Open Source Python Software Development Software - Page 45

Previously codenamed "FlasCC" and "Alchemy," CrossBridge provides a complete BSD-like C/C++ development environment based on GCC that lets you compile your C/C++ code to target the Adobe Flash Runtimes (Flash Player and AIR). With CrossBridge you can port almost any existing C/C++ code to the web, across browsers. Forum: http://forum.crossbridge.io/ GitHub - SDK Source: https://github.com/crossbridge-community/crossbridge/ GitHub - Issue Tracker: https://github.com/crossbridge-community/crossbridge/issues GitHub - Wiki: https://github.com/crossbridge-community/crossbridge/wiki GitHub - Examples: https://github.com/crossbridge-community Demo: http://www.vpmedia.hu/crossbridge/neverball Tutorials: https://www.adobe.com/devnet/tagsearch.tsb.prod-_sl_flascc_v_.tse.html http://bruce-lab.blogspot.hu/2014/01/crossbridgeflascc-resources-collection.html https://tv.adobe.com/watch/max-2013/from-c-to-flash-the-power-of-the-adobe-flash-c-compiler/

Crusty the Package. Is a package generator for build systems. It uses upstream version controls to create snapshots that could be build automaticly in build systems.

CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data.

A hook script usable with TortoiseSVN (and possible other subversion clients/GUIs) providing transparent repository encryption (encryption/decryption at the client side).

A plugin for gedit that uses ctags to create a menu that can be used to easily navigate a code file in gedit.

The Cumulative Include Dependency Checker will help you measure the quality of the include hierarchy in a software project. It will also attempt to identify problem header files that have the most impact on your include hierarchy.

A compact compiled templating language in Python which implements just about the features of TAL, METAL together with Zope's i18n extensions and whose aims are simplicity, full test coverage and complete documentation.

Curtains is an easy to use, well-documented GUI toolkit with a pythonic API for creating beautiful applications on Windows.

CvsShell is a console-based cvs frontend. It provides convenient access to the cvs commands through a shell-like user interface. CvsShell is written in Python.

Please take a look at our project wiki for more detailed information: https://github.com/monsdar/CxxProf/wiki/What-is-CxxProf%3F

Cyclone is java-based rich GUI web tool for automating tasks with ease. Cyclone is a project that was borne out of necessity. Agreed, there are many native tools that allow task to be scheduled or automated on most operating systems, however, Cyclone is more than just an automation tool. In many programming languages, there are batch module/libraries available to schedule jobs, but need quite a bit of enhancements to provide similar feature set as Cyclone. Cyclone comes with a number of fascinating features Real-time progress and status montoring for jobs Pause, resume and stop an executing job Multi-user environment with in-built authentication and authorization modules Polygot - jobs can be implemented in java, groovy, ruby, javascript and python Easy administration and configuration - user management etc. Extensible security module

This project provides a system tray application that monitors the status of a project which uses a DART dashboard. Status is displayed by color-coded icons, and message dialogs alert the user when the build status changes.

DAXFi is a Python package that helps configure several different kinds of firewalls in a consistent way. The rules are described in XML. It comes with a Python package, useful to build other applications and includes some useful example programs.

Db Api client is a simple Python DB Api v2.0 client. It shows some information about database, tables, table\'s description. Db Api Client provides sql queries executing, shows results, and exports to some formats (html, csv).

DBFrames is an application framework for building data aware applications for Windows Mobile devices. It uses PythonCE, SQLite and PocketPyGui. Version for Android (writen in Java): https://github.com/yurtk/dbfragments

DC/OS (Datacenter Operating System) is an open-source distributed operating system based on the Apache Mesos kernel. It abstracts a cluster of machines into a single logical computer, simplifying the deployment and management of containerized applications and services. DC/OS provides a comprehensive platform for running modern applications with features like container orchestration, service discovery, and scalability.​

A DCPU-16 Assembler in Python. This assembler is based on the specifications published at http://0x10c.com/doc/dcpu-16.txt

Standard linux distributions are not very optimized when used as linux storage appliances. DCT decided to create a linux distrubution based on Debian which has been adjusted to server better as an appliance platform with as main goal storage purposes.

DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in the sample. The user manual of DEBay: https://sourceforge.net/projects/debay/files/UserManual.pdf Sample data: https://sourceforge.net/projects/debay/files/Test_data/ Citation Information: Vimalathithan Devaraj, Biplab Bose. DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population. Heliyon, 2020, 6(7), e04489. https://doi.org/10.1016/j.heliyon.2020.e04489

DEE Excellent Editor is a simple source code editor whit syntax highlighting for many languages with a file browser panel to quickly select files. DEE can open files from the local system or directly from an ftp server.

PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.

Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms.

This is a command line tool that list all registered occurences of a DLL from the windows registry and allows to unregister them all with regsvr32.

This project contains some test scripts and software for Analog Devices' DSP chip emulators. This verifies whether a particular emulator is working correctly with a particular PC and target board.