def demo_plot_map(): map = np.zeros((182, 218, 182)) # Color a asymetric rectangle around Broadman area 26: x, y, z = -6, -53, 9 x_map, y_map, z_map = coord_transform(x, y, z, mni_sform_inv) map[x_map-30:x_map+30, y_map-3:y_map+3, z_map-10:z_map+10] = 1 plot_map(map, mni_sform, cut_coords=(x, y, z), vmin=0.5, figure_num=512)
def auto_plot_map(map, sform, vmin=None, cut_coords=None, do3d=False, anat=None, anat_sform=None, title='', figure_num=None, mask=None, auto_sign=True): """ Automatic plotting of an activation map. Plot a together a 3D volume rendering view of the activation, with an outline of the brain, and 2D cuts. If Mayavi is not installed, falls back to 2D views only. 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. vmin : float, optional The lower threshold of the positive activation. This parameter is used to threshold the activation map. cut_coords: 3-tuple of floats, optional The MNI coordinates of the point where the cut is performed, in MNI coordinates and order. If None is given, the cut_coords are automaticaly estimated. do3d : boolean, optional If do3d is True, a 3D plot is created if Mayavi is installed. anat : 3D ndarray, optional The anatomical image to be used as a background. If None, the MNI152 T1 1mm template is used. 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. title : string, optional The title dispayed on the figure. figure_num : integer, optional The number of the matplotlib and Mayavi figures used. If None is given, a new figure is created. mask : 3D ndarray, boolean, optional The brain mask. If None, the mask is computed from the map. auto_sign : boolean, optional If auto_sign is True, the sign of the activation is automaticaly computed: negative activation can thus be plotted. Returns ------- vmin : float The lower threshold of the activation used. cut_coords : 3-tuple of floats The Talairach coordinates of the cut performed for the 2D view. 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 do3d: if do3d == 'offscreen': try: from enthought.mayavi import mlab mlab.options.offscreen = True except: pass plotter = plot_map else: plotter = plot_map_2d if mask is None: mask = compute_mask(map) if vmin is None: vmin = np.inf pvalue = 0.04 while not np.isfinite(vmin): pvalue *= 1.25 vmax, vmin = find_activation(map, mask=mask, pvalue=pvalue) if not np.isfinite(vmin) and auto_sign: if np.isfinite(vmax): vmin = -vmax if mask is not None: map[mask] *= -1 else: map *= -1 if cut_coords is None: x, y, z = find_cut_coords(map, activation_threshold=vmin) # XXX: Careful with Voxel/MNI ordering y, x, z = coord_transform(x, y, z, sform) cut_coords = (x, y, z) plotter(map, sform, vmin=vmin, cut_coords=cut_coords, anat=anat, anat_sform=anat_sform, title=title, figure_num=figure_num, mask=mask) return vmin, cut_coords
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')