--- a
+++ b/plot_luna_roi.py
@@ -0,0 +1,61 @@
+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
+import utils_lung
+import data_iterators
+
+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_fpred_scan', 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)
+
+# candidates after segmentations path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+segmentation_outputs_path = predictions_dir + '/%s' % config_name
+id2candidates_path = utils_lung.get_candidates_paths(segmentation_outputs_path)
+
+data_iterator = data_iterators.FixedCandidatesLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                transform_params=config().p_transform,
+                                                                data_prep_fun=config().data_prep_function,
+                                                                id2candidates_path=id2candidates_path,
+                                                                top_n=4)
+
+print
+print 'Data'
+print 'n samples: %d' % data_iterator.nsamples
+
+prev_pid = None
+i = 0
+for (x_chunk_train, y_chunk_train, id_train) in data_iterator.generate():
+    print id_train
+    pid = id_train[0]
+    if pid == prev_pid:
+        i += 1
+    else:
+        i = 0
+
+    utils_plots.plot_slice_3d_3axis(input=x_chunk_train[0, 0],
+                                    pid='-'.join([str(pid), str(i)]),
+                                    img_dir=outputs_path,
+                                    idx=np.array(x_chunk_train[0, 0].shape) / 2)
+    prev_pid = pid