yafl/tests/src/yaflpy_compile_robust.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
    Copyright 2020 anonimous <shkolnick-kun@gmail.com> and contributors.

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing,
    software distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

    See the License for the specific language governing permissions
"""
import matplotlib.pyplot as plt
import numpy as np
import pyximport
import scipy.stats
import sys
import time


sys.path.insert(0,'../../src/yaflpy')

pyximport.install(
    build_dir='../projects/obj',
    pyimport=True,
    reload_support=True,
    language_level=3,
    setup_args={
        'include_dirs': [np.get_include(), '../../src', '../../src/yaflpy'],
        }
    )

#from yaflpy import RobustJoseph as KF
#from yaflpy import RobustBierman as KF
#from yaflpy import AdaptiveRobustJoseph as KF
from yaflpy import AdaptiveRobustBierman as KF

def _fx(x, dt, **fx_args):
    x = x.copy()
    x[0] += x[1] * dt
    x[2] += x[3] * dt
    return x

def _jfx(x, dt, **fx_args):
    F = np.array([
        [1., dt, 0., 0.],
        [0., 1., 0., 0.],
        [0., 0., 1., dt],
        [0., 0., 0., 1.],
        ])
    return F

def _hx(x, **hx_args):
    if hx_args:
        print(hx_args)
    return np.array([x[0], x[2]])

def _jhx(x, **hx_args):
    H = np.array([
        [1., 0., 0., 0.],
        [0., 0., 1., 0.],
        ])
    return H

#Default noice model is Normal with Poisson outliers
def _gz(beta):
    # +- 3*sigma
    if 3.0 >= np.abs(beta):
        return float(beta)

    # +- 6*sigma - uncertain measurements
    if 6.0 >= np.abs(beta):
        return float(beta/3.0)

    # outliers
    return float(np.sign(beta))

def _gdotz(beta):
    # +- 3*sigma
    if 3.0 >= np.abs(beta):
        return 1.0

    # +- 6*sigma - uncertain measurements
    if 6.0 >= np.abs(beta):
        return 1.0/3.0

    # outliers
    return 0.0

def _zrf(a,b):
    return a - b

STD = 100.

#kf = KF(4, 2, 1., _fx, _jfx, _hx, _jhx, residual_z=_zrf)
#kf = KF(4, 2, 1., _fx, _jfx, _hx, _jhx, gz=_gz, gdotz=_gdotz)
kf = KF(4, 2, 1., _fx, _jfx, _hx, _jhx)
kf.x[0] = 1000.
kf.x[1] = -0.5
kf.Dp *= .00001
kf.Dq *= 1.0e-8
#This is robust filter, so no square here
kf.Dr *= STD
kf.Dr[0] *= .87
kf.Ur += 0.5

#kf.chi2 = 8.807468393511947

N = 12000

clean = np.zeros((N, 2))
noisy = np.zeros((N, 2))
t     = np.zeros((N,), dtype=np.float)
st    = np.zeros((N,), dtype=np.int)
for i in range(1, len(clean)//2):
    clean[i] = clean[i-1] + np.array([1.5,1.])
    noisy[i] = clean[i]   + np.random.normal(scale=STD, size=2)
    t[i] = i

for i in range(i, len(clean)):
    clean[i] = clean[i-1] + np.array([1.,3.])
    noisy[i] = clean[i]   + np.random.normal(scale=STD, size=2)
    t[i] = i

kf_out = np.zeros((N, 2))

start = time.time()
for i, z in enumerate(noisy):
    kf.predict()
    st[i] = kf.update(z)
    kf_out[i] = kf.x[::2]
end = time.time()
print(end - start)

plt.plot(t, noisy - kf_out)
plt.show()

plt.plot(t, clean - kf_out)
plt.show()

plt.plot(clean[:,0], clean[:,1], kf_out[:,0], kf_out[:,1])
plt.show()

plt.plot(noisy[:,0], noisy[:,1], kf_out[:,0], kf_out[:,1])
plt.show()

plt.plot(t, noisy[:,1], t, kf_out[:,1], t, clean[:,1])
plt.show()

plt.plot(t, noisy[:,0], t, kf_out[:,0], t, clean[:,0])
plt.show()

plt.plot(t, [st[i] & yaflpy.ST_MSK_ANOMALY for i in range(N)])
plt.show()

plt.plot(t, [st[i] & yaflpy.ST_MSK_REGULARIZED for i in range(N)])
plt.show()

plt.plot(t, [st[i] & yaflpy.ST_MSK_GLITCH_SMALL for i in range(N)])
plt.show()

plt.plot(t, [st[i] & yaflpy.ST_MSK_GLITCH_LARGE for i in range(N)])
plt.show()


print('Done!')