def compute_dt_matrices(A, B, h, TV=False):
# variable declaration
t0 = 0.0 # start time
T = 1 # end time
n, m = B.shape
# Matrix declaration
x0 = np.random.random(n)
Csurface = np.random.random((m, n))
# Declaration of the Dynamical System
if TV:
process_ds = SK.FirstOrderLinearDS(x0, A)
else:
process_ds = SK.FirstOrderLinearTIDS(x0, A)
# Model
process = SK.Model(t0, T)
process.nonSmoothDynamicalSystem().insertDynamicalSystem(process_ds)
# time discretisation
process_time_discretisation = SK.TimeDiscretisation(t0, h)
# Creation of the Simulation
process_simu = SK.TimeStepping(process_time_discretisation, 0)
process_simu.setName("plant simulation")
# Declaration of the integrator
process_integrator = SK.ZeroOrderHoldOSI()
process_integrator.insertDynamicalSystem(process_ds)
process_simu.insertIntegrator(process_integrator)
rel = SK.FirstOrderLinearTIR(Csurface, B)
nslaw = SK.RelayNSL(m)
inter = SK.Interaction(m, nslaw, rel)
#process.nonSmoothDynamicalSystem().insertInteraction(inter, True)
process.nonSmoothDynamicalSystem().link(inter, process_ds)
process.nonSmoothDynamicalSystem().setControlProperty(inter, True)
# Initialization
process.initialize(process_simu)
# Main loop
process_simu.computeOneStep()
Ad = SK.getMatrix(process_integrator.Ad(process_ds)).copy()
Bd = SK.getMatrix(process_integrator.Bd(process_ds)).copy()
return (Ad, Bd)
def __init__(self, strings, frets, time_range, fs):
"""
"""
super(Guitar, self).__init__(time_range[0], time_range[1])
if not isinstance(strings, list):
strings = [strings]
if not isinstance(frets, list):
frets = [frets]
self.modal_form = strings[0].modal_form
self.frets = frets
self.strings = strings
for string in strings:
self.nonSmoothDynamicalSystem().insertDynamicalSystem(string)
# link the interaction(s) and the dynamical system(s)
for fret in frets:
self.nonSmoothDynamicalSystem().link(fret, string)
# -- Simulation --
# (1) OneStepIntegrators
theta = 0.5001
osi = sk.MoreauJeanOSI(theta)
t0 = time_range[0]
tend = time_range[1]
# (2) Time discretisation --
self.fs = fs # 1960
self.time_step = 1. / fs
t = sk.TimeDiscretisation(t0, self.time_step)
self.nb_time_steps = (int)((tend - t0) / self.time_step)
# (3) one step non smooth problem
osnspb = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
self.simu = sk.TimeStepping(t, osi, osnspb)
# simulation initialization
self.setSimulation(self.simu)
self.initialize()
ndof = strings[0].ndof
self.fret_position = frets[0].contact_index
self.data = npw.zeros((self.nb_time_steps + 1, 3 + ndof))
x0 = [10., 10.] C = [[1., 0]] # we have to specify ndmin=2, so it's understood as K = [.25, .125, 2] # Declaration of the Dynamical System doubleIntegrator = sk.FirstOrderLinearTIDS(x0, A) # Model process = sk.Model(t0, T) process.nonSmoothDynamicalSystem().insertDynamicalSystem(doubleIntegrator) # Declaration of the integrator OSI = sk.EulerMoreauOSI(theta) # time discretisation t = sk.TimeDiscretisation(t0, h) tSensor = sk.TimeDiscretisation(t0, h) tActuator = sk.TimeDiscretisation(t0, h) s = sk.TimeStepping(t, 0) s.insertIntegrator(OSI) # Actuator, Sensor & ControlManager control = ControlManager(s) sens = LinearSensor(doubleIntegrator, C) control.addSensorPtr(sens, tSensor) act = PID(sens) act.setB(B) control.addActuatorPtr(act, tActuator) # Initialization process.setSimulation(s) process.initialize() control.initialize(process) act.setRef(xFinal)
blockModel.nonSmoothDynamicalSystem().link(inter[i], block) # ======================================= # The Simulation # ======================================= # (1) OneStepIntegrators OSI = kernel.Moreau(theta) # (2) Time discretisation -- t = kernel.TimeDiscretisation(t0, h) osnspb = kernel.LCP() # (4) Simulation setup with (1) (2) (3) s = kernel.TimeStepping(t) s.insertIntegrator(OSI) s.insertNonSmoothProblem(osnspb) blockModel.setSimulation(s) # simulation initialization blockModel.initialize() # the number of time steps N = (T - t0) / h # Get the values to be plotted # ->saved in a matrix dataPlot dataPlot = np.empty((N + 1, 9))
# Simulation # h = 0.005 # time step theta = 0.5 # theta scheme # (1) OneStepIntegrators OSI = sk.MoreauJeanOSI(theta) # (2) Time discretisation -- t = sk.TimeDiscretisation(t0, h) # (3) one step non smooth problem osnspb = sk.LCP() # (4) Simulation setup with (1) (2) (3) s = sk.TimeStepping(bouncingBall, t, OSI, osnspb) s.setNewtonTolerance(1e-10) s.setNewtonMaxIteration(10) # end of model definition # # computation # # the number of time steps N = int((T - t0) // h) + 1 # Get the values to be plotted # ->saved in a matrix dataPlot dataPlot = np.empty((N + 1, 16))
myNslaw = sk.RelayNSL(2)
myProcessRelation = sk.FirstOrderLinearR(C, B)
myProcessRelation.setComputeEFunction('plugins', 'computeE')
# myProcessRelation.setDPtr(D)
myProcessInteraction = sk.Interaction(myNslaw, myProcessRelation)
# NSDS
simplerelay = sk.NonSmoothDynamicalSystem(t0, T)
simplerelay.insertDynamicalSystem(process)
simplerelay.link(myProcessInteraction, process)
# myProcessRelation.computeJachx(0, x0, x0 , x0, C)
# Simulation
s = sk.TimeStepping(simplerelay, sk.TimeDiscretisation(t0, h),
sk.EulerMoreauOSI(theta), sk.Relay())
# s.setComputeResiduY(True)
# s.setComputeResiduR(True)
# matrix to save data
dataPlot = np.empty((N + 1, 7))
k = 0
dataPlot[k, 0] = t0
dataPlot[k, 1:3] = process.x()
dataPlot[k, 3] = myProcessInteraction.lambda_(0)[0]
dataPlot[k, 4] = myProcessInteraction.lambda_(0)[1]
dataPlot[k, 5] = myProcessInteraction.y(0)[0]
dataPlot[k, 6] = myProcessInteraction.y(0)[1]
# time loop
k = 1
print('start computation')
C[0, 0] = C[1, 1] = -1. particle_relation = sk.FirstOrderLinearR(C, B) nslaw = sk.RelayNSL(ninter) particle_interaction = sk.Interaction(nslaw, particle_relation) # -- The Model -- filippov = sk.NonSmoothDynamicalSystem(t0, T) filippov.insertDynamicalSystem(particle) filippov.link(particle_interaction, particle) # -- Simulation -- td = sk.TimeDiscretisation(t0, h) simu = sk.TimeStepping(filippov, td) # osi theta = 0.5 myIntegrator = sk.EulerMoreauOSI(theta) simu.insertIntegrator(myIntegrator) # osns osnspb = sk.Relay(sn.SICONOS_RELAY_LEMKE) simu.insertNonSmoothProblem(osnspb) # -- Get the values to be plotted -- output_size = 1 + ndof + 2 * ninter nb_time_steps = int((T - t0) / h) + 1 data_plot = np.empty((nb_time_steps, output_size)) data_plot[0, 0] = filippov.t0() data_plot[0, 1:4] = particle.x()
def test_bouncing_ball2():
"""Run a complete simulation (Bouncing ball example)
LagrangianLinearTIDS, plugged Fext.
"""
t0 = 0 # start time
T = 5 # end time
h = 0.005 # time step
r = 0.1 # ball radius
g = 9.81 # gravity
m = 1 # ball mass
e = 0.9 # restitution coeficient
theta = 0.5 # theta scheme
#
# dynamical system
#
x = np.zeros(3, dtype=np.float64)
x[0] = 1.
v = np.zeros_like(x)
# mass matrix
mass = np.eye(3, dtype=np.float64)
mass[2, 2] = 3. / 5 * r * r
# the dynamical system
ball = sk.LagrangianLinearTIDS(x, v, mass)
weight = np.zeros(ball.dimension())
weight[0] = -m * g
ball.setFExtPtr(weight)
# a ball with its own computeFExt
class Ball(sk.LagrangianLinearTIDS):
def computeFExt(self, t):
"""External forces operator computation
"""
print("computing FExt at t=", t)
#self._fExt[0] = -m * g
weight = np.zeros(self.dimension())
weight[0] = -m * g
self.setFExtPtr(weight)
ball_d = Ball(x.copy(), v.copy(), mass)
ball_d.computeFExt(t0)
# Interactions
#
# ball-floor
H = np.zeros((1, 3), dtype=np.float64)
H[0, 0] = 1.
nslaw = sk.NewtonImpactNSL(e)
nslaw_d = sk.NewtonImpactNSL(e)
relation = sk.LagrangianLinearTIR(H)
relation_d = sk.LagrangianLinearTIR(H)
inter = sk.Interaction(nslaw, relation)
inter_d = sk.Interaction(nslaw_d, relation_d)
#
# NSDS
#
bouncing_ball = sk.NonSmoothDynamicalSystem(t0, T)
bouncing_ball_d = sk.NonSmoothDynamicalSystem(t0, T)
# add the dynamical system to the non smooth dynamical system
bouncing_ball.insertDynamicalSystem(ball)
bouncing_ball_d.insertDynamicalSystem(ball_d)
# link the interaction and the dynamical system
bouncing_ball.link(inter, ball)
bouncing_ball_d.link(inter_d, ball_d)
#
# Simulation
#
# (1) OneStepIntegrators
OSI = sk.MoreauJeanOSI(theta)
OSI_d = sk.MoreauJeanOSI(theta)
# (2) Time discretisation --
t = sk.TimeDiscretisation(t0, h)
t_d = sk.TimeDiscretisation(t0, h)
# (3) one step non smooth problem
osnspb = sk.LCP()
osnspb_d = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
s = sk.TimeStepping(bouncing_ball,t, OSI, osnspb)
s_d = sk.TimeStepping(bouncing_ball_d,t_d, OSI_d, osnspb_d)
# end of model definition
#
# computation
#
# the number of time steps
nb_time_steps = int((T - t0) / h + 1)
# Get the values to be plotted
# ->saved in a matrix data
s_d.computeOneStep()
data = np.empty((nb_time_steps + 1, 5))
data_d = np.empty((nb_time_steps + 1, 5))
data[0, 0] = t0
data[0, 1] = ball.q()[0]
data[0, 2] = ball.velocity()[0]
data[0, 3] = ball.p(1)[0]
data[0, 4] = inter.lambda_(1)
data_d[0, 0] = t0
data_d[0, 1] = ball_d.q()[0]
data_d[0, 2] = ball_d.velocity()[0]
data_d[0, 3] = ball_d.p(1)[0]
data_d[0, 4] = inter_d.lambda_(1)
k = 1
# time loop
while(s.hasNextEvent()):
s.computeOneStep()
s_d.computeOneStep()
data[k, 0] = s.nextTime()
data[k, 1] = ball.q()[0]
data[k, 2] = ball.velocity()[0]
data[k, 3] = ball.p(1)[0]
data[k, 4] = inter.lambda_(1)[0]
data_d[k, 0] = s_d.nextTime()
data_d[k, 1] = ball_d.q()[0]
data_d[k, 2] = ball_d.velocity()[0]
data_d[k, 3] = ball_d.p(1)[0]
data_d[k, 4] = inter_d.lambda_(1)[0]
assert np.allclose(data[k, 1], data_d[k, 1])
#print(s.nextTime())
k += 1
s.nextStep()
s_d.nextStep()
#
# Simulation
#
# (1) OneStepIntegrators
theta = 0.5
aOSI = sk.EulerMoreauOSI(theta)
# (2) Time discretisation
aTiDisc = sk.TimeDiscretisation(t0, h_step)
# (3) Non smooth problem
aLCP = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
aTS = sk.TimeStepping(DiodeBridge, aTiDisc, aOSI, aLCP)
# end of model definition
#
# computation
#
k = 0
h = aTS.timeStep()
print("Timestep : ", h)
# Number of time steps
N = int((T - t0) / h)
print("Number of steps : ", N)
# Get the values to be plotted
def run_simulation_with_two_ds(ball, ball_d, t0):
T = 5 # end time
h = 0.005 # time step
e = 0.9 # restitution coeficient
theta = 0.5 # theta scheme
# ball-floor
H = np.zeros((1, 3), dtype=np.float64)
H[0, 0] = 1.
nslaw = sk.NewtonImpactNSL(e)
nslaw_d = sk.NewtonImpactNSL(e)
relation = sk.LagrangianLinearTIR(H)
relation_d = sk.LagrangianLinearTIR(H)
inter = sk.Interaction(nslaw, relation)
inter_d = sk.Interaction(nslaw_d, relation_d)
#
# NSDS
#
bouncing_ball = sk.NonSmoothDynamicalSystem(t0, T)
bouncing_ball_d = sk.NonSmoothDynamicalSystem(t0, T)
# add the dynamical system to the non smooth dynamical system
bouncing_ball.insertDynamicalSystem(ball)
bouncing_ball_d.insertDynamicalSystem(ball_d)
# link the interaction and the dynamical system
bouncing_ball.link(inter, ball)
bouncing_ball_d.link(inter_d, ball_d)
#
# Simulation
#
# (1) OneStepIntegrators
OSI = sk.MoreauJeanOSI(theta)
OSI_d = sk.MoreauJeanOSI(theta)
# (2) Time discretisation --
t = sk.TimeDiscretisation(t0, h)
t_d = sk.TimeDiscretisation(t0, h)
# (3) one step non smooth problem
osnspb = sk.LCP()
osnspb_d = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
s = sk.TimeStepping(bouncing_ball, t, OSI, osnspb)
s_d = sk.TimeStepping(bouncing_ball_d, t_d, OSI_d, osnspb_d)
# end of model definition
#
# computation
#
# the number of time steps
nb_time_steps = int((T - t0) / h + 1)
# Get the values to be plotted
# ->saved in a matrix data
s_d.computeOneStep()
data = np.empty((nb_time_steps + 1, 5))
data_d = np.empty((nb_time_steps + 1, 5))
data[0, 0] = t0
data[0, 1] = ball.q()[0]
data[0, 2] = ball.velocity()[0]
data[0, 3] = ball.p(1)[0]
data[0, 4] = inter.lambda_(1)
data_d[0, 0] = t0
data_d[0, 1] = ball_d.q()[0]
data_d[0, 2] = ball_d.velocity()[0]
data_d[0, 3] = ball_d.p(1)[0]
data_d[0, 4] = inter_d.lambda_(1)
k = 1
# time loop
while (s.hasNextEvent()):
s.computeOneStep()
s_d.computeOneStep()
data[k, 0] = s.nextTime()
data[k, 1] = ball.q()[0]
data[k, 2] = ball.velocity()[0]
data[k, 3] = ball.p(1)[0]
data[k, 4] = inter.lambda_(1)[0]
data_d[k, 0] = s_d.nextTime()
data_d[k, 1] = ball_d.q()[0]
data_d[k, 2] = ball_d.velocity()[0]
data_d[k, 3] = ball_d.p(1)[0]
data_d[k, 4] = inter_d.lambda_(1)[0]
assert np.allclose(data[k, 1], data_d[k, 1])
#print(s.nextTime())
k += 1
s.nextStep()
s_d.nextStep()
data.resize(k, 5)
view = False
if view:
import matplotlib.pyplot as plt
fig_size = [14, 14]
plt.rcParams["figure.figsize"] = fig_size
plt.subplot(411)
plt.title('displacement')
plt.plot(data[:, 0], data[:, 1])
plt.grid()
plt.subplot(412)
plt.title('velocity')
plt.plot(data[:, 0], data[:, 2])
plt.grid()
plt.subplot(413)
plt.plot(data[:, 0], data[:, 3])
plt.title('reaction')
plt.grid()
plt.subplot(414)
plt.plot(data[:, 0], data[:, 4])
plt.title('lambda')
plt.grid()
plt.show()
x0 = [10., 10.] C = [[1., 0]] # we have to specify ndmin=2, so it's understood as K = [.25, .125, 2] # Declaration of the Dynamical System doubleIntegrator = sk.FirstOrderLinearTIDS(x0, A) # Model process = sk.NonSmoothDynamicalSystem(t0, T) process.insertDynamicalSystem(doubleIntegrator) # Declaration of the integrator OSI = sk.EulerMoreauOSI(theta) # time discretisation t = sk.TimeDiscretisation(t0, h) tSensor = sk.TimeDiscretisation(t0, h) tActuator = sk.TimeDiscretisation(t0, h) s = sk.TimeStepping(process, t, 0) s.insertIntegrator(OSI) # Actuator, Sensor & ControlManager control = ControlManager(s) sens = LinearSensor(doubleIntegrator, C) control.addSensorPtr(sens, tSensor) act = PID(sens) act.setB(B) control.addActuatorPtr(act, tActuator) # Initialization control.initialize(process) act.setRef(xFinal) act.setK(K) act.setDeltaT(h)
particle_relation = sk.FirstOrderLinearR(C, B) nslaw = sk.RelayNSL(ninter) particle_interaction = sk.Interaction(nslaw, particle_relation) # -- The Model -- filippov = sk.Model(t0, T) nsds = filippov.nonSmoothDynamicalSystem() nsds.insertDynamicalSystem(particle) nsds.link(particle_interaction, particle) # -- Simulation -- td = sk.TimeDiscretisation(t0, h) simu = sk.TimeStepping(td) # osi theta = 0.5 myIntegrator = sk.EulerMoreauOSI(theta) simu.insertIntegrator(myIntegrator) # osns osnspb = sk.Relay(sn.SICONOS_RELAY_LEMKE) simu.insertNonSmoothProblem(osnspb) filippov.setSimulation(simu) filippov.initialize() # -- Get the values to be plotted -- output_size = 1 + ndof + 2 * ninter nb_time_steps = int((T - t0) / h) + 1
def __init__(self,
strings_and_frets,
time_range,
fs,
output_freq=1,
interactions_output=0,
integrators=None,
default_integrator=None):
"""
Parameters
----------
strings_and_frets : python dictionnary
keys = interactions (Fret). See notes below.
values = dynamical systems (StringDS)
time_range : list
time range for the simulation.
fs : double
sampling frequency
integrators : dictionnary, optional
integrators[some_ds] = osi class name
(MoreauJeanOSI or MoreauJeanBilbaoOSI).
Default: Bilbao for all ds.
default_integrator: sk.OneStepIntegrator
default osi type (if integrators is not set
or for ds not present in integrators).
Default = Bilbao.
output_freq: int, optional
output frequency for times steps
interactions_output: int, optional
0: save nothing, 1: only y, 2: y + lambda(vel) 3: y + ydot + lambda
default = 0
Notes
-----
* strings_and_frets.keys() : interactions
* strings_and_frets.values() : dynamical systems
* so, strings_and_frets[some_interaction] returns the ds
linked to some_interaction.
* For integrators:
- to use Bilbao for all ds : do not set
integrators and default_integrator params
- to choose the integrator: set default_integrator=OSI
OSI being some siconos OneStepIntegrator class.
- to explicitely set the integrator for each ds:
set integrators dictionnary, with
integrators[some_ds] = OSI (OneStepIntegrator class)
all ds not set in integrators will be integrated
with default_integrator.
"""
# iterate through ds and interactions in the input dictionnary
# - insert ds into the nonsmooth dynamical system
# - link each ds to its interaction
self.nsds = sk.NonSmoothDynamicalSystem(time_range[0], time_range[1])
if None in strings_and_frets:
# No interactions in the model
ds = strings_and_frets[None]
assert isinstance(ds, StringDS)
self.nsds.insertDynamicalSystem(ds)
else:
#self.nsds.insertDynamicalSystem(list(strings_and_frets.values())[0])
for interaction in strings_and_frets:
ds = strings_and_frets[interaction]
assert isinstance(interaction, Fret)
assert isinstance(ds, StringDS)
self.nsds.insertDynamicalSystem(ds)
# link the interaction and the dynamical system
self.nsds.link(interaction, ds)
self.strings_and_frets = strings_and_frets
# -- Simulation --
moreau_bilbao = sk.MoreauJeanBilbaoOSI()
moreau_jean = sk.MoreauJeanOSI(0.500001)
default_integrator = moreau_bilbao
if default_integrator == 'MoreauJean':
default_integrator = moreau_jean
# (2) Time discretisation --
t0 = time_range[0]
tend = time_range[1]
self.fs = fs
self.time_step = 1. / fs
t = sk.TimeDiscretisation(t0, self.time_step)
self.nb_time_steps = (int)((tend - t0) * fs)
self.output_freq = output_freq
self.nb_time_steps_output = (int)(self.nb_time_steps / output_freq)
# (3) one step non smooth problem
self.osnspb = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
self.default_integrator = default_integrator
self.simulation = sk.TimeStepping(self.nsds, t, default_integrator,
self.osnspb)
#self.simulation = sk.TimeStepping(self.nsds, t, moreau_bilbao, self.osnspb)
if integrators is not None:
for ds in integrators:
self.simulation.associate(integrators[ds], ds)
# internal buffers, used to save data for each time step.
# A dict of buffers to save ds variables for all time steps
self.data_ds = {}
for ds in self.strings_and_frets.values():
ndof = ds.dimension()
self.data_ds[ds] = npw.zeros((ndof, self.nb_time_steps_output + 1))
if None in self.strings_and_frets:
self.strings_and_frets = {}
# A dict of buffers to save interactions variables for all time steps
self.data_interactions = {}
self.save_interactions = interactions_output > 0
self.interactions_output = interactions_output
if self.save_interactions:
for interaction in self.strings_and_frets:
self.data_interactions[interaction] = []
for i in range(interactions_output):
self.data_interactions[interaction].append(
npw.zeros(self.nb_time_steps_output + 1))
# time instants
self.time = npw.zeros(self.nb_time_steps_output + 1)
# saved data state (modal or nodal)
self.modal_values = True
def __init__(self,
strings_and_frets,
time_range,
fs,
integrators=None,
default_integrator=None):
"""
Parameters
----------
strings_and_frets : python dictionnary
keys = interactions (Fret). See notes below.
values = dynamical systems (StringDS)
time_range : list
time range for the simulation.
fs : double
sampling frequency
integrators : dictionnary, optional
integrators[some_ds] = osi class name
(MoreauJeanOSI or MoreauJeanBilbaoOSI).
Default: Bilbao for all ds.
default_integrator: sk.OneStepIntegrator
default osi type (if integrators is not set
or for ds not present in integrators).
Default = Bilbao.
Notes
-----
* strings_and_frets.keys() : interactions
* strings_and_frets.values() : dynamical systems
* so, strings_and_frets[some_interaction] returns the ds
linked to some_interaction.
* For integrators:
- to use Bilbao for all ds : do not set
integrators and default_integrator params
- to choose the integrator: set default_integrator=OSI
OSI being some siconos OneStepIntegrator class.
- to explicitely set the integrator for each ds:
set integrators dictionnary, with
integrators[some_ds] = OSI (OneStepIntegrator class)
all ds not set in integrators will be integrated
with default_integrator.
"""
# Build siconos model object (input = time range)
super(Guitar, self).__init__(time_range[0], time_range[1])
# iterate through ds and interactions in the input dictionnary
# - insert ds into the nonsmooth dynamical system
# - link each ds to its interaction
for interaction in strings_and_frets:
ds = strings_and_frets[interaction]
assert isinstance(interaction, Fret)
assert isinstance(ds, StringDS)
self.insertDynamicalSystem(ds)
# link the interaction and the dynamical system
self.link(interaction, ds)
self.strings_and_frets = strings_and_frets
# -- Simulation --
moreau_bilbao = sk.MoreauJeanBilbaoOSI()
moreau_jean = sk.MoreauJeanOSI(0.500001)
default_integrator = moreau_bilbao
if default_integrator is 'MoreauJean':
default_integrator = moreau_jean
# (2) Time discretisation --
t0 = time_range[0]
tend = time_range[1]
self.fs = fs
self.time_step = 1. / fs
t = sk.TimeDiscretisation(t0, self.time_step)
self.nb_time_steps = (int)((tend - t0) / self.time_step) + 1
# (3) one step non smooth problem
osnspb = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
self.simu = sk.TimeStepping(self, t, default_integrator, osnspb)
if integrators is not None:
for ds in integrators:
self.simu.associate(integrators[ds], ds)
# internal buffers, used to save data for each time step.
# A dict of buffers to save ds variables for all time steps
self.data_ds = {}
for ds in self.strings_and_frets.values():
ndof = ds.dimension()
self.data_ds[ds] = npw.zeros((self.nb_time_steps + 1, ndof))
# A dict of buffers to save interactions variables for all time steps
self.data_interactions = {}
for interaction in self.strings_and_frets:
nbc = interaction.getSizeOfY()
self.data_interactions[interaction] = \
npw.zeros((self.nb_time_steps + 1, 3 * nbc))
# time instants
self.time = npw.zeros(self.nb_time_steps + 1)
def test_bouncing_ball2():
import siconos.kernel as K
from numpy import array, eye, empty
t0 = 0 # start time
T = 5 # end time
h = 0.005 # time step
r = 0.1 # ball radius
g = 9.81 # gravity
m = 1 # ball mass
e = 0.9 # restitution coeficient
theta = 0.5 # theta scheme
#
# dynamical system
#
x = array([1, 0, 0]) # initial position
v = array([0, 0, 0]) # initial velocity
mass = eye(3) # mass matrix
mass[2, 2] = 3./5 * r * r
# the dynamical system
ball = K.LagrangianLinearTIDS(x, v, mass)
# set external forces
weight = array([-m * g, 0, 0])
ball.setFExtPtr(weight)
# a ball with its own computeFExt
class Ball(K.LagrangianLinearTIDS):
def computeFExt(self, t):
#print("computing FExt at t=", t)
weight = array([-m * g, 0, 0])
self.setFExtPtr(weight)
ball_d = Ball(array([1, 0, 0]), array([0, 0, 0]), mass)
ball_d.setFExtPtr(array([0, 0, 0]))
#
# Interactions
#
# ball-floor
H = array([[1, 0, 0]])
nslaw = K.NewtonImpactNSL(e)
nslaw_d = K.NewtonImpactNSL(e)
relation = K.LagrangianLinearTIR(H)
relation_d = K.LagrangianLinearTIR(H)
inter = K.Interaction(1, nslaw, relation)
inter_d = K.Interaction(1, nslaw_d, relation_d)
#
# Model
#
bouncingBall = K.Model(t0, T)
bouncingBall_d = K.Model(t0, T)
# add the dynamical system to the non smooth dynamical system
bouncingBall.nonSmoothDynamicalSystem().insertDynamicalSystem(ball)
bouncingBall_d.nonSmoothDynamicalSystem().insertDynamicalSystem(ball_d)
# link the interaction and the dynamical system
bouncingBall.nonSmoothDynamicalSystem().link(inter, ball)
bouncingBall_d.nonSmoothDynamicalSystem().link(inter_d, ball_d)
#
# Simulation
#
# (1) OneStepIntegrators
OSI = K.MoreauJeanOSI(theta)
OSI.insertDynamicalSystem(ball)
OSI_d = K.MoreauJeanOSI(theta)
OSI_d.insertDynamicalSystem(ball_d)
# (2) Time discretisation --
t = K.TimeDiscretisation(t0, h)
t_d = K.TimeDiscretisation(t0, h)
# (3) one step non smooth problem
osnspb = K.LCP()
osnspb_d = K.LCP()
# (4) Simulation setup with (1) (2) (3)
s = K.TimeStepping(t)
s.insertIntegrator(OSI)
s.insertNonSmoothProblem(osnspb)
s_d = K.TimeStepping(t_d)
s_d.insertIntegrator(OSI_d)
s_d.insertNonSmoothProblem(osnspb_d)
# end of model definition
#
# computation
#
# simulation initialization
bouncingBall.initialize(s)
bouncingBall_d.initialize(s_d)
# the number of time steps
N = (T-t0)/h+1
# Get the values to be plotted
# ->saved in a matrix dataPlot
s_d.computeOneStep()
dataPlot = empty((N+1, 5))
dataPlot_d = empty((N+1, 5))
dataPlot[0, 0] = t0
dataPlot[0, 1] = ball.q()[0]
dataPlot[0, 2] = ball.velocity()[0]
dataPlot[0, 3] = ball.p(1)[0]
dataPlot[0, 4] = inter.lambda_(1)
dataPlot_d[0, 0] = t0
dataPlot_d[0, 1] = ball_d.q()[0]
dataPlot_d[0, 2] = ball_d.velocity()[0]
dataPlot_d[0, 3] = ball_d.p(1)[0]
dataPlot_d[0, 4] = inter_d.lambda_(1)
k = 1
# time loop
while(s.hasNextEvent()):
s.computeOneStep()
s_d.computeOneStep()
dataPlot[k, 0] = s.nextTime()
dataPlot[k, 1] = ball.q()[0]
dataPlot[k, 2] = ball.velocity()[0]
dataPlot[k, 3] = ball.p(1)[0]
dataPlot[k, 4] = inter.lambda_(1)[0]
dataPlot_d[k, 0] = s_d.nextTime()
dataPlot_d[k, 1] = ball_d.q()[0]
dataPlot_d[k, 2] = ball_d.velocity()[0]
dataPlot_d[k, 3] = ball_d.p(1)[0]
dataPlot_d[k, 4] = inter_d.lambda_(1)[0]
assert dataPlot[k, 1] == dataPlot_d[k, 1]
#print(s.nextTime())
k += 1
s.nextStep()
s_d.nextStep()
#
# Simulation
#
# (1) OneStepIntegrators
theta = 0.5
aOSI = sk.EulerMoreauOSI(theta)
# (2) Time discretisation
aTiDisc = sk.TimeDiscretisation(t0, h_step)
# (3) Non smooth problem
aLCP = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
aTS = sk.TimeStepping(aTiDisc, aOSI, aLCP)
# end of model definition
#
# computation
#
# simulation initialization
DiodeBridge.setSimulation(aTS)
DiodeBridge.initialize()
k = 0
h = aTS.timeStep()
print("Timestep : ", h)
# Number of time steps
def test_lagrangian_and_osis():
"""Tests osi/lagrangian combinations
"""
# --- The dynamical systems ---
# dimension
ndof = 3
# initial conditons
q0 = np.zeros(ndof, dtype=np.float64)
v0 = np.zeros_like(q0)
q0[0] = 1.
# mass, stiffness and damping matrices
# (diagonal values only)
mass_diag = np.ones(ndof, dtype=np.float64)
k_diag = np.zeros_like(mass_diag)
c_diag = np.zeros_like(mass_diag)
sigma = 0.2
omega2 = 1.2
k_diag[...] = omega2
c_diag[...] = sigma
ds_list = {}
# - LagrangianLinearTIDS -
ds_list['LTIDS+MJ'] = sk.LagrangianLinearTIDS(q0, v0, np.diag(mass_diag),
np.diag(k_diag),
np.diag(c_diag))
# - LagrangianLinearDiagonalDS -
ds_list['LLDDS+MJ'] = sk.LagrangianLinearDiagonalDS(
q0, v0, k_diag, c_diag, mass_diag)
ds_list['LLDDS+MJB'] = sk.LagrangianLinearDiagonalDS(
q0, v0, k_diag, c_diag, mass_diag)
# no mass (i.e. implicitely equal to Id)
ds_list['LLDDS+MJB2'] = sk.LagrangianLinearDiagonalDS(
q0, v0, k_diag, c_diag)
nb_ds = len(ds_list)
for ds in ds_list.values():
ds.computeForces(0., q0, v0)
assert np.allclose(ds_list['LTIDS+MJ'].forces(),
ds_list['LTIDS+MJ'].forces())
# --- Interactions ---
cor = 0.9
nslaw = sk.NewtonImpactNSL(cor)
hmat = np.zeros((1, ndof), dtype=np.float64)
hmat[0, 0] = 1.
relation = sk.LagrangianLinearTIR(hmat)
interactions = []
for k in range(nb_ds):
interactions.append(sk.Interaction(nslaw, relation))
# --- NSDS ---
tinit = 0.
tend = 3.
nsds = sk.NonSmoothDynamicalSystem(tinit, tend)
# - insert ds into the model and link them with their interaction -
ninter = 0
for ds in ds_list.values():
nsds.insertDynamicalSystem(ds)
nsds.link(interactions[ninter], ds)
ninter += 1
# --- Simulation ---
# - (1) OneStepIntegrators --
theta = 0.5000001
standard = sk.MoreauJeanOSI(theta)
bilbao = sk.MoreauJeanBilbaoOSI()
#-- (2) Time discretisation --
time_step = 1e-4
td = sk.TimeDiscretisation(tinit, time_step)
# -- (3) one step non smooth problem
lcp = sk.LCP()
# -- (4) Simulation setup with (1) (2) (3)
simu = sk.TimeStepping(nsds, td, standard, lcp)
# extra osi must be explicitely inserted into simu and linked to ds
simu.associate(standard, ds_list['LTIDS+MJ'])
simu.associate(standard, ds_list['LLDDS+MJ'])
simu.associate(bilbao, ds_list['LLDDS+MJB'])
simu.associate(bilbao, ds_list['LLDDS+MJB2'])
positions = []
velocities = []
for ds in ds_list.values():
positions.append(ds.q())
velocities.append(ds.velocity())
# number of time steps and buffer used to save results
nb_steps = int((tend - tinit) / time_step) + 1
data = np.zeros((nb_steps, 1 + 2 * len(ds_list)), dtype=np.float64)
data[0, 0] = tinit
for k in range(nb_ds):
data[0, k * 2 + 1] = positions[k][0]
data[0, k * 2 + 2] = velocities[k][0]
current_iter = 1
# --- Time loop ---
while simu.hasNextEvent():
simu.computeOneStep()
data[current_iter, 0] = simu.nextTime()
for k in range(nb_ds):
data[current_iter, k * 2 + 1] = positions[k][0]
data[current_iter, k * 2 + 2] = velocities[k][0]
simu.nextStep()
current_iter += 1
# -- check results
ref_pos = data[:, 1]
ref_vel = data[:, 2]
for k in range(1, nb_ds):
assert np.allclose(ref_pos, data[:, k * 2 + 1], atol=time_step)
assert np.allclose(ref_vel, data[:, k * 2 + 2], atol=time_step)
#
# Simulation
#
# (1) OneStepIntegrators
theta = 0.500000001
aOSI = sk.EulerMoreauOSI(theta)
# (2) Time discretisation
aTiDisc = sk.TimeDiscretisation(t0, h_step)
# (3) Non smooth problem
aLCP = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
aTS = sk.TimeStepping(CircuitRLCD, aTiDisc, aOSI, aLCP)
# end of model definition
#
# computation
#
k = 0
h = aTS.timeStep()
print("Timestep : ", h)
# Number of time steps
N = int((T - t0) / h)
print("Number of steps : ", N)
# Get the values to be plotted
def test_bouncing_ball1():
"""Run a complete simulation (Bouncing ball example)
LagrangianLinearTIDS, no plugins.
"""
t0 = 0. # start time
tend = 10. # end time
h = 0.005 # time step
r = 0.1 # ball radius
g = 9.81 # gravity
m = 1 # ball mass
e = 0.9 # restitution coeficient
theta = 0.5 # theta scheme
#
# dynamical system
#
x = np.zeros(3, dtype=np.float64)
x[0] = 1.
v = np.zeros_like(x)
# mass matrix
mass = np.eye(3, dtype=np.float64)
mass[2, 2] = 3. / 5 * r * r
# the dynamical system
ball = sk.LagrangianLinearTIDS(x, v, mass)
# set external forces
weight = np.zeros_like(x)
weight[0] = -m * g
ball.setFExtPtr(weight)
#
# Interactions
#
# ball-floor
H = np.zeros((1, 3), dtype=np.float64)
H[0, 0] = 1.
nslaw = sk.NewtonImpactNSL(e)
relation = sk.LagrangianLinearTIR(H)
inter = sk.Interaction(nslaw, relation)
#
# NSDS
#
bouncing_ball = sk.NonSmoothDynamicalSystem(t0, tend)
# add the dynamical system to the non smooth dynamical system
bouncing_ball.insertDynamicalSystem(ball)
# link the interaction and the dynamical system
bouncing_ball.link(inter, ball)
#
# Simulation
#
# (1) OneStepIntegrators
OSI = sk.MoreauJeanOSI(theta)
# (2) Time discretisation --
t = sk.TimeDiscretisation(t0, h)
# (3) one step non smooth problem
osnspb = sk.LCP()
# (4) Simulation setup with (1) (2) (3)
s = sk.TimeStepping(bouncing_ball,t, OSI, osnspb)
# end of model definition
#
# computation
#
#
# save and load data from xml and .dat
#
try:
from siconos.io import save
save(bouncing_ball, "bouncingBall.xml")
save(bouncing_ball, "bouncingBall.bin")
except:
print("Warning : could not import save from siconos.io")
# the number of time steps
nb_time_steps = int((tend - t0) / h + 1)
# Get the values to be plotted
# ->saved in a matrix dataPlot
data = np.empty((nb_time_steps, 5))
#
# numpy pointers on dense Siconos vectors
#
q = ball.q()
v = ball.velocity()
p = ball.p(1)
lambda_ = inter.lambda_(1)
#
# initial data
#
data[0, 0] = t0
data[0, 1] = q[0]
data[0, 2] = v[0]
data[0, 3] = p[0]
data[0, 4] = lambda_[0]
k = 1
# time loop
while(s.hasNextEvent()):
s.computeOneStep()
data[k, 0] = s.nextTime()
data[k, 1] = q[0]
data[k, 2] = v[0]
data[k, 3] = p[0]
data[k, 4] = lambda_[0]
k += 1
#print(s.nextTime())
s.nextStep()
#
# comparison with the reference file
#
ref = sk.getMatrix(sk.SimpleMatrix(
os.path.join(working_dir, "data/result.ref")))
assert (np.linalg.norm(data - ref) < 1e-12)
relation = sk.FirstOrderLinearTIR(N, B)
relation.setDPtr(M)
nslaw = sk.ComplementarityConditionNSL(ninter)
interaction = sk.Interaction(nslaw, relation)
# -- The Model --
circuit = sk.NonSmoothDynamicalSystem(t0, T)
circuit.setTitle('train')
circuit.insertDynamicalSystem(RC)
circuit.link(interaction, RC)
# -- Simulation --
td = sk.TimeDiscretisation(t0, tau)
simu = sk.TimeStepping(circuit, td)
# osi
theta = 0.50000000000001
osi = sk.EulerMoreauOSI(theta)
simu.insertIntegrator(osi)
# osns
osnspb = sk.LCP()
simu.insertNonSmoothProblem(osnspb)
# -- Get the values to be plotted --
output_size = 2 * number_of_cars + 1
nb_time_steps = int((T - t0) / tau) + 1
data_plot = np.empty((nb_time_steps, output_size))
data_plot[0, 0] = circuit.t0() / tscale
data_plot[0, 1:] = RC.x()
