issue #383 modifications to support multiple plot types in a single c…

…onfig file and logging replaces printing to stdout

metplotpy/plots/tcmpr_plots/skill/median/tcmpr_skill_median.py

@@ -1,42 +1,48 @@
 import os
+from datetime import datetime
 
 from metcalcpy.util import utils
 from metplotpy.plots.tcmpr_plots.skill.median.tcmpr_series_skill_median import TcmprSeriesSkillMedian
 from metplotpy.plots.tcmpr_plots.skill.tcmpr_skill import TcmprSkill
+import metplotpy.plots.util as util
 
 
 class TcmprSkillMedian(TcmprSkill):
-    def __init__(self, config_obj, column_info, col, case_data, input_df):
-        super().__init__(config_obj, column_info, col, case_data, input_df, None)
-        print("--------------------------------------------------------")
-        print(f"Plotting SKILL_MD time series by {self.config_obj.series_val_names[0]}")
+    def __init__(self, config_obj, column_info, col, case_data, input_df, stat_name):
+        super().__init__(config_obj, column_info, col, case_data, input_df, stat_name, None)
 
-        print("Plot HFIP Baseline:" + self.cur_baseline)
+        # Set up Logging
+        self.skillmd_logger = util.get_common_logger(self.config_obj.log_level, self.config_obj.log_filename)
 
-        self._adjust_titles()
-        self.series_list = self._create_series(self.input_df)
+        self.skillmd_logger.info("--------------------------------------------------------")
+        self.skillmd_logger.info(f"Plotting SKILL_MD time series by {self.config_obj.series_val_names[0]}")
+
+        self.skillmd_logger.info("Plot HFIP Baseline:" + self.cur_baseline)
+
+        self._adjust_titles(stat_name)
+        self.series_list = self._create_series(self.input_df, stat_name)
         self.case_data = None
 
         if self.config_obj.prefix is None or len(self.config_obj.prefix) == 0:
-            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.list_stat_1[0]}_skill_md.png"
+            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{stat_name}_skill_md.png"
         else:
-            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.prefix}.png"
+            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.prefix}_{stat_name}_skill_md.png"
 
-        # remove the old file if it exist
+        # remove the old file if it exists
         if os.path.exists(self.plot_filename):
             os.remove(self.plot_filename)
-        self._create_figure()
+        self._create_figure(stat_name)
 
-    def _adjust_titles(self):
+    def _adjust_titles(self, stat_name):
         if self.yaxis_1 is None or len(self.yaxis_1) == 0:
-            self.yaxis_1 = self.config_obj.list_stat_1[0] + '(' + self.col['units'] + ')'
+            self.yaxis_1 =  'Skill  for ' + stat_name + ' (' + self.col['units'] + ')'
 
         if self.title is None or len(self.title) == 0:
             self.title = 'Median Skill Scores of ' + self.col['desc'] + ' by ' \
                          + self.column_info[self.column_info['COLUMN'] == self.config_obj.series_val_names[0]][
                              "DESCRIPTION"].tolist()[0]
 
-    def _create_series(self, input_data):
+    def _create_series(self, input_data, stat_name):
         """
            Generate all the series objects that are to be displayed as specified by the plot_disp
            setting in the config file.  The points are all ordered by datetime.  Each series object
@@ -47,26 +53,38 @@ def _create_series(self, input_data):
                input_data:  The input data in the form of a Pandas dataframe.
                             This data will be subset to reflect the series data of interest.
 
+               stat_name:   The current name of the "statistic" specified in list_stat_1 of the config file
+
            Returns:
                a list of series objects that are to be displayed
 
 
         """
+
+        start_time = datetime.now()
+
         all_fields_values = {'AMODEL': [utils.GROUP_SEPARATOR.join(self.config_obj.skill_ref)],
                              'fcst_var': self.config_obj.list_stat_1}
         permutations = utils.create_permutations_mv(all_fields_values, 0)
         ref_model_data_series = TcmprSeriesSkillMedian(self.config_obj, 0,
-                                                       input_data, [], permutations[0])
+                                                       input_data, [], permutations[0], stat_name)
         ref_model_data = ref_model_data_series.series_data
 
         series_list = []
 
         # add series for y1 axis
-        num_series_y1 = len(self.config_obj.get_series_y(1))
-        for i, name in enumerate(self.config_obj.get_series_y(1)):
+        unique_series = []
+        for cur in self.config_obj.get_series_y(1):
+            if cur[0] not in unique_series:
+                unique_series.append(cur[0])
+
+        num_series_y1 = len(unique_series)
+        # num_series_y1 = len(self.config_obj.get_series_y(1))
+        # for i, name in enumerate(self.config_obj.get_series_y(1)):
+        for i, name in enumerate(unique_series):
             if not isinstance(name, list):
                 name = [name]
-            series_obj = TcmprSeriesSkillMedian(self.config_obj, i, input_data, series_list, name, ref_model_data)
+            series_obj = TcmprSeriesSkillMedian(self.config_obj, i, input_data, series_list, name, stat_name, ref_model_data)
             series_list.append(series_obj)
 
         # add derived for y1 axis
@@ -76,10 +94,13 @@ def _create_series(self, input_data):
                 name.append("DIFF")
             # include the series only if the name is valid
             if len(name) == 3:
-                series_obj = TcmprSeriesSkillMedian(self.config_obj, num_series_y1 + i, input_data, series_list, name)
+                series_obj = TcmprSeriesSkillMedian(self.config_obj, num_series_y1 + i, input_data, series_list, name, stat_name)
                 series_list.append(series_obj)
 
         # reorder series
         series_list = self.config_obj.create_list_by_series_ordering(series_list)
+        end_time = datetime.now()
+        total_time = end_time - start_time
+        self.skillmd_logger.info(f"Took {total_time} milliseconds to create series for {stat_name}")
 
         return series_list