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 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 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 atlas_label(
atlas,
alpha=0.7,
anat="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii",
ax=None,
color="#E69F00",
fig=None,
label_names=[],
mapping="",
annotate=True,
black_bg=False,
draw_cross=True,
threshold=None,
roi=False,
subplot_titles=[],
scale=1.,
dim=0,
**kwargs):
"""Plot a region of interest based on an atlas and a label."""
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
anat = path.abspath(path.expanduser(anat))
if mapping and label_names:
roi = roi_from_atlaslabel(atlas,
mapping=mapping,
label_names=label_names,
**kwargs)
elif isinstance(atlas, str):
atlas = path.abspath(path.expanduser(atlas))
roi = nib.load(atlas)
else:
roi = atlas
cm = ListedColormap([color], name="my_atlas_label_cmap", N=None)
display = nilearn.plotting.plot_roi(
roi,
alpha=alpha,
annotate=False,
axes=ax,
bg_img=anat,
black_bg=black_bg,
draw_cross=False,
cmap=cm,
dim=dim,
figure=fig,
)
if draw_cross:
display.draw_cross(linewidth=scale * 1.6, alpha=0.4)
if annotate:
display.annotate(size=2 + scale * 18)
if subplot_titles:
display.title(title, size=2 + scale * 26)
return display
def plot_roi_by_label(label_names,
save_as="",
):
roi = roi_from_atlaslabel("~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=label_names,
save_as=save_as
)
maps.atlas_label(roi)
plt.show()
def atlas_label(atlas, alpha=0.7, anat="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii", ax=None, color="#E69F00", fig=None, label_names=[], mapping="", annotate=True, black_bg=False, draw_cross=True, threshold=None, roi=False, subplot_titles=[], scale=1., dim=0, **kwargs ): """Plot a region of interest based on an atlas and a label.""" from matplotlib.colors import LinearSegmentedColormap, ListedColormap anat = path.abspath(path.expanduser(anat)) if mapping and label_names: roi = roi_from_atlaslabel(atlas, mapping=mapping, label_names=label_names, **kwargs) elif isinstance(atlas, str): atlas = path.abspath(path.expanduser(atlas)) roi = nib.load(atlas) else: roi = atlas cm = ListedColormap([color], name="my_atlas_label_cmap", N=None) display = nilearn.plotting.plot_roi(roi, alpha=alpha, annotate=False, axes=ax, bg_img=anat, black_bg=black_bg, draw_cross=False, cmap=cm, dim=dim, figure=fig, ) if draw_cross: display.draw_cross(linewidth=scale*1.6, alpha=0.4) if annotate: display.annotate(size=2+scale*18) if subplot_titles: display.title(title, size=2+scale*26) return display
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 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 test_roi_from_atlaslabel():
from samri.fetch.local import roi_from_atlaslabel
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'],
)
roi_data = my_roi.get_data()
output_labels = np.unique(roi_data).tolist()
assert output_labels == [0, 1]
my_roi = roi_from_atlaslabel(
atlas,
mapping=mapping,
label_names=['cortex'],
output_label=3,
)
roi_data = my_roi.get_data()
output_labels = np.unique(roi_data).tolist()
assert output_labels == [0, 3]
def anova():
from samri.fetch.local import roi_from_atlaslabel
roi = roi_from_atlaslabel(
"~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=["cortex"],
save_as="/tmp/ctx.nii.gz")
glm.l2_anova(
"~/ni_data/ofM.dr/l1/composite/",
workflow_name="anova_ctx",
keep_work=False,
mask="/tmp/ctx.nii.gz",
include={
'session': ['ofM', 'ofMaF', 'ofMcF1', 'ofMcF2', 'ofMpF'],
'subject': ['5691', "5689", "5690", "5700"],
},
)
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 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 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 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 atlas_label(atlas,
alpha=1,
overlay_alpha=1.0,
anat='/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii',
ax=None,
color="#E69F00",
fig=None,
label_names=[],
mapping='/usr/share/mouse-brain-atlases/dsurqe_labels.csv',
annotate=True,
black_bg=False,
draw_cross=True,
threshold=1,
roi=False,
subplot_titles=[],
scale=1.,
dim=0,
anat_cmap='binary',
display_mode='yx',
**kwargs
):
"""Plot a region of interest based on an atlas and a label.
Parameters
----------
display_mode : {‘ortho’, ‘tiled’, ‘x’, ‘y’, ‘z’, ‘yx’, ‘xz’, ‘yz’}
Which slides to display, parameter passed to `nilearn.plotting.plt_roi()`
"""
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
anat = path.abspath(path.expanduser(anat))
if label_names:
roi = roi_from_atlaslabel(atlas, mapping=mapping, label_names=label_names, **kwargs)
elif isinstance(atlas, str):
atlas = path.abspath(path.expanduser(atlas))
roi = nib.load(atlas)
else:
roi = atlas
cm = ListedColormap([color], name="my_atlas_label_cmap", N=None)
cut = nilearn.plotting.plot_stat_map(roi,anat,threshold=None,colorbar=False).cut_coords
plt.close()
# more of these will need to be added
if display_mode == 'yx':
cut = cut[:2]
display = nilearn.plotting.plot_anat(anat,
alpha=alpha,
dim=dim,
axes=ax,
threshold=threshold,
figure=fig,
cmap=anat_cmap,
title=None,
annotate=False,
draw_cross=False,
black_bg=black_bg,
colorbar=False,
display_mode=display_mode,
cut_coords=cut,
)
display.add_overlay(roi,
alpha=overlay_alpha,
colorbar=False,
cmap=cm,
)
if draw_cross:
display.draw_cross(linewidth=scale*1.6, alpha=0.4)
if annotate:
display.annotate(size=2+scale*18)
if subplot_titles:
display.title(title, size=2+scale*26)
return display
