Release early, release often, then hide in a foreign country and wait one year

Thanks to waiting one year, Google and the Python Software Foundation have substantially improved jax and Python, making lsqfitgp faster and supported on Windows, through no effort of my own.

Release highlights

• Improved gvar formatting.
• Data transformations in bayestree.bcf.

Improved formatting of uncertainties

Improved gvar formatting. Disabled by default, can be enabled with the context manager gvar_format. The new format has more options than the one provided by gvar. In particular the default setting has 1.5 error digits instead of 2, and never shows non-significant digits.

• Fractional error digits: with 1.5 digits, 2 error digits are shown up to $\sqrt{10}$, then 1. So 12.3 +/- 1.5 becomes '12.3(1.5), while 12.3 +/- 4.5 becomes '12(4)'.
• Never show non-significant digits: the gvar formatter displays 1 +/- 234 as '1(234)', showing more digits than the first 2 error digits. The new formatter instead writes 'oo(23o)', using o as a "small zero" symbol to represent a rounding zero rather than a significant zero. This also allows to avoid exponential notation for small numbers: 1234 +/- 567 is shown as '1.23(57)e+03' with the default formatter, and as '12oo(6oo)' with the new formatter.

The new formatter can also be used manually without gvars through uformat.

BCF

• Data transformations, with free parameters inferred together with the other hyperparameters, thus accounting for the additional degrees of freedom due to the transformation. The predefined transformations are standardization and Yeo-Johnson. The user can pass arbitrary callables, and easily stack their own transformations with the predefined ones.
• The hyperparameters are always expressed in the transformed model, instead of rescaled for standardization, since now the data transformation is under control of the user.
• bcf.pred can sample the predictive posterior, and return results in the transformed space instead of data space.

empbayes_fit

• New parameter empbayesfit(..., additional_loss=<func>) to add an user-specified function to the minus log marginal posterior of the hyperparameters used to find the MAP.
• Fix show-stopping bug with old numpy versions if there were more than 1 hyperparameters.

Dependencies

As usual I run tests with the oldest supported versions of all dependencies. Additionally, I now check that the versions pre-installed on Google Colab are supported.

• Minimum required versions bumped to:
  • Python 3.8 -> 3.9
  • numpy 1.20 -> 1.22
  • scipy 1.5 -> 1.10
  • jax 0.4.6 -> 0.4.26
  • gvar 11.10.1 -> 12.0
• New supported versions/platforms:
  • Python 3.12
  • numpy 2
  • Windows fully supported instead of experimental

StructuredArray

• Implemented StructuredArray.nbytes.