turbustat.statistics.
VCS
(cube, header=None, vel_units=False)[source] [edit on github]¶Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn
The VCS technique (Lazarian & Pogosyan, 2004).
Parameters: | cube : numpy.ndarray or astropy.io.fits.PrimaryHDU or SpectralCube
header : FITS header, optional
vel_units : bool, optional
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Attributes Summary
brk |
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brk_err |
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slope_errs |
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slopes |
Methods Summary
compute_pspec () |
Take the FFT of each spectrum in velocity dimension. |
fit_pspec ([breaks, log_break, lg_scale_cut, ...]) |
Fit the 1D Power spectrum using a segmented linear model. |
run ([verbose, save_name, breaks]) |
Run the entire computation. |
Attributes Documentation
brk
¶brk_err
¶slope_errs
¶slopes
¶Methods Documentation
compute_pspec
()[source] [edit on github]¶Take the FFT of each spectrum in velocity dimension.
fit_pspec
(breaks=None, log_break=True, lg_scale_cut=2, verbose=False)[source] [edit on github]¶Fit the 1D Power spectrum using a segmented linear model. Note that the current implementation allows for only 1 break point in the model. If the break point is estimated via a spline, the breaks are tested, starting from the largest, until the model finds a good fit.
Parameters: | breaks : float or None, optional
log_break : bool, optional
lg_scale_cut : int, optional
verbose : bool, optional
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run
(verbose=False, save_name=None, breaks=None)[source] [edit on github]¶Run the entire computation.
Parameters: | verbose: bool, optional
save_name : str,optional
breaks : float, optional
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