import earthdata idea

py_earth_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GIBS Earth Science Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Motive: How well can we streamline aquasition of Earth Science Data?\n",
    "\n",
    "Pretend task: Where's the comforable swimming location in Washington?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import earthdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What sensor data is available?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Selecting a Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BRUVQVEQRQUVFVOQ155wVc845izkHVBCF14QJFTOYE36CoihBEcSECcFA/vbfWPP29s3s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\nHIERQKAr5ahCsSpl5/8BZ0/8mvD5ihgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "image/png": {
       "height": 77,
       "width": 425
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import earthdata.Datasets.<tab>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import earthdata.Datasets.SeaSurfaceTemperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "earthdata.Datasets.SeaSurfaceTemperature.ModisAqua5DayInfrared?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Docstring: The Moderate-resolution Imaging Spectroradiometer (MODIS) is a scientific instrument (radiometer) on board the NASA Terra and Aqua satellite platforms, launched in 1999 and 2002[...]\n",
    "    Type: file\n",
    "\n",
    "Looks good, let's check the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sst = earthdata.Datasets.SeaSurfaceTemperature.ModisAqua5DayInfrared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "earthdata.Datasets.SeaSurfaceTemperature.ModisAqua5DayInfrared"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Global'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst.region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8 Days'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst.temporal_resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.083 degrees (Latitude) x 0.083 degrees (Longitude)'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst.spatial_resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also query for Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[earthdata.Datasets.SeaSurfaceTemperature.ModisAqua5DayInfrared]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthdata.Datsets.find(data_type=\"sea surface temperature\", temporal_resolution_lt=timedelta(year=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Selecting a Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time = datetime.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Selecting a Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from geopy.geocoders import Nominatim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geolocator = Nominatim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location(Washington, United States of America, (47.2868352, -120.2126139, 0.0))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "washington = geolocator.geocode(\"Washington State, USA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from geopy.distance import distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't feel like swimming in the ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "long_beach = geolocator.geocode(\"Long Beach, Washington\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Distance(310.9894578347312)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance = distance(washington.point, long_beach.point) # Calculates distance using the vincenty method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Putting it together and preview with GIBS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selection = earthdata.Selection(dataset=sst, time=time, location=(washington.point, distance.km))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {