diff --git a/current/LAU_CM/collection.json b/current/LAU_CM/collection.json new file mode 100644 index 0000000000..e51143539d --- /dev/null +++ b/current/LAU_CM/collection.json @@ -0,0 +1,178 @@ +{ + "type": "Collection", + "id": "LAU_CM", + "stac_version": "1.0.0", + "description": "The LAUs are a administrative to help ensure that data are readily available and policies can be implemented effectively using these units, a subdivision of the NUTS 3 regions covering the EU's whole economic territory and appropriate for implementing local level typologies included in Tercet, namely the coastal area and the degree of urbanisation (DEGURBA) classification, including city and functional urban areas (FUA) definitions.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "item", + "href": "./items/LAU_2020_NUTS_2021_01M_3035_CM.json", + "type": "application/json" + }, + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + } + ], + "stac_extensions": [ + "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", + "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/version/v1.2.0/schema.json" + ], + "item_assets": { + "data": { + "title": "LAU", + "description": "A system of local administrative units (LAUs) compatible with NUTS, clipped only to the coastal area", + "roles": [ + "data" + ], + "type": "application/vnd.apache.parquet", + "table:storage_options": { + "account_name": "coclico" + }, + "table:columns": [ + { + "name": "GISCO_ID", + "type": "string", + "description": "" + }, + { + "name": "CNTR_CODE", + "type": "string", + "description": "" + }, + { + "name": "nuts_2", + "type": "string", + "description": "" + }, + { + "name": "nuts_3", + "type": "string", + "description": "" + }, + { + "name": "LAU_ID", + "type": "string", + "description": "" + }, + { + "name": "LAU_NAME", + "type": "string", + "description": "" + }, + { + "name": "POP_2020", + "type": "double", + "description": "" + }, + { + "name": "POP_DENS_2", + "type": "double", + "description": "" + }, + { + "name": "AREA_KM2", + "type": "double", + "description": "" + }, + { + "name": "YEAR", + "type": "int64", + "description": "" + }, + { + "name": "FID", + "type": "string", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "__index_level_0__", + "type": "int64", + "description": "" + } + ] + } + }, + "base_url": "gs://coclico-data-public/coclico/LAU_CM", + "sci:citation": "None.", + "version": "1", + "title": "Local administrative units, clipped by Coastal Mask", + "extent": { + "spatial": { + "bbox": [ + [ + -81.12146941539005, + -29.12357316038395, + 93.1752791130622, + 37.855412297701754 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2024-01-22T00:00:00Z", + "2024-01-22T00:00:00Z" + ] + ] + } + }, + "license": "CC-BY-4.0", + "keywords": [ + "Local", + "Adminstrative", + "Units", + "European Union", + "Full-Track", + "Background Layers" + ], + "providers": [ + { + "name": "Eurostat", + "roles": [ + "producer", + "licensor" + ], + "url": "https://ec.europa.eu/eurostat/web/nuts/local-administrative-units" + }, + { + "name": "Deltares", + "roles": [ + "processor", + "host" + ], + "url": "https://deltares.nl" + } + ], + "assets": { + "thumbnail": { + "href": "https://storage.googleapis.com/download/storage/v1/b/coclico-data-public/o/coclico%2Fassets%2Fthumbnails%2FLAU_CM.png?alt=media", + "type": "image/png", + "title": "Thumbnail" + }, + "geoparquet-stac-items": { + "href": "gs://coclico-data-public/coclico/LAU_CM/LAU_2020_NUTS_2021_01M_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "GeoParquet STAC items", + "description": "Snapshot of the collection's STAC items exported to GeoParquet format.", + "roles": [ + "data" + ] + } + } +} \ No newline at end of file diff --git a/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json b/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json new file mode 100644 index 0000000000..d683206aac --- /dev/null +++ b/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json @@ -0,0 +1,155 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "LAU_2020_NUTS_2021_01M_3035_CM", + "properties": { + "title": "LAU", + "description": "A system of local administrative units (LAUs) compatible with NUTS, clipped only to the coastal area", + "table:columns": [ + { + "name": "GISCO_ID", + "type": "string", + "description": "" + }, + { + "name": "CNTR_CODE", + "type": "string", + "description": "" + }, + { + "name": "nuts_2", + "type": "string", + "description": "" + }, + { + "name": "nuts_3", + "type": "string", + "description": "" + }, + { + "name": "LAU_ID", + "type": "string", + "description": "" + }, + { + "name": "LAU_NAME", + "type": "string", + "description": "" + }, + { + "name": "POP_2020", + "type": "double", + "description": "" + }, + { + "name": "POP_DENS_2", + "type": "double", + "description": "" + }, + { + "name": "AREA_KM2", + "type": "double", + "description": "" + }, + { + "name": "YEAR", + "type": "int64", + "description": "" + }, + { + "name": "FID", + "type": "string", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "__index_level_0__", + "type": "int64", + "description": "" + } + ], + "proj:bbox": [ + -2710033.3959, + -3076198.0823, + 10026005.2215, + 5415709.913699999 + ], + "proj:epsg": 3035, + "table:row_count": 11965, + "created": "2024-12-12T13:10:41.632774Z", + "datetime": "2024-01-22T00:00:00Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + 93.1752791130622, + -29.12357316038395 + ], + [ + 93.1752791130622, + 37.855412297701754 + ], + [ + -81.12146941539005, + 37.855412297701754 + ], + [ + -81.12146941539005, + -29.12357316038395 + ], + [ + 93.1752791130622, + -29.12357316038395 + ] + ] + ] + }, + "links": [ + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Local administrative units, clipped by Coastal Mask" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Local administrative units, clipped by Coastal Mask" + } + ], + "assets": { + "data": { + "href": "https://storage.googleapis.com/coclico-data-public/coclico/LAU_CM/LAU_2020_NUTS_2021_01M_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "LAU", + "description": "A system of local administrative units (LAUs) compatible with NUTS, clipped only to the coastal area", + "roles": [ + "data" + ] + } + }, + "bbox": [ + -81.12146941539005, + -29.12357316038395, + 93.1752791130622, + 37.855412297701754 + ], + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/projection/v1.1.0/schema.json" + ], + "collection": "LAU_CM" +} \ No newline at end of file diff --git a/current/NUTS_CM/collection.json b/current/NUTS_CM/collection.json new file mode 100644 index 0000000000..a8b0f8da75 --- /dev/null +++ b/current/NUTS_CM/collection.json @@ -0,0 +1,163 @@ +{ + "type": "Collection", + "id": "NUTS_CM", + "stac_version": "1.0.0", + "description": "The GISCO statistical unit dataset represents the NUTS (nomenclature of territorial units for statistics) and statistical regions by means of multipart polygon, polyline and point topology. The NUTS geographical information is completed by attribute tables and a set of cartographic help lines to better visualise multipart polygonal regions.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "item", + "href": "./items/NUTS_RG_01M_2021_3035_CM.json", + "type": "application/json" + }, + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + } + ], + "stac_extensions": [ + "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", + "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/version/v1.2.0/schema.json" + ], + "item_assets": { + "data": { + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "roles": [ + "data" + 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territorial units for statistics, clipped by Coastal Mask", + "extent": { + "spatial": { + "bbox": [ + [ + -90.23528381409483, + -29.857082115784582, + 103.456913582018, + 39.79889221318682 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2024-01-22T00:00:00Z", + "2024-01-22T00:00:00Z" + ] + ] + } + }, + "license": "CC-BY-4.0", + "keywords": [ + "Local", + "Adminstrative", + "Units", + "European Union", + "Full-Track", + "Background Layers" + ], + "providers": [ + { + "name": "Eurostat", + "roles": [ + "producer", + "licensor" + ], + "url": "https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics" + }, + { + "name": "Deltares", + "roles": [ + "processor", + "host" + ], + "url": "https://deltares.nl" + } + ], + "assets": { + "thumbnail": { + "href": "https://storage.googleapis.com/download/storage/v1/b/coclico-data-public/o/coclico%2Fassets%2Fthumbnails%2FNUTS_CM.png?alt=media", + "type": "image/png", + "title": "Thumbnail" + }, + "geoparquet-stac-items": { + "href": "gs://coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "GeoParquet STAC items", + "description": "Snapshot of the collection's STAC items exported to GeoParquet format.", + "roles": [ + "data" + ] + } + } +} \ No newline at end of file diff --git a/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json new file mode 100644 index 0000000000..37c24f1947 --- /dev/null +++ b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json @@ -0,0 +1,140 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "NUTS_RG_01M_2021_3035_CM", + "properties": { + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "table:columns": [ + { + "name": "NUTS_ID", + "type": "string", + "description": "" + }, + { + "name": "LEVL_CODE", + "type": "int64", + "description": "" + }, + { + "name": "CNTR_CODE", + "type": "string", + "description": "" + }, + { + "name": "NAME_LATN", + "type": "binary", + "description": "" + }, + { + "name": "NUTS_NAME", + "type": "binary", + "description": "" + }, + { + "name": "MOUNT_TYPE", + "type": "double", + "description": "" + }, + { + "name": "URBN_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "COAST_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "__index_level_0__", + "type": "int64", + "description": "" + } + ], + "proj:bbox": [ + -2824230.8134000003, + -3076162.6068, + 10026010.8334, + 6405005.3475 + ], + "proj:epsg": 3035, + "table:row_count": 921, + "created": "2024-12-12T13:09:43.503823Z", + "datetime": "2024-01-22T00:00:00Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + 103.456913582018, + -29.857082115784582 + ], + [ + 103.456913582018, + 39.79889221318682 + ], + [ + -90.23528381409483, + 39.79889221318682 + ], + [ + -90.23528381409483, + -29.857082115784582 + ], + [ + 103.456913582018, + -29.857082115784582 + ] + ] + ] + }, + "links": [ + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + } + ], + "assets": { + "data": { + "href": "https://storage.googleapis.com/coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "roles": [ + "data" + ] + } + }, + "bbox": [ + -90.23528381409483, + -29.857082115784582, + 103.456913582018, + 39.79889221318682 + ], + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/projection/v1.1.0/schema.json" + ], + "collection": "NUTS_CM" +} \ No newline at end of file diff --git a/current/catalog.json b/current/catalog.json index 1802f3c601..91cf657e67 100644 --- a/current/catalog.json +++ b/current/catalog.json @@ -166,6 +166,18 @@ "type": "application/json", "title": "Coastal Hazard Flood Projections" }, + { + "rel": "child", + "href": "./NUTS_CM/collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + }, + { + "rel": "child", + "href": "./LAU_CM/collection.json", + "type": "application/json", + "title": "Local administrative units, clipped by Coastal Mask" + }, { "rel": "child", "href": "./NUTS/collection.json", diff --git a/notebooks/99_LAU_NUTS.ipynb b/notebooks/99_LAU_NUTS.ipynb index 96c606721d..cf6eeb92ff 100644 --- a/notebooks/99_LAU_NUTS.ipynb +++ b/notebooks/99_LAU_NUTS.ipynb @@ -9,9 +9,24 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_14636\\3768201458.py:12: DeprecationWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas still uses PyGEOS by default. However, starting with version 0.14, the default will switch to Shapely. To force to use Shapely 2.0 now, you can either uninstall PyGEOS or set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n", + "\n", + "import os\n", + "os.environ['USE_PYGEOS'] = '0'\n", + "import geopandas\n", + "\n", + "In the next release, GeoPandas will switch to using Shapely by default, even if PyGEOS is installed. If you only have PyGEOS installed to get speed-ups, this switch should be smooth. However, if you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n", + " import geopandas as gpd\n" + ] + } + ], "source": [ "# Load software\n", "import os\n", @@ -43,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -305,7 +320,7 @@ "[98613 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -316,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -532,7 +547,7 @@ "[2010 rows x 9 columns]" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -543,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -555,9 +570,16 @@ "NUTS.to_parquet(nuts_parquet_file)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work on masking the LAU data " + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -852,18 +874,19 @@ "[98613 rows x 12 columns]" ] }, - "execution_count": 8, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "LAU_NUTS_data" + "LAU_NUTS_data\n", + "LAU_NUTS_data.to_parquet(LAU_dir.join_path('LAU_2020_NUTS_2021_01M_3035.parquet'))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -6446,13 +6469,13 @@ "source": [ "# After LAU and NUTS are match we will only select the LAU's that overlap with the Coastal Mask\n", "\n", - "# masking the data with the Coastal Mask (takes abou 45 min)\n", + "# masking the data with the Coastal Mask (takes about 45 min)\n", "folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", "\n", "# Retrieve total bounds in CRS of coastal mask\n", "totalbbox = LAU_NUTS_data.to_crs(\"EPSG:4326\").total_bounds\n", "\n", - "# Polygonized, empty vecotr for storing the Coastal Mask Polygons\n", + "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", "pgnzd = []\n", "# read tif files in the coastal mask folder\n", "for idx, file in enumerate(os.listdir(folder_mask)):\n", @@ -6482,7 +6505,27 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1008" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -6491,7 +6534,7 @@ "167957" ] }, - "execution_count": 14, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -6504,7 +6547,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -6514,22 +6557,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", + "shapes_gdf.to_parquet(LAU_dir.joinpath('CoastalMask_3035_polygonized_LAU.parquet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\soest\\AppData\\Local\\Temp\\ipykernel_62380\\1320939396.py:3: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: EPSG:4326\n", - "Right CRS: None\n", - "\n", - " joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n" - ] - }, { "data": { "text/html": [ @@ -6563,8 +6604,6 @@ " YEAR\n", " FID\n", " geometry\n", - " index_right\n", - " value\n", " \n", " \n", " \n", @@ -6581,9 +6620,7 @@ " 67.719735\n", " 2020\n", " DE_03361001\n", - " POLYGON ((9.07578 52.98510, 9.07958 52.99275, ...\n", - " 29151\n", - " 1.0\n", + " POLYGON ((4258927.295 3320011.519, 4259193.583...\n", " \n", " \n", " 1193\n", @@ -6598,9 +6635,7 @@ " 38.448908\n", " 2020\n", " DE_03361002\n", - " POLYGON ((9.16004 52.96257, 9.17682 52.95673, ...\n", - " 29151\n", - " 1.0\n", + " POLYGON ((4264556.862 3317435.819, 4265677.354...\n", " \n", " \n", " 1195\n", @@ -6615,9 +6650,7 @@ " 83.462333\n", " 2020\n", " DE_03361003\n", - " POLYGON ((9.32560 52.76771, 9.32330 52.77258, ...\n", - " 29151\n", - " 1.0\n", + " POLYGON ((4275479.269 3295636.790, 4275328.930...\n", " \n", " \n", " 1196\n", @@ -6632,9 +6665,7 @@ " 21.349247\n", " 2020\n", " DE_03361004\n", - " POLYGON ((8.95213 52.88891, 8.95141 52.89676, ...\n", - " 29151\n", - " 1.0\n", + " POLYGON ((4250468.027 3309420.666, 4250432.180...\n", " \n", " \n", " 1197\n", @@ -6649,9 +6680,7 @@ " 174.871744\n", " 2020\n", " DE_03361005\n", - " POLYGON ((9.27815 52.90163, 9.30342 52.91093, ...\n", - " 29151\n", - " 1.0\n", + " POLYGON ((4272425.673 3310568.711, 4274136.202...\n", " \n", " \n", " ...\n", @@ -6667,8 +6696,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 97667\n", @@ -6683,9 +6710,7 @@ " 18.356042\n", " 2020\n", " FR_97614\n", - " POLYGON ((45.13836 -12.86943, 45.13394 -12.867...\n", - " 165557\n", - " 1.0\n", + " POLYGON ((8727685.023 -2785490.190, 8727139.84...\n", " \n", " \n", " 97669\n", @@ -6700,9 +6725,7 @@ " 11.016715\n", " 2020\n", " FR_97616\n", - " POLYGON ((45.13226 -12.88889, 45.11965 -12.877...\n", - " 165557\n", - " 1.0\n", + " POLYGON ((8727114.505 -2787670.745, 8725517.75...\n", " \n", " \n", " 97670\n", @@ -6717,9 +6740,7 @@ " 34.284037\n", " 2020\n", " FR_97617\n", - " POLYGON ((45.17306 -12.76405, 45.17253 -12.772...\n", - " 165557\n", - " 1.0\n", + " POLYGON ((8730964.436 -2773602.954, 8730972.75...\n", " \n", " \n", " 97660\n", @@ -6734,9 +6755,7 @@ " 6.372824\n", " 2020\n", " FR_97608\n", - " MULTIPOLYGON (((45.29645 -12.78407, 45.27135 -...\n", - " 165549\n", - " 1.0\n", + " MULTIPOLYGON (((8745852.102 -2769772.861, 8742...\n", " \n", " \n", " 97668\n", @@ -6751,13 +6770,11 @@ " 4.367692\n", " 2020\n", " FR_97615\n", - " POLYGON ((45.29645 -12.78407, 45.29559 -12.796...\n", - " 165549\n", - " 1.0\n", + " POLYGON ((8745852.102 -2769772.861, 8745849.32...\n", " \n", " \n", "\n", - "

11965 rows × 14 columns

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11965 rows × 12 columns

\n", "" ], "text/plain": [ @@ -6787,30 +6804,30 @@ "97660 2797.974566 6.372824 2020 FR_97608 \n", "97668 2619.690480 4.367692 2020 FR_97615 \n", "\n", - " geometry index_right value \n", - "1192 POLYGON ((9.07578 52.98510, 9.07958 52.99275, ... 29151 1.0 \n", - "1193 POLYGON ((9.16004 52.96257, 9.17682 52.95673, ... 29151 1.0 \n", - "1195 POLYGON ((9.32560 52.76771, 9.32330 52.77258, ... 29151 1.0 \n", - "1196 POLYGON ((8.95213 52.88891, 8.95141 52.89676, ... 29151 1.0 \n", - "1197 POLYGON ((9.27815 52.90163, 9.30342 52.91093, ... 29151 1.0 \n", - "... ... ... ... \n", - "97667 POLYGON ((45.13836 -12.86943, 45.13394 -12.867... 165557 1.0 \n", - "97669 POLYGON ((45.13226 -12.88889, 45.11965 -12.877... 165557 1.0 \n", - "97670 POLYGON ((45.17306 -12.76405, 45.17253 -12.772... 165557 1.0 \n", - "97660 MULTIPOLYGON (((45.29645 -12.78407, 45.27135 -... 165549 1.0 \n", - "97668 POLYGON ((45.29645 -12.78407, 45.29559 -12.796... 165549 1.0 \n", + " geometry \n", + "1192 POLYGON ((4258927.295 3320011.519, 4259193.583... \n", + "1193 POLYGON ((4264556.862 3317435.819, 4265677.354... \n", + "1195 POLYGON ((4275479.269 3295636.790, 4275328.930... \n", + "1196 POLYGON ((4250468.027 3309420.666, 4250432.180... \n", + "1197 POLYGON ((4272425.673 3310568.711, 4274136.202... \n", + "... ... \n", + "97667 POLYGON ((8727685.023 -2785490.190, 8727139.84... \n", + "97669 POLYGON ((8727114.505 -2787670.745, 8725517.75... \n", + "97670 POLYGON ((8730964.436 -2773602.954, 8730972.75... \n", + "97660 MULTIPOLYGON (((8745852.102 -2769772.861, 8742... \n", + "97668 POLYGON ((8745852.102 -2769772.861, 8745849.32... \n", "\n", - "[11965 rows x 14 columns]" + "[11965 rows x 12 columns]" ] }, - "execution_count": 16, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Perform a spatial join to find intersections\n", - "LAU_NUTS_reproj = LAU_NUTS_data.to_crs(\"EPSG:4326\")\n", + "LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", "joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", "\n", "# remove columns added by shapes_gdf\n", @@ -6818,322 +6835,6368 @@ "\n", "# Remove duplicate rows based on the index of gdf1\n", "LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", - "LAU_NUTS_CLEANCM" + "\n", + "# Remove extra columns resulting from the spatial join\n", + "LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "LAU_NUTS_CLEANCM\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "# Write geodataframe to parquet\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "out_file= ds_dir.joinpath('LAU_NUTS_CFHP.parquet')\n", + "out_file= LAU_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", "\n", "# altered_lau_data.to_parquet(out_file_altered)\n", - "LAU_NUTS_data.to_parquet(out_file)" + "LAU_NUTS_CLEANCM.to_parquet(out_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work on masking the NUTS data " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'P:\\\\11207608-coclico\\\\FASTTRACK_DATA\\\\XX_NUTS\\\\NUTS_RG_01M_2021_3035.parquet'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "nuts_parquet_file" + "# shapefile\n", + "NUTS\n", + "\n", + "# open parquet file with geopandas\n", + "NUTS_parq = gpd.read_parquet(nuts_parquet_file)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-5.92_y74.76.tif\n", + "4506\n", + "4507\n", + "4508\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-60.53_y-24.23.tif\n", + "4509\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x14.56_y-17.4.tif\n", + "4510\n", + "4511\n", + "4512\n", + "4513\n", + "4514\n", + "4515\n", + "4516\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-53.71_y33.8.tif\n", + "4517\n", + "4518\n", + "4519\n", + "4520\n", + "4521\n", + "4522\n", + "4523\n", + "4524\n", + "4525\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-12.75_y16.73.tif\n", + "4526\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x28.21_y16.73.tif\n", + "4527\n", + "4528\n", + "4529\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-19.57_y-7.16.tif\n", + "4530\n", + "4531\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x17.97_y16.73.tif\n", + "4532\n", + "4533\n", + "4534\n", + "4535\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x55.52_y30.39.tif\n", + "4536\n", + "4537\n", + "4538\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-33.23_y44.04.tif\n", + "4539\n", + "4540\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x0.91_y71.35.tif\n", + "4541\n", + "4542\n", + "4543\n", + "4544\n", + "4545\n", + "4546\n", + "4547\n", + "4548\n", + "4549\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x7.73_y-3.75.tif\n", + "4550\n", + "4551\n", + "4552\n", + "4553\n", + "4554\n", + "4555\n", + "4556\n", + "4557\n", + "4558\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-43.47_y78.17.tif\n", + "4559\n", + "4560\n", + "4561\n" + ] + } + ], + "source": [ + "# masking the data with the Coastal Mask (takes about 45 min)\n", + "folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", + "\n", + "# Retrieve total bounds in CRS of coastal mask\n", + "totalbbox = NUTS.to_crs(\"EPSG:4326\").total_bounds\n", + "\n", + "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", + "pgnzd = []\n", + "# read tif files in the coastal mask folder\n", + "for idx, file in enumerate(os.listdir(folder_mask)):\n", + " print(idx)\n", + " if file.endswith(\".tif\"):\n", + " img = xr.open_dataset(os.path.join(folder_mask, file), engine=\"rasterio\", mask_and_scale=False) \n", + " if geometry.box(*img.rio.bounds()).intersects(geometry.box(*totalbbox)): # only do the ones in the NUTS bbox --> speed up to 17 min\n", + " print(os.path.join(folder_mask, file))\n", + "\n", + " # get the data\n", + " data_array = img.band_data.values\n", + " mask = data_array == 1\n", + " \n", + " # Split the string into a list of values and correct the gdal affine transformation\n", + " geotrans = list(map(float, img.spatial_ref.GeoTransform.split(' ')))\n", + " afn = Affine.from_gdal(*geotrans)\n", + "\n", + " # Extract shapes\n", + " results = (\n", + " {'properties': {'value': v}, 'geometry': s}\n", + " for i, (s, v) in enumerate(\n", + " shapes(data_array, mask=mask, transform=afn))\n", + " )\n", + "\n", + " pgnzd.append(list(results))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1116" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "170926" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# flatten the list\n", + "fpgnzd = list(itertools.chain(*pgnzd))\n", + "len(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert shapes to a GeoDataFrame\n", + "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", + "shapes_gdf.to_parquet(NUTS_dir.joinpath('CoastalMask_3035_polygonized_NUTS.parquet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GISCO_IDCNTR_CODELAU_IDLAU_NAMEPOP_2020POP_DENS_2AREA_KM2YEARFIDgeometry
0CZ_579475CZ579475Libňatov363.062.2401585.8322472020CZ_579475b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00...
1CZ_579483CZ579483Libotov178.041.7747594.2609462020CZ_579483b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x08\\x00...
2CZ_579491CZ579491Vranov181.037.4088734.8384242020CZ_579491b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\n\\x00\\x...
3CZ_579505CZ579505Malá Úpa142.05.31851026.6992072020CZ_579505b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x17\\x00...
4AT_41004AT41004Eggendorf im Traunkreis1041.0116.4817478.9370232020AT_41004b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00...
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98608SK_599816SK599816Košice - mestská časť Nad jazerom24443.06686.2115513.6557322020SK_599816b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\t\\x00\\x...
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98611SK_599859SK599859Košice - mestská časť Poľov1219.094.04951112.9612582020SK_599859b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0e\\x00...
98612SK_599875SK599875Košice - mestská časť Sídlisko Ťahanovce21936.02655.8225238.2595882020SK_599875b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x12\\x00...
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NUTS_IDLEVL_CODECNTR_CODENAME_LATNNUTS_NAMEMOUNT_TYPEURBN_TYPECOAST_TYPEgeometry
0AL0ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
1CZ0CZ?esko?esko0.000POLYGON ((4624843.654 3112209.741, 4625546.618...
2DE0DEDeutschlandDeutschland0.000MULTIPOLYGON (((4355225.365 2715902.993, 43541...
3DK0DKDanmarkDanmark0.000MULTIPOLYGON (((4650502.736 3591342.844, 46503...
4CY0CYb'4bfd70726f73'??????0.000MULTIPOLYGON (((6527040.718 1762367.593, 65267...
..............................
2005NO0B13NOJan MayenJan Mayen3.031POLYGON ((3623747.621 5400386.841, 3624031.138...
2006EE0093EEKesk-EestiKesk-Eesti4.031MULTIPOLYGON (((5216227.688 4159212.769, 52172...
2007NO01NONorgeNorge0.000MULTIPOLYGON (((4961367.759 5413266.131, 49622...
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98613 rows × 10 columns

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921 rows × 9 columns

\n", + "
" ], "text/plain": [ - "" + " NUTS_ID LEVL_CODE CNTR_CODE \\\n", + "0 AL 0 AL \n", + "49 AL0 1 AL \n", + "153 AL03 2 AL \n", + "363 AL035 3 AL \n", + "10 EL 0 EL \n", + "... ... ... ... \n", + "328 PT20 2 PT \n", + "1825 PT200 3 PT \n", + "230 PT3 1 PT \n", + "329 PT30 2 PT \n", + "1826 PT300 3 PT \n", + "\n", + " NAME_LATN \\\n", + "0 b'5368716970eb726961' \n", + "49 b'5368716970eb726961' \n", + "153 Jug \n", + "363 b'566c6f72eb' \n", + "10 b'456c6ce16461' \n", + "... ... \n", + "328 b'52656769e36f20417574f36e6f6d6120646f732041e7... \n", + "1825 b'52656769e36f20417574f36e6f6d6120646f732041e7... \n", + "230 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "329 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "1826 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "\n", + " NUTS_NAME MOUNT_TYPE \\\n", + "0 b'5368716970eb726961' 0.0 \n", + "49 b'5368716970eb726961' 0.0 \n", + "153 Jug 0.0 \n", + "363 b'566c6f72eb' 4.0 \n", + "10 ?????? 0.0 \n", + "... ... ... \n", + "328 b'52656769e36f20417574f36e6f6d6120646f732041e7... 0.0 \n", + "1825 b'52656769e36f20417574f36e6f6d6120646f732041e7... 2.0 \n", + "230 b'52656769e36f20417574f36e6f6d61206461204d6164... 0.0 \n", + "329 b'52656769e36f20417574f36e6f6d61206461204d6164... 0.0 \n", + "1826 b'52656769e36f20417574f36e6f6d61206461204d6164... 3.0 \n", + "\n", + " URBN_TYPE COAST_TYPE geometry \n", + "0 0 0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", + "49 0 0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", + "153 0 0 MULTIPOLYGON (((5211962.696 2061668.541, 52149... \n", + "363 2 1 MULTIPOLYGON (((5117808.521 1998738.510, 51189... \n", + "10 0 0 MULTIPOLYGON (((6083864.252 1676459.388, 60836... \n", + "... ... ... ... \n", + "328 0 0 MULTIPOLYGON (((1282493.513 2380255.965, 12840... \n", + "1825 2 1 MULTIPOLYGON (((1282493.513 2380255.965, 12840... \n", + "230 0 0 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "329 0 0 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "1826 1 1 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "\n", + "[921 rows x 9 columns]" ] }, - "execution_count": 18, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import shapely\n", - "shapely.Polygon(LAU.geometry[0])" + "# Perform a spatial join to find intersections\n", + "NUTS_reproj = NUTS.set_crs(\"EPSG:3035\")\n", + "joined_gdf = gpd.sjoin(NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", + "\n", + "# remove columns added by shapes_gdf\n", + "joined_gdf.drop(columns=['index_right', 'value'])\n", + "\n", + "# Remove duplicate rows based on the index of gdf1\n", + "NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", + "\n", + "# Remove extra columns resulting from the spatial join\n", + "NUTS_CLEANCM = NUTS_CLEANCM[NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "NUTS_CLEANCM" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "1514" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# testern = NUTS[NUTS.LEVL_CODE == 3]\n", + "# len(testern)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "610" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tester = NUTS_CLEANCM[NUTS_CLEANCM.LEVL_CODE == 3]\n", + "# len(tester)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Write geodataframe to parquet\n", + "out_file= NUTS_dir.joinpath('NUTS_RG_01M_2021_3035_CM.parquet')\n", + "\n", + "# altered_lau_data.to_parquet(out_file_altered)\n", + "NUTS_CLEANCM.to_parquet(out_file)" + ] } ], "metadata": { diff --git a/scripts/create_stacs/99_LAU_CM_stacs.py b/scripts/create_stacs/99_LAU_CM_stacs.py new file mode 100644 index 0000000000..28c47ded72 --- /dev/null +++ b/scripts/create_stacs/99_LAU_CM_stacs.py @@ -0,0 +1,536 @@ +# %% +# ## Load software +import sys + +branch = "dev" +sys.path.insert(0, "../src") + +from coastmonitor.io.drive_config import configure_instance + +is_local_instance = configure_instance(branch=branch) + +import dataclasses +import datetime +import logging +import os +import cv2 +import pathlib +import re +import json +import pyarrow +import gcsfs +import geopandas as gpd +import pandas as pd +import numpy as np +from typing import Any + +import fsspec +import pystac +import stac_geoparquet +from typing import List +from posixpath import join as urljoin +from dotenv import load_dotenv +from pystac.stac_io import DefaultStacIO + +from coclicodata.etl.cloud_utils import ( + load_google_credentials, + dir_to_google_cloud, + file_to_google_cloud, +) +from coclicodata.drive_config import p_drive +from coclicodata.coclico_stac.reshape_im import reshape_aspectratio_image + +from coastmonitor import stac_table +from coastmonitor.stac.layouts import ParquetLayout + +# %% +# ## Define variables +# hard-coded input params at project level +GCS_PROTOCOL = "https://storage.googleapis.com" +GCS_PROJECT = "coclico-11207608-002" +BUCKET_NAME = "coclico-data-public" +BUCKET_PROJ = "coclico" +PROJ_NAME = "LAU_CM" + +# hard-coded STAC templates +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent + +# hard-coded input params which differ per dataset +DATASET_DIR = "XX_LAU" +# CF_FILE = "Global_merit_coastal_mask_landwards.tif" +COLLECTION_ID = "LAU_CM" # name of stac collection +MAX_FILE_SIZE = 500 # max file size in MB + +# define local directories +home = pathlib.Path().home() +tmp_dir = home.joinpath("data", "tmp") +coclico_data_dir = p_drive.joinpath( + "11207608-coclico", "FASTTRACK_DATA" +) # remote p drive +cred_data_dir = p_drive.joinpath("11207608-coclico", "FASTTRACK_DATA") + +# use local or remote data dir +use_local_data = False + +if use_local_data: + ds_dir = tmp_dir +else: + ds_dir = coclico_data_dir + +if not ds_dir.exists(): + raise FileNotFoundError(f"Data dir does not exist, {str(ds_dir)}") + +# # directory to export result +# cog_dirs = ds_dir.joinpath("cogs") +ds_path = ds_dir.joinpath("XX_LAU") +ds_fp = ds_path.joinpath("LAU_2020_NUTS_2021_01M_3035_CM.parquet") # file directory + +# # load metadata template +metadata_fp = ds_path.joinpath("metadata", ds_fp.name).with_suffix(".json") +with open(metadata_fp, "r") as f: + metadata = json.load(f) + +# # extend keywords +metadata["KEYWORDS"].extend(["Full-Track", "Background Layers"]) + +# # data output configurations +HREF_PREFIX = urljoin( + GCS_PROTOCOL, BUCKET_NAME, BUCKET_PROJ, PROJ_NAME +) # cloud export directory +TMP_DIR = pathlib.Path.home() / "tmp" + +PARQUET_MEDIA_TYPE = "application/vnd.apache.parquet" + +# CONTAINER_NAME = "transects" +# PREFIX = f"gcts-{TRANSECT_LENGTH}m.parquet" +# BASE_URL = f"gs://{CONTAINER_NAME}/{PREFIX}" +GEOPARQUET_STAC_ITEMS_HREF = ( + f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{COLLECTION_ID}/{ds_fp.name}" +) + + +# %% +# %% +def read_parquet_schema_df(uri: str) -> List: # pd.DataFrame: + """Return a Pandas dataframe corresponding to the schema of a local URI of a parquet file. + + The returned dataframe has the columns: column, pa_dtype + """ + # Ref: https://stackoverflow.com/a/64288036/ + # Ref: https://stackoverflow.com/questions/41567081/get-schema-of-parquet-file-in-python + schema = pyarrow.parquet.read_schema(uri, memory_map=True) + # schema = pd.DataFrame(({"name": name, "type": str(pa_dtype)} for name, pa_dtype in zip(schema.names, schema.types))) + schema = [ + { + "name": name, + "type": str(pa_dtype), + "description": "", + } # TODO: add column descriptions once received from the VU + for name, pa_dtype in zip(schema.names, schema.types) + ] + # schema = schema.reindex(columns=["name", "type"], fill_value=pd.NA) # Ensures columns in case the parquet file has an empty dataframe. + return schema + + +def partition_dataframe(df: pd.DataFrame, batch_size: int) -> list[pd.DataFrame]: + """ + Splits a DataFrame into partitions approximately equal to or smaller than the specified batch size. + + Args: + df (pd.DataFrame): The DataFrame to be partitioned. + batch_size (int): The maximum number of rows each partition should have. + + Returns: + List[pd.DataFrame]: A list of DataFrames, each with a size up to the specified batch size. + """ + n_rows = len(df) + if n_rows <= batch_size: + return [df] + + num_partitions = (n_rows + batch_size - 1) // batch_size + partition_size = (n_rows + num_partitions - 1) // num_partitions + + partitions = [ + df.iloc[i : i + partition_size] for i in range(0, n_rows, partition_size) + ] + return partitions + + +@dataclasses.dataclass +class PathParts: + """ + Parses a path into its component parts, supporting variations with and without hive partitioning, + and with and without geographical bounds. + """ + + path: str + container: str | None = None + prefix: str | None = None + name: str | None = None + stac_item_id: str | None = None + + def __post_init__(self) -> None: + # Strip any protocol pattern like "xyz://" + stripped_path = re.sub(r"^\w+://", "", self.path) + split = stripped_path.rstrip("/").split("/") + + # Extract container + self.container = split[0] + + # Determine if there is hive partitioning and extract it + hive_partition_info = [ + "_".join(part.split("=")) for part in split[1:-1] if "=" in part + ] + + # Extract name, which is the filename with the .parquet extension + self.name = split[-1] + + # Construct the stac_item_id + # Include hive partitioning info if present, then add the file name, replacing ".parquet" and ensuring underscores + parts_to_join = hive_partition_info + [self.name.replace(".parquet", "")] + self.stac_item_id = "_".join(parts_to_join) + + +def create_collection( + description: str | None = None, extra_fields: dict[str, Any] | None = None +) -> pystac.Collection: + + providers = [ + pystac.Provider( + name=metadata["PROVIDERS"]["name"], + roles=[ + pystac.provider.ProviderRole.PRODUCER, + pystac.provider.ProviderRole.LICENSOR, + ], + url=metadata["PROVIDERS"]["url"], + ), + pystac.Provider( + name="Deltares", + roles=[ + pystac.provider.ProviderRole.PROCESSOR, + pystac.provider.ProviderRole.HOST, + ], + url="https://deltares.nl", + ), + ] + + start_datetime = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + + extent = pystac.Extent( + pystac.SpatialExtent([metadata["SPATIAL_EXTENT"]]), + pystac.TemporalExtent([[start_datetime, None]]), + ) + + # double check, this is hard-coded! + # links = [ + # pystac.Link( + # pystac.RelType.LICENSE, + # target="https://creativecommons.org/publicdomain/zero/1.0/", + # media_type="text/html", + # title="CC License", + # ) + # ] + + if "Creative Commons" in metadata["LICENSE"] and "4.0" in metadata["LICENSE"]: + metadata["LICENSE"] = "CC-BY-4.0" + + collection = pystac.Collection( + id=COLLECTION_ID, + title=metadata["TITLE"], + description=metadata["DESCRIPTION"], + license=metadata["LICENSE"], + providers=providers, + extent=extent, + catalog_type=pystac.CatalogType.RELATIVE_PUBLISHED, + ) + + collection.add_asset( + "thumbnail", + pystac.Asset( + f"https://storage.googleapis.com/coclico-data-public/coclico/assets/thumbnails/{COLLECTION_ID}.jpeg", + title="Thumbnail", + media_type=pystac.MediaType.JPEG, + ), + ) + # collection.links = links + collection.keywords = metadata["KEYWORDS"] + + pystac.extensions.item_assets.ItemAssetsExtension.add_to(collection) + + collection.extra_fields["item_assets"] = { + "data": { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + "roles": ["data"], + "type": stac_table.PARQUET_MEDIA_TYPE, + **ASSET_EXTRA_FIELDS, + } + } + + if extra_fields: + collection.extra_fields.update(extra_fields) + + pystac.extensions.scientific.ScientificExtension.add_to(collection) + collection.extra_fields["sci:citation"] = metadata["CITATION"] + + collection.stac_extensions.append(stac_table.SCHEMA_URI) + + pystac.extensions.version.VersionExtension.add_to(collection) + collection.extra_fields["version"] = "1" + + return collection + + +def create_item( + asset_href: str, + storage_options: dict[str, Any] | None = None, + asset_extra_fields: dict[str, Any] | None = None, +) -> pystac.Item: + """Create a STAC Item + + For + + Args: + asset_href (str): The HREF pointing to an asset associated with the item + + Returns: + Item: STAC Item object + """ + + parts = PathParts(asset_href) + + properties = { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + } + + dt = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + # shape = shapely.box(*bbox) + # geometry = shapely.geometry.mapping(shape) + template = pystac.Item( + id=parts.stac_item_id, + properties=properties, + geometry=None, + bbox=None, + datetime=dt, + stac_extensions=[], + ) + + item = stac_table.generate( + uri=asset_href, + template=template, + infer_bbox=True, + infer_geometry=None, + datetime_column=None, + infer_datetime=stac_table.InferDatetimeOptions.no, + count_rows=True, + asset_key="data", + asset_extra_fields=asset_extra_fields, + proj=True, + storage_options=storage_options, + validate=False, + ) + assert isinstance(item, pystac.Item) + + item.common_metadata.created = datetime.datetime.utcnow() + + # add descriptions to item properties + if "table:columns" in ASSET_EXTRA_FIELDS and "table:columns" in item.properties: + source_lookup = { + col["name"]: col for col in ASSET_EXTRA_FIELDS["table:columns"] + } + + for target_col in item.properties["table:columns"]: + source_col = source_lookup.get(target_col["name"]) + if source_col: + target_col.setdefault("description", source_col.get("description")) + + # TODO: make configurable upstream + item.assets["data"].title = metadata["TITLE_ABBREVIATION"] + item.assets["data"].description = metadata["SHORT_DESCRIPTION"] + + return item + + +# %% +# ## Do the work +if __name__ == "__main__": + log = logging.getLogger() + log.setLevel(logging.ERROR) + + # loading credentials + load_google_credentials( + google_token_fp=cred_data_dir.joinpath("google_credentials_new.json") + ) + + # %% test if file is multi-indexed, if we need to write to the cloud and whether we need to split files + dum = gpd.read_parquet(ds_fp) # read parquet file + split = "N" # value to determine if we need to split the files + for file in os.listdir(ds_path): + if os.path.getsize(ds_path.joinpath(file)) / 10**6 < MAX_FILE_SIZE: + split = "Y" # change slit to Yes + break + + # bucket content + uri = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{PROJ_NAME}" + # storage_options = {"account_name": "coclico", "credential": sas_token} + # fs, token, [root] = fsspec.get_fs_token_paths(uri, storage_options=storage_options) + fs = gcsfs.GCSFileSystem( + gcs_project=GCS_PROJECT, token=os.environ["GOOGLE_APPLICATION_CREDENTIALS"] + ) + paths = fs.glob(uri + "/*.parquet") + uris = ["gs://" + p for p in paths] + + # TODO: build something in for assessing size of parquet data, do this in both the if and elif statements + # if ( + # dum.index.nlevels > 1 or split == "Y" + # ) and paths == []: # if multi-indexed or split and there is nothing in the cloud + # files = os.listdir(ds_path) # list all files in the directory + # files_clean = [k for k in files if ".parquet" in k] # only select parquet files + + # for file in files_clean: + # print(file) + # file_size = os.path.getsize(ds_path.joinpath(file)) / 10**6 + + # if file_size < MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + + # # write to the cloud, single file + # dspd.to_parquet( + # f"{uri}/{file}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif file_size > MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + + # batch_size = int( + # np.ceil(len(dspd) / np.ceil(file_size / MAX_FILE_SIZE)) + # ) # calc batch size (max number of rows per partition) + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + # splitted_dspd = partition_dataframe(dspd, batch_size) # calc partitions + + # # write to the cloud, all split files + # for idx, split_dspd in enumerate(splitted_dspd): + # file_name = ( + # file.split(".")[0] + # + "_{:02d}.".format(idx + 1) + # + file.split(".")[1] + # ) # add zero-padded index (+1 to start at 1) to file name + # split_dspd.to_parquet( + # f"{uri}/{file_name}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif ( + # dum.index.nlevels == 1 and split == "N" and paths == [] + # ): # if not multi-indexed and no need to split and cloud file does not exist + + # upload directory to the cloud (files already parquet) + file_to_google_cloud( + file_path=str(ds_fp), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + file_name=ds_fp.name, + ) + + # %% get descriptions + COLUMN_DESCRIPTIONS = read_parquet_schema_df( + uris[0] + ) # select first file of the cloud directory + + ASSET_EXTRA_FIELDS = { + "table:storage_options": {"account_name": "coclico"}, + "table:columns": COLUMN_DESCRIPTIONS, + } + + # %% add to STAC + catalog = pystac.Catalog.from_file(str(STAC_DIR / "catalog.json")) + + stac_io = DefaultStacIO() + layout = ParquetLayout() + + collection = create_collection(extra_fields={"base_url": uri}) + + for uri in uris: + GCS_url = urljoin(HREF_PREFIX, uri.split("/")[-1]) + print(GCS_url) + item = create_item(uri) + item.assets["data"].href = GCS_url # replace with https link iso gs uri + collection.add_item(item) + + collection.update_extent_from_items() + + items = list(collection.get_all_items()) + items_as_json = [i.to_dict() for i in items] + item_extents = stac_geoparquet.to_geodataframe(items_as_json) + + with fsspec.open(GEOPARQUET_STAC_ITEMS_HREF, mode="wb") as f: + item_extents.to_parquet(f) + + collection.add_asset( + "geoparquet-stac-items", + pystac.Asset( + GEOPARQUET_STAC_ITEMS_HREF, + title="GeoParquet STAC items", + description="Snapshot of the collection's STAC items exported to GeoParquet format.", + media_type=PARQUET_MEDIA_TYPE, + roles=["data"], + ), + ) + + # Set thumbnail directory + THUMB_DIR = pathlib.Path(__file__).parent.parent.joinpath("thumbnails") + THUMB_FILE = THUMB_DIR.joinpath(COLLECTION_ID + ".png") + + # Make sure image is reshaped to desired aspect ratio (default = 16/9) + cropped_im = reshape_aspectratio_image(str(THUMB_FILE)) + + # Overwrite image with cropped version + cv2.imwrite(str(THUMB_FILE), cropped_im) + + # Upload thumbnail to cloud + THUMB_URL = file_to_google_cloud( + str(THUMB_FILE), + GCS_PROJECT, + BUCKET_NAME, + BUCKET_PROJ, + "assets/thumbnails", + THUMB_FILE.name, + return_URL=True, + ) + + # Add thumbnail + collection.add_asset( + "thumbnail", + pystac.Asset( + THUMB_URL, # noqa: E501 + title="Thumbnail", + media_type=pystac.MediaType.PNG, + ), + ) + + if catalog.get_child(collection.id): + catalog.remove_child(collection.id) + print(f"Removed child: {collection.id}.") + + catalog.add_child(collection) + + collection.normalize_hrefs(str(STAC_DIR / collection.id), layout) + + collection.validate_all() + + catalog.save( + catalog_type=pystac.CatalogType.SELF_CONTAINED, + dest_href=str(STAC_DIR), + stac_io=stac_io, + ) + +# %% diff --git a/scripts/create_stacs/99_NUTS_CM_stacs.py b/scripts/create_stacs/99_NUTS_CM_stacs.py new file mode 100644 index 0000000000..d27d458dfa --- /dev/null +++ b/scripts/create_stacs/99_NUTS_CM_stacs.py @@ -0,0 +1,536 @@ +# %% +# ## Load software +import sys + +branch = "dev" +sys.path.insert(0, "../src") + +from coastmonitor.io.drive_config import configure_instance + +is_local_instance = configure_instance(branch=branch) + +import dataclasses +import datetime +import logging +import os +import cv2 +import pathlib +import re +import json +import pyarrow +import gcsfs +import geopandas as gpd +import pandas as pd +import numpy as np +from typing import Any + +import fsspec +import pystac +import stac_geoparquet +from typing import List +from posixpath import join as urljoin +from dotenv import load_dotenv +from pystac.stac_io import DefaultStacIO + +from coclicodata.etl.cloud_utils import ( + load_google_credentials, + dir_to_google_cloud, + file_to_google_cloud, +) +from coclicodata.drive_config import p_drive +from coclicodata.coclico_stac.reshape_im import reshape_aspectratio_image + +from coastmonitor import stac_table +from coastmonitor.stac.layouts import ParquetLayout + +# %% +# ## Define variables +# hard-coded input params at project level +GCS_PROTOCOL = "https://storage.googleapis.com" +GCS_PROJECT = "coclico-11207608-002" +BUCKET_NAME = "coclico-data-public" +BUCKET_PROJ = "coclico" +PROJ_NAME = "NUTS_CM" + +# hard-coded STAC templates +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent + +# hard-coded input params which differ per dataset +DATASET_DIR = "XX_NUTS" +# CF_FILE = "Global_merit_coastal_mask_landwards.tif" +COLLECTION_ID = "NUTS_CM" # name of stac collection +MAX_FILE_SIZE = 500 # max file size in MB + +# define local directories +home = pathlib.Path().home() +tmp_dir = home.joinpath("data", "tmp") +coclico_data_dir = p_drive.joinpath( + "11207608-coclico", "FASTTRACK_DATA" +) # remote p drive +cred_data_dir = p_drive.joinpath("11207608-coclico", "FASTTRACK_DATA") + +# use local or remote data dir +use_local_data = False + +if use_local_data: + ds_dir = tmp_dir +else: + ds_dir = coclico_data_dir + +if not ds_dir.exists(): + raise FileNotFoundError(f"Data dir does not exist, {str(ds_dir)}") + +# # directory to export result +# cog_dirs = ds_dir.joinpath("cogs") +ds_path = ds_dir.joinpath("XX_NUTS") +ds_fp = ds_path.joinpath("NUTS_RG_01M_2021_3035_CM.parquet") # file directory + +# # load metadata template +metadata_fp = ds_path.joinpath("metadata", ds_fp.name).with_suffix(".json") +with open(metadata_fp, "r") as f: + metadata = json.load(f) + +# # extend keywords +metadata["KEYWORDS"].extend(["Full-Track", "Background Layers"]) + +# # data output configurations +HREF_PREFIX = urljoin( + GCS_PROTOCOL, BUCKET_NAME, BUCKET_PROJ, PROJ_NAME +) # cloud export directory +TMP_DIR = pathlib.Path.home() / "tmp" + +PARQUET_MEDIA_TYPE = "application/vnd.apache.parquet" + +# CONTAINER_NAME = "transects" +# PREFIX = f"gcts-{TRANSECT_LENGTH}m.parquet" +# BASE_URL = f"gs://{CONTAINER_NAME}/{PREFIX}" +GEOPARQUET_STAC_ITEMS_HREF = ( + f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{COLLECTION_ID}/{ds_fp.name}" +) + + +# %% +# %% +def read_parquet_schema_df(uri: str) -> List: # pd.DataFrame: + """Return a Pandas dataframe corresponding to the schema of a local URI of a parquet file. + + The returned dataframe has the columns: column, pa_dtype + """ + # Ref: https://stackoverflow.com/a/64288036/ + # Ref: https://stackoverflow.com/questions/41567081/get-schema-of-parquet-file-in-python + schema = pyarrow.parquet.read_schema(uri, memory_map=True) + # schema = pd.DataFrame(({"name": name, "type": str(pa_dtype)} for name, pa_dtype in zip(schema.names, schema.types))) + schema = [ + { + "name": name, + "type": str(pa_dtype), + "description": "", + } # TODO: add column descriptions once received from the VU + for name, pa_dtype in zip(schema.names, schema.types) + ] + # schema = schema.reindex(columns=["name", "type"], fill_value=pd.NA) # Ensures columns in case the parquet file has an empty dataframe. + return schema + + +def partition_dataframe(df: pd.DataFrame, batch_size: int) -> list[pd.DataFrame]: + """ + Splits a DataFrame into partitions approximately equal to or smaller than the specified batch size. + + Args: + df (pd.DataFrame): The DataFrame to be partitioned. + batch_size (int): The maximum number of rows each partition should have. + + Returns: + List[pd.DataFrame]: A list of DataFrames, each with a size up to the specified batch size. + """ + n_rows = len(df) + if n_rows <= batch_size: + return [df] + + num_partitions = (n_rows + batch_size - 1) // batch_size + partition_size = (n_rows + num_partitions - 1) // num_partitions + + partitions = [ + df.iloc[i : i + partition_size] for i in range(0, n_rows, partition_size) + ] + return partitions + + +@dataclasses.dataclass +class PathParts: + """ + Parses a path into its component parts, supporting variations with and without hive partitioning, + and with and without geographical bounds. + """ + + path: str + container: str | None = None + prefix: str | None = None + name: str | None = None + stac_item_id: str | None = None + + def __post_init__(self) -> None: + # Strip any protocol pattern like "xyz://" + stripped_path = re.sub(r"^\w+://", "", self.path) + split = stripped_path.rstrip("/").split("/") + + # Extract container + self.container = split[0] + + # Determine if there is hive partitioning and extract it + hive_partition_info = [ + "_".join(part.split("=")) for part in split[1:-1] if "=" in part + ] + + # Extract name, which is the filename with the .parquet extension + self.name = split[-1] + + # Construct the stac_item_id + # Include hive partitioning info if present, then add the file name, replacing ".parquet" and ensuring underscores + parts_to_join = hive_partition_info + [self.name.replace(".parquet", "")] + self.stac_item_id = "_".join(parts_to_join) + + +def create_collection( + description: str | None = None, extra_fields: dict[str, Any] | None = None +) -> pystac.Collection: + + providers = [ + pystac.Provider( + name=metadata["PROVIDERS"]["name"], + roles=[ + pystac.provider.ProviderRole.PRODUCER, + pystac.provider.ProviderRole.LICENSOR, + ], + url=metadata["PROVIDERS"]["url"], + ), + pystac.Provider( + name="Deltares", + roles=[ + pystac.provider.ProviderRole.PROCESSOR, + pystac.provider.ProviderRole.HOST, + ], + url="https://deltares.nl", + ), + ] + + start_datetime = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + + extent = pystac.Extent( + pystac.SpatialExtent([metadata["SPATIAL_EXTENT"]]), + pystac.TemporalExtent([[start_datetime, None]]), + ) + + # double check, this is hard-coded! + # links = [ + # pystac.Link( + # pystac.RelType.LICENSE, + # target="https://creativecommons.org/publicdomain/zero/1.0/", + # media_type="text/html", + # title="CC License", + # ) + # ] + + if "Creative Commons" in metadata["LICENSE"] and "4.0" in metadata["LICENSE"]: + metadata["LICENSE"] = "CC-BY-4.0" + + collection = pystac.Collection( + id=COLLECTION_ID, + title=metadata["TITLE"], + description=metadata["DESCRIPTION"], + license=metadata["LICENSE"], + providers=providers, + extent=extent, + catalog_type=pystac.CatalogType.RELATIVE_PUBLISHED, + ) + + collection.add_asset( + "thumbnail", + pystac.Asset( + f"https://storage.googleapis.com/coclico-data-public/coclico/assets/thumbnails/{COLLECTION_ID}.jpeg", + title="Thumbnail", + media_type=pystac.MediaType.JPEG, + ), + ) + # collection.links = links + collection.keywords = metadata["KEYWORDS"] + + pystac.extensions.item_assets.ItemAssetsExtension.add_to(collection) + + collection.extra_fields["item_assets"] = { + "data": { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + "roles": ["data"], + "type": stac_table.PARQUET_MEDIA_TYPE, + **ASSET_EXTRA_FIELDS, + } + } + + if extra_fields: + collection.extra_fields.update(extra_fields) + + pystac.extensions.scientific.ScientificExtension.add_to(collection) + collection.extra_fields["sci:citation"] = metadata["CITATION"] + + collection.stac_extensions.append(stac_table.SCHEMA_URI) + + pystac.extensions.version.VersionExtension.add_to(collection) + collection.extra_fields["version"] = "1" + + return collection + + +def create_item( + asset_href: str, + storage_options: dict[str, Any] | None = None, + asset_extra_fields: dict[str, Any] | None = None, +) -> pystac.Item: + """Create a STAC Item + + For + + Args: + asset_href (str): The HREF pointing to an asset associated with the item + + Returns: + Item: STAC Item object + """ + + parts = PathParts(asset_href) + + properties = { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + } + + dt = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + # shape = shapely.box(*bbox) + # geometry = shapely.geometry.mapping(shape) + template = pystac.Item( + id=parts.stac_item_id, + properties=properties, + geometry=None, + bbox=None, + datetime=dt, + stac_extensions=[], + ) + + item = stac_table.generate( + uri=asset_href, + template=template, + infer_bbox=True, + infer_geometry=None, + datetime_column=None, + infer_datetime=stac_table.InferDatetimeOptions.no, + count_rows=True, + asset_key="data", + asset_extra_fields=asset_extra_fields, + proj=True, + storage_options=storage_options, + validate=False, + ) + assert isinstance(item, pystac.Item) + + item.common_metadata.created = datetime.datetime.utcnow() + + # add descriptions to item properties + if "table:columns" in ASSET_EXTRA_FIELDS and "table:columns" in item.properties: + source_lookup = { + col["name"]: col for col in ASSET_EXTRA_FIELDS["table:columns"] + } + + for target_col in item.properties["table:columns"]: + source_col = source_lookup.get(target_col["name"]) + if source_col: + target_col.setdefault("description", source_col.get("description")) + + # TODO: make configurable upstream + item.assets["data"].title = metadata["TITLE_ABBREVIATION"] + item.assets["data"].description = metadata["SHORT_DESCRIPTION"] + + return item + + +# %% +# ## Do the work +if __name__ == "__main__": + log = logging.getLogger() + log.setLevel(logging.ERROR) + + # loading credentials + load_google_credentials( + google_token_fp=cred_data_dir.joinpath("google_credentials_new.json") + ) + + # %% test if file is multi-indexed, if we need to write to the cloud and whether we need to split files + dum = gpd.read_parquet(ds_fp) # read parquet file + split = "N" # value to determine if we need to split the files + for file in os.listdir(ds_path): + if os.path.getsize(ds_path.joinpath(file)) / 10**6 < MAX_FILE_SIZE: + split = "Y" # change slit to Yes + break + + # bucket content + uri = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{PROJ_NAME}" + # storage_options = {"account_name": "coclico", "credential": sas_token} + # fs, token, [root] = fsspec.get_fs_token_paths(uri, storage_options=storage_options) + fs = gcsfs.GCSFileSystem( + gcs_project=GCS_PROJECT, token=os.environ["GOOGLE_APPLICATION_CREDENTIALS"] + ) + paths = fs.glob(uri + "/*.parquet") + uris = ["gs://" + p for p in paths] + + # TODO: build something in for assessing size of parquet data, do this in both the if and elif statements + # if ( + # dum.index.nlevels > 1 or split == "Y" + # ) and paths == []: # if multi-indexed or split and there is nothing in the cloud + # files = os.listdir(ds_path) # list all files in the directory + # files_clean = [k for k in files if ".parquet" in k] # only select parquet files + + # for file in files_clean: + # print(file) + # file_size = os.path.getsize(ds_path.joinpath(file)) / 10**6 + + # if file_size < MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + + # # write to the cloud, single file + # dspd.to_parquet( + # f"{uri}/{file}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif file_size > MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + + # batch_size = int( + # np.ceil(len(dspd) / np.ceil(file_size / MAX_FILE_SIZE)) + # ) # calc batch size (max number of rows per partition) + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + # splitted_dspd = partition_dataframe(dspd, batch_size) # calc partitions + + # # write to the cloud, all split files + # for idx, split_dspd in enumerate(splitted_dspd): + # file_name = ( + # file.split(".")[0] + # + "_{:02d}.".format(idx + 1) + # + file.split(".")[1] + # ) # add zero-padded index (+1 to start at 1) to file name + # split_dspd.to_parquet( + # f"{uri}/{file_name}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif ( + # dum.index.nlevels == 1 and split == "N" and paths == [] + # ): # if not multi-indexed and no need to split and cloud file does not exist + + # upload directory to the cloud (files already parquet) + file_to_google_cloud( + file_path=str(ds_fp), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + file_name=ds_fp.name, + ) + + # %% get descriptions + COLUMN_DESCRIPTIONS = read_parquet_schema_df( + uris[0] + ) # select first file of the cloud directory + + ASSET_EXTRA_FIELDS = { + "table:storage_options": {"account_name": "coclico"}, + "table:columns": COLUMN_DESCRIPTIONS, + } + + # %% add to STAC + catalog = pystac.Catalog.from_file(str(STAC_DIR / "catalog.json")) + + stac_io = DefaultStacIO() + layout = ParquetLayout() + + collection = create_collection(extra_fields={"base_url": uri}) + + for uri in uris: + GCS_url = urljoin(HREF_PREFIX, uri.split("/")[-1]) + print(GCS_url) + item = create_item(uri) + item.assets["data"].href = GCS_url # replace with https link iso gs uri + collection.add_item(item) + + collection.update_extent_from_items() + + items = list(collection.get_all_items()) + items_as_json = [i.to_dict() for i in items] + item_extents = stac_geoparquet.to_geodataframe(items_as_json) + + with fsspec.open(GEOPARQUET_STAC_ITEMS_HREF, mode="wb") as f: + item_extents.to_parquet(f) + + collection.add_asset( + "geoparquet-stac-items", + pystac.Asset( + GEOPARQUET_STAC_ITEMS_HREF, + title="GeoParquet STAC items", + description="Snapshot of the collection's STAC items exported to GeoParquet format.", + media_type=PARQUET_MEDIA_TYPE, + roles=["data"], + ), + ) + + # Set thumbnail directory + THUMB_DIR = pathlib.Path(__file__).parent.parent.joinpath("thumbnails") + THUMB_FILE = THUMB_DIR.joinpath(COLLECTION_ID + ".png") + + # Make sure image is reshaped to desired aspect ratio (default = 16/9) + cropped_im = reshape_aspectratio_image(str(THUMB_FILE)) + + # Overwrite image with cropped version + cv2.imwrite(str(THUMB_FILE), cropped_im) + + # Upload thumbnail to cloud + THUMB_URL = file_to_google_cloud( + str(THUMB_FILE), + GCS_PROJECT, + BUCKET_NAME, + BUCKET_PROJ, + "assets/thumbnails", + THUMB_FILE.name, + return_URL=True, + ) + + # Add thumbnail + collection.add_asset( + "thumbnail", + pystac.Asset( + THUMB_URL, # noqa: E501 + title="Thumbnail", + media_type=pystac.MediaType.PNG, + ), + ) + + if catalog.get_child(collection.id): + catalog.remove_child(collection.id) + print(f"Removed child: {collection.id}.") + + catalog.add_child(collection) + + collection.normalize_hrefs(str(STAC_DIR / collection.id), layout) + + collection.validate_all() + + catalog.save( + catalog_type=pystac.CatalogType.SELF_CONTAINED, + dest_href=str(STAC_DIR), + stac_io=stac_io, + ) + +# %% diff --git a/scripts/thumbnails/LAU_CM.png b/scripts/thumbnails/LAU_CM.png new file mode 100644 index 0000000000..3b2b4365cd Binary files /dev/null and b/scripts/thumbnails/LAU_CM.png differ diff --git a/scripts/thumbnails/NUTS_CM.png b/scripts/thumbnails/NUTS_CM.png new file mode 100644 index 0000000000..3b2b4365cd Binary files /dev/null and b/scripts/thumbnails/NUTS_CM.png differ