''' '''
'''
ISC License
Copyright (c) 2016, Autonomous Vehicle Systems Lab, University of Colorado at Boulder
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
import sys, os, inspect
import numpy as np
from Basilisk.utilities import unitTestSupport
filename = inspect.getframeinfo(inspect.currentframe()).filename
path = os.path.dirname(os.path.abspath(filename))
import matplotlib.pyplot as plt
[docs]def StateErrorCovarPlot(x, Pflat, FilterType, show_plots, saveFigures):
"""
Support method to plot the state error covariances
:param x: states
:param Pflat: Variable
:param FilterType: specifies the filter type
:param show_plots: flag
:param saveFigures: flag
:return:
"""
nstates = int(np.sqrt(len(Pflat[0,:])-1))
P = np.zeros([len(Pflat[:,0]),nstates,nstates])
t= np.zeros(len(Pflat[:,0]))
for i in range(len(Pflat[:,0])):
t[i] = x[i, 0]*1E-9
P[i,:,:] = Pflat[i,1:37].reshape([nstates,nstates])
for j in range(len(P[0,0,:])):
P[i,j,j] = np.sqrt(P[i,j,j])
if nstates == 6:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(t , x[:, 1], "b", label='Error Filter')
plt.plot(t , 3 * np.sqrt(P[:, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t , -3 * np.sqrt(P[:, 0, 0]), 'r--')
plt.legend(loc='upper right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(322)
plt.plot(t , x[:, 4], "b")
plt.plot(t , 3 * np.sqrt(P[:, 3, 3]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 3, 3]), 'r--')
plt.ylabel(r'$\dot{d}_x$(m)')
plt.title('First rate component')
plt.grid()
plt.subplot(323)
plt.plot(t , x[:, 2], "b")
plt.plot(t , 3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.ylabel(r'$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(t , x[:, 5], "b")
plt.plot(t , 3 * np.sqrt(P[:, 4, 4]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 4, 4]), 'r--')
plt.ylabel(r'$\dot{d}_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(t , x[:, 3], "b")
plt.plot(t , 3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
if FilterType == 'SuKF':
plt.subplot(326)
plt.plot(t, x[:, 6], "b")
plt.plot(t, x[:, 6] + 3 * np.sqrt(P[:, 5, 5]), 'r--')
plt.plot(t, x[:, 6] -3 * np.sqrt(P[:, 5, 5]), 'r--')
plt.ylabel(r'$\dot{d}_z$(m)')
plt.xlabel('t(s)')
plt.title('Sun Intensity')
plt.grid()
else:
plt.subplot(326)
plt.plot(t , x[:, 6], "b")
plt.plot(t , 3 * np.sqrt(P[:, 5, 5]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 5, 5]), 'r--')
plt.ylabel(r'$\dot{d}_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if nstates == 3:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(311)
plt.plot(t, x[:, 1], "b", label='Error Filter')
plt.plot(t, 3 * np.sqrt(P[:, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t, -3 * np.sqrt(P[:, 0, 0]), 'r--')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(312)
plt.plot(t, x[:, 2], "b")
plt.plot(t, 3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.plot(t, -3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.ylabel('$d_y$(m)')
plt.title('Second LOST component')
plt.grid()
plt.subplot(313)
plt.plot(t, x[:, 3], "b")
plt.plot(t, 3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.plot(t, -3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.ylabel('$d_z$(m)')
plt.title('Third LOS component')
plt.grid()
if nstates == 5:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(t , x[:, 1], "b", label='Error Filter')
plt.plot(t , 3 * np.sqrt(P[:, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t , -3 * np.sqrt(P[:, 0, 0]), 'r--')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(323)
plt.plot(t , x[:, 2], "b")
plt.plot(t , 3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 1, 1]), 'r--')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(t , x[:, 3], "b")
plt.plot(t , 3 * np.sqrt(P[:, 3, 3]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 3, 3]), 'r--')
plt.ylabel(r'$\omega_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(t , x[:, 3], "b")
plt.plot(t , 3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 2, 2]), 'r--')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
plt.subplot(326)
plt.plot(t , x[:, 5], "b")
plt.plot(t , 3 * np.sqrt(P[:, 4, 4]), 'r--')
plt.plot(t , -3 * np.sqrt(P[:, 4, 4]), 'r--')
plt.ylabel(r'$\omega_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_StatesPlot'+FilterType, plt, path)
if show_plots:
plt.show()
plt.close('all')
def StatesPlotCompare(x, x2, Pflat, Pflat2, FilterType, show_plots, saveFigures):
nstates = int(np.sqrt(len(Pflat[0,:])-1))
P = np.zeros([len(Pflat[:,0]),nstates,nstates])
P2 = np.zeros([len(Pflat[:,0]),nstates,nstates])
t= np.zeros(len(Pflat[:,0]))
for i in range(len(Pflat[:,0])):
t[i] = x[i, 0]*1E-9
P[i,:,:] = Pflat[i,1:(nstates*nstates +1)].reshape([nstates,nstates])
P2[i, :, :] = Pflat2[i, 1:(nstates*nstates +1)].reshape([nstates, nstates])
if nstates == 6:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(t[0:30] , x[0:30, 1], "b", label='Error Filter')
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 0, 0]), 'r--')
plt.plot(t[0:30] , x2[0:30, 1], "g", label='Error Expected')
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 0, 0]), 'c--', label='Covar Expected')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 0, 0]), 'c--')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(322)
plt.plot(t[0:30] , x[0:30, 4], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 3, 3]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 3, 3]), 'r--')
plt.plot(t[0:30] , x2[0:30, 4], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 3, 3]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 3, 3]), 'c--')
plt.ylabel(r'$\dot{d}_x$(m)')
plt.title('First rate component')
plt.grid()
plt.subplot(323)
plt.plot(t[0:30] , x[0:30, 2], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30] , x2[0:30, 2], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(t[0:30] , x[0:30, 5], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 4, 4]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 4, 4]), 'r--')
plt.plot(t[0:30] , x2[0:30, 5], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 4, 4]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 4, 4]), 'c--')
plt.ylabel(r'$\dot{d}_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(t[0:30] , x[0:30, 3], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30] , x2[0:30, 3], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
if FilterType == 'SuKF':
plt.subplot(326)
plt.plot(t[0:30], x[0:30, 6], "b")
plt.plot(t[0:30], x[0:30, 6] + 3 * np.sqrt(P[0:30, 5, 5]), 'r--')
plt.plot(t[0:30], x[0:30, 6] -3 * np.sqrt(P[0:30, 5, 5]), 'r--')
plt.plot(t[0:30], x2[0:30, 6], "g")
plt.plot(t[0:30], x2[0:30, 6]+ 3 * np.sqrt(P2[0:30, 5, 5]), 'c--')
plt.plot(t[0:30], x2[0:30, 6]-3 * np.sqrt(P2[0:30, 5, 5]), 'c--')
plt.ylabel('S')
plt.xlabel('t(s)')
plt.title('Solar Intensity')
plt.grid()
else:
plt.subplot(326)
plt.plot(t[0:30] , x[0:30, 6], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 5, 5]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 5, 5]), 'r--')
plt.plot(t[0:30] , x2[0:30, 6], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 5, 5]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 5, 5]), 'c--')
plt.ylabel(r'$\dot{d}_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if nstates == 3:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(311)
plt.plot(t[0:30], x[0:30, 1], "b", label='Error Filter')
plt.plot(t[0:30], 3 * np.sqrt(P[0:30, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t[0:30], -3 * np.sqrt(P[0:30, 0, 0]), 'r--')
plt.plot(t[0:30], x2[0:30, 1], "g", label='Error Expected')
plt.plot(t[0:30], 3 * np.sqrt(P2[0:30, 0, 0]), 'c--', label='Covar Expected')
plt.plot(t[0:30], -3 * np.sqrt(P2[0:30, 0, 0]), 'c--')
plt.ylabel('$d_x$(m)')
plt.legend(loc='lower right')
plt.title('First LOS component')
plt.grid()
plt.subplot(312)
plt.plot(t[0:30], x[0:30, 2], "b")
plt.plot(t[0:30], 3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30], -3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30], x2[0:30, 2], "g")
plt.plot(t[0:30], 3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.plot(t[0:30], -3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(313)
plt.plot(t[0:30], x[0:30, 3], "b")
plt.plot(t[0:30], 3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30], -3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30], x2[0:30, 3], "g")
plt.plot(t[0:30], 3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.plot(t[0:30], -3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.ylabel('$d_z$(m)')
plt.title('Third LOS component')
plt.grid()
if nstates == 5:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(t[0:30] , x[0:30, 1], "b", label='Error Filter')
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 0, 0]), 'r--', label='Covar Filter')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 0, 0]), 'r--')
plt.plot(t[0:30] , x2[0:30, 1], "g", label='Error Expected')
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 0, 0]), 'c--', label='Covar Expected')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 0, 0]), 'c--')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(323)
plt.plot(t[0:30] , x[0:30, 2], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 1, 1]), 'r--')
plt.plot(t[0:30] , x2[0:30, 2], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 1, 1]), 'c--')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(t[0:30] , x[0:30, 4], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 3, 3]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 3, 3]), 'r--')
plt.plot(t[0:30] , x2[0:30, 4], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 3, 3]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 3, 3]), 'c--')
plt.ylabel(r'$\omega_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(t[0:30] , x[0:30, 3], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 2, 2]), 'r--')
plt.plot(t[0:30] , x2[0:30, 3], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 2, 2]), 'c--')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
plt.subplot(326)
plt.plot(t[0:30] , x[0:30, 5], "b")
plt.plot(t[0:30] , 3 * np.sqrt(P[0:30, 4, 4]), 'r--')
plt.plot(t[0:30] , -3 * np.sqrt(P[0:30, 4, 4]), 'r--')
plt.plot(t[0:30] , x2[0:30, 5], "g")
plt.plot(t[0:30] , 3 * np.sqrt(P2[0:30, 4, 4]), 'c--')
plt.plot(t[0:30] , -3 * np.sqrt(P2[0:30, 4, 4]), 'c--')
plt.ylabel(r'$\omega_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_StatesCompare'+FilterType, plt, path)
if show_plots:
plt.show()
plt.close()
def numMeasurements(numObs, FilterType, show_plots, saveFigures):
plt.plot(111)
plt.plot(numObs[:,0]*(1E-9) , numObs[:, 1], "b")
plt.ylim([0,5])
plt.xlabel('t(s)')
plt.title('Number of Activated CSS')
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_Obs'+ FilterType, plt, path)
if show_plots:
plt.show()
plt.close()
def PostFitResiduals(Res, noise, FilterType, show_plots, saveFigures):
MeasNoise = np.zeros(len(Res[:,0]))
t= np.zeros(len(Res[:,0]))
constantVal = np.array([np.nan]*4)
for i in range(len(Res[:,0])):
t[i] = Res[i, 0]*1E-9
MeasNoise[i] = 3*noise
# Don't plot constant values, they mean no measurement is taken
if i>0:
for j in range(1,5):
with np.errstate(invalid='ignore'):
constantRes = np.abs(Res[i,j]-Res[i-1,j])
if constantRes < 1E-10 or np.abs(constantVal[j-1] - Res[i,j])<1E-10:
constantVal[j-1] = Res[i, j]
Res[i, j] = np.nan
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(411)
plt.plot(t , Res[:, 1], "b.", label='Residual')
plt.plot(t , MeasNoise, 'r--', label='Covar')
plt.plot(t , -MeasNoise, 'r--')
plt.legend(loc='lower right')
plt.ylabel('$r_1$(m)')
plt.ylim([-5*noise, 5*noise])
plt.title('First CSS')
plt.subplot(412)
plt.plot(t , Res[:, 2], "b.")
plt.plot(t , MeasNoise, 'r--')
plt.plot(t , -MeasNoise, 'r--')
plt.ylabel('$r_2$(m)')
plt.ylim([-5*noise, 5*noise])
plt.title('Second CSS')
plt.subplot(413)
plt.plot(t , Res[:, 3], "b.")
plt.plot(t , MeasNoise, 'r--')
plt.plot(t , -MeasNoise, 'r--')
plt.ylabel('$r_3$(m)')
plt.ylim([-5*noise, 5*noise])
plt.title('Third CSS')
plt.subplot(414)
plt.plot(t , Res[:, 4], "b.")
plt.plot(t , MeasNoise, 'r--')
plt.plot(t , -MeasNoise, 'r--')
plt.ylim([-5*noise, 5*noise])
plt.ylabel('$r_4$(m)')
plt.xlabel('t(s)')
plt.title('Fourth CSS')
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_PostFit'+ FilterType, plt, path)
if show_plots:
plt.show()
plt.close()
def StatesVsExpected(stateLog, Pflat, expectedStateArray, FilterType, show_plots, saveFigures):
nstates = int(np.sqrt(len(Pflat[0,:])-1))
P = np.zeros([len(Pflat[:, 0]), nstates, nstates])
for i in range(len(Pflat[:, 0])):
P[i, :, :] = Pflat[i, 1:(nstates*nstates +1)].reshape([nstates, nstates])
for j in range(len(P[0,0,:])):
P[i,j,j] = np.sqrt(P[i,j,j])
if nstates ==6:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 1], 'k--', label='Expected')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1], 'b', label='Filter')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] + P[:,0,0], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] - P[:,0,0], 'r--', label='Covar')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(322)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 4], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4] + P[:,3,3], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4] - P[:,3,3], 'r--', label='Covar')
plt.ylabel(r'$\dot{d}_x$(m)')
plt.title('First rate component')
plt.grid()
plt.subplot(323)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 2], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] + P[:,1,1], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] - P[:,1,1], 'r--', label='Covar')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 5], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5] + P[:,4,4], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5] - P[:,4,4], 'r--', label='Covar')
plt.ylabel(r'$\dot{d}_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 3], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] + P[:,2,2], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] - P[:,2,2], 'r--', label='Covar')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
plt.subplot(326)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 6], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 6], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 6] + P[:,5,5], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 6] - P[:,5,5], 'r--', label='Covar')
plt.ylabel(r'$\dot{d}_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if nstates ==3:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(311)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 1], 'k--', label='Expected')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1], 'b', label='Filter')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] + P[:, 0, 0], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] - P[:, 0, 0], 'r--', label='Covar')
plt.ylabel('$d_x$(m)')
plt.legend(loc='lower right')
plt.title('First LOS component')
plt.grid()
plt.subplot(312)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 2], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] + P[:, 1, 1], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] - P[:, 1, 1], 'r--', label='Covar')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(313)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 3], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] + P[:, 2, 2], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] - P[:, 2, 2], 'r--', label='Covar')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
if nstates ==5:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 1], 'k--', label='Expected')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1], 'b', label='Filter')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] + P[:,0,0], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1] - P[:,0,0], 'r--', label='Covar')
plt.legend(loc='lower right')
plt.ylabel('$d_x$(m)')
plt.title('First LOS component')
plt.grid()
plt.subplot(323)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 2], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] + P[:,1,1], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2] - P[:,1,1], 'r--', label='Covar')
plt.ylabel('$d_y$(m)')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 4], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4] + P[:,3,3], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4] - P[:,3,3], 'r--', label='Covar')
plt.ylabel(r'$\omega_y$(m)')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 3], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] + P[:,2,2], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3] - P[:,2,2], 'r--', label='Covar')
plt.ylabel('$d_z$(m)')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
plt.subplot(326)
plt.plot(stateLog[:, 0] * 1.0E-9, expectedStateArray[:, 5], 'k--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5] + P[:,4,4], 'r--')
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5] - P[:,4,4], 'r--', label='Covar')
plt.ylabel(r'$\omega_z$(m)')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_StatesExpected' + FilterType , plt, path)
if show_plots:
plt.show()
plt.close()
def StatesVsTargets(target1, target2, stateLog, FilterType, show_plots, saveFigures):
nstates = int(stateLog[0,:])
target = np.ones([len(stateLog[:, 0]),nstates])
for i in range((len(stateLog[:, 0])-1)/2):
target[i, :] = target1
target[i+(len(stateLog[:, 0]) - 1) / 2,:] = target2
if nstates == 6:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(321)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1], 'b', label='Filter')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 0], 'r--', label='Expected')
plt.legend(loc='lower right')
plt.title('First LOS component')
plt.grid()
plt.subplot(322)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 4], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 3], 'r--')
plt.title('First rate component')
plt.grid()
plt.subplot(323)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 1], 'r--')
plt.title('Second LOS component')
plt.grid()
plt.subplot(324)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 5], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 4], 'r--')
plt.title('Second rate component')
plt.grid()
plt.subplot(325)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 2], 'r--')
plt.xlabel('t(s)')
plt.title('Third LOS component')
plt.grid()
plt.subplot(326)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 6], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 5], 'r--')
plt.xlabel('t(s)')
plt.title('Third rate component')
plt.grid()
if nstates == 3:
plt.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(311)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 1], 'b', label='Filter')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 0], 'r--', label='Expected')
plt.legend(loc='lower right')
plt.title('First LOS component')
plt.grid()
plt.subplot(312)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 2], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 1], 'r--')
plt.title('Second rate component')
plt.grid()
plt.subplot(313)
plt.plot(stateLog[:, 0] * 1.0E-9, stateLog[:, 3], 'b')
plt.plot(stateLog[:, 0] * 1.0E-9, target[:, 2], 'r--')
plt.title('Third LOS component')
plt.grid()
if saveFigures:
unitTestSupport.saveScenarioFigure('scenario_Filters_StatesTarget' + FilterType, plt, path)
if show_plots:
plt.show()
plt.close()