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Hotfix: polygon table coverage #35

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merged 1 commit into from
Dec 24, 2024
Merged

Hotfix: polygon table coverage #35

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134 changes: 79 additions & 55 deletions src/utils/metadata_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,21 @@
coloredlogs.install(level=LOG_LEVEL, logger=logger)


def check_coverage(row, dataset):
"""
Given an input row from the iso3 table and a dataset name,
returns whether or not there should be stats coverage.
Assumes that no dataset column present means that there's
full coverage.

"""
try:
coverage = row[dataset]
except KeyError:
coverage = True
return coverage


def get_available_datasets():
"""
Get list of available datasets from config directory.
Expand Down Expand Up @@ -69,6 +84,7 @@ def get_single_cog(dataset, mode):
return rxr.open_rasterio(cog_url, chunks="auto")


# TODO: This could really use some refactoring...
def process_polygon_metadata(
engine, mode, upsampled_resolution, sel_iso3s=None
):
Expand Down Expand Up @@ -104,64 +120,72 @@ def process_polygon_metadata(
for i in range(0, max_adm + 1):
gdf = gpd.read_file(f"{td}/{iso3.lower()}_adm{i}.shp")
for dataset in datasets:
da = get_single_cog(dataset, mode)
input_resolution = da.rio.resolution()
gdf_adm0 = gpd.read_file(
f"{td}/{iso3.lower()}_adm0.shp"
)
# We want all values to be unique, so that we can count the total
# number of unique cells from the raw source that contribute to the stats
da.values = np.arange(da.size).reshape(da.shape)
da = da.astype(np.float32)
# Dummy `date` dimension to pass `validate_dims`
da = da.expand_dims({"date": 1})

try:
da_clipped = prep_raster(da, gdf_adm0)
except NoDataInBounds:
logger.error(
f"{dataset} has no coverage at adm level {i}"
coverage = check_coverage(row, dataset)
if coverage:
da = get_single_cog(dataset, mode)
input_resolution = da.rio.resolution()
gdf_adm0 = gpd.read_file(
f"{td}/{iso3.lower()}_adm0.shp"
)
# We want all values to be unique, so that we can count the total
# number of unique cells from the raw source that contribute to the stats
da.values = np.arange(da.size).reshape(da.shape)
da = da.astype(np.float32)
# Dummy `date` dimension to pass `validate_dims`
da = da.expand_dims({"date": 1})

try:
da_clipped = prep_raster(da, gdf_adm0)
except NoDataInBounds:
logger.error(
f"{dataset} has no coverage at adm level {i}"
)
continue

da_clipped = prep_raster(
da, gdf_adm0, logger=logger
)
output_resolution = da_clipped.rio.resolution()
upscale_factor = (
input_resolution[0] / output_resolution[0]
)
continue

da_clipped = prep_raster(da, gdf_adm0, logger=logger)
output_resolution = da_clipped.rio.resolution()
upscale_factor = (
input_resolution[0] / output_resolution[0]
)

src_transform = da_clipped.rio.transform()
src_width = da_clipped.rio.width
src_height = da_clipped.rio.height
src_transform = da_clipped.rio.transform()
src_width = da_clipped.rio.width
src_height = da_clipped.rio.height

admin_raster = rasterize_admin(
gdf,
src_width,
src_height,
src_transform,
all_touched=False,
)
adm_ids = gdf[f"ADM{i}_PCODE"]
n_adms = len(adm_ids)

results = fast_zonal_stats(
da_clipped.values[0][0],
admin_raster,
n_adms,
stats=["count", "unique"],
rast_fill=np.nan,
)
df_results = pd.DataFrame.from_dict(results)
df_results[f"{dataset}_frac_raw_pixels"] = df_results[
"count"
] / (upscale_factor**2)
df_results = df_results.rename(
columns={
"unique": f"{dataset}_n_intersect_raw_pixels",
"count": f"{dataset}_n_upsampled_pixels",
}
)
gdf = gdf.join(df_results)
admin_raster = rasterize_admin(
gdf,
src_width,
src_height,
src_transform,
all_touched=False,
)
adm_ids = gdf[f"ADM{i}_PCODE"]
n_adms = len(adm_ids)

results = fast_zonal_stats(
da_clipped.values[0][0],
admin_raster,
n_adms,
stats=["count", "unique"],
rast_fill=np.nan,
)
df_results = pd.DataFrame.from_dict(results)
df_results[
f"{dataset}_frac_raw_pixels"
] = df_results["count"] / (upscale_factor**2)
df_results = df_results.rename(
columns={
"unique": f"{dataset}_n_intersect_raw_pixels",
"count": f"{dataset}_n_upsampled_pixels",
}
)
gdf = gdf.join(df_results)
else:
logger.info(
f"Skipping calculation for {dataset} for {iso3}"
)

gdf = gdf.to_crs("ESRI:54009")
gdf["area"] = gdf.geometry.area / 1_000_000
Expand Down
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