Altered colors of plots.

Maybe not perfect yet, but I have signicantly changed the colors
and various other look-and-fell aspects of the plots.

book_format.py

@@ -6,18 +6,16 @@ def load_style():
     styles = open("./styles/custom2.css", "r").read()
     return HTML(styles)
 
-pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
-pylab.rcParams['lines.linewidth'] = 1
-
+pylab.rcParams['lines.linewidth'] = 2
 pylab.rcParams['lines.antialiased'] = True
 pylab.rcParams['patch.linewidth'] = 0.5
 pylab.rcParams['patch.facecolor'] = '348ABD' #blue
 pylab.rcParams['patch.edgecolor'] = 'eeeeee'
 pylab.rcParams['patch.antialiased'] = True
 pylab.rcParams['font.family'] = 'monospace'
-pylab.rcParams['font.size'] = 10.0
+pylab.rcParams['font.size'] = 12.0
 #pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
-pylab.rcParams['axes.facecolor'] = 'eeeeee'
+pylab.rcParams['axes.facecolor'] = 'E5E5E5'
 pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
 pylab.rcParams['axes.linewidth'] = 1
 pylab.rcParams['axes.grid'] = True
@@ -25,7 +23,7 @@ pylab.rcParams['axes.titlesize'] = 'x-large'
 pylab.rcParams['axes.labelsize'] = 'large'
 pylab.rcParams['axes.labelcolor'] = '555555'
 pylab.rcParams['axes.axisbelow'] = True
-pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
+pylab.rcParams['axes.color_cycle'] = '004080, 8EBA42, E24A33, 348ABD, 777760, 988ED5, FDC15E'
 pylab.rcParams['xtick.major.pad'] = 6
 pylab.rcParams['xtick.minor.size'] = 0
 pylab.rcParams['xtick.minor.pad'] = 6
@@ -36,4 +34,8 @@ pylab.rcParams['figure.facecolor'] = '0.85'
 pylab.rcParams['figure.edgecolor'] = '0.50'
 pylab.rcParams['figure.subplot.hspace'] = 0.5
 pylab.rcParams['figure.figsize'] = 12,6
+pylab.rcParams['grid.color'] = 'ffffff'
+pylab.rcParams['grid.linestyle'] = 'solid'
+pylab.rcParams['grid.linewidth'] = 1.5
+
 

mkf_internal.py

@@ -20,16 +20,19 @@ def show_residual_chart():
     plt.scatter ([2],[2.8],marker='o')
     ax = plt.axes()
     ax.annotate('', xy=(2,3), xytext=(1,2),
-                arrowprops=dict(arrowstyle='->', ec='b',shrinkA=3, shrinkB=4))
-    ax.annotate('prediction', xy=(2.04,3.), color='b')
+                arrowprops=dict(arrowstyle='->', ec='#004080',
+                                lw=2,
+                                shrinkA=3, shrinkB=4))
+    ax.annotate('prediction', xy=(2.04,3.), color='#004080')
     ax.annotate('measurement', xy=(2.05, 2.28))
     ax.annotate('prior estimate', xy=(1, 1.9))
-    ax.annotate('residual', xy=(2.04,2.6), color='r')
+    ax.annotate('residual', xy=(2.04,2.6), color='#e24a33')
     ax.annotate('new estimate', xy=(2,2.8),xytext=(2.1,2.8),
                 arrowprops=dict(arrowstyle='->', ec="k", shrinkA=3, shrinkB=4))
     ax.annotate('', xy=(2,3), xytext=(2,2.3),
                 arrowprops=dict(arrowstyle="-",
-                                ec="r",
+                                ec="#e24a33",
+                                lw=2,
                                 shrinkA=5, shrinkB=5))
     plt.title("Kalman Filter Prediction Update Step")
     plt.axis('equal')
@@ -39,7 +42,7 @@ def show_residual_chart():
 def show_position_chart():
     """ Displays 3 measurements at t=1,2,3, with x=1,2,3"""
 
-    plt.scatter ([1,2,3], [1,2,3], s=128)
+    plt.scatter ([1,2,3], [1,2,3], s=128, color='#004080')
     plt.xlim([0,4]);
     plt.ylim([0,4])
 
@@ -54,7 +57,7 @@ def show_position_chart():
 def show_position_prediction_chart():
     """ displays 3 measurements, with the next position predicted"""
 
-    plt.scatter ([1,2,3], [1,2,3], s=128)
+    plt.scatter ([1,2,3], [1,2,3], s=128, color='#004080')
 
     plt.xlim([0,5])
     plt.ylim([0,5])
@@ -65,7 +68,7 @@ def show_position_prediction_chart():
     plt.xticks(np.arange(1,5,1))
     plt.yticks(np.arange(1,5,1))
 
-    plt.scatter ([4], [4], c='g',s=128)
+    plt.scatter ([4], [4], c='g',s=128, color='#8EBA42')
     ax = plt.axes()
     ax.annotate('', xy=(4,4), xytext=(3,3),
                 arrowprops=dict(arrowstyle='->',
@@ -181,6 +184,6 @@ def plot_3d_covariance(mean, cov):
 
 
 if __name__ == "__main__":
-
-    plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
-    #show_residual_chart()
+    #show_position_chart()
+    #plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
+    show_residual_chart()

stats.py

@@ -226,7 +226,7 @@ def is_inside_ellipse(x,y, ex, ey, orientation, width, height):
 
 def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
              ellipse=None, title=None, axis_equal=True,
-             facecolor='none', edgecolor='blue'):
+             facecolor='none', edgecolor='#004080'):
     """ plots the covariance ellipse where
 
     mean is a (x,y) tuple for the mean of the covariance (center of ellipse)
@@ -273,7 +273,7 @@ def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
         e = Ellipse(xy=mean, width=sd*width, height=sd*height, angle=angle,
                     facecolor=facecolor,
                     edgecolor=edgecolor,
-                    lw=1)
+                    lw=2)
         ax.add_patch(e)
     plt.scatter(mean[0], mean[1], marker='+') # mark the center
 
@@ -330,7 +330,7 @@ if __name__ == '__main__':
 
     do_plot_test()
 
-    test_gaussian()
+    #test_gaussian()
 
     # test conversion of scalar to covariance matrix
     x  = multivariate_gaussian(np.array([1,1]), np.array([3,4]), np.eye(2)*1.4)