More Human Failure Analyses

[1]:

from fmdtools.sim.sample import ParameterDomain, ParameterSample, FaultDomain, FaultSample
import fmdtools.analyze as an
import fmdtools.sim.propagate as prop
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42
import multiprocessing as mp
import scipy.stats as stats


import multiprocessing as mp

[2]:

from examples.rover.rover_model_human import RoverHuman, RoverHumanParam, asg_pos
from examples.rover.rover_model import plot_map

[3]:

mdl = RoverHuman()
g = mdl.as_modelgraph()
fig, ax = g.draw()
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_3_0.png 
[4]:

g.set_exec_order(mdl)
fig, ax = g.draw()
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_4_0.png 
[5]:

ag = mdl.fxns['operator'].aa.as_modelgraph()
ag.set_pos(**asg_pos)
fig, ax = ag.draw()
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_5_0.png 
[6]:

fig.savefig("action_graph.pdf", format="pdf", bbox_inches = 'tight', pad_inches = 0)

[7]:

mdl = RoverHuman(p={'linetype': 'sine', 'period': 10, 'amp': 3, 'drive_modes': {'mode_args':'manual'}})
er, hist = prop.nominal(mdl)
fig, ax = plot_map(mdl, hist)
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_7_0.png 
[8]:

mdl.fxns['drive'].m.faultmodes

[8]:

{'elec_open': Fault(prob=0.25, cost=0.0, phases={'drive': 1.0, 'start': 1.0}, units='sim'),
 'stuck': Fault(prob=0.25, cost=0.0, phases={'drive': 1.0, 'start': 1.0}, units='sim'),
 'stuck_right': Fault(prob=0.25, cost=0.0, phases={'drive': 1.0, 'start': 1.0}, units='sim'),
 'stuck_left': Fault(prob=0.25, cost=0.0, phases={'drive': 1.0, 'start': 1.0}, units='sim')}

[9]:

fd_hum = FaultDomain(mdl)
fd_hum.add_all_fxn_modes('operator')
fd_hum

[9]:

FaultDomain with faults:
 -('operator', 'look_failed_no_action')
 -('operator', 'percieve_failed_no_action')
 -('operator', 'percieve_not_visible')
 -('operator', 'percieve_wrong_position')
 -('operator', 'comprehend_failed_no_action')
 -('operator', 'project_failed_turn_left')
 -('operator', 'project_failed_turn_right')
 -('operator', 'project_failed_slow')
 -('operator', 'project_failed_fast')
 -('operator', 'project_failed_no_action')
 -...more

[10]:

fs_hum = FaultSample(fd_hum)
fs_hum.add_fault_phases("start")
fs_hum

[10]:

FaultSample of scenarios:
 - operator_look_failed_no_action_t15p0
 - operator_percieve_failed_no_action_t15p0
 - operator_percieve_not_visible_t15p0
 - operator_percieve_wrong_position_t15p0
 - operator_comprehend_failed_no_action_t15p0
 - operator_project_failed_turn_left_t15p0
 - operator_project_failed_turn_right_t15p0
 - operator_project_failed_slow_t15p0
 - operator_project_failed_fast_t15p0
 - operator_project_failed_no_action_t15p0
 - ... (19 total)

[11]:

fs_hum.scenarios()

[11]:

[SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['look_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='look_failed_no_action', rate=1.0, name='operator_look_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['percieve_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='percieve_failed_no_action', rate=0.3333333333333333, name='operator_percieve_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['percieve_not_visible']}, disturbances={})}, times=(15.0,), function='operator', fault='percieve_not_visible', rate=0.3333333333333333, name='operator_percieve_not_visible_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['percieve_wrong_position']}, disturbances={})}, times=(15.0,), function='operator', fault='percieve_wrong_position', rate=0.3333333333333333, name='operator_percieve_wrong_position_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['comprehend_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='comprehend_failed_no_action', rate=1.0, name='operator_comprehend_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['project_failed_turn_left']}, disturbances={})}, times=(15.0,), function='operator', fault='project_failed_turn_left', rate=0.2, name='operator_project_failed_turn_left_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['project_failed_turn_right']}, disturbances={})}, times=(15.0,), function='operator', fault='project_failed_turn_right', rate=0.2, name='operator_project_failed_turn_right_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['project_failed_slow']}, disturbances={})}, times=(15.0,), function='operator', fault='project_failed_slow', rate=0.2, name='operator_project_failed_slow_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['project_failed_fast']}, disturbances={})}, times=(15.0,), function='operator', fault='project_failed_fast', rate=0.2, name='operator_project_failed_fast_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['project_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='project_failed_no_action', rate=0.2, name='operator_project_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['decide_failed_turn_left']}, disturbances={})}, times=(15.0,), function='operator', fault='decide_failed_turn_left', rate=0.2, name='operator_decide_failed_turn_left_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['decide_failed_turn_right']}, disturbances={})}, times=(15.0,), function='operator', fault='decide_failed_turn_right', rate=0.2, name='operator_decide_failed_turn_right_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['decide_failed_slow']}, disturbances={})}, times=(15.0,), function='operator', fault='decide_failed_slow', rate=0.2, name='operator_decide_failed_slow_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['decide_failed_fast']}, disturbances={})}, times=(15.0,), function='operator', fault='decide_failed_fast', rate=0.2, name='operator_decide_failed_fast_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['decide_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='decide_failed_no_action', rate=0.2, name='operator_decide_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['reach_failed_no_action']}, disturbances={})}, times=(15.0,), function='operator', fault='reach_failed_no_action', rate=1.0, name='operator_reach_failed_no_action_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['press_failed_left']}, disturbances={})}, times=(15.0,), function='operator', fault='press_failed_left', rate=0.3333333333333333, name='operator_press_failed_left_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['press_failed_right']}, disturbances={})}, times=(15.0,), function='operator', fault='press_failed_right', rate=0.3333333333333333, name='operator_press_failed_right_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'operator': ['press_no_press']}, disturbances={})}, times=(15.0,), function='operator', fault='press_no_press', rate=0.3333333333333333, name='operator_press_no_press_t15p0', time=15.0, phase='start')]

[12]:

ecs, hists = prop.fault_sample(mdl, fs_hum)

SCENARIOS COMPLETE: 100%|██████████| 19/19 [00:03<00:00,  6.26it/s]

[13]:

fig, ax = plot_map(mdl, hists)
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_13_0.png 
[14]:

figs, axs = hists.plot_line('flows.pos.s.vel','flows.pos.s.x', 'flows.pos.s.y',
                            'flows.switch.s.power', 'fxns.power.s.charge', time_slice=[14],
                            title="Rover Response to Human Fault Modes at t=14",
                            indiv_kwargs={'faulty':{'alpha':0.5, 'color':'red'}},
                            ylabels={'flows.pos.s.x':'meters','flows.pos.s.y':'meters', 'flows.ground.s.x':'m/s',
                                   'fxns.power.s.charge': '%'}, xlabel='time (s)',
                            titles={'flows.pos.s.x':'x position','flows.pos.s.y':'y position', 'flows.pos.s.vel':'velocity',
                                   'flows.s.switch': 'on/off switch', 'fxns.power.s.charge': 'state of charge'}, h_padding=0.3)
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_14_0.png 
[15]:

fig.savefig("rover_fault_behavior.pdf", format="pdf", bbox_inches = 'tight', pad_inches = 0)

[16]:

from examples.rover.rover_degradation import PSFDegradationShort, PSFShortParams

[17]:

pd_deg = ParameterDomain(PSFShortParams)
pd_deg.add_constant('stress_param', 0.0)
pd_deg.add_variable('experience', var_lim=(1.0, 5.0))
pd_deg(2,2)

[17]:

PSFShortParams(experience=2.0, stress_param=0.0, fatigue_param=1.0)

[18]:

ps_deg = ParameterSample(pd_deg)
ps_deg.add_variable_ranges(replicates = 10)
ps_deg.scenarios()

[18]:

[ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 2121425827}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep0_range_0'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 3118789205}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep1_range_1'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 1460251320}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep2_range_2'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 3448493913}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep3_range_3'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 2067320777}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep4_range_4'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 2580642624}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep5_range_5'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 2475035383}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep6_range_6'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 338745618}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep7_range_7'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 56084742}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep8_range_8'),
 ParameterScenario(sequence={}, times=(), p={'experience': 1.0, 'stress_param': 0.0}, r={'seed': 689999441}, sp={}, prob=0.02, inputparams={0: 1.0}, rangeid='', name='rep9_range_9'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 2121425827}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep0_range_10'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 3118789205}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep1_range_11'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 1460251320}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep2_range_12'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 3448493913}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep3_range_13'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 2067320777}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep4_range_14'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 2580642624}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep5_range_15'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 2475035383}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep6_range_16'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 338745618}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep7_range_17'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 56084742}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep8_range_18'),
 ParameterScenario(sequence={}, times=(), p={'experience': 2.0, 'stress_param': 0.0}, r={'seed': 689999441}, sp={}, prob=0.02, inputparams={0: 2.0}, rangeid='', name='rep9_range_19'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 2121425827}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep0_range_20'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 3118789205}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep1_range_21'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 1460251320}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep2_range_22'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 3448493913}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep3_range_23'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 2067320777}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep4_range_24'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 2580642624}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep5_range_25'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 2475035383}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep6_range_26'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 338745618}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep7_range_27'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 56084742}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep8_range_28'),
 ParameterScenario(sequence={}, times=(), p={'experience': 3.0, 'stress_param': 0.0}, r={'seed': 689999441}, sp={}, prob=0.02, inputparams={0: 3.0}, rangeid='', name='rep9_range_29'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 2121425827}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep0_range_30'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 3118789205}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep1_range_31'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 1460251320}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep2_range_32'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 3448493913}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep3_range_33'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 2067320777}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep4_range_34'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 2580642624}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep5_range_35'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 2475035383}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep6_range_36'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 338745618}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep7_range_37'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 56084742}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep8_range_38'),
 ParameterScenario(sequence={}, times=(), p={'experience': 4.0, 'stress_param': 0.0}, r={'seed': 689999441}, sp={}, prob=0.02, inputparams={0: 4.0}, rangeid='', name='rep9_range_39'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 2121425827}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep0_range_40'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 3118789205}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep1_range_41'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 1460251320}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep2_range_42'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 3448493913}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep3_range_43'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 2067320777}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep4_range_44'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 2580642624}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep5_range_45'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 2475035383}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep6_range_46'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 338745618}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep7_range_47'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 56084742}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep8_range_48'),
 ParameterScenario(sequence={}, times=(), p={'experience': 5.0, 'stress_param': 0.0}, r={'seed': 689999441}, sp={}, prob=0.02, inputparams={0: 5.0}, rangeid='', name='rep9_range_49')]

[19]:

deg_mdl = PSFDegradationShort()
ec_psf_short, hist_psf_short = prop.parameter_sample(deg_mdl, ps_deg, run_stochastic=True)

SCENARIOS COMPLETE: 100%|██████████| 50/50 [00:00<00:00, 497.14it/s]

[20]:

hist_psf_short.keys()

[20]:

dict_keys(['rep0_range_0.s.fatigue', 'rep0_range_0.s.stress', 'rep0_range_0.s.experience', 'rep0_range_0.r.s.fatigue_param', 'rep0_range_0.r.probdens', 'rep0_range_0.time', 'rep1_range_1.s.fatigue', 'rep1_range_1.s.stress', 'rep1_range_1.s.experience', 'rep1_range_1.r.s.fatigue_param', 'rep1_range_1.r.probdens', 'rep1_range_1.time', 'rep2_range_2.s.fatigue', 'rep2_range_2.s.stress', 'rep2_range_2.s.experience', 'rep2_range_2.r.s.fatigue_param', 'rep2_range_2.r.probdens', 'rep2_range_2.time', 'rep3_range_3.s.fatigue', 'rep3_range_3.s.stress', 'rep3_range_3.s.experience', 'rep3_range_3.r.s.fatigue_param', 'rep3_range_3.r.probdens', 'rep3_range_3.time', 'rep4_range_4.s.fatigue', 'rep4_range_4.s.stress', 'rep4_range_4.s.experience', 'rep4_range_4.r.s.fatigue_param', 'rep4_range_4.r.probdens', 'rep4_range_4.time', 'rep5_range_5.s.fatigue', 'rep5_range_5.s.stress', 'rep5_range_5.s.experience', 'rep5_range_5.r.s.fatigue_param', 'rep5_range_5.r.probdens', 'rep5_range_5.time', 'rep6_range_6.s.fatigue', 'rep6_range_6.s.stress', 'rep6_range_6.s.experience', 'rep6_range_6.r.s.fatigue_param', 'rep6_range_6.r.probdens', 'rep6_range_6.time', 'rep7_range_7.s.fatigue', 'rep7_range_7.s.stress', 'rep7_range_7.s.experience', 'rep7_range_7.r.s.fatigue_param', 'rep7_range_7.r.probdens', 'rep7_range_7.time', 'rep8_range_8.s.fatigue', 'rep8_range_8.s.stress', 'rep8_range_8.s.experience', 'rep8_range_8.r.s.fatigue_param', 'rep8_range_8.r.probdens', 'rep8_range_8.time', 'rep9_range_9.s.fatigue', 'rep9_range_9.s.stress', 'rep9_range_9.s.experience', 'rep9_range_9.r.s.fatigue_param', 'rep9_range_9.r.probdens', 'rep9_range_9.time', 'rep0_range_10.s.fatigue', 'rep0_range_10.s.stress', 'rep0_range_10.s.experience', 'rep0_range_10.r.s.fatigue_param', 'rep0_range_10.r.probdens', 'rep0_range_10.time', 'rep1_range_11.s.fatigue', 'rep1_range_11.s.stress', 'rep1_range_11.s.experience', 'rep1_range_11.r.s.fatigue_param', 'rep1_range_11.r.probdens', 'rep1_range_11.time', 'rep2_range_12.s.fatigue', 'rep2_range_12.s.stress', 'rep2_range_12.s.experience', 'rep2_range_12.r.s.fatigue_param', 'rep2_range_12.r.probdens', 'rep2_range_12.time', 'rep3_range_13.s.fatigue', 'rep3_range_13.s.stress', 'rep3_range_13.s.experience', 'rep3_range_13.r.s.fatigue_param', 'rep3_range_13.r.probdens', 'rep3_range_13.time', 'rep4_range_14.s.fatigue', 'rep4_range_14.s.stress', 'rep4_range_14.s.experience', 'rep4_range_14.r.s.fatigue_param', 'rep4_range_14.r.probdens', 'rep4_range_14.time', 'rep5_range_15.s.fatigue', 'rep5_range_15.s.stress', 'rep5_range_15.s.experience', 'rep5_range_15.r.s.fatigue_param', 'rep5_range_15.r.probdens', 'rep5_range_15.time', 'rep6_range_16.s.fatigue', 'rep6_range_16.s.stress', 'rep6_range_16.s.experience', 'rep6_range_16.r.s.fatigue_param', 'rep6_range_16.r.probdens', 'rep6_range_16.time', 'rep7_range_17.s.fatigue', 'rep7_range_17.s.stress', 'rep7_range_17.s.experience', 'rep7_range_17.r.s.fatigue_param', 'rep7_range_17.r.probdens', 'rep7_range_17.time', 'rep8_range_18.s.fatigue', 'rep8_range_18.s.stress', 'rep8_range_18.s.experience', 'rep8_range_18.r.s.fatigue_param', 'rep8_range_18.r.probdens', 'rep8_range_18.time', 'rep9_range_19.s.fatigue', 'rep9_range_19.s.stress', 'rep9_range_19.s.experience', 'rep9_range_19.r.s.fatigue_param', 'rep9_range_19.r.probdens', 'rep9_range_19.time', 'rep0_range_20.s.fatigue', 'rep0_range_20.s.stress', 'rep0_range_20.s.experience', 'rep0_range_20.r.s.fatigue_param', 'rep0_range_20.r.probdens', 'rep0_range_20.time', 'rep1_range_21.s.fatigue', 'rep1_range_21.s.stress', 'rep1_range_21.s.experience', 'rep1_range_21.r.s.fatigue_param', 'rep1_range_21.r.probdens', 'rep1_range_21.time', 'rep2_range_22.s.fatigue', 'rep2_range_22.s.stress', 'rep2_range_22.s.experience', 'rep2_range_22.r.s.fatigue_param', 'rep2_range_22.r.probdens', 'rep2_range_22.time', 'rep3_range_23.s.fatigue', 'rep3_range_23.s.stress', 'rep3_range_23.s.experience', 'rep3_range_23.r.s.fatigue_param', 'rep3_range_23.r.probdens', 'rep3_range_23.time', 'rep4_range_24.s.fatigue', 'rep4_range_24.s.stress', 'rep4_range_24.s.experience', 'rep4_range_24.r.s.fatigue_param', 'rep4_range_24.r.probdens', 'rep4_range_24.time', 'rep5_range_25.s.fatigue', 'rep5_range_25.s.stress', 'rep5_range_25.s.experience', 'rep5_range_25.r.s.fatigue_param', 'rep5_range_25.r.probdens', 'rep5_range_25.time', 'rep6_range_26.s.fatigue', 'rep6_range_26.s.stress', 'rep6_range_26.s.experience', 'rep6_range_26.r.s.fatigue_param', 'rep6_range_26.r.probdens', 'rep6_range_26.time', 'rep7_range_27.s.fatigue', 'rep7_range_27.s.stress', 'rep7_range_27.s.experience', 'rep7_range_27.r.s.fatigue_param', 'rep7_range_27.r.probdens', 'rep7_range_27.time', 'rep8_range_28.s.fatigue', 'rep8_range_28.s.stress', 'rep8_range_28.s.experience', 'rep8_range_28.r.s.fatigue_param', 'rep8_range_28.r.probdens', 'rep8_range_28.time', 'rep9_range_29.s.fatigue', 'rep9_range_29.s.stress', 'rep9_range_29.s.experience', 'rep9_range_29.r.s.fatigue_param', 'rep9_range_29.r.probdens', 'rep9_range_29.time', 'rep0_range_30.s.fatigue', 'rep0_range_30.s.stress', 'rep0_range_30.s.experience', 'rep0_range_30.r.s.fatigue_param', 'rep0_range_30.r.probdens', 'rep0_range_30.time', 'rep1_range_31.s.fatigue', 'rep1_range_31.s.stress', 'rep1_range_31.s.experience', 'rep1_range_31.r.s.fatigue_param', 'rep1_range_31.r.probdens', 'rep1_range_31.time', 'rep2_range_32.s.fatigue', 'rep2_range_32.s.stress', 'rep2_range_32.s.experience', 'rep2_range_32.r.s.fatigue_param', 'rep2_range_32.r.probdens', 'rep2_range_32.time', 'rep3_range_33.s.fatigue', 'rep3_range_33.s.stress', 'rep3_range_33.s.experience', 'rep3_range_33.r.s.fatigue_param', 'rep3_range_33.r.probdens', 'rep3_range_33.time', 'rep4_range_34.s.fatigue', 'rep4_range_34.s.stress', 'rep4_range_34.s.experience', 'rep4_range_34.r.s.fatigue_param', 'rep4_range_34.r.probdens', 'rep4_range_34.time', 'rep5_range_35.s.fatigue', 'rep5_range_35.s.stress', 'rep5_range_35.s.experience', 'rep5_range_35.r.s.fatigue_param', 'rep5_range_35.r.probdens', 'rep5_range_35.time', 'rep6_range_36.s.fatigue', 'rep6_range_36.s.stress', 'rep6_range_36.s.experience', 'rep6_range_36.r.s.fatigue_param', 'rep6_range_36.r.probdens', 'rep6_range_36.time', 'rep7_range_37.s.fatigue', 'rep7_range_37.s.stress', 'rep7_range_37.s.experience', 'rep7_range_37.r.s.fatigue_param', 'rep7_range_37.r.probdens', 'rep7_range_37.time', 'rep8_range_38.s.fatigue', 'rep8_range_38.s.stress', 'rep8_range_38.s.experience', 'rep8_range_38.r.s.fatigue_param', 'rep8_range_38.r.probdens', 'rep8_range_38.time', 'rep9_range_39.s.fatigue', 'rep9_range_39.s.stress', 'rep9_range_39.s.experience', 'rep9_range_39.r.s.fatigue_param', 'rep9_range_39.r.probdens', 'rep9_range_39.time', 'rep0_range_40.s.fatigue', 'rep0_range_40.s.stress', 'rep0_range_40.s.experience', 'rep0_range_40.r.s.fatigue_param', 'rep0_range_40.r.probdens', 'rep0_range_40.time', 'rep1_range_41.s.fatigue', 'rep1_range_41.s.stress', 'rep1_range_41.s.experience', 'rep1_range_41.r.s.fatigue_param', 'rep1_range_41.r.probdens', 'rep1_range_41.time', 'rep2_range_42.s.fatigue', 'rep2_range_42.s.stress', 'rep2_range_42.s.experience', 'rep2_range_42.r.s.fatigue_param', 'rep2_range_42.r.probdens', 'rep2_range_42.time', 'rep3_range_43.s.fatigue', 'rep3_range_43.s.stress', 'rep3_range_43.s.experience', 'rep3_range_43.r.s.fatigue_param', 'rep3_range_43.r.probdens', 'rep3_range_43.time', 'rep4_range_44.s.fatigue', 'rep4_range_44.s.stress', 'rep4_range_44.s.experience', 'rep4_range_44.r.s.fatigue_param', 'rep4_range_44.r.probdens', 'rep4_range_44.time', 'rep5_range_45.s.fatigue', 'rep5_range_45.s.stress', 'rep5_range_45.s.experience', 'rep5_range_45.r.s.fatigue_param', 'rep5_range_45.r.probdens', 'rep5_range_45.time', 'rep6_range_46.s.fatigue', 'rep6_range_46.s.stress', 'rep6_range_46.s.experience', 'rep6_range_46.r.s.fatigue_param', 'rep6_range_46.r.probdens', 'rep6_range_46.time', 'rep7_range_47.s.fatigue', 'rep7_range_47.s.stress', 'rep7_range_47.s.experience', 'rep7_range_47.r.s.fatigue_param', 'rep7_range_47.r.probdens', 'rep7_range_47.time', 'rep8_range_48.s.fatigue', 'rep8_range_48.s.stress', 'rep8_range_48.s.experience', 'rep8_range_48.r.s.fatigue_param', 'rep8_range_48.r.probdens', 'rep8_range_48.time', 'rep9_range_49.s.fatigue', 'rep9_range_49.s.stress', 'rep9_range_49.s.experience', 'rep9_range_49.r.s.fatigue_param', 'rep9_range_49.r.probdens', 'rep9_range_49.time'])

[21]:

fig, axs = hist_psf_short.plot_line('s.fatigue', 's.stress', aggregation='mean_bound', xlabel="time (hours)",
                           ylabels={'s.fatigue': 'level','s.stress': 'level (%)'}, h_padding=0.3,
                           titles={'s.fatigue': 'fatigue', 's.stress': 'stress'})
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_21_0.png 
[22]:

fig.savefig("degraded_PSFs.pdf", format="pdf", bbox_inches = 'tight', pad_inches = 0)

[23]:

RoverHumanParam().psfs

[23]:

PSFParam(fatigue=0.0, stress=0.0)

[24]:

from fmdtools.sim.sample import ParameterHistSample
pd_psf = ParameterDomain(RoverHumanParam)
pd_psf.add_variables('psfs.fatigue', 'psfs.stress')
phs = ParameterHistSample(hist_psf_short, 's.fatigue', 's.stress', paramdomain=pd_psf)
# phs.add_hist_times('default', 1, ts=(0, 10, 1))
phs.add_hist_groups(ts=(0, 10, 1), reps=5)
# phs.add_hist_scenario(rep=4, t=1)
len(phs.scenarios())

[24]:

50

[25]:

ec_hum, hist_hum = prop.parameter_sample(mdl, phs)

SCENARIOS COMPLETE: 100%|██████████| 50/50 [00:18<00:00,  2.75it/s]

[26]:

from fmdtools.analyze.tabulate import NominalEnvelope
na = NominalEnvelope(phs, ec_hum, 'at_finish', 'p.psfs.fatigue', 'p.psfs.stress')
fig, ax = na.as_plot()
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_26_0.png 
[27]:

na = NominalEnvelope(phs, ec_hum, 'at_finish', 'inputparams.t', 'inputparams.rep')
fig, ax = na.as_plot()
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_27_0.png 
[28]:

fig.savefig("nominal_PSF_completions.pdf", format="pdf", bbox_inches = 'tight', pad_inches = 0)

[29]:

from fmdtools.sim.sample import SampleApproach
sa = SampleApproach(mdl)
sa.add_faultdomains(**{'drive': (('all_fxn_modes', 'drive'), {})})
sa.add_faultsamples(**{'drive': (('fault_phases', 'drive', 'start'), {})})
sa.scenarios()

[29]:

[SingleFaultScenario(sequence={15.0: Injection(faults={'drive': ['elec_open']}, disturbances={})}, times=(15.0,), function='drive', fault='elec_open', rate=0.25, name='drive_elec_open_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'drive': ['stuck']}, disturbances={})}, times=(15.0,), function='drive', fault='stuck', rate=0.25, name='drive_stuck_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'drive': ['stuck_right']}, disturbances={})}, times=(15.0,), function='drive', fault='stuck_right', rate=0.25, name='drive_stuck_right_t15p0', time=15.0, phase='start'),
 SingleFaultScenario(sequence={15.0: Injection(faults={'drive': ['stuck_left']}, disturbances={})}, times=(15.0,), function='drive', fault='stuck_left', rate=0.25, name='drive_stuck_left_t15p0', time=15.0, phase='start')]

[30]:

f_ecs, f_hists, apps = prop.nested_sample(mdl, phs,
                                          faultdomains={'drive': (('all_fxn_modes', 'drive'), {})},
                                          faultsamples={'drive': (('fault_phases', 'drive', 'start'), {})})

NESTED SCENARIOS COMPLETE:   4%|▍         | 2/50 [00:03<01:16,  1.59s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:   6%|▌         | 3/50 [00:05<01:27,  1.86s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:   8%|▊         | 4/50 [00:07<01:31,  2.00s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  10%|█         | 5/50 [00:09<01:32,  2.05s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  12%|█▏        | 6/50 [00:11<01:31,  2.09s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  14%|█▍        | 7/50 [00:14<01:32,  2.14s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  16%|█▌        | 8/50 [00:16<01:30,  2.16s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  18%|█▊        | 9/50 [00:18<01:28,  2.17s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  24%|██▍       | 12/50 [00:23<01:11,  1.88s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  26%|██▌       | 13/50 [00:26<01:13,  1.97s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  28%|██▊       | 14/50 [00:28<01:13,  2.03s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  30%|███       | 15/50 [00:30<01:12,  2.07s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  32%|███▏      | 16/50 [00:32<01:11,  2.10s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  34%|███▍      | 17/50 [00:34<01:09,  2.12s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  36%|███▌      | 18/50 [00:36<01:08,  2.13s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  38%|███▊      | 19/50 [00:39<01:06,  2.15s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  42%|████▏     | 21/50 [00:42<00:57,  1.98s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  44%|████▍     | 22/50 [00:44<00:56,  2.02s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  46%|████▌     | 23/50 [00:47<00:55,  2.06s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  48%|████▊     | 24/50 [00:49<00:54,  2.08s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  50%|█████     | 25/50 [00:51<00:52,  2.10s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  52%|█████▏    | 26/50 [00:53<00:51,  2.13s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  54%|█████▍    | 27/50 [00:55<00:49,  2.14s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  56%|█████▌    | 28/50 [00:57<00:47,  2.15s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  58%|█████▊    | 29/50 [01:00<00:45,  2.15s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  68%|██████▊   | 34/50 [01:08<00:27,  1.70s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  70%|███████   | 35/50 [01:10<00:27,  1.84s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  72%|███████▏  | 36/50 [01:12<00:26,  1.92s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  74%|███████▍  | 37/50 [01:14<00:25,  1.98s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  76%|███████▌  | 38/50 [01:16<00:24,  2.03s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  78%|███████▊  | 39/50 [01:19<00:22,  2.07s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  92%|█████████▏| 46/50 [01:30<00:06,  1.64s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  94%|█████████▍| 47/50 [01:32<00:05,  1.79s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  96%|█████████▌| 48/50 [01:34<00:03,  1.89s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE:  98%|█████████▊| 49/50 [01:37<00:01,  1.97s/it]

Faults found during the nominal run {'operator': ['percieve_failed_no_action']}

NESTED SCENARIOS COMPLETE: 100%|██████████| 50/50 [01:39<00:00,  1.99s/it]

[31]:

from fmdtools.analyze.tabulate import NestedComparison
nc = NestedComparison(f_ecs, phs, samp_factors=['inputparams.t'], samps=apps, samps_factors=['fault'], metrics=['end_dist', 'tot_deviation'])
nc.sort_by_factor('fault')
nc.sort_by_factor('inputparams.t')
nc.as_table()

[31]:
end_dist tot_deviation
0 stuck_left 30.044497 0.388610
1 stuck_left 31.264909 0.310888
0 stuck_right 30.519136 0.270632
1 stuck_right 31.644620 0.216505
2 stuck_left 33.705732 0.155444
3 stuck_left 33.705732 0.155444
0 elec_open 31.752378 0.148246
stuck 14.310387 0.148246
1 stuck 18.677621 0.118596
elec_open 32.631213 0.118596
2 stuck_right 33.895587 0.108253
3 stuck_right 33.895587 0.108253
5 stuck_left 34.926143 0.077722
4 stuck_left 34.926143 0.077722
2 elec_open 34.388884 0.059298
stuck 27.412088 0.059298
3 elec_open 34.388884 0.059298
stuck 27.412088 0.059298
5 stuck_right 35.021071 0.054126
4 stuck_right 35.021071 0.054126
5 stuck 31.779321 0.029649
elec_open 35.267719 0.029649
4 stuck 31.779321 0.029649
elec_open 35.267719 0.029649
9 stuck_left 36.146555 0.000000
stuck 36.146555 0.000000
elec_open 36.146555 0.000000
8 stuck_right 36.146555 0.000000
stuck_left 36.146555 0.000000
stuck 36.146555 0.000000
elec_open 36.146555 0.000000
7 stuck_right 36.146555 0.000000
stuck_left 36.146555 0.000000
stuck 36.146555 0.000000
elec_open 36.146555 0.000000
6 stuck_right 36.146555 0.000000
stuck_left 36.146555 0.000000
stuck 36.146555 0.000000
elec_open 36.146555 0.000000
9 stuck_right 36.146555 0.000000 
[32]:

fig, axs = nc.as_plots('end_dist', 'tot_deviation', figsize = (8, 4))
../../../_images/examples_rover_HFAC_Analyses_HFAC_Analyses_32_0.png