def import_data(cls, filename, project=None, delim=' | '):
r"""
Opens a 'csv' file, reads in the data, and adds it to the **Network**
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project object is supplied then one will be created and
returned.
Returns
-------
project : list
An OpenPNM project containing the data assigned to Generic
versions of the objects from which it was exported.
"""
from pandas import read_table
if project is None:
project = ws.new_project()
fname = cls._parse_filename(filename, ext='csv')
a = read_table(filepath_or_buffer=fname,
sep=',',
skipinitialspace=True,
index_col=False,
true_values=['T', 't', 'True', 'true', 'TRUE'],
false_values=['F', 'f', 'False', 'false', 'FALSE'])
dct = {}
# First parse through all the items and re-merge columns
keys = sorted(list(a.keys()))
for item in keys:
m = re.search(r'\[.\]', item) # The dot '.' is a wildcard
if m: # m is None if pattern not found, otherwise merge cols
pname = re.split(r'\[.\]', item)[0] # Get base propname
# Find all other keys with same base propname
merge_keys = [k for k in a.keys() if k.startswith(pname)]
# Rerieve and remove arrays with same base propname
merge_cols = [a.pop(k) for k in merge_keys]
# Merge arrays into multi-column array and store in DataFrame
dct[pname] = np.vstack(merge_cols).T
# Remove key from list of keys
for k in keys:
if k.startswith(pname):
keys.pop(keys.index(k))
else:
dct[item] = np.array(a.pop(item))
project = Dict.from_dict(dct, project=project, delim=delim)
return project
def test_from_dict_not_interleaved_not_flatted_not_categorized(self):
D = Dict.to_dict(network=self.net, phases=[self.phase_1],
flatten=False, interleave=False,
categorize_by=[])
proj = Dict.from_dict(D)
assert len(proj) == 6
assert len(proj.geometries().values()) == 0
assert len(proj.phases().values()) == 0
assert len(proj.physics().values()) == 0
def test_from_dict_interleaved_categorized_by_object(self):
D = Dict.to_dict(network=self.net, phases=[self.phase_1],
flatten=False, interleave=True,
categorize_by=['object'])
proj = Dict.from_dict(D)
assert len(proj) == 2
assert len(proj.geometries().values()) == 0
assert len(proj.phases().values()) == 1
assert len(proj.physics().values()) == 0
def test_from_dict_not_interleaved_not_flatted_cat_by_obj_data_elem(self):
D = Dict.to_dict(network=self.net, phases=[self.phase_1],
flatten=False, interleave=False,
categorize_by=['object', 'element', 'data'])
# Ensure that data and element categorizations are ripped out
proj = Dict.from_dict(D)
assert len(proj) == 6
assert len(proj.geometries().values()) == 2
assert len(proj.phases().values()) == 1
assert len(proj.physics().values()) == 2
def test_from_dict_not_interleaved_not_flatted_not_categorized(self):
D = Dict.to_dict(network=self.net,
phases=[self.phase_1],
flatten=False,
interleave=False,
categorize_by=[])
proj = Dict.from_dict(D)
assert len(proj) == 6
assert len(proj.geometries().values()) == 0
assert len(proj.phases().values()) == 0
assert len(proj.physics().values()) == 0
def test_from_dict_interleaved_categorized_by_object(self):
D = Dict.to_dict(network=self.net,
phases=[self.phase_1],
flatten=False,
interleave=True,
categorize_by=['object'])
proj = Dict.from_dict(D)
assert len(proj) == 2
assert len(proj.geometries().values()) == 0
assert len(proj.phases().values()) == 1
assert len(proj.physics().values()) == 0
def load(cls, filename, project=None, delim=' | '):
r"""
Read in pore and throat data from a saved VTK file.
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project is supplied then one will be created and returned.
"""
net = {}
filename = cls._parse_filename(filename, ext='vtp')
tree = ET.parse(filename)
piece_node = tree.find('PolyData').find('Piece')
# Extract connectivity
conn_element = piece_node.find('Lines').find('DataArray')
conns = VTK._element_to_array(conn_element, 2)
# Extract coordinates
coord_element = piece_node.find('Points').find('DataArray')
coords = VTK._element_to_array(coord_element, 3)
# Extract pore data
for item in piece_node.find('PointData').iter('DataArray'):
key = item.get('Name')
array = VTK._element_to_array(item)
net[key] = array
# Extract throat data
for item in piece_node.find('CellData').iter('DataArray'):
key = item.get('Name')
array = VTK._element_to_array(item)
net[key] = array
if project is None:
project = ws.new_project()
project = Dict.from_dict(dct=net, project=project, delim=delim)
# Clean up data values, if necessary, like convert array's of
# 1's and 0's into boolean.
project = cls._convert_data(project)
# Add coords and conns to network
network = project.network
network.update({'throat.conns': conns})
network.update({'pore.coords': coords})
return project
def test_from_dict_not_interleaved_not_flatted_cat_by_obj_data_elem(self):
D = Dict.to_dict(network=self.net,
phases=[self.phase_1],
flatten=False,
interleave=False,
categorize_by=['object', 'element', 'data'])
# Ensure that data and element categorizations are ripped out
proj = Dict.from_dict(D)
assert len(proj) == 6
assert len(proj.geometries().values()) == 2
assert len(proj.phases().values()) == 1
assert len(proj.physics().values()) == 2
def load(cls, filename, project=None, delim=' | '):
r"""
Read in pore and throat data from a saved VTK file.
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project is supplied then one will be created and returned.
"""
net = {}
filename = cls._parse_filename(filename, ext='vtp')
tree = ET.parse(filename)
piece_node = tree.find('PolyData').find('Piece')
# Extract connectivity
conn_element = piece_node.find('Lines').find('DataArray')
conns = VTK._element_to_array(conn_element, 2)
# Extract coordinates
coord_element = piece_node.find('Points').find('DataArray')
coords = VTK._element_to_array(coord_element, 3)
# Extract pore data
for item in piece_node.find('PointData').iter('DataArray'):
key = item.get('Name')
array = VTK._element_to_array(item)
net[key] = array
# Extract throat data
for item in piece_node.find('CellData').iter('DataArray'):
key = item.get('Name')
array = VTK._element_to_array(item)
net[key] = array
if project is None:
project = ws.new_project()
project = Dict.from_dict(dct=net, project=project, delim=delim)
# Clean up data values, if necessary, like convert array's of
# 1's and 0's into boolean.
project = cls._convert_data(project)
# Add coords and conns to network
network = project.network
network.update({'throat.conns': conns})
network.update({'pore.coords': coords})
return project
def load(cls, filename, project=None, delim=' | '):
r"""
Opens a 'csv' file, reads in the data, and adds it to the **Network**
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project object is supplied then one will be created and
returned.
"""
if project is None:
project = ws.new_project()
fname = cls._parse_filename(filename, ext='csv')
a = pd.read_table(filepath_or_buffer=fname,
sep=',',
skipinitialspace=True,
index_col=False,
true_values=['T', 't', 'True', 'true', 'TRUE'],
false_values=['F', 'f', 'False', 'false', 'FALSE'])
dct = {}
# First parse through all the items and re-merge columns
keys = sorted(list(a.keys()))
for item in keys:
m = re.search(r'\[.\]', item) # The dot '.' is a wildcard
if m: # m is None if pattern not found, otherwise merge cols
pname = re.split(r'\[.\]', item)[0] # Get base propname
# Find all other keys with same base propname
merge_keys = [k for k in a.keys() if k.startswith(pname)]
# Rerieve and remove arrays with same base propname
merge_cols = [a.pop(k) for k in merge_keys]
# Merge arrays into multi-column array and store in DataFrame
dct[pname] = sp.vstack(merge_cols).T
# Remove key from list of keys
[keys.pop(keys.index(k)) for k in keys if k.startswith(pname)]
else:
dct[item] = sp.array(a.pop(item))
project = Dict.from_dict(dct, project=project, delim=delim)
return project
def load(cls, filename, project=None):
r"""
Loads data onto the given network from an appropriately formatted
'mat' file (i.e. MatLAB output).
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project object is supplied then one will be created and
returned.
Returns
-------
If no project object is supplied then one will be created and returned.
"""
filename = cls._parse_filename(filename=filename, ext='mat')
data = spio.loadmat(filename)
# Reinsert the '.' separator into the array names
for item in list(data.keys()):
if item in ['__header__', '__version__', '__globals__']:
data.pop(item)
continue
elif '_pore_' in item:
path, prop = item.split('_pore_')
new_key = path + '|pore.' + prop
elif '_throat_' in item:
path, prop = item.split('_throat_')
new_key = path + '|throat.' + prop
data[new_key] = data.pop(item)
if project is None:
project = ws.new_project()
project = Dict.from_dict(data, project=project, delim='|')
project = cls._convert_data(project)
return project
def load(cls, filename, project=None):
r"""
Loads data onto the given network from an appropriately formatted
'mat' file (i.e. MatLAB output).
Parameters
----------
filename : string (optional)
The name of the file containing the data to import. The formatting
of this file is outlined below.
project : OpenPNM Project object
A GenericNetwork is created and added to the specified Project.
If no Project object is supplied then one will be created and
returned.
Returns
-------
If no project object is supplied then one will be created and returned.
"""
filename = cls._parse_filename(filename=filename, ext='mat')
data = spio.loadmat(filename)
# Reinsert the '.' separator into the array names
for item in list(data.keys()):
if item in ['__header__', '__version__', '__globals__']:
data.pop(item)
continue
elif '_pore_' in item:
path, prop = item.split('_pore_')
new_key = path + '|pore.' + prop
elif '_throat_' in item:
path, prop = item.split('_throat_')
new_key = path + '|throat.' + prop
data[new_key] = data.pop(item)
if project is None:
project = ws.new_project()
project = Dict.from_dict(data, project=project, delim='|')
project = cls._convert_data(project)
return project