diff --git a/workflows/archive/macav2_historical_monthly_create_collection_from_zarr.ipynb b/workflows/archive/macav2_historical_monthly_create_collection_from_zarr.ipynb
index 7cfacd9c78f58ca4aee1664ff6a8ecab53ca5306..a2a3cfae07bcd41cf1117bd62f32baa0d3b37e9f 100644
--- a/workflows/archive/macav2_historical_monthly_create_collection_from_zarr.ipynb
+++ b/workflows/archive/macav2_historical_monthly_create_collection_from_zarr.ipynb
@@ -33,7 +33,8 @@
"import pandas as pd\n",
"import json\n",
"import numpy as np\n",
- "import metpy\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
"import cartopy.crs as ccrs\n",
"import cfunits\n",
"import json\n",
@@ -213,8 +214,37 @@
"metadata": {},
"outputs": [],
"source": [
- "ds = ds.metpy.parse_cf()\n",
- "crs = ds[list(ds.keys())[0]].metpy.cartopy_crs"
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = 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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e4b4c28-1a02-4fdf-8498-274728ea0d2c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8566c769-1638-48eb-8717-fccdb6cfc0c1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
]
},
{
@@ -245,14 +275,63 @@
"# 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",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_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",
+ "#spatial_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",
+ "spatial_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(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1fa7c721-aecd-4ea6-8f8d-226cc75e10bb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5fa66fb7-ea2f-4a09-b5db-7c6f42a2694c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c28a05a0-8bde-4a99-80ee-f0385cfed090",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84a630f9-c527-43c5-8da4-1ee386c78c59",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
"# create a spatial extent object \n",
- "spatial_extent = pystac.SpatialExtent(bboxes=[coord_bounds])"
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
]
},
{
@@ -439,17 +518,6 @@
"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,
@@ -666,8 +734,8 @@
"# 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",
" }"
]
},