Altered plot styles.

Lines seemed to thick to see what is happening when they are close
to each other. Changed line width to 2.

code/538.json

@@ -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",

code/book_plots.py

@@ -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)
 

code/mkf_internal.py

@@ -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()
 

code/nonlinear_plots.py

@@ -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)

code/particle_filter.py

@@ -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)