def demo_ortho_slicer():
""" A small demo of the OrthoSlicer functionality.
"""
pl.clf()
oslicer = OrthoSlicer(cut_coords=(0, 0, 0))
from anat_cache import _AnatCache
map, affine, _ = _AnatCache.get_anat()
oslicer.plot_map(map, affine, cmap=pl.cm.gray)
return oslicer
def demo_ortho_slicer():
""" A small demo of the OrthoSlicer functionality.
"""
pl.clf()
oslicer = OrthoSlicer(cut_coords=(0, 0, 0))
from anat_cache import _AnatCache
map, affine, _ = _AnatCache.get_anat()
oslicer.plot_map(map, affine, cmap=pl.cm.gray)
return oslicer
def plot_map_2d(map, sform, cut_coords, anat=None, anat_sform=None,
vmin=None, figure_num=None, axes=None, title='',
mask=None, **kwargs):
""" Plot three cuts of a given activation map (Frontal, Axial, and Lateral)
Parameters
----------
map : 3D ndarray
The activation map, as a 3D image.
sform : 4x4 ndarray
The affine matrix going from image voxel space to MNI space.
cut_coords: 3-tuple of floats
The MNI coordinates of the point where the cut is performed, in
MNI coordinates and order.
anat : 3D ndarray, optional or False
The anatomical image to be used as a background. If None, the
MNI152 T1 1mm template is used. If False, no anat is displayed.
anat_sform : 4x4 ndarray, optional
The affine matrix going from the anatomical image voxel space to
MNI space. This parameter is not used when the default
anatomical is used, but it is compulsory when using an
explicite anatomical image.
vmin : float, optional
The lower threshold of the positive activation. This
parameter is used to threshold the activation map.
figure_num : integer, optional
The number of the matplotlib figure used. If None is given, a
new figure is created.
axes : 4 tuple of float: (xmin, xmax, ymin, ymin), optional
The coordinates, in matplotlib figure space, of the axes
used to display the plot. If None, the complete figure is
used.
title : string, optional
The title dispayed on the figure.
mask : 3D ndarray, boolean, optional
The brain mask. If None, the mask is computed from the map.*
kwargs: extra keyword arguments, optional
Extra keyword arguments passed to pylab.imshow
Notes
-----
All the 3D arrays are in numpy convention: (x, y, z)
Cut coordinates are in Talairach coordinates. Warning: Talairach
coordinates are (y, x, z), if (x, y, z) are in voxel-ordering
convention.
"""
if anat is None:
anat, anat_sform, vmax_anat = _AnatCache.get_anat()
elif anat is not False:
vmax_anat = anat.max()
if mask is not None and (
np.all(mask) or np.all(np.logical_not(mask))):
mask = None
vmin_map = map.min()
vmax_map = map.max()
if vmin is not None and np.isfinite(vmin):
map = np.ma.masked_less(map, vmin)
elif mask is not None and not isinstance(map, np.ma.masked_array):
map = np.ma.masked_array(map, np.logical_not(mask))
vmin_map = map.min()
vmax_map = map.max()
if isinstance(map, np.ma.core.MaskedArray):
use_mask = False
if map._mask is False or np.all(np.logical_not(map._mask)):
map = np.asarray(map)
elif map._mask is True or np.all(map._mask):
map = np.asarray(map)
if use_mask and mask is not None:
map = np.ma.masked_array(map, np.logical_not(mask))
# Calculate the bounds
if anat is not False:
anat_bounds = np.zeros((4, 6))
anat_bounds[:3, -3:] = np.identity(3)*anat.shape
anat_bounds[-1, :] = 1
anat_bounds = np.dot(anat_sform, anat_bounds)
map_bounds = np.zeros((4, 6))
map_bounds[:3, -3:] = np.identity(3)*map.shape
map_bounds[-1, :] = 1
map_bounds = np.dot(sform, map_bounds)
# The coordinates of the center of the cut in different spaces.
y, x, z = cut_coords
x_map, y_map, z_map = [int(round(c)) for c in
coord_transform(x, y, z,
np.linalg.inv(sform))]
if anat is not False:
x_anat, y_anat, z_anat = [int(round(c)) for c in
coord_transform(x, y, z,
np.linalg.inv(anat_sform))]
fig = pl.figure(figure_num, figsize=(6.6, 2.6))
if axes is None:
axes = (0., 1., 0., 1.)
pl.clf()
ax_xmin, ax_xmax, ax_ymin, ax_ymax = axes
ax_width = ax_xmax - ax_xmin
ax_height = ax_ymax - ax_ymin
# Calculate the axes ratio size in a 'clever' way
if anat is not False:
shapes = np.array(anat.shape, 'f')
else:
shapes = np.array(map.shape, 'f')
shapes *= ax_width/shapes.sum()
###########################################################################
# Frontal
pl.axes([ax_xmin, ax_ymin, shapes[0], ax_height])
if anat is not False:
if y_anat < anat.shape[1]:
pl.imshow(np.rot90(anat[:, y_anat, :]),
cmap=pl.cm.gray,
vmin=-.5*vmax_anat,
vmax=vmax_anat,
extent=(anat_bounds[0, 3],
anat_bounds[0, 0],
anat_bounds[2, 0],
anat_bounds[2, 5]))
if y_map < map.shape[1]:
pl.imshow(np.rot90(map[:, y_map, :]),
vmin=vmin_map,
vmax=vmax_map,
extent=(map_bounds[0, 3],
map_bounds[0, 0],
map_bounds[2, 0],
map_bounds[2, 5]),
**kwargs)
pl.text(ax_xmin +shapes[0] + shapes[1] - 0.01, ax_ymin + 0.07, '%i' % x,
horizontalalignment='right',
verticalalignment='bottom',
transform=fig.transFigure)
xmin, xmax = pl.xlim()
ymin, ymax = pl.ylim()
pl.hlines(z, xmin, xmax, color=(.5, .5, .5))
pl.vlines(-x, ymin, ymax, color=(.5, .5, .5))
pl.axis('off')
###########################################################################
# Lateral
pl.axes([ax_xmin + shapes[0], ax_ymin, shapes[1], ax_height])
if anat is not False:
if x_anat < anat.shape[0]:
pl.imshow(np.rot90(anat[x_anat, ...]), cmap=pl.cm.gray,
vmin=-.5*vmax_anat,
vmax=vmax_anat,
extent=(anat_bounds[1, 0],
anat_bounds[1, 4],
anat_bounds[2, 0],
anat_bounds[2, 5]))
if x_map < map.shape[0]:
pl.imshow(np.rot90(map[x_map, ...]),
vmin=vmin_map,
vmax=vmax_map,
extent=(map_bounds[1, 0],
map_bounds[1, 4],
map_bounds[2, 0],
map_bounds[2, 5]),
**kwargs)
pl.text(ax_xmin + shapes[-1] - 0.01, ax_ymin + 0.07, '%i' % y,
horizontalalignment='right',
verticalalignment='bottom',
transform=fig.transFigure)
xmin, xmax = pl.xlim()
ymin, ymax = pl.ylim()
pl.hlines(z, xmin, xmax, color=(.5, .5, .5))
pl.vlines(y, ymin, ymax, color=(.5, .5, .5))
pl.axis('off')
###########################################################################
# Axial
pl.axes([ax_xmin + shapes[0] + shapes[1], ax_ymin, shapes[-1],
ax_height])
if anat is not False:
if z_anat < anat.shape[2]:
pl.imshow(np.rot90(anat[..., z_anat]),
cmap=pl.cm.gray,
vmin=-.5*vmax_anat,
vmax=vmax_anat,
extent=(anat_bounds[0, 0],
anat_bounds[0, 3],
anat_bounds[1, 0],
anat_bounds[1, 4]))
if z_map < map.shape[2]:
pl.imshow(np.rot90(map[..., z_map]),
vmin=vmin_map,
vmax=vmax_map,
extent=(map_bounds[0, 0],
map_bounds[0, 3],
map_bounds[1, 0],
map_bounds[1, 4]),
**kwargs)
pl.text(ax_xmax - 0.01, ax_ymin + 0.07, '%i' % z,
horizontalalignment='right',
verticalalignment='bottom',
transform=fig.transFigure)
xmin, xmax = pl.xlim()
ymin, ymax = pl.ylim()
pl.hlines(y, xmin, xmax, color=(.5, .5, .5))
pl.vlines(x, ymin, ymax, color=(.5, .5, .5))
pl.axis('off')
pl.text(ax_xmin + 0.01, ax_ymax - 0.01, title,
horizontalalignment='left',
verticalalignment='top',
transform=fig.transFigure)
pl.axis('off')