Merge pull request #4 from hydroEng/ensemble-missing-tps-fix

Ensemble missing tps fix

src/logger.py

@@ -21,7 +21,13 @@ def _write_sr(msg: pandas.Series):
             self.log_string += '\n' + msg.to_string() + '\n'
 
         def _write_df(msg: pandas.DataFrame):
-            self.log_string += '\n' + msg.to_string() + '\n'
+
+            try:
+                name = str(msg.name) + "\n"
+            except AttributeError:
+                name = ""
+
+            self.log_string += '\n' + name + msg.to_string() + '\n'
 
         def _write_none():
             self.log_string += '\n' + "None" + '\n'
@@ -57,7 +63,7 @@ def print_log(self):
     def write_to_txt(self, output_dir, filename):
         """ Output log as a text file to specified folder.  """
 
-        filepath= os.path.join(output_dir, filename)
+        filepath = os.path.join(output_dir, filename)
 
         with open(filepath, 'w+') as f:
             f.write(self.log_string)

src/tuflow_ensemble.py

@@ -294,6 +294,10 @@ def _get_crit_tp(row: pd.Series) -> str:
     median = row['Median']
 
     diffs = {}
+
+    # Skip nan values that occur because of missing results for a particular temporal pattern / duration csv.
+    row = row.dropna()
+
     for cell in row:
         col_name = _get_col_name(cell, row)
         if 'tp' in col_name:
@@ -328,7 +332,6 @@ def _tp_vs_max_flow_df(df: pd.DataFrame) -> tuple:
             po_line = str(column)
 
     dur_tp_df = df.pivot(index='Duration', columns='Temporal Pattern', values=po_line)
-    dur_tp_df = dur_tp_df.fillna(0)
     dur_tp_df = _drop_sort_duration(dur_tp_df)
 
     tp_cols = [col for col in dur_tp_df.columns if 'tp' in col]