[JTH] add better logs and docs

Author: tausiaj

bluemath_tk/datamining/pca.py

@@ -319,7 +319,7 @@ def _generate_stacked_data(self, data: xr.Dataset) -> np.ndarray:
                 np.mean(~np.isnan(var_to_clean_values), axis=0) > var_to_clean_threshold
             )[0]
             self.logger.warning(
-                f"Using {len(not_nan_positions)} out of {var_to_clean_values.shape[1]} available variables"
+                f"Using {len(not_nan_positions)} out of {var_to_clean_values.shape[1]} available variables \n"
                 "If this is originated by using few times, please check 'nan_threshold_to_drop' parameter in fit method"
             )
             var_value_to_replace_nans = self.value_to_replace_nans.get(

bluemath_tk/predictor/xwt.py

@@ -37,10 +37,7 @@ def get_dynamic_estela_predictor(
     time - t, where t is specified in the estela dataset.
 
     Parameters
-    ----------ltimes = estela.where(estela.F >= 0, np.nan).traveltime.astype(int)
-    estela_max_traveltime = estela_traveltimes.max().values
-    for traveltime in range(estela_max_traveltime):
-        data = data.w
+    ----------
     data : xr.Dataset
         The input dataset with dimensions longitude, latitude, and time.
     estela : xr.Dataset
@@ -82,9 +79,11 @@ def get_dynamic_estela_predictor(
         if "longitude" not in estela.dims or "latitude" not in estela.dims:
             raise ValueError("Estela must have longitude and latitude dimensions.")
         estela = estela.interp_like(data)  # TODO: Check NaNs interpolation
+
     data = data.chunk({"time": 365}).where(estela.F >= 0.0, np.nan)
     estela_traveltimes = estela.where(estela.F >= 0, np.nan).traveltime.astype(int)
     estela_max_traveltime = estela_traveltimes.max().values
+
     for traveltime in range(estela_max_traveltime):
         data = data.where(estela_traveltimes != traveltime, data.shift(time=traveltime))