v0.6.1

Fixed

• BUILD: added rich library.

Removed

• BUILD: scipy Dependency

v0.6.0

Added

• Added nspp_plot and nspp_simulation functions to time_dependent module.
• DOCS: added nspp_plot and nspp_simulation examples to time dependent notebook
• DOCS: simple trace notebook

Changed

• BREAKING: to prototype trace functionality. config name -> class breaks with v0.5.0

Fixed

• THINNING: patched compatibility of thinning algorithm to work with numpy >= v2. np.Inf -> np.inf

v0.5.0

Added

• EXPERIMENTAL: added trace module with Traceable class for colour coding output from different processes and tracking individual patients.

Fixed

• DIST: fix to NSPPThinning sampling to pre-calcualte mean IAT to ensure that correct exponential mean is used.
• DIST: normal distribution uses minimum value instead of resampling on negative value.

v0.4.0

Changes

• BUILD: Dropped legacy setuptools and migrated package build to hatch
• BUILD: Removed setup.py, requirements.txt and MANIFEST in favour of pyproject.toml

v0.3.3

PATCH:

• PATCH: distributions.Discrete was not returning numpy arrays.

v0.3.2

PATCH:

• Update Github action to publish to pypi. Use setuptools instead of build

v0.3.1

PATCH:

• PYPI has deprecated username and password. PYPI Publish Github action no works with API Token

v0.3.0

Changes:

• Distributions classes now have python type hints.
• Added distributions and time dependent arrivals via thinning example notebooks.
• Added datasets module and function to load example NSPP dataset.
• Distributions added
  • Erlang (mean and stdev parameters)
  • ErlangK (k and theta parameters)
  • Poisson
  • Beta
  • Gamma
  • Weibull
  • PearsonV
  • PearsonVI
  • Discrete (values and observed frequency parameters)
  • ContinuousEmpirical (linear interpolation between groups)
  • RawEmpirical (resample with replacement from individual X's)
  • TruncatedDistribution (arbitrary truncation of any distribution)
• Added sim_tools.time_dependent module that contains NSPPThinning class for modelling time dependent arrival processes.
• Updated test suite for distributions and thinning
• Basic Jupyterbook of documentation.

v0.2.1

Changes

Patches

• Modified Setup tools to avoid numpy import error on build.
• Updated github action to use up to date actions.

v0.2.0

• Added sim_tools.distribution module. This contains classes representing popular sampling distributions for Discrete-event simulation. All classes encapsulate a numpy.random.Generator object, a random seed, and the parameters of a sampling distribution.

• Python has been updated, tested, and patched for 3.10 and 3.11 as well as numpy 1.20+

• Minor linting and code formatting improvement.