diff --git a/catalog/LOCA_historical/collection.json b/catalog/LOCA_historical/collection.json
index 4b00181959fbb926c6ce1d0b248ae2209efe4491..13bbd8011a072d95743ff10f53de6ba1c66a8f06 100644
--- a/catalog/LOCA_historical/collection.json
+++ b/catalog/LOCA_historical/collection.json
@@ -37,7 +37,7 @@
293.96875
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- "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"ProjectedCRS\",\"name\":\"unknown\",\"base_crs\":{\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"unknown\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"name\":\"Equidistant Cylindrical\",\"id\":{\"authority\":\"EPSG\",\"code\":1028}},\"parameters\":[{\"name\":\"Latitude of 1st standard parallel\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8823}},{\"name\":\"Latitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8801}},{\"name\":\"Longitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8802}},{\"name\":\"False easting\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8806}},{\"name\":\"False northing\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8807}}]},\"coordinate_system\":{\"subtype\":\"Cartesian\",\"axis\":[{\"name\":\"Easting\",\"abbreviation\":\"E\",\"direction\":\"east\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Northing\",\"abbreviation\":\"N\",\"direction\":\"north\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}}"
+ "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",
@@ -48,7 +48,7 @@
53.96875
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- "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"ProjectedCRS\",\"name\":\"unknown\",\"base_crs\":{\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"unknown\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"name\":\"Equidistant Cylindrical\",\"id\":{\"authority\":\"EPSG\",\"code\":1028}},\"parameters\":[{\"name\":\"Latitude of 1st standard parallel\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8823}},{\"name\":\"Latitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8801}},{\"name\":\"Longitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8802}},{\"name\":\"False easting\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8806}},{\"name\":\"False northing\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8807}}]},\"coordinate_system\":{\"subtype\":\"Cartesian\",\"axis\":[{\"name\":\"Easting\",\"abbreviation\":\"E\",\"direction\":\"east\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Northing\",\"abbreviation\":\"N\",\"direction\":\"north\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}}"
+ "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",
@@ -60,6 +60,12 @@
}
},
"cube:variables": {
+ "crs": {
+ "dimensions": [],
+ "type": "auxiliary",
+ "description": null,
+ "unit": null
+ },
"lat_bnds": {
"dimensions": [
"lat",
@@ -842,9 +848,9 @@
"spatial": {
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[
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+ -66.03124999999991,
53.96875
]
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diff --git a/catalog/bcca_cmip3_future/collection.json b/catalog/bcca_cmip3_future/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bacdc0637e95fe93ca26c1a1dbdd7872f3cbb7f
--- /dev/null
+++ b/catalog/bcca_cmip3_future/collection.json
@@ -0,0 +1,1714 @@
+{
+ "type": "Collection",
+ "id": "bcca_cmip3_future",
+ "stac_version": "1.0.0",
+ "description": "Statistically downscaled GCM data using Bias Corrected Constructed Analogs (BCCA)",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.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": [
+ "2046-01-01T12:00:00Z",
+ "2100-12-31T12:00:00Z"
+ ],
+ "step": "NaT"
+ },
+ "lon": {
+ "type": "spatial",
+ "axis": "x",
+ "description": "Longitude",
+ "extent": [
+ -124.6875,
+ -67.0625
+ ],
+ "step": 0.125,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"undefined\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"undefined\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]}}"
+ },
+ "lat": {
+ "type": "spatial",
+ "axis": "y",
+ "description": "Latitude",
+ "extent": [
+ 25.1875,
+ 52.8125
+ ],
+ "step": 0.125,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"undefined\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"undefined\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]}}"
+ }
+ },
+ "cube:variables": {
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+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
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+ "unit": "mm/d"
+ },
+ "cccma_cgcm3_1-gregorian-sresa1b-run1-tasmax-BCCA_0-125deg": {
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+ "time",
+ "lat",
+ "lon"
+ ],
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+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
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+ "dimensions": [
+ "time",
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+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "cccma_cgcm3_1-gregorian-sresa1b-run2-pr-BCCA_0-125deg": {
+ "dimensions": [
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+ "dimensions": [
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+ ],
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+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
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+ ],
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+ "time",
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+ ],
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+}
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diff --git a/catalog/bcca_cmip3_historic/collection.json b/catalog/bcca_cmip3_historic/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..87985601b028d7639b707e56db8c6ca631c8c3e7
--- /dev/null
+++ b/catalog/bcca_cmip3_historic/collection.json
@@ -0,0 +1,664 @@
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+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "miub_echo_g-gregorian-20c3m-run2-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "miub_echo_g-gregorian-20c3m-run3-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "miub_echo_g-gregorian-20c3m-run3-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "miub_echo_g-gregorian-20c3m-run3-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mpi_echam5-gregorian-20c3m-run1-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mpi_echam5-gregorian-20c3m-run1-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mpi_echam5-gregorian-20c3m-run1-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run1-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run1-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run1-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run2-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run2-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run2-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run3-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run3-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run3-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run4-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run4-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run4-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run5-pr-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run5-tasmax-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily maximum surface (2m) temperature",
+ "unit": "C"
+ },
+ "mri_cgcm2_3_2a-gregorian-20c3m-run5-tasmin-BCCA_0-125deg": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily minimum surface (2m) temperature",
+ "unit": "C"
+ }
+ },
+ "extent": {
+ "spatial": {
+ "bbox": [
+ [
+ -124.6875,
+ 25.1875,
+ -67.0625,
+ 52.8125
+ ]
+ ]
+ },
+ "temporal": {
+ "interval": [
+ [
+ "1961-01-01T12:00:00Z",
+ "2000-12-31T12:00:00Z"
+ ]
+ ]
+ }
+ },
+ "license": "CC0-1.0",
+ "assets": {
+ "zarr-s3-osn": {
+ "href": "s3://mdmf/gdp/bcca_cmip3_historic.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/bcca_cmip3_historic.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/catalog/catalog.json b/catalog/catalog.json
index 4807913c57e6045ad7538c3ae3397a1eb82c14d3..0cd440b25090f1190753b88ac6c67ea8dd1220aa 100644
--- a/catalog/catalog.json
+++ b/catalog/catalog.json
@@ -29,11 +29,6 @@
"href": "./alaska_et_2020_gfdl_historical_simulation/collection.json",
"type": "application/json"
},
- {
- "rel": "child",
- "href": "./cprep_tasmax_day_I35txdetrp1-DeltaSD-A12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
- "type": "application/json"
- },
{
"rel": "child",
"href": "./macav2_historical_daily/collection.json",
@@ -206,8 +201,124 @@
},
{
"rel": "child",
- "href": "./sambi_urb/collection.json",
+ "href": "./topowx/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./topowx_monthly/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./topowx_normals/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./bcca_cmip3_future/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./bcca_cmip3_historic/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_ACCESS_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_ACCESS_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_ACCESS_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_CNRM_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_CNRM_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_CNRM_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_GFDL_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_GFDL_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_GFDL_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_IPSL_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_IPSL_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_IPSL_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MIROC_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MIROC_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MIROC_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MRI_1980_1999/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MRI_2040_2059/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./notaro_MRI_2080_2099/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep/collection.json",
+>>>>>>> catalog/catalog.json
"type": "application/json"
}
]
-}
\ No newline at end of file
+}
diff --git a/catalog/cprep/collection.json b/catalog/cprep/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..27de05f81700e67ca9187c866a1ed6baf3882803
--- /dev/null
+++ b/catalog/cprep/collection.json
@@ -0,0 +1,1524 @@
+{
+ "type": "Collection",
+ "id": "cprep",
+ "stac_version": "1.0.0",
+ "description": "South Central Climate Projections Evaluation Project (C-PrEP)",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_tasmax_day_I35txdetrp1-DeltaSD-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A12P01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A14D01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A14L01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A14P01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A18D01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A18L01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A18P01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A22D01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A22L01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A22P01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A24D01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A24L01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A24P01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A28D01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A28L01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A28P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A32D01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A32L01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A32P01K00_rcp26_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A34D01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A34L01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A34P01K00_rcp45_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A38D01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A38L01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-DeltaSD-A38P01K00_rcp85_r1i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B10D01K00_historical_r6i1p1_I35Land_19810101-20051231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B10L01K00_historical_r6i1p1_I35Land_19810101-20051231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B10P01K00_historical_r6i1p1_I35Land_19810101-20051231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B12P01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B14D01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B14L01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B14P01K00_rcp45_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B18D01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B18L01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B18P01K00_rcp85_r6i1p1_I35Land_20060101-20991231/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./cprep_pr_day_I35prp1-PARM-B20D01K00_historical_r1i1p1_I35Land_19810101-20051231/collection.json",
+ "type": "application/json"
+ },
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diff --git a/catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json b/catalog/cprep/cprep_pr_day_I35prp1-DeltaSD-A12D01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..a0a44e7c7021dbe1745f11eb2d567e564e8703ff
--- /dev/null
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similarity index 98%
rename from catalog/cprep_tasmax_day_I35txdetrp1-DeltaSD-A12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json
rename to catalog/cprep/cprep_tasmax_day_I35txdetrp1-DeltaSD-A12L01K00_rcp26_r6i1p1_I35Land_20060101-20991231/collection.json
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..0b8216bba790530d3ec4adf329e780730ac99b83
--- /dev/null
+++ b/catalog/topowx_monthly/collection.json
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+ },
+ "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/topowx_monthly.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/catalog/topowx_normals/collection.json b/catalog/topowx_normals/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..4b2fd00f7e9ab02042d33973f717aced277882db
--- /dev/null
+++ b/catalog/topowx_normals/collection.json
@@ -0,0 +1,181 @@
+{
+ "type": "Collection",
+ "id": "topowx_normals",
+ "stac_version": "1.0.0",
+ "description": "Interpolated 1981-2010 Monthly Normals for Topoclimatic Temperature",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.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": [
+ "1981-01-16T00:00:00Z",
+ "1981-12-16T00:00:00Z"
+ ],
+ "step": "P0Y1M0DT0H0M0S"
+ },
+ "lon": {
+ "type": "spatial",
+ "axis": "x",
+ "description": "longitude",
+ "extent": [
+ -125.00000055343521,
+ -66.67500078682558
+ ],
+ "step": 0.008333333299987089,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"undefined\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"undefined\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563},\"prime_meridian\":{\"name\":\"undefined\",\"longitude\":0}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]}}"
+ },
+ "lat": {
+ "type": "spatial",
+ "axis": "y",
+ "description": "latitude",
+ "extent": [
+ 24.116666563540484,
+ 51.1916664552432
+ ],
+ "step": -0.0083333333,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"GeographicCRS\",\"name\":\"undefined\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"undefined\",\"ellipsoid\":{\"name\":\"undefined\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563},\"prime_meridian\":{\"name\":\"undefined\",\"longitude\":0}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"}]}}"
+ },
+ "nv": {
+ "type": "count",
+ "description": null,
+ "extent": [
+ 0,
+ 1
+ ]
+ }
+ },
+ "cube:variables": {
+ "climatology_bounds": {
+ "dimensions": [
+ "time",
+ "nv"
+ ],
+ "type": "data",
+ "description": null,
+ "unit": null
+ },
+ "crs": {
+ "dimensions": [],
+ "type": "auxiliary",
+ "description": null,
+ "unit": null
+ },
+ "tmax_normal": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "normal maximum air temperature",
+ "unit": "C"
+ },
+ "tmax_se": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "maximum air temperature kriging standard error",
+ "unit": "C"
+ },
+ "tmin_normal": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "normal minimum air temperature",
+ "unit": "C"
+ },
+ "tmin_se": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "minimum air temperature kriging standard error",
+ "unit": "C"
+ }
+ },
+ "extent": {
+ "spatial": {
+ "bbox": [
+ [
+ -125.00000055343521,
+ 24.116666563540484,
+ -66.67500078682558,
+ 51.1916664552432
+ ]
+ ]
+ },
+ "temporal": {
+ "interval": [
+ [
+ "1981-01-16T00:00:00Z",
+ "1981-12-16T00:00:00Z"
+ ]
+ ]
+ }
+ },
+ "license": "CC-BY-SA-4.0",
+ "assets": {
+ "zarr-s3-osn": {
+ "href": "s3://mdmf/gdp/topowx_normals.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/topowx_normals.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/bcca_cmip3_future_create_collection_from_zarr.ipynb b/workflows/archive/bcca_cmip3_future_create_collection_from_zarr.ipynb
index 6c6ba38aa6ab53df7c8f7598e649724c7201858e..41e619849c997aa383c024bdacfa868d437d5602 100644
--- a/workflows/archive/bcca_cmip3_future_create_collection_from_zarr.ipynb
+++ b/workflows/archive/bcca_cmip3_future_create_collection_from_zarr.ipynb
@@ -545,7 +545,7 @@
"\n",
"If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations\n",
"\n",
- "##### data go from 2046-2065 then 2081-2100"
+ "##### data go from 2046-2065 then 2081-2100 - cannot input time step because irregular"
]
},
{
@@ -568,10 +568,10 @@
"metadata": {},
"outputs": [],
"source": [
- "# optional debugging for time steps:\n",
- "# check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
- "# please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
- "time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
]
},
{
@@ -581,11 +581,11 @@
"metadata": {},
"outputs": [],
"source": [
- "# debugging for time steps, cont:\n",
- "# please choose one of the index locations printed above\n",
- "# this will print the time steps adjacent to it\n",
- "ix = 2\n",
- "ds.isel(time=slice(ix-1,ix+3)).time"
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
]
},
{
@@ -740,13 +740,6 @@
"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",
- " 'bottom_top_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top_stag')}),\n",
- " 'bottom_top': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top')}),\n",
- " 'soil_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layers_stag')}),\n",
- " 'x_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, 'x_stag')}),\n",
- " 'y_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, 'y_stag')}),\n",
- " 'snow_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snow_layers_stag')}),\n",
- " 'snso_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snso_layers_stag')}),\n",
" }"
]
},
diff --git a/workflows/archive/bcca_cmip3_historic_create_collection_from_zarr.ipynb b/workflows/archive/bcca_cmip3_historic_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bf9122fa30e8f32a61353f8d84dc428f68100969
--- /dev/null
+++ b/workflows/archive/bcca_cmip3_historic_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# bcca_cmip3_historic Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'bcca_cmip3_historic'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "#collection_license = stac_helpers.license_picker(ds.attrs['license'])\n",
+ "# no license - using CC0-1.0 because govt institutions helped in this work\n",
+ "collection_license = 'CC0-1.0'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/bcca_cmip5_future_create_collection_from_zarr.ipynb b/workflows/archive/bcca_cmip5_future_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5a8d339a9c04a259463f7f888c321457c2a574db
--- /dev/null
+++ b/workflows/archive/bcca_cmip5_future_create_collection_from_zarr.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# bcca_cmip5_future Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'bcca_cmip5_future'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "#collection_description = ds.attrs['title']\n",
+ "# no attribute on dataset with a good description, so manually filling this field with the same information as was used on the bcca_cmip3 datasets\n",
+ "collection_description = \"Statistically downscaled GCM data using Bias Corrected Constructed Analogs (BCCA)\"\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "#collection_license = stac_helpers.license_picker(ds.attrs['license'])\n",
+ "# no license - using CC0-1.0 because govt institutions helped in this work\n",
+ "collection_license = 'CC0-1.0'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/bcca_cmip5_historical_create_collection_from_zarr.ipynb b/workflows/archive/bcca_cmip5_historical_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bebbdf36914fe5b5a0dba7d9901c7808611aec2c
--- /dev/null
+++ b/workflows/archive/bcca_cmip5_historical_create_collection_from_zarr.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# bcca_cmip5_historical Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'bcca_cmip5_historical'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "#collection_description = ds.attrs['title']\n",
+ "# no attribute on dataset with a good description, so manually filling this field with the same information as was used on the bcca_cmip3 datasets\n",
+ "collection_description = \"Statistically downscaled GCM data using Bias Corrected Constructed Analogs (BCCA)\"\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "#collection_license = stac_helpers.license_picker(ds.attrs['license'])\n",
+ "# no license - using CC0-1.0 because govt institutions helped in this work\n",
+ "collection_license = 'CC0-1.0'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/cprep_create_collection_from_zarr.ipynb b/workflows/archive/cprep_create_collection_from_zarr.ipynb
index df0be7b8d2d3dae9878db12a38cfe38f8b70f929..62531c30255bc46bc4825f4fe9cbbd5d6c4d450b 100644
--- a/workflows/archive/cprep_create_collection_from_zarr.ipynb
+++ b/workflows/archive/cprep_create_collection_from_zarr.ipynb
@@ -8,7 +8,6 @@
},
"source": [
"# C-PrEP Zarr -> Collection Workflow\n",
- "## In progress - figuring out how to label bnds dim of dataset\n",
"\n",
"This is a workflow to build [STAC collections](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr assets for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
"\n",
@@ -119,7 +118,10 @@
"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 +199,10 @@
"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 +434,10 @@
"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 +480,60 @@
"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 +543,8 @@
},
"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 +700,8 @@
"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 +999,19 @@
"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 +1023,226 @@
"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": []
},
{
diff --git a/workflows/archive/notaro_ACCESS_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_ACCESS_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b5e7a0b0e5f50db4fef31dfd537fd145f4c120d5
--- /dev/null
+++ b/workflows/archive/notaro_ACCESS_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_ACCESS_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_ACCESS_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_ACCESS_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_ACCESS_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8f16133dac5f2990e8aa7a3018cb3a59b2093bd3
--- /dev/null
+++ b/workflows/archive/notaro_ACCESS_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_ACCESS_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_ACCESS_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_ACCESS_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_ACCESS_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5b4b7abbe6e9feed0d9823490002766d012c8be8
--- /dev/null
+++ b/workflows/archive/notaro_ACCESS_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_ACCESS_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_ACCESS_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_CNRM_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_CNRM_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..289d94c6b79b8a1165f1237207f0cb6590188cd3
--- /dev/null
+++ b/workflows/archive/notaro_CNRM_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_CNRM_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_CNRM_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_CNRM_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_CNRM_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5b085bb1fcc93bb65752f5dd7381a63b17d9a30d
--- /dev/null
+++ b/workflows/archive/notaro_CNRM_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_CNRM_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_CNRM_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_CNRM_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_CNRM_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5902ba9bbfc8be496073060804d5e08dc88a0e47
--- /dev/null
+++ b/workflows/archive/notaro_CNRM_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_CNRM_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_CNRM_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_GFDL_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_GFDL_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3f26eb81ebbb30f8be914f9b207eca2d6e392fac
--- /dev/null
+++ b/workflows/archive/notaro_GFDL_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_GFDL_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_GFDL_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_GFDL_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_GFDL_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f00300492c4c0888183d6157670a3c96a300ed91
--- /dev/null
+++ b/workflows/archive/notaro_GFDL_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_GFDL_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_GFDL_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_GFDL_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_GFDL_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ce0584b132d5a73dda9eec0ff005d8644ea62c9a
--- /dev/null
+++ b/workflows/archive/notaro_GFDL_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_GFDL_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_GFDL_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_IPSL_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_IPSL_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6327782e089502da88b095635af48b18803afb94
--- /dev/null
+++ b/workflows/archive/notaro_IPSL_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_IPSL_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_IPSL_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_IPSL_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_IPSL_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ad9aa96d12a178e6182724e2d8a585fe2ad34aa5
--- /dev/null
+++ b/workflows/archive/notaro_IPSL_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_IPSL_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_IPSL_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_IPSL_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_IPSL_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..88ec4c5cbc9a4380eff8357a49f3ce11690a8d6c
--- /dev/null
+++ b/workflows/archive/notaro_IPSL_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_IPSL_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_IPSL_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MIROC_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MIROC_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1c0edb8f3ae82b1d4440f5b5aa8b3a3fa0729bcd
--- /dev/null
+++ b/workflows/archive/notaro_MIROC_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MIROC_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MIROC_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MIROC_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MIROC_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..710b8b879e82914213cae05a1a98419c0b3bd016
--- /dev/null
+++ b/workflows/archive/notaro_MIROC_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MIROC_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MIROC_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MIROC_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MIROC_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4a3f1164de38cb648471735b7127699c832e1390
--- /dev/null
+++ b/workflows/archive/notaro_MIROC_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MIROC_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MIROC_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MRI_1980_1999_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MRI_1980_1999_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c92900b39dfe541806bec5e861b807b27a3d73ce
--- /dev/null
+++ b/workflows/archive/notaro_MRI_1980_1999_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MRI_1980_1999 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MRI_1980_1999'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MRI_2040_2059_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MRI_2040_2059_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c900be38506f7893a9a62a1d2b6f1540c4db3994
--- /dev/null
+++ b/workflows/archive/notaro_MRI_2040_2059_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MRI_2040_2059 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MRI_2040_2059'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/notaro_MRI_2080_2099_create_collection_from_zarr.ipynb b/workflows/archive/notaro_MRI_2080_2099_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d9a56b62d89d635ebbaf157217a0c8d711c7353d
--- /dev/null
+++ b/workflows/archive/notaro_MRI_2080_2099_create_collection_from_zarr.ipynb
@@ -0,0 +1,880 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# notaro_MRI_2080_2099 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'notaro_MRI_2080_2099'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'rcm_map'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'soil_layer': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layer'), 'extent': [ds.soil_layer.min().item(), ds.soil_layer.max().item()]}),\n",
+ " 'time_bounds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'time_bounds'), 'extent': [ds.time_bounds.min().item(), ds.time_bounds.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/topowx_create_collection_from_zarr.ipynb b/workflows/archive/topowx_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a63f389a1af26362029f09a1d8bfab4db6e5ba6d
--- /dev/null
+++ b/workflows/archive/topowx_create_collection_from_zarr.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# topowx Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'topowx'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ "dim_names_dict = {'X': 'lon', 'Y': 'lat', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P0Y1M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 11\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "had to round to 12th decimal due to slight variations in rounding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=12)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'nv': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'nv'), 'extent': [ds.nv.min().item(), ds.nv.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/topowx_monthly_create_collection_from_zarr.ipynb b/workflows/archive/topowx_monthly_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c682df98d7546fef609d6428e7909ecb4ebffce8
--- /dev/null
+++ b/workflows/archive/topowx_monthly_create_collection_from_zarr.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# topowx_monthly Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'topowx_monthly'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ "dim_names_dict = {'X': 'lon', 'Y': 'lat', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "time_step = \"P0Y1M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 11\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "had to round to 13th decimal due to slight variations in rounding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'nv': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'nv'), 'extent': [ds.nv.min().item(), ds.nv.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/topowx_normals_create_collection_from_zarr.ipynb b/workflows/archive/topowx_normals_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..18b2d7dbed2ecd56330d0ae09895d61cc0aef039
--- /dev/null
+++ b/workflows/archive/topowx_normals_create_collection_from_zarr.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# topowx_normals Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'topowx_normals'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{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\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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",
+ "dim_names_dict = {'X': 'lon', 'Y': 'lat', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\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",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "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",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\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]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "time_step = \"P0Y1M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 11\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "had to round to 13th decimal due to slight variations in rounding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the 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",
+ " 'nv': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'nv'), 'extent': [ds.nv.min().item(), ds.nv.max().item()]})\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/stac_helpers.py b/workflows/stac_helpers.py
index 55801bc9c7eb320ca7fcfa3fd4ee40d2da38c1eb..db030937d6b849550b49de7da0ae597ac5fc0f86 100644
--- a/workflows/stac_helpers.py
+++ b/workflows/stac_helpers.py
@@ -10,7 +10,9 @@ def license_picker(license_text):
'Creative Commons CC0 1.0 Universal Dedication(http://creativecommons.org/publicdomain/zero/1.0/legalcode)': 'CC0-1.0',
'Freely available': 'Unlicense',
'Freely Available: Oregon State University retains rights to ownership of the data and information.': 'Unlicense',
- 'No restrictions': 'Unlicense'
+ 'No restrictions': 'Unlicense',
+ 'Creative Commons Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0/)': 'CC-BY-SA-4.0',
+ 'This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/).': 'CC-BY-SA-4.0'
}
try:
license = license_mapper[license_text]