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(
np.array(data, dtype=np.double), kde=False, bins=100, ax=ax
) #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, dtype=np.double), ax=ax)
#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_frame_displacement(realignment_parameters_file,
parameter_source,
mean_FD_distribution=None,
figsize=(11.7, 8.3)):
FD_power = calc_frame_dispalcement(realignment_parameters_file,
parameter_source)
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 Displacement (over all subjects) [mm]")
MeanFD = FD_power.mean()
label = "MeanFD = %g" % MeanFD
plot_vline(MeanFD, label, ax=ax)
fig.suptitle('motion', fontsize='14')
return fig
def plot_epi_T1_corregistration(
mean_epi_file,
wm_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, 2)
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, 1) #changed from (2,1,0) to (2,1,1)
else:
ax = plt.subplot(1, 1, 1)
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()
wm_nii = nb.load(wm_file)
wm_data = wm_nii.get_data()
wm_data[wm_data > 1] = 1
wm_affine = wm_nii.get_affine()
slicer = viz.plot_anat(
np.asarray(func),
np.asarray(func_affine),
black_bg=True,
cmap=cm.Greys_r, # @UndefinedVariable
figure=fig,
axes=ax,
draw_cross=False)
slicer.contour_map(np.asarray(wm_data),
np.asarray(wm_affine),
linewidths=[0.1],
colors=[
'r',
])
fig.suptitle('coregistration', fontsize='14')
return fig
plt.show(fig)