semvec/pypi-packages huggingface dataset:
tendril-iotedge 0.4.4
Tendril IoT Edge Server Infrastructure
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7
Keywords: tendril



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    :target: https://pypi.org/project/tendril-iotedge

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    :target: https://www.codacy.com/app/chintal/tendril-iotedge

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    :target: https://www.gnu.org/licenses/agpl-3.0.en.html



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Introduction
------------

TODO Some brief introduction


Package Information
-------------------

The latest version of the documentation, including installation, usage, and
API/developer notes can be found at
`ReadTheDocs <https://tendril-iotedge.readthedocs.io/en/latest/index.html>`_.

The latest version of the sources can be found at
`GitHub <https://github.com/tendril-framework/tendril-iotedge>`_. Please use 
GitHub's features to report bugs, request features, or submit pull/merge requests.

The principle author for ``tendril-iotedge`` is Chintalagiri Shashank. The 
author can be contacted if necessary via the information on the
`author's github profile <https://github.com/chintal>`_ . See the AUTHORS file
for a full list of collaborators and/or contributing authors, if any.

``tendril-iotedge`` is distributed under the terms of the
`AGPLv3 license <https://www.gnu.org/licenses/agpl-3.0.en.html>`_ .
A copy of the text of the license is included along with the sources.
NeuronLib 0.0.0
This is a package I am developing to support the creation and training of neural networks in Python. It is currently not ready for release, but I hope to have an early version ready by May 2024.
Requires Python: >=3.9

# NeuronLib
This is a library I am developing based on my linear algebra library for the purposes of instantiating, training, and saving neural networks in Python. Presently, the networks can be created and trained without any issue, but the efficacy of the networks is unclear. Linear regression functions perfectly, but networks involving a more complex architecture have demonstrated limited functionality at best. Quadratic regression and the capturing of even slightly more complex relationships, despite droves of training data has proved fruitless.

At the moment it is unclear if this is a result of a disfunctional training algorithm or simply the result of poor network architecture. Consequently, further testing is demanded. If the networks can prove to be effective in classification applications, I will prepare the library to be released, ideally some time in May 2024. Before this package can be cleaned up and prepared for a first official release, it will be necessary to develop methods for saving and loading previously trained neural networks, revise the instantiation method to allow for more customizable and versatile network creation, and develop at least one optimizer for training. 

Prior to a first release, an official documentation will be written. In the meantime feel free to look at the source code to see what I'm currently testing and working on!
basic-bar 0.5
A simple and efficient progress bar

# Basic Bar

Basic Bar is a simple and effective text-based progress bar for Python applications. It's lightweight and easy to integrate, making it perfect for monitoring the progress of iterable operations with visual feedback and estimated completion times.

## Installation

You can install Basic Bar directly from PyPI:

```bash
pip install basic-bar
```

## Usage

To use Basic Bar, simply wrap any iterable in the `bar()` function. This function will yield from the iterable and display a progress bar in the terminal.

### Example

Here's a basic example of how to use Basic Bar:

```python
from basic_bar import bar
import time

# Example iterable
data = range(100)

# Wrapping the iterable with the bar function
for item in bar(data):
    time.sleep(0.01)  # Simulate some work
```

This will display a progress bar in your terminal, updating with each iteration.

### Customization

You can customize the length of the progress bar by providing a `length` parameter:

```python
for item in bar(data, length=50):
    time.sleep(0.01)  # Simulate some work
```

## Features

- **Simple Integration**: Just wrap your iterable with `bar()`.
- **Customizable Bar Length**: Adjust the length of the progress bar to fit your needs.
- **Real-Time Updates**: Provides real-time feedback on progress with percentage completion and estimated time remaining.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.