/
_brain.py
1819 lines (1665 loc) · 74.5 KB
/
_brain.py
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Oleh Kozynets <ok7mailbox@gmail.com>
# Guillaume Favelier <guillaume.favelier@gmail.com>
# jona-sassenhagen <jona.sassenhagen@gmail.com>
# Joan Massich <mailsik@gmail.com>
#
# License: Simplified BSD
from functools import partial
import os
import os.path as op
import numpy as np
from scipy import sparse
from .colormap import calculate_lut
from .surface import Surface
from .view import views_dicts
from .._3d import _process_clim, _handle_time, _check_views
from ...defaults import _handle_default
from ...surface import mesh_edges
from ...source_space import SourceSpaces
from ...transforms import apply_trans
from ...utils import (_check_option, logger, verbose, fill_doc, _validate_type,
use_log_level, Bunch)
@fill_doc
class Brain(object):
"""Class for visualizing a brain.
.. warning::
The API for this class is not currently complete. We suggest using
:meth:`mne.viz.plot_source_estimates` with the PyVista backend
enabled to obtain a ``Brain`` instance.
Parameters
----------
subject_id : str
Subject name in Freesurfer subjects dir.
hemi : str
Hemisphere id (ie 'lh', 'rh', 'both', or 'split'). In the case
of 'both', both hemispheres are shown in the same window.
In the case of 'split' hemispheres are displayed side-by-side
in different viewing panes.
surf : str
FreeSurfer surface mesh name (ie 'white', 'inflated', etc.).
title : str
Title for the window.
cortex : str or None
Specifies how the cortical surface is rendered.
The name of one of the preset cortex styles can be:
``'classic'`` (default), ``'high_contrast'``,
``'low_contrast'``, or ``'bone'`` or a valid color name.
Setting this to ``None`` is equivalent to ``(0.5, 0.5, 0.5)``.
alpha : float in [0, 1]
Alpha level to control opacity of the cortical surface.
size : int | array-like, shape (2,)
The size of the window, in pixels. can be one number to specify
a square window, or a length-2 sequence to specify (width, height).
background : tuple(int, int, int)
The color definition of the background: (red, green, blue).
foreground : matplotlib color
Color of the foreground (will be used for colorbars and text).
None (default) will use black or white depending on the value
of ``background``.
figure : list of Figure | None | int
If None (default), a new window will be created with the appropriate
views. For single view plots, the figure can be specified as int to
retrieve the corresponding Mayavi window.
subjects_dir : str | None
If not None, this directory will be used as the subjects directory
instead of the value set using the SUBJECTS_DIR environment
variable.
views : list | str
The views to use.
offset : bool
If True, aligs origin with medial wall. Useful for viewing inflated
surface where hemispheres typically overlap (Default: True).
show_toolbar : bool
If True, toolbars will be shown for each view.
offscreen : bool
If True, rendering will be done offscreen (not shown). Useful
mostly for generating images or screenshots, but can be buggy.
Use at your own risk.
interaction : str
Can be "trackball" (default) or "terrain", i.e. a turntable-style
camera.
units : str
Can be 'm' or 'mm' (default).
%(view_layout)s
show : bool
Display the window as soon as it is ready. Defaults to True.
Attributes
----------
geo : dict
A dictionary of pysurfer.Surface objects for each hemisphere.
overlays : dict
The overlays.
Notes
-----
This table shows the capabilities of each Brain backend ("✓" for full
support, and "-" for partial support):
.. table::
:widths: auto
+---------------------------+--------------+---------------+
| 3D function: | surfer.Brain | mne.viz.Brain |
+===========================+==============+===============+
| add_annotation | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_data | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_foci | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_label | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_text | ✓ | ✓ |
+---------------------------+--------------+---------------+
| close | ✓ | ✓ |
+---------------------------+--------------+---------------+
| data | ✓ | ✓ |
+---------------------------+--------------+---------------+
| foci | ✓ | |
+---------------------------+--------------+---------------+
| labels | ✓ | |
+---------------------------+--------------+---------------+
| labels_dict | ✓ | |
+---------------------------+--------------+---------------+
| remove_data | ✓ | |
+---------------------------+--------------+---------------+
| remove_foci | ✓ | |
+---------------------------+--------------+---------------+
| remove_labels | ✓ | ✓ |
+---------------------------+--------------+---------------+
| save_image | ✓ | ✓ |
+---------------------------+--------------+---------------+
| save_movie | ✓ | ✓ |
+---------------------------+--------------+---------------+
| screenshot | ✓ | ✓ |
+---------------------------+--------------+---------------+
| show_view | ✓ | ✓ |
+---------------------------+--------------+---------------+
| TimeViewer | ✓ | ✓ |
+---------------------------+--------------+---------------+
| enable_depth_peeling | | ✓ |
+---------------------------+--------------+---------------+
| get_picked_points | | ✓ |
+---------------------------+--------------+---------------+
| add_data(volume) | | ✓ |
+---------------------------+--------------+---------------+
| view_layout | | ✓ |
+---------------------------+--------------+---------------+
| flatmaps | | ✓ |
+---------------------------+--------------+---------------+
"""
def __init__(self, subject_id, hemi, surf, title=None,
cortex="classic", alpha=1.0, size=800, background="black",
foreground=None, figure=None, subjects_dir=None,
views='auto', offset=True, show_toolbar=False,
offscreen=False, interaction='trackball', units='mm',
view_layout='vertical', show=True):
from ..backends.renderer import backend, _get_renderer, _get_3d_backend
from matplotlib.colors import colorConverter
from matplotlib.cm import get_cmap
if hemi in ('both', 'split'):
self._hemis = ('lh', 'rh')
elif hemi in ('lh', 'rh'):
self._hemis = (hemi, )
else:
raise KeyError('hemi has to be either "lh", "rh", "split", '
'or "both"')
_check_option('view_layout', view_layout, ('vertical', 'horizontal'))
self._view_layout = view_layout
if figure is not None and not isinstance(figure, int):
backend._check_3d_figure(figure)
if title is None:
self._title = subject_id
else:
self._title = title
self._interaction = 'trackball'
if isinstance(background, str):
background = colorConverter.to_rgb(background)
self._bg_color = background
if foreground is None:
foreground = 'w' if sum(self._bg_color) < 2 else 'k'
if isinstance(foreground, str):
foreground = colorConverter.to_rgb(foreground)
self._fg_color = foreground
if isinstance(views, str):
views = [views]
views = _check_views(surf, views, hemi)
col_dict = dict(lh=1, rh=1, both=1, split=2)
shape = (len(views), col_dict[hemi])
if self._view_layout == 'horizontal':
shape = shape[::-1]
self._subplot_shape = shape
size = tuple(np.atleast_1d(size).round(0).astype(int).flat)
if len(size) not in (1, 2):
raise ValueError('"size" parameter must be an int or length-2 '
'sequence of ints.')
self._size = size if len(size) == 2 else size * 2 # 1-tuple to 2-tuple
self._notebook = (_get_3d_backend() == "notebook")
self._hemi = hemi
self._units = units
self._alpha = float(alpha)
self._subject_id = subject_id
self._subjects_dir = subjects_dir
self._views = views
self._times = None
self._label_data = list()
self._hemi_actors = {}
self._hemi_meshes = {}
# for now only one color bar can be added
# since it is the same for all figures
self._colorbar_added = False
# for now only one time label can be added
# since it is the same for all figures
self._time_label_added = False
# array of data used by TimeViewer
self._data = {}
self.geo, self._overlays = {}, {}
self.set_time_interpolation('nearest')
geo_kwargs = self._cortex_colormap(cortex)
# evaluate at the midpoint of the used colormap
val = -geo_kwargs['vmin'] / (geo_kwargs['vmax'] - geo_kwargs['vmin'])
self._brain_color = get_cmap(geo_kwargs['colormap'])(val)
# load geometry for one or both hemispheres as necessary
offset = None if (not offset or hemi != 'both') else 0.0
self._renderer = _get_renderer(name=self._title, size=self._size,
bgcolor=background,
shape=shape,
fig=figure)
for h in self._hemis:
# Initialize a Surface object as the geometry
geo = Surface(subject_id, h, surf, subjects_dir, offset,
units=self._units)
# Load in the geometry and curvature
geo.load_geometry()
geo.load_curvature()
self.geo[h] = geo
for ri, ci, v in self._iter_views(h):
self._renderer.subplot(ri, ci)
kwargs = {
"color": None,
"scalars": self.geo[h].bin_curv,
"vmin": geo_kwargs["vmin"],
"vmax": geo_kwargs["vmax"],
"colormap": geo_kwargs["colormap"],
"opacity": alpha,
"pickable": False,
}
if self._hemi_meshes.get(h) is None:
mesh_data = self._renderer.mesh(
x=self.geo[h].coords[:, 0],
y=self.geo[h].coords[:, 1],
z=self.geo[h].coords[:, 2],
triangles=self.geo[h].faces,
normals=self.geo[h].nn,
**kwargs,
)
if isinstance(mesh_data, tuple):
actor, mesh = mesh_data
# add metadata to the mesh for picking
mesh._hemi = h
else:
actor, mesh = mesh_data.actor, mesh_data
self._hemi_meshes[h] = mesh
self._hemi_actors[h] = actor
else:
self._renderer.polydata(
self._hemi_meshes[h],
**kwargs,
)
del kwargs
self._renderer.set_camera(**views_dicts[h][v])
self.interaction = interaction
self._closed = False
if show:
self._renderer.show()
# update the views once the geometry is all set
for h in self._hemis:
for ri, ci, v in self._iter_views(h):
self.show_view(v, row=ri, col=ci, hemi=h)
if surf == 'flat':
self._renderer.set_interaction("rubber_band_2d")
if hemi == 'rh' and hasattr(self._renderer, "_orient_lights"):
self._renderer._orient_lights()
@property
def interaction(self):
"""The interaction style."""
return self._interaction
@interaction.setter
def interaction(self, interaction):
"""Set the interaction style."""
_validate_type(interaction, str, 'interaction')
_check_option('interaction', interaction, ('trackball', 'terrain'))
for ri, ci, _ in self._iter_views('vol'): # will traverse all
self._renderer.subplot(ri, ci)
self._renderer.set_interaction(interaction)
def _cortex_colormap(self, cortex):
"""Return the colormap corresponding to the cortex."""
colormap_map = dict(classic=dict(colormap="Greys",
vmin=-1, vmax=2),
high_contrast=dict(colormap="Greys",
vmin=-.1, vmax=1.3),
low_contrast=dict(colormap="Greys",
vmin=-5, vmax=5),
bone=dict(colormap="bone_r",
vmin=-.2, vmax=2),
)
return colormap_map[cortex]
@verbose
def add_data(self, array, fmin=None, fmid=None, fmax=None,
thresh=None, center=None, transparent=False, colormap="auto",
alpha=1, vertices=None, smoothing_steps=None, time=None,
time_label="auto", colorbar=True,
hemi=None, remove_existing=None, time_label_size=None,
initial_time=None, scale_factor=None, vector_alpha=None,
clim=None, src=None, volume_options=0.4, colorbar_kwargs=None,
verbose=None):
"""Display data from a numpy array on the surface or volume.
This provides a similar interface to
:meth:`surfer.Brain.add_overlay`, but it displays
it with a single colormap. It offers more flexibility over the
colormap, and provides a way to display four-dimensional data
(i.e., a timecourse) or five-dimensional data (i.e., a
vector-valued timecourse).
.. note:: ``fmin`` sets the low end of the colormap, and is separate
from thresh (this is a different convention from
:meth:`surfer.Brain.add_overlay`).
Parameters
----------
array : numpy array, shape (n_vertices[, 3][, n_times])
Data array. For the data to be understood as vector-valued
(3 values per vertex corresponding to X/Y/Z surface RAS),
then ``array`` must be have all 3 dimensions.
If vectors with no time dimension are desired, consider using a
singleton (e.g., ``np.newaxis``) to create a "time" dimension
and pass ``time_label=None`` (vector values are not supported).
%(fmin_fmid_fmax)s
%(thresh)s
%(center)s
%(transparent)s
colormap : str, list of color, or array
Name of matplotlib colormap to use, a list of matplotlib colors,
or a custom look up table (an n x 4 array coded with RBGA values
between 0 and 255), the default "auto" chooses a default divergent
colormap, if "center" is given (currently "icefire"), otherwise a
default sequential colormap (currently "rocket").
alpha : float in [0, 1]
Alpha level to control opacity of the overlay.
vertices : numpy array
Vertices for which the data is defined (needed if
``len(data) < nvtx``).
smoothing_steps : int or None
Number of smoothing steps (smoothing is used if len(data) < nvtx)
The value 'nearest' can be used too. None (default) will use as
many as necessary to fill the surface.
time : numpy array
Time points in the data array (if data is 2D or 3D).
%(time_label)s
colorbar : bool
Whether to add a colorbar to the figure. Can also be a tuple
to give the (row, col) index of where to put the colorbar.
hemi : str | None
If None, it is assumed to belong to the hemisphere being
shown. If two hemispheres are being shown, an error will
be thrown.
remove_existing : bool
Not supported yet.
Remove surface added by previous "add_data" call. Useful for
conserving memory when displaying different data in a loop.
time_label_size : int
Font size of the time label (default 14).
initial_time : float | None
Time initially shown in the plot. ``None`` to use the first time
sample (default).
scale_factor : float | None (default)
The scale factor to use when displaying glyphs for vector-valued
data.
vector_alpha : float | None
Alpha level to control opacity of the arrows. Only used for
vector-valued data. If None (default), ``alpha`` is used.
clim : dict
Original clim arguments.
%(src_volume_options)s
colorbar_kwargs : dict | None
Options to pass to :meth:`pyvista.BasePlotter.add_scalar_bar`
(e.g., ``dict(title_font_size=10)``).
%(verbose)s
Notes
-----
If the data is defined for a subset of vertices (specified
by the "vertices" parameter), a smoothing method is used to interpolate
the data onto the high resolution surface. If the data is defined for
subsampled version of the surface, smoothing_steps can be set to None,
in which case only as many smoothing steps are applied until the whole
surface is filled with non-zeros.
Due to a Mayavi (or VTK) alpha rendering bug, ``vector_alpha`` is
clamped to be strictly < 1.
"""
_validate_type(transparent, bool, 'transparent')
_validate_type(vector_alpha, ('numeric', None), 'vector_alpha')
_validate_type(scale_factor, ('numeric', None), 'scale_factor')
# those parameters are not supported yet, only None is allowed
_check_option('thresh', thresh, [None])
_check_option('remove_existing', remove_existing, [None])
_validate_type(time_label_size, (None, 'numeric'), 'time_label_size')
if time_label_size is not None:
time_label_size = float(time_label_size)
if time_label_size < 0:
raise ValueError('time_label_size must be positive, got '
f'{time_label_size}')
hemi = self._check_hemi(hemi, extras=['vol'])
array = np.asarray(array)
vector_alpha = alpha if vector_alpha is None else vector_alpha
self._data['vector_alpha'] = vector_alpha
self._data['scale_factor'] = scale_factor
# Create time array and add label if > 1D
if array.ndim <= 1:
time_idx = 0
else:
# check time array
if time is None:
time = np.arange(array.shape[-1])
else:
time = np.asarray(time)
if time.shape != (array.shape[-1],):
raise ValueError('time has shape %s, but need shape %s '
'(array.shape[-1])' %
(time.shape, (array.shape[-1],)))
self._data["time"] = time
if self._n_times is None:
self._times = time
elif len(time) != self._n_times:
raise ValueError("New n_times is different from previous "
"n_times")
elif not np.array_equal(time, self._times):
raise ValueError("Not all time values are consistent with "
"previously set times.")
# initial time
if initial_time is None:
time_idx = 0
else:
time_idx = self._to_time_index(initial_time)
# time label
time_label, _ = _handle_time(time_label, 's', time)
y_txt = 0.05 + 0.1 * bool(colorbar)
if array.ndim == 3:
if array.shape[1] != 3:
raise ValueError('If array has 3 dimensions, array.shape[1] '
'must equal 3, got %s' % (array.shape[1],))
fmin, fmid, fmax = _update_limits(
fmin, fmid, fmax, center, array
)
if colormap == 'auto':
colormap = 'mne' if center is not None else 'hot'
if smoothing_steps is None:
smoothing_steps = 7
elif smoothing_steps == 'nearest':
smoothing_steps = 0
elif isinstance(smoothing_steps, int):
if smoothing_steps < 0:
raise ValueError('Expected value of `smoothing_steps` is'
' positive but {} was given.'.format(
smoothing_steps))
else:
raise TypeError('Expected type of `smoothing_steps` is int or'
' NoneType but {} was given.'.format(
type(smoothing_steps)))
self._data['smoothing_steps'] = smoothing_steps
self._data['clim'] = clim
self._data['time'] = time
self._data['initial_time'] = initial_time
self._data['time_label'] = time_label
self._data['initial_time_idx'] = time_idx
self._data['time_idx'] = time_idx
self._data['transparent'] = transparent
# data specific for a hemi
self._data[hemi] = dict()
self._data[hemi]['actors'] = None
self._data[hemi]['mesh'] = None
self._data[hemi]['glyph_dataset'] = None
self._data[hemi]['glyph_mapper'] = None
self._data[hemi]['glyph_actor'] = None
self._data[hemi]['array'] = array
self._data[hemi]['vertices'] = vertices
self._data['alpha'] = alpha
self._data['colormap'] = colormap
self._data['center'] = center
self._data['fmin'] = fmin
self._data['fmid'] = fmid
self._data['fmax'] = fmax
self.update_lut()
# 1) add the surfaces first
actor = None
for ri, ci, _ in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
if hemi in ('lh', 'rh'):
if self._data[hemi]['actors'] is None:
self._data[hemi]['actors'] = list()
actor, mesh = self._add_surface_data(hemi)
self._data[hemi]['actors'].append(actor)
self._data[hemi]['mesh'] = mesh
else:
actor, _ = self._add_volume_data(hemi, src, volume_options)
assert actor is not None # should have added one
# 2) update time and smoothing properties
# set_data_smoothing calls "set_time_point" for us, which will set
# _current_time
self.set_time_interpolation(self.time_interpolation)
self.set_data_smoothing(self._data['smoothing_steps'])
# 3) add the other actors
if colorbar is True:
# botto left by default
colorbar = (self._subplot_shape[0] - 1, 0)
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
# Add the time label to the bottommost view
do = (ri, ci) == colorbar
if not self._time_label_added and time_label is not None and do:
time_actor = self._renderer.text2d(
x_window=0.95, y_window=y_txt,
color=self._fg_color,
size=time_label_size,
text=time_label(self._current_time),
justification='right'
)
self._data['time_actor'] = time_actor
self._time_label_added = True
if colorbar and not self._colorbar_added and do:
kwargs = dict(source=actor, n_labels=8, color=self._fg_color,
bgcolor=self._brain_color[:3])
kwargs.update(colorbar_kwargs or {})
self._renderer.scalarbar(**kwargs)
self._colorbar_added = True
self._renderer.set_camera(**views_dicts[hemi][v])
self._update()
def _iter_views(self, hemi):
# which rows and columns each type of visual needs to be added to
if self._hemi == 'split':
hemi_dict = dict(lh=[0], rh=[1], vol=[0, 1])
else:
hemi_dict = dict(lh=[0], rh=[0], vol=[0])
for vi, view in enumerate(self._views):
if self._hemi == 'split':
view_dict = dict(lh=[vi], rh=[vi], vol=[vi, vi])
else:
view_dict = dict(lh=[vi], rh=[vi], vol=[vi])
if self._view_layout == 'vertical':
rows = view_dict # views are rows
cols = hemi_dict # hemis are columns
else:
rows = hemi_dict # hemis are rows
cols = view_dict # views are columns
for ri, ci in zip(rows[hemi], cols[hemi]):
yield ri, ci, view
def _add_surface_data(self, hemi):
rng = self._cmap_range
kwargs = {
"color": None,
"colormap": self._data['ctable'],
"vmin": rng[0],
"vmax": rng[1],
"opacity": self._data['alpha'],
"scalars": np.zeros(len(self.geo[hemi].coords)),
}
if self._data[hemi]['mesh'] is not None:
actor, mesh = self._renderer.polydata(
self._data[hemi]['mesh'],
**kwargs,
)
return actor, mesh
mesh_data = self._renderer.mesh(
x=self.geo[hemi].coords[:, 0],
y=self.geo[hemi].coords[:, 1],
z=self.geo[hemi].coords[:, 2],
triangles=self.geo[hemi].faces,
normals=self.geo[hemi].nn,
polygon_offset=-2,
**kwargs,
)
if isinstance(mesh_data, tuple):
actor, mesh = mesh_data
# add metadata to the mesh for picking
mesh._hemi = hemi
else:
actor, mesh = mesh_data, None
return actor, mesh
def remove_labels(self):
"""Remove all the ROI labels from the image."""
for data in self._label_data:
self._renderer.remove_mesh(data)
self._label_data.clear()
self._update()
def _add_volume_data(self, hemi, src, volume_options):
from ..backends._pyvista import _volume
_validate_type(src, SourceSpaces, 'src')
_check_option('src.kind', src.kind, ('volume',))
_validate_type(
volume_options, (dict, 'numeric', None), 'volume_options')
assert hemi == 'vol'
if not isinstance(volume_options, dict):
volume_options = dict(
resolution=float(volume_options) if volume_options is not None
else None)
volume_options = _handle_default('volume_options', volume_options)
allowed_types = (
['resolution', (None, 'numeric')],
['blending', (str,)],
['alpha', ('numeric', None)],
['surface_alpha', (None, 'numeric')],
['silhouette_alpha', (None, 'numeric')],
['silhouette_linewidth', ('numeric',)],
)
for key, types in allowed_types:
_validate_type(volume_options[key], types,
f'volume_options[{repr(key)}]')
extra_keys = set(volume_options) - set(a[0] for a in allowed_types)
if len(extra_keys):
raise ValueError(
f'volume_options got unknown keys {sorted(extra_keys)}')
_check_option('volume_options["blending"]', volume_options['blending'],
('composite', 'mip'))
blending = volume_options['blending']
alpha = volume_options['alpha']
if alpha is None:
alpha = 0.4 if self._data[hemi]['array'].ndim == 3 else 1.
alpha = np.clip(float(alpha), 0., 1.)
resolution = volume_options['resolution']
surface_alpha = volume_options['surface_alpha']
if surface_alpha is None:
surface_alpha = min(alpha / 2., 0.1)
silhouette_alpha = volume_options['silhouette_alpha']
if silhouette_alpha is None:
silhouette_alpha = surface_alpha / 4.
silhouette_linewidth = volume_options['silhouette_linewidth']
del volume_options
volume_pos = self._data[hemi].get('grid_volume_pos')
volume_neg = self._data[hemi].get('grid_volume_neg')
center = self._data['center']
if volume_pos is None:
xyz = np.meshgrid(
*[np.arange(s) for s in src[0]['shape']], indexing='ij')
dimensions = np.array(src[0]['shape'], int)
mult = 1000 if self._units == 'mm' else 1
src_mri_t = src[0]['src_mri_t']['trans'].copy()
src_mri_t[:3] *= mult
if resolution is not None:
resolution = resolution * mult / 1000. # to mm
del src, mult
coords = np.array([c.ravel(order='F') for c in xyz]).T
coords = apply_trans(src_mri_t, coords)
self.geo[hemi] = Bunch(coords=coords)
vertices = self._data[hemi]['vertices']
assert self._data[hemi]['array'].shape[0] == len(vertices)
# MNE constructs the source space on a uniform grid in MRI space,
# but mne coreg can change it to be non-uniform, so we need to
# use all three elements here
assert np.allclose(
src_mri_t[:3, :3], np.diag(np.diag(src_mri_t)[:3]))
spacing = np.diag(src_mri_t)[:3]
origin = src_mri_t[:3, 3] - spacing / 2.
scalars = np.zeros(np.prod(dimensions))
scalars[vertices] = 1. # for the outer mesh
grid, grid_mesh, volume_pos, volume_neg = \
_volume(dimensions, origin, spacing, scalars, surface_alpha,
resolution, blending, center)
self._data[hemi]['alpha'] = alpha # incorrectly set earlier
self._data[hemi]['grid'] = grid
self._data[hemi]['grid_mesh'] = grid_mesh
self._data[hemi]['grid_coords'] = coords
self._data[hemi]['grid_src_mri_t'] = src_mri_t
self._data[hemi]['grid_shape'] = dimensions
self._data[hemi]['grid_volume_pos'] = volume_pos
self._data[hemi]['grid_volume_neg'] = volume_neg
actor_pos, _ = self._renderer.plotter.add_actor(
volume_pos, reset_camera=False, name=None, culling=False)
if volume_neg is not None:
actor_neg, _ = self._renderer.plotter.add_actor(
volume_neg, reset_camera=False, name=None, culling=False)
else:
actor_neg = None
grid_mesh = self._data[hemi]['grid_mesh']
if grid_mesh is not None:
import vtk
_, prop = self._renderer.plotter.add_actor(
grid_mesh, reset_camera=False, name=None, culling=False,
pickable=False)
prop.SetColor(*self._brain_color[:3])
prop.SetOpacity(surface_alpha)
if silhouette_alpha > 0 and silhouette_linewidth > 0:
for ri, ci, v in self._iter_views('vol'):
self._renderer.subplot(ri, ci)
grid_silhouette = vtk.vtkPolyDataSilhouette()
grid_silhouette.SetInputData(grid_mesh.GetInput())
grid_silhouette.SetCamera(
self._renderer.plotter.renderer.GetActiveCamera())
grid_silhouette.SetEnableFeatureAngle(0)
grid_silhouette_mapper = vtk.vtkPolyDataMapper()
grid_silhouette_mapper.SetInputConnection(
grid_silhouette.GetOutputPort())
_, prop = self._renderer.plotter.add_actor(
grid_silhouette_mapper, reset_camera=False, name=None,
culling=False, pickable=False)
prop.SetColor(*self._brain_color[:3])
prop.SetOpacity(silhouette_alpha)
prop.SetLineWidth(silhouette_linewidth)
return actor_pos, actor_neg
def add_label(self, label, color=None, alpha=1, scalar_thresh=None,
borders=False, hemi=None, subdir=None):
"""Add an ROI label to the image.
Parameters
----------
label : str | instance of Label
Label filepath or name. Can also be an instance of
an object with attributes "hemi", "vertices", "name", and
optionally "color" and "values" (if scalar_thresh is not None).
color : matplotlib-style color | None
Anything matplotlib accepts: string, RGB, hex, etc. (default
"crimson").
alpha : float in [0, 1]
Alpha level to control opacity.
scalar_thresh : None | float
Threshold the label ids using this value in the label
file's scalar field (i.e. label only vertices with
scalar >= thresh).
borders : bool | int
Show only label borders. If int, specify the number of steps
(away from the true border) along the cortical mesh to include
as part of the border definition.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown.
subdir : None | str
If a label is specified as name, subdir can be used to indicate
that the label file is in a sub-directory of the subject's
label directory rather than in the label directory itself (e.g.
for ``$SUBJECTS_DIR/$SUBJECT/label/aparc/lh.cuneus.label``
``brain.add_label('cuneus', subdir='aparc')``).
Notes
-----
To remove previously added labels, run Brain.remove_labels().
"""
from matplotlib.colors import colorConverter
from ...label import read_label
if isinstance(label, str):
if color is None:
color = "crimson"
if os.path.isfile(label):
filepath = label
label = read_label(filepath)
hemi = label.hemi
label_name = os.path.basename(filepath).split('.')[1]
else:
hemi = self._check_hemi(hemi)
label_name = label
label_fname = ".".join([hemi, label_name, 'label'])
if subdir is None:
filepath = op.join(self._subjects_dir, self._subject_id,
'label', label_fname)
else:
filepath = op.join(self._subjects_dir, self._subject_id,
'label', subdir, label_fname)
if not os.path.exists(filepath):
raise ValueError('Label file %s does not exist'
% filepath)
label = read_label(filepath)
ids = label.vertices
scalars = label.values
else:
# try to extract parameters from label instance
try:
hemi = label.hemi
ids = label.vertices
if label.name is None:
label_name = 'unnamed'
else:
label_name = str(label.name)
if color is None:
if hasattr(label, 'color') and label.color is not None:
color = label.color
else:
color = "crimson"
if scalar_thresh is not None:
scalars = label.values
except Exception:
raise ValueError('Label was not a filename (str), and could '
'not be understood as a class. The class '
'must have attributes "hemi", "vertices", '
'"name", and (if scalar_thresh is not None)'
'"values"')
hemi = self._check_hemi(hemi)
if scalar_thresh is not None:
ids = ids[scalars >= scalar_thresh]
# XXX: add support for label_name
self._label_name = label_name
label = np.zeros(self.geo[hemi].coords.shape[0])
label[ids] = 1
color = colorConverter.to_rgba(color, alpha)
cmap = np.array([(0, 0, 0, 0,), color])
ctable = np.round(cmap * 255).astype(np.uint8)
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
if borders:
surface = {
'rr': self.geo[hemi].coords,
'tris': self.geo[hemi].faces,
}
mesh_data = self._renderer.contour(
surface=surface,
scalars=label,
contours=[1.0],
color=color,
kind='tube',
)
else:
mesh_data = self._renderer.mesh(
x=self.geo[hemi].coords[:, 0],
y=self.geo[hemi].coords[:, 1],
z=self.geo[hemi].coords[:, 2],
triangles=self.geo[hemi].faces,
scalars=label,
color=None,
colormap=ctable,
backface_culling=False,
polygon_offset=-2,
)
self._label_data.append(mesh_data)
self._renderer.set_camera(**views_dicts[hemi][v])
self._update()
def add_foci(self, coords, coords_as_verts=False, map_surface=None,
scale_factor=1, color="white", alpha=1, name=None,
hemi=None):
"""Add spherical foci, possibly mapping to displayed surf.
The foci spheres can be displayed at the coordinates given, or
mapped through a surface geometry. In other words, coordinates
from a volume-based analysis in MNI space can be displayed on an
inflated average surface by finding the closest vertex on the
white surface and mapping to that vertex on the inflated mesh.
Parameters
----------
coords : ndarray, shape (n_coords, 3)
Coordinates in stereotaxic space (default) or array of
vertex ids (with ``coord_as_verts=True``).
coords_as_verts : bool
Whether the coords parameter should be interpreted as vertex ids.
map_surface : None
Surface to map coordinates through, or None to use raw coords.
scale_factor : float
Controls the size of the foci spheres (relative to 1cm).
color : matplotlib color code
HTML name, RBG tuple, or hex code.
alpha : float in [0, 1]
Opacity of focus gylphs.
name : str
Internal name to use.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown. If two hemispheres are being shown, an error will
be thrown.
"""
from matplotlib.colors import colorConverter
hemi = self._check_hemi(hemi)
# those parameters are not supported yet, only None is allowed
_check_option('map_surface', map_surface, [None])
# Figure out how to interpret the first parameter
if coords_as_verts:
coords = self.geo[hemi].coords[coords]
# Convert the color code
if not isinstance(color, tuple):
color = colorConverter.to_rgb(color)
if self._units == 'm':
scale_factor = scale_factor / 1000.
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
self._renderer.sphere(center=coords, color=color,
scale=(10. * scale_factor),
opacity=alpha)
self._renderer.set_camera(**views_dicts[hemi][v])
def add_text(self, x, y, text, name=None, color=None, opacity=1.0,
row=-1, col=-1, font_size=None, justification=None):
"""Add a text to the visualization.
Parameters
----------
x : float
X coordinate.
y : float
Y coordinate.
text : str
Text to add.
name : str
Name of the text (text label can be updated using update_text()).
color : tuple
Color of the text. Default is the foreground color set during
initialization (default is black or white depending on the
background color).
opacity : float
Opacity of the text (default 1.0).
row : int
Row index of which brain to use.
col : int
Column index of which brain to use.
font_size : float | None
The font size to use.
justification : str | None
The text justification.
"""
# XXX: support `name` should be added when update_text/remove_text
# are implemented
# _check_option('name', name, [None])
self._renderer.text2d(x_window=x, y_window=y, text=text, color=color,
size=font_size, justification=justification)
def add_annotation(self, annot, borders=True, alpha=1, hemi=None,
remove_existing=True, color=None, **kwargs):
"""Add an annotation file.
Parameters
----------
annot : str | tuple
Either path to annotation file or annotation name. Alternatively,
the annotation can be specified as a ``(labels, ctab)`` tuple per
hemisphere, i.e. ``annot=(labels, ctab)`` for a single hemisphere
or ``annot=((lh_labels, lh_ctab), (rh_labels, rh_ctab))`` for both
hemispheres. ``labels`` and ``ctab`` should be arrays as returned
by :func:`nibabel.freesurfer.io.read_annot`.
borders : bool | int
Show only label borders. If int, specify the number of steps
(away from the true border) along the cortical mesh to include
as part of the border definition.
alpha : float in [0, 1]
Alpha level to control opacity.