From 5b6962407826c571b8d19deecf93dbff41c1629c Mon Sep 17 00:00:00 2001
From: Nicolas Barthes <nicolas.barthes@cnrs.fr>
Date: Wed, 5 Jun 2024 12:18:16 +0200
Subject: [PATCH] added automated simple interface with default values
---
src/pages/1-samples_selection.py | 56 +++++++++++++++++++-------------
1 file changed, 33 insertions(+), 23 deletions(-)
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index a30e15e..aae214b 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -1,8 +1,9 @@
from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
-repertoire_a_vider = 'D:/Mouhcine/nirs_workflow/src/Report/figures'
+# empty temp figures
+repertoire_a_vider = 'D:/Mouhcine/nirs_workflow/src/Report/figures'
if os.path.exists(repertoire_a_vider):
for fichier in os.listdir(repertoire_a_vider):
chemin_fichier = os.path.join(repertoire_a_vider, fichier)
@@ -11,27 +12,40 @@ if os.path.exists(repertoire_a_vider):
elif os.path.isdir(chemin_fichier):
shutil.rmtree(chemin_fichier)
# HTML pour le bandeau "CEFE - CNRS"
-# bandeau_html = """
-# <div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
-# <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
-# </div>
-# """
-# # Injecter le code HTML du bandeau
-# st.markdown(bandeau_html, unsafe_allow_html=True)
add_header()
+#load specific model page css
+local_css(css_file / "style_model.css")
+
+#define some variables
tcr=pd.DataFrame()
sam=pd.DataFrame()
sam1=pd.DataFrame()
+dim_red_methods=['', 'PCA','UMAP', 'NMF'] # List of dimensionality reduction algos
+cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP'] # List of clustering algos
+selec_strategy = ['center','random']
-# path = os.path.dirname(os.path.abspath(__file__)).replace('\\','/')
-# css_file = path[:path.find('/pages')]+'/style'
-# local_css(css_file +"/style_model.css")
-local_css(css_file / "style_model.css")
-
+# check session state and define default values if simple interface to automate processing
st.session_state["interface"] = st.session_state.get('interface')
if st.session_state["interface"] == '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))
+
+if st.session_state["interface"] == 'advanced':
+ default_reduction_option = 0
+ default_clustering_option = 0
+ default_sample_selection_option = 0
################################### I - Data Loading and Visualization ########################################
st.header("I - Spectral Data Visualization", divider='blue')
@@ -111,9 +125,6 @@ scores, loadings, pc = st.columns([2, 3, 0.5])
influence, hotelling, qexp = st.columns([2, 2, 1])
st.header('III - Selected samples for chemical analysis', divider='blue')
-dim_red_methods=['', 'PCA','UMAP', 'NMF'] # List of dimensionality reduction algos
-cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP'] # List of clustering algos
-
dr_model = None # dimensionality reduction model
cl_model = None # clustering model
@@ -122,8 +133,8 @@ t = pd.DataFrame # scores
p = pd.DataFrame # loadings
labels = []
if not spectra.empty:
- dim_red_method = pc.selectbox("Dimensionality reduction techniques: ", options = dim_red_methods, key = 37)
- clus_method = pc.selectbox("Clustering techniques: ", options = cluster_methods, key = 38)
+ dim_red_method = pc.selectbox("Dimensionality reduction techniques: ", options = dim_red_methods, index = default_reduction_option, key = 37)
+ clus_method = pc.selectbox("Clustering techniques: ", options = cluster_methods, index = default_clustering_option, key = 38)
xc = standardize(spectra, center=True, scale=False)
@@ -196,7 +207,6 @@ if not t.empty:
#################################################### III - Samples selection using the reduced data preentation ######
-selec_strategy = ['center','random']
samples_df_chem = pd.DataFrame
selected_samples = []
selected_samples_idx = []
@@ -205,7 +215,7 @@ selected_samples_idx = []
if labels:
if clus_method:
selection = scores.radio('Select samples selection strategy:',
- options = selec_strategy)
+ options = selec_strategy, index = default_sample_selection_option, key=102)
# Strategy 0
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
@@ -499,8 +509,8 @@ Ac_Km = ['Spectra_Plot.png', 'Elbow.png', 'graphe_loadings.png', 'plot_axe1_axe2
# Streamlit container
with st.container():
- header3, header4 = st.columns(2)
- if header3.button("Exporter le rapport"):
+ header3, = st.columns(1)
+ if header3.button("Export report"):
if test == '.csv':
if dim_red_method == dim_red_methods[1] and clus_method == cluster_methods[1]:
latex_report = report.report(sam, tcr, Nb_ech, nb_clu, 'sample', Ac_Km, 'csv', 'kmeans')
--
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