mne.bem.ConductorModel#

class mne.bem.ConductorModel[source]#

BEM or sphere model.

See make_bem_model() and make_bem_solution() to create a mne.bem.ConductorModel.

Attributes:
radius

Sphere radius if an EEG sphere model.

Methods

__contains__(key, /)

True if the dictionary has the specified key, else False.

__getitem__(key, /)

Return self[key].

__iter__(/)

Implement iter(self).

__len__(/)

Return len(self).

clear(/)

Remove all items from the dict.

copy()

Return copy of ConductorModel instance.

fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items(/)

Return a set-like object providing a view on the dict's items.

keys(/)

Return a set-like object providing a view on the dict's keys.

pop(key[, default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values(/)

Return an object providing a view on the dict's values.

__contains__(key, /)#

True if the dictionary has the specified key, else False.

__getitem__(key, /)#

Return self[key].

__iter__(/)#

Implement iter(self).

__len__(/)#

Return len(self).

clear(/)#

Remove all items from the dict.

copy()[source]#

Return copy of ConductorModel instance.

Returns:
beminstance of ConductorModel

The copied conductor model.

classmethod fromkeys(iterable, value=None, /)#

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#

Return the value for key if key is in the dictionary, else default.

items(/)#

Return a set-like object providing a view on the dict’s items.

keys(/)#

Return a set-like object providing a view on the dict’s keys.

pop(key, default=<unrepresentable>, /)#

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

property radius#

Sphere radius if an EEG sphere model.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) None.  Update D from mapping/iterable E and F.#

If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values(/)#

Return an object providing a view on the dict’s values.

Examples using mne.bem.ConductorModel#

Kernel OPM phantom data

Kernel OPM phantom data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Plotting sensor layouts of EEG systems

Plotting sensor layouts of EEG systems

Source alignment and coordinate frames

Source alignment and coordinate frames

Head model and forward computation

Head model and forward computation

EEG forward operator with a template MRI

EEG forward operator with a template MRI

Source localization by guided equivalent current dipole (ECD) fitting

Source localization by guided equivalent current dipole (ECD) fitting

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

KIT phantom dataset tutorial

KIT phantom dataset tutorial

Setting the EEG reference

Setting the EEG reference