def test_hill_branch_wt_diversity(primal_graph):
for distances, betas in network_generator():
G = primal_graph.copy()
data_dict = mock.mock_data_dict(G)
landuse_labels = mock.mock_categorical_data(len(data_dict))
# easy version
N_easy = networks.NetworkLayerFromNX(G, distances=distances)
D_easy = layers.DataLayerFromDict(data_dict)
D_easy.assign_to_network(N_easy, max_dist=500)
D_easy.hill_branch_wt_diversity(landuse_labels, qs=[0, 1, 2])
# custom version
N_full = networks.NetworkLayerFromNX(G, distances=distances)
D_full = layers.DataLayerFromDict(data_dict)
D_full.assign_to_network(N_full, max_dist=500)
D_full.compute_landuses(landuse_labels,
mixed_use_keys=['hill_branch_wt'],
qs=[0, 1, 2])
# compare
for d in distances:
for q in [0, 1, 2]:
assert np.allclose(
N_easy.metrics['mixed_uses']['hill_branch_wt'][q][d],
N_full.metrics['mixed_uses']['hill_branch_wt'][q][d],
atol=0.001,
rtol=0)
def test_Data_Layer_From_Dict(primal_graph):
data_dict = mock.mock_data_dict(primal_graph)
data_uids, data_map = layers.data_map_from_dict(data_dict)
x_arr = data_map[:, 0]
y_arr = data_map[:, 1]
# test against DataLayerFromDict's internal process
D = layers.DataLayerFromDict(data_dict)
assert D.uids == data_uids
assert np.allclose(D._data, data_map, equal_nan=True)
assert np.allclose(D.data_x_arr, x_arr, atol=0.001, rtol=0)
assert np.allclose(D.data_y_arr, y_arr, atol=0.001, rtol=0)
def test_compute_accessibilities(primal_graph):
for distances, betas in network_generator():
G = primal_graph.copy()
data_dict = mock.mock_data_dict(G)
landuse_labels = mock.mock_categorical_data(len(data_dict))
# easy version
N_easy = networks.NetworkLayerFromNX(G, distances=distances)
D_easy = layers.DataLayerFromDict(data_dict)
D_easy.assign_to_network(N_easy, max_dist=500)
D_easy.compute_accessibilities(landuse_labels, ['c'])
# custom version
N_full = networks.NetworkLayerFromNX(G, distances=distances)
D_full = layers.DataLayerFromDict(data_dict)
D_full.assign_to_network(N_full, max_dist=500)
D_full.compute_landuses(landuse_labels, accessibility_keys=['c'])
# compare
for d in distances:
for wt in ['weighted', 'non_weighted']:
assert np.allclose(N_easy.metrics['accessibility'][wt]['c'][d],
N_full.metrics['accessibility'][wt]['c'][d],
atol=0.001,
rtol=0)
def test_find_nearest(primal_graph):
N = networks.NetworkLayerFromNX(primal_graph, distances=[100])
# generate some data
data_dict = mock.mock_data_dict(primal_graph)
D = layers.DataLayerFromDict(data_dict)
# test the filter - iterating each point in data map
for d in D._data:
d_x = d[0]
d_y = d[1]
# find the closest point on the network
min_idx, min_dist = data.find_nearest(d_x, d_y, N.node_x_arr, N.node_y_arr, max_dist=500)
# check that no other indices are nearer
for i, n in enumerate(N._node_data):
n_x = n[0]
n_y = n[1]
dist = np.sqrt((d_x - n_x) ** 2 + (d_y - n_y) ** 2)
if i == min_idx:
assert round(dist, 8) == round(min_dist, 8)
else:
assert dist > min_dist
plt.style.use('matplotlibrc')
###
# INTRO PLOT
G = mock.mock_graph()
plot.plot_nX(G, labels=True, node_size=80, path='images/graph.png', dpi=150)
# INTRO EXAMPLE PLOTS
G = graphs.nX_simple_geoms(G)
G = graphs.nX_decompose(G, 20)
N = networks.NetworkLayerFromNX(G, distances=[400, 800])
N.segment_centrality(measures=['segment_harmonic'])
data_dict = mock.mock_data_dict(G, random_seed=25)
D = layers.DataLayerFromDict(data_dict)
D.assign_to_network(N, max_dist=400)
landuse_labels = mock.mock_categorical_data(len(data_dict), random_seed=25)
D.hill_branch_wt_diversity(landuse_labels, qs=[0])
G_metrics = N.to_networkX()
segment_harmonic_vals = []
mixed_uses_vals = []
for node, data in G_metrics.nodes(data=True):
segment_harmonic_vals.append(
data['metrics']['centrality']['segment_harmonic'][800])
mixed_uses_vals.append(
data['metrics']['mixed_uses']['hill_branch_wt'][0][400])
# custom colourmap
cmap = colors.LinearSegmentedColormap.from_list('cityseer',
def test_nX_from_graph_maps(primal_graph):
# also see test_networks.test_to_networkX for tests on implementation via Network layer
# check round trip to and from graph maps results in same graph
# explicitly set live params for equality checks
# graph_maps_from_networkX generates these implicitly if missing
for n in primal_graph.nodes():
primal_graph.nodes[n]['live'] = bool(np.random.randint(0, 1))
# test directly from and to graph maps
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(primal_graph)
G_round_trip = graphs.nX_from_graph_maps(node_uids, node_data, edge_data, node_edge_map)
assert list(G_round_trip.nodes) == list(primal_graph.nodes)
assert list(G_round_trip.edges) == list(primal_graph.edges)
# check with metrics dictionary
N = networks.NetworkLayerFromNX(primal_graph, distances=[500, 1000])
N.node_centrality(measures=['node_harmonic'])
data_dict = mock.mock_data_dict(primal_graph)
landuse_labels = mock.mock_categorical_data(len(data_dict))
D = layers.DataLayerFromDict(data_dict)
D.assign_to_network(N, max_dist=400)
D.compute_landuses(landuse_labels,
mixed_use_keys=['hill', 'shannon'],
accessibility_keys=['a', 'c'],
qs=[0, 1])
metrics_dict = N.metrics_to_dict()
# without backbone
G_round_trip_data = graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
metrics_dict=metrics_dict)
for uid, metrics in metrics_dict.items():
assert G_round_trip_data.nodes[uid]['metrics'] == metrics
# with backbone
G_round_trip_data = graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
networkX_multigraph=primal_graph,
metrics_dict=metrics_dict)
for uid, metrics in metrics_dict.items():
assert G_round_trip_data.nodes[uid]['metrics'] == metrics
# test with decomposed
G_decomposed = graphs.nX_decompose(primal_graph, decompose_max=20)
# set live explicitly
for n in G_decomposed.nodes():
G_decomposed.nodes[n]['live'] = bool(np.random.randint(0, 1))
node_uids_d, node_data_d, edge_data_d, node_edge_map_d = graphs.graph_maps_from_nX(G_decomposed)
G_round_trip_d = graphs.nX_from_graph_maps(node_uids_d, node_data_d, edge_data_d, node_edge_map_d)
assert list(G_round_trip_d.nodes) == list(G_decomposed.nodes)
for n, iter_node_data in G_round_trip.nodes(data=True):
assert n in G_decomposed
assert iter_node_data['live'] == G_decomposed.nodes[n]['live']
assert iter_node_data['x'] == G_decomposed.nodes[n]['x']
assert iter_node_data['y'] == G_decomposed.nodes[n]['y']
assert G_round_trip_d.edges == G_decomposed.edges
# error checks for when using backbone graph:
# mismatching numbers of nodes
corrupt_G = primal_graph.copy()
corrupt_G.remove_node(0)
with pytest.raises(ValueError):
graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
networkX_multigraph=corrupt_G)
# mismatching node uid
with pytest.raises(KeyError):
corrupt_node_uids = list(node_uids)
corrupt_node_uids[0] = 'boo'
graphs.nX_from_graph_maps(corrupt_node_uids,
node_data,
edge_data,
node_edge_map,
networkX_multigraph=primal_graph)
# missing edge
with pytest.raises(KeyError):
corrupt_primal_graph = primal_graph.copy()
corrupt_primal_graph.remove_edge(0, 1)
graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
networkX_multigraph=corrupt_primal_graph)
def test_compute_stats(primal_graph):
"""
Test stats component
"""
betas = np.array([0.01, 0.005])
distances = networks.distance_from_beta(betas)
# network layer
N_single = networks.NetworkLayerFromNX(primal_graph, distances=distances)
N_multi = networks.NetworkLayerFromNX(primal_graph, distances=distances)
node_map = N_multi._node_data
edge_map = N_multi._edge_data
node_edge_map = N_multi._node_edge_map
# data layer
data_dict = mock.mock_data_dict(primal_graph)
D_single = layers.DataLayerFromDict(data_dict)
D_multi = layers.DataLayerFromDict(data_dict)
# check single metrics independently against underlying for some use-cases, e.g. hill, non-hill, accessibility...
D_single.assign_to_network(N_single, max_dist=500)
D_multi.assign_to_network(N_multi, max_dist=500)
# generate some mock landuse data
mock_numeric = mock.mock_numerical_data(len(data_dict), num_arrs=2)
# generate stats
D_single.compute_stats(stats_keys='boo', stats_data_arrs=mock_numeric[0])
D_single.compute_stats(stats_keys='baa', stats_data_arrs=mock_numeric[1])
D_multi.compute_stats(stats_keys=['boo', 'baa'],
stats_data_arrs=mock_numeric)
# test against underlying method
data_map = D_single._data
stats_sum, stats_sum_wt, stats_mean, stats_mean_wt, stats_variance, stats_variance_wt, stats_max, stats_min = \
data.aggregate_stats(node_map,
edge_map,
node_edge_map,
data_map,
distances,
betas,
numerical_arrays=mock_numeric)
stats_keys = [
'max', 'min', 'sum', 'sum_weighted', 'mean', 'mean_weighted',
'variance', 'variance_weighted'
]
stats_data = [
stats_max, stats_min, stats_sum, stats_sum_wt, stats_mean,
stats_mean_wt, stats_variance, stats_variance_wt
]
for num_idx, num_label in enumerate(['boo', 'baa']):
for s_key, stats in zip(stats_keys, stats_data):
for d_idx, d_key in enumerate(distances):
# check one-at-a-time computed vs multiply computed
assert np.allclose(
N_single.metrics['stats'][num_label][s_key][d_key],
N_multi.metrics['stats'][num_label][s_key][d_key],
atol=0.001,
rtol=0,
equal_nan=True)
# check one-at-a-time against manual
assert np.allclose(
N_single.metrics['stats'][num_label][s_key][d_key],
stats[num_idx][d_idx],
atol=0.001,
rtol=0,
equal_nan=True)
# check multiply computed against manual
assert np.allclose(
N_multi.metrics['stats'][num_label][s_key][d_key],
stats[num_idx][d_idx],
atol=0.001,
rtol=0,
equal_nan=True)
# check that problematic keys and data arrays are caught
for labels, arrs, err in (
(['a'], mock_numeric, ValueError), # mismatching lengths
(['a', 'b'], None, TypeError), # missing arrays
(['a', 'b'], [], ValueError), # missing arrays
(None, mock_numeric, TypeError), # missing labels
([], mock_numeric, ValueError)): # missing labels
with pytest.raises(err):
D_multi.compute_stats(stats_keys=labels, stats_data_arrs=arrs)
def test_compute_landuses(primal_graph):
betas = np.array([0.01, 0.005])
distances = networks.distance_from_beta(betas)
# network layer
N = networks.NetworkLayerFromNX(primal_graph, distances=distances)
node_map = N._node_data
edge_map = N._edge_data
node_edge_map = N._node_edge_map
# data layer
data_dict = mock.mock_data_dict(primal_graph)
qs = np.array([0, 1, 2])
D = layers.DataLayerFromDict(data_dict)
# check single metrics independently against underlying for some use-cases, e.g. hill, non-hill, accessibility...
D.assign_to_network(N, max_dist=500)
# generate some mock landuse data
landuse_labels = mock.mock_categorical_data(len(data_dict))
landuse_classes, landuse_encodings = layers.encode_categorical(
landuse_labels)
# compute hill mixed uses
D.compute_landuses(landuse_labels,
mixed_use_keys=['hill_branch_wt'],
qs=qs)
# test against underlying method
data_map = D._data
mu_data_hill, mu_data_other, ac_data, ac_data_wt = data.aggregate_landuses(
node_map,
edge_map,
node_edge_map,
data_map,
distances,
betas,
landuse_encodings,
qs=qs,
mixed_use_hill_keys=np.array([1]))
for q_idx, q_key in enumerate(qs):
for d_idx, d_key in enumerate(distances):
assert np.allclose(
N.metrics['mixed_uses']['hill_branch_wt'][q_key][d_key],
mu_data_hill[0][q_idx][d_idx],
atol=0.001,
rtol=0)
# gini simpson
D.compute_landuses(landuse_labels, mixed_use_keys=['gini_simpson'])
# test against underlying method
data_map = D._data
mu_data_hill, mu_data_other, ac_data, ac_data_wt = data.aggregate_landuses(
node_map,
edge_map,
node_edge_map,
data_map,
distances,
betas,
landuse_encodings,
mixed_use_other_keys=np.array([1]))
for d_idx, d_key in enumerate(distances):
assert np.allclose(N.metrics['mixed_uses']['gini_simpson'][d_key],
mu_data_other[0][d_idx],
atol=0.001,
rtol=0)
# accessibilities
D.compute_landuses(landuse_labels, accessibility_keys=['c'])
# test against underlying method
data_map = D._data
mu_data_hill, mu_data_other, ac_data, ac_data_wt = data.aggregate_landuses(
node_map,
edge_map,
node_edge_map,
data_map,
distances,
betas,
landuse_encodings,
accessibility_keys=np.array([landuse_classes.index('c')]))
for d_idx, d_key in enumerate(distances):
assert np.allclose(
N.metrics['accessibility']['non_weighted']['c'][d_key],
ac_data[0][d_idx],
atol=0.001,
rtol=0)
assert np.allclose(N.metrics['accessibility']['weighted']['c'][d_key],
ac_data_wt[0][d_idx],
atol=0.001,
rtol=0)
# also check the number of returned types for a few assortments of metrics
mixed_uses_hill_types = np.array([
'hill', 'hill_branch_wt', 'hill_pairwise_wt', 'hill_pairwise_disparity'
])
mixed_use_other_types = np.array(
['shannon', 'gini_simpson', 'raos_pairwise_disparity'])
ac_codes = np.array(landuse_classes)
# mixed uses hill
mu_hill_random = np.arange(len(mixed_uses_hill_types))
np.random.shuffle(mu_hill_random)
# mixed uses other
mu_other_random = np.arange(len(mixed_use_other_types))
np.random.shuffle(mu_other_random)
# accessibility
ac_random = np.arange(len(landuse_classes))
np.random.shuffle(ac_random)
# mock disparity matrix
mock_disparity_wt_matrix = np.full(
(len(landuse_classes), len(landuse_classes)), 1)
# not necessary to do all labels, first few should do
for mu_h_min in range(3):
mu_h_keys = np.array(mu_hill_random[mu_h_min:])
for mu_o_min in range(3):
mu_o_keys = np.array(mu_other_random[mu_o_min:])
for ac_min in range(3):
ac_keys = np.array(ac_random[ac_min:])
# in the final case, set accessibility to a single code otherwise an error would be raised
if len(mu_h_keys) == 0 and len(mu_o_keys) == 0 and len(
ac_keys) == 0:
ac_keys = np.array([0])
# randomise order of keys and metrics
mu_h_metrics = mixed_uses_hill_types[mu_h_keys]
mu_o_metrics = mixed_use_other_types[mu_o_keys]
ac_metrics = ac_codes[ac_keys]
# prepare network and compute
N_temp = networks.NetworkLayerFromNX(primal_graph,
distances=distances)
D_temp = layers.DataLayerFromDict(data_dict)
D_temp.assign_to_network(N_temp, max_dist=500)
D_temp.compute_landuses(
landuse_labels,
mixed_use_keys=list(mu_h_metrics) + list(mu_o_metrics),
accessibility_keys=ac_metrics,
cl_disparity_wt_matrix=mock_disparity_wt_matrix,
qs=qs)
# test against underlying method
mu_data_hill, mu_data_other, ac_data, ac_data_wt = \
data.aggregate_landuses(node_map,
edge_map,
node_edge_map,
data_map,
distances,
betas,
landuse_encodings,
qs=qs,
mixed_use_hill_keys=mu_h_keys,
mixed_use_other_keys=mu_o_keys,
accessibility_keys=ac_keys,
cl_disparity_wt_matrix=mock_disparity_wt_matrix)
for mu_h_idx, mu_h_met in enumerate(mu_h_metrics):
for q_idx, q_key in enumerate(qs):
for d_idx, d_key in enumerate(distances):
assert np.allclose(
N_temp.metrics['mixed_uses'][mu_h_met][q_key]
[d_key],
mu_data_hill[mu_h_idx][q_idx][d_idx],
atol=0.001,
rtol=0)
for mu_o_idx, mu_o_met in enumerate(mu_o_metrics):
for d_idx, d_key in enumerate(distances):
assert np.allclose(
N_temp.metrics['mixed_uses'][mu_o_met][d_key],
mu_data_other[mu_o_idx][d_idx],
atol=0.001,
rtol=0)
for ac_idx, ac_met in enumerate(ac_metrics):
for d_idx, d_key in enumerate(distances):
assert np.allclose(N_temp.metrics['accessibility']
['non_weighted'][ac_met][d_key],
ac_data[ac_idx][d_idx],
atol=0.001,
rtol=0)
assert np.allclose(N_temp.metrics['accessibility']
['weighted'][ac_met][d_key],
ac_data_wt[ac_idx][d_idx],
atol=0.001,
rtol=0)
# most integrity checks happen in underlying method, though check here for mismatching labels length and typos
with pytest.raises(ValueError):
D.compute_landuses(landuse_labels[-1], mixed_use_keys=['shannon'])
with pytest.raises(ValueError):
D.compute_landuses(landuse_labels, mixed_use_keys=['spelling_typo'])
# don't check accessibility_labels for typos - because only warning is triggered (not all labels will be in all data)
# check that unassigned data layer flags
with pytest.raises(ValueError):
D_new = layers.DataLayerFromDict(data_dict)
D_new.compute_landuses(landuse_labels, mixed_use_keys=['shannon'])