feat: reduce memory footprint #55
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Windowing can now unload results to a memmap on disk. The tensor conversions now use as_tensor that avoids copying numpy arrays if possible.
Concatenating the runs is memory intensive and can be avoided by putting them into a custom dataset instead of a TensorDataset.
This enables using occult Numpy arrays to hold data, e.g., `lib.stride_tricks.sliding_window_view` which can lower memory consumption.
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