8000 2 new examples · matplotlib/matplotlib@e6cb8f4 · GitHub
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2 new examples
svn path=/trunk/matplotlib/; revision=88
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‎examples/csd_demo.py

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"""
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Compute the cross spectral density of two signals
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"""
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from __future__ import division
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import sys
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from matplotlib.matlab import *
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dt = 0.01
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t = arange(0, 30, dt)
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nse1 = randn(len(t)) # white noise 1
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nse2 = randn(len(t)) # white noise 2
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r = exp(divide(-t,0.05))
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cnse1 = convolve(nse1, r, mode=2)*dt # colored noise 1
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cnse1 = cnse1[:len(t)]
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cnse2 = convolve(nse2, r, mode=2)*dt # colored noise 2
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cnse2 = cnse2[:len(t)]
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# two signals with a coherent part and a random part
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s1 = 0.01*sin(2*pi*10*t) + cnse1
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s2 = 0.01*sin(2*pi*10*t) + cnse2
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subplot(211)
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plot(t, s1, 'b-', t, s2, 'g-')
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set(gca(), 'xlim', [0,5])
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xlabel('time')
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ylabel('s1 and s2')
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subplot(212)
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cxy, f = cohere(s1, s2, 256, 1/dt)
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show()
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‎examples/figtext.py

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plot(t1, f(t1), 'bo', t2, f(t2), 'k')
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title('subplot 1')
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ylabel('Damped oscillation')
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figtitle = 'This is a somehwhat long figure title'
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figtitle = 'This is a somewhat long figure title'
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t = gcf().text(0.5, 0.95, figtitle,
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horizontalalignment='center', fontsize=20,)
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horizontalalignment='center', fontsize=16,)
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subplot(122)
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title('subplot 2')
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ylabel('Undamped')
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#savefig('figtext')
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savefig('figtext')
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show()
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‎examples/psd_demo.py

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"""
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Compute the power spectral density of a signal
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"""
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from matplotlib.matlab import *
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dt = 0.01
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t = arange(0,10,dt)
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nse = randn(len(t)) # white noise
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r = exp(-t/0.05)
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cnse = convolve(nse, r, mode=2)*dt # colored noise
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cnse = cnse[:len(t)]
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s = 0.1*sin(2*pi*t) + cnse
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figure(1)
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plot(t,s)
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figure(2)
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psd(s, 512, 1/dt)
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savefig('psd_demo')
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show()
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"""
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% compare with matlab
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dt = 0.01;
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t = [0:dt:10];
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s = sin(2*pi*t);
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nse = randn(size(t));
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r = exp(-t/0.05);
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cnse = conv(nse, r)*dt;
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cnse = cnse(1:length(t));
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s = 0.1*sin(2*pi*t) + cnse;
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figure(1)
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plot(t,s)
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figure(2)
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psd(s, 512, 1/dt)
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savefig('psd_demo')
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"""

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