--- a
+++ b/generate_features_dsb.py
@@ -0,0 +1,77 @@
+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: generate_features_dsb.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_gen_features', 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_featuremap = 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
+
+prev_pid = None
+candidates = []
+patients_count = 0
+patch_size = 48
+stride = 16
+for n, (x, id) in enumerate(data_iterator.generate()):
+    pid = id
+
+    print(pid)
+    print model.l_out.output_shape
+    predictions = np.empty(((x.shape[2]-patch_size+1)//stride, (x.shape[3]-patch_size+1)//stride, (x.shape[4]-patch_size+1)//stride,) + (model.l_out.output_shape[1],))
+    print predictions.shape
+    print 'x.shape', x.shape
+    for idxi, i in enumerate(np.arange(0,x.shape[2]-patch_size,stride)):  
+        print 'slice idxi', idxi 
+	for idxj, j in enumerate(np.arange(0,x.shape[3]-patch_size,stride)):  
+            for idxk, k in enumerate(np.arange(0,x.shape[4]-patch_size,stride)):
+                #print i, j, k, '|', idxi, idxj, idxk, x.shape[4], x.shape[4]-patch_size+1
+		x_in = x[0,0,i:i+patch_size,j:j+patch_size,k:k+patch_size]
+                #print x_in.shape
+                x_shared.set_value(x_in[None,:,:,:])
+                fm = get_featuremap()
+		#print fm.shape
+                predictions[idxi,idxj,idxk] = fm[0]
+
+    result = np.concatenate(predictions,axis=0)
+
+    utils.save_pkl(result, outputs_path + '/%s.pkl' % pid)