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 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