running_median

eazy.utils.running_median(xi, yi, NBIN=10, reverse=False, bins=None, x_func=<function median>, y_func=<function median>, std_func=<function mad_std>, x_kwargs={}, y_kwargs={}, std_kwargs={}, use_biweight=False, integrate=False, **kwargs)

Binned median/biweight/nmad statistics

Parameters:

xiarray-like

Data of independent variable

yiarray-like

Data of dependent variable

NBINint

Number of bins along xi

reversebool

Calculate bins starting at largest values of xi

binsarray-like

Fixed bins, rather than calculating with NBIN

x_funcfunction

Function to compute moments of xi

y_func, std_funcfunction

Functions to compute moments of yi. Assumed to be the central value and dispersion, but don’t have to be

x_kwargs, y_kwargs, std_kwargsdict

Keyword arguments to pass to moment functions

use_biweightbool

Use robust biweight estimators:

• x_func : astropy.stats.biweight_location

• y_func : astropy.stats.biweight_location

• std_func : astropy.stats.biweight_midvariance

integratebool

Numerically integrate yi with the trapezoidal rule within the bins

Returns:

xm, ym, ysarray-like

Binned moments of xi and yi

ynarray-like

Number of entries per bin