def fromarray(data, innames, outnames, coordmap=None):
"""Create an image from a numpy array.
Parameters
----------
data : numpy array
A numpy array of three dimensions.
innames : sequence
a list of input axis names
innames : sequence
a list of output axis names
coordmap : A `CoordinateMap`
If not specified, an identity coordinate map is created.
Returns
-------
image : An `Image` object
See Also
--------
load : function for loading images
save : function for saving images
"""
ndim = len(data.shape)
if not coordmap:
coordmap = Affine.from_start_step(innames,
outnames,
(0.,)*ndim,
(1.,)*ndim)
return Image(data, coordmap)
def test_affine_start_step():
incs, outcs, aff = affine_v2w()
start = aff[:3, 3]
step = aff.diagonal()[:3]
cm = Affine.from_start_step(incs.coord_names, outcs.coord_names,
start, step)
yield assert_equal, cm.affine, aff
yield assert_raises, ValueError, Affine.from_start_step, 'ijk', 'xy', \
start, step
def test_kernel():
# Verify the the 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,))
ii, jj, kk = randint(11,17, (3,))
coordmap = Affine.from_start_step('ijk', 'xyz',
randint(5,20,(3,))*0.25,
randint(5,10,(3,))*0.5)
signal = np.zeros(shape)
signal[ii,jj,kk] = 1.
signal = Image(signal, coordmap=coordmap)
kernel = LinearFilter(coordmap, shape,
fwhm=randint(50,100)/10.)
ssignal = kernel.smooth(signal)
ssignal = np.asarray(ssignal)
ssignal[:] *= kernel.norms[kernel.normalization]
I = np.indices(ssignal.shape)
I.shape = (kernel.coordmap.ndim[0], np.product(shape))
i, j, k = I[:,np.argmax(ssignal[:].flat)]
yield assert_equal, (i,j,k), (ii,jj,kk)
Z = kernel.coordmap(I) - kernel.coordmap([i,j,k])
_k = kernel(Z)
_k.shape = ssignal.shape
yield assert_true, (np.corrcoef(_k[:].flat, ssignal[:].flat)[0,1] > tol)
yield 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
vx = ssignal[i,j,(k-10):(k+10)]
vvx = coordmap(_indices(i,j,k,2)) - coordmap([[i],[j],[k]])
yield 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)) - coordmap([[i],[j],[k]])
yield 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)) - coordmap([[i],[j],[k]])
yield assert_true, (np.corrcoef(vz, kernel(vvz))[0,1] > tol)
