labs.utils.simul_multisubject_fmri_dataset¶
Module: labs.utils.simul_multisubject_fmri_dataset¶
This module contains a function to produce a dataset which simulates a collection of 2D images This dataset is saved as a 3D image (each slice being a subject) and a 3D array
Author : Bertrand Thirion, 2008-2010
Functions¶
- nipy.labs.utils.simul_multisubject_fmri_dataset.surrogate_2d_dataset(n_subj=10, shape=(30, 30), sk=1.0, noise_level=1.0, pos=array([[6, 7], [10, 10], [15, 10]]), ampli=array([3, 4, 4]), spatial_jitter=1.0, signal_jitter=1.0, width=5.0, width_jitter=0, out_text_file=None, out_image_file=None, seed=False)¶
Create surrogate (simulated) 2D activation data with spatial noise
- Parameters:
- n_subj: integer, optional
The number of subjects, ie the number of different maps generated.
- shape=(30,30): tuple of integers,
the shape of each image
- sk: float, optional
Amount of spatial noise smoothness.
- noise_level: float, optional
Amplitude of the spatial noise. amplitude=noise_level)
- pos: 2D ndarray of integers, optional
x, y positions of the various simulated activations.
- ampli: 1D ndarray of floats, optional
Respective amplitude of each activation
- spatial_jitter: float, optional
Random spatial jitter added to the position of each activation, in pixel.
- signal_jitter: float, optional
Random amplitude fluctuation for each activation, added to the amplitude specified by ampli
- width: float or ndarray, optional
Width of the activations
- width_jitter: float
Relative width jitter of the blobs
- out_text_file: string or None, optional
If not None, the resulting array is saved as a text file with the given file name
- out_image_file: string or None, optional
If not None, the resulting is saved as a nifti file with the given file name.
- seed=False: int, optional
If seed is not False, the random number generator is initialized at a certain value
- Returns:
- dataset: 3D ndarray
The surrogate activation map, with dimensions
(n_subj,) + shape
- nipy.labs.utils.simul_multisubject_fmri_dataset.surrogate_3d_dataset(n_subj=1, shape=(20, 20, 20), mask=None, sk=1.0, noise_level=1.0, pos=None, ampli=None, spatial_jitter=1.0, signal_jitter=1.0, width=5.0, out_text_file=None, out_image_file=None, seed=False)¶
Create surrogate (simulated) 3D activation data with spatial noise.
- Parameters:
- n_subj: integer, optional
The number of subjects, ie the number of different maps generated.
- shape=(20,20,20): tuple of 3 integers,
the shape of each image
- mask=None: Nifti1Image instance,
referential- and mask- defining image (overrides shape)
- sk: float, optional
Amount of spatial noise smoothness.
- noise_level: float, optional
Amplitude of the spatial noise. amplitude=noise_level)
- pos: 2D ndarray of integers, optional
x, y positions of the various simulated activations.
- ampli: 1D ndarray of floats, optional
Respective amplitude of each activation
- spatial_jitter: float, optional
Random spatial jitter added to the position of each activation, in pixel.
- signal_jitter: float, optional
Random amplitude fluctuation for each activation, added to the amplitude specified by ampli
- width: float or ndarray, optional
Width of the activations
- out_text_file: string or None, optional
If not None, the resulting array is saved as a text file with the given file name
- out_image_file: string or None, optional
If not None, the resulting is saved as a nifti file with the given file name.
- seed=False: int, optional
If seed is not False, the random number generator is initialized at a certain value
- Returns:
- dataset: 3D ndarray
The surrogate activation map, with dimensions
(n_subj,) + shape
- nipy.labs.utils.simul_multisubject_fmri_dataset.surrogate_4d_dataset(shape=(20, 20, 20), mask=None, n_scans=1, n_sess=1, dmtx=None, sk=1.0, noise_level=1.0, signal_level=1.0, out_image_file=None, seed=False)¶
Create surrogate (simulated) 3D activation data with spatial noise.
- Parameters:
- shape = (20, 20, 20): tuple of integers,
the shape of each image
- mask=None: brifti image instance,
referential- and mask- defining image (overrides shape)
- n_scans: int, optional,
number of scans to be simlulated overridden by the design matrix
- n_sess: int, optional,
the number of simulated sessions
- dmtx: array of shape(n_scans, n_rows),
the design matrix
- sk: float, optional
Amount of spatial noise smoothness.
- noise_level: float, optional
Amplitude of the spatial noise. amplitude=noise_level)
- signal_level: float, optional,
Amplitude of the signal
- out_image_file: string or list of strings or None, optional
If not None, the resulting is saved as (set of) nifti file(s) with the given file path(s)
- seed=False: int, optional
If seed is not False, the random number generator is initialized at a certain value
- Returns:
- dataset: a list of n_sess ndarray of shape
(shape[0], shape[1], shape[2], n_scans) The surrogate activation map