def bm29ize(self):
"""transform the data of id24 in a list of bm29 instance"""
self.spectra=[]
for n,item in enumerate(scipy.hsplit(self.data[:,1:], self.data.shape[1]-1)):
self.spectra.append(bm29file([self.energy, item[:,0]]))
self.spectra[n].comments= list(self.comments)
self.spectra[n].comments.append("# spectra number "+str(n)+ "\n")
self.spectra[n].comments.append("# ---------------------------------"+ "\n")
self.spectra[n].comments.append("#L E Mu"+ "\n")
def openSinglefile(filename):
inFile = open(filename, 'r')
buffero= scipy.loadtxt(inFile)
inFile.close()
x_array=buffero[:,0]
spectra=[]
for item in scipy.hsplit(buffero[:,1:], buffero.shape[1]-1):
spectra.append(generic(x_array,scipy.ravel(item)))
return spectra
def openSinglefile(filename):
inFile = open(filename, 'r')
buffero = scipy.loadtxt(inFile)
inFile.close()
x_array = buffero[:, 0]
spectra = []
for item in scipy.hsplit(buffero[:, 1:], buffero.shape[1] - 1):
spectra.append(generic(x_array, scipy.ravel(item)))
return spectra
def idct2(X, blksize):
"""Calculate the inverse DCT transform of a 2D array, X"""
dctm = dct_mat(blksize)
#try:
blks = [sp.vsplit(x, X.shape[1]/blksize) for x in sp.hsplit(X, X.shape[0]/blksize)]
#except:
# print "Some error occurred"
blks = [sp.dot(sp.dot(dctm.T, b), dctm) for b in blks]
return sp.concatenate([blk for blk in blks]).reshape(X.shape)
def bm29ize(self):
"""transform the data of id24 in a list of bm29 instance"""
self.spectra = []
for n, item in enumerate(
scipy.hsplit(self.data[:, 1:], self.data.shape[1] - 1)):
self.spectra.append(bm29file([self.energy, item[:, 0]]))
self.spectra[n].comments = list(self.comments)
self.spectra[n].comments.append("# spectra number " + str(n) +
"\n")
self.spectra[n].comments.append(
"# ---------------------------------" + "\n")
self.spectra[n].comments.append("#L E Mu" + "\n")
def dct2(X, blksize):
"""Calculate DCT transform of a 2D array, X
In order for this work, we have to split X into blksize chunks"""
dctm = dct_mat(blksize)
#try:
blks = [sp.vsplit(x, X.shape[1]/blksize) for x in sp.hsplit(X, X.shape[0]/blksize)]
#except:
# print "Some error occurred"
blks = [sp.dot(sp.dot(dctm, b), dctm.T) for b in blks]
return sp.concatenate([blk for blk in blks]).reshape(X.shape)
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
def generate_node_connectivity_array(index_map, data_array):
r"""
Generates a node connectivity array based on faces, edges and corner
adjacency
"""
#
logger.info('generating network connections...')
#
# setting up some constants
x_dim, y_dim, z_dim = data_array.shape
conn_map = list(product([0, -1, 1], [0, -1, 1], [0, -1, 1]))
#
conn_map = sp.array(conn_map, dtype=int)
conn_map = conn_map[1:]
#
# creating slice list to process data chunks
slice_list = [slice(0, 10000)]
for i in range(slice_list[0].stop, index_map.shape[0], slice_list[0].stop):
slice_list.append(slice(i, i + slice_list[0].stop))
slice_list[-1] = slice(slice_list[-1].start, index_map.shape[0])
#
conns = sp.ones((0, 2), dtype=data_array.index_int_type)
logger.debug('\tnumber of slices to process: {}'.format(len(slice_list)))
percent = 10
for n, sect in enumerate(slice_list):
# getting coordinates of nodes and their neighbors
nodes = index_map[sect]
inds = sp.repeat(nodes, conn_map.shape[0], axis=0)
inds += sp.tile(conn_map, (nodes.shape[0], 1))
#
# calculating the flattened index of the central nodes and storing
nodes = sp.ravel_multi_index(sp.hsplit(nodes, 3), data_array.shape)
inds = sp.hstack([inds, sp.repeat(nodes, conn_map.shape[0], axis=0)])
#
# removing neigbors with negative indicies
mask = ~inds[:, 0:3] < 0
inds = inds[sp.sum(mask, axis=1) == 3]
# removing neighbors with indicies outside of bounds
mask = (inds[:, 0] < x_dim, inds[:, 1] < y_dim, inds[:, 2] < z_dim)
mask = sp.stack(mask, axis=1)
inds = inds[sp.sum(mask, axis=1) == 3]
# removing indices with zero-weight connection
mask = data_array[inds[:, 0], inds[:, 1], inds[:, 2]]
inds = inds[mask]
if inds.size:
# calculating flattened index of remaining nieghbor nodes
nodes = sp.ravel_multi_index(sp.hsplit(inds[:, 0:3], 3),
data_array.shape)
inds = sp.hstack([sp.reshape(inds[:, -1], (-1, 1)), nodes])
# ensuring conns[0] is always < conns[1] for duplicate removal
mask = inds[:, 0] > inds[:, 1]
inds[mask] = inds[mask][:, ::-1]
# appending section connectivity data to conns array
conns = sp.append(conns, inds.astype(sp.uint32), axis=0)
if int(n / len(slice_list) * 100) == percent:
logger.debug('\tprocessed slice {:5d}, {}% complete'.format(
n, percent))
percent += 10
#
# using scipy magic from stackoverflow to remove dupilcate connections
logger.info('removing duplicate connections...')
dim0 = conns.shape[0]
conns = sp.ascontiguousarray(conns)
dtype = sp.dtype((sp.void, conns.dtype.itemsize * conns.shape[1]))
dim1 = conns.shape[1]
conns = sp.unique(conns.view(dtype)).view(conns.dtype).reshape(-1, dim1)
logger.debug('\tremoved {} duplicates'.format(dim0 - conns.shape[0]))
#
return conns
def generate_node_connectivity_array(index_map, data_array):
r"""
Generates a node connectivity array based on faces, edges and corner
adjacency
"""
#
logger.info('generating network connections...')
#
# setting up some constants
x_dim, y_dim, z_dim = data_array.shape
conn_map = list(product([0, -1, 1], [0, -1, 1], [0, -1, 1]))
conn_map = sp.array(conn_map, dtype=int)
conn_map = conn_map[1:]
#
# creating slice list to process data chunks
slice_list = [slice(0, 10000)]
for i in range(slice_list[0].stop, index_map.shape[0], slice_list[0].stop):
slice_list.append(slice(i, i+slice_list[0].stop))
slice_list[-1] = slice(slice_list[-1].start, index_map.shape[0])
#
conns = sp.ones((0, 2), dtype=sp.uint32)
logger.debug(' number of slices to process: {}'.format(len(slice_list)))
for sect in slice_list:
# getting coordinates of nodes and their neighbors
nodes = index_map[sect]
inds = sp.repeat(nodes, conn_map.shape[0], axis=0)
inds += sp.tile(conn_map, (nodes.shape[0], 1))
#
# calculating the flattened index of the central nodes and storing
nodes = sp.ravel_multi_index(sp.hsplit(nodes, 3), data_array.shape)
inds = sp.hstack([inds, sp.repeat(nodes, conn_map.shape[0], axis=0)])
#
# removing neigbors with negative indicies
mask = ~inds[:, 0:3] < 0
inds = inds[sp.sum(mask, axis=1) == 3]
# removing neighbors with indicies outside of bounds
mask = (inds[:, 0] < x_dim, inds[:, 1] < y_dim, inds[:, 2] < z_dim)
mask = sp.stack(mask, axis=1)
inds = inds[sp.sum(mask, axis=1) == 3]
# removing indices with zero-weight connection
mask = data_array[inds[:, 0], inds[:, 1], inds[:, 2]]
inds = inds[mask]
if inds.size:
# calculating flattened index of remaining nieghbor nodes
nodes = sp.ravel_multi_index(sp.hsplit(inds[:, 0:3], 3),
data_array.shape)
inds = sp.hstack([sp.reshape(inds[:, -1], (-1, 1)), nodes])
# ensuring conns[0] is always < conns[1] for duplicate removal
mask = inds[:, 0] > inds[:, 1]
inds[mask] = inds[mask][:, ::-1]
# appending section connectivity data to conns array
conns = sp.append(conns, inds.astype(sp.uint32), axis=0)
#
# using scipy magic from stackoverflow to remove dupilcate connections
logger.info('removing duplicate connections...')
dim0 = conns.shape[0]
conns = sp.ascontiguousarray(conns)
dtype = sp.dtype((sp.void, conns.dtype.itemsize*conns.shape[1]))
dim1 = conns.shape[1]
conns = sp.unique(conns.view(dtype)).view(conns.dtype).reshape(-1, dim1)
logger.debug(' removed {} duplicates'.format(dim0 - conns.shape[0]))
#
return conns
