--- a
+++ b/test_fpred_scan.py
@@ -0,0 +1,69 @@
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+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_scan.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_fpred_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
+
+print
+print 'Data'
+print 'n samples: %d' % data_iterator.nsamples
+
+nblob2prob, nblob2label = {}, {}
+pid2candidates = defaultdict(list)
+for n, (x, candidate_zyxd, id) in enumerate(data_iterator.generate()):
+    pid = id[0]
+    x_shared.set_value(x)
+    predictions = get_predictions_patch()
+    label = candidate_zyxd[-1]
+    p1 = predictions[0][1]
+    nblob2prob[n] = p1
+    nblob2label[n] = label
+    candidate_zyxdp = np.append(candidate_zyxd, [[p1]])
+    pid2candidates[pid].append(candidate_zyxdp)
+
+
+for k in pid2candidates.iterkeys():
+    candidates = np.asarray(pid2candidates[k])
+    candidates_wo_dupes = utils_lung.filter_close_neighbors(candidates)
+    a = np.asarray(sorted(candidates_wo_dupes, key=lambda x: x[-1], reverse=True))
+    utils.save_pkl(a, outputs_path + '/%s.pkl' % k)