def run_randomise_summary_basics(args: object) \
-> Tuple[List[CorrpMap], pd.DataFrame]:
'''Run the randomise output summary (ran from commandline)'''
# Get a list of corrp map paths based on the arg parse inputs
corrp_map_locs = get_corrp_map_locs(args)
# Get a list of corrpMap objects ** this is where corrpMaps are formed
corrp_map_classes = get_corrp_map_classes(corrp_map_locs, args)
# Print a matrix information of the first corrpMap in the corrp_map_classes
# assuming all of corrpMaps in the list have the same matrix and contrast
print_head('Matrix information')
corrp_map_classes[0].print_matrix_info()
# TODO: copy this to html summary
if args.print_cov_info and \
hasattr(corrp_map_classes[0], 'covar_info_dict'):
print_head('Covariate summary')
for col, table in corrp_map_classes[0].covar_info_dict.items():
print(col)
print_df(table)
# Print information of each corrpMap
df = print_and_return_corrpMaps_summary(corrp_map_classes,
sig_only=args.sig_only)
return corrp_map_classes, df
def run_atlas_query(args: object, corrp_map_classes: List[object]) -> None:
'''Atlas query'''
# if atlas query option is on
if args.atlasquery:
print_head('Atlas query of the significant cluster')
print_head('(atlasquery from FSL)')
for corrpMap in corrp_map_classes:
if corrpMap.significant:
corrpMap.get_atlas_query()
print_df(corrpMap.df_query)
def print_matrix_info(self):
print_head('Matrix summary')
print(f'Contrast file : {self.contrast_file}')
print(f'Matrix file : {self.matrix_file}')
print()
if hasattr(self, 'matrix_df'):
print(f'total number of data point : {len(self.matrix_df)}')
if hasattr(self, 'group_cols'):
print(f'Group columns are : ' + ', '.join(self.group_cols))
if hasattr(self, 'matrix_info'):
print_df(self.matrix_info)
def get_corrp_map_classes(corrp_map_locs: List, args: object) -> List[object]:
'''Return corrpMaps from the corrp_map_locs and additional args info'''
print_head('Summarizing information for files below')
corrp_map_classes = []
# set caselist as empty string if the caselist is not given
if args.caselist:
caselist = args.caselist
else:
caselist = ''
for corrp_map_loc in corrp_map_locs:
print(f'\t{corrp_map_loc}')
if args.merged_img_dir:
corrpMap = CorrpMap(corrp_map_loc,
threshold=args.threshold,
contrast_file=args.contrast,
matrix_file=args.matrix,
merged_img_dir=args.merged_img_dir,
template=args.template,
caselist=caselist,
group_labels=args.grouplabels)
else:
corrpMap = CorrpMap(corrp_map_loc,
threshold=args.threshold,
contrast_file=args.contrast,
matrix_file=args.matrix,
template=args.template,
caselist=caselist,
group_labels=args.grouplabels)
corrp_map_classes.append(corrpMap)
# if no corrpMap is defined
try:
corrpMap
except NameError:
sys.exit('Please check there is corrp file')
return corrp_map_classes
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 print_and_return_corrpMaps_summary(corrp_map_classes: List[object],
sig_only: bool = False) -> pd.DataFrame:
"""Print the information of each corrpMap in the corrp_map_classes list"""
print_head('Result summary')
# concatenate corrpMap.df
df = pd.concat([x.df for x in corrp_map_classes], sort=False)
df = df.sort_values('file name')
if sig_only:
print_head('Only showing significant maps')
try:
df_sig = df.groupby('Significance').get_group(True)
print_df(df_sig.set_index(df_sig.columns[0]))
except KeyError:
print('There is no significant corrp map. Please return withtout '
'the -so option')
else:
print_df(df.set_index(df.columns[0]))
return df
def get_skeleton_summary(args: object,
corrp_map_classes: List[object]) -> None:
'''skeleton summary'''
# skeleton summary parts
if args.skeleton_summary:
print_head('Running skeleton summary')
summarized_merged_maps = []
for corrpMap in corrp_map_classes:
if not hasattr(corrpMap, 'matrix_df'):
print('Please provide correct design matrix. The file is '
'required to read in the group infromation.')
pass
elif corrpMap.modality == 'unknown':
print(f'The modality for {corrpMap.location} is unknown to '
'the current version of randomise_summary. Please check '
'the modality is in the list below.')
print(' ' + ' '.join(corrpMap.modality_full_list))
elif corrpMap.merged_4d_file == 'missing':
print(f'Merged 4d file for {corrpMap.location} is missing. '
f'Please check there are all_{corrpMap.modality}'
'_skeleton.nii.gz in the same directory.')
elif hasattr(corrpMap, 'merged_4d_file') and \
corrpMap.merged_4d_file not in summarized_merged_maps and \
corrpMap.merged_4d_file != 'missing' and args.tbss_all_loc:
print_head("Summarizing merged 4d file:"
f"{corrpMap.merged_4d_file}")
warp_dir = str(
Path(args.tbss_all_loc) / corrpMap.modality / 'warped')
print(warp_dir)
caselist = str(Path(args.tbss_all_loc) / 'log/caselist.txt')
skeleton_summary(corrpMap,
warp_dir=warp_dir,
caselist=caselist)
summarized_merged_maps.append(corrpMap.merged_4d_file)
print()
elif hasattr(corrpMap, 'merged_4d_file') and \
corrpMap.merged_4d_file not in summarized_merged_maps and \
corrpMap.merged_4d_file != 'missing':
print_head("Summarizing merged 4d file:"
f"{corrpMap.merged_4d_file}")
skeleton_summary(corrpMap)
summarized_merged_maps.append(corrpMap.merged_4d_file)
print()
def __init__(self,
location: Union[str, Path],
threshold=0.95,
contrast_file=False,
matrix_file=False,
**kwargs):
#TODO add group labels
#TODO add merged image location
#TODO add randomise script location and contents
self.location = Path(location)
self.name = self.location.name
self.threshold = threshold
if contrast_file == False: # contrast not specified
self.contrast_file = ''
else:
self.contrast_file = contrast_file
if matrix_file == False: # matrix file not specified
self.matrix_file = ''
else:
self.matrix_file = matrix_file
# group labels
if 'group_labels' in kwargs:
self.group_labels = kwargs.get('group_labels')
else:
self.group_labels = False
# if caselist is given
# in `get_corrp_map_classes`, '' is given as the caselist
# when there is no caelist is given to the randomise_summary.py
if 'caselist' in kwargs:
caselist = kwargs.get('caselist')
if Path(caselist).is_file():
with open(caselist, 'r') as f:
self.caselist = [x.strip() for x in f.readlines()]
if not Path(self.contrast_file).is_file():
self.contrast_file = search_and_select_one('contrast_file',
self.location.parent,
['*.con', 'contrast*'],
depth=0)
if not Path(self.matrix_file).is_file():
self.matrix_file = search_and_select_one('matrix_file',
self.location.parent,
['*.mat', 'matrix*'],
depth=0)
# Modality
# modality must be included in its name
self.modality_full_list = [
'FW', 'FA', 'FAt', 'FAc', 'FAk', 'iFW', 'MK', 'MKc', 'MKk', 'MD',
'MDt', 'RD', 'RDt', 'AD', 'ADt'
]
try:
self.modality = re.search(
'(' + '|'.join(self.modality_full_list) + ')_',
self.location.name).group(1)
except AttributeError:
print_head(f'No modality is detected in the file: {self.name}\n'
'Please add modality in the file name')
self.modality = 'unknown'
# Merged skeleton file
# find merged skeleton file
merged_skel_pattern = [
f'*all*_{self.modality}[_.]*skel*nii.gz',
f'*{self.modality}*merged*.nii.gz', f'all_{self.modality}_*'
]
if 'merged_img_dir' in kwargs:
self.merged_4d_file = search_and_select_one(
'merged_skeleton',
kwargs.get('merged_img_dir'),
merged_skel_pattern,
depth=0)
else:
self.merged_4d_file = search_and_select_one('merged_skeleton',
self.location.parent,
merged_skel_pattern,
depth=0)
# information from the file name
self.test_kind = re.search(r'(\w)stat\d+.nii.gz', self.name).group(1)
self.stat_num = re.search(r'(\d+).nii.gz', self.name).group(1)
# Below variables are to estimate number of significant voxels in each
# hemisphere
# checking significance
self.check_significance()
# template settings: If not specified use ENIGMA
if 'template' in kwargs:
self.template = kwargs.get('template')
else:
self.template = 'enigma'
self.template_settings()
if self.significant:
# if significant read in skeleton mask
#TODO
# enigma settings
skel_img, self.skel_mask_data = get_nifti_img_data(
self.skel_mask_loc)
self.get_significant_info()
self.get_significant_overlap()
# uncache the skeleton data matrix
skel_img.uncache()
# summary in pandas DataFrame
self.make_df()
# if matrix or contrast file is given
if self.matrix_file != 'missing':
self.get_matrix_info()
if self.contrast_file != 'missing':
self.get_contrast_info()
self.get_contrast_info_english()
self.update_with_contrast()