semvec/pypi-packages huggingface dataset:
fast-n 0.0.2
A fast noise library for game dev.
Requires Python: >=3.11

***Fast_n*** is a fast and easy to use python library to generate noise. **(It currently only supports perlin noise.)**

## Installation

Type `pip install fast_n` in your command prompt to install ***fast_n***. If you do not have python installed, download it on the [official website](https://www.python.org/downloads/).

## Use cases

Noise is very useful for anything procedural, being especially used for procedural world generation in video games. ***Fast_n*** is fast enough for most use cases.

## Features

At the moment, only perlin noise is implemented but I'm sure I'll add more at some point, and I already plan on improving the speed of the noise generation by writing it in C rather than pure python.

## How to use

Create a noise object, passing in a seed and some noise-dependant parameters, then call its Sample() method with x and y 2D coordinates to access the value at the given position.

This exemple shows how to draw perlin noise using ***PIL*** to handle the image stuff:

```python
from fastn import noise
from PIL import Image

perlin = noise.PerlinNoise(23) # creates a PerlinNoise object with a seed of 23
img = Image.new("L", (128, 128)) # creates an image using only a grayscale channel
for y in range(128):
    for x in range(128):
        # with perlin noise you want to avoid only using integer coordinates 
        # because they always return the same value
        noiseValue = perlin.Sample(x / 32, y / 32)
        pixelBrightness = round((noiseValue * 0.5 + 0.5) * 255) # transforms the output from a [-1, 1] range to a [0, 255] range
        img.putpixel((x, y), pixelBrightness)
img.show() # opens the image in an image viewer
```

## Credits

- [Raouf](https://rtouti.github.io/): My perlin noise implementation is based on [this blog post](https://rtouti.github.io/graphics/perlin-noise-algorithm) he made.
latex-generator-VILGIZ 0.1.6
LaTeX table and image generator

## hw_2

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- Р—Р°РїСѓСЃРє С‡РµСЂРµР· Docker РіРµРЅРµСЂРёСЂСѓРµС‚ РєРѕРґ Рё РёР· РЅРµРіРѕ PDF СЃ С‚Р°Р±Р»РёС†РµР№ Рё РєР°СЂС‚РёРЅРєРѕР№.
- Р’СЃРµ СЂРµР·СѓР»СЊС‚Р°С‚С‹ Р»РµР¶Р°С‚ РІ `hw_2/artifacts/`.
configfacets 0.0.5
Package for generating application configuration
Requires Python: >=3.6

# Configfacets - Python Client Library

## Overview

As applications scale and integrate with multiple systems, managing configurations becomes increasingly complex. Configfacets simplifies this with a Low-Code/No-Code configuration management system using plain JSON—no custom verbs, no complicated syntax. This Python client library facilitates seamless interaction with the Configfacets API, enabling efficient retrieval and management of configuration data.

Our key features are...

**Repositories & Versioning:**
Design configurations as modular, reusable components, store them in a centralized repository, and maintain full version control for better organization and tracking.

**Reusability:**
Add provider and community-contributed repositories as dependencies, reuse configuration templates, pass in customizable values to effortlessly set up and manage your application configurations.

**Collaboration:**
Invite users and teams to repository with precise role-based permissions—Admin, Collaborator, or Viewer—to control access and streamline contributions.

**REST APIs:**
Expose configurations through REST API endpoints. Generate static and dynamic configurations by leveraging facet filters and runtime configuration values in the request context.

**Organization Management:**
Our hierarchical design simplifies managing multi-level organizational structures, team hierarchies, roles, and responsibilities.

## Usage

### Installation

Ensure you have Python 3.6 or higher installed. Install the library using pip:

```
pip install configfacets
```

```
from configfacets.configuration import Configuration

config = Configuration(
    source="https://configfacets.com/apis/repos/configfacets/core-concepts/appconfigs/resources/collections/api-configurations/exec?format=json",
    sourceType="url",
    apiKey="<your_api_key>",
    postBody={"facets": ["env:prod", "cluster:aws", "region:east"]},
)
config.fetch()
resp = config.get_resp()

rabbitMQHost = config.get_value("rabbitmq.host")
rabbitMQPort = config.get_value("rabbitmq.port")

print("RabbitMQ Host:{}, Port:{}".format(rabbitMQHost, rabbitMQPort))
```

## API Reference

**Configuration**

- `__init__(self, source, sourceType, apiKey=None, postBody=None):` Initializes the configuration object with a source (URL or file) and source type.
- `fetch(self):` Fetches the configuration data from the source.
- `get_resp(self):` Returns the fetched configuration data.
- `get_value(self, key_path):` Retrieves the value for the specified key path.

## Contributing

We welcome contributions! Feel free to connect with us in our [Discord community](https://discord.gg/zWj3Rzud5s).
titerplot 0.1.0
A Python package for plotting titer data with factorial/combinatorial designs.
Requires Python: >=3.12

# `Titerplot`

Author: [Will Hannon](https://willhannon.com/)

Plotting viral titers from experiments with a factorial/combinatorial design with [Altair](https://altair-viz.github.io/).

## Install

```bash
# Requires Python > 3.12
pip install titerplot
```

## Use

The primary function of this library is `plot_titers`, which can be imported directly from the package:

```python
from titerplot import plot_titers

# Example usage
plot_titers(
    data,
    titer_col="Titer",
    condition_cols=["Condition_A", "Condition_B", "Condition_C", "Condition_D"],
    facet_col="Facet",
    color_col="Virus"
)
```

For a complete demonstration of the library's capabilities, check out the [plotting demo notebook](docs/plotting-demo.ipynb). For an example of the ideal data format, check out the [dummy_data](data/dummy_titers.csv). 

## Develop

This project uses [Poetry](https://python-poetry.org/) for dependency management and packaging.

### Setup development environment

```bash
# Clone the repository
git clone git@github.com:WillHannon-MCB/titerplot.git
cd titerplot

# Install dependencies with Poetry
poetry install

# Activate the virtual environment
poetry shell
```

### Run tests

Pytest runs the demo notebooks and saves the rendered notebooks to `/docs/`.

```bash
# Run pytest
pytest
```

### Project structure

```
.
├── data/               # Sample data files
├── docs/               # Rendered Demo notebooks
├── notebooks/          # Development notebooks
├── src/                # Source code
│   └── titerplot/      # Main package
├── tests/              # Test files
├── pyproject.toml      # Package configuration
└── poetry.lock         # Locked dependencies
```

## Publish

`titerplot` is published on [PyPI](https://pypi.org/) so that it can be installed with `pip`. A [worflow](.github/workflows/publish.yml) run by Github Actions publishes to PyPI when the tag is updated:

```bash
git tag -a v0.1.0 -m "First release version"
git push origin v0.1.0 
```

Ensure that you've updated the [`CHANGELOG`](CHANGELOG) with specific changes and [pyproject.toml](pyproject.toml) with the same version as the tag. For now, this is handled manually.