def cut_slit(data,slit_number): slit_number -= 1 data[scipy.isnan(data)] = 0. slice = data.sum(axis=1) # Find bottom of good data bottom = 0 while slice[bottom]==0: bottom += 1 slice = scipy.trim_zeros(slice) zeros = scipy.where(slice==0) zeros = zeros[0]+bottom # Special case for images with only one slit if zeros.size==0: # Deal with upper zeros slice = data.sum(axis=1) top = bottom while top<slice.size and slice[top]!=0: top += 1 slice = data.sum(axis=0) indx = scipy.where(slice!=0) start = indx[0][0] end = indx[0][-1]+1 return data[bottom:top,start:end],start,bottom borders = scipy.array_split(zeros,zeros.size/5) if slit_number>len(borders): return scipy.asarray(0),0,0 if slit_number==0: start = 0 end = borders[0][0] elif slit_number==len(borders): start = borders[slit_number-1][4]+1 end = slice.size else: start = borders[slit_number-1][4]+1 end = borders[slit_number][0] data = data[start:end] bottom = start slice = data.sum(axis=0) indx = scipy.where(slice!=0) start = indx[0][0] end = indx[0][-1]+1 return data[:,start:end],start,bottom
def _create_alias_map(im, alias=None):
r"""
Creates an alias mapping between phases in original image and identifyable
names. This mapping is used during network extraction to label
interconnection between and properties of each phase.
Parameters
----------
im : ND-array
Image of porous material where each phase is represented by unique
integer. Phase integer should start from 1. Boolean image will extract
only one network labeled with True's only.
alias : dict (Optional)
A dictionary that assigns unique image label to specific phase.
For example {1: 'Solid'} will show all structural properties associated
with label 1 as Solid phase properties.
If ``None`` then default labelling will be used i.e {1: 'Phase1',..}.
Returns
-------
A dictionary with numerical phase labels as key, and readable phase names
as valuies. If no alias is provided then default labelling is used
i.e {1: 'Phase1',..}
"""
# -------------------------------------------------------------------------
# Get alias if provided by user
phases_num = sp.unique(im * 1)
phases_num = sp.trim_zeros(phases_num)
al = {}
for values in phases_num:
al[values] = 'phase{}'.format(values)
if alias is not None:
alias_sort = dict(sorted(alias.items()))
phase_labels = sp.array([*alias_sort])
al = alias
if set(phase_labels) != set(phases_num):
raise Exception('Alias labels does not match with image labels '
'please provide correct image labels')
return al
def solve(d,orders):
path = os.path.split(__file__)[0]
lines = {}
lines['cuar'] = numpy.loadtxt(path+"/data/cuar.lines")
lines['hgne'] = numpy.loadtxt(path+"/data/hgne.lines")
lines['xe'] = numpy.loadtxt(path+"/data/xe.lines")
#startsoln = numpy.load(path+"/data/test_wavesol.dat",
# allow_pickle=True)
startsoln = numpy.load(path+"/data/esi_wavesolution.dat",
allow_pickle=True)
#arclist = d.keys() # Under python 3 this does not produce a list
# and so arclist[0] below fails
arclist = list(d)
arclist.sort()
soln = []
if d[arclist[0]].shape[1]>3000:
xvals = numpy.arange(4096.)
cuslice = slice(3860,3940)
fw1 = 75.
fw2 = 9
else:
xvals = numpy.arange(1.,4096.,2.)
cuslice = slice(1930,1970)
fw1 = 37.
fw2 = 5
"""
Do a temporary kludge. In some cases, the finding of the orders
fails, and only 9 orders are found. In this case, we need to skip
the first of the orders
"""
if len(orders) == 9:
ordstart = 1
dord = 1
else:
ordstart = 0
dord = 0
for i in range(ordstart, 10):
solution = startsoln[i]
start,end = orders[(i-dord)]
peaks = {}
trace = {}
fitD = {}
WIDTH = 4
import pylab
for arc in arclist:
data = numpy.nanmedian(d[arc][start:end],axis=0)
data[scipy.isnan(data)] = 0.
if i==0 and arc=='cuar':
data[cuslice] = numpy.median(data)
trace[arc] = data.copy()
bak = ndimage.percentile_filter(data,50.,int(fw1))
bak = getContinuum(bak,40.)
data -= bak
fitD[arc] = data/d[arc][start:end].std(0)
p = ndimage.maximum_filter(data,fw2)
std = clip(scipy.trim_zeros(data),3.)[1]
nsig = ndimage.uniform_filter((data>7.*std)*1.,3)
peak = scipy.where((nsig==1)&(p>10.*std)&(p==data))[0]
peaks[arc] = []
for p in peak:
if p-WIDTH<0 or p+WIDTH+1>xvals.size:
continue
x = xvals[p-WIDTH:p+WIDTH+1].copy()#-xvals[p]
f = data[p-WIDTH:p+WIDTH+1].copy()
fitdata = scipy.array([x,f]).T
fit = scipy.array([0.,f.max(),xvals[p],1.])
fit,chi = sf.ngaussfit(fitdata,fit,weight=1)
peaks[arc].append(fit[2])#+xvals[p])
for converge in range(15):
wave = 10**sf.genfunc(xvals,0.,solution)
refit = []
corrA = {}
err = wave[int(wave.size/2)]-wave[int(wave.size/2-1)]
for arc in arclist:
corr = []
p = 10.**sf.genfunc(peaks[arc],0.,solution)
for k in range(p.size):
cent = p[k]
diff = cent-lines[arc]
corr.append(diff[abs(diff).argmin()])
corr = numpy.array(corr)
if corr.size<4:
continue
m,s = clip(corr)
corr = numpy.median(corr[abs(corr-m)<5.*s])
print(corr)
corrA[arc] = corr
# corr = m
#for arc in arclist:
p = 10.**sf.genfunc(peaks[arc],0.,solution)
for k in range(p.size):
pos = peaks[arc][k]
cent = p[k]
diff = abs(cent-lines[arc]-corr)
if diff.min()<2.*err:
refit.append([pos,lines[arc][diff.argmin()]])
refit = scipy.asarray(refit)
solution = sf.lsqfit(refit,'polynomial',3)
refit = []
err = solution['coeff'][1]
for arc in arclist:
data = trace[arc]
for pos in peaks[arc]:
cent = sf.genfunc(pos,0.,solution)
delta = 1e9
match = None
for j in lines[arc]:
diff = abs(cent-j)
if diff<delta and diff<1.*err:
delta = diff
match = j
if match is not None:
refit.append([pos,match])
refit = scipy.asarray(refit)
refit[:,1] = numpy.log10(refit[:,1])
solution = sf.lsqfit(refit,'chebyshev',3)
#refit[:,0],refit[:,1] = refit[:,1].copy(),refit[:,0].copy()
#refit = numpy.array([refit[:,1],refit[:,0]]).T
#refit = refit[:,::-1]
solution2 = sf.lsqfit(refit[:,::-1],'chebyshev',3)
#soln.append([solution,solution2])
w = 10**sf.genfunc(xvals,0.,solution)
if (w==wave).all() or converge>8:
print("Order %d converged in %d iterations"%(i,converge))
soln.append([solution,solution2])
break
for arc in arclist:
pylab.plot(w,trace[arc])
pylab.plot(w,fitD[arc])
pp = 10**sf.genfunc(peaks[arc],0.,solution)
for p in pp:
pylab.axvline(p,c='k')
for j in 10**refit[:,1]:
if j>w[0] and j<w[-1]:
pylab.axvline(j)
pylab.show()
break
return soln
def snow_dual(im, voxel_size=1,
boundary_faces=['top', 'bottom', 'left', 'right', 'front', 'back'],
marching_cubes_area=False):
r"""
Extracts a dual pore and solid network from a binary image using a modified
version of the SNOW algorithm
Parameters
----------
im : ND-array
Binary image in the Boolean form with True’s as void phase and False’s
as solid phase. It can process the inverted configuration of the
boolean image as well, but output labelling of phases will be inverted
and solid phase properties will be assigned to void phase properties
labels which will cause confusion while performing the simulation.
voxel_size : scalar
The resolution of the image, expressed as the length of one side of a
voxel, so the volume of a voxel would be **voxel_size**-cubed. The
default is 1, which is useful when overlaying the PNM on the original
image since the scale of the image is alway 1 unit lenth per voxel.
boundary_faces : list of strings
Boundary faces labels are provided to assign hypothetical boundary
nodes having zero resistance to transport process. For cubical
geometry, the user can choose ‘left’, ‘right’, ‘top’, ‘bottom’,
‘front’ and ‘back’ face labels to assign boundary nodes. If no label is
assigned then all six faces will be selected as boundary nodes
automatically which can be trimmed later on based on user requirements.
marching_cubes_area : bool
If ``True`` then the surface area and interfacial area between regions
will be using the marching cube algorithm. This is a more accurate
representation of area in extracted network, but is quite slow, so
it is ``False`` by default. The default method simply counts voxels
so does not correctly account for the voxelated nature of the images.
Returns
-------
A dictionary containing all the void and solid phase size data, as well as
the network topological information. The dictionary names use the OpenPNM
convention (i.e. 'pore.coords', 'throat.conns') so it may be converted
directly to an OpenPNM network object using the ``update`` command.
References
----------
[1] Gostick, J. "A versatile and efficient network extraction algorithm
using marker-based watershed segmenation". Phys. Rev. E 96, 023307 (2017)
[2] Khan, ZA et al. "Dual network extraction algorithm to investigate
multiple transport processes in porous materials: Image-based modeling
of pore and grain-scale processes. Computers and Chemical Engineering.
123(6), 64-77 (2019)
"""
# -------------------------------------------------------------------------
# SNOW void phase
pore_regions = snow_partitioning(im, return_all=True)
# SNOW solid phase
solid_regions = snow_partitioning(~im, return_all=True)
# -------------------------------------------------------------------------
# Combined Distance transform of two phases.
pore_dt = pore_regions.dt
solid_dt = solid_regions.dt
dt = pore_dt + solid_dt
pore_peaks = pore_regions.peaks
solid_peaks = solid_regions.peaks
peaks = pore_peaks + solid_peaks
# Calculates combined void and solid regions for dual network extraction
pore_regions = pore_regions.regions
solid_regions = solid_regions.regions
pore_region = pore_regions*im
solid_region = solid_regions*~im
solid_num = sp.amax(pore_regions)
solid_region = solid_region + solid_num
solid_region = solid_region * ~im
regions = pore_region + solid_region
b_num = sp.amax(regions)
# -------------------------------------------------------------------------
# Boundary Conditions
regions = add_boundary_regions(regions=regions, faces=boundary_faces)
# -------------------------------------------------------------------------
# Padding distance transform to extract geometrical properties
f = boundary_faces
if f is not None:
if im.ndim == 2:
faces = [(int('left' in f)*3, int('right' in f)*3),
(int(('front') in f)*3 or int(('bottom') in f)*3,
int(('back') in f)*3 or int(('top') in f)*3)]
if im.ndim == 3:
faces = [(int('left' in f)*3, int('right' in f)*3),
(int('front' in f)*3, int('back' in f)*3),
(int('top' in f)*3, int('bottom' in f)*3)]
dt = sp.pad(dt, pad_width=faces, mode='edge')
else:
dt = dt
# -------------------------------------------------------------------------
# Extract void,solid and throat information from image
net = regions_to_network(im=regions, dt=dt, voxel_size=voxel_size)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# Extract marching cube surface area and interfacial area of regions
if marching_cubes_area:
areas = region_surface_areas(regions=regions)
interface_area = region_interface_areas(regions=regions, areas=areas,
voxel_size=voxel_size)
net['pore.surface_area'] = areas * voxel_size**2
net['throat.area'] = interface_area.area
# -------------------------------------------------------------------------
# Find void to void, void to solid and solid to solid throat conns
loc1 = net['throat.conns'][:, 0] < solid_num
loc2 = net['throat.conns'][:, 1] >= solid_num
loc3 = net['throat.conns'][:, 1] < b_num
pore_solid_labels = loc1 * loc2 * loc3
loc4 = net['throat.conns'][:, 0] >= solid_num
loc5 = net['throat.conns'][:, 0] < b_num
solid_solid_labels = loc4 * loc2 * loc5 * loc3
loc6 = net['throat.conns'][:, 1] < solid_num
pore_pore_labels = loc1 * loc6
loc7 = net['throat.conns'][:, 1] >= b_num
boundary_throat_labels = loc5 * loc7
solid_labels = ((net['pore.label'] > solid_num) * ~
(net['pore.label'] > b_num))
boundary_labels = net['pore.label'] > b_num
b_sa = sp.zeros(len(boundary_labels[boundary_labels == 1.0]))
# -------------------------------------------------------------------------
# Calculates void interfacial area that connects with solid and vice versa
p_conns = net['throat.conns'][:, 0][pore_solid_labels]
ps = net['throat.area'][pore_solid_labels]
p_sa = sp.bincount(p_conns, ps)
s_conns = net['throat.conns'][:, 1][pore_solid_labels]
s_pa = sp.bincount(s_conns, ps)
s_pa = sp.trim_zeros(s_pa) # remove pore surface area labels
p_solid_surf = sp.concatenate((p_sa, s_pa, b_sa))
# -------------------------------------------------------------------------
# Calculates interfacial area using marching cube method
if marching_cubes_area:
ps_c = net['throat.area'][pore_solid_labels]
p_sa_c = sp.bincount(p_conns, ps_c)
s_pa_c = sp.bincount(s_conns, ps_c)
s_pa_c = sp.trim_zeros(s_pa_c) # remove pore surface area labels
p_solid_surf = sp.concatenate((p_sa_c, s_pa_c, b_sa))
# -------------------------------------------------------------------------
# Adding additional information of dual network
net['pore.solid_void_area'] = (p_solid_surf * voxel_size**2)
net['throat.void'] = pore_pore_labels
net['throat.interconnect'] = pore_solid_labels
net['throat.solid'] = solid_solid_labels
net['throat.boundary'] = boundary_throat_labels
net['pore.void'] = net['pore.label'] <= solid_num
net['pore.solid'] = solid_labels
net['pore.boundary'] = boundary_labels
class network_dict(dict):
pass
net = network_dict(net)
net.im = im
net.dt = dt
net.regions = regions
net.peaks = peaks
net.pore_dt = pore_dt
net.pore_regions = pore_region
net.pore_peaks = pore_peaks
net.solid_dt = solid_dt
net.solid_regions = solid_region
net.solid_peaks = solid_peaks
return net
def cut_slit(data, slit_number):
"""
cut_slit(data,slit_number)
Find slit in 2d mask image.
Inputs:
data - 2d array of mask image
slit_number - the number of the slit to cut out (with 1 as the bottom)
Outputs:
2d array of slit data, left index of slit, bottom index of slit
"""
data = data.copy()
slit_number -= 1
data[scipy.isnan(data)] = 0.
slice = data.sum(axis=1)
# Find bottom of good data
bottom = 0
while slice[bottom] == 0:
bottom += 1
slice = scipy.trim_zeros(slice)
zeros = scipy.where(slice == 0)
zeros = zeros[0] + bottom
# Special case for images with only one slit
if zeros.size == 0:
# Deal with upper zeros
slice = data.sum(axis=1)
top = bottom
while top < slice.size and slice[top] != 0:
top += 1
slice = data.sum(axis=0)
indx = scipy.where(slice != 0)
start = indx[0][0]
end = indx[0][-1] + 1
return data[bottom:top, start:end], start, bottom
borders = []
start = 0
for i in range(zeros.size - 1):
if zeros[i] + 1 != zeros[i + 1]:
borders.append(zeros[start:i + 1].copy())
start = i + 1
borders.append(zeros[start:].copy())
if slit_number > len(borders):
return scipy.asarray(0), 0, 0
if slit_number == 0:
start = 0
end = borders[0][0]
elif slit_number == len(borders):
start = borders[slit_number - 1][4] + 1
end = slice.size
else:
start = borders[slit_number - 1][4] + 1
end = borders[slit_number][0]
data = data[start:end]
bottom = start
slice = data.sum(axis=0)
indx = scipy.where(slice != 0)
start = indx[0][0]
end = indx[0][-1] + 1
return data[:, start:end], start, bottom
def add_phase_interconnections(net, snow_partitioning_n, voxel_size=1,
marching_cubes_area=False,
alias=None):
r"""
This function connects networks of two or more phases together by
interconnecting neighbouring nodes inside different phases.
The resulting network can be used for the study of transport and kinetics
at interphase of two phases.
Parameters
----------
network : 2D or 3D network
A dictoionary containing structural information of two or more
phases networks. The dictonary format must be same as porespy
region_to_network function.
snow_partitioning_n : tuple
The output generated by snow_partitioning_n function. The tuple should
have phases_max_labels and original image of material.
voxel_size : scalar
The resolution of the image, expressed as the length of one side of a
voxel, so the volume of a voxel would be **voxel_size**-cubed. The
default is 1, which is useful when overlaying the PNM on the original
image since the scale of the image is alway 1 unit lenth per voxel.
marching_cubes_area : bool
If ``True`` then the surface area and interfacial area between regions
will be causing the marching cube algorithm. This is a more accurate
representation of area in extracted network, but is quite slow, so
it is ``False`` by default. The default method simply counts voxels
so does not correctly account for the voxelated nature of the images.
alias : dict (Optional)
A dictionary that assigns unique image label to specific phase.
For example {1: 'Solid'} will show all structural properties associated
with label 1 as Solid phase properties.
If ``None`` then default labelling will be used i.e {1: 'Phase1',..}.
Returns
-------
A dictionary containing network information of individual and connected
networks. The dictionary names use the OpenPNM convention so it may be
converted directly to an OpenPNM network object using the ``update``
command.
"""
# -------------------------------------------------------------------------
# Get alias if provided by user
im = snow_partitioning_n.im
al = _create_alias_map(im, alias=alias)
# -------------------------------------------------------------------------
# Find interconnection and interfacial area between ith and jth phases
conns1 = net['throat.conns'][:, 0]
conns2 = net['throat.conns'][:, 1]
label = net['pore.label'] - 1
num = snow_partitioning_n.phase_max_label
num = [0, *num]
phases_num = sp.unique(im * 1)
phases_num = sp.trim_zeros(phases_num)
for i in phases_num:
loc1 = sp.logical_and(conns1 >= num[i - 1], conns1 < num[i])
loc2 = sp.logical_and(conns2 >= num[i - 1], conns2 < num[i])
loc3 = sp.logical_and(label >= num[i - 1], label < num[i])
net['throat.{}'.format(al[i])] = loc1 * loc2
net['pore.{}'.format(al[i])] = loc3
if i == phases_num[-1]:
loc4 = sp.logical_and(conns1 < num[-1], conns2 >= num[-1])
loc5 = label >= num[-1]
net['throat.boundary'] = loc4
net['pore.boundary'] = loc5
for j in phases_num:
if j > i:
pi_pj_sa = sp.zeros_like(label)
loc6 = sp.logical_and(conns2 >= num[j - 1], conns2 < num[j])
pi_pj_conns = loc1 * loc6
net['throat.{}_{}'.format(al[i], al[j])] = pi_pj_conns
if any(pi_pj_conns):
# ---------------------------------------------------------
# Calculates phase[i] interfacial area that connects with
# phase[j] and vice versa
p_conns = net['throat.conns'][:, 0][pi_pj_conns]
s_conns = net['throat.conns'][:, 1][pi_pj_conns]
ps = net['throat.area'][pi_pj_conns]
p_sa = sp.bincount(p_conns, ps)
# trim zeros at head/tail position to avoid extra bins
p_sa = sp.trim_zeros(p_sa)
i_index = sp.arange(min(p_conns), max(p_conns) + 1)
j_index = sp.arange(min(s_conns), max(s_conns) + 1)
s_pa = sp.bincount(s_conns, ps)
s_pa = sp.trim_zeros(s_pa)
pi_pj_sa[i_index] = p_sa
pi_pj_sa[j_index] = s_pa
# ---------------------------------------------------------
# Calculates interfacial area using marching cube method
if marching_cubes_area:
ps_c = net['throat.area'][pi_pj_conns]
p_sa_c = sp.bincount(p_conns, ps_c)
p_sa_c = sp.trim_zeros(p_sa_c)
s_pa_c = sp.bincount(s_conns, ps_c)
s_pa_c = sp.trim_zeros(s_pa_c)
pi_pj_sa[i_index] = p_sa_c
pi_pj_sa[j_index] = s_pa_c
net['pore.{}_{}_area'.format(al[i],
al[j])] = (pi_pj_sa *
voxel_size ** 2)
return net
def snow_n(im,
voxel_size=1,
boundary_faces=['top', 'bottom', 'left', 'right', 'front', 'back'],
marching_cubes_area=False,
alias=None):
r"""
Analyzes an image that has been segemented into N phases and extracts all
a network for each of the N phases, including geometerical information as
well as network connectivity between each phase.
Parameters
----------
im : ND-array
Image of porous material where each phase is represented by unique
integer. Phase integer should start from 1 (0 is ignored)
voxel_size : scalar
The resolution of the image, expressed as the length of one side of a
voxel, so the volume of a voxel would be **voxel_size**-cubed. The
default is 1, which is useful when overlaying the PNM on the original
image since the scale of the image is always 1 unit lenth per voxel.
boundary_faces : list of strings
Boundary faces labels are provided to assign hypothetical boundary
nodes having zero resistance to transport process. For cubical
geometry, the user can choose ‘left’, ‘right’, ‘top’, ‘bottom’,
‘front’ and ‘back’ face labels to assign boundary nodes. If no label is
assigned then all six faces will be selected as boundary nodes
automatically which can be trimmed later on based on user requirements.
marching_cubes_area : bool
If ``True`` then the surface area and interfacial area between regions
will be calculated using the marching cube algorithm. This is a more
accurate representation of area in extracted network, but is quite
slow, so it is ``False`` by default. The default method simply counts
voxels so does not correctly account for the voxelated nature of the
images.
alias : dict (Optional)
A dictionary that assigns unique image label to specific phases. For
example {1: 'Solid'} will show all structural properties associated
with label 1 as Solid phase properties. If ``None`` then default
labelling will be used i.e {1: 'Phase1',..}.
Returns
-------
A dictionary containing all N phases size data, as well as the
network topological information. The dictionary names use the OpenPNM
convention (i.e. 'pore.coords', 'throat.conns') so it may be converted
directly to an OpenPNM network object using the ``update`` command.
"""
# -------------------------------------------------------------------------
# Get alias if provided by user
al = _create_alias_map(im, alias=alias)
# -------------------------------------------------------------------------
# Perform snow on each phase and merge all segmentation and dt together
snow = snow_partitioning_n(im,
r_max=4,
sigma=0.4,
return_all=True,
mask=True,
randomize=False,
alias=al)
# -------------------------------------------------------------------------
# Add boundary regions
f = boundary_faces
regions = add_boundary_regions(regions=snow.regions, faces=f)
# -------------------------------------------------------------------------
# Padding distance transform to extract geometrical properties
dt = pad_faces(im=snow.dt, faces=f)
# -------------------------------------------------------------------------
# For only one phase extraction with boundary regions
phases_num = sp.unique(im).astype(int)
phases_num = sp.trim_zeros(phases_num)
if len(phases_num) == 1:
if f is not None:
snow.im = pad_faces(im=snow.im, faces=f)
regions = regions * (snow.im.astype(bool))
regions = make_contiguous(regions)
# -------------------------------------------------------------------------
# Extract N phases sites and bond information from image
net = regions_to_network(im=regions, dt=dt, voxel_size=voxel_size)
# -------------------------------------------------------------------------
# Extract marching cube surface area and interfacial area of regions
if marching_cubes_area:
areas = region_surface_areas(regions=regions)
interface_area = region_interface_areas(regions=regions,
areas=areas,
voxel_size=voxel_size)
net['pore.surface_area'] = areas * voxel_size**2
net['throat.area'] = interface_area.area
# -------------------------------------------------------------------------
# Find interconnection and interfacial area between ith and jth phases
net = add_phase_interconnections(net=net,
snow_partitioning_n=snow,
marching_cubes_area=marching_cubes_area,
alias=al)
# -------------------------------------------------------------------------
# label boundary cells
net = label_boundary_cells(network=net, boundary_faces=f)
# -------------------------------------------------------------------------
temp = _net_dict(net)
temp.im = im.copy()
temp.dt = dt
temp.regions = regions
return temp
def snow_partitioning_n(im, r_max=4, sigma=0.4, return_all=True,
mask=True, randomize=False, alias=None):
r"""
This function partitions an imaging oontain an arbitrary number of phases
into regions using a marker-based watershed segmentation. Its an extension
of snow_partitioning function with all phases partitioned together.
Parameters
----------
im : ND-array
Image of porous material where each phase is represented by unique
integer starting from 1 (0's are ignored).
r_max : scalar
The radius of the spherical structuring element to use in the Maximum
filter stage that is used to find peaks. The default is 4.
sigma : scalar
The standard deviation of the Gaussian filter used. The default is
0.4. If 0 is given then the filter is not applied, which is useful if a
distance transform is supplied as the ``im`` argument that has already
been processed.
return_all : boolean (default is False)
If set to ``True`` a named tuple is returned containing the original
image, the combined distance transform, list of each phase max label,
and the final combined regions of all phases.
mask : boolean (default is True)
Apply a mask to the regions which are not under concern.
randomize : boolean
If ``True`` (default), then the region colors will be randomized before
returning. This is helpful for visualizing otherwise neighboring
regions have similar coloring and are hard to distinguish.
alias : dict (Optional)
A dictionary that assigns unique image label to specific phases. For
example {1: 'Solid'} will show all structural properties associated
with label 1 as Solid phase properties. If ``None`` then default
labelling will be used i.e {1: 'Phase1',..}.
Returns
-------
An image the same shape as ``im`` with the all phases partitioned into
regions using a marker based watershed with the peaks found by the
SNOW algorithm [1]. If ``return_all`` is ``True`` then a **named tuple**
is returned with the following attribute:
* ``im`` : The actual image of the porous material
* ``dt`` : The combined distance transform of the image
* ``phase_max_label`` : The list of max label of each phase in order to
distinguish between each other
* ``regions`` : The partitioned regions of n phases using a marker
based watershed with the peaks found by the SNOW algorithm
References
----------
[1] Gostick, J. "A versatile and efficient network extraction algorithm
using marker-based watershed segmentation". Physical Review E. (2017)
[2] Khan, ZA et al. "Dual network extraction algorithm to investigate
multiple transport processes in porous materials: Image-based modeling
of pore and grain-scale processes". Computers in Chemical Engineering.
(2019)
See Also
----------
snow_partitioning
Notes
-----
In principle it is possible to perform a distance transform on each
phase separately, merge these into a single image, then apply the
watershed only once. This, however, has been found to create edge artifacts
between regions arising from the way watershed handles plateaus in the
distance transform. To overcome this, this function applies the watershed
to each of the distance transforms separately, then merges the segmented
regions back into a single image.
"""
# Get alias if provided by user
al = _create_alias_map(im=im, alias=alias)
# Perform snow on each phase and merge all segmentation and dt together
phases_num = sp.unique(im * 1)
phases_num = sp.trim_zeros(phases_num)
combined_dt = 0
combined_region = 0
num = [0]
for i in phases_num:
print('_' * 60)
if alias is None:
print('Processing Phase {}'.format(i))
else:
print('Processing Phase {}'.format(al[i]))
phase_snow = snow_partitioning(im == i,
dt=None, r_max=r_max, sigma=sigma,
return_all=return_all, mask=mask,
randomize=randomize)
if len(phases_num) == 1 and phases_num == 1:
combined_dt = phase_snow.dt
combined_region = phase_snow.regions
else:
combined_dt += phase_snow.dt
phase_snow.regions *= phase_snow.im
phase_snow.regions += num[i - 1]
phase_ws = phase_snow.regions * phase_snow.im
phase_ws[phase_ws == num[i - 1]] = 0
combined_region += phase_ws
num.append(sp.amax(combined_region))
if return_all:
tup = namedtuple('results', field_names=['im', 'dt', 'phase_max_label',
'regions'])
tup.im = im
tup.dt = combined_dt
tup.phase_max_label = num[1:]
tup.regions = combined_region
return tup
else:
return combined_region
def snow_dual(
im,
voxel_size=1,
boundary_faces=['top', 'bottom', 'left', 'right', 'front', 'back']):
r"""
Analyzes an image that has been partitioned into void and solid regions
and extracts the void and solid phase geometry as well as network
connectivity.
Parameters
----------
im : ND-array
Binary image in the Boolean form with True’s as void phase and False’s
as solid phase. It can process the inverted configuration of the
boolean image as well, but output labelling of phases will be inverted
and solid phase properties will be assigned to void phase properties
labels which will cause confusion while performing the simulation.
voxel_size : scalar
The resolution of the image, expressed as the length of one side of a
voxel, so the volume of a voxel would be **voxel_size**-cubed. The
default is 1, which is useful when overlaying the PNM on the original
image since the scale of the image is alway 1 unit lenth per voxel.
boundary_faces : list of strings
Boundary faces labels are provided to assign hypothetical boundary
nodes having zero resistance to transport process. For cubical
geometry, the user can choose ‘left’, ‘right’, ‘top’, ‘bottom’,
‘front’ and ‘back’ face labels to assign boundary nodes. If no label is
assigned then all six faces will be selected as boundary nodes
automatically which can be trimmed later on based on user requirements.
Returns
-------
A dictionary containing all the void and solid phase size data, as well as
the network topological information. The dictionary names use the OpenPNM
convention (i.e. 'pore.coords', 'throat.conns') so it may be converted
directly to an OpenPNM network object using the ``update`` command.
"""
# -------------------------------------------------------------------------
# SNOW void phase
pore_regions = snow_partitioning(im, return_all=True)
# SNOW solid phase
solid_regions = snow_partitioning(~im, return_all=True)
# -------------------------------------------------------------------------
# Combined Distance transform of two phases.
pore_dt = pore_regions.dt
solid_dt = solid_regions.dt
dt = pore_dt + solid_dt
# Calculates combined void and solid regions for dual network extraction
pore_regions = pore_regions.regions
solid_regions = solid_regions.regions
pore_region = pore_regions * im
solid_region = solid_regions * ~im
solid_num = sp.amax(pore_regions)
solid_region = solid_region + solid_num
solid_region = solid_region * ~im
regions = pore_region + solid_region
b_num = sp.amax(regions)
# -------------------------------------------------------------------------
# Boundary Conditions
regions = add_boundary_regions(regions=regions, faces=boundary_faces)
# -------------------------------------------------------------------------
# Padding distance transform to extract geometrical properties
f = boundary_faces
if f is not None:
faces = [(int('top' in f) * 3, int('bottom' in f) * 3),
(int('left' in f) * 3, int('right' in f) * 3)]
if im.ndim == 3:
faces = [(int('front' in f) * 3, int('back' in f) * 3)] + faces
dt = sp.pad(dt, pad_width=faces, mode='edge')
else:
dt = dt
# -------------------------------------------------------------------------
# Extract void,solid and throat information from image
net = regions_to_network(im=regions, dt=dt, voxel_size=voxel_size)
# -------------------------------------------------------------------------
# Find void to void, void to solid and solid to solid throat conns
loc1 = net['throat.conns'][:, 0] < solid_num
loc2 = net['throat.conns'][:, 1] >= solid_num
loc3 = net['throat.conns'][:, 1] < b_num
pore_solid_labels = loc1 * loc2 * loc3
loc4 = net['throat.conns'][:, 0] >= solid_num
loc5 = net['throat.conns'][:, 0] < b_num
solid_solid_labels = loc4 * loc2 * loc5 * loc3
loc6 = net['throat.conns'][:, 1] < solid_num
pore_pore_labels = loc1 * loc6
loc7 = net['throat.conns'][:, 1] >= b_num
boundary_throat_labels = loc5 * loc7
solid_labels = ((net['pore.label'] > solid_num) *
~(net['pore.label'] > b_num))
boundary_labels = net['pore.label'] > b_num
b_sa = sp.zeros(len(boundary_labels[boundary_labels == 1.0]))
# -------------------------------------------------------------------------
# Calculates void interfacial area that connects with solid and vice versa
p_conns = net['throat.conns'][:, 0][pore_solid_labels]
ps = net['throat.area'][pore_solid_labels]
p_sa = sp.bincount(p_conns, ps)
s_conns = net['throat.conns'][:, 1][pore_solid_labels]
s_pa = sp.bincount(s_conns, ps)
s_pa = sp.trim_zeros(s_pa) # remove pore surface area labels
p_solid_surf = sp.concatenate((p_sa, s_pa, b_sa))
# -------------------------------------------------------------------------
# Calculates interfacial area using marching cube method
ps_c = net['throat.area_mc'][pore_solid_labels]
p_sa_c = sp.bincount(p_conns, ps_c)
s_pa_c = sp.bincount(s_conns, ps_c)
s_pa_c = sp.trim_zeros(s_pa_c) # remove pore surface area labels
p_solid_surf_c = sp.concatenate((p_sa_c, s_pa_c, b_sa))
# -------------------------------------------------------------------------
# Adding additional information of dual network
net['pore.solid_IFA'] = p_solid_surf * voxel_size**2
net['pore.solid_IFA_mc'] = (p_solid_surf_c * voxel_size**2)
net['throat.void'] = pore_pore_labels
net['throat.interconnect'] = pore_solid_labels
net['throat.solid'] = solid_solid_labels
net['throat.boundary'] = boundary_throat_labels
net['pore.void'] = net['pore.label'] <= solid_num
net['pore.solid'] = solid_labels
net['pore.boundary'] = boundary_labels
return net