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Description
Currently our OHLC sampling (bars) graphics convert to a line curve when the user zooms out enough that you can't visually distinguish a bar's shape (uppx is way too high to represent them per horizonal pixel).
To convert to this line we currently use a structured array flattener routine which uses np.recfunctions.structure_to_unstructured(): ohlc_to_line().
In theory we can actually just use our m4 implementation to post-process either this flattened output or write an OHLC aware version which iterates the ['high', 'low'] fields of the input array when computing max/min values. In other words the y input could be pre-processed from another (numba) routine which flattens only the high/low values from the OHLC struct array and then feeds them into this same function. The caveat will be making sure the x input is sized to match 🤔
This is definitely not a high priority task but may be interesting to tinker with especially in the context of overloading ds_m4() for other types of aggregation schemes (eg. as we still need with vlm in #326).