bump vega versions

splink/params.py

@@ -200,7 +200,7 @@ def _convert_params_dict_to_bayes_factor_data(self):
 
                 data.append(row)
         return data
-    
+
     def _convert_params_dict_to_bayes_factor_iteration_history(self):
         """
         Get the data needed for a chart that shows which comparison
@@ -227,9 +227,9 @@ def _convert_params_dict_to_bayes_factor_iteration_history(self):
                     row["u"] = this_gamma["prob_dist_non_match"][level]["probability"]
                     try:
                         row["bayes_factor"] = row["m"] / row["u"]
-                    except ZeroDivisionError: 
+                    except ZeroDivisionError:
                         row["bayes_factor"] = None
-                    
+
                     if it_num == len(self.param_history)-1:
                         row["final"]=True
                     else:
@@ -371,11 +371,11 @@ def is_converged(self):
             if new_change > biggest_change:
                 biggest_change = new_change
                 biggest_change_key = key
-        
+
         logger.info(f"The maximum change in parameters was {biggest_change} for key {biggest_change_key}")
 
         return(all(diff))
-    
+
     def get_settings_with_current_params(self):
         return get_or_update_settings(self)
 
@@ -390,10 +390,10 @@ def field_value_to_probs(fv):
             u_probs = []
             for key, val in fv['prob_dist_non_match'].items():
                 u_probs.append(val["probability"])
-            
+
             print(f'"m_probabilities": {m_probs},')
             print(f'"u_probabilities": {u_probs}')
-                
+
         for field, value in self.params['π'].items():
             print(field)
             field_value_to_probs(value)
@@ -438,7 +438,7 @@ def probability_distribution_chart(self):  # pragma: no cover
             return alt.Chart.from_dict(probability_distribution_chart)
         else:
             return probability_distribution_chart
-        
+
     def gamma_distribution_chart(self):  # pragma: no cover
         """
         If altair is installed, returns the chart
@@ -466,34 +466,34 @@ def bayes_factor_chart(self):  # pragma: no cover
             return alt.Chart.from_dict(bayes_factor_chart_def)
         else:
             return bayes_factor_chart_def
-        
+
     def bayes_factor_history_charts(self):
         """
         If altair is installed, returns the chart
         Otherwise will return the chart spec as a dictionary
         """
         # Empty list of chart definitions
         chart_defs = []
-    
+
         # Full iteration history
         data = self._convert_params_dict_to_bayes_factor_iteration_history()
-    
+
         # Create charts for each column
         for col_dict in self.settings["comparison_columns"]:
-        
+
             # Get column name
             if "col_name" in col_dict:
                 col_name = col_dict["col_name"]
             elif "custom_name" in col_dict:
-                col_name = col_dict["custom_name"] 
-           
+                col_name = col_dict["custom_name"]
+
             chart_def = copy.deepcopy(bayes_factor_history_chart_def)
             # Assign iteration history to values of chart_def
             chart_def["data"]["values"] = [d for d in data if d['column']==col_name]
             chart_def["title"]["text"] = col_name
             chart_def["hconcat"][1]["layer"][0]["encoding"]["color"]["legend"]["tickCount"] = col_dict["num_levels"]-1
             chart_defs.append(chart_def)
-        
+
         combined_charts = {
             "config": {
                 "view": {"width": 400, "height": 120},
@@ -503,12 +503,12 @@ def bayes_factor_history_charts(self):
             "resolve": {"scale":{"color": "independent"}},
             '$schema': 'https://vega.github.io/schema/vega-lite/v4.8.1.json'
         }
-        
+
         if altair_installed:
             return alt.Chart.from_dict(combined_charts)
         else:
             return combined_charts
-        
+
 
     def all_charts_write_html_file(self, filename="splink_charts.html", overwrite=False):
 
@@ -530,7 +530,7 @@ def all_charts_write_html_file(self, filename="splink_charts.html", overwrite=Fa
 
             c5 = self.bayes_factor_history_charts().to_json(indent=None)
             c6 = self.gamma_distribution_chart().to_json(indent=None)
-            
+
             with open(filename, "w") as f:
                 f.write(
                     multi_chart_template.format(
@@ -554,16 +554,16 @@ def all_charts_write_html_file(self, filename="splink_charts.html", overwrite=Fa
                 c4 = json.dumps(self.ll_iteration_chart())
             else:
                 c4 = ""
-            
+
             c5 = json.dumps(self.bayes_factor_history_charts())
             c6 = json.dumps(self.gamma_distribution_chart())
-            
+
             with open(filename, "w") as f:
                 f.write(
                     multi_chart_template.format(
-                        vega_version="5",
-                        vegalite_version="3.3.0",
-                        vegaembed_version="4",
+                        vega_version="5.17.0",
+                        vegalite_version="4.17.0",
+                        vegaembed_version="6",
                         spec1=c1,
                         spec2=c6,
                         spec3=c2,
@@ -680,34 +680,34 @@ def _flatten_dict(dictionary, accumulator=None, parent_key=None, separator="_"):
 
 @check_types
 def get_or_update_settings(params: Params, settings: dict = None):
-    
+
     if not settings:
         settings = params.settings
-    
-    settings["proportion_of_matches"] = params.params['λ']                     
-                         
+
+    settings["proportion_of_matches"] = params.params['λ']
+
     for comp in settings["comparison_columns"]:
         if "col_name" in comp.keys():
             label = "gamma_"+comp["col_name"]
         else:
             label = "gamma_"+comp["custom_name"]
-            
+
         if "num_levels" in comp.keys():
             num_levels = comp["num_levels"]
         else:
             num_levels = _get_default_value("num_levels", is_column_setting=True)
-        
-        
+
+
         if label in params.params["π"].keys():
             saved = params.params["π"][label]
-    
+
             if num_levels == saved["num_levels"]:
                 m_probs = [val['probability'] for key, val in saved["prob_dist_match"].items()]
                 u_probs = [val['probability'] for key, val in saved["prob_dist_non_match"].items()]
-    
+
                 comp["m_probabilities"] = m_probs
                 comp["u_probabilities"] = u_probs
             else:
                 warnings.warn(f"{label}: Saved m and u probabilities do not match the specified number of levels ({num_levels}) - default probabilities will be used")
-    
+
     return(settings)