diff --git a/App/Readme.md b/App/Readme.md deleted file mode 100644 index f5db2a2db..000000000 --- a/App/Readme.md +++ /dev/null @@ -1,75 +0,0 @@ -# Nuxt 3 Minimal Starter - -Look at the [Nuxt 3 documentation](https://nuxt.com/docs/getting-started/introduction) to learn more. - -## Setup - -Make sure to install the dependencies: - -```bash -# npm -npm install - -# pnpm -pnpm install - -# yarn -yarn install - -# bun -bun install -``` - -## Development Server - -Start the development server on `http://localhost:3000`: - -```bash -# npm -npm run dev - -# pnpm -pnpm run dev - -# yarn -yarn dev - -# bun -bun run dev -``` - -## Production - -Build the application for production: - -```bash -# npm -npm run build - -# pnpm -pnpm run build - -# yarn -yarn build - -# bun -bun run build -``` - -Locally preview production build: - -```bash -# npm -npm run preview - -# pnpm -pnpm run preview - -# yarn -yarn preview - -# bun -bun run preview -``` - -Check out the [deployment documentation](https://nuxt.com/docs/getting-started/deployment) for more information. diff --git a/Readme.md b/Readme.md index f337fab3d..048e6009f 100644 --- a/Readme.md +++ b/Readme.md @@ -16,6 +16,9 @@ The repository is structured as follows: ## Way of working When adding a dataset, please make sure to open a branch and follow the entire data workflow as described in `STAC/docs`. + +Check whether your changes to the STAC are OK by running `python -m pytest` in the root folder of this branch in the (Miniforge) prompt. +Note, make sure you activated the correct environment ("globalcoastalatlas" in this case). You can also do this in VS Code 'testing' by adding the branch. Only when all components are correctly ingested and visualized, open a pull request to main in order to update the platform. # local development guide Horizon VM @@ -30,4 +33,4 @@ Only when all components are correctly ingested and visualized, open a pull requ - NUXT_STAC_ROOT - make sure you are in the `app` folder: `cd app` - Install required dependencies: `npm install` -- Start the development server: `npm run dev` +- Start the development server: `npm run dev` \ No newline at end of file diff --git a/STAC/data/current/catalog.json b/STAC/data/current/catalog.json index 06c9bf51c..7e9db3743 100644 --- a/STAC/data/current/catalog.json +++ b/STAC/data/current/catalog.json @@ -60,9 +60,9 @@ }, { "rel": "child", - "href": "./esl_gwl/collection.json", + "href": "./sub_threat/collection.json", "type": "application/json", - "title": "ESL by GWL" + "title": "Land Subsidence Threat" } ], "assets": { diff --git a/STAC/data/current/esl_gwl/collection.json b/STAC/data/current/esl_gwl/collection.json index ad429ace1..68a8f7b0b 100644 --- a/STAC/data/current/esl_gwl/collection.json +++ b/STAC/data/current/esl_gwl/collection.json @@ -1,410 +1,376 @@ { - "type": "Collection", - "id": "esl_gwl", - "stac_version": "1.0.0", - "description": "Extreme sea level projections for five return periods (5, 10, 20 50 and 100 year) for different global warming levels (present-day, 1.5 degree, 3 degree and 5 degree difference) for five different delta's across the world for the year 2100.", - "links": [ - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-0.0.json", - "type": "application/json", - "properties": { - "rp": 5.0, - "gwl": 0.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-1.5.json", - "type": "application/json", - "properties": { - "rp": 5.0, - "gwl": 1.5 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-3.0.json", - "type": "application/json", - "properties": { - "rp": 5.0, - "gwl": 3.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-5.0.json", - "type": "application/json", - "properties": { - "rp": 5.0, - "gwl": 5.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-0.0.json", - "type": "application/json", - "properties": { - "rp": 10.0, - "gwl": 0.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-1.5.json", - "type": "application/json", - "properties": { - "rp": 10.0, - "gwl": 1.5 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-3.0.json", - "type": "application/json", - "properties": { - "rp": 10.0, - "gwl": 3.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-5.0.json", - "type": "application/json", - "properties": { - "rp": 10.0, - "gwl": 5.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-0.0.json", - "type": "application/json", - "properties": { - "rp": 20.0, - "gwl": 0.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-1.5.json", - "type": "application/json", - "properties": { - "rp": 20.0, - "gwl": 1.5 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-3.0.json", - "type": "application/json", - "properties": { - "rp": 20.0, - "gwl": 3.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-5.0.json", - "type": "application/json", - "properties": { - "rp": 20.0, - "gwl": 5.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-0.0.json", - "type": "application/json", - "properties": { - "rp": 50.0, - "gwl": 0.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-1.5.json", - "type": "application/json", - "properties": { - "rp": 50.0, - "gwl": 1.5 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-3.0.json", - "type": "application/json", - "properties": { - "rp": 50.0, - "gwl": 3.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-5.0.json", - "type": "application/json", - "properties": { - "rp": 50.0, - "gwl": 5.0 - } - }, - { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-0.0.json", - "type": "application/json", - "properties": { - "rp": 100.0, - "gwl": 0.0 - } - }, + "type": "Collection", + "id": "esl_gwl", + "stac_version": "1.0.0", + "description": "Extreme sea level projections for five return periods (5, 10, 20 50 and 100 year) for different global warming levels (present-day, 1.5 degree, 3 degree and 5 degree difference) for five different delta's across the world for the year 2100.", + "links": [ + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-0.0.json", + "type": "application/json", + "properties": { + "rp": 5.0, + "gwl": 0.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-1.5.json", + "type": "application/json", + "properties": { + "rp": 5.0, + "gwl": 1.5 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-3.0.json", + "type": "application/json", + "properties": { + "rp": 5.0, + "gwl": 3.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-5.0-gwl-5.0.json", + "type": "application/json", + "properties": { + "rp": 5.0, + "gwl": 5.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-0.0.json", + "type": "application/json", + "properties": { + "rp": 10.0, + "gwl": 0.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-1.5.json", + "type": "application/json", + "properties": { + "rp": 10.0, + "gwl": 1.5 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-3.0.json", + "type": "application/json", + "properties": { + "rp": 10.0, + "gwl": 3.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-10.0-gwl-5.0.json", + "type": "application/json", + "properties": { + "rp": 10.0, + "gwl": 5.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-0.0.json", + "type": "application/json", + "properties": { + "rp": 20.0, + "gwl": 0.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-1.5.json", + "type": "application/json", + "properties": { + "rp": 20.0, + "gwl": 1.5 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-3.0.json", + "type": "application/json", + "properties": { + "rp": 20.0, + "gwl": 3.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-20.0-gwl-5.0.json", + "type": "application/json", + "properties": { + "rp": 20.0, + "gwl": 5.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-0.0.json", + "type": "application/json", + "properties": { + "rp": 50.0, + "gwl": 0.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-1.5.json", + "type": "application/json", + "properties": { + "rp": 50.0, + "gwl": 1.5 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-3.0.json", + "type": "application/json", + "properties": { + "rp": 50.0, + "gwl": 3.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-50.0-gwl-5.0.json", + "type": "application/json", + "properties": { + "rp": 50.0, + "gwl": 5.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-0.0.json", + "type": "application/json", + "properties": { + "rp": 100.0, + "gwl": 0.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-1.5.json", + "type": "application/json", + "properties": { + "rp": 100.0, + "gwl": 1.5 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-3.0.json", + "type": "application/json", + "properties": { + "rp": 100.0, + "gwl": 3.0 + } + }, + { + "rel": "item", + "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-5.0.json", + "type": "application/json", + "properties": { + "rp": 100.0, + "gwl": 5.0 + } + }, + { + "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", + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "deltares:units": "m", + "deltares:plotSeries": "esl", + "deltares:plotxAxis": "rp", + "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": { + "rp": { + "values": [5.0, 10.0, 20.0, 50.0, 100.0], + "description": "return period", + "reference_system": "EPSG:4326", + "type": "temporal" + }, + "lon": { + "axis": "x", + "extent": [-92.959, 108.193], + "description": "longitude", + "reference_system": "EPSG:4326", + "type": "spatial" + }, + "lat": { + "axis": "y", + "extent": [4.323, 56.589], + "description": "latitude", + "reference_system": "EPSG:4326", + "type": "spatial" + }, + "gwl": { + "values": [0.0, 1.5, 3.0, 5.0], + "description": "global warming level", + "reference_system": "EPSG:4326", + "type": "temporal" + } + }, + "cube:variables": { + "esl": { + "type": "data", + "description": "extreme sea level", + "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] + }, + "lat": { + "type": "auxiliary", + "description": "latitude", + "dimensions": ["nstations"], + "unit": "degrees_north", + "attrs": { + "long_name": "latitude", + "standard_name": "longitude", + "units": "degrees_north" + }, + "shape": [230], + "chunks": [230] + }, + "lon": { + "type": "auxiliary", + "description": "longitude", + "dimensions": ["nstations"], + "unit": "degrees_east", + "attrs": { + "long_name": "longitude", + "standard_name": "longitude", + "units": "degrees_east" + }, + "shape": [230], + "chunks": [230] + }, + "stations": { + "type": "auxiliary", + "dimensions": ["nstations"], + "attrs": {}, + "shape": [230] + } + }, + "title": "ESL by GWL", + "extent": { + "spatial": { + "bbox": [[-180.0, -90.0, 180.0, 90.0]] + }, + "temporal": { + "interval": [[null, null]] + } + }, + "license": "proprietary", + "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" + } + ], + "summaries": { + "rp": { + "label": "Risk Profile", + "options": [ { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-1.5.json", - "type": "application/json", - "properties": { - "rp": 100.0, - "gwl": 1.5 - } + "label": "5.0", + "value": 5.0 }, { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-3.0.json", - "type": "application/json", - "properties": { - "rp": 100.0, - "gwl": 3.0 - } + "label": "10.0", + "value": 10.0 }, { - "rel": "item", - "href": "./esl-mapbox/esl-mapbox-rp-100.0-gwl-5.0.json", - "type": "application/json", - "properties": { - "rp": 100.0, - "gwl": 5.0 - } + "label": "20.0", + "value": 20.0 }, { - "rel": "root", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" + "label": "50.0", + "value": 50.0 }, { - "rel": "parent", - "href": "../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" + "label": "100.0", + "value": 100.0 } - ], - "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", - "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" - ], - "deltares:units": "m", - "deltares:plotSeries": "esl", - "deltares:plotxAxis": "rp", - "deltares:plotType": "line", - "deltares:min": 0, - "deltares:max": 3, - "deltares:linearGradient": [ + ] + }, + "gwl": { + "label": "Global Water Level", + "options": [ { - "color": "hsl(110,90%,80%)", - "offset": "0.000%", - "opacity": 100 + "label": "0.0", + "value": 0.0 }, { - "color": "hsla(55,88%,53%,0.5)", - "offset": "50.000%", - "opacity": 100 + "label": "1.5", + "value": 1.5 }, { - "color": "hsl(0,90%,70%)", - "offset": "100.000%", - "opacity": 100 - } - ], - "cube:dimensions": { - "rp": { - "values": [ - 5.0, - 10.0, - 20.0, - 50.0, - 100.0 - ], - "description": "return period", - "reference_system": "EPSG:4326", - "type": "temporal" - }, - "lon": { - "axis": "x", - "extent": [ - -92.959, - 108.193 - ], - "description": "longitude", - "reference_system": "EPSG:4326", - "type": "spatial" - }, - "lat": { - "axis": "y", - "extent": [ - 4.323, - 56.589 - ], - "description": "latitude", - "reference_system": "EPSG:4326", - "type": "spatial" - }, - "gwl": { - "values": [ - 0.0, - 1.5, - 3.0, - 5.0 - ], - "description": "global warming level", - "reference_system": "EPSG:4326", - "type": "temporal" - } - }, - "cube:variables": { - "esl": { - "type": "data", - "description": "extreme sea level", - "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 - ] + "label": "3.0", + "value": 3.0 }, - "lat": { - "type": "auxiliary", - "description": "latitude", - "dimensions": [ - "nstations" - ], - "unit": "degrees_north", - "attrs": { - "long_name": "latitude", - "standard_name": "longitude", - "units": "degrees_north" - }, - "shape": [ - 230 - ], - "chunks": [ - 230 - ] - }, - "lon": { - "type": "auxiliary", - "description": "longitude", - "dimensions": [ - "nstations" - ], - "unit": "degrees_east", - "attrs": { - "long_name": "longitude", - "standard_name": "longitude", - "units": "degrees_east" - }, - "shape": [ - 230 - ], - "chunks": [ - 230 - ] - }, - "stations": { - "type": "auxiliary", - "dimensions": [ - "nstations" - ], - "attrs": {}, - "shape": [ - 230 - ] - } - }, - "title": "ESL by GWL", - "extent": { - "spatial": { - "bbox": [ - [ - -180.0, - -90.0, - 180.0, - 90.0 - ] - ] - }, - "temporal": { - "interval": [ - [ - null, - null - ] - ] - } - }, - "license": "proprietary", - "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" - } - ], - "summaries": { - "rp": [ - 5.0, - 10.0, - 20.0, - 50.0, - 100.0 - ], - "gwl": [ - 0.0, - 1.5, - 3.0, - 5.0 - ] - }, - "assets": { - "data": { - "href": "https://storage.googleapis.com/dgds-data-public/gca/ESLbyGWL.zarr", - "title": "esl_gwl zarr root", - "description": "The root of the esl_gwl zarr dataset on public Google Cloud Storage.", - "roles": [ - "data", - "zarr-root", - "gcs" - ] + "label": "5.0", + "value": 5.0 } + ] + } + }, + "assets": { + "data": { + "href": "https://storage.googleapis.com/dgds-data-public/gca/ESLbyGWL.zarr", + "title": "esl_gwl zarr root", + "description": "The root of the esl_gwl zarr dataset on public Google Cloud Storage.", + "roles": ["data", "zarr-root", "gcs"] } -} \ No newline at end of file + } +} diff --git a/STAC/data/current/sub_threat/collection.json b/STAC/data/current/sub_threat/collection.json new file mode 100644 index 000000000..b5870a4a2 --- /dev/null +++ b/STAC/data/current/sub_threat/collection.json @@ -0,0 +1,333 @@ +{ + "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": "./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" + } + }, + { + "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", + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "deltares:units": "m", + "deltares:plotSeries": "", + "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": { + "time": { + "values": [ + 1262304000000000000, + 2208988800000000000 + ], + "description": "Time", + "reference_system": "EPSG:4326", + "type": "temporal" + }, + "lon": { + "axis": "x", + "extent": [ + -112.49572607506327, + 171.47479227444026 + ], + "description": "Longitude", + "reference_system": "EPSG:4326", + "type": "spatial" + }, + "lat": { + "axis": "y", + "extent": [ + -41.829914775737656, + 61.987864223960244 + ], + "description": "Latitude", + "reference_system": "EPSG:4326", + "type": "spatial" + } + }, + "cube:variables": { + "country": { + "type": "auxiliary", + "dimensions": [ + "stations" + ], + "attrs": {}, + "shape": [ + 108 + ] + }, + "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", + "description": "Latitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_north", + "attrs": { + "long_name": "Latitude", + "standard_name": "longitude", + "units": "degrees_north" + }, + "shape": [ + 108 + ], + "chunks": [ + 108 + ] + }, + "lon": { + "type": "auxiliary", + "description": "Longitude", + "dimensions": [ + "stations" + ], + "unit": "degrees_east", + "attrs": { + "long_name": "Longitude", + "standard_name": "longitude", + "units": "degrees_east" + }, + "shape": [ + 108 + ], + "chunks": [ + 108 + ] + } + }, + "title": "Land Subsidence Threat", + "extent": { + "spatial": { + "bbox": [ + [ + -180.0, + -90.0, + 180.0, + 90.0 + ] + ] + }, + "temporal": { + "interval": [ + [ + null, + null + ] + ] + } + }, + "license": "proprietary", + "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" + } + ], + "summaries": { + "time": { + "label": "Time", + "options": [ + { + "label": "2010", + "value": 2010 + }, + { + "label": "2040", + "value": 2040 + } + ] + }, + "variables": { + "label": "Variables", + "options": [ + { + "label": "Expected annual people affected", + "value": "eapa" + }, + { + "label": "Exposed GDP (in US$)", + "value": "egdp" + }, + { + "label": "Potential subsidence index", + "value": "epsi" + } + ] + } + }, + "assets": { + "data": { + "href": "https://storage.googleapis.com/dgds-data-public/gca/Global_TLS.zarr", + "title": "sub_threat zarr root", + "description": "The root of the sub_threat zarr dataset on public Google Cloud Storage.", + "roles": [ + "data", + "zarr-root", + "gcs" + ] + } + } +} \ No newline at end of file diff --git a/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2010.json b/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2010.json index 54a5d144a..48805ec00 100644 --- a/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2010.json +++ b/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2010.json @@ -1,73 +1,101 @@ { - "type": "Feature", - "stac_version": "1.0.0", - "id": "eapa-mapbox-time-2010", - "properties": { - "deltares:item_key": "time-2010", - "deltares:paint": { - "fill-color": [ - "interpolate", - ["linear"], - ["get", "time-2010"], - 0, - "rgba(59, 217, 90, 0.3)", - 277.5, - "rgba(245, 188, 66, 0.3)", - 555, - "rgba(217, 59, 59, 0.3)" - ] + "type": "Feature", + "stac_version": "1.0.0", + "id": "eapa-mapbox-time-2010", + "properties": { + "deltares:item_key": "time-2010", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2010" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2010, + "variables": "eapa", + "datetime": "2023-12-13T11:00:58.960320Z" }, - "deltares:type": "fill", - "deltares:stations": "locationId", - "deltares:onclick": {}, - "time": 2010, - "datetime": "2023-11-07T13:34:44.212216Z" - }, - "geometry": { - "type": "Polygon", - "coordinates": [ - [ - [-180.0, -90.0], - [180.0, -90.0], - [180.0, 90.0], - [-180.0, 90.0], - [-180.0, -90.0] - ] - ] - }, - "links": [ - { - "rel": "collection", - "href": "../collection.json", - "type": "application/json", - "title": "Land Subsidence Threat" + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ + 180.0, + -90.0 + ], + [ + 180.0, + 90.0 + ], + [ + -180.0, + 90.0 + ], + [ + -180.0, + -90.0 + ] + ] + ] }, - { - "rel": "root", - "href": "../../catalog.json", - "type": "application/json", - "title": "GlobalCoastalAtlas STAC Catalog" + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + }, + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + } + ], + "assets": { + "mapbox": { + "href": "mapbox://global-data-viewer.Global_TLS_eapa", + "title": "Point locations", + "description": "Mapbox url", + "type": "vector", + "source": "Global_TLS_eapa", + "roles": [ + "mapbox" + ] + } }, - { - "rel": "parent", - "href": "../collection.json", - "type": "application/json", - "title": "Land Subsidence Threat" - } - ], - "assets": { - "mapbox": { - "href": "mapbox://global-data-viewer.Global_TLS_eapa", - "title": "Point locations", - "description": "Mapbox url", - "type": "vector", - "source": "Global_TLS_eapa", - "roles": ["mapbox"] - } - }, - "bbox": [-180, -90, 180, 90], - "stac_extensions": [ - "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" - ], - "collection": "sub_threat" -} + "bbox": [ + -180, + -90, + 180, + 90 + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "collection": "sub_threat" +} \ No newline at end of file diff --git a/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2040.json b/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2040.json new file mode 100644 index 000000000..490b0d78d --- /dev/null +++ b/STAC/data/current/sub_threat/eapa-mapbox/eapa-mapbox-time-2040.json @@ -0,0 +1,101 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "eapa-mapbox-time-2040", + "properties": { + "deltares:item_key": "time-2040", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2040" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2040, + "variables": "eapa", + "datetime": "2023-12-13T11:00:58.965405Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ + 180.0, + -90.0 + ], + [ + 180.0, + 90.0 + ], + [ + -180.0, + 90.0 + ], + [ + -180.0, + -90.0 + ] + ] + ] + }, + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + }, + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + } + ], + "assets": { + "mapbox": { + "href": "mapbox://global-data-viewer.Global_TLS_eapa", + "title": "Point locations", + "description": "Mapbox url", + "type": "vector", + "source": "Global_TLS_eapa", + "roles": [ + "mapbox" + ] + } + }, + "bbox": [ + -180, + -90, + 180, + 90 + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "collection": "sub_threat" +} \ No newline at end of file diff --git a/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2010.json b/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2010.json new file mode 100644 index 000000000..8c111e343 --- /dev/null +++ b/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2010.json @@ -0,0 +1,101 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "egdp-mapbox-time-2010", + "properties": { + "deltares:item_key": "time-2010", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2010" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2010, + "variables": "egdp", + "datetime": "2023-12-13T11:00:58.965405Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ + 180.0, + -90.0 + ], + [ + 180.0, + 90.0 + ], + [ + -180.0, + 90.0 + ], + [ + -180.0, + -90.0 + ] + ] + ] + }, + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + }, + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + } + ], + "assets": { + "mapbox": { + "href": "mapbox://global-data-viewer.Global_TLS_egdp", + "title": "Point locations", + "description": "Mapbox url", + "type": "vector", + "source": "Global_TLS_egdp", + "roles": [ + "mapbox" + ] + } + }, + "bbox": [ + -180, + -90, + 180, + 90 + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "collection": "sub_threat" +} \ No newline at end of file diff --git a/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2040.json b/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2040.json new file mode 100644 index 000000000..4a6fe15c1 --- /dev/null +++ b/STAC/data/current/sub_threat/egdp-mapbox/egdp-mapbox-time-2040.json @@ -0,0 +1,101 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "egdp-mapbox-time-2040", + "properties": { + "deltares:item_key": "time-2040", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2040" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2040, + "variables": "egdp", + "datetime": "2023-12-13T11:00:58.966407Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ 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a/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2010.json b/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2010.json new file mode 100644 index 000000000..96c284f41 --- /dev/null +++ b/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2010.json @@ -0,0 +1,101 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "epsi-mapbox-time-2010", + "properties": { + "deltares:item_key": "time-2010", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2010" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2010, + "variables": "epsi", + "datetime": "2023-12-13T11:00:58.967405Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ + 180.0, + -90.0 + ], + [ + 180.0, + 90.0 + ], + [ + -180.0, + 90.0 + ], + [ + -180.0, + -90.0 + ] + ] + ] + }, + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + }, + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + } + ], + "assets": { + "mapbox": { + "href": "mapbox://global-data-viewer.Global_TLS_epsi", + "title": "Point locations", + "description": "Mapbox url", + "type": "vector", + "source": "Global_TLS_epsi", + "roles": [ + "mapbox" + ] + } + }, + "bbox": [ + -180, + -90, + 180, + 90 + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "collection": "sub_threat" +} \ No newline at end of file diff --git a/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2040.json b/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2040.json new file mode 100644 index 000000000..0c1477ac5 --- /dev/null +++ b/STAC/data/current/sub_threat/epsi-mapbox/epsi-mapbox-time-2040.json @@ -0,0 +1,101 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "epsi-mapbox-time-2040", + "properties": { + "deltares:item_key": "time-2040", + "deltares:paint": { + "fill-color": [ + "interpolate", + [ + "linear" + ], + [ + "get", + "time-2040" + ], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)" + ] + }, + "deltares:type": "fill", + "deltares:stations": "locationId", + "deltares:onclick": {}, + "time": 2040, + "variables": "epsi", + "datetime": "2023-12-13T11:00:58.967405Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -180.0, + -90.0 + ], + [ + 180.0, + -90.0 + ], + [ + 180.0, + 90.0 + ], + [ + -180.0, + 90.0 + ], + [ + -180.0, + -90.0 + ] + ] + ] + }, + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + }, + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "GlobalCoastalAtlas STAC Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Land Subsidence Threat" + } + ], + "assets": { + "mapbox": { + "href": "mapbox://global-data-viewer.Global_TLS_epsi", + "title": "Point locations", + "description": "Mapbox url", + "type": "vector", + "source": "Global_TLS_epsi", + "roles": [ + "mapbox" + ] + } + }, + "bbox": [ + -180, + -90, + 180, + 90 + ], + "stac_extensions": [ + "https://raw.githubusercontent.com/openearth/coclicodata/main/json-schema/schema.json" + ], + "collection": "sub_threat" +} \ No newline at end of file diff --git a/STAC/data/notebooks/09_Subsidence.ipynb b/STAC/data/notebooks/09_Subsidence.ipynb new file mode 100644 index 000000000..dab8330b4 --- /dev/null +++ b/STAC/data/notebooks/09_Subsidence.ipynb @@ -0,0 +1,2527 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Subsidence\n", + "\n", + "Notebook to migrate xlsx files to CF compliant .." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 1;\n var nbb_unformatted_code = \"# Optional; code formatter, installed as jupyter lab extension\\n#%load_ext lab_black\\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# 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": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Optional; code formatter, installed as jupyter lab extension\n", + "#%load_ext lab_black\n", + "# Optional; code formatter, installed as jupyter notebook extension\n", + "%load_ext nb_black" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure OS dependent paths" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_4024\\3046747413.py:6: DeprecationWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas still uses PyGEOS by default. However, starting with version 0.14, the default will switch to Shapely. To force to use Shapely 2.0 now, you can either uninstall PyGEOS or set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n", + "\n", + "import os\n", + "os.environ['USE_PYGEOS'] = '0'\n", + "import geopandas\n", + "\n", + "In the next release, GeoPandas will switch to using Shapely by default, even if PyGEOS is installed. If you only have PyGEOS installed to get speed-ups, this switch should be smooth. However, if you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n", + " import geopandas as gpd\n" + ] + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 2;\n var nbb_unformatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\n\\nimport numpy as np\\nimport geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\\n\\n# Define (local and) remote drives\\ngca_data_dir = p_drive.joinpath(\\\"11208003-latedeo2022\\\",\\\"020_InternationalDeltaPortfolio\\\",\\\"datasets\\\")\\ncoclico_data_dir = p_drive.joinpath(\\\"11205479-coclico\\\", \\\"FASTTRACK_DATA\\\")\\n\\n# 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-h892ecd3_0\\\\Library\\\\share\\\\udunits\\\\udunits2.xml\\\"\\n )\\n)\";\n var nbb_formatted_code = \"# Import standard packages\\nimport os\\nimport pathlib\\n\\nimport numpy as np\\nimport geopandas as gpd\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport xarray as xr\\nimport json\\nimport copy\\nfrom itertools import chain\\nfrom shapely import wkb\\n\\n# Import custom functionality\\nfrom coclicodata.drive_config import p_drive\\nfrom coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\\n\\n# Define (local and) remote drives\\ngca_data_dir = p_drive.joinpath(\\n \\\"11208003-latedeo2022\\\", \\\"020_InternationalDeltaPortfolio\\\", \\\"datasets\\\"\\n)\\ncoclico_data_dir = p_drive.joinpath(\\\"11205479-coclico\\\", \\\"FASTTRACK_DATA\\\")\\n\\n# 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-h892ecd3_0\\\\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": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Import standard packages\n", + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import json\n", + "import copy\n", + "from itertools import chain\n", + "from shapely import wkb\n", + "\n", + "# Import custom functionality\n", + "from coclicodata.drive_config import p_drive\n", + "from coclicodata.etl.cf_compliancy_checker import check_compliancy, save_compliancy\n", + "\n", + "# Define (local and) remote drives\n", + "gca_data_dir = p_drive.joinpath(\"11208003-latedeo2022\",\"020_InternationalDeltaPortfolio\",\"datasets\")\n", + "coclico_data_dir = p_drive.joinpath(\"11205479-coclico\", \"FASTTRACK_DATA\")\n", + "\n", + "# Workaround to the Windows OS (10) udunits error after installation of cfchecker: https://github.com/SciTools/iris/issues/404\n", + "os.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-h892ecd3_0\\Library\\share\\udunits\\udunits2.xml\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 4;\n var nbb_unformatted_code = \"# Project paths & files (manual input)\\ndataset_dir = gca_data_dir.joinpath(r\\\"00_mapping_global_threat_of_land_subsidence\\\")\\ndataset_dir_path = dataset_dir.joinpath(\\\"abb8549_data_s3_red.nc\\\")\\ndataset_out_file = \\\"Global_TLS\\\" # threat of land subsidence\\nCF_dir = gca_data_dir.joinpath(r\\\"CF\\\") # directory to save output CF check files\";\n var nbb_formatted_code = \"# Project paths & files (manual input)\\ndataset_dir = gca_data_dir.joinpath(r\\\"00_mapping_global_threat_of_land_subsidence\\\")\\ndataset_dir_path = dataset_dir.joinpath(\\\"abb8549_data_s3_red.nc\\\")\\ndataset_out_file = \\\"Global_TLS\\\" # threat of land subsidence\\nCF_dir = gca_data_dir.joinpath(r\\\"CF\\\") # directory to save output CF check files\";\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": [ + "# Project paths & files (manual input)\n", + "dataset_dir = gca_data_dir.joinpath(r\"00_mapping_global_threat_of_land_subsidence\")\n", + "dataset_dir_path = dataset_dir.joinpath(\"abb8549_data_s3_red.nc\")\n", + "dataset_out_file = \"Global_TLS\" # threat of land subsidence\n", + "CF_dir = gca_data_dir.joinpath(r\"CF\") # directory to save output CF check files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write XLSX to NetCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 5;\n var nbb_unformatted_code = \"# write xlsx to netcdf\\n\\n# open the XLSX dataset as pandas dataframe\\ndf = pd.read_excel(str(dataset_dir_path).replace(\\\"_red.nc\\\", \\\".xlsx\\\"), index_col=None, header=0)\\n\\n# Rename columns that cause errors\\nkey_list_corr = {}\\nfor idx, i in enumerate(df.keys()):\\n if '/' in i:\\n key_list_corr[i] = i.replace(\\\"/\\\", \\\"per\\\")\\n\\ndf = df.rename(columns=key_list_corr)\\n\\n# Select relevant columns (info from Gilles --> decreases the datasets)\\ncolumns=['Country',\\n 'Potential exposed population in 2010 (Million)',\\n 'Exposed GDP (EGDP) (Billion US$)',\\n 'Potential global subsidence index in 2010 (PGSI)',\\n 'Potential exposed population in 2040 (Million)',\\n 'Exposed GDP (EGDP) (Billion US$) in 2040',\\n 'Potential subsidence index 2040',\\n ]\\n\\ndf = df[columns]\\n\\n# Convert the pandas dataframe to an xarray dataset\\nds = xr.Dataset.from_dataframe(df)\\n\\n# # Write the xarray dataset to a netCDF file\\nds.to_netcdf(dataset_dir_path)\";\n var nbb_formatted_code = \"# write xlsx to netcdf\\n\\n# open the XLSX dataset as pandas dataframe\\ndf = pd.read_excel(\\n str(dataset_dir_path).replace(\\\"_red.nc\\\", \\\".xlsx\\\"), index_col=None, header=0\\n)\\n\\n# Rename columns that cause errors\\nkey_list_corr = {}\\nfor idx, i in enumerate(df.keys()):\\n if \\\"/\\\" in i:\\n key_list_corr[i] = i.replace(\\\"/\\\", \\\"per\\\")\\n\\ndf = df.rename(columns=key_list_corr)\\n\\n# Select relevant columns (info from Gilles --> decreases the datasets)\\ncolumns = [\\n \\\"Country\\\",\\n \\\"Potential exposed population in 2010 (Million)\\\",\\n \\\"Exposed GDP (EGDP) (Billion US$)\\\",\\n \\\"Potential global subsidence index in 2010 (PGSI)\\\",\\n \\\"Potential exposed population in 2040 (Million)\\\",\\n \\\"Exposed GDP (EGDP) (Billion US$) in 2040\\\",\\n \\\"Potential subsidence index 2040\\\",\\n]\\n\\ndf = df[columns]\\n\\n# Convert the pandas dataframe to an xarray dataset\\nds = xr.Dataset.from_dataframe(df)\\n\\n# # Write the xarray dataset to a netCDF file\\nds.to_netcdf(dataset_dir_path)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# write xlsx to netcdf\n", + "\n", + "# open the XLSX dataset as pandas dataframe\n", + "df = pd.read_excel(str(dataset_dir_path).replace(\"_red.nc\", \".xlsx\"), index_col=None, header=0)\n", + "\n", + "# Rename columns that cause errors\n", + "key_list_corr = {}\n", + "for idx, i in enumerate(df.keys()):\n", + " if '/' in i:\n", + " key_list_corr[i] = i.replace(\"/\", \"per\")\n", + "\n", + "df = df.rename(columns=key_list_corr)\n", + "\n", + "# Select relevant columns (info from Gilles --> decreases the datasets)\n", + "columns=['Country',\n", + " 'Potential exposed population in 2010 (Million)',\n", + " 'Exposed GDP (EGDP) (Billion US$)',\n", + " 'Potential global subsidence index in 2010 (PGSI)',\n", + " 'Potential exposed population in 2040 (Million)',\n", + " 'Exposed GDP (EGDP) (Billion US$) in 2040',\n", + " 'Potential subsidence index 2040',\n", + " ]\n", + "\n", + "df = df[columns]\n", + "\n", + "# Convert the pandas dataframe to an xarray dataset\n", + "ds = xr.Dataset.from_dataframe(df)\n", + "\n", + "# # Write the xarray dataset to a netCDF file\n", + "ds.to_netcdf(dataset_dir_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check CF compliancy original NetCDF files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:                                           (index: 110)\n",
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+       "    Country                                           (index) object ...\n",
+       "    Potential exposed population in 2010 (Million)    (index) float64 ...\n",
+       "    Exposed GDP (EGDP) (Billion US$)                  (index) float64 ...\n",
+       "    Potential global subsidence index in 2010 (PGSI)  (index) float64 ...\n",
+       "    Potential exposed population in 2040 (Million)    (index) float64 ...\n",
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" + ], + "text/plain": [ + "\n", + "Dimensions: (index: 110)\n", + "Coordinates:\n", + " * index (index) int64 0 1 ... 109\n", + "Data variables:\n", + " Country (index) object ...\n", + " Potential exposed population in 2010 (Million) (index) float64 ...\n", + " Exposed GDP (EGDP) (Billion US$) (index) float64 ...\n", + " Potential global subsidence index in 2010 (PGSI) (index) float64 ...\n", + " Potential exposed population in 2040 (Million) (index) float64 ...\n", + " Exposed GDP (EGDP) (Billion US$) in 2040 (index) float64 ...\n", + " Potential subsidence index 2040 (index) float64 ..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"# open datasets\\nds = xr.open_dataset(dataset_dir_path)\\n\\n# check original dataset\\nds\";\n var nbb_formatted_code = \"# open datasets\\nds = xr.open_dataset(dataset_dir_path)\\n\\n# check original dataset\\nds\";\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": [ + "# open datasets\n", + "ds = xr.open_dataset(dataset_dir_path)\n", + "\n", + "# check original dataset\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 7;\n var nbb_unformatted_code = \"%%capture cap --no-stderr\\n# check original CF compliancy\\n\\ncheck_compliancy(testfile=dataset_dir_path, \\n working_dir=CF_dir\\n )\";\n var nbb_formatted_code = \"%%capture cap --no-stderr\\n# check original CF compliancy\\n\\ncheck_compliancy(testfile=dataset_dir_path, \\n working_dir=CF_dir\\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": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%capture cap --no-stderr\n", + "# check original CF compliancy\n", + "\n", + "check_compliancy(testfile=dataset_dir_path, \n", + " working_dir=CF_dir\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'P:\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\\00_mapping_global_threat_of_land_subsidence\\\\abb8549_data_s3_red.nc': {'warnings': '17', 'errors': '1'}}\n" + ] + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 8;\n var nbb_unformatted_code = \"# save original CF compliancy\\nsave_compliancy(cap, testfile=dataset_dir_path, working_dir=CF_dir)\";\n var nbb_formatted_code = \"# save original CF compliancy\\nsave_compliancy(cap, testfile=dataset_dir_path, working_dir=CF_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": [ + "# save original CF compliancy\n", + "save_compliancy(cap, testfile=dataset_dir_path, working_dir=CF_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add NUTS regions (or equivalent)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The nuts regions are not included as attributes in the netCDF files. Also this dataset is global and hence NUTS cannot be used. The NetCDF files only contain country names so we retrieve that information the [World Bank](https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_4024\\1661254418.py:9: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " glob_regions[\"lon\"] = glob_regions.centroid.x # set the lon\n", + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_4024\\1661254418.py:10: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " glob_regions[\"lat\"] = glob_regions.centroid.y # set the lat\n" + ] + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 9;\n var nbb_unformatted_code = \"# load country regions\\nglob_regions = gpd.read_file(\\n coclico_data_dir.joinpath(\\\"XX_Global\\\", \\\"WB_countries_Admin0.geojson\\\")\\n)\\nglob_regions = glob_regions.to_crs(\\\"EPSG:4326\\\")\\n\\nglob_regions[\\\"geometry\\\"] = glob_regions.geometry # rename the geometry\\n#glob_regions[\\\"geometry\\\"] = glob_regions.centroid.buffer(0.5) # set the buffered centroid as geometry (for simplicity)\\nglob_regions[\\\"lon\\\"] = glob_regions.centroid.x # set the lon\\nglob_regions[\\\"lat\\\"] = glob_regions.centroid.y # set the lat\\n#glob_regions.representative_point() # usefull if for instance Aruba was part of NL\\n\\n# plot \\n# %matplotlib ipympl\\n# %matplotlib inline\\n\\n# fig, ax = plt.subplots(figsize=(14,7))\\n# glob_regions[\\\"geometry\\\"].plot(ax=ax) # all countries that could be plotted\\n# glob_regions.centroid.plot(ax=ax, markersize=5, color=\\\"red\\\")\\n# glob_regions.representative_point().plot(ax=ax, markersize=5, color=\\\"green\\\")\\n\\n# plt.grid(alpha=0.5)\";\n var nbb_formatted_code = \"# load country regions\\nglob_regions = gpd.read_file(\\n coclico_data_dir.joinpath(\\\"XX_Global\\\", \\\"WB_countries_Admin0.geojson\\\")\\n)\\nglob_regions = glob_regions.to_crs(\\\"EPSG:4326\\\")\\n\\nglob_regions[\\\"geometry\\\"] = glob_regions.geometry # rename the geometry\\n# glob_regions[\\\"geometry\\\"] = glob_regions.centroid.buffer(0.5) # set the buffered centroid as geometry (for simplicity)\\nglob_regions[\\\"lon\\\"] = glob_regions.centroid.x # set the lon\\nglob_regions[\\\"lat\\\"] = glob_regions.centroid.y # set the lat\\n# glob_regions.representative_point() # usefull if for instance Aruba was part of NL\\n\\n# plot\\n# %matplotlib ipympl\\n# %matplotlib inline\\n\\n# fig, ax = plt.subplots(figsize=(14,7))\\n# glob_regions[\\\"geometry\\\"].plot(ax=ax) # all countries that could be plotted\\n# glob_regions.centroid.plot(ax=ax, markersize=5, color=\\\"red\\\")\\n# glob_regions.representative_point().plot(ax=ax, markersize=5, color=\\\"green\\\")\\n\\n# plt.grid(alpha=0.5)\";\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": [ + "# load country regions\n", + "glob_regions = gpd.read_file(\n", + " coclico_data_dir.joinpath(\"XX_Global\", \"WB_countries_Admin0.geojson\")\n", + ")\n", + "glob_regions = glob_regions.to_crs(\"EPSG:4326\")\n", + "\n", + "glob_regions[\"geometry\"] = glob_regions.geometry # rename the geometry\n", + "#glob_regions[\"geometry\"] = glob_regions.centroid.buffer(0.5) # set the buffered centroid as geometry (for simplicity to test at first, but never use in real dataset)\n", + "glob_regions[\"lon\"] = glob_regions.centroid.x # set the lon\n", + "glob_regions[\"lat\"] = glob_regions.centroid.y # set the lat\n", + "#glob_regions.representative_point() # usefull if for instance Aruba was part of NL\n", + "\n", + "# plot \n", + "# %matplotlib ipympl\n", + "# %matplotlib inline\n", + "\n", + "# fig, ax = plt.subplots(figsize=(14,7))\n", + "# glob_regions[\"geometry\"].plot(ax=ax) # all countries that could be plotted\n", + "# glob_regions.centroid.plot(ax=ax, markersize=5, color=\"red\")\n", + "# glob_regions.representative_point().plot(ax=ax, markersize=5, color=\"green\")\n", + "\n", + "# plt.grid(alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 10;\n var nbb_unformatted_code = \"ds_old = [\\\"China\\\", \\\"United States\\\", \\\"Taiwan\\\", \\\"Palestina\\\", \\\"Macedonia\\\", \\\"Caspian Sea\\\", \\\"Republic of Congo\\\"] # non WB compliant country names\\nds_new = [\\\"People's Republic of China\\\", \\\"United States of America\\\", \\\"\\\", \\\"Palestine\\\", \\\"Republic of Macedonia\\\", \\\"\\\", \\\"Republic of the Congo\\\"] # WB country name\\n\\n# replace countries with WB compliant country names\\nidx_rep = [idx for idx, x in enumerate(ds['Country'].values) if x in ds_old] # get ids for replacement task\\nds['Country'].values[idx_rep] = ds_new\";\n var nbb_formatted_code = \"ds_old = [\\n \\\"China\\\",\\n \\\"United States\\\",\\n \\\"Taiwan\\\",\\n \\\"Palestina\\\",\\n \\\"Macedonia\\\",\\n \\\"Caspian Sea\\\",\\n \\\"Republic of Congo\\\",\\n] # non WB compliant country names\\nds_new = [\\n \\\"People's Republic of China\\\",\\n \\\"United States of America\\\",\\n \\\"\\\",\\n \\\"Palestine\\\",\\n \\\"Republic of Macedonia\\\",\\n \\\"\\\",\\n \\\"Republic of the Congo\\\",\\n] # WB country name\\n\\n# replace countries with WB compliant country names\\nidx_rep = [\\n idx for idx, x in enumerate(ds[\\\"Country\\\"].values) if x in ds_old\\n] # get ids for replacement task\\nds[\\\"Country\\\"].values[idx_rep] = ds_new\";\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": [ + "ds_old = [\"China\", \"United States\", \"Taiwan\", \"Palestina\", \"Macedonia\", \"Caspian Sea\", \"Republic of Congo\"] # non WB compliant country names\n", + "ds_new = [\"People's Republic of China\", \"United States of America\", \"\", \"Palestine\", \"Republic of Macedonia\", \"\", \"Republic of the Congo\"] # WB country name\n", + "\n", + "# replace countries with WB compliant country names\n", + "idx_rep = [idx for idx, x in enumerate(ds['Country'].values) if x in ds_old] # get ids for replacement task\n", + "ds['Country'].values[idx_rep] = ds_new" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 11;\n var nbb_unformatted_code = \"# join datasets by keeping correct countries\\n\\n# check = []\\n# for i in ds[\\\"Country\\\"].values:\\n# if i in list(glob_regions[\\\"NAME_EN\\\"]):\\n# check.append(i)\\n# #print(\\\"in\\\", i)\\n# else:\\n# print(\\\"not in\\\", i)\\n\\n# make xarray dataframe\\ndspd = ds.to_dataframe()\\n\\n# note, this automatically removes country = \\\"\\\" from the dataframe (Taiwan; is part of China, and Caspian Sea)\\ndsgr = dspd.merge(glob_regions, left_on='Country', right_on='NAME_EN')\\n\\n# note, duplications in NL and NZ, we will only keep the biggest area polygons (the actual countries)\\ndef dup_idx(lst, item):\\n return [i for i, x in enumerate(lst) if x == item]\\n\\nrem_list = []\\nfor i in np.unique(dsgr.Country):\\n idx_dupl = dup_idx(dsgr.Country, i) # check if duplicate items in the list\\n if len(idx_dupl) > 1: # only retrieve area when more than one\\n area_dupl = [dsgr.Shape_Area[j] for j in idx_dupl] # get area of duplicate items\\n idx_dupl.pop(np.argmax(area_dupl)) # remove the one with the largest area\\n rem_list.append(idx_dupl)\\n\\ndsgr_red = dsgr.drop(list(chain.from_iterable(rem_list))) # reduce dataframe with duplicate idx \\n\\n# plot \\n# %matplotlib ipympl\\n# %matplotlib inline\\n\\n# fig, ax = plt.subplots(figsize=(14,7))\\n# gdf = gpd.GeoDataFrame(dsgr_red, geometry=\\\"geometry\\\")\\n# gdf.plot(ax=ax) # reduced countries with data\\n# plt.scatter(gdf.lon, gdf.lat, s=5, color=\\\"red\\\")\\n# plt.grid(alpha=0.5)\";\n var nbb_formatted_code = \"# join datasets by keeping correct countries\\n\\n# check = []\\n# for i in ds[\\\"Country\\\"].values:\\n# if i in list(glob_regions[\\\"NAME_EN\\\"]):\\n# check.append(i)\\n# #print(\\\"in\\\", i)\\n# else:\\n# print(\\\"not in\\\", i)\\n\\n# make xarray dataframe\\ndspd = ds.to_dataframe()\\n\\n# note, this automatically removes country = \\\"\\\" from the dataframe (Taiwan; is part of China, and Caspian Sea)\\ndsgr = dspd.merge(glob_regions, left_on=\\\"Country\\\", right_on=\\\"NAME_EN\\\")\\n\\n\\n# note, duplications in NL and NZ, we will only keep the biggest area polygons (the actual countries)\\ndef dup_idx(lst, item):\\n return [i for i, x in enumerate(lst) if x == item]\\n\\n\\nrem_list = []\\nfor i in np.unique(dsgr.Country):\\n idx_dupl = dup_idx(dsgr.Country, i) # check if duplicate items in the list\\n if len(idx_dupl) > 1: # only retrieve area when more than one\\n area_dupl = [\\n dsgr.Shape_Area[j] for j in idx_dupl\\n ] # get area of duplicate items\\n idx_dupl.pop(np.argmax(area_dupl)) # remove the one with the largest area\\n rem_list.append(idx_dupl)\\n\\ndsgr_red = dsgr.drop(\\n list(chain.from_iterable(rem_list))\\n) # reduce dataframe with duplicate idx\\n\\n# plot\\n# %matplotlib ipympl\\n# %matplotlib inline\\n\\n# fig, ax = plt.subplots(figsize=(14,7))\\n# gdf = gpd.GeoDataFrame(dsgr_red, geometry=\\\"geometry\\\")\\n# gdf.plot(ax=ax) # reduced countries with data\\n# plt.scatter(gdf.lon, gdf.lat, s=5, color=\\\"red\\\")\\n# plt.grid(alpha=0.5)\";\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": [ + "# join datasets by keeping correct countries\n", + "\n", + "# check = []\n", + "# for i in ds[\"Country\"].values:\n", + "# if i in list(glob_regions[\"NAME_EN\"]):\n", + "# check.append(i)\n", + "# #print(\"in\", i)\n", + "# else:\n", + "# print(\"not in\", i)\n", + "\n", + "# make xarray dataframe\n", + "dspd = ds.to_dataframe()\n", + "\n", + "# note, this automatically removes country = \"\" from the dataframe (Taiwan; is part of China, and Caspian Sea)\n", + "dsgr = dspd.merge(glob_regions, left_on='Country', right_on='NAME_EN')\n", + "\n", + "# note, duplications in NL and NZ, we will only keep the biggest area polygons (the actual countries)\n", + "def dup_idx(lst, item):\n", + " return [i for i, x in enumerate(lst) if x == item]\n", + "\n", + "rem_list = []\n", + "for i in np.unique(dsgr.Country):\n", + " idx_dupl = dup_idx(dsgr.Country, i) # check if duplicate items in the list\n", + " if len(idx_dupl) > 1: # only retrieve area when more than one\n", + " area_dupl = [dsgr.Shape_Area[j] for j in idx_dupl] # get area of duplicate items\n", + " idx_dupl.pop(np.argmax(area_dupl)) # remove the one with the largest area\n", + " rem_list.append(idx_dupl)\n", + "\n", + "dsgr_red = dsgr.drop(list(chain.from_iterable(rem_list))) # reduce dataframe with duplicate idx \n", + "\n", + "# plot \n", + "# %matplotlib ipympl\n", + "# %matplotlib inline\n", + "\n", + "# fig, ax = plt.subplots(figsize=(14,7))\n", + "# gdf = gpd.GeoDataFrame(dsgr_red, geometry=\"geometry\")\n", + "# gdf.plot(ax=ax) # reduced countries with data\n", + "# plt.scatter(gdf.lon, gdf.lat, s=5, color=\"red\")\n", + "# plt.grid(alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 12;\n var nbb_unformatted_code = \"# drop countries with \\\"\\\" from the dataframe\\nidx_drop = [idx for idx, i in enumerate(ds.Country) if i == \\\"\\\"] \\nds = ds.drop_sel(index=idx_drop)\";\n var nbb_formatted_code = \"# drop countries with \\\"\\\" from the dataframe\\nidx_drop = [idx for idx, i in enumerate(ds.Country) if i == \\\"\\\"]\\nds = ds.drop_sel(index=idx_drop)\";\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": [ + "# drop countries with \"\" from the dataframe\n", + "idx_drop = [idx for idx, i in enumerate(ds.Country) if i == \"\"] \n", + "ds = ds.drop_sel(index=idx_drop)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 13;\n var nbb_unformatted_code = \"# add geometries and lon, lat to file\\n\\n# extract geometries of nut2 regions in well-known binary format\\ngeoms = dsgr_red[\\\"geometry\\\"].apply(lambda x: wkb.dumps(x))\\n\\n# rename dims and add new data to dataset\\nds = ds.assign_coords({\\\"geometry\\\": (\\\"index\\\", geoms)})\\nds = ds.assign_coords(lon=(\\\"index\\\", dsgr_red.lon))\\nds = ds.assign_coords(lat=(\\\"index\\\", dsgr_red.lat))\";\n var nbb_formatted_code = \"# add geometries and lon, lat to file\\n\\n# extract geometries of nut2 regions in well-known binary format\\ngeoms = dsgr_red[\\\"geometry\\\"].apply(lambda x: wkb.dumps(x))\\n\\n# rename dims and add new data to dataset\\nds = ds.assign_coords({\\\"geometry\\\": (\\\"index\\\", geoms)})\\nds = ds.assign_coords(lon=(\\\"index\\\", dsgr_red.lon))\\nds = ds.assign_coords(lat=(\\\"index\\\", dsgr_red.lat))\";\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": [ + "# add geometries and lon, lat to file\n", + "\n", + "# extract geometries of nut2 regions in well-known binary format\n", + "geoms = dsgr_red[\"geometry\"].apply(lambda x: wkb.dumps(x))\n", + "\n", + "# rename dims and add new data to dataset\n", + "ds = ds.assign_coords({\"geometry\": (\"index\", geoms)})\n", + "ds = ds.assign_coords(lon=(\"index\", dsgr_red.lon))\n", + "ds = ds.assign_coords(lat=(\"index\", dsgr_red.lat))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 14;\n var nbb_unformatted_code = \"# populate the geometry coordinate with metadata\\nadd_geom_attrs = {\\n \\\"geometry\\\": {\\n \\\"long_name\\\": \\\"World administrative boundaries (polygons)\\\",\\n \\\"geometry_type\\\": \\\"polygon\\\",\\n \\\"units\\\": \\\"degree\\\",\\n \\\"comment\\\": \\\"These world boundaries (2020 version) are available from the World Bank, distributed in well-known binary format (wkb)\\\",\\n \\\"crs_wkt\\\": f\\\"{glob_regions.crs.to_epsg()}\\\",\\n },\\n}\\n\\nfor k, v in add_geom_attrs.items():\\n ds[k].attrs = add_geom_attrs[k]\";\n var nbb_formatted_code = \"# populate the geometry coordinate with metadata\\nadd_geom_attrs = {\\n \\\"geometry\\\": {\\n \\\"long_name\\\": \\\"World administrative boundaries (polygons)\\\",\\n \\\"geometry_type\\\": \\\"polygon\\\",\\n \\\"units\\\": \\\"degree\\\",\\n \\\"comment\\\": \\\"These world boundaries (2020 version) are available from the World Bank, distributed in well-known binary format (wkb)\\\",\\n \\\"crs_wkt\\\": f\\\"{glob_regions.crs.to_epsg()}\\\",\\n },\\n}\\n\\nfor k, v in add_geom_attrs.items():\\n ds[k].attrs = add_geom_attrs[k]\";\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": [ + "# populate the geometry coordinate with metadata\n", + "add_geom_attrs = {\n", + " \"geometry\": {\n", + " \"long_name\": \"World administrative boundaries (polygons)\",\n", + " \"geometry_type\": \"polygon\",\n", + " \"units\": \"degree\",\n", + " \"comment\": \"These world boundaries (2020 version) are available from the World Bank, distributed in well-known binary format (wkb)\",\n", + " \"crs_wkt\": f\"{glob_regions.crs.to_epsg()}\",\n", + " },\n", + "}\n", + "\n", + "for k, v in add_geom_attrs.items():\n", + " ds[k].attrs = add_geom_attrs[k]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make CF compliant alterations to the NetCDF files (dataset dependent)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:                                           (index: 108)\n",
+       "Coordinates:\n",
+       "  * index                                             (index) int64 0 1 ... 109\n",
+       "    geometry                                          (index) object b'\\x01\\x...\n",
+       "    lon                                               (index) float64 104.0 ....\n",
+       "    lat                                               (index) float64 36.52 ....\n",
+       "Data variables:\n",
+       "    Country                                           (index) object "People'...\n",
+       "    Potential exposed population in 2010 (Million)    (index) float64 335.9 ....\n",
+       "    Exposed GDP (EGDP) (Billion US$)                  (index) float64 1.532e+...\n",
+       "    Potential global subsidence index in 2010 (PGSI)  (index) float64 60.79 ....\n",
+       "    Potential exposed population in 2040 (Million)    (index) float64 345.0 ....\n",
+       "    Exposed GDP (EGDP) (Billion US$) in 2040          (index) float64 1.573e+...\n",
+       "    Potential subsidence index 2040                   (index) float64 51.25 ....
" + ], + "text/plain": [ + "\n", + "Dimensions: (index: 108)\n", + "Coordinates:\n", + " * index (index) int64 0 1 ... 109\n", + " geometry (index) object b'\\x01\\x...\n", + " lon (index) float64 104.0 ....\n", + " lat (index) float64 36.52 ....\n", + "Data variables:\n", + " Country (index) object \"People'...\n", + " Potential exposed population in 2010 (Million) (index) float64 335.9 ....\n", + " Exposed GDP (EGDP) (Billion US$) (index) float64 1.532e+...\n", + " Potential global subsidence index in 2010 (PGSI) (index) float64 60.79 ....\n", + " Potential exposed population in 2040 (Million) (index) float64 345.0 ....\n", + " Exposed GDP (EGDP) (Billion US$) in 2040 (index) float64 1.573e+...\n", + " Potential subsidence index 2040 (index) float64 51.25 ...." + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 15;\n var nbb_unformatted_code = \"# open datasets\\n# ds = xr.open_dataset(dataset_dir_path)\\n\\n# check original dataset\\nds\";\n var nbb_formatted_code = \"# open datasets\\n# ds = xr.open_dataset(dataset_dir_path)\\n\\n# check original dataset\\nds\";\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": [ + "# open datasets\n", + "# ds = xr.open_dataset(dataset_dir_path)\n", + "\n", + "# check original dataset\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 16;\n var nbb_unformatted_code = \"# NetCDF attribute alterations by means of metadata template\\nf_global = open(dataset_dir.joinpath(\\\"metadata_subsidence.json\\\"))\\nmeta_global = json.load(f_global)\\n\\nfor attr_name, attr_val in meta_global.items():\\n if attr_name == 'PROVIDERS':\\n attr_val = json.dumps(attr_val)\\n ds.attrs[attr_name] = attr_val\\n\\nds.attrs['Conventions'] = \\\"CF-1.8\\\"\";\n var nbb_formatted_code = \"# NetCDF attribute alterations by means of metadata template\\nf_global = open(dataset_dir.joinpath(\\\"metadata_subsidence.json\\\"))\\nmeta_global = json.load(f_global)\\n\\nfor attr_name, attr_val in meta_global.items():\\n if attr_name == \\\"PROVIDERS\\\":\\n attr_val = json.dumps(attr_val)\\n ds.attrs[attr_name] = attr_val\\n\\nds.attrs[\\\"Conventions\\\"] = \\\"CF-1.8\\\"\";\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": [ + "# NetCDF attribute alterations by means of metadata template\n", + "f_global = open(dataset_dir.joinpath(\"metadata_subsidence.json\"))\n", + "meta_global = json.load(f_global)\n", + "\n", + "for attr_name, attr_val in meta_global.items():\n", + " if attr_name == 'PROVIDERS':\n", + " attr_val = json.dumps(attr_val)\n", + " ds.attrs[attr_name] = attr_val\n", + "\n", + "ds.attrs['Conventions'] = \"CF-1.8\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 17;\n var nbb_unformatted_code = \"dsn = copy.deepcopy(ds)\\n\\n# rename dims and vars\\ndsn = dsn.rename_dims({\\\"index\\\": \\\"stations\\\"})\\ndsn = dsn.rename_vars({\\\"Country\\\": \\\"country\\\"})\\n\\n# move variable to coords to avoid duplication in dimensions\\n#dsn = dsn.set_coords([\\\"country\\\"])\\ndsn = dsn.assign_coords({\\\"country\\\": (\\\"stations\\\", np.array(dsn[\\\"country\\\"].values, dtype=\\\"S\\\"))})\\n\\n# merge variables for 2010 and 2040 into one variable\\ndata_array_eapa = np.concatenate([dsn[\\\"Potential exposed population in 2010 (Million)\\\"].values, dsn[\\\"Potential exposed population in 2040 (Million)\\\"].values])\\ndata_array_eapar = data_array_eapa.reshape((2,len(dsn[\\\"Potential exposed population in 2010 (Million)\\\"].values)))\\ndata_array_egdp = np.concatenate([dsn[\\\"Exposed GDP (EGDP) (Billion US$)\\\"].values, dsn[\\\"Exposed GDP (EGDP) (Billion US$) in 2040\\\"].values])\\ndata_array_egdpr = data_array_egdp.reshape((2,len(dsn[\\\"Exposed GDP (EGDP) (Billion US$)\\\"].values)))\\ndata_array_epsi = np.concatenate([dsn[\\\"Potential global subsidence index in 2010 (PGSI)\\\"].values, dsn[\\\"Potential subsidence index 2040\\\"].values])\\ndata_array_epsir = data_array_epsi.reshape((2,len(dsn[\\\"Potential global subsidence index in 2010 (PGSI)\\\"].values)))\\n\\n# remove variables\\ndsn = dsn.drop([\\\"index\\\", \\\"Potential exposed population in 2010 (Million)\\\", \\\"Potential exposed population in 2040 (Million)\\\", \\\"Exposed GDP (EGDP) (Billion US$)\\\", \\n \\\"Exposed GDP (EGDP) (Billion US$) in 2040\\\", \\\"Potential global subsidence index in 2010 (PGSI)\\\", \\\"Potential subsidence index 2040\\\"])\\n\\n# add dimensions\\ndsn = dsn.expand_dims(dim={\\\"time\\\": [2010, 2040]})\\n\\n# add variables\\ndsn = dsn.assign(eapa=([\\\"time\\\", \\\"stations\\\"], data_array_eapar))\\ndsn = dsn.assign(egdp=([\\\"time\\\", \\\"stations\\\"], data_array_egdpr))\\ndsn = dsn.assign(epsi=([\\\"time\\\", \\\"stations\\\"], data_array_epsir))\\n\\n# add or change certain variable / coordinate attributes\\ndataset_attributes = {\\n \\\"time\\\": {\\\"long_name\\\": \\\"Time\\\", \\\"units\\\": \\\"yr\\\"}, # TODO: year as unit raises a warning for not being exactly a calender year\\n \\\"lon\\\": {\\\"standard_name\\\": \\\"longitude\\\", \\\"long_name\\\": \\\"Longitude\\\", \\\"units\\\": \\\"degrees_east\\\"},\\n \\\"lat\\\": {\\\"standard_name\\\": \\\"longitude\\\", \\\"long_name\\\": \\\"Latitude\\\", \\\"units\\\": \\\"degrees_north\\\"},\\n \\\"country\\\": {\\\"long_name\\\": \\\"Country\\\", \\\"units\\\": \\\"1\\\"},\\n \\\"eapa\\\": {\\\"long_name\\\": \\\"Expected annual people affected\\\", \\\"units\\\": \\\"1e6\\\"},\\n \\\"egdp\\\": {\\\"long_name\\\": \\\"Exposed GDP (in US$)\\\", \\\"units\\\": \\\"1e9\\\"}, # TODO: check how unit works in the F/E, compare to CFR CoCliCo dataset\\n \\\"epsi\\\": {\\\"long_name\\\": \\\"Potential subsidence index\\\", \\\"units\\\": \\\"1\\\"}, # set to 1 if no unit\\n} # specify custom (CF convention) attributes\\n\\n# add / overwrite attributes\\nfor k, v in dataset_attributes.items():\\n try:\\n dsn[k].attrs = dataset_attributes[k]\\n except:\\n continue\";\n var nbb_formatted_code = \"dsn = copy.deepcopy(ds)\\n\\n# rename dims and vars\\ndsn = dsn.rename_dims({\\\"index\\\": \\\"stations\\\"})\\ndsn = dsn.rename_vars({\\\"Country\\\": \\\"country\\\"})\\n\\n# move variable to coords to avoid duplication in dimensions\\n# dsn = dsn.set_coords([\\\"country\\\"])\\ndsn = dsn.assign_coords(\\n {\\\"country\\\": (\\\"stations\\\", np.array(dsn[\\\"country\\\"].values, dtype=\\\"S\\\"))}\\n)\\n\\n# merge variables for 2010 and 2040 into one variable\\ndata_array_eapa = np.concatenate(\\n [\\n dsn[\\\"Potential exposed population in 2010 (Million)\\\"].values,\\n dsn[\\\"Potential exposed population in 2040 (Million)\\\"].values,\\n ]\\n)\\ndata_array_eapar = data_array_eapa.reshape(\\n (2, len(dsn[\\\"Potential exposed population in 2010 (Million)\\\"].values))\\n)\\ndata_array_egdp = np.concatenate(\\n [\\n dsn[\\\"Exposed GDP (EGDP) (Billion US$)\\\"].values,\\n dsn[\\\"Exposed GDP (EGDP) (Billion US$) in 2040\\\"].values,\\n ]\\n)\\ndata_array_egdpr = data_array_egdp.reshape(\\n (2, len(dsn[\\\"Exposed GDP (EGDP) (Billion US$)\\\"].values))\\n)\\ndata_array_epsi = np.concatenate(\\n [\\n dsn[\\\"Potential global subsidence index in 2010 (PGSI)\\\"].values,\\n dsn[\\\"Potential subsidence index 2040\\\"].values,\\n ]\\n)\\ndata_array_epsir = data_array_epsi.reshape(\\n (2, len(dsn[\\\"Potential global subsidence index in 2010 (PGSI)\\\"].values))\\n)\\n\\n# remove variables\\ndsn = dsn.drop(\\n [\\n \\\"index\\\",\\n \\\"Potential exposed population in 2010 (Million)\\\",\\n \\\"Potential exposed population in 2040 (Million)\\\",\\n \\\"Exposed GDP (EGDP) (Billion US$)\\\",\\n \\\"Exposed GDP (EGDP) (Billion US$) in 2040\\\",\\n \\\"Potential global subsidence index in 2010 (PGSI)\\\",\\n \\\"Potential subsidence index 2040\\\",\\n ]\\n)\\n\\n# add dimensions\\ndsn = dsn.expand_dims(dim={\\\"time\\\": [2010, 2040]})\\n\\n# add variables\\ndsn = dsn.assign(eapa=([\\\"time\\\", \\\"stations\\\"], data_array_eapar))\\ndsn = dsn.assign(egdp=([\\\"time\\\", \\\"stations\\\"], data_array_egdpr))\\ndsn = dsn.assign(epsi=([\\\"time\\\", \\\"stations\\\"], data_array_epsir))\\n\\n# add or change certain variable / coordinate attributes\\ndataset_attributes = {\\n \\\"time\\\": {\\n \\\"long_name\\\": \\\"Time\\\",\\n \\\"units\\\": \\\"yr\\\",\\n }, # TODO: year as unit raises a warning for not being exactly a calender year\\n \\\"lon\\\": {\\n \\\"standard_name\\\": \\\"longitude\\\",\\n \\\"long_name\\\": \\\"Longitude\\\",\\n \\\"units\\\": \\\"degrees_east\\\",\\n },\\n \\\"lat\\\": {\\n \\\"standard_name\\\": \\\"longitude\\\",\\n \\\"long_name\\\": \\\"Latitude\\\",\\n \\\"units\\\": \\\"degrees_north\\\",\\n },\\n \\\"country\\\": {\\\"long_name\\\": \\\"Country\\\", \\\"units\\\": \\\"1\\\"},\\n \\\"eapa\\\": {\\\"long_name\\\": \\\"Expected annual people affected\\\", \\\"units\\\": \\\"1e6\\\"},\\n \\\"egdp\\\": {\\n \\\"long_name\\\": \\\"Exposed GDP (in US$)\\\",\\n \\\"units\\\": \\\"1e9\\\",\\n }, # TODO: check how unit works in the F/E, compare to CFR CoCliCo dataset\\n \\\"epsi\\\": {\\n \\\"long_name\\\": \\\"Potential subsidence index\\\",\\n \\\"units\\\": \\\"1\\\",\\n }, # set to 1 if no unit\\n} # specify custom (CF convention) attributes\\n\\n# add / overwrite attributes\\nfor k, v in dataset_attributes.items():\\n try:\\n dsn[k].attrs = dataset_attributes[k]\\n except:\\n continue\";\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": [ + "dsn = copy.deepcopy(ds)\n", + "\n", + "# rename dims and vars\n", + "dsn = dsn.rename_dims({\"index\": \"stations\"})\n", + "dsn = dsn.rename_vars({\"Country\": \"country\"})\n", + "\n", + "# move variable to coords to avoid duplication in dimensions\n", + "#dsn = dsn.set_coords([\"country\"])\n", + "dsn = dsn.assign_coords({\"country\": (\"stations\", np.array(dsn[\"country\"].values, dtype=\"S\"))})\n", + "\n", + "# merge variables for 2010 and 2040 into one variable\n", + "data_array_eapa = np.concatenate([dsn[\"Potential exposed population in 2010 (Million)\"].values, dsn[\"Potential exposed population in 2040 (Million)\"].values])\n", + "data_array_eapar = data_array_eapa.reshape((2,len(dsn[\"Potential exposed population in 2010 (Million)\"].values)))\n", + "data_array_egdp = np.concatenate([dsn[\"Exposed GDP (EGDP) (Billion US$)\"].values, dsn[\"Exposed GDP (EGDP) (Billion US$) in 2040\"].values])\n", + "data_array_egdpr = data_array_egdp.reshape((2,len(dsn[\"Exposed GDP (EGDP) (Billion US$)\"].values)))\n", + "data_array_epsi = np.concatenate([dsn[\"Potential global subsidence index in 2010 (PGSI)\"].values, dsn[\"Potential subsidence index 2040\"].values])\n", + "data_array_epsir = data_array_epsi.reshape((2,len(dsn[\"Potential global subsidence index in 2010 (PGSI)\"].values)))\n", + "\n", + "# remove variables\n", + "dsn = dsn.drop([\"index\", \"Potential exposed population in 2010 (Million)\", \"Potential exposed population in 2040 (Million)\", \"Exposed GDP (EGDP) (Billion US$)\", \n", + " \"Exposed GDP (EGDP) (Billion US$) in 2040\", \"Potential global subsidence index in 2010 (PGSI)\", \"Potential subsidence index 2040\"])\n", + "\n", + "# add dimensions\n", + "dsn = dsn.expand_dims(dim={\"time\": [2010, 2040]})\n", + "\n", + "# add variables\n", + "dsn = dsn.assign(eapa=([\"time\", \"stations\"], data_array_eapar))\n", + "dsn = dsn.assign(egdp=([\"time\", \"stations\"], data_array_egdpr))\n", + "dsn = dsn.assign(epsi=([\"time\", \"stations\"], data_array_epsir))\n", + "\n", + "# add or change certain variable / coordinate attributes\n", + "dataset_attributes = {\n", + " \"time\": {\"long_name\": \"Time\", \"units\": \"yr\"}, # TODO: year as unit raises a warning for not being exactly a calender year\n", + " \"lon\": {\"standard_name\": \"longitude\", \"long_name\": \"Longitude\", \"units\": \"degrees_east\"},\n", + " \"lat\": {\"standard_name\": \"longitude\", \"long_name\": \"Latitude\", \"units\": \"degrees_north\"},\n", + " \"country\": {\"long_name\": \"Country\", \"units\": \"1\"},\n", + " \"eapa\": {\"long_name\": \"Expected annual people affected\", \"units\": \"1e6\"},\n", + " \"egdp\": {\"long_name\": \"Exposed GDP (in US$)\", \"units\": \"1e9\"}, # TODO: check how unit works in the F/E, compare to CFR CoCliCo dataset\n", + " \"epsi\": {\"long_name\": \"Potential subsidence index\", \"units\": \"1\"}, # set to 1 if no unit\n", + "} # specify custom (CF convention) attributes\n", + "\n", + "# add / overwrite attributes\n", + "for k, v in dataset_attributes.items():\n", + " try:\n", + " dsn[k].attrs = dataset_attributes[k]\n", + " except:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:   (time: 2, stations: 108)\n",
+       "Coordinates:\n",
+       "  * time      (time) int32 2010 2040\n",
+       "    country   (stations) |S26 b"People's Republic of China" ... b'Zambia'\n",
+       "    geometry  (stations) object b'\\x01\\x06\\x00\\x00\\x00G\\x00\\x00\\x00\\x01\\x03\\x...\n",
+       "    lon       (stations) float64 104.0 -112.5 138.0 29.86 ... 20.79 45.85 27.77\n",
+       "    lat       (stations) float64 36.52 45.69 37.55 26.49 ... 44.22 6.063 -13.46\n",
+       "Dimensions without coordinates: stations\n",
+       "Data variables:\n",
+       "    eapa      (time, stations) float64 335.9 39.31 27.22 ... 0.2337 0.002928\n",
+       "    egdp      (time, stations) float64 1.532e+03 1.902e+03 ... 0.0 0.004284\n",
+       "    epsi      (time, stations) float64 60.79 54.23 40.96 40.52 ... 0.0 0.0 0.0\n",
+       "Attributes: (12/21)\n",
+       "    TITLE:               Land Subsidence Threat\n",
+       "    TITLE_ABBREVIATION:  sub_threat\n",
+       "    DESCRIPTION:         Subsidence, the lowering of Earth's land surface, is...\n",
+       "    SHORT_DESCRIPTION:   To raise awareness and inform decision-making, we ev...\n",
+       "    INSTITUTION:         Instituto Geológico y Minero de España\n",
+       "    PROVIDERS:           {"name": "Instituto Geol\\u00f3gico y Minero de Espa\\...\n",
+       "    ...                  ...\n",
+       "    DOI:                 https://www.science.org/doi/10.1126/science.abb8549\n",
+       "    LONG_NAME:           sub_threat\n",
+       "    UNITS:               \n",
+       "    COMMENT:             \n",
+       "    CRS:                 EPSG:4326\n",
+       "    Conventions:         CF-1.8
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2, stations: 108)\n", + "Coordinates:\n", + " * time (time) int32 2010 2040\n", + " country (stations) |S26 b\"People's Republic of China\" ... b'Zambia'\n", + " geometry (stations) object b'\\x01\\x06\\x00\\x00\\x00G\\x00\\x00\\x00\\x01\\x03\\x...\n", + " lon (stations) float64 104.0 -112.5 138.0 29.86 ... 20.79 45.85 27.77\n", + " lat (stations) float64 36.52 45.69 37.55 26.49 ... 44.22 6.063 -13.46\n", + "Dimensions without coordinates: stations\n", + "Data variables:\n", + " eapa (time, stations) float64 335.9 39.31 27.22 ... 0.2337 0.002928\n", + " egdp (time, stations) float64 1.532e+03 1.902e+03 ... 0.0 0.004284\n", + " epsi (time, stations) float64 60.79 54.23 40.96 40.52 ... 0.0 0.0 0.0\n", + "Attributes: (12/21)\n", + " TITLE: Land Subsidence Threat\n", + " TITLE_ABBREVIATION: sub_threat\n", + " DESCRIPTION: Subsidence, the lowering of Earth's land surface, is...\n", + " SHORT_DESCRIPTION: To raise awareness and inform decision-making, we ev...\n", + " INSTITUTION: Instituto Geológico y Minero de España\n", + " PROVIDERS: {\"name\": \"Instituto Geol\\u00f3gico y Minero de Espa\\...\n", + " ... ...\n", + " DOI: https://www.science.org/doi/10.1126/science.abb8549\n", + " LONG_NAME: sub_threat\n", + " UNITS: \n", + " COMMENT: \n", + " CRS: EPSG:4326\n", + " Conventions: CF-1.8" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 18;\n var nbb_unformatted_code = \"# check the xarray dataset, best practice is to have as many as possible bold dimensions (dimension == coordinate name).\\n# in this way, the Front-End can access the variable directly without having to index the variable first\\n\\ndsn\";\n var nbb_formatted_code = \"# check the xarray dataset, best practice is to have as many as possible bold dimensions (dimension == coordinate name).\\n# in this way, the Front-End can access the variable directly without having to index the variable first\\n\\ndsn\";\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": [ + "# check the xarray dataset, best practice is to have as many as possible bold dimensions (dimension == coordinate name).\n", + "# in this way, the Front-End can access the variable directly without having to index the variable first\n", + "\n", + "dsn" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 19;\n var nbb_unformatted_code = \"# write to NetCDF file to check compliancy\\n\\n# prevent file locking, see: https://github.com/pydata/xarray/issues/2376\\nimport os\\nos.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'\\n\\ndsn.to_netcdf(path=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"))\";\n var nbb_formatted_code = \"# write to NetCDF file to check compliancy\\n\\n# prevent file locking, see: https://github.com/pydata/xarray/issues/2376\\nimport os\\n\\nos.environ[\\\"HDF5_USE_FILE_LOCKING\\\"] = \\\"FALSE\\\"\\n\\ndsn.to_netcdf(path=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"))\";\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": [ + "# write to NetCDF file to check compliancy\n", + "\n", + "# prevent file locking, see: https://github.com/pydata/xarray/issues/2376\n", + "import os\n", + "os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'\n", + "\n", + "dsn.to_netcdf(path=dataset_dir.joinpath(dataset_out_file + \"_CF.nc\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check CF compliancy altered NetCDF files" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 20;\n var nbb_unformatted_code = \"%%capture cap --no-stderr\\n# check altered CF compliancy\\n\\ncheck_compliancy(testfile=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"), working_dir=CF_dir)\";\n var nbb_formatted_code = \"%%capture cap --no-stderr\\n# check altered CF compliancy\\n\\ncheck_compliancy(testfile=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"), working_dir=CF_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": [ + "%%capture cap --no-stderr\n", + "# check altered CF compliancy\n", + "\n", + "check_compliancy(testfile=dataset_dir.joinpath(dataset_out_file + \"_CF.nc\"), working_dir=CF_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'P:\\\\11208003-latedeo2022\\\\020_InternationalDeltaPortfolio\\\\datasets\\\\00_mapping_global_threat_of_land_subsidence\\\\Global_TLS_CF.nc': {'warnings': '0', 'errors': '0'}}\n" + ] + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 21;\n var nbb_unformatted_code = \"# save altered CF compliancy\\nsave_compliancy(\\n cap,\\n testfile=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"),\\n working_dir=CF_dir,\\n)\";\n var nbb_formatted_code = \"# save altered CF compliancy\\nsave_compliancy(\\n cap,\\n testfile=dataset_dir.joinpath(dataset_out_file + \\\"_CF.nc\\\"),\\n working_dir=CF_dir,\\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": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# save altered CF compliancy\n", + "save_compliancy(\n", + " cap,\n", + " testfile=dataset_dir.joinpath(dataset_out_file + \"_CF.nc\"),\n", + " working_dir=CF_dir,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write data to Zarr files" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 23;\n var nbb_unformatted_code = \"# export to zarr in write mode (to overwrite if exists)\\ndsn.to_zarr(dataset_dir.joinpath(\\\"%s.zarr\\\" % dataset_out_file), mode=\\\"w\\\")\";\n var nbb_formatted_code = \"# export to zarr in write mode (to overwrite if exists)\\ndsn.to_zarr(dataset_dir.joinpath(\\\"%s.zarr\\\" % dataset_out_file), mode=\\\"w\\\")\";\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": [ + "# export to zarr in write mode (to overwrite if exists)\n", + "dsn.to_zarr(dataset_dir.joinpath(\"%s.zarr\" % dataset_out_file), mode=\"w\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/STAC/data/scripts/09_subsidence_stacs.py b/STAC/data/scripts/09_subsidence_stacs.py new file mode 100644 index 000000000..c1d008cad --- /dev/null +++ b/STAC/data/scripts/09_subsidence_stacs.py @@ -0,0 +1,294 @@ +# %% +import os +import pathlib +import sys +import json +from posixpath import join as urljoin + +import pystac +from coclicodata.drive_config import p_drive +from coclicodata.etl.cloud_utils import dataset_from_google_cloud +from coclicodata.etl.extract import get_mapbox_url, zero_terminated_bytes_as_str +from pystac import Catalog, CatalogType, Collection, Summaries +from coclicodata.coclico_stac.io import CoCliCoStacIO +from coclicodata.coclico_stac.layouts import CoCliCoZarrLayout +from coclicodata.coclico_stac.templates import ( + extend_links, + gen_default_collection_props, + gen_default_item, + gen_default_item_props, + gen_default_summaries, + gen_mapbox_asset, + gen_zarr_asset, + get_template_collection, +) +from coclicodata.coclico_stac.extension import CoclicoExtension +from coclicodata.coclico_stac.datacube import add_datacube +from coclicodata.coclico_stac.utils import ( + get_dimension_dot_product, + get_dimension_values, + get_mapbox_item_id, + rm_special_characters, +) + +if __name__ == "__main__": + # hard-coded input params at project level + BUCKET_NAME = "dgds-data-public" + BUCKET_PROJ = "gca" + MAPBOX_PROJ = "global-data-viewer" + + # hard-coded input params at project level + gca_data_dir = pathlib.Path( + p_drive, + "11208003-latedeo2022", + "020_InternationalDeltaPortfolio", + "datasets", + ) + dataset_dir = gca_data_dir.joinpath(r"00_mapping_global_threat_of_land_subsidence") + + # opening metadata + metadata_fp = dataset_dir.joinpath("metadata_subsidence.json") + with open(metadata_fp, "r") as f: + metadata = json.load(f) + + # STAC configs + STAC_DIR = "current" + TEMPLATE_COLLECTION = "template" # stac template for dataset collection + COLLECTION_TITLE = metadata["TITLE"] + COLLECTION_ID = metadata["TITLE_ABBREVIATION"] # name of stac collection + DATASET_DESCRIPTION = metadata["DESCRIPTION"] + + # hard-coded input params which differ per dataset + DATASET_FILENAME = "Global_TLS.zarr" + VARIABLES = ["eapa", "egdp", "epsi"] # xarray variables in dataset + X_DIMENSION = "lon" # False, None or str; spatial lon dim used by datacube + Y_DIMENSION = "lat" # False, None or str; spatial lat dim "" + TEMPORAL_DIMENSION = "time" # False, None or str; temporal "" + ADDITIONAL_DIMENSIONS = ["time"] # Empty list or list of str; additional dims "" + DIMENSIONS_TO_IGNORE = ["stations"] # List of str; dims ignored by datacube + MAP_SELECTION_DIMS = { + "time": [2010, 2040], + } + # hard-coded frontend properties + STATIONS = "locationId" + TYPE = "fill" + ON_CLICK = {} + + # these are added at collection level (for graph plot in the dashboard) + UNITS = "m" + PLOT_SERIES = ( # leave empty if not applicable (i.e. the dimensions are already span up) + "" + ) + PLOT_X_AXIS = "time" + PLOT_TYPE = "line" + MIN = 0 + MAX = 3 + LINEAR_GRADIENT = [ + {"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}, + ] + + # functions to generate properties that vary per dataset but cannot be hard-coded because + # they also require input arguments + def get_paint_props(item_key: str): + return { + "fill-color": [ + "interpolate", + ["linear"], + ["get", item_key], + 0, + "hsla(110, 90%, 80%, 0.25)", + 1.5, + "hsla(55, 88%, 53%, 0.25)", + 3.0, + "hsla(0, 90%, 70%, 0.25)", + ], + } + + # semi hard-coded input params + gcs_zarr_store = os.path.join("gcs://", BUCKET_NAME, BUCKET_PROJ, DATASET_FILENAME) + gcs_api_zarr_store = os.path.join( + "https://storage.googleapis.com", BUCKET_NAME, BUCKET_PROJ, DATASET_FILENAME + ) + + # read data from gcs zarr store + ds = dataset_from_google_cloud( + bucket_name=BUCKET_NAME, bucket_proj=BUCKET_PROJ, zarr_filename=DATASET_FILENAME + ) + + # import xarray as xr + + # fpath = pathlib.Path.home().joinpath("data", "tmp", "europe_storm_surge_level.zarr") + # ds = xr.open_zarr(fpath) + + # cast zero terminated bytes to str because json library cannot write handle bytes + ds = zero_terminated_bytes_as_str(ds) + + # remove characters that cause problems in the frontend. + ds = rm_special_characters( + ds, dimensions_to_check=ADDITIONAL_DIMENSIONS, characters=["%"] + ) + + title = ds.attrs.get("title", COLLECTION_ID) + + # load coclico data catalog + catalog = Catalog.from_file( + os.path.join(pathlib.Path(__file__).parent.parent, STAC_DIR, "catalog.json") + ) + + template_fp = os.path.join( + pathlib.Path(__file__).parent.parent, + STAC_DIR, + TEMPLATE_COLLECTION, + "collection.json", + ) + + # generate collection for dataset + collection = get_template_collection( + template_fp=template_fp, + collection_id=COLLECTION_ID, + title=COLLECTION_TITLE, + description=DATASET_DESCRIPTION, + keywords=[], + ) + + # add datacube dimensions derived from xarray dataset to dataset stac_obj + collection = add_datacube( + stac_obj=collection, + ds=ds, + x_dimension=X_DIMENSION, + y_dimension=Y_DIMENSION, + temporal_dimension=TEMPORAL_DIMENSION, + additional_dimensions=ADDITIONAL_DIMENSIONS, + reference_system=ds.CRS, + ) + + # This dataset has quite some dimensions, so if we would parse all information the end-user + # would be overwhelmed by all options. So for the stac items that we generate for the frontend + # visualizations a subset of the data is selected. Of course, this operation is dataset specific. + for k, v in MAP_SELECTION_DIMS.items(): + if k in ds.dims and ds.coords: + ds = ds.sel({k: v}) + else: + try: + # assume that coordinates with strings always have same dim name but with n + ds = ds.sel({"n" + k: k == v}) + except: + raise ValueError(f"Cannot find {k}") + + # generate stac feature keys (strings which will be stac item ids) for mapbox layers + if len(ADDITIONAL_DIMENSIONS) > 0: + dimvals = get_dimension_values(ds, dimensions_to_ignore=DIMENSIONS_TO_IGNORE) + dimcombs = get_dimension_dot_product(dimvals) + else: + dimvals = {} + dimcombs = [] + + # TODO: check what can be customized in the layout + layout = CoCliCoZarrLayout() + + # create stac collection per variable and add to dataset collection + for var in VARIABLES: + # add zarr store as asset to stac_obj + collection.add_asset("data", gen_zarr_asset(title, gcs_api_zarr_store)) + + # stac items are generated per AdditionalDimension (non spatial) + for dimcomb in dimcombs: + mapbox_url = get_mapbox_url(MAPBOX_PROJ, DATASET_FILENAME, var) + + # generate stac item key and add link to asset to the stac item + item_id = get_mapbox_item_id(dimcomb) + feature = gen_default_item(f"{var}-mapbox-{item_id}") + feature.add_asset("mapbox", gen_mapbox_asset(mapbox_url)) + + # This calls ItemCoclicoExtension and links CoclicoExtension to the stac item + coclico_ext = CoclicoExtension.ext(feature, add_if_missing=True) + + coclico_ext.item_key = item_id + coclico_ext.paint = get_paint_props(item_id) + coclico_ext.type_ = TYPE + coclico_ext.stations = STATIONS + coclico_ext.on_click = ON_CLICK + + # TODO: include this in our datacube? + # add dimension key-value pairs to stac item properties dict + for k, v in dimcomb.items(): + feature.properties[k] = v + + # added specifically for GCA + feature.properties["variables"] = var + + # add stac item to collection + collection.add_item(feature, strategy=layout) + + # if no variables present we still need to add zarr reference at collection level + if not VARIABLES: + collection.add_asset("data", gen_zarr_asset(title, gcs_api_zarr_store)) + + # TODO: use gen_default_summaries() from blueprint.py after making it frontend compliant. + collection.summaries = Summaries({}) + # TODO: check if maxcount is required (inpsired on xstac library) + # stac_obj.summaries.maxcount = 50 + + # added specifically for GCA + # extend dimvals with data variables + dimvals["variables"] = VARIABLES + + # for GCA specifically extend v to dict with more info to be able to have labels in drop downs + for k, v in dimvals.items(): + try: + add_dict = {"label": ds[k].long_name, "options": []} + for sv in v: + add_vals = {"label": str(sv), "value": sv} + add_dict["options"].append(add_vals) + + except: # if variable added + add_dict = {"label": "Variables", "options": []} + for sv in v: + add_vals = {"label": ds[sv].long_name, "value": sv} + add_dict["options"].append(add_vals) + collection.summaries.add(k, add_dict) + + # this calls CollectionCoclicoExtension since stac_obj==pystac.Collection + coclico_ext = CoclicoExtension.ext(collection, add_if_missing=True) + + # Add frontend properties defined above to collection extension properties. The + # properties attribute of this extension is linked to the extra_fields attribute of + # the stac collection. + coclico_ext.units = UNITS + coclico_ext.plot_series = PLOT_SERIES + coclico_ext.plot_x_axis = PLOT_X_AXIS + coclico_ext.plot_type = PLOT_TYPE + coclico_ext.min_ = MIN + coclico_ext.max_ = MAX + coclico_ext.linear_gradient = LINEAR_GRADIENT + + # set extra link properties + extend_links(collection, dimvals.keys()) + + # Add thumbnail + # collection.add_asset( + # "thumbnail", + # pystac.Asset( + # "https://storage.googleapis.com/dgds-data-public/gca/assets/thumbnails/" + # + COLLECTION_ID + # + ".png", # noqa: E501 + # title="Thumbnail", + # media_type=pystac.MediaType.PNG, + # ), + # ) + + # save and limit number of folders + catalog.add_child(collection) + + collection.normalize_hrefs( + os.path.join(pathlib.Path(__file__).parent.parent, STAC_DIR, COLLECTION_ID), + strategy=layout, + ) + + catalog.save( + catalog_type=CatalogType.SELF_CONTAINED, + dest_href=os.path.join(pathlib.Path(__file__).parent.parent, STAC_DIR), + stac_io=CoCliCoStacIO(), + ) diff --git a/STAC/data/scripts/stac_to_cloud.py b/STAC/data/scripts/stac_to_cloud.py index e980359b6..b75af2b75 100644 --- a/STAC/data/scripts/stac_to_cloud.py +++ b/STAC/data/scripts/stac_to_cloud.py @@ -2,14 +2,18 @@ import pystac import pystac_client import os -from coclicodata.etl.cloud_utils import dir_to_google_cloud, load_google_credentials, p_drive +from coclicodata.etl.cloud_utils import ( + dir_to_google_cloud, + load_google_credentials, + p_drive, +) if __name__ == "__main__": # hard-coded input params GCS_PROJECT = "DGDS - I1000482-002" BUCKET_NAME = "dgds-data-public" BUCKET_PROJ = "gca" - STAC_NAME = "gca-stac" + STAC_NAME = "gca-stac2" IN_DIRNAME = "current" # hard-coded input params at project level diff --git a/STAC/data/scripts/upload_and_generate_geojson.py b/STAC/data/scripts/upload_and_generate_geojson.py index f0ef55995..1bdc39165 100644 --- a/STAC/data/scripts/upload_and_generate_geojson.py +++ b/STAC/data/scripts/upload_and_generate_geojson.py @@ -39,40 +39,33 @@ # hard-coded input params at project level gca_data_dir = pathlib.Path( - # p_drive, "1000545-054-globalbeaches", "15_GlobalCoastalAtlas", "datasets" p_drive, "11208003-latedeo2022", "020_InternationalDeltaPortfolio", "datasets", - "04_extreme_sea_levels_at_different_global_warming_levels", ) - dataset_dir = gca_data_dir.joinpath("5DeltasESL") - - credentials_dir = pathlib.Path(p_drive, "11205479-coclico", "FASTTRACK_DATA") - - IN_FILENAME = "ESLbyGWL.zarr" # original filename as on P drive - OUT_FILENAME = "ESLbyGWL.zarr" # file name in the cloud and on MapBox - - # what variable(s) do you want to show as marker color? - VARIABLES = ["esl"] - - # what are the dimensions that you want to use as to affect the marker color (never include stations). These will be the drop down menu's. - ADDITIONAL_DIMENSIONS = ["gwl", "rp"] - - # which of these dimensions do you want to use, i.e. also specify the subsets (if there are a lot maybe make a selection). These will be the values in the drop down menu's. If only one (like mean), specify a value without a list to squeeze the dataset. + dataset_dir = gca_data_dir.joinpath(r"00_mapping_global_threat_of_land_subsidence") + cred_dir = pathlib.Path(p_drive, "11205479-coclico", "FASTTRACK_DATA") + IN_FILENAME = "Global_TLS.zarr" # original filename as on P drive + OUT_FILENAME = "Global_TLS.zarr" # file name in the cloud and on MapBox + VARIABLES = [ + "eapa", + "egdp", + "epsi", + ] # what variable(s) do you want to show as marker color? + # dimensions to include, i.e. what are the dimensions that you want to use as to affect the marker color (never include stations). These will be the drop down menu's. Note down without n.. in front. + ADDITIONAL_DIMENSIONS = ["time"] + # use these to reduce dimension like {ensemble: "mean", "time": [1995, 2020, 2100]}, i.e. which of the dimensions do you want to use. Also specify the subsets (if there are a lot maybe make a selection). These will be the values in the drop down menu's. If only one (like mean), specify a value without a list to squeeze the dataset. Needs to span the entire dim space (except for (n)stations). MAP_SELECTION_DIMS = { - "gwl": [0.0, 1.5, 3.0, 5.0], - "rp": [5.0, 10.0, 20.0, 50.0, 100.0], - "ensemble": 50, + "time": [2010, 2040], } - - # which dimensions to ignore (if n... in front of dim, it goes searching in additional_dimension for dim without n in front (ntime -> time)). This spans up the remainder of the dimension space. + # which dimensions to ignore (if n... in front of dim, it goes searching in additional_dimension for dim without n in front (ntime -> time). Except for nstations, just specify station in this case). This spans up the remainder of the dimension space. DIMENSIONS_TO_IGNORE = ["stations"] # dimensions to ignore # TODO: safe cloud creds in password client load_env_variables(env_var_keys=["MAPBOX_ACCESS_TOKEN"]) load_google_credentials( - google_token_fp=credentials_dir.joinpath("google_credentials.json") + google_token_fp=cred_dir.joinpath("google_credentials.json") ) # TODO: come up with checks for data @@ -94,16 +87,17 @@ bucket_name=BUCKET_NAME, bucket_proj=BUCKET_PROJ, zarr_filename=OUT_FILENAME ) - # read data from local source + # # read data from local source # fpath = pathlib.Path.home().joinpath( # "data", "tmp", "shoreline_change_projections.zarr" # ) - fpath = dataset_dir.joinpath("ESLbyGWL.zarr") - ds = xr.open_zarr(fpath) + # fpath = dataset_dir.joinpath(IN_FILENAME) + # ds = xr.open_zarr(fpath) ds = zero_terminated_bytes_as_str(ds) # remove characters that cause problems in the frontend. + ds = rm_special_characters( ds=ds, dimensions_to_check=ADDITIONAL_DIMENSIONS, characters=["%"] ) @@ -133,7 +127,7 @@ variable=var, dimension_combinations=dimcombs, stations_dim=( # note, make nstations if stations has string, else make stations - "nstations" + "stations" ), ) @@ -148,6 +142,7 @@ ) fn = mapbox_url.split(".")[1] + fp = pathlib.Path(outdir, fn).with_suffix(".geojson") with open(fp, "w") as f: @@ -156,6 +151,8 @@ geojson.dump(collection, f) print("Done!") - # Note, if mapbox cli raises en util collection error, this should be monkey + # Note, if mapbox cli raises an util collection error, this should be monkey # patched. Instructions are in documentation of the function. geojson_to_mapbox(source_fpath=fp, mapbox_url=mapbox_url) + +# %% diff --git a/STAC/visualization/09_subsidence.ipynb b/STAC/visualization/09_subsidence.ipynb new file mode 100644 index 000000000..c63b728d8 --- /dev/null +++ b/STAC/visualization/09_subsidence.ipynb @@ -0,0 +1,1044 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import pystac_client\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import pathlib\n", + "import os\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import matplotlib as mpl\n", + "from matplotlib import colors\n", + "from shapely import wkb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\kras\\AppData\\Local\\mambaforge\\envs\\coclico_new\\Lib\\site-packages\\pystac_client\\client.py:186: NoConformsTo: Server does not advertise any conformance classes.\n", + " warnings.warn(NoConformsTo())\n" + ] + } + ], + "source": [ + "# opening STAC catalog, either a url, cloud storage link or a local (cloned) folder\n", + "cwd = pathlib.Path().resolve()\n", + "\n", + "catalog = pystac_client.Client.open(\n", + " \"https://storage.googleapis.com/dgds-data-public/gca/gca-stac2/catalog.json\" # cloud API\n", + " #os.path.join(os.path.dirname(cwd), \"stac_folder\", \"current\", \"catalog.json\") # local clone \n", + ")\n", + "#catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(catalog.get_children())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "href = catalog.get_child(\"sub_threat\").assets[\"data\"].href" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:   (stations: 108, time: 2)\n",
+       "Coordinates:\n",
+       "    country   (stations) |S26 dask.array<chunksize=(108,), meta=np.ndarray>\n",
+       "    geometry  (stations) |S1095835 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    lat       (stations) float64 dask.array<chunksize=(108,), meta=np.ndarray>\n",
+       "    lon       (stations) float64 dask.array<chunksize=(108,), meta=np.ndarray>\n",
+       "  * time      (time) int32 2010 2040\n",
+       "Dimensions without coordinates: stations\n",
+       "Data variables:\n",
+       "    eapa      (time, stations) float64 dask.array<chunksize=(2, 108), meta=np.ndarray>\n",
+       "    egdp      (time, stations) float64 dask.array<chunksize=(2, 108), meta=np.ndarray>\n",
+       "    epsi      (time, stations) float64 dask.array<chunksize=(2, 108), meta=np.ndarray>\n",
+       "Attributes: (12/21)\n",
+       "    AUTHOR:              Herrera, G., Ezquerro, P., Tomás, R., Bejar, M., Lop...\n",
+       "    CITATION:            Herrera, G. & Ezquerro, Pablo & Tomás, Roberto & Bej...\n",
+       "    COMMENT:             \n",
+       "    CRS:                 EPSG:4326\n",
+       "    Conventions:         CF-1.8\n",
+       "    DESCRIPTION:         Subsidence, the lowering of Earth's land surface, is...\n",
+       "    ...                  ...\n",
+       "    SPATIAL_EXTENT:      [-180, -90, 180, 90]\n",
+       "    TAGS:                ['land subsidence', 'groundwater depletion', 'drought']\n",
+       "    TEMPORAL_EXTENT:     ['2010', '2040']\n",
+       "    TITLE:               Land Subsidence Threat\n",
+       "    TITLE_ABBREVIATION:  sub_threat\n",
+       "    UNITS:
" + ], + "text/plain": [ + "\n", + "Dimensions: (stations: 108, time: 2)\n", + "Coordinates:\n", + " country (stations) |S26 dask.array\n", + " geometry (stations) |S1095835 dask.array\n", + " lat (stations) float64 dask.array\n", + " lon (stations) float64 dask.array\n", + " * time (time) int32 2010 2040\n", + "Dimensions without coordinates: stations\n", + "Data variables:\n", + " eapa (time, stations) float64 dask.array\n", + " egdp (time, stations) float64 dask.array\n", + " epsi (time, stations) float64 dask.array\n", + "Attributes: (12/21)\n", + " AUTHOR: Herrera, G., Ezquerro, P., Tomás, R., Bejar, M., Lop...\n", + " CITATION: Herrera, G. & Ezquerro, Pablo & Tomás, Roberto & Bej...\n", + " COMMENT: \n", + " CRS: EPSG:4326\n", + " Conventions: CF-1.8\n", + " DESCRIPTION: Subsidence, the lowering of Earth's land surface, is...\n", + " ... ...\n", + " SPATIAL_EXTENT: [-180, -90, 180, 90]\n", + " TAGS: ['land subsidence', 'groundwater depletion', 'drought']\n", + " TEMPORAL_EXTENT: ['2010', '2040']\n", + " TITLE: Land Subsidence Threat\n", + " TITLE_ABBREVIATION: sub_threat\n", + " UNITS: " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.open_zarr(href)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# plot input params\n", + "var = \"epsi\" # look at ds's data variables for options\n", + "time = 2010 # look at ds.time.values for options\n", + "\n", + "# world shapefile\n", + "shapefile = r'p:\\11208003-latedeo2022\\020_InternationalDeltaPortfolio\\datasets\\05_world-administrative-boundaries\\world-administrative-boundaries.shp'\n", + "world = gpd.read_file(shapefile)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# make geodataframe by converting wkb back to polygons\n", + "df = ds[var].sel(time=time).to_dataframe().reset_index()\n", + "geometry = [wkb.loads(i) for i in df.geometry]\n", + "gdf = gpd.GeoDataFrame(df, geometry=geometry)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# settings\n", + "vmin = 0\n", + "vmax = 61\n", + "steps = 7\n", + "\n", + "# plotting\n", + "cmap = mpl.cm.RdYlGn_r\n", + "norm = colors.BoundaryNorm(np.linspace(vmin, vmax, steps), cmap.N)\n", + "cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)\n", + "\n", + "fig, ax = plt.subplots()\n", + "fig.set_size_inches(15, 8)\n", + "base = world.boundary.plot(ax=ax, edgecolor='grey',facecolor='grey',alpha=0.1,zorder=0)\n", + "plot = gdf.plot(var, ax=ax, cmap=cmap, norm=norm)\n", + "ax.grid(alpha=0.5)\n", + "ax.set_title(\"%s for %s\"%(ds[var].long_name, time))\n", + "\n", + "cax = fig.add_axes([ax.get_position().x1+0.01,ax.get_position().y0,0.02,ax.get_position().height]) # to give colorbar own axes\n", + "cb = plt.colorbar(cbar, cax=cax) # Similar to fig.colorbar(im, cax = cax)\n", + "cb.set_ticks([])\n", + "for j, lab in enumerate(['VL','L','ML','MH','H','VH']):\n", + " cb.ax.text(.5, vmin+((vmax-vmin)/(steps-1))*j+((vmax-vmin)/(steps-1))/2, lab, ha='center', va='center')\n", + "cb.ax.get_yaxis().labelpad = 15\n", + "#cb.set_label('Potential Subsidence Index (Very Low to Very High)');" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "60.786857759586766\n" + ] + } + ], + "source": [ + "print(np.min(df[var].values))\n", + "print(np.max(df[var].values))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "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": 2 +} diff --git a/app/README.md b/app/README.md deleted file mode 100644 index f5db2a2db..000000000 --- a/app/README.md +++ /dev/null @@ -1,75 +0,0 @@ -# Nuxt 3 Minimal Starter - -Look at the [Nuxt 3 documentation](https://nuxt.com/docs/getting-started/introduction) to learn more. - -## Setup - -Make sure to install the dependencies: - -```bash -# npm -npm install - -# pnpm -pnpm install - -# yarn -yarn install - -# bun -bun install -``` - -## Development Server - -Start the development server on `http://localhost:3000`: - -```bash -# npm -npm run dev - -# pnpm -pnpm run dev - -# yarn -yarn dev - -# bun -bun run dev -``` - -## Production - -Build the application for production: - -```bash -# npm -npm run build - -# pnpm -pnpm run build - -# yarn -yarn build - -# bun -bun run build -``` - -Locally preview production build: - -```bash -# npm -npm run preview - -# pnpm -pnpm run preview - -# yarn -yarn preview - -# bun -bun run preview -``` - -Check out the [deployment documentation](https://nuxt.com/docs/getting-started/deployment) for more information. diff --git a/app/pages/index.vue b/app/pages/index.vue index 724914fcb..373f9c6ef 100644 --- a/app/pages/index.vue +++ b/app/pages/index.vue @@ -10,6 +10,16 @@ type ItemType = typeof itemShape type CatalogType = typeof catalogShape type CollectionType = typeof collectionShape +type Option = + | { + label: string + value: number + } + | { + label: string + value: string + } + let url = useRequestURL() let { @@ -61,24 +71,24 @@ let summaries = computed(() => { // let activeCollection = ref(currentCollection); let variables = ref( - Object.entries(summaries.value ?? {}).reduce((acc, [key, values]) => { + Object.entries(summaries.value ?? {}).reduce((acc, [key, summary]) => { return { ...acc, - [key]: values[0], + [key]: summary.options[0], } - }, {} as Record), + }, {} as Record), ) watchEffect( () => { variables.value = Object.entries(summaries.value ?? {}).reduce( - (acc, [key, values]) => { + (acc, [key, summary]) => { return { ...acc, - [key]: values[0], + [key]: summary.options[0], } }, - {} as Record, + {} as Record, ) }, { flush: 'pre' }, @@ -91,8 +101,9 @@ let activeItemUrl = computed(() => { .filter((l) => l.rel === 'item') .find((link) => { return Object.entries(variables.value).every( - ([key, value]) => - link.properties?.[key as keyof typeof link.properties] === value, + ([key, option]) => + link.properties?.[key as keyof typeof link.properties] === + option.value, ) }) ?? activeCollection.value.links.filter((l) => l.rel === 'item')[0] @@ -154,22 +165,25 @@ let geojson = computed(() => { - +