def afni_BlurToFWHM(self, input_path, fwhm,
regex=None, img_ext='nii.gz', file_idx=None,
step_idx=None, sub_code=None, suffix=None):
""" FWHM based spatial gaussian smoothing using 3dmerge command of Afni
Args:
input_path(str): datatype or stepcode of input data
fwhm(float): full width half maximum value
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='BlurToFWHM', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, idx=file_idx,
filter_dict=filter_dict, group_input=False)
itf.set_var(label='fwhm', value=str(fwhm))
itf.set_output(label='output')
itf.set_cmd("3dmerge -prefix *[output] -doall -1blur_fwhm *[fwhm] *[input]")
itf.set_output_checker(label='output')
itf.run()
def ants_SpatialNorm(self, input_path, ref_path,
img_ext='nii.gz', file_idx=None,
step_idx=None, sub_code=None, suffix=None):
""" realign subject brain image into standard space using antsRegistrationSyN.sh command
of ants package
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): path for brain template image
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self, n_threads=1)
itf.init_step(title='SpatialNorm', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=dict(ext=img_ext))
itf.set_var(label='ref', value=ref_path)
itf.set_var(label='thread', value=self._n_threads)
itf.set_output(label='output', suffix='_', ext=False)
itf.set_cmd("antsRegistrationSyN.sh -f *[ref] -m *[input] -o *[output] -n *[thread]")
itf.set_output_checker(suffix='Warped', ext='nii.gz')
itf.run()
def ants_ApplySpatialNorm(self, input_path, ref_path,
file_idx=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" apply transform matrix generated by antsRegistrationSyN.sh along other images
using WarpTimeSeriesImageMultiTransform command of ants package
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): path for brain template image
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='ApplySpatialNorm', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
itf.set_input(label='input', input_path=input_path, group_input=False,
idx=file_idx, filter_dict=dict(ext=img_ext))
itf.set_static_input(label='base', input_path=ref_path,
idx=0, filter_dict=dict(regex=r'.*_Warped$', ext='nii.gz'))
itf.set_static_input(label='tfmorph', input_path=ref_path,
idx=0, filter_dict=dict(regex=r'.*_1Warp$', ext='nii.gz'))
itf.set_static_input(label='tfmat', input_path=ref_path,
idx=0, filter_dict=dict(ext='mat'))
itf.set_output(label='output')
itf.set_cmd("WarpTimeSeriesImageMultiTransform 4 *[input] *[output] -R "
"*[base] *[tfmorph] *[tfmat]")
itf.set_output_checker()
itf.run()
def camri_TSNR(self,
input_path,
mask_path,
regex=None,
img_ext='nii.gz',
file_idx=None,
step_idx=None,
sub_code=None,
suffix=None):
from .funcs import tsnr_func
itf = InterfaceBuilder(self)
itf.init_step(title='tSNR',
mode='processing',
type='python',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
idx=file_idx,
filter_dict=filter_dict,
group_input=False)
itf.set_var(label='mask', value=mask_path)
itf.set_output(label='output')
itf.set_func(tsnr_func)
itf.set_output_checker(label='output')
itf.run()
def afni_ApplySpatialNorm(self, input_path, ref_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" apply transform matrix generated by 3dAllineate along other images
using same command of afni package
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): path for brain template image
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='ApplySpatialNorm', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_static_input(label='base', input_path=ref_path,
idx=0, filter_dict=dict(ext='nii.gz'))
itf.set_static_input(label='tfmat', input_path=ref_path,
idx=0, filter_dict=dict(ext='aff12.1D'))
itf.set_output(label='output')
cmd = '3dAllineate -prefix *[output] -master *[base] -warp shr -1Dmatrix_apply *[tfmat] *[input]'
itf.set_cmd(cmd)
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def afni_SliceTimingCorrection(self, input_path,
tr=None, tpattern=None,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
"""Correct slice timing using afni's 3dTshift command
Args:
input_path(str): datatype or stepcode of input data
tr(int or float): sampling rate
tpattern(str): slice timing pattern available in 3dTshift
(e.g. altplus, altminus, seqplut, seqminus)
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='SliceTimingCorrection',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(ext=img_ext, regex=regex)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_output(label='output')
cmd = ['3dTshift -prefix *[output]']
if tr is not None:
itf.set_var(label='tr', value=tr)
cmd.append('-TR *[tr]')
if tpattern is not None:
itf.set_var(label='tpattern', value=tpattern)
cmd.append('-tpattern *[tpattern]')
cmd.append('*[input]')
itf.set_cmd(' '.join(cmd))
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def afni_MultiVarModeling(self, input_path, regex, regex_label, subbrick_idx=1, mask_path=None,
output_filename=None, bsVars=None, wsVars=None, glt_codes=None, glf_codes=None,
reindex_subj_by=False, n_threads=None,
img_ext='nii.gz', step_idx=None, sub_code=None, suffix=None):
""" perform Group Analysis with Multi-Variable Modeling approach using 3dMVM in Afni
this interface currently compatible with only within variable and between variable options.
Args:
input_path(str): datatype or stepcode of input data
regex(raw str): regular express pattern to filter dataset
(please refer tutorial)
regex_label(dict): label and group number to specify columns of data table
(please refer document)
subbrick_idx(int): index of sub-brick wants to input
mask_path(str): path for brain mask image
output_filename(str): output filename
bsVars(str): between subject variables
wsVars(str): within subject variables
glt_codes(dict): glt codes dict(title=code, ...)
glf_codes(dict): glf codes dict(title=code, ...)
reindex_subj_by(str): one of between subject variable if want to reindex subject id
n_threads(int): number of thread for multicore processing
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
import re
import numpy as np
import pandas as pd
from collections import OrderedDict
itf = InterfaceBuilder(self)
itf.init_step('MultiVarModeling',
idx=step_idx, subcode=sub_code, suffix=suffix, mode='reporting')
itf.set_input(label='input', input_path=input_path,
filter_dict=dict(regex=regex, ext=img_ext),
group_input=True)
# Prepare data table (advanced usage of complex input structure)
p = re.compile(regex)
inputs = itf.get_inputs('input')
df_dict = OrderedDict()
subj_idx = regex_label.pop('Subj')
df_dict['Subj'] = [p.search(f).group(subj_idx) for f in inputs]
for label, idx in regex_label.items():
df_dict[label] = [p.search(f).group(idx) for f in inputs]
if reindex_subj_by is not False:
subjs = np.array(df_dict['Subj'], dtype=object)
reindex_base = np.array(df_dict[reindex_subj_by])
reindex_items = list(set(reindex_base))
offset = 0
for item in reindex_items:
sub_set = subjs[reindex_base == item]
sub_num = sorted(list(set(sub_set)))
sub_set = ['s{}'.format(i + 1 + offset) for i, n in enumerate(sub_num) for s in sub_set if s == n]
subjs[reindex_base == item] = sub_set
offset += len(sub_num)
df_dict['Subj'] = list(subjs)
inputs = [f.replace('nii.gz', 'nii.gz[{}]'.format(subbrick_idx)) for f in inputs]
df_dict['InputFile'] = inputs
group_df = pd.DataFrame(df_dict)
datatable_path = 'dataTable_{}.txt'.format(suffix)
group_df.iloc[0:len(group_df) - 1].to_csv(datatable_path, index=None,
sep='\t', line_terminator=' \\\n')
group_df.iloc[[len(group_df) - 1]].to_csv(datatable_path, index=None,
header=None, sep='\t', mode='a',
line_terminator='')
itf.set_var(label='datatable', value=datatable_path)
cmd = ['3dMVM']
if n_threads is not None:
itf.set_var(label='n_thread', value=8)
cmd.append('-jobs *[n_thread]')
if mask_path is not None:
itf.set_var(label='mask', value=mask_path)
cmd.append('-mask *[mask]')
itf.set_output(label='output', modifier=output_filename, ext='nii.gz')
cmd.append('-prefix *[output]')
if bsVars is not None:
cmd.append('-bsVars "{}"'.format(bsVars))
if wsVars is not None:
cmd.append('-wsVars "{}"'.format(wsVars))
if glt_codes is not None:
cmd.append('-num_glt {}'.format(len(glt_codes)))
for glt_label, (title, code) in enumerate(sorted(glt_codes.items())):
cmd.append('-gltLabel {0} {1} -gltCode {0} "{2}"'.format(glt_label + 1, title, code))
if glf_codes is not None:
cmd.append('-num_glf {}'.format(len(glf_codes)))
for glf_label, (title, code) in enumerate(sorted(glf_codes.items())):
cmd.append('-glfLabel {0} {1} -glfCode {0} "{2}"'.format(glf_label + 1, title, code))
cmd.append('-dataTable @*[datatable]')
itf.set_cmd(' '.join(cmd))
itf.set_errterm(['ERROR'])
itf.set_output_checker(label='output')
itf.run()
def afni_SkullStripping(self, input_path, mask_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" stripping the skull using brain mask
Args:
input_path(str): datatype or stepcode of input data
mask_path(str): stepcode of mask_path
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='SkullStripping', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_static_input(label='mask', input_path=mask_path,
idx=0, mask=True, filter_dict=dict(regex=r'.*_mask$', ext=img_ext))
itf.set_output(label='output')
itf.set_cmd("3dcalc -prefix *[output] -expr 'a*step(b)' -a *[input] -b *[mask]")
itf.set_errterm(['ERROR'])
itf.set_output_checker() # default label='output'
itf.run()
def ants_N4BiasFieldCorrection(self, input_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" correcting bias field using N4 algorithm of ants package
Args:
input_path(str): datatype or stepcode of input data
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='N4BiasFieldCorrection', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_output(label='output')
itf.set_cmd("N4BiasFieldCorrection -i *[input] -o *[output]")
itf.set_output_checker() # default label='output'
itf.run()
def camri_Periodogram(self,
input_path,
mask_path=None,
dt=None,
nfft=None,
file_idx=None,
regex=None,
img_ext='nii.gz',
step_idx=None,
sub_code=None,
suffix=None):
"""
Args:
input_path(str): datatype or stepcode of input data
mask_path(str): mask
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
mode: mode
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
from .funcs import periodogram_func
itf = InterfaceBuilder(self)
itf.init_step(title='Periodogram',
mode='processing',
type='python',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
idx=file_idx,
filter_dict=filter_dict,
group_input=False)
itf.set_var(label='mask', value=mask_path)
itf.set_var(label='dt', value=dt)
itf.set_var(label='nfft', value=nfft)
itf.set_func(periodogram_func)
itf.set_output(label='output')
itf.set_output_checker(label='output')
itf.run()
def afni_MeanImageCalc(self, input_path, range=None,
file_idx=0, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
"""Calculate mean intensity image using 3dTstat
Args:
input_path(str): datatype or stepcode of input data
range(list): range for averaging (default=None)
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='MeanImageCalculation',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_output(label='output')
cmd = ["3dTstat -prefix *[output] -mean"]
if range is not None:
if isinstance(range, list) and len(range) is 2:
start, end = range
if isinstance(start, int) and isinstance(end, int):
itf.set_var(label='start', value=start)
itf.set_var(label='end', value=end)
else:
self.logging('warn', 'incorrect range values.')
else:
self.logging('warn', 'incorrect range values.')
cmd.append("*[input]'[*[start]..*[end]]'")
else:
cmd.append("*[input]")
itf.set_cmd(' '.join(cmd))
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def camri_ReHo(self,
input_path,
mask_path,
nn_level=3,
file_idx=None,
regex=None,
img_ext='nii.gz',
step_idx=None,
sub_code=None,
suffix=None):
"""
Args:
input_path:
mask_path:
nn_level:
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx:
sub_code:
suffix:
Returns:
"""
from .funcs import reho_func
itf = InterfaceBuilder(self)
itf.init_step(title='ReHo',
mode='processing',
type='python',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
idx=file_idx,
filter_dict=filter_dict,
group_input=False)
itf.set_var(label='mask', value=mask_path)
itf.set_var(label='nn_level', value=nn_level)
itf.set_output(label='output')
itf.set_func(reho_func)
itf.set_output_checker(label='output')
itf.run()
def camri_NuisanceRegression(self,
input_path,
dt,
mask_path=None,
file_idx=None,
regex=None,
img_ext='nii.gz',
fwhm=None,
highpass=None,
lowpass=None,
ort=None,
ort_regex=None,
ort_ext=None,
step_idx=None,
sub_code=None,
suffix=None):
"""
Args:
input_path:
dt: sample time in second
mask_path: mask path
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
fwhm:
highpass:
lowpass:
ort:
ort_regex:
step_idx:
sub_code:
suffix:
"""
from .funcs import nuisance_regression_func
itf = InterfaceBuilder(self)
itf.init_step(title='NuisanceRegression',
mode='processing',
type='python',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
idx=file_idx,
filter_dict=filter_dict,
group_input=False)
itf.set_var(label='mask', value=mask_path)
itf.set_var(label='fwhm', value=fwhm)
itf.set_var(label='highpass', value=highpass)
itf.set_var(label='lowpass', value=lowpass)
itf.set_var(label='dt', value=dt)
itf.set_var(label='bp_order', value=5)
if ort:
if not ort_regex:
raise InvalidApproach(
'Regex pattern must provided for ort option.')
if not ort_ext:
raise InvalidApproach(
'Extension hint of ort must be provided.')
itf.set_input(label='ort',
input_path=ort,
filter_dict=dict(regex=ort_regex, ext=ort_ext))
else:
itf.set_var(label='ort', value=ort)
itf.set_var(label='pn_order', value=3)
itf.set_func(nuisance_regression_func)
itf.set_output(label='output')
itf.set_output_checker(label='output')
itf.run()
def camri_BrainMasking(self,
input_path,
file_idx=None,
regex=None,
img_ext='nii.gz',
step_idx=None,
sub_code=None,
suffix=None):
""" Estimate brain mask using 2D-UNET,
Args:
input_path(str): datatype or stepcode of input data
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self, n_threads=1)
itf.init_step(title='BrainMaskEstimate',
mode='masking',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
group_input=False,
idx=file_idx,
filter_dict=filter_dict)
itf.set_output(label='mask', suffix='_mask')
itf.set_output(label='copy')
itf.set_cmd("rbm *[input] *[mask]")
if sys.platform == 'win32':
itf.set_cmd("copy *[input] *[copy]")
else:
itf.set_cmd("cp *[input] *[copy]")
itf.set_output_checker(label='mask')
itf.run()
def afni_Scailing(self, input_path, mask_path, mean=100, max=200,
regex=None, img_ext='nii.gz', file_idx=None,
step_idx=None, sub_code=None, suffix=None):
""" Scaling the time series dataset to have given mean in the mask
If max value is inputted, the max value will be cut at given value.
Args:
input_path(str): datatype or stepcode of input data
mask_path(str): path for brain mask image
mean(int, float): desired mean value
max(int, float): desired max value
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='Scaling', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, idx=file_idx,
filter_dict=filter_dict, group_input=False)
itf.set_temporary(label='meanimg')
itf.set_var(label='mask', value=mask_path)
itf.set_var(label='mean', value=mean)
if max is not None:
itf.set_var(label='max', value=max)
expr_block = 'c * min(*[max], a/b**[mean])'
else:
expr_block = 'c * min(a/b**[mean])'
itf.set_output(label='output')
itf.set_cmd("3dTstat -mean -prefix *[meanimg] *[input]")
itf.set_cmd("3dcalc -a *[input] -b *[meanimg] -c *[mask] -expr '{}' -prefix *[output]".format(expr_block))
itf.set_errterm(['ERROR'])
itf.set_output_checker(label='output')
itf.run()
def afni_Coregistration(self, input_path, ref_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" realign the functional image into anatomical image using 3dAllineate command of
afni package.
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): stepcode of reference data
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='Coregistration', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=dict(ext=img_ext))
itf.set_static_input(label='ref', input_path=ref_path,
idx=0, filter_dict=filter_dict)
itf.set_output(label='output')
itf.set_output(label='tfmat', ext='aff12.1D')
itf.set_cmd("3dAllineate -prefix *[output] -onepass -EPI -base *[ref] -cmass+xy "
"-1Dmatrix_save *[tfmat] *[input]")
itf.set_errterm(['ERROR'])
itf.set_output_checker() # default label='output'
itf.run()
def afni_Deconvolution(self, input_path, mask_path,
onset_time, model, parameters, polort=2,
regex=None, img_ext='nii.gz', file_idx=None,
step_idx=None, sub_code=None, suffix=None):
""" General Linear Model analysis using 3dDeconvolve of Afni package
this interface is for use of single stimulation model only.
Args:
input_path(str): datatype or stepcode of input data
mask_path(str): path for brain mask image
polort(int): polynomial regressor for detrending
onset_time(list): stimulation onset time, list of int (e.g. [10, 50, 90])
model(str): response model
parameters(list): parameters for response model
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='Deconvolve', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
# set input
itf.set_input(label='input', input_path=input_path, idx=file_idx,
filter_dict=filter_dict, group_input=False)
# set variables
itf.set_var(label='mask', value=mask_path)
itf.set_var(label='polort', value=polort)
itf.set_var(label='onset_time', value=' '.join(map(str, onset_time)))
itf.set_var(label='model', value=model)
itf.set_var(label='parameters', value=','.join(map(str, parameters)))
# set temporary output
itf.set_temporary(label='bucket')
# set main output
itf.set_output(label='output')
itf.set_output(label='matrix', ext=False)
itf.set_cmd("3dDeconvolve -input *[input] -mask *[mask] "
"-num_stimts 1 -polort *[polort] -stim_times 1 '1D: *[onset_time]' "
"'*[model](*[parameters])' -stim_label 1 STIM -tout -bucket *[bucket] -x1D *[matrix]")
itf.set_cmd("3dREMLfit -matrix *[matrix].xmat.1D -input *[input] -tout -Rbuck *[output] -verb")
itf.set_errterm(['ERROR'])
itf.set_output_checker(label='output')
itf.run()
def afni_ApplyTransform(self, input_path, ref_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" apply the transform matrix that acquired from 3dAllineate command
along all input data using 3dAllineate command of afni package.
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): stepcode of reference data
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self, n_threads=1)
itf.init_step(title='ApplyTransform', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_static_input(label='ref', input_path=ref_path,
idx=0, filter_dict=dict(ext=img_ext))
itf.set_static_input(label='tfmat', input_path=ref_path,
idx=0, filter_dict=dict(ext='aff12.1D'))
itf.set_output(label='output')
itf.set_cmd("3dAllineate -prefix *[output] -master *[ref] -1Dmatrix_apply *[tfmat] *[input]")
itf.set_errterm(['ERROR'])
itf.set_output_checker() # default label='output'
itf.run()
def afni_TTest(self, input_a, regex_a, data_idx_a=1, output_filename=None, mask_path=None,
clustsim=True,
input_b=None, regex_b=None, data_idx_b=1,
img_ext='nii.gz', step_idx=None, sub_code=None, suffix=None):
""" perform student t-test using 3dttest++ in Afni
Args:
input_a(str): datatype or stepcode of input data of group A
regex_a(str): regular express pattern to filter group A
input_b(str): datatype or stepcode of input data of group B
regex_b(str): regular express pattern to filter group B
output_filename(str): output filename
data_idx_a(int): index of sub-brick wants to input for group A
data_idx_b(int): index of sub-brick wants to input for group B
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self, relpath=True)
if input_b is None:
title = 'OneSampleTTest'
else:
title = 'TwoSampleTTest'
itf.init_step(title=title,
idx=step_idx, subcode=sub_code, suffix=suffix,
mode='reporting')
itf.set_input(label='groupA', input_path=input_a, group_input=True,
join_modifier=dict(suffix="'[{}]'".format(data_idx_a)),
filter_dict=dict(regex=regex_a, ext=img_ext))
if input_b is not None:
itf.set_input(label='groupB', input_path=input_b, group_input=True,
join_modifier=dict(suffix=f"'[{data_idx_b}]'"),
filter_dict=dict(regex=regex_b, ext=img_ext))
input_sets = '-setA *[groupA] -setB *[groupB]'
else:
input_sets = '-setA *[groupA]'
if clustsim is True:
option = '-Clustsim'
# option = '-tempdir *[tempdir] -prefix_clustsim *[temp_prefix] -CLUSTSIM'
# itf.set_var(label='temp_prefix', value='temporary')
# itf.set_temporary(label='tempdir', path_only=True)
else:
option = ''
itf.set_output(label='output', modifier=output_filename,
ext='nii.gz')
itf.set_var(label='mask', value=mask_path)
itf.set_output(label='resid', modifier=output_filename,
suffix='_resid', ext='nii.gz')
itf.set_cmd(f'3dttest++ -mask *[mask] -prefix *[output] {input_sets} '
f'-resid *[resid] -ACF {option}')
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def afni_SpatialNorm(self, input_path, ref_path,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
""" realign subject brain image into standard space using 3dAllineate command
of afni package
Args:
input_path(str): datatype or stepcode of input data
ref_path(str): path for brain template image
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
step_idx(int): stepcode index (positive integer lower than 99)
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
itf.init_step(title='SpatialNorm', mode='processing',
idx=step_idx, subcode=sub_code, suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex,
ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_var(label='ref', value=ref_path)
itf.set_output(label='output')
itf.set_output(label='tfmat', ext='aff12.1D')
cmd = '3dAllineate -prefix *[output] -twopass -cmass+xy -zclip -conv 0.01 -base *[ref] ' \
'-cost crM -check nmi -warp shr -1Dmatrix_save *[tfmat] *[input]'
itf.set_cmd(cmd)
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def afni_MotionCorrection(self, input_path,
base=0, fourier=True, verbose=True, mparam=True,
file_idx=None, regex=None, img_ext='nii.gz',
step_idx=None, sub_code=None, suffix=None):
"""Correct head motion using afni's 3dvolreg command
Args:
input_path(str): datatype or stepcode of input data
step_idx(int): stepcode index (positive integer lower than 99)
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='nii.gz')
base(int or str): reference image
fourier(bool): Fourior option on for 3dvolreg
verbose(bool): if True, print out all processing messages on STERR.log
mparam(bool): if True, generate motion parameter file(1D) using same filename
sub_code(str): sub stepcode, one character, 0 or A-Z
suffix(str): suffix to identify the current step
"""
itf = InterfaceBuilder(self)
if regex is not None:
filter_dict = dict(ext=img_ext, regex=regex)
else:
filter_dict = dict(ext=img_ext)
itf.init_step(title='MotionCorrection',
idx=step_idx, subcode=sub_code, suffix=suffix)
itf.set_input(label='input', input_path=input_path, group_input=False, idx=file_idx,
filter_dict=filter_dict)
itf.set_output(label='output')
cmd = ["3dvolreg -prefix *[output]"]
if mparam is True:
itf.set_output(label='mparam', ext='1D')
cmd.append("-1Dfile *[mparam]")
if fourier is True:
cmd.append("-Fourier")
if verbose is True:
cmd.append("-verbose")
if isinstance(base, int):
# use frame number for the reference
itf.set_var(label='base', value=base)
elif isinstance(base, str):
# use input_path for the reference
itf.set_static_input(label='base', input_path=base, idx=0,
filter_dict=dict(ext=img_ext))
cmd.append('-base *[base] *[input]')
itf.set_cmd(' '.join(cmd))
itf.set_errterm(['ERROR'])
itf.set_output_checker()
itf.run()
def camri_FramewiseDisplacement(self,
input_path,
mean_radius=None,
file_idx=None,
regex=None,
img_ext='1D',
step_idx=None,
sub_code=None,
suffix=None):
"""
Args:
input_path:
mean_radius:
file_idx(int): index of file if the process need to be executed on a specific file
in session folder.
regex(str): regular express pattern to filter dataset
img_ext(str): file extension (default='1D')
step_idx:
sub_code:
suffix:
Returns:
"""
from .funcs import fd_func
itf = InterfaceBuilder(self)
itf.init_step(title='FramewiseDisplacement',
mode='processing',
type='python',
idx=step_idx,
subcode=sub_code,
suffix=suffix)
if regex is not None:
filter_dict = dict(regex=regex, ext=img_ext)
else:
filter_dict = dict(ext=img_ext)
itf.set_input(label='input',
input_path=input_path,
idx=file_idx,
filter_dict=filter_dict,
group_input=False)
itf.set_var(label='mean_radius', value=mean_radius)
itf.set_output(label='output', ext='tsv')
itf.set_func(fd_func)
itf.set_output_checker(label='output')
itf.run()