def plotTrajectoryProfiles(pathToSave,
folderName,
trajectories,
origin,
end,
savefig=True,
showfig=True):
if (not os.path.isdir("./{path}/{folderToSave}".format(
path=pathToSave, folderToSave=folderName))):
os.makedirs("./{path}/{folderToSave}".format(path=pathToSave,
folderToSave=folderName))
for i in range(0, len(trajectories)):
last_point_in_trajectory = trajectories[i][len(trajectories[i]) - 1]
# end[0] is end latitude, end[1] is end longitude
if (
utils.nearOrigin(
end[0], end[1],
last_point_in_trajectory[utils.dataDict["latitude"]],
last_point_in_trajectory[utils.dataDict["longitude"]])
): # only plot profile for those trajectories between origin and end
# 1. acceleration profile from start point to end point
accelerations, timeAxis, distanceAxis = getSpeedAccelerations(
trajectories[i], utils.dataDict)
plt.scatter(
distanceAxis,
accelerations,
label="trajectory_acceleration_profile_{i}".format(i=i))
if (savefig):
plt.savefig(
"./{path}/{folderToSave}/trajectory_acceleration_profile_{i}.png"
.format(path=pathToSave, folderToSave=folderName, i=i))
if (showfig):
plt.show()
plt.clf()
# 2. speed profile from start point to end point
speeds, timeAxis, distanceAxis = getSpeeds(trajectories[i],
utils.dataDict)
plt.scatter(distanceAxis,
speeds,
label="trajectory_speed_profile_{i}".format(i=i))
if (savefig):
plt.savefig(
"./{path}/{folderToSave}/trajectory_speed_profile_{i}.png".
format(path=pathToSave, folderToSave=folderName, i=i))
if (showfig):
plt.show()
plt.clf()
def plotTrajectoryProfiles(pathToSave, folderName, trajectories, origin, end, savefig = True,showfig = True):
if(not os.path.isdir("./{path}/{folderToSave}".format(path = pathToSave, folderToSave = folderName))):
os.makedirs("./{path}/{folderToSave}".format(path = pathToSave, folderToSave = folderName))
for i in range(0, len(trajectories)):
last_point_in_trajectory = trajectories[i][len(trajectories[i]) -1]
# end[0] is end latitude, end[1] is end longitude
if(utils.nearOrigin(end[0],end[1],last_point_in_trajectory[utils.dataDict["latitude"]], last_point_in_trajectory[utils.dataDict["longitude"]])): # only plot profile for those trajectories between origin and end
# 1. acceleration profile from start point to end point
accelerations, timeAxis, distanceAxis = getSpeedAccelerations(trajectories[i], utils.dataDict)
plt.scatter(distanceAxis, accelerations, label = "trajectory_acceleration_profile_{i}".format(i = i))
if(savefig):
plt.savefig("./{path}/{folderToSave}/trajectory_acceleration_profile_{i}.png".format(path = pathToSave, folderToSave = folderName, i = i))
if(showfig):
plt.show()
plt.clf()
# 2. speed profile from start point to end point
speeds, timeAxis , distanceAxis= getSpeeds(trajectories[i], utils.dataDict)
plt.scatter(distanceAxis, speeds, label = "trajectory_speed_profile_{i}".format(i = i))
if(savefig):
plt.savefig("./{path}/{folderToSave}/trajectory_speed_profile_{i}.png".format(path = pathToSave, folderToSave = folderName, i = i))
if(showfig):
plt.show()
plt.clf()
def extractTrajectoriesUntilOD(data, originTS, originLatitude, originLongtitude, endTS, endLatitude, endLongtitude, show = True, save = False, clean = False, fname = "", path = "plots"):
"""
returns: OD_trajectories: in x,y coordinate;
OD_trajectories_lat_lon: in lat, lon coordinate;
"""
maxSpeed = 0
for i in range(0, data.shape[0]):
speed_over_ground = data[i][utils.dataDict["speed_over_ground"]]
if(speed_over_ground > maxSpeed and speed_over_ground != 102.3): #1023 indicates speed not available
maxSpeed = speed_over_ground
print "This tanker maxSpeed:", maxSpeed, " knot"
OD_trajectories = [] # origin destination endpoints trajectory
i = 0
while(i< data.shape[0]):
cur_pos = data[i]
if(utils.nearOrigin( \
originLatitude, \
originLongtitude, \
cur_pos[utils.dataDict["latitude"]], \
cur_pos[utils.dataDict["longitude"]], \
thresh = 0.0) and \
cur_pos[utils.dataDict["ts"]] == originTS): # must be exact point
this_OD_trajectory = []
this_OD_trajectory.append(cur_pos)
i += 1
while(i < data.shape[0] and \
(not utils.nearOrigin( \
endLatitude, \
endLongtitude, \
data[i][utils.dataDict["latitude"]], \
data[i][utils.dataDict["longitude"]], \
thresh = 0.0))):
this_OD_trajectory.append(data[i])
i += 1
if(i < data.shape[0]):
this_OD_trajectory.append(data[i]) # append the destination endpoint
this_OD_trajectory = np.asarray(this_OD_trajectory) # make it to be an np 2D array
""" box/radius approach in cleaning of points around origin"""
j = 1
print "checking points around origin:", j
while(j < this_OD_trajectory.shape[0] and \
utils.nearOrigin( \
originLatitude, \
originLongtitude, \
this_OD_trajectory[j][utils.dataDict["latitude"]], \
this_OD_trajectory[j][utils.dataDict["longitude"]], \
thresh = utils.NEIGHBOURHOOD_ORIGIN)):
j += 1
print "last point around origin:", j
this_OD_trajectory_around_origin = this_OD_trajectory[0:j]
"""Take the box mean, treat timestamp as averaged as well"""
this_OD_trajectory_mean_origin = boxMeanTrajectoryPoints(this_OD_trajectory_around_origin, originLatitude, originLongtitude)
print "mean start point x,y : ", utils.LatLonToXY( \
originLatitude, \
originLongtitude, \
this_OD_trajectory_mean_origin[utils.dataDict["latitude"]], \
this_OD_trajectory_mean_origin[utils.dataDict["longitude"]])
OD_trajectories.append(np.insert(this_OD_trajectory[j:],0,this_OD_trajectory_mean_origin, axis = 0))
break # only one trajectory per pair OD, since OD might be duplicated
i += 1
OD_trajectories = np.array(OD_trajectories)
OD_trajectories_lat_lon = copy.deepcopy(OD_trajectories)
for i in range(0, len(OD_trajectories)):
for j in range(0, len(OD_trajectories[i])):
x, y = utils.LatLonToXY(originLatitude, originLongtitude, OD_trajectories[i][j][utils.dataDict["latitude"]], OD_trajectories[i][j][utils.dataDict["longitude"]])
OD_trajectories[i][j][utils.data_dict_x_y_coordinate["y"]] = y
OD_trajectories[i][j][utils.data_dict_x_y_coordinate["x"]] = x
# plotting purpose
plt.scatter(OD_trajectories[i][0:len(OD_trajectories[i]),utils.data_dict_x_y_coordinate["x"]], \
OD_trajectories[i][0:len(OD_trajectories[i]),utils.data_dict_x_y_coordinate["y"]])
if(not plt.gca().yaxis_inverted()):
plt.gca().invert_yaxis()
if(save):
plt.savefig("./{path}/{fname}.png".format(path = path, fname = fname))
if(show):
plt.show()
if(clean):
plt.clf()
return OD_trajectories, OD_trajectories_lat_lon