| Name | stats-arrays JSON |
| Version |
0.7
JSON |
| download |
| home_page | None |
| Summary | Standard NumPy array interface for defining uncertain parameters |
| upload_time | 2024-08-19 16:46:49 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
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](https://docs.brightway.dev/), but can be applied to any stochastic model.
# Example
```python
>>> 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://github.com/brightway-lca/stats_arrays
* Online documentation: https://stats-arrays.readthedocs.io/en/latest/
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"description": "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.\n\n# Motivation\n\n* Want a consistent interface to SciPy and NumPy statistical function\n* Want to be able to quickly load and save many parameter uncertainty distribution definitions in a portable format\n* Want to manipulate and switch parameter uncertainty distributions and variables\n* Want simple Monte Carlo random number generators that return a vector of parameter values to be fed into uncertainty or sensitivity analysis\n* Want something simple, extensible, documented and tested\n\nThe `stats_arrays package was originally developed for the [Brightway2 life cycle assessment framework](https://docs.brightway.dev/), but can be applied to any stochastic model.\n\n# Example\n\n```python\n\n>>> from stats_arrays import *\n>>> my_variables = UncertaintyBase.from_dicts(\n... {'loc': 2, 'scale': 0.5, 'uncertainty_type': NormalUncertainty.id},\n... {'loc': 1.5, 'minimum': 0, 'maximum': 10, 'uncertainty_type': TriangularUncertainty.id}\n... )\n>>> my_variables\narray([(2.0, 0.5, nan, nan, nan, False, 3),\n (1.5, nan, nan, 0.0, 10.0, False, 5)],\n dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),\n ('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),\n ('uncertainty_type', 'u1')])\n>>> my_rng = MCRandomNumberGenerator(my_variables)\n>>> my_rng.next()\narray([ 2.74414022, 3.54748507])\n>>> # can also be used as an interator\n>>> zip(my_rng, xrange(10))\n[(array([ 2.96893108, 2.90654471]), 0),\n (array([ 2.31190619, 1.49471845]), 1),\n (array([ 3.02026168, 3.33696367]), 2),\n (array([ 2.04775418, 3.68356226]), 3),\n (array([ 2.61976694, 7.0149952 ]), 4),\n (array([ 1.79914025, 6.55264372]), 5),\n (array([ 2.2389968 , 1.11165296]), 6),\n (array([ 1.69236527, 3.24463981]), 7),\n (array([ 1.77750176, 1.90119991]), 8),\n (array([ 2.32664152, 0.84490754]), 9)]\n\n```\n\n# More\n\n* Source code: https://github.com/brightway-lca/stats_arrays\n* Online documentation: https://stats-arrays.readthedocs.io/en/latest/\n",
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