Simulated epochs data

Use fitgrid.generate to quickly generate small or large random data sets with categorical and random continuous predictor variables. The random values can be seeded for replicability.

The data can be returned in fitgrid Epochs format for immediate modeling or as a pandas.DataFrame. The latter is useful for mocking up the conversion from a dataframe to fitgrid.Epochs when developing an analysis pipeline.

Small data sets are useful for trying out features and functions. Larger sets are useful for testing system performance and limitations.

import fitgrid

Small random data set as fitgrid.Epochs

fitgrid.generate(n_samples=8, n_channels=4, seed=32)

Out:

<fitgrid.epochs.Epochs object at 0x7f4bf51a7eb0>

The same data as a pandas.DataFrame

fitgrid.generate(n_samples=8, n_channels=4, seed=32, return_type="dataframe")
epoch_id time categorical continuous channel0 channel1 channel2 channel3
0 0 0 cat0 0.858889 43.475142 -5.176046 -30.477447 6.407577
1 0 1 cat0 0.372711 -30.204601 10.380524 -24.299167 -41.349312
2 0 2 cat0 0.555129 37.757038 6.190869 3.025954 40.897177
3 0 3 cat0 0.955657 15.874407 14.819547 36.902390 68.487095
4 0 4 cat0 0.736670 -15.427042 32.314754 9.701035 -35.299150
... ... ... ... ... ... ... ... ...
155 19 3 cat1 0.213619 -27.412434 -26.668317 24.662472 16.017164
156 19 4 cat1 0.467320 22.395003 -0.198398 -35.641279 15.776159
157 19 5 cat1 0.808112 14.750858 30.162813 40.701753 7.671025
158 19 6 cat1 0.601196 -38.540116 63.694680 14.552887 8.973484
159 19 7 cat1 0.413561 -25.620330 38.373017 11.135731 -16.534360

160 rows × 8 columns



Total running time of the script: ( 0 minutes 0.027 seconds)

Gallery generated by Sphinx-Gallery