emiliom/IOOSAAT_ERDDAPdataaccess_ex1.ipynb

## IOOSAAT_ERDDAPdataaccess_ex1.ipynb
{
 "metadata": {
  "name": "AAT_ERDDAP_Example1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "IOOS Animal Acoustic Telemetry Data Project. ERDDAP Data Access Example 1"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "For detections from one project (at one array), join in all corresponding animal tag data, then plot detections by tagging project and calendar month. 2013-9-30"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# IPython session run with --pylab=inline; \"import matplotlib.pyplot as plt\" was run during startup\nimport pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Read data from ERDDAP (two requests) into Pandas DataFrames, then post-process the data (join, clean up, enhance, etc)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# ERDDAP data request URL's (decided in advance), selecting only a subset of variables\n# Each request returns a csv table with 2 header rows: variable names and units\nwilldet_url = \"http://MYSERVER/erddap/tabledap/otnnepWILLDetects.csv?time,transmittername,platform_name,platform_latitude,platform_longitude\"\nantag_url = \"http://MYSERVER/erddap/tabledap/otnnepAnTags.csv?transmittername,vernacularname,project_reference\"",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# read detection data from \"will\" receivers (Willapa Bay) into pandas dataframe\nwilldet = pd.read_csv(willdet_url, skiprows=[1], parse_dates=['time'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# read all animal tags into pandas dataframe\nantag = pd.read_csv(antag_url, skiprows=[1])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# create month variable on will det, then merge the two dataframes\nwilldet['month'] = willdet['time'].apply(lambda dt: dt.month)\nwilldetantag = pd.merge(willdet, antag, how='left')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "willdetantag",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 100367 entries, 0 to 100366\nData columns (total 8 columns):\ntime                  100367  non-null values\ntransmittername       100367  non-null values\nplatform_name         100367  non-null values\nplatform_latitude     100367  non-null values\nplatform_longitude    100367  non-null values\nmonth                 100367  non-null values\nvernacularname        53035  non-null values\nproject_reference     53035  non-null values\ndtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 100367 entries, 0 to 100366\nData columns (total 8 columns):\ntime                  100367  non-null values\ntransmittername       100367  non-null values\nplatform_name         100367  non-null values\nplatform_latitude     100367  non-null values\nplatform_longitude    100367  non-null values\nmonth                 100367  non-null values\nvernacularname        53035  non-null values\nproject_reference     53035  non-null values\ndtypes: datetime64[ns](1), float64(2), int64(1), object(4)"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# create new dataframe where records without an identified tagging project are removed\nd = willdetantag[willdetantag.project_reference.notnull()]\nd.vernacularname.value_counts()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "green sturgeon    53022\nsockeye salmon       13\ndtype: int64"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# total number of detections, by tagging project\nd.project_reference.value_counts()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "VOGL     31146\nLIND     15335\nMOSER     6541\nPSS2        13\ndtype: int64"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# aggregate (count) detections by calendar month (1-12), and split off by tagging project \ndvogl  = d[d.project_reference=='VOGL'].groupby('month').time.count()\ndlind  = d[d.project_reference=='LIND'].groupby('month').time.count()\ndmoser = d[d.project_reference=='MOSER'].groupby('month').time.count()\ndvogl",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "month\n1         774\n2         999\n3         941\n4        3000\n5        6433\n6        9524\n7        2809\n8        1067\n9        1875\n10       2773\n11        462\n12        489\ndtype: int64"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Plot some results, by calendar month (1-12)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# plot number of detected Vogl tags vs month (plotted independently of year)\ndvogl.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "<matplotlib.axes.AxesSubplot at 0x4335850>"
      },
      {
       "output_type": "display_data",
       "png": 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QkmJ0NP6nshJ69dKFYdw4o6Mxh+Ji/dzuykr9POrrr6/9PdXVkJCgi8LQod6P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\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# now compare the number of detected tags vs month (independent of year), for each of the 3 tagging projects\n# blue line is Vogl (same plot as above)\nplt.plot(dvogl.index,dvogl.values,'b-', dlind.index,dlind.values,'r-', dmoser.index,dmoser.values,'g-')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "[<matplotlib.lines.Line2D at 0x52464d0>,\n <matplotlib.lines.Line2D at 0x5246990>,\n <matplotlib.lines.Line2D at 0x5246e90>]"
      },
      {
       "output_type": "display_data",
       "png": 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T+Osv/ccnNZQQiNaOHQPOnOF3WvVy5Aj/K+3YUbAYCksKMWPPDPw09if8k/oP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\nE1xr0xRZrZogqyWQ1aIcWc3KkGtahjZ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      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}