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| import pandas as pd | |
| from pathlib import Path | |
| BASE_DIR = Path(r"c:\Users\harsh\incois\dashboard") | |
| PROF_FILE = BASE_DIR / "ar_index_global_prof.txt" | |
| def check_land_points(): | |
| print(f"Reading {PROF_FILE}...") | |
| df = pd.read_csv(PROF_FILE, comment="#") | |
| df.columns = df.columns.str.strip() | |
| # India land area approx: 8-36N, 68-95E | |
| india_land = df[ | |
| (df["latitude"] > 8) & (df["latitude"] < 36) & | |
| (df["longitude"] > 68) & (df["longitude"] < 95) | |
| ].copy() | |
| india_land["wmo_id"] = india_land["file"].str.extract(r"/(\d+)/") | |
| latest = india_land.sort_values("date").groupby("wmo_id").tail(1) | |
| # Specific search for JA floats in this box | |
| ja_in_india = latest[latest["institution"] == "JA"] | |
| print(f"JA floats in India box: {len(ja_in_india)}") | |
| if len(ja_in_india) > 0: | |
| print(ja_in_india[["wmo_id", "latitude", "longitude", "institution", "date"]].to_string()) | |
| # All floats in India land box | |
| print(f"\nAll unique floats in India box: {len(latest)}") | |
| # Print top 20 suspicious ones (high latitude, inland) | |
| suspicious = latest[latest["latitude"] > 10] | |
| print(suspicious[["wmo_id", "latitude", "longitude", "institution", "date"]].head(20)) | |
| if __name__ == "__main__": | |
| check_land_points() | |