matplotlib-devel Mailing List for matplotlib

On Sat, Feb 19, 2011 at 1:03 PM, Jarrod Millman <mi...@be...> wrote:
> On Fri, Feb 18, 2011 at 8:55 PM, Darren Dale wrote:
>> * Jarrod offered to contribute to the docs to describe the recommended
>> workflow.
>
> I did a first pass at changing the documentation from describing svn
> to git (including adding gitwash) and generated a pull request:
>  https://github.com/matplotlib/matplotlib/pull/3

Lets continue discussion at the pull request.

On Fri, Feb 18, 2011 at 8:55 PM, Darren Dale wrote:
> * Jarrod offered to contribute to the docs to describe the recommended
> workflow.

I did a first pass at changing the documentation from describing svn
to git (including adding gitwash) and generated a pull request:
  https://github.com/matplotlib/matplotlib/pull/3

Best,
Jarrod

On Fri, Feb 18, 2011 at 4:53 PM, John Hunter <jd...@gm...> wrote:
> I have removed everyone's commit access to the mpl svn server in
> advance of the github migration, which is underway.  Darren will reply
> when github is live.
>
> svn is dead, long live github!

I just pushed the repositories up to github.com/matplotlib .

A few notes:

* Jarrod offered to contribute to the docs to describe the recommended
workflow. For the impatient:

  - Visit https://github.com/matplotlib/matplotlib and hit the "fork" button
  - clone the resulting repository, eg "git clone
gi...@gi...:username/matplotlib.git"
  - make a new branch (git checkout -b new_feature) and hack away
  - don't merge upstream changes into your feature branch (it makes
the history graph look ugly)
  - when you are ready to have your changes merged upstream, push your
branch to your own repository at github
  - switch to that branch in your github profile, and select the "pull
request" button. This starts the code review process, which also gives
others a chance to inspect the git history graph before it gets merged
into the official mpl repo.

* python-3 support should happen in the matploltib-py3 repo, the
master branch in that repository contains Michael's changes from the
py3k svn branch, but rebased on top of the more recent changes to the
master branch in the main matplotlib repo.

Darren

On Fri, Feb 18, 2011 at 8:16 PM, Jarrod Millman <mi...@be...> wrote:
> On Fri, Feb 18, 2011 at 1:53 PM, John Hunter wrote:
>> I have removed everyone's commit access to the mpl svn server in
>> advance of the github migration, which is underway.  Darren will reply
>> when github is live.
>
> Excellent!  I am glad to see that all the core scipy-related projects
> will soon be on github (scipy will move to github as soon as the 0.9
> release is finalized).
>
> I am sure you know about this; but just as a reminder, Matthew and
> Fernando made a set of generic instruction for using git/github:
>  https://github.com/matthew-brett/gitwash
> The idea being that all the core projects share a standard set of
> procedures for using git/github and adopting similar workflow
> policies.  So far NumPy, IPython, and NIPY all use these instructions:
>
>  https://github.com/numpy/numpy/tree/master/doc/source/dev/gitwash
>  https://github.com/ipython/ipython/tree/master/docs/source/development/gitwash
>  https://github.com/nipy/nipy/tree/master/doc/devel/guidelines/gitwash
>
> If you want I am happy to take care of adding gitwash to the
> matplotlib docs as soon as the move to git/github is done (and
> generating a pull request).

Jarrod, that would be greatly appreciated.

On Fri, Feb 18, 2011 at 1:53 PM, John Hunter wrote:
> I have removed everyone's commit access to the mpl svn server in
> advance of the github migration, which is underway.  Darren will reply
> when github is live.

Excellent!  I am glad to see that all the core scipy-related projects
will soon be on github (scipy will move to github as soon as the 0.9
release is finalized).

I am sure you know about this; but just as a reminder, Matthew and
Fernando made a set of generic instruction for using git/github:
  https://github.com/matthew-brett/gitwash
The idea being that all the core projects share a standard set of
procedures for using git/github and adopting similar workflow
policies.  So far NumPy, IPython, and NIPY all use these instructions:

  https://github.com/numpy/numpy/tree/master/doc/source/dev/gitwash
  https://github.com/ipython/ipython/tree/master/docs/source/development/gitwash
  https://github.com/nipy/nipy/tree/master/doc/devel/guidelines/gitwash

If you want I am happy to take care of adding gitwash to the
matplotlib docs as soon as the move to git/github is done (and
generating a pull request).

Best,
Jarrod

Hi all,


I did not see any voronoi diagram in matplotlib examples so I created a simple one from the available tri.Triangulation function (I hope I did not miss something evident).


Nicolas



#!/usr/bin/env python
# -----------------------------------------------------------------------------
# Voronoi diagram from a list of points
# Copyright (C) 2011  Nicolas P. Rougier
#
# Distributed under the terms of the BSD License.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib
import matplotlib.pyplot as plt


def circumcircle(P1, P2, P3):
    ''' 
    Return center of the circle containing P1, P2 and P3

    If P1, P2 and P3 are colinear, return None

    Adapted from:
    http://local.wasp.uwa.edu.au/~pbourke/geometry/circlefrom3/Circle.cpp
    '''
    delta_a = P2 - P1
    delta_b = P3 - P2
    if np.abs(delta_a[0]) <= 0.000000001 and np.abs(delta_b[1]) <= 0.000000001:
        center_x = 0.5*(P2[0] + P3[0])
        center_y = 0.5*(P1[1] + P2[1])
    else:
        aSlope = delta_a[1]/delta_a[0]
        bSlope = delta_b[1]/delta_b[0]
        if np.abs(aSlope-bSlope) <= 0.000000001:
            return None
        center_x= (aSlope*bSlope*(P1[1] - P3[1]) + bSlope*(P1[0] + P2 [0]) \
                        - aSlope*(P2[0]+P3[0]))/(2.*(bSlope-aSlope))
        center_y = -(center_x - (P1[0]+P2[0])/2.)/aSlope + (P1[1]+P2[1])/2.
    return center_x, center_y

def voronoi(X,Y):
    ''' Return line segments describing the voronoi diagram of X and Y '''
    P = np.zeros((X.size+4,2))
    P[:X.size,0], P[:Y.size,1] = X, Y
    # We add four points at (pseudo) "infinity"
    m = max(np.abs(X).max(), np.abs(Y).max())*1e5
    P[X.size:,0] = -m, -m, +m, +m
    P[Y.size:,1] = -m, +m, -m, +m
    D = matplotlib.tri.Triangulation(P[:,0],P[:,1])
    T = D.triangles
    n = T.shape[0]
    C = np.zeros((n,2))
    for i in range(n):
        C[i] = circumcircle(P[T[i,0]],P[T[i,1]],P[T[i,2]])
    X,Y = C[:,0], C[:,1]
    segments = []
    for i in range(n):
        for k in D.neighbors[i]:
            if k != -1:
                segments.append([(X[i],Y[i]), (X[k],Y[k])])
    return segments

# -----------------------------------------------------------------------------
if __name__ == '__main__':
    P = np.random.random((2,256))
    X,Y = P[0],P[1]
    fig = plt.figure(figsize=(10,10))
    axes = plt.subplot(1,1,1)
    plt.scatter(X,Y, s=5)
    segments = voronoi(X,Y)
    lines = matplotlib.collections.LineCollection(segments, color='0.75')
    axes.add_collection(lines)
    plt.axis([0,1,0,1])
    plt.show()