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Update flags for specifying independent leads, update eva.gev_confidence_interval and fileio._fix_metadata #89

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Merged
stellema merged 6 commits into from
May 6, 2026
Merged

Update flags for specifying independent leads, update eva.gev_confidence_interval and fileio._fix_metadata #89

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1288e61
Remove retry_fit option from unseen.eva.fit_gev
stellema May 6, 2026
9057513
Update eva.gev_confidence_interval
stellema May 6, 2026
2a914d5
Update independence cmd args to pass kws to fileio.open_dataset
stellema May 6, 2026
09294f4
Update fileio to allow passing generic Python code to _fix_metadata
stellema May 6, 2026
2a904a3
Update cmd line args for minimum independent leads
stellema May 6, 2026
52ead0b
Add add_legend option to timeseries plot
stellema May 6, 2026
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34 changes: 21 additions & 13 deletions unseen/bias_correction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,12 +213,20 @@ def _parse_command_line():
)
parser.add_argument(
"--min_lead",
type=int,
default=None,
help="Minimum lead time to include in analysis (int or filename)",
help="Minimum lead time to include in analysis",
)
parser.add_argument(
"--min_lead_kwargs",
"--min_lead_file",
type=str,
default=None,
help="Name of file containing the minimum lead time to include in analysis",
)
parser.add_argument(
"--min_lead_file_kwargs",
nargs="*",
default={},
action=general_utils.store_dict,
help="Optional fileio.open_dataset kwargs for lead independence (e.g., spatial_agg=median)",
)
Expand Down Expand Up @@ -270,17 +278,17 @@ def _main():

# Mask lead times below min_lead
if args.min_lead:
if isinstance(args.min_lead, str):
# Load min_lead from file
ds_min_lead = fileio.open_dataset(args.min_lead, **args.min_lead_kwargs)
min_lead = ds_min_lead["min_lead"].load()
da_fcst = da_fcst.groupby(f"{args.init_dim}.month").where(
da_fcst[args.lead_dim] >= min_lead
)
da_fcst = da_fcst.drop_vars("month")
else:
min_lead = args.min_lead
da_fcst = da_fcst.where(da_fcst[args.lead_dim] >= min_lead)
min_lead = int(args.min_lead)
da_fcst = da_fcst.where(da_fcst[args.lead_dim] >= min_lead)

elif args.min_lead_file:
# Load min_lead from file
ds_min_lead = fileio.open_dataset(args.min_lead_file, **args.min_lead_kwargs)
min_lead = ds_min_lead["min_lead"].load()
da_fcst = da_fcst.groupby(f"{args.init_dim}.month").where(
da_fcst[args.lead_dim] >= min_lead
)
da_fcst = da_fcst.drop_vars("month")

# Calculate bias
bias = get_bias(
Expand Down
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