Coding norms

ush/SpatialTemporalStatsTool/SpatialTemporalStats.py

@@ -235,21 +235,24 @@ def plot_obs(self, selected_var_gdf, var_name, region, resolution, output_path):
             )
 
             if item == "Obs_Minus_Forecast_adjusted_Average":
-                max_val_cbar = 5.0*std_val
-                min_val_cbar = -5.0*std_val
+                max_val_cbar = 5.0 * std_val
+                min_val_cbar = -5.0 * std_val
                 cmap = "bwr"
             else:
                 max_val_cbar = max_val
                 min_val_cbar = min_val
                 cmap = "jet"
 
-            cbar_label = "grid=%dx%d,   min=%.3lf,   max=%.3lf,   bias=%.3lf,   std=%.3lf\n" % (
-                resolution,
-                resolution,
-                min_val,
-                max_val,
-                avg_val,
-                std_val,
+            cbar_label = (
+                "grid=%dx%d,   min=%.3lf,   max=%.3lf,   bias=%.3lf,   std=%.3lf\n"
+                % (
+                    resolution,
+                    resolution,
+                    min_val,
+                    max_val,
+                    avg_val,
+                    std_val,
+                )
             )
 
             filtered_gdf.plot(
@@ -320,14 +323,14 @@ def make_summary_plots(
         self.sensor = sensor
         # read all obs files
         all_files = os.listdir(obs_files_path)
-        # obs_files = [os.path.join(obs_files_path, file) for file in all_files if file.endswith('.nc4')]
         obs_files = [
             os.path.join(obs_files_path, file)
             for file in all_files
             if file.endswith(".nc4") and "diag_%s_ges" % sensor in file
         ]
 
-        # get date time from file names. alternatively could get from attribute but that needs reading the entire nc4
+        # get date time from file names.
+        # alternatively could get from attribute but that needs reading the entire nc4
         files_date_times_df = pd.DataFrame()
 
         files_date_times = self._extract_date_times(obs_files)
@@ -354,51 +357,47 @@ def make_summary_plots(
         # get unique channels from one of the files
         ds = xarray.open_dataset(studied_cycle_files[index[0]])
         unique_channels = np.unique(ds["Channel_Index"].data).tolist()
-        print('Total Number of Channels ', len(unique_channels))
-
-        Allchannels_data={}
+        print("Total Number of Channels ", len(unique_channels))
+        Allchannels_data = {}
         for this_channel in unique_channels:
-           Allchannels_data[this_channel] = np.empty(shape=(0,))
+            Allchannels_data[this_channel] = np.empty(shape=(0,))
         for this_cycle_obs_file in studied_cycle_files:
             ds = xarray.open_dataset(this_cycle_obs_file)
             if QC_filter:
                 QC_bool = ds["QC_Flag"].data == 0
-
             for this_channel in unique_channels:
                 channel_bool = ds["Channel_Index"].data == this_channel
 
-                this_cycle_channel_var_values = ds[var_name].data[channel_bool*QC_bool]
+                this_cycle_channel_var_values = ds[var_name].data[
+                    channel_bool * QC_bool
+                ]
                 Allchannels_data[this_channel] = np.append(
                     Allchannels_data[this_channel], this_cycle_channel_var_values
                 )
 
         for this_channel in unique_channels:
-            this_channel_values=Allchannels_data[this_channel]
+            this_channel_values = Allchannels_data[this_channel]
             squared_values = [x**2 for x in this_channel_values]
             mean_of_squares = sum(squared_values) / len(squared_values)
-            rms_value=mean_of_squares ** 0.5
+            rms_value = mean_of_squares ** 0.5
             Summary_results.append(
                 [
                     this_channel,
                     np.size(this_channel_values),
                     np.std(this_channel_values),
                     np.mean(this_channel_values),
-                    rms_value               
+                    rms_value,
                 ]
             )
 
-
         Summary_resultsDF = pd.DataFrame(
-            Summary_results, columns=["channel", "count", "std", "mean", "rms"]
-        )
+            Summary_results, columns=["channel", "count", "std", "mean", "rms"])
         # Plotting
         plt.figure(figsize=(10, 6))
         plt.scatter(Summary_resultsDF["channel"], Summary_resultsDF["count"], s=50)
         plt.xlabel("Channel")
         plt.ylabel("Count")
         plt.title("%s %s" % ((self.sensor, var_name)))
-        #plt.xticks(Summary_resultsDF["channel"])
-        #plt.xticks(rotation=45)
         plt.grid(True)
         plt.tight_layout()
         plt.savefig(
@@ -429,14 +428,12 @@ def make_summary_plots(
             Summary_resultsDF["rms"],
             s=50,
             label="Rms",
-            facecolors='none',
-            edgecolors='blue'
+            facecolors="none",
+            edgecolors="blue",
         )
         plt.xlabel("Channel")
         plt.ylabel("Statistics")
         plt.title("%s %s" % ((self.sensor, var_name)))
-        #plt.xticks(Summary_resultsDF["channel"])
-        #plt.xticks(rotation=45)
         plt.grid(True)
         plt.tight_layout()
         plt.legend()

ush/SpatialTemporalStatsTool/user_Analysis.py

@@ -2,9 +2,7 @@
 
 # Set input and output paths
 input_path = "/PATH/TO/Input/Files"
-
-#output_path = r'./Results'
-
+output_path = r'./Results'
 
 # Set sensor name
 sensor = "iasi_metop-c"
@@ -14,7 +12,7 @@
 channel_no = 1
 
 # Set start and end dates
-start_date, end_date = '2024-01-01', '2024-01-31'
+start_date, end_date = "2024-01-01", "2024-01-31"
 
 # Set region
 # 1: global, 2: polar region, 3: mid-latitudes region,
@@ -69,5 +67,4 @@
     input_path, sensor, var_name, start_date, end_date, QC_filter, output_path
 )
 print("Summary plots created!")
-
 # Print summary results