Removed unused code and added test for shiftdim

master
Per A Brodtkorb 9 years ago
parent e59eb05226
commit e2323eab87

@ -351,51 +351,7 @@ def test_shiftdim():
print(c == a)
def test_dct3():
a = np.array([[[0.51699637, 0.42946223, 0.89843545],
[0.27853391, 0.8931508, 0.34319118],
[0.51984431, 0.09217771, 0.78764716]],
[[0.25019845, 0.92622331, 0.06111409],
[0.81363641, 0.06093368, 0.13123373],
[0.47268657, 0.39635091, 0.77978269]],
[[0.86098829, 0.07901332, 0.82169182],
[0.12560088, 0.78210188, 0.69805434],
[0.33544628, 0.81540172, 0.9393219]]])
d = dct(dct(dct(a).transpose(0, 2, 1)).transpose(2, 1, 0)
).transpose(2, 1, 0).transpose(0, 2, 1)
d0 = dctn(a)
e = idct(idct(idct(d).transpose(0, 2, 1)).transpose(2, 1, 0)
).transpose(2, 1, 0).transpose(0, 2, 1)
print(d)
print(d0)
print(np.allclose(d, d0))
print(np.allclose(a, e))
def test_dctn():
a = np.arange(12).reshape((3, -1))
print('a = ', a)
print(' ')
y = dct(a, n=10)
x = idct(y)
print('y = dct(a)')
print(y)
print('x = idct(y)')
print(x)
print(' ')
yn = dctn(a) # , shape=(10,), axes=(1,))
xn = idctn(yn) # , axes=(1,))
print('yn = dctn(a)')
print(yn)
print('xn = idctn(yn)')
print(xn)
print(' ')
print(xn-a)
def test_dct2():
def example_dct2():
import scipy.ndimage as sn
import matplotlib.pyplot as plt
name = os.path.join(path, 'autumn.gif')
@ -430,18 +386,5 @@ def test_docstrings():
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
def test_commands():
import commands
commands.getstatusoutput('preprocess -DFORMAT=html -DDEVICE=screen ' +
'tutorial.do.txt > ' +
'tmp_preprocess__tutorial.do.txt')
if __name__ == '__main__':
# print(test_commands())
# test_dct2()
test_docstrings()
# test_dctn()
# test_shiftdim()
# test_dct3()
# test()

@ -10,6 +10,14 @@ import wafo.dctpack as wd
class Test(unittest.TestCase):
def test_shiftdim(self):
a = np.arange(6).reshape((1, 1, 3, 1, 2))
b = wd.shiftdim(a)
c = wd.shiftdim(b, -2)
assert_array_almost_equal(b.shape, (3, 1, 2))
assert_array_almost_equal(c.shape, a.shape)
assert_array_almost_equal(c, a)
def test_dct3(self):
a = np.array([[[0.51699637, 0.42946223, 0.89843545],
[0.27853391, 0.8931508, 0.34319118],

Loading…
Cancel
Save