PowerSpectrum¶

class turbustat.statistics.PowerSpectrum(img, header=None, weights=None)[source] [edit on github]¶

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn, turbustat.statistics.base_pspec2.StatisticBase_PSpec2D

Compute the power spectrum of a given image. (e.g., Burkhart et al., 2010)

Parameters:	img : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject 2D image. header : FITS header, optional The image header. Needed for the pixel scale. weights : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject Weights to be applied to the image.

Methods Summary

compute_pspec()	Compute the 2D power spectrum.
run([verbose, logspacing, return_stddev, ...])	Full computation of the spatial power spectrum.

Methods Documentation

compute_pspec()[source] [edit on github]¶

Compute the 2D power spectrum.

run(verbose=False, logspacing=False, return_stddev=True, low_cut=None, high_cut=0.5, ang_units=False, unit=Unit("deg"), save_name=None, use_wavenumber=False)[source] [edit on github]¶

Full computation of the spatial power spectrum.

Parameters:

verbose: bool, optional Enables plotting. logspacing : bool, optional Return logarithmically spaced bins for the lags. return_stddev : bool, optional Return the standard deviation in the 1D bins. low_cut : float, optional Low frequency cut off in frequencies used in the fitting. high_cut : float, optional High frequency cut off in frequencies used in the fitting. ang_units : bool, optional Convert frequencies to angular units using the given header. unit : u.Unit, optional Choose the angular unit to convert to when ang_units is enabled. save_name : str,optional Save the figure when a file name is given. use_wavenumber : bool, optional Plot the x-axis as the wavenumber rather than spatial frequency.