def test_saver_kf():
kf = kinematic_kf(3, 2, dt=0.1, dim_z=3)
s = Saver(kf)
for i in range(10):
kf.predict()
kf.update([i, i, i])
s.save()
# this will assert if the KalmanFilter did not properly assert
s.to_array()
assert len(s.x) == 10
assert len(s.y) == 10
assert len(s.K) == 10
assert (len(s) == len(s.x))
# Force an exception to occur my malforming K
kf = kinematic_kf(3, 2, dt=0.1, dim_z=3)
kf.K = 0.
s2 = Saver(kf)
for i in range(10):
kf.predict()
kf.update([i, i, i])
s2.save()
# this should raise an exception because K is malformed
try:
s2.to_array()
assert False, "Should not have been able to convert s.K into an array"
except:
pass
def test_saver_UKF():
def fx(x, dt):
F = np.array([[1, dt, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, dt],
[0, 0, 0, 1]], dtype=float)
return np.dot(F, x)
def hx(x):
return np.array([x[0], x[2]])
dt = 0.1
points = MerweScaledSigmaPoints(4, .1, 2., -1)
kf = UnscentedKalmanFilter(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, points=points)
z_std = 0.1
kf.R = np.diag([z_std**2, z_std**2]) # 1 standard
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 1.
zs = [[i, i] for i in range(40)]
s = Saver(kf, skip_private=False, skip_callable=False, ignore=['z_mean'])
for z in zs:
kf.predict()
kf.update(z)
#print(kf.x, kf.log_likelihood, kf.P.diagonal())
s.save()
s.to_array()
def test_1d_array():
f1 = GHFilter (0, 0, 1, .8, .2)
f2 = GHFilter (array([0]), array([0]), 1, .8, .2)
str(f1)
str(f2)
# test both give same answers, and that we can
# use a scalar for the measurment
for i in range(1,10):
f1.update(i)
f2.update(i)
assert f1.x == f2.x[0]
assert f1.dx == f2.dx[0]
assert f1.VRF() == f2.VRF()
# test using an array for the measurement
s1 = Saver(f1)
s2 = Saver(f2)
for i in range(1,10):
f1.update(i)
f2.update(array([i]))
s1.save()
s2.save()
assert f1.x == f2.x[0]
assert f1.dx == f2.dx[0]
assert f1.VRF() == f2.VRF()
s1.to_array()
s2.to_array()
def test_saver_kf():
kf = kinematic_kf(3, 2, dt=0.1, dim_z=3)
s = Saver(kf)
for i in range(10):
kf.predict()
kf.update([i, i, i])
s.save()
# this will assert if the KalmanFilter did not properly assert
s.to_array()
assert len(s.x) == 10
assert len(s.y) == 10
assert len(s.K) == 10
assert (len(s) == len(s.x))
# Force an exception to occur my malforming K
kf = kinematic_kf(3, 2, dt=0.1, dim_z=3)
kf.K = 0.
s2 = Saver(kf)
for i in range(10):
kf.predict()
kf.update([i, i, i])
s2.save()
# this should raise an exception because K is malformed
try:
s2.to_array()
assert False, "Should not have been able to convert s.K into an array"
except:
pass
def run_filter(list_args: List[float],
data: pd.DataFrame) -> Tuple[KalmanFilter, Saver]:
if isinstance(data, tuple):
# data is the first *args
data = data[0]
# store the parameters in the list_args
s_noise_Q00, r_noise_Q11, q_meas_error_R00, r_meas_error_R11 = list_args
kf = init_filter(
r0=0.0,
S0=5.74,
s_variance_P00=1,
r_variance_P11=100,
s_noise_Q00=s_noise_Q00,
r_noise_Q11=r_noise_Q11,
q_meas_error_R00=q_meas_error_R00,
r_meas_error_R11=r_meas_error_R11,
)
s = Saver(kf)
lls = []
for z in np.vstack([data["q_obs"], data["r_obs"]]).T:
kf.predict()
kf.update(z)
log_likelihood = kf.log_likelihood_of(z)
lls.append(log_likelihood)
s.save()
s.to_array()
lls = np.array(lls)
return kf, s, lls
def _test_log_likelihood():
from filterpy.common import Saver
def fx(x, dt):
F = np.array(
[[1, dt, 0, 0], [0, 1, 0, 0], [0, 0, 1, dt], [0, 0, 0, 1]],
dtype=float)
return np.dot(F, x)
def hx(x):
return np.array([x[0], x[2]])
dt = 0.1
points = MerweScaledSigmaPoints(4, .1, 2., -1)
kf = UKF(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, points=points)
z_std = 0.1
kf.R = np.diag([z_std**2, z_std**2]) # 1 standard
kf.Q = Q_discrete_white_noise(dim=2, dt=dt, var=1.1**2, block_size=2)
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 1.
zs = [[i + randn() * z_std, i + randn() * z_std] for i in range(40)]
s = Saver(kf)
for z in zs:
kf.predict()
kf.update(z)
print(kf.x, kf.log_likelihood, kf.P.diagonal())
s.save()
s.to_array()
plt.plot(s.x[:, 0], s.x[:, 2])
def myKalmanFilter(Kalman_filter=None, zs=None):
s = Saver(Kalman_filter)
Kalman_filter.batch_filter(zs, saver=s)
s.to_array()
zs_filtered = s.x[:, 0]
err = s.x[:, 1]
return zs_filtered, err
def test_saver_UKF():
def fx(x, dt):
F = np.array(
[[1, dt, 0, 0], [0, 1, 0, 0], [0, 0, 1, dt], [0, 0, 0, 1]],
dtype=float)
return np.dot(F, x)
def hx(x):
return np.array([x[0], x[2]])
dt = 0.1
points = MerweScaledSigmaPoints(4, .1, 2., -1)
kf = UnscentedKalmanFilter(dim_x=4,
dim_z=2,
dt=dt,
fx=fx,
hx=hx,
points=points)
z_std = 0.1
kf.R = np.diag([z_std**2, z_std**2]) # 1 standard
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 1.
zs = [[i, i] for i in range(40)]
s = Saver(kf, skip_private=False, skip_callable=False, ignore=['z_mean'])
for z in zs:
kf.predict()
kf.update(z)
#print(kf.x, kf.log_likelihood, kf.P.diagonal())
s.save()
s.to_array()
def test_noisy_1d():
f = KalmanFilter(dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = np.array([[1., 1.],
[0., 1.]]) # state transition matrix
f.H = np.array([[1., 0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R = 5 # state uncertainty
f.Q = 0.0001 # process uncertainty
measurements = []
results = []
zs = []
for t in range(100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
# save data
results.append(f.x[0, 0])
measurements.append(z)
# test mahalanobis
a = np.zeros(f.y.shape)
maha = scipy_mahalanobis(a, f.y, f.SI)
assert f.mahalanobis == approx(maha)
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.x = np.array([[2., 0]]).T
f.P = np.eye(2) * 100.
s = Saver(f)
m, c, _, _ = f.batch_filter(zs, update_first=False, saver=s)
s.to_array()
assert len(s.x) == len(zs)
assert len(s.x) == len(s)
# plot data
if DO_PLOT:
p1, = plt.plot(measurements, 'r', alpha=0.5)
p2, = plt.plot(results, 'b')
p4, = plt.plot(m[:, 0], 'm')
p3, = plt.plot([0, 100], [0, 100], 'g') # perfect result
plt.legend([p1, p2, p3, p4],
["noisy measurement", "KF output", "ideal", "batch"], loc=4)
plt.show()
def test_noisy_1d():
f = KalmanFilter(dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = np.array([[1., 1.],
[0., 1.]]) # state transition matrix
f.H = np.array([[1., 0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R = 5 # state uncertainty
f.Q = 0.0001 # process uncertainty
measurements = []
results = []
zs = []
for t in range(100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
# save data
results.append(f.x[0, 0])
measurements.append(z)
# test mahalanobis
a = np.zeros(f.y.shape)
maha = scipy_mahalanobis(a, f.y, f.SI)
assert f.mahalanobis == approx(maha)
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.x = np.array([[2., 0]]).T
f.P = np.eye(2) * 100.
s = Saver(f)
m, c, _, _ = f.batch_filter(zs, update_first=False, saver=s)
s.to_array()
assert len(s.x) == len(zs)
assert len(s.x) == len(s)
# plot data
if DO_PLOT:
p1, = plt.plot(measurements, 'r', alpha=0.5)
p2, = plt.plot(results, 'b')
p4, = plt.plot(m[:, 0], 'm')
p3, = plt.plot([0, 100], [0, 100], 'g') # perfect result
plt.legend([p1, p2, p3, p4],
["noisy measurement", "KF output", "ideal", "batch"], loc=4)
plt.show()
def test_MMAE2():
dt = 0.1
pos, zs = generate_data(120, noise_factor=0.6)
z_xs = zs[:, 0]
dt = 0.1
ca = make_ca_filter(dt, noise_factor=0.6)
cv = make_ca_filter(dt, noise_factor=0.6)
cv.F[:, 2] = 0 # remove acceleration term
cv.P[2, 2] = 0
cv.Q[2, 2] = 0
filters = [cv, ca]
bank = MMAEFilterBank(filters, (0.5, 0.5), dim_x=3, H=ca.H)
xs, probs = [], []
cvxs, caxs = [], []
s = Saver(bank)
for i, z in enumerate(z_xs):
bank.predict()
bank.update(z)
xs.append(bank.x[0])
cvxs.append(cv.x[0])
caxs.append(ca.x[0])
print(i, cv.likelihood, ca.likelihood, bank.p)
s.save()
probs.append(bank.p[0] / bank.p[1])
s.to_array()
if DO_PLOT:
plt.subplot(121)
plt.plot(xs)
plt.plot(pos[:, 0])
plt.subplot(122)
plt.plot(probs)
plt.title('probability ratio p(cv)/p(ca)')
plt.figure()
plt.plot(cvxs, label='CV')
plt.plot(caxs, label='CA')
plt.plot(pos[:, 0])
plt.legend()
plt.figure()
plt.plot(xs)
plt.plot(pos[:, 0])
return bank
def test_1d_const_vel():
def hx(x):
return np.array([x[0]])
F = np.array([[1., 1.],[0., 1.]])
def fx(x, dt):
return np.dot(F, x)
x = np.array([0., 1.])
P = np.eye(2)* 100.
f = EnKF(x=x, P=P, dim_z=1, dt=1., N=8, hx=hx, fx=fx)
std_noise = 10.
f.R *= std_noise**2
f.Q = Q_discrete_white_noise(2, 1., .001)
measurements = []
results = []
ps = []
zs = []
s = Saver(f)
for t in range (0,100):
# create measurement = t plus white noise
z = t + randn()*std_noise
zs.append(z)
f.predict()
f.update(np.asarray([z]))
# save data
results.append (f.x[0])
measurements.append(z)
ps.append(3*(f.P[0,0]**.5))
s.save()
s.to_array()
results = np.asarray(results)
ps = np.asarray(ps)
if DO_PLOT:
plt.plot(results, label='EnKF')
plt.plot(measurements, c='r', label='z')
plt.plot (results-ps, c='k',linestyle='--', label='3$\sigma$')
plt.plot(results+ps, c='k', linestyle='--')
plt.legend(loc='best')
#print(ps)
return f
def test_MMAE2():
dt = 0.1
pos, zs = generate_data(120, noise_factor=0.6)
z_xs = zs[:, 0]
dt = 0.1
ca = make_ca_filter(dt, noise_factor=0.6)
cv = make_ca_filter(dt, noise_factor=0.6)
cv.F[:, 2] = 0 # remove acceleration term
cv.P[2, 2] = 0
cv.Q[2, 2] = 0
filters = [cv, ca]
bank = MMAEFilterBank(filters, (0.5, 0.5), dim_x=3, H=ca.H)
xs, probs = [], []
cvxs, caxs = [], []
s = Saver(bank)
for i, z in enumerate(z_xs):
bank.predict()
bank.update(z)
xs.append(bank.x[0])
cvxs.append(cv.x[0])
caxs.append(ca.x[0])
print(i, cv.likelihood, ca.likelihood, bank.p)
s.save()
probs.append(bank.p[0] / bank.p[1])
s.to_array()
if DO_PLOT:
plt.subplot(121)
plt.plot(xs)
plt.plot(pos[:, 0])
plt.subplot(122)
plt.plot(probs)
plt.title('probability ratio p(cv)/p(ca)')
plt.figure()
plt.plot(cvxs, label='CV')
plt.plot(caxs, label='CA')
plt.plot(pos[:, 0])
plt.legend()
plt.figure()
plt.plot(xs)
plt.plot(pos[:, 0])
return bank
def test_1d():
def fx(x, dt):
F = np.array([[1., dt],
[0, 1]])
return np.dot(F, x)
def hx(x):
return x[0:1]
ckf = CKF(dim_x=2, dim_z=1, dt=0.1, hx=hx, fx=fx)
ckf.x = np.array([[1.], [2.]])
ckf.P = np.array([[1, 1.1],
[1.1, 3]])
ckf.R = np.eye(1) * .05
ckf.Q = np.array([[0., 0], [0., .001]])
dt = 0.1
points = MerweScaledSigmaPoints(2, .1, 2., -1)
kf = UKF(dim_x=2, dim_z=1, dt=dt, fx=fx, hx=hx, points=points)
kf.x = np.array([1, 2])
kf.P = np.array([[1, 1.1],
[1.1, 3]])
kf.R *= 0.05
kf.Q = np.array([[0., 0], [0., .001]])
s = Saver(kf)
for i in range(50):
z = np.array([[i+randn()*0.1]])
ckf.predict()
ckf.update(z)
kf.predict()
kf.update(z[0])
assert abs(ckf.x[0] - kf.x[0]) < 1e-10
assert abs(ckf.x[1] - kf.x[1]) < 1e-10
s.save()
# test mahalanobis
a = np.zeros(kf.y.shape)
maha = scipy_mahalanobis(a, kf.y, kf.SI)
assert kf.mahalanobis == approx(maha)
s.to_array()
def test_1d():
def fx(x, dt):
F = np.array([[1., dt], [0, 1]])
return np.dot(F, x)
def hx(x):
return x[0:1]
ckf = CKF(dim_x=2, dim_z=1, dt=0.1, hx=hx, fx=fx)
ckf.x = np.array([[1.], [2.]])
ckf.P = np.array([[1, 1.1], [1.1, 3]])
ckf.R = np.eye(1) * .05
ckf.Q = np.array([[0., 0], [0., .001]])
dt = 0.1
points = MerweScaledSigmaPoints(2, .1, 2., -1)
kf = UKF(dim_x=2, dim_z=1, dt=dt, fx=fx, hx=hx, points=points)
kf.x = np.array([1, 2])
kf.P = np.array([[1, 1.1], [1.1, 3]])
kf.R *= 0.05
kf.Q = np.array([[0., 0], [0., .001]])
s = Saver(kf)
for i in range(50):
z = np.array([[i + randn() * 0.1]])
ckf.predict()
ckf.update(z)
kf.predict()
kf.update(z[0])
assert abs(ckf.x[0] - kf.x[0]) < 1e-10
assert abs(ckf.x[1] - kf.x[1]) < 1e-10
s.save()
# test mahalanobis
a = np.zeros(kf.y.shape)
maha = scipy_mahalanobis(a, kf.y, kf.SI)
assert kf.mahalanobis == approx(maha)
s.to_array()
def test_saver_ekf():
def HJ(x):
return np.array([[1, 1]])
def hx(x):
return np.array([x[0]])
kf = ExtendedKalmanFilter(2, 1)
s = Saver(kf)
for i in range(3):
kf.predict()
kf.update([[i]], HJ, hx)
s.save()
# this will assert if the KalmanFilter did not properly assert
s.to_array()
assert len(s.x) == 3
assert len(s.y) == 3
assert len(s.K) == 3
def test_saver_ekf():
def HJ(x):
return np.array([[1, 1]])
def hx(x):
return np.array([x[0]])
kf = ExtendedKalmanFilter(2, 1)
s = Saver(kf)
for i in range(3):
kf.predict()
kf.update([[i]], HJ, hx)
s.save()
# this will assert if the KalmanFilter did not properly assert
s.to_array()
assert len(s.x) == 3
assert len(s.y) == 3
assert len(s.K) == 3
def test_1d():
def fx(x, dt):
F = np.array([[1., dt], [0, 1]])
return np.dot(F, x)
def hx(x):
return x[0:1]
ckf = CKF(dim_x=2, dim_z=1, dt=0.1, hx=hx, fx=fx)
ckf.x = np.array([[1.], [2.]])
ckf.P = np.array([[1, 1.1], [1.1, 3]])
ckf.R = np.eye(1) * .05
ckf.Q = np.array([[0., 0], [0., .001]])
dt = 0.1
points = MerweScaledSigmaPoints(2, .1, 2., -1)
kf = UKF(dim_x=2, dim_z=1, dt=dt, fx=fx, hx=hx, points=points)
kf.x = np.array([1, 2])
kf.P = np.array([[1, 1.1], [1.1, 3]])
kf.R *= 0.05
kf.Q = np.array([[0., 0], [0., .001]])
s = Saver(kf)
for i in range(50):
z = np.array([[i + randn() * 0.1]])
#xx, pp, Sx = predict(f, x, P, Q)
#x, P = update(h, z, xx, pp, R)
ckf.predict()
ckf.update(z)
kf.predict()
kf.update(z[0])
assert abs(ckf.x[0] - kf.x[0]) < 1e-10
assert abs(ckf.x[1] - kf.x[1]) < 1e-10
s.save()
s.to_array()
def smooth(self, t, z):
dt = t[1:] - t[:-1]
if np.any(dt < 0):
raise ValueError('Times must be sorted in accedning order.')
t = t.copy()
z = z.copy()
if self.kf.x is None:
self.kf.x = self.init_x(z[0])
s = Saver(self.kf)
#s.save()
for i, ti in enumerate(t[1:]):
self.kf.predict(dt=dt[i])
self.kf.update(z[i + 1], epoch=ti)
s.save()
s.to_array()
xs, _, _ = self.kf.rts_smoother(s.x, s.P)
return xs[:, :6]
s = Saver(kf)
# ------ RUN FILTER ------
if observe_every > 1:
data.loc[data.index % observe_every != 0, "q_obs"] = np.nan
# Iterate over the Kalman Filter
for time_ix, z in enumerate(np.vstack([data["q_obs"], data["r_obs"]]).T):
kf.predict()
# only make update steps every n timesteps
if time_ix % observe_every == 0:
kf.update(z)
s.save()
s.to_array()
# only observe every n values
# data["q_true_original"] = data["q_true"]
# update data with POSTERIOR estimates
# Calculate the DISCHARGE (measurement operator * \bar{x})
data["q_filtered"] = (s.H @ s.x)[:, 0]
data["q_variance"] = (s.H @ s.P)[:, 0, 0]
return kf, s, data
def calculate_r2_metrics(data):
data = data.dropna()
prior_r2 = r2_score(data["q_true"], data["q_prior"])
def run_ukf(
data: pd.DataFrame,
S0_est: float,
s_variance: float,
r_variance: Optional[float],
Q00_s_noise: float,
Q11_r_noise: Optional[float],
R: float,
params: Dict[str, float],
alpha: float,
beta: float,
kappa: float,
dimension: int = 2,
observe_every: int = 1,
) -> Tuple[UKF.UnscentedKalmanFilter, pd.DataFrame, Saver]:
# --- INIT FILTER --- #
if dimension == 1:
ukf = init_filter(
S0=S0_est,
s_variance=s_variance,
Q00_s_noise=Q00_s_noise,
R=R,
h=hx,
f=abc,
params=params, # dict(a=a_est, b=b_est, c=c_est)
alpha=alpha,
beta=beta,
kappa=kappa,
)
elif dimension == 2:
ukf = init_2D_filter(
S0=S0_est,
r0=data["r_obs"][0],
s_variance=s_variance,
r_variance=r_variance,
Q00_s_noise=Q00_s_noise,
Q11_r_noise=Q11_r_noise,
R=R,
params=params,
alpha=alpha,
beta=beta,
kappa=kappa,
)
s = Saver(ukf)
# --- RUN FILTER --- #
for time_ix, (q, r) in enumerate(np.vstack([data["q_obs"], data["r_obs"]]).T):
# NOTE: z is the discharge (q)
if dimension == 1:
# TODO: how include measurement accuracy of rainfall ?
fx_args = hx_args = {"r": r} # rainfall inputs to the model
# predict
ukf.predict(**fx_args)
# update
if time_ix % observe_every == 0:
ukf.update(q, **hx_args)
elif dimension == 2:
z2d = np.array([q, r])
ukf.predict()
if time_ix % observe_every == 0:
ukf.update(z2d)
else:
assert False, "Have only implemented [1, 2] dimensions"
s.save()
# save the output data
s.to_array()
data_ukf = update_data_columns(data.copy(), s, dimension=dimension)
return ukf, data_ukf, s
def test_imm():
""" this test is drawn from Crassidis [1], example 4.6.
** References**
[1] Crassidis. "Optimal Estimation of Dynamic Systems", CRC Press,
Second edition.
"""
r = 1.
dt = 1.
phi_sim = np.array([[1, dt, 0, 0], [0, 1, 0, 0], [0, 0, 1, dt],
[0, 0, 0, 1]])
gam = np.array([[dt**2 / 2, 0], [dt, 0], [0, dt**2 / 2], [0, dt]])
x = np.array([[2000, 0, 10000, -15.]]).T
simxs = []
N = 600
for i in range(N):
x = np.dot(phi_sim, x)
if i >= 400:
x += np.dot(gam, np.array([[.075, .075]]).T)
simxs.append(x)
simxs = np.array(simxs)
#x = np.genfromtxt('c:/users/rlabbe/dropbox/Crassidis/mycode/x.csv', delimiter=',')
zs = np.zeros((N, 2))
for i in range(len(zs)):
zs[i, 0] = simxs[i, 0] + randn() * r
zs[i, 1] = simxs[i, 2] + randn() * r
try:
#data to test against crassidis' IMM matlab code
zs_tmp = np.genfromtxt(
'c:/users/rlabbe/dropbox/Crassidis/mycode/xx.csv',
delimiter=',')[:-1]
zs = zs_tmp
except:
pass
ca = KalmanFilter(6, 2)
cano = KalmanFilter(6, 2)
dt2 = (dt**2) / 2
ca.F = np.array([[1, dt, dt2, 0, 0, 0], [0, 1, dt, 0, 0, 0],
[0, 0, 1, 0, 0, 0], [0, 0, 0, 1, dt, dt2],
[0, 0, 0, 0, 1, dt], [0, 0, 0, 0, 0, 1]])
cano.F = ca.F.copy()
ca.x = np.array([[2000., 0, 0, 10000, -15, 0]]).T
cano.x = ca.x.copy()
ca.P *= 1.e-12
cano.P *= 1.e-12
ca.R *= r**2
cano.R *= r**2
cano.Q *= 0
q = np.array([[.05, .125, 1 / 6], [.125, 1 / 3, .5], [1 / 6, .5, 1]
]) * 1.e-3
ca.Q[0:3, 0:3] = q
ca.Q[3:6, 3:6] = q
ca.H = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]])
cano.H = ca.H.copy()
filters = [ca, cano]
trans = np.array([[0.97, 0.03], [0.03, 0.97]])
bank = IMMEstimator(filters, (0.5, 0.5), trans)
# ensure __repr__ doesn't have problems
str(bank)
xs, probs = [], []
cvxs, caxs = [], []
s = Saver(bank)
for i, z in enumerate(zs[0:10]):
z = np.array([z]).T
bank.update(z)
#print(ca.likelihood, cano.likelihood)
#print(ca.x.T)
xs.append(bank.x.copy())
cvxs.append(ca.x.copy())
caxs.append(cano.x.copy())
#print(i, ca.likelihood, cano.likelihood, bank.w)
#print('p', bank.p)
probs.append(bank.mu.copy())
s.save()
s.to_array()
if DO_PLOT:
xs = np.array(xs)
cvxs = np.array(cvxs)
caxs = np.array(caxs)
probs = np.array(probs)
plt.subplot(121)
plt.plot(xs[:, 0], xs[:, 3], 'k')
#plt.plot(cvxs[:, 0], caxs[:, 3])
#plt.plot(simxs[:, 0], simxs[:, 2], 'g')
plt.scatter(zs[:, 0], zs[:, 1], marker='+', alpha=0.2)
plt.subplot(122)
plt.plot(probs[:, 0])
plt.plot(probs[:, 1])
plt.ylim(-1.5, 1.5)
plt.title('probability ratio p(cv)/p(ca)')
'''plt.figure()
plt.plot(cvxs, label='CV')
plt.plot(caxs, label='CA')
plt.plot(xs[:, 0], label='GT')
plt.legend()
plt.figure()
plt.plot(xs)
plt.plot(xs[:, 0])'''
return bank
import matplotlib.pyplot as plt
import numpy as np
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise, Saver
r_std, q_std = 2., 0.003
cv = KalmanFilter(dim_x=2, dim_z=1)
cv.x = np.array([[0., 1.]]) # position, velocity
cv.F = np.array([[1, dt],[0, 1]])
cv.R = np.array([[r_std*r_std]])
f.H = np.array([[1., 0.]])
f.P = np.diag([0.1*0.1, 0.03*0.03])
f.Q = Q_discrete_white_noise(2, dt, q_std**2)
saver = Saver(cv)
for z in range(100):
cv.predict()
cv.update([z + randn() * r_std])
saver.save() # save the filter's state
saver.to_array()
plt.plot(saver.x[:, 0])
# plot all of the priors
plt.plot(saver.x_prior[:, 0])
# plot mahalanobis distance
plt.figure()
plt.plot(saver.mahalanobis)
def test_ekf():
def H_of(x):
""" compute Jacobian of H matrix for state x """
horiz_dist = x[0]
altitude = x[2]
denom = sqrt(horiz_dist**2 + altitude**2)
return array([[horiz_dist/denom, 0., altitude/denom]])
def hx(x):
""" takes a state variable and returns the measurement that would
correspond to that state.
"""
return sqrt(x[0]**2 + x[2]**2)
dt = 0.05
proccess_error = 0.05
rk = ExtendedKalmanFilter(dim_x=3, dim_z=1)
rk.F = eye(3) + array ([[0, 1, 0],
[0, 0, 0],
[0, 0, 0]])*dt
def fx(x, dt):
return np.dot(rk.F, x)
rk.x = array([-10., 90., 1100.])
rk.R *= 10
rk.Q = array([[0, 0, 0],
[0, 1, 0],
[0, 0, 1]]) * 0.001
rk.P *= 50
rs = []
xs = []
radar = RadarSim(dt)
ps = []
pos = []
s = Saver(rk)
for i in range(int(20/dt)):
z = radar.get_range(proccess_error)
pos.append(radar.pos)
rk.update(asarray([z]), H_of, hx, R=hx(rk.x)*proccess_error)
ps.append(rk.P)
rk.predict()
xs.append(rk.x)
rs.append(z)
s.save()
# test mahalanobis
a = np.zeros(rk.y.shape)
maha = scipy_mahalanobis(a, rk.y, rk.SI)
assert rk.mahalanobis == approx(maha)
s.to_array()
xs = asarray(xs)
ps = asarray(ps)
rs = asarray(rs)
p_pos = ps[:, 0, 0]
p_vel = ps[:, 1, 1]
p_alt = ps[:, 2, 2]
pos = asarray(pos)
if DO_PLOT:
plt.subplot(311)
plt.plot(xs[:, 0])
plt.ylabel('position')
plt.subplot(312)
plt.plot(xs[:, 1])
plt.ylabel('velocity')
plt.subplot(313)
#plt.plot(xs[:,2])
#plt.ylabel('altitude')
plt.plot(p_pos)
plt.plot(-p_pos)
plt.plot(xs[:, 0] - pos)
def test_linear_rts():
""" for a linear model the Kalman filter and UKF should produce nearly
identical results.
Test code mostly due to user gboehl as reported in GitHub issue #97, though
I converted it from an AR(1) process to constant velocity kinematic
model.
"""
dt = 1.0
F = np.array([[1., dt], [.0, 1]])
H = np.array([[1., .0]])
def t_func(x, dt):
F = np.array([[1., dt], [.0, 1]])
return np.dot(F, x)
def o_func(x):
return np.dot(H, x)
sig_t = .1 # peocess
sig_o = .00000001 # measurement
N = 50
X_true, X_obs = [], []
for i in range(N):
X_true.append([i + 1, 1.])
X_obs.append(i + 1 + np.random.normal(scale=sig_o))
X_true = np.array(X_true)
X_obs = np.array(X_obs)
oc = np.ones((1, 1)) * sig_o**2
tc = np.zeros((2, 2))
tc[1, 1] = sig_t**2
tc = Q_discrete_white_noise(dim=2, dt=dt, var=sig_t**2)
points = MerweScaledSigmaPoints(n=2, alpha=.1, beta=2., kappa=1)
ukf = UKF(dim_x=2, dim_z=1, dt=dt, hx=o_func, fx=t_func, points=points)
ukf.x = np.array([0., 1.])
ukf.R = oc[:]
ukf.Q = tc[:]
s = Saver(ukf)
s.save()
s.to_array()
kf = KalmanFilter(dim_x=2, dim_z=1)
kf.x = np.array([[0., 1]]).T
kf.R = oc[:]
kf.Q = tc[:]
kf.H = H[:]
kf.F = F[:]
mu_ukf, cov_ukf = ukf.batch_filter(X_obs)
x_ukf, _, _ = ukf.rts_smoother(mu_ukf, cov_ukf)
mu_kf, cov_kf, _, _ = kf.batch_filter(X_obs)
x_kf, _, _, _ = kf.rts_smoother(mu_kf, cov_kf)
# check results of filtering are correct
kfx = mu_kf[:, 0, 0]
ukfx = mu_ukf[:, 0]
kfxx = mu_kf[:, 1, 0]
ukfxx = mu_ukf[:, 1]
dx = kfx - ukfx
dxx = kfxx - ukfxx
# error in position should be smaller then error in velocity, hence
# atol is different for the two tests.
assert np.allclose(dx, 0, atol=1e-7)
assert np.allclose(dxx, 0, atol=1e-6)
# now ensure the RTS smoothers gave nearly identical results
kfx = x_kf[:, 0, 0]
ukfx = x_ukf[:, 0]
kfxx = x_kf[:, 1, 0]
ukfxx = x_ukf[:, 1]
dx = kfx - ukfx
dxx = kfxx - ukfxx
assert np.allclose(dx, 0, atol=1e-7)
assert np.allclose(dxx, 0, atol=1e-6)
return ukf
def test_ekf():
def H_of(x):
""" compute Jacobian of H matrix for state x """
horiz_dist = x[0]
altitude = x[2]
denom = sqrt(horiz_dist**2 + altitude**2)
return array([[horiz_dist / denom, 0., altitude / denom]])
def hx(x):
""" takes a state variable and returns the measurement that would
correspond to that state.
"""
return sqrt(x[0]**2 + x[2]**2)
dt = 0.05
proccess_error = 0.05
rk = ExtendedKalmanFilter(dim_x=3, dim_z=1)
rk.F = eye(3) + array([[0, 1, 0], [0, 0, 0], [0, 0, 0]]) * dt
def fx(x, dt):
return np.dot(rk.F, x)
rk.x = array([-10., 90., 1100.])
rk.R *= 10
rk.Q = array([[0, 0, 0], [0, 1, 0], [0, 0, 1]]) * 0.001
rk.P *= 50
rs = []
xs = []
radar = RadarSim(dt)
ps = []
pos = []
s = Saver(rk)
for i in range(int(20 / dt)):
z = radar.get_range(proccess_error)
pos.append(radar.pos)
rk.update(asarray([z]), H_of, hx, R=hx(rk.x) * proccess_error)
ps.append(rk.P)
rk.predict()
xs.append(rk.x)
rs.append(z)
s.save()
# test mahalanobis
a = np.zeros(rk.y.shape)
maha = scipy_mahalanobis(a, rk.y, rk.SI)
assert rk.mahalanobis == approx(maha)
s.to_array()
xs = asarray(xs)
ps = asarray(ps)
rs = asarray(rs)
p_pos = ps[:, 0, 0]
p_vel = ps[:, 1, 1]
p_alt = ps[:, 2, 2]
pos = asarray(pos)
if DO_PLOT:
plt.subplot(311)
plt.plot(xs[:, 0])
plt.ylabel('position')
plt.subplot(312)
plt.plot(xs[:, 1])
plt.ylabel('velocity')
plt.subplot(313)
#plt.plot(xs[:,2])
#plt.ylabel('altitude')
plt.plot(p_pos)
plt.plot(-p_pos)
plt.plot(xs[:, 0] - pos)
def test_noisy_1d():
f = KalmanFilter (dim_x=2, dim_z=1)
f.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
f.H = np.array([[1.,0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R *= 5 # state uncertainty
f.Q *= 0.0001 # process uncertainty
fsq = SquareRootKalmanFilter (dim_x=2, dim_z=1)
fsq.x = np.array([[2.],
[0.]]) # initial state (location and velocity)
fsq.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
fsq.H = np.array([[1.,0.]]) # Measurement function
fsq.P = np.eye(2) * 1000. # covariance matrix
fsq.R *= 5 # state uncertainty
fsq.Q *= 0.0001 # process uncertainty
# does __repr__ work?
str(fsq)
measurements = []
results = []
zs = []
s = Saver(fsq)
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
fsq.update(z)
fsq.predict()
assert abs(f.x[0,0] - fsq.x[0,0]) < 1.e-12
assert abs(f.x[1,0] - fsq.x[1,0]) < 1.e-12
# save data
results.append (f.x[0,0])
measurements.append(z)
s.save()
s.to_array()
for i in range(f.P.shape[0]):
assert abs(f.P[i,i] - fsq.P[i,i]) < 0.01
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.x = np.array([[2.,0]]).T
f.P = np.eye(2)*100.
m,c,_,_ = f.batch_filter(zs,update_first=False)
# plot data
if DO_PLOT:
p1, = plt.plot(measurements,'r', alpha=0.5)
p2, = plt.plot (results,'b')
p4, = plt.plot(m[:,0], 'm')
p3, = plt.plot ([0,100],[0,100], 'g') # perfect result
plt.legend([p1,p2, p3, p4],
["noisy measurement", "KF output", "ideal", "batch"], loc=4)
plt.show()
def test_imm():
""" This test is drawn from Crassidis [1], example 4.6.
** References**
[1] Crassidis. "Optimal Estimation of Dynamic Systems", CRC Press,
Second edition.
"""
r = 100.
dt = 1.
phi_sim = np.array(
[[1, dt, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, dt],
[0, 0, 0, 1]])
gam = np.array([[dt**2/2, 0],
[dt, 0],
[0, dt**2/2],
[0, dt]])
x = np.array([[2000, 0, 10000, -15.]]).T
simxs = []
N = 600
for i in range(N):
x = np.dot(phi_sim, x)
if i >= 400:
x += np.dot(gam, np.array([[.075, .075]]).T)
simxs.append(x)
simxs = np.array(simxs)
zs = np.zeros((N, 2))
for i in range(len(zs)):
zs[i, 0] = simxs[i, 0] + randn()*r
zs[i, 1] = simxs[i, 2] + randn()*r
'''
try:
# data to test against crassidis' IMM matlab code
zs_tmp = np.genfromtxt('c:/users/rlabbe/dropbox/Crassidis/mycode/xx.csv', delimiter=',')[:-1]
zs = zs_tmp
except:
pass
'''
ca = KalmanFilter(6, 2)
cano = KalmanFilter(6, 2)
dt2 = (dt**2)/2
ca.F = np.array(
[[1, dt, dt2, 0, 0, 0],
[0, 1, dt, 0, 0, 0],
[0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, dt, dt2],
[0, 0, 0, 0, 1, dt],
[0, 0, 0, 0, 0, 1]])
cano.F = ca.F.copy()
ca.x = np.array([[2000., 0, 0, 10000, -15, 0]]).T
cano.x = ca.x.copy()
ca.P *= 1.e-12
cano.P *= 1.e-12
ca.R *= r**2
cano.R *= r**2
cano.Q *= 0
q = np.array([[.05, .125, 1./6],
[.125, 1/3, .5],
[1./6, .5, 1.]])*1.e-3
ca.Q[0:3, 0:3] = q
ca.Q[3:6, 3:6] = q
ca.H = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0]])
cano.H = ca.H.copy()
filters = [ca, cano]
trans = np.array([[0.97, 0.03],
[0.03, 0.97]])
bank = IMMEstimator(filters, (0.5, 0.5), trans)
# ensure __repr__ doesn't have problems
str(bank)
s = Saver(bank)
ca_s = Saver(ca)
cano_s = Saver(cano)
for i, z in enumerate(zs):
z = np.array([z]).T
bank.update(z)
bank.predict()
s.save()
ca_s.save()
cano_s.save()
if DO_PLOT:
s.to_array()
ca_s.to_array()
cano_s.to_array()
plt.figure()
plt.subplot(121)
plt.plot(s.x[:, 0], s.x[:, 3], 'k')
#plt.plot(cvxs[:, 0], caxs[:, 3])
#plt.plot(simxs[:, 0], simxs[:, 2], 'g')
plt.scatter(zs[:, 0], zs[:, 1], marker='+', alpha=0.2)
plt.subplot(122)
plt.plot(s.mu[:, 0])
plt.plot(s.mu[:, 1])
plt.ylim(0, 1)
plt.title('probability ratio p(cv)/p(ca)')
'''plt.figure()