semvec/pypi-packages huggingface dataset:
text-clean 0.1.5
Process text for NLP
Requires Python: >=3.6

#text-clean
Process text for NLP.


## how to use
```python
from text_clean import TextClean


tc = TextClean()

_str = "繁 體 字是smasd ❶ ❷ ❸ ❹ 艹艹艹艹 艹艹艹艹nm 夶彩呗我 幹什麼 □ ■ ◇ ◆ − ++++++++ ⑪ ⑫ ⑬  ⒍ ⒎ ⒏ ⒐ W,X  asd鬼东 西錯 鍼٩(๑ᵒ̴̶͈᷄ᗨᵒ̴̶͈᷅)و q����"

print(tc.clean(_str))

# output
# 繁体字是smasd1234艹艹艹nm大大彩呗我干什么+++1112136789wxasd鬼东西错针q
```
IDA-pkg 0.0.2
Python package for efficient dispersal of information that breaks a file of length N into n pieces, each of length N/m, so that every m pieces are sufficient to reconstruct the original file.
Requires Python: >=3.6

# IDA

This is a library that implements the Information Dispersal Algorithm (IDA) introduced in [1].
IDA breaks up a file of size N into n fragments, each of size N/m such that the original file can be recovered from any m of these fragments.
Note that after IDA splits the file, the total size of all the fragments is(n/m)N. 
The parameters n and m can be chosen such that their ratio is close to 1, and hence, the total size of all the fragments after splitting the original file is close to the size of the original file, making IDA space-efficient. 

[1] Rabin, Michael O. "Efficient dispersal of information for security, load balancing, and fault tolerance." Journal of the ACM (JACM) 36.2 (1989): 335-348.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install IDA.

```
pip install IDA-pkg
```

## Issues
* Warning: currently only works with small files!

## Examples
The following is a simple example. That is, we split a file into 10 fragments such that any 5 of them are sufficient to recover the original data. 

```python
import IDA
fragments = IDA.split("test.txt", 10, 5) 
```

To reassemble the file, we take any 5 of the fragments, e.g., we take the first five fragments, then the following will return the content of the original file as a string. 

```python
import IDA
IDA.assemble(fragments[:5]) 
```

You can also write the output to a file. 

```python
IDA.assemble(fragments[:5], "output.txt") 
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.


## License
[MIT](https://choosealicense.com/licenses/mit/)
grains-cow-fortune 2
Vertically extends the POP-based grains project and adds the cowsay and fortune GRAINS
Requires Python: >=3.6

=============
GRAINS_COWSAY
=============

** A simple example of a vertically app merged grains project **


INSTALLATION
============



With git::

    git clone https://gitlab.com/saltstack/pop/grains.git
    pip install -e grains

With pip::

    pip install corn_cowsay


EXECUTION
=========
After installation the `grains` command should now be available if it wasn't already


TESTING
=======
install `requirements-test.txt` with pip and run pytest::

    pip install -r grains/requirements-test.txt
    pytest grains/tests

VERTICAL APP-MERGING
====================
Instructions for extending grains like this project does

Install pop::

    pip install --upgrade pop

Create a new directory for the project::

    mkdir grains_{project}
    cd grains_{project}


Use `pop-seed` to generate the structure of a project that extends `grains`::

    pop-seed -t v pop_{kernel} -d grains

* "-t v" specifies that this is a vertically app-merged project
*  "-d grains" says that we want to implement the dynamic name of "grains"

Add "grainsv2" to the requirements.txt::

    echo "grainsv2" >> requirements.txt

Note* url based reqs aren't supported on older versions of setuptools
To pip install your vertically app-merged project install grains manually::

    pip install -e git+https://gitlab.com/saltstack/pop/grains.git#egg=grainsv2

And that's it!  Go to town extending grains
Follow the conventions you see in gitlab.com/satlstack/pop/grains