def setUp(self):
self.network = copy.deepcopy(network_empirical_simplified_wcm)
# empirical origins
obs1 = tigernet.testing_data("CensusBlocks_Leon_FL_2010")
# empirical destinations
obs2 = tigernet.testing_data("WeightedParcels_Leon_FL_2010")
# associate origins with the network
args = self.network, obs1.copy()
kwargs = {"df_name": "obs1", "df_key": "GEOID", "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": "PARCEL_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.testing_data("WeightedParcels_Leon_FL_2010")
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "PARCEL_ID"}
self.net_obs = tigernet.Observations(*args, **kwargs)
def test_no_cost_matrix(self):
network = copy.deepcopy(network_lattice_1x1_geomelem)
pts = [Point(1, 1), Point(3, 1), Point(1, 3), Point(3, 3)]
od_data = {"obs_id": ["a", "b", "c", "d"]}
obs = geopandas.GeoDataFrame(od_data, geometry=pts)
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "obs_id"}
observations = tigernet.Observations(*args, **kwargs)
with self.assertRaises(AttributeError):
tigernet.obs2obs_cost_matrix(observations, network)
def setUp(self):
self.network = copy.deepcopy(network_empirical_simplified_wcm)
# empirical observations
obs = tigernet.testing_data("CensusBlocks_Leon_FL_2010")
# associate observations with the network
args = self.network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "GEOID"}
self.net_obs = tigernet.Observations(*args, **kwargs)
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.testing_data("WeightedParcels_Leon_FL_2010")
# associate observations with the network
args = network, obs.copy()
kwargs = {"df_name": "obs1", "df_key": "PARCEL_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):
self.network = copy.deepcopy(network_lattice_1x1_small)
# generate synthetic observations
pts = [Point(1, 1), Point(3, 1), Point(1, 3), Point(3, 3)]
obs = geopandas.GeoDataFrame({"obs_id": ["a", "b", "c", "d"]},
geometry=pts)
# associate observations with the network
args = self.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 ---------------------------------------- 1
self.obs1 = tigernet.testing_data("WeightedParcels_Leon_FL_2010")
# associate observations with the network
args = network, self.obs1.copy()
kwargs = {"df_name": "obs1", "df_key": "PARCEL_ID", "snap_to": "segments"}
kwargs.update({"obs_pop": "SUM_EST_PO", "restrict_col": "MTFCC"})
kwargs.update({"remove_restricted": ["S1100", "S1630", "S1640"]})
self.net_obs1 = tigernet.Observations(*args, **kwargs)
# generate synthetic observations ---------------------------------------- 2
self.obs2 = tigernet.testing_data("CensusBlocks_Leon_FL_2010")
# associate observations with the network
args = network, self.obs2.copy()
kwargs = {"df_name": "obs2", "df_key": "GEOID", "snap_to": "segments"}
kwargs.update({"obs_pop": "POP100", "restrict_col": "MTFCC"})
kwargs.update({"remove_restricted": ["S1100", "S1630", "S1640"]})
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"}
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_bad_snap_to(self):
network = copy.deepcopy(network_lattice_1x1_geomelem)
with self.assertRaises(ValueError):
tigernet.Observations(network, None, None, snap_to="network")
def test_no_segm2geom(self):
network = copy.deepcopy(network_lattice_1x1_no_args)
with self.assertRaises(AttributeError):
tigernet.Observations(network, None, None)