DeePaC-Live | DeePaC plugin for live analysis as the sequencer

by JakubBartoszewicz Python Version: Current License: MIT

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

kandi X-RAY | DeePaC-Live Summary

DeePaC-Live is a Python library. DeePaC-Live 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.

DeePaC plugin for live analysis as the sequencer is running.

Support

Quality

Security

License

Reuse

Support

DeePaC-Live has a low active ecosystem.

It has 1 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

DeePaC-Live has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DeePaC-Live is current.

Quality

DeePaC-Live has no bugs reported.

Security

DeePaC-Live has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DeePaC-Live 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

DeePaC-Live 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.

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DeePaC-Live Key Features

No Key Features are available at this moment for DeePaC-Live.

DeePaC-Live Examples and Code Snippets

No Code Snippets are available at this moment for DeePaC-Live.

Community Discussions

No Community Discussions are available at this moment for DeePaC-Live.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install DeePaC-Live

We recommend using Bioconda (based on the conda package manager) or custom Docker images based on official Tensorflow images. Alternatively, a pip installation is possible as well.

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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