Kalman-and-Bayesian-Filters.../code/book_plots.py
2015-08-01 08:52:48 -07:00

230 lines
6.0 KiB
Python

# -*- coding: utf-8 -*-
"""Copyright 2015 Roger R Labbe Jr.
Code supporting the book
Kalman and Bayesian Filters in Python
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
This is licensed under an MIT license. See the LICENSE.txt file
for more information.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import matplotlib.pyplot as plt
import numpy as np
def plot_errorbars(bars, xlims):
i = 1.0
for bar in bars:
plt.errorbar([bar[0]], [i], xerr=[bar[1]], fmt='o', label=bar[2] , capthick=2, capsize=10)
i += 0.2
plt.ylim(0, 2)
plt.xlim(xlims[0], xlims[1])
show_legend()
plt.gca().axes.yaxis.set_ticks([])
plt.show()
def show_legend():
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
def bar_plot(pos, ylim=(0,1), title=None):
plt.cla()
ax = plt.gca()
x = np.arange(len(pos))
ax.bar(x, pos, color='#30a2da')
if ylim:
plt.ylim(ylim)
plt.xticks(x+0.4, x)
if title is not None:
plt.title(title)
def set_labels(title=None, x=None, y=None):
""" helps make code in book shorter. Optional set title, xlabel and ylabel
"""
if x is not None:
plt.xlabel(x)
if y is not None:
plt.ylabel(y)
if title is not None:
plt.title(title)
def set_limits(x, y):
""" helper function to make code in book shorter. Set the limits for the x
and y axis.
"""
plt.gca().set_xlim(x)
plt.gca().set_ylim(y)
def plot_predictions(p, rng=None):
if rng is None:
rng = range(len(p))
plt.scatter(rng, p, marker='v', s=40, edgecolor='r',
facecolor='None', lw=2, label='prediction')
def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
plot_filter(filter_xs[:, 0])
plot_track(xs[:, 0])
if zs is not None:
plot_measurements(zs)
show_legend()
set_labels(title=title, x='meters', y='time (sec)')
if aspect_equal:
plt.gca().set_aspect('equal')
plt.xlim((-1, len(xs)))
plt.show()
def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
lines=False, **kwargs):
""" Helper function to give a consistant way to display
measurements in the book.
"""
plt.autoscale(tight=True)
'''if ys is not None:
plt.scatter(xs, ys, marker=marker, c=c, s=s,
label=label, alpha=alpha)
if connect:
plt.plot(xs, ys, c=c, lw=1, alpha=alpha)
else:
plt.scatter(range(len(xs)), xs, marker=marker, c=c, s=s,
label=label, alpha=alpha)
if connect:
plt.plot(range(len(xs)), xs, lw=1, c=c, alpha=alpha)'''
if lines:
if ys is not None:
plt.plot(xs, ys, color=color, lw=lw, ls='--', label=label, **kwargs)
else:
plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
else:
if ys is not None:
plt.scatter(xs, ys, edgecolor=color, facecolor='none',
lw=2, label=label, **kwargs)
else:
plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none',
lw=2, label=label, **kwargs)
def plot_residual_limits(Ps, stds=1.):
""" plots standand deviation given in Ps as a yellow shaded region. One std
by default, use stds for a different choice (e.g. stds=3 for 3 standard
deviations.
"""
std = np.sqrt(Ps) * stds
plt.plot(-std, color='k', ls=':', lw=2)
plt.plot(std, color='k', ls=':', lw=2)
plt.fill_between(range(len(std)), -std, std,
facecolor='#ffff00', alpha=0.3)
def plot_track(xs, ys=None, label='Track', c='k', lw=2, **kwargs):
if ys is not None:
plt.plot(xs, ys, color=c, lw=lw, ls=':', label=label, **kwargs)
else:
plt.plot(xs, color=c, lw=lw, ls=':', label=label, **kwargs)
def plot_filter(xs, ys=None, c='#013afe', label='Filter', var=None, **kwargs):
#def plot_filter(xs, ys=None, c='#6d904f', label='Filter', vars=None, **kwargs):
if ys is None:
ys = xs
xs = range(len(ys))
plt.plot(xs, ys, color=c, label=label, **kwargs)
if var is None:
return
var = np.asarray(var)
std = np.sqrt(var)
std_top = ys+std
std_btm = ys-std
plt.plot(xs, ys+std, linestyle=':', color='k', lw=2)
plt.plot(xs, ys-std, linestyle=':', color='k', lw=2)
plt.fill_between(xs, std_btm, std_top,
facecolor='yellow', alpha=0.2)
def _blob(x, y, area, colour):
"""
Draws a square-shaped blob with the given area (< 1) at
the given coordinates.
"""
hs = np.sqrt(area) / 2
xcorners = np.array([x - hs, x + hs, x + hs, x - hs])
ycorners = np.array([y - hs, y - hs, y + hs, y + hs])
plt.fill(xcorners, ycorners, colour, edgecolor=colour)
def hinton(W, maxweight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if plt.isinteractive():
plt.ioff()
plt.clf()
height, width = W.shape
if not maxweight:
maxweight = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))
plt.fill(np.array([0, width, width, 0]),
np.array([0, 0, height, height]),
'gray')
plt.axis('off')
plt.axis('equal')
for x in range(width):
for y in range(height):
_x = x+1
_y = y+1
w = W[y, x]
if w > 0:
_blob(_x - 0.5,
height - _y + 0.5,
min(1, w/maxweight),
'white')
elif w < 0:
_blob(_x - 0.5,
height - _y + 0.5,
min(1, -w/maxweight),
'black')
if reenable:
plt.ion()
if __name__ == "__main__":
p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,
0.06288015, 0.06109133, 0.0581008, 0.09334062]*2
bar_plot(p)
plot_measurements(p)