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(np.arange(8), [1, 2], mode=VALID) test = np.array([1, 4, 7, 10, 13, 16, 19]) assert_equal(result, test)
def test_convolve9(): result = 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, test)
def test_convolve8(): result = 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, test)
def test_convolve7(): result = convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) test = np.array([35, 56, 77]) assert_equal(result, test)
def test_convolve6(): result = 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, test)
def test_convolve5(): result = convolve(np.arange(8), [1, 2, 3], mode=SAME) test = np.array([1, 4, 10, 16, 22, 28, 34, 32]) assert_equal(result, test)
def test_convolve2(): result = convolve(np.arange(8), [1, 2], mode=SAME) test = np.array([0, 1, 4, 7, 10, 13, 16, 19]) assert_equal(result, test)
def test_convolve10(): result = convolve([1., 2.], np.arange(10.)) test = np.array([0., 1., 4., 7., 10., 13., 16., 19., 22., 25., 18.]) assert_equal(result, test)
def test_convolve10(): result = convolve([1.0, 2.0], np.arange(10.0)) test = np.array([0.0, 1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 19.0, 22.0, 25.0, 18.0]) assert_equal(result, test)
def test_convolve3(): result = convolve(np.arange(8), [1, 2], mode=FULL) test = np.array([0, 1, 4, 7, 10, 13, 16, 19, 14]) assert_equal(result, test)
def test_convolve4(): result = convolve(np.arange(8), [1, 2, 3], mode=VALID) test = np.array([4, 10, 16, 22, 28, 34]) assert_equal(result, test)