matplotlib
diff --git a/‎examples/animation/animate_decay.py
Lines changed: 4 additions & 3 deletions b/‎examples/animation/animate_decay.py
Lines changed: 4 additions & 3 deletions
diff --git a/‎examples/animation/bayes_update.py
Lines changed: 1 addition & 9 deletions b/‎examples/animation/bayes_update.py
Lines changed: 1 addition & 9 deletions
diff --git a/‎examples/animation/double_pendulum_sgskip.py
Lines changed: 2 additions & 8 deletions b/‎examples/animation/double_pendulum_sgskip.py
Lines changed: 2 additions & 8 deletions
diff --git a/‎examples/animation/simple_anim.py
Lines changed: 1 addition & 6 deletions b/‎examples/animation/simple_anim.py
Lines changed: 1 addition & 6 deletions
@@ -8,13 +8,15 @@
 - changing axes limits during an animation.
 """
 
+import itertools
+
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation
 
 
 def data_gen():
-    for cnt in range(1000):
+    for cnt in itertools.count():
         t = cnt / 10
         yield t, np.sin(2*np.pi*t) * np.exp(-t/10.)
 
@@ -47,6 +49,5 @@ def run(data):
 
     return line,
 
-ani = animation.FuncAnimation(fig, run, data_gen, blit=False, interval=10,
-                              repeat=False, init_func=init)
+ani = animation.FuncAnimation(fig, run, data_gen, interval=10, init_func=init)
 plt.show()
@@ -38,16 +38,9 @@ def __init__(self, ax, prob=0.5):
         # which the plotted distribution should converge.
         self.ax.axvline(prob, linestyle='--', color='black')
 
-    def init(self):
-        self.success = 0
-        self.line.set_data([], [])
-        return self.line,
-
     def __call__(self, i):
         # This way the plot can continuously run and we just keep
         # watching new realizations of the process
-        if i == 0:
-            return self.init()
 
         # Choose success based on exceed a threshold with a uniform pick
         if np.random.rand(1,) < self.prob:
@@ -62,6 +55,5 @@ def __call__(self, i):
 
 fig, ax = plt.subplots()
 ud = UpdateDist(ax, prob=0.7)
-anim = FuncAnimation(fig, ud, frames=np.arange(100), init_func=ud.init,
-                     interval=100, blit=True)
+anim = FuncAnimation(fig, ud, frames=100, interval=100, blit=True)
 plt.show()
@@ -79,12 +79,6 @@ def derivs(state, t):
 time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)
 
 
-def init():
-    line.set_data([], [])
-    time_text.set_text('')
-    return line, time_text
-
 def animate(i):
     thisx = [0, x1[i], x2[i]]
     thisy = [0, y1[i], y2[i]]
@@ -94,6 +88,6 @@ def animate(i):
     return line, time_text
 
 
-ani = animation.FuncAnimation(fig, animate, range(1, len(y)),
-                              interval=dt*1000, blit=True, init_func=init)
+ani = animation.FuncAnimation(
+    fig, animate, len(y), interval=dt*1000, blit=True)
 plt.show()
@@ -15,18 +15,13 @@
 line, = ax.plot(x, np.sin(x))
 
 
-def init():  # only required for blitting to give a clean slate.
-    line.set_ydata([np.nan] * len(x))
-    return line,
-
-
 def animate(i):
     line.set_ydata(np.sin(x + i / 100))  # update the data.
     return line,
 
 
 ani = animation.FuncAnimation(
-    fig, animate, init_func=init, interval=2, blit=True, save_count=50)
+    fig, animate, interval=2, blit=True, save_count=50)
 
 # To save the animation, use e.g.
 #