refactoring of caption generation

changed the way and place are captions are generated, which not only simplifies the code in some ways but allows for regeneration of captions and opens up the possibility of caption customization based on user style edits
msrosenberg · Sep 7, 2022 · 71b170a · 71b170a
1 parent bc46859
commit 71b170a
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Showing 6 changed files with 483 additions and 291 deletions.
diff --git a/src/MetaWinAnalysis.py b/src/MetaWinAnalysis.py
@@ -1798,62 +1798,56 @@ def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05
     output, all_citations = options.report_choices()
     output_blocks.extend(output)
     if options.structure == SIMPLE_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.simple_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == GROUPED_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.grouped_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == CUMULATIVE_MA:
-        output, figure, fig_caption, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options,
-                                                                                                    decimal_places,
-                                                                                                    alpha, norm_ci)
+        output, figure, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options,
+                                                                                       decimal_places, alpha, norm_ci)
         analysis_values = None
         citations = []
     elif options.structure == REGRESSION_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.regression_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == COMPLEX_MA:
         output, analysis_values, citations = MetaWinAnalysisFunctions.complex_meta_analysis(data, options,
                                                                                             decimal_places, alpha,
                                                                                             norm_ci)
         figure = None
-        fig_caption = None
         chart_data = None
     elif options.structure == NESTED_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.nested_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == TRIM_FILL:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.trim_and_fill_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == JACKKNIFE:
-        (output, figure, fig_caption,
-         chart_data, citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places,
-                                                                                   alpha, norm_ci)
+        (output, figure,  chart_data,
+         citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places, alpha, norm_ci)
         analysis_values = None
     elif options.structure == PHYLOGENETIC_MA:
         output, citations = MetaWinAnalysisFunctions.phylogenetic_meta_analysis(data, options, tree, decimal_places,
                                                                                 alpha, norm_ci)
         analysis_values = None
         figure = None
-        fig_caption = None
         chart_data = None
     elif options.structure == RANKCOR:
         output, citations = MetaWinAnalysisFunctions.rank_correlation_analysis(data, options, decimal_places)
         figure = None
-        fig_caption = None
         chart_data = None
         analysis_values = None
     else:
         output = []
         analysis_values = None
         figure = None
-        fig_caption = None
         chart_data = None
         citations = []
     all_citations.extend(citations)
     output_blocks.extend(output)
     output_blocks.extend(create_reference_list(all_citations))
-    return output_blocks, figure, fig_caption, chart_data, analysis_values
+    return output_blocks, figure, chart_data, analysis_values
 
 
 def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4, alpha: float = 0.05,
@@ -1909,12 +1903,9 @@ def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4,
                 meta_analysis_options.structure = None
 
         if meta_analysis_options.structure is not None:
-            output, figure, fig_caption, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places,
-                                                                          alpha, tree, norm_ci)
+            output, figure, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places, alpha, tree,
+                                                             norm_ci)
             sender.last_effect = meta_analysis_options.effect_data
             sender.last_var = meta_analysis_options.effect_vars
-            return output, figure, fig_caption, chart_data
-        else:
-            return None, None, None, None
-    else:
-        return None, None, None, None
+            return output, figure, chart_data
+    return None, None, None