EarthObservationWebServices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Earth Observation data access tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://publicwiki.deltares.nl/download/attachments/42401809/OpenEarthBuildingBlocks_standards_client_server.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Tutorial on how to work with different flavours of web services for Earth Obersvation data\n",
    "# 1. netCDF-CF-OPeNDAP using netcdf4 package\n",
    "# 2. OGC WCS using owslib package\n",
    "# 3. OGC WMS using owslib package\n",
    "# For overview see OpenEarrth.eu > Data\n",
    "from IPython.display import Image\n",
    "Image(url='https://publicwiki.deltares.nl/download/attachments/42401809/OpenEarthBuildingBlocks_standards_client_server.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset # includes OPeNDAP library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. OPeNDAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. OPeNDAP\n",
    "# define url and open dataset\n",
    "url = 'http://thredds.jpl.nasa.gov/thredds/dodsC/ncml_aggregation/Chlorophyll/seawifs/aggregate__SEAWIFS_L3_CHLA_MONTHLY_9KM_R.ncml'\n",
    "ds = Dataset(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "243 days since 1997-01-01\n",
      "1997-09-01 00:00:00 1997-10-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# get time\n",
    "print(ds.variables['time'][0],ds.variables['time'].units)\n",
    "from netCDF4 import num2date\n",
    "time = num2date(ds.variables['time'][:], units=ds.variables['time'].units)\n",
    "print(time[0],time[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-179.95833332999999, 89.958333330000002, 179.95831894, -89.958326139999983]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get full coordinate sticks so be able to make geographical selection\n",
    "lon = ds.variables['lon'][:]\n",
    "lat = ds.variables['lat'][:]\n",
    "[lon[0],lat[0],lon[-1],lat[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2040, 360, 2339, 479]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from bbox get netCDF-OPeNDAP subsetting indices\n",
    "import numpy as np\n",
    "bbox = (-10., 50., 15., 60.)\n",
    "ilon = []\n",
    "for i,il  in enumerate(lon):\n",
    "    if (il >= bbox[0]) & (il <= bbox[2]):\n",
    "        ilon.append(i)\n",
    "ilat = []\n",
    "for i,il  in enumerate(lat):\n",
    "    if (il >= bbox[1]) & (il <= bbox[3]):\n",
    "        ilat.append(i)\n",
    "[ilon[0],ilat[0],ilon[-1],ilat[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "it = 0 # first\n",
    "l3m_data = ds.variables['l3m_data'][it,ilat[0]:ilat[-1],ilon[0]:ilon[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data =\n",
       " [[0.9353600144386292 0.9353600144386292 0.9677000045776367 ..., -- -- --]\n",
       " [1.0851999521255493 1.0851999521255493 0.7522500157356262 ..., -- -- --]\n",
       " [1.1441500186920166 1.0524699687957764 1.0524699687957764 ..., -- -- --]\n",
       " ..., \n",
       " [-- -- -- ..., -- -- --]\n",
       " [-- -- -- ..., -- -- --]\n",
       " [-- -- -- ..., -- -- --]],\n",
       "             mask =\n",
       " [[False False False ...,  True  True  True]\n",
       " [False False False ...,  True  True  True]\n",
       " [False False False ...,  True  True  True]\n",
       " ..., \n",
       " [ True  True  True ...,  True  True  True]\n",
       " [ True  True  True ...,  True  True  True]\n",
       " [ True  True  True ...,  True  True  True]],\n",
       "       fill_value = -32767.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data is numpy masked array\n",
    "l3m_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.28729999, 88.393517]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim = [0, 10]\n",
    "print(clim)\n",
    "[np.nanmin(l3m_data),np.max(l3m_data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# close dataset\n",
    "ds.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x666ffd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LlFjUXFQVUzKpHO1HoPKZElsluKpxpAbG2RutRenYhLbmrzbCdkgsLA4+DivH36FApTMl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Y+X7hsUHOYvRf5ERGpFZr3rLS8UqpTWp71lQ7f1QlEURZKTTUm3o0KJj73blTtM5ZQqOm2jKIqi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\nUYoCFUoURVEURSkK/j8LN69wtIEIwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x504c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure()\n",
    "fig.set_figwidth(10)\n",
    "ax = plt.axes([0.1,.8,0.8,0.8])\n",
    "dd = 8\n",
    "h = plt.pcolor(lon[ilon[0]:ilon[-1]],lat[ilat[0]:ilat[-1]],l3m_data)\n",
    "h.set_clim([0, 2])\n",
    "plt.colorbar()\n",
    "plt.title(str(time[it]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.WCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 2. WCS\n",
    "#see also: http://stommel.tamu.edu/~baum/wcs.html\n",
    "import owslib.wcs\n",
    "url = 'http://thredds.jpl.nasa.gov/thredds/wcs/ncml_aggregation/Chlorophyll/seawifs/aggregate__SEAWIFS_L3_CHLA_MONTHLY_9KM_R.ncml?'\n",
    "wcs = owslib.wcs.WebCoverageService(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'l3m_data': <owslib.coverage.wcs100.ContentMetadata at 0x5eac8d0>}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcs.contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-179.9583282470703, -89.95833587646484, 179.95835876464844, 89.95833587646484)\n",
      "<owslib.coverage.wcs100.RectifiedGrid object at 0x0000000005EBF8D0>\n",
      "['GeoTIFF', 'GeoTIFF_Float', 'NetCDF3']\n"
     ]
    }
   ],
   "source": [
    "meta = wcs.contents['l3m_data']\n",
    "print(meta.boundingBoxWGS84)\n",
    "print(meta.grid)\n",
    "print(meta.supportedFormats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bbox = (-10., 50., 15., 60.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method getCoverage in module owslib.coverage.wcs100:\n",
      "\n",
      "getCoverage(identifier=None, bbox=None, time=None, format=None, crs=None, width=None, height=None, resx=None, resy=None, resz=None, parameter=None, method='Get', **kwargs) method of owslib.coverage.wcs100.WebCoverageService_1_0_0 instance\n",
      "    Request and return a coverage from the WCS as a file-like object\n",
      "    note: additional **kwargs helps with multi-version implementation\n",
      "    core keyword arguments should be supported cross version\n",
      "    example:\n",
      "    cvg=wcs.getCoverage(identifier=['TuMYrRQ4'], timeSequence=['2792-06-01T00:00:00.0'], bbox=(-112,36,-106,41),format='cf-netcdf')\n",
      "    \n",
      "    is equivalent to:\n",
      "    http://myhost/mywcs?SERVICE=WCS&REQUEST=GetCoverage&IDENTIFIER=TuMYrRQ4&VERSION=1.1.0&BOUNDINGBOX=-180,-90,180,90&TIME=2792-06-01T00:00:00.0&FORMAT=cf-netcdf\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(wcs.getCoverage)\n",
    "output = wcs.getCoverage(identifier='l3m_data',\n",
    "                bbox=bbox, \n",
    "                format='GeoTIFF',\n",
    "                crs='OGC:CRS84', \n",
    "                version=\"1.0.0\")\n",
    "# save to file\n",
    "f=open('wcs_test.tiff','wb')\n",
    "f.write(output.read())\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'PIL'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-2d5529452ee8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# no PIL for python 3.4.*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'wcs_test.tiff'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mextent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named 'PIL'"
     ]
    }
   ],
   "source": [
    "# no PIL for python 3.4.*\n",
    "from PIL import Image\n",
    "im = Image.open('wcs_test.tiff')\n",
    "%matplotlib inline\n",
    "plt.imshow(img,extent=[bbox[0],bbox[1],bbox[2],bbox[3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.WMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3. WMS\n",
    "# see also: http://nbviewer.ipython.org/github/Unidata/tds-python-workshop/blob/master/wms_sample.ipynb\n",
    "import owslib.wms\n",
    "url = 'http://thredds.jpl.nasa.gov/thredds/wms/ncml_aggregation/Chlorophyll/seawifs/aggregate__SEAWIFS_L3_CHLA_MONTHLY_9KM_R.ncml?'\n",
    "wms = owslib.wms.WebMapService(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('\\n                          1997-09-01T00:00:00.000Z',\n",
       " '2010-12-01T00:00:00.000Z')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see what the server offers.\n",
    "wms.contents\n",
    "wms.contents['l3m_data'].timepositions[0], wms.contents['l3m_data'].timepositions[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_children',\n",
       " 'abstract',\n",
       " 'boundingBox',\n",
       " 'boundingBoxWGS84',\n",
       " 'cascaded',\n",
       " 'children',\n",
       " 'crsOptions',\n",
       " 'dataUrls',\n",
       " 'defaulttimeposition',\n",
       " 'elevations',\n",
       " 'fixedHeight',\n",
       " 'fixedWidth',\n",
       " 'id',\n",
       " 'index',\n",
       " 'keywords',\n",
       " 'layers',\n",
       " 'metadataUrls',\n",
       " 'name',\n",
       " 'noSubsets',\n",
       " 'opaque',\n",
       " 'parent',\n",
       " 'queryable',\n",
       " 'scaleHint',\n",
       " 'styles',\n",
       " 'timepositions',\n",
       " 'title']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(wms.contents['l3m_data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bbox = (-10., 50., 15., 60.)\n",
    "f = wms.getmap(['l3m_data'], \\\n",
    "               srs='EPSG:4326', \\\n",
    "               bbox=bbox, \\\n",
    "               size=(1024, 1024), \\\n",
    "               format='image/png', \\\n",
    "               time='1997-09-01T00:00:00.000Z',colorscalerange='0,10')\n",
    "# colorscalerange does not work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert binary data to IO stream\n",
    "import matplotlib.pyplot as plt\n",
    "from io import BytesIO, StringIO\n",
    "f_io = BytesIO(f.read())\n",
    "img = plt.imread(f_io)\n",
    "# x = bbox[0] + (bbox[2]-bbox[0])*np.arange(0,img.shape[0]+1)/img.shape[0]\n",
    "# y = bbox[1] + (bbox[3]-bbox[1])*np.arange(0,img.shape[1]+1)/img.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa2d7f28>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JZtlh2v3FLzH4fomZFW21M+wFJS5/f5RXAvBBPgbADfwPb+OMxO03g79xF/dzNxpFiQxD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\nIAiCBH4AMSbfTnzs8hMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x443b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.imshow(img,extent=[bbox[0],bbox[1],bbox[2],bbox[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}