Metadata-Version: 2.1
Name: SweeplineVT
Version: 0.0.7
Summary: Voronoi Tessellation using Sweep-line algorithm
Home-page: https://github.com/lewtonstein/SweeplineVT
Author: Teng Liu
Author-email: lewtonstein@gmail.com
License: UNKNOWN
Description: # 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)
        
        
        
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
