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Allow for processing of floodscan polygon metadata #25

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Nov 28, 2024
Merged

Allow for processing of floodscan polygon metadata #25

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41 changes: 34 additions & 7 deletions src/utils/metadata_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,13 +7,18 @@
import numpy as np
import pandas as pd
import rioxarray as rxr
from rioxarray.exceptions import NoDataInBounds

from src.config.settings import LOG_LEVEL, load_pipeline_config
from src.utils.cloud_utils import get_container_client
from src.utils.cog_utils import get_cog_url
from src.utils.database_utils import create_polygon_table, postgres_upsert
from src.utils.iso3_utils import get_iso3_data, load_shp_from_azure
from src.utils.raster_utils import fast_zonal_stats, prep_raster, rasterize_admin
from src.utils.raster_utils import (
fast_zonal_stats,
prep_raster,
rasterize_admin,
)

logger = logging.getLogger(__name__)
coloredlogs.install(level=LOG_LEVEL, logger=logger)
Expand Down Expand Up @@ -57,12 +62,16 @@ def get_single_cog(dataset, mode):
container_client = get_container_client(mode, "raster")
config = load_pipeline_config(dataset)
prefix = config["blob_prefix"]
cogs_list = [x.name for x in container_client.list_blobs(name_starts_with=prefix)]
cogs_list = [
x.name for x in container_client.list_blobs(name_starts_with=prefix)
]
cog_url = get_cog_url(mode, cogs_list[0])
return rxr.open_rasterio(cog_url, chunks="auto")


def process_polygon_metadata(engine, mode, upsampled_resolution, sel_iso3s=None):
def process_polygon_metadata(
engine, mode, upsampled_resolution, sel_iso3s=None
):
"""
Process and store polygon metadata for all administrative levels and datasets.

Expand Down Expand Up @@ -97,28 +106,46 @@ def process_polygon_metadata(engine, mode, upsampled_resolution, sel_iso3s=None)
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")
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]
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

admin_raster = rasterize_admin(
gdf, src_width, src_height, src_transform, all_touched=False
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],
da_clipped.values[0][0],
admin_raster,
n_adms,
stats=["count", "unique"],
Expand Down