semvec/pypi-packages huggingface dataset:
directory-downloader 2.0.1
A python Package for downloading files from websites using the directory listing function
Requires Python: >=3.6

# Directory Downloader

Directory Downloader
is a Python library for downloading files from websites using the *directory listing* function used mostly in apache/nginx and ftp an example can be seen [here](https://ftp.mozilla.org/)

## Dependencies

Before installation, make sure you have the required dependencies of asyncio, aiohttp, bs4 and colorama.




## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install directory_downloader.

```bash
pip install directory-downloader 
```

## Usage

```python

import asyncio
from directory_downloader import DDownloader


async def main():
    url = "https://example.com/directory/"
    downloader = DDownloader()
    await downloader.crawl(url)  # fetch all the links from /directory/
    await downloader.download_files()  # download all files to current directory


if __name__ == '__main__':
    asyncio.run(main())
```
## Advanced Usage

### to disable coloring:
```python
downloader = DDownloader(coloring=False)
```
### to disable verbose mode:
```python
downloader = DDownloader(verbose=False)
```
### to change the directory simply add:
```python
downloader.download_files(full_directory=r"C:\Users\User\Desktop")
```
### to make downloads faster you can increase the number of workers (by default 5):
```python
downloader.download_files(workers=5)
```
#### note : increasing the number of workers too much can lead to unstable behavior use at your own risk
### to fetch file links that have extension of pdf only you can use:
```python
await downloader.crawl(extensions=[".pdf"])
```
### you can also use regex like the following:
```python
await downloader.crawl(filter=r"test\d\d\d\.apk")   
```
### to download an existing list of urls you can do as follow:
```python
urls = set(["www.example/example/file1.pdf","www.example/example/file2.pdf",...])
await downloader.download_files(urls=urls)
```
### also you can use filter on downloadable files:
```python
await downloader.download_files(urls=urls,filter=r"test\d\d\d\.apk")
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
hycli 0.6.0
Hypatos cli tool to batch extract documents through the API and to compare the results.
Requires Python: >=3.7,<4.0
Keywords: hypatos,document,etraction,ocr

# Hycli

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hycli)](https://pypi.org/project/hycli/)
[![PyPI version](https://badge.fury.io/py/hycli.svg)](https://pypi.org/project/hycli/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![Main workflow](https://github.com/hypatos/hycli/workflows/Main%20workflow/badge.svg)

This repository contains a Python package/CLI tool to batch extract documents through the Hypatos API and to compare the results.

## Quickstart

```
pip install hycli
hycli extract path-to-files [to-xlsx|to-csv|to-json] url
hycli compare workbook1 workbook2
hycli evaluate -gt ground-truth-workbook -m1 model1-workbook -m2 model2-workbook
```

For more information without leaving the cli use ```hycli --help```

## API Reference and User Guide [available](https://hypatos.github.io/hycli/)


---

## Development

### Requirement

- [poetry](https://python-poetry.org/)

### Installing

```
poetry install
poetry shell
hycli --help
```

### Running the tests

```
poetry run pytest
```

### Versioning

Uses [bump2version](https://github.com/c4urself/bump2version):

```
bump2version --verbose [patch|minor|major]  # add --dry-run to have a preview
git push --follow-tags
```

### Documentation

Is build with sphinx, can be seen/worked on locally by:

```
cd docs
make html
open build/html/index.html
```

### License

Hypatos
py-uwerr 0.1
Pure Python library that implements Wolff's method to compute autocorrelation timesof Monte Carlo series.
Requires Python: >=3.6
Keywords: Monte Carlo,autocorrelation time,tau int,statistics

============================
Analysis of Monte Carlo data 
============================

Author: Dirk Hesse <herr.dirk.hesse@gmail.com>

We implement the method to estimate autocorrelation times of Monte
Carlo data presented in 

U. Wolff [ALPHA Collaboration], *Monte Carlo errors with less errors*,
Comput. Phys. Commun.  **156**, 143 (2004) ``[hep-lat/0306017]``.

**PUBLICATIONS MAKING USE OF THIS CODE MUST CITE THE PAPER.**

The main objective is the following: Data coming from a Monte Carlo
simulation usually suffers from autocorrelation. It is not
straight-forward to estimate this autocorrelation, which is required
to give robust estimates for errors. This program implements a method
proposed by Wolff to estimate autocorrelations in a safe way.


Quick start
===========

This package contains code to generate correlated data, so we can
conveniently demonstrate the basic functionality of the code in a
short example::

  >>> from puwr import tauint, correlated_data
  >>> correlated_data(2, 10)
  [[array([ 1.02833043,  1.08615234,  1.16421776,  1.15975754,
            1.23046603,  1.13941114,  1.1485227 ,  1.13464388,
            1.12461557,  1.15413354])]]
  >>> mean, delta, tint, d_tint = tauint(correlated_data(10, 200), 0)
  >>> print "mean = {0} +/- {1}".format(mean, delta)
  mean = 1.42726267057 +/- 0.03013853
  >>> print "tau_int = {0} +/- {1}".format(tint, d_tint)
  tau_int = 9.89344869217 +/- 4.10466090332

The data is expected to be in the format
``data[observable][replicum][measurement]``. See the documentation
that comes with this code for more information.


License
=======

See LICENSE file.
my-pickledb 0.0.6
Oh-My-PickleDB is an open source key-value store using Python's json module
Requires Python: >=3.6

# What is PickleDB
[PickleDB](https://github.com/patx/pickledb) is a lightweight, fast, and simple database based on
[json](https://docs.python.org/3/library/json.html) module. And it's BSD licensed!

## Oh-My-PickleDB
Oh-My-PickleDB is an improved version of [PickleDB](https://github.com/patx/pickledb) with more database concepts than original one. Most important concepts are:

* Cryptography module - Encrypt/Decrypt database content using [FERNET](https://cryptography.io/en/latest/fernet/) encoding
* Code improvements - More readable documentation, improved code...
* Database Conversions - Works with bytes, json or str databases
* More flexibility - Save/load database as bytes, json or str, you decide!
* Statistics Tools - HopperDB works as a data analyzer

## Oh-My-PickleDB is fun and easy to use
```python
>>> from my_pickledb import PickleDB

>>> database = PickleDB("test.db", load=True, auto_dump=True)

>>> db.set('key', 'value')

>>> db.get('key')
>>> 'value'

>>> db.dump()
>>> True
```

## Easy to Install
```python
# Using python pip
$ pip install my-pickledb
```

```python
# Using git
$ git clone https://github.com/tory1103/oh-my-pickledb.git
$ cd oh-my-pickledb
$ pip install -r requirements.txt
```

<h3 align="right">Useful Links</h3>
<p align="right">
<a href="https://tory1103.github.io/oh-my-pickledb/">
Website<br>
</a>
<a href="https://tory1103.github.io/oh-my-pickledb/docs.html">
Documentation<br>
</a>
<a href="https://pypi.org/project/my-pickledb/">
PyPi<br>
</a>

</p>