fixes due to code directory

code/ukf_internal.py

@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 27 21:21:19 2014
+
+@author: rlabbe
+"""
+import matplotlib.pyplot as plt
+from matplotlib.patches import Ellipse,Arrow
+import stats
+import numpy as np
+import math
+
+
+def arrow(x1,y1,x2,y2):
+    return Arrow(x1,y1, x2-x1, y2-y1, lw=2, width=0.1, ec='k', color='k')
+
+def show_2d_transform():
+    ax=plt.gca()
+
+    ax.add_artist(Ellipse(xy=(2,5), width=2, height=3,angle=70,linewidth=1,ec='k'))
+    ax.add_artist(Ellipse(xy=(7,5), width=2.2, alpha=0.3, height=3.8,angle=150,linewidth=1,ec='k'))
+
+    ax.add_artist(arrow(2, 5, 6, 4.8))
+    ax.add_artist(arrow(1.5, 5.5, 7, 3.8))
+    ax.add_artist(arrow(2.3, 4.1, 8, 6))
+
+    ax.axes.get_xaxis().set_visible(False)
+    ax.axes.get_yaxis().set_visible(False)
+
+    plt.axis('equal')
+    plt.xlim(0,10); plt.ylim(0,10)
+    plt.show()
+
+
+def show_3_sigma_points():
+    xs = np.arange(-4, 4, 0.1)
+    var = 1.5
+    ys = [stats.gaussian(x, 0, var) for x in xs]
+    samples = [0, 1.2, -1.2]
+    for x in samples:
+        plt.scatter ([x], [stats.gaussian(x, 0, var)], s=80)
+
+    plt.plot(xs, ys)
+    plt.show()
+
+def show_sigma_selections():
+    ax=plt.gca()
+    ax.add_artist(Ellipse(xy=(2,5), alpha=0.5, width=2, height=3,angle=0,linewidth=1,ec='k'))
+    ax.add_artist(Ellipse(xy=(5,5), alpha=0.5, width=2, height=3,angle=0,linewidth=1,ec='k'))
+    ax.add_artist(Ellipse(xy=(8,5), alpha=0.5, width=2, height=3,angle=0,linewidth=1,ec='k'))
+    ax.axes.get_xaxis().set_visible(False)
+    ax.axes.get_yaxis().set_visible(False)
+
+    plt.scatter([1.5,2,2.5],[5,5,5],c='k', s=50)
+    plt.scatter([2,2],[4.5, 5.5],c='k', s=50)
+
+    plt.scatter([4.8,5,5.2],[5,5,5],c='k', s=50)
+    plt.scatter([5,5],[4.8, 5.2],c='k', s=50)
+
+    plt.scatter([7.2,8,8.8],[5,5,5],c='k', s=50)
+    plt.scatter([8,8],[4,6],c='k' ,s=50)
+
+    plt.axis('equal')
+    plt.xlim(0,10); plt.ylim(0,10)
+    plt.show()
+
+
+
+def show_sigmas_for_2_kappas():
+    # generate the Gaussian data
+    
+    xs = np.arange(-4, 4, 0.1)
+    mean = 0
+    sigma = 1.5
+    ys = [stats.gaussian(x, mean, sigma*sigma) for x in xs]
+    
+    def sigma_points(mean, sigma, kappa):
+        sigma1 = mean + math.sqrt((1+kappa)*sigma) 
+        sigma2 = mean - math.sqrt((1+kappa)*sigma) 
+        return mean, sigma1, sigma2
+        
+    #generate our samples
+    kappa = 2
+    x0,x1,x2 = sigma_points(mean, sigma, kappa)
+    
+    samples = [x0,x1,x2]
+    for x in samples:
+        p1 = plt.scatter([x], [stats.gaussian(x, mean, sigma*sigma)], s=80, color='k')
+    
+    kappa = -.5
+    x0,x1,x2 = sigma_points(mean, sigma, kappa)
+    
+    samples = [x0,x1,x2]
+    for x in samples:
+        p2 = plt.scatter([x], [stats.gaussian(x, mean, sigma*sigma)], s=80, color='b')
+    
+    plt.legend([p1,p2], ['$kappa$=2', '$kappa$=-0.5'])
+    plt.plot(xs, ys)
+    plt.show()
+
+
+
+if __name__ == '__main__':
+    show_sigma_selections()
+