diff --git a/src/Class_Mod/DxReader.py b/src/Class_Mod/DxReader.py
index 08bf1d175764bc08d93ca0bd241fc4af7aad5843..973372738142a6d6a1233146fef512d6c5f86461 100644
--- a/src/Class_Mod/DxReader.py
+++ b/src/Class_Mod/DxReader.py
@@ -13,42 +13,38 @@ class DxRead:
self.__nb = self.__dxfile['blocks'] # Get the total number of blocks = The total number of scanned samples
self.__list_of_blocks = self.__dxfile['children'] # Store all blocks within a a list
self.__wl = self.__list_of_blocks[0]["x"] # Wavelengths/frequencies/range
-
-
-
+
# Start retreiving the data
specs = np.zeros((self.__nb, len(self.__list_of_blocks[0]["y"])), dtype=float) # preallocate a np matrix for sotoring spectra
self.idx = np.arange(self.__nb) # This list is designed to store samples name
self.__met = {}
-
-
for i in range(self.__nb): # Loop over the blocks
specs[i] = self.__list_of_blocks[i]['y']
-
block = self.__list_of_blocks[i]
block_met = { 'name': block['title'],
'origin': block['origin'],
'date': block['date'],
- # 'time': block['time'],
- # 'spectrometer/data system': block['spectrometer/data system'],
- # 'instrumental parameters': block['instrumental parameters'],
+ #'time': block['time'],
+ 'spectrometer': block['spectrometer/data system'].split('\n$$')[0],
+ 'n_scans':block['spectrometer/data system'].split('\n$$')[6].split('=')[1],
+ 'resolution': block['spectrometer/data system'].split('\n$$')[8].split('=')[1],
+ #'instrumental parameters': block['instrumental parameters'],
'xunits': block['xunits'],
'yunits': block['yunits'],
- # 'xfactor': block['xfactor'],
- # 'yfactor': block['yfactor'],
- # 'firstx': block['firstx'],
- # 'lastx': block['lastx'],
- # 'firsty':block['firsty'],
- # 'miny': block['miny'],
- # 'maxy': block['maxy'],
- # 'npoints': block['npoints'],
+ #'xfactor': block['xfactor'],
+ #'yfactor': block['yfactor'],
+ 'firstx': block['firstx'],
+ 'lastx': block['lastx'],
+ #'firsty':block['firsty'],
+ #'miny': block['miny'],
+ #'maxy': block['maxy'],
+ 'npoints': block['npoints'],
'concentrations':block['concentrations'],
- # 'deltax':block['deltax']
+ #'deltax':block['deltax']
}
+
self.__met[f'{i}'] = block_met
self.metadata_ = pd.DataFrame(self.__met).T
-
-
self.spectra = pd.DataFrame(np.fliplr(specs), columns= self.__wl[::-1], index = self.metadata_['name']) # Storing spectra in a pd.dataframe
@@ -91,7 +87,12 @@ class DxRead:
me = self.metadata_.drop("concentrations", axis = 1)
me = me.drop(me.columns[(me == '').all()], axis = 1)
return me
-
+ @property
+ def md_df_st_(self):
+ rt = ['origin','date']
+ cl = self.metadata_.loc[:,rt]
+ return cl
+
@property
def chem_data_(self):
return self.chem_data
@@ -99,4 +100,4 @@ class DxRead:
@st.cache_data
def read_dx(file):
M = DxRead(file)
- return M.chem_data, M.specs_df_, M.md_df_
+ return M.chem_data, M.specs_df_, M.md_df_, M.md_df_st_
\ No newline at end of file
diff --git a/src/Class_Mod/UMAP_.py b/src/Class_Mod/UMAP_.py
index 28d0436e6efe90fb251e60627234376cc10467d0..1b95e14cf0148fb13df6341aaf84ec8e9d31b23b 100644
--- a/src/Class_Mod/UMAP_.py
+++ b/src/Class_Mod/UMAP_.py
@@ -20,7 +20,7 @@ class Umap:
self.model = UMAP(n_neighbors=20, n_components=3, min_dist=0.0, random_state=42,)
self.model.fit(self.numerical_data, y = self.categorical_data_encoded)
self.scores_raw = self.model.transform(self.numerical_data)
- self.scores = pd.DataFrame(self.scores_raw, index = self.numerical_data.index)
+ self.scores = pd.DataFrame(self.scores_raw)
@property
def scores_(self):
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index de03b98e62d2866f40650c73da0120eba4377694..f99b7bd2b08bbbc5b918619921bad471ba83f5e2 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -2,8 +2,6 @@ from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
-
-
# HTML pour le bandeau "CEFE - CNRS"
# bandeau_html = """
# <div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
@@ -63,7 +61,7 @@ if data_file:
tmp.write(data_file.read())
tmp_path = tmp.name
with col1:
- _, spectra, meta_data = read_dx(file = tmp_path)
+ _, spectra, meta_data, md_df_st_ = read_dx(file = tmp_path)
st.success("The data have been loaded successfully", icon="✅")
os.unlink(tmp_path)
@@ -119,9 +117,9 @@ if not spectra.empty:
elif dim_red_method == dim_red_methods[2]:
if not meta_data.empty:
- filter = meta_data.columns[1:]
+ filter = md_df_st_.columns
col = pc.selectbox('Supervised UMAP by:', options= filter, key=108)
- supervised = meta_data[col]
+ supervised = md_df_st_[col]
else:
supervised = None
dr_model = Umap(numerical_data = MinMaxScale(spectra), cat_data = supervised)
@@ -136,10 +134,11 @@ if not spectra.empty:
t = pd.concat([dr_model.scores_.loc[:,axis1], dr_model.scores_.loc[:,axis2], dr_model.scores_.loc[:,axis3]], axis = 1)
-###### 2- clustering #######
+###### II - clustering #######
if not t.empty:
tcr = standardize(t)
# Clustering
+ # 1- K-MEANS Clustering
if clus_method == cluster_methods[1]:
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')
@@ -151,27 +150,35 @@ if not t.empty:
f.write(img)
data, labels, clu_centers = cl_model.fit_optimal(nclusters = ncluster)
+ # 2- HDBSCAN clustering
elif clus_method == cluster_methods[2]:
optimized_hdbscan = Hdbscan(np.array(t))
labels, hdbscan_score = optimized_hdbscan.HDBSCAN_scores_
non_clustered = np.where(labels == -1)
- labels[non_clustered] = 1000
- labels = labels.tolist()
-
+ labels[non_clustered] = np.max(labels)+2
+ labels = [f'cluster#{i+1}' for i in labels.tolist()]
+
+ # 3- Affinity propagation
elif clus_method == cluster_methods[3]:
cl_model = AP(X=tcr)
data, labels, clu_centers = cl_model.fit_optimal_
-###### 3- Samples selection using the reduced data preentation ######
+###### III - Samples selection using the reduced data preentation ######
selec_strategy = ['center','random']
samples_df_chem = pd.DataFrame
selected_samples = []
selected_samples_idx = []
+
if labels:
- selection = scores.radio('Select samples selection strategy:',
- options = selec_strategy)
+ if clus_method:
+ if clus_method == cluster_methods[2]:
+ selection = scores.radio('Select samples selection strategy:',
+ options = ['random'])
+ else:
+ selection = scores.radio('Select samples selection strategy:',
+ options = selec_strategy)
if selection == selec_strategy[0]:
# 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, tcr)
@@ -183,7 +190,7 @@ if labels:
for i in np.unique(labels):
C = np.where(np.array(labels) == i)[0]
if C.shape[0] >= selection_number:
- #scores.write(list(tcr.index)[labels== i])
+ # 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,:])
@@ -223,28 +230,28 @@ if not t.empty:
# scores plot with metadata
elif len(list(labels)) == 0 and not meta_data.empty:
- filter = meta_data.columns[1:]
+ 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,meta_data[col])), ax = ax3)
+ 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,meta_data[col])) )
- sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,meta_data[col])), ax = ax1)
- sns.scatterplot(data = tcr, x = axis2, y =axis3 , hue = list(map(str.lower,meta_data[col])), ax = ax2)
- sns.scatterplot(data = tcr, x = axis1, y =axis3 , hue = list(map(str.lower,meta_data[col])), ax = ax3)
+ 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(meta_data.columns[1:])
+ filter.extend(md_df_st_.columns)
else:
- filter = meta_data.columns[1:].insert(0,'None')
+ filter = md_df_st_.columns.insert(0,'None')
col = st.selectbox('Color by:', options= filter)
if col == "None":
@@ -254,10 +261,10 @@ if not t.empty:
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,meta_data[col])))
- sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,meta_data[col])), ax = ax1)
- sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,meta_data[col])), ax = ax2)
- sns.scatterplot(data = tcr, x = axis1, y =axis2 , hue = list(map(str.lower,meta_data[col])), ax = ax3)
+ 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)
@@ -267,7 +274,7 @@ if not t.empty:
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 = 7, color = 'black'),
+ z = tt.loc[:,axis3], mode ='markers', marker = dict(size = 5, color = 'black'),
name = 'selected samples')
plt.savefig("./Report/Figures/test.png")
@@ -354,20 +361,4 @@ 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, use_container_width=True)
- fig.write_image("./Report/figures/graphe_hotelling.png", format="png")
-
- if dim_red_method == dim_red_methods[2] and clus_method == cluster_methods[2]: # UMAP clustered by HDBSCAN
- with loadings: # Display some clustering metrics
- st.write('Clustering metrics:')
- clusters_number = set(labels)
- clusters_number.remove(-1)
- st.write('Optimal number of clusters = ' + str(len(clusters_number)))
- st.write('DBCV score (-1 to 1 - higher is better) = ' + str(round(hdbscan_score,3)))
- st.write('Unclassified samples: ' + str(len(t[labels==-1])) + ' on ' + str(len(t)) + ' samples (' + str(round(len(t[labels==-1])/len(t)*100, 1)) + '%).')
-
-
-
-
-
-
-
\ No newline at end of file
+ fig.write_image("./Report/figures/graphe_hotelling.png", format="png")
\ No newline at end of file
diff --git a/src/pages/4-inputs.py b/src/pages/4-inputs.py
index 4540d4d7f4b3618eb4d1c9e366d28275f36060f8..671182332587000596b195cbeb46c1a573dd0895 100644
--- a/src/pages/4-inputs.py
+++ b/src/pages/4-inputs.py
@@ -70,7 +70,7 @@ with st.container():
# Save the form data here
st.session_state['form_submitted'] = True
- st.success('Form sent successfully!')
+ st.success('Form sent successfully!', icon="✅")
# Création du dictionnaire avec les données du formulaire
form_data = {