corrections

src/Report/report.py

@@ -11,21 +11,21 @@ def intersect(l1, l2):
 def check(file):
     return os.path.isfile(file)
 def report(*args):
-    signal_preprocess = {'Snv':'Standard Normal Variate (SNV) \cite{barnes1989standard}',
-                         'SG': 'Savitzky-Golay (SG) \cite{savitzky1964smoothing}'}
-    dim_red_methods= {'PCA':'Principal Components Analysis (PCA) \cite{wold1987principal,ringner2008principal,greenacre2022principal,JMLR:v12:pedregosa11a}',
-                      'UMAP':'Uniform Manifold Approximation and Projection (UMAP) \cite{ghojogh2021uniform,JMLR:v12:pedregosa11a}',
-                        'NMF':'Non-negative Matrix Factorization (NMF) \cite{lopes2015non}'}  # List of dimensionality reduction algos
-    cluster_methods = {'Kmeans':'Kmeans \cite{chong2021k,JMLR:v12:pedregosa11a}',
-                       'HDBSCAN':'Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) \cite{mcinnes2017hdbscan}',
-                        'AP':'Affinity Propagation (AP) \cite{dueck2009affinity,JMLR:v12:pedregosa11a}',
-                        'KS':'Kennard-Stone algorithm (KS)',
-                        'RDM': 'random approach'} # List of clustering algos
+    signal_preprocess = {'Snv':r'''Standard Normal Variate (SNV) \cite{barnes1989standard}''',
+                         'SG': r'''Savitzky-Golay (SG) \cite{savitzky1964smoothing}'''}
+    dim_red_methods= {'PCA':r'''Principal Components Analysis (PCA) \cite{wold1987principal,ringner2008principal,greenacre2022principal,JMLR:v12:pedregosa11a}''',
+                      'UMAP':r'''Uniform Manifold Approximation and Projection (UMAP) \cite{ghojogh2021uniform,JMLR:v12:pedregosa11a}''',
+                        'NMF':r'''Non-negative Matrix Factorization (NMF) \cite{lopes2015non}'''}  # List of dimensionality reduction algos
+    cluster_methods = {'Kmeans':r'''Kmeans \cite{chong2021k,JMLR:v12:pedregosa11a}''',
+                       'HDBSCAN':r'''Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) \cite{mcinnes2017hdbscan}''',
+                        'AP':r'''Affinity Propagation (AP) \cite{dueck2009affinity,JMLR:v12:pedregosa11a}''',
+                        'KS':r'''Kennard-Stone algorithm (KS)''',
+                        'RDM': r'''random approach'''} # List of clustering algos
     
     selec_strategy = {'center':'PCA','random':'PCA'}
-    reg_algo ={"PLS":'Partial Least Squares (PLS) \cite{Wold2001,JMLR:v12:pedregosa11a}',
-                "LW-PLS": 'Locally Weighted-Partial Least Squares (LW-PLS) \cite{Lesnoff2020}',
-                "TPE-iPLS": "Tree-structured Parzen estimator-interval Partial Least Squares (TPE-iPLS)"} 
+    reg_algo ={"PLS":r'''Partial Least Squares (PLS) \cite{Wold2001,JMLR:v12:pedregosa11a}''',
+                "LW-PLS": r'''Locally Weighted-Partial Least Squares (LW-PLS) \cite{Lesnoff2020}''',
+                "TPE-iPLS": r'''Tree-structured Parzen estimator-interval Partial Least Squares (TPE-iPLS)'''} 
 
     to_report=[]
     j=0
@@ -176,19 +176,19 @@ def report(*args):
                     large residual distance to the subspace, but whose projection is on the subspace; and, finally, bad leverage
                       points, which have a large residual distance such that the projection on the subspace is away from regular observations.\par"""
               
-        latex_report += """ Results of applying this workflow are displayed in""" 
+        latex_report += r""" Results of applying this workflow are displayed in""" 
         if 'PCA' in to_report:
-            latex_report += """ (\cref{pcaplots, hotelling_and_influence, loadings})."""
+            latex_report += r""" (\cref{pcaplots, hotelling_and_influence, loadings})."""
         elif 'NMF' in to_report:
-            latex_report += """ (\cref{pcaplots, loadings})."""
+            latex_report += r""" (\cref{pcaplots, loadings})."""
         else:
-            latex_report += """ (\cref{pcaplots})."""
+            latex_report += r""" (\cref{pcaplots})."""
 
         if 'KS' in to_report or 'RDM' in to_report:
-            latex_report += """ Based of the features extracted using {"""+to_report[2]+ r"""}, """
+            latex_report += r""" Based of the features extracted using {"""+to_report[2]+ r"""}, """
         
         else:
-            latex_report += """ Based of the features extracted using {"""+to_report[2]+ r"""},
+            latex_report += r""" Based of the features extracted using {"""+to_report[2]+ r"""},
             {"""+to_report[3]+ r"""} revealed the  existance of {"""+to_report[5] + r"""} data clusters, visualized with different colors, from which """
         
         latex_report += r"""a subset of {"""+to_report[8]+ r"""} samples was selected"""

src/pages/1-samples_selection.py

@@ -414,6 +414,7 @@ if not t.empty:
         st.write('Scores plot')
         tcr_plot = tcr.copy()
         colfilter = st.selectbox('Color by:', options= filter,format_func = lambda x: x if x else "<Select>")
+        p_hash(colfilter)
         if colfilter in cluster_methods:
             tcr_plot[colfilter] = labels
         elif not meta_data.empty and colfilter in md_df_st_.columns.tolist():

src/pages/3-prediction.py

@@ -158,8 +158,8 @@ try:
         with M4:
             @st.cache_data
             def prep_info(change):
-                SG = f'- Savitzky-Golay derivative parameters \:(Window_length:{prep['window_length']};  polynomial order: {prep['polyorder']};  Derivative order : {prep['deriv']})'
-                Norm = f'- Spectral Normalization \: {norm}'
+                SG = r'''- Savitzky-Golay derivative parameters \:(Window_length:{prep['window_length']};  polynomial order: {prep['polyorder']};  Derivative order : {prep['deriv']})'''
+                Norm = r'''- Spectral Normalization \: {norm}'''
                 return SG, Norm
             SG, Norm = prep_info(change = hash_)
             st.info('The spectra were preprocessed using:\n'+SG+"\n"+Norm)

src/pages/4-inputs.py

@@ -69,30 +69,31 @@ with st.container():
             }
 
         submitted = st.form_submit_button(label='Save')
-        if submitted:
-            if '' not in form_data.values(): 
-                # Save the form data here
-                st.session_state['form_submitted'] = True
-                st.success('Form was saved successfully!', icon="✅")
-                # Enregistrement des données dans un fichier JSON
-                with open('form_data.json', 'w') as json_file:
-                    json.dump(form_data, json_file)
-
-                with st.container():
-                    if st.session_state['interface'] == 'simple':
-                        header3, header4 = st.columns(2)
-                        if header3.button("Samples Selection"):
-                            st.switch_page(pages_folder / '1-samples_selection.py')
-                        if header4.button("Model Creation"):
-                            st.switch_page(pages_folder / '2-model_creation.py')
-                    elif st.session_state['interface'] == 'advanced':
-                        header3, header4, header5 = st.columns(3)
-                        if header3.button("Samples Selection"):
-                            st.switch_page(pages_folder / '1-samples_selection.py')
-                        if header4.button("Model Creation"):
-                            st.switch_page(pages_folder / '2-model_creation.py')
-                        if header5.button("Prediction"):
-                            st.switch_page(pages_folder / '3-prediction.py')
-            
-            else:
-                st.error('Error: The form was not saved, please ensure the required fields are filled!')
\ No newline at end of file
+    if submitted:
+        if '' not in form_data.values(): 
+            # Save the form data here
+            st.session_state['form_submitted'] = True
+            st.success('Form was saved successfully!', icon="✅")
+            # Enregistrement des données dans un fichier JSON
+            with open('form_data.json', 'w') as json_file:
+                json.dump(form_data, json_file)
+
+            if st.session_state['interface'] == 'simple':
+                header3, header4 = st.columns(2)
+                if header3.button("Samples Selection"):
+                    st.switch_page(pages_folder / '1-samples_selection.py')
+                if header4.button("Model Creation"):
+                    st.switch_page(pages_folder / '2-model_creation.py')
+            elif st.session_state['interface'] == 'advanced':
+                header3, header4, header5 = st.columns(3)
+                if header3.button("Samples Selection"):
+                    st.switch_page(pages_folder / '1-samples_selection.py')
+                if header4.button("Model Creation"):
+                    st.switch_page(pages_folder / '2-model_creation.py')
+                if header5.button("Prediction"):
+                    st.switch_page(pages_folder / '3-prediction.py')
+
+        else:
+            st.error('Error: The form was not saved, please ensure the required fields are filled!')
+
+