semvec/pypi-packages huggingface dataset:
artksii 1.0
artskii is CLI tool to convert images and videos to ASCII art.

# mascii

`mascii` is a command-line tool to convert images and videos to ASCII art.

## Features

- Convert images to ASCII art
- Convert videos to ASCII art
- Adjustable playback speed for videos
- Option to invert the ASCII character set

## Installation

To install `mascii`, clone the repository and install it using `pip`:

```
pip install mascii
```

or

```
git  clone  https://github.com/joelcrasta/mascii.git
cd  mascii
pip  install  -e  .
```

## Usage

### Convert image to ASCII art

To display an ASCII art, use the `-p` or `--path` option to specify the output file name (without extension):

```
mascii  -p  path/to/image.jpg
```

### Convert a Video to ASCII Art

To convert a video to ASCII art, use the `-p` or `--path` option to specify the path to the input video, and the `-s` or `--speed` option to specify the playback speed (1-10):

```
mascii  -p  path/to/video.mp4  -s  5
```

### Save ASCII art

To convert an image to ASCII art and save it, use the `-o` or `--out` option to specify the output file name (without extension):

```
mascii  -p  path/to/image.jpg  -o  output_filename
```

### Full Command-Line Options

```
usage:  mascii  [-h]  [-p PATH]  [-o OUT]  [-s SPEED]  [-i]
```

## Future Updates

- Save your own characters sets and use them to make art.
- Coloured ASCII art.
- Save videos as ASCII arts and play them anytime.
fourtwonine 0.1.0
Simple, flexible rate limits for Starlette and FastAPI
Requires Python: >=3.9
Keywords: fastapi, limits, rate limit, starlette

# fourtwonine

Yet another rate limiter for Starlette and FastAPI.

This is simply a thin layer over the [limits](https://limits.readthedocs.io/en/stable/quickstart.html)
library, making it easy to define flexible rate limits.

## Features
- Fully async and typed
- Supports any data store the `limits` library supports: `redis`, `mongodb`, `memcached`, etc.
- Supports legacy rate limit headers, as well as IETF [draft 06](https://datatracker.ietf.org/doc/html/draft-ietf-httpapi-ratelimit-headers-06)
and [draft 07](https://datatracker.ietf.org/doc/html/draft-ietf-httpapi-ratelimit-headers-07) headers.
- Exposes rate limit state (under `request.state.rate_limit` by default)

## Examples
Check the [`examples`](examples) directory for how to integrate this into your app.
ganita 0.1.0
A package for mathematical calculations using Indian Knowledge System including Lilavati's methods
Requires Python: >=3.7

# Ganita

Ganita is a Python package for mathematical calculations, including implementations of Lilavati's methods.

## Installation

You can install the Ganita package using pip:
pip install ganita

## Usage

Here's a quick example of how to use the Lilavati square function:
python
from ganitha.lilavati.squares import optimized_lilavati_square
result = optimized_lilavati_square(123)
print(f"The square of 123 is: {result}")
mlagents-envs 1.1.0
Unity Machine Learning Agents Interface
Requires Python: <=3.10.12,>=3.10.1

# Unity ML-Agents Python Interface

The `mlagents_envs` Python package is part of the
[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
`mlagents_envs` provides three Python APIs that allows direct interaction with the
Unity game engine:
- A single agent API (Gym API)
- A gym-like multi-agent API (PettingZoo API)
- A low-level API (LLAPI)

The LLAPI is used by the trainer implementation in `mlagents`.
`mlagents_envs` can be used independently of `mlagents` for Python
communication.

## Installation

Install the `mlagents_envs` package with:

```sh
python -m pip install mlagents_envs==1.1.0
```

## Usage & More Information

See
- [Gym API Guide](../docs/Python-Gym-API.md)
- [PettingZoo API Guide](../docs/Python-PettingZoo-API.md)
- [Python API Guide](../docs/Python-LLAPI.md)

for more information on how to use the API to interact with a Unity environment.

For more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the main
[ML-Agents Toolkit documentation](../docs/Readme.md).

## Limitations

- `mlagents_envs` uses localhost ports to exchange data between Unity and
  Python. As such, multiple instances can have their ports collide, leading to
  errors. Make sure to use a different port if you are using multiple instances
  of `UnityEnvironment`.
- Communication between Unity and the Python `UnityEnvironment` is not secure.
- On Linux, ports are not released immediately after the communication closes.
  As such, you cannot reuse ports right after closing a `UnityEnvironment`.
mlagents 1.1.0
Unity Machine Learning Agents
Requires Python: <=3.10.12,>=3.10.1

# Unity ML-Agents Trainers

The `mlagents` Python package is part of the
[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents). `mlagents`
provides a set of reinforcement and imitation learning algorithms designed to be
used with Unity environments. The algorithms interface with the Python API
provided by the `mlagents_envs` package. See [here](../docs/Python-LLAPI.md) for
more information on `mlagents_envs`.

The algorithms can be accessed using the: `mlagents-learn` access point. See
[here](../docs/Training-ML-Agents.md) for more information on using this
package.

## Installation

Install the `mlagents` package with:

```sh
python -m pip install mlagents==1.1.0
```

## Usage & More Information

For more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the main
[ML-Agents Toolkit documentation](../docs/Readme.md).

## Limitations

- Resuming self-play from a checkpoint resets the reported ELO to the default
  value.
django-ui-forms 1.1
A Django reusable app to extend forms with Bootstrap and Tailwind CSS support

# Django UI Forms

A Django reusable app to extend forms and provide UI with templates available for Bootstrap and Tailwind CSS.

https://pypi.org/project/django-ui-forms/

## Features

- Custom form handling with Bootstrap and Tailwind CSS support.
- Easy to use and integrate into existing Django projects.
- Flexible and reusable components for better form management.

## Installation

You can install this package via pip:

```bash
pip install django-ui-forms
```

## Usage

To use this package in your Django project, follow these steps:

1. Add `django_ui_forms` to your `INSTALLED_APPS` in your Django settings:

```python
INSTALLED_APPS = [
    ...
    'django_ui_forms',
]
```

2. Create your forms by extending `BootstrapBaseUiForms` or `TailwindBaseUiForms`:

```python
from django import forms
from django_ui_forms.forms import BootstrapBaseUiForms, TailwindBaseUiForms

class MyForm(BootstrapBaseUiForms, forms.Form):
    my_field = forms.CharField(label='My Field')

class MyModelForm(TailwindBaseUiForms, forms.ModelForm):
    class Meta:
        model = MyModel
        fields = '__all__'
```

3.  Update custom form renderer in your settings:

```python
from django.forms.renderers import TemplatesSetting

class CustomFormRenderer(TemplatesSetting):
form_template_name = "bootstrap_forms_snipprt.html" # for Bootstrap
form_template_name = "tailwind_forms_snipprt.html" # for Tailwind

FORM_RENDERER = 'your_project_name.settings.CustomFormRenderer'
```

4. Use the forms in your views and templates.

## Example

See the `examples/` directory for a complete example of how to use this package in a Django project.

## Running Tests

Tests are Under Development

## Contributing

Contributions are welcome! Please submit a pull request or open an issue.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.