issue_comments

Could you share an small example of what you’d like to do, ideally on synthetic data?

Hi Scott,

I can't think of a generic situation where text labels have a numerical weight that is hardcoded to their position on the alphabet, e.g. mean("A", "C") = "B". What one typically does is map the labels (any string) to their (arbitrary) weights, interpolate the weights, and then do a nearest-neighbour interpolation (or floor or ceil, depending on the preference) back to the label. Which is what you described but with the special caveat that your weights are the ASCII codes for your labels.

On Sat, 8 Feb 2020 at 20:43, scottcanoe notifications@github.com wrote:

I'd like to suggest an improvement to enable a repeat-based interpolation mechanism for non-numerical data. In my use case, I have time series data (dim='t'), where each timepoint is associated with a measured variable (e.g., fluorescence) as well as a label indicating the stimulus being presented (e.g., "A"). However, if and when I need to upsample my data, the string-valued stimulus information is lost, and its imperative that the stimulus information is still present when working on the resampled data.

My solution to this problem has been to map the labels to integers, use nearest-neighbor interpolation on the integer-valued representation, and finally map the integers back to labels. (I'm willing to bet there's a name for this technique, but I wasn't able to find it by googling around for it.)

I'm new to xarray, but so far as I can tell this functionality is not provided. More specifically, calling DataArray.interp on a string-valued array results in a type error (<builtins.TypeError: interp only works for a numeric type array. Given <U1.>).

Finally, I'd like to applaud you for your work on xarray. I only wish I had found it sooner!

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