Open Source Python Software Development Software - Page 75

A message passing library addressing the special needs/capabilities of Qt applications. The basic building block for classic 3-tier applications where both client and server are Qt applications.

MRV is a python framework based on wrapping the Maya API to speed up development and ease of use within pipelines using Autodesk Maya.

This code is another way to create SOA applications through HTTP. Can execute Database SPs, MSMQ, Assemblies, IronPython script and generates RSS, JSON, Web Service, Text or Excel file through HTTP. The system has authentication and logging support.

MSA, or Memory Sparse Attention, is a research framework for scaling language-model memory to extremely long contexts. It replaces full attention over all tokens with sparse selection of compressed latent memory states. Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers only selected memory back to the accelerator. Memory Interleave alternates retrieval, context expansion, and generation to improve multi-hop reasoning across distant segments. The project reports experiments extending from 16K to 100M tokens, including inference on two A800 GPUs.

MSCViewer is a tool intended for debugging of control flows in concurrent, distributed systems. The tool loads logs generated by various entities in the system and visualize a sequence diagram chart for events and interactions. The diagram is fully interactive: entity can be added/removed from the diagram and shuffled; events can be filtered, searched, highlighted and annotated with comments. MSCViewer features integration with a Python interpreter which allows writing Python scripts interacting with the model. This powerful feature can be used to automate validatation of distributed control flows, integrate with graphing infrastructure, etc.

Automatically Get Official Product Updates on munki Server. This MUNKI SERVER Command line Tool (Munki Updater) will check for available MUNKI Product Updates on official Servers (example Mozilla) and will allow to Download and Update older Product Versions on MUNKI Repository and distribute them on clients having older versions. munki updater readme: http://svn.code.sourceforge.net/p/munkiupdater/code-0/trunk/readme.txt ############# managed software centre installer including public repo with freeware: /bin/bash -c "$(curl -fsSL http://svn.code.sourceforge.net/p/munkiupdater/code-0/trunk/online/install-munki.sh)"

MVCPython is a framework which automatically generates a MVC architecture (MVC= Model View Controller) just from Python Classes and Object defintions. MVCPython achieves this through a strong use of Python's introspection capability.

This project is redundant. All files have been copied to MaMo Py: https://sourceforge.net/projects/marimorepy/

A collection of python libraries used by MARIMORE Inc. http://www.marimore.co.jp THIS PROJECT HAS MOVED TO https://github.com/marimore/marimorepy

The project moved here: https://github.com/vbatoufflet/machette

You assemble *blocks* in your django template, with each block wired to a tastypie resource, which in turn is mapped to a django model. Each *block* will reside in a dijit ContentPane which you must define in your template. You have a choice of 2 layouts for each *block*, Form or Grid based. Form displays a single record in either a 1, 2 or 3 column layout while Grid displays many rows in a spreadsheet like grid. You can perform edits in place on fields in both layouts. You can define the master-detail relationship between the *blocks* and enblok will automatically display the related detail *block* rows as the user navigates the master *block*'s rows. You get for free the default *CRUD* functionality for each block, together with Search. You are not limited to just a 2 level master-detail relationship, in fact enblok can support n-th level relationships but we do not advise you to go beyond 3. Read the distribution's enblok/readme.txt for further instructions.

This is a Python interface for reading and writing Magellan GPS unit map files. There is also a command line tool for creating GPS maps from OpenStreetMap data.

Make It heavy is a Python framework for producing deeper AI analysis through multi-agent orchestration. It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow.

This script will automatically decode an obfuscated JavaScript or VBScript in HTML and scan for suspicious/malicious codes in the page.

Mallard is a graphical game design package, akin to many applications for Windows such as The Games Factory, RPG Maker, Game Maker, etc. It is written in Python with wxWidgets and PyGame, and therefore should support any OS which supports Python and SDL.

Adobe Malware Classifier is a command-line tool that lets antivirus analysts, IT administrators, and security researchers quickly and easily determine if a binary file contains malware, so they can develop malware detection signatures faster, reducing the time in which users' systems are vulnerable. Malware Classifier uses machine learning algorithms to classify Win32 binaries – EXEs and DLLs – into three classes: 0 for “clean,” 1 for “malicious,” or “UNKNOWN.” The tool was developed using models resultant from running the J48, J48 Graft, PART, and Ridor machine-learning algorithms on a dataset of approximately 100,000 malicious programs and 16,000 clean programs. The tool extracts seven key features from an unknown binary, feeds them to one of the four classifiers or all of them, and presents its classification of the unknown binary.

The Mandragora World Engine is a C++/Python engine which allows the easy creation of programs, including applications, simulations, and games. Able to load and run specific program modules. Will handle objects, graphics, sound, physics, GUI, and even AI.

Manticore helps us quickly take advantage of symbolic execution, taint analysis, and instrumentation to analyze binaries. Parts of Manticore underpinned our symbolic execution capabilities in the Cyber Grand Challenge. As an open-source tool, we hope that others can take advantage of these capabilities in their own projects. We prioritized simplicity and usability while building Manticore. We used minimal external dependencies and our API should look familiar to anyone with exploitation or reversing background. If you have never used such a tool before, give Manticore a try. Manticore comes with an easy-to-use command line tool that quickly generates new program “test cases” (or sample inputs) with symbolic execution. Each test case results in a unique outcome when running the program, like a normal process exit or crash (e.g., invalid program counter, invalid memory read/write).

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.

En una pequena guia para la los estudiantes de programacion para iniciarlos en el mundo de C/C++

Marin is an open-source research platform and community for developing foundation models through transparent, reproducible experimentation. It covers the complete model-building pipeline from data curation and filtering through tokenization, pretraining, post-training, and evaluation. Experiments and decisions are documented as they occur, including unsuccessful approaches. The framework is primarily used for large language models but has also supported audio-text, DNA, and protein modeling research. Experiments are expressed as dependent steps that execute in topological order, enabling reproducible training workflows. Marin also provides model checkpoints, training recipes, scaling research, documentation, and reusable infrastructure for large-scale experiments.

The market assumes an environment in which farmers sell their produce through brokers and traders locate produce to buy through brokers. The major aim of the simulator is to experiment with various reputation mechanisms to manage bottlenecks and to model various adversarial scenarios. The market is aimed to simulate agricultural trade in developing countries. It is written in python and mysql database on Linux.

The Markovski project implements an interpreter/compiler for a language named Markovski, used to write Markov algorithms (see http://en.wikipedia.org/wiki/Markov_algorithm).

Markovify is an open-source Python library for generating random text using Markov chain models trained on existing writing. It analyzes large text collections to identify word sequences and their statistical relationships. The software uses these patterns to construct new sentences that resemble the vocabulary and structure of the original material. Users can customize the number of preceding words considered during generation to influence coherence and variation. Built-in safeguards reduce excessive repetition of sentences from the training corpus. Markovify supports newline-separated text, custom parsing rules, and extensible generation methods. Models can be combined, serialized into JSON, and reused without repeating the training process. Its lightweight architecture makes it suitable for creative writing experiments, procedural content generation, and text-based applications.

Marvin3 is a new cross-platform stack-based language developed in Python. Marvin3 features object-oriented-programming, anonymous routines, eager lists, variables, lexical scope, combinators and support for modules.