statisticalAPI/Statistical API Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "numerous-bracket",
   "metadata": {},
   "source": [
    "# Visualize statistical results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d0f4a2-c65c-4566-83fe-7417652c115e",
   "metadata": {},
   "source": [
    "This notebook was created as a companion notebook to the [Statitical API webinar](https://youtu.be/4JviobxqTO8)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metropolitan-challenge",
   "metadata": {},
   "source": [
    "## Import modules and authenticate at Sentinel Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "modular-mediterranean",
   "metadata": {},
   "outputs": [],
   "source": [
    "from oauthlib.oauth2 import BackendApplicationClient\n",
    "from requests_oauthlib import OAuth2Session\n",
    "import requests\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from sentinelhub import SentinelHubStatistical, DataCollection, CRS, BBox, bbox_to_dimensions, \\\n",
    "    Geometry, SHConfig, parse_time, parse_time_interval, SentinelHubStatisticalDownloadClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "338cf4a4-8e3e-4a09-8940-ece903977c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the client ID and client secret below with your own\n",
    "config = SHConfig()\n",
    "client_id = \"<your_client_id>\"\n",
    "client_secret = \"<your_client_id>\"\n",
    "\n",
    "client = BackendApplicationClient(client_id=client_id)\n",
    "oauth = OAuth2Session(client=client)\n",
    "token = oauth.fetch_token(token_url='https://services.sentinel-hub.com/oauth/token',\n",
    "                          client_id=client_id, client_secret=client_secret)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9eb120e-5626-4213-aadd-6cde61aa80ad",
   "metadata": {},
   "source": [
    "## Run a request: NDVI and BSI statistics with water pixels excluded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd2f735-e8e4-477e-95f5-5d36a36bf647",
   "metadata": {},
   "source": [
    "First, we run a statistical API request, to get the JSON result with statistics. The request  covers 1 year and has 10 day aggregation intervals. Statistics is calculated for 2 indices, NDVI and BSI (barren soil index), each returned in its own output. Additionally, water pixels were excluded from the calculation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a848cc2b-5883-4da2-9d34-9c77a8ebb55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': [{'interval': {'from': '2020-08-01T00:00:00Z',\n",
       "    'to': '2020-08-11T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4044482707977295,\n",
       "        'max': -0.09483614563941956,\n",
       "        'mean': -0.27758398120354194,\n",
       "        'stDev': 0.044207114056467496,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.750795304775238,\n",
       "        'max': 0.8925060033798218,\n",
       "        'mean': 0.8437445996058478,\n",
       "        'stDev': 0.017705930134783242,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2020-09-10T00:00:00Z', 'to': '2020-09-20T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4406079649925232,\n",
       "        'max': -0.05410122126340866,\n",
       "        'mean': -0.2468917381963866,\n",
       "        'stDev': 0.050662497357656294,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.7094070315361023,\n",
       "        'max': 0.8823529481887817,\n",
       "        'mean': 0.8138526516467535,\n",
       "        'stDev': 0.020639924263070358,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2020-10-30T00:00:00Z', 'to': '2020-11-09T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.2906162440776825,\n",
       "        'max': 0.26512789726257324,\n",
       "        'mean': -0.017572231820642846,\n",
       "        'stDev': 0.07808352779999517,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.48571428656578064,\n",
       "        'max': 0.8345679044723511,\n",
       "        'mean': 0.6830975254775392,\n",
       "        'stDev': 0.04857720106332705,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2020-11-19T00:00:00Z', 'to': '2020-11-29T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4057008922100067,\n",
       "        'max': 0.2971014380455017,\n",
       "        'mean': 0.000844104062628714,\n",
       "        'stDev': 0.09802444404898893,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.5101796388626099,\n",
       "        'max': 0.9005076289176941,\n",
       "        'mean': 0.6844527571100473,\n",
       "        'stDev': 0.06226184582080005,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-01-08T00:00:00Z', 'to': '2021-01-18T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4158285856246948,\n",
       "        'max': 0.16101695597171783,\n",
       "        'mean': -0.11789178762385046,\n",
       "        'stDev': 0.09024302112269823,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.13919052481651306,\n",
       "        'max': 0.8164188861846924,\n",
       "        'mean': 0.516412826646887,\n",
       "        'stDev': 0.10304578351969004,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-02-07T00:00:00Z', 'to': '2021-02-17T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.28271549940109253,\n",
       "        'max': 0.3853868246078491,\n",
       "        'mean': 0.06241801905887621,\n",
       "        'stDev': 0.08601077759031144,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.34170636534690857,\n",
       "        'max': 0.8730158805847168,\n",
       "        'mean': 0.609049283630678,\n",
       "        'stDev': 0.07943249194310842,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-02-17T00:00:00Z', 'to': '2021-02-27T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.08473683893680573,\n",
       "        'max': 0.17836764454841614,\n",
       "        'mean': 0.07100009319231898,\n",
       "        'stDev': 0.04108465495646482,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.30276185274124146,\n",
       "        'max': 0.5237265825271606,\n",
       "        'mean': 0.3974006404037584,\n",
       "        'stDev': 0.03567912810324863,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-02-27T00:00:00Z', 'to': '2021-03-09T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.19803714752197266,\n",
       "        'max': 0.3813062310218811,\n",
       "        'mean': 0.11320474375979504,\n",
       "        'stDev': 0.07474566733113758,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.3722943663597107,\n",
       "        'max': 0.7956989407539368,\n",
       "        'mean': 0.5439309637969332,\n",
       "        'stDev': 0.07015380863189329,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-03-19T00:00:00Z', 'to': '2021-03-29T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.12186046689748764,\n",
       "        'max': 0.2973730266094208,\n",
       "        'mean': 0.08046419203701934,\n",
       "        'stDev': 0.059170548014775344,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.3653225898742676,\n",
       "        'max': 0.75,\n",
       "        'mean': 0.5521596287116568,\n",
       "        'stDev': 0.054455156355168406,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-03-29T00:00:00Z', 'to': '2021-04-08T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.22019289433956146,\n",
       "        'max': 0.22972136735916138,\n",
       "        'mean': -0.003824531577289263,\n",
       "        'stDev': 0.06670562131027041,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.48723331093788147,\n",
       "        'max': 0.8358531594276428,\n",
       "        'mean': 0.6712415874359907,\n",
       "        'stDev': 0.05519874419477429,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-05-08T00:00:00Z', 'to': '2021-05-18T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4052489399909973,\n",
       "        'max': 0.020908838137984276,\n",
       "        'mean': -0.19874355976910238,\n",
       "        'stDev': 0.05624135734392454,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.6965517401695251,\n",
       "        'max': 0.8915068507194519,\n",
       "        'mean': 0.800618395435578,\n",
       "        'stDev': 0.027760141839832445,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-06-07T00:00:00Z', 'to': '2021-06-17T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4969586431980133,\n",
       "        'max': -0.12409603595733643,\n",
       "        'mean': -0.383820254953655,\n",
       "        'stDev': 0.04099402994229729,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.8239485025405884,\n",
       "        'max': 0.933567225933075,\n",
       "        'mean': 0.9079555950557855,\n",
       "        'stDev': 0.012508545798593384,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-06-17T00:00:00Z', 'to': '2021-06-27T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.4123913645744324,\n",
       "        'max': -0.1787378191947937,\n",
       "        'mean': -0.30929980835919707,\n",
       "        'stDev': 0.03371816984923077,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.719004213809967,\n",
       "        'max': 0.867723822593689,\n",
       "        'mean': 0.8276296372721363,\n",
       "        'stDev': 0.01777508865525441,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}},\n",
       "  {'interval': {'from': '2021-06-27T00:00:00Z', 'to': '2021-07-07T00:00:00Z'},\n",
       "   'outputs': {'bsi': {'bands': {'BSI': {'stats': {'min': -0.436344176530838,\n",
       "        'max': -0.1778450906276703,\n",
       "        'mean': -0.3245005471603598,\n",
       "        'stDev': 0.04033602461684905,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}},\n",
       "    'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.7428354024887085,\n",
       "        'max': 0.9023941159248352,\n",
       "        'mean': 0.8611833145458123,\n",
       "        'stDev': 0.01760092049823873,\n",
       "        'sampleCount': 3036,\n",
       "        'noDataCount': 1192}}}}}}],\n",
       " 'status': 'OK'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evalscript = \"\"\"\n",
    "//VERSION=3\n",
    "function setup() {\n",
    "  return {\n",
    "    input: [{\n",
    "      bands: [\n",
    "        \"B02\",\n",
    "        \"B04\",\n",
    "        \"B08\",\n",
    "        \"B11\",\n",
    "        \"SCL\",\n",
    "        \"CLM\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"ndvi\",\n",
    "        bands: [\"NDVI\"]\n",
    "      },\n",
    "      {\n",
    "        id: \"bsi\",\n",
    "        bands: [\"BSI\"]\n",
    "      },\n",
    "      {\n",
    "        id: \"dataMask\",\n",
    "        bands: 1\n",
    "      }]\n",
    "  }\n",
    "}\n",
    "\n",
    "function evaluatePixel(samples) {\n",
    "    let ndvi = (samples.B08 - samples.B04)/(samples.B08 + samples.B04)\n",
    "    let bsi = ((samples.B11 + samples.B04)-(samples.B08 + samples.B02))/((samples.B11 + samples.B04)+(samples.B08 + samples.B02));\n",
    "     \n",
    "    var validNDVIMask = 1\n",
    "    if (samples.B08 + samples.B04 + samples.B11 + samples.B02 == 0 ){\n",
    "        validNDVIMask = 0\n",
    "    }\n",
    "    \n",
    "    var noWaterMask = 1\n",
    "    if (samples.SCL == 6 ){\n",
    "        noWaterMask = 0\n",
    "    }\n",
    "\n",
    "    return {\n",
    "        ndvi: [ndvi],\n",
    "        bsi: [bsi],\n",
    "        // Exclude nodata pixels, pixels where ndvi is not defined and water pixels from statistics:\n",
    "        dataMask: [samples.dataMask * validNDVIMask * noWaterMask]\n",
    "    }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "stats_request = {\n",
    "  \"input\": {\n",
    "   \"bounds\": {\n",
    "      \"geometry\": {\n",
    "          \"type\": \"Polygon\",\n",
    "          \"coordinates\": [\n",
    "            [\n",
    "              [\n",
    "                458085.878866,\n",
    "                5097236.833044\n",
    "              ],\n",
    "              [\n",
    "                457813.834156,\n",
    "                5096808.351383\n",
    "              ],\n",
    "              [\n",
    "                457979.897062,\n",
    "                5096313.767184\n",
    "              ],\n",
    "              [\n",
    "                458146.639373,\n",
    "                5096405.411294\n",
    "              ],\n",
    "              [\n",
    "                458085.878866,\n",
    "                5097236.833044\n",
    "              ]\n",
    "            ]\n",
    "          ]\n",
    "        },\n",
    "    \"properties\": {\n",
    "        \"crs\": \"http://www.opengis.net/def/crs/EPSG/0/32633\"\n",
    "        }\n",
    "    },\n",
    "    \"data\": [\n",
    "      {\n",
    "        \"type\": \"sentinel-2-l2a\",\n",
    "        \"dataFilter\": {\n",
    "            \"mosaickingOrder\": \"mostRecent\",\n",
    "            \"maxCloudCoverage\": 5\n",
    "        }\n",
    "      }\n",
    "    ]\n",
    "  },\n",
    "  \"aggregation\": {\n",
    "    \"timeRange\": {\n",
    "        \"from\": \"2020-08-01T00:00:00Z\",\n",
    "        \"to\": \"2021-08-01T00:00:00Z\"\n",
    "      },\n",
    "    \"aggregationInterval\": {\n",
    "        \"of\": \"P10D\"\n",
    "    },\n",
    "    \"evalscript\": evalscript,\n",
    "    \"resx\": 10,\n",
    "    \"resy\": 10\n",
    "  }\n",
    "}\n",
    "\n",
    "headers = {\n",
    "  'Content-Type': 'application/json',\n",
    "  'Accept': 'application/json'\n",
    "}\n",
    "\n",
    "url = \"https://services.sentinel-hub.com/api/v1/statistics\"\n",
    "\n",
    "response = oauth.request(\"POST\", url=url, headers=headers, json=stats_request)\n",
    "sh_statistics = response.json()\n",
    "sh_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5998d7-4c64-479a-8b9d-ad15571c1aab",
   "metadata": {},
   "source": [
    "## Find any value in JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab94716e-fd65-4068-bf5f-03c03d1ed251",
   "metadata": {},
   "source": [
    "### Follow the JSON nesting structure to return any value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90d9bf8-78f1-4c38-8525-c92ef1e0369c",
   "metadata": {},
   "source": [
    "To find any specific value, it's important to understand the nesting structure of the resulting JSON. Note that the path changes with different outputs and naming. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50361050-b6e0-4189-8f79-cfad079c5b21",
   "metadata": {},
   "source": [
    "**Third mean BSI value**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37a9cd4-3138-4e3e-895f-fbdc3e538a34",
   "metadata": {},
   "source": [
    "Here we find the mean BSI value of the third interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8dab75e7-ad5f-4fe8-911f-cae0a8d208b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.017572231820642846"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh_statistics['data'][2]['outputs']['bsi']['bands']['BSI']['stats']['mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f329caf-e921-4ee8-a531-9a7706c8d36f",
   "metadata": {},
   "source": [
    "**First outputs content**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f849870e-14a8-490a-9686-e4f205edf631",
   "metadata": {},
   "source": [
    "By ending our search sooner, we can check the content included in our JSON at any level. The following request returns the content of outputs in the first available interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6607ebdb-2ef6-4fd7-85c6-53b698939fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bsi': {'bands': {'BSI': {'stats': {'min': -0.4044482707977295,\n",
       "     'max': -0.09483614563941956,\n",
       "     'mean': -0.27758398120354194,\n",
       "     'stDev': 0.044207114056467496,\n",
       "     'sampleCount': 3036,\n",
       "     'noDataCount': 1192}}}},\n",
       " 'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.750795304775238,\n",
       "     'max': 0.8925060033798218,\n",
       "     'mean': 0.8437445996058478,\n",
       "     'stDev': 0.017705930134783242,\n",
       "     'sampleCount': 3036,\n",
       "     'noDataCount': 1192}}}}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh_statistics['data'][0]['outputs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec98e29-c1a7-4a49-b953-6fbf7cca752b",
   "metadata": {},
   "source": [
    "**From-To dates of the sixth interval**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd31d83-6443-40ac-92f8-94ba3e9b262c",
   "metadata": {},
   "source": [
    "The following request returns the from-to dates of the sixth available interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4fc4ecdd-539a-4b39-9be1-deef4154e5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'from': '2021-02-07T00:00:00Z', 'to': '2021-02-17T00:00:00Z'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh_statistics['data'][5]['interval']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78296905-3494-4cc3-8448-d9fc660796bd",
   "metadata": {},
   "source": [
    "### Create loops to access all value instances "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bacbcea-4136-4eb9-8f26-91e3e1d99de4",
   "metadata": {},
   "source": [
    "To create a chart, that displays temporal NDVI changes for our parcel, we need to find every instance of e.g. mean NDVI and every corresponding date. To do so, we create a loop to check the content for all intervals and return the list of values we need."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0ea7b5-78f4-46b0-85e7-bb62dcaeb9b1",
   "metadata": {},
   "source": [
    "**Find every instance of mean NDVI**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3084d233-e8a3-4d81-81ce-79b4bf54a617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8437445996058478\n",
      "0.8138526516467535\n",
      "0.6830975254775392\n",
      "0.6844527571100473\n",
      "0.516412826646887\n",
      "0.609049283630678\n",
      "0.3974006404037584\n",
      "0.5439309637969332\n",
      "0.5521596287116568\n",
      "0.6712415874359907\n",
      "0.800618395435578\n",
      "0.9079555950557855\n",
      "0.8276296372721363\n",
      "0.8611833145458123\n"
     ]
    }
   ],
   "source": [
    "for data_item in sh_statistics['data']:\n",
    "    for band, value in data_item['outputs']['ndvi']['bands'].items(): \n",
    "        print (value['stats']['mean'])    # Returns the contents of each band"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90dded8f-b5bf-42af-83c2-2474e88869ca",
   "metadata": {},
   "source": [
    "**Retrieve all the available TO dates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e14b10e5-d9f0-4ad2-8e63-cb18d06090e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-08-11T00:00:00Z\n",
      "2020-09-20T00:00:00Z\n",
      "2020-11-09T00:00:00Z\n",
      "2020-11-29T00:00:00Z\n",
      "2021-01-18T00:00:00Z\n",
      "2021-02-17T00:00:00Z\n",
      "2021-02-27T00:00:00Z\n",
      "2021-03-09T00:00:00Z\n",
      "2021-03-29T00:00:00Z\n",
      "2021-04-08T00:00:00Z\n",
      "2021-05-18T00:00:00Z\n",
      "2021-06-17T00:00:00Z\n",
      "2021-06-27T00:00:00Z\n",
      "2021-07-07T00:00:00Z\n"
     ]
    }
   ],
   "source": [
    "for data_item in sh_statistics['data']:\n",
    "    for band in data_item['outputs']['ndvi']['bands'].items():  # For each band found\n",
    "        print(data_item['interval']['to'])   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b706296f-1199-40a1-bec4-219b496aaff9",
   "metadata": {},
   "source": [
    "## Display statistical data in a table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8f5157-3fe5-47d5-90b2-1d914c26c8aa",
   "metadata": {},
   "source": [
    "**Create empty arrays that will be filled with data later**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "590975a0-5c67-4bb4-9e8a-79e603c4edfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = []\n",
    "Mean = []\n",
    "Min = []\n",
    "Max = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde9ad4b-cf59-4c58-abb6-b3e24058e306",
   "metadata": {},
   "source": [
    "**Get NDVI statistics and TO dates**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54805bf8-91c5-4182-a26d-045fb9248263",
   "metadata": {},
   "source": [
    "Next, we create loops to access mean, max and min NDVI values for each interval, and append each to the previously created arrays. We do the same for TO dates. Note that we also formatted our dates to only include the first 10 strings. \n",
    "\n",
    "Finally, we print the arrays to inspect the values retrieved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b505565a-da42-48cb-b852-324ce4513883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-08-11', '2020-09-20', '2020-11-09', '2020-11-29', '2021-01-18', '2021-02-17', '2021-02-27', '2021-03-09', '2021-03-29', '2021-04-08', '2021-05-18', '2021-06-17', '2021-06-27', '2021-07-07']\n",
      "[0.8437445996058478, 0.8138526516467535, 0.6830975254775392, 0.6844527571100473, 0.516412826646887, 0.609049283630678, 0.3974006404037584, 0.5439309637969332, 0.5521596287116568, 0.6712415874359907, 0.800618395435578, 0.9079555950557855, 0.8276296372721363, 0.8611833145458123]\n",
      "[0.8925060033798218, 0.8823529481887817, 0.8345679044723511, 0.9005076289176941, 0.8164188861846924, 0.8730158805847168, 0.5237265825271606, 0.7956989407539368, 0.75, 0.8358531594276428, 0.8915068507194519, 0.933567225933075, 0.867723822593689, 0.9023941159248352]\n",
      "[0.750795304775238, 0.7094070315361023, 0.48571428656578064, 0.5101796388626099, 0.13919052481651306, 0.34170636534690857, 0.30276185274124146, 0.3722943663597107, 0.3653225898742676, 0.48723331093788147, 0.6965517401695251, 0.8239485025405884, 0.719004213809967, 0.7428354024887085]\n"
     ]
    }
   ],
   "source": [
    "# Find ndvi values\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band, value in data_item['outputs']['ndvi']['bands'].items(): \n",
    "        Mean.append(value ['stats']['mean'])            # Find mean values and append them to the mean array  \n",
    "        Max.append(value ['stats']['max'])              # Find max values and append them to the max array                                  \n",
    "        Min.append(value ['stats']['min'])              # Find min values and append them to the min array\n",
    "\n",
    "# Find \"date to\" for each ndvi entry\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band in data_item['outputs']['ndvi']['bands'].items():  # For each band found\n",
    "        date.append(data_item['interval']['to'])                # Find and append \"date to\" values to the date array\n",
    "        date[:] = (elem[:10] for elem in date)                  # Limit each date string to the first 10 characters to keep a readable date format\n",
    "        \n",
    "# Print out the data arrays\n",
    "print (date)\n",
    "print (Mean)\n",
    "print (Max)\n",
    "print (Min)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7772bbf-6867-4a1c-984e-f27004d0d59a",
   "metadata": {},
   "source": [
    "**Create an object with the data arrays**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7ac641-25f5-47a3-bb84-e3de049c9f46",
   "metadata": {},
   "source": [
    "We create a simple object to make it easier to create a table. Key values of this object are names of the columns we want o see in our table, and the values are our data arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c617163d-ce7e-4515-a884-243c7f8577c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "elements = {\n",
    "    'Date': date,\n",
    "    'Mean': Mean,\n",
    "    'Max' : Max,\n",
    "    'Min' : Min,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414ae32e-cc0e-4323-9fc2-85ea5f166808",
   "metadata": {},
   "source": [
    "**Plot the data arrays into a table using Data Frame**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9bc3c7c-24b0-41fa-a830-abb59113ccfc",
   "metadata": {},
   "source": [
    "We simply create the table using pandas DataFrame from our elements object above. This returns a nice orderly table, displaying our dates and statistical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bec56327-23c1-438f-bd69-675d4e085c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date      Mean       Max       Min\n",
      "0   2020-08-11  0.843745  0.892506  0.750795\n",
      "1   2020-09-20  0.813853  0.882353  0.709407\n",
      "2   2020-11-09  0.683098  0.834568  0.485714\n",
      "3   2020-11-29  0.684453  0.900508  0.510180\n",
      "4   2021-01-18  0.516413  0.816419  0.139191\n",
      "5   2021-02-17  0.609049  0.873016  0.341706\n",
      "6   2021-02-27  0.397401  0.523727  0.302762\n",
      "7   2021-03-09  0.543931  0.795699  0.372294\n",
      "8   2021-03-29  0.552160  0.750000  0.365323\n",
      "9   2021-04-08  0.671242  0.835853  0.487233\n",
      "10  2021-05-18  0.800618  0.891507  0.696552\n",
      "11  2021-06-17  0.907956  0.933567  0.823949\n",
      "12  2021-06-27  0.827630  0.867724  0.719004\n",
      "13  2021-07-07  0.861183  0.902394  0.742835\n"
     ]
    }
   ],
   "source": [
    "table = pd.DataFrame(elements)\n",
    "print (table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c9f994-2201-403d-a7be-29cd84cc76ee",
   "metadata": {},
   "source": [
    "## Create a line chart from the table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0e5d94-726b-4c19-8aee-8cdc9382dcf5",
   "metadata": {},
   "source": [
    "When we have a table with data, creating charts is easy. We just reference the table name and decide what we want displayed on x and y axis. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d3a622-396c-479a-8895-e397ce68bf2d",
   "metadata": {},
   "source": [
    "**The simplest chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b22561ce-7d75-4b7f-8e56-916b67e0f234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "table.plot(x='Date', y='Mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86cba03-2da9-4559-878c-ab7062e81e09",
   "metadata": {},
   "source": [
    "**Visualize mean, maximum and minimum NDVI values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "87a8a510-2b7d-4d09-8d90-7752f15808ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0bb5cd9e20>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(20,8)\n",
    "ax.ticklabel_format(style='plain')\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "line1 = table.plot(color='#4d6600', kind='line',x='Date',y='Max',ax=ax, label='Maximum NDVI', linewidth=6)\n",
    "line2 = table.plot(color='#739900', kind='line',x='Date',y='Mean',ax=ax, label='Mean NDVI', linewidth=6)\n",
    "line3 = table.plot(color='#99cc00', kind='line',x='Date',y='Min',ax=ax, label='Minimum NDVI', linewidth=6)\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('NDVI value')\n",
    "plt.title('NDVI')\n",
    "plt.legend(loc='best', bbox_to_anchor=(0.5, 0.2, 0.45, 0.2), shadow=True, prop={'size': 18})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7231c4f-38cf-411f-a206-3af5597a2574",
   "metadata": {},
   "source": [
    "**Fill in the area between max and min**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35c5a185-409b-429d-852e-306c8c639886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "factor = 1000000\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(20,8)\n",
    "ax.ticklabel_format(style='plain')\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "x = table['Date']\n",
    "y1 = table['Max']\n",
    "y4 = table['Min']\n",
    "\n",
    "line1 = table.plot(color='#4d6600', alpha=0.3, kind='line',x='Date',y='Max',ax=ax, label='Maximum NDVI', linewidth=6)\n",
    "line2 = table.plot(color='#739900', kind='line',x='Date',y='Mean',ax=ax, label='Mean NDVI', linewidth=6, marker='o', markersize=15)\n",
    "line3 = table.plot(color='#99cc00', alpha = 0.3, kind='line',x='Date',y='Min',ax=ax, label='Minimum NDVI', linewidth=6)\n",
    "\n",
    "plt.fill_between(x, y1, y4, color='#99cc00', alpha=0.1)\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('NDVI value')\n",
    "plt.title('NDVI')\n",
    "plt.legend(loc='best', bbox_to_anchor=(0.5, 0.2, 0.45, 0.2), shadow=True, prop={'size': 18})\n",
    "\n",
    "plt.savefig('ndvi-mix-1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5550e86e-cddd-4e48-be05-5e19ff467929",
   "metadata": {},
   "source": [
    "## Retrieve and visualize statistics for both indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3ea426-1f69-4f08-99a1-4a00e44e2da8",
   "metadata": {},
   "source": [
    "Here we wanted to show how you can append statistical data for several outputs into the same table. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f752e5-9dd1-45ec-81f4-e8f68a46bc89",
   "metadata": {},
   "source": [
    "### Retrieve statistics for NDVI and BSI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfe3e9c-9d9e-48c4-8f2f-d8fd12e500b3",
   "metadata": {},
   "source": [
    "In this part we did the same thing as before, just that we repeated the process for both indices and appended BSI data to our arrays as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1098708f-f77a-48a6-9ff8-b0140200454f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-08-11', '2020-09-20', '2020-11-09', '2020-11-29', '2021-01-18', '2021-02-17', '2021-02-27', '2021-03-09', '2021-03-29', '2021-04-08', '2021-05-18', '2021-06-17', '2021-06-27', '2021-07-07', '2020-08-11', '2020-09-20', '2020-11-09', '2020-11-29', '2021-01-18', '2021-02-17', '2021-02-27', '2021-03-09', '2021-03-29', '2021-04-08', '2021-05-18', '2021-06-17', '2021-06-27', '2021-07-07']\n",
      "['NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'NDVI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI', 'BSI']\n",
      "[0.8437445996058478, 0.8138526516467535, 0.6830975254775392, 0.6844527571100473, 0.516412826646887, 0.609049283630678, 0.3974006404037584, 0.5439309637969332, 0.5521596287116568, 0.6712415874359907, 0.800618395435578, 0.9079555950557855, 0.8276296372721363, 0.8611833145458123, -0.27758398120354194, -0.2468917381963866, -0.017572231820642846, 0.000844104062628714, -0.11789178762385046, 0.06241801905887621, 0.07100009319231898, 0.11320474375979504, 0.08046419203701934, -0.003824531577289263, -0.19874355976910238, -0.383820254953655, -0.30929980835919707, -0.3245005471603598]\n",
      "[0.8925060033798218, 0.8823529481887817, 0.8345679044723511, 0.9005076289176941, 0.8164188861846924, 0.8730158805847168, 0.5237265825271606, 0.7956989407539368, 0.75, 0.8358531594276428, 0.8915068507194519, 0.933567225933075, 0.867723822593689, 0.9023941159248352, -0.09483614563941956, -0.05410122126340866, 0.26512789726257324, 0.2971014380455017, 0.16101695597171783, 0.3853868246078491, 0.17836764454841614, 0.3813062310218811, 0.2973730266094208, 0.22972136735916138, 0.020908838137984276, -0.12409603595733643, -0.1787378191947937, -0.1778450906276703]\n",
      "[0.750795304775238, 0.7094070315361023, 0.48571428656578064, 0.5101796388626099, 0.13919052481651306, 0.34170636534690857, 0.30276185274124146, 0.3722943663597107, 0.3653225898742676, 0.48723331093788147, 0.6965517401695251, 0.8239485025405884, 0.719004213809967, 0.7428354024887085, -0.4044482707977295, -0.4406079649925232, -0.2906162440776825, -0.4057008922100067, -0.4158285856246948, -0.28271549940109253, -0.08473683893680573, -0.19803714752197266, -0.12186046689748764, -0.22019289433956146, -0.4052489399909973, -0.4969586431980133, -0.4123913645744324, -0.436344176530838]\n"
     ]
    }
   ],
   "source": [
    "# Create empty arrays that will be filled in later\n",
    "index = []\n",
    "date = []\n",
    "Mean = []\n",
    "Min = []\n",
    "Max = []\n",
    "\n",
    "# Find ndvi values\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band, value in data_item['outputs']['ndvi']['bands'].items(): \n",
    "        index.append(band)\n",
    "        Mean.append(value ['stats']['mean'])            # Find mean values and append them to the mean array  \n",
    "        Min.append(value ['stats']['max'])              # Find max values and append them to the max array                                  \n",
    "        Max.append(value ['stats']['min'])              # Find min values and append them to the min array\n",
    "        \n",
    "# Find bsi values\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band, value in data_item['outputs']['bsi']['bands'].items(): \n",
    "        index.append(band)\n",
    "        Mean.append(value ['stats']['mean'])            # Find mean values and append them to the mean array  \n",
    "        Min.append(value ['stats']['max'])              # Find max values and append them to the max array                                  \n",
    "        Max.append(value ['stats']['min'])              # Find min values and append them to the min array\n",
    "\n",
    "# Find \"date to\" for each ndvi entry\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band in data_item['outputs']['ndvi']['bands'].items():  # For each band found\n",
    "        date.append(data_item['interval']['to'])                # Find and append \"date to\" values to the date array\n",
    "        date[:] = (elem[:10] for elem in date)                  # Limit each date string to the first 10 characters to keep a readable date format\n",
    "\n",
    "# Find \"date to\" for each bsi entry\n",
    "for data_item in sh_statistics['data']:\n",
    "    for band in data_item['outputs']['bsi']['bands'].items():  # For each band found\n",
    "        date.append(data_item['interval']['to'])                # Find and append \"date to\" values to the date array\n",
    "        date[:] = (elem[:10] for elem in date)                  # Limit each date string to the first 10 characters to keep a readable date format\n",
    "        \n",
    "# Print out the data arrays\n",
    "print (date)\n",
    "print(index)\n",
    "print (Mean)\n",
    "print (Min)\n",
    "print (Max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a371fc7b-ad1f-49dd-8724-a290864f6480",
   "metadata": {},
   "source": [
    "### Display the table with statistics for both indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1541c552-b816-411d-9cc5-de8632737499",
   "metadata": {},
   "outputs": [],
   "source": [
    "elements = {\n",
    "    'Index': index,\n",
    "    'Date': date,\n",
    "    'Mean': Mean,\n",
    "    'Max' : Max,\n",
    "    'Min' : Min,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3daf2c1c-5e4d-47f5-9ac3-92269a63a47b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Index        Date      Mean       Max       Min\n",
      "0   NDVI  2020-08-11  0.843745  0.750795  0.892506\n",
      "1   NDVI  2020-09-20  0.813853  0.709407  0.882353\n",
      "2   NDVI  2020-11-09  0.683098  0.485714  0.834568\n",
      "3   NDVI  2020-11-29  0.684453  0.510180  0.900508\n",
      "4   NDVI  2021-01-18  0.516413  0.139191  0.816419\n",
      "5   NDVI  2021-02-17  0.609049  0.341706  0.873016\n",
      "6   NDVI  2021-02-27  0.397401  0.302762  0.523727\n",
      "7   NDVI  2021-03-09  0.543931  0.372294  0.795699\n",
      "8   NDVI  2021-03-29  0.552160  0.365323  0.750000\n",
      "9   NDVI  2021-04-08  0.671242  0.487233  0.835853\n",
      "10  NDVI  2021-05-18  0.800618  0.696552  0.891507\n",
      "11  NDVI  2021-06-17  0.907956  0.823949  0.933567\n",
      "12  NDVI  2021-06-27  0.827630  0.719004  0.867724\n",
      "13  NDVI  2021-07-07  0.861183  0.742835  0.902394\n",
      "14   BSI  2020-08-11 -0.277584 -0.404448 -0.094836\n",
      "15   BSI  2020-09-20 -0.246892 -0.440608 -0.054101\n",
      "16   BSI  2020-11-09 -0.017572 -0.290616  0.265128\n",
      "17   BSI  2020-11-29  0.000844 -0.405701  0.297101\n",
      "18   BSI  2021-01-18 -0.117892 -0.415829  0.161017\n",
      "19   BSI  2021-02-17  0.062418 -0.282715  0.385387\n",
      "20   BSI  2021-02-27  0.071000 -0.084737  0.178368\n",
      "21   BSI  2021-03-09  0.113205 -0.198037  0.381306\n",
      "22   BSI  2021-03-29  0.080464 -0.121860  0.297373\n",
      "23   BSI  2021-04-08 -0.003825 -0.220193  0.229721\n",
      "24   BSI  2021-05-18 -0.198744 -0.405249  0.020909\n",
      "25   BSI  2021-06-17 -0.383820 -0.496959 -0.124096\n",
      "26   BSI  2021-06-27 -0.309300 -0.412391 -0.178738\n",
      "27   BSI  2021-07-07 -0.324501 -0.436344 -0.177845\n"
     ]
    }
   ],
   "source": [
    "table = pd.DataFrame(elements)\n",
    "print (table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9aeb64-313c-4ea3-956c-115aa4337c7d",
   "metadata": {},
   "source": [
    "### Split the table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134b096e-6908-4afd-b9a8-7962b8ba0db0",
   "metadata": {},
   "source": [
    "This step is needed if you have a table with statistics for several outputs and bands. To visualize specific parts of the table on a chart, the table needs to be split. However, this is just illustratory, as for our example, we could simply just create 2 separate tables in the first place. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b9faf0e-2bce-447c-900e-8b20d1f03174",
   "metadata": {},
   "outputs": [],
   "source": [
    "NDVItable = table.loc[table['Index'] == 'NDVI']\n",
    "BSItable = table.loc[table['Index'] == 'BSI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdf6d43f-6114-45fa-be6c-2040a387339d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Index        Date      Mean       Max       Min\n",
      "0   NDVI  2020-08-11  0.843745  0.750795  0.892506\n",
      "1   NDVI  2020-09-20  0.813853  0.709407  0.882353\n",
      "2   NDVI  2020-11-09  0.683098  0.485714  0.834568\n",
      "3   NDVI  2020-11-29  0.684453  0.510180  0.900508\n",
      "4   NDVI  2021-01-18  0.516413  0.139191  0.816419\n",
      "5   NDVI  2021-02-17  0.609049  0.341706  0.873016\n",
      "6   NDVI  2021-02-27  0.397401  0.302762  0.523727\n",
      "7   NDVI  2021-03-09  0.543931  0.372294  0.795699\n",
      "8   NDVI  2021-03-29  0.552160  0.365323  0.750000\n",
      "9   NDVI  2021-04-08  0.671242  0.487233  0.835853\n",
      "10  NDVI  2021-05-18  0.800618  0.696552  0.891507\n",
      "11  NDVI  2021-06-17  0.907956  0.823949  0.933567\n",
      "12  NDVI  2021-06-27  0.827630  0.719004  0.867724\n",
      "13  NDVI  2021-07-07  0.861183  0.742835  0.902394\n",
      "   Index        Date      Mean       Max       Min\n",
      "14   BSI  2020-08-11 -0.277584 -0.404448 -0.094836\n",
      "15   BSI  2020-09-20 -0.246892 -0.440608 -0.054101\n",
      "16   BSI  2020-11-09 -0.017572 -0.290616  0.265128\n",
      "17   BSI  2020-11-29  0.000844 -0.405701  0.297101\n",
      "18   BSI  2021-01-18 -0.117892 -0.415829  0.161017\n",
      "19   BSI  2021-02-17  0.062418 -0.282715  0.385387\n",
      "20   BSI  2021-02-27  0.071000 -0.084737  0.178368\n",
      "21   BSI  2021-03-09  0.113205 -0.198037  0.381306\n",
      "22   BSI  2021-03-29  0.080464 -0.121860  0.297373\n",
      "23   BSI  2021-04-08 -0.003825 -0.220193  0.229721\n",
      "24   BSI  2021-05-18 -0.198744 -0.405249  0.020909\n",
      "25   BSI  2021-06-17 -0.383820 -0.496959 -0.124096\n",
      "26   BSI  2021-06-27 -0.309300 -0.412391 -0.178738\n",
      "27   BSI  2021-07-07 -0.324501 -0.436344 -0.177845\n"
     ]
    }
   ],
   "source": [
    "print (NDVItable)\n",
    "print (BSItable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3e2c1f-48bc-43cf-b92f-cba0749394fa",
   "metadata": {},
   "source": [
    "### Visualize both mean NDVI and BSI values on a line chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d7d653f-e7d6-4ca6-8056-539fa8f0641c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "factor = 1000000\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(20,8)\n",
    "ax.ticklabel_format(style='plain')\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "line1 = NDVItable.plot(color='#99cc00', kind='line',x='Date',y='Mean',ax=ax, label='NDVI', linewidth=6)\n",
    "line1 = BSItable.plot(color='#B344C6', kind='line',x='Date',y='Mean',ax=ax, label='BSI', linewidth=6)\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Index value')\n",
    "plt.title('Mean BSI and NDVI values')\n",
    "plt.legend(loc=\"best\", shadow=True, prop={'size': 18})\n",
    "\n",
    "plt.savefig('ndvi-1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f843ce-b5eb-4962-9c21-f350d9b4fa03",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1487cded-ee43-4b98-aeb6-8a0a5a3f53b0",
   "metadata": {},
   "source": [
    "### NDVI statistics with a histogram for 1 month, 10 bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef27b48-d4af-42c7-92ca-900c793032ff",
   "metadata": {},
   "source": [
    "The following is a JSON response of the statistical request, that calculates NDVI statistics for one month. A histogram with 10 bins has also been requested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "971065b1-aa77-414a-b402-1f92e2712275",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentinelhub import SHConfig, SentinelHubStatistical, BBox, Geometry, DataCollection, CRS\n",
    "\n",
    "# Credentials\n",
    "config = SHConfig()\n",
    "config.sh_client_id = client_id\n",
    "config.sh_client_secret = client_secret\n",
    "\n",
    "evalscript = \"\"\"\n",
    "//VERSION=3\n",
    "function setup() {\n",
    "  return {\n",
    "    input: [{\n",
    "      bands: [\n",
    "        \"B04\",\n",
    "        \"B08\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"data\",\n",
    "        bands: [\"NDVI\"]\n",
    "      },\n",
    "      {\n",
    "        id: \"dataMask\",\n",
    "        bands: 1\n",
    "      }]\n",
    "  }\n",
    "}\n",
    "\n",
    "function evaluatePixel(samples) {\n",
    "    let ndvi = (samples.B08 - samples.B04)/(samples.B08 + samples.B04)\n",
    "    \n",
    "    var validNDVIMask = 1\n",
    "    if (samples.B08 + samples.B04 == 0 ){\n",
    "        validNDVIMask = 0\n",
    "    }\n",
    "    \n",
    "    var noWaterMask = 1\n",
    "    if (samples.SCL == 6 ){\n",
    "        noWaterMask = 0\n",
    "    }\n",
    "\n",
    "    return {\n",
    "        data: [ndvi],\n",
    "        // Exclude nodata pixels, pixels where ndvi is not defined and water pixels from statistics:\n",
    "        dataMask: [samples.dataMask * validNDVIMask * noWaterMask]\n",
    "    }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "calculations = {\n",
    "    \"default\": {\n",
    "        \"histograms\": {\n",
    "            \"default\": {\n",
    "                \"nBins\": 10\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "bbox = BBox(bbox=[14.43807, 45.978388, 14.449997, 45.986768], crs=CRS.WGS84)\n",
    "geometry = Geometry(geometry={\"type\":\"Polygon\",\"coordinates\":[[[14.43807,45.985337],[14.442789,45.978716],[14.444548,45.979045],[14.445106,45.978388],[14.449997,45.980118],[14.445707,45.983846],[14.443089,45.98322],[14.44236,45.984204],[14.444591,45.984621],[14.442789,45.986768],[14.43807,45.985337]]]}, crs=CRS.WGS84)\n",
    "\n",
    "request = SentinelHubStatistical(\n",
    "    aggregation=SentinelHubStatistical.aggregation(\n",
    "        evalscript=evalscript,\n",
    "        time_interval=('2021-01-01T00:00:00Z', '2021-02-01T23:59:59Z'),\n",
    "        aggregation_interval='P1M',\n",
    "        size=[512, 517.657],        \n",
    "    ),\n",
    "    input_data=[\n",
    "        SentinelHubStatistical.input_data(\n",
    "            DataCollection.SENTINEL2_L2A,                        \n",
    "      ),\n",
    "    ],\n",
    "    bbox=bbox,\n",
    "    geometry=geometry,\n",
    "    calculations=calculations,\n",
    "    config=config\n",
    ")\n",
    "\n",
    "response = request.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af7ceb8-4f17-478d-a327-4affcb2682fa",
   "metadata": {},
   "source": [
    "Just to illustrate the possibility, if you saved the API response, you can simply paste it in and name it as a variable instead of running the request all over again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c780868-8230-44f6-9fb0-c4ec09c26c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "json = {'data': [{'interval': {'from': '2021-01-01T00:00:00Z',\n",
    "     'to': '2021-02-01T00:00:00Z'},\n",
    "    'outputs': {'data': {'bands': {'NDVI': {'stats': {'min': -0.009306260384619236,\n",
    "         'max': 0.05112960934638977,\n",
    "         'mean': 0.019889125026077203,\n",
    "         'stDev': 0.007759267052060924,\n",
    "         'sampleCount': 264704,\n",
    "         'noDataCount': 141236},\n",
    "        'histogram': {'bins': [{'lowEdge': -0.009306260384619236,\n",
    "           'highEdge': -0.0032626734115183357,\n",
    "           'count': 181},\n",
    "          {'lowEdge': -0.0032626734115183357,\n",
    "           'highEdge': 0.0027809135615825646,\n",
    "           'count': 1432},\n",
    "          {'lowEdge': 0.0027809135615825646,\n",
    "           'highEdge': 0.008824500534683463,\n",
    "           'count': 7084},\n",
    "          {'lowEdge': 0.008824500534683463,\n",
    "           'highEdge': 0.014868087507784365,\n",
    "           'count': 23004},\n",
    "          {'lowEdge': 0.014868087507784365,\n",
    "           'highEdge': 0.020911674480885267,\n",
    "           'count': 37390},\n",
    "          {'lowEdge': 0.020911674480885267,\n",
    "           'highEdge': 0.026955261453986162,\n",
    "           'count': 33437},\n",
    "          {'lowEdge': 0.026955261453986162,\n",
    "           'highEdge': 0.032998848427087064,\n",
    "           'count': 15122},\n",
    "          {'lowEdge': 0.032998848427087064,\n",
    "           'highEdge': 0.039042435400187966,\n",
    "           'count': 4310},\n",
    "          {'lowEdge': 0.039042435400187966,\n",
    "           'highEdge': 0.04508602237328887,\n",
    "           'count': 1203},\n",
    "          {'lowEdge': 0.04508602237328887,\n",
    "           'highEdge': 0.05112960934638977,\n",
    "           'count': 305}],\n",
    "         'overflowCount': 0,\n",
    "         'underflowCount': 0}}}}}}],\n",
    "  'status': 'OK'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0cbc908-d4e3-4dd5-9340-ad39ddd474bb",
   "metadata": {},
   "source": [
    "### Retrieve the histogram and its values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad574f0-1fbc-4bfa-b2b4-330d052cab59",
   "metadata": {},
   "source": [
    "Here we searched the resulting JSON to find the histogram part of the response. As you can see, each bin has a number of pixels that fall into it, as well as lowEdge and highEdge values, which specify where each bin begins and ends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c6992d32-e031-45bd-a5ae-50f9f23d93cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'lowEdge': -0.009306260384619236,\n",
       "  'highEdge': -0.0032626734115183357,\n",
       "  'count': 181},\n",
       " {'lowEdge': -0.0032626734115183357,\n",
       "  'highEdge': 0.0027809135615825646,\n",
       "  'count': 1432},\n",
       " {'lowEdge': 0.0027809135615825646,\n",
       "  'highEdge': 0.008824500534683463,\n",
       "  'count': 7084},\n",
       " {'lowEdge': 0.008824500534683463,\n",
       "  'highEdge': 0.014868087507784365,\n",
       "  'count': 23004},\n",
       " {'lowEdge': 0.014868087507784365,\n",
       "  'highEdge': 0.020911674480885267,\n",
       "  'count': 37390},\n",
       " {'lowEdge': 0.020911674480885267,\n",
       "  'highEdge': 0.026955261453986162,\n",
       "  'count': 33437},\n",
       " {'lowEdge': 0.026955261453986162,\n",
       "  'highEdge': 0.032998848427087064,\n",
       "  'count': 15122},\n",
       " {'lowEdge': 0.032998848427087064,\n",
       "  'highEdge': 0.039042435400187966,\n",
       "  'count': 4310},\n",
       " {'lowEdge': 0.039042435400187966,\n",
       "  'highEdge': 0.04508602237328887,\n",
       "  'count': 1203},\n",
       " {'lowEdge': 0.04508602237328887,\n",
       "  'highEdge': 0.05112960934638977,\n",
       "  'count': 305}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json['data'][0]['outputs']['data']['bands']['NDVI']['histogram']['bins']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdddc85d-19d3-4b7f-b530-cc72b4cb17dd",
   "metadata": {},
   "source": [
    "**Retrieve lowEdge values**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b396b404-3ef4-4f11-8829-ae4d7d59ea4d",
   "metadata": {},
   "source": [
    "To visualize a histogram, we need its lowEdge values. To do so, we specify empty arrays, then append the values to them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fd48ed0-5ec6-4556-8e4c-1ecc6b99cfef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.009306260384619236, -0.0032626734115183357, 0.0027809135615825646, 0.008824500534683463, 0.014868087507784365, 0.020911674480885267, 0.026955261453986162, 0.032998848427087064, 0.039042435400187966, 0.04508602237328887]\n"
     ]
    }
   ],
   "source": [
    "# Get lowEdge values and append them to the lowEdge array\n",
    "array=[]\n",
    "array2 = []\n",
    "lowEdge = []\n",
    "\n",
    "for data_item in json['data']:\n",
    "    for item, value in data_item['outputs']['data']['bands']['NDVI']['histogram'].items():\n",
    "        array.append(value)  \n",
    "        array2 = array[0]\n",
    "for i in array2:\n",
    "    lowEdge.append(i['lowEdge'])      \n",
    "print (lowEdge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fcff7f-16a6-4ef0-a23a-327ba81c31e5",
   "metadata": {},
   "source": [
    "**Retrieve count values**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f704e80a-6475-4571-8131-7a04460fe505",
   "metadata": {},
   "source": [
    "To visualize a histogram, we need its count values. To do so, we specify empty arrays, then append the values to them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f376c999-62a7-45dd-b933-9516f903efc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get count values and append them to the count array\n",
    "array3=[]\n",
    "array4 = []\n",
    "count = []\n",
    "\n",
    "for data_item in json['data']:\n",
    "    for item, value in data_item['outputs']['data']['bands']['NDVI']['histogram'].items():\n",
    "        array3.append(value)  \n",
    "        array4 = array[0]\n",
    "for i in array4:\n",
    "    count.append(i['count'])   \n",
    "print (count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b585d8a-0fbd-4398-89af-b17423521274",
   "metadata": {},
   "source": [
    "**Round lowEdge values, then print out final lowEdge and count values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "35636060-f774-4b11-abeb-d750d1a8fd41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[181, 1432, 7084, 23004, 37390, 33437, 15122, 4310, 1203, 305]\n",
      "[-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "print(count)\n",
    "lowEdge = [round(num, 1) for num in lowEdge]\n",
    "print(lowEdge) #bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48af7363-d7b7-4f2b-abee-04a7bb4b00c3",
   "metadata": {},
   "source": [
    "### Visualize the NDVI histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e5eeda1b-e4f7-416e-898a-82c9d9e3c673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = lowEdge\n",
    "number = count\n",
    "\n",
    "bins_pos = [i for i, _ in enumerate(bins)]\n",
    "\n",
    "plt.style.use('seaborn-whitegrid') # nice and clean grid\n",
    "\n",
    "plt.bar(bins_pos, number, facecolor = '#BDDD2B', linewidth=0.5, alpha=0.8)\n",
    "plt.xlabel(\"NDVI value\", fontsize=15)\n",
    "plt.ylabel(\"Pixel count\", fontsize=15)\n",
    "plt.title(\"NDVI histogram\", fontsize=20)\n",
    "\n",
    "\n",
    "plt.xticks(bins_pos,bins, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}