Cython wrapper for the Boost Voronoi library (version 1.59.0)
Project description
pyvoronoi
A wrapper for Boost's Voronoi diagram library. The full documentation of the Boost Voronoi API is available here.
Install
The installation have been tested on Windows and Linux Ubuntu. If you notice any issue on Mac, reach out to us, we are interested in making sure it works for you.
Windows users will need Microsoft Visual C++ installed on their machine. You can find information about the version needed on this link. Python version from 3.5 to 3.12 rely on Visual C++ 14.x.
Dependencies
Cython dependency is optional. Cpp sources generated with Cython are available in releases.
Note on using the setup.py:
setup.py operates in 2 modes that are based on the presence of the dev file in the root of the project.
-
When dev is present, Cython will be used to compile the .pyx sources. This is the development mode (as you get it in the git repository).
-
When dev is absent, C/C++ compiler will be used to compile the .cpp sources (that were prepared in in the development mode). This is the distribution mode (as you get it on PyPI).
This way the package can be used without or with an incompatible version of Cython.
The idea comes from Matt Shannon's bandmat library.
From PyPI
Cython not required.
pip install pyvoronoi
From source
Cython required.
Clone the repository:
git clone https://github.com/fabanc/pyvoronoi.git
Install:
python setup.py install
After every modification of .pyx files compile with Cython:
python setup.py build_ext --inplace
Note in order to build the wheels, you will need to also install wheel
pip install wheel
Using
Create a new instance, passing the scaling factor into the constructor:
import pyvoronoi
pv = pyvoronoi.Pyvoronoi(10)
Since the voronoi library uses integer representation for points, the scaling factor chosen must be high enough to avoid roundoff error when converting from point coordinates to integers.
Add points and segments:
pv.AddPoint([0, 0])
pv.AddSegment([[1,5],[2,2]])
Call Construct()
and get the edges and vertices:
pv.Construct()
edges = pv.GetEdges()
vertices = pv.GetVertices()
cells = pv.GetCells()
Note that vertices, edges, and cells, can be accessed individually. The methods above are just convenience wrappers around the following functions:
-
GetVertex
-
GetEdge
-
Get Cell
def GetVertices(self):
count = self.CountVertices()
output = []
for index in range(count):
output.append(self.GetVertex(index))
return output
def GetEdges(self):
count = self.CountEdges()
output = []
for index in range(count):
output.append(self.GetEdge(index))
return output
def GetCells(self):
count = self.CountCells()
output = []
for index in range(count):
output.append(self.GetCell(index))
return output
If you are running python 2.x, you might want to write your own wrappers using xrange. This will be more efficient.
Edges have the following properties:
start, end
contain the indices of the start and end vertices or-1
if the edge is infinite at that end.is_primary
is true if the edge is not coincident with any of the source inputs.is_linear
is true if the edge is linear (not curved).cell
is the identifier of the cell this segment is part of.twin
is the identifier of the twin segment as defined in the boost voronoi API.
Cells have the following properties:
cell_identifier
is the index of the cell.site
is the index of the site which generated this cell (same as site1, site2 on the edges).contains_point
is true if the site was generated by a point.contains_segment
is true if the site was generated by a segment.is_open
is true if any of the cell's edges is infinite.is_degenerate
is true if the cell doesn't have an incident edge. Can happen if a few input segments share a common endpoint.vertices
contains indices into the vertex array.edges
contains indices into the edge array.
pv = pyvoronoi.Pyvoronoi(100)
pv.AddSegment([[0.1,0.8],[0.3,0.6]])
pv.AddSegment([[0.3,0.6],[0.4,0.6]])
pv.AddSegment([[0.4,0.6],[0.4,0.5]])
pv.AddSegment([[0.4,0.6],[0.4,0.7]])
pv.AddSegment([[0.4,0.7],[0.5,0.8]])
pv.AddSegment([[0.4,0.7],[0.5,0.6]])
pv.AddSegment([[0.5,0.6],[0.7,0.7]])
pv.Construct()
edges = pv.GetEdges()
vertices = pv.GetVertices()
cells = pv.GetCells()
print("Cell Count: {0}".format(len(cells)))
for c in cells:
print("Cell contains point: {0}. Contains segment: {1}. Is open: {2}, Site Index: {3}".format(c.contains_point, c.contains_segment, c.is_open, c.site))
print(",".join(map(str,c.vertices)))
for sIndex in c.edges:
print("Start Index: {0}, End Index = {1}".format(edges[sIndex].start, edges[sIndex].end))
Some output edges returned by the boost voronoi API are suposed to be curved. In the C++ API, it is up to you to code it. Luckily, you can do it in python using the following the function DiscretizeCurvedEdge. The sample below shows you how to do that:
for cIndex in range(len(cells)):
cell = cells[cIndex]
if cell.is_open == False:
for i in range(len(cell.edges)):
e = edges[cell.edges[i]]
startVertex = vertices[e.start]
endVertex = vertices[e.end]
max_distance = distance([startVertex.X, startVertex.Y], [endVertex.X, endVertex.Y]) / 10
if startVertex != -1 and endVertex != -1:
if(e.is_linear == True):
array = [[startVertex.X, startVertex.Y],[endVertex.X, endVertex.Y]]
else:
points = pv.DiscretizeCurvedEdge(i, max_distance)
for p in points:
print "{0},{1}".format(p[0], p[1])
The curve interpolation code can return 2 exceptions.
-
FocusOnDirectixException: this happens when the input point is on the segment side. In that cases, it makes no sense to interpolate a parabola between those two geometries since a parabola equation is supposed to find an equidistant point between the two geometries.
-
UnsolvableParabolaEquation: there are cases where the point returned by boost does not fit with the parabola equation (for a same position on the x-axis, we get 2 different points, both equidistant). Understanding this issue is still under investigation. It is possible to mitigate this issue by setting an optional 3rd parameter of the function DiscretizeCurvedEdge). A higher value means more tolerance to this exception. The recommended value would be 1 / Scaling Factor.
License
- Pyvoronoi is available under
MIT license <http://opensource.org/licenses/MIT>
__. - The core Voronoi library is available under
Boost Software License <http://www.boost.org/LICENSE_1_0.txt>
__. Freeware for both open source and commercial applications.
Development
Build tools
This project uses cibuildwheel to build wheels on multiple platforms.
Project details
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https://docs.pypi.org/attestations/publish/v1
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pyvoronoi-1.1.5-cp36-cp36m-macosx_10_9_x86_64.whl
- Subject digest:
e9e733501419b437f03aceec3450f62fcfce4d879e37d9e07b899a2f8fc4d36f
- Sigstore transparency entry: 148032671
- Sigstore integration time:
- Predicate type: