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        "author": "Jan Freyberg",
        "author_email": "jan.freyberg@gmail.com",
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        "description": "# ssvepy\n\nA package to analyse MNE-formatted EEG data for steady-state visually evoked potentials (SSVEPs).\n\n### Install:\n\n`pip install git+https://github.com/janfreyberg/ssvepy.git`\n\nAs always with pip packages, you can install a \"development\" version of this package by (forking and) cloning the git repository and installing it via `pip install -e /path/to/package`. Please do open a pull request if you make improvements.\n\n### Documentation:\n\nThe docs for this package are at http://www.janfreyberg.com/ssvepy. There, you'll find the API and an example notebook.\n\n### Usage:\n\nYou should load, preprocess and epoch your data using [MNE](https://github.com/mne-tools/mne-python).\n\nTake a look at a notebook that sets up an SSVEP analysis structure with the example data in this package:\nhttps://github.com/janfreyberg/ssvepy/blob/master/example.ipynb\n\nOnce you have a data structure of the class `Epoch`, you can use `ssvepy.Ssvep(epoch_data, stimulation_frequency)`, where `stimulation_frequency` is the frequency (or list of frequencies) at which you stimulated your participants.\n\nOther input parameters and their defaults are:\n- The following parameters, which are equivalent to the parameters in `mne.time_frequency.psd_multitaper`:\n  - `fmin=0.1`, the low end of the frequency range\n  - `fmax=50`, the high end of the frequency range\n  - `tmin=None`, the start time of the segment you want to analyse\n  - `tmax=None`, the end time of the segment you want to analyse\n- `noisebandwidth=1.0`, what bandwidth around a frequency should be used to calculate its signal-to-noise-ratio\n-  Whether you want to compute the following nonlinearity frequencies:\n  - `compute_harmonics=True`\n  - `compute_subharmonics=False`\n  - `compute_intermodulation=True` (NB: only when there's more than one input frequency)\n- You can also provide your own Power-spectrum data, if you have worked it out using another method.\n  - `psd=None` The powerspectrum. Needs to be a numpy array with dimensions: (epochs, channels, frequency)\n  - `freqs=None` The frequencys at which the powerspectrum was evaluated. Needs to be a one-dimensional numpy array.\n\nThe resulting data has the following attributes:\n\n- `stimulation`: a data structure with the following attributes:\n  - `stimulation.frequencies`, `stimulation.power`, `stimulation.snr`\n- `harmonics`, `subharmonics`, `intermodulations`: non-linear combination of your input stimulus frequencies, all with the attributes:\n  - `_.frequencies`, `_.power`, `_.snr`, `_.order`\n- `psd`: the Power-spectrum\n- `freqs`: the frequencies at which the psd was evaluated\n\nAnd the following methods:\n\n- `plot_psd()`: Plot the power spectrum\n- `plot_snr()`: Plot the SNR spectrum\n- `save(filename)`: Saves an `hdf5` file that can be loaded with `ssvepy.load_ssvep(filename)` <sup>1</sup>\n\nMore to come.\n\n---\n\n<sup>1</sup>: This package currently uses hierarchical data files (hdf5) because it seems to lend itself to the different data stored in ssvep classes, but I know it's less than ideal to have different data structures from MNE. I'm still thinking about improvements.\n",
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