xcp_d.config module

A Python module to maintain unique, run-wide XCP-D settings.

This module implements the memory structures to keep a consistent, singleton config. Settings are passed across processes via filesystem, and a copy of the settings for each run and subject is left under <xcp_d_dir>/sub-<participant_id>/log/<run_unique_id>/xcp_d.toml. Settings are stored using ToML. The module has a to_filename() function to allow writing out the settings to hard disk in ToML format, which looks like:

This config file is used to pass the settings across processes, using the load() function.

Configuration sections

class xcp_d.config.environment

Read-only options regarding the platform and environment.

Crawls runtime descriptive settings (e.g., default FreeSurfer license, execution environment, nipype and XCP-D versions, etc.). The environment section is not loaded in from file, only written out when settings are exported. This config section is useful when reporting issues, and these variables are tracked whenever the user does not opt-out using the --notrack argument.

cpu_count = 2

Number of available CPUs.

exec_docker_version = None

Version of Docker Engine.

exec_env = 'posix'

A string representing the execution platform.

free_mem = 6.2

Free memory at start.

nipype_version = '1.8.6'

Nipype’s current version.

overcommit_limit = '50%'

Linux’s kernel virtual memory overcommit limits.

overcommit_policy = 'heuristic'

Linux’s kernel virtual memory overcommit policy.

templateflow_version = '24.2.0'

The TemplateFlow client version installed.

version = '0.7.4.dev4+g2160bea'

XCP-D’s version.

class xcp_d.config.execution

Configure run-level settings.

atlases = []

Selection of atlases to apply to the data.

bids_database_dir = None

Path to the directory containing SQLite database indices for the input BIDS dataset.

bids_description_hash = None

Checksum (SHA256) of the dataset_description.json of the BIDS dataset.

bids_filters = None

A dictionary of BIDS selection filters.

boilerplate_only = False

Only generate a boilerplate.

custom_confounds = None

A path to a folder containing custom confounds to include in the postprocessing.

debug = []

Debug mode(s).

fmri_dir = None

An existing path to the preprocessing derivatives dataset, which must be BIDS-compliant.

fs_license_file = None

An existing file containing a FreeSurfer license.

classmethod init()

Create a new BIDS Layout accessible with layout.

layout = None

A BIDSLayout object, see init().

log_dir = None

The path to a directory that contains execution logs.

log_level = 25

Output verbosity.

low_mem = None

Utilize uncompressed NIfTIs and other tricks to minimize memory allocation.

md_only_boilerplate = False

Do not convert boilerplate from MarkDown to LaTex and HTML.

notrack = False

Do not collect telemetry information for XCP-D.

output_dir = None

Folder where derivatives will be stored.

participant_label = None

List of participant identifiers that are to be preprocessed.

reports_only = False

Only build the reports, based on the reportlets found in a cached working directory.

run_uuid = '20240513-160406_19ddd88a-12c6-4e64-b87e-4aff1cab830b'

Unique identifier of this particular run.

task_id = None

Select a particular task from all available in the dataset.

templateflow_home = PosixPath('/home/docs/.cache/templateflow')

The root folder of the TemplateFlow client.

work_dir = PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/xcp-d/checkouts/latest/docs/work')

Path to a working directory where intermediate results will be available.

write_graph = False

Write out the computational graph corresponding to the planned preprocessing.

xcp_d_dir = None

Root of XCP-D BIDS Derivatives dataset.

class xcp_d.config.workflow

Configure the particular execution graph of this workflow.

band_stop_max = None

High cutoff frequency for the band-stop filter.

band_stop_min = None

Low cutoff frequency for the band-stop filter.

bandpass_filter = True

Apply a band-pass filter to the data.

bpf_order = 2

Order of the band-pass filter.

cifti = False

Postprocess CIFTI inputs instead of NIfTIs.

combineruns = False

Combine runs of the same task.

dcan_qc = True

Run DCAN QC.

despike = False

Despike the BOLD data before postprocessing.

dummy_scans = 0

Number of label-control volume pairs to delete before CBF computation.

exact_time = []

Produce correlation matrices limited to each requested amount of time.

fd_thresh = 0.3

Framewise displacement threshold for censoring.

head_radius = 50

Radius of the head in mm.

high_pass = 0.01

Lower bound of the band-pass filter.

input_type = 'fmriprep'

Postprocessing pipeline type.

low_pass = 0.1

Upper bound of the band-pass filter.

min_coverage = 0.5

Coverage threshold to apply to parcels in each atlas.

min_time = 240

Post-scrubbing threshold to apply to individual runs in the dataset.

motion_filter_order = 4

Order of the filter to apply to the motion regressors.

motion_filter_type = None

Type of filter to apply to the motion regressors.

params = '36P'

Nuisance regressors to include in the postprocessing.

process_surfaces = False

Warp FreeSurfer’s surfaces to the MNI space.

smoothing = 6

Full-width at half-maximum (FWHM) of the smoothing kernel.

class xcp_d.config.nipype

Nipype settings.

crashfile_format = 'txt'

The file format for crashfiles, either text or pickle.

get_linked_libs = False

Run NiPype’s tool to enlist linked libraries for every interface.

classmethod get_plugin()

Format a dictionary for Nipype consumption.

classmethod init()

Set NiPype configurations.

memory_gb = None

Estimation in GB of the RAM this workflow can allocate at any given time.

nprocs = 2

Number of processes (compute tasks) that can be run in parallel (multiprocessing only).

omp_nthreads = None

Number of CPUs a single process can access for multithreaded execution.

plugin = 'MultiProc'

NiPype’s execution plugin.

plugin_args = {'maxtasksperchild': 1, 'raise_insufficient': False}

Settings for NiPype’s execution plugin.

resource_monitor = False

Enable resource monitor.

stop_on_first_crash = True

Whether the workflow should stop or continue after the first error.

Usage

A config file is used to pass settings and collect information as the execution graph is built across processes.

from xcp_d import config
config_file = config.execution.work_dir / '.xcp_d.toml'
config.to_filename(config_file)
# Call build_workflow(config_file, retval) in a subprocess
with Manager() as mgr:
    from xcp_d.cli.workflow import build_workflow
    retval = mgr.dict()
    p = Process(target=build_workflow, args=(str(config_file), retval))
    p.start()
    p.join()
config.load(config_file)
# Access configs from any code section as:
value = config.section.setting

Logging

class xcp_d.config.loggers

Keep loggers easily accessible (see init()).

cli = <Logger cli (WARNING)>

Command-line interface logging.

default = <RootLogger root (WARNING)>

The root logger.

classmethod init()

Set the log level, initialize all loggers into loggers.

• Add new logger levels (25: IMPORTANT, and 15: VERBOSE).

• Add a new sub-logger (cli).

• Logger configuration.

interface = <Logger nipype.interface (INFO)>

NiPype’s interface logger.

utils = <Logger nipype.utils (INFO)>

NiPype’s utils logger.

workflow = <Logger nipype.workflow (INFO)>

NiPype’s workflow logger.

Other responsibilities

The config is responsible for other conveniency actions.

• Switching Python’s multiprocessing to forkserver mode.

• Set up a filter for warnings as early as possible.

• Automated I/O magic operations. Some conversions need to happen in the store/load processes (e.g., from/to Path <-> str, BIDSLayout, etc.)

xcp_d.config.dumps()

Format config into toml.

class xcp_d.config.environment

Bases: _Config

Read-only options regarding the platform and environment.

Crawls runtime descriptive settings (e.g., default FreeSurfer license, execution environment, nipype and XCP-D versions, etc.). The environment section is not loaded in from file, only written out when settings are exported. This config section is useful when reporting issues, and these variables are tracked whenever the user does not opt-out using the --notrack argument.

cpu_count = 2

Number of available CPUs.

exec_docker_version = None

Version of Docker Engine.

exec_env = 'posix'

A string representing the execution platform.

free_mem = 6.2

Free memory at start.

nipype_version = '1.8.6'

Nipype’s current version.

overcommit_limit = '50%'

Linux’s kernel virtual memory overcommit limits.

overcommit_policy = 'heuristic'

Linux’s kernel virtual memory overcommit policy.

templateflow_version = '24.2.0'

The TemplateFlow client version installed.

version = '0.7.4.dev4+g2160bea'

XCP-D’s version.

class xcp_d.config.execution

Bases: _Config

Configure run-level settings.

atlases = []

Selection of atlases to apply to the data.

bids_database_dir = None

Path to the directory containing SQLite database indices for the input BIDS dataset.

bids_description_hash = None

Checksum (SHA256) of the dataset_description.json of the BIDS dataset.

bids_filters = None

A dictionary of BIDS selection filters.

boilerplate_only = False

Only generate a boilerplate.

custom_confounds = None

A path to a folder containing custom confounds to include in the postprocessing.

debug = []

Debug mode(s).

fmri_dir = None

An existing path to the preprocessing derivatives dataset, which must be BIDS-compliant.

fs_license_file = None

An existing file containing a FreeSurfer license.

classmethod init()

Create a new BIDS Layout accessible with layout.

layout = None

A BIDSLayout object, see init().

log_dir = None

The path to a directory that contains execution logs.

log_level = 25

Output verbosity.

low_mem = None

Utilize uncompressed NIfTIs and other tricks to minimize memory allocation.

md_only_boilerplate = False

Do not convert boilerplate from MarkDown to LaTex and HTML.

notrack = False

Do not collect telemetry information for XCP-D.

output_dir = None

Folder where derivatives will be stored.

participant_label = None

List of participant identifiers that are to be preprocessed.

reports_only = False

Only build the reports, based on the reportlets found in a cached working directory.

run_uuid = '20240513-160406_19ddd88a-12c6-4e64-b87e-4aff1cab830b'

Unique identifier of this particular run.

task_id = None

Select a particular task from all available in the dataset.

templateflow_home = PosixPath('/home/docs/.cache/templateflow')

The root folder of the TemplateFlow client.

work_dir = PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/xcp-d/checkouts/latest/docs/work')

Path to a working directory where intermediate results will be available.

write_graph = False

Write out the computational graph corresponding to the planned preprocessing.

xcp_d_dir = None

Root of XCP-D BIDS Derivatives dataset.

xcp_d.config.from_dict(settings, init=True, ignore=None)

Read settings from a flat dictionary.

Parameters:

• setting (dict) – Settings to apply to any configuration

• init (bool or Container) – Initialize all, none, or a subset of configurations.

• ignore (Container) – Collection of keys in setting to ignore

xcp_d.config.get(flat=False)

Get config as a dict.

xcp_d.config.load(filename, skip=None, init=True)

Load settings from file.

Parameters:

• filename (os.PathLike) – TOML file containing XCP-D configuration.

• skip (dict or None) – Sets of values to ignore during load, keyed by section name

• init (bool or Container) – Initialize all, none, or a subset of configurations.

class xcp_d.config.loggers

Bases: object

Keep loggers easily accessible (see init()).

cli = <Logger cli (WARNING)>

Command-line interface logging.

default = <RootLogger root (WARNING)>

The root logger.

classmethod init()

Set the log level, initialize all loggers into loggers.

• Add new logger levels (25: IMPORTANT, and 15: VERBOSE).

• Add a new sub-logger (cli).

• Logger configuration.

interface = <Logger nipype.interface (INFO)>

NiPype’s interface logger.

utils = <Logger nipype.utils (INFO)>

NiPype’s utils logger.

workflow = <Logger nipype.workflow (INFO)>

NiPype’s workflow logger.

class xcp_d.config.nipype

Bases: _Config

Nipype settings.

crashfile_format = 'txt'

The file format for crashfiles, either text or pickle.

get_linked_libs = False

Run NiPype’s tool to enlist linked libraries for every interface.

classmethod get_plugin()

Format a dictionary for Nipype consumption.

classmethod init()

Set NiPype configurations.

memory_gb = None

Estimation in GB of the RAM this workflow can allocate at any given time.

nprocs = 2

Number of processes (compute tasks) that can be run in parallel (multiprocessing only).

omp_nthreads = None

Number of CPUs a single process can access for multithreaded execution.

plugin = 'MultiProc'

NiPype’s execution plugin.

plugin_args = {'maxtasksperchild': 1, 'raise_insufficient': False}

Settings for NiPype’s execution plugin.

resource_monitor = False

Enable resource monitor.

stop_on_first_crash = True

Whether the workflow should stop or continue after the first error.

class xcp_d.config.seeds

Bases: _Config

Initialize the PRNG and track random seed assignments.

classmethod init()

Initialize a seeds object.

master = None

Master random seed to initialize the Pseudorandom Number Generator (PRNG).

xcp_d.config.to_filename(filename)

Write settings to file.

class xcp_d.config.workflow

Bases: _Config

Configure the particular execution graph of this workflow.

band_stop_max = None

High cutoff frequency for the band-stop filter.

band_stop_min = None

Low cutoff frequency for the band-stop filter.

bandpass_filter = True

Apply a band-pass filter to the data.

bpf_order = 2

Order of the band-pass filter.

cifti = False

Postprocess CIFTI inputs instead of NIfTIs.

combineruns = False

Combine runs of the same task.

dcan_qc = True

Run DCAN QC.

despike = False

Despike the BOLD data before postprocessing.

dummy_scans = 0

Number of label-control volume pairs to delete before CBF computation.

exact_time = []

Produce correlation matrices limited to each requested amount of time.

fd_thresh = 0.3

Framewise displacement threshold for censoring.

head_radius = 50

Radius of the head in mm.

high_pass = 0.01

Lower bound of the band-pass filter.

classmethod init()

input_type = 'fmriprep'

Postprocessing pipeline type.

low_pass = 0.1

Upper bound of the band-pass filter.

min_coverage = 0.5

Coverage threshold to apply to parcels in each atlas.

min_time = 240

Post-scrubbing threshold to apply to individual runs in the dataset.

motion_filter_order = 4

Order of the filter to apply to the motion regressors.

motion_filter_type = None

Type of filter to apply to the motion regressors.

params = '36P'

Nuisance regressors to include in the postprocessing.

process_surfaces = False

Warp FreeSurfer’s surfaces to the MNI space.

smoothing = 6

Full-width at half-maximum (FWHM) of the smoothing kernel.