# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)
import numpy as np
from mne import verbose
from mne.viz import plot_alignment
[docs]
@verbose
def plot_nirs_source_detector(
data,
info=None,
radius=0.001,
trans=None,
subject=None,
subjects_dir=None,
surfaces="head",
coord_frame="head",
meg=None,
eeg="original",
fwd=None,
dig=False,
ecog=True,
src=None,
mri_fiducials=False,
bem=None,
seeg=True,
fnirs=False,
show_axes=False,
fig=None,
cmap=None,
interaction="trackball",
verbose=None,
):
"""
3D visualisation of fNIRS response magnitude.
This function plots the response amplitude for each channel.
Each channel is represented by a line between the source and detector,
the color of the line reflects the response magnitude.
Parameters
----------
data : array
Array of values to be plotted between source and detectors.
One value should be specified per channel in the same order
as `info.chs`.
info : dict | None
The measurement info.
If None (default), no sensor information will be shown.
radius : numbers
Tube radius for connecting links.
%(trans)s
subject : str | None
The subject name corresponding to FreeSurfer environment
variable SUBJECT. Can be omitted if ``src`` is provided.
%(subjects_dir)s
surfaces : str | list
Surfaces to plot. Supported values:
* scalp: one of 'head', 'outer_skin' (alias for 'head'),
'head-dense', or 'seghead' (alias for 'head-dense')
* skull: 'outer_skull', 'inner_skull', 'brain' (alias for
'inner_skull')
* brain: one of 'pial', 'white', 'inflated', or 'brain'
(alias for 'pial').
Defaults to 'head'.
.. note:: For single layer BEMs it is recommended to use 'brain'.
coord_frame : str
Coordinate frame to use, 'head', 'meg', or 'mri'.
meg : str | list | bool | None
Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or
reference sensors respectively, or a combination like
``('helmet', 'sensors')`` (same as None, default). True translates to
``('helmet', 'sensors', 'ref')``.
eeg : bool | str | list
String options are:
- "original" (default; equivalent to ``True``)
Shows EEG sensors using their digitized locations (after
transformation to the chosen ``coord_frame``)
- "projected"
The EEG locations projected onto the scalp, as is done in forward
modeling
Can also be a list of these options, or an empty list (``[]``,
equivalent of ``False``).
fwd : instance of Forward
The forward solution. If present, the orientations of the dipoles
present in the forward solution are displayed.
dig : bool | 'fiducials'
If True, plot the digitization points; 'fiducials' to plot fiducial
points only.
ecog : bool
If True (default), show ECoG sensors.
src : instance of SourceSpaces | None
If not None, also plot the source space points.
mri_fiducials : bool | str
Plot MRI fiducials (default False). If ``True``, look for a file with
the canonical name (``bem/{subject}-fiducials.fif``). If ``str`` it
should provide the full path to the fiducials file.
bem : list of dict | instance of ConductorModel | None
Can be either the BEM surfaces (list of dict), a BEM solution or a
sphere model. If None, we first try loading
`'$SUBJECTS_DIR/$SUBJECT/bem/$SUBJECT-$SOURCE.fif'`, and then look for
`'$SUBJECT*$SOURCE.fif'` in the same directory. For `'outer_skin'`,
the subjects bem and bem/flash folders are searched. Defaults to None.
seeg : bool
If True (default), show sEEG electrodes.
fnirs : bool
If True (default), show fNIRS electrodes.
show_axes : bool
If True (default False), coordinate frame axis indicators will be
shown:
* head in pink.
* MRI in gray (if ``trans is not None``).
* MEG in blue (if MEG sensors are present).
.. versionadded:: 0.16
fig : mayavi.mlab.Figure | None
Mayavi Scene in which to plot the alignment.
If ``None``, creates a new 600x600 pixel figure with black background.
.. versionadded:: 0.16
cmap : str
Colormap to be used.
interaction : str
Can be "trackball" (default) or "terrain", i.e. a turntable-style
camera.
.. versionadded:: 0.16
%(verbose)s
Returns
-------
fig : Figure
The 3D figure.
Notes
-----
For more information see :func:`mne.viz.plot_alignment`.
.. versionadded:: 0.15
"""
# Determine range of values for creating colormap
vmin = np.min(data)
vmax = np.max(data)
# If no colormap is specified choose depending on range of data
if cmap is None:
if (vmin >= 0) & (vmax >= 0):
# For positive only data use magma
cmap = "Oranges"
else:
# Otherwise use blue to red and ensure zero sits at white
vmin = -1.0 * np.max(np.abs(data))
vmax = np.max(np.abs(data))
cmap = "RdBu_r"
if isinstance(radius, (int, float)):
radius = np.ones(len(info["chs"])) * radius
# Plot requested alignment
fig = plot_alignment(
info=info,
trans=trans,
subject=subject,
subjects_dir=subjects_dir,
surfaces=surfaces,
coord_frame=coord_frame,
meg=meg,
eeg=eeg,
fwd=fwd,
dig=dig,
ecog=ecog,
src=src,
mri_fiducials=mri_fiducials,
bem=bem,
seeg=seeg,
fnirs=fnirs,
show_axes=show_axes,
fig=fig,
interaction=interaction,
verbose=verbose,
)
from mne.viz.backends.renderer import _get_renderer
renderer = _get_renderer(fig)
# Overlay channels between source and detectors
for idx, ch in enumerate(info["chs"]):
locs = ch["loc"]
renderer.tube(
origin=[np.array([locs[3], locs[4], locs[5]])],
destination=[np.array([locs[6], locs[7], locs[8]])],
scalars=np.array([[1.0, 1.0]]) * data[idx],
radius=radius[idx],
colormap=cmap,
vmin=vmin,
vmax=vmax,
)
t = renderer.tube(
origin=[np.array([0, 0, 0])],
destination=[np.array([0, 0, 0.001])],
scalars=np.array([[vmin, vmax]]),
radius=0.0001,
colormap=cmap,
vmin=vmin,
vmax=vmax,
)
renderer.scalarbar(t)
return fig