| ... | ... | @@ -6,7 +6,7 @@ Moreover, the python percentile calculation 'ecosystem' becomes more diverse wit |
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This getting even more 'interesting' when also considering [Dask's percentile](https://docs.dask.org/en/latest/array-api.html#dask.array.percentile), which is not without problems, see e.g. [dask issue #1225](https://github.com/dask/dask/issues/1225).
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The [Iris percentile function](https://scitools.org.uk/iris/docs/latest/iris/iris/analysis.html#iris.analysis.PERCENTILE) is divided (depending on arguments) into a **fast method** (cf. [Iris issue #3294](https://github.com/SciTools/iris/issues/3294)) using numpy.percentile, and a **normal method** using scipy.stats.mstats.mquantiles with default **kwargs corresponding to method H&F#7. However, according to the Iris documentation it seems that main distinction between the fast and the normal method is that the former does not handle masked data and the latter does. The fact that the normal method have all (continuous) H&F methods implemented is not well documented.
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The [Iris percentile function](https://scitools.org.uk/iris/docs/latest/iris/iris/analysis.html#iris.analysis.PERCENTILE) is divided (depending on calling arguments) into a **fast method** (cf. [Iris issue #3294](https://github.com/SciTools/iris/issues/3294)) using numpy.percentile, and a **'normal' method** using scipy.stats.mstats.mquantiles with default `**kwargs` corresponding to method H&F#7. However, according to the Iris documentation it seems that main distinction between the fast and the normal method is that the former does not handle masked data and the latter does. The fact that the normal method have all (continuous) H&F methods implemented is not well documented.
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[1] Hyndman, R.J.; Fan, Y., 1996. American Statistician, 50 (4): 361–365. doi:10.2307/2684934. JSTOR 2684934. |
