Run yapf on all python files in the repo
Signed-off-by: Ravago Jones <ravagojones@gmail.com>
Change-Id: I221e04c3f517fab8535b22551553799e0fee7a80
diff --git a/y2017/control_loops/python/indexer.py b/y2017/control_loops/python/indexer.py
index 7312e57..d4c3fe6 100755
--- a/y2017/control_loops/python/indexer.py
+++ b/y2017/control_loops/python/indexer.py
@@ -12,188 +12,203 @@
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
gflags.DEFINE_bool('stall', False, 'If true, stall the indexer.')
+
class VelocityIndexer(control_loop.ControlLoop):
- def __init__(self, name='VelocityIndexer'):
- super(VelocityIndexer, self).__init__(name)
- # Stall Torque in N m
- self.stall_torque = 0.71
- # Stall Current in Amps
- self.stall_current = 134
- self.free_speed_rpm = 18730.0
- # Free Speed in rotations/second.
- self.free_speed = self.free_speed_rpm / 60.0
- # Free Current in Amps
- self.free_current = 0.7
- # Moment of inertia of the indexer halves in kg m^2
- # This is measured as Iyy in CAD (the moment of inertia around the Y axis).
- # Inner part of indexer -> Iyy = 59500 lb * mm * mm
- # Inner spins with 12 / 48 * 18 / 48 * 24 / 36 * 16 / 72
- # Outer part of indexer -> Iyy = 210000 lb * mm * mm
- # 1 775 pro -> 12 / 48 * 18 / 48 * 30 / 422
- self.J_inner = 0.0269
- self.J_outer = 0.0952
- # Gear ratios for the inner and outer parts.
- self.G_inner = (12.0 / 48.0) * (20.0 / 34.0) * (18.0 / 36.0) * (12.0 / 84.0)
- self.G_outer = (12.0 / 48.0) * (20.0 / 34.0) * (18.0 / 36.0) * (24.0 / 420.0)
+ def __init__(self, name='VelocityIndexer'):
+ super(VelocityIndexer, self).__init__(name)
+ # Stall Torque in N m
+ self.stall_torque = 0.71
+ # Stall Current in Amps
+ self.stall_current = 134
+ self.free_speed_rpm = 18730.0
+ # Free Speed in rotations/second.
+ self.free_speed = self.free_speed_rpm / 60.0
+ # Free Current in Amps
+ self.free_current = 0.7
+ # Moment of inertia of the indexer halves in kg m^2
+ # This is measured as Iyy in CAD (the moment of inertia around the Y axis).
+ # Inner part of indexer -> Iyy = 59500 lb * mm * mm
+ # Inner spins with 12 / 48 * 18 / 48 * 24 / 36 * 16 / 72
+ # Outer part of indexer -> Iyy = 210000 lb * mm * mm
+ # 1 775 pro -> 12 / 48 * 18 / 48 * 30 / 422
- # Motor inertia in kg m^2
- self.motor_inertia = 0.00001187
+ self.J_inner = 0.0269
+ self.J_outer = 0.0952
+ # Gear ratios for the inner and outer parts.
+ self.G_inner = (12.0 / 48.0) * (20.0 / 34.0) * (18.0 / 36.0) * (12.0 /
+ 84.0)
+ self.G_outer = (12.0 / 48.0) * (20.0 / 34.0) * (18.0 / 36.0) * (24.0 /
+ 420.0)
- # The output coordinate system is in radians for the inner part of the
- # indexer.
- # Compute the effective moment of inertia assuming all the mass is in that
- # coordinate system.
- self.J = (
- self.J_inner * self.G_inner * self.G_inner +
- self.J_outer * self.G_outer * self.G_outer) / (self.G_inner * self.G_inner) + \
- self.motor_inertia * ((1.0 / self.G_inner) ** 2.0)
- glog.debug('Indexer J is %f', self.J)
- self.G = self.G_inner
+ # Motor inertia in kg m^2
+ self.motor_inertia = 0.00001187
- # Resistance of the motor, divided by 2 to account for the 2 motors
- self.resistance = 12.0 / self.stall_current
- # Motor velocity constant
- self.Kv = ((self.free_speed * 2.0 * numpy.pi) /
- (12.0 - self.resistance * self.free_current))
- # Torque constant
- self.Kt = self.stall_torque / self.stall_current
- # Control loop time step
- self.dt = 0.005
+ # The output coordinate system is in radians for the inner part of the
+ # indexer.
+ # Compute the effective moment of inertia assuming all the mass is in that
+ # coordinate system.
+ self.J = (
+ self.J_inner * self.G_inner * self.G_inner +
+ self.J_outer * self.G_outer * self.G_outer) / (self.G_inner * self.G_inner) + \
+ self.motor_inertia * ((1.0 / self.G_inner) ** 2.0)
+ glog.debug('Indexer J is %f', self.J)
+ self.G = self.G_inner
- # State feedback matrices
- # [angular velocity]
- self.A_continuous = numpy.matrix(
- [[-self.Kt / self.Kv / (self.J * self.G * self.G * self.resistance)]])
- self.B_continuous = numpy.matrix(
- [[self.Kt / (self.J * self.G * self.resistance)]])
- self.C = numpy.matrix([[1]])
- self.D = numpy.matrix([[0]])
+ # Resistance of the motor, divided by 2 to account for the 2 motors
+ self.resistance = 12.0 / self.stall_current
+ # Motor velocity constant
+ self.Kv = ((self.free_speed * 2.0 * numpy.pi) /
+ (12.0 - self.resistance * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Control loop time step
+ self.dt = 0.005
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ # State feedback matrices
+ # [angular velocity]
+ self.A_continuous = numpy.matrix([[
+ -self.Kt / self.Kv / (self.J * self.G * self.G * self.resistance)
+ ]])
+ self.B_continuous = numpy.matrix(
+ [[self.Kt / (self.J * self.G * self.resistance)]])
+ self.C = numpy.matrix([[1]])
+ self.D = numpy.matrix([[0]])
- self.PlaceControllerPoles([.75])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.PlaceObserverPoles([0.3])
+ self.PlaceControllerPoles([.75])
- self.U_max = numpy.matrix([[12.0]])
- self.U_min = numpy.matrix([[-12.0]])
+ self.PlaceObserverPoles([0.3])
- qff_vel = 8.0
- self.Qff = numpy.matrix([[1.0 / (qff_vel ** 2.0)]])
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
- self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
- self.InitializeState()
+ qff_vel = 8.0
+ self.Qff = numpy.matrix([[1.0 / (qff_vel**2.0)]])
+
+ self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+ self.InitializeState()
class Indexer(VelocityIndexer):
- def __init__(self, name='Indexer'):
- super(Indexer, self).__init__(name)
- self.A_continuous_unaugmented = self.A_continuous
- self.B_continuous_unaugmented = self.B_continuous
+ def __init__(self, name='Indexer'):
+ super(Indexer, self).__init__(name)
- self.A_continuous = numpy.matrix(numpy.zeros((2, 2)))
- self.A_continuous[1:2, 1:2] = self.A_continuous_unaugmented
- self.A_continuous[0, 1] = 1
+ self.A_continuous_unaugmented = self.A_continuous
+ self.B_continuous_unaugmented = self.B_continuous
- self.B_continuous = numpy.matrix(numpy.zeros((2, 1)))
- self.B_continuous[1:2, 0] = self.B_continuous_unaugmented
+ self.A_continuous = numpy.matrix(numpy.zeros((2, 2)))
+ self.A_continuous[1:2, 1:2] = self.A_continuous_unaugmented
+ self.A_continuous[0, 1] = 1
- # State feedback matrices
- # [position, angular velocity]
- self.C = numpy.matrix([[1, 0]])
- self.D = numpy.matrix([[0]])
+ self.B_continuous = numpy.matrix(numpy.zeros((2, 1)))
+ self.B_continuous[1:2, 0] = self.B_continuous_unaugmented
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ # State feedback matrices
+ # [position, angular velocity]
+ self.C = numpy.matrix([[1, 0]])
+ self.D = numpy.matrix([[0]])
- self.rpl = .45
- self.ipl = 0.07
- self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
- self.rpl - 1j * self.ipl])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.K_unaugmented = self.K
- self.K = numpy.matrix(numpy.zeros((1, 2)))
- self.K[0, 1:2] = self.K_unaugmented
- self.Kff_unaugmented = self.Kff
- self.Kff = numpy.matrix(numpy.zeros((1, 2)))
- self.Kff[0, 1:2] = self.Kff_unaugmented
+ self.rpl = .45
+ self.ipl = 0.07
+ self.PlaceObserverPoles(
+ [self.rpl + 1j * self.ipl, self.rpl - 1j * self.ipl])
- self.InitializeState()
+ self.K_unaugmented = self.K
+ self.K = numpy.matrix(numpy.zeros((1, 2)))
+ self.K[0, 1:2] = self.K_unaugmented
+ self.Kff_unaugmented = self.Kff
+ self.Kff = numpy.matrix(numpy.zeros((1, 2)))
+ self.Kff[0, 1:2] = self.Kff_unaugmented
+
+ self.InitializeState()
class IntegralIndexer(Indexer):
- def __init__(self, name="IntegralIndexer", voltage_error_noise=None):
- super(IntegralIndexer, self).__init__(name=name)
- self.A_continuous_unaugmented = self.A_continuous
- self.B_continuous_unaugmented = self.B_continuous
+ def __init__(self, name="IntegralIndexer", voltage_error_noise=None):
+ super(IntegralIndexer, self).__init__(name=name)
- self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
- self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
- self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+ self.A_continuous_unaugmented = self.A_continuous
+ self.B_continuous_unaugmented = self.B_continuous
- self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
- self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+ self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+ self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+ self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
- self.C_unaugmented = self.C
- self.C = numpy.matrix(numpy.zeros((1, 3)))
- self.C[0:1, 0:2] = self.C_unaugmented
+ self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+ self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.C_unaugmented = self.C
+ self.C = numpy.matrix(numpy.zeros((1, 3)))
+ self.C[0:1, 0:2] = self.C_unaugmented
- q_pos = 0.01
- q_vel = 2.0
- q_voltage = 0.6
- if voltage_error_noise is not None:
- q_voltage = voltage_error_noise
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
- [0.0, (q_vel ** 2.0), 0.0],
- [0.0, 0.0, (q_voltage ** 2.0)]])
+ q_pos = 0.01
+ q_vel = 2.0
+ q_voltage = 0.6
+ if voltage_error_noise is not None:
+ q_voltage = voltage_error_noise
- r_pos = 0.001
- self.R = numpy.matrix([[(r_pos ** 2.0)]])
+ self.Q = numpy.matrix([[(q_pos**2.0), 0.0, 0.0],
+ [0.0, (q_vel**2.0), 0.0],
+ [0.0, 0.0, (q_voltage**2.0)]])
- self.KalmanGain, self.Q_steady = controls.kalman(
- A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
- self.L = self.A * self.KalmanGain
+ r_pos = 0.001
+ self.R = numpy.matrix([[(r_pos**2.0)]])
- self.K_unaugmented = self.K
- self.K = numpy.matrix(numpy.zeros((1, 3)))
- self.K[0, 0:2] = self.K_unaugmented
- self.K[0, 2] = 1
- self.Kff_unaugmented = self.Kff
- self.Kff = numpy.matrix(numpy.zeros((1, 3)))
- self.Kff[0, 0:2] = self.Kff_unaugmented
+ self.KalmanGain, self.Q_steady = controls.kalman(A=self.A,
+ B=self.B,
+ C=self.C,
+ Q=self.Q,
+ R=self.R)
+ self.L = self.A * self.KalmanGain
- self.InitializeState()
+ self.K_unaugmented = self.K
+ self.K = numpy.matrix(numpy.zeros((1, 3)))
+ self.K[0, 0:2] = self.K_unaugmented
+ self.K[0, 2] = 1
+ self.Kff_unaugmented = self.Kff
+ self.Kff = numpy.matrix(numpy.zeros((1, 3)))
+ self.Kff[0, 0:2] = self.Kff_unaugmented
+
+ self.InitializeState()
class ScenarioPlotter(object):
- def __init__(self):
- # Various lists for graphing things.
- self.t = []
- self.x = []
- self.v = []
- self.a = []
- self.stall_ratio = []
- self.x_hat = []
- self.u = []
- self.offset = []
- def run_test(self, indexer, goal, iterations=200, controller_indexer=None,
- observer_indexer=None):
- """Runs the indexer plant with an initial condition and goal.
+ def __init__(self):
+ # Various lists for graphing things.
+ self.t = []
+ self.x = []
+ self.v = []
+ self.a = []
+ self.stall_ratio = []
+ self.x_hat = []
+ self.u = []
+ self.offset = []
+
+ def run_test(self,
+ indexer,
+ goal,
+ iterations=200,
+ controller_indexer=None,
+ observer_indexer=None):
+ """Runs the indexer plant with an initial condition and goal.
Args:
indexer: Indexer object to use.
@@ -205,141 +220,148 @@
should use the actual state.
"""
- if controller_indexer is None:
- controller_indexer = indexer
+ if controller_indexer is None:
+ controller_indexer = indexer
- vbat = 12.0
+ vbat = 12.0
- if self.t:
- initial_t = self.t[-1] + indexer.dt
- else:
- initial_t = 0
-
- for i in range(iterations):
- X_hat = indexer.X
-
- if observer_indexer is not None:
- X_hat = observer_indexer.X_hat
- observer_indexer.Y = indexer.Y
- observer_indexer.CorrectObserver(numpy.matrix([[0.0]]))
- self.x_hat.append(observer_indexer.X_hat[1, 0])
- self.offset.append(observer_indexer.X_hat[2, 0])
-
- ff_U = controller_indexer.Kff * (goal - observer_indexer.A * goal)
-
- U = controller_indexer.K * (goal - X_hat) + ff_U
- U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
- self.x.append(indexer.X[0, 0])
-
- if self.v:
- last_v = self.v[-1]
- else:
- last_v = 0
-
- self.v.append(indexer.X[1, 0])
- self.a.append((self.v[-1] - last_v) / indexer.dt)
-
- applied_U = U.copy()
- if i >= 40:
- applied_U -= 2
-
- if FLAGS.stall and i >= 40:
- indexer.X[1, 0] = 0.0
- else:
- indexer.Update(applied_U)
-
- if observer_indexer is not None:
- clipped_u = U[0, 0]
- clip_u_value = 3.0
- if clipped_u < 0:
- clipped_u = min(clipped_u, -clip_u_value)
+ if self.t:
+ initial_t = self.t[-1] + indexer.dt
else:
- clipped_u = max(clipped_u, clip_u_value)
+ initial_t = 0
- self.stall_ratio.append(10 * (-self.offset[-1] / clipped_u))
+ for i in range(iterations):
+ X_hat = indexer.X
- observer_indexer.PredictObserver(U)
+ if observer_indexer is not None:
+ X_hat = observer_indexer.X_hat
+ observer_indexer.Y = indexer.Y
+ observer_indexer.CorrectObserver(numpy.matrix([[0.0]]))
+ self.x_hat.append(observer_indexer.X_hat[1, 0])
+ self.offset.append(observer_indexer.X_hat[2, 0])
- self.t.append(initial_t + i * indexer.dt)
- self.u.append(U[0, 0])
+ ff_U = controller_indexer.Kff * (goal - observer_indexer.A * goal)
- def Plot(self):
- pylab.subplot(3, 1, 1)
- pylab.plot(self.t, self.v, label='x')
- pylab.plot(self.t, self.x_hat, label='x_hat')
- pylab.legend()
+ U = controller_indexer.K * (goal - X_hat) + ff_U
+ U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+ self.x.append(indexer.X[0, 0])
- pylab.subplot(3, 1, 2)
- pylab.plot(self.t, self.u, label='u')
- pylab.plot(self.t, self.offset, label='voltage_offset')
- pylab.plot(self.t, self.stall_ratio, label='stall_ratio')
- pylab.plot(self.t,
- [10.0 if x > 6.0 else 0.0 for x in self.stall_ratio],
- label='is_stalled')
- pylab.legend()
+ if self.v:
+ last_v = self.v[-1]
+ else:
+ last_v = 0
- pylab.subplot(3, 1, 3)
- pylab.plot(self.t, self.a, label='a')
- pylab.legend()
+ self.v.append(indexer.X[1, 0])
+ self.a.append((self.v[-1] - last_v) / indexer.dt)
- pylab.show()
+ applied_U = U.copy()
+ if i >= 40:
+ applied_U -= 2
+
+ if FLAGS.stall and i >= 40:
+ indexer.X[1, 0] = 0.0
+ else:
+ indexer.Update(applied_U)
+
+ if observer_indexer is not None:
+ clipped_u = U[0, 0]
+ clip_u_value = 3.0
+ if clipped_u < 0:
+ clipped_u = min(clipped_u, -clip_u_value)
+ else:
+ clipped_u = max(clipped_u, clip_u_value)
+
+ self.stall_ratio.append(10 * (-self.offset[-1] / clipped_u))
+
+ observer_indexer.PredictObserver(U)
+
+ self.t.append(initial_t + i * indexer.dt)
+ self.u.append(U[0, 0])
+
+ def Plot(self):
+ pylab.subplot(3, 1, 1)
+ pylab.plot(self.t, self.v, label='x')
+ pylab.plot(self.t, self.x_hat, label='x_hat')
+ pylab.legend()
+
+ pylab.subplot(3, 1, 2)
+ pylab.plot(self.t, self.u, label='u')
+ pylab.plot(self.t, self.offset, label='voltage_offset')
+ pylab.plot(self.t, self.stall_ratio, label='stall_ratio')
+ pylab.plot(self.t,
+ [10.0 if x > 6.0 else 0.0 for x in self.stall_ratio],
+ label='is_stalled')
+ pylab.legend()
+
+ pylab.subplot(3, 1, 3)
+ pylab.plot(self.t, self.a, label='a')
+ pylab.legend()
+
+ pylab.show()
def main(argv):
- scenario_plotter = ScenarioPlotter()
+ scenario_plotter = ScenarioPlotter()
- indexer = Indexer()
- indexer_controller = IntegralIndexer()
- observer_indexer = IntegralIndexer()
+ indexer = Indexer()
+ indexer_controller = IntegralIndexer()
+ observer_indexer = IntegralIndexer()
- initial_X = numpy.matrix([[0.0], [0.0]])
- R = numpy.matrix([[0.0], [20.0], [0.0]])
- scenario_plotter.run_test(indexer, goal=R, controller_indexer=indexer_controller,
- observer_indexer=observer_indexer, iterations=200)
+ initial_X = numpy.matrix([[0.0], [0.0]])
+ R = numpy.matrix([[0.0], [20.0], [0.0]])
+ scenario_plotter.run_test(indexer,
+ goal=R,
+ controller_indexer=indexer_controller,
+ observer_indexer=observer_indexer,
+ iterations=200)
- if FLAGS.plot:
- scenario_plotter.Plot()
+ if FLAGS.plot:
+ scenario_plotter.Plot()
- scenario_plotter = ScenarioPlotter()
+ scenario_plotter = ScenarioPlotter()
- indexer = Indexer()
- indexer_controller = IntegralIndexer(voltage_error_noise=1.5)
- observer_indexer = IntegralIndexer(voltage_error_noise=1.5)
+ indexer = Indexer()
+ indexer_controller = IntegralIndexer(voltage_error_noise=1.5)
+ observer_indexer = IntegralIndexer(voltage_error_noise=1.5)
- initial_X = numpy.matrix([[0.0], [0.0]])
- R = numpy.matrix([[0.0], [20.0], [0.0]])
- scenario_plotter.run_test(indexer, goal=R, controller_indexer=indexer_controller,
- observer_indexer=observer_indexer, iterations=200)
+ initial_X = numpy.matrix([[0.0], [0.0]])
+ R = numpy.matrix([[0.0], [20.0], [0.0]])
+ scenario_plotter.run_test(indexer,
+ goal=R,
+ controller_indexer=indexer_controller,
+ observer_indexer=observer_indexer,
+ iterations=200)
- if FLAGS.plot:
- scenario_plotter.Plot()
+ if FLAGS.plot:
+ scenario_plotter.Plot()
- if len(argv) != 7:
- glog.fatal('Expected .h file name and .cc file names')
- else:
- namespaces = ['y2017', 'control_loops', 'superstructure', 'indexer']
- indexer = Indexer('Indexer')
- loop_writer = control_loop.ControlLoopWriter('Indexer', [indexer],
- namespaces=namespaces)
- loop_writer.AddConstant(control_loop.Constant(
- 'kFreeSpeed', '%f', indexer.free_speed))
- loop_writer.AddConstant(control_loop.Constant(
- 'kOutputRatio', '%f', indexer.G))
- loop_writer.Write(argv[1], argv[2])
+ if len(argv) != 7:
+ glog.fatal('Expected .h file name and .cc file names')
+ else:
+ namespaces = ['y2017', 'control_loops', 'superstructure', 'indexer']
+ indexer = Indexer('Indexer')
+ loop_writer = control_loop.ControlLoopWriter('Indexer', [indexer],
+ namespaces=namespaces)
+ loop_writer.AddConstant(
+ control_loop.Constant('kFreeSpeed', '%f', indexer.free_speed))
+ loop_writer.AddConstant(
+ control_loop.Constant('kOutputRatio', '%f', indexer.G))
+ loop_writer.Write(argv[1], argv[2])
- integral_indexer = IntegralIndexer('IntegralIndexer')
- integral_loop_writer = control_loop.ControlLoopWriter(
- 'IntegralIndexer', [integral_indexer], namespaces=namespaces)
- integral_loop_writer.Write(argv[3], argv[4])
+ integral_indexer = IntegralIndexer('IntegralIndexer')
+ integral_loop_writer = control_loop.ControlLoopWriter(
+ 'IntegralIndexer', [integral_indexer], namespaces=namespaces)
+ integral_loop_writer.Write(argv[3], argv[4])
- stuck_integral_indexer = IntegralIndexer('StuckIntegralIndexer',
- voltage_error_noise=1.5)
- stuck_integral_loop_writer = control_loop.ControlLoopWriter(
- 'StuckIntegralIndexer', [stuck_integral_indexer], namespaces=namespaces)
- stuck_integral_loop_writer.Write(argv[5], argv[6])
+ stuck_integral_indexer = IntegralIndexer('StuckIntegralIndexer',
+ voltage_error_noise=1.5)
+ stuck_integral_loop_writer = control_loop.ControlLoopWriter(
+ 'StuckIntegralIndexer', [stuck_integral_indexer],
+ namespaces=namespaces)
+ stuck_integral_loop_writer.Write(argv[5], argv[6])
if __name__ == '__main__':
- argv = FLAGS(sys.argv)
- glog.init()
- sys.exit(main(argv))
+ argv = FLAGS(sys.argv)
+ glog.init()
+ sys.exit(main(argv))