This documentation is for astroML version 0.2

This page


astroML Mailing List

GitHub Issue Tracker


Scipy 2012 (15 minute talk)

Scipy 2013 (20 minute talk)


If you use the software, please consider citing astroML.

11.9.3. astroML.filters.min_component_filter

astroML.filters.min_component_filter(x, y, feature_mask, p=1, fcut=None, Q=None)[source]

Minimum component filtering

Minimum component filtering is useful for determining the background component of a signal in the presence of spikes

Parameters :

x : array_like

1D array of evenly spaced x values

y : array_like

1D array of y values corresponding to x

feature_mask : array_like

1D mask array giving the locations of features in the data which should be ignored for smoothing

p : integer (optional)

polynomial degree to be used for the fit (default = 1)

fcut : float (optional)

the cutoff frequency for the low-pass filter. Default value is f_nyq / sqrt(N)

Q : float (optional)

the strength of the low-pass filter. Larger Q means a steeper cutoff default value is 0.1 * fcut

Returns :

y_filtered : ndarray

The filtered version of y.


This code follows the procedure explained in the book “Practical Statistics for Astronomers” by Wall & Jenkins book, as well as in Wall, J, A&A 122:371, 1997