statisticalAPI/Statistical API Requests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "numerous-bracket",
   "metadata": {},
   "source": [
    "# Statistical API - Request Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c4c39-b16e-4fc6-82af-ac44c2ed8d8d",
   "metadata": {},
   "source": [
    "This notebook was created as a companion notebook to the [Statitical API webinar](https://youtu.be/4JviobxqTO8). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab082e5c-a618-4115-bfc1-b9ac7f561db6",
   "metadata": {},
   "source": [
    "## Import modules and authenticate at Sentinel Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e0abed13-690a-4949-902e-8a961fbc8401",
   "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": 95,
   "id": "65ca9355-ba33-45e7-853d-3c99ec3c5c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the client ID and client secret below with your own\n",
    "client_id = \"<your_c>\"\n",
    "client_secret = \"6RdPgeW7bOtzRb4m7Sul\"\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": "prescription-bunch",
   "metadata": {},
   "source": [
    "**Note: Statistical API uses your processing units. It's easy to set a long time-range and daily aggregations, so keep this in mind.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f036f05-d603-4b10-a920-fea6a4b76c96",
   "metadata": {},
   "source": [
    "## How does SH Statistical API calculate statistics?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084cb712-8a90-46fd-9a16-3a79baeae0a5",
   "metadata": {},
   "source": [
    "In this part, we would like to demonstrate how Statistical API calculates statistics. To do so, we first calculate statistics \"manually\" by first requesting an image with Process API and then calculating statistics for the image using numpy. Next, we run the statistical API request and show that the results of the request are the same as the results of the manual calculation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incomplete-commodity",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define AOI and evalscript"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lasting-bradley",
   "metadata": {},
   "source": [
    "Here we define AOI and evalscript which we will use in both requests; Statistical API and Process API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "sitting-bangladesh",
   "metadata": {},
   "outputs": [],
   "source": [
    "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: 1,\n",
    "        sampleType: \"FLOAT32\"  //FLOAT32 is used in process API to get floating NDVI values for statistics\n",
    "      },\n",
    "      {\n",
    "        id: \"dataMask\",\n",
    "        bands: 1\n",
    "      }]\n",
    "  }\n",
    "}\n",
    "\n",
    "function evaluatePixel(samples) {\n",
    "    return {\n",
    "        data: [index(samples.B08, samples.B04)],\n",
    "        dataMask: [samples.dataMask]\n",
    "        };\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "bounds = {\"geometry\":{\n",
    "  \"type\": \"Polygon\",\n",
    "  \"coordinates\": [\n",
    "    [\n",
    "      [\n",
    "        14.311462,\n",
    "        45.79756\n",
    "      ],\n",
    "      [\n",
    "        14.31188,\n",
    "        45.797111\n",
    "      ],\n",
    "      [\n",
    "        14.311977,\n",
    "        45.79658\n",
    "      ],\n",
    "      [\n",
    "        14.312653,\n",
    "        45.795839\n",
    "      ],\n",
    "      [\n",
    "        14.313372,\n",
    "        45.795443\n",
    "      ],\n",
    "      [\n",
    "        14.314305,\n",
    "        45.795346\n",
    "      ],\n",
    "      [\n",
    "        14.31496,\n",
    "        45.795091\n",
    "      ],\n",
    "      [\n",
    "        14.31525,\n",
    "        45.795278\n",
    "      ],\n",
    "      [\n",
    "        14.31585,\n",
    "        45.795368\n",
    "      ],\n",
    "      [\n",
    "        14.315818,\n",
    "        45.795593\n",
    "      ],\n",
    "      [\n",
    "        14.315335,\n",
    "        45.795967\n",
    "      ],\n",
    "      [\n",
    "        14.314895,\n",
    "        45.796453\n",
    "      ],\n",
    "      [\n",
    "        14.31393,\n",
    "        45.796939\n",
    "      ],\n",
    "      [\n",
    "        14.312492,\n",
    "        45.797425\n",
    "      ],\n",
    "      [\n",
    "        14.311462,\n",
    "        45.79756\n",
    "      ]\n",
    "    ]\n",
    "  ]\n",
    "}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-shareware",
   "metadata": {},
   "source": [
    "### Process API + statistics on image with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "twelve-pastor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Request an image\n",
    "process_request = {\n",
    "  \"input\": {\n",
    "    \"bounds\": bounds,\n",
    "    \"data\": [\n",
    "      {\n",
    "        \"type\": \"S2L2A\",\n",
    "        \"dataFilter\": {\n",
    "            \"timeRange\": {\n",
    "                \"from\": \"2021-10-02T00:00:00Z\",\n",
    "                \"to\": \"2021-10-03T00:00:00Z\"\n",
    "              },\n",
    "            \"mosaickingOrder\": \"leastRecent\"\n",
    "        },\n",
    "        \"processing\": {\n",
    "            \"upsampling\": \"BICUBIC\"\n",
    "        }\n",
    "      }\n",
    "    ]\n",
    "  },\n",
    "  \"evalscript\": evalscript,\n",
    "  \"output\":{\n",
    "    \"resx\": 0.00010,\n",
    "    \"resy\": 0.00010,\n",
    "    \"responses\": [\n",
    "         {\n",
    "            \"identifier\": \"data\",\n",
    "            \"format\": {\n",
    "                \"type\": \"image/tiff\"\n",
    "            }\n",
    "        },\n",
    "        {\n",
    "            \"identifier\": \"dataMask\",\n",
    "            \"format\": {\n",
    "                \"type\": \"image/tiff\"\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "  }\n",
    "}\n",
    "\n",
    "headers = {\n",
    "  'Content-Type': 'application/json',\n",
    "  'Accept': 'application/x-tar'\n",
    "}\n",
    "\n",
    "response = oauth.request(\"POST\", url=\"https://services.sentinel-hub.com/api/v1/process\", headers=headers, json=process_request)\n",
    "tar = tarfile.open(fileobj=io.BytesIO(response.content))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aware-adams",
   "metadata": {},
   "source": [
    "Let's display the requested image. It will show NDVI values in the requested polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "funny-chain",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Extract our data.tif output form the tar file and save it as numpy array\n",
    "im_array = imread(BytesIO(tar.extractfile(tar.getmember(\"data.tif\")).read(-1)))\n",
    "im_array = im_array.astype('float')\n",
    "# Treat value 0 as nans\n",
    "im_array[im_array == 0] = np.nan\n",
    "plt.imshow(im_array, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "patent-conducting",
   "metadata": {},
   "source": [
    "Let's check the size of the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "early-proceeding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape = im_array.shape\n",
    "shape[0] * shape [1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36683257-2486-4e1a-9b5e-65299c06d345",
   "metadata": {},
   "source": [
    "The image consist of 1100 pixels. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yellow-linux",
   "metadata": {},
   "source": [
    "Now we will manually calculate the same statistics that we requested with Statistical API above, i.e.:  \n",
    "- minimum value \n",
    "- maximum value\n",
    "- mean value\n",
    "- standard deviation\n",
    "- histogram with 5 bins\n",
    "- 10th, 30th, 40th, 50th, and 90th percentils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "extra-harassment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min': 0.12717677652835846,\n",
       " 'max': 0.5160512328147888,\n",
       " 'mean': 0.28425120391382225,\n",
       " 'stDev': 0.10754383799787968,\n",
       " 'hist': (array([150, 211,  83,  37,  90]),\n",
       "  array([0.12717678, 0.20495167, 0.28272656, 0.36050145, 0.43827634,\n",
       "         0.51605123])),\n",
       " 'percentiles': array([0.17404507, 0.21141253, 0.23049518, 0.2516419 , 0.48868692])}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manual_stattics = {\n",
    "    \"min\": np.nanmin(im_array),\n",
    "    \"max\": np.nanmax(im_array),\n",
    "    \"mean\": np.nanmean(im_array),\n",
    "    \"stDev\": np.nanstd(im_array),\n",
    "    \"hist\": np.histogram(im_array[~np.isnan(im_array)], bins=5),\n",
    "    \"percentiles\": np.percentile(im_array[~np.isnan(im_array)], [10, 30, 40, 50, 90])\n",
    "}\n",
    "manual_stattics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ae0040-8b4e-4775-9e6a-585fe75b0af9",
   "metadata": {},
   "source": [
    "### Statistics with Statistical API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c1dcc480-3146-4fd1-b748-2829e344efce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': [{'interval': {'from': '2021-10-02T00:00:00Z',\n",
       "    'to': '2021-10-03T00:00:00Z'},\n",
       "   'outputs': {'data': {'bands': {'B0': {'stats': {'min': 0.12717677652835846,\n",
       "        'max': 0.5160512328147888,\n",
       "        'mean': 0.2842512039138222,\n",
       "        'stDev': 0.10754383799787971,\n",
       "        'sampleCount': 1100,\n",
       "        'noDataCount': 529,\n",
       "        'percentiles': {'50.0': 0.2516418993473053,\n",
       "         '10.0': 0.17404507100582123,\n",
       "         '40.0': 0.23049518465995789,\n",
       "         '90.0': 0.4886869192123413,\n",
       "         '30.0': 0.21141253411769867}},\n",
       "       'histogram': {'bins': [{'lowEdge': 0.12717677652835846,\n",
       "          'highEdge': 0.20495166778564453,\n",
       "          'count': 150},\n",
       "         {'lowEdge': 0.20495166778564453,\n",
       "          'highEdge': 0.2827265590429306,\n",
       "          'count': 211},\n",
       "         {'lowEdge': 0.2827265590429306,\n",
       "          'highEdge': 0.36050145030021663,\n",
       "          'count': 83},\n",
       "         {'lowEdge': 0.36050145030021663,\n",
       "          'highEdge': 0.4382763415575027,\n",
       "          'count': 37},\n",
       "         {'lowEdge': 0.4382763415575027,\n",
       "          'highEdge': 0.5160512328147888,\n",
       "          'count': 90}],\n",
       "        'overflowCount': 0,\n",
       "        'underflowCount': 0}}}}}}],\n",
       " 'status': 'OK'}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistical_request = {\n",
    "  \"input\": {\n",
    "    \"bounds\": bounds,\n",
    "    \"data\": [\n",
    "      {\n",
    "        \"type\": \"S2L2A\",\n",
    "        \"dataFilter\": {\n",
    "            \"mosaickingOrder\": \"leastRecent\"\n",
    "        },\n",
    "        \"processing\": {\n",
    "            \"upsampling\": \"BICUBIC\"\n",
    "        }\n",
    "      }\n",
    "    ]\n",
    "  },\n",
    "  \"aggregation\": {\n",
    "    \"timeRange\": {\n",
    "            \"from\": \"2021-10-02T00:00:00Z\",\n",
    "            \"to\": \"2021-10-03T00:00:00Z\"\n",
    "      },\n",
    "      \"aggregationInterval\": {\n",
    "          \"of\": \"P1D\"\n",
    "      },\n",
    "    \"evalscript\": evalscript,\n",
    "    \"resx\": 0.00010,\n",
    "    \"resy\": 0.00010,\n",
    "  },\n",
    "  \"calculations\": {\n",
    "      \"default\": {\n",
    "         \"statistics\": {\n",
    "              \"default\": {\n",
    "                  \"percentiles\": {\n",
    "                      \"k\": [10, 30, 40, 50, 90],\n",
    "                      \"interpolation\": \"higher\"\n",
    "                  }\n",
    "              }\n",
    "          },\n",
    "          \"histograms\": {\n",
    "              \"default\": {\n",
    "                  \"nBins\": 5\n",
    "              }\n",
    "          }\n",
    "      }\n",
    "  }\n",
    "}\n",
    "\n",
    "headers = {\n",
    "  'Content-Type': 'application/json',\n",
    "   'Accept': 'application/json'\n",
    "}\n",
    "\n",
    "response = oauth.request(\"POST\", url=\"https://services.sentinel-hub.com/api/v1/statistics\" , headers=headers, json=statistical_request)\n",
    "sh_statistics = response.json()\n",
    "sh_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-static",
   "metadata": {},
   "source": [
    "### Compare the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "unnecessary-commissioner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': [{'interval': {'from': '2021-10-02T00:00:00Z',\n",
       "    'to': '2021-10-03T00:00:00Z'},\n",
       "   'outputs': {'data': {'bands': {'B0': {'stats': {'min': 0.12717677652835846,\n",
       "        'max': 0.5160512328147888,\n",
       "        'mean': 0.2842512039138222,\n",
       "        'stDev': 0.10754383799787971,\n",
       "        'sampleCount': 1100,\n",
       "        'noDataCount': 529,\n",
       "        'percentiles': {'50.0': 0.2516418993473053,\n",
       "         '10.0': 0.17404507100582123,\n",
       "         '40.0': 0.23049518465995789,\n",
       "         '90.0': 0.4886869192123413,\n",
       "         '30.0': 0.21141253411769867}},\n",
       "       'histogram': {'bins': [{'lowEdge': 0.12717677652835846,\n",
       "          'highEdge': 0.20495166778564453,\n",
       "          'count': 150},\n",
       "         {'lowEdge': 0.20495166778564453,\n",
       "          'highEdge': 0.2827265590429306,\n",
       "          'count': 211},\n",
       "         {'lowEdge': 0.2827265590429306,\n",
       "          'highEdge': 0.36050145030021663,\n",
       "          'count': 83},\n",
       "         {'lowEdge': 0.36050145030021663,\n",
       "          'highEdge': 0.4382763415575027,\n",
       "          'count': 37},\n",
       "         {'lowEdge': 0.4382763415575027,\n",
       "          'highEdge': 0.5160512328147888,\n",
       "          'count': 90}],\n",
       "        'overflowCount': 0,\n",
       "        'underflowCount': 0}}}}}}],\n",
       " 'status': 'OK'}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical api\n",
    "sh_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "healthy-reduction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min': 0.12717677652835846,\n",
       " 'max': 0.5160512328147888,\n",
       " 'mean': 0.28425120391382225,\n",
       " 'stDev': 0.10754383799787968,\n",
       " 'hist': (array([150, 211,  83,  37,  90]),\n",
       "  array([0.12717678, 0.20495167, 0.28272656, 0.36050145, 0.43827634,\n",
       "         0.51605123])),\n",
       " 'percentiles': array([0.17404507, 0.21141253, 0.23049518, 0.2516419 , 0.48868692])}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process API + numpay\n",
    "manual_stattics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "official-consolidation",
   "metadata": {},
   "source": [
    "We see that we get the same values in both cases, which is exactly what we wanted to show. \n",
    "Note that sampleCount is 1100, which corresponds to the number of all pixels in the image. However, only pixels within the polygon were used for calcualting the statistics, because we excluded pixels outside of the polygon in our evalscript with this line `dataMask: [samples.dataMask]`. 529 pixels were excluded, which is reported as `noDataCount` by the Statistical API.  \n",
    "\n",
    "To conclude, Statistical API generates the image based on requested parameters and evalscript (in the same ways as Process API) and then calculates the statistics based on this image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a29ce6-8ca4-4a14-bc87-346b9562e6bc",
   "metadata": {},
   "source": [
    "## Run any statistical API request from Requests Builder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cae158d-155c-4f76-bf9d-4e306b40c8f7",
   "metadata": {},
   "source": [
    "It's easiest to construct API requests using [Requests Builder's](https://apps.sentinel-hub.com/requests-builder/) interface."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff1f724-69a1-4b40-abd1-b78710756924",
   "metadata": {},
   "source": [
    "To run the request from [Requests Builder](https://apps.sentinel-hub.com/requests-builder/), do the following: \n",
    "1. navigate to Requests Builder, log in and toggle on the _Statistical_ API option.\n",
    "2. use the user interface to build your request\n",
    "3. copy the request in the sh-py format from the _Request Preview_ window\n",
    "4. replace the client ID and client Secret at the top with either your client ID and client secret, or, in this notebook, just with client_id and cliend_secret variables, as we have them defined above. \n",
    "5. add `response` at the bottom to run the request"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5634ef3-bd30-4f5e-aea2-ee2c6fc71825",
   "metadata": {},
   "source": [
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d228e8b0-17e7-40eb-9ba2-cd4bfea5d2e0",
   "metadata": {},
   "source": [
    "The requests below have been copied from the Requests Builder Preview Window."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c86033-5624-4ef7-a377-b03b129fd689",
   "metadata": {},
   "source": [
    "### NDVI statistics for a single day - Sentinel-2 L2A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2166376-558d-44ba-9b18-06a4892a8f6f",
   "metadata": {},
   "source": [
    "Time interval for this request was set from 2022-01-25 to 2022-01-26. \n",
    "Aggregations are set to P1D. \n",
    "\n",
    "Note that in case there's no data available in the specific time frame, the request will return an error.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "58d688fb-5347-41d1-a93e-9ab776a8f48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'data': [{'interval': {'from': '2022-01-25T00:00:00Z',\n",
       "     'to': '2022-01-26T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.11903064697980881,\n",
       "         'max': 0.5644171833992004,\n",
       "         'mean': 0.19506650719444532,\n",
       "         'stDev': 0.06335584426277424,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}}],\n",
       "  'status': 'OK'}]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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: \"NDVI\",\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",
    "    return {\n",
    "        NDVI: [ndvi],\n",
    "        dataMask: [samples.dataMask]\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=('2022-01-25T00:00:00Z', '2022-01-26T00:00:00Z'),\n",
    "        aggregation_interval='P1D',\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",
    "    config=config\n",
    ")\n",
    "\n",
    "response = request.get_data()\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e11b6d-3975-4fef-af2a-c5bd4b87f32e",
   "metadata": {},
   "source": [
    "### NDVI statistics for 6 months, monthly aggregations - Sentinel-2 L2A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f2b3de-4e5b-41fb-8cb4-1ce55543711b",
   "metadata": {},
   "source": [
    "Time interval for this request was set from 2021-01-01 to 2021-07-30. \n",
    "Aggregations are set to P1M, returning 6 intervals, each covering one month. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b2b8037c-9548-4cd8-8442-79a42048db30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'data': [{'interval': {'from': '2021-01-01T00:00:00Z',\n",
       "     'to': '2021-02-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'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",
       "   {'interval': {'from': '2021-02-01T00:00:00Z', 'to': '2021-03-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.16998468339443207,\n",
       "         'max': 0.46969306468963623,\n",
       "         'mean': 0.26567304173508294,\n",
       "         'stDev': 0.042864532271783634,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-03-01T00:00:00Z', 'to': '2021-04-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.12366737425327301,\n",
       "         'max': 0.6077755689620972,\n",
       "         'mean': 0.24903059921593887,\n",
       "         'stDev': 0.06863118938717337,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-04-01T00:00:00Z', 'to': '2021-05-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': -0.46362340450286865,\n",
       "         'max': 0.6997806429862976,\n",
       "         'mean': 0.03175643059686588,\n",
       "         'stDev': 0.05907010805375239,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-05-01T00:00:00Z', 'to': '2021-06-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': -0.5080345869064331,\n",
       "         'max': 0.7137176990509033,\n",
       "         'mean': 0.08103933926760669,\n",
       "         'stDev': 0.09585210756562319,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-06-01T00:00:00Z', 'to': '2021-07-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.21539345383644104,\n",
       "         'max': 0.892277717590332,\n",
       "         'mean': 0.5166371539773398,\n",
       "         'stDev': 0.1265581153141522,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}}],\n",
       "  'status': 'OK'}]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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: \"NDVI\",\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",
    "    return {\n",
    "        NDVI: [ndvi],\n",
    "        dataMask: [samples.dataMask]\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', '2021-07-30T23:59:59Z'),\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",
    "    config=config\n",
    ")\n",
    "\n",
    "response = request.get_data()\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd37a530-67f5-4b1e-9431-bc5416d7d1e8",
   "metadata": {},
   "source": [
    "### NDVI statistics for 6 months, monthly aggregations - Landsat 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a92abe-b332-4743-97fc-2e64950ec757",
   "metadata": {},
   "source": [
    "This request is the same as above, but the collection is changed to Landsat 8. To change the collection, one only has to:\n",
    "- replace the collection in Requests Builder or directly in the request\n",
    "- replace the NDVI bands to match Landsat 8. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bb87f8a9-1e35-4aa1-8c54-ddd640eaff75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'data': [{'interval': {'from': '2021-01-01T00:00:00Z',\n",
       "     'to': '2021-01-31T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.2804112136363983,\n",
       "         'max': 0.600660502910614,\n",
       "         'mean': 0.4074056929175689,\n",
       "         'stDev': 0.05899712086356643,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-01-31T00:00:00Z', 'to': '2021-03-02T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.21140876412391663,\n",
       "         'max': 0.44373634457588196,\n",
       "         'mean': 0.29828984253068563,\n",
       "         'stDev': 0.04167081259233397,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-03-02T00:00:00Z', 'to': '2021-04-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': -0.03023025020956993,\n",
       "         'max': -0.009512590244412422,\n",
       "         'mean': -0.019385796673209298,\n",
       "         'stDev': 0.004005856347569613,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-04-01T00:00:00Z', 'to': '2021-05-01T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': -0.010567218996584415,\n",
       "         'max': 0.012558533810079098,\n",
       "         'mean': -0.0019122025428034283,\n",
       "         'stDev': 0.005124834123268298,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-05-01T00:00:00Z', 'to': '2021-05-31T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.292960524559021,\n",
       "         'max': 0.9069891571998596,\n",
       "         'mean': 0.4522225201384797,\n",
       "         'stDev': 0.13305718475460937,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-05-31T00:00:00Z', 'to': '2021-06-30T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.3139234483242035,\n",
       "         'max': 0.8527226448059082,\n",
       "         'mean': 0.4672039818173966,\n",
       "         'stDev': 0.10338300125580333,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}},\n",
       "   {'interval': {'from': '2021-06-30T00:00:00Z', 'to': '2021-07-30T00:00:00Z'},\n",
       "    'outputs': {'NDVI': {'bands': {'NDVI': {'stats': {'min': 0.5255835652351379,\n",
       "         'max': 0.8736894726753235,\n",
       "         'mean': 0.7979113405465506,\n",
       "         'stDev': 0.07301493312197062,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}}],\n",
       "  'status': 'OK'}]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "        \"B05\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"NDVI\",\n",
    "        bands: [\"NDVI\"]\n",
    "      },\n",
    "      {\n",
    "        id: \"dataMask\",\n",
    "        bands: 1\n",
    "      }]\n",
    "  };\n",
    "}\n",
    "\n",
    "function evaluatePixel(samples) {\n",
    "    let ndvi = (samples.B05 - samples.B04) / (samples.B05+samples.B04);\n",
    "    return {\n",
    "        NDVI: [ndvi],\n",
    "        dataMask: [samples.dataMask]\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', '2021-07-30T23:59:59Z'),\n",
    "        aggregation_interval='P30D',\n",
    "        size=[512, 517.657],        \n",
    "    ),\n",
    "    input_data=[\n",
    "        SentinelHubStatistical.input_data(\n",
    "            DataCollection.LANDSAT_OT_L2,                        \n",
    "      ),\n",
    "    ],\n",
    "    bbox=bbox,\n",
    "    geometry=geometry,\n",
    "    config=config\n",
    ")\n",
    "\n",
    "response = request.get_data()\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e2fad2-85e4-40bb-a620-449e5198928e",
   "metadata": {},
   "source": [
    "### Run a multi-band statistical request"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d1ea6d-cde3-4d06-baa2-f552155b5cf1",
   "metadata": {},
   "source": [
    "This request shows how you can manipulate your outputs and their names. The request has 2 general outputs, called `NDVIbands` and `NDMIbands`, each returning 2 bands and the index itself. Outputs and bands are named in the `function setup()`, then called and returned in the `function evaluatePixel(samples)`. Inspect the results to see how naming your bands changes the resulting JSON. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "495d15ac-c3ea-4bcf-8865-6f9054359347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'data': [{'interval': {'from': '2021-02-01T00:00:00Z',\n",
       "     'to': '2021-03-01T00:00:00Z'},\n",
       "    'outputs': {'NDMIbands': {'bands': {'B03': {'stats': {'min': 0.053199999034404755,\n",
       "         'max': 0.13420000672340393,\n",
       "         'mean': 0.09822341737125981,\n",
       "         'stDev': 0.013013923400378708,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}},\n",
       "       'B11': {'stats': {'min': 0.17630000412464142,\n",
       "         'max': 0.34630000591278076,\n",
       "         'mean': 0.2774841450814966,\n",
       "         'stDev': 0.04031132063588604,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}},\n",
       "       'NDMI': {'stats': {'min': -0.6306146383285522,\n",
       "         'max': -0.27061426639556885,\n",
       "         'mean': -0.47529485860199644,\n",
       "         'stDev': 0.038161040750366824,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}},\n",
       "     'NDVIbands': {'bands': {'B04': {'stats': {'min': 0.06469999998807907,\n",
       "         'max': 0.16439999639987946,\n",
       "         'mean': 0.11773806747663258,\n",
       "         'stDev': 0.017140112862927288,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}},\n",
       "       'B08': {'stats': {'min': 0.12290000170469284,\n",
       "         'max': 0.30379998683929443,\n",
       "         'mean': 0.2037825785257703,\n",
       "         'stDev': 0.03288817299815174,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}},\n",
       "       'NDVI': {'stats': {'min': 0.16998468339443207,\n",
       "         'max': 0.46969306468963623,\n",
       "         'mean': 0.26567304173508294,\n",
       "         'stDev': 0.042864532271783634,\n",
       "         'sampleCount': 264704,\n",
       "         'noDataCount': 141236}}}}}}],\n",
       "  'status': 'OK'}]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "        \"B03\",\n",
    "        \"B04\",\n",
    "        \"B08\",\n",
    "        \"B11\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"NDVIbands\",\n",
    "        bands: [\"B04\", \"B08\", \"NDVI\"]\n",
    "      },\n",
    "      {\n",
    "        id: \"NDMIbands\",\n",
    "        bands: [\"B03\",\"B11\",\"NDMI\"]\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 ndmi = (samples.B03 - samples.B11) / (samples.B03+samples.B11);\n",
    "    return {\n",
    "        NDVIbands: [samples.B04, samples.B08, ndvi],\n",
    "        NDMIbands: [samples.B03, samples.B11, ndmi],\n",
    "        dataMask: [samples.dataMask]\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=('2021-02-01T00:00:00Z', '2021-03-30T23:59:59Z'),\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",
    "    config=config\n",
    ")\n",
    "\n",
    "response = request.get_data()\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b28c4d-a29c-4fe8-8332-37dbfca34286",
   "metadata": {},
   "source": [
    "## Run a request from documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34473bce-ea5e-4620-bcd4-807638328b05",
   "metadata": {},
   "source": [
    "With our authentication above, we can simply paste in any example request from documentation and run it. Example requests from documentation are in CURL format. \n",
    "Find documentation examples [here](https://docs.sentinel-hub.com/api/latest/api/statistical/examples/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5bcaff-94b3-4b4c-a7a1-7666a4c293fc",
   "metadata": {},
   "source": [
    "This request names the output `output_B04` in `function setup()` and returns a single band B04 as its band in `function evaluatePixel()`. \n",
    "Time-range is set to one day 2020-07-04/2020-07-05, and aggregation is set to P1D. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6c0cdfbe-f9a6-41eb-85e1-13a353da7c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': [{'interval': {'from': '2020-07-04T00:00:00Z',\n",
       "    'to': '2020-07-05T00:00:00Z'},\n",
       "   'outputs': {'output_B04': {'bands': {'B0': {'stats': {'min': 0.07970000058412552,\n",
       "        'max': 0.30959999561309814,\n",
       "        'mean': 0.11471141986778864,\n",
       "        'stDev': 0.034298170449733226,\n",
       "        'sampleCount': 3240,\n",
       "        'noDataCount': 0}}}}}}],\n",
       " 'status': 'OK'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evalscript = \"\"\"\n",
    "//VERSION=3\n",
    "function setup() {\n",
    "  return {\n",
    "    input: [{\n",
    "      bands: [\n",
    "        \"B04\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"output_B04\",\n",
    "        bands: 1,\n",
    "        sampleType: \"FLOAT32\"\n",
    "      },\n",
    "      {\n",
    "        id: \"dataMask\",\n",
    "        bands: 1\n",
    "      }]\n",
    "  }\n",
    "}\n",
    "function evaluatePixel(samples) {\n",
    "    return {\n",
    "        output_B04: [samples.B04],\n",
    "        dataMask: [samples.dataMask]\n",
    "        }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "stats_request = {\n",
    "  \"input\": {\n",
    "    \"bounds\": {\n",
    "      \"bbox\": [414315, 4958219, 414859, 4958819],\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\": \"leastRecent\"\n",
    "        },\n",
    "      }\n",
    "    ]\n",
    "  },\n",
    "  \"aggregation\": {\n",
    "    \"timeRange\": {\n",
    "            \"from\": \"2020-07-04T00:00:00Z\",\n",
    "            \"to\": \"2020-07-05T00:00:00Z\"\n",
    "      },\n",
    "    \"aggregationInterval\": {\n",
    "        \"of\": \"P1D\"\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",
    "response = oauth.request(\"POST\", url=url , headers=headers, json=stats_request)\n",
    "sh_statistics = response.json()\n",
    "sh_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d7fd4f-5af3-478c-af3c-cbe5e3a6ecb5",
   "metadata": {},
   "source": [
    "## Exluding pixels - NDVI statistics with invalid, no-data and water pixels excluded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a186aa2e-f020-47f4-a838-a18c0de4144d",
   "metadata": {},
   "source": [
    "The following request uses dataMask output to exclude specific pixels. If dataMask = 0, the pixel is considered to have no-data, and if it's 1, the pixel is considered to have data. Pixels that are considered to not have data are excluded from the calculation. \n",
    "\n",
    "The user can add additional pixels to the dataMask output to also exclude water pixels, invalid pixels, etc. \n",
    "\n",
    "- no-data pixels are excluded by simply outputting dataMask, which is mandatory\n",
    "- water pixels are excluded by creating a variable, checking if SCL band == 6 and assigning 0 to the variable\n",
    "- invalid pixels are excluded by creating a variable, checking if all the bands = 0, then assigning 0 to the variable\n",
    "\n",
    "Finally, all three are multiplied in the dataMask output. If any of the three = 0, the result will be 0 and the pixel is excluded from the calculation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a98efdd9-b14d-4b3c-ab68-bc1c70f961fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': [{'interval': {'from': '2020-01-01T00:00:00Z',\n",
       "    'to': '2020-01-31T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.7018803358078003,\n",
       "        'max': 0.7018803358078003,\n",
       "        'mean': 0.7018803358078003,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-01-31T00:00:00Z', 'to': '2020-03-01T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.7313191294670105,\n",
       "        'max': 0.7313191294670105,\n",
       "        'mean': 0.7313191294670105,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-03-01T00:00:00Z', 'to': '2020-03-31T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.43195265531539917,\n",
       "        'max': 0.43195265531539917,\n",
       "        'mean': 0.43195265531539917,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-03-31T00:00:00Z', 'to': '2020-04-30T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.24942965805530548,\n",
       "        'max': 0.24942965805530548,\n",
       "        'mean': 0.24942965805530548,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-04-30T00:00:00Z', 'to': '2020-05-30T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.11939461529254913,\n",
       "        'max': 0.11939461529254913,\n",
       "        'mean': 0.11939461529254913,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-05-30T00:00:00Z', 'to': '2020-06-29T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.07164248079061508,\n",
       "        'max': 0.07164248079061508,\n",
       "        'mean': 0.07164248079061508,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-06-29T00:00:00Z', 'to': '2020-07-29T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.08363234996795654,\n",
       "        'max': 0.08363234996795654,\n",
       "        'mean': 0.08363234996795654,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-07-29T00:00:00Z', 'to': '2020-08-28T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': -0.03349282220005989,\n",
       "        'max': -0.03349282220005989,\n",
       "        'mean': -0.03349282220005989,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-08-28T00:00:00Z', 'to': '2020-09-27T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': -0.06077348068356514,\n",
       "        'max': -0.06077348068356514,\n",
       "        'mean': -0.06077348068356514,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-09-27T00:00:00Z', 'to': '2020-10-27T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.007115489803254604,\n",
       "        'max': 0.007115489803254604,\n",
       "        'mean': 0.007115489803254604,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-10-27T00:00:00Z', 'to': '2020-11-26T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.09821882843971252,\n",
       "        'max': 0.09821882843971252,\n",
       "        'mean': 0.09821882843971252,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}},\n",
       "  {'interval': {'from': '2020-11-26T00:00:00Z', 'to': '2020-12-26T00:00:00Z'},\n",
       "   'outputs': {'ndvi': {'bands': {'NDVI': {'stats': {'min': 0.1497216522693634,\n",
       "        'max': 0.1497216522693634,\n",
       "        'mean': 0.1497216522693634,\n",
       "        'stDev': 0.0,\n",
       "        'sampleCount': 1,\n",
       "        'noDataCount': 0}}}}}}],\n",
       " 'status': 'OK'}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evalscript = \"\"\"\n",
    "//VERSION=3\n",
    "function setup() {\n",
    "  return {\n",
    "    input: [{\n",
    "      bands: [\n",
    "        \"B04\",\n",
    "        \"B08\",\n",
    "        \"SCL\",\n",
    "        \"dataMask\"\n",
    "      ]\n",
    "    }],\n",
    "    output: [\n",
    "      {\n",
    "        id: \"ndvi\",\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.B04 + samples.B08 == 0 ){ //check if all the bands are 0\n",
    "        validNDVIMask = 0\n",
    "    }\n",
    "    \n",
    "    var noWaterMask = 1\n",
    "    if (samples.SCL == 6 ){ //check if pixels are classified as water using SCL band\n",
    "        noWaterMask = 0\n",
    "    }\n",
    "\n",
    "    return {\n",
    "        ndvi: [ndvi],\n",
    "        dataMask: [samples.dataMask * validNDVIMask * noWaterMask]  //if even one of the three exclusion criteria is 0, the pixel will be excluded\n",
    "    }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "stats_request = {\n",
    "  \"input\": {\n",
    "   \"bounds\": {\n",
    "      \"geometry\": {\n",
    "          \"type\": \"Polygon\",\n",
    "          \"coordinates\": [\n",
    "            [\n",
    "               [\n",
    "        14.43807,\n",
    "        45.985337\n",
    "      ],\n",
    "      [\n",
    "        14.442789,\n",
    "        45.978716\n",
    "      ],\n",
    "      [\n",
    "        14.444548,\n",
    "        45.979045\n",
    "      ],\n",
    "      [\n",
    "        14.445106,\n",
    "        45.978388\n",
    "      ],\n",
    "      [\n",
    "        14.449997,\n",
    "        45.980118\n",
    "      ],\n",
    "      [\n",
    "        14.445707,\n",
    "        45.983846\n",
    "      ],\n",
    "      [\n",
    "        14.443089,\n",
    "        45.98322\n",
    "      ],\n",
    "      [\n",
    "        14.44236,\n",
    "        45.984204\n",
    "      ],\n",
    "      [\n",
    "        14.444591,\n",
    "        45.984621\n",
    "      ],\n",
    "      [\n",
    "        14.442789,\n",
    "        45.986768\n",
    "      ],\n",
    "      [\n",
    "        14.43807,\n",
    "        45.985337\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\": \"leastCC\"\n",
    "        }\n",
    "      }\n",
    "    ]\n",
    "  },\n",
    "  \"aggregation\": {\n",
    "    \"timeRange\": {\n",
    "        \"from\": \"2020-01-01T00:00:00Z\",\n",
    "        \"to\": \"2020-12-31T00:00:00Z\"\n",
    "      },\n",
    "    \"aggregationInterval\": {\n",
    "        \"of\": \"P30D\"\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"
   ]
  }
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