graphpype.nodes.graph_stats.PrepareCormat¶

class graphpype.nodes.graph_stats.PrepareCormat(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)[source]¶

Average correlation matrices, within a common reference (based on labels, or coordinates)

Inputs:

cor_mat_files:

type = List of File, (exists=True), desc=’list of all correlation matrice files (in npy format) for each subject’, mandatory=True

coords_files:

type = List of File, (exists=True), desc=’list of all coordinates in numpy space files for each subject’, mandatory=True, xor = [‘labels_files’]

labels_files:

type = List of File, (exists=True),

desc=’list of labels (in txt format) for each subject’, mandatory=True, xor = [‘coords_files’])

gm_mask_coords_file:

type = File(exists=True, desc=’Coordinates in numpy space, corresponding to all possible nodes in the original space’, mandatory=False, xor = [‘gm_mask_labels_file’])

gm_mask_labels_file :

type = File, (exists=True), desc=’Labels for all possible nodes - in case coords are varying from one indiv to the other (source space for example)’, mandatory=False, xor = [‘gm_mask_coords_file’])

Attributes:	always_run Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context. can_resume Defines if the interface can reuse partial results after interruption. version interfaces should implement a version property

Methods

aggregate_outputs([runtime, needed_outputs])	Collate expected outputs and check for existence
help([returnhelp])	Prints class help
input_spec	alias of PrepareCormatInputSpec
load_inputs_from_json(json_file[, overwrite])	A convenient way to load pre-set inputs from a JSON file.
output_spec	alias of PrepareCormatOutputSpec
run([cwd, ignore_exception])	Execute this interface.
save_inputs_to_json(json_file)	A convenient way to save current inputs to a JSON file.

__init__(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

Subclasses must implement __init__

Methods

__init__([from_file, resource_monitor, …])	Subclasses must implement __init__
aggregate_outputs([runtime, needed_outputs])	Collate expected outputs and check for existence
help([returnhelp])	Prints class help
load_inputs_from_json(json_file[, overwrite])	A convenient way to load pre-set inputs from a JSON file.
run([cwd, ignore_exception])	Execute this interface.
save_inputs_to_json(json_file)	A convenient way to save current inputs to a JSON file.

Attributes

always_run	Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context.
can_resume	Defines if the interface can reuse partial results after interruption.
references_
resource_monitor
version	interfaces should implement a version property