semvec/pypi-packages huggingface dataset:
4dfpy 0.0.1
A simple package for converting a 4dfp brain image into a Numpy array.
Requires Python: >=3.8

# 4dfpy

This is a (very!) simple package for converting binary data contained in Avi Snider's 4dfp neuroimaging file format into a Numpy array, that can then be used in your Python scripts.

## Install

```
pip install 4dfpy
```

## Running

```
import 4dfpy

my_4dfp_data = 4dfpy.load("/path/to/myimg")
```

The path to the 4dfp files should **only include the root name** without the
extension (example: "/path/to/myimg.4dfp.img" should instead be "/path/to/myimg").
Make sure the .4dfp.img and .4dfp.hdr files are in the same directory.

[ ] TODO: add support for including file extension
4dfpy-washu-seal 0.0.1
A simple package for converting a 4dfp brain image into a Numpy array.
Requires Python: >=3.8

# 4dfpy

This is a (very!) simple package for converting binary data contained in Avi Snider's 4dfp neuroimaging file format into a Numpy array, that can then be used in your Python scripts.

## Install

```
pip install 4dfpy
```

## Running

```
import 4dfpy

my_4dfp_data = 4dfpy.load("/path/to/myimg")
```

The path to the 4dfp files should **only include the root name** without the
extension (example: "/path/to/myimg.4dfp.img" should instead be "/path/to/myimg").
Make sure the .4dfp.img and .4dfp.hdr files are in the same directory.

[ ] TODO: add support for including file extension
slotted-aloha-simulator 0.1.0
SAS provides simulation and mean field models for slotted Aloha with talkative users.
Requires Python: >=3.9,<4.0

# Slotted Aloha Simulator


[![PyPI Status](https://img.shields.io/pypi/v/slotted-aloha-simulator.svg)](https://pypi.python.org/pypi/slotted-aloha-simulator)
[![Build Status](https://github.com/balouf/slotted-aloha-simulator/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/balouf/slotted-aloha-simulator/actions?query=workflow%3Abuild)
[![Documentation Status](https://github.com/balouf/slotted-aloha-simulator/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/balouf/slotted-aloha-simulator/actions?query=workflow%3Adocs)
[![License](https://img.shields.io/github/license/balouf/slotted-aloha-simulator)](https://github.com/balouf/slotted-aloha-simulator/blob/main/LICENSE)
[![Code Coverage](https://codecov.io/gh/balouf/slotted-aloha-simulator/branch/main/graphs/badge.svg)](https://codecov.io/gh/balouf/slotted-aloha-simulator/tree/main)

SAS provides simulation and mean field models for slotted Aloha with talkative users.


- Free software: MIT license
- Documentation: https://balouf.github.io/slotted-aloha-simulator/.


## Features

- High-speed simulator for simplified slotted aloha with unbounded window growth
- Also provides deterministic approximations
- Easy parallelization
- Display functions

## Credits

This package was created with [Cookiecutter][CC] and the [Package Helper 3][PH3] project template.

[CC]: https://github.com/audreyr/cookiecutter
[PH3]: https://balouf.github.io/package-helper-3/
supertts 0.0.19
A super TTS package supporting multiple providers.

## SuperTTS

#### Star the repository if this was useful. ⭐️

[![PyPI version](https://badge.fury.io/py/supertts.svg)](https://badge.fury.io/py/supertts)
[![Downloads](https://pepy.tech/badge/supertts)](https://pepy.tech/project/supertts)
[![Downloads](https://pepy.tech/badge/supertts/month)](https://pepy.tech/project/supertts/month)
[![Downloads](https://pepy.tech/badge/supertts/week)](https://pepy.tech/project/supertts/week)

![GitHub license](https://img.shields.io/github/license/onlyoneaman/supertts)

Single TTS Package to use multiple TTS engines.

## Installation

```bash
pip install supertts
```

## Usage

Add Keys to Environment Variables
```bash
# For Openai
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"

# For Azure
export AZURE_KEY="YOUR_AZURE_KEY"
export AZURE_REGION="YOUR_AZURE_REGION"
```

TTS
```python
from supertts import SuperTTS

supertts = SuperTTS()
supertts.tts("Hello World")
```

Voices
```python
from supertts import SuperTTS

supertts = SuperTTS()
supertts.voices()
```

### Configuration

#### tts

Openai TTS
- voice: Voice to use for TTS (Default: "alloy")
- model: Model to use for TTS (Default: "tts-1")

```python
from supertts import SuperTTS

supertts = SuperTTS()

supertts.tts("Hello World", model="tts-1-hd")

supertts.tts("Hello World", voice="nova")

supertts.tts("Hello World", model="tts-1-hd", voice="nova")
```


## Supported TTS Engines

- Openai
- Azure

### Upcoming TTS Engines

- Google

## License

MIT

## Author

[Aman](https://amankumar.ai) | [X (Twitter)](https://twitter.com/onlyoneaman)

Enjoy using SuperTTS! 🚀
cagey 1.0.4
Streamlined automated data analysis.
Requires Python: ==3.11.*

:Docs: https://cagey.readthedocs.io

Cagey
=====

First and foremost ``cagey`` is a command line tool for creating and managing a cage
database. With ``cagey`` you will extract a a range of experimental and computational
data in a variety of formats and place it into a single file. You can then use this file
as input for your scripts which perform data analysis. You can install ``cagey`` with:

.. code-block:: bash

  pip install cagey

Once installed you can use the command line tool to create a new database:

.. code-block:: bash

  cagey new path/to/data path/to/cagey.db

This will create a new database at ``path/to/cagey.db`` and populate it with data from
``path/to/data``. There are in fact a lot more things you can do with ``cagey``, and
the best way to learn about them is to use it interactively, starting with:

.. code-block:: bash

  cagey --help

::

   Usage: cagey [OPTIONS] COMMAND [ARGS]...

   A cage database tool.
   Run cagey help intro for an introduction.

  ╭─ Options ───────────────────────────────────────────────────────────────────────────╮
  │ --install-completion          Install completion for the current shell.             │
  │ --show-completion             Show completion for the current shell, to copy it or  │
  │                               customize the installation.                           │
  │ --help                        Show this message and exit.                           │
  ╰─────────────────────────────────────────────────────────────────────────────────────╯
  ╭─ Commands ──────────────────────────────────────────────────────────────────────────╮
  │ help         Get help on how to use cagey.                                          │
  │ insert       Insert new data into the cagey database.                               │
  │ ms           Mass spectrum analysis.                                                │
  │ new          Create a new database.                                                 │
  │ nmr          Extract NMR peaks.                                                     │
  ╰─────────────────────────────────────────────────────────────────────────────────────╯

Working with the database
-------------------------

Once you've used ``cagey`` to create a database, you can use the ``cagey.queries`` module
to extract data from it in your Python scripts. This step is best understood by
reading our documentation, which you can find at https://cagey.readthedocs.io.