semvec/pypi-packages huggingface dataset:
abl-quantconnect-stubs 13987.7
Type stubs for QuantConnect's Lean

# QuantConnect Stubs

This package contains type stubs for QuantConnect's [Lean](https://github.com/QuantConnect/Lean) algorithmic trading engine and for parts of the .NET library that are used by Lean.

These stubs can be used by editors to provide type-aware features like autocomplete and auto-imports in QuantConnect strategies written in Python.

After installing the stubs, you can copy the following line to the top of every Python file to have the same imports as the ones that are added by default in the cloud:
```py
from AlgorithmImports import *
```

This line imports [all common QuantConnect members](https://github.com/QuantConnect/Lean/blob/master/Common/AlgorithmImports.py) and provides autocomplete for them.
image-to-text-egs 0.1
Image to text convertion package

This is image to text convertion package from egs.


Example:
--------

import image_to_text_egs

text_data = image_to_text_egs.image_to_text("image_url.jpg")

print("Text data:",text_data)

version
==============


Image_OCR(0.1)(22/06/2023)
-----------------------------

   - First release of image to text convertion EGS
kurvy 1.0.4
Curve approximation toolkit
Requires Python: >=3.9

![pyfer_cover](aux/kurvy.png)

-----------------

# Kurvy ~ Trigonometric Curve Approximation Toolkit

[![PyPI - Version](https://img.shields.io/pypi/v/kurvy.svg)](https://pypi.org/project/kurvy/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kurvy.svg)](https://pypi.org/project/kurvy/)
[![License: MIT](https://img.shields.io/badge/license-MIT-C06524)](https://github.com/celerygemini/kurvy/blob/main/LICENSE)

## 💡 What is it?

**kurvy** is a Python package that helps approximate trigonometric curves by implementing a gradient descent algorithm.

### Features

- Find the optimal parameters for trigonometric-like data.
- Customize training loops.
- Add momentum to parameter updates.
- Apply L2 regularization.
- Randomly generate trigonometric data and errors.

## 🛠️ Setup 

Install it from **PyPI** by running `pip install kurvy`.

### Dependencies 

The code was written with **Python 3.9.16**, and it is recommended to run it in a virtual environment. 
All the required libraries are listed in the `requirements.txt` file in this repo.

## 🚀 Execution

Once the package has been installed, it is simply a case of experimenting with the various classes and functions. For a quickstart, please see the [demo](https://github.com/celerygemini/kurvy/blob/main/demo.ipynb).

## 📝 Documentation

Documentation is currently available in the form of docstrings.

## ⚖️ License

The project is licensed under the MIT license.
imgo 2.5.9
Image Dataset Management Toolkit
Requires Python: >=3.7

# imgo

## Process, Augment, and Balance Image Data

[![PyPI - Version](https://img.shields.io/pypi/v/imgo.svg)](https://pypi.org/project/imgo/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/imgo.svg)](https://pypi.org/project/imgo/)
[![License: MIT](https://img.shields.io/badge/license-MIT-C06524)](https://github.com/celerygemini/imgo/blob/main/LICENSE)

## 💡 What is it?

This library is designed to facilitate the preprocessing phase of image classification projects in order to get into the fun part: training the models!

### Features:

Imgo is composed of two modules: **uptools** and **augtools**.

**Uptools** helps to streamline various image data preprocessing tasks, such as:

 - Reading images from a local disk
 - Rescaling images
 - Normalizing and standardizing pixel values
 - Converting image datasets into numpy-arrays
 - One-hot-encoding label data
 - Splitting image datasets into training, validation, and testing subsets
 - Merging data subsets into a single dataset
 - Saving numpy-arrays as images in class subdirectories
 
![imgo_up_demo](aux/imgo_up_demo.jpg)
 
**Augtools** allows the user to quickly and efficiently apply augmentation to image data. With Augtools, users can perform the following augmentation tasks using very few lines of code:

 - Apply a powerful collection of transformation and corruption functions
 - Augment images saved on a local disk
 - Save augmented images in class subdirectories
 - Augment entire image datasets
 - Augment training data in place in preparation for machine learning projects
 - Rebalance class sizes by generating new training images

![imgo_aug_demo](aux/imgo_aug_demo.jpg)

## 🛠️ Setup 

Install it from **PyPI** by running `pip install imgo`.

### Dependencies 

The code was written with **Python 3.6**, and it is recommended to run it in a virtual environment. 
All the required libraries are listed in the `requirements.txt` file in this repo.

## 🚀 Execution

Once the package has been installed, it is simply a case of experimenting with the various classes and functions. For a quickstart, please see the [demo](https://github.com/celerygemini/imgo/tree/master/demos).

## 📝 Documentation

Documentation is currently available in the form of docstrings.

## ⚖️ License

The project is licensed under the MIT license.

### Acknowledgements

Some of the **augtools** library is built as a wrapper around **Imgaug**, a powerful image augmentation library. For more information, please see https://imgaug.readthedocs.io/en/latest/.