Updated the Plots tab of Dashboard. It can now plot values in a list(s).

Author: hajimeMU

lib/Sisyphus/Gui/Dashboard/callbacks/callbacks_conditions.py

@@ -1,7 +1,7 @@
 import pandas as pd, ast
 from dash import dcc, html, Input, Output, State, ctx
 import dash
-import base64
+import base64, pickle
 import json
 
 
@@ -12,6 +12,12 @@
 logger = config.getLogger(__name__)
 
 
+def resolve_df_from_store(store):
+    if isinstance(store, dict) and "path" in store:
+        with open(store["path"], "rb") as f:
+            return pickle.load(f)
+    return pd.DataFrame(store)
+
 # Add/remove condition input rows
 def register_callbacks(app):
     @app.callback(
@@ -24,7 +30,11 @@ def register_callbacks(app):
         prevent_initial_call=True
     )
     def update_conditions(data, add_clicks, remove_clicks, children, logic_operator):
-        df = pd.DataFrame(data)
+        
+        #df = pd.DataFrame(data)
+        df = resolve_df_from_store(data)
+
+        
         triggered = ctx.triggered_id
         children = list(children) if children else []
 

lib/Sisyphus/Gui/Dashboard/callbacks/callbacks_filter.py

@@ -38,7 +38,7 @@ def register_callbacks(app):
         State("preferences-store", "data"),
         State("typeid-input", "value"),
         State("testtype-input", "value"),
-        State("condition-container","children"), # <--- testing
+        State("condition-container","children"),
         prevent_initial_call=True
     )
     def download_filtered(n_clicks, fields, thresholds, operators, logic_operator, filtered_data, filename, preferences, typeid, testtype,conds):
@@ -64,7 +64,13 @@ def download_filtered(n_clicks, fields, thresholds, operators, logic_operator, f
             filename += ".csv"
 
         if n_clicks and filtered_data:
-            df = pd.DataFrame(filtered_data)
+            #df = pd.DataFrame(filtered_data)
+            from Sisyphus.Gui.Dashboard.utils.data_resolver import (
+                load_df_from_store,
+                apply_row_indices,
+            )
+            df_full = load_df_from_store(filtered_data)
+            df = apply_row_indices(df_full, filtered_data.get("row_indices"))
 
             # --- Local save ---
             save_dir = preferences.get("working_dir", os.getcwd())