From 5bf9a8238f112a5a0fc3f12ac0be44d37436b641 Mon Sep 17 00:00:00 2001
From: amsnyder <asnyder@usgs.gov>
Date: Wed, 17 Jan 2024 12:01:57 -0600
Subject: [PATCH] add maca_vic_past and remove outputs from macav2_future_daily
nb
---
catalog/catalog.json | 5 +
catalog/maca_vic_past/collection.json | 394 +
...vic_past_create_collection_from_zarr.ipynb | 919 +
...re_daily_create_collection_from_zarr.ipynb | 49617 +---------------
4 files changed, 1371 insertions(+), 49564 deletions(-)
create mode 100644 catalog/maca_vic_past/collection.json
create mode 100644 workflows/archive/maca_vic_past_create_collection_from_zarr.ipynb
diff --git a/catalog/catalog.json b/catalog/catalog.json
index 9a0f6448..0a502ab6 100644
--- a/catalog/catalog.json
+++ b/catalog/catalog.json
@@ -58,6 +58,11 @@
"rel": "child",
"href": "./macav2_future_daily/collection.json",
"type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./maca_vic_past/collection.json",
+ "type": "application/json"
}
]
}
\ No newline at end of file
diff --git a/catalog/maca_vic_past/collection.json b/catalog/maca_vic_past/collection.json
new file mode 100644
index 00000000..ec202d38
--- /dev/null
+++ b/catalog/maca_vic_past/collection.json
@@ -0,0 +1,394 @@
+{
+ "type": "Collection",
+ "id": "maca_vic_past",
+ "stac_version": "1.0.0",
+ "description": "maca_vic_past",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.json",
+ "type": "application/json"
+ }
+ ],
+ "stac_extensions": [
+ "https://stac-extensions.github.io/datacube/v2.2.0/schema.json"
+ ],
+ "cube:dimensions": {
+ "time": {
+ "type": "temporal",
+ "description": null,
+ "extent": [
+ "1971-01-01T00:00:00Z",
+ "1999-12-31T00:00:00Z"
+ ],
+ "step": "P1DT0H0M0S"
+ },
+ "lon": {
+ "type": "spatial",
+ "axis": "x",
+ "description": "longitude",
+ "extent": [
+ 235.40625,
+ 250.21875
+ ],
+ "step": 0.0625,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"ProjectedCRS\",\"name\":\"unknown\",\"base_crs\":{\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"unknown\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"name\":\"Equidistant Cylindrical\",\"id\":{\"authority\":\"EPSG\",\"code\":1028}},\"parameters\":[{\"name\":\"Latitude of 1st standard parallel\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8823}},{\"name\":\"Latitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8801}},{\"name\":\"Longitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8802}},{\"name\":\"False easting\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8806}},{\"name\":\"False northing\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8807}}]},\"coordinate_system\":{\"subtype\":\"Cartesian\",\"axis\":[{\"name\":\"Easting\",\"abbreviation\":\"E\",\"direction\":\"east\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Northing\",\"abbreviation\":\"N\",\"direction\":\"north\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}}"
+ },
+ "lat": {
+ "type": "spatial",
+ "axis": "y",
+ "description": "latitude",
+ "extent": [
+ 41.21875,
+ 52.84375
+ ],
+ "step": 0.0625,
+ "reference_system": "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"ProjectedCRS\",\"name\":\"unknown\",\"base_crs\":{\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"unknown\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"name\":\"Equidistant Cylindrical\",\"id\":{\"authority\":\"EPSG\",\"code\":1028}},\"parameters\":[{\"name\":\"Latitude of 1st standard parallel\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8823}},{\"name\":\"Latitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8801}},{\"name\":\"Longitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8802}},{\"name\":\"False easting\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8806}},{\"name\":\"False northing\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8807}}]},\"coordinate_system\":{\"subtype\":\"Cartesian\",\"axis\":[{\"name\":\"Easting\",\"abbreviation\":\"E\",\"direction\":\"east\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Northing\",\"abbreviation\":\"N\",\"direction\":\"north\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}}"
+ }
+ },
+ "cube:variables": {
+ "crs": {
+ "dimensions": [],
+ "type": "auxiliary",
+ "description": null,
+ "unit": null
+ },
+ "dbcsd_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Minimum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "dbcsd_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "dbcsd_specific_humidity": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Specific Humidity",
+ "unit": "1"
+ },
+ "dbcsd_surface_downwelling_shortwave_flux_in_air": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Surface Downwelling Shortwave Radiation",
+ "unit": "W m-2"
+ },
+ "dbcsd_wind_speed": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Wind Speed",
+ "unit": "m s-1"
+ },
+ "macav2_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Minimum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "macav2_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "macav2_specific_humidity": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Specific Humidity",
+ "unit": "1"
+ },
+ "macav2_surface_downwelling_shortwave_flux_in_air": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Surface Downwelling Shortwave Radiation",
+ "unit": "W m-2"
+ },
+ "macav2_wind_speed": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Wind Speed",
+ "unit": "m s-1"
+ },
+ "mbcsd_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Maximum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "mbcsd_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "sdbcNARCCAP_CRCM_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Maximum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "sdbcNARCCAP_CRCM_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "sdbcNARCCAP_CRCM_specific_humidity": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Specific Humidity",
+ "unit": "1"
+ },
+ "sdbcNARCCAP_CRCM_surface_downwelling_shortwave_flux_in_air": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Surface Downwelling Shortwave Radiation",
+ "unit": "W m-2"
+ },
+ "sdbcNARCCAP_CRCM_wind_speed": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Wind Speed",
+ "unit": "m s-1"
+ },
+ "sdbcNARCCAP_MM5I_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Maximum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "sdbcNARCCAP_MM5I_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "sdbcNARCCAP_MM5I_specific_humidity": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Specific Humidity",
+ "unit": "1"
+ },
+ "sdbcNARCCAP_MM5I_surface_downwelling_shortwave_flux_in_air": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Surface Downwelling Shortwave Radiation",
+ "unit": "W m-2"
+ },
+ "sdbcNARCCAP_MM5I_wind_speed": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Wind Speed",
+ "unit": "m s-1"
+ },
+ "sdbcNARCCAP_WRFG_air_temperature": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Minimum Near-Surface Air Temperature",
+ "unit": "K"
+ },
+ "sdbcNARCCAP_WRFG_precipitation_amount": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Precipitation",
+ "unit": "mm/d"
+ },
+ "sdbcNARCCAP_WRFG_specific_humidity": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Specific Humidity",
+ "unit": "1"
+ },
+ "sdbcNARCCAP_WRFG_surface_downwelling_shortwave_flux_in_air": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Surface Downwelling Shortwave Radiation",
+ "unit": "W m-2"
+ },
+ "sdbcNARCCAP_WRFG_wind_speed": {
+ "dimensions": [
+ "time",
+ "lat",
+ "lon"
+ ],
+ "type": "data",
+ "description": "Daily Mean Near-Surface Wind Speed",
+ "unit": "m s-1"
+ }
+ },
+ "extent": {
+ "spatial": {
+ "bbox": [
+ [
+ 235.40625,
+ 41.21875,
+ 250.21875,
+ 52.84375
+ ]
+ ]
+ },
+ "temporal": {
+ "interval": [
+ [
+ "1971-01-01T00:00:00Z",
+ "1999-12-31T00:00:00Z"
+ ]
+ ]
+ }
+ },
+ "license": "CC0-1.0",
+ "assets": {
+ "zarr-s3-osn": {
+ "href": "s3://mdmf/gdp/maca_vic_past.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "Open Storage Network Pod S3 API access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "anon": true,
+ "client_kwargs": {
+ "endpoint_url": "https://usgs.osn.mghpcc.org/"
+ }
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ },
+ "zarr-s3": {
+ "href": "s3://nhgf-development/workspace/DataConversion/maca_vic_past.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "S3 access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "requester_pays": true
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ }
+ }
+}
\ No newline at end of file
diff --git a/workflows/archive/maca_vic_past_create_collection_from_zarr.ipynb b/workflows/archive/maca_vic_past_create_collection_from_zarr.ipynb
new file mode 100644
index 00000000..b7c0f96b
--- /dev/null
+++ b/workflows/archive/maca_vic_past_create_collection_from_zarr.ipynb
@@ -0,0 +1,919 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# maca_vic_past Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import metpy\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up NHGF STAC Catalog"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection\n",
+ "collection_id = 'maca_vic_past'\n",
+ "# description of STAC collection\n",
+ "collection_description = 'maca_vic_past'\n",
+ "# license for dataset\n",
+ "collection_license = 'CC0-1.0'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = 's3://mdmf/gdp/maca_vic_past.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\": {\"anon\": True, \"client_kwargs\": {\"endpoint_url\":\"https://usgs.osn.mghpcc.org/\"}}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description = \"Open Storage Network Pod S3 API access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = 's3://nhgf-development/workspace/DataConversion/maca_vic_past.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs2 = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\":{\"requester_pays\":True}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description2 = \"S3 access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles2 = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# open and view zarr dataset\n",
+ "fs2 = fsspec.filesystem('s3', anon=True, endpoint_url='https://usgs.osn.mghpcc.org/')\n",
+ "ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n",
+ " backend_kwargs={'consolidated':True}, chunks={})\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dims_auto_extract = ['X', 'Y', 'T']\n",
+ "# def extract_dim(ds, d):\n",
+ "# try:\n",
+ "# dim_list = ds.cf.axes[d]\n",
+ "# assert len(dim_list)==1, f'There are too many {d} dimensions in this dataset.'\n",
+ "# dim = dim_list[0]\n",
+ "# except KeyError:\n",
+ "# print(f\"Could not auto-extract {d} dimension name.\")\n",
+ "# print(\"Look at the xarray output above showing the dataset dimensions.\")\n",
+ "# dim = str(input(f\"What is the name of the {d} dimension of this dataset?\"))\n",
+ "# assert dim in ds.dims, \"That is not a valid dimension name for this dataset\"\n",
+ "# print(f\"name of {d} dimension: {dim}\\n\")\n",
+ "# return dim\n",
+ "\n",
+ "# dim_names_dict = {}\n",
+ "# for d in dims_auto_extract:\n",
+ "# dim_names_dict[d] = extract_dim(ds, d)\n",
+ "dim_names_dict = {'X': 'lon', 'Y': 'lat', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = ds.metpy.parse_cf()\n",
+ "crs = ds[list(ds.keys())[0]].metpy.cartopy_crs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8fbfecfb-9886-4d06-a34c-6471cb0a6053",
+ "metadata": {},
+ "source": [
+ "## Plot a map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4eb4d027-4266-4a0b-8f16-bacfbef06242",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # plot a map of a single variable\n",
+ "# var_to_plot = 'SNOW'\n",
+ "# da = ds[var_to_plot].sel(time='2014-03-01 00:00').load()\n",
+ "# da.hvplot.quadmesh(x='lon', y='lat', rasterize=True,\n",
+ "# geo=True, tiles='OSM', alpha=0.7, cmap='turbo')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e057a6c-06fb-4406-823b-e81c58e520e4",
+ "metadata": {},
+ "source": [
+ "## Plot a time series at a specific point\n",
+ "This can help you verify a variable's values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7de2681-88c2-4597-857c-8f169c596f8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # enter lat, lon of point you want to plot time series for\n",
+ "# lat,lon = 39.978322,-105.2772194\n",
+ "# time_start = '2013-01-01 00:00'\n",
+ "# time_end = '2013-12-31 00:00'\n",
+ "# x, y = crs.transform_point(lon, lat, src_crs=ccrs.PlateCarree()) # PlateCaree = Lat,Lon\n",
+ "# da = ds[var_to_plot].sel(x=x, y=y, method='nearest').sel(time=slice(time_start,time_end)).load()\n",
+ "# da.hvplot(x=dim_names_dict['T'], grid=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float ratherthan a numpy float\n",
+ "#coord_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "#coord_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\n",
+ "coord_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
+ "print(coord_bounds)\n",
+ "print(f'\\ncoord_bounds data type: {type(coord_bounds[0])}')\n",
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[coord_bounds])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out first and last timestamps\n",
+ "# temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n",
+ "# temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n",
+ "# had to use another method for this dataset due to error in converting to pandas timestamp\n",
+ "temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n",
+ "# create a temporal extent object\n",
+ "temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfb71202-03df-45b5-ac2f-0dc2ee1ab780",
+ "metadata": {},
+ "source": [
+ "## Create pystac collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection = catalog.get_child(collection_id)\n",
+ " print(\"existing collection opened\")\n",
+ " collection.extent=collection_extent\n",
+ " collection.description=collection_description\n",
+ " collection.license=collection_license\n",
+ "else:\n",
+ " collection = pystac.Collection(id=collection_id,\n",
+ " description=collection_description,\n",
+ " extent=collection_extent,\n",
+ " license=collection_license)\n",
+ " print(\"new collection created\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "asset_id = \"zarr-s3-osn\"\n",
+ "asset = pystac.Asset(href=zarr_url,\n",
+ " description=asset_description,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles,\n",
+ " extra_fields = xarray_opendataset_kwargs)\n",
+ "collection.add_asset(asset_id, asset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "asset_id2 = \"zarr-s3\"\n",
+ "asset2 = pystac.Asset(href=zarr_url2,\n",
+ " description=asset_description2,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles2,\n",
+ " extra_fields = xarray_opendataset_kwargs2)\n",
+ "collection.add_asset(asset_id2, asset2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "120a4914-3302-44a5-a282-0308ac84f040",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# list out dataset dimensions\n",
+ "# When writing data to Zarr, Xarray sets this attribute on all variables based on the variable dimensions. When reading a Zarr group, Xarray looks for this attribute on all arrays,\n",
+ "# raising an error if it can’t be found.\n",
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00a18a29-fb9a-4b56-8009-493122997b16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# get x, y bounds for extent of those dimensions (required)\n",
+ "xy_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
+ "print(xy_bounds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0f49d6d7-9e30-4144-909b-fa1238e6c77a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_step(ds, dim_name, time_dim=False, debug=False, step_ix=0, round_dec=None):\n",
+ " dim_vals = ds[dim_name].values\n",
+ " diffs = [d2 - d1 for d1, d2 in zip(dim_vals, dim_vals[1:])]\n",
+ " # option to round number of decimals\n",
+ " # sometimes there are different steps calculated due to small rounding errors coming out of the diff\n",
+ " # calculation, rounding these can correct for that\n",
+ " if round_dec:\n",
+ " unique_steps = np.unique(np.array(diffs).round(decimals=round_dec), return_counts=True)\n",
+ " else:\n",
+ " unique_steps = np.unique(diffs, return_counts=True)\n",
+ " step_list = unique_steps[0]\n",
+ " # optional - for invesitgating uneven steps\n",
+ " if debug:\n",
+ " print(f'step_list: {step_list}')\n",
+ " print(f'step_count: {unique_steps[1]}')\n",
+ " indices = [i for i, x in enumerate(diffs) if x == step_list[step_ix]]\n",
+ " print(f'index locations of step index {step_ix} in step_list: {indices}')\n",
+ " # set step - if all steps are the same length\n",
+ " # datacube spec specifies to use null for irregularly spaced steps\n",
+ " if len(step_list)==1:\n",
+ " if time_dim:\n",
+ " # make sure time deltas are in np timedelta format\n",
+ " step_list = [np.array([step], dtype=\"timedelta64[ns]\")[0] for step in step_list]\n",
+ " step = step_list[0].astype(float).item()\n",
+ " else:\n",
+ " step = None\n",
+ " return(step)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a20d12bf-a511-4c5e-84d0-77e2ec551518",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_long_name(ds, v):\n",
+ " # try to get long_name attribute from variable\n",
+ " try:\n",
+ " long_name = ds[v].attrs['long_name']\n",
+ " # otherwise, leave empty\n",
+ " except:\n",
+ " long_name = None\n",
+ " return long_name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user input needed - you will need to look at the crs information and create a cartopy crs object after identifying the projection type:\n",
+ "reference list of cartopy projections: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "print(crs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # create the appropriate cartopy projection\n",
+ "# lcc = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)\n",
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(projjson)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed - looks at the steps pulled out and make sure they make sense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps: get all step values and locations\n",
+ "# time_step = get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 3343\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = get_step(ds, dim_names_dict['X'])\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps: get all step values and locations\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = get_step(ds, x_dim, debug=True, step_ix=1)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = get_step(ds, dim_names_dict['Y'])\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps: get all step values and locations\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = get_step(ds, y_dim, debug=True, step_ix=1)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed - you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes(type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the dataset\n",
+ "dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': get_long_name(ds, dim_names_dict['X']), 'extent': [xy_bounds[0], xy_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': get_long_name(ds, dim_names_dict['Y']), 'extent': [xy_bounds[1], xy_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "92510876-7853-4d24-8563-c69f9012aeb6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define functions to pull out datacube attributes and validate format\n",
+ "def get_unit(ds, v):\n",
+ " # check if unit is defined for variable\n",
+ " try:\n",
+ " unit = ds[v].attrs['units']\n",
+ " except:\n",
+ " unit = None\n",
+ " # check if unit comes from https://docs.unidata.ucar.edu/udunits/current/#Database\n",
+ " # datacube extension specifies: The unit of measurement for the data, preferably compliant to UDUNITS-2 units (singular).\n",
+ " # gdptools expects this format as well\n",
+ " try:\n",
+ " cfunits.Units(unit).isvalid\n",
+ " except:\n",
+ " print(\"Unit is not valid as a UD unit.\")\n",
+ " unit = str(input(\"Please enter a valid unit for {v} from here: https://docs.unidata.ucar.edu/udunits/current/#Database\"))\n",
+ " assert cfunits.Units(unit).isvalid\n",
+ " return unit\n",
+ "\n",
+ "def get_var_type(ds, v):\n",
+ " if v in ds.coords:\n",
+ " # type = auxiliary for a variable that contains coordinate data, but isn't a dimension in cube:dimensions.\n",
+ " # For example, the values of the datacube might be provided in the projected coordinate reference system, \n",
+ " # but the datacube could have a variable lon with dimensions (y, x), giving the longitude at each point.\n",
+ " var_type = 'auxiliary'\n",
+ " # type = data for a variable indicating some measured value, for example \"precipitation\", \"temperature\", etc.\n",
+ " else:\n",
+ " var_type = 'data'\n",
+ " return var_type"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop metpy_crs coordinate we have added\n",
+ "if 'metpy_crs' in ds.coords:\n",
+ " ds = ds.drop_vars('metpy_crs')\n",
+ "\n",
+ "# pull list of vars from dataset\n",
+ "vars = list(ds.variables)\n",
+ "\n",
+ "# spec says that the keys of cube:dimensions and cube:variables should be unique together; a key like lat should not be both a dimension and a variable.\n",
+ "# we will drop all values in dims from vars\n",
+ "vars = [v for v in vars if v not in dims]\n",
+ "\n",
+ "# Microsoft Planetary Computer includes coordinates and crs as variables here:\n",
+ "# https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# we will keep those in the var list\n",
+ "\n",
+ "# create dictionary of dataset variables and associated dimensions\n",
+ "vars_dict={}\n",
+ "for v in vars:\n",
+ " unit = get_unit(ds, v)\n",
+ " var_type = get_var_type(ds, v)\n",
+ " long_name = get_long_name(ds, v)\n",
+ " vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# def find_paths(nested_dict, prepath=()):\n",
+ "# for k, v in nested_dict.items():\n",
+ "# try:\n",
+ "# path = prepath + (k,)\n",
+ "# if type(v) is np.float64: # found value\n",
+ "# yield path\n",
+ "# elif hasattr(v, 'items'): # v is a dict\n",
+ "# yield from find_paths(v, path) \n",
+ "# except:\n",
+ "# print(prepath)\n",
+ "\n",
+ "# print(*find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n",
+ "else:\n",
+ " catalog.add_child(collection)\n",
+ " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/macav2_future_daily_create_collection_from_zarr.ipynb b/workflows/archive/macav2_future_daily_create_collection_from_zarr.ipynb
index 3a3cfd11..a7f18c53 100644
--- a/workflows/archive/macav2_future_daily_create_collection_from_zarr.ipynb
+++ b/workflows/archive/macav2_future_daily_create_collection_from_zarr.ipynb
@@ -17,648 +17,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "201e0945-de55-45ff-b095-c2af009a4e62",
"metadata": {},
- "outputs": [
- {
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- "application/javascript": [
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- " function now() {\n",
- " return new Date();\n",
- " }\n",
- "\n",
- " var force = true;\n",
- " var py_version = '3.3.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
- " var reloading = false;\n",
- " var Bokeh = root.Bokeh;\n",
- "\n",
- " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
- " root._bokeh_timeout = Date.now() + 5000;\n",
- " root._bokeh_failed_load = false;\n",
- " }\n",
- "\n",
- " function run_callbacks() {\n",
- " try {\n",
- " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
- " if (callback != null)\n",
- " callback();\n",
- " });\n",
- " } finally {\n",
- " delete root._bokeh_onload_callbacks;\n",
- " }\n",
- " console.debug(\"Bokeh: all callbacks have finished\");\n",
- " }\n",
- "\n",
- " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
- " if (css_urls == null) css_urls = [];\n",
- " if (js_urls == null) js_urls = [];\n",
- " if (js_modules == null) js_modules = [];\n",
- " if (js_exports == null) js_exports = {};\n",
- "\n",
- " root._bokeh_onload_callbacks.push(callback);\n",
- "\n",
- " if (root._bokeh_is_loading > 0) {\n",
- " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
- " return null;\n",
- " }\n",
- " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
- " run_callbacks();\n",
- " return null;\n",
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- " if (!reloading) {\n",
- " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
- " }\n",
- "\n",
- " function on_load() {\n",
- " root._bokeh_is_loading--;\n",
- " if (root._bokeh_is_loading === 0) {\n",
- " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
- " run_callbacks()\n",
- " }\n",
- " }\n",
- " window._bokeh_on_load = on_load\n",
- "\n",
- " function on_error() {\n",
- " console.error(\"failed to load \" + url);\n",
- " }\n",
- "\n",
- " var skip = [];\n",
- " if (window.requirejs) {\n",
- " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
- " require([\"jspanel\"], function(jsPanel) {\n",
- "\twindow.jsPanel = jsPanel\n",
- "\ton_load()\n",
- " })\n",
- " require([\"jspanel-modal\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " require([\"jspanel-tooltip\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " require([\"jspanel-hint\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " require([\"jspanel-layout\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " require([\"jspanel-contextmenu\"], function() {\n",
- "\ton_load()\n",
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- " require([\"jspanel-dock\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " require([\"gridstack\"], function(GridStack) {\n",
- "\twindow.GridStack = GridStack\n",
- "\ton_load()\n",
- " })\n",
- " require([\"notyf\"], function() {\n",
- "\ton_load()\n",
- " })\n",
- " root._bokeh_is_loading = css_urls.length + 9;\n",
- " } else {\n",
- " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
- " }\n",
- "\n",
- " var existing_stylesheets = []\n",
- " var links = document.getElementsByTagName('link')\n",
- " for (var i = 0; i < links.length; i++) {\n",
- " var link = links[i]\n",
- " if (link.href != null) {\n",
- "\texisting_stylesheets.push(link.href)\n",
- " }\n",
- " }\n",
- " for (var i = 0; i < css_urls.length; i++) {\n",
- " var url = css_urls[i];\n",
- " if (existing_stylesheets.indexOf(url) !== -1) {\n",
- "\ton_load()\n",
- "\tcontinue;\n",
- " }\n",
- " const element = document.createElement(\"link\");\n",
- " element.onload = on_load;\n",
- " element.onerror = on_error;\n",
- " element.rel = \"stylesheet\";\n",
- " element.type = \"text/css\";\n",
- " element.href = url;\n",
- " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
- " document.body.appendChild(element);\n",
- " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
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- " skip.push(urls[i])\n",
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- " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
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- " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
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- " } var existing_scripts = []\n",
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- " var url = js_urls[i];\n",
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- "\t}\n",
- "\tcontinue;\n",
- " }\n",
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- " element.onload = on_load;\n",
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- " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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- " element.onerror = on_error;\n",
- " element.async = false;\n",
- " element.type = \"module\";\n",
- " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " element.textContent = `\n",
- " import ${name} from \"${url}\"\n",
- " window.${name} = ${name}\n",
- " window._bokeh_on_load()\n",
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- "\n",
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- " const element = document.createElement(\"style\");\n",
- " element.appendChild(document.createTextNode(css));\n",
- " document.body.appendChild(element);\n",
- " }\n",
- "\n",
- " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.holoviz.org/panel/1.3.6/dist/panel.min.js\"];\n",
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- " Bokeh.set_log_level(\"info\");\n",
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- " for (var i = 0; i < inline_js.length; i++) {\n",
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- "\tvar NewBokeh = root.Bokeh;\n",
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- "\t Bokeh.versions = new Map();\n",
- "\t}\n",
- "\tif (NewBokeh.version !== Bokeh.version) {\n",
- "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
- "\t}\n",
- "\troot.Bokeh = Bokeh;\n",
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- " } else if (!root._bokeh_failed_load) {\n",
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- " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
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- " if (!reloading && !bokeh_loaded) {\n",
- "\troot.Bokeh = undefined;\n",
- " }\n",
- " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
- "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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- " // Give older versions of the autoload script a head-start to ensure\n",
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- " setTimeout(load_or_wait, 100)\n",
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- "<script type=\"application/javascript\">(function(root) {\n",
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- " var render_items = [{\"docid\":\"7f2514fc-b2dc-41a2-abb6-d833339aa585\",\"roots\":{\"p1002\":\"aef1fa7d-a91f-44d1-a081-9099b2b4081c\"},\"root_ids\":[\"p1002\"]}];\n",
- " var docs = Object.values(docs_json)\n",
- " if (!docs) {\n",
- " return\n",
- " }\n",
- " const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
- " function embed_document(root) {\n",
- " var Bokeh = get_bokeh(root)\n",
- " Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
- " for (const render_item of render_items) {\n",
- " for (const root_id of render_item.root_ids) {\n",
- "\tconst id_el = document.getElementById(root_id)\n",
- "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
- "\t const root_el = id_el.children[0]\n",
- "\t root_el.id = root_el.id + '-rendered'\n",
- "\t}\n",
- " }\n",
- " }\n",
- " }\n",
- " function get_bokeh(root) {\n",
- " if (root.Bokeh === undefined) {\n",
- " return null\n",
- " } else if (root.Bokeh.version !== py_version) {\n",
- " if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
- "\treturn null\n",
- " }\n",
- " return root.Bokeh.versions.get(py_version);\n",
- " } else if (root.Bokeh.version === py_version) {\n",
- " return root.Bokeh\n",
- " }\n",
- " return null\n",
- " }\n",
- " function is_loaded(root) {\n",
- " var Bokeh = get_bokeh(root)\n",
- " return (Bokeh != null && Bokeh.Panel !== undefined)\n",
- " }\n",
- " if (is_loaded(root)) {\n",
- " embed_document(root);\n",
- " } else {\n",
- " var attempts = 0;\n",
- " var timer = setInterval(function(root) {\n",
- " if (is_loaded(root)) {\n",
- " clearInterval(timer);\n",
- " embed_document(root);\n",
- " } else if (document.readyState == \"complete\") {\n",
- " attempts++;\n",
- " if (attempts > 200) {\n",
- " clearInterval(timer);\n",
- "\t var Bokeh = get_bokeh(root)\n",
- "\t if (Bokeh == null || Bokeh.Panel == null) {\n",
- " console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
- "\t } else {\n",
- "\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
- "\t embed_document(root)\n",
- "\t }\n",
- " }\n",
- " }\n",
- " }, 25, root)\n",
- " }\n",
- "})(window);</script>"
- ]
- },
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- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1002"
- }
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import pystac\n",
"from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
@@ -687,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
"metadata": {},
"outputs": [],
@@ -708,7 +70,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "482d204d-b5b6-40e5-ac42-55b459be1097",
"metadata": {},
"outputs": [],
@@ -731,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "dd6fa323-132a-4794-8c80-576933f547a0",
"metadata": {
"tags": []
@@ -753,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
"metadata": {
"tags": []
@@ -799,48784 +161,10 @@
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- "execution_count": 6,
+ "execution_count": null,
"id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
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- "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
- "Dimensions: (time: 34333, lat: 585, lon: 1386)\n",
- "Coordinates:\n",
- " crs float64 ...\n",
- " * lat (lat) float64 25.06 25.1 ... 49.35 49.4\n",
- " * lon (lon) float64 235.2 235.3 ... 292.9\n",
- " * time (time) datetime64[ns] 2006-01-01 ... ...\n",
- "Data variables: (12/350)\n",
- " huss_BNU-ESM_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " huss_BNU-ESM_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " huss_CCSM4_r6i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " huss_CCSM4_r6i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " huss_CNRM-CM5_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " huss_CNRM-CM5_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " ... ...\n",
- " vas_bcc-csm1-1-m_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_bcc-csm1-1-m_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_bcc-csm1-1_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_bcc-csm1-1_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_inmcm4_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_inmcm4_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- "Attributes: (12/45)\n",
- " Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
- " Metadata_Link: \n",
- " cdm_data_type: GRID\n",
- " comment: Total daily precipitation at surface; in...\n",
- " contributor_name: Katherine C. Hegewisch\n",
- " contributor_role: Postdoctoral Fellow\n",
- " ... ...\n",
- " summary: This archive contains daily downscaled m...\n",
- " time_coverage_duration: P5Y\n",
- " time_coverage_end: 2010-12-31T00:00\n",
- " time_coverage_resolution: P1D\n",
- " time_coverage_start: 2006-01-01T00:0\n",
- " title: Downscaled daily meteorological data of ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f965f029-3201-4c91-b39a-21865aad175b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f965f029-3201-4c91-b39a-21865aad175b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 34333</li><li><span class='xr-has-index'>lat</span>: 585</li><li><span class='xr-has-index'>lon</span>: 1386</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-420ed9d1-68e1-40d2-b72f-5a92e28dd366' class='xr-section-summary-in' type='checkbox' checked><label for='section-420ed9d1-68e1-40d2-b72f-5a92e28dd366' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df7b160f-82cb-447a-95f6-dbcc725e577e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df7b160f-82cb-447a-95f6-dbcc725e577e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2a73256-74fd-4765-acd0-a8deb6d48db0' class='xr-var-data-in' type='checkbox'><label for='data-b2a73256-74fd-4765-acd0-a8deb6d48db0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.06 25.1 25.15 ... 49.35 49.4</div><input id='attrs-3daa252a-fdae-4f06-800f-52d1f834691d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3daa252a-fdae-4f06-800f-52d1f834691d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11bf36b3-e31a-4cf5-adf1-4ac27231a7a8' class='xr-var-data-in' type='checkbox'><label for='data-11bf36b3-e31a-4cf5-adf1-4ac27231a7a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([25.063078, 25.104744, 25.14641 , ..., 49.312691, 49.354359, 49.396023])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>235.2 235.3 235.3 ... 292.9 292.9</div><input id='attrs-8210671c-11eb-489f-82d7-ac445c7a7829' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8210671c-11eb-489f-82d7-ac445c7a7829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00e8f235-6f88-414e-9576-6c5ae3419646' class='xr-var-data-in' type='checkbox'><label for='data-00e8f235-6f88-414e-9576-6c5ae3419646' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([235.227844, 235.269501, 235.311157, ..., 292.851929, 292.893585,\n",
- " 292.935242])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2006-01-01 ... 2099-12-31</div><input id='attrs-95f7de45-469a-4625-abcc-4661a3d410cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f7de45-469a-4625-abcc-4661a3d410cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85b376ec-e8a6-4989-9e2a-d0ba50cc57a6' class='xr-var-data-in' type='checkbox'><label for='data-85b376ec-e8a6-4989-9e2a-d0ba50cc57a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>days since 1900-01-01</dd></dl></div><div class='xr-var-data'><pre>array(['2006-01-01T00:00:00.000000000', '2006-01-02T00:00:00.000000000',\n",
- " '2006-01-03T00:00:00.000000000', ..., '2099-12-29T00:00:00.000000000',\n",
- " '2099-12-30T00:00:00.000000000', '2099-12-31T00:00:00.000000000'],\n",
- " dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2e224a6-5725-43a9-aca3-5af22f109cb5' class='xr-section-summary-in' type='checkbox' ><label for='section-e2e224a6-5725-43a9-aca3-5af22f109cb5' class='xr-section-summary' >Data variables: <span>(350)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>huss_BNU-ESM_r1i1p1_rcp45</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(745, 86, 204), meta=np.ndarray></div><input id='attrs-07a11e78-95ac-48ef-b4ae-f5f62f31232a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07a11e78-95ac-48ef-b4ae-f5f62f31232a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43e4d3d7-6cdc-4394-ac70-4eaf8ffedfd7' class='xr-var-data-in' type='checkbox'><label for='data-43e4d3d7-6cdc-4394-ac70-4eaf8ffedfd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean(interval: 24 hours)</dd><dt><span>comments :</span></dt><dd>Surface specific humidity</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>height :</span></dt><dd>0 m</dd><dt><span>long_name :</span></dt><dd>Daily Mean Near-Surface Specific Humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd></dl></div><div class='xr-var-data'><table>\n",
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- " <tbody>\n",
- " \n",
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- " <td> 103.70 GiB </td>\n",
- " <td> 49.86 MiB </td>\n",
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- " \n",
- " <tr>\n",
- " <th> Shape </th>\n",
- " <td> (34333, 585, 1386) </td>\n",
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- " <tr>\n",
- " <th> Dask graph </th>\n",
- " <td colspan=\"2\"> 2303 chunks in 2 graph layers </td>\n",
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- " <tr>\n",
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- " <tr>\n",
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- " <tr>\n",
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- "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f0c5ef9-a864-4762-b9b5-650799431f0b' class='xr-section-summary-in' type='checkbox' ><label for='section-2f0c5ef9-a864-4762-b9b5-650799431f0b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ded60a77-deab-4be5-9e25-e2f47dfb20f1' class='xr-index-data-in' type='checkbox'/><label for='index-ded60a77-deab-4be5-9e25-e2f47dfb20f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([25.063077926635742, 25.10474395751953, 25.14640998840332,\n",
- " 25.18807601928711, 25.2297420501709, 25.271408081054688,\n",
- " 25.313074111938477, 25.354740142822266, 25.396406173706055,\n",
- " 25.438072204589844,\n",
- " ...\n",
- " 49.02103042602539, 49.06269454956055, 49.10436248779297,\n",
- " 49.146026611328125, 49.18769454956055, 49.2293586730957,\n",
- " 49.271026611328125, 49.31269073486328, 49.3543586730957,\n",
- " 49.39602279663086],\n",
- " dtype='float64', name='lat', length=585))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-18ce9b42-adc9-4c72-b7b1-de581be8a1a4' class='xr-index-data-in' type='checkbox'/><label for='index-18ce9b42-adc9-4c72-b7b1-de581be8a1a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([235.22784423828125, 235.26950073242188, 235.3111572265625,\n",
- " 235.35284423828125, 235.39450073242188, 235.4361572265625,\n",
- " 235.4778289794922, 235.51950073242188, 235.5611572265625,\n",
- " 235.6028289794922,\n",
- " ...\n",
- " 292.56024169921875, 292.6019287109375, 292.6435852050781,\n",
- " 292.68524169921875, 292.7269287109375, 292.7685852050781,\n",
- " 292.81024169921875, 292.8519287109375, 292.8935852050781,\n",
- " 292.93524169921875],\n",
- " dtype='float64', name='lon', length=1386))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25f5216a-ca68-4726-a648-95317d842cc8' class='xr-index-data-in' type='checkbox'/><label for='index-25f5216a-ca68-4726-a648-95317d842cc8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2006-01-01', '2006-01-02', '2006-01-03', '2006-01-04',\n",
- " '2006-01-05', '2006-01-06', '2006-01-07', '2006-01-08',\n",
- " '2006-01-09', '2006-01-10',\n",
- " ...\n",
- " '2099-12-22', '2099-12-23', '2099-12-24', '2099-12-25',\n",
- " '2099-12-26', '2099-12-27', '2099-12-28', '2099-12-29',\n",
- " '2099-12-30', '2099-12-31'],\n",
- " dtype='datetime64[ns]', name='time', length=34333, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f08f7e5f-0cf2-4e17-9073-2a89e67763cf' class='xr-section-summary-in' type='checkbox' ><label for='section-f08f7e5f-0cf2-4e17-9073-2a89e67763cf' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Metadata_Link :</span></dt><dd></dd><dt><span>cdm_data_type :</span></dt><dd>GRID</dd><dt><span>comment :</span></dt><dd>Total daily precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</dd><dt><span>contributor_name :</span></dt><dd>Katherine C. Hegewisch</dd><dt><span>contributor_role :</span></dt><dd>Postdoctoral Fellow</dd><dt><span>creator_email :</span></dt><dd>jabatzoglou@uidaho.edu</dd><dt><span>creator_name :</span></dt><dd>John Abatzoglou</dd><dt><span>creator_url :</span></dt><dd>http://maca.northwestknowledge.net</dd><dt><span>date_created :</span></dt><dd>2014-05-15</dd><dt><span>date_issued :</span></dt><dd>2014-05-15</dd><dt><span>date_modified :</span></dt><dd>2014-05-15</dd><dt><span>description :</span></dt><dd>Multivariate Adaptive Constructed Analogs (MACA) method, version 2.3,Dec 2013.</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-124.7722 25.0631,-124.7722 49.3960, -67.0648 49.3960,-67.0648, 25.0631, -124.7722,25.0631))</dd><dt><span>geospatial_lat_max :</span></dt><dd>49.3960</dd><dt><span>geospatial_lat_min :</span></dt><dd>25.0631</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.0417</dd><dt><span>geospatial_lat_units :</span></dt><dd>decimal degrees north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-67.0648</dd><dt><span>geospatial_lon_min :</span></dt><dd>-124.7722</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.0417</dd><dt><span>geospatial_lon_units :</span></dt><dd>decimal degrees east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>0.0</dd><dt><span>history :</span></dt><dd>No revisions.</dd><dt><span>id :</span></dt><dd>MACAv2-METDATA</dd><dt><span>institution :</span></dt><dd>University of Idaho</dd><dt><span>keywords :</span></dt><dd>daily precipitation, daily maximum temperature, daily minimum temperature, daily downward shortwave solar radiation, daily specific humidity, daily wind velocity, CMIP5, Gridded Meteorological Data</dd><dt><span>keywords_vocabulary :</span></dt><dd></dd><dt><span>license :</span></dt><dd>Creative Commons CC0 1.0 Universal Dedication(http://creativecommons.org/publicdomain/zero/1.0/legalcode)</dd><dt><span>naming_authority :</span></dt><dd>edu.uidaho.reacch</dd><dt><span>processing_level :</span></dt><dd>GRID</dd><dt><span>project :</span></dt><dd></dd><dt><span>publisher_email :</span></dt><dd></dd><dt><span>publisher_name :</span></dt><dd></dd><dt><span>publisher_url :</span></dt><dd></dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF-1.0</dd><dt><span>summary :</span></dt><dd>This archive contains daily downscaled meteorological and hydrological projections for the Conterminous United States at 1/24-deg resolution utilizing the Multivariate Adaptive Constructed Analogs (MACA, Abatzoglou, 2012) statistical downscaling method with the METDATA (Abatzoglou,2013) training dataset. The downscaled meteorological variables are maximum/minimum temperature(tasmax/tasmin), maximum/minimum relative humidity (rhsmax/rhsmin)precipitation amount(pr), downward shortwave solar radiation(rsds), eastward wind(uas), northward wind(vas), and specific humidity(huss). The downscaling is based on the 365-day model outputs from different global climate models (GCMs) from Phase 5 of the Coupled Model Inter-comparison Project (CMIP3) utlizing the historical (1950-2005) and future RCP4.5/8.5(2006-2099) scenarios. Leap days have been added to the dataset from the average values between Feb 28 and Mar 1 in order to aid modellers.</dd><dt><span>time_coverage_duration :</span></dt><dd>P5Y</dd><dt><span>time_coverage_end :</span></dt><dd>2010-12-31T00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>2006-01-01T00:0</dd><dt><span>title :</span></dt><dd>Downscaled daily meteorological data of Precipitation from College of Global Change and Earth System Science, Beijing Normal University (BNU-ESM) using the run r1i1p1 of the rcp45 scenario.</dd></dl></div></li></ul></div></div>"
- ],
- "text/plain": [
- "<xarray.Dataset>\n",
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- "Coordinates:\n",
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- " * time (time) datetime64[ns] 2006-01-01 ... ...\n",
- "Data variables: (12/350)\n",
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- " ... ...\n",
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- " vas_inmcm4_r1i1p1_rcp45 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
- " vas_inmcm4_r1i1p1_rcp85 (time, lat, lon) float32 dask.array<chunksize=(745, 86, 204), meta=np.ndarray>\n",
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- " cdm_data_type: GRID\n",
- " comment: Total daily precipitation at surface; in...\n",
- " contributor_name: Katherine C. Hegewisch\n",
- " contributor_role: Postdoctoral Fellow\n",
- " ... ...\n",
- " summary: This archive contains daily downscaled m...\n",
- " time_coverage_duration: P5Y\n",
- " time_coverage_end: 2010-12-31T00:00\n",
- " time_coverage_resolution: P1D\n",
- " time_coverage_start: 2006-01-01T00:0\n",
- " title: Downscaled daily meteorological data of ..."
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# open and view zarr dataset\n",
"fs2 = fsspec.filesystem('s3', requester_pays=True)\n",
@@ -49596,18 +184,10 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dimension dictionary: {'X': 'lon', 'Y': 'lat', 'T': 'time'}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# dims_auto_extract = ['X', 'Y', 'T']\n",
"# def extract_dim(ds, d):\n",
@@ -49640,7 +220,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
"metadata": {},
"outputs": [],
@@ -49659,7 +239,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "4eb4d027-4266-4a0b-8f16-bacfbef06242",
"metadata": {},
"outputs": [],
@@ -49682,7 +262,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "c7de2681-88c2-4597-857c-8f169c596f8b",
"metadata": {},
"outputs": [],
@@ -49715,20 +295,10 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[235.22784423828125, 25.063077926635742, 292.93524169921875, 49.39602279663086]\n",
- "\n",
- "coord_bounds data type: <class 'float'>\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# pull out lat/lon bbox for data\n",
"# coordinates must be from WGS 84 datum\n",
@@ -49754,19 +324,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "41a84995-867c-4152-8c57-85e3758bbb77",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min: 2006-01-01 00:00:00 \n",
- "max: 2099-12-31 00:00:00\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# pull out first and last timestamps\n",
"temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n",
@@ -49778,7 +339,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
"metadata": {},
"outputs": [],
@@ -49796,18 +357,10 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "new collection created\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"if catalog.get_child(collection_id):\n",
" collection = catalog.get_child(collection_id)\n",
@@ -49833,7 +386,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "094832af-d22b-4359-b0f6-cf687acce5cc",
"metadata": {},
"outputs": [],
@@ -49849,7 +402,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
"metadata": {
"tags": []
@@ -49875,7 +428,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
"metadata": {},
"outputs": [],
@@ -49894,18 +447,10 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"id": "120a4914-3302-44a5-a282-0308ac84f040",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['lat', 'lon', 'time']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# list out dataset dimensions\n",
"# When writing data to Zarr, Xarray sets this attribute on all variables based on the variable dimensions. When reading a Zarr group, Xarray looks for this attribute on all arrays,\n",
@@ -49916,18 +461,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"id": "00a18a29-fb9a-4b56-8009-493122997b16",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[235.22784423828125, 25.063077926635742, 292.93524169921875, 49.39602279663086]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# get x, y bounds for extent of those dimensions (required)\n",
"xy_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
@@ -49936,7 +473,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "0f49d6d7-9e30-4144-909b-fa1238e6c77a",
"metadata": {},
"outputs": [],
@@ -49972,7 +509,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"id": "a20d12bf-a511-4c5e-84d0-77e2ec551518",
"metadata": {},
"outputs": [],
@@ -49998,18 +535,10 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "+proj=eqc +ellps=WGS84 +a=6378137.0 +lon_0=0.0 +to_meter=111319.4907932736 +vto_meter=1 +no_defs +type=crs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# print out crs information in dataset\n",
"print(crs)"
@@ -50017,18 +546,10 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\"$schema\":\"https://proj.org/schemas/v0.5/projjson.schema.json\",\"type\":\"ProjectedCRS\",\"name\":\"unknown\",\"base_crs\":{\"name\":\"unknown\",\"datum\":{\"type\":\"GeodeticReferenceFrame\",\"name\":\"unknown\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"coordinate_system\":{\"subtype\":\"ellipsoidal\",\"axis\":[{\"name\":\"Longitude\",\"abbreviation\":\"lon\",\"direction\":\"east\",\"unit\":\"degree\"},{\"name\":\"Latitude\",\"abbreviation\":\"lat\",\"direction\":\"north\",\"unit\":\"degree\"},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}},\"conversion\":{\"name\":\"unknown\",\"method\":{\"name\":\"Equidistant Cylindrical\",\"id\":{\"authority\":\"EPSG\",\"code\":1028}},\"parameters\":[{\"name\":\"Latitude of 1st standard parallel\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8823}},{\"name\":\"Latitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8801}},{\"name\":\"Longitude of natural origin\",\"value\":0,\"unit\":\"degree\",\"id\":{\"authority\":\"EPSG\",\"code\":8802}},{\"name\":\"False easting\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8806}},{\"name\":\"False northing\",\"value\":0,\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274},\"id\":{\"authority\":\"EPSG\",\"code\":8807}}]},\"coordinate_system\":{\"subtype\":\"Cartesian\",\"axis\":[{\"name\":\"Easting\",\"abbreviation\":\"E\",\"direction\":\"east\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Northing\",\"abbreviation\":\"N\",\"direction\":\"north\",\"unit\":{\"type\":\"LinearUnit\",\"name\":\"unknown\",\"conversion_factor\":111319.490793274}},{\"name\":\"Ellipsoidal height\",\"abbreviation\":\"h\",\"direction\":\"up\",\"unit\":\"metre\"}]}}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# # create the appropriate cartopy projection\n",
"# lcc = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
@@ -50064,18 +585,10 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time step: P1DT0H0M0S\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"time_step = pd.Timedelta(get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
"print(f'time step: {time_step}')"
@@ -50083,7 +596,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
"metadata": {},
"outputs": [],
@@ -50094,7 +607,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
"metadata": {},
"outputs": [],
@@ -50120,18 +633,10 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x step: 0.0417\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x_step = get_step(ds, dim_names_dict['X'], round_dec=4)\n",
"print(f'x step: {x_step}')"
@@ -50139,7 +644,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
"metadata": {},
"outputs": [],
@@ -50152,7 +657,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
"metadata": {},
"outputs": [],
@@ -50178,18 +683,10 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"id": "7405583b-ecb9-44b0-8815-048e42e55a42",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "y step: 0.0417\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"y_step = get_step(ds, dim_names_dict['Y'], round_dec=4)\n",
"print(f'y step: {y_step}')"
@@ -50197,7 +694,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
"metadata": {},
"outputs": [],
@@ -50210,7 +707,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
"metadata": {},
"outputs": [],
@@ -50236,25 +733,17 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['lat', 'lon', 'time']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(dims)"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
"metadata": {},
"outputs": [],
@@ -50283,7 +772,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"id": "92510876-7853-4d24-8563-c69f9012aeb6",
"metadata": {},
"outputs": [],
@@ -50320,7 +809,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
"metadata": {},
"outputs": [],
@@ -50360,7 +849,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"id": "10141fd4-91d6-491d-878b-02653720891d",
"metadata": {},
"outputs": [],
@@ -50379,7 +868,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
"metadata": {
"tags": []
@@ -50404,7 +893,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
"metadata": {},
"outputs": [],
--
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