def plotRepresentativeTrajectory(cluster_label, data, fname="", path="", show=False):
"""
cluster_label: length n
data: length n, needs to be in X, Y coordinate
plots the cluster centroids of the current clustering
"""
centroids = getClusterCentroids(cluster_label, data)
centroids_arr = []
for class_label, centroid in centroids.iteritems():
centroids_arr.append(centroid)
plotter.plotListOfTrajectories(
centroids_arr, show=show, clean=True, save=(fname != "" and path != ""), fname=fname, path=path
)
def plotRepresentativeTrajectory(cluster_label,
data,
fname="",
path="",
show=False):
"""
cluster_label: length n
data: length n, needs to be in X, Y coordinate
plots the cluster centroids of the current clustering
"""
centroids = getClusterCentroids(cluster_label, data)
centroids_arr = []
for class_label, centroid in centroids.iteritems():
centroids_arr.append(centroid)
plotter.plotListOfTrajectories(centroids_arr,
show=show,
clean=True,
save=(fname != "" and path != ""),
fname=fname,
path=path)
def main():
root_folder = raw_input("Input the root_folder name:")
"""
Firstly, extract all .csv input file names from {root_folder}/input/*.csv
"""
# filenames = ["8514019.csv", "9116943.csv", "9267118.csv", "9443140.csv", "9383986.csv", "9343340.csv", "9417464.csv", "9664225.csv", "9538440.csv", "9327138.csv"]
# filenames = ["9664225.csv"]
# filenames = ["8514019.csv"]
filenames = []
for input_filename in os.listdir("{root_folder}/input/".format(root_folder = root_folder)):
if (input_filename.find(".csv") != -1):
filenames.append(input_filename)
"""
Get min distance between vessels
"""
need_compute_mindistance = raw_input("Need to compute min_distance_matrix for vessel interaction? (y/n) :") == 'y'
if (need_compute_mindistance):
"""sort the aggregateData with MMSI based on TS"""
data_with_mmsi = writeToCSV.readDataFromCSVWithMMSI(path = root_folder + "/cleanedData", filename = "aggregateData_with_mmsi.csv")
data_with_mmsi_sorted = compute_mindistance.sortDataBasedOnTS(data_with_mmsi)
writeToCSV.writeDataToCSVWithMMSI(data_with_mmsi_sorted, root_folder + "/cleanedData", "aggregateData_with_mmsi_sorted")
"""Apply the computing of min distance using a timed window"""
data_with_mmsi_sorted = writeToCSV.readDataFromCSVWithMMSI(path = root_folder + "/cleanedData", filename = "aggregateData_with_mmsi_sorted.csv")
mmsi_set = compute_mindistance.getSetOfMMSI(data_with_mmsi_sorted)
print mmsi_set
print list(mmsi_set)
start_time = time.time()
mmsi_list_dict, min_distance_matrix, vessel_distance_speed_dict = \
compute_mindistance.computeVesselMinDistanceMatrix(data_with_mmsi_sorted, TIME_WINDOW = 1800)
writeToCSV.saveData([{ \
'mmsi_list_dict': mmsi_list_dict, \
'min_distance_matrix': min_distance_matrix, \
'vessel_distance_speed_dict': vessel_distance_speed_dict
}], filename = root_folder + "/cleanedData" + "/min_distance_matrix_with_mmsi_time_window_1800_sec")
print "time spent:", time.time() - start_time
"""From already computed"""
# min_distance_matrix_result = writeToCSV.loadData(\
# root_folder + "/cleanedData" + "/min_distance_matrix_with_mmsi_time_window_1800_sec.npz")
# print "min_distance_matrix_result type:\n", type(min_distance_matrix_result)
# mmsi_list_dict = min_distance_matrix_result[0]["mmsi_list_dict"]
# min_distance_matrix = min_distance_matrix_result[0]["min_distance_matrix"]
# vessel_distance_speed_dict = min_distance_matrix_result[0]["vessel_distance_speed_dict"]
# print "min_distance_matrix loaded:\n", min_distance_matrix
# min_of_min_distance = sys.maxint
# for i in range(0, min_distance_matrix.shape[0]):
# for j in range(i + 1, min_distance_matrix.shape[1]):
# if (min_distance_matrix[i][j] < min_of_min_distance):
# min_of_min_distance = min_distance_matrix[i][j]
# print "min_distance_matrix min of 10 tankers:", min_of_min_distance
"""write min distance records for Agent Based Simulator"""
writeToCSV.writeVesselSpeedToDistance(\
path = utils.queryPath(root_folder+"LearningResult"),\
file_name = "vessel_speed_to_distance", \
vessel_distance_speed_dict = vessel_distance_speed_dict)
writeToCSV.writeVesselMinDistanceMatrix(\
path = utils.queryPath(root_folder+"LearningResult"), \
file_name = "vessel_min_distance_matrix", \
mmsi_list_dict = mmsi_list_dict, \
min_distance_matrix = min_distance_matrix)
writeToCSV.writeMMSIs(\
path = utils.queryPath(root_folder+"LearningResult"), \
file_name = "mmsi_list", \
mmsi_list = [key for key, index in mmsi_list_dict.iteritems()])
"""
Test Clustering
"""
# trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_with_1D_data_refined.npz")
# # trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_cleaned.npz")
# # trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_9664225.npz")
# print "trajectories_to_cluster.shape: ", trajectories_to_cluster.shape
# print "type(trajectories_to_cluster): ", type(trajectories_to_cluster)
# print "len(trajectories_to_cluster): ", len(trajectories_to_cluster)
# # convert Lat, Lon to XY for clustering
# all_OD_trajectories_XY = convertListOfTrajectoriesToXY(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, trajectories_to_cluster)
# executeClustering(root_folder = root_folder, \
# all_OD_trajectories_XY = all_OD_trajectories_XY, \
# reference_lat = utils.CENTER_LAT_SG, \
# reference_lon = utils.CENTER_LON_SG, \
# filenames = filenames)
# raise ValueError("purpose stop for testing clustering")
"""
plot out the value space of the features, speed, accelerations, etc, for the aggregateData
"""
# filename = "aggregateData.npz"
# path = "tankers/cleanedData"
# data = writeToCSV.loadArray("{p}/{f}".format(p = path, f=filename))
# for trajectory in trajectories_to_cluster:
# plotter.plotFeatureSpace(trajectory)
# raise ValueError("For plotting feature space only")
"""
Read the cleaned .csv input files form {root_folder}/cleanedData/
Extract endpoints
"""
endpoints = None
all_OD_trajectories = []
utils.queryPath("{root_folder}/endpoints".format(root_folder = root_folder))
utils.queryPath("{root_folder}/trajectories".format(root_folder = root_folder))
for i in range(0, len(filenames)):
this_vessel_trajectory_points = writeToCSV.readDataFromCSV(root_folder + "/cleanedData", filenames[i])
# Extract end points, along with MMSI
this_vessel_endpoints = np.asarray(extractEndPoints(writeToCSV.readDataFromCSVWithMMSI(root_folder + "/cleanedData", filenames[i])))
# Save end points, along with MMSI
writeToCSV.writeDataToCSVWithMMSI( \
this_vessel_endpoints, \
root_folder + "/endpoints", \
"{filename}_endpoints".format(filename = filenames[i][:filenames[i].find(".")]))
print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape
# Append to the total end points
if(endpoints is None):
endpoints = this_vessel_endpoints
else:
endpoints = np.concatenate((endpoints, this_vessel_endpoints), axis=0)
for s in range (0, len(this_vessel_endpoints) - 1):
originLatitude = this_vessel_endpoints[s][utils.dataDict["latitude"]]
originLongtitude = this_vessel_endpoints[s][utils.dataDict["longitude"]]
origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]]
endLatitude = this_vessel_endpoints[s + 1][utils.dataDict["latitude"]]
endLongtitude = this_vessel_endpoints[s + 1][utils.dataDict["longitude"]]
end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]]
"""Extracting trajectory between a pair of OD"""
print "\n\nextracting endpoints between ", s, " and ", s + 1
OD_trajectories, OD_trajectories_lat_lon = extractTrajectoriesUntilOD(\
this_vessel_trajectory_points, \
origin_ts, \
originLatitude, \
originLongtitude, \
end_ts, \
endLatitude, \
endLongtitude, \
show = False, save = True, clean = False, \
fname = filenames[i][:filenames[i].find(".")] + "_trajectory_between_endpoint{s}_and{e}".format(s = s, e = s + 1))
# there will be one trajectory between each OD
assert (len(OD_trajectories) > 0), "OD_trajectories extracted must have length > 0"
print "number of trajectory points extracted : ", len(OD_trajectories[0])
if(len(OD_trajectories[0]) > 2): # more than just the origin and destination endpoints along the trajectory
writeToCSV.writeDataToCSV( \
data = OD_trajectories_lat_lon[0],
path = root_folder + "/trajectories", \
file_name = "{filename}_trajectory_endpoint_{s}_to_{e}".format(filename = filenames[i][:filenames[i].find(".")], \
s = s, \
e = s + 1))
"""
Interpolation based on pure geographical trajectory, ignore temporal information
"""
interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation(OD_trajectories)
plotter.plotListOfTrajectories( \
interpolated_OD_trajectories, \
show = False, \
clean = True, \
save = True, \
fname = filenames[i][:filenames[i].find(".")] + "_interpolated_algo_3_between_endpoint{s}_and{e}".format(\
s = s, \
e = s + 1))
"""
Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates
"""
if(len(interpolated_OD_trajectories) > 0):
interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures( \
interpolated_OD_trajectories[0], \
OD_trajectories[0])
# change X, Y coordinate to Lat, Lon
interpolated_OD_trajectories_lat_lon = convertListOfTrajectoriesToLatLon( \
originLatitude, originLongtitude, interpolated_OD_trajectories)
if(len(interpolated_OD_trajectories_lat_lon) > 0):
# since there should be only one trajectory between each pair of OD
all_OD_trajectories.append(interpolated_OD_trajectories_lat_lon[0])
else:
print "no trajectories extracted between endpoints ", s , " and ", s + 1
plt.clf()
assert (not endpoints is None), "Error!: No endpoints extracted from the historial data of vessels" + "_".join(filenames)
print "Final endpoints.shape:", endpoints.shape
print "number of interpolated all_OD_trajectories:", len(all_OD_trajectories)
"""
save the augmented trajectories between endpoints as npz data file and the plot
"""
# remove error trajectories that are too far from Singapore
all_OD_trajectories = utils.removeErrorTrajectoryFromList(all_OD_trajectories)
writeToCSV.saveData(all_OD_trajectories, root_folder + "/all_OD_trajectories_with_1D_data")
# convert Lat, Lon to XY for displaying
all_OD_trajectories_XY = convertListOfTrajectoriesToXY(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories)
plotter.plotListOfTrajectories(all_OD_trajectories_XY, show = False, clean = True, save = True, \
fname = "{root_folder}_all_OD_trajectories".format(root_folder = root_folder))
"""
Execute Clustering
"""
executeClustering(root_folder = root_folder, \
all_OD_trajectories_XY = all_OD_trajectories_XY, \
reference_lat = utils.CENTER_LAT_SG, \
reference_lon = utils.CENTER_LON_SG, \
filenames = filenames)
def main():
metric_to_use = int(
raw_input("use metric?\n" + "1. l2\n" + "2. center of mass\n"))
root_folder = "tankers/out_sample_test"
"""read centroids"""
centroids = None
if (metric_to_use == 1):
centroids = writeToCSV.loadData(
"tankers/cleanedData/centroids_arr_l2.npz")
elif (metric_to_use == 2):
centroids = writeToCSV.loadData(
"tankers/cleanedData/centroids_arr_center_mass.npz")
"""Extract endpoints, trajectories, augmentation"""
filenames = [
"9050462.csv", "9259769.csv", "9327138.csv", "9408475.csv",
"9417464.csv", "9548440.csv"
] # for out sample test
# filenames = ["9408475.csv"]
endpoints = None
all_OD_trajectories = []
"""Do the augmentation if not yet done"""
if (not os.path.exists(root_folder +
"/all_OD_trajectories_with_1D_data.npz")):
for i in range(0, len(filenames)):
this_vessel_trajectory_points = writeToCSV.readDataFromCSV(
root_folder + "/cleanedData", filenames[i])
# Extract end points, along with MMSI
this_vessel_endpoints = np.asarray(
trajectory_modeller.extractEndPoints(
writeToCSV.readDataFromCSVWithMMSI(
root_folder + "/cleanedData", filenames[i])))
# Save end points, along with MMSI
writeToCSV.writeDataToCSVWithMMSI( \
this_vessel_endpoints, \
utils.queryPath(root_folder + "/endpoints"), \
"{filename}_endpoints".format(filename = filenames[i][:filenames[i].find(".")]))
print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape
# Append to the total end points
if (endpoints is None):
endpoints = this_vessel_endpoints
else:
endpoints = np.concatenate((endpoints, this_vessel_endpoints),
axis=0)
for s in range(0, len(this_vessel_endpoints) - 1):
originLatitude = this_vessel_endpoints[s][
utils.dataDict["latitude"]]
originLongtitude = this_vessel_endpoints[s][
utils.dataDict["longitude"]]
origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]]
endLatitude = this_vessel_endpoints[s + 1][
utils.dataDict["latitude"]]
endLongtitude = this_vessel_endpoints[s + 1][
utils.dataDict["longitude"]]
end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]]
"""Extracting trajectory between a pair of OD"""
print "\n\nextracting endpoints between ", s, " and ", s + 1
OD_trajectories, OD_trajectories_lat_lon = trajectory_modeller.extractTrajectoriesUntilOD(\
this_vessel_trajectory_points, \
origin_ts, \
originLatitude, \
originLongtitude, \
end_ts, \
endLatitude, \
endLongtitude, \
show = False, save = True, clean = False, \
fname = filenames[i][:filenames[i].find(".")] + "_trajectory_between_endpoint{s}_and{e}".format(s = s, e = s + 1), \
path = utils.queryPath(root_folder + "/plots"))
# there will be one trajectory between each OD
assert (len(OD_trajectories) >
0), "OD_trajectories extracted must have length > 0"
print "number of trajectory points extracted : ", len(
OD_trajectories[0])
if (
len(OD_trajectories[0]) > 2
): # more than just the origin and destination endpoints along the trajectory
writeToCSV.writeDataToCSV( \
data = OD_trajectories_lat_lon[0],
path = utils.queryPath(root_folder + "/trajectories"), \
file_name = "{filename}_trajectory_endpoint_{s}_to_{e}".format(filename = filenames[i][:filenames[i].find(".")], \
s = s, \
e = s + 1))
"""
Interpolation based on pure geographical trajectory, ignore temporal information
"""
interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation(
OD_trajectories)
plotter.plotListOfTrajectories( \
interpolated_OD_trajectories, \
show = False, \
clean = True, \
save = True, \
fname = filenames[i][:filenames[i].find(".")] + "_interpolated_algo_3_between_endpoint{s}_and{e}".format(\
s = s, \
e = s + 1), \
path = utils.queryPath(root_folder + "/plots"))
"""
Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates
"""
if (len(interpolated_OD_trajectories) > 0):
interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures( \
interpolated_OD_trajectories[0], \
OD_trajectories[0])
# change X, Y coordinate to Lat, Lon
interpolated_OD_trajectories_lat_lon = trajectory_modeller.convertListOfTrajectoriesToLatLon( \
originLatitude, originLongtitude, interpolated_OD_trajectories)
if (len(interpolated_OD_trajectories_lat_lon) > 0):
# since there should be only one trajectory between each pair of OD
all_OD_trajectories.append(
interpolated_OD_trajectories_lat_lon[0])
else:
print "no trajectories extracted between endpoints ", s, " and ", s + 1
plt.clf()
assert (
not endpoints is None
), "Error!: No endpoints extracted from the historial data of vessels" + "_".join(
filenames)
print "Final endpoints.shape:", endpoints.shape
print "number of interpolated all_OD_trajectories:", len(
all_OD_trajectories)
all_OD_trajectories = utils.removeErrorTrajectoryFromList(
all_OD_trajectories)
writeToCSV.saveData(all_OD_trajectories,
root_folder + "/all_OD_trajectories_with_1D_data")
else:
all_OD_trajectories = writeToCSV.loadData(
root_folder + "/all_OD_trajectories_with_1D_data.npz")
"""convert Lat, Lon to XY for displaying"""
all_OD_trajectories_XY = trajectory_modeller.convertListOfTrajectoriesToXY(
utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories)
plotter.plotListOfTrajectories(\
all_OD_trajectories_XY, \
show = True, \
clean = True, \
save = False, \
fname = "out_sample_tanker_all_OD_trajectories", path = utils.queryPath(root_folder + "/plots"))
"""Test distance to cluster centroids"""
centroids_XY = trajectory_modeller.convertListOfTrajectoriesToXY(\
utils.CENTER_LAT_SG, utils.CENTER_LON_SG, centroids)
for i in range(0, len(all_OD_trajectories_XY)):
this_tr_XY = all_OD_trajectories_XY[i]
if (metric_to_use == 1):
this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(\
this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityL2)
print "augmented trajectories[{i}]".format(i = i), \
"'s best l2 distance is against cluster centroids[{i}], = ".format(i = according_pattern_index), \
this_tr_centroids_dist, ", max allowed distance = ", 1000
elif (metric_to_use == 2):
this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(\
this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityCenterMass)
print "augmented trajectories[{i}]".format(i = i), \
"'s best center of mass distance is against cluster centroids[{i}], = ".format(i = according_pattern_index), \
this_tr_centroids_dist, ", max allowed distance = ", 1.5
# plotter.plotFeatureSpace(centroids[according_pattern_index])
# plotter.plotFeatureSpace(\
# trajectory_modeller.convertListOfTrajectoriesToLatLon(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, [this_tr_XY])[0])
return
def main():
metric_to_use = int(raw_input("use metric?\n" + "1. l2\n" + "2. center of mass\n"))
root_folder = "tankers/out_sample_test"
"""read centroids"""
centroids = None
if metric_to_use == 1:
centroids = writeToCSV.loadData("tankers/cleanedData/centroids_arr_l2.npz")
elif metric_to_use == 2:
centroids = writeToCSV.loadData("tankers/cleanedData/centroids_arr_center_mass.npz")
"""Extract endpoints, trajectories, augmentation"""
filenames = [
"9050462.csv",
"9259769.csv",
"9327138.csv",
"9408475.csv",
"9417464.csv",
"9548440.csv",
] # for out sample test
# filenames = ["9408475.csv"]
endpoints = None
all_OD_trajectories = []
"""Do the augmentation if not yet done"""
if not os.path.exists(root_folder + "/all_OD_trajectories_with_1D_data.npz"):
for i in range(0, len(filenames)):
this_vessel_trajectory_points = writeToCSV.readDataFromCSV(root_folder + "/cleanedData", filenames[i])
# Extract end points, along with MMSI
this_vessel_endpoints = np.asarray(
trajectory_modeller.extractEndPoints(
writeToCSV.readDataFromCSVWithMMSI(root_folder + "/cleanedData", filenames[i])
)
)
# Save end points, along with MMSI
writeToCSV.writeDataToCSVWithMMSI(
this_vessel_endpoints,
utils.queryPath(root_folder + "/endpoints"),
"{filename}_endpoints".format(filename=filenames[i][: filenames[i].find(".")]),
)
print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape
# Append to the total end points
if endpoints is None:
endpoints = this_vessel_endpoints
else:
endpoints = np.concatenate((endpoints, this_vessel_endpoints), axis=0)
for s in range(0, len(this_vessel_endpoints) - 1):
originLatitude = this_vessel_endpoints[s][utils.dataDict["latitude"]]
originLongtitude = this_vessel_endpoints[s][utils.dataDict["longitude"]]
origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]]
endLatitude = this_vessel_endpoints[s + 1][utils.dataDict["latitude"]]
endLongtitude = this_vessel_endpoints[s + 1][utils.dataDict["longitude"]]
end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]]
"""Extracting trajectory between a pair of OD"""
print "\n\nextracting endpoints between ", s, " and ", s + 1
OD_trajectories, OD_trajectories_lat_lon = trajectory_modeller.extractTrajectoriesUntilOD(
this_vessel_trajectory_points,
origin_ts,
originLatitude,
originLongtitude,
end_ts,
endLatitude,
endLongtitude,
show=False,
save=True,
clean=False,
fname=filenames[i][: filenames[i].find(".")]
+ "_trajectory_between_endpoint{s}_and{e}".format(s=s, e=s + 1),
path=utils.queryPath(root_folder + "/plots"),
)
# there will be one trajectory between each OD
assert len(OD_trajectories) > 0, "OD_trajectories extracted must have length > 0"
print "number of trajectory points extracted : ", len(OD_trajectories[0])
if (
len(OD_trajectories[0]) > 2
): # more than just the origin and destination endpoints along the trajectory
writeToCSV.writeDataToCSV(
data=OD_trajectories_lat_lon[0],
path=utils.queryPath(root_folder + "/trajectories"),
file_name="{filename}_trajectory_endpoint_{s}_to_{e}".format(
filename=filenames[i][: filenames[i].find(".")], s=s, e=s + 1
),
)
"""
Interpolation based on pure geographical trajectory, ignore temporal information
"""
interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation(OD_trajectories)
plotter.plotListOfTrajectories(
interpolated_OD_trajectories,
show=False,
clean=True,
save=True,
fname=filenames[i][: filenames[i].find(".")]
+ "_interpolated_algo_3_between_endpoint{s}_and{e}".format(s=s, e=s + 1),
path=utils.queryPath(root_folder + "/plots"),
)
"""
Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates
"""
if len(interpolated_OD_trajectories) > 0:
interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures(
interpolated_OD_trajectories[0], OD_trajectories[0]
)
# change X, Y coordinate to Lat, Lon
interpolated_OD_trajectories_lat_lon = trajectory_modeller.convertListOfTrajectoriesToLatLon(
originLatitude, originLongtitude, interpolated_OD_trajectories
)
if len(interpolated_OD_trajectories_lat_lon) > 0:
# since there should be only one trajectory between each pair of OD
all_OD_trajectories.append(interpolated_OD_trajectories_lat_lon[0])
else:
print "no trajectories extracted between endpoints ", s, " and ", s + 1
plt.clf()
assert not endpoints is None, "Error!: No endpoints extracted from the historial data of vessels" + "_".join(
filenames
)
print "Final endpoints.shape:", endpoints.shape
print "number of interpolated all_OD_trajectories:", len(all_OD_trajectories)
all_OD_trajectories = utils.removeErrorTrajectoryFromList(all_OD_trajectories)
writeToCSV.saveData(all_OD_trajectories, root_folder + "/all_OD_trajectories_with_1D_data")
else:
all_OD_trajectories = writeToCSV.loadData(root_folder + "/all_OD_trajectories_with_1D_data.npz")
"""convert Lat, Lon to XY for displaying"""
all_OD_trajectories_XY = trajectory_modeller.convertListOfTrajectoriesToXY(
utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories
)
plotter.plotListOfTrajectories(
all_OD_trajectories_XY,
show=True,
clean=True,
save=False,
fname="out_sample_tanker_all_OD_trajectories",
path=utils.queryPath(root_folder + "/plots"),
)
"""Test distance to cluster centroids"""
centroids_XY = trajectory_modeller.convertListOfTrajectoriesToXY(
utils.CENTER_LAT_SG, utils.CENTER_LON_SG, centroids
)
for i in range(0, len(all_OD_trajectories_XY)):
this_tr_XY = all_OD_trajectories_XY[i]
if metric_to_use == 1:
this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(
this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityL2
)
print "augmented trajectories[{i}]".format(
i=i
), "'s best l2 distance is against cluster centroids[{i}], = ".format(
i=according_pattern_index
), this_tr_centroids_dist, ", max allowed distance = ", 1000
elif metric_to_use == 2:
this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(
this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityCenterMass
)
print "augmented trajectories[{i}]".format(
i=i
), "'s best center of mass distance is against cluster centroids[{i}], = ".format(
i=according_pattern_index
), this_tr_centroids_dist, ", max allowed distance = ", 1.5
# plotter.plotFeatureSpace(centroids[according_pattern_index])
# plotter.plotFeatureSpace(\
# trajectory_modeller.convertListOfTrajectoriesToLatLon(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, [this_tr_XY])[0])
return