def crop(network, shape, mode='full'):
r"""
Removes vertices that lie outside the specifed shape
Parameters
----------
network : dict
Dictionary containing ``vert.coords`` and ``edge.conns`` arrays
shape : array_like
The [x, y, z] shape of the domain beyond which trimming should be
applied
mode : str
Controls how vertices to be trimmed is determined. Options are:
* 'full':
Any vertices lying outside the domain are trimmed
* 'mixed'
Vertices with at least one neighbor lying inside the domain
are kept
"""
Pdrop = isoutside(network['vert.coords'], shape=shape, thresh=0)
if mode == 'full':
network = trim(network=network, pores=np.where(Pdrop)[0])
elif mode == 'mixed':
# Find throats connecting internal to external pores
Ts = np.sum(Pdrop[network['edge.conns']], axis=1) == 1
# Keep the pores on the ends of these throats
Pkeep = np.unique(network['edge.conns'][Ts])
Ps = np.array(list(set(np.where(Pdrop)[0]).difference(set(Pkeep)))).astype(int)
network = trim(network=network, pores=Ps)
# Remove throats between these surviving external pores
Pdrop = isoutside(network['vert.coords'], shape=shape)
Ts = np.all(Pdrop[network['edge.conns']], axis=1)
network = trim(network=network, throats=Ts)
# Lastly label the surviving pores as outside for further processing
network['vert.outside'] = np.zeros(network['vert.coords'].shape[0], dtype=bool)
network['vert.outside'][Pdrop] = True
return network
def __init__(self, shape=[1, 1, 1], num_points=None, points=None, **kwargs):
# Clean-up input points
points = self._parse_points(shape=shape,
num_points=num_points,
points=points)
super().__init__(shape=shape, points=points, **kwargs)
# Initialize network object
topotools.trim(network=self, pores=self.pores(['voronoi']))
pop = ['pore.voronoi', 'throat.voronoi', 'throat.interconnect',
'pore.delaunay', 'throat.delaunay']
for item in pop:
del self[item]
# Trim additional pores that are missed by the parent class's trimming
Ps = topotools.isoutside(coords=self['pore.coords'], shape=shape)
topotools.trim(network=self, pores=Ps)
def __init__(self, shape=None, num_points=None, **kwargs):
# Clean-up input points
points = kwargs.pop('points', None)
points = self._parse_points(shape=shape,
num_points=num_points,
points=points)
# Initialize network object
super().__init__(shape=shape, points=points, **kwargs)
topotools.trim(network=self, pores=self.pores(['voronoi']))
pop = ['pore.voronoi', 'throat.voronoi', 'throat.interconnect',
'pore.delaunay', 'throat.delaunay']
for item in pop:
del self[item]
# Trim additional pores that are missed by the parent class's trimming
Ps = topotools.isoutside(coords=self['pore.coords'], shape=shape)
topotools.trim(network=self, pores=Ps)
def _trim_external_pores(self, shape):
r'''
'''
# Find all pores within the domain
Ps = topotools.isoutside(coords=self['pore.coords'], shape=shape)
self['pore.external'] = False
self['pore.external'][Ps] = True
# Find which internal pores are delaunay
Ps = (~self['pore.external'])*self['pore.delaunay']
# Find all pores connected to an internal delaunay pore
Ps = self.find_neighbor_pores(pores=Ps, include_input=True)
# Mark them all as keepers
self['pore.keep'] = False
self['pore.keep'][Ps] = True
# Trim all bad pores
topotools.trim(network=self, pores=~self['pore.keep'])
# Now label boundary pores
self['pore.boundary'] = False
self['pore.boundary'] = self['pore.delaunay']*self['pore.external']
# Label Voronoi pores on boundary
Ps = self.find_neighbor_pores(pores=self.pores('boundary'))
Ps = self['pore.voronoi']*self.tomask(pores=Ps)
self['pore.boundary'][Ps] = True
# Label Voronoi and interconnect throats on boundary
self['throat.boundary'] = False
Ps = self.pores('boundary')
Ts = self.find_neighbor_throats(pores=Ps, mode='xnor')
self['throat.boundary'][Ts] = True
# Trim throats between Delaunay boundary pores
Ps = self.pores(labels=['boundary', 'delaunay'], mode='xnor')
Ts = self.find_neighbor_throats(pores=Ps, mode='xnor')
topotools.trim(network=self, throats=Ts)
# Move Delaunay boundary pores to centroid of Voronoi facet
Ps = self.pores(labels=['boundary', 'delaunay'], mode='xnor')
for P in Ps:
Ns = self.find_neighbor_pores(pores=P)
Ns = Ps = self['pore.voronoi']*self.tomask(pores=Ns)
coords = sp.mean(self['pore.coords'][Ns], axis=0)
self['pore.coords'][P] = coords
self['pore.internal'] = ~self['pore.boundary']
Ps = self.pores('internal')
Ts = self.find_neighbor_throats(pores=Ps, mode='xnor')
self['throat.internal'] = False
self['throat.internal'][Ts] = True
# Label surface pores and throats between boundary and internal
Ts = self.throats(['boundary', 'internal'], mode='not')
self['throat.surface'] = False
self['throat.surface'][Ts] = True
surf_pores = self['throat.conns'][Ts].flatten()
surf_pores = sp.unique(surf_pores[~self['pore.boundary'][surf_pores]])
self['pore.surface'] = False
self['pore.surface'][surf_pores] = True
# Clean-up
del self['pore.external']
del self['pore.keep']
def voronoi_delaunay_dual(points, shape, crop=False):
r"""
Generate a dual Voronoi-Delaunay network from given base points
Parameters
----------
points : array_like or scalar
The points to be tessellated. If a scalar is given a set of points
of that size is generated.
shape : array_like
The size of the domain in which the points lie
crop : bool, optional (default is ``False``)
If ``True`` then all points lying beyond the given domain shape will
be removed
Returns
-------
network : dict
A dictionary containing 'vert.coords' and 'edge.conns'
vor : Voronoi object
The Voronoi tessellation object produced by ``scipy.spatial.Voronoi``
tri : Delaunay object
The Delaunay triangulation object produced ``scipy.spatial.Delaunay``
"""
# Generate a set of base points if number was given
points = tools.parse_points(points=points, shape=shape)
mask = ~np.all(points == 0, axis=0)
# Perform tessellations
vor = sptl.Voronoi(points=points[:, mask])
tri = sptl.Delaunay(points=points[:, mask])
# Combine points
pts_all = np.vstack((vor.points, vor.vertices))
Nall = np.shape(pts_all)[0]
# Create adjacency matrix in lil format for quick construction
am = sprs.lil_matrix((Nall, Nall))
for ridge in vor.ridge_dict.keys():
# Make Delaunay-to-Delaunay connections
for i in ridge:
am.rows[i].extend([ridge[0], ridge[1]])
# Get Voronoi vertices for current ridge
row = vor.ridge_dict[ridge].copy()
# Index Voronoi vertex numbers by number of Delaunay points
row = [i + vor.npoints for i in row if i > -1]
# Make Voronoi-to-Delaunay connections
for i in ridge:
am.rows[i].extend(row)
# Make Voronoi-to-Voronoi connections
row.append(row[0])
for i in range(len(row)-1):
am.rows[row[i]].append(row[i+1])
# Finalize adjacency matrix by assigning data values
am.data = am.rows # Values don't matter, only shape, so use 'rows'
# Convert to COO format for direct acces to row and col
am = am.tocoo()
# Extract rows and cols
conns = np.vstack((am.row, am.col)).T
# Convert to sanitized adjacency matrix
am = conns_to_am(conns)
# Finally, retrieve conns back from am
conns = np.vstack((am.row, am.col)).T
# Convert coords to 3D by adding col of 0's if necessary
coords = np.around(pts_all, decimals=10)
if np.any(mask == False):
verts = np.zeros([np.shape(coords)[0], 3])
for i, col in enumerate(np.where(mask)[0]):
verts[:, col] = coords[:, i]
else:
verts = np.copy(coords)
# Assign coords and conns to network dict
network = {}
network['vert.coords'] = verts
network['edge.conns'] = conns
# Identify and trim pores outside the domain if requested
if crop:
Ps = isoutside(verts, shape=shape)
network = tools.trim(network=network, vert_ids=np.where(Ps)[0])
return network, vor, tri
