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Changelog

v0.4.8: Remove Python version upper bound¤

Removes an erroneous upper bound limit on Python version (was previously <3.13, which is super restrictive.)

v0.4.7: Relax numba version constraints¤

Maintenance release that allows for higher numba versions that previously, thanks to a bugfix now in numba.

v0.4.6: Relax version constraints¤

Bugfix release that relaxes some version constraints, including allowing for support of e.g. astropy 7.0.

v0.4.5: Bugfix to cluster simulation¤

Fixes a regression added in a previous release that prevented simulation of clusters observed with Gaia.

... remember: always run your unit tests! 🥴

v0.4.4: Bugfix for cluster simulation when zero stars simulated¤

Two changes to how simulated clusters handle having zero stars were made.

  • When zero stars are simulated, ocelot.simulate.SimulatedCluster now raises a NotEnoughStarsError (defined in ocelot.simulate.errors), making it easier to catch when no stars were simulated.
  • When cluster pre-pruning causes zero stars to be simulated, ocelot.simulate.SimulatedCluster now also raises a NotEnoughStarsError (previously, this behaviour was undefined.)

v0.4.3: Improvements to (Gaia) cluster simulation¤

A couple of improvements were made to cluster simulation:

  • ocelot.model.observation.BaseObservation now has two additional default methods for applying photometric and astrometric errors. These can be overwritten for observation types that require additional functionality beyond simple Gaussian errors.
  • The GaiaDR3ObservationModel class now uses this photometric data overwrite to model underestimated BP and RP fluxes in Gaia DR3, slightly improving the realism of cluster CMDs further.

v0.4.2: Limit Python version¤

This is a quick release to limit our Python version to below 3.13, as it seems some of our dependencies don't support it yet.

v0.4.1 - Move IMF to optional dependency¤

The imf library has been moved to be an optional dependency to try and fix issues with PyPI upload failing.

v0.4.0 - Addition of cluster simulation code and models¤

This release brings a brand new, sophisticated API for cluster simulation, in addition to optimized models covering a number of different aspects of star cluster science. Additions include:

  • The new ocelot.simulate submodule for simulating clusters, including:
    • The flexible and hackable SimulatedCluster class. Extensive code optimizations allow for most open clusters to have full simulations (even randomly sampling the orbits of their binary stars to see if they would or wouldn't be resolved!) in less than a second
    • New SimulatedClusterParamters, SimulatedClusterModels, and SimulatedClusterFeatures dataclasses for controlling SimulatedCluster functionality
    • Ability to simulate observations of simulated clusters, allowing the same simulated cluster to be 'observed' by many different telescopes
  • The new ocelot.model submodule for star cluster models, including:
    • A model of King 1962 empirical star clusters. Currently limited to sampling clusters in 3D
    • A bespoke model of differential extinction that approximates dust structure with fractal noise
    • A heavily optimized implementation of the Moe & DiStefano 2017 binary star models
    • Models for selection functions due to observations, such as an optimized implementation of the Castro-Ginard 2023 subsample selection function that can be used to model any generic subsample of stars
    • A model for star cluster observations with Gaia
  • Extensive unit tests to assure the reliability and accuracy of simulated clusters and their models
  • Documentation improvements, including the first tutorial in the module
  • Refactoring of some old aspects of the module

The APIs of ocelot.simulate and ocelot.model are now the first stable APIs of ocelot, and are ready to use in production code.