48f7eadc3e
Still a lot of work to be done, but I'm writing more realistic examples, and reordering the material.
62 lines
1.2 KiB
Python
62 lines
1.2 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Sun May 18 11:09:23 2014
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@author: rlabbe
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"""
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from __future__ import division
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import numpy as np
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import matplotlib.pyplot as plt
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def plot_transfer_func(data, f, lims, num_bins=1000):
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ys = f(data)
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h = np.histogram(ys, num_bins, density=False)
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#plot output
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plt.subplot(2,2,1)
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plt.plot(h[0], h[1][1:], lw=4)
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plt.ylim(lims)
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plt.gca().xaxis.set_ticklabels([])
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plt.title('output')
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plt.axhline(np.mean(ys), ls='--', lw=2)
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# plot transfer function
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plt.subplot(2,2,2)
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x = np.arange(lims[0], lims[1],0.1)
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y = f(x)
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plt.plot (x,y)
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isct = f(0)
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plt.plot([0,0,lims[0]],[lims[0],isct,isct],c='r')
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plt.xlim(lims)
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plt.ylim(lims)
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plt.title('transfer function')
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# plot input
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h = np.histogram(data, num_bins, density=True)
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plt.subplot(2,2,4)
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plt.plot(h[1][1:], h[0], lw=4)
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plt.xlim(lims)
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plt.gca().yaxis.set_ticklabels([])
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plt.title('input')
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plt.show()
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if __name__ == "__main__":
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from numpy.random import normal
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import numpy as np
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data = normal(loc=0.0, scale=1, size=500000)
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def g(x):
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return (np.cos(4*(x/2+0.7)))*np.sin(0.3*x)-1.6*x
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plot_transfer_func (data, g, lims=(-3,3), num_bins=100) |