daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Added chart of multiple Gaussians

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Roger Labbe
2015-06-24 21:00:57 -07:00
parent 46b3be3139
commit 10a62649df
4 changed files with 247 additions and 117 deletions
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code/nonlinear_plots.py
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@@ -6,11 +6,14 @@ Created on Sun May 18 11:09:23 2014
"""
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import normal
import scipy.stats
from filterpy.common import multivariate_gaussian
from matplotlib import cm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from numpy.random import normal, multivariate_normal
import scipy.stats
def plot_nonlinear_func(data, f, gaussian, num_bins=300):
@@ -215,7 +218,7 @@ def plot_bivariate_colormap(xs, ys):
def plot_monte_carlo_mean(xs, ys, f, mean_fx, label):
def plot_monte_carlo_mean(xs, ys, f, mean_fx, label, plot_colormap=True):
fxs, fys = f(xs, ys)
computed_mean_x = np.average(fxs)
@@ -225,6 +228,7 @@ def plot_monte_carlo_mean(xs, ys, f, mean_fx, label):
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)
@@ -237,6 +241,7 @@ def plot_monte_carlo_mean(xs, ys, f, mean_fx, label):
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])
@@ -261,6 +266,28 @@ def plot_cov_ellipse_colormap(cov=[[1,1],[1,1]]):
def plot_multiple_gaussians(xs, ps, x_range, y_range, N):
""" given a list of 2d states (x,y) and 2x2 covariance matrices, produce
a surface plot showing all of the gaussians"""
xs = np.asarray(xs)
x = np.linspace (x_range[0], x_range[1], N)
y = np.linspace (y_range[0], y_range[1], N)
xx, yy = np.meshgrid(x, y)
zv = np.zeros((N, N))
for mean, cov in zip(xs, ps):
zs = np.array([multivariate_gaussian(np.array([i ,j]), mean, cov)
for i, j in zip(np.ravel(xx), np.ravel(yy))])
zv += zs.reshape(xx.shape)
ax = plt.figure().add_subplot(111, projection='3d')
ax.plot_surface(xx, yy, zv, rstride=1, cstride=1, lw=.5, edgecolors='#191919',
antialiased=True, shade=True, cmap=cm.autumn)
ax.view_init(elev=40., azim=250)
if __name__ == "__main__":
plot_cov_ellipse_colormap(cov=[[2, 1.2], [1.2, 2]])
'''