C-PAC v.1.8
2020 November 18 ‒ ???
Changelog:
- Removed support for median angle correction
C-PAC is the main product of the Computational Neuroimaging Lab.
Changelog:
Run
rest_test_CPACv1.6.2_nuis1-3a-lower-thresh.yml
without error in a Singularity image on Habanero with full dataset.
Planned release: Friday, 2020 July 10 alongside C-PAC v1.7
installation method | includes | size |
---|---|---|
Neurodocker | ANTs 2.3.4 (2.3.3.dev168-g29bdf) FreeSurfer 6.0.0-min (v6.0.0-2beb96c) |
2.97GB |
Neurodocker | ANTs 2.3.4 (2.3.3.dev168-g29bdf) | 2.02GB |
Neurodocker | FreeSurfer 6.0.0-min (v6.0.0-2beb96c) | 1.05GB |
from source | ANTS 2.3.4 (2.3.4.dev170-g11953) | 7.61GB |
installation method | includes | size |
---|---|---|
Neurodocker | ANTs 2.3.4 (2.3.3.dev168-g29bdf) FreeSurfer 6.0.0-min (v6.0.0-2beb96c) |
2.97GB |
Neurodocker | ANTs 2.3.4 (2.3.3.dev168-g29bdf) | 2.02GB |
Neurodocker | FreeSurfer 6.0.0-min (v6.0.0-2beb96c) | 1.05GB |
from source | ANTS 2.3.4 (2.3.4.dev170-g11953) | 7.61GB |
our copy of niworkflows v0.10.2 | niworkflows v1.3.2 | ||
---|---|---|---|
ANTs v2.1.0 | Docker | cpac-docker-image.tar.gz | cpac-docker-image.tar.gz |
Singularity | cpac-singularity-image.simg | cpac-singularity-image.simg | |
ANTs v2.3.4 | Docker | cpac-docker-image.tar.gz | cpac-docker-image.tar.gz |
Singularity | cpac-singularity-image.simg | cpac-singularity-image.simg |
Difference between niworkflows and C-PAC niworkflows
I don’t see anything obviously hanging.
One level higher workflow just adds an input and output node:
Original niworkflows-ants was refactored in September…
Extra parameters for C-PAC niworkflows-ants:
tpl_target_path
tpl_target_path, common_spec = get_template_specs(
in_template, template_spec=template_spec
)
tpl_mask_path
# Get probabilistic brain mask if available
tpl_mask_path = get_template(
in_template, label="brain", suffix="probseg", **common_spec
) or get_template(in_template, desc="brain", suffix="mask", **common_spec)
tpl_regmask_path
# Try to find a registration mask, set if available
tpl_regmask_path = get_template(
in_template, desc="BrainCerebellumExtraction", suffix="mask", **common_spec
)
# Inserting Median Angle Correction Workflow
new_strat_list = []
if '3-median-angle-correction' in c.nuisance_corrections:
# this is just a shorter variable for a config variable that is
# called multiple times
median_angle_correction = c.nuisance_corrections[
'3-median-angle-correction'
]
if True in median_angle_correction['run']:
for num_strat, strat in enumerate(strat_list):
# for each strategy, create a new one without median angle
if False in median_angle_correction['run']:
new_strat_list.append(strat.fork())
median_angle_corr = create_median_angle_correction(
'median_angle_corr_%d' % num_strat
)
median_angle_corr.get_node(
'median_angle_correct'
).iterables = (
'target_angle_deg',
median_angle_correction['target_angle_deg'])
node, out_file = strat.get_leaf_properties()
workflow.connect(node, out_file,
median_angle_corr, 'inputspec.subject')
strat.append_name(median_angle_corr.name)
strat.set_leaf_properties(median_angle_corr,
'outputspec.subject')
strat.update_resource_pool({
'functional_median_angle_corrected': (
median_angle_corr,
'outputspec.subject')
})
# We don't need this shorthand variable anymore after this loop
del median_angle_correction
strat_list += new_strat_list
Planned release: Friday, 2020 July 10
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