numba-scipy | numba_scipy extends Numba to make it aware of SciPy | Machine Learning library

by numba Python Version: 0.4.0.dev0 License: BSD-2-Clause

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | numba-scipy Summary

numba-scipy is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. numba-scipy has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install numba-scipy' or download it from GitHub, PyPI.

numba_scipy extends Numba to make it aware of SciPy

Support

Quality

Security

License

Reuse

Support

numba-scipy has a low active ecosystem.

It has 119 star(s) with 18 fork(s). There are 14 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 13 open issues and 15 have been closed. On average issues are closed in 120 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of numba-scipy is 0.4.0.dev0

Quality

numba-scipy has 0 bugs and 26 code smells.

Security

numba-scipy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

numba-scipy code analysis shows 0 unresolved vulnerabilities.

There are 3 security hotspots that need review.

License

numba-scipy is licensed under the BSD-2-Clause License. This license is Permissive.

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

Reuse

numba-scipy releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

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

numba-scipy saves you 1002 person hours of effort in developing the same functionality from scratch.

It has 2032 lines of code, 79 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

Returns a dict of the cmdclass to use
Create a ConfigParser from a root directory
Get the project root directory
Extract the version information
Returns a dictionary mapping the names of the keys to their names
Parse a capsule name
Add overloaded overload
Return a function to choose a kernel
Generates a signature file
Given a mangled function name return its name
Create the versioneer config file
Install versioneer
Scans the contents of the setup py file
Generate a dictionary of signature keys
Generate special documentation

Get all kandi verified functions for this library.

numba-scipy Key Features

No Key Features are available at this moment for numba-scipy.

numba-scipy Examples and Code Snippets

No Code Snippets are available at this moment for numba-scipy.

Community Discussions

Trending Discussions on numba-scipy

How to call Scipy Numba functions on GPU?

Scipy special functions with numba

QUESTION

How to call Scipy Numba functions on GPU?

Asked 2022-Jan-16 at 17:45

After installing the numba-scipy package, the following code snippet works:

...

ANSWER

Answered 2022-Jan-16 at 17:45

scipy.special.stdtrit is actually not supported by Numba in nopython mode. Only the fallback implementation can be used (which mostly defeat the purpose of using Numba). In fact, Scipy is mostly unsupported by Numba yet. Thus, you cannot use it in Numba CUDA-targeted functions too. You could try to implement this yourself but this is pretty difficult to do.

Source https://stackoverflow.com/questions/70731068

QUESTION

Scipy special functions with numba

Asked 2020-Aug-17 at 07:11

This code fails:

...

ANSWER

Answered 2020-Aug-16 at 09:36

Since you're having install issues according to a comment, I would especially suggest using numba-scipy instead, from the creators of numba itself (link to pypi). It's always a good idea to use first-party extensions to packages.

The very documentation you linked relates to numba_special. If you look at the main page you'll see the first example:

Source https://stackoverflow.com/questions/63434971

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

Vulnerabilities

No vulnerabilities reported

Install numba-scipy

You can install using 'pip install numba-scipy' or download it from GitHub, PyPI.
You can use numba-scipy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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