--- a
+++ b/test_fpred_scan_dsb.py
@@ -0,0 +1,135 @@
+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_scan.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_fpred_scan', config_name)
+
+tta = sys.argv[2] if len(sys.argv) == 3 else None
+
+# 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')
+
+
+if tta == 'tta':
+    data_iterator = config().tt_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
+    for n, (x, candidate_zyxd, pid) in enumerate(data_iterator.generate()):
+
+        if pid != prev_pid and prev_pid is not None:
+            print patients_count, prev_pid, len(candidates)
+            candidates = np.asarray(candidates)
+            a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
+            utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
+            print 'saved predictions'
+            patients_count += 1
+            candidates = []
+        
+        preds = []
+        for bidx, pos in enumerate(range(0,x.shape[0],16)):
+            print bidx
+            x_batch = x[pos:pos+16]
+            x_shared.set_value(x)
+            predictions = get_predictions_patch()
+            predictions = predictions[:, 1] if predictions.shape[-1] == 2 else predictions
+            #print "predictions", predictions
+            preds.append(predictions)
+        
+        preds = np.concatenate(preds)
+        pred = np.average(preds)
+        candidate_zyxdp = np.append(candidate_zyxd, [[pred]])
+        candidates.append(candidate_zyxdp)
+
+        prev_pid = pid
+
+    # save the last one
+    print patients_count, prev_pid, len(candidates)
+    candidates = np.asarray(candidates)
+    a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
+    utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
+    print 'saved predictions'
+else:
+    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
+    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)
+            a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
+            utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
+            print 'saved predictions'
+            patients_count += 1
+            candidates = []
+
+        x_shared.set_value(x)
+        predictions = get_predictions_patch()
+        p1 = predictions[0][1]
+        candidate_zyxdp = np.append(candidate_zyxd, [[p1]])
+        candidates.append(candidate_zyxdp)
+
+        prev_pid = pid
+
+    # save the last one
+    print patients_count, prev_pid, len(candidates)
+    candidates = np.asarray(candidates)
+    a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
+    utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
+    print 'saved predictions'