daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Altered plot styles.

Browse Source 
Lines seemed to thick to see what is happening when they are close
to each other. Changed line width to 2.
This commit is contained in:
Roger Labbe
2015-06-22 17:56:14 -07:00
parent 51c9a8283e
commit 5f593639d8
14 changed files with 755 additions and 1021 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
code/538.json
View File

@@ -34,7 +34,7 @@
"ytick.minor.size": 0,
"axes.linewidth": 3.0,
"font.size":14.0,
"lines.linewidth": 4,
"lines.linewidth": 3,
"lines.solid_capstyle": "butt",
"savefig.edgecolor": "#f0f0f0",
"savefig.facecolor": "#f0f0f0",

32
code/book_plots.py
View File

@@ -19,8 +19,8 @@ def plot_errorbars(bars, xlims):
show_legend()
plt.gca().axes.yaxis.set_ticks([])
plt.show()
def show_legend():
@@ -39,7 +39,7 @@ def bar_plot(pos, ylim=(0,1), title=None):
plt.title(title)
def plot_measurements(xs, ys=None, c='r', lw=2, label='Measurements', **kwargs):
def plot_measurements(xs, ys=None, color='r', lw=2, label='Measurements', **kwargs):
""" Helper function to give a consistant way to display
measurements in the book.
"""
@@ -57,47 +57,47 @@ def plot_measurements(xs, ys=None, c='r', lw=2, label='Measurements', **kwargs):
plt.plot(range(len(xs)), xs, lw=1, c=c, alpha=alpha)'''
if ys is not None:
plt.plot(xs, ys, c=c, lw=lw, linestyle='--', label=label, **kwargs)
plt.plot(xs, ys, color=color, lw=lw, ls='--', label=label, **kwargs)
else:
plt.plot(xs, c=c, lw=lw, linestyle='--', label=label, **kwargs)
plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
def plot_residual_limits(Ps):
std = np.sqrt(Ps)
plt.plot(-std, c='k', ls=':', lw=2)
plt.plot(std, c='k', ls=':', lw=2)
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, c=c, lw=lw, label=label, **kwargs)
plt.plot(xs, ys, color=c, lw=lw, ls=':', label=label, **kwargs)
else:
plt.plot(xs, c=c, lw=lw, label=label, **kwargs)
plt.plot(xs, color=c, lw=lw, ls=':', label=label, **kwargs)
#c='#013afe'
def plot_filter(xs, ys=None, c='#6d904f', label='Filter', vars=None, **kwargs):
def plot_filter(xs, ys=None, c='#013afe', label='Filter', vars=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, c=c, label=label, **kwargs)
plt.plot(xs, ys, color=c, label=label, **kwargs)
if vars is None:
return
vars = np.asarray(vars)
std = np.sqrt(vars)
std_top = ys+std
std_btm = ys-std
plt.plot(xs, ys+std, linestyle=':', c='k', lw=2)
plt.plot(xs, ys-std, linestyle=':', c='k', lw=2)
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)

13
code/mkf_internal.py
View File

@@ -370,12 +370,12 @@ def plot_track(ps, zs, cov,
std_top = actual+std
std_btm = actual-std
bp.plot_track(actual)
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=2)
plt.plot(std_btm, linestyle=':', color='k', lw=2)
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)
@@ -404,13 +404,9 @@ def plot_covariance(P, index=(0, 0)):
plt.plot(ps)
def test_fill():
plt.fill_between([0,1,2,3,4], [1,1,1,1,1], [4,4,4,4,4])
plt.plot([0,6], [6,1])
# plt.ylim(2,5)
plt.show()
if __name__ == "__main__":
pass
#show_position_chart()
#plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
#plot_3d_sampled_covariance([2,7], [[8.,0],[0,4.]])
@@ -418,5 +414,4 @@ if __name__ == "__main__":
#show_position_chart()
#show_x_error_chart(4)
test_fill()

68
code/nonlinear_plots.py
View File

@@ -197,11 +197,77 @@ def test_plot():
def plot_bivariate_colormap(xs, ys):
xs = np.asarray(xs)
ys = np.asarray(ys)
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
values = np.vstack([xs, ys])
kernel = scipy.stats.gaussian_kde(values)
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
Z = np.reshape(kernel.evaluate(positions).T, X.shape)
plt.gca().imshow(np.rot90(Z), cmap=plt.cm.Greys,
extent=[xmin, xmax, ymin, ymax])
def plot_monte_carlo_mean(xs, ys, f, mean_fx, label):
fxs, fys = f(xs, ys)
computed_mean_x = np.average(fxs)
computed_mean_y = np.average(fys)
plt.subplot(121)
plt.gca().grid(b=False)
plot_bivariate_colormap(xs, ys)
plt.scatter(xs, ys, marker='.', alpha=0.02, color='k')
plt.xlim(-20, 20)
plt.ylim(-20, 20)
plt.subplot(122)
plt.gca().grid(b=False)
plt.scatter(fxs, fys, marker='.', alpha=0.02, color='k')
plt.scatter(mean_fx[0], mean_fx[1],
marker='v', s=300, c='r', label='Linearized Mean')
plt.scatter(computed_mean_x, computed_mean_y,
marker='*',s=120, c='r', label='Computed Mean')
plot_bivariate_colormap(fxs, fys)
plt.ylim([-10, 200])
plt.xlim([-100, 100])
plt.legend(loc='best', scatterpoints=1)
print ('Difference in mean x={:.3f}, y={:.3f}'.format(
computed_mean_x-mean_fx[0], computed_mean_y-mean_fx[1]))
def plot_cov_ellipse_colormap(cov=[[1,1],[1,1]]):
side = np.linspace(-3,3,24)
X,Y = np.meshgrid(side,side)
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X;
pos[:, :, 1] = Y
plt.axes(xticks=[], yticks=[], frameon=True)
rv = scipy.stats.multivariate_normal((0,0), cov)
plt.gca().grid(b=False)
plt.gca().imshow(rv.pdf(pos), cmap=plt.cm.Greys, origin='lower')
plt.show()
if __name__ == "__main__":
plot_cov_ellipse_colormap(cov=[[2, 1.2], [1.2, 2]])
'''
from numpy.random import normal
import numpy as np
plot_ukf_vs_mc()
plot_ukf_vs_mc()'''
'''x0 = (1, 1)
data = normal(loc=x0[0], scale=x0[1], size=500000)

11
code/particle_filter.py
View File

@@ -151,7 +151,11 @@ if __name__ == '__main__':
mu0 = np.array([0., 0.])
plot(pf, weights=False)
for x in range(10):
fig = plt.gcf()
fig.show()
fig.canvas.draw()
for x in range(50):
z[0] += 1.0 + randn()*0.3
z[1] += 1.0 + randn()*0.3
@@ -169,12 +173,11 @@ if __name__ == '__main__':
#print(var)
plot(pf, weights=False)
plt.scatter(z[0], z[1], c='r', s=40)
plt.plot(z[0], z[1], marker='v', c='r', ms=10)
plt.scatter(mu[0], mu[1], c='g', s=100)#,
#s=min(500, abs((1./np.sum(var)))*20), alpha=0.5)
plt.tight_layout()
plt.pause(.22)
fig.canvas.draw()
#pf.assign_speed_by_gaussian(1, 1.5)
#pf.move(h=[1,1], v=1.4, t=1)