def test_discard_segments(self):
# 1-------------------------------------------------------------------------
known_shape_after_discard = 47
kws = {"bbox": "discard", "direc": "test_data"}
gdf = tigernet.testing_data("Edges_Leon_FL_2010", **kws)
discard = tigernet.get_discard_segms("2010", "12", "073")
observed_shape_after_discard = gdf[~gdf["TLID"].isin(discard)].shape[0]
self.assertEqual(observed_shape_after_discard,
known_shape_after_discard)
# 2-------------------------------------------------------------------------
known_shape_after_discard = 23
# filter out only roads
gdf = tigernet.testing_data("Edges_Leon_FL_2010", **kws)
yes_roads = gdf["MTFCC"].str.startswith("S")
roads = gdf[yes_roads].copy()
# Tiger attributes primary and secondary
ATTR1, ATTR2 = "MTFCC", "TLID"
# segment welding and splitting stipulations
INTRST = "S1100" # interstates mtfcc code
RAMP = "S1630" # ramp mtfcc code
SERV_DR = "S1640" # service drive mtfcc code
SPLIT_GRP = "FULLNAME" # grouped by this variable
SPLIT_BY = [RAMP, SERV_DR] # split interstates by ramps & service
SKIP_RESTR = True # no weld retry if still MLS
kwargs = {"from_raw": True, "attr1": ATTR1, "attr2": ATTR2}
comp_kws = {"record_components": True, "largest_component": True}
kwargs.update(comp_kws)
geom_kws = {"record_geom": True, "calc_len": True}
kwargs.update(geom_kws)
mtfcc_kws = {"skip_restr": SKIP_RESTR}
mtfcc_kws.update({"mtfcc_split": INTRST, "mtfcc_intrst": INTRST})
mtfcc_kws.update({"mtfcc_split_grp": SPLIT_GRP, "mtfcc_ramp": RAMP})
mtfcc_kws.update({"mtfcc_split_by": SPLIT_BY, "mtfcc_serv": SERV_DR})
kwargs.update(mtfcc_kws)
network = tigernet.Network(roads,
discard_segs=("2010", "12", "073"),
**kwargs)
network.simplify_network(
record_components=True,
record_geom=True,
largest_component=False,
def_graph_elems=True,
inplace=True,
)
observed_shape_after_discard = network.s_data.shape[0]
self.assertEqual(observed_shape_after_discard,
known_shape_after_discard)
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 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):
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):
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 test_read_data_bbox_5_int_coords(self):
with self.assertRaises(ValueError):
tigernet.testing_data(self.dset, bbox=[1, 1, 2, 2, 2])
def test_read_data_bbox_5_coords(self):
with self.assertRaises(ValueError):
tigernet.testing_data(self.dset, bbox=[1.0, 1.0, 2.0, 2.0, 2.0])
def test_read_data_bbox_not_iterable(self):
with self.assertRaises(ValueError):
tigernet.testing_data(self.dset, bbox=2020)
def test_read_data_bbox_bad_str(self):
with self.assertRaises(ValueError):
tigernet.testing_data(self.dset, bbox="bad_description")
def test_good_read_from_coords(self):
known_recs = 4
bbox = (-84.34, 30.4935, -84.3378, 30.494)
observed_recs = tigernet.testing_data(self.dset, bbox=bbox).shape[0]
self.assertEqual(observed_recs, known_recs)
def test_good_read_no_bbox(self):
known_recs = 37798
bbox = None
observed_recs = tigernet.testing_data(self.dset, bbox=bbox).shape[0]
self.assertEqual(observed_recs, known_recs)
# used in:
# - test_obs2obs_synthetic.TestSyntheticObservationsOrigToXXXXSegments
# - test_obs2obs_synthetic.TestSyntheticObservationsOrigToXXXXNodes
# - test_obs2obs_synthetic.TestSyntheticObservationsOrigToDestSegments
# - test_obs2obs_synthetic.TestSyntheticObservationsOrigToDestNodes
h1v1 = {"n_hori_lines": 1, "n_vert_lines": 1}
lattice = tigernet.generate_lattice(bounds=[0, 0, 4, 4], **h1v1)
network_lattice_1x1_small = tigernet.Network(lattice, **kws)
network_lattice_1x1_small.cost_matrix()
###############################################################################
############################### Empirical networks ############################
###############################################################################
# get the roads shapefile as a GeoDataFrame
gdf = tigernet.testing_data("Edges_Leon_FL_2010")
# filter out only roads
yes_roads = gdf["MTFCC"].str.startswith("S")
roads = gdf[yes_roads].copy()
# Tiger attributes primary and secondary
ATTR1, ATTR2 = "MTFCC", "TLID"
# segment welding and splitting stipulations --------------------------------------------
INTRST = "S1100" # interstates mtfcc code
RAMP = "S1630" # ramp mtfcc code
SERV_DR = "S1640" # service drive mtfcc code
SPLIT_GRP = "FULLNAME" # grouped by this variable
SPLIT_BY = [RAMP, SERV_DR] # split interstates by ramps & service
SKIP_RESTR = True # no weld retry if still MLS
