semvec/pypi-packages huggingface dataset:
sphinx-notfound-page 1.1.0
Sphinx extension to build a 404 page with absolute URLs
Requires Python: >=3.8
Keywords: notfound, 404, page, sphinx, documentation

|Build| |PyPI version| |Docs badge| |License|

sphinx-notfound-page
====================

Create a custom 404 page with absolute URLs hardcoded.


Installation
------------

.. code-block:: bash

   pip install sphinx-notfound-page


Configuration
-------------

Add this extension in your ``conf.py`` file as:

.. code-block:: python

   extensions = [
    # ... other extensions here

    'notfound.extension',
   ]


Documentation
-------------

Check out the full documentation at https://sphinx-notfound-page.readthedocs.io/


Thanks
------

**Strongly** based on @ericholscher's solution from https://github.com/readthedocs/readthedocs.org/issues/353

.. |Build| image:: https://circleci.com/gh/readthedocs/sphinx-notfound-page.svg?style=svg
   :target: https://circleci.com/gh/readthedocs/sphinx-notfound-page
   :alt: Build status
.. |PyPI version| image:: https://img.shields.io/pypi/v/sphinx-notfound-page.svg
   :target: https://pypi.org/project/sphinx-notfound-page
   :alt: Current PyPI version
.. |Docs badge| image:: https://readthedocs.org/projects/sphinx-notfound-page/badge/?version=latest
   :target: https://sphinx-notfound-page.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation status
.. |License| image:: https://img.shields.io/github/license/readthedocs/sphinx-notfound-page.svg
   :target: LICENSE
   :alt: Repository license
Turbo-Flask 0.8.6
Use Hotwire Turbo in your Flask application.
Requires Python: >=3.6

# turbo-flask

[![Build status](https://github.com/miguelgrinberg/turbo-flask/workflows/build/badge.svg)](https://github.com/miguelgrinberg/turbo-flask/actions) [![codecov](https://codecov.io/gh/miguelgrinberg/turbo-flask/branch/main/graph/badge.svg)](https://codecov.io/gh/miguelgrinberg/turbo-flask)

Integration of Hotwire's Turbo library with Flask, to allow you to create
applications that look and feel like single-page apps without using
JavaScript.

![Todo App Demo](examples/todos/todo-demo.gif)

Resources
---------

-  [Documentation](https://turbo-flask.readthedocs.io/en/latest/)
-  [PyPI](https://pypi.python.org/pypi/turbo-flask)
-  [Change Log](https://github.com/miguelgrinberg/turbo-flask/blob/main/CHANGES.md)
dqm 0.1.0
A library to analyze the data quality metrics collected via Smasung galaxy smartwatch and computer mouse data
Requires Python: >=3.0


# DQM Library for Smartwatch
## How to deploy:

1. ``` python3 -m build  ```

2. ``` python3 -m twine upload dist/* -u __token__ -p $PYPI_TOKEN ```
## Overview

The DQM (Data Quality Monitoring) library for smartwatches is designed to monitor and ensure the quality of biosensing data collected from wearable devices. This library provides tools and algorithms to assess and enhance the reliability and accuracy of the data collected from smartwatch sensors.

## Features

- **Real-time Data Quality Assessment**: Continuously monitor the quality of biosensing data in real-time.
- **Anomaly Detection**: Detect anomalies and irregularities in the data to ensure accuracy.
- **Data Cleaning**: Automatically clean and preprocess data to remove noise and artifacts.
- **Compatibility**: Compatible with various smartwatch models and sensor types.

## Installation

To install the DQM library, you can use pip:

```pip install --upgrade dqm```
## Usage 
Here is a basic example of how to use the DQM library for monitoring data quality:


from dqm_lib_smartwatch import DQM

# Initialize the DQM object
```
import dqm
# Load your smartwatch data
data = load_smartwatch_data('path_to_data_file')
# Assess data quality
 sample_rate_consistency = dqm.calculate_src(data)
# Print the quality report
print(sample_rate_consistency)
```

## Contribution
For detailed documentation and API reference, please visit the official documentation.


We welcome contributions from the community. To contribute, follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
Create a pull request.
altcolor 0.0.5
A package designed to add color to text in console based applications. **[Credit to [colorama]('https://pypi.org/project/colorama/') for a few of the colors**
Requires Python: >=3.6


# AltColor 

**AltColor** is a Python package designed to add color to text in console-based applications, making it easy to create visually appealing outputs.

---

## Predefined Colors
AltColor provides a variety of predefined colors for your text, some of which are credited to [Colorama](https://pypi.org/project/colorama/). These predefined colors include:

- **Standard Colors**:  
  `BLACK`, `RED`, `GREEN`, `YELLOW`, `BLUE`, `MAGENTA`, `CYAN`, `WHITE`  

- **Light Colors**:  
  `LIGHTBLACK`, `LIGHTRED`, `LIGHTGREEN`, `LIGHTYELLOW`, `LIGHTBLUE`, `LIGHTMAGENTA`, `LIGHTCYAN`, `LIGHTWHITE`  

Additionally, you can define custom RGB colors for even more flexibility!

---

## Installation

Install AltColor via pip:

```bash
pip install altcolor
```

---

## Example Code

```python
# Imports
# Imports
from altcolor import colored_text, leaked_text, reset, cPrint, init; init(show_credits=True)

# Code

# Colored text allows you to paint your words with RGB
print(colored_text((244, 5, 7), "Hello World!"))

# Here's a regular print
print("Hello World!")

# We even have predefined colors
print(colored_text("BLUE", "Hello World!"))


# Leaked text splills into other print statements until you use reset()
print(leaked_text("RED", "Hey,"))
print("you")
print("are" + reset())

# Leaked text can even spill into the background
print(leaked_text("WHITE", "cool!", "Back"))
print("How")
print("are")
print("you?" + reset())

# You can also use cPrint
cPrint(color="RED", text="Hello World!", objective="controlled")
print("Hello World!")
cPrint(color="RED", text="Hello World!", objective="leaked")
print("Hello World!" + reset())
print("Hello World!")
```

---

### Key Features:

1. **Colored Text with RGB**: Use custom RGB values to style your text.
2. **Predefined Colors**: Quickly style text with easy-to-use predefined colors.
3. **Leaked Text**: Color spills into subsequent output until explicitly reset.
4. **Background Color**: Apply color to text backgrounds.
5. **Controlled Printing**: Use `cPrint` for precise and formatted color control.
6. **Credits Display**: Show credits for the package. (optional using `init(show_credits=True)`)

---

### Links
- **PyPI:** [AltColor on PyPI](https://pypi.org/project/altcolor/)
- **GitHub:** [AltColor Repository](https://github.com/your-repository-url)

---

Happy coding, and bring your console to life with **AltColor**!
topf 1.0.4
TOpological Point Features: Node-Level Topological Representation Learning on Point Clouds
Requires Python: >=3.9

# Topological Point Features 🪴

This is the python package for topological point features (TOPF), enabling the construction of point-level features in point clouds stemming from algebraic topology and differential geometry as described in [Node-Level Topological Representation Learning on Point Clouds](https://arxiv.org/abs/2406.02300). 🪴

![Example of TOPF on three point clouds](https://github.com/vincent-grande/topf/blob/main/examples/teaserfigure.png?raw=True)

## Installation

Although being a python package, TOPF requires an installation [Julia](https://julialang.org/downloads/) because it uses the wonderful package [`Ripserer.jl`](https://mtsch.github.io/Ripserer.jl/dev/). After having installed Julia and set up PATH variables, you can install TOPF simply by running

    pip install topf

TOPF currently works under `macOS` and `Linux`. `Windows` is not supported.

## Usage

Two Jupyter-Notebooks with example usage of TOPF with [basic examples](https://github.com/vincent-grande/topf/blob/main/examples/topf_basic_examples.ipynb) and [3d examples](https://github.com/vincent-grande/topf/blob/main/examples/topf_examples3d.ipynb) can be found in the examples folder.

## Citation

TOPF is based on the paper 'Node-Level Topological Representation Learning on Point Clouds', Vincent P. Grande and Michael T. Schaub, 2024.
If you find TOPF useful, please consider citing the paper:

    @misc{grande2024topf,
      title={Node-Level Topological Representation Learning on Point Clouds}, 
      author={Vincent P. Grande and Michael T. Schaub},
      year={2024},
      eprint={2406.02300},
      archivePrefix={arXiv},
      primaryClass={math.AT}
    }

## Dependencies

TOPF depends on [`Julia`](https://julialang.org), the Julia package [`Ripserer.jl`](https://mtsch.github.io/Ripserer.jl/dev/), [`Python`](https://www.python.org) and the Python packages [`numpy`](https://numpy.org), [`gudhi`](https://gudhi.inria.fr), [`matplotlib`](https://matplotlib.org), [`scikit-learn`](https://scikit-learn.org/stable/), [`scipy`](https://scipy.org), [`pandas`](https://pandas.pydata.org), and [`plotly`](https://plotly.com). The idea of how to fix Z/3Z cycles with faulty lifts to real coefficients was inspired by [DreiMac](https://dreimac.scikit-tda.org/en/latest/)'s solution to the problem (for cocycles).

## Feedback

Any feedback, comments, or bug reports are welcome! Simply write an email to [Vincent](https://vincent-grande.github.io).