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