Formatting

bestrobot.py

@@ -11,11 +11,13 @@ def isstable(model):
     """
     return 2 * model[1] * model[2] + model[2] * model[2] - 2 * model[0]
 
-def tf((f,g,h), w):
+
+def tf((f, g, h), w):
     """
     Return the complex value of the linearized transfer function T(jw)
     """
-    return (g*1j*w + f)/(-w*w + (g+h)*1j*w + f)
+    return (g * 1j * w + f) / (-w * w + (g + h) * 1j * w + f)
+
 
 def tflogmag(model, w):
     """
@@ -24,12 +26,14 @@ def tflogmag(model, w):
     """
     return log(absolute(tf(model, w)))
 
+
 def htflogmag(model, w):
     """
     Return the log of the magnitude of the linearized headway transfer function
         log[ |1-T(jw)| ]
     """
-    return log(absolute(1-tf(model,w)))
+    return log(absolute(1 - tf(model, w)))
+
 
 def btf(human, robot, gamma, w):
     """
@@ -42,9 +46,10 @@ def btf(human, robot, gamma, w):
     """
     tr = tf(robot, w)
     th = tf(human, w)
-    t = gamma * tr / (1 - (1-gamma)*tr*th)
+    t = gamma * tr / (1 - (1 - gamma) * tr * th)
     return t
 
+
 def btflogmag(human, robot, gamma, w):
     """
     Return the log of the magnitude of the linearized transfer function
@@ -53,13 +58,15 @@ def btflogmag(human, robot, gamma, w):
     """
     return log(absolute(btf(human, robot, gamma, w)))
 
+
 def bhtflogmag(human, robot, gamma, w):
     """
     Return the log of the magnitude of the linearized headway transfer function
         log[ |1-T(jw)| ]
     of a bilateral robot controller [ followed by a human ]
     """
-    return log(absolute(1-btf(human, robot, gamma, w)))
+    return log(absolute(1 - btf(human, robot, gamma, w)))
+
 
 def kstable(human, robot, w, eta=2):
     """
@@ -87,6 +94,7 @@ def ksafe(human, robot, w, eta):
 
 TOL = 1e-6
 
+
 def maxkfn(human, robot, eta, kfn):
     """
     Find the maximum number of humans cars that can be handled by a
@@ -97,9 +105,11 @@ def maxkfn(human, robot, eta, kfn):
     :param kfn: function { ksafe for safety | kstable for string stability }
     :return:
     """
-    f,g,h = human
+    f, g, h = human
     maxw = sqrt(-isstable(human))
-    return minimize(lambda w : kfn(human, robot, w, eta), 1, bounds=[(TOL,maxw-TOL)])#.fun[0]
+    return minimize(lambda w: kfn(human, robot, w, eta), 1,
+                    bounds=[(TOL, maxw - TOL)])  # .fun[0]
+
 
 def maxk(human, robot, eta):
     """
@@ -109,7 +119,8 @@ def maxk(human, robot, eta):
     :param eta:
     :return:
     """
-    return min(maxkfn(human, robot, eta, kstable), maxkfn(human, robot, eta, ksafe))
+    return min(maxkfn(human, robot, eta, kstable),
+               maxkfn(human, robot, eta, ksafe))
 
 
 def best_robot_at_w(human, w, optfun, eta=2, fbound=(0, 0.2), gbound=(0, 0.2),
@@ -178,7 +189,8 @@ def plot_kmaxs_vs_FGH(human, robotopt, eta=2, res=100, fbound=(0, 0.2),
         robots[:, i] = robotXs
         kstablerobots = np.array(
             [maxkfn(human, robot, eta, kstable).fun[0] for robot in robots])
-        ksaferobots = [maxkfn(human, robot, eta, ksafe).fun[0] for robot in robots]
+        ksaferobots = [maxkfn(human, robot, eta, ksafe).fun[0] for robot in
+                       robots]
         kstablerobots[kstablerobots < 0] = 0
         plt.plot(robotXs, kstablerobots, 'k.')
         plt.plot(robotXs, ksaferobots, 'r.')
@@ -202,4 +214,3 @@ if __name__ == "__main__":
 
     robotopt = bestrobot(human, eta)
     plot_kmaxs_vs_FGH(human, robotopt.x)
-