Merge pull request #14 from msrosenberg/dev-branch

Progress Bars
msrosenberg · May 12, 2023 · 40123bd · 40123bd
2 parents 96930ad + ba149ac
commit 40123bd
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Showing 5 changed files with 198 additions and 38 deletions.
diff --git a/resources/metawin_help.html b/resources/metawin_help.html
@@ -1054,7 +1054,7 @@ <h3>Running a Rank Correlation Analysis</h3>
                 Additionally, there is an option to perform the rank correlation using Kendall's &tau;
                 (<a href="#kendall_1938">Kendall 1938</a>) or Spearman's &rho; (<a href="#spearman_1904">Spearman
                 1904</a>). Because the distribution of these metrics can be complicated, particularly when the
-                number of studies is low, <span class="metawin">MetaWin</span> autmoatically uses a randomization
+                number of studies is low, <span class="metawin">MetaWin</span> automatically uses a randomization
                 procedure to thest their significance, with the number of iterations user-specifiable.
             </p>
 

diff --git a/src/MetaWinAnalysis.py b/src/MetaWinAnalysis.py
@@ -1786,7 +1786,7 @@ def add_resampling_options_to_dialog(sender, test_model: bool = False):
 
 
 def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05, tree: Optional = None,
-                     norm_ci: bool = True):
+                     norm_ci: bool = True, sender=None):
     """
     primary function controlling the execution of an analysis
 
@@ -1799,42 +1799,48 @@ def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05
     output_blocks.extend(output)
     if options.structure == SIMPLE_MA:
         (output, figure, chart_data, analysis_values,
-         citations) = MetaWinAnalysisFunctions.simple_meta_analysis(data, options, decimal_places, alpha, norm_ci)
+         citations) = MetaWinAnalysisFunctions.simple_meta_analysis(data, options, decimal_places, alpha, norm_ci,
+                                                                    sender=sender)
     elif options.structure == GROUPED_MA:
         (output, figure, chart_data, analysis_values,
-         citations) = MetaWinAnalysisFunctions.grouped_meta_analysis(data, options, decimal_places, alpha, norm_ci)
+         citations) = MetaWinAnalysisFunctions.grouped_meta_analysis(data, options, decimal_places, alpha, norm_ci,
+                                                                     sender=sender)
     elif options.structure == CUMULATIVE_MA:
-        output, figure, 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, sender=sender)
         analysis_values = None
         citations = []
     elif options.structure == REGRESSION_MA:
         (output, figure, chart_data, analysis_values,
-         citations) = MetaWinAnalysisFunctions.regression_meta_analysis(data, options, decimal_places, alpha, norm_ci)
+         citations) = MetaWinAnalysisFunctions.regression_meta_analysis(data, options, decimal_places, alpha, norm_ci,
+                                                                        sender=sender)
     elif options.structure == COMPLEX_MA:
         output, analysis_values, citations = MetaWinAnalysisFunctions.complex_meta_analysis(data, options,
                                                                                             decimal_places, alpha,
-                                                                                            norm_ci)
+                                                                                            norm_ci, sender=sender)
         figure = None
         chart_data = None
     elif options.structure == NESTED_MA:
         (output, figure, chart_data, analysis_values,
-         citations) = MetaWinAnalysisFunctions.nested_meta_analysis(data, options, decimal_places, alpha, norm_ci)
+         citations) = MetaWinAnalysisFunctions.nested_meta_analysis(data, options, decimal_places, alpha, norm_ci,
+                                                                    sender=sender)
     elif options.structure == TRIM_FILL:
         (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,  chart_data,
-         citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places, alpha, norm_ci)
+         citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places, alpha, norm_ci,
+                                                                       sender=sender)
         analysis_values = None
     elif options.structure == PHYLOGENETIC_MA:
         output, citations = MetaWinAnalysisFunctions.phylogenetic_meta_analysis(data, options, tree, decimal_places,
-                                                                                alpha, norm_ci)
+                                                                                alpha, norm_ci, sender=sender)
         analysis_values = None
         figure = None
         chart_data = None
     elif options.structure == RANKCOR:
-        output, citations = MetaWinAnalysisFunctions.rank_correlation_analysis(data, options, decimal_places)
+        output, citations = MetaWinAnalysisFunctions.rank_correlation_analysis(data, options, decimal_places,
+                                                                               sender=sender)
         figure = None
         chart_data = None
         analysis_values = None
@@ -1904,7 +1910,7 @@ def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4,
 
         if meta_analysis_options.structure is not None:
             output, figure, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places, alpha, tree,
-                                                             norm_ci)
+                                                             norm_ci, sender=sender)
             sender.last_effect = meta_analysis_options.effect_data
             sender.last_var = meta_analysis_options.effect_vars
             return output, figure, chart_data