added cumsum and %

app.py

@@ -10,6 +10,7 @@ from pathlib import Path
 from typing import Literal
 
 import gradio as gr
+import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
 import tenacity
@@ -197,6 +198,8 @@ def plot_scatter(
         log_scale_y: bool,
         normalization: bool,
         rounding: int,
+        cumsum: bool,
+        perc: bool,
         progress: gr.Progress,
 ):
     fig = go.Figure()
@@ -207,6 +210,10 @@ def plot_scatter(
         histogram_prepared = prepare_for_non_grouped_plotting(histogram, normalization, rounding)
         x = sorted(histogram_prepared.keys())
         y = [histogram_prepared[k] for k in x]
+        if cumsum:
+            y = np.cumsum(y).tolist()
+        if perc:
+            y = (np.array(y) * 100).tolist()
 
         fig.add_trace(
             go.Scatter(
@@ -274,6 +281,7 @@ def plot_bars(
 
     return fig
 
+
 def get_desc(data):
     res = {name: list(dt.to_dict().keys()) for name, dt in data.items()}
     return "\n".join([
@@ -293,6 +301,8 @@ def update_graph(
         top_k,
         direction,
         regex,
+        cumsum,
+        perc,
         progress=gr.Progress(),
 ):
     if len(datasets) <= 0 or not metric_name or not grouping:
@@ -313,15 +323,16 @@ def update_graph(
 
     data = {path: result for path, result in zip(datasets, data)}
     return plot_data(data, metric_name, normalization, rounding, grouping, top_k, direction, regex, log_scale_x,
-                     log_scale_y, progress), data, export_data(data, metric_name), get_desc(data)
+                     log_scale_y, cumsum, perc, progress), data, export_data(data, metric_name), get_desc(data)
 
 
 def plot_data(data, metric_name, normalization, rounding, grouping, top_k, direction, regex, log_scale_x, log_scale_y,
+              cumsum, perc,
               progress=gr.Progress()):
     if rounding is None or top_k is None:
         return None
     graph_fc = (
-        partial(plot_scatter, normalization=normalization, rounding=rounding)
+        partial(plot_scatter, normalization=normalization, rounding=rounding, cumsum=cumsum, perc=perc)
         if grouping == "histogram"
         else partial(plot_bars, top_k=top_k, direction=direction, regex=regex, rounding=rounding)
     )
@@ -399,6 +410,14 @@ The data might not be 100% representative, due to the sampling and optimistic me
 
     with gr.Row():
         with gr.Column(scale=1):
+            cumsum_checkbox = gr.Checkbox(
+                label="Cumsum",
+                value=False,
+            )
+            perc_checkbox = gr.Checkbox(
+                label="%",
+                value=False,
+            )
             log_scale_x_checkbox = gr.Checkbox(
                 label="Log scale x",
                 value=False,
@@ -491,28 +510,33 @@ The data might not be 100% representative, due to the sampling and optimistic me
             top_select,
             direction_checkbox,
             group_regex,
+            cumsum_checkbox,
+            perc_checkbox
         ],
         outputs=[graph_output, exported_data, export_data_json, min_max_hist_data],
     )
 
-    for inp in [normalization_checkbox, rounding, group_regex, direction_checkbox, top_select, log_scale_x_checkbox,
-                log_scale_y_checkbox]:
-        inp.change(
-            fn=plot_data,
-            inputs=[
-                exported_data,
-                metric_name_dropdown,
-                normalization_checkbox,
-                rounding,
-                grouping_dropdown,
-                top_select,
-                direction_checkbox,
-                group_regex,
-                log_scale_x_checkbox,
-                log_scale_y_checkbox,
-            ],
-            outputs=[graph_output],
-        )
+    gr.on(
+        triggers=[normalization_checkbox.change, rounding.change, group_regex.change, direction_checkbox.change,
+                  top_select.change, log_scale_x_checkbox.change,
+                  log_scale_y_checkbox.change, cumsum_checkbox.change, perc_checkbox.change],
+        fn=plot_data,
+        inputs=[
+            exported_data,
+            metric_name_dropdown,
+            normalization_checkbox,
+            rounding,
+            grouping_dropdown,
+            top_select,
+            direction_checkbox,
+            group_regex,
+            log_scale_x_checkbox,
+            log_scale_y_checkbox,
+            cumsum_checkbox,
+            perc_checkbox
+        ],
+        outputs=[graph_output],
+    )
 
     datasets_selected.change(
         fn=fetch_groups,