def read_matsim_network(path_to_network: str, epsg: str):
"""
Reads MATSim's network.xml to genet.Network object
:param path_to_network: path to MATSim's network.xml file
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
n = core.Network(epsg=epsg)
n.graph, n.link_id_mapping, duplicated_nodes, duplicated_links = \
matsim_reader.read_network(path_to_network, n.transformer)
n.graph.graph['name'] = 'Network graph'
n.graph.graph['crs'] = n.epsg
if 'simplified' not in n.graph.graph:
n.graph.graph['simplified'] = False
for node_id, duplicated_node_attribs in duplicated_nodes.items():
for duplicated_node_attrib in duplicated_node_attribs:
n.change_log.remove(
object_type='node',
object_id=node_id,
object_attributes=duplicated_node_attrib
)
for link_id, reindexed_duplicated_links in duplicated_links.items():
for duplicated_link in reindexed_duplicated_links:
n.change_log.modify(
object_type='link',
old_id=link_id,
old_attributes=n.link(duplicated_link),
new_id=duplicated_link,
new_attributes=n.link(duplicated_link)
)
return n
def read_json_network(network_path: str, epsg: str):
"""
Reads Network graph from JSON file.
:param network_path: path to json or geojson network file
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading Network from {network_path}')
with open(network_path) as json_file:
json_data = json.load(json_file)
for node, data in json_data['nodes'].items():
try:
del data['geometry']
except KeyError:
pass
for link, data in json_data['links'].items():
try:
data['geometry'] = spatial.decode_polyline_to_shapely_linestring(data['geometry'])
except KeyError:
pass
try:
data['modes'] = set(data['modes'].split(','))
except KeyError:
pass
n = core.Network(epsg=epsg)
n.add_nodes(json_data['nodes'])
n.add_links(json_data['links'])
n.change_log = change_log.ChangeLog()
return n
def read_geojson_network(nodes_path: str, links_path: str, epsg: str):
"""
Reads Network graph from JSON file.
:param nodes_path: path to geojson network nodes file
:param links_path: path to geojson network links file
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading Network nodes from {nodes_path}')
nodes = gpd.read_file(nodes_path)
nodes = nodes.drop('geometry', axis=1)
nodes['id'] = nodes['id'].astype(int).astype(str)
nodes = nodes.set_index('id', drop=False)
if 'index' in nodes.columns:
nodes = nodes.drop('index', axis=1)
logging.info(f'Reading Network links from {links_path}')
links = gpd.read_file(links_path).to_crs(epsg)
links['modes'] = links['modes'].apply(lambda x: set(x.split(',')))
links['id'] = links['id'].astype(int).astype(str)
links = links.set_index('id', drop=False)
if 'index' in links.columns:
links = links.drop('index', axis=1)
n = core.Network(epsg=epsg)
n.add_nodes(nodes.T.to_dict())
n.add_links(links.T.to_dict())
n.change_log = change_log.ChangeLog()
return n
def read_osm(osm_file_path, osm_read_config, num_processes: int = 1, epsg=None):
"""
Reads OSM data into a graph of the Network object
:param osm_file_path: path to .osm or .osm.pbf file
:param osm_read_config: config file (see configs folder in genet for examples) which informs for example which
highway types to read (in case of road network) and what modes to assign to them
:param num_processes: number of processes to split parallelisable operations across
:param epsg: projection for the output Network, e.g. 'epsg:27700'. If not provided, defaults to epsg:4326
:return: genet.Network object
"""
if epsg is None:
epsg = 'epsg:4326'
config = osm_reader.Config(osm_read_config)
n = core.Network(epsg)
nodes, edges = osm_reader.generate_osm_graph_edges_from_file(
osm_file_path, config, num_processes)
nodes_and_attributes = parallel.multiprocess_wrap(
data=nodes,
split=parallel.split_dict,
apply=osm_reader.generate_graph_nodes,
combine=parallel.combine_dict,
epsg=epsg,
processes=num_processes
)
reindexing_dict, nodes_and_attributes = n.add_nodes(nodes_and_attributes, ignore_change_log=True)
edges_attributes = parallel.multiprocess_wrap(
data=edges,
split=parallel.split_list,
apply=osm_reader.generate_graph_edges,
combine=parallel.combine_list,
reindexing_dict=reindexing_dict,
nodes_and_attributes=nodes_and_attributes,
config_path=osm_read_config,
processes=num_processes
)
n.add_edges(edges_attributes, ignore_change_log=True)
logging.info('Deleting isolated nodes which have no edges.')
n.remove_nodes(list(nx.isolates(n.graph)))
return n
def read_csv(path_to_network_nodes: str, path_to_network_links: str, epsg: str):
"""
Reads CSV data into a genet.Network object
:param path_to_network_nodes: CSV file describing nodes. Should at least include columns:
- id: unique ID for the node
- x: spatial coordinate in given epsg
- y: spatial coordinate in given epsg
:param path_to_network_links: CSV file describing links.
Should at least include columns:
- from - source Node ID
- to - target Node ID
Optional columns, but strongly encouraged
- id - unique ID for link
- length - link length in metres
- freespeed - meter/seconds speed
- capacity - vehicles/hour
- permlanes - number of lanes
- modes - set of modes
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading nodes from {path_to_network_nodes}')
df_nodes = pd.read_csv(path_to_network_nodes)
if {'index', 'id'}.issubset(set(df_nodes.columns)):
df_nodes = df_nodes.drop('index', axis=1)
elif 'id' not in df_nodes.columns:
raise NetworkSchemaError('Expected `id` column in the nodes.csv is missing. This need to be the IDs to which '
'links.csv refers to in `from` and `to` columns.')
df_nodes['id'] = df_nodes['id'].astype(int).astype(str)
df_nodes = df_nodes.set_index('id', drop=False)
try:
df_nodes = df_nodes.drop('geometry', axis=1)
except KeyError:
pass
logging.info(f'Reading links from {path_to_network_nodes}')
df_links = pd.read_csv(path_to_network_links)
if {'index', 'id'}.issubset(set(df_links.columns)):
df_links = df_links.drop('index', axis=1)
elif 'id' not in df_links.columns:
if 'index' in df_links.columns:
if not df_links['index'].duplicated().any():
df_links['id'] = df_links['index']
else:
df_links = df_links.drop('index', axis=1)
else:
df_links['id'] = range(len(df_links))
df_links['id'] = df_links['id'].astype(int).astype(str)
df_links['from'] = df_links['from'].astype(int).astype(str)
df_links['to'] = df_links['to'].astype(int).astype(str)
df_links = df_links.set_index('id', drop=False)
# recover encoded geometry
try:
df_links['geometry'] = df_links['geometry'].apply(lambda x: spatial.decode_polyline_to_shapely_linestring(x))
except KeyError:
pass
df_links['attributes'] = _literal_eval_col(df_links['attributes'])
df_links['modes'] = _literal_eval_col(df_links['modes'])
n = core.Network(epsg=epsg)
n.add_nodes(df_nodes.T.to_dict())
n.add_links(df_links.T.to_dict())
n.change_log = change_log.ChangeLog()
return n