diff --git a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
index 61d5f552e791cb638e3f34a5652c545e1343f04d..51923ae5d0f09e8c02828833c980ce15c247ab0b 100644
--- a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
@@ -45,46 +45,21 @@
},
{
"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",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
"metadata": {},
"source": [
- "## Collection Metadata Input"
+ "## Collection ID"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
"metadata": {},
"outputs": [],
"source": [
- "# name for STAC collection\n",
- "collection_id = 'conus404-daily'\n",
- "# description of STAC collection\n",
- "collection_description = 'CONUS404 40 years of daily values for subset of model output variables derived from hourly values on cloud storage'\n",
- "# license for dataset\n",
- "collection_license = 'CC0-1.0'"
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'conus404-daily'"
]
},
{
@@ -163,6 +138,30 @@
"ds"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = 'CONUS404 40 years of daily values for subset of model output variables derived from hourly values on cloud storage'\n",
+ "# you can consider pulling this fram dataset attributes instead of manually typing it:\n",
+ "# collection_description = ds.attrs['title']\n",
+ "\n",
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
{
"cell_type": "markdown",
"id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
@@ -407,10 +406,23 @@
},
{
"cell_type": "markdown",
- "id": "cfb71202-03df-45b5-ac2f-0dc2ee1ab780",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
"metadata": {},
+ "outputs": [],
"source": [
- "## Create pystac collection"
+ "# 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'))"
]
},
{
@@ -556,7 +568,9 @@
"id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
"metadata": {},
"source": [
- "**Time**"
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
]
},
{
@@ -567,6 +581,8 @@
"outputs": [],
"source": [
"time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
"print(f'time step: {time_step}')"
]
},
@@ -699,26 +715,6 @@
"# ds.isel(y=slice(ix-1,ix+3)).y"
]
},
- {
- "cell_type": "markdown",
- "id": "dc9c149c-c2ba-4c5f-b48b-7b1b88a0309c",
- "metadata": {},
- "source": [
- "#### extract x, y dimension lower and upper bounds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "773d2e70-9bc5-4f37-96d0-5ff980ae0c2d",
- "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": "markdown",
"id": "00a5e041-081d-428d-ac2e-75d16de205e6",
@@ -729,7 +725,15 @@
"\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)."
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\n",
+ " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]})\n",
+ " }\n",
+ "```"
]
},
{
@@ -759,8 +763,8 @@
"# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
"# note that the extent of each dimension should be pulled from the dataset\n",
"dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
- " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [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': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [xy_bounds[1], xy_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
" 'bottom_top_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top_stag')}),\n",
" 'bottom_top': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top')}),\n",
" 'soil_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layers_stag')}),\n",
@@ -768,8 +772,18 @@
" 'y_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, 'y_stag')}),\n",
" 'snow_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snow_layers_stag')}),\n",
" 'snso_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snso_layers_stag')}),\n",
- " }\n",
- "display(dims_dict)"
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert dims_dict.keys() == dims"
]
},
{
diff --git a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
index 5c91bcca4d8eb22975b2a17520b6147152844055..80ac6143334bedae04c439b97060becc7a73d98d 100644
--- a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
@@ -49,45 +49,20 @@
},
{
"cell_type": "markdown",
- "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "id": "f8c93dbc-174c-4387-be7a-00eccf004509",
"metadata": {},
"source": [
- "## Open up NHGF STAC Catalog"
+ "## Item ID"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define folder location where your STAC catalog json file is\n",
- "catalog_path = os.path.join('..', '..', 'catalog_items')\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",
+ "id": "65b8979c-a462-4f68-8912-e82158d8811e",
"metadata": {},
- "source": [
- "## Item Metadata Input"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
- "metadata": {
- "tags": []
- },
"outputs": [],
"source": [
- "# name for STAC collection\n",
+ "# name for STAC item - should match name of zarr dataset\n",
"item_id = 'conus404-daily'"
]
},
@@ -109,7 +84,7 @@
"outputs": [],
"source": [
"# url to zarr store that you want to create a collection for\n",
- "zarr_url = 's3://hytest/conus404/conus404_daily.zarr/'\n",
+ "zarr_url = f's3://mdmf/gdp/{item_id}.zarr/'\n",
"\n",
"# define keyword arguments needed for opening the dataset with xarray\n",
"# ref: https://github.com/stac-extensions/xarray-assets\n",
@@ -416,10 +391,25 @@
},
{
"cell_type": "markdown",
- "id": "cfb71202-03df-45b5-ac2f-0dc2ee1ab780",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
"metadata": {},
"source": [
- "## Create pystac item"
+ "## Open up STAC Catalog and create an item"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog_items')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
]
},
{
@@ -565,7 +555,9 @@
"id": "18b8950f-030f-4f78-b9ac-799dc9263cb6",
"metadata": {},
"source": [
- "**Time**"
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
]
},
{
@@ -576,6 +568,8 @@
"outputs": [],
"source": [
"time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
"print(f'time step: {time_step}')"
]
},
@@ -708,26 +702,6 @@
"# ds.isel(y=slice(ix-1,ix+3)).y"
]
},
- {
- "cell_type": "markdown",
- "id": "1cf871b8-7eee-4c47-81b4-a42583dd60b0",
- "metadata": {},
- "source": [
- "#### extract x, y dimension lower and upper bounds"
- ]
- },
- {
- "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": "markdown",
"id": "00a5e041-081d-428d-ac2e-75d16de205e6",
@@ -738,7 +712,15 @@
"\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)."
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\n",
+ " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]})\n",
+ " }\n",
+ "```"
]
},
{
@@ -768,8 +750,8 @@
"# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
"# note that the extent of each dimension should be pulled from the dataset\n",
"dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step': time_step}),\n",
- " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [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': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [xy_bounds[1], xy_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
" 'bottom_top_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top_stag')}),\n",
" 'bottom_top': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top')}),\n",
" 'soil_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layers_stag')}),\n",
@@ -777,8 +759,18 @@
" 'y_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, 'y_stag')}),\n",
" 'snow_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snow_layers_stag')}),\n",
" 'snso_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snso_layers_stag')}),\n",
- " }\n",
- "display(dims_dict)"
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "816ed76c-c287-4116-a507-6a5c2734e24b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert dims_dict.keys() == dims"
]
},
{