8305200ff0
due to running them. In the process of adding a bunch of new .py scripts to support the book. Most important is the DiscreteBayes1D.py class, which implements a Discrete Bayesian filter in a generalized way.
27 lines
950 B
Python
27 lines
950 B
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu May 1 16:56:49 2014
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@author: rlabbe
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"""
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def show_residual_chart():
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xlim([0.9,2.5]);ylim([0.5,2.5])
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scatter ([1,2,2],[1,2,1.3])
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scatter ([2],[1.8],marker='o')
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ax = plt.axes()
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ax.annotate('', xy=(2,2), xytext=(1,1),
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arrowprops=dict(arrowstyle='->', ec='b',shrinkA=3, shrinkB=4))
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ax.annotate('prediction', xy=(1.7,2), color='b')
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ax.annotate('measurement', xy=(2.05, 1.28))
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ax.annotate('prior measurement', xy=(1, 0.9))
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ax.annotate('residual', xy=(2.04,1.6), color='r')
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ax.annotate('new estimate', xy=(2,1.8),xytext=(2.15,1.9),
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arrowprops=dict(arrowstyle='->', shrinkA=3, shrinkB=4))
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ax.annotate('', xy=(2,2), xytext=(2,1.3),
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arrowprops=dict(arrowstyle="<->",
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ec="r",
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shrinkA=5, shrinkB=5))
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title("Kalman Filter Prediction Update Step")
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show() |