semvec/pypi-packages huggingface dataset:
gamspec 0.1.1
Gamut is a Python package for the analysis of 1-d gamma spectrum.

![Gamut_logo](https://github.com/Albert-Zhangweijian/Gamut/assets/91001624/eff31c24-9b49-432c-a77a-5d53a189aff3)
# Gamut
Gamut (GAMma Utils) is a python-based gamma spectrum analysis program.

Submodules:
-PeakRegion: basic objects, including: `Region`, `Calibration`.
-Spectrum: `Spectrum`, `SimulatedSpectrum` and `simuspecs`.
-Operator: `Operator`, `Pipe`, `Node`, `Flow` and `PipiNet` objects.

-operators:
    - Smoother
    - PeakSearcher
    - AreaCalculator
    - OtherOpeartor
    - BasicOperator
j2express 0.5.0
CLI for Jinja2 written from the ground up
Requires Python: >=3.9

# j2express
CLI for Jinja written from the ground up

# Motivation
This will provide more feedback than other implementations of j2 templating on the command line.

## Installation

After cloning the repo, activate a virtual environment and run

`pip install .`

## Running

Process template:

`j2x -f template.j2`

Show help and options:

`j2x --help`

### Flags

- `--trim-blocks / --no-trim-blocks`: Set Jinja's environment to trim whitespace. (default: --no-trim-blocks) 
- `--lstrip-blocks / --no-lstrip-blocks`: Set Jinja's environment to remove whitespace from the left. (default: --lstrip-blocks)
- `--strict / --no-strict`: Set to fail the render process if an environmental variable is referenced that is not set (default: --no-strict)

### Goals

- [X] process templates with exported env variables being represented as: `{{ VARIABLE_NAME }}`
- [X] install and make available on the command line
- [X] make available on PyPI
- [ ] colorized output for troubleshooting variables
- [ ] support for arrays from command line
halftones 0.1.1
Python package for processing and creating halftone images

# Halftones Package

`halftones` is a Python package for generating halftone versions of images. It provides an easy-to-use interface for transforming images into their halftone representations.

## Example Output

![Halftone Image Example](images/halftone_image.png)

*Example of a halftone image generated by the `halftones` package.*


## Installation

Install the package using pip:

```bash
pip install halftones
```


## Example Usage
Here's a simple example to demonstrate how to use the halftones package:
```python
import halftones as ht
from PIL import Image

# Load your image (replace 'path/to/your/image.jpg' with your image file)
image = Image.open('path/to/your/image.jpg')

# Generate a halftone version of the image
image_color_info = ht.generate_halftone_image(image, display_flag=True, grid_width=5)

# The resulting 'image_color_info' contains information about the processed image
```
This example demonstrates loading an image using the PIL library, processing it with the halftones package, and retrieving the result.


## Features

The `halftones` package offers a range of functionalities for creating and manipulating halftone images. Here are some of the key features:
- **Halftone Pattern Generation**: Generate halftone patterns from images, a popular artistic effect that simulates continuous tone imagery through the use of dots.
- **Flexible Grid and Tile Configurations**: Create grids and tiles with user-specified angles and distances, offering flexibility in designing the halftone effect.
- **Command-Line Interface**: In addition to being a library, `halftones` can be used as a command-line tool, making it easy to process images directly from the terminal.
- **Customizable Halftone Effects**: Parameters for adjusting the halftone effect, such as alpha transparency and black channel threshold, allow for a wide range of artistic expressions.

These features make `halftones` a versatile tool for artists, designers, and anyone interested in image processing and halftone art.


## License

The `halftones` package is open-source software licensed under the MIT License.


## Contact
[Email hi@eotles.com](mailto:hi@eotles.com) for questions.
neoml 2.0.210
NeoML python bindings

# NeoML

NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. It provides many traditional ML algorithms and neural networks with support for over 100 layer types.

NeoML is designed to minimize your code's dependence on the platform and devices used for calculations. The low-level math functions are isolated in a separate math engine module that has different implementations for different platforms, and the high-level code only has to select an appropriate math engine implementation to work with.

![Desktop Build Status](https://img.shields.io/azure-devops/build/abbyyopensource/401f7fe0-92d9-411d-9839-60d3455fa1c0/2/master?label=Desktop%20Build)
![Python Build](https://img.shields.io/azure-devops/build/abbyyopensource/401f7fe0-92d9-411d-9839-60d3455fa1c0/14/master?label=Python%20Build)
![iOS Build](https://img.shields.io/azure-devops/build/abbyyopensource/401f7fe0-92d9-411d-9839-60d3455fa1c0/15/master?label=iOS%20build)
[![Documentation Status](https://readthedocs.org/projects/neoml/badge/?version=latest)](https://neoml.readthedocs.io/en/latest/?badge=latest)

## Documentation

See our documentation that includes detailed API reference and tutorials demonstrating how to use the library at https://neoml.readthedocs.io

## Install

    pip3 install neoml