semvec/pypi-packages huggingface dataset:
terrastack 1.0.0
Create terraform specifications with code
License: MIT
Requires Python: >=3.6.0


Terrastack
========================

This simple project provides a way to define terrastack specifications through the Python language,
which in turn can be used to output Terraform json specifications.

The library itself does not try to create abstractions for every type of resource and attribute that
can be defined in Terraform. The goal for the API is to be as thin as possible - with the help of
using standard python objects and kwarg handling. Sacrificing type safety in order to be feature
compatible with Terraform.

Example use:
```
import terrastack as ts

stack = ts.Stack()
stack.extend(ts.Provider("aws",
    region  = "eu-west-1",
    version = "1.30.0",
))

# extend supports variable number of components
stack.extend(
    ts.Resource("aws_instance", "my-instance-1",
        ami="some-ami",
    ),
    ts.Resource("aws_instance", "my-instance-2",
        ami="some-ami",
    ),
    ts.Output("my_output", "some_value"),
)

print(stack.render_json())

# output:
        {
            "output": {
                "my_output": {
                    "value": "some_value"
                }
            },
            "provider": [
                {
                    "aws": {
                        "region": "eu-west-1",
                        "version": "1.30.0"
                    }
                }
            ],
            "resource": {
                "aws_instance": {
                    "my-instance-1": {
                        "ami": "some-ami"
                    },
                    "my-instance-2": {
                        "ami": "some-ami"
                    }
                }
            }
        }
```
german-lemmatizer 0.1.1
A Python package (using a Docker image under the hood) to lemmatize German texts.
License: MIT

<div align="center">
  <img src="matt-artz-353291-unsplash.jpg" alt="Scissors">
</div>

# German Lemmatizer

A Python package (using a Docker image under the hood) to [lemmatize](https://en.wikipedia.org/wiki/Lemmatisation) German texts.

Built upon:

-   [IWNLP](https://github.com/Liebeck/spacy-iwnlp) uses the crowd-generated token tables on [de.wikitionary](https://de.wiktionary.org/).
-   [GermaLemma](https://github.com/WZBSocialScienceCenter/germalemma): Looks up lemmas in the [TIGER Corpus](http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/) and uses [Pattern](https://www.clips.uantwerpen.be/pattern) as a fallback for some rule-based lemmatizations.

It works as follows. First [spaCy](https://spacy.io/) tags the token with POS. Then `German Lemmatizer` looks up lemmas on IWNLP and GermanLemma. If they disagree, choose the one from IWNLP. If they agree or only one tool finds it, take it. Try to preserve the casing of the original token.

You may want to use underlying Docker image: [german-lemmatizer-docker](https://github.com/jfilter/german-lemmatizer-docker)

## Installation

1. Install [Docker](https://docs.docker.com/).
2. `pip install german-lemmatizer`

## Usage

1. Read and accept the [license terms of the TIGER Corpus](http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/license/htmlicense.html) (free to use for non-commercial purposes).
2. Make sure the Docker daemons runs.
3. Write some Python code

```python
from german_lemmatizer import lemmatize

lemmatize(
    ['Johannes war ein guter Schüler', 'Sabiene sang zahlreiche Lieder'],
    working_dir='*',
    chunk_size=10000,
    n_jobs=1,
    escape=False,
    remove_stop=False)
```

The list of texts is split into chunks (`chunk_size`) and processed in parallel (`n_jobs`).

Enable the `escape` parameter if your text contains newslines. `remove_stop` removes stopwords as defined by spaCy.

## License

MIT.
invenio-marc21 1.0.0a9
Invenio module with nice defaults for MARC21 overlay.
License: MIT
Keywords: invenio MARC21

..
    This file is part of Invenio.
    Copyright (C) 2016-2018 CERN.

    Invenio is free software; you can redistribute it and/or modify it
    under the terms of the MIT License; see LICENSE file for more details.

================
 Invenio-MARC21
================

.. image:: https://img.shields.io/github/license/inveniosoftware/invenio-marc21.svg
        :target: https://github.com/inveniosoftware/invenio-marc21/blob/master/LICENSE

.. image:: https://img.shields.io/travis/inveniosoftware/invenio-marc21.svg
        :target: https://travis-ci.org/inveniosoftware/invenio-marc21

.. image:: https://img.shields.io/coveralls/inveniosoftware/invenio-marc21.svg
        :target: https://coveralls.io/r/inveniosoftware/invenio-marc21

.. image:: https://img.shields.io/pypi/v/invenio-marc21.svg
        :target: https://pypi.org/pypi/invenio-marc21


Invenio module with nice defaults for MARC21 overlay.

*This is an experimental developer preview release.*

* Free software: MIT license
* Documentation: https://invenio-marc21.readthedocs.io/


..
    This file is part of Invenio.
    Copyright (C) 2016-2019 CERN.

    Invenio is free software; you can redistribute it and/or modify it
    under the terms of the MIT License; see LICENSE file for more details.

Changes
=======

Version 1.0.0 (released 2018-07-19)

- Initial public release.
pycopy-future 0.1
Dummy __future__ module for Pycopy
License: MIT

This is a dummy implementation of a module for the standard library of
Pycopy project (https://github.com/pfalcon/pycopy).
It contains zero or very little functionality, and primarily intended to
avoid import errors (using idea that even if an application imports a
module, it may be not using it onevery code path, so may work at least
partially). It is expected that more complete implementation of the module
will be provided later. Please help with the development if you are
interested in this module.