Initial Climate Mood Analysis project
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Justin Fisher
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#!/usr/bin/env python3
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"""
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Add wettest/driest precipitation month metrics to data/climate-data.csv.
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The metrics use the existing monthly NOAA nClimGrid precipitation file. They
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are categorical month labels derived from 1991-2020 county-area means.
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"""
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from __future__ import annotations
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import argparse
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import csv
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from pathlib import Path
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import numpy as np
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import xarray as xr
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from build_county_climate_data import (
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MONTH_NAMES,
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_as_monthly_climatology,
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_extract_grid_2d,
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_load_counties,
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_select_data_var,
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_zonal_mean,
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)
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REPO_ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_CLIMATE_DATA = REPO_ROOT / "data" / "climate-data.csv"
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DEFAULT_COUNTIES_GEOJSON = REPO_ROOT / "data" / "geojson-counties-fips.json"
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DEFAULT_MONTHLY_PRCP_NC = REPO_ROOT / "data" / "noaa" / "nclimgrid" / "nclimgrid_prcp.nc"
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PRECIP_MONTH_FIELDS = ["wettestPrecipMonth", "driestPrecipMonth"]
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def normalize_fips(value: object) -> str:
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"""Return a 5-digit county FIPS string from mixed text or numeric input."""
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digits = "".join(ch for ch in str(value).strip() if ch.isdigit())
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return digits.zfill(5)[-5:] if digits else ""
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def read_csv_rows(path: Path) -> tuple[list[str], list[dict]]:
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"""Read a CSV while preserving the source field order."""
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with path.open("r", encoding="utf-8-sig", newline="") as csv_file:
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reader = csv.DictReader(csv_file)
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if reader.fieldnames is None:
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raise ValueError(f"{path} has no CSV header.")
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return list(reader.fieldnames), list(reader)
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def fieldnames_with_precip_months(original_fields: list[str]) -> list[str]:
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"""Place the new fields next to the existing precipitation metrics."""
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fields = [field for field in original_fields if field not in PRECIP_MONTH_FIELDS]
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insert_after = "seasonalityIndex"
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insert_at = fields.index(insert_after) + 1 if insert_after in fields else len(fields)
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return fields[:insert_at] + PRECIP_MONTH_FIELDS + fields[insert_at:]
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def build_precip_month_lookup(
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*,
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counties_geojson: Path,
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monthly_prcp_nc: Path,
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climatology_start_year: int,
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climatology_end_year: int,
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) -> dict[str, tuple[str, str]]:
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"""Calculate wettest and driest precipitation month for each county."""
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counties = _load_counties(counties_geojson)
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monthly_prcp = xr.open_dataset(monthly_prcp_nc, decode_times=True)
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try:
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prcp_var = _select_data_var(monthly_prcp, "mlyprcp_norm")
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prcp_monthly = _as_monthly_climatology(
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monthly_prcp[prcp_var],
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start_year=climatology_start_year,
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end_year=climatology_end_year,
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)
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prcp_by_month: list[list[float]] = []
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for month in range(12):
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prcp_arr, prcp_transform = _extract_grid_2d(prcp_monthly.isel(time=month))
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prcp_by_month.append(_zonal_mean(prcp_arr, prcp_transform, counties))
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finally:
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monthly_prcp.close()
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lookup: dict[str, tuple[str, str]] = {}
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for idx, row in counties.iterrows():
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county_fips = str(row["county_fips"])
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prcp_months_mm = np.array([prcp_by_month[m][idx] for m in range(12)], dtype=np.float64)
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if not np.any(np.isfinite(prcp_months_mm)):
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continue
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wettest_month = MONTH_NAMES[int(np.nanargmax(prcp_months_mm))]
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driest_month = MONTH_NAMES[int(np.nanargmin(prcp_months_mm))]
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lookup[county_fips] = (wettest_month, driest_month)
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return lookup
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def apply_precipitation_month_metrics(
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*,
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climate_data: Path,
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counties_geojson: Path,
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monthly_prcp_nc: Path,
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out: Path,
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climatology_start_year: int,
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climatology_end_year: int,
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) -> None:
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"""Merge precipitation month categories into the app climate CSV."""
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original_fields, rows = read_csv_rows(climate_data)
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lookup = build_precip_month_lookup(
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counties_geojson=counties_geojson,
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monthly_prcp_nc=monthly_prcp_nc,
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climatology_start_year=climatology_start_year,
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climatology_end_year=climatology_end_year,
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)
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updated_count = 0
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missing_count = 0
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for row in rows:
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county_fips = normalize_fips(row.get("countyFips", ""))
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values = lookup.get(county_fips)
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if values:
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row["wettestPrecipMonth"], row["driestPrecipMonth"] = values
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updated_count += 1
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else:
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row["wettestPrecipMonth"] = ""
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row["driestPrecipMonth"] = ""
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missing_count += 1
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source = (row.get("source") or "").strip() or "unknown-source"
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tag = f"precip-month-extremes=noaa-nclimgrid-monthly-{climatology_start_year}-{climatology_end_year}"
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if tag not in source:
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row["source"] = f"{source} + {tag}"
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fieldnames = fieldnames_with_precip_months(original_fields)
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with out.open("w", encoding="utf-8", newline="") as csv_file:
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writer = csv.DictWriter(csv_file, fieldnames=fieldnames, extrasaction="ignore")
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writer.writeheader()
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writer.writerows(rows)
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print(f"Wrote {len(rows)} rows to {out}")
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print(f"Updated precipitation month metrics for {updated_count} rows.")
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print(f"Left precipitation month metrics blank for {missing_count} rows.")
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def parse_args() -> argparse.Namespace:
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"""Parse command-line paths for the precipitation month update."""
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parser = argparse.ArgumentParser(
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description="Add wettestPrecipMonth and driestPrecipMonth to climate-data.csv."
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)
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parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
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parser.add_argument("--counties-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON)
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parser.add_argument("--monthly-prcp-nc", type=Path, default=DEFAULT_MONTHLY_PRCP_NC)
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parser.add_argument("--out", type=Path, default=DEFAULT_CLIMATE_DATA)
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parser.add_argument("--climatology-start-year", type=int, default=1991)
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parser.add_argument("--climatology-end-year", type=int, default=2020)
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return parser.parse_args()
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def main() -> None:
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"""Run the CSV update."""
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args = parse_args()
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apply_precipitation_month_metrics(
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climate_data=args.climate_data,
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counties_geojson=args.counties_geojson,
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monthly_prcp_nc=args.monthly_prcp_nc,
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out=args.out,
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climatology_start_year=args.climatology_start_year,
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climatology_end_year=args.climatology_end_year,
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)
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if __name__ == "__main__":
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main()
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