def test_main(mock_server, test_client_config):
mock_server.reset()
client = Dispatcher(test_client_config)
res = client.distance_matrix(origins=ORIGS,
destinations=DESTS,
provider='google')
assert len(res) == 1975
assert res.isnull().sum().sum() == 0
def main():
client = Dispatcher()
s = time.time()
res, inv = client.distance_matrix(origins=ORIGS,
destinations=DESTS,
provider='google',
return_inverse=True)
t = time.time() - s
prettyprinter.pprint(res.take(inv.flat))
print('Duration: %dms' % (t * 1000))
def main():
client = Dispatcher()
s = time.time()
res = client.distance_matrix(
origins=np.random.rand(10, 2) * [20, 20] + [25, -90],
destinations=np.random.rand(10, 2) * [20, 20] + [25, -90],
provider='google')
t = time.time() - s
prettyprinter.pprint(res)
print('Duration: %dms' % (t * 1000))
def test_square(mock_server):
mock_server.reset()
client = Dispatcher({
'providers': {
'google': {
'type_': 'google',
'key': 'test123',
'base_url': 'http://localhost:8080/'
}
}
})
res = client.distance_matrix(
origins=LOCS,
destinations=LOCS,
provider='google',
max_meters=np.Infinity
)
assert len(res) == 2500
assert res.isnull().sum().sum() == 0
def test_main(mock_server, test_client_config):
mock_server.reset()
client = Dispatcher(test_client_config)
res, inv = client.distance_matrix(origins=np.array([[37.2, -88.2],
[37.1, -88.1],
[42.5, -97.5]]),
destinations=np.array([[37.1, -88.1],
[45.5, -97.5],
[37.1, -86.1],
[41.9, -97.2]]),
provider='google',
return_inverse=True)
assert res.isnull().sum().sum() == 0
# np.unique will end up sorting index
pd.testing.assert_index_equal(
res.index,
pd.MultiIndex.from_tuples([
(37.1, -88.1, 37.1, -88.1),
(37.1, -88.1, 37.1, -86.1),
(37.1, -88.1, 41.9, -97.2),
(37.1, -88.1, 45.5, -97.5),
(37.2, -88.2, 37.1, -88.1),
(37.2, -88.2, 37.1, -86.1),
(37.2, -88.2, 41.9, -97.2),
(37.2, -88.2, 45.5, -97.5),
(42.5, -97.5, 37.1, -88.1),
(42.5, -97.5, 37.1, -86.1),
(42.5, -97.5, 41.9, -97.2),
(42.5, -97.5, 45.5, -97.5),
],
names=['olat', 'olon', 'dlat', 'dlon']))
# double check these values if they should need to change.
# should be able to eyeball if they look reasonable.
expected_meters = [
0., 444779., 1143293.274236, 1400881.808377, 44477., 489257.,
1128289.345949, 1385191.366365, 1204670.037841, 1401763.032474,
200150., 667169.
]
np.testing.assert_array_almost_equal(res.meters.values, expected_meters)
# restore index with inverse key
# should be origin-major traversal
pd.testing.assert_index_equal(
res.take(inv.flat).index,
pd.MultiIndex.from_tuples([
(37.2, -88.2, 37.1, -88.1),
(37.2, -88.2, 45.5, -97.5),
(37.2, -88.2, 37.1, -86.1),
(37.2, -88.2, 41.9, -97.2),
(37.1, -88.1, 37.1, -88.1),
(37.1, -88.1, 45.5, -97.5),
(37.1, -88.1, 37.1, -86.1),
(37.1, -88.1, 41.9, -97.2),
(42.5, -97.5, 37.1, -88.1),
(42.5, -97.5, 45.5, -97.5),
(42.5, -97.5, 37.1, -86.1),
(42.5, -97.5, 41.9, -97.2),
],
names=['olat', 'olon', 'dlat', 'dlon']))