def test_stepout(self):
data = numpy.zeros([100, 100])
check_data = data + 1.0
# Make adjacent traces dissimilar, next nearest trace
# similar
data += 11.
data[:, ::2] = -11.0
window_size = 20
step_out = 2
# Check with a step out of 2
output = similarity(data, window_size, step_out=step_out)
same = numpy.allclose(check_data[:, step_out:], output[:, step_out:],
.001)
self.assertTrue(same)
# Check with a step out of 1
step_out = 1
output = similarity(data, window_size, step_out=step_out)
# Everything should be zero
check_data -= 1
same = numpy.allclose(check_data[:, step_out:], output[:, step_out:],
.001)
self.assertTrue(same)
def test_lag(self):
data = numpy.zeros([100, 100])
check_data = data + 1.0
# Make an off by 1 similarity that can be corrected
# with lag
data += 11
data[::2, ::2] = -11.
data[1::2, 1::2] = -11
lag = 2
window_size = 20
output = similarity(data, window_size, lag=lag)
same = numpy.allclose(check_data[window_size / 2:, 1:],
output[window_size / 2:, 1:],
atol=.01)
self.assertTrue(same)
# Should be zero with no lag
lag = 0
window_size = 20
output = similarity(data, window_size, lag=lag)
check_data[:, 1:] -= 1.
same = numpy.allclose(check_data[:, 1:], output[:, 1:], .001)
self.assertTrue(same)
def test_lag(self):
data = numpy.zeros([100, 100])
check_data = data + 1.0
# Make an off by 1 similarity that can be corrected
# with lag
data += 11
data[::2, ::2] = -11.0
data[1::2, 1::2] = -11.0
lag = 2
window_size = 20
output = similarity(data, window_size, lag=lag)
same = numpy.allclose(check_data[window_size//2:, 1:], output[window_size//2:, 1:], atol=.01)
self.assertTrue(same)
# Should be zero with no lag
lag = 0
window_size = 20
output = similarity(data, window_size, lag=lag)
check_data[:, 1:] -= 1.0
same = numpy.allclose(check_data[:, 1:], output[:, 1:], .001)
self.assertTrue(same)
def test_stepout(self):
data = numpy.zeros([100, 100])
check_data = data + 1.0
# Make adjacent traces dissimilar, next nearest trace similar.
data += 11.
data[:, ::2] = -11.0
window_size = 20
step_out = 2
# Check with a step out of 2.
output = similarity(data, window_size, step_out=step_out)
same = numpy.allclose(check_data[:, step_out:], output[:, step_out:], .001)
self.assertTrue(same)
# Check with a step out of 1
step_out = 1
output = similarity(data, window_size, step_out=step_out)
# Everything should be zero
check_data -= 1
same = numpy.allclose(check_data[:, step_out:], output[:, step_out:], .001)
self.assertTrue(same)
def test_same_data(self):
"""
Simple test to check if the algorithm works for the trivial case.
"""
# data2d = numpy.ones([3, 100])
# output2d = similarity(data2d, duration=0.01, dt=0.001, kind='marfurt')
# self.assertTrue(output2d.dims == 2)
data3d = numpy.ones([3, 3, 100])
output3d = similarity(data3d, duration=0.01, dt=0.001, kind='marfurt')
self.assertTrue(output3d.ndim == 3)
def test_same_data(self):
"""
Simple test to check if the algorithm works for the trivial case.
"""
data = numpy.zeros([100, 100])
check_data = data + 1.0
data += 10.0
window_size = 20
output = similarity(data, window_size)
same = numpy.allclose(check_data[:, 1:], output[:, 1:], .001)
self.assertTrue(same)
def test_same_data(self):
"""
Simple test to check if the algorithm works for the trivial case.
"""
data = numpy.zeros([100, 100])
check_data = data + 1.0
data += 10.0
window_size = 20
output = similarity(data, window_size)
same = numpy.allclose(check_data[:, 1:], output[:, 1:], .001)
self.assertTrue(same)
