v0.6.1

• Use optimized version of skimage.feature.peak_local_max (see scikit-image/scikit-image#7548). This slightly changes the intensity measurements.

v0.6.0

• Update to photutils v2.0.

v0.5.7

• Change rms_asymmetry to rms_asymmetry2, since this quantity actually represents the square of A_rms.

v0.5.6

• Implement RMS asymmetry (Sazonova et al. 2024).

v0.5.5

• More robust search for asymmetry center.
• Attempt Sersic fit even when basic measurements were not successful (flag >= 2).

v0.5.4

• Make statmorph slightly more memory efficient.

v0.5.3

• An improved initial guess for the double Sersic fit is now obtained by performing single Sersic fits to to the inner and outer regions of the source of interest, which are assumed to be separated by an ellipse of a given size.
• The separating ellipse is by default twice the half-light ellipse, but this can be controlled by the user with the argument doublesersic_rsep_over_rhalf
• The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are now calculated for the single and double Sersic fits.

v0.5.2

• Users can now perform double 2D Sersic fits by including the option include_doublesersic = True.
• Users can (optionally) customize the fits via the arguments doublesersic_model_args and doublesersic_fitting_args.
• Furthermore, the option doublesersic_tied_ellip = True can be used to ensure that both components have the same ellipticity and position angle, if desired.
• The quality flags flag_sersic and flag_doublesersic now take values 0-4, just like flag (see v0.5.0 release notes).
• Added reduced chi^2 statistics (sersic_chi2_dof and doublesersic_chi2_dof) to measure goodness of fit.
• Added an example notebook about double Sersic fitting to the documentation.
• Updated tutorial.

v0.5.1

• Added support for constrained Sersic fits via the sersic_model_args keyword.
• The input argument sersic_maxiter is deprecated. Please use sersic_fitting_args instead.
• Default value of cutout_extent increased from 1.5 to 2.5 (more "space" to make measurements by default, although this also depends on the input segmap).

v0.5.0

The "bad measurement" flag now takes values between 0 and 4 (formerly 0 and 1), as explained in the documentation. In summary, a flag of 1 now indicates mild issues with the measurements, while a flag of 2 is reserved for more serious problems. In practice, users of previous versions of statmorph need to know the following when upgrading to v0.5.0:

• The statement flag == 0 has the same effect as before, since a flag of 0 indicates good measurements.
• The statement flag != 1 should be replaced with flag == 0 (to avoid erroneously selecting flag == 2 cases).
• The statement flag == 1 (or flag != 0) should be replaced with either flag >= 1 (to restore the functionality of previous versions) or with flag == 2, which should result in a smaller fraction of "bad" measurements.
• Users who are dealing with a high fraction of "bad" measurements should consider replacing flag == 0 with the less strict flag <= 1 condition.
• The flag_catastrophic property was removed and is now represented by flag == 4 (note that all calculations are aborted when this happens).