semvec/pypi-packages huggingface dataset:
python-kanban 0.2.0
Text-based interface for a Kanban board in pure Python
License: MIT
Requires Python: >=3.7,<4.0

=============
Python Kanban
=============

A terminal-based interface written in pure Python for a very plain Kanban board.

Installation
============

.. code:: bash

    pip install python-kanban


Usage
=====

The simplest way to execute it is to run 

.. code:: bash

    python_kanban


in the command line. The navigation keys are (or should be) self-explanatory in the
application.

This will create a ``kanban.db`` database file in the current folder. After
quitting and running ``python_kanban`` again the same database will be loaded
and the tasks are persisted.

If you wish to customize the database file or simply manage multiple files, you
can set an environment variable with:

.. code:: bash

    export DYNACONF_DB_FILE=another_database_file.db

and it will either create another ``another_database_file.db`` file or load it
if already existing.

This is still a work in progress, the looks may be rough in the edges, but most of the main functionality is there already.

Check the `repository page <https://github.com/fillipe-gsm/python-kanban>`_ for more information.
bias-correction 0.4
python library for bias_correction
License: MIT
Requires Python: >=3.6

_________________

[![PyPI version](https://badge.fury.io/py/bias-correction.svg)](http://badge.fury.io/py/bias-correction)
[![License](https://img.shields.io/github/license/mashape/apistatus.svg)](https://pypi.python.org/pypi/bias-correction/)
[![Downloads](https://pepy.tech/badge/bias-correction)](https://pepy.tech/project/bias-correction)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
_________________

### About

The module `bias_correction` consists of functions to perform bias correction of datasets to remove biases across datasets. Implemented methods include [quantile mapping](https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.2168), [modified quantile mapping](https://www.sciencedirect.com/science/article/abs/pii/S0034425716302000?via%3Dihub) , [scaled distribution mapping (Gamma and Normal Corrections)](https://www.hydrol-earth-syst-sci.net/21/2649/2017/). 

### Installation

```
pip install bias-correction
```

## Documentation

Latest documentation is available [here](https://pankajkarman.github.io/bias_correction/index.html).

### Usage

`bias_correction` is easy to use. Just import:

```python
from bias_correction import BiasCorrection
```
Instantiate the bias correction class as:
```python
bc = BiasCorrection(reference, model, data_to_be_corrected)
```

Perform correction specifying the method to be used:
```python
corrected = bc.correct(method='gamma_mapping')
```
dj-channels-pubsub 0.1.0
Django Channels Layer for Google PubSub
License: MIT
Keywords: dj-channels-pubsub

=============================
Django Channels PubSub
=============================

.. image:: https://badge.fury.io/py/dj-channels-pubsub.svg
    :target: https://badge.fury.io/py/dj-channels-pubsub

.. image:: https://travis-ci.org/bgervan/dj-channels-pubsub.svg?branch=master
    :target: https://travis-ci.org/bgervan/dj-channels-pubsub

.. image:: https://codecov.io/gh/bgervan/dj-channels-pubsub/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/bgervan/dj-channels-pubsub

Django Channels Layer for Google PubSub

Documentation
-------------

The full documentation is at https://dj-channels-pubsub.readthedocs.io.

Quickstart
----------

Install Django Channels PubSub::

    pip install dj-channels-pubsub


Add Environment variables:

::

    export GOOGLE_CLOUD_PROJECT="" # Real project ID required for local emulator too


For the local PubSub Emulator, add:

::

    export PUBSUB_EMULATOR_HOST=localhost:8085

For the local PubSub Emulator with Docker Compose:

::

    export PUBSUB_EMULATOR_HOST=pubsub:8085

::

    pubsub:
    image: google/cloud-sdk:331.0.0
    command: gcloud beta emulators pubsub start --host-port=0.0.0.0:8085
    ports:
      - "8085:8085"
    restart: unless-stopped

Features
--------

* TODO

Running Tests
-------------

Does the code actually work?

::

    source <YOURVIRTUALENV>/bin/activate
    (myenv) $ pip install tox
    (myenv) $ tox


Development commands
---------------------

::

    pip install -r requirements_dev.txt
    invoke -l


Credits
-------

Tools used in rendering this package:

*  Cookiecutter_
*  `cookiecutter-djangopackage`_

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`cookiecutter-djangopackage`: https://github.com/pydanny/cookiecutter-djangopackage




History
-------

0.1.0 (2021-08-04)
++++++++++++++++++

* First release on PyPI.
cosSim 0.1.0
This CLI tool compares files or directories with cosine similarity.
License: MIT
Requires Python: >=3.6
Keywords: cosine Similarity,file comparison,nltk,CLI programm,vectorisation

# cosSim - See how similar your files are

It is hard to determine how similar two text files are.  Without much complication, cosSim uses simple tokenization and vectorization with which a word similarity can be calculated.

This is very usefull in cases where the context is not important, but the spelling has a big impact (as in OCR with pdf files).

This Project has been brought to life with the help of the [AfZ](https://www.afz.ethz.ch/) (Archive of Contemporary History) at the [ETH Zürich](https://ethz.ch/en.html).

# Overview

The tool is suited to compare texts that do not depend on context, but rather rely on correct spelling. The output is presented in percent. Some use cases could be:

- comparing two different OCR outputs to a ground truth

- comparing hand written digitalized text with a ground truth

- checking if your AI has a correct spelling regarding your ground truth

So if you want to get a similarity in terms of semantics, this is not the right tool for you.

The CLI tool uses the [NLTK Library](https://www.nltk.org/) to tokenize the texts, Numpy to store the vector data and the cosine similarity to compare the vectors.

## Guide
The following shows how to get and use cosSim.

### Installation

    $ pip install cosSim

If you would rather like to customize the code to your needs, grab a stable version under "Releases".

### Usage

The CLI can be used in two ways. It is able to *compare two files or directories to a ground truth*. It can also *compare one file or directory to a ground truth*. The amout of files or directories is specified in the **positional argument** behind the command:

    $ cosSim path_to_dir_or_file

or

    $ cosSim path_to_dir_or_file another_path

The programm recognises with the **--dir** or **--file** flag, which kind of parsing you would like to do. So if you desire to compare two files to the integrated corpus, simply type:

    $ cosSim path1 path2 --file

Because the integrated corpus mostly generates an output, that represents language similarty (that is not useful in many cases), cosSim accepts your ground truth under the **--base** flag:

    $ cosSim path1 path2 --file --base path_to_ground_truth


Regarding language support right now, cosSim supports
- german
- english

tokenization as well as corpora. If neede, more language support will be added in the future.
You can specify the language by adding *de* or *en* to the **--lang** flag. If no language is explicitly stated, the program defaults to *german*. 

Of course you can access a help menu in within the CLI by adding **--help** or **-h** to the end of the line.

### Common error messages

Because the program uses the nltk library, there is a possibility that an error occurs, which notes a missing installation. In order to prevent this from happening again, see their dedicated [documentation](https://www.nltk.org/data.html) regarding these rather small problems.
noizy 0.1.0
Log function calls: from noizy import noizy
License: MIT
Requires Python: >=3.6,<4.0

# noizy

> Log function calls: from noizy import noizy

WARNING: This repo is in development. It was automatically generated with [mkpylib](https://github.com/shawwn/scrap/blob/master/mkpylib). If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality is `print('TODO')`.

If you really want to try it out, feel free. I recommend reading through the code and commit history to see if it does what you need, or [ask me](#contact) for status updates.

Stay tuned!

## Install

```
python3 -m pip install -U noizy
```

## Usage

```py
import noizy

print('TODO')
```

## License

MIT

## Contact

A library by [Shawn Presser](https://www.shawwn.com). If you found it useful, please consider [joining my patreon](https://www.patreon.com/shawwn)!

My Twitter DMs are always open; you should [send me one](https://twitter.com/theshawwn)! It's the best way to reach me, and I'm always happy to hear from you.

- Twitter: [@theshawwn](https://twitter.com/theshawwn)
- Patreon: [https://www.patreon.com/shawwn](https://www.patreon.com/shawwn)
- HN: [sillysaurusx](https://news.ycombinator.com/threads?id=sillysaurusx)
- Website: [shawwn.com](https://www.shawwn.com)