Floodscan stats

run_raster_stats.py

@@ -45,7 +45,7 @@ def setup_logger(name, level=logging.INFO):
     return logger
 
 
-def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
+def process_chunk(start, end, dataset, mode, df_iso3s, engine_url, chunksize):
     process_name = current_process().name
     logger = setup_logger(f"{process_name}: {dataset}_{start}")
     logger.info(f"Starting processing for {dataset} from {start} to {end}")
@@ -56,8 +56,13 @@ def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
     try:
         for _, row in df_iso3s.iterrows():
             iso3 = row["iso3"]
-            # shp_url = row["o_shp"]
             max_adm = row["max_adm_level"]
+
+            # Coverage check for specific datasets
+            if dataset in df_iso3s.keys():
+                if not row[dataset]:
+                    logger.info(f"Skipping {iso3}...")
+                    continue
             logger.info(f"Processing data for {iso3}...")
 
             with tempfile.TemporaryDirectory() as td:
@@ -95,6 +100,7 @@ def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
                         con=engine,
                         if_exists="append",
                         index=False,
+                        chunksize=chunksize,
                         method=postgres_upsert,
                     )
                 except Exception as e:
@@ -130,10 +136,10 @@ def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
     logger.info(f"Updating data for {dataset}...")
 
     create_qa_table(engine)
-    start, end, is_forecast, sel_iso3s = parse_pipeline_config(
+    start, end, is_forecast, sel_iso3s, extra_dims = parse_pipeline_config(
         dataset, args.test, args.update_stats, args.mode
     )
-    create_dataset_table(dataset, engine, is_forecast)
+    create_dataset_table(dataset, engine, is_forecast, extra_dims)
 
     df_iso3s = get_iso3_data(sel_iso3s, engine)
     date_ranges = split_date_range(start, end)
@@ -145,7 +151,7 @@ def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
         )
 
         process_args = [
-            (start, end, dataset, args.mode, df_iso3s, engine_url)
+            (start, end, dataset, args.mode, df_iso3s, engine_url, args.chunksize)
             for start, end in date_ranges
         ]
 
@@ -154,6 +160,8 @@ def process_chunk(start, end, dataset, mode, df_iso3s, engine_url):
 
     else:
         logger.info("Processing entire date range in a single chunk")
-        process_chunk(start, end, dataset, args.mode, df_iso3s, engine_url)
+        process_chunk(
+            start, end, dataset, args.mode, df_iso3s, engine_url, args.chunksize
+        )
 
     logger.info("Done calculating and saving stats.")

src/config/floodscan.yml

@@ -0,0 +1,11 @@
+blob_prefix: floodscan/daily/v5/processed/aer_area_300s_
+start_date: 1998-01-12
+end_date: Null
+forecast: False
+extra_dims:
+  - band : str
+test:
+  start_date: 2023-12-01
+  end_date: 2024-01-31
+  iso3s: ["ETH"]
+coverage: ["DZA", "AGO", "BEN", "BWA", "BFA", "BDI", "CPV", "CMR", "CAF", "TCD", "COM", "COG", "CIV", "CAP", "DJI", "EGY", "GNQ", "ERI", "SWZ", "ETH", "GAB", "GMB", "GHA", "GIN", "GNB", "KEN", "LS0", "LBR", "LBY", "MDG", "MWI", "MLI", "MRT", "MUS", "MAR", "MOZ", "NAM", "NER", "NGA", "RWA", "STP", "SEN", "SYC", "SLE", "SOM", "ZAF", "SSD", "SDN", "TGO", "TUN", "UGA", "TZA", "ZMB", "ZWE"]

src/config/imerg.yml

@@ -3,6 +3,6 @@ start_date: 2000-06-01
 end_date: Null
 forecast: False
 test:
-  start_date: 2000-06-01
-  end_date: 2024-10-01
+  start_date: 2020-01-01
+  end_date: 2020-01-15
   iso3s: ["ETH"]

src/config/seas5.yml

@@ -2,7 +2,9 @@ blob_prefix: seas5/monthly/processed/precip_em_i
 start_date: 1981-01-01
 end_date: Null
 forecast: True
+extra_dims:
+  - leadtime : int
 test:
   start_date: 2024-01-01
-  end_date: 2024-10-01
+  end_date: 2024-02-01
   iso3s: ["AFG"]

src/config/settings.py

@@ -4,7 +4,7 @@
 import yaml
 from dotenv import load_dotenv
 
-from src.utils.general_utils import get_most_recent_date
+from src.utils.general_utils import get_most_recent_date, parse_extra_dims
 
 load_dotenv()
 
@@ -37,10 +37,11 @@ def parse_pipeline_config(dataset, test, update, mode):
         end_date = config["end_date"]
         sel_iso3s = None
     forecast = config["forecast"]
+    extra_dims = parse_extra_dims(config.get("extra_dims"))
     if not end_date:
         end_date = date.today() - timedelta(days=1)
     if update:
         last_update = get_most_recent_date(mode, config["blob_prefix"])
         start_date = last_update
         end_date = last_update
-    return start_date, end_date, forecast, sel_iso3s
+    return start_date, end_date, forecast, sel_iso3s, extra_dims

src/utils/cog_utils.py

@@ -109,6 +109,23 @@ def process_seas5(cog_name, mode):
     return da_in
 
 
+def process_floodscan(cog_name, mode):
+    cog_url = get_cog_url(mode, cog_name)
+    da_in = rxr.open_rasterio(cog_url, chunks="auto")
+
+    year_valid = da_in.attrs["year_valid"]
+    month_valid = str(da_in.attrs["month_valid"]).zfill(2)
+    date_valid = str(da_in.attrs["date_valid"]).zfill(2)
+    date_in = f"{year_valid}-{month_valid}-{date_valid}"
+
+    da_in = da_in.squeeze(drop=True)
+    da_in["date"] = date_in
+    da_in = da_in.expand_dims(["date"])
+
+    da_in = da_in.persist()
+    return da_in
+
+
 def stack_cogs(start_date, end_date, dataset="era5", mode="dev"):
     """
     Stack Cloud Optimized GeoTIFFs (COGs) for a specified date range into an xarray Dataset.
@@ -124,7 +141,7 @@ def stack_cogs(start_date, end_date, dataset="era5", mode="dev"):
     end_date : str or datetime-like
         The end date of the date range for stacking the COGs. This can be a string or a datetime object.
     dataset : str, optional
-        The name of the dataset to retrieve COGs from. Options include "era5", "imerg", and "seas5".
+        The name of the dataset to retrieve COGs from. Options include "floodscan", "era5", "imerg", and "seas5".
         Default is "era5".
     mode : str, optional
         The environment mode to use when accessing the cloud storage container. May be "dev", "prod", or "local".
@@ -149,13 +166,16 @@ def stack_cogs(start_date, end_date, dataset="era5", mode="dev"):
         config = load_pipeline_config(dataset)
         prefix = config["blob_prefix"]
     except Exception:
-        logger.error("Input `dataset` must be one of `era5`, `seas5`, or `imerg`.")
+        logger.error(
+            "Input `dataset` must be one of `floodscan`, `era5`, `seas5`, or `imerg`."
+        )
 
     cogs_list = [
         x.name
         for x in container_client.list_blobs(name_starts_with=prefix)
         if (parse_date(x.name) >= start_date) & (parse_date(x.name) <= end_date)  # noqa
     ]
+
     if len(cogs_list) == 0:
         raise Exception("No COGs found to process")
 
@@ -171,6 +191,8 @@ def stack_cogs(start_date, end_date, dataset="era5", mode="dev"):
             da_in = process_seas5(cog, mode)
         elif dataset == "imerg":
             da_in = process_imerg(cog, mode)
+        elif dataset == "floodscan":
+            da_in = process_floodscan(cog, mode)
         das.append(da_in)
 
     # Note that we're dropping all attributes here

src/utils/database_utils.py

@@ -35,7 +35,7 @@ def db_engine_url(mode):
     return DATABASES[mode]
 
 
-def create_dataset_table(dataset, engine, is_forecast=False):
+def create_dataset_table(dataset, engine, is_forecast=False, extra_dims={}):
     """
     Create a table for storing dataset statistics in the database.
 
@@ -48,11 +48,16 @@ def create_dataset_table(dataset, engine, is_forecast=False):
     is_forecast : Bool
         Whether or not the dataset is a forecast. Will include `leadtime` and
         `issued_date` columns if so.
+    extra_dims : dict
+        Dictionary where the keys are names of any extra dimensions that need to be added to the
+        dataset table and the values are the type.
 
     Returns
     -------
     None
     """
+    if extra_dims is None:
+        extra_dims = {}
     metadata = MetaData()
     columns = [
         Column("iso3", CHAR(3)),
@@ -71,8 +76,9 @@ def create_dataset_table(dataset, engine, is_forecast=False):
     unique_constraint_columns = ["valid_date", "pcode"]
     if is_forecast:
         columns.insert(3, Column("issued_date", Date))
-        columns.insert(4, Column("leadtime", Integer))
-        unique_constraint_columns.append("leadtime")
+    for idx, dim in enumerate(extra_dims):
+        columns.insert(idx + 4, Column(dim, extra_dims[dim]))
+        unique_constraint_columns.append(dim)
 
     Table(
         f"{dataset}",
@@ -127,6 +133,7 @@ def create_iso3_table(engine):
         Column("max_adm_level", Integer),
         Column("stats_last_updated", Date),
         Column("shp_url", String),
+        Column("floodscan", Boolean),
     )
     metadata.create_all(engine)
 

src/utils/general_utils.py

@@ -3,6 +3,7 @@
 
 import pandas as pd
 from dateutil.relativedelta import relativedelta
+from sqlalchemy import VARCHAR, Integer
 
 from src.utils.cloud_utils import get_container_client
 
@@ -110,3 +111,15 @@ def parse_date(filename):
     """
     res = re.search("([0-9]{4}-[0-9]{2}-[0-9]{2})", filename)
     return pd.to_datetime(res[0])
+
+
+def parse_extra_dims(extra_dims):
+    parsed_extra_dims = {}
+    for extra_dim in extra_dims:
+        dim = next(iter(extra_dim))
+        if extra_dim[dim] == "str":
+            parsed_extra_dims[dim] = VARCHAR
+        else:
+            parsed_extra_dims[dim] = Integer
+
+    return parsed_extra_dims

src/utils/inputs.py

@@ -9,7 +9,7 @@ def cli_args():
     parser.add_argument(
         "dataset",
         help="Dataset for which to calculate raster stats",
-        choices=["seas5", "era5", "imerg"],
+        choices=["seas5", "era5", "imerg", "floodscan"],
         default=None,
         nargs="?",
     )
@@ -36,4 +36,10 @@ def cli_args():
         help="Update the iso3 and polygon metadata tables.",
         action="store_true",
     )
+    parser.add_argument(
+        "--chunksize",
+        help="Limit the SQL insert batches to an specific chunksize.",
+        type=int,
+        default=100000,
+    )
     return parser.parse_args()

src/utils/iso3_utils.py

@@ -8,6 +8,7 @@
 import requests
 from sqlalchemy import text
 
+from src.config.settings import load_pipeline_config
 from src.utils.cloud_utils import get_container_client
 from src.utils.database_utils import create_iso3_table
 
@@ -144,6 +145,19 @@ def determine_max_adm_level(row):
         return min(1, row["src_lvl"])
 
 
+def load_coverage():
+    pipelines = ["seas5", "era5", "imerg", "floodscan"]
+    coverage = {}
+
+    for dataset in pipelines:
+        config = load_pipeline_config(dataset)
+        if "coverage" in config:
+            dataset_coverage = config["coverage"]
+            coverage[dataset] = dataset_coverage
+
+    return coverage
+
+
 def create_iso3_df(engine):
     """
     Create and populate an ISO3 table in the database with country information.
@@ -178,6 +192,8 @@ def create_iso3_df(engine):
         )
         & (df_hrp["endDate"] >= current_date)  # noqa
     ]
+    dataset_coverage = load_coverage()
+
     iso3_codes = set()
     for locations in df_active_hrp["locations"]:
         iso3_codes.update(locations.split("|"))
@@ -187,6 +203,9 @@ def create_iso3_df(engine):
     df["max_adm_level"] = df.apply(determine_max_adm_level, axis=1)
     df["stats_last_updated"] = None
 
+    for dataset in dataset_coverage:
+        df[dataset] = df["iso_3"].isin(dataset_coverage[dataset])
+
     # TODO: This list seems to have some inconsistencies when compared against the
     # contents of all polygons
     # Also need global p-codes list from https://fieldmaps.io/data/cod