--- a
+++ b/test_luna_props_scan_dsb.py
@@ -0,0 +1,85 @@
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+import os
+
+import pathfinder
+import utils
+from configuration import config, set_configuration
+from utils_plots import plot_slice_3d_3
+import theano.tensor as T
+import utils_lung
+import blobs_detection
+import logger
+from collections import defaultdict
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: test_luna_props_scan.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_luna_props_scan', config_name)
+
+# predictions path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/%s' % config_name
+utils.auto_make_dir(outputs_path)
+
+# logs
+logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
+sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
+sys.stderr = sys.stdout
+
+# builds model and sets its parameters
+model = config().build_model()
+
+x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
+givens_valid = {}
+givens_valid[model.l_in.input_var] = x_shared
+
+get_predictions_patch = theano.function([],
+                                        nn.layers.get_output(model.l_out, deterministic=True),
+                                        givens=givens_valid,
+                                        on_unused_input='ignore')
+
+data_iterator = config().data_iterator
+
+#existing_preds = [f.rsplit('.') for f in os.listdir(outputs_path)]
+#print existing_preds
+
+print
+print 'Data'
+print 'n samples: %d' % data_iterator.nsamples
+
+prev_pid = None
+candidates = []
+patients_count = 0
+max_malignancy = 0.
+for n, (x, candidate_zyxd, id) in enumerate(data_iterator.generate()):
+    pid = id[0]
+
+    if pid != prev_pid and prev_pid is not None:
+        print patients_count, prev_pid, len(candidates)
+        candidates = np.asarray(candidates)
+        utils.save_pkl(candidates, outputs_path + '/%s.pkl' % prev_pid)
+        patients_count += 1
+        candidates = []
+
+    #print 'x.shape', x.shape
+    x_shared.set_value(x)
+    predictions = get_predictions_patch()
+    #print 'predictions.shape', predictions.shape
+    #print 'candidate_zyxd', candidate_zyxd.shape
+
+    candidate_zyxd_pred = np.append(candidate_zyxd, [predictions])
+    #print 'candidate_zyxd_pred', candidate_zyxd_pred
+    candidates.append(candidate_zyxd_pred)
+
+    prev_pid = pid
+
+# save the last one
+print patients_count, prev_pid, len(candidates)
+candidates = np.asarray(candidates)
+utils.save_pkl(candidates, outputs_path + '/%s.pkl' % prev_pid)