From f71a2342e6ef939cd33a8e0a762c7af8f343d9aa Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Thu, 11 Apr 2024 16:53:50 +0200
Subject: [PATCH] PCA color filter prediction using ipls model
---
pages/1-samples_selection.py | 23 +++++++++++++++--------
pages/3-prediction.py | 2 +-
2 files changed, 16 insertions(+), 9 deletions(-)
diff --git a/pages/1-samples_selection.py b/pages/1-samples_selection.py
index 63e4a43..f7ca85a 100644
--- a/pages/1-samples_selection.py
+++ b/pages/1-samples_selection.py
@@ -6,6 +6,8 @@ from Class_Mod.DATA_HANDLING import *
################################### Data Loading and Visualization ########################################
container1 = st.container(border=True)
col2, col1 = st.columns([3, 1])
+col1.header("Data Loading", divider='blue')
+col2.header("Spectral Data Visualization", divider='blue')
container2 = st.container(border=True)
@@ -15,8 +17,6 @@ influence, hotelling, qexp = st.columns([2, 2, 1])
with container1:
- col1.header("Data Loading", divider='blue')
- col2.header("Spectral Data Visualization", divider='blue')
# loader for csv file containing NIRS spectra
sselectx_csv = col1.file_uploader("Load NIRS Data", type=["csv","dx"], help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns", key=5)
if sselectx_csv is not None:
@@ -59,9 +59,10 @@ with container1:
data = DxRead(path = tmp_path)
data_import = data.specs_df_
st.success("The data have been loaded successfully", icon="✅")
+
## Visualize spectra
-
+
with col2:
fig, ax = plt.subplots(figsize = (30,7))
data_import.T.plot(legend=False, ax = ax, color = 'blue')
@@ -85,11 +86,10 @@ with container1:
######################################################################################
############################## Exploratory data analysis ###############################
+plot_type=['', 'PCA','UMAP', 'NMF']
+cluster_methods = ['', 'Kmeans','UMAP', 'AP']
with container2:
if sselectx_csv is not None:
- plot_type=['', 'PCA','UMAP', 'NMF']
- cluster_methods = ['', 'Kmeans','UMAP', 'AP']
-
with pc:
type_plot = st.selectbox("Dimensionality reduction techniques: ", options=plot_type, key=37)
type_cluster = st.selectbox("Clustering techniques: ", options=cluster_methods, key=38)
@@ -128,8 +128,15 @@ with container2:
else:
- fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3)
- fig.update_traces(marker=dict(size=4))
+ if test == '.dx':
+ filter = ['origin', 'date', 'time', 'spectrometer/data system']
+ col = st.selectbox('filter', options= filter)
+
+ fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = data.md_df_[col])
+ fig.update_traces(marker=dict(size=4))
+ else:
+ fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3 )
+ fig.update_traces(marker=dict(size=4))
st.plotly_chart(fig)
diff --git a/pages/3-prediction.py b/pages/3-prediction.py
index e7f32f7..6fba851 100644
--- a/pages/3-prediction.py
+++ b/pages/3-prediction.py
@@ -42,7 +42,7 @@ if NIRS_csv:
if index:
idx = pd.read_csv(index, sep=';', index_col=0).iloc[:,0].to_numpy()
-result = ''
+#result = ''
if st.button("Predict"):
if s:
--
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