diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000000000000000000000000000000000..35eb1ddfbbc029bcab630581847471d7f238ec53
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="VcsDirectoryMappings">
+ <mapping directory="" vcs="Git" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/app.py b/app.py
index 6cc896d1fb8ea68b59d881c7aba8ad3cac75aafe..0bac962ed2f64a1255e59383a9eee65725291175 100644
--- a/app.py
+++ b/app.py
@@ -48,8 +48,7 @@ with st.container():
col = 0
else:
col = False
- import_button = settings_column.button('Import')
- if import_button:
+
data_import = pd.read_csv(sselectx_csv, sep=psep, index_col=col)
# compute PCA - pca_maker function in application_functions.py
pca_data, cat_cols, pca_cols = pca_maker(data_import)
@@ -58,11 +57,11 @@ with st.container():
pca_2 = settings_column.selectbox("Second Principle Component", options=pca_cols, index=1)
# if categorical variables exist, add 2 select lists to choose the categorical variables to color the PCA
if cat_cols[0] == "no categories":
- scatter_column.plotly_chart(px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, hover_name=pca_data.index, title="PCA plot of sample spectra"))
+ plot_pca = scatter_column.plotly_chart(px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, hover_name=pca_data.index, title="PCA plot of sample spectra"))
else:
categorical_variable = settings_column.selectbox("Variable Select", options = cat_cols)
categorical_variable_2 = settings_column.selectbox("Second Variable Select (hover data)", options = cat_cols)
- scatter_column.plotly_chart(px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, color=categorical_variable, hover_data = [categorical_variable_2], hover_name=pca_data.index, title="PCA plot of sample spectra"))
+ plot_pca = scatter_column.plotly_chart(px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, color=categorical_variable, hover_data = [categorical_variable_2], hover_name=pca_data.index, title="PCA plot of sample spectra"))
#K-Means
## K-Means choose number of clusters
wcss_samples = []
@@ -72,14 +71,14 @@ with st.container():
kmeans_samples = km(n_clusters = i, init = 'k-means++', random_state = 42)
kmeans_samples.fit(pca_data.loc[:,[pca_1,pca_2]])
wcss_samples.append(kmeans_samples.inertia_)
- settings_column.plotly_chart(px.line(x=clusters_sample, y=wcss_samples, title="K-Means clusters number selection", width=200))
- # scatter_column.plotly_chart(px.line(x=clusters_sample, y=wcss_samples, title="K-Means clusters number selection"))
+ settings_column.plotly_chart(px.line(x=clusters_sample, y=wcss_samples, title="K-Means clusters nb sel", width=200))
## Draw clustering
nb_select = settings_column.slider("Choose cluster number (K-Means)", min_value=2, max_value=cluster_max, value=5, format="%i")
kmeans_samples = km(n_clusters=nb_select, random_state=42)
kmeans_samples.fit(pca_data.loc[:,[pca_1,pca_2]])
- # kmeans_samples.labels_
- plot = scatter_column.plotly_chart(px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, color=kmeans_samples.labels_, hover_name=pca_data.index, title="PCA projection with K-Means Clusters"))
+ # plot the pca with clustering only (no selected samples)
+ # graph = px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, color=kmeans_samples.labels_, hover_name=pca_data.index, title="PCA projection with K-Means Clusters")
+ # plot = scatter_column.plotly_chart(graph)
# choose between cluster centered sample and random samples
selection = settings_column.select_slider('Centered samples or random ones', options=['center','random'])
export = []
@@ -89,25 +88,30 @@ with st.container():
closest, _ = pairwise_distances_argmin_min(kmeans_samples.cluster_centers_, pca_data.loc[:,[pca_1,pca_2]])
scatter_column.dataframe(pca_data.loc[pca_data.index[closest],[pca_1,pca_2]], use_container_width=True)
export.append(pca_data.loc[pca_data.index[closest],[pca_1,pca_2]].index.T)
- # plot.empty()
+ # list indexes of selected samples for colored plot
+ te = pca_data.loc[pca_data.index[closest],[pca_1,pca_2]].index.values.tolist()
elif selection == 'random':
selection_number = settings_column.number_input('How many samples per cluster?', step=1, value = 3)
for i in np.unique(kmeans_samples.labels_):
if len(pd.DataFrame(pca_data.loc[pca_data.index[kmeans_samples.labels_==i],[pca_1,pca_2]])) >= selection_number:
- # scatter_column.write('cluster number - ')
- # scatter_column.write(i)
- # scatter_column.write('_samples in this cluster_')
- # scatter_column.write(len(pd.DataFrame(pca_data.loc[pca_data.index[kmeans_samples.labels_==i],[pca_1,pca_2]])))
- # scatter_column.dataframe(pd.DataFrame(pca_data.loc[pca_data.index[kmeans_samples.labels_==i],[pca_1,pca_2]]).sample(n=selection_number))
export.append(pca_data.loc[pca_data.index[kmeans_samples.labels_==i]].sample(n=selection_number).index)
else:
- # scatter_column.write('cluster number - ')
- # scatter_column.write(i)
- # scatter_column.write("_whole cluster (not enough samples)_")
- # scatter_column.write(len(pd.DataFrame(pca_data.loc[pca_data.index[kmeans_samples.labels_==i],[pca_1,pca_2]])))
- # scatter_column.dataframe(pd.DataFrame(pca_data.loc[pca_data.index[kmeans_samples.labels_==i],[pca_1,pca_2]]))
export.append(pca_data.loc[pca_data.index[kmeans_samples.labels_==i]].index)
+ # list indexes of selected samples for colored plot
+ te = []
+ for sublist in export:
+ for item in sublist:
+ te.append(item)
+ # display a matrix of selected samples
scatter_column.write(pd.DataFrame(export).T)
+ # convert cluster number to text for optimized coloring
+ kmeans_samples.labels_ = kmeans_samples.labels_.astype(str)
+ for j in te:
+ kmeans_samples.labels_[pca_data.index.get_loc(j)] = 'selected'
+ # plot de pca with colored clusters and selected samples
+ graph_selected = px.scatter(data_frame=pca_data, x=pca_1, y=pca_2, template="simple_white", height=800, color=kmeans_samples.labels_, hover_name=pca_data.index, title="PCA projection with K-Means Clusters and selected samples")
+ plot = scatter_column.plotly_chart(graph_selected)
+ # button to export the names of selected samples - by cluster if random - in a csv
if scatter_column.button('Export'):
pd.DataFrame(export).T.to_csv('./data/Samples_for_Chemical_Analysis.csv')
else: