def polyline_encoded_table(requests, port):
"""
Function that uses the python wrapper
for osrm in order to get the distance
matrix between given input points by
using the table service of the osrm server
hosted on the input host.
Args:
requests: A list of objects of the class
Request corresponding to each rider in the
system.
port: The full link to the localhost port on
which osrm server is running.
"""
osrm.RequestConfig.host = port
sources_data = [(i.source_long, i.source_lat) for i in requests]
destinations_data = [(i.dest_long, i.dest_lat) for i in requests]
coordinates = []
for element in sources_data:
coordinates.append(element)
for element in destinations_data:
coordinates.append(element)
response = osrm.table(coordinates, output='raw', annotations='distance')
return response['distances']
def compute_distance_matrix(csv_nodes_path, titles='', annotations='distance'):
""" Method to compute and return distance matrix given a set of nodes of interest
Args:
csv_nodes_path (str]): Path to a csv containing info about nodes.
titles (str, optional): Titles of the od matrix to be produced. Defaults to ''.
annotations (str, optional): distance or time. Defaults to 'distance'.
"""
supermarkets = pd.read_csv(csv_nodes_path, delimiter='\t')
coords = [
','.join(str(x) for x in y)
for y in map(tuple, supermarkets[['latitude', 'longitude']].values)
]
numerical_coords = []
for elem in coords:
long_lat_elems = elem.split(',')
numerical_coords.append(
(float(long_lat_elems[1]), float(long_lat_elems[0])))
# create titles
if not titles or len(titles) != len(numerical_coords):
titles = [i for i in range(len(numerical_coords))]
# compute distance matrix and return it
dist_od = osrm.table(numerical_coords,
ids_origin=titles,
output='pandas',
annotations=annotations)
return dist_od
def make_experiment():
MyConfig = osrm.RequestConfig("localhost:5000/v1/driving")
for size in range(2000, 2001, 1):
points = [random_point() for i in range(size)]
st = time()
res = osrm.table(points, points, url_config=MyConfig)
print(res[0])
print(size, round(time() - st, 4))
def test_table_only_origins(self, mock_urlopen):
mock_urlopen.return_value = MockReadable(
u'''{"code":"Ok","durations":[[0,1559.9,4192.8,9858.4,7679.7],[1579.3,0,5300.6,8735.2,6507.6],[4214.7,5334,0,5671.5,3972.1],[9496.8,8354.6,5689.7,0,2643.2],[7270.1,6127.9,3971.5,2624.5,0]],"destinations":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}],"sources":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}]}'''
)
names = ['name1', 'name2', 'name3', 'name4', 'name5']
coords = [[21.0566163803209, 42.004088575972],
[21.3856064050746, 42.0094518118189],
[20.9574645547597, 41.5286973392856],
[21.1477394809847, 41.0691482795275],
[21.5506463080973, 41.3532256406286]]
durations, new_coords, _ = osrm.table(coords,
ids_origin=names,
output="pandas")
self.assertIsInstance(durations, DataFrame)
durations2, new_coords2, _ = osrm.table(coords,
ids_origin=names,
output="np")
self.assertIsInstance(durations2, numpy.ndarray)
self.assertEqual(durations.values.tolist(), durations2.tolist())
def test_table_only_origins(self, mock_urlopen):
mock_urlopen.return_value = MockReadable(
u'''{"code":"Ok","durations":[[0,1559.9,4192.8,9858.4,7679.7],[1579.3,0,5300.6,8735.2,6507.6],[4214.7,5334,0,5671.5,3972.1],[9496.8,8354.6,5689.7,0,2643.2],[7270.1,6127.9,3971.5,2624.5,0]],"destinations":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}],"sources":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}]}'''
)
names = ['name1', 'name2', 'name3', 'name4', 'name5']
coords = [[21.0566163803209, 42.004088575972],
[21.3856064050746, 42.0094518118189],
[20.9574645547597, 41.5286973392856],
[21.1477394809847, 41.0691482795275],
[21.5506463080973, 41.3532256406286]]
durations, new_coords, _ = osrm.table(coords,
ids_origin=names,
output="pandas")
self.assertIsInstance(durations, DataFrame)
durations2, new_coords2, _ = osrm.table(coords,
ids_origin=names,
output="np")
self.assertIsInstance(durations2, numpy.ndarray)
self.assertEqual(durations.values.tolist(), durations2.tolist())
def test_non_existing_host(self):
Profile = osrm.RequestConfig("localhost/v1/flying")
self.assertEqual(Profile.host, "localhost")
with self.assertRaises(URLError):
osrm.nearest((12.36, 45.36), url_config=Profile)
with self.assertRaises(URLError):
osrm.trip([(13.38886, 52.51703), (10.00, 53.55),
(52.374444, 9.738611)],
url_config=Profile)
with self.assertRaises(URLError):
osrm.simple_route((13.38886, 52.51703), (10.00, 53.55),
url_config=Profile)
with self.assertRaises(URLError):
osrm.AccessIsochrone((13.38886, 52.51703),
points_grid=100,
url_config=Profile)
with self.assertRaises(URLError):
osrm.match([(10.00, 53.55), (52.374444, 9.738611)],
url_config=Profile)
with self.assertRaises(URLError):
osrm.table([(10.00, 53.55), (52.374444, 9.738611)],
[(10.00, 53.55), (52.374444, 9.738611)],
url_config=Profile)
def test_non_existing_host(self):
Profile = osrm.RequestConfig("localhost/v1/flying")
self.assertEqual(Profile.host, "localhost")
with self.assertRaises(URLError):
osrm.nearest((12.36, 45.36), url_config=Profile)
with self.assertRaises(URLError):
osrm.trip(
[(13.38886, 52.51703), (10.00, 53.55), (52.374444, 9.738611)],
url_config=Profile)
with self.assertRaises(URLError):
osrm.simple_route(
(13.38886, 52.51703), (10.00, 53.55), url_config=Profile)
with self.assertRaises(URLError):
osrm.AccessIsochrone(
(13.38886, 52.51703), points_grid=100, url_config=Profile)
with self.assertRaises(URLError):
osrm.match(
[(10.00, 53.55), (52.374444, 9.738611)], url_config=Profile)
with self.assertRaises(URLError):
osrm.table(
[(10.00, 53.55), (52.374444, 9.738611)],
[(10.00, 53.55), (52.374444, 9.738611)],
url_config=Profile)
def _get_travel_durations(self):
""" Use OSRM to find the travel durations between every set of demand
locations and stations.
"""
progress("Creating matrix of travel times...", verbose=self.verbose)
coord_list = list(self.demand_locs.values()) + list(
self.station_locs.values())
id_list = list(self.demand_locs.keys()) + list(
self.station_locs.keys())
time_matrix, _, _ = osrm.table(coord_list,
coords_dest=coord_list,
ids_origin=id_list,
ids_dest=id_list,
output='dataframe',
url_config=self.osrm_config)
return time_matrix
def list_matching(list1, list2, metric, maxMat=50000):
numberOfSources = len(list1)
numberOfTargets = len(list2)
if numberOfSources + numberOfTargets > maxMat:
print('The list call needs to be split to fit the maximum allowed of ' + str(maxMat))
print('(Calling with ' + str(numberOfSources) + ' sources and ' + str(numberOfTargets) + ' targets)')
if numberOfTargets < maxMat - 1:
nel1 = maxMat - numberOfTargets - 1
print('Making calls with ' + str(nel1) + ' elements from list1 and ' + str(numberOfTargets) + ' from list2')
nSplits = float(numberOfSources)/float(nel1)
nSplits = int(np.floor(nSplits))
print('Expecting ' + str(nSplits) + ' recursive calls.')
matchListGo = []
matchListReturn = []
stored = 0
nCalls = 0
while stored < numberOfSources:
nCalls += 1
print('\tProcessing call number ' + str(nCalls) + '...')
miniList1 = list1[stored:int(min(stored+nel1,numberOfSources))]
print('\t(call with minlist: ' + str(len(miniList1)) + ' targets: ' + str(len(list2)) + ')')
miniGo, miniReturn = list_matching(miniList1, list2, metric,maxMat=maxMat)
matchListGo += copy.deepcopy(miniGo)
matchListReturn += copy.deepcopy(miniReturn)
stored += nel1
print('\tDone.')
print('Performed ' + str(nCalls) + ' calls.')
print('> :split exit')
return [matchListGo, matchListReturn]
else:
print('ERROR: Nedd to split second / both lists, not yet implemented!')
exit(-1)
superList = reverse_list_coords(list1 + list2)
l2idx = len(list1)
[theMatrix, dummy, dummy] = osrm.table(superList, output='np', annotations=metric)
matchListGo = []
matchListReturn = []
for i in range(len(list1)):
matchListGo.append(theMatrix[i,l2idx:])
matchListReturn.append(theMatrix[l2idx:,i])
return [matchListGo, matchListReturn]
def test_table_OD(self, mock_urlopen):
mock_urlopen.return_value = MockReadable(
u'''{"code":"Ok","durations":[[10785.3,9107,14619.6,5341.2],[9546.8,7934.9,15473,4054.3],[14559.4,12440.7,18315.9,9115.3],[14463.4,10768.4,22202.6,9904],[12236.7,8541.7,19975.9,7677.3]],"destinations":[{"hint":"XAiehPiqpY4_JQUAZQAAAA0BAACzBQAAVwEAALxM2gRcSLAIbrcAABnMXwGE7IAC2NBfASDugAINAAEBfDhq3w==","name":"1061","location":[23.055385,42.003588]},{"hint":"qdjCi1mz-4wAAAAAFgAAAAAAAABzEgAAKQgAABae8wfvnfMHbrcAAHmQVQErD4ECwJNVATgDgQK6AAEBfDhq3w==","name":"","location":[22.384761,42.012459]},{"hint":"lfafieL2n4kAAAAAAAAAADIAAABbAAAAPQYAAEz6EwdiWKIFbrcAADTGPwF-M5gC2Mg_AZgxmAIDAAEBfDhq3w==","name":"","location":[20.956724,43.529086]},{"hint":"8gQxizUOMYsAAAAAAAAAABsAAAAAAAAAQRkAANIcvgd1Hb4HbrcAADQQSQEqrn0CCNZIAdinfQIAAAEBfDhq3w==","name":"","location":[21.565492,41.791018]}],"sources":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}]}'''
)
origins = [[21.0566163803209, 42.004088575972],
[21.3856064050746, 42.0094518118189],
[20.9574645547597, 41.5286973392856],
[21.1477394809847, 41.0691482795275],
[21.5506463080973, 41.3532256406286]]
destinations = [[23.0566, 42.004], [22.3856, 42.0094],
[20.9574, 43.5286], [21.5506, 41.7894]]
durations, snapped_origins, snapped_destinations = \
osrm.table(origins, destinations)
self.assertIsInstance(durations, numpy.ndarray)
expected_shape = (len(origins), len(destinations))
self.assertEqual(durations.shape, expected_shape)
self.assertTrue(durations.any())
def test_table_OD(self, mock_urlopen):
mock_urlopen.return_value = MockReadable(
u'''{"code":"Ok","durations":[[10785.3,9107,14619.6,5341.2],[9546.8,7934.9,15473,4054.3],[14559.4,12440.7,18315.9,9115.3],[14463.4,10768.4,22202.6,9904],[12236.7,8541.7,19975.9,7677.3]],"destinations":[{"hint":"XAiehPiqpY4_JQUAZQAAAA0BAACzBQAAVwEAALxM2gRcSLAIbrcAABnMXwGE7IAC2NBfASDugAINAAEBfDhq3w==","name":"1061","location":[23.055385,42.003588]},{"hint":"qdjCi1mz-4wAAAAAFgAAAAAAAABzEgAAKQgAABae8wfvnfMHbrcAAHmQVQErD4ECwJNVATgDgQK6AAEBfDhq3w==","name":"","location":[22.384761,42.012459]},{"hint":"lfafieL2n4kAAAAAAAAAADIAAABbAAAAPQYAAEz6EwdiWKIFbrcAADTGPwF-M5gC2Mg_AZgxmAIDAAEBfDhq3w==","name":"","location":[20.956724,43.529086]},{"hint":"8gQxizUOMYsAAAAAAAAAABsAAAAAAAAAQRkAANIcvgd1Hb4HbrcAADQQSQEqrn0CCNZIAdinfQIAAAEBfDhq3w==","name":"","location":[21.565492,41.791018]}],"sources":[{"hint":"-hmZhIIUJo8AAAAAAQAAAAoAAAAAAAAANwUAAP6jLAP9oywDbrcAAGdMQQF37oACaExBAXjugAIAAAEBfDhq3w==","name":"","location":[21.056615,42.004087]},{"hint":"P7yyg-Xnvo-GOH0ALAAAACYAAAAAAAAA-QAAAMut1QNf1AgDbrcAAIZRRgFrA4EChlFGAWsDgQIAAAEBfDhq3w==","name":"\xd0\x91\xd1\x83\xd0\xbb\xd0\xb5\xd0\xb2\xd0\xb0\xd1\x80 \xd0\x98\xd0\xbb\xd0\xb8\xd0\xbd\xd0\xb4\xd0\xb5\xd0\xbd","location":[21.385606,42.009451]},{"hint":"SzHlia8HEorwPf4AAQAAAFYAAAAAAAAA8wAAAIPVMgcv1TIHbrcAABfJPwF4rXkCGMk_AXmteQIAAAEBfDhq3w==","name":"R2231","location":[20.957463,41.528696]},{"hint":"VktjiIkUGY_-bPEACgAAAA8AAAAXAQAAWwQAAGHfwwb1cVUGbrcAAFqwQgFcqnICW7BCAVyqcgIJAAEBfDhq3w==","name":"R2347","location":[21.147738,41.069148]},{"hint":"dku7jXlLu43E7icBcQAAABYAAAAAAAAAAAAAAK8RqA-tEagPbrcAADXWSAEJAHcCNtZIAQkAdwIAAAEBfDhq3w==","name":"\xd0\xa2\xd1\x80\xd0\xb8\xd0\xb7\xd0\xbb\xd0\xb0","location":[21.550645,41.353225]}]}'''
)
origins = [[21.0566163803209, 42.004088575972],
[21.3856064050746, 42.0094518118189],
[20.9574645547597, 41.5286973392856],
[21.1477394809847, 41.0691482795275],
[21.5506463080973, 41.3532256406286]]
destinations = [[23.0566, 42.004], [22.3856, 42.0094],
[20.9574, 43.5286], [21.5506, 41.7894]]
durations, snapped_origins, snapped_destinations = \
osrm.table(origins, destinations)
self.assertIsInstance(durations, numpy.ndarray)
expected_shape = (len(origins), len(destinations))
self.assertEqual(durations.shape, expected_shape)
self.assertTrue(durations.any())
def calcODMatrix(self, longName="Long", latName="Lat", idName="id"):
'''
Generates the OSRM table based on the origins and destinations. The x and y locations
need to be extracted from the shapefiles
longName - column name of longitude
latName - column name of latitude
idName - column name of id
'''
list_coord = [
tuple(x) for x in self.origins[[longName, latName]].values
]
list_id = self.origins[[idName]].values
print list_coord
print list_id
time_matrix, snapped_coords = osrm.table(list_coord,
ids_origin=list_id,
output="dataframe")
self.od_matrix = time_matrix
self.snappedOrigins = snapped_coords
return self.odMatrix
import pandas as pd
import osrm
coords = pd.read_csv(r'./inputs/boundaries_coords.csv', index_col='BoundaryId')
boundaries = pd.read_csv(r'./inputs/boundaries_used.csv',
index_col='BoundaryId')
df = boundaries.join(coords)[['X', 'Y']]
coords_src = df.apply(list, axis=1).tolist()
ids_origin = df.index.tolist()
dists = osrm.table(coords_src, ids_origin, output='df', annotations='duration')
def od_matrix_latlong(pointList, metric='distance'):
odmatrix, dummy, dummy = osrm.table(reverse_list_coords(pointList), output='np', annotations=metric)
return odmatrix
import osrm
osrm.RequestConfig.host = "router.project-osrm.org"
list_coord = [[21.0566163803209, 42.004088575972],
[21.3856064050746, 42.0094518118189],
[20.9574645547597, 41.5286973392856],
[21.1477394809847, 41.0691482795275],
[21.5506463080973, 41.3532256406286]]
list_id = ['name1', 'name2', 'name3', 'name4', 'name5']
time_matrix, snapped_coords = osrm.table(list_coord,
ids_origin=list_id,
output='dataframe')
print(time_matrix)
def compute(df):
group_10m = df.groupby(pd.Grouper(freq=str(MAX_WAITING_TIME) + 'Min'))
collapsedJobs = []
sharedJobs = []
doable = 0
total = 0
dfg = 0
rides = []
for key, item in group_10m:
try:
# cluster here!
group = group_10m.get_group(key)
# empty groups cannot be shared...
if group.shape[0] == 1:
continue
# list_coord = [[21.0566163803209, 42.004088575972],
# [21.3856064050746, 42.0094518118189],
# [20.9574645547597, 41.5286973392856],
# [21.1477394809847, 41.0691482795275],
# [21.5506463080973, 41.3532256406286]]
# list_id = ['name1', 'name2', 'name3', 'name4', 'name5']
# time_matrix, snapped_coords = osrm.table(list_coord,
# ids_origin=list_id,
# output='dataframe')
dropoffCoords = group.as_matrix(
columns=['dropoff_longitude', 'dropoff_latitude'])
# print(len(dropoffCoords))
time_matrix = osrm.table(dropoffCoords,
ids_origin=np.arange(len(dropoffCoords)),
ids_dest=np.arange(len(dropoffCoords)),
output='dataframe',
send_as_polyline=False)
# print(time_matrix)
time_matrix_delay = np.add(time_matrix, +LATENESS_ADJUSTER)
print("OSRM Table Returned")
x = 0
dfg = 0
marked = []
for i in time_matrix:
# tollerable means different between pickup and dropoff is minimal
y = 0
for j in i:
if x == y or time_matrix_delay[x][
y] == 0 or time_matrix_delay[x][
y] == LATENESS_ADJUSTER:
y = y + 1
# if x != y:
# print(str(x) + " " + str(y))
continue
first = group.iloc[[x]]
second = group.iloc[[y]]
# is first pickup before second
# consider change of duration for second ride aswell!
if pd.Timedelta(second["pickup_datetime"].values[0] -
first["pickup_datetime"].values[0]
).seconds >= 0 and pd.Timedelta(
second.index.values[0] -
first.index.values[0]
).seconds < time_matrix_delay[x][y]:
# is pickup time realistic?
result = osrm.simple_route([
first["pickup_longitude"].values[0],
first["pickup_latitude"].values[0]
], [
second["pickup_longitude"].values[0],
second["pickup_latitude"].values[0]
],
output='route',
geometry='wkt',
send_as_polyline=True)
extended_trip_time = result[0]["duration"] * 2
if pd.Timedelta(
first["pickup_datetime"].values[0] -
second["pickup_datetime"].values[0]
).seconds <= extended_trip_time + EARLY_PICKUP_ADJUSTER:
# if True:
# print("Tollerable")
# how long does journey take?
# pickup first, pickup_second, dropoff_first, dropoff_second,
first_result = osrm.simple_route(
[
first["pickup_longitude"].values[0],
first["pickup_latitude"].values[0]
], [
first["dropoff_longitude"].values[0],
first["dropoff_latitude"].values[0]
], [[
second["pickup_longitude"].values[0],
second["pickup_latitude"].values[0]
]],
output='route',
geometry='wkt',
send_as_polyline=True)
first_trip_time = first[
"trip_time_in_secs"].values[0]
extended_first_trip_time = first_result[0][
"duration"]
if extended_first_trip_time < first_trip_time + LATENESS_ADJUSTER:
# print("no friggin way")
second_result = osrm.simple_route(
[
second["pickup_longitude"].values[0],
second["pickup_latitude"].values[0]
], [
second["dropoff_longitude"].values[0],
second["dropoff_latitude"].values[0]
], [[
first["dropoff_longitude"].values[0],
first["dropoff_latitude"].values[0]
]],
output='route',
geometry='wkt',
send_as_polyline=True)
second_trip_time = second[
"trip_time_in_secs"].values[0]
extended_second_trip_time = second_result[0][
"duration"]
if extended_second_trip_time < second_trip_time + LATENESS_ADJUSTER:
# print("no friggin way")
dfg += 1
result = osrm.simple_route(
[
first["pickup_longitude"].
values[0],
first["pickup_latitude"].values[0]
], [
second["dropoff_longitude"].
values[0],
second["dropoff_latitude"].
values[0]
], [[
second["pickup_longitude"].
values[0],
second["pickup_latitude"].values[0]
],
[
first["dropoff_longitude"].
values[0],
first["dropoff_latitude"].
values[0]
]],
output='route',
geometry='geojson',
send_as_polyline=True)
collapsedJobs.append(group.iloc[[x]])
collapsedJobs.append(group.iloc[[y]])
ride = {}
ride["pickup_first"] = {}
ride["pickup_first"]["type"] = "point"
ride["pickup_first"]["latitude"] = first[
"pickup_latitude"].values[0]
ride["pickup_first"]["longitude"] = first[
"pickup_longitude"].values[0]
ride["dropoff_first"] = {}
ride["dropoff_first"]["type"] = "point"
ride["dropoff_first"]["latitude"] = first[
"dropoff_latitude"].values[0]
ride["dropoff_first"]["longitude"] = first[
"dropoff_longitude"].values[0]
ride["pickup_second"] = {}
ride["pickup_second"]["type"] = "point"
ride["pickup_second"]["latitude"] = second[
"pickup_latitude"].values[0]
ride["pickup_second"][
"longitude"] = second[
"pickup_longitude"].values[0]
ride["dropoff_second"] = {}
ride["dropoff_second"]["type"] = "point"
ride["dropoff_second"][
"latitude"] = second[
"dropoff_latitude"].values[0]
ride["dropoff_second"][
"longitude"] = second[
"dropoff_longitude"].values[0]
# print(second_result)
ride["geometry"] = result[0]["geometry"]
rides.append(ride)
marked.append(x)
marked.append(y)
g = 0
for k in i:
print(x)
time_matrix_delay[x][g] = 0
time_matrix_delay[y][g] = 0
time_matrix_delay[g][x] = 0
time_matrix_delay[g][y] = 0
g += 1
# print(g)
else:
l = 0
else:
l = 1
else:
l = 3
else:
l = 2
y = y + 1
x = x + 1
total += group.shape[0]
doable += dfg
print("Total")
print(total)
print("Doable")
print(doable)
if (doable > 20):
break
except Exception:
print(traceback.format_exc())
# continue
# break
# break
# print(group_10m.get_group(key)["dropoff_latitude"])
#
# break
collapsedDropoffArray = []
collapsedPickupArray = []
for collapsedJob in collapsedJobs:
collapsedJobDropoffDict = {}
collapsedJobDropoffDict["type"] = "point"
collapsedJobDropoffDict["longitude"] = collapsedJob[
"dropoff_longitude"].values[0]
collapsedJobDropoffDict["latitude"] = collapsedJob[
"dropoff_latitude"].values[0]
collapsedDropoffArray.append(collapsedJobDropoffDict)
collapsedJobPickupDict = {}
collapsedJobPickupDict["type"] = "point"
collapsedJobPickupDict["longitude"] = collapsedJob[
"pickup_longitude"].values[0]
collapsedJobPickupDict["latitude"] = collapsedJob[
"pickup_latitude"].values[0]
collapsedPickupArray.append(collapsedJobPickupDict)
dataDict = {
'collapsedDropoffArray': collapsedDropoffArray,
'collapsedPickupArray': collapsedPickupArray,
'rides': rides,
'totalRides': total,
'collapsedRides': doable * 2,
'sharedRides': doable,
'algorithm': "Minimal Delay"
}
# print(json.dumps(dataDict))
data = json.dumps(dataDict)
return data
# print(len(collapsedJobs))
# print(len(sharedJobs))
# collapsedDropoffArray = []
# collapsedPickupArray = []
# sharedDropoffArray = []
# sharedPickupArray = []
# for collapsedJob in collapsedJobs:
# collapsedJobDropoffDict = {}
# collapsedJobDropoffDict["type"] = "point"
# collapsedJobDropoffDict["longitude"] = collapsedJob["dropoff_longitude"].values[0]
# collapsedJobDropoffDict["latitude"] = collapsedJob["dropoff_latitude"].values[0]
# collapsedDropoffArray.append(collapsedJobDropoffDict)
# collapsedJobPickupDict = {}
# collapsedJobPickupDict["type"] = "point"
# collapsedJobPickupDict["longitude"] = collapsedJob["pickup_longitude"].values[0]
# collapsedJobPickupDict["latitude"] = collapsedJob["pickup_latitude"].values[0]
# collapsedPickupArray.append(collapsedJobPickupDict)
# for sharedJob in sharedJobs:
# sharedJobPickupDict = {}
# sharedJobPickupDict["type"] = "point"
# sharedJobPickupDict["longitude"] = sharedJob["pickup_longitude"].values[0]
# sharedJobPickupDict["latitude"] = sharedJob["pickup_latitude"].values[0]
# sharedPickupArray.append(sharedJobPickupDict)
# sharedJobDropoffDict = {}
# sharedJobDropoffDict["type"] = "point"
# sharedJobDropoffDict["longitude"] = sharedJob["dropoff_longitude"].values[0]
# sharedJobDropoffDict["latitude"] = sharedJob["dropoff_latitude"].values[0]
# sharedDropoffArray.append(sharedJobDropoffDict)
# dataDict = {
# 'collapsedDropoffArray' : collapsedDropoffArray,
# 'collapsedPickupArray' : collapsedPickupArray,
# 'sharedDropoffArray' : sharedDropoffArray,
# 'sharedPickupArray' : sharedPickupArray,
# 'totalRides' : df.shape[0],
# 'collapsedRides' : len(collapsedJobs),
# 'sharedRides' : len(sharedJobs),
# 'algorithm' : "naiveN"
# }
# data = json.dumps(dataDict)
# return data
return
