def test_reg_qc(
radius=5,
autofind=False,
plot=False,
):
"""This could be used as a continuous integration test function once we can distribute demo data."""
from samri.utilities import bids_autofind
from samri.plotting.aggregate import registration_qc
from samri.report.registration import get_scores
from samri.typesetting import inline_anova
from samri.utilities import bids_substitution_iterator
if autofind:
path_template, substitutions = bids_autofind("~/ni_data/ofM.dr/preprocessing/composite","func")
else:
path_template = "{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv.nii.gz"
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["5689","5690","5691","5694","5706","5700","5704","6255","6262"],
["CogB"],
"~/ni_data/ofM.dr/",
"composite",
acquisitions=['EPI'],
check_file_format=path_template,
)
df = get_scores(path_template, substitutions,
"~/ni_data/templates/DSURQEc_200micron_average.nii",
metric="CC",
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as="f_reg_quality.csv",
)
def test_autofind():
"""We may be able to turn this into a CI function, if we put together a data fetching script for dummy (empty) BIDS-formatted data.
"""
from samri.utilities import bids_autofind
from samri.plotting.aggregate import registration_qc
from samri.report.registration import get_scores
from samri.typesetting import inline_anova
from samri.utilities import bids_substitution_iterator
path_template, substitutions = bids_autofind("~/ni_data/ofM.dr/preprocessing/composite","func")
df = get_scores(path_template, substitutions,
"~/ni_data/templates/DSURQEc_200micron_average.nii",
metric="CC",
radius_or_number_of_bins=10,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as=False,
)
anova_summary = registration_qc(df,
value={"similarity":"Similarity"},
group={"sub":"Subject"},
repeat={"ses":"Session"},
show=False,
save_as="/tmp/f_reg_cc10.png",
)
subject_effect = inline_anova(anova_summary,"C(Subject)",style="python")
print("Subject Main Effect: {}".format(subject_effect))
session_effect = inline_anova(anova_summary,"C(Session)",style="python")
print("Session Main Effect: {}".format(session_effect))
def reg_cc(
radius=5,
autofind=True,
):
from samri.utilities import bids_autofind
from samri.plotting.aggregate import registration_qc
from samri.report.registration import get_scores
from samri.typesetting import inline_anova
from samri.utilities import bids_substitution_iterator
if autofind:
path_template, substitutions = bids_autofind(
"~/ni_data/ofM.dr/preprocessing/composite", "func")
else:
substitutions = bids_substitution_iterator(
["ofM", "ofM_aF", "ofM_cF1", "ofM_cF2", "ofM_pF"],
[
"4001", "4007", "4008", "4011", "5692", "5694", "5699", "5700",
"5704", "6255", "6262"
],
["EPI_CBV_chr_longSOA", "EPI_CBV_jb_long"],
"~/ni_data/ofM.dr/",
"composite",
)
path_template = "~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_trial-{trial}.nii.gz"
df = get_scores(
path_template,
substitutions,
"~/ni_data/templates/DSURQEc_200micron_average.nii",
metric="CC",
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as="f_reg_quality.csv",
)
anova_summary = registration_qc(
df,
value={"similarity": "Similarity"},
group={"sub": "Subject"},
repeat={"ses": "Session"},
extra={"trial": "Type"},
model="{value} ~ C({extra}) + C({repeat}) + C({group}) -1",
save_as="registration_qc.png",
print_model=True,
print_anova=True,
show=False,
)
subject_effect = inline_anova(anova_summary, "C(Subject)", style="python")
print("Subject Main Effect: {}".format(subject_effect))
session_effect = inline_anova(anova_summary,
"C(Session)",
style="python",
max_len=2)
print("Session Main Effect: {}".format(session_effect))
type_effect = inline_anova(anova_summary, "C(Type)", style="python")
print("Scan Type Main Effect: {}".format(type_effect))
def total_significance():
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
substitutions = bids_autofind('~/ni_data/ofM.dr/bids/l1/generic/',
path_template="{bids_dir}/sub-{{subject}}/ses-{{session}}/sub-{{subject}}_ses-{{session}}_acq-{{acquisition}}_task-{{task}}_cbv_pfstat.nii.gz",
match_regex='.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/.*?_acq-(?P<acquisition>.+).*?_task-(?P<task>.+)_cbv_pfstat\.nii.gz',
)
iter_significant_signal('~/ni_data/ofM.dr/bids/l1/generic/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_pfstat.nii.gz',
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/bids/l1/generic/total_significance.csv'
)
def dr_cont():
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'])
# 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',
)
print(substitutions)
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 vta_full(workflow_name='generic', ):
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import full_prep
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.vta/bids'
#full_prep(bids_base, "/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
# registration_mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# functional_match={'type':['cbv',],},
# structural_match={'acquisition':['TurboRARE']},
# actual_size=True,
# functional_registration_method='composite',
# negative_contrast_agent=True,
# out_dir='~/ni_data/ofM.vta/preprocessing',
# workflow_name=workflow_name,
# )
#glm.l1('~/ni_data/ofM.vta/preprocessing/generic',
# out_dir='~/ni_data/ofM.vta/l1',
# workflow_name=workflow_name,
# 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
path_template, substitutions = bids_autofind(
'~/ni_data/ofM.vta/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',
)
print(substitutions)
iter_significant_signal(
path_template,
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/vta/generic/total_significance.csv')
def reg_cc(
path="~/ni_data/ofM.dr/preprocessing/composite",
template="/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
radius=8,
autofind=False,
plot=False,
save="f_reg_quality",
metrics=['CC', 'GC', 'MI'],
):
from samri.utilities import bids_autofind
from samri.plotting.aggregate import registration_qc
from samri.report.registration import iter_measure_sim
from samri.typesetting import inline_anova
from samri.utilities import bids_substitution_iterator
if autofind:
path_template, substitutions = bids_autofind(path, "func")
else:
path_template = "{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz"
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
[
"4001", "4007", "4008", "4011", "5692", "5694", "5699", "5700",
"5704", "6255", "6262"
],
["CogB", "JogB"],
"~/ni_data/ofM.dr/",
"composite",
acquisitions=['EPI', 'EPIlowcov'],
validate_for_template=path_template,
)
for metric in metrics:
df = iter_measure_sim(
path_template,
substitutions,
template,
metric=metric,
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as=save + "_" + metric + ".csv",
)
def vta_full(
workflow_name='generic',
):
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import full_prep
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.vta/bids'
#full_prep(bids_base, "/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
# registration_mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# functional_match={'type':['cbv',],},
# structural_match={'acquisition':['TurboRARE']},
# actual_size=True,
# functional_registration_method='composite',
# negative_contrast_agent=True,
# out_dir='~/ni_data/ofM.vta/preprocessing',
# workflow_name=workflow_name,
# )
#glm.l1('~/ni_data/ofM.vta/preprocessing/generic',
# out_dir='~/ni_data/ofM.vta/l1',
# workflow_name=workflow_name,
# 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
path_template, substitutions = bids_autofind('~/ni_data/ofM.vta/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',
)
print(substitutions)
iter_significant_signal(path_template,
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/vta/generic/total_significance.csv'
)
def reg_cc(
path = "~/ni_data/ofM.dr/preprocessing/composite",
template = "/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
radius=8,
autofind=False,
plot=False,
save = "f_reg_quality",
metrics = ['CC','GC','MI'],
):
from samri.utilities import bids_autofind
from samri.plotting.aggregate import registration_qc
from samri.report.registration import iter_measure_sim
from samri.typesetting import inline_anova
from samri.utilities import bids_substitution_iterator
if autofind:
path_template, substitutions = bids_autofind(path,"func")
else:
path_template = "{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz"
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["4001","4007","4008","4011","5692","5694","5699","5700","5704","6255","6262"],
["CogB","JogB"],
"~/ni_data/ofM.dr/",
"composite",
acquisitions=['EPI','EPIlowcov'],
validate_for_template=path_template,
)
for metric in metrics:
df = iter_measure_sim(path_template, substitutions,
template,
metric=metric,
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as= save + "_" + metric + ".csv",
)
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",
)
