2009/7/22 Pierre GM : You could try scipy.stats.scoreatpercentile, scipy.stats.mstats.plottingposition or scipy.stats.mstats.mquantiles, which will all approximate quantiles of your distribution.

It seems that mquantiles doesn't do what you'd expect when the limit keyword argument is specified. There's a patch for review here: http://codereview.appspot.com/97077 Cheers, Scott

2009/7/23 Pierre GM :

On Jul 23, 2009, at 6:07 AM, Scott Sinclair wrote:

...

...

2009/7/22 Pierre GM : You could try scipy.stats.scoreatpercentile, scipy.stats.mstats.plottingposition or scipy.stats.mstats.mquantiles, which will all approximate quantiles of your distribution.

It seems that mquantiles doesn't do what you'd expect when the limit keyword argument is specified. There's a patch for review here:

Thx for the patch, I'll port it in the next few hours. However, I disagree with the last few lines (where the quantiles are transformed to a standard ndarray if the mask is nomask. For consistency, we should always have a MaskedArray, don't you think ? (And anyway, taking a view as a ndarray is faster than using np.asarray...)

Agree it's more consistent to always return a MaskedArray. I don't remember why I chose to return an ndarray. I think that it was probably to do with the fact that an ndarray is returned when 'axis' isn't specified...

...

...

import numpy as np import scipy as sp sp.__version__ '0.8.0.dev5874' from scipy.stats.mstats import mquantiles a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36.]) type(mquantiles(a)) type(mquantiles(np.ma.masked_array(a))) type(mquantiles(a, axis=0))

This could be fixed by forcing _quantiles1D() to always return a MaskedArray. Cheers, Scott