cs670 assignment1

assignment1.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Probability Assignment"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get full credit in this assignment you need to use `numpy` libraries  and include adequate explanation of the code in either markdown cells or inline code comments. Sometimes you need to type equations - type equations in Latex math notation. \n",
-    "\n",
-    "PS: Please note that we run through chatGPT the questions and you will be referred to the Dean if we find that a robot answered your questions."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 1a (10 points)\n",
-    "\n",
-    "In a private subreddit people are posting their opinions on the CEO of the company you work for. Lets assume that the employees that are posting are random logging in to that subreddit and that each post indicates whether the employee approves or not the job that the CEO is doing. Let $x_i$ be the binary random variable where $x_i=1$ indicates approval. You can assume that $x$ is distributed according to a Bernoulli distribution with parameter $p=1/2$.\n",
-    "\n",
-    "Your job is to sample $n=50$ posts and estimate the approval rate of the CEO by considering the statistics of $y=x_1+x_2+ \\dots + x_n$. What is the probability that 25 employees approve the CEO?\n",
-    "\n",
-    "## Question 1b (15 points)\n",
-    "\n",
-    "Following your findings in Q1a, read about the [Cenral Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem) and recognize that \n",
-    "\n",
-    "$$z=\\frac{y-\\mu_y}{\\sigma_y}$$ \n",
-    "\n",
-    "is normally distributed with mean 0 and variance 1.\n",
-    "\n",
-    "Can you find the  probability that 25 employees approve the CEO using the Gaussian approximation?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Answer 1a\n",
-    "\n",
-    "Given that the probability of\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Type the answer here using the [latex syntax](https://wch.github.io/latexsheet/) or handwrite the answer, upload the picture in the same folder and use a new markdown cell with markdown syntax `![title](image_name.png)`"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 2 (25 points)\n",
-    "\n",
-    "A sequential experiment involves repeatedly drawing a ball from one of the two urns, noting the number on the ball and replacing the ball in the urn. Urn 0 contains a ball with the number 0 and two balls with the number 1. Urn 1 contains five balls with the number 0 and one ball with the number 1. \n",
-    "\n",
-    "The urn from which the first ball is drawn is selected by flipping a fair coin. Urn 0 is used if the outcome is H and urn 1 is used if the outcome is T. **The urn used in a subsequent draws corresponds to the number on the ball drawn in the previous draw.** \n",
-    "\n",
-    "What is the probability of a specific sequence of the numbers on drawn balls being 0011 ? "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Type the answer here using the [latex syntax](https://wch.github.io/latexsheet/) or handwrite the answer, upload the picture in the same folder and use a new markdown cell with markdown syntax `![title](image_name.png)`\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 3 (25 points) \n",
-    "\n",
-    "Referring to Example 6.6 of the  [Math for ML book](https://mml-book.github.io/book/mml-book.pdf), simulate and plot the bivariate normal distribution with the shown parameters using the [Cholesky factorization](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cholesky.html) for the simulation. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Type the Python code here and ensure you save the notebook with the results of the code execution."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 4 (25 points)\n",
-    "\n",
-    "Go through the provided links on [Poisson](https://dlsun.github.io/probability/poisson.html) and [exponential distributions](https://dlsun.github.io/probability/exponential.html) as the `Math for ML` textbook in your course site  is not covering enough these important distributions.\n",
-    "\n",
-    "Watch this video https://www.youtube.com/watch?v=Asto3RS46ks where the author is explaining how to simulate a Poisson distribution from scratch. \n",
-    "\n",
-    "1. Using the Kaggle API download [this dataset](https://www.kaggle.com/datasets/new-york-city/nyc-east-river-bicycle-crossings) and plot the histogram of the number of cyclists that cross the Brooklyn bridge per day.  \n",
-    "2. Simulate the number of cyclists that cross the Brooklyn bridge per day using the Poisson distribution. Ensure that the simulated counts are similar distribution-wise to the observed counts.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Type the Python code here and ensure you save the notebook with the results of the code execution."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
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assignment1/assignment1.ipynb

@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/umangsoni22/cs670-project/blob/assignment1/assignment1/assignment1.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Probability Assignment"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get full credit in this assignment you need to use `numpy` libraries  and include adequate explanation of the code in either markdown cells or inline code comments. Sometimes you need to type equations - type equations in Latex math notation. \n",
+    "\n",
+    "PS: Please note that we run through chatGPT the questions and you will be referred to the Dean if we find that a robot answered your questions."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 1a (10 points)\n",
+    "\n",
+    "In a private subreddit people are posting their opinions on the CEO of the company you work for. Lets assume that the employees that are posting are random logging in to that subreddit and that each post indicates whether the employee approves or not the job that the CEO is doing. Let $x_i$ be the binary random variable where $x_i=1$ indicates approval. You can assume that $x$ is distributed according to a Bernoulli distribution with parameter $p=1/2$.\n",
+    "\n",
+    "Your job is to sample $n=50$ posts and estimate the approval rate of the CEO by considering the statistics of $y=x_1+x_2+ \\dots + x_n$. What is the probability that 25 employees approve the CEO?\n",
+    "\n",
+    "## Question 1b (15 points)\n",
+    "\n",
+    "Following your findings in Q1a, read about the [Cenral Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem) and recognize that \n",
+    "\n",
+    "$$z=\\frac{y-\\mu_y}{\\sigma_y}$$ \n",
+    "\n",
+    "is normally distributed with mean 0 and variance 1.\n",
+    "\n",
+    "Can you find the  probability that 25 employees approve the CEO using the Gaussian approximation?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Answer 1a\n",
+    "\n",
+    "Given p = 1/2\n",
+    "\n",
+    "We know that the binomial probability distribution is\n",
+    "\n",
+    "$$P(r) = (\\binom{n}{r})·p^r(1−p)^{n−r}$$\n",
+    "\n",
+    "Using above we can calculate probability where 25 employees approve the CEO out of total 50 posts is\n",
+    "\n",
+    "$$ P(25) = \\frac{50!}{25!(50-25)!}.(1/2)^{25}.(1-1/2)^{50-25} $$\n",
+    "\n",
+    "$$ P(25) = \\frac{126410606437752}{1125899906842624} = 0.1123 $$\n",
+    "\n",
+    "Result = 11.23 %\n",
+    "\n",
+    "## Answer 1b\n",
+    "\n",
+    "$$ \\mu_y = np = 25$$\n",
+    "$$ \\sigma_y = \\sqrt{np(1-p)} = \\sqrt{50*0.5*0.5} = 3.54 $$\n",
+    "\n",
+    "To determine the minimum area under curve, we'll calculate z-score i.e. distance from mean with std. as base for employees < 24.5 and z < 25.5 to get approximate area under curve and estimate probability for exactly 25 employees.\n",
+    "\n",
+    "$$ z1 < \\frac{y_1-\\mu_y}{\\sigma_y} = \\frac{24.5-25}{3.54} = -0.141 $$\n",
+    "$$ z2 < \\frac{y_2-\\mu_y}{\\sigma_y} = \\frac{25-25.5}{3.54} = 0.141 $$\n",
+    "\n",
+    "From z-table, we calculate the Probability of area under curve for using z1 and z2 as\n",
+    "\n",
+    "$$ P(Z<-0.14) = 0.4443 $$\n",
+    "$$ P(Z< 0.14) = 0.5557 $$\n",
+    "\n",
+    "$$ Approx P(25) = 0.5557 - 0.4443 = 0.1114 $$\n",
+    "\n",
+    "Approx Result = 11.14 %"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 2 (25 points)\n",
+    "\n",
+    "A sequential experiment involves repeatedly drawing a ball from one of the two urns, noting the number on the ball and replacing the ball in the urn. Urn 0 contains a ball with the number 0 and two balls with the number 1. Urn 1 contains five balls with the number 0 and one ball with the number 1. \n",
+    "\n",
+    "The urn from which the first ball is drawn is selected by flipping a fair coin. Urn 0 is used if the outcome is H and urn 1 is used if the outcome is T. **The urn used in a subsequent draws corresponds to the number on the ball drawn in the previous draw.** \n",
+    "\n",
+    "What is the probability of a specific sequence of the numbers on drawn balls being 0011 ? "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Answer 2\n",
+    "\n",
+    "Random variable for container\n",
+    "$$y ∈ \\{0, 1\\}$$\n",
+    "\n",
+    "Random variable for balls\n",
+    "$$y ∈ \\{0, 1\\}$$\n",
+    "\n",
+    "Given Sequence : 0011\n",
+    "\n",
+    "For the first trial, probability of selecting Ball 0 is <br>\n",
+    "$$P(X=0)=P(Y=0).P(X=0/Y=0)+P(Y=1).P(X=0/Y=1)$$\n",
+    "$$ = (1/2).(1/3) + (1/2).(5/6) = 2/3$$\n",
+    "\n",
+    "For second trial, urn 0, would be selected.\n",
+    "$$P(X=0/Y=0) = 1/3$$\n",
+    "\n",
+    "For third trial, urn 0, would be selected.\n",
+    "$$P(X=1/Y=0) = 2/3$$\n",
+    "\n",
+    "For fourth trial, urn 1, would be selected.\n",
+    "$$P(X=1/Y=1) = 1/6$$\n",
+    "\n",
+    "Using Product rule, the probability of the sequence would be:\n",
+    "$$(2/3).(1/3).(2/3).(1/6) = 0.246 $$"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 3 (25 points) \n",
+    "\n",
+    "Referring to Example 6.6 of the  [Math for ML book](https://mml-book.github.io/book/mml-book.pdf), simulate and plot the bivariate normal distribution with the shown parameters using the [Cholesky factorization](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cholesky.html) for the simulation. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-07T07:46:08.569681Z",
+     "start_time": "2023-06-07T07:46:08.491296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<matplotlib.collections.PathCollection at 0x145c24bd0>"
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
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+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Type the Python code here and ensure you save the notebook with the results of the code execution.\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "mean = np.array([0, 2])\n",
+    "A = np.array([[0.3,-1],[-1,5]])\n",
+    "L = np.linalg.cholesky(A)\n",
+    "X = np.random.normal(size=(5000, 2)) # 5000 Samples\n",
+    "Y = np.dot(L, X.T).T + mean\n",
+    "plt.scatter(Y[:, 0], Y[:, 1])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 4 (25 points)\n",
+    "\n",
+    "Go through the provided links on [Poisson](https://dlsun.github.io/probability/poisson.html) and [exponential distributions](https://dlsun.github.io/probability/exponential.html) as the `Math for ML` textbook in your course site  is not covering enough these important distributions.\n",
+    "\n",
+    "Watch this video https://www.youtube.com/watch?v=Asto3RS46ks where the author is explaining how to simulate a Poisson distribution from scratch. \n",
+    "\n",
+    "1. Using the Kaggle API download [this dataset](https://www.kaggle.com/datasets/new-york-city/nyc-east-river-bicycle-crossings) and plot the histogram of the number of cyclists that cross the Brooklyn bridge per day.  \n",
+    "2. Simulate the number of cyclists that cross the Brooklyn bridge per day using the Poisson distribution. Ensure that the simulated counts are similar distribution-wise to the observed counts.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"nyc-east-river-bicycle-counts.csv\")\n",
+    "df.set_index('Day', inplace=True)\n",
+    "\n",
+    "# Plot the histogram of the number of cyclists that cross the Brooklyn bridge per day\n",
+    "df['Brooklyn Bridge'].hist(label='Observed', alpha=0.5)\n",
+    "\n",
+    "# Generating new data with randomly choosing cyclists counts from dataframe & simulating using poisson distribution.\n",
+    "# This is to ensure the simulated counts are similar distribution-wise to observed counts\n",
+    "df['Brooklyn Bridge Random'] = np.random.choice(df['Brooklyn Bridge'], size=len(df))\n",
+    "df['Brooklyn Bridge Simulated'] = np.random.poisson(df['Brooklyn Bridge Random'], size=len(df))\n",
+    "\n",
+    "# Plot the histogram of the simulated number of cyclists that cross the Brooklyn bridge per day\n",
+    "df['Brooklyn Bridge Simulated'].hist(label='Simulated', alpha=0.5)\n",
+    "\n",
+    "plt.ylabel('Frequency of Days')\n",
+    "plt.xlabel('Number of Cyclist/Day')\n",
+    "plt.title('Histogram of Cyclists that cross the Brooklyn bridge/day')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Uncomment to see the random choice and simulated values.\n",
+    "# df[['Brooklyn Bridge', 'Brooklyn Bridge Random', 'Brooklyn Bridge Simulated']]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-07T07:51:04.620432Z",
+     "start_time": "2023-06-07T07:51:04.528377Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "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": "3.10.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

assignment1/nyc-east-river-bicycle-counts.csv

@@ -0,0 +1,211 @@
+,Date,Day,High Temp (°F),Low Temp (°F),Precipitation,Brooklyn Bridge,Manhattan Bridge,Williamsburg Bridge,Queensboro Bridge,Total
+0,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+1,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+2,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+3,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+4,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+5,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+6,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+7,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+8,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+9,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+10,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+11,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+12,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+13,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+14,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+15,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+16,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+17,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+18,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+19,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+20,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+21,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+22,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+23,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+24,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+25,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+26,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+27,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+28,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+29,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+30,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+31,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+32,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+33,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+34,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+35,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+36,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+37,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+38,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+39,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+40,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+41,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+42,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+43,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+44,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+45,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+46,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+47,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+48,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+49,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+50,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+51,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+52,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+53,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+54,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+55,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+56,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+57,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+58,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+59,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+60,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+61,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+62,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+63,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+64,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+65,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+66,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+67,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+68,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+69,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+70,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+71,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+72,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+73,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+74,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+75,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+76,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+77,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+78,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+79,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+80,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+81,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+82,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+83,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+84,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+85,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+86,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+87,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+88,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+89,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+90,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+91,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+92,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+93,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+94,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+95,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+96,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+97,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+98,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+99,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+100,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+101,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+102,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+103,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+104,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+105,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+106,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+107,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+108,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+109,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+110,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+111,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+112,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+113,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+114,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+115,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+116,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+117,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+118,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+119,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+120,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+121,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+122,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+123,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+124,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+125,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+126,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+127,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+128,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+129,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+130,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+131,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+132,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+133,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+134,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+135,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+136,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+137,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+138,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+139,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+140,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+141,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+142,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+143,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+144,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+145,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+146,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+147,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+148,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+149,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+150,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+151,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+152,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+153,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+154,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+155,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+156,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+157,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+158,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+159,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+160,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+161,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+162,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+163,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+164,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+165,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+166,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+167,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+168,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+169,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+170,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+171,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+172,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+173,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+174,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+175,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+176,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+177,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+178,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+179,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432
+180,2016-04-01 00:00:00,2016-04-01 00:00:00,78.1,66,0.01,1704.0,3126,4115.0,2552.0,11497
+181,2016-04-02 00:00:00,2016-04-02 00:00:00,55.0,48.9,0.15,827.0,1646,2565.0,1884.0,6922
+182,2016-04-03 00:00:00,2016-04-03 00:00:00,39.9,34,0.09,526.0,1232,1695.0,1306.0,4759
+183,2016-04-04 00:00:00,2016-04-04 00:00:00,44.1,33.1,0.47 (S),521.0,1067,1440.0,1307.0,4335
+184,2016-04-05 00:00:00,2016-04-05 00:00:00,42.1,26.1,0,1416.0,2617,3081.0,2357.0,9471
+185,2016-04-06 00:00:00,2016-04-06 00:00:00,45.0,30,0,1885.0,3329,3856.0,2849.0,11919
+186,2016-04-07 00:00:00,2016-04-07 00:00:00,57.0,53.1,0.09,1276.0,2581,3282.0,2457.0,9596
+187,2016-04-08 00:00:00,2016-04-08 00:00:00,46.9,44.1,0.01,1982.0,3455,4113.0,3194.0,12744
+188,2016-04-09 00:00:00,2016-04-09 00:00:00,43.0,37.9,0.09,504.0,997,1507.0,1502.0,4510
+189,2016-04-10 00:00:00,2016-04-10 00:00:00,48.9,30.9,0,1447.0,2387,3132.0,2160.0,9126
+190,2016-04-11 00:00:00,2016-04-11 00:00:00,62.1,46,0.01,2005.0,3791,4334.0,3182.0,13312
+191,2016-04-12 00:00:00,2016-04-12 00:00:00,57.0,45,0.2,1045.0,2178,2762.0,2082.0,8067
+192,2016-04-13 00:00:00,2016-04-13 00:00:00,57.0,39.9,0,2840.0,5395,5995.0,4192.0,18422
+193,2016-04-14 00:00:00,2016-04-14 00:00:00,62.1,44.6,0,2861.0,5309,6030.0,4115.0,18315
+194,2016-04-15 00:00:00,2016-04-15 00:00:00,64.0,44.1,0,2770.0,5072,5816.0,3912.0,17570
+195,2016-04-16 00:00:00,2016-04-16 00:00:00,66.0,45,0,2384.0,4316,5624.0,4051.0,16375
+196,2016-04-17 00:00:00,2016-04-17 00:00:00,73.9,46,0,3147.0,4969,5867.0,4197.0,18180
+197,2016-04-18 00:00:00,2016-04-18 00:00:00,81.0,52,0,3871.0,6823,7432.0,4964.0,23090
+198,2016-04-19 00:00:00,2016-04-19 00:00:00,71.1,63,0,3501.0,6951,7834.0,5032.0,23318
+199,2016-04-20 00:00:00,2016-04-20 00:00:00,68.0,50,0,3450.0,6574,7639.0,4928.0,22591
+200,2016-04-21 00:00:00,2016-04-21 00:00:00,71.1,50,0,3436.0,6452,7426.0,4813.0,22127
+201,2016-04-22 00:00:00,2016-04-22 00:00:00,78.1,63,T,2975.0,4907,6093.0,3862.0,17837
+202,2016-04-23 00:00:00,2016-04-23 00:00:00,70.0,61,0.16,2055.0,3276,4856.0,3239.0,13426
+203,2016-04-24 00:00:00,2016-04-24 00:00:00,68.0,48,0,2798.0,4650,5335.0,3957.0,16740
+204,2016-04-25 00:00:00,2016-04-25 00:00:00,66.9,54,0,3463.0,5978,6845.0,4564.0,20850
+205,2016-04-26 00:00:00,2016-04-26 00:00:00,60.1,46.9,0.24,1997.0,3520,4559.0,2929.0,13005
+206,2016-04-27 00:00:00,2016-04-27 00:00:00,62.1,46.9,0,3343.0,5606,6577.0,4388.0,19914
+207,2016-04-28 00:00:00,2016-04-28 00:00:00,57.9,48,0,2486.0,4152,5336.0,3657.0,15631
+208,2016-04-29 00:00:00,2016-04-29 00:00:00,57.0,46.9,0.05,2375.0,4178,5053.0,3348.0,14954
+209,2016-04-30 00:00:00,2016-04-30 00:00:00,64.0,48,0,3199.0,4952,5675.0,3606.0,17432