Added custom marker color and y-axis (only for 1D)

app.py

@@ -28,9 +28,8 @@ df = solara.reactive(df_original[features.value].copy)
 @solara.component
 def process_data():
     df_out = df_original[features.value].copy()
-    df_out = df_out.dropna() if drop_nans.value else df_out
     df_out = df_out.sample(frac=training_ratio.value/100.0, random_state=42)
-    print('processing')
+    df_out = df_out.dropna() if drop_nans.value else df_out
     info_string = ""
     for f in features.value:
         x = df_out[f].to_numpy()
@@ -52,9 +51,6 @@ def process_data():
             info_string = str(e)
     df.set(df_out)
     solara.DataFrame(df_out, items_per_page=5) 
-    #info_message.set(info_string)
-
-
 
 def reset():
     features.value = default_features
@@ -64,7 +60,6 @@ def reset():
         feature_maps[f].set('x')
         scaler.set('none')
 
-
 @solara.component
 def InfoBox():
     if len(info_message.value) > 0:
@@ -72,37 +67,30 @@ def InfoBox():
 
 @solara.component 
 def BoxPlot():
-    if df.value is None:
+    if df.value is None or len(df.value) == 0:
         return
     fig = Figure()
     ax = fig.subplots()
     df.value.plot.box(figsize=(20,5), ax=ax)
-
     ymin = min(df.value.min(numeric_only=True))
     ymax = max(df.value.max(numeric_only=True))
-
     yrange = solara.use_reactive((ymin, ymax))
     ax.set_ylim(yrange.value[0], yrange.value[1])
-
     solara.SliderRangeFloat("yrange min and max", value=yrange,min=ymin, max=ymax)
     solara.FigureMatplotlib(fig)
     solara.Markdown(f'{yrange}')
 
-
-
-
 @solara.component
 def EmbeddingPlot():
-    print('embedding plot')
-
     embedding_method, set_embedding_method = solara.use_state('PCA')
     n_components, set_n_component = solara.use_state(2)
-    #power_iteration_normalizer, set_power_iteration_normalizer = solara.use_state('auto')
     n_neighbors, set_n_neighbors = solara.use_state(5)
     lle_methods = ['standard', 'hessian', 'modified', 'ltsa']
     lle_method, set_lle_method = solara.use_state('standard')
     eigen_solver, set_eigen_solver = solara.use_state('auto')
     eigen_solver_se, set_eigen_solver_se = solara.use_state(None)
+    marker_color, set_marker_color = solara.use_state('pha')
+    y_axis, set_y_axis = solara.use_state('moid')
 
     with solara.Row():
         solara.ToggleButtonsSingle(value=embedding_method, values=['PCA','TSNE','ISOMAP','LLE','SE','MDS'], dense=True, on_value=set_embedding_method)
@@ -112,8 +100,6 @@ def EmbeddingPlot():
         solara.Select(label='eigen_solver (LLE, Isomap)', value=eigen_solver, values=['auto', 'arpack', 'dense'], on_value=set_eigen_solver)
         solara.Select(label='eigen_solver (SE)', value=eigen_solver_se, values=[None, 'arpack', 'lobpcg', 'amg'], on_value=set_eigen_solver_se)
 
-        
-
     if embedding_method == 'PCA':
         embedder = PCA(n_components=n_components)
     elif embedding_method == 'SE':
@@ -139,18 +125,38 @@ def EmbeddingPlot():
         df_embedded = pd.DataFrame(data_embedded)
         df_embedded['pha'] = list(df_original.loc[df.value.index,'pha'])
         df_embedded['moid'] = list(df_original.loc[df.value.index,'moid'])
-
-        print('embedding done')
-
-        #print(df_embedded.head())
+        df_embedded['H'] = list(df_original.loc[df.value.index,'H'])
+        df_embedded['pdes'] = list(df_original.loc[df.value.index,'pdes'])
+        df_embedded['a'] = list(df_original.loc[df.value.index,'a'])
+        df_embedded['e'] = list(df_original.loc[df.value.index,'e'])
+        df_embedded['i'] = list(df_original.loc[df.value.index,'i'])
+        df_embedded['om'] = list(df_original.loc[df.value.index,'om'])
+        df_embedded['w'] = list(df_original.loc[df.value.index,'w'])
+
+        df_embedded_clean = df_embedded.dropna()
         if n_components == 1:
-            fig = px.scatter(df_embedded, x=0, y='moid', color='pha', width=1024, height=768)
+            fig = px.scatter(df_embedded, x=0, y=y_axis, color=marker_color, 
+                            hover_name='pdes',width=1024, height=768)
         elif n_components == 2:
-            fig = px.scatter(df_embedded, x=0,y=1,color='pha', width=1024, height=768)
+
+            fig = px.scatter(df_embedded_clean, x=0,y=1,color=marker_color, 
+                            hover_name='pdes',width=1024, height=768)
         elif n_components == 3:
-            fig = px.scatter_3d(df_embedded, x=0,y=1,z=2,color='moid',symbol='pha', width=1024, height=768)
-        
-        solara.FigurePlotly(fig)
+            fig = px.scatter_3d(df_embedded, x=0,y=1,z=2,color=marker_color,
+                            hover_name='pdes',width=1024, height=768)
+
+        with solara.Row():
+            with solara.Column():
+                solara.Select(label='Marker Color',
+                              value=marker_color,
+                              values=[None,'pha','moid','H','a','e','i','om','w'],
+                              on_value=set_marker_color)
+                solara.Select(label='Y-Axis (only in 1D plot)',
+                              value=y_axis,
+                              values=['moid','H','a','e','i','om','w'],
+                              on_value=set_y_axis)
+            solara.FigurePlotly(fig)
+
     except Exception as e:
         solara.Error(str(e))
 
@@ -174,18 +180,8 @@ def Page():
         solara.ToggleButtonsSingle(value=scaler, values=scalers)
         solara.SliderInt(label='Training Ratio %', value=training_ratio, min=1, max=99)
     
-    #solara.Markdown(f"""
-    #                # Drop_nans: {drop_nans.value} 
-    #                # trn_ratio {training_ratio.value} 
-    #                # features: {features.value}
-    #                # scaler: {scaler.value} 
-    #                # feature_maps: {feature_maps}
-    #                # info_message: {info_message.value}
-    #                """)
     InfoBox()
     EmbeddingPlot()
     BoxPlot() 
-    
-
-Page()
 
+Page()