Update process_multiSample.py

mNSF/process_multiSample.py

@@ -108,7 +108,7 @@ def get_D_fromAnnData(ad):
 	return D
 
 
-def get_listDtrain(list_D_,nbatch=1):
+def get_listDtrain(list_D_,nbatch=1,list_nchunk=[False]*len(list_D_)):
 	"""
 	Prepare the training data by creating TensorFlow Datasets.
     
@@ -124,12 +124,32 @@ def get_listDtrain(list_D_,nbatch=1):
 	"""
 	list_Dtrain=list()
 	nsample=len(list_D_)
+	# data chunking
+	nsample_splitted = sum(list_nchunk)
+	list_D_chunk = list()
 	for ksample in range(0,nsample):
 		D=list_D_[ksample]
-		Ntr = D["Y"].shape[0] # Number of observations in this sample
+		nchunk = list_nchunk[ksample]
+		X = D['X']
+		Y = D['Y']
+		nspot = X.shape[1]
+		nspot_perChunk = int(nspot/nchunk)
+		for kchunk in range(0,nchunk):
+			st = (kchunk-1)*nspot_perChunk
+			end_ = kchunk*nspot_perChunk
+			if (kchunk==nchunk-1):end_=nspot
+			X_chunk=X[st:end_,]
+			Y_chunk=Y[st:end_,]
+			D_chunk = list()
+			D_chunk["X"]=X
+			D_chunk["Y"]=Y
+			list_D_chunk.append(D_chunk)
+	for ksample_splitted in range(0,nsample_splitted):
+		D_chunk=list_D_chunk[ksample]
+		Ntr = D_chunk["Y"].shape[0] # Number of observations in this sample
 
 		# Convert dictionary to TensorFlow Dataset
-		Dtrain = Dataset.from_tensor_slices(D)
+		Dtrain = Dataset.from_tensor_slices(D_chunk)
 
 		# Batch the data
 		if (nbatch==1): D_train = Dtrain.batch(round(Ntr)+1)