def test_non_integer_nodeids(request):
store = pd.HDFStore(
os.path.join(os.path.dirname(__file__), 'osm_sample.h5'), "r")
nodes, edges = store.nodes, store.edges
# convert to string!
nodes.index = nodes.index.astype("str")
edges["from"] = edges["from"].astype("str")
edges["to"] = edges["to"].astype("str")
net = pdna.Network(nodes.x, nodes.y, edges["from"], edges.to,
edges[["weight"]])
def fin():
store.close()
request.addfinalizer(fin)
# test accuracy compared to pandas functions
ssize = 50
r = random_data(ssize)
connected_nodes = get_connected_nodes(net)
random_nodes = random_connected_nodes(net, ssize)
net.set(random_nodes, variable=r)
s = net.aggregate(100000, type="count").loc[connected_nodes]
assert list(nodes.index), list(s.index)
def make_network(name, weight_col, max_distance):
st = pd.HDFStore(os.path.join(misc.data_dir(), name), "r")
nodes, edges = st.nodes, st.edges
net = pdna.Network(nodes["x"], nodes["y"], edges["from"], edges["to"],
edges[[weight_col]])
net.precompute(max_distance)
return net
def sample_osm(request):
store = pd.HDFStore(
os.path.join(os.path.dirname(__file__), 'osm_sample.h5'), "r")
nodes, edges = store.nodes, store.edges
net = pdna.Network(nodes.x, nodes.y, edges["from"], edges.to,
edges[["weight"]])
# try again to test for crash
with pytest.raises(AssertionError):
net = pdna.Network(nodes.x, nodes.y, edges["from"], edges.to,
edges[["weight"]])
net.precompute(2000)
def fin():
store.close()
request.addfinalizer(fin)
return net
def make_network(edge_name, node_name, weight_col, max_distance):
if edge_name == node_name:
st = pd.HDFStore(os.path.join(DATA_DIR, edge_name), "r")
nodes, edges = st.nodes, st.edges
else:
edges = pd.read_csv(os.path.join(DATA_DIR, edge_name))
nodes = pd.read_csv(os.path.join(DATA_DIR, node_name), index_col="ID")
net = pdna.Network(nodes["x"], nodes["y"], edges["from"], edges["to"],
edges[[weight_col]])
net.precompute(max_distance)
return net
def second_sample_osm(request):
store = pd.HDFStore(
os.path.join(os.path.dirname(__file__), 'osm_sample.h5'), "r")
nodes, edges = store.nodes, store.edges
net = pdna.Network(nodes.x, nodes.y, edges["from"], edges.to,
edges[["weight"]])
net.precompute(2000)
def fin():
store.close()
request.addfinalizer(fin)
return net
# allow test file to be passed as an argument
storef = sys.argv[1]
else:
# if no argument provided look for it in the test data
storef = os.path.normpath(os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'../pandana/tests/osm_sample.h5'))
if not os.path.isfile(storef):
raise IOError('Could not find test input file: {!r}'.format(storef))
print('Building network from file: {!r}'.format(storef))
store = pd.HDFStore(storef, "r")
nodes, edges = store.nodes, store.edges
net = pdna.Network(nodes.x, nodes.y, edges["from"], edges.to,
edges[["weight"]])
store.close()
print()
# Demonstrate shortest path code - the largest connected subgraph here has 477 nodes,
# per the unit tests
net.set(pd.Series(net.node_ids))
s = net.aggregate(10000, type='count')
connected_nodes = s[s==477]
n = 10000
nodes_a = np.random.choice(connected_nodes.index, n)
nodes_b = np.random.choice(connected_nodes.index, n)
print('Shortest path 1:')
