def bundle_of_tubes(shape: List[int], spacing: int):
r"""
Create a 3D image of a bundle of tubes, in the form of a rectangular
plate with randomly sized holes through it.
Parameters
----------
shape : list
The size the image, with the 3rd dimension indicating the plate
thickness. If the 3rd dimension is not given then a thickness of
1 voxel is assumed.
spacing : scalar
The center to center distance of the holes. The hole sizes will be
randomly distributed between this values down to 3 voxels.
Returns
-------
image : ND-array
A boolean array with ``True`` values denoting the pore space
"""
shape = sp.array(shape)
if sp.size(shape) == 1:
shape = sp.full((3, ), int(shape))
if sp.size(shape) == 2:
shape = sp.hstack((shape, [1]))
temp = sp.zeros(shape=shape[:2])
Xi = sp.ceil(sp.linspace(spacing/2,
shape[0]-(spacing/2)-1,
int(shape[0]/spacing)))
Xi = sp.array(Xi, dtype=int)
Yi = sp.ceil(sp.linspace(spacing/2,
shape[1]-(spacing/2)-1,
int(shape[1]/spacing)))
Yi = sp.array(Yi, dtype=int)
temp[tuple(sp.meshgrid(Xi, Yi))] = 1
inds = sp.where(temp)
for i in range(len(inds[0])):
r = sp.random.randint(1, (spacing/2))
try:
s1 = slice(inds[0][i]-r, inds[0][i]+r+1)
s2 = slice(inds[1][i]-r, inds[1][i]+r+1)
temp[s1, s2] = ps_disk(r)
except ValueError:
odd_shape = sp.shape(temp[s1, s2])
temp[s1, s2] = ps_disk(r)[:odd_shape[0], :odd_shape[1]]
im = sp.broadcast_to(array=sp.atleast_3d(temp), shape=shape)
return im
def RSA(im: array, radius: int, volume_fraction: int = 1,
mode: str = 'extended'):
r"""
Generates a sphere or disk packing using Random Sequential Addition
This which ensures that spheres do not overlap but does not guarantee they
are tightly packed.
Parameters
----------
im : ND-array
The image into which the spheres should be inserted. By accepting an
image rather than a shape, it allows users to insert spheres into an
already existing image. To begin the process, start with an array of
zero such as ``im = np.zeros([200, 200], dtype=bool)``.
radius : int
The radius of the disk or sphere to insert.
volume_fraction : scalar
The fraction of the image that should be filled with spheres. The
spheres are addeds 1's, so each sphere addition increases the
``volume_fraction`` until the specified limit is reach.
mode : string
Controls how the edges of the image are handled. Options are:
'extended' - Spheres are allowed to extend beyond the edge of the image
'contained' - Spheres are all completely within the image
'periodic' - The portion of a sphere that extends beyond the image is
inserted into the opposite edge of the image (Not Implemented Yet!)
Returns
-------
image : ND-array
A copy of ``im`` with spheres of specified radius *added* to the
background.
Notes
-----
Each sphere is filled with 1's, but the center is marked with a 2. This
allows easy boolean masking to extract only the centers, which can be
converted to coordinates using ``scipy.where`` and used for other purposes.
The obtain only the spheres, use``im = im == 1``.
This function adds spheres to the background of the received ``im``, which
allows iteratively adding spheres of different radii to the unfilled space.
References
----------
[1] Random Heterogeneous Materials, S. Torquato (2001)
"""
# Note: The 2D vs 3D splitting of this just me being lazy...I can't be
# bothered to figure it out programmatically right now
# TODO: Ideally the spheres should be added periodically
print(78*'―')
print('RSA: Adding spheres of size ' + str(radius))
d2 = len(im.shape) == 2
mrad = 2*radius
if d2:
im_strel = ps_disk(radius)
mask_strel = ps_disk(mrad)
else:
im_strel = ps_ball(radius)
mask_strel = ps_ball(mrad)
if sp.any(im > 0):
# Dilate existing objects by im_strel to remove pixels near them
# from consideration for sphere placement
mask = ps.tools.fftmorphology(im > 0, im_strel > 0, mode='dilate')
mask = mask.astype(int)
else:
mask = sp.zeros_like(im)
if mode == 'contained':
mask = _remove_edge(mask, radius)
elif mode == 'extended':
pass
elif mode == 'periodic':
raise Exception('Periodic edges are not implemented yet')
else:
raise Exception('Unrecognized mode: ' + mode)
vf = im.sum()/im.size
free_spots = sp.argwhere(mask == 0)
i = 0
while vf <= volume_fraction and len(free_spots) > 0:
choice = sp.random.randint(0, len(free_spots), size=1)
if d2:
[x, y] = free_spots[choice].flatten()
im = _fit_strel_to_im_2d(im, im_strel, radius, x, y)
mask = _fit_strel_to_im_2d(mask, mask_strel, mrad, x, y)
im[x, y] = 2
else:
[x, y, z] = free_spots[choice].flatten()
im = _fit_strel_to_im_3d(im, im_strel, radius, x, y, z)
mask = _fit_strel_to_im_3d(mask, mask_strel, mrad, x, y, z)
im[x, y, z] = 2
free_spots = sp.argwhere(mask == 0)
vf = im.sum()/im.size
i += 1
if vf > volume_fraction:
print('Volume Fraction', volume_fraction, 'reached')
if len(free_spots) == 0:
print('No more free spots', 'Volume Fraction', vf)
return im
def overlapping_spheres(shape: List[int], radius: int, porosity: float,
iter_max: int = 10, tol: float = 0.01):
r"""
Generate a packing of overlapping mono-disperse spheres
Parameters
----------
shape : list
The size of the image to generate in [Nx, Ny, Nz] where Ni is the
number of voxels in the i-th direction.
radius : scalar
The radius of spheres in the packing.
porosity : scalar
The porosity of the final image, accurate to the given tolerance.
iter_max : int
Maximum number of iterations for the iterative algorithm that improves
the porosity of the final image to match the given value.
tol : float
Tolerance for porosity of the final image compared to the given value.
Returns
-------
image : ND-array
A boolean array with ``True`` values denoting the pore space
Notes
-----
This method can also be used to generate a dispersion of hollows by
treating ``porosity`` as solid volume fraction and inverting the
returned image.
"""
shape = sp.array(shape)
if sp.size(shape) == 1:
shape = sp.full((3, ), int(shape))
ndim = (shape != 1).sum()
s_vol = ps_disk(radius).sum() if ndim == 2 else ps_ball(radius).sum()
bulk_vol = sp.prod(shape)
N = int(sp.ceil((1 - porosity)*bulk_vol/s_vol))
im = sp.random.random(size=shape)
# Helper functions for calculating porosity: phi = g(f(N))
f = lambda N: spim.distance_transform_edt(im > N/bulk_vol) < radius
g = lambda im: 1 - im.sum() / sp.prod(shape)
# # Newton's method for getting image porosity match the given
# w = 1.0 # Damping factor
# dN = 5 if ndim == 2 else 25 # Perturbation
# for i in range(iter_max):
# err = g(f(N)) - porosity
# d_err = (g(f(N+dN)) - g(f(N))) / dN
# if d_err == 0:
# break
# if abs(err) <= tol:
# break
# N2 = N - int(err/d_err) # xnew = xold - f/df
# N = w * N2 + (1-w) * N
# Bisection search: N is always undershoot (bc. of overlaps)
N_low, N_high = N, 4*N
for i in range(iter_max):
N = sp.mean([N_high, N_low], dtype=int)
err = g(f(N)) - porosity
if err > 0:
N_low = N
else:
N_high = N
if abs(err) <= tol:
break
return ~f(N)
def RSA(im: array, radius: int, volume_fraction: int = 1, n_max: int = None,
mode: str = 'contained'):
r"""
Generates a sphere or disk packing using Random Sequential Addition
This algorithm ensures that spheres do not overlap but does not
guarantee they are tightly packed.
This function adds spheres to the background of the received ``im``, which
allows iteratively adding spheres of different radii to the unfilled space,
be repeatedly passing in the result of previous calls to RSA.
Parameters
----------
im : ND-array
The image into which the spheres should be inserted. By accepting an
image rather than a shape, it allows users to insert spheres into an
already existing image. To begin the process, start with an array of
zeros such as ``im = np.zeros([200, 200, 200], dtype=bool)``.
radius : int
The radius of the disk or sphere to insert.
volume_fraction : scalar (default is 1.0)
The fraction of the image that should be filled with spheres. The
spheres are added as 1's, so each sphere addition increases the
``volume_fraction`` until the specified limit is reach. Note that if
``n_max`` is reached first, then ``volume_fraction`` will not be
acheived.
n_max : int (default is 10,000)
The maximum number of spheres to add. By default the value of
``n_max`` is high so that the addition of spheres will go indefinately
until ``volume_fraction`` is met, but specifying a smaller value
will halt addition after the given number of spheres are added.
mode : string (default is 'contained')
Controls how the edges of the image are handled. Options are:
'contained' - Spheres are all completely within the image
'extended' - Spheres are allowed to extend beyond the edge of the
image. In this mode the volume fraction will be less that requested
since some spheres extend beyond the image, but their entire volume
is counted as added for computational efficiency.
Returns
-------
image : ND-array
A handle to the input ``im`` with spheres of specified radius
*added* to the background.
Notes
-----
This function uses Numba to speed up the search for valid sphere insertion
points. It seems that Numba does not look at the state of the scipy
random number generator, so setting the seed to a known value has no
effect on the output of this function. Each call to this function will
produce a unique image. If you wish to use the same realization multiple
times you must save the array (e.g. ``numpy.save``).
References
----------
[1] Random Heterogeneous Materials, S. Torquato (2001)
"""
print(80*'-')
print(f'RSA: Adding spheres of size {radius}')
im = im.astype(bool)
if n_max is None:
n_max = 10000
vf_final = volume_fraction
vf_start = im.sum()/im.size
print('Initial volume fraction:', vf_start)
if im.ndim == 2:
template_lg = ps_disk(radius*2)
template_sm = ps_disk(radius)
else:
template_lg = ps_ball(radius*2)
template_sm = ps_ball(radius)
vf_template = template_sm.sum()/im.size
# Pad image by the radius of large template to enable insertion near edges
im = np.pad(im, pad_width=2*radius, mode='edge')
# Depending on mode, adjust mask to remove options around edge
if mode == 'contained':
border = get_border(im.shape, thickness=2*radius, mode='faces')
elif mode == 'extended':
border = get_border(im.shape, thickness=radius+1, mode='faces')
else:
raise Exception('Unrecognized mode: ', mode)
# Remove border pixels
im[border] = True
# Dilate existing objects by strel to remove pixels near them
# from consideration for sphere placement
print('Dilating foreground features by sphere radius')
dt = edt(im == 0)
options_im = (dt >= radius)
# ------------------------------------------------------------------------
# Begin inserting the spheres
vf = vf_start
free_sites = np.flatnonzero(options_im)
i = 0
while (vf <= vf_final) and (i < n_max) and (len(free_sites) > 0):
c, count = _make_choice(options_im, free_sites=free_sites)
# The 100 below is arbitrary and may change performance
if count > 100:
# Regenerate list of free_sites
print('Regenerating free_sites after', i, 'iterations')
free_sites = np.flatnonzero(options_im)
if all(np.array(c) == -1):
break
s_sm = tuple([slice(x - radius, x + radius + 1, None) for x in c])
s_lg = tuple([slice(x - 2*radius, x + 2*radius + 1, None) for x in c])
im[s_sm] += template_sm # Add ball to image
options_im[s_lg][template_lg] = False # Update extended region
vf += vf_template
i += 1
print('Number of spheres inserted:', i)
# ------------------------------------------------------------------------
# Get slice into returned image to retain original size
s = tuple([slice(2*radius, d-2*radius, None) for d in im.shape])
im = im[s]
vf = im.sum()/im.size
print('Final volume fraction:', vf)
return im