correction

src/pages/1-samples_selection.py

@@ -267,7 +267,15 @@ if not t.empty:
 
         case 'RDM':
             rset = scores.number_input(min_value=0, max_value=100, value=20, label = 'The ratio of data to be sampled (%)')
-            cl_model = RDM(x = tcr, rset = rset)         
+            cl_model = RDM(x = tcr, rset = rset)
+        
+    if clus_method in ['KS', 'RDM']:
+            calset = cl_model.calset
+            labels = ["ind"]*n_samples
+            ncluster = "1"
+            selection_number = 'None'
+            selected_samples_idx = calset[1]
+            selection = 'None'
 
     new_tcr = tcr.iloc[clustered,:]    
     
@@ -283,41 +291,41 @@ elif labels:
     num_clusters = len(np.unique(labels))
     custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
     if clus_method:
-        if clus_method in ['KS', 'RDM']:
-            calset = cl_model.calset
-            labels = ["ind"]*n_samples
-            ncluster = "1"
-            selection_number = 'None'
-            selected_samples_idx = calset[1]
-            selection = 'None'
-        else:
-            selection = scores.radio('Select samples selection strategy:',
-                                        options = selec_strategy, index = default_sample_selection_option, key=102)
-        
-            match selection:
-            # Strategy 0
-                case 'center':
-                    # list samples at clusters centers - Use sklearn.metrics.pairwise_distances_argmin if you want more than 1 sample per cluster
-                    closest, _ = pairwise_distances_argmin_min(clu_centers, new_tcr)
-                    selected_samples_idx = np.array(new_tcr.index)[list(closest)]
-                    selected_samples_idx = selected_samples_idx.tolist()
-                    
-                #### Strategy 1
-                case 'random':
-                    selection_number = scores.number_input('How many samples per cluster?',
-                                                            min_value = 1, step=1, value = round(n_samples*0.1))
-                    s = np.array(labels)[np.where(np.array(labels) !='Non clustered')[0]]
-                    for i in np.unique(s):
-                        C = np.where(np.array(labels) == i)[0]
-                        if C.shape[0] >= selection_number:
-                            # scores.write(list(tcr.index)[labels== i])
-                            km2 = KMeans(n_clusters = selection_number)
-                            km2.fit(tcr.iloc[C,:])
-                            clos, _ = pairwise_distances_argmin_min(km2.cluster_centers_, tcr.iloc[C,:])
-                            selected_samples_idx.extend(tcr.iloc[C,:].iloc[list(clos)].index)
-                        else:
-                            selected_samples_idx.extend(new_tcr.iloc[C,:].index.to_list())
-                    # list indexes of selected samples for colored plot    
+        # if clus_method in ['KS', 'RDM']:
+        #     calset = cl_model.calset
+        #     labels = ["ind"]*n_samples
+        #     ncluster = "1"
+        #     selection_number = 'None'
+        #     selected_samples_idx = calset[1]
+        #     selection = 'None'
+        # else:
+        selection = scores.radio('Select samples selection strategy:',
+                                options = selec_strategy, index = default_sample_selection_option, key=102)
+
+        match selection:
+        # Strategy 0
+            case 'center':
+                # list samples at clusters centers - Use sklearn.metrics.pairwise_distances_argmin if you want more than 1 sample per cluster
+                closest, _ = pairwise_distances_argmin_min(clu_centers, new_tcr)
+                selected_samples_idx = np.array(new_tcr.index)[list(closest)]
+                selected_samples_idx = selected_samples_idx.tolist()
+                
+            #### Strategy 1
+            case 'random':
+                selection_number = scores.number_input('How many samples per cluster?',
+                                                        min_value = 1, step=1, value = round(n_samples*0.1))
+                s = np.array(labels)[np.where(np.array(labels) !='Non clustered')[0]]
+                for i in np.unique(s):
+                    C = np.where(np.array(labels) == i)[0]
+                    if C.shape[0] >= selection_number:
+                        # scores.write(list(tcr.index)[labels== i])
+                        km2 = KMeans(n_clusters = selection_number)
+                        km2.fit(tcr.iloc[C,:])
+                        clos, _ = pairwise_distances_argmin_min(km2.cluster_centers_, tcr.iloc[C,:])
+                        selected_samples_idx.extend(tcr.iloc[C,:].iloc[list(clos)].index)
+                    else:
+                        selected_samples_idx.extend(new_tcr.iloc[C,:].index.to_list())
+                # list indexes of selected samples for colored plot    
 
 ################################      Plots visualization          ############################################