semvec/pypi-packages huggingface dataset:
delaunay 1.0.5
a lightweight 2d delaunay triangulator based on algorithm by Guibas & Stolfi
Requires Python: >=3.6

## What?

This is a pure python library for finding the
delaunay triangulation on given pointsets.
Maybe one day voronoi tessellation will be added, since its based on
the [quad-edge datastructure](https://en.wikipedia.org/wiki/Quad-edge),
which makes finding the dual to each representations
easy.

<img src="./images/plot.png" alt="delaunay triangulation" width="500"/>

## Installation && Usage

Either clone this repository or install via
pip:

`pip install delaunay`

An example usage looks like this:

```python
from random import seed, uniform
from delaunay.quadedge.mesh import Mesh
from delaunay.quadedge.point import Vertex
from delaunay.delaunay import delaunay

if __name__ == "__main__":

    seed(123123123)

    N = 44 # number of vertices

    vertices = [Vertex(uniform(0, 100), uniform(0, 100)) for v in range(N)]

    m = Mesh() # this object holds the edges and vertices

    m.loadVertices(vertices)

    end = N - 1

    delaunay(m, 0, end) # computes the triangulation

    # populates a list of [org, dest], values for further manipulation
    lines = []
    for qe in m.quadEdges:
        if qe.org is not None:
            lines += [[[qe.org.x, qe.dest.x], [qe.org.y, qe.dest.y]]]

    # plotting, for example:

    # import matplotlib.pyplot as plt

    # fig, ax = plt.subplots()

    # for line in lines:
    #     start, end = line

    #     ax.plot(start, end)

    # plt.show()

```

## How?

In their paper *'Primitives for the Manipulation
of General Subdivisions and the Computation of Voronoi Diagrams'*[0]
from 1985, L. Guibas & J. Stolfi propose a divide-and-conquer-algorithm
with all the rigor one can hope for.
The algorithm runs in O(n log(n)), which should be fine,
but for really huge sets R. Dwyers modification [1] of the
original algo from 1986 should provide a significant
improvement. For now i'll stick with the first one
mentioned, but later maybe this work will progress.

## Why?

In comparison with scipy[2] this library is
consirably more lightweight. Of course scipy's delaunay is
based on QHull[3], a library written in c, which means it
runs ~40 times faster than a python implementation [4].

## References

[0] Guibas, Leonidas and Stolfi, Jorge
'Primitives for the Manipulation of General Subdivisions and the Computation of Voronoi'
In: ACM Trans. Graph.4.2 (Apr.1985), pp. 74–123. issn: 0730-0301 doi:10.1145/282918.282923

[1] - [Dwyer's Algorithm](https://github.com/rexdwyer/DelaunayTriangulation)

[2] - [Scipy Delaunay Implementation](https://scipy.org/)

[3] - [QHull Delaunay Implementation](http://www.qhull.org/html/qdelaun.htm)

[4] - [V-hill's Delaunay Implementation](https://github.com/V-Hill/delaunay-triangulation)
mcaptcha-api 0.1.0
A library to interact with mCaptcha API

[![status-badge](https://ci.batsense.net/api/badges/106/status.svg)](https://ci.batsense.net/repos/106)

---

# mcaptcha_api: Python library to interact with mCaptcha server

## Installation

```bash
pip install mcaptcha_api
```

## Usage

| Parameter      | Info                                                                                                                  |
| -------------- | --------------------------------------------------------------------------------------------------------------------- |
| `instance_url` | the URL of the mCaptcha instance you are using                                                                        |
| `sitekey`      | The captcha identifier; can be obtained from dashboard or from widget URL (`http://hostname/widget?sitekey=<sitekey>` |
| `secret`       | Account secret; can be obtained from mCaptcha dashboard account settings page                                         |

```python
mcaptcha = MCaptcha(instance_url=INSTANCE_URL, sitekey=SITEKEY, secret=SECRET)
assert mcaptcha.verify(token=TOKEN) is True
```