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app.py

@@ -22,9 +22,6 @@ import pandas as pd
 from sklearn.manifold import TSNE
 from sklearn.decomposition import PCA
 import plotly.express as px
-import plotly.graph_objects as go
-import itertools
-from plotly.colors import qualitative
 
 # Load data
 def load_data():
@@ -128,32 +125,29 @@ def plot_tsne(tech_filter, snr_filter, mod_filter, mob_filter, representation, c
     x_pad = max(1e-3, (x_max - x_min) * 0.05)
     y_pad = max(1e-3, (y_max - y_min) * 0.05)
 
-    # Plot (explicit Scattergl to ensure points render)
-    palette = itertools.cycle(qualitative.Set2 + qualitative.Set3 + qualitative.D3)
-    fig = go.Figure()
-    for cls in sorted(filtered_df[color_by].unique()):
-        mask = filtered_df[color_by] == cls
-        color = next(palette)
-        fig.add_trace(go.Scattergl(
-            x=filtered_df.loc[mask, 'x'],
-            y=filtered_df.loc[mask, 'y'],
-            mode='markers',
-            name=str(cls),
-            marker=dict(size=6, opacity=0.8, color=color, line=dict(width=0)),
-            hovertext=filtered_df.loc[mask, ['tech', 'snr', 'mod', 'mob']].astype(str).agg('<br>'.join, axis=1),
-            hoverinfo='text'
-        ))
-    fig.update_layout(
+    # Plot (force serialization-friendly types)
+    plot_df = filtered_df.copy()
+    plot_df['x'] = plot_df['x'].astype(float)
+    plot_df['y'] = plot_df['y'].astype(float)
+
+    fig = px.scatter(
+        plot_df,
+        x='x',
+        y='y',
+        color=color_by,
+        hover_data=['tech', 'snr', 'mod', 'mob'],
         title=f"t-SNE of {representation} ({len(filtered_df)} samples)",
         template="plotly_white",
-        legend_title_text=color_by.capitalize(),
-        margin=dict(l=40, r=20, t=60, b=40),
+        render_mode="webgl",
     )
+    fig.update_layout(legend_title_text=color_by.capitalize(), margin=dict(l=40, r=20, t=60, b=40))
+    fig.update_traces(mode='markers', marker=dict(size=6, opacity=0.8, line=dict(width=0)))
     fig.update_xaxes(range=[x_min - x_pad, x_max + x_pad], zeroline=False)
     fig.update_yaxes(range=[y_min - y_pad, y_max + y_pad], zeroline=False)
     
     coord_info = f"x[{x_min:.3f},{x_max:.3f}] y[{y_min:.3f},{y_max:.3f}]"
-    return fig, f"{status_msg} | filtered samples: {len(filtered_df)} | {coord_info}"
+    trace_info = f"traces: {len(fig.data)}"
+    return fig, f"{status_msg} | filtered samples: {len(filtered_df)} | {coord_info} | {trace_info}"
 
 # Convenience preset: single-tech, modulation-colored embedding view
 def quick_modulation_plot():