semvec/pypi-packages huggingface dataset:
nv21 1.0.0
Functions for reading and decoding the data of frames in NV21 format.
Requires Python: >=3.6, <4
Keywords: nv21,yuv420sp,yuv

# nv21
Functions for reading and decoding the data of frames in NV21 format.

Basically it wraps the cv2-based conversion with some check in order to avoid some confusing cv2 errors.

## Available functions

* `nv21_to_rgb`
* `read_nv21_from_file`
* `yuv_to_nv21`

Example
```python
import cv2
import matplotlib.pyplot as plt  # do not forget to install `matplotlib` first

import nv21


# Check the "data" folder of this repository.
img = cv2.imread('./data/example.png')[..., ::-1]  # to RGB
h, w = img.shape[:2]

nv21_data = nv21.yuv_to_nv21(
    cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
)

restored_img = nv21.nv21_to_rgb(nv21_data, w, h)

# Draw results
plt.subplot(1, 3, 1)
plt.axis('off')
plt.imshow(img)

plt.subplot(1, 3, 2)
plt.axis('off')
plt.imshow(nv21_data, cmap='gray')

plt.subplot(1, 3, 3)
plt.axis('off')
plt.imshow(restored_img)

plt.show()
```
robustgp 2.1
Robust Gaussian process regression based on iterative trimming.
Keywords: Gaussian process,regression,robust statistics,outlier detection

# robustgp

[Robust Gaussian Process Regression Based on Iterative Trimming (ITGP)](https://arxiv.org/abs/2011.11057)

The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers. ITGP is a new robust GP regression algorithm that iteratively trims the most extreme data points. While the new algorithm retains the attractive properties of the standard GP as a nonparametric and flexible regression method, it can greatly improve the model accuracy for contaminated data even in the presence of extreme or abundant outliers. It is also easier to implement compared with previous robust GP variants that rely on approximate inference. Applied to a wide range of experiments with different contamination levels, the proposed method significantly outperforms the standard GP and the popular robust GP variant with the Student-t likelihood in most test cases. In addition, as a practical example in the astrophysical study, we show that this method can precisely determine the main-sequence ridge line in the color-magnitude diagram of star clusters.


## Install

```
pip install robustgp
```

Dependency:
  - [GPy](https://github.com/SheffieldML/GPy/)


## Quick start

One can start with examples in [this notebook](https://nbviewer.jupyter.org/github/syrte/robustgp/blob/master/notebook/Example_Neal_Dataset.ipynb).


## Usage

```python
from robustgp import ITGP

# train ITGP
res = ITGP(X, Y, alpha1=0.5, alpha2=0.975, nsh=2, ncc=2, nrw=1)
gp, consistency = res.gp, res.consistency

# make prediction
y_avg, y_var = gp.predict(x_new)
y_var *= consistency
```
Here `gp` is a `GPy.core.GP` object, whose usage is further illustrated [here](https://nbviewer.jupyter.org/github/SheffieldML/notebook/blob/master/GPy/basic_gp.ipynb).

- See the [docstring of ITGP](https://github.com/syrte/robustgp/blob/master/robustgp/robustgp.py) for detailed usage and full API.

- See this [notebook](https://github.com/syrte/robustgp/blob/master/notebook/Example_Neal_Dataset.ipynb) for a complete example.


## References

- Algorithm paper:
  [Robust Gaussian Process Regression Based on Iterative Trimming](https://arxiv.org/abs/2011.11057),
  Zhao-Zhou Li, Lu Li, & Zhengyi Shao, 2020

- First application:
  [Modeling Unresolved Binaries of Open Clusters in the Color-Magnitude Diagram. I. Method and Application of NGC 3532](https://ui.adsabs.harvard.edu/abs/2020ApJ...901...49L/),
  Li et al., 2020

## License

The MIT License
PyMonitorLib 0.3.0
Library for creating simple interval processes. This is especially useful for interval based monitoring application that generate telemetry at a set interval.
Requires Python: >=3.6

# PyMonitorLib
Lightweight python library for collecting metrics and outputing to influx DB on an interval.

This library is designed for creating interval based monitoring applications quickly and easily. a Main function initializes the application and a Process function is called at the regular interval.

An example for all that is required:

```python
from monitor.lib import Execute, Result

def Main(config, logger, pipeline):
    return Result.SUCCESS

if __name__ == "__main__":
     Execute(Main, 'service')
```

The example will create an ArgumentParser for the necessary argument collect and spin up a process.

## Notes
This library currently only supports linux/posix based systems for use in process daemonization. Windows services are not currently supported. Basic functionality for building OS independent monitors should still function.
cuplcodec 2.1.0
Package for creating and decoding URLs that contain temperature and humidity samples.

# cuplcodec
[cuplcodec](https://cupl.co.uk/index.php/software/cuplcodec/) encodes environmental sensor data into a URL. Each sample includes two 12-bit readings (temperature and humidity). These are converted to base64 and written into a circular buffer. A HMAC is written after the most recent sample in the buffer. The URL can be rendered as a QR code or stored on an NFC tag. If a tag with a URL is tapped 
by a mobile phone, it opens automatically in a web browser. This allows the user to view sensor data **without installing an app on their phones**. A web application comprising [cuplfrontend](https://github.com/cuplsensor/cuplfrontend) and [cuplbackend](https://github.com/cuplsensor/cuplbackend) runs the decoder in cuplcodec, unwraps the circular buffer and displays a list of samples in the browser.

## Tests

![GitHub Workflow Status](https://github.com/cuplsensor/cuplcodec/workflows/Python%20package/badge.svg)

The codec is tested with [pytest](https://docs.pytest.org/en/stable/). The encoder written in C can be tested with the decoder written in Python using [CFFI](https://cffi.readthedocs.io/en/latest/).

## Documentation 

[![Documentation Status](https://readthedocs.org/projects/wscodec/badge/?version=latest)](https://readthedocs.org/projects/wscodec/badge/?version=latest) 

Hosted on [ReadTheDocs](https://readthedocs.org/projects/wscodec/badge/?version=latest). This includes information on how to run the encoder on an MSP430.

## PyPI Package

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/ansicolortags.svg)](https://pypi.python.org/pypi/cuplcodec/)

Hosted on [PyPI](https://pypi.org/project/cuplcodec/). Install the package with: 

    pip install cuplcodec

## Licence

### cuplcodec

[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

### Documentation

[![License: CC BY-SA 4.0](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-sa/4.0/)