Initial Climate Mood Analysis project

scripts/rebuild_nsrdb_representative_point_ghi_summary.py

@@ -0,0 +1,163 @@
+#!/usr/bin/env python3
+"""
+Rebuild county representative-point GHI summaries from cached NSRDB raw CSV responses.
+
+This does not call the NSRDB API. It reads data/nrel/representative_point_csv/*.csv, computes:
+
+avgSolarGhiKwhM2Day = sum(hourly GHI) / 1000 / 365
+
+and writes a county-keyed CSV compatible with build_county_climate_data.py.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+from pathlib import Path
+
+
+DEFAULT_POINTS_CSV = Path("data/nrel/county_representative_points.csv")
+DEFAULT_REPRESENTATIVE_POINT_CSV_DIR = Path("data/nrel/representative_point_csv")
+DEFAULT_OUTPUT_CSV = Path("data/nrel/county_representative_point_ghi_summary.csv")
+
+
+def read_county_points(points_csv: Path) -> list[dict[str, str]]:
+    """Read county representative points keyed by county FIPS."""
+    with points_csv.open(newline="", encoding="utf-8") as handle:
+        return list(csv.DictReader(handle))
+
+
+def extract_ghi_values(csv_path: Path) -> list[float]:
+    """Extract numeric hourly GHI values from a cached NSRDB CSV response."""
+    with csv_path.open(newline="", encoding="utf-8-sig") as handle:
+        reader = csv.reader(handle)
+        ghi_index: int | None = None
+        values: list[float] = []
+
+        for row in reader:
+            if ghi_index is None:
+                if {"Year", "Month", "Day", "Hour", "Minute", "GHI"}.issubset(set(row)):
+                    ghi_index = row.index("GHI")
+                continue
+
+            if len(row) <= ghi_index or not row[ghi_index].strip():
+                continue
+            values.append(float(row[ghi_index]))
+
+    if ghi_index is None:
+        raise ValueError(f"Could not find NSRDB data header with a GHI column in {csv_path}.")
+    if not values:
+        raise ValueError(f"No GHI values were found in {csv_path}.")
+    return values
+
+
+def select_raw_csv(representative_point_csv_dir: Path, county_fips: str) -> Path | None:
+    """Return the cached raw CSV for a county, preferring polar data when both exist."""
+    matches = sorted(representative_point_csv_dir.glob(f"{county_fips}_*_ghi.csv"))
+    if not matches:
+        return None
+
+    polar_matches = [path for path in matches if "_polar-tmy_" in path.name]
+    return polar_matches[0] if polar_matches else matches[0]
+
+
+def source_label(raw_csv: Path) -> str:
+    """Return a readable source label from the cached filename."""
+    if "_polar-tmy_" in raw_csv.name:
+        return "NSRDB Polar TMY PSM v4 representative point"
+    if "_goes-tmy_" in raw_csv.name:
+        return "NSRDB GOES TMY PSM v4 representative point"
+    return "NSRDB TMY representative point"
+
+
+def summarize_county(point: dict[str, str], raw_csv: Path) -> dict[str, str]:
+    """Summarize one county raw CSV into the app solar metric."""
+    ghi_values = extract_ghi_values(raw_csv)
+    avg_daily_ghi = sum(ghi_values) / 1000 / 365
+
+    return {
+        "county_fips": point["county_fips"],
+        "county_name": point["county_name"],
+        "state_fips": point["state_fips"],
+        "state_abbr": point["state_abbr"],
+        "lat": point["lat"],
+        "lon": point["lon"],
+        "avgSolarGhiKwhM2Day": f"{avg_daily_ghi:.3f}",
+        "ghi_rows": str(len(ghi_values)),
+        "ghi_min": f"{min(ghi_values):.1f}",
+        "ghi_max": f"{max(ghi_values):.1f}",
+        "source": source_label(raw_csv),
+        "raw_csv": str(raw_csv),
+    }
+
+
+def write_summary_csv(rows: list[dict[str, str]], output_csv: Path) -> None:
+    """Write rebuilt county solar summaries."""
+    output_csv.parent.mkdir(parents=True, exist_ok=True)
+    with output_csv.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(
+            handle,
+            fieldnames=[
+                "county_fips",
+                "county_name",
+                "state_fips",
+                "state_abbr",
+                "lat",
+                "lon",
+                "avgSolarGhiKwhM2Day",
+                "ghi_rows",
+                "ghi_min",
+                "ghi_max",
+                "source",
+                "raw_csv",
+            ],
+        )
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--points-csv", type=Path, default=DEFAULT_POINTS_CSV)
+    parser.add_argument(
+        "--representative-point-csv-dir",
+        dest="representative_point_csv_dir",
+        type=Path,
+        default=DEFAULT_REPRESENTATIVE_POINT_CSV_DIR,
+    )
+    parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Run the cached raw CSV summarizer."""
+    args = parse_args()
+    rows: list[dict[str, str]] = []
+    missing: list[str] = []
+    failed: list[str] = []
+
+    for point in read_county_points(args.points_csv):
+        county_fips = point["county_fips"]
+        raw_csv = select_raw_csv(args.representative_point_csv_dir, county_fips)
+        if raw_csv is None:
+            missing.append(county_fips)
+            continue
+
+        try:
+            rows.append(summarize_county(point, raw_csv))
+        except (OSError, ValueError) as exc:
+            failed.append(f"{county_fips}: {exc}")
+
+    write_summary_csv(rows, args.output_csv)
+    print(f"Wrote {len(rows)} county representative-point solar summaries to {args.output_csv}")
+    if missing:
+        print(f"Missing raw CSVs for {len(missing)} counties: {', '.join(missing[:20])}")
+    if failed:
+        print(f"Failed to summarize {len(failed)} counties:")
+        for message in failed[:20]:
+            print(f"  {message}")
+
+
+if __name__ == "__main__":
+    main()