Open Source Python Mathematics Software
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Python Mathematics Software
View 209 business solutionsBrowse free open source Python Mathematics Software and projects below. Use the toggles on the left to filter open source Python Mathematics Software by OS, license, language, programming language, and project status.
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SymPy
A computer algebra system written in pure Python
SymPy is an open source Python library for symbolic mathematics. Its goal is to become a full-featured computer algebra system (CAS) while maintaining the simplicity of its code. Written entirely in Python, SymPy is easy to use, comprehensible and easily extensible. It’s also very lightweight as it solely depends on mpmath, a pure Python library for arbitrary floating point arithmetic. SymPy has participated in every Google Summer of Code since 2007 and because of this has continuously improved. A lot more people have also contributed to it, and used it in their projects. Beyond being an interactive tool, it can be embedded in other apps and extended with custom functions. -
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SciPy
SciPy library main repository
This is the main repository for the SciPy library, one of the core packages that make up the SciPy stack. SciPy is an open source software used in the fields of mathematics, science, and engineering, with modules for statistics, optimization, integration, linear algebra, signal and image processing, and many more. The SciPy library contains many of the user-friendly and efficient numerical routines, including those for numerical integration, interpolation, and optimization. SciPy is built to work with NumPy, a software that provides convenient and fast N-dimensional array manipulation. Both SciPy and NumPy run on all popular operating systems, are fast and easy to install, and are powerful yet easy to use. They’re currently depended upon by numerous leading scientists and engineers all over the world. Try them for yourself! -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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RPy is a very simple, yet robust, Python interface to the R Programming Language. It can manage all kinds of R objects and can execute arbitrary R functions (including the graphic functions). RPy has been superseded by the RPy2 project: https://rpy2.github.io/
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. Based on one of the most widely-used Python tensor libraries: Theano. -
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The Generic Graphics Toolkit is a library of generic programming primitives that allow applications to be written that adapt to whatever underlying graphics system the developer is using.
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Digraph3
A collection of python3 modules for Algorithmic Decision Theory
This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs. -
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xLaDe
A Lean 4 Ecosystem Framework!
xLaDe is a simple Python-based CLI tool built for executing and preserving Lean 4 projects. It is an ecosystem-level tool, which records the toolchain and other metadata of projects and allows the reconstruction and rebuilding of that exact environment later. Lean 4 undergoes rapid development, which can introduce backward-compatibility issues. This problem becomes more difficult as the versions accumulate over time. xLaDe is built to mitigate the practical issues of backward-compatibility problems and improve ecosystem-level tooling for the Lean 4 theorem prover. Instead of directly solving backward-compatibility issues by storing every version of Lean 4 and other tools or providing cross-version compatibility, xLaDe tries to store sufficient environment metadata such as toolchains, dependencies, and other context, and then recreates the environment for running Lean 4 projects upon request, also termed as experiments in xLaDe. -
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The NUMIPAD library implements several methods/algorithms to solve inverse problems and adaptive decomposition (i.e. Tikhonov regularization, Total Variation, Basis Pursuit, etc.)
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Compilation of Physics Calculators
A compliation of Physics derived claculators
A NEW GUI VERSION WILL BE RELEASED SOON! Thank you for visiting the Physics Calculator, provided by PIezoSystems Inc., please visit their company website at https://piezosystems.org. For other downloads, please visit our github page, https://github.com/PiezoSystems. We hope that you find this program useful and easy to use, for troubleshooting, please email us, contact@piezosystems.org. If any bugs were found, please email us at bugs@piezosystems.org. -
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GluCat: Clifford algebra templates
Calculation with Clifford algebras: C++ library and Python module
GluCat is a generic library of C++ templates that implement universal Clifford algebras over the field of real numbers. The PyClical extension module for Python gives users an easy Python scripting interface for calculations in Clifford algebras. The name PyClical is an homage to Pertti Lounesto's CLICAL. -
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The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
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TAROT is a easy-to-use framework for Monte Carlo simulations in python. Calculations between different kinds of randomly distributed numbers are made as easy as basic arithmetics. Tarot provides an interactive graphical interface for interpretation.
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That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline-related Inventory Management system. That library uses the Standard Airline IT C++ object model (http://sf.net/projects/stdair).
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H9A
H9A counts how many times a digit appears in any range, instantly.
H9A is an installable Python package that counts how many times a given digit appears in a range of numbers (by default: the digit 9 between 1 and 100). It provides both a h9a command-line tool and an importable library, and it prints each step of the calculation — the per-place counts and the combined total — as styled, colorized output. It is built with rich for console formatting, pyfiglet for an ASCII-art banner, and Pillow for optional terminal-style screenshots. -
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PyVision Computer Vision Toolkit
A Python computer vision library
PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries. -
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Simple Python Fixed-Point Module
Pure-python binary fixed-point arithmetic library
This Python module provides basic facilities for mathematics on fixed-point numbers. The number of fractional binary digits is tunable, allowing near-arbitrary precision arithmetic. Arithmetic & functions such as sqrt, exp, log, sin & cos are available. For the latest releases please visit https://github.com/rwpenney/spfpm -
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Travel Market Simulator
Travel Market Simulator
That project aims at studying the impact of IT systems interactions on traveller demand and airline revenues. Passenger demand is generated (Monte Carlo) and injected into simulated CRS and airline IT systems. Differential analysis is then performed on various changes compared to a bottom line scenario. -
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binary Turing Machine
A Turing Machine Simulator
Free binary Turing Machine simulator with a graphical user interface and possibility to save your files. Language : French (no translation available) -
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pure python polyfit
python2/3: compute polyfit (1D, 2D, N-D) without thirdparty libraries
python2/3: compute polyfit (1D, 2D, N-D) without any thirdparty library like numpy, scipy etc. also can be used for least squares solution computation and for A=QR matrix decomposition. Tested with python 2.7 and 3.4 Consider donating to this project: https://sourceforge.net/p/purepythonpolyfit/donation For a Sample use, refer to the WIKI -
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AirTSP
Simulated Airline Travel Solution Provider Library
AirTSP is also named AirTSP (TSP standing for Travel Service Provider). That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline Schedule Management System. It is intended to be used in simulated environments only: it is not designed to work in the real-world of Airline IT operations. AirTSP/AirTSP makes an extensive use of existing open-source libraries for increased functionality, speed and accuracy. In particular the Boost (C++ Standard Extensions: http://www.boost.org) library is used. AirTSP/AirTSP is the one of the components of the Travel Market Simulator (http://www.travel-market-simulator). However, it may be used in a stand-alone mode.
