def test_l1():
l1(PREPROCESS_BASE,
mask='mouse',
match={'session': ['ofMaF'], 'acq':['EPIlowcov']},
out_base='/var/tmp/samri_testing/pytest/',
workflow_name='l1',
)
def aic():
preprocessing.bruker(
'~/ni_data/test/',
functional_match={'trial': ['CogB', 'CogB2m', 'JogB']},
structural_match={'acquisition': ['TurboRARE', 'TurboRARElowcov']},
workflow_name='composite',
lowpass_sigma=2,
highpass_sigma=225,
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_registration_method="composite",
keep_work=True,
template="~/ni_data/templates/DSURQEc_200micron_average.nii",
registration_mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
actual_size=True,
verbose=True,
)
glm.l1(
'~/ni_data/test/preprocessing/composite',
workflow_name='composite',
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
keep_work=True,
)
def run_level1_glm():
glm.l1(
preprocessing_dir='~/bandpass_ni_data/rsfM/preprocessing/composite',
workflow_name='as_composite',
habituation='confound',
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
keep_work=True)
def aic():
from samri.pipelines import preprocess, glm
preprocessing.bruker(
'~/ni_data/test/',
functional_match={'task': ['CogB', 'CogB2m', 'JogB']},
structural_match={'acquisition': ['TurboRARE', 'TurboRARElowcov']},
workflow_name='composite',
lowpass_sigma=2,
highpass_sigma=225,
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_registration_method="composite",
keep_work=True,
template="/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
registration_mask=
"/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
actual_size=True,
verbose=True,
)
glm.l1(
'~/ni_data/test/preprocessing/composite',
workflow_name='composite',
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
keep_work=True,
)
def test_l1():
l1(PREPROCESS_BASE,
mask='mouse',
match={"acq":["EPIlowcov"]},
out_base='/var/tmp/samri_testing/pytest/',
workflow_name='l1',
)
def run_level1_glm():
from samri.pipelines import glm, preprocess
glm.l1(preprocessing_dir='~/bandpass_ni_data/rsfM/preprocessing/composite',
workflow_name='as_composite',
habituation='confound',
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
keep_work=True)
def higher():
glm.l1(
'~/ni_data/test/preprocessing/composite',
workflow_name='higher',
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
keep_work=True,
)
def dr_only():
glm.l1(
"~/ni_data/ofM.dr/preprocessing/_composite",
mask="~/ni_data/templates/roi/f_dr_chr.nii.gz",
workflow_name="dr",
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
keep_work=True,
)
def glm_only():
from samri.pipelines import glm
glm.l1(
'~/ni_data/ofM.vta/bids/',
workflow_name='composite',
include={"subject": ["SN2143", "SN2145", "SN3974", "SN3975"]},
habituation="confound",
keep_work=True,
mask="",
)
def test_l1(tmp_path):
l1(
PREPROCESS_BASE,
mask='mouse',
match={
'session': ['ofMaF'],
'acq': ['EPIlowcov']
},
out_base=tmp_path,
workflow_name='l1',
)
def test_l1_generic(self):
with tempfile.TemporaryDirectory() as tmpdirname:
workflow_name = 'generic'
mask = '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii'
preprocess_base = '{}/preprocessing/'.format(TEST_DATA_DIR)
bf_path = TEST_DATA_DIR / 'chr_beta1.txt'
glm.l1(os.path.join(preprocess_base, workflow_name),
bf_path=bf_path,
workflow_name=workflow_name,
habituation="confound",
mask=mask,
keep_work=False,
match={'suffix': ['bold']},
exclude={'task': ['rest']},
invert=False,
out_base='{}/l1'.format(tmpdirname)
)
print('finished test_l1_generic successfully.')
def dr_composite():
from samri.pipelines import glm, preprocess
preprocessing.bruker("~/ni_data/ofM.dr/",
exclude_measurements=['20151027_121613_4013_1_1'],
workflow_name="composite",
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_blur_xy=4,
functional_registration_method="composite")
glm.l1("~/ni_data/ofM.dr/preprocessing/composite",
workflow_name="composite",
include={"subjects": [i for i in range(4001, 4010)] + [4011, 4012]},
habituation="confound",
mask="~/ni_data/templates/ds_QBI_chr_bin.nii.gz",
keep_work=True)
glm.l1(
"~/ni_data/ofM.dr/preprocessing/composite",
workflow_name="composite_dr",
include={"subjects": [i for i in range(4001, 4010)] + [4011, 4012]},
habituation="confound",
mask="~/ni_data/templates/roi/f_dr_chr_bin.nii.gz",
)
glm.l2_common_effect("~/ni_data/ofM.dr/l1/composite",
workflow_name="subjectwise_composite",
groupby="subject")
glm.l2_common_effect("~/ni_data/ofM.dr/l1/composite",
workflow_name="sessionwise_composite",
groupby="session",
exclude={
"subjects": [
"4001", "4002", "4003", "4004", "4005",
"4006", "4009", "4011", "4013"
]
})
glm.l2_common_effect(
"~/ni_data/ofM.dr/l1/composite",
workflow_name="sessionwise_composite_w4011",
groupby="session",
exclude={
"subjects":
["4001", "4002", "4003", "4004", "4005", "4006", "4009", "4013"]
})
def dr_full():
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import bruker
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.dr/bids'
# Preprocess
animal_list = animal_multiselect(
cage_treatments=['cFluDW', 'cFluDW_', 'cFluIP'])
# Animal list selection needs fixing in LabbookDB database, so we add the following animals manually
animal_list.extend([
'4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009',
'4011', '4012', '4013', '6557'
])
full_prep(
bids_base,
"~/ni_data/templates/dsurqec_200micron.nii",
registration_mask="~/ni_data/templates/dsurqec_200micron_mask.nii",
functional_match={
'type': [
'cbv',
],
},
structural_match={
'acquisition': ['TurboRARE', 'TurboRARElowcov'],
},
subjects=animal_list,
actual_size=True,
functional_registration_method="composite",
negative_contrast_agent=True,
out_dir='~/ni_data/ofM.dr/preprocessing',
)
#bruker(bids_base, "~/ni_data/templates/dsurqec_200micron.nii",
# registration_mask="~/ni_data/templates/dsurqec_200micron_mask.nii",
# functional_match={'type':['bold',],},
# structural_match={'acquisition':['TurboRARE','TurboRARElowcov'],},
# subjects=animal_list,
# actual_size=True,
# functional_registration_method="composite",
# negative_contrast_agent=False,
# out_dir='~/ni_data/ofM.dr/preprocessing',
# )
# Model fitting
glm.l1(
'~/ni_data/ofM.dr/preprocessing/generic',
out_dir='~/ni_data/ofM.dr/l1',
workflow_name='generic',
habituation="confound",
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# We need the workdir to extract the betas
keep_work=True,
)
# Determining Responders by Significance
substitutions = bids_autofind(
'~/ni_data/ofM.dr/l1/generic/',
path_template=
"{bids_dir}/sub-{{subject}}/ses-{{session}}/sub-{{subject}}_ses-{{session}}_task-{{task}}_acq-{{acquisition}}_cbv_pfstat.nii.gz",
match_regex=
'.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/.*?_task-(?P<task>.+).*?_acq-(?P<acquisition>.+)_cbv_pfstat\.nii.gz',
)
iter_significant_signal(
'~/ni_data/ofM.dr/l1/generic/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{{acquisition}}_cbv_pfstat.nii.gz',
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/l1/generic/total_significance.csv')
# Determining Responders by a priori pattern
glm.l2_common_effect(
'~/ni_data/ofM.dr/l1/',
workflow_name="a_priori_responders",
include={
'subject': [
'4001', '4005', '4006', '4007', '4008', '4009', '4011', '4012',
'4013'
],
},
groupby="session",
keep_work=True,
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
)
def cbv_composite(
data_path,
workflow_name,
preprocessing_dir="preprocessing",
l1_dir="l1",
):
preprocessing.bruker(
data_path,
#exclude_measurements=['20151027_121613_4013_1_1'],
functional_scan_types=["EPI_CBV_chr_longSOA", "EPI_CBV_jb_long"],
#subjects=["4007","4008","4011","4012","5687","5688","5695","5689","5690","5691","5703","5704","5706"],
subjects=["4007", "4008", "4011", "5687", "5688", "5704"],
#subjects=["4007","4008","4009","4011","4012","5689","5690","5691","5703","5704","5706"],
workflow_name=workflow_name,
lowpass_sigma=2,
highpass_sigma=225,
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_blur_xy=.4,
functional_registration_method="composite",
keep_work=True,
template="~/ni_data/templates/DSURQEc_200micron_average.nii",
registration_mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
actual_size=True,
)
glm.l1(
path.join(data_path, preprocessing_dir, workflow_name),
workflow_name=workflow_name,
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
keep_work=True,
)
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="composite_sessions_best_responders",
exclude={
"scans": ["EPI_BOLD_"],
"subjects": [
"4001", "4002", "4003", "4004", "4006", "4008", "4009", "5674",
"5703", "5704", "5706", "4005", "5687"
]
},
groupby="session",
keep_work=True,
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="composite_sessions_responders",
exclude={
"scans": ["EPI_BOLD_"],
"subjects": [
"4001", "4002", "4003", "4004", "4006", "4008", "4009", "5674",
"5703", "5704", "5706"
]
},
groupby="session",
keep_work=True,
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
preprocess_base = '{}/preprocessing/'.format(scratch_dir)
masks = {
'generic': '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
'masked': '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
}
for key in masks:
glm.l1(path.join(preprocess_base, key),
bf_path='../data/chr_beta1.txt',
workflow_name=key,
habituation="confound",
mask=masks[key],
keep_work=False,
n_jobs_percentage=.33,
match={'suffix': ['cbv']},
exclude={'task': ['rest']},
invert=True,
out_base='{}/l1'.format(scratch_dir)
)
glm.l1(path.join(preprocess_base, key),
bf_path='../data/chr_beta1.txt',
workflow_name=key,
habituation="confound",
mask=masks[key],
keep_work=False,
n_jobs_percentage=.33,
match={'suffix': ['bold']},
exclude={'task': ['rest']},
invert=False,
