def test_outker_shape_0_div_std_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 0.94326323, -0.89923584,
-0.28315943, -0.30885619],
[ 1.27807069, 0.63492846,
-1.23798132, -0.50979644]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_input_d_4_div_euclidean_remove_mean_false_default_rest():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.13273999, -0.09456467,
-0.01975331, -0.04648187],
[ 0.00148955, -0.00257985,
0.02118244, -0.01543736]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_mag_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.18866782, -0.17986178,
-0.05663793, -0.06177634],
[-0.00420652, -0.03951693,
-0.0673274 , -0.05859426]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_std_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.26222396, -0.90901738,
-0.24902068, -0.45406818],
[ 1.54160333, 0.49371463,
-0.80440265, -0.05310058]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_outker_shape_0_div_mag_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
outker_shape = 0, 0
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.24052431, -0.18180957,
-0.04978044, -0.0898783 ],
[ 0.00301287, -0.00500357,
0.04109935, -0.03260877]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=outker_shape,
div_method='euclidean', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_std_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.27801514, -0.99776751,
-0.2487534 , -0.51636076],
[ 1.42037416, 0.16307378,
-0.9643169 , -0.31313351]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_std_remove_mean_false():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 8], [20, 12]]
gt = np.array([[ 1.32899761, -0.94678491,
-0.19777086, -0.46537822],
[ 1.67757177, 0.42027149,
-0.70711917, -0.05593578]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='std', remove_mean=False,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_euclidean_remove_mean_true():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 1.27813682e-01, -9.97862518e-02,
-2.48777084e-02, -5.16409911e-02],
[ -2.00690944e-02, -2.42322776e-02,
7.76741435e-05, -3.73861268e-02]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=True,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_div_euclidean_remove_mean_false_threshold_1_stretch_1e_2():
arr_in = np.zeros((20, 30, 4), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 4), dtype=DTYPE)
np.random.seed(42)
arr_in[:] = np.random.randn(np.prod(arr_in.shape)).reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[ 0.01255756, -0.00894607,
-0.00186872, -0.00439731],
[ 0.00013929, -0.00024125,
0.00198085, -0.0014436 ]],
dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False, threshold=1, stretch=1e-2,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
div_method='euclidean', remove_mean=False, threshold=1, stretch=1e-2,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_lena_npy_array():
arr_in = lena()[::32, ::32].astype(DTYPE)
idx = [[4, 2], [4, 2]]
gt = np.array([0.2178068, 0.30647671],
dtype=DTYPE)
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_input_d_1_default_remove_mean_threshold_stretch():
arr_in = np.zeros((20, 30, 1), dtype=DTYPE)
inker_shape = 5, 5
arr_out = np.zeros((16, 26, 1), dtype=DTYPE)
np.random.seed(42)
data = np.random.randn(np.prod(arr_in.shape))
arr_in[:] = data.reshape(arr_in.shape)
idx = [[4, 3], [20, 12]]
gt = np.array([[0.20177312], [0.21249016]], dtype=DTYPE)
lnorm(arr_in, arr_out=arr_out,
inker_shape=inker_shape, outker_shape=inker_shape,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
arr_out = lnorm(arr_in,
inker_shape=inker_shape, outker_shape=inker_shape,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_lena_pt3_array():
lena32 = lena()[::32, ::32].astype(DTYPE)/255.
arr_in = Array(lena32.shape, dtype=DTYPE)
arr_in[:] = lena32
idx = [[4, 2], [4, 2]]
gt = np.array([0.21779411, 0.30645376],
dtype=DTYPE)
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
def test_lena_npy_array_non_C_contiguous():
arr_in = lena()[::32, ::32].astype(DTYPE)
arr_in = np.asfortranarray(arr_in)
idx = [[4, 2], [4, 2]]
gt = np.array([0.2178068, 0.30647671],
dtype=DTYPE)
try:
arr_out = lnorm(arr_in,
plugin=plugin, plugin_kwargs=plugin_kwargs)
gv = arr_out[idx]
assert_allclose(gv, gt, rtol=RTOL, atol=ATOL)
except NotImplementedError:
raise SkipTest
