Metadata-Version: 2.4
Name: psevencore
Version: 2026.5.20
Summary: pSeven Core is an integrated toolkit for design space exploration, optimization, and predictive modeling.
Author-email: pSeven SAS <info@pseven.io>
Maintainer-email: pSeven SAS <info@pseven.io>
License-Expression: LicenseRef-pSeven-Proprietary
Project-URL: Homepage, https://www.pseven.io/product/pseven-core/
Project-URL: Documentation, https://www.pseven.io/product/pseven-core/manual/
Project-URL: Changelog, https://www.pseven.io/product/pseven-core/manual/latest/support/changelog.html
Project-URL: Support, https://www.pseven.io/support.html
Keywords: adaptive design,adaptive doe,approximation,approximation model,approximation modeling,approximator,box-behnken,correlation analysis,design exploration,design of experiments,design optimization,design space,difference approximation,dimension reduction,doe,engineering,engineering optimization,feature importance,feature scoring,feature selection,fmi,fmu,fractional factorial,gaussian processes,gradient optimization,kendall,latin hypercube sampling,low discrepancy sequence,mixed integer,mixture of approximators,modeling,multiple fidelity,mutual information,optimization,optimizer,orthogonal array,parametric study,pearson,predictive modeling,principal component analysis,regression,regression tree,response surface,robust optimization,screening index,screening indices,sensitivity analysis,shap,shapley additive explanations,sobol index,sobol indices,spearman,surrogate based optimization,taguchi index,taguchi indices,tensor approximation,variable fidelity
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Manufacturing
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: C
Classifier: Programming Language :: Java
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Java Libraries
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<2,>=1.16; python_version ~= "2.7"
Requires-Dist: numpy>=1.16; python_version >= "3.6"
Dynamic: license-file


pSeven Core is an integrated toolkit for predictive modeling, data analysis
and optimization. It provides a variety of proprietary and classical algorithms
for local and global optimization, approximation, dimension reduction, design
of experiments, and sensitivity analysis. See the homepage and documentation
for full details.

-   Homepage: https://www.pseven.io/product/pseven-core/
-   Documentation: https://www.pseven.io/product/pseven-core/manual/
-   Support: https://www.pseven.io/support.html
-   Contacts: https://www.pseven.io/contacts/


## Requirements

-   Python 3.6 or newer.
    -   pSeven Core also maintains compatibility with the final Python 2.7 version.
-   [NumPy](https://pypi.org/project/numpy/) 1.16 or newer.
    -   pSeven Core v2024.06 and older versions are not compatible with NumPy 2.
        pSeven Core v2024.07 and newer are up to date with NumPy 2.

Additionally recommended:

- [pandas](https://pypi.org/project/pandas/), any up to date version.
- [SciPy](https://pypi.org/project/scipy/), any up to date version.
- [Matplotlib](https://pypi.org/project/matplotlib/) 1.1 or newer.

While the above are not required, they are widely used in pSeven Core examples
and guides.

Optional:

- [SHAP](https://pypi.org/project/shap/) - implements a game theoretic approach
  to explain model output.

SHAP is required only by some pSeven Core approximation models and only if you
are going to use the SHAP evaluation feature for that certain kind of models.


### Windows requirements

pSeven Core is tested on Windows 10, 64-bit desktop editions. Newer versions
and corresponding server editions are also supported but not regularly tested.


### Linux requirements

pSeven Core works on any Linux x86_64 with:

* Linux kernel 2.6.18 or newer.
* GNU C Library (glibc) 2.5 or newer.
