Merge branch 'master' of https://src.koda.cnrs.fr/cefe/pace/nirs_workflow

src/pages/1-samples_selection.py

@@ -313,7 +313,7 @@ elif labels:
             #### 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))
+                                                        min_value = 1, step=1, value = round(n_samples*0.1/ncluster))
                 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]
@@ -496,7 +496,7 @@ if not spectra.empty:
                         ann =  meta_data.loc[:,'name'][i]
                     else:
                         ann = t.index[i]
-                    fig.add_annotation(dict(x = leverage[i], y = residuals[i], showarrow=True, text = ann,font= dict(color= "black", size= 15),
+                    fig.add_annotation(dict(x = leverage[i], y = residuals[i], showarrow=True, text = str(ann),font= dict(color= "black", size= 15),
                                 xanchor = 'auto', yanchor = 'auto'))
             
             fig.update_traces(marker=dict(size= 6), showlegend= True)
@@ -541,7 +541,7 @@ if not spectra.empty:
                         ann =  meta_data.loc[:,'name'][i]
                     else:
                         ann = t.index[i]
-                    fig.add_annotation(dict(x = hotelling[i], y = residuals[i], showarrow=True, text = ann, font= dict(color= "black", size= 15),
+                    fig.add_annotation(dict(x = hotelling[i], y = residuals[i], showarrow=True, text = str(ann), font= dict(color= "black", size= 15),
                                 xanchor = 'auto', yanchor = 'auto'))
                     
             fig.update_traces(marker=dict(size= 6), showlegend= True)
@@ -605,7 +605,7 @@ if not sam.empty:
     ## generate report
     latex_report = report.report('Representative subset selection', data_file.name, dim_red_method,
                                   clus_method, Nb_ech, ncluster, selection, selection_number, nb_clu,tcr, sam)
-    
+
    
     @st.cache_data
     def download_res(file,sam):