VCA¶

class turbustat.statistics.VCA(cube, header=None, slice_size=None)[source] [edit on github]¶

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

The VCA technique (Lazarian & Pogosyan, 2004).

Parameters:	cube : numpy.ndarray or astropy.io.fits.PrimaryHDU or SpectralCube Data cube. header : FITS header, optional Corresponding FITS header. slice_sizes : float or int, optional Slices to degrade the cube to.

Methods Summary

compute_pspec()	Compute the 2D power spectrum.
run([verbose, save_name, brk, ...])	Full computation of VCA.

Methods Documentation

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

Compute the 2D power spectrum.

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

Full computation of VCA.

Parameters:

verbose : bool, optional Enables plotting. save_name : str,optional Save the figure when a file name is given. brk : float, optional Initial guess for the break point. return_stddev : bool, optional Return the standard deviation in the 1D bins. logspacing : bool, optional Return logarithmically spaced bins for the lags. 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. use_wavenumber : bool, optional Plot the x-axis as the wavenumber rather than spatial frequency.