def plot_data(data,affine,plot_title,thresh):
minval = np.min(data)
maxval = np.max(data)
img = nib.Nifti1Image(data,affine)
img.header["cal_min"] = minval
img.header["cal_max"] = maxval
fsaverage = datasets.fetch_surf_fsaverage()
texture_right = surface.vol_to_surf(img, fsaverage.pial_right)
texture_left = surface.vol_to_surf(img, fsaverage.pial_left)
plotting.plot_surf_stat_map(fsaverage.infl_right, texture_right, hemi='right', title=plot_title, colorbar=True, threshold=thresh, bg_map=fsaverage.sulc_right)
plotting.plot_surf_stat_map(fsaverage.infl_left, texture_left, hemi='left', title=plot_title, threshold=thresh, bg_map=fsaverage.sulc_left)
plt.show()
def surface_plots(self, perf_img):
print("Creating surface plots")
figure, axes = plt.subplots(2,
2,
subplot_kw={"projection": "3d"},
figsize=(12, 12))
axes = axes.ravel()
big_fsaverage = datasets.fetch_surf_fsaverage("fsaverage")
cnt = 0
for hemi, infl, sulc, pial in [
(
"left",
big_fsaverage.infl_left,
big_fsaverage.sulc_left,
big_fsaverage.pial_left,
),
(
"right",
big_fsaverage.infl_right,
big_fsaverage.sulc_right,
big_fsaverage.pial_right,
),
]:
print("Hemi {}".format(hemi))
big_texture = surface.vol_to_surf(perf_img,
pial,
interpolation="nearest")
for view in ["lateral", "medial"]:
print(" View {}".format(view))
if cnt == 3:
output_file = os.path.join(
self.folder, "importance_scores_surface.png")
else:
output_file = None
plotting.plot_surf_stat_map(
infl,
big_texture,
hemi=hemi,
colorbar=True,
title="{} hemisphere {} view".format(hemi, view),
threshold=0.0001,
bg_map=sulc,
view=view,
output_file=output_file,
axes=axes[cnt],
)
cnt += 1
def surf_stat_maps(self, data, data_type, map_index):
data, title, threshold, output_folder = self.prepare_plots(
data, data_type, map_index, "Surf_Stat_Maps")
fsaverage = datasets.fetch_surf_fsaverage()
texture = surface.vol_to_surf(data, fsaverage.pial_left)
plotting.plot_surf_stat_map(
fsaverage.infl_left,
texture,
hemi='left',
view='lateral',
title=title,
colorbar=True,
threshold=threshold,
bg_map=fsaverage.sulc_left,
bg_on_data=True,
cmap='Spectral',
output_file=(output_folder + 'feature_' + str(map_index) +
'-left-lateral-' + self.category + '_category.png'))
plotting.plot_surf_stat_map(
fsaverage.infl_left,
texture,
hemi='left',
view='medial',
title=title,
colorbar=True,
threshold=threshold,
bg_map=fsaverage.sulc_left,
bg_on_data=True,
cmap='Spectral',
output_file=(output_folder + 'feature_' + str(map_index) +
'-left-medial-' + self.category + '_category.png'))
texture = surface.vol_to_surf(data, fsaverage.pial_right)
plotting.plot_surf_stat_map(
fsaverage.infl_right,
texture,
hemi='right',
view='lateral',
title=title,
colorbar=True,
threshold=threshold,
bg_map=fsaverage.sulc_right,
bg_on_data=True,
cmap='Spectral',
output_file=(output_folder + 'feature_' + str(map_index) +
'-right-lateral-' + self.category + '_category.png'))
plotting.plot_surf_stat_map(
fsaverage.infl_right,
texture,
hemi='right',
view='medial',
title=title,
colorbar=True,
threshold=threshold,
bg_map=fsaverage.sulc_right,
bg_on_data=True,
cmap='Spectral',
output_file=(output_folder + 'feature_' + str(map_index) +
'-right-medial-' + self.category + '_category.png'))
def plot_surfaces(grad_dict, index, outdir):
grad_lh = grad_dict["grads_lh"][:, index]
if np.max(grad_lh[grad_lh > 0]) > np.abs(np.min(grad_lh[grad_lh < 0])):
grad_lh[grad_lh < 0] = grad_lh[grad_lh < 0] / np.abs(np.min(grad_lh[grad_lh < 0]))
grad_lh[grad_lh < 0] = grad_lh[grad_lh < 0] * np.max(grad_lh[grad_lh > 0])
else:
grad_lh[grad_lh > 0] = grad_lh[grad_lh > 0] / np.max(grad_lh[grad_lh > 0])
grad_lh[grad_lh > 0] = grad_lh[grad_lh > 0] * np.abs(np.min(grad_lh[grad_lh < 0]))
plotting.plot_surf_stat_map(
grad_dict["pial_left"],
grad_lh,
hemi="left",
bg_map=grad_dict["sulc_left"],
bg_on_data=True,
colorbar=False,
view="medial",
cmap="jet",
output_file=op.join(outdir, "gradient-{0}_left_medial.png".format(index)),
)
plotting.plot_surf_stat_map(
grad_dict["pial_left"],
grad_lh,
hemi="left",
bg_map=grad_dict["sulc_left"],
bg_on_data=True,
colorbar=False,
view="lateral",
cmap="jet",
output_file=op.join(outdir, "gradient-{0}_left_lateral.png".format(index)),
)
grad_rh = grad_dict["grads_rh"][:, index]
if np.max(grad_rh[grad_rh > 0]) > np.abs(np.min(grad_rh[grad_rh < 0])):
grad_rh[grad_rh < 0] = grad_rh[grad_rh < 0] / np.abs(np.min(grad_rh[grad_rh < 0]))
grad_rh[grad_rh < 0] = grad_rh[grad_rh < 0] * np.max(grad_rh[grad_rh > 0])
else:
grad_rh[grad_rh > 0] = grad_rh[grad_rh > 0] / np.max(grad_rh[grad_rh > 0])
grad_rh[grad_rh > 0] = grad_rh[grad_rh > 0] * np.abs(np.min(grad_rh[grad_rh < 0]))
plotting.plot_surf_stat_map(
grad_dict["pial_right"],
grad_rh,
hemi="right",
bg_map=grad_dict["sulc_right"],
bg_on_data=True,
colorbar=False,
view="medial",
cmap="jet",
output_file=op.join(outdir, "gradient-{0}_right_medial.png".format(index)),
)
plotting.plot_surf_stat_map(
grad_dict["pial_right"],
grad_rh,
hemi="right",
bg_map=grad_dict["sulc_right"],
bg_on_data=True,
colorbar=True,
view="lateral",
cmap="jet",
output_file=op.join(outdir, "gradient-{0}_right_lateral.png".format(index)),
)
def make_surf_plot(data, surf='inflated', version='fsaverage5', **kwargs):
"""
Generates 2 x 2 surface plot of `data`
Parameters
----------
data : array_like
Data to be plotted; should be left hemisphere first
surf : {'orig', 'white', 'smoothwm', 'pial', 'inflated', 'sphere'}
Which surface plot should be displaye don
version : {'fsaverage', 'fsaverage5', 'fsaverage6'}
Which fsaverage surface to use for plots. Dimensionality of `data`
should match resolution of surface
Returns
-------
fig : matplotlib.figure.Figure
Figure object
"""
# get plotting kwargs
data = np.nan_to_num(data)
opts = dict(colorbar=False, vmax=data.max())
opts.update(kwargs)
for key in ['hemi', 'view', 'axes']:
if key in opts:
del opts[key]
data = np.split(data, 2)
fs = fetch_fsaverage(version)[surf]
fig, axes = plt.subplots(2, 2, subplot_kw={'projection': '3d'})
for row, view in zip(axes, ['lateral', 'medial']):
for n, (col, hemi) in enumerate(zip(row, ['lh', 'rh'])):
fn = getattr(fs, hemi)
hemi = 'left' if hemi == 'lh' else 'right'
plot_surf_stat_map(fn,
data[n],
hemi=hemi,
view=view,
axes=col,
**opts)
fig.tight_layout()
return fig
def plot_data(data, affine, thresh):
minval = np.min(data)
maxval = np.max(data)
img = nib.Nifti1Image(data, affine)
img.header["cal_min"] = minval
img.header["cal_max"] = maxval
fsaverage = datasets.fetch_surf_fsaverage()
texture_right = surface.vol_to_surf(img, fsaverage.pial_right)
texture_left = surface.vol_to_surf(img, fsaverage.pial_left)
# plot right lateral
plotting.plot_surf_stat_map(fsaverage.infl_right,
texture_right,
hemi='right',
view='lateral',
title="right lateral",
threshold=thresh,
bg_map=fsaverage.sulc_right)
plt.tight_layout()
# plot left lateral
plotting.plot_surf_stat_map(fsaverage.infl_left,
texture_left,
hemi='left',
view='lateral',
title="left lateral",
threshold=thresh,
bg_map=fsaverage.sulc_left)
plt.tight_layout()
# plot right medial
plotting.plot_surf_stat_map(fsaverage.infl_right,
texture_right,
hemi='right',
view='medial',
title="right medial",
threshold=thresh,
bg_map=fsaverage.sulc_right)
plt.tight_layout()
# plot left medial
plotting.plot_surf_stat_map(fsaverage.infl_left,
texture_left,
hemi='left',
view='medial',
title="left medial",
threshold=thresh,
bg_map=fsaverage.sulc_left)
plt.tight_layout()
plt.show()
def surface_plots(self, perf_img):
print('Creating surface plots')
figure, axes = plt.subplots(2,
2,
subplot_kw={'projection': '3d'},
figsize=(12, 12))
axes = axes.ravel()
big_fsaverage = datasets.fetch_surf_fsaverage('fsaverage')
cnt = 0
for hemi, infl, sulc, pial in [
('left', big_fsaverage.infl_left, big_fsaverage.sulc_left,
big_fsaverage.pial_left),
('right', big_fsaverage.infl_right, big_fsaverage.sulc_right,
big_fsaverage.pial_right)
]:
print('Hemi {}'.format(hemi))
big_texture = surface.vol_to_surf(perf_img,
pial,
interpolation='nearest')
for view in ['lateral', 'medial']:
print(' View {}'.format(view))
if cnt == 3:
output_file = os.path.join(
self.folder, 'importance_scores_surface.png')
else:
output_file = None
plotting.plot_surf_stat_map(
infl,
big_texture,
hemi=hemi,
colorbar=True,
title='{} hemisphere {} view'.format(hemi, view),
threshold=0.0001,
bg_map=sulc,
view=view,
output_file=output_file,
axes=axes[cnt])
cnt += 1
def make_thumbnail_surface(func,
hemi,
threshold=3.0,
vmax=10.,
output_dir='/tmp'):
if os.path.exists('/neurospin/ibc'):
dir_ = '/neurospin/ibc/derivatives/sub-01/ses-00/anat/fsaverage/surf'
else:
dir_ = '/storage/store/data/ibc/derivatives/sub-01/ses-00/anat/' + \
'fsaverage/surf'
if hemi == 'right':
mesh = os.path.join(dir_, 'rh.inflated')
bg_map = os.path.join(dir_, 'rh.sulc')
else:
mesh = os.path.join(dir_, 'lh.inflated')
bg_map = os.path.join(dir_, 'lh.sulc')
medial = '/tmp/surf_medial_%s.png' % hemi
lateral = '/tmp/surf_lateral_%s.png' % hemi
# threshold = fdr_threshold(func, .05)
plotting.plot_surf_stat_map(mesh,
func,
hemi=hemi,
vmax=vmax,
threshold=threshold,
bg_map=bg_map,
view='lateral',
output_file=lateral)
plotting.plot_surf_stat_map(mesh,
func,
hemi=hemi,
vmax=vmax,
threshold=threshold,
bg_map=bg_map,
view='medial',
output_file=medial)
return medial, lateral
def plot_dscalar(
img,
colorbar=True,
plot_abs=False,
vmax=None,
threshold=None,
cmap='cold_hot',
output_file=None,
):
import matplotlib as mpl
from matplotlib import pyplot as plt
subcort, ltexture, rtexture = decompose_dscalar(img)
fig = plt.figure(figsize=(11, 9))
ax1 = plt.subplot2grid((3, 2), (0, 0), projection='3d')
ax2 = plt.subplot2grid((3, 2), (0, 1), projection='3d')
ax3 = plt.subplot2grid((3, 2), (1, 0), projection='3d')
ax4 = plt.subplot2grid((3, 2), (1, 1), projection='3d')
ax5 = plt.subplot2grid((3, 2), (2, 0), colspan=2)
surf_fmt = 'data/conte69/tpl-conte69_hemi-{hemi}_space-fsLR_den-32k_inflated.surf.gii'.format
lsurf = nb.load(resource_filename('fitlins',
surf_fmt(hemi='L'))).agg_data()
rsurf = nb.load(resource_filename('fitlins',
surf_fmt(hemi='R'))).agg_data()
kwargs = {
'threshold': None if threshold == 'auto' else threshold,
'colorbar': False,
'plot_abs': plot_abs,
'cmap': cmap,
'vmax': vmax,
}
nlp.plot_surf_stat_map(lsurf, ltexture, view='lateral', axes=ax1, **kwargs)
nlp.plot_surf_stat_map(rsurf, rtexture, view='medial', axes=ax2, **kwargs)
nlp.plot_surf_stat_map(lsurf, ltexture, view='medial', axes=ax3, **kwargs)
nlp.plot_surf_stat_map(rsurf, rtexture, view='lateral', axes=ax4, **kwargs)
nlp.plot_glass_brain(subcort, display_mode='lyrz', axes=ax5, **kwargs)
if colorbar:
data = img.get_fdata(dtype=np.float32)
if vmax is None:
vmax = max(-data.min(), data.max())
norm = mpl.colors.Normalize(vmin=-vmax if data.min() < 0 else 0,
vmax=vmax)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
fig.colorbar(sm, ax=fig.axes, location='right', aspect=50)
if output_file:
fig.savefig(output_file)
plt.close(fig)
ax.set_yticklabels(nice_contrasts[half_contrasts:])
ax.plot([0.017, 0.017], [0, len(contrasts)], 'g', linewidth=2)
#
#plt.subplots_adjust(left=.4, right=.99, bottom=.03, top=.99)
plt.savefig(os.path.join(write_dir, 'score_per_contrast.png'))
plt.show(block=False)
print(n_dim, n_parcellations, np.mean(con_scores))
fsaverage = fetch_surf_fsaverage()
texture = vol_to_surf(mean_vox_scores_img, fsaverage.pial_right)
output_file = os.path.join(write_dir, 'score_rh.png')
plot_surf_stat_map(fsaverage.infl_right,
texture,
hemi='right',
colorbar=False,
output_file=output_file,
threshold=0.,
bg_map=fsaverage.sulc_right,
vmax=.25)
texture = vol_to_surf(mean_vox_scores_img, fsaverage.pial_left)
output_file = os.path.join(write_dir, 'score_lh.png')
plot_surf_stat_map(fsaverage.infl_left,
texture,
hemi='left',
colorbar=False,
output_file=output_file,
threshold=0.,
bg_map=fsaverage.sulc_left,
vmax=.25)
###############################################################################
##############################################################################
# Sample the 3D data around each node of the mesh
# -----------------------------------------------
from nilearn import surface
texture = surface.vol_to_surf(stat_img, fsaverage.pial_right)
##############################################################################
# Plot the result
# ---------------
from nilearn import plotting
plotting.plot_surf_stat_map(fsaverage.infl_right, texture, hemi='right',
title='Surface right hemisphere', colorbar=True,
threshold=1., bg_map=fsaverage.sulc_right)
##############################################################################
# Plot 3D image for comparison
# ----------------------------
plotting.plot_glass_brain(stat_img, display_mode='r', plot_abs=False,
title='Glass brain', threshold=2.)
plotting.plot_stat_map(stat_img, display_mode='x', threshold=1.,
cut_coords=range(0, 51, 10), title='Slices')
##############################################################################
# Plot with higher-resolution mesh
if hemi == 'left':
coords_x, faces_x, beta_mesh_x, sulc_x =\
coords_l, faces_l, beta_mesh_l, sulc_l
elif hemi == 'right':
coords_x, faces_x, beta_mesh_x, sulc_x =\
coords_r, faces_r, beta_mesh_r, sulc_r
vmax_beta = np.max(np.abs(beta)) / 10
vmax_beta = np.max(np.abs(beta_mesh_x) * 1000) / 10
plotting.plot_surf_stat_map(
[coords_x, faces_x],
stat_map=1000 * beta_mesh_x,
hemi=hemi,
view=view,
bg_map=sulc_x, #bg_on_data=True,
#vmax = vmax_beta,#stat[2] / 10,#vmax=vmax_beta,
darkness=.5,
cmap=plt.cm.seismic,
#symmetric_cbar=True,
#output_file=output_filename
)
print(pd.Series((beta_mesh_l[beta_mesh_l != 0]).ravel()).describe())
print(pd.Series((beta_mesh_r[beta_mesh_r != 0]).ravel()).describe())
mesh_processing.save_texture(os.path.join(WD_CLUST, "beta_lh.gii"),
beta_mesh_l)
mesh_processing.save_texture(os.path.join(WD_CLUST, "beta_rh.gii"),
beta_mesh_r)
def plot_full_surf_stat_map(stat,
title=None,
ts=None,
mask=None,
inflate=False,
outfile=None,
add_colorbar=True,
vmax=None,
**kwargs):
"""Use nilearn's plot_surf_stat_map to plot volume data in the surface.
Plots a collage of both hemispheres and both medial and lateral views of
the brain. The surface mesh used for plotting is freesurfer's fsaverage.
:param stat: A 3D statistical map (Nilearn's niimg object)
:param title: A title for the image
:param ts: Optional timeseries to be plotted in the background.
:param mask: Optional mask passed to nilearn's vol_to_surf.
:param inflate: If True, plot on inflated, if False, on pial.
:param outfile: Optional output filename to save figure.
:param vmax: Colormap vmax limit
Other kwargs are passed to Nilearn's plot_surf_stat_map.
"""
fig, axes = plt.subplots(dpi=100,
nrows=2,
ncols=2,
subplot_kw={'projection': '3d'})
((ax_ll, ax_rl), (ax_lm, ax_rm)) = axes
# Compute the surface textures from the statistical map.
texture_r = surface.vol_to_surf(stat, FSAVERAGE.pial_right, mask_img=mask)
texture_l = surface.vol_to_surf(stat, FSAVERAGE.pial_left, mask_img=mask)
# Vmax scaled for optimal dynamic range.
if vmax is None:
vmax = fast_abs_percentile(stat.dataobj, 99.8)
# Plot on inflated brain or on pial surface?
if inflate:
leftsurf = FSAVERAGE.infl_left
rightsurf = FSAVERAGE.infl_right
else:
leftsurf = FSAVERAGE.pial_left
rightsurf = FSAVERAGE.pial_right
plotting.plot_surf_stat_map(rightsurf,
texture_r,
colorbar=False,
hemi='right',
axes=ax_rl,
bg_map=FSAVERAGE.sulc_right,
vmax=vmax,
**kwargs)
plotting.plot_surf_stat_map(rightsurf,
texture_r,
colorbar=False,
hemi='right',
view='medial',
axes=ax_rm,
bg_map=FSAVERAGE.sulc_right,
vmax=vmax,
**kwargs)
plotting.plot_surf_stat_map(leftsurf,
texture_l,
colorbar=False,
hemi='left',
axes=ax_ll,
bg_map=FSAVERAGE.sulc_left,
vmax=vmax,
**kwargs)
plotting.plot_surf_stat_map(leftsurf,
texture_l,
colorbar=False,
hemi='left',
view='medial',
axes=ax_lm,
bg_map=FSAVERAGE.sulc_left,
vmax=vmax,
**kwargs)
# The default camera distance in the 3D plot makes the surfaces look small.
# The fix is simple, bring the camera closer to the object.
for ax in axes.flatten():
ax.dist = 6
# Alpha set to 0 so that the timeseries is visible.
ax.patch.set_alpha(0.)
# Remove whitespace between subplots.
fig.subplots_adjust(wspace=-0.02, hspace=0.0)
if ts is not None:
# x0, y0, width, height = 0.34, 0.465, 0.38, 0.06
# x0 left -> right, y0 top -> down.
x0, y0 = 0.15, 0.445
width, height = (1.05 - 2 * x0), 0.12
ax5 = fig.add_axes([x0, y0, width, height], zorder=-1)
ax5.plot(ts[::2], 'dimgray', linewidth=0.8)
ax5.axis('off')
# Only necessary if ax5 is on top. (zorder larger than other axes)
# ax5.patch.set_alpha(0.)
# Add colorbar
if add_colorbar:
if 'threshold' in kwargs:
if kwargs['threshold'] is not None:
threshold = kwargs['threshold']
else:
threshold = 0
vmin = -vmax # when also displaying negative values, change to -vmax
if 'cmap' in kwargs:
cmap = nilearn_cmaps[kwargs['cmap']]
else:
cmap = nilearn_cmaps['cold_hot']
norm = Normalize(vmin=vmin, vmax=vmax)
cmaplist = [cmap(i) for i in range(cmap.N)]
# set colors to grey for absolute values < threshold
istart = int(norm(-threshold, clip=True) * (cmap.N - 1))
istop = int(norm(threshold, clip=True) * (cmap.N - 1))
for i in range(istart, istop):
cmaplist[i] = (0.5, 0.5, 0.5, 1.)
our_cmap = LinearSegmentedColormap.from_list('Custom cmap', cmaplist,
cmap.N)
sm = plt.cm.ScalarMappable(cmap=our_cmap,
norm=plt.Normalize(vmin=vmin, vmax=vmax))
# fake up the array of the scalar mappable.
sm._A = []
# fig.subplots_adjust(bottom=0.05)
cbar_ax = fig.add_subplot(32, 1,
32) # fig.add_axes([0.3, 0.9, 0.4, 0.03])
fig.colorbar(sm, cax=cbar_ax, orientation='horizontal')
if title is not None:
# y defaults to 0.98. The value of 0.93 lowers it a bit.
fig.suptitle(title, fontsize=12, y=0.93)
if outfile is not None:
plt.savefig(outfile, dpi=100, bbox_inches='tight')
##############################################################################
# Sample the 3D data around each node of the mesh
# -----------------------------------------------
from nilearn import surface
texture = surface.vol_to_surf(localizer_tmap, fsaverage.pial_right)
##############################################################################
# Plot the result
# ---------------
from nilearn import plotting
plotting.plot_surf_stat_map(fsaverage.infl_right, texture, hemi='right',
title='Surface right hemisphere',
threshold=1., bg_map=fsaverage.sulc_right)
##############################################################################
# Plot 3D image for comparison
# ----------------------------
plotting.plot_glass_brain(localizer_tmap, display_mode='r', plot_abs=False,
title='Glass brain', threshold=2.)
plotting.plot_stat_map(localizer_tmap, display_mode='x', threshold=1.,
cut_coords=range(0, 51, 10), title='Slices')
plotting.show()
plotting.plot_surf_roi(fsaverage['pial_left'],
roi_map=pcc_map,
hemi='left',
view='medial',
bg_map=fsaverage['sulc_left'],
bg_on_data=True,
title='PCC Seed')
###############################################################################
# Display unthresholded stat map with dimmed background
plotting.plot_surf_stat_map(fsaverage['pial_left'],
stat_map=stat_map,
hemi='left',
view='medial',
colorbar=True,
bg_map=fsaverage['sulc_left'],
bg_on_data=True,
darkness=.5,
title='Correlation map')
###############################################################################
# Display unthresholded stat map without background map, transparency is
# automatically set to .5, but can also be controlled with the alpha parameter
plotting.plot_surf_stat_map(fsaverage['pial_left'],
stat_map=stat_map,
hemi='left',
view='medial',
colorbar=True,
title='Plotting without background')
# -----------
#
# Prepare figure for concurrent plot of individual maps
# compute population-level maps for left and right hemisphere
# we directetly do that on the values arrays
from scipy.stats import ttest_1samp, norm
t_left, pval_left = ttest_1samp(np.array(z_scores_left), 0)
t_right, pval_right = ttest_1samp(np.array(z_scores_right), 0)
############################################################################
# What we have so far are p-values: we convert them to z-values for plotting
z_val_left = norm.isf(pval_left)
z_val_right = norm.isf(pval_right)
############################################################################
# Plot the resulting maps.
# Left hemipshere
from nilearn import plotting
plotting.plot_surf_stat_map(
fsaverage.infl_left, z_val_left, hemi='left',
title="language-string, left hemisphere", colorbar=True,
threshold=3., bg_map=fsaverage.sulc_left)
############################################################################
# Right hemisphere
plotting.plot_surf_stat_map(
fsaverage.infl_right, z_val_left, hemi='right',
title="language-string, right hemisphere", colorbar=True,
threshold=3., bg_map=fsaverage.sulc_right)
plotting.show()
texture = surface.vol_to_surf(stat_img, fsaverage.pial_right)
##############################################################################
# Plot the result
# ---------------
#
# You can visualize the texture on the surface using the function
# :func:`~nilearn.plotting.plot_surf_stat_map` which uses ``matplotlib``
# as the default plotting engine:
from nilearn import plotting
fig = plotting.plot_surf_stat_map(fsaverage.infl_right,
texture,
hemi='right',
title='Surface right hemisphere',
colorbar=True,
threshold=1.,
bg_map=fsaverage.sulc_right)
fig.show()
##############################################################################
# Interactive plotting with Plotly
# --------------------------------
#
# If you have a recent version of Nilearn (>=0.8.2), and if you have
# ``plotly`` installed, you can easily configure
# :func:`~nilearn.plotting.plot_surf_stat_map` to use ``plotly`` instead
# of ``matplotlib``:
engine = 'plotly'
# get surface mesh (fsaverage)
fsaverage = datasets.fetch_surf_fsaverage()
#%%
# project volume onto surface
# Caution: the projection is done for one hemisphere only, make sure to plot
# (below) for the same hemisphere
img_to_plot = surface.vol_to_surf(rho_map_pos, fsaverage.pial_right)
#%%
# plot surface
# note: the bg_map and hemi should be the same hemisphere as fsaverage.???
plotting.plot_surf_stat_map(fsaverage.infl_right,
img_to_plot,
hemi='right',
bg_map=fsaverage.sulc_right,
view='lateral',
title='Surface plot',
colorbar=True,
threshold=0.01)
#%%
# plot glass brain
plotting.plot_glass_brain(rho_map_pos,
display_mode='lyrz',
plot_abs=False,
title='Glass brain',
threshold=0.005)
#%%
## Plot Group
p_val = 0.05
# project volume onto surface
# Caution: the projection is done for one hemisphere only, make sure to plot
# (below) for the same hemisphere
img_to_plot = surface.vol_to_surf(z_map, fsaverage.pial_left)
#%%
p_val = 0.05
p_unc = norm.isf(p_val)
#%%
# plot surface
# note: the bg_map and hemi should be the same hemisphere as fsaverage.
plotting.plot_surf_stat_map(fsaverage.infl_left,
img_to_plot,
hemi='left',
bg_map=fsaverage.sulc_left,
view='lateral',
title=f"{model1}-{model2}, p<{p_val}",
colorbar=True,
threshold=p_unc)
#%%
# Plot glass brain of the z_map
display = plotting.plot_glass_brain(
z_map,
threshold=p_unc,
colorbar=True,
display_mode='lyr',
plot_abs=False,
title=f"group {model1}-{model2} (unc p<{p_val})")
#display.savefig(os.path.join(rootdir, "brain_plots", f"group_{model1}_{model2}.png"))
#########################################################################
# iterate over contrasts
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
print(' Contrast % i out of %i: %s, right hemisphere' %
(index + 1, len(contrasts), contrast_id))
# compute contrast-related statistics
contrast = compute_contrast(labels, estimates, contrast_val,
contrast_type='t')
# we present the Z-transform of the t map
z_score = contrast.z_score()
# we plot it on the surface, on the inflated fsaverage mesh,
# together with a suitable background to give an impression
# of the cortex folding.
plotting.plot_surf_stat_map(
fsaverage.infl_right, z_score, hemi='right',
title=contrast_id, colorbar=True,
threshold=3., bg_map=fsaverage.sulc_right)
#########################################################################
# Analysing the left hemisphere
# -----------------------------
#
# Note that it requires little additional code!
#########################################################################
# Project the fMRI data to the mesh
texture = surface.vol_to_surf(fmri_img, fsaverage.pial_left)
#########################################################################
# Estimate the General Linear Model
labels, estimates = run_glm(texture.T, design_matrix.values)
# load and smooth, when convert to nifti object
file = glob.glob(os.path.join(rootdir, 'subj01_diff_test_rho.nii.gz'))
rho_map = masker.transform(file)
rho_map = masker.inverse_transform(rho_map)
# get surface mesh(fsaverage)
fsaverage = datasets.fetch_surf_fsaverage()
# project volume onto surface
# Caution: the projection is done for one hemisphere only, make sure to plot
# (below) for the same hemisphere
img_to_plot = surface.vol_to_surf(rho_map, fsaverage.pial_right)
# plot surface
# note: the bg_map and hemi should be the same hemisphere as fsaverage.???
plotting.plot_surf_stat_map(fsaverage.infl_right,
img_to_plot,
hemi='right',
bg_map=fsaverage.sulc_right,
view='medial',
title='Surface plot',
colorbar=True,
threshold=0.005)
# plot glass brain
plotting.plot_glass_brain(rho_map,
display_mode='lyrz',
plot_abs=False,
title='Glass brain',
threshold=0.005)
save_obj(d_parcel_fsaverage,
os.path.join(path_to_masks, 'ROIS_in_fsaverge.pkl'))
do_plotting = False
if do_plotting:
fsaverage = datasets.fetch_surf_fsaverage(mesh='fsaverage')
for ROI_file in os.listdir(
os.path.join(path_to_masks,
'language_separFiles_in_fsaverage')):
if ROI_file.__contains__('_LH_'):
lh_surf = nib.load(
os.path.join(path_to_masks,
'language_separFiles_in_fsaverage', ROI_file))
figure = plotting.plot_surf_stat_map(
fsaverage.infl_left,
lh_surf.agg_data(),
hemi='left',
title='Surface left hemisphere',
colorbar=True,
threshold=1.,
bg_map=fsaverage.sulc_left)
plotting.plot_surf_contours(
fsaverage.infl_left,
lh_surf.agg_data(),
levels=[
1,
],
figure=figure,
legend=False,
colors=[
'g',
],
output_file=os.path.join(
def __update_views__(self, new_vertex):
if new_vertex is None or new_vertex == self._current_vertex:
return
axes = [
(self._ax[0], self.__lateral_cut__),
# (self._ax[1], self.__medial_cut__),
# (self._ax[2], self.__dorsal_cut__),
# (self._ax[3], self.__ventral_cut__),
# (self._ax[4], self.__anterior_cut__),
# (self._ax[5], self.__posterior_cut__)
]
for i in range(len(axes)):
ax, cut = axes[i]
ax.clear()
stat_map, view = cut()
plotting.plot_surf_stat_map(self._template,
stat_map=stat_map,
hemi=self._hemisphere,
view=view,
colorbar=True,
bg_map=self._bg_image,
bg_on_data=True,
cmap=self._rgba_cmap,
threshold=.5,
title='Threshold and colormap',
axes=ax)
ax.tick_params(direction='in')
for item in ax.get_xticklabels():
item.set_fontsize(8)
for item in ax.get_yticklabels():
item.set_fontsize(8)
self._ax[-1].clear()
x = new_vertex
colors = ['r', 'c', 'g', 'm', 'y', 'k']
if self._correction_processor is not None:
correction_processor, correction_parameters = self._correction_processor
cdata = correction_processor.corrected_values(
correction_parameters, x1=x, x2=x + 1)
self._ax[1].plot(correction_processor.predictors[:, 0],
cdata[:, 0], 'bo')
for processor, prediction_parameters, correction_parameters, label in self._processors:
cdata = processor.corrected_values(correction_parameters,
x1=x,
x2=x + 1)
self._ax[1].plot(processor.predictors[:, 0],
cdata[:, 0],
'bo',
color=colors[0])
axis, curve = processor.curve(prediction_parameters,
x1=x,
x2=x + 1,
tpoints=self._num_points)
self._ax[1].plot(axis.T,
curve[:, 0],
label=label,
color=colors[0],
marker='d')
colors.append(colors[0])
del colors[0]
if len(self._processors) > 0 or hasattr(self, '_correction_processor'):
self._ax[1].legend()
# Compute mm coordinates with affine
# c_vertex = [new_vertex, 1]
# c_vertex = np.array(c_vertex, dtype=np.float32)
# mm_coords = self._affine.dot(c_vertex)[:-1]
# self._figure.canvas.set_window_title('Coordinates {}, {}, {}'.format(*mm_coords))
self._figure.canvas.draw()
self._current_vertex = new_vertex
fsaverage = datasets.fetch_surf_fsaverage('fsaverage')
####### Plot state 1
texturestate1_l = surface.vol_to_surf(atlas_0state1, fsaverage.pial_left)
texturestate1_r = surface.vol_to_surf(atlas_0state1, fsaverage.pial_right)
fig, axs = plt.subplots(ncols=4,
nrows=1,
subplot_kw={'projection': '3d'},
figsize=(22, 5))
fig.tight_layout()
plotting.plot_surf_stat_map(surf_mesh=fsaverage.infl_right,
stat_map=texturestate1_r,
bg_map=fsaverage.sulc_right,
hemi='right',
view='lateral',
threshold=.084,
colorbar=False,
axes=axs[0])
plotting.plot_surf_stat_map(surf_mesh=fsaverage.infl_left,
stat_map=texturestate1_l,
bg_map=fsaverage.sulc_left,
hemi='left',
view='lateral',
threshold=.084,
colorbar=False,
axes=axs[1])
plotting.plot_surf_stat_map(surf_mesh=fsaverage.infl_left,
stat_map=texturestate1_l,
bg_map=fsaverage.sulc_left,
hemi='left',
'%s.sl_distinct%.1f.func.gii' % (hemi, radius))
cons_path_fn = os.path.join(surfpath,
'%s.sl_consist%.1f.func.gii' % (hemi, radius))
spread_path_fn = os.path.join(
surfpath, '%s.sl_spread_correlation_%.1f.func.gii' % (hemi, radius))
# plot the surface data
hemi_text = 'right' if hemi == 'rh' else 'left'
for view in ['medial', 'lateral']:
plotting.plot_surf_stat_map(
surf_mesh=os.path.join(surfpath, '%s.inflated' % (hemi)),
stat_map=load_surf_data(dist_path_fn)[:, 1],
bg_map=os.path.join(surfpath, '%s.sulc' % (hemi)),
hemi=hemi_text,
title='Distinctiveness %s hemisphere' % (hemi_text),
view=view,
output_file=os.path.join(datapath, 'results',
'%s.distinct.%s.png' % (hemi, view)),
alpha=0.7,
bg_on_data=True,
colorbar=True,
threshold=0.2,
cmap='cold_hot')
plotting.plot_surf_stat_map(
surf_mesh=os.path.join(surfpath, '%s.inflated' % (hemi)),
stat_map=load_surf_data(cons_path_fn)[:, 1],
bg_map=os.path.join(surfpath, '%s.sulc' % (hemi)),
hemi=hemi_text,
title='Consistency %s hemisphere' % (hemi_text),
view=view,
output_file=os.path.join(datapath, 'results',
'%s.consist.%s.png' % (hemi, view)),
def plot_brainrsa_surface(img, threshold=None, type='r'):
"""
Plot the RSA-result into a brain surface
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_data()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_data()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
fsaverage = datasets.fetch_surf_fsaverage(mesh='fsaverage')
texture_left = surface.vol_to_surf(img, fsaverage.pial_left)
texture_right = surface.vol_to_surf(img, fsaverage.pial_right)
# type='r'
if type == 'r':
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_left, hemi='left', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=False, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_right, hemi='right', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=True, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_left, hemi='left', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=False, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_right, hemi='right', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=True, vmax=0.8, darkness=0.7)
plt.show()
# type='t'
if type == 't':
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_left, hemi='left', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=False, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_right, hemi='right', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=True, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_left, hemi='left', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=False, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_right, hemi='right', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=True, darkness=0.7)
plt.show()
# -----------
#
# Prepare figure for concurrent plot of individual maps
# compute population-level maps for left and right hemisphere
# we directetly do that on the values arrays
from scipy.stats import ttest_1samp, norm
t_left, pval_left = ttest_1samp(np.array(z_scores_left), 0)
t_right, pval_right = ttest_1samp(np.array(z_scores_right), 0)
############################################################################
# What we have so far are p-values: we convert them to z-values for plotting
z_val_left = norm.isf(pval_left)
z_val_right = norm.isf(pval_right)
############################################################################
# Plot the resulting maps.
# Left hemipshere
from nilearn import plotting
plotting.plot_surf_stat_map(
fsaverage.infl_left, z_val_left, hemi='left',
title="language-string, left hemisphere", colorbar=True,
threshold=3., bg_map=fsaverage.sulc_left)
############################################################################
# Right hemisphere
plotting.plot_surf_stat_map(
fsaverage.infl_right, z_val_left, hemi='right',
title="language-string, right hemisphere", colorbar=True,
threshold=3., bg_map=fsaverage.sulc_right)
plotting.show()
nilearn.plotting.plot_stat_map(con_image, display_mode='ortho',
threshold=2.3)#, cut_coords=(20,55)) #, 10, 15), dim=1)
#%% Surface display
import nibabel as nib
from nilearn.plotting import plot_stat_map
from nilearn import datasets, surface, plotting
from nilearn import datasets
fsaverage = datasets.fetch_surf_fsaverage()
from nilearn import surface
texture = surface.vol_to_surf('/media/Data/work/KPE_SPM/Sink_ses-2/2ndLevel/_contrast_id_con_0001/spmT_0001_thr.nii', fsaverage.pial_right)
from nilearn import plotting
a = '/media/Data/KPE_fmriPrep_preproc/kpeOutput/derivatives/fmriprep/sub-1263/ses-1/func/sub-1263_ses-1_task-Memory_space-fsaverage5_hemi-R.func.gii'
plotting.plot_surf_stat_map(fsaverage.infl_right, texture, hemi='right',
title='Surface right hemisphere', colorbar=True,
threshold=0., bg_map=a)#fsaverage.sulc_right)
big_fsaverage = datasets.fetch_surf_fsaverage('fsaverage')
fmri_img = nib.load('/media/Data/work/KPE_SPM/Sink_ses-2/2ndLevel/_contrast_id_con_0001/spmT_0001_thr.nii')
big_texture_right = surface.vol_to_surf(fmri_img, big_fsaverage.pial_right)
%matplotlib inline
plotting.plot_surf_stat_map(big_fsaverage.infl_right,
big_texture_right,
hemi='right', colorbar=True,
title='',
threshold=3,
bg_map=big_fsaverage.sulc_right)
plotting.show()
%matplotlib qt
plotting.view_surf(big_fsaverage.infl_left, big_texture_right, threshold='95%',
def plot_surf_stat(lh_surf,
lh_stat_map,
lh_bg_map,
rh_surf,
rh_stat_map,
rh_bg_map,
out_fname,
cmap='coolwarm',
symmetric_cbar='auto',
upper_lim=None,
threshold=None):
'''Use Nilearn to plot statistical surface map for L lat, med, R lat, med view.
'''
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import nibabel.freesurfer.io as fsio
import nibabel.freesurfer.mghformat as fsmgh
from nilearn import plotting
# Get max and min value across stat maps from the two hemi
lh_stat_dat = fsmgh.load(lh_stat_map).get_data()
rh_stat_dat = fsmgh.load(rh_stat_map).get_data()
flat_dat = np.hstack((lh_stat_dat.flatten(), rh_stat_dat.flatten()))
max_val = np.maximum(np.abs(np.nanmax(flat_dat)),
np.abs(np.nanmin(flat_dat)))
if upper_lim is not None:
vmax = upper_lim if max_val > upper_lim else max_val
else:
vmax = max_val
fig, axs = plt.subplots(2,
2,
figsize=(8, 6),
subplot_kw={'projection': '3d'})
# Get threshold if txt is specified
if isinstance(threshold, str):
if op.exists(threshold):
thresh_arr = np.loadtxt(threshold)
thresh = thresh_arr if thresh_arr.shape == (
) and thresh_arr != np.inf else None
else:
thresh = None
elif isinstance(threshold, int) or isinstance(threshold, float):
thresh = threshold
else:
thresh = None
# Add threshold to title if not None
if thresh is not None:
if float(thresh) >= 1e-2 and float(thresh) < 1e2:
title_txt = 'Threshold = {:.2f}'.format(float(thresh))
else:
title_txt = 'Threshold = {:.2e}'.format(float(thresh))
else:
title_txt = ''
for i, ax in enumerate(fig.axes):
if i <= 1:
hemi = 'left'
surf = lh_surf
stat_map = lh_stat_dat
bg = lh_bg_map
else:
hemi = 'right'
surf = rh_surf
stat_map = rh_stat_dat
bg = rh_bg_map
view = 'lateral' if i % 2 == 0 else 'medial'
colorbar = True if i == 3 else False
title = title_txt if i == 0 else ''
plotting.plot_surf_stat_map(surf,
stat_map,
hemi=hemi,
bg_map=bg,
view=view,
vmax=vmax,
threshold=thresh,
title=title,
cmap=cmap,
symmetric_cbar=symmetric_cbar,
colorbar=colorbar,
axes=ax,
figure=fig)
fig.savefig(out_fname, dpi=200, bbox_inches='tight')
return op.abspath(out_fname)
# Searchlight computation
# -----------------------
from sklearn.model_selection import KFold
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import RidgeClassifier
from nilearn.decoding.searchlight import search_light
# Simple linear estimator preceeded by a normalization step
estimator = make_pipeline(StandardScaler(), RidgeClassifier(alpha=10.))
# Define cross-validation scheme
cv = KFold(n_splits=3, shuffle=False)
# Cross-validated search light
scores = search_light(X, y, estimator, adjacency, cv=cv, n_jobs=1)
#########################################################################
# Visualization
# -------------
from nilearn import plotting
chance = .5
plotting.plot_surf_stat_map(infl_mesh,
scores - chance,
view='medial',
colorbar=True,
threshold=0.1,
bg_map=fsaverage['sulc_' + hemi],
title='Accuracy map, left hemisphere')
plotting.show()
def ViewSurface(image, threshold=1.94):
fsaverageL = r"E:\brain\atlas\fsaverage\pial_left.gii"
fsaverageR = r"E:\brain\atlas\fsaverage\pial_right.gii"
textureL = surface.vol_to_surf(image,
fsaverage.pial_left,
inner_mesh=fsaverage.white_left)
textureR = surface.vol_to_surf(image,
fsaverage.pial_right,
inner_mesh=fsaverage.white_right)
fig, axes = plt.subplots(1,
2,
figsize=[10, 6],
subplot_kw={'projection': '3d'})
vmax = np.max([np.abs(textureL), np.abs(textureR)])
plotting.plot_surf_stat_map(
fsaverage.pial_left,
textureL,
hemi='left',
threshold=threshold,
bg_map=fsaverage.sulc_right,
cmap=sns.color_palette('cold_hot', as_cmap=True),
darkness=0.5,
colorbar=False,
#symmetric_cbar=True,
bg_on_data=True,
axes=axes[0],
vmax=vmax,
figure=fig)
plotting.plot_surf_stat_map(
fsaverage.pial_right,
textureR,
hemi='right',
threshold=threshold,
bg_map=fsaverage.sulc_right,
cmap=sns.color_palette('cold_hot', as_cmap=True),
darkness=0.5,
colorbar=False,
#symmetric_cbar=True,
bg_on_data=True,
axes=axes[1],
vmax=vmax,
figure=fig)
plt.show()
fig2, axes2 = plt.subplots(1,
2,
figsize=[10, 6],
subplot_kw={'projection': '3d'})
plotting.plot_surf_stat_map(
fsaverage.pial_left,
textureL,
hemi='right',
threshold=threshold,
bg_map=fsaverage.sulc_right,
cmap=sns.color_palette('cold_hot', as_cmap=True),
darkness=0.5,
colorbar=False,
#symmetric_cbar=True,
bg_on_data=True,
axes=axes2[0],
vmax=vmax,
figure=fig2)
plotting.plot_surf_stat_map(
fsaverage.pial_right,
textureR,
hemi='left',
threshold=threshold,
bg_map=fsaverage.sulc_right,
cmap=sns.color_palette('cold_hot', as_cmap=True),
darkness=0.5,
colorbar=True,
#symmetric_cbar=True,
bg_on_data=True,
axes=axes2[1],
vmax=vmax,
figure=fig2)
plt.show()
print(' Contrast % i out of %i: %s, right hemisphere' %
(index + 1, len(contrasts), contrast_id))
# compute contrast-related statistics
contrast = compute_contrast(labels,
estimates,
contrast_val,
contrast_type='t')
# we present the Z-transform of the t map
z_score = contrast.z_score()
# we plot it on the surface, on the inflated fsaverage mesh,
# together with a suitable background to give an impression
# of the cortex folding.
plotting.plot_surf_stat_map(fsaverage.infl_right,
z_score,
hemi='right',
title=contrast_id,
colorbar=True,
threshold=3.,
bg_map=fsaverage.sulc_right)
#########################################################################
# Analysing the left hemisphere
# -----------------------------
#
# Note that re-creating the above analysis for the left hemisphere requires little additional code!
#########################################################################
# We project the fMRI data to the mesh.
texture = surface.vol_to_surf(fmri_img, fsaverage.pial_left)
#########################################################################
def plot_surfaces(grad_dict,
index,
outdir,
prefix,
normalize=False,
cmap="jet"):
grad_lh = grad_dict["grads_lh"][:, index]
grad_rh = grad_dict["grads_rh"][:, index]
if normalize:
if np.max(grad_lh[grad_lh > 0]) > np.abs(np.min(grad_lh[grad_lh < 0])):
grad_lh[grad_lh < 0] = grad_lh[grad_lh < 0] / np.abs(
np.min(grad_lh[grad_lh < 0]))
grad_lh[grad_lh < 0] = grad_lh[grad_lh < 0] * np.max(
grad_lh[grad_lh > 0])
else:
grad_lh[grad_lh > 0] = grad_lh[grad_lh > 0] / np.max(
grad_lh[grad_lh > 0])
grad_lh[grad_lh > 0] = grad_lh[grad_lh > 0] * np.abs(
np.min(grad_lh[grad_lh < 0]))
if np.max(grad_rh[grad_rh > 0]) > np.abs(np.min(grad_rh[grad_rh < 0])):
grad_rh[grad_rh < 0] = grad_rh[grad_rh < 0] / np.abs(
np.min(grad_rh[grad_rh < 0]))
grad_rh[grad_rh < 0] = grad_rh[grad_rh < 0] * np.max(
grad_rh[grad_rh > 0])
else:
grad_rh[grad_rh > 0] = grad_rh[grad_rh > 0] / np.max(
grad_rh[grad_rh > 0])
grad_rh[grad_rh > 0] = grad_rh[grad_rh > 0] * np.abs(
np.min(grad_rh[grad_rh < 0]))
plotting.plot_surf_stat_map(
grad_dict["pial_left"],
grad_lh,
hemi="left",
bg_map=grad_dict["sulc_left"],
bg_on_data=True,
threshold=np.finfo(np.float32).eps,
colorbar=False,
view="medial",
cmap=cmap,
output_file=op.join(outdir,
"{0}-{1}_left_medial.png".format(prefix, index)),
)
plotting.plot_surf_stat_map(
grad_dict["pial_left"],
grad_lh,
hemi="left",
bg_map=grad_dict["sulc_left"],
bg_on_data=True,
threshold=np.finfo(np.float32).eps,
colorbar=False,
view="lateral",
cmap=cmap,
output_file=op.join(outdir,
"{0}-{1}_left_lateral.png".format(prefix, index)),
)
plotting.plot_surf_stat_map(
grad_dict["pial_right"],
grad_rh,
hemi="right",
bg_map=grad_dict["sulc_right"],
bg_on_data=True,
threshold=np.finfo(np.float32).eps,
colorbar=False,
view="medial",
cmap=cmap,
output_file=op.join(outdir,
"{0}-{1}_right_medial.png".format(prefix, index)),
)
plotting.plot_surf_stat_map(
grad_dict["pial_right"],
grad_rh,
hemi="right",
bg_map=grad_dict["sulc_right"],
bg_on_data=True,
threshold=np.finfo(np.float32).eps,
colorbar=True,
view="lateral",
cmap=cmap,
output_file=op.join(outdir,
"{0}-{1}_right_lateral.png".format(prefix, index)),
)
# Transform ROI indices in ROI map
pcc_map = np.zeros(parcellation.shape[0], dtype=int)
pcc_map[pcc_labels] = 1
from nilearn import plotting
plotting.plot_surf_roi(fsaverage['pial_left'], roi_map=pcc_map,
hemi='left', view='medial',
bg_map=fsaverage['sulc_left'], bg_on_data=True,
title='PCC Seed')
###############################################################################
# Display unthresholded stat map with a slightly dimmed background
plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
hemi='left', view='medial', colorbar=True,
bg_map=fsaverage['sulc_left'], bg_on_data=True,
darkness=.3, title='Correlation map')
###############################################################################
# Many different options are available for plotting, for example thresholding,
# or using custom colormaps
plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
hemi='left', view='medial', colorbar=True,
bg_map=fsaverage['sulc_left'], bg_on_data=True,
cmap='Spectral', threshold=.5,
title='Threshold and colormap')
###############################################################################
# Here the surface is plotted in a lateral view without a background map.
# To capture 3D structure without depth information, the default is to plot a
# half transparent surface.
avg /= n_images
return avg
def get_collection_counts(img_fnames):
counts = dict()
for ii, image in enumerate(img_fnames):
collection, name = image.split('/')[-2:]
counts[collection] = counts.get(collection, 0) + 1
return counts
# counts = get_collection_counts(images)
target_img = nib.load(images[0])
desc = 'Average maps for %s' % search_term
avg_effect = average_maps(images_effect, target_img, desc)
desc = 'Average other maps'
avg_other = average_maps(images_other, target_img, desc)
contrast_img = nib.Nifti1Image(avg_effect - avg_other, target_img.affine)
avg_img = nib.Nifti1Image(avg_effect, target_img.affine)
fig = plot_glass_brain(contrast_img, plot_abs=False)
fig.savefig('figures/encoder_%s.png' % search_term)
# surface
fsaverage = datasets.fetch_surf_fsaverage5()
texture = surface.vol_to_surf(contrast_img, fsaverage.pial_right)
plot_surf_stat_map(fsaverage.infl_right, texture, symmetric_cbar=True)
plt.show()