本文整理汇总了Python中nipy.testing.assert_equal函数的典型用法代码示例。如果您正苦于以下问题:Python assert_equal函数的具体用法?Python assert_equal怎么用?Python assert_equal使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了assert_equal函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_resid
def test_resid():
# Data is projected onto k=10 dimensional subspace then has its mean
# removed. Should still have rank 10.
k = 10
ncomp = 5
ntotal = k
X = np.random.standard_normal((data['nimages'], k))
p = pca(data['fmridata'], -1, ncomp=ncomp, design_resid=X)
yield assert_equal(
p['basis_vectors'].shape,
(data['nimages'], ntotal))
yield assert_equal(
p['basis_projections'].shape,
data['mask'].shape + (ncomp,))
yield assert_equal(p['pcnt_var'].shape, (ntotal,))
yield assert_almost_equal(p['pcnt_var'].sum(), 100.)
# if design_resid is None, we do not remove the mean, and we get
# full rank from our data
p = pca(data['fmridata'], -1, design_resid=None)
rank = p['basis_vectors'].shape[1]
yield assert_equal(rank, data['nimages'])
rarr = reconstruct(p['basis_vectors'], p['basis_projections'], -1)
# add back the sqrt MSE, because we standardized
rmse = root_mse(data['fmridata'], axis=-1)[...,None]
yield assert_array_almost_equal(rarr * rmse, data['fmridata'])
示例2: test_scaling_io_dtype
def test_scaling_io_dtype():
# Does data dtype get set?
# Is scaling correctly applied?
rng = np.random.RandomState(19660520) # VBD
ulp1_f32 = np.finfo(np.float32).eps
types = (np.uint8, np.uint16, np.int16, np.int32, np.float32)
with InTemporaryDirectory():
for in_type in types:
for out_type in types:
data, _ = randimg_in2out(rng, in_type, out_type, 'img.nii')
img = load_image('img.nii')
# Check the output type is as expected
hdr = img.metadata['header']
assert_equal(hdr.get_data_dtype().type, out_type)
# Check the data is within reasonable bounds. The exact bounds
# are a little annoying to calculate - see
# nibabel/tests/test_round_trip for inspiration
data_back = img.get_data().copy() # copy to detach from file
del img
top = np.abs(data - data_back)
nzs = (top !=0) & (data !=0)
abs_err = top[nzs]
if abs_err.size != 0: # all exact, that's OK.
continue
rel_err = abs_err / data[nzs]
if np.dtype(out_type).kind in 'iu':
slope, inter = hdr.get_slope_inter()
abs_err_thresh = slope / 2.0
rel_err_thresh = ulp1_f32
elif np.dtype(out_type).kind == 'f':
abs_err_thresh = big_bad_ulp(data.astype(out_type))[nzs]
rel_err_thresh = ulp1_f32
assert_true(np.all(
(abs_err <= abs_err_thresh) |
(rel_err <= rel_err_thresh)))
示例3: test_series_from_mask
def test_series_from_mask():
""" Test the smoothing of the timeseries extraction
"""
# A delta in 3D
data = np.zeros((40, 40, 40, 2))
data[20, 20, 20] = 1
mask = np.ones((40, 40, 40), dtype=np.bool)
with InTemporaryDirectory():
for affine in (np.eye(4), np.diag((1, 1, -1, 1)),
np.diag((.5, 1, .5, 1))):
img = nib.Nifti1Image(data, affine)
nib.save(img, 'testing.nii')
series, header = series_from_mask('testing.nii', mask, smooth=9)
series = np.reshape(series[:, 0], (40, 40, 40))
vmax = series.max()
# We are expecting a full-width at half maximum of
# 9mm/voxel_size:
above_half_max = series > .5*vmax
for axis in (0, 1, 2):
proj = np.any(np.any(np.rollaxis(above_half_max,
axis=axis), axis=-1), axis=-1)
assert_equal(proj.sum(), 9/np.abs(affine[axis, axis]))
# Check that NaNs in the data do not propagate
data[10, 10, 10] = np.NaN
img = nib.Nifti1Image(data, affine)
nib.save(img, 'testing.nii')
series, header = series_from_mask('testing.nii', mask, smooth=9)
assert_true(np.all(np.isfinite(series)))
示例4: test_Rintercept
def test_Rintercept():
x = F.Term('x')
y = F.Term('x')
xf = x.formula
yf = y.formula
newf = (xf+F.I)*(yf+F.I)
assert_equal(set(newf.terms), set([x,y,x*y,sympy.Number(1)]))
示例5: test_largest_cc
def test_largest_cc():
""" Check the extraction of the largest connected component.
"""
a = np.zeros((6, 6, 6))
a[1:3, 1:3, 1:3] = 1
assert_equal(a, largest_cc(a))
b = a.copy()
b[5, 5, 5] = 1
assert_equal(a, largest_cc(b))
示例6: test_kernel
def test_kernel():
# Verify that convolution with a delta function gives the correct
# answer.
tol = 0.9999
sdtol = 1.0e-8
for x in range(6):
shape = randint(30,60,(3,))
# pos of delta
ii, jj, kk = randint(11,17, (3,))
# random affine coordmap (diagonal and translations)
coordmap = AffineTransform.from_start_step('ijk', 'xyz',
randint(5,20,(3,))*0.25,
randint(5,10,(3,))*0.5)
# delta function in 3D array
signal = np.zeros(shape)
signal[ii,jj,kk] = 1.
signal = Image(signal, coordmap=coordmap)
# A filter with coordmap, shape matched to image
kernel = LinearFilter(coordmap, shape,
fwhm=randint(50,100)/10.)
# smoothed normalized 3D array
ssignal = kernel.smooth(signal).get_data()
ssignal[:] *= kernel.norms[kernel.normalization]
# 3 points * signal.size array
I = np.indices(ssignal.shape)
I.shape = (kernel.coordmap.ndims[0], np.product(shape))
# location of maximum in smoothed array
i, j, k = I[:, np.argmax(ssignal[:].flat)]
# same place as we put it before smoothing?
assert_equal((i,j,k), (ii,jj,kk))
# get physical points position relative to position of delta
Z = kernel.coordmap(I.T) - kernel.coordmap([i,j,k])
_k = kernel(Z)
_k.shape = ssignal.shape
assert_true((np.corrcoef(_k[:].flat, ssignal[:].flat)[0,1] > tol))
assert_true(((_k[:] - ssignal[:]).std() < sdtol))
def _indices(i,j,k,axis):
I = np.zeros((3,20))
I[0] += i
I[1] += j
I[2] += k
I[axis] += np.arange(-10,10)
return I.T
vx = ssignal[i,j,(k-10):(k+10)]
xformed_ijk = coordmap([i, j, k])
vvx = coordmap(_indices(i,j,k,2)) - xformed_ijk
assert_true((np.corrcoef(vx, kernel(vvx))[0,1] > tol))
vy = ssignal[i,(j-10):(j+10),k]
vvy = coordmap(_indices(i,j,k,1)) - xformed_ijk
assert_true((np.corrcoef(vy, kernel(vvy))[0,1] > tol))
vz = ssignal[(i-10):(i+10),j,k]
vvz = coordmap(_indices(i,j,k,0)) - xformed_ijk
assert_true((np.corrcoef(vz, kernel(vvz))[0,1] > tol))
示例7: _test_clamping
def _test_clamping(I, thI=0.0, clI=256):
regie = IconicRegistration(I, I, bins=clI)
Ic = regie._source
Ic2 = regie._target[1:I.shape[0]+1,1:I.shape[1]+1,1:I.shape[2]+1]
assert_equal(Ic, Ic2.squeeze())
dyn = Ic.max() + 1
assert_equal(dyn, regie._joint_hist.shape[0])
assert_equal(dyn, regie._joint_hist.shape[1])
assert_equal(dyn, regie._source_hist.shape[0])
assert_equal(dyn, regie._target_hist.shape[0])
return Ic, Ic2
示例8: test_save1
def test_save1():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
img = load_image(funcfile)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
示例9: test_update_labels
def test_update_labels():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20, dim=20, r=3,
amplitude=5, noise=1, jitter=1, prng=prng)
P = os.multivariate_stat(data, vardata, XYZ)
P.init_hidden_variables()
p = P.data.shape[1]
P.labels_prior = np.ones((1, p), float)
P.label_values = np.zeros((1, p), int)
P.labels_prior_mask = np.arange(p)
P.update_labels()
assert_equal(max(abs(P.labels - np.zeros(p, int))), 0)
示例10: test_mod
def test_mod():
from nipy.core.reference.coordinate_map import _matching_orth_dim
aff = np.diag([1,2,3,1])
for i in range(3):
yield assert_equal(_matching_orth_dim(i, aff), (i, ''))
aff = np.diag([-1,-2,-3,1])
for i in range(3):
yield assert_equal(_matching_orth_dim(i, aff), (i, ''))
aff = np.ones((4,4))
for i in range(3):
val, msg = _matching_orth_dim(i, aff)
yield assert_equal(val, None)
aff = np.zeros((3,3))
for i in range(2):
val, msg = _matching_orth_dim(i, aff)
yield assert_equal(val, None)
aff = np.array([[1, 0, 0, 1],
[0, 0, 2, 1],
[0, 3, 0, 1],
[0, 0, 0, 1]])
val, msg = _matching_orth_dim(1, aff)
yield assert_equal(_matching_orth_dim(0, aff), (0, ''))
yield assert_equal(_matching_orth_dim(1, aff), (2, ''))
yield assert_equal(_matching_orth_dim(2, aff), (1, ''))
aff = np.diag([1, 2, 0, 1])
aff[:,3] = 1
yield assert_equal(_matching_orth_dim(2, aff), (2, ''))
示例11: test_save2
def test_save2():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
shape = (13,5,7,3)
step = np.array([3.45,2.3,4.5,6.93])
cmap = api.AffineTransform.from_start_step('ijkt', 'xyzt', [1,3,5,0], step)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
示例12: test_names
def test_names():
# Check that the design column names are what we expect
X = twoway.design(D, return_float=False)
assert_equal(
set(X.dtype.names),
set(
(
"Duration_1*Weight_1",
"Duration_1*Weight_2",
"Duration_1*Weight_3",
"Duration_2*Weight_1",
"Duration_2*Weight_2",
"Duration_2*Weight_3",
)
),
)
示例13: test_calling_shapes
def test_calling_shapes():
cs2d = CS("ij")
cs1d = CS("i")
cm2d = CoordinateMap(cs2d, cs2d, lambda x: x + 1)
cm1d2d = CoordinateMap(cs1d, cs2d, lambda x: np.concatenate((x, x), axis=-1))
at2d = AffineTransform(cs2d, cs2d, np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1]]))
at1d2d = AffineTransform(cs1d, cs2d, np.array([[1, 0], [0, 1], [0, 1]]))
# test coordinate maps and affine transforms
for xfm2d, xfm1d2d in ((cm2d, cm1d2d), (at2d, at1d2d)):
arr = np.array([0, 1])
yield assert_array_equal(xfm2d(arr), [1, 2])
# test lists work too
res = xfm2d([0, 1])
yield assert_array_equal(res, [1, 2])
# and return arrays (by checking shape attribute)
yield assert_equal(res.shape, (2,))
# maintaining input shape
arr_long = arr[None, None, :]
yield assert_array_equal(xfm2d(arr_long), arr_long + 1)
# wrong shape array raises error
yield assert_raises(CoordinateSystemError, xfm2d, np.zeros((3,)))
yield assert_raises(CoordinateSystemError, xfm2d, np.zeros((3, 3)))
# 1d to 2d
arr = np.array(1)
yield assert_array_equal(xfm1d2d(arr), [1, 1])
arr_long = arr[None, None, None]
yield assert_array_equal(xfm1d2d(arr_long), np.ones((1, 1, 2)))
# wrong shape array raises error. Note 1d input requires size 1
# as final axis
yield assert_raises(CoordinateSystemError, xfm1d2d, np.zeros((3,)))
yield assert_raises(CoordinateSystemError, xfm1d2d, np.zeros((3, 2)))
示例14: test_PCAMask
def test_PCAMask():
# for 2 and 4D case
ntotal = data['nimages'] - 1
ncomp = 5
arr4d = data['fmridata']
mask3d = data['mask']
arr2d = arr4d.reshape((-1, data['nimages']))
mask1d = mask3d.reshape((-1))
for arr, mask in (arr4d, mask3d), (arr2d, mask1d):
p = pca(arr, -1, mask, ncomp=ncomp)
yield assert_equal(
p['basis_vectors'].shape,
(data['nimages'], ntotal))
yield assert_equal(
p['basis_projections'].shape,
mask.shape + (ncomp,))
yield assert_equal(p['pcnt_var'].shape, (ntotal,))
yield assert_almost_equal(p['pcnt_var'].sum(), 100.)
示例15: test_PCANoMask_nostandardize
def test_PCANoMask_nostandardize():
ntotal = data['nimages'] - 1
ncomp = 5
p = pca(data['fmridata'], -1, ncomp=ncomp, standardize=False)
assert_equal(p['basis_vectors'].shape, (data['nimages'], ntotal))
assert_equal(p['basis_projections'].shape, data['mask'].shape + (ncomp,))
assert_equal(p['pcnt_var'].shape, (ntotal,))
assert_almost_equal(p['pcnt_var'].sum(), 100.)