jackparmer/plotly, cron jobs, and weather underground api

## plotly, cron jobs, and weather underground api
{
 "metadata": {
  "name": "plotly and weather underground api"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "SF and Montr\u00e9al weather using plotly and wunderground APIs"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "This IPython notebook was prepared for this article in <em>Modern Data</em>: http://mod.plot.ly/update-plotly-charts-with-cron-jobs-and-python"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import urllib2\nimport json\nimport plotly\nimport datetime\nimport plotly.plotly as py\nfrom plotly.graph_objs import *\npy.sign_in('Python-Demo-Account', 'gwt101uhh0')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "api_key = 'XXXX' # Grab your own, free wunderground API key by signing up here: http://www.wunderground.com/weather/api \nsf_lookup_url = 'http://api.wunderground.com/api/' + api_key + '/geolookup/conditions/q/CA/San_Francisco.json'\nmtl_lookup_url = 'http://api.wunderground.com/api/' + api_key + '/conditions/q/Canada/Montreal.json'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Retrieve current temperature in Montr\u00e9al and San Francisco "
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Code modified from: http://www.wunderground.com/weather/api/d/docs?d=resources/code-samples&MR=1"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "urls = { 'MTL': mtl_lookup_url, 'SF': sf_lookup_url }\ntemps = { 'MTL': [], 'SF': [] }\n\nfor city in temps.keys():\n    f = urllib2.urlopen(urls[city])\n    json_string = f.read()\n    parsed_json = json.loads(json_string)\n    temps[city].append( parsed_json['current_observation']['temp_c'] )\n    temps[city].append( parsed_json['current_observation']['temp_f'] )\n    print \"Current temperature in %s is: %s C, %s F\" % (city, temps[city][0], temps[city][1] )\n    f.close()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Current temperature in SF is: 11.7 C, 53.1 F\nCurrent temperature in MTL is: -2 C, 28 F"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Graph temperature data with Plotly Python client"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Get started with the Plotly Python client here: https://plot.ly/python/getting-started/"
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Create a pretty chart layout object (the code block below is only for styling)"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "(<strong>Tip</strong>: Its easiest to style charts in Plotly's online GUI, then copy-paste the styling code from the plot's \"CODE\" tab, ie https://plot.ly/~jackp/1837)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "layout = Layout(\n    title='Current temperature in Montr\u00e9al and San Francisco',\n    titlefont=Font(\n        family='\"Open sans\", verdana, arial, sans-serif',\n        size=17,\n        color='#444'\n    ),\n    font=Font(\n        family='\"Open sans\", verdana, arial, sans-serif',\n        size=12,\n        color='#444'\n    ),\n    showlegend=True,\n    autosize=True,\n    width=803,\n    height=566,\n    xaxis=XAxis(\n        title='Click to enter X axis title',\n        titlefont=Font(\n            family='\"Open sans\", verdana, arial, sans-serif',\n            size=14,\n            color='#444'\n        ),\n        range=[1418632334984.89, 1418632334986.89],\n        domain=[0, 1],\n        type='date',\n        rangemode='normal',\n        autorange=True,\n        showgrid=False,\n        zeroline=False,\n        showline=True,\n        autotick=True,\n        nticks=0,\n        ticks='inside',\n        showticklabels=True,\n        tick0=0,\n        dtick=1,\n        ticklen=5,\n        tickwidth=1,\n        tickcolor='#444',\n        tickangle='auto',\n        tickfont=Font(\n            family='\"Open sans\", verdana, arial, sans-serif',\n            size=12,\n            color='#444'\n        ),\n        mirror='allticks',\n        linecolor='rgb(34,34,34)',\n        linewidth=1,\n        anchor='y',\n        side='bottom'\n    ),\n    yaxis=YAxis(\n        title='Temperature (degrees)',\n        titlefont=Font(\n            family='\"Open sans\", verdana, arial, sans-serif',\n            size=14,\n            color='#444'\n        ),\n        range=[-5.968375815056313, 57.068375815056314],\n        domain=[0, 1],\n        type='linear',\n        rangemode='normal',\n        autorange=True,\n        showgrid=False,\n        zeroline=False,\n        showline=True,\n        autotick=True,\n        nticks=0,\n        ticks='inside',\n        showticklabels=True,\n        tick0=0,\n        dtick=1,\n        ticklen=5,\n        tickwidth=1,\n        tickcolor='#444',\n        tickangle='auto',\n        tickfont=Font(\n            family='\"Open sans\", verdana, arial, sans-serif',\n            size=12,\n            color='#444'\n        ),\n        exponentformat='B',\n        showexponent='all',\n        mirror='allticks',\n        linecolor='rgb(34,34,34)',\n        linewidth=1,\n        anchor='x',\n        side='left'\n    ),\n    legend=Legend(\n        x=1.00,\n        y=1.02,\n        traceorder='normal',\n        font=Font(\n            family='\"Open sans\", verdana, arial, sans-serif',\n            size=12,\n            color='#444'\n        ),\n        bgcolor='rgba(255, 255, 255, 0.5)',\n        bordercolor='#444',\n        borderwidth=0,\n        xanchor='left',\n        yanchor='auto'\n    )\n)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Graph the temperature data"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cur_time = datetime.datetime.now() # current date and time\ndata=[]\ntemp_types = ['C','F']\nfor city in temps.keys():\n    for i in range(len(temp_types)):\n        data.append( Scatter( x=[cur_time], y=[temps[city][i]], \\\n                             line=Line(dash='dot') if i==0 else Line(),\n                             mode='lines+markers', \\\n                             name='{0} ({1})'.format(city,temp_types[i]) ) )\n\ndata = Data( data )\nfig = Figure(data=data, layout=layout)\npy.iplot(fig, filename='montreal-and-san-francisco-temperatures')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~Python-Demo-Account/1343\" height=\"525\" width=\"100%\"></iframe>",
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       "metadata": {},
       "output_type": "pyout",
       "png": 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GG8M1atTI9hgn9Iac1wqHr7zySj+LSOD1UzO7cxF4kyXNdRBAAAEEEMhbAr4E\n3t1rdJ3vkBz58fTN6wowLf8elrTrPGPoa4FHjhwZGj16dJYjrzpam13gPXTokDRv3jw0derUsL5B\nLbuQPGPGDNm5c2eoU6dOfo7yEng99wKXJyDwuoRiNwQQQAABBPKZQJaBd+nAkIRC7kPfv6eEzlgF\npoSzFOINTd2fQ92r9jxD//jx49K2bdvQnXfeGa5Tp47olIX0W7zAu3LlSnnnnXdCgwcPjrYl1jGb\nNm2SAQMGhCZMmJCzdmffZwi8yfqeIvAmS5rrIIAAAgggkLcEsgy8fy2Ro5Dm2x3/9ccsg6ZOYZgy\nZUro448/lurVq0uzZs3CkeCbeT7uDTfcIE8//XT0PDNnzjSjth07dswQeNPP+40cc+zYMaldu3Zo\nwYIF4VDIN4IcnSgUdjbfQPPZiQi8+azg3C4CCCCAAAIuBWKO8Lo83uy22lniMPM63yV/K3LNAzk5\ni0i1XtlmPZ2eMH78+NCePXvkueeeM/vGG+GdPHmynDhxItSqVau4I7x6vpo1a4amT58eLlq0aM7a\nHntvAq9fkvHOQ+CNJ8TXEUAAAQQQyJ8Cvs3hnXhPKPo2x8LFRVq978sc3sxVOXjwoDgrNITmzp3r\nKvC+/fbb+qBbqF27dnEDrxOMTeCdP39+uGDBgn51CAKvX5LxzkPgjSfE1xFAAAEEEMifAr4EXqUz\nqzR8fBqx4j2+rdLw7bffysmTJ6VMmTKif+yfM2eOLF26NPTSSy+5CrwfffSRLFq0KNSvX7+4gXfH\njh3SvXv3UOZlzTz2DAKvR0DXhxN4XVOxIwIIIIAAAvlKwLfAmyC1L774QoYNGxbat2+f6Bq6lSpV\nEh2tTUtLM1eMN6VBlzF79NFHQ86Da+HIWr2xjpk3b56sWrXKrPrg4+0QeH3EzPZUBN5kSXMdBBBA\nAAEE8paA7YHXD03nZRKhunXrhm+99dZsT+eswRtypkuEb7rpJj8uGzkHgTcnmk6xZN26dc4qIafd\n7r33Xnn44YfN/71hwwYZOnSo6G8/Gm579uwpJUuWjJ6ewJsTafZFAAEEEEAg/wjkh8Cra/mOGTMm\nNHz48JirL6xdu1YmTpwYcvKUnys0aEci8Obk28l5+4c4a8jJ+eefn+GwU6dOSYsWLaRz585SpUoV\nmTVrlg7HizNXhcCbE2D2RQABBBBAIB8K5MXwLA4AACAASURBVIfAq2Xdvn276DQInRaR1aZTH/TF\nFMWKFfO7FxB4cyLaqFEjcd4SEh3hjRy7fv16cd4+Ik6HNf+kAbhhw4YyadIkiSypoSO8zn9nzEf5\n4IMPclSEnLSXfRFAAAEEEEDALoG77rrrjCzgrDsrzpvF7GposFqTo6yV79fhvf/+++XCCy+Un3/+\nWfQdz85bR8xvKosXLzYjus68k2j3cBZXlg4dOkiFChXMv2ng3bhx4xmd3FlyI0dFCFb/424QQAAB\nBBDIXwLOagdnZIGXX36ZwJvYbpCjrJXvA+/hw4flnHPOMUtzzJ49W5zRWRk7dqw469CJvgov/W9n\nzpIa0rRpU6lcuXI08G7evDmx5eTsCCCAAAIIIJDnBPLLlIYUFobA6wVfpy3ob2Vr1qyRFStWSK9e\nvaKn09HfLl26SMWKFQm8XpA5FgEEEEAAgYALEHgTXmACrxfiBx54QJ8mlN27d+v6dGYer246Alyv\nXj3RV+lFJl6zSoMXaY5FAAEEEEAguAIE3oTXlsDrlliXG9P/ypcvb94y4iyeLJ999plZikwfUmvT\npo20b98+ukrD8uXLZciQIdHTE3jdSrMfAggggAAC+UuAwJvwehN43RLv2bNH+vfvb0Zzzz77bLnq\nqqvksccei661u2XLFrNk2d69e6Vs2bJmHd7IG0j0GgRet9LshwACCCCAQP4SIPAmvN4E3oQT/3IB\nAm+ypLkOAggggAACeUsgLwRefRXwiRMndMnVDKtM6AP9zpvRQvoGtT59+oSdv3ibVxDr9M6jR4/K\nueeea4rhTPUMN2vWLO5riHVfHTx86qmnQqNGjQpH1uzVZ6WcpWFD27ZtE2eFK7NaVt++fcP6759/\n/nnIeTlYdq8iJvAm61uCwJssaa6DAAIIIIBA3hLwK/DuPLhTPt/zubn5apdWk+JnF/cNQgPvWWed\npStShStVqhQ97/z582XOnDmhiy++2ATeyBd0ZSrnL9+hV155JUMQ1fP07t07XK5cuZhtGzRoUOjG\nG28M16hRw+yzZMkScc4T6tq1a9j5dzOVVN/cdsMNN5ivOy8GCzlLw4Y1BMfYCLy+9YQ4JyLwJkua\n6yCAAAIIIJC3BPwIvOv3r5fWC1qHDhw7YG6+2NnFZMLdE8IVLzi9WpTXTYNqtWrVws7b0ELOm2Wj\nIbZbt26hm2++OewszxryI/AeOnRImjdvHnJGc8P61jV9bsp58VfIuU7YuU6WtzFjxgzZuXNnSMM4\ngddrpT0eT+D1CMjhCCCAAAIIBFQgq8A7evXokLOZAPfY9Y9F7zzWv989/e7QrkO7MghVKV1Fbkq7\nSd8Q6/o8sYg18OoUAmckNTRlyhQz1UCfa3Kebwo5y7SGP/roI18C78qVK3VhgJAzOmza/M0334gT\nsEPO+w9iTlnQdyEMGDAgNGHCBAJvqr9HCLyprgDXRwABBBBAwE6BrALvta9dG/0z/BctvogGOTf/\nHusu3Zwnu8CrIXTkyJGh6tWrh++4447o8qsXXHCBLFu2zJfAO3PmTDNa67yx1tyzvuvA8QmNGzcu\nZuA9duyYOK9nDi1YsCDshPusboEpDcnq+gTeZElzHQQQQAABBPKWQKwR3shdtKvcLhr2dIQ3q3/P\naoRX5/Gmn9Lg5jzxAu/69evFmbcbevbZZ80Das57CMKrV6/2LfDqOwych+NCrVq1Mvesc4F1VNkJ\nwtk9lCY1a9YMTZ8+PVy0aFECbyq7P4E3lfpcGwEEEEAAAXsF/JrD22p+q9DB4wfNjZ5X6DyZ+D8T\nfZ3DqyO8xYsXl8aNG5v5sgsXLgw988wzYWd017fA+/bbb4vOE27X7nTI15Uh6tevb65z3XXXZVlE\n3UcDrxPEw7qCQxYbI7zJ6v4E3mRJcx0EEEAAAQTyloAfgVfvWFdpWLl7pbn56mWr+75KgwZenb6g\n0xqWLl0q+iDZ7bffrmHXt8DrzAWWRYsWhfr16xcd0dWXfTlzeEOPP/54+Nprr5UCBQrotAfz3gPd\nduzYId27dw9NmzaNObyp7voE3lRXgOsjgAACCCBgp4BfgTeRd6cPrUUCry4J5iwtFnrrrbfMw2s5\nDbwaViNzbYsUKSLppys4o7vy6KOPhpyQG9ZgG9nee+89mTVrVkgflNPVG3R5MmetXhNw582bJ6tW\nrTLLncUwYIQ3kZ0j/bkJvMmS5joIIIAAAgjkLYG8EHiTKeq8RCJUt27dsL7Mws2mc3ydl1+Eb7rp\npli7E3jdQPqxD4HXD0XOgQACCCCAQPAECLwZa6ojyGPGjAkNHz481qoL0QPWrl0rEydODA0dOjS7\nfQm8yfq2IfAmS5rrIIAAAgggkLcECLxn1mv79u2SlpYmkVcLx6qoToHQKQ7FihXLrugE3mR9SxB4\nkyXNdRBAAAEEEMhbAgTehNeLwJtw4l8uQOBNljTXQQABBBBAIG8JEHgTXi8Cb8KJCbzJIuY6CCCA\nAAII5EkBAm/Cy0bgTTgxgTdZxFwHAQQQQACBPClA4E142Qi8CScm8CaLmOsggAACCCCQJwUIvAkv\nG4E34cQE3mQRcx0EEEAAAQTypACBN+FlI/AmnJjAmyxiroMAAggggECeFCDwJrxsBN6EExN4k0XM\ndRBAAAEEEMiTAnkh8OqrhU+cOCGTJk3K8Prew4cPi/OWs5C+Fa1Pnz7hNm3ahPbt2ycnT56Uo0eP\nyrnnnmtqUq9evXCzZs1Ez6OvAC5XrlzMWu3du1dfGxwaNWqUeXWxHqP/FnnVsL6O+JFHHgl//vnn\nIeetbLFeJ5z+/ATeZH1nsCxZsqS5DgIIIIAAAnlLwK/Ae3znTjn82Upz88X+XF0KFC/uG4SGzrPO\nOks6deoUrlSpUvS88+fPlzlz5oQuvvhiE3gjX9i8ebMMHjw49Morr2QIpG4C76BBg0I33nhjuEaN\nGuZ0sY5xQm/Iea1w+Morr4x3nwTeeEJ+fZ3A65ck50EAAQQQQCBYAn4E3iPO63i/ad4idOrAAYNT\nwHnz2GWvvxYuctVVvmBp6KxWrVrYebNZqHPnztEQ261bt9DNN98c3rRpU8iPwHvo0CFp3rx5aOrU\nqWF9g1p2gXfGjBmyc+fOkIbwODdJ4PWlF7g4CYHXBRK7IIAAAgggkA8Fsgq8/x05KuRs8YJcVOuH\nmTNDx3ftyqBXyBl1Pb9uXdfn0IMv7NA+ywpo4O3bt2/YGVENTZkyxUw12L17t/Tv3z/UsGHD8Ecf\nfeRL4F25cqW88847IWd0ONruWCO8TsiWAQMGhCZMmBDvHgm8yfq+IvAmS5rrIIAAAgggkLcEsgq8\nX1W8Kkchza87vmr9V1mGRw2dGkJHjhwZql69eviOO+6QyZMnSzFnJPmCCy6QZcuW+RJ4Z86caUZt\nO3bsmCHwpp/De8MNN8jTTz8dPnbsmNSuXTu0YMGCsPPLQXYEObIMhZ3NL9D8dh4Cb36rOPeLAAII\nIICAO4FYI7zujj6914+zZknmEd6zL79cit9zT05OI7/u2CHbwLt+/Xpx5u2Gnn32WfOA2rBhw8Kr\nV6/2LfBqiHYejgu1atUq7giv3ljNmjVD06dPDxctWpTAm6NKJ2hnAm+CYDktAggggAACeVzAtzm8\nzZqHTh08aDQKnHeeXDb5dV/n8OoIb3HnQbjGjRubebMLFy4MPfPMM2FndNe3wPv222+LzhNu165d\n3MCrq0Zo4HUCeLhgwYIEXhu+Dwi8NlSBNiCAAAIIIGCfgB+BV+/KrNKw4jNzg8Xu/LPvqzRo4NXp\nCzqtYenSpeI8sBa+/fbbNez6FniducCyaNGiUL9+/eIG3h07dkj37t1D06ZNizcDgSkNyer2BN5k\nSXMdBBBAAAEE8paAX4E3kXcdmcOrgfcrZ0UIZy3d0FtvvWUeXstp4HXm6Epkzq2uqevM240GVmd0\nVx599NGQ8+BaOLLubqyH1ubNmyerVq0y6/rGuXcCbyI7R/pzE3iTJc11EEAAAQQQyFsCeSHwJlPU\neZlEqK6zuoS+zCK7TVeMcF56Eb7pppviNY/AG0/Ir68TeP2S5DwIIIAAAggES4DAm7GeOoI8ZsyY\n0PDhw2OuvrB27VqZOHFiaOjQofFWaNCTE3iT9S1D4E2WNNdBAAEEEEAgbwkQeM+s1/bt2yUtLU10\nykRWm0590BdT6LJoLjYCrwskX3Yh8PrCyEkQQAABBBAInACBN+ElJfAmnPiXCxB4kyXNdRBAAAEE\nEMhbAgTehNeLwJtwYgJvsoi5DgIIIIAAAnlSgMCb8LIReBNOTOBNFjHXQQABBBBAIE8KEHgTXjYC\nb8KJCbzJIuY6CCCAAAII5EkBAm/Cy0bgTTgxgTdZxFwHAQQQQACBPClA4E142Qi8CScm8CaLmOsg\ngAACCCCQJwUIvAkvG4E34cQE3mQRcx0EEEAAAQTypAArOdlVtlDY2exqUt5pDZ0579SKliKAAAII\nIJBMATJCMrXjX4vAG98o5h50Zg94HIoAAggggECABcgIdhXXmsB76tQp2bZtm6xbt0727dsnhw8f\nlqJFi0qpUqWkUqVKUrZs2YTK7d+/Xx566CHp2LGjVKtWzVxrw4YN4rzP2bRHO27Pnj2lZMmS0XbQ\nmRNaEk6OAAIIIIBAnhUgI9hVupQH3uPHj8vs2bPl3XffNcHyiiuukF//+tdy7rnnysGDB2X37t0m\nCF9++eVSv359qVGjRkIEn376aTl06JDcc889JvBqAG/RooV07txZqlSpIrNmzZJVq1ZJv379CLwJ\nqQAnRQABBBBAIDgCBF67apnywKujqhdeeKHUrl1bbr75Zjn77LPPENLgu2zZMpkzZ45cdtll0qtX\nL18VFy5cKGvXrpXChQvLVVddZQLv+vXrZfTo0eI8ZWmupQG4YcOGMmnSJDPyrJt25jVr1pwxB9r5\neo6eHPT1ZjgZAggggAACCCRVwBkwOyMLXHfddaHNmzcntR1cLLZAygPvypUr5aabbnJVI32+7t//\n/rfceOONrvZ3s5OOKvfu3VtefPFFGT9+vFxzzTUm8C5evNiM6Pbo0SN6Gp3u0KFDB6lQoUI08Dr7\nn9HJndFqAq8bfPZBAAEEEEAgAAL33XffGVnAGUgj8FpU25QH3uwsNOCGQonNjk899ZTUq1dPKleu\nLCNHjowG3rlz58qmTZukU6dO0SZ2795dmjZtavaNjPDy25tFvZmmIIAAAgggYIkAUxosKcQvzbAm\n8L7++uvyu9/9Tq6++mrTtDfeeMP8pw+taSjVjuP3tmDBAvn666/NqK1u6QPvkiVLZMWKFRmmT7Rt\n21a6dOkiFStWJPD6XQzOhwACCCCAQIAECLx2FdOawNu4cWMZOHCgmaOro6Y6mjp48GD58ssv5eOP\nPzarJfi96aoLX331VXQU+ejRo1KwYEG56667pGbNmjJs2DAzj1e3kydPmpHgyZMnS7FixQi8fheD\n8yGAAAIIIBAgAQKvXcW0JvDWqlXLrNRQqFAhGTJkiKSlpUmzZs1EV3F44IEHzNcSvaUf4dWH1Nq0\naSPt27ePrtKwfPly07bIRmdOdEU4PwIIIIAAAnlTgIxgV92sCby6BNgzzzxjVkrQqQO6GsKvfvUr\nOXDggDRp0sSs0JDoLX3g1Wtt2bLFjDLv3bvXrAOsI8IaxAm8ia4E50cAAQQQQCBvCxB47aqfNYFX\nHxJ79dVXzfJfDz74oDRq1MhI6ajqtGnTosuD2cRHZ7apGrQFAQQQQAABewTICPbUQltiTeDVxvzn\nP/8RXZlB5/FGtl27dpn/8+KLL7ZLzmkNndm6ktAgBBBAAAEErBAgI1hRhmgjrAq82qoTJ06IvuZX\nV2ewfaMz214h2ocAAggggEBqBMgIqXGPdVVrAq/O1dW3mukb1XTTJcN0W7Rokegob/Pmze2SY4TX\nunrQIAQQQAABBGwRIPDaUonT7bAm8A4aNEh0WTANtu3atROd06vb1q1bpU+fPmZNXts2OrNtFaE9\nCCCAAAII2CFARrCjDpFWWBN469atKxMnTpQSJUqILlEWCbw68tugQQOZN2+eXXKM8FpXDxqEAAII\nIICALQIEXlsqYdkIb506dWTcuHFSsmTJDIF3zZo1MmDAALNSg20bndm2itAeBBBAAAEE7BAgI9hR\nB+tGeEeNGiV79uyRjh07SsuWLWXmzJmydu1aM6+3atWq0rp1a7vkGOG1rh40CAEEEEAAAVsECLy2\nVMKyEV59o5q+bELfqKZzeXXTt67p63w1AOsrf23b6My2VYT2IIAAAgggYIcAGcGOOlg3whtpkC5L\npiO9J0+elIsuusiEXls3OrOtlaFdCCCAAAIIpFaAjJBa/8xXt+ahtfSBl3V47eoktAYBBBBAAAEE\nciZA4M2ZV6L3tibwsg5vokvN+RFAAAEEEEAgWQIE3mRJu7uONYGXdXjdFYy9EEAAAQQQQMB+AQKv\nXTWyJvCyDq9dHYPWIIAAAggggEDuBQi8ubdLxJHWBF7W4U1EeTknAggggAACCKRCgMCbCvXY17Qm\n8LIOr10dg9YggAACCCCAQO4FCLy5t0vEkdYEXtbhTUR5OScCCCCAAAIIpEKAwJsK9TwwwhtpIuvw\n2tVBaA0CCCCAAAII5FyAwJtzs0QeYc0I7+zZs6VGjRpStGjRRN6vr+emM/vKyckQQAABBBAIjAAZ\nwa5SWhN4a9WqJRMnTpTSpUvbJZRNa+jMeaZUNBQBBBBAAIGkCpARksod92LWBN7nnntOypcvL/Xr\n14/baFt2oDPbUgnagQACCCCAgF0CZAS76mFN4F23bp0MHz5crrjiCrn++uulSJEiGaSqVatml5zT\nGjqzdSWhQQgggAACCFghQEawogzRRlgTeNu1a5etzOjRo+2SI/BaVw8ahAACCCCAgC0CBF5bKnG6\nHdYEXrtY3LWGzuzOib0QQAABBBDIbwJkBLsqbk3g/fTTT+XWW2+NqbN37145cuSIXHLJJVKgQAEr\nFOnMVpSBRiCAAAIIIGCdABnBrpJYE3jvuusuGTdunGzYsEEKFiwolStXlgsuuMBoTZ06VaZMmWLm\n9ZYsWVKef/5587+p3ujMqa4A10cAAQQQQMBOATKCXXWxJvDqGryFChWSSpUqGaGtW7fKX//6V7nm\nmmukdu3aMmzYMLOKg4bigwcPSpcuXVIuSWdOeQloAAIIIIAAAlYKkBHsKos1gffuu++WV199VcqW\nLWuENm/eLKNGjZJnn31W6tatKwsWLDBTGQ4dOiRt27aVyZMnp1ySzpzyEtAABBBAAAEErBQgI9hV\nFmsCb7NmzTKE2HA4LC1btpSXXnrJrM27cOHCqFyDBg3k7bffTrkknTnlJaABCCCAAAIIWClARrCr\nLNYE3u7du8vtt98uNWvWlFAoJPqq4ddee00aN24skyZNkrfeesvM6dUR3ocfftjM6031RmdOdQW4\nPgIIIIAAAnYKkBHsqos1gXfHjh0ycOBA2bhxoxG67LLL5PHHH5f3339f0tLSZNWqVVK9enX55JNP\nzOuHO3XqlHJJOnPKS0ADEEAAAQQQsFKAjGBXWawJvBEWHcE9evRodIWGyL/rlIaPP/7YBOEmTZpI\n4cKFUy5JZ055CWgAAggggAACVgqQEewqi3WB98SJE7J//34pVaqUXVJZtIbObH2JaCACCCCAAAIp\nESAjpIQ95kWtCbwHDhyQESNGyLJly0xjdVUG3RYtWiS7du2S5s2b2yXntIbObF1JaBACCCCAAAJW\nCJARrChDtBHWBN5BgwaZqQwabNu1aydz5841jdT1ePv06SNvvPGGXXIEXuvqQYMQQAABBBCwRYDA\na0slTrfDmsCra+1OnDhRSpQoIbVq1YoGXh351WXI5s2bZ5ccgde6etAgBBBAAAEEbBEg8NpSCcsC\nb506dcxb1PSVwekD75o1a2TAgAEybdo0u+QIvNbVgwYhgAACCCBgiwCB15ZKWBZ49a1qe/bskY4d\nO5oXTsycOVPWrl1r5vVWrVpVWrdubZccgde6etAgBBBAAAEEbBEg8NpSCcsC7/Hjx80LJt59910z\nl1e3QoUKSb169UwALliwoF1yBF7r6kGDEEAAAQQQsEWAwGtLJSwLvBEWXZZMR3pPnjwpF110kQm9\ntm50ZlsrQ7sQQAABBBBIrQAZIbX+ma9uzUNrdrG4aw2d2Z0TeyGAAAIIIJDfBMgIdlU8pYE3stau\nG5K7777bzW5J3YfOnFRuLoYAAggggECeESAj2FWqlAbeLl26RDUKFCggX3zxhZx77rlSunRpM5Xh\n22+/lSNHjkj16tWle/fuvssdPHhQpkyZIh9++KE59yWXXCJdu3aVMmXKmP9/w4YNMnToUNm3b595\nyUTPnj3NKhKRjc7se0k4IQIIIIAAAoEQICPYVcaUBt70FGPHjhV9cO3hhx+Ws88+23xJ5/GOHz9e\nChcuLC1atPBdTl9h/Mknn8idd95prqErQ6xcuVIGDhwop06dMtfs3LmzVKlSRWbNmiWrVq2Sfv36\nEXh9rwQnRAABBBBAIFgCBF676mlN4G3UqJGMGTPGvHgi/fbTTz+ZJcmmT5+ecLlt27bJs88+KxMm\nTJD169fL6NGjzbJoumkAbtiwoVlJomjRoubftDM7o8PhzA279NJLQwlvLBdAAAEEEEAAASsEtm/f\nfkYWcJZUDW3evNmK9tEIi960Vrt2bRM0S5UqlaEue/fuNYH373//e0Lr9eOPP8rIkSOlXLly0qRJ\nE1m8eLEZ0e3Ro0f0urpGcIcOHaRChQrRwHvHHXec0cmdUEzgTWi1ODkCCCCAAAL2CDjLp56RBf7x\nj38QeO0pkT2vFu7fv7+ZK9u2bVu54oorJBQKyTfffCN/+9vf5Pzzz5c+ffokhG337t0mxGrg1akL\nTz75pLne3LlzZdOmTdKpU6fodXUecdOmTaVy5crRwMtvbwkpCydFAAEEEEAgTwswpcGu8lkzpeHA\ngQNmSsOiRYvM9AHd9EG2P//5z9KuXTs577zzEip37NgxWbp0qbz55ptm3vBHH30kK1askF69ekWv\nq2FcH7SrWLEigTeh1eDkCCCAAAII5G0BAq9d9bMm8EZY9C1r+uIJ3XR6Q5EiRZIq9uCDD4q+5vj7\n77+XYcOGmXm8uukDdPrWt8mTJ0uxYsUIvEmtChdDAAEEEEAgbwkQeO2qV8oD7+zZs+Xee+81o7nx\ntp9//tksIVazZs14u7r6ujPJ3CyDFllqzJlvY0aZdamycDgsbdq0kfbt20dXaVi+fLkMGTIkem46\nsytmdkIAAQQQQCDfCZAR7Cp5ygOvzs3duXOn3HPPPXLbbbeZ1wmn33R6g66eoEF3/vz5oi+g0CDq\nx/bvf//bzBHW+btnnXWWlC1bVh555BHz4JpuW7ZskcGDB4s+OKdf03V409LSCLx+4HMOBBBAAAEE\nAixA4LWruCkPvMqhI6c60rt69WozV/fCCy80I6+HDh0y0xt0msMtt9wiDRo0kGuuucYaQTqzNaWg\nIQgggAACCFglQEawqhz2rNKgLBpw9e1m3333nej0hchb18qXL5/0ubxuykRndqPEPggEX+CUs174\ngcVLzI0W+3N1KVC8ePBvmjtEAIFsBcgIdnUQK0Z47SJx3xo6s3sr9kQgqAJHvvpKvmneQk45K83o\nVsB5qPWy11+TIlddFdRb5r4QQMCFABnBBVISdyHwesCmM3vA41AEAiKwvV17Objk9OhuZDuvenW5\ndPTLAblDbgMBBHIjQEbIjVrijiHwerClM3vA41AEAiLwVcWsR3KvWv9VQO6Q20AAgdwIkBFyo5a4\nYwi8HmzpzB7wOBSBgAhsue9+Oeo8e5B+K+y8fvyKd2cH5A65DQQQyI0AGSE3aok7xrrAe+LECdm/\nf7956YTtG53Z9grRPgQSL3B4xWey3Vmv+9TBg+ZiBZyVZi59+WU595abE39xroAAAtYKkBHsKo01\ngVdfLTxixAhZtmyZEVqwYIH5X33V8K5du6R58+Z2yTmtoTNbVxIahEBKBI47a4kfcD6rdCt2551S\nqEyZlLSDiyKAgD0CZAR7aqEtsSbwDho0yKy3q8G2Xbt2MnfuXCO1detW0ZdTvPHGG3bJEXitqwcN\nQgABBBBAwBYBAq8tlTjdDmsCb926dWXixIlSokQJqVWrVjTw6sivvnBi3rx5dskReK2rBw1CAAEE\nEEDAFgECry2VsCzw1qlTR8aNGyclS5bMEHjXrFkjAwYMkGnTptklR+C1rh40CAEEEEAAAVsECLy2\nVMKywDtq1CjzGuGOHTtKy5YtZebMmbJ27Vozr7dq1arSunVru+QIvNbVgwYhgAACCCBgiwCB15ZK\nWBZ4jx8/LpMmTZJ3333XzOXVrVChQlKvXj0TgAsWLGiXHIHXunrQIAQQQAABBGwRIPDaUgnLAm+E\nRZcl05HekydPykUXXWRCr60bndnWytAuBBBAAAEEUitARkitf+arW/PQ2uzZs6VGjRpStGhRu4Sy\naQ2dOc+UioYigAACCCCQVAEyQlK5417MmsCrKzPoKg2lS5eO22hbdqAz21IJ2oEAAggggIBdAmQE\nu+phTeB97rnnpHz58lK/fn27hBjhzTP1oKEIIIAAAgjYIkDgtaUSp9thTeBdt26dDB8+XK644gq5\n/vrrpUiRIhmkqlWrZpec0xo6s3UloUEIIIAAAghYIUBGsKIM0UZYE3j17WrZbaNHj7ZLjsBrXT1o\nEAIIIIAAArYIEHhtqYRlI7x2sbhrDZ3ZnRN7IYAAAgggkN8EyAh2VdyaEV67WNy1hs7szom9EEAA\nAQQQyG8CZAS7Km5N4J07d262MrqKg20bndm2itAeBBBAAAEE7BAgI9hRh0grrAm8jz/+eAaZY8eO\nye7du+WHH36Q6tWrS69eveySc1pDZ7auJDQIAQQQQAABKwTICFaUIdoIawJvViynTp2SGTNmyN69\ne6V9+/Z2yRF4rasHDUIAAQQQQMAW1JeSKgAAIABJREFUAQKvLZU43Q6rA2+EqlmzZjJ58mS75Ai8\n1tWDBiGAAAIIIGCLAIHXlkrkkcCro7xNmzaVqVOn2iVH4LWuHjQIAQQQQAABWwQIvLZUwrLAu2rV\nqjNkjh49KkuXLpVDhw6JvonNto3ObFtFaA8CCCCAAAJ2CJAR7KhDpBXWTGlo0qTJGTLnnnuuXHnl\nldKmTRspWbKkXXKM8FpXDxqEAAIIIICALQIEXlsqYdkIr10s7lpDZ3bnxF4IIIAAAgjkNwEygl0V\nt2aE95VXXpFHH330DJ0jR47I+PHjWaXBrn5DaxBAAAEEEEAgGwECr13dw5rAW7duXZk5c+YZOroe\n7/333y/xXkyRClY6cyrUuSYCCCCAAAL2C5AR7KpRygPv7NmzjYiO4upc3fSbrtCgD7P9+OOPMnLk\nSLvknNbQma0rCQ1CAAEEEEDACgEyghVliDYi5YF34cKFsmLFCvn444/lhhtuyKBToEABSUtLkwYN\nGkjp0qXtkiPwWlcPGoQAAggggIAtAgReWypxuh0pD7wRjmHDhknXrl3t0onTGjpznioXjUUAAQQQ\nQCBpAmSEpFG7upA1gddVay3bic5sWUFoDgIIIIAAApYIkBEsKcQvzbAm8OpqDO+9955s3bpV9EG1\nzFufPn3sknNaQ2e2riQ0CAEEEEAAASsEyAhWlCHaCGsCr75JbdeuXfKHP/xB5syZI7Vr15YdO3bI\nypUrpVu3bnL77bfbJUfgta4eNAgBBBBAAAFbBAi8tlTidDusCby69NikSZPk/PPPN+vx6rq8ui1e\nvFg+++wz6dWrl11yBF7r6kGDEEAAAQQQsEWAwGtLJSwMvNOmTZMiRYpI27ZtZfTo0aKrNBw9elQe\neOABM93Bto3ObFtFaA8CCCCAAAJ2CJAR7KhDpBXWjPA+8cQTZvmxKlWqSP/+/c3UhurVq8v69etF\n5+9Onz7dLjlGeK2rBw1CAAEEEEDAFgECry2VsGyEd+PGjXLeeefJxRdfLBs2bBANwPr/79+/X5o3\nby6NGjWyS47Aa109aBACCCCAAAK2CBB4bamEZYE3M8t3330nGoL1hRPaaWzc6Mw2VoU2IYAAAggg\nkHoBMkLqa5C+BdZMadBXDNeoUUOKFi2aNKETJ07I1KlTZcGCBXLy5Em5/PLLzYoQpUqVMm3Qkeah\nQ4fKvn37TOju2bOnlCxZMto+OnPSSsWFEEAAAQQQyFMCZAS7ymVN4K1Vq5ZMnDgxqa8QPnDggFkC\nTVeI0KA9efJksw5w37595dSpU9KiRQvp3LmzmVc8a9YsWbVqlfTr14/Aa1cfpjUIIIAAAghYJ0Dg\ntask1gReXYe3fPnyUr9+/ZQJbd68WQYNGiRjx441D8vpShEjRoww7dEA3LBhQ7N0WmQUWjuzE4TD\nmRt83XXXhVJ2E1wYAQQQQAABBJIqsGbNmjOyQJ06dUKaK9jsELAm8K5bt06GDx8uV1xxhVx//fVm\nebL0W7Vq1RIu9u6778qWLVuka9euZv1fHdHt0aNH9LodO3aUDh06SIUKFcy/aeC97777zujkL774\nIoE34dXiAggggAACCNgh4EyHPCMLOJmCwGtHeUwrrAm87dq1y5ZFR1sTue3du1eefPJJM8Krc3jn\nzp0rmzZtkk6dOkUv2717d2natKlUrlw5Gnj57S2RVeHcCCCAAAII5E0BpjTYVTdrAm8qWX744Qfz\nQJq+8CISZpcsWSIrVqzI8IY3/XqXLl2kYsWKBN5UFoxrI4AAAgggYLkAgdeuAlkXeHXlBF17N7JS\nQqK59ME1fW1x48aNzcsuItvXX38tw4YNM/N4ddNVHOrVq2cebCtWrBiBN9GF4fwIIIAAAgjkYQEC\nr13FsybwavDUB8SWLVtmhHSpMN0WLVoku3btMi+f8Hs7fPiw9O7d27y6+Lbbbstwen1IrU2bNtK+\nffvoKg3Lly+XIUOGRPejM/tdEc6HAAIIIIBAMATICHbV0ZrAq3Nnjx49aoKtzufVObS66TJh+mrh\nN954w3e5999/3zwoV6hQoQzndh46M9MW9AG2wYMHi87vLVu2rJn2kJaWRuD1vRKcEAEEEEAAgWAJ\nEHjtqqc1gbdu3bpmHd4SJUqIrskbCbw68tugQQOZN2+eXXJOa+jM1pWEBiGAAAIIIGCFABnBijJE\nG2FN4HXWq5Nx48aZN5mlD7zO2nYyYMAAmTZtml1yBF7r6kGDEEAAAQQQsEWAwGtLJU63w5rAO2rU\nKNmzZ4/oWrctW7aUmTNnytq1a8283qpVq0rr1q3tkiPwWlcPGoQAAggggIAtAgReWyphWeA9fvy4\neYuZvvxB5/LqpnNrdWUEDcAFCxa0S47Aa109aBACCCCAAAK2CBB4bamEZYE3wqLLkulIry4DdtFF\nF53xQJlNfHRmm6pBWxBAAAEEELBHgIxgTy20JdZMabCLxV1r6MzunNgLAQQQQACB/CZARrCr4lYF\n3o0bN4ouFfbtt98apTJlykjt2rXNagg2bnRmG6tCmxBAAAEEEEi9ABkh9TVI3wJrAq8uQzZ27Fip\nXr26XHbZZWZKw4YNG8yLKLp16yY1atSwS85pDZ3ZupLQIAQQQAABBKwQICNYUYZoI6wJvE2aNJFn\nn332jNHcpUuXmuXKEvHiCa+loDN7FeR4BBBAAAEEgilARrCrrtYE3mbNmsnkyZPP0NHX/+pKDbx4\nwq6OQ2sQQAABBBBAILYAgdeu3mFN4NV1eOvXry+lS5fOIPT555/L66+/btbjtW2jM9tWEdqDAAII\nIICAHQJkBDvqEGmFNYF369atotMXdO3dtLQ00XV5v/nmG1mwYIEJwpdccomcOnUqqletWrWUS9KZ\nU14CGoAAAggggICVAmQEu8piTeBt165djmRGjx6do/0TsTOdORGqnBMBBBBAAIG8L0BGsKuG1gRe\nu1jctYbO7M6JvRBAAAEEEMhvAmQEuypO4PVQDzqzBzwORQABBBBAIMACZAS7imtN4N29e7eMHz9e\nNm/eLLoyQ+Zt2rRpdsk5raEzW1cSGoQAAggggIAVAmQEK8oQbYQ1gbdr167mwbQ77rhDChcufIbS\n9ddfb5ccgde6etAgBBBAAAEEbBEg8NpSidPtsCbwNm7cWKZOnWqXTpzW0JnzVLloLAIIIIAAAkkT\nICMkjdrVhawJvC1btjSvFj777LNdNdyGnejMNlSBNiCAAAIIIGCfABnBrppYE3hnzJghq1evloYN\nG8qvf/1rKVCgQAYp/TfbNjqzbRWhPQgggAACCNghQEawow6RVlgTeBctWiSDBw+WcDicpdDChQvt\nknNaQ2e2riQ0CAEEEEAAASsEyAhWlCHaCGsCb5MmTUT/i/XQmo1THejMdnVmWoMAAggggIAtAmQE\nWypxuh1WBd433njDLp04raEz56ly0VgEEEAAAQSSJkBGSBq1qwtZE3g7d+4sffv2lZIlS7pquA07\n0ZltqAJtQAABBBBAwD4BMoJdNbEm8M6bN0/efPNNueuuu6RUqVJSqFChDFLVqlWzS85pDZ3ZupLQ\nIAQQQAABBKwQICNYUYZoI6wJvO3atctWZvTo0XbJEXitqwcNQgABBBBAwBYBAq8tlTjdDmsCr10s\n7lpDZ3bnxF4IIIAAAgjkNwEygl0Vty7wnjhxQvbv32+mNdi+0ZltrxDtQwABBBBAIDUCZITUuMe6\nqjWB98CBAzJixAhZtmyZaeuCBQvM/+r6vLt27ZLmzZvbJee0hs5sXUloEAIIIIAAAlYIkBGsKEO0\nEdYE3kGDBsnRo0dNsNX5vHPnzjWN3Lp1q/Tp00dsXLKMzmxXZ6Y1CCCAAAII2CJARrClEqfbYU3g\nrVu3rkycOFFKlCghtWrVigZeHflt0KCB6CoOtm10ZtsqQnsQQAABBBCwQ4CMYEcdIq2wJvDWqVNH\nxo0bZ9bhTR9416xZIwMGDJBp06bZJee0hs5sXUloEAIIIIAAAlYIkBGsKEO0EdYE3lGjRsmePXuk\nY8eO0rJlS5k5c6asXbvWzOutWrWqtG7d2i45Aq919aBBCCCAAAII2CJA4LWlEqfbYU3gPX78uEya\nNEneffddM5dXN335RL169UwALliwoF1yBF7r6kGDEEiVwE8/H5cP1u0xl7+rUmkpfk7GF+ekql1c\nFwEEUidA4E2dfVZXtibwRhqny5LpSO/JkyfloosuOuONazbx0ZltqgZtQSA1Al/u+kkajf1Efjpy\nwjSgeJGz5M1Hfi9XX1w8NQ3iqgggYIUAGcGKMkQbkfLA+8ILL5hVGc455xy7ZFy0hs7sAoldEAi4\nwMOvfy4LfxndjdxqDWeU99XmVQJ+59weAghkJ0BGsKt/pDzw1qhRQ6ZPn25WZ8hrG505r1WM9iLg\nv8DlPd/P8qTbnr/H/4txRgQQyDMCZAS7SkXg9VAPOrMHPA5FICACNV9aJl99eyDD3dxS7gJ569Hf\nB+QOuQ0EEMiNABkhN2qJO8aKwKsvmyhSpEi2d1m/fv3EKeTyzHTmXMJxGAIBEvhkyz55xJnWcOCX\nObzFnDm8Y53pDL+/omSA7pJbQQCBnAqQEXIqltj9rQi8lStXlrPOOivbOx04cGBiJXJxdjpzLtA4\nBIEACmz//mf54Mvd5s7uujpNLv1V3nsmIYBl4ZYQSKkAGSGl/Gdc3IrAyxxeuzoFrUEAAQQQQAAB\nbwIEXm9+fh9N4PUgSmf2gMehCCCAAAIIBFiAjGBXcVMeeJs0aSJjxoyRYsWKpUxmxYoV5vXFL774\nonldcGTbsGGDDB06VPbt22f+vWfPnubVx5GNzpyyknFhBBBAAAEErBYgI9hVnpQH3lRzvPPOO/Lp\np5/KkSNHpFu3btHAe+rUKWnRooV07txZqlSpIrNmzZJVq1ZJv379CLypLhrXRwABBBBAwHIBAq9d\nBcr3gXf16tVy9dVXyxNPPCEdOnSIBt7169fL6NGjZcSIEaZiGoAbNmxoXn9ctGhR82/amcePHx/O\nXNKqVauG7CozrUEAAQQQQACBRAl8+OGHZ2SBNm3ahDZv3pyoS3LeHArk+8Ab8erSpYt07NgxGngX\nL15sRnR79OgRJdWvayiuUKFCNPC2atXqjE7ep08fAm8OOyK7I4AAAgggkFcF+vfvf0YWmDhxIoHX\nooISeH8pRubAO3fuXNm0aZN06tQpWq7u3btL06ZNRZdRi4zw8tubRb2ZpiCAAAIIIGCJAFMaLCnE\nL80g8MYIvEuWLBF9mK1Xr17RirVt21Y0GFesWJHAa1c/pjUIIIAAAghYJUDgtaocQuCNEXi//vpr\nGTZsmJnHq9vJkyelXr16Mnny5OiKEnRmuzozrUEAAQQQQMAWATKCLZU43Q4Cb4zAqw+pORPOpX37\n9tFVGpYvXy5DhgyJVpDObFdnpjUIIIAAAgjYIkBGsKUSBN4Mlcg8h1e/uGXLFhk8eLDs3btXypYt\na9bhTUtLI/Da1YdpDQIIIIAAAtYJEHjtKgkjvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aA\nx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiA\njGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2\ngMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARY\ngIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn\n9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggE\nWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupB\nZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAII\nBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7q\nQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAAC\nCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe\n6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAA\nAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8\nHupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvB7qQWf2gMehCCCA\nAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgggAACCARYgIxgV3EJ\nvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdxCbwe6kFn9oDHoQgg\ngAACCARYgIxgV3EJvB7qQWf2gMehCCCAAAIIBFiAjGBXcQm8HupBZ/aAx6EIIIAAAggEWICMYFdx\nCbwe6kFn9oDHoQgggAACCARYgIxgV3EJvNnUY8OGDTJ06FDZt2+faMft2bOnlCxZMnoEndmuzkxr\nEEAAAQQQsEWAjGBLJU63g8Abox6nTp2SFi1aSOfOnaVKlSoya9YsWbVqlfTr14/Aa1cfpjUIIIAA\nAghYJ0DgtaskBN4Y9Vi/fr2MHj1aRowYYfbQANywYUOZNGmSFC1a1PybduYhQ4aEM5+ibt26IbvK\nTGsQQAABBBBAIFECM2fOPCML9OjRI7R58+ZEXZLz5lCAwBsDbPHixWZE1+mw0T06duwoHTp0kAoV\nKkQD7+OPP35GJ2/Xrh2BN4cdkd0RQAABBBDIqwLOANkZWeCFF14g8FpUUAJvjGLMnTtXNm3aJJ06\ndYru0b17d2natKlUrlw5Gnj57c2i3kxTEEAAAQQQsESAKQ2WFOKXZhB4Y9RjyZIlsmLFCunVq1d0\nj7Zt20qXLl2kYsWKBF67+jGtQQABBBBAwCoBAq9V5eChtVjl+Prrr2XYsGFmHq9uJ0+elHr16snk\nyZOlWLFiBF67+jGtQQABBBBAwCoBAq9V5SDwxiqHPqTWpk0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     "source": "<strong>Notes on automating this script</strong>\n\nThis Python script can be automated using cron jobs (https://help.ubuntu.com/community/CronHowto) and setting the \"fileopt\" kwarg in py.plot() to \"extend\" (https://plot.ly/python/file-options/). Also, make sure that you set the \"auto_open\" kwarg to False, so that your server does not try to open your plot as an HTML page (https://plot.ly/python/overview/).\n\nIn your Python script, replace the py.iplot() call above with:\n<code>py.plot(fig, filename='montreal-and-san-francisco-temperatures', fileopt='extend', auto_open=False)</code>. You can copy the full Python script from here: https://gist.github.com/jackparmer/9de837d2ccc24b483a2e#file-graph-weather-underground-data-with-plotly-python-client\n\nFinally, make sure to include <code>#!/usr/bin/env python</code> as the first line at the top of your Python script. Cron will not be able to run your Python script if this is not the first line."
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