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217 | class CamelsSe(HydroDataset):
_COL_MAP = {
"Qobs_m3s": "q_cms_obs",
"Qobs_mm": "q_mm_obs",
"Pobs_mm": "pcp_mm",
"Tobs_C": "airtemp_c_mean",
}
# (filename, column suffix) — AquaFetch suffixes soil/signature columns so
# that names shared across files (e.g. water_percentage, the Sxx signatures)
# do not collide when concatenated.
_ATTR_FILES = [
("catchments_physical_properties.csv", ""),
("catchments_landcover.csv", ""),
("catchments_soil_classes.csv", "_sc"),
("catchments_hydrological_signatures_1961_2020.csv", "_hs"),
("catchments_hydrological_signatures_CNP1_1961_1990.csv", "_CNP_61_90"),
("catchments_hydrological_signatures_CNP2_1991_2020.csv", "_CNP_91_20"),
]
_TS_REL = "CAMELS_SE/catchment time series/catchment time series"
_ATT_REL = "CAMELS_SE/catchment properties/catchment properties"
"""CAMELS_SE dataset class extending RainfallRunoff.
This class provides access to the CAMELS_SE dataset, which contains hourly
hydrological and meteorological data for various watersheds.
Attributes:
region: Geographic region identifier
download: Whether to download data automatically
ds_description: Dictionary containing dataset file paths
"""
def __init__(
self, uri: str, region: Optional[str] = None, download: bool = False
) -> None:
"""Initialize CAMELS_SE dataset.
Args:
uri: Path to the data directory
region: Geographic region identifier (optional)
download: Whether to download data automatically (default: False)
"""
super().__init__(uri)
self.region = region
self.download = download
if not str(uri).startswith("s3://"):
self.aqua_fetch = CAMELS_SE(uri)
def read_object_ids(self) -> np.ndarray:
uri = str(self.data_source_dir).rstrip("/")
if self._is_cloud():
fs = self._make_s3fs()
names = [p.split("/")[-1] for p in fs.ls(f"{uri}/{self._TS_REL}".removeprefix("s3://"))]
else:
names = os.listdir(os.path.join(uri, *self._TS_REL.split("/")))
ids = sorted(
(n.replace("catchment_id_", "").split("_")[0]
for n in names if n.startswith("catchment_id_")),
key=lambda x: int(x),
)
return np.array(ids)
def cache_attributes_to_zarr(self) -> None:
import zarr
fs = self._make_s3fs()
uri = str(self.data_source_dir).rstrip("/")
base = f"{uri}/{self._ATT_REL}"
dfs = []
for fname, suffix in self._ATTR_FILES:
path = f"{base}/{fname}".removeprefix("s3://")
try:
with fs.open(path) as fh:
df = pd.read_csv(fh, index_col="ID", dtype={"ID": str})
df.index = df.index.astype(str)
if suffix:
df.columns = [f"{c}{suffix}" for c in df.columns]
dfs.append(df)
except Exception as e:
print(f" WARN {fname}: {e}")
static = pd.concat(dfs, axis=1)
static = static.loc[~static.index.duplicated(keep="first")]
stations = self.read_object_ids().tolist()
static = static.reindex(stations)
static.columns = self._clean_feature_names(list(static.columns))
static = static.rename(columns={"latitude_wgs84": "lat"})
zarr_name = self._attributes_cache_filename.replace(".nc", ".zarr")
out, opts = self._zarr_path_and_opts(zarr_name)
n = len(stations)
root = zarr.open_group(out, mode="w", storage_options=opts, zarr_format=2)
for col in static.columns:
vals = static[col].values.astype(str) if static[col].dtype == object else static[col].values
arr = root.create_array(col, shape=(n,), chunks=(n,), dtype=vals.dtype)
arr[:] = vals
arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]
basin_arr = root.create_array("basin", shape=(n,), chunks=(n,), dtype=str)
basin_arr[:] = stations
basin_arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]
root.attrs["coordinates"] = "basin"
self._write_zarr_units(root, "static")
print(f"Attributes zarr written to: {out}")
def cache_timeseries_to_zarr(self) -> None:
import zarr
from tqdm import tqdm
fs = self._make_s3fs()
uri = str(self.data_source_dir).rstrip("/")
ts_base = f"{uri}/{self._TS_REL}"
# Build a map from station ID →?filename (IDs can have spaces in name part)
if self._is_cloud():
fs2 = self._make_s3fs()
all_names = [p.split("/")[-1] for p in fs2.ls(ts_base.removeprefix("s3://"))]
else:
all_names = os.listdir(os.path.join(uri, *self._TS_REL.split("/")))
id_to_fname = {
n.replace("catchment_id_", "").split("_")[0]: n
for n in all_names if n.startswith("catchment_id_")
}
stations = self.read_object_ids().tolist()
all_times = pd.date_range(self.default_t_range[0], self.default_t_range[1], freq="D")
n, nt = len(stations), len(all_times)
times_ns = all_times.asi8
all_vars = list(self._COL_MAP.values())
data: dict[str, np.ndarray] = {vn: np.full((n, nt), np.nan) for vn in all_vars}
print(f"Reading {n} stations from OSS...")
for i, stn in enumerate(tqdm(stations, desc="CAMELS_SE zarr")):
fname = id_to_fname.get(stn)
if fname is None:
continue
path = f"{ts_base}/{fname}".removeprefix("s3://")
try:
with fs.open(path) as fh:
df = pd.read_csv(fh)
df.index = pd.to_datetime(
{"year": df["Year"], "month": df["Month"], "day": df["Day"]}
)
df = df.reindex(all_times)
for raw_col, zarr_vn in self._COL_MAP.items():
if raw_col in df.columns:
data[zarr_vn][i] = pd.to_numeric(df[raw_col], errors="coerce").values
except Exception as e:
print(f" WARN {stn}: {e}")
zarr_name = self._timeseries_cache_filename.replace(".nc", ".zarr")
out, opts = self._zarr_path_and_opts(zarr_name)
root = zarr.open_group(out, mode="w", storage_options=opts, zarr_format=2)
for vn in all_vars:
arr = root.create_array(vn, shape=(n, nt), chunks=(n, min(nt, 365)), dtype="float64")
arr[:] = data[vn]
arr.attrs["_ARRAY_DIMENSIONS"] = ["basin", "time"]
time_arr = root.create_array("time", shape=(nt,), chunks=(min(nt, 365),), dtype="int64")
time_arr[:] = times_ns
time_arr.attrs["_ARRAY_DIMENSIONS"] = ["time"]
time_arr.attrs["units"] = "nanoseconds since 1970-01-01"
time_arr.attrs["calendar"] = "proleptic_gregorian"
basin_arr = root.create_array("basin", shape=(n,), chunks=(n,), dtype=str)
basin_arr[:] = stations
basin_arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]
root.attrs["coordinates"] = "basin time"
self._write_zarr_units(root, "dynamic")
print(f"Timeseries zarr written to: {out}")
@property
def _attributes_cache_filename(self):
return "camels_se_attributes.nc"
@property
def _timeseries_cache_filename(self):
return "camels_se_timeseries.nc"
@property
def default_t_range(self):
return ["1961-01-01", "2020-12-31"]
# get the information of features from dataset file"Documentation_2024-01-02.pdf"
_subclass_static_definitions = {
"p_mean": {"specific_name": "pmean_mm_year", "unit": "mm/year"},
"area": {"specific_name": "area_km2", "unit": "km^2"},
"urban_percentage": {"specific_name": "urban_percentage", "unit": "%"},
}
_dynamic_variable_mapping = {
StandardVariable.STREAMFLOW: {
"default_source": "obs_cms",
"sources": {
"obs_cms": {"specific_name": "q_cms_obs", "unit": "m^3/s"},
"obs_mm": {"specific_name": "q_mm_obs", "unit": "mm/day"},
},
},
StandardVariable.PRECIPITATION: {
"default_source": "default",
"sources": {
"default": {"specific_name": "pcp_mm", "unit": "mm/day"},
},
},
StandardVariable.TEMPERATURE_MEAN: {
"default_source": "default",
"sources": {
"default": {"specific_name": "airtemp_C_mean", "unit": "°C"},
},
},
}
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