Change log

Change log#

v0.7.1 DOI#

Fixed#

  • Patched ReplicationsAlgorithm look ahead will now correctly use _klimit() to calculate extra no. replications to run.

v0.7.0 DOI#

Added#

  • output_analysis module - focussed at the moment on selecting the number of replications

  • ReplicationsAlgorithm that implements the automated approach to selecting the number of replications for a single performance measures.

  • ReplicationsAlgorithmModelAdapter - a Protocol to adapt any model to work with with ReplicationsAlgorithm

  • confidence_interval_method - select the number of replication using the classical confidence interval method

  • plotly_confidence_interval_method - visualise the confidence interval method using plotly.

  • ReplicationObserver a Protocol for observering the replications algorithm

  • ReplicationTabulizer record replications algorithm in a pandas dataframe.

  • Documentation for ReplicationsAlgorithm

Updated#

  • sim-tools dev conda environment now pip installs local python package in editable model.

v0.6.1 DOI#

Fixed#

  • BUILD: added rich library.

Removed#

  • Scipy Dependency

v0.6.0 DOI#

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 DOI#

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 DOI#

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 DOI#

Fixed#

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

v0.3.2 DOI#

Changed#

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

v0.3.1 DOI#

Fixed:#

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

v0.3.0#

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.

v0.2.1#

Fixed#

  • Modified Setup tools to avoid numpy import error on build.

  • Updated github action to use up to date actions.

v0.2.0 DOI#

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.