daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Roger Labbe
2014-09-01 19:26:50 -07:00
parent 7a56f2263d
commit fc993eff4c
6 changed files with 0 additions and 257 deletions
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41
code/book_format.py Normal file
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# -*- coding: utf-8 -*-
from IPython.core.display import HTML
import matplotlib.pylab as pylab
def load_style():
styles = open("../styles/custom2.css", "r").read()
return HTML(styles)
pylab.rcParams['lines.linewidth'] = 2
pylab.rcParams['lines.antialiased'] = True
pylab.rcParams['patch.linewidth'] = 0.5
pylab.rcParams['patch.facecolor'] = '348ABD' #blue
pylab.rcParams['patch.edgecolor'] = 'eeeeee'
pylab.rcParams['patch.antialiased'] = True
pylab.rcParams['font.family'] = 'monospace'
pylab.rcParams['font.size'] = 12.0
#pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
pylab.rcParams['axes.facecolor'] = 'E5E5E5'
pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
pylab.rcParams['axes.linewidth'] = 1
pylab.rcParams['axes.grid'] = True
pylab.rcParams['axes.titlesize'] = 'x-large'
pylab.rcParams['axes.labelsize'] = 'large'
pylab.rcParams['axes.labelcolor'] = '555555'
pylab.rcParams['axes.axisbelow'] = True
pylab.rcParams['axes.color_cycle'] = '004080, 8EBA42, E24A33, 348ABD, 777760, 988ED5, FDC15E'
pylab.rcParams['xtick.major.pad'] = 6
pylab.rcParams['xtick.minor.size'] = 0
pylab.rcParams['xtick.minor.pad'] = 6
pylab.rcParams['xtick.color'] = '555555'
pylab.rcParams['ytick.direction'] = 'in'
pylab.rcParams['legend.fancybox'] = True
pylab.rcParams['figure.facecolor'] = '0.85'
pylab.rcParams['figure.edgecolor'] = '0.50'
pylab.rcParams['figure.subplot.hspace'] = 0.5
pylab.rcParams['figure.figsize'] = 12,6
pylab.rcParams['grid.color'] = 'ffffff'
pylab.rcParams['grid.linestyle'] = 'solid'
pylab.rcParams['grid.linewidth'] = 1.5

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code/gh_internal.py Normal file
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import numpy as np
import pylab as plt
from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
def create_predict_update_chart(box_bg = '#CCCCCC',
arrow1 = '#88CCFF',
arrow2 = '#88FF88'):
plt.figure(figsize=(6,6), facecolor='w')
ax = plt.axes((0, 0, 1, 1),
xticks=[], yticks=[], frameon=False)
#ax.set_xlim(0, 10)
#ax.set_ylim(0, 10)
pc = Circle((4,5), 0.5, fc=box_bg)
uc = Circle((6,5), 0.5, fc=box_bg)
ax.add_patch (pc)
ax.add_patch (uc)
plt.text(4,5, "Predict\nStep",ha='center', va='center', fontsize=14)
plt.text(6,5, "Update\nStep",ha='center', va='center', fontsize=14)
#btm
ax.annotate('',
xy=(4.1, 4.5), xycoords='data',
xytext=(6, 4.5), textcoords='data',
size=20,
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none",
patchB=pc,
patchA=uc,
connectionstyle="arc3,rad=-0.5"))
#top
ax.annotate('',
xy=(6, 5.5), xycoords='data',
xytext=(4.1, 5.5), textcoords='data',
size=20,
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none",
patchB=uc,
patchA=pc,
connectionstyle="arc3,rad=-0.5"))
ax.annotate('Measurement ($\mathbf{z_k}$)',
xy=(6.3, 5.4), xycoords='data',
xytext=(6,6), textcoords='data',
size=18,
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none"))
ax.annotate('',
xy=(4.0, 3.5), xycoords='data',
xytext=(4.0,4.5), textcoords='data',
size=18,
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none"))
ax.annotate('Initial\nConditions ($\mathbf{x_0}$)',
xy=(4.0, 5.5), xycoords='data',
xytext=(2.5,6.5), textcoords='data',
size=18,
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none"))
plt.text (4,3.4,'State Estimate ($\mathbf{\hat{x}_k}$)',
ha='center', va='center', fontsize=18)
plt.axis('equal')
#plt.axis([0,8,0,8])
plt.show()
def plot_estimate_chart_1():
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
plt.scatter ([0], [158], c='b')
plt.scatter ([1], [159], c='r')
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.show()
def plot_estimate_chart_2():
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
plt.scatter ([0], [158.0], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
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, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.show()
def plot_estimate_chart_3():
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
ax.annotate('', xy=[1,159], xytext=[1,164.2],
arrowprops=dict(arrowstyle='-',
ec='k', lw=1, shrinkA=8, shrinkB=8))
est_y = ((164.2-158)*.8 + 158)
plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
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, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
plt.text (0.95, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.show()
if __name__ == '__main__':
create_predict_update_chart()