{ "cells": [ { "cell_type": "markdown", "id": "179d753d-6c51-4e6c-88e0-be44818a0eef", "metadata": {}, "source": [ "# JupyterLab conectándose a openEO" ] }, { "cell_type": "markdown", "id": "d4ee417d-1a2b-4b74-9fac-2b2acad4a0ca", "metadata": {}, "source": [ "Importa el paquete `openeo` y conéctate al back-end de openEO del Copernicus Data Space Ecosystem." ] }, { "cell_type": "code", "execution_count": 10, "id": "f7f09e6a-bd6b-48b6-8819-f60909f45722", "metadata": {}, "outputs": [], "source": [ "import openeo" ] }, { "cell_type": "code", "execution_count": 11, "id": "4b809f3b-434e-41a3-832e-ccaacea54d13", "metadata": {}, "outputs": [], "source": [ "connection = openeo.connect(url=\"openeo.dataspace.copernicus.eu\",)" ] }, { "cell_type": "markdown", "id": "371198ee-db1b-43fa-941a-8ddb9f0c0561", "metadata": {}, "source": [ "Puedes obtener todas las colecciones existentes dentro de openEO" ] }, { "cell_type": "code", "execution_count": 3, "id": "da476336-8519-44f4-9c8f-a6deb3850e7b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['SENTINEL3_OLCI_L1B', 'SENTINEL3_SLSTR', 'SENTINEL_5P_L2', 'COPERNICUS_VEGETATION_PHENOLOGY_PRODUCTIVITY_10M_SEASON1', 'COPERNICUS_VEGETATION_PHENOLOGY_PRODUCTIVITY_10M_SEASON2', 'COPERNICUS_PLANT_PHENOLOGY_INDEX', 'ESA_WORLDCOVER_10M_2020_V1', 'ESA_WORLDCOVER_10M_2021_V2', 'COPERNICUS_VEGETATION_INDICES', 'SENTINEL2_L1C', 'SENTINEL2_L2A', 'SENTINEL1_GRD', 'COPERNICUS_30', 'LANDSAT8_L2', 'SENTINEL3_SYN_L2_SYN', 'SENTINEL3_SLSTR_L2_LST', 'SENTINEL1_GLOBAL_MOSAICS', 'SENTINEL3_OLCI_L2_LAND', 'SENTINEL3_OLCI_L2_WATER', 'SENTINEL3_SYN_L2_AOD']\n" ] } ], "source": [ "print(connection.list_collection_ids())" ] }, { "cell_type": "markdown", "id": "c6ac9c05-1f6a-4fd8-8b02-8ca3726d4454", "metadata": {}, "source": [ "Y obtener mas detalle de alguna de ellas que te interese:" ] }, { "cell_type": "code", "execution_count": 4, "id": "698ee5ff-d68d-4ca9-b325-96b3262848ad", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " " ], "text/plain": [ "{'assets': {'thumbnail': {'href': 'https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2022/07/tallinn_estonia/24383281-1-eng-GB/Tallinn_Estonia_pillars.jpg',\n", " 'roles': ['thumbnail'],\n", " 'title': 'Sentinel 2 L2A',\n", " 'type': 'image/png'}},\n", " 'cube:dimensions': {'bands': {'type': 'bands',\n", " 'values': ['B01',\n", " 'B02',\n", " 'B03',\n", " 'B04',\n", " 'B05',\n", " 'B06',\n", " 'B07',\n", " 'B08',\n", " 'B8A',\n", " 'B09',\n", " 'B11',\n", " 'B12',\n", " 'WVP',\n", " 'AOT',\n", " 'SCL',\n", " 'sunAzimuthAngles',\n", " 'sunZenithAngles',\n", " 'viewAzimuthMean',\n", " 'viewZenithMean',\n", " 'CLD',\n", " 'SNW']},\n", " 't': {'extent': ['2015-07-04T00:00:00Z', None], 'type': 'temporal'},\n", " 'x': {'axis': 'x',\n", " 'extent': [-180, 180],\n", " 'reference_system': {'$schema': 'https://proj.org/schemas/v0.2/projjson.schema.json',\n", " 'area': 'World',\n", " 'bbox': {'east_longitude': 180,\n", " 'north_latitude': 90,\n", " 'south_latitude': -90,\n", " 'west_longitude': -180},\n", " 'coordinate_system': {'axis': [{'abbreviation': 'Lat',\n", " 'direction': 'north',\n", " 'name': 'Geodetic latitude',\n", " 'unit': 'degree'},\n", " {'abbreviation': 'Lon',\n", " 'direction': 'east',\n", " 'name': 'Geodetic longitude',\n", " 'unit': 'degree'}],\n", " 'subtype': 'ellipsoidal'},\n", " 'datum': {'ellipsoid': {'inverse_flattening': 298.257223563,\n", " 'name': 'WGS 84',\n", " 'semi_major_axis': 6378137},\n", " 'name': 'World Geodetic System 1984',\n", " 'type': 'GeodeticReferenceFrame'},\n", " 'id': {'authority': 'OGC', 'code': 'Auto42001', 'version': '1.3'},\n", " 'name': 'AUTO 42001 (Universal Transverse Mercator)',\n", " 'type': 'GeodeticCRS'},\n", " 'step': 10,\n", " 'type': 'spatial'},\n", " 'y': {'axis': 'y',\n", " 'extent': [-56, 83],\n", " 'reference_system': {'$schema': 'https://proj.org/schemas/v0.2/projjson.schema.json',\n", " 'area': 'World',\n", " 'bbox': {'east_longitude': 180,\n", " 'north_latitude': 90,\n", " 'south_latitude': -90,\n", " 'west_longitude': -180},\n", " 'coordinate_system': {'axis': [{'abbreviation': 'Lat',\n", " 'direction': 'north',\n", " 'name': 'Geodetic latitude',\n", " 'unit': 'degree'},\n", " {'abbreviation': 'Lon',\n", " 'direction': 'east',\n", " 'name': 'Geodetic longitude',\n", " 'unit': 'degree'}],\n", " 'subtype': 'ellipsoidal'},\n", " 'datum': {'ellipsoid': {'inverse_flattening': 298.257223563,\n", " 'name': 'WGS 84',\n", " 'semi_major_axis': 6378137},\n", " 'name': 'World Geodetic System 1984',\n", " 'type': 'GeodeticReferenceFrame'},\n", " 'id': {'authority': 'OGC', 'code': 'Auto42001', 'version': '1.3'},\n", " 'name': 'AUTO 42001 (Universal Transverse Mercator)',\n", " 'type': 'GeodeticCRS'},\n", " 'step': 10,\n", " 'type': 'spatial'}},\n", " 'description': 'SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The SENTINEL-2 Multispectral Instrument (MSI) samples 13 spectral bands: four bands at 10 metres, six bands at 20 metres and three bands at 60 metres spatial resolution.\\n The level 2A data is atmospherically corrected using Sen2Cor.',\n", " 'extent': {'spatial': {'bbox': [[-180, -56, 180, 83]]},\n", " 'temporal': {'interval': [['2015-07-04T00:00:00Z', None]]}},\n", " 'id': 'SENTINEL2_L2A',\n", " 'keywords': ['COPERNICUS',\n", " 'ESA',\n", " 'Orthoimagery',\n", " 'Sentinel-2',\n", " 'MSI',\n", " 'Level-2A',\n", " 'Radiometry',\n", " 'Plant Resource',\n", " 'TOC'],\n", " 'license': 'proprietary',\n", " 'links': [{'href': 'https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice',\n", " 'rel': 'license',\n", " 'type': 'application/pdf'},\n", " {'href': 'https://documentation.dataspace.copernicus.eu/Data/SentinelMissions/Sentinel2.html#sentinel-2-level-2a-top-of-canopy-toc',\n", " 'rel': 'alternate',\n", " 'type': 'application/html'},\n", " {'href': 'https://dataspace.copernicus.eu/browser/?zoom=11&lat=45.36638&lng=12.49832&themeId=DEFAULT-THEME&visualizationUrl=https%3A%2F%2Fsh.dataspace.copernicus.eu%2Fogc%2Fwms%2F28b654e7-8912-4e59-9e58-85b58d768b3a&datasetId=S2_L2A_CDAS&fromTime=2023-02-07T00%3A00%3A00.000Z&toTime=2023-02-07T23%3A59%3A59.999Z&layerId=1_TRUE_COLOR&demSource3D=%22MAPZEN%22&cloudCoverage=10',\n", " 'rel': 'browser',\n", " 'title': 'CDSE Data-Browser'},\n", " {'href': 'https://catalogue.dataspace.copernicus.eu/resto/api/collections/Sentinel2/search.json?cloudCover=[0,10]&productType=S2MSI2A&startDate=2022-06-11&completionDate=2022-06-22&maxRecords=10',\n", " 'rel': 'opensearch',\n", " 'title': 'Opensearch product query'},\n", " {'href': 'https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a',\n", " 'rel': 'about',\n", " 'title': 'User guide'},\n", " {'href': 'https://services.sentinel-hub.com/ogc/wmts/7d34803f-511c-4caf-9438-6d72f32c8174',\n", " 'rel': 'wmts',\n", " 'title': 'Web Map Tile Service',\n", " 'wmts:dimensions': {'warnings': True},\n", " 'wmts:layer': 'TRUE-COLOR'},\n", " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections',\n", " 'rel': 'root'},\n", " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections',\n", " 'rel': 'parent'},\n", " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections/SENTINEL2_L2A',\n", " 'rel': 'self'}],\n", " 'mission': 'Sentinel-2',\n", " 'name': 'SENTINEL2_L2A',\n", " 'providers': [{'name': 'ESA',\n", " 'roles': ['producer'],\n", " 'url': 'https://esa.int/'},\n", " {'name': 'CDSE',\n", " 'roles': ['processor', 'host'],\n", " 'url': 'https://dataspace.copernicus.eu/'}],\n", " 'stac_extensions': ['https://stac-extensions.github.io/datacube/v2.2.0/schema.json',\n", " 'https://stac-extensions.github.io/eo/v1.1.0/schema.json'],\n", " 'stac_version': '0.9.0',\n", " 'summaries': {'bands': [{'eo:center_wavelength': 0.4427,\n", " 'eo:common_name': 'coastal',\n", " 'name': 'B01'},\n", " {'eo:center_wavelength': 0.4924, 'eo:common_name': 'blue', 'name': 'B02'},\n", " {'eo:center_wavelength': 0.5598, 'eo:common_name': 'green', 'name': 'B03'},\n", " {'eo:center_wavelength': 0.6646, 'eo:common_name': 'red', 'name': 'B04'},\n", " {'eo:center_wavelength': 0.7041, 'name': 'B05'},\n", " {'eo:center_wavelength': 0.7405, 'name': 'B06'},\n", " {'eo:center_wavelength': 0.7828, 'name': 'B07'},\n", " {'eo:center_wavelength': 0.8328, 'eo:common_name': 'nir', 'name': 'B08'},\n", " {'eo:center_wavelength': 0.8647, 'eo:common_name': 'nir08', 'name': 'B8A'},\n", " {'eo:center_wavelength': 0.9451, 'eo:common_name': 'nir09', 'name': 'B09'},\n", " {'eo:center_wavelength': 1.6137, 'eo:common_name': 'swir16', 'name': 'B11'},\n", " {'eo:center_wavelength': 2.2024, 'eo:common_name': 'swir22', 'name': 'B12'},\n", " {'name': 'WVP'},\n", " {'name': 'AOT'},\n", " {'name': 'SCL'},\n", " {'name': 'sunAzimuthAngles'},\n", " {'name': 'sunZenithAngles'},\n", " {'name': 'viewAzimuthMean'},\n", " {'name': 'viewZenithMean'},\n", " {'name': 'CLD'},\n", " {'name': 'SNW'}],\n", " 'constellation': ['sentinel-2'],\n", " 'eo:bands': [{'aliases': ['IMG_DATA_Band_B01_60m_Tile1_Data'],\n", " 'center_wavelength': 0.4427,\n", " 'common_name': 'coastal',\n", " 'full_width_half_max': 0.021,\n", " 'gsd': 60,\n", " 'name': 'B01',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B02_10m_Tile1_Data'],\n", " 'center_wavelength': 0.4924,\n", " 'common_name': 'blue',\n", " 'full_width_half_max': 0.066,\n", " 'gsd': 10,\n", " 'name': 'B02',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B03_10m_Tile1_Data'],\n", " 'center_wavelength': 0.5598,\n", " 'common_name': 'green',\n", " 'full_width_half_max': 0.036,\n", " 'gsd': 10,\n", " 'name': 'B03',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B04_10m_Tile1_Data'],\n", " 'center_wavelength': 0.6646,\n", " 'common_name': 'red',\n", " 'full_width_half_max': 0.031,\n", " 'gsd': 10,\n", " 'name': 'B04',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B05_20m_Tile1_Data'],\n", " 'center_wavelength': 0.7041,\n", " 'full_width_half_max': 0.015,\n", " 'gsd': 20,\n", " 'name': 'B05',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B06_20m_Tile1_Data'],\n", " 'center_wavelength': 0.7405,\n", " 'full_width_half_max': 0.015,\n", " 'gsd': 20,\n", " 'name': 'B06',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B07_20m_Tile1_Data'],\n", " 'center_wavelength': 0.7828,\n", " 'full_width_half_max': 0.02,\n", " 'gsd': 20,\n", " 'name': 'B07',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B08_10m_Tile1_Data'],\n", " 'center_wavelength': 0.8328,\n", " 'common_name': 'nir',\n", " 'full_width_half_max': 0.106,\n", " 'gsd': 10,\n", " 'name': 'B08',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B8A_20m_Tile1_Data'],\n", " 'center_wavelength': 0.8647,\n", " 'common_name': 'nir08',\n", " 'full_width_half_max': 0.021,\n", " 'gsd': 20,\n", " 'name': 'B8A',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B09_60m_Tile1_Data'],\n", " 'center_wavelength': 0.9451,\n", " 'common_name': 'nir09',\n", " 'full_width_half_max': 0.02,\n", " 'gsd': 60,\n", " 'name': 'B09',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B11_20m_Tile1_Data'],\n", " 'center_wavelength': 1.6137,\n", " 'common_name': 'swir16',\n", " 'full_width_half_max': 0.091,\n", " 'gsd': 20,\n", " 'name': 'B11',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_B12_20m_Tile1_Data'],\n", " 'center_wavelength': 2.2024,\n", " 'common_name': 'swir22',\n", " 'full_width_half_max': 0.175,\n", " 'gsd': 20,\n", " 'name': 'B12',\n", " 'offset': 0,\n", " 'scale': 0.0001,\n", " 'type': 'int16',\n", " 'unit': '1'},\n", " {'aliases': ['IMG_DATA_Band_WVP_10m_Tile1_Data'], 'name': 'WVP'},\n", " {'aliases': ['IMG_DATA_Band_AOT_20m_Tile1_Data'], 'name': 'AOT'},\n", " {'aliases': ['IMG_DATA_Band_SCL_20m_Tile1_Data'], 'gsd': 20, 'name': 'SCL'},\n", " {'aliases': ['S2_Level-2A_Tile1_Metadata##0', 'SAA'],\n", " 'name': 'sunAzimuthAngles',\n", " 'unit': 'deg'},\n", " {'aliases': ['S2_Level-2A_Tile1_Metadata##1', 'SZA'],\n", " 'name': 'sunZenithAngles',\n", " 'unit': 'deg'},\n", " {'aliases': ['S2_Level-2A_Tile1_Metadata##2',\n", " 'VAA',\n", " 'approximateViewAzimuth'],\n", " 'name': 'viewAzimuthMean',\n", " 'unit': 'deg'},\n", " {'aliases': ['S2_Level-2A_Tile1_Metadata##3',\n", " 'VZA',\n", " 'approximateViewZenith'],\n", " 'name': 'viewZenithMean',\n", " 'unit': 'deg'},\n", " {'aliases': ['CloudPrbMask_Tile1_Data'],\n", " 'gsd': 20,\n", " 'name': 'CLD',\n", " 'statistics': {'maximum': [100], 'minimum': [0]},\n", " 'type': 'uint8',\n", " 'unit': '1'},\n", " {'aliases': ['SnowPrbMask_Tile1_Data'],\n", " 'gsd': 20,\n", " 'name': 'SNW',\n", " 'statistics': {'maximum': [100], 'minimum': [0]},\n", " 'type': 'uint8',\n", " 'unit': '1'}],\n", " 'eo:cloud_cover': {'maximum': 100, 'minimum': 0},\n", " 'gsd': [10],\n", " 'instrument': ['msi'],\n", " 'platform': ['sentinel-2a', 'sentinel-2b'],\n", " 'processingBaseline': {},\n", " 'productIdentifier': {},\n", " 'tileId': {}},\n", " 'title': 'Sentinel-2 L2A'}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "connection.describe_collection(\"SENTINEL2_L2A\")" ] }, { "cell_type": "markdown", "id": "0b3212fa-72d5-4ff1-908c-3b9005a4fb71", "metadata": {}, "source": [ "También los procesos disponibles:" ] }, { "cell_type": "code", "execution_count": 5, "id": "2ae4548f-854a-436c-8e59-a14b98d0f392", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " " ], "text/plain": [ "[{'categories': ['math', 'math > exponential & logarithmic'],\n", " 'description': 'Computes the square root of a real number `x`, which is equal to calculating `x` to the power of *0.5*.\\n\\nA square root of x is a number a such that *`a² = x`*. Therefore, the square root is the inverse function of a to the power of 2, but only for *a >= 0*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': 1}, 'returns': 1},\n", " {'arguments': {'x': 9}, 'returns': 3},\n", " {'arguments': {'x': None}, 'returns': None}],\n", " 'id': 'sqrt',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/SquareRoot.html',\n", " 'rel': 'about',\n", " 'title': 'Square root explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'power': {'arguments': {'base': {'from_parameter': 'x'},\n", " 'p': 0.5},\n", " 'process_id': 'power',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed square root.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Square root'},\n", " {'categories': ['comparison'],\n", " 'description': 'Checks whether the specified data is missing data, i.e. equals to `null` or any of the no-data values specified in the metadata.\\n\\nThe special numerical value `NaN` (not a number) as defined by the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935) is only considered as no-data value if specified as no-data value in the metadata.',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': False},\n", " {'arguments': {'x': 'Test'}, 'returns': False},\n", " {'arguments': {'x': None}, 'returns': True},\n", " {'arguments': {'x': [None, None]}, 'returns': False}],\n", " 'id': 'is_nodata',\n", " 'parameters': [{'description': 'The data to check.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type is allowed.'}}],\n", " 'returns': {'description': '`true` if the data is a no-data value, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'summary': 'Value is a no-data value'},\n", " {'categories': ['arrays', 'sorting'],\n", " 'description': 'Rearranges an array based on a ranked list of element positions in the original list (i.e., a permutation). The positions must be zero-based. The process ``order()`` can compute such a permutation.',\n", " 'examples': [{'arguments': {'data': [5, 4, 3], 'order': [2, 1, 0]},\n", " 'returns': [3, 4, 5],\n", " 'title': 'Reverse a list'},\n", " {'arguments': {'data': [5, 4, 3, 2], 'order': [1, 3]},\n", " 'returns': [4, 2],\n", " 'title': 'Remove two elements'},\n", " {'arguments': {'data': [5, 4, 3, 2], 'order': [0, 2, 1, 3]},\n", " 'returns': [5, 3, 4, 2],\n", " 'title': 'Swap two elements'}],\n", " 'id': 'rearrange',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Permutation.html',\n", " 'rel': 'about',\n", " 'title': 'Permutation explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'The array to rearrange.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'The permutation used for rearranging.',\n", " 'name': 'order',\n", " 'schema': {'items': {'minimum': 0, 'type': 'integer'}, 'type': 'array'}}],\n", " 'returns': {'description': 'The rearranged array.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Sort an array based on a permutation'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 1}],\n", " 'id': 'cos',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Cosine.html',\n", " 'rel': 'about',\n", " 'title': 'Cosine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed cosine of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Cosine'},\n", " {'categories': ['arrays', 'sorting'],\n", " 'description': 'Sorts an array into ascending (default) or descending order.\\n\\n**Remarks:**\\n\\n* The ordering of ties is implementation-dependent.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone/time-offset representations.',\n", " 'examples': [{'arguments': {'data': [6,\n", " -1,\n", " 2,\n", " None,\n", " 7,\n", " 4,\n", " None,\n", " 8,\n", " 3,\n", " 9,\n", " 9]},\n", " 'returns': [-1, 2, 3, 4, 6, 7, 8, 9, 9]},\n", " {'arguments': {'asc': False,\n", " 'data': [6, -1, 2, None, 7, 4, None, 8, 3, 9, 9],\n", " 'nodata': True},\n", " 'returns': [9, 9, 8, 7, 6, 4, 3, 2, -1, None, None]}],\n", " 'id': 'sort',\n", " 'parameters': [{'description': 'An array with data to sort.',\n", " 'name': 'data',\n", " 'schema': {'items': {'anyOf': [{'type': 'number'},\n", " {'type': 'null'},\n", " {'format': 'date-time', 'subtype': 'date-time', 'type': 'string'},\n", " {'format': 'date', 'subtype': 'date', 'type': 'string'}]},\n", " 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'The default sort order is ascending, with smallest values first. To sort in reverse (descending) order, set this parameter to `false`.',\n", " 'name': 'asc',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': None,\n", " 'description': 'Controls the handling of no-data values (`null`). By default, they are removed. If set to `true`, missing values in the data are put last; if set to `false`, they are put first.',\n", " 'name': 'nodata',\n", " 'optional': True,\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'process_graph': {'order': {'arguments': {'asc': {'from_parameter': 'asc'},\n", " 'data': {'from_parameter': 'data'},\n", " 'nodata': {'from_parameter': 'nodata'}},\n", " 'process_id': 'order'},\n", " 'rearrange': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'order': {'from_node': 'order'}},\n", " 'process_id': 'rearrange',\n", " 'result': True}},\n", " 'returns': {'description': 'The sorted array.',\n", " 'schema': {'items': {'anyOf': [{'type': 'number'},\n", " {'type': 'null'},\n", " {'format': 'date-time', 'subtype': 'date-time', 'type': 'string'},\n", " {'format': 'date', 'subtype': 'date', 'type': 'string'}]},\n", " 'type': 'array'}},\n", " 'summary': 'Sort data'},\n", " {'categories': ['arrays', 'reducer'],\n", " 'description': 'Gives the last element of an array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 2},\n", " {'arguments': {'data': ['A', 'B', None]}, 'returns': 'B'},\n", " {'arguments': {'data': [0, 1, None], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None}],\n", " 'id': 'last',\n", " 'parameters': [{'description': 'An array with elements of any data type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if the last value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The last element of the input array.',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " 'summary': 'Last element'},\n", " {'categories': ['math > statistics', 'reducer'],\n", " 'description': 'Computes the sample standard deviation, which quantifies the amount of variation of an array of numbers. It is defined to be the square root of the corresponding variance (see ``variance()``).\\n\\nA low standard deviation indicates that the values tend to be close to the expected value, while a high standard deviation indicates that the values are spread out over a wider range.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [-1, 1, 3, None]}, 'returns': 2},\n", " {'arguments': {'data': [-1, 1, 3, None], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None}],\n", " 'id': 'sd',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/StandardDeviation.html',\n", " 'rel': 'about',\n", " 'title': 'Standard deviation explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'power': {'arguments': {'base': {'from_node': 'variance'},\n", " 'p': 0.5},\n", " 'process_id': 'power',\n", " 'result': True},\n", " 'variance': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'ignore_nodata': {'from_parameter': 'ignore_nodata'}},\n", " 'process_id': 'variance'}},\n", " 'returns': {'description': 'The computed sample standard deviation.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Standard deviation'},\n", " {'categories': ['logic'],\n", " 'description': 'Checks if **exactly one** of the values is true. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | -----\\nnull || null | null | null\\nfalse || null | false | true\\ntrue || null | true | false\\n```',\n", " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': False},\n", " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", " {'arguments': {'x': True, 'y': False}, 'returns': True},\n", " {'arguments': {'x': True, 'y': None}, 'returns': None},\n", " {'arguments': {'x': False, 'y': None}, 'returns': None}],\n", " 'id': 'xor',\n", " 'parameters': [{'description': 'A boolean value.',\n", " 'name': 'x',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " {'description': 'A boolean value.',\n", " 'name': 'y',\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'process_graph': {'and1': {'arguments': {'x': {'from_node': 'not_x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'and'},\n", " 'and2': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_node': 'not_y'}},\n", " 'process_id': 'and'},\n", " 'not_x': {'arguments': {'x': {'from_parameter': 'x'}}, 'process_id': 'not'},\n", " 'not_y': {'arguments': {'x': {'from_parameter': 'y'}}, 'process_id': 'not'},\n", " 'or': {'arguments': {'x': {'from_node': 'and1'},\n", " 'y': {'from_node': 'and2'}},\n", " 'process_id': 'or',\n", " 'result': True}},\n", " 'returns': {'description': 'Boolean result of the logical XOR.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Logical XOR (exclusive or)'},\n", " {'categories': ['comparison'],\n", " 'description': 'Checks whether the specified value `x` is valid. The following values are considered valid:\\n\\n* Any finite numerical value (integers and floating-point numbers). The definition of finite numbers follows the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935) and excludes the special value `NaN` (not a number).\\n* Any other value that is not a no-data value according to ``is_nodata()``. Thus all arrays, objects and strings are valid, regardless of their content.',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': True},\n", " {'arguments': {'x': 'Test'}, 'returns': True},\n", " {'arguments': {'x': None}, 'returns': False},\n", " {'arguments': {'x': [None, None]}, 'returns': True}],\n", " 'id': 'is_valid',\n", " 'links': [{'href': 'https://ieeexplore.ieee.org/document/4610935',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2008 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The data to check.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type is allowed.'}}],\n", " 'returns': {'description': '`true` if the data is valid, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'summary': 'Value is valid data'},\n", " {'categories': ['math'],\n", " 'description': 'Multiplies the two numbers `x` and `y` (*`x * y`*) and returns the computed product.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 12.5},\n", " {'arguments': {'x': -2, 'y': -4}, 'returns': 8},\n", " {'arguments': {'x': 1, 'y': None}, 'returns': None}],\n", " 'exceptions': {'MultiplicandMissing': {'message': 'Multiplication requires at least two numbers.'}},\n", " 'id': 'multiply',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Product.html',\n", " 'rel': 'about',\n", " 'title': 'Product explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The multiplier.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The multiplicand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'product': {'arguments': {'data': [{'from_parameter': 'x'},\n", " {'from_parameter': 'y'}],\n", " 'ignore_nodata': False},\n", " 'process_id': 'product',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed product of the two numbers.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Multiplication of two numbers'},\n", " {'categories': ['math > constants', 'math > trigonometric'],\n", " 'description': 'The real number Pi (π) is a mathematical constant that is the ratio of the circumference of a circle to its diameter. The numerical value is approximately *3.14159*.',\n", " 'id': 'pi',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Pi.html',\n", " 'rel': 'about',\n", " 'title': 'Mathematical constant Pi explained by Wolfram MathWorld'}],\n", " 'parameters': [],\n", " 'returns': {'description': 'The numerical value of Pi.',\n", " 'schema': {'type': 'number'}},\n", " 'summary': 'Pi (π)'},\n", " {'categories': ['arrays'],\n", " 'description': 'Gives all labels for a labeled array or gives all indices for an array without labels. If the array is not labeled, an array with the zero-based indices is returned. The labels or indices have the same order as in the array.',\n", " 'id': 'array_labels',\n", " 'parameters': [{'description': 'An array.',\n", " 'name': 'data',\n", " 'schema': {'type': 'array'}}],\n", " 'returns': {'description': 'The labels or indices as array.',\n", " 'schema': {'items': {'type': ['number', 'string']}, 'type': 'array'}},\n", " 'summary': 'Get the labels for an array'},\n", " {'categories': ['comparison', 'math > constants'],\n", " 'description': 'Checks whether the specified value `x` is *not* a number. Numbers are all integers and floating-point numbers, except for the special value `NaN` as defined by the [IEEE Standard 754](https://ieeexplore.ieee.org/document/4610935).',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': False},\n", " {'arguments': {'x': 'Test'}, 'returns': True},\n", " {'arguments': {'x': None}, 'returns': True}],\n", " 'id': 'is_nan',\n", " 'links': [{'href': 'https://ieeexplore.ieee.org/document/4610935',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2008 for Floating-Point Arithmetic'},\n", " {'href': 'http://mathworld.wolfram.com/NaN.html',\n", " 'rel': 'about',\n", " 'title': 'NaN explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'The data to check.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type is allowed.'}}],\n", " 'returns': {'description': 'Returns `true` for `NaN` and all non-numeric data types, otherwise returns `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'summary': 'Value is not a number'},\n", " {'categories': ['arrays', 'comparison', 'reducer'],\n", " 'description': \"Checks whether the array specified for `data` contains the value specified in `value`. Returns `true` if there's a match, otherwise `false`.\\n\\n**Remarks:**\\n\\n* To get the index or the label of the value found, use ``array_find()``.\\n* All definitions for the process ``eq()`` regarding the comparison of values apply here as well. A `null` return value from ``eq()`` is handled exactly as `false` (no match).\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*.\\n* An integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`. Still, this process may return unexpectedly `false` when comparing floating-point numbers due to floating-point inaccuracy in machine-based computation.\\n* Temporal strings are treated as normal strings and MUST NOT be interpreted.\",\n", " 'examples': [{'arguments': {'data': [1, 2, 3], 'value': 2}, 'returns': True},\n", " {'arguments': {'data': ['A', 'B', 'C'], 'value': 'b'}, 'returns': False},\n", " {'arguments': {'data': [1, 2, 3], 'value': '2'}, 'returns': False},\n", " {'arguments': {'data': [1, 2, None], 'value': None}, 'returns': False},\n", " {'arguments': {'data': [[1, 2], [3, 4]], 'value': 2}, 'returns': False}],\n", " 'id': 'array_contains',\n", " 'links': [{'href': 'https://raw.githubusercontent.com/Open-EO/openeo-community-examples/main/processes/array_contains_nodata.json',\n", " 'rel': 'example',\n", " 'title': 'Check for no-data values in arrays',\n", " 'type': 'application/json'}],\n", " 'parameters': [{'description': 'List to find the value in.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'Value to find in `data`. If the value is `null`, this process returns always `false`.',\n", " 'name': 'value',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'process_graph': {'find': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'value': {'from_parameter': 'value'}},\n", " 'process_id': 'array_find'},\n", " 'is_nodata': {'arguments': {'x': {'from_node': 'find'}},\n", " 'process_id': 'is_nodata'},\n", " 'not': {'arguments': {'x': {'from_node': 'is_nodata'}},\n", " 'process_id': 'not',\n", " 'result': True}},\n", " 'returns': {'description': '`true` if the list contains the value, false` otherwise.',\n", " 'schema': {'type': 'boolean'}},\n", " 'summary': 'Check whether the array contains a given value'},\n", " {'categories': ['math'],\n", " 'description': 'Sums up the two numbers `x` and `y` (*`x + y`*) and returns the computed sum.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 7.5},\n", " {'arguments': {'x': -2, 'y': -4}, 'returns': -6},\n", " {'arguments': {'x': 1, 'y': None}, 'returns': None}],\n", " 'id': 'add',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Sum.html',\n", " 'rel': 'about',\n", " 'title': 'Sum explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The first summand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The second summand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'sum': {'arguments': {'data': [{'from_parameter': 'x'},\n", " {'from_parameter': 'y'}],\n", " 'ignore_nodata': False},\n", " 'process_id': 'sum',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed sum of the two numbers.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Addition of two numbers'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the hyperbolic tangent of `x`. The tangent is defined to be the hyperbolic sine of x divided by the hyperbolic cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'tanh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/HyperbolicTangent.html',\n", " 'rel': 'about',\n", " 'title': 'Hyperbolic tangent explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed hyperbolic tangent of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Hyperbolic tangent'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the arc tangent of `x`. The arc tangent is the inverse function of the tangent so that *`arctan(tan(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'arctan',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseTangent.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse tangent explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse tangent'},\n", " {'categories': ['math', 'math > rounding'],\n", " 'description': 'The integer part of the real number `x`.\\n\\nThis process is *not* an alias for the ``floor()`` process as defined by some mathematicians, see the examples for negative numbers in both processes for differences.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': 3.5}, 'returns': 3},\n", " {'arguments': {'x': -0.4}, 'returns': 0},\n", " {'arguments': {'x': -3.5}, 'returns': -3}],\n", " 'id': 'int',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/IntegerPart.html',\n", " 'rel': 'about',\n", " 'title': 'Integer Part explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'Integer part of the number.',\n", " 'schema': {'type': ['integer', 'null']}},\n", " 'summary': 'Integer part of a number'},\n", " {'categories': ['math', 'math > statistics', 'reducer'],\n", " 'description': 'Computes the largest value of an array of numbers, which is equal to the first element of a sorted (i.e., ordered) version of the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 3},\n", " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': 5},\n", " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None}],\n", " 'id': 'max',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Maximum.html',\n", " 'rel': 'about',\n", " 'title': 'Maximum explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The maximum value.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Maximum value'},\n", " {'categories': ['math', 'math > statistics', 'reducer'],\n", " 'description': 'Computes the smallest value of an array of numbers, which is equal to the last element of a sorted (i.e., ordered) version of the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 0},\n", " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': -0.7},\n", " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []}, 'returns': None}],\n", " 'id': 'min',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Minimum.html',\n", " 'rel': 'about',\n", " 'title': 'Minimum explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The minimum value.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Minimum value'},\n", " {'categories': ['math > exponential & logarithmic'],\n", " 'description': 'Logarithm to the base `base` of the number `x` is defined to be the inverse function of taking b to the power of x.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `log(0, 2)` results in ±infinity if the processing environment supports it or otherwise an exception is thrown.',\n", " 'examples': [{'arguments': {'base': 10, 'x': 10}, 'returns': 1},\n", " {'arguments': {'base': 2, 'x': 2}, 'returns': 1},\n", " {'arguments': {'base': 2, 'x': 4}, 'returns': 2},\n", " {'arguments': {'base': 16, 'x': 1}, 'returns': 0}],\n", " 'id': 'log',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Logarithm.html',\n", " 'rel': 'about',\n", " 'title': 'Logarithm explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'A number to compute the logarithm for.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The numerical base.',\n", " 'name': 'base',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed logarithm.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Logarithm to a base'},\n", " {'categories': ['math'],\n", " 'description': 'The signum (also known as *sign*) of `x` is defined as:\\n\\n* *1* if *x > 0*\\n* *0* if *x = 0*\\n* *-1* if *x < 0*\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': -2}, 'returns': -1},\n", " {'arguments': {'x': 3.5}, 'returns': 1},\n", " {'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': None}, 'returns': None}],\n", " 'id': 'sgn',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Sign.html',\n", " 'rel': 'about',\n", " 'title': 'Sign explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'gt0': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': 0},\n", " 'process_id': 'gt'},\n", " 'if_gt0': {'arguments': {'accept': 1,\n", " 'reject': {'from_parameter': 'x'},\n", " 'value': {'from_node': 'gt0'}},\n", " 'process_id': 'if'},\n", " 'if_lt0': {'arguments': {'accept': -1,\n", " 'reject': {'from_node': 'if_gt0'},\n", " 'value': {'from_node': 'lt0'}},\n", " 'process_id': 'if',\n", " 'result': True},\n", " 'lt0': {'arguments': {'x': {'from_parameter': 'x'}, 'y': 0},\n", " 'process_id': 'lt'}},\n", " 'returns': {'description': 'The computed signum value of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Signum'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the arc tangent of two numbers `x` and `y`. It is similar to calculating the arc tangent of *`y / x`*, except that the signs of both arguments are used to determine the quadrant of the result.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.',\n", " 'examples': [{'arguments': {'x': 0, 'y': 0}, 'returns': 0},\n", " {'arguments': {'x': 1.5, 'y': None}, 'returns': None}],\n", " 'id': 'arctan2',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Atan2',\n", " 'rel': 'about',\n", " 'title': 'Two-argument inverse tangent explained by Wikipedia'}],\n", " 'parameters': [{'description': 'A number to be used as the dividend.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'A number to be used as the divisor.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse tangent of two numbers'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the tangent of `x`. The tangent is defined to be the sine of x divided by the cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'tan',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Tangent.html',\n", " 'rel': 'about',\n", " 'title': 'Tangent explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed tangent of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Tangent'},\n", " {'categories': ['arrays', 'math > statistics', 'reducer'],\n", " 'description': 'Gives the number of elements in an array that matches the specified condition.\\n\\n**Remarks:**\\n\\n* Counts the number of valid elements by default (`condition` is set to `null`). A valid element is every element for which ``is_valid()`` returns `true`.\\n* To count all elements in a list set the `condition` parameter to boolean `true`.',\n", " 'examples': [{'arguments': {'data': []}, 'returns': 0},\n", " {'arguments': {'data': [1, 0, 3, 2]}, 'returns': 4},\n", " {'arguments': {'data': ['ABC', None]}, 'returns': 1},\n", " {'arguments': {'condition': True, 'data': [False, None]}, 'returns': 2},\n", " {'arguments': {'condition': {'gt': {'arguments': {'x': {'from_parameter': 'element'},\n", " 'y': 2},\n", " 'process_id': 'gt',\n", " 'result': True}},\n", " 'data': [0, 1, 2, 3, 4, 5, None]},\n", " 'returns': 3}],\n", " 'id': 'count',\n", " 'parameters': [{'description': 'An array with elements of any data type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'A condition consists of one or more processes, which in the end return a boolean value. It is evaluated against each element in the array. An element is counted only if the condition returns `true`. Defaults to count valid elements in a list (see ``is_valid()``). Setting this parameter to boolean `true` counts all elements in the list. `false` is not a valid value for this parameter.',\n", " 'name': 'condition',\n", " 'optional': True,\n", " 'schema': [{'description': 'A logical expression that is evaluated against each element in the array.',\n", " 'parameters': [{'description': 'The value of the current element being processed.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': '`true` if the element should increase the counter, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'subtype': 'process-graph',\n", " 'title': 'Condition',\n", " 'type': 'object'},\n", " {'const': True,\n", " 'description': 'Boolean `true` counts all elements in the list.',\n", " 'title': 'All elements',\n", " 'type': 'boolean'},\n", " {'description': '`null` counts valid elements in the list.',\n", " 'title': 'Valid elements',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the condition.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The counted number of elements.',\n", " 'schema': {'type': 'number'}},\n", " 'summary': 'Count the number of elements'},\n", " {'categories': ['comparison'],\n", " 'description': 'By default, this process checks whether `x` is greater than or equal to `min` and lower than or equal to `max`, which is the same as computing `and(gte(x, min), lte(x, max))`. Therefore, all definitions from ``and()``, ``gte()`` and ``lte()`` apply here as well.\\n\\nIf `exclude_max` is set to `true` the upper bound is excluded so that the process checks whether `x` is greater than or equal to `min` and lower than `max`. In this case, the process works the same as computing `and(gte(x, min), lt(x, max))`.\\n\\nLower and upper bounds are not allowed to be swapped. So `min` MUST be lower than or equal to `max` or otherwise the process always returns `false`.',\n", " 'examples': [{'arguments': {'max': 1, 'min': 0, 'x': None}, 'returns': None},\n", " {'arguments': {'max': 1, 'min': 0, 'x': 1}, 'returns': True},\n", " {'arguments': {'exclude_max': True, 'max': 1, 'min': 0, 'x': 1},\n", " 'returns': False},\n", " {'arguments': {'max': 0, 'min': 1, 'x': 0.5},\n", " 'description': 'Swapped bounds (min is greater than max) MUST always return `false`.',\n", " 'returns': False},\n", " {'arguments': {'max': 0, 'min': -1, 'x': -0.5}, 'returns': True}],\n", " 'id': 'between',\n", " 'parameters': [{'description': 'The value to check.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " {'description': 'Lower boundary (inclusive) to check against.',\n", " 'name': 'min',\n", " 'schema': {'type': 'number'}},\n", " {'description': 'Upper boundary (inclusive) to check against.',\n", " 'name': 'max',\n", " 'schema': {'type': 'number'}},\n", " {'default': False,\n", " 'description': 'Exclude the upper boundary `max` if set to `true`. Defaults to `false`.',\n", " 'name': 'exclude_max',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'and': {'arguments': {'x': {'from_node': 'gte'},\n", " 'y': {'from_node': 'if'}},\n", " 'process_id': 'and',\n", " 'result': True},\n", " 'gte': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'min'}},\n", " 'process_id': 'gte'},\n", " 'if': {'arguments': {'accept': {'from_node': 'lte'},\n", " 'reject': {'from_node': 'lt'},\n", " 'value': {'from_parameter': 'exclude_max'}},\n", " 'process_id': 'if'},\n", " 'lt': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'max'}},\n", " 'process_id': 'lt'},\n", " 'lte': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'max'}},\n", " 'process_id': 'lte'}},\n", " 'returns': {'description': '`true` if `x` is between the specified bounds, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Between comparison'},\n", " {'categories': ['texts', 'comparison'],\n", " 'description': 'Compares whether `x` is strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 1, 'y': 1}, 'returns': True},\n", " {'arguments': {'x': 1, 'y': '1'}, 'returns': False},\n", " {'arguments': {'x': 0, 'y': False}, 'returns': False},\n", " {'arguments': {'delta': 0.01, 'x': 1.02, 'y': 1}, 'returns': False},\n", " {'arguments': {'delta': 0.01, 'x': -1, 'y': -1.001}, 'returns': True},\n", " {'arguments': {'delta': 10, 'x': 115, 'y': 110}, 'returns': True},\n", " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': False},\n", " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", " 'returns': True},\n", " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", " 'returns': True},\n", " {'arguments': {'x': '2018-01-01T00:00:00Z',\n", " 'y': '2018-01-01T00:00:00+00:00'},\n", " 'returns': False},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'eq',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'default': None,\n", " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating-point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `lte(abs(minus([x, y]), delta)`',\n", " 'name': 'delta',\n", " 'optional': True,\n", " 'schema': {'type': ['number', 'null']}},\n", " {'default': True,\n", " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", " 'name': 'case_sensitive',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': '`true` if `x` is equal to `y`, `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Equal to comparison'},\n", " {'categories': ['math > statistics'],\n", " 'description': 'Two element array containing the minimum and the maximum values of `data`.\\n\\nThis process is basically an alias for calling both ``min()`` and ``max()``, but may be implemented more performant by back-ends as it only needs to iterate over the data once instead of twice.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': [0, 3]},\n", " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': [-0.7, 5]},\n", " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False},\n", " 'returns': [None, None]},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return two `null` values.',\n", " 'returns': [None, None]}],\n", " 'id': 'extrema',\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that an array with two `null` values is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'An array containing the minimum and maximum values for the specified numbers. The first element is the minimum, the second element is the maximum. If the input array is empty both elements are set to `null`.',\n", " 'schema': [{'items': {'type': 'number'},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'type': 'array'},\n", " {'items': {'type': 'null'},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'type': 'array'}]},\n", " 'summary': 'Minimum and maximum values'},\n", " {'categories': ['math > statistics', 'reducer'],\n", " 'description': 'The statistical median of an array of numbers is the value separating the higher half from the lower half of the data.\\n\\nAn array without non-`null` elements resolves always with `null`.\\n\\n**Remarks:**\\n\\n* For symmetric arrays, the result is equal to the ``mean()``.\\n* The median can also be calculated by computing the ``quantiles()`` with a probability of *0.5*.',\n", " 'examples': [{'arguments': {'data': [1, 3, 3, 6, 7, 8, 9]}, 'returns': 6},\n", " {'arguments': {'data': [1, 2, 3, 4, 5, 6, 8, 9]}, 'returns': 4.5},\n", " {'arguments': {'data': [-1, -0.5, None, 1]}, 'returns': -0.5},\n", " {'arguments': {'data': [-1, 0, None, 1], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None},\n", " {'arguments': {'data': [None, None]},\n", " 'description': 'The input array has only `null` elements: return `null`.',\n", " 'returns': None}],\n", " 'id': 'median',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/StatisticalMedian.html',\n", " 'rel': 'about',\n", " 'title': 'Statistical Median explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'array_element': {'arguments': {'data': {'from_node': 'quantiles'},\n", " 'index': 0,\n", " 'return_nodata': True},\n", " 'process_id': 'array_element',\n", " 'result': True},\n", " 'quantiles': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'ignore_nodata': {'from_parameter': 'ignore_nodata'},\n", " 'probabilities': [0.5]},\n", " 'process_id': 'quantiles'}},\n", " 'returns': {'description': 'The computed statistical median.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Statistical median'},\n", " {'categories': ['math > rounding'],\n", " 'description': 'The least integer greater than or equal to the number `x`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': 3.5}, 'returns': 4},\n", " {'arguments': {'x': -0.4}, 'returns': 0},\n", " {'arguments': {'x': -3.5}, 'returns': -3}],\n", " 'id': 'ceil',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/CeilingFunction.html',\n", " 'rel': 'about',\n", " 'title': 'Ceiling explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number to round up.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The number rounded up.',\n", " 'schema': {'type': ['integer', 'null']}},\n", " 'summary': 'Round fractions up'},\n", " {'categories': ['comparison'],\n", " 'description': 'Compares whether `x` is strictly less than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is not a `number`, the process returns `false`.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", " {'arguments': {'x': '2018-01-01T00:00:00Z', 'y': '2018-01-02T00:00:00Z'},\n", " 'returns': False},\n", " {'arguments': {'x': 0, 'y': True}, 'returns': False},\n", " {'arguments': {'x': False, 'y': True}, 'returns': False},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'lt',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'returns': {'description': '`true` if `x` is strictly less than `y`, `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Less than comparison'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the inverse hyperbolic cosine of `x`. It is the inverse function of the hyperbolic cosine so that *`arcosh(cosh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': 0}],\n", " 'id': 'arcosh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseHyperbolicCosine.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse hyperbolic cosine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse hyperbolic cosine'},\n", " {'categories': ['logic'],\n", " 'description': 'Checks if **both** values are true.\\n\\nEvaluates parameter `x` before `y` and stops once the outcome is unambiguous. If any argument is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```',\n", " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", " {'arguments': {'x': False, 'y': None}, 'returns': False},\n", " {'arguments': {'x': True, 'y': None}, 'returns': None}],\n", " 'id': 'and',\n", " 'parameters': [{'description': 'A boolean value.',\n", " 'name': 'x',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " {'description': 'A boolean value.',\n", " 'name': 'y',\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'process_graph': {'all': {'arguments': {'data': [{'from_parameter': 'x'},\n", " {'from_parameter': 'y'}],\n", " 'ignore_nodata': False},\n", " 'process_id': 'all',\n", " 'result': True}},\n", " 'returns': {'description': 'Boolean result of the logical AND.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Logical AND'},\n", " {'categories': ['arrays', 'reducer'],\n", " 'description': 'Gives the element with the specified index or label from the array.\\n\\nEither the parameter `index` or `label` must be specified, otherwise the `ArrayElementParameterMissing` exception is thrown. If both parameters are set the `ArrayElementParameterConflict` exception is thrown.',\n", " 'examples': [{'arguments': {'data': [9, 8, 7, 6, 5], 'index': 2},\n", " 'returns': 7},\n", " {'arguments': {'data': ['A', 'B', 'C'], 'index': 0}, 'returns': 'A'},\n", " {'arguments': {'data': [], 'index': 0, 'return_nodata': True},\n", " 'returns': None}],\n", " 'exceptions': {'ArrayElementNotAvailable': {'message': 'The array has no element with the specified index or label.'},\n", " 'ArrayElementParameterConflict': {'message': 'The process `array_element` only allows that either the `index` or the `labels` parameter is set.'},\n", " 'ArrayElementParameterMissing': {'message': 'The process `array_element` requires either the `index` or `labels` parameter to be set.'},\n", " 'ArrayNotLabeled': {'message': 'The array is not a labeled array, but the `label` parameter is set. Use the `index` instead.'}},\n", " 'id': 'array_element',\n", " 'parameters': [{'description': 'An array.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'The zero-based index of the element to retrieve.',\n", " 'name': 'index',\n", " 'optional': True,\n", " 'schema': {'minimum': 0, 'type': 'integer'}},\n", " {'description': 'The label of the element to retrieve. Throws an `ArrayNotLabeled` exception, if the given array is not a labeled array and this parameter is set.',\n", " 'name': 'label',\n", " 'optional': True,\n", " 'schema': [{'type': 'number'}, {'type': 'string'}]},\n", " {'default': False,\n", " 'description': 'By default this process throws an `ArrayElementNotAvailable` exception if the index or label is invalid. If you want to return `null` instead, set this flag to `true`.',\n", " 'name': 'return_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The value of the requested element.',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " 'summary': 'Get an element from an array'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the inverse hyperbolic sine of `x`. It is the inverse function of the hyperbolic sine so that *`arsinh(sinh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'arsinh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseHyperbolicSine.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse hyperbolic sine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse hyperbolic sine'},\n", " {'categories': ['math > rounding'],\n", " 'description': 'Rounds a real number `x` to specified precision `p`.\\n\\nIf `x` is halfway between closest numbers of precision `p`, it is rounded to the closest even number of precision `p`.\\nThis behavior follows [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) and is often called \"round to nearest (even)\" or \"banker\\'s rounding\". It minimizes rounding errors that result from consistently rounding a midpoint value in a single direction.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'p': 1, 'x': 3.56}, 'returns': 3.6},\n", " {'arguments': {'p': 2, 'x': -0.4444444}, 'returns': -0.44},\n", " {'arguments': {'x': -2.5}, 'returns': -2},\n", " {'arguments': {'x': -3.5}, 'returns': -4},\n", " {'arguments': {'p': 1, 'x': 0.25}, 'returns': 0.2},\n", " {'arguments': {'p': 1, 'x': 0.35}, 'returns': 0.4},\n", " {'arguments': {'p': -2, 'x': 1234.5}, 'returns': 1200}],\n", " 'id': 'round',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/AbsoluteValue.html',\n", " 'rel': 'about',\n", " 'title': 'Absolute value explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'A number to round.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'default': 0,\n", " 'description': 'A positive number specifies the number of digits after the decimal point to round to. A negative number means rounding to a power of ten, so for example *-2* rounds to the nearest hundred. Defaults to *0*.',\n", " 'name': 'p',\n", " 'optional': True,\n", " 'schema': {'type': 'integer'}}],\n", " 'returns': {'description': 'The rounded number.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Round to a specified precision'},\n", " {'categories': ['math > statistics', 'reducer'],\n", " 'description': 'Computes the sample variance of an array of numbers by calculating the square of the standard deviation (see ``sd()``). It is defined to be the expectation of the squared deviation of a random variable from its expected value. Basically, it measures how far the numbers in the array are spread out from their average value.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [-1, 1, 3]}, 'returns': 4},\n", " {'arguments': {'data': [2, 3, 3, None, 4, 4, 5]}, 'returns': 1.1},\n", " {'arguments': {'data': [-1, 1, None, 3], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None}],\n", " 'id': 'variance',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Variance.html',\n", " 'rel': 'about',\n", " 'title': 'Variance explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'apply': {'arguments': {'context': {'from_node': 'mean'},\n", " 'data': {'from_parameter': 'data'},\n", " 'process': {'process-graph': {'power': {'arguments': {'base': {'from_node': 'subtract'},\n", " 'p': 2},\n", " 'process_id': 'power',\n", " 'result': True},\n", " 'subtract': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'context'}},\n", " 'process_id': 'subtract'}}}},\n", " 'process_id': 'apply'},\n", " 'mean': {'arguments': {'data': {'from_parameter': 'data'}},\n", " 'process_id': 'mean'},\n", " 'mean2': {'arguments': {'data': {'from_node': 'apply'},\n", " 'ignore_nodata': {'from_parameter': 'ignore_nodata'}},\n", " 'process_id': 'mean',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed sample variance.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Variance'},\n", " {'categories': ['arrays', 'reducer'],\n", " 'description': 'Returns the zero-based index of the first (or last) occurrence of the value specified by `value` in the array specified by `data` or `null` if there is no match. Use the parameter `reverse` to switch from the first to the last match.\\n\\n**Remarks:**\\n\\n* Use ``array_contains()`` to check if an array contains a value regardless of the position.\\n* Use ``array_find_label()`` to find the index for a label.\\n* All definitions for the process ``eq()`` regarding the comparison of values apply here as well. A `null` return value from ``eq()`` is handled exactly as `false` (no match).\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*.\\n* An integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`. Still, this process may return unexpectedly `false` when comparing floating-point numbers due to floating-point inaccuracy in machine-based computation.\\n* Temporal strings are treated as normal strings and MUST NOT be interpreted.\\n* If the specified value is an array, object or null, the process always returns `null`. See the examples for one to find `null` values.',\n", " 'examples': [{'arguments': {'data': [1, 2, 3, 2, 3], 'value': 2},\n", " 'returns': 1},\n", " {'arguments': {'data': [1, 2, 3, 2, 3], 'reverse': True, 'value': 2},\n", " 'returns': 3},\n", " {'arguments': {'data': ['A', 'B', 'C'], 'value': 'b'}, 'returns': None},\n", " {'arguments': {'data': [1, 2, 3], 'value': '2'}, 'returns': None},\n", " {'arguments': {'data': [1, None, 2, None], 'value': None}, 'returns': None},\n", " {'arguments': {'data': [[1, 2], [3, 4]], 'value': [1, 2]}, 'returns': None},\n", " {'arguments': {'data': [[1, 2], [3, 4]], 'value': 2}, 'returns': None},\n", " {'arguments': {'data': [{'a': 'b'}, {'c': 'd'}], 'value': {'a': 'b'}},\n", " 'returns': None}],\n", " 'id': 'array_find',\n", " 'links': [{'href': 'https://raw.githubusercontent.com/Open-EO/openeo-community-examples/main/processes/array_find_nodata.json',\n", " 'rel': 'example',\n", " 'title': 'Find no-data values in arrays',\n", " 'type': 'application/json'}],\n", " 'parameters': [{'description': 'List to find the value in.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'Value to find in `data`. If the value is `null`, this process returns always `null`.',\n", " 'name': 'value',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " {'default': False,\n", " 'description': 'By default, this process finds the index of the first match. To return the index of the last match instead, set this flag to `true`.',\n", " 'name': 'reverse',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The index of the first element with the specified value. If no element was found, `null` is returned.',\n", " 'schema': [{'type': 'null'}, {'minimum': 0, 'type': 'integer'}]},\n", " 'summary': 'Get the index for a value in an array'},\n", " {'categories': ['math', 'math > exponential & logarithmic'],\n", " 'description': 'Computes the exponentiation for the base `base` raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.',\n", " 'examples': [{'arguments': {'base': 0, 'p': 2}, 'returns': 0},\n", " {'arguments': {'base': 2.5, 'p': 0}, 'returns': 1},\n", " {'arguments': {'base': 3, 'p': 3}, 'returns': 27},\n", " {'arguments': {'base': 5, 'p': -1}, 'returns': 0.2},\n", " {'arguments': {'base': 1, 'p': 0.5}, 'returns': 1},\n", " {'arguments': {'base': 1, 'p': None}, 'returns': None},\n", " {'arguments': {'base': None, 'p': 2}, 'returns': None}],\n", " 'id': 'power',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Power.html',\n", " 'rel': 'about',\n", " 'title': 'Power explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'The numerical base.',\n", " 'name': 'base',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The numerical exponent.',\n", " 'name': 'p',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed value for `base` raised to the power of `p`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Exponentiation'},\n", " {'categories': ['math'],\n", " 'description': 'Divides argument `x` by the argument `y` (*`x / y`*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, a division by zero results in ±infinity if the processing environment supports it. Otherwise, a `DivisionByZero` exception must the thrown.',\n", " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2},\n", " {'arguments': {'x': -2, 'y': 4}, 'returns': -0.5},\n", " {'arguments': {'x': 1, 'y': None}, 'returns': None}],\n", " 'exceptions': {'DivisionByZero': {'message': 'Division by zero is not supported.'}},\n", " 'id': 'divide',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Division.html',\n", " 'rel': 'about',\n", " 'title': 'Division explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The dividend.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The divisor.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed result.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Division of two numbers'},\n", " {'categories': ['arrays', 'filter'],\n", " 'description': 'Filters the array elements based on a logical expression so that afterwards an array is returned that only contains the values, indices and/or labels conforming to the condition.',\n", " 'id': 'array_filter',\n", " 'parameters': [{'description': 'An array.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'A condition that is evaluated against each value, index and/or label in the array. Only the array elements for which the condition returns `true` are preserved.',\n", " 'name': 'condition',\n", " 'schema': {'parameters': [{'description': 'The value of the current element being processed.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'description': 'The zero-based index of the current element being processed.',\n", " 'name': 'index',\n", " 'schema': {'minimum': 0, 'type': 'integer'}},\n", " {'default': None,\n", " 'description': 'The label of the current element being processed. Only populated for labeled arrays.',\n", " 'name': 'label',\n", " 'optional': True,\n", " 'schema': [{'type': 'number'}, {'type': 'string'}, {'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': '`true` if the value should be kept in the array, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the condition.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'An array filtered by the specified condition. The number of elements are less than or equal compared to the original array.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Filter an array based on a condition'},\n", " {'categories': ['arrays', 'reducer'],\n", " 'description': 'Gives the first element of an array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 1},\n", " {'arguments': {'data': [None, 'A', 'B']}, 'returns': 'A'},\n", " {'arguments': {'data': [None, 2, 3], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None}],\n", " 'id': 'first',\n", " 'parameters': [{'description': 'An array with elements of any data type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if the first value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The first element of the input array.',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " 'summary': 'First element'},\n", " {'categories': ['arrays', 'sorting'],\n", " 'description': 'Computes the ranked (sorted) element positions in the original list (i.e., a permutation), either in ascending or descending order. The process ``rearrange()`` allows sorting the data based on the computed permutation.\\n\\n**Remarks:**\\n\\n* The positions in the result are zero-based.\\n* The ordering of ties is implementation-dependent.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone/time-offset representations.',\n", " 'examples': [{'arguments': {'data': [6,\n", " -1,\n", " 2,\n", " None,\n", " 7,\n", " 4,\n", " None,\n", " 8,\n", " 3,\n", " 9,\n", " 9]},\n", " 'returns': [1, 2, 8, 5, 0, 4, 7, 9, 10]},\n", " {'arguments': {'data': [6, -1, 2, None, 7, 4, None, 8, 3, 9, 9],\n", " 'nodata': True},\n", " 'returns': [1, 2, 8, 5, 0, 4, 7, 9, 10, 3, 6]},\n", " {'arguments': {'asc': False,\n", " 'data': [6, -1, 2, None, 7, 4, None, 8, 3, 9, 9],\n", " 'nodata': True},\n", " 'returns': [9, 10, 7, 4, 0, 5, 8, 2, 1, 3, 6]},\n", " {'arguments': {'asc': False,\n", " 'data': [6, -1, 2, None, 7, 4, None, 8, 3, 9, 9],\n", " 'nodata': False},\n", " 'returns': [3, 6, 9, 10, 7, 4, 0, 5, 8, 2, 1]}],\n", " 'id': 'order',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Permutation.html',\n", " 'rel': 'about',\n", " 'title': 'Permutation explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array to compute the order for.',\n", " 'name': 'data',\n", " 'schema': {'items': {'anyOf': [{'type': 'number'},\n", " {'type': 'null'},\n", " {'format': 'date-time', 'subtype': 'date-time', 'type': 'string'},\n", " {'format': 'date', 'subtype': 'date', 'type': 'string'}]},\n", " 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'The default sort order is ascending, with smallest values first. To sort in reverse (descending) order, set this parameter to `false`.',\n", " 'name': 'asc',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': None,\n", " 'description': 'Controls the handling of no-data values (`null`). By default, they are removed. If set to `true`, missing values in the data are put last; if set to `false`, they are put first.',\n", " 'name': 'nodata',\n", " 'optional': True,\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'returns': {'description': 'The computed permutation.',\n", " 'schema': {'items': {'minimum': 0, 'type': 'integer'}, 'type': 'array'}},\n", " 'summary': 'Get the order of array elements'},\n", " {'categories': ['logic'],\n", " 'description': 'Checks if **at least one** of the values is true. Evaluates parameter `x` before `y` and stops once the outcome is unambiguous. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```',\n", " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", " {'arguments': {'x': True, 'y': None}, 'returns': True},\n", " {'arguments': {'x': None, 'y': True}, 'returns': True},\n", " {'arguments': {'x': False, 'y': None}, 'returns': None}],\n", " 'id': 'or',\n", " 'parameters': [{'description': 'A boolean value.',\n", " 'name': 'x',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " {'description': 'A boolean value.',\n", " 'name': 'y',\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'process_graph': {'any': {'arguments': {'data': [{'from_parameter': 'x'},\n", " {'from_parameter': 'y'}],\n", " 'ignore_nodata': False},\n", " 'process_id': 'any',\n", " 'result': True}},\n", " 'returns': {'description': 'Boolean result of the logical OR.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Logical OR'},\n", " {'categories': ['math'],\n", " 'description': 'Clips a number between specified minimum and maximum values. A value larger than the maximum value is set to the maximum value, a value lower than the minimum value is set to the minimum value.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'max': 1, 'min': -1, 'x': -5}, 'returns': -1},\n", " {'arguments': {'max': 10, 'min': 1, 'x': 10.001}, 'returns': 10},\n", " {'arguments': {'max': 0.02, 'min': 0, 'x': 1e-06}, 'returns': 1e-06},\n", " {'arguments': {'max': 1, 'min': 0, 'x': None}, 'returns': None}],\n", " 'id': 'clip',\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'Minimum value. If the value is lower than this value, the process will return the value of this parameter.',\n", " 'name': 'min',\n", " 'schema': {'type': 'number'}},\n", " {'description': 'Maximum value. If the value is greater than this value, the process will return the value of this parameter.',\n", " 'name': 'max',\n", " 'schema': {'type': 'number'}}],\n", " 'process_graph': {'max': {'arguments': {'data': [{'from_parameter': 'min'},\n", " {'from_node': 'min'}]},\n", " 'process_id': 'max',\n", " 'result': True},\n", " 'min': {'arguments': {'data': [{'from_parameter': 'max'},\n", " {'from_parameter': 'x'}]},\n", " 'process_id': 'min'}},\n", " 'returns': {'description': 'The value clipped to the specified range.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Clip a value between a minimum and a maximum'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the hyperbolic cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 1}],\n", " 'id': 'cosh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/HyperbolicCosine.html',\n", " 'rel': 'about',\n", " 'title': 'Hyperbolic cosine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed hyperbolic cosine of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Hyperbolic cosine'},\n", " {'categories': ['texts', 'comparison'],\n", " 'description': 'Compares whether `x` is **not** strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly. For example, a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating-point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 1, 'y': 1}, 'returns': False},\n", " {'arguments': {'x': 1, 'y': '1'}, 'returns': True},\n", " {'arguments': {'x': 0, 'y': False}, 'returns': True},\n", " {'arguments': {'delta': 0.01, 'x': 1.02, 'y': 1}, 'returns': True},\n", " {'arguments': {'delta': 0.01, 'x': -1, 'y': -1.001}, 'returns': False},\n", " {'arguments': {'delta': 10, 'x': 115, 'y': 110}, 'returns': False},\n", " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': True},\n", " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", " 'returns': False},\n", " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", " 'returns': False},\n", " {'arguments': {'x': '2018-01-01T00:00:00Z',\n", " 'y': '2018-01-01T00:00:00+00:00'},\n", " 'returns': True},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'neq',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'default': None,\n", " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the non-equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating-point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `gt(abs(minus([x, y]), delta)`',\n", " 'name': 'delta',\n", " 'optional': True,\n", " 'schema': {'type': ['number', 'null']}},\n", " {'default': True,\n", " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", " 'name': 'case_sensitive',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'eq': {'arguments': {'case_sensitive': {'from_parameter': 'case_sensitive'},\n", " 'delta': {'from_parameter': 'delta'},\n", " 'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'eq'},\n", " 'not': {'arguments': {'x': {'from_node': 'eq'}},\n", " 'process_id': 'not',\n", " 'result': True}},\n", " 'returns': {'description': '`true` if `x` is *not* equal to `y`, `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Not equal to comparison'},\n", " {'categories': ['math > statistics', 'reducer'],\n", " 'description': 'The arithmetic mean of an array of numbers is the quantity commonly called the average. It is defined as the sum of all elements divided by the number of elements.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 1.5},\n", " {'arguments': {'data': [9, 2.5, None, -2.5]}, 'returns': 3},\n", " {'arguments': {'data': [1, None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': []},\n", " 'description': 'The input array is empty: return `null`.',\n", " 'returns': None},\n", " {'arguments': {'data': [None, None]},\n", " 'description': 'The input array has only `null` elements: return `null`.',\n", " 'returns': None}],\n", " 'id': 'mean',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/ArithmeticMean.html',\n", " 'rel': 'about',\n", " 'title': 'Arithmetic mean explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'process_graph': {'count': {'arguments': {'condition': {'from_node': 'count_condition'},\n", " 'data': {'from_parameter': 'data'}},\n", " 'process_id': 'count'},\n", " 'count_condition': {'arguments': {'accept': None,\n", " 'reject': True,\n", " 'value': {'from_parameter': 'ignore_nodata'}},\n", " 'process_id': 'if'},\n", " 'divide': {'arguments': {'x': {'from_node': 'sum'},\n", " 'y': {'from_node': 'count'}},\n", " 'process_id': 'divide'},\n", " 'if': {'arguments': {'accept': {'from_node': 'divide'},\n", " 'value': {'from_node': 'neq'}},\n", " 'process_id': 'if',\n", " 'result': True},\n", " 'neq': {'arguments': {'x': {'from_node': 'count'}, 'y': 0},\n", " 'process_id': 'neq'},\n", " 'sum': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'ignore_nodata': {'from_parameter': 'ignore_nodata'}},\n", " 'process_id': 'sum'}},\n", " 'returns': {'description': 'The computed arithmetic mean.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Arithmetic mean (average)'},\n", " {'categories': ['math'],\n", " 'description': 'Computes the absolute value of a real number `x`, which is the \"unsigned\" portion of x and often denoted as *|x|*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': 3.5}, 'returns': 3.5},\n", " {'arguments': {'x': -0.4}, 'returns': 0.4},\n", " {'arguments': {'x': -3.5}, 'returns': 3.5}],\n", " 'id': 'absolute',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/AbsoluteValue.html',\n", " 'rel': 'about',\n", " 'title': 'Absolute value explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'if': {'arguments': {'accept': {'from_node': 'multiply'},\n", " 'reject': {'from_parameter': 'x'},\n", " 'value': {'from_node': 'lt'}},\n", " 'process_id': 'if',\n", " 'result': True},\n", " 'lt': {'arguments': {'x': {'from_parameter': 'x'}, 'y': 0},\n", " 'process_id': 'lt'},\n", " 'multiply': {'arguments': {'x': {'from_parameter': 'x'}, 'y': -1},\n", " 'process_id': 'multiply'}},\n", " 'returns': {'description': 'The computed absolute value.',\n", " 'schema': {'minimum': 0, 'type': ['number', 'null']}},\n", " 'summary': 'Absolute value'},\n", " {'categories': ['logic', 'reducer'],\n", " 'description': 'Checks if **any** (i.e. at least one) value in `data` is `true`. If no value is given (i.e. the array is empty) the process returns `null`.\\n\\nBy default all no-data values are ignored so that the process returns `null` if all values are no-data, `true` if at least one value is true and `false` otherwise. Setting the `ignore_nodata` flag to `false` takes no-data values into account and the array values are reduced pairwise according to the following truth table:\\n\\n```\\n || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```\\n\\n**Remark:** The process evaluates all values from the first to the last element and stops once the outcome is unambiguous. A result is ambiguous unless a value is `true`.',\n", " 'examples': [{'arguments': {'data': [False, None]}, 'returns': False},\n", " {'arguments': {'data': [True, None]}, 'returns': True},\n", " {'arguments': {'data': [False, None], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': [True, None], 'ignore_nodata': False},\n", " 'returns': True},\n", " {'arguments': {'data': [True, False, True, False]}, 'returns': True},\n", " {'arguments': {'data': [True, False]}, 'returns': True},\n", " {'arguments': {'data': [False, False]}, 'returns': False},\n", " {'arguments': {'data': [True]}, 'returns': True},\n", " {'arguments': {'data': [None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': []}, 'returns': None}],\n", " 'id': 'any',\n", " 'parameters': [{'description': 'A set of boolean values.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['boolean', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not and ignores them by default.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'Boolean result of the logical operation.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Is at least one value true?'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the hyperbolic sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'sinh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/HyperbolicSine.html',\n", " 'rel': 'about',\n", " 'title': 'Hyperbolic sine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed hyperbolic sine of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Hyperbolic sine'},\n", " {'categories': ['comparison'],\n", " 'description': 'Compares whether `x` is strictly greater than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is not a `number`, the process returns `false`.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", " {'arguments': {'x': 2, 'y': 1}, 'returns': True},\n", " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", " {'arguments': {'x': '2018-01-02T00:00:00Z', 'y': '2018-01-01T00:00:00Z'},\n", " 'returns': False},\n", " {'arguments': {'x': True, 'y': 0}, 'returns': False},\n", " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'gt',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'returns': {'description': '`true` if `x` is strictly greater than `y` or `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Greater than comparison'},\n", " {'categories': ['comparison'],\n", " 'description': 'Compares whether `x` is less than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number`, the process returns `false`.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", " {'arguments': {'x': '2018-01-01T00:00:00Z',\n", " 'y': '2018-01-01T00:00:00+00:00'},\n", " 'returns': False},\n", " {'arguments': {'x': False, 'y': True}, 'returns': False},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'lte',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'process_graph': {'eq': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'eq'},\n", " 'lt': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'lt'},\n", " 'or': {'arguments': {'x': {'from_node': 'lt'}, 'y': {'from_node': 'eq'}},\n", " 'process_id': 'or',\n", " 'result': True}},\n", " 'returns': {'description': '`true` if `x` is less than or equal to `y`, `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Less than or equal to comparison'},\n", " {'categories': ['math', 'reducer'],\n", " 'description': 'Multiplies all elements in a sequential array of numbers and returns the computed product.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'data': [5, 0]}, 'returns': 0},\n", " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': -20},\n", " {'arguments': {'data': [1, None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': [-1]}, 'returns': -1},\n", " {'arguments': {'data': [None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': []}, 'returns': None}],\n", " 'id': 'product',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Product.html',\n", " 'rel': 'about',\n", " 'title': 'Product explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The computed product of the sequence of numbers.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Compute the product by multiplying numbers'},\n", " {'categories': ['math'],\n", " 'description': 'Remainder after a division of `x` by `y` for both integers and floating-point numbers.\\n\\nThe result of a modulo operation has the sign of the divisor. The handling regarding the sign of the result [differs between programming languages](https://en.wikipedia.org/wiki/Modulo_operation#In_programming_languages) and needs careful consideration to avoid unexpected results.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`. A modulo by zero results in ±infinity if the processing environment supports it. Otherwise, a `DivisionByZero` exception must the thrown.',\n", " 'examples': [{'arguments': {'x': 27, 'y': 5}, 'returns': 2},\n", " {'arguments': {'x': -27, 'y': 5}, 'returns': 3},\n", " {'arguments': {'x': 3.14, 'y': -2}, 'returns': -0.86},\n", " {'arguments': {'x': -27, 'y': -5}, 'returns': -2},\n", " {'arguments': {'x': 27, 'y': None}, 'returns': None},\n", " {'arguments': {'x': None, 'y': 5}, 'returns': None}],\n", " 'exceptions': {'DivisionByZero': {'message': 'Division by zero is not supported.'}},\n", " 'id': 'mod',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Modulo_operation',\n", " 'rel': 'about',\n", " 'title': 'Modulo explained by Wikipedia'}],\n", " 'parameters': [{'description': 'A number to be used as the dividend.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'A number to be used as the divisor.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The remainder after division.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Modulo'},\n", " {'categories': ['math'],\n", " 'description': 'Subtracts argument `y` from the argument `x` (*`x - y`*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2.5},\n", " {'arguments': {'x': -2, 'y': 4}, 'returns': -6},\n", " {'arguments': {'x': 1, 'y': None}, 'returns': None}],\n", " 'id': 'subtract',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Subtraction.html',\n", " 'rel': 'about',\n", " 'title': 'Subtraction explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The minuend.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The subtrahend.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed result.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Subtraction of two numbers'},\n", " {'categories': ['logic', 'reducer'],\n", " 'description': 'Checks if **all** of the values in `data` are true. If no value is given (i.e. the array is empty) the process returns `null`.\\n\\nBy default all no-data values are ignored so that the process returns `null` if all values are no-data, `true` if all values are true and `false` otherwise. Setting the `ignore_nodata` flag to `false` takes no-data values into account and the array values are reduced pairwise according to the following truth table:\\n\\n```\\n || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```\\n\\n**Remark:** The process evaluates all values from the first to the last element and stops once the outcome is unambiguous. A result is ambiguous unless a value is `false` or all values have been taken into account.',\n", " 'examples': [{'arguments': {'data': [False, None]}, 'returns': False},\n", " {'arguments': {'data': [True, None]}, 'returns': True},\n", " {'arguments': {'data': [False, None], 'ignore_nodata': False},\n", " 'returns': False},\n", " {'arguments': {'data': [True, None], 'ignore_nodata': False},\n", " 'returns': None},\n", " {'arguments': {'data': [True, False, True, False]}, 'returns': False},\n", " {'arguments': {'data': [True, False]}, 'returns': False},\n", " {'arguments': {'data': [True, True]}, 'returns': True},\n", " {'arguments': {'data': [True]}, 'returns': True},\n", " {'arguments': {'data': [None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': []}, 'returns': None}],\n", " 'id': 'all',\n", " 'parameters': [{'description': 'A set of boolean values.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['boolean', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not and ignores them by default.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'Boolean result of the logical operation.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Are all of the values true?'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the inverse hyperbolic tangent of `x`. It is the inverse function of the hyperbolic tangent so that *`artanh(tanh(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'artanh',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseHyperbolicTangent.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse hyperbolic tangent explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse hyperbolic tangent'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the arc sine of `x`. The arc sine is the inverse function of the sine so that *`arcsin(sin(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'arcsin',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseSine.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse sine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse sine'},\n", " {'categories': ['comparison'],\n", " 'description': 'Compares whether `x` is greater than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number`, the process returns `false`.\\n* Temporal strings are normal strings. To compare temporal strings as dates/times, use ``date_difference()``.',\n", " 'examples': [{'arguments': {'x': 1, 'y': None}, 'returns': None},\n", " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", " {'arguments': {'x': 1, 'y': 2}, 'returns': False},\n", " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", " {'arguments': {'x': '2018-01-01T00:00:00Z',\n", " 'y': '2018-01-01T00:00:00+00:00'},\n", " 'returns': False},\n", " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", " {'arguments': {'x': None, 'y': None}, 'returns': None}],\n", " 'id': 'gte',\n", " 'parameters': [{'description': 'First operand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}},\n", " {'description': 'Second operand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'process_graph': {'eq': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'eq'},\n", " 'gt': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'gt'},\n", " 'or': {'arguments': {'x': {'from_node': 'gt'}, 'y': {'from_node': 'eq'}},\n", " 'process_id': 'or',\n", " 'result': True}},\n", " 'returns': {'description': '`true` if `x` is greater than or equal to `y`, `null` if any operand is `null`, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Greater than or equal to comparison'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the arc cosine of `x`. The arc cosine is the inverse function of the cosine so that *`arccos(cos(x)) = x`*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': 0}],\n", " 'id': 'arccos',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/InverseCosine.html',\n", " 'rel': 'about',\n", " 'title': 'Inverse cosine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed angle in radians.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Inverse cosine'},\n", " {'categories': ['math > exponential & logarithmic'],\n", " 'description': 'The natural logarithm is the logarithm to the base *e* of the number `x`, which equals to using the *log* process with the base set to *e*. The natural logarithm is the inverse function of taking *e* to the power x.\\n\\nThe no-data value `null` is passed through.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, *`ln(0)`* results in ±infinity if the processing environment supports it or otherwise an exception is thrown.',\n", " 'examples': [{'arguments': {'x': 1}, 'returns': 0}],\n", " 'id': 'ln',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/NaturalLogarithm.html',\n", " 'rel': 'about',\n", " 'title': 'Natural logarithm explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'A number to compute the natural logarithm for.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'e': {'arguments': {}, 'process_id': 'e'},\n", " 'log': {'arguments': {'base': {'from_node': 'e'},\n", " 'x': {'from_parameter': 'x'}},\n", " 'process_id': 'log',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed natural logarithm.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Natural logarithm'},\n", " {'categories': ['math > trigonometric'],\n", " 'description': 'Computes the sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0}],\n", " 'id': 'sin',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Sine.html',\n", " 'rel': 'about',\n", " 'title': 'Sine explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'An angle in radians.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The computed sine of `x`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Sine'},\n", " {'categories': ['math', 'reducer'],\n", " 'description': 'Sums up all elements in a sequential array of numbers and returns the computed sum.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'data': [5, 1]}, 'returns': 6},\n", " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': 4.5},\n", " {'arguments': {'data': [1, None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': [100]}, 'returns': 100},\n", " {'arguments': {'data': [None], 'ignore_nodata': False}, 'returns': None},\n", " {'arguments': {'data': []}, 'returns': None}],\n", " 'id': 'sum',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Sum.html',\n", " 'rel': 'about',\n", " 'title': 'Sum explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'The computed sum of the sequence of numbers.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Compute the sum by adding up numbers'},\n", " {'categories': ['math > statistics'],\n", " 'description': 'Calculates quantiles, which are cut points dividing the range of a sample distribution into either\\n\\n1. intervals corresponding to the given probabilities *or*\\n2. equal-sized intervals (q-quantiles).\\n\\nEither the parameter `probabilities` or `q` must be specified, otherwise the `QuantilesParameterMissing` exception is thrown. If both parameters are set the `QuantilesParameterConflict` exception is thrown.\\n\\nSample quantiles can be computed with several different algorithms. Hyndman and Fan (1996) have concluded on nine different types, which are commonly implemented in statistical software packages. This process is implementing type 7, which is implemented widely and often also the default type (e.g. in Excel, Julia, Python, R and S).',\n", " 'examples': [{'arguments': {'data': [2, 4, 4, 4, 5, 5, 7, 9],\n", " 'probabilities': [0.005, 0.01, 0.02, 0.05, 0.1, 0.5]},\n", " 'returns': [2.07, 2.14, 2.28, 2.7, 3.4, 4.5]},\n", " {'arguments': {'data': [2, 4, 4, 4, 5, 5, 7, 9], 'probabilities': 4},\n", " 'returns': [4, 4.5, 5.5]},\n", " {'arguments': {'data': [-1, -0.5, None, 1], 'probabilities': 2},\n", " 'returns': [-0.5]},\n", " {'arguments': {'data': [-1, -0.5, None, 1],\n", " 'ignore_nodata': False,\n", " 'probabilities': 4},\n", " 'returns': [None, None, None]},\n", " {'arguments': {'data': [], 'probabilities': [0.1, 0.5]},\n", " 'returns': [None, None],\n", " 'title': 'Empty array'}],\n", " 'exceptions': {'AscendingProbabilitiesRequired': {'message': 'The values passed for parameter `probabilities` must be sorted in ascending order.'},\n", " 'QuantilesParameterConflict': {'message': 'The process `quantiles` only allows that either the `probabilities` or the `q` parameter is set.'},\n", " 'QuantilesParameterMissing': {'message': 'The process `quantiles` requires either the `probabilities` or `q` parameter to be set.'}},\n", " 'id': 'quantiles',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Quantile',\n", " 'rel': 'about',\n", " 'title': 'Quantiles explained by Wikipedia'},\n", " {'href': 'https://www.amherst.edu/media/view/129116/original/Sample+Quantiles.pdf',\n", " 'rel': 'about',\n", " 'title': 'Hyndman and Fan (1996): Sample Quantiles in Statistical Packages',\n", " 'type': 'application/pdf'}],\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'description': 'Quantiles to calculate. Either a list of probabilities or the number of intervals:\\n\\n* Provide an array with a sorted list of probabilities in ascending order to calculate quantiles for. The probabilities must be between 0 and 1 (inclusive). If not sorted in ascending order, an `AscendingProbabilitiesRequired` exception is thrown.\\n* Provide an integer to specify the number of intervals to calculate quantiles for. Calculates q-quantiles with equal-sized intervals.',\n", " 'name': 'probabilities',\n", " 'optional': True,\n", " 'schema': [{'items': {'maximum': 1, 'minimum': 0, 'type': 'number'},\n", " 'title': 'List of probabilities',\n", " 'type': 'array',\n", " 'uniqueItems': True},\n", " {'minimum': 2,\n", " 'title': 'Number of intervals (q-quantiles)',\n", " 'type': 'integer'}]},\n", " {'deprecated': True,\n", " 'description': 'Number of intervals to calculate quantiles for. Calculates q-quantiles with equal-sized intervals.\\n\\nThis parameter has been **deprecated**. Please use the parameter `probabilities` instead.',\n", " 'name': 'q',\n", " 'optional': True,\n", " 'schema': {'minimum': 2, 'type': 'integer'}},\n", " {'default': True,\n", " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that an array with `null` values is returned if any element is such a value.',\n", " 'name': 'ignore_nodata',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': \"An array with the computed quantiles. The list has either\\n\\n* as many elements as the given list of `probabilities` had or\\n* *`q`-1* elements.\\n\\nIf the input array is empty the resulting array is filled with as many `null` values as required according to the list above. See the 'Empty array' example for an example.\",\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " 'summary': 'Quantiles'},\n", " {'categories': ['logic'],\n", " 'description': 'Inverts a single boolean so that `true` gets `false` and `false` gets `true`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': None}, 'returns': None},\n", " {'arguments': {'x': False}, 'returns': True},\n", " {'arguments': {'x': True}, 'returns': False}],\n", " 'id': 'not',\n", " 'parameters': [{'description': 'Boolean value to invert.',\n", " 'name': 'x',\n", " 'schema': {'type': ['boolean', 'null']}}],\n", " 'returns': {'description': 'Inverted boolean value.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Inverting a boolean'},\n", " {'categories': ['math > constants', 'math > exponential & logarithmic'],\n", " 'description': 'The real number *e* is a mathematical constant that is the base of the natural logarithm such that *`ln(e) = 1`*. The numerical value is approximately *2.71828*.',\n", " 'id': 'e',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/e.html',\n", " 'rel': 'about',\n", " 'title': 'Mathematical constant e explained by Wolfram MathWorld'}],\n", " 'parameters': [],\n", " 'returns': {'description': \"The numerical value of Euler's number.\",\n", " 'schema': {'type': 'number'}},\n", " 'summary': \"Euler's number (e)\"},\n", " {'categories': ['math > rounding'],\n", " 'description': 'The greatest integer less than or equal to the number `x`.\\n\\nThis process is *not* an alias for the ``int()`` process as defined by some mathematicians, see the examples for negative numbers in both processes for differences.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", " {'arguments': {'x': 3.5}, 'returns': 3},\n", " {'arguments': {'x': -0.4}, 'returns': -1},\n", " {'arguments': {'x': -3.5}, 'returns': -4}],\n", " 'id': 'floor',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/FloorFunction.html',\n", " 'rel': 'about',\n", " 'title': 'Floor explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'A number to round down.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'returns': {'description': 'The number rounded down.',\n", " 'schema': {'type': ['integer', 'null']}},\n", " 'summary': 'Round fractions down'},\n", " {'categories': ['math > exponential & logarithmic'],\n", " 'description': 'Exponential function to the base *e* raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'p': 0}, 'returns': 1},\n", " {'arguments': {'p': None}, 'returns': None}],\n", " 'id': 'exp',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/ExponentialFunction.html',\n", " 'rel': 'about',\n", " 'title': 'Exponential function explained by Wolfram MathWorld'}],\n", " 'parameters': [{'description': 'The numerical exponent.',\n", " 'name': 'p',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'e': {'arguments': {}, 'process_id': 'e'},\n", " 'power': {'arguments': {'base': {'from_node': 'e'},\n", " 'p': {'from_parameter': 'p'}},\n", " 'process_id': 'power',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed value for *e* raised to the power of `p`.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Exponentiation to the base e'},\n", " {'categories': ['cubes', 'import'],\n", " 'description': 'Loads a collection from the current back-end by its id and returns it as a processable data cube. The data that is added to the data cube can be restricted with the parameters `spatial_extent`, `temporal_extent`, `bands` and `properties`. If no data is available for the given extents, a `NoDataAvailable` exception is thrown.\\n\\n**Remarks:**\\n\\n* The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as specified in the metadata if the `bands` parameter is set to `null`.\\n* If no additional parameter is specified this would imply that the whole data set is expected to be loaded. Due to the large size of many data sets, this is not recommended and may be optimized by back-ends to only load the data that is actually required after evaluating subsequent processes such as filters. This means that the values in the data cube should be processed only after the data has been limited to the required extent and as a consequence also to a manageable size.',\n", " 'examples': [{'arguments': {'id': 'Sentinel-2',\n", " 'properties': {'eo:cloud_cover': {'process_graph': {'cc': {'arguments': {'max': 50,\n", " 'min': 0,\n", " 'x': {'from_parameter': 'value'}},\n", " 'process_id': 'between',\n", " 'result': True}}},\n", " 'platform': {'process_graph': {'pf': {'arguments': {'case_sensitive': False,\n", " 'x': {'from_parameter': 'value'},\n", " 'y': 'Sentinel-2B'},\n", " 'process_id': 'eq',\n", " 'result': True}}}},\n", " 'spatial_extent': {'east': 16.6,\n", " 'north': 48.6,\n", " 'south': 47.2,\n", " 'west': 16.1},\n", " 'temporal_extent': ['2018-01-01', '2019-01-01']},\n", " 'description': 'Loading `Sentinel-2B` data from a `Sentinel-2` collection for 2018, but only with cloud cover between 0 and 50%.'}],\n", " 'exceptions': {'NoDataAvailable': {'message': 'There is no data available for the given extents.'},\n", " 'TemporalExtentEmpty': {'message': 'The temporal extent is empty. The second instant in time must always be greater/later than the first instant in time.'}},\n", " 'id': 'load_collection',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html',\n", " 'rel': 'about',\n", " 'title': 'Data Cubes explained in the openEO documentation'},\n", " {'href': 'https://proj.org/usage/projections.html',\n", " 'rel': 'about',\n", " 'title': 'PROJ parameters for cartographic projections'},\n", " {'href': 'http://www.epsg-registry.org',\n", " 'rel': 'about',\n", " 'title': 'Official EPSG code registry'},\n", " {'href': 'http://www.epsg.io',\n", " 'rel': 'about',\n", " 'title': 'Unofficial EPSG code database'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'},\n", " {'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", " 'rel': 'about',\n", " 'title': 'List of common band names as specified by the STAC specification'},\n", " {'href': 'https://www.rfc-editor.org/rfc/rfc3339.html',\n", " 'rel': 'about',\n", " 'title': 'RFC3339: Details about formatting temporal strings'}],\n", " 'parameters': [{'description': 'The collection id.',\n", " 'name': 'id',\n", " 'schema': {'pattern': '^[\\\\w\\\\-\\\\.~/]+$',\n", " 'subtype': 'collection-id',\n", " 'type': 'string'}},\n", " {'description': 'Limits the data to load from the collection to the specified bounding box or polygons.\\n\\n* For raster data, the process loads the pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\\n* For vector data, the process loads the geometry into the data cube if the geometry is fully *within* the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC). Empty geometries may only be in the data cube if no spatial extent has been provided.\\n\\nThe GeoJSON can be one of the following feature types:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry, or\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries.\\n* Empty geometries are ignored.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_bbox()`` or ``filter_spatial()`` directly after loading unbounded data.',\n", " 'name': 'spatial_extent',\n", " 'schema': [{'properties': {'base': {'default': None,\n", " 'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'crs': {'anyOf': [{'examples': [3857],\n", " 'minimum': 1000,\n", " 'subtype': 'epsg-code',\n", " 'title': 'EPSG Code',\n", " 'type': 'integer'},\n", " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'}],\n", " 'default': 4326,\n", " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/) or [WKT2 CRS string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.'},\n", " 'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", " 'type': 'number'},\n", " 'height': {'default': None,\n", " 'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", " 'type': 'number'}},\n", " 'required': ['west', 'south', 'east', 'north'],\n", " 'subtype': 'bounding-box',\n", " 'title': 'Bounding Box',\n", " 'type': 'object'},\n", " {'deprecated': True,\n", " 'description': 'Deprecated in favor of ``load_geojson()``. Limits the data cube to the bounding box of the given geometries. For raster data, all pixels inside the bounding box that do not intersect with any of the polygons will be set to no data (`null`).\\n\\nThe GeoJSON type `GeometryCollection` is not supported. Empty geometries are ignored.',\n", " 'subtype': 'geojson',\n", " 'title': 'GeoJSON',\n", " 'type': 'object'},\n", " {'description': 'Limits the data cube to the bounding box of the given geometries in the vector data cube. For raster data, all pixels inside the bounding box that do not intersect with any of the polygons will be set to no data (`null`). Empty geometries are ignored.',\n", " 'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector data cube',\n", " 'type': 'object'},\n", " {'description': \"Don't filter spatially. All data is included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'description': 'Limits the data to load from the collection to the specified left-closed temporal interval. Applies to all temporal dimensions. The interval has to be specified as an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified time instant is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified time instant is **excluded** from the interval.\\n\\nThe second element must always be greater/later than the first element. Otherwise, a `TemporalExtentEmpty` exception is thrown.\\n\\nAlso supports unbounded intervals by setting one of the boundaries to `null`, but never both.\\n\\nSet this parameter to `null` to set no limit for the temporal extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_temporal()`` directly after loading unbounded data.',\n", " 'name': 'temporal_extent',\n", " 'schema': [{'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", " ['2015-01-01', '2016-01-01']],\n", " 'items': {'anyOf': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'type': 'null'}]},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'subtype': 'temporal-interval',\n", " 'type': 'array',\n", " 'uniqueItems': True},\n", " {'description': \"Don't filter temporally. All data is included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': \"Only adds the specified bands into the data cube so that bands that don't match the list of band names are not available. Applies to all dimensions of type `bands`.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.\\n\\nIt is recommended to use this parameter instead of using ``filter_bands()`` directly after loading unbounded data.\",\n", " 'name': 'bands',\n", " 'optional': True,\n", " 'schema': [{'items': {'subtype': 'band-name', 'type': 'string'},\n", " 'minItems': 1,\n", " 'type': 'array'},\n", " {'description': \"Don't filter bands. All bands are included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Limits the data by metadata properties to include only data in the data cube which all given conditions return `true` for (AND operation).\\n\\nSpecify key-value-pairs with the key being the name of the metadata property, which can be retrieved with the openEO Data Discovery for Collections. The value must be a condition (user-defined process) to be evaluated against the collection metadata, see the example.',\n", " 'name': 'properties',\n", " 'optional': True,\n", " 'schema': [{'additionalProperties': {'parameters': [{'description': 'The property value to be checked against.',\n", " 'name': 'value',\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': '`true` if the data should be loaded into the data cube, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'},\n", " 'description': 'A list of filters to check against. Specify key-value-pairs with the key being the name of the metadata property name and the value being a process evaluated against the metadata values.',\n", " 'subtype': 'metadata-filter',\n", " 'title': 'Filters',\n", " 'type': 'object'},\n", " {'description': \"Don't filter by metadata properties.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]}],\n", " 'returns': {'description': \"A data cube for further processing. The dimensions and dimension properties (name, type, labels, reference system and resolution) correspond to the collection's metadata, but the dimension labels are restricted as specified in the parameters.\",\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Load a collection'},\n", " {'description': 'Loads arbitrary from disk. This process is deprecated, considering using load_uploaded_files or load_stac.',\n", " 'id': 'load_disk_data',\n", " 'parameters': [{'description': \"the file format, e.g. 'GTiff'\",\n", " 'name': 'format',\n", " 'optional': False,\n", " 'schema': {'type': 'string'}},\n", " {'description': 'a glob pattern that matches the files to load from disk',\n", " 'name': 'glob_pattern',\n", " 'optional': False,\n", " 'schema': {'type': 'string'}},\n", " {'description': 'options specific to the file format',\n", " 'name': 'options',\n", " 'optional': False,\n", " 'schema': {'type': 'object'}}],\n", " 'returns': {'description': 'the data as a data cube', 'schema': {}}},\n", " {'description': 'Add a buffer around a geometry.',\n", " 'id': 'vector_buffer',\n", " 'parameters': [{'description': 'Input geometry to add buffer to, vector-cube or GeoJSON(deprecated).',\n", " 'name': 'geometries',\n", " 'optional': False,\n", " 'schema': [{'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'},\n", " {'subtype': 'geojson', 'type': 'object'}]},\n", " {'description': 'The distance of the buffer in meters. A positive distance expands the geometries, resulting in outward buffering (dilation), while a negative distance shrinks the geometries, resulting in inward buffering (erosion).\\n\\nIf the unit of the spatial reference system is not meters, a `UnitMismatch` error is thrown. Use ``vector_reproject()`` to convert the geometries to a suitable spatial reference system.',\n", " 'name': 'distance',\n", " 'optional': False,\n", " 'schema': {'type': 'number'}}],\n", " 'returns': {'description': 'Output geometry (GeoJSON object) with the added or subtracted buffer',\n", " 'schema': {'subtype': 'geojson', 'type': 'object'}}},\n", " {'categories': ['cubes'],\n", " 'description': \"Applies a focal process to a data cube.\\n\\nA focal process is a process that works on a 'neighborhood' of pixels. The neighborhood can extend into multiple dimensions, this extent is specified by the `size` argument. It is not only (part of) the size of the input window, but also the size of the output for a given position of the sliding window. The sliding window moves with multiples of `size`.\\n\\nAn overlap can be specified so that neighborhoods can have overlapping boundaries. This allows for continuity of the output. The overlap region must be included in the data cube or array returned by `process`, but any changed values will be ignored. The missing overlap at the borders of the original data cube is made available as no-data (`null`) in the sub-data cubes.\\n\\nThe neighborhood size should be kept small enough, to avoid running beyond computational resources, but a too-small size will result in a larger number of process invocations, which may slow down processing. Window sizes for spatial dimensions typically range from 64 to 512 pixels, while overlaps of 8 to 32 pixels are common.\\n\\nFor the special case of 2D convolution, it is recommended to use ``apply_kernel()``.\",\n", " 'examples': [{'arguments': {'data': {'from_parameter': 'data'},\n", " 'overlap': [{'dimension': 'x', 'unit': 'px', 'value': 16},\n", " {'dimension': 'y', 'unit': 'px', 'value': 16},\n", " {'dimension': 't', 'value': 'P3D'}],\n", " 'process': {'process_graph': {'udf': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'runtime': 'Python',\n", " 'udf': 'ml.py'},\n", " 'process_id': 'run_udf',\n", " 'result': True}}},\n", " 'size': [{'dimension': 'x', 'unit': 'px', 'value': 128},\n", " {'dimension': 'y', 'unit': 'px', 'value': 128},\n", " {'dimension': 't', 'value': 'P5D'}]}}],\n", " 'exceptions': {'DataCubePropertiesImmutable': {'message': 'The dimension properties (name, type, labels, reference system and resolution) must remain unchanged.'},\n", " 'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'apply_neighborhood',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", " 'rel': 'about',\n", " 'title': 'Apply explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'Process to be applied on all neighborhoods.',\n", " 'name': 'process',\n", " 'schema': {'parameters': [{'description': 'The input data, which is a subset of the data cube as specified in `size` and `overlap`. If the given size and overlap result in a one-dimensional data cube it is converted to a labeled array.',\n", " 'name': 'data',\n", " 'schema': [{'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Multi-dimensional data',\n", " 'type': 'object'},\n", " {'subtype': 'labeled-array',\n", " 'title': 'One-dimensional data',\n", " 'type': 'array'}]},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'An array or data cube with the newly computed values. The data type and dimensionality must correspond to the input data.\\n\\n* Data cubes must have the same dimensions and the dimension properties (name, type, labels, reference system and resolution) must remain unchanged. Otherwise, a `DataCubePropertiesImmutable` exception will be thrown.\\n* Arrays can be returned with or without labels.',\n", " 'schema': [{'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Multi-dimensional data',\n", " 'type': 'object'},\n", " {'title': 'One-dimensional data', 'type': 'array'}]},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'description': 'Neighborhood sizes along each dimension.\\n\\nThis object maps dimension names to either a physical measure (e.g. 100 m, 10 days) or pixels (e.g. 32 pixels). For dimensions not specified, the default is to provide all values. Be aware that including all values from overly large dimensions may not be processed at once.',\n", " 'name': 'size',\n", " 'schema': {'items': {'properties': {'dimension': {'type': 'string'},\n", " 'unit': {'anyOf': [{'title': 'Default unit', 'type': 'null'},\n", " {'enum': ['px', 'm'], 'title': 'Specific unit', 'type': 'string'}],\n", " 'default': None,\n", " 'description': 'The unit the values are given in, either in meters (`m`) or pixels (`px`). If no unit or `null` is given, uses the unit specified for the dimension or otherwise the default unit of the reference system.'},\n", " 'value': {'anyOf': [{'title': 'All values', 'type': 'null'},\n", " {'description': 'See the `unit` parameter for more information.',\n", " 'minimum': 0,\n", " 'type': 'number'},\n", " {'description': '[ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations), e.g. `P1D` for one day.',\n", " 'pattern': '^(-?)P(?=\\\\d|T\\\\d)(?:(\\\\d+)Y)?(?:(\\\\d+)M)?(?:(\\\\d+)([DW]))?(?:T(?:(\\\\d+)H)?(?:(\\\\d+)M)?(?:(\\\\d+(?:\\\\.\\\\d+)?)S)?)?$',\n", " 'subtype': 'duration',\n", " 'type': 'string'}],\n", " 'default': None}},\n", " 'required': ['dimension', 'value'],\n", " 'subtype': 'chunk-size',\n", " 'type': 'object'},\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Overlap of neighborhoods along each dimension to avoid border effects. By default no overlap is provided.\\n\\nFor instance a temporal dimension can add 1 month before and after a neighborhood. In the spatial dimensions, this is often a number of pixels. The overlap specified is added before and after, so an overlap of 8 pixels will add 8 pixels on both sides of the window, so 16 in total.\\n\\nBe aware that large overlaps increase the need for computational resources and modifying overlapping data in subsequent operations have no effect.',\n", " 'name': 'overlap',\n", " 'optional': True,\n", " 'schema': [{'title': 'Without overlap', 'type': 'null'},\n", " {'items': {'properties': {'dimension': {'type': 'string'},\n", " 'unit': {'anyOf': [{'title': 'Default unit', 'type': 'null'},\n", " {'enum': ['px', 'm'], 'title': 'Specific unit', 'type': 'string'}],\n", " 'default': None,\n", " 'description': 'The unit the values are given in, either in meters (`m`) or pixels (`px`). If no unit or `null` is given, uses the unit specified for the dimension or otherwise the default unit of the reference system.'},\n", " 'value': {'anyOf': [{'title': 'No values', 'type': 'null'},\n", " {'description': 'See the `unit` parameter for more information.',\n", " 'minimum': 0,\n", " 'type': 'number'},\n", " {'description': '[ISO 8601 duration](https://en.wikipedia.org/wiki/ISO_8601#Durations), e.g. `P1D` for one day.',\n", " 'pattern': '^(-?)P(?=\\\\d|T\\\\d)(?:(\\\\d+)Y)?(?:(\\\\d+)M)?(?:(\\\\d+)([DW]))?(?:T(?:(\\\\d+)H)?(?:(\\\\d+)M)?(?:(\\\\d+(?:\\\\.\\\\d+)?)S)?)?$',\n", " 'subtype': 'duration',\n", " 'type': 'string'}],\n", " 'default': None}},\n", " 'required': ['dimension', 'value'],\n", " 'subtype': 'chunk-size',\n", " 'type': 'object'},\n", " 'title': 'With overlap',\n", " 'type': 'array'}]},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the process.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A raster data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Apply a process to pixels in a n-dimensional neighborhood'},\n", " {'categories': ['cubes'],\n", " 'description': 'Applies a process to all values along a dimension of a data cube. For example, if the temporal dimension is specified the process will work on the values of a time series.\\n\\nThe process ``reduce_dimension()`` also applies a process to values along a dimension, but drops the dimension afterwards. The process ``apply()`` applies a process to each value in the data cube.\\n\\nThe target dimension is the source dimension if not specified otherwise in the `target_dimension` parameter. The values in the target dimension get replaced by the computed values. The name, type and reference system are preserved.\\n\\nThe dimension labels are preserved when the target dimension is the source dimension and the number of values in the source dimension is equal to the number of values computed by the process. Otherwise, the dimension labels will be incrementing integers starting from zero, which can be changed using ``rename_labels()`` afterwards. The number of labels will be equal to the number of values computed by the process.',\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'apply_dimension',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", " 'rel': 'about',\n", " 'title': 'Apply explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'Process to be applied on all values along the given dimension. The specified process needs to accept an array and must return an array with at least one element. A process may consist of multiple sub-processes.',\n", " 'name': 'process',\n", " 'schema': {'parameters': [{'description': 'A labeled array with elements of any type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type.'},\n", " 'subtype': 'labeled-array',\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new data cube.',\n", " 'schema': {'items': {'description': 'Any data type.'}, 'type': 'array'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'description': 'The name of the source dimension to apply the process on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'schema': {'type': 'string'}},\n", " {'default': None,\n", " 'description': \"The name of the target dimension or `null` (the default) to use the source dimension specified in the parameter `dimension`.\\n\\nBy specifying a target dimension, the source dimension is removed. The target dimension with the specified name and the type `other` (see ``add_dimension()``) is created, if it doesn't exist yet.\",\n", " 'name': 'target_dimension',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the process.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': \"A data cube with the newly computed values.\\n\\nAll dimensions stay the same, except for the dimensions specified in corresponding parameters. There are three cases how the dimensions can change:\\n\\n1. The source dimension is the target dimension:\\n - The (number of) dimensions remain unchanged as the source dimension is the target dimension.\\n - The source dimension properties name and type remain unchanged.\\n - The dimension labels, the reference system and the resolution are preserved only if the number of values in the source dimension is equal to the number of values computed by the process. Otherwise, all other dimension properties change as defined in the list below.\\n2. The source dimension is not the target dimension. The target dimension exists with a single label only:\\n - The number of dimensions decreases by one as the source dimension is 'dropped' and the target dimension is filled with the processed data that originates from the source dimension.\\n - The target dimension properties name and type remain unchanged. All other dimension properties change as defined in the list below.\\n3. The source dimension is not the target dimension and the latter does not exist:\\n - The number of dimensions remain unchanged, but the source dimension is replaced with the target dimension.\\n - The target dimension has the specified name and the type other. All other dimension properties are set as defined in the list below.\\n\\nUnless otherwise stated above, for the given (target) dimension the following applies:\\n\\n- the number of dimension labels is equal to the number of values computed by the process,\\n- the dimension labels are incrementing integers starting from zero,\\n- the resolution changes, and\\n- the reference system is undefined.\",\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Apply a process to all values along a dimension'},\n", " {'categories': ['cubes', 'export'],\n", " 'description': 'Makes the processed data available in the given file format to the corresponding medium that is relevant for the context this processes is applied in:\\n\\n* For **batch jobs** the data is stored on the back-end. STAC-compatible metadata is usually made available with the processed data.\\n* For **synchronous processing** the data is sent to the client as a direct response to the request.\\n* **Secondary web services** are provided with the processed data so that it can make use of it (e.g., visualize it). Web service may require the data in a certain format. Please refer to the documentation of the individual service types for details.',\n", " 'exceptions': {'DataCubeEmpty': {'message': \"The file format doesn't support storing empty data cubes.\"},\n", " 'FormatUnsuitable': {'message': \"Data can't be transformed into the requested output format.\"}},\n", " 'id': 'save_result',\n", " 'links': [{'href': 'https://gdal.org/drivers/raster/index.html',\n", " 'rel': 'about',\n", " 'title': 'GDAL Raster Formats'},\n", " {'href': 'https://gdal.org/drivers/vector/index.html',\n", " 'rel': 'about',\n", " 'title': 'OGR Vector Formats'}],\n", " 'parameters': [{'description': 'The data to deliver in the given file format.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': \"The file format to use. It must be one of the values that the server reports as supported output file formats, which usually correspond to the short GDAL/OGR codes. This parameter is *case insensitive*.\\n\\n* If the data cube is empty and the file format can't store empty data cubes, a `DataCubeEmpty` exception is thrown.\\n* If the file format is otherwise not suitable for storing the underlying data structure, a `FormatUnsuitable` exception is thrown.\",\n", " 'name': 'format',\n", " 'schema': {'subtype': 'output-format', 'type': 'string'}},\n", " {'default': {},\n", " 'description': 'The file format parameters to be used to create the file(s). Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.',\n", " 'name': 'options',\n", " 'optional': True,\n", " 'schema': {'subtype': 'output-format-options', 'type': 'object'}}],\n", " 'returns': {'description': 'Always returns `true` as in case of an error an exception is thrown which aborts the execution of the process.',\n", " 'schema': {'const': True, 'type': 'boolean'}},\n", " 'summary': 'Save processed data'},\n", " {'categories': ['machine learning', 'import'],\n", " 'description': 'Saves a machine learning model as part of a batch job.\\n\\nThe model will be accompanied by a separate STAC Item that implements the [ml-model extension](https://github.com/stac-extensions/ml-model).',\n", " 'experimental': True,\n", " 'id': 'save_ml_model',\n", " 'links': [{'href': 'https://github.com/stac-extensions/ml-model',\n", " 'rel': 'about',\n", " 'title': 'STAC ml-model extension',\n", " 'type': 'text/html'}],\n", " 'parameters': [{'description': 'The data to store as a machine learning model.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'ml-model', 'type': 'object'}},\n", " {'default': {},\n", " 'description': 'Additional parameters to create the file(s).',\n", " 'name': 'options',\n", " 'optional': True,\n", " 'schema': {'additionalParameters': False, 'type': 'object'}}],\n", " 'returns': {'description': 'Returns `false` if the process failed to store the model, `true` otherwise.',\n", " 'schema': {'type': 'boolean'}},\n", " 'summary': 'Save a ML model'},\n", " {'categories': ['machine learning', 'import'],\n", " 'description': 'Loads a machine learning model from a STAC Item.\\n\\nSuch a model could be trained and saved as part of a previous batch job with processes such as ``fit_random_forest()`` and ``save_ml_model()``.',\n", " 'experimental': True,\n", " 'id': 'load_ml_model',\n", " 'links': [{'href': 'https://github.com/stac-extensions/ml-model',\n", " 'rel': 'about',\n", " 'title': 'STAC ml-model extension',\n", " 'type': 'text/html'}],\n", " 'parameters': [{'description': 'The STAC Item to load the machine learning model from. The STAC Item must implement the `ml-model` extension.',\n", " 'name': 'id',\n", " 'schema': [{'format': 'uri',\n", " 'pattern': '^https?://',\n", " 'subtype': 'uri',\n", " 'title': 'URL',\n", " 'type': 'string'},\n", " {'description': 'Loading a model by batch job ID is possible only if a single model has been saved by the job. Otherwise, you have to load a specific model from a batch job by URL.',\n", " 'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", " 'subtype': 'job-id',\n", " 'title': 'Batch Job ID',\n", " 'type': 'string'},\n", " {'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", " 'subtype': 'file-path',\n", " 'title': 'User-uploaded Files',\n", " 'type': 'string'}]}],\n", " 'returns': {'description': 'A machine learning model to be used with machine learning processes such as ``predict_random_forest()``.',\n", " 'schema': {'subtype': 'ml-model', 'type': 'object'}},\n", " 'summary': 'Load a ML model'},\n", " {'categories': ['cubes'],\n", " 'description': 'Applies a process to each value in the data cube (i.e. a local operation). In contrast, the process ``apply_dimension()`` applies a process to all values along a particular dimension.',\n", " 'id': 'apply',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", " 'rel': 'about',\n", " 'title': 'Apply explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'A process that accepts and returns a single value and is applied on each individual value in the data cube. The process may consist of multiple sub-processes and could, for example, consist of processes such as ``absolute()`` or ``linear_scale_range()``.',\n", " 'name': 'process',\n", " 'schema': {'parameters': [{'description': 'The value to process.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the process.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Apply a process to each value'},\n", " {'categories': ['cubes', 'reducer'],\n", " 'description': 'Applies a reducer to a data cube dimension by collapsing all the values along the specified dimension into an output value computed by the reducer.\\n\\nThe dimension is dropped. To avoid this, use ``apply_dimension()`` instead.',\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'reduce_dimension',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#reduce',\n", " 'rel': 'about',\n", " 'title': 'Reducers explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': \"A reducer to apply on the specified dimension. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\",\n", " 'name': 'reducer',\n", " 'schema': {'parameters': [{'description': 'A labeled array with elements of any type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type.'},\n", " 'subtype': 'labeled-array',\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'description': 'The name of the dimension over which to reduce. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'schema': {'type': 'string'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the reducer.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A data cube with the newly computed values. It is missing the given dimension, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Reduce dimensions'},\n", " {'categories': ['cubes'],\n", " 'description': 'Applies a process to segments of the data cube that are defined by the given polygons. For each polygon provided, all pixels for which the point at the pixel center intersects with the polygon (as defined in the Simple Features standard by the OGC) are collected into sub data cubes. If a pixel is part of multiple of the provided polygons (e.g., when the polygons overlap), the `GeometriesOverlap` exception is thrown. Each sub data cube is passed individually to the given process.',\n", " 'exceptions': {'GeometriesOverlap': {'message': 'Geometries are not allowed to overlap to avoid that pixel values are processed multiple times.'}},\n", " 'experimental': True,\n", " 'id': 'apply_polygon',\n", " 'links': [{'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " {'description': 'A vector data cube containing at least one polygon. The provided vector data can be one of the following:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry, or\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries.\\n* Empty geometries are ignored.',\n", " 'name': 'polygons',\n", " 'schema': {'dimensions': [{'geometry_type': ['Polygon', 'MultiPolygon'],\n", " 'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'A process that accepts and returns a single data cube and is applied on each individual sub data cube. The process may consist of multiple sub-processes.',\n", " 'name': 'process',\n", " 'schema': {'parameters': [{'description': 'A sub data cube of the original data cube. The sub data cubes provided cover the smallest possible grid-aligned extent of the corresponding polygon and all pixels outside of the polygon are replaced with the value given in `mask_value`.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The updated sub data cube with the newly computed values and the same dimensions. The dimension properties (name, type, reference system and resolution) must remain unchanged. The labels can change, but the number of labels must remain unchanged.',\n", " 'schema': {'description': 'A data cube.',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'All pixels for which the point at the pixel center **does not** intersect with the polygon are replaced with the given value, which defaults to `null` (no data).\\n\\nIt can provide a distinction between no data values within the polygon and masked pixels outside of it.',\n", " 'name': 'mask_value',\n", " 'optional': True,\n", " 'schema': [{'type': 'number'},\n", " {'type': 'boolean'},\n", " {'type': 'string'},\n", " {'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the process.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " 'summary': 'Apply a process to segments of the data cube'},\n", " {'categories': ['machine learning'],\n", " 'description': 'Executes the fit of a random forest classification based on training data. The process does not include a separate split of the data in test, validation and training data. The Random Forest classification model is based on the approach by Breiman (2001).',\n", " 'experimental': True,\n", " 'id': 'fit_class_random_forest',\n", " 'links': [{'href': 'https://doi.org/10.1023/A:1010933404324',\n", " 'rel': 'about',\n", " 'title': 'Breiman (2001): Random Forests',\n", " 'type': 'text/html'}],\n", " 'parameters': [{'description': 'The predictors for the classification model as a vector data cube. Aggregated to the features (vectors) of the target input variable.',\n", " 'name': 'predictors',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}},\n", " {'description': 'The training sites for the classification model as a vector data cube. This is associated with the target variable for the Random Forest model. The geometry has to be associated with a value to predict (e.g. fractional forest canopy cover).',\n", " 'name': 'target',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}},\n", " {'description': 'Specifies how many split variables will be used at a node.\\n\\nThe following options are available:\\n\\n- *integer*: The given number of variables are considered for each split.\\n- `all`: All variables are considered for each split.\\n- `log2`: The logarithm with base 2 of the number of variables are considered for each split.\\n- `onethird`: A third of the number of variables are considered for each split.\\n- `sqrt`: The square root of the number of variables are considered for each split. This is often the default for classification.',\n", " 'name': 'max_variables',\n", " 'schema': [{'minimum': 1, 'type': 'integer'},\n", " {'enum': ['all', 'log2', 'onethird', 'sqrt'], 'type': 'string'}]},\n", " {'default': 100,\n", " 'description': 'The number of trees build within the Random Forest classification.',\n", " 'name': 'num_trees',\n", " 'optional': True,\n", " 'schema': {'minimum': 1, 'type': 'integer'}},\n", " {'default': None,\n", " 'description': 'A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.',\n", " 'name': 'seed',\n", " 'optional': True,\n", " 'schema': {'type': ['integer', 'null']}}],\n", " 'returns': {'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.',\n", " 'schema': {'subtype': 'ml-model', 'type': 'object'}},\n", " 'summary': 'Train a random forest classification model'},\n", " {'categories': ['machine learning'],\n", " 'description': 'Executes the fit of a catboost classification based on training data. The process does not include a separate split of the data in test, validation and training data.',\n", " 'experimental': True,\n", " 'id': 'fit_class_catboost',\n", " 'links': [],\n", " 'parameters': [{'description': 'The predictors for the classification model as a vector data cube. Aggregated to the features (vectors) of the target input variable.',\n", " 'name': 'predictors',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}},\n", " {'description': 'The training sites for the classification model as a vector data cube. This is associated with the target variable for the Catboost model. The geometry has to be associated with a value to predict (e.g. fractional forest canopy cover).',\n", " 'name': 'target',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}},\n", " {'default': 5,\n", " 'description': 'The maximum number of trees that can be built during the training process.',\n", " 'name': 'iterations',\n", " 'optional': True,\n", " 'schema': {'maximum': 500, 'minimum': 1, 'type': 'integer'}},\n", " {'default': 5,\n", " 'description': 'Depth of the trees.',\n", " 'name': 'depth',\n", " 'optional': True,\n", " 'schema': {'maximum': 16, 'minimum': 1, 'type': 'integer'}},\n", " {'default': 0,\n", " 'description': 'The random seed used for training, for reproducibility.',\n", " 'name': 'seed',\n", " 'optional': True,\n", " 'schema': {'maximum': 2147483647, 'minimum': 0, 'type': 'integer'}}],\n", " 'returns': {'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.',\n", " 'schema': {'subtype': 'ml-model', 'type': 'object'}},\n", " 'summary': 'Train a catboost classification model'},\n", " {'categories': ['machine learning', 'reducer'],\n", " 'description': 'Applies a Random Forest machine learning model to an array and predicts a value for it.',\n", " 'experimental': True,\n", " 'id': 'predict_random_forest',\n", " 'parameters': [{'description': 'An array of numbers.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " {'description': 'A model object that can be trained with the processes ``fit_regr_random_forest()`` (regression) and ``fit_class_random_forest()`` (classification).',\n", " 'name': 'model',\n", " 'schema': {'subtype': 'ml-model', 'type': 'object'}}],\n", " 'returns': {'description': 'The predicted value. Returns `null` if any of the given values in the array is a no-data value.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Predict values from a Random Forest model'},\n", " {'categories': ['cubes'],\n", " 'description': 'Adds a new named dimension to the data cube.\\n\\nAfterwards, the dimension can be referred to with the specified `name`. If a dimension with the specified name exists, the process fails with a `DimensionExists` exception. The dimension label of the dimension is set to the specified `label`.',\n", " 'exceptions': {'DimensionExists': {'message': 'A dimension with the specified name already exists.'}},\n", " 'id': 'add_dimension',\n", " 'parameters': [{'description': 'A data cube to add the dimension to.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'Name for the dimension.',\n", " 'name': 'name',\n", " 'schema': {'type': 'string'}},\n", " {'description': 'A dimension label.',\n", " 'name': 'label',\n", " 'schema': [{'type': 'number'}, {'type': 'string'}]},\n", " {'default': 'other',\n", " 'description': 'The type of dimension, defaults to `other`.',\n", " 'name': 'type',\n", " 'optional': True,\n", " 'schema': {'enum': ['bands', 'geometry', 'spatial', 'temporal', 'other'],\n", " 'type': 'string'}}],\n", " 'returns': {'description': 'The data cube with a newly added dimension. The new dimension has exactly one dimension label. All other dimensions remain unchanged.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Add a new dimension'},\n", " {'categories': ['cubes'],\n", " 'description': 'Drops a dimension from the data cube.\\n\\nDropping a dimension only works on dimensions with a single dimension label left, otherwise the process fails with a `DimensionLabelCountMismatch` exception. Dimension values can be reduced to a single value with a filter such as ``filter_bands()`` or the ``reduce_dimension()`` process. If a dimension with the specified name does not exist, the process fails with a `DimensionNotAvailable` exception.',\n", " 'exceptions': {'DimensionLabelCountMismatch': {'message': 'The number of dimension labels exceeds one, which requires a reducer.'},\n", " 'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'drop_dimension',\n", " 'parameters': [{'description': 'The data cube to drop a dimension from.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'Name of the dimension to drop.',\n", " 'name': 'name',\n", " 'schema': {'type': 'string'}}],\n", " 'returns': {'description': 'A data cube without the specified dimension. The number of dimensions decreases by one, but the dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Remove a dimension'},\n", " {'categories': ['cubes'],\n", " 'description': 'Gives all labels for a dimension in the data cube. The labels have the same order as in the data cube.\\n\\nIf a dimension with the specified name does not exist, the process fails with a `DimensionNotAvailable` exception.',\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'dimension_labels',\n", " 'parameters': [{'description': 'The data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'The name of the dimension to get the labels for.',\n", " 'name': 'dimension',\n", " 'schema': {'type': 'string'}}],\n", " 'returns': {'description': 'The labels as an array.',\n", " 'schema': {'items': {'type': ['number', 'string']}, 'type': 'array'}},\n", " 'summary': 'Get the dimension labels'},\n", " {'categories': ['cubes'],\n", " 'description': 'Renames a dimension in the data cube while preserving all other properties.',\n", " 'exceptions': {'DimensionExists': {'message': 'A dimension with the specified name already exists.'},\n", " 'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'id': 'rename_dimension',\n", " 'parameters': [{'description': 'The data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'The current name of the dimension. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'source',\n", " 'schema': {'type': 'string'}},\n", " {'description': 'A new Name for the dimension. Fails with a `DimensionExists` exception if a dimension with the specified name exists.',\n", " 'name': 'target',\n", " 'schema': {'type': 'string'}}],\n", " 'returns': {'description': 'A data cube with the same dimensions, but the name of one of the dimensions changes. The old name can not be referred to any longer. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Rename a dimension'},\n", " {'categories': ['cubes'],\n", " 'description': \"Renames the labels of the specified dimension in the data cube from `source` to `target`.\\n\\nIf the array for the source labels is empty (the default), the dimension labels are expected to be enumerated with zero-based numbering (0,1,2,3,...) so that the dimension labels directly map to the indices of the array specified for the parameter `target`. Otherwise, the number of the source and target labels must be equal. If none of these requirements is fulfilled, the `LabelMismatch` exception is thrown.\\n\\nThis process doesn't change the order of the labels and their corresponding data.\",\n", " 'examples': [{'arguments': {'data': {'from_parameter': 'data'},\n", " 'dimension': 'bands',\n", " 'source': ['B1', 'B2', 'B3'],\n", " 'target': ['red', 'green', 'blue']},\n", " 'description': 'Renaming the bands from `B1` to `red`, from `B2` to `green` and from `B3` to `blue`.',\n", " 'title': 'Rename named labels'}],\n", " 'exceptions': {'LabelExists': {'message': 'A label with the specified name exists.'},\n", " 'LabelMismatch': {'message': \"The number of labels in the parameters `source` and `target` don't match.\"},\n", " 'LabelNotAvailable': {'message': 'A label with the specified name does not exist.'},\n", " 'LabelsNotEnumerated': {'message': 'The dimension labels are not enumerated.'}},\n", " 'id': 'rename_labels',\n", " 'parameters': [{'description': 'The data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'The name of the dimension to rename the labels for.',\n", " 'name': 'dimension',\n", " 'schema': {'type': 'string'}},\n", " {'description': 'The new names for the labels.\\n\\nIf a target dimension label already exists in the data cube, a `LabelExists` exception is thrown.',\n", " 'name': 'target',\n", " 'schema': {'items': {'type': ['number', 'string']}, 'type': 'array'}},\n", " {'default': [],\n", " 'description': \"The original names of the labels to be renamed to corresponding array elements in the parameter `target`. It is allowed to only specify a subset of labels to rename, as long as the `target` and `source` parameter have the same length. The order of the labels doesn't need to match the order of the dimension labels in the data cube. By default, the array is empty so that the dimension labels in the data cube are expected to be enumerated.\\n\\nIf the dimension labels are not enumerated and the given array is empty, the `LabelsNotEnumerated` exception is thrown. If one of the source dimension labels doesn't exist, the `LabelNotAvailable` exception is thrown.\",\n", " 'name': 'source',\n", " 'optional': True,\n", " 'schema': {'items': {'type': ['number', 'string']}, 'type': 'array'}}],\n", " 'returns': {'description': 'The data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that for the given dimension the labels change. The old labels can not be referred to any longer. The number of labels remains the same.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Rename dimension labels'},\n", " {'categories': ['cubes', 'aggregate'],\n", " 'description': 'Computes a temporal aggregation based on an array of temporal intervals.\\n\\nFor common regular calendar hierarchies such as year, month, week or seasons ``aggregate_temporal_period()`` can be used. Other calendar hierarchies must be transformed into specific intervals by the clients.\\n\\nFor each interval, all data along the dimension will be passed through the reducer.\\n\\nThe computed values will be projected to the labels. If no labels are specified, the start of the temporal interval will be used as label for the corresponding values. In case of a conflict (i.e. the user-specified values for the start times of the temporal intervals are not distinct), the user-defined labels must be specified in the parameter `labels` as otherwise a `DistinctDimensionLabelsRequired` exception would be thrown. The number of user-defined labels and the number of intervals need to be equal.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension.',\n", " 'examples': [{'arguments': {'data': {'from_parameter': 'data'},\n", " 'intervals': [['2015-01-01', '2016-01-01'],\n", " ['2016-01-01', '2017-01-01'],\n", " ['2017-01-01', '2018-01-01'],\n", " ['2018-01-01', '2019-01-01'],\n", " ['2019-01-01', '2020-01-01']],\n", " 'labels': ['2015', '2016', '2017', '2018', '2019'],\n", " 'reducer': {'process_graph': {'mean1': {'arguments': {'data': {'from_parameter': 'data'}},\n", " 'process_id': 'mean',\n", " 'result': True}}}}}],\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", " 'DistinctDimensionLabelsRequired': {'message': 'The dimension labels have duplicate values. Distinct labels must be specified.'},\n", " 'TemporalExtentEmpty': {'message': 'At least one of the intervals is empty. The second instant in time must always be greater/later than the first instant.'},\n", " 'TooManyDimensions': {'message': 'The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified.'}},\n", " 'id': 'aggregate_temporal',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", " 'rel': 'about',\n", " 'title': 'Aggregation explained in the openEO documentation'},\n", " {'href': 'https://www.rfc-editor.org/rfc/rfc3339.html',\n", " 'rel': 'about',\n", " 'title': 'RFC3339: Details about formatting temporal strings'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'Left-closed temporal intervals, which are allowed to overlap. Each temporal interval in the array has exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified time instant is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified time instant is **excluded** from the interval.\\n\\nThe second element must always be greater/later than the first element, except when using time without date. Otherwise, a `TemporalExtentEmpty` exception is thrown.',\n", " 'name': 'intervals',\n", " 'schema': {'examples': [[['2015-01-01', '2016-01-01'],\n", " ['2016-01-01', '2017-01-01'],\n", " ['2017-01-01', '2018-01-01']],\n", " [['06:00:00', '18:00:00'], ['18:00:00', '06:00:00']]],\n", " 'items': {'items': {'anyOf': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'description': 'Time only, formatted as `HH:MM:SS`. The time zone is UTC.',\n", " 'pattern': '^\\\\d{2}:\\\\d{2}:\\\\d{2}$',\n", " 'subtype': 'time',\n", " 'type': 'string'},\n", " {'type': 'null'}]},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'subtype': 'temporal-interval',\n", " 'type': 'array',\n", " 'uniqueItems': True},\n", " 'minItems': 1,\n", " 'subtype': 'temporal-intervals',\n", " 'type': 'array'}},\n", " {'description': \"A reducer to be applied for the values contained in each interval. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes. Intervals may not contain any values, which for most reducers leads to no-data (`null`) values by default.\",\n", " 'name': 'reducer',\n", " 'schema': {'parameters': [{'description': \"A labeled array with elements of any type. If there's no data for the interval, the array is empty.\",\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type.'},\n", " 'subtype': 'labeled-array',\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': [],\n", " 'description': 'Distinct labels for the intervals, which can contain dates and/or times. Is only required to be specified if the values for the start of the temporal intervals are not distinct and thus the default labels would not be unique. The number of labels and the number of groups need to be equal.',\n", " 'name': 'labels',\n", " 'optional': True,\n", " 'schema': {'items': {'type': ['number', 'string']}, 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the reducer.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension.',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Temporal aggregations'},\n", " {'categories': ['aggregate', 'climatology', 'cubes'],\n", " 'description': 'Computes a temporal aggregation based on calendar hierarchies such as years, months or seasons. For other calendar hierarchies ``aggregate_temporal()`` can be used.\\n\\nFor each interval, all data along the dimension will be passed through the reducer.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension.',\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", " 'DistinctDimensionLabelsRequired': {'message': 'The dimension labels have duplicate values. Distinct labels must be specified.'},\n", " 'TooManyDimensions': {'message': 'The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified.'}},\n", " 'id': 'aggregate_temporal_period',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", " 'rel': 'about',\n", " 'title': 'Aggregation explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'The source data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'The time intervals to aggregate. The following pre-defined values are available:\\n\\n* `hour`: Hour of the day\\n* `day`: Day of the year\\n* `week`: Week of the year\\n* `dekad`: Ten day periods, counted per year with three periods per month (day 1 - 10, 11 - 20 and 21 - end of month). The third dekad of the month can range from 8 to 11 days. For example, the third dekad of a year spans from January 21 till January 31 (11 days), the fourth dekad spans from February 1 till February 10 (10 days) and the sixth dekad spans from February 21 till February 28 or February 29 in a leap year (8 or 9 days respectively).\\n* `month`: Month of the year\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).\\n* `year`: Proleptic years\\n* `decade`: Ten year periods ([0-to-9 decade](https://en.wikipedia.org/wiki/Decade#0-to-9_decade)), from a year ending in a 0 to the next year ending in a 9.\\n* `decade-ad`: Ten year periods ([1-to-0 decade](https://en.wikipedia.org/wiki/Decade#1-to-0_decade)) better aligned with the anno Domini (AD) calendar era, from a year ending in a 1 to the next year ending in a 0.',\n", " 'name': 'period',\n", " 'schema': {'enum': ['hour',\n", " 'day',\n", " 'week',\n", " 'dekad',\n", " 'month',\n", " 'season',\n", " 'tropical-season',\n", " 'year',\n", " 'decade',\n", " 'decade-ad'],\n", " 'type': 'string'}},\n", " {'description': \"A reducer to be applied for the values contained in each period. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes. Periods may not contain any values, which for most reducers leads to no-data (`null`) values by default.\",\n", " 'name': 'reducer',\n", " 'schema': {'parameters': [{'description': \"A labeled array with elements of any type. If there's no data for the period, the array is empty.\",\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type.'},\n", " 'subtype': 'labeled-array',\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the source data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the reducer.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension. The specified temporal dimension has the following dimension labels (`YYYY` = four-digit year, `MM` = two-digit month, `DD` two-digit day of month):\\n\\n* `hour`: `YYYY-MM-DD-00` - `YYYY-MM-DD-23`\\n* `day`: `YYYY-001` - `YYYY-365`\\n* `week`: `YYYY-01` - `YYYY-52`\\n* `dekad`: `YYYY-00` - `YYYY-36`\\n* `month`: `YYYY-01` - `YYYY-12`\\n* `season`: `YYYY-djf` (December - February), `YYYY-mam` (March - May), `YYYY-jja` (June - August), `YYYY-son` (September - November).\\n* `tropical-season`: `YYYY-ndjfma` (November - April), `YYYY-mjjaso` (May - October).\\n* `year`: `YYYY`\\n* `decade`: `YYY0`\\n* `decade-ad`: `YYY1`\\n\\nThe dimension labels in the new data cube are complete for the whole extent of the source data cube. For example, if `period` is set to `day` and the source data cube has two dimension labels at the beginning of the year (`2020-01-01`) and the end of a year (`2020-12-31`), the process returns a data cube with 365 dimension labels (`2020-001`, `2020-002`, ..., `2020-365`). In contrast, if `period` is set to `day` and the source data cube has just one dimension label `2020-01-05`, the process returns a data cube with just a single dimension label (`2020-005`).',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Temporal aggregations based on calendar hierarchies'},\n", " {'categories': ['cubes', 'aggregate'],\n", " 'description': \"Aggregates statistics for one or more geometries (e.g. zonal statistics for polygons) over the spatial dimensions. The given data cube can have multiple additional dimensions and for all these dimensions results will be computed individually.\\n\\nAn 'unbounded' aggregation over the full extent of the horizontal spatial dimensions can be computed with the process ``reduce_spatial()``.\\n\\nThis process passes a list of values to the reducer. The list of values has an undefined order, therefore processes such as ``last()`` and ``first()`` that depend on the order of the values will lead to unpredictable results.\",\n", " 'exceptions': {'TargetDimensionExists': {'message': 'A dimension with the specified target dimension name already exists.'}},\n", " 'id': 'aggregate_spatial',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", " 'rel': 'about',\n", " 'title': 'Aggregation explained in the openEO documentation'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'A raster data cube with at least two spatial dimensions.\\n\\nThe data cube implicitly gets restricted to the bounds of the geometries as if ``filter_spatial()`` would have been used with the same values for the corresponding parameters immediately before this process.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'Geometries for which the aggregation will be computed. Feature properties are preserved for vector data cubes and all GeoJSON Features.\\n\\nOne value will be computed per label in the dimension of type `geometries`, GeoJSON `Feature` or `Geometry`. For a `FeatureCollection` multiple values will be computed, one value per contained `Feature`. No values will be computed for empty geometries. For example, a single value will be computed for a `MultiPolygon`, but two values will be computed for a `FeatureCollection` containing two polygons.\\n\\n- For **polygons**, the process considers all pixels for which the point at the pixel center intersects with the corresponding polygon (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nThus, pixels may be part of multiple geometries and be part of multiple aggregations. No operation is applied to geometries that are outside of the bounds of the data.',\n", " 'name': 'geometries',\n", " 'schema': [{'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector Data Cube',\n", " 'type': 'object'},\n", " {'deprecated': True,\n", " 'description': 'Deprecated in favor of ``load_geojson()``. The GeoJSON type `GeometryCollection` is not supported.',\n", " 'subtype': 'geojson',\n", " 'title': 'GeoJSON',\n", " 'type': 'object'}]},\n", " {'description': \"A reducer to be applied on all values of each geometry. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\",\n", " 'name': 'reducer',\n", " 'schema': {'parameters': [{'description': 'An array with elements of any type.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type.'},\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the vector data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': \"By default (which is `null`), the process only computes the results and doesn't add a new dimension.\\n\\nIf this parameter contains a new dimension name, the computation also stores information about the total count of pixels (valid + invalid pixels) and the number of valid pixels (see ``is_valid()``) for each computed value. These values are added as a new dimension. The new dimension of type `other` has the dimension labels `value`, `total_count` and `valid_count`.\\n\\nFails with a `TargetDimensionExists` exception if a dimension with the specified name exists.\",\n", " 'name': 'target_dimension',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the reducer.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': \"A vector data cube with the computed results. Empty geometries still exist but without any aggregated values (i.e. no-data). The spatial dimensions are replaced by a dimension of type 'geometries' and if `target_dimension` is not `null`, a new dimension is added.\",\n", " 'schema': {'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Zonal statistics for geometries'},\n", " {'categories': ['cubes', 'masks'],\n", " 'description': \"Applies a mask to a raster data cube. To apply a polygon as a mask, use ``mask_polygon()``.\\n\\nA mask is a raster data cube for which corresponding pixels among `data` and `mask` are compared and those pixels in `data` are replaced whose pixels in `mask` are non-zero (for numbers) or `true` (for boolean values). The pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data).\\n\\nThe data cubes have to be compatible except that the horizontal spatial dimensions (axes `x` and `y`) will be aligned implicitly by ``resample_cube_spatial()``. `data` is the target data cube for resampling and the default parameters of ``resample_cube_spatial()`` apply. All other dimensions in the mask must also be available in the raster data cube with the same name, type, reference system, resolution and labels. Dimensions can be missing in the mask with the result that the mask is applied to each label of the dimension in `data` that is missing in the data cube of the mask. The process fails if there's an incompatibility found between the raster data cube and the mask.\",\n", " 'id': 'mask',\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'A mask as a raster data cube. Every pixel in `data` must have a corresponding element in `mask`.',\n", " 'name': 'mask',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'The value used to replace masked values with.',\n", " 'name': 'replacement',\n", " 'optional': True,\n", " 'schema': {'type': ['number', 'boolean', 'string', 'null']}}],\n", " 'returns': {'description': 'A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Apply a raster mask'},\n", " {'categories': ['cubes', 'masks'],\n", " 'description': 'Applies a (multi) polygon mask to a raster data cube. To apply a raster mask use ``mask()``.\\n\\nAll pixels for which the point at the pixel center **does not** intersect with any polygon (as defined in the Simple Features standard by the OGC) are replaced. This behavior can be inverted by setting the parameter `inside` to `true`. The pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data). No data values in `data` will be left untouched by the masking operation.',\n", " 'id': 'mask_polygon',\n", " 'links': [{'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'A GeoJSON object or a vector data cube containing at least one polygon. The provided vector data can be one of the following:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry, or\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries.\\n* Empty geometries are ignored.',\n", " 'name': 'mask',\n", " 'schema': [{'dimensions': [{'geometry_type': ['Polygon', 'MultiPolygon'],\n", " 'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector Data Cube',\n", " 'type': 'object'},\n", " {'deprecated': True,\n", " 'description': 'Deprecated in favor of ``load_geojson()``. The GeoJSON type `GeometryCollection` is not supported.',\n", " 'subtype': 'geojson',\n", " 'title': 'GeoJSON',\n", " 'type': 'object'}]},\n", " {'default': None,\n", " 'description': 'The value used to replace masked values with.',\n", " 'name': 'replacement',\n", " 'optional': True,\n", " 'schema': [{'type': 'number'},\n", " {'type': 'boolean'},\n", " {'type': 'string'},\n", " {'type': 'null'}]},\n", " {'default': False,\n", " 'description': 'If set to `true` all pixels for which the point at the pixel center **does** intersect with any polygon are replaced.',\n", " 'name': 'inside',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': 'A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Apply a polygon mask'},\n", " {'categories': ['cubes', 'filter'],\n", " 'description': 'Limits the data cube to the specified interval of dates and/or times.\\n\\nMore precisely, the filter checks whether each of the temporal dimension labels is greater than or equal to the lower boundary (start date/time) and less than the value of the upper boundary (end date/time). This corresponds to a left-closed interval, which contains the lower boundary but not the upper boundary.',\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", " 'TemporalExtentEmpty': {'message': 'The temporal extent is empty. The second instant in time must always be greater/later than the first instant in time.'}},\n", " 'id': 'filter_temporal',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", " 'rel': 'about',\n", " 'title': 'Filters explained in the openEO documentation'},\n", " {'href': 'https://www.rfc-editor.org/rfc/rfc3339.html',\n", " 'rel': 'about',\n", " 'title': 'RFC3339: Details about formatting temporal strings'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'Left-closed temporal interval, i.e. an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified time instant is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified time instant is **excluded** from the interval.\\n\\nThe second element must always be greater/later than the first element. Otherwise, a `TemporalExtentEmpty` exception is thrown.\\n\\nAlso supports unbounded intervals by setting one of the boundaries to `null`, but never both.',\n", " 'name': 'extent',\n", " 'schema': {'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", " ['2015-01-01', '2016-01-01']],\n", " 'items': {'anyOf': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'type': 'null'}]},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'subtype': 'temporal-interval',\n", " 'type': 'array'}},\n", " {'default': None,\n", " 'description': 'The name of the temporal dimension to filter on. If no specific dimension is specified, the filter applies to all temporal dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}}],\n", " 'returns': {'description': 'A data cube restricted to the specified temporal extent. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the temporal dimensions (determined by `dimensions` parameter) may have less dimension labels.',\n", " 'schema': {'dimensions': [{'type': 'temporal'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Temporal filter based on temporal intervals'},\n", " {'categories': ['cubes', 'filter'],\n", " 'description': 'Filters the dimension labels in the data cube for the given dimension. Only the dimension labels that match the specified condition are preserved, all other labels with their corresponding data get removed.',\n", " 'examples': [{'arguments': {'condition': {'process_graph': {'eq': {'arguments': {'case_sensitive': False,\n", " 'x': {'from_parameter': 'value'},\n", " 'y': 'Sentinel-2A'},\n", " 'process_id': 'eq',\n", " 'result': True}}},\n", " 'data': {'from_parameter': 'sentinel2_data'},\n", " 'dimension': 'platform'},\n", " 'description': 'Filters the data cube to only contain data from platform Sentinel-2A. This example assumes that the data cube has a dimension `platform` so that computations can distinguish between Sentinel-2A and Sentinel-2B data.'}],\n", " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}},\n", " 'experimental': True,\n", " 'id': 'filter_labels',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", " 'rel': 'about',\n", " 'title': 'Filters explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'A condition that is evaluated against each dimension label in the specified dimension. A dimension label and the corresponding data is preserved for the given dimension, if the condition returns `true`.',\n", " 'name': 'condition',\n", " 'schema': {'parameters': [{'description': 'A single dimension label to compare against. The data type of the parameter depends on the dimension labels set for the dimension. Please note that for some dimension types a representation is used, e.g.\\n\\n* dates and/or times are usually strings compliant to [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601),\\n* geometries can be a WKT string or an identifier.',\n", " 'name': 'value',\n", " 'schema': [{'type': 'number'}, {'type': 'string'}]},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': '`true` if the dimension label should be kept in the data cube, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'description': 'The name of the dimension to filter on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", " 'name': 'dimension',\n", " 'schema': {'type': 'string'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the condition.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the given dimension has less (or the same) dimension labels.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Filter dimension labels based on a condition'},\n", " {'categories': ['cubes', 'filter'],\n", " 'description': 'Limits the data cube to the specified bounding box.\\n\\n* For raster data cubes, the filter retains a pixel in the data cube if the point at the pixel center intersects with the bounding box (as defined in the Simple Features standard by the OGC). Alternatively, ``filter_spatial()`` can be used to filter by geometry.\\n* For vector data cubes, the filter retains the geometry in the data cube if the geometry is fully within the bounding box (as defined in the Simple Features standard by the OGC). All geometries that were empty or not contained fully within the bounding box will be removed from the data cube.\\n\\nAlternatively, ``filter_vector()`` can be used to filter by geometry.',\n", " 'id': 'filter_bbox',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", " 'rel': 'about',\n", " 'title': 'Filters explained in the openEO documentation'},\n", " {'href': 'https://proj.org/usage/projections.html',\n", " 'rel': 'about',\n", " 'title': 'PROJ parameters for cartographic projections'},\n", " {'href': 'http://www.epsg-registry.org',\n", " 'rel': 'about',\n", " 'title': 'Official EPSG code registry'},\n", " {'href': 'http://www.epsg.io',\n", " 'rel': 'about',\n", " 'title': 'Unofficial EPSG code database'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'A data cube.',\n", " 'name': 'data',\n", " 'schema': [{'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Raster data cube',\n", " 'type': 'object'},\n", " {'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector data cube',\n", " 'type': 'object'}]},\n", " {'description': 'A bounding box, which may include a vertical axis (see `base` and `height`).',\n", " 'name': 'extent',\n", " 'schema': {'properties': {'base': {'default': None,\n", " 'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'crs': {'anyOf': [{'examples': [3857],\n", " 'minimum': 1000,\n", " 'subtype': 'epsg-code',\n", " 'title': 'EPSG Code',\n", " 'type': 'integer'},\n", " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'}],\n", " 'default': 4326,\n", " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/) or [WKT2 CRS string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.'},\n", " 'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", " 'type': 'number'},\n", " 'height': {'default': None,\n", " 'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", " 'type': 'number'}},\n", " 'required': ['west', 'south', 'east', 'north'],\n", " 'subtype': 'bounding-box',\n", " 'type': 'object'}}],\n", " 'returns': {'description': 'A data cube restricted to the bounding box. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.',\n", " 'schema': [{'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Raster data cube',\n", " 'type': 'object'},\n", " {'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector data cube',\n", " 'type': 'object'}]},\n", " 'summary': 'Spatial filter using a bounding box'},\n", " {'categories': ['cubes', 'filter'],\n", " 'description': 'Limits the raster data cube over the spatial dimensions to the specified geometries.\\n\\n- For **polygons**, the filter retains a pixel in the data cube if the point at the pixel center intersects with at least one of the polygons (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nMore specifically, pixels outside of the bounding box of the given geometry will not be available after filtering. All pixels inside the bounding box that are not retained will be set to `null` (no data).\\n\\n Alternatively, use ``filter_bbox()`` to filter by bounding box.',\n", " 'id': 'filter_spatial',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", " 'rel': 'about',\n", " 'title': 'Filters explained in the openEO documentation'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'One or more geometries used for filtering, given as GeoJSON or vector data cube. If multiple geometries are provided, the union of them is used. Empty geometries are ignored.\\n\\nLimits the data cube to the bounding box of the given geometries. No implicit masking gets applied. To mask the pixels of the data cube use ``mask_polygon()``.',\n", " 'name': 'geometries',\n", " 'schema': [{'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector Data Cube',\n", " 'type': 'object'},\n", " {'deprecated': True,\n", " 'description': 'Deprecated in favor of ``load_geojson()``. The GeoJSON type `GeometryCollection` is not supported.',\n", " 'subtype': 'geojson',\n", " 'title': 'GeoJSON',\n", " 'type': 'object'}]}],\n", " 'returns': {'description': 'A raster data cube restricted to the specified geometries. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Spatial filter raster data cubes using geometries'},\n", " {'categories': ['cubes', 'filter'],\n", " 'description': \"Filters the bands in the data cube so that bands that don't match any of the criteria are dropped from the data cube. The data cube is expected to have only one dimension of type `bands`. Fails with a `DimensionMissing` exception if no such dimension exists.\\n\\nThe following criteria can be used to select bands:\\n\\n* `bands`: band name or common band name (e.g. `B01`, `B8A`, `red` or `nir`)\\n* `wavelengths`: ranges of wavelengths in micrometers (μm) (e.g. 0.5 - 0.6)\\n\\nAll these information are exposed in the band metadata of the collection. To keep algorithms interoperable it is recommended to prefer the common band names or the wavelengths over band names that are specific to the collection and/or back-end.\\n\\nIf multiple criteria are specified, any of them must match and not all of them, i.e. they are combined with an OR-operation. If no criteria are specified, the `BandFilterParameterMissing` exception must be thrown.\\n\\n**Important:** The order of the specified array defines the order of the bands in the data cube, which can be important for subsequent processes. If multiple bands are matched by a single criterion (e.g. a range of wavelengths), they stay in the original order.\",\n", " 'exceptions': {'BandFilterParameterMissing': {'message': 'The process `filter_bands` requires any of the parameters `bands`, `common_names` or `wavelengths` to be set.'},\n", " 'DimensionMissing': {'message': 'A band dimension is missing.'}},\n", " 'id': 'filter_bands',\n", " 'links': [{'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", " 'rel': 'about',\n", " 'title': 'List of common band names as specified by the STAC specification'},\n", " {'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", " 'rel': 'about',\n", " 'title': 'Filters explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A data cube with bands.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'type': 'bands'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': [],\n", " 'description': 'A list of band names. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If the unique band name and the common name conflict, the unique band name has a higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.',\n", " 'name': 'bands',\n", " 'optional': True,\n", " 'schema': {'items': {'subtype': 'band-name', 'type': 'string'},\n", " 'type': 'array'}},\n", " {'default': [],\n", " 'description': 'A list of sub-lists with each sub-list consisting of two elements. The first element is the minimum wavelength and the second element is the maximum wavelength. Wavelengths are specified in micrometers (μm).\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match the wavelengths, all matched bands are included in the original order.',\n", " 'name': 'wavelengths',\n", " 'optional': True,\n", " 'schema': {'items': {'examples': [[[0.45, 0.5], [0.6, 0.7]]],\n", " 'items': {'type': 'number'},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'type': 'array'},\n", " 'type': 'array'}}],\n", " 'returns': {'description': 'A data cube limited to a subset of its original bands. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the dimension of type `bands` has less (or the same) dimension labels.',\n", " 'schema': {'dimensions': [{'type': 'bands'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Filter the bands by names'},\n", " {'categories': ['cubes', 'math > image filter'],\n", " 'description': \"Applies a 2D convolution (i.e. a focal operation with a weighted kernel) on the horizontal spatial dimensions (axes `x` and `y`) of a raster data cube.\\n\\nEach value in the kernel is multiplied with the corresponding pixel value and all products are summed up afterwards. The sum is then multiplied with the factor.\\n\\nThe process can't handle non-numerical or infinite numerical values in the data cube. Boolean values are converted to integers (`false` = 0, `true` = 1), but all other non-numerical or infinite values are replaced with zeroes by default (see parameter `replace_invalid`).\\n\\nFor cases requiring more generic focal operations or non-numerical values, see ``apply_neighborhood()``.\",\n", " 'exceptions': {'KernelDimensionsUneven': {'message': 'Each dimension of the kernel must have an uneven number of elements.'}},\n", " 'id': 'apply_kernel',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", " 'rel': 'about',\n", " 'title': 'Apply explained in the openEO documentation'},\n", " {'href': 'http://www.songho.ca/dsp/convolution/convolution.html',\n", " 'rel': 'about',\n", " 'title': 'Convolutions explained'},\n", " {'href': 'http://www.songho.ca/dsp/convolution/convolution2d_example.html',\n", " 'rel': 'about',\n", " 'title': 'Example of 2D Convolution'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'Kernel as a two-dimensional array of weights. The inner level of the nested array aligns with the `x` axis and the outer level aligns with the `y` axis. Each level of the kernel must have an uneven number of elements, otherwise the process throws a `KernelDimensionsUneven` exception.',\n", " 'name': 'kernel',\n", " 'schema': {'description': 'A two-dimensional array of numbers.',\n", " 'items': {'items': {'type': 'number'}, 'type': 'array'},\n", " 'subtype': 'kernel',\n", " 'type': 'array'}},\n", " {'default': 1,\n", " 'description': 'A factor that is multiplied to each value after the kernel has been applied.\\n\\nThis is basically a shortcut for explicitly multiplying each value by a factor afterwards, which is often required for some kernel-based algorithms such as the Gaussian blur.',\n", " 'name': 'factor',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}},\n", " {'default': 0,\n", " 'description': 'Determines how the data is extended when the kernel overlaps with the borders. Defaults to fill the border with zeroes.\\n\\nThe following options are available:\\n\\n* *numeric value* - fill with a user-defined constant number `n`: `nnnnnn|abcdefgh|nnnnnn` (default, with `n` = 0)\\n* `replicate` - repeat the value from the pixel at the border: `aaaaaa|abcdefgh|hhhhhh`\\n* `reflect` - mirror/reflect from the border: `fedcba|abcdefgh|hgfedc`\\n* `reflect_pixel` - mirror/reflect from the center of the pixel at the border: `gfedcb|abcdefgh|gfedcb`\\n* `wrap` - repeat/wrap the image: `cdefgh|abcdefgh|abcdef`',\n", " 'name': 'border',\n", " 'optional': True,\n", " 'schema': [{'enum': ['replicate', 'reflect', 'reflect_pixel', 'wrap'],\n", " 'type': 'string'},\n", " {'type': 'number'}]},\n", " {'default': 0,\n", " 'description': 'This parameter specifies the value to replace non-numerical or infinite numerical values with. By default, those values are replaced with zeroes.',\n", " 'name': 'replace_invalid',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}}],\n", " 'returns': {'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Apply a spatial convolution with a kernel'},\n", " {'categories': ['cubes', 'math > indices', 'vegetation indices'],\n", " 'description': \"Computes the Normalized Difference Vegetation Index (NDVI). The NDVI is computed as *`(nir - red) / (nir + red)`*.\\n\\nThe `data` parameter expects a raster data cube with a dimension of type `bands` or a `DimensionAmbiguous` exception is thrown otherwise. By default, the dimension must have at least two bands with the common names `red` and `nir` assigned. Otherwise, the user has to specify the parameters `nir` and `red`. If neither is the case, either the exception `NirBandAmbiguous` or `RedBandAmbiguous` is thrown. The common names for each band are specified in the collection's band metadata and are *not* equal to the band names.\\n\\nBy default, the dimension of type `bands` is dropped by this process. To keep the dimension specify a new band name in the parameter `target_band`. This adds a new dimension label with the specified name to the dimension, which can be used to access the computed values. If a band with the specified name exists, a `BandExists` is thrown.\\n\\nThis process is very similar to the process ``normalized_difference()``, but determines the bands automatically based on the common names (`red`/`nir`) specified in the metadata.\",\n", " 'exceptions': {'BandExists': {'message': 'A band with the specified target name exists.'},\n", " 'DimensionAmbiguous': {'message': 'dimension of type `bands` is not available or is ambiguous..'},\n", " 'NirBandAmbiguous': {'message': \"The NIR band can't be resolved, please specify the specific NIR band name.\"},\n", " 'RedBandAmbiguous': {'message': \"The red band can't be resolved, please specify the specific red band name.\"}},\n", " 'id': 'ndvi',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index',\n", " 'rel': 'about',\n", " 'title': 'NDVI explained by Wikipedia'},\n", " {'href': 'https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php',\n", " 'rel': 'about',\n", " 'title': 'NDVI explained by NASA'},\n", " {'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", " 'rel': 'about',\n", " 'title': 'List of common band names as specified by the STAC specification'}],\n", " 'parameters': [{'description': 'A raster data cube with two bands that have the common names `red` and `nir` assigned.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'},\n", " {'type': 'bands'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': 'nir',\n", " 'description': 'The name of the NIR band. Defaults to the band that has the common name `nir` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.',\n", " 'name': 'nir',\n", " 'optional': True,\n", " 'schema': {'subtype': 'band-name', 'type': 'string'}},\n", " {'default': 'red',\n", " 'description': 'The name of the red band. Defaults to the band that has the common name `red` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.',\n", " 'name': 'red',\n", " 'optional': True,\n", " 'schema': {'subtype': 'band-name', 'type': 'string'}},\n", " {'default': None,\n", " 'description': 'By default, the dimension of type `bands` is dropped. To keep the dimension specify a new band name in this parameter so that a new dimension label with the specified name will be added for the computed values.',\n", " 'name': 'target_band',\n", " 'optional': True,\n", " 'schema': [{'pattern': '^\\\\w+$', 'type': 'string'}, {'type': 'null'}]}],\n", " 'returns': {'description': 'A raster data cube containing the computed NDVI values. The structure of the data cube differs depending on the value passed to `target_band`:\\n\\n* `target_band` is `null`: The data cube does not contain the dimension of type `bands`, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.\\n* `target_band` is a string: The data cube keeps the same dimensions. The dimension properties remain unchanged, but the number of dimension labels for the dimension of type `bands` increases by one. The additional label is named as specified in `target_band`.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Normalized Difference Vegetation Index'},\n", " {'categories': ['cubes', 'reproject'],\n", " 'description': 'Resamples the spatial dimensions (x,y) of the data cube to a specified resolution and/or warps the data cube to the target projection. At least `resolution` or `projection` must be specified.\\n\\nRelated processes:\\n\\n* Use ``filter_bbox()`` to set the target spatial extent.\\n* To spatially align two data cubes with each other (e.g. for merging), better use the process ``resample_cube_spatial()``.',\n", " 'id': 'resample_spatial',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#resample',\n", " 'rel': 'about',\n", " 'title': 'Resampling explained in the openEO documentation'},\n", " {'href': 'https://proj.org/usage/projections.html',\n", " 'rel': 'about',\n", " 'title': 'PROJ parameters for cartographic projections'},\n", " {'href': 'http://www.epsg-registry.org',\n", " 'rel': 'about',\n", " 'title': 'Official EPSG code registry'},\n", " {'href': 'http://www.epsg.io',\n", " 'rel': 'about',\n", " 'title': 'Unofficial EPSG code database'},\n", " {'href': 'https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r',\n", " 'rel': 'about',\n", " 'title': 'gdalwarp resampling methods'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': 0,\n", " 'description': \"Resamples the data cube to the target resolution, which can be specified either as separate values for x and y or as a single value for both axes. Specified in the units of the target projection. Doesn't change the resolution by default (`0`).\",\n", " 'name': 'resolution',\n", " 'optional': True,\n", " 'schema': [{'description': 'A single number used as the resolution for both x and y.',\n", " 'minimum': 0,\n", " 'type': 'number'},\n", " {'description': 'A two-element array to specify separate resolutions for x (first element) and y (second element).',\n", " 'items': {'minimum': 0, 'type': 'number'},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'type': 'array'}]},\n", " {'default': None,\n", " 'description': 'Warps the data cube to the target projection, specified as as [EPSG code](http://www.epsg-registry.org/) or [WKT2 CRS string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html). By default (`null`), the projection is not changed.',\n", " 'name': 'projection',\n", " 'optional': True,\n", " 'schema': [{'examples': [3857],\n", " 'minimum': 1000,\n", " 'subtype': 'epsg-code',\n", " 'title': 'EPSG Code',\n", " 'type': 'integer'},\n", " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", " {'title': \"Don't change projection\", 'type': 'null'}]},\n", " {'default': 'near',\n", " 'description': 'Resampling method to use. The following options are available and are meant to align with [`gdalwarp`](https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r):\\n\\n* `average`: average (mean) resampling, computes the weighted average of all valid pixels\\n* `bilinear`: bilinear resampling\\n* `cubic`: cubic resampling\\n* `cubicspline`: cubic spline resampling\\n* `lanczos`: Lanczos windowed sinc resampling\\n* `max`: maximum resampling, selects the maximum value from all valid pixels\\n* `med`: median resampling, selects the median value of all valid pixels\\n* `min`: minimum resampling, selects the minimum value from all valid pixels\\n* `mode`: mode resampling, selects the value which appears most often of all the sampled points\\n* `near`: nearest neighbour resampling (default)\\n* `q1`: first quartile resampling, selects the first quartile value of all valid pixels\\n* `q3`: third quartile resampling, selects the third quartile value of all valid pixels\\n* `rms` root mean square (quadratic mean) of all valid pixels\\n* `sum`: compute the weighted sum of all valid pixels\\n\\nValid pixels are determined based on the function ``is_valid()``.',\n", " 'name': 'method',\n", " 'optional': True,\n", " 'schema': {'enum': ['average',\n", " 'bilinear',\n", " 'cubic',\n", " 'cubicspline',\n", " 'lanczos',\n", " 'max',\n", " 'med',\n", " 'min',\n", " 'mode',\n", " 'near',\n", " 'q1',\n", " 'q3',\n", " 'rms',\n", " 'sum'],\n", " 'type': 'string'}},\n", " {'default': 'upper-left',\n", " 'description': 'Specifies to which corner of the spatial extent the new resampled data is aligned to.',\n", " 'name': 'align',\n", " 'optional': True,\n", " 'schema': {'enum': ['lower-left',\n", " 'upper-left',\n", " 'lower-right',\n", " 'upper-right'],\n", " 'type': 'string'}}],\n", " 'returns': {'description': 'A raster data cube with values warped onto the new projection. It has the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-spatial or vertical spatial dimensions. For the horizontal spatial dimensions the name and type remain unchanged, but reference system, labels and resolution may change depending on the given parameters.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Resample and warp the spatial dimensions'},\n", " {'categories': ['cubes', 'reproject'],\n", " 'description': 'Resamples the spatial dimensions (x,y) from a source data cube to align with the corresponding dimensions of the given target data cube. Returns a new data cube with the resampled dimensions.\\n\\nTo resample a data cube to a specific resolution or projection regardless of an existing target data cube, refer to ``resample_spatial()``.',\n", " 'id': 'resample_cube_spatial',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html#resample',\n", " 'rel': 'about',\n", " 'title': 'Resampling explained in the openEO documentation'}],\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'description': 'A raster data cube that describes the spatial target resolution.',\n", " 'name': 'target',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': 'near',\n", " 'description': 'Resampling method to use. The following options are available and are meant to align with [`gdalwarp`](https://gdal.org/programs/gdalwarp.html#cmdoption-gdalwarp-r):\\n\\n* `average`: average (mean) resampling, computes the weighted average of all valid pixels\\n* `bilinear`: bilinear resampling\\n* `cubic`: cubic resampling\\n* `cubicspline`: cubic spline resampling\\n* `lanczos`: Lanczos windowed sinc resampling\\n* `max`: maximum resampling, selects the maximum value from all valid pixels\\n* `med`: median resampling, selects the median value of all valid pixels\\n* `min`: minimum resampling, selects the minimum value from all valid pixels\\n* `mode`: mode resampling, selects the value which appears most often of all the sampled points\\n* `near`: nearest neighbour resampling (default)\\n* `q1`: first quartile resampling, selects the first quartile value of all valid pixels\\n* `q3`: third quartile resampling, selects the third quartile value of all valid pixels\\n* `rms` root mean square (quadratic mean) of all valid pixels\\n* `sum`: compute the weighted sum of all valid pixels\\n\\nValid pixels are determined based on the function ``is_valid()``.',\n", " 'name': 'method',\n", " 'optional': True,\n", " 'schema': {'enum': ['average',\n", " 'bilinear',\n", " 'cubic',\n", " 'cubicspline',\n", " 'lanczos',\n", " 'max',\n", " 'med',\n", " 'min',\n", " 'mode',\n", " 'near',\n", " 'q1',\n", " 'q3',\n", " 'rms',\n", " 'sum'],\n", " 'type': 'string'}}],\n", " 'returns': {'description': 'A raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the spatial dimensions.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Resample the spatial dimensions to match a target data cube'},\n", " {'categories': ['cubes'],\n", " 'description': \"The process merges two 'compatible' data cubes.\\n\\nThe data cubes have to be compatible, which means that they must share a common subset of equal dimensions. To conveniently get to such a subset of equal dimensions, the process tries to align the horizontal spatial dimensions (axes `x` and `y`) implicitly with ``resample_cube_spatial()`` if required. `cube1` is the target data cube for resampling and the default parameters of ``resample_cube_spatial()`` apply. The equality for geometries follows the definition in the Simple Features standard by the OGC.\\n\\nAll dimensions share the same properties, such as name, type, reference system, and resolution. Dimensions can have disjoint or overlapping labels. If there is any overlap between the dimension labels, the parameter `overlap_resolver` must be specified to combine the two values for these overlapping labels. A merge operation without overlap should be reversible with (a set of) filter operations for each of the two cubes, if no implicit resampling was applied.\\n\\nIt is not possible to merge a vector and a raster data cube. Merging vector data cubes with different base geometry types (points, lines/line strings, polygons) is not possible and throws the `IncompatibleGeometryTypes` exception. The base geometry types can be merged with their corresponding multi geometry types.\\n\\nAfter the merge, the dimensions with a natural/inherent label order (with a reference system this is each spatial and temporal dimensions) still have all dimension labels sorted. For other dimensions without inherent order, including bands, the dimension labels keep the order in which they are present in the original data cubes, and the dimension labels of `cube2` get appended to the dimension labels of `cube1`.\\n\\n**Examples for merging two data cubes:**\\n\\n1. Data cubes with the dimensions (`x`, `y`, `t`, `bands`) have the same dimension labels in `x`, `y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the base data cube and `B3` and `B4` for the other. An overlap resolver is *not needed*. The merged data cube has the dimensions `x`, `y`, `t`, `bands`, and the dimension `bands` has four dimension labels: `B1`, `B2`, `B3`, `B4`.\\n2. Data cubes with the dimensions (`x`, `y`, `t`, `bands`) have the same dimension labels in `x`, `y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the base data cube and `B2` and `B3` for the other. An overlap resolver is *required* to resolve overlap in band `B2`. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has three dimension labels: `B1`, `B2`, `B3`.\\n3. Data cubes with the dimensions (`x`, `y`, `t`) have the same dimension labels in `x`, `y` and `t`. There are two options:\\n 1. Keep the overlapping values separately in the merged data cube: An overlap resolver is *not needed*, but for each data cube you need to add a new dimension using ``add_dimension()``. The new dimensions must be equal, except that the labels for the new dimensions must differ. The merged data cube has the same dimensions and labels as the original data cubes, plus the dimension added with ``add_dimension()``, which has the two dimension labels after the merge.\\n 2. Combine the overlapping values into a single value: An overlap resolver is *required* to resolve the overlap for all values. The merged data cube has the same dimensions and labels as the original data cubes, but all values have been processed by the overlap resolver.\\n4. A data cube with dimensions (`x`, `y`, `t` / `bands`) or (`x`, `y`, `t`, `bands`) and another data cube with dimensions (`x`, `y`) have the same dimension labels in `x` and `y`. Merging them will join dimensions `x` and `y`, so the lower dimension cube is merged with each time step and band available in the higher dimensional cube. A use case for this is applying a digital elevation model to a spatio-temporal data cube. An overlap resolver is *required* to resolve the overlap for all pixels.\",\n", " 'exceptions': {'IncompatibleGeometryTypes': {'message': \"The geometry types are not compatible and can't be merged.\"},\n", " 'OverlapResolverMissing': {'message': 'Overlapping data cubes, but no overlap resolver has been specified.'}},\n", " 'id': 'merge_cubes',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Reduction_Operator',\n", " 'rel': 'about',\n", " 'title': 'Background information on reduction operators (binary reducers) by Wikipedia'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'}],\n", " 'parameters': [{'description': 'The base data cube.',\n", " 'name': 'cube1',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'description': 'The other data cube to be merged with the base data cube.',\n", " 'name': 'cube2',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'A reduction operator that resolves the conflict if the data overlaps. The reducer must return a value of the same data type as the input values are. The reduction operator may be a single process such as ``multiply()`` or consist of multiple sub-processes. `null` (the default) can be specified if no overlap resolver is required.',\n", " 'name': 'overlap_resolver',\n", " 'optional': True,\n", " 'schema': {'parameters': [{'description': 'The overlapping value from the base data cube `cube1`.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'description': 'The overlapping value from the other data cube `cube2`.',\n", " 'name': 'y',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the merged data cube.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the overlap resolver.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The merged data cube. See the process description for details regarding the dimensions and dimension properties (name, type, labels, reference system and resolution).',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Merge two data cubes'},\n", " {'categories': ['cubes', 'import', 'udf'],\n", " 'description': \"Runs a UDF in one of the supported runtime environments.\\n\\nThe process can either:\\n\\n1. load and run a UDF stored in a file on the server-side workspace of the authenticated user. The path to the UDF file must be relative to the root directory of the user's workspace.\\n2. fetch and run a remotely stored and published UDF by absolute URI.\\n3. run the source code specified inline as string.\\n\\nThe loaded UDF can be executed in several processes such as ``aggregate_spatial()``, ``apply()``, ``apply_dimension()`` and ``reduce_dimension()``. The user must ensure that the data is given in a way that the UDF code can make sense of it.\",\n", " 'exceptions': {'InvalidRuntime': {'message': 'The specified UDF runtime is not supported.'},\n", " 'InvalidVersion': {'message': 'The specified UDF runtime version is not supported.'}},\n", " 'id': 'run_udf',\n", " 'parameters': [{'description': 'The data to be passed to the UDF.',\n", " 'name': 'data',\n", " 'schema': [{'items': {'description': 'Any data type.'},\n", " 'minItems': 1,\n", " 'title': 'Array',\n", " 'type': 'array'},\n", " {'description': 'A single value of any data type.',\n", " 'title': 'Single Value'}]},\n", " {'description': 'Either source code, an absolute URL or a path to a UDF script.',\n", " 'name': 'udf',\n", " 'schema': [{'description': 'Absolute URL to a UDF',\n", " 'format': 'uri',\n", " 'pattern': '^https?://',\n", " 'subtype': 'uri',\n", " 'type': 'string'},\n", " {'description': 'Path to a UDF uploaded to the server.',\n", " 'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", " 'subtype': 'file-path',\n", " 'type': 'string'},\n", " {'description': 'The multi-line source code of a UDF, must contain a newline/line-break.',\n", " 'pattern': '(\\r\\n|\\r|\\n)',\n", " 'subtype': 'udf-code',\n", " 'type': 'string'}]},\n", " {'description': 'A UDF runtime identifier available at the back-end.',\n", " 'name': 'runtime',\n", " 'schema': {'subtype': 'udf-runtime', 'type': 'string'}},\n", " {'default': None,\n", " 'description': 'An UDF runtime version. If set to `null`, the default runtime version specified for each runtime is used.',\n", " 'name': 'version',\n", " 'optional': True,\n", " 'schema': [{'subtype': 'udf-runtime-version', 'type': 'string'},\n", " {'title': 'Default runtime version', 'type': 'null'}]},\n", " {'default': {},\n", " 'description': 'Additional data such as configuration options to be passed to the UDF.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The data processed by the UDF. The returned value can be of any data type and is exactly what the UDF code returns.',\n", " 'schema': {'description': 'Any data type.', 'title': 'Any'}},\n", " 'summary': 'Run a UDF'},\n", " {'categories': ['math'],\n", " 'description': 'Performs a linear transformation between the input and output range.\\n\\nThe given number in `x` is clipped to the bounds specified in `inputMin` and `inputMax` so that the underlying formula *`((x - inputMin) / (inputMax - inputMin)) * (outputMax - outputMin) + outputMin`* never returns any value lower than `outputMin` or greater than `outputMax`.\\n\\nPotential use case include\\n\\n* scaling values to the 8-bit range (0 - 255) often used for numeric representation of values in one of the channels of the [RGB colour model](https://en.wikipedia.org/wiki/RGB_color_model#Numeric_representations) or\\n* calculating percentages (0 - 100).\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'inputMax': 1,\n", " 'inputMin': -1,\n", " 'outputMax': 255,\n", " 'outputMin': 0,\n", " 'x': 0.3},\n", " 'returns': 165.75},\n", " {'arguments': {'inputMax': 255, 'inputMin': 0, 'x': 25.5}, 'returns': 0.1},\n", " {'arguments': {'inputMax': 100, 'inputMin': 0, 'x': None}, 'returns': None},\n", " {'arguments': {'inputMax': 1,\n", " 'inputMin': 0,\n", " 'outputMax': 255,\n", " 'outputMin': 0,\n", " 'x': 1.12},\n", " 'description': 'Shows that the input data is clipped.',\n", " 'returns': 255}],\n", " 'id': 'linear_scale_range',\n", " 'parameters': [{'description': 'A number to transform. The number gets clipped to the bounds specified in `inputMin` and `inputMax`.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'Minimum value the input can obtain.',\n", " 'name': 'inputMin',\n", " 'schema': {'type': 'number'}},\n", " {'description': 'Maximum value the input can obtain.',\n", " 'name': 'inputMax',\n", " 'schema': {'type': 'number'}},\n", " {'default': 0,\n", " 'description': 'Minimum value of the desired output range.',\n", " 'name': 'outputMin',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}},\n", " {'default': 1,\n", " 'description': 'Maximum value of the desired output range.',\n", " 'name': 'outputMax',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}}],\n", " 'process_graph': {'add': {'arguments': {'x': {'from_node': 'multiply'},\n", " 'y': {'from_parameter': 'outputMin'}},\n", " 'process_id': 'add',\n", " 'result': True},\n", " 'divide': {'arguments': {'x': {'from_node': 'subtract1'},\n", " 'y': {'from_node': 'subtract2'}},\n", " 'process_id': 'divide'},\n", " 'multiply': {'arguments': {'x': {'from_node': 'divide'},\n", " 'y': {'from_node': 'subtract3'}},\n", " 'process_id': 'multiply'},\n", " 'subtract1': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'inputMin'}},\n", " 'process_id': 'subtract'},\n", " 'subtract2': {'arguments': {'x': {'from_parameter': 'inputMax'},\n", " 'y': {'from_parameter': 'inputMin'}},\n", " 'process_id': 'subtract'},\n", " 'subtract3': {'arguments': {'x': {'from_parameter': 'outputMax'},\n", " 'y': {'from_parameter': 'outputMin'}},\n", " 'process_id': 'subtract'}},\n", " 'returns': {'description': 'The transformed number.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Linear transformation between two ranges'},\n", " {'categories': ['math > constants'],\n", " 'description': 'Defines a constant value that can be reused in multiple places of a process.',\n", " 'id': 'constant',\n", " 'parameters': [{'description': 'The value of the constant.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value of the constant.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'summary': 'Define a constant value'},\n", " {'categories': ['arrays'],\n", " 'description': 'Applies a process to each individual value in the array. This is basically what other languages call either a `for each` loop or a `map` function.',\n", " 'id': 'array_apply',\n", " 'parameters': [{'description': 'An array.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'A process that accepts and returns a single value and is applied on each individual value in the array. The process may consist of multiple sub-processes and could, for example, consist of processes such as ``absolute()`` or ``linear_scale_range()``.',\n", " 'name': 'process',\n", " 'schema': {'parameters': [{'description': 'The value of the current element being processed.',\n", " 'name': 'x',\n", " 'schema': {'description': 'Any data type.'}},\n", " {'description': 'The zero-based index of the current element being processed.',\n", " 'name': 'index',\n", " 'schema': {'minimum': 0, 'type': 'integer'}},\n", " {'default': None,\n", " 'description': 'The label of the current element being processed. Only populated for labeled arrays.',\n", " 'name': 'label',\n", " 'optional': True,\n", " 'schema': [{'type': 'number'}, {'type': 'string'}, {'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Additional data passed by the user.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'The value to be set in the new array.',\n", " 'schema': {'description': 'Any data type.'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'Additional data to be passed to the process.',\n", " 'name': 'context',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': 'An array with the newly computed values. The number of elements are the same as for the original array.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Apply a process to each array element'},\n", " {'description': 'Reads vector data from a file or a URL.',\n", " 'id': 'read_vector',\n", " 'parameters': [{'description': 'Vector file reference: a HTTP(S) URL or a file path.',\n", " 'name': 'filename',\n", " 'optional': False,\n", " 'schema': [{'description': 'Public URL to a vector file.',\n", " 'pattern': '^https?://',\n", " 'subtype': 'uri',\n", " 'type': 'string'},\n", " {'description': 'File path (resolvable back-end-side) to a vector file.',\n", " 'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", " 'subtype': 'file-path',\n", " 'type': 'string'}]}],\n", " 'returns': {'description': 'GeoJSON-style feature collection',\n", " 'schema': {'subtype': 'geojson', 'type': 'object'}}},\n", " {'categories': ['cubes', 'import'],\n", " 'description': 'Loads one or more user-uploaded files from the server-side workspace of the authenticated user and returns them as a single data cube. The files must have been stored by the authenticated user on the back-end currently connected to.',\n", " 'exceptions': {'FormatUnsuitable': {'message': \"Data can't be loaded with the requested input format.\"}},\n", " 'experimental': True,\n", " 'id': 'load_uploaded_files',\n", " 'parameters': [{'description': \"The files to read. Folders can't be specified, specify all files instead. An exception is thrown if a file can't be read.\",\n", " 'name': 'paths',\n", " 'schema': {'items': {'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", " 'subtype': 'file-path',\n", " 'type': 'string'},\n", " 'subtype': 'file-paths',\n", " 'type': 'array'}},\n", " {'description': 'The file format to read from. It must be one of the values that the server reports as supported input file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for loading the data, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.',\n", " 'name': 'format',\n", " 'schema': {'subtype': 'input-format', 'type': 'string'}},\n", " {'default': {},\n", " 'description': 'The file format parameters to be used to read the files. Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.',\n", " 'name': 'options',\n", " 'optional': True,\n", " 'schema': {'subtype': 'input-format-options', 'type': 'object'}}],\n", " 'returns': {'description': 'A data cube for further processing.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Load files from the user workspace'},\n", " {'description': '[EXPERIMENTAL:] Converts given data (e.g. GeoJson object) to a vector cube.',\n", " 'id': 'to_vector_cube',\n", " 'parameters': [{'description': 'GeoJson object.',\n", " 'name': 'data',\n", " 'optional': False,\n", " 'schema': {'subtype': 'geojson', 'type': 'object'}}],\n", " 'returns': {'description': 'vector-cube',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}}},\n", " {'categories': ['import', 'vector'],\n", " 'description': 'Converts GeoJSON data as defined by [RFC 7946](https://www.rfc-editor.org/rfc/rfc7946.html) into a vector data cube. Feature properties are preserved.',\n", " 'experimental': True,\n", " 'id': 'load_geojson',\n", " 'links': [{'href': 'https://www.rfc-editor.org/rfc/rfc7946.html',\n", " 'rel': 'about',\n", " 'title': 'RFC 7946: The GeoJSON Format',\n", " 'type': 'text/html'}],\n", " 'parameters': [{'description': 'A GeoJSON object to convert into a vector data cube. The GeoJSON type `GeometryCollection` is not supported. Each geometry in the GeoJSON data results in a dimension label in the `geometries` dimension.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'geojson', 'type': 'object'}},\n", " {'default': [],\n", " 'description': 'A list of properties from the GeoJSON file to construct an additional dimension from. A new dimension with the name `properties` and type `other` is created if at least one property is provided. Only applies for GeoJSON Features and FeatureCollections. Missing values are generally set to no-data (`null`).\\n\\nDepending on the number of properties provided, the process creates the dimension differently:\\n\\n- Single property with scalar values: A single dimension label with the name of the property and a single value per geometry.\\n- Single property of type array: The dimension labels correspond to the array indices. There are as many values and labels per geometry as there are for the largest array.\\n- Multiple properties with scalar values: The dimension labels correspond to the property names. There are as many values and labels per geometry as there are properties provided here.',\n", " 'name': 'properties',\n", " 'optional': True,\n", " 'schema': {'items': {'type': 'string'},\n", " 'type': 'array',\n", " 'uniqueItems': True}}],\n", " 'returns': {'description': 'A vector data cube containing the geometries, either one or two dimensional.',\n", " 'schema': {'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Converts GeoJSON into a vector data cube'},\n", " {'categories': ['cubes', 'import'],\n", " 'description': 'Loads a file from a URL (supported protocols: HTTP and HTTPS).',\n", " 'exceptions': {'FormatUnsuitable': {'message': \"Data can't be loaded with the requested input format.\"}},\n", " 'experimental': True,\n", " 'id': 'load_url',\n", " 'parameters': [{'description': 'The URL to read from. Authentication details such as API keys or tokens may need to be included in the URL.',\n", " 'name': 'url',\n", " 'schema': {'format': 'uri',\n", " 'pattern': '^https?://',\n", " 'subtype': 'uri',\n", " 'title': 'URL',\n", " 'type': 'string'}},\n", " {'description': 'The file format to use when loading the data. It must be one of the values that the server reports as supported input file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for loading the data, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.',\n", " 'name': 'format',\n", " 'schema': {'subtype': 'input-format', 'type': 'string'}},\n", " {'default': {},\n", " 'description': 'The file format parameters to use when reading the data. Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.',\n", " 'name': 'options',\n", " 'optional': True,\n", " 'schema': {'subtype': 'input-format-options', 'type': 'object'}}],\n", " 'returns': {'description': 'A data cube for further processing.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Load data from a URL'},\n", " {'description': 'Reads vector data from a file or a URL or get geometries from a FeatureCollection',\n", " 'id': 'get_geometries',\n", " 'parameters': [{'description': 'filename or http url of a vector file',\n", " 'name': 'filename',\n", " 'optional': True,\n", " 'schema': {'type': 'string'}},\n", " {'description': 'feature collection',\n", " 'name': 'feature_collection',\n", " 'optional': True,\n", " 'schema': {'type': 'object'}}],\n", " 'returns': {'description': 'TODO',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}}},\n", " {'description': 'Converts this raster data cube into a vector data cube. The bounding polygon of homogenous areas of pixels is constructed.\\nOnly the first band is considered the others are ignored.',\n", " 'experimental': True,\n", " 'id': 'raster_to_vector',\n", " 'parameters': [{'description': 'A raster data cube.',\n", " 'name': 'data',\n", " 'optional': False,\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}],\n", " 'returns': {'description': 'vector-cube',\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}}},\n", " {'description': 'Creates a raster cube as output based on a vector cube. The values in the output raster cube are based on the numeric properties in the input vector cube.',\n", " 'experimental': True,\n", " 'id': 'vector_to_raster',\n", " 'parameters': [{'description': 'A vector data cube.',\n", " 'name': 'data',\n", " 'optional': False,\n", " 'schema': {'subtype': 'vector-cube', 'type': 'object'}},\n", " {'description': 'A raster data cube used as reference.',\n", " 'name': 'target',\n", " 'optional': False,\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}],\n", " 'returns': {'description': 'raster-cube',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}},\n", " {'description': 'Sleep for given amount of seconds (and just pass-through given data).',\n", " 'id': 'sleep',\n", " 'parameters': [{'description': 'Data to pass through.',\n", " 'name': 'data',\n", " 'optional': True,\n", " 'schema': {}},\n", " {'description': 'Number of seconds to sleep.',\n", " 'name': 'seconds',\n", " 'optional': False,\n", " 'schema': {'type': 'number'}}],\n", " 'returns': {'description': 'Original data', 'schema': {}}},\n", " {'categories': ['cubes', 'sar'],\n", " 'description': 'Computes backscatter from SAR input.\\n\\nNote that backscatter computation may require instrument specific metadata that is tightly coupled to the original SAR products. As a result, this process may only work in combination with loading data from specific collections, not with general data cubes.\\n\\nThis process uses bilinear interpolation, both for resampling the DEM and the backscatter.\\n\\n\\n\\n## Backend notes\\nThe implementation in this backend is based on Orfeo Toolbox.\\n',\n", " 'exceptions': {'DigitalElevationModelInvalid': {'message': \"The digital elevation model specified is either not a DEM or can't be used with the data cube given.\"}},\n", " 'experimental': False,\n", " 'id': 'sar_backscatter',\n", " 'links': [{'href': 'https://bok.eo4geo.eu/PP2-2-4-3',\n", " 'rel': 'about',\n", " 'title': 'Gamma nought (0) explained by EO4GEO body of knowledge.'},\n", " {'href': 'https://bok.eo4geo.eu/PP2-2-4-2',\n", " 'rel': 'about',\n", " 'title': 'Sigma nought (0) explained by EO4GEO body of knowledge.'},\n", " {'href': 'https://www.geo.uzh.ch/microsite/rsl-documents/research/publications/peer-reviewed-articles/201108-TGRS-Small-tcGamma-3809999360/201108-TGRS-Small-tcGamma.pdf',\n", " 'rel': 'about',\n", " 'title': 'Flattening Gamma: Radiometric Terrain Correction for SAR Imagery'},\n", " {'href': 'https://doi.org/10.3390/data4030093',\n", " 'rel': 'about',\n", " 'title': 'Reasoning behind the choice of bilinear resampling'},\n", " {'href': 'https://www.orfeo-toolbox.org/CookBook/Applications/app_SARCalibration.html',\n", " 'rel': 'about',\n", " 'title': 'Orfeo toolbox backscatter processor.'}],\n", " 'parameters': [{'description': 'The source data cube containing SAR input.',\n", " 'name': 'data',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'},\n", " {'type': 'bands'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " {'default': 'sigma0-ellipsoid',\n", " 'description': 'The radiometric correction coefficient.\\nOn this backend, only the following option is available:\\n\\n* `sigma0-ellipsoid`: ground area computed with ellipsoid earth model\\n',\n", " 'name': 'coefficient',\n", " 'optional': True,\n", " 'schema': {'enum': ['sigma0-ellipsoid'], 'type': 'string'}},\n", " {'default': None,\n", " 'description': 'The digital elevation model to use. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility.',\n", " 'name': 'elevation_model',\n", " 'optional': True,\n", " 'schema': [{'subtype': 'collection-id', 'type': 'string'},\n", " {'type': 'null'}]},\n", " {'default': False,\n", " 'description': 'If set to `true`, a data mask is added to the bands with the name `mask`. It indicates which values are valid (1), invalid (0) or contain no-data (null).',\n", " 'name': 'mask',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': False,\n", " 'description': 'If set to `true`, a DEM-based local contributing area band named `contributing_area` is added. The values are given in square meters.',\n", " 'name': 'contributing_area',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': False,\n", " 'description': 'If set to `true`, a DEM-based local incidence angle band named `local_incidence_angle` is added. The values are given in degrees.',\n", " 'name': 'local_incidence_angle',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': False,\n", " 'description': 'If set to `true`, an ellipsoidal incidence angle band named `ellipsoid_incidence_angle` is added. The values are given in degrees.',\n", " 'name': 'ellipsoid_incidence_angle',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': True,\n", " 'description': 'If set to `false`, no noise removal is applied. Defaults to `true`, which removes noise.',\n", " 'name': 'noise_removal',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}},\n", " {'default': {},\n", " 'description': 'Proprietary options for the backscatter computations. Specifying proprietary options will reduce portability.',\n", " 'name': 'options',\n", " 'optional': True,\n", " 'schema': {'additionalProperties': False, 'type': 'object'}}],\n", " 'returns': {'description': 'Backscatter values corresponding to the chosen parametrization. The values are given in linear scale.',\n", " 'schema': {'dimensions': [{'axis': ['x', 'y'], 'type': 'spatial'},\n", " {'type': 'bands'}],\n", " 'subtype': 'datacube',\n", " 'type': 'object'}},\n", " 'summary': 'Computes backscatter from SAR input'},\n", " {'categories': ['cubes', 'optical'],\n", " 'description': 'Resolution merging algorithms try to improve the spatial resolution of lower resolution bands (e.g. Sentinel-2 20M) based on higher resolution bands. (e.g. Sentinel-2 10M).\\n ',\n", " 'experimental': True,\n", " 'id': 'resolution_merge',\n", " 'links': [{'href': 'https://bok.eo4geo.eu/IP2-1-3',\n", " 'rel': 'about',\n", " 'title': 'Pansharpening explained by EO4GEO body of knowledge.'},\n", " {'href': 'https://doi.org/10.1109/TGRS.2016.2537929',\n", " 'rel': 'about',\n", " 'title': 'Scientific publication: Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs'}],\n", " 'parameters': [{'description': 'Data cube containing multiple spectral bands, with different source resolutions.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'The method to use.\\\\n\\\\nThe supported algorithms can vary between back-ends. Set to `null` (the default) to allow the back-end to choose, which will improve portability, but reduce reproducibility..',\n", " 'name': 'method',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}},\n", " {'default': [],\n", " 'description': \"A list of band names to use as 'high-resolution' band. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.\\n These bands will remain unmodified.\",\n", " 'name': 'high_resolution_bands',\n", " 'optional': False,\n", " 'schema': {'items': {'subtype': 'band-name', 'type': 'string'},\n", " 'type': 'array'}},\n", " {'default': [],\n", " 'description': 'A list of band names for which the spatial resolution should be increased. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.\\n These bands will be modified by the process.',\n", " 'name': 'low_resolution_bands',\n", " 'optional': False,\n", " 'schema': {'items': {'subtype': 'band-name', 'type': 'string'},\n", " 'type': 'array'}}],\n", " 'returns': {'description': 'A datacube with the same bands and metadata as the input, but algorithmically increased spatial resolution for the selected bands.',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " 'summary': 'Resolution merging or pansharpening: increase the spatial resolution of low-resolution bands, based on higher resolution bands.'},\n", " {'description': 'Discards given data. Used for side-effecting purposes.',\n", " 'id': 'discard_result',\n", " 'parameters': [{'description': 'Data to discard.',\n", " 'name': 'data',\n", " 'optional': True,\n", " 'schema': {}}],\n", " 'returns': {'description': 'Nothing', 'schema': {}}},\n", " {'categories': ['cubes', 'ard'],\n", " 'description': '[Deprecated: consider using to_scl_dilation_mask] Mask clouds by dilating Sen2Cor sceneclassification. This dilation algorithm removes pixels in the neighbourhood of clouds in a fairly aggressive manner, to avoid any type of contamination. Nevertheless, some outliers can sometimes still remain.',\n", " 'experimental': True,\n", " 'id': 'mask_scl_dilation',\n", " 'parameters': [{'description': 'The source data cube containing Sentinel-2 input.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'default': None,\n", " 'description': 'The name of the band containing the Sen2Cor sceneclassification.',\n", " 'name': 'scl_band_name',\n", " 'optional': True,\n", " 'schema': [{'type': 'string'}, {'type': 'null'}]}],\n", " 'process_graph': {'apply1': {'arguments': {'data': {'from_node': 'applykernel1'},\n", " 'process': {'process_graph': {'gt1': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': 0.057},\n", " 'process_id': 'gt',\n", " 'result': True}}}},\n", " 'process_id': 'apply'},\n", " 'apply2': {'arguments': {'data': {'from_node': 'applykernel2'},\n", " 'process': {'process_graph': {'gt2': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': 0.025},\n", " 'process_id': 'gt',\n", 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'process_id': 'apply_kernel'},\n", " 'filterbands1': {'arguments': {'bands': [{'from_parameter': 'scl_band_name'}],\n", " 'data': {'from_parameter': 'data'}},\n", " 'process_id': 'filter_bands'},\n", " 'mergecubes1': {'arguments': {'cube1': {'from_node': 'apply1'},\n", " 'cube2': {'from_node': 'apply2'},\n", " 'overlap_resolver': {'process_graph': {'or9': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'or',\n", " 'result': True}}}},\n", " 'process_id': 'merge_cubes',\n", " 'result': True},\n", " 'reducedimension1': {'arguments': {'data': {'from_node': 'filterbands1'},\n", " 'dimension': 'bands',\n", " 'reducer': {'process_graph': {'arrayelement1': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'index': 0},\n", " 'process_id': 'array_element'},\n", " 'eq1': {'arguments': {'x': {'from_node': 'arrayelement1'}, 'y': 2},\n", " 'process_id': 'eq'},\n", " 'eq2': {'arguments': {'x': {'from_node': 'arrayelement1'}, 'y': 4},\n", " 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{'from_node': 'filterbands1'},\n", " 'dimension': 'bands',\n", " 'reducer': {'process_graph': {'arrayelement2': {'arguments': {'data': {'from_parameter': 'data'},\n", " 'index': 0},\n", " 'process_id': 'array_element'},\n", " 'eq10': {'arguments': {'x': {'from_node': 'arrayelement2'}, 'y': 11},\n", " 'process_id': 'eq'},\n", " 'eq6': {'arguments': {'x': {'from_node': 'arrayelement2'}, 'y': 3},\n", " 'process_id': 'eq'},\n", " 'eq7': {'arguments': {'x': {'from_node': 'arrayelement2'}, 'y': 8},\n", " 'process_id': 'eq'},\n", " 'eq8': {'arguments': {'x': {'from_node': 'arrayelement2'}, 'y': 9},\n", " 'process_id': 'eq'},\n", " 'eq9': {'arguments': {'x': {'from_node': 'arrayelement2'}, 'y': 10},\n", " 'process_id': 'eq'},\n", " 'or5': {'arguments': {'x': {'from_node': 'eq6'},\n", " 'y': {'from_node': 'eq7'}},\n", " 'process_id': 'or'},\n", " 'or6': {'arguments': {'x': {'from_node': 'or5'},\n", " 'y': {'from_node': 'eq8'}},\n", " 'process_id': 'or'},\n", " 'or7': {'arguments': {'x': {'from_node': 'or6'},\n", " 'y': {'from_node': 'eq9'}},\n", " 'process_id': 'or'},\n", " 'or8': {'arguments': {'x': {'from_node': 'or7'},\n", " 'y': {'from_node': 'eq10'}},\n", " 'process_id': 'or',\n", " 'result': True}}}},\n", " 'process_id': 'reduce_dimension'}},\n", " 'returns': {'description': 'Masked data cube',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Mask clouds by dilating Sen2Cor sceneclassification'},\n", " {'categories': ['cubes', 'ard'],\n", " 'description': \"Create a mask for clouds by dilating Sen2Cor sceneclassification. Requires a datacube with only Sentinel-2 L2A 'SCL' band as input. This dilation algorithm removes pixels in the neighbourhood of clouds in a fairly aggressive manner, to avoid any type of contamination. Nevertheless, some outliers can sometimes still remain.\",\n", " 'experimental': True,\n", " 'id': 'to_scl_dilation_mask',\n", " 'parameters': [{'description': 'The source data cube containing only the Sentinel-2 SCL band.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " {'default': 0,\n", " 'description': '',\n", " 'name': 'erosion_kernel_size',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}},\n", " {'default': [2, 4, 5, 6, 7],\n", " 'description': '',\n", " 'name': 'mask1_values',\n", " 'optional': True,\n", " 'schema': {'items': {'type': 'number'}, 'type': 'array'}},\n", " {'default': [3, 8, 9, 10, 11],\n", " 'description': '',\n", " 'name': 'mask2_values',\n", " 'optional': True,\n", " 'schema': {'items': {'type': 'number'}, 'type': 'array'}},\n", " {'default': 17,\n", " 'description': 'Size of the first convolution kernel.',\n", " 'name': 'kernel1_size',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}},\n", " {'default': 201,\n", " 'description': 'Size of the second convolution kernel.',\n", " 'name': 'kernel2_size',\n", " 'optional': True,\n", " 'schema': {'type': 'number'}}],\n", " 'returns': {'description': 'SCL dilation mask.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Create a mask for clouds by dilating Sen2Cor sceneclassification'},\n", " {'categories': ['cubes', 'ard'],\n", " 'description': 'Mask high probability clouds from Sentinel-2 L1C data.',\n", " 'experimental': True,\n", " 'id': 'mask_l1c',\n", " 'parameters': [{'description': 'The source data cube containing Sentinel-2 L1C input.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}],\n", " 'returns': {'description': 'Masked data cube',\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " 'summary': 'Mask high probability clouds from Sentinel-2 L1C data.'},\n", " {'categories': ['arrays'],\n", " 'description': 'Appends a new value to the end of the array, which may also include a new label for labeled arrays.',\n", " 'examples': [{'arguments': {'data': [1, 2], 'value': 3},\n", " 'returns': [1, 2, 3]}],\n", " 'exceptions': {'LabelExists': {'message': 'An array element with the specified label already exists.'}},\n", " 'id': 'array_append',\n", " 'parameters': [{'description': 'An array.',\n", " 'name': 'data',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'Value to append to the array.',\n", " 'name': 'value',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " {'default': None,\n", " 'description': 'If the given array is a labeled array, a new label for the new value should be given. If not given or `null`, the array index as string is used as the label. If in any case the label exists, a `LabelExists` exception is thrown.',\n", " 'name': 'label',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}}],\n", " 'returns': {'description': 'The new array with the value being appended.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Append a value to an array'},\n", " {'categories': ['arrays', 'math', 'math > interpolation'],\n", " 'description': 'Performs a linear interpolation for each of the no-data values (`null`) in the array given, except for leading and trailing no-data values.\\n\\nThe linear interpolants are defined by the array indices or labels (x) and the values in the array (y).',\n", " 'examples': [{'arguments': {'data': [None, 1, None, 6, None, -8]},\n", " 'returns': [None, 1, 3.5, 6, -1, -8]},\n", " {'arguments': {'data': [None, 1, None, None]},\n", " 'returns': [None, 1, None, None]}],\n", " 'id': 'array_interpolate_linear',\n", " 'links': [{'href': 'https://en.wikipedia.org/wiki/Linear_interpolation',\n", " 'rel': 'about',\n", " 'title': 'Linear interpolation explained by Wikipedia'}],\n", " 'parameters': [{'description': 'An array of numbers and no-data values.\\n\\nIf the given array is a labeled array, the labels must have a natural/inherent label order and the process expects the labels to be sorted accordingly. This is the default behavior in openEO for spatial and temporal dimensions.',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}}],\n", " 'returns': {'description': 'An array with no-data values being replaced with interpolated values. If not at least 2 numerical values are available in the array, the array stays the same.',\n", " 'schema': {'items': {'type': ['number', 'null']}, 'type': 'array'}},\n", " 'summary': 'One-dimensional linear interpolation for arrays'},\n", " {'categories': ['date & time'],\n", " 'description': \"Based on a given date (and optionally time), calculates a new date (and time if given) by adding or subtracting a given temporal period.\\n\\nSome specifics about dates and times need to be taken into account:\\n\\n* This process doesn't have any effect on the time zone.\\n* It doesn't take daylight saving time (DST) into account as only dates and times in UTC (with potential numerical time zone modifier) are supported.\\n* Leap years are implemented in a way that computations handle them gracefully (see parameter `unit` for details).\\n* Leap seconds are mostly ignored in manipulations as they don't follow a regular pattern. Leap seconds can be passed to the process, but will never be returned.\",\n", " 'examples': [{'arguments': {'date': '2020-02-01T17:22:45Z',\n", " 'unit': 'month',\n", " 'value': 6},\n", " 'returns': '2020-08-01T17:22:45Z'},\n", " {'arguments': {'date': '2021-03-31T00:00:00+02:00',\n", " 'unit': 'day',\n", " 'value': -7},\n", " 'returns': '2021-03-24T00:00:00+02:00'},\n", " {'arguments': {'date': '2020-02-29T17:22:45Z', 'unit': 'year', 'value': 1},\n", " 'description': 'Adding a year to February 29th in a leap year will result in February 28th in the next (non-leap) year.',\n", " 'returns': '2021-02-28T17:22:45Z'},\n", " {'arguments': {'date': '2020-01-31', 'unit': 'month', 'value': 1},\n", " 'description': 'Adding a month to January 31th will result in February 29th in leap years.',\n", " 'returns': '2020-02-29'},\n", " {'arguments': {'date': '2016-12-31T23:59:59Z',\n", " 'unit': 'second',\n", " 'value': 1},\n", " 'description': 'The process skips over the leap second `2016-12-31T23:59:60Z`.',\n", " 'returns': '2017-01-01T00:00:00Z'},\n", " {'arguments': {'date': '2018-12-31T17:22:45Z',\n", " 'unit': 'millisecond',\n", " 'value': 1150},\n", " 'description': 'Milliseconds can be added or subtracted. If not given, the default value is `0`.',\n", " 'returns': '2018-12-31T17:22:46.150Z'},\n", " {'arguments': {'date': '2018-01-01', 'unit': 'hour', 'value': 25},\n", " 'returns': '2018-01-02'},\n", " {'arguments': {'date': '2018-01-01', 'unit': 'hour', 'value': -1},\n", " 'returns': '2017-12-31'}],\n", " 'experimental': True,\n", " 'id': 'date_shift',\n", " 'parameters': [{'description': \"The date (and optionally time) to manipulate.\\n\\nIf the given date doesn't include the time, the process assumes that the time component is `00:00:00Z` (i.e. midnight, in UTC). The millisecond part of the time is optional and defaults to `0` if not given.\",\n", " 'name': 'date',\n", " 'schema': [{'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'format': 'date', 'subtype': 'date', 'type': 'string'}]},\n", " {'description': \"The period of time in the unit given that is added (positive numbers) or subtracted (negative numbers). The value `0` doesn't have any effect.\",\n", " 'name': 'value',\n", " 'schema': {'type': 'integer'}},\n", " {'description': 'The unit for the value given. The following pre-defined units are available:\\n\\n- millisecond: Milliseconds\\n- second: Seconds - leap seconds are ignored in computations.\\n- minute: Minutes\\n- hour: Hours\\n- day: Days - changes only the the day part of a date\\n- week: Weeks (equivalent to 7 days)\\n- month: Months\\n- year: Years\\n\\nManipulations with the unit `year`, `month`, `week` or `day` do never change the time. If any of the manipulations result in an invalid date or time, the corresponding part is rounded down to the next valid date or time respectively. For example, adding a month to `2020-01-31` would result in `2020-02-29`.',\n", " 'name': 'unit',\n", " 'schema': {'enum': ['millisecond',\n", " 'second',\n", " 'minute',\n", " 'hour',\n", " 'day',\n", " 'week',\n", " 'month',\n", " 'year'],\n", " 'type': 'string'}}],\n", " 'returns': {'description': 'The manipulated date. If a time component was given in the parameter `date`, the time component is returned with the date.',\n", " 'schema': [{'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'format': 'date', 'subtype': 'date', 'type': 'string'}]},\n", " 'summary': 'Manipulates dates and times by addition or subtraction'},\n", " {'categories': ['comparison', 'date & time'],\n", " 'description': 'By default, this process checks whether `x` is later than or equal to `min` and before or equal to `max`.\\n\\nIf `exclude_max` is set to `true` the upper bound is excluded so that the process checks whether `x` is later than or equal to `min` and before `max`.\\n\\nLower and upper bounds are not allowed to be swapped. So `min` MUST be before or equal to `max` or otherwise the process always returns `false`.',\n", " 'examples': [{'arguments': {'max': '2022-01-01',\n", " 'min': '2021-01-01',\n", " 'x': '2020-01-01'},\n", " 'returns': False}],\n", " 'experimental': True,\n", " 'id': 'date_between',\n", " 'parameters': [{'description': 'The value to check.',\n", " 'name': 'x',\n", " 'schema': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'description': 'Time only, formatted as HH:MM:SS. The time zone is UTC.',\n", " 'pattern': '^\\\\d{2}:\\\\d{2}:\\\\d{2}$',\n", " 'subtype': 'time',\n", " 'type': 'string'}]},\n", " {'description': 'Lower boundary (inclusive) to check against.',\n", " 'name': 'min',\n", " 'schema': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'description': 'Time only, formatted as HH:MM:SS. The time zone is UTC.',\n", " 'pattern': '^\\\\d{2}:\\\\d{2}:\\\\d{2}$',\n", " 'subtype': 'time',\n", " 'type': 'string'}]},\n", " {'description': 'Upper boundary (inclusive) to check against.',\n", " 'name': 'max',\n", " 'schema': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'description': 'Time only, formatted as HH:MM:SS. The time zone is UTC.',\n", " 'pattern': '^\\\\d{2}:\\\\d{2}:\\\\d{2}$',\n", " 'subtype': 'time',\n", " 'type': 'string'}]},\n", " {'default': False,\n", " 'description': 'Exclude the upper boundary `max` if set to `true`. Defaults to `false`.',\n", " 'name': 'exclude_max',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': '`true` if `x` is between the specified bounds, otherwise `false`.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Between comparison for dates and times'},\n", " {'categories': ['arrays'],\n", " 'description': 'Concatenates two arrays into a single array by appending the second array to the first array.\\n\\nArray labels are kept only if both given arrays are labeled. Otherwise, the labels get discarded from both arrays. The process fails with an `ArrayLabelConflict` exception if a label is present in both arrays. Conflicts must be resolved beforehand.',\n", " 'examples': [{'arguments': {'array1': [1.5, 2.5], 'array2': [5]},\n", " 'description': 'Concatenates two numerical arrays.',\n", " 'returns': [1.5, 2.5, 5]},\n", " {'arguments': {'array1': ['a', 'b'], 'array2': [1, 2]},\n", " 'description': 'Concatenates two arrays containing different data type, may not always be supported.',\n", " 'returns': ['a', 'b', 1, 2]}],\n", " 'exceptions': {'ArrayLabelConflict': {'message': 'At least one label exists in both arrays and the conflict must be resolved before.'}},\n", " 'id': 'array_concat',\n", " 'parameters': [{'description': 'The first array.',\n", " 'name': 'array1',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'description': 'The second array.',\n", " 'name': 'array2',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}}],\n", " 'returns': {'description': 'The merged array.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Merge two arrays'},\n", " {'categories': ['arrays'],\n", " 'description': 'Creates a new array, which by default is empty.\\n\\nThe second parameter `repeat` allows to add the given array multiple times to the new array.\\n\\nIn most cases you can simply pass a (native) array to processes directly, but this process is especially useful to create a new array that is getting returned by a child process, for example in ``apply_dimension()``.',\n", " 'examples': [{'arguments': {}, 'returns': []},\n", " {'arguments': {'data': ['this', 'is', 'a', 'test']},\n", " 'returns': ['this', 'is', 'a', 'test']},\n", " {'arguments': {'data': [None], 'repeat': 3}, 'returns': [None, None, None]},\n", " {'arguments': {'data': [1, 2, 3], 'repeat': 2},\n", " 'returns': [1, 2, 3, 1, 2, 3]}],\n", " 'id': 'array_create',\n", " 'parameters': [{'default': [],\n", " 'description': 'A (native) array to fill the newly created array with. Defaults to an empty array.',\n", " 'name': 'data',\n", " 'optional': True,\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " {'default': 1,\n", " 'description': 'The number of times the (native) array specified in `data` is repeatedly added after each other to the new array being created. Defaults to `1`.',\n", " 'name': 'repeat',\n", " 'optional': True,\n", " 'schema': {'minimum': 1, 'type': 'integer'}}],\n", " 'returns': {'description': 'The newly created array.',\n", " 'schema': {'items': {'description': 'Any data type is allowed.'},\n", " 'type': 'array'}},\n", " 'summary': 'Create an array'},\n", " {'categories': ['development'],\n", " 'description': 'This process can be used to add runtime information to the logs, e.g. for debugging purposes. This process should be used with caution and it is recommended to remove the process in production workflows. For example, logging each value or array individually in a process such as ``apply()`` or ``reduce_dimension()`` could lead to a (too) large number of log entries. Several data structures (e.g. data cubes) are too large to log and will only return summaries of their contents.\\n\\nThe data provided in the parameter `data` is returned without changes.',\n", " 'experimental': True,\n", " 'id': 'inspect',\n", " 'parameters': [{'description': 'Data to log.',\n", " 'name': 'data',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " {'default': '',\n", " 'description': 'A message to send in addition to the data.',\n", " 'name': 'message',\n", " 'optional': True,\n", " 'schema': {'type': 'string'}},\n", " {'default': 'User',\n", " 'description': 'A label to help identify one or more log entries originating from this process in the list of all log entries. It can help to group or filter log entries and is usually not unique.',\n", " 'name': 'code',\n", " 'optional': True,\n", " 'schema': {'type': 'string'}},\n", " {'default': 'info',\n", " 'description': 'The severity level of this message, defaults to `info`.',\n", " 'name': 'level',\n", " 'optional': True,\n", " 'schema': {'enum': ['error', 'warning', 'info', 'debug'],\n", " 'type': 'string'}}],\n", " 'returns': {'description': 'The data as passed to the `data` parameter without any modification.',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " 'summary': 'Add information to the logs'},\n", " {'categories': ['texts', 'comparison'],\n", " 'description': 'Checks whether the text (also known as *string*) specified for `data` contains the text specified for `pattern` at the beginning. Both are expected to be encoded in UTF-8 by default. The no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'amet'},\n", " 'returns': False},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet', 'pattern': 'Lorem'},\n", " 'returns': True},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet', 'pattern': 'lorem'},\n", " 'returns': False},\n", " {'arguments': {'case_sensitive': False,\n", " 'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'lorem'},\n", " 'returns': True},\n", " {'arguments': {'case_sensitive': False, 'data': 'Ä', 'pattern': 'ä'},\n", " 'returns': True},\n", " {'arguments': {'data': None, 'pattern': 'null'}, 'returns': None}],\n", " 'id': 'text_begins',\n", " 'parameters': [{'description': 'Text in which to find something at the beginning.',\n", " 'name': 'data',\n", " 'schema': {'type': ['string', 'null']}},\n", " {'description': 'Text to find at the beginning of `data`. Regular expressions are not supported.',\n", " 'name': 'pattern',\n", " 'schema': {'type': 'string'}},\n", " {'default': True,\n", " 'description': 'Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", " 'name': 'case_sensitive',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': '`true` if `data` begins with `pattern`, false` otherwise.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Text begins with another text'},\n", " {'categories': ['texts', 'comparison'],\n", " 'description': 'Checks whether the text (also known as *string*) specified for `data` contains the text specified for `pattern`. Both are expected to be encoded in UTF-8 by default. The no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'openEO'},\n", " 'returns': False},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'ipsum dolor'},\n", " 'returns': True},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'Ipsum Dolor'},\n", " 'returns': False},\n", " {'arguments': {'case_sensitive': False,\n", " 'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'SIT'},\n", " 'returns': True},\n", " {'arguments': {'case_sensitive': False, 'data': 'ÄÖÜ', 'pattern': 'ö'},\n", " 'returns': True},\n", " {'arguments': {'data': None, 'pattern': 'null'}, 'returns': None}],\n", " 'id': 'text_contains',\n", " 'parameters': [{'description': 'Text in which to find something in.',\n", " 'name': 'data',\n", " 'schema': {'type': ['string', 'null']}},\n", " {'description': 'Text to find in `data`. Regular expressions are not supported.',\n", " 'name': 'pattern',\n", " 'schema': {'type': 'string'}},\n", " {'default': True,\n", " 'description': 'Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", " 'name': 'case_sensitive',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': '`true` if `data` contains the `pattern`, false` otherwise.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Text contains another text'},\n", " {'categories': ['texts', 'comparison'],\n", " 'description': 'Checks whether the text (also known as *string*) specified for `data` contains the text specified for `pattern` at the end. Both are expected to be encoded in UTF-8 by default. The no-data value `null` is passed through and therefore gets propagated.',\n", " 'examples': [{'arguments': {'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'amet'},\n", " 'returns': True},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet', 'pattern': 'AMET'},\n", " 'returns': False},\n", " {'arguments': {'data': 'Lorem ipsum dolor sit amet', 'pattern': 'Lorem'},\n", " 'returns': False},\n", " {'arguments': {'case_sensitive': False,\n", " 'data': 'Lorem ipsum dolor sit amet',\n", " 'pattern': 'AMET'},\n", " 'returns': True},\n", " {'arguments': {'case_sensitive': False, 'data': 'Ä', 'pattern': 'ä'},\n", " 'returns': True},\n", " {'arguments': {'data': None, 'pattern': 'null'}, 'returns': None}],\n", " 'id': 'text_ends',\n", " 'parameters': [{'description': 'Text in which to find something at the end.',\n", " 'name': 'data',\n", " 'schema': {'type': ['string', 'null']}},\n", " {'description': 'Text to find at the end of `data`. Regular expressions are not supported.',\n", " 'name': 'pattern',\n", " 'schema': {'type': 'string'}},\n", " {'default': True,\n", " 'description': 'Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", " 'name': 'case_sensitive',\n", " 'optional': True,\n", " 'schema': {'type': 'boolean'}}],\n", " 'returns': {'description': '`true` if `data` ends with `pattern`, false` otherwise.',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " 'summary': 'Text ends with another text'},\n", " {'categories': ['texts'],\n", " 'description': 'Merges text representations (also known as *string*) of a set of elements to a single text, having the separator between each element.',\n", " 'examples': [{'arguments': {'data': ['Hello', 'World'], 'separator': ' '},\n", " 'returns': 'Hello World'},\n", " {'arguments': {'data': [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]},\n", " 'returns': '1234567890'},\n", " {'arguments': {'data': [None, True, False, 1, -1.5, 'ß'],\n", " 'separator': '\\n'},\n", " 'returns': 'null\\ntrue\\nfalse\\n1\\n-1.5\\nß'},\n", " {'arguments': {'data': [2, 0], 'separator': 1}, 'returns': '210'},\n", " {'arguments': {'data': []}, 'returns': ''}],\n", " 'id': 'text_concat',\n", " 'parameters': [{'description': 'A set of elements. Numbers, boolean values and null values get converted to their (lower case) string representation. For example: `1` (integer), `-1.5` (number), `true` / `false` (boolean values)',\n", " 'name': 'data',\n", " 'schema': {'items': {'type': ['string', 'number', 'boolean', 'null']},\n", " 'type': 'array'}},\n", " {'default': '',\n", " 'description': 'A separator to put between each of the individual texts. Defaults to an empty string.',\n", " 'name': 'separator',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'number', 'boolean', 'null']}}],\n", " 'returns': {'description': 'A string containing a string representation of all the array elements in the same order, with the separator between each element.',\n", " 'schema': {'type': 'string'}},\n", " 'summary': 'Concatenate elements to a single text'},\n", " {'categories': ['cubes', 'import'],\n", " 'description': 'Loads data from a static STAC catalog or a STAC API Collection and returns the data as a processable data cube. A batch job result can be loaded by providing a reference to it.\\n\\nIf supported by the underlying metadata and file format, the data that is added to the data cube can be restricted with the parameters `spatial_extent`, `temporal_extent` and `bands`. If no data is available for the given extents, a `NoDataAvailable` exception is thrown.\\n\\n**Remarks:**\\n\\n* The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as specified in the metadata if the `bands` parameter is set to `null`.\\n* If no additional parameter is specified this would imply that the whole data set is expected to be loaded. Due to the large size of many data sets, this is not recommended and may be optimized by back-ends to only load the data that is actually required after evaluating subsequent processes such as filters. This means that the values should be processed only after the data has been limited to the required extent and as a consequence also to a manageable size.',\n", " 'examples': [{'arguments': {'url': 'https://example.com/api/v1.0/jobs/123/results'},\n", " 'title': 'Load from a static STAC / batch job result'},\n", " {'arguments': {'properties': {'eo:cloud_cover': {'process_graph': {'cc': {'arguments': {'max': 50,\n", " 'min': 0,\n", " 'x': {'from_parameter': 'value'}},\n", " 'process_id': 'between',\n", " 'result': True}}},\n", " 'platform': {'process_graph': {'pf': {'arguments': {'case_sensitive': False,\n", " 'x': {'from_parameter': 'value'},\n", " 'y': 'Sentinel-2B'},\n", " 'process_id': 'eq',\n", " 'result': True}}}},\n", " 'spatial_extent': {'east': 16.6,\n", " 'north': 48.6,\n", " 'south': 47.2,\n", " 'west': 16.1},\n", " 'temporal_extent': ['2018-01-01', '2019-01-01'],\n", " 'url': 'https://example.com/collections/SENTINEL2'},\n", " 'title': 'Load from a STAC API'}],\n", " 'exceptions': {'NoDataAvailable': {'message': 'There is no data available for the given extents.'},\n", " 'TemporalExtentEmpty': {'message': 'The temporal extent is empty. The second instant in time must always be greater/later than the first instant in time.'}},\n", " 'experimental': True,\n", " 'id': 'load_stac',\n", " 'links': [{'href': 'https://openeo.org/documentation/1.0/datacubes.html',\n", " 'rel': 'about',\n", " 'title': 'Data Cubes explained in the openEO documentation'},\n", " {'href': 'https://proj.org/usage/projections.html',\n", " 'rel': 'about',\n", " 'title': 'PROJ parameters for cartographic projections'},\n", " {'href': 'http://www.epsg-registry.org',\n", " 'rel': 'about',\n", " 'title': 'Official EPSG code registry'},\n", " {'href': 'http://www.epsg.io',\n", " 'rel': 'about',\n", " 'title': 'Unofficial EPSG code database'},\n", " {'href': 'http://www.opengeospatial.org/standards/sfa',\n", " 'rel': 'about',\n", " 'title': 'Simple Features standard by the OGC'},\n", " {'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", " 'rel': 'about',\n", " 'title': 'List of common band names as specified by the STAC specification'},\n", " {'href': 'https://www.rfc-editor.org/rfc/rfc3339.html',\n", " 'rel': 'about',\n", " 'title': 'RFC3339: Details about formatting temporal strings'}],\n", " 'parameters': [{'description': 'The URL to a static STAC catalog (STAC Item, STAC Collection, or STAC Catalog) or a specific STAC API Collection that allows to filter items and to download assets. This includes batch job results, which itself are compliant to STAC. For external URLs, authentication details such as API keys or tokens may need to be included in the URL.\\n\\nBatch job results can be specified in two ways:\\n\\n- For Batch job results at the same back-end, a URL pointing to the corresponding batch job results endpoint should be provided. The URL usually ends with `/jobs/{id}/results` and `{id}` is the corresponding batch job ID.\\n- For external results, a signed URL must be provided. Not all back-ends support signed URLs, which are provided as a link with the link relation `canonical` in the batch job result metadata.',\n", " 'name': 'url',\n", " 'schema': {'format': 'uri',\n", " 'pattern': '^https?://',\n", " 'subtype': 'uri',\n", " 'title': 'URL',\n", " 'type': 'string'}},\n", " {'default': None,\n", " 'description': 'Limits the data to load to the specified bounding box or polygons.\\n\\n* For raster data, the process loads the pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\\n* For vector data, the process loads the geometry into the data cube if the geometry is fully within the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC). Empty geometries may only be in the data cube if no spatial extent has been provided.\\n\\nThe GeoJSON can be one of the following feature types:\\n\\n* A `Polygon` or `MultiPolygon` geometry,\\n* a `Feature` with a `Polygon` or `MultiPolygon` geometry, or\\n* a `FeatureCollection` containing at least one `Feature` with `Polygon` or `MultiPolygon` geometries.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_bbox()`` or ``filter_spatial()`` directly after loading unbounded data.',\n", " 'name': 'spatial_extent',\n", " 'optional': True,\n", " 'schema': [{'properties': {'base': {'default': None,\n", " 'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'crs': {'anyOf': [{'examples': [3857],\n", " 'minimum': 1000,\n", " 'subtype': 'epsg-code',\n", " 'title': 'EPSG Code',\n", " 'type': 'integer'},\n", " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'}],\n", " 'default': 4326,\n", " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/) or [WKT2 CRS string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.'},\n", " 'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", " 'type': 'number'},\n", " 'height': {'default': None,\n", " 'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", " 'type': ['number', 'null']},\n", " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", " 'type': 'number'},\n", " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", " 'type': 'number'}},\n", " 'required': ['west', 'south', 'east', 'north'],\n", " 'subtype': 'bounding-box',\n", " 'title': 'Bounding Box',\n", " 'type': 'object'},\n", " {'description': 'Limits the data cube to the bounding box of the given geometries. For raster data, all pixels inside the bounding box that do not intersect with any of the polygons will be set to no data (`null`).\\n\\nThe GeoJSON type `GeometryCollection` is not supported. Empty geometries are ignored.',\n", " 'subtype': 'geojson',\n", " 'title': 'GeoJSON',\n", " 'type': 'object'},\n", " {'description': 'Limits the data cube to the bounding box of the given geometries in the vector data cube. All pixels inside the bounding box that do not intersect with any of the polygons will be set to no data (`null`). Empty geometries are ignored.',\n", " 'dimensions': [{'type': 'geometry'}],\n", " 'subtype': 'datacube',\n", " 'title': 'Vector data cube',\n", " 'type': 'object'},\n", " {'description': \"Don't filter spatially. All data is included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Limits the data to load to the specified left-closed temporal interval. Applies to all temporal dimensions. The interval has to be specified as an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe second element must always be greater/later than the first element. Otherwise, a `TemporalExtentEmpty` exception is thrown.\\n\\nAlso supports open intervals by setting one of the boundaries to `null`, but never both.\\n\\nSet this parameter to `null` to set no limit for the temporal extent. Be careful with this when loading large datasets! It is recommended to use this parameter instead of using ``filter_temporal()`` directly after loading unbounded data.',\n", " 'name': 'temporal_extent',\n", " 'optional': True,\n", " 'schema': [{'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", " ['2015-01-01', '2016-01-01']],\n", " 'items': {'anyOf': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'type': 'null'}]},\n", " 'maxItems': 2,\n", " 'minItems': 2,\n", " 'subtype': 'temporal-interval',\n", " 'type': 'array',\n", " 'uniqueItems': True},\n", " {'description': \"Don't filter temporally. All data is included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': \"Only adds the specified bands into the data cube so that bands that don't match the list of band names are not available. Applies to all dimensions of type `bands`.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If the unique band name and the common name conflict, the unique band name has a higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.\\n\\nIt is recommended to use this parameter instead of using ``filter_bands()`` directly after loading unbounded data.\",\n", " 'name': 'bands',\n", " 'optional': True,\n", " 'schema': [{'items': {'subtype': 'band-name', 'type': 'string'},\n", " 'minItems': 1,\n", " 'type': 'array'},\n", " {'description': \"Don't filter bands. All bands are included in the data cube.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]},\n", " {'default': None,\n", " 'description': 'Limits the data by metadata properties to include only data in the data cube which all given conditions return `true` for (AND operation).\\n\\nSpecify key-value-pairs with the key being the name of the metadata property, which can be retrieved with the openEO Data Discovery for Collections. The value must be a condition (user-defined process) to be evaluated against a STAC API. This parameter is not supported for static STAC.',\n", " 'name': 'properties',\n", " 'optional': True,\n", " 'schema': [{'additionalProperties': {'parameters': [{'description': 'The property value to be checked against.',\n", " 'name': 'value',\n", " 'schema': {'description': 'Any data type.'}}],\n", " 'returns': {'description': '`true` if the data should be loaded into the data cube, otherwise `false`.',\n", " 'schema': {'type': 'boolean'}},\n", " 'subtype': 'process-graph',\n", " 'type': 'object'},\n", " 'description': 'A list of filters to check against. Specify key-value-pairs with the key being the name of the metadata property name and the value being a process evaluated against the metadata values.',\n", " 'subtype': 'metadata-filter',\n", " 'title': 'Filters',\n", " 'type': 'object'},\n", " {'description': \"Don't filter by metadata properties.\",\n", " 'title': 'No filter',\n", " 'type': 'null'}]}],\n", " 'returns': {'description': 'A data cube for further processing.',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}},\n", " 'summary': 'Loads data from STAC'},\n", " {'categories': ['logic', 'comparison', 'masks'],\n", " 'description': 'If the value passed is `true`, returns the value of the `accept` parameter, otherwise returns the value of the `reject` parameter.\\n\\nThis is basically an if-then-else construct as in other programming languages.',\n", " 'examples': [{'arguments': {'accept': 'A', 'reject': 'B', 'value': True},\n", " 'returns': 'A'},\n", " {'arguments': {'accept': 'A', 'reject': 'B', 'value': None},\n", " 'returns': 'B'},\n", " {'arguments': {'accept': [1, 2, 3], 'reject': [4, 5, 6], 'value': False},\n", " 'returns': [4, 5, 6]},\n", " {'arguments': {'accept': 123, 'value': True}, 'returns': 123},\n", " {'arguments': {'accept': 1, 'value': False}, 'returns': None}],\n", " 'id': 'if',\n", " 'parameters': [{'description': 'A boolean value.',\n", " 'name': 'value',\n", " 'schema': {'type': ['boolean', 'null']}},\n", " {'description': 'A value that is returned if the boolean value is `true`.',\n", " 'name': 'accept',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " {'default': None,\n", " 'description': 'A value that is returned if the boolean value is **not** `true`. Defaults to `null`.',\n", " 'name': 'reject',\n", " 'optional': True,\n", " 'schema': {'description': 'Any data type is allowed.'}}],\n", " 'returns': {'description': 'Either the `accept` or `reject` argument depending on the given boolean value.',\n", " 'schema': {'description': 'Any data type is allowed.'}},\n", " 'summary': 'If-Then-Else conditional'},\n", " {'categories': ['export', 'stac'],\n", " 'description': 'Exports the given processing results made available through a STAC resource (e.g., a STAC Collection) to the given user workspace. The STAC resource itself is exported with all STAC resources and assets underneath.',\n", " 'experimental': True,\n", " 'id': 'export_workspace',\n", " 'parameters': [{'description': 'The data to export to the user workspace as a STAC resource.',\n", " 'name': 'data',\n", " 'schema': {'subtype': 'stac', 'type': 'object'}},\n", " {'description': 'The identifier of the workspace to export to.',\n", " 'name': 'workspace',\n", " 'schema': {'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", " 'subtype': 'workspace-id',\n", " 'type': 'string'}},\n", " {'default': None,\n", " 'description': 'Provides a cloud-specific path identifier to a STAC resource to merge the given STAC resource into. If not provided, the STAC resource is kept separate from any other STAC resources in the workspace.',\n", " 'name': 'merge',\n", " 'optional': True,\n", " 'schema': {'type': ['string', 'null']}}],\n", " 'returns': {'description': 'Returns the potentially updated STAC resource.',\n", " 'schema': {'subtype': 'stac', 'type': 'object'}},\n", " 'summary': 'Export data to a cloud user workspace'},\n", " {'categories': ['cubes', 'elevation'],\n", " 'description': \"Computes the aspect from elevation data. Aspect is the direction component of a gradient vector. It is the direction in degrees of which direction the maximum change in direction is pointing at a given point. Horn's method is used to compute the aspect based on estimates of the partial derivatives dz/dx and dz/dy.\",\n", " 'experimental': True,\n", " 'id': 'aspect',\n", " 'parameters': [{'description': 'Data cube containing elevation data.',\n", " 'name': 'data',\n", " 'optional': False,\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}],\n", " 'returns': {'description': \"A data cube with calculated aspects for each band, the band names are the original band names with a '_aspect' suffix.\",\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " 'summary': 'Compute aspect on elevation data'},\n", " {'categories': ['cubes', 'elevation'],\n", " 'description': \"Computes the slope from elevation data. Slope is the magnitude portion of the gradient vector. It is the maximum change of elevation from a raster cell to any immediate neighbor. Horn's method is used to compute the slope based on estimates of the partial derivatives dz/dx and dz/dy.\",\n", " 'experimental': True,\n", " 'id': 'slope',\n", " 'parameters': [{'description': 'Data cube containing elevation data.',\n", " 'name': 'data',\n", " 'optional': False,\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}}],\n", " 'returns': {'description': \"A data cube with calculated slopes for each band, the band names are the original band names with a '_slope' suffix.\",\n", " 'schema': {'subtype': 'raster-cube', 'type': 'object'}},\n", " 'summary': 'Compute slope on elevation data'},\n", " {'categories': ['math > indices', 'vegetation indices'],\n", " 'description': 'Computes the normalized difference for two bands. The normalized difference is computed as *`(x - y) / (x + y)`*.\\n\\nThis process could be used for a number of remote sensing indices such as:\\n\\n* [NDVI](https://eos.com/ndvi/): `x` = NIR band, `y` = red band\\n* [NDWI](https://eos.com/ndwi/): `x` = NIR band, `y` = SWIR band\\n* [NDSI](https://eos.com/ndsi/): `x` = green band, `y` = SWIR band\\n\\nSome back-ends may have native processes such as ``ndvi()`` available for convenience.',\n", " 'id': 'normalized_difference',\n", " 'links': [{'href': 'https://eos.com/ndvi/',\n", " 'rel': 'related',\n", " 'title': 'NDVI explained by EOS'},\n", " {'href': 'https://eos.com/ndwi/',\n", " 'rel': 'related',\n", " 'title': 'NDWI explained by EOS'},\n", " {'href': 'https://eos.com/ndsi/',\n", " 'rel': 'related',\n", " 'title': 'NDSI explained by EOS'}],\n", " 'parameters': [{'description': 'The value for the first band.',\n", " 'name': 'x',\n", " 'schema': {'type': 'number'}},\n", " {'description': 'The value for the second band.',\n", " 'name': 'y',\n", " 'schema': {'type': 'number'}}],\n", " 'process_graph': {'add': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'add'},\n", " 'divide': {'arguments': {'x': {'from_node': 'subtract'},\n", " 'y': {'from_node': 'add'}},\n", " 'process_id': 'divide',\n", " 'result': True},\n", " 'subtract': {'arguments': {'x': {'from_parameter': 'x'},\n", " 'y': {'from_parameter': 'y'}},\n", " 'process_id': 'subtract'}},\n", " 'returns': {'description': 'The computed normalized difference.',\n", " 'schema': {'maximum': 1, 'minimum': -1, 'type': 'number'}},\n", " 'summary': 'Normalized difference'},\n", " {'description': 'Proof-of-concept process to run CWL in parallel.',\n", " 'id': '_cwl_dummy_stac_parallel',\n", " 'parameters': [{'description': 'request_dates. Choose from 2023-06-01, 2023-06-04 and / or 2023-06-04.',\n", " 'name': 'request_dates',\n", " 'optional': False,\n", " 'schema': {'items': {'anyOf': [{'description': 'Date and time with a time zone.',\n", " 'format': 'date-time',\n", " 'subtype': 'date-time',\n", " 'type': 'string'},\n", " {'description': 'Date only, formatted as `YYYY-MM-DD`. The time zone is UTC. Missing time components are all 0.',\n", " 'format': 'date',\n", " 'subtype': 'date',\n", " 'type': 'string'},\n", " {'description': 'Time only, formatted as `HH:MM:SS`. The time zone is UTC.',\n", " 'pattern': '^\\\\d{2}:\\\\d{2}:\\\\d{2}$',\n", " 'subtype': 'time',\n", " 'type': 'string'},\n", " {'type': 'null'}]},\n", " 'minItems': 1,\n", " 'subtype': 'temporal-intervals',\n", " 'type': 'array'}}],\n", " 'returns': {'description': 'the data as a data cube',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}}},\n", " {'description': 'Proof-of-concept process to run CWL based inSAR. More info here: https://github.com/cloudinsar/s1-workflows',\n", " 'id': 'sar_coherence',\n", " 'parameters': [],\n", " 'returns': {'description': 'the data as a data cube',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}}},\n", " {'description': 'Proof-of-concept process to run CWL based inSAR. More info here: https://github.com/cloudinsar/s1-workflows',\n", " 'id': 'sar_interferogram',\n", " 'parameters': [],\n", " 'returns': {'description': 'the data as a data cube',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}}},\n", " {'description': 'Proof-of-concept process to run CWL based inSAR. More info here: https://github.com/cloudinsar/s1-workflows',\n", " 'id': 'sar_slc_preprocessing',\n", " 'parameters': [],\n", " 'returns': {'description': 'the data as a data cube',\n", " 'schema': {'subtype': 'datacube', 'type': 'object'}}}]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "connection.list_processes()" ] }, { "cell_type": "markdown", "id": "d8787459-d72c-4e27-9b74-bd7d1e0915c0", "metadata": {}, "source": [ "Y mayor detalle si te interesa alguno:" ] }, { "cell_type": "code", "execution_count": 6, "id": "7df7a55f-b127-42b3-8293-3c79ce3e7510", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " " ], "text/plain": [ "{'categories': ['math'],\n", " 'description': 'Sums up the two numbers `x` and `y` (*`x + y`*) and returns the computed sum.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 7.5},\n", " {'arguments': {'x': -2, 'y': -4}, 'returns': -6},\n", " {'arguments': {'x': 1, 'y': None}, 'returns': None}],\n", " 'id': 'add',\n", " 'links': [{'href': 'http://mathworld.wolfram.com/Sum.html',\n", " 'rel': 'about',\n", " 'title': 'Sum explained by Wolfram MathWorld'},\n", " {'href': 'https://ieeexplore.ieee.org/document/8766229',\n", " 'rel': 'about',\n", " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", " 'parameters': [{'description': 'The first summand.',\n", " 'name': 'x',\n", " 'schema': {'type': ['number', 'null']}},\n", " {'description': 'The second summand.',\n", " 'name': 'y',\n", " 'schema': {'type': ['number', 'null']}}],\n", " 'process_graph': {'sum': {'arguments': {'data': [{'from_parameter': 'x'},\n", " {'from_parameter': 'y'}],\n", " 'ignore_nodata': False},\n", " 'process_id': 'sum',\n", " 'result': True}},\n", " 'returns': {'description': 'The computed sum of the two numbers.',\n", " 'schema': {'type': ['number', 'null']}},\n", " 'summary': 'Addition of two numbers'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "connection.describe_process(\"add\")" ] }, { "cell_type": "markdown", "id": "006dedcb-11f5-4e4a-bb6c-9b6a65771ea6", "metadata": {}, "source": [ "## Autenticación" ] }, { "cell_type": "markdown", "id": "e5535b04-cf1c-4d57-97db-e45d042724c9", "metadata": {}, "source": [ "Primero debemos autenticarnos en el CDSE." ] }, { "cell_type": "code", "execution_count": 14, "id": "a1b09645-c360-4527-bcdc-b9b35b04825b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Authenticated using refresh token.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "connection.authenticate_oidc()" ] }, { "cell_type": "markdown", "id": "04065c5e-cda2-46bc-ae39-f47ef1f0ca67", "metadata": {}, "source": [ "Comienza con un cubo de datos simple: una pequeña porción espacio-temporal de datos de `SENTINEL2_L2A`:" ] }, { "cell_type": "code", "execution_count": 15, "id": "ea7bdbf4-a9ac-4898-8aa2-7fb5d192aa1b", "metadata": {}, "outputs": [], "source": [ "cube = connection.load_collection(\n", " \"SENTINEL2_L2A\",\n", " bands=[\"B04\", \"B03\", \"B02\", \"B08\", \"SCL\"],\n", " temporal_extent=(\"2025-07-01\", \"2025-10-31\"),\n", " spatial_extent={\n", " \"west\": -75,\n", " \"south\": 5,\n", " \"east\": -74,\n", " \"north\": 6,\n", " \"crs\": \"EPSG:4326\",\n", " },\n", " max_cloud_cover=50,\n", ")\n" ] }, { "cell_type": "markdown", "id": "71e0da57-c9e9-46e4-841e-8a212eadde10", "metadata": {}, "source": [ "## Máscara para nubes\n", "\n", "En general, para hacer que los datos crudos sean más útiles, normalmente queremos eliminar los píxeles de nubes y trabajar solo con datos sin nubes. Es muy común que los datos de observación de la Tierra tengan capas de enmascaramiento separadas que, por ejemplo, indican si un píxel está cubierto por un (tipo de) nube o no. Para Sentinel-2, una de esas capas es la capa de \"clasificación de escena\" (nombre de banda \"SCL\") generada por el algoritmo Sen2Cor.\n", "\n", "Con openEO y la biblioteca cliente de openEO para Python, podemos tomar esta banda \"SCL\" (que ya incluimos antes en la llamada a `load_collection`) y aplicar el enmascaramiento de nubes de la siguiente manera.\n", "\n", "Primero construimos una máscara de nubes binaria a partir de los valores 3 (sombras de nubes), 8 (probabilidad media de nubes) y 9 (probabilidad alta de nubes) de la SCL:" ] }, { "cell_type": "code", "execution_count": 16, "id": "a639e2e4-4b79-4873-935c-cb1719c76dfd", "metadata": {}, "outputs": [], "source": [ "# Aislamos la banda SCL\n", "scl_band = cube.band(\"SCL\")\n", "\n", "# Creamos una máscara booleana: True donde SCL es 4 (veg) O 5 (suelo)\n", "cloud_mask = (scl_band == 3) | (scl_band == 8) | (scl_band == 9)\n" ] }, { "cell_type": "markdown", "id": "4b701521-cbaf-4bac-b61f-70d3181fee82", "metadata": {}, "source": [ "Aplica la máscara de nubes, y construye el compuesto de nuevo:" ] }, { "cell_type": "code", "execution_count": 17, "id": "37516936-ce79-41eb-9f74-adebffa2d2ad", "metadata": {}, "outputs": [], "source": [ "cube_masked = cube.mask(cloud_mask)" ] }, { "cell_type": "markdown", "id": "70e26298-4202-4b18-9aef-b264b89b7a3d", "metadata": {}, "source": [ "## Cálculo del NDVI" ] }, { "cell_type": "code", "execution_count": 20, "id": "b02cce85-24e8-4e64-95ce-60a71055aeeb", "metadata": {}, "outputs": [], "source": [ "# Para crear un cubo con un NDVI por cada imagen válida.\n", "ndvi_cube = cube_masked.ndvi(nir=\"B08\", red=\"B04\")" ] }, { "cell_type": "markdown", "id": "e55f4532-210a-4b62-a5f7-ababa9e71268", "metadata": {}, "source": [ "Podemos crear un \"compuesto\" (un solo mapa) tomando el MÁXIMO NDVI de cada píxel a lo largo del mes. Esto ayuda a obtener la vegetación más saludable\n", "y reduce aún más los efectos residuales de nubes/bruma." ] }, { "cell_type": "code", "execution_count": 21, "id": "12988503-a124-40c0-871e-600dd594988f", "metadata": {}, "outputs": [], "source": [ "ndvi_composite = ndvi_cube.reduce_dimension(dimension=\"t\", reducer=\"max\")" ] }, { "cell_type": "markdown", "id": "faa4c445-4b35-4f94-858e-f34516beec9a", "metadata": {}, "source": [ "Todo lo anterior solo ha definido el flujo de trabajo. Ahora, enviamos el trabajo al backend de CDSE para que se procese y descargamos el resultado como un GeoTIFF." ] }, { "cell_type": "code", "execution_count": 22, "id": "22624b3e-0305-40b0-b870-eb833bc044ba", "metadata": {}, "outputs": [], "source": [ "ndvi_composite.download(\"ndvi.tif\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "fea58025-3f4f-43eb-a1f2-8443bccc0fe0", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from rasterio.plot import show\n", "with rasterio.open(\"ndvi.tif\") as src:\n", " \n", " # Configuramos el tamaño del gráfico\n", " fig, ax = plt.subplots(figsize=(10, 10))\n", " \n", " # Usamos la función 'show' de rasterio, que se integra con matplotlib\n", " # cmap='RdYlGn' (Red-Yellow-Green) es el mapa de color\n", " # estándar para NDVI.\n", " show(\n", " src, \n", " ax=ax, \n", " cmap='RdYlGn', \n", " title='NDVI Máximo',\n", " vmin=0, # Forzamos el rango de color (0 = sin vegetación)\n", " vmax=1 # (1 = vegetación muy saludable)\n", " )\n", " \n", " # Añadimos etiquetas a los ejes\n", " ax.set_xlabel('Longitud')\n", " ax.set_ylabel('Latitud')\n", " \n", " # Mostramos el gráfico\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "56469a8c-591c-4b37-b8ed-5f07fc7d5529", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "OpenEO", "language": "python", "name": "openeo" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }