# Source code for openpnm.network.GenericNetwork

```
import numpy as np
import scipy.sparse as sprs
import scipy.spatial as sptl
from openpnm.core import Base, ModelsMixin, LegacyMixin, LabelMixin, ParamMixin
from openpnm import topotools
from openpnm.utils import Workspace, logging
import openpnm.models.topology as tm
logger = logging.getLogger(__name__)
ws = Workspace()
[docs]class GenericNetwork(ParamMixin, Base, ModelsMixin, LegacyMixin, LabelMixin):
r"""
This generic class contains the main functionality used by all networks
Parameters
----------
coords : array_like
An Np-by-3 array of [x, y, z] coordinates for each pore.
conns : array_like
An Nt-by-2 array of [head, tail] connections between pores.
Notes
-----
The GenericNetwork class houses a number of methods used for querying and
managing the network's spatial and topological information. The following
table gives a very short overview of the methods added those already found
on the ``openpnm.core.Base`` class.
+-----------------------------+-------------------------------------------+
| Method or Attribute | Functionality |
+=============================+===========================================+
| ``create_adjacency_matrix`` | Create an adjacency matrix using given |
| | weights in a specified format |
+-----------------------------+-------------------------------------------+
| ``create_incidence_matrix`` | Create an incidence matrix using given |
| | weights in a specified format |
+-----------------------------+-------------------------------------------+
| ``get_adjacency_matrix`` | Retrieve an existing adjacency matrix in |
| | the specified format (from ``am``) |
+-----------------------------+-------------------------------------------+
| ``get_incidence_matrix`` | Retrieve an existing incidence matrix in |
| | the specified format (from ``im``) |
+-----------------------------+-------------------------------------------+
| ``am`` | Returns the adjacency matrix in COO format|
+-----------------------------+-------------------------------------------+
| ``im`` | Returns the incidence matrix in COO format|
+-----------------------------+-------------------------------------------+
| ``find_neighbor_pores`` | For a given set of pores, find all |
| | neighboring pores |
+-----------------------------+-------------------------------------------+
| ``find_neighbor_throats`` | For a given set of pores, find all |
| | neighboring throats |
+-----------------------------+-------------------------------------------+
| ``find_connecting_throat`` | For each pair of throats find the pores |
| | they connect |
+-----------------------------+-------------------------------------------+
| ``find_connected_pores`` | For each throat, find the pores which it |
| | connects |
+-----------------------------+-------------------------------------------+
| ``num_neighbors`` | For a given set of pores find the number |
| | of neighbors for each |
+-----------------------------+-------------------------------------------+
| ``find_nearby_pores`` | For a given set of pores, find pores that |
| | are within a certain distance |
+-----------------------------+-------------------------------------------+
| ``check_network_health`` | Check the topology for any problems such |
| | as isolated pores |
+-----------------------------+-------------------------------------------+
Examples
--------
>>> import openpnm as op
Create some pore coordinates and connections manually and assign to a
GenericNetwork instance. Consider a linear network of 4 pores and 3
throats:
::
0 ―― 1 ―― 3 ―― 2
>>> coords = [[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0]]
>>> conns = [[0, 1], [1, 3], [2, 3]]
>>> pn = op.network.GenericNetwork(conns=conns, coords=coords)
Networks have two required properties: 'pore.coords' and 'throat.conns'.
These arrays indicate the spatial location of each pore, and which pores
are connected to which. Without these the Network object cannot function.
>>> print(pn.props())
――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――
1 : pore.coords
2 : throat.conns
――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――
The GenericNetwork class has several methods for querying the topology.
>>> Ps = pn.find_neighbor_pores(pores=1)
>>> print(Ps)
[0 3]
>>> Ts = pn.find_neighbor_throats(pores=[0, 1])
>>> print(Ts)
[0 1]
>>> print(pn.num_neighbors(2))
[1]
All of the topological queries are accomplished by inspecting the adjacency
and incidence matrices. They are created on demand, and are stored for
future use to save construction time.
"""
def __new__(cls, *args, **kwargs):
instance = super(GenericNetwork, cls).__new__(cls, *args, **kwargs)
# Initialize adjacency and incidence matrix dictionaries
instance._im = {}
instance._am = {}
return instance
def __init__(self, conns=None, coords=None, project=None, settings={},
**kwargs):
self.settings.setdefault('prefix', 'net')
self.settings.update(settings)
super().__init__(project=project, **kwargs)
if coords is not None:
Np = np.shape(coords)[0]
self['pore.all'] = np.ones(Np, dtype=bool)
self['pore.coords'] = np.array(coords)
if conns is not None:
Nt = np.shape(conns)[0]
self['throat.all'] = np.ones(Nt, dtype=bool)
self['throat.conns'] = np.array(conns)
self.add_model(propname='pore.coordination_number',
model=tm.coordination_number,
regen_mode='explicit')
def __setitem__(self, key, value):
if key == 'throat.conns':
if np.shape(value)[1] != 2:
logger.error('Wrong size for throat conns!')
else:
if np.any(value[:, 0] > value[:, 1]):
logger.debug('Converting throat.conns to be upper triangular')
value = np.sort(value, axis=1)
super().__setitem__(key, value)
def __getitem__(self, key):
element, prop = key.split('.', 1)
# Deal with special keys first
if key.split('.')[-1] == self.name:
element = key.split('.')[0]
return self[f"{element}.all"]
if key.split('.')[-1] == '_id':
self._gen_ids()
return self.get(f"{element}._id")
vals = super().__getitem__(key)
return vals
def _gen_ids(self):
IDs = self.get('pore._id', np.array([], ndmin=1, dtype=np.int64))
if len(IDs) < self.Np:
temp = ws._gen_ids(size=self.Np - len(IDs))
self['pore._id'] = np.concatenate((IDs, temp))
IDs = self.get('throat._id', np.array([], ndmin=1, dtype=np.int64))
if len(IDs) < self.Nt:
temp = ws._gen_ids(size=self.Nt - len(IDs))
self['throat._id'] = np.concatenate((IDs, temp))
[docs] def get_adjacency_matrix(self, fmt='coo'):
r"""
Returns an adjacency matrix in the specified sparse format, with throat
IDs indicating the non-zero values.
Parameters
----------
fmt : string, optional
The sparse storage format to return. Options are:
**'coo'** : (default) This is the native format of OpenPNM data
**'lil'** : Enables row-wise slice of the matrix
**'csr'** : Favored by most linear algebra routines
**'dok'** : Enables subscript access of locations
Notes
-----
This method will only create the requested matrix in the specified
format if one is not already saved on the object. If not present,
this method will create and return the matrix, as well as store it
for future use.
To obtain a matrix with weights other than throat IDs at each non-zero
location use ``create_adjacency_matrix``.
To obtain the non-directed graph, with only upper-triangular entries,
use ``sp.sparse.triu(am, k=1)``.
"""
# Retrieve existing matrix if available
if fmt in self._am.keys():
am = self._am[fmt]
else:
am = self.create_adjacency_matrix(weights=self.Ts, fmt=fmt)
self._am[fmt] = am
return am
[docs] def get_incidence_matrix(self, fmt='coo'):
r"""
Returns an incidence matrix in the specified sparse format, with pore
IDs indicating the non-zero values.
Parameters
----------
fmt : string, optional
The sparse storage format to return. Options are:
**'coo'** : (default) This is the native format of OpenPNM data
**'lil'** : Enables row-wise slice of the matrix
**'csr'** : Favored by most linear algebra routines
**'dok'** : Enables subscript access of locations
Notes
-----
This method will only create the requested matrix in the specified
format if one is not already saved on the object. If not present,
this method will create and return the matrix, as well as store it
for future use.
To obtain a matrix with weights other than pore IDs at each non-zero
location use ``create_incidence_matrix``.
"""
if fmt in self._im.keys():
im = self._im[fmt]
elif self._im.keys():
im = self._im[list(self._im.keys())[0]]
tofmt = getattr(im, f"to{fmt}")
im = tofmt()
self._im[fmt] = im
else:
im = self.create_incidence_matrix(weights=self.Ts, fmt=fmt)
self._im[fmt] = im
return im
im = property(fget=get_incidence_matrix)
am = property(fget=get_adjacency_matrix)
[docs] def create_adjacency_matrix(self, weights=None, fmt='coo', triu=False,
drop_zeros=False):
r"""
Generates a weighted adjacency matrix in the desired sparse format
Parameters
----------
weights : array_like, optional
An array containing the throat values to enter into the matrix
(in graph theory these are known as the 'weights').
If the array is Nt-long, it implies that the matrix is symmetric,
so the upper and lower triangular regions are mirror images. If
it is 2*Nt-long then it is assumed that the first Nt elements are
for the upper triangle, and the last Nt element are for the lower
triangular.
If omitted, ones are used to create a standard adjacency matrix
representing connectivity only.
fmt : string, optional
The sparse storage format to return. Options are:
**'coo'** : (default) This is the native format of OpenPNM data
**'lil'** : Enables row-wise slice of the matrix
**'csr'** : Favored by most linear algebra routines
**'dok'** : Enables subscript access of locations
triu : boolean (default is ``False``)
If ``True``, the returned sparse matrix only contains the upper-
triangular elements. This argument is ignored if the ``weights``
array is 2*Nt-long.
drop_zeros : boolean (default is ``False``)
If ``True``, applies the ``eliminate_zeros`` method of the sparse
array to remove all zero locations.
Returns
-------
An adjacency matrix in the specified Scipy sparse format.
Notes
-----
The adjacency matrix is used by OpenPNM for finding the pores
connected to a give pore or set of pores. Specifically, an adjacency
matrix has Np rows and Np columns. Each row represents a pore,
containing non-zero values at the locations corresponding to the
indices of the pores connected to that pore. The ``weights`` argument
indicates what value to place at each location, with the default
being 1's to simply indicate connections. Another useful option is
throat indices, such that the data values on each row indicate which
throats are connected to the pore.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> weights = np.random.rand(pn.num_throats(), ) < 0.5
>>> am = pn.create_adjacency_matrix(weights=weights, fmt='csr')
"""
allowed_weights = [(self.Nt,), (2 * self.Nt,), (self.Nt, 2)]
# Check if provided data is valid
if weights is None:
weights = np.ones((self.Nt,), dtype=int)
elif np.shape(weights) not in allowed_weights:
raise Exception('Received weights are of incorrect length')
weights = np.array(weights)
# Append row & col to each other, and data to itself
conn = self['throat.conns']
row = conn[:, 0]
col = conn[:, 1]
if weights.shape == (2 * self.Nt):
# The flip is necessary since we want [conns.T, reverse(conns).T].T
row = np.append(row, conn[:, 1])
col = np.append(col, conn[:, 0])
elif weights.shape == (self.Nt, 2):
# The flip is necessary since we want [conns.T, reverse(conns).T].T
row = np.append(row, conn[:, 1])
col = np.append(col, conn[:, 0])
weights = weights.flatten(order='F')
elif not triu:
# The flip is necessary since we want [conns.T, reverse(conns).T].T
row = np.append(row, conn[:, 1])
col = np.append(col, conn[:, 0])
weights = np.append(weights, weights)
# Generate sparse adjacency matrix in 'coo' format
temp = sprs.coo_matrix((weights, (row, col)), (self.Np, self.Np))
if drop_zeros:
temp.eliminate_zeros()
# Convert to requested format
if fmt == 'coo':
pass # temp is already in coo format
elif fmt == 'csr':
temp = temp.tocsr()
elif fmt == 'lil':
temp = temp.tolil()
elif fmt == 'dok':
temp = temp.todok()
return temp
[docs] def create_incidence_matrix(self, weights=None, fmt='coo',
drop_zeros=False):
r"""
Creates a weighted incidence matrix in the desired sparse format
Parameters
----------
weights : array_like, optional
An array containing the throat values to enter into the matrix (In
graph theory these are known as the 'weights'). If omitted, ones
are used to create a standard incidence matrix representing
connectivity only.
fmt : string, optional
The sparse storage format to return. Options are:
**'coo'** : (default) This is the native format of OpenPNMs data
**'lil'** : Enables row-wise slice of the matrix
**'csr'** : Favored by most linear algebra routines
**'dok'** : Enables subscript access of locations
drop_zeros : boolean (default is ``False``)
If ``True``, applies the ``eliminate_zeros`` method of the sparse
array to remove all zero locations.
Returns
-------
An incidence matrix in the specified sparse format
Notes
-----
The incidence matrix is a cousin to the adjacency matrix, and used by
OpenPNM for finding the throats connected to a give pore or set of
pores. Specifically, an incidence matrix has Np rows and Nt columns,
and each row represents a pore, containing non-zero values at the
locations corresponding to the indices of the throats connected to that
pore. The ``weights`` argument indicates what value to place at each
location, with the default being 1's to simply indicate connections.
Another useful option is throat indices, such that the data values
on each row indicate which throats are connected to the pore, though
this is redundant as it is identical to the locations of non-zeros.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> weights = np.random.rand(pn.num_throats(), ) < 0.5
>>> im = pn.create_incidence_matrix(weights=weights, fmt='csr')
"""
# Check if provided data is valid
if weights is None:
weights = np.ones((self.Nt,), dtype=int)
elif np.shape(weights)[0] != self.Nt:
raise Exception('Received dataset of incorrect length')
conn = self['throat.conns']
row = conn[:, 0]
row = np.append(row, conn[:, 1])
col = np.arange(self.Nt)
col = np.append(col, col)
weights = np.append(weights, weights)
temp = sprs.coo.coo_matrix((weights, (row, col)), (self.Np, self.Nt))
if drop_zeros:
temp.eliminate_zeros()
# Convert to requested format
if fmt == 'coo':
pass # temp is already in coo format
elif fmt == 'csr':
temp = temp.tocsr()
elif fmt == 'lil':
temp = temp.tolil()
elif fmt == 'dok':
temp = temp.todok()
return temp
[docs] def find_connected_pores(self, throats=[], flatten=False, mode='union'):
r"""
Return a list of pores connected to the given list of throats
Parameters
----------
throats : array_like
List of throats numbers
flatten : boolean, optional
If ``True`` (default) a 1D array of unique pore numbers is
returned. If ``False`` each location in the the returned array
contains a sub-arras of neighboring pores for each input throat,
in the order they were sent.
mode : string
Specifies logic to filter the resulting list. Options are:
**'or'** : (default) All neighbors of the input throats. This is
also known as the 'union' in set theory or 'any' in boolean logic.
Both keywords are accepted and treated as 'or'.
**'xor'** : Only neighbors of one and only one input throat. This
is useful for finding the sites that are not shared by any of the
input throats.
**'xnor'** : Neighbors that are shared by two or more input
throats. This is equivalent to finding all neighbors with 'or',
minus those found with 'xor', and is useful for finding neighbors
that the inputs have in common.
**'and'** : Only neighbors shared by all input throats. This is
also known as 'intersection' in set theory and (somtimes) as 'all'
in boolean logic. Both keywords are accepted and treated as 'and'.
Returns
-------
1D array (if ``flatten`` is ``True``) or ndarray of arrays (if
``flatten`` is ``False``)
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> Ps = pn.find_connected_pores(throats=[0, 1])
>>> print(Ps)
[[0 1]
[1 2]]
>>> Ps = pn.find_connected_pores(throats=[0, 1], flatten=True)
>>> print(Ps)
[0 1 2]
"""
Ts = self._parse_indices(throats)
am = self.get_adjacency_matrix(fmt='coo')
pores = topotools.find_connected_sites(bonds=Ts, am=am,
flatten=flatten, logic=mode)
return pores
[docs] def find_connecting_throat(self, P1, P2):
r"""
Return the throat index connecting pairs of pores
Parameters
----------
P1 , P2 : array_like
The indices of the pores whose throats are sought. These can be
vectors of indices, but must be the same length
Returns
-------
Returns a list the same length as P1 (and P2) with the each element
containing the throat index that connects the corresponding pores,
or `None`` if pores are not connected.
Notes
-----
The returned list can be converted to an ND-array, which will convert
the ``None`` values to ``nan``. These can then be found using
``numpy.isnan``.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> Ts = pn.find_connecting_throat([0, 1, 2], [2, 2, 2])
>>> print(Ts)
[None, 1, None]
"""
am = self.create_adjacency_matrix(weights=self.Ts, fmt='coo')
sites = np.vstack((P1, P2)).T
Ts = topotools.find_connecting_bonds(sites=sites, am=am)
return Ts
[docs] def find_neighbor_pores(self, pores, mode='union', flatten=True,
include_input=False):
r"""
Returns a list of pores that are direct neighbors to the given pore(s)
Parameters
----------
pores : array_like
Indices of the pores whose neighbors are sought
flatten : boolean
If ``True`` (default) the returned result is a compressed array of
all neighbors. If ``False``, a list of lists with each sub-list
containing the neighbors for each input site. Note that an
*unflattened* list might be slow to generate since it is a Python
``list`` rather than a Numpy ``array``.
include_input : bool
If ``False`` (default) then the input pores are not included in
the returned list(s). Note that since pores are not neighbors of
themselves, the neighbors of pore N will not include N, even if
this flag is ``True``.
mode : string
Specifies logic to filter the resulting list. Options are:
**'or'** : (default) All neighbors of the input pores. This is
also known as the 'union' in set theory or 'any' in boolean logic.
Both keywords are accepted and treated as 'or'.
**'xor'** : Only neighbors of one and only one input pore. This
is useful for finding the pores that are not shared by any of the
input pores. This is known as 'exclusive_or' in set theory, and
is an accepted input.
**'xnor'** : Neighbors that are shared by two or more input pores.
This is equivalent to finding all neighbors with 'or', minus those
found with 'xor', and is useful for finding neighbors that the
inputs have in common.
**'and'** : Only neighbors shared by all input pores. This is also
known as 'intersection' in set theory and (somtimes) as 'all' in
boolean logic. Both keywords are accepted and treated as 'and'.
Returns
-------
If ``flatten`` is ``True``, returns a 1D array of pore indices filtered
according to the specified mode. If ``flatten`` is ``False``, returns
a list of lists, where each list contains the neighbors of the
corresponding input pores.
Notes
-----
The ``logic`` options are applied to neighboring pores only, thus it
is not possible to obtain pores that are part of the global set but
not neighbors. This is because (a) the list of global pores might be
very large, and (b) it is not possible to return a list of neighbors
for each input pores if global pores are considered.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> Ps = pn.find_neighbor_pores(pores=[0, 2])
>>> print(Ps)
[ 1 3 5 7 25 27]
>>> Ps = pn.find_neighbor_pores(pores=[0, 1])
>>> print(Ps)
[ 2 5 6 25 26]
>>> Ps = pn.find_neighbor_pores(pores=[0, 1], mode='union',
... include_input=True)
>>> print(Ps)
[ 0 1 2 5 6 25 26]
>>> Ps = pn.find_neighbor_pores(pores=[0, 2], flatten=False)
>>> print(Ps[0])
[ 1 5 25]
>>> print(Ps[1])
[ 1 3 7 27]
>>> Ps = pn.find_neighbor_pores(pores=[0, 2], mode='xnor')
>>> print(Ps)
[1]
>>> Ps = pn.find_neighbor_pores(pores=[0, 2], mode='xor')
>>> print(Ps)
[ 3 5 7 25 27]
"""
pores = self._parse_indices(pores)
if np.size(pores) == 0:
return np.array([], ndmin=1, dtype=int)
if 'lil' not in self._am.keys():
self.get_adjacency_matrix(fmt='lil')
neighbors = topotools.find_neighbor_sites(sites=pores, logic=mode,
am=self._am['lil'],
flatten=flatten,
include_input=include_input)
return neighbors
[docs] def find_neighbor_throats(self, pores, mode='union', flatten=True):
r"""
Returns a list of throats neighboring the given pore(s)
Parameters
----------
pores : array_like
Indices of pores whose neighbors are sought
flatten : boolean, optional
If ``True`` (default) a 1D array of unique throat indices is
returned. If ``False`` the returned array contains arrays of
neighboring throat indices for each input pore, in the order
they were sent.
mode : string
Specifies logic to filter the resulting list. Options are:
**'or'** : (default) All neighbors of the input pores. This is
also known as the 'union' in set theory or 'any' in boolean logic.
Both keywords are accepted and treated as 'or'.
**'xor'** : Only neighbors of one and only one input pore. This
is useful for finding the thraots that are not shared by any of the
input pores.
**'xnor'** : Neighbors that are shared by two or more input pores.
This is equivalent to finding all neighbors with 'or', minus those
found with 'xor', and is useful for finding neighbors that the
inputs have in common.
**'and'** : Only neighbors shared by all input pores. This is also
known as 'intersection' in set theory and (somtimes) as 'all' in
boolean logic. Both keywords are accepted and treated as 'and'.
Returns
-------
If ``flatten`` is ``True``, returns a 1D array of throat indices
filtered according to the specified mode. If ``flatten`` is ``False``,
returns a list of lists, where each list contains the neighbors of the
corresponding input pores.
Notes
-----
The ``logic`` options are applied to neighboring bonds only, thus it
is not possible to obtain bonds that are part of the global set but
not neighbors. This is because (a) the list of global bonds might be
very large, and (b) it is not possible to return a list of neighbors
for each input site if global sites are considered.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> Ts = pn.find_neighbor_throats(pores=[0, 1])
>>> print(Ts)
[ 0 1 100 101 200 201]
>>> Ts = pn.find_neighbor_throats(pores=[0, 1], flatten=False)
>>> print(Ts[0])
[ 0 100 200]
>>> print(Ts[1])
[ 0 1 101 201]
"""
pores = self._parse_indices(pores)
if np.size(pores) == 0:
return np.array([], ndmin=1, dtype=int)
if flatten is False:
if 'lil' not in self._im.keys():
self.get_incidence_matrix(fmt='lil')
neighbors = topotools.find_neighbor_bonds(sites=pores, logic=mode,
im=self._im['lil'],
flatten=flatten)
else:
am = self.create_adjacency_matrix(fmt='coo', triu=True)
neighbors = topotools.find_neighbor_bonds(sites=pores, logic=mode,
am=am, flatten=True)
return neighbors
def _find_neighbors(self, pores, element, **kwargs):
element = self._parse_element(element=element, single=True)
if np.size(pores) == 0:
return np.array([], ndmin=1, dtype=int)
if element == 'pore':
neighbors = self.find_neighbor_pores(pores=pores, **kwargs)
else:
neighbors = self.find_neighbor_throats(pores=pores, **kwargs)
return neighbors
[docs] def num_neighbors(self, pores, mode='or', flatten=False):
r"""
Returns the number of neigbhoring pores for each given input pore
Parameters
----------
pores : array_like
Pores whose neighbors are to be counted
flatten : boolean (optional)
If ``False`` (default) the number of pores neighboring each input
pore as an array the same length as ``pores``. If ``True`` the
sum total number of is counted.
mode : string
The logic to apply to the returned count of pores.
**'or'** : (default) All neighbors of the input pores. This is
also known as the 'union' in set theory or 'any' in boolean logic.
Both keywords are accepted and treated as 'or'.
**'xor'** : Only neighbors of one and only one input pore. This
is useful for counting the pores that are not shared by any of the
input pores. This is known as 'exclusive_or' in set theory, and
is an accepted input.
**'xnor'** : Neighbors that are shared by two or more input pores.
This is equivalent to counting all neighbors with 'or', minus those
found with 'xor', and is useful for finding neighbors that the
inputs have in common.
**'and'** : Only neighbors shared by all input pores. This is also
known as 'intersection' in set theory and (somtimes) as 'all' in
boolean logic. Both keywords are accepted and treated as 'and'.
Returns
-------
If ``flatten`` is False, a 1D array with number of neighbors in each
element, otherwise a scalar value of the number of neighbors.
Notes
-----
This method literally just counts the number of elements in the array
returned by ``find_neighbor_pores`` using the same logic. Explore
those methods if uncertain about the meaning of the ``mode`` argument
here.
See Also
--------
find_neighbor_pores
find_neighbor_throats
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[5, 5, 5])
>>> Np = pn.num_neighbors(pores=[0, 1], flatten=False)
>>> print(Np)
[3 4]
>>> Np = pn.num_neighbors(pores=[0, 2], flatten=True)
>>> print(Np)
6
>>> Np = pn.num_neighbors(pores=[0, 2], mode='and', flatten=True)
>>> print(Np)
1
"""
pores = self._parse_indices(pores)
if flatten:
# Count number of neighbors
num = self.find_neighbor_pores(pores, flatten=flatten,
mode=mode, include_input=True)
num = np.size(num)
else: # Could be done much faster if flatten == False
am = self.create_adjacency_matrix(fmt="csr")
num = am[pores].sum(axis=1).A1
return num
[docs] def find_nearby_pores(self, pores, r, flatten=False, include_input=False):
r"""
Find all pores within a given radial distance of the input pore(s)
regardless of whether or not they are toplogically connected.
Parameters
----------
pores : array_like
The list of pores for whom nearby neighbors are to be found
r : scalar
The maximum radius within which the search should be performed
include_input : bool
Controls whether the input pores should be included in the list of
pores nearby the *other pores* in the input list. So if
``pores=[1, 2]`` and 1 and 2 are within ``r`` of each other,
then 1 will be included in the returned for pores near 2, and
vice-versa *if* this argument is ``True``. The default is
``False``.
flatten : bool
If ``True`` returns a single list of all pores that match the
criteria, otherwise returns an array containing a sub-array for
each input pore, where each sub-array contains the pores that
are nearby to each given input pore. The default is False.
Returns
-------
A list of pores which are within the given spatial distance. If a
list of N pores is supplied, then a an N-long list of such lists is
returned. The returned lists each contain the pore for which the
neighbors were sought.
Examples
--------
>>> import openpnm as op
>>> pn = op.network.Cubic(shape=[3, 3, 3])
>>> Ps = pn.find_nearby_pores(pores=[0, 1], r=1)
>>> print(Ps[0])
[3 9]
>>> print(Ps[1])
[ 2 4 10]
>>> Ps = pn.find_nearby_pores(pores=[0, 1], r=0.5)
>>> print(Ps)
[array([], dtype=int64), array([], dtype=int64)]
>>> Ps = pn.find_nearby_pores(pores=[0, 1], r=1, flatten=True)
>>> print(Ps)
[ 2 3 4 9 10]
"""
pores = self._parse_indices(pores)
# Handle an empty array if given
if np.size(pores) == 0:
return np.array([], dtype=np.int64)
if r <= 0:
raise Exception('Provided distances should be greater than 0')
# Create kdTree objects
kd = sptl.cKDTree(self['pore.coords'])
kd_pores = sptl.cKDTree(self['pore.coords'][pores])
# Perform search
Ps_within_r = kd_pores.query_ball_tree(kd, r=r)
# Remove self from each list
for i, P in enumerate(Ps_within_r):
Ps_within_r[i].remove(pores[i])
# Convert to flattened list by default
temp = np.concatenate((Ps_within_r))
Pn = np.unique(temp).astype(np.int64)
# Remove inputs if necessary
if include_input is False:
Pn = Pn[~np.in1d(Pn, pores)]
# Convert list of lists to a list of nd-arrays
if flatten is False:
if len(Pn) == 0: # Deal with no nearby neighbors
Pn = [np.array([], dtype=np.int64) for i in pores]
else:
mask = np.zeros(shape=np.amax((Pn.max(), pores.max())) + 1, dtype=bool)
mask[Pn] = True
temp = []
for item in Ps_within_r:
temp.append(np.array(item, dtype=np.int64)[mask[item]])
Pn = temp
return Pn
@property
def conns(self):
return self['throat.conns']
@property
def coords(self):
return self['pore.coords']
[docs] def check_network_health(self):
r"""
This method check the network topological health by checking for:
(1) Isolated pores
(2) Islands or isolated clusters of pores
(3) Duplicate throats
(4) Bidirectional throats (ie. symmetrical adjacency matrix)
(5) Headless throats
Returns
-------
A dictionary containing the offending pores or throat numbers under
each named key.
It also returns a list of which pores and throats should be trimmed
from the network to restore health. This list is a suggestion only,
and is based on keeping the largest cluster and trimming the others.
Notes
-----
- Does not yet check for duplicate pores
- Does not yet suggest which throats to remove
- This is just a 'check' and does not 'fix' the problems it finds
"""
health = self.project.check_network_health()
return health
```