shiftung radar to chaoss page

Signed-off-by: Akshat Baranwal <[email protected]>

Author: Akshatb2006

8Knot/pages/chaoss/chaoss.py

@@ -6,6 +6,7 @@
 # import visualization cards
 from .visualizations.project_velocity import gc_project_velocity
 from .visualizations.contrib_importance_pie import gc_contrib_importance_pie
+from .visualizations.contributor_radar import gc_contributor_radar
 
 warnings.filterwarnings("ignore")
 
@@ -21,6 +22,13 @@
             align="center",
             style={"marginBottom": ".5%"},
         ),
+        dbc.Row(
+            [
+                dbc.Col(gc_contributor_radar, width=6),
+            ],
+            align="center",
+            style={"marginBottom": ".5%"},
+        ),
     ],
     fluid=True,
 )

…tors/visualizations/contributor_radar.py …aoss/visualizations/contributor_radar.py8Knot/pages/contributors/visualizations/contributor_radar.py renamed to 8Knot/pages/chaoss/visualizations/contributor_radar.py 8Knot/pages/contributors/visualizations/contributor_radar.py renamed to 8Knot/pages/chaoss/visualizations/contributor_radar.py

@@ -149,15 +149,15 @@ def generate_radar_chart_from_data(start_date, end_date, repolist, bot_switch):
     start = time.perf_counter()
 
     # GET ALL DATA FROM POSTGRES CACHE
-    df_raw = cf.retrieve_from_cache(tablename=func_name, repolist=repolist)
+    df = cf.retrieve_from_cache(tablename=func_name, repolist=repolist)
 
     # test if there is data
-    if df_raw.empty:
+    if df.empty:
         logging.warning(f"{VIZ_ID} - NO DATA AVAILABLE")
         return nodata_graph()
 
     # function for all data pre processing
-    df = process_data(df_raw, start_date, end_date, bot_switch)
+    df = process_data(df, start_date, end_date, bot_switch)
 
     if df.empty:
         logging.warning(f"{VIZ_ID} - NO DATA AVAILABLE AFTER PROCESSING")
@@ -169,13 +169,13 @@ def generate_radar_chart_from_data(start_date, end_date, repolist, bot_switch):
     return fig
 
 
-def process_data(df_raw: pd.DataFrame, start_date, end_date, bot_switch):
+def process_data(df: pd.DataFrame, start_date, end_date, bot_switch):
     """Process the raw contributor data and filter by date range and bot switch."""
-    df_raw['created_at'] = pd.to_datetime(df_raw['created_at'], utc=True)
+    df['created_at'] = pd.to_datetime(df['created_at'], utc=True)
     start_date_dt = pd.to_datetime(start_date, utc=True)
     end_date_dt = pd.to_datetime(end_date, utc=True)
 
-    df_filtered = df_raw[(df_raw['created_at'] >= start_date_dt) & (df_raw['created_at'] <= end_date_dt)]
+    df_filtered = df[(df['created_at'] >= start_date_dt) & (df['created_at'] <= end_date_dt)]
     if df_filtered.empty:
         return pd.DataFrame()
 

8Knot/pages/contributors/cntrb_behavior.py

@@ -9,7 +9,6 @@
 from .visualizations.contributors_types_over_time import gc_contributors_over_time
 from .visualizations.active_drifting_contributors import gc_active_drifting_contributors
 from .visualizations.new_contributor import gc_new_contributor
-from .visualizations.contributor_radar import gc_contributor_radar
 from .visualizations.contributor_funnel import gc_contributor_funnel, gc_contributor_dropoff
 
 
@@ -38,7 +37,6 @@
         dbc.Row(
             [
                 dbc.Col(gc_contributors_over_time, width=6),
-                dbc.Col(gc_contributor_radar, width=6),
             ],
             align="center",
             style={"marginBottom": ".5%"},