How to use the porespy.networks.regions_to_network function in porespy
To help you get started, we’ve selected a few porespy examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
PMEAL / porespy / test / unit / test_network_extraction.py View on Github 
np.random.seed(1)
snow_out1 = ps.filters.snow_partitioning(im1, return_all=True)
pore_map1 = snow_out1.im * snow_out1.regions
net1 = ps.networks.regions_to_network(im=pore_map1,
dt=snow_out1.dt,
voxel_size=1)
np.random.seed(1)
snow_out2 = ps.filters.snow_partitioning(im2, return_all=True)
pore_map2 = snow_out2.im * snow_out2.regions
net2 = ps.networks.regions_to_network(im=pore_map2,
dt=snow_out2.dt,
voxel_size=1)
np.random.seed(1)
snow_out3 = ps.filters.snow_partitioning(im3, return_all=True)
pore_map3 = snow_out3.im * snow_out3.regions
net3 = ps.networks.regions_to_network(im=pore_map3,
dt=snow_out3.dt,
voxel_size=1)
assert np.allclose(net1['pore.coords'][:, 0],
net2['pore.coords'][:, 0])
assert np.allclose(net1['pore.coords'][:, 1],
net2['pore.coords'][:, 2])
assert np.allclose(net1['pore.coords'][:, 0],
net3['pore.coords'][:, 1])def test_regions_to_network(self):
im = self.snow.regions*self.im
net = ps.networks.regions_to_network(im)
found_nans = False
for key in net.keys():
if np.any(np.isnan(net[key])):
found_nans = True
assert found_nans is Falsedef test_planar_2d_image(self):
np.random.seed(1)
im1 = ps.generators.blobs([100, 100, 1])
np.random.seed(1)
im2 = ps.generators.blobs([100, 1, 100])
np.random.seed(1)
im3 = ps.generators.blobs([1, 100, 100])
np.random.seed(1)
snow_out1 = ps.filters.snow_partitioning(im1, return_all=True)
pore_map1 = snow_out1.im * snow_out1.regions
net1 = ps.networks.regions_to_network(im=pore_map1,
dt=snow_out1.dt,
voxel_size=1)
np.random.seed(1)
snow_out2 = ps.filters.snow_partitioning(im2, return_all=True)
pore_map2 = snow_out2.im * snow_out2.regions
net2 = ps.networks.regions_to_network(im=pore_map2,
dt=snow_out2.dt,
voxel_size=1)
np.random.seed(1)
snow_out3 = ps.filters.snow_partitioning(im3, return_all=True)
pore_map3 = snow_out3.im * snow_out3.regions
net3 = ps.networks.regions_to_network(im=pore_map3,
dt=snow_out3.dt,
voxel_size=1)
assert np.allclose(net1['pore.coords'][:, 0],
net2['pore.coords'][:, 0])def test_extract_pore_network_3d(self):
im = self.snow3d.regions*self.im3d
net = ps.networks.regions_to_network(im)
found_nans = False
for key in net.keys():
if np.any(np.isnan(net[key])):
found_nans = True
assert found_nans is Falsenp.random.seed(1)
im1 = ps.generators.blobs([100, 100, 1])
np.random.seed(1)
im2 = ps.generators.blobs([100, 1, 100])
np.random.seed(1)
im3 = ps.generators.blobs([1, 100, 100])
np.random.seed(1)
snow_out1 = ps.filters.snow_partitioning(im1, return_all=True)
pore_map1 = snow_out1.im * snow_out1.regions
net1 = ps.networks.regions_to_network(im=pore_map1,
dt=snow_out1.dt,
voxel_size=1)
np.random.seed(1)
snow_out2 = ps.filters.snow_partitioning(im2, return_all=True)
pore_map2 = snow_out2.im * snow_out2.regions
net2 = ps.networks.regions_to_network(im=pore_map2,
dt=snow_out2.dt,
voxel_size=1)
np.random.seed(1)
snow_out3 = ps.filters.snow_partitioning(im3, return_all=True)
pore_map3 = snow_out3.im * snow_out3.regions
net3 = ps.networks.regions_to_network(im=pore_map3,
dt=snow_out3.dt,
voxel_size=1)
assert np.allclose(net1['pore.coords'][:, 0],
net2['pore.coords'][:, 0])
assert np.allclose(net1['pore.coords'][:, 1],
net2['pore.coords'][:, 2])
assert np.allclose(net1['pore.coords'][:, 0],
net3['pore.coords'][:, 1])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
dt = pad_faces(im=dt, faces=boundary_faces)
# -------------------------------------------------------------------------
# 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_numregions = 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)im = regions.im
dt = regions.dt
regions = regions.regions
b_num = sp.amax(regions)
# -------------------------------------------------------------------------
# Boundary Conditions
regions = add_boundary_regions(regions=regions, faces=boundary_faces)
# -------------------------------------------------------------------------
# Padding distance transform and image to extract geometrical properties
dt = pad_faces(im=dt, faces=boundary_faces)
im = pad_faces(im=im, faces=boundary_faces)
regions = regions*im
regions = make_contiguous(regions)
# -------------------------------------------------------------------------
# Extract void 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 connections of boundary and internal voids
boundary_labels = net['pore.label'] > b_num
loc1 = net['throat.conns'][:, 0] < b_num
loc2 = net['throat.conns'][:, 1] >= b_num
pore_labels = net['pore.label'] <= b_num
loc3 = net['throat.conns'][:, 0] < b_num
loc4 = net['throat.conns'][:, 1] < b_numporespy
A set of tools for analyzing 3D images of porous materials
Package Health Score
75 / 100Popular porespy functions
- porespy.filters
- porespy.filters.apply_chords
- porespy.filters.find_disconnected_voxels
- porespy.filters.porosimetry
- porespy.filters.snow_partitioning
- porespy.generators
- porespy.generators.blobs
- porespy.generators.insert_shape
- porespy.generators.lattice_spheres
- porespy.generators.RSA
- porespy.io.evtk.vtk.VtkCellType
- porespy.io.evtk.vtk.VtkDataType
- porespy.metrics.porosity
- porespy.metrics.region_surface_areas
- porespy.networks.regions_to_network
- porespy.tools.extend_slice
- porespy.tools.extract_subsection
- porespy.tools.fftmorphology
- porespy.tools.get_planes
- porespy.tools.make_contiguous