Apply some automatic (pep8) whitespace formating changes.

MPLAnimator.py

@@ -38,7 +38,7 @@ def __init__(self, name=None, setup_cb=None):
         if name == None:
             self.tmpdir = tempfile.TemporaryDirectory()
             self.dir = self.tmpdir.name
-            self.name = 'animator_'+self.dir
+            self.name = 'animator_' + self.dir
         else:
             self.dir = '.prerendered/' + name
             if not os.path.exists(self.dir):
@@ -47,7 +47,6 @@ def __init__(self, name=None, setup_cb=None):
         if setup_cb:
             setup_cb()
 
-
     def initUI(self):
         """Initialize the UI."""
 
@@ -78,7 +77,6 @@ def initUI(self):
         self.prerender_checkbox = QtWidgets.QCheckBox('Pre-rendered')
         self.layout.addWidget(self.prerender_checkbox)
 
-
     def setFrameCallback(self, frame_cb, max_frame):
         """Set frame-callback relevant attributes.
 
@@ -92,7 +90,6 @@ def setFrameCallback(self, frame_cb, max_frame):
         self.max_frame = max_frame
         self.slider.setMaximum(max_frame - 1)
 
-
     def setClickCallback(self, click_cb):
         """Set click-callback relevant attributes.
 
@@ -102,7 +99,6 @@ def setClickCallback(self, click_cb):
         """
         self.click_cb = click_cb
 
-
     def prerender(self):
         """Prerender the animation."""
         if len(os.listdir(self.dir)) == 0:
@@ -112,13 +108,11 @@ def prerender(self):
                 self.frame_cb(i)
                 plt.savefig('{}/{}.png'.format(self.dir, i))
 
-
     def handleCanvasClick(self, event: matplotlib.backend_bases.MouseEvent):
         """Unpack canvas click event to click callback function."""
         self.click_cb(**(event.__dict__))
         self.visualize()
 
-
     def visualize(self, i=None):
         """Update visualization for set frame.
 
@@ -142,7 +136,6 @@ def visualize(self, i=None):
             self.frame_cb(i)
             self.canvas.draw()
 
-
     def clear(self):
         """Clear pre-rendered images."""
 

example.py

@@ -4,31 +4,35 @@
 import matplotlib.pyplot as plt
 from MPLAnimator import Animator
 
+
 def naive_estimator(x, data, h):
-    n = sum(1 for d in data if x-h/2 < d <= x+h/2)
+    n = sum(1 for d in data if x - h / 2 < d <= x + h / 2)
     N = len(data)
-    return n/(N*h)
+    return n / (N * h)
+
 
 data = [0.5, 0.7, 0.8, 1.9, 2.4, 6.1, 6.2, 7.3]
 xs = np.arange(0, 8, 0.01)
 h = 2
 hist = [naive_estimator(x, data, h) for x in xs]
 
+
 def setup():
     plt.gcf()
     plt.suptitle("Naive Estimator for h = {}".format(h))
 
+
 def frame(i):
     plt.cla()
 
     # plot original data
     plt.plot(xs, hist)
-    plt.plot(data, [0]*len(data), 'xk')
+    plt.plot(data, [0] * len(data), 'xk')
     plt.axhline(0, color='k', linewidth=0.5)
 
     # calculate current interval
     x = i / 10
-    x1, x2 = x-h/2, x+h/2
+    x1, x2 = x - h / 2, x + h / 2
 
     # calculate relative width for visualization
     axis_to_data = plt.gca().transAxes + plt.gca().transData.inverted()
@@ -45,7 +49,7 @@ def frame(i):
 
     # highlight data in interval
     highlight_data = [d for d in data if x1 < d <= x2]
-    plt.plot(highlight_data, [0]*len(highlight_data), 'oC3')
+    plt.plot(highlight_data, [0] * len(highlight_data), 'oC3')
 
     plt.xlim(-0.5, 8.5)