Metadata-Version: 2.1
Name: SweeplineVT
Version: 0.0.8
Summary: Voronoi Tessellation using Sweep-line algorithm
Home-page: https://github.com/lewtonstein/SweeplineVT
Author: Teng Liu
Author-email: lewtonstein@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: astropy
Requires-Dist: matplotlib

# SweeplineVT -- Voronoi Tessellation using sweep-line algorithm

[SweeplineVT github](https://github.com/lewtonstein/SweeplineVT)

```
pip install SweeplineVT
```

## Description
* Voronoi Tessellation on the basis of the sweep-line algorithm developed by Steven Fortune in 1986
* Make centroidal Voronoi Tessellation (CVT)
* Make Delaunay triangulation
* Take accurate positions of points with no need of pixelation / binning.
* Confine Voronoi diagram to a rectangular region
## Examples
### Make Voronoi Tessellation for a list of points
* The content of "example.dat"

```
-0.5 1.5
1.1 3.3
2.9 5.7
3.1 4.3
5.4 2.6
6.3 3.1
```

```
slvt.py example.dat --calCentroid
pl_VT.py example_VT.dat example_ctd.dat -s
```

The red points are the Voronoi sites (input points from "example.dat"). The green lines connect each sites with the corresponding cell centroid.

![example](https://github.com/lewtonstein/SweeplineVT/blob/master/SweeplineVT/doc/example_VT.png?raw=true)

### Make centroidal Voronoi Tessellation (CVT) of 14 points in 0<x<7, 0<y<5.
```
slvt.py --makeCVT 14 --border 0,7,0,5 -s
pl_VT.py CVT14_VT.dat -s
```

"-s" of slvt.py means "silent". "-s" of pl_VT.py means "step".

* One possible result you might see:   
![CVT14](https://github.com/lewtonstein/SweeplineVT/blob/master/SweeplineVT/doc/CVT14_VT.png?raw=true)


### Show the distribution of cell area

```
from SweeplineVT import Voronoi
import pylab as pl
x=np.random.random(size=10)
y=np.random.random(size=10)
vor=Voronoi(events=np.vstack((x,y)).T,calArea=True,autoscale=False)
Area,ind,cts=vor.getarealist()
Area/=np.array(cts)
pl.hist(Area)
```


## Output files
* {FileName}_VT.dat: each item corresponds to one cell edge. The 9 columns are:
 + 1: index of edge
 + 2-3 and 3-4: coordinates of the two Voronoi vertices (nodes) of the edge
 + 5-6 and 7-8: coordiantes of the two nearest Voronoi sites (The Delaunay diagram)

* With "--calCentroid", {FileName}_ctd.dat: each item corresponds to one cell. The 6 columns are:
 + 1: index of cell / site.
 + 2-3: Voronoi site of the cell
 + 4-5: centroid of the cell
 + 6: area of the cell
 + 7: site duplication number (>1 means duplicated)

* With "--calArea", {FileName}_area.dat: each item corresponds to one cell. The 6 columns are:
 + 1: index of cell / site.
 + 2-3: Voronoi site of the cell
 + 4: area of the cell
 + 5: site duplication number (>1 means duplicated)




