Comparison and Summary Statistics

Tools for calculating derived quantities (image2 images or reductions) based on an image. Should all be in the form def q(image) to be accessible as image.

imtools.stats.dssim(var1, var2)

Image dissimilarity DSSIM is 1/abs(SSIM) - 1

imtools.stats.dssims(image1, image2)

DSSIM for each variable

imtools.stats.mle(var1, var2)

Mean “Linear” error (normalized L1 norm) Follows GRRT paper definition in dividing by the sum(var) of first image

imtools.stats.mles(image1, image2)

MSE of this image vs animage2. Follows GRRT paper definition in dividing by the sum(var) of first image

imtools.stats.mse(var1, var2)

MSE between images. Follows GRRT paper definition in dividing by the sum(var) of first image

imtools.stats.mses(image1, image2)

MSE for each variable.

imtools.stats.ncc(var1, var2)

Normalized Cross-Correlation NCC (like ZNCC without mean subtraction)

imtools.stats.nccs(image1, image2)

NCC for each variable

imtools.stats.polar_abs_integrated(image1, image2)

Absolute differences in the total flux, integrated & averaged linear polarization fractions, and circular polarization fraction.

imtools.stats.polar_rels_integrated(image1, image2)

Relative differences in the total flux, integrated & averaged linear polarization fractions, and circular polarization fraction. (That is, a “polar” breakdown with magnitudes instead of a “Cartesian” breakdown with Stokes Parameters)

imtools.stats.rel_integrated(var1, var2)

Relative difference of the sum of a variable

imtools.stats.rels_integrated(image1, image2)

Integrated relative errors for each variable

imtools.stats.ssim(var1, var2)

Image structural similarity SSIM as defined in Gold et. al, eq. 14

imtools.stats.ssims(image1, image2)

SSIM for each variable

imtools.stats.zncc(var1, var2)

Zero-Normalized Cross-Correlation ZNCC (normalized correlation of deviation from mean)

imtools.stats.znccs(image1, image2)

ZNCC for each variable