Change log

Change log#

[v0.9.0]#

Added#

  • distributions.DistributionRegistry - for batch creation of standard distributions from a dictionary or list.

  • DOCS: DistributionRegistry explaination and examples including use of JSON to store configs.

  • distributions.spawn_seeds function to support creation of PRNG streams.

  • Hyperexponential - A continuous probability distribution that is a mixture (weighted sum) of exponential distributions. It has a higher coefficient of variation than a single exponential distribution, making it useful for modeling highly variable processes or heavy-tailed phenomena.

  • RawContinuousEmpirical - A distribution that performs linear interpolation between data points according to the algorithm described in Law & Kelton’s “Simulation Modeling and Analysis”.

  • sim_tools._validation: internal module that contains common validation routines for sim_tools functions and classes.

  • All distribution classes updated to include valudation of input parameters.

  • DOCS: Dedicated page for using empirical distributions.

Changed#

  • Distribution changed from abstract base class to Protocol. All inheritance removed from concrete classes.

  • Added __repr__() to all distribution classes.

  • DOCS: improved docstrings for all distribution classes

  • BREAKING: Discrete -> DiscreteEmpirical

  • BREAKING: RawEmpirical -> RawDiscreteEmpirical

  • BREAKING: ContinuousEmpirical -> GroupedContinuousEmpirical`. To clarify the purpose of the emprical distribution

  • BREAKING: NSPPThinning: class now only requires “mean_iat” column in data. Acceptance/rejection calcualted using $iat_{min} / iat_(t)$

Fixed#

  • Gamma fix of the calculation of the mean based on alpha and beta.

  • GroupedContinuousEmpirical: silent bug when u selects the first group. Interpolation did not work correctly and sampled out of range. This now been handled by logic pre-sample.

  • NSPPThinning: removed redundant outer loop from sampling.

v0.8.0#

Added#

  • Add simpy and treat-sim to the environment, as these were required in the notebooks in docs/.

  • Add nbqa and pylint to the environment for linting, plus a relevant files lint.sh and .pylintrc.

  • Add tests for output_analysis functions (functional, unit and back tests).

  • Add validation of parameters in ReplicationsAlgorithm.

  • Add validation of data type in OnlineStatistics.

Changed#

  • Simplified distribution value type tests to a single test where possible using pytest.mark.parametrize.

  • Linted .py and .ipynb files using pylint (most addressed, some remain unresolved).

  • Provided advice on tests, building docs and linting in the README.md.

  • 00_front_page.md now just imported README.md (reducing duplication, and keping it up-to-date).

Removed#

  • Removed duplicate sw21_tutorial.ipynb.

Fixed#

  • Within confidence_interval_method, convert data provided to OnlineStatistics to np.array so that it is actually used to update the class (when before, it was not, as it was a list).

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.

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