From 8915f27a3e4aa259d09e8d4d3fac82d1e9a7eb09 Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Mon, 15 Apr 2024 09:48:34 +0200
Subject: [PATCH] color filters
---
pages/1-samples_selection.py | 65 ++++++++++++++++++------------------
1 file changed, 33 insertions(+), 32 deletions(-)
diff --git a/pages/1-samples_selection.py b/pages/1-samples_selection.py
index 76c2454..121d467 100644
--- a/pages/1-samples_selection.py
+++ b/pages/1-samples_selection.py
@@ -12,58 +12,57 @@ if st.session_state["interface"] == 'simple':
hide_pages("Predictions")
################################### Data Loading and Visualization ########################################
-# container1 = st.header("Data loading",border=True)
col2, col1 = st.columns([3, 1])
col1.header("Data Loading", divider='blue')
col2.header("Spectral Data Visualization", divider='blue')
## Preallocation of data structure
-data_import = pd.DataFrame
+spectra = pd.DataFrame
meta_data = pd.DataFrame
selected_samples = pd.DataFrame
-# 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)
+# loader for datafile
+data_file = 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)
-#with container1:
-if sselectx_csv:
- test = sselectx_csv.name[sselectx_csv.name.find('.'):]
+if data_file:
+ # Retrieve the extension of the file
+ test = data_file.name[data_file.name.find('.'):]
+ ## Load .csv file
if test== '.csv':
with col1:
# Select list for CSV delimiter
- psep = st.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+sselectx_csv.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+sselectx_csv.name))), key=9)
+ psep = st.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+data_file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+data_file.name))), key=9)
# Select list for CSV header True / False
- phdr = st.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+sselectx_csv.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+sselectx_csv.name))), key=31)
+ phdr = st.selectbox("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))), key=31)
if phdr == 'yes':
col = 0
else:
col = False
- imp = pd.read_csv(sselectx_csv, sep=psep, index_col=col)
- data_import = col_cat(imp)[0]
+ imp = pd.read_csv(data_file, sep=psep, index_col=col)
+ spectra = col_cat(imp)[0]
meta_data = col_cat(imp)[1]
st.success("The data have been loaded successfully", icon="✅")
+ ## Load .dx file
elif test == '.dx':
# Create a temporary file to save the uploaded file
with NamedTemporaryFile(delete=False, suffix=".dx") as tmp:
- tmp.write(sselectx_csv.read())
+ tmp.write(data_file.read())
tmp_path = tmp.name
with col1:
- _, data_import, meta_data = read_dx(file = tmp_path)
+ _, spectra, meta_data = read_dx(file = tmp_path)
st.success("The data have been loaded successfully", icon="✅")
os.unlink(tmp_path)
-if not data_import.empty:
- ## Visualize spectra
+## Visualize spectra
+if not spectra.empty:
with col2:
- fig = plot_spectra(data_import)
-
- #plt.annotate(text = info.T, xy =(m, info.loc[:,"Max"]), size=20, color = 'black', backgroundcolor='red')
+ fig = plot_spectra(spectra)
st.pyplot(fig)
@@ -73,20 +72,25 @@ container2.header("Exploratory Data Analysis-Multivariable Data Analysis", divid
scores, loadings, pc = st.columns([2, 3, 0.5])
influence, hotelling, qexp = st.columns([2, 2, 1])
-dim_red_methods=['', 'PCA','UMAP', 'NMF']
-cluster_methods = ['', 'Kmeans','HDBSCAN', 'AP']
-dr_model = None
-cl_model = None
+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
# Dimensionality reduction
-t = pd.DataFrame
-if not data_import.empty:
+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)
+ xc = standardize(spectra)
+
if dim_red_method == dim_red_methods[1]:
- dr_model = LinearPCA(data_import, Ncomp=5)
+ dr_model = LinearPCA(xc, Ncomp=5)
elif dim_red_method == dim_red_methods[2]:
- dr_model = Umap(x = data_import, n_components = 5, n_neighbors = 20 , min_dist = 0)
+ dr_model = Umap(x = xc, n_components = 5, n_neighbors = 20 , min_dist = 0)
if dr_model:
axis1 = pc.selectbox("x-axis", options = dr_model.scores_.columns, index=0)
@@ -96,7 +100,6 @@ if not data_import.empty:
# clustering
-labels = pd.DataFrame
if not t.empty:
# Clustering
if clus_method == cluster_methods[1]:
@@ -106,21 +109,19 @@ if not t.empty:
scores.plotly_chart(fig2)
data, labels = cl_model.fit_optimal(nclusters = ncluster)
- elif clus_method == cluster_methods[1]:
+ elif clus_method == cluster_methods[2]:
from hdbscan import HDBSCAN_function
labels, hdbscan_score = HDBSCAN_function(t, min_cluster_size=10)
##### Plots
-
## Scores
-
if not t.empty:
with scores:
st.write('Scores plot')
# scores plot with clustering
- if not pd.DataFrame(labels).empty:
+ if list(labels):
fig = px.scatter_3d(t, x=axis1, y=axis2, z = axis3, color = labels)
else:
# scores plot with metadata
@@ -140,7 +141,7 @@ if not t.empty:
-if not data_import.empty:
+if not spectra.empty:
if dim_red_method == dim_red_methods[1]:
with loadings:
st.write('Loadings plot')
--
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