Ajout de l'export des fiigures pour le rapport

Packages.py

@@ -64,6 +64,8 @@ import joblib
 
 from hyperopt import fmin, hp, tpe, Trials, space_eval, STATUS_OK, anneal
 
-
-
+import plotly.graph_objects as go
+import plotly.express as px
+import plotly.io as pio
+import streamlit as st
 st.set_option('deprecation.showPyplotGlobalUse', False)

app.py

@@ -6,6 +6,16 @@ from Modules import *
 from Class_Mod.DATA_HANDLING import *
 
 
+# HTML pour le bandeau "CEFE - CNRS"
+bandeau_html = """
+<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
+  <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
+</div>
+"""
+
+# Injecter le code HTML du bandeau
+st.markdown(bandeau_html, unsafe_allow_html=True)
+
 # # TOC menu on the left
 show_pages(
     [Page("app.py", "Home"),
@@ -18,6 +28,7 @@ hide_pages("Samples Selection")
 hide_pages("Models Creation")
 hide_pages("Predictions")
 
+
 with st.sidebar:
     interface = st.selectbox(label="Interface", options=['simple', 'advanced'], key='interface')
     st.page_link('pages\\1-samples_selection.py')
@@ -35,17 +46,35 @@ with st.sidebar:
         st.page_link('pages\\2-model_creation.py')
         st.page_link('pages\\3-prediction.py')
 
+
 # Page header
 with st.container():
-    st.subheader("Plateforme d'Analyses Chimiques pour l'Ecologie-PACE :goat:")
-    st.title("NIRS Utils")
-    st.write("Samples selection (PCA, [UMAP](https://umap-learn.readthedocs.io/en/latest/how_umap_works.html), ...), Predictive Modelling ([Pinard](https://github.com/GBeurier/pinard), [LWPLSR](https://doi.org/10.1002/cem.3209), ...), and Predictions using your data (CSV or DX files) and/or PACE NIRS Database.")
-    #st.image(img_general)
-    st.markdown("### We could add documentation here ###")
+
+    # Centrer les boutons
+    st.markdown(
+        """
+        <style>
+        .stButton>button {
+            display: block;
+            margin: 0 auto;
+            width: 200px; 
+            height: 50px; 
+            font-size: 16px; 
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
     header1, header2, header3 = st.columns(3)
     if header1.button("Samples Selection"):
         st.switch_page('pages\\1-samples_selection.py')
     if header2.button("Model Creation"):
         st.switch_page('pages\\2-model_creation.py')
     if header3.button("Predictions"):
-        st.switch_page('pages\\3-prediction.py')
\ No newline at end of file
+        st.switch_page('pages\\3-prediction.py')
+    st.subheader("Plateforme d'Analyses Chimiques pour l'Ecologie-PACE :goat:")
+    st.title("NIRS Utils")
+    st.write("Samples selection (PCA, [UMAP](https://umap-learn.readthedocs.io/en/latest/how_umap_works.html), ...), Predictive Modelling ([Pinard](https://github.com/GBeurier/pinard), [LWPLSR](https://doi.org/10.1002/cem.3209), ...), and Predictions using your data (CSV or DX files) and/or PACE NIRS Database.")
+    #st.image(img_general)
+    st.markdown("### We could add documentation here ###")
\ No newline at end of file

pages/1-samples_selection.py

@@ -3,6 +3,19 @@ st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
 from Modules import *
 from Class_Mod.DATA_HANDLING import *
 
+
+
+# HTML pour le bandeau "CEFE - CNRS"
+bandeau_html = """
+<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
+  <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
+</div>
+"""
+
+
+# Injecter le code HTML du bandeau
+st.markdown(bandeau_html, unsafe_allow_html=True)
+
 st.session_state["interface"] = st.session_state.get('interface')
 if st.session_state["interface"] == 'simple':
     hide_pages("Predictions")
@@ -54,13 +67,12 @@ if data_file:
                 st.success("The data have been loaded successfully", icon="✅")
         os.unlink(tmp_path)
 
-
 ## Visualize spectra
 if not spectra.empty:
     with col2:
         fig = plot_spectra(spectra)
         st.pyplot(fig)
-
+        fig.savefig("./Report/figures/Spectra_Plot.png")
 
 ############################## Exploratory data analysis ###############################
 container2 = st.container(border=True)
@@ -102,8 +114,13 @@ if not t.empty:
     if clus_method == cluster_methods[1]:
         ncluster = scores.number_input(min_value=2, max_value=30, value=3, label = 'Select the desired number of clusters')
         cl_model = Sk_Kmeans(tcr, max_clusters = 30)
-        fig2 = px.scatter(cl_model.inertia_.T, y = 'inertia')
+        fig2 = px.scatter(cl_model.inertia_.T, y = 'inertia')        
         scores.plotly_chart(fig2)
+        img = pio.to_image(fig2, format="png")
+        with open("./Report/figures/Elbow.png", "wb") as f:
+                f.write(img)    
+
+
         data, labels = cl_model.fit_optimal(nclusters = ncluster)
 
     elif clus_method == cluster_methods[2]:
@@ -119,7 +136,7 @@ if not t.empty:
         # scores plot with clustering
         if list(labels) and meta_data.empty:
             fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = labels)
-
+    
         # scores plot with metadata
         elif len(list(labels)) == 0 and not meta_data.empty:
             filter = meta_data.columns[1:]
@@ -151,26 +168,63 @@ if not t.empty:
         st.plotly_chart(fig)
 
 
+## Export en 2d Axe1..Axe3
+if not t.empty:
+    if dim_red_method == dim_red_methods[1]: 
+
+        # nombre de clusters
+        num_clusters = len(np.unique(labels))
+
+        # Une couleur par cluster
+        custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
+
+        # Graphique pour les dimensions (axis1, axis2)
+        fig_2d_axis1_axis2 = px.scatter(t, x=axis1, y=axis2, color=labels, color_discrete_sequence=custom_color_palette) 
+        img_2d_axis1_axis2 = pio.to_image(fig_2d_axis1_axis2, format="png")
+        with open("./Report/figures/scores_plot_2d_axis1_axis2.png", "wb") as f:
+            f.write(img_2d_axis1_axis2)
+
+        # Graphique pour les dimensions (axis1, axis3)
+        fig_2d_axis1_axis3 = px.scatter(t, x=axis1, y=axis3, color=labels, color_discrete_sequence=custom_color_palette) 
+        img_2d_axis1_axis3 = pio.to_image(fig_2d_axis1_axis3, format="png")
+        with open("./Report/figures/scores_plot_2d_axis1_axis3.png", "wb") as f:
+            f.write(img_2d_axis1_axis3)
+
+        # Graphique pour les dimensions (axis2, axis3)
+        fig_2d_axis2_axis3 = px.scatter(t, x=axis2, y=axis3, color=labels, color_discrete_sequence=custom_color_palette) 
+        img_2d_axis2_axis3 = pio.to_image(fig_2d_axis2_axis3, format="png")
+        with open("./Report/figures/scores_plot_2d_axis2_axis3.png", "wb") as f:
+            f.write(img_2d_axis2_axis3)
 
 if not spectra.empty:
     if dim_red_method == dim_red_methods[1]:
+
         with loadings:
             st.write('Loadings plot')
             p = dr_model.loadings_
-            pp = pd.concat([p, pd.DataFrame(np.arange(p.shape[0]), index=p.index, columns=['wl'])], axis =1)
+            pp = pd.concat([p, pd.DataFrame(np.arange(p.shape[0]), index=p.index, columns=['wl'])], axis=1)
             df1 = pp.melt(id_vars="wl")
-            fig = px.line(df1, x = 'wl', y = 'value', color='variable')
-            fig.update_layout(legend=dict(x=1, y=0,font=dict(family="Courier", size=12, color="black"),
-                                        bordercolor="Black", borderwidth=2))
-            st.plotly_chart(fig, use_container_width = True)
+            fig = px.line(df1, x='wl', y='value', color='variable', color_discrete_sequence=px.colors.qualitative.Plotly)
+            fig.update_layout(legend=dict(x=1, y=0, font=dict(family="Courier", size=12, color="black"),
+                                        bordercolor="black", borderwidth=2))
+            st.plotly_chart(fig, use_container_width=True)
+
+            # Export du graphique
+            img = pio.to_image(fig, format="png")
+            with open("./Report/figures/graphe_loadings.png", "wb") as f:
+                f.write(img)
 
         with influence:
             st.write('Influence plot')
             ax1 = st.selectbox("Component", options=dr_model.scores_.columns, index=3)
             leverage = dr_model.leverage_
             residuals = dr_model.residuals_
-            fig = px.scatter(x=leverage[ax1], y=residuals[ax1], color = leverage[ax1]*residuals[ax1]).update_layout(xaxis_title="Leverage",yaxis_title="Residuals")
+            fig = px.scatter(x=leverage[ax1], y=residuals[ax1], color=leverage[ax1]*residuals[ax1], color_continuous_scale='Blues')
+            fig.update_layout(xaxis_title="Leverage", yaxis_title="Residuals")
             st.plotly_chart(fig)
+            img = pio.to_image(fig, format="png")
+            with open("./Report/figures/Influence_plot.png", "wb") as f:
+                f.write(img)
 
         with hotelling:
                 st.write('T²-Hotelling vs Q residuals plot')
@@ -179,4 +233,5 @@ if not spectra.empty:
 
                 hotelling = dr_model.hotelling_
                 fig = px.scatter(t, x=hotelling[ax2], y=residuals[ax2]).update_layout(xaxis_title="T²",yaxis_title="Residuals")
-                st.plotly_chart(fig)
\ No newline at end of file
+                st.plotly_chart(fig)
+                fig.write_image("./Report/figures/graphe_hotelling.png", format="png")

pages/2-model_creation.py

@@ -2,7 +2,15 @@ from Packages import *
 st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
 from Modules import *
 from Class_Mod.DATA_HANDLING import *
-
+# HTML pour le bandeau "CEFE - CNRS"
+bandeau_html = """
+<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
+  <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
+</div>
+"""
+
+# Injecter le code HTML du bandeau
+st.markdown(bandeau_html, unsafe_allow_html=True)
 
 st.session_state["interface"] = st.session_state.get('interface')
 if st.session_state["interface"] == 'simple':

pages/3-prediction.py

@@ -2,7 +2,15 @@ from Packages import *
 st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
 from Modules import *
 from Class_Mod.DATA_HANDLING import *
+# HTML pour le bandeau "CEFE - CNRS"
+bandeau_html = """
+<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
+  <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
+</div>
+"""
 
+# Injecter le code HTML du bandeau
+st.markdown(bandeau_html, unsafe_allow_html=True)
 st.session_state["interface"] = st.session_state.get('interface')