Issue #383 modifications to support multiple plot types, list_stat_1 …

…values

metplotpy/plots/tcmpr_plots/line/mean/tcmpr_line_mean.py

@@ -5,30 +5,32 @@
 
 
 class TcmprLineMean(TcmprLine):
-    def __init__(self, config_obj, column_info, col, case_data, input_df, baseline_data):
-        super().__init__(config_obj, column_info, col, case_data, input_df, None)
+    def __init__(self, config_obj, column_info, col, case_data, input_df, baseline_data, stat_name):
+        super().__init__(config_obj, column_info, col, case_data, input_df, None, stat_name)
         print("--------------------------------------------------------")
         print(f"Plotting MEAN time series by {self.config_obj.series_val_names[0]}")
 
-        self._adjust_titles()
-        self.series_list = self._create_series(self.input_df)
+        self._adjust_titles(stat_name)
+        self.series_list = self._create_series(self.input_df, stat_name)
         self.case_data = None
         self.cur_baseline = baseline_data['cur_baseline']
         self.cur_baseline_data = baseline_data['cur_baseline_data']
         self.baseline_lead_time = 'ind'
         self._init_hfip_baseline_for_plot()
         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]}_mean.png"
+            # self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.list_stat_1[0]}_{stat_name}_mean.png"
+            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{stat_name}_mean.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}_mean.png"
         # remove the old file if it exist
         if os.path.exists(self.plot_filename):
             os.remove(self.plot_filename)
         self._create_figure()
 
-    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 = self.config_obj.list_stat_1[0] + '(' + self.col['units'] + ')'
+            self.yaxis_1 = stat_name + '(' + self.col['units'] + ')'
 
         if self.title is None or len(self.title) == 0:
             self.title = 'Mean of ' + self.col['desc'] + ' by ' \
@@ -42,17 +44,19 @@ def _init_hfip_baseline_for_plot(self):
             self.cur_baseline_data = self.cur_baseline_data[(self.cur_baseline_data['TYPE'] == 'CONS')]
             print('Plot HFIP Baseline:' + self.cur_baseline.replace('Error ', ''))
 
-    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
-           is represented by a box in the diagram, so they also contain information
+           is represented by a line in the diagram, so they also contain information
            for  plot-related/appearance-related settings (which were defined in the config file).
+           A series object is defined by the series_val_1 setting in the config file.
 
            Args:
                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:   Corresponds to the current list_stat_1 value (e.g. TK_ERR, ABS(AMAX_WIND-BMAX_WIND), etc.)
+                            in the config file.
            Returns:
                a list of series objects that are to be displayed
 
@@ -61,10 +65,16 @@ def _create_series(self, input_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)):
+        all_series = self.config_obj.get_series_y(1)
+
+        # Limit the series to only the current statistic, list_stat_1 in config file
+        series_by_stat = [cur for cur in all_series if stat_name in cur]
+
+        num_series_y1 = len(series_by_stat)
+        for i, name in enumerate(series_by_stat):
             if not isinstance(name, list):
                 name = [name]
+
             series_obj = TcmprSeriesLineMean(self.config_obj, i, input_data, series_list, name)
             series_list.append(series_obj)
 

metplotpy/plots/tcmpr_plots/line/mean/tcmpr_series_line_mean.py

@@ -22,7 +22,7 @@
 
 class TcmprSeriesLineMean(TcmprSeries):
     """
-        Represents a Box plot series object
+        Represents a TCMPR mean  series object
         of data points and their plotting style
         elements (line colors,  etc.)
 

metplotpy/plots/tcmpr_plots/line/median/tcmpr_line_median.py

@@ -5,20 +5,20 @@
 
 
 class TcmprLineMedian(TcmprLine):
-    def __init__(self, config_obj, column_info, col, case_data, input_df):
-        super().__init__(config_obj, column_info, col, case_data, input_df, None)
+    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, None, stat_name)
         print("--------------------------------------------------------")
         print(f"Plotting MEDIAN time series by {self.config_obj.series_val_names[0]}")
 
         print("Plot HFIP Baseline:" + self.cur_baseline)
         self._adjust_titles()
-        self.series_list = self._create_series(self.input_df)
+        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]}_median.png"
+            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{stat_name}_median.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}.png"
+            self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.prefix}_{stat_name}_median.png"
         # remove the old file if it exist
         if os.path.exists(self.plot_filename):
             os.remove(self.plot_filename)
@@ -33,7 +33,7 @@ def _adjust_titles(self):
                          + 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
@@ -52,10 +52,16 @@ def _create_series(self, input_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)):
+        all_series = self.config_obj.get_series_y(1)
+
+        # Limit the series to only the current statistic, list_stat_1 in config file
+        series_by_stat = [cur for cur in all_series if stat_name in cur]
+
+        num_series_y1 = len(series_by_stat)
+        for i, name in enumerate(series_by_stat):
             if not isinstance(name, list):
                 name = [name]
+
             series_obj = TcmprSeriesLineMedian(self.config_obj, i, input_data, series_list, name)
             series_list.append(series_obj)
 

metplotpy/plots/tcmpr_plots/line/tcmpr_line.py

@@ -5,7 +5,7 @@
 
 
 class TcmprLine(Tcmpr):
-    def __init__(self, config_obj, column_info, col, case_data, input_df, baseline_data):
+    def __init__(self, config_obj, column_info, col, case_data, input_df, baseline_data, stat_name):
         super().__init__(config_obj, column_info, col, case_data, input_df)
 
     def _create_figure(self):

metplotpy/plots/tcmpr_plots/tcmpr_config.py

@@ -36,7 +36,7 @@ def __init__(self, parameters: dict) -> None:
         super().__init__(parameters)
 
         ##############################################
-        self.plot_list = self._get_plot()
+        self.plot_type = self._get_plot()
         self.tcst_files = self._get_tcst_files()
         self.tcst_dir = self._get_tcst_dir()
         self.rp_diff = self._get_rp_diff()
@@ -219,9 +219,9 @@ def _get_markers_size(self) -> list:
         return self.create_list_by_series_ordering(markers_size)
 
     def _get_plot(self) -> list:
-        plot_list = self.get_config_value('plot_list')
+        plot_type = self.get_config_value('plot_type')
         # TODO validate plots BOXPLOT, POINT, MEAN, MEDIAN, RELPERF, RANK, SKILL_MN, SKILL_MD
-        return plot_list
+        return plot_type
 
     def _get_tcst_files(self) -> list:
         tcst_files = self.get_config_value('tcst_files')

metplotpy/plots/tcmpr_plots/tcmpr_series.py

@@ -36,7 +36,8 @@ def __init__(self, config, idx: int, input_data, series_list: list,
         self.series_list = series_list
         self.series_name = series_name
         self.rank_min_val = []
-        self.series_len = len(config.get_series_y(1)) + len(config.get_config_value('derived_series_1'))
+        # self.series_len = len(config.get_series_y(1)) + len(config.get_config_value('derived_series_1'))
+        self.series_len = len(self.series_list) + len(config.get_config_value('derived_series_1'))
         self.skill_ref_data = skill_ref_data
         if idx >= self.series_len:
             super().__init__(config, 0, input_data, 1)

metplotpy/plots/tcmpr_plots/tcmpr_util.py

@@ -120,13 +120,18 @@ def init_hfip_baseline(config, baseline_file, input_df):
                 all_filters = [baseline['BASIN'].isin(input_df['BASIN']), baseline['VARIABLE'].isin([stat]),
                                baseline['LEAD_HR'].isin(input_df['LEAD_HR'])]
                 mask = np.array(all_filters).all(axis=0)
-                cur_baseline_data = baseline.loc[mask]
+                cur_baseline_data_filtered = baseline.loc[mask]
+                # work on a copy to prevent the SettingWithCopyWarning
+                cur_baseline_data = cur_baseline_data_filtered.copy()
+
+                # cur_baseline_data = cur_baseline_data_filtered.copy(deep=True)
                 if config.hfip_bsln == "0":
                     cur_baseline = "HFIP Baseline"
                 elif config.hfip_bsln == "5":
                     cur_baseline = 'Error Target for 20% HFIP Goal'
                     cur_baseline_data['VALUE'] = cur_baseline_data['VALUE'].apply(
                         lambda x: calc_util.round_half_up(x * 0.8, 1))
+
                 else:  # config.hfip_bsln == "10":
                     cur_baseline = 'Error Target for 50% HFIP Goal'
                     cur_baseline_data['VALUE'] = cur_baseline_data['VALUE'].apply(