def plot_reg(image1, image2, name_source, out_dir):
import os
import pathlib
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import matplotlib.pyplot as plt
from rabies.visualization import plot_3d, otsu_scaling
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12 * 3, 2 * 2))
plt.tight_layout()
scaled = otsu_scaling(image1)
display1, display2, display3 = plot_3d(scaled, axes[0, :], cmap='gray')
display1.add_edges(image2)
display2.add_edges(image2)
display3.add_edges(image2)
scaled = otsu_scaling(image2)
display1, display2, display3 = plot_3d(scaled, axes[1, :], cmap='gray')
display1.add_edges(image1)
display2.add_edges(image1)
display3.add_edges(image1)
fig.savefig(f'{prefix}_registration.png', bbox_inches='tight')
def template_masking(template, mask, out_dir):
import os
import SimpleITK as sitk
# set default threader to platform to avoid freezing with MultiProc https://github.com/SimpleITK/SimpleITK/issues/1239
sitk.ProcessObject_SetGlobalDefaultThreader('Platform')
from nilearn import plotting
import matplotlib.pyplot as plt
from rabies.visualization import plot_3d, otsu_scaling
os.makedirs(out_dir, exist_ok=True)
scaled = otsu_scaling(template)
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(4, 2 * 2))
# plot brain mask
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
plot_3d(axes[:], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.3,
cbar=False)
plt.tight_layout()
fig.savefig(out_dir + '/template_masking.png', bbox_inches='tight')
def inho_cor_diagnosis(raw_img, init_denoise, warped_mask, final_denoise,
name_source, out_dir):
import os
import pathlib
import SimpleITK as sitk
# set default threader to platform to avoid freezing with MultiProc https://github.com/SimpleITK/SimpleITK/issues/1239
sitk.ProcessObject_SetGlobalDefaultThreader('Platform')
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import matplotlib.pyplot as plt
from rabies.visualization import plot_3d, otsu_scaling
fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(12 * 4, 2 * 3))
scaled = otsu_scaling(raw_img)
axes[0, 0].set_title('Raw Image', fontsize=30, color='white')
#add_filenames(axes[-1,0], {'File':raw_img})
plot_3d(axes[:, 0], scaled, fig=fig, vmin=0, vmax=1, cmap='viridis')
axes[0, 2].set_title('Resampled Mask', fontsize=30, color='white')
#add_filenames(axes[-1,2], {'Mask File':warped_mask,'EPI File':raw_img})
plot_3d(axes[:, 2], scaled, fig=fig, vmin=0, vmax=1, cmap='viridis')
sitk_mask = sitk.ReadImage(warped_mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
plot_3d(axes[:, 2],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.3,
cbar=False)
scaled = otsu_scaling(init_denoise)
axes[0, 1].set_title('Initial Correction', fontsize=30, color='white')
#add_filenames(axes[-1,1], {'File':init_denoise})
plot_3d(axes[:, 1], scaled, fig=fig, vmin=0, vmax=1, cmap='viridis')
scaled = otsu_scaling(final_denoise)
axes[0, 3].set_title('Final Correction', fontsize=30, color='white')
#add_filenames(axes[-1,3], {'File':final_denoise})
plot_3d(axes[:, 3], scaled, fig=fig, vmin=0, vmax=1, cmap='viridis')
plt.tight_layout()
fig.savefig(f'{prefix}_inho_cor.png', bbox_inches='tight')
def masked_plot(fig, axes, img, scaled, percentile=0.01, vmax=None):
mask = percent_masking(img, percentile=percentile)
masked = sitk.GetImageFromArray(sitk.GetArrayFromImage(img) * mask)
masked.CopyInformation(img)
data = sitk.GetArrayFromImage(img)
if vmax is None:
vmax = data.max()
if not (type(axes) is np.ndarray or type(axes) is list):
axes = [axes]
planes = ('coronal')
else:
planes = ('sagittal', 'coronal', 'horizontal')
plot_3d(axes,
scaled,
fig,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6,
planes=planes)
# resample to match template
sitk_img = sitk.Resample(masked, scaled)
cbar_list = plot_3d(axes,
sitk_img,
fig,
vmin=-vmax,
vmax=vmax,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=vmax * 0.001,
num_slices=6,
planes=planes)
return cbar_list
def scan_diagnosis(bold_file, mask_file_dict, temporal_info, spatial_info, CR_data_dict, regional_grayplot=False):
template_file = mask_file_dict['template_file']
fig = plt.figure(figsize=(6, 18))
#fig.suptitle(name, fontsize=30, color='white')
ax0 = fig.add_subplot(3,1,1)
ax1 = fig.add_subplot(12,1,5)
ax1_ = fig.add_subplot(12,1,6)
ax2 = fig.add_subplot(6,1,4)
ax3 = fig.add_subplot(6,1,5)
ax4 = fig.add_subplot(6,1,6)
# disable function
regional_grayplot=False
if regional_grayplot:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax0)
im, slice_alt, region_mask_label = grayplot_regional(
bold_file, mask_file_dict, fig, ax0)
ax0.yaxis.labelpad = 40
ax_slice = divider.append_axes('left', size='5%', pad=0.0)
ax_label = divider.append_axes('left', size='5%', pad=0.0)
ax_slice.imshow(slice_alt.reshape(-1, 1), cmap='gray',
vmin=0, vmax=1.1, aspect='auto')
ax_label.imshow(region_mask_label.reshape(-1, 1),
cmap='Spectral', aspect='auto')
ax_slice.axis('off')
ax_label.axis('off')
else:
im = grayplot(bold_file, mask_file_dict, fig, ax0)
ax0.set_ylabel('Voxels', fontsize=20)
ax0.spines['right'].set_visible(False)
ax0.spines['top'].set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.axes.get_yaxis().set_ticks([])
plt.setp(ax1.get_xticklabels(), visible=False)
y = temporal_info['FD_trace'].to_numpy()
x = range(len(y))
ax0.set_xlim([0, len(y)-1])
ax1.set_xlim([0, len(y)-1])
ax1_.set_xlim([0, len(y)-1])
ax2.set_xlim([0, len(y)-1])
ax3.set_xlim([0, len(y)-1])
ax4.set_xlim([0, len(y)-1])
# plot the motion timecourses
confounds_csv = CR_data_dict['confounds_csv']
time_range = CR_data_dict['time_range']
frame_mask = CR_data_dict['frame_mask']
df = pd.read_csv(confounds_csv)
# take proper subset of timepoints
ax1.plot(x,df['mov1'].to_numpy()[time_range][frame_mask])
ax1.plot(x,df['mov2'].to_numpy()[time_range][frame_mask])
ax1.plot(x,df['mov3'].to_numpy()[time_range][frame_mask])
ax1.legend(['translation 1', 'translation 2', 'translation 3'],
loc='center left', bbox_to_anchor=(1.15, 0.5))
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1_.plot(x,df['rot1'].to_numpy()[time_range][frame_mask])
ax1_.plot(x,df['rot2'].to_numpy()[time_range][frame_mask])
ax1_.plot(x,df['rot3'].to_numpy()[time_range][frame_mask])
ax1_.legend(['rotation 1', 'rotation 2', 'rotation 3'],
loc='center left', bbox_to_anchor=(1.15, 0.5))
plt.setp(ax1_.get_xticklabels(), visible=False)
ax1_.spines['right'].set_visible(False)
ax1_.spines['top'].set_visible(False)
y = temporal_info['FD_trace'].to_numpy()
ax2.plot(x,y, 'r')
ax2.set_ylabel('FD in mm', fontsize=15)
DVARS = temporal_info['DVARS']
DVARS[0] = None
ax2_ = ax2.twinx()
y2 = DVARS
ax2_.plot(x,y2, 'b')
ax2_.set_ylabel('DVARS', fontsize=15)
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2_.spines['top'].set_visible(False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax2_.get_xticklabels(), visible=False)
ax2.legend(['Framewise \nDisplacement (FD)'
], loc='center left', bbox_to_anchor=(1.15, 0.6))
ax2_.legend(['DVARS'
], loc='center left', bbox_to_anchor=(1.15, 0.4))
ax3.plot(x,temporal_info['edge_trace'])
ax3.plot(x,temporal_info['WM_trace'])
ax3.plot(x,temporal_info['CSF_trace'])
ax3.plot(x,temporal_info['predicted_time'])
ax3.set_ylabel('Mask L2-norm', fontsize=15)
ax3_ = ax3.twinx()
ax3_.plot(x,temporal_info['VE_temporal'], 'darkviolet')
ax3_.set_ylabel('CR R^2', fontsize=15)
ax3_.spines['right'].set_visible(False)
ax3_.spines['top'].set_visible(False)
plt.setp(ax3_.get_xticklabels(), visible=False)
ax3.legend(['Edge Mask', 'WM Mask', 'CSF Mask', 'CR prediction'
], loc='center left', bbox_to_anchor=(1.15, 0.7))
ax3_.legend(['CR R^2'
], loc='center left', bbox_to_anchor=(1.15, 0.3))
ax3_.set_ylim([0,1])
y = temporal_info['signal_trace']
ax4.plot(x,y)
ax4.plot(x,temporal_info['noise_trace'])
ax4.legend(['BOLD components', 'Confound components'
], loc='center left', bbox_to_anchor=(1.15, 0.5))
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_xlabel('Timepoint', fontsize=15)
ax4.set_ylabel('Abs. Beta \ncoefficients (Avg.)', fontsize=15)
dr_maps = spatial_info['DR_BOLD']
mask_file = mask_file_dict['brain_mask']
nrows = 6+dr_maps.shape[0]
fig2, axes2 = plt.subplots(nrows=nrows, ncols=3, figsize=(12*3, 2*nrows))
plt.tight_layout()
from rabies.visualization import otsu_scaling, plot_3d
axes = axes2[0, :]
scaled = otsu_scaling(template_file)
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
temporal_std = spatial_info['temporal_std']
analysis_functions.recover_3D(
mask_file, temporal_std).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
# select vmax at 95th percentile value
vector = temporal_std.flatten()
vector.sort()
vmax = vector[int(len(vector)*0.95)]
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=0, vmax=vmax,
cmap='inferno', alpha=1, cbar=True, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 35
cbar.set_label('Standard \n Deviation', fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('BOLD-Temporal s.d.', fontsize=30, color='white')
axes = axes2[1, :]
scaled = otsu_scaling(template_file)
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
predicted_std = spatial_info['predicted_std']
analysis_functions.recover_3D(
mask_file, predicted_std).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
# select vmax at 95th percentile value
vector = predicted_std.flatten()
vector.sort()
vmax = vector[int(len(vector)*0.95)]
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=0, vmax=vmax,
cmap='inferno', alpha=1, cbar=True, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 35
cbar.set_label('Standard \n Deviation', fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('CR-Temporal s.d.', fontsize=30, color='white')
axes = axes2[2, :]
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['VE_spatial']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=0, vmax=1, cmap='inferno',
alpha=1, cbar=True, threshold=0.1, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 20
cbar.set_label('CR R^2', fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('CR R^2', fontsize=30, color='white')
axes = axes2[3, :]
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['GS_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=-1, vmax=1, cmap='cold_hot',
alpha=1, cbar=True, threshold=0.1, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 20
cbar.set_label("Pearson's' r", fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('Global Signal Correlation', fontsize=30, color='white')
axes = axes2[4, :]
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['DVARS_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=-1, vmax=1, cmap='cold_hot',
alpha=1, cbar=True, threshold=0.1, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 20
cbar.set_label("Pearson's' r", fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('DVARS Correlation', fontsize=30, color='white')
axes = axes2[5, :]
plot_3d(axes, scaled, fig2, vmin=0, vmax=1,
cmap='gray', alpha=1, cbar=False, num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['FD_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = plot_3d(axes, sitk_img, fig2, vmin=-1, vmax=1, cmap='cold_hot',
alpha=1, cbar=True, threshold=0.1, num_slices=6)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 20
cbar.set_label("Pearson's' r", fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title('FD Correlation', fontsize=30, color='white')
for i in range(dr_maps.shape[0]):
axes = axes2[i+6, :]
analysis_functions.recover_3D(
mask_file, dr_maps[i, :]).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = masked_plot(fig2,axes, sitk_img, scaled, percentile=0.015, vmax=None)
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 35
cbar.set_label("Beta \nCoefficient", fontsize=15, rotation=270, color='white')
for ax in axes:
ax.set_title(f'BOLD component {i}', fontsize=30, color='white')
return fig, fig2
def spatial_crosscorrelations(merged, scaled, mask_file, fig_path):
dict_keys = [
'temporal_std', 'predicted_std', 'VE_spatial', 'GS_corr', 'DVARS_corr',
'FD_corr', 'DR_BOLD', 'dual_ICA_maps'
]
voxelwise_list = []
for scan_data in merged:
sub_list = [scan_data[key] for key in dict_keys]
voxelwise_sub = np.array(sub_list[:6])
if len(sub_list[7]) > 0:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[6]), np.array(sub_list[7])),
axis=0)
else:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[6])), axis=0)
voxelwise_list.append(voxelwise_sub)
num_prior_maps = len(sub_list[6])
voxelwise_array = np.array(voxelwise_list)
label_name = [
'BOLD-Temporal s.d.', 'CR-Temporal s.d.', 'CR R^2', 'GS corr',
'DVARS corr', 'FD corr'
]
label_name += [
f'BOLD Dual Regression map {i}' for i in range(num_prior_maps)
]
label_name += [f'BOLD Dual ICA map {i}' for i in range(num_prior_maps)]
ncols = 6
fig, axes = plt.subplots(nrows=voxelwise_array.shape[1],
ncols=ncols,
figsize=(12 * ncols,
2 * voxelwise_array.shape[1]))
for i, x_label in zip(range(voxelwise_array.shape[1]), label_name):
for j, y_label in zip(range(ncols), label_name[:ncols]):
ax = axes[i, j]
if i <= j:
ax.axis('off')
continue
X = voxelwise_array[:, i, :]
Y = voxelwise_array[:, j, :]
corr = elementwise_spearman(X, Y)
plot_3d([ax],
scaled,
fig,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6,
planes=('coronal'))
recover_3D(mask_file, corr).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
cbar_list = plot_3d([ax],
sitk_img,
fig,
vmin=-1.0,
vmax=1.0,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6,
planes=('coronal'))
ax.set_title(f'Cross-correlation for \n{x_label} and {y_label}',
fontsize=20,
color='white')
for cbar in cbar_list:
cbar.ax.get_yaxis().labelpad = 20
cbar.set_label("Spearman rho",
fontsize=12,
rotation=270,
color='white')
fig.savefig(fig_path, bbox_inches='tight')
def template_info(anat_template, opts, out_dir):
import os
import SimpleITK as sitk
# set default threader to platform to avoid freezing with MultiProc https://github.com/SimpleITK/SimpleITK/issues/1239
sitk.ProcessObject_SetGlobalDefaultThreader('Platform')
from nilearn import plotting
import matplotlib.pyplot as plt
from rabies.visualization import plot_3d, otsu_scaling
brain_mask = str(opts.brain_mask)
WM_mask = str(opts.WM_mask)
CSF_mask = str(opts.CSF_mask)
vascular_mask = str(opts.vascular_mask)
labels = str(opts.labels)
os.makedirs(out_dir, exist_ok=True)
scaled = otsu_scaling(anat_template)
fig, axes = plt.subplots(nrows=3, ncols=6, figsize=(4 * 6, 2 * 2))
axes[0, 0].set_title('Anatomical Template', fontsize=30, color='white')
plot_3d(axes[:, 0], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
# plot brain mask
mask = brain_mask
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
axes[0, 1].set_title('Brain Mask', fontsize=30, color='white')
plot_3d(axes[:, 1], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:, 1],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.3,
cbar=False)
# plot WM mask
mask = WM_mask
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
axes[0, 2].set_title('WM Mask', fontsize=30, color='white')
plot_3d(axes[:, 2], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:, 2],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.5,
cbar=False)
# plot CSF mask
mask = CSF_mask
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
axes[0, 3].set_title('CSF Mask', fontsize=30, color='white')
plot_3d(axes[:, 3], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:, 3],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.5,
cbar=False)
# plot VASC mask
mask = vascular_mask
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
axes[0, 4].set_title('Vascular Mask', fontsize=30, color='white')
plot_3d(axes[:, 4], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:, 4],
sitk_mask,
fig=fig,
vmin=-1,
vmax=1,
cmap='bwr',
alpha=0.5,
cbar=False)
# plot labels
mask = labels
sitk_mask = sitk.ReadImage(mask, sitk.sitkFloat32)
# resample mask to match template
sitk_mask = sitk.Resample(sitk_mask, scaled)
axes[0, 5].set_title('Atlas Labels', fontsize=30, color='white')
plot_3d(axes[:, 5], scaled, fig=fig, vmin=0, vmax=1, cmap='gray')
plot_3d(axes[:, 5],
sitk_mask,
fig=fig,
vmin=1,
vmax=sitk.GetArrayFromImage(sitk_mask).max(),
cmap='rainbow',
alpha=0.5,
cbar=False)
plt.tight_layout()
fig.savefig(out_dir + '/template_files.png', bbox_inches='tight')
def temporal_features(bold_file, confounds_csv, FD_csv, rabies_data_type,
name_source, out_dir):
import os
import pathlib
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from rabies.visualization import plot_3d
from rabies.utils import copyInfo_3DImage
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(20, 5))
# plot the motion timecourses
import pandas as pd
df = pd.read_csv(confounds_csv)
ax = axes[0, 0]
ax.plot(df['mov1'])
ax.plot(df['mov2'])
ax.plot(df['mov3'])
ax.legend(['mov1', 'mov2', 'mov3'])
ax.set_title('Translation parameters', fontsize=30, color='white')
ax = axes[1, 0]
ax.plot(df['rot1'])
ax.plot(df['rot2'])
ax.plot(df['rot3'])
ax.legend(['rot1', 'rot2', 'rot3'])
ax.set_title('Rotation parameters', fontsize=30, color='white')
df = pd.read_csv(FD_csv)
ax = axes[2, 0]
ax.plot(df['Mean'], color='r')
ax.set_title('Framewise Displacement', fontsize=30, color='white')
plt.tight_layout()
# calculate STD and tSNR map on preprocessed timeseries
img = sitk.ReadImage(bold_file, rabies_data_type)
array = sitk.GetArrayFromImage(img)
mean = array.mean(axis=0)
std = array.std(axis=0)
std_filename = os.path.abspath('tSTD.nii.gz')
std_image = copyInfo_3DImage(sitk.GetImageFromArray(std, isVector=False),
img)
sitk.WriteImage(std_image, std_filename)
tSNR = np.divide(mean, std)
tSNR[np.isnan(tSNR)] = 0
tSNR_filename = os.path.abspath('tSNR.nii.gz')
tSNR_image = copyInfo_3DImage(sitk.GetImageFromArray(tSNR, isVector=False),
img)
sitk.WriteImage(tSNR_image, tSNR_filename)
axes[0, 1].set_title('Temporal STD', fontsize=30, color='white')
std = std.flatten()
std.sort()
std_vmax = std[int(len(std) * 0.95)]
plot_3d(axes[:, 1],
std_image,
fig=fig,
vmin=0,
vmax=std_vmax,
cmap='inferno',
cbar=True)
axes[0, 2].set_title('Temporal SNR', fontsize=30, color='white')
plot_3d(axes[:, 2],
tSNR_image,
fig=fig,
vmin=0,
vmax=tSNR.max(),
cmap='Spectral',
cbar=True)
fig.savefig(f'{prefix}_temporal_features.png', bbox_inches='tight')
return std_filename, tSNR_filename