USGS_water.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:3d75c88928d12286c2a3eb5ab6b0ae46817ad1284c946ba4d1392f936cde9765"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Read USGS streamflow using PYOOS "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "import pandas as pd\n",
      "import datetime as dt\n",
      "from pyoos.collectors.usgs.usgs_rest import UsgsRest\n"
     ],
     "language": "python",
     "metadata": {
      "input_collapsed": false
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Specify a bounding box and time range of interest:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box=[-87,46,-85,48]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# last few days\n",
      "jd_start = dt.datetime.utcnow()- dt.timedelta(days=3)\n",
      "jd_stop = dt.datetime.utcnow() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# hurricane sandy time period\n",
      "jd_start = dt.datetime(2012,10,26)\n",
      "jd_stop = dt.datetime(2012,11,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print jd_start\n",
      "print jd_stop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2012-10-26 00:00:00\n",
        "2012-11-02 00:00:00\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Get observations from USGS Rest"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = UsgsRest()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.list_variables()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "'Find variables using this website: http://help.waterdata.usgs.gov/codes-and-parameters/parameters'"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.filter(bbox=tuple(box), start=jd_start, end=jd_stop)\n",
      "col = c.collect()\n",
      "col.calculate_bounds()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sta in col.elements: print sta.name,sta.location.x,sta.location.y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "AU TRAIN RIVER AT FOREST LAKE, MI -86.8501514 46.34077908\n",
        "TAHQUAMENON RIVER NEAR PARADISE, MI -85.2695548 46.57501478\n",
        "BLACK RIVER NEAR GARNET, MI -85.3653694 46.1180556\n",
        "MANISTIQUE RIVER NEAR MANISTIQUE, MI -86.161249 46.03052857\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(sta)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "paegan.cdm.dsg.features.station.Station"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta.get_unique_members()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[{'description': 'Discharge, cubic feet per second',\n",
        "  'name': 'Streamflow, ft&#179;/s',\n",
        "  'standard': '00060',\n",
        "  'unit': 'ft3/s'}]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta.properties()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{'county': '26153',\n",
        " 'horizontal_crs': 'EPSG:4326',\n",
        " 'huc': '04060106',\n",
        " 'location_description': [],\n",
        " 'state': '26',\n",
        " 'station_type': [],\n",
        " 'vertical_crs': None,\n",
        " 'vertical_units': 'm'}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Read Data from a Single Station"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(sta)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "paegan.cdm.dsg.features.station.Station"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta.elements[0].time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "datetime.datetime(2012, 10, 29, 3, 15, tzinfo=<UTC>)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sta.elements[0].members"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[{'description': 'Discharge, cubic feet per second',\n",
        "  'name': 'Streamflow, ft&#179;/s',\n",
        "  'standard': '00060',\n",
        "  'unit': 'ft3/s',\n",
        "  'value': '1270'}]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = [float(ele.members[0]['value']) for ele in sta.elements]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jd = [ele.time for ele in sta.elements]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.Series(d,index=jd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x33f5410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DoKZG1ktK4HvfkwnXb7zRrK58QhtZ0yBoPt4330hw//hjqW1t2+aMHJhrgnbuvUYm9H/8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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x367d450>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "count     673.000000\n",
        "mean     1181.292719\n",
        "std        83.462774\n",
        "min      1020.000000\n",
        "25%      1120.000000\n",
        "50%      1190.000000\n",
        "75%      1260.000000\n",
        "max      1290.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "795010.0"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}