Automated download of barometer data into IPython Notebook

BarometerDownload

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  "signature": "sha256:d53411c6867a1ab9eaf600b142942f9a026b9c7218cb905e56750bad84f915ad"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<p>For Windows, install the following and add C:\\Program Files (x86)\\GnuWin32\\bin to your PATH</p>\n",
      "http://gnuwin32.sourceforge.net/packages/wget.htm<br />\n",
      "http://gnuwin32.sourceforge.net/packages/unzip.htm<br />\n",
      "http://gnuwin32.sourceforge.net/packages/coreutils.htm<br />"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import os\n",
      "import datetime\n",
      "import numpy\n",
      "import pandas\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def tofloat(x):\n",
      "    try:\n",
      "        return float(x)\n",
      "    except ValueError:\n",
      "        return None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cities were hand-selected, with WBAN manually looked up from http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfcities = pandas.DataFrame([{'City':'Centennial', 'WBAN':93067},\n",
      "                             {'City':'San Diego', 'WBAN':3131}])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "os.mkdir(\"TempBarometerFiles\")\n",
      "os.chdir(\"TempBarometerFiles\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "processingyear = datetime.date.today().year\n",
      "processingmonth = datetime.date.today().month\n",
      "dfdiff=pandas.DataFrame(numpy.zeros(0,dtype=[('WBAN', 'i4'),('Range', 'f8')]))\n",
      "for x in range(0, 12):\n",
      "    dt = datetime.datetime(processingyear, processingmonth, 1) - datetime.timedelta(days=1)\n",
      "    processingyear = dt.year\n",
      "    processingmonth = dt.month\n",
      "    os.system(\"wget -q http://cdo.ncdc.noaa.gov/qclcd_ascii/QCLCD\" + str(processingyear) + str(processingmonth).zfill(2) + \".zip\")\n",
      "    os.system(\"unzip QCLCD\" + str(processingyear) + str(processingmonth).zfill(2) + \".zip\")\n",
      "    df=pandas.read_csv(str(processingyear) + str(processingmonth).zfill(2) + \"hourly.txt\",low_memory=False)\n",
      "    dfsp = df.merge(dfcities, on=\"WBAN\").ix[:,(\"WBAN\", \"Date\", \"StationPressure\")]\n",
      "    dfsp[\"StationPressureFloat\"] = dfsp[\"StationPressure\"].apply(lambda x: tofloat(x))\n",
      "    del dfsp[\"StationPressure\"]\n",
      "    dfsp = dfsp.ix[dfsp[\"StationPressureFloat\"].apply(lambda x: not math.isnan(x))]\n",
      "    gb = dfsp.groupby([\"WBAN\",\"Date\"])\n",
      "    dfminmax = gb.min().join(gb.max(), lsuffix=\"Min\", rsuffix=\"Max\")\n",
      "    dfdiffcur = pandas.DataFrame(dfminmax[\"StationPressureFloatMax\"] - dfminmax[\"StationPressureFloatMin\"], columns=[\"Range\"])\n",
      "    dfdiffcur.reset_index(level=0, inplace=True)\n",
      "    dfdiff = dfdiff.append(dfdiffcur)\n",
      "    os.system(\"rm *.txt\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist = numpy.histogram(dfdiff.ix[dfdiff[\"WBAN\"]==93067,\"Range\"],range=(0,0.6))\n",
      "pandas.DataFrame({'delta inches':hist[1][1:],'Denver (Days)':hist[0]}).plot(x='delta inches',kind='bar', ylim=(0,300))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xedcde10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fGbRRnxnUr6+LRk5E5Mo4IycichKckRMR6ZQuGrnsWRgzaKM+M2ijPjOoX18XjZyIyJVx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\nW55jlPfbzQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf257f98>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist = numpy.histogram(dfdiff.ix[dfdiff[\"WBAN\"]==3131,\"Range\"],range=(0,0.6))\n",
      "pandas.DataFrame({'delta inches':hist[1][1:],'San Diego (Days)':hist[0]}).plot(x='delta inches',kind='bar', ylim=(0,300))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xee77a20>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tMoM66jOD7etrpJETETkujlakZZBdv+UMRKQuGh+tEBE5Lo00cqPsAGAG+TNJ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\nIiI7x0ZORGTn2MiJiOwcGzkRkZ37f2BK3Uu/BuzKAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf2e8ba8>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "os.chdir(\"..\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm -rf TempBarometerFiles"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}