77ba700e39
Pretty happy with it now. Needs copy editing, and probably an easier introduction to convey the basic idea. Moved from a class based approach to a procedural approach, and I like that very much.
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
# -*- coding: utf-8 -*-
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"""Copyright 2015 Roger R Labbe Jr.
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Code supporting the book
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Kalman and Bayesian Filters in Python
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https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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This is licensed under an MIT license. See the LICENSE.txt file
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for more information.
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"""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import book_plots as bp
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import matplotlib.pyplot as plt
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def plot_dog_track(xs, measurement_var, process_var):
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N = len(xs)
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bp.plot_track([0, N-1], [1, N])
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bp.plot_measurements(xs, label='Sensor')
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bp.set_labels('variance = {}, process variance = {}'.format(
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measurement_var, process_var), 'time', 'pos')
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plt.ylim([0, N])
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bp.show_legend()
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plt.show()
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def print_gh(predict, update, z):
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predict_template = ' {: 7.3f} {: 8.3f}'
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update_template = '{: 7.3f} {: 7.3f}\t {:.3f}'
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print(predict_template.format(predict[0], predict[1]),end='\t')
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print(update_template.format(update[0], update[1], z))
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def print_variance(positions):
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print('Variance:')
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for i in range(0, len(positions), 5):
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print('\t{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
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*[v[1] for v in positions[i:i+5]])) |