def testCooper():
ies.setDataUri(
dataUri
) #Make sure that any auto-generated IDs follow the URI stub we're using.
mmsis = ['234234000', '235083854', '356793000']
startTime = "2021-08-11T10:47:48"
endTime = "2021-08-11T14:01:22"
vesselSTs = [
"2021-08-11T10:47:48", "2021-08-11T10:47:48", "2021-08-11T11:18:15"
] #The third vessel joins late
vesselETs = [
"2021-08-11T14:01:22", "2021-08-11T12:50:41", "2021-08-11T14:01:22"
] #The second vessel leaves early
cooper = cooperingAtSea(iesGraph, startTime, endTime, mmsis, 0.3,
"2021-08-12T09:00:00", True, vesselSTs, vesselETs)
ies.saveRdf(iesGraph, "track-analytics.ttl")
def testDestination():
predictedDestination(
iesGraph, '234234000',
[["Pontefract", "GB POF", "GBR", 0.5], ["Hull", "GB HUL", "GBR", 0.3],
["Ackworth", "GB AMT ", "GBR", 0.2]], "2021-08-12T09:00:00")
ies.saveRdf(iesGraph, "ta-pred-dest.ttl")
def exportTrack(iesGraph, track, output="file", epsgCode="4326"):
#The track dictionary should already have min and max timestamps for the pings it contains
ies.initialiseGraph(iesGraph=iesGraph)
#Add a parent observation
obs = ies.instantiate(iesGraph=iesGraph, _class=ies.observation)
ies.startsIn(iesGraph=iesGraph,
item=obs,
timeString=track["minDateTime"].isoformat())
ies.endsIn(iesGraph=iesGraph,
item=obs,
timeString=track["maxDateTime"].isoformat())
measures = dict({})
#Now add the measure classes for Speed Over Ground and Course Over Ground...and knots for the Unit Of Measure
measures["sogClass"] = ies.instantiate(iesGraph=iesGraph,
_class=ies.classOfMeasure,
instance=URIRef(ies.dataUri +
"SpeedOverGround"))
measures["cogClass"] = ies.instantiate(iesGraph=iesGraph,
_class=ies.classOfMeasure,
instance=URIRef(ies.dataUri +
"CourseOverGround"))
measures["knots"] = ies.instantiate(iesGraph=iesGraph,
_class=ies.unitOfMeasure,
instance=URIRef(ies.dataUri + "Knots"))
measures["degTN"] = ies.instantiate(iesGraph=iesGraph,
_class=ies.unitOfMeasure,
instance=URIRef(ies.dataUri +
"DegreesTrueNorth"))
ies.addName(iesGraph=iesGraph,
item=measures["sogClass"],
nameString="Speed Over Ground")
ies.addName(iesGraph=iesGraph,
item=measures["cogClass"],
nameString="Course Over Ground")
ies.addName(iesGraph=iesGraph, item=measures["knots"], nameString="knots")
ies.addName(iesGraph=iesGraph,
item=measures["degTN"],
nameString="degrees true North")
#add the location transponder - We don't know this is necessarily a vessel. All we know is that we have a LocationTransponder.
lt = ies.createLocationTransponder(iesGraph=iesGraph, mmsi=track["id"])
obsvd = ies.instantiate(iesGraph=iesGraph, _class=ies.observedTarget)
ies.addToGraph(iesGraph=iesGraph, subject=obsvd, predicate=ies.ipo, obj=lt)
ies.addToGraph(iesGraph=iesGraph,
subject=obsvd,
predicate=ies.ipi,
obj=obs)
#now go through the individual location observations and add those...
for ping in track["pings"]:
createLocationObservation(iesGraph=iesGraph,
ping=ping,
transponder=lt,
obs=obs,
measures=measures)
if output == "kafka":
ies.sendToKafka(iesGraph=iesGraph,
kProducer=kafkaBroker,
kTopic=iesKafkaTopic)
else:
ies.saveRdf(
iesGraph, './data/track' + track["id"] + '-' +
str(track["counter"]) + '.ies.ttl')
import pickle
import ies_stone_soup as iesss
import ies_functions as ies
import stonesoup
import numpy as np
from stonesoup.models.measurement.linear import LinearGaussian
measurement_model = LinearGaussian(ndim_state=4,
mapping=[0, 2],
noise_covar=np.diag([15, 15]))
file = open("./ss.pkl", "rb")
tracks = pickle.load(file)
iesGraph = ies.initialiseGraph()
for track in tracks:
print(track.metadata)
iesss.exportStoneSoupTrack(iesGraph,
track.id,
track.states,
measurement_model.mapping,
targetId=track.metadata["MMSI"],
epsgCode=32630)
ies.saveRdf(iesGraph, "./ss.ttl")
assr = ies.instantiate(iesGraph=iesGraph, _class=ies.assessor)
ies.addToGraph(iesGraph=iesGraph,
subject=assr,
predicate=ies.ipi,
obj=assessment)
ies.addToGraph(iesGraph=iesGraph,
subject=assr,
predicate=ies.ipo,
obj=inferenceSystem)
#Now put the assessment in a period - i.e. when the assessment was done
ies.putInPeriod(iesGraph=iesGraph,
item=assessment,
timeString="2007-01-02T09:17:04")
#Set up the rdf graph
graph = ies.initialiseGraph(None)
#Now say one is following the other (they're not, but I didn't have any data where ships followed each other)
#First we create an object for the system that detected it.
hal = ies.instantiate(iesGraph=graph, _class=ies.system)
ies.addName(iesGraph=graph, item=hal, nameString="HAL")
#now create the following pattern, with our system included as the assessor
following(iesGraph=graph,
followerMMSI="367000150",
followedMMSI="366952890",
startTimeStamp="2007-01-01T00:00:09",
endTimeStamp="2007-01-01T00:05:40",
inferenceSystem=hal)
ies.saveRdf(graph=graph, filename='following.ies.ttl')