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_as="f_reg_quality",
# metrics = ['CC','GC'],
metrics=['CC', 'GC', 'MI'],
):
from samri.report.registration import iter_measure_sim
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,
)
df = iter_measure_sim(
path_template,
template,
substitutions,
metric=metrics[0],
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.95,
save_as=False,
)
df = df.rename(columns={
'similarity': metrics[0],
})
for metric in metrics[1:]:
_df = iter_measure_sim(
path_template,
template,
substitutions,
metric=metric,
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.95,
save_as=False,
)
df[metric] = _df['similarity'].values
if save_as:
df.to_csv(save_as + ".csv")
return df
def reg_gc():
from samri.plotting.aggregate import registration_qc
from samri.report.registration import iter_measure_sim
from samri.typesetting import inline_anova
df = iter_measure_sim(
"~/ni_data/ofM.dr/preprocessing/composite",
"~/.samri_files/templates/mouse/DSURQE/DSURQEc_200micron_average.nii",
#modality="anat",
metric="GC",
radius_or_number_of_bins=0,
sampling_strategy="Regular",
sampling_percentage=0.5,
save_as=False,
)
anova_summary = registration_qc(
df,
value={"similarity": "Similarity"},
group={"sub": "Subject"},
repeat={"ses": "Session"},
show=False,
save_as="/tmp/f_reg_gc.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_gc():
from samri.plotting.aggregate import registration_qc
from samri.report.registration import iter_measure_sim
from samri.typesetting import inline_anova
df = iter_measure_sim(
"~/ni_data/ofM.dr/preprocessing/composite",
"~/.samri_files/templates/mouse/DSURQE/DSURQEc_200micron_average.nii",
#modality="anat",
metric="GC",
radius_or_number_of_bins = 0,
sampling_strategy = "Regular",
sampling_percentage=0.5,
save_as=False,
)
anova_summary = registration_qc(df,
value={"similarity":"Similarity"},
group={"sub":"Subject"},
repeat={"ses":"Session"},
show=False,
save_as='/tmp/f_reg_gc.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(
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 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 iter_measure_sim
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}_task-{task}_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 = iter_measure_sim(
path_template,
substitutions,
"/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
metric="CC",
radius_or_number_of_bins=radius,
sampling_strategy="Regular",
sampling_percentage=0.33,
save_as="f_reg_quality.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 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 iter_measure_sim
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 = iter_measure_sim(
path_template,
substitutions,
"/usr/share/mouse-brain-atlases/dsurqec_200micron.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))