Search Results for "no code"
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Showing 386 open source projects for "no code"
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Architecture as a code
Visualize, collaborate, and evolve the software architecture
Architecture as a code is an open-source modeling language and toolkit that enables software teams to describe, visualize, collaborate on, and maintain software architecture as code. Inspired by the C4 Model and other architectural DSLs, LikeC4 lets you define your system’s structure in a textual DSL and then automatically generate consistent diagrams that reflect that design, ensuring that architecture documentation stays in sync with source code changes. -
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Julia VS Code
Julia extension for Visual Studio Code
This VS Code extension provides support for the Julia programming language. We build on Julia’s unique combination of ease-of-use and performance. Beginners and experts can build better software more quickly, and get to a result faster. With a completely live environment, Julia for VS Code aims to take the frustration and guesswork out of programming and put the fun back in. -
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Candle
GRBL controller application with G-Code visualizer written in Qt
GRBL controller application with G-Code visualizer written in Qt. -
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Azure Data Studio
A data management tool that enables working with other SQL tools
...Azure Data Studio offers a modern, keyboard-focused SQL coding experience that makes your everyday tasks easier with built-in features, such as multiple tab windows, a rich SQL editor, IntelliSense, keyword completion, code snippets, code navigation, and source control integration (Git). -
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PythonCall & JuliaCall
Python and Julia in harmony
Bringing Python® and Julia together in seamless harmony. Call Python code from Julia and Julia code from Python via a symmetric interface. Simple syntax, so the Python code looks like Python and the Julia code looks like Julia. Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control. Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g. bytes, array. array, numpy.ndarray) from Julia or Julia arrays from Python. ... -
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MacroTools.jl
MacroTools provides a library of tools for working with Julia code
MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system and code-walking tools that let you do deep transformations of code in a few lines. See the docs for more info. -
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LatticeQCD.jl
A native Julia code for lattice QCD with dynamical fermions in 4D
This code enables you to perform lattice QCD calculations! A native Julia code for Lattice QCD. -
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Pluto.jl
Simple reactive notebooks for Julia plutojl.org
We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there. -
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Automa.jl
A julia code generator for regular expressions
Automa is a regex-to-Julia compiler. By compiling regex to Julia code in the form of Expr objects, Automa provides facilities to create efficient and robust regex-based lexers, tokenizers and parsers using Julia's metaprogramming capabilities. You can view Automa as a regex engine that can insert arbitrary Julia code into its input-matching process, which will be executed when certain parts of the regex match an input. -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. -
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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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Mage.ai
Build, run, and manage data pipelines for integrating data
...Have you met anyone who said they loved developing in Airflow? That’s why we designed an easy developer experience that you’ll enjoy. Each step in your pipeline is a standalone file containing modular code that’s reusable and testable with data validations. No more DAGs with spaghetti code. Start developing locally with a single command or launch a dev environment in your cloud using Terraform. Write code in Python, SQL, or R in the same data pipeline for ultimate flexibility. -
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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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LineSearches.jl
Line search methods for optimization and root-finding
Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package. -
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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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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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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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JET.jl
An experimental code analyzer for Julia
JET employs Julia's type inference system to detect potential bugs and type instabilities. JET is tightly coupled to the Julia compiler, and so each JET release supports a limited range of Julia versions. See the Project.toml file for the range of supported Julia versions. The Julia package manager should install a version of JET compatible with the Julia version you are running. If you want to use JET on unreleased version of Julia where compatibility with JET is yet unknown, clone this git... -
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Dash
Build beautiful web-based analytic apps, no JavaScript required
...Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. Dash apps are very lightweight, requiring only a limited number of lines of Python or R code; and every aesthetic element can be customized and rendered in the web. It’s also not just for dashboards. You have full control over the look and feel of your apps, so you can style them to look any way you want. -
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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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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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gophernotes
The Go kernel for Jupyter notebooks and nteract
gophernotes is a Go kernel for Jupyter notebooks and nteract. It lets you use Go interactively in a browser-based notebook or desktop app. Use gophernotes to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the Jupyter Notebook Viewer. Go forth and do data science, or anything else interesting, with Go notebooks! This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. ... -
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ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for running only part of the test suite of a package. For example, if you made a change related to addition, and included "addition" in the description of the corresponding testsets, you can easily run only these tests. -
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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.
