semvec/pypi-packages huggingface dataset:
PDF4Cat 0.5.0
PDF4Cat Simple and Power tool for processing pdf docs using PyMuPDF
Requires Python: >=3.7

# PDF4Cat
PDF4Cat Simple and Power tool for processing pdf docs using PyMuPDF 

[![Documentation Status](https://readthedocs.org/projects/pdf4cat/badge/?version=latest)](https://pdf4cat.readthedocs.io/en/latest/?badge=latest)

[Docs](https://pdf4cat.readthedocs.io)

# Planing add
- [ ] CLI
- [ ] Async work & optimizations
## PDF:
- [X] Merge
- [X] Split
- [X] Rotate
- [ ] Edit Pages
- [X] Delete Pages and save to pdf(from pdf)
- [X] Extract Pages and save to pdf(from pdf)
- [X] Protect (Encrypt)
- [X] Unlock (Decrypt)
- [X] Compress (Flate)

## Other things:
- [X] OCR pdf
- [X] Pdf to Images
- [X] Images to pdf
### Add actions with docs:
- [X] DOCX
- [X] POWER POINT
- [X] OPEN OFFICE DOCS

## Note: before use OCR run: 

### Install Tesseract.

### Locate Tesseract’s language support folder. Typically you will find it here:
Windows: C:\Program Files\Tesseract-OCR\tessdata
#
Unix systems: /usr/share/tesseract-ocr/4.00/tessdata

### Set the environment variable TESSDATA_PREFIX
Windows: set TESSDATA_PREFIX=C:\Program Files\Tesseract-OCR\tessdata
#
Unix systems: export TESSDATA_PREFIX=/usr/share/tesseract-ocr/4.00/tessdata
crimpl 0.2.0
Compute Resources Made Simple

<p align="center"><a href="http://crimpl.readthedocs.io"><img src="./docs/images/crimpl.png" alt="crimpl logo" width="300px" align="center"/></a></p>

<p align="center" style="text-align:center"><i>Connecting to Compute Resources made Simple(r)</i></p>

<pre align="center" style="text-align:center; font-family:monospace; margin: 30px">
  pip install crimpl
</pre>



[![badge](https://img.shields.io/badge/github-kecnry%2Fcrimpl-blue.svg)](https://github.com/kecnry/crimpl)
[![badge](https://img.shields.io/badge/pip-crimpl-lightgray.svg)](https://pypi.org/project/crimpl/)
![badge](https://img.shields.io/badge/python-3.6+-blue.svg)
[![badge](https://img.shields.io/badge/license-GPL3-blue.svg)](https://github.com/kecnry/crimpl/blob/master/LICENSE)
[![badge](https://travis-ci.com/kecnry/crimpl.svg?branch=master)](https://travis-ci.com/kecnry/crimpl)
[![badge](https://img.shields.io/codecov/c/github/kecnry/crimpl)](https://codecov.io/gh/kecnry/crimpl)
[![badge](https://readthedocs.org/projects/crimpl/badge/?version=latest)](https://crimpl.readthedocs.io/en/latest/?badge=latest)


**IMPORTANT**: **crimpl** is currently still under development, is not yet well-tested, and is subject to significant API changes.  Please keep posted until an official release is ready.

Read the [latest documentation on readthedocs](https://crimpl.readthedocs.io) or [browse the current documentation](./docs/index.md).


**crimpl** provides high-level python object-oriented interfaces to manage running scripts within conda environments on remote compute resources.

Each type of server implements a `run_script` which runs a given set of commands remotely on the server, showing the output, and waiting for completion, and `submit_script` which starts the script running on the server and detaches while allowing for monitoring its progress remotely.  They also each include a `s.check_output` for copying expected output files back to the local machine.


## Documentation and API Docs

The configuration, options, and capabilities of each type of server are explored in [the latest documentation on readthedocs](https://crimpl.readthedocs.io) or [browse the current documentation](./docs/index.md).

## Contributors

[Kyle Conroy](https://github.com/kecnry)

Contributions are welcome!  Feel free to file an issue or fork and create a pull-request.
HomoPy 0.2
Your solution for stiffness problems

# HomoPy
*Your solution for stiffness problems*

HomoPy is a Python package to perform caluclations of effective stiffness properties in homogenized materials, with an emphasize on fiber reinforced polymers.
Currently, HomoPy offers two types of homogenization procedures:
- Halpin-Tsai with a Shear-Lag modification
- Mori-Tanaka

## Halpin-Tsai
The Halpin-Tsai method is an empirical approach to homogenize two isotropic materials. Our approach is modified with the Shear-Lag model after Cox (xxxx), which is also used in ___ (xxxx) and ___ (xxxx). Being a scalar homeginzation scheme, it allows to calculate the effective stiffness in the plane which is orthogonal to the isotropic plane within transverse isotropic materials, as it is the case for unidirectional reinforced polymers. The effective stiffness, or Young's modulus, is then a function of the angle to the reinforcing direction. A fiber distrubtion within the plane is recognized by volume averaging of imaginary plies of individual orientations in analogy to the laminate theory.

## Mori-Tanaka
The Mori-Tanaka scheme goes back to Mori and Tanaka (xxxx) and is a mean-field homogenization scheme based on Eshelby's solution (xxxx). The implementation so far only allows spheroidal inclusions, which in fact is an ellispoid with identical minor axes or ellipsoid of revolution, respectively. Our algorithm allows to homogenize materials with different types of fibers, each possibily having an individual fiber distrubtion. Being a tensorial homogenization scheme, the fiber orientation tensor is directly included in the calculation and the result is an effective stiffness tensor. The authors would like to emphasize that for multi-inclusion problems or non-isotropic inclusions, the effective stiffness tensor may violate thermodynamic requirements, such as a symmetric stiffness tensor. Further readings of this attribute are given in ___ (xxxx) and ___ (xxxx). It is in the remit of the user to keep this in mind and act accordingly.

***
Further topic related methods of the package:
- Closures to calculate orientation tensors of forth order from an orientation tensor of second order are implemented by a dependence on the package [fiberoripy](https://github.com/nilsmeyerkit/fiberoripy)
- Determining fiber orientation tensors from micrographs is possible by the implemented method of ellipses (yet to come)
sfas 0.0.2
Efficient implementation of a greedy algorithm for computing small feedback arc sets in directed weighted multi-graphs.
Requires Python: >=3.6

# Small Feedback Arc Set (sfas)
Efficient implementation of a greedy algorithm for computing small feedback arc sets in directed weighted multi-graphs.
This implementation is an adaptation of the algorithm described in Section 2.3 of [this](http://www.vldb.org/pvldb/vol10/p133-simpson.pdf) article, with additional generalization to support weights and parallel edges.
jupyter-datasette-proxy 1.1
Run datasette on JupyterHub with jupyter-server-proxy

# jupyter-datasette-proxy

Using [datasette](https://datasette.io/) in JupyterHub or Binder with
[jupyter-server-proxy](https://github.com/jupyterhub/jupyter-server-proxy)

Launch into notebook interface (run `datasette` from the notebook `New` menu): [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/psychemedia/jupyterserverproxy-datasette-demo/master)

Launch directly into datasette: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/psychemedia/jupyterserverproxy-datasette-demo/master?urlpath=datasette)

The `datasette` port is assigned dynamically, although a known port could be specified if datasette is used to provide an API.

## Installation

You can install from PyPI:

```bash
pip install jupyter-datasette-proxy
```

This will also bring in datasette.
datamaps 1.1.9
Collect and clean data using Excel spreadsheets.
Requires Python: >=3.6
Keywords: data console commandline excel

![Tests](https://github.com/hammerheadlemon/datamaps/workflows/Tests/badge.svg)

## Introduction

`datamaps` is a command line tool, written in Python, which facilitates the collection of data using Excel as the 'form'. It accepts that Excel files are freely distributable, easy to understand and already well integrated in the non-technical office environment.

The tool uses a concept a *datamap*, which is simple, user-created CSV file which maps cell references within Excel 'templates'. The datamap concept makes it easy to design pretty Excel spreadsheets with blank cells (the 'template') intended for the user to complete - the cell references in the datamap inform the `datamaps` program where it should expect to find user-entered data. At this point, the output is another *master* spreadsheet which contains all the data from each completed template compiled into a single table. This is then used by other tools for further analysis.

The documentation found at [Datamaps - Twenty Four Software](https://datamaps.twentyfoursoftware.com) gives a more detailed explanation.

## Installation

`pip install datamaps`

## Bugs and issues

Please report bugs as [Github issues](https://github.com/yulqen/datamaps/issues).
releases-manager 0.0.6
Manage python published release for packages that you owns on PyPI.

# Python releases manager - pyrman

Python library that can be used to manage releases for packages that you owns on PyPI.

## Features

* list packages that you own
* list releases for packages that you own
* unpublish (remove) a specific release for one of your packages

## Usage (CLI)

* Unpublish a release

```bash
pyrman release rm <package> <version>
```

* You can find out more information on how to use CLI by using `--help` option

```bash
pyrman --help
```

## Usage (Python)

```python
from pyrman.client import ReleaseManagerClient

pyrman = ReleaseManagerClient(url)
pyrman.authenticate(username, password)
releases = pyrman.list_releases("my-package")
for release in releases:
    pyrman.remove_release("my-package", release.version)
```