{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kOn6Dorgx2wk" }, "source": [ "--------\n", "\n", "# Search and fuse S1, S2 and weather data with custom raster or vector\n", "\n", "--------\n", "\n", "**Short description**\n", "\n", "This notebook introduces the Fuse functionality in the SpaceSense library.\n", "\n", "In this notebook, you will search for, filter, and fuse Sentinel-1, Sentinel-2 and ERA5 weather data with user-provided raster and vector files.\n", "\n", "--------\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o4gN2FtIx2wl" }, "source": [ "### 0 - Install Spacesense Client Library and dependencies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please follow the [installation process](../gettingstarted/installation.rst) and use the virtual environment in this notebook." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "keC_14irx2wq" }, "source": [ "### 1 - Import spacesense object(s) and other dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "l1WpZ6RSx2wr" }, "outputs": [], "source": [ "from spacesense import Client, Raster, Vector\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import json\n", "import datetime\n", "iso = datetime.date.fromisoformat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2 - Configure the API Key by setting the `SS_API_KEY` environment variable" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "if \"SS_API_KEY\" not in os.environ:\n", " from getpass import getpass\n", " api_key = getpass('Enter your api key : ')\n", " os.environ[\"SS_API_KEY\"] = api_key" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3 - Define AOI and output options\n", "\n", "The GeoJSON of this AOI, containing an agricultural field in Sardinia, can be downloaded [here](../resources/aois/sardinia_field.geojson)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Define the AOI\n", "with open(\"../resources/aois/sardinia_field.geojson\", mode=\"r\") as file:\n", " aoi = json.load(file)\n", "\n", "# Get an instance of the SpaceSense Client object\n", "client = Client(id=\"fuse_with_custom_data\")\n", "\n", "# Enable to save data in local files\n", "client.enable_local_output()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4 - Search S1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titledateswath_coverage_percentagerelativeorbitnumberlastrelativeorbitnumberproducttypesensoroperationalmodeacquisitiontypepolarisationmodebeginpositionplatformnamemissiondatatakeidorbitdirectionorbitnumberinstrumentnamelastorbitnumberendpositioningestiondateslicenumberplatformidentifier
0S1B_IW_GRDH_1SDV_20210101T172105_20210101T1721...2021-01-01100.088.088.0GRDIWNOMINALVV VH2021-01-01T17:21:05.249000Sentinel-1194718.0ASCENDING24964.0Synthetic Aperture Radar (C-band)24964.02021-01-01T17:21:30.2480002021-01-01T21:41:44.62600010.02016-025A
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" ], "text/plain": [ " title date \\\n", "0 S1B_IW_GRDH_1SDV_20210101T172105_20210101T1721... 2021-01-01 \n", "\n", " swath_coverage_percentage relativeorbitnumber lastrelativeorbitnumber \\\n", "0 100.0 88.0 88.0 \n", "\n", " producttype sensoroperationalmode acquisitiontype polarisationmode \\\n", "0 GRD IW NOMINAL VV VH \n", "\n", " beginposition platformname missiondatatakeid orbitdirection \\\n", "0 2021-01-01T17:21:05.249000 Sentinel-1 194718.0 ASCENDING \n", "\n", " orbitnumber instrumentname lastorbitnumber \\\n", "0 24964.0 Synthetic Aperture Radar (C-band) 24964.0 \n", "\n", " endposition ingestiondate slicenumber \\\n", "0 2021-01-01T17:21:30.248000 2021-01-01T21:41:44.626000 10.0 \n", "\n", " platformidentifier \n", "0 2016-025A " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date = \"2021-01-01\"\n", "end_date = \"2021-01-02\"\n", "# Retrieves all S1 images corresponding to the aoi, start date, and end date\n", "res_S1 = client.s1_search(aoi=aoi, start_date=start_date, end_date=end_date)\n", "res_S1.filter_duplicate_dates()\n", "res_S1.dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5 - Search S2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddatetilevalid_pixel_percentageplatformrelative_orbit_numberproduct_iddatetimeswath_coverage_percentageno_datacloud_shadowsvegetationnot_vegetatedwatercloud_medium_probabilitycloud_high_probabilitythin_cirrussnow
6S2B_32TMK_20210101_0_L2A2021-01-0132TMK0.00sentinel-2b065S2B_MSIL2A_20210101T102329_N0214_R065_T32TMK_2...2021-01-01T10:29:42Z100.00.00.00.000.00.00.00100.000.00.0
5S2A_32TMK_20210103_0_L2A2021-01-0332TMK0.00sentinel-2a022S2A_MSIL2A_20210103T101411_N0214_R022_T32TMK_2...2021-01-03T10:19:48Z100.00.00.00.000.00.00.00100.000.00.0
4S2A_32TMK_20210106_0_L2A2021-01-0632TMK0.00sentinel-2a065S2A_MSIL2A_20210106T102411_N0214_R065_T32TMK_2...2021-01-06T10:29:44Z100.00.00.00.000.00.00.00100.000.00.0
3S2B_32TMK_20210108_0_L2A2021-01-0832TMK50.73sentinel-2b022S2B_MSIL2A_20210108T101319_N0214_R022_T32TMK_2...2021-01-08T10:19:47Z100.00.00.00.000.00.049.270.000.00.0
2S2B_32TMK_20210111_0_L2A2021-01-1132TMK5.08sentinel-2b065S2B_MSIL2A_20210111T102309_N0214_R065_T32TMK_2...2021-01-11T10:29:43Z100.00.00.00.000.00.016.6977.530.70.0
1S2A_32TMK_20210113_0_L2A2021-01-1332TMK0.00sentinel-2a022S2A_MSIL2A_20210113T101401_N0214_R022_T32TMK_2...2021-01-13T10:19:48Z100.00.00.00.000.00.00.00100.000.00.0
0S2A_32TMK_20210116_0_L2A2021-01-1632TMK100.00sentinel-2a065S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2...2021-01-16T10:29:44Z100.00.00.097.560.00.00.000.000.00.0
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" ], "text/plain": [ " id date tile valid_pixel_percentage \\\n", "6 S2B_32TMK_20210101_0_L2A 2021-01-01 32TMK 0.00 \n", "5 S2A_32TMK_20210103_0_L2A 2021-01-03 32TMK 0.00 \n", "4 S2A_32TMK_20210106_0_L2A 2021-01-06 32TMK 0.00 \n", "3 S2B_32TMK_20210108_0_L2A 2021-01-08 32TMK 50.73 \n", "2 S2B_32TMK_20210111_0_L2A 2021-01-11 32TMK 5.08 \n", "1 S2A_32TMK_20210113_0_L2A 2021-01-13 32TMK 0.00 \n", "0 S2A_32TMK_20210116_0_L2A 2021-01-16 32TMK 100.00 \n", "\n", " platform relative_orbit_number \\\n", "6 sentinel-2b 065 \n", "5 sentinel-2a 022 \n", "4 sentinel-2a 065 \n", "3 sentinel-2b 022 \n", "2 sentinel-2b 065 \n", "1 sentinel-2a 022 \n", "0 sentinel-2a 065 \n", "\n", " product_id datetime \\\n", "6 S2B_MSIL2A_20210101T102329_N0214_R065_T32TMK_2... 2021-01-01T10:29:42Z \n", "5 S2A_MSIL2A_20210103T101411_N0214_R022_T32TMK_2... 2021-01-03T10:19:48Z \n", "4 S2A_MSIL2A_20210106T102411_N0214_R065_T32TMK_2... 2021-01-06T10:29:44Z \n", "3 S2B_MSIL2A_20210108T101319_N0214_R022_T32TMK_2... 2021-01-08T10:19:47Z \n", "2 S2B_MSIL2A_20210111T102309_N0214_R065_T32TMK_2... 2021-01-11T10:29:43Z \n", "1 S2A_MSIL2A_20210113T101401_N0214_R022_T32TMK_2... 2021-01-13T10:19:48Z \n", "0 S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2... 2021-01-16T10:29:44Z \n", "\n", " swath_coverage_percentage no_data cloud_shadows vegetation \\\n", "6 100.0 0.0 0.0 0.00 \n", "5 100.0 0.0 0.0 0.00 \n", "4 100.0 0.0 0.0 0.00 \n", "3 100.0 0.0 0.0 0.00 \n", "2 100.0 0.0 0.0 0.00 \n", "1 100.0 0.0 0.0 0.00 \n", "0 100.0 0.0 0.0 97.56 \n", "\n", " not_vegetated water cloud_medium_probability cloud_high_probability \\\n", "6 0.0 0.0 0.00 100.00 \n", "5 0.0 0.0 0.00 100.00 \n", "4 0.0 0.0 0.00 100.00 \n", "3 0.0 0.0 49.27 0.00 \n", "2 0.0 0.0 16.69 77.53 \n", "1 0.0 0.0 0.00 100.00 \n", "0 0.0 0.0 0.00 0.00 \n", "\n", " thin_cirrus snow \n", "6 0.0 0.0 \n", "5 0.0 0.0 \n", "4 0.0 0.0 \n", "3 0.0 0.0 \n", "2 0.7 0.0 \n", "1 0.0 0.0 \n", "0 0.0 0.0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date = \"2021-01-01\"\n", "end_date = \"2021-01-17\"\n", "# Retrieves all S2 images corresponding to the aoi, start date, and end date\n", "res_S2 = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date)\n", "res_S2.dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6 - Search Weather" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateavgtempprec
02021-01-019.38860514.856473
12021-01-029.04799812.129927
22021-01-037.0304813.415852
32021-01-047.3527479.735805
42021-01-057.53179313.345673
52021-01-066.9309020.578632
62021-01-076.7750794.149154
72021-01-089.1841379.411143
82021-01-0912.0557809.186649
92021-01-1011.52041010.954764
102021-01-118.97451210.341069
112021-01-129.0479980.744074
122021-01-1312.3574774.554079
132021-01-1412.4221131.272354
142021-01-1511.4825998.809325
152021-01-169.1948182.330621
162021-01-1711.1722351.816961
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" ], "text/plain": [ " date avgtemp prec\n", "0 2021-01-01 9.388605 14.856473\n", "1 2021-01-02 9.047998 12.129927\n", "2 2021-01-03 7.030481 3.415852\n", "3 2021-01-04 7.352747 9.735805\n", "4 2021-01-05 7.531793 13.345673\n", "5 2021-01-06 6.930902 0.578632\n", "6 2021-01-07 6.775079 4.149154\n", "7 2021-01-08 9.184137 9.411143\n", "8 2021-01-09 12.055780 9.186649\n", "9 2021-01-10 11.520410 10.954764\n", "10 2021-01-11 8.974512 10.341069\n", "11 2021-01-12 9.047998 0.744074\n", "12 2021-01-13 12.357477 4.554079\n", "13 2021-01-14 12.422113 1.272354\n", "14 2021-01-15 11.482599 8.809325\n", "15 2021-01-16 9.194818 2.330621\n", "16 2021-01-17 11.172235 1.816961" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date = \"2021-01-01\"\n", "end_date = \"2021-01-17\"\n", "# Retrieves temperature extrema corresponding to the aoi, start date, and end date\n", "res_weather = client.weather_search(aoi=aoi, start_date=start_date, end_date=end_date, variables = [\"AVGTEMP\", \"PREC\"])\n", "res_weather.dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7 - Filter search results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select only the results which you require. Learn more about how to filter and manipulate search results with [this example](./3_filter_s1_s2.ipynb)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddatetilevalid_pixel_percentageplatformrelative_orbit_numberproduct_iddatetimeswath_coverage_percentageno_datacloud_shadowsvegetationnot_vegetatedwatercloud_medium_probabilitycloud_high_probabilitythin_cirrussnow
0S2A_32TMK_20210116_0_L2A2021-01-1632TMK100.0sentinel-2a065S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2...2021-01-16T10:29:44Z100.00.00.097.560.00.00.00.00.00.0
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" ], "text/plain": [ " id date tile valid_pixel_percentage \\\n", "0 S2A_32TMK_20210116_0_L2A 2021-01-16 32TMK 100.0 \n", "\n", " platform relative_orbit_number \\\n", "0 sentinel-2a 065 \n", "\n", " product_id datetime \\\n", "0 S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2... 2021-01-16T10:29:44Z \n", "\n", " swath_coverage_percentage no_data cloud_shadows vegetation \\\n", "0 100.0 0.0 0.0 97.56 \n", "\n", " not_vegetated water cloud_medium_probability cloud_high_probability \\\n", "0 0.0 0.0 0.0 0.0 \n", "\n", " thin_cirrus snow \n", "0 0.0 0.0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_S2.dataframe = res_S2.dataframe[res_S2.dataframe[\"valid_pixel_percentage\"] > 80]\n", "res_S2.dataframe" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "res_weather.dataframe = res_weather.dataframe[[date in list(res_S2.dataframe[\"date\"])+list(res_S1.dataframe[\"date\"]) for date in list(res_weather.dataframe[\"date\"])]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8 - Select S2 bands" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "res_S2.output_bands = [\"RED\", \"GREEN\", \"BLUE\", \"NIR\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9 - Provide custom data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Example vector of land use over the AOI\n", "# The \"attributes\" parameter can be used to select individual layers to fuse\n", "land_use = Vector(\"../resources/example_files/Sardinia_land_use.shp\", attributes=[\"IDFEATURE\"])\n", "\n", "# Example raster of NDVI (say for comparison) over the AOI\n", "ndvi = Raster(\"../resources/example_files/ndvi_crop_sardinia.tif\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10 - Obtain all S1 and S2 images, and Fuse with custom files" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-11-28 09:53:21,611 INFO spacesense.core : created everything\n" ] } ], "source": [ "fusion = client.fuse(\n", " catalogs_list=[res_S1,res_S2,res_weather],\n", " to_fuse=[land_use, ndvi],\n", " additional_info={\"Info\": \"You can put any additional information, such as custom non-raster or non-vector data, in this parameter\"}\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 11 - See the fused dataset " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result of the Fusion is an [Xarray Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html). This is a 3 dimensional array with a structure and in-memory representation of a NetCDF file. \n", "\n", "[Here](https://docs.xarray.dev/en/stable/tutorials-and-videos.html) is a link to several tutorials from Xarray. And [this link](http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/xarray.html) gives some introductory tutorials including basic data structure organization, selection, and computation.\n", "\n", "For this example, the Dataset has dimensions of latitude, longitude, and time, with the Data Variables being the data layers of S1, S2, and the custom raster and vector that we fused together. As custom rasters and vectors do not currently support association with a date, they are fused as time-independant variables, thus only having an x (latitude) and y (longitude) dimension. S1 and S2 bands were not changed from their default values, which can be found [here for S1](../apidocs/searchresults.html#models.Sentinel1SearchResult.output_bands), and [here for S2](apidocs/searchresults.html#models.Sentinel2SearchResult.output_bands). " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
                            "Dimensions:                      (time: 2, y: 43, x: 88)\n",
                            "Coordinates:\n",
                            "  * time                         (time) datetime64[ns] 2021-01-01 2021-01-16\n",
                            "  * y                            (y) float32 39.83 39.83 39.83 ... 39.83 39.83\n",
                            "  * x                            (x) float64 8.623 8.623 8.623 ... 8.63 8.63\n",
                            "Data variables:\n",
                            "    S1_VH                        (time, y, x) float32 0.0 0.0206 ... nan nan\n",
                            "    S1_VV                        (time, y, x) float32 0.0 0.1091 ... nan nan\n",
                            "    S1_DV                        (time, y, x) float32 nan 5.294 ... nan nan\n",
                            "    S1_LIA                       (time, y, x) float32 0.0 38.73 ... nan nan\n",
                            "    S2_RED                       (time, y, x) float32 nan nan nan ... 0.0417 0.0\n",
                            "    S2_GREEN                     (time, y, x) float32 nan nan nan ... 0.0371 0.0\n",
                            "    S2_BLUE                      (time, y, x) float32 nan nan nan ... 0.0255 0.0\n",
                            "    S2_NIR                       (time, y, x) float32 nan nan nan ... 0.2182 0.0\n",
                            "    WEATHER_AVGTEMP              (time) float32 9.389 9.195\n",
                            "    WEATHER_PREC                 (time) float32 14.86 2.331\n",
                            "    Sardinia_land_use_IDFEATURE  (y, x) float32 1.361e+05 ... 1.361e+05\n",
                            "    ndvi_crop_sardinia_BAND1     (y, x) float32 nan nan nan nan ... nan nan nan\n",
                            "Attributes:\n",
                            "    crs:                   +init=epsg:4326\n",
                            "    transform:             [ 8.97434772e-05  0.00000000e+00  8.62246354e+00  ...\n",
                            "    res:                   [8.97434772e-05 9.03483104e-05]\n",
                            "    ulx, uly:              [ 8.62246354 39.83104648]\n",
                            "    weather_data_lineage:  {"Data origin": "ERA5 reanalysis by ECMWF download...\n",
                            "    s1_data_lineage:       {"Data origin": "S3 bucket (ARN=arn:aws:s3:::senti...\n",
                            "    s2_data_lineage:       {"Data origin": "S3 bucket (ARN=arn:aws:s3:::senti...\n",
                            "    additional_info:       {'Info': 'You can put any additional information, ...
" ], "text/plain": [ "\n", "Dimensions: (time: 2, y: 43, x: 88)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2021-01-01 2021-01-16\n", " * y (y) float32 39.83 39.83 39.83 ... 39.83 39.83\n", " * x (x) float64 8.623 8.623 8.623 ... 8.63 8.63\n", "Data variables:\n", " S1_VH (time, y, x) float32 0.0 0.0206 ... nan nan\n", " S1_VV (time, y, x) float32 0.0 0.1091 ... nan nan\n", " S1_DV (time, y, x) float32 nan 5.294 ... nan nan\n", " S1_LIA (time, y, x) float32 0.0 38.73 ... nan nan\n", " S2_RED (time, y, x) float32 nan nan nan ... 0.0417 0.0\n", " S2_GREEN (time, y, x) float32 nan nan nan ... 0.0371 0.0\n", " S2_BLUE (time, y, x) float32 nan nan nan ... 0.0255 0.0\n", " S2_NIR (time, y, x) float32 nan nan nan ... 0.2182 0.0\n", " WEATHER_AVGTEMP (time) float32 9.389 9.195\n", " WEATHER_PREC (time) float32 14.86 2.331\n", " Sardinia_land_use_IDFEATURE (y, x) float32 1.361e+05 ... 1.361e+05\n", " ndvi_crop_sardinia_BAND1 (y, x) float32 nan nan nan nan ... nan nan nan\n", "Attributes:\n", " crs: +init=epsg:4326\n", " transform: [ 8.97434772e-05 0.00000000e+00 8.62246354e+00 ...\n", " res: [8.97434772e-05 9.03483104e-05]\n", " ulx, uly: [ 8.62246354 39.83104648]\n", " weather_data_lineage: {\"Data origin\": \"ERA5 reanalysis by ECMWF download...\n", " s1_data_lineage: {\"Data origin\": \"S3 bucket (ARN=arn:aws:s3:::senti...\n", " s2_data_lineage: {\"Data origin\": \"S3 bucket (ARN=arn:aws:s3:::senti...\n", " additional_info: {'Info': 'You can put any additional information, ..." ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fusion.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12 - Plot the fused dataset with matplotlib" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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eAAAAkUR/1MRpngAAAAAAAIBDzEwDACCK8UsgAAAAIon+qImZaQAAAAAAAIBDzEwDgFNM51VeI9vQuy4CLTl5LigZbmSrcxdGoCXhxy+BwHfrnUNdjewMb7mR9UvYZGQ9fdsd7aNv8ue2+ZbadCNbUtbRyBosZ79/1/s9RtYysdrRtme33Webf1mVYmQJMfVG5vOYWUPAbE9W3AFH7bHzWVWm47IZcZVGdjhg/n38qralo/r2uxONrN4yH19FfYKj+iQpy1dhZF3jv3K0baqnysg+qm5vZK1jDxpZZoz9c1BtxdmUrXDUHjtJ7hojq7ecff3M9ByyzdM85t+yqkDAUZ1fNCQbWZ3Nc1hyMMdRfbsPJ9nmE856z9H2gxI3G5nPZb7XM2PM5/CP5X2NbO6eS8y2pL/rqC2N6Z/0mZHtSTcf91vnphpZpxmPGtnGqb87ofYgcuiPmhhMAwAgitF5AQAAQCTRHzVxmicAAAAAAADgEDPTAACIYpblkhWGX/HCUQcAAABOP/RHTQymAQAQxQJyKaAwTKsPQx0AAAA4/dAfNXGaJwAAAAAAAOAQM9MAAIhiLPgKAACASKI/amIwDQCAKMYaFQAAAIgk+qMmTvMEAAAAAAAAHGJmGgAAUYxp9QAAAIgk+qMmBtMAAFErfegGI9vzaucTqnN21odGNnHnhSdU53eJafVA88xY+xNH5TyugJF9WZdmZNmx+4yslafK0T5qrFjbPCG+1iybbpZdsTfH0X5ibB5LZa3P0ba7a1rY5rlpO4xsV02KkbXw1BlZvds8CWbr4VaO2mMnLbbaNq+3zP3UWx4j6xr/lZFVB7yO9n0wYB7Han+ckdUGnH+9Kq1NNrIEt3kc7fjc9UaWEXvAyD6tzjIyj8zXiSTlePcaWY3l7PjYSdVhI6uyeV52NrQ0Mp/LfHySlGbzOmvriXfUnk/rzffWs1/1N7IWsc6eg1+2e982vzS+1NH2dieJVQQsIwvYvL4Hp3xsZEUHehjZK5XnO2yLvZ8kf2Rkw1qVGNmnHTKMbHdZ2xPaN6IL/VETp3kCAAAAAAAADjEzDQCAKGaFaVr9qfRLIAAAAE4e+qMmZqYBAAAAAAAADjGYBgBAFLMkWVYYbpF+IAAAAPheinR/9IknnlD79u3l8/mUl5enlStXNlr20ksvlcvlMm4/+tGPgmV++ctfGvcPHjy4SW3iNE8AAKJYQC65FIarJ4WhDgAAAJx+ItkfXbhwoSZNmqTCwkLl5eVp1qxZGjRokDZu3Kg2bdoY5V9++WXV1X19IZF9+/apZ8+euuaaa0LKDR48WP/7v/8b/H9cnHlRmeNhZhoAAAAAAACizsyZMzV+/HiNHTtWXbt2VWFhoRISEjRv3jzb8mlpacrMzAzeFi9erISEBGMwLS4uLqRcy5bmlYWPh8E0AACi2NFLkYfjBgAAADRVuPujlZWVIbfa2lrb/dbV1amkpET5+fnBzO12Kz8/X8uXL3fU9meeeUYjRoxQYmJiSL5kyRK1adNGnTp10g033KB9+/Y16ZgwmAYAQBQL/PvqSeG4AQAAAE0V7v5odna2UlJSgrf777/fdr979+6V3+9XRkZGSJ6RkaHS0tJvbffKlSu1du1aXX/99SH54MGD9fzzz6u4uFgPPPCA3n33XV1xxRXy+/2OjwlrpgHAaaDzKq+jcht61317oQhLH7rByPa82tnILigZbrv96tyFYW8TgOizqcpcR8VOx8TdRnbIb66b8mltOyPrl7DZ0T5S3dW2+Y6GVkaWm7jFyHzuekf7eXf3OUaWnnjI0bZ1fo9tfthv/v1o6ztgZJUNPiMLWObv9vEeZ4/FTmlNsm3e0mse38P+WCOrt8zHmBW739G+S6o6OCrXIWGvo3KSVFprPp6valMdbet2BYwsPeagkbX3mTMtPq+xf2/Yvc6yY5s2U+NYfpu1kVp5qows2V3juM6tDebzuqku3dG2kz+80sgyWlUa2cA2nzqqr7O3zDbf2eBsvorPZX5pj3WZy7NXBOKNzG/z3vpRyr+MbFm1+ZnQFB8eNl/3lyZsMrKb2r9jZFMO/MTIut71qON9f/r73zkui++fHTt2KDn568/Apq5X5tQzzzyj7t27q0+fPiH5iBEjgv/u3r27evToobPPPltLlizR5Zdf7qhuZqYBABDFwnLlpH/fAAAAgKYKd380OTk55NbYYFrr1q3l8XhUVhY6eF1WVqbMzMzjtrmqqkovvPCCxo0b962P76yzzlLr1q21ebOzH8kkBtMAAIhqrJkGAACASIpUf9Tr9So3N1fFxcXBLBAIqLi4WH379j3uti+++KJqa2t13XXXfet+vvzyS+3bt09t27Z13DYG0wAAAAAAABB1Jk2apKefflrPPfec1q9frxtuuEFVVVUaO3asJGn06NG64447jO2eeeYZDRs2TK1ahS6pcOjQId1yyy364IMPtHXrVhUXF+vKK6/UOeeco0GDBjluF2umAQAQxcI1q4yZaQAAAGiOSPZHhw8frj179mjq1KkqLS1Vr169VFRUFLwowfbt2+V2h84T27hxo/75z3/qrbfeMurzeDz6+OOP9dxzz6miokJZWVkaOHCg7rnnniat3cZgGgAAUSxgueQKQ+eFq3kCAACgOSLdHy0oKFBBQYHtfUuWLDGyTp06yWpkweD4+Hi9+eabzWrHsTjNEwAAAAAAAHCImWkAAESxcF2Jk6t5AgAAoDnoj5qYmQYAAAAAAAA4xMw0AMBpb3bWh0Y2ceeFEWiJ6cgvgeFY8DUMjQG+R/6Y9z9GNujdm41sd00LI9t/OMHIYj1+IzvQ1ixnp2fCdts83VNpZAcDPiM7w1vuaD9ZiWZ9FbXxjrZtEVdnm9cGzK8L2T6zPfWWx8i+rG5pZB0S9zpqj51ET61tXl6X6Gj7TTWZRlYTF+to27beCiP7si7NyOLU4Kg+SUr3HjIyu+Ntx+5420lwm8csJeawbdltta2NLMld42g/dlLd5n58LvN91C3WfMwf1wds61xRfZaRPbH+EkftCRzwGtmZ7SuMbNn+c4ysoO0/jKw6YP/a8cvZ3+way3zcaTavcbvjWCfz+U901RtZXsLnjtrSmH8dzjGyktpsIzs7dreRXZqz2cj+sb2n4313/69HjeyTR37neHuEF/1RE4NpAABEMa7mCQAAgEiiP2riNE8AAGBYunSphg4dqqysLLlcLi1atCh4X319vW677TZ1795diYmJysrK0ujRo7Vz586QOsrLyzVq1CglJycrNTVV48aN06FDoTMxPv74Y1100UXy+XzKzs7Wgw8+aLTlxRdfVOfOneXz+dS9e3e98cYbIfdblqWpU6eqbdu2io+PV35+vjZt2hS+gwEAAAAcg8E0AACimBXGW1NUVVWpZ8+eeuKJJ4z7qqurtXr1ak2ZMkWrV6/Wyy+/rI0bN+onP/lJSLlRo0Zp3bp1Wrx4sV577TUtXbpUEyZMCN5fWVmpgQMHKicnRyUlJXrooYc0ffp0zZ07N1hm2bJlGjlypMaNG6ePPvpIw4YN07Bhw7R27dpgmQcffFBz5sxRYWGhVqxYocTERA0aNEg1Nc0/PQkAAABHRKo/Gs04zRMAgCgWqWn1V1xxha644grb+1JSUrR48eKQ7PHHH1efPn20fft2nXnmmVq/fr2Kior04Ycfqnfv3pKkxx57TEOGDNHDDz+srKwszZ8/X3V1dZo3b568Xq+6deumNWvWaObMmcFBt9mzZ2vw4MG65ZZbJEn33HOPFi9erMcff1yFhYWyLEuzZs3S5MmTdeWVV0qSnn/+eWVkZGjRokUaMWJEkx43AAAAQnGap4mZaQAAnEYqKytDbrW19gt6N9WBAwfkcrmUmpoqSVq+fLlSU1ODA2mSlJ+fL7fbrRUrVgTLXHzxxfJ6v14UetCgQdq4caP2798fLJOfnx+yr0GDBmn58uWSpC1btqi0tDSkTEpKivLy8oJlAAAAgHBiMA0AgGgW5nn12dnZSklJCd7uv//+E25iTU2NbrvtNo0cOVLJycmSpNLSUrVp0yakXExMjNLS0lRaWhosk5GREVLm6P+/rcyx9x+7nV0ZAAAAnADO8zRwmicAANEsTNPq9e86duzYERzwkqS4uLgTqra+vl4///nPZVmWnnrqqROqCwAAAFEozP3RUwEz0wAAOI0kJyeH3E5kMO3oQNq2bdu0ePHikEG6zMxM7d69O6R8Q0ODysvLlZmZGSxTVlYWUubo/7+tzLH3H7udXRkAAAAgnJiZBgAI6rzKa2QbetdFoCXfnQtKhhvZ6tyFEWiJM5Z15BaOesLp6EDapk2b9M4776hVq1Yh9/ft21cVFRUqKSlRbm6uJOntt99WIBBQXl5esMxdd92l+vp6xcbGSpIWL16sTp06qWXLlsEyxcXFuvnmm4N1L168WH379pUkdejQQZmZmSouLlavXr0kHVkXbsWKFbrhhhvC+6DxvRew+UV854EUIzu8JdnIXAGzvk8SKx3tN9bdYJvnePcZWU0g1sjKG1o42k9abLWRVTeYn+t2YuweoKRUmzrt2pjgNv9W5CSWG9m26lZG5lSm74BtnuatMrKK+gSbLN7I4lzmc20n1u03soxY8/lP8ZjHqzE1lnkcPbJ/Hr6prN583Vb4zcecatMeu/1Kks9Vb2RVgeb/4FLqN9vodlUY2b6AeeXlFdWdbeuc+VG+kcWtN59XO65089h+UubsR5fXE3sZ2RXJH9uWjXXZv9+/ye0y/zDXWOZclxrL/Mput22VzfOaHXPQUVsak5iwycj+frCHkdm99n7Ycq2Rreh4puN9N5SmOS6L71609kcjiZlpAABEsaNXTwrHrSkOHTqkNWvWaM2aNZKOLPS/Zs0abd++XfX19frZz36mVatWaf78+fL7/SotLVVpaanq6o58oe7SpYsGDx6s8ePHa+XKlXr//fdVUFCgESNGKCsrS5J07bXXyuv1aty4cVq3bp0WLlyo2bNna9KkScF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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_p = fusion.dataset.drop([\"WEATHER_AVGTEMP\", \"WEATHER_PREC\"])\n", "\n", "# Configure matplotlib to show the 11 layers of fused dataset in a 6x2 grid\n", "fig, axs = plt.subplots(nrows=5,ncols=2, figsize=(16, 30))\n", "\n", "# Loop over the 11 variables in the fused dataset, select a date, and plot the layer\n", "for idx, data_var in enumerate(ds_p.data_vars):\n", " # Set the variable to plot, and take the first date\n", " var = ds_p.isel(time=0)[data_var]\n", " # If the first date is all nans (no data for that variable), attempt to plot the next date\n", " for i, time in enumerate(ds_p.time.values):\n", " if np.isnan(var.mean().values) == True:\n", " var = ds_p.isel(time=i+1)[data_var]\n", " if \"WEATHER\" not in data_var:\n", " ax = fig.axes[idx]\n", " var.plot(ax=ax,cbar_kwargs={'label':\"\"})\n", " ax.set_title(data_var)\n", " ax.axis('off')\n", " plt.draw()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Select and plot individual layer and time\n", "fusion.dataset.S2_RED.sel(time=\"2021-01-16\").plot()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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