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Generating simulated activation maps

The module nipy.labs.utils.simul_multisubject_fmri_dataset contains a various functions to create simulated activation maps in two, three and four dimensions. A 2D example is surrogate_2d_dataset(). The functions can position various activations and add noise, both as background noise and jitter in the activation positions and amplitude.

These functions can be useful to test methods.

Function documentation

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, optionnal :

The number of subjects, ie the number of different maps generated.

shape=(30,30): tuple of integers, :

the shape of each image

sk: float, optionnal :

Amount of spatial noise smoothness.

noise_level: float, optionnal :

Amplitude of the spatial noise. amplitude=noise_level)

pos: 2D ndarray of integers, optionnal :

x, y positions of the various simulated activations.

ampli: 1D ndarray of floats, optionnal :

Respective amplitude of each activation

spatial_jitter: float, optionnal :

Random spatial jitter added to the position of each activation, in pixel.

signal_jitter: float, optionnal :

Random amplitude fluctuation for each activation, added to the amplitude specified by ampli

width: float or ndarray, optionnal :

Width of the activations

width_jitter: float :

Relative width jitter of the blobs

out_text_file: string or None, optionnal :

If not None, the resulting array is saved as a text file with the given file name

out_image_file: string or None, optionnal :

If not None, the resulting is saved as a nifti file with the given file name.

seed=False: int, optionnal :

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, optionnal :

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, optionnal :

Amount of spatial noise smoothness.

noise_level: float, optionnal :

Amplitude of the spatial noise. amplitude=noise_level)

pos: 2D ndarray of integers, optionnal :

x, y positions of the various simulated activations.

ampli: 1D ndarray of floats, optionnal :

Respective amplitude of each activation

spatial_jitter: float, optionnal :

Random spatial jitter added to the position of each activation, in pixel.

signal_jitter: float, optionnal :

Random amplitude fluctuation for each activation, added to the amplitude specified by ampli

width: float or ndarray, optionnal :

Width of the activations

out_text_file: string or None, optionnal :

If not None, the resulting array is saved as a text file with the given file name

out_image_file: string or None, optionnal :

If not None, the resulting is saved as a nifti file with the given file name.

seed=False: int, optionnal :

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 overrided 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, optionnal :

Amount of spatial noise smoothness.

noise_level: float, optionnal :

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, optionnal :

If not None, the resulting is saved as (set of) nifti file(s) with the given file path(s)

seed=False: int, optionnal :

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