daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Wording improvements, and plot changes

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This commit is contained in:
Roger Labbe 2018-03-29 16:50:10 -07:00
parent 4289a5d2a0
commit 9b80d231e2
3 changed files with 303 additions and 2697 deletions
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2912
01-g-h-filter.ipynb
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File diff suppressed because one or more lines are too long

46
kf_book/book_plots.py
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@ -101,7 +101,31 @@ def interactive_plot(close=True, fig=None):
def plot_errorbars(bars, xlims, ylims=(-1, 1)):
"""Plots a list of error bars with optional x and y limits.
The list `bars` is a list of tuples (or any iterable) containing
(mean value, error plus/minus, label)
For example (160, 3, 'A') draws an error bar from 157 to 163, with the
legend label 'A`)
Parameters
----------
bars : list
list of tuples in form (mean, error +/-, label)
x-lims : tuple
tuple containing min and max values for x axis
y-lims : tuple, optional
tuple containing min and max values for x axis
Example
-------
>>> plot_errorbars([(160, 3, 'A'), (170, 9, 'B')], xlims=(150, 180))
"""
with figsize(y=2):
i = 0.0
for bar in bars:
@ -146,7 +170,8 @@ def plot_hypothesis1():
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4); plt.ylim(150, 185)
plt.xlim(0,4)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.tight_layout()
@ -158,7 +183,9 @@ def plot_hypothesis2():
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11); plt.ylim(150, 185)
plt.xlim(0, 11)
plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
@ -173,7 +200,8 @@ def plot_hypothesis3():
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
@ -188,7 +216,8 @@ def plot_hypothesis4():
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
@ -206,7 +235,8 @@ def plot_hypothesis5():
plt.errorbar(range(1, 13), weights, label='weights',
yerr=5, fmt='o', capthick=2, capsize=10)
plt.plot (xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
@ -239,7 +269,7 @@ def plot_estimate_chart_2():
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z_t$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0.0, 159.8, "estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.text (0.0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
@ -265,8 +295,8 @@ def plot_estimate_chart_3():
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 159.8, "estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.text (0.95, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.text (0, 159.8, "last estimate ($\hat{x}_{t-1}$)", ha='left', va='top',fontsize=18)
plt.text (0.95, est_y, "estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')

42
kf_book/gh_internal.py
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@ -20,57 +20,31 @@ from __future__ import (absolute_import, division, print_function,
import kf_book.book_plots as book_plots
import numpy as np
from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
import pylab as plt
import time
import matplotlib.pylab as plt
def plot_gh_results(weights, estimates, predictions, time_step=0):
plt.figure(figsize=(9,4))
n = len(weights)
if time_step > 0:
rng = range(1, n+1)
else:
rng = range(n, n+1)
xs = range(n+1)
pred, = book_plots.plot_track(xs[1:], predictions, c='r', marker='v')
scale, = book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
est, = book_plots.plot_filter(xs, estimates, marker='o')
act, = book_plots.plot_track([0, n], [160, 160+n], c='k')
plt.gcf().canvas.draw()
for i in rng:
xs = list(range(i+1))
pred, = book_plots.plot_track(xs[1:], predictions[:i], c='r', marker='v')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
scale, = book_plots.plot_measurements(xs[1:], weights[:i], color='k', lines=False)
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
est, = book_plots.plot_filter(xs[:i+1], estimates[:i+1], marker='o')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
plt.gcf().canvas.draw()
time.sleep(time_step)
plt.legend([act, scale, est, pred], ['Actual Weight', 'Measurement', 'Estimates', 'Predictions'], loc=4)
plt.legend([scale, est, pred], ['Measurement', 'Estimates', 'Predictions'], loc=4)
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([-1, n+1])
plt.ylim([156.0, 173])
def print_results(estimates, prediction, weight):
print('previous: {:.2f}, prediction: {:.2f} estimate {:.2f}'.format(
estimates[-2], prediction, weight))
def plot_g_h_results(measurements, filtered_data,
title='', z_label='Measurements',
**kwargs):
@ -100,9 +74,5 @@ def plot_g_h_results(measurements, filtered_data,
plt.gca().canvas.draw()
time.sleep(0.5)
if __name__ == '__main__':
import seaborn
plot_errorbar1()
#create_predict_update_chart()