The stats_arrays package provides a standard NumPy array interface for defining uncertain parameters used in models, and classes for Monte Carlo sampling. It also plays well with others.
Motivation
- Want a consistent interface to SciPy and NumPy statistical function
- Want to be able to quickly load and save many parameter uncertainty distribution definitions in a portable format
- Want to manipulate and switch parameter uncertainty distributions and variables
- Want simple Monte Carlo random number generators that return a vector of parameter values to be fed into uncertainty or sensitivity analysis
- Want something simple, extensible, documented and tested
The `stats_arrays package was originally developed for the Brightway2 life cycle assessment framework, but can be applied to any stochastic model.
Example
>>> from stats_arrays import *
>>> my_variables = UncertaintyBase.from_dicts(
... {'loc': 2, 'scale': 0.5, 'uncertainty_type': NormalUncertainty.id},
... {'loc': 1.5, 'minimum': 0, 'maximum': 10, 'uncertainty_type': TriangularUncertainty.id}
... )
>>> my_variables
array([(2.0, 0.5, nan, nan, nan, False, 3),
(1.5, nan, nan, 0.0, 10.0, False, 5)],
dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),
('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),
('uncertainty_type', 'u1')])
>>> my_rng = MCRandomNumberGenerator(my_variables)
>>> my_rng.next()
array([ 2.74414022, 3.54748507])
>>> # can also be used as an interator
>>> zip(my_rng, xrange(10))
[(array([ 2.96893108, 2.90654471]), 0),
(array([ 2.31190619, 1.49471845]), 1),
(array([ 3.02026168, 3.33696367]), 2),
(array([ 2.04775418, 3.68356226]), 3),
(array([ 2.61976694, 7.0149952 ]), 4),
(array([ 1.79914025, 6.55264372]), 5),
(array([ 2.2389968 , 1.11165296]), 6),
(array([ 1.69236527, 3.24463981]), 7),
(array([ 1.77750176, 1.90119991]), 8),
(array([ 2.32664152, 0.84490754]), 9)]More
- Source code: https://bitbucket.org/cmutel/stats_arrays
- Online documentation: https://stats_arrays.readthedocs.io/en/latest/
