Open Source Python Software Development Software - Page 39
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Python Software Development Software
View 6114 business solutionsBrowse free open source Python Software Development Software and projects below. Use the toggles on the left to filter open source Python Software Development Software by OS, license, language, programming language, and project status.
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Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset. -
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An AI sandbox for learning game programming. It includes an integrated development environment, and functionality for rapid development of virtual characters.
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts. -
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It is a Process Monitoring Toolkit with the ability of Extend. E.g. You can use it to monitor a specified process. And set a triger like (over 50MB mem use) then do something like (report to DB).
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AlluraSkel
Allura Tool Skeletons
Grab this to try your hand at developing a tool for Allura. Currently supports: - python package setup - simple per-project-installable Tool - simple Ming/MongoDB model - simple TurboGears Controllers - simple Jinja templates -
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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Alphafold
Open source code for AlphaFold
This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions. The total download size for the full databases is around 415 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. We recommend using an SSD for better genetic search performance. -
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AlsaPlayer-Python is a set of python bindings. It allows you to write custom interfaces for AlsaPlayer using the full power of the Python language. Also provided is a Python module to control AlsaPlayer from external programs.
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Our goal is to develop a full working solver for ATA (with 1 clock) in Python, with MTL to ATA support. The decidability for the emptiness problem was proposed by Lasota and Walukiewicz. The MTL to ATA was proposed by Ouaknine and Worrell.
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Amazon Braket Ocean Plugin
A Python plugin for using Ocean with Amazon Braket
The Amazon Braket Ocean Plugin is an open-source library in Python that provides a framework that you can use to interact with Ocean tools on top of Amazon Braket. Before you begin working with the Amazon Braket Ocean Plugin, make sure that you've installed or configured the following prerequisites. Download and install Python 3.7.2 or greater from Python.org. If you are using Windows, choose Add Python to environment variables before you begin the installation. Make sure that your AWS account is onboarded to Amazon Braket. The Amazon Braket Ocean Plugin can be installed with pip. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. This package provides samplers which use Braket solvers. These samplers extend abstract base classes provided in Ocean's dimod and thus have the same interfaces as other samplers in Ocean. -
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Amazon Braket PennyLane Plugin
A plugin for allowing Xanadu PennyLane to use Amazon Braket devices
The Amazon Braket PennyLane plugin offers two Amazon Braket quantum devices to work with PennyLane. The Amazon Braket Python SDK is an open-source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations. Once the Pennylane-Braket plugin is installed, the provided Braket devices can be accessed straight away in PennyLane, without the need to import any additional packages. While the local device helps with small-scale simulations and rapid prototyping, the remote device allows you to run larger simulations or access quantum hardware via the Amazon Braket service. -
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Amazon Braket Python Schemas
A library that contains schemas for Amazon Braket
Amazon Braket Python Schemas is an open source library that contains the schemas for Braket, including intermediate representations (IR) for Amazon Braket quantum tasks and offers serialization and deserialization of those IR payloads. Think of the IR as the contract between the Amazon Braket SDK and Amazon Braket API for quantum programs. Schemas for the S3 results of each quantum task. Schemas for the device capabilities of each device. The preferred way to get Amazon Braket Python Schemas is by installing the Amazon Braket Python SDK, which will pull in the schemas. You can install from source by cloning this repository and running a pip install command in the root directory of the repository. There are currently two types of IR, including jaqcd (JsonAwsQuantumCircuitDescription) and annealing. -
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Amazon Braket Strawberry Fields Plugin
An open source framework for using Amazon Braket devices
An open-source framework for using Amazon Braket devices with the Strawberry Fields photonic device programming library. This plugin provides a BraketEngine class for running photonic quantum circuits created in Strawberry Fields on the Amazon Braket service. The Amazon Braket Python SDK is an open source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. This plugin provides the classes BraketEngine for submitting photonic circuits to Amazon Braket and BraketJob for tracking the status of the Braket task. Strawberry Fields is an open source library for writing and running programs for photonic quantum computers. BraketEngine and BraketJob have the same interfaces as RemoteEngine in Strawberry Fields and Job in the Xanadu Cloud Client, respectively, and can be used as drop-in replacements. -
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APLEpy stands for Algebraic Programming Language Extension for Python. It is an open source alternative to commercial products such as AMPL and GAMS. It offers the same high level of abstraction while keeping the advantages of using Python environment.
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AnAviPreviewGUI is an avi preview GUI for the command line tool AviPreviewC (from www.funkster.org) AviPreviewC make avi from tempory file ".part" of eDonkey software. The GUI set size, frame, codec, resolution, ... before sand command to create preview.
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These are Python bindings for Anjuta DevStudio version 2. This allows you to write Anjuta plugins in Python.
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In Systems Biology models are created in various formats (Matlab, Java, C/C++, Python, ...). "Annotate Your Model" will help you to link your model to biological web resources by creating a CSV file containing MIRIAM annotations.
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This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.
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Ansible AUR
Ansible module to manage packages from the AUR
ansible-aur is an Ansible module designed to manage packages from the Arch User Repository (AUR). It supports various AUR helpers such as yay, paru, pacaur, trizen, pikaur, and aurman, automatically selecting the first available helper or falling back to makepkg if none are found. This module facilitates the installation and upgrading of AUR packages within Ansible playbooks, streamlining package management for Arch Linux systems. -
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Ansible Molecule
Molecule aids in the development and testing of Ansible roles
Molecule project is designed to aid in the development and testing of Ansible roles. Molecule provides support for testing with multiple instances, operating systems and distributions, virtualization providers, test frameworks and testing scenarios. Molecule encourages an approach that results in consistently developed roles that are well-written, easily understood and maintained. Molecule supports only the latest two major versions of Ansible (N/N-1), meaning that if the latest version is 2.9.x, we will also test our code with 2.8.x. Depending on the driver chosen, you may need to install additional OS packages. See INSTALL.rst, which is created when initializing a new scenario. Ansible is not listed as a direct dependency of molecule package because we only call it as a command-line tool. You may want to install it using your distribution package installer. It is your responsibility to assure that soft dependencies of Ansible are available on your controller or host machines. -
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Ansible for DevOps
Ansible for DevOps examples
Ansible for DevOps is a collection of Ansible playbooks, roles, and infrastructure-as-code examples that accompany the book Ansible for DevOps by Jeff Geerling. Rather than being theoretical, the examples span real-world infrastructure setups: multi-server orchestration, LAMP stacks, Docker deployments, Kubernetes cluster spins, rolling updates, and security hardening. You can clone the repo and play with actual scenarios using Vagrant, VirtualBox, or cloud hosts, making it ideal for both learning and reference in production readiness. The code is structured by chapter/topic, so you can pick a scenario (for example “nodejs deployment” or “ELK stack”) and dive into a fully featured Ansible solution rather than starting from scratch. Because Ansible is popular for provisioning and configuration management, this repository lowers the barrier to experimenting with real infra patterns. -
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Antares es un lenguaje de programación relizado dentro de la materia de Proyecto de Desarrollo de Software con el profesor Ariel Ortiz. Los responsables del proyecto son: Luis Ángel De Niz Eduardo Fuentes Alberto Sobrino
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AnyStatus
Get your own sweet custom status for discord
Get your own sweet custom discord status! Paste your account token and your desired prefix in `config.json`. The file can be opened using the normal Notepad. You can find your token here. You need to have Python installed to use AnyStatus. Next, open a new terminal in the current folder and execute the following command: pip install -r requirements.txt Then open main.py and your custom status is ready :) Send `<prefix><activity> = <text>` in any discord channel to change your status. Example: `!playing = Minecraft` You can alternatively run `chmod +x run.sh` in the console and open run.sh afterwards or open run.bat if you're a windows user for automatic package installation and quick launch. Also remember that it can take up to 5 minutes to update your status.
