arXivist | Command line interface for managing papers | Development Tools library
kandi X-RAY | arXivist Summary
kandi X-RAY | arXivist Summary
arXivist is a command line interface allowing for the management of pre-print research papers and other publications from repositories such as arXiv on a local machine.
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Top functions reviewed by kandi - BETA
- Enter view mode
- Returns the sql query as a sql query
- Gather search results
- Aggregate search results
- Generic search mode
- Create a new CmdEnumEnum that matches the given parameters
- Add a result to the list
- Insert a search query
- Execute an INSERT statement
- Insert authors into the database
- Adds command line arguments to save query
- Enter search mode
- Open PDF file
- Handle more specific actions
- Generic query
- Delete a paper
- Overrides user options
- Wraps a db query
- Retrieve search results
- Keywords insert
- Execute an insertion query
- Retrieve ARX IDs from a search result
- Load env file
- Get formatted user input
- Download and save a PDF from arXiv
- Get suggested papers from the database
- Get suggested suggestion mode
arXivist Key Features
arXivist Examples and Code Snippets
Community Discussions
Trending Discussions on arXivist
QUESTION
I have a docker-compose file that looks like this
...ANSWER
Answered 2020-Jun-27 at 14:56I thought I had Docker setup on my machine, which runs Fedora 32. However as I came to realize from this article, setting up Docker on Fedora 32 requires some extra steps I was not previously aware of.
Specifically for this issue, the command listed in the article to add Docker to whitelist Docker on the local network's firewall with the command
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install arXivist
Command line interface which can run Bash scripts
Git to clone the repo
Docker
Docker-Compose
search mode search for new materials to download
view mode search for previously downloaded materials to modify them
suggest mode suggest papers based off of previously downloaded materials
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