def test_properties(self):
# Test setters/getters for cross class properties.
# This implicitly tests to_tf() and to_zpk()
# Getters
s = StateSpace(1, 1, 1, 1, dt=0.05)
assert_equal(s.poles, [1])
assert_equal(s.zeros, [0])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
assert_equal(s.gain, 1)
assert_equal(s.num, [1, 0])
assert_equal(s.den, [1, -1])
# transfer function setters
s2 = StateSpace(2, 2, 2, 2, dt=0.05)
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
s2.num = [1, 0]
s2.den = [1, -1]
self._compare_systems(s, s2)
# zpk setters
s2 = StateSpace(2, 2, 2, 2, dt=0.05)
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
s2.poles = 1
s2.zeros = 0
s2.gain = 1
self._compare_systems(s, s2)
def test_initialization(self):
# Check that all initializations work
dt = 0.05
s = StateSpace(1, 1, 1, 1, dt=dt)
s = StateSpace([1], [2], [3], [4], dt=dt)
s = StateSpace(np.array([[1, 2], [3, 4]]), np.array([[1], [2]]),
np.array([[1, 0]]), np.array([[0]]), dt=dt)
s = StateSpace(1, 1, 1, 1, dt=True)
def test_conversion(self):
# Check the conversion functions
s = StateSpace(1, 2, 3, 4)
assert_(isinstance(s.to_ss(), StateSpace))
assert_(isinstance(s.to_tf(), TransferFunction))
assert_(isinstance(s.to_zpk(), ZerosPolesGain))
# Make sure copies work
assert_(StateSpace(s) is not s)
assert_(s.to_ss() is not s)
def tfSim(inputType, mag, wn, zeta, init_xdot, init_x, step_increment,
endtime):
num = [0, 0, wn**2]
den = [1, 2 * zeta * wn, wn**2]
timesteps = np.arange(0, endtime, step_increment)
if inputType == "sine":
u = mag * np.sin(2 * np.pi * timesteps)
elif inputType == "step":
u = mag * np.ones(timesteps.size)
else:
raise ValueException("unrecognized input type")
[A, B, C, D] = tf2ss(num, den)
sys = StateSpace(A, B, C, D)
print("C: {} ; shape: {}".format(C, C.shape))
init_xdot = init_xdot / C[0][1]
init_x = init_x / C[0][1]
init_cond = np.array([init_xdot, init_x])
print(init_cond)
print("initial conditions shape: {}".format(init_cond.shape))
y = lsim(sys, u, timesteps, init_cond)
return y
def test_properties(self):
# Test setters/getters for cross class properties.
# This implicitly tests to_tf() and to_zpk()
# Getters
s = StateSpace(1, 1, 1, 1, dt=0.05)
assert_equal(s.poles, [1])
assert_equal(s.zeros, [0])
def test_properties(self):
# Test setters/getters for cross class properties.
# This implicitly tests to_tf() and to_zpk()
# Getters
s = StateSpace(1, 1, 1, 1, dt=0.05)
assert_equal(s.poles, [1])
assert_equal(s.zeros, [0])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
assert_equal(s.gain, 1)
assert_equal(s.num, [1, 0])
assert_equal(s.den, [1, -1])
# transfer function setters
s2 = StateSpace(2, 2, 2, 2, dt=0.05)
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
s2.num = [1, 0]
s2.den = [1, -1]
self._compare_systems(s, s2)
# zpk setters
s2 = StateSpace(2, 2, 2, 2, dt=0.05)
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
s2.poles = 1
s2.zeros = 0
s2.gain = 1
self._compare_systems(s, s2)
def test_conversion(self):
# Check the conversion functions
s = StateSpace(1, 2, 3, 4, dt=0.05)
assert_(isinstance(s.to_ss(), StateSpace))
assert_(isinstance(s.to_tf(), TransferFunction))
assert_(isinstance(s.to_zpk(), ZerosPolesGain))
# Make sure copies work
assert_(StateSpace(s) is not s)
assert_(s.to_ss() is not s)
def get_model():
# Build model: R3C3
# =================
# Constants
Vi = 400. # Indoor volume [m3]
# Parameters
Ri = 0.25 / (3. * Vi ** (2./3.)) # Int. wall resistance [K/W]
Re1 = 1.00 / (3. * Vi ** (2./3.)) # Ext. wall resistance [K/W]
Re2 = 0.25 / (3. * Vi ** (2./3.)) # Ext. wall resistance [K/W]
Ci = 30.0 * Vi * 1.2 * 1005. # Int. wall capacitance [J/K]
Ce = 20.0 * Vi * 1.2 * 1005. # Ext. wall capacitance [J/K]
Cr = 10.0 * Vi * 1.2 * 1005. # Zone capacitance [J/K]
fsol = 1.8 # Solar coeff.
qmet = 100. # Metabolic heat gain [W/person]
# States, inputs and outputs:
# n (states) : [Tr, Ti, Te]
# p (inputs) : [Tout, Hglo, qhvac, nocc]
# q (outputs) : [Tr, Ti, Te, qhvac]
# State matrix (n x n)
A = np.array([
[-1/Ri/Cr - 1/Re1/Cr, 1/Ri/Cr, 1/Re1/Cr],
[1/Ri/Ci, -1/Ri/Ci, 0],
[1/Re1/Ce, 0, -1/Re1/Ce - 1/Re2/Ce]
]) # [Tr, Ti, Te]^T
# Input matrix (n x p)
B = np.array([
[0., fsol/Cr, 1/Cr, qmet/Cr],
[0., 0., 0., 0.],
[1/Re2/Ce, 0., 0., 0.]
])
# Output matrix (q x n)
C = np.array([
[1., 0., 0.], # Tr
[0., 1., 0.], # Ti
[0., 0., 1.], # Te
[0., 0., 0.] # qhvac
])
# Feedthrough matrix (q x p)
D = np.array([
[0., 0., 0., 0.], # Tr
[0., 0., 0., 0.], # Ti
[0., 0., 0., 0.], # Te
[0., 0., 1., 0.] # qhvac
])
return StateSpace(A, B, C, D)
def maxent(P,A,B,omega=None):
assert P.ndim==2 and P.shape[0] == P.shape[1], 'P must be a square matrix'
if omega is None:
omega=linspace(0,2*pi,200)
N = omega.size
iP = inv(P)
e = inv(B.T @ iP @ B)
C1 = e @ B.T @ iP
n,m = B.shape
# Phi=C1*G(z); max entropy spectrum = Phi^{-1} e Phi^{-1}^*
rA = A.real
iA = A.imag
AA = array([[rA, -iA],[iA, rA]])
rB = B.real
iB = B.imag
BB = array([[rB, -iB],[iB, rB]])
rC = C1.real
iC = C1.imag
CC1= array([[rC, -iC],[iC, rC]])
Phi_inv = StateSpace(AA-BB*CC1*AA, BB, -CC1*AA, eye(2*m,2*m));
HH = freqresp(Phi_inv,omega)
H = HH[:m,:m,:] + 1j*HH[m:2*m,:m,:]
if m==1:
h = H.squeeze()
spectrum = diag(h @ e @ h.T).real.T
else:
print('The spectrum is matricial m*m, where m=',m)
spectrum = zeros((m,N*m))
halfspectrum = zeros((m,N*m))
[Ue,svdOmega] = svd(e)
sqrtsvdOmega = sqrt(svdOmega)
for i in range(N):
temp=H[:,:,i] #H(:,:,i)=temp*e*temp.T
spectrum[:,(i-1)*m:i*m] = temp @ e @ temp.T
halfspectrum[:,(i-1)*m:i*m] = temp @ Ue @ sqrtsvdOmega
return spectrum, halfspectrum, omega
# In[14]:
Cp = C
print(Cp)
# In[15]:
Ep = array([E, dot((C1 - C2), X) + dot((E1 - E2), U)]).transpose()
print(Ep)
# Com as matrizes ``Ap``, ``Bp``, ``Cp``, ``Ep`` calculadas o modelo de espaço de estado é montado (a partir daqui o notebook assume o que o ``buckboost.mdl`` no Simulink faz)
# In[16]:
ss_buckboost = StateSpace(Ap, Bp, Cp, Ep)
# Um sinal de tempo (``t_in``) e os steps de entrada (``u`` e ``d``) são gerados utilizando a função [step](#step_function) criada.
# In[17]:
t_in = arange(0, t_final, t_step)
u = step(t_in, .03, 0, v_f, '$\hat{u}$')
d = step(t_in, .03, 0, d_f, '$\hat{d}$')
up = array([u, d]).transpose()
# Nesse momento, com as entradas definidas e o modelo pronto podemos evolui-lo no espaço de estados utilizando a função ``lsim``:
# In[18]:
C = np.matrix([
[1, 0, 0, 0],
[0, 1, 0, 0],
])
#C = np.concatenate((C,np.zeros((4,4))),axis=1)
D = np.zeros((2, 2))
top = np.concatenate((A, np.zeros((4, 2))), axis=1)
bottom = np.concatenate((-C, np.zeros((2, 2))), axis=1)
Ahat = np.concatenate((top, bottom), axis=0)
Bhat = np.concatenate((B, np.zeros((2, 2))), axis=0)
Q = np.diag([1, 1, .0000001, .0000001, 1, 1]) * np.eye(6)
R = np.eye(2)
sys = StateSpace(Ahat, Bhat, np.concatenate((C, np.zeros((2, 2))), axis=1), D)
#sys_d = sys.to_discrete(1)
#Ahat, Bhat = sys_d.A, sys_d.B
K, S, E = lqr(Ahat, Bhat, Q, R)
""" Control """
x = np.matrix([[1], [-1], [2], [3], [0], [0]]) # initial state
ref = np.matrix([[0], [0]])
cont = 0
record_x = []
record_u = []
record_SP = []
y = [[0], [0]]
e_dot = [[0], [0], [0], [0]]
xsi = 0
# This is the code for Exercise 5.
import numpy as np
from scipy.signal import StateSpace, dlsim
A = np.asarray([[0.,1.],
[1.,1.]])
B = np.asarray([[0.],[0.]])
C = np.asarray([0.,0.])
D = np.asarray([0.])
plane_sys = StateSpace(A, B, C, D, dt = 1)
t = np.arange(0, 20, 1)
input = np.zeros(len(t))
_, y, x = dlsim(plane_sys, input, t, x0=[0, 1])
F20 = int(x[19, 1])
print("F20 is:", F20)
def test_initialization(self):
# Check that all initializations work
s = StateSpace(1, 1, 1, 1)
s = StateSpace([1], [2], [3], [4])
s = StateSpace(np.array([[1, 2], [3, 4]]), np.array([[1], [2]]),
np.array([[1, 0]]), np.array([[0]]))
def test_operators(self):
# Test +/-/* operators on systems
class BadType(object):
pass
s1 = StateSpace(np.array([[-0.5, 0.7], [0.3, -0.8]]),
np.array([[1], [0]]),
np.array([[1, 0]]),
np.array([[0]]),
)
s2 = StateSpace(np.array([[-0.2, -0.1], [0.4, -0.1]]),
np.array([[1], [0]]),
np.array([[1, 0]]),
np.array([[0]])
)
s_discrete = s1.to_discrete(0.1)
s2_discrete = s2.to_discrete(0.2)
# Impulse response
t = np.linspace(0, 1, 100)
u = np.zeros_like(t)
u[0] = 1
# Test multiplication
for typ in six.integer_types + (float, complex, np.float32,
np.complex128, np.array):
assert_allclose(lsim(typ(2) * s1, U=u, T=t)[1],
typ(2) * lsim(s1, U=u, T=t)[1])
assert_allclose(lsim(s1 * typ(2), U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] * typ(2))
assert_allclose(lsim(s1 / typ(2), U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] / typ(2))
with assert_raises(TypeError):
typ(2) / s1
assert_allclose(lsim(s1 * 2, U=u, T=t)[1],
lsim(s1, U=2 * u, T=t)[1])
assert_allclose(lsim(s1 * s2, U=u, T=t)[1],
lsim(s1, U=lsim(s2, U=u, T=t)[1], T=t)[1],
atol=1e-5)
with assert_raises(TypeError):
s1 / s1
with assert_raises(TypeError):
s1 * s_discrete
with assert_raises(TypeError):
# Check different discretization constants
s_discrete * s2_discrete
with assert_raises(TypeError):
s1 * BadType()
with assert_raises(TypeError):
BadType() * s1
with assert_raises(TypeError):
s1 / BadType()
with assert_raises(TypeError):
BadType() / s1
# Test addition
assert_allclose(lsim(s1 + 2, U=u, T=t)[1],
2 * u + lsim(s1, U=u, T=t)[1])
# Check for dimension mismatch
with assert_raises(ValueError):
s1 + np.array([1, 2])
with assert_raises(ValueError):
np.array([1, 2]) + s1
with assert_raises(TypeError):
s1 + s_discrete
with assert_raises(ValueError):
s1 / np.array([[1, 2], [3, 4]])
with assert_raises(TypeError):
# Check different discretization constants
s_discrete + s2_discrete
with assert_raises(TypeError):
s1 + BadType()
with assert_raises(TypeError):
BadType() + s1
assert_allclose(lsim(s1 + s2, U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] + lsim(s2, U=u, T=t)[1])
# Test substraction
assert_allclose(lsim(s1 - 2, U=u, T=t)[1],
-2 * u + lsim(s1, U=u, T=t)[1])
assert_allclose(lsim(2 - s1, U=u, T=t)[1],
2 * u + lsim(-s1, U=u, T=t)[1])
assert_allclose(lsim(s1 - s2, U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] - lsim(s2, U=u, T=t)[1])
with assert_raises(TypeError):
s1 - BadType()
with assert_raises(TypeError):
BadType() - s1
def test_operators(self):
# Test +/-/* operators on systems
class BadType(object):
pass
s1 = StateSpace(
np.array([[-0.5, 0.7], [0.3, -0.8]]),
np.array([[1], [0]]),
np.array([[1, 0]]),
np.array([[0]]),
)
s2 = StateSpace(np.array([[-0.2, -0.1], [0.4, -0.1]]),
np.array([[1], [0]]), np.array([[1, 0]]),
np.array([[0]]))
s_discrete = s1.to_discrete(0.1)
s2_discrete = s2.to_discrete(0.2)
s3_discrete = s2.to_discrete(0.1)
# Impulse response
t = np.linspace(0, 1, 100)
u = np.zeros_like(t)
u[0] = 1
# Test multiplication
for typ in (int, float, complex, np.float32, np.complex128, np.array):
assert_allclose(
lsim(typ(2) * s1, U=u, T=t)[1],
typ(2) * lsim(s1, U=u, T=t)[1])
assert_allclose(
lsim(s1 * typ(2), U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] * typ(2))
assert_allclose(
lsim(s1 / typ(2), U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] / typ(2))
with assert_raises(TypeError):
typ(2) / s1
assert_allclose(lsim(s1 * 2, U=u, T=t)[1], lsim(s1, U=2 * u, T=t)[1])
assert_allclose(lsim(s1 * s2, U=u, T=t)[1],
lsim(s1, U=lsim(s2, U=u, T=t)[1], T=t)[1],
atol=1e-5)
with assert_raises(TypeError):
s1 / s1
with assert_raises(TypeError):
s1 * s_discrete
with assert_raises(TypeError):
# Check different discretization constants
s_discrete * s2_discrete
with assert_raises(TypeError):
s1 * BadType()
with assert_raises(TypeError):
BadType() * s1
with assert_raises(TypeError):
s1 / BadType()
with assert_raises(TypeError):
BadType() / s1
# Test addition
assert_allclose(
lsim(s1 + 2, U=u, T=t)[1], 2 * u + lsim(s1, U=u, T=t)[1])
# Check for dimension mismatch
with assert_raises(ValueError):
s1 + np.array([1, 2])
with assert_raises(ValueError):
np.array([1, 2]) + s1
with assert_raises(TypeError):
s1 + s_discrete
with assert_raises(ValueError):
s1 / np.array([[1, 2], [3, 4]])
with assert_raises(TypeError):
# Check different discretization constants
s_discrete + s2_discrete
with assert_raises(TypeError):
s1 + BadType()
with assert_raises(TypeError):
BadType() + s1
assert_allclose(
lsim(s1 + s2, U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] + lsim(s2, U=u, T=t)[1])
# Test subtraction
assert_allclose(
lsim(s1 - 2, U=u, T=t)[1], -2 * u + lsim(s1, U=u, T=t)[1])
assert_allclose(
lsim(2 - s1, U=u, T=t)[1], 2 * u + lsim(-s1, U=u, T=t)[1])
assert_allclose(
lsim(s1 - s2, U=u, T=t)[1],
lsim(s1, U=u, T=t)[1] - lsim(s2, U=u, T=t)[1])
with assert_raises(TypeError):
s1 - BadType()
with assert_raises(TypeError):
BadType() - s1
s = s_discrete + s3_discrete
assert_(s.dt == 0.1)
s = s_discrete * s3_discrete
assert_(s.dt == 0.1)
s = 3 * s_discrete
assert_(s.dt == 0.1)
s = -s_discrete
assert_(s.dt == 0.1)
import numpy as np
from scipy.signal import StateSpace, lsim, dlsim
from scipy.linalg import expm
import matplotlib.pyplot as plt
# Build the CT system.
A = np.asarray([[0., 1.],
[-2., -2.]])
B = np.asarray([[1.],[1.]])
C = np.asarray([2.,3.])
D = np.asarray([0.])
t_CT = np.arange(0, 5.01, 0.01)
input_CT = np.ones(len(t_CT))
sys_CT = StateSpace(A, B, C, D)
_, y_CT, x_CT = lsim(sys_CT, input_CT, t_CT, X0=[0., 0.])
y5_CT = y_CT[-1]
print("In the CT system, y(5) is:", y5_CT)
# Calculate the DT system.
G = expm(A);
H = np.asarray([[0.,0.],[0.,0.]])
step = np.arange(0,1.001,0.001)
for i in step:
H += expm(A*i) * 0.001
H = np.dot(H, B)
# Build the DT system.
sys_DT = StateSpace(G, H, C, D, dt = 1)
print("The discretized state space representation of this system is:")
print(sys_DT)
t_DT = np.arange(0, 6, 1);
def __init__(self, Y0, Y1, U0, q=10, u_nominal=1, dt=1):
if Y0.shape[1] != U0.shape[1]:
raise ValueError(
"The number of snapshot pairs Y0, U0 should be equal to the number of training inputs."
)
if Y1.shape[1] != U0.shape[1]:
raise ValueError(
"The number of snapshot pairs Y1, U0 should be equal to the number of training inputs."
)
# Parameters
self.ny = Y0.shape[0] # Number of outputs
self.nu = U0.shape[0] # Number of inputs
self.q = q # Number of POD modes kept
self.p = Y0.shape[1] # Number of training snapshots available
self.dt = dt # Time step
# Save snapshots
self.Y0 = Y0
self.Y1 = Y1
self.U0 = U0
# Compute POD Modes
self.Uq = self.compute_POD_basis(Y0, self.q)
# Project snapshots
H0 = self.Uq.conj().T @ Y0
H1 = self.Uq.conj().T @ Y1
# POD projection error
pod_error = np.linalg.norm((Y0 - self.Uq @ H0), ord='fro')/ \
np.linalg.norm(Y0, ord='fro')*100
print(' POD projection error = ', pod_error, '%')
# Run DMDc
self.F, self.G = self.DMDc(H0, H1, U0)
# Eigendecomposition of A
lamb, self.W = np.linalg.eig(self.F)
self.Lambda = np.diag(lamb)
self.Gamma = np.linalg.inv(self.W) @ self.G
# DMD modes
self.Phi = self.Uq @ self.W
# Setup matrices for sparsity-promoting optimization
#self.R = np.zeros((self.q, self.p), dtype=np.complex)
R = self.Lambda @ (
np.linalg.pinv(self.Phi) @ self.Y0) + self.Gamma @ U0
L = self.Phi
#Y1 = self.Y[:,1:]
# Now cast into quadratic form
self.P = np.multiply(L.conj().T @ L, (R @ R.conj().T).conj())
self.d = np.diag(R @ Y1.conj().T @ L).conj()
self.s = np.trace(Y1.conj().T @ Y1)
print('s.shape = ', self.s.shape)
self.sys_pod = StateSpace(self.F, self.G, self.Uq)
self.sys_dmd = StateSpace(self.Lambda, self.Gamma, self.Phi)
print('DMD model error:')
self.sys_dmd.error(Y0, Y1, U0)
class DMDcsp(object):
"""
Sparsity-Promoting Dynamic Mode Decomposition with Control Class
Initialization: Simply provide data matrices Y0, Y1, U0
"""
def __init__(self, Y0, Y1, U0, q=10, u_nominal=1, dt=1):
if Y0.shape[1] != U0.shape[1]:
raise ValueError(
"The number of snapshot pairs Y0, U0 should be equal to the number of training inputs."
)
if Y1.shape[1] != U0.shape[1]:
raise ValueError(
"The number of snapshot pairs Y1, U0 should be equal to the number of training inputs."
)
# Parameters
self.ny = Y0.shape[0] # Number of outputs
self.nu = U0.shape[0] # Number of inputs
self.q = q # Number of POD modes kept
self.p = Y0.shape[1] # Number of training snapshots available
self.dt = dt # Time step
# Save snapshots
self.Y0 = Y0
self.Y1 = Y1
self.U0 = U0
# Compute POD Modes
self.Uq = self.compute_POD_basis(Y0, self.q)
# Project snapshots
H0 = self.Uq.conj().T @ Y0
H1 = self.Uq.conj().T @ Y1
# POD projection error
pod_error = np.linalg.norm((Y0 - self.Uq @ H0), ord='fro')/ \
np.linalg.norm(Y0, ord='fro')*100
print(' POD projection error = ', pod_error, '%')
# Run DMDc
self.F, self.G = self.DMDc(H0, H1, U0)
# Eigendecomposition of A
lamb, self.W = np.linalg.eig(self.F)
self.Lambda = np.diag(lamb)
self.Gamma = np.linalg.inv(self.W) @ self.G
# DMD modes
self.Phi = self.Uq @ self.W
# Setup matrices for sparsity-promoting optimization
#self.R = np.zeros((self.q, self.p), dtype=np.complex)
R = self.Lambda @ (
np.linalg.pinv(self.Phi) @ self.Y0) + self.Gamma @ U0
L = self.Phi
#Y1 = self.Y[:,1:]
# Now cast into quadratic form
self.P = np.multiply(L.conj().T @ L, (R @ R.conj().T).conj())
self.d = np.diag(R @ Y1.conj().T @ L).conj()
self.s = np.trace(Y1.conj().T @ Y1)
print('s.shape = ', self.s.shape)
self.sys_pod = StateSpace(self.F, self.G, self.Uq)
self.sys_dmd = StateSpace(self.Lambda, self.Gamma, self.Phi)
print('DMD model error:')
self.sys_dmd.error(Y0, Y1, U0)
def DMDc(self, H0, H1, U0):
"""
Inputs:
H0 : first snapshot matrix
H1 : second snapshot matrix (after one time step)
U0 : corresponding input
Outputs:
A, B : state and output matrices fitted to the reduced-order data
"""
q = H0.shape[0]
nu = U0.shape[0]
p = U0.shape[1]
U, Sig, VT = np.linalg.svd(np.vstack((H0, U0)), full_matrices=False)
thres = 1.0e-10
rtil = np.min((np.sum(np.diag(Sig) > thres), q))
print(' rtil = ', rtil)
Util = U[:, :rtil]
Sigtil = np.diag(Sig)[:rtil, :rtil]
Vtil = VT.T[:, :rtil]
U_F = Util[:q, :]
U_G = Util[q:q + nu, :]
F = H1 @ Vtil @ np.linalg.inv(Sigtil) @ U_F.conj().T
G = H1 @ Vtil @ np.linalg.inv(Sigtil) @ U_G.conj().T
dmdc_error = np.linalg.norm((H1 - (F @ H0 + G @ U0)), ord='fro')/ \
np.linalg.norm(H1, ord='fro')*100
print(' DMDc error = ', dmdc_error, '%')
print(' Maximum eigenvalue: ', np.max(np.abs(np.linalg.eig(F)[0])))
return F, G
def sparse(self, gamma, niter):
zero_thres = 1.e-6
print("gamma = %e | number of modes = " % (gamma))
# Define and solve the sparsity-promoting optimization problem
# Weighted L1 norm is updated iteratively
alpha = cp.Variable(self.q)
weights = np.ones(self.q)
for i in range(niter):
objective_sparse = cp.Minimize(
cp.quad_form(alpha, self.P) -
2. * cp.real(self.d.conj().T @ alpha) + self.s +
gamma * cp.pnorm(np.diag(weights) @ alpha, p=1))
prob_sparse = cp.Problem(objective_sparse)
sol_sparse = prob_sparse.solve(verbose=False, solver=cp.SCS)
alpha_sp = alpha.value # Sparse solution
if alpha_sp is None:
alpha_sp = np.ones(self.q)
# Update weights
weights = 1.0 / (np.abs(alpha_sp) + np.finfo(float).eps)
nonzero = np.abs(alpha_sp) > zero_thres # Nonzero modes
print(" %d of %d" %
(np.sum(nonzero), self.q))
J_sp = np.real(alpha_sp.conj().T @ self.P @ alpha_sp -
2 * np.real(self.d.conj().T @ alpha_sp) +
self.s) # Square error
nx = np.sum(
nonzero
) # Number of nonzero modes - order of the sparse/reduced model
Ez = np.eye(self.q)[:, ~nonzero]
# Define and solve the refinement optimization problem
#alpha = cp.Variable(self.q)
objective_refine = cp.Minimize(
cp.quad_form(alpha, self.P) -
2. * cp.real(self.d.conj().T @ alpha) + self.s)
if np.sum(~nonzero):
constraint_refine = [Ez.T @ alpha == 0]
prob_refine = cp.Problem(objective_refine, constraint_refine)
else:
prob_refine = cp.Problem(objective_refine)
sol_refine = prob_refine.solve()
alpha_ref = alpha.value
J_ref = np.real(alpha_ref.conj().T @ self.P @ alpha_ref -
2 * np.real(self.d.conj().T @ alpha_ref) +
self.s) # Square error
P_loss = 100 * np.sqrt(J_ref / self.s)
# Truncate modes
E = np.eye(self.q)[:, nonzero]
Lambda_bar = E.T @ self.Lambda @ E
Beta_bar = E.T @ self.Gamma
Phi_bar = self.Phi @ np.diag(alpha_ref) @ E
# Save data
stats = {}
stats["nx"] = nx
stats["alpha_sp"] = alpha_sp
stats["alpha_ref"] = alpha_ref
stats["z_0"] = (np.linalg.pinv(self.Phi) @ self.Y0[:, 0])[nonzero]
stats["E"] = E
stats["J_sp"] = J_sp
stats["J_ref"] = J_ref
stats["P_loss"] = P_loss
if nx != 0:
print("Rank of controllability matrix: %d of %d" %
(np.linalg.matrix_rank(control.ctrb(Lambda_bar,
Beta_bar)), nx))
# Convert system from complex modal form to real block-modal form
lambda_bar = np.diag(Lambda_bar)
A = np.zeros((nx, nx))
B = np.zeros((nx, self.nu))
Theta = np.zeros((self.ny, nx))
i = 0
while i < nx:
if np.isreal(lambda_bar[i]):
A[i, i] = lambda_bar[i]
B[i] = Beta_bar[i]
Theta[:, i] = Phi_bar[:, i]
i += 1
elif i == nx - 1:
# In case only one of the conjugate pairs is truncated - this rarely happens
A[i, i] = np.real(lambda_bar[i])
B[i] = np.real(Beta_bar[i])
Theta[:, i] = np.real(Phi_bar[:, i])
i += 1
elif np.isreal(np.isreal(lambda_bar[i] + lambda_bar[i + 1])):
A[i:i + 2, i:i + 2] = np.array(
[[np.real(lambda_bar[i]),
np.imag(lambda_bar[i])],
[-np.imag(lambda_bar[i]),
np.real(lambda_bar[i])]])
B[i] = np.real(Beta_bar[i])
B[i + 1] = -np.imag(Beta_bar[i])
Theta[:, i] = 2 * np.real(Phi_bar[:, i])
Theta[:, i + 1] = 2 * np.imag(Phi_bar[:, i])
i += 2
else:
raise ValueError(
"Eigenvalues are not grouped in conjugate pairs")
#return StateSpace(Lambda_bar, Beta_bar, Phi_bar), lambda_bar, stats
return StateSpace(A, B, Theta), lambda_bar, stats
def sparse_batch(self, gamma, niter):
num = gamma.shape[0]
self.rsys = num * [None]
self.sys_eig = num * [None]
stats = {}
stats["alpha_sp"] = num * [None]
stats["alpha_ref"] = num * [None]
stats["z_0"] = num * [None]
stats["E"] = num * [None]
stats['nx'] = np.zeros(num)
stats["J_sp"] = np.zeros(num)
stats["J_ref"] = np.zeros(num)
stats["P_loss"] = np.zeros(num)
for i in range(num):
print('Model # %d' % i)
self.rsys[i], self.sys_eig[i], stats_tmp = self.sparse(
gamma[i], niter)
# Save stats
stats["alpha_sp"][i] = stats_tmp["alpha_sp"]
stats["alpha_ref"][i] = stats_tmp["alpha_ref"]
stats["z_0"][i] = stats_tmp["z_0"]
stats["E"][i] = stats_tmp["E"]
stats['nx'][i] = stats_tmp['nx']
stats["J_sp"][i] = stats_tmp["J_sp"]
stats["J_ref"][i] = stats_tmp["J_ref"]
stats["P_loss"][i] = stats_tmp["P_loss"]
self.sp_stats = stats
return stats
def compute_noise_cov(self, sys_i, sens):
# Measurement output matrix
C = self.rsys[sys_i].C[sens, :]
ThetaInv = np.linalg.pinv(self.rsys[sys_i].C)
Qe = np.cov(ThetaInv @ self.Y1 -
self.rsys[sys_i].A @ (ThetaInv @ self.Y0 -
self.rsys[sys_i].B @ self.U0))
Re = np.cov(self.Y1[sens] - C @ (ThetaInv @ self.Y1))
return C, Qe, Re
def compute_POD_basis(self, Y, q):
return np.linalg.svd(Y, full_matrices=False)[0][:, :q]
"""
Plot functions
"""
def plot_dmd_modes(self, grid, E=None):
if E is None:
E = np.eye(self.q)
nlevels = 41
wymin = -0.1
wymax = 0.1
nx = E.shape[1]
X = grid.X()
Z = grid.Z()
xmax = np.max(X)
def Phi(k):
return np.real(self.Phi @ E)[:, k].reshape((grid.npx, grid.npz))
fig, axs = plt.subplots(1,
figsize=(10, 5),
facecolor='w',
edgecolor='k')
cont = axs.contourf(X,
Z,
Phi(0),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
cbar = fig.colorbar(cont, ax=axs, orientation='vertical')
cbar.set_ticks(np.linspace(wymin, wymax, num=6, endpoint=True))
axs.set_title('$Phi_0$')
axs.set_xlim([1, xmax])
# Flat plate patch
delx = 5.0 / 768.0
delz = 2.0 / 384.0
xc = 249 * delx
zc = 192 * delz
alpha = 20.0 * np.pi / 180.0
DL = 80 * delx
DT = 6 * delz
flat_plate = patches.Rectangle(
(xc - DL * np.cos(alpha) / 2. - DT * np.sin(alpha) / 2.,
zc + DL * np.sin(alpha) / 2. - DT * np.cos(alpha) / 2.),
DL,
DT,
angle=-(alpha * 180.0 / np.pi),
linewidth=1,
edgecolor='black',
facecolor='black')
axs.add_patch(flat_plate)
def animate(k):
axs.clear()
cont = axs.contourf(X,
Z,
Phi(k),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
axs.set_xlabel('$x$')
axs.set_ylabel('$z$')
axs.set_title('$Phi_{%d}$' % k)
axs.set_aspect('equal', 'box')
axs.add_patch(flat_plate)
axs.set_xlim([1, xmax])
return cont
anim = animation.FuncAnimation(fig,
animate,
frames=range(0, nx, 1),
interval=500)
return anim
def plot_model_response(self, sys, grid):
x0 = np.linalg.pinv(sys.C) @ self.Y0[:, 0]
xdmd, ydmd = sys.lsim(x0, self.U0)
nlevels = 41
wymin = -10
wymax = 10
X = grid.X()
Z = grid.Z()
xmax = np.max(X)
def WY(k):
return self.Y0[:, k].reshape((grid.npx, grid.npz))
def WY_dmd(k):
return ydmd[:, k].reshape((grid.npx, grid.npz))
fig, axs = plt.subplots(2,
figsize=(10, 8),
facecolor='w',
edgecolor='k')
cont = axs[0].contourf(X,
Z,
WY(0),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
cont = axs[1].contourf(X,
Z,
WY_dmd(0),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
#plt.clim(wymin, wymax)
cbar = fig.colorbar(cont, ax=axs, orientation='vertical')
#cbar.ax.set_autoscale_on(True)
cbar.set_ticks(np.linspace(wymin, wymax, num=6, endpoint=True))
# Flat plate patch
delx = 5.0 / 768.0
delz = 2.0 / 384.0
xc = 249 * delx
zc = 192 * delz
alpha = 20.0 * np.pi / 180.0
DL = 80 * delx
DT = 6 * delz
flat_plate = patches.Rectangle(
(xc - DL * np.cos(alpha) / 2. - DT * np.sin(alpha) / 2.,
zc + DL * np.sin(alpha) / 2. - DT * np.cos(alpha) / 2.),
DL,
DT,
angle=-(alpha * 180.0 / np.pi),
linewidth=1,
edgecolor='black',
facecolor='black')
flat_plate_2 = patches.Rectangle(
(xc - DL * np.cos(alpha) / 2. - DT * np.sin(alpha) / 2.,
zc + DL * np.sin(alpha) / 2. - DT * np.cos(alpha) / 2.),
DL,
DT,
angle=-(alpha * 180.0 / np.pi),
linewidth=1,
edgecolor='black',
facecolor='black')
axs[0].add_patch(flat_plate)
axs[1].add_patch(flat_plate_2)
axs[0].set_xlim([1, xmax])
axs[1].set_xlim([1, xmax])
axs[0].set_title('Time step $k = %d$' % (0))
#cbar.ax.locator_params(nbins=6)
def animate(k):
# Clear axes
axs[0].clear()
axs[1].clear()
# Training Data
cont = axs[0].contourf(X,
Z,
WY(k),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
axs[0].set_xlabel('$x$')
axs[0].set_ylabel('$z$')
axs[0].set_aspect('equal', 'box')
axs[0].add_patch(flat_plate)
axs[0].set_xlim([1, xmax])
axs[0].set_title('Time step $k = %d$' % (k))
# Simulation Results
cont = axs[1].contourf(X,
Z,
WY_dmd(k),
nlevels,
cmap='coolwarm',
vmin=wymin,
vmax=wymax)
axs[1].set_xlabel('$x$')
axs[1].set_ylabel('$z$')
axs[1].set_aspect('equal', 'box')
axs[1].add_patch(flat_plate_2)
axs[1].set_xlim([1, xmax])
return cont
anim = animation.FuncAnimation(fig,
animate,
frames=range(0, self.p, 4),
interval=200)
return anim
def sparse(self, gamma, niter):
zero_thres = 1.e-6
print("gamma = %e | number of modes = " % (gamma))
# Define and solve the sparsity-promoting optimization problem
# Weighted L1 norm is updated iteratively
alpha = cp.Variable(self.q)
weights = np.ones(self.q)
for i in range(niter):
objective_sparse = cp.Minimize(
cp.quad_form(alpha, self.P) -
2. * cp.real(self.d.conj().T @ alpha) + self.s +
gamma * cp.pnorm(np.diag(weights) @ alpha, p=1))
prob_sparse = cp.Problem(objective_sparse)
sol_sparse = prob_sparse.solve(verbose=False, solver=cp.SCS)
alpha_sp = alpha.value # Sparse solution
if alpha_sp is None:
alpha_sp = np.ones(self.q)
# Update weights
weights = 1.0 / (np.abs(alpha_sp) + np.finfo(float).eps)
nonzero = np.abs(alpha_sp) > zero_thres # Nonzero modes
print(" %d of %d" %
(np.sum(nonzero), self.q))
J_sp = np.real(alpha_sp.conj().T @ self.P @ alpha_sp -
2 * np.real(self.d.conj().T @ alpha_sp) +
self.s) # Square error
nx = np.sum(
nonzero
) # Number of nonzero modes - order of the sparse/reduced model
Ez = np.eye(self.q)[:, ~nonzero]
# Define and solve the refinement optimization problem
#alpha = cp.Variable(self.q)
objective_refine = cp.Minimize(
cp.quad_form(alpha, self.P) -
2. * cp.real(self.d.conj().T @ alpha) + self.s)
if np.sum(~nonzero):
constraint_refine = [Ez.T @ alpha == 0]
prob_refine = cp.Problem(objective_refine, constraint_refine)
else:
prob_refine = cp.Problem(objective_refine)
sol_refine = prob_refine.solve()
alpha_ref = alpha.value
J_ref = np.real(alpha_ref.conj().T @ self.P @ alpha_ref -
2 * np.real(self.d.conj().T @ alpha_ref) +
self.s) # Square error
P_loss = 100 * np.sqrt(J_ref / self.s)
# Truncate modes
E = np.eye(self.q)[:, nonzero]
Lambda_bar = E.T @ self.Lambda @ E
Beta_bar = E.T @ self.Gamma
Phi_bar = self.Phi @ np.diag(alpha_ref) @ E
# Save data
stats = {}
stats["nx"] = nx
stats["alpha_sp"] = alpha_sp
stats["alpha_ref"] = alpha_ref
stats["z_0"] = (np.linalg.pinv(self.Phi) @ self.Y0[:, 0])[nonzero]
stats["E"] = E
stats["J_sp"] = J_sp
stats["J_ref"] = J_ref
stats["P_loss"] = P_loss
if nx != 0:
print("Rank of controllability matrix: %d of %d" %
(np.linalg.matrix_rank(control.ctrb(Lambda_bar,
Beta_bar)), nx))
# Convert system from complex modal form to real block-modal form
lambda_bar = np.diag(Lambda_bar)
A = np.zeros((nx, nx))
B = np.zeros((nx, self.nu))
Theta = np.zeros((self.ny, nx))
i = 0
while i < nx:
if np.isreal(lambda_bar[i]):
A[i, i] = lambda_bar[i]
B[i] = Beta_bar[i]
Theta[:, i] = Phi_bar[:, i]
i += 1
elif i == nx - 1:
# In case only one of the conjugate pairs is truncated - this rarely happens
A[i, i] = np.real(lambda_bar[i])
B[i] = np.real(Beta_bar[i])
Theta[:, i] = np.real(Phi_bar[:, i])
i += 1
elif np.isreal(np.isreal(lambda_bar[i] + lambda_bar[i + 1])):
A[i:i + 2, i:i + 2] = np.array(
[[np.real(lambda_bar[i]),
np.imag(lambda_bar[i])],
[-np.imag(lambda_bar[i]),
np.real(lambda_bar[i])]])
B[i] = np.real(Beta_bar[i])
B[i + 1] = -np.imag(Beta_bar[i])
Theta[:, i] = 2 * np.real(Phi_bar[:, i])
Theta[:, i + 1] = 2 * np.imag(Phi_bar[:, i])
i += 2
else:
raise ValueError(
"Eigenvalues are not grouped in conjugate pairs")
#return StateSpace(Lambda_bar, Beta_bar, Phi_bar), lambda_bar, stats
return StateSpace(A, B, Theta), lambda_bar, stats
