def __init__(
self,
track,
res=1,
buffer=10,
prop=0.5,
length=0,
time=None,
mode=MODE_PARALLEL,
type=TYPE_SELECT,
):
"""TODO"""
self.track = track
self.time = time
self.type = type
self.prop = prop
self.mode = mode
self.length = length
self.segments = []
for i in range(1, len(track), 1):
pt1 = track[i].position.copy()
pt2 = pt1.copy()
dx = track[i].position.getX() - track[i - 1].position.getX()
dy = track[i].position.getY() - track[i - 1].position.getY()
R = (dx * dx + dy * dy)**(0.5)
if R == 0:
continue
pt1.translate(+buffer * dy / R, -buffer * dx / R)
pt2.translate(-buffer * dy / R, +buffer * dx / R)
self.segments.append(Track([Obs(pt1), Obs(pt2)]))
def test_create_index(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(550, 320), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(610, 325), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track.addObs(p2)
p3 = Obs(ENUCoords(610, 330), GPSTime.readTimestamp('2020-01-01 10:17:00'))
track.addObs(p3)
p4 = Obs(ENUCoords(650, 330), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p4)
p5 = Obs(ENUCoords(675, 340), GPSTime.readTimestamp('2020-01-01 10:25:00'))
track.addObs(p5)
#track.plot()
#track.plotAsMarkers()
TRACES = []
TRACES.append(track)
collection = TrackCollection(TRACES)
res = (25, 4)
index = SpatialIndex(collection, res, 0.05, True)
index.plot()
# =====================================================================
self.assertEqual(index.request(0, 0), [0])
self.assertEqual(index.request(1, 0), [0])
self.assertEqual(index.request(0, 1), [])
self.assertEqual(index.request(1, 1), [0])
self.assertEqual(index.request(2, 0), [])
self.assertEqual(index.request(2, 1), [0])
def test_write_csv_path(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(0, 1), GPSTime.readTimestamp('2020-01-01 10:00:01'))
track.addObs(p2)
p3 = Obs(ENUCoords(1, 1), GPSTime.readTimestamp('2020-01-01 10:00:02'))
track.addObs(p3)
p4 = Obs(ENUCoords(1, 2), GPSTime.readTimestamp('2020-01-01 10:00:03'))
track.addObs(p4)
p5 = Obs(ENUCoords(2, 2), GPSTime.readTimestamp('2020-01-01 10:00:04'))
track.addObs(p5)
csvpath = os.path.join(self.resource_path, 'data/test/test_write_csv_path.wkt')
FileWriter.writeToFile(track, csvpath)
contents = open(csvpath).read()
txt = "0.000,0.000,0.000,01/01/2020 10:00:00\n"
txt += "0.000,1.000,0.000,01/01/2020 10:00:01\n"
txt += "1.000,1.000,0.000,01/01/2020 10:00:02\n"
txt += "1.000,2.000,0.000,01/01/2020 10:00:03\n"
txt += "2.000,2.000,0.000,01/01/2020 10:00:04\n"
self.assertEqual(contents.strip(), txt.strip())
def test_write_csv_2AF_desordre(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(0, 1), GPSTime.readTimestamp('2020-01-01 10:00:01'))
track.addObs(p2)
p3 = Obs(ENUCoords(1, 1), GPSTime.readTimestamp('2020-01-01 10:00:02'))
track.addObs(p3)
p4 = Obs(ENUCoords(1, 2), GPSTime.readTimestamp('2020-01-01 10:00:03'))
track.addObs(p4)
p5 = Obs(ENUCoords(2, 2), GPSTime.readTimestamp('2020-01-01 10:00:04'))
track.addObs(p5)
track.addAnalyticalFeature(Analytics.speed)
track.compute_abscurv()
csvpath = os.path.join(self.resource_path, 'data/test/test_write_csv_2AF_desordre.wkt')
af_names = ['speed', 'abs_curv']
FileWriter.writeToFile(track, csvpath, id_E=3, id_N=2, id_U=0, id_T=1, h=1,
separator=";", af_names=af_names)
contents = open(csvpath).read()
txt = "#srid: ENU\n"
txt += "#U;time;N;E;speed;abs_curv\n"
txt += "0.000;01/01/2020 10:00:00;0.000;0.000;1.0;0\n"
txt += "0.000;01/01/2020 10:00:01;1.000;0.000;0.7071067811865476;1.0\n"
txt += "0.000;01/01/2020 10:00:02;1.000;1.000;0.7071067811865476;2.0\n"
txt += "0.000;01/01/2020 10:00:03;2.000;1.000;0.7071067811865476;3.0\n"
txt += "0.000;01/01/2020 10:00:04;2.000;2.000;1.0;4.0\n"
self.assertEqual(contents.strip(), txt.strip())
def setUp(self): self.track = Track() p1 = Obs(ENUCoords(0, 0), "2020-01-01 10:00:00") self.track.addObs(p1) p2 = Obs(ENUCoords(0, 1), "2020-01-01 10:00:01") self.track.addObs(p2) p3 = Obs(ENUCoords(1, 2), "2020-01-01 10:00:02") self.track.addObs(p3)
def __mapOnNetwork(
track, network, obs_noise=50, transition_cost=10, search_radius=50, debug=False
):
"""TODO"""
if debug:
f1 = open("observation.dat", "a")
f2 = open("transition.dat", "a")
track.createAnalyticalFeature("obs_noise", obs_noise)
verbose = True
global STATES
global net
STATES = []
net = network
to_run = range(len(track))
if verbose:
print("Map-matching preparation...")
to_run = progressbar.progressbar(to_run)
for i in to_run:
STATES.append([])
E = network.spatial_index.neighborhood(track[i].position, unit=1)
for elem in E:
eg = network.EDGES[network.getEdgeId(elem)].geom
p, d, v = __projOnTrack(track[i].position, eg)
if d < search_radius:
STATES[-1].append(
(p, elem, __distToNode(eg, p, v, 0), __distToNode(eg, p, v, 1))
)
if debug:
wkt = Track([Obs(track[i].position), Obs(p)]).toWKT()
f1.write(str(i) + ' "' + wkt + '" ' + str(d) + "\n")
model = Dynamics.HMM()
model.setStates(__states)
model.setTransitionModel(__tst_log)
model.setObservationModel(__obs_log)
model.estimate(
track,
obs=["x", "y"],
mode=Dynamics.MODE_OBS_AS_2D_POSITIONS,
verbose=verbose * Dynamics.MODE_VERBOSE_PROGRESS,
)
for k in progressbar.progressbar(range(len(track))):
X = [track[k].position.getX(), track["hmm_inference", k][0].getX()]
Y = [track[k].position.getY(), track["hmm_inference", k][0].getY()]
plt.plot(X, Y, "g-")
track[k].position.setX(track["hmm_inference", k][0].getX())
track[k].position.setY(track["hmm_inference", k][0].getY())
def run_routing_backward(self, target: Union[int, Node]) -> Union[Track, None]:
"""Computes shortest path between the source node used in forward step
:func:`run_routing_forward` and any target node.
If target node has not been reached during forward search, a None object is
returned by the function.
:param target: A target node
:return: A track between the source node specified in
:func:`run_routing_forward` and a target node. The track contains topologic
and non-topologic vertices. If the node target has not been reached during
forward step, None object is output
"""
target = self.__correctInputNode(target)
node = self.NODES[target]
track = Track()
track.addObs(Obs(node.coord))
if node.antecedent == "":
return None
while (node.poids != 0) and (node.antecedent != ""):
e = self.EDGES[node.antecedent_edge]
edge_geom = e.geom.copy()
if e.source != node:
edge_geom = edge_geom.reverse()
track = track + (edge_geom > 1)
node = node.antecedent
return track
def test_write_csv_minim(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(0, 1), GPSTime.readTimestamp('2020-01-01 10:00:01'))
track.addObs(p2)
csvpath = os.path.join(self.resource_path, 'data/test/test_write_csv_minim.wkt')
FileWriter.writeToFile(track, csvpath, id_E=0,id_N=1,id_U=2,id_T=3,h=1, separator=";")
contents = open(csvpath).read()
txt = "#srid: ENU\n"
txt += "#E;N;U;time\n"
txt += "0.000;0.000;0.000;01/01/2020 10:00:00\n"
txt += "0.000;1.000;0.000;01/01/2020 10:00:01\n"
self.assertEqual(contents.strip(), txt.strip())
def findStopsLocal(track, speed=1, duration=10):
track = track.copy()
stops = Track()
# track.segmentation("speed", "#mark", speed)
track.operate(Operator.Operator.DIFFERENTIATOR, "#mark")
track.operate(Operator.Operator.RECTIFIER, "#mark")
TRACES = split(track, "#mark")
TMP_MEAN_X = []
TMP_MEAN_Y = []
TMP_MEAN_Z = []
TMP_STD_X = []
TMP_STD_Y = []
TMP_STD_Z = []
TMP_DURATION = []
TMP_NBPOINTS = []
TMP_SIGMA_X = []
TMP_SIGMA_Y = []
TMP_SIGMA_Z = []
for i in range(0, len(TRACES), 2):
if TRACES[i].duration() < duration:
continue
stops.addObs(
Obs(TRACES[i].getCentroid().copy(),
TRACES[i].getFirstObs().timestamp.copy()))
TMP_SIGMA_X.append(TRACES[i].operate(Operator.Operator.AVERAGER, "x"))
TMP_SIGMA_Y.append(TRACES[i].operate(Operator.Operator.AVERAGER, "y"))
TMP_SIGMA_Z.append(TRACES[i].operate(Operator.Operator.AVERAGER, "z"))
TMP_SIGMA_X.append(TRACES[i].operate(Operator.Operator.STDDEV, "x"))
TMP_SIGMA_Y.append(TRACES[i].operate(Operator.Operator.STDDEV, "y"))
TMP_SIGMA_Z.append(TRACES[i].operate(Operator.Operator.STDDEV, "z"))
TMP_NBPOINTS.append(TRACES[i].size())
TMP_DURATION.append(TRACES[i].duration())
if stops.size() == 0:
return stops
# stops.createAnalyticalFeature("radius", TMP_RADIUS)
stops.createAnalyticalFeature("mean_x", TMP_MEAN_X)
stops.createAnalyticalFeature("mean_y", TMP_MEAN_Y)
stops.createAnalyticalFeature("mean_z", TMP_MEAN_Z)
stops.createAnalyticalFeature("sigma_x", TMP_STD_X)
stops.createAnalyticalFeature("sigma_y", TMP_STD_Y)
stops.createAnalyticalFeature("sigma_z", TMP_STD_Z)
stops.createAnalyticalFeature("duration", TMP_DURATION)
stops.createAnalyticalFeature("nb_points", TMP_NBPOINTS)
stops.operate(Operator.Operator.QUAD_ADDER, "sigma_x", "sigma_y", "rmse")
return stops
def test_create_index_collection2(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(3.1, 3), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track.addObs(p2)
p3 = Obs(ENUCoords(3.1, 4.5), GPSTime.readTimestamp('2020-01-01 10:17:00'))
track.addObs(p3)
p4 = Obs(ENUCoords(4.5, 4.5), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p4)
p5 = Obs(ENUCoords(6, 5.5), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p5)
p6 = Obs(ENUCoords(7, 4.5), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p6)
p7 = Obs(ENUCoords(11, 5.5), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p7)
p8 = Obs(ENUCoords(13, 10), GPSTime.readTimestamp('2020-01-01 10:25:00'))
track.addObs(p8)
#track.plot()
#track.plotAsMarkers()
TRACES = []
TRACES.append(track)
collection = TrackCollection(TRACES)
index = SpatialIndex(collection, (2, 2))
index.plot()
# =====================================================================
# =====================================================================
self.assertEqual(index.request(0, 0), [0])
self.assertEqual(index.request(1, 0), [0])
self.assertEqual(index.request(0, 1), [])
self.assertEqual(index.request(1, 1), [0])
self.assertEqual(index.request(2, 0), [])
self.assertEqual(index.request(2, 1), [])
self.assertEqual(index.request(1, 2), [0])
self.assertEqual(index.request(2, 2), [0])
self.assertEqual(index.request(3, 2), [0])
self.assertEqual(index.request(3, 3), [])
self.assertEqual(index.request(4, 2), [0])
self.assertEqual(index.request(4, 3), [])
self.assertEqual(index.request(4, 4), [])
self.assertEqual(index.request(5, 2), [0])
self.assertEqual(index.request(5, 3), [0])
self.assertEqual(index.request(5, 4), [])
def wktLineStringToObs(wkt, srid):
"""
Une polyligne de n points est modélisée par une Track (timestamp = 1970/01/01 00 :00 :00)
Cas LINESTRING()
"""
# Creation d'une liste vide
TAB_OBS = list()
# Separation de la chaine
coords_string = wkt.split("(")
coords_string = coords_string[1]
coords_string = coords_string.split(")")[0]
coords = coords_string.split(",")
for i in range(0, len(coords)):
sl = coords[i].strip().split(" ")
x = float(sl[0])
y = float(sl[1])
if len(sl) == 3:
z = float(sl[2])
else:
z = 0.0
if not srid.upper() in [
"ENUCOORDS",
"ENU",
"GEOCOORDS",
"GEO",
"ECEFCOORDS",
"ECEF",
]:
print("Error: unknown coordinate type [" + str(srid) + "]")
exit()
if srid.upper() in ["ENUCOORDS", "ENU"]:
point = ENUCoords(x, y, z)
if srid.upper() in ["GEOCOORDS", "GEO"]:
point = GeoCoords(x, y, z)
if srid.upper() in ["ECEFCOORDS", "ECEF"]:
point = ECEFCoords(x, y, z)
TAB_OBS.append(Obs(point, GPSTime()))
return TAB_OBS
def setUp(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
self.track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.track.addObs(p1)
p2 = Obs(ENUCoords(0, 1), GPSTime.readTimestamp('2020-01-01 10:00:01'))
self.track.addObs(p2)
p3 = Obs(ENUCoords(1, 1), GPSTime.readTimestamp('2020-01-01 10:00:02'))
self.track.addObs(p3)
p4 = Obs(ENUCoords(1, 2), GPSTime.readTimestamp('2020-01-01 10:00:03'))
self.track.addObs(p4)
p5 = Obs(ENUCoords(2, 2), GPSTime.readTimestamp('2020-01-01 10:00:04'))
self.track.addObs(p5)
p6 = Obs(ENUCoords(2, 3), GPSTime.readTimestamp('2020-01-01 10:00:06'))
self.track.addObs(p6)
p7 = Obs(ENUCoords(3, 3), GPSTime.readTimestamp('2020-01-01 10:00:08'))
self.track.addObs(p7)
p8 = Obs(ENUCoords(3, 4), GPSTime.readTimestamp('2020-01-01 10:00:10'))
self.track.addObs(p8)
p9 = Obs(ENUCoords(4, 4), GPSTime.readTimestamp('2020-01-01 10:00:12'))
self.track.addObs(p9)
def readFromGpx(path, srid="GEO"):
"""
Reads (multiple) tracks in .gpx file
"""
tracks = TrackCollection()
format_old = GPSTime.getReadFormat()
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
doc = minidom.parse(path)
trks = doc.getElementsByTagName("trk")
for trk in trks:
trace = t.Track()
trkpts = trk.getElementsByTagName("trkpt")
for trkpt in trkpts:
lon = float(trkpt.attributes["lon"].value)
lat = float(trkpt.attributes["lat"].value)
hgt = utils.NAN
eles = trkpt.getElementsByTagName("ele")
if eles.length > 0:
hgt = float(eles[0].firstChild.data)
time = ""
times = trkpt.getElementsByTagName("time")
if times.length > 0:
time = GPSTime(times[0].firstChild.data)
else:
time = GPSTime()
point = Obs(utils.makeCoords(lon, lat, hgt, srid), time)
trace.addObs(point)
tracks.addTrack(trace)
# pourquoi ?
# --> pour remettre le format comme il etait avant la lectre :)
GPSTime.setReadFormat(format_old)
collection = TrackCollection(tracks)
return collection
def tabCoordsLineStringToObs(coords, srid):
"""TODO"""
# Creation d'une liste vide
TAB_OBS = list()
for i in range(0, len(coords)):
sl = coords[i]
x = float(sl[0])
y = float(sl[1])
if len(sl) == 3:
z = float(sl[2])
else:
z = 0.0
if not srid.upper() in [
"ENUCOORDS",
"ENU",
"GEOCOORDS",
"GEO",
"ECEFCOORDS",
"ECEF",
]:
print("Error: unknown coordinate type [" + str(srid) + "]")
exit()
if srid.upper() in ["ENUCOORDS", "ENU"]:
point = ENUCoords(x, y, z)
if srid.upper() in ["GEOCOORDS", "GEO"]:
point = GeoCoords(x, y, z)
if srid.upper() in ["ECEFCOORDS", "ECEF"]:
point = ECEFCoords(x, y, z)
TAB_OBS.append(Obs(point, GPSTime()))
return TAB_OBS
def findStopsGlobal(track,
diameter=20,
duration=60,
downsampling=1,
verbose=True):
"""Find stop points in a track based on two parameters:
Maximal size of a stop (as the diameter of enclosing circle,
in ground units) and minimal time duration (in seconds)
Use downsampling parameter > 1 to speed up the process"""
# If down-sampling is required
if downsampling > 1:
track = track.copy()
track **= track.size() / downsampling
# ---------------------------------------------------------------------------
# Computes cost matrix as :
# Cij = 0 if size of enclosing circle of pi, pi+1, ... pj-1 is > diameter
# Cij = 0 if time duration between pi and p-1 is < duration
# Cij = (j-i)**2 = square of the number of points of segment otherwise
# ---------------------------------------------------------------------------
C = np.zeros((track.size(), track.size()))
RANGE = range(track.size() - 2)
if verbose:
print("Minimal enclosing circles computation:")
RANGE = progressbar.progressbar(RANGE)
for i in RANGE:
for j in range(i + 1, track.size() - 1):
if track[i].distance2DTo(track[j - 1]) > diameter:
C[i, j] = 0
break
if track[j - 1].timestamp - track[i].timestamp <= duration:
C[i, j] = 0
continue
C[i, j] = 2 * minCircle(track.extract(i, j - 1))[1]
C[i, j] = (C[i, j] < diameter) * (j - i)**2
C = C + np.transpose(C)
# ---------------------------------------------------------------------------
# Computes optimal partition with dynamic programing
# ---------------------------------------------------------------------------
segmentation = optimalPartition(C, MODE_SEGMENTATION_MAXIMIZE, verbose)
stops = Track()
TMP_RADIUS = []
TMP_MEAN_X = []
TMP_MEAN_Y = []
TMP_MEAN_Z = []
TMP_IDSTART = []
TMP_IDEND = []
TMP_STD_X = []
TMP_STD_Y = []
TMP_STD_Z = []
TMP_DURATION = []
TMP_NBPOINTS = []
for i in range(len(segmentation) - 1):
portion = track.extract(segmentation[i], segmentation[i + 1] - 1)
C = minCircle(portion)
if (C[1] > diameter / 2) or (portion.duration() < duration):
continue
stops.addObs(Obs(C[0], portion.getFirstObs().timestamp))
TMP_RADIUS.append(C[1])
TMP_MEAN_X.append(portion.operate(Operator.Operator.AVERAGER, "x"))
TMP_MEAN_Y.append(portion.operate(Operator.Operator.AVERAGER, "y"))
TMP_MEAN_Z.append(portion.operate(Operator.Operator.AVERAGER, "z"))
TMP_STD_X.append(portion.operate(Operator.Operator.STDDEV, "x"))
TMP_STD_Y.append(portion.operate(Operator.Operator.STDDEV, "y"))
TMP_STD_Z.append(portion.operate(Operator.Operator.STDDEV, "z"))
TMP_IDSTART.append(segmentation[i] * downsampling)
TMP_IDEND.append((segmentation[i + 1] - 1) * downsampling)
TMP_NBPOINTS.append(segmentation[i + 1] - segmentation[i])
TMP_DURATION.append(portion.duration())
if stops.size() == 0:
return stops
stops.createAnalyticalFeature("radius", TMP_RADIUS)
stops.createAnalyticalFeature("mean_x", TMP_MEAN_X)
stops.createAnalyticalFeature("mean_y", TMP_MEAN_Y)
stops.createAnalyticalFeature("mean_z", TMP_MEAN_Z)
stops.createAnalyticalFeature("id_ini", TMP_IDSTART)
stops.createAnalyticalFeature("id_end", TMP_IDEND)
stops.createAnalyticalFeature("sigma_x", TMP_STD_X)
stops.createAnalyticalFeature("sigma_y", TMP_STD_Y)
stops.createAnalyticalFeature("sigma_z", TMP_STD_Z)
stops.createAnalyticalFeature("duration", TMP_DURATION)
stops.createAnalyticalFeature("nb_points", TMP_NBPOINTS)
stops.operate(Operator.Operator.QUAD_ADDER, "sigma_x", "sigma_y", "rmse")
stops.base = track.base
return stops
def setUp(self):
#----------------------------------------------------------------------
# 4 sommets sur axes du cercle trigonométrique
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
self.trace1 = Track()
c1 = ENUCoords(1, 0, 0)
p1 = Obs(c1, GPSTime.readTimestamp("2018-01-01 10:00:00"))
self.trace1.addObs(p1)
c2 = ENUCoords(0, 1, 0)
p2 = Obs(c2, GPSTime.readTimestamp("2018-01-01 10:00:12"))
self.trace1.addObs(p2)
c3 = ENUCoords(-1, 0, 0)
p3 = Obs(c3, GPSTime.readTimestamp("2018-01-01 10:00:40"))
self.trace1.addObs(p3)
c4 = ENUCoords(0, -1, 0)
p4 = Obs(c4, GPSTime.readTimestamp("2018-01-01 10:01:50"))
self.trace1.addObs(p4)
self.trace1.addObs(p1)
# ---------------------------------------------------------------------
# Un escalier
self.trace2 = Track()
pm3 = Obs(ENUCoords(-2, -1),
GPSTime.readTimestamp('2020-01-01 09:59:44'))
self.trace2.addObs(pm3)
pm2 = Obs(ENUCoords(-1, -1),
GPSTime.readTimestamp('2020-01-01 09:59:48'))
self.trace2.addObs(pm2)
pm1 = Obs(ENUCoords(-1, 0),
GPSTime.readTimestamp('2020-01-01 09:59:55'))
self.trace2.addObs(pm1)
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace2.addObs(p1)
p2 = Obs(ENUCoords(0, 2), GPSTime.readTimestamp('2020-01-01 10:00:01'))
self.trace2.addObs(p2)
p3 = Obs(ENUCoords(1, 2), GPSTime.readTimestamp('2020-01-01 10:00:02'))
self.trace2.addObs(p3)
p4 = Obs(ENUCoords(1, 5), GPSTime.readTimestamp('2020-01-01 10:00:03'))
self.trace2.addObs(p4)
p5 = Obs(ENUCoords(2, 5), GPSTime.readTimestamp('2020-01-01 10:00:04'))
self.trace2.addObs(p5)
p6 = Obs(ENUCoords(2, 9), GPSTime.readTimestamp('2020-01-01 10:00:06'))
self.trace2.addObs(p6)
p7 = Obs(ENUCoords(3, 9), GPSTime.readTimestamp('2020-01-01 10:00:08'))
self.trace2.addObs(p7)
p8 = Obs(ENUCoords(3, 14),
GPSTime.readTimestamp('2020-01-01 10:00:10'))
self.trace2.addObs(p8)
p9 = Obs(ENUCoords(4, 14),
GPSTime.readTimestamp('2020-01-01 10:00:12'))
self.trace2.addObs(p9)
p10 = Obs(ENUCoords(4, 20),
GPSTime.readTimestamp('2020-01-01 10:00:15'))
self.trace2.addObs(p10)
# ---------------------------------------------------------------------
#
self.trace3 = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p1)
p2 = Obs(ENUCoords(1.5, 0.5),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p2)
p3 = Obs(ENUCoords(2, 2), GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p3)
p4 = Obs(ENUCoords(3.75, 0.6),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p4)
p5 = Obs(ENUCoords(5, 0.5),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p5)
p6 = Obs(ENUCoords(3.55, -0.5),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p6)
p7 = Obs(ENUCoords(1.8, -1.2),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p7)
p8 = Obs(ENUCoords(1, -3),
GPSTime.readTimestamp('2020-01-01 10:00:00'))
self.trace3.addObs(p8)
def noise(track,
sigma=[1],
kernel=[Kernel.DiracKernel()],
distribution=DISTRIBUTION_NORMAL,
mode='linear',
force=False):
"""Track noising with Cholesky factorization of gaussian process covariance matrix:
.. math::
h(x2-x1)=\\exp-\\left(\\frac{x2-x1}{scope}\\right)^2
If :math:`X` is a gaussian white noise, :math:`Cov(LX) = L^t*L` => if :math:`L` is a
Cholesky factorization of a semi-postive-definite matrix :math:`S`,
:math:`then Cov(LX) = L^T*L = S` and :math:`Y=LX`` has :math:`S` as covariance matrix.
:param track: the track to be smoothed (input track is not modified)
:param sigma: noise amplitude(s) (in observation coordinate units)
:param kernel: noise autocovariance function(s)
:param mode: 'linear' (default), 'circular' or 'euclidian'
:param force: force definite-positive matrix with removal of negative eigen values"""
sigma = utils.listify(sigma)
kernel = utils.listify(kernel)
if len(sigma) != len(kernel):
sys.exit(
"Error: amplitude and kernel arrays must have same size in 'noise' function"
)
N = track.size()
track.compute_abscurv()
noised_track = track.copy()
for n in range(len(sigma)):
SIGMA_S = utils.makeCovarianceMatrixFromKernel(kernel[n],
track,
force=force,
mode=mode)
SIGMA_S += np.identity(N) * 1e-12
SIGMA_S *= sigma[n]**2 / SIGMA_S[0, 0]
# Cholesky decomposition
L = np.linalg.cholesky(SIGMA_S)
# Noise simulation
if distribution == DISTRIBUTION_NORMAL:
Xx = np.random.normal(0.0, 1.0, N)
Xy = np.random.normal(0.0, 1.0, N)
Xz = np.random.normal(0.0, 1.0, N)
if distribution == DISTRIBUTION_UNIFORM:
Xx = np.random.uniform(-1.73205, 1.73205, N)
Xy = np.random.uniform(-1.73205, 1.73205, N)
Xz = np.random.uniform(-1.73205, 1.73205, N)
if distribution == DISTRIBUTION_LAPLACE:
Xx = np.random.laplace(0.0, 0.5, N)
Xy = np.random.laplace(0.0, 0.5, N)
Xz = np.random.laplace(0.0, 0.5, N)
Yx = np.matmul(L, Xx)
Yy = np.matmul(L, Xy)
Yz = np.matmul(L, Xz)
# Building noised track
for i in range(N):
pt = noised_track.getObs(i).position
pt.setX(pt.getX() + Yx[i])
pt.setY(pt.getY() + Yy[i])
pt.setZ(pt.getZ() + Yz[i])
obs = Obs(pt, track.getObs(i).timestamp)
if mode == 'circular':
noised_track.loop()
return noised_track
def test_create_index_collection1(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
track = Track()
p1 = Obs(ENUCoords(0, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track.addObs(p1)
p2 = Obs(ENUCoords(2.5, 3), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track.addObs(p2)
p3 = Obs(ENUCoords(2.5, 5), GPSTime.readTimestamp('2020-01-01 10:17:00'))
track.addObs(p3)
p4 = Obs(ENUCoords(7, 5), GPSTime.readTimestamp('2020-01-01 10:21:00'))
track.addObs(p4)
p5 = Obs(ENUCoords(10, 10), GPSTime.readTimestamp('2020-01-01 10:25:00'))
track.addObs(p5)
#track.plot()
#track.plotAsMarkers()
TRACES = []
TRACES.append(track)
collection = TrackCollection(TRACES)
index = SpatialIndex(collection, (2, 2))
index.plot()
# =====================================================================
# =====================================================================
self.assertEqual(index.request(0, 0), [0])
self.assertEqual(index.request(1, 0), [])
self.assertEqual(index.request(0, 1), [0])
self.assertEqual(index.request(1, 1), [0])
self.assertEqual(index.request(2, 0), [])
self.assertEqual(index.request(2, 1), [])
self.assertEqual(index.request(1, 2), [0])
self.assertEqual(index.request(2, 2), [0])
self.assertEqual(index.request(3, 2), [0])
self.assertEqual(index.request(3, 3), [0])
self.assertEqual(index.request(4, 3), [0])
self.assertEqual(index.request(4, 4), [0])
# # =====================================================================
self.assertEqual(index.request(ENUCoords(0, 0)), [0])
self.assertEqual(index.request(ENUCoords(2.5, 3)), [0])
self.assertEqual(index.request(ENUCoords(2.5, 5)), [0])
self.assertEqual(index.request(ENUCoords(7, 5)), [0])
self.assertEqual(index.request(ENUCoords(10, 10)), [0])
self.assertEqual(index.request(ENUCoords(0.5, 2.5)), [0])
self.assertEqual(index.request(ENUCoords(4.2, 5.8)), [0])
# # =====================================================================
self.assertEqual(index.request([ENUCoords(2.1, 0.5), ENUCoords(1.1, 1.1)]), [0])
self.assertEqual(index.request([ENUCoords(2.1, 0.5), ENUCoords(7.1, 3.5)]), [])
self.assertEqual(index.request([ENUCoords(5.8, 5.8), ENUCoords(2.1, 1.1)]), [0])
# # =====================================================================
self.assertEqual(index.request(track), [0])
track2 = Track()
p6 = Obs(ENUCoords(2.2, 0), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track2.addObs(p6)
p7 = Obs(ENUCoords(2.2, 3.8), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track2.addObs(p7)
p8 = Obs(ENUCoords(6.5, 3.8), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track2.addObs(p8)
self.assertEqual(index.request(track2), [0])
track3 = Track()
p9 = Obs(ENUCoords(6.5, 3.8), GPSTime.readTimestamp('2020-01-01 10:00:00'))
track3.addObs(p9)
p10 = Obs(ENUCoords(6.5, 7), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track3.addObs(p10)
p11 = Obs(ENUCoords(10, 7), GPSTime.readTimestamp('2020-01-01 10:08:00'))
track3.addObs(p11)
self.assertEqual(index.request(track3), [0])
# # =====================================================================
# # =====================================================================
self.assertCountEqual(index.neighborhood(0, 4, 0), [])
self.assertCountEqual(index.neighborhood(0, 4, 1), [])
self.assertCountEqual(index.neighborhood(0, 4, 2), [0])
self.assertCountEqual(index.neighborhood(0, 4, 3), [0])
self.assertCountEqual(index.neighborhood(3, 0, 0), [])
self.assertCountEqual(index.neighborhood(3, 0, 1), [])
self.assertCountEqual(index.neighborhood(3, 0, 2), [0])
self.assertCountEqual(index.neighborhood(3, 0, 3), [0])
self.assertCountEqual(index.neighborhood(2, 2, 0), [0])
self.assertCountEqual(index.neighborhood(2, 2, 1), [0])
self.assertCountEqual(index.neighborhood(2, 2, 2), [0])
#self.assertCountEqual(index.neighborhood(2, 2, 3), [0])
# # UNIT = -1
self.assertCountEqual(index.neighborhood(2, 1, -1), [0])
self.assertCountEqual(index.neighborhood(2, 0, -1), [0])
self.assertCountEqual(index.neighborhood(0, 1, -1), [0])
self.assertCountEqual(index.neighborhood(1, 1, -1), [0])
self.assertCountEqual(index.neighborhood(0, 4, -1), [0])
self.assertCountEqual(index.neighborhood(3, 4, -1), [0])
self.assertCountEqual(index.neighborhood(4, 4, -1), [0])
self.assertCountEqual(index.neighborhood(2, 4, -1), [0])
# # =====================================================================
self.assertCountEqual(index.neighborhood(ENUCoords(0, 0.1)), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.5, 3)), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.5, 5)), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(7, 5)), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(10, 10)), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(6.5, 3.8), None, 0), [])
self.assertCountEqual(index.neighborhood(ENUCoords(6.5, 3.8), None, 1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(6.5, 3.8), None, 2), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(6.5, 3.8), None, 3), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.2, 3.8), None, 0), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.2, 3.8), None, 1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.2, 3.8), None, 2), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.2, 3.8), None, 3), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(9.9, 7), None, 0), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(9.9, 7), None, 1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(9.9, 7), None, 2), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(9.9, 7), None, 3), [0])
# # UNIT = -1
self.assertCountEqual(index.neighborhood(ENUCoords(0, 0), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.5, 3), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.5, 5), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(7, 5), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(10, 10), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(6.5, 3.8), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(2.2, 3.8), None, -1), [0])
self.assertCountEqual(index.neighborhood(ENUCoords(9.9, 7), None, -1), [0])
# # =====================================================================
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(0.1, 2.1)], None, 0), [0])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(0.1, 2.1)], None, 1), [0])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(0.1, 2.1)], None, 2), [0])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(0.1, 2.1)], None, -1), [0])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(7.1, 3.5)]), [])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(7.1, 3.5)], None, 2), [0])
self.assertEqual(index.neighborhood([ENUCoords(2.1, 0.5), ENUCoords(7.1, 3.5)], None, -1), [0])
self.assertEqual(index.neighborhood([ENUCoords(5.8, 5.8), ENUCoords(2.1, 1.1)]), [0])
self.assertEqual(index.neighborhood([ENUCoords(5.8, 5.8), ENUCoords(2.1, 1.1)], None, 1), [0])
self.assertEqual(index.neighborhood([ENUCoords(5.8, 5.8), ENUCoords(2.1, 1.1)], None, 2), [0])
self.assertEqual(index.neighborhood([ENUCoords(5.8, 5.8), ENUCoords(2.1, 1.1)], None, -1), [0])
# # =====================================================================
self.assertEqual(index.neighborhood(track), [0])
self.assertEqual(index.neighborhood(track, None, 1), [0])
self.assertEqual(index.neighborhood(track, None, 3), [0])
self.assertEqual(index.neighborhood(track, None, -1), [0])
self.assertEqual(index.neighborhood(track2), [0])
self.assertEqual(index.neighborhood(track2, None, 0), [0])
self.assertEqual(index.neighborhood(track2, None, 1), [0])
self.assertEqual(index.neighborhood(track2, None, 3), [0])
self.assertEqual(index.neighborhood(track2, None, -1), [0])
self.assertEqual(index.neighborhood(track3), [0])
self.assertEqual(index.neighborhood(track3, None, 0), [0])
self.assertEqual(index.neighborhood(track3, None, 1), [0])
self.assertEqual(index.neighborhood(track3, None, 2), [0])
self.assertEqual(index.neighborhood(track3, None, 3), [0])
self.assertEqual(index.neighborhood(track3, None, -1), [0])
def test_selection_track_constraint(self):
GPSTime.setReadFormat("4Y-2M-2D 2h:2m:2s")
chemin = os.path.join(self.resource_path, './data/trace1.dat')
trace = FileReader.readFromFile(chemin, 2, 3, -1, 4, separator=",")
trace.plot()
# =====================================================================
trace1 = Track()
c1 = trace.getObs(1350).position
c0 = ENUCoords(c1.getX() + 5000, c1.getY())
c2 = ENUCoords(c1.getX() - 5000, c1.getY())
p1 = Obs(c0, GPSTime.readTimestamp("2018-07-31 14:00:00"))
p2 = Obs(c1, GPSTime.readTimestamp("2018-07-31 14:01:00"))
p3 = Obs(c2, GPSTime.readTimestamp("2018-07-31 14:02:00"))
trace1.addObs(p1)
trace1.addObs(p2)
trace1.addObs(p3)
plt.plot(trace1.getX(), trace1.getY(), 'r-')
plt.show()
c3 = TrackConstraint(trace1, mode=MODE_PARALLEL)
s = Selector([c3])
selector = GlobalSelector([s])
isSelection = selector.contains(trace)
self.assertFalse(isSelection)
c4 = TrackConstraint(trace1, mode=MODE_CROSSES)
s = Selector([c4])
selector = GlobalSelector([s])
isSelection = selector.contains(trace)
self.assertTrue(isSelection)
# =====================================================================
trace1 = Track()
c0 = ENUCoords(
trace.getObs(1349).position.getX(),
trace.getObs(1349).position.getY())
c1 = ENUCoords(
trace.getObs(1350).position.getX(),
trace.getObs(1350).position.getY())
c2 = ENUCoords(
trace.getObs(1351).position.getX(),
trace.getObs(1351).position.getY())
p1 = Obs(c0, GPSTime.readTimestamp("2018-07-31 14:00:00"))
p2 = Obs(c1, GPSTime.readTimestamp("2018-07-31 14:01:00"))
p3 = Obs(c2, GPSTime.readTimestamp("2018-07-31 14:02:00"))
trace1.addObs(p1)
trace1.addObs(p2)
trace1.addObs(p3)
trace.plot()
plt.plot(trace1.getX(), trace1.getY(), 'r-')
plt.show()
c3 = TrackConstraint(trace1, mode=MODE_PARALLEL)
s = Selector([c3])
selector = GlobalSelector([s])
isSelection = selector.contains(trace)
self.assertTrue(isSelection)
c4 = TrackConstraint(trace1, mode=MODE_CROSSES)
s = Selector([c4])
selector = GlobalSelector([s])
isSelection = selector.contains(trace)
self.assertTrue(isSelection)
def __init__(self, pt1, pt2, time=None, type=TYPE_SELECT):
"""TODO"""
self.gate = Track([Obs(pt1), Obs(pt2)])
self.time = time
self.type = type
def example3():
start = GeoCoords(2.4320023, 48.84298, 100).toECEFCoords()
print(start)
path = "data/psr.dat"
track = Track()
for i in range(47):
track.addObs(Obs(ECEFCoords(0, 0, 0)))
track.createAnalyticalFeature("m0", [0] * 47)
track.createAnalyticalFeature("sx0", [0] * 47)
track.createAnalyticalFeature("sy0", [0] * 47)
track.createAnalyticalFeature("sz0", [0] * 47)
track.createAnalyticalFeature("m1", [0] * 47)
track.createAnalyticalFeature("sx1", [0] * 47)
track.createAnalyticalFeature("sy1", [0] * 47)
track.createAnalyticalFeature("sz1", [0] * 47)
track.createAnalyticalFeature("m2", [0] * 47)
track.createAnalyticalFeature("sx2", [0] * 47)
track.createAnalyticalFeature("sy2", [0] * 47)
track.createAnalyticalFeature("sz2", [0] * 47)
track.createAnalyticalFeature("m3", [0] * 47)
track.createAnalyticalFeature("sx3", [0] * 47)
track.createAnalyticalFeature("sy3", [0] * 47)
track.createAnalyticalFeature("sz3", [0] * 47)
track.createAnalyticalFeature("m4", [0] * 47)
track.createAnalyticalFeature("sx4", [0] * 47)
track.createAnalyticalFeature("sy4", [0] * 47)
track.createAnalyticalFeature("sz4", [0] * 47)
with open(path) as fp:
line = True
for i in range(47):
for j in range(5):
line = fp.readline()
vals = line[:-2].split(",")
track.setObsAnalyticalFeature("sx" + str(j), i, float(vals[1]))
track.setObsAnalyticalFeature("sy" + str(j), i, float(vals[2]))
track.setObsAnalyticalFeature("sz" + str(j), i, float(vals[3]))
track.setObsAnalyticalFeature("m" + str(j), i, float(vals[4]))
line = fp.readline()
def F(x):
plan = ECEFCoords(x[0, 0], x[1, 0], x[2, 0]).toENUCoords(start)
plan.E += x[5, 0] * math.sin(x[4, 0])
plan.N += x[5, 0] * math.cos(x[4, 0])
xyz = plan.toECEFCoords(start)
return np.array([[xyz.X], [xyz.Y], [xyz.Z], [x[3, 0] + x[6, 0]],
[x[4, 0]], [x[5, 0]], [x[6, 0]]])
def H(x, k, track):
return np.array([
[((x[0, 0] - track["sx0", k])**2 + (x[1, 0] - track["sy0", k])**2 +
(x[2, 0] - track["sz0", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx1", k])**2 + (x[1, 0] - track["sy1", k])**2 +
(x[2, 0] - track["sz1", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx2", k])**2 + (x[1, 0] - track["sy2", k])**2 +
(x[2, 0] - track["sz2", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx3", k])**2 + (x[1, 0] - track["sy3", k])**2 +
(x[2, 0] - track["sz3", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx4", k])**2 + (x[1, 0] - track["sy4", k])**2 +
(x[2, 0] - track["sz4", k])**2)**0.5 + x[3, 0]]
])
Q = 1e0 * np.eye(7, 7)
Q[3, 3] = 0
Q[4, 4] = 1e-10
Q[5, 5] = 1e-1
Q[6, 6] = 1e-1
R = 1e1 * np.eye(5, 5)
X0 = np.array([[start.getX()], [start.getY()], [start.getZ()], [0], [0],
[0], [0]])
P0 = 1e5 * np.eye(7, 7)
P0[3, 3] = 1e6
P0[4, 4] = 1e1
P0[5, 5] = 1e1
P0[6, 6] = 1e3
UKF = Kalman(spreading=1)
UKF.setTransition(F, Q)
UKF.setObservation(H, R)
UKF.setInitState(X0, P0)
UKF.summary()
UKF.estimate(track, ["m0", "m1", "m2", "m3", "m4"],
mode=Dynamics.MODE_STATES_AS_3D_POSITIONS)
track.toGeoCoords()
track.plot('r-')
plt.show()
KmlWriter.writeToKml(track, path="couplage.kml", type="LINE")
def example4():
start = GeoCoords(2.4320023, 48.84298, 100).toECEFCoords()
path = "data/psr_all.dat"
track = Track()
Nepochs = 534
for i in range(Nepochs):
track.addObs(Obs(ECEFCoords(0, 0, 0)))
track.createAnalyticalFeature("m0", [0] * Nepochs)
track.createAnalyticalFeature("sx0", [0] * Nepochs)
track.createAnalyticalFeature("sy0", [0] * Nepochs)
track.createAnalyticalFeature("sz0", [0] * Nepochs)
track.createAnalyticalFeature("m1", [0] * Nepochs)
track.createAnalyticalFeature("sx1", [0] * Nepochs)
track.createAnalyticalFeature("sy1", [0] * Nepochs)
track.createAnalyticalFeature("sz1", [0] * Nepochs)
track.createAnalyticalFeature("m2", [0] * Nepochs)
track.createAnalyticalFeature("sx2", [0] * Nepochs)
track.createAnalyticalFeature("sy2", [0] * Nepochs)
track.createAnalyticalFeature("sz2", [0] * Nepochs)
track.createAnalyticalFeature("m3", [0] * Nepochs)
track.createAnalyticalFeature("sx3", [0] * Nepochs)
track.createAnalyticalFeature("sy3", [0] * Nepochs)
track.createAnalyticalFeature("sz3", [0] * Nepochs)
track.createAnalyticalFeature("m4", [0] * Nepochs)
track.createAnalyticalFeature("sx4", [0] * Nepochs)
track.createAnalyticalFeature("sy4", [0] * Nepochs)
track.createAnalyticalFeature("sz4", [0] * Nepochs)
track.createAnalyticalFeature("m5", [0] * Nepochs)
track.createAnalyticalFeature("sx5", [0] * Nepochs)
track.createAnalyticalFeature("sy5", [0] * Nepochs)
track.createAnalyticalFeature("sz5", [0] * Nepochs)
with open(path) as fp:
line = True
for i in range(Nepochs):
for j in range(6):
line = fp.readline()
vals = line[:-1].split(",")
track.setObsAnalyticalFeature("sx" + str(j), i, float(vals[1]))
track.setObsAnalyticalFeature("sy" + str(j), i, float(vals[2]))
track.setObsAnalyticalFeature("sz" + str(j), i, float(vals[3]))
track.setObsAnalyticalFeature("m" + str(j), i, float(vals[4]))
line = fp.readline()
track = track % [False, True]
def F(x):
return np.array([[x[0, 0]], [x[1, 0]], [x[2, 0]], [x[3, 0] + x[4, 0]],
[x[4, 0]]])
def H(x, k, track):
return np.array([
[((x[0, 0] - track["sx0", k])**2 + (x[1, 0] - track["sy0", k])**2 +
(x[2, 0] - track["sz0", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx1", k])**2 + (x[1, 0] - track["sy1", k])**2 +
(x[2, 0] - track["sz1", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx2", k])**2 + (x[1, 0] - track["sy2", k])**2 +
(x[2, 0] - track["sz2", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx3", k])**2 + (x[1, 0] - track["sy3", k])**2 +
(x[2, 0] - track["sz3", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx4", k])**2 + (x[1, 0] - track["sy4", k])**2 +
(x[2, 0] - track["sz4", k])**2)**0.5 + x[3, 0]],
[((x[0, 0] - track["sx5", k])**2 + (x[1, 0] - track["sy5", k])**2 +
(x[2, 0] - track["sz5", k])**2)**0.5 + x[3, 0]]
])
Q = 1e0 * np.eye(5, 5)
Q[3, 3] = 0
Q[4, 4] = 1e0
def R(k):
P = 1e1 * np.eye(6, 6)
if (k >= 70) and (k < 267):
for i in range(3, 6):
P[i, i] = 1e16
return P
for k in range(70, 267):
for i in range(3, 6):
track.setObsAnalyticalFeature("m" + str(i), k, 20000000)
X0 = np.array([[start.X], [start.Y], [start.Z], [0], [0]])
P0 = 1e5 * np.eye(5, 5)
P0[3, 3] = 1e8
P0[4, 4] = 1e6
UKF = Kalman(spreading=1)
UKF.setTransition(F, Q)
UKF.setObservation(H, R)
UKF.setInitState(X0, P0)
UKF.summary()
UKF.estimate(track, ["m0", "m1", "m2", "m3", "m4", "m5"],
mode=Dynamics.MODE_STATES_AS_3D_POSITIONS)
track.toGeoCoords()
KmlWriter.writeToKml(track,
path="couplage.kml",
type="POINT",
c1=[0, 1, 0, 1])
track.plot('r+')
plt.show()
def intersection(track1, track2, withTime=-1):
if not (track1.getSRID() == track2.getSRID()):
print("Error: tracks must have same SRID to compute intersections")
exit()
I = Track()
TMP_I = []
TMP_J = []
TMP_TPS2 = []
for i in range(len(track1) - 1):
x11 = track1[i].position.getX()
y11 = track1[i].position.getY()
x12 = track1[i + 1].position.getX()
y12 = track1[i + 1].position.getY()
seg1 = [x11, y11, x12, y12]
for j in range(len(track2) - 1):
x21 = track2[j].position.getX()
y21 = track2[j].position.getY()
x22 = track2[j + 1].position.getX()
y22 = track2[j + 1].position.getY()
seg2 = [x21, y21, x22, y22]
if isSegmentIntersects(seg1, seg2):
P1 = cartesienne(seg1)
P2 = cartesienne(seg2)
A = np.zeros((2, 2))
B = np.zeros((2, 1))
A[0, 0] = P1[0]
A[0, 1] = P1[1]
B[0, 0] = -P1[2]
A[1, 0] = P2[0]
A[1, 1] = P2[1]
B[1, 0] = -P2[2]
X = np.linalg.solve(A, B)
x = X[0, 0]
y = X[1, 0]
p = Utils.makeCoords(x, y, 0, track1.getSRID())
# Linear interpolation on track 1
w1 = p.distance2DTo(track1[i].position)
w2 = p.distance2DTo(track1[i + 1].position)
p.setZ((w1 * track1[i + 1].position.getZ() +
w2 * track1[i].position.getZ()) / (w1 + w2))
t1 = track1[i].timestamp.toAbsTime()
t2 = track1[i].timestamp.toAbsTime()
ta = (w1 * t2 + w2 * t1) / (w1 + w2)
# Linear interpolation on track 2
w1 = p.distance2DTo(track2[j].position)
w2 = p.distance2DTo(track2[j + 1].position)
t1 = track2[i].timestamp.toAbsTime()
t2 = track2[i].timestamp.toAbsTime()
tb = (w1 * t2 + w2 * t1) / (w1 + w2)
# Add intersection
if (withTime == -1) or (abs(tb - ta) < withTime):
I.addObs(Obs(p, GPSTime.readUnixTime(ta)))
TMP_TPS2.append(GPSTime.readUnixTime(tb))
TMP_I.append(i)
TMP_J.append(j)
if I.size() > 0:
I.createAnalyticalFeature("timestamp2", TMP_TPS2)
I.createAnalyticalFeature("id1", TMP_I)
I.createAnalyticalFeature("id2", TMP_J)
return I
def findStopsGlobalForRTK(track,
std_max=2e-2,
duration=5,
downsampling=1,
verbose=True):
"""Find stop points in a track based on maximal size of a stop and minimal
time duration
Two parameters:
- Maximal size of a stop (as the standard deviation per axis, in ground units)
- Minimal time duration (in seconds)
Use downsampling parameter > 1 to speed up the process.
Default is set for precise RTK GNSS survey (2 cm for 5 sec)
"""
# If down-sampling is required
if downsampling > 1:
track = track.copy()
track **= track.size() / downsampling
# ---------------------------------------------------------------------------
# Computes cost matrix as :
# Cij = 0 if sqrt(0.33*(std_x^2 + std_y^2 + std_Z^2)) > std_max
# Cij = 0 if time duration between pi and p-1 is < duration
# Cij = (j-i)**2 = square of the number of points of segment otherwise
# ---------------------------------------------------------------------------
C = np.zeros((track.size(), track.size()))
RANGE = range(track.size() - 2)
if verbose:
print("Minimal enclosing circles computation:")
RANGE = progressbar.progressbar(RANGE)
for i in RANGE:
for j in range(i + 1, track.size() - 1):
if track[i].distanceTo(track[j - 1]) > 3 * std_max:
C[i, j] = 0
break
if track[j - 1].timestamp - track[i].timestamp <= duration:
C[i, j] = 0
continue
portion = track.extract(i, j - 1)
varx = portion.operate(Operator.Operator.VARIANCE, "x")
vary = portion.operate(Operator.Operator.VARIANCE, "y")
varz = portion.operate(Operator.Operator.VARIANCE, "z")
C[i, j] = math.sqrt(varx + vary + varz)
C[i, j] = (C[i, j] < std_max) * (j - i)**2
C = C + np.transpose(C)
# ---------------------------------------------------------------------------
# Computes optimal partition with dynamic programing
# ---------------------------------------------------------------------------
segmentation = optimalPartition(C, MODE_SEGMENTATION_MAXIMIZE, verbose)
stops = Track()
TMP_RADIUS = []
TMP_MEAN_X = []
TMP_MEAN_Y = []
TMP_MEAN_Z = []
TMP_IDSTART = []
TMP_IDEND = []
TMP_STD_X = []
TMP_STD_Y = []
TMP_STD_Z = []
TMP_DURATION = []
TMP_NBPOINTS = []
for i in range(len(segmentation) - 1):
portion = track.extract(segmentation[i], segmentation[i + 1] - 1)
radius = C[segmentation[i], segmentation[i + 1]]
if radius == 0:
continue
xm = portion.operate(Operator.Operator.AVERAGER, "x")
ym = portion.operate(Operator.Operator.AVERAGER, "y")
zm = portion.operate(Operator.Operator.AVERAGER, "z")
xv = portion.operate(Operator.Operator.VARIANCE, "x")
yv = portion.operate(Operator.Operator.VARIANCE, "y")
zv = portion.operate(Operator.Operator.VARIANCE, "z")
pt = portion[0].position.copy()
pt.setX(xm)
pt.setY(ym)
pt.setZ(zm)
stops.addObs(Obs(pt, portion[0].timestamp))
TMP_RADIUS.append(math.sqrt(xv + yv + zv))
TMP_MEAN_X.append(xm)
TMP_MEAN_Y.append(ym)
TMP_MEAN_Z.append(zm)
TMP_STD_X.append(xv**0.5)
TMP_STD_Y.append(yv**0.5)
TMP_STD_Z.append(zv**0.5)
TMP_IDSTART.append(segmentation[i] * downsampling)
TMP_IDEND.append((segmentation[i + 1] - 1) * downsampling)
TMP_NBPOINTS.append(segmentation[i + 1] - segmentation[i])
TMP_DURATION.append(portion.duration())
if stops.size() == 0:
return stops
stops.createAnalyticalFeature("radius", TMP_RADIUS)
stops.createAnalyticalFeature("mean_x", TMP_MEAN_X)
stops.createAnalyticalFeature("mean_y", TMP_MEAN_Y)
stops.createAnalyticalFeature("mean_z", TMP_MEAN_Z)
stops.createAnalyticalFeature("id_ini", TMP_IDSTART)
stops.createAnalyticalFeature("id_end", TMP_IDEND)
stops.createAnalyticalFeature("sigma_x", TMP_STD_X)
stops.createAnalyticalFeature("sigma_y", TMP_STD_Y)
stops.createAnalyticalFeature("sigma_z", TMP_STD_Z)
stops.createAnalyticalFeature("duration", TMP_DURATION)
stops.createAnalyticalFeature("nb_points", TMP_NBPOINTS)
stops.operate(Operator.Operator.QUAD_ADDER, "sigma_x", "sigma_y", "rmse")
stops.base = track.base
return stops