def test_dual_regression(group_level="migp"):
from samri.analysis import fc
substitutions_a = bids_substitution_iterator(
[
"ofM",
],
["5689", "5690", "5691"],
["EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"as_composite",
)
substitutions_b = bids_substitution_iterator(
[
"ofM_cF2",
],
["5689", "5690", "5691"],
["EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"as_composite",
)
fc.dual_regression(
substitutions_a,
substitutions_b,
group_level=group_level,
)
def label_activity(label):
"""
Create a DataFrame containing the per-session per-subject mean values for an autogenerated ROI based on the given label.
Other parameter customizations are hard-coded below.
"""
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
from os.path import basename, splitext
mapping='/usr/share/mouse-brain-atlases/dsurqec_mapping.csv'
atlas='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii'
my_roi = roi_from_atlaslabel(atlas,
mapping=mapping,
label_names=[label],
)
workflow_name = 'generic'
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
["CogB",],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df = roi.per_session(substitutions,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[label],
atlas=atlas,
mapping=mapping,
)
df['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704','6455','6459'],
["CogB",],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df_ = roi.per_session(substitutions_,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[label],
atlas=atlas,
mapping=mapping,
)
df_['treatment']='Vehicle'
df=pd.concat([df_,df])
df=df.rename(columns={'session': 'Session',})
df.to_csv('~/ni_data/ofM.dr/bids/l1/{}/{}.csv'.format(workflow_name, label))
def dr_activity():
import pandas as pd
from behaviopy.plotting import qualitative_times
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'composite'
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
['5691', "5689", "5690", "5700"],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
)
fit, anova, subjectdf, voxeldf = roi.per_session(
substitutions,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
roi_mask="~/ni_data/templates/roi/DSURQEc_dr.nii.gz",
)
subjectdf['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["6262", "6255", "5694", "5706", '5704'],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
)
fit_, anova_, subjectdf_, voxeldf_ = roi.per_session(
substitutions_,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
roi_mask="~/ni_data/templates/roi/DSURQEc_dr.nii.gz",
)
subjectdf_['treatment'] = 'Vehicle'
subjectdf = pd.concat([subjectdf_, subjectdf])
subjectdf = subjectdf.rename(columns={
'session': 'Session',
})
subjectdf.to_csv(
'~/ni_data/ofM.dr/l1/{}/dr_summary.csv'.format(workflow_name))
def label_connectivity(workflow_name='DSURQEc_drp', labels=['cortex']):
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
mapping='/usr/share/mouse-brain-atlases/dsurqec_mapping.csv'
atlas='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii'
my_roi = roi_from_atlaslabel(atlas,
mapping=mapping,
label_names=labels,
)
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
["CogB",],
"~/ni_data/ofM.dr/bids",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
df = roi.per_session(substitutions,
filename_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
roi_mask=my_roi,
feature=labels,
atlas=atlas,
mapping=mapping,
)
df['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704','6455','6459'],
["CogB",],
"~/ni_data/ofM.dr/bids",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
df_ = roi.per_session(substitutions_,
filename_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
roi_mask=my_roi,
feature=labels,
atlas=atlas,
mapping=mapping,
)
df_['treatment']='Vehicle'
df = pd.concat([df_,df])
df = df.rename(columns={'session': 'Session','t':'z'})
df.to_csv('~/ni_data/ofM.dr/bids/fc/{}/{}_summary.csv'.format(workflow_name,'-'.join(labels)))
def pattern_fc():
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'drs_seed'
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700"],
["CogB",],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=["EPI",],
check_file_format='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
subjectdf = summary.analytic_pattern_per_session(substitutions,
'~/ni_data/ofM.dr/l2/anova_ctx/anova_zfstat.nii.gz',
t_file_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
figure="per-participant",
color="#309079",
xy_label=["Session","Pattern Score"],
)
subjectdf['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704'],
["CogB",],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=["EPI",],
check_file_format='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
subjectdf_ = summary.analytic_pattern_per_session(substitutions_,
'~/ni_data/ofM.dr/l2/anova_ctx/anova_zfstat.nii.gz',
t_file_template='{data_dir}/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
figure="per-participant",
color="#309079",
xy_label=["Session","Pattern Score"],
)
subjectdf_['treatment']='Vehicle'
subjectdf=pd.concat([subjectdf_,subjectdf])
subjectdf=subjectdf.rename(columns={'session': 'Session',})
subjectdf.to_csv('~/ni_data/ofM.dr/fc/{}/ctx_pattern_summary.csv'.format(workflow_name))
def p_clusters(mask):
from samri.plotting import summary, timeseries
substitutions = bids_substitution_iterator(
["ofM", "ofM_aF", "ofM_cF1", "ofM_cF2", "ofM_pF"],
["4011", "4012", "5689", "5690", "5691"],
# ["4007","4008","4011","4012","5689","5690","5691"],
["EPI_CBV_jb_long", "EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"composite",
l1_dir="dr",
)
timecourses, designs, stat_maps, events_dfs, subplot_titles = summary.p_filtered_ts(
substitutions,
ts_file_template=
"{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}.nii.gz",
beta_file_template=
"{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_cope.nii.gz",
# p_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_pstat.nii.gz",
p_file_template=
"{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_pfstat.nii.gz",
design_file_template=
"{data_dir}/l1/{l1_workdir}/_subject_session_scan_{subject}.{session}.{scan}/modelgen/run0.mat",
event_file_template=
"{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}_events.tsv",
brain_mask=mask,
p_level=0.05,
)
timeseries.multi(timecourses,
designs,
stat_maps,
events_dfs,
subplot_titles,
figure="timecourses")
plt.show()
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 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 roi_per_session(l1_dir, roi, color,
roi_mask_normalize="",
):
from samri.plotting import summary
substitutions = bids_substitution_iterator(
["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
# ["5689","5690","5691"],
["4005","5687","4007","4011","4012","5689","5690","5691"],
# ["4007","4011","4012","5689","5690","5691"],
# ["4009","4011","4012","5689","5690","5691"],
# ["4008","4009","4011","4012",],
["EPI_CBV_jb_long","EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"",
l1_dir=l1_dir,
)
if isinstance(roi, list) and not "/" in roi[0]:
roi = roi_from_atlaslabel("~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=roi,
)
fit, anova = summary.roi_per_session(substitutions,
t_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_trial-{scan}_tstat.nii.gz",
legend_loc=2,
# figure="per-voxel",
figure="per-participant",
roi_mask=roi,
roi_mask_normalize=roi_mask_normalize,
color=color,
)
print(anova)
def temporal_qc_separate():
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import numpy as np
import pandas as pd
from samri.report.snr import base_metrics
from samri.plotting.timeseries import multi
from samri.utilities import bids_substitution_iterator
substitutions = bids_substitution_iterator(
['testSTIM'],
['COILphantom'],
['CcsI'],
'/home/chymera/ni_data/phantoms/',
'bids',
acquisitions=['EPIalladj','EPIcopyadjNODUM','EPIcopyadj','EPImoveGOP'],
)
for i in substitutions:
timecourses = base_metrics('{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}.nii', i)
events_df = pd.read_csv('{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_events.tsv'.format(**i), sep='\t')
multi(timecourses,
designs=[],
events_dfs=[events_df],
subplot_titles='acquisition',
quantitative=False,
save_as='temp_{acquisition}.pdf'.format(**i),
samri_style=True,
ax_size=[16,6],
unit_ticking=True,
)
def plot_roi_per_session(l1_dir, roi_mask, color,
roi_mask_normalize="",
):
from samri.plotting import summary
from samri.report import roi
substitutions = bids_substitution_iterator(
["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
# ["5689","5690","5700"],
["6262","6255","5694","5706",'5704'],
# ["4007","4011","4012","5689","5690","5691"],
# ["4009","4011","4012","5689","5690","5691"],
# ["4008","4009","4011","4012",],
["EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"",
l1_dir=l1_dir,
)
if isinstance(roi, list) and not "/" in roi[0]:
roi = roi_from_atlaslabel("~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=roi,
)
fit, anova, subjectdf, voxeldf = roi.roi_per_session(substitutions,
t_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_trial-{trial}_tstat.nii.gz",
roi_mask=roi_mask,
roi_mask_normalize=roi_mask_normalize,
)
summary.plot_roi_per_session(subjectdf, voxeldf, legend_loc=2, figure="per-participant", color=color, save_as="~/test.png")
print(anova)
def dr_seed_fc():
import numpy as np
from os import path
from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
db_path = '~/syncdata/meta.db'
groups = treatment_group(db_path, ['cFluDW','cFluDW_'], 'cage')
groups = append_external_identifiers(db_path, groups, ['Genotype_code'])
all_subjects = groups['ETH/AIC'].unique()
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
all_subjects,
["CogB",],
"~/ni_data/ofM.dr/",
"composite",
acquisitions=["EPI",],
check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
fc_results = fc.seed_based(substitutions, "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", "/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results="~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
)
def temporal_qc_separate():
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import numpy as np
import pandas as pd
from samri.report.snr import base_metrics
from samri.plotting.timeseries import multi
from samri.utilities import bids_substitution_iterator
substitutions = bids_substitution_iterator(
['testSTIM'],
['COILphantom'],
['CcsI'],
'/home/chymera/ni_data/phantoms/',
'bids',
acquisitions=['EPIalladj','EPIcopyadjNODUM','EPIcopyadj','EPImoveGOP'],
)
for i in substitutions:
timecourses = base_metrics('{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}.nii', i)
events_df = pd.read_csv('{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_events.tsv'.format(**i), sep='\t')
multi(timecourses,
designs=[],
events_dfs=[events_df],
subplot_titles='acquisition',
quantitative=False,
save_as='temp_{acquisition}.pdf'.format(**i),
samri_style=True,
ax_size=[16,6],
unit_ticking=True,
)
def temporal_qc_al_in_one():
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import numpy as np
from samri.report.snr import iter_base_metrics
from samri.plotting.timeseries import multi
from samri.utilities import bids_substitution_iterator
substitutions = bids_substitution_iterator(
['testSTIM'],
['COILphantom'],
['CcsI'],
'/home/chymera/ni_data/phantoms/',
'bids',
acquisitions=['EPIalladj','EPIcopyadjNODUM','EPIcopyadj','EPImoveGOP'],
)
timecourses = iter_base_metrics('{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}.nii', substitutions)
multi(timecourses,
designs=[],
events_dfs=[],
subplot_titles='acquisition',
quantitative=False,
save_as="temp_qc.pdf",
samri_style=True,
ax_size=[16,6],
unit_ticking=True,
)
def temporal_qc_al_in_one():
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import numpy as np
from samri.report.snr import iter_base_metrics
from samri.plotting.timeseries import multi
from samri.utilities import bids_substitution_iterator
substitutions = bids_substitution_iterator(
['testSTIM'],
['COILphantom'],
['CcsI'],
'/home/chymera/ni_data/phantoms/',
'bids',
acquisitions=[
'EPIalladj', 'EPIcopyadjNODUM', 'EPIcopyadj', 'EPImoveGOP'
],
)
timecourses = iter_base_metrics(
'{data_dir}/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}.nii',
substitutions)
multi(
timecourses,
designs=[],
events_dfs=[],
subplot_titles='acquisition',
quantitative=False,
save_as="temp_qc.pdf",
samri_style=True,
ax_size=[16, 6],
unit_ticking=True,
)
def dr_seed_fc():
import numpy as np
from os import path
from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
db_path = '~/syncdata/meta.db'
groups = treatment_group(db_path, ['cFluDW','cFluDW_'], 'cage')
groups = append_external_identifiers(db_path, groups, ['Genotype_code'])
all_subjects = groups['ETH/AIC'].unique()
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
all_subjects,
["CogB",],
"~/ni_data/ofM.dr/",
"composite",
acquisitions=["EPI",],
check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
fc_results = fc.seed_based(substitutions, "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
ts_file_template='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results="~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
)
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 ctx_pattern_mask_drs_sfc():
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'drs_seed'
my_roi = '~/ni_data/ofM.dr/l2/anova_ctx/anova_fstat.nii.gz'
my_roi = roi.from_img_threshold(my_roi, 2., save_as='~/ni_data/ofM.dr/fc/{}/ctx_pattern_mask.nii.gz'.format(workflow_name))
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700"],
["CogB",],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=["EPI",],
check_file_format='~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
subjectdf, voxeldf = roi.per_session(substitutions,
filename_template='~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
roi_mask=my_roi,
)
subjectdf['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704'],
["CogB",],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=["EPI",],
check_file_format='~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
)
subjectdf_, voxeldf_ = roi.per_session(substitutions_,
filename_template='~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz',
roi_mask=my_roi,
)
subjectdf_['treatment']='Vehicle'
subjectdf=pd.concat([subjectdf_,subjectdf])
subjectdf=subjectdf.rename(columns={'session': 'Session','t':'z'})
subjectdf.to_csv('~/ni_data/ofM.dr/fc/{}/ctx_pattern_mask_summary.csv'.format(workflow_name))
def pattern_activity():
import pandas as pd
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'generic'
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
["CogB",],
"~/ni_data/ofM.dr/bids",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df = roi.analytic_pattern_per_session(substitutions,
'~/ni_data/ofM.dr/bids/l2/best_responders/sessionofM/tstat1.nii.gz',
t_file_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704','6455','6459'],
["CogB",],
"~/ni_data/ofM.dr/bids",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df_ = roi.analytic_pattern_per_session(substitutions_,
'~/ni_data/ofM.dr/bids/l2/best_responders/sessionofM/tstat1.nii.gz',
t_file_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
df_['treatment']='Vehicle'
df=pd.concat([df_,df])
df=df.rename(columns={'session': 'Session',})
df.to_csv('~/ni_data/ofM.dr/bids/l1/{}/pattern_summary.csv'.format(workflow_name))
def roi(roi_path="~/ni_data/templates/roi/f_dr_chr.nii.gz"): from samri.plotting import summary, timeseries substitutions = bids_substitution_iterator( ["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"], [4007,4008,4009,4011,4012], ["EPI_CBV_jb_long"], "~/ni_data/ofM.dr/", "composite", ) timecourses, designs, stat_maps, subplot_titles = summary.roi_ts(substitutions, roi_path=roi_path,) timeseries.multi(timecourses, designs, stat_maps, subplot_titles, figure="timecourses") plt.show()
def roi_teaching(roi_path="~/ni_data/templates/roi/f_dr_chr.nii.gz"):
from samri.plotting import timeseries
design_file_template="~/ni_data/ofM.dr/l1/{l1_workdir}/_subject_session_scan_{subject}.{session}.{scan}/modelgen/run0.mat"
substitutions = bids_substitution_iterator(
["ofM_cF2"],
[4008],
["EPI_CBV_jb_long"],
"~/ni_data/ofM.dr/"
"composite",
)
timeseries.roi_based(substitutions[0], design_file_template=design_file_template, flip=True, plot_design_regressors=[0])
plt.show()
def test_multi_roi_timeseries():
import matplotlib.pyplot as plt
from os import path
from samri.plotting import summary, timeseries
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
substitutions = bids_substitution_iterator(
sessions=[
'ofM',
'ofMaF',
],
subjects=[
'4007',
],
runs=[
'0',
'1',
],
modalities=[
'bold',
'cbv',
],
data_dir='/usr/share/samri_bidsdata',
# BOLD scans are not recognized, since the current (=sci-biology/samri_bidsdata-0.2) filt file suffix also contains `_maths_` for CBV, but not for BOLD.
validate_for_template="{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}.nii.gz",
)
mapping='/usr/share/mouse-brain-atlases/dsurqe_labels.csv'
atlas='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii'
my_roi = roi_from_atlaslabel(atlas,
mapping=mapping,
label_names=['cortex'],
)
timecourses, designs, _, events_dfs, subplot_titles = summary.ts_overviews(substitutions, my_roi,
ts_file_template="{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}.nii.gz",
betas_file_template="{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}_betas.nii.gz",
design_file_template="{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}_design.mat",
event_file_template='{data_dir}/preprocessing/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_events.tsv',
n_jobs_percentage=0.5,
)
style_file = path.join(path.dirname(path.realpath(__file__)),'../samri_multiple-ts.conf')
plt.style.use(style_file)
timeseries.multi(timecourses, designs, events_dfs, subplot_titles,
quantitative=False,
save_as='_multi_roi_timeseries.pdf',
)
def seed_connectivity_overview(
template="~/ni_data/templates/DSURQEc_40micron_masked.nii.gz",
):
substitutions = bids_substitution_iterator(
["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
# ["5689","5690","5691"],
["4005","5687","4007","4011","4012","5689","5690","5691"],
# ["4007","4011","4012","5689","5690","5691"],
# ["4009","4011","4012","5689","5690","5691"],
# ["4008","4009","4011","4012",],
["EPI_CBV_jb_long","EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"as_composite",
)
subjectdf, voxeldf = aggregate.seed_fc_rois(substitutions, "~/ni_data/templates/roi/DSURQEc_dr.nii.gz", "~/ni_data/templates/roi/DSURQEc_ctx.nii.gz",
ts_file_template="~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_trial-{scan}.nii.gz",
)
def test_contours_substitutions():
import itertools
import matplotlib.pyplot as plt
from samri.plotting.maps import contour_slices
from samri.utilities import bids_substitution_iterator
subjects = [
'4007',
]
contrasts = [
#'bold',
'cbv',
]
for i in list(itertools.product(subjects, contrasts)):
file_template = '{{data_dir}}/preprocessing/sub-{{subject}}/ses-{{session}}/func/sub-{{subject}}_ses-{{session}}_task-JogB_acq-EPIlowcov_run-{{run}}_{}.nii.gz'.format(
i[1])
substitutions = bids_substitution_iterator(
sessions=[
'ofM',
'ofMaF',
'ofMcF1',
'ofMcF2',
],
subjects=[i[0]],
runs=[0, 1],
data_dir='/usr/share/samri_bidsdata',
validate_for_template=file_template,
)
cmap = plt.get_cmap('tab20').colors
contour_slices(
'/usr/share/mouse-brain-atlases/dsurqec_40micron.nii',
alpha=[0.6],
colors=cmap[::2],
figure_title='Multi-Session Fit and Coherence Control',
file_template=file_template,
force_reverse_slice_order=True,
legend_template='{session} session',
levels_percentile=[77],
save_as='_contours_multi_{}_{}.pdf'.format(i[0], i[1]),
slice_spacing=0.45,
substitutions=substitutions,
)
def test_contours_substitutions():
import itertools
import matplotlib.pyplot as plt
from samri.plotting.maps import contour_slices
from samri.utilities import bids_substitution_iterator
subjects = [
'4007',
]
contrasts= [
#'bold',
'cbv',
]
for i in list(itertools.product(subjects, contrasts)):
file_template='{{data_dir}}/preprocessing/sub-{{subject}}/ses-{{session}}/func/sub-{{subject}}_ses-{{session}}_task-JogB_acq-EPIlowcov_run-{{run}}_{}.nii.gz'.format(i[1])
substitutions = bids_substitution_iterator(
sessions=[
'ofM',
'ofMaF',
'ofMcF1',
'ofMcF2',
],
subjects=[i[0]],
runs=[0,1],
data_dir='/usr/share/samri_bidsdata',
validate_for_template=file_template,
)
cmap = plt.get_cmap('tab20').colors
contour_slices('/usr/share/mouse-brain-atlases/dsurqec_40micron.nii',
alpha=[0.6],
colors=cmap[::2],
figure_title='Multi-Session Fit and Coherence Control',
file_template=file_template,
force_reverse_slice_order=True,
legend_template='{session} session',
levels_percentile=[77],
save_as='_contours_multi_{}_{}.pdf'.format(i[0],i[1]),
slice_spacing=0.45,
substitutions=substitutions,
)
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 qc_regressor(sessions, subjects, scans, workflow_name, mask,
data_dir="~/ni_data/ofM.dr",
save_as="",
):
from samri.plotting import summary, timeseries
plt.style.use('samri_multiple-ts.conf')
substitutions = bids_substitution_iterator(sessions,subjects,scans,data_dir,workflow_name)
timecourses, designs, stat_maps, events_dfs, subplot_titles = summary.ts_overviews(substitutions, mask,
ts_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}.nii.gz",
beta_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_cope.nii.gz",
design_file_template="{data_dir}/l1/{l1_workdir}/_subject_session_scan_{subject}.{session}.{scan}/modelgen/run0.mat",
event_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}_events.tsv",
)
timeseries.multi(timecourses, designs, stat_maps, events_dfs, subplot_titles,
figure="timecourses",
quantitative=False,
save_as=save_as,
)
def qc_regressor(sessions, subjects, scans, workflow_name, mask,
data_dir="~/ni_data/ofM.dr",
save_as="",
):
from samri.plotting import summary, timeseries
plt.style.use('samri_multiple-ts.conf')
substitutions = bids_substitution_iterator(sessions,subjects,scans,data_dir,workflow_name)
timecourses, designs, stat_maps, events_dfs, subplot_titles = summary.ts_overviews(substitutions, mask,
ts_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_trial-{scan}.nii.gz",
beta_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_trial-{scan}_cope.nii.gz",
design_file_template="{data_dir}/l1/{l1_workdir}/_subject_session_scan_{subject}.{session}.{scan}/modelgen/run0.mat",
event_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_trial-{scan}_events.tsv",
)
timeseries.multi(timecourses, designs, stat_maps, events_dfs, subplot_titles,
figure="timecourses",
quantitative=False,
save_as=save_as,
)
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 p_clusters(mask):
from samri.plotting import summary, timeseries
substitutions = bids_substitution_iterator(
["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
["4011","4012","5689","5690","5691"],
# ["4007","4008","4011","4012","5689","5690","5691"],
["EPI_CBV_jb_long","EPI_CBV_chr_longSOA"],
"~/ni_data/ofM.dr/",
"composite",
l1_dir="dr",
)
timecourses, designs, stat_maps, events_dfs, subplot_titles = summary.p_filtered_ts(substitutions,
ts_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}.nii.gz",
beta_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_cope.nii.gz",
# p_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_pstat.nii.gz",
p_file_template="{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{scan}_pfstat.nii.gz",
design_file_template="{data_dir}/l1/{l1_workdir}/_subject_session_scan_{subject}.{session}.{scan}/modelgen/run0.mat",
event_file_template="{data_dir}/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{scan}_events.tsv",
brain_mask=mask,
p_level=0.05,
)
timeseries.multi(timecourses, designs, stat_maps, events_dfs, subplot_titles, figure="timecourses")
plt.show()
def drs_activity(
roi,
atlas='/usr/share/mouse-brain-templates/dsurqec_40micron_labels.nii',
mapping='/usr/share/mouse-brain-templates/dsurqe_labels.csv',
):
"""
TODO : make more generalizable, too many hardcoded values
TODO : Make more elegant, atlas/split could be (partly) relegated to masking function instead of being done here.
Create a DataFrame containing the per-session per-subject mean values for an autogenerated ROI based on the given label.
Other parameter customizations are hard-coded below.
"""
import pandas as pd
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
from os.path import basename, splitext
if mapping and atlas:
mapping = path.abspath(path.expanduser(mapping))
atlas = path.abspath(path.expanduser(atlas))
my_roi = roi_from_atlaslabel(
atlas,
mapping=mapping,
label_names=[roi],
)
roi_name = roi
else:
roi = path.abspath(path.expanduser(roi))
roi_name = splitext(basename(roi))[0]
if roi_name[-4:] == '.nii':
roi_name = roi_name[:-4]
source_workflow = 'generic'
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
[
'5691', "5689", "5690", "5700", "6451", "6460", "6456", "6461",
"6462"
],
[
"CogB",
],
"~/ni_data/ofM.dr",
source_workflow,
acquisitions=[
"EPI",
],
validate_for_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
if mapping and atlas:
df = roi.per_session(
substitutions,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[roi],
atlas=atlas,
mapping=mapping,
)
else:
df = roi.per_session(
substitutions,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi,
)
df['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["6262", "6255", "5694", "5706", '5704', '6455', '6459'],
[
"CogB",
],
"~/ni_data/ofM.dr",
source_workflow,
acquisitions=[
"EPI",
],
validate_for_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
if mapping and atlas:
df_ = roi.per_session(
substitutions_,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[roi],
atlas=atlas,
mapping=mapping,
)
else:
df_ = roi.per_session(
substitutions_,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi,
)
df_['treatment'] = 'Vehicle'
df = pd.concat([df_, df])
df = df.rename(columns={
'session': 'Session',
})
df.to_csv('~/ni_data/ofM.dr/l1/{}/{}.csv'.format(source_workflow,
roi_name))
def seed_connectivity_overview(
template='/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii',
cut_coords=[None, [0, -4.9, -3.3]],
plot=False,
):
import numpy as np
from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
db_path = '~/syncdata/meta.db'
groups = treatment_group(db_path, ['cFluDW', 'cFluDW_'], 'cage')
groups = append_external_identifiers(db_path, groups, ['Genotype_code'])
all_subjects = groups['ETH/AIC'].unique()
treatment = groups[(groups['Genotype_code'] == "eptg")
& (groups['Cage_TreatmentProtocol_code'] == "cFluDW"
)]['ETH/AIC'].tolist()
no_treatment = groups[(groups['Genotype_code'] == "eptg")
& (groups['Cage_TreatmentProtocol_code'] == "cFluDW_"
)]['ETH/AIC'].tolist()
negative_controls = groups[groups['Genotype_code'] ==
"epwt"]['ETH/AIC'].tolist()
print(treatment, no_treatment, negative_controls)
substitutions = bids_substitution_iterator(
["ofM", "ofM_aF", "ofM_cF1", "ofM_cF2", "ofM_pF"],
all_subjects,
[
"EPI_CBV_chr_longSOA",
],
"~/ni_data/ofM.dr/",
"composite",
)
fc_results = fc.seed_based(
substitutions,
"~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz",
'/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
ts_file_template=
"~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{task}.nii.gz",
)
print([i['subject'] for i in fc_results])
return
if plot:
multipage_plot(
fc_results,
treatment,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_treatment.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
multipage_plot(
fc_results,
no_treatment,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_no_treatment.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
multipage_plot(
fc_results,
negative_controls,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_negative_control.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
def roi_activity(roi_mask="~/ni_data/templates/roi/DSURQEc_drp.nii.gz"):
"""
Create a DataFrame containing the per-session per-subject mean values for the specified ROI.
Other parameter customizations are hard-coded below.
"""
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
from os.path import basename, splitext
workflow_name = 'generic'
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
#["5689","5690","6456","6461"],
['5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
#['5691',"5689","5690","5700",],
["CogB",],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df = roi.per_session(substitutions,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
#["5694","5706",'5704','6455','6459','5794'],
["6262","6255","5694","5706",'5704','6455','6459'],
#["6262","6255","5694","5706",'5704',],
["CogB",],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df_ = roi.per_session(substitutions_,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df_['treatment'] = 'Vehicle'
substitutions__ = bids_substitution_iterator(
["ofM"],
#["5694","5706",'5704','6455','6459','5794'],
["6530","6532","6542","6548",'6549','6552','6553','6556','6557'],
#["6262","6255","56","5706",'5704',],
["CogB",],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df__ = roi.per_session(substitutions__,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df__['treatment'] = 'New'
df = pd.concat([df__,df_,df])
df = df.rename(columns={'session': 'Session',})
roi_name = splitext(basename(roi_mask))[0]
if roi_name[-4:] == '.nii':
roi_name = roi_name[:-4]
df.to_csv('~/ni_data/ofM.dr/bids/l1/{}/{}.csv'.format(workflow_name, roi_name))
def seed_connectivity_overview(
template="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii",
cut_coords=[None,[0,-4.9,-3.3]],
plot=False,
):
import numpy as np
from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
db_path = '~/syncdata/meta.db'
groups = treatment_group(db_path, ['cFluDW','cFluDW_'], 'cage')
groups = append_external_identifiers(db_path, groups, ['Genotype_code'])
all_subjects = groups['ETH/AIC'].unique()
treatment = groups[
(groups['Genotype_code']=="eptg")&
(groups['Cage_TreatmentProtocol_code']=="cFluDW")
]['ETH/AIC'].tolist()
no_treatment = groups[
(groups['Genotype_code']=="eptg")&
(groups['Cage_TreatmentProtocol_code']=="cFluDW_")
]['ETH/AIC'].tolist()
negative_controls = groups[groups['Genotype_code']=="epwt"]['ETH/AIC'].tolist()
print(treatment, no_treatment, negative_controls)
substitutions = bids_substitution_iterator(
["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
all_subjects,
["EPI_CBV_chr_longSOA",],
"~/ni_data/ofM.dr/",
"composite",
)
fc_results = fc.seed_based(substitutions, "~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz", "/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template="~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-{task}.nii.gz",
)
print([i['subject'] for i in fc_results])
return
if plot:
multipage_plot(fc_results, treatment,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_treatment.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
multipage_plot(fc_results, no_treatment,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_no_treatment.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
multipage_plot(fc_results, negative_controls,
figure_title="Chronic Fluoxetine (drinking water) Treatment Group",
template=template,
threshold=0.1,
base_cut_coords=cut_coords,
save_as="fc_negative_control.pdf",
overlays=['~/ni_data/templates/roi/DSURQEc_dr_xs.nii.gz'],
scale=0.4,
)
struct_registration.inputs.output_warped_image = os.path.join(
workdir, '{subject}_{session}.nii.gz'.format(**substitution))
struct_registration.inputs.moving_image = n4_out
print("Running registration:\n{}".format(
struct_registration.cmdline))
struct_registration_run = struct_registration.run()
if __name__ == '__main__':
# substitutions = bids_substitution_iterator(
# ["ofM","ofM_aF","ofM_cF1","ofM_cF2","ofM_pF"],
# ["4011","4012","5689","5690","5691"],
# ["EPI_CBV_jb_long","EPI_CBV_chr_longSOA"],
# "composite")
substitutions = bids_substitution_iterator(
["ofM", "ofM_aF", "ofM_cF1", "ofM_cF2", "ofM_pF"], ["5690"],
["TurboRARE"], "~/ni_data/ofM.dr", "composite")
structural(
substitutions,
[PHASES["rigid"], PHASES["affine"], PHASES["syn"]],
structural_file_template=
"~/ni_data/ofM.dr/preprocessing/{preprocessing_workdir}/_subject_session_{subject}.{session}/_scan_type_{scan}/s_bru2nii/",
)
# structural_to_functional_per_participant_test(
# subjects_participants = [{'subjfect' : 11, 'session': 'rstFMRI_with_medetadomine'}],
# template = "~/GitHub/mriPipeline/templates/waxholm/WHS_SD_rat_T2star_v1.01_downsample3.nii.gz",
# f_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_SE_EPI/f_bru2nii/",
# s_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_T2_TurboRARE/s_bru2nii/",
# )
def drs_activity(roi,
atlas='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii',
mapping='/usr/share/mouse-brain-atlases/dsurqe_labels.csv',
):
"""
TODO : make more generalizable, too many hardcoded values
TODO : Make more elegant, atlas/split could be (partly) relegated to masking function instead of being done here.
Create a DataFrame containing the per-session per-subject mean values for an autogenerated ROI based on the given label.
Other parameter customizations are hard-coded below.
"""
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
from os.path import basename, splitext
if mapping and atlas:
mapping = path.abspath(path.expanduser(mapping))
atlas = path.abspath(path.expanduser(atlas))
my_roi = roi_from_atlaslabel(atlas,
mapping=mapping,
label_names=[roi],
)
roi_name = roi
else:
roi = path.abspath(path.expanduser(roi))
roi_name = splitext(basename(roi))[0]
if roi_name[-4:] == '.nii':
roi_name = roi_name[:-4]
source_workflow = 'generic'
substitutions = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
['5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
["CogB",],
"~/ni_data/ofM.dr",
source_workflow,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
if mapping and atlas:
df = roi.per_session(substitutions,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[roi],
atlas=atlas,
mapping=mapping,
)
else:
df = roi.per_session(substitutions,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi,
)
df['treatment']='Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM","ofMaF","ofMcF1","ofMcF2","ofMpF"],
["6262","6255","5694","5706",'5704','6455','6459'],
["CogB",],
"~/ni_data/ofM.dr",
source_workflow,
acquisitions=["EPI",],
validate_for_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
)
if mapping and atlas:
df_ = roi.per_session(substitutions_,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_tstat.nii.gz',
roi_mask=my_roi,
feature=[roi],
atlas=atlas,
mapping=mapping,
)
else:
df_ = roi.per_session(substitutions_,
filename_template='{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi,
)
df_['treatment']='Vehicle'
df=pd.concat([df_,df])
df=df.rename(columns={'session': 'Session',})
df.to_csv('~/ni_data/ofM.dr/l1/{}/{}.csv'.format(source_workflow, roi_name))
def test_multi_roi_timeseries():
import matplotlib.pyplot as plt
from os import path
from samri.plotting import summary, timeseries
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
substitutions = bids_substitution_iterator(
sessions=[
'ofM',
'ofMaF',
],
subjects=[
'4007',
],
runs=[
'0',
'1',
],
modalities=[
'bold',
'cbv',
],
data_dir='/usr/share/samri_bidsdata',
# BOLD scans are not recognized, since the current (=sci-biology/samri_bidsdata-0.2) filt file suffix also contains `_maths_` for CBV, but not for BOLD.
validate_for_template=
"{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}.nii.gz",
)
mapping = '/usr/share/mouse-brain-atlases/dsurqe_labels.csv'
atlas = '/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii'
my_roi = roi_from_atlaslabel(
atlas,
mapping=mapping,
label_names=['cortex'],
)
timecourses, designs, _, events_dfs, subplot_titles = summary.ts_overviews(
substitutions,
my_roi,
ts_file_template=
"{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}.nii.gz",
betas_file_template=
"{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}_betas.nii.gz",
design_file_template=
"{data_dir}/l1/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_{modality}_design.mat",
event_file_template=
'{data_dir}/preprocessing/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_task-JogB_acq-EPIlowcov_run-{run}_events.tsv',
n_jobs_percentage=0.5,
)
style_file = path.join(path.dirname(path.realpath(__file__)),
'../samri_multiple-ts.conf')
plt.style.use(style_file)
timeseries.multi(
timecourses,
designs,
events_dfs,
subplot_titles,
quantitative=False,
save_as='_multi_roi_timeseries.pdf',
)
def roi_activity(roi_mask="~/ni_data/templates/roi/DSURQEc_drp.nii.gz"):
"""
Create a DataFrame containing the per-session per-subject mean values for the specified ROI.
Other parameter customizations are hard-coded below.
"""
import pandas as pd
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
from os.path import basename, splitext
workflow_name = 'generic'
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
#["5689","5690","6456","6461"],
[
'5691', "5689", "5690", "5700", "6451", "6460", "6456", "6461",
"6462"
],
#['5691',"5689","5690","5700",],
[
"CogB",
],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=[
"EPI",
],
validate_for_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df = roi.per_session(
substitutions,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
#["5694","5706",'5704','6455','6459','5794'],
["6262", "6255", "5694", "5706", '5704', '6455', '6459'],
#["6262","6255","5694","5706",'5704',],
[
"CogB",
],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=[
"EPI",
],
validate_for_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df_ = roi.per_session(
substitutions_,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df_['treatment'] = 'Vehicle'
substitutions__ = bids_substitution_iterator(
["ofM"],
#["5694","5706",'5704','6455','6459','5794'],
[
"6530", "6532", "6542", "6548", '6549', '6552', '6553', '6556',
'6557'
],
#["6262","6255","56","5706",'5704',],
[
"CogB",
],
"~/ni_data/ofM.dr/bids/",
workflow_name,
acquisitions=[
"EPI",
],
validate_for_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
)
df__ = roi.per_session(
substitutions__,
filename_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_cbv_tstat.nii.gz',
roi_mask=roi_mask,
)
df__['treatment'] = 'New'
df = pd.concat([df__, df_, df])
df = df.rename(columns={
'session': 'Session',
})
roi_name = splitext(basename(roi_mask))[0]
if roi_name[-4:] == '.nii':
roi_name = roi_name[:-4]
df.to_csv('~/ni_data/ofM.dr/bids/l1/{}/{}.csv'.format(
workflow_name, roi_name))
def ctx_drs_connectivity():
import pandas as pd
from behaviopy.plotting import qualitative_times
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'drs_seed'
my_roi = roi_from_atlaslabel(
"~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=["cortex"],
)
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
['5691', "5689", "5690", "5700"],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_zstat.nii.gz',
)
fit, anova, subjectdf, voxeldf = roi.per_session(
substitutions,
filename_template=
'~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_zstat.nii.gz',
roi_mask=my_roi,
)
subjectdf['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["6262", "6255", "5694", "5706", '5704'],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_zstat.nii.gz',
)
fit_, anova_, subjectdf_, voxeldf_ = roi.per_session(
substitutions_,
filename_template=
'~/ni_data/ofM.dr/fc/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_zstat.nii.gz',
roi_mask=my_roi,
)
subjectdf_['treatment'] = 'Vehicle'
subjectdf = pd.concat([subjectdf_, subjectdf])
subjectdf = subjectdf.rename(columns={'session': 'Session', 't': 'z'})
subjectdf.to_csv(
'~/ni_data/ofM.dr/fc/{}/ctx_summary.csv'.format(workflow_name))
def pattern_activity():
import pandas as pd
from behaviopy.plotting import qualitative_times
from samri.plotting import summary
from samri.report import roi
from samri.utilities import bids_substitution_iterator
from samri.fetch.local import roi_from_atlaslabel
workflow_name = 'composite'
substitutions = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
['5691', "5689", "5690", "5700"],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
)
subjectdf = summary.analytic_pattern_per_session(
substitutions,
'~/ni_data/ofM.dr/l2/best_responders/sessionofM/tstat1.nii.gz',
t_file_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
legend_loc=2,
figure="per-participant",
color="#309079",
xy_label=["Session", "Pattern Score"],
)
subjectdf['treatment'] = 'Fluoxetine'
substitutions_ = bids_substitution_iterator(
["ofM", "ofMaF", "ofMcF1", "ofMcF2", "ofMpF"],
["6262", "6255", "5694", "5706", '5704'],
[
"CogB",
],
"~/ni_data/ofM.dr/",
workflow_name,
acquisitions=[
"EPI",
],
check_file_format=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
)
subjectdf_ = summary.analytic_pattern_per_session(
substitutions_,
'~/ni_data/ofM.dr/l2/best_responders/sessionofM/tstat1.nii.gz',
t_file_template=
'{data_dir}/l1/{l1_dir}/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_trial-{trial}_cbv_tstat.nii.gz',
legend_loc=2,
figure="per-participant",
color="#309079",
xy_label=["Session", "Pattern Score"],
)
subjectdf_['treatment'] = 'Vehicle'
subjectdf = pd.concat([subjectdf_, subjectdf])
subjectdf = subjectdf.rename(columns={
'session': 'Session',
})
subjectdf.to_csv(
'~/ni_data/ofM.dr/l1/{}/pattern_summary.csv'.format(workflow_name))
