Sim-tools

Fixed

* BUILD: added rich library.

Removed

* Scipy Dependency

[v0.6.0](https://github.com/TomMonks/sim-tools/releases/tag/v0.6.0) [](https://doi.org/10.5281/zenodo.13122484)

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](https://github.com/TomMonks/sim-tools/releases/tag/v0.5.0) [](https://doi.org/10.5281/zenodo.12204481)

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 allows minimum value and truncates automaticalled instead of resampling.

[v0.4.0](https://github.com/TomMonks/sim-tools/releases/tag/v0.4.0) [](https://doi.org/10.5281/zenodo.10987685)

Changed

* 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](https://github.com/TomMonks/sim-tools/releases/tag/v0.3.3) [](https://doi.org/10.5281/zenodo.10629861)

Fixed

* PATCH: `distributions.Discrete` was not returning numpy arrays.

[v0.3.2](https://github.com/TomMonks/sim-tools/releases/tag/v0.3.2) [](https://doi.org/10.5281/zenodo.10625581)

Changed

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

[v0.3.1](https://github.com/TomMonks/sim-tools/releases/tag/v0.3.1) [](https://doi.org/10.5281/zenodo.10625470)

Fixed:

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

Added

* 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.

Fixed

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

Added

* 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.

Changed

* 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.