def test_linear_2d_simplex():
""" should work like a linear KF if problem is linear """
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 = SimplexSigmaPoints(n=4)
kf = UKF(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, points=points)
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 0.0001
zs = []
for i in range(20):
z = np.array([i + randn() * 0.1, i + randn() * 0.1])
zs.append(z)
Ms, Ps = kf.batch_filter(zs)
smooth_x, _, _ = kf.rts_smoother(Ms, Ps, dt=dt)
if DO_PLOT:
zs = np.asarray(zs)
#plt.plot(zs[:,0])
plt.plot(Ms[:, 0])
plt.plot(smooth_x[:, 0], smooth_x[:, 2])
print(smooth_x)
def test_batch_missing_data():
""" batch filter should accept missing data with None in the measurements """
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)
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 0.0001
zs = []
for i in range(20):
z = np.array([i + randn() * 0.1, i + randn() * 0.1])
zs.append(z)
zs[2] = None
Rs = [1] * len(zs)
Rs[2] = None
Ms, Ps = kf.batch_filter(zs)
def test_linear_2d():
""" should work like a linear KF if problem is linear """
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)
kf.x = np.array([-1., 1., -1., 1])
kf.P*=0.0001
#kf.R *=0
#kf.Q
zs = []
for i in range(20):
z = np.array([i+randn()*0.1, i+randn()*0.1])
zs.append(z)
Ms, Ps = kf.batch_filter(zs)
smooth_x, _, _ = kf.rts_smoother(Ms, Ps, dt=dt)
def test_batch_missing_data():
""" batch filter should accept missing data with None in the measurements """
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)
kf.x = np.array([-1., 1., -1., 1])
kf.P*=0.0001
zs = []
for i in range(20):
z = np.array([i+randn()*0.1, i+randn()*0.1])
zs.append(z)
zs[2] = None
Rs = [1]*len(zs)
Rs[2] = None
Ms, Ps = kf.batch_filter(zs)
def test_linear_2d_merwe():
""" should work like a linear KF if problem is linear """
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)
kf.x = np.array([-1., 1., -1., 1])
kf.P *= 1.1
# test __repr__ doesn't crash
str(kf)
zs = [[i + randn() * 0.1, i + randn() * 0.1] for i in range(20)]
Ms, Ps = kf.batch_filter(zs)
smooth_x, _, _ = kf.rts_smoother(Ms, Ps, dt=dt)
if DO_PLOT:
plt.figure()
zs = np.asarray(zs)
plt.plot(zs[:, 0], marker='+')
plt.plot(Ms[:, 0], c='b')
plt.plot(smooth_x[:, 0], smooth_x[:, 2], c='r')
print(smooth_x)
def test_rts():
def fx(x, dt):
A = np.eye(3) + dt * np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
f = np.dot(A, x)
return f
def hx(x):
return np.sqrt(x[0]**2 + x[2]**2)
dt = 0.05
sp = JulierSigmaPoints(n=3, kappa=1.)
kf = UKF(3, 1, dt, fx=fx, hx=hx, points=sp)
kf.Q *= 0.01
kf.R = 10
kf.x = np.array([0., 90., 1100.])
kf.P *= 100.
radar = RadarSim(dt)
t = np.arange(0, 20 + dt, dt)
n = len(t)
xs = np.zeros((n, 3))
random.seed(200)
rs = []
#xs = []
for i in range(len(t)):
r = radar.get_range()
#r = GetRadar(dt)
kf.predict()
kf.update(z=[r])
xs[i, :] = kf.x
rs.append(r)
kf.x = np.array([0., 90., 1100.])
kf.P = np.eye(3) * 100
M, P = kf.batch_filter(rs)
assert np.array_equal(M, xs), "Batch filter generated different output"
Qs = [kf.Q] * len(t)
M2, P2, K = kf.rts_smoother(Xs=M, Ps=P, Qs=Qs)
if DO_PLOT:
print(xs[:, 0].shape)
plt.figure()
plt.subplot(311)
plt.plot(t, xs[:, 0])
plt.plot(t, M2[:, 0], c='g')
plt.subplot(312)
plt.plot(t, xs[:, 1])
plt.plot(t, M2[:, 1], c='g')
plt.subplot(313)
plt.plot(t, xs[:, 2])
plt.plot(t, M2[:, 2], c='g')
def test_rts():
def fx(x, dt):
A = np.eye(3) + dt * np.array ([[0, 1, 0],
[0, 0, 0],
[0, 0, 0]])
f = np.dot(A, x)
return f
def hx(x):
return np.sqrt (x[0]**2 + x[2]**2)
dt = 0.05
sp = JulierSigmaPoints(n=3, kappa=1.)
kf = UKF(3, 1, dt, fx=fx, hx=hx, points=sp)
kf.Q *= 0.01
kf.R = 10
kf.x = np.array([0., 90., 1100.])
kf.P *= 100.
radar = RadarSim(dt)
t = np.arange(0,20+dt, dt)
n = len(t)
xs = np.zeros((n,3))
random.seed(200)
rs = []
#xs = []
for i in range(len(t)):
r = radar.get_range()
#r = GetRadar(dt)
kf.predict()
kf.update(z=[r])
xs[i,:] = kf.x
rs.append(r)
kf.x = np.array([0., 90., 1100.])
kf.P = np.eye(3)*100
M, P = kf.batch_filter(rs)
assert np.array_equal(M, xs), "Batch filter generated different output"
Qs = [kf.Q]*len(t)
M2, P2, K = kf.rts_smoother(Xs=M, Ps=P, Qs=Qs)
def Unscentedfilter(zs): # Filter function
points = MerweScaledSigmaPoints(2, alpha=.1, beta=2., kappa=1)
ukf = UnscentedKalmanFilter(dim_x=2,
dim_z=1,
fx=fx,
hx=hx,
points=points,
dt=dt)
ukf.Q = array(([50, 0], [0, 50]))
ukf.R = 100
ukf.P = eye(2) * 2
mu, cov = ukf.batch_filter(zs)
x, _, _ = ukf.rts_smoother(mu, cov)
return x[:, 0]
def test_linear_2d():
""" should work like a linear KF if problem is linear """
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
kf = UKF(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, kappa=0)
kf.x = np.array([-1., 1., -1., 1])
kf.P*=0.0001
#kf.R *=0
#kf.Q
zs = []
for i in range(20):
z = np.array([i+randn()*0.1, i+randn()*0.1])
zs.append(z)
Ms, Ps = kf.batch_filter(zs)
smooth_x, _, _ = kf.rts_smoother(Ms, Ps, dt=dt)
if DO_PLOT:
zs = np.asarray(zs)
#plt.plot(zs[:,0])
plt.plot(Ms[:,0])
plt.plot(smooth_x[:,0], smooth_x[:,2])
print(smooth_x)
def smooth(data: ndarray, dt: float):
points = MerweScaledSigmaPoints(3, alpha=1e-3, beta=2.0, kappa=0)
noisy_kalman = UnscentedKalmanFilter(
dim_x=3,
dim_z=1,
dt=dt,
hx=state_to_measurement,
fx=state_transition,
points=points,
)
noisy_kalman.x = array([0, data[1], data[1] - data[0]], dtype="float32")
noisy_kalman.R *= 20**2 # sensor variance
noisy_kalman.P = diag([5**2, 5**2, 1**2]) # variance of the system
noisy_kalman.Q = Q_discrete_white_noise(3, dt=dt, var=0.05)
means, covariances = noisy_kalman.batch_filter(data)
means[:, 1][means[:, 1] < 0] = 0 # clip velocity
return means[:, 1]
def test_fixed_lag():
def fx(x, dt):
A = np.eye(3) + dt * np.array ([[0, 1, 0],
[0, 0, 0],
[0, 0, 0]])
f = np.dot(A, x)
return f
def hx(x):
return np.sqrt (x[0]**2 + x[2]**2)
dt = 0.05
kf = UKF(3, 1, dt, fx=fx, hx=hx, kappa=0.)
kf.Q *= 0.01
kf.R = 10
kf.x = np.array([0., 90., 1100.])
kf.P *= 1.
radar = RadarSim(dt)
t = np.arange(0,20+dt, dt)
n = len(t)
xs = np.zeros((n,3))
random.seed(200)
rs = []
#xs = []
M = []
P = []
N =10
flxs = []
for i in range(len(t)):
r = radar.get_range()
#r = GetRadar(dt)
kf.predict()
kf.update(z=[r])
xs[i,:] = kf.x
flxs.append(kf.x)
rs.append(r)
M.append(kf.x)
P.append(kf.P)
print(i)
#print(i, np.asarray(flxs)[:,0])
if i == 20 and len(M) >= N:
try:
M2, P2, K = kf.rts_smoother(Xs=np.asarray(M)[-N:], Ps=np.asarray(P)[-N:])
flxs[-N:] = M2
#flxs[-N:] = [20]*N
except:
print('except', i)
#P[-N:] = P2
kf.x = np.array([0., 90., 1100.])
kf.P = np.eye(3)*100
M, P = kf.batch_filter(rs)
Qs = [kf.Q]*len(t)
M2, P2, K = kf.rts_smoother(Xs=M, Ps=P, Qs=Qs)
flxs = np.asarray(flxs)
print(xs[:,0].shape)
plt.figure()
plt.subplot(311)
plt.plot(t, xs[:,0])
plt.plot(t, flxs[:,0], c='r')
plt.plot(t, M2[:,0], c='g')
plt.subplot(312)
plt.plot(t, xs[:,1])
plt.plot(t, flxs[:,1], c='r')
plt.plot(t, M2[:,1], c='g')
plt.subplot(313)
plt.plot(t, xs[:,2])
plt.plot(t, flxs[:,2], c='r')
plt.plot(t, M2[:,2], c='g')
def test_rts():
def fx(x, dt):
A = np.eye(3) + dt * np.array ([[0, 1, 0],
[0, 0, 0],
[0, 0, 0]])
f = np.dot(A, x)
return f
def hx(x):
return np.sqrt (x[0]**2 + x[2]**2)
dt = 0.05
kf = UKF(3, 1, dt, fx=fx, hx=hx, kappa=1.)
kf.Q *= 0.01
kf.R = 10
kf.x = np.array([0., 90., 1100.])
kf.P *= 100.
radar = RadarSim(dt)
t = np.arange(0,20+dt, dt)
n = len(t)
xs = np.zeros((n,3))
random.seed(200)
rs = []
#xs = []
for i in range(len(t)):
r = radar.get_range()
#r = GetRadar(dt)
kf.predict()
kf.update(z=[r])
xs[i,:] = kf.x
rs.append(r)
kf.x = np.array([0., 90., 1100.])
kf.P = np.eye(3)*100
M, P = kf.batch_filter(rs)
assert np.array_equal(M, xs), "Batch filter generated different output"
Qs = [kf.Q]*len(t)
M2, P2, K = kf.rts_smoother(Xs=M, Ps=P, Qs=Qs)
if DO_PLOT:
print(xs[:,0].shape)
plt.figure()
plt.subplot(311)
plt.plot(t, xs[:,0])
plt.plot(t, M2[:,0], c='g')
plt.subplot(312)
plt.plot(t, xs[:,1])
plt.plot(t, M2[:,1], c='g')
plt.subplot(313)
plt.plot(t, xs[:,2])
plt.plot(t, M2[:,2], c='g')
def two_radar():
# code is not complete - I was using to test RTS smoother. very similar
# to two_radary.py in book.
import numpy as np
import matplotlib.pyplot as plt
from numpy import array
from numpy.linalg import norm
from numpy.random import randn
from math import atan2
from filterpy.common import Q_discrete_white_noise
class RadarStation(object):
def __init__(self, pos, range_std, bearing_std):
self.pos = asarray(pos)
self.range_std = range_std
self.bearing_std = bearing_std
def reading_of(self, ac_pos):
""" Returns range and bearing to aircraft as tuple. bearing is in
radians.
"""
diff = np.subtract(self.pos, ac_pos)
rng = norm(diff)
brg = atan2(diff[1], diff[0])
return rng, brg
def noisy_reading(self, ac_pos):
rng, brg = self.reading_of(ac_pos)
rng += randn() * self.range_std
brg += randn() * self.bearing_std
return rng, brg
class ACSim(object):
def __init__(self, pos, vel, vel_std):
self.pos = asarray(pos, dtype=float)
self.vel = asarray(vel, dtype=float)
self.vel_std = vel_std
def update(self):
vel = self.vel + (randn() * self.vel_std)
self.pos += vel
return self.pos
dt = 1.
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1] * dt
x_est[2] += x[3] * dt
return x_est
vx, vy = 0.1, 0.1
f = UnscentedKalmanFilter(dim_x=4, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=0)
aircraft = ACSim((100, 100), (vx * dt, vy * dt), 0.00000002)
range_std = 0.001 # 1 meter
bearing_std = 1. / 1000 # 1mrad
R1 = RadarStation((0, 0), range_std, bearing_std)
R2 = RadarStation((200, 0), range_std, bearing_std)
hx.R1 = R1
hx.R2 = R2
f.x = array([100, vx, 100, vy])
f.R = np.diag([range_std**2, bearing_std**2, range_std**2, bearing_std**2])
q = Q_discrete_white_noise(2, var=0.0002, dt=dt)
f.Q[0:2, 0:2] = q
f.Q[2:4, 2:4] = q
f.P = np.diag([.1, 0.01, .1, 0.01])
track = []
zs = []
for i in range(int(300 / dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2 = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
zs.append(z)
track.append(pos.copy())
zs = asarray(zs)
xs, Ps, Pxz, pM, pP = f.batch_filter(zs)
ms, _, _ = f.rts_smoother(xs, Ps)
track = asarray(track)
time = np.arange(0, len(xs) * dt, dt)
if DO_PLOT:
plt.figure()
plt.subplot(411)
plt.plot(time, track[:, 0])
plt.plot(time, xs[:, 0])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('x position (m)')
plt.tight_layout()
plt.subplot(412)
plt.plot(time, track[:, 1])
plt.plot(time, xs[:, 2])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('y position (m)')
plt.tight_layout()
plt.subplot(413)
plt.plot(time, xs[:, 1])
plt.plot(time, ms[:, 1])
plt.legend(loc=4)
plt.ylim([0, 0.2])
plt.xlabel('time (sec)')
plt.ylabel('x velocity (m/s)')
plt.tight_layout()
plt.subplot(414)
plt.plot(time, xs[:, 3])
plt.plot(time, ms[:, 3])
plt.ylabel('y velocity (m/s)')
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.tight_layout()
plt.show()
def two_radar_constalt():
dt = 0.05
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
pass
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1] * dt
return x_est
vx = 100 / 1000 # meters/sec
vz = 0.0
f = UKF(dim_x=3, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=0)
aircraft = ACSim((0, 1), (vx * dt, vz * dt), 0.00)
range_std = 1 / 1000.0
bearing_std = 1 / 1000000.0
R1 = RadarStation((0, 0), range_std, bearing_std)
R2 = RadarStation((60, 0), range_std, bearing_std)
hx.R1 = R1
hx.R2 = R2
f.x = array([aircraft.pos[0], vx, aircraft.pos[1]])
f.R = np.diag([range_std ** 2, bearing_std ** 2, range_std ** 2, bearing_std ** 2])
q = Q_discrete_white_noise(2, var=0.0002, dt=dt)
# q = np.array([[0,0],[0,0.0002]])
f.Q[0:2, 0:2] = q
f.Q[2, 2] = 0.0002
f.P = np.diag([0.1, 0.01, 0.1]) * 0.1
track = []
zs = []
for i in range(int(500 / dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2 = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
zs.append(z)
track.append(pos.copy())
zs = asarray(zs)
xs, Ps = f.batch_filter(zs)
ms, _, _ = f.rts_smoother(xs, Ps)
track = asarray(track)
time = np.arange(0, len(xs) * dt, dt)
plt.figure()
plt.subplot(311)
plt.plot(time, track[:, 0])
plt.plot(time, xs[:, 0])
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.ylabel("x position (m)")
plt.subplot(312)
plt.plot(time, xs[:, 1] * 1000, label="UKF")
plt.plot(time, ms[:, 1] * 1000, label="RTS")
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.ylabel("velocity (m/s)")
plt.subplot(313)
plt.plot(time, xs[:, 2] * 1000, label="UKF")
plt.plot(time, ms[:, 2] * 1000, label="RTS")
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.ylabel("altitude (m)")
plt.ylim([900, 1100])
for z in zs[:10]:
p = R1.z_to_x(z[0], z[1])
# plt.scatter(p[0], p[1], marker='+', c='k')
p = R2.z_to_x(z[2], z[3])
# plt.scatter(p[0], p[1], marker='+', c='b')
plt.show()
# calculate weight update
synapse_1_update = np.dot(np.atleast_2d(layer_1).T, (layer_2_delta))
synapse_h_update = np.dot(
np.atleast_2d(context_layer).T, (layer_1_delta))
synapse_0_update = np.dot(X.T, (layer_1_delta))
# concatenate weight
synapse_0_c = np.reshape(synapse_0, (-1, 1))
synapse_h_c = np.reshape(synapse_h, (-1, 1))
synapse_1_c = np.reshape(synapse_1, (-1, 1))
w_concat = np.concatenate((synapse_0_c, synapse_h_c, synapse_1_c),
axis=0)
zs = data
Xs, Ps = UKF.batch_filter(zs=w_concat)
# =============================================================================
# w_concat_eye = np.eye(w_concat.size)
# # sigma points dari mean dan kovarian pada synapse_1
#
# # ukf points (mean dan kovarian) setiap synapse layer
# synapse_0_sig = np.zeros((len(synapse_0_c),dim_x,1))
# synapse_h_sig = np.zeros((len(synapse_h_c),dim_x,1))
# synapse_1_sig = np.zeros((len(synapse_1_c),dim_x,1))
# w_concat_sig = np.concatenate((synapse_0_sig,synapse_h_sig,synapse_1_sig), axis=0)
#
# # hitung bobot tiap points tersebut
# # Unscented transform untuk trasformasi mean dan kovarian ke tuple (measurement space)
# # new mean dan sigmas
# # update pengukuran
# # kalman gain
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 fit(sysargv):
"""
Program to process Covid Data.
:Example:
For countries (European database)
>> python ProcessSEIR1R2D.py
>> python ProcessSEIR1R2D.py France 0 1 0 0 1 1
>> python ProcessSEIR1R2D.py France 2 3 8 0 1 1 # 3 périodes pour les femmes en France avec un décalage de 8 jours
>> python ProcessSEIR1R2D.py France,Germany 1 1 0 0 1 1 # 1 période pour les hommes francais et les hommes allemands
For French Region (French database)
>> python ProcessSEIR1R2D.py FRANCE,D69 0 -1 13 0 1 1 # Code Insee Dpt 69 (Rhône)
>> python ProcessSEIR1R2D.py FRANCE,R84 0 -1 13 0 1 1 # Tous les dpts de la Région dont le code Insee est
>> python ProcessSEIR1R2D.py FRANCE,R32+ 0 -1 13 0 1 1 # Somme de tous les dpts de la Région 32 (Hauts-de-France)
>> python ProcessSEIR1R2D.py FRANCE,MetropoleD 0 -1 13 0 1 1 # Tous les départements de la France métropolitaine
>> python ProcessSEIR1R2D.py FRANCE,MetropoleD+ 0 -1 13 0 1 1 # Toute la France métropolitaine (en sommant les dpts)
>> python ProcessSEIR1R2D.py FRANCE,MetropoleR+ 0 -1 13 0 1 1 # Somme des dpts de toutes les régions françaises
Toute combinaison est possible de lieu : exemple FRANCE,R32+,D05,R84
argv[1] : List of countries (ex. France,Germany,Italy), or see above. Default: France
argv[2] : Sex (male:1, female:2, male+female:0). Only for french database Default: 0
argv[3] : Periods ('1' -> 1 period ('all-in-on'), '!=1' -> severall periods). Default: -1
argv[4] : Delay (in days). Default: 13
argv[5] : UKF filtering of data (0/1). Default: 0
argv[6] : Verbose level (debug: 3, ..., almost mute: 0). Default: 1
argv[7] : Plot graphique (0/1). Default: 1
"""
#Austria,Belgium,Croatia,Czechia,Finland,France,Germany,Greece,Hungary,Ireland,Italy,Lithuania,Poland,Portugal,Romania,Serbia,Spain,Switzerland,Ukraine
#Austria,Belgium,Croatia,Czechia,Finland,France,Germany,Greece,Hungary,Ireland,Italy,Poland,Portugal,Romania,Serbia,Spain,Switzerland,Ukraine
# Il y a 18 pays
if len(sysargv) > 7:
print(' CAUTION : bad number of arguments - see help')
exit(1)
# Constantes
######################################################@
fileLocalCopy = True # if we upload the file from the url (to get latest data) or from a local copy file
readStartDateStr = "2020-03-01" # "2020-03-01" Le 8 mars, pour inclure un grand nombre de pays européens dont la date de premier était postérieur au 1er mars
readStopDateStr = None
recouvrement = -1
dt = 1
France = 'France'
thresholdSignif = 1.5E-6
# Interpetation of arguments - reparation
######################################################@
# Default value for parameters
listplaces = ['France']
sexe, sexestr = 0, 'male+female'
nbperiodes = -1
decalage = 13
UKF_filt = False
verbose = 1
plot = True
# Parameters from argv
if len(sysargv) > 0: liste = list(sysargv[0].split(','))
if len(sysargv) > 1: sexe = int(sysargv[1])
if len(sysargv) > 2: nbperiodes = int(sysargv[2])
if len(sysargv) > 3: decalage = int(sysargv[3])
if len(sysargv) > 4 and int(sysargv[4]) == 1: UKF_filt = True
if len(sysargv) > 5: verbose = int(sysargv[5])
if len(sysargv) > 6 and int(sysargv[6]) == 0: plot = False
if nbperiodes == 1:
decalage = 0 # nécessairement pas de décalage (on compense le recouvrement)
if sexe not in [0, 1, 2]:
sexe, sexestr = 0, 'male+female' # sexe indiférencié
if sexe == 1: sexestr = 'male'
if sexe == 2: sexestr = 'female'
listplaces = []
listnames = []
if liste[0] == 'FRANCE':
FrDatabase = True
liste = liste[1:]
for el in liste:
l, n = getPlace(el)
if el == 'MetropoleR+':
for l1, n1 in zip(l, n):
listplaces.extend(l1)
listnames.extend([n1])
else:
listplaces.extend(l)
listnames.extend(n)
else:
listplaces = liste[:]
FrDatabase = False
if verbose > 0:
print(' Full command line : ' + sysargv[0] + ' ' + str(nbperiodes) +
' ' + str(decalage) + ' ' + str(UKF_filt) + ' ' + str(verbose) +
' ' + str(plot),
flush=True)
# Data reading to get first and last date available in the data set
######################################################@
if FrDatabase == True:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, _ = readDataFrance(
['D69'],
readStartDateStr,
readStopDateStr,
fileLocalCopy,
sexe,
verbose=0)
else:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, _ = readDataEurope(
France,
readStartDateStr,
readStopDateStr,
fileLocalCopy,
verbose=0)
dataLength = pd_exerpt.shape[0]
readStartDate = datetime.strptime(readStartDateStr, strDate)
if readStartDate < pd_exerpt.index[0]:
readStartDate = pd_exerpt.index[0]
readStartDateStr = pd_exerpt.index[0].strftime(strDate)
readStopDate = datetime.strptime(readStopDateStr, strDate)
if readStopDate < pd_exerpt.index[-1]:
readStopDate = pd_exerpt.index[-1]
readStopDateStr = pd_exerpt.index[-1].strftime(strDate)
dataLength = pd_exerpt.shape[0]
if verbose > 1:
print('readStartDateStr=', readStartDateStr, ', readStopDateStr=',
readStopDateStr)
print('readStartDate =', readStartDate, ', readStopDate =',
readStopDate)
print('dataLength =', dataLength)
#input('pause')
# Collections of data return by this function
modelSEIR1R2D = np.zeros(shape=(len(listplaces), dataLength, 6))
data_deriv = np.zeros(shape=(len(listplaces), dataLength, 2))
modelR1_deriv = np.zeros(shape=(len(listplaces), dataLength, 2))
data_all = np.zeros(shape=(len(listplaces), dataLength, 2))
modelR1_all = np.zeros(shape=(len(listplaces), dataLength, 2))
Listepd = []
ListetabParamModel = []
# data observed
data = np.zeros(shape=(dataLength, 2))
# Paramètres sous forme de chaines de caractères
ListeTextParam = []
ListeDateI0 = []
# Loop on the places to process
for indexplace, place in enumerate(listplaces):
# Get the full name of the place to process, and the special dates corresponding to the place
if FrDatabase == True:
placefull = 'France-' + listnames[indexplace][0]
DatesString = readDates(France, verbose)
else:
placefull = place
DatesString = readDates(place, verbose)
print('PROCESSING of', placefull, 'in', listnames)
# data reading of the observations
#############################################################################
if FrDatabase == True:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, dateFirstNonZeroStr = readDataFrance(
place,
readStartDateStr,
readStopDateStr,
fileLocalCopy,
sexe,
verbose=0)
else:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, dateFirstNonZeroStr = readDataEurope(
place,
readStartDateStr,
readStopDateStr,
fileLocalCopy,
verbose=0)
shift_0value = getNbDaysBetweenDateFromString(readStartDateStr,
dateFirstNonZeroStr)
# UKF Filtering ?
if UKF_filt == True:
data2Filt = pd_exerpt[[HeadData[0],
HeadData[1]]].to_numpy(copy=True)
sigmas = MerweScaledSigmaPoints(n=2, alpha=.5, beta=2.,
kappa=1.) #1-3.)
ukf = UKF(dim_x=2, dim_z=2, fx=fR1D, hx=hR1D, dt=dt, points=sigmas)
# Filter init
ukf.x[:] = data2Filt[0, :]
ukf.Q = np.diag([30., 15.])
ukf.R = np.diag([170., 100.])
if verbose > 1:
print('ukf.x[:]=', ukf.x[:])
print('ukf.R =', ukf.R)
print('ukf.Q =', ukf.Q)
# UKF filtering and smoothing, batch mode
R1Ffilt, _ = ukf.batch_filter(data2Filt)
HeadData[0] += ' filt'
HeadData[1] += ' filt'
pd_exerpt[HeadData[0]] = R1Ffilt[:, 0]
pd_exerpt[HeadData[1]] = R1Ffilt[:, 1]
# Get the list of dates to process
ListDates, ListDatesStr = GetPairListDates(readStartDate, readStopDate,
DatesString, decalage,
nbperiodes, recouvrement)
if verbose > 1:
#print('ListDates =', ListDates)
print('ListDatesStr=', ListDatesStr)
#input('pause')
# Solveur edo
solveur = SolveEDO_SEIR1R2D(N, dt, verbose)
indexdata = solveur.indexdata
E0, I0, R10, R20, D0 = 0, 1, 0, 0, 0
# Repertoire des figures
if plot == True:
repertoire = getRepertoire(
UKF_filt, './figures/SEIR1R2D_UKFilt/' + placefull + '/sexe_' +
str(sexe) + '_delay_' + str(decalage), './figures/SEIR1R2D/' +
placefull + '/sexe_' + str(sexe) + '_delay_' + str(decalage))
prefFig = repertoire + '/Process_'
# Remise à 0 des données
data.fill(0.)
# Boucle pour traiter successivement les différentes fenêtres
###############################################################
ListeTextParamPlace = []
ListetabParamModelPlace = []
ListeEQM = []
DEGENERATE_CASE = False
for i in range(len(ListDatesStr)):
# dates of the current period
fitStartDate, fitStopDate = ListDates[i]
fitStartDateStr, fitStopDateStr = ListDatesStr[i]
# Est-on dans un CAS degénéré?
# print(getNbDaysBetweenDateFromString(dateFirstNonZeroStr, fitStopDateStr))
if getNbDaysBetweenDateFromString(
dateFirstNonZeroStr, fitStopDateStr
) < 5: # Il faut au moins 5 données pour fitter
DEGENERATE_CASE = True
if i > 0:
DatesString.addOtherDates(fitStartDateStr)
# Récupérations des données observées
dataLengthPeriod = 0
indMinPeriod = (fitStartDate - readStartDate).days
for j, z in enumerate(pd_exerpt.loc[
fitStartDateStr:addDaystoStrDate(fitStopDateStr, -1),
HeadData[0]]):
data[indMinPeriod + j, 0] = z
dataLengthPeriod += 1
for j, z in enumerate(pd_exerpt.loc[
fitStartDateStr:addDaystoStrDate(fitStopDateStr, -1),
HeadData[1]]):
data[indMinPeriod + j, 1] = z
slicedata = slice(indMinPeriod, indMinPeriod + dataLengthPeriod)
slicedataderiv = slice(slicedata.start + 1, slicedata.stop)
if verbose > 0:
print(' dataLength =', dataLength)
print(' indMinPeriod =', indMinPeriod)
print(' dataLengthPeriod=', dataLengthPeriod)
print(' fitStartDateStr =', fitStartDateStr)
print(' fitStopDateStr =', fitStopDateStr)
#input('attente')
# Set initialisation data for the solveur
############################################################################
# paramètres initiaux à optimiser
if i == 0:
datelegend = fitStartDateStr
# ts=getNbDaysBetweenDateFromString(DatesString.listFirstCaseDates[0], readStartDateStr)
# En premiere approximation, on prend la date du premier cas estimé pour le pays (même si c'est faux pour les régions et dpts)
ts = getNbDaysBetweenDateFromString(
DatesString.listFirstCaseDates[0], dateFirstNonZeroStr)
if ts < 0:
continue # On passe au pays suivant
if nbperiodes != 1: # pour plusieurs périodes
#l, b0, c0, f0 = 0.255, 1./5.2, 1./12, 0.08
#a0 = (l+c0)*(1.+l/b0)
#a0, b0, c0, f0, mu0, xi0 = 0.55, 0.34, 0.12, 0.25, 0.0005, 0.0001
a0, b0, c0, f0, mu0, xi0 = 0.60, 0.55, 0.30, 0.50, 0.0005, 0.0001
T = 150
else: # pour une période
#a0, b0, c0, f0, mu0, xi0 = 0.10, 0.29, 0.10, 0.0022, 0.00004, 0.
a0, b0, c0, f0, mu0, xi0 = 0.70, 0.25, 0.05, 0.003, 0.0005, 0.0001
T = 350
if i == 1 or i == 2:
datelegend = None
_, a0, b0, c0, f0, mu0, xi0 = solveur.modele.getParam()
R10 = int(data[indMinPeriod,
0]) # on corrige R1 à la valeur numérique
F0 = int(data[indMinPeriod,
1]) # on corrige F à la valeur numérique
if i == 1:
a0 /= 4. # le confinement réduit drastiquement (pour aider l'optimisation)
T = 120
ts = 0
time = np.linspace(0, T - 1, T)
solveur.modele.setParam(N=N,
a=a0,
b=b0,
c=c0,
f=f0,
mu=mu0,
xi=xi0)
solveur.setParamInit(N=N, E0=E0, I0=I0, R10=R10, R20=R20, D0=D0)
# Before optimization
###############################
# Solve ode avant optimization
sol_ode = solveur.solveEDO(time)
# calcul time shift initial (ts) with respect to data
if i == 0:
ts = solveur.compute_tsfromEQM(data[slicedata, :], T,
indexdata)
else:
solveur.TS = ts = 0
sliceedo = slice(ts, min(ts + dataLengthPeriod, T))
if verbose > 0:
print(solveur)
print(' ts=' + str(ts))
# plot
if plot == True and DEGENERATE_CASE == False:
commontitre = placefull + '- Period ' + str(i) + '\\' + str(
len(ListDatesStr) - 1
) + ' - [' + fitStartDateStr + '\u2192' + addDaystoStrDate(
fitStopDateStr, -1)
if sewe == 0:
titre = commontitre + '] (Delay (delta)=' + str(
decalage) + ')'
else:
titre = commontitre + '] (Sex=', +sexestr + ', Delay (delta)=' + str(
decalage) + ')'
listePlot = indexdata
filename = prefFig + str(decalage) + '_Period' + str(
i) + '_' + ''.join(map(str, listePlot)) + 'Init.png'
solveur.plotEDO(filename,
titre,
sliceedo,
slicedata,
plot=listePlot,
data=data,
text=solveur.getTextParam(datelegend,
Period=i))
# Parameters optimization
############################################################################
solveur.paramOptimization(
data[slicedata, :],
time) # version lorsque ts est calculé automatiquement
#solveur.paramOptimization(data[slicedata, :], time, ts) # version lorsque l'on veut fixer ts
_, a1, b1, c1, f1, mu1, xi1 = solveur.modele.getParam()
R0 = solveur.modele.getR0()
if verbose > 0:
print('Solver' 's state after optimization=', solveur)
print(' Reproductivité après: ', R0)
# After optimization
###############################
# Solve ode avant optimization
sol_ode = solveur.solveEDO(time)
# calcul time shift with respect to data
if i == 0:
ts = solveur.compute_tsfromEQM(data[slicedata, :], T,
indexdata)
else:
solveur.TS = ts = 0
sliceedo = slice(ts, min(ts + dataLengthPeriod, T))
sliceedoderiv = slice(sliceedo.start + 1, sliceedo.stop)
if verbose > 0:
print(solveur)
print(' ts=' + str(ts))
if i == 0: # on se souvient de la date du premier infesté
dateI0 = addDaystoStrDate(fitStartDateStr, -ts + shift_0value)
if verbose > 2:
print('dateI0=', dateI0)
input('attente')
# sauvegarde des param (tableau et texte)
seuil = (data[slicedata.stop - 1, 0] - data[slicedata.start, 0]
) / getNbDaysBetweenDateFromString(
fitStartDateStr, fitStopDateStr) / N
#print('seuil=', seuil)
#print('DEGENERATE_CASE=', DEGENERATE_CASE)
if DEGENERATE_CASE == True:
ROsignificatif = False
ListetabParamModelPlace.append(
[-1., -1., -1., -1., -1., -1., -1.])
else:
if seuil < thresholdSignif:
ROsignificatif = False
ListetabParamModelPlace.append(
[a1, b1, c1, f1, mu1, xi1, -1.])
else:
ROsignificatif = True
ListetabParamModelPlace.append(
[a1, b1, c1, f1, mu1, xi1, R0])
# print('seuil=', seuil)
# print('ROsignificatif=', ROsignificatif)
# print('R0=', R0)
# input('pause')
ListeTextParamPlace.append(
solveur.getTextParamWeak(datelegend, ROsignificatif, Period=i))
data_deriv_period = (
data[slicedataderiv, :] -
data[slicedataderiv.start - 1:slicedataderiv.stop - 1, :]) / dt
modelR1_deriv_period = (
sol_ode[sliceedoderiv, indexdata] -
sol_ode[sliceedoderiv.start - 1:sliceedoderiv.stop - 1,
indexdata]) / dt
data_all_period = data[slicedataderiv, :]
modelR1_all_period = sol_ode[sliceedoderiv, indexdata]
if plot == True and DEGENERATE_CASE == False:
commontitre = placefull + '- Period ' + str(i) + '\\' + str(
len(ListDatesStr) - 1
) + ' - [' + fitStartDateStr + '\u2192' + addDaystoStrDate(
fitStopDateStr, -1)
if sexe == 0:
titre = commontitre + '] (Delay (delta)=' + str(
decalage) + ')'
else:
titre = commontitre + '] (Sex=', +sexestr + ', Delay (delta)=' + str(
decalage) + ')'
# listePlot = [0,1,2,3,4,5]
# filename = prefFig + str(decalage) + '_Period' + str(i) + '_' + ''.join(map(str, listePlot)) + '.png'
# solveur.plotEDO(filename, titre, sliceedo, slicedata, plot=listePlot, data=data, text=solveur.getTextParam(datelegend, ROsignificatif, Period=i))
listePlot = [1, 2, 3, 5]
filename = prefFig + str(decalage) + '_Period' + str(
i) + '_' + ''.join(map(str, listePlot)) + 'Final.png'
solveur.plotEDO(filename,
titre,
sliceedo,
slicedata,
plot=listePlot,
data=data,
text=solveur.getTextParam(datelegend,
ROsignificatif,
Period=i))
listePlot = indexdata
filename = prefFig + str(decalage) + '_Period' + str(
i) + '_' + ''.join(map(str, listePlot)) + 'Final.png'
solveur.plotEDO(filename,
titre,
sliceedo,
slicedata,
plot=listePlot,
data=data,
text=solveur.getTextParam(datelegend,
ROsignificatif,
Period=i))
# dérivée numérique de R1 et F
filename = prefFig + str(decalage) + '_Period' + str(
i) + '_' + ''.join(map(str, listePlot)) + 'Deriv.png'
solveur.plotEDO_deriv(filename,
titre,
sliceedoderiv,
slicedataderiv,
data_deriv_period,
indexdata,
text=solveur.getTextParam(datelegend,
ROsignificatif,
Period=i))
# sol_ode_withSwitch = solveur.solveEDO_withSwitch(T, timeswitch=ts+dataLengthPeriod)
# ajout des données dérivées
data_all[indexplace, slicedataderiv, :] = data_all_period
modelR1_all[indexplace, slicedataderiv, :] = modelR1_all_period
data_deriv[indexplace, slicedataderiv, :] = data_deriv_period
modelR1_deriv[indexplace, slicedataderiv, :] = modelR1_deriv_period
# ajout des SEIR1R2D
modelSEIR1R2D[indexplace,
slicedata.start:slicedata.stop, :] = sol_ode[
ts:ts + sliceedo.stop - sliceedo.start, :]
# preparation for next iteration
_, E0, I0, R10, R20, D0 = map(
int, sol_ode[ts + dataLengthPeriod + recouvrement, :])
#print('A LA FIN : E0, I0, R10, R20, D0=', E0, I0, R10, R20, D0)
if verbose > 1:
input('next step')
Listepd.append(pd_exerpt)
ListeDateI0.append(dateI0)
# calcul de l'EQM sur les données (et non sur les dérivées des données)
#EQM = mean_squared_error(data_deriv[indexplace, :], modelR1_deriv[indexplace, :])
EQM = mean_squared_error(data_all[indexplace, :],
modelR1_all[indexplace, :])
ListeEQM.append(EQM)
# udpate des listes pour transmission
ListeTextParam.append(ListeTextParamPlace)
ListetabParamModel.append(ListetabParamModelPlace)
return modelSEIR1R2D, ListeTextParam, Listepd, data_deriv, modelR1_deriv, ListetabParamModel, ListeEQM, ListeDateI0
def main(sysargv):
"""
Program to plot data and generate figures on Covid Data.
:Example:
For country all around the world (European database)
>> python PlotDataCovid.py
>> python PlotDataCovid.py United_Kingdom
>> python PlotDataCovid.py Italy 2 1 # Only Italian women
>> python PlotDataCovid.py France,Germany 1 # Shortcut for processing the two countries successively
>> python PlotDataCovid.py France,Spain,Italy,United_Kingdom,Germany,Belgium 0
For French geographical areas (French database)
>> python PlotDataCovid.py FRANCE,D69 # Department 69 (Rhône), INSEE numbering
>> python PlotDataCovid.py FRANCE,R84 # Process successively all the dpts of region #84 ('Auvergne-Rhone-Alpes')
>> python PlotDataCovid.py FRANCE,R32+ # Sum od all dpts of Région 32 ('Hauts de France')
>> python PlotDataCovid.py FRANCE,MetropoleD # All the dpts of the metropolitan France
>> python PlotDataCovid.py FRANCE,MetropoleD+ # France (by summing dpts)
>> python PlotDataCovid.py FRANCE,MetropoleR+ # All the regions by summing their dpts
Every combination is possible, e.g.: FRANCE,R32+,D05,R84
argv[1] : List of countries (ex. France,Germany,Italy), or see above for France. Default: France
argv[2] : Sex (male:1, female:2, male+female:0). Only for french database Default: 0
argv[3] : Verbose level (debug: 3, ..., almost mute: 0). Default: 1
"""
#Austria,Belgium,Croatia,Czechia,Finland,France,Germany,Greece,Hungary,Ireland,Italy,Lithuania,Poland,Portugal,Romania,Serbia,Spain,Switzerland,Ukraine
print('Command line : ', sysargv, flush=True)
if len(sysargv) > 4:
print(' CAUTION : bad number of arguments - see help')
exit(1)
# Constants
######################################################@
dt = 1
readStartDateStr = "2020-03-01"
readStopDateStr = None
France = 'France'
# Interpetation of arguments - reparation
######################################################@
# Default value for parameters
listplaces = ['France']
sexe, sexestr = 0, 'male+female'
verbose = 1
# Parameters from argv
if len(sysargv) > 1: liste = list(sysargv[1].split(','))
if len(sysargv) > 2: sexe = int(sysargv[2])
if len(sysargv) > 3: verbose = int(sysargv[3])
if sexe not in [0, 1, 2]: sexe, sexestr = 0, 'male+female'
if sexe == 1: sexestr = 'male'
if sexe == 2: sexestr = 'female'
# List iof places to process
listplaces = []
listnames = []
if liste[0] == 'FRANCE':
FrDatabase = True
liste = liste[1:]
for el in liste:
l, n = getPlace(el)
if el == 'MetropoleR+':
for l1, n1 in zip(l, n):
listplaces.extend(l1)
listnames.extend([n1])
else:
listplaces.extend(l)
listnames.extend(n)
else:
listplaces = liste[:]
FrDatabase = False
# Loop for all places
############################################################@
for indexplace, place in enumerate(listplaces):
# Get the full name of the place to process, and the special dates corresponding to the place
if FrDatabase == True:
placefull = 'France-' + listnames[indexplace][0]
DatesString = readDates(France, verbose)
else:
placefull = place
DatesString = readDates(place, verbose)
# Figures repository
repertoire = getRepertoire(
True, './figures/data/' + placefull + '/sexe_' + str(sexe))
prefFig = repertoire + '/'
# Data reading and plot
##########################################################
if FrDatabase == True:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, dateFirstNonZeroStr = readDataFrance(
place,
readStartDateStr,
readStopDateStr,
fileLocalCopy,
sexe,
verbose=0)
else:
pd_exerpt, HeadData, N, readStartDateStr, readStopDateStr, dateFirstNonZeroStr = readDataEurope(
place,
readStartDateStr,
readStopDateStr,
fileLocalCopy,
verbose=0)
readStartDate = datetime.strptime(readStartDateStr, "%Y-%m-%d")
readStopDate = datetime.strptime(readStopDateStr, "%Y-%m-%d")
dataLength = pd_exerpt.shape[0]
if verbose > 0:
print('readStartDateStr=', readStartDateStr, ', readStopDateStr=',
readStopDateStr)
print('readStartDate =', readStartDate, ', readStopDate =',
readStopDate)
print('dateFirstNonZeroStr=', dateFirstNonZeroStr)
#input('pause')
# Adding the gradient
pd_exerpt['Diff ' + HeadData[0]] = pd_exerpt[HeadData[0]].diff()
pd_exerpt['Diff ' + HeadData[1]] = pd_exerpt[HeadData[1]].diff()
pd_exerpt['Diff ' + HeadData[2]] = pd_exerpt[HeadData[2]].diff()
# Plot and store the figures in the directory
if sexe == 0:
titre = placefull
else:
titre = placefull + ' - Sex=' + sexestr
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + HeadData[0] + '.png',
y=HeadData[0],
color='red',
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + HeadData[1] + '.png',
y=HeadData[1],
color='black',
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + HeadData[2] + '.png',
y=HeadData[2],
color='black',
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + HeadData[0] + HeadData[1] + '.png',
y=[HeadData[0], HeadData[1]],
color=['red', 'black'],
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'Diff' + HeadData[0] + '.png',
y=['Diff ' + HeadData[0]],
color='red',
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'Diff' + HeadData[1] + '.png',
y=['Diff ' + HeadData[1]],
color='black',
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'Diff' + HeadData[0] + HeadData[1] +
'.png',
y=['Diff ' + HeadData[0], 'Diff ' + HeadData[1]],
color=['red', 'black'],
Dates=DatesString)
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'Diff' + HeadData[0] + HeadData[1] +
HeadData[2] + '.png',
y=[
'Diff ' + HeadData[0], 'Diff ' + HeadData[1],
'Diff ' + HeadData[2]
],
color=['red', 'black', 'blue'],
Dates=DatesString)
# Data filtering and plot
######################################################
# R1+D filtering by UKF
data = pd_exerpt[HeadData[2]].tolist()
dt = 1
sigmas = MerweScaledSigmaPoints(n=1, alpha=.5, beta=2.,
kappa=0.) #1-3.)
ukf = UKF(dim_x=1, dim_z=1, fx=fR1, hx=hR1, dt=dt, points=sigmas)
# Filter init
ukf.x[0] = data[0]
ukf.Q = np.diag([30.])
ukf.R = np.diag([170.])
if verbose > 1:
print('ukf.x[0]=', ukf.x[0])
print('ukf.R =', ukf.R)
print('ukf.Q =', ukf.Q)
# UKF filtering and smoothing, batch mode
R1filt, _ = ukf.batch_filter(data)
# plotting
pd_exerpt[HeadData[2] + ' filt'] = R1filt
if sexe == 0:
titre = placefull
else:
titre = placefull + ' - Sex=' + sexestr
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'filt' + HeadData[2] + '.png',
y=[HeadData[2], HeadData[2] + ' filt'],
color=['red', 'darkred'],
Dates=DatesString)
pd_exerpt['Diff ' + HeadData[2] + ' filt'] = pd_exerpt[HeadData[2] +
' filt'].diff()
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'diff_filt' + HeadData[2] + '.png',
y=['Diff ' + HeadData[2], 'Diff ' + HeadData[2] + ' filt'],
color=['red', 'darkred'],
Dates=DatesString)
# Diff R1 filtering by UKF
# It works but identicial to previous plot
#############################################################################
# data = pd_exerpt['Diff cases'].tolist()
# data[0] = data[1]
# print('data=', data)
# dt = 1
# sigmas = MerweScaledSigmaPoints(n=1, alpha=.5, beta=2., kappa=1.) #1-3.)
# ukf = UKF(dim_x=1, dim_z=1, fx=fR1, hx=hR1, dt=dt, points=sigmas)
# # Filter init
# ukf.x[0] = data[0]
# ukf.Q = np.diag([30.])
# ukf.R = np.diag([170.])
# if verbose>1:
# print('ukf.x[0]=', ukf.x[0])
# print('ukf.R =', ukf.R)
# print('ukf.Q =', ukf.Q)
# # UKF filtering and smoothing, batch mode
# diffR1filt, _ = ukf.batch_filter(data)
# pd_exerpt['diffR1 filt'] = diffR1filt
# PlotData(pd_exerpt, titre=titre, filenameFig=prefFig+'diffcases_filt'+HeadData[0]+'.png', y=['Diff cases', 'diffR1 filt'], color=['red', 'darkred'], Dates=DatesString)
# F filtering by UKF
#############################################################################
data = pd_exerpt[HeadData[1]].tolist()
dt = 1
sigmas = MerweScaledSigmaPoints(n=1, alpha=.5, beta=2.,
kappa=0.) #1-3.)
ukf = UKF(dim_x=1, dim_z=1, fx=fF, hx=hF, dt=dt, points=sigmas)
# Filter init
ukf.x[0] = data[0]
ukf.Q = np.diag([15.])
ukf.R = np.diag([100.])
if verbose > 1:
print('ukf.x[0]=', ukf.x[0])
print('ukf.R =', ukf.R)
print('ukf.Q =', ukf.Q)
# UKF filtering and smoothing, batch mode
Ffilt, _ = ukf.batch_filter(data)
# plotting
pd_exerpt[HeadData[1] + ' filt'] = Ffilt
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'filt' + HeadData[1] + '.png',
y=[HeadData[1], HeadData[1] + ' filt'],
color=['gray', 'black'],
Dates=DatesString)
pd_exerpt['Diff ' + HeadData[1] + ' filt'] = pd_exerpt[HeadData[1] +
' filt'].diff()
PlotData(pd_exerpt,
titre=titre,
filenameFig=prefFig + 'diff_filt' + HeadData[1] + '.png',
y=['Diff ' + HeadData[1], 'Diff ' + HeadData[1] + ' filt'],
color=['gray', 'black'],
Dates=DatesString)
# R1 and F simultaneous filtering by UKF
#############################################################################
data = pd_exerpt[[HeadData[0], HeadData[1]]].to_numpy(copy=True)
dt = 1
sigmas = MerweScaledSigmaPoints(n=2, alpha=.5, beta=2.,
kappa=1.) #1-3.)
ukf = UKF(dim_x=2, dim_z=2, fx=fR1F, hx=hR1F, dt=dt, points=sigmas)
# Filter init
ukf.x[:] = data[0, :]
ukf.Q = np.diag([30., 15.])
ukf.R = np.diag([170., 100.])
if verbose > 1:
print('ukf.x[:]=', ukf.x[:])
print('ukf.R =', ukf.R)
print('ukf.Q =', ukf.Q)
# UKF filtering and smoothing, batch mode
R1Ffilt, _ = ukf.batch_filter(data)
# plotting
pd_exerpt[HeadData[0] + ' filtboth'] = R1Ffilt[:, 0]
pd_exerpt[HeadData[1] + ' filtboth'] = R1Ffilt[:, 1]
PlotData(pd_exerpt, titre=titre, filenameFig=prefFig+'filtboth'+HeadData[0]+HeadData[1]+'.png', \
y=[HeadData[0], HeadData[0]+' filtboth', HeadData[1], HeadData[1]+' filtboth'], color=['red', 'darkred', 'gray', 'black'], Dates=DatesString)
pd_exerpt['Diff ' + HeadData[0] +
' filtboth'] = pd_exerpt[HeadData[0] + ' filtboth'].diff()
pd_exerpt['Diff ' + HeadData[1] +
' filtboth'] = pd_exerpt[HeadData[1] + ' filtboth'].diff()
PlotData(pd_exerpt, titre=titre, filenameFig=prefFig+'diff_filt'+HeadData[0]+HeadData[1]+'.png', \
y=['Diff '+HeadData[0], 'Diff '+HeadData[0]+' filtboth', 'Diff '+HeadData[1], 'Diff '+HeadData[1]+' filtboth', ], color=['red', 'darkred', 'gray', 'black'], Dates=DatesString)
def two_radar_constalt():
dt = .05
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
pass
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1]*dt
return x_est
vx = 100/1000 # meters/sec
vz = 0.
f = UKF(dim_x=3, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=0)
aircraft = ACSim ((0, 1), (vx*dt, vz*dt), 0.00)
range_std = 1/1000.
bearing_std =1/1000000.
R1 = RadarStation (( 0, 0), range_std, bearing_std)
R2 = RadarStation ((60, 0), range_std, bearing_std)
hx.R1 = R1
hx.R2 = R2
f.x = array([aircraft.pos[0], vx, aircraft.pos[1]])
f.R = np.diag([range_std**2, bearing_std**2, range_std**2, bearing_std**2])
q = Q_discrete_white_noise(2, var=0.0002, dt=dt)
#q = np.array([[0,0],[0,0.0002]])
f.Q[0:2, 0:2] = q
f.Q[2,2] = 0.0002
f.P = np.diag([.1, 0.01, .1])*0.1
track = []
zs = []
for i in range(int(500/dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2 = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
zs.append(z)
track.append(pos.copy())
zs = asarray(zs)
xs, Ps = f.batch_filter(zs)
ms, _, _ = f.rts_smoother(xs, Ps)
track = asarray(track)
time = np.arange(0,len(xs)*dt, dt)
plt.figure()
plt.subplot(311)
plt.plot(time, track[:,0])
plt.plot(time, xs[:,0])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('x position (m)')
plt.subplot(312)
plt.plot(time, xs[:,1]*1000, label="UKF")
plt.plot(time, ms[:,1]*1000, label='RTS')
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('velocity (m/s)')
plt.subplot(313)
plt.plot(time, xs[:,2]*1000, label="UKF")
plt.plot(time, ms[:,2]*1000, label='RTS')
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('altitude (m)')
plt.ylim([900,1100])
for z in zs[:10]:
p = R1.z_to_x(z[0], z[1])
#plt.scatter(p[0], p[1], marker='+', c='k')
p = R2.z_to_x(z[2], z[3])
#plt.scatter(p[0], p[1], marker='+', c='b')
plt.show()
def two_radar_constvel():
dt = 5
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
pass
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1] * dt
x_est[2] += x[3] * dt
return x_est
f = UKF(dim_x=4, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=0)
aircraft = ACSim((100, 100), (0.1 * dt, 0.02 * dt), 0.002)
range_std = 0.2
bearing_std = radians(0.5)
R1 = RadarStation((0, 0), range_std, bearing_std)
R2 = RadarStation((200, 0), range_std, bearing_std)
hx.R1 = R1
hx.R2 = R2
f.x = array([100, 0.1, 100, 0.02])
f.R = np.diag([range_std ** 2, bearing_std ** 2, range_std ** 2, bearing_std ** 2])
q = Q_discrete_white_noise(2, var=0.002, dt=dt)
# q = np.array([[0,0],[0,0.0002]])
f.Q[0:2, 0:2] = q
f.Q[2:4, 2:4] = q
f.P = np.diag([0.1, 0.01, 0.1, 0.01])
track = []
zs = []
for i in range(int(300 / dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2 = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
zs.append(z)
track.append(pos.copy())
zs = asarray(zs)
xs, Ps, Pxz = f.batch_filter(zs)
ms, _, _ = f.rts_smoother2(xs, Ps, Pxz)
track = asarray(track)
time = np.arange(0, len(xs) * dt, dt)
plt.figure()
plt.subplot(411)
plt.plot(time, track[:, 0])
plt.plot(time, xs[:, 0])
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.ylabel("x position (m)")
plt.subplot(412)
plt.plot(time, track[:, 1])
plt.plot(time, xs[:, 2])
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.ylabel("y position (m)")
plt.subplot(413)
plt.plot(time, xs[:, 1])
plt.plot(time, ms[:, 1])
plt.legend(loc=4)
plt.ylim([0, 0.2])
plt.xlabel("time (sec)")
plt.ylabel("x velocity (m/s)")
plt.subplot(414)
plt.plot(time, xs[:, 3])
plt.plot(time, ms[:, 3])
plt.ylabel("y velocity (m/s)")
plt.legend(loc=4)
plt.xlabel("time (sec)")
plt.show()
def two_radar_constvel():
dt = 5
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
pass
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1]*dt
x_est[2] += x[3]*dt
return x_est
f = UKF(dim_x=4, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=0)
aircraft = ACSim ((100,100), (0.1*dt,0.02*dt), 0.002)
range_std = 0.2
bearing_std = radians(0.5)
R1 = RadarStation ((0,0), range_std, bearing_std)
R2 = RadarStation ((200,0), range_std, bearing_std)
hx.R1 = R1
hx.R2 = R2
f.x = array([100, 0.1, 100, 0.02])
f.R = np.diag([range_std**2, bearing_std**2, range_std**2, bearing_std**2])
q = Q_discrete_white_noise(2, var=0.002, dt=dt)
#q = np.array([[0,0],[0,0.0002]])
f.Q[0:2, 0:2] = q
f.Q[2:4, 2:4] = q
f.P = np.diag([.1, 0.01, .1, 0.01])
track = []
zs = []
for i in range(int(300/dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2 = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
zs.append(z)
track.append(pos.copy())
zs = asarray(zs)
xs, Ps, Pxz = f.batch_filter(zs)
ms, _, _ = f.rts_smoother2(xs, Ps, Pxz)
track = asarray(track)
time = np.arange(0,len(xs)*dt, dt)
plt.figure()
plt.subplot(411)
plt.plot(time, track[:,0])
plt.plot(time, xs[:,0])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('x position (m)')
plt.subplot(412)
plt.plot(time, track[:,1])
plt.plot(time, xs[:,2])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('y position (m)')
plt.subplot(413)
plt.plot(time, xs[:,1])
plt.plot(time, ms[:,1])
plt.legend(loc=4)
plt.ylim([0, 0.2])
plt.xlabel('time (sec)')
plt.ylabel('x velocity (m/s)')
plt.subplot(414)
plt.plot(time, xs[:,3])
plt.plot(time, ms[:,3])
plt.ylabel('y velocity (m/s)')
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.show()
class Model:
"""
SIRD model of Covid-19.
"""
# __CONFIRMED_URL = 'https://bit.ly/35yJO0d'
__CONFIRMED_URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
__CONFIRMED_URL_DATA = pd.read_csv(__CONFIRMED_URL)
# __RECOVERED_URL = 'https://bit.ly/2L6jLE9'
__RECOVERED_URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
__RECOVERED_URL_DATA = pd.read_csv(__RECOVERED_URL)
# __DEATHS_URL = 'https://bit.ly/2L0hzxQ'
__DEATHS_URL = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
__DEATHS_URL_DATA = pd.read_csv(__DEATHS_URL)
__POPULATION_URL = 'https://bit.ly/2WYjZCD'
__JHU_DATA_SHIFT = 4
__N_FILTERED = 7 # Number of state variables to filter (I, R, D, β, γ, μ and n, the population).
__N_MEASURED = 3 # Number of measured variables (I, R and D).
__NB_OF_STEPS = 100
__DELTA_T = 1 / __NB_OF_STEPS
__FIG_SIZE = (11, 13)
__S_COLOR = '#0072bd'
__I_COLOR = '#d95319'
__R_COLOR = '#edb120'
__D_COLOR = '#7e2f8e'
__BETA_COLOR = '#77ac30'
__GAMMA_COLOR = '#4dbeee'
__MU_COLOR = '#a2142f'
__DATA_ALPHA = 0.3
__DATA = None
__POPULATION = None
class Use(Enum):
WIKIPEDIA = auto()
DATA = auto()
def __init__(self,
use=Use.DATA,
country='Bangladesh',
max_data=0,
people=1e6,
tag=1):
"""
Initialise our Model object.
"""
# Retrieve the data (if requested and needed).
if use == Model.Use.DATA and Model.__DATA is None:
confirmed_data, confirmed_data_start = self.__jhu_data(
Model.__CONFIRMED_URL_DATA, country)
recovered_data, recovered_data_start = self.__jhu_data(
Model.__RECOVERED_URL_DATA, country)
deaths_data, deaths_data_start = self.__jhu_data(
Model.__DEATHS_URL_DATA, country)
data_start = min(confirmed_data_start, recovered_data_start,
deaths_data_start) - Model.__JHU_DATA_SHIFT
start_date = confirmed_data.columns[data_start].split('/')
for i in range(data_start, confirmed_data.shape[1]):
c = confirmed_data.iloc[0][i]
r = recovered_data.iloc[0][i]
d = deaths_data.iloc[0][i]
data = [c - r - d, r, d]
# print(c, r, d)
if Model.__DATA is None:
Model.__DATA = np.array(data)
else:
Model.__DATA = np.vstack((Model.__DATA, data))
# print(Model.__DATA)
# Model.__DATA = smooth_data(Model.__DATA)
if use == Model.Use.DATA:
self.__data = Model.__DATA
else:
self.__data = None
if self.__data is not None:
if not isinstance(max_data, int):
sys.exit('Error: \'max_data\' must be an integer value.')
if max_data > 0:
self.__data = self.__data[:max_data]
# Retrieve the population (if needed).
if (tag == 0):
if use == Model.Use.DATA:
Model.__POPULATION = {}
# print(tag)
tag = 1
# response = requests.get(Model.__POPULATION_URL)
# soup = BeautifulSoup(response.text, 'html.parser')
# data = soup.select('div div div div div tbody tr')
# for i in range(len(data)):
# country_soup = BeautifulSoup(data[i].prettify(), 'html.parser')
# country_value = country_soup.select('tr td a')[0].get_text().strip()
# population_value = country_soup.select('tr td')[2].get_text().strip().replace(',', '')
# # Model.__POPULATION[country_value] = int(population_value)
# # print(people)
# # Model.__POPULATION[country_value] = int(people)
Model.__POPULATION[country] = int(people)
if use == Model.Use.DATA:
if country in Model.__POPULATION:
self.__population = Model.__POPULATION[country]
else:
sys.exit('Error: no population data is available for {}.'.
format(country))
# Keep track of whether to use the data.
self.__use_data = use == Model.Use.DATA
# Declare some internal variables (that will then be initialised through our call to reset()).
self.__beta = None
self.__gamma = None
self.__mu = None
self.__ukf = None
self.__x = None
self.__n = None
self.__data_s_values = None
self.__data_i_values = None
self.__data_r_values = None
self.__data_d_values = None
self.__s_values = None
self.__i_values = None
self.__r_values = None
self.__d_values = None
self.__beta_values = None
self.__gamma_values = None
self.__mu_values = None
# Initialise (i.e. reset) our SIRD model.
self.__start_date = pd.to_datetime(start_date[0] + '-' +
start_date[1] + '-' + start_date[2])
self.__confirmed_data = confirmed_data.iloc[0][data_start:-1]
self.__recovered_data = recovered_data.iloc[0][data_start:-1]
self.__deaths_data = deaths_data.iloc[0][data_start:-1]
self.reset()
@staticmethod
def __jhu_data(data, country):
# data = pd.read_csv(url)
# data = data[(data['Country/Region'] == country) & data['Province/State'].isnull()]
data_1 = data[(data['Country/Region'] == country)
& data['Province/State'].isnull()]
if data_1.shape[0] == 0:
data = data[(data['Country/Region'] == country
)].groupby('Country/Region').sum()
data.to_csv('test_data.csv')
data = pd.read_csv('test_data.csv')
else:
data = data_1
if data.shape[0] == 0:
sys.exit(
'Error: no Covid-19 data is available for {}.'.format(country))
data = data.drop(data.columns[list(range(Model.__JHU_DATA_SHIFT))],
axis=1) # Skip non-data columns.
start = None
for i in range(data.shape[1]):
if data.iloc[0][i] != 0:
start = Model.__JHU_DATA_SHIFT + i
break
return data, start
def __data_x(self, day, index):
"""
Return the I/R/D value for the given day.
"""
return self.__data[day][index] if self.__use_data else math.nan
def __data_s(self, day):
"""
Return the S value for the given day.
"""
if self.__use_data:
return self.__population - self.__data_i(day) - self.__data_r(
day) - self.__data_d(day)
else:
return math.nan
def __data_i(self, day):
"""
Return the I value for the given day.
"""
return self.__data_x(day, 0)
def __data_r(self, day):
"""
Return the R value for the given day.
"""
return self.__data_x(day, 1)
def __data_d(self, day):
"""
Return the D value for the given day.
"""
return self.__data_x(day, 2)
def __data_available(self, day):
"""
Return whether some data is available for the given day.
"""
return day <= self.__data.shape[0] - 1 if self.__use_data else False
def __s_value(self):
"""
Return the S value based on the values of I, R, D and N.
"""
return self.__n - self.__x.sum()
def __i_value(self):
"""
Return the I value.
"""
return self.__x[0]
def __r_value(self):
"""
Return the R value.
"""
return self.__x[1]
def __d_value(self):
"""
Return the D value.
"""
return self.__x[2]
@staticmethod
def __f(x, dt, **kwargs):
"""
State function.
The ODE system to solve is:
dI/dt = βIS/N - γI - μI
dR/dt = γI
dD/dt = μI
"""
model_self = kwargs.get('model_self')
with_ukf = kwargs.get('with_ukf', True)
if with_ukf:
s = x[6] - x[:3].sum()
beta = x[3]
gamma = x[4]
mu = x[5]
n = x[6]
else:
s = model_self.__n - x.sum()
beta = model_self.__beta
gamma = model_self.__gamma
mu = model_self.__mu
n = model_self.__n
a = np.array([[1 + dt * (beta * s / n - gamma - mu), 0, 0, 0, 0, 0, 0],
[dt * gamma, 1, 0, 0, 0, 0, 0],
[dt * mu, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
if with_ukf:
return a @ x
else:
return a[:3, :3] @ x
@staticmethod
def __h(x):
"""
Measurement function.
"""
return x[:Model.__N_MEASURED]
def reset(self):
"""
Reset our SIRD model.
"""
# Reset β, γ and μ to the values mentioned on Wikipedia (see https://bit.ly/2VMvb6h).
Model.__DATA = None
self.__beta = 0.4
self.__gamma = 0.035
self.__mu = 0.005
# Reset I, R and D to the data at day 0 or the values mentioned on Wikipedia (see https://bit.ly/2VMvb6h).
if self.__use_data:
self.__x = np.array(
[self.__data_i(0),
self.__data_r(0),
self.__data_d(0)])
self.__n = self.__population
else:
self.__x = np.array([3, 0, 0])
self.__n = 1000
# Reset our Unscented Kalman filter (if required). Note tat we use a dt value of 1 (day) and not the value of
# Model.__DELTA_T.
if self.__use_data:
points = MerweScaledSigmaPoints(
Model.__N_FILTERED,
1e-3, # Alpha value (usually a small positive value like 1e-3).
2, # Beta value (a value of 2 is optimal for a Gaussian distribution).
0, # Kappa value (usually, either 0 or 3-n).
)
self.__ukf = UnscentedKalmanFilter(Model.__N_FILTERED,
Model.__N_MEASURED, 1, self.__h,
Model.__f, points)
self.__ukf.x = np.array([
self.__data_i(0),
self.__data_r(0),
self.__data_d(0), self.__beta, self.__gamma, self.__mu,
self.__n
])
self.__ukf.P *= 15
# Reset our data (if requested).
if self.__use_data:
self.__data_s_values = np.array([self.__data_s(0)])
self.__data_i_values = np.array([self.__data_i(0)])
self.__data_r_values = np.array([self.__data_r(0)])
self.__data_d_values = np.array([self.__data_d(0)])
# Reset our predicted/estimated values.
self.__s_values = np.array([self.__s_value()])
self.__i_values = np.array([self.__i_value()])
self.__r_values = np.array([self.__r_value()])
self.__d_values = np.array([self.__d_value()])
# Reset our estimated SIRD model parameters.
self.__beta_values = np.array([self.__beta])
self.__gamma_values = np.array([self.__gamma])
self.__mu_values = np.array([self.__mu])
def run(self, batch_filter=True, nb_of_days=100):
"""
Run our SIRD model for the given number of days, taking advantage of the data (if requested) to estimate the
values of β, γ and μ.
"""
# Make sure that we were given a valid number of days.
if not isinstance(nb_of_days, int) or nb_of_days <= 0:
sys.exit(
'Error: \'nb_of_days\' must be an integer value greater than zero.'
)
# Run our SIRD simulation, which involves computing our predicted/estimated state by computing our SIRD model /
# Unscented Kalman filter in batch filter mode, if required.
if self.__use_data and batch_filter:
mu, cov = self.__ukf.batch_filter(self.__data)
batch_filter_x, _, _ = self.__ukf.rts_smoother(mu, cov)
# Override the first value of S, I, R and D.
x = batch_filter_x[0][:3]
self.__s_values = np.array([self.__n - x.sum()])
self.__i_values = np.array([x[0]])
self.__r_values = np.array([x[1]])
self.__d_values = np.array([x[2]])
for i in range(1, nb_of_days + 1):
# Compute our predicted/estimated state by computing our SIRD model / Unscented Kalman filter for one day.
if self.__use_data and self.__data_available(i):
if batch_filter:
self.__x = batch_filter_x[i][:3]
self.__beta = batch_filter_x[i][3]
self.__gamma = batch_filter_x[i][4]
self.__mu = batch_filter_x[i][5]
else:
self.__ukf.predict(model_self=self)
self.__ukf.update(
np.array([
self.__data_i(i),
self.__data_r(i),
self.__data_d(i)
]))
self.__x = self.__ukf.x[:3]
self.__beta = self.__ukf.x[3]
self.__gamma = self.__ukf.x[4]
self.__mu = self.__ukf.x[5]
else:
for j in range(1, Model.__NB_OF_STEPS + 1):
self.__x = Model.__f(self.__x,
Model.__DELTA_T,
model_self=self,
with_ukf=False)
# Keep track of our data (if requested).
if self.__use_data:
if self.__data_available(i):
self.__data_s_values = np.append(self.__data_s_values,
self.__data_s(i))
self.__data_i_values = np.append(self.__data_i_values,
self.__data_i(i))
self.__data_r_values = np.append(self.__data_r_values,
self.__data_r(i))
self.__data_d_values = np.append(self.__data_d_values,
self.__data_d(i))
else:
self.__data_s_values = np.append(self.__data_s_values,
math.nan)
self.__data_i_values = np.append(self.__data_i_values,
math.nan)
self.__data_r_values = np.append(self.__data_r_values,
math.nan)
self.__data_d_values = np.append(self.__data_d_values,
math.nan)
# Keep track of our predicted/estimated values.
self.__s_values = np.append(self.__s_values, self.__s_value())
self.__i_values = np.append(self.__i_values, self.__i_value())
self.__r_values = np.append(self.__r_values, self.__r_value())
self.__d_values = np.append(self.__d_values, self.__d_value())
# Keep track of our estimated SIRD model parameters.
self.__beta_values = np.append(self.__beta_values, self.__beta)
self.__gamma_values = np.append(self.__gamma_values, self.__gamma)
self.__mu_values = np.append(self.__mu_values, self.__mu)
def plot(self, figure=None, two_axes=False):
"""
Plot the results using five subplots for 1) S, 2) I and R, 3) D, 4) β, and 5) γ and μ. In each subplot, we plot
the data (if requested) as bars and the computed value as a line.
"""
# days = range(self.__s_values.size)
days = convert_to_date(self.__start_date, self.__s_values)
nrows = 5 if self.__use_data else 3
ncols = 1
if figure is None:
show_figure = True
figure, axes = plt.subplots(nrows,
ncols,
figsize=Model.__FIG_SIZE,
sharex=True)
else:
figure.clf()
show_figure = False
axes = figure.subplots(nrows, ncols, sharex=True)
figure.canvas.set_window_title('SIRD model fitted to data' if self.
__use_data else 'Wikipedia SIRD model')
# First subplot: S.
axis1 = axes[0]
axis1.plot(days, self.__s_values, Model.__S_COLOR, label='S')
axis1.legend(loc='best')
if self.__use_data:
axis2 = axis1.twinx() if two_axes else axis1
axis2.bar(days,
self.__data_s_values,
color=Model.__S_COLOR,
alpha=Model.__DATA_ALPHA)
data_s_range = self.__population - min(self.__data_s_values)
data_block = 10**(math.floor(math.log10(data_s_range)) - 1)
s_values_shift = data_block * math.ceil(data_s_range / data_block)
axis2.set_ylim(
min(min(self.__s_values), self.__population - s_values_shift),
self.__population)
# Second subplot: I and R.
axis1 = axes[1]
axis1.plot(days, self.__i_values, Model.__I_COLOR, label='I')
axis1.plot(days, self.__r_values, Model.__R_COLOR, label='R')
axis1.legend(loc='best')
if self.__use_data:
axis2 = axis1.twinx() if two_axes else axis1
axis2.bar(days,
self.__data_i_values,
color=Model.__I_COLOR,
alpha=Model.__DATA_ALPHA)
axis2.bar(days,
self.__data_r_values,
color=Model.__R_COLOR,
alpha=Model.__DATA_ALPHA)
# Third subplot: D.
axis1 = axes[2]
axis1.plot(days, self.__d_values, Model.__D_COLOR, label='D')
axis1.legend(loc='best')
if self.__use_data:
axis2 = axis1.twinx() if two_axes else axis1
axis2.bar(days,
self.__data_d_values,
color=Model.__D_COLOR,
alpha=Model.__DATA_ALPHA)
# Fourth subplot: β.
if self.__use_data:
axis1 = axes[3]
axis1.plot(days, self.__beta_values, Model.__BETA_COLOR, label='β')
axis1.legend(loc='best')
# Fourth subplot: γ and μ.
if self.__use_data:
axis1 = axes[4]
axis1.plot(days,
self.__gamma_values,
Model.__GAMMA_COLOR,
label='γ')
axis1.plot(days, self.__mu_values, Model.__MU_COLOR, label='μ')
axis1.legend(loc='best')
plt.xlabel('time (day)')
if show_figure:
plt.show()
def movie(self, filename, batch_filter=True, nb_of_days=100):
"""
Generate, if using the data, a movie showing the evolution of our SIRD model throughout time.
"""
if self.__use_data:
data_size = Model.__DATA.shape[0]
figure = plt.figure(figsize=Model.__FIG_SIZE)
backend = matplotlib.get_backend()
writer = manimation.writers['ffmpeg']()
matplotlib.use("Agg")
with writer.saving(figure, filename, 96):
for i in range(1, data_size + 1):
print('Processing frame #',
i,
'/',
data_size,
'...',
sep='')
self.__data = Model.__DATA[:i]
self.reset()
self.run(batch_filter=batch_filter, nb_of_days=nb_of_days)
self.plot(figure=figure)
writer.grab_frame()
print('All done!')
matplotlib.use(backend)
def s(self, day=-1):
"""
Return all the S values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__s_values
else:
return self.__s_values[day]
def i(self, day=-1):
"""
Return all the I values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__i_values
else:
return self.__i_values[day]
def r(self, day=-1):
"""
Return all the R values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__r_values
else:
return self.__r_values[day]
def d(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__d_values
else:
return self.__d_values[day]
def beta(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__beta_values
else:
return self.__beta_values[day]
def gamma(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__gamma_values
else:
return self.__gamma_values[day]
def mu(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
if day == -1:
return self.__mu_values
else:
return self.__mu_values[day]
def days_array(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
days = convert_to_date(self.__start_date, self.__s_values)
return days
def days_cases(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
days = convert_to_date(self.__start_date, self.__confirmed_data)
return days
def confirmed_data(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
return self.__confirmed_data
def recovered_data(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
return self.__recovered_data
def deaths_data(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
return self.__deaths_data
def active_data(self, day=-1):
"""
Return all the D values (if day=-1) or its value for a given day.
"""
return self.__confirmed_data - self.__recovered_data - self.__deaths_data
def test_fixed_lag():
def fx(x, dt):
A = np.eye(3) + dt * np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
f = np.dot(A, x)
return f
def hx(x):
return np.sqrt(x[0]**2 + x[2]**2)
dt = 0.05
sp = JulierSigmaPoints(n=3, kappa=0)
kf = UKF(3, 1, dt, fx=fx, hx=hx, points=sp)
kf.Q *= 0.01
kf.R = 10
kf.x = np.array([0., 90., 1100.])
kf.P *= 1.
radar = RadarSim(dt)
t = np.arange(0, 20 + dt, dt)
n = len(t)
xs = np.zeros((n, 3))
random.seed(200)
rs = []
#xs = []
M = []
P = []
N = 10
flxs = []
for i in range(len(t)):
r = radar.get_range()
#r = GetRadar(dt)
kf.predict()
kf.update(z=[r])
xs[i, :] = kf.x
flxs.append(kf.x)
rs.append(r)
M.append(kf.x)
P.append(kf.P)
print(i)
#print(i, np.asarray(flxs)[:,0])
if i == 20 and len(M) >= N:
try:
M2, P2, K = kf.rts_smoother(Xs=np.asarray(M)[-N:],
Ps=np.asarray(P)[-N:])
flxs[-N:] = M2
#flxs[-N:] = [20]*N
except:
print('except', i)
#P[-N:] = P2
kf.x = np.array([0., 90., 1100.])
kf.P = np.eye(3) * 100
M, P = kf.batch_filter(rs)
Qs = [kf.Q] * len(t)
M2, P2, K = kf.rts_smoother(Xs=M, Ps=P, Qs=Qs)
flxs = np.asarray(flxs)
print(xs[:, 0].shape)
plt.figure()
plt.subplot(311)
plt.plot(t, xs[:, 0])
plt.plot(t, flxs[:, 0], c='r')
plt.plot(t, M2[:, 0], c='g')
plt.subplot(312)
plt.plot(t, xs[:, 1])
plt.plot(t, flxs[:, 1], c='r')
plt.plot(t, M2[:, 1], c='g')
plt.subplot(313)
plt.plot(t, xs[:, 2])
plt.plot(t, flxs[:, 2], c='r')
plt.plot(t, M2[:, 2], c='g')
def ukf(pr, fs, meas_noise=3):
"""Use an unscented Kalman filter to smooth the data and predict
positions px, py, pz when we have missing data.
Parameters:
pr = (ntime x nmark x 3) raw data array in mm
fs = sampling rate
meas_noise = measurement noise (from Qualisys), default=3
Returns:
out = dict that holds filtered position and calculated velocity
and accleration. Keys are as follows:
p, v, a: pos, vel, acc after RTS smoothing. These are the values
to fill in the missing data gaps
nans: (ntime x nmark) bool array that stores where we have bad values
"""
ntime, nmark, ncoords = pr.shape
g = 9810 # mm/s^2
dim_x = 9 # tracked variables px, vx, ax, py, vy, ay, pz, vz, az
dim_z = 3 # measured variables px, py, pz
dt = 1 / fs
# state uncertainty matrix (measurement noise)
R = meas_noise**2 * np.eye(dim_z)
# process uncertainty matrix (effect of unmodeled behavior)
sigx, sigy, sigz = .5 * g, .5 * g, .5 * g
qx = Q_discrete_white_noise(3, dt=dt, var=sigx**2)
qy = Q_discrete_white_noise(3, dt=dt, var=sigy**2)
qz = Q_discrete_white_noise(3, dt=dt, var=sigz**2)
Q = _sub3(qx, qy, qz)
# uncertainty covariance matrix
p0 = np.diag([1, 500, 2 * g])**2
P = _sub3(p0, p0, p0)
# store the data in 3D arrays
pf, vf, af = pr.copy(), pr.copy(), pr.copy() # for after RTS smoothing
pf0, vf0, af0 = pr.copy(), pr.copy(), pr.copy() # for original pass
nans = np.zeros((ntime, nmark)).astype(np.int)
# indices for ntime x 9 arrays to get pos, vel, and acc
pos_idx, vel_idx, acc_idx = [0, 3, 6], [1, 4, 7], [2, 5, 8]
for j in np.arange(nmark):
zs = pr[:, j].copy()
# store the nan values
idx = np.where(np.isnan(zs[:, 0]))[0]
nans[idx, j] = 1
# batch_filter needs a list, with a nan values as None
zs = zs.tolist()
for ii in idx:
zs[ii] = None
# initial conditions for the smoother
x0 = np.zeros(9)
x0[pos_idx] = zs[0]
x0[vel_idx] = 0, 500, 1000 # guess v0, mm/s
x0[acc_idx] = .1 * g, .1 * g, -.5 * g # guess a0, mm/s^2
# how to calculate the sigma points
msp = MerweScaledSigmaPoints(n=dim_x, alpha=1e-3, beta=2, kappa=0)
# setup the filter with our values
kf = UKF(dim_x, dim_z, dt, _hx, _fx, msp)
kf.x, kf.P, kf.R, kf.Q = x0, P, R, Q
# filter 'forward'
xs, covs = kf.batch_filter(zs)
# apply RTS smoothing
Ms, Ps, Ks = kf.rts_smoother(xs, covs, dt=dt)
# get data out of the (ntime x 9) array
pf0[:, j] = xs[:, pos_idx]
vf0[:, j] = xs[:, vel_idx]
af0[:, j] = xs[:, acc_idx]
pf[:, j] = Ms[:, pos_idx]
vf[:, j] = Ms[:, vel_idx]
af[:, j] = Ms[:, acc_idx]
# finally store everything in a dictionary
out = {'p': pf, 'v': vf, 'a': af, 'pf0': pf0, 'vf0': vf0, 'af0': af0,
'nans': nans, 'xs': xs, 'covs': covs, 'zs': zs, 'x0': x0}
return out