semvec/pypi-packages huggingface dataset:
breakpoint-regression 0.1.1
Breakpoint (segmented) regression in python
License: MIT

# breakpoint-regression

Breakpoint (aka segmented) regression in Python. Simultaneously find breakpoints and straightline segments between those breakpoints. Based on Muggeo "Estimating regression models with unknown break-points" (2003)


## Installation

You can install breakpoint-regression from [PyPI](https://pypi.org/project/breakpoint-regression/):

    pip install breakpoint-regression

The package is supported on Python 3.7 and above.

## How To Use

The package requires some x and y data to fit. You also need to tell it how many breakpoints you want, and starting guesses for those breakpoints. 

The package includes tools for summarising the fitted model and plotting.

	import breakpoint_regression
	import matplotlib.pyplot as plt
	import numpy as np

	# Generate some test data with 1 breakpoint
	alpha_1 = -4
	alpha_2 = 1
	intercept = 100
	breakpoint_1 = 7
	n_points = 200

	xx = np.linspace(0, 20, n_points)
	yy = intercept + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)

	# Given some data, fit the model
	bp_fit = breakpoint_regression.Fit(xx, yy, n_breakpoints=1, start_values=[5])

	# Print a summary of the fit
	bp_fit.summary()

	# Plot the data, fit, breakpoints and confidence intervals
	bp_fit.plot_data()
	# Pass in standard matplotlib keywords to control any of the plots
	bp_fit.plot_fit(color="red", linewidth=4) 
	bp_fit.plot_breakpoints()
	bp_fit.plot_breakpoint_confidence_intervals()
	plt.show()
	plt.close()

	# Plot the history of the breakpoints during the algorithm 
	bp_fit.plot_breakpoint_history()
	plt.show()
fast-deskew 1.0
Deskew any images faster than ever
License: MIT
Requires Python: >=3
Keywords: python,deskew,open cv,skew


# fast_deskew
An optimised way to deskew images faster.

• Available on :The Python Package Index (PyPI)  :<br /> https://pypi.org/
## Installation
```pip install fast_deskew```

## How to use it?
import fast_deskew

INPUT_IMAGE_PATH = "example/input_image.png"

img_input = cv2.imread(INPUT_IMAGE_PATH,cv2.IMREAD_GRAYSCALE)

result_image, best_angle = fast_deskew.deskew_image(img_input)

OR 

import fast_deskew

INPUT_IMAGE_PATH = "example/input_image.png"

result_image, best_angle = fast_deskew.deskew_image(INPUT_IMAGE_PATH, False) #0 for image array and 1 for image_path.

<br /> 
<br /> 

## Input Image:

 ![alt tag](https://github.com/Subhamp7/fast_deskew/blob/master/readme_resources/input_image.png)

<br /> 
<br /> 

 ## Output Image:

 ![alt tag](https://github.com/Subhamp7/fast_deskew/blob/master/readme_resources/result_image.png)


 ## License

© 2021 Subham Prasad

This repository is licensed under the MIT license. See LICENSE for details.
scrawler 0.3.1
Tool for General Purpose Web Scraping and Crawling
License: MIT
Requires Python: >=3.6
Keywords: Web Scraping,Crawling,asyncio,multithreading

scrawler
========

*"scrawler" = "scraper" + "crawler"*

Provides functionality for the automatic collection of website data
(`web scraping <https://en.wikipedia.org/wiki/Web_scraping>`__) and
following links to map an entire domain
(`crawling <https://en.wikipedia.org/wiki/Web_crawler>`__). It can
handle these tasks individually, or process several websites/domains in
parallel using ``asyncio`` and ``multithreading``.

This project was initially developed while working at the `Fraunhofer
Institute for Systems and Innovation
Research <https://www.isi.fraunhofer.de/en.html>`__. Many thanks for the
opportunity and support!

Installation
------------

You can install scrawler from PyPI:

::

    pip install scrawler

.. note::
    Alternatively, you can find the ``.whl`` and ``.tar.gz`` files on GitHub
    for each respective `release <https://github.com/dglttr/scrawler/releases>`__.

Getting Started
---------------

Check out the `Getting Started Guide <https://scrawler.readthedocs.io/en/latest/getting_started.html>`__.

Documentation
-------------

Documentation is available at `Read the Docs <https://scrawler.readthedocs.io/en/latest/>`__.