{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import psycopg2\n",
      "import numpy\n",
      "ASOS = psycopg2.connect(database='asos', user='nobody', host='iemdb')\n",
      "cursor = ASOS.cursor()\n",
      "\n",
      "tmp_hits = numpy.zeros( (121-50,), 'f')\n",
      "tmp_cnts = numpy.zeros( (121-50,), 'f')\n",
      "doy_hits = numpy.zeros( (366,), 'f')\n",
      "doy_cnts = numpy.zeros( (366,), 'f')\n",
      "\n",
      "cursor.execute(\"\"\"SELECT extract(doy from valid), round(tmpf::numeric,0), dwpf from alldata\n",
      "  WHERE station = 'DSM' and extract(hour from valid + '10 minutes'::interval) in (13,14,15,16)\n",
      "  and tmpf is not null and dwpf is not null\"\"\")\n",
      "for row in cursor:\n",
      "    doy_cnts[ row[0] - 1 ] += 1.0\n",
      "    if row[1] >= 55:\n",
      "        tmp_cnts[ row[1] - 55 ] += 1.0\n",
      "        if row[2] < 55:\n",
      "            tmp_hits[ row[1] - 55 ] += 1.0\n",
      "    if row[2] < 55:\n",
      "        doy_hits[ row[0] - 1 ] += 1.0\n",
      "        \n",
      "cursor.close()\n",
      "ASOS.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "(fig, ax) = plt.subplots(2,1)\n",
      "doy_cnts = numpy.where( doy_cnts < 1, 1, doy_cnts)\n",
      "\n",
      "ax[0].bar( numpy.arange(55,101)-0.4, tmp_hits[:46] / tmp_cnts[:46] * 100.0, width=1.0,\n",
      "  fc='r', ec='r')\n",
      "ax[0].set_xlim(54.5,100.5)\n",
      "ax[0].grid(True)\n",
      "ax[0].set_title(\"1933-2012 Des Moines Afternoon (1-4 PM) Frequency\\nof sub 55$^{\\circ}\\mathrm{F}$ Dew Points\")\n",
      "ax[0].set_ylabel(\"Freq.[%] by Air Temp\")\n",
      "ax[0].set_yticks([0,25,50,75,100])\n",
      "\n",
      "ax[1].bar( numpy.arange(50,321), doy_hits[50:321] / doy_cnts[50:321] * 100.0, width=1.0,\n",
      "  fc='g', ec='g')\n",
      "ax[1].set_xticks( (1,32,60,91,121,152,182,213,244,274,305,335,365) )\n",
      "ax[1].set_xticklabels( ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec') )\n",
      "ax[1].set_xlim(50,320)\n",
      "ax[1].grid(True)\n",
      "ax[1].set_ylabel(\"Freq.[%] by Day of Year\")\n",
      "ax[1].set_yticks([0,25,50,75,100])\n",
      "\n",
      "fig.savefig('test.svg')\n",
      "import iemplot\n",
      "iemplot.makefeature('test')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "File test.ps is missing!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib64/python2.6/site-packages/matplotlib/__init__.py:1141: UserWarning:  This call to matplotlib.use() has no effect\n",
        "because the the backend has already been chosen;\n",
        "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
        "or matplotlib.backends is imported for the first time.\n",
        "\n",
        "  warnings.warn(_use_error_msg)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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89dVXXC1h586dePnll5Gfnw8A3HgvNVE0IBcA/PXXXwqX6222L1WxCbEYKCjQ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\nrN+z9QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3effe90>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}