Load peaks from kilosort output

[JoeZiminski]

[In progress]
continuation from #3499

        # TODO: a couple of approaches. 1) do everything in 3D, draw a sphere around prediction, take spikes only within the sphere
        # 2) do separate for x, y. But resolution will be much lower, making things noisier, also harder to determine threshold.
        # 3) just use depth. Probably go for that. check with others.

    norm_weights = (
        pc_features / np.sum(pc_features, axis=1)[:, np.newaxis]
    )  # TOOD: discuss use of square. Probbaly do not use to keep in line with COM in SI.

    # TODO:  now max site per spike is computed from PCs, not as the channel max site as previous
    spike_max_sites = spike_features_indices[
        np.arange(spike_features_indices.shape[0]), np.argmax(norm_weights, axis=1)
    ]

    #  Zero any small channel amplitudes  TODO: removed this.
    #    threshold_values = 0.3 * template_amplitudes_unscaled  TODO: remove this to be more general. Agree?
    #    template_amplitudes_per_channel[template_amplitudes_per_channel < threshold_values[:, np.newaxis]] = 0

    # Get channel with the largest amplitude (take that as the waveform)
    template_max_site = np.argmax(
        np.max(np.abs(unwhite_templates), axis=1), axis=1
    )  # TODO: i changed this to use unwhitened templates instead of templates. This okay?

    # Use template channel with max signal as waveform
   waveforms = np.empty(
       unwhite_templates.shape[:2]
   )  # TODO: i changed this to use unwhitened templates instead of templates. This okay?
   ```