arrayish | downstream compatibility of Numpy-compatible arrays | Data Manipulation library

 by   hameerabbasi Python Version: 0.0.1 License: BSD-3-Clause

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kandi X-RAY | arrayish Summary

kandi X-RAY | arrayish Summary

arrayish is a Python library typically used in Utilities, Data Manipulation, Numpy applications. arrayish 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 arrayish' or download it from GitHub, PyPI.

A library for downstream compatibility of Numpy-compatible arrays.
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            kandi-support Support

              arrayish has a low active ecosystem.
              It has 11 star(s) with 0 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 2 open issues and 0 have been closed. On average issues are closed in 874 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of arrayish is 0.0.1

            kandi-Quality Quality

              arrayish has 0 bugs and 25 code smells.

            kandi-Security Security

              arrayish has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              arrayish code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              arrayish is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              arrayish 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.
              It has 1598 lines of code, 52 functions and 5 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed arrayish and discovered the below as its top functions. This is intended to give you an instant insight into arrayish implemented functionality, and help decide if they suit your requirements.
            • Return a dict containing the command - line arguments
            • Create a ConfigParser from a root directory
            • Get the project root directory
            • Get the version information
            • Create the versioneer config file
            • Install versioneer
            • Extract the version information from the VCS
            • Scans the setup py py file and checks if it is missing
            Get all kandi verified functions for this library.

            arrayish Key Features

            No Key Features are available at this moment for arrayish.

            arrayish Examples and Code Snippets

            No Code Snippets are available at this moment for arrayish.

            Community Discussions

            Trending Discussions on arrayish

            Troubles to deploy from Strapi on Heroku

            QUESTION

            Troubles to deploy from Strapi on Heroku
            Asked 2020-Nov-08 at 18:14

            I have created a project on Strapi (CMS) which is linked to MongoDB but I have some trouble to deploy it on Heroku.

            I am trying to deploy a project I created on Heroku and I have some trouble to do it... Anyone has any idea of what is going on ? It seems to do with sharp 'darwin-x64' but I really don't know what it is.

            Build Log

            ...

            ANSWER

            Answered 2020-Nov-08 at 18:14

            It looks like there is a mismatch between the environments you use. Try the following:

            1. Remove sharp completely from your app.

            Source https://stackoverflow.com/questions/64741366

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

            Vulnerabilities

            No vulnerabilities reported

            Install arrayish

            You can install using 'pip install arrayish' or download it from GitHub, PyPI.
            You can use arrayish 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 .
            Find more information at:

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            Install
          • PyPI

            pip install arrayish

            • CLONE
          • HTTPS

            https://github.com/hameerabbasi/arrayish.git

          • CLI

            gh repo clone hameerabbasi/arrayish

          • sshUrl

            git@github.com:hameerabbasi/arrayish.git

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