semvec/pypi-packages huggingface dataset:
owoifyer 1.0.0
Turn your bland old english into a fuwwy poetic mastewpiece
Requires Python: >=3.6

# Owoifyer

Owoify your text quickly with this python CLI tool

## Installation

```
python3 -m pip install owoifyer
```

## Usage

```py
from owoifyer import owoify

text = "Hello there, how are you on this fine evening?"

owo_text = owoify(text)

print(owo_text)
# Hewwo thewe, how awe you on this finy evenyng?
```

_Yes I was very bored,_ 
_no I am not a furry._
meta1-testnet 0.2.11
Python library for meta1
Keywords: meta1,library,api,testnet

# Python Library for Meta1

![](https://img.shields.io/pypi/v/meta1.svg?style=for-the-badge)
![](https://img.shields.io/github/release/meta1/python-meta1.svg?style=for-the-badge)
![](https://img.shields.io/github/downloads/meta1/python-meta1/total.svg?style=for-the-badge)
![](https://img.shields.io/pypi/pyversions/meta1.svg?style=for-the-badge)
![](https://img.shields.io/pypi/l/meta1.svg?style=for-the-badge)
![](https://cla-assistant.io/readme/badge/meta1/python-meta1)

**Stable**

[![docs master](https://readthedocs.org/projects/python-meta1/badge/?version=latest)](http://python-meta1.readthedocs.io/en/latest/)
[![Travis master](https://travis-ci.org/meta1/python-meta1.png?branch=master)](https://travis-ci.org/meta1/python-meta1)
[![codecov](https://codecov.io/gh/meta1/python-meta1/branch/master/graph/badge.svg)](https://codecov.io/gh/meta1/python-meta1)

**Develop**

[![docs develop](https://readthedocs.org/projects/python-meta1/badge/?version=develop)](http://python-meta1.readthedocs.io/en/develop/)
[![Travis develop](https://travis-ci.org/meta1/python-meta1.png?branch=develop)](https://travis-ci.org/meta1/python-meta1)
[![codecov develop](https://codecov.io/gh/meta1/python-meta1/branch/develop/graph/badge.svg)](https://codecov.io/gh/meta1/python-meta1)

---

## Documentation

Visit the [python website](http://docs.pybitshares.com/en/latest/) for in depth documentation on this Python library.

## Installation

### Install with pip3:

    $ sudo apt-get install libffi-dev libssl-dev python-dev python3-dev python3-pip
    $ pip3 install meta1

### Manual installation:

    $ git clone https://github.com/meta-source/meta1-python
    $ cd meta1-python
    $ python3 setup.py install --user

### Upgrade

    $ pip3 install --user --upgrade meta1

## Contributing

python-meta1 welcomes contributions from anyone and everyone. Please
see our [guidelines for contributing](CONTRIBUTING.md) and the [code of
conduct](CODE_OF_CONDUCT.md).

### Discussion and Developers

Discussions around development and use of this library can be found in a
[dedicated Telegram Channel](hhttps://t.me/meta1exchange)

### License

A copy of the license is available in the repository's
[LICENSE](LICENSE.txt) file.

### Bounties

As part of [HackTheDex](https://hackthedex.io), security issues found in this
library are potentially eligible for a bounty.
postman2markdown 1.0.2
Generate markdown api document from postman.

postman2markdown
=====================

|Python Version| |PyPI version|

    *Generate markdown api document from postman.*

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

.. code:: shell

    pip install postman2markdown

Usage
------

* **Export json from postman**
    Export your collection from postman(only support v2.1), you could get a json file.

* **Run** ``p2m`` **to generate markdown api document**::

        # specify input file
        p2m xxx.json

        # specify multi input files
        p2m xxx.json yyy.json zzz.json

* **You will get your markdown api document.**

.. |Python Version| image:: https://img.shields.io/badge/python-3.7-brightgreen.svg?style=flat-square
    :target: https://www.python.org/
.. |PyPI version| image:: https://img.shields.io/pypi/v/postman2markdown.svg?style=flat-square
    :target: https://pypi.org/project/postman2markdown/
continuous-peak-fit 0.0.1
Fits azimuth/time dependency of peaks with Fourier Series descriptions
Requires Python: >=3.6

# Continuous-Peak-Fit

## Installation

Installation is available through git or via pip

From git use

`git clone https://github.com/ExperimentalMineralPhysics/Continuous-Peak-Fit.git`

To install the required dependencies use, in the Continuous-Peak-Fit directory use 

`pip install requirements.txt`

# 

For pip installation use

`pip install continuous-peak-fit`

this should automatically install the required dependencies.


## Usage

Continuous-Peak-Fit can be run in a number of ways but requires an inputs file containing information about where your 
data files are stored as well as information of the number of peak and appropriate ranges for the fit. Example data and 
inputs file are available in the Example1-Fe directory from the git repository. 

If using a pip install an example inputs file can be generated using the 'CPF_generate_inputs' executable or from within
 python using

`import cpf`

`cpf.generate_inputs()`

Fitting can then be performed using 

`CPF_XRD_FitPattern <inputs_file>` from the command line

or from within python

`import cpf`

`cpf.XRD_FitPattern.execute(settings_file=<inputs_file>)`
Scopeobj 0.2.1
Scope object in Python
Requires Python: >=3.0

Scopeobj is a Python module. You can use it as a scope.
For example:
>>> from scopeobj import Scope
>>> s = Scope()
>>> s.exec("x = 3")
>>> s.eval("x ** 2")
9
>>> s.clear()
>>> s.getitem("x")
None
SciFin 0.0.5
SciFin is a python package for Science and Finance.
Requires Python: >=3.6


<p align="center">
  <img src="./docs/logos/logo_scifin_github.jpg" width=300 title="hover text">
</p>



# SciFin

SciFin is a python package for Science and Finance.

## Summary

The SciFin package is a Python package designed to gather and develop methods for scientific studies and financial services. It originates from the observation that numerous methods developed in scientific fields (such as mathematics, physics, biology and climate sciences) have direct applicability in finance and that, conversely, multiple methods developed in finance can benefit science.

The development goal of this package is to offer a toolbox that can be used to derive specific applications both in research and business. Its purpose is not only to bring these fields together, but also to increase interoperability between them, helping science turn into business and finance to get new insights from science. Some functions will thus be neutral to any scientific or economic fields, while others will be more specialized for precise tasks. The motivationg behind this design is to provide tools that perform advanced tasks without depending on too many parameters.


## Contents

The current development is focused on the following topics:
- `classifier`: Classification techniques
- `fouriertrf`: Fourier transforms
- `geneticalg`: Genetic algorithms
- `marketdata`: Reading market data
- `montecarlo`: Monte Carlo simulations
- `neuralnets`: Neural networks
- `statistics`: Basic statistics
- `timeseries`: Time series analysis

Other topics will later follow.


## Installation

Installing SciFin on Linux or Mac is very easy, you can simply run `pip install SciFin` on the Terminal command line.

If you encounter problems during installation or after and think you know how the problem can be improved, please share it with me.


## Contact

- If you have comments or suggestions, you can reach Fabien Nugier.
- If you wish to contribute, please contact me through GitHub.
kata-baku-checkr-po-file 0.1.2
Checker for Kata Baku for PO File reader
Requires Python: >=3.6

# Kata Baku Checker for PO File

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/209d0abd94964e0380c2ed897acd052d)](https://app.codacy.com/app/berviantoleo/Kata-Baku-Checker-for-PO-File?utm_source=github.com&utm_medium=referral&utm_content=berviantoleo/Kata-Baku-Checker-for-PO-File&utm_campaign=Badge_Grade_Settings)

## Build Status

| Travis | Github Action - Docker | Python Package |
|:------:|:----------------------:|:--------------:|
| [![Build Status](https://travis-ci.com/bervProject/Kata-Baku-Checker-for-PO-File.svg?branch=master)](https://travis-ci.com/bervProject/Kata-Baku-Checker-for-PO-File) | ![Docker](https://github.com/bervProject/Kata-Baku-Checker-for-PO-File/workflows/Docker/badge.svg) | ![Upload Python Package](https://github.com/bervProject/Kata-Baku-Checker-for-PO-File/workflows/Upload%20Python%20Package/badge.svg) |

## Usage

```bash
  python check.py -i [Input File] -t po
```

### Docker Image & Docker Compose

#### Build

```bash
docker-compose build
```

#### Running

```bash
docker-compose run console python check.py -i example/id.po -t po
```

## Forked from

https://github.com/turfaa/IndonesianKataBakuChecker

## Data Example Source

From Geany Repository.

## LICENSE

```
MIT License

Copyright (c) 2018 Bervianto Leo Pratama

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

```
cwlkernel 0.0.4
CWL Jupyter Notebook Kernel
Requires Python: >=3.6

# Jupyter Notebook Kernel for Common Workflow Language 

[![Build Status](https://travis-ci.com/giannisdoukas/CWLJNIKernel.svg)](https://travis-ci.com/giannisdoukas/CWLJNIKernel)
[![Coverage Status](https://coveralls.io/repos/github/giannisdoukas/CWLJNIKernel/badge.svg?t=AHSikx)](https://coveralls.io/github/giannisdoukas/CWLJNIKernel)
[![Gitter chat](https://badges.gitter.im/CWLJNIKernel/gitter.png)](https://gitter.im/CWLJNIKernel/community)
[![Documentation Status](https://readthedocs.org/projects/cwljnikernel/badge/?version=latest)](https://cwljnikernel.readthedocs.io/en/latest/?badge=latest)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/6c7585bdf708450d92d97c9e1add4633)](https://www.codacy.com/manual/giannisdoukas/CWLJNIKernel?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=giannisdoukas/CWLJNIKernel&amp;utm_campaign=Badge_Grade)

This is a jupyter notebook kernel which enables running Common Workflow Language. It uses 
[cwltool](https://github.com/common-workflow-language/cwltool) as the execution engine. The goal of the kernel is to 
improve the human-in-the-loop interaction. The goal of the kernel is to improve human-in-the-loop by improving the 
following:
* Documenting the workflow
* Enable the developer to execute a workflow with multiple steps splited in multiple JN cells
* Enable the user to execute multiple workflows stitched together
* Ensembl workflows
    * Run the same workflow many times over different datasets or with different settings over the same data sets
    * Run different workflows over the same datasets

`Currently windows are not supported`

## Examples
In examples directory there are many examples which illustrate how to use the kernel. 
![example](examples/example.png)

## How to contribute
If you are a CWL developer and you would like to contribute feel free to open an issue and ask for new features you 
would like to see.
roundface 0.0.17
Detect Faces In Images and Generate Profile Photos
Requires Python: >=3.6
Keywords: face detect roundface profile photo

# Roundface
Generate profile photos for all faces in a given image

**Input**

![Source image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/sample.jpg?raw=true)

**Output**

![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/1.jpg?raw=true) ![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/2.jpg?raw=true) ![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/3.jpg?raw=true)

**Greyed**

![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/1-grey.jpg?raw=true) ![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/2-grey.jpg?raw=true) ![demo image](https://github.com/Edward-K1/roundface/blob/develop/resources/images/3-grey.jpg?raw=true)

## Requirements:
- Python 3
- OpenCV
- NumPy

## Installation
```
pip install opencv-python roundface
```

## Project Setup
- Clone repo
- CD in project
- Create python virtual environment: `python -m venv venv`
- Activate virtual environment: `venv/bin/activate` or `venv\Scripts\activate`
- Install requirements with pip: `pip install -r requirement.txt`

## Usage:
`roundface - s <path to image file or folder>`

`python roundface.py - s <path to image file or folder>`

## More Options:

**-g**  or  **--grey**   Integer. 1 or 0. Specifies whether resulting photos should be greyed out.

**- sz**  or  **--size**   Integer. Desired output size in pixels

**- r**  or  **--radius**  Integer (Float). e.g 1.2 for 20% more than the default radius.

**Faces that can't fit a particular radius will be skipped**


## Examples

```
roundface -s z://home/photos
roundface -s z://home/photos/myself.jpg
roundface -s vacation/photos - sz 400 -g 1
roundface -s vacation/photos - sz 400 -r 1.0
```
---

```
python roundface.py -s z://home/photos
python roundface.py -s z://home/photos/myself.jpg
python roundface.py -s vacation/photos - sz 400 -g 1
python roundface.py -s vacation/photos - sz 400 -r 1.0
```
A folder named **roundface** will in created in the same location as the image source. It will contain the output photos.

## Credits:
[Pexels](https://www.pexels.com/photo/three-children-sitting-on-stairs-1212805/)   
[Towards Data Science](https://towardsdatascience.com/extracting-faces-using-opencv-face-detection-neural-network-475c5cd0c260)
neatly 0.0.21.3
A NEAT package
Requires Python: >=3.8

# neatly

**NOTE: *neatly* is not yet functional**  
  
*neatly* is a package designed to facilitate the implementation of [NeuroEvolution of Augmenting Topologies (NEAT)](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf) in Python.

## Installation
*neatly* can be installed with pip via:
```bash
pip install neatly
```

## Usage/Example

### Configuration
When using *neatly*, although a config file isn't strictly necessary, it's highly highly recommended that you use one to streamline *neatly*'s implementation process. In the same folder as the python file that is using neatly functions, create a `config.toml` file. To get started, simply copy-paste this in:

```toml
i_count = 2     # Number of inputs
o_count = 1     # Number of outputs
pop_size = 30   # Population size of a single generation
```

This is an extremely basic `config.toml` file, but it gets the job done. Feel free to adjust the default values in order to steer *neatly* in the direction of your desired network/structure. There are many other useful parameters that can (and should) be tweaked, so make sure to check out <link-to-docs>

### Hello, world!

Here's an example of how to implement a very simple *neatly* network:

```python
import neatly

# Initialize a population and sort into a list of extant species
pop = neatly.initialize_population()
almanac = neatly.speciate(pop)
```

For more details and tutorials, see <link-to-docs>

## Contributing
To be completely honest, I don't know why anybody would want to contribute to this pile of hot garbage, and I don't really know how contributing works, but if you're interested email [me](mailto:seanrhee2024@u.northwestern.edu?subject=[Github]%20contributing%20to%20neatly).

## License
[MIT](https://choosealicense.com/licenses/mit/)