matplotlib
diff --git a/‎examples/api/filled_step.py
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+3-2Lines changed: 3 additions & 2 deletions b/‎examples/api/filled_step.py
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diff --git a/‎examples/pylab_examples/boxplot_demo2.py
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diff --git a/‎examples/pylab_examples/spectrum_demo.py
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diff --git a/‎examples/pylab_examples/system_monitor.py
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+10-10Lines changed: 10 additions & 10 deletions b/‎examples/pylab_examples/system_monitor.py
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diff --git a/‎examples/pylab_examples/xcorr_demo.py
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@@ -1,4 +1,5 @@
 import itertools
+from collections import OrderedDict
 from functools import partial
 
 import numpy as np
@@ -174,9 +175,9 @@ def stack_hist(ax, stacked_data, sty_cycle, bottoms=None,
 hatch_cycle = cycler('hatch', ['/', '*', '+', '|'])
 
 # make some synthetic data
+np.random.seed(0)
 stack_data = np.random.randn(4, 12250)
-dict_data = {lab: d for lab, d in zip(list(c['label'] for c in label_cycle),
-                                      stack_data)}
+dict_data = OrderedDict(zip((c['label'] for c in label_cycle), stack_data))
 
 # work with plain arrays
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4.5), tight_layout=True)
 
@@ -16,6 +16,7 @@
 randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
                'Triangular(2,9,11)']
 N = 500
+np.random.seed(0)
 norm = np.random.normal(1, 1, N)
 logn = np.random.lognormal(1, 1, N)
 expo = np.random.exponential(1, N)
 
@@ -1,6 +1,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+
+np.random.seed(0)
+
 dt = 0.01
 Fs = 1/dt
 t = np.arange(0, 10, dt)
 
@@ -3,23 +3,23 @@
 import numpy as np
 
 
-def get_memory():
+def get_memory(t):
     "Simulate a function that returns system memory"
-    return 100*(0.5 + 0.5*np.sin(0.5*np.pi*time.time()))
+    return 100 * (0.5 + 0.5 * np.sin(0.5 * np.pi * t))
 
 
-def get_cpu():
+def get_cpu(t):
     "Simulate a function that returns cpu usage"
-    return 100*(0.5 + 0.5*np.sin(0.2*np.pi*(time.time() - 0.25)))
+    return 100 * (0.5 + 0.5 * np.sin(0.2 * np.pi * (t - 0.25)))
 
 
-def get_net():
+def get_net(t):
     "Simulate a function that returns network bandwidth"
-    return 100*(0.5 + 0.5*np.sin(0.7*np.pi*(time.time() - 0.1)))
+    return 100 * (0.5 + 0.5 * np.sin(0.7 * np.pi * (t - 0.1)))
 
 
-def get_stats():
-    return get_memory(), get_cpu(), get_net()
+def get_stats(t):
+    return get_memory(t), get_cpu(t), get_net(t)
 
 fig, ax = plt.subplots()
 ind = np.arange(1, 4)
@@ -28,7 +28,7 @@ def get_stats():
 plt.show(block=False)
 
 
-pm, pc, pn = plt.bar(ind, get_stats())
+pm, pc, pn = plt.bar(ind, get_stats(0))
 centers = ind + 0.5*pm.get_width()
 pm.set_facecolor('r')
 pc.set_facecolor('g')
@@ -42,7 +42,7 @@ def get_stats():
 
 start = time.time()
 for i in range(200):  # run for a little while
-    m, c, n = get_stats()
+    m, c, n = get_stats(i / 10.0)
 
     # update the animated artists
     pm.set_height(m)
 
@@ -1,6 +1,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+
+np.random.seed(0)
+
 x, y = np.random.randn(2, 100)
 fig = plt.figure()
 ax1 = fig.add_subplot(211)