2 files changed, 66 insertions, 49 deletions
diff --git a/gr-blocks/examples/ctrlport/simple_copy.grc b/gr-blocks/examples/ctrlport/simple_copy.grc
index a52c350ded..f61c6cd18a 100644
--- a/gr-blocks/examples/ctrlport/simple_copy.grc
+++ b/gr-blocks/examples/ctrlport/simple_copy.grc
@@ -1,5 +1,5 @@
 <?xml version='1.0' encoding='utf-8'?>
-<?grc format='1' created='3.7.11'?>
+<?grc format='1' created='3.7.12'?>
 <flow_graph>
   <timestamp>Sat Mar 16 22:03:14 2013</timestamp>
   <block>
@@ -393,48 +393,6 @@ to enable/disablethis block</value>
     </param>
   </block>
   <block>
-    <key>epy_block</key>
-    <param>
-      <key>alias</key>
-      <value></value>
-    </param>
-    <param>
-      <key>_io_cache</key>
-      <value>('Null Msg Source', 'blk', [], [], [('fake_output', 'message', 1)], '', [])</value>
-    </param>
-    <param>
-      <key>_source_code</key>
-      <value># Block that doesn't do anything, just used to get a msg input port on another block exposed to ControlPort
-from gnuradio import gr
-import pmt
-class blk(gr.basic_block):
-    def __init__(self): 
-        gr.basic_block.__init__(self,name='Null Msg Source',in_sig=[],out_sig=[])
-        self.message_port_register_out(pmt.intern("fake_output"))
-</value>
-    </param>
-    <param>
-      <key>comment</key>
-      <value></value>
-    </param>
-    <param>
-      <key>_enabled</key>
-      <value>True</value>
-    </param>
-    <param>
-      <key>_coordinate</key>
-      <value>(357, 218)</value>
-    </param>
-    <param>
-      <key>_rotation</key>
-      <value>0</value>
-    </param>
-    <param>
-      <key>id</key>
-      <value>epy_block_0</value>
-    </param>
-  </block>
-  <block>
     <key>note</key>
     <param>
       <key>alias</key>
@@ -861,10 +819,4 @@ python simple_copy_controller.py 127.0.0.1 &lt;PORT&gt; true</value>
     <source_key>0</source_key>
     <sink_key>0</sink_key>
   </connection>
-  <connection>
-    <source_block_id>epy_block_0</source_block_id>
-    <sink_block_id>blocks_copy_0</sink_block_id>
-    <source_key>fake_output</source_key>
-    <sink_key>en</sink_key>
-  </connection>
 </flow_graph>
diff --git a/gr-blocks/python/blocks/qa_moving_average.py b/gr-blocks/python/blocks/qa_moving_average.py
index 2c58805925..53b240fe7f 100644
--- a/gr-blocks/python/blocks/qa_moving_average.py
+++ b/gr-blocks/python/blocks/qa_moving_average.py
@@ -45,6 +45,10 @@ class test_moving_average(gr_unittest.TestCase):
     def tearDown(self):
         self.tb = None
 
+    # These tests will always pass and are therefore useless. 100 random numbers [-1,1) are
+    # getting summed up and scaled with 0.001. Then, an assertion verifies a result near 0,
+    # which is the case even if the block is malfunctioning.
+
     def test_01(self):
         tb = self.tb
 
@@ -87,5 +91,66 @@ class test_moving_average(gr_unittest.TestCase):
         # make sure result is close to zero
         self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
 
+    # This tests implement own moving average to verify correct behaviour of the block
+
+    def test_03(self):
+        tb = self.tb
+
+        N = 10000  # number of samples
+        history = 100  # num of samples to average
+        data = make_random_float_tuple(N, 1)  # generate random data
+
+        #  pythonic MA filter
+        data_padded = (history-1)*[0.0]+list(data)  # history  
+        expected_result = []
+        moving_sum = sum(data_padded[:history-1])
+        for i in range(N):
+            moving_sum += data_padded[i+history-1]
+            expected_result.append(moving_sum)
+            moving_sum -= data_padded[i]
+
+        src = blocks.vector_source_f(data, False)
+        op  = blocks.moving_average_ff(history, 1)
+        dst = blocks.vector_sink_f()
+        
+        tb.connect(src, op)
+        tb.connect(op, dst)
+        tb.run()
+    
+        dst_data = dst.data()
+
+        # make sure result is close to zero
+        self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 4)
+
+    def test_04(self):
+        tb = self.tb
+
+        N = 10000  # number of samples
+        history = 100  # num of samples to average
+        data = make_random_complex_tuple(N, 1)  # generate random data
+
+        #  pythonic MA filter
+        data_padded = (history-1)*[0.0+1j*0.0]+list(data)  # history  
+        expected_result = []
+        moving_sum = sum(data_padded[:history-1])
+        for i in range(N):
+            moving_sum += data_padded[i+history-1]
+            expected_result.append(moving_sum)
+            moving_sum -= data_padded[i]
+
+        src = blocks.vector_source_c(data, False)
+        op  = blocks.moving_average_cc(history, 1)
+        dst = blocks.vector_sink_c()
+        
+        tb.connect(src, op)
+        tb.connect(op, dst)
+        tb.run()
+    
+        dst_data = dst.data()
+
+        # make sure result is close to zero
+        self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 4)
+
+
 if __name__ == '__main__':
     gr_unittest.run(test_moving_average, "test_moving_average.xml")