I just saw the email below from John, and I was wondering why using compound
paths are "goo-gobs" faster than using rectangles(patches)?
I've been using the candlestick function quite a bit lately. I guess using
compound paths could make the candlestick function faster. I'll give it a try.
Regards,
Christophe
-----Original Message-----
From: John Hunter [mailto:jdh2...@gmail.com]
Sent: Sat 08/08/2009 09:00
To: Kaushik Ghose
Cc: matplotlib-users@lists.sourceforge.net
Subject: Re: [Matplotlib-users] animate histogram
On Sat, Aug 8, 2009 at 6:17 AM, Kaushik
Ghose<kaushik_gh...@hms.harvard.edu> wrote:
> (http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.patches.Rectangle)
> e.g. set_height() to change the rectangles?
>
> e.g. code
> ------------------------------
> import pylab
> x = pylab.rand(20)
> h = pylab.hist(x)
> h[2][0].set_height(1)
> pylab.draw()
> --------------------------------
This is the right idea, but it is likely to be slow for animation,
since each rectangle is a separate mpl object, each with its own
graphics contexs, rendering step, etc. bar and hist were very early
functions which I wrote before we had collections and compound paths.
If I were rewriting it from scratch, I would use a compound path.
It's a little more work upfront because you have to manually compute
the vertices and path codes, but it will be goo-gobs faster. Here is
the animated histogram for tk using compound paths
"""
This example shows how to use a path patch to draw a bunch of
rectangles for an animated histogram
"""
import numpy as np
import matplotlib
matplotlib.use('TkAgg') # do this before importing pylab
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.path as path
fig = plt.figure()
ax = fig.add_subplot(111)
# histogram our data with numpy
data = np.random.randn(1000)
n, bins = np.histogram(data, 100)
# get the corners of the rectangles for the histogram
left = np.array(bins[:-1])
right = np.array(bins[1:])
bottom = np.zeros(len(left))
top = bottom + n
nrects = len(left)
# here comes the tricky part -- we have to set up the vertex and path
# codes arrays using moveto, lineto and closepoly
# for each rect: 1 for the MOVETO, 3 for the LINETO, 1 for the
# CLOSEPOLY; the vert for the closepoly is ignored but we still need
# it to keep the codes aligned with the vertices
nverts = nrects*(1+3+1)
verts = np.zeros((nverts, 2))
codes = np.ones(nverts, int) * path.Path.LINETO
codes[0::5] = path.Path.MOVETO
codes[4::5] = path.Path.CLOSEPOLY
verts[0::5,0] = left
verts[0::5,1] = bottom
verts[1::5,0] = left
verts[1::5,1] = top
verts[2::5,0] = right
verts[2::5,1] = top
verts[3::5,0] = right
verts[3::5,1] = bottom
barpath = path.Path(verts, codes)
patch = patches.PathPatch(barpath, facecolor='green',
edgecolor='yellow', alpha=0.5)
ax.add_patch(patch)
ax.set_xlim(left[0], right[-1])
ax.set_ylim(bottom.min(), top.max())
def animate():
# simulate new data coming in
data = np.random.randn(1000)
n, bins = np.histogram(data, 100)
top = bottom + n
verts[1::5,1] = top
verts[2::5,1] = top
fig.canvas.draw()
def run():
for i in range(100):
fig.canvas.manager.window.after(100, animate)
fig.canvas.manager.window.after(100, run)
plt.show()
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