def network_analyze(
traces: gpd.GeoDataFrame,
area: gpd.GeoDataFrame,
name: str,
coverage_gdf: gpd.GeoDataFrame,
circle_radius: float,
) -> Tuple[Network, pd.Series]:
"""
Analyze traces and area for results.
"""
network = Network(
trace_gdf=traces,
area_gdf=area,
name=name,
determine_branches_nodes=True,
snap_threshold=0.001,
)
points = network.representative_points()
if not len(points) == 1:
raise ValueError("Expected one target area representative point.")
point = points[0]
amount_of_coverage = assess_coverage(target_centroid=point,
coverage_gdf=coverage_gdf,
radius=circle_radius)
describe_df = describe_random_network(
network=network,
target_centroid=point,
name=name,
amount_of_coverage=amount_of_coverage,
radius=circle_radius,
)
return network, describe_df.iloc[0]
def test_network(
traces,
area,
name,
determine_branches_nodes,
truncate_traces,
snap_threshold,
circular_target_area,
file_regression,
data_regression,
):
"""
Test Network object creation and attributes with general datasets.
Tests for regression and general assertions.
"""
network = Network(
trace_gdf=traces,
area_gdf=area,
name=name,
determine_branches_nodes=determine_branches_nodes,
truncate_traces=truncate_traces,
snap_threshold=snap_threshold,
trace_length_set_names=("a", "b"),
branch_length_set_names=("A", "B"),
trace_length_set_ranges=((0.1, 1), (1, 2)),
branch_length_set_ranges=((0.1, 1), (1, 2)),
circular_target_area=circular_target_area,
)
assert area.shape[0] == len(network.representative_points())
numerical_description = network.numerical_network_description()
assert isinstance(numerical_description, dict)
for key, item in numerical_description.items():
assert isinstance(key, str)
if not isinstance(item, (int, float, str)):
assert isinstance(item.item(), (int, float))
cut_off = 1.0
num_description_explicit_cut_offs = network.numerical_network_description(
trace_lengths_cut_off=cut_off, branch_lengths_cut_off=cut_off
)
for label in ("branch", "trace"):
key = label + (
" "
+ length_distributions.Dist.POWERLAW.value
+ " "
+ length_distributions.CUT_OFF
)
assert num_description_explicit_cut_offs[key] == 1.0
network_target_areas = network.target_areas
assert isinstance(network_target_areas, list)
assert all(
[isinstance(val, (Polygon, MultiPolygon)) for val in network_target_areas]
)
network_attributes = dict()
for attribute in ("node_counts", "branch_counts"):
# network_attributes[attribute] = getattr(network, attribute)
for key, value in getattr(network, attribute).items():
if not np.isnan(value):
network_attributes[key] = int(value)
for key, value in numerical_description.items():
if isinstance(value, (float, int)):
network_attributes[key] = round(
value.item() if hasattr(value, "item") else value, 2
)
data_regression.check(network_attributes)
if determine_branches_nodes and network.branch_gdf.shape[0] < 500:
sorted_branch_gdf = network.branch_gdf.sort_index()
assert isinstance(sorted_branch_gdf, gpd.GeoDataFrame)
# Do not check massive branch counts
file_regression.check(sorted_branch_gdf.to_json(indent=1))
network_extensive_testing(
network=network, traces=traces, area=area, snap_threshold=snap_threshold
)
trace_intersects = network.trace_intersects_target_area_boundary
assert isinstance(trace_intersects, np.ndarray)
assert trace_intersects.dtype in ("int32", "int64")
branch_intersects = network.branch_intersects_target_area_boundary
assert isinstance(branch_intersects, np.ndarray)
assert network.branch_intersects_target_area_boundary.dtype in ("int32", "int64")