scattering: improve the code

scatcluster/processing/scattering.py

@@ -5,7 +5,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import scipy as sp
+from scipy import stats as sp_stats
 import xarray as xr
 from matplotlib import dates as mdates
 from obspy.clients.filesystem.sds import Client
@@ -22,24 +22,22 @@ def reduce_type(self):
         """
         Pooling operation performed on the last axis.
         """
-        pooling_options = [
-            ('avg', np.mean),
-            ('max', np.max),
-            ('median', np.median),
-            ('std', np.std),
-            ('gmean', sp.stats.gmean),
-            ('hmean', sp.stats.hmean),
-            ('pmean', sp.stats.pmean),
-            ('kurtosis', sp.stats.kurtosis),
-            ('skew', sp.stats.skew),
-            ('entropy', sp.stats.entropy),
-            ('sem', sp.stats.sem),
-            ('differential_entropy', sp.stats.differential_entropy),
-            ('median_abs_deviation', sp.stats.median_abs_deviation),
-        ]
-        for po in pooling_options:
-            if self.network_pooling == po[0]:
-                return po[1]
+        pooling_options = {
+            'avg': np.mean,
+            'max': np.max,
+            'median': np.median,
+            'std': np.std,
+            'gmean': sp_stats.gmean,
+            'hmean': sp_stats.hmean,
+            'pmean': sp_stats.hmean,
+            'kurtosis': sp_stats.kurtosis,
+            'skew': sp_stats.skew,
+            'entropy': sp_stats.entropy,
+            'sem': sp_stats.sem,
+            'differential_entropy': sp_stats.differential_entropy,
+            'median_abs_deviation': sp_stats.median_abs_deviation,
+        }
+        return pooling_options.get(self.network_pooling, None)
 
     def load_data_times(self):
         """
@@ -50,21 +48,29 @@ def load_data_times(self):
         `{self.network_name}_times.npy` and stores them in the `data_times` attribute.
 
         """
-        self.data_times = np.load(
-            f'{self.data_savepath}data/{self.data_network}_{self.data_station}_{self.data_location}_'
-            f'{self.network_name}_times.npy')
+        try:
+            file_path = f'{self.data_savepath}data/{self.data_network}_{self.data_station}_{self.data_location}_' \
+                        f'{self.network_name}_times.npy'
+            self.data_times = np.load(file_path)
+        except FileNotFoundError:
+            print(f"File not found: {file_path}")
+        except Exception as e:
+            print(f"An error occurred while loading data times: {e}")        
 
     def build_day_list(self) -> None:
         """Build data_day_list object
         """
-        day_list = [
-            day_start for day_start in pd.date_range(
-                UTCDateTime(self.data_starttime).strftime('%Y%m%d'), (
-                    UTCDateTime(self.data_endtime) - (60 * 60 * 24)).strftime('%Y%m%d')).strftime('%Y-%m-%d').tolist()
-            if day_start not in [UTCDateTime(day_exc).strftime('%Y-%m-%d') for day_exc in self.data_exclude_days]
-        ]
-
-        self.data_day_list = day_list
+        try:
+            start_time = UTCDateTime(self.data_starttime)
+            end_time = UTCDateTime(self.data_endtime)
+            exclude_days = [UTCDateTime(day).strftime('%Y-%m-%d') for day in self.data_exclude_days]
+            day_list = [
+                day_start for day_start in pd.date_range(start_time.strftime('%Y%m%d'), end_time.strftime('%Y%m%d')).strftime('%Y-%m-%d').tolist()
+                if day_start not in exclude_days
+            ]
+            self.data_day_list = day_list
+        except Exception as e:
+            print(f"An error occurred while building day list: {e}")
 
     def build_channel_list(self) -> None:
         if self.sample_stream is None: