semvec/pypi-packages huggingface dataset:
django-oauth2-provider-ng 0.5.0
Provide OAuth2 access to your app

django-oauth2-provider
======================

.. image:: https://travis-ci.org/caffeinehit/django-oauth2-provider.png?branch=master

*django-oauth2-provider* is a Django application that provides
customizable OAuth2\-authentication for your Django projects.

`Documentation <http://readthedocs.org/docs/django-oauth2-provider/en/latest/>`_

`Help <https://groups.google.com/d/forum/django-oauth2-provider>`_

Running Tests
=============

.. code-block:: bash

   $ pip install -r requirements.txt
   $ bash test.sh

License
=======

*django-oauth2-provider* is released under the MIT License. Please see the LICENSE file for details.
fuzzylab 0.13
Python Fuzzy Logic library

# fuzzylab
Python Fuzzy Logic library based on Octave Fuzzy Logic Toolkit 0.4.6, considered as a mostly MATLAB-compatible fuzzy logic toolkit for Octave.

The way to use the fuzzylab functions is based on Matlab R2022a Fuzzy Logic Toolbox functions.

This library is more mature compared to the first version and will receive periodic updates.

## Research work

[Fuzzy Logic Controller with Fuzzylab Python Library and the Robot Operating System for Autonomous Robot Navigation: A Practical Approach](https://doi.org/10.1007/978-3-030-35445-9_27)

## Installation

```
pip install fuzzylab
```

## Usage

In the folder [examples](https://github.com/ITTcs/fuzzylab/tree/master/examples) there are some code snippets and the equivalent matlab code for a starting point using the library.

We use `fl` as the alias for `fuzzylab`.

```python
import fuzzylab as fl
```

## Example

```python
import fuzzylab as fl
import matplotlib.pyplot as plt

x = fl.arange(0, 0.1, 10)
y = fl.trimf(x, [3, 6, 8])

plt.plot(x,y)
plt.title('trimf, P = [3, 6, 8]')
plt.xlabel('x')
plt.ylabel('Degree of Membership')
plt.ylim([-0.05, 1.05])
plt.show()
```

![trimf](https://raw.githubusercontent.com/ITTcs/fuzzylab/master/images/trimf.png)

Also we can use a more matlab code style

```python
from fuzzylab import arange, trimf
from matplotlib.pyplot import plot, title, xlabel, ylabel, ylim, show

x = arange(0, 0.1, 10)
y = trimf(x, [3, 6, 8])

plot(x,y)
title('trimf, P = [3, 6, 8]')
xlabel('x')
ylabel('Degree of Membership')
ylim([-0.05, 1.05])
show()
```

## Citing fuzzylab

    @Inbook{Avelar2020,
      author="Avelar, Eduardo
      and Castillo, Oscar
      and Soria, Jos{\'e}",
      editor="Castillo, Oscar
      and Melin, Patricia
      and Kacprzyk, Janusz",
      title="Fuzzy Logic Controller with Fuzzylab Python Library and the Robot Operating System for Autonomous Robot Navigation: A Practical Approach",
      bookTitle="Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications",
      year="2020",
      publisher="Springer International Publishing",
      address="Cham",
      pages="355--369",
      isbn="978-3-030-35445-9",
      doi="10.1007/978-3-030-35445-9_27",
      url="https://doi.org/10.1007/978-3-030-35445-9_27"
    }
vod-tudelft 1.0.3
View of Delft dataset devkit
Keywords: python,video,ai,lidar,radar,camera


# The View-of-Delft dataset
<div align="center">
<figure>
<img src="figures/example_frame_2.png" alt="Prius sensor setup" width="800"/>
<figcaption align = "center"><b>Example frame from our dataset with camera, LiDAR, 3+1D radar, and annotations overlaid.</b></figcaption>
<\figure>
</div>

 We present the novel View-of-Delft (VoD) automotive dataset. It contains 10.000 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of more than 76000 3D bounding box annotations, including more than 15000 pedestrian, 7100 cyclist and 17000 car labels.

## Sensors and data
The LiDAR sensor is a Velodyne 64 operating at 10 Hz. The camera provides colored images of 1936 × 1216 pixels at around 12 Hz. The horizontal field of view is ~64° (± 32°), vertical field of view is ~ 44° (± 22°). Odometry is a filtered combination of several inputs: RTK GPS, IMU, and wheel odometry with a frame rate around 100 Hz. Odometry is given relative to the starting position at the beginning of the current sequence, not in absolute coordinates. Note that while camera and odometry operate at a higher frequency than the LiDAR sensor, timestamps of the LiDAR sensor were chosen as “lead”, and we provide the closest camera frame and odometry information available. 

The provided LiDAR point-clouds are already ego-motion compensated. More specifically, misalignment of LiDAR and camera data originating from ego-motion during the scan (i.e. one full rotation of the LiDAR sensor) and ego-motion between the capture of LiDAR and camera data has been compensated. See an example frame for our data with calibrated and ego-motion compensated LiDAR point-cloud overlaid on the camera image. 

We also provide intrinsic calibration for the camera and extrinsic calibration of all three sensors in a format specified by the annotating party.

<img src="figures/Prius_sensor_setup_5.png" alt="Prius sensor setup" width="800"/>

## Introduction

<img src="figures/sensors.gif" alt="Prius sensor setup" width="600"/>

<img src="figures/labels.gif" alt="Prius sensor setup" width="600"/>

## Goal 

## Use-Cases

## Annotation intstructions
pylint-elasticsearch-dsl 0.1.1
pylint plugin for the elasticsearch-dsl package

# pylint-elasticsearch-dsl

[![Build Status](https://travis-ci.com/glostis/pylint-elasticsearch-dsl.svg?branch=master)](https://travis-ci.com/glostis/pylint-elasticsearch-dsl)
[![PyPI](https://img.shields.io/pypi/v/pylint-elasticsearch-dsl)](https://pypi.org/project/pylint-elasticsearch-dsl)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[`pylint`](https://www.pylint.org/) plugin for the
[`elasticsearch-dsl`](https://elasticsearch-dsl.readthedocs.io/en/latest/) package

This plugin is needed to help `pylint` understand the types of some objects of the `elasticsearch-dsl`
package.

## Installation

```
pip install pylint-elasticsearch-dsl
```

## Scope

Currently, this plugin only patches the false positive `invalid-unary-operand-type` error
that is triggered when writing for example:
```python
from elasticsearch_dsl import Q

~Q("exists", field="foo")
```
See [this issue](https://github.com/PyCQA/pylint/issues/3258) for more details.

## Contributing

To work on this project, install the development requirements by running:
```
make install
```

The tests can be run with:
```
make test
```