PCA color filter

prediction using ipls model

pages/1-samples_selection.py

@@ -6,6 +6,8 @@ from Class_Mod.DATA_HANDLING import *
 ################################### Data Loading and Visualization ########################################
 container1 = st.container(border=True)
 col2, col1 = st.columns([3, 1])
+col1.header("Data Loading", divider='blue')
+col2.header("Spectral Data Visualization", divider='blue')
 
 
 container2 = st.container(border=True)
@@ -15,8 +17,6 @@ influence, hotelling, qexp = st.columns([2, 2, 1])
 
 
 with container1:
-    col1.header("Data Loading", divider='blue')
-    col2.header("Spectral Data Visualization", divider='blue')
     # loader for csv file containing NIRS spectra
     sselectx_csv = col1.file_uploader("Load NIRS Data", type=["csv","dx"], help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns", key=5)
     if sselectx_csv is not None:
@@ -59,9 +59,10 @@ with container1:
                         data = DxRead(path = tmp_path)
                         data_import = data.specs_df_
                         st.success("The data have been loaded successfully", icon="✅")
+                        
 
                     ## Visualize spectra
-                
+
                 with col2:
                     fig, ax = plt.subplots(figsize = (30,7))
                     data_import.T.plot(legend=False, ax = ax, color = 'blue')
@@ -85,11 +86,10 @@ with container1:
 ######################################################################################
 
 ############################## Exploratory data analysis ###############################
+plot_type=['', 'PCA','UMAP', 'NMF']
+cluster_methods = ['', 'Kmeans','UMAP', 'AP']
 with container2:
     if sselectx_csv is not None:
-        plot_type=['', 'PCA','UMAP', 'NMF']
-        cluster_methods = ['', 'Kmeans','UMAP', 'AP']
-
         with pc:
             type_plot = st.selectbox("Dimensionality reduction techniques: ", options=plot_type, key=37)
             type_cluster = st.selectbox("Clustering techniques: ", options=cluster_methods, key=38)
@@ -128,8 +128,15 @@ with container2:
 
 
                 else:
-                    fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3)
-                    fig.update_traces(marker=dict(size=4))
+                    if test == '.dx':
+                        filter = ['origin', 'date', 'time', 'spectrometer/data system']
+                        col = st.selectbox('filter', options= filter)
+
+                        fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = data.md_df_[col])
+                        fig.update_traces(marker=dict(size=4))
+                    else:
+                        fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3 )
+                        fig.update_traces(marker=dict(size=4))
 
                 st.plotly_chart(fig)
 

pages/3-prediction.py

@@ -42,7 +42,7 @@ if NIRS_csv:
                       if index:
                         idx = pd.read_csv(index, sep=';', index_col=0).iloc[:,0].to_numpy()
 
-result = ''
+#result = ''
 
 if st.button("Predict"):
         if s: