Metadata-Version: 2.1
Name: aesara
Version: 2.9.3
Summary: A library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
Project-URL: Homepage, https://github.com/aesara-devs/aesara
Author-email: aesara-devs <aesara.devs@gmail.com>
License: BSD-3-Clause
License-File: LICENSE.txt
Keywords: aesara,autodiff,blas,differentiation,math,numerical,numpy,symbolic
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX :: SunOS/Solaris
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Compilers
Requires-Python: >=3.8
Requires-Dist: cons
Requires-Dist: etuples
Requires-Dist: filelock
Requires-Dist: logical-unification
Requires-Dist: minikanren
Requires-Dist: numpy>=1.17.0
Requires-Dist: scipy>=0.14
Requires-Dist: setuptools>=48.0.0
Requires-Dist: typing-extensions
Description-Content-Type: text/x-rst

Aesara is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy_. Aesara features:

 * **tight integration with NumPy:** a similar interface to NumPy's. numpy.ndarrays are also used internally in Aesara-compiled functions.
 * **efficient symbolic differentiation:** Aesara can compute derivatives for functions of one or many inputs.
 * **speed and stability optimizations:** avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.
 * **dynamic C code generation:** evaluate expressions faster.
 * **extensive unit-testing and self-verification:** includes tools for detecting and diagnosing bugs and/or potential problems.

.. _NumPy: http://numpy.scipy.org/