--- a
+++ b/plot_dsb_roi.py
@@ -0,0 +1,52 @@
+import cPickle as pickle
+import string
+import sys
+import time
+from itertools import izip
+import lasagne as nn
+import numpy as np
+import theano
+from datetime import datetime, timedelta
+import utils
+import logger
+import theano.tensor as T
+import buffering
+from configuration import config, set_configuration
+import pathfinder
+import utils_plots
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: train.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_class_dsb', config_name)
+
+predictions_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/%s' % config_name
+utils.auto_make_dir(outputs_path)
+
+train_data_iterator = config().train_data_iterator
+valid_data_iterator = config().valid_data_iterator
+test_data_iterator = config().test_data_iterator
+
+
+print
+print 'Data'
+print 'n train: %d' % train_data_iterator.nsamples
+print 'n validation: %d' % valid_data_iterator.nsamples
+print 'n chunks per epoch', config().nchunks_per_epoch
+
+# use buffering.buffered_gen_threaded()
+for (x_chunk_train, y_chunk_train, id_train) in test_data_iterator.generate():
+    print id_train
+    print x_chunk_train.shape
+
+    for i in xrange(x_chunk_train.shape[0]):
+        pid = id_train[i]
+        for j in xrange(x_chunk_train.shape[1]):
+            utils_plots.plot_slice_3d_3axis(input=x_chunk_train[i, j, 0],
+                                            pid='-'.join([str(pid), str(j)]),
+                                            img_dir=outputs_path,
+                                            idx=np.array(x_chunk_train[i, j, 0].shape) / 2)