Multivariate Pattern Analysis in Python

# mvpa2.misc.data_generators

Miscellaneous data generators for unittests and demos

Functions

`autocorrelated_noise`(ds, sr, cutoff[, lfnl, ...]) | Generate a dataset with samples being temporally autocorrelated noise.
`chirp_linear`(n_instances[, n_features, ...]) | Generates simple dataset for linear regressions
`dumb_feature_binary_dataset`() | Very simple binary (2 labels) dataset
`dumb_feature_dataset`() | Create a very simple dataset with 2 features and 3 labels
`get_mv_pattern`(s2n) | Simple multivariate dataset
`linear1d_gaussian_noise`([size, slope, ...]) | A straight line with some Gaussian noise.
`linear_awgn`([size, intercept, slope, ...]) | Generate a dataset from a linear function with AWGN (Added White Gaussian Noise).
`multiple_chunks`(func, n_chunks, *args, **kwargs) | Replicate datasets multiple times raising different chunks
`noisy_2d_fx`(size_per_fx, dfx, sfx, center[, ...]) | Yet another generator of random dataset
`normal_feature_dataset`([perlabel, nlabels, ...]) | Generate a univariate dataset with normal noise and specified means.
`pure_multivariate_signal`(patterns[, ...]) | Create a 2d dataset with a clear purely multivariate signal.
`random_affine_transformation`(ds[, ...]) | Distort a dataset by random scale, shift, and rotation.
`simple_hrf_dataset`([events, hrf_gen, ...]) | events: list of Events or ndarray of onsets for simple(r) designs
`sin_modulated`(n_instances, n_features[, ...]) | Generate a (quite) complex multidimensional non-linear dataset
`wr1996`([size]) | Generate ‘6d robot arm’ dataset (Williams and Rasmussen 1996)

Classes