From 2b15d435af67728929dd57043bf946f131274753 Mon Sep 17 00:00:00 2001 From: amsnyder <asnyder@usgs.gov> Date: Tue, 9 Apr 2024 13:25:41 -0500 Subject: [PATCH] add full cprep collection --- catalog/collection.json | 44 + catalog/cprep/collection.json | 1524 +++++++++++++++++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ .../collection.json | 181 ++ 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.../cprep_create_collection_from_zarr.ipynb | 306 +++- 300 files changed, 56808 insertions(+), 11 deletions(-) create mode 100644 catalog/collection.json create mode 100644 catalog/cprep/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A12P01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A14D01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A14L01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A14P01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 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create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A24P01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A28D01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A28L01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A28P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A32D01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A32L01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A32P01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 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create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B10P01K00_historical_r6i1p1_I35Land_19810101-20051231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B12P01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B14D01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B14L01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B14P01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B18D01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B18L01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B18P01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B20D01K00_historical_r1i1p1_I35Land_19810101-20051231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B20L01K00_historical_r1i1p1_I35Land_19810101-20051231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B20P01K00_historical_r1i1p1_I35Land_19810101-20051231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B22D01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 catalog/cprep/cprep_pr_day_I35prp1-PARM-B22L01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json create mode 100644 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"engine": "zarr", + "consolidated": true + }, + "xarray:storage_options": { + "requester_pays": true + }, + "roles": [ + "data", + "zarr", + "s3" + ] + } + } +} \ No newline at end of file diff --git a/catalog/cprep/cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json b/catalog/cprep/cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json new file mode 100644 index 00000000..7fbe6cf9 --- /dev/null +++ b/catalog/cprep/cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json @@ -0,0 +1,187 @@ +{ + "type": "Collection", + "id": "cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231", + "stac_version": "1.0.0", + "description": "South Central Climate Projections Evaluation Project (C-PrEP)", + "links": [ + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json" + } + ], + "stac_extensions": [ + "https://stac-extensions.github.io/datacube/v2.2.0/schema.json" + ], + "cube:dimensions": { + "time": { + "type": "temporal", + "description": "time", + "extent": [ + "2006-01-01T12:00:00Z", + "2099-12-31T12:00:00Z" + ], + "step": "P1DT0H0M0S" + }, + "lon": { + "type": "spatial", + "axis": "x", + "description": "longitude", + "extent": [ + 251.05, + 269.95 + ], + "step": 0.1, + "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"Unknown based on WGS 84 ellipsoid\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563,\"id\":{\"authority\":\"EPSG\",\"code\":7030}}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]},\"remarks\":\"PROJ CRS string: +proj=longlat +ellps=WGS84 +lon_wrap=180 +no_defs\"}" + }, + "lat": { + "type": "spatial", + "axis": "y", + "description": "latitude", + "extent": [ + 26.05, + 39.95 + ], + "step": 0.1, + "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"Unknown based on WGS 84 ellipsoid\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563,\"id\":{\"authority\":\"EPSG\",\"code\":7030}}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]},\"remarks\":\"PROJ CRS string: +proj=longlat +ellps=WGS84 +lon_wrap=180 +no_defs\"}" + }, + "bnds": { + "type": "count", + "description": "bnds", + "extent": [ + 1.0, + 2.0 + ] + } + }, + "cube:variables": { + "crs": { + "dimensions": [], + "type": "auxiliary", + "description": null, + "unit": null + }, + "height": { + "dimensions": [], + "type": "data", + "description": "height", + "unit": "m" + }, + "i_offset": { + "dimensions": [], + "type": "data", + "description": null, + "unit": null + }, + "j_offset": { + "dimensions": [], + "type": "data", + "description": null, + "unit": null + }, + "lat_bnds": { + "dimensions": [ + "lat", + "bnds" + ], + "type": "data", + "description": null, + "unit": null + }, + "lon_bnds": { + "dimensions": [ + "lon", + "bnds" + ], + "type": "data", + "description": null, + "unit": null + }, + "tasmin": { + "dimensions": [ + "time", + "lat", + "lon" + ], + "type": "data", + "description": "Downscaled Daily Minimum Near-Surface Air Temperature", + "unit": "K" + }, + "time_bnds": { + "dimensions": [ + "time", + "bnds" + ], + "type": "data", + "description": "time axis boundaries", + "unit": null + } + }, + "extent": { + "spatial": { + "bbox": [ + [ + -108.94999999999999, + 26.05, + -90.05, + 39.95 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2006-01-01T12:00:00Z", + "2099-12-31T12:00:00Z" + ] + ] + } + }, + "license": "Unlicense", + "assets": { + "zarr-s3-osn": { + "href": "s3://mdmf/gdp/cprep/cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231.zarr/", + "type": "application/vnd+zarr", + "description": "Open Storage Network Pod S3 API access to collection zarr group", + "xarray:open_kwargs": { + "chunks": {}, + "engine": "zarr", + "consolidated": true + }, + "xarray:storage_options": { + "anon": true, + "client_kwargs": { + "endpoint_url": "https://usgs.osn.mghpcc.org/" + } + }, + "roles": [ + "data", + "zarr", + "s3" + ] + }, + "zarr-s3": { + "href": "s3://nhgf-development/workspace/DataConversion/gdp/cprep/cprep_tasmin_day_I35tnp1-EDQM-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231.zarr/", + "type": "application/vnd+zarr", + "description": "S3 access to collection zarr group", + "xarray:open_kwargs": { + "chunks": {}, + "engine": "zarr", + "consolidated": true + }, + "xarray:storage_options": { + "requester_pays": true + }, + "roles": [ + "data", + "zarr", + "s3" + ] + } + } +} \ No newline at end of file diff --git a/workflows/archive/cprep_create_collection_from_zarr.ipynb b/workflows/archive/cprep_create_collection_from_zarr.ipynb index df0be7b8..1ed26c7c 100644 --- a/workflows/archive/cprep_create_collection_from_zarr.ipynb +++ b/workflows/archive/cprep_create_collection_from_zarr.ipynb @@ -119,7 +119,10 @@ "outputs": [], "source": [ "# name for STAC collection\n", - "collection_id = os.path.splitext(zarr_list[100])[0]" + "# most of them look like this one\n", + "collection_id = os.path.splitext(zarr_list[100])[0]\n", + "# this one fails on decode times\n", + "collection_id = \"cprep_tasmax_day_I35txdetrp1-DeltaSD-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231\"" ] }, { @@ -197,8 +200,10 @@ "source": [ "# open and view zarr dataset\n", "fs2 = fsspec.filesystem('s3', anon=True, endpoint_url='https://usgs.osn.mghpcc.org/')\n", + "# ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n", + "# backend_kwargs={'consolidated':True}, chunks={})\n", "ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n", - " backend_kwargs={'consolidated':True}, chunks={})\n", + " backend_kwargs={'consolidated':True}, chunks={}, decode_times=False)\n", "ds" ] }, @@ -430,10 +435,10 @@ "outputs": [], "source": [ "# pull out first and last timestamps\n", - "# temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n", - "# temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n", - "temporal_extent_lower = ds.indexes[dim_names_dict['T']].to_datetimeindex().min()\n", - "temporal_extent_upper = ds.indexes[dim_names_dict['T']].to_datetimeindex().max()\n", + "temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n", + "temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n", + "# temporal_extent_lower = ds.indexes[dim_names_dict['T']].to_datetimeindex().min()\n", + "# temporal_extent_upper = ds.indexes[dim_names_dict['T']].to_datetimeindex().max()\n", "print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n", "# create a temporal extent object\n", "temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])" @@ -476,6 +481,60 @@ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "e56853f8-1cb1-46ea-b6ee-5c9295b20311", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#create cprep overall collection\n", + "# note: each dataset has the same spatial extent, so the extent extracted from one dataset is used here\n", + "# 241 ocurrences: \n", + "# min: 2006-01-01 12:00:00\n", + "# max: 2099-12-31 12:00:00\n", + "# 54 occurences:\n", + "# min: 1981-01-01 12:00:00 \n", + "# max: 2005-12-31 12:00:00\n", + "# 2 occurrences\n", + "# min: 1970-01-01 00:00:00.000016425 \n", + "# max: 1983-11-03 21:13:54.048972992\n", + "overall_temporal_extent= pystac.TemporalExtent(intervals=[[pd.Timestamp('1970-01-01 00:00:00.000016425'), pd.Timestamp('2099-12-31 12:00:00')]])\n", + "overall_collection_extent = pystac.Extent(spatial=spatial_extent, temporal=overall_temporal_extent)\n", + "overall_collection_id = 'cprep'\n", + "collection_extent\n", + "if catalog.get_child(overall_collection_id):\n", + " cprep_collection = catalog.get_child(overall_collection_id)\n", + " print(\"existing collection opened\")\n", + " cprep_collection.extent=overall_collection_extent\n", + " cprep_collection.description=collection_description\n", + " cprep_collection.license=collection_license\n", + "else:\n", + " cprep_collection = pystac.Collection(id=overall_collection_id,\n", + " description=collection_description,\n", + " extent=overall_collection_extent,\n", + " license=collection_license)\n", + " print(\"new collection created\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f039d8aa-f385-4660-a662-dfe363cc25b2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if catalog.get_child(overall_collection_id):\n", + " cprep_collection.normalize_and_save(root_href=os.path.join(catalog_path, overall_collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n", + "else:\n", + " catalog.add_child(cprep_collection)\n", + " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -485,7 +544,8 @@ }, "outputs": [], "source": [ - "if catalog.get_child(collection_id):\n", + "# create collection for current zarr\n", + "if cprep_collection.get_child(collection_id):\n", " collection = catalog.get_child(collection_id)\n", " print(\"existing collection opened\")\n", " collection.extent=collection_extent\n", @@ -641,6 +701,8 @@ "outputs": [], "source": [ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n", + "if time_step=='NaT' and ((ds.isel(time=1).time - ds.isel(time=0).time)==1) and (ds.time.units=='days since 1961-01-01 00:00'):\n", + " time_step=\"P1DT0H0M0S\"\n", "print(f'time step: {time_step}')" ] }, @@ -938,17 +1000,19 @@ "metadata": {}, "outputs": [], "source": [ - "if catalog.get_child(collection_id):\n", + "if cprep_collection.get_child(collection_id):\n", " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n", "else:\n", - " catalog.add_child(collection)\n", - " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)" + " cprep_collection.add_child(collection)\n", + " cprep_collection.normalize_and_save(root_href=catalog_path+f'/{overall_collection_id}', catalog_type=pystac.CatalogType.SELF_CONTAINED)" ] }, { "cell_type": "markdown", "id": "c734db5d-a9b4-42b4-88fb-50d84493f694", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## The big job\n", "Now that we have tested the workflow, we will implement it in a big for loop for all 297 zarrs." @@ -960,6 +1024,226 @@ "id": "929a90d4-2ae1-480a-aec3-348d19a667d8", "metadata": {}, "outputs": [], + "source": [ + "fs2 = fsspec.filesystem('s3', anon=True, endpoint_url='https://usgs.osn.mghpcc.org/')\n", + "\n", + "# define folder location where your STAC catalog json file is\n", + "catalog_path = os.path.join('..', '..', 'catalog')\n", + "# open catalog\n", + "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))\n", + "\n", + "for zarr in zarr_list:\n", + " collection_id = os.path.splitext(zarr)[0]\n", + " \n", + " # url to zarr store that you want to create a collection for\n", + " zarr_url = f's3://mdmf/gdp/cprep/{collection_id}.zarr/'\n", + "\n", + " # define keyword arguments needed for opening the dataset with xarray\n", + " # ref: https://github.com/stac-extensions/xarray-assets\n", + " xarray_opendataset_kwargs = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n", + " \"xarray:storage_options\": {\"anon\": True, \"client_kwargs\": {\"endpoint_url\":\"https://usgs.osn.mghpcc.org/\"}}}\n", + " # description for zarr url asset attached to collection (zarr_url)\n", + " asset_description = \"Open Storage Network Pod S3 API access to collection zarr group\"\n", + " # roles to tag zarr url asset with\n", + " asset_roles = [\"data\",\"zarr\",\"s3\"]\n", + " \n", + " # url to zarr store that you want to create a collection for\n", + " zarr_url2 = f's3://nhgf-development/workspace/DataConversion/gdp/cprep/{collection_id}.zarr/'\n", + "\n", + " # define keyword arguments needed for opening the dataset with xarray\n", + " # ref: https://github.com/stac-extensions/xarray-assets\n", + " xarray_opendataset_kwargs2 = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n", + " \"xarray:storage_options\":{\"requester_pays\":True}}\n", + " # description for zarr url asset attached to collection (zarr_url)\n", + " asset_description2 = \"S3 access to collection zarr group\"\n", + " # roles to tag zarr url asset with\n", + " asset_roles2 = [\"data\",\"zarr\",\"s3\"]\n", + " \n", + " # open and view zarr dataset\n", + " try:\n", + " ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n", + " backend_kwargs={'consolidated':True}, chunks={})\n", + " decode_times=True\n", + "\n", + " except:\n", + " ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n", + " backend_kwargs={'consolidated':True}, chunks={}, decode_times=False)\n", + " decode_times=False\n", + " \n", + " # description of STAC collection\n", + " collection_description = ds.attrs['title']\n", + " \n", + " # license for dataset\n", + " collection_license = stac_helpers.license_picker(ds.attrs['license'])\n", + " \n", + " dims_auto_extract = ['X', 'Y', 'T']\n", + " dim_names_dict = {}\n", + " for d in dims_auto_extract:\n", + " dim_names_dict[d] = stac_helpers.extract_dim(ds, d)\n", + " \n", + " crs_var = 'crs'\n", + " # use pyproj to automatically extract crs info\n", + " crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n", + " \n", + " # create a spatial extent object \n", + " spatial_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n", + " XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)\n", + " transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n", + " lon, lat = transformer.transform(XX.ravel(), YY.ravel())\n", + " print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n", + " print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')\n", + " spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])\n", + " \n", + " # pull out first and last timestamps\n", + " if decode_times:\n", + " temporal_extent_lower = ds.indexes[dim_names_dict['T']].to_datetimeindex().min()\n", + " temporal_extent_upper = ds.indexes[dim_names_dict['T']].to_datetimeindex().max()\n", + " else:\n", + " temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n", + " temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n", + " print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n", + " # create a temporal extent object\n", + " temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])\n", + " \n", + " collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)\n", + " \n", + " # create collection for current zarr\n", + " if cprep_collection.get_child(collection_id):\n", + " collection = catalog.get_child(collection_id)\n", + " print(\"existing collection opened\")\n", + " collection.extent=collection_extent\n", + " collection.description=collection_description\n", + " collection.license=collection_license\n", + " else:\n", + " collection = pystac.Collection(id=collection_id,\n", + " description=collection_description,\n", + " extent=collection_extent,\n", + " license=collection_license)\n", + " print(\"new collection created\")\n", + " \n", + " asset_id = \"zarr-s3-osn\"\n", + " asset = pystac.Asset(href=zarr_url,\n", + " description=asset_description,\n", + " media_type=\"application/vnd+zarr\",\n", + " roles=asset_roles,\n", + " extra_fields = xarray_opendataset_kwargs)\n", + " collection.add_asset(asset_id, asset)\n", + " \n", + " asset_id2 = \"zarr-s3\"\n", + " asset2 = pystac.Asset(href=zarr_url2,\n", + " description=asset_description2,\n", + " media_type=\"application/vnd+zarr\",\n", + " roles=asset_roles2,\n", + " extra_fields = xarray_opendataset_kwargs2)\n", + " collection.add_asset(asset_id2, asset2)\n", + " \n", + " # instantiate extention on collection\n", + " dc = DatacubeExtension.ext(collection, add_if_missing=True)\n", + " \n", + " # list out dataset dimensions\n", + " dims = list(ds.dims)\n", + " \n", + " projjson = crs.to_json()\n", + "\n", + " time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n", + " x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n", + " y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n", + " print(f'x step: {x_step}')\n", + " print(f'y step: {y_step}')\n", + " \n", + " # create a dictionary of datacube dimensions you would like to assign to this dataset\n", + " dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n", + " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n", + " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n", + " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]}),\n", + " }\n", + " # make sure you added all the right dims\n", + " assert sorted(list(dims_dict.keys())) == sorted(dims)\n", + " \n", + " # drop metpy_crs coordinate we have added\n", + " if 'metpy_crs' in ds.coords:\n", + " ds = ds.drop_vars('metpy_crs')\n", + "\n", + " # pull list of vars from dataset\n", + " vars = list(ds.variables)\n", + "\n", + " # spec says that the keys of cube:dimensions and cube:variables should be unique together; a key like lat should not be both a dimension and a variable.\n", + " # we will drop all values in dims from vars\n", + " vars = [v for v in vars if v not in dims]\n", + "\n", + " # Microsoft Planetary Computer includes coordinates and crs as variables here:\n", + " # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n", + " # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n", + " # we will keep those in the var list\n", + "\n", + " # create dictionary of dataset variables and associated dimensions\n", + " vars_dict={}\n", + " for v in vars:\n", + " unit = stac_helpers.get_unit(ds, v)\n", + " var_type = stac_helpers.get_var_type(ds, v, crs_var)\n", + " long_name = stac_helpers.get_long_name(ds, v)\n", + " vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})\n", + " \n", + " # add dimesions and variables to collection extension\n", + " dc.apply(dimensions=dims_dict, variables=vars_dict)\n", + " \n", + " if cprep_collection.get_child(collection_id):\n", + " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n", + " else:\n", + " cprep_collection.add_child(collection)\n", + " cprep_collection.normalize_and_save(root_href=catalog_path+f'/{overall_collection_id}', catalog_type=pystac.CatalogType.SELF_CONTAINED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca1bd339-a580-48fe-af4e-c1de7c8cbc48", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "zarr_url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "334d826f-df7f-4b9c-82de-1354352945f0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a60045a8-1496-47e0-9d3d-e4bf02bbfbc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e68c395-5cc2-478e-bfb9-57e2bc22a47c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cbfd7ae-5eda-46be-a978-7b9e32306cb2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b868cf72-86a6-4fc0-84a6-58633700e2eb", + "metadata": {}, + "outputs": [], "source": [] }, { -- GitLab