# Plot 1 point at a time, results in very bad performance and the figuring entering a state of non-response

Hello Everyone
This is my first post, I really enjoy using python/numpy/scipy/matplotlib - great replacement for matlab. I am having a problem, with plot 1 point at a time. i am attempt to emulate an issue in the lab, where we get samples every second and plot them. The figure becomes non-responsive and eventually stops plotting, does anyone have an idea.
Cheers
Ray
Code

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from numpy import *
from scipy import *
from pylab import *
import time;
ion()
figure(1)

subplot(412);plot([925],[0.296000],‘o’)

subplot(413);plot([925],[0.247000],‘o’)
subplot(414);plot([925],[0.145000],‘o’)
subplot(411);plot([926],[0.516000],‘o’)
time.sleep(1)
subplot(412);plot([926],[0.770000],‘o’)

subplot(413);plot([926],[0.820000],‘o’)
subplot(414);plot([926],[0.405000],‘o’)
subplot(411);plot([927],[0.704000],‘o’)
time.sleep(1)
subplot(412);plot([927],[0.721000],‘o’)

subplot(413);plot([927],[0.996000],‘o’)
subplot(414);plot([927],[0.219000],‘o’)
subplot(411);plot([928],[0.414000],‘o’)
time.sleep(1)
subplot(412);plot([928],[0.281000],‘o’)

subplot(413);plot([928],[0.066000],‘o’)
subplot(414);plot([928],[0.398000],‘o’)
subplot(411);plot([929],[0.718000],‘o’)

subplot(412);plot([929],[0.247000],‘o’)
subplot(413);plot([929],[0.887000],‘o’)

subplot(414);plot([929],[0.562000],‘o’)
subplot(411);plot([930],[0.386000],‘o’)
time.sleep(1)
subplot(412);plot([930],[0.173000],‘o’)
subplot(413);plot([930],[0.678000],‘o’)

subplot(414);plot([930],[0.635000],‘o’)
subplot(411);plot([931],[0.746000],‘o’)
time.sleep(1)
subplot(412);plot([931],[0.332000],‘o’)
subplot(413);plot([931],[0.468000],‘o’)

subplot(414);plot([931],[0.373000],‘o’)
subplot(411);plot([932],[0.568000],‘o’)
time.sleep(1)
subplot(412);plot([932],[0.929000],‘o’)
subplot(413);plot([932],[0.096000],‘o’)

subplot(414);plot([932],[0.132000],‘o’)
subplot(411);plot([933],[0.840000],‘o’)
time.sleep(1)
subplot(412);plot([933],[0.946000],‘o’)
subplot(413);plot([933],[0.607000],‘o’)

subplot(414);plot([933],[0.094000],‘o’)
subplot(411);plot([934],[0.690000],‘o’)
time.sleep(1)
subplot(412);plot([934],[0.330000],‘o’)
subplot(413);plot([934],[0.955000],‘o’)

subplot(414);plot([934],[0.101000],‘o’)
subplot(411);plot([935],[0.931000],‘o’)
time.sleep(1)
subplot(412);plot([935],[0.515000],‘o’)
subplot(413);plot([935],[0.444000],‘o’)

subplot(414);plot([935],[0.425000],‘o’)
subplot(411);plot([936],[0.960000],‘o’)
time.sleep(1)
subplot(412);plot([936],[0.676000],‘o’)
subplot(413);plot([936],[0.295000],‘o’)

subplot(414);plot([936],[0.861000],‘o’)
subplot(411);plot([937],[0.564000],‘o’)
time.sleep(1)
subplot(412);plot([937],[0.948000],‘o’)
subplot(413);plot([937],[0.159000],‘o’)

subplot(414);plot([937],[0.512000],‘o’)
subplot(411);plot([938],[0.884000],‘o’)
time.sleep(1)
subplot(412);plot([938],[0.542000],‘o’)
subplot(413);plot([938],[0.409000],‘o’)

subplot(414);plot([938],[0.079000],‘o’)
subplot(411);plot([939],[0.194000],‘o’)
time.sleep(1)
subplot(412);plot([939],[0.910000],‘o’)
subplot(413);plot([939],[0.901000],‘o’)

subplot(414);plot([939],[0.153000],‘o’)
subplot(411);plot([940],[0.183000],‘o’)
time.sleep(1)
subplot(412);plot([940],[0.578000],‘o’)
subplot(413);plot([940],[0.241000],‘o’)

subplot(414);plot([940],[0.484000],‘o’)
subplot(411);plot([941],[0.261000],‘o’)
time.sleep(1)
subplot(412);plot([941],[0.436000],‘o’)
subplot(413);plot([941],[0.120000],‘o’)

subplot(414);plot([941],[0.798000],‘o’)
subplot(411);plot([942],[0.339000],‘o’)
time.sleep(1)
subplot(412);plot([942],[0.927000],‘o’)
subplot(413);plot([942],[0.964000],‘o’)

subplot(414);plot([942],[0.526000],‘o’)
subplot(411);plot([943],[0.289000],‘o’)
time.sleep(1)
subplot(412);plot([943],[0.167000],‘o’)
subplot(413);plot([943],[0.695000],‘o’)

subplot(414);plot([943],[0.788000],‘o’)
subplot(411);plot([944],[0.863000],‘o’)
time.sleep(1)
subplot(412);plot([944],[0.054000],‘o’)
subplot(413);plot([944],[0.568000],‘o’)

subplot(414);plot([944],[0.054000],‘o’)
subplot(411);plot([945],[0.858000],‘o’)
time.sleep(1)
subplot(412);plot([945],[0.461000],‘o’)
subplot(413);plot([945],[0.916000],‘o’)

subplot(414);plot([945],[0.549000],‘o’)
subplot(411);plot([946],[0.575000],‘o’)
time.sleep(1)
subplot(412);plot([946],[0.056000],‘o’)
subplot(413);plot([946],[0.331000],‘o’)

subplot(414);plot([946],[0.864000],‘o’)
subplot(411);plot([947],[0.650000],‘o’)
time.sleep(1)
subplot(412);plot([947],[0.205000],‘o’)
subplot(413);plot([947],[0.036000],‘o’)

subplot(414);plot([947],[0.951000],‘o’)
subplot(411);plot([948],[0.289000],‘o’)
time.sleep(1)
subplot(412);plot([948],[0.446000],‘o’)
subplot(413);plot([948],[0.132000],‘o’)

subplot(414);plot([948],[0.562000],‘o’)
subplot(411);plot([949],[0.084000],‘o’)
time.sleep(1)
subplot(412);plot([949],[0.429000],‘o’)
subplot(413);plot([949],[0.451000],‘o’)

subplot(414);plot([949],[0.994000],‘o’)
subplot(411);plot([950],[0.520000],‘o’)
time.sleep(1)
subplot(412);plot([950],[0.280000],‘o’)
subplot(413);plot([950],[0.575000],‘o’)

subplot(414);plot([950],[0.152000],‘o’)
subplot(411);plot([951],[0.253000],‘o’)
time.sleep(1)
subplot(412);plot([951],[0.845000],‘o’)
subplot(413);plot([951],[0.973000],‘o’)

subplot(414);plot([951],[0.062000],‘o’)
subplot(411);plot([952],[0.669000],‘o’)
time.sleep(1)
subplot(412);plot([952],[0.583000],‘o’)
subplot(413);plot([952],[0.037000],‘o’)

subplot(414);plot([952],[0.179000],‘o’)
subplot(411);plot([953],[0.886000],‘o’)
time.sleep(1)
subplot(412);plot([953],[0.577000],‘o’)
subplot(413);plot([953],[0.670000],‘o’)

subplot(414);plot([953],[0.635000],‘o’)
subplot(411);plot([954],[0.609000],‘o’)
time.sleep(1)
subplot(412);plot([954],[0.058000],‘o’)
subplot(413);plot([954],[0.978000],‘o’)

subplot(414);plot([954],[0.341000],‘o’)
subplot(411);plot([955],[0.643000],‘o’)
time.sleep(1)
subplot(412);plot([955],[0.130000],‘o’)
subplot(413);plot([955],[0.563000],‘o’)

subplot(414);plot([955],[0.127000],‘o’)
subplot(411);plot([956],[0.068000],‘o’)
time.sleep(1)
subplot(412);plot([956],[0.405000],‘o’)
subplot(413);plot([956],[0.574000],‘o’)

subplot(414);plot([956],[0.135000],‘o’)
subplot(411);plot([957],[0.967000],‘o’)
time.sleep(1)
subplot(412);plot([957],[0.382000],‘o’)
subplot(413);plot([957],[0.191000],‘o’)

subplot(414);plot([957],[0.331000],‘o’)
subplot(411);plot([958],[0.409000],‘o’)
time.sleep(1)
subplot(412);plot([958],[0.053000],‘o’)
subplot(413);plot([958],[0.597000],‘o’)

subplot(414);plot([958],[0.516000],‘o’)
subplot(411);plot([959],[0.102000],‘o’)
time.sleep(1)
subplot(412);plot([959],[0.972000],‘o’)
subplot(413);plot([959],[0.199000],‘o’)

subplot(414);plot([959],[0.678000],‘o’)
subplot(411);plot([960],[0.200000],‘o’)
time.sleep(1)
subplot(412);plot([960],[0.070000],‘o’)
subplot(413);plot([960],[0.935000],‘o’)

subplot(414);plot([960],[0.803000],‘o’)
subplot(411);plot([961],[0.441000],‘o’)
time.sleep(1)
subplot(412);plot([961],[0.881000],‘o’)
subplot(413);plot([961],[0.094000],‘o’)

subplot(414);plot([961],[0.640000],‘o’)
subplot(411);plot([962],[0.955000],‘o’)
time.sleep(1)
subplot(412);plot([962],[0.997000],‘o’)
subplot(413);plot([962],[0.664000],‘o’)

subplot(414);plot([962],[0.536000],‘o’)
subplot(411);plot([963],[0.669000],‘o’)
time.sleep(1)
subplot(412);plot([963],[0.389000],‘o’)
subplot(413);plot([963],[0.633000],‘o’)

subplot(414);plot([963],[0.488000],‘o’)
subplot(411);plot([964],[0.825000],‘o’)
time.sleep(1)
subplot(412);plot([964],[0.502000],‘o’)
subplot(413);plot([964],[0.689000],‘o’)

subplot(414);plot([964],[0.804000],‘o’)
subplot(411);plot([965],[0.068000],‘o’)
time.sleep(1)
subplot(412);plot([965],[0.542000],‘o’)
subplot(413);plot([965],[0.860000],‘o’)

subplot(414);plot([965],[0.672000],‘o’)
subplot(411);plot([966],[0.070000],‘o’)
time.sleep(1)
subplot(412);plot([966],[0.247000],‘o’)
subplot(413);plot([966],[0.613000],‘o’)

subplot(414);plot([966],[0.288000],‘o’)
subplot(411);plot([967],[0.315000],‘o’)
time.sleep(1)
subplot(412);plot([967],[0.228000],‘o’)
subplot(413);plot([967],[0.452000],‘o’)

subplot(414);plot([967],[0.283000],‘o’)
subplot(411);plot([968],[0.241000],‘o’)
time.sleep(1)
subplot(412);plot([968],[0.267000],‘o’)
subplot(413);plot([968],[0.269000],‘o’)

subplot(414);plot([968],[0.558000],‘o’)
subplot(411);plot([969],[0.282000],‘o’)
time.sleep(1)
subplot(412);plot([969],[0.748000],‘o’)
subplot(413);plot([969],[0.323000],‘o’)

subplot(414);plot([969],[0.190000],‘o’)
subplot(411);plot([970],[0.236000],‘o’)
time.sleep(1)
subplot(412);plot([970],[0.193000],‘o’)
subplot(413);plot([970],[0.244000],‘o’)

subplot(414);plot([970],[0.464000],‘o’)
subplot(411);plot([971],[0.728000],‘o’)
time.sleep(1)
subplot(412);plot([971],[0.174000],‘o’)
subplot(413);plot([971],[0.791000],‘o’)

subplot(414);plot([971],[0.988000],‘o’)
subplot(411);plot([972],[0.984000],‘o’)
time.sleep(1)
subplot(412);plot([972],[0.890000],‘o’)
subplot(413);plot([972],[0.140000],‘o’)

subplot(414);plot([972],[0.463000],‘o’)
subplot(411);plot([973],[0.284000],‘o’)
time.sleep(1)
subplot(412);plot([973],[0.110000],‘o’)
subplot(413);plot([973],[0.139000],‘o’)

subplot(414);plot([973],[0.400000],‘o’)
subplot(411);plot([974],[0.919000],‘o’)
time.sleep(1)
subplot(412);plot([974],[0.038000],‘o’)
subplot(413);plot([974],[0.519000],‘o’)

subplot(414);plot([974],[0.206000],‘o’)
subplot(411);plot([975],[0.645000],‘o’)
time.sleep(1)
subplot(412);plot([975],[0.854000],‘o’)
subplot(413);plot([975],[0.276000],‘o’)

subplot(414);plot([975],[0.975000],‘o’)
subplot(411);plot([976],[0.624000],‘o’)
time.sleep(1)
subplot(412);plot([976],[0.031000],‘o’)
subplot(413);plot([976],[0.359000],‘o’)

subplot(414);plot([976],[0.908000],‘o’)
subplot(411);plot([977],[0.675000],‘o’)
time.sleep(1)
subplot(412);plot([977],[0.810000],‘o’)
subplot(413);plot([977],[0.349000],‘o’)

subplot(414);plot([977],[0.355000],‘o’)
subplot(411);plot([978],[0.430000],‘o’)
time.sleep(1)
subplot(412);plot([978],[0.445000],‘o’)
subplot(413);plot([978],[0.423000],‘o’)

subplot(414);plot([978],[0.813000],‘o’)
subplot(411);plot([979],[0.787000],‘o’)
time.sleep(1)
subplot(412);plot([979],[0.467000],‘o’)
subplot(413);plot([979],[0.276000],‘o’)

subplot(414);plot([979],[0.402000],‘o’)
subplot(411);plot([980],[0.089000],‘o’)
time.sleep(1)
subplot(412);plot([980],[0.664000],‘o’)
subplot(413);plot([980],[0.671000],‘o’)

subplot(414);plot([980],[0.913000],‘o’)
subplot(411);plot([981],[0.247000],‘o’)
time.sleep(1)
subplot(412);plot([981],[0.382000],‘o’)
subplot(413);plot([981],[0.939000],‘o’)

subplot(414);plot([981],[0.277000],‘o’)
subplot(411);plot([982],[0.259000],‘o’)
time.sleep(1)
subplot(412);plot([982],[0.777000],‘o’)
subplot(413);plot([982],[0.524000],‘o’)

subplot(414);plot([982],[0.068000],‘o’)
subplot(411);plot([983],[0.580000],‘o’)
time.sleep(1)
subplot(412);plot([983],[0.346000],‘o’)
subplot(413);plot([983],[0.607000],‘o’)

subplot(414);plot([983],[0.833000],‘o’)
subplot(411);plot([984],[0.300000],‘o’)
time.sleep(1)
subplot(412);plot([984],[0.626000],‘o’)
subplot(413);plot([984],[0.837000],‘o’)

subplot(414);plot([984],[0.685000],‘o’)
subplot(411);plot([985],[0.386000],‘o’)
time.sleep(1)
subplot(412);plot([985],[0.957000],‘o’)
subplot(413);plot([985],[0.701000],‘o’)

subplot(414);plot([985],[0.657000],‘o’)
subplot(411);plot([986],[0.631000],‘o’)
time.sleep(1)
subplot(412);plot([986],[0.121000],‘o’)
subplot(413);plot([986],[0.912000],‘o’)

subplot(414);plot([986],[0.567000],‘o’)
subplot(411);plot([987],[0.088000],‘o’)
time.sleep(1)
subplot(412);plot([987],[0.279000],‘o’)
subplot(413);plot([987],[0.024000],‘o’)

subplot(414);plot([987],[0.114000],‘o’)
subplot(411);plot([988],[0.035000],‘o’)
time.sleep(1)
subplot(412);plot([988],[0.025000],‘o’)
subplot(413);plot([988],[0.267000],‘o’)

subplot(414);plot([988],[0.251000],‘o’)
subplot(411);plot([989],[0.861000],‘o’)
time.sleep(1)
subplot(412);plot([989],[0.909000],‘o’)
subplot(413);plot([989],[0.564000],‘o’)

subplot(414);plot([989],[0.582000],‘o’)
subplot(411);plot([990],[0.211000],‘o’)
time.sleep(1)
subplot(412);plot([990],[0.921000],‘o’)
subplot(413);plot([990],[0.915000],‘o’)

subplot(414);plot([990],[0.044000],‘o’)
subplot(411);plot([991],[0.808000],‘o’)
time.sleep(1)
subplot(412);plot([991],[0.455000],‘o’)
subplot(413);plot([991],[0.846000],‘o’)

subplot(414);plot([991],[0.385000],‘o’)
subplot(411);plot([992],[0.647000],‘o’)
time.sleep(1)
subplot(412);plot([992],[0.723000],‘o’)
subplot(413);plot([992],[0.112000],‘o’)

subplot(414);plot([992],[0.017000],‘o’)
subplot(411);plot([993],[0.689000],‘o’)
time.sleep(1)
subplot(412);plot([993],[0.635000],‘o’)
subplot(413);plot([993],[0.225000],‘o’)

subplot(414);plot([993],[0.599000],‘o’)
subplot(411);plot([994],[0.056000],‘o’)
time.sleep(1)
subplot(412);plot([994],[0.534000],‘o’)
subplot(413);plot([994],[0.271000],‘o’)

subplot(414);plot([994],[0.774000],‘o’)
subplot(411);plot([995],[0.837000],‘o’)
time.sleep(1)
subplot(412);plot([995],[0.408000],‘o’)
subplot(413);plot([995],[0.363000],‘o’)

subplot(414);plot([995],[0.796000],‘o’)
subplot(411);plot([996],[0.839000],‘o’)
time.sleep(1)
subplot(412);plot([996],[0.581000],‘o’)
subplot(413);plot([996],[0.063000],‘o’)

subplot(414);plot([996],[0.404000],‘o’)
subplot(411);plot([997],[0.230000],‘o’)
time.sleep(1)
subplot(412);plot([997],[0.731000],‘o’)
subplot(413);plot([997],[0.478000],‘o’)

subplot(414);plot([997],[0.274000],‘o’)
subplot(411);plot([998],[0.319000],‘o’)
time.sleep(1)
subplot(412);plot([998],[0.870000],‘o’)
subplot(413);plot([998],[0.220000],‘o’)

subplot(414);plot([998],[0.364000],‘o’)
subplot(411);plot([999],[0.592000],‘o’)
time.sleep(1)
subplot(412);plot([999],[0.494000],‘o’)
subplot(413);plot([999],[0.320000],‘o’)

subplot(414);plot([999],[0.143000],‘o’)

Yep, this is a bad idea. The line object in matplotlib is a fairly
heavy object which has a lot of stuff under the hood. Each plot
command creates a new line object. What you want to do is create just
one (or a few) line objects, each of which handles many points. Below
is the first pass naive implementation to show you how to do this --
you will probably want to improve on this by using a better data
structure to store the growing points, eg a buffer that drops old
points off the end, or a dynamically resizing numpy array which grows
intelligently when it gets full. The main point is that we create a
single line object and add the new data to it....

import time
import matplotlib
matplotlib.use('TkAgg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.collections as collections

plt.ion() # interactive plotting

fig = plt.figure()

xdata, ydata = [0.], [0.]
line, = ax.plot(xdata, ydata, 'o')

dt = 0.1
for i in range(100):
x = xdata[-1] + dt
y = np.sin(2*np.pi*x)
xdata.append(x)
ydata.append(y)
line.set_data(xdata, ydata)
ax.set_xlim(x-1, x)
ax.set_ylim(-1.1, 1.1)
fig.canvas.draw()
time.sleep(0.1)

plt.show()

···

On Fri, Aug 29, 2008 at 12:01 PM, Ray Salem <raysalem@...149...> wrote:

This is my first post, I really enjoy using python/numpy/scipy/matplotlib
- great replacement for matlab. I am having a problem, with plot 1 point at
a time. i am attempt to emulate an issue in the lab, where we get samples
every second and plot them. The figure becomes non-responsive and eventually
stops plotting, does anyone have an idea.