minor fixes to plotting graph

README.rst

@@ -2,7 +2,7 @@ DoWhy | Making causal inference easy
 ====================================
 
 Amit Sharma (`amshar@microsoft.com <mailto:amshar@microsoft.com>`_),
-Emre Kiciman (`emrek@micosoft.com <mailto:emrek@microsoft.com>`_)
+Emre Kiciman (`emrek@microsoft.com <mailto:emrek@microsoft.com>`_)
 
 As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning. 
 

dowhy/causal_graph.py

@@ -30,18 +30,21 @@ class CausalGraph:
         self.logger = logging.getLogger(__name__)
 
     def view_graph(self, layout="dot"):
+        out_filename="causal_model.png"
         try:
             import pygraphviz as pgv
             agraph = nx.drawing.nx_agraph.to_agraph(self._graph)
-            agraph.draw("causal_model.png", format="png", prog=layout)
+            agraph.draw(out_filename, format="png", prog=layout)
         except:    
             print("Error in loading pygraphviz library. Ensure that graphviz and pygraphviz are installed.")
             print("Using Matplotlib for plotting")
             import matplotlib.pyplot as plt 
-            plt.ion()
+            plt.clf()
             nx.draw_networkx(self._graph, pos=nx.shell_layout(self._graph))
+            plt.axis('off')
+            plt.savefig(out_filename)
             plt.draw()
-            #plt.show()
+            
 
     def build_graph(self, common_cause_names, instrument_names):
         self._graph.add_node(self.treatment_name, observed="yes")