1 files changed, 91 insertions, 0 deletions
diff --git a/gr-blocks/python/qa_moving_average.py b/gr-blocks/python/qa_moving_average.py
new file mode 100644
index 0000000000..169b4746c2
--- /dev/null
+++ b/gr-blocks/python/qa_moving_average.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+#
+# Copyright 2013 Free Software Foundation, Inc.
+#
+# This file is part of GNU Radio
+#
+# GNU Radio is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 3, or (at your option)
+# any later version.
+#
+# GNU Radio is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with GNU Radio; see the file COPYING.  If not, write to
+# the Free Software Foundation, Inc., 51 Franklin Street,
+# Boston, MA 02110-1301, USA.
+#
+
+from gnuradio import gr, gr_unittest
+import blocks_swig as blocks
+import math, random
+
+def make_random_complex_tuple(L, scale=1):
+    result = []
+    for x in range(L):
+        result.append(scale*complex(2*random.random()-1,
+                                    2*random.random()-1))
+    return tuple(result)
+
+def make_random_float_tuple(L, scale=1):
+    result = []
+    for x in range(L):
+        result.append(scale*(2*random.random()-1))
+    return tuple(result)
+
+class test_moving_average(gr_unittest.TestCase):
+
+    def setUp(self):
+        self.tb = gr.top_block()
+
+    def tearDown(self):
+        self.tb = None
+
+    def test_01(self):
+        tb = self.tb
+
+        N = 10000
+        seed = 0
+        data = make_random_float_tuple(N, 1)
+        expected_result = N*[0,]
+
+        src = gr.vector_source_f(data, False)
+        op  = blocks.moving_average_ff(100, 0.001)
+        dst = gr.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, 1)
+
+    def test_02(self):
+        tb = self.tb
+
+        N = 10000
+        seed = 0
+        data = make_random_complex_tuple(N, 1)
+        expected_result = N*[0,]
+
+        src = gr.vector_source_c(data, False)
+        op  = blocks.moving_average_cc(100, 0.001)
+        dst = gr.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, 1)
+
+if __name__ == '__main__':
+    gr_unittest.run(test_moving_average, "test_moving_average.xml")