Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -59,6 +59,25 @@ tab0_content = html.Div([
                  options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
 ])
 
+def update_dataset(dataset_chosen): #batch_chosen, 
+    filepath = f"az://data10xflex/{dataset_chosen}"
+    df = pl.read_parquet(filepath,storage_options=storage_options)
+    
+    min_value = df[col_features].min()
+    max_value = df[col_features].max()
+    
+    min_value_2 = df[col_counts].min()
+    min_value_2 = round(min_value_2)
+    max_value_2 = df[col_counts].max()
+    max_value_2 = round(max_value_2)
+    
+    min_value_3 = df[col_mt].min()
+    min_value_3 = round(min_value_3, 1)
+    max_value_3 = df[col_mt].max()
+    max_value_3 = round(max_value_3, 1)    
+
+    return df, min_value, max_value, min_value_2, max_value_2, min_value_3, max_value_3
+    
 # Loads in the conditions specified in the yaml file
 
 # Note: Future version perhaps all values from a column in the dataframe of the parquet file
@@ -302,23 +321,7 @@ def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
     Input(component_id='range-slider-3', component_property='value')
 )
 
-def update_graph_and_pie_chart(dataset_chosen, col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen, 
-    filepath = f"az://data10xflex/{dataset_chosen}"
-    df = pl.read_parquet(filepath,storage_options=storage_options)
-    
-    min_value = df[col_features].min()
-    max_value = df[col_features].max()
-    
-    min_value_2 = df[col_counts].min()
-    min_value_2 = round(min_value_2)
-    max_value_2 = df[col_counts].max()
-    max_value_2 = round(max_value_2)
-    
-    min_value_3 = df[col_mt].min()
-    min_value_3 = round(min_value_3, 1)
-    max_value_3 = df[col_mt].max()
-    max_value_3 = round(max_value_3, 1)    
-    
+def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen, 
     batch_chosen = df[col_chosen].unique().to_list()
     dff = df.filter(
         (pl.col(col_chosen).cast(str).is_in(batch_chosen)) &