MMMBackground#

class photutils.background.MMMBackground(sigma_clip=<default: SigmaClip(sigma=3.0, maxiters=10)>)[source]#

Bases: ModeEstimatorBackground

Class to calculate the background in an array using the DAOPHOT MMM algorithm.

The background is calculated using a mode estimator of the form (3 * median) - (2 * mean).

Parameters:

sigma_clipSigmaClip or None, optional: A SigmaClip object that defines the sigma clipping parameters. If None then no sigma clipping will be performed. The default is a SigmaClip instance with sigma=3.0 and maxiters=10.

Examples

>>> import numpy as np
>>> from astropy.stats import SigmaClip
>>> from photutils.background import MMMBackground
>>> data = np.arange(100.0)
>>> sigma_clip = SigmaClip(sigma=3.0)
>>> bkg = MMMBackground(sigma_clip=sigma_clip)

The background value can be calculated by using the calc_background method, e.g.:

>>> bkg_value = bkg.calc_background(data)
>>> print(bkg_value)
49.5

Alternatively, the background value can be calculated by calling the class instance as a function, e.g.:

>>> bkg_value = bkg(data)
>>> print(bkg_value)
49.5

Methods Summary

`__call__`(data[, axis, masked])	Call self as a function.
`calc_background`(data[, axis, masked])	Calculate the background value.

Methods Documentation

__call__(data, axis=None, masked=False)#: Call self as a function.

calc_background(data, axis=None, masked=False)#

Calculate the background value.

Parameters:

datandarray or MaskedArray: The array for which to calculate the background value.
axisint, tuple of int, or None, optional: The array axis along which the background is calculated. If None, then the entire array is used.
maskedbool, optional: If True, then a MaskedArray is returned. If False, then a ndarray is returned, where masked values have a value of NaN. The default is False.

Returns:

resultfloat, ndarray, or MaskedArray: The calculated background value. If masked is False, then a ndarray is returned, otherwise a MaskedArray is returned. A scalar result is always returned as a NumPy floating-point scalar. The result is float32 or float64, depending on the dtype of the input data and on the sigma clipping. For example, sigma-clipped integer data give a float32 result.