def test_convolve1():
"""
convolve(data, kernel, mode=FULL)
Returns the discrete, linear convolution of 1-D
sequences a and v; mode can be 0 (VALID), 1 (SAME), or 2 (FULL)
to specify size of the resulting sequence.
"""
result = convolve.convolve(np.arange(8), [1, 2], mode=VALID)
test = np.array([ 1, 4, 7, 10, 13, 16, 19])
assert_equal(result.all(),test.all())
def test_convolve6():
result = convolve.convolve(np.arange(8), [1, 2, 3], mode=FULL)
test = np.array([ 0, 1, 4, 10, 16, 22, 28, 34, 32, 21])
assert_equal(result.all(),test.all())
def test_convolve5():
result = convolve.convolve(np.arange(8), [1, 2, 3], mode=SAME)
test = np.array([ 1, 4, 10, 16, 22, 28, 34, 32])
assert_equal(result.all(),test.all())
def test_convolve4():
result = convolve.convolve(np.arange(8), [1, 2, 3], mode=VALID)
test = np.array([ 4, 10, 16, 22, 28, 34])
assert_equal(result.all(),test.all())
def test_convolve3():
result = convolve.convolve(np.arange(8), [1, 2], mode=FULL)
test = np.array([ 0, 1, 4, 7, 10, 13, 16, 19, 14])
assert_equal(result.all(),test.all())
def test_convolve10():
result = convolve.convolve([1.,2.], np.arange(10.))
test = np.array([ 0., 1., 4., 7., 10., 13., 16., 19., 22., 25., 18.])
assert_equal(result.all(),test.all())
def test_convolve9():
result = convolve.convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL)
test = np.array([ 0, 1, 4, 10, 20, 35, 56, 77, 90, 94, 88, 71, 42])
assert_equal(result.all(),test.all())
def test_convolve8():
result = convolve.convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME)
test = np.array([ 4, 10, 20, 35, 56, 77, 90, 94])
assert_equal(result.all(),test.all())
def test_convolve7():
result = convolve.convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID)
test = np.array([35, 56, 77])
assert_equal(result.all(),test.all())