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wmvanvliet
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wmvanvliet
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Some pitpicks on the code. But uhm, where is the actual CSP happening in this code?
| from mne.filter import filter_data | ||
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| class TimeFrequencyCSP(object): |
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Since this class follows the scikit-learn API, can we make this a subclass of sklearn.base.BaseEstimator and add the sklearn.base.TransformerMixin?
| Times in seconds. Upper bound of the time over which to estimate | ||
| signals. | ||
| freqs : array of floats | ||
| Frequency values over which to compute time-frequency decomposition. |
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Can you elaborate a bit on how exactly to pass the frequency ranges? Maybe give an example here?
| from sklearn import clone | ||
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| # Crop data into time-window of interest | ||
| Xt = epochs.copy().crop(w_tmin, w_tmax).get_data() |
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Why Xt and not just X? I read it first as meaning X.T, but below, it is passed directly to the sklearn estimators...
| self._window_spacing)[1:] # noqa | ||
| self._n_windows = len(self._centered_w_times) | ||
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| def _transform(self, epochs, y, w_tmin, w_tmax, method='', pos=[]): |
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Why do the method and pos params have default values? The defaults don't make much sense to me.
| # filter the data at the desired frequency | ||
| epochs_bp = epochs.copy() | ||
| epochs_bp._data = filter_data(epochs_bp.get_data(), self.sfreq, | ||
| fmin, fmax, verbose='CRITICAL') |
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A thought: maybe it is helpful to expose some parameters for the filtering? Perhaps not, as the number of parameters to this class will grow quite large.
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Developing a Class from the time-frequency CSP decoding example
(https://4177-1301584-gh.circle-artifacts.com/0/home/ubuntu/mne-python/doc/_build/html/auto_examples/decoding/plot_decoding_csp_timefreq.html#sphx-glr-auto-examples-decoding-plot-decoding-csp-timefreq-py)
cc @kingjr