Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -72,13 +72,13 @@ max_value_3 = round(max_value_3, 1)
 
 # Note: Future version perhaps all values from a column in the dataframe of the parquet file
 # Note 2: This could also be a tsv of the categories and own specified colors
-conditions = df[col_batch].unique().to_list()
+#conditions = df[col_batch].unique().to_list()
 # Create the first tab content
 # Add Sliders for three QC params: N genes by counts, total amount of reads and pct MT reads
 
 tab1_content = html.Div([
-    dcc.Dropdown(id='dpdn2', value=conditions, multi=True,
-                 options=conditions),
+    #dcc.Dropdown(id='dpdn2', value=conditions, multi=True,
+    #             options=conditions),
     html.Label("N Genes by Counts"),
     dcc.RangeSlider(
         id='range-slider-1',
@@ -287,7 +287,7 @@ def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
     Output(component_id='scatter-plot-11', component_property='figure'),
     Output(component_id='scatter-plot-12', component_property='figure'),
     Output(component_id='my-graph2', component_property='figure'),
-    Input(component_id='dpdn2', component_property='value'),
+    #Input(component_id='dpdn2', component_property='value'),
     Input(component_id='dpdn3', component_property='value'),
     Input(component_id='dpdn4', component_property='value'),
     Input(component_id='dpdn5', component_property='value'),
@@ -298,9 +298,9 @@ 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(batch_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3):
+def update_graph_and_pie_chart(s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen, 
     dff = df.filter(
-        (pl.col(condition1_chosen).cast(str).is_in(batch_chosen)) &
+        (pl.col(condition1_chosen).cast(str)) & #.is_in(batch_chosen)
         (pl.col(col_features) >= range_value_1[0]) &
         (pl.col(col_features) <= range_value_1[1]) &
         (pl.col(col_counts) >= range_value_2[0]) &