Open Source Python UML Tools - Page 2

Mantikhor is an *information representation* language. Like RDF, it models information as directed-edge arc-node graphs, although Mantikhor's use of such constructs is more structured and constrained.

Ocular is a spreadsheet written entirely in python. Cell contents are evaluated by python after any standard spreadsheet coordinates are parsed. This allows the full Monty from Python to be implemented in a visual environment.

O PETRILab é um software multiplataforma desenvolvido inteiramente em Python. Ele permite a modelagem e simulação de Redes de Petri Interpretadas para Controle (RPIC), tendo suporte a todos seus elementos: lugares, transições, arcos ordinários e inibidores, eventos, condições e ações. Com uma interface gráfica simples e intuitiva, o usuário consegue modelar e simular passo-a-passo sua RPIC de forma rápida e prática, afim de estudá-la e aprimorá-la. Além disso, o software conta com uma conversão automática de RPICs em diagramas Ladder, como proposto no artigo de M. V. Moreira e J. C. Basílio [1]. Para utilizar o programa, baixe a versão mais recente em https://sourceforge.net/projects/petrilab/files/, extraia no local de preferência e execute o arquivo 'petrilab.pyw'. [1] M. V. Moreira e J. C. Basílio, “Bridging the Gap Between Design and Implementation of Discrete-Event Controllers”.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERIN

Application used to create models based on the MOF and uses them as new meta-models within the same application.

Moved to Google Code, see http://code.google.com/p/pythonequations/downloads/list

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

vipera is a designer of applications for the programming language Python. It has two main objectives: 1) The design of an application from an educative point of view, paying special attention to the documentation and design of classes. 2) The automatic generation of base code for software projects. vipera is a combination of basic tools for the design of applications in the early stages of development, allowing the creation of modules (libraries) and definition of their basic characteristics, such as classes, functions, records, constants and import modules. The design of classes is done graphically, by means of a code similar to UML. It includes an inverse engineering option, that is, from Python scripts, classes and relationships are identified and represented graphically.

Pythonisa is a Python code analyzer is a simple code analyzer for Python that creates a class diagram after executing your code.

Snot The Second, the rubber boxes King, declared a holy war against evil plastic balls to defeat himself and his crew lives. Excentric crossplatform 2D labyrinth game written in Python. Just try to reach exit of each level.

SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python version and processor type. The Docker images, used to run training & inference jobs, are built from both corresponding "base" and "final" Dockerfiles. The "base" Dockerfile encompasses the installation of the framework and all of the dependencies needed. All "final" Dockerfiles build images using base images that use the tagging scheme.

Most known is SPE, a python IDE with auto indentation&completion,call tips,syntax coloring&highlighting,uml viewer,class explorer,source index,todo list,pycrust shell,file browsers,drag&drop,Blender support.Spe ships with wxGlade,PyChecker and Kiki.

The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU).

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.

DIA plugin for automatic UML Class Diagrams generation out of Java source files.

This project contains a set of tools for formal verification and static analysis of VHDL design.

itemis CREATE - formerly known as Yakindu Statechart Tools (SCT) - is a tool for the specification and development of reactive, event-driven systems with the help of state machines. It consists of an easy-to-use tool for graphical editing and provides validation, simulation and code generators for different target platforms. Visit http://www.statecharts.org for more information! !! YAKINDU SCT HAS MOVED !! DOWNLOAD FROM https://info.itemis.com/download-yakindu-statechart-tools

An AToM3 definition of a CASE framework for creating Zope products with files in the filesystem. The framework consists of a modelling notation ZProduct and a suit of related transformations.

DEVSimPy is an advanced wxPython GUI for the modeling and simulation of systems based on the DEVS (Discrete EVent system Specification) formalism. Features include powerful built-in editor, advanced modeling approach, powerful discrete event simulation algorithm, import/export DEVS components library and more.

Convert dia design to C++ files.

Launch a customizable list of shell script from Dia

Implemented classes of 3d objects: * Vector * Point * Line * Plane * LineSegment Yet incompletely implemented classes: * Triangle * Disk (closed circle) * Union (a collection of 3d objects) Each object has methods for finding its sizes, containing box or containing sphere. It finds intersection and distance or closest to another object part of itself. It also can tell if it contains the other object or is it contained by that. Where appropriate, it's easy to check orthogonality and parallelism. Vectors are sub-typed from numpy ndarray class. Extensive unit tests are included. Test coverage exceeds 95%. See documentation of the library internals in section Files ( https://sourceforge.net/projects/geometry3d/files/ ).

Modelio-Open is a project hosting a set of open source extensions (SoaML, SysML and UML Testing Profile) for a previous version (1.2) of the Modelio Free tool . Currently, the lastest version (2.x) of Modelio modeling and generation tool is available at http://modelio.org/downloads/download-modelio.html. All extensions are downloadable at http://forge.modelio.org/projects.

The ‘setupdocx‘ provides a control layer for continuous documentation by the simplified creation, packaging, and installation of documentation. The provided commands are distributed as entry points and optional base classes for further customization into 'setup.py' - setuptools / distutils. Manages arbitrary document templates for the supported builder, supports multiple builds with arbitrary document layouts, designs, and patched contents. The current release supports the following commands: - build_docx - Enhanced documentation. - install_docx - Installs local documentation. - dist_docx - Documentation packaging. - build_apidoc - Standalone Generator for API Documentation - build_apiref - Standalone Generator for API Reference

Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome. A bias measure is a function that returns a bias metric. A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure. A collection of bias metrics for a given dataset or a combination of a dataset and model.

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. It supports libjpeg-turbo as well. libpng and libjpeg must be available at compilation time in order to be available. TorchVision also offers a C++ API that contains C++ equivalent of python models.