SCF¶

class turbustat.statistics.SCF(cube, header=None, size=11, roll_lags=None)[source] [edit on github]¶

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn

Computes the Spectral Correlation Function of a data cube (Rosolowsky et al, 1999).

Parameters:	cube : numpy.ndarray or astropy.io.fits.PrimaryHDU or SpectralCube Data cube. header : FITS header, optional Header for the cube. size : int, optional Total size of the lags used in one dimension. roll_lags : ndarray, optional Pass a custom array of lag values. These will be the pixel size of the lags used in each dimension. Ideally, these should have symmetric positive and negative values.

Attributes Summary

lags	Values of the lags, in pixels, to compute SCF at
scf_spectrum	Azimuthally averaged 1D SCF spectrum
scf_spectrum_stddev	Standard deviation of the scf_spectrum
scf_surface	SCF correlation array
slope	SCF spectrum slope
slope_err	1-sigma error on the SCF spectrum slope

Methods Summary

compute_spectrum([logspacing, return_stddev])	Compute the 1D spectrum as a function of lag.
compute_surface([boundary])	Computes the SCF up to the given lag value.
fit_plaw([xlow, xhigh, verbose])	Fit a power-law to the SCF spectrum.
fitted_model(xvals)	Computes the fitted power-law in log-log space using the given x values.
load_results(pickle_file)	Load in a saved pickle file.
run([logspacing, return_stddev, boundary, ...])	Computes all SCF outputs.
save_results([output_name, keep_data])	Save the results of the SCF to avoid re-computing.

Attributes Documentation

lags¶

Values of the lags, in pixels, to compute SCF at

scf_spectrum¶

Azimuthally averaged 1D SCF spectrum

scf_spectrum_stddev¶

Standard deviation of the scf_spectrum

scf_surface¶

SCF correlation array

slope¶

SCF spectrum slope

slope_err¶

1-sigma error on the SCF spectrum slope

Methods Documentation

compute_spectrum(logspacing=False, return_stddev=True, **kwargs)[source] [edit on github]¶

Compute the 1D spectrum as a function of lag. Can optionally use log-spaced bins. kwargs are passed into the pspec function, which provides many options. The default settings are applicable in nearly all use cases.

Parameters:	logspacing : bool, optional Return logarithmically spaced bins for the lags. return_stddev : bool, optional Return the standard deviation in the 1D bins. Default is True. kwargs : passed to turbustat.statistics.psds.pspec

compute_surface(boundary='continuous')[source] [edit on github]¶

Computes the SCF up to the given lag value.

Parameters:	boundary : {“continuous”, “cut”} Treat the boundary as continuous (wrap-around) or cut values beyond the edge (i.e., for most observational data).

fit_plaw(xlow=None, xhigh=None, verbose=False)[source] [edit on github]¶

Fit a power-law to the SCF spectrum.

Parameters:	xlow : float, optional Lower lag value limit in log-scale to consider in the fit. xhigh : float, optional Upper lag value limit in log-scale to consider in the fit. verbose : bool, optional Show fit summary when enabled.

fitted_model(xvals)[source] [edit on github]¶

Computes the fitted power-law in log-log space using the given x values.

Parameters:	xvals : ndarray Values of log(lags) to compute the model at (base 10 log).
Returns:	model_values : ndarray Values of the model at the given values. Equivalent to log values of the SCF spectrum.

static load_results(pickle_file)[source] [edit on github]¶

Load in a saved pickle file.

Parameters:	pickle_file : str Name of filename to load in.
Returns:	self : SCF instance SCF instance with saved results.

Examples

Load saved results. >>> scf = SCF.load_results(“scf_saved.pkl”) # doctest: +SKIP

run(logspacing=False, return_stddev=True, boundary='continuous', xlow=None, xhigh=None, save_results=False, output_name=None, verbose=False, ang_units=False, unit=Unit("deg"), save_name=None)[source] [edit on github]¶

Computes all SCF outputs.

Parameters:

logspacing : bool, optional Return logarithmically spaced bins for the lags. return_stddev : bool, optional Return the standard deviation in the 1D bins. boundary : {“continuous”, “cut”} Treat the boundary as continuous (wrap-around) or cut values beyond the edge (i.e., for most observational data). xlow : float, optional See fit_plaw. xhigh : float, optional See fit_plaw. save_results : bool, optional Pickle the results. output_name : str, optional Name of the outputted pickle file. verbose : bool, optional Enables plotting. 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.

save_results(output_name=None, keep_data=False)[source] [edit on github]¶

Save the results of the SCF to avoid re-computing. The pickled file will not include the data cube by default.

Parameters:	output_name : str, optional Name of the outputted pickle file. keep_data : bool, optional Save the data cube in the pickle file when enabled.