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 = 1
window_size = 20
output = compute_similarity( data, window_size,
lag=lag, test=True )
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 = compute_similarity( data, window_size,
lag=lag )
check_data[:,1:] -= 1.
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 = compute_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 = compute_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.
"""
data = numpy.zeros( [100, 100] )
check_data = data + 1.0
data +=10.
window_size = 20
output = compute_similarity( data, window_size )
same = numpy.allclose( check_data[:,1:],
output[:,1:], .001 )
self.assertTrue( same )
