from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
# empty temp figures
for i in ['Report/figures','Report/datasets']:
repertoire_a_vider = Path(i)
if os.path.exists(repertoire_a_vider):
for fichier in os.listdir(repertoire_a_vider):
chemin_fichier = os.path.join(repertoire_a_vider, fichier)
if os.path.isfile(chemin_fichier) or os.path.islink(chemin_fichier):
os.unlink(chemin_fichier)
elif os.path.isdir(chemin_fichier):
shutil.rmtree(chemin_fichier)
# HTML pour le bandeau "CEFE - CNRS"
add_header()
#load specific model page css
local_css(css_file / "style_model.css")
add_sidebar(pages_folder)
# algorithms available in our app
dim_red_methods=['', 'PCA','UMAP', 'NMF'] # List of dimensionality reduction algos
cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP', 'KS', 'RDM'] # List of clustering algos
selec_strategy = ['center','random']
match st.session_state["interface"]:
case 'simple':
st.write(':red[Automated Simple Interface]')
# hide_pages("Predictions")
if 37 not in st.session_state:
default_reduction_option = 1
else:
default_reduction_option = dim_red_methods.index(st.session_state.get(37))
if 38 not in st.session_state:
default_clustering_option = 1
else:
default_clustering_option = cluster_methods.index(st.session_state.get(38))
if 102 not in st.session_state:
default_sample_selection_option = 1
else:
default_sample_selection_option = selec_strategy.index(st.session_state.get(102))
case'advanced':
default_reduction_option = 0
default_clustering_option = 0
default_sample_selection_option = 0
################################### I - Data Loading and Visualization ########################################
date_time = datetime.datetime.now().strftime('_%y_%m_%d_%H_%M_')
st.title("Calibration Subset Selection")
col2, col1 = st.columns([3, 1])
col2.image("./images/sample selection.png", use_column_width=True)
## Preallocation of data structure
spectra = pd.DataFrame()
meta_data = pd.DataFrame()
tcr=pd.DataFrame()
sam=pd.DataFrame()
sam1=pd.DataFrame()
selected_samples = pd.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
# loader for datafile
data_file = col1.file_uploader("Data file", type=["csv","dx"], help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns", key=5)
if not data_file:
col1.warning('⚠️ Please load data file !')
else:
# Retrieve the extension of the file
# test = data_file.name[data_file.name.find('.'):]
extension = data_file.name.split(".")[-1]
userfilename = data_file.name.replace(f".{extension}", '')
match extension:
## Load .csv file
case 'csv':
with col1:
# Select list for CSV delimiter
psep = st.radio("Select csv separator - _detected_: " + str(find_delimiter('data/'+data_file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+data_file.name))),horizontal=True, key=9)
# Select list for CSV header True / False
phdr = st.radio("indexes column in csv? - _detected_: " + str(find_col_index('data/'+data_file.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+data_file.name))),horizontal=True, key=31)
if phdr == 'yes':
col = 0
else:
col = False
imp = pd.read_csv(data_file, sep=psep, index_col=col)
imp.to_csv("./Report/datasets/"+data_file.name,sep = ';', encoding='utf-8', mode='a')
# spectra = col_cat(imp)[0]
# meta_data = col_cat(imp)[1]
spectra, md_df_st_ = col_cat(imp)
meta_data = md_df_st_
st.success("The data have been loaded successfully", icon="✅")
## Load .dx file
case 'dx':
# Create a temporary file to save the uploaded file
with NamedTemporaryFile(delete=False, suffix=".dx") as tmp:
tmp.write(data_file.read())
with open(tmp.name, 'r') as dd:
dxdata = dd.read()
with open('Report/datasets/'+data_file.name, 'w') as dd:
dd.write(dxdata)
tmp_path = tmp.name
with col1:
_, spectra, meta_data, md_df_st_ = read_dx(file = tmp_path)
st.success("The data have been loaded successfully", icon="✅")
os.unlink(tmp_path)
## Visualize spectra
st.header("I - Spectral Data Visualization", divider='blue')
if not spectra.empty:
n_samples = spectra.shape[0]
nwl = spectra.shape[1]
# retrieve columns name and rows name of spectra
colnames = list(spectra.columns)
rownames = [str(i) for i in list(spectra.index)]
spectra.index = rownames
col2, col1 = st.columns([3, 1])
with col2:
fig, ax = plt.subplots(figsize = (30,7))
if extension =='dx':
lab = ['Wavenumber (1/cm)' if meta_data.loc[:,'xunits'][0] == '1/cm' else 'Wavelength (nm)']
if lab[0] =='Wavenumber (1/cm)':
spectra.T.plot(legend=False, ax = ax).invert_xaxis()
else :
spectra.T.plot(legend=False, ax = ax)
ax.set_xlabel(lab[0], fontsize=18)
else:
spectra.T.plot(legend=False, ax = ax)
ax.set_xlabel('Wavelength/Wavenumber', fontsize=18)
ax.set_ylabel('Signal intensity', fontsize=18)
plt.margins(x = 0)
plt.tight_layout()
st.pyplot(fig)
# update lines size
for line in ax.get_lines():
line.set_linewidth(0.8) # Set the desired line width here
# Update the size of plot axis for exprotation to report
l, w = fig.get_size_inches()
fig.set_size_inches(8, 3)
for label in (ax.get_xticklabels()+ax.get_yticklabels()):
ax.xaxis.label.set_size(9.5)
ax.yaxis.label.set_size(9.5)
plt.tight_layout()
fig.savefig("./Report/figures/spectra_plot.png", dpi=400) ## Export report
fig.set_size_inches(l, w)# reset the plot size to its original size
data_info = pd.DataFrame({'Name': [data_file.name],
'Number of scanned samples': [n_samples]},
index = ['Input file'])
with col1:
st.info('Information on the loaded data file')
st.write(data_info) ## table showing the number of samples in the data file
############################## Exploratory data analysis ###############################
st.header("II - Exploratory Data Analysis-Multivariable Data Analysis", divider='blue')
###### 1- Dimensionality reduction ######
t = pd.DataFrame # scores
p = pd.DataFrame # loadings
if not spectra.empty:
bb1, bb2, bb3, bb4, bb5, bb6, bb7 = st.columns([1,1,0.6,0.6,0.6,1.5,1.5])
dim_red_method = bb1.selectbox("Dimensionality reduction techniques: ", options = dim_red_methods, index = default_reduction_option, key = 37)
clus_method = bb2.selectbox("Clustering/sampling techniques: ", options = cluster_methods, index = default_clustering_option, key = 38)
xc = standardize(spectra, center=True, scale=False)
match dim_red_method:
case "":
bb1.warning('⚠️ Please choose an algorithm !')
case "PCA":
@st.cache_data
def dr_model_(change):
dr_model = LinearPCA(xc, Ncomp=8)
return dr_model
dr_model = dr_model_(change = hash_data(xc))
case "UMAP":
if not meta_data.empty:
filter = md_df_st_.columns
filter = filter.insert(0, 'Nothing')
col = bb1.selectbox('Supervised UMAP by:', options= filter, key=108)
if col == 'Nothing':
supervised = None
else:
supervised = md_df_st_[col]
else:
supervised = None
@st.cache_data
def dr_model_(change):
dr_model = Umap(numerical_data = MinMaxScale(spectra), cat_data = supervised)
return dr_model
dr_model = dr_model_(change = hash_data(spectra))
case 'NMF':
@st.cache_data
def dr_model_(change):
dr_model = Nmf(spectra, Ncomp= 3)
return dr_model
dr_model = dr_model_(change = hash_data(spectra))
if dr_model:
axis1 = bb3.selectbox("x-axis", options = dr_model.scores_.columns, index=0)
axis2 = bb4.selectbox("y-axis", options = dr_model.scores_.columns, index=1)
axis3 = bb5.selectbox("z-axis", options = dr_model.scores_.columns, index=2)
t = pd.concat([dr_model.scores_.loc[:,axis1], dr_model.scores_.loc[:,axis2], dr_model.scores_.loc[:,axis3]], axis = 1)
###### II - clustering #######
if not t.empty:
clustered = np.arange(n_samples)
non_clustered = None
if dim_red_method == 'UMAP':
scores = st.container()
else:
scores, loadings= st.columns([3,3])
tcr = standardize(t)
# Clustering
match clus_method:
case '':
bb2.warning('⚠️ Please choose an algothithm !')
case 'Kmeans':
cl_model = Sk_Kmeans(tcr, max_clusters = 25)
ncluster = scores.number_input(min_value=2, max_value=25, value=cl_model.suggested_n_clusters_, label = 'Select the desired number of clusters')
data, labels, clu_centers = cl_model.fit_optimal(nclusters = ncluster)
# 2- HDBSCAN clustering
case 'HDBSCAN':
optimized_hdbscan = Hdbscan(np.array(tcr))
all_labels, clu_centers = optimized_hdbscan.HDBSCAN_scores_
labels = [f'cluster#{i+1}' if i !=-1 else 'Non clustered' for i in all_labels]
ncluster = len(clu_centers)
non_clustered = np.where(np.array(labels) == 'Non clustered')[0]
# 3- Affinity propagation
case 'AP':
cl_model = AP(X = tcr)
data, labels, clu_centers = cl_model.fit_optimal_
ncluster = len(clu_centers)
case 'KS':
rset = scores.number_input(min_value=0, max_value=100, value=20, label = 'The ratio of data to be sampled (%)')
cl_model = KS(x = tcr, rset = rset)
case 'RDM':
rset = scores.number_input(min_value=0, max_value=100, value=20, label = 'The ratio of data to be sampled (%)')
cl_model = RDM(x = tcr, rset = rset)
new_tcr = tcr.iloc[clustered,:]
#################################################### III - Samples selection using the reduced data preentation ######
samples_df_chem = pd.DataFrame
selected_samples = []
selected_samples_idx = []
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:
if clus_method in ['KS', 'RDM']:
calset = cl_model.calset
labels = ["ind"]*n_samples
ncluster = "1"
selection_number = 'None'
selected_samples_idx = calset[1]
selection = 'None'
else:
selection = scores.radio('Select samples selection strategy:',
options = selec_strategy, index = default_sample_selection_option, key=102)
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
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 = scores.number_input('How many samples per cluster?',
min_value = 1, step=1, value = 3)
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:
# scores.write(list(tcr.index)[labels== i])
km2 = KMeans(n_clusters = selection_number)
km2.fit(tcr.iloc[C,:])
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())
# list indexes of selected samples for colored plot
################################ Plots visualization ############################################
## Scores
if not t.empty:
with scores:
fig1, ((ax1, ax2),(ax3,ax4)) = plt.subplots(2,2)
st.write('Scores plot')
# scores plot with clustering
if list(labels) and meta_data.empty:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color=labels ,color_discrete_sequence= custom_color_palette)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = labels, ax = ax1)
# scores plot with metadata
elif len(list(labels)) == 0 and not meta_data.empty:
filter = md_df_st_.columns
col = st.selectbox('Color by:', options= filter)
if col == 0:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , ax = ax1)
sns.scatterplot(data = tcr, x = axis2, y =axis3 , ax = ax2)
sns.scatterplot(data = tcr, x = axis1, y =axis3 , hue = list(map(str.lower,md_df_st_[col])), ax = ax3)
else:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,md_df_st_[col])) )
sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,md_df_st_[col])), ax = ax1)
sns.scatterplot(data = tcr, x = axis2, y =axis3 , hue = list(map(str.lower,md_df_st_[col])), ax = ax2)
sns.scatterplot(data = tcr, x = axis1, y =axis3 , hue = list(map(str.lower,md_df_st_[col])), ax = ax3)
# color with scores and metadata
elif len(list(labels)) > 0 and not meta_data.empty:
if clus_method in cluster_methods[1:]:
filter = ['None', clus_method]
filter.extend(md_df_st_.columns)
else:
filter = md_df_st_.columns.insert(0,'None')
col = st.selectbox('Color by:', options= filter)
if col == "None":
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , ax = ax1)
elif col == clus_method:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = labels)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , ax = ax1)
else:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color = list(map(str.lower,md_df_st_[col])))
sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,md_df_st_[col])), ax = ax1)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,md_df_st_[col])), ax = ax2)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,md_df_st_[col])), ax = ax3)
else:
fig = px.scatter_3d(tcr, x=axis1, y=axis2, z = axis3, color=labels if list(labels) else None,color_discrete_sequence= custom_color_palette)
sns.scatterplot(data = tcr, x = axis1, y =axis2 , ax = ax1)
fig.update_traces(marker=dict(size=4))
if selected_samples_idx:
tt = tcr.iloc[selected_samples_idx,:]
fig.add_scatter3d(x = tt.loc[:,axis1], y = tt.loc[:,axis2],z = tt.loc[:,axis3],
mode ='markers', marker = dict(size = 5, color = 'black'),
name = 'selected samples')
st.plotly_chart(fig, use_container_width = True)
if labels:
# export 2D scores plot
comb = [i for i in combinations([1,2,3], 2)]
subcap = ['a','b','c']
for i in range(len(comb)):
fig_export = px.scatter(tcr, x = eval(f'axis{str(comb[i][0])}'), y=eval(f'axis{str(comb[i][1])}'),
color = labels if list(labels) else None,
color_discrete_sequence = custom_color_palette)
fig_export.add_scatter(x = tt.loc[:,eval(f'axis{str(comb[i][0])}')], y = tt.loc[:,eval(f'axis{str(comb[i][1])}')],
mode ='markers', marker = dict(size = 5, color = 'black'),
name = 'selected samples')
fig_export.update_layout(font=dict(size=23))
fig_export.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_export.update_traces(marker=dict(size= 10), showlegend= False)
fig_export.write_image(f'./Report/Figures/scores_pc{str(comb[i][0])}_pc{str(comb[i][1])}.png')
if not spectra.empty:
if dim_red_method in ['PCA','NMF']:
with loadings:
st.write('Loadings plot')
p = dr_model.loadings_
freq = pd.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 = pd.concat([p, freq], axis=1)
#########################################
df1 = pp.melt(id_vars=freq.columns)
fig = px.line(df1, x=freq.columns, 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))
fig.update_layout(xaxis_title = xlab,yaxis_title = "Intensity" ,xaxis = dict(autorange= inv))
st.plotly_chart(fig, use_container_width=True)
# Export du graphique
img = pio.to_image(fig, format="png")
with open("./Report/figures/loadings_plot.png", "wb") as f:
f.write(img)
#############################################################################################################
if dim_red_method == 'PCA':
influence, hotelling = st.columns([3, 3])
with influence:
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 = pd.concat([dr_model.loadings_.loc[:,axis1], dr_model.loadings_.loc[:,axis2], dr_model.loadings_.loc[:,axis3]], axis = 1)
# 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)
tresh4 = sc.stats.chi2.ppf(0.05, df = 3)
# color with metadata
if not meta_data.empty and clus_method:
if col == "None":
l1 = ["Samples"]* n_samples
elif col == clus_method:
l1 = labels
else:
l1 = list(map(str.lower,md_df_st_[col]))
elif meta_data.empty and clus_method:
l1 = labels
elif meta_data.empty and not clus_method:
l1 = ["Samples"]* n_samples
elif not meta_data.empty and not clus_method:
l1 = list(map(str.lower,md_df_st_[col]))
fig = px.scatter(x = leverage, y = residuals, color=labels if list(labels) else None,
color_discrete_sequence= custom_color_palette)
fig.add_vline(x = tresh3, line_width = 1, line_dash = 'solid', line_color = 'red')
fig.add_hline(y=tresh4, line_width=1, line_dash='solid', line_color='red')
fig.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]
fig.add_annotation(dict(x = leverage[i], y = residuals[i], showarrow=True, text = ann,font= dict(color= "black", size= 15),
xanchor = 'auto', yanchor = 'auto'))
fig.update_traces(marker=dict(size= 6), showlegend= True)
fig.update_layout(font=dict(size=23), width=800, height=500)
st.plotly_chart(fig, use_container_width=True)
for annotation in fig.layout.annotations:
annotation.font.size = 35
fig.update_layout(font=dict(size=23), width=800, height=600)
fig.update_traces(marker=dict(size= 10), showlegend= False)
fig.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)
fig.write_image('./Report/figures/influence_plot.png', engine = 'kaleido')
with hotelling:
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)
fcri = sc.stats.f.isf(0.05, 3, n_samples)
tresh0 = (3 * (n_samples ** 2 - 1) * fcri) / (n_samples * (n_samples - 3))
tresh1 = sc.stats.chi2.ppf(0.05, df = 3)
fig = px.scatter(t, x = hotelling, y = residuals, color=labels if list(labels) else None,
color_discrete_sequence= custom_color_palette)
fig.update_layout(xaxis_title="Hotelling-T² distance",yaxis_title="Q-residuals")
fig.add_vline(x=tresh0, line_width=1, line_dash='solid', line_color='red')
fig.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]
fig.add_annotation(dict(x = hotelling[i], y = residuals[i], showarrow=True, text = ann, font= dict(color= "black", size= 15),
xanchor = 'auto', yanchor = 'auto'))
fig.update_traces(marker=dict(size= 6), showlegend= True)
fig.update_layout(font=dict(size=23), width=800, height=500)
st.plotly_chart(fig, use_container_width=True)
for annotation in fig.layout.annotations:
annotation.font.size = 35
fig.update_layout(font=dict(size=23), width=800, height=600)
fig.update_traces(marker=dict(size= 10), showlegend= False)
fig.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)
fig.write_image("./Report/figures/hotelling_plot.png", format="png")
st.header('III - Selected Samples for Reference Analysis', divider='blue')
if labels:
sel, info = st.columns([3, 1])
sel.write("Tabular identifiers of selected samples for reference analysis:")
if selected_samples_idx:
if meta_data.empty:
sam1 = pd.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
info.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 == cluster_methods[2]:
# unclus = sel.checkbox("Include non clustered samples (for HDBSCAN clustering)", value=True)
if clus_method == cluster_methods[2]:
unclus = sel.checkbox("Include non clustered samples (for HDBSCAN clustering)", value=True)
if selected_samples_idx:
if unclus:
if meta_data.empty:
sam2 = pd.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 = pd.concat([sam1, sam2], axis = 0)
sam.index = np.arange(sam.shape[0])+1
info.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
sel.write(sam)
Nb_ech = str(n_samples)
nb_clu = str(sam1.shape[0])
for i in ['Representative subset selection', data_file.name, dim_red_method,
clus_method, Nb_ech, ncluster, selection, selection_number, nb_clu]:
st.write(i)
# st.write(hash_data(change = './Report/report.tex'))
# file_pathtex = Path('./Report/report.tex')
# if file_pathtex.is_file():
# with open('./Report/report.tex') as myfile:
# filehash = hash_data(myfile.read())
# figs_list = os.listdir("./Report/figures")
if not (sam.empty and sam1.empty):
Nb_ech = str(n_samples)
nb_clu = str(sam1.shape[0])
###################################################
## 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):
zipname = f'results{date_time}subset_selection_{file.name.split('.')[0]}.zip' # name of the zipfile
with open('./temp/fname.json', 'w') as f: # dump filename and save it as a .json file
json.dump(zipname, f)
shutil.make_archive(base_name = zipname.split('.')[0],format = "zip",root_dir = "./Report", base_dir = "figures")# create zip containing figures and report
file_path = Path("./temp/"+zipname)
sam.to_csv("./"+zipname,sep = ';',
encoding='utf-8', mode='a',
compression=dict(method='zip',archive_name=f"selected subset for reference analysis_{userfilename}_{date_time}_.csv"))
with zipfile.ZipFile("./"+zipname, 'a') as newzip:
newzip.write("./Report/report.pdf", arcname="report.pdf")
newzip.write("./Report/datasets/"+os.listdir("./Report/datasets")[0], arcname=os.listdir("./Report/datasets")[0])
# #### add data to zip
# match data_file.name:
# case 'csv':
# with open(data_file.name, 'wb') as cs:
# st.write(data_file.getbuffer())
# case 'dx':
# st.write(4)
### move the .zip file to the temp directory
shutil.move('./'+zipname,'./temp/'+ zipname)
a =''
for i in (data_file.name, dim_red_method,clus_method, Nb_ech, tcr.astype(str)):
a += str(i)
myfilepdf = Path("./Report/report.pdf")
if 'htest' not in st.session_state:
st.session_state.htest = '0'
report.compile_latex(change =hash_data(a))
st.write(hash_data(a))
if myfilepdf.is_file():
download_res(file = data_file, sam = sam)
elif st.session_state['htest'] != hash_data(a):
st.session_state['htest'] = hash_data(a)
report.compile_latex(change =hash_data(a))
st.write(hash_data(a))
if myfilepdf.is_file():
download_res(file = data_file, sam = sam)
else:
pass
list_of_files = glob.glob(r"./temp/*.zip")
if len(list_of_files) >3:
oldest_file = min(list_of_files, key=os.path.getctime)
os.remove(oldest_file)
list_of_files = glob.glob(r"./temp/*.zip")
recent_file = max(list_of_files, key=os.path.getctime)
with open('./temp/fname.json', 'r') as f:
zipname = json.load(f)
if os.path.split(recent_file)[1] == os.path.split(zipname)[1]:
with open("./temp/"+zipname, "rb") as fp:
st.download_button('Download', data = fp, file_name=zipname, mime="application/zip",
args=None, kwargs=None,type="primary",use_container_width=True)