more loggings

mlair/helpers/filter.py

@@ -212,20 +212,36 @@ class ClimateFIRFilter:
 
         # apriori starts after data
         if dates[0] < apriori.coords[time_dim].values[0]:
+            logging.info(f"{data.coords['Stations'].values[0]}: apriori starts after data")
             # add difference in full years
             date_diff = abs(dates[0] - apriori.coords[time_dim].values[0]).astype("timedelta64[D]")
             extend_range = np.ceil(date_diff / (np.timedelta64(1, "D") * 365)).astype(int) * 365
             coords = apriori.coords
 
             # create new time axis
-            start = coords[time_dim][0].values.astype("datetime64[%s]" % td_type) - np.timedelta64(extend_range, "D")
+            # start = coords[time_dim][0].values.astype("datetime64[%s]" % td_type) - np.timedelta64(extend_range, "D")
+            # end = coords[time_dim][0].values.astype("datetime64[%s]" % td_type)
+            # new_time_axis = np.arange(start, end).astype("datetime64[ns]")
+
+            factor = 1 if td_type == "D" else 24
+            start = coords[time_dim][0].values.astype("datetime64[%s]" % td_type) - np.timedelta64(
+                extend_range * factor + 1,
+                td_type)
             end = coords[time_dim][0].values.astype("datetime64[%s]" % td_type)
             new_time_axis = np.arange(start, end).astype("datetime64[ns]")
+            logging.info(f"{data.coords['Stations'].values[0]}: shape of new_time_axis = {new_time_axis.shape}")
 
             # extract old values to use with new axis
-            start = coords[time_dim][0].values.astype("datetime64[D]")
-            end = coords[time_dim][0].values.astype("datetime64[D]") + np.timedelta64(extend_range - 1, "D")
+            # start = coords[time_dim][0].values.astype("datetime64[D]")
+            # end = coords[time_dim][0].values.astype("datetime64[D]") + np.timedelta64(extend_range - 1, "D")
+            # new_values = apriori.sel({time_dim: slice(start, end)})
+            # new_values.coords[time_dim] = new_time_axis
+
+            start = coords[time_dim][0].values.astype("datetime64[%s]" % td_type)
+            end = coords[time_dim][0].values.astype("datetime64[%s]" % td_type) + np.timedelta64(
+                extend_range * factor - 1, td_type)
             new_values = apriori.sel({time_dim: slice(start, end)})
+            logging.info(f"{data.coords['Stations'].values[0]}: shape of new_values = {new_values.shape}")
             new_values.coords[time_dim] = new_time_axis
 
             # add new values to apriori
@@ -233,6 +249,7 @@ class ClimateFIRFilter:
 
         # apriori ends before data
         if dates[-1] + np.timedelta64(365, "D") > apriori.coords[time_dim].values[-1]:
+            logging.info(f"{data.coords['Stations'].values[0]}: apriori ends before data")
             # add difference in full years + 1 year (because apriori is used as future estimate)
             date_diff = abs(dates[-1] - apriori.coords[time_dim].values[-1]).astype("timedelta64[D]")
             extend_range = np.ceil(date_diff / (np.timedelta64(1, "D") * 365)).astype(int) * 365 + 365
@@ -245,6 +262,7 @@ class ClimateFIRFilter:
                 extend_range * factor + 1,
                 td_type)
             new_time_axis = np.arange(start, end).astype("datetime64[ns]")
+            logging.info(f"{data.coords['Stations'].values[0]}: shape of new_time_axis = {new_time_axis.shape}")
 
             # extract old values to use with new axis
             start = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) - np.timedelta64(
@@ -253,6 +271,7 @@ class ClimateFIRFilter:
             #     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)})
+            logging.info(f"{data.coords['Stations'].values[0]}: shape of new_values = {new_values.shape}")
             new_values.coords[time_dim] = new_time_axis
 
             # add new values to apriori