfjfcSmN8DYMj+W\ne7CptmwnQlOsvSnNSDRT45dN5irqKOBseim4klP4SOg4HkDoUJ95vHtvJ0ZJZh0gY/YJZpLN1gZ1\nHJO+6f3eet5vwiAUd2a+Q+JnYOYkOLuZTpLHsb+Ms5M+hmnLWcxb5gtVpCQszJ7aPfCWW5/YOW+S\nh45vmcdHEOa7eA74lLsvTDDiQBkXAad56DSeqEEcy9OBhV6iD8DYsa4qvvYqQjOMfxOa1S0CPuru\nLaXIMlhZo2DlLt+dMMFjN/A54F4vcYf1rCzKWCBpyKmMhWVmk9y92cLM1h1mtjdhziUDvu/uV8bt\nKnzTKIfKmOKcxaLChEgZyf0iyjkJ/ifwOS/hEKu9SUNGKM+cZjaD0NHyaUJzuteA493936XMMVzx\nxOhmwnDPH3H3e5JNtCVlLJw05FTGIeXYosCTOcnNOgnuBn7u7r9Qxr6lJWexaWhYkTKS+6EUr2D8\nBTgF+A/goqzh5hKRhoxQfjnjEJCPE5pivINwQvHNTEEia9jIshdrn04gdCJcmnCcXilj4aQhpzIO\nKccWV5F907CwzxAGpdgGmG1mE0oekHRkjJlSkbPYVDMhkgLxRPNQYFnSTZ36koaMkEzOzNUrM6sl\n9C+pBHYhdPo+y92fKkWOQjOzanfvTDpHf5SxcNKQUxmHJ+uq+p5Ap7u/mHSmXGnICOnJWQgqTIiI\nlECcuOoZwuhRBwCvJ3QSfx74dKy1KFWWzdrt5p7c9NXuu5SUsXDSkFMZiyP217JM7sG02S91u/40\nZIyvmYqcSUhNtbqISBrEL5ze1AEXE2aSdXd/DjiMMLv5qBLFAzZWw081s+kWBkXoNLNRZna2mY0r\nhxMiZSycNORUxsLINJc0s7GZZTF3ZebngfZR7JPfNGSE9OQsBypMiIgUUF8nFO6+Crjc3ZfGL6Rq\nd3+aMJnfQ6XKZ0E1obnVXGCqmdUATxLm50j8e0EZCycNOZWxcOJnyzRgrpkdH5tXVgH/NLNzks4H\n6cgI6clZDtTMSURkGOJVq88RJpWrJEza96y7tw/y+Zn+FCVtImEpGFlKGQsnDTmVsTDM7B2EWbif\ni/fnA+uAE919SZLZMtKQEdKTM2kqTIiI5MnMxgP3A5nx4icA9YQ5QW4q1/ayFkaWajWzfYGHCV+O\nH3H3P8X1iedWxsJJQ05lLCwzOwD4GWECtaXA29x9dbKpNpeGjJCenEkqi2o5EZG0idXdVxMmoTuR\nMNzswcCzwPlmNrpcTiyyxRqQVgsjS/2GMOdGBfAVM3sjJN/OVxkLJw05lbFwYtMr3P1hYDLghNqT\nt2Rt01e/rpJIQ8aYIRU5y4EKEyIi+ZlCGJFprrs/7+4bPMy4fTWwF7Bfoun6EJtUVQKPEr4D9gcO\nAvYFLo/NOBKljIWThpzKWDgeZl8eY2bPAC8BpwPVwBfM7Ji4TaJNUtKQMWZIRc5yoMKEiEh+qgkF\nihrY7ArV3wEjNHlKVD9XzcpmZCllLJw05FTGkvgcUAWc6u6/Bc4j9O84y8y2STTZJmnICOnJmSj1\nmRARyYOZNRAmnfszcE6mDa2Z7R+Xv8vd70wwYr+yO3xbHC/fzGrdfX3S2TKUsXDSkFMZB50hMxla\nn4M2mNkEd1+T9fidQKu7/1UZhyYtOZOkwoSISJ7MbD9gT3e/NmvZDEJTiHe7+5/jsrHADOBpd28p\nYp6yH1lKGQuasxb4OuFKaRVwnrs3DuH5pTiWyli4nFXAWGCiZ40kZFkdv23LifVKPQFd2WeMr1lL\n6Ej95362STxnWlQlHUBEJK3c/VFCwQELk1h1A5mq73Vx+XjCSCD7A+8oVhbrfWSp44BPAYMaWSpz\nIlTEkzZlLFzOcYQasFbCrOp1wHSgMa5PPKcyFjTneGAeoT/WJDN7ELgMuMvd2zI5c7OWuCBR9hmz\nfBs428ze5+6397ZBmeRMBdVMiIgUkJmdAswB3gC8SLiqfTJwqIdRQYrxmlXA7wgnQp8ClhOuDt5M\nGKp2f3dvK8ZrD5YyFk5s0/9LwknvR4CmcmoKBMpYSGY2BngAaAH+SBhR6MvAOOBa4NvuvjbJK+dp\nyJjNzD4P/JhQgJzt7jcmHCnV1AFbRKSwqghDCI4DfgScAryjWAWJKA0jSylj4VQQcv7V3Ze4+3oz\nO9rMbjOzp8zsTjP7gJlNUsbUZwQ4BGgAvuzuF7r7r4A3AfcSCkE/NLPxsY9CUud1aciY7e+EOSMe\nBK41sw9BqInKbBALmzII5fCGioikXtYXT6ZPxOXAJwgFiceK/PJlP7IUylhImSxjAczsaOA2oBN4\nklCQ/S3w6diMRxnTmxFgB0LOJwAszGHTDnyckPco4JsWJtVL6qp/GjJmewWoBa4BbgKuNrMTs/p1\njC5ms7WRRoUJEZECyPrieYpwkrI3cFDsV1FsqwmT5+1nZhOzslTG+44SZBiIMhZOD+Fk9wAz2wk4\nDfghcKK7z3L3twDXEYax3AsSucqqjIXzGKHAcyJA7H9QHU/WvwDcR7j6f6QyDizWPrxKmHhwOfBF\n4A/AnFgzdTnw/TKpQUkFHSgRkcJ6kdC5b4a7P1WKF3T3JuCDwD0eh6iNuuN9dtX9WDN7m5nVlSKb\nMhZevHr6E8IIYV8AxgMPZDq5xs3OAF4GPhufU9KrrMpYGPGkewVwD/BJMzso5ujMOlk/jVAjqoyD\nEDuBtwLNwHHuvhT4CvB7YD5wEvDrMqlBSQUVJkRECsjdu4DzPUxoVcrXfdTjELUWZuqF3keW+gWh\n02nJR/NTxsJx90cIHVw/TbjaWx+XZ06A2oGVhKYciVDGoYuF1KPNbFTM4e6+HPg5odDzRTPbJ67r\njM1xOoCvAm81s+nFvuqfhoy95cxanjn3/Sewe8y5kNCsrZMwAeFuxc43kqgwISJSYB6GiC2H198+\n3jebWTVwEXA88DF3b04kXKSMBfFL4ALCCe+JZjY9a91EwkAAiyHRZiXKODSzCH0MjjGz0ZmF7n4L\noTnOh4D/NrMD4vLM6GINhJGJVpXgqn8aMvaXM1NQvAvYzswqzewa4C2Efm6/B240s+NKkHFE0DwT\nIiIjV28jS80sQYfwoVDGPMXRh34cs30DuMLMbiUMy3kYsA/wybhtIs1KlHHIMpPl/RSoMbPrY20n\n7v6reFX9R0CDmV3u7teb2e6EOWwWE+bLKLY0ZOw3Z7QamEwY0nY34Bh3f8jMniT0jypp7XKaqTAh\nIjLCmG2ciTd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f03402470d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['ctc', 'ctt']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=4)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run4_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run4_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([4, 3 * len(mutants)])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['A', 'B'])\n", "axcount = 0\n", "for mutant in mutants:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(len(mutants), 1, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_double_mutants.tsv')\n", " summarydata['pauselocation'] = summarydata['pauselocation'].apply(\n", " lambda x: x.split(','))\n", " summarydata['sortcolumn'] = summarydata['pauselocation'].apply(\n", " return_pos_for_ordering)\n", " summarydata['mutant'] = summarydata.apply(get_mutant, axis=1)\n", " summarydata = summarydata.sort_values(by=['sortcolumn'])\n", " summarydata = summarydata.set_index('mutant')\n", "\n", " # make xtick labels nice\n", " xticklabels = []\n", " for location in summarydata['pauselocation']:\n", " xticklabels.append(' + '.join(sorted(location, key=int)))\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[\n", " pretermtype] == pretermrate) & (simulationdata['mutant'].apply(\n", " lambda string: string.find(mutant.lower()) != -1))]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.set_index('mutant')\n", " subset.index = map(return_mutant_for_ordering, subset.index)\n", " subset = subset.ix[summarydata.index]\n", " predicted = np.array(subset['ps_ratio'])[5:]\n", " measured = np.array(summarydata['starverate_mean'])[5:]\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " np.arange(\n", " len(subset)), # no simulation data for No Stall control\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=np.arange(len(summarydata)),\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata[('starverate_err')],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8,\n", " capsize=1.0, )\n", " ax.set(xlabel='Location of stall sites',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.set_xlim(left=-0.5, right=len(summarydata) - 0.5)\n", " ax.set_xticks(np.arange(len(summarydata)))\n", " ax.set_xticklabels(\n", " xticklabels,\n", " rotation=45,\n", " ha='right', )\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1, 1.3))\n", " ax.set_title(mutant, y=1.1, weight='bold')\n", " ax.text(\n", " -0.2,\n", " 1.2,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig5_s1ab.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Fig. 5--Figure supplement 1C" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QK9Ga1tZYwqTKzxK+pLwC/Dx6Tq4A9ZK7aw6FSAZ1tWmdmR1C2JjyUMJ+MXcC34mWce6s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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034e5420d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['tcg', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=15)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run15_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run15_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([4, 3 * len(mutants)])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['C', ''])\n", "axcount = 0\n", "for mutant in mutants[:-1]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(len(mutants), 1, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_double_mutants.tsv')\n", " summarydata['pauselocation'] = summarydata['pauselocation'].apply(\n", " lambda x: x.split(','))\n", " summarydata['sortcolumn'] = summarydata['pauselocation'].apply(\n", " return_pos_for_ordering)\n", " summarydata['mutant'] = summarydata.apply(get_mutant, axis=1)\n", " summarydata = summarydata.sort_values(by=['sortcolumn'])\n", " summarydata = summarydata.set_index('mutant')\n", "\n", " # make xtick labels nice\n", " xticklabels = []\n", " for location in summarydata['pauselocation']:\n", " xticklabels.append(' + '.join(sorted(location, key=int)))\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[\n", " pretermtype] == pretermrate) & (simulationdata['mutant'].apply(\n", " lambda string: string.find(mutant.lower()) != -1))]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.set_index('mutant')\n", " subset.index = map(return_mutant_for_ordering, subset.index)\n", " subset = subset.ix[summarydata.index]\n", " predicted = np.array(subset['ps_ratio'])[6:]\n", " measured = np.array(summarydata['starverate_mean'])[6:]\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " np.arange(\n", " len(subset)), # no simulation data for No Stall control\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=np.arange(len(summarydata)),\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata[('starverate_err')],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8,\n", " capsize=1.0, )\n", " ax.set(xlabel='Location of stall sites',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.set_xlim(left=-0.5, right=len(summarydata) - 0.5)\n", " ax.set_xticks(np.arange(len(summarydata)))\n", " ax.set_xticklabels(\n", " xticklabels,\n", " rotation=45,\n", " ha='right', )\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1, 1.3))\n", " ax.set_title(mutant, y=1.1, weight='bold')\n", " ax.text(\n", " -0.2,\n", " 1.2,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig5_s1c.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Fig. 6" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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xeAMFBSXMmfMGjY21PPlkqDNlSn7iFJHOmzwZFlZv4p2Np9Cvrom336rmITyc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034315d250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['cta', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=11)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run11_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run11_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([4, 3 * len(mutants)])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['C', ''])\n", "axcount = 0\n", "for mutant in mutants[:-1]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(len(mutants), 1, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_distance_mutants.tsv')\n", " summarydata['pauselocation'] = summarydata['pauselocation'].apply(\n", " lambda x: x.split(','))\n", " summarydata['mutant'] = summarydata.apply(get_mutant, axis=1)\n", " summarydata['sortcolumn'] = summarydata['pauselocation'].apply(\n", " return_interpausedistance_for_ordering)\n", " summarydata = summarydata.sort_values(by=['sortcolumn'])\n", " summarydata = summarydata.set_index('mutant')\n", "\n", " # make xtick labels nice\n", " xticklabels = []\n", " for location in summarydata['pauselocation']:\n", " xticklabels.append(' + '.join(sorted(location, key=int)))\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[\n", " pretermtype] == pretermrate) & (simulationdata['mutant'].apply(\n", " lambda string: string.find(mutant.lower()) != -1))]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.set_index('mutant')\n", " subset.index = map(return_mutant_for_ordering, subset.index)\n", " subset = subset.ix[summarydata.index]\n", " predicted = np.array(subset['ps_ratio'])[7:]\n", " measured = np.array(summarydata['starverate_mean'])[7:]\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " np.arange(\n", " len(subset)), # no simulation data for No Stall control\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=np.arange(len(summarydata)),\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata[('starverate_err')],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8,\n", " capsize=1.0, )\n", " ax.set(xlabel='Location of stall sites',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.set_xlim(left=-0.5, right=len(summarydata) - 0.5)\n", " ax.set_xticks(np.arange(len(summarydata)))\n", " ax.set_xticklabels(\n", " xticklabels,\n", " rotation=45,\n", " ha='right', )\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1, 1.3))\n", " ax.set_title(mutant, y=1.1, weight='bold')\n", " ax.text(\n", " -0.2,\n", " 1.2,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig6.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 6--Figure supplement 1" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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+sr0aGqCmhtb4Jh5f+zNa33gllImIiIh0QcnEEHH32919H3cvcvfd3f1nKdce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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034095f110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutants = ['ctc', '']\n", "\n", "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=5)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run5_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run5_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm']\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([4, 3 * len(mutants)])\n", "fig.subplots_adjust(wspace=1, hspace=1)\n", "\n", "panellabels = iter(['A', 'B'])\n", "axcount = 0\n", "for mutant in mutants[:-1]:\n", " rmserror = dict()\n", " axcount += 1\n", " ax = fig.add_subplot(len(mutants), 1, axcount)\n", "\n", " summarydata = pd.read_table(\n", " '../processeddata/platereader/measured_yfprates_' + mutant +\n", " '_distance_mutants.tsv')\n", " summarydata['pauselocation'] = summarydata['pauselocation'].apply(\n", " lambda x: x.split(','))\n", " summarydata['mutant'] = summarydata.apply(get_mutant, axis=1)\n", " summarydata['sortcolumn'] = summarydata['pauselocation'].apply(\n", " return_interpausedistance_for_ordering)\n", " summarydata = summarydata.sort_values(by=['sortcolumn'])\n", " summarydata = summarydata.set_index('mutant')\n", "\n", " # make xtick labels nice\n", " xticklabels = []\n", " for location in summarydata['pauselocation']:\n", " xticklabels.append(' + '.join(sorted(location, key=int)))\n", "\n", " for pretermtype in pretermtypes:\n", " pretermrates = np.unique(simulationdata[pretermtype])\n", " for pretermrate in pretermrates:\n", " fitresults = dict()\n", " if pretermtype == 'selpreterm' and pretermrate == 0:\n", " continue\n", " subset = simulationdata[(simulationdata[\n", " pretermtype] == pretermrate) & (simulationdata['mutant'].apply(\n", " lambda string: string.find(mutant.lower()) != -1))]\n", " model = pretermtype\n", " # if pretermrate is 0, make sure all other preterm rates are also 0\n", " if pretermrate == 0:\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[(subset[innerpretermtype] == 0)]\n", " model = 'trafficjam'\n", " subset = subset.set_index('mutant')\n", " subset.index = map(return_mutant_for_ordering, subset.index)\n", " subset = subset.ix[summarydata.index]\n", " predicted = np.array(subset['ps_ratio'])[5:]\n", " measured = np.array(summarydata['starverate_mean'])[5:]\n", " rmserror[model] = int(\n", " np.sqrt(mean_squared_error(measured, predicted)) /\n", " measured.mean() * 100)\n", "\n", " ax.plot(\n", " np.arange(\n", " len(subset)), # no simulation data for No Stall control\n", " subset['ps_ratio'],\n", " marker=modelmarkers[model],\n", " linestyle='None',\n", " markerfacecolor=modelcolors[model],\n", " alpha=0.6,\n", " markeredgecolor='None',\n", " label=modellabels[model] + \" %d%%\" % rmserror[model])\n", "\n", " ax.errorbar(\n", " x=np.arange(len(summarydata)),\n", " y=summarydata['starverate_mean'],\n", " yerr=summarydata[('starverate_err')],\n", " marker='^',\n", " linestyle='None',\n", " color='black',\n", " linewidth=0.5,\n", " markeredgecolor='black',\n", " label='Measured',\n", " alpha=0.8,\n", " capsize=1.0, )\n", " ax.set(xlabel='Location of stall sites',\n", " ylabel='YFP synthesis rate\\n(Relative to no stall site)')\n", " clean_axis(ax)\n", " ax.yaxis.set(major_locator=MaxNLocator(5))\n", " ax.set_xlim(left=-0.5, right=len(summarydata) - 0.5)\n", " ax.set_xticks(np.arange(len(summarydata)))\n", " ax.set_xticklabels(\n", " xticklabels,\n", " rotation=45,\n", " ha='right', )\n", " handles, legendlabels = ax.get_legend_handles_labels()\n", " handles = [handles[n] for n in [3, 0, 1, 2]]\n", " legendlabels = [legendlabels[n] for n in [3, 0, 1, 2]]\n", " ax.legend(handles, legendlabels, loc=1, bbox_to_anchor=(1, 1.3))\n", " ax.set_title(mutant, y=1.1, weight='bold')\n", " ax.text(\n", " -0.2,\n", " 1.2,\n", " panellabels.next(),\n", " weight='bold',\n", " transform=ax.transAxes,\n", " fontsize='large')\n", "\n", "fig.savefig('../figures/fig6_s1.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 7A top panel" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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LKQ54qmsjjxRu58CZBuLc3Guzra7t5507HmZjQ4UOptevhw98YB5KOiOFGyIi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"text/plain": [ "<matplotlib.figure.Figure at 0x7f034052f590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=9)\n", "\n", "simulationdata.drop(\n", " ['files'], axis=1).to_csv(\n", " '../rawdata/simulations/run9_data.tsv', sep='\\t', index_label='index')\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run9_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm', 'bkgdpreterm']\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "for pretermtype in pretermtypes:\n", " subset = simulationdata[(simulationdata[pretermtype] != 0)]\n", " model = pretermtype\n", " sortedindices = sorted(\n", " subset.index, key=lambda x: subset.ix[x][pretermtype])\n", " subset = subset.ix[sortedindices]\n", " #subset = subset[2:-2]\n", " temp = ax.semilogx(\n", " subset[pretermtype],\n", " subset['proteins_yfp0'] / subset['proteins_yfp0'].max() /\n", " subset['simulation_time'] * subset['simulation_time'].min(),\n", " '-',\n", " label=modellabels[model],\n", " marker='None',\n", " color=modelcolors[model],\n", " alpha=0.6,\n", " basex=2)\n", " if pretermtype == 'selpreterm':\n", " temp[0].set_label(modellabels[model] + ' (selective)')\n", " if pretermtype == 'bkgdpreterm':\n", " temp[0].set_label(modellabels[model] + ' (non-selective)')\n", "clean_axis(ax)\n", "ax.legend(loc=2, bbox_to_anchor=(1.2, 0.7))\n", "ax.yaxis.set(major_locator=MaxNLocator(3))\n", "ax.set(\n", " xlabel=u'Abortive termination rate (s-1)',\n", " ylabel='Protein synthesis rate (a.u.)\\nNo stall site', )\n", "fig.set_size_inches([4, 1.6])\n", "fig.savefig('../figures/fig7a_top.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 7A bottom panel" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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jIJWq7rUiItInFKJF6izqEL0ZWOzuh5Ye7w88B7zH3f9f6dgBwBzg/7n7e8uc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\n1aVSIUivXg2rVoXR1ys5CJtZ6NSbned95swwXdy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y3+giDaDSzblnAfcBU4H3ufu3zOy0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0340731890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "\n", "simulationdata = simulation_utils.get_simulation_data(runnumber=9)\n", "'''\n", "\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run9_data.tsv', index_col=0)\n", "\n", "pretermtypes = ['5primepreterm', 'selpreterm', 'bkgdpreterm']\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "for pretermtype in pretermtypes:\n", " subset = simulationdata[(simulationdata[pretermtype] != 0)]\n", " model = pretermtype\n", " sortedindices = sorted(\n", " subset.index, key=lambda x: subset.ix[x][pretermtype])\n", " subset = subset.ix[sortedindices]\n", " subset = subset[2:-2]\n", " ax.semilogx(\n", " subset[pretermtype],\n", " subset['proteins_mutant'] / subset['proteins_yfp0'].max() /\n", " subset['simulation_time'] * subset['simulation_time'].min(),\n", " '-',\n", " label=modellabels[model],\n", " marker='None',\n", " color=modelcolors[model],\n", " alpha=0.6,\n", " basex=2)\n", "\n", " if pretermtype == 'selpreterm':\n", " ax.set_label(modellabels[model] + ' (selective)')\n", " if pretermtype == 'bkgdpreterm':\n", " ax.set_label(modellabels[model] + ' (non-selective)')\n", "\n", "clean_axis(ax)\n", "ax.legend(loc=2, bbox_to_anchor=(1.2, 0.7))\n", "ax.yaxis.set(major_locator=MaxNLocator(3))\n", "ax.set(\n", " xlabel=u'Abortive termination rate (s⁻¹)',\n", " ylabel='Protein synthesis rate (a.u.)\\nSingle stall site', )\n", "fig.set_size_inches([4, 1.6])\n", "fig.savefig('../figures/fig7a_bottom.svg')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot Fig. 7B" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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vquqjXtsw4GPgUVW9vANjMQzDMIyUhB2AdEKIfXehNQ0fOI83ChwUa1DVT0Rk\nEi7zkmEYhmGEQqBiKiLjgbdV9QavaSFQp6rrgzyPx3ri0u+parOILAX2T7BbCgwP4fyGYRiGAWRO\nb+eXkbTd4rIQCCvwZypwtIjsFNf2KXCYiMTXChpKa+5boxMhIoeIyBMislhEtorIfBH5q4jsnmA3\nTESeEZGVItIkIitE5FkvQC5V3zeJiIrIPUmOXe4dS/ewvb2GsR0RtJg2AYeKyBBv/6cAZSKS1cPn\nuR4FugETReS/vLaXccnkHxKRA0Xk18AInMganQgR+QEwEbfH+BfAqbji6scDk2NCKSIHeHZ9gKuB\nrwI/xaWz/MAr1p7YdxlwETATuNAr1BDPy8CRcY8/eO3fjGs7OqCPahhGCRBoAJKIvAJ8g9xy5qqq\n+pp29qKFfwiMUtVzvXqhnwB7JIzhFFX9dw5jMooQETka+A9wr6r+OOFYX2AasEpVR4rIw8BJwJ7x\nmaC8bVmfAR+r6tcT+vgaMBo4BpcD+gpVfTjNeC7HRZJb6kbD2E4JOgDph7g9nofh6nbGqrpkg+/C\nrap6jYg8BuzgvW4UkWNxnsIRuC03d5qQBoDIhbjyeUGzHNUnfL7nOtze4l8lHlDVNSJyLbCPJ5gD\n8GZIEuy2iMiPcbMbiVwGfKKq74nI27jEICnF1DAMI1AxVdXFuL2jwLatMU+q6kVBnifhnFMTXq8E\nLg3rfNsxg3DrzwXFy3x1CvBKqvSLqvpcnP2rwGm45YBHgLeAOeoYlaT/HXHTtf/Pa3oMeEJERuRQ\nJtAwjO2EoNdME/kPMDvkcxjbFzvhci4vzMZYVe8Hfo+L8r4XmAWsFpEnReTQJG/5NlAOxLzlF3DF\n3K/s4LgNw+jEhC2mC3BrTh3Gi9hcFIvU9F5n+/BTJ9UobmLrnlnnWlbV3+A86wtw07WbcaI5SUSu\nSTC/DHgbaBSR3rjlileA80UksQ6tYRgGEH7ShkuBS0RkHt50WWJ+Xh8Mxq3BVsa9zpZSKyJupEBV\nN4hILS4aNyneWmmVqm6Ifx/wtPeIFa5/ErhVRP6hquu8toO9t2ygPd8B7g/mkxiG0ZkIW0wvAC4G\nvgLcDPzeqyTzKPCyqjb56Cu2d3BZwmsjPywvon7HAieISI2qNiQ5/j/A7SLyZeB54PrEaFxVneZt\nnXoRF/29DnfzVwecgcumFc+DuEAkE1PDMNoRam7ebScRGQBciBPW/XGe4kbgKeAxVZ0S+iCMToO3\nN/R94A4hmIcnAAAgAElEQVRV/WnCsQHAZJxneTBubXUtrhh7Q4Ltz4Hf4qaAt+CEfayqfjvJOX+N\nq0V7pKp+kHDMtsYYxnZO2GumgIuwVdU/qeow4FDgHqAe+AHwoYjMEJEfe5GUhpEWT8yuB/5XRF4T\nkXNF5ERv/fMjXN7mc1U1AlwFHIhL5HCliBwnIqeKyJ04cfytNwV8Ji6xw9MpTvsE7ibQApEMw2hH\nXjzTpCd2wR2/xe1NjYl6A+5i9jtvm028/VsdOJ2q6kkdeL9RhIjIqbjfz3BgR1wt238DN6vqkji7\nEbi9qccAfYFGXDrKe1T1Bc9mNHA40F9Vm1Oc723PZuf49VjzTA3DyKuYikgNcDYukONEXDCR4i6A\n7wL/hfMiaoGvq+q7ce9NVc4tXWKI2LG05dwMwzAMoyPka830eFyu03OA7jiB+wIXiPRYghdxLXAb\nMD1Wq9RrT/Qsy4E7gL2AB4CXcOtjLbg1sG8A1wLTgUutOLhhGIYRFmEXB78Rt59vF5yANuCiJx9W\n1ZTTtiKyFUBVu6Sx+RkuQvjrqjo2hc0xuD2Dt6rqr3P9HIZhGIaRjrDFNDY1Ow23Wf4pVd2Y4T01\nuKLf76vquWnsPgeWqurxGfobB+yjqrv4GbthGIZhZEvY+0zvxXmhH2f7Bm/7QjYJGQbiRDoTdbjg\nFMMwDMMIhYJF83YUEfkUF5m5h6omLf4tIgNxZbbmqerIfI6vkIhIBa5aCsDK+NJjhmEYRvDkZZ9p\nSPwdl/R8jFcAug0iciQwDldi64E8j63QDMBtE1lCq6gahmEYIVHKnmklMAY4AbcFZjmtqQZ3Bfrh\ngp6eUtXvFGSQBUJEBuOEFHLb+1iaPwrDMLJn7ly45x5o8rK6HnAAXHUVVFamf1/p4rtmth9K1jP1\nNtafgtuMvwDYGVeU/DCgP6702/9sb0JqGIaRFaNGtQopwKefwsyZhRtPiRN2AFKoeGuBt+OSmg/C\n7S9VYJlXJNwwDMNIJBqFRUkqU44aBSNGtG83MlLSYprAClyaOFXV9YUejGEYRtHSkiImcd26/I6j\nE1Gy07wxvATnr+MKPq8G7vTanxeR27x9q4ZhGEaMVGJq5EyonqlXTzITCjQDm4CFKepTpur/N8AN\nuIXlqPccW2Q+GJcH+FAROVlVG/2M3TAMo9NiYho4YU/zvoO/yNCI52X+QFWXpTMUkW/gqs4sAn6C\nS5a/Oc7kfFzWpWNwxaLv9TEOwzCMzkskUugRdDrCnuZ9BJhBq+f4IfAs8BwwEZeUXoA1uIT0m4Bv\nAhNEpFeGvn+CWyP9iqq+pKp18QdVdTLwVVzd1IuC+kCGYRglj3mmgRO2mN4L7I0Tzr1U9UhVvUBV\nz1fVY3D7Qf8N1AAX4hIM3Arshqv4ko6RwHhVnZ/KQFVXA+OBPTr6QQzDMDoNJqaBE7aY3oSrFPMN\nVf0i8aC3feVsoAlX0Dmiqr8AFgNnZei7EueZZkKAaj+DNgzD6NSYmAZO2GJ6DPCOqm5IZeBNz44H\njo9rno7zWtMxDzhMRNKVaesOHIqrQmMYhmGArZmGQNhi2gxkWvsE2CFhLBEyBy49hUsZ+Ndk21+8\ntr/iKsY8l9VoDcMwtgfMMw2csMV0OvBlETkilYGIHA582bON8SUgUz7Zu4CPcMXH54vIi177cBH5\nO65azH8Ds4A/5zZ8wzCMToiJaeCELaZ/BMpxlV2uE5G9RKRaRLqIyL4ich0uWX0ZLiVguYjcBewO\nvJauY2/f6Fdw1WP6AWd4hw4AvgMMAV4GTlTV+jA+nGEYRkliYho4oe4zVdVxInINcAdwi/dIJAL8\nTFVfFpHdgauBdcDdWfRfC1wiIr/Eebe74MR7BS7Sd2Ewn8QwDKMTYWumgRN6OkFVvQ84ELdNZjaw\nBRe9Ox+3pnmwqt7umQsuAviQTEkbEs6xQlWfVdU/qeotqvp4IYRURMpE5KciMk9EtorIxyLy7QSb\nQ0TkHRGpE5HlInKziFTle6yGYWzHmGcaOHlJdK+qc4EfZWG3ALjeT98i0ge3jzRtDl5VHe+n3xz5\nP+BnwG9w67mnAU+KSFRVnxaRobh9tROBc4H9cDcPOwJX5mF8hmEYJqYhULJVY0SkK2699EwyF31V\nws9D3BX4MXCXqsams98UkZHANcDTwM+BWuAMVW0CXheReuBeEblZVReHOUbDMAzAxDQEQhdTEdkV\n+D4wDOhG6qllVdXjfHR9Ey7hQwSYA2zEXx7goGkEjsJVromnidbtQacAr3lCGmMU8Bfv2ENhD9Iw\nDMPWTIMnbG9tf+A9oCfZeY9+OAPn5R2pqrNyGF6gqGoEl4cYERFchPGluIjj73nJJXYF5ia8b42I\nbAb2yfZcIjI4g8kAH0M3DGN7wzzTwAnbM/0/nFf2Hi7YaDkuuX0Q9AfGFoOQJuG/cUklwG3xeZJW\n73RzEvta3A1HtizJfWiGYWz3mJgGTthiegLuwn9SwtRmEMwEBgXcZ1B8CBwHHAT8HreX9oIM74mG\nPSjDMAwgvZhGo1AW+kaPTkfYYloNvBWCkAL8AXhRRM5X1adD6D9nvEo284Hx3hTu47RWrumR5C09\nceXnsmVIhuMDcNHEhmEY7Um3ZhqJmJjmQNhiOouQyp95SR6uxW09+T7wMbA+tbneEMY4YohIX+BU\nYIxX+i3GVO95ELAM2DPhff1wAjs723OpatpUi27J1jAMIwXpPNOWFqiszN9YOglhi+ndwN9F5Fuq\nOirIjr09m9fiApuO9h6JqHdcgVDFFOiC80B/hfOaY5zsPc8A3gC+ISLXeukQAc7BRSS/FfL4DMMw\nHJnE1PBN2GI6A3gReEpEzgMmARtIEbmrqo/46PsOYDBuTfZVYE2qfvOBqi4WkUeA34hIMzANOBb4\nBfCwqs4SkVuB84HRInIHrnD6zcBfbY+pYRh5I51g2raZnAhbTKfT6h2eg9sXmg4/YvplnJAO83L0\nFgNXAQuAK3DbYJbgsiHdBqCqc0TkZOBPuP2la4E7PRvDMIz8kE4wzTPNibDF9O+E5y2WAx8VkZDi\nBVrd5D1S2UwAUpakMwzDCB3zTAMn7Koxl4TY/SRcuTXDMAzDD7ZmGjilHP98A7CniNwiIiWbY9gw\nDCPvmJgGTl5ESES+hIu8PR63B7IJWAW8DTyoqpNz6HY4Ljr2OuC7IjIVtzWmOYmtqurFOZzDMAyj\n82FrpoGTj0T3lwP3AfEblyqBod7jYhG5RlUf8Nn1vbQGN/UBvprGVgETU8MwDLA10xAIO9H94cAD\nOE/0JuAZYCEueGgocB7Os7xbRCb79FAvDXi4hmEY2wc2zRs4YXumP8d5jmep6ti49mZcdqQbRGQi\n8DrwE+Db2Xasqo8HOVDDMIySJhqFV16BKVOge3c49VQ46KDktiamgRN2ANIxwAcJQtoGVR0DTMTt\nGzUMwzBy4ZVXYPRoWL0aFiyA+++HRYuS22bKzWv4Jmwx7QWkzSPrsRTYKeSxGIZhdF4++KDt62gU\nJqdYOTPPNHDCFtPlwMFZ2B2Mi+41DMMw/BKJwIYN7dvfeCO5vYlp4IQtpqNxe0F/mcpARH6Fq6Qy\nJuSxGIZhdE62bPFnb1tjAifsAKSbcYndbxSRk4B/Al94x3YDvoXbe7qJtpVWDMMwjGypq/Nnb1tj\nAifsdIJLReQU4AXgROCEBBPBTQX/l6qmWCk3DMMw0hKkmJpnmhOhJ21Q1Q9FZA/gXOA4XJHsmIiO\nB55T1a0dPY+IVAJ9gUZVXdfR/gzDMEqGdGKqCiJt28wzDZy8pBP0CmE/4T0CRUS+DvwMV4mlwmuL\nAP8B7lXVl4M+p2EYRlGRTkwbG6Gmpm2brZkGTiknukdEfge8givCXQasxBUJLwdOAl4QkZTl0AzD\nMDoF6cQ08ZiqTfOGQKCeqYgs7sDbVVV39XGurwHXA+uA/wWej00Xi0h34L9wRbh/ISJvqupbHRib\nYRhG/lm1CiZNgspK+PKXoVu35HaZxHSnuG38qu6RChPTnAh6mndwB97rt4j4j4EW4GuqOqVNR6p1\nwKMiMgP4APgRYGJqGEbpMHcu/PnPrVOyb74JN9wAPXq0t00nprW1bV9nEktbM82JoMV094D7S8eh\nwLuJQhqPqk4RkfHAYfkblmEYRgC89FJbYautdUkYzjmnvW26faaJQptJTM0zzYlAxTTP21u6AWuz\nsFsH9A55LL4QkZNxVXQOwGV+ug+4XTXd3IthGJ2ChgYoK4OqqtQ29fUwf3779nffTS6mftZMTUxD\nIS/RvJkQkWrgNFV90cfbFgFHiEi5qiadlxCRClyU75IAhhkIInIE8CrwLG7N9xjgVtzf4pYCDs0w\njFyIRt3Wk8TtJ4k0NsLjj8PUqc72mGPgvPOgIslleMGC5H3U1zsvNHHt1I+YZprGtWnenMhHcfDT\ngR/ipoCrcXtMY5QBNbiE+GW4KNxseYnWWqhXq2o04bxlwN3AzsAdOX+A4PkdME1VL/Rej/H2yP5K\nRO4KYs9tQairg/ffh/Xr3euyMnfBiERg6VJoboa+fV2IflmZe5SXw9at7iLT1OT66NrV2a1Y4e7g\n+/Z1F45Yf01Nrs+qKndRqalxd9ItLa19irjn2HliD2gbfBH/3NgIK1e6vmMXxmjU9dOlCwwY4Owi\nkdaLZyTixhmNQv/+UF3tLnaVla12dXXuc/TuDT17uj5WrXLjHTDAfd6GBnduERg0qO3FNX6Mqq7P\nhgb3+aurW9vin6H1wh5/kffTpuqmFVta3LkqK90j2bji2xJfpzrW2Ahr17q+Bw1y3zO4z9DU5L5X\ncN9rZWXr3z/2N1Bt/bvGxq3qvseGBleCrKrKjX/9ends0CDXnoz4Ptavd+fYcUfXfzTa3i72+1i5\n0gmfKuy2W9tAn0Tef7/t9zF+vHscdljb72ftWvjii9T93Hef+z9RXu5+K5WV7j2pGDPGCXiPHu4z\nZUrwYJ5pTkiYM4si8lVczt0Mt2zUAW+r6hk++t4R+BiXBOJznLh+4R3eDTgTl/N3GTBCVbOZEg4V\nzwPfDNygqrfEtR8KfAicrKrjAjjPYFq98SGqmk3lnnj8/Sjmz4e//MV/FhbDMIqP6mro1cv9e+tW\nd9MQu6FKJJ03HrtJbW52fcbenymaOBptfcTfJIq4G9ARI2DkSNhhB7+fLJMOdYiwxfQ14FTgL8Bf\ngXOAX+OmXluAU3BTnauBg1V1s8/+hwLPASO8ptiHiX1p04D/VtV5HfgYgSEi++GKop+jqi/Ete8A\nrAeuVtV7s+gnU9T0AOAj79/hiumUKfDII3Y3axhGfhk6FK64wo+ohiqmYU/zHorzFq9WVRWRGpx4\n7qqq/wSmi8g8YBTwU+A3fjpX1QXAISJyLElSFarqfwL7JMHg3e6ReNMQi13vmWU/xbEG/MYb8M9/\nFnoUhmFsj6xa5ZZOioSwxbQ38F5clOos73kEroIMqvqCiHwGnIUPMRWRXYA6VV2vqhOACSnsdgP2\nUdWxOX2CYMmUcSqa4Xhx0dBQ6BEYhrG9MmJE61p7ERC2mNYSJyCqWicia4D9Euw+Bb7ms++FwJPA\nxRns/oRLLbijz/7DYJP3nLjrumfC8UwMyXA8fpo3PE4/3QU+TJrU/lgsWATcekksGCQabQ3OiQX4\nxIKIysvd+kpzc2vAS3Nz2/WTWNBFU5MLDGpsdK+rqtqutcSfJxOx4JtYcFQsuESkdTyxfmKBTbG1\nnC5dWgOpWlpcQFHss8SoqHDjjAXOVFa2rifFiLWlmi6PD3ypqXE3MrExibSOO34NKzF4KbEtE5WV\n7m+X+B2k+x6TjTfVc2Wl+zs2N7fvp6bGPTc0tF9jiwWZxf7O8VRUuEcsSC0add9NJJL9zV9VlXtP\nzD72vSZ+Z4mvY/lvE9cR4+1E3G8FWoOhEr+b2NpiLAahsdF95t692/6NIxH33bW0uH/H/m/U1rrf\nZYxBg9zfceNGV0A8toZZUwPrSrgmyMiRhR5BG8IW09nAyITtK3OAQxLsepPBK/PWR9s0AT2StMfT\nC+cFp9nQlVfmAxFcYFQ8sdezs+kk0xqoZArRDwoRuOgi9x907lzXdtJJ8K1vtY2eTTWexMjTxGN+\n2tMRiwBNjFjNtp/YxTzTe/yOLV4MY++Lb4t/TnaudMezJZnIxv4dE61k440RxPnj/zbpvuNk32/8\n38bPeFLdTMSihFOdrzPyxRfuhjgmrLHvVNUJbnm5u+lJvNHL5oYsFngUC2TK5rcTuzFMfF69GhYu\ndOft3h322SenjxsWYQcgXQvcBrwI/FJV54rIb3Hrpj9Q1QdE5BjgbeATVR2epq/XcQFLvocBvKOq\nJ+bw3sARkbeALsBRselvEfkj8D1gkKrWB3COjkbzGoZhGD4IW0xrcDVLDwFeU9XTRaQfzkPrCmyk\nNTvRD1X1/jR97U3bbTa7APWkzoKkQAMwD/iJF6xUcETkRODfuDXjR4CjcBHOv1DVWwM6RwVuqhdg\npapaqK1hGEaIhCqmsG1v5feBJlW9z2s7HngYl8ihCXgAJ3hZD0ZEosCTqnpR4IMOGRE5C5e8YR/c\nPtj7VPX2wo7KMAzDyJXQxTTtyUX6A5tU1XdYqIgcB6xS1TnBj8wwDMMwsqegYmoYhmEYnYFM+x4N\nwzAMw8iAialhGIZhdBATU8MwDMPoICamRkkiIsNE5BkRWSkiTSKyQkSeFZEvpbC/SURURO5Jcuxy\n71i6h20vMgwjJRaAZJQcInIA8IH3+Cuu6tBg4GrgS8AJqvpBnH0Zrpj8Btz+5DbJMUSkL7BH3Cm+\nCfzSe17jtamqJsmbaBiGEX7ShoeBh1X1/YzGhpEl3u/qJGDP+IQUItIN+Az4WFW/Htf+NWA0cAwu\nicgVqvpwmv4vBx7CskcZhpElYefmvRS4xCuz9hjwhKouy6UjEXmkA+NQVf1uB96/3SPChbgSd0Gz\nXJUnfL5nAC4TVptlClXdIiI/Brol2F+GS1f5noi8jUvdmFJMDcMw/BK2mF6Aq+ryFeBm4PciMg54\nFHhZVZt89HVJkrZ4tzpZ1mT12hUwMe0Yg4B0RQXyyavAacBE7ybrLWCOOkbFG4rIjrjp2v/nNT0G\nPCEiI1R1ah7HbBhGJyZUMVXVZ4BnRGQAcCFOWL+GS1i/UUSeAh5T1SlZdPc/Sdp+AuwP/At4CVeW\nrQV34f8GTswn0nohNToBqnq/iAwErgPu9ZrXishY4C5VjS8/922gHLZ5vy8A9wFXAlfkaciGYXRy\n8h6AJCIjccJ6DrAzzmv8FJf0/e+quj7Lfr6LCz65XFUfTWFzDvA8cJ3lvu0YIvyccDzTBar8MZc3\nisgOuJuzk4ATcONT4MeqerdnMw0XRHRu3FvvAc7EBSLVJunX1kwNw/BFwaJ5RaQ38Fvgh7SufTUA\nTwO/U9XFGd7/CbBFVQ/PYDcBGKCqe3V40NsxxSimiYjIcFzB+D1wN2q7AOmmcr+frFKRialhGH4J\ne820DV5JtrOB7wAnApU4T+IN4F3gv3BBS98Ska+r6rtpuhuKm97NxCpcgXCjYywvhn5FZGfgI+D6\nxIhcVZ0mIr/G1c/dA/c7qwPOoH3x+QdxgUgpy/4ZhmFkS17E1Cu5dhFuarc7LijoC1wg0mOqGitk\nfWNcQfG7SS+Cy4AjRaQqVSCTiPQAvuydy+gAOUTchsVK3Lr4D0TkH0kqDu2Dm+FYhFszf0VV30rs\nRET+jvu9HRG/J9UwDCMXQs2AJCI3ishC4E1cNG4l8AzwFVUdqqq/jxNSAFT1DqAR2C9D98/jNuo/\n7UVsJp57MPAK0Ad4vKOfxSgOVDUCXAUcCEwWkStF5DgROVVE7gRuxC0fHIf72z+doqsncLMiV4Y/\nasMwOjthJ22ITa1Nw+3re0pVN2Z4Tw3wOfC+qp6bxq4nMAF3UW0EJuO8VYBdgZE4z/tt4Guq2tyB\nj2IUGSIyAhfNewzQF/cbmArco6oviMho4HCgf6q/vbfn9HBgZ1XdENdua6aGYfgibDG9G5cB6eOQ\n+u8N/B7n9SZu1N+I2wJxo6o2hnF+wzAMw4DwxfQiYL6qvpfB7pvASFW9IcfzVAGH4PaXKs5DnRyf\nas4wDMMwwiIf07xPqOrFGexG4aZiu4c2GMMwDMMIiUCjeUXkQlyQUTx7ishlad7WC7fp3k9qwfhz\n7gtcAxwPDAH+qaqXiMi9wBzgPrXSOIZhGEaIBL01ZiRO2GLipcAR3iMdQg4RtyJyKfAXoDquORah\nfCIu6vNYETlfVRP3GRqGYRhGIAQtpjcAPWlNOn8xMB+XkCEZitsTOA+fm+dF5GhcxOVm4FfAWOCT\nOJNf4/aqfguXGL1Y9kmGjohU4CqrAKy0tWPDMIxwycea6ZOqelEIff8Ll5f1qFhi88TzicieuLy/\nH6rqsUGPoVjx9tjG9u/a9g7DMIyQCbtqTJhJIY4E3k2oEJJ4/s9FZDxwUIjj6IzYGrNhdHLq6+Hh\nh2HWLOjeHc46C446qtCjCpVkZToDI+gApFgi9EWqGol7nRWqusCHeTdgQ0Yr2Ar08DMOwzCMzs4/\n/gGfeAtjmzfD44/DoEGw224FHVbJErRn+jkuofj+wFzvdbZejvoczxfASBEp91LMtUNEKnH5fRf5\n6NcwDKNTowqTJ7dvf/ttuPTS/I+nMxD0NOxi3Fpdc9zrbB9LEjvLwAu43Lw3p7H5AzAQeNln34Zh\nGJ2WlhQhiR9YyYecCdQzVdXd0r0OmFtxBZ9/KiJfAcZ77XuLyP/hgpNG4rIh/SnEcRiGYZQUqcTU\nyJ281jMNElXd5JV2+weuzNpw79Bh3gNgOnCeqq7L/wgNwzCKExPT4MlXPdNdgVpVXe+9Hgr8Apex\naBJwp6pu8tuvqi4DjheRw4ATgF2AcmAF8B9VfSeYT2AYhtF5MDENnlDFVETKgb/hCoNfCDzlVXp5\nD+iHC1U+GfiWV6S5LpfzqOqHwIfBjNowDKNzY2IaPKEWBwe+h8uCtBGICeUVQH9c/dEzgWdx0b/X\n+elYRB4RkWuzsHtCROynYxiG4WFiGjxhi+m3cfs8D1HVV7y2b+G2wVzrtV2Ii+Y922fflwB/EpEX\nRSRTtZlQN+sahmGUEiamwRO2mB6AW7tcCCAiO+EibDfGapx6e0SnArvn0L8CZwAfiMgewQzZMAyj\ncxNJujPf6Ahhi2kFUB/3+qs4L/E/CXbV5OY9Pgc8iJsm/lBETsllkIZhGNsTzc2ZbQx/hC2mC2ib\nF/cMnDc5OtYgIj1wJdq+yKH/FlW9CvgJrlrNqyLys5xHaxiGsR1gnmnwhC2mbwB7iMjjInITbr20\nEXgRQESOAV4DegMv5XoSVb0LOB3nBf9BRJ4WkS7e4YL8bERksIhs9PbCxrfvKSL/8o6tFZH7RaRn\nIcZoGMb2ia2ZBk/Y+0x/j9v6cmFc2y9Vda337+dwdTc/AG7pyIlUdYyIHIGrXXousI+InIkLgMor\nIjIEV1+1V0J7b+AtYCUuyrkfLpPT7riMTYZhGKFjYho8YZdg2+QlVPgWLkfueFWdFGfyDC4J/QOq\n2hjA+WaLyKE4z/dYnEjP6mi/2SIiZbg9tbeRfA34KqAPMCJ2QyEiS4HXReToWFCWYRhGmJiYBk/o\nGZBUtQF4MsWxjPtEczjfei9X7wPApbg9rfniIO+8fwH+jZvCjucUYEKcZw5uKrwWOA2XzMIwDCNU\nTEyDJ++5eT3vLSmqGvXR1X+A2Sn6aQa+KyKzcdPH+dpnuhjYU1WXJq6VeuyHS1KxDa/u60Jgn2xP\nIiKDM5gMyLYvwzC2P0xMgyd0MRWRs3B5eIcBNWlMfdUzVdUTsrC5TURGAztl229H8HIPr09j0gvY\nnKS9FheNnC1+y9UZhmFsw8Q0eMLOzfsNYBTZeYaheI+q+mkY/eZIuuhpP165YRhGzpiYBk/YnunP\ncSJ5B3AXsNzLeOQbEVmM816PV9WF3utsUVXdNZfzBswmoEeS9p64uqvZMiTD8QHARz76MwxjO8LE\nNHjCFtPhwHRV/WkAfQ3GiWll3Ots0QDOHwSfAXvGN3iVdXYHXsi2E1Vdmu64iKUiNgwjNenENBqF\nsrAzEHRCwhbTJmBhQH3FcvcuS3hdSrwB/ExE+qrqGq/tZKC7d8wwDCN00olpJGJimgthi+kkYISI\nlOc6vRtDVRele10i3A9cDYwTkd/h9pzeCoxW1fcLOjLDMLYb0qUTjESgsjL1cSM5Yd9/3AAMAm5J\ntyVme8HzRk8A1gL/AG4CngfOK+S4DMPYvkiX6N6S4OdG2J7pEcAY4FrgOyIyBdhA8jVMVdWLU3Uk\nIuM7MA5V1eM68P5cTvgOSSKUVfUT4Cv5HIthGEY8mTxTwz9hi+mfccIpuExEp6WxVVy+2lQc04Fx\nFEsAkmEYRsFJt2Zqkb65EbaYXhpgXxmTNBiGYRiZyRSAZPgn7ET3jwfYV2JBccMwDCMH0omprZnm\nRl6DgkSkn4iMFJG9vddd83Te4fk4j2EYRilgnmnw5CXRvYhcBvyU1mTuT+LWR18WkU3AlQmVVLLt\ndwTwPdye02raBvyU4XIB98eVf8t7Un/DMIxixNZMgycfie4fwQmnAKtxBbFjorcrLiPQ/iJyhKom\nSwKfqt9DgAlAVVx/SltBjb2e2ZHPYBiG0ZkwzzR4Qp3mFZGLgUuA6biC2ImlwU4A3sR5rNf47P7n\nOG/0FeBMXB1RBc4AzgYe9F5/Chya2ycwDMPofNiaafCEvWb6PaAO+JqqTk88qKrLcEK4AfiWz76P\nBlYC56nqK8DTuM+jqvqSql4F/BDYH/9CbRiG0WkxzzR4whbTA4F34vLQtkNVtwDv4T/Xbh9giqo2\nea9jU7mHxPX9AK7253/77NswDKPk2LIFpk6FTz4BTbO73tZMgyfsNdP4Ki/p6Ib/eqZbcYn03YlU\nN4rIBmDfBLupwEk++zYMwygpvvgC7rsPNnuRJ3vvDddckzzPrnmmwRO2ZzobOFxEdkxlICJ9cWua\nsyrCMwcAACAASURBVHz2PQ/4UkLbXGBkQlsNFslrGEYn51//ahVSgLlzYcqU5LbpBNPWTHMjbDF9\nGOgNPCMi/RIPikh/3Fpnd9x2GT+8BuwuIn8WkV5e23vAUBE53et/b+B4gisDZxiGUXSouqndRP71\nr+T26QTTPNPcCFtM/4YTva8Ai0RkGm7q91gvcf084ERgPC4a1w9/xonk1cBTXtt9QAT4p5dUfyou\n4vfpDn4OwzCMoqWuLnn72hS799MJpq2Z5kaoYqqqUVy07v8BDbhpWcHtLz0GKAfuAk5VVV9/QlXd\nCByJE9APvbaFuD2tDcBwoCvwL+C2AD6OYRhGUbJpkz97WzMNntDXEr2i4L8VkZuAEcAuOBFdAXyk\nqvUd6HsNCdteVPVpEXkFGAasUdUFOQ/eMAyjBAhSTG3NNDfyFpijqs3AJO8R9rm25OM8hmEYxcDG\njamPNTRATU3ra1WrZxoGgYqpiOzSkfer6mKf5xsInI9LSViTxlRV9bsdGZthGEaxks4z3bgRBsTl\nnssklrZmmhtBe6ZfkHshbsXHeETkIFzgUg8y71FVwMTUMIySobkZRo2CDz5we0VPOw1OPDG5bTrP\nNFFMM4mleaa5EbSYLqe9mHYHYltXluAicFuAQbicvIKL6l3u81y3AT2Bd3H5eTcmObdhGEZJ8tpr\n8M477t8NDfDss9C3Lxx4YHvbTGIaTyYxtTXT3AhUTFV1cPxrERkCvA8sAC5X1WkJx/cEHsWJ6jd9\nnm4k8BlwvBc1bBQhTU0uvVljIxx8MPTqldxu7VqYNw/69IE994QZM9wG9GHDYMeUKT+Co7YWpk93\nHkBNjUvL1r8/rFvnzr/nniAJ8x/NzW5vXyTixlmTsNCgCgsWwKpVsMcerr9oFGbPhvp62H9/6NbN\n/XvaNCgvhxEjoKrK//hV24+vmKmvh1mzoEsX2G8/KIvbV9DQ4H4zqu776NIl+36XL3e/pd13hx49\n3O9v2jT3/KUvQc+ewX8WP6i63xTATju1P97SAp9+CqtXw+jR7Y+PG5dcTNNN8z7/vNs6s8su7nds\nnmk4iKZL4NjRzkWeBk4B9lTV9SlsegKfA5NU9XQffa8Fxqvq2YEMthMhIoNxswAAQ1R1qc8ufP8o\nGhtdtpV169yFsazMicjEiW3t9toLdt65VXhWr3YXznQMGuQuhOD6rK93zzU17kJbVuZEqnv31ums\nrl1bBaZLF3cxbW5ufY56t18iLg3bgixivocPd0JXVeXeP3UqbN3qjlVWwsCB7rzNzW6MK1Zk9dW1\no08f54HssAMMHuwufps3tz62boXevaFfP/d9L1zovI9evdwYevVy46uqcu/dtMmNq1cv911VV7tj\nDQ3usXWre92vH1R4t9fr1sGyZe7v2qeP+66rqlrfW1fn+u7Rw/Up4m5AFi1yn3/IEHcT0tjobKuq\nWsVt6VKYObNt7thhw9yFfulSmDy57fdx0EFOHGPjikScKHbt6sa/ZYtr//TTzN/twIHuPN26uc+6\naZN71Ne730mPHu5RU+PO09LinmP/3rLFfbddu7r+ysvds2rbPmOC1KWL+1utXAmLF8OSJW3HM2yY\n+31XV7vvZsqU1HtDY3Tr5v6Wqm4sXbq433A29Ovnxpzut3nUUXDxxdn1V2KEersZtphuAN5S1XMy\n2D0PnKyqKfyWpO95CrdXdS9VbezYSPOLiJwM3AQcAKzC7ZW9XQP6Y+RbTOvr4Q9/cMJoGEZpc/jh\ncNllhR5FKIQqpvlIdN87C7uBgF9B/BkuWcOzInJtqewnFZEjgFeBZ4HrcTcEt+L+FrcUcGg5M2aM\nCalhdBbmz3czBKru/7VI66xFbClBpO0j1hYjNp29erXznnv3bp3F6N691Va11cOORp1H39TkvOfK\nSnfOsjJYv94txfTt62Y9ipGwPdNxuNy4J6rqhBQ2ZwOjgJf8TtmKyA+Bu3GiXY+ri5oMVdVd/fQd\nFiIyFuitqofHtf0RuAror6pbAzhH3jzTaBR++cv0ARCGYRhBccIJcN55OcUIlLRnegsu9+5YEbkP\nGAMs847tCpwNXIZL/3ejn45F5L9xqQjBfUndvEcyiiLKV0SqcTcXNyQcGoXztI8BxuV5WB1i7lwT\nUsMw8sfbbzsP9oILiivoLlQxVdU3ReR7ONH7X+DaBBPBeZOXqGqGMJR2/MJ7/93AP4A1FIlopmEo\nUIUrFRfP597zPmQhpp7nmY4BGY4HxiQfeaZ69HDBKY2N6QsX+6Gy0k0b9ejhppQaGrJ7X/w0k59z\nVVW5gJ1oQvx4eXlr0ElNTevr+PHU1LgglkzRlP+/vfMOl6Wq8vb76xNuvlwyEiQKIqgYCAYU1EGQ\nMSsYMA4j+hnGiAEDDoYZTGPAMTIoIIqCJFGCSBBJIgqoiCBRuJcL3BxO6vX9sXbdrlOn+6Tu6j7n\n3PU+Tz3VvfeuXbtWhbXD2mvPmuXdW2vWDA+fO7dmxLNsWe28c+d6F1jGvHneNdbf72WQGk93qFS8\nXOvW1ZdFdi2Z4VYeaXzyy8tmoscWkfw+9PfXwjIZZMZBeRYsGC6bYnkqFTfmyayqV62qLyvJj8uM\nuvLnL5J1UZr5s57R0+Pny4cV5ZAdlz0j3d2134sWuTFYT0+te9XMjfGWL6+VccECN0paudJlsuee\nbnR1xx1uMd83rSxM6nPllX4vXvWqqaNQ2+Gb93uSfgW8GXgOPr/U8BbqpcAPko/dibI7cI2ZvbdV\nZW0DmYHVykJ49rqP13D/vrGTlM/AQH1L3AUL4JOfrI2FzJ8/fIFiM/8Y9fW54pg1q/ZBGRryj8Ds\n2f6BHBysjauAf1yqVf/oLVo0cpymv98/WKtX1z6a/f01K9yentrHKM9oU0uysZzsGqpVL+OsWV5O\nqVbOegsx18OspsDWr3fr1K4ul0mWZ1+fX2f2gezOva2ZZXJmzWzmaTN5Fhka8jSZkh0Y8HSZLPr6\n/MObl/UmmzS+b5k8166txYHfs2rVlX1vr5evt9eP6e+vWQNnss4qF93dfs+yfDJ5DAzUrGmHhjy/\nnh7Pb926Wv6SnzfLN4vLV5oyq+WurlpFZd68kc9QX1+tTF1dvuWn7mTKrqvLLXe7u/0Z7+/3LavM\nZOVYt86ve+7c2rn6+nxbsMCvq6+vZnmepcmupxllUXyus/uble+73x1pPT1duPRSH8t97nM7XRKn\nLb5505jdhLpxx8GjwHQzexlrlZ5pNV/25pvrtwSf/vTR5/NJIz/62Qvf3T18Xml3nSe0UvFpI43y\nhfrxozHaByub6pP/v6hgVlevnGOdL5teMS83OJGfU9lIMUKtUpDPbzSZZ1M4wJV1cU7saOfKn6OY\nbv78+ufaqrB6cb1zgsstk11RpjDyOkcrb/4eZbLNlz0fVq/cWbp65SymycqUf87qlWnOnPrzZPNp\n8zLIUxnrazEOis91UTkfeaRP11mypPlztZtddoF99+10KWq0zdF9CZwNvFHS5mb2SKcLM06yqdUL\nCuELC/FjMZY92zbADeMt1GRZtMjnXt5yy/Cuy/33b3xMEARTh4ULvRfpjjtg6VJvJc+eXeuNySoC\n+WGArCs9a+VmW1Fx9/T4nPLubrcOrla9gpCvgGet5GzLusiHhmq9EtWqO/jIL36+++7wzneOXfFp\nJ6Va85aJpEXANbhrwk/gq8Q8CtQdHZoKXpIkzQZWAx81sy/kwvfDy3+wmV3egvM0a80bBEEQTIDp\nrEyvx1t0jxtHcjOzKdEKl3QZMAd4ZuakIU2NOQbYtpn1XXPn6KZmhLR4oguvB0EQBBNjOivTCbU0\nzawFIxDNI+l5uOHVWcDJwDOB44CPmNmJnSxbEARBMDmmrTKdzkh6OfBpfCrMP4GTzOxLnS1VEARB\nMFmmrTJNXaN/MbMfdLosQRAEwcZN25SppB7gKbgl6mIzu1rSY83s3knm9yhwt5k9tZXlDIIgCIKJ\nUvo4oqRuSf+Jr45yDXAmbmwDcKqkGyTtOomse4B7WlTMIAiCIJg0pSrTZFV6AW5gMxf4PcOdDS/C\nF/m+StLWE8z+DOAQSU9rRVmDIAiCYLKUPV3kncAhwEXAW8xsccEK9wDgW8AbcN+9x04g70tw94TX\nSboO+BM+z7Sela+ZWdG5fBAEQRC0hLKXYLsJ2B7Y2cxWp7AqcJqZvTH97wHuApab2d4TyLuK+/gd\nzXNlFm9m1jVKumCaIWlv4OP4KjybAY8AVwKfM7M/1Un/WeBjwDfM7N2FuKOB745xyqGpMlc5CIKp\nR9kfh92BizJFWg8zG0gOGA6ZYN7/ydRfJSYoAUl74ePv1wLvxn00b59+XyvpYDO7Npe+ArwRuAV4\ng6QPF5xjnAvknJXxEuCjaZ8twhDPWhAEDSlbmQ7g46JjsTkN3AA2wsyOn0yBghnB+/GW6GF5706S\nzgH+hruXPDyX/hBc2b4Gb72+Fvh+FplWLVqayyfrIbkpXDEGQTAeylamfwT2k7SDmdVdNkzSLsC+\nwPXNnCh1F28J9E0jx/fThmUnLHsDvnxeq3lg009seuoEj9kG774fZkBnZmskvZeRi8S/Fbg1Tcf6\nDW5N/n2CIAhaRNnK9JvAj4FzJb2lOJYl6YnAD4FZwPcmcwJJh+OGSweQrkfSEHAFPj527uSLH+TY\nFl/cfCpwAfAi4BpJJwOXAbeZ87N8Qkmb4d21H09Bp+BTsp46iQXpgyAI6lLq1BgzOxP4DrAP8Ifk\naMGAQyXdi7dcnwz8xMxOm2j+kj4NnAcciF/LYry7rgt4PnB2MjwJZhBm9r/ACcATgG8AfwEeknSa\npOIKh6/Hn4es9Xs2vjj729tU3CAINgJKd9pgZm/Hu9lux8dPBWyBj2HdB7wP/+BNCEmH4mNjjwJv\nBhaa2XZmtg2wCfBvwDLgI8m5fDCDMLNP4q3l1+Fdtivx5+g6Se/JJX0r8BugLy3b14tXwF4rqbiu\nbBAEwaRoi6m/mZ0CnJIcMzwWbyk8aGbNeDB6L76W6aFmdmPhfKuB/5N0M27x+R94V2AwgzCzZbjz\njjMAJD0FOA04UdLp+LO2T0q+rE4WRwH/24aiBkEww2nrvDkzW4K7FWwF+wK/LSrSwvlulHQlsF+L\nzrkx88BUyFfSdsANwCfMbJgRkZndJOk44OfArriyXA28lJHOPL6NGyKFMg2CoGlKV6aSZuPGIrsB\ns0dJamZ2wgSyngc8PI50jzC+6TnBKEzC4rYsFuM9Eu+UdLqZrS/E7wGsx/02vw44z8xG9EpI+iHw\nGUkH5OekBkEQTIZSlamkHfF5fdvng3O/LRdmuFHJeLkHOEBSl5kNNTh/N27lW3daTjD9MLMhSe8A\nzgF+L+kbwF9x38+HAO/CLXefi89fPqNBVqfiz9vb8aGAIAiCSVN2y/Qr+JJrdwAXA8tpnSeZc4AP\nAV+T9G4zG9aNl7zefA3YDvhyi84ZTAHM7BeS9sfv/3Gk+cXAH4AjzexsSb/Ex0kvapDHvZKuAI6Q\n9L40/hoEQTApyvbNuxjvcntCwX1bK/LeDHduvy2urM8B7k7ROwEvw7uW/wk81czG0yUcBEEQBBOm\nbGW6HLjUzF5VUv674OujZguE57uNAW4CXmNmfy/j/EEQBEEA5SvTC3Bn93tYiSeSdCA+RrYtrkgf\nAK40syvKOmcQBEEQZJStTPfCjTtOBY4dbfWYIAiCIJiulKpMASS9Ffe7uw5ft/TRBknNzJ5bamGC\nIAiCoATKbpn+C/ALxmc1POoC3sn5wmTZqBR1mhK0Tfq7OL9MWRAEQdB6yp4a85/pHOfg8/2WMvmp\nMc+exDFGbQ7rxsQ21ObW7gDEmpxBEAQlUrYyfSLwJzN7RQvyOngCabcDTgQek/5f3ILzb0xsbJWP\nINjoGLhrgP6/9G/4r9lizrPnoFka5ahpTakXVrYyXQP8oxUZjdcyV9KbcCcNmwIrgA+Y2cmtKEMQ\nBMFMwMwYvGv46I+tNwYXD9KzY0+HSjW9KXsJtl8Bz5Y0p+TzIGnbNBXnZFyR/hLYOxRpEARBAYPq\nuuLaD9B/a3+dxMF4KFuZfgxfreMCSfuMlXiySHoLcCvuUH8F8FYzO9zM/lnWOYMgCKYtI/Vo0CTt\n8M17D3AQcKOkPlzZDdRJa2a240QyT8txfRd4Id4ffgFwjJk92EyhgyAIZjShTFtO2cq06EZwNo2X\nYZuQ0YukfwO+CGyCOzR/r5lNlWXCgiAIpixWDRvDVlO2Mt251RlK2h53AvEveGv0fLw1urjV5wqC\nIJiRRMu05ZSqTM3snlbmJ+nfgS8AC3FPSu8xsx+18hxBEAQznlCmLafslukG0govz8XnfvYBS4Ar\nzGxcC3dLuhh4Pt4avQ/4KLBY0vPGc7yZXTaZcgdBEMw4Qpm2nNKVqaRNge8AdR03SDoP+PdxrDf6\ngtzv7XHn+ePFaGPFIQiCYCoTY6atp1QFk+aXXgY8Gbfi/SXu7L4L2AU4BHgpsKOkA8xstElOVzJD\nPPOk7ur34ouY3wt8A/hmmcvUBUEQbCBapi2n7Nba+3BFegFwlJmtzEdKWgicjs8PfRfuuaguZnZQ\necVsH5KOxlvqXwfOBQ5Mv2cDX+pg0YIg2FgIZdpyyl415mbc6frOZramQZp5eGv1PjN7WmmFmSJI\n+h0wZGYH5sLOAA4ws5ZYPyeL5w2O7s1soo7uo4UcBDOYoYeGWH/D+rpx8w6f1+bStI1SffOW7QFp\nN+DKRooUIMVdBTxutIwkFeesThpJr21VXpNgNrCyEPYIsHkHyhIEwUZIjJm2nrKV6QAwfxzp5jF2\na+gHki6R9KTJFkbSsyRdjXtN6hRfBV4o6ShJm0h6IfAmJmBQJWn70TZqa5kGQRCMJHRpyyl7zPRP\nwHMk7dhozqmknfEpMzeMkde++Jqof5B0Ce644VejtXpT/psDrwSOAfYB/gh0sjv5DNy9Yl55XoQb\nJI2XcU0nCoIgqEuMmbacspXpt4DTgIskHW1mv81HSjoQV4q9ad8QM/uLpKcC78fnmB4CDEi6CVfa\nd+MWw13AFviaps8A9kxZPAJ8GPiqmdXzDdwuzsUXOj8WuB5f8/V44KeSXh4WvUEQlE4o05ZTqgES\ngKRTgdfjHQtLccf34NNCtsAHhX9iZuMex5S0CHgH8GZqY63FC8kGm/+OK+pvjtWKLRtJzwSuxufV\nfi8X/iLgF8CLzeyCceSz/RhJtqHW0g8DpCAIhjFwz0DD5dbCAGlylO7IwMzeIOl6fJrMTsBWueh7\n8JVlvj7BPJcDnwc+L2lH4GDgsSnvHtzV4O3A78zsb81eQwvJVsW5uhB+ZdrvhU8jGpWxlKNU6jMT\nBMF0J1qmLactXoHM7OvA11OLalu8hvDAeF0JjpH3PcApzebTJm5L+wOBv+bCn5X2/2hvcYIg2CgJ\nZdpy2upiL7Wo7geQ1CNpazNb0s4ydBIzu0nSWcCXk5vF6/DW6PHAjcDPO1i8IAg2FkZRpmYWvVuT\noOypMUjaStInJT0lF/Yu3CDoAUn/kHRY2eWYQrwO9/T0dmpWvP8HHGRmg50sWBAEGwejzjMNi4lJ\nUbZv3u1xQ5itgMXATcki96t4V+9yfBz1XEn7mdkfyyzPVCD5H/5k2oIgCNrPaN28VdrQzJp5lC2y\njwBb492Xv05hb8MV6ZfMbDPgxbhS/1DJZQmCIAhgbGUaTJiyx0xfiM//PMLMslv0Yrwj4asAZvYL\nSdfijhuCIAiCsgll2nLKbpluB9yYKVJJe+OLg99emN5xP7BlyWUJgiAIGH3MNPz2To6ylelKYG7u\nf2Zo9OtCum2B1SWXJQiCIIDRjYxCl06KspXp34ADk0VvFzVPSOdnCZJXoGfgPnODIAiCsolu3pZT\ntjL9Dr5qzK3AncCTgDuASwAkfSv7jfvxDYIgCEpm1K7cUKaTolRlaman41NA5uLu/m4DXpkzRnoO\n7v7vfWb20zLLEgRBECRGc9oQY6aToh2+eT8j6URgEzNbWoh+J3CLmT1cdjmCIAiCRHTztpx2+ebt\nx1eMKYb/ph3nD4IgCHKEMm05bfFzIenJkn4g6R5JfZJWSbpD0nclPb0dZQiCIAicGDNtPe3wzXs0\nvgj2G4Ad8DHSecAuwL8Bv5P09rLLEQRBECRizLTllKpMJe2PW+kO4SujPB6YhRsk7Q2cAAwCX4sW\nahAEQZsYrfUZunRSlN0y/TDuh/flZvafZna7mQ2Y2Xoz+4uZfQp4BT52+76SyxIEQRBAjJmWQNnK\n9NnAtWZ2UaMEZvYr4Bp8mkwQBEFQMjFm2nrKVqabkBYDH4P7gS1KLksQBEEAo3blxpjp5ChbmT4A\n7DOOdPsAS0ouSxAEQQAxZloCZSvTXwK7SfpoowSSPgbsBvyq5LIEQRAEEGOmJVC204bPAa8FPiPp\n+cBZ+PqmADsBrwIOAlYAny+5LEEHMDOqK6owBJVFFdSlUdNKjeODIGiM9Rv9f+5n8IFB1C169uih\nZ6eekenMYsy0BEpVpmZ2v6QXAmcDzwMOLiQR3hX8ajO7p8yyBO3HBo2+3/cx9MgQAJW5FWbtO4vK\n/OEdItZn9P2xj+qjVTRXdG3ZxdCjQ9h6o2vrLnqf0DuqEm5VWauPVKEbKvMrWL+h+YJ+oJdQ8sGU\nZ+D2AQYfGAT8ee7/cz+VhRW6NusannCMbtwYM50c7fDNe72kXYEjcYvdbakp0SuBM81sXdnlCMrF\n+ozB+weprq6CgeaKgb8PDEtTXVtl3RXr6H1CL5X5FYaWDVFdWmVo+VAtn9XmeSQG7x3E1hqznjYL\n63eFZ30GAhsyP1ePK7rKvAqVzSs+eKHxK8Dqqip91/dRXV+/Sq5u0fuEXrp3qL0uZsbQA0MM3DkA\nBt07ddP92O5h5zQzBu/z8lcWVejeptuPWzIEVejavAvNkivyR6tQgcqmo7feg6AeZv7+FRm8d3Ck\nMh2r5Rkt00khs6iFzDQkbQ/cl/7uYGbjsajOM6GHorq+yvqr12Prp86zpIrQbLm/rQwDW2sblC1D\nNYU8rjxnia6tukAwtHgI6x95YNeiLswMqq6k81TmVLBBwwb8OPV6V9zgnYNU13raysIKvY/vxQaN\nyvwKlQWFVvyQYWsMzRbqFTZkDD00tEFhVzarTItWtA2kSkU3dG3dNaISYisNM6OyycSuJ/ueTQcZ\ntJLqyirrrqrfJpl3+Lxh/63fWHvJ2oZ59ezUQ+9evS0t3xSh1IeiLY7uASQdhI+PbgcMAPcCl5vZ\nte0qQ1AOg/cMTilFCt5VZWtbWybr85bmaORb2UWq64YrV+s3+m/pH55mZZX116/f8L+yoOJKs8+V\nsPXVrkk98gpBoVtOs+RbarFbv6EeD6PH4zH8nqXj1S0qm1Q2VDiqq6oeL1yh9QjNSXlUwNZ4edQr\n/4oM+Hms3ysnlU0qaG4q34Dnr1nyMfRHqt7jMJQUX0X07tNLZVEFhvAu/xUuq65FXfQ+tdcrIkOG\nrUvHzBWqDP82Dj4wyMBtA1TXVela1EXP7j1Ul1cZfHAQhqB7+266d+0ecZxVzf2w9XReCZsZ1Ydd\n9pXNKmiWGFo65Pdni8YVi0xe9RhaOuSVr/ny46NlWgqlt0yTm8BTgD0ZWTMw3GHDUWZ2d6kF2Yho\nd8t0/TXrGXq0sRIJglIQGxRjppjHdVhPqmhU2FBJAVBXqlD0MEyhqEvQldL2ewVDPa6UskqGDXlv\nBF1Av6elgiuxucJWmVdOunw4gm4fPrBBr2jQAwzC4D8Hhw1zFOnavIvKwnS85BU0o24Xb73r7n5s\nN5oj+m/tb5iue/tuZj151viEOb2Yvi1TSY8DLgPmA38CLqD2kd8JeBnwTOBySfuZ2UNllmeqIOkQ\n4LPAXvj82pOAL9k07HM3sxHdmUHQFmxiSnTDYQM1BTosfMjaUinMDPIme+xkj7cB8zH+MRNOKvuN\nnrK7eT+FK9JPmdkJxUhJHwe+gPvlPR74fyWXp+NIOgCvVPwE+ATucvFE/F78VweLNjn6qPthqkdl\nTmVEV2cQBFOMeEUnRandvJKWAIvN7MmjpBHwV2CBmW1XWmGmCJIuAhaZ2f65sP8G3gFs3QrL5nZ2\n8w4tHRo2xpfR+/heuh7TBUNpfCtZqFpfaslWvVVbmV1B8+Xjm11QfdStdbPxuery6obuMb+41IVm\n3k2m2Wl8sNfHlqprqiPGBJFfkVka40udPRvGItfbhvFFKj72V11ZRRUNG4/ccA2TaA0FwXSia9Ou\nmt2BfLpYw07SeuHy98/WpW7xOf6OYvi7PIh3g3dpXK6DsvH4yvwKPXv0eFf5xJm+3bx4q/S20RKY\nmUm6GfjXksvScSTNwo2wPlWI+hlwLN5KvaTNxWqKRl28XVt0UZk78oHXLNE1q2tk+II0vaVwTNfm\nI9M2omur8aedCJl17gZlOmgbrII115WzrXWlXFnoxjpmaYysH+iB6nKvQCBX4tXVVWyN1boqMzFW\nPb2tsQ0fJKBmvVs1bJVtGNfLxuSqK6pu1tedDIwGU49BMgDaYLWcKhaQxhuTtXPesKlINrYXbDwM\nLRveldxs9/d4e6/GorqqytDDQ8x62qwJfRvaQdnK9CZgP0ndZjbaCPkTgT+XXJapwC5AL3B7IfyO\ntN+DcSjT1PIcjW0mXrTJ0UiZav7MmZogqdYyJrWIFw6/Ps2TL3mfO0ZzBHP8/4hKwpbt/RCY5aYA\nZW9iznrVBmyY4rZ+b8VvsAge8ngbSJavmXOLQXMl3sMGZc5Qqgx0UTO06Uu9BBW8wtDr3f5DDw15\nHvLjs56yymyfL1xdVfWyVNxqOG+hvMGSWdTmFffUei4gnTtZAW/Ip8fLCDXLZ4RXcFb7lBx1qdaj\nMVgzLqrMqbis+j3NhqlOYsP/rKeEKlTXDH8/VNHwezFRcpWhjRUbMPqu76N3n166H9O2CSljUnZJ\nPgZcCpws6V1mtrKYQNIXgccBLy25LFOBTdK+KIdVab9wnPncN3aS9lBdOVKZVuaF44GphlTryVSk\nLgAAGc1JREFU3qbOFMINFq65/8Piu+QVhuJx3YLZ6c/sXMT8QsLi/0TeEcZ0ZbS5rTZktalB3aql\nH8QVdDfei5AqFJrlQyJZz4Yq8vs1lLbs3g3UpjxZv1FdW91Q4agsqPiQyXKviHRt0YW6RHV9FVvh\nrgQ1y5X9+utGDtFMB6xq9P2hD/aB7u2mxjPU0lJI+mGd4HuA1wOHS7oQ9827HveE9ALcyf11wJOA\nX7SyPFOQsTr6p9XQv5lhq0dWk4uOBoJgJjPa3FR1aUTFMmtBb6Ardc0X0mhOLqyb4V/rXh8uAD+2\n3jvXtenw3o/K7MrwCg8we7/ZrL9h/bRs7WqWqGw6db41rVbpR40StymuVOtxALA/M9/Z/Yq0X1AI\nX1iIH4sdxojfBrhhvIWaLLamvsPsbPwzCIKpTdeWXczefzaD97vby6Jxnbq9BTtizHws5ZvsAzIl\nn7kZ3RCXzeUdrLXsR8urOOaqbjF7v9l17TI6RauV6VtanN9M4068s2a3Qnj2/6/jyWQs69wWeHEZ\nVwaV+ZURrsqCIJhedG3eNeWMeaYj4Zu3zUi6DDdLeWbmpCFNjTkG2NbMGjvNHP85uqkZIS0ew/gr\nCIIgaJKpMXK7cfEZ3CjrTEkn4x6gPgR8pBWKFCApz4nOLQ2CIAgmSUtbpskAyYBjzWxJA4OkRpiZ\nvallhZnCSHo58Gl8Ksw/gZPM7EudLVUQBEEwWVqtTKu4Mt3TzG5P/8eLmVl03AdBEATTjrIMkB4s\n/A+CIAiCGUsYIAVBEARBk0yJSTqSeiQd1+lyBEEQBMFkKKVlKmk74FBgc+BW4JeN1uqU9Dx8Pc/d\nY8w0CIIgmI60fGqMpI/glqr5vH8v6XAzeziXbmvgK8CRuJOA6ekkMgiCINjoaWk3b5ry8TncWdSN\nwFnAQ8C+wMm5dEcAf6GmSH8NNFzzNAiCIAimMq1umb4DnxrzQTP7CoCkefiyYodL2hU4AndcIGAx\n8AEzO6PF5QiCIAiCttHqeaYPAmvNbNdC+PNxhXoG8BpckX4fV7rjde4elEzBDWEQBMFMozT3qq1u\nmW4G/L5O+I1p/xp87c43mtl5LT530DzbMIXWSg2CIGgxO1CSq9VWK9MeYFmd8Hzr8zAzu6bF5w2C\nIAiCjtEWR/dmZmlZsKtCkU5pFjP2Wqn1yK+fum/KJ6gR8mlMyGZ0Qj6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0340824ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Uncomment this region if run_simulations_based_on_experiment_fits.ipynb\n", "# was run to generate new simulation data\n", "'''\n", "simulationdata = simulation_utils.get_simulation_data(\n", " runnumber=9, ribodensity=True)\n", "\n", "'''\n", "# Comment this region if new simulation data was generated\n", "simulationdata = pd.read_table(\n", " '../rawdata/simulations/run9_data.tsv', index_col=0)\n", "\n", "\n", "fig = plt.figure()\n", "fig.set_size_inches([2, 2])\n", "# common axis below used for a shared x and y label across panels \n", "commonaxis = plt.axes(frameon=False)\n", "commonaxis.set_xticks([])\n", "commonaxis.set_yticks([])\n", "commonaxis.yaxis.labelpad = 25 # move the labels a bit away from panels\n", "commonaxis.xaxis.labelpad = 25 # move the labels a bit away from panels\n", "commonaxis.set(\n", " xlabel='Distance from start (codons)',\n", " ylabel='Ribosome density along yfp mRNA\\n(Normalized to median)', )\n", "\n", "pretermtypes = [\n", " 'trafficjam',\n", " '5primepreterm',\n", " 'selpreterm',\n", " 'bkgdpreterm',\n", "]\n", "\n", "axcount = 0\n", "for pretermtype in pretermtypes:\n", " axcount += 1\n", " subset = simulationdata[(simulationdata['mutant'] == 'cta18')]\n", " model = pretermtype\n", " if pretermtype == 'trafficjam':\n", " for innerpretermtype in pretermtypes:\n", " if innerpretermtype == pretermtype:\n", " continue\n", " subset = subset[subset[innerpretermtype] == 0]\n", " # this choice ensures that the traffic jam model is considered\n", " # as the limit of the CSAT model.\n", " subset = subset.reset_index().ix[2]\n", " else:\n", " subset = subset[subset[pretermtype] == 1.0]\n", " subset = subset.reset_index().ix[0]\n", " ribodensityfile = ('../rawdata/simulations/run9_' + pretermtype +\n", " '_ribodensity_mutant.csv')\n", " if 'ribodensity_mutant' not in subset: # using pre-run simulation\n", " subset['ribodensity_mutant'] = np.array(\n", " open(ribodensityfile).read().strip().split(','))\n", " ax = fig.add_subplot(4, 1, axcount)\n", " normalization = np.median(subset['ribodensity_mutant'].astype(float))\n", " ax.plot(\n", " np.arange(len(subset['ribodensity_mutant'])),\n", " subset['ribodensity_mutant'].astype(float) / normalization,\n", " label=modellabels[pretermtype],\n", " color=modelcolors[pretermtype],\n", " alpha=0.6)\n", " if pretermtype == 'selpreterm':\n", " ax.set_label(modellabels[model] + ' (selective)')\n", " if pretermtype == 'bkgdpreterm':\n", " ax.set_label(modellabels[model] + ' (non-selective)')\n", " clean_axis(ax)\n", " ax.xaxis.set(major_locator=MaxNLocator(5))\n", " ax.yaxis.set(major_locator=MaxNLocator(2))\n", " if axcount != len(pretermtypes):\n", " ax.xaxis.set_visible(False)\n", " [\n", " spine.set_color('none') for loc, spine in ax.spines.items()\n", " if loc in ['bottom']\n", " ]\n", " ax.legend(loc=2, bbox_to_anchor=(0, 1.5))\n", " fh = open(ribodensityfile, 'w')\n", " output = ','.join(subset['ribodensity_mutant']) + '\\n'\n", " fh.write(output)\n", " fh.close()\n", "fig.savefig('../figures/fig7b.svg')" ] } ], "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 }

gpl-3.0

jgpavez/MedicalDiagnosis

exploratory_analysis.ipynb

1

39770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory analysis\n", "\n", "This notebook includes some exploratory analysis on the data set for medical diagnosis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import urllib\n", "import pdb\n", "import json\n", "import os\n", "import pandas as pd\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-\n", "('Vocab size:', 21520, 'unique words')\n", "('Story max length:', 1647, 'words')\n", "('Number of training stories:', 133093)\n", "('Number of test stories:', 59394)\n", "-\n", "Here's what a \"story\" tuple looks like (input, query, answer):\n", "([u'barrett', u'esophagus', u'itself', u'does', u'not', u'cause', u'symptoms', u'.', u'many', u'people', u'with', u'this', u'condition', u'do', u'not', u'have', u'any', u'symptoms', u'.', u'the', u'acid', u'reflux', u'that', u'causes', u'barrett', u'esophagus', u'often', u'leads', u'to', u'symptoms', u'of', u'heartburn', u'.'], u'barrett esophagus')\n", "-\n", "Vectorizing the word sequences...\n" ] } ], "source": [ "train_facts = pickle.load(open('data/training_set.dat','r'))\n", "\n", "test_facts = pickle.load(open('data/test_set.dat','r'))\n", "\n", "train_stories = [(reduce(lambda x,y: x + y, map(list,fact)),q) for fact,q in train_facts]\n", "test_stories = [(reduce(lambda x,y: x + y, map(list,fact)),q) for fact,q in test_facts]\n", "\n", "\n", "vocab = sorted(reduce(lambda x, y: x | y, (set(story + [answer]) for story, answer in train_stories + test_stories)))\n", "story_vocab = sorted(reduce(lambda x, y: x | y, (set(story) for story, answer in train_stories + test_stories)))\n", "\n", "# Reserve 0 for masking via pad_sequences\n", "vocab_size = len(vocab) + 1\n", "story_maxlen = max(map(len, (x for x, _ in train_stories + test_stories)))\n", "\n", "\n", "print('-')\n", "print('Vocab size:', vocab_size, 'unique words')\n", "print('Story max length:', story_maxlen, 'words')\n", "print('Number of training stories:', len(train_stories))\n", "print('Number of test stories:', len(test_stories))\n", "print('-')\n", "print('Here\\'s what a \"story\" tuple looks like (input, query, answer):')\n", "print(train_stories[0])\n", "print('-')\n", "print('Vectorizing the word sequences...')\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Answers dict len: 2350\n" ] } ], "source": [ "answer_vocab = sorted(reduce(lambda x, y: x | y, (set([answer]) for _, answer in train_stories + test_stories)))\n", "# Reserve 0 for masking via pad_sequences\n", "answer_dict = dict((word, i) for i, word in enumerate(answer_vocab))\n", "print('Answers dict len: {0}'.format(len(answer_dict)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DescribeResult(nobs=192487, minmax=(4, 1647), mean=122.61276865450654, variance=24135.934935201523, skewness=3.780458033729767, kurtosis=19.974163240940545)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fafc3877d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "import numpy as np\n", "\n", "lens = map(len, (x for x, _ in train_stories + test_stories))\n", "print stats.describe(lens)\n", "plt.xlabel('#words x story')\n", "plt.hist(lens, bins=30,alpha=0.5)\n", "plt.axvline(np.array(lens).mean(), color='black', linestyle='dashed', linewidth=2)\n", "plt.savefig('plots/word_by_story.png')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DescribeResult(nobs=192487, minmax=(2, 118), mean=11.537999968829064, variance=153.05061042999489, skewness=2.9519698043011893, kurtosis=12.82327875307992)\n" ] }, { "data": { "image/png": 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mVshBYWZmhRwUZmZWyEFhZmaF/j+bsAEB1lOwIAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fafc8392bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lens = map(len, (x for x, _ in train_facts + test_facts))\n", "print stats.describe(lens)\n", "plt.xlabel('#facts')\n", "plt.hist(lens, bins=30,alpha=0.5)\n", "plt.axvline(np.array(lens).mean(), color='black', linestyle='dashed', linewidth=2)\n", "plt.savefig('plots/facts_by_disease.png')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n", "DescribeResult(nobs=2220915, minmax=(2, 111), mean=10.626865053367643, variance=63.421087757828843, skewness=1.34814656662994, kurtosis=3.0186643322827527)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fafc871f1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lens = map(len, (x for h,_ in train_facts + test_facts for x in h))\n", "print lens[0]\n", "print stats.describe(lens)\n", "plt.xlabel('#words x facts')\n", "plt.hist(lens, bins=30,alpha=0.5)\n", "plt.axvline(np.array(lens).mean(), color='black', linestyle='dashed', linewidth=2)\n", "plt.savefig('plots/word_by_fact.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#print story_vocab" ] }, { "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit