daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Change plots to interactive plots

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This commit is contained in:
Roger Labbe
2016-02-27 20:52:36 -08:00
parent f3358f44d6
commit ef67326af6
8 changed files with 64871 additions and 577 deletions
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8
code/book_plots.py
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@@ -339,8 +339,11 @@ def show_residual_chart(show_eq=True, show_H=False):
plt.xlabel('time')
ax.yaxis.set_label_position("right")
plt.ylabel('state')
plt.xlim(-0.5, 1.5)
plt.savefig('../figs/residual_chart.png', pad_inches=0.1)
plt.xlim(-0.1, 1.5)
if show_H:
plt.savefig('../figs/residual_chart_with_h.png', pad_inches=0.1)
else:
plt.savefig('../figs/residual_chart.png', pad_inches=0.1)
@@ -589,6 +592,7 @@ if __name__ == "__main__":
plot_estimate_chart_3()
create_predict_update_chart()
show_residual_chart()
show_residual_chart(True, True)
plt.close('all')
'''p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,

66
code/mkf_internal.py
View File

@@ -417,51 +417,53 @@ def plot_correlation_covariance():
plt.title('|4.0 3.9|\n|3.9 4.0|')
plt.show()
from book_plots import interactive_plot
def plot_track(ps, actual, zs, cov, std_scale=1,
plot_P=True, y_lim=None, dt=1.,
xlabel='time', ylabel='position',
title='Kalman Filter'):
count = len(zs)
zs = np.asarray(zs)
with interactive_plot():
count = len(zs)
zs = np.asarray(zs)
cov = np.asarray(cov)
std = std_scale*np.sqrt(cov[:,0,0])
std_top = np.minimum(actual+std, [count + 10])
std_btm = np.maximum(actual-std, [-50])
cov = np.asarray(cov)
std = std_scale*np.sqrt(cov[:,0,0])
std_top = np.minimum(actual+std, [count + 10])
std_btm = np.maximum(actual-std, [-50])
std_top = actual + std
std_btm = actual - std
std_top = actual + std
std_btm = actual - std
bp.plot_track(actual,c='k')
bp.plot_measurements(range(1, count + 1), zs)
bp.plot_filter(range(1, count + 1), ps)
bp.plot_track(actual,c='k')
bp.plot_measurements(range(1, count + 1), zs)
bp.plot_filter(range(1, count + 1), ps)
plt.plot(std_top, linestyle=':', color='k', lw=1, alpha=0.4)
plt.plot(std_btm, linestyle=':', color='k', lw=1, alpha=0.4)
plt.fill_between(range(len(std_top)), std_top, std_btm,
facecolor='yellow', alpha=0.2, interpolate=True)
plt.legend(loc=4)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if y_lim is not None:
plt.ylim(y_lim)
else:
plt.ylim((-50, count + 10))
plt.plot(std_top, linestyle=':', color='k', lw=1, alpha=0.4)
plt.plot(std_btm, linestyle=':', color='k', lw=1, alpha=0.4)
plt.fill_between(range(len(std_top)), std_top, std_btm,
facecolor='yellow', alpha=0.2, interpolate=True)
plt.legend(loc=4)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if y_lim is not None:
plt.ylim(y_lim)
else:
plt.ylim((-50, count + 10))
plt.xlim((0,count))
plt.title(title)
plt.show()
plt.xlim((0,count))
plt.title(title)
if plot_P:
ax = plt.subplot(121)
ax.set_title("$\sigma^2_x$ (pos variance)")
plot_covariance(cov, (0, 0))
ax = plt.subplot(122)
ax.set_title("$\sigma^2_\dot{x}$ (vel variance)")
plot_covariance(cov, (1, 1))
plt.show()
with interactive_plot():
ax = plt.subplot(121)
ax.set_title("$\sigma^2_x$ (pos variance)")
plot_covariance(cov, (0, 0))
ax = plt.subplot(122)
ax.set_title("$\sigma^2_\dot{x}$ (vel variance)")
plot_covariance(cov, (1, 1))
def plot_covariance(P, index=(0, 0)):
ps = []