semvec/pypi-packages huggingface dataset:
Invoice-GUBS 0.0.0.2
An invoice ocr package
Requires Python: >=3.6

## Data extractor for PDF invoices 

To run the code:

1. Install the package using using the command:  
   `pip install invoiceocr-Gubs`
2. In your script, write:
    `from InvoiceOcrGubs import script`
3. To run the script, run command:
   `python <file_name> --pdf <pdf_name>`
2. A window will open up, select the desired rectangle by dragging with help of   mouse and then press c on keyboard
3. The window will close and the etxt will be showed on the terminal.
scrapy-tor-proxy-rotator 0.0.2
Rotate TOR IPs with Scrapy (forked from scrapy-tor-proxy-rotation 0.0.1)
Requires Python: >=3.6

# Scrapy Tor Proxy Rotation
This module allows Scrapy to rotate Tor IPs.

## Install

Simple install, via **pip**:
```bash
pip install scrapy-tor-proxy-rotation
```

## Config Tor
To configure **Tor**. First, install :

```bash
sudo apt-get install tor
```

Stop its execution to make configurations:

```bash
sudo service tor stop
```

Open your configuration file as root, available in */etc/tor/torrc*, for example, using nano:

```bash
sudo nano /etc/tor/torrc
```
Place the lines below and save:

```
ControlPort 9051
CookieAuthentication 0
```

Restart Tor:

```bash
sudo service tor start
```


It is possible to verify the IP of your machine and compare it as Tor in the following way:
- To see your IP:
    ```bash
    curl http://icanhazip.com/
    ```
- To see the ip of Tor:
    ```bash
    torify curl http://icanhazip.com/   
    ```

For Scrapy it is necessary to use an intermediary, in this case or **[Privoxy](https://www.privoxy.org/)**. 

> Tor Default Proxy Server: 127.0.0.1:9050

## Install and Config **Privoxy**:
- Install: 
    ```bash
    sudo apt install privoxy
    ```
- Stop the service:
    ```bash
    sudo service privoxy stop
    ```
- Open the config file:
    ```bash
    sudo nano /etc/privoxy/config
    ```
- Add the following lines:
    ```bash
    forward-socks5t / 127.0.0.1:9050 .
    ``` 
- Start the service: 
    ```
    service privoxy start
    ```

Test: 
```bash
torify curl http://icanhazip.com/
```
```bash
curl -x 127.0.0.1:8118 http://icanhazip.com/
```

## Use

After performing these configurations, it is possible to integrate Tor with Scrapy.
- Configure the middleware in your settings file (**settings.py**):
    ```python
    DOWNLOADER_MIDDLEWARES = {
        ...,
        'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware': 110,
        'tor_ip_rotator.middlewares.TorProxyMiddleware': 100
    }
    ```
    
- Add those in your custom_settings in your spider or in (**settings.py**) if you want to use them on all spiders from the project:  
    ```python
    TOR_IPROTATOR_ENABLED = True
    TOR_IPROTATOR_CHANGE_AFTER = #número de requisições feitas em um mesmo endereço IP
    ```
By default, an IP can be reused after 10 other uses. This value can be altered by the variable TOR_IPROTATOR_ALLOW_REUSE_IP_AFTER, as below:

```python
TOR_IPROTATOR_ALLOW_REUSE_IP_AFTER = #
```

A large number can also make it slower to retrieve a new IP to use or find. If the value is 0, there will be no record of used IPs.
pysimrel-pkg-therimalaya 0.6.3
Simulation of Linear Model Data
Requires Python: >=3.7.13

# `pysimrel`: Simulation of Linear Model Data

[![Documentation Status](https://readthedocs.org/projects/pysimrel/badge/?version=latest)](http://pysimrel.readthedocs.io/?badge=latest)
[![Build Status](https://travis-ci.org/simulatr/pysimrel.svg?branch=master)](https://travis-ci.org/simulatr/pysimrel)
[![codecov](https://codecov.io/gh/simulatr/pysimrel/branch/master/graph/badge.svg)](https://codecov.io/gh/simulatr/pysimrel)


This package lets user to simulate data from a liner model controlling some important parameters. Only tuning few parameters, user can simulate data with wide range of properties.
WeChatEnterprise-hahaleyile 0.0.6
WeChatEnterpriseApi
Requires Python: >=3.6

# 项目说明

企业微信 api 的 python 实现。

目前已实现应用的发文本、markdown、文件和上传文件。

其中 access_token 和 media_id 通过 redis 缓存，缓存标识加有前缀。
pycaltransfer 0.1.8
Calibration transfer for chemometrics and spectral data applications
Requires Python: >=3.7

# Calibration transfer for chemometrics and spectral data applications

This package contains methods to perform calibration transfer based on bilinear models, mainly Partial Least Squares Regression.
Numpy and Sci-Kit Learn are mandatory dependencies

The methods included are:

(Piecewise) Direct standardization (PDS, DS) (Wang 1991, Bouveresse1996)

Orthogonal projection (EPO transfer) (Zeaiter 2006, Roger 2003)

Domain invariant PLS (Nikzad-Langerodi 2018, 2020)

Joint Y PLS (Folch-Fortuny 2017, Garcia Munoz 2005)

Spectral Space Transformation (SST) (W. Du, 2011)

Transfer by orthogonal projection (TOP) (A. Andrew and T. Fearn, 2004)

Dynamic orthogonal projection (DOP) (Zeater, et al 2006)

Transfer component analysis (TCA) (Pan, et at 2011)

Unsupervised dynamic orthogonal projection (uDOP) (Fonseca Diaz, et al 2022)


## Installation options

### Option 1. Install via pip

```python
pip install pycaltransfer
```

### Option 2. Clone repository

```git
git clone https://gitlab.com/chemosoftware/python/pycaltransfer.git
```

To start using this package and get the documentation of the methods, do:

```python
import pycaltransfer.caltransfer as caltransfer
help(caltransfer.ds_pc_transfer_fit)
help(caltransfer.pds_pls_transfer_fit)
help(caltransfer.epo_fit)
help(caltransfer.jointypls_regression)
help(caltransfer.slope_bias_correction)
help(caltransfer.dipals)
help(caltransfer.sst)
help(caltransfer.top)
help(caltransfer.dop)
help(caltransfer.linear_tca)
help(caltransfer.udop)
```
nice-prompts 0.4.3
Use nice prompts in your terminal e.g. multiple choice
Requires Python: >=3.6

# Nice-prompts.

Generate nice looking prompts for your cli applications.

[![PyPI](https://img.shields.io/pypi/v/nice-prompts?style=for-the-badge)](https://pypi.org/project/nice-prompts)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nice-prompts?style=for-the-badge)](https://pypi.org/project/nice-prompts)
[![Read the Docs](https://img.shields.io/readthedocs/nice-prompts?style=for-the-badge)](https://nice-prompts.readthedocs.io)

Install from PyPi:
```bash
$ pip3 install nice-prompts
```

### Demo (select 1, select at least 2 with max 2, select any amount):

```python3
import nice_prompts

n = nice_prompts.NicePrompt()

print(n.selection({"I like pizza": "Good taste",
    "I respectfully disagree with the opinion of liking pizza": "Fair enough, good day",
    "I hate pizza": "Bad sport 👎"})) # Select one from the keys, return the value

print(n.multiselection({"I like pizza": "Good taste",
    "I respectfully disagree with the opinion of liking pizza": "Fair enough, good day",
    "I hate pizza": "Bad sport 👎"}, amount=2, required=2)) # Select multiple from the keys, return the values. You must select 2


print(n.multiselection({"I like pizza": "Good taste",
    "I respectfully disagree with the opinion of liking pizza": "Fair enough, good day",
    "I hate pizza": "Bad sport 👎"}, required=0))  # Select multiple from the keys, return the values. No max, can be left blank

```

![Demo](https://raw.githubusercontent.com/kuroyuki-simp/nice-prompts/master/media/demo.gif)
SalamFake 1.0.2
Fake Email & Generate Email
Requires Python: >=3

# SalamFake
### to install:
```bash
pip install SalamFake  
```
### Example
```python
from SalamFake import Email


mail=Email().Mail()
print(mail)



while True:
	mass=Email(mail["session"]).inbox()
	if mass:
		print(mass)
		break
	
```

### Follow us on social media accounts

* telegram : @T5B55
pysimrel 0.6.1
Simulation of Linear Model Data
Requires Python: >=3.6

# `pysimrel`: Simulation of Linear Model Data

[![Documentation Status](https://readthedocs.org/projects/pysimrel/badge/?version=latest)](http://pysimrel.readthedocs.io/?badge=latest)
[![Build Status](https://travis-ci.org/simulatr/pysimrel.svg?branch=master)](https://travis-ci.org/simulatr/pysimrel)
[![codecov](https://codecov.io/gh/simulatr/pysimrel/branch/master/graph/badge.svg)](https://codecov.io/gh/simulatr/pysimrel)

The package allows users to simulate data from a linear model. With few tuning parameters, users can simulate data with wide range of properties.
calibpy 1.0.0
A simple camera calibration toolkit based on opencv to do intrinsic and extrinsic camera calibration as well as depth registration tasks.
Requires Python: >=3.9

# Calipy

Calipy is a camera calibration and registration toolbox. It is based on [OpenCV](https://opencv.org/) and implements an easy access to internal and  external camera calibration using [ChArUco Boards](https://calib.io/pages/camera-calibration-pattern-generator). The external calibration routines are tested and guaranteed to be consistent with the Blender coordinate conventions. 
MIT License

Copyright (c) [2022] [Dr. Sven Wanner & Ben Niehaus]

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.