def plot_mask_image(self):
m = Mosaic(self.inputs.orig_file, self.inputs.mask_file,
self.inputs.mask_file, show_mask=False, step=1)
m.plot_mask()
m.savefig("functional_mask.png")
m.close()
def plot_mean_image(self):
cmap = sns.cubehelix_palette(as_cmap=True, reverse=True, light=1, dark=0)
m = Mosaic(self.inputs.mean_file, self.inputs.mean_file, self.inputs.mask_file, step=1)
m.plot_overlay(vmin=0, cmap=cmap, fmt="%d")
m.savefig("mean_func.png")
m.close()
def write_tsnr_image(self):
"""Show the total subject SNR."""
m = Mosaic(self.inputs.anatomy,
self.inputs.tsnr_file,
self.every_mask,
step=self.step)
m.plot_overlay("cube:1.9:.5", 0, alpha=1, fmt="%d")
out_fname = op.abspath("tsnr.png")
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_r2_images(self):
"""Show the fixed effects model fit."""
cmaps = ["cube:2:0", "cube:2.6:0"]
for fname, cmap in zip(self.inputs.r2_files, cmaps):
m = Mosaic(self.inputs.anatomy, fname,
self.every_mask, step=self.step)
m.plot_overlay(cmap, 0, alpha=.85)
out_fname = op.abspath(nii_to_png(fname))
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def plot_watershed(self, peaks):
"""Plot the watershed segmentation."""
palette = sns.husl_palette(len(peaks))
cmap = mpl.colors.ListedColormap(palette)
m = Mosaic(stat=self.inputs.seg_file, mask=self.inputs.mask_file)
m.plot_overlay(thresh=.5, cmap=cmap, vmin=1, vmax=len(peaks))
out_fname = nii_to_png(self.inputs.seg_file)
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def plot_mask(self):
"""Plot the analysis mask."""
m = Mosaic(stat=self.inputs.mask_file)
m.plot_mask()
out_fname = nii_to_png(self.inputs.mask_file)
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def plot_full_zstat(self):
"""Plot the unthresholded zstat."""
m = Mosaic(stat=self.inputs.zstat_file, mask=self.inputs.mask_file)
m.plot_overlay(cmap="coolwarm", center=True, alpha=.9)
out_fname = nii_to_png(self.inputs.zstat_file)
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def plot_peaks(self, peaks):
"""Plot the peaks."""
palette = sns.husl_palette(len(peaks))
cmap = mpl.colors.ListedColormap(palette)
disk_img = self._peaks_to_disks(peaks)
m = Mosaic(stat=disk_img, mask=self.inputs.mask_file)
m.plot_overlay(thresh=.5, cmap=cmap, vmin=1, vmax=len(peaks))
out_fname = nii_to_png(self.inputs.zstat_thresh_file, "_peaks")
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def _run_interface(self, runtime):
subjects_dir = os.environ["SUBJECTS_DIR"]
wm_file = op.join(subjects_dir, self.inputs.subject_id, "mri/wm.mgz")
wm_data = nib.load(wm_file).get_data().astype(bool).astype(int)
m = Mosaic(self.inputs.in_file, wm_data, step=3)
m.plot_contours(["#DD2222"])
m.savefig("func2anat.png")
m.close()
return runtime
def plot_zstats(self):
"""Plot the positive and negative z stats with a low threshold."""
for z_file in self.inputs.zstat_files:
m = Mosaic(self.mean, z_file, self.mask, step=1)
m.plot_activation(pos_cmap="Reds_r",
neg_cmap="Blues",
thresh=1.7,
alpha=.85)
png_name = nii_to_png(z_file)
m.savefig(png_name)
m.close()
self.out_files.append(png_name)
def _run_interface(self, runtime):
target_brain = fsl.Info.standard_image("avg152T1_brain.nii.gz")
m = Mosaic(self.inputs.in_file, target_brain)
m.plot_contours("Reds", 2)
m.savefig("warp_report.png")
m.close()
return runtime
def plot_thresh_zstat(self):
"""Plot the thresholded zstat."""
m = Mosaic(stat=self.inputs.zstat_thresh_file,
mask=self.inputs.mask_file)
m.plot_activation(pos_cmap="OrRd_r", vfloor=3.3, alpha=.9)
out_fname = nii_to_png(self.inputs.zstat_thresh_file)
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def plot_tsnr(self):
tsnr = self.inputs.tsnr_file
m = Mosaic(self.mean, tsnr, self.mask, step=1)
m.plot_overlay("cube:1.9:.5", 0, alpha=1, fmt="%d")
png_name = nii_to_png(tsnr)
m.savefig(png_name)
m.close()
self.out_files.append(png_name)
def plot_residuals(self):
"""Plot the variance of the model residuals across time."""
ss = self.inputs.sigmasquareds_file
m = Mosaic(self.mean, ss, self.mask, step=1)
m.plot_overlay("cube:.8:.2", 0, alpha=.6, fmt="%d")
png_name = nii_to_png(ss)
m.savefig(png_name)
m.close()
self.out_files.append(png_name)
def write_zstat_images(self):
"""Show the fixed effects inference maps."""
for fname in self.inputs.zstat_files:
m = Mosaic(self.inputs.anatomy, fname,
self.every_mask, step=self.step)
m.plot_activation(pos_cmap="Reds_r", neg_cmap="Blues",
thresh=2.3, alpha=.85)
contrast = fname.split("/")[-2]
os.mkdir(contrast)
out_fname = op.join(contrast, "zstat1.png")
self.zstat_files.append(op.abspath(contrast))
m.savefig(out_fname)
m.close()
def plot_mean_image(self):
cmap = sns.cubehelix_palette(as_cmap=True,
reverse=True,
light=1,
dark=0)
m = Mosaic(self.inputs.mean_file,
self.inputs.mean_file,
self.inputs.mask_file,
step=1)
m.plot_overlay(vmin=0, cmap=cmap, fmt="%d")
m.savefig("mean_func.png")
m.close()
def plot_rsquareds(self):
"""Plot the full, main, and confound R squared maps."""
cmaps = ["cube:2:0", "cube:2.6:0", "cube:1.5:0"]
for r2_file, cmap in zip(self.inputs.r2_files, cmaps):
m = Mosaic(self.mean, r2_file, self.mask, step=1)
m.plot_overlay(cmap, 0, alpha=.6)
png_name = nii_to_png(r2_file)
m.savefig(png_name)
m.close()
self.out_files.append(png_name)
def plot_zstats(self):
"""Plot the positive and negative z stats with a low threshold."""
for z_file in self.inputs.zstat_files:
m = Mosaic(self.mean, z_file, self.mask, step=1)
m.plot_activation(pos_cmap="Reds_r", neg_cmap="Blues",
thresh=1.7, alpha=.85)
png_name = nii_to_png(z_file)
m.savefig(png_name)
m.close()
self.out_files.append(png_name)
def plot_watershed(self, peaks):
"""Plot the watershed segmentation."""
palette = sns.husl_palette(len(peaks))
cmap = mpl.colors.ListedColormap(palette)
m = Mosaic(stat=self.inputs.seg_file, mask=self.inputs.mask_file)
m.plot_overlay(thresh=.5, cmap=cmap, vmin=1, vmax=len(peaks))
out_fname = nii_to_png(self.inputs.seg_file)
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_tsnr_image(self):
"""Show the total subject SNR."""
m = Mosaic(self.inputs.anatomy,
self.inputs.tsnr_file,
self.every_mask,
step=self.step)
m.plot_overlay("cube:1.9:.5", 0, alpha=1, fmt="%d")
out_fname = op.abspath("tsnr.png")
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_r2_images(self):
"""Show the fixed effects model fit."""
cmaps = ["cube:2:0", "cube:2.6:0"]
for fname, cmap in zip(self.inputs.r2_files, cmaps):
m = Mosaic(self.inputs.anatomy, fname,
self.every_mask, step=self.step)
m.plot_overlay(cmap, 0, alpha=.85)
out_fname = op.abspath(nii_to_png(fname))
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_mask_image(self):
"""Show the overlap of each run's mask."""
mask_imgs = [nib.load(f) for f in self.inputs.masks]
mask_data = [img.get_data()[..., np.newaxis] for img in mask_imgs]
each_mask = np.concatenate(mask_data, axis=-1)
any_mask = each_mask.max(axis=-1)
self.every_mask = each_mask.min(axis=-1)
m = Mosaic(self.inputs.anatomy, each_mask, any_mask,
step=self.step, show_mask=False)
m.plot_mask_edges()
out_fname = op.abspath("mask_overlap.png")
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_png(self):
"""Write a mosiac png showing the masked voxels."""
slices_temp = op.join(self.data_dir, "%(subj)s/masks",
self.roi_name + ".png")
for subj in self.subject_list:
args = dict(subj=subj)
m = Mosaic(self.epi_template % args,
self.out_template % args,
self.fov_template % args,
step=1, show_mask=False)
cmap = mpl.colors.ListedColormap(["#C41E3A"])
m.plot_overlay(cmap, thresh=.5, alpha=.9, colorbar=False)
m.savefig(slices_temp % args)
m.close()
def plot_peaks(self, peaks):
"""Plot the peaks."""
palette = sns.husl_palette(len(peaks))
cmap = mpl.colors.ListedColormap(palette)
disk_img = self._peaks_to_disks(peaks)
m = Mosaic(stat=disk_img, mask=self.inputs.mask_file)
m.plot_overlay(thresh=.5, cmap=cmap, vmin=1, vmax=len(peaks))
out_fname = nii_to_png(self.inputs.zstat_thresh_file, "_peaks")
self.out_files.append(out_fname)
m.savefig(out_fname)
m.close()
def _run_interface(self, runtime):
subjects_dir = os.environ["SUBJECTS_DIR"]
wm_file = op.join(subjects_dir, self.inputs.subject_id, "mri/wm.mgz")
wm_data = nib.load(wm_file).get_data().astype(bool).astype(int)
m = Mosaic(self.inputs.in_file, wm_data, step=3)
m.plot_contours(["#DD2222"])
m.savefig("func2anat.png")
m.close()
return runtime
def write_zstat_images(self):
"""Show the fixed effects inference maps."""
for fname in self.inputs.zstat_files:
m = Mosaic(self.inputs.anatomy, fname,
self.every_mask, step=self.step)
m.plot_activation(pos_cmap="Reds_r", neg_cmap="Blues",
thresh=2.3, alpha=.85)
contrast = fname.split("/")[-2]
os.mkdir(contrast)
out_fname = op.join(contrast, "zstat1.png")
self.zstat_files.append(op.abspath(contrast))
m.savefig(out_fname)
m.close()
def write_mask_image(self):
"""Show the overlap of each run's mask."""
mask_imgs = [nib.load(f) for f in self.inputs.masks]
mask_data = [img.get_data()[..., np.newaxis] for img in mask_imgs]
each_mask = np.concatenate(mask_data, axis=-1)
any_mask = each_mask.max(axis=-1)
self.every_mask = each_mask.min(axis=-1)
m = Mosaic(self.inputs.anatomy, each_mask, any_mask,
stat_interp="nearest", step=self.step, show_mask=False)
m.plot_mask_edges()
out_fname = op.abspath("mask_overlap.png")
self.summary_files.append(out_fname)
m.savefig(out_fname)
m.close()
def write_png(self):
"""Write a mosiac png showing the masked voxels."""
slices_temp = op.join(self.data_dir, "%(subj)s/masks", self.roi_name + ".png")
for subj in self.subject_list:
args = dict(subj=subj)
m = Mosaic(
self.epi_template % args,
self.out_template % args,
self.fov_template % args,
step=1,
show_mask=False,
stat_interp="nearest",
)
cmap = mpl.colors.ListedColormap(["#C41E3A"])
m.plot_overlay(cmap, thresh=0.5, alpha=0.9, colorbar=False)
m.savefig(slices_temp % args)
m.close()
def volume_images(data_dir, subj):
"""Plot a mosiac of the brainmask and aseg volumes."""
# Plot the volume slices
brain_file = op.join(data_dir, subj, "mri/brainmask.mgz")
# Load the cortical ribbon file and use same index for both hemis
ribbon_file = op.join(data_dir, subj, "mri/ribbon.mgz")
ribbon_data = nib.load(ribbon_file).get_data().astype(float)
ribbon_data[ribbon_data == 3] = 1
ribbon_data[ribbon_data == 42] = 1
ribbon_data[ribbon_data != 1] = np.nan
# Load the aseg file and use it to derive a FOV mask
aseg_file = op.join(data_dir, subj, "mri/aseg.mgz")
aseg_data = nib.load(aseg_file).get_data()
aseg_data[aseg_data == 41] = 2
mask_data = (aseg_data > 0).astype(int)
# Load the lookup table for the aseg volume
aseg_lut = np.genfromtxt(op.join(os.environ["FREESURFER_HOME"],
"FreeSurferColorLUT.txt"))
# Draw the brainmask and cortical ribbon
m = Mosaic(brain_file, ribbon_data, mask_data, step=3)
m.plot_mask("#C41E3A")
m.savefig(op.join(data_dir, subj, "snapshots/volume.png"))
m.close()
# Draw the brainmask and cortical ribbon
aseg_cmap = mpl.colors.ListedColormap(aseg_lut[:64, 2:5] / 255)
m = Mosaic(brain_file, aseg_data, mask_data, step=3)
m.plot_overlay(aseg_cmap, vmin=0, vmax=63, alpha=.75, colorbar=False)
m.savefig(op.join(data_dir, subj, "snapshots/aseg.png"))
m.close()
def plot_target(self):
"""Plot a mosaic of the motion correction target image."""
m = Mosaic(self.inputs.target_file, step=1)
return m
def volume_images(data_dir, subj):
"""Plot a mosiac of the brainmask and aseg volumes."""
# Plot the volume slices
brain_file = op.join(data_dir, subj, "mri/brainmask.mgz")
# Load the cortical ribbon file and use same index for both hemis
ribbon_file = op.join(data_dir, subj, "mri/ribbon.mgz")
ribbon_data = nib.load(ribbon_file).get_data().astype(float)
ribbon_data[ribbon_data == 3] = 1
ribbon_data[ribbon_data == 42] = 1
ribbon_data[ribbon_data != 1] = np.nan
# Load the aseg file and use it to derive a FOV mask
aseg_file = op.join(data_dir, subj, "mri/aseg.mgz")
aseg_data = nib.load(aseg_file).get_data()
aseg_data[aseg_data == 41] = 2
mask_data = (aseg_data > 0).astype(int)
# Load the lookup table for the aseg volume
aseg_lut = np.genfromtxt(
op.join(os.environ["FREESURFER_HOME"], "FreeSurferColorLUT.txt"))
# Draw the brainmask and cortical ribbon
m = Mosaic(brain_file, ribbon_data, mask_data, step=3)
m.plot_mask("#C41E3A")
m.savefig(op.join(data_dir, subj, "snapshots/volume.png"))
m.close()
# Draw the brainmask and cortical ribbon
aseg_cmap = mpl.colors.ListedColormap(aseg_lut[:64, 2:5] / 255)
m = Mosaic(brain_file, aseg_data, mask_data, step=3)
m.plot_overlay(aseg_cmap, vmin=0, vmax=63, alpha=.75, colorbar=False)
m.savefig(op.join(data_dir, subj, "snapshots/aseg.png"))
m.close()
