--- a
+++ b/scripts/utils/identify_resamplingShape.py
@@ -0,0 +1,97 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2020 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+import tensorflow as tf
+from miscnn.data_loading.interfaces import NIFTI_interface
+from miscnn import Data_IO, Preprocessor, Data_Augmentation, Neural_Network
+from miscnn.processing.subfunctions import Normalization, Clipping, Resampling
+from miscnn.neural_network.architecture.unet.standard import Architecture
+from miscnn.neural_network.metrics import tversky_crossentropy, dice_soft, \
+                                          dice_crossentropy, tversky_loss
+from miscnn.evaluation.cross_validation import cross_validation
+from tensorflow.keras.callbacks import ReduceLROnPlateau, TensorBoard, \
+                                       EarlyStopping, CSVLogger
+from miscnn.evaluation.cross_validation import run_fold, load_csv2fold
+import os
+import numpy as np
+
+#-----------------------------------------------------#
+#      Tensorflow Configuration for GPU Cluster       #
+#-----------------------------------------------------#
+# physical_devices = tf.config.list_physical_devices('GPU')
+# tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+#-----------------------------------------------------#
+#               Setup of MIScnn Pipeline              #
+#-----------------------------------------------------#
+# Initialize Data IO Interface for NIfTI data
+## We are using 4 classes due to [background, lung_left, lung_right, covid-19]
+interface = NIFTI_interface(channels=1, classes=4)
+
+# Create Data IO object to load and write samples in the file structure
+data_io = Data_IO(interface, input_path="data", delete_batchDir=False)
+
+# Access all available samples in our file structure
+sample_list = data_io.get_indiceslist()
+sample_list.sort()
+
+# Create and configure the Data Augmentation class
+data_aug = Data_Augmentation(cycles=1, scaling=True, rotations=True,
+                             elastic_deform=True, mirror=True,
+                             brightness=True, contrast=True, gamma=True,
+                             gaussian_noise=True)
+
+# Create a clipping Subfunction to the lung window of CTs (-1250 and 250)
+sf_clipping = Clipping(min=-1250, max=250)
+# Create a pixel value normalization Subfunction to scale between 0-255
+sf_normalize = Normalization(mode="grayscale")
+# Create a resampling Subfunction to voxel spacing 1.58 x 1.58 x 2.70
+sf_resample = Resampling((1.58, 1.58, 2.70))
+# Create a pixel value normalization Subfunction for z-score scaling
+sf_zscore = Normalization(mode="z-score")
+
+# Assemble Subfunction classes into a list
+sf = [sf_clipping, sf_normalize, sf_resample, sf_zscore]
+
+# Create and configure the Preprocessor class
+pp = Preprocessor(data_io, data_aug=data_aug, batch_size=2, subfunctions=sf,
+                  prepare_subfunctions=True, prepare_batches=False,
+                  analysis="fullimage", patch_shape=(160, 160, 80))
+# Adjust the patch overlap for predictions
+pp.patchwise_overlap = (80, 80, 40)
+
+
+# Initialize Keras Data Generator for generating batches
+from miscnn.neural_network.data_generator import DataGenerator
+dataGen = DataGenerator(sample_list, pp, training=False, validation=False, shuffle=False)
+
+x = []
+y = []
+z = []
+for batch in dataGen:
+    print("Batch:", batch.shape)
+    x.append(batch.shape[1])
+    y.append(batch.shape[2])
+    z.append(batch.shape[3])
+
+print("Mean:")
+print(np.mean(x), np.mean(y), np.mean(z))
+print(np.median(x), np.median(y), np.median(z))