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