fixes in dependency handling

vd

@@ -68,7 +68,7 @@ if __name__ == "__main__":
       traces = traces_path
     else:
 
-      app = Process(program, envs, timeout, ["libcairo"], [], True)  
+      app = Process(program, envs, timeout, [], [], True)  
       prt = TypePrinter(traces_path, program, 0)
       traces = []
       all_files = []
@@ -101,6 +101,5 @@ if __name__ == "__main__":
       clustered_traces = ClusterScikit(vectorizer, traces, None, "dynamic", None)
     else:
       clustered_traces = ClusterConv(vectorizer, traces, None, "dynamic", None, None)
-
-    cluster_sampler(clustered_traces,1)
-    #print clusters
+      cluster_sampler(clustered_traces,1)
+      #print clusters

vdiscover/Cluster.py

@@ -26,7 +26,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib as mpl
-import pylab as plb
+#import pylab as plb
 
 from Utils import *
 from Pipeline import *
@@ -269,7 +269,7 @@ def ClusterScikit(model_file, train_file, valid_file, ftype, nsamples):
   model = make_cluster_pipeline_bow(ftype)
   X_red = model.fit_transform(train_dict)
 
-  mpl.rcParams.update({'font.size': 10})
+  #mpl.rcParams.update({'font.size': 10})
   plt.figure()
   colors = 'brgcmykbgrcmykbgrcmykbgrcmyk'
 

vpredictor

@@ -30,7 +30,6 @@ sys.setrecursionlimit(1024*1024*1024)
 from vdiscover.Pipeline import *
 from vdiscover.Recall  import Recall
 from vdiscover.Train  import Train
-from vdiscover.Cluster  import ClusterScikit, ClusterConv
 
 if __name__ == "__main__":
 
@@ -128,9 +127,13 @@ if __name__ == "__main__":
       #elif training_mode_:
       #  Train(out_file, in_file, valid_file, "lstm", ftype, nsamples)
       elif training_mode_cluster_bow:
+        from vdiscover.Cluster  import ClusterScikit
+
         #Cluster(in_file, valid_file, ftype, nsamples)
         ClusterScikit(None, in_file, valid_file, ftype, nsamples)
       elif training_mode_cluster_conv:
+        from vdiscover.Cluster  import ClusterConv
+
         #Cluster(in_file, valid_file, ftype, nsamples)
         if (model_file is None):
           print "Clustering using a convolutional model requires a pre-trained model"