)
table = table.loc[table.Value > 0].copy()
table.Priority = table.Priority.astype("uint8")
table["category"] = table.Descrpt.str[0].astype("uint8")
remap_table = table[["Value", "Priority"]].values.astype("uint8")
priority_data = remap(data, remap_table, nodata=nodata)
write_raster(outfilename, priority_data, transform, DATA_CRS, nodata=nodata)
print("Adding overviews and masks...")
add_overviews(outfilename)
create_lowres_mask(
outfilename,
str(outfilename).replace(".tif", "_mask.tif"),
factor=MASK_FACTOR,
ignore_zero=False,
)
### Process indicators
# Extract cores from the categories
table["aquatic_cores"] = table.Descrpt.str.contains("Aquatic")
table["terrestrial_cores"] = table.Descrpt.str.contains("Terrestrial")
table["imperiled_cores"] = table.Descrpt.str.contains("Imperiled")
for core_type in ["aquatic_cores", "terrestrial_cores", "imperiled_cores"]:
print(f"Processing {core_type}")
cores_data = remap(data,
table[["Value", core_type]].values.astype("uint8"),
nodata=nodata)
ix = (
slice(row_off, row_off + data_height, None),
slice(col_off, col_off + data_width, None),
)
out[ix] = np.where((data >= 0) & (data < 255), data.astype("uint8"),
out[ix])
meta = {
"driver": "GTiff",
"crs": DATA_CRS,
"transform": transform,
"width": width,
"height": height,
"count": 1,
"nodata": 255,
"dtype": "uint8",
"compress": "lzw",
}
with rasterio.open(out_dir / f"urban_{year}.tif", "w", **meta) as outfile:
outfile.write(out, 1)
outfile.build_overviews(OVERVIEW_FACTORS, Resampling.nearest)
create_lowres_mask(out_dir / "urban_2100.tif",
out_dir / "urban_mask.tif",
factor=8,
ignore_zero=True)