semvec/pypi-packages huggingface dataset:
tmtk 0.5.8
A toolkit for ETL curation for the tranSMART data warehouse.
Keywords: transmart,etl,arborist,concept tree

====
tmtk
====

Master:

.. image:: https://travis-ci.org/thehyve/tmtk.svg?branch=master
    :target: https://travis-ci.org/thehyve/tmtk

.. image:: https://codecov.io/gh/thehyve/tmtk/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/thehyve/tmtk

.. image:: https://readthedocs.org/projects/tmtk/badge/?version=latest
    :target: http://tmtk.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

Develop:

.. image:: https://travis-ci.org/thehyve/tmtk.svg?branch=develop
    :target: https://travis-ci.org/thehyve/tmtk

.. image:: https://codecov.io/gh/thehyve/tmtk/branch/develop/graph/badge.svg
    :target: https://codecov.io/gh/thehyve/tmtk


Anaconda Cloud latest package:

.. image:: https://anaconda.org/conda-forge/tmtk/badges/version.svg
    :target: https://anaconda.org/conda-forge/tmtk


A toolkit for ETL curation for the tranSMART data warehouse. The
TranSMART curation toolkit (``tmtk``) can be used to edit and validate
studies prior to loading them with `transmart-batch`_.

For general documentation visit `readthedocs`_.

Installation
------------

Installing via Anaconda Cloud or Pip package managers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Anaconda:

.. code:: sh

    conda install -c conda-forge tmtk

Pip:

.. code:: sh

    pip install tmtk

Installing manually
^^^^^^^^^^^^^^^^^^^

Initialize a virtualenv
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: bash

    python3 -m venv env
    source env/bin/activate


Installation from source
~~~~~~~~~~~~~~~~~~~~~~~~

To install *tmtk* and all dependencies into your Python environment,
and enable the Arborist Jupyter notebook extension, run:

.. code:: sh

    pip install -r requirements.txt
    python setup.py install

or if you want to run the tool from code in development mode:

.. code:: sh

    pip install -r requirements.txt
    python setup.py develop
    jupyter-nbextension install --py tmtk.arborist
    jupyter-serverextension enable tmtk.arborist


Requirements
^^^^^^^^^^^^

The dependencies are in ``requirements.txt``,
optional dependencies are in ``requirements-dev.txt``.


Licence
-------

LGPL-3.0

.. _transmart-batch: https://github.com/thehyve/transmart-batch/
.. _virtualenvwrapper: https://virtualenvwrapper.readthedocs.io
.. _readthedocs: https://tmtk.readthedocs.io/en/latest/
.. _examples: examples
vlibras-deeplearning 1.0.9
VLibras (LAViD-UFPB) neural translation module for translating brazilian portuguese to LIBRAS.

<div align="center">
  <a href="https://www.vlibras.gov.br/">
    <img
      alt="VLibras"
      src="https://vlibras.gov.br/assets/imgs/IcaroGrande.png"
    />
  </a>
</div>

# VLibras Translate (Deep Learning Module)

Neural translation module for VLibras Translate.

![Version](https://img.shields.io/badge/version-v1.0.9-blue.svg)
![License](https://img.shields.io/badge/license-LGPLv3-blue.svg)
![VLibras](https://img.shields.io/badge/vlibras%20suite-2020-green.svg?logo=data:image/png;base64,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)

## Table of Contents

- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Installing](#installing)
- [Contributors](#contributors)
- [License](#license)


## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

### Prerequisites

- [Python >=3.5](https://www.python.org/)
- [pip > 19.0.0](https://pypi.org/project/pip/)
- [git LFS](https://git-lfs.github.com/)

### Installing

After all the prerequisites are satisfied, install as normal through `pip`. For example, to install an editable version of the package for development:
```bash
pip install -e .
```

After the initial installation (and after each update), it is usually a good idea to request a translation through the `vlibras-translate` module. This will trigger the model download process, which can take a bit of time depending on your internet connection.

## Contributors

* Samuel Moura - <samuel.moura@lavid.ufpb.br> / <samueldemouramoreira@gmail.com>
* Caio Moraes - <caiomoraes.cesar@gmail.com>

## License

This project is licensed under the LGPLv3 License - see the [LICENSE](LICENSE) file for details.
cointacparsers 0.0.2
fsl and vbm parsers

# cointacparsers 

This package includes fsl_parser and vbm_parser. fsl_parser parses the freesurfer files (specified in inputspec.json) as dependencies and returns 
the covariates (X) and dependent (y) matrices as dataframes. The format of inputspec.json should be as shown in the example inputspec.json here.
Similarly vbm_parser parses NIfTI images as dependencies and returns the covariates (X) and dependent (y) matrices as dataframes. 
# How to install
pip install cointacparsers
# How to run
for fsl parser: \
 &nbsp;&nbsp;&nbsp;&nbsp; import cointacparsers \
 &nbsp;&nbsp;&nbsp;&nbsp; from cointacparsers import parsers \
 &nbsp;&nbsp;&nbsp;&nbsp; args = json.loads(sys.stdin.read()) \
 &nbsp;&nbsp;&nbsp;&nbsp; (X, y) = parsers.fsl_parser(args) \
for vbm parser: \
 &nbsp;&nbsp;&nbsp;&nbsp; import cointacparsers \
 &nbsp;&nbsp;&nbsp;&nbsp; from cointacparsers import parsers \
 &nbsp;&nbsp;&nbsp;&nbsp; args = json.loads(sys.stdin.read()) \
 &nbsp;&nbsp;&nbsp;&nbsp; mask = os.path.join('/computation', mask_directory, mask_file_name) \
 &nbsp;&nbsp;&nbsp;&nbsp; (X, y) = parsers.vbm_parser(args, mask)
cusbase64 0.2
Customized base64 algorithm

Customize the indexing string of base64 encoding.

You can set you own indexing string using the `config()` method.



Usage: 

```python
import cusbase64

b = cusbase64.CusBase64()
b.encode('binary\x00string') # Output: YmluYXJ5AHN0cmluZw==
b.decode('YmluYXJ5AHN0cmluZw==') # Output: binary\x00string

b.config('aABCDEFGHIJKLMNOPQRSTUVWXYZbcdefghijklmnopqrstuvwxyz0123456789+/')
b.decode('c2UsYi1kYWM0cnUjdFlvbiAjb21wbFU0YP==') # Output: self-destruction complete
```
pottok 0.1rc2
Optimal Transport with raster image, including an optimal transport validation
Requires Python: >=3.5

[![Documentation status](https://readthedocs.org/projects/pottok/badge/?version=latest)](https://pottok.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/pottok.svg)](https://badge.fury.io/py/pottok)
[![Build status](https://api.travis-ci.com/obernardoff/pottok.svg?branch=master)](https://travis-ci.com/obernardoff/pottok)
[![Downloads](https://pepy.tech/badge/pottok)](https://pepy.tech/project/pottok)

# Pottok - Python Optimal Transport for Terrestrial Observation Knowledge

Pottok is a python library based on POT Python Optimal Transport. It provides a grid search for POT with two different method : crossed and circular.

# Classes description

The two avalable classes are :
 - pottok.OptimalTransportGridsearch : Create a gris dearch compatible with POT. The user can use the circular grid search based on the circular validation and crossed grid search based on crossed validation. This one can use scikit learn or museotoolbox GridSearchCV.
 - pottok.RasterOptimalTransport : this class is specifically for raster use. 

# How it works ? 

Since a picture is worth more than a thousand words, we will use a concrete example to illustrate these two grid search
>Add tetris draw

# Pottok installation