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Python

import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
from pytest import raises as assert_raises
from numpy.fft import fft, ifft
from scipy.signal import max_len_seq
class TestMLS(object):
def test_mls_inputs(self):
# can't all be zero state
assert_raises(ValueError, max_len_seq,
10, state=np.zeros(10))
# wrong size state
assert_raises(ValueError, max_len_seq, 10,
state=np.ones(3))
# wrong length
assert_raises(ValueError, max_len_seq, 10, length=-1)
assert_array_equal(max_len_seq(10, length=0)[0], [])
# unknown taps
assert_raises(ValueError, max_len_seq, 64)
# bad taps
assert_raises(ValueError, max_len_seq, 10, taps=[-1, 1])
def test_mls_output(self):
# define some alternate working taps
alt_taps = {2: [1], 3: [2], 4: [3], 5: [4, 3, 2], 6: [5, 4, 1], 7: [4],
8: [7, 5, 3]}
# assume the other bit levels work, too slow to test higher orders...
for nbits in range(2, 8):
for state in [None, np.round(np.random.rand(nbits))]:
for taps in [None, alt_taps[nbits]]:
if state is not None and np.all(state == 0):
state[0] = 1 # they can't all be zero
orig_m = max_len_seq(nbits, state=state,
taps=taps)[0]
m = 2. * orig_m - 1. # convert to +/- 1 representation
# First, make sure we got all 1's or -1
err_msg = "mls had non binary terms"
assert_array_equal(np.abs(m), np.ones_like(m),
err_msg=err_msg)
# Test via circular cross-correlation, which is just mult.
# in the frequency domain with one signal conjugated
tester = np.real(ifft(fft(m) * np.conj(fft(m))))
out_len = 2**nbits - 1
# impulse amplitude == test_len
err_msg = "mls impulse has incorrect value"
assert_allclose(tester[0], out_len, err_msg=err_msg)
# steady-state is -1
err_msg = "mls steady-state has incorrect value"
assert_allclose(tester[1:], np.full(out_len - 1, -1),
err_msg=err_msg)
# let's do the split thing using a couple options
for n in (1, 2**(nbits - 1)):
m1, s1 = max_len_seq(nbits, state=state, taps=taps,
length=n)
m2, s2 = max_len_seq(nbits, state=s1, taps=taps,
length=1)
m3, s3 = max_len_seq(nbits, state=s2, taps=taps,
length=out_len - n - 1)
new_m = np.concatenate((m1, m2, m3))
assert_array_equal(orig_m, new_m)