[JTH] warning to info in normalize and hide warnings in estela predictr calculation

Author: tausiaj

bluemath_tk/core/operations.py

@@ -95,7 +95,7 @@ def normalize(
         if custom_scale_factor.get(data_var):
             if custom_scale_factor.get(data_var)[0] > data_var_min:
                 if logger is not None:
-                    logger.warning(
+                    logger.info(
                         f"Proposed min custom scaler for {data_var} is bigger than datapoint"  # , using smallest datapoint
                     )
                 else:
@@ -107,7 +107,7 @@ def normalize(
             data_var_min = custom_scale_factor.get(data_var)[0]
             if custom_scale_factor.get(data_var)[1] < data_var_max:
                 if logger is not None:
-                    logger.warning(
+                    logger.info(
                         f"Proposed max custom scaler for {data_var} is lower than datapoint"  # , using biggest datapoint
                     )
                 else:

bluemath_tk/predictor/xwt.py

@@ -1,3 +1,4 @@
+import logging
 import warnings
 from datetime import datetime, timedelta
 from typing import Any, Dict
@@ -28,6 +29,7 @@ def get_dynamic_estela_predictor(
     data: xr.Dataset,
     estela: xr.Dataset,
     check_interpolation: bool = True,
+    verbose: bool = False,
 ) -> xr.Dataset:
     """
     Transform an xarray dataset of longitude, latitude, and time into one where
@@ -45,13 +47,29 @@ def get_dynamic_estela_predictor(
         The dataset containing the F values with dimensions longitude and latitude.
     check_interpolation : bool, optional
         Whether to check if the data is interpolated. Default is True.
+    verbose : bool, optional
+        Whether to print verbose output. Default is False.
+        If False, Dask logs are suppressed.
+        If True, Dask logs are shown.
 
     Returns
     -------
     xr.Dataset
         The transformed dataset.
     """
 
+    if not verbose:
+        # Suppress Dask logs
+        logging.getLogger("distributed").setLevel(logging.ERROR)
+        logging.getLogger("distributed.client").setLevel(logging.ERROR)
+        logging.getLogger("distributed.scheduler").setLevel(logging.ERROR)
+        logging.getLogger("distributed.worker").setLevel(logging.ERROR)
+        logging.getLogger("distributed.nanny").setLevel(logging.ERROR)
+        # Also suppress bokeh and tornado logs that Dask uses
+        logging.getLogger("bokeh").setLevel(logging.ERROR)
+        logging.getLogger("tornado").setLevel(logging.ERROR)
+
+    # TODO: Add customization for dask client
     _dask_client = setup_dask_client(n_workers=4, memory_limit=0.25)
 
     if check_interpolation:

bluemath_tk/teslakit/climate_emulator.py

@@ -1175,7 +1175,7 @@ def GenerateWaves(
                 # solve normal inverse CDF for each active chromosome
                 ipbs = 0  # prob_sim aux. index
                 sim_row = np.zeros(srl)
-                for i_c in np.where(crm == 1)[0]:
+                for i_c in np.where(np.atleast_1d(crm) == 1)[0]:
                     # random sampled GEV
                     rd = np.random.randint(0, len(xds_GEV_Par_Sampled.simulation))
                     xds_GEV_Par = xds_GEV_Par_Sampled.isel(simulation=rd)

bluemath_tk/waves/binwaves.py

@@ -141,6 +141,10 @@ def reconstruc_spectra(
         The memory limit to use. Default is 0.5.
     chunk_sizes : dict, optional
         The chunk sizes to use. Default is {"time": 24}.
+    verbose : bool, optional
+        Whether to print verbose output. Default is False.
+        If False, Dask logs are suppressed.
+        If True, Dask logs are shown.
 
     Returns
     -------