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245 | class simbi(HydroDataset):
"""simbi dataset class extending RainfallRunoff.
This class provides access to the simbi 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
"""
# OSS dataset folder (uploaded raw data)
_DATA_REL = "Simbi"
_ATTR_REL = "Simbi/03_SIMBI_ATTRIBUTE"
_OTHERS_REL = "Simbi/03_SIMBI_ATTRIBUTE/02_OTHERS"
_DAILY_REL = "Simbi/03_SIMBI_ATTRIBUTE/01_CLIMATIC_SIGNATURE/02_DAILY"
_MONTHLY_REL = "Simbi/03_SIMBI_ATTRIBUTE/01_CLIMATIC_SIGNATURE/01_MONTHLY"
_Q_REL = "Simbi/00_SIMBI_OBSERVED_DATA/02_DAILY_STREAMFLOW"
_PCP_REL = "Simbi/00_SIMBI_OBSERVED_DATA/01_DAILY_RAINFALL"
_TEMP_REL = "Simbi/00_SIMBI_OBSERVED_DATA/05_DAILY_LONG_TERM_AVERAGE_TEMPERATURE"
# static attribute files mirroring AquaFetch Simbi._static_data:
# other_attributes() then clim_sigs() (daily then monthly).
# each is (relative_dir, filename, column_suffix); every file is read with
# index_col=0 and the index normalised via id.split("-")[1].
_STATIC_FILES = [
(_OTHERS_REL, "stream_density.csv", ""),
(_OTHERS_REL, "Percent_land_cover_98.csv", "_lc_98"),
(_OTHERS_REL, "Percent_land_cover_95.csv", "_lc_95"),
(_OTHERS_REL, "Percent_geologic_class.csv", "_geol"),
(_OTHERS_REL, "location_and_topography.csv", ""),
(_OTHERS_REL, "hypsometric_curve.csv", ""),
(_OTHERS_REL, "Percent_aquifer_class.csv", ""),
(_OTHERS_REL, "Percent_carb_sediment_magma.csv", ""),
(_DAILY_REL, "baseflow_index.csv", "_d"),
(_DAILY_REL, "high_q_dur.csv", "_d_hq_dur"),
(_DAILY_REL, "high_q_freq.csv", "_d_hq_freq"),
(_DAILY_REL, "low_q_dur.csv", "_d_lq_dur"),
(_DAILY_REL, "low_q_freq.csv", "_d_lq_freq"),
(_DAILY_REL, "q_mean.csv", "_d_mean"),
(_DAILY_REL, "quantile_5.csv", "_d_q5"),
(_DAILY_REL, "quantile_95.csv", "_d_q95"),
(_MONTHLY_REL, "aridity_runoff.csv", "_mon_arid"),
(_MONTHLY_REL, "average.csv", "_mon_avg"),
(_MONTHLY_REL, "QMNA5.csv", "_mon_QMNA5"),
(_MONTHLY_REL, "QMXA10.csv", "_mon_QMXA10"),
(_MONTHLY_REL, "quantile_5.csv", "_mon_q5"),
(_MONTHLY_REL, "quantile_95.csv", "_mon_q95"),
]
# AquaFetch Simbi.static_map (raw column -> standard name)
_STATIC_RENAME = {
"Area": "area_km2",
"Lat_Cent": "lat",
"Lon_Cent": "long",
"Slope": "slope_degrees",
}
def __init__(
self, uri: str, region: Optional[str] = None, download: bool = False
) -> None:
"""Initialize simbi 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
# aqua_fetch only supports local paths
if not str(uri).startswith("s3://"):
self.aqua_fetch = Simbi(uri)
def read_object_ids(self) -> np.ndarray:
"""Station IDs: the 24 catchments with boundary + static data.
Derived from the topography attribute file index (id.split('-')[1]),
which matches AquaFetch Simbi.boundary_stations().
"""
if self._is_cloud():
fs = self._make_s3fs()
uri = str(self.data_source_dir).rstrip("/")
path = f"{uri}/{self._OTHERS_REL}/location_and_topography.csv".removeprefix(
"s3://"
)
with fs.open(path) as fh:
idx = pd.read_csv(fh, index_col=0).index
ids = sorted(str(i).split("-")[1] for i in idx)
return np.array(ids)
return super().read_object_ids()
def cache_attributes_to_zarr(self) -> None:
import zarr
fs = self._make_s3fs()
uri = str(self.data_source_dir).rstrip("/")
dfs = []
for rel, fname, suffix in self._STATIC_FILES:
path = f"{uri}/{rel}/{fname}".removeprefix("s3://")
try:
with fs.open(path) as fh:
df = pd.read_csv(fh, index_col=0)
df.index = [str(i).split("-")[1] for i in df.index]
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")]
static = static.rename(columns=self._STATIC_RENAME)
static.index = static.index.astype(str)
static.columns = self._clean_feature_names(list(static.columns))
static = static.loc[:, ~static.columns.duplicated(keep="first")]
zarr_name = self._attributes_cache_filename.replace(".nc", ".zarr")
out, opts = self._zarr_path_and_opts(zarr_name)
ids = static.index.tolist()
n = len(ids)
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[:] = ids
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("/")
q_base = f"{uri}/{self._Q_REL}"
pcp_dirs = [f"{uri}/{self._PCP_REL}/1920_1940", f"{uri}/{self._PCP_REL}/1948_1966"]
temp_base = f"{uri}/{self._TEMP_REL}"
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
def _read_one(path, col_out):
try:
with fs.open(path.removeprefix("s3://")) as fh:
df = pd.read_csv(fh, index_col=0, parse_dates=True)
s = df.iloc[:, 0]
s.index = pd.to_datetime(s.index)
s = s[~s.index.duplicated(keep="first")]
return s
except Exception:
return None
# cleaned zarr var names from dynamic mapping
q_vn, pcp_vn, temp_vn = "q_cms_obs", "pcp_mm", "airtemp_c_mean"
data = {vn: np.full((n, nt), np.nan) for vn in (q_vn, pcp_vn, temp_vn)}
for i, stn in enumerate(tqdm(stations, desc="simbi")):
q = _read_one(f"{q_base}/Q_{stn}.csv", q_vn)
if q is not None:
data[q_vn][i] = pd.to_numeric(q.reindex(all_times), errors="coerce").values
# precipitation: concat the two period folders
parts = [_read_one(f"{d}/P_{stn}.csv", pcp_vn) for d in pcp_dirs]
parts = [p for p in parts if p is not None]
if parts:
pcp = pd.concat(parts)
pcp = pcp[~pcp.index.duplicated(keep="first")]
data[pcp_vn][i] = pd.to_numeric(pcp.reindex(all_times), errors="coerce").values
t = _read_one(f"{temp_base}/P_{stn}.csv", temp_vn)
if t is not None:
data[temp_vn][i] = pd.to_numeric(t.reindex(all_times), errors="coerce").values
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 (q_vn, pcp_vn, temp_vn):
arr = root.create_array(vn, shape=(n, nt), chunks=(n, min(nt, 365)),
dtype="float64", fill_value=np.nan)
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 "simbi_attributes.nc"
@property
def _timeseries_cache_filename(self):
return "simbi_timeseries.nc"
@property
def default_t_range(self):
return ["1920-01-01", "2005-12-31"]
# get the information of features from dataset file "SIMBI_README"
_subclass_static_definitions = {
"area": {"specific_name": "area_km2", "unit": "km^2"},
"p_mean": {"specific_name": "p_mon_avg", "unit": "mm/month"},
}
_dynamic_variable_mapping = {
StandardVariable.STREAMFLOW: {
"default_source": "observations",
"sources": {
"observations": {"specific_name": "q_cms_obs", "unit": "m^3/s"}
},
},
StandardVariable.PRECIPITATION: {
"default_source": "observations",
"sources": {"observations": {"specific_name": "pcp_mm", "unit": "mm/day"}},
},
StandardVariable.TEMPERATURE_MEAN: {
"default_source": "observations",
"sources": {
"observations": {"specific_name": "airtemp_c_mean", "unit": "°C"}
},
},
}
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