daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Reran with tight_layout for interactive plots

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This is just so everything looks nice in nbviewer. I added
plt.tight_layout() to the interactive_plot context manager,
which makes plots fill the output cell better.
This commit is contained in:
Roger Labbe
2016-02-28 09:46:06 -08:00
parent 2594d8905c
commit f62fb8bbe8
16 changed files with 3720 additions and 3717 deletions
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13
code/book_plots.py
View File

@@ -17,28 +17,27 @@ for more information.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from book_format import figsize
from contextlib import contextmanager
import matplotlib.pyplot as plt
import numpy as np
from contextlib import contextmanager
import sys
sys.path.insert(0, '..')
from book_format import figsize
import time
try:
import seaborn
except:
pass
sys.path.insert(0, '..')
def end_interactive(fig):
""" end interaction in a plot created with %matplotlib notebook """
import time
plt.gcf().canvas.draw()
time.sleep(0.1)
time.sleep(0.2)
plt.close(fig)

10
code/gaussian_internal.py
View File

@@ -34,17 +34,16 @@ def plot_height_std(x, lw=10):
facecolor='yellow', alpha=0.4)
plt.xlabel('student')
plt.ylabel('height (m)')
plt.show()
def plot_correlated_data(X, Y, xlabel=None,
def plot_correlated_data(X, Y, xlabel=None,
ylabel=None, equal=True):
plt.scatter(X, Y)
if xlabel is not None:
plt.xlabel('Height (in)');
plt.xlabel('Height (in)');
if ylabel is not None:
plt.ylabel('Weight (lbs)')
@@ -112,7 +111,6 @@ def display_stddev_plot():
ax.xaxis.set_ticklabels(['$-2\sigma$', '$-1\sigma$','$\mu$','$1\sigma$', '$2\sigma$'])
ax.yaxis.set_ticks([])
ax.grid(None, 'both', lw=0)
plt.show()
if __name__ == '__main__':
display_stddev_plot()

166
code/pf_internal.py
View File

@@ -17,6 +17,7 @@ from __future__ import (absolute_import, division, print_function,
unicode_literals)
from book_plots import figsize, end_interactive
from filterpy.monte_carlo import stratified_resample, residual_resample
import matplotlib as mpl
import matplotlib.pyplot as plt
@@ -309,31 +310,32 @@ def test_pf2():
plt.show()
from book_plots import figsize, interactive_plot
def plot_cumsum(a):
with figsize(y=2):
with interactive_plot():
N = len(a)
fig = plt.figure()
N = len(a)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
#fig = plt.figure(figsize=(6,3))
fig=plt.gcf()
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
if N > 10:
bar.set_ticks([])
#fig = plt.figure(figsize=(6,3))
fig=plt.gcf()
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
if N > 10:
bar.set_ticks([])
end_interactive(fig)
def plot_stratified_resample(a):
@@ -347,22 +349,23 @@ def plot_stratified_resample(a):
cumsum = np.insert(cumsum, 0, 0)
with figsize(y=2):
with interactive_plot():
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
# make N subdivisions, and chose a random position within each one
b = (random(N) + range(N)) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('stratified resampling')
# make N subdivisions, and chose a random position within each one
b = (random(N) + range(N)) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('stratified resampling')
end_interactive(fig)
def plot_systematic_resample(a):
@@ -376,22 +379,23 @@ def plot_systematic_resample(a):
cumsum = np.insert(cumsum, 0, 0)
with figsize(y=2):
with interactive_plot():
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
# make N subdivisions, and chose a random position within each one
b = (random() + np.array(range(N))) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('systematic resampling')
# make N subdivisions, and chose a random position within each one
b = (random() + np.array(range(N))) / N
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('systematic resampling')
end_interactive(fig)
def plot_multinomial_resample(a):
@@ -405,20 +409,21 @@ def plot_multinomial_resample(a):
cumsum = np.insert(cumsum, 0, 0)
with figsize(y=2):
with interactive_plot():
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
# make N subdivisions, and chose a random position within each one
b = random(N)
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('multinomial resampling')
# make N subdivisions, and chose a random position within each one
b = random(N)
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('multinomial resampling')
end_interactive(fig)
def plot_residual_resample(a):
@@ -434,24 +439,25 @@ def plot_residual_resample(a):
[1., .4, 0.]]*(int(N/4) + 1))
with figsize(y=2):
with interactive_plot():
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
indexes = residual_resample(a_norm)
bins = np.bincount(indexes)
for i in range(1, N):
n = bins[i-1] # number particles in this sample
if n > 0:
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('residual resampling')
indexes = residual_resample(a_norm)
bins = np.bincount(indexes)
for i in range(1, N):
n = bins[i-1] # number particles in this sample
if n > 0:
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('residual resampling')
end_interactive(fig)
if __name__ == '__main__':