def plot_frame_displacement(realignment_parameters_file,
mean_FD_distribution=None,
figsize=(11.7, 8.3)):
FD_power = calc_frame_dispalcement(realignment_parameters_file)
fig = Figure(figsize=figsize)
FigureCanvas(fig)
if mean_FD_distribution:
grid = GridSpec(2, 4)
else:
grid = GridSpec(1, 4)
ax = fig.add_subplot(grid[0, :-1])
ax.plot(FD_power)
ax.set_xlim((0, len(FD_power)))
ax.set_ylabel("Frame Displacement [mm]")
ax.set_xlabel("Frame number")
ylim = ax.get_ylim()
ax = fig.add_subplot(grid[0, -1])
sns.distplot(FD_power, vertical=True, ax=ax)
ax.set_ylim(ylim)
if mean_FD_distribution:
ax = fig.add_subplot(grid[1, :])
sns.distplot(mean_FD_distribution, ax=ax)
ax.set_xlabel("Mean Frame Dispalcement (over all subjects) [mm]")
MeanFD = FD_power.mean()
label = "MeanFD = %g" % MeanFD
plot_vline(MeanFD, label, ax=ax)
return fig
def plot_frame_displacement(realignment_parameters_file, mean_FD_distribution=None, figsize=(11.7,8.3)):
FD_power = calc_frame_dispalcement(realignment_parameters_file)
fig = Figure(figsize=figsize)
FigureCanvas(fig)
if mean_FD_distribution:
grid = GridSpec(2, 4)
else:
grid = GridSpec(1, 4)
ax = fig.add_subplot(grid[0,:-1])
ax.plot(FD_power)
ax.set_xlim((0, len(FD_power)))
ax.set_ylabel("Frame Displacement [mm]")
ax.set_xlabel("Frame number")
ylim = ax.get_ylim()
ax = fig.add_subplot(grid[0,-1])
sns.distplot(FD_power, vertical=True, ax=ax)
ax.set_ylim(ylim)
if mean_FD_distribution:
ax = fig.add_subplot(grid[1,:])
sns.distplot(mean_FD_distribution, ax=ax)
ax.set_xlabel("Mean Frame Dispalcement (over all subjects) [mm]")
MeanFD = FD_power.mean()
label = "MeanFD = %g"%MeanFD
plot_vline(MeanFD, label, ax=ax)
return fig
def plot_epi_T1_corregistration(
mean_epi_file,
reg_file,
fssubjects_dir,
subject_id,
similarity_distribution=None,
figsize=(11.7, 8.3),
):
fig = plt.figure(figsize=figsize)
if similarity_distribution:
ax = plt.subplot(2, 1, 1)
sns.distplot(similarity_distribution.values(), ax=ax)
ax.set_xlabel(
"EPI-T1 similarity after coregistration (over all subjects)")
cur_similarity = similarity_distribution[subject_id]
label = "similarity = %g" % cur_similarity
plot_vline(cur_similarity, label, ax=ax)
ax = plt.subplot(2, 1, 0)
else:
ax = plt.subplot(1, 1, 0)
res = ApplyVolTransform(source_file=mean_epi_file,
reg_file=reg_file,
fs_target=True,
subjects_dir=fssubjects_dir,
terminal_output="none").run()
func = nb.load(res.outputs.transformed_file).get_data()
func_affine = nb.load(res.outputs.transformed_file).get_affine()
ribbon_file = "%s/%s/mri/ribbon.mgz" % (fssubjects_dir, subject_id)
ribbon_nii = nb.load(ribbon_file)
ribbon_data = ribbon_nii.get_data()
ribbon_data[ribbon_data > 1] = 1
ribbon_affine = ribbon_nii.get_affine()
slicer = viz.plot_anat(
np.asarray(func),
np.asarray(func_affine),
black_bg=True,
cmap=cm.Greys_r, # @UndefinedVariable
cut_coords=(-6, 3, 32),
figure=fig,
axes=ax,
draw_cross=False)
slicer.contour_map(np.asarray(ribbon_data),
np.asarray(ribbon_affine),
levels=[.51],
colors=[
'r',
])
return fig
def plot_distrbution_of_values(main_file, mask_file, xlabel, distribution=None, xlabel2=None, figsize=(11.7,8.3)):
data = _get_values_inside_a_mask(main_file, mask_file)
fig = Figure(figsize=figsize)
FigureCanvas(fig)
gs = GridSpec(2, 1)
ax = fig.add_subplot(gs[0, 0])
sns.distplot(data.astype(np.double), kde=False, bins=100, ax=ax)
ax.set_xlabel(xlabel)
ax = fig.add_subplot(gs[1, 0])
sns.distplot(np.array(distribution).astype(np.double), ax=ax)
cur_val = np.median(data)
label = "%g"%cur_val
plot_vline(cur_val, label, ax=ax)
ax.set_xlabel(xlabel2)
return fig
def plot_distrbution_of_values(main_file, mask_file, xlabel, distribution=None, xlabel2=None, figsize=(11.7,8.3)):
data = _get_values_inside_a_mask(main_file, mask_file)
fig = Figure(figsize=figsize)
FigureCanvas(fig)
gs = GridSpec(2, 1)
ax = fig.add_subplot(gs[0, 0])
sns.distplot(data.astype(np.double), kde=False, bins=100, ax=ax)
ax.set_xlabel(xlabel)
ax = fig.add_subplot(gs[1, 0])
sns.distplot(np.array(distribution).astype(np.double), ax=ax)
cur_val = np.median(data)
label = "%g"%cur_val
plot_vline(cur_val, label, ax=ax)
ax.set_xlabel(xlabel2)
return fig
def plot_FD(fd1d, mean_FD_distribution, subject,figsize = (8.3,8.3)):
threshold = 0.2
FD_power = np.genfromtxt(fd1d)
meanFD = np.round(np.mean(FD_power), decimals = 2)
rmsFD = np.sqrt(np.mean(FD_power))
count = np.int(FD_power[FD_power>threshold].size)
percentFD = (count*100/(len(FD_power)+1))
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(wspace=0.3)
fig.set_size_inches(12, 8)
grid = GridSpec(2, 4)
ax = plt.subplot(grid[0,:-1])
ax.plot(FD_power)
ylim = ax.get_ylim()
ax.set_xlim((0, len(FD_power)))
ax.set_ylabel("%s Frame Displacement [mm]" %subject)
ax.set_xlabel("Frame #")
ax.text(20, (ylim[1] - ylim[1]*0.05), 'FD mean = %s'%meanFD, va='center', size = 18, color = 'r')
ax.text(20, (ylim[1] - ylim[1]*0.2), '%s%% (%s) > threshold '%(percentFD, count), va='center', size = 18, color = 'r')
#ax.text(20, (ylim[1] - ylim[1]*0.15), 'are above the threshold '%, va='center', size = 18, color = 'r')
plt.axhline(threshold, linestyle='dashed', linewidth=2)#,color='r')
ax = plt.subplot(grid[0,-1])
sns.distplot(FD_power, vertical = True, ax = ax)
ax.set_ylim(ylim)
ax= plt.subplot(grid[1,:])
sns.distplot(mean_FD_distribution, ax=ax)
ax.set_xlabel("%s Mean Frame Dispalcement (over all subjects) [mm]"%subject)
label = "MeanFD = %g"%meanFD
plot_vline(meanFD, label, ax=ax)
png_name = str(os.path.join(os.getcwd(), 'qc_fd_plot.png'))
plt.savefig(png_name, dpi=190, bbox_inches='tight')
return fig
def plot_epi_T1_corregistration(mean_epi_file, reg_file, fssubjects_dir, subject_id, similarity_distribution=None, figsize=(11.7,8.3),):
fig = plt.figure(figsize=figsize)
if similarity_distribution:
ax = plt.subplot(2,1,1)
sns.distplot(similarity_distribution.values(), ax=ax)
ax.set_xlabel("EPI-T1 similarity after coregistration (over all subjects)")
cur_similarity = similarity_distribution[subject_id]
label = "similarity = %g"%cur_similarity
plot_vline(cur_similarity, label, ax=ax)
ax = plt.subplot(2,1,0)
else:
ax = plt.subplot(1,1,0)
res = ApplyVolTransform(source_file = mean_epi_file,
reg_file = reg_file,
fs_target = True,
subjects_dir = fssubjects_dir,
terminal_output = "none").run()
func = nb.load(res.outputs.transformed_file).get_data()
func_affine = nb.load(res.outputs.transformed_file).get_affine()
ribbon_file = "%s/%s/mri/ribbon.mgz"%(fssubjects_dir, subject_id)
ribbon_nii = nb.load(ribbon_file)
ribbon_data = ribbon_nii.get_data()
ribbon_data[ribbon_data > 1] = 1
ribbon_affine = ribbon_nii.get_affine()
slicer = viz.plot_anat(np.asarray(func), np.asarray(func_affine), black_bg=True,
cmap = cm.Greys_r, # @UndefinedVariable
cut_coords = (-6,3,32),
figure = fig,
axes = ax,
draw_cross = False)
slicer.contour_map(np.asarray(ribbon_data), np.asarray(ribbon_affine), levels=[.51], colors=['r',])
return fig
def plot_distrbution_of_values(main_file, mask_file, xlabel, distribution=None, xlabel2=None, figsize=(11.7,8.3)):
data = _get_values_inside_a_mask(main_file, mask_file)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(wspace=0.3)
fig.set_size_inches(12, 8)
gs = GridSpec(2, 1)
ax = plt.subplot(gs[0, 0])
sns.distplot(data.astype(np.double), kde=False, bins=100, ax=ax)
ax.set_xlabel(xlabel)
ax = plt.subplot(gs[1, 0])
sns.distplot(np.array(distribution).astype(np.double), ax=ax)
cur_val = np.median(data)
label = "%g"%cur_val
plot_vline(cur_val, label, ax=ax)
ax.set_xlabel(xlabel2)
png_name = str(os.path.join(os.getcwd(), 'qc_tsnr_plot.png'))
plt.savefig(png_name, dpi=190, bbox_inches='tight')
return fig