Note
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Create project from GMNS#
In this example, we import a simple network in GMNS format. The source files of this network are publicly available in the GMNS GitHub repository itself.
References
See also
Several functions, methods, classes and modules are used in this example:
# Imports
from uuid import uuid4
from os.path import join
from tempfile import gettempdir
from aequilibrae.project import Project
from aequilibrae.parameters import Parameters
import folium
# We load the example file from the GMNS GitHub repository
link_file = "https://raw.githubusercontent.com/zephyr-data-specs/GMNS/main/examples/Arlington_Signals/link.csv"
node_file = "https://raw.githubusercontent.com/zephyr-data-specs/GMNS/main/examples/Arlington_Signals/node.csv"
use_group_file = "https://raw.githubusercontent.com/zephyr-data-specs/GMNS/main/examples/Arlington_Signals/use_group.csv"
# We create the example project inside our temp folder
fldr = join(gettempdir(), uuid4().hex)
project = Project()
project.new(fldr)
No pre-existing parameter file exists for this project. Will use default
No pre-existing parameter file exists for this project. Will use default
In this cell, we modify the AequilibraE parameters.yml file so it contains additional fields to be read in the GMNS link and/or node tables. Remember to always keep the “required” key set to False, since we are adding a non-required field.
new_link_fields = {
"bridge": {"description": "bridge flag", "type": "text", "required": False},
"tunnel": {"description": "tunnel flag", "type": "text", "required": False},
}
new_node_fields = {
"port": {"description": "port flag", "type": "text", "required": False},
"hospital": {"description": "hospital flag", "type": "text", "required": False},
}
par = Parameters()
par.parameters["network"]["gmns"]["link"]["fields"].update(new_link_fields)
par.parameters["network"]["gmns"]["node"]["fields"].update(new_node_fields)
par.write_back()
As it is specified that the geometries are in the coordinate system EPSG:32619, which is different than the system supported by AequilibraE (EPSG:4326), we inform the srid in the method call:
project.network.create_from_gmns(
link_file_path=link_file, node_file_path=node_file, use_group_path=use_group_file, srid=32619
)
Fields not imported from node table: wkt_coord. If you want them to be imported, please modify the parameters.yml file.
Now, let’s plot a map. This map can be compared with the images of the README.md file located in this example repository on GitHub: zephyr-data-specs/GMNS
links = project.network.links.data
nodes = project.network.nodes.data
We create our Folium layers
network_links = folium.FeatureGroup("links")
network_nodes = folium.FeatureGroup("nodes")
layers = [network_links, network_nodes]
We do some Python magic to transform this dataset into the format required by Folium We are only getting link_id and link_type into the map, but we could get other pieces of info as well
for i, row in links.iterrows():
points = row.geometry.wkt.replace("LINESTRING ", "").replace("(", "").replace(")", "").split(", ")
points = "[[" + "],[".join([p.replace(" ", ", ") for p in points]) + "]]"
# we need to take from x/y to lat/long
points = [[x[1], x[0]] for x in eval(points)]
_ = folium.vector_layers.PolyLine(
points, popup=f"<b>link_id: {row.link_id}</b>", tooltip=f"{row.modes}", color="black", weight=2
).add_to(network_links)
And now we get the nodes
for i, row in nodes.iterrows():
point = (row.geometry.y, row.geometry.x)
_ = folium.vector_layers.CircleMarker(
point,
popup=f"<b>link_id: {row.node_id}</b>",
tooltip=f"{row.modes}",
color="red",
radius=5,
fill=True,
fillColor="red",
fillOpacity=1.0,
).add_to(network_nodes)
We get the center of the region
curr = project.conn.cursor()
curr.execute("select avg(xmin), avg(ymin) from idx_links_geometry")
long, lat = curr.fetchone()
# We create the map
map_gmns = folium.Map(location=[lat, long], zoom_start=17)
# Add all layers
for layer in layers:
layer.add_to(map_gmns)
# And Add layer control before we display it
folium.LayerControl().add_to(map_gmns)
map_gmns
project.close()
Total running time of the script: (0 minutes 1.668 seconds)