eMODIS API/aganalytics-emodis-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ag Analytics-eMODIS-demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This eMODIS API provides a Normalized Difference Vegetation Index (NDVI), which quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import time\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%autosave 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API Details\n",
    "\n",
    "####Request URL:https://ag-analytics.azure-api.net/eMODIS/[?year][&shape][&filtered][&startYear]\n",
    "\n",
    "####Request parameters\n",
    "\n",
    "***year - ***\n",
    "The year of which you would like to receive data up to. (e.g. – 2018)\n",
    "\n",
    "***shape - ***\n",
    "(ESRI Polygon shape): The shape information for field in esriGeometryPolygon format. See example request below. Standard open source JavaScript front-end libraries (e.g., Leaflet) can be used to structure the shape.\n",
    "\n",
    "***filtered - ***\n",
    "Filter values using modified SG filter.\n",
    "\n",
    "***startYear - ***\n",
    "The year you would like the historical range to start. (e.g. 2002 -)\n",
    "\n",
    "\n",
    "In order to get Ocp-Apim-Subscription-Key, please click on this link https://analytics.ag/Session/SignInToAPIPayment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Request Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "shape={\"rings\":[[[-91.42396,38.285882],[-91.424146,38.285908],[-91.424585,38.285883],[-91.424669,38.285878],[-91.424981,38.2857],[-91.425093,38.285636],[-91.425162,38.285572],[-91.425365,38.285384],[-91.425408,38.285312],[-91.425564,38.285048],[-91.425676,38.284729],[-91.425753,38.284391],[-91.425933,38.284091],[-91.426397,38.283576],[-91.426753,38.283242],[-91.42733,38.282874],[-91.427799,38.28267],[-91.428179,38.282556],[-91.428529,38.282496],[-91.428971,38.282456],[-91.429343,38.282461],[-91.429609,38.282529],[-91.430162,38.282738],[-91.431095,38.283107],[-91.431361,38.283148],[-91.431722,38.283107],[-91.432515,38.282696],[-91.433079,38.282402],[-91.433506,38.281959],[-91.433796,38.28145],[-91.433935,38.280939],[-91.434023,38.28062],[-91.434217,38.280247],[-91.434515,38.279884],[-91.435083,38.279398],[-91.435737,38.278775],[-91.435842,38.278675],[-91.436245,38.278296],[-91.436674,38.278036],[-91.436825,38.277933],[-91.436832,38.278407],[-91.437145,38.278468],[-91.437252,38.278341],[-91.437768,38.278302],[-91.437892,38.284998],[-91.437906,38.285771],[-91.437906,38.285773],[-91.433113,38.285862],[-91.423955,38.28603],[-91.423961,38.286021],[-91.42396,38.285882]],[[-91.436093,38.278766],[-91.43616,38.278913],[-91.43631,38.278942],[-91.436473,38.278917],[-91.436465,38.278761],[-91.436374,38.278687],[-91.436166,38.278647],[-91.436093,38.278766]]],\"spatialReference\":{\"wkid\":4326}}\n",
    "filtered=True\n",
    "startyear=2002\n",
    "headers={'Ocp-Apim-Subscription-Key': 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'}\n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  API Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_NDVI(year,shape,filtered,startyear,headers):\n",
    "    try:\n",
    "        url = \"https://ag-analytics.azure-api.net/eMODIS/?year={}&shape={}&filtered={}&startYear={}\".format(year,shape,filtered,startyear)\n",
    "        response = (requests.get(url,headers=headers).text)\n",
    "\n",
    "        \n",
    "        \n",
    "        response_json = json.loads(response)\n",
    "        \n",
    "        print(response_json)\n",
    "        \n",
    "\n",
    "        time.sleep(0.5)\n",
    "        return response_json\n",
    "    \n",
    "    except Exception as e:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling API Function and print output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'CurNDVI': [{'NDVI Value': 4017.97747129826, 'Week': 1}, {'NDVI Value': 3614.87551980224, 'Week': 2}, {'NDVI Value': 3193.50196191025, 'Week': 3}, {'NDVI Value': 2847.46007316502, 'Week': 4}, {'NDVI Value': 2662.69455238688, 'Week': 5}, {'NDVI Value': 2621.87097705604, 'Week': 6}, {'NDVI Value': 2813.58810059798, 'Week': 7}, {'NDVI Value': 3176.84356025482, 'Week': 8}, {'NDVI Value': 3603.10396641094, 'Week': 9}, {'NDVI Value': 4037.48416595199, 'Week': 10}, {'NDVI Value': 4425.09900576362, 'Week': 11}, {'NDVI Value': 4711.06333273149, 'Week': 12}]}, {'AveNDVI': [{'NDVI Value': 4341.936119717646, 'Week': 1}, {'NDVI Value': 4114.843487907076, 'Week': 2}, {'NDVI Value': 3938.5288637016956, 'Week': 3}, {'NDVI Value': 3808.9999458726243, 'Week': 4}, {'NDVI Value': 3731.5204558856476, 'Week': 5}, {'NDVI Value': 3728.8679441808963, 'Week': 6}, {'NDVI Value': 3803.897973866361, 'Week': 7}, {'NDVI Value': 3958.523914885678, 'Week': 8}, {'NDVI Value': 4159.395457096689, 'Week': 9}, {'NDVI Value': 4411.453676128342, 'Week': 10}, {'NDVI Value': 4705.37326079276, 'Week': 11}, {'NDVI Value': 5042.056357388052, 'Week': 12}, {'NDVI Value': 5450.190571648609, 'Week': 13}, {'NDVI Value': 5862.904283066361, 'Week': 14}, {'NDVI Value': 6270.3525239772525, 'Week': 15}, {'NDVI Value': 6670.28208443528, 'Week': 16}, {'NDVI Value': 7046.067986682804, 'Week': 17}, {'NDVI Value': 7367.9032705406735, 'Week': 18}, {'NDVI Value': 7618.394868338504, 'Week': 19}, {'NDVI Value': 7799.11245479096, 'Week': 20}, {'NDVI Value': 7906.9250398691465, 'Week': 21}, {'NDVI Value': 7952.311437484153, 'Week': 22}, {'NDVI Value': 7962.849509461701, 'Week': 23}, {'NDVI Value': 7938.451601650267, 'Week': 24}, {'NDVI Value': 7884.635713422165, 'Week': 25}, {'NDVI Value': 7821.752309564762, 'Week': 26}, {'NDVI Value': 7768.42689409892, 'Week': 27}, {'NDVI Value': 7638.687868159732, 'Week': 28}, {'NDVI Value': 7532.750811772843, 'Week': 29}, {'NDVI Value': 7450.754808123467, 'Week': 30}, {'NDVI Value': 7491.019954940423, 'Week': 31}, {'NDVI Value': 7355.365811323417, 'Week': 32}, {'NDVI Value': 7382.650883774727, 'Week': 33}, {'NDVI Value': 7451.7132306369385, 'Week': 34}, {'NDVI Value': 7539.841543263134, 'Week': 35}, {'NDVI Value': 7625.370235058307, 'Week': 36}, {'NDVI Value': 7689.088107503085, 'Week': 37}, {'NDVI Value': 7703.001657714648, 'Week': 38}, {'NDVI Value': 7653.341354288034, 'Week': 39}, {'NDVI Value': 7537.768074960112, 'Week': 40}, {'NDVI Value': 7357.138113232642, 'Week': 41}, {'NDVI Value': 7110.1868259209505, 'Week': 42}, {'NDVI Value': 6820.51942444537, 'Week': 43}, {'NDVI Value': 6524.506363370118, 'Week': 44}, {'NDVI Value': 6222.682257371009, 'Week': 45}, {'NDVI Value': 5929.338880909852, 'Week': 46}, {'NDVI Value': 5657.247096263972, 'Week': 47}, {'NDVI Value': 5414.1680144870115, 'Week': 48}, {'NDVI Value': 5187.490952459275, 'Week': 49}, {'NDVI Value': 4983.395620526804, 'Week': 50}, {'NDVI Value': 4778.154468910642, 'Week': 51}, {'NDVI Value': 4517.58381850387, 'Week': 52}, {'NDVI Value': 4390.593993032612, 'Week': 53}]}, {'MinNDVI': [{'Week': 1, 'NDVI Value': 3271.180486018626}, {'Week': 2, 'NDVI Value': 3066.2798355850064}, {'Week': 3, 'NDVI Value': 2934.036649938896}, {'Week': 4, 'NDVI Value': 2824.350096235904}, {'Week': 5, 'NDVI Value': 2834.799863599922}, {'Week': 6, 'NDVI Value': 2978.728823822338}, {'Week': 7, 'NDVI Value': 3070.2179360058317}, {'Week': 8, 'NDVI Value': 3296.968402955782}, {'Week': 9, 'NDVI Value': 3551.2038417222957}, {'Week': 10, 'NDVI Value': 3801.201925412094}, {'Week': 11, 'NDVI Value': 3797.549276133952}, {'Week': 12, 'NDVI Value': 4002.866913857044}, {'Week': 13, 'NDVI Value': 4487.74745523915}, {'Week': 14, 'NDVI Value': 5079.92462992226}, {'Week': 15, 'NDVI Value': 5508.700281403045}, {'Week': 16, 'NDVI Value': 5724.5363680197}, {'Week': 17, 'NDVI Value': 6169.435057624385}, {'Week': 18, 'NDVI Value': 6600.50413302574}, {'Week': 19, 'NDVI Value': 6937.8045285105945}, {'Week': 20, 'NDVI Value': 7330.917339377254}, {'Week': 21, 'NDVI Value': 7601.64794062396}, {'Week': 22, 'NDVI Value': 7604.6948247183045}, {'Week': 23, 'NDVI Value': 7696.015584748206}, {'Week': 24, 'NDVI Value': 7588.487745491555}, {'Week': 25, 'NDVI Value': 7372.700055947889}, {'Week': 26, 'NDVI Value': 7205.62384007528}, {'Week': 27, 'NDVI Value': 7171.859193415112}, {'Week': 28, 'NDVI Value': 6921.396640835146}, {'Week': 29, 'NDVI Value': 6525.2340084912685}, {'Week': 30, 'NDVI Value': 6272.950893558309}, {'Week': 31, 'NDVI Value': 6640.505222449129}, {'Week': 32, 'NDVI Value': 6170.187882500662}, {'Week': 33, 'NDVI Value': 6392.269621149819}, {'Week': 34, 'NDVI Value': 6638.128122065656}, {'Week': 35, 'NDVI Value': 6911.375049257405}, {'Week': 36, 'NDVI Value': 6973.7228419316425}, {'Week': 37, 'NDVI Value': 7142.181374780518}, {'Week': 38, 'NDVI Value': 7342.926457922336}, {'Week': 39, 'NDVI Value': 7241.478497514644}, {'Week': 40, 'NDVI Value': 7078.169588277526}, {'Week': 41, 'NDVI Value': 6778.660349998222}, {'Week': 42, 'NDVI Value': 6479.765277141025}, {'Week': 43, 'NDVI Value': 6097.293342751528}, {'Week': 44, 'NDVI Value': 5775.028143679382}, {'Week': 45, 'NDVI Value': 5491.266250547278}, {'Week': 46, 'NDVI Value': 5270.414164980486}, {'Week': 47, 'NDVI Value': 4973.231255372574}, {'Week': 48, 'NDVI Value': 4780.407558465632}, {'Week': 49, 'NDVI Value': 4618.53598442143}, {'Week': 50, 'NDVI Value': 4503.72933411852}, {'Week': 51, 'NDVI Value': 4134.703551371402}, {'Week': 52, 'NDVI Value': 3593.05689401916}, {'Week': 53, 'NDVI Value': 3663.91266697778}]}, {'MaxNDVI': [{'Week': 1, 'NDVI Value': 5336.139158932699}, {'Week': 2, 'NDVI Value': 5024.152426577266}, {'Week': 3, 'NDVI Value': 4809.949316524358}, {'Week': 4, 'NDVI Value': 4638.361833056034}, {'Week': 5, 'NDVI Value': 4496.703759141397}, {'Week': 6, 'NDVI Value': 4472.976418053067}, {'Week': 7, 'NDVI Value': 4465.190907930718}, {'Week': 8, 'NDVI Value': 4617.109361730036}, {'Week': 9, 'NDVI Value': 4803.0905847155445}, {'Week': 10, 'NDVI Value': 5092.47902019874}, {'Week': 11, 'NDVI Value': 5494.029438199982}, {'Week': 12, 'NDVI Value': 5890.973611025458}, {'Week': 13, 'NDVI Value': 6278.1638309295995}, {'Week': 14, 'NDVI Value': 6628.40975899747}, {'Week': 15, 'NDVI Value': 7125.137333530305}, {'Week': 16, 'NDVI Value': 7540.70648601319}, {'Week': 17, 'NDVI Value': 7871.79126244711}, {'Week': 18, 'NDVI Value': 8099.089996426835}, {'Week': 19, 'NDVI Value': 8235.20971687966}, {'Week': 20, 'NDVI Value': 8210.596097913985}, {'Week': 21, 'NDVI Value': 8232.713494115445}, {'Week': 22, 'NDVI Value': 8230.61667704988}, {'Week': 23, 'NDVI Value': 8297.969416854816}, {'Week': 24, 'NDVI Value': 8301.948393208444}, {'Week': 25, 'NDVI Value': 8260.262118157989}, {'Week': 26, 'NDVI Value': 8241.524421310485}, {'Week': 27, 'NDVI Value': 8237.610695641533}, {'Week': 28, 'NDVI Value': 8183.1863623712725}, {'Week': 29, 'NDVI Value': 8146.861350399394}, {'Week': 30, 'NDVI Value': 8081.061578896308}, {'Week': 31, 'NDVI Value': 8037.279435200898}, {'Week': 32, 'NDVI Value': 8131.6675550693035}, {'Week': 33, 'NDVI Value': 8160.393414364317}, {'Week': 34, 'NDVI Value': 8123.375427337762}, {'Week': 35, 'NDVI Value': 8081.18112252609}, {'Week': 36, 'NDVI Value': 8049.227080488385}, {'Week': 37, 'NDVI Value': 8059.697398070968}, {'Week': 38, 'NDVI Value': 8094.7962626011085}, {'Week': 39, 'NDVI Value': 8069.253684013358}, {'Week': 40, 'NDVI Value': 7978.798496966801}, {'Week': 41, 'NDVI Value': 7875.601235490685}, {'Week': 42, 'NDVI Value': 7725.4564809727}, {'Week': 43, 'NDVI Value': 7494.557967370174}, {'Week': 44, 'NDVI Value': 7314.719374419328}, {'Week': 45, 'NDVI Value': 7108.1388882446245}, {'Week': 46, 'NDVI Value': 6792.225777065422}, {'Week': 47, 'NDVI Value': 6521.776823100134}, {'Week': 48, 'NDVI Value': 6342.594943457994}, {'Week': 49, 'NDVI Value': 6144.906534486134}, {'Week': 50, 'NDVI Value': 5885.753488342816}, {'Week': 51, 'NDVI Value': 5549.316822223263}, {'Week': 52, 'NDVI Value': 5107.587927751525}, {'Week': 53, 'NDVI Value': 4938.77894510775}]}]\n"
     ]
    }
   ],
   "source": [
    "ndvidata=get_NDVI(year,shape,filtered,startyear,headers)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ee7eca3da0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "res=pd.DataFrame()\n",
    "\n",
    "for i,j in enumerate(ndvidata):\n",
    "    \n",
    "    for index in ndvidata[i].keys():\n",
    "            \n",
    "        \n",
    "        df = pd.DataFrame.from_records(j[index])\n",
    "        df.rename(columns={'NDVI Value':index},inplace=True)\n",
    "        if res.empty:\n",
    "            res=df\n",
    "        else:\n",
    "            res=res.merge(df,on=\"Week\",how=\"outer\")\n",
    "        \n",
    "res.set_index('Week',inplace=True)\n",
    "res=res.sort_index()\n",
    "plt.plot(res)\n",
    "plt.xlabel(\"week\")\n",
    "plt.ylabel(\"NDVI Values\")\n",
    "plt.title(\"Emodis NDVI\")\n",
    "plt.legend(res.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}