It has been a long time in the making, but now the day has finally come where the 1D Monte Carlo method has been published! To top it off, the publication is open access (courtesy of my current institute: NIMS), and has a wicked showcase document as supplementary material. Feel (very) free to check it out here!
In short: the MC method can retrieve form-free particle size distributions from isotropic scattering patterns, complete with uncertainties. There is more to it than that, but the details are in the paper. The Python 2.7 code is available in a Git repository as indicated in this post, available under a creative commons attribution sharealike license.
In the same J. Appl. Cryst. edition that my paper appears in, there is another paper which caught my interest. This is a paper by E. B. Knudsen, et al., which presents a bunch of code under the name of FabIO that can be used in Python to read in detector images (under a GPL license). There is even some code there for the more obscure of image formats, with improvements to follow. For me, this is part of what I was looking for to augment my own procedures, so I really appreciate it when others make their stuff available with a suitable license!
That FabIO package is part of larger programs for XRD and data reduction, respectively. Given that this is a program which is sustained by the efforts of a variety of user-driven institutes (ESRF, for example), I am very much looking forward to see which direction it will be taken into. One of the authors has assured me that if there is a need and an example (say, for supporting an imageplate image format or some of the stranger wire-arrays), they are more than willing to implement that particular image format. Naturally, that open-source project is available for collaborative efforts as well!