Taxiway Model Overview
The taxiway model has three main agents: - Helicopter, which lands and takes off from a helipad - Aircraft, which lands at a runway, taxis to a gate, and takes off from a runway (and may be UAVs or Piloted Aircraft) - ATC, which coordinates operations
These agents interact via the flows: - Ground, a MultiFlow tracking the map as well as agent assignments/allocations - Location, a MultiFlow tracking the position/velocity of each route on the map, and - Requests, a CommsFlow tracking the messages sent between the ATC and the Aircraft/Helicopters
Copyright © 2024, United States Government, as represented by the Administrator of the National Aeronautics and Space Administration. All rights reserved.
The “"Fault Model Design tools - fmdtools version 2"” software is 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 and limitations under the License.
[1]:
from model import taxiway_model, create_fault_scen_metrics
from fmdtools.sim import propagate as prop
import networkx as nx
from common import plot_tstep, plot_course
[2]:
import pandas as pd
[3]:
from fmdtools.define.architecture.function import FunctionArchitectureTypeGraph
[4]:
mdl = taxiway_model()
[5]:
tg = FunctionArchitectureTypeGraph(mdl)
[6]:
tg.set_edge_labels(title='')
[7]:
fig, ax = tg.draw(figsize=(7,5), withlegend=True, legend_bbox=(0, .2))
[8]:
fig.savefig("modelstructure.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[9]:
mdl.flows['location']
[9]:
location Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
ma1 Location flow: LocationState(x=10.0, y=20.0, xd=0.0, yd=0.0, speed=0.0, stage='takeoff', mode='standby')
percieved Location flow: LocationState(x=10.0, y=20.0, xd=0.0, yd=0.0, speed=0.0, stage='takeoff', mode='standby')
closest Location flow: LocationState(x=10.0, y=20.0, xd=0.0, yd=0.0, speed=0.0, stage='takeoff', mode='standby')
ma2 Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
percieved Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
closest Location flow: LocationState(x=10.0, y=20.0, xd=0.0, yd=0.0, speed=0.0, stage='takeoff', mode='standby')
ma3 Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
percieved Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
closest Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
ua1 Location flow: LocationState(x=45.0, y=-5.0, xd=0.0, yd=0.0, speed=0.0, stage='park', mode='standby')
percieved Location flow: LocationState(x=45.0, y=-5.0, xd=0.0, yd=0.0, speed=0.0, stage='park', mode='standby')
closest Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
ua2 Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
percieved Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
closest Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
ua3 Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
percieved Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
closest Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
h1 Location flow: LocationState(x=65.0, y=15.0, xd=0.0, yd=0.0, speed=0.0, stage='land', mode='standby')
percieved Location flow: LocationState(x=65.0, y=15.0, xd=0.0, yd=0.0, speed=0.0, stage='land', mode='standby')
closest Location flow: LocationState(x=45.0, y=-5.0, xd=0.0, yd=0.0, speed=0.0, stage='park', mode='standby')
h2 Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
percieved Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
closest Location flow: LocationState(x=30.0, y=25.0, xd=0.0, yd=0.0, speed=0.0, stage='flight', mode='standby')
[10]:
mdl.flows['requests']
[10]:
requests Requests flow: RequestState(atc_com='None', asset_req='None', route=' ')
atc: atc Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: atc_out Requests flow: RequestState(atc_com='None', asset_req='None', route=' ')
in: {'ma1': ('asset_req',), 'ma2': ('asset_req',), 'ma3': ('asset_req',), 'ua1': ('asset_req',), 'ua2': ('asset_req',), 'ua3': ('asset_req',), 'h1': ('asset_req',), 'h2': ('asset_req',)}
received: {}
h1: h1 Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
h2: h2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
ua1: ua1 Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
ua2: ua2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
ua3: ua3 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
ma1: ma1 Requests flow: RequestState(atc_com='None', asset_req='takeoff', route=' ')
ma2: ma2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
ma3: ma3 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
ma1: ma1 Requests flow: RequestState(atc_com='None', asset_req='takeoff', route=' ')
out: ma1_out Requests flow: RequestState(atc_com='None', asset_req='takeoff', route=' ')
in: {}
received: {}
ma2: ma2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: ma2_out Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
in: {}
received: {}
ma3: ma3 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: ma3_out Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
in: {}
received: {}
ua1: ua1 Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
out: ua1_out Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
in: {}
received: {}
ua2: ua2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: ua2_out Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
in: {}
received: {}
ua3: ua3 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: ua3_out Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
in: {}
received: {}
h1: h1 Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
out: h1_out Requests flow: RequestState(atc_com='None', asset_req='taxi', route=' ')
in: {}
received: {}
h2: h2 Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
out: h2_out Requests flow: RequestState(atc_com='None', asset_req='land', route=' ')
in: {}
received: {}
[11]:
mdl.flows['ground']
[11]:
ground Environment flow: TaxiwayStates(area_allocation={'takeoff1': {'ma1'}, 'landing1': set(), 'helipad1': {'h1'}, 'gate1': set(), 'gate2': set(), 'gate3': set(), 'gate4': set(), 'gate5': set(), 'gate6': {'ua1'}, 'air_loc': set()}, asset_area={'h1': 'helipad1', 'h2': 'air_loc', 'ua1': 'gate6', 'ua2': 'air_loc', 'ua3': 'air_loc', 'ma1': 'takeoff1', 'ma2': 'air_loc', 'ma3': 'air_loc'}, asset_assignment={'h1': 'helipad1', 'h2': 'air_loc', 'ua1': 'gate6', 'ua2': 'air_loc', 'ua3': 'air_loc', 'ma1': 'takeoff1', 'ma2': 'air_loc', 'ma3': 'air_loc'})
atc Environment flow: TaxiwayStates(area_allocation={'takeoff1': {'ma1'}, 'landing1': set(), 'helipad1': {'h1'}, 'gate1': set(), 'gate2': set(), 'gate3': set(), 'gate4': set(), 'gate5': set(), 'gate6': {'ua1'}, 'air_loc': set()}, asset_area={'h1': 'helipad1', 'h2': 'air_loc', 'ua1': 'gate6', 'ua2': 'air_loc', 'ua3': 'air_loc', 'ma1': 'takeoff1', 'ma2': 'air_loc', 'ma3': 'air_loc'}, asset_assignment={'h1': 'helipad1', 'h2': 'air_loc', 'ua1': 'gate6', 'ua2': 'air_loc', 'ua3': 'air_loc', 'ma1': 'takeoff1', 'ma2': 'air_loc', 'ma3': 'air_loc'})
Model Simulation
[12]:
fig, ax = mdl.flows['ground'].show_map()
[13]:
endresults, mdlhist = prop.nominal(mdl)
[14]:
fig, ax=plot_tstep(mdl, mdlhist, 16, show_area_allocation=False, locattr="stage", title="Taxiway Activity ")
[15]:
fig.savefig("modelactivity.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[16]:
fig, ax = plot_course(mdl, mdlhist, "ma2", title="One aircraft's route over time")
[17]:
fig.savefig("assetroute.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[18]:
from fmdtools.analyze import phases
phasemaps = phases.from_hist(mdlhist)
phases_to_plot ={"ma2": phasemaps["ma2"], 'h2': phasemaps['h2']}
fig = phases.phaseplot(phases_to_plot, figsize=(4,4), title_padding=-0.02, title="Asset Operational Phases", phase_ticks="both")
[19]:
fig.savefig("assetmodes.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
Fault Simulation and Analysis
Perception Fault - AC Vision Fault
[20]:
endresults, mdlhist = prop.one_fault(mdl, "ma3", "lost_sight",
desired_result={93: {"graph.flows.location":{'include_glob':False, 'with_methods': False}},
110:{"graph.flows.location":{'include_glob':False, 'with_methods': False}},
20:["graph"], 120:['graph', "endclass"]})
[21]:
fig, ax = plot_tstep(mdl, mdlhist.faulty, 93, locattr="stage", assets_to_label=["ma3", "ma2"], areas_to_label=[],
title="MA3 approaches MA2 with no vision cone ", show_area_allocation=False)
[22]:
fig.savefig("ac_vision_map.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[23]:
endresults.t120p0.endclass #table should also include local, global metrics
[23]:
num_cycled: 4
perc_cycled: 0.5
num_crashed: 2
[24]:
endresults.t93p0.graph.flows.location
endresults.t93p0.graph.flows.location.set_edge_labels(title="")
[25]:
#%matplotlib qt
#endresults.t93p0.graph.flows.location.move_nodes()
[26]:
%matplotlib inline
[27]:
pos = {'ma1': [-0.51, -0.08], 'ma1_percieved': [-0.88, 0.03],
'ma1_closest': [-0.3, 0.12], 'ma2': [-0.66, 0.59],
'ma2_percieved': [-0.81, 0.84], 'ma2_closest': [-0.67, 0.26],
'ma3': [0.77, 0.49], 'ma3_percieved': [0.81, 0.83],
'ma3_closest': [0.51, 0.28], 'ua1': [0.0, 0.62],
'ua1_percieved': [0.0, 0.91], 'ua1_closest': [-0.02, 0.34],
'ua2': [0.02, -0.64], 'ua2_percieved': [0.0, -0.88],
'ua2_closest': [0.04, -0.38], 'ua3': [0.66, -0.48],
'ua3_percieved': [0.8, -0.84], 'ua3_closest': [0.42, -0.23],
'h1': [0.64, 0.11], 'h1_percieved': [0.93, -0.15],
'h1_closest': [0.3, 0.07], 'h2': [-0.69, -0.52],
'h2_percieved': [-0.79, -0.85], 'h2_closest': [-0.45, -0.32]}
endresults.t93p0.graph.flows.location.set_pos(**pos)
endresults.t93p0.graph.flows.location.set_node_labels(title="shortname")
[28]:
fig, ax = endresults.t93p0.graph.flows.location.draw(figsize=(8,8), title="t=93", legend_bbox=(0.84,0.27))
[29]:
fig.savefig("ac_loc_93.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[30]:
#%matplotlib qt
#endresults.t120p0.graph.move_nodes()
[31]:
%matplotlib inline
[32]:
pos = {'atc': [-1.0, -0.15], 'ma1': [-0.86, 0.52], 'ma2': [-0.33, 0.95],
'ma3': [0.62, -0.78], 'ua1': [-0.66, -0.76], 'ua2': [0.36, 0.94],
'ua3': [-0.02, -1.0], 'h1': [0.89, 0.48], 'h2': [1.0, -0.2],
'ground': [0.31, -0.26], 'location': [-0.31, -0.24], 'requests': [-0.01, 0.43]}
endresults.t120p0.graph.set_pos(**pos)
[33]:
endresults.t120p0.graph.set_edge_labels(title="")
endresults.t120p0.graph.set_node_styles(nodetype=dict(FxnBlock=dict(nx_node_size=2000),
MultiFlow=dict(nx_node_size=2000),
CommsFlow=dict(nx_node_size=2000)),
degraded={}, faulty={})
[34]:
fig, ax = endresults.t120p0.graph.draw(figsize=(5,5), withlegend=False)
[35]:
fig.savefig("ac_faultprop_120.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[36]:
ind_hist = create_fault_scen_metrics(mdlhist)
[37]:
fig, ax = ind_hist.plot_line("degraded_fields",
"cycled_assets",
"unsafe_distances",
"assets_without_sight",
"faulty_functions",
time_slice=[0,93],
time_slice_label = "fault injection/occurence",
ylabels={'off-nominal fields':'%'})
[38]:
fig.savefig("fault_history_ac_vision.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[39]:
final_values = ind_hist.faulty.get_slice(-1)
final_values
[39]:
incorrect_fields: 0
assets_without_sight: 0
unsafe_distances: 0
overbooked_locations: 0
incorrect_perception: 0
duplicate_land_commands: 0
cycled_assets: 4
degraded_fields: 25.7396449704142
faulty_functions: 0.2222222222222222
time: 120.0
[40]:
final_res = pd.DataFrame.from_dict({k: [v] for k, v in final_values.items()}, orient='index')
final_res
[40]:
| 0 | |
|---|---|
| incorrect_fields | 0.000000 |
| assets_without_sight | 0.000000 |
| unsafe_distances | 0.000000 |
| overbooked_locations | 0.000000 |
| incorrect_perception | 0.000000 |
| duplicate_land_commands | 0.000000 |
| cycled_assets | 4.000000 |
| degraded_fields | 25.739645 |
| faulty_functions | 0.222222 |
| time | 120.000000 |
[41]:
print(final_res.to_latex())
\begin{tabular}{lr}
\toprule
& 0 \\
\midrule
incorrect_fields & 0.000000 \\
assets_without_sight & 0.000000 \\
unsafe_distances & 0.000000 \\
overbooked_locations & 0.000000 \\
incorrect_perception & 0.000000 \\
duplicate_land_commands & 0.000000 \\
cycled_assets & 4.000000 \\
degraded_fields & 25.739645 \\
faulty_functions & 0.222222 \\
time & 120.000000 \\
\bottomrule
\end{tabular}
Communications Fault - Poor land command by ATC
[109]:
from fmdtools.define.flow.multiflow import MultiFlowGraph
from fmdtools.define.flow.commsflow import CommsFlowGraph
ground_args = {'with_root':True, "role_nodes": ['container', 'local']}
# todo: re-add send connections
# 'send_connections':{"asset_area":"asset_area", "area_allocation":"area_allocation", "asset_assignment":"asset_assignment"}
req_args = {'include_glob': False, "ports_only": True, 'with_methods': False}
endresults, mdlhist = prop.sequence(mdl, faultseq={8:{"atc":["wrong_land_command"]},10:{"ua2":["lost_sight"]}},
desired_result={10:{"graph.flows.requests":(CommsFlowGraph, req_args)},
11:{"graph.flows.requests":(CommsFlowGraph, req_args),
"graph.flows.ground":(MultiFlowGraph, ground_args)},
19:{"graph.flows.requests":{'include_glob':False, "ports_only":True}},
20:["graph"], 120:"endclass"})
[90]:
fig, ax = plot_tstep(mdl, mdlhist.faulty, 19, title="Aircraft crashed", areas_to_label=[])
[44]:
fig.savefig("atc_comms_map.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[78]:
endresults.t120p0.endclass
[78]:
num_cycled: 4
perc_cycled: 0.5
num_crashed: 2
[79]:
endresults.t11p0.graph.flows.ground
[79]:
<fmdtools.define.flow.multiflow.MultiFlowGraph at 0x2b74abd9290>
[80]:
import networkx as nx
[91]:
pos = nx.spring_layout(nx.MultiGraph(endresults.t11p0.graph.flows.ground.g))
endresults.t11p0.graph.flows.ground.set_pos(**pos)
endresults.t11p0.graph.flows.ground.set_node_labels(title="last", subtext="indicators")
endresults.t11p0.graph.flows.ground.set_edge_labels(title="")
endresults.t11p0.graph.flows.ground.set_node_styles(nodetype={'State':dict(nx_node_size=800),
'MultiFlow':dict(nx_node_size=800)},
degraded={}, faulty={})
[94]:
fig, ax = endresults.t11p0.graph.flows.ground.draw(figsize=(7,7), legend_labelspacing=3, legend_bbox=(0.79,0.6))
[50]:
fig.savefig("atc_comms_ground_11.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[98]:
pos = nx.spring_layout(nx.MultiGraph(endresults.t10p0.graph.flows.requests.g), iterations=500)
endresults.t10p0.graph.flows.requests.set_pos(**pos)
[117]:
endresults.t10p0.graph.flows.requests.set_edge_labels(title="")
endresults.t10p0.graph.flows.requests.set_node_labels(title="shortname")
endresults.t10p0.graph.flows.requests.set_node_styles(nodetype={'CommsFlow':dict(nx_node_size=900)},
degraded={}, faulty={})
[118]:
fig, ax = endresults.t10p0.graph.flows.requests.draw(figsize=(10,10), legend_bbox=(0.7,0.9))
[115]:
fig.savefig("atc_comms_requests_10.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[119]:
pos = {'atc': [-1.0, -0.22], 'ma1': [-0.86, 0.52], 'ma2': [-0.33, 0.95],
'ma3': [0.62, -0.78], 'ua1': [-0.66, -0.76], 'ua2': [0.36, 0.94],
'ua3': [-0.02, -1.0], 'h1': [0.89, 0.48], 'h2': [1.0, -0.2],
'ground': [0.31, -0.26], 'location': [-0.31, -0.24], 'requests': [-0.01, 0.43]}
endresults.t20p0.graph.set_pos(**pos)
endresults.t20p0.graph.set_edge_labels(title="")
endresults.t20p0.graph.set_node_labels(title="shortname")
endresults.t20p0.graph.set_node_styles(nodetype=dict(FxnBlock=dict(nx_node_size=2000),
MultiFlow=dict(nx_node_size=2000),
CommsFlow=dict(nx_node_size=2000)),
degraded={}, faulty={})
[120]:
fig, ax = endresults.t20p0.graph.draw(withlegend=False, figsize=(5,5))
[57]:
fig.savefig("atc_comms_resgraph.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
[58]:
ind_hist = create_fault_scen_metrics(mdlhist)
[59]:
fig, ax = ind_hist.plot_line("degraded_fields",
"cycled_assets",
"unsafe_distances",
"assets_without_sight",
"faulty_functions",
time_slice=[8, 10],
time_slice_label = "fault injection/occurence",
ylabels={'off-nominal fields':'%'})
[60]:
fig.savefig("fault_history_atc_comms.eps", format="eps", bbox_inches = 'tight', pad_inches = 0)
WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
[61]:
final_values = ind_hist.faulty.get_slice(-1)
final_values
[61]:
incorrect_fields: 0
assets_without_sight: 0
unsafe_distances: 0
overbooked_locations: 0
incorrect_perception: 0
duplicate_land_commands: 0
cycled_assets: 4
degraded_fields: 35.798816568047336
faulty_functions: 0.3333333333333333
time: 120.0
[62]:
final_res = pd.DataFrame.from_dict({k: [v] for k, v in final_values.items()}, orient='index')
final_res
[62]:
| 0 | |
|---|---|
| incorrect_fields | 0.000000 |
| assets_without_sight | 0.000000 |
| unsafe_distances | 0.000000 |
| overbooked_locations | 0.000000 |
| incorrect_perception | 0.000000 |
| duplicate_land_commands | 0.000000 |
| cycled_assets | 4.000000 |
| degraded_fields | 35.798817 |
| faulty_functions | 0.333333 |
| time | 120.000000 |
[63]:
print(final_res.to_latex())
\begin{tabular}{lr}
\toprule
& 0 \\
\midrule
incorrect_fields & 0.000000 \\
assets_without_sight & 0.000000 \\
unsafe_distances & 0.000000 \\
overbooked_locations & 0.000000 \\
incorrect_perception & 0.000000 \\
duplicate_land_commands & 0.000000 \\
cycled_assets & 4.000000 \\
degraded_fields & 35.798817 \\
faulty_functions & 0.333333 \\
time & 120.000000 \\
\bottomrule
\end{tabular}
[ ]: