1-samples_selection.py

from common import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")





# layout
UiComponents(pagespath = pages_folder, csspath= css_file,imgpath=image_path ,
             header=True, sidebar= True, bgimg=False, colborders=True)
st.header("Calibration Subset Selection") # page title
st.markdown("Create a predictive model, then use it for predicting your target variable (chemical data) from NIRS spectra")
c1, c2 = st.columns([3, 1])
c1.image("./images/sample selection.png", use_column_width=True) # graphical abstract



# empty temp figures
report_path = Path("report")
report_path_rel = Path("./report")

def delete_files(keep):
    from os import walk, remove
    supp = []
    # Walk through the directory
    for root, dirs, files in os.walk(report_path, topdown=False):
        for file in files:
            if file != 'logo_cefe.png' and not any(file.endswith(ext) for ext in keep):
                os.remove(os.path.join(root, file))




if Path('report/out/model').exists() and Path('report/out/model').is_dir():
    rmtree(Path('report/out/model'))

# algorithms available on our app
match st.session_state["interface"]:
    case 'simple':
        dim_red_methods = ['PCA']
        cluster_methods = ['KS'] # List of clustering algos
        selec_strategy = ['random']

    case 'advanced':
        dim_red_methods=['PCA','UMAP', 'NMF']  # List of dimensionality reduction algos
        cluster_methods = ['Kmeans','HDBSCAN', 'AP'] # List of clustering algos
        selec_strategy = ['center','random']

# ~~~~~~~~~~~~~~~~ clean the analysis results dir ~~~~~~~~~~~~~~~~
delete_files(keep = ['.py', '.pyc','.bib'])

################################### I - Data Loading and Visualization ########################################
files_format = ['csv', 'dx'] # Supported files format
# loader for datafile
file = c2.file_uploader("Data file", type = ["csv", "dx"], help = " :mushroom: select a csv matrix with samples as rows and lambdas as columns", key = 5)

## Preallocation of data structure
spectra = DataFrame()
meta_data = DataFrame()
tcr=DataFrame()
sam=DataFrame()
sam1=DataFrame()
selected_samples = DataFrame()
non_clustered = None
l1 = []
labels = []
color_palette = None
dr_model = None # dimensionality reduction model
cl_model = None # clustering model
selection = None
selection_number = "None"
samples_df_chem = DataFrame
selected_samples = []
selected_samples_idx = []






hash_ = ''



if not file:
    c2.info('Info: Please load data file !')

else:
    extension = file.name.split(".")[-1]
    userfilename = file.name.replace(f".{extension}", '')

    match extension:
        case 'csv':# Load .csv file
            with c2:
                c2_1, c2_2 = st.columns([.5, .5])
                with c2_1:
                    dec = st.radio('decimal:', options= [".", ","], horizontal = True)
                    sep = st.radio("separator:", options = [";", ","], horizontal = True)
                with c2_2:
                    phdr = st.radio("header: ", options = ["yes", "no"], horizontal = True)
                    pnames = st.radio("samples name:", options = ["yes", "no"], horizontal = True)

                hdr = 0 if phdr =="yes" else None
                names = 0 if pnames =="yes" else None
                hash_ = ObjectHash(current=hash_, add= [userfilename, hdr, names, dec, sep])

                from io import StringIO
                stringio = StringIO(file.getvalue().decode("utf-8"))
                data_str = str(stringio.read())
                
                @st.cache_data
                def read_csv(file = file, change = None):
                    from utils.data_parsing import CsvParser
                    par = CsvParser(file= file)
                    par.parse(decimal = dec, separator = sep, index_col = names, header = hdr)
                    return par.float, par.meta_data, par.meta_data_st_, par.df
                
                spectra, meta_data, md_df_st_, imp = read_csv(file= file, change = hash_)

                try :
                    spectra, meta_data, md_df_st_, imp = read_csv(file= file)
                    st.success("The data have been loaded successfully", icon="✅")

                except:
                    st.error('''Error: The format of the file does not correspond to the expected dialect settings.
                              To read the file correctly, please adjust the separator parameters.''')

        ## Load .dx file
        case 'dx':
            with c2:
                # Create a temporary file to save the uploaded file
                with NamedTemporaryFile(delete = False, suffix = ".dx") as tmp:
                    tmp.write(file.read())
                    tmp_path = tmp.name
                    with open(tmp.name, 'r') as dd:
                        dxdata = dd.read()
                        
                    ## load and parse the temp dx file
                    @st.cache_data
                    def read_dx(tmp_path, change = None):
                        M = JcampParser(path = tmp_path)
                        M.parse()
                        return M.chem_data, M.specs_df_, M.meta_data, M.meta_data_st_
                    
                    hash_ = ObjectHash(current=hash_, add= dxdata)
                    _, spectra, meta_data, md_df_st_ = read_dx(tmp_path = tmp_path)

                    st.success("The data have been loaded successfully", icon="✅")

################################################### END : I- Data loading and preparation ####################################################
if not spectra.empty:
    with c2:
        st.write('Data summary:')
        st.write(f'- the number of spectra:{spectra.shape[0]}')
        st.write(f'- the number of wavelengths:{spectra.shape[1]}')
        st.write(f'- the number of categorical variables:{meta_data.shape[1]}')

################################################### BEGIN : visualize and split the data ####################################################
st.subheader("I - Spectral Data Visualization", divider='blue')
if not spectra.empty:
    ObjectHash(np.mean(spectra))
    n_samples = spectra.shape[0]
    nwl = spectra.shape[1]
    # retrieve columns name and rows name of the dataframe
    colnames = list(spectra.columns)
    rownames = [str(i) for i in list(spectra.index)]
    spectra.index = rownames

    @st.cache_data
    def spectra_visualize(variable):# this method takes spectra as input
        fig = plot_spectra(spectra, xunits = 'Wavelength/Wavenumber', yunits = "Signal intensity")
        
        data_info = DataFrame({'Name': [file.name],
                                'Number of scanned samples': [n_samples]},
                                index = ['Input file'])
        return fig, data_info
    fig_spectra, data_info = spectra_visualize(variable = spectra)

    c3, c4 = st.columns([3, 1])
    with c3:
        st.pyplot(fig_spectra)

    with c4:
        st.info('Information on the loaded data file')
        st.write(data_info) ## table showing the number of samples in the data file

################################################### END : visualize and split the data ####################################################

############################## Exploratory data analysis ###############################
st.subheader("II - Exploratory Data Analysis-Multivariable Data Analysis", divider='blue')

###### 1- Dimensionality reduction ######
t = DataFrame # scores
p = DataFrame # loadings
if not spectra.empty:
    xc = standardize(spectra, center=True, scale=False)

    c5, c6, c7, c8, c9, c10, c11 = st.columns([1, 1, 0.6, 0.6, 0.6, 1.5, 1.5])
    with c5:
        dim_red_method = st.selectbox("Dimensionality reduction techniques: ",
         options = ['']+dim_red_methods if len(dim_red_methods)>2 else dim_red_methods,
         key = 37, format_func = lambda x: x if x else "<Select>", disabled = False if len(dim_red_methods)>2 else True)
        if dim_red_method == '':
            st.info('Info: Select a dimensionality reduction technique!')
        ObjectHash(dim_red_method)


        if dim_red_method == "UMAP":
            if not meta_data.empty:
                filter = md_df_st_.columns.tolist()
                supervised = st.selectbox('Supervised UMAP by(optional):', options = ['']+filter, format_func = lambda x: x if x else "<Select>", key=108)
                umapsupervisor = [None if supervised == '' else md_df_st_[supervised]][0]

            else:
                supervised = st.selectbox('Supervised UMAP by:', options = ["Meta-data is not available"], disabled=True, format_func = lambda x: x if x else "<Select>", key=108)
                umapsupervisor = None
            ObjectHash(supervised)

        disablewidgets = [False if (dim_red_method and st.session_state.interface == 'advanced') else True][0]
        clus_method = st.selectbox("Clustering techniques(optional): ",
         options = ['']+cluster_methods if len(cluster_methods)>2 else cluster_methods,
         key = 38, format_func = lambda x: x if x else "<Select>", disabled= disablewidgets)

        
        # if disablewidgets == False and dim_red_method in dim_red_methods:
        #     inf = st.info('Info: Select a clustering technique!')

        if dim_red_method:
            @st.cache_data
            def dimensionality_reduction(variable):
                match dim_red_method:
                    case "PCA":
                            from utils.dim_reduction import LinearPCA
                            dr_model = LinearPCA(xc, Ncomp=8)
                    case "UMAP":
                            from utils.dim_reduction import Umap
                            dr_model = Umap(numerical_data = MinMaxScale(spectra), cat_data = umapsupervisor)   
                    case 'NMF':
                            from utils.dim_reduction import Nmf
                            dr_model = Nmf(spectra, Ncomp= 3)
                return dr_model
            
            dr_model = dimensionality_reduction(variable = xc)
            

        if dr_model:
            axis1 = c7.selectbox("x-axis", options = dr_model.scores_.columns, index=0)
            axis2 = c8.selectbox("y-axis", options = dr_model.scores_.columns, index=1)
            axis3 = c9.selectbox("z-axis", options = dr_model.scores_.columns, index=2)
            axis = np.unique([axis1, axis2, axis3])

            t = dr_model.scores_.loc[:,np.unique(axis)]
            tcr = standardize(t)

###### II - clustering #######
if not t.empty:
    clustered = np.arange(n_samples)
    non_clustered = None

    if dim_red_method == 'UMAP':
        c12 = st.container()
    else:
        c12, c13 = st.columns([3,3])

if not spectra.empty:
    with c6:
        sel_ratio = st.number_input('Enter the number/fraction of samples to be selected:',min_value=0.01, max_value=float("{:.2f}".format(spectra.shape[0])), value=0.20, format="%.2f", disabled= disablewidgets)
        if sel_ratio:
            if sel_ratio > 1.00:
                ratio = int(sel_ratio)
            elif sel_ratio < 1.00:
                ratio = int(sel_ratio*spectra.shape[0])
            ObjectHash(sel_ratio)
        if st.session_state["interface"] =='simple':
            clus_method = 'KS'

        else:
            if dr_model and not clus_method:
                clus_method = st.radio('Select samples selection strategy:', options = ['RDM', 'KS'])

            elif dr_model and clus_method:
                disabled1 = False if clus_method in cluster_methods else True
                selection = st.radio('Select samples selection strategy:', options = selec_strategy, disabled = disabled1)



if dr_model and sel_ratio:
    # Clustering
    match clus_method:
        case 'Kmeans':
            from utils.clustering import Sk_Kmeans
            cl_model = Sk_Kmeans(tcr, max_clusters = ratio)
            data, labels, clu_centers = cl_model.fit_optimal_
            ncluster = clu_centers.shape[0]

        # 2- HDBSCAN clustering
        case 'HDBSCAN':
            from utils.clustering import Hdbscan
            cl_model = Hdbscan(np.array(tcr))
            labels, clu_centers, non_clustered = cl_model.labels_,cl_model.centers_, cl_model.non_clustered
            ncluster = len(clu_centers)

        # 3- Affinity propagation
        case 'AP':
            from utils.clustering import AP
            cl_model = AP(X = tcr)
            data, labels, clu_centers = cl_model.fit_optimal_
            ncluster = len(clu_centers)

        case 'KS':
            cl_model = KS(x = tcr, rset = ratio)

        case 'RDM':
            cl_model = RDM(x = tcr, rset = ratio)

    if clus_method in ['KS', 'RDM']:
        _, selected_samples_idx = cl_model.calset
        labels = ["ind"]*n_samples
        ncluster = "1"
        selection_number = 'None'
        selection = 'None'

    new_tcr = tcr.iloc[clustered,:]
    

# #################################################### III - Samples selection using the reduced data presentation ######
if not labels:
    custom_color_palette = px.colors.qualitative.Plotly[:1]
elif labels:
    num_clusters = len(np.unique(labels))
    custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
    if clus_method:
        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
                from sklearn.metrics import pairwise_distances_argmin_min
                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 = int(ratio/num_clusters)
                ObjectHash(selection_number)
                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:
                        from sklearn.cluster import KMeans
                        km2 = KMeans(n_clusters = selection_number)
                        km2.fit(tcr.iloc[C,:])
                        from sklearn.metrics import pairwise_distances_argmin_min
                        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())

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ results visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    ## Scores
if not t.empty:
    if meta_data.empty and clus_method in cluster_methods:
        filter = clus_method
    elif not meta_data.empty and clus_method in cluster_methods:
        filter = [clus_method] + md_df_st_.columns.tolist()
    elif not meta_data.empty and clus_method not in cluster_methods:
        filter = [''] + md_df_st_.columns.tolist()
    elif meta_data.empty and not clus_method in cluster_methods:
        filter = []
    
    if st.session_state["interface"] =='simple':
        desactivatelist = True
        if meta_data.empty:
            desactivatelist = True
            filter = ['']
        elif not meta_data.empty:
            filter = [''] + md_df_st_.columns.tolist()
            desactivatelist = False
    else:
        desactivatelist = False
    

    with c12:
        st.write('Scores plot')
        tcr_plot = tcr.copy()
        if len(axis)== 1:
            tcr_plot['1d'] = np.random.uniform(-.5, .5, tcr_plot.shape[0])

        colfilter = st.selectbox('Color by:', options= filter,format_func = lambda x: x if x else "<Select>", disabled = desactivatelist)
        ObjectHash(colfilter)
        if colfilter in cluster_methods:
            tcr_plot[colfilter] = labels
        elif not meta_data.empty and colfilter in md_df_st_.columns.tolist():
            tcr_plot[f'{colfilter} :'] = list(map(str.lower,md_df_st_.loc[:,colfilter]))
        else:
            tcr_plot[f'{colfilter} :'] = ['sample'] * tcr_plot.shape[0]
        
        col_var_name = tcr_plot.columns.tolist()[-1]
        n_categories = len(np.unique(tcr_plot[col_var_name]))
        custom_color_palette = px.colors.qualitative.Plotly[:n_categories]

        if selected_samples_idx:# color selected samples
            t_selected = tcr_plot.iloc[selected_samples_idx,:]
        match t.shape[1]:
            case 3:
                fig = px.scatter_3d(tcr_plot, x = axis[0], y = axis[1], z = axis[2], color = col_var_name ,color_discrete_sequence = custom_color_palette)
                fig.update_traces(marker=dict(size=4))
                if selected_samples_idx:# color selected samples
                    fig.add_scatter3d(x = t_selected.loc[:,axis[0]], y = t_selected.loc[:,axis[1]], z = t_selected.loc[:,axis[2]],
                                    mode ='markers', marker = dict(size = 5, color = 'black'), name = 'selected samples')
                
            case 2:
                fig = px.scatter(tcr_plot, x = axis[0], y = axis[1], color = col_var_name ,color_discrete_sequence = custom_color_palette)
                if selected_samples_idx:# color selected samples
                    fig.add_scatter(x = t_selected.loc[:,axis[0]], y = t_selected.loc[:,axis[1]],
                                    mode ='markers', marker = dict(size = 5, color = 'black'), name = 'selected samples')

            
            case 1:
                yy = np.random.uniform(-.5, .5, tcr_plot.shape[0])
                fig = px.scatter(tcr_plot, x = axis[0], y = '1d', color = col_var_name ,color_discrete_sequence = custom_color_palette)
                fig.add_scatter(x = t_selected.loc[:,axis[0]], y = t_selected['1d'],
                                    mode ='markers', marker = dict(size = 5, color = 'black'), name = 'selected samples')
                fig.update_layout( yaxis_range=[-1.6, 1.6])
                fig.update_yaxes(visible=False)

        st.plotly_chart(fig, use_container_width = True)

        if labels:
            fig_export = {}
            # export 2D scores plot
            if len(axis)== 3:
                from itertools import combinations
                comb = [i for i in combinations(np.arange(len(axis)), 2)]
                subcap = ['a','b','c']
                for i in range(len(comb)):
                    fig_= px.scatter(tcr_plot, x = axis[(comb[i][0])], y=axis[(comb[i][1])],color = labels if list(labels) else None,color_discrete_sequence = custom_color_palette)
                    fig_.add_scatter(x = t_selected.loc[:,axis[(comb[i][0])]], y = t_selected.loc[:,axis[(comb[i][1])]], mode ='markers', marker = dict(size = 5, color = 'black'),
                                name = 'selected samples')
                    fig_.update_layout(font=dict(size=23))
                    fig_.add_annotation(text= f'({subcap[i]})', align='center', showarrow= False, xref='paper', yref='paper', x=-0.13, y= 1,
                                                font= dict(color= "black", size= 35), bgcolor ='white', borderpad= 2, bordercolor= 'black', borderwidth= 3)
                    fig_.update_traces(marker=dict(size= 10), showlegend= False)
                    fig_export[f'scores_pc{comb[i][0]}_pc{comb[i][1]}'] = fig_
                    # fig_export.write_image(f'./report/out/figures/scores_pc{str(comb[i][0])}_pc{str(comb[i][1])}.png')
            else:
                fig_export['fig'] = fig
            


if not spectra.empty:
    if dim_red_method in ['PCA','NMF']:
        with c13:
            st.write('Loadings plot')
            p = dr_model.loadings_
            freq = DataFrame(colnames, index=p.index)
            if extension =='dx':
                if meta_data.loc[:,'xunits'][0] == '1/cm':
                    freq.columns = ['Wavenumber (1/cm)']
                    xlab = "Wavenumber (1/cm)"
                    inv = 'reversed'
                else:
                    freq.columns = ['Wavelength (nm)']
                    xlab = 'Wavelength (nm)'
                    inv = None
            else:
                freq.columns = ['Wavelength/Wavenumber']
                xlab = 'Wavelength/Wavenumber'
                inv = None
                
            pp = concat([p, freq], axis=1)
            #########################################
            df1 = pp.melt(id_vars=freq.columns)
            loadingsplot = px.line(df1, x=freq.columns, y='value', color='variable', color_discrete_sequence=px.colors.qualitative.Plotly)
            loadingsplot.update_layout(legend=dict(x=1, y=0, font=dict(family="Courier", size=12, color="black"),
                                        bordercolor="black", borderwidth=2))
            loadingsplot.update_layout(xaxis_title = xlab,yaxis_title = "Intensity" ,xaxis = dict(autorange= inv))

            
            st.plotly_chart(loadingsplot, use_container_width=True)
    
#############################################################################################################
    if dim_red_method == 'PCA':
        c14, c15 = st.columns([3, 3])
        with c14:
            st.write('Influence plot')
            # Laverage
            Hat =  t.to_numpy() @ np.linalg.inv(np.transpose(t.to_numpy()) @ t.to_numpy()) @ np.transpose(t.to_numpy())
            leverage = np.diag(Hat) / np.trace(Hat)
            tresh3 = 2 * tcr.shape[1]/n_samples
            # Loadings
            p = dr_model.loadings_.loc[:,axis]
            # Matrix reconstruction
            xp = np.dot(t,p.T)
            # Q residuals: Q residuals represent the magnitude of the variation remaining in each sample after projection through the model
            residuals = np.diag(np.subtract(xc.to_numpy(), xp)@ np.subtract(xc.to_numpy(), xp).T)
            from scipy.stats import chi2
            tresh4 = chi2.ppf(0.05, df = len(axis))

            # color with metadata
            if colfilter:
                if colfilter == "":
                    l1 = ["Samples"]* n_samples

                elif colfilter == clus_method:
                    l1 = labels

                else:
                    l1 = tcr_plot[f'{colfilter} :']


            tcr_plot["leverage"] = leverage
            tcr_plot["residuals"] = residuals
            influence_plot = px.scatter(data_frame =tcr_plot, x = "leverage", y = "residuals", color=col_var_name,
                                            color_discrete_sequence= custom_color_palette)
            influence_plot.add_scatter(x = leverage[selected_samples_idx] , y = residuals[selected_samples_idx],
                                       mode ='markers', marker = dict(size = 5, color = 'black'), name = 'selected samples')
            
            influence_plot.add_vline(x = tresh3, line_width = 1, line_dash = 'solid', line_color = 'red')
            influence_plot.add_hline(y=tresh4, line_width=1, line_dash='solid', line_color='red')
            influence_plot.update_layout(xaxis_title="Leverage", yaxis_title = "Q-residuals", font=dict(size=20), width=800, height=600)

            out3 = leverage > tresh3
            out4 = residuals > tresh4

            # for i in range(n_samples):
            #     if out3[i]:
            #         if not meta_data.empty:
            #             ann =  meta_data.loc[:,'name'][i]
            #         else:
            #             ann = t.index[i]
            #         influence_plot.add_annotation(dict(x = leverage[i], y = residuals[i], showarrow=True, text = str(ann),font= dict(color= "black", size= 15),
            #                     xanchor = 'auto', yanchor = 'auto'))
            
            influence_plot.update_traces(marker=dict(size= 6), showlegend= True)
            influence_plot.update_layout(font=dict(size=23), width=800, height=500)
            st.plotly_chart(influence_plot, use_container_width=True)


            
            for annotation in influence_plot.layout.annotations:
                annotation.font.size = 35
            influence_plot.update_layout(font=dict(size=23), width=800, height=600)
            influence_plot.update_traces(marker=dict(size= 10), showlegend= False)
            influence_plot.add_annotation(text= '(a)', align='center', showarrow= False, xref='paper', yref='paper', x=-0.125, y= 1,
                                             font= dict(color= "black", size= 35), bgcolor ='white', borderpad= 2, bordercolor= 'black', borderwidth= 3)
            # influence_plot.write_image('./report/out/figures/influence_plot.png', engine = 'kaleido')
        
        
        with c15:
            st.write('T²-Hotelling vs Q-residuals plot')
            # Hotelling
            hotelling  = t.var(axis = 1)
            # Q residuals: Q residuals represent the magnitude of the variation remaining in each sample after projection through the model
            residuals = np.diag(np.subtract(xc.to_numpy(), xp)@ np.subtract(xc.to_numpy(), xp).T)
            
            from scipy.stats import f, chi2
            fcri = f.isf(0.05, 3, n_samples)
            tresh0 = (3 * (n_samples ** 2 - 1) * fcri) / (n_samples * (n_samples - 3))
            tresh1 = chi2.ppf(0.05, df = 3)
            hotelling_plot = px.scatter(t, x = hotelling, y = residuals, color=labels if list(labels) else None,
                                            color_discrete_sequence= custom_color_palette)
            hotelling_plot.add_scatter(x = hotelling[selected_samples_idx] , y = residuals[selected_samples_idx],
                                       mode ='markers', marker = dict(size = 5, color = 'black'), name = 'selected samples')
            hotelling_plot.update_layout(xaxis_title="Hotelling-T² distance",yaxis_title="Q-residuals")
            hotelling_plot.add_vline(x=tresh0, line_width=1, line_dash='solid', line_color='red')
            hotelling_plot.add_hline(y=tresh1, line_width=1, line_dash='solid', line_color='red')

            out0 = hotelling > tresh0
            out1 = residuals > tresh1

            
            for i in range(n_samples):
                if out0[i]:
                    if not meta_data.empty:
                        ann =  meta_data.loc[:,'name'][i]
                    else:
                        ann = t.index[i]
                    hotelling_plot.add_annotation(dict(x = hotelling[i], y = residuals[i], showarrow=True, text = str(ann), font= dict(color= "black", size= 15),
                                xanchor = 'auto', yanchor = 'auto'))
                    
            hotelling_plot.update_traces(marker=dict(size= 6), showlegend= True)
            hotelling_plot.update_layout(font=dict(size=23), width=800, height=500)
            st.plotly_chart(hotelling_plot, use_container_width=True)


            for annotation in hotelling_plot.layout.annotations:
                annotation.font.size = 35
            hotelling_plot.update_layout(font=dict(size=23), width=800, height=600)
            hotelling_plot.update_traces(marker=dict(size= 10), showlegend= False)
            hotelling_plot.add_annotation(text= '(b)', align='center', showarrow= False, xref='paper', yref='paper', x=-0.125, y= 1,
                                             font= dict(color= "black", size= 35), bgcolor ='white', borderpad= 2, bordercolor= 'black', borderwidth= 3)
            # hotelling_plot.write_image("./report/out/figures/hotelling_plot.png", format="png")

st.subheader('III - Selected Samples for Reference Analysis', divider='blue')
if labels:
    c16, c17 = st.columns([3, 1])
    c16.write("Tabular identifiers of selected samples for reference analysis:")
    if selected_samples_idx:
        if meta_data.empty:
            sam1 = DataFrame({'name': spectra.index[clustered][selected_samples_idx],
                                'cluster':np.array(labels)[clustered][selected_samples_idx]},
                                index = selected_samples_idx)
        else:
            sam1 = meta_data.iloc[clustered,:].iloc[selected_samples_idx,:]
            sam1.insert(loc=0, column='index', value=selected_samples_idx)
            sam1.insert(loc=1, column='cluster', value=np.array(labels)[selected_samples_idx])
        sam1.index = np.arange(len(selected_samples_idx))+1
        with c17:
            st.info(f'Information !\n - The total number of samples: {n_samples}.\n- The number of samples selected for reference analysis: {sam1.shape[0]}.\n - The proportion of samples selected for reference analysis: {round(sam1.shape[0]/n_samples*100)}%.')
        sam = sam1

        if clus_method =='HDBSCAN':
            with c16:
                unclus = st.checkbox("Include non clustered samples (for HDBSCAN clustering)", value=True)

            if selected_samples_idx:
                if unclus:
                    if meta_data.empty:
                        sam2 = DataFrame({'name': spectra.index[non_clustered],
                                            'cluster':['Non clustered']*len(spectra.index[non_clustered])},
                                            index = spectra.index[non_clustered])
                    else :
                        sam2 = meta_data.iloc[non_clustered,:]
                        sam2.insert(loc=0, column='index', value= spectra.index[non_clustered])
                        sam2.insert(loc=1, column='cluster', value=['Non clustered']*len(spectra.index[non_clustered]))
                    
                    sam = concat([sam1, sam2], axis = 0)
                    sam.index = np.arange(sam.shape[0])+1
                    with c17:
                        st.info(f'- The number of Non-clustered samples: {sam2.shape[0]}.\n - The proportion of Non-clustered samples: {round(sam2.shape[0]/n_samples*100)}%')
        else:
            sam = sam1
        with c16:
            st.write(sam)


if not sam.empty:
    zip_data = ""
    Nb_ech = str(n_samples)
    nb_clu = str(sam1.shape[0])
    st.subheader('Download the analysis results')
    st.write("**Note:** Please check the box only after you have finished processing your data and are satisfied with the results. Checking the box prematurely may slow down the app and could lead to crashes.")
    decis = st.checkbox("Yes, I want to download the results")
    if decis:
        ###################################################
        # ## generate report
        @st.cache_data
        def export_report(change):
            latex_report = report.report('Representative subset selection', file.name, dim_red_method,
                                        clus_method, Nb_ech, ncluster, selection, selection_number, nb_clu,tcr, sam)

        @st.cache_data
        def preparing_results_for_downloading(change):
            # path_to_report = Path("report")############################### i am here
            match extension:
                # load csv file
                case 'csv':
                    imp.to_csv('report/out/dataset/'+ file.name, sep = ';', encoding = 'utf-8', mode = 'a')
                case 'dx':
                    with open('report/out/dataset/'+file.name, 'w') as dd:
                        dd.write(dxdata)

            fig_spectra.savefig(report_path_rel/"out/figures/spectra_plot.png", dpi = 400) ## Export report

            if len(axis) == 3:
                for i in range(len(comb)):
                    fig_export[f'scores_pc{comb[i][0]}_pc{comb[i][1]}'].write_image(report_path_rel/f'out/figures/scores_pc{str(comb[i][0]+1)}_pc{str(comb[i][1]+1)}.png')
            elif len(axis)==2 :
                fig_export['fig'].write_image(report_path_rel/'out/figures/scores_plot2D.png')
            elif len(axis)==1 :
                fig_export['fig'].write_image(report_path_rel/'out/figures/scores_plot1D.png')
                    
            # Export du graphique
            if dim_red_method in ['PCA','NMF']:
                import plotly.io as pio
                img = pio.to_image(loadingsplot, format="png")
                with open(report_path_rel/"out/figures/loadings_plot.png", "wb") as f:
                    f.write(img)
            if dim_red_method == 'PCA': 
                hotelling_plot.write_image(report_path_rel/"out/figures/hotelling_plot.png", format="png")
                influence_plot.write_image(report_path_rel/'out/figures/influence_plot.png', engine = 'kaleido')
            
            sam.to_csv(report_path_rel/'out/Selected_subset_for_calib_development.csv', sep = ';')
            export_report(change = hash_)
            if Path(report_path_rel/"report.tex").exists():
                report.generate_report(change = hash_)
            if Path(report_path_rel/"report.pdf").exists():
                move(report_path_rel/"report.pdf", "./report/out/report.pdf")
            return change


        preparing_results_for_downloading(change = hash_)
        report.generate_report(change = hash_)

        

        @st.cache_data
        def tempdir(change):
            from tempfile import TemporaryDirectory
            with  TemporaryDirectory( prefix="results", dir="./report") as temp_dir:# create a temp directory
                tempdirname = os.path.split(temp_dir)[1]

                if len(os.listdir(report_path_rel/'out/figures/'))>=2:
                    
                    make_archive(base_name= report_path_rel/"Results", format="zip", base_dir="out", root_dir = "./report")# create a zip file
                    move(report_path_rel/"Results.zip", f"./report/{tempdirname}/Results.zip")# put the inside the temp dir
                    with open(report_path_rel/f"{tempdirname}/Results.zip", "rb") as f:
                        zip_data = f.read()
            return tempdirname, zip_data
        
        try :
            tempdirname, zip_data = tempdir(change = hash_)
            # st.download_button(label = 'Download', data = zip_data, file_name = f'Nirs_Workflow_{date_time}_SamSel_.zip', mime ="application/zip",
            #             args = None, kwargs = None,type = "primary",use_container_width = True)
        except:
            pass
    date_time = datetime.now().strftime('%y%m%d%H%M')
    disabled_down = True if zip_data == '' else False
    st.download_button(label = 'Download', data = zip_data, file_name = f'Nirs_Workflow_{date_time}_SamSel_.zip', mime ="application/zip",
                args = None, kwargs = None,type = "primary",use_container_width = True, disabled = disabled_down)


    delete_files(keep = ['.py', '.pyc','.bib'])