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#!/usr/bin/env python
#
# Copyright 2013,2017 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, 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
# 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
N = 10000
seed = 0
data = make_random_float_tuple(N, 1)
expected_result = N*[0,]
src = blocks.vector_source_f(data, False)
op = blocks.moving_average_ff(100, 0.001)
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, 1)
def test_02(self):
tb = self.tb
N = 10000
seed = 0
data = make_random_complex_tuple(N, 1)
expected_result = N*[0,]
src = blocks.vector_source_c(data, False)
op = blocks.moving_average_cc(100, 0.001)
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, 1)
# This tests implement own moving average to verify correct behaviour of the block
def test_03(self):
tb = self.tb
vlen = 5
N = 10*vlen
seed = 0
data = make_random_float_tuple(N, 2**10)
data = [int(d*1000) for d in data]
src = blocks.vector_source_i(data, False)
one_to_many = blocks.stream_to_streams(gr.sizeof_int, vlen)
one_to_vector = blocks.stream_to_vector(gr.sizeof_int, vlen)
many_to_vector = blocks.streams_to_vector(gr.sizeof_int, vlen)
isolated = [ blocks.moving_average_ii(100, 1) for i in range(vlen)]
dut = blocks.moving_average_ii(100, 1, vlen=vlen)
dut_dst = blocks.vector_sink_i(vlen=vlen)
ref_dst = blocks.vector_sink_i(vlen=vlen)
tb.connect(src, one_to_many)
tb.connect(src, one_to_vector, dut, dut_dst)
tb.connect(many_to_vector, ref_dst)
for idx, single in enumerate(isolated):
tb.connect((one_to_many,idx), single, (many_to_vector,idx))
tb.run()
dut_data = dut_dst.data()
ref_data = ref_dst.data()
# make sure result is close to zero
self.assertTupleEqual(dut_data, ref_data)
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")
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