daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Made plots interactive

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This commit is contained in:
Roger Labbe 2016-02-27 21:40:21 -08:00
parent ef67326af6
commit a647f96388
8 changed files with 72765 additions and 721 deletions
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26468
10-Unscented-Kalman-Filter.ipynb
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11-Extended-Kalman-Filters.ipynb
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12-Particle-Filters.ipynb
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13-Smoothing.ipynb
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14-Adaptive-Filtering.ipynb
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4
code/adaptive_internal.py
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@ -40,8 +40,9 @@ def plot_track_and_residuals(t, xs, z_xs, res):
plt.title('residuals')
plt.show()
def plot_markov_chain():
plt.figure(figsize=(4,4), facecolor='w')
fig = plt.figure(figsize=(4,4), facecolor='w')
ax = plt.axes((0, 0, 1, 1),
xticks=[], yticks=[], frameon=False)
#ax.set_xlim(0, 10)
@ -98,6 +99,7 @@ def plot_markov_chain():
plt.axis('equal')
plt.show()
bp.end_interactive(fig)
def turning_target(N=600, turn_start=400):

167
code/pf_internal.py
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@ -309,28 +309,31 @@ def test_pf2():
plt.show()
from book_plots import figsize, interactive_plot
def plot_cumsum(a):
N = len(a)
with figsize(y=2):
with interactive_plot():
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))
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([])
plt.show()
#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([])
def plot_stratified_resample(a):
@ -343,23 +346,23 @@ def plot_stratified_resample(a):
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
fig = plt.figure(figsize=(6,3))
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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
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)
# 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')
plt.show()
# 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')
def plot_systematic_resample(a):
@ -372,23 +375,23 @@ def plot_systematic_resample(a):
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
fig = plt.figure(figsize=(6,3))
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')
xs = np.linspace(0., 1.-1./N, N)
ax.vlines(xs, 0, 1, lw=2)
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)
# 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')
plt.show()
# 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')
def plot_multinomial_resample(a):
@ -401,21 +404,21 @@ def plot_multinomial_resample(a):
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
cumsum = np.insert(cumsum, 0, 0)
fig = plt.figure(figsize=(6,3))
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')
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')
# 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')
plt.show()
# 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')
def plot_residual_resample(a):
@ -430,25 +433,25 @@ def plot_residual_resample(a):
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
fig = plt.figure(figsize=(6,3))
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')
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')
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')
plt.show()
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')
if __name__ == '__main__':

2
code/ukf_internal.py
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@ -93,7 +93,7 @@ def show_four_gps():
def show_sigma_transform(with_text=False):
fig = plt.figure()
fig = plt.gcf()
ax=fig.gca()
x = np.array([0, 5])