From 282b3b4318e1ebdc78495e96e8752b2cb4c15d42 Mon Sep 17 00:00:00 2001
From: amsnyder <asnyder@usgs.gov>
Date: Mon, 8 Apr 2024 12:43:35 -0500
Subject: [PATCH] fix crs var detection
---
...reate_collection_from_zarr_conus404-daily.ipynb | 14 ++++++++++++--
.../create_item_from_zarr_conus404-daily.ipynb | 14 ++++++++++++--
2 files changed, 24 insertions(+), 4 deletions(-)
diff --git a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
index 29e27fc1..58161e5a 100644
--- a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
@@ -194,6 +194,16 @@
"If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -202,7 +212,7 @@
"outputs": [],
"source": [
"# use pyproj to automatically extract crs info\n",
- "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
"\n",
"# alternatively, create the appropriate cartopy projection\n",
"# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
@@ -821,7 +831,7 @@
"vars_dict={}\n",
"for v in vars:\n",
" unit = stac_helpers.get_unit(ds, v)\n",
- " var_type = stac_helpers.get_var_type(ds, v)\n",
+ " var_type = stac_helpers.get_var_type(ds, v, crs_var)\n",
" long_name = stac_helpers.get_long_name(ds, v)\n",
" vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
]
diff --git a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
index 250d7781..ad517d53 100644
--- a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
@@ -178,6 +178,16 @@
"If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bae3ab0-b0cc-4486-aaaf-209c4d30ec1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -186,7 +196,7 @@
"outputs": [],
"source": [
"# use pyproj to automatically extract crs info\n",
- "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
"\n",
"# alternatively, create the appropriate cartopy projection\n",
"# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
@@ -808,7 +818,7 @@
"vars_dict={}\n",
"for v in vars:\n",
" unit = stac_helpers.get_unit(ds, v)\n",
- " var_type = stac_helpers.get_var_type(ds, v)\n",
+ " var_type = stac_helpers.get_var_type(ds, v, crs_var)\n",
" long_name = stac_helpers.get_long_name(ds, v)\n",
" vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
]
--
GitLab