Search Results for "g-code" - Page 2
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Showing 476 open source projects for "g-code"
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miepython
Mie scattering of light by perfect spheres
miepython is a pure Python module to calculate light scattering for non-absorbing, partially-absorbing, or perfectly-conducting spheres. Mie theory is used, following the procedure described by Wiscombe. This code has been validated against his results. This code provides functions for calculating the extinction efficiency, scattering efficiency, backscattering, and scattering asymmetry. Moreover, a set of angles can be given to calculate the scattering for a sphere at each of those angles. -
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Positron
Positron, a next-generation data science IDE
...The IDE supports notebook and script workflows, integration of data-app frameworks (such as Shiny, Streamlit, Dash), database and cloud connections, and built-in AI-assisted capabilities to help write code, explore data, and build models. -
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JavaParser
Java 1-17 Parser and Abstract Syntax Tree for Java
This project contains a set of libraries implementing a Java 1.0 - Java 17 Parser with advanced analysis functionalities. The project binaries are available in Maven Central. We strongly advise users to adopt Maven, Gradle or another build system for their projects. If you are not familiar with them we suggest taking a look at the maven quickstart projects. Since Version 3.5.10, the JavaParser project includes the JavaSymbolSolver. While JavaParser generates an Abstract Syntax Tree,... -
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esquisse
RStudio add-in to make plots interactively with ggplot2
...It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. -
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LanguageServer.jl
An implementation of the Microsoft Language Server Protocol
This package implements the Microsoft Language Server Protocol for the Julia programming language. Text editors with a client for the Language Server Protocol are able to make use of the Julia Language Server for various code editing features. -
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gtsummary
Presentation-Ready Data Summary and Analytic Result Tables
gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting. -
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geemap
A Python package for interactive geospaital analysis and visualization
...GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation and interactive IDE (i.e., GEE JavaScript Code Editor) of the GEE JavaScript API, the GEE Python API has relatively little documentation and limited functionality for visualizing results interactively. The geemap Python package was created to fill this gap. -
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Finch.jl
Sparse tensors in Julia and more
Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code. -
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Infiltrator.jl
No-overhead breakpoints in Julia
This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you into an interactive REPL session that lets you inspect local variables and the call stack as well as execute arbitrary statements in the context of the current local and global scope. -
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Plots
Powerful convenience for Julia visualizations and data analysis
...If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn. -
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ggpubr
'ggplot2' Based Publication Ready Plots
ggpubr is an R package that provides easy-to-use wrapper functions around ggplot2 to create publication-ready visualizations with minimal code. It streamlines plot creation for researchers and analysts, allowing features such as statistical annotation, theme customization, and plot arrangement with fewer lines of code. -
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KernelAbstractions.jl
Heterogeneous programming in Julia
KernelAbstractions (KA) is a package that enables you to write GPU-like kernels targetting different execution backends. KA is intended to be a minimal and performant library that explores ways to write heterogeneous code. -
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Obsidian Visual Skills Pack
Generate Canvas, Excalidraw, and Mermaid diagrams from text
LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking input size by up to 95 %. ... -
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SciML Style Guide for Julia
A style guide for stylish Julia developers
The SciML Style Guide is a style guide for the Julia programming language. It is used by the SciML Open Source Scientific Machine Learning Organization. As such, it is open to discussion with the community. If the standard for code contributions is that every PR needs to support every possible input type that anyone can think of, the barrier would be too high for newcomers. Instead, the principle is to be as correct as possible to begin with, and grow the generic support over time. All recommended functionality should be tested, and any known generality issues should be documented in an issue (and with a @test_broken test when possible). ... -
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Gnuplot.jl
Julia interface to gnuplot
...Its main purpose it to provide a fast and powerful data visualization framework, using an extremely concise Julia syntax. It also has automatic display of plots in Jupyter, Juno and VS Code. -
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CausalInference.jl
Causal inference, graphical models and structure learning in Julia
Julia package for causal inference and analysis, graphical models and structure learning. This package contains code for the PC algorithm and the extended FCI algorithm, the score based greedy equivalence search (GES) algorithm, the Bayesian Causal Zig-Zag sampler and a function suite for adjustment set search. -
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Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a... -
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Mercury
Convert Python notebook to web app and share with non-technical users
...You define interactive widgets for your notebook with the YAML header. Your users can change the widgets values, execute the notebook and save result (as PDF or html file). You can hide your code to not scare your (non-coding) collaborators. Easily deploy to any server. Mercury is dual-licensed. Looking for dedicated support, a commercial-friendly license, and more features? The Mercury Pro is for you. -
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dlib
Toolkit for making machine learning and data analysis applications
...It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. -
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oneAPI.jl
Julia support for the oneAPI programming toolkit.
Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features. -
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Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
...This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be quite time-consuming. How many times have you conducted the same action to pre-process a raw dataset? ... -
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Quadratic
Data science spreadsheet with Python & SQL
...This makes our grids completely portable and very easy to share. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. Quickly write code and see the output in full detail. No more squinting into a tiny terminal to see your data output. -
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Superset
Apache Superset is a data visualization and data exploration platform
...Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. Quickly and easily integrate and explore your data, using either our simple no-code viz builder or state-of-the-art SQL IDE. Superset can connect to any SQL-based data source through SQLAlchemy, including modern cloud-native databases and engines at a petabyte-scale. Superset is lightweight and highly scalable, leveraging the power of your existing data infrastructure without requiring yet another ingestion layer. ... -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
...When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. ... -
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Malli
High-performance data-driven data specification library
Malli is a powerful, data-driven schema library for Clojure and ClojureScript, offering rich support for specification, validation, parsing, error reporting, and generative testing. Designed for performance, Malli leverages efficient runtime representations and code generation, seamlessly integrating with Clojure’s data-oriented architecture. It supports function schemas, JSON transformation, and OpenAPI generation for strong API contracts.
