pyns.endpoints.analysis.Analyses

class pyns.endpoints.analysis.Analyses(client)

Bases: Base

Analyses endpoint class

__init__(client)

Initialize a Model instance.

Parameters:: client (Neuroscout) – base client instance

Methods

`__init__`(client)	Initialize a Model instance.
`clone`(id)	Clone analysis
`compile`(id[, build])	Submit analysis for complilation
`create_analysis`(*, name, dataset_name, ...)	Analysis creation "wizard".
`delete`(id)	Delete analysis
`fill`(id[, partial, dryrun])	Fill missing fields
`generate_report`(id[, run_id, sampling_rate, ...])	Submit analysis for report generation
`get_analysis`(id)	Convenience function to fetch and create Analysis object from a known analysis id
`get_bundle`(id[, filename])	Get analysis bundle
`get_design_matrix`(id[, run_id, loop_wait])	Get report design_matrix
`get_full`(id)	Get full analysis object (including runs and predictors)
`get_report`(id[, run_id, loop_wait])	Get generated reports for analysis
`get_resources`(id)	Get analysis resources
`get_status`(id)	Get analysis status
`get_uploads`(id[, select])	Get NeuroVault uploads associated with this analysis
`load_uploads`(id[, select, download_dir, ...])	Load collection upload as NiBabel images and associated meta-data You can filter which images are loaded based on either collection level attributes or statmap image level attributes.
`plot_report`(id[, run_id, plot_type, loop_wait])	Uses altair to plot design_matrix plot generated by report
`plot_uploads`(id[, plot_args])	Plot uploads for matching collections using nilearn
`put`(id, **kwargs)	Put analysis
`upload_neurovault`(id, validation_hash[, ...])	Submit analysis for report generation

clone(id)

Clone analysis

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

compile(id, build=True)

Submit analysis for complilation

Parameters:

id (str) – Analysis hash_id
build (bool) – Build pybids object and verify compilation

Returns:

Requests response object

Rype:

requests.Response

create_analysis(*, name, dataset_name, predictor_names, tasks=None, subjects=None, runs=None, session=None, hrf_variables=None, contrasts=None, dummy_contrasts=True, transformations=None, **kwargs)

Analysis creation “wizard”. Builds analysis with a pre-populated BIDS Stats Model.

Parameters:

name (str) – analysis name
dataset_name (str) – dataset name
predictor_names (list) – predictor names to include in model
tasks (list) – list of tasks to include
subjects (list) – list of subject identifiers
runs (list) – list of run ids
session (str) – session name
hrf_variables (list) – subset of predictor_names to convolve with HRF
contrasts (list) – list of contrast dictionaries
dummy_contrasts (list) – subset of predictor_names to create dummy contrast for
transformations (list) – list of transformations
kwargs (dict) – arguments to pass to Analysis class

Returns:

Analysis object

Rype:

Analysis

delete(id)

Delete analysis

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

fill(id, partial=True, dryrun=False)

Fill missing fields

Parameters:

id (str) – Analysis hash_id
partial (bool) – Partial fill.
dryrun (bool) – Do not commit to database.

Returns:

Requests response object

Rype:

requests.Response

generate_report(id, run_id=None, sampling_rate=None, scale=False)

Submit analysis for report generation

Parameters:

id (str) – Analysis hash_id
run_id (list) – Optional run_id to constrain report.
sampling_rate (float) – Sampling rate for design matrix
scale (bool) – Scale design matrix.

Returns:

Requests response object

Rype:

requests.Response

get_analysis(id)

Convenience function to fetch and create Analysis object from a known analysis id

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

get_bundle(id, filename=None)

Get analysis bundle

Parameters:

id (str) – Analysis hash_id
filename (str) – Optional filename to save bundle to

Returns:

Requests response object

Rype:

requests.Response

get_design_matrix(id, run_id=None, loop_wait=True)

Get report design_matrix

Parameters:

id (str) – Analysis hash_id
run_id (list) – Optional run_id to constrain report.
loop_wait (bool) – Wait until report completes before returning response.

Returns:

Requests response object

Rype:

requests.Response

get_full(id)

Get full analysis object (including runs and predictors)

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

get_report(id, run_id=None, loop_wait=True)

Get generated reports for analysis

Parameters:

id (str) – Analysis hash_id
run_id (list) – Optional run_id to constrain report.
loop_wait (bool) – Wait until report completes before returning response.

Returns:

Requests response object

Rype:

requests.Response

get_resources(id)

Get analysis resources

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

get_status(id)

Get analysis status

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

get_uploads(id, select='latest', **kwargs)

Get NeuroVault uploads associated with this analysis

Parameters:

id (str) – Analysis hash_id
select (str) – How to select from multiple collections Options: “latest”, “oldest” or None. If None, returns all results.
kwargs (dict) – Attributes to filter collections on. If any attributes are not found, they are ignored.

Returns:

Requests response object

Rype:

requests.Response

load_uploads(id, select='latest', download_dir=None, collection_filters={}, image_filters={})

Load collection upload as NiBabel images and associated meta-data You can filter which images are loaded based on either collection level attributes or statmap image level attributes. These correspond to field returns for get_uploads at the collection level or file level. In addition for images, BIDS entities are parsed and available to filter on.

Parameters:

id (str) – Analysis hash_id
select (str) – How to select from multiple collections Options: “latest”, “oldest” or None. If None, returns all results.
download_dir – Path to download images. If None, tempdir.
collection_filters (dict) – Attributes to filter collections on.
image_filters (dict) – Attributes to filter images on. If any attributes are not found, they are ignored.

Returns:

list list of tuples of format (Nifti1Image, kwargs).

Rype:

list

plot_report(id, run_id=None, plot_type='design_matrix_plot', loop_wait=True)

Uses altair to plot design_matrix plot generated by report

Parameters:

id (str) – Analysis hash_id
run_id (list) – Optional run_id to constrain report.
plot_type (str) – design_matrix_plot or corr_matrix_plot
loop_wait (bool) – Wait until report completes before returning response.

Returns:

Requests response object

Rype:

requests.Response

plot_uploads(id, plot_args={}, **kwargs)

Plot uploads for matching collections using nilearn

Parameters:

id (str) – Analysis hash_id
plot_args (dict) – Plot arguments for nilearn.plotting
kwargs (dict) – Arguments for load_uploads

Returns:

list of matplotlib objects.

Rype:

list

put(id, **kwargs)

Put analysis

Parameters:: id (str) – Analysis hash_id
Returns:: Requests response object
Rype:: requests.Response

upload_neurovault(id, validation_hash, subject_paths=None, group_paths=None, collection_id=None, force=False, cli_version=None, fmriprep_version=None, estimator=None, n_subjects=None, cli_args=None)

Submit analysis for report generation

Parameters:

id (str) – Analysis hash_id
validation_hash (str) – Validation hash string.
subject_paths (list) – List of image paths.
group_paths (list) – List of image paths.
force (bool) – Force upload with unique timestamped name.
cli_version (str) – neuroscout-cli version at runtime
fmriprep_version (str) – fmriprep version at runtime
estimator (str) – estimator used in fitlins (anfi/nilearn)
n_subjects (int) – Number of subjects in analysis.
cli_args (dict) – Run time CLI args

Returns:

Arguments specified to CLI at runtime

Rype:

dict