diff --git a/mouse_sagittal_data_analysis/fit.py b/mouse_sagittal_data_analysis/fit.py
deleted file mode 100644
index cc630bd..0000000
--- a/mouse_sagittal_data_analysis/fit.py
+++ /dev/null
@@ -1,167 +0,0 @@
-
-
-dir_mNSF_functions='/users/ywang/Hansen_projects/scRNA/mNSFH_2023_06_14/mNSF-main'
-dir_output='/dcs04/hansen/data/ywang/ST/data_10X_ST/mouse_Sagittal_spaceRanger1_1_0/out/'
-
-########################################################################
-########################################################################
-import sys
-sys.path.append(dir_mNSF_functions)
-
-#from scanpy import read_h5ad
-
-
-import mNSF
-
-from mNSF import process_multiSample
-
-from mNSF.NSF import preprocess
-from mNSF.NSF import misc
-#from mNSF.NSF import visualize
-#from mNSF import training_multiSample
-from mNSF import training_multiSample
-from mNSF import process_multiSample
-from mNSF.NSF import visualize
-
-#from tensorflow.data import Dataset
-
-from os import path
-#import pandas
-import os
-import numpy as np
-import tensorflow as tf
-import pandas as pd
-import sys 
-import pickle
-
-
-
-sys.path.append(dir_output)
-os.chdir(dir_output)
-
-
-
-########################################################################
-########################################################################
-L=12
-
-## nsample = 2:
-#worked on L12
-
-##nsample = 4:
-#worked on L12
-
-
-
-nsample=4
-
-dpth='data'
-
-pth="/dcs04/hansen/data/ywang/ST/data_10X_ST/mouse_Sagittal_spaceRanger1_1_0/out/"
-mpth = path.join(pth,"models")
-misc.mkdir_p(mpth)
-pp = path.join(mpth,"pp")#list_fit[0].generate_pickle_path("constant",base=mpth)
-misc.mkdir_p(pp)
-
-
-
-
-########################################################################3
-################### step 0  Data loading
-########################################################################
-
-list_D=list()
-list_X=list()
-for ksample in range(0,nsample):
-	Y=pd.read_csv(path.join(dpth,'/dcs04/hansen/data/ywang/ST/data_10X_ST/mouse_Sagittal_spaceRanger1_1_0/out/Y_sample'+ str(ksample+1) +'.csv'))
-	X=pd.read_csv(path.join(dpth,'/dcs04/hansen/data/ywang/ST/data_10X_ST/mouse_Sagittal_spaceRanger1_1_0/out/X_sample'+ str(ksample+1) +'.csv'))
-	D=process_multiSample.get_D(X,Y)
-	list_D.append(D)
-	list_X.append(X)
-
-list_D=[list_D[3],list_D[2],list_D[1],list_D[0]]
-list_X=[list_X[3],list_X[2],list_X[1],list_X[0]]
-
-	
-list_Dtrain=process_multiSample.get_listDtrain(list_D)
-list_sampleID=process_multiSample.get_listSampleID(list_D)
-
-
-
-
-########################################################################3
-################### step 1 initialize model
-########################################################################
-
-list_fit = process_multiSample.ini_multiSample(list_D,L)
-
-
-########################################################################
-################### step 2 fit model
-########################################################################
-
-
-list_fit=training_multiSample.train_model_mNSF(list_fit,pp,list_Dtrain,list_D)
-
-gc.collect()
-
-
-#during traning:
-#print(tf.config.experimental.get_memory_info('GPU:0'))
-#{'current': 4684887040, 'peak': 29394932480}
-
-
-# save the fitted model
-process_multiSample.save_object(list_fit, 'list_fit.pkl') 
-
-
-
-
-########################################################################3
-################### step 3 save and plot results
-########################################################################
-## save the loadings
-#loadings=visualize.get_loadings(list_fit[0])
-#DF = pd.DataFrame(loadings) 
-#DF.to_csv(("loadings.csv"))
-inpf12=process_multiSample.interpret_npf_v3(list_fit,list_X,S=100,lda_mode=False)
-
-
-W = inpf12["loadings"]
-#Wdf=pd.DataFrame(W*inpf12["totals1"][:,None], index=ad.var.index, columns=range(1,L+1))
-
-Wdf=pd.DataFrame(W*inpf12["totals1"][:,None],  columns=range(1,L+1))
-Wdf.to_csv(path.join("loadings_spde.csv"))
-
-
-
-## save the factors
-#inpf12 = process_multiSample.interpret_npf_v3(list_fit,list_X,S=100,lda_mode=False)
-Factors = inpf12["factors"][:,0:L]
-
-for k in range(0,nsample):
-	indices=list_sampleID[k]
-	indices=indices.astype(int)
-	Factors_df = pd.DataFrame(Factors[indices,:]) 
-	Factors_df.to_csv(path.join("factors_sample"+str(k+1)+".csv"))
-
-
-#
-
-
-
-## plot the factors
-hmkw = {"figsize":(7,.9),"bgcol":"white","subplot_space":0.1,"marker":"s","s":10}
-for ksample in range(0,nsample):
-	indices=list_sampleID[k]
-	indices=indices.astype(int)
-	fig,axes=visualize.multiheatmap(list_D[ksample]["X"],Factors[indices,:], (1,L), cmap="Blues", **hmkw)
-	fig.savefig(path.join(dir_output,"sample"+str(ksample+1)+".png"),bbox_inches='tight')
-
-
-
-
-
-
-	
-