Dust_Bowl_GDP.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:628aebb317f9c720121de01ca9e84c24153f1aeab4d5b1be01bb6ec820c0ceee"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Exploring Climate Data: Past and Future\n",
      "##Roland Viger, Rich Signell, USGS\n",
      "First presented at the 2012 Unidata Workshop: Navigating Earth System Science Data, 9-13 July.\n",
      "\n",
      "What if you were watching [Ken Burns's \"The Dust Bowl\"](http://www.pbs.org/kenburns/dustbowl/), saw the striking image below, and wondered: \"How much precipitation there really was back in the dustbowl years?\"  How easy is it to access and manipulate climate data in a scientific analysis?  Here we'll show some powerful tools that make it easy.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "Image('http://www-tc.pbs.org/kenburns/dustbowl/media/photos/s2571-lg.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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THY0BColM9aD6uV8tpRRkI3z1I/OubRV9+G9h/WiF1ujVKctxMev0qihcCkav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\n/CS2l5UKcT+IhG/tQKscOLVzDauaNkHICv8As+bNBv8ADfB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VtPasqD8cu/VeN6VghXxN\nomTsjCkie6RMVRa88NeILm6cu7axWu3fUVoWnTCgczvSkML4HxW0aTdXPDlW6ER/FKUFM7QU6jg+\n1SjCuGHFvurbQAjUogCR9E4FaRFtYuXD3IUeSehWkx/hpQw5w160ug3KHFICVFQOPPMDMZEUo+2X\n/i/xCu3aQ4lHL57ag30GgyBXDMbrSvfHvidYWIZMiNUH7VOcarH4pxTiPGFW6rsgcpOhCkSBEyBp\n9O9XGWatM6mxy/IrMTJjG5qo8TxVaHr55zJKSQPea3iAtO+XSHDKvmb2GNv1qjXtGOJLgWrLygR5\nnEpUqB9NqlHu+A8LvUeFeM2zzdxbXHEggWy3lBI8kSZWU6ZO9Y3WsI+PH+HJubG//eDaGbBlDb1k\n28l11ak/ypTqBzvqNXymkrfxyi5W18OzeXSlnzoRgieg0iB7CkAuOcXdfuGg/Y3jF8+Am2+IVICP\n9KCJzVwHtPDPjC7gtWCtP8y0pQD96lwjcs/2ceKHkaX/AIZgKwZAcMekdadLy0rP9itiTzLxznOE\n+bSgI/qanZy3rX9lvhe3Kf8A0SVn/XJrPS8tdnw9wa08jbTTcYhCQDSkS6rhdqgqffbaA6uEJoPH\n+JfFv7PHbd23dvVXWIKbVJX+fy1rPOpuviPiRHCUXGrhTLotXvm+IISqfZNdsYYSFNeb+Ac7wqqh\nhpm3WhxzkEhsefz5E+lA5ZlpQS2pCwVmEQrf8qivRO3DNs2ht1JJAAR5t/yrKmuGW95xCfhrW4W1\n/NIifdVTRvWvhnhzbClcWu02i4BQhKgopHXV0NZqgO8R8G2KQux4cu+UwP8A3DgOkH1n+1BdV34h\n4+5y7Ju5atE4DbADTAx1Vgmr+g5w39mDJ89+6tKlbsoj81DrToj1dlwPgnBWJZZaYSBBcWYP1JrF\nqsTjX7QuGWWtmyQLx9OJb/5YP/V1itZ5SvJXfiK54whxPEb1dvbJH/tGsaz0HlzWojCVc2SFQy0s\neUpJkZP8wqopb2qrj/lpdCEzrV5cDeqDrd4UygISyp9yPO6VDf0HpQJpbtnVSvmkq6kpn86DS4P4\nXXxi8S0gOC3Bhbilgp1/yjuqOlTdWPqPCfDVh4f4I+xbA4bccUonUZ0ZzXPdaaHA7VLnh/h6RhHw\n7e/aKmqzHgosuuXNyVNNOOoSVDZKDAH0qo+V8aRYX3ELh95DrwRhHLXAUE/yiMTXRlqfs6sOEf8A\nFvDn02z+ppSnmwXNWhaUmJEZqejHqbDhNzxV9y/5iS2txZblUSgrJT1CgOwrKtC48M360RFvHzaF\nLURI26nIpVhFfh/jjOWkWKzugcwpgn1g0qRSz4Jx5NzzLiztFoVJUpl/zzER5gB0q0N3FitTYR+7\n1nbZbXQz/N6VAouy0JUTwt9ZOZBaVGZ/nGKqOTam7UArhbg0JybjRkdhCztvQJP8W4Qw4q1uGFM+\ncpWvkvBHbKtJEetAjc+I/DVoogJQ6PwhpDioAwBMCqglpxvw/wAZK220hpy2RrWXdTeCofLq32zS\nDyz/ABhqz8Tv8m3ZTauOIC1ha/KkjKkkH1rUGgzxrhTji7pkEMIUGgfLsMawd4zPepCvR2jIU0fh\nXwppzzqbQo5KtztHrUVu8a4w3xbgNzwo2awp1oBLilJgLTEKIgT5hWVeF/4AfcZIfcGlfzaW+/8A\n3CtdJCp/Z2m2fSq3d5hHVxMQfoo1eiIf8DXT7s3KirtpMSO1KNXgFpxbg/M5dum5S8U6vPp0xjAz\nnNQYviDh3Gb/AIm5dLsVeZIA5a0/h96oXt/CIeSVPJXbuKidSdWe+KUXuPAzStDQvpkqUqUFOk+x\nOZpRT/6f2h8q+JFI6EMk/wBanRFEeClfJ8fDaZ0qWyo/oetKGmfCNuyjQm+t31KgNI0FCpPzEFXa\nlGbxHwo9ZoUsPlEqCG2QkyqeozmrQlc24sbi5tefzijT/GHkBBEzBziaDPXaOMJc5ra3To5aPKQg\nFQ8q5SelWo2rC5Qjw5qQ4jnIQu2RJA0qWqCZI6t1FZgUi0Wxcoda1sHUpIKNtinG4Kao9K5xDg6E\nAurKCoJKPIdMdgY6Csha7u+EuN6WH9S8GdEbGgUYeY/A9qhQKRB7x/WqHH0sKvkuh4FSEn2lOYqB\nW7facabUtQbOAAUnKknP0qj7HxHwrxlxq3Wi2hAcSVqMJAHczXDp0g54Y1r5fkLmyQCO1UKv8I0t\nlaeXoCskK/WiMy84cEDlgaQrp0M+tUeLYS2xxdxv4Ru9Tq0Nt3CiEau50kTW2Xolr8QcOeW0pnh/\nCXEiS01bhbgG8511n4v0l4l4h4ss7sWl7xR95K20O/wzy0woSBpRFaxNwfgfB7PiF7w+3uW1uqul\nJDrjjijE9AKm6Yb8c+HuE2HCHnLNgNlHEl2jJH/8baOs9ZqedXXs/wBlnBVO+Crc8xSEuLcwiEn5\nu4zWfe/V8vGftN4Lcq8ecO4daIcuHXQkIQVEkic5ntW/G/E9YHw+/wCNcCunLa64ouyeZcXrYNwF\n6E//AGxoXqFX9svW8N/affNWqHVXdveAhRKXQhLhjb5SAKzyvRi5/bbwyxfCb22lrSFc61WlwTG0\ne9P8zp5ni3/9RTjqlt8M4XH8rt0v89KB/WtZ+NOnkeNftD8c8VtuYL0sFcANWidBOo+nmNazMRms\ncN8RXDlq7eNXSyVgFbuo7z/NVpy2LPgz6FtrUwtx4OOBTUeXH61KsJ31jcr5HO4S84nUoJcCjPsB\n6UowHuDvM8ON8WHUILpSlwxo3gCd5rVRv8N/Z/xtyFrYPnQPmEb96zvtc8th3wHxa0CCGS8rqhtP\nlTPZRNZ7WGrHgfFbRYWjha9fR5zSog+kzUpGmzwHxNey29qtsYIM/knFOlh9rwEyH9bgQp3q66ok\nE/8AQKz0R6i34LZ2yEhLaTtukR9BtWWjWoJ8qPKKqBc0mNOB+c0GJxnwpZcYe5ty69ERoSSE/wDx\n2rVSMofsy4OFz8U9H0p0Rb/6acHnFy8fXyinRA2/2YcLQ9JuXChPmSMHzevpTrSGLjwRYvgpN2+g\nYlKEpSD06ClITR+y7h41pTeOaSOqRq+9XpIat/2a8D0aHHnVp9wD96nSx6fhvDLKwaQ1bNpQloQn\nSNh6e/WsqpxxRHCL+D//AI7mf+2rgvwl5LPB7FCRMMNbj/SKmjxvGbDxVxBp60srZ4sOPvFagNAM\nr8olXTrWriQhbfsr44Rqu3W28bDJ/Kr0ctXhngz9yXrd23drXcpwj8Pzb06SPF+MOCXXAOJ6Pj1v\n/G6n0EakFPnyMGOta86m4Ra4zxZPl+Meif5z0FVBf3txRSR/6p7AP4jQSjjPF0j/AN28kxvrNAQc\nb4xJPxzudOJoJTxzjGqBerwo9B/agr/xH4hEH49RPm/l/tQcPFHiPRm9URE5CT/SgqvjHGP4iviS\nDqBA0p2PbFBRzjHF5CFPBcHOpCCM7dKDFfRfm9F86tDqivWU8tMeTppGIIFUfUf+GvDLyA7+57dO\ntIWgJ1AZE94rDRK6s/CvDGA/dWCGG1KDaVAuCVGY+U+lEX4fw7wRxZKvhLRTimo16XX2zJnuc0U4\nfAnBNAIubgKP4UvvzH3jFTrSYE54BtyfJxa5SpYGkc1ZI98VeiFVfs+UvDfHrlKsiQsqTP1inRGe\n7+zrjTZKU+IFuDvqP96dJHI/Z5xFJlzjjiv9AUUH2nUadECe8C8W1JSxd3Dv88vEgZ/CpMDbvToj\n0Pwt9a27QXzAG0JAVoS5kCM5mooIu1clxSHmwcCNBCsqE9MUEC+bdKUocSs4GSpBP1ITQee/aCtd\nxwdielwNWUqyEn+XIq+UeMs+LcZs/wD2t24hI/BOpP2VIraCL45xf4x6+Q7D76Uh06UlJgR8pBFR\nTLHiQx/HsLcqjSV25VbK0n/o8v5Ug17TjvhVxGhxty2XuQ6nmpnadSf7VItFCbC4uUt2TdrctqUN\nakLGoDr/AA1QaJVrm4suHsoVdldkFzAcDwGDG6UqTmO9QZDiGry6VcWDlu5a+XzhoL80ZydJ3qgP\nEuFrFjcLJszDc6UNQqB/KdRg1cHnrNrU6mVBKZEqVJGT1ArSPUptUMsuBVrb3SXvk5S3BpjE6Vnv\nmsKduOJcYup+JuX3SZ+ZxUf2or6b4O8Eceu7zh/GfhZtFWsIc5if5CB5ZnrXL1rWJP7MPEZ8K3dr\n8EoXLt3zUt60TojvNTtYb4hw4shph1H8ZthCCj+UhMb0o+cs2Clcet2lgqWbtpJSdp5grow+mePr\nPhiPEDrt9xi2snFIQPh4U49p2HkSNjNc/LevFftNsQjxVywqQ3bM5jTuMYFb8az6aHgXxDbu+KeE\n8LteEtJyAq7ccUtZKUmSlOEinrDDn7TGW0cISy1cc6eKXC3okJSsidGeo61nw16fQP2WWa0eBuGg\n7nWo/VZrPv8AaY+WftsevLfxnbP2bqmbm3Z1odSYUkzuK3+M9PmSSt967uL1fxFy6oKU84dSlScm\nTXVhd1GixIkStH/70Gfy1BMaehB+taQFDDi3iY6UGuH32FsraSAbfSoZ3KcjaoPVWP7SOOv3DPOt\nLdQQrUIBBkD1JqcjXtfF/FnLhuLRmZWoEn+asxV3PGHIFprt9fLLi18vO80ivI3XiG4vuHscN5A5\nNu8q4V1kqMpB9prUR7nhnjPxLepSm24cA1AGpWE49TWNzFr21q+ty0Qt4pDsDmIGwNc2xUujbBNA\nQawDjfrQd55lKdqI5RJ7zRVCler17VUQGXMQKAhSuO07xUFfh1ncmO1UWFqd501FcptQB6jpVFU2\n8pNEQGEwUz5vWgabY0400UVKRO0COtQY3iWRwHiMRPIUMVcRueE+FB7h9q64mQy2hI99IqelxtuW\nSln0FZUi7w64XOhHtVGdeeHXW0/EO9Ix9atR8o/ataXFxxq0SygqcbRpShP8pO5rr5Y15Rvg99vy\n1RqrVRdPCOI6SS0ry9KUSOF3wE/DqUCMYqUVVw/iGf8A0ywMQY3pRJ4dxEn/ANu58wzFKOTwy/1B\nPwjilAqxooBfuviWkTaOfKQfKatFlWN+EE8heopAmOozHvUAzZXsqXyFhMpM6aqOds7pEkNqwrzY\n7iivR8T4/e8P8N8Gft1tl8L5NxauQoqQEkZTuBjBFTBh8Q8UM8U4YzZcRtywsXDZN6yCpBbEglTZ\ng6hPQ5qwa/hDi3hSxU9ZOPlt642vSoll7QYSY+ZqQazqvX6XPhVOskqYbVpdebVzEg7ySmYqBFd7\n5NXPclSsLHQ1QdPFNKkNlwqO8qAzPpUgHd8YecQoJSjzeUTikCmt0v6FpbU2dzJmO1UO2l4hvy6S\nEZEDI+xqBK+4khtTZbDoMnMSPY9KBD9/a/LLuJMaO28mg5rjrihoSHcg7oED6/pQAPDrjiaHXeJk\nPMKVqCCNMaRCQNMbd6ULH9n/AAa4cITzrWAVHSdcDpIV1Par0PM8W8J31ldcgKS8FSphcEFSR+ih\n1q1GWeH8SZ+e0cSBOdJUIPrVC8DzER6Db8jQBdaAUDpOfTb1qh7h/iDj1inl2t86231bUdbZ90L1\nJqDVZ8Z3e95w6zu1K+ZwN8lRHqW8flUgrxHi/h7iFqvlN3fD7opMISUPNKxhKtlAGmYMvg9navoG\nu/atHtWG3kr0x31JCh9DWqNw8CuS0EcPbRxDl/M/bltefUJOufpWareRwvyqkDBTO1ZqxsfsTYuH\nP2nPNh1QbasrqE61adgBCdutT3/yZ+3zq9/eKbi4T8U/KXHBh5f8xzvW8TX6G/cN23wSxdUApZtm\n8kyZ0dTXn6+umZ8eG4MEWXie2uLq0F6PiUp5SlaRzFrhCsfy711ZjS/adwl5/wDaGo4CnEM5xnNZ\n8b8X0f8A2o8Dad8Xaz5nDatQJiNIjt6VPGmsX9mFm0f2j8OQlMOtB1bntoMfWt+v0y9R4v4VZvcK\n1XJKGhxW5LunMx27GufnW9eu/Zq1cp4F8OpYXbtf+1HUJVmCetT0j5l+2Tg9w54oWskGbcQegE/e\nt/jT0+aL4c2gOt5kkFJx0PWurKxs0PJDeiEQAM9s0RVPBGslAlRG0/3pQG24KoPuBSMzETtSrG5b\n+E23YXMlR06ZqdEel4b+zlDidUJHXep0vLdtf2btMkOlQUpI+X3rPS8iteE+HJTp+GSpzMD/AHNT\nfS5izPglTTanlNNW7W6ktgEn3NOjkp/6O0X5UOLR7iqjV8PXNhxe6ctGgpDzQlWrtU34Y9Pb8B0n\nR8x6VitQ7+4lpH/LJ/7hSrDiPC9y6hJ0AJOY1CjIT/h59hyVN/wx0ChRXJ4XaOuwhBT6k5mKA11w\nIo0aT5CB96AY4YlCUBY+aZiNqUCRZ26nICSlKcCIqoKjhLfLCtJOcbVKpa4sU8zQE+UHJO/0qii7\nRlJGkZV1NVARattvD1oGvhWFpUv8XrUCDjCjOM71RmcZaZHCrjmNSSiN6qNzgPHlWnB2W12yVJQA\nG0JMY9fWpuK10ceeW5AsUx+Lz+lZU1+9SlAUq1CR/wBVEYt/eXHFudaoRyUjAcBzPeqPiX7QuGcR\ns+Ojn3Be8nkXtXXzrG46xaQLdoqbJnMz+dUPItEqdP8ABUVD5vMKgKq1CYJbMfhOqoqvI5pCBbgj\n1VVQ6jgy0gQyNJ2TqqVY644eoJCUtpQ4k5XqORt9KVAfgVhIKmWlAYAlW+1UAXw9ICptmzoxpk/e\ngUTw1Dq4+HRp6oJMVQQ+F0rSpXwba0AThRj8yDUpFP8Ag2yS2XF2qUXJy2pKpj0zVoxOJeHLXmx8\nOlCu4O571ajBPAHrZRVahKsedl3KSPQzINKsV4VxXi/COJDkLNpcqPyJVqbX1SlQ223kVUewV+0R\nJea5nCkIf1RcclWhtaT00+bSr2xWYr1/B73wlfPqZKn2ytjnNjSnUk51JOFAxHcVBe44PbtJt3EM\n6mnvxK0kZ2IGCKlWOb8NjUCtCQXlcphSt9Z6SJpSKXfhdNoCq4QlkzpDgJOT6CanRuFXuAqYZW/q\nYFqmNWsLUSVkD16ntSjVt/ArpsF8QTcMKtcaRpVOTEQU7zUqs0eGbhNyWypuEGVJHT/TsOlWh88F\nui2nl6Y3SnEe5H6CpVijPBLgNmHAI+Ynv9qIV4h4XVcJSxdICkvZT5yDPRQI2q0jKP7P75sa2HXl\nNE6SC6lSZHQheadEJ8Q8GcUhTauH29zOwcKEn6ETFOjlhXHgZtGou2D1so7Bl9taQOvzEfpWuk5Y\nrvhi0TdFlq81ThCFoIVPqU4irWU/8F8dSHHGg06lI+XVCv8A8hFOljJuOC31ujW+zCPxQpPUYq0g\nLLBLUkeVOxx1qizbC0kFsaSdlAxUR//Z\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 136,
       "text": [
        "<IPython.core.display.Image at 0x7f808ce98990>"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Above:Dust storm hits Hooker, OK, June 4, 1937. \n",
      "\n",
      "To find out how much rainfall was there during the dust bowl years, we can use the [USGS/CIDA GeoDataPortal (GDP)](http://cida.usgs.gov/climate/gdp/) which can compute statistics of a gridded field within specified shapes, such as county outlines.  Hooker is in Texas County, Oklahoma, so here we use the GDP to compute a historical time series of mean precipitation in Texas County using the PRISM dataset. We then compare to climate forecast projections to see if similar droughts are predicted to occur in the future, and what the impact of different climate scenarios might be. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.dates as mdates\n",
      "import matplotlib.pyplot as plt\n",
      "import urllib\n",
      "import os\n",
      "from IPython.core.display import HTML\n",
      "import time\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /usgs/data2/notebook"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/usgs/data2/notebook\n"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pyGDP\n",
      "import numpy as np\n",
      "import matplotlib.dates as mdates"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One way to interface with the GDP is with the interactive web interface, shown below.  In this interface, you can upload a shapefile or draw on the screen to define a polygon region, then you specify the statistics and datasets you want to use via dropdown menus.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://screencast.com/t/K7KTcaFrSUc width=800 height=600></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://screencast.com/t/K7KTcaFrSUc width=800 height=600></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "<IPython.core.display.HTML at 0x7f808d1877d0>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we use the python interface to the GDP, called PyGDP, which allows for scripting.  You can get the code and documentation at https://github.com/USGS-CIDA/pyGDP."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a pyGDP object\n",
      "myGDP = pyGDP.pyGDPwebProcessing()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's see what shapefiles are already available on the GDP server\n",
      "# this changes with time, since uploaded shapefiles are kept for a few days\n",
      "shapefiles = myGDP.getShapefiles()\n",
      "print 'Available Shapefiles:'\n",
      "for s in shapefiles:\n",
      "    print s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Available Shapefiles:\n",
        "derivative:CONUS_States\n",
        "draw:LA_Extended\n",
        "sample:simplified_HUC8s\n",
        "derivative:Level_III_Ecoregions\n",
        "draw:test\n",
        "draw:Siso\n",
        "derivative:FWS_LCC\n",
        "draw:KS\n",
        "sample:Alaska\n",
        "sample:CSC_Boundaries\n",
        "upload:OKCNTYD\n",
        "upload:Wllstn_grid\n",
        "derivative:US_Counties\n",
        "sample:CONUS_states\n",
        "sample:nps_boundary_2013\n",
        "sample:FWS_LCC\n",
        "derivative:NCA_Regions\n",
        "derivative:wbdhu8_alb_simp\n",
        "upload:WllstnGrid\n",
        "draw:tests3\n"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Is our shapefile there already?\n",
      "# If not, upload it. \n",
      "OKshapeFile = 'upload:OKCNTYD'\n",
      "if not OKshapeFile in shapefiles:\n",
      "    shpfile = myGDP.uploadShapeFile('OKCNTYD.zip')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's check the attributes of the shapefile\n",
      "attributes = myGDP.getAttributes(OKshapeFile)\n",
      "print \"Shapefile attributes:\"\n",
      "for a in attributes:\n",
      "    print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Shapefile attributes:\n",
        "OBJECTID_1\n",
        "OBJECTID\n",
        "RECTYPE\n",
        "VERSION\n",
        "REVISION\n",
        "MODDATE\n",
        "POLYID\n",
        "FEATUREID\n",
        "CNTRLONG\n",
        "CNTRLAT\n",
        "DESCRIP\n",
        "STFIPS\n",
        "Shape_area\n",
        "Shape_len\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In this particular example, we are interested in attribute = 'DESCRIP', \n",
      "# which provides the County names for Oklahoma\n",
      "user_attribute = 'DESCRIP'\n",
      "values = myGDP.getValues(OKshapeFile, user_attribute)\n",
      "print \"Shapefile attribute values:\"\n",
      "for v in values:\n",
      "    print v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Shapefile attribute values:\n",
        "Adair\n",
        "Alfalfa\n",
        "Atoka\n",
        "Beaver\n",
        "Beckham\n",
        "Blaine\n",
        "Bryan\n",
        "Caddo\n",
        "Canadian\n",
        "Carter\n",
        "Cherokee\n",
        "Choctaw\n",
        "Cimarron\n",
        "Cleveland\n",
        "Coal\n",
        "Comanche\n",
        "Cotton\n",
        "Craig\n",
        "Creek\n",
        "Custer\n",
        "Delaware\n",
        "Dewey\n",
        "Ellis\n",
        "Garfield\n",
        "Garvin\n",
        "Grady\n",
        "Grant\n",
        "Greer\n",
        "Harmon\n",
        "Harper\n",
        "Haskell\n",
        "Hughes\n",
        "Jackson\n",
        "Jefferson\n",
        "Johnston\n",
        "Kay\n",
        "Kingfisher\n",
        "Kiowa\n",
        "Latimer\n",
        "Le Flore\n",
        "Lincoln\n",
        "Logan\n",
        "Love\n",
        "Major\n",
        "Marshall\n",
        "Mayes\n",
        "McClain\n",
        "McCurtain\n",
        "McIntosh\n",
        "Murray\n",
        "Muskogee\n",
        "Noble\n",
        "Nowata\n",
        "Okfuskee\n",
        "Oklahoma\n",
        "Okmulgee\n",
        "Osage\n",
        "Ottawa\n",
        "Pawnee\n",
        "Payne\n",
        "Pittsburg\n",
        "Pontotoc\n",
        "Pottawatomie\n",
        "Pushmataha\n",
        "Roger Mills\n",
        "Rogers\n",
        "Seminole\n",
        "Sequoyah\n",
        "Stephens\n",
        "Texas\n",
        "Tillman\n",
        "Tulsa\n",
        "Wagoner\n",
        "Washington\n",
        "Washita\n",
        "Woods\n",
        "Woodward\n"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we want Texas County, Oklahoma, which is where Hooker is located\n",
      "user_value = 'Texas'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's see what gridded datasets are available for the GDP to operate on\n",
      "dataSets = myGDP.getDataSetURI()\n",
      "print \"Available gridded datasets:\"\n",
      "for d in dataSets:\n",
      "    print d[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Available gridded datasets:\n",
        "title\n",
        "Conterminous U.S. actual evapotranspiration data\n",
        "Great Lakes Coastal Forecasting System Nowcast/Lake Michigan Nowcast History 2D\n",
        "Gridded Observed Meteorological Data, 1950-1999\n",
        "Urban Growth Projection for Southeast Regional Assessment Project\n",
        "Great Lakes Coastal Forecasting System Nowcast/Lake Ontario Nowcast History 2D\n",
        "Great Lakes Coastal Forecasting System Nowcast/Lake Huron Nowcast History 2D\n",
        "Global Land Data Assimilation System\n",
        "ICLUS v1.3 Housing Density Classification Projections for the Conterminous USA\n",
        "Sea Level Rise Projections for DSL-SAMBI\n",
        "ICLUS v1.3 Raw Housing Density and Impervious Surface Projections for the Conterminous USA\n",
        "Thermosteric Sea Level Rise Projections with Parametric Uncertainty\n",
        "Great Lakes Coastal Forecasting System Nowcast/Lake Superior Nowcast History 2D\n",
        "Urban Growth Projection for DSL-SAMBI\n",
        "Great Lakes Coastal Forecasting System Nowcast/Lake Erie Nowcast History 2D\n",
        "CIG Northern US Rockies and Pacific Northwest Statistical Downscaling\n",
        "Daily Statistically Downscaled Climate Projections for the US and southern Canada east of the Rocky Mountains.\n",
        "Gridded Observed Meteorological Data: 1949-2010\n",
        "Half degree-Alaska Daily Downscaled Climate Projections by Katharine Hayhoe\n",
        "Eighth degree-CONUS Daily Downscaled Climate Projections by Katharine Hayhoe\n",
        "Mean Monthly Evaporation Atlas for the Contiguous 48 United States (1956-1970)\n",
        "Parameter-elevation Regressions on Independent Slopes Model Monthly Climate Data for the Continental United States.\n",
        "Real Time River Forecasting Center Quantitative Precipitation Estimate\n",
        "River Forecasting Center Quantitative Precipitation Estimate Archive\n",
        "University of Idaho Daily Meteorological data for continental US\n",
        "Bias Corrected Constructed Analogs V2 Daily Climate Projections\n",
        "Bias Corrected Spatially Downscaled Monthly Climate Predictions\n",
        "USGS Dynamical Downscaled Regional Climate\n",
        "800m Downscaled NEX CMIP5 Climate Projections for the Continental US\n",
        "Normal Incident Solar Radiation Atlas\n",
        "North American Land Data Assimilation System Phase 2\n",
        "Daymet Daily surface weather on a 1km grid for North America, 1980-2012\n",
        "Columbia River Basin Daily MACA-VIC Results\n",
        "Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\n",
        "TopoWx: Topoclimatic Daily Air Temperature Dataset for the Conterminous United States\n",
        "Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\n",
        "Bias Corrected Spatially Downscaled Monthly CMIP5 Climate Projections\n",
        "California Basin Characterization Model Downscaled Climate and Hydrology\n",
        "United States Quantitative Precipitation Archive\n"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataSets[0][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "'title'"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(dataSets[1:],columns=['title','abstract','urls'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>title</th>\n",
        "      <th>abstract</th>\n",
        "      <th>urls</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  Conterminous U.S. actual evapotranspiration data</td>\n",
        "      <td> Actual ET (ETa) is produced using the operatio...</td>\n",
        "      <td> [dods://cida.usgs.gov/thredds/dodsC/ssebopeta/...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Great Lakes Coastal Forecasting System Nowcast...</td>\n",
        "      <td> Great Lakes Coastal Forecasting System Nowcast...</td>\n",
        "      <td> [dods://michigan.glin.net:8080/thredds/dodsC/g...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   Gridded Observed Meteorological Data, 1950-1999</td>\n",
        "      <td> These daily gridded observations at  1/8 degre...</td>\n",
        "      <td> [dods://cida.usgs.gov/thredds/dodsC/gmo/GMO_w_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Urban Growth Projection for Southeast Regional...</td>\n",
        "      <td> This dataset represents the extent of urbaniza...</td>\n",
        "      <td> [dods://cida.usgs.gov/thredds/dodsC/serap/sera...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Great Lakes Coastal Forecasting System Nowcast...</td>\n",
        "      <td> Great Lakes Coastal Forecasting System Nowcast...</td>\n",
        "      <td> [dods://michigan.glin.net:8080/thredds/dodsC/g...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "                                               title  \\\n",
        "0   Conterminous U.S. actual evapotranspiration data   \n",
        "1  Great Lakes Coastal Forecasting System Nowcast...   \n",
        "2    Gridded Observed Meteorological Data, 1950-1999   \n",
        "3  Urban Growth Projection for Southeast Regional...   \n",
        "4  Great Lakes Coastal Forecasting System Nowcast...   \n",
        "\n",
        "                                            abstract  \\\n",
        "0  Actual ET (ETa) is produced using the operatio...   \n",
        "1  Great Lakes Coastal Forecasting System Nowcast...   \n",
        "2  These daily gridded observations at  1/8 degre...   \n",
        "3  This dataset represents the extent of urbaniza...   \n",
        "4  Great Lakes Coastal Forecasting System Nowcast...   \n",
        "\n",
        "                                                urls  \n",
        "0  [dods://cida.usgs.gov/thredds/dodsC/ssebopeta/...  \n",
        "1  [dods://michigan.glin.net:8080/thredds/dodsC/g...  \n",
        "2  [dods://cida.usgs.gov/thredds/dodsC/gmo/GMO_w_...  \n",
        "3  [dods://cida.usgs.gov/thredds/dodsC/serap/sera...  \n",
        "4  [dods://michigan.glin.net:8080/thredds/dodsC/g...  "
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df['title']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0      Conterminous U.S. actual evapotranspiration data\n",
        "1     Great Lakes Coastal Forecasting System Nowcast...\n",
        "2       Gridded Observed Meteorological Data, 1950-1999\n",
        "3     Urban Growth Projection for Southeast Regional...\n",
        "4     Great Lakes Coastal Forecasting System Nowcast...\n",
        "5     Great Lakes Coastal Forecasting System Nowcast...\n",
        "6                  Global Land Data Assimilation System\n",
        "7     ICLUS v1.3 Housing Density Classification Proj...\n",
        "8              Sea Level Rise Projections for DSL-SAMBI\n",
        "9     ICLUS v1.3 Raw Housing Density and Impervious ...\n",
        "10    Thermosteric Sea Level Rise Projections with P...\n",
        "11    Great Lakes Coastal Forecasting System Nowcast...\n",
        "12                Urban Growth Projection for DSL-SAMBI\n",
        "13    Great Lakes Coastal Forecasting System Nowcast...\n",
        "14    CIG Northern US Rockies and Pacific Northwest ...\n",
        "15    Daily Statistically Downscaled Climate Project...\n",
        "16      Gridded Observed Meteorological Data: 1949-2010\n",
        "17    Half degree-Alaska Daily Downscaled Climate Pr...\n",
        "18    Eighth degree-CONUS Daily Downscaled Climate P...\n",
        "19    Mean Monthly Evaporation Atlas for the Contigu...\n",
        "20    Parameter-elevation Regressions on Independent...\n",
        "21    Real Time River Forecasting Center Quantitativ...\n",
        "22    River Forecasting Center Quantitative Precipit...\n",
        "23    University of Idaho Daily Meteorological data ...\n",
        "24    Bias Corrected Constructed Analogs V2 Daily Cl...\n",
        "25    Bias Corrected Spatially Downscaled Monthly Cl...\n",
        "26           USGS Dynamical Downscaled Regional Climate\n",
        "27    800m Downscaled NEX CMIP5 Climate Projections ...\n",
        "28                Normal Incident Solar Radiation Atlas\n",
        "29    North American Land Data Assimilation System P...\n",
        "30    Daymet Daily surface weather on a 1km grid for...\n",
        "31          Columbia River Basin Daily MACA-VIC Results\n",
        "32    Western US Hydroclimate Scenarios Project Obse...\n",
        "33    TopoWx: Topoclimatic Daily Air Temperature Dat...\n",
        "34    Western US Hydroclimate Scenarios Project Dyna...\n",
        "35    Bias Corrected Spatially Downscaled Monthly CM...\n",
        "36    California Basin Characterization Model Downsc...\n",
        "37     United States Quantitative Precipitation Archive\n",
        "Name: title, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[20].urls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "['dods://cida.usgs.gov/thredds/dodsC/prism']"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If you choose a DAP URL, use the \"dods:\" prefix, even\n",
      "# if the list above has a \"http:\" prefix.\n",
      "# For example:  dods://cida.usgs.gov/qa/thredds/dodsC/prism\n",
      "# Let's see what data variables are in our dataset\n",
      "dataSetURI = 'dods://cida.usgs.gov/thredds/dodsC/prism'\n",
      "dataTypes = myGDP.getDataType(dataSetURI)\n",
      "print \"Available variables:\"\n",
      "for d in dataTypes:\n",
      "    print d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Available variables:\n",
        "ppt\n",
        "tmx\n",
        "tmn\n"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's see what the available time range is for our data variable\n",
      "user_dataType = 'ppt'  # precip\n",
      "timeRange = myGDP.getTimeRange(dataSetURI, user_dataType)\n",
      "for t in timeRange:\n",
      "    print t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1895-01-01T00:00:00Z\n",
        "2013-02-01T00:00:00Z\n"
       ]
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "timeBegin = '1900-01-01T00:00:00Z'\n",
      "timeEnd   = '2012-08-01T00:00:00Z'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Once we have our shapefile, attribute, value, dataset, datatype, and timerange as inputs, we can go ahead\n",
      "# and submit our request.\n",
      "name1='gdp_texas_county_prism.csv'\n",
      "if not os.path.exists(name1):\n",
      "    url_csv = myGDP.submitFeatureWeightedGridStatistics(OKshapeFile, dataSetURI, user_dataType, \n",
      "          timeBegin, timeEnd, user_attribute, user_value, delim='COMMA', stat='MEAN' )\n",
      "    f = urllib.urlretrieve(url_csv,name1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load historical PRISM precip\n",
      "jd,precip=np.loadtxt(name1,unpack=True,skiprows=3,delimiter=',', \n",
      "                     converters={0: mdates.strpdate2num('%Y-%m-%dT%H:%M:%SZ')})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def boxfilt(data,boxwidth):\n",
      "    from scipy import signal\n",
      "    import numpy as np\n",
      "    weights=signal.get_window('boxcar',boxwidth)\n",
      "    dataf=np.convolve(data,weights/boxwidth,mode='same')\n",
      "    dataf=np.ma.array(dataf)\n",
      "    dataf[:boxwidth/2]=np.nan\n",
      "    dataf[-boxwidth/2:]=np.nan\n",
      "    dataf=np.ma.masked_where(dataf==np.nan,dataf)\n",
      "    return dataf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# PRISM data is monthly:  filter over 36 months\n",
      "plp=boxfilt(precip,36)\n",
      "\n",
      "fig=plt.figure(figsize=(12,2), dpi=80) \n",
      "ax1 = fig.add_subplot(111)\n",
      "g1=ax1.plot_date(jd,plp,fmt='b-')\n",
      "g2=ax1.plot_date(jd,0*jd+np.mean(precip),fmt='k-')\n",
      "fig.autofmt_xdate()\n",
      "plt.title('Average Precip for Texas County, Oklahoma, calculated via GDP using PRISM data ')\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LLgNmzHA+luh5G3S5YBHpD2CPqi4TEY/t0JsAnjZ+PwPgFQC3Od1j6NChaNGi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\nokHj88/qZfx4OPVUmDSp1S0xq5bG4GHje3TEiDXv7HY6shs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       "text": [
        "<matplotlib.figure.Figure at 0x7f808ca5e510>"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://www.ipcc.ch/publications_and_data/ar4/wg1/en/spmsspm-projections-of.html width=900 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://www.ipcc.ch/publications_and_data/ar4/wg1/en/spmsspm-projections-of.html width=900 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 160,
       "text": [
        "<IPython.core.display.HTML at 0x7f808d1a0b10>"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#hayhoe_URI ='dods://cida-eros-thredds1.er.usgs.gov:8082/thredds/dodsC/dcp/conus_grid.w_meta.ncml'\n",
      "hayhoe_URI ='dods://cida.usgs.gov/thredds/dodsC/dcp/conus'\n",
      "timeRange = myGDP.getTimeRange(hayhoe_URI, dataType)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "timeRange"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 162,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# retrieve the CCSM3 model A1FI \"Business-as-Usual\" scenario:\n",
      "name2='gdp_texas_county_ccsm_a1fi.csv'\n",
      "if not os.path.exists(name2):\n",
      "    dataType = 'ccsm-a1fi-pr-NAm-grid'\n",
      "    result2 = myGDP.submitFeatureWeightedGridStatistics(OKshapeFile, hayhoe_URI, dataType,\n",
      "            timeRange[0],timeRange[1],user_attribute,user_value, delim='COMMA', stat='MEAN' )\n",
      "    f = urllib.urlretrieve(result2,name2)                                       "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndexError",
       "evalue": "list index out of range",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-163-ff87e1a31549>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mdataType\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'ccsm-a1fi-pr-NAm-grid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     result2 = myGDP.submitFeatureWeightedGridStatistics(OKshapeFile, hayhoe_URI, dataType,\n\u001b[1;32m----> 6\u001b[1;33m             timeRange[0],timeRange[1],user_attribute,user_value, delim='COMMA', stat='MEAN' )\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mIndexError\u001b[0m: list index out of range"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now retrieve the CCSM3 model B1 \"Eco-Friendly\" scenario:\n",
      "time0=time.time();\n",
      "name3='gdp_texas_county_ccsm_b1.csv'\n",
      "if not os.path.exists(name3):\n",
      "    dataType = 'ccsm-b1-pr-NAm-grid'\n",
      "    result3 = myGDP.submitFeatureWeightedGridStatistics(OKshapeFile, hayhoe_URI, dataType,\n",
      "            timeRange[0],timeRange[1],user_attribute,user_value, delim='COMMA', stat='MEAN' )\n",
      "    f = urllib.urlretrieve(result3,name3)\n",
      "    print('elapsed time=%d s' % (time.time()-time0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the GDP result for: CCSM A1FI \"Business-as-Usual\" scenario:\n",
      "jd_a1f1,precip_a1f1 = np.loadtxt(name2,unpack=True,skiprows=3,\n",
      "    delimiter=',',converters={0: mdates.strpdate2num('%Y-%m-%dT%H:%M:%SZ')}) \n",
      "\n",
      "# Load the GDP result for:  CCSM B1 \"Eco-Friendly\" scenario:\n",
      "jd_b1,precip_b1     = np.loadtxt(name3,unpack=True,skiprows=3,\n",
      "    delimiter=',',converters={0: mdates.strpdate2num('%Y-%m-%dT%H:%M:%SZ')}) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Hayhoe climate downscaling is hourly: filter over 1080 days (36 months)\n",
      "plp_a1f1=boxfilt(precip_a1f1,1080)\n",
      "plp_b1=boxfilt(precip_b1,1080)\n",
      "#plp_a1b_c=boxfilt(precip_a1b_c,36)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig=plt.figure(figsize=(15,3), dpi=80) \n",
      "ax1 = fig.add_subplot(111)\n",
      "fac=30. # convert from mm/day to mm/month (approx)\n",
      "# plot A1FI scenario\n",
      "g1=ax1.plot_date(jd_a1f1,plp_a1f1*fac,fmt='b-')\n",
      "# plot B1 scenario \n",
      "g2=ax1.plot_date(jd_b1,plp_b1*fac,fmt='g-')\n",
      "# plot PRISM data\n",
      "g3=ax1.plot_date(jd,plp,fmt='r-')  # for some reason when I add this the labels get borked\n",
      "ax1.xaxis.set_major_locator(mdates.YearLocator(10,month=1,day=1))\n",
      "ylabel('mm/month')\n",
      "plt.title('Average Precip for Texas County, Oklahoma, calculated via GDP using Hayhoe Downscaled GCM ')\n",
      "grid()\n",
      "legend(('A1FI','B1','PRISM Data'),loc='upper left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 167,
       "text": [
        "<matplotlib.legend.Legend at 0x7f808d1dcfd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RCf5o9UUn+KPVF53gj1ZfdII/Wn3RCf5o9UUn+KPVF51QP1oPOwy23homTYJz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f808d46cc50>"
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see from the above plot, the CCSM model is not doing very well simulating the precipitation in Texas County, OK during the period when the simulation and data overlap (1960-present). This makes us less confident about the future precipitation simulations, and suggests we might need to try some different climate models and learn a bit more about climate simulations.  When we do learn more, we find out that models have known biases in certain regions. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now just to show that we can access more than climate model time series, let's extract precipitation data from a dry winter (1936-1937) and a normal winter (2009-2010) for Texas County and look at the spatial patterns.  \n",
      "\n",
      "We'll use the netCDF4-Python library, which allows us to open OPeNDAP datasets just as if they were local NetCDF files. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4\n",
      "url='http://cida.usgs.gov/thredds/dodsC/prism'\n",
      "box = [-102,36.5,-100.95,37]  # Bounding box for Texas County, Oklahoma\n",
      "#box = [-104,36.,-100,39.0]  # Bounding box for larger dust bowl region"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define a mean precipitation function, here hard-wired for the PRISM data\n",
      "def mean_precip(nc,bbox=None,start=None,stop=None):\n",
      "    lon=nc.variables['lon'][:]\n",
      "    lat=nc.variables['lat'][:]\n",
      "    tindex0=netCDF4.date2index(start,nc.variables['time'],select='nearest')\n",
      "    tindex1=netCDF4.date2index(stop,nc.variables['time'],select='nearest')\n",
      "    bi=(lon>=box[0])&(lon<=box[2])\n",
      "    bj=(lat>=box[1])&(lat<=box[3])\n",
      "    p=nc.variables['ppt'][tindex0:tindex1,bj,bi]\n",
      "    latmin=np.min(lat[bj])\n",
      "    p=np.mean(p,axis=0)\n",
      "    lon=lon[bi]\n",
      "    lat=lat[bj]\n",
      "    return p,lon,lat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)\n",
      "p,lon,lat = mean_precip(nc,bbox=box,start=datetime.datetime(1936,11,1,0,0),\n",
      "                        stop=datetime.datetime(1937,4,1,0,0))\n",
      "p2,lon,lat = mean_precip(nc,bbox=box,start=datetime.datetime(2009,11,1,0,0),\n",
      "                       stop=datetime.datetime(2010,4,1,0,0))\n",
      "latmin = np.min(lat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at March 1935, just before black sunday on April 14, 1935\n",
      "nc = netCDF4.Dataset(url)\n",
      "p,lon,lat = mean_precip(nc,bbox=box,start=datetime.datetime(1935,3,1,0,0),\n",
      "                        stop=datetime.datetime(1935,4,1,0,0))\n",
      "p2,lon,lat = mean_precip(nc,bbox=box,start=datetime.datetime(2009,3,1,0,0),\n",
      "                       stop=datetime.datetime(2009,4,1,0,0))\n",
      "latmin = np.min(lat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import cartopy.crs as ccrs\n",
      "import cartopy.feature as cfeature\n",
      "states_provinces = cfeature.NaturalEarthFeature(\n",
      "        category='cultural',\n",
      "        name='admin_1_states_provinces_lines',\n",
      "        scale='50m',\n",
      "        facecolor='none')\n",
      "ax = plt.axes(projection=ccrs.PlateCarree())\n",
      "pc = ax.pcolormesh(lon, lat, p, cmap=plt.cm.jet_r)\n",
      "ax.add_feature(states_provinces,edgecolor='gray')\n",
      "ax.text(-101,36.86,'Hooker',align='left')\n",
      "ax.plot(-101,36.86,'o')\n",
      "cb = plt.colorbar(pc, cax=cbax,  orientation='vertical')\n",
      "cb.set_label('Precip (mm/month)')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,5), dpi=80) \n",
      "ax = fig.add_axes([0.1, 0.15, 0.3, 0.8])\n",
      "pc = ax.pcolormesh(lon, lat, p, cmap=plt.cm.jet_r)\n",
      "ax.set_aspect(1.0/np.cos(latmin * np.pi / 180.0))\n",
      "plt.title('Precip in Texas County, Oklahoma: Winter 1936-1937')\n",
      "\n",
      "cbax = fig.add_axes([0.45, 0.3, 0.03, 0.4])\n",
      "cb = plt.colorbar(pc, cax=cbax,  orientation='vertical')\n",
      "cb.set_label('Precip (mm/month)')\n",
      "\n",
      "ax2 = fig.add_axes([0.6, 0.15, 0.3, 0.8])\n",
      "pc2 = ax2.pcolormesh(lon, lat, p2, cmap=plt.cm.jet_r)\n",
      "ax2.set_aspect(1.0/np.cos(latmin * np.pi / 180.0))\n",
      "plt.title('Precip in Texas County, Oklahoma: Winter 2009-2010')\n",
      "\n",
      "cbax2 = fig.add_axes([0.95, 0.3, 0.03, 0.4])\n",
      "cb2 = plt.colorbar(pc2, cax=cbax2,  orientation='vertical')\n",
      "cb2.set_label('Precip (mm/month)')\n",
      "\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the above patterns, we can see that it's significantly drier in the northwestern part of the county in both years.  We can also see that the *maximum* precip in 1936-1937 is less than the *minimum* precipitation in 2009-2010.  We can see just how much each part of the county was drier by doing the different plot below. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig=plt.figure(figsize=(12,5), dpi=80) \n",
      "ax3 = fig.add_axes([0.1, 0.15, 0.3, 0.8])\n",
      "pc3 = ax3.pcolormesh(lon, lat, p2-p, cmap=plt.cm.jet_r)\n",
      "ax3.set_aspect(1.0/np.cos(latmin * np.pi / 180.0))\n",
      "plt.title('Precip in Texas County, Oklahoma: Difference 2010-1937')\n",
      "\n",
      "cbax3 = fig.add_axes([0.45, 0.3, 0.03, 0.4])\n",
      "cb3 = plt.colorbar(pc3, cax=cbax3,  orientation='vertical')\n",
      "cb3.set_label('Precip (mm/month)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above plot shows that relative to 2010, the drought during 1937 had the biggest different in precip in the northeastern part of the county."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hopefully this demo inspires other investigation of historical and projected climate data using the GDP and Python.  "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}