Function for creating toy data

I often find myself generating toy data (e.g. for educational or testing purposes), so I thought a dedicated function might be useful.

It should be as simple as possible, for example:

from numpy.random import default_rng

from mne import create_info
from mne.io import RawArray


def create_toy_data(n_channels=3, duration=25, sfreq=250, seed=None):
    rng = default_rng(seed)
    data = rng.standard_normal(size=(n_channels, int(duration * sfreq))) * 5e-6
    info = create_info(n_channels, sfreq, "eeg")
    return RawArray(data, info)

It is important that there are sensible defaults for all parameters, which makes it possible to generate toy data very quickly:

raw = create_toy_data()

If people think this would be useful, I can go ahead and submit a PR.

Of course, this function could have a lot of additional parameters, such as

• the kind of generated data (raw, epochs, evoked),
• the probability distribution to sample from,
• the channel type,
• the data scaling,
• ...

However, I'd say YAGNI until someone really needs a particular feature.

If there is interest, I have two questions:

1. Where should this function live? I'd put it in mne.misc and export it to mne, but there might be a better place.
2. The recommended method for generating random numbers is numpy.random.default_rng(seed), but the seed is not compatible with how we are dealing with random state (check_random_state()). If the function gets a seed or random_state parameter, how should we handle this?

So – yay or nay?

Start by reviewing the proposed create_toy_data entry point in mne.misc, its export to mne, and the existing create_info and RawArray APIs. Compare the suggested numpy.random.default_rng(seed) approach with the project's check_random_state() handling. Done means the API location, random-state behavior, defaults, and supported output are agreed and implemented with appropriate tests.