jupyter-c-kernel | Minimal Jupyter C kernel | Code Editor library

by brendan-rius Python Version: 1.2.2 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | jupyter-c-kernel Summary

jupyter-c-kernel is a Python library typically used in Editor, Code Editor, Jupyter applications. jupyter-c-kernel has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Minimal Jupyter C kernel

Support

Quality

Security

License

Reuse

Support

jupyter-c-kernel has a low active ecosystem.

It has 546 star(s) with 143 fork(s). There are 20 watchers for this library.

It had no major release in the last 6 months.

There are 34 open issues and 28 have been closed. On average issues are closed in 135 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of jupyter-c-kernel is 1.2.2

Quality

jupyter-c-kernel has 0 bugs and 0 code smells.

Security

jupyter-c-kernel has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

jupyter-c-kernel code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

jupyter-c-kernel is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

jupyter-c-kernel releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

jupyter-c-kernel saves you 54 person hours of effort in developing the same functionality from scratch.

It has 141 lines of code, 14 functions and 4 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed jupyter-c-kernel and discovered the below as its top functions. This is intended to give you an instant insight into jupyter-c-kernel implemented functionality, and help decide if they suit your requirements.

Execute the given code
Filters out magic flags
Write the contents of the queue
Create a temporary temporary file
Compile the source file using gcc
Create a jupyter subprocess
Write contents to stderr
Write contents to stdout
Perform cleanup
Removes all files from the master

Get all kandi verified functions for this library.

jupyter-c-kernel Key Features

No Key Features are available at this moment for jupyter-c-kernel.

jupyter-c-kernel Examples and Code Snippets

No Code Snippets are available at this moment for jupyter-c-kernel.

Community Discussions

Trending Discussions on jupyter-c-kernel

Error when I try install a C kernel for jupyter

QUESTION

Error when I try install a C kernel for jupyter

Asked 2019-Dec-17 at 02:09

I'm trying to install the C kernel for jupyter, following the steps on the documentation. Wich is

...

ANSWER

Answered 2019-Dec-17 at 02:09

install_c_kernel --user is the command that worked for me and is recommended on Github issues for the repo here: https://github.com/brendan-rius/jupyter-c-kernel/issues/38

It also shows up as bash if you read the issue carefully but when you open the notebook it shows up as C.

If you already have a .ipynb notebook open you can choose Kernel from the menu and at the bottom it gives you the option to change kernels.

Source https://stackoverflow.com/questions/59332118

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install jupyter-c-kernel

Works only on Linux and OS X. Windows is not supported yet. If you want to use this project on Windows, please use Docker.
Make sure you have the following requirements installed:
gcc
jupyter
python 3
pip

Support

The docker image installs the kernel in editable mode, meaning that you can change the code in real-time in Docker. For that, just run the docker box like that:. This clones the source, run the kernel, and binds the current folder (the one you just cloned) to the corresponding folder in Docker. Now, if you change the source, it will be reflected in http://localhost:8888 instantly. Do not forget to click "restart" the kernel on the page as it does not auto-restart.

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