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+ "extent": [ + -92.959, + 108.193 + ], "description": "longitude", "reference_system": "EPSG:4326", "type": "spatial" }, "lat": { "axis": "y", - "extent": [4.323, 56.589], + "extent": [ + 4.323, + 56.589 + ], "description": "latitude", "reference_system": "EPSG:4326", "type": "spatial" }, "gwl": { - "values": [0.0, 1.5, 3.0, 5.0], + "values": [ + 0.0, + 1.5, + 3.0, + 5.0 + ], "description": "global warming level", "reference_system": "EPSG:4326", "type": "temporal" @@ -257,55 +274,98 @@ "esl": { "type": "data", "description": "extreme sea level", - "dimensions": ["rp", "gwl", "ensemble", "nstations"], + "dimensions": [ + "rp", + "gwl", + "ensemble", + "nstations" + ], "unit": "m", "attrs": { "long_name": "extreme sea level", "units": "m" }, - "shape": [5, 4, 3, 230], - "chunks": [5, 4, 3, 230] + "shape": [ + 5, + 4, + 3, + 230 + ], + "chunks": [ + 5, + 4, + 3, + 230 + ] }, "lat": { "type": "auxiliary", "description": "latitude", - "dimensions": ["nstations"], + "dimensions": [ + "nstations" + ], "unit": "degrees_north", "attrs": { "long_name": "latitude", "standard_name": "longitude", "units": "degrees_north" }, - "shape": [230], - "chunks": [230] + "shape": [ + 230 + ], + "chunks": [ + 230 + ] }, "lon": { "type": "auxiliary", "description": "longitude", - "dimensions": ["nstations"], + "dimensions": [ + "nstations" + ], "unit": "degrees_east", "attrs": { "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" }, - "shape": [230], - "chunks": [230] + "shape": [ + 230 + ], + "chunks": [ + 230 + ] }, "stations": { "type": "auxiliary", - "dimensions": ["nstations"], + "dimensions": [ + "nstations" + ], "attrs": {}, - "shape": [230] + "shape": [ + 230 + ] } }, "title": "ESL by GWL", "extent": { "spatial": { - "bbox": [[-180.0, -90.0, 180.0, 90.0]] + "bbox": [ + [ + -180.0, + -90.0, + 180.0, + 90.0 + ] + ] }, "temporal": { - "interval": [[null, null]] + "interval": [ + [ + null, + null + ] + ] } }, "license": "proprietary", @@ -313,7 +373,10 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During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion and, when exacerbated by droughts, will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide.", - "links": [ - { - "rel": "item", - "href": "./eapa-mapbox/eapa-mapbox-time-2010.json", - "type": "application/json", - "properties": { - "time": 2010, - "variables": "eapa" - } - }, - { - "rel": "item", - "href": "./eapa-mapbox/eapa-mapbox-time-2040.json", - "type": "application/json", - "properties": { - "time": 2040, - "variables": "eapa" - } - }, - { - "rel": "item", - "href": "./egdp-mapbox/egdp-mapbox-time-2010.json", - "type": "application/json", - "properties": { - "time": 2010, - "variables": "egdp" - } - }, - { - "rel": "item", - "href": "./egdp-mapbox/egdp-mapbox-time-2040.json", - "type": "application/json", - "properties": { - "time": 2040, - "variables": "egdp" - } - }, - { - "rel": "item", - "href": "./epsi-mapbox/epsi-mapbox-time-2010.json", - "type": "application/json", - "properties": { - "time": 2010, - "variables": "epsi" - } - }, - { - "rel": "item", - "href": "./epsi-mapbox/epsi-mapbox-time-2040.json", - "type": "application/json", - "properties": { - "time": 2040, - "variables": "epsi" - } - }, + "type": "Collection", + "id": "sub_threat", + "stac_version": "1.0.0", + "description": "Subsidence, the lowering of Earth's land surface, is a potentially destructive hazard that can be caused by a wide range of natural or anthropogenic triggers but mainly results from solid or fluid mobilization underground. During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion and, when exacerbated by droughts, will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide.", + "links": [ + { + "rel": "item", + "href": "./eapa-mapbox/eapa-mapbox-time-2010.json", + "type": "application/json", + "properties": { + "time": 2010, + "variables": "eapa" + } + }, + { + "rel": "item", + "href": "./eapa-mapbox/eapa-mapbox-time-2040.json", + "type": "application/json", + "properties": { + "time": 2040, + "variables": "eapa" + } + }, + { + "rel": "item", + "href": "./egdp-mapbox/egdp-mapbox-time-2010.json", + "type": "application/json", + "properties": { + "time": 2010, + "variables": "egdp" + } + }, + { + "rel": "item", + "href": "./egdp-mapbox/egdp-mapbox-time-2040.json", + "type": "application/json", + "properties": { + "time": 2040, + "variables": "egdp" + } + }, + { + "rel": "item", + "href": 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}, - "eapa": { - "type": "data", - "description": "Expected annual people affected", - "dimensions": [ - "time", - "stations" - ], - "unit": "1e6", - "attrs": { - "long_name": "Expected annual people affected", - "units": "1e6" - }, - "shape": [ - 2, - 108 - ], - "chunks": [ - 2, - 108 - ] - }, - "egdp": { - "type": "data", - "description": "Exposed GDP (in US$)", - "dimensions": [ - "time", - "stations" - ], - "unit": "1e9", - "attrs": { - "long_name": "Exposed GDP (in US$)", - "units": "1e9" - }, - "shape": [ - 2, - 108 - ], - "chunks": [ - 2, - 108 - ] - }, - "epsi": { - "type": "data", - "description": "Potential subsidence index", - "dimensions": [ - "time", - "stations" - ], - "unit": "1", - "attrs": { - "long_name": "Potential subsidence index", - "units": "1" - }, - "shape": [ - 2, - 108 - ], - "chunks": [ - 2, - 108 - ] - }, - "geometry": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 108 - ] - }, - "lat": { - "type": "auxiliary", - 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public Google Cloud Storage.", + "roles": [ + "data", + "zarr-root", + "gcs" + ] } + } } \ No newline at end of file diff --git a/STAC/data/current/world_gdp/collection.json b/STAC/data/current/world_gdp/collection.json index 917d88cc5..add105d07 100644 --- a/STAC/data/current/world_gdp/collection.json +++ b/STAC/data/current/world_gdp/collection.json @@ -1,214 +1,214 @@ { - "type": "Collection", - "id": "world_gdp", - "stac_version": "1.0.0", - "description": "The World Gridded GDP dataset contatins GDP per capita estimates at grids with a spatial resolution of 0.5 degree (approximately 50 km at the equator). The GDP data was obtained by integrating sub-national GDP data from various sources such as national statistics offices, the World Bank, and other international organizations. Also spatial data such as night-time light data and land cover data was used to estimate GDP values in areas where no sub-national data was available.", - "links": [ - { - "rel": "root", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" - }, - { - "rel": "parent", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" - } - ], - "stac_extensions": [ - "https://raw.githubusercontent.com/openearth/coclicodata/feat/update-deltares-stac-properties/json-schema/schema.json", - "https://stac-extensions.github.io/datacube/v2.0.0/schema.json" - ], - "deltares:units": "m", - "deltares:plotSeries": "sp", - "deltares:plotxAxis": "time", - "deltares:plotType": "line", - "deltares:min": 0, - "deltares:max": 3, - "deltares:linearGradient": [ - { - "color": "hsl(110,90%,80%)", - "offset": "0.000%", - "opacity": 100 - }, - { - "color": "hsla(55,88%,53%,0.5)", - "offset": "50.000%", - "opacity": 100 - }, - { - "color": "hsl(0,90%,70%)", - "offset": "100.000%", - "opacity": 100 - } - ], - "cube:dimensions": { - "lon": { - "axis": "x", - "extent": [ - -179.33421444099997, - 179.665408928 - ], - "description": "Longitude", - "reference_system": "EPSG:4326", - "type": "spatial" - }, - "lat": { - "axis": "y", - "extent": [ - -55.97349167300001, - 66.4159042598 - ], - "description": "Latitude", - "reference_system": "EPSG:4326", - "type": "spatial" - } + "type": "Collection", + "id": "world_gdp", + "stac_version": "1.0.0", + "description": "The World Gridded GDP dataset contatins GDP per capita estimates at grids with a spatial resolution of 0.5 degree (approximately 50 km at the equator). The GDP data was obtained by integrating sub-national GDP data from various sources such as national statistics offices, the World Bank, and other international organizations. Also spatial data such as night-time light data and land cover data was used to estimate GDP values in areas where no sub-national data was available.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" }, - "cube:variables": { - "continent": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1709733 - ] - }, - "country": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1709733 - ] - }, - "gdp": { - "type": "data", - "description": "GDP per capita", - "dimensions": [ - "stations" - ], - "attrs": { - "long_name": "GDP per capita" - }, - "shape": [ - 1709733 - ], - "chunks": [ - 53430 - ] - }, - "lat": { - "type": "auxiliary", - "description": "Latitude", - "dimensions": [ - "stations" - ], - "unit": "degrees_north", - "attrs": { - "long_name": "Latitude", - "standard_name": "latitude", - "units": "degrees_north" - }, - "shape": [ - 1709733 - ], - "chunks": [ - 53430 - ] - }, - "lon": { - "type": "auxiliary", - "description": "Longitude", - "dimensions": [ - "stations" - ], - "unit": "degrees_east", - "attrs": { - "long_name": "Longitude", - "standard_name": "longitude", - "units": "degrees_east" - }, - "shape": [ - 1709733 - ], - "chunks": [ - 53430 - ] - }, - "transect_geom": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1709733 - ] - }, - "transect_id": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1709733 - ] - } + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + } + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/feat/update-deltares-stac-properties/json-schema/schema.json", + "https://stac-extensions.github.io/datacube/v2.2.0/schema.json" + ], + "deltares:units": "m", + "deltares:plotSeries": "sp", + "deltares:plotxAxis": "time", + "deltares:plotType": "line", + "deltares:min": 0, + "deltares:max": 3, + "deltares:linearGradient": [ + { + "color": "hsl(110,90%,80%)", + "offset": "0.000%", + "opacity": 100 + }, + { + "color": "hsla(55,88%,53%,0.5)", + "offset": "50.000%", + "opacity": 100 + }, + { + "color": "hsl(0,90%,70%)", + "offset": "100.000%", + "opacity": 100 + } + ], + "cube:dimensions": { + "lon": { + "axis": "x", + "extent": [ + -179.33421444099997, + 179.665408928 + ], + "description": "Longitude", + "reference_system": "EPSG:4326", + "type": "spatial" + }, + "lat": { + "axis": "y", + "extent": [ + -55.97349167300001, + 66.4159042598 + ], + "description": "Latitude", + "reference_system": "EPSG:4326", + "type": "spatial" + } + }, + "cube:variables": { + "continent": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1709733 + ] + }, + "country": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1709733 + ] }, - "title": "World Gridded GDP", - "extent": { - "spatial": { - "bbox": [ - [ - -180.0, - -90.0, - 180.0, - 90.0 - ] - ] - }, - "temporal": { - "interval": [ - [ - null, - null - ] - ] - } + "gdp": { + "type": "data", + "description": "GDP per capita", + "dimensions": [ + "stations" + ], + "attrs": { + "long_name": "GDP per capita" + }, + "shape": [ + 1709733 + ], + "chunks": [ + 53430 + ] }, - "license": "proprietary", - "keywords": [ - "GlobalCoastalAtlas", - "DeltaPortfolio" - ], - "providers": [ - { - "name": "Deltares", - "description": "Deltares is an independent institute for applied research in the field of water and subsurface.", - "roles": [ - "producer", - "processor" - ], - "url": "https://www.deltares.nl" - } - ], - "assets": { - "data": { - "href": "https://storage.googleapis.com/dgds-data-public/gca/world_gdp.zarr", - "title": "world_gdp zarr root", - "description": "The root of the world_gdp zarr dataset on public Google Cloud Storage.", - "roles": [ - "data", - "zarr-root", - "gcs" - ] - } + "lat": { + "type": "auxiliary", + "description": "Latitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_north", + "attrs": { + "long_name": "Latitude", + "standard_name": "latitude", + "units": "degrees_north" + }, + "shape": [ + 1709733 + ], + "chunks": [ + 53430 + ] + }, + "lon": { + "type": "auxiliary", + "description": "Longitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_east", + "attrs": { + "long_name": "Longitude", + "standard_name": "longitude", + "units": "degrees_east" + }, + "shape": [ + 1709733 + ], + "chunks": [ + 53430 + ] + }, + "transect_geom": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1709733 + ] + }, + "transect_id": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1709733 + ] + } + }, + "title": "World Gridded GDP", + "extent": { + "spatial": { + "bbox": [ + [ + -180.0, + -90.0, + 180.0, + 90.0 + ] + ] + }, + "temporal": { + "interval": [ + [ + null, + null + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "GlobalCoastalAtlas", + "DeltaPortfolio" + ], + "providers": [ + { + "name": "Deltares", + "description": "Deltares is an independent institute for applied research in the field of water and subsurface.", + "roles": [ + "producer", + "processor" + ], + "url": "https://www.deltares.nl" + } + ], + "assets": { + "data": { + "href": "https://storage.googleapis.com/dgds-data-public/gca/world_gdp.zarr", + "title": "world_gdp zarr root", + "description": "The root of the world_gdp zarr dataset on public Google Cloud Storage.", + "roles": [ + "data", + "zarr-root", + "gcs" + ] } + } } \ No newline at end of file diff --git a/STAC/data/current/world_pop/collection.json b/STAC/data/current/world_pop/collection.json index b54fd9182..e05530896 100644 --- a/STAC/data/current/world_pop/collection.json +++ b/STAC/data/current/world_pop/collection.json @@ -1,262 +1,262 @@ { - "type": "Collection", - "id": "world_pop", - "stac_version": "1.0.0", - "description": "The World Population dataset provides a global population count per pixel at ~100 m resolution. The dataset is based on UNDP 2020 estimates for in total 183 countries.", - "links": [ - { - "rel": "root", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" - }, - { - "rel": "parent", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" - } - ], - "stac_extensions": [ - "https://raw.githubusercontent.com/openearth/coclicodata/feat/update-deltares-stac-properties/json-schema/schema.json", - "https://stac-extensions.github.io/datacube/v2.0.0/schema.json" - ], - "deltares:units": "m", - "deltares:plotSeries": "sp", - "deltares:plotxAxis": "time", - "deltares:plotType": "line", - "deltares:min": 0, - "deltares:max": 3, - "deltares:linearGradient": [ - { - "color": "hsl(110,90%,80%)", - "offset": "0.000%", - "opacity": 100 - }, - { - "color": "hsla(55,88%,53%,0.5)", - "offset": "50.000%", - "opacity": 100 - }, - { - "color": "hsl(0,90%,70%)", - "offset": "100.000%", - "opacity": 100 - } - ], - "cube:dimensions": { - "lon": { - "axis": "x", - "extent": [ - -179.33421444099997, - 179.665408928 - ], - "description": "Longitude", - "reference_system": "EPSG:4326", - "type": "spatial" - }, - "lat": { - "axis": "y", - "extent": [ - -55.97349167300001, - 66.4159042598 - ], - "description": "Latitude", - "reference_system": "EPSG:4326", - "type": "spatial" - } + "type": "Collection", + "id": "world_pop", + "stac_version": "1.0.0", + "description": "The World Population dataset provides a global population count per pixel at ~100 m resolution. The dataset is based on UNDP 2020 estimates for in total 183 countries.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" }, - "cube:variables": { - "continent": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1723302 - ] - }, - "country": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1723302 - ] - }, - "lat": { - "type": "auxiliary", - "description": "Latitude", - "dimensions": [ - "stations" - ], - "unit": "degrees_north", - "attrs": { - "long_name": "Latitude", - "standard_name": "latitude", - "units": "degrees_north" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "lon": { - "type": "auxiliary", - "description": "Longitude", - "dimensions": [ - "stations" - ], - "unit": "degrees_east", - "attrs": { - "long_name": "Longitude", - "standard_name": "longitude", - "units": "degrees_east" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "pop_10_m": { - "type": "data", - "description": "Population below 10 m MSL", - "dimensions": [ - "stations" - ], - "attrs": { - "long_name": "Population below 10 m MSL" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "pop_1_m": { - "type": "data", - "description": "Population below 1 m MSL", - "dimensions": [ - "stations" - ], - "attrs": { - "long_name": "Population below 1 m MSL" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "pop_5_m": { - "type": "data", - "description": "Population below 5 m MSL", - "dimensions": [ - "stations" - ], - "attrs": { - "long_name": "Population below 5 m MSL" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "pop_tot": { - "type": "data", - "description": "Total population", - "dimensions": [ - "stations" - ], - "attrs": { - "long_name": "Total population" - }, - "shape": [ - 1723302 - ], - "chunks": [ - 53854 - ] - }, - "transect_geom": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1723302 - ] - }, - "transect_id": { - "type": "auxiliary", - "dimensions": [ - "stations" - ], - "attrs": {}, - "shape": [ - 1723302 - ] - } + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + } + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/feat/update-deltares-stac-properties/json-schema/schema.json", + "https://stac-extensions.github.io/datacube/v2.2.0/schema.json" + ], + "deltares:units": "m", + "deltares:plotSeries": "sp", + "deltares:plotxAxis": "time", + "deltares:plotType": "line", + "deltares:min": 0, + "deltares:max": 3, + "deltares:linearGradient": [ + { + "color": "hsl(110,90%,80%)", + "offset": "0.000%", + "opacity": 100 + }, + { + "color": "hsla(55,88%,53%,0.5)", + "offset": "50.000%", + "opacity": 100 + }, + { + "color": "hsl(0,90%,70%)", + "offset": "100.000%", + "opacity": 100 + } + ], + "cube:dimensions": { + "lon": { + "axis": "x", + "extent": [ + -179.33421444099997, + 179.665408928 + ], + "description": "Longitude", + "reference_system": "EPSG:4326", + "type": "spatial" + }, + "lat": { + "axis": "y", + "extent": [ + -55.97349167300001, + 66.4159042598 + ], + "description": "Latitude", + "reference_system": "EPSG:4326", + "type": "spatial" + } + }, + "cube:variables": { + "continent": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1723302 + ] + }, + "country": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1723302 + ] + }, + "lat": { + "type": "auxiliary", + "description": "Latitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_north", + "attrs": { + "long_name": "Latitude", + "standard_name": "latitude", + "units": "degrees_north" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] + }, + "lon": { + "type": "auxiliary", + "description": "Longitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_east", + "attrs": { + "long_name": "Longitude", + "standard_name": "longitude", + "units": "degrees_east" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] }, - "title": "World Population", - "extent": { - "spatial": { - "bbox": [ - [ - -180.0, - -90.0, - 180.0, - 90.0 - ] - ] - }, - "temporal": { - "interval": [ - [ - null, - null - ] - ] - } + "pop_10_m": { + "type": "data", + "description": "Population below 10 m MSL", + "dimensions": [ + "stations" + ], + "attrs": { + "long_name": "Population below 10 m MSL" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] }, - "license": "proprietary", - "keywords": [ - "GlobalCoastalAtlas", - "DeltaPortfolio" - ], - "providers": [ - { - "name": "Deltares", - "description": "Deltares is an independent institute for applied research in the field of water and subsurface.", - "roles": [ - "producer", - "processor" - ], - "url": "https://www.deltares.nl" - } - ], - "assets": { - "data": { - "href": "https://storage.googleapis.com/dgds-data-public/gca/world_pop.zarr", - "title": "world_pop zarr root", - "description": "The root of the world_pop zarr dataset on public Google Cloud Storage.", - "roles": [ - "data", - "zarr-root", - "gcs" - ] - } + "pop_1_m": { + "type": "data", + "description": "Population below 1 m MSL", + "dimensions": [ + "stations" + ], + "attrs": { + "long_name": "Population below 1 m MSL" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] + }, + "pop_5_m": { + "type": "data", + "description": "Population below 5 m MSL", + "dimensions": [ + "stations" + ], + "attrs": { + "long_name": "Population below 5 m MSL" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] + }, + "pop_tot": { + "type": "data", + "description": "Total population", + "dimensions": [ + "stations" + ], + "attrs": { + "long_name": "Total population" + }, + "shape": [ + 1723302 + ], + "chunks": [ + 53854 + ] + }, + "transect_geom": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1723302 + ] + }, + "transect_id": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 1723302 + ] + } + }, + "title": "World Population", + "extent": { + "spatial": { + "bbox": [ + [ + -180.0, + -90.0, + 180.0, + 90.0 + ] + ] + }, + "temporal": { + "interval": [ + [ + null, + null + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "GlobalCoastalAtlas", + "DeltaPortfolio" + ], + "providers": [ + { + "name": "Deltares", + "description": "Deltares is an independent institute for applied research in the field of water and subsurface.", + "roles": [ + "producer", + "processor" + ], + "url": "https://www.deltares.nl" + } + ], + "assets": { + "data": { + "href": "https://storage.googleapis.com/dgds-data-public/gca/world_pop.zarr", + "title": "world_pop zarr root", + "description": "The root of the world_pop zarr dataset on public Google Cloud Storage.", + "roles": [ + "data", + "zarr-root", + "gcs" + ] } + } } \ No newline at end of file diff --git a/STAC/data/notebooks/10_Land_subsidence_prediction_maps_updated.ipynb b/STAC/data/notebooks/10_Land_subsidence_prediction_maps_updated.ipynb index 38a1e637c..162bc5fd9 100644 --- a/STAC/data/notebooks/10_Land_subsidence_prediction_maps_updated.ipynb +++ b/STAC/data/notebooks/10_Land_subsidence_prediction_maps_updated.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "id": "f1aef40e", "metadata": {}, "outputs": [ @@ -27,7 +27,7 @@ }, { "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 34;\n var nbb_unformatted_code = \"# Optional; code formatter, installed as jupyter lab extension\\n# %load_ext lab_black\\n\\n# Optional; code formatter, installed as jupyter notebook extension\\n%load_ext nb_black\";\n var nbb_formatted_code = \"# Optional; code formatter, installed as jupyter lab extension\\n# %load_ext lab_black\\n\\n# Optional; code formatter, installed as jupyter notebook extension\\n%load_ext nb_black\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 15;\n var nbb_unformatted_code = \"# Optional; code formatter, installed as jupyter lab extension\\n# %load_ext lab_black\\n\\n# Optional; code formatter, installed as jupyter notebook extension\\n%load_ext nb_black\";\n var nbb_formatted_code = \"# Optional; code formatter, installed as jupyter lab extension\\n# %load_ext lab_black\\n\\n# Optional; code formatter, installed as jupyter notebook extension\\n%load_ext nb_black\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", "text/plain": [ "" ] @@ -46,13 +46,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "id": "80287b05", "metadata": {}, "outputs": [ { "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 35;\n var nbb_unformatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\nfrom pathlib import Path\\n\\nimport numpy as np\\n#import geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport glob\\nimport itertools\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\nimport json\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\";\n var nbb_formatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\nfrom pathlib import Path\\n\\nimport numpy as np\\n\\n# import geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport glob\\nimport itertools\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\nimport json\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 16;\n var nbb_unformatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\nfrom pathlib import Path\\n\\nimport numpy as np\\n#import geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport glob\\nimport itertools\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\nimport json\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\";\n var nbb_formatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\nfrom pathlib import Path\\n\\nimport numpy as np\\n\\n# import geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport glob\\nimport itertools\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\nimport json\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", "text/plain": [ "" ] @@ -96,13 +96,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "id": "2d5692d8", "metadata": {}, "outputs": [ { "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 36;\n var nbb_unformatted_code = \"# Workaround to the Windows OS (10) udunits error after installation of cfchecker: https://github.com/SciTools/iris/issues/404\\nos.environ[\\\"UDUNITS2_XML_PATH\\\"] = str(\\n pathlib.Path().home().joinpath( # change to the udunits2.xml file dir in your Python installation\\n r\\\"Anaconda3\\\\pkgs\\\\udunits2-2.2.28-hfda9870_3\\\\Library\\\\share\\\\udunits\\\\udunits2.xml\\\"\\n )\\n)\";\n var nbb_formatted_code = \"# Workaround to the Windows OS (10) udunits error after installation of cfchecker: https://github.com/SciTools/iris/issues/404\\nos.environ[\\\"UDUNITS2_XML_PATH\\\"] = str(\\n pathlib.Path()\\n .home()\\n .joinpath( # change to the udunits2.xml file dir in your Python installation\\n r\\\"Anaconda3\\\\pkgs\\\\udunits2-2.2.28-hfda9870_3\\\\Library\\\\share\\\\udunits\\\\udunits2.xml\\\"\\n )\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 17;\n var nbb_unformatted_code = \"# Workaround to the Windows OS (10) udunits error after installation of cfchecker: https://github.com/SciTools/iris/issues/404\\nos.environ[\\\"UDUNITS2_XML_PATH\\\"] = str(\\n pathlib.Path().home().joinpath( # change to the udunits2.xml file dir in your Python installation\\n r\\\"Anaconda3\\\\pkgs\\\\udunits2-2.2.28-hfda9870_3\\\\Library\\\\share\\\\udunits\\\\udunits2.xml\\\"\\n )\\n)\";\n var nbb_formatted_code = \"# Workaround to the Windows OS (10) udunits error after installation of cfchecker: https://github.com/SciTools/iris/issues/404\\nos.environ[\\\"UDUNITS2_XML_PATH\\\"] = str(\\n pathlib.Path()\\n .home()\\n .joinpath( # change to the udunits2.xml file dir in your Python installation\\n r\\\"Anaconda3\\\\pkgs\\\\udunits2-2.2.28-hfda9870_3\\\\Library\\\\share\\\\udunits\\\\udunits2.xml\\\"\\n )\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", "text/plain": [ "" ] @@ -130,13 +130,13 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "id": "ef7e94bc", "metadata": {}, "outputs": [ { "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 37;\n var nbb_unformatted_code = \"# Define (local and) remote drives\\n# raw_data_dir = p_drive.joinpath(\\\"archivedprojects\\\", \\\"11208003-latedeo2022\\\", \\\"020_InternationalDeltaPortfolio\\\", \\\"datasets\\\", \\\"00_bodemdalingsvoorspellingskaarten\\\")\\nraw_data_dir = p_drive.joinpath(r\\\"archivedprojects\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\") \\nprocessed_data_dir = p_drive.joinpath(r\\\"11210264-003-delta-portal\\\\data\\\") \";\n var nbb_formatted_code = \"# Define (local and) remote drives\\n# raw_data_dir = p_drive.joinpath(\\\"archivedprojects\\\", \\\"11208003-latedeo2022\\\", \\\"020_InternationalDeltaPortfolio\\\", \\\"datasets\\\", \\\"00_bodemdalingsvoorspellingskaarten\\\")\\nraw_data_dir = p_drive.joinpath(\\n r\\\"archivedprojects\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\"\\n)\\nprocessed_data_dir = p_drive.joinpath(r\\\"11210264-003-delta-portal\\\\data\\\")\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 18;\n var nbb_unformatted_code = \"# Define (local and) remote drives\\n# raw_data_dir = p_drive.joinpath(\\\"archivedprojects\\\", \\\"11208003-latedeo2022\\\", \\\"020_InternationalDeltaPortfolio\\\", \\\"datasets\\\", \\\"00_bodemdalingsvoorspellingskaarten\\\")\\nraw_data_dir = p_drive.joinpath(r\\\"archivedprojects\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\") \\nprocessed_data_dir = p_drive.joinpath(r\\\"11210264-003-delta-portal\\\\data\\\") \";\n var nbb_formatted_code = \"# Define (local and) remote drives\\n# raw_data_dir = p_drive.joinpath(\\\"archivedprojects\\\", \\\"11208003-latedeo2022\\\", \\\"020_InternationalDeltaPortfolio\\\", \\\"datasets\\\", \\\"00_bodemdalingsvoorspellingskaarten\\\")\\nraw_data_dir = p_drive.joinpath(\\n r\\\"archivedprojects\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\"\\n)\\nprocessed_data_dir = p_drive.joinpath(r\\\"11210264-003-delta-portal\\\\data\\\")\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", "text/plain": [ "" ] @@ -152,6 +152,37 @@ "processed_data_dir = p_drive.joinpath(r\"11210264-003-delta-portal\\data\") " ] }, + { + "cell_type": "code", + "execution_count": 19, + "id": "149abcc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "WindowsPath('P:/11210264-003-delta-portal/data')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 19;\n var nbb_unformatted_code = \"processed_data_dir\";\n var nbb_formatted_code = \"processed_data_dir\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "processed_data_dir" + ] + }, { "cell_type": "markdown", "id": "d594aa92", diff --git a/STAC/data/scripts/10_Land_subsidence_prediction_maps_updated_stacs.py b/STAC/data/scripts/10_Land_subsidence_prediction_maps_updated_stacs.py index 3076a0ad5..f6722b647 100644 --- a/STAC/data/scripts/10_Land_subsidence_prediction_maps_updated_stacs.py +++ b/STAC/data/scripts/10_Land_subsidence_prediction_maps_updated_stacs.py @@ -56,7 +56,7 @@ # ## Define variables # hard-coded input params at project level GCS_PROTOCOL = "https://storage.googleapis.com" -# GCS_PROJECT = "coclico-11207608-002" +GCS_PROJECT = "DGDS - I1000482-002" BUCKET_NAME = "dgds-data-public" BUCKET_PROJ = "gca" PROJ_NAME = "Land_subsidence_prediction_maps_updated" @@ -89,7 +89,7 @@ "11210264-003-delta-portal", "data" ) # remote p drive google_cred_dir = p_drive.joinpath( - "11207608-coclico", "FASTTRACK_DATA", "google_credentials_new.json" + "11207608-coclico", "FASTTRACK_DATA", "google_credentials.json" ) ds_dir = coclico_data_dir.joinpath(DATASET_DIR) @@ -496,13 +496,13 @@ def get_paths(folder_structure, base_dir=""): # # upload directory with cogs to google cloud load_google_credentials(google_token_fp=google_cred_dir) - # dir_to_google_cloud( - # dir_path=str(cog_dirs), - # gcs_project=GCS_PROJECT, - # bucket_name=BUCKET_NAME, - # bucket_proj=BUCKET_PROJ, - # dir_name=PROJ_NAME, - # ) + dir_to_google_cloud( + dir_path=str(cog_dirs), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + ) # %% # stac_io = CoCliCoStacIO() diff --git a/STAC/data/scripts/stac_to_cloud.py b/STAC/data/scripts/stac_to_cloud.py index b75af2b75..52766c839 100644 --- a/STAC/data/scripts/stac_to_cloud.py +++ b/STAC/data/scripts/stac_to_cloud.py @@ -17,7 +17,7 @@ IN_DIRNAME = "current" # hard-coded input params at project level - coclico_data_dir = pathlib.Path(p_drive, "11205479-coclico", "FASTTRACK_DATA") + coclico_data_dir = pathlib.Path(p_drive, "11207608-coclico", "FASTTRACK_DATA") # upload dir to gcs from local drive source_dir_fp = str(pathlib.Path(__file__).parent.parent.joinpath(IN_DIRNAME))