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Moved python files from sw to sw/python; Moved scritps into sw/python/scripts

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This commit is contained in:
Andreas Tsouchlos
2022-12-08 14:31:23 +01:00
parent 7c01f0a7e3
commit 3938c4aa31
37 changed files with 136 additions and 421 deletions
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1
sw/python/utility/test/__init__.py Normal file
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"""This package contains unit tests."""

72
sw/python/utility/test/test_proximal.py Normal file
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import unittest
import numpy as np
from decoders import proximal
class CheckParityTestCase(unittest.TestCase):
"""Test case for the check_parity function."""
def test_check_parity(self):
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = proximal.ProximalDecoder(H)
d1 = np.array([0, 1, 0, 1])
c1 = np.dot(np.transpose(G), d1) % 2
d2 = np.array([0, 0, 0, 0])
c2 = np.dot(np.transpose(G), d2) % 2
d3 = np.array([1, 1, 1, 1])
c3 = np.dot(np.transpose(G), d3) % 2
invalid_codeword = np.array([0, 1, 1, 0, 1, 1, 1])
self.assertEqual(decoder._check_parity(c1), True)
self.assertEqual(decoder._check_parity(c2), True)
self.assertEqual(decoder._check_parity(c3), True)
self.assertEqual(decoder._check_parity(invalid_codeword), False)
class GradientTestCase(unittest.TestCase):
"""Test case for the calculation of the gradient of the
code-constraint-polynomial."""
def test_grad_h(self):
"""Test the gradient of the code-constraint polynomial."""
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
x = np.array([1, 2, -1, -2, 2, 1, -1]) # Some randomly chosen vector
expected_grad_h = np.array(
[4, 26, -8, -36, 38, 28, -32]) # Manually calculated result
decoder = proximal.ProximalDecoder(H)
grad_h = decoder._grad_h(x)
self.assertEqual(np.array_equal(grad_h, expected_grad_h), True)
if __name__ == "__main__":
unittest.main()

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sw/python/utility/test/test_soft_decision.py Normal file
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import unittest
import numpy as np
from decoders import maximum_likelihood
class CodewordGenerationTestCase(unittest.TestCase):
def test_codeword_generation(self):
"""Test case for data word and code word generation."""
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
decoder = maximum_likelihood.MLDecoder(G, H)
expected_datawords = np.array([[0, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 0, 1, 1],
[0, 1, 0, 0],
[0, 1, 0, 1],
[0, 1, 1, 0],
[0, 1, 1, 1],
[1, 0, 0, 0],
[1, 0, 0, 1],
[1, 0, 1, 0],
[1, 0, 1, 1],
[1, 1, 0, 0],
[1, 1, 0, 1],
[1, 1, 1, 0],
[1, 1, 1, 1]])
expected_codewords = np.array([[0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 1],
[0, 1, 0, 1, 0, 1, 0],
[1, 0, 0, 0, 0, 1, 1],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1],
[1, 0, 1, 1, 0, 1, 0],
[0, 1, 1, 0, 0, 1, 1],
[0, 1, 1, 1, 1, 0, 0],
[1, 0, 1, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1]])
self.assertEqual(np.array_equal(decoder._datawords, expected_datawords), True)
self.assertEqual(np.array_equal(decoder._codewords, expected_codewords), True)

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sw/python/utility/test/test_utility.py Normal file
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import unittest
import numpy as np
from utility import noise, codes
# TODO: Rewrite tests for new SNR calculation
class NoiseAmpFromSNRTestCase(unittest.TestCase):
"""Test case for noise amplitude calculation."""
def test_get_noise_amp_from_SNR(self):
SNR1 = 0
SNR2 = 3
SNR3 = 20
SNR4 = -20
var1 = noise.get_noise_variance_from_SNR(SNR1, n=8, k=8)
var2 = noise.get_noise_variance_from_SNR(SNR2, n=8, k=8)
var3 = noise.get_noise_variance_from_SNR(SNR3, n=8, k=8)
var4 = noise.get_noise_variance_from_SNR(SNR4, n=8, k=8)
self.assertEqual(var1, 1 * 0.5)
self.assertAlmostEqual(var2, 0.5 * 0.5, places=2)
self.assertEqual(var3, 0.01 * 0.5)
self.assertEqual(var4, 100 * 0.5)
class CodesTestCase(unittest.TestCase):
"""Tests relating to the 'codes' utilities."""
def test_get_systematic_H(self):
# Hamming(7,4) code
G = np.array([[1, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1]])
expected_H = np.array([[0, 1, 1, 1, 1, 0, 0],
[1, 0, 1, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = codes.get_systematic_H(G)
self.assertEqual(np.array_equal(expected_H, H), True)
if __name__ == '__main__':
unittest.main()

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sw/python/utility/test/test_visualization.py Normal file
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import unittest
from utility import visualization
class NumRowsTestCase(unittest.TestCase):
def test_get_num_rows(self):
"""Test case for number of row calculation."""
num_rows1 = visualization._get_num_rows(num_graphs=4, num_cols=3)
expected_rows1 = 2
num_rows2 = visualization._get_num_rows(num_graphs=5, num_cols=2)
expected_rows2 = 3
num_rows3 = visualization._get_num_rows(num_graphs=4, num_cols=4)
expected_rows3 = 1
num_rows4 = visualization._get_num_rows(num_graphs=4, num_cols=5)
expected_rows4 = 1
self.assertEqual(num_rows1, expected_rows1)
self.assertEqual(num_rows2, expected_rows2)
self.assertEqual(num_rows3, expected_rows3)
self.assertEqual(num_rows4, expected_rows4)