def setUp(self):
self.network = copy.deepcopy(network_lattice_1x1_small)
# generate synthetic origins
obs1 = tigernet.generate_obs(5, self.network.s_data)
obs1["obs_id"] = ["a", "b", "c", "d", "e"]
# generate synthetic destinations
obs2 = tigernet.generate_obs(3, self.network.s_data, seed=1)
obs2["obs_id"] = ["z", "y", "x"]
# associate origins with the network
args = self.network, obs1.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id", "snap_to": "nodes"}
self.net_obs1 = tigernet.Observations(*args, **kwargs)
# associate destinations with the network
args = self.network, obs2.copy()
kwargs = {"df_name": "obs2", "df_key": "obs_id", "snap_to": "nodes"}
self.net_obs2 = tigernet.Observations(*args, **kwargs)
def setUp(self):
network = copy.deepcopy(network_empirical_simplified)
# generate synthetic observations
obs = tigernet.generate_obs(500, network.s_data)
obs["obs_id"] = obs.index
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id", "snap_to": "nodes"}
self.net_obs = tigernet.Observations(*args, **kwargs)
def setUp(self):
network = copy.deepcopy(network_lattice_1x1_geomelem)
# generate synthetic observations
obs = tigernet.generate_obs(5, network.s_data)
obs["obs_id"] = ["a", "b", "c", "d", "e"]
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id"}
self.net_obs = tigernet.Observations(*args, **kwargs)
def setUp(self):
network = copy.deepcopy(network_empirical_simplified)
# generate synthetic observations
obs = tigernet.generate_obs(500, network.s_data)
obs["obs_id"] = obs.index
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id", "snap_to": "nodes"}
kwargs.update({"restrict_col": "MTFCC"})
kwargs.update({"remove_restricted": ["S1100", "S1630", "S1640"]})
self.net_obs = tigernet.Observations(*args, **kwargs)
def setUp(self):
network = copy.deepcopy(network_lattice_1x1_geomelem)
network.s_data.loc[1, "MTFCC"] = "S1100"
network.s_data.loc[3, "MTFCC"] = "S1100"
# generate synthetic observations
obs = tigernet.generate_obs(5, network.s_data)
obs["obs_id"] = ["a", "b", "c", "d", "e"]
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id", "snap_to": "nodes"}
kwargs.update({"restrict_col": "MTFCC"})
kwargs.update({"remove_restricted": ["S1100", "S1630", "S1640"]})
self.net_obs = tigernet.Observations(*args, **kwargs)
def test_generate_observations_totalbounds(self):
n_obs = 5
obs = tigernet.generate_obs(n_obs, self.network.s_data)
known_n_obs = n_obs
observed_n_obs = obs.shape[0]
self.assertEqual(observed_n_obs, known_n_obs)
known_coords = numpy.array([
(4.939321535345923, 6.436704297351775),
(5.4248703846447945, 4.903948646972072),
(3.8128931940501425, 5.813047017599905),
(3.9382849013642325, 8.025957007038718),
(8.672964844509263, 3.4509736694319995),
])
observed_coords = numpy.array([(p.x, p.y) for p in obs.geometry])
numpy.testing.assert_array_almost_equal(observed_coords, known_coords)
def test_generate_observations_inbuffer(self):
n_obs = 5
obs = tigernet.generate_obs(n_obs, self.network.s_data, near_net=0.5)
known_n_obs = n_obs
observed_n_obs = obs.shape[0]
self.assertEqual(observed_n_obs, known_n_obs)
known_coords = numpy.array([
(4.939321535345923, 6.436704297351775),
(5.4248703846447945, 4.903948646972072),
(7.125525342743981, 4.76005427777614),
(4.153314260276387, 7.024762586578099),
(4.696634895750645, 3.731957459914712),
])
observed_coords = numpy.array([(p.x, p.y) for p in obs.geometry])
numpy.testing.assert_array_almost_equal(observed_coords, known_coords)
def test_generate_observations_inbuffer(self):
n_obs = 500
obs = tigernet.generate_obs(n_obs, self.network.s_data, near_net=30)
known_n_obs = n_obs
observed_n_obs = obs.shape[0]
self.assertEqual(observed_n_obs, known_n_obs)
known_coords = numpy.array([
(622571.9108776418, 166711.9648736473),
(624474.552580049, 165130.38564160923),
(623803.6385443554, 166644.26001242414),
(622876.6555154349, 165227.52219813256),
(623400.7775349629, 166023.94388077687),
])
observed_coords = numpy.array([(p.x, p.y) for p in obs.geometry])[:5]
numpy.testing.assert_array_almost_equal(observed_coords,
known_coords,
decimal=DECIMAL)
def test_generate_observations_totalbounds(self):
n_obs = 500
obs = tigernet.generate_obs(n_obs, self.network.s_data)
known_n_obs = n_obs
observed_n_obs = obs.shape[0]
self.assertEqual(observed_n_obs, known_n_obs)
known_coords = numpy.array([
(622974.1796832045, 166162.55760926675),
(623169.2985719838, 165632.6814708112),
(622521.5219401979, 165946.95826632337),
(622571.9108776418, 166711.9648736473),
(624474.552580049, 165130.38564160923),
])
observed_coords = numpy.array([(p.x, p.y) for p in obs.geometry])[:5]
numpy.testing.assert_array_almost_equal(observed_coords,
known_coords,
decimal=DECIMAL)