semvec/pypi-packages huggingface dataset:
source-infuser 0.0.3
A package to prepare your software project for continuous infusion into your AI assistant
Requires Python: >=3.9

# Source Infuser

<img src="docs/source_infuser_logo.png" alt="Source Infuser Logo" width="200" height="200">

Source Infuser helps keep your AI assistant updated with the changes you perform in your software project. It generates detailed reports of your project's structure, which can be continuously infused into your AI assistant.

## Features

- Generate markdown reports of your project structure.
- Supports customizable ignore patterns similar to `.gitignore` in `.psi-ignore`.
- Easy integration with your existing CI/CD pipelines.

## Installation

### From Source

You can install the package directly from the GitHub repository:

```shell
git clone https://github.com/ivanzhovannik/source-infuser.git
cd source-infuser
pip install .
```

### From PyPi (Feature Under Development)

```shell
pip install source_infuser
```

## Usage

To generate a report of the current directory and write it into `report.md`:

```shell
psi -o report.md
```

or alternatevely

```shell
source_infuser -r . -o report.md
```

**Command-line Options**: 
* -r, --root: Root directory of the project (default: current directory).
* -o, --output: Output markdown file (optional).

**Ignore some elements of your project**: 

In case you want to ignore parts of your root using `.gitignore` patters, just add `.psi-ignore` file to your current directory.

## Contributions
We welcome contributions! Please follow these steps:

1. Go to [source-infuser/issues](https://github.com/ivanzhovannik/source-infuser/issues).
2. If there is no issue similar to the one you encountered, create a new issue.
3. Describe your case in detail to help us better reproduce and understand the problem.
4. If you solved the problem yourself, please go ahead and create a pull-request, tag @ivanzhovannik to review.

## License
This package is free to use for research or commercial purposes. Any part can be reproduced; just mention the source.

## Acknowledgements
Thanks to all users and contributors who made this project better!
reinfocus 0.0.2
Environments where agents can focus cameras on rendered objects.
Requires Python: >=3.11

Reinfocus
------------------

![Python versions](https://img.shields.io/pypi/pyversions/reinfocus)
[![PyPI](https://img.shields.io/pypi/v/reinfocus)](https://pypi.org/project/reinfocus/)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](
    https://github.com/jeffwhunter/reinfocus/blob/main/LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](
    https://github.com/psf/black)

`reinfocus` is a python package that makes it easy to create reinforcement learning
environments that use ray tracing to simulate camera focus. See the
[examples](https://github.com/jeffwhunter/reinfocus/examples) for an impression of how it
can be used.

<p align="center">
    <img src="./ppo-DiscreteSteps-v0.gif">
    <br/>
    <em>
        A ppo trained agent acting in DiscreteSteps-v0. The checkerboard target is
        positioned at some depth, and the agent decides what depth to focus on. It
        receives as input the focus depth, focus value, and the change in both those since
        the last time step.
    </em>
</p>

Installation
------------------
To install `reinfocus`, use `pip install reinfocus`.

For GPU support, install
[the lastest NVIDIA graphics drivers](https://www.nvidia.com/Download/index.aspx). Next
you will need to install `cudatoolkit`; how you do that depends on what type of python
installation you use:
 * [anaconda](https://www.anaconda.com/) or
 [variants](https://docs.anaconda.com/free/miniconda/) (recommended): use
 `conda install cudatoolkit`
 * [plain python](https://www.python.org/downloads/release/python-3110/) (untested):
 install [`cudatoolkit`](https://developer.nvidia.com/cuda-downloads), then set
 [`CUDA_HOME`](
    https://numba.readthedocs.io/en/stable/cuda/overview.html#cudatoolkit-lookup)

Special Thanks
------------------
`reinfocus.graphics` is a [`numba`](
https://numba.readthedocs.io/en/stable/cuda/index.html) translation of the wonderful [Ray
Tracing in One Weekend in CUDA](https://github.com/rogerallen/raytracinginoneweekendincuda
) by [Roger Allan](https://github.com/rogerallen).
data-preprocessing-lib-fb 0.0.1b0
A data preprocessing library in Python
Requires Python: >=3.6

# Data Preprocessing Library

A comprehensive data preprocessing library in Python. This library provides various functions for data cleaning, transformation, and manipulation operations, designed to make your data preprocessing tasks easier and more efficient.

## Features

- **Handling Missing Values**: Mean, median, constant imputation, and deletion.
- **Outlier Detection and Correction**: IQR method with threshold.
- **Data Standardization and Normalization**: Min-Max and Standard normalization.
- **Text Cleaning and Manipulation**: Remove stopwords, lowercase conversion, punctuation removal, lemmatization (requires NLTK).
- **Feature Engineering**: Create new features from existing ones.
- **Data Type Conversion**: Convert to numeric, categorical, and datetime.
- **Encoding Categorical Data**: One-hot encoding and label encoding.
- **Date and Time Manipulation**: Extract date components, calculate date differences.

## Installation

To install the library, use the following command:

```bash
pip install data_preprocessing_library
vin-validator 0.2
A package to validate Vehicle Identification Numbers (VINs)
Requires Python: >=3.6

# VIN Validator

`vin-validator` is a Python package to validate Vehicle Identification Numbers (VINs) using the NHTSA API.

## Installation

To install the package, use the following command:

```bash
pip install vin-validator
```

## Usage

Here's an example of how to use the `vin-validator` package to validate a VIN and retrieve vehicle details:

```python
from vin_validator.validator import VINValidator

vin = "1HGCM82633A004352"
is_valid, details = VINValidator.validate_vin(vin)

if is_valid:
    print(f"VIN '{vin}' is valid.")
    print("Vehicle Details:")
    print(f"Make: {details.get('Make')}")
    print(f"Model: {details.get('Model')}")
    print(f"Model Year: {details.get('ModelYear')}")
    print(f"Body Class: {details.get('BodyClass')}")
else:
    print(f"VIN '{vin}' is invalid.")
```

## Example Script

You can also find an example script in the [examples](examples/validate_vin.py) directory.

## License

This project is licensed under the MIT License. See the `LICENSE` file for more details.
PyCHZZK 0.1.2
NAVER CHZZK Python SDK
Requires Python: >=3.10

# PyCHZZK
NAVER CHZZK Python SDK


## Usage
```
pip install PyCHZZK
```

## Example
#### **PyCHZZK.api.Channels**
**channel.search**
```python
import asyncio
from PyCHHZK.api import Channels

async def print_search_channels():
    channel = Channels()
    channels = await channel.search("녹두로")
    print(channels)

asyncio.run(print_search_channels())
```
---
**channel.get_data**
```python
import asyncio
from PyCHHZK.api import Channels
from PyCHZZK.enums import Fields

async def print_channel_data():
    channel = Channels()
    channel_data = await channel.get_data(channel_id, Fields.description)
    print(channel_data)

asyncio.run(print_channel_data())
```
---
**channel.get_info**
```python
import asyncio
from PyCHHZK.api import Channels

async def print_channel_info():
    channel = Channels()
    channel_info = await channel.get_info(channel_id)
    print(channel_info)

asyncio.run(print_channel_info())
```
---
**channel.get_live_status**
```python
import asyncio
from PyCHHZK.api import Channels

async def print_live_status():
    channel = Channels()
    live_status = await channel.get_live_status(channel_id)
    print(live_status)

asyncio.run(print_live_status())
```

## License
Licensed under the [MIT](./LICENSE)