def test_extract_graph_links_with_flat_condition():
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 'primary'
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 'primary'
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': 'yes'})
links = graph_operations.extract_on_attributes(
n.links(),
conditions={'attributes': 'yes'},
)
assert links == ['2']
def test_extract_graph_links_with_flat_condition_and_list_value_specifying_to_ignore_mixed_types(
):
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 'primary'
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 'primary'
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': ['yes', 'no', 'bobby']})
links = graph_operations.extract_on_attributes(
n.links(), conditions={'attributes': 'yes'}, mixed_dtypes=False)
assert links == []
def test_extract_graph_links_with_callable_condition_and_list_value_specifying_to_ignore_mixed_types(
):
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': {9, 10}
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': [1, 2]
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': 'yes'})
def condition(val):
return val == 9
links = graph_operations.extract_on_attributes(
n.links(),
conditions={'attributes': {
'osm:way:highway': {
'text': condition
}
}},
mixed_dtypes=False)
assert links == []
def test_extract_graph_links_with_callable_condition():
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 9
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': 1
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': 'yes'})
def condition(val):
return val == 9
links = graph_operations.extract_on_attributes(
n.links(),
conditions={'attributes': {
'osm:way:highway': {
'text': condition
}
}})
assert links == ['0']
def test_extract_graph_links_with_list_condition_and_list_value():
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': ['primary', 'secondary']
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': {'primary', 'other'}
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': 'yes'})
links = graph_operations.extract_on_attributes(
n.links(),
conditions={
'attributes': {
'osm:way:highway': {
'text': ['primary', 'some_other_highway']
}
}
})
assert links == ['0', '1']
def test_extract_graph_links_with_bound_condition_and_list_value():
n = Network('epsg:27700')
n.add_link('0',
1,
2,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': [9, 1]
}
}
})
n.add_link('1',
2,
3,
attribs={
'attributes': {
'osm:way:highway': {
'name': 'osm:way:highway',
'class': 'java.lang.String',
'text': [0, 1]
}
}
})
n.add_link('2', 3, 4, attribs={'attributes': 'yes'})
links = graph_operations.extract_on_attributes(
n.links(),
conditions={'attributes': {
'osm:way:highway': {
'text': (2, 10)
}
}})
assert links == ['0']
def generate_problem_graph(self):
# build the problem graph
# build up the nodes, edges and shortest path lengths (will be used to compute weight coefficient later on)
self.nodes.loc[:,
'problem_nodes'] = self.nodes.loc[:,
'id'] + '.link:' + self.nodes.loc[:,
'link_id']
self.edges = self.edges.merge(
self.nodes[['id', 'link_id', 'problem_nodes'
]].rename(columns={
'link_id': 'link_id_u',
'problem_nodes': 'problem_nodes_u'
}),
left_on='u',
right_on='id')
self.edges = self.edges.merge(
self.nodes[['id', 'link_id', 'problem_nodes'
]].rename(columns={
'link_id': 'link_id_v',
'problem_nodes': 'problem_nodes_v'
}),
left_on='v',
right_on='id')
self.edges = self.network_spatial_tree.shortest_path_lengths(
df_pt_edges=self.edges,
from_col='link_id_u',
to_col='link_id_v',
weight='length')
# build the problem graph
problem_graph = nx.DiGraph()
problem_nodes = self.nodes.set_index('problem_nodes').T.to_dict()
problem_graph.add_nodes_from(problem_nodes)
path_length_coeff = self.edges.loc[self.edges['path_lengths'].notna(
), :].groupby('problem_nodes_u').mean()
path_length_coeff = path_length_coeff.rename(
columns={
'path_lengths': 'path_lengths_u'
}).merge(
self.edges.loc[self.edges['path_lengths'].notna(), :].rename(
columns={
'path_lengths': 'path_lengths_v'
}).groupby('problem_nodes_v').mean(),
how='outer',
left_index=True,
right_index=True)
path_length_coeff = (
1 / path_length_coeff[['path_lengths_u', 'path_lengths_v']].mean(
axis=1, skipna=True)).to_dict()
nx.set_node_attributes(problem_graph, problem_nodes)
nx.set_node_attributes(problem_graph, path_length_coeff, 'coeff')
problem_edges = self.edges.loc[self.edges['path_lengths'].isna(), :]
problem_graph.add_edges_from(
zip(problem_edges['problem_nodes_u'],
problem_edges['problem_nodes_v']))
# connect all catchment pools
[
problem_graph.add_edges_from(
itertools.combinations(group['problem_nodes'], 2))
for name, group in self.nodes.groupby('id')
]
nodes_without_paths = set(problem_graph.nodes()) - set(
graph_operations.extract_on_attributes(
problem_graph.nodes(data=True),
conditions={'coeff': has_attrib}))
problem_graph.remove_nodes_from(nodes_without_paths)
return problem_graph
