def tbss_fill(self):
if self.template == 'enigma':
command = f'tbss_fill \
{self.location} \
{self.threshold} \
{self.mean_fa} {self.tbss_fill_out}'
# {self.enigma_fa_loc} {self.tbss_fill_out}'
else:
command = f'tbss_fill \
{self.location} \
{self.threshold} \
{self.fa_bg_loc} {self.tbss_fill_out}'
print(re.sub('\s+', ' ', command))
print(os.popen(command).read())
def create_figure(args: object, corrp_map_classes: List[object]) -> None:
if args.figure or args.tbss_fill:
print_head('Saving figures')
for corrpMap in corrp_map_classes:
if corrpMap.significant is True:
# tbss_fill if tbss_fill=True
if args.tbss_fill:
print_head(f'Estimating tbss_fill for {corrpMap.location}')
# run tbss_fill
corrpMap.tbss_fill_out = re.sub('.nii.gz',
'_filled.nii.gz',
str(corrpMap.location))
corrpMap.tbss_fill()
corrpMap.get_figure(
figure_same_slice=args.figure_same_slice)
plt.close()
if args.figure:
corrpMap.get_figure(
figure_same_slice=args.figure_same_slice)
plt.close()
def template_settings(self):
"""Set TBSS template settings"""
# if given TBSS template is enigma
if self.template == 'enigma':
self.fsl_dir = Path(environ['FSLDIR'])
self.fsl_data_dir = self.fsl_dir / 'data'
self.enigma_dir = Path(
'/data/pnl/soft/pnlpipe3/tbss/data/enigmaDTI')
self.enigma_table = self.enigma_dir / 'ENIGMA_look_up_table.txt'
# self.fa_bg_loc = Path(self.template).absolute() / 'mean_FA.nii.gz'
self.fa_bg_loc = self.enigma_dir / 'ENIGMA_DTI_FA.nii.gz'
self.mean_fa = self.location.parent / 'mean_FA.nii.gz'
self.skel_mask_loc = self.enigma_dir / \
'ENIGMA_DTI_FA_skeleton_mask.nii.gz'
# if given TBSS template is a string of the FA template
# eg) mean_FA.nii.gz
else:
self.fa_bg_loc = Path(self.template).absolute()
self.skel_mask_loc = re.sub('.nii.gz', '_skeleton_mask.nii.gz',
str(self.fa_bg_loc))
def get_figure_enigma(self, **kwargs):
# TODO replace this function with nifti_snapshot
# TODO add skeleton check functions to the randomise_summary
"""Fig and axes attribute to CorrpMap"""
# if study template is not ENIGMA
if 'mean_fa' in kwargs:
mean_fa_loc = kwargs.get('mean_fa')
print(f'background image : {mean_fa_loc}')
self.enigma_fa_data = get_nifti_data(mean_fa_loc)
mean_fa_skel_loc = re.sub('.nii.gz', '_skeleton.nii.gz',
mean_fa_loc)
print(f'background skeleton image: {mean_fa_skel_loc}')
self.enigma_skeleton_data = get_nifti_data(mean_fa_skel_loc)
else:
# self.enigma_fa_data = get_nifti_data(self.enigma_fa_loc)
self.enigma_fa_data = get_nifti_data(self.fa_bg_loc)
self.enigma_skeleton_data = get_nifti_data(self.skel_mask_loc)
# figure settings
self.ncols = 5
self.nrows = 4
size_w = 4
size_h = 4
# When study template is used, slice_gap=3 is too wide)
if self.data_shape[-1] < 100:
slice_gap = 2
else:
slice_gap = 3
# Get the center of data
center_of_data = np.array(
ndimage.measurements.center_of_mass(
self.enigma_fa_data)).astype(int)
# Get the center slice number
z_slice_center = center_of_data[-1]
# Get the slice numbers in array
nslice = self.ncols * self.nrows
slice_nums = np.arange(z_slice_center - (nslice * slice_gap),
z_slice_center + (nslice * slice_gap),
slice_gap)[::2]
# if corrpMap.corrp_data_filled exist
if hasattr(self, 'corrp_data_filled'):
data = np.where(self.corrp_data_filled == 0, np.nan,
self.corrp_data_filled)
elif hasattr(self, 'type'):
if self.type in ['average', 'std', 'bin_sum', 'bin_sum_diff']:
# for skeleton std data plot
data = np.where(self.corrp_data == 0, np.nan, self.corrp_data)
else:
# Make voxels with their intensities lower than data_vmin
# transparent
data = np.where(self.corrp_data < self.threshold, np.nan,
self.corrp_data)
# TODO put below to above
if hasattr(self, 'vmin'):
vmin = self.vmin
else:
vmin = self.threshold
if hasattr(self, 'vmax'):
if self.vmax == 'free':
vmax = self.corrp_data.max()
else:
vmax = self.vmax
else:
vmax = 1
self.tbssFigure = nifti_snapshot.TbssFigure(
template=self.template,
image_data_list=[data],
output_file=self.out_image_loc,
cmap_list=['autumn'],
cbar_titles=[self.cbar_title],
alpha_list=[1],
title=self.title)
# below is self.tbssFigure.create_figure_one_map()
self.tbssFigure.images_mask_out_the_zero()
self.tbssFigure.images_mask_by_threshold(0.95)
# self.tbssFigure.loop_through_axes_draw_bg()
self.tbssFigure.loop_through_axes_draw_bg_tbss()
self.tbssFigure.annotate_with_z()
self.tbssFigure.loop_through_axes_draw_images_corrp_map(0.95)
self.tbssFigure.cbar_x = 0.25
self.tbssFigure.cbar_width = 0.5
self.tbssFigure.add_cbars_horizontal()
# self.fig = self.tbssFigure.fig
self.tbssFigure.fig.suptitle(self.tbssFigure.title,
y=0.92,
fontsize=25)
self.tbssFigure.fig.savefig(self.tbssFigure.output_file, dpi=200)
def get_figure(self, **kwargs):
"""Get corrpMap figure"""
self.cbar_title = f'{self.modality} {self.contrast_text}'
# same slice
if 'figure_same_slice' in kwargs:
same_slice = kwargs.get('figure_same_slice')
if hasattr(self, 'tbss_fill_out'): # for tbss fill option
self.out_image_loc = re.sub('.nii.gz', '.png',
str(self.tbss_fill_out))
self.title = f'{self.modality} {self.contrast_text}\n' \
f'{self.tbss_fill_out}'
# vmin and vmax list given
self.tbssFigure = nifti_snapshot.TbssFigure(
image_files=[self.tbss_fill_out],
fa_bg=self.fa_bg_loc,
skeleton_bg=self.skel_mask_loc,
output_file=self.out_image_loc,
cmap_list=['autumn'],
cbar_titles=[self.cbar_title],
alpha_list=[1],
title=self.title,
tbss_filled=True,
same_slice=same_slice)
# below is self.tbssFigure.create_figure_one_map()
# self.tbssFigure.images_mask_out_the_zero()
# self.tbssFigure.loop_through_axes_draw_bg()
self.tbssFigure.loop_through_axes_draw_bg_tbss()
self.tbssFigure.annotate_with_z()
self.tbssFigure.loop_through_axes_draw_images()
self.tbssFigure.cbar_x = 0.25
self.tbssFigure.cbar_width = 0.5
self.tbssFigure.add_cbars_horizontal_tbss_filled()
self.tbssFigure.fig.suptitle(self.tbssFigure.title,
y=0.92,
fontsize=25)
self.tbssFigure.fig.savefig(self.tbssFigure.output_file, dpi=200)
else:
self.out_image_loc = re.sub('.nii.gz', '.png', str(self.location))
self.title = f'{self.modality} {self.contrast_text}\n' \
f'{self.location}'
self.tbssFigure = nifti_snapshot.TbssFigure(
image_files=[str(self.location)],
fa_bg=self.fa_bg_loc,
skeleton_bg=self.skel_mask_loc,
output_file=self.out_image_loc,
cmap_list=['autumn'],
cbar_titles=[self.cbar_title],
alpha_list=[1],
cbar_ticks=[0.95, 1],
title=self.title,
same_slice=same_slice)
# below is self.tbssFigure.create_figure_one_map()
self.tbssFigure.images_mask_out_the_zero()
self.tbssFigure.images_mask_by_threshold(0.95)
# self.tbssFigure.loop_through_axes_draw_bg()
self.tbssFigure.loop_through_axes_draw_bg_tbss()
self.tbssFigure.annotate_with_z()
self.tbssFigure.loop_through_axes_draw_images_corrp_map(0.95)
self.tbssFigure.cbar_x = 0.25
self.tbssFigure.cbar_width = 0.5
self.tbssFigure.add_cbars_horizontal()
self.tbssFigure.fig.suptitle(self.tbssFigure.title,
y=0.92,
fontsize=25)
self.tbssFigure.fig.savefig(self.tbssFigure.output_file, dpi=200)
def get_atlas_query(self):
"""Return pandas dataframe summary of atlas_query outputs"""
# threshold corrp file according to the threshold
thresholded_map = tempfile.NamedTemporaryFile(suffix='tmp.nii.gz')
command = f'fslmaths {self.location} \
-thr {self.threshold} -bin \
{thresholded_map.name}'
# TODO change below to kcho_util run
print('Command used')
print('\t' + re.sub('\s+', ' ', command))
os.popen(command).read()
# run atlas query from FSL
# label
command = f'atlasquery \
-m {thresholded_map.name} \
-a "JHU ICBM-DTI-81 White-Matter Labels"'
text_label = os.popen(command).read()
print('\t' + re.sub('\s+', ' ', command))
print('\t\t' + re.sub('\n', '\n\t\t', text_label))
# tract
command = f'atlasquery \
-m {thresholded_map.name} \
-a "JHU White-Matter Tractography Atlas"'
text_tract = os.popen(command).read()
print('\t' + re.sub('\s+', ' ', command))
print('\t\t' + re.sub('\n', '\n\t\t', text_tract))
# Make pandas dataframe
df_query_label = pd.read_csv(pd.compat.StringIO(text_label),
sep=':',
names=['Structure', 'Percentage'])
df_query_label['atlas'] = 'Labels'
df_query_tract = pd.read_csv(pd.compat.StringIO(text_tract),
sep=':',
names=['Structure', 'Percentage'])
df_query_tract['atlas'] = 'Tracts'
df_query = pd.concat([df_query_label, df_query_tract])
df_query['file_name'] = self.name
df_query = df_query[['file_name', 'Structure', 'Percentage', 'atlas']]
df_query = df_query.sort_values(['file_name', 'atlas', 'Percentage'],
ascending=False)
# Remove texts bound by parentheses
df_query['Structure'] = df_query['Structure'].apply(
lambda x: re.sub(r'\(.*\)', '', x))
# Adding 'Side' column
df_query['Side'] = df_query['Structure'].str.extract('(L|R)$')
# Remove side information from Structure column
df_query['Structure'] = df_query['Structure'].str.replace(
'(L|R)$', '').str.strip()
df_query.loc[df_query['Side'].isnull(), 'Side'] = 'M'
# Side column to wide format
self.df_query = pd.pivot_table(
index=['file_name', 'Structure', 'atlas'],
columns='Side',
values='Percentage',
data=df_query).reset_index()
# TODO: change here later
# self.df_query = self.df_query.groupby('atlas').get_group('Labels')
self.df_query = self.df_query.sort_values('atlas')