diff --git a/y2018/control_loops/python/intake.py b/y2018/control_loops/python/intake.py
index 1a7df6a..8879dff 100755
--- a/y2018/control_loops/python/intake.py
+++ b/y2018/control_loops/python/intake.py
@@ -12,214 +12,229 @@
 FLAGS = gflags.FLAGS
 
 try:
-  gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+    gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
 except gflags.DuplicateFlagError:
-  pass
+    pass
+
 
 class Intake(control_loop.ControlLoop):
-  def __init__(self, name="Intake"):
-    super(Intake, self).__init__(name)
-    self.motor = control_loop.BAG()
-    # TODO(constants): Update all of these & retune poles.
-    # Stall Torque in N m
-    self.stall_torque = self.motor.stall_torque
-    # Stall Current in Amps
-    self.stall_current = self.motor.stall_current
-    # Free Speed in RPM
-    self.free_speed = self.motor.free_speed
-    # Free Current in Amps
-    self.free_current = self.motor.free_current
+    def __init__(self, name="Intake"):
+        super(Intake, self).__init__(name)
+        self.motor = control_loop.BAG()
+        # Stall Torque in N m
+        self.stall_torque = self.motor.stall_torque
+        # Stall Current in Amps
+        self.stall_current = self.motor.stall_current
+        # Free Speed in RPM
+        self.free_speed = self.motor.free_speed
+        # Free Current in Amps
+        self.free_current = self.motor.free_current
 
-    # Resistance of the motor
-    self.resistance = self.motor.resistance
-    # Motor velocity constant
-    self.Kv = self.motor.Kv
-    # Torque constant
-    self.Kt = self.motor.Kt
-    # Gear ratio
-    self.G = 1.0 / 100.0
+        # Resistance of the motor
+        self.resistance = self.motor.resistance
+        # Motor velocity constant
+        self.Kv = self.motor.Kv
+        # Torque constant
+        self.Kt = self.motor.Kt
+        # Gear ratio
+        self.G = 1.0 / 102.6
 
-    self.motor_inertia = 0.000006
+        self.motor_inertia = 0.00000589 * 1.2
 
-    # Series elastic moment of inertia
-    self.Je = self.motor_inertia / (self.G * self.G)
-    # Grabber moment of inertia
-    self.Jo = 0.295
+        # Series elastic moment of inertia
+        self.Je = self.motor_inertia / (self.G * self.G)
+        # Grabber moment of inertia
+        self.Jo = 0.0363
 
-    # Spring constant (N m / radian)
-    self.Ks = 30.0
+        # Bot has a time constant of 0.22
+        # Current physics has a time constant of 0.18
 
-    # Control loop time step
-    self.dt = 0.00505
+        # Spring constant (N m / radian)
+        self.Ks = 32.74
 
-    # State is [output_position, output_velocity,
-    #           elastic_position, elastic_velocity]
-    # The output position is the absolute position of the intake arm.
-    # The elastic position is the absolute position of the motor side of the
-    # series elastic.
-    # Input is [voltage]
+        # Control loop time step
+        self.dt = 0.00505
 
-    self.A_continuous = numpy.matrix(
-        [[0.0, 1.0, 0.0, 0.0],
-         [(-self.Ks / self.Jo), 0.0, (self.Ks / self.Jo), 0.0],
-         [0.0, 0.0, 0.0, 1.0],
-         [(self.Ks / self.Je), 0.0, (-self.Ks / self.Je), \
-          -self.Kt / (self.Je * self.resistance * self.Kv * self.G * self.G)]])
+        # State is [output_position, output_velocity,
+        #           elastic_position, elastic_velocity]
+        # The output position is the absolute position of the intake arm.
+        # The elastic position is the absolute position of the motor side of the
+        # series elastic.
+        # Input is [voltage]
 
-    # Start with the unmodified input
-    self.B_continuous = numpy.matrix(
-        [[0.0],
-         [0.0],
-         [0.0],
-         [self.Kt / (self.G * self.Je * self.resistance)]])
+        self.A_continuous = numpy.matrix(
+            [[0.0, 1.0, 0.0, 0.0],
+             [(-self.Ks / self.Jo), 0.0, (self.Ks / self.Jo), 0.0],
+             [0.0, 0.0, 0.0, 1.0],
+             [(self.Ks / self.Je), 0.0, (-self.Ks / self.Je), \
+              -self.Kt / (self.Je * self.resistance * self.Kv * self.G * self.G)]])
 
-    self.C = numpy.matrix([[1.0, 0.0, -1.0, 0.0],
-                           [0.0, 0.0, 1.0, 0.0]])
-    self.D = numpy.matrix([[0.0],
-                           [0.0]])
+        # Start with the unmodified input
+        self.B_continuous = numpy.matrix(
+            [[0.0], [0.0], [0.0],
+             [self.Kt / (self.G * self.Je * self.resistance)]])
 
-    self.A, self.B = self.ContinuousToDiscrete(
-        self.A_continuous, self.B_continuous, self.dt)
+        self.C = numpy.matrix([[1.0, 0.0, -1.0, 0.0], [0.0, 0.0, 1.0, 0.0]])
+        self.D = numpy.matrix([[0.0], [0.0]])
 
-    controllability = controls.ctrb(self.A, self.B)
-    glog.debug('ctrb: ' + repr(numpy.linalg.matrix_rank(controllability)))
+        self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+                                                   self.B_continuous, self.dt)
 
-    observability = controls.ctrb(self.A.T, self.C.T)
-    glog.debug('obs: ' + repr(numpy.linalg.matrix_rank(observability)))
+        #controllability = controls.ctrb(self.A, self.B)
+        #glog.debug('ctrb: ' + repr(numpy.linalg.matrix_rank(controllability)))
 
-    glog.debug('A_continuous ' + repr(self.A_continuous))
-    glog.debug('B_continuous ' + repr(self.B_continuous))
+        #observability = controls.ctrb(self.A.T, self.C.T)
+        #glog.debug('obs: ' + repr(numpy.linalg.matrix_rank(observability)))
 
-    self.K = numpy.matrix(numpy.zeros((1, 4)))
+        glog.debug('A_continuous ' + repr(self.A_continuous))
+        glog.debug('B_continuous ' + repr(self.B_continuous))
 
-    q_pos = 0.05
-    q_vel = 2.65
-    self.Q = numpy.matrix(numpy.diag([(q_pos ** 2.0), (q_vel ** 2.0),
-                                      (q_pos ** 2.0), (q_vel ** 2.0)]))
+        self.K = numpy.matrix(numpy.zeros((1, 4)))
 
-    r_nm = 0.025
-    self.R = numpy.matrix(numpy.diag([(r_nm ** 2.0), (r_nm ** 2.0)]))
+        q_pos = 0.05
+        q_vel = 2.65
+        self.Q = numpy.matrix(
+            numpy.diag([(q_pos**2.0), (q_vel**2.0), (q_pos**2.0), (q_vel
+                                                                   **2.0)]))
 
-    self.KalmanGain, self.Q_steady = controls.kalman(
-        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+        r_nm = 0.025
+        self.R = numpy.matrix(numpy.diag([(r_nm**2.0), (r_nm**2.0)]))
 
-    # The box formed by U_min and U_max must encompass all possible values,
-    # or else Austin's code gets angry.
-    self.U_max = numpy.matrix([[12.0]])
-    self.U_min = numpy.matrix([[-12.0]])
+        self.KalmanGain, self.Q_steady = controls.kalman(
+            A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
 
-    self.InitializeState()
+        # The box formed by U_min and U_max must encompass all possible values,
+        # or else Austin's code gets angry.
+        self.U_max = numpy.matrix([[12.0]])
+        self.U_min = numpy.matrix([[-12.0]])
+
+        self.InitializeState()
 
 
 class DelayedIntake(Intake):
-  def __init__(self, name="DelayedIntake"):
-    super(DelayedIntake, self).__init__(name=name)
+    def __init__(self, name="DelayedIntake"):
+        super(DelayedIntake, self).__init__(name=name)
 
-    self.A_undelayed = self.A
-    self.B_undelayed = self.B
+        self.A_undelayed = self.A
+        self.B_undelayed = self.B
 
-    self.C_unaugmented = self.C
-    self.C = numpy.matrix(numpy.zeros((2, 5)))
-    self.C[0:2, 0:4] = self.C_unaugmented
+        self.C_unaugmented = self.C
+        self.C = numpy.matrix(numpy.zeros((2, 5)))
+        self.C[0:2, 0:4] = self.C_unaugmented
 
-    # Model this as X[4] is the last power.  And then B applies to the last
-    # power.  This lets us model the 1 cycle PWM delay accurately.
-    self.A = numpy.matrix(numpy.zeros((5, 5)))
-    self.A[0:4, 0:4] = self.A_undelayed
-    self.A[0:4, 4] = self.B_undelayed
-    self.B = numpy.matrix(numpy.zeros((5, 1)))
-    self.B[4, 0] = 1.0
+        # Model this as X[4] is the last power.  And then B applies to the last
+        # power.  This lets us model the 1 cycle PWM delay accurately.
+        self.A = numpy.matrix(numpy.zeros((5, 5)))
+        self.A[0:4, 0:4] = self.A_undelayed
+        self.A[0:4, 4] = self.B_undelayed
+        self.B = numpy.matrix(numpy.zeros((5, 1)))
+        self.B[4, 0] = 1.0
 
-    # Coordinate transform fom absolute angles to relative angles.
-    # [output_position, output_velocity, spring_angle, spring_velocity, voltage]
-    abs_to_rel = numpy.matrix([[ 1.0,  0.0, 0.0, 0.0, 0.0],
-                               [ 0.0,  1.0, 0.0, 0.0, 0.0],
-                               [-1.0,  0.0, 1.0, 0.0, 0.0],
-                               [ 0.0, -1.0, 0.0, 1.0, 0.0],
-                               [ 0.0,  0.0, 0.0, 0.0, 1.0]])
-    # and back again.
-    rel_to_abs = numpy.matrix(numpy.linalg.inv(abs_to_rel))
+        # Coordinate transform fom absolute angles to relative angles.
+        # [output_position, output_velocity, spring_angle, spring_velocity, voltage]
+        abs_to_rel = numpy.matrix(
+            [[1.0, 0.0, 0.0, 0.0, 0.0],
+             [0.0, 1.0, 0.0, 0.0, 0.0],
+             [1.0, 0.0, -1.0, 0.0, 0.0],
+             [0.0, 1.0, 0.0, -1.0, 0.0],
+             [0.0, 0.0, 0.0, 0.0, 1.0]])
+        # and back again.
+        rel_to_abs = numpy.matrix(numpy.linalg.inv(abs_to_rel))
 
-    # Now, get A and B in the relative coordinate system.
-    self.A_transformed_full = abs_to_rel * self.A * rel_to_abs
-    self.B_transformed_full = abs_to_rel * self.B
+        # Now, get A and B in the relative coordinate system.
+        self.A_transformed_full = numpy.matrix(numpy.zeros((5, 5)))
+        self.B_transformed_full = numpy.matrix(numpy.zeros((5, 1)))
+        (self.A_transformed_full[0:4, 0:4],
+         self.A_transformed_full[0:4, 4]) = self.ContinuousToDiscrete(
+             abs_to_rel[0:4, 0:4] * self.A_continuous * rel_to_abs[0:4, 0:4],
+             abs_to_rel[0:4, 0:4] * self.B_continuous, self.dt)
+        self.B_transformed_full[4, 0] = 1.0
 
-    # Pull out the components of the dynamics which don't include the spring
-    # output positoin so we can do partial state feedback on what we care about.
-    self.A_transformed = self.A_transformed_full[1:5, 1:5]
-    self.B_transformed = self.B_transformed_full[1:5, 0]
+        # Pull out the components of the dynamics which don't include the spring
+        # output position so we can do partial state feedback on what we care about.
+        self.A_transformed = self.A_transformed_full[1:5, 1:5]
+        self.B_transformed = self.B_transformed_full[1:5, 0]
 
-    glog.debug('A_transformed_full ' + str(self.A_transformed_full))
-    glog.debug('B_transformed_full ' + str(self.B_transformed_full))
-    glog.debug('A_transformed ' + str(self.A_transformed))
-    glog.debug('B_transformed ' + str(self.B_transformed))
+        glog.debug('A_transformed_full ' + str(self.A_transformed_full))
+        glog.debug('B_transformed_full ' + str(self.B_transformed_full))
+        glog.debug('A_transformed ' + str(self.A_transformed))
+        glog.debug('B_transformed ' + str(self.B_transformed))
 
-    # Now, let's design a controller in
-    #   [output_velocity, spring_position, spring_velocity, delayed_voltage]
-    # space.
+        # Now, let's design a controller in
+        #   [output_velocity, spring_position, spring_velocity, delayed_voltage]
+        # space.
 
-    q_output_vel = 0.20
-    q_spring_pos = 0.05
-    q_spring_vel = 3.0
-    q_voltage = 100.0
-    self.Q_lqr = numpy.matrix(numpy.diag(
-        [1.0 / (q_output_vel ** 2.0),
-         1.0 / (q_spring_pos ** 2.0),
-         1.0 / (q_spring_vel ** 2.0),
-         1.0 / (q_voltage ** 2.0)]))
+        q_output_vel = 1.0
+        q_spring_pos = 0.5
+        q_spring_vel = 2.0
+        q_voltage = 1000000000000.0
+        self.Q_lqr = numpy.matrix(
+            numpy.diag([
+                1.0 / (q_output_vel**2.0), 1.0 / (q_spring_pos**2.0),
+                1.0 / (q_spring_vel**2.0), 1.0 / (q_voltage**2.0)
+            ]))
 
-    self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0))]])
+        self.R = numpy.matrix([[(1.0 / (12.0**2.0))]])
 
-    self.K_transformed = controls.dlqr(self.A_transformed, self.B_transformed,
-                                       self.Q_lqr, self.R)
+        self.K_transformed = controls.dlqr(
+            self.A_transformed, self.B_transformed, self.Q_lqr, self.R)
 
-    # Write the controller back out in the absolute coordinate system.
-    self.K = numpy.hstack((numpy.matrix([[0.0]]),
-                           self.K_transformed)) * abs_to_rel
+        # Write the controller back out in the absolute coordinate system.
+        self.K = numpy.hstack((numpy.matrix([[0.0]]),
+                               self.K_transformed)) * abs_to_rel
 
-    glog.debug('Poles are %s for %s',
-        repr(numpy.linalg.eig(
-            self.A_transformed -
-            self.B_transformed * self.K_transformed)[0]), self._name)
-    glog.debug('K is %s', repr(self.K_transformed))
+        controllability = controls.ctrb(self.A_transformed, self.B_transformed)
+        glog.debug('ctrb: ' + repr(numpy.linalg.matrix_rank(controllability)))
 
-    # Design a kalman filter here as well.
-    q_pos = 0.05
-    q_vel = 2.65
-    q_volts = 0.005
-    self.Q = numpy.matrix(numpy.diag([(q_pos ** 2.0), (q_vel ** 2.0),
-                                      (q_pos ** 2.0), (q_vel ** 2.0),
-                                      (q_volts ** 2.0)]))
+        w, v = numpy.linalg.eig(
+            self.A_transformed - self.B_transformed * self.K_transformed)
+        glog.debug('Poles are %s, for %s', repr(w), self._name)
 
-    r_nm = 0.025
-    self.R = numpy.matrix(numpy.diag([(r_nm ** 2.0), (r_nm ** 2.0)]))
+        for i in range(len(w)):
+            glog.debug('  Pole %s -> %s', repr(w[i]), v[:, i])
 
-    self.KalmanGain, self.Q_steady = controls.kalman(
-        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+        glog.debug('K is %s', repr(self.K_transformed))
 
-    # The box formed by U_min and U_max must encompass all possible values,
-    # or else Austin's code gets angry.
-    self.U_max = numpy.matrix([[12.0]])
-    self.U_min = numpy.matrix([[-12.0]])
+        # Design a kalman filter here as well.
+        q_pos = 0.05
+        q_vel = 2.65
+        q_volts = 0.005
+        self.Q = numpy.matrix(
+            numpy.diag([(q_pos**2.0), (q_vel**2.0), (q_pos**2.0), (q_vel**2.0),
+                        (q_volts**2.0)]))
 
-    self.InitializeState()
+        r_nm = 0.025
+        self.R = numpy.matrix(numpy.diag([(r_nm**2.0), (r_nm**2.0)]))
+
+        glog.debug('Overall poles are %s, for %s',
+                   repr(numpy.linalg.eig(self.A - self.B * self.K)[0]),
+                   self._name)
+
+        self.KalmanGain, self.Q_steady = controls.kalman(
+            A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+
+        self.InitializeState()
 
 
 class ScenarioPlotter(object):
-  def __init__(self):
-    # Various lists for graphing things.
-    self.t = []
-    self.x = []
-    self.v = []
-    self.goal_v = []
-    self.a = []
-    self.spring = []
-    self.x_hat = []
-    self.u = []
+    def __init__(self):
+        # Various lists for graphing things.
+        self.t = []
+        self.x_motor = []
+        self.x_output = []
+        self.v = []
+        self.goal_v = []
+        self.a = []
+        self.spring = []
+        self.x_hat = []
+        self.u = []
 
-  def run_test(self, intake, iterations=400, controller_intake=None,
-             observer_intake=None):
-    """Runs the intake plant with an initial condition and goal.
+    def run_test(self,
+                 intake,
+                 iterations=400,
+                 controller_intake=None,
+                 observer_intake=None):
+        """Runs the intake plant with an initial condition and goal.
 
       Test for whether the goal has been reached and whether the separation
       goes outside of the initial and goal values by more than
@@ -236,127 +251,137 @@
             should use the actual state.
     """
 
-    if controller_intake is None:
-      controller_intake = intake
+        if controller_intake is None:
+            controller_intake = intake
 
-    vbat = 12.0
+        vbat = 12.0
 
-    if self.t:
-      initial_t = self.t[-1] + intake.dt
-    else:
-      initial_t = 0
+        if self.t:
+            initial_t = self.t[-1] + intake.dt
+        else:
+            initial_t = 0
 
-    # Delay U by 1 cycle in our simulation to make it more realistic.
-    last_U = numpy.matrix([[0.0]])
+        # Delay U by 1 cycle in our simulation to make it more realistic.
+        last_U = numpy.matrix([[0.0]])
+        intake.Y = intake.C * intake.X
 
-    for i in xrange(iterations):
-      X_hat = intake.X
+        for i in xrange(iterations):
+            X_hat = intake.X
 
-      if observer_intake is not None:
-        X_hat = observer_intake.X_hat
-        self.x_hat.append(observer_intake.X_hat[0, 0])
+            if observer_intake is not None:
+                X_hat = observer_intake.X_hat
+                self.x_hat.append(observer_intake.X_hat[0, 0])
 
-      goal_angle = 3.0
-      goal_velocity = numpy.clip((goal_angle - X_hat[0, 0]) * 6.0, -10.0, 10.0)
+            goal_angle = 3.0
+            goal_velocity = numpy.clip((goal_angle - X_hat[0, 0]) * 6.0, -1.0,
+                                       1.0)
 
-      self.goal_v.append(goal_velocity)
+            self.goal_v.append(goal_velocity)
 
-      # Nominal: 1.8 N at 0.25 m -> 0.45 N m
-      # Nominal: 13 N at 0.25 m at 0.5 radians -> 3.25 N m -> 6 N m / radian
+            # Nominal: 1.8 N at 0.25 m -> 0.45 N m
+            # Nominal: 13 N at 0.25 m at 0.5 radians -> 3.25 N m -> 6 N m / radian
 
-      R = numpy.matrix([[0.0],
-                        [goal_velocity],
-                        [0.0],
-                        [goal_velocity],
-                        [goal_velocity / (intake.G * intake.Kv)]])
-      U = controller_intake.K * (R - X_hat) + R[4, 0]
+            R = numpy.matrix([[0.0], [goal_velocity], [0.0], [goal_velocity],
+                              [goal_velocity / (intake.G * intake.Kv)]])
+            U = controller_intake.K * (R - X_hat) + R[4, 0]
 
-      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+            U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat) # * 0.0
 
-      self.x.append(intake.X[0, 0])
-      self.spring.append((intake.X[2, 0] - intake.X[0, 0]) * intake.Ks)
+            self.x_output.append(intake.X[0, 0])
+            self.x_motor.append(intake.X[2, 0])
+            self.spring.append(intake.X[0, 0] - intake.X[2, 0])
 
-      if self.v:
-        last_v = self.v[-1]
-      else:
-        last_v = 0
+            if self.v:
+                last_v = self.v[-1]
+            else:
+                last_v = 0
 
-      self.v.append(intake.X[1, 0])
-      self.a.append((self.v[-1] - last_v) / intake.dt)
+            self.v.append(intake.X[1, 0])
+            self.a.append((self.v[-1] - last_v) / intake.dt)
 
-      if observer_intake is not None:
-        observer_intake.Y = intake.Y
-        observer_intake.CorrectObserver(U)
+            if observer_intake is not None:
+                observer_intake.Y = intake.Y
+                observer_intake.CorrectObserver(U)
 
-      intake.Update(last_U + 0.0)
+            intake.Update(last_U + 0.0)
 
-      if observer_intake is not None:
-        observer_intake.PredictObserver(U)
+            if observer_intake is not None:
+                observer_intake.PredictObserver(U)
 
-      self.t.append(initial_t + i * intake.dt)
-      self.u.append(U[0, 0])
-      last_U = U
+            self.t.append(initial_t + i * intake.dt)
+            self.u.append(U[0, 0])
+            last_U = U
 
-  def Plot(self):
-    pylab.subplot(3, 1, 1)
-    pylab.plot(self.t, self.x, label='x')
-    pylab.plot(self.t, self.x_hat, label='x_hat')
-    pylab.legend()
+    def Plot(self):
+        pylab.subplot(3, 1, 1)
+        pylab.plot(self.t, self.x_output, label='x output')
+        pylab.plot(self.t, self.x_motor, label='x motor')
+        pylab.plot(self.t, self.x_hat, label='x_hat')
+        pylab.legend()
 
-    spring_ax1 = pylab.subplot(3, 1, 2)
-    spring_ax1.plot(self.t, self.u, 'k', label='u')
-    spring_ax2 = spring_ax1.twinx()
-    spring_ax2.plot(self.t, self.spring, label='spring_angle')
-    spring_ax1.legend(loc=2)
-    spring_ax2.legend()
+        spring_ax1 = pylab.subplot(3, 1, 2)
+        spring_ax1.plot(self.t, self.u, 'k', label='u')
+        spring_ax2 = spring_ax1.twinx()
+        spring_ax2.plot(self.t, self.spring, label='spring_angle')
+        spring_ax1.legend(loc=2)
+        spring_ax2.legend()
 
-    accel_ax1 = pylab.subplot(3, 1, 3)
-    accel_ax1.plot(self.t, self.a, 'r', label='a')
+        accel_ax1 = pylab.subplot(3, 1, 3)
+        accel_ax1.plot(self.t, self.a, 'r', label='a')
 
-    accel_ax2 = accel_ax1.twinx()
-    accel_ax2.plot(self.t, self.v, label='v')
-    accel_ax2.plot(self.t, self.goal_v, label='goal_v')
-    accel_ax1.legend(loc=2)
-    accel_ax2.legend()
+        accel_ax2 = accel_ax1.twinx()
+        accel_ax2.plot(self.t, self.v, label='v')
+        accel_ax2.plot(self.t, self.goal_v, label='goal_v')
+        accel_ax1.legend(loc=2)
+        accel_ax2.legend()
 
-    pylab.show()
+        pylab.show()
 
 
 def main(argv):
-  scenario_plotter = ScenarioPlotter()
+    scenario_plotter = ScenarioPlotter()
 
-  intake = Intake()
-  intake_controller = DelayedIntake()
-  observer_intake = DelayedIntake()
+    intake = Intake()
+    intake.X[0, 0] = 0.0
+    intake_controller = DelayedIntake()
+    observer_intake = DelayedIntake()
+    observer_intake.X_hat[0, 0] = intake.X[0, 0]
 
-  # Test moving the intake with constant separation.
-  scenario_plotter.run_test(intake, controller_intake=intake_controller,
-                            observer_intake=observer_intake, iterations=200)
+    # Test moving the intake with constant separation.
+    scenario_plotter.run_test(
+        intake,
+        controller_intake=intake_controller,
+        observer_intake=observer_intake,
+        iterations=200)
 
-  if FLAGS.plot:
-    scenario_plotter.Plot()
+    if FLAGS.plot:
+        scenario_plotter.Plot()
 
-  # Write the generated constants out to a file.
-  if len(argv) != 5:
-    glog.fatal('Expected .h file name and .cc file name for intake and delayed_intake.')
-  else:
-    namespaces = ['y2018', 'control_loops', 'superstructure', 'intake']
-    intake = Intake('Intake')
-    loop_writer = control_loop.ControlLoopWriter(
-        'Intake', [intake], namespaces=namespaces)
-    loop_writer.AddConstant(control_loop.Constant('kGearRatio', '%f', intake.G))
-    loop_writer.AddConstant(
-        control_loop.Constant('kMotorVelocityConstant', '%f', intake.Kv))
-    loop_writer.AddConstant(
-        control_loop.Constant('kFreeSpeed', '%f', intake.free_speed))
-    loop_writer.Write(argv[1], argv[2])
+    # Write the generated constants out to a file.
+    if len(argv) != 5:
+        glog.fatal(
+            'Expected .h file name and .cc file name for intake and delayed_intake.'
+        )
+    else:
+        namespaces = ['y2018', 'control_loops', 'superstructure', 'intake']
+        intake = Intake('Intake')
+        loop_writer = control_loop.ControlLoopWriter(
+            'Intake', [intake], namespaces=namespaces)
+        loop_writer.AddConstant(
+            control_loop.Constant('kGearRatio', '%f', intake.G))
+        loop_writer.AddConstant(
+            control_loop.Constant('kMotorVelocityConstant', '%f', intake.Kv))
+        loop_writer.AddConstant(
+            control_loop.Constant('kFreeSpeed', '%f', intake.free_speed))
+        loop_writer.Write(argv[1], argv[2])
 
-    delayed_intake = DelayedIntake('DelayedIntake')
-    loop_writer = control_loop.ControlLoopWriter(
-    'DelayedIntake', [delayed_intake], namespaces=namespaces)
-    loop_writer.Write(argv[3], argv[4])
+        delayed_intake = DelayedIntake('DelayedIntake')
+        loop_writer = control_loop.ControlLoopWriter(
+            'DelayedIntake', [delayed_intake], namespaces=namespaces)
+        loop_writer.Write(argv[3], argv[4])
+
 
 if __name__ == '__main__':
-  argv = FLAGS(sys.argv)
-  glog.init()
-  sys.exit(main(argv))
+    argv = FLAGS(sys.argv)
+    glog.init()
+    sys.exit(main(argv))