saltstat contains statistical functions

saltstat.iterstat(arr, sig, niter, verbose=False)

iterstas calculates an arrays statistics using a sigma clipped values


Calculated the Median Absolute Deviation defined as: MAD=median(|x - median(x)|)


calculate mean of numeric list


calculate mean of 2d array

saltstat.mean2dclip(arrays, mean, std, sig)

calculate the sigma clipped mean of 2d array

saltstat.median(x, logfile=None)

calculate median of numeric list

logfile–depreciated variable

saltstat.median2d(arrays, logfile=None)

calculate median of 2d array

logfile–depreciated variable

saltstat.median2dclip(arr, mean, std, sig)

calculate the sigma clipped median of 2d array

saltstat.median_absolute_deviation(a, axis=None)

Compute the median absolute deviation

Returns the median absolute deviation of the array elements. The MAD is defined as median(|a-median(a)|).

a : array_like
Input array or object that can be converted to an array.
axis : int, optional
Axis along which the medians are computed. The default (axis=None) is to compute the median along a flattened version of the array.
median_absolute_deviation : ndarray
A new array holding the result. If the input contains integers, or floats of smaller precision than 64, then the output

This will generate random variates from a Gaussian distribution and return the median absolute deviation for that distribution:

>>> from astropy.stats import median_aboslute_deviation
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> mad = median_absolute_deviation(randvar)


saltstat.median_combine(arrays, logfile=None, axis=0)

Median combine a set of arrays

logfile–depreciated variable

saltstat.median_image(arr, nbin)

Median smooth an image with a filter size set by bin

returns arr

saltstat.std2dclip(arrays, mean, std, sig)

calculate clipped std of 2d array

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