robintibor

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Second vote: `n_in_chan[nel]s, n_out_chan[nel]s` ![1](https://github.githubassets.com/images/icons/emoji/unicode/1f389.png "1") `n_chan[nels], n_classes` ![2](https://github.githubassets.com/images/icons/emoji/unicode/1f680.png "2") `in_chan[nel]s, out_chan[nel]s` ![3](https://github.githubassets.com/images/icons/emoji/unicode/2764.png "3")

What a marvelous way to vote! @hubertjb @sliwy @gemeinl @agramfort notice second vote also up

keep in mind in the end n_classes loses meaning for regression tasks obviously

In the same pass as code style, we can also do naming stuff. There we have to decide about supercrop-language, if we want to keep it and where etc.

Another Point: * Expression -> Lambda?

I think one issues is we have multiple things which refer to timewindows and differ in the meaning of the timewindows: * Trials, as @hubertjb said, tied to a specific...

So my view atm: * Trial * ComputeWindow * ReceptiveField (possibly only needed in very few places) Then we use the established terms "trial" and "receptivefield", and have a new...

Thinking about it if we use ComputeCrop and this is only time we use crop, inside the code we could shorten to crop?

yes it is a single input to the network. the larger you make this input, the more predictions you will get -> the more are computed in parallel, saving you...

Unfortunately, "we have predictions for all receptive_fields and average them to obtain final window prediction" is not correct in general. For example, one use case that we had before and...