--- a
+++ b/analyze_dsb_pixel_spacings.py
@@ -0,0 +1,45 @@
+import cPickle as pickle
+import string
+import sys
+import time
+from itertools import izip
+import numpy as np
+from datetime import datetime, timedelta
+import utils
+import utils_lung
+import logger
+import buffering
+import pathfinder
+import data_iterators
+
+
+candidates_config = 'dsb_c3_s5_p8a1'
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+candidates_path = predictions_dir + '/%s' % candidates_config
+id2candidates_path = utils_lung.get_candidates_paths(candidates_path)
+
+train_valid_ids = utils.load_pkl(pathfinder.VALIDATION_SPLIT_PATH)
+train_pids, valid_pids, test_pids = train_valid_ids['training'], train_valid_ids['validation'], train_valid_ids['test']
+all_pids = train_pids + valid_pids + test_pids
+
+data_iterator = data_iterators.DSBPixelSpacingsGenerator(pathfinder.DATA_PATH, id2candidates_path, all_pids)
+
+
+z = []
+y = []
+x = []
+pixel_spacings = {}
+
+# use buffering.buffered_gen_threaded()
+for idx, (pid, pixel_spacing) in enumerate(data_iterator.generate()):
+    print idx, pid, pixel_spacing
+    z.append(pixel_spacing[0])
+    y.append(pixel_spacing[1])
+    x.append(pixel_spacing[2])
+    pixel_spacings[pid] = pixel_spacing
+
+
+utils.save_pkl(pixel_spacings, 'pixel_spacings_dsb.pkl')
+print 'z', min(z), max(z)
+print 'y', min(y), max(y)
+print 'x', min(x), max(x)