Coder Social home page Coder Social logo

ckanext-metaexport's Introduction

ckanext-metaexport

ckanext-metaexport proviedes universal way to export dataset's metadata into different metadata standards.

All formats should be registered before using. In order to do this, perform next steps:

  1. Implement ckanext.metaexport.interfaces.IMetaexport interface.
  2. Add register_metadata_format, that returns dictonary with format names as keys and FormatInstances as values.

3. Add dataExtractors - method that provides function for generating data, that'll be passed into templates. It can be done via register_data_extractors method, that receives current formatters list and should pick desired format from collection and call set_data_extractor of this format. set_data_extractor expects to receive one argument - function, that will receive package_id and should return dictonary with template variables. 5. In order to create your own format, you have to create class, inherited from ckanext.metaexport.formatters.Format

All export views are available under /dataset/{id}/metaexport/{format} URL.

Requirements

For example, you might want to mention here which versions of CKAN this extension works with.

Installation

To install ckanext-metaexport:

  1. Activate your CKAN virtual environment, for example:

    . /usr/lib/ckan/default/bin/activate
    
  2. Install the ckanext-metaexport Python package into your virtual environment:

    pip install ckanext-metaexport
    
  3. Add metaexport to the ckan.plugins setting in your CKAN config file (by default the config file is located at /etc/ckan/default/production.ini).

  4. Restart CKAN. For example if you've deployed CKAN with Apache on Ubuntu:

    sudo service apache2 reload
    

Config Settings

Document any optional config settings here. For example:

# The minimum number of hours to wait before re-checking a resource
# (optional, default: 24).
ckanext.metaexport.some_setting = some_default_value

Development Installation

To install ckanext-metaexport for development, activate your CKAN virtualenv and do:

git clone https://github.com/DataShades/ckanext-metaexport.git
cd ckanext-metaexport
python setup.py develop
pip install -r dev-requirements.txt

Running the Tests

To run the tests, do:

nosetests --nologcapture --with-pylons=test.ini

To run the tests and produce a coverage report, first make sure you have coverage installed in your virtualenv (pip install coverage) then run:

nosetests --nologcapture --with-pylons=test.ini --with-coverage --cover-package=ckanext.metaexport --cover-inclusive --cover-erase --cover-tests

Registering ckanext-metaexport on PyPI

ckanext-metaexport should be availabe on PyPI as https://pypi.python.org/pypi/ckanext-metaexport. If that link doesn't work, then you can register the project on PyPI for the first time by following these steps:

  1. Create a source distribution of the project:

    python setup.py sdist
    
  2. Register the project:

    python setup.py register
    
  3. Upload the source distribution to PyPI:

    python setup.py sdist upload
    
  4. Tag the first release of the project on GitHub with the version number from the setup.py file. For example if the version number in setup.py is 0.0.1 then do:

    git tag 0.0.1
    git push --tags
    

Releasing a New Version of ckanext-metaexport

ckanext-metaexport is availabe on PyPI as https://pypi.python.org/pypi/ckanext-metaexport. To publish a new version to PyPI follow these steps:

  1. Update the version number in the setup.py file. See PEP 440 for how to choose version numbers.

  2. Create a source distribution of the new version:

    python setup.py sdist
    
  3. Upload the source distribution to PyPI:

    python setup.py sdist upload
    
  4. Tag the new release of the project on GitHub with the version number from the setup.py file. For example if the version number in setup.py is 0.0.2 then do:

    git tag 0.0.2
    git push --tags
    

ckanext-metaexport's People

Contributors

smotornyuk avatar engerrs avatar aleks-iv avatar iaroslav13 avatar agmorev avatar tomecirun avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.