kinisi-0.3.4a

See kinisi-0.3.4. This release is to trigger the PyPI distribution release.

kinisi-0.3.4

Changes

• Improve stability of likelihood sampling by using hand rolled likelihood function.
• Reverted the removal the rtol parameter from the bootstrap method.

kinisi-0.3.3

Changes

• Improve stability of covariance matrix
• Remove the rtol parameter from the bootstrap method.

kinisi-0.3.2

Changes

• Reverted to using likelihood sampling, which with the fixes to the covariance matrix gives the same results as the bootstrap approach.

kinisi-0.3.1

Changes

• SciPy 1.7.0 is now required on installation

kinisi-0.3.0

Changes

• The variance is now determined from using generalised least squares on a number of random samples from the Gaussian process, this has the benefit of being more accurate and efficient.
• As a result of the above there is no longer a "Sampling Likelihood" tqdm bar.
• There is now a rtol kwarg for the bootstap_GLS method, this controls the rtol in the call to the scipy.linalg.pinvh that is used to stabilise the large matrices.
• All documentation has been adapted to reflect this methodological change.

kinisi-0.2.2

Changes

• Add the ability to use multiple identical simulations from an MDAnalysis.Universe class object.

kinisi-0.2.1

Changes

• Add the flatchain property to the Analyzer classes, this returns the samples from the MCMC sampling of the diffusion parameter and intercept.
• Include the use of the new flatchain property in the documentation.
• Pin to the latest (1.2.5) version of uravu, which includes the flatchain property for Relationship class objects.
• Use the flatchain property from uravu in the Arrhenius tutorial.

kinisi-0.2.0

Changes

• Significant improvements to documentation, including a detailed description of the methodology used by kinisi, a tutorial showing the use of the Arrhenius functionality and the addition of a covariance matrix derivation document.
• Running and testing on python 3.10 (via CI and support in setup.py).
• A transition in the API to follow a more pymatgen style, where Analyzer objects are accessed by calling a from_* method based on the inputs.
• Addition of a logo file to the repository.

kinsi-0.1.1

Changes

• Previously, the sampling of the diffusion coefficient allowed for negative values, this was determined to be unphysical. Therefore, a Bayesian prior of a Heaviside step function at 0 has been assigned to the gradient/diffusion coefficient to stop this.
• It was noted that for very large number of atoms/timesteps (which are typically handled using MDAnalysis), there could be a memory overflow. This is due to the creation of a list of numpy arrays, for which the largest could be in the gigabyte range.

  item_size * atoms * timesteps * dimensions * to Gb
  8 * 20000 * 10000 * 3 * 1e-9 = 4.8 #Gb
  

  Therefore, in addition to being able to sub_sample_traj, it is also possible to sub_sample_atoms (current this is only supported of MDAnalysis objects), which performed the same function as sub_sample_traj but on the atoms instead of timesteps.
• The code is now fully typed and formatted to follow PEP8 formatted guidelines (with the exception of a 120 max line length).