Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -36,16 +36,18 @@ def read_config(filename):
     return config
 
 config = read_config(config_path)
-#path_parquet = config.get("path_parquet")
+path_parquet = config.get("path_parquet")
 col_batch = config.get("col_batch")
 col_features = config.get("col_features")
 col_counts = config.get("col_counts")
 col_mt = config.get("col_mt")
 
+filepath = f"az://{path_parquet}"
+
 storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY,'anon': False}
 #azfs = AzureBlobFileSystem(**storage_options )
 
-
+df = pl.read_parquet(filepath,storage_options=storage_options)
 #abfs = AzureBlobFileSystem(account_name=accountname,account_key=accountkey)
 #df = df.rename({"__index_level_0__": "Unnamed: 0"})
 
@@ -53,39 +55,18 @@ storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STO
 external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
 app = dash.Dash(__name__, external_stylesheets=external_stylesheets) #, requests_pathname_prefix='/dashboard1/'
 
-tab0_content = html.Div([
-    html.Label("Dataset chosen"),
-    dcc.Dropdown(id='dpdn1', value="d1011/10xflexd1011_umap_clusres", multi=False,
-                 options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
-])
-
-@app.callback(
-    Output('dynamic-table', 'children'),
-    Input(component_id='dpdn1', component_property='value')
-)
-def update_table(dataset_chosen):
-    filepath = f"az://data10xflex/{dataset_chosen}"
-    df = pl.read_parquet(filepath, storage_options=storage_options)
-
-    min_value = df[col_features].min().item()
-    max_value = df[col_features].max().item()
-
-    min_value_2 = round(df[col_counts].min())
-    max_value_2 = round(df[col_counts].max())
-
-    min_value_3 = round(df[col_mt].min(), 1)
-    max_value_3 = round(df[col_mt].max(), 1)
+min_value = df[col_features].min()
+max_value = df[col_features].max()
 
-    # Create other visualizations or perform calculations below using the updated df variable
+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)
 
-    return [
-        html.H5(f'Minimum Value - {col_features}: {min_value}'),
-        html.H5(f'Maximum Value - {col_features}: {max_value}'),
-        html.H5(f'Minimum Value - {col_counts}: {min_value_2}'),
-        html.H5(f'Maximum Value - {col_counts}: {max_value_2}'),
-        html.H5(f'Minimum Value - {col_mt}: {min_value_3}'),
-        html.H5(f'Maximum Value - {col_mt}: {max_value_3}'),
-    ]
+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)
 
 # Loads in the conditions specified in the yaml file
 
@@ -262,7 +243,6 @@ app.layout = html.Div([
     dcc.Tabs(id='tabs', style= {'width': 600,
         'font-size': '100%',
         'height': 50}, value='tab1',children=[
-        dcc.Tab(label='Dataset', value='tab0', children=tab0_content),
         dcc.Tab(label='QC', value='tab1', children=tab1_content),
         dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content),
         dcc.Tab(label='Custom', value='tab3', children=tab3_content),