DeltaVariance¶

class turbustat.statistics.DeltaVariance(img, header=None, weights=None, diam_ratio=1.5, lags=None, nlags=25)[source] [edit on github]¶

Bases: turbustat.statistics.base_statistic.BaseStatisticMixIn

The delta-variance technique as described in Ossenkopf et al. (2008).

Parameters:

img : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject The image calculate the delta-variance of. header : FITS header, optional Image header. Required when img is a ndarray. weights : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject Weights to be used. diam_ratio : float, optional The ratio between the kernel sizes. lags : numpy.ndarray or list, optional The pixel scales to compute the delta-variance at. nlags : int, optional Number of lags to use.

Attributes Summary

delta_var	Delta Variance values.
delta_var_error	1-sigma errors on the Delta variance values.
fit_range
slope
slope_err
weights	Array of weights.

Methods Summary

compute_deltavar()	Computes the delta-variance values and errors.
do_convolutions([allow_huge, boundary])	Perform the convolutions at all lags.
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.
run([verbose, ang_units, unit, allow_huge, ...])	Compute the delta-variance.

Attributes Documentation

delta_var¶

Delta Variance values.

delta_var_error¶

1-sigma errors on the Delta variance values.

fit_range¶

slope¶

slope_err¶

weights¶

Array of weights.

Methods Documentation

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

Computes the delta-variance values and errors.

do_convolutions(allow_huge=False, boundary='wrap')[source] [edit on github]¶

Perform the convolutions at all lags.

Parameters:	allow_huge : bool, optional Passed to convolve_fft. Allows operations on images larger than 1 Gb. boundary : {“wrap”, “fill”}, optional Use “wrap” for periodic boundaries, and “fill” for non-periodic.

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 to consider in the fit. xhigh : float, optional Upper lag value 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.

run(verbose=False, ang_units=False, unit=Unit("deg"), allow_huge=False, boundary='wrap', xlow=None, xhigh=None, save_name=None)[source] [edit on github]¶

Compute the delta-variance.

Parameters:

verbose : bool, optional Plot delta-variance transform. 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. allow_huge : bool, optional See do_convolutions. boundary : {“wrap”, “fill”}, optional Use “wrap” for periodic boundaries, and “cut” for non-periodic. xlow : float, optional Lower lag value to consider in the fit. xhigh : float, optional Upper lag value to consider in the fit. save_name : str,optional Save the figure when a file name is given.