{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentinel-2 L2A Analytic (GeoTIFF)\n",
    "This notebook provides details on how to access Sentinel-2 data (L2A pre-processing or Bottom Of Atmosphere (BOA) reflectance images) with 13 bands. The 13 bands include 4 visible bands (10 m resolution), 6 Near-Infrared bands (20 m resolution), and 3 Short-Wave Infrared bands (60 m resolution). The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies.Important application areas for Sentinel-2 imagery are: land cover monitoring (agriculture, forestry), coastal area monitoring, inland water monitoring, glacier monitoring and flood mapping.\n",
    "Sentinel-2 Level-2A products have been available over Europe since March 2018, and later extended gloablly as of December 2018.\n",
    "\n",
    "In the example, the workflow, the area of interest and the workflow parameters are defined. After running the job, the results are downloaded and visualized. For more information, refer to the block's [UP42 Marketplace page](https://marketplace.up42.com/block/e13d8e92-2763-4640-80d6-1501b2729707) and [Documentation](https://docs.up42.com/up42-blocks/data/esa-s2-l2a-gtiff-analytic.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import up42\n",
    "\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-26 14:39:24,521 - Authentication with UP42 successful!\n",
      "2021-03-26 14:39:25,664 - Initialized Project(name: S2l2a-Analytics-data-example, project_id: 1f2bad99-daad-4a60-9192-5169263caff6, description: , createdAt: 2021-03-18T14:39:08.255254Z)\n"
     ]
    }
   ],
   "source": [
    "# Authenticate user and initialise project.\n",
    "up42.authenticate(project_id=\"1234\", \n",
    "                  project_api_key=\"5678\")\n",
    "project = up42.initialize_project()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the aoi\n",
    "aoi = {\"type\": \"FeatureCollection\",\"features\": [{\"type\": \"Feature\",\"properties\": {},\n",
    "    \"geometry\": {\"type\": \"Polygon\",\"coordinates\": [[[14.610298,-24.516841],\n",
    "                                                    [14.622478,-24.516841],\n",
    "                                                    [14.622478,-24.50833],\n",
    "                                                    [14.610298,-24.50833],\n",
    "                                                    [14.610298,-24.516841]]]}}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-26 14:39:25,686 - Getting existing workflows in project ...\n",
      "2021-03-26 14:39:26,185 - Got 2 workflows for project 1f2bad99-daad-4a60-9192-5169263caff6.\n",
      "100%|██████████| 2/2 [00:01<00:00,  1.86it/s]\n",
      "2021-03-26 14:39:27,271 - Using existing workflow: Sentinel2-l2-analytic-example - 00e94c32-7cfe-40b8-85b6-cc2ffa980c75\n",
      "2021-03-26 14:39:30,984 - Added tasks to workflow: [{'name': 'esa-s2-l2a-gtiff-analytic:1', 'parentName': None, 'blockId': 'e13d8e92-2763-4640-80d6-1501b2729707'}]\n"
     ]
    }
   ],
   "source": [
    "# Construct the workflow.\n",
    "workflow = project.create_workflow(name=\"Sentinel2-l2-analytic-example\", use_existing=True)\n",
    "input_tasks = [\"Sentinel-2 L2A Analytic (GeoTIFF)\"]\n",
    "workflow.add_workflow_tasks(input_tasks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the aoi and input parameters of the workflow to run it.\n",
    "input_parameters = workflow.construct_parameters(geometry=aoi, \n",
    "                                                 geometry_operation='intersects', \n",
    "                                                 start_date=\"2018-11-07\", \n",
    "                                                 end_date=\"2020-12-31\",\n",
    "                                                 limit=1)\n",
    "input_parameters[\"esa-s2-l2a-gtiff-analytic:1\"].update({\"max_cloud_cover\":10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-26 14:39:39,820 - Estimated: 1-1 Credits, Duration: 0-0 min.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'esa-s2-l2a-gtiff-analytic:1': {'blockConsumption': {'resources': {'unit': 'SQUARE_KM',\n",
       "    'min': 0,\n",
       "    'max': 0},\n",
       "   'credit': {'min': 0, 'max': 0}},\n",
       "  'machineConsumption': {'duration': {'min': 0, 'max': 0},\n",
       "   'credit': {'min': 1, 'max': 1}}}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Price Estimation.\n",
    "workflow.estimate_job(input_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-26 14:39:39,835 - +++++++++++++++++++++++++++++++++\n",
      "2021-03-26 14:39:39,836 - Running this job as Test Query...\n",
      "2021-03-26 14:39:39,837 - +++++++++++++++++++++++++++++++++\n",
      "2021-03-26 14:39:39,838 - Selected input_parameters: {'esa-s2-l2a-gtiff-analytic:1': {'time': '2018-11-07T00:00:00Z/2020-12-31T23:59:59Z', 'limit': 1, 'max_cloud_cover': 10, 'intersects': {'type': 'Polygon', 'coordinates': (((14.610298, -24.516841), (14.622478, -24.516841), (14.622478, -24.50833), (14.610298, -24.50833), (14.610298, -24.516841)),)}}, 'config': {'mode': 'DRY_RUN'}}\n",
      "2021-03-26 14:41:55,500 - Created and running new job: 62a1a4d8-d05a-4d11-b2c5-c36cbcc8ac71.\n",
      "2021-03-26 14:41:56,168 - Tracking job status continuously, reporting every 30 seconds...\n",
      "2021-03-26 14:42:19,774 - Job finished successfully! - 62a1a4d8-d05a-4d11-b2c5-c36cbcc8ac71\n"
     ]
    }
   ],
   "source": [
    "# Run test job to query data availability and check the configuration.\n",
    "test_job = workflow.test_job(input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-26 14:42:24,789 - Selected input_parameters: {'esa-s2-l2a-gtiff-analytic:1': {'time': '2018-11-07T00:00:00Z/2020-12-31T23:59:59Z', 'limit': 1, 'max_cloud_cover': 10, 'intersects': {'type': 'Polygon', 'coordinates': (((14.610298, -24.516841), (14.622478, -24.516841), (14.622478, -24.50833), (14.610298, -24.50833), (14.610298, -24.516841)),)}}}\n",
      "2021-03-26 14:42:29,390 - Created and running new job: ac01ba64-5610-424c-8a7e-933c511a2f7c.\n",
      "2021-03-26 14:42:29,881 - Tracking job status continuously, reporting every 30 seconds...\n",
      "2021-03-26 14:43:04,487 - Job is PENDING! - ac01ba64-5610-424c-8a7e-933c511a2f7c\n",
      "2021-03-26 14:43:38,533 - Job is RUNNING! - ac01ba64-5610-424c-8a7e-933c511a2f7c\n",
      "2021-03-26 14:44:12,538 - Job is RUNNING! - ac01ba64-5610-424c-8a7e-933c511a2f7c\n",
      "2021-03-26 14:44:46,415 - Job is RUNNING! - ac01ba64-5610-424c-8a7e-933c511a2f7c\n",
      "2021-03-26 14:45:14,293 - Job finished successfully! - ac01ba64-5610-424c-8a7e-933c511a2f7c\n"
     ]
    }
   ],
   "source": [
    "# Run actual Job.\n",
    "job = workflow.run_job(input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downlod Results.\n",
    "job.download_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the RGB bands\n",
    "This block delivers each band image as a single file, `job.plot_results()` does not yet cover this case. We will visualize the RGB bands manually, also see this [tutorial](https://automating-gis-processes.github.io/site/notebooks/Raster/plotting-raster.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the RGB bands.\n",
    "red = 'project_1f2bad99-daad-4a60-9192-5169263caff6/job_01bdf71e-7c35-4454-844b-809900bcef64/S2B_33JVN_20201215_0_L2A_B04.tif'\n",
    "green = 'project_1f2bad99-daad-4a60-9192-5169263caff6/job_01bdf71e-7c35-4454-844b-809900bcef64/S2B_33JVN_20201215_0_L2A_B03.tif'\n",
    "blue = 'project_1f2bad99-daad-4a60-9192-5169263caff6/job_01bdf71e-7c35-4454-844b-809900bcef64/S2B_33JVN_20201215_0_L2A_B02.tif'\n",
    "file_list = [red, green, blue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(array):\n",
    "    \"\"\"Normalizes numpy arrays into scale 0.0 - 1.0\"\"\"\n",
    "    array_min, array_max = array.min(), array.max()\n",
    "    return ((array - array_min)/(array_max - array_min))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgb = []\n",
    "for file in file_list:\n",
    "    with rasterio.open(file) as src:\n",
    "        rgb.append(normalize(src.read(1)))\n",
    "rgb = np.dstack(rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11f9b6210>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results.\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(rgb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "UP42-py-dev",
   "language": "python",
   "name": "up42-py-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}