Reference
Classes
Main classes provided by the package
- distributions.Bernoulli
-
Bernoulli distribution implementation.
- distributions.Beta
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Beta distribution implementation.
- distributions.CombinationDistribution
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Combination distribution implementation.
- distributions.DiscreteEmpirical
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DiscreteEmpirical distribution implementation.
- distributions.DistributionRegistry
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Registry for probability distribution classes with batch creation
- distributions.Erlang
-
Erlang distribution implementation.
- distributions.ErlangK
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Erlang distribution where k and theta are specified.
- distributions.Exponential
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Exponential distribution implementation.
- distributions.FixedDistribution
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Fixed distribution implementation.
- distributions.Gamma
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Gamma distribution implementation with shape (alpha) and scale (beta)
- distributions.GroupedContinuousEmpirical
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Continuous Empirical Distribution for Grouped Data implementation.
- distributions.Hyperexponential
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Hyperexponential distribution implementation.
- distributions.Lognormal
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Lognormal distribution implementation.
- distributions.Normal
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Normal distribution implementation with optional truncation.
- distributions.PearsonV
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Pearson Type V distribution implementation (inverse Gamma distribution).
- distributions.PearsonVI
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Pearson Type VI distribution implementation (inverted beta distribution).
- distributions.Poisson
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Poisson distribution implementation.
- distributions.RawContinuousEmpirical
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Continuous Empirical Distribution for Raw Data using Law and Kelton’s
- distributions.RawDiscreteEmpirical
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Raw Empirical distribution implementation.
- distributions.Triangular
-
Triangular distribution implementation.
- distributions.TruncatedDistribution
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Truncated Distribution implementation.
- distributions.Uniform
-
Uniform distribution implementation.
- distributions.Weibull
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Weibull distribution implementation.
- output_analysis.OnlineStatistics
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Computes running sample mean and variance using Welford’s algorithm.
- output_analysis.ReplicationTabulizer
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Observer class for recording replication results from an
- output_analysis.ReplicationsAlgorithm
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Automatically determine the number of simulation replications needed
- time_dependent.DistributionRegistry
-
Registry for probability distribution classes with batch creation
- time_dependent.NSPPThinning
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Non Stationary Poisson Process via Thinning.
Abstract Classes
Abstract base classes
- trace.Traceable
-
Provides basic trace functionality for a process to subclass.
Protocols
Protocol / structural-typing interfaces
- distributions.Distribution
-
Distribution protocol defining the interface for probability distributions.
- output_analysis.AlgorithmObserver
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Protocol for observer classes used in ReplicationsAlgorithm.
- output_analysis.ReplicationObserver
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Interface (protocol) for observers that track simulation replication
- output_analysis.ReplicationsAlgorithmModelAdapter
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Adapter pattern for the “Replications Algorithm”.
Functions
Utility functions
- datasets.load_banks_et_al_nspp()
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Load example Non-stationary poisson process data from Banks et al.
- distributions.is_integer()
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Validates that a value is an integer.
- distributions.is_non_negative()
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Validates that a value is greater than or equal to 0.
- distributions.is_numeric()
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Validates that a value is a number (int or float).
- distributions.is_ordered_pair()
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Validates that two values are in ascending order (low < high).
- distributions.is_ordered_triplet()
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Validates that three values are in ascending order.
- distributions.is_positive()
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Validates that a value is positive (> 0).
- distributions.is_positive_array()
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Validates that all elements in the array are positive.
- distributions.is_probability()
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Validates that a value is a valid probability (between 0 and 1).
- distributions.is_probability_vector()
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Validates that the array is a valid probability vector.
- distributions.spawn_seeds()
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Generate multiple statistically independent random seeds.
- distributions.validate()
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Applies multiple validators to a value.
- output_analysis.confidence_interval_method()
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Determine the minimum number of simulation replications required to achieve
- output_analysis.plotly_confidence_interval_method()
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Create an interactive Plotly visualisation of the cumulative mean and
- time_dependent.nspp_plot()
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Generate a matplotlib chart to visualise a non-stationary poisson process
- time_dependent.nspp_simulation()
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Generate a pandas dataframe that contains multiple replications of
Constants
Module-level constants and data
Other
Additional exports