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

by JakubBartoszewicz Python Version: 0.3.0 License: MIT License

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 a Permissive License and it has low support. However DeePaC-Live build file is not available. You can download it from GitLab.

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 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 12 months.

DeePaC-Live has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeePaC-Live is 0.3.0

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 License. This license is Permissive.

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

Reuse

DeePaC-Live releases are available to install and integrate.

DeePaC-Live has no build file. You will be need to create the build yourself to 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 GitLab. If you have any questions check and ask questions on community page Stack Overflow .

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