diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index 03f10eb85ec81648585b9bc6b830ad71cc7828a7..00408684379c31dc6e7d3c18cf8c8bcf7bf52778 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -150,7 +150,7 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
self.station_type, self.network, self.store_data_locally, self.data_origin,
start, end)
data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method[ind],
- limit=self.interpolation_limit[ind])
+ limit=self.interpolation_limit[ind], sampling=self.sampling[ind])
return data
def _extract_lazy(self, lazy_data):
diff --git a/mlair/data_handler/data_handler_single_station.py b/mlair/data_handler/data_handler_single_station.py
index 25822762e6f0bc682ee78374be79bcdf86345a07..4330efd9ee5d3ae8a64c6eb9b95a0c58e18b3c36 100644
--- a/mlair/data_handler/data_handler_single_station.py
+++ b/mlair/data_handler/data_handler_single_station.py
@@ -280,7 +280,7 @@ class DataHandlerSingleStation(AbstractDataHandler):
self.station_type, self.network, self.store_data_locally, self.data_origin,
self.start, self.end)
self._data = self.interpolate(data, dim=self.time_dim, method=self.interpolation_method,
- limit=self.interpolation_limit)
+ limit=self.interpolation_limit, sampling=self.sampling)
self.set_inputs_and_targets()
def set_inputs_and_targets(self):
@@ -469,9 +469,8 @@ class DataHandlerSingleStation(AbstractDataHandler):
all_vars = sorted(statistics_per_var.keys())
return os.path.join(path, f"{''.join(station)}_{'_'.join(all_vars)}_meta.csv")
- @staticmethod
- def interpolate(data, dim: str, method: str = 'linear', limit: int = None, use_coordinate: Union[bool, str] = True,
- **kwargs):
+ def interpolate(self, data, dim: str, method: str = 'linear', limit: int = None,
+ use_coordinate: Union[bool, str] = True, sampling="daily", **kwargs):
"""
Interpolate values according to different methods.
@@ -508,8 +507,22 @@ class DataHandlerSingleStation(AbstractDataHandler):
:return: xarray.DataArray
"""
+ data = self.create_full_time_dim(data, dim, sampling)
return data.interpolate_na(dim=dim, method=method, limit=limit, use_coordinate=use_coordinate, **kwargs)
+ @staticmethod
+ def create_full_time_dim(data, dim, sampling):
+ """Ensure time dimension to be equidistant. Sometimes dates if missing values have been dropped."""
+ start = data.coords[dim].values[0]
+ end = data.coords[dim].values[-1]
+ freq = {"daily": "1D", "hourly": "1H"}.get(sampling)
+ datetime_index = pd.DataFrame(index=pd.date_range(start, end, freq=freq))
+ t = data.sel({dim: start}, drop=True)
+ res = xr.DataArray(coords=[datetime_index.index, *[t.coords[c] for c in t.coords]], dims=[dim, *t.coords])
+ res = res.transpose(*data.dims)
+ res.loc[data.coords] = data
+ return res
+
def make_history_window(self, dim_name_of_inputs: str, window: int, dim_name_of_shift: str) -> None:
"""
Create a xr.DataArray containing history data.
diff --git a/mlair/helpers/filter.py b/mlair/helpers/filter.py
index 4a6a5044d35d82eaea5a5b7e454771838375b3c5..b6c27cbcb781e4593a8533ab2ac72928fecadb67 100644
--- a/mlair/helpers/filter.py
+++ b/mlair/helpers/filter.py
@@ -92,6 +92,7 @@ class ClimateFIRFilter:
if apriori is None:
apriori = self.create_monthly_mean(data, sel_opts=sel_opts, sampling=sampling,
time_dim=time_dim) + diurnal_anomalies
+ logging.info(f"{plot_name}: apriori shape = {apriori.shape}")
apriori_list = to_list(apriori)
input_data = data.__deepcopy__()
for i in range(len(order)):
@@ -137,7 +138,7 @@ class ClimateFIRFilter:
self._apriori = apriori_list
@staticmethod
- def create_unity_array(data, time_dim, extend_range=365):
+ def create_unity_array(data, time_dim, extend_range=366):
"""Create a xr data array filled with ones. time_dim is extended by extend_range days in future and past."""
coords = data.coords
@@ -261,7 +262,7 @@ class ClimateFIRFilter:
end = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) + np.timedelta64(
extend_range * factor + 1,
td_type)
- new_time_axis = np.arange(start, end).astype("datetime64[ns]")
+ new_time_axis = np.arange(start, end).astype("datetime64[ns]") # hint: arange does not include end date
logging.info(f"{data.coords['Stations'].values[0]}: shape of new_time_axis = {new_time_axis.shape}")
logging.info(f"{data.coords['Stations'].values[0]}: start of new_time_axis = {start}")
logging.info(f"{data.coords['Stations'].values[0]}: end of new_time_axis = {end}")
@@ -273,14 +274,17 @@ class ClimateFIRFilter:
# start = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) - np.timedelta64(
# extend_range * factor, td_type)
end = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type)
- new_values = apriori.sel({time_dim: slice(start, end)})
+ new_values = apriori.sel({time_dim: slice(start, end)}) # hint: slice includes end date
logging.info(f"{data.coords['Stations'].values[0]}: shape of new_values = {new_values.shape}")
logging.info(f"{data.coords['Stations'].values[0]}: start of new_values = {start}")
logging.info(f"{data.coords['Stations'].values[0]}: end of new_values = {end}")
logging.info(f"{data.coords['Stations'].values[0]}: delta of new_values = {end - start}")
+
+ logging.info(f"{data.coords['Stations'].values[0]}: set new_time_axis")
new_values.coords[time_dim] = new_time_axis
# add new values to apriori
+ logging.info(f"{data.coords['Stations'].values[0]}: add to apriori")
apriori = apriori.combine_first(new_values)
return apriori