def __init__(self,
shapes,
spacings,
origins,
labels=None,
name=None,
project=None):
# If no labels given, then use default
if labels is None:
labels = [None for i in shapes]
# Generate cubic networks of specified properties and store in a list
nets = []
for n in range(len(shapes)):
net = Cubic(shape=shapes[n], spacing=spacings[n], name=labels[n])
# label_faces(net)
net['pore.coords'] += origins[n]
nets.append(net)
# Begin the process of merging the networks, but no stitching yet
main_net = nets.pop(0)
for net in nets:
extend(network=main_net,
pore_coords=net['pore.coords'],
throat_conns=net['throat.conns'] + main_net.Np)
for net in nets:
P1 = main_net.pores('surface')
P1 = net.pores('surface')
stitch(network=main_net)
def test_check_network_health_duplicate_throat(self):
net = op.network.Cubic(shape=[5, 5, 5])
P12 = net['throat.conns'][0]
extend(network=net, throat_conns=[P12])
a = net.check_network_health()
assert len(a['duplicate_throats']) == 1
assert len(a['duplicate_throats'][0]) == 2
def __init__(self, shapes, spacings, origins, labels=None, name=None,
project=None):
# If no labels given, then use default
if labels is None:
labels = [None for i in shapes]
# Generate cubic networks of specified properties and store in a list
nets = []
for n in range(len(shapes)):
net = Cubic(shape=shapes[n], spacing=spacings[n], name=labels[n])
# label_faces(net)
net['pore.coords'] += origins[n]
nets.append(net)
# Begin the process of merging the networks, but no stitching yet
main_net = nets.pop(0)
for net in nets:
extend(network=main_net, pore_coords=net['pore.coords'],
throat_conns=net['throat.conns'] + main_net.Np)
for net in nets:
P1 = main_net.pores('surface')
P1 = net.pores('surface')
stitch(network=main_net)
def test_check_network_health_headless_throats(self):
net = op.network.Cubic(shape=[5, 5, 5])
with pytest.raises(Exception):
extend(network=net, throat_conns=[[5, 5555]])
net['throat.conns'][0] = [5, 5555]
a = net.check_network_health()
assert a['headless_throats'] == np.array([0])
def load(cls,
path,
node_file="throats_cellsThroatsGraph_Nodes.txt",
graph_file="throats_cellsThroatsGraph.txt",
network=None,
voxel_size=None,
return_geometry=False):
r"""
Loads network data from an iMorph processed image stack
Parameters
----------
path : string
The path of the folder where the subfiles are held
node_file : string
The file that describes the pores and throats, the
default iMorph name is: throats_cellsThroatsGraph_Nodes.txt
graph_file : string
The file that describes the connectivity of the network, the
default iMorph name is: throats_cellsThroatsGraph.txt
network : OpenPNM Network Object
The OpenPNM Network onto which the data should be loaded. If no
network is supplied then an empty import network is created and
returned.
voxel_size : float
Allows the user to define a voxel size different than what is
contained in the node_file. The value must be in meters.
return_geometry : Boolean
If True, then all geometrical related properties are removed from
the Network object and added to a GenericGeometry object. In this
case the method returns a tuple containing (network, geometry). If
False (default) then the returned Network will contain all
properties that were in the original file. In this case, the user
can call the ```split_geometry``` method explicitly to perform the
separation.
Returns
-------
If no Network object is supplied then one will be created and returned.
If return_geometry is True, then a tuple is returned containing both
the network and a geometry object.
"""
#
path = Path(path)
node_file = os.path.join(path.resolve(), node_file)
graph_file = os.path.join(path.resolve(), graph_file)
# parsing the nodes file
with open(node_file, 'r') as file:
Np = sp.fromstring(file.readline().rsplit('=')[1],
sep='\t',
dtype=int)[0]
vox_size = sp.fromstring(
file.readline().rsplit(')')[1],
sep='\t',
)[0]
# network always recreated to prevent errors
network = GenericNetwork(Np=Np, Nt=0)
# Define expected properies
network['pore.volume'] = sp.nan
scrap_lines = [file.readline() for line in range(4)]
while True:
vals = file.readline().split('\t')
if len(vals) == 1:
break
network['pore.volume'][int(vals[0])] = float(vals[3])
if 'pore.' + vals[2] not in network.labels():
network['pore.' + vals[2]] = False
network['pore.' + vals[2]][int(vals[0])] = True
if voxel_size is None:
voxel_size = vox_size * 1.0E-6 # file stores value in microns
if voxel_size < 0:
raise (Exception('Error - Voxel size must be specfied in ' +
'the Nodes file or as a keyword argument.'))
# parsing the graph file
with open(graph_file, 'r') as file:
# Define expected properties
network['pore.coords'] = sp.zeros((Np, 3)) * sp.nan
network['pore.types'] = sp.nan
network['pore.color'] = sp.nan
network['pore.radius'] = sp.nan
network['pore.dmax'] = sp.nan
network['pore.node_number'] = sp.nan
# Scan file to get pore coordinate data
scrap_lines = [file.readline() for line in range(3)]
line = file.readline()
xmax = 0.0
ymax = 0.0
zmax = 0.0
node_num = 0
while line != 'connectivity table\n':
vals = sp.fromstring(line, sep='\t')
xmax = vals[1] if vals[1] > xmax else xmax
ymax = vals[2] if vals[2] > ymax else ymax
zmax = vals[3] if vals[3] > zmax else zmax
network['pore.coords'][int(vals[0]), :] = vals[1:4]
network['pore.types'][int(vals[0])] = vals[4]
network['pore.color'][int(vals[0])] = vals[5]
network['pore.radius'][int(vals[0])] = vals[6]
network['pore.dmax'][int(vals[0])] = vals[7]
network['pore.node_number'][int(vals[0])] = node_num
node_num += 1
line = file.readline()
# Scan file to get to connectivity data
scrap_lines.append(file.readline()) # Skip line
# Create sparse lil array incrementally build adjacency matrix
lil = sp.sparse.lil_matrix((Np, Np), dtype=int)
while True:
vals = sp.fromstring(file.readline(), sep='\t', dtype=int)
if len(vals) <= 1:
break
lil.rows[vals[0]] = vals[2:]
lil.data[vals[0]] = sp.ones(vals[1])
# fixing any negative volumes or distances so they are 1 voxel/micron
network['pore.volume'][sp.where(network['pore.volume'] < 0)[0]] = 1.0
network['pore.radius'][sp.where(network['pore.radius'] < 0)[0]] = 1.0
network['pore.dmax'][sp.where(network['pore.dmax'] < 0)[0]] = 1.0
# Add adjacency matrix to OpenPNM network
conns = sp.sparse.triu(lil, k=1, format='coo')
network.update({'throat.all': sp.ones(len(conns.col), dtype=bool)})
network['throat.conns'] = sp.vstack([conns.row, conns.col]).T
network['pore.to_trim'] = False
network['pore.to_trim'][network.pores('*throat')] = True
Ts = network.pores('to_trim')
new_conns = network.find_neighbor_pores(pores=Ts, flatten=False)
extend(network=network, throat_conns=new_conns, labels='new_conns')
for item in network.props('pore'):
item = item.split('.')[1]
arr = sp.ones_like(network['pore.' + item])[0]
arr = sp.tile(A=arr, reps=[network.Nt, 1]) * sp.nan
network['throat.' + item] = sp.squeeze(arr)
network['throat.'+item][network.throats('new_conns')] = \
network['pore.'+item][Ts]
trim(network=network, pores=Ts)
# setting up boundary pores
x_coord, y_coord, z_coord = sp.hsplit(network['pore.coords'], 3)
network['pore.front_boundary'] = sp.ravel(x_coord == 0)
network['pore.back_boundary'] = sp.ravel(x_coord == xmax)
network['pore.left_boundary'] = sp.ravel(y_coord == 0)
network['pore.right_boundary'] = sp.ravel(y_coord == ymax)
network['pore.bottom_boundary'] = sp.ravel(z_coord == 0)
network['pore.top_boundary'] = sp.ravel(z_coord == zmax)
# removing any pores that got classified as a boundary pore but
# weren't labled a border_cell_face
ps = sp.where(~sp.in1d(network.pores('*_boundary'),
network.pores('border_cell_face')))[0]
ps = network.pores('*_boundary')[ps]
for side in ['front', 'back', 'left', 'right', 'top', 'bottom']:
network['pore.' + side + '_boundary'][ps] = False
# setting internal label
network['pore.internal'] = False
network['pore.internal'][network.pores('*_boundary',
mode='not')] = True
# adding props to border cell face throats and from pores
Ts = sp.where(
network['throat.conns'][:,
1] > network.pores('border_cell_face')[0] -
1)[0]
faces = network['throat.conns'][Ts, 1]
for item in network.props('pore'):
item = item.split('.')[1]
network['throat.' + item][Ts] = network['pore.' + item][faces]
network['pore.volume'][faces] = 0.0
# applying unit conversions
# TODO: Determine if radius and dmax are indeed microns and not voxels
network['pore.coords'] = network['pore.coords'] * 1e-6
network['pore.radius'] = network['pore.radius'] * 1e-6
network['pore.dmax'] = network['pore.dmax'] * 1e-6
network['pore.volume'] = network['pore.volume'] * voxel_size**3
network['throat.coords'] = network['throat.coords'] * 1e-6
network['throat.radius'] = network['throat.radius'] * 1e-6
network['throat.dmax'] = network['throat.dmax'] * 1e-6
network['throat.volume'] = network['throat.volume'] * voxel_size**3
return network.project
def import_data(cls,
path,
node_file="throats_cellsThroatsGraph_Nodes.txt",
graph_file="throats_cellsThroatsGraph.txt",
voxel_size=None):
r"""
Loads network data from an iMorph processed image stack
Parameters
----------
path : string
The path of the folder where the subfiles are held
node_file : string
The file that describes the pores and throats, the
default iMorph name is: throats_cellsThroatsGraph_Nodes.txt
graph_file : string
The file that describes the connectivity of the network, the
default iMorph name is: throats_cellsThroatsGraph.txt
voxel_size : float
Allows the user to define a voxel size different than what is
contained in the node_file. The value must be in meters.
Returns
-------
project : list
An OpenPNM project object containing a network and a geometry
object. The geometry-related data are automatically placed on the
geometry object using the ``Imported`` geometry class.
"""
path = Path(path)
node_file = os.path.join(path.resolve(), node_file)
graph_file = os.path.join(path.resolve(), graph_file)
# Parsing the nodes file
with open(node_file, "r") as file:
Np = np.fromstring(file.readline().rsplit("=")[1],
sep="\t",
dtype=int)[0]
vox_size = np.fromstring(
file.readline().rsplit(")")[1],
sep="\t",
)[0]
# Network always recreated to prevent errors
network = GenericNetwork(Np=Np, Nt=0)
# Define expected properies
network["pore.volume"] = np.nan
scrap_lines = [file.readline() for line in range(4)]
while True:
vals = file.readline().split("\t")
if len(vals) == 1:
break
network["pore.volume"][int(vals[0])] = float(vals[3])
if "pore." + vals[2] not in network.labels():
network["pore." + vals[2]] = False
network["pore." + vals[2]][int(vals[0])] = True
if voxel_size is None:
voxel_size = vox_size * 1.0e-6 # File stores value in microns
if voxel_size < 0:
raise Exception("Error - Voxel size must be specfied in " +
"the Nodes file or as a keyword argument.")
# Parsing the graph file
with open(graph_file, "r") as file:
# Define expected properties
network["pore.coords"] = np.zeros((Np, 3)) * np.nan
network["pore.types"] = np.nan
network["pore.color"] = np.nan
network["pore.radius"] = np.nan
network["pore.dmax"] = np.nan
network["pore.node_number"] = np.nan
# Scan file to get pore coordinate data
scrap_lines = [file.readline() for line in range(3)]
line = file.readline()
xmax = 0.0
ymax = 0.0
zmax = 0.0
node_num = 0
while line != "connectivity table\n":
vals = np.fromstring(line, sep="\t")
xmax = vals[1] if vals[1] > xmax else xmax
ymax = vals[2] if vals[2] > ymax else ymax
zmax = vals[3] if vals[3] > zmax else zmax
network["pore.coords"][int(vals[0]), :] = vals[1:4]
network["pore.types"][int(vals[0])] = vals[4]
network["pore.color"][int(vals[0])] = vals[5]
network["pore.radius"][int(vals[0])] = vals[6]
network["pore.dmax"][int(vals[0])] = vals[7]
network["pore.node_number"][int(vals[0])] = node_num
node_num += 1
line = file.readline()
# Scan file to get to connectivity data
scrap_lines.append(file.readline()) # Skip line
# Create sparse lil array incrementally build adjacency matrix
lil = sp.sparse.lil_matrix((Np, Np), dtype=int)
while True:
vals = np.fromstring(file.readline(), sep="\t", dtype=int)
if len(vals) <= 1:
break
lil.rows[vals[0]] = vals[2:].tolist()
lil.data[vals[0]] = np.ones(vals[1]).tolist()
# Fixing any negative volumes or distances so they are 1 voxel/micron
network["pore.volume"][np.where(network["pore.volume"] < 0)[0]] = 1.0
network["pore.radius"][np.where(network["pore.radius"] < 0)[0]] = 1.0
network["pore.dmax"][np.where(network["pore.dmax"] < 0)[0]] = 1.0
# Add adjacency matrix to OpenPNM network
conns = sp.sparse.triu(lil, k=1, format="coo")
network.update({"throat.all": np.ones(len(conns.col), dtype=bool)})
network["throat.conns"] = np.vstack([conns.row, conns.col]).T
network["pore.to_trim"] = False
network["pore.to_trim"][network.pores("*throat")] = True
Ts = network.pores("to_trim")
new_conns = network.find_neighbor_pores(pores=Ts, flatten=False)
extend(network=network, throat_conns=new_conns, labels="new_conns")
for item in network.props("pore"):
item = item.split(".")[1]
arr = np.ones_like(network["pore." + item])[0]
arr = np.tile(A=arr, reps=[network.Nt, 1]) * np.nan
network["throat." + item] = np.squeeze(arr)
network["throat." +
item][network.throats("new_conns")] = network["pore." +
item][Ts]
trim(network=network, pores=Ts)
# Setting up boundary pores
x_coord, y_coord, z_coord = np.hsplit(network["pore.coords"], 3)
network["pore.front_boundary"] = np.ravel(x_coord == 0)
network["pore.back_boundary"] = np.ravel(x_coord == xmax)
network["pore.left_boundary"] = np.ravel(y_coord == 0)
network["pore.right_boundary"] = np.ravel(y_coord == ymax)
network["pore.bottom_boundary"] = np.ravel(z_coord == 0)
network["pore.top_boundary"] = np.ravel(z_coord == zmax)
# Removing any pores that got classified as a boundary pore but
# Weren't labled a border_cell_face
ps = np.where(~np.in1d(network.pores("*_boundary"),
network.pores("border_cell_face")))[0]
ps = network.pores("*_boundary")[ps]
for side in ["front", "back", "left", "right", "top", "bottom"]:
network["pore." + side + "_boundary"][ps] = False
# Setting internal label
network["pore.internal"] = False
network["pore.internal"][network.pores("*_boundary",
mode="not")] = True
# Adding props to border cell face throats and from pores
Ts = np.where(
network["throat.conns"][:,
1] > network.pores("border_cell_face")[0] -
1)[0]
faces = network["throat.conns"][Ts, 1]
for item in network.props("pore"):
item = item.split(".")[1]
network["throat." + item][Ts] = network["pore." + item][faces]
network["pore.volume"][faces] = 0.0
# Applying unit conversions
# TODO: Determine if radius and dmax are indeed microns and not voxels
network["pore.coords"] = network["pore.coords"] * 1e-6
network["pore.radius"] = network["pore.radius"] * 1e-6
network["pore.dmax"] = network["pore.dmax"] * 1e-6
network["pore.volume"] = network["pore.volume"] * voxel_size**3
network["throat.coords"] = network["throat.coords"] * 1e-6
network["throat.radius"] = network["throat.radius"] * 1e-6
network["throat.dmax"] = network["throat.dmax"] * 1e-6
network["throat.volume"] = network["throat.volume"] * voxel_size**3
return network.project
def load(cls, path,
node_file="throats_cellsThroatsGraph_Nodes.txt",
graph_file="throats_cellsThroatsGraph.txt",
network=None, voxel_size=None, return_geometry=False):
r"""
Loads network data from an iMorph processed image stack
Parameters
----------
path : string
The path of the folder where the subfiles are held
node_file : string
The file that describes the pores and throats, the
default iMorph name is: throats_cellsThroatsGraph_Nodes.txt
graph_file : string
The file that describes the connectivity of the network, the
default iMorph name is: throats_cellsThroatsGraph.txt
network : OpenPNM Network Object
The OpenPNM Network onto which the data should be loaded. If no
network is supplied then an empty import network is created and
returned.
voxel_size : float
Allows the user to define a voxel size different than what is
contained in the node_file. The value must be in meters.
return_geometry : Boolean
If True, then all geometrical related properties are removed from
the Network object and added to a GenericGeometry object. In this
case the method returns a tuple containing (network, geometry). If
False (default) then the returned Network will contain all
properties that were in the original file. In this case, the user
can call the ```split_geometry``` method explicitly to perform the
separation.
Returns
-------
If no Network object is supplied then one will be created and returned.
If return_geometry is True, then a tuple is returned containing both
the network and a geometry object.
"""
#
path = Path(path)
node_file = os.path.join(path.resolve(), node_file)
graph_file = os.path.join(path.resolve(), graph_file)
# parsing the nodes file
with open(node_file, 'r') as file:
Np = sp.fromstring(file.readline().rsplit('=')[1], sep='\t',
dtype=int)[0]
vox_size = sp.fromstring(file.readline().rsplit(')')[1], sep='\t',)[0]
# network always recreated to prevent errors
network = GenericNetwork(Np=Np, Nt=0)
# Define expected properies
network['pore.volume'] = sp.nan
scrap_lines = [file.readline() for line in range(4)]
while True:
vals = file.readline().split('\t')
if len(vals) == 1:
break
network['pore.volume'][int(vals[0])] = float(vals[3])
if 'pore.'+vals[2] not in network.labels():
network['pore.'+vals[2]] = False
network['pore.'+vals[2]][int(vals[0])] = True
if voxel_size is None:
voxel_size = vox_size * 1.0E-6 # file stores value in microns
if voxel_size < 0:
raise(Exception('Error - Voxel size must be specfied in ' +
'the Nodes file or as a keyword argument.'))
# parsing the graph file
with open(graph_file, 'r') as file:
# Define expected properties
network['pore.coords'] = sp.zeros((Np, 3))*sp.nan
network['pore.types'] = sp.nan
network['pore.color'] = sp.nan
network['pore.radius'] = sp.nan
network['pore.dmax'] = sp.nan
network['pore.node_number'] = sp.nan
# Scan file to get pore coordinate data
scrap_lines = [file.readline() for line in range(3)]
line = file.readline()
xmax = 0.0
ymax = 0.0
zmax = 0.0
node_num = 0
while line != 'connectivity table\n':
vals = sp.fromstring(line, sep='\t')
xmax = vals[1] if vals[1] > xmax else xmax
ymax = vals[2] if vals[2] > ymax else ymax
zmax = vals[3] if vals[3] > zmax else zmax
network['pore.coords'][int(vals[0]), :] = vals[1:4]
network['pore.types'][int(vals[0])] = vals[4]
network['pore.color'][int(vals[0])] = vals[5]
network['pore.radius'][int(vals[0])] = vals[6]
network['pore.dmax'][int(vals[0])] = vals[7]
network['pore.node_number'][int(vals[0])] = node_num
node_num += 1
line = file.readline()
# Scan file to get to connectivity data
scrap_lines.append(file.readline()) # Skip line
# Create sparse lil array incrementally build adjacency matrix
lil = sp.sparse.lil_matrix((Np, Np), dtype=int)
while True:
vals = sp.fromstring(file.readline(), sep='\t', dtype=int)
if len(vals) <= 1:
break
lil.rows[vals[0]] = vals[2:]
lil.data[vals[0]] = sp.ones(vals[1])
# fixing any negative volumes or distances so they are 1 voxel/micron
network['pore.volume'][sp.where(network['pore.volume'] < 0)[0]] = 1.0
network['pore.radius'][sp.where(network['pore.radius'] < 0)[0]] = 1.0
network['pore.dmax'][sp.where(network['pore.dmax'] < 0)[0]] = 1.0
# Add adjacency matrix to OpenPNM network
conns = sp.sparse.triu(lil, k=1, format='coo')
network.update({'throat.all': sp.ones(len(conns.col), dtype=bool)})
network['throat.conns'] = sp.vstack([conns.row, conns.col]).T
network['pore.to_trim'] = False
network['pore.to_trim'][network.pores('*throat')] = True
Ts = network.pores('to_trim')
new_conns = network.find_neighbor_pores(pores=Ts, flatten=False)
extend(network=network, throat_conns=new_conns, labels='new_conns')
for item in network.props('pore'):
item = item.split('.')[1]
arr = sp.ones_like(network['pore.'+item])[0]
arr = sp.tile(A=arr, reps=[network.Nt, 1])*sp.nan
network['throat.'+item] = sp.squeeze(arr)
network['throat.'+item][network.throats('new_conns')] = \
network['pore.'+item][Ts]
trim(network=network, pores=Ts)
# setting up boundary pores
x_coord, y_coord, z_coord = sp.hsplit(network['pore.coords'], 3)
network['pore.front_boundary'] = sp.ravel(x_coord == 0)
network['pore.back_boundary'] = sp.ravel(x_coord == xmax)
network['pore.left_boundary'] = sp.ravel(y_coord == 0)
network['pore.right_boundary'] = sp.ravel(y_coord == ymax)
network['pore.bottom_boundary'] = sp.ravel(z_coord == 0)
network['pore.top_boundary'] = sp.ravel(z_coord == zmax)
# removing any pores that got classified as a boundary pore but
# weren't labled a border_cell_face
ps = sp.where(~sp.in1d(network.pores('*_boundary'),
network.pores('border_cell_face')))[0]
ps = network.pores('*_boundary')[ps]
for side in ['front', 'back', 'left', 'right', 'top', 'bottom']:
network['pore.'+side+'_boundary'][ps] = False
# setting internal label
network['pore.internal'] = False
network['pore.internal'][network.pores('*_boundary', mode='not')] = True
# adding props to border cell face throats and from pores
Ts = sp.where(network['throat.conns'][:, 1] >
network.pores('border_cell_face')[0] - 1)[0]
faces = network['throat.conns'][Ts, 1]
for item in network.props('pore'):
item = item.split('.')[1]
network['throat.'+item][Ts] = network['pore.'+item][faces]
network['pore.volume'][faces] = 0.0
# applying unit conversions
# TODO: Determine if radius and dmax are indeed microns and not voxels
network['pore.coords'] = network['pore.coords'] * 1e-6
network['pore.radius'] = network['pore.radius'] * 1e-6
network['pore.dmax'] = network['pore.dmax'] * 1e-6
network['pore.volume'] = network['pore.volume'] * voxel_size**3
network['throat.coords'] = network['throat.coords'] * 1e-6
network['throat.radius'] = network['throat.radius'] * 1e-6
network['throat.dmax'] = network['throat.dmax'] * 1e-6
network['throat.volume'] = network['throat.volume'] * voxel_size**3
return network.project
def test_check_network_health_looped_throats(self):
net = op.network.Cubic(shape=[5, 5, 5])
extend(network=net, throat_conns=[[5, 5]])
a = net.check_network_health()
assert a['looped_throats'] == np.array([300])
def spider_web_network(im_soft,
mhs,
cc_im,
dtheta=10,
pixel_size=10.4e-6,
path=None,
filename='spider_net'):
# Make spiderweb dividing lines
(inds_x, inds_y) = np.indices(im_soft.shape)
inds_x -= mhs
inds_y -= mhs
theta = np.around(np.arctan2(inds_y, inds_x) * 360 / (2 * np.pi), 2)
remainder = theta % dtheta
lines = np.logical_or((remainder < 1.0), (remainder > (dtheta - 1.0)))
med_ax = medial_axis(lines)
plt.figure()
plt.imshow(med_ax)
med_ax = binary_dilation(med_ax, selem=square(2))
# Multiply cc image by -1 along the dividing lines to define nodes
cc_im[med_ax] *= -1
# Extract coordinates of nodes on the current collectors
cc_coords = []
for i in np.unique(cc_im)[np.unique(cc_im) < 0]:
lab, N = label(cc_im == i, return_num=True)
coords = np.zeros([N, 2], dtype=int)
for l_int in np.arange(N):
x, y = np.where(lab == l_int + 1)
if len(x) > 0:
coords[l_int] = [
np.int(np.around(np.mean(x), 0)),
np.int(np.around(np.mean(y), 0))
]
cc_coords.append(coords)
plt.figure()
plt.imshow(cc_im)
plt.scatter(cc_coords[0][:, 1], cc_coords[0][:, 0], c='r', s=50)
plt.scatter(cc_coords[1][:, 1], cc_coords[1][:, 0], c='b', s=50)
# Collect coords together
coords = np.vstack((cc_coords[0], cc_coords[1]))
# Record which belongs to same cc
first_cc = np.zeros(len(coords), dtype=bool)
first_cc[:len(cc_coords[0])] = True
# Find which theta bin coords are in
point_thetas = theta[coords[:, 0], coords[:, 1]]
bins = np.arange(-180, 180 + 2 * dtheta, dtheta) - dtheta / 2
point_group_theta = np.zeros(len(coords[:, 0]), dtype=int)
for i in range(len(bins) - 1):
lower = bins[i]
upper = bins[i + 1]
sel = (point_thetas > lower) * (point_thetas < upper)
point_group_theta[sel] = i
# Get radial position of coords
rads = np.linalg.norm(coords - mhs, axis=1)
groups, counts = np.unique(point_group_theta, return_counts=True)
sorted_groups = np.zeros([len(groups), counts.max()], dtype=int)
sorted_groups.fill(-1)
for g in range(len(groups)):
Ps = np.arange(0, len(point_group_theta), 1)[point_group_theta == g]
group_rads = rads[Ps]
sorted_rads = group_rads.argsort()
sorted_Ps = Ps[sorted_rads]
sorted_groups[g, :len(sorted_Ps)] = sorted_Ps
plt.figure()
cc_groups = first_cc[sorted_groups].astype(int)
cc_groups[sorted_groups == -1] = -1
plt.imshow(cc_groups)
inner_Ps = sorted_groups[:, 0]
inner_cc = first_cc[inner_Ps]
change_cc = inner_cc != np.roll(inner_cc, -1)
sorted_cc = first_cc[sorted_groups].astype(int)
arc_indices = []
cc_roll = [[], []]
for cc_num, this_cc in enumerate([True, False]):
start_group = np.argwhere(
np.logical_and(inner_cc == this_cc, change_cc)).flatten()[0]
layer_group = np.arange(0, len(groups), 1)
layer_group = np.roll(layer_group, -start_group)
i = 0
layer = 0
numbered_cc = first_cc[sorted_groups].astype(int)
while layer < counts.max():
g = layer_group[0]
if sorted_cc[g, layer] == this_cc:
pass
else:
layer += 1
if layer < counts.max():
if sorted_groups[g, layer] > -1:
cc_roll[cc_num].append(sorted_groups[g, layer])
numbered_cc[g, layer] = i
arc_indices.append(g)
i += 1
else:
pass
layer_group = np.roll(layer_group, 1)
plt.figure()
plt.imshow(numbered_cc)
print(len(np.unique(cc_roll[0] + cc_roll[1])))
ordered_neg_Ps = np.asarray(cc_roll[0])
ordered_pos_Ps = np.asarray(cc_roll[1])
neg_coords = coords[ordered_neg_Ps]
neg_conns = np.vstack(
(np.arange(0,
len(ordered_neg_Ps) - 1,
1), np.arange(0,
len(ordered_neg_Ps) - 1, 1) + 1)).T
pos_coords = coords[ordered_pos_Ps]
pos_conns = np.vstack(
(np.arange(0,
len(ordered_pos_Ps) - 1,
1), np.arange(0,
len(ordered_pos_Ps) - 1, 1) + 1)).T
pos_conns += len(ordered_neg_Ps)
neg_inner_conns = []
for i, p_neg in enumerate(ordered_neg_Ps):
g, layer = np.argwhere(sorted_groups == p_neg).flatten()
if layer < counts.max() - 1:
neighbor = sorted_groups[g, layer + 1]
if neighbor > -1:
sorted_neighbor = np.argwhere(
ordered_pos_Ps == neighbor).flatten()[0]
neg_inner_conns.append(
[i, sorted_neighbor + len(ordered_neg_Ps)])
pos_inner_conns = []
for i, p_pos in enumerate(ordered_pos_Ps):
g, layer = np.argwhere(sorted_groups == p_pos).flatten()
if layer < counts.max() - 1:
neighbor = sorted_groups[g, layer + 1]
if neighbor > -1:
sorted_neighbor = np.argwhere(
ordered_neg_Ps == neighbor).flatten()
if len(sorted_neighbor) > 0:
sorted_neighbor = sorted_neighbor[0]
pos_inner_conns.append(
[i + len(ordered_neg_Ps), sorted_neighbor])
neg_inner_conns = np.asarray(neg_inner_conns)
pos_inner_conns = np.asarray(pos_inner_conns)
new_coords = np.vstack((neg_coords, pos_coords))
coords_3d = np.vstack(
(new_coords[:, 0], new_coords[:, 1], np.zeros(new_coords.shape[0]))).T
new_conns = np.vstack(
(neg_conns, pos_conns, neg_inner_conns, pos_inner_conns))
net = op.network.GenericNetwork(conns=new_conns, coords=coords_3d)
Ps, counts = np.unique(np.hstack(
(net['throat.conns'][:, 0], net['throat.conns'][:, 1])),
return_counts=True)
net['pore.surface'] = False
net['pore.terminal'] = False
net['pore.neg_cc'] = False
net['pore.pos_cc'] = False
net['pore.inner'] = False
net['pore.outer'] = False
net['pore.surface'][Ps[counts < 4]] = True
net['pore.terminal'][Ps[counts == 2]] = True
net['pore.neg_cc'][:len(ordered_neg_Ps)] = True
net['pore.pos_cc'][len(ordered_neg_Ps):] = True
net['pore.cell_id'] = np.arange(0, net.Np, 1).astype(int)
net['pore.arc_index'] = np.asarray(arc_indices)
net['pore.radial_position'] = np.linalg.norm(new_coords - mhs, axis=1)
rad_pos = net['pore.radial_position']
inner_mask = np.logical_and(net['pore.surface'], rad_pos < mhs / 2)
outer_mask = np.logical_and(net['pore.surface'], rad_pos > mhs / 2)
net['pore.inner'][inner_mask] = True
net['pore.outer'][outer_mask] = True
net['throat.neg_cc'] = False
net['throat.pos_cc'] = False
net['throat.spm_resistor'] = True
pos_cc_Ts = net.find_neighbor_throats(net.pores("pos_cc"), mode="xnor")
neg_cc_Ts = net.find_neighbor_throats(net.pores("neg_cc"), mode="xnor")
net['throat.neg_cc'][neg_cc_Ts] = True
net['throat.pos_cc'][pos_cc_Ts] = True
net['throat.spm_resistor'][neg_cc_Ts] = False
net['throat.spm_resistor'][pos_cc_Ts] = False
net['throat.spm_resistor_order'] = -1
spm_res = net['throat.spm_resistor']
n_spm = np.sum(spm_res)
net['throat.spm_resistor_order'][spm_res] = np.arange(0,
n_spm,
1,
dtype=int)
net['throat.spm_neg_inner'] = False
net['throat.spm_pos_inner'] = False
res_order = net['throat.spm_resistor_order']
net['throat.spm_neg_inner'][spm_res *
(res_order < len(neg_inner_conns))] = True
net['throat.spm_pos_inner'][spm_res *
(res_order >= len(neg_inner_conns))] = True
net['pore.neg_tab'] = False
net['pore.pos_tab'] = False
net['pore.neg_tab'][net.pores(['inner', 'neg_cc', 'terminal'],
mode='and')] = True
net['pore.pos_tab'][net.pores(['outer', 'pos_cc', 'terminal'],
mode='and')] = True
num_free = net.num_pores('outer')
outer_pos = net['pore.coords'][net.pores('outer')]
x = outer_pos[:, 0] - mhs
y = outer_pos[:, 1] - mhs
r, t = ecm.polar_transform(x, y)
r_new = np.ones(num_free) * (r + 50)
new_x, new_y = ecm.cartesian_transform(r_new, t)
free_coords = outer_pos.copy()
free_coords[:, 0] = new_x + mhs
free_coords[:, 1] = new_y + mhs
start = len(ordered_neg_Ps) + len(ordered_pos_Ps) - num_free
free_conns = np.vstack(
(np.arange(0, num_free, 1), np.arange(0, num_free, 1) + num_free)).T
free_conns += start
tt.extend(net,
pore_coords=free_coords,
throat_conns=free_conns,
labels=['free_stream'])
free_Ps = net['pore.free_stream']
net['pore.arc_index'][free_Ps] = net['pore.arc_index'][net['pore.outer']]
net['pore.cell_id'][net.pores('free_stream')] = -1
net['pore.cell_id'] = net['pore.cell_id'].astype(int)
fig, ax = plt.subplots(figsize=(12, 12))
ax = ecm.plot_resistors(net, net.throats('neg_cc'), c='r', ax=ax)
ax = ecm.plot_resistors(net, net.throats('pos_cc'), c='b', ax=ax)
ax = ecm.plot_resistors(net, net.throats('spm_resistor'), c='k', ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('neg_cc'), c='r', ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('pos_cc'), c='b', ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('surface'), c='k', ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('outer'), c='pink', ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('inner'), c='purple', ax=ax)
ax = tt.plot_coordinates(net,
pores=net.pores('terminal'),
c='y',
s=100,
ax=ax)
ax = tt.plot_coordinates(net, pores=net.pores('free_stream'), c='g', ax=ax)
ax = tt.plot_connections(net,
throats=net.throats('free_stream'),
c='g',
ax=ax)
fig, ax = plt.subplots(figsize=(12, 12))
ax = ecm.plot_resistors(net, net.throats('neg_cc'), c='r', ax=ax)
ax = ecm.plot_resistors(net, net.throats('pos_cc'), c='b', ax=ax)
ax = ecm.plot_resistors(net, net.throats('spm_resistor'), c='k', ax=ax)
plt.imshow(im_soft.T)
# Scale and save net
prj = wrk['proj_01']
net['pore.coords'] *= pixel_size
mean = mhs * pixel_size
net['pore.coords'][:, 0] -= mean
net['pore.coords'][:, 1] -= mean
net['pore.radial_position'] = np.linalg.norm(net['pore.coords'], axis=1)
net['throat.radial_position'] = net.interpolate_data(
'pore.radial_position')
net['throat.arc_length'] = net['throat.radial_position'] * np.deg2rad(
dtheta)
if path is None:
path = ecm.INPUT_DIR
wrk.save_project(project=prj, filename=os.path.join(path, filename))
return net