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Resolve "robust apriori estimate for short timeseries"

+ 2
2
@@ -382,7 +382,7 @@ class ClimateFIRFilter(FIRFilter):
@@ -382,7 +382,7 @@ class ClimateFIRFilter(FIRFilter):
monthly_mean.sel(month=month, drop=True),
monthly_mean.sel(month=month, drop=True),
monthly)
monthly)
# transform monthly information into original sampling rate
# transform monthly information into original sampling rate
return monthly.resample({time_dim: sampling}).interpolate()
return monthly.dropna(dim=time_dim).resample({time_dim: sampling}).interpolate()
@staticmethod
@staticmethod
def _compute_hourly_mean_per_month(data: xr.DataArray, time_dim: str, as_anomaly: bool) -> Dict[int, xr.DataArray]:
def _compute_hourly_mean_per_month(data: xr.DataArray, time_dim: str, as_anomaly: bool) -> Dict[int, xr.DataArray]:
@@ -422,7 +422,7 @@ class ClimateFIRFilter(FIRFilter):
@@ -422,7 +422,7 @@ class ClimateFIRFilter(FIRFilter):
for month in means.keys():
for month in means.keys():
hourly_mean_single_month = means[month].sel(hour=hour, drop=True)
hourly_mean_single_month = means[month].sel(hour=hour, drop=True)
h_coll = xr.where((h_coll[f"{time_dim}.month"] == month), hourly_mean_single_month, h_coll)
h_coll = xr.where((h_coll[f"{time_dim}.month"] == month), hourly_mean_single_month, h_coll)
h_coll = h_coll.resample({time_dim: sampling}).interpolate()
h_coll = h_coll.dropna(time_dim).resample({time_dim: sampling}).interpolate()
h_coll = h_coll.sel({time_dim: (h_coll[f"{time_dim}.hour"] == hour)})
h_coll = h_coll.sel({time_dim: (h_coll[f"{time_dim}.hour"] == hour)})
return h_coll
return h_coll
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