add iteration over year

Author: NicolasColombi

climada/hazard/tc_tracks.py

@@ -1685,74 +1685,85 @@ def from_FAST(cls, folder_name: str):
             if Path(file).suffix != ".nc":
                 continue
             with xr.open_dataset(file) as dataset:
-                for i in dataset.n_trk:
-
-                    # Select track
-                    track = dataset.sel(n_trk=i)
-                    # chunk dataset at first NaN value
-                    lon = track.lon_trks.data
-                    last_valid_index = np.where(np.isnan(lon))[0][0]
-                    track = track.isel(time=slice(0, last_valid_index))
-                    # Select lat, lon
-                    lat = track.lat_trks.data
-                    lon = track.lon_trks.data
-                    # Convert lon from 0-360 to -180 - 180
-                    lon = ((lon + 180) % 360) - 180
-                    # Convert time to pandas Datetime "yyyy.mm.dd"
-                    reference_time = (
-                        f"{track.tc_years.item()}-{int(track.tc_month.item())}-01"
-                    )
-                    time = pd.to_datetime(
-                        track.time.data, unit="s", origin=reference_time
-                    ).astype("datetime64[s]")
-                    # Define variables
-                    max_sustained_wind_knots = track.vmax_trks.data * 1.943844
-                    env_pressure = BASIN_ENV_PRESSURE[track.tc_basins.data.item()]
-                    cen_pres = _estimate_pressure(
-                        np.full(lat.shape, np.nan), lat, lon, max_sustained_wind_knots
-                    )
+                for year in dataset.year:
+                    for i in dataset.n_trk:
+
+                        # Select track
+                        track = dataset.sel(n_trk=i, year=year)
+                        # chunk dataset at first NaN value
+                        lon = track.lon_trks.data
+                        last_valid_index = np.where(np.isfinite(lon))[0][-1]
+                        track = track.isel(time=slice(0, last_valid_index + 1))
+                        # Select lat, lon
+                        lat = track.lat_trks.data
+                        lon = track.lon_trks.data
+                        # Convert lon from 0-360 to -180 - 180
+                        lon = ((lon + 180) % 360) - 180
+                        # Convert time to pandas Datetime "yyyy.mm.dd"
+                        reference_time = (
+                            f"{track.tc_years.item()}-{int(track.tc_month.item())}-01"
+                        )
+                        time = pd.to_datetime(
+                            track.time.data, unit="s", origin=reference_time
+                        ).astype("datetime64[s]")
+                        # Define variables
+                        max_sustained_wind_knots = track.vmax_trks.data * 1.943844
+                        env_pressure = BASIN_ENV_PRESSURE[track.tc_basins.data.item()]
+                        cen_pres = _estimate_pressure(
+                            np.full(lat.shape, np.nan),
+                            lat,
+                            lon,
+                            max_sustained_wind_knots,
+                        )
 
-                    data.append(
-                        xr.Dataset(
-                            {
-                                "time_step": (
-                                    "time",
-                                    np.full(time.shape[0], track.time.data[1]),
-                                ),
-                                "max_sustained_wind": ("time", track.vmax_trks.data),
-                                "central_pressure": ("time", cen_pres),
-                                "radius_max_wind": (
-                                    "time",
-                                    estimate_rmw(np.full(lat.shape, np.nan), cen_pres),
-                                ),
-                                "environmental_pressure": (
-                                    "time",
-                                    np.full(time.shape[0], env_pressure),
-                                ),
-                                "basin": (
-                                    "time",
-                                    np.full(time.shape[0], track.tc_basins.data.item()),
-                                ),
-                            },
-                            coords={
-                                "time": ("time", time),
-                                "lat": ("time", lat),
-                                "lon": ("time", lon),
-                            },
-                            attrs={
-                                "max_sustained_wind_unit": "m/s",
-                                "central_pressure_unit": "hPa",
-                                "name": f"storm_{track.n_trk.item()}",
-                                "sid": track.n_trk.item(),
-                                "orig_event_flag": True,
-                                "data_provider": "FAST",
-                                "id_no": track.n_trk.item(),
-                                "category": TCTracks.define_tc_category_FAST(
-                                    max_sustained_wind_knots
-                                ),
-                            },
+                        data.append(
+                            xr.Dataset(
+                                {
+                                    "time_step": (
+                                        "time",
+                                        np.full(time.shape[0], track.time.data[1]),
+                                    ),
+                                    "max_sustained_wind": (
+                                        "time",
+                                        track.vmax_trks.data,
+                                    ),
+                                    "central_pressure": ("time", cen_pres),
+                                    "radius_max_wind": (
+                                        "time",
+                                        estimate_rmw(
+                                            np.full(lat.shape, np.nan), cen_pres
+                                        ),
+                                    ),
+                                    "environmental_pressure": (
+                                        "time",
+                                        np.full(time.shape[0], env_pressure),
+                                    ),
+                                    "basin": (
+                                        "time",
+                                        np.full(
+                                            time.shape[0], track.tc_basins.data.item()
+                                        ),
+                                    ),
+                                },
+                                coords={
+                                    "time": ("time", time),
+                                    "lat": ("time", lat),
+                                    "lon": ("time", lon),
+                                },
+                                attrs={
+                                    "max_sustained_wind_unit": "m/s",
+                                    "central_pressure_unit": "hPa",
+                                    "name": f"storm_{track.n_trk.item()}",
+                                    "sid": track.n_trk.item(),
+                                    "orig_event_flag": True,
+                                    "data_provider": "FAST",
+                                    "id_no": track.n_trk.item(),
+                                    "category": TCTracks.define_tc_category_FAST(
+                                        max_sustained_wind_knots
+                                    ),
+                                },
+                            )
                         )
-                    )
 
         return cls(data)