# 8. Use examples¶

This page is still under development, so if you have developed interesting use cases, please consider contributing them.

Note

The examples provided here are not meant as a through description of AequilibraE’s capabilities. For that, please look into the API documentation or email aequilibrae@googlegroups.com

## 8.1. Sample Data¶

We have compiled two very distinct example datasets imported from the TNTP instances.

While the Sioux Falls network is probably the most traditional example network available for evaluating network algorithms, the Chicago Regional model is a good example of a real-world sized model, with roughly 1,800 zones.

Each instance contains the following folder structure and contents:

0_tntp_data:

1_project

• AequilibraE project result of the import of the links and nodes layers

2_skim_results:

• Skim results for distance and free_flow_travel_time computed by minimizing free_flow_travel_time

• Result matrices in openmatrix and AequilibraE formats

3_desire_lines

• Layers for desire lines and delaunay lines, each one in a separate geopackage file

• Desire lines flow map

• Delaunay Lines flow map

4_assignment_results

• Outputs from traffic assignment to a relative gap of 1e-5 and with skimming enabled

• Link flows in csv and AequilibraE formats

• Skim matrices in openmatrix and AequilibraE formats

• Assignment flow map in png format

5_distribution_results

• Models calibrated for inverse power and negative exponential deterrence functions

• Convergence logs for the calibration of each model

• Trip length frequency distribution chart for original matrix

• Trip length frequency distribution chart for model with negative exponential deterrence function

• Trip length frequency distribution chart for model with inverse power deterrence function

• Inputs are the original demand matrix and the skim for TIME (final iteration) from the ASSIGNMENT

6_forecast

• Synthetic future vectors generated with a random growth from 0 to 10% in each cell on top of the original matrix vectors

• Application of both gravity models calibrated plus IPF to the synthetic future vectors

7_future_year_assignment

• Traffic assignment

• Outputs from traffic assignment to a relative gap of 1e-5 and with skimming enabled

• Link flows in csv and AequilibraE formats

• Skim matrices in openmatrix and AequilibraE formats

• Scenario comparison flow map of absolute differences

• Composite scenario comparison flow map (gray is flow maintained in both scenarios, red is flow growth and green is flow decline)

## 8.2. Comprehensive example¶

The process of generating the data provided in the sample data above from the data downloaded from the TNTP instances was similar than a natural workflow one would find in a traditional model, and it was developed as a Jupyter notebook, which is available on Github

Below we have that same workflow as a single script

import sys
from os.path import join
import numpy as np
from math import log10, floor
import matplotlib.pyplot as plt
from aequilibrae.distribution import GravityCalibration, Ipf, GravityApplication, SyntheticGravityModel
from aequilibrae import Parameters
from aequilibrae.project import Project
from aequilibrae.paths import PathResults, SkimResults
from aequilibrae.matrix import AequilibraeData, AequilibraeMatrix
from aequilibrae import logger
from aequilibrae.paths import TrafficAssignment, TrafficClass

import logging

######### FILES AND FOLDER #########

fldr = 'D:/release/Sample models/sioux_falls_2020_02_15'
proj_name = 'SiouxFalls.sqlite'

# remove the comments for the lines below to run the Chicago model example instead
# fldr = 'D:/release/Sample models/Chicago_2020_02_15'
# proj_name = 'chicagomodel.sqlite'

dt_fldr = '0_tntp_data'
prj_fldr = '1_project'
skm_fldr = '2_skim_results'
assg_fldr = '4_assignment_results'
dstr_fldr = '5_distribution_results'
frcst_fldr = '6_forecast'
ftr_fldr = '7_future_year_assignment'

########### LOGGING #################

p = Parameters()
p.parameters['system']['logging_directory'] = fldr
p.write_back()
# To make sure the logging will go where it should, stop the script here and
# re-run it

# Because assignment takes a long time, we want the log to be shown here
stdout_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(asctime)s;%(name)s;%(levelname)s ; %(message)s")
stdout_handler.setFormatter(formatter)

########### PROJECT #################

project = Project()

########### PATH COMPUTATION #################

# we build all graphs
project.network.build_graphs()
# We get warnings that several fields in the project are filled with NaNs.  Which is true, but we won't
# use those fields

# we grab the graph for cars
graph = project.network.graphs['c']

# let's say we want to minimize distance
graph.set_graph('distance')

# And will skim time and distance while we are at it
graph.set_skimming(['free_flow_time', 'distance'])

# And we will allow paths to be compute going through other centroids/centroid connectors
# required for the Sioux Falls network, as all nodes are centroids
graph.set_blocked_centroid_flows(False)

# instantiate a path results object and prepare it to work with the graph
res = PathResults()
res.prepare(graph)

# compute a path from node 2 to 13
res.compute_path(2, 13)

# We can get the sequence of nodes we traverse
res.path_nodes

# We can get the link sequence we traverse
res.path

# We can get the mileposts for our sequence of nodes
res.milepost

# And We can the skims for our tree
res.skims

# If we want to compute the path for a different destination and same origin, we can just do this
# It is way faster when you have large networks
res.update_trace(4)

########## SKIMMING ###################

# setup the object result
res = SkimResults()
res.prepare(graph)

# And run the skimming
res.compute_skims()

# The result is an AequilibraEMatrix object
skims = res.skims

# We can export to AEM and OMX
skims.export(join(fldr, skm_fldr, 'skimming_on_time.aem'))
skims.export(join(fldr, skm_fldr, 'skimming_on_time.omx'))

######### TRAFFIC ASSIGNMENT WITH SKIMMING

demand = AequilibraeMatrix()
demand.computational_view(['matrix'])  # We will only assign one user class stored as 'matrix' inside the OMX file

assig = TrafficAssignment()

# Creates the assignment class
assigclass = TrafficClass(graph, demand)

# The first thing to do is to add at list of traffic classes to be assigned
assig.set_classes([assigclass])

assig.set_vdf("BPR")  # This is not case-sensitive # Then we set the volume delay function

assig.set_vdf_parameters({"alpha": "b", "beta": "power"})  # And its parameters

assig.set_capacity_field("capacity")  # The capacity and free flow travel times as they exist in the graph
assig.set_time_field("free_flow_time")

# And the algorithm we want to use to assign
assig.set_algorithm('bfw')

# since I haven't checked the parameters file, let's make sure convergence criteria is good
assig.max_iter = 1000
assig.rgap_target = 0.00001

assig.execute()  # we then execute the assignment

# Convergence report is easy to see
import pandas as pd
convergence_report = pd.DataFrame(assig.assignment.convergence_report)

# The link flows are easy to export.
# we do so for csv and AequilibraEData

# the skims are easy to get.

# The blended one are here
avg_skims = assigclass.results.skims

# The ones for the last iteration are here
last_skims = assigclass._aon_results.skims

# Assembling a single final skim file can be done like this
# We will want only the time for the last iteration and the distance averaged out for all iterations
kwargs = {'file_name': join(fldr, assg_fldr, 'skims.aem'),
'zones': graph.num_zones,
'matrix_names': ['time_final', 'distance_blended']}

# Create the matrix file
out_skims = AequilibraeMatrix()
out_skims.create_empty(**kwargs)
out_skims.index[:] = avg_skims.index[:]

# Transfer the data
# The names of the skims are the name of the fields
out_skims.matrix['time_final'][:, :] = last_skims.matrix['free_flow_time'][:, :]
# It is CRITICAL to assign the matrix values using the [:,:]
out_skims.matrix['distance_blended'][:, :] = avg_skims.matrix['distance'][:, :]

out_skims.matrices.flush()  # Make sure that all data went to the disk

# Export to OMX as well
out_skims.export(join(fldr, assg_fldr, 'skims.omx'))

#############    TRIP DISTRIBUTION #################

# The demand is already in memory

# Need the skims
imped = AequilibraeMatrix()

# But before using the data, let's get some impedance for the intrazonals
# Let's assume it is 75% of the closest zone

# If we run the code below more than once, we will be overwriting the diagonal values with non-sensical data
# so let's zero it first
np.fill_diagonal(imped.matrix['time_final'], 0)

# We compute it with a little bit of NumPy magic
intrazonals = np.amin(imped.matrix['time_final'], where=imped.matrix['time_final'] > 0,
initial=imped.matrix['time_final'].max(), axis=1)
intrazonals *= 0.75

# Then we fill in the impedance matrix
np.fill_diagonal(imped.matrix['time_final'], intrazonals)

# We set the matrices for use in computation
imped.computational_view(['time_final'])
demand.computational_view(['matrix'])

# Little function to plot TLFDs
def plot_tlfd(demand, skim, name):
# No science here. Just found it works well for Sioux Falls & Chicago
b = floor(log10(skim.shape[0]) * 10)
n, bins, patches = plt.hist(np.nan_to_num(skim.flatten(), 0), bins=b,
weights=np.nan_to_num(demand.flatten()),
density=False, facecolor='g', alpha=0.75)

plt.xlabel('Trip length')
plt.ylabel('Probability')
plt.title('Trip-length frequency distribution')
plt.savefig(name, format="png")
plt.clf()

# Calibrate models with the two functional forms
for function in ['power', 'expo']:
model = GravityCalibration(matrix=demand, impedance=imped, function=function, nan_as_zero=True)
model.calibrate()

# we save the model
model.model.save(join(fldr, dstr_fldr, f'{function}_model.mod'))

# We save a trip length frequency distribution image
plot_tlfd(model.result_matrix.matrix_view, imped.matrix_view,
join(fldr, dstr_fldr, f'{function}_tfld.png'))

# We can save the result of applying the model as well
# we can also save the calibration report
with open(join(fldr, dstr_fldr, f'{function}_convergence.log'), 'w') as otp:
for r in model.report:
otp.write(r + '\n')

# We save a trip length frequency distribution image
plot_tlfd(demand.matrix_view, imped.matrix_view, join(fldr, dstr_fldr, 'demand_tfld.png'))

################  FORECAST #############################

# We compute the vectors from our matrix
mat = AequilibraeMatrix()

mat.computational_view()
origins = np.sum(mat.matrix_view, axis=1)
destinations = np.sum(mat.matrix_view, axis=0)

args = {'file_path':join(fldr,  frcst_fldr, 'synthetic_future_vector.aed'),
"entries": mat.zones,
"field_names": ["origins", "destinations"],
"data_types": [np.float64, np.float64],
"memory_mode": False}

vectors = AequilibraeData()
vectors.create_empty(**args)

vectors.index[:] =mat.index[:]

# Then grow them with some random growth between 0 and 10% - Plus balance them
vectors.origins[:] = origins * (1+ np.random.rand(vectors.entries)/10)
vectors.destinations[:] = destinations * (1+ np.random.rand(vectors.entries)/10)
vectors.destinations *= vectors.origins.sum()/vectors.destinations.sum()

# Impedance matrix is already in memory

# We want the main diagonal to be zero, as the original matrix does
# not have intrazonal trips
np.fill_diagonal(imped.matrix_view, np.nan)

# Apply the gravity models
for function in ['power', 'expo']:
model = SyntheticGravityModel()

outmatrix = join(fldr,frcst_fldr, f'demand_{function}_model.aem')
apply = GravityApplication()
args = {"impedance": imped,
"rows": vectors,
"row_field": "origins",
"model": model,
"columns": vectors,
"column_field": "destinations",
"output": outmatrix,
"nan_as_zero":True
}

gravity = GravityApplication(**args)
gravity.apply()

#We get the output matrix and save it to OMX too
resm = AequilibraeMatrix()
resm.export(join(fldr,frcst_fldr, f'demand_{function}_model.omx'))

# APPLY IPF
demand = AequilibraeMatrix()
demand.computational_view()

args = {'matrix': demand,
'rows': vectors,
'columns': vectors,
'column_field': "destinations",
'row_field': "origins",
'nan_as_zero': True}

ipf = Ipf(**args)
ipf.fit()

output = AequilibraeMatrix()

output.export(join(fldr,frcst_fldr, 'demand_ipf.aem'))
output.export(join(fldr,frcst_fldr, 'demand_ipf.omx'))

logger.info('\n\n\n TRAFFIC ASSIGNMENT FOR FUTURE YEAR')

# Let's use the IPF matrix
demand = AequilibraeMatrix()
demand.computational_view() # There is only one matrix there, so don;t even worry about its core name

assig = TrafficAssignment()

# Creates the assignment class
assigclass = TrafficClass(graph, demand)

# The first thing to do is to add at list of traffic classes to be assigned
assig.set_classes([assigclass])

assig.set_vdf("BPR")  # This is not case-sensitive # Then we set the volume delay function

assig.set_vdf_parameters({"alpha": "b", "beta": "power"}) # And its parameters

assig.set_capacity_field("capacity") # The capacity and free flow travel times as they exist in the graph
assig.set_time_field("free_flow_time")

# And the algorithm we want to use to assign
assig.set_algorithm('bfw')

# since I haven't checked the parameters file, let's make sure convergence criteria is good
assig.max_iter = 1000
assig.rgap_target = 0.00001

assig.execute() # we then execute the assignment


## 8.3. Logging¶

AequilibraE uses Python’s standard logging library to a file called aequilibrae.log, but the output folder for this logging can be changed to a custom system folder by altering the parameter system –> logging_directory on the parameters file.

As an example, one could do programatically change the output folder to ‘D:/myProject/logs’ by doing the following:

from aequilibrae import Parameters

fldr = 'D:/myProject/logs'

p = Parameters()
p.parameters['system']['logging_directory'] =  fldr
p.write_back()


The other useful resource, especially during model debugging it to also show all log messages directly on the screen. Doing that requires a little knowledge of the Python Logging library, but it is just as easy:

from aequilibrae import logger
import logging

stdout_handler = logging.StreamHandler(sys.stdout)


## 8.4. Parameters module¶

Several examples on how to manipulate the parameters within AequilibraE have been shown in other parts of this tutorial.

However, in case you ever have trouble with parameter changes you have made, you can always revert them to their default values. But remember, ALL CHANGES WILL BE LOST.

from aequilibrae import Parameters

fldr = 'D:/myProject/logs'

p = Parameters()
p.reset_default()


## 8.5. Matrix module¶

Let’s see two cases where we work with the matrix module

### 8.5.1. Extracting vectors¶

Let’s extract the vectors for total origins and destinations for the Chicago model demand matrix:

from aequilibrae.matrix import AequilibraeData, AequilibraeMatrix
import numpy as np

mat = AequilibraeMatrix()
m = mat.get_matrix("matrix")

vectors = "D:/release/Sample models/Chicago_2020_02_15/vectors.aed"
args = {
"file_path": vectors,
"entries": vec_1.shape[0],
"field_names": ["origins", "destinations"],
"data_types": [np.float64, np.float64],
}
dataset = AequilibraeData()
dataset.create_empty(**args)

# Transfer the data
dataset.index[:] =mat.index[:]
dataset.origins[:] = np.sum(m, axis=1)[:]
dataset.destinations[:] = np.sum(m, axis=0)[:]


### 8.5.2. Comprehensive example¶

Lets say we want to Import the freight matrices provided with FAF into AequilibraE’s matrix format in order to create some Delaunay Lines in QGIS or to perform traffic assignment

#### 8.5.2.3. The code¶

We import all libraries we will need, including the AequilibraE

import pandas as pd
import numpy as np
import os
from aequilibrae.matrix import AequilibraeMatrix
from scipy.sparse import coo_matrix


Now we set all the paths for files and parameters we need and import the matrices into a Pandas DataFrame

data_folder = 'Y:/ALL DATA/DATA/Pedro/Professional/Data/USA/FAF/4.4'
data_file = 'FAF4.4_HiLoForecasts.csv'
sctg_names_file = 'sctg_codes.csv'  # Simplified to 50 characters, which is AequilibraE's limit
output_folder = data_folder



We import the sctg codes

sctg_names = pd.read_csv(os.path.join(data_folder, sctg_names_file), low_memory=False)
sctg_names.set_index('Code', inplace=True)
sctg_descr = list(sctg_names['Commodity Description'])


We now process the matrices to collect all the data we need, such as:

• List of zones

• CSTG codes

• Matrices/scenarios we are importing

all_zones = np.array(sorted(list(set( list(matrices.dms_orig.unique()) + list(matrices.dms_dest.unique())))))

# Count them and create a 0-based index
num_zones = all_zones.shape[0]
idx = np.arange(num_zones)

# Creates the indexing dataframes
origs = pd.DataFrame({"from_index": all_zones, "from":idx})
dests = pd.DataFrame({"to_index": all_zones, "to":idx})

# adds the new index columns to the pandas dataframe
matrices = matrices.merge(origs, left_on='dms_orig', right_on='from_index', how='left')
matrices = matrices.merge(dests, left_on='dms_dest', right_on='to_index', how='left')

# Lists sctg codes and all the years/scenarios we have matrices for
mat_years = [x for x in matrices.columns if 'tons' in x]
sctg_codes = matrices.sctg2.unique()


We now import one matrix for each year, saving all the SCTG codes as different matrix cores in our zoning system

# aggregate the matrix according to the relevant criteria
agg_matrix = matrices.groupby(['from', 'to', 'sctg2'])[mat_years].sum()

# returns the indices
agg_matrix.reset_index(inplace=True)

for y in mat_years:
mat = AequilibraeMatrix()

# Here it does not make sense to use OMX
# If one wants to create an OMX from other data sources, openmatrix is
# the library to use
kwargs = {'file_name': os.path.join(output_folder, y + '.aem'),
'zones': num_zones,
'matrix_names': sctg_descr}

mat.create_empty(**kwargs)
mat.index[:] = all_zones[:]
# for all sctg codes
for i in sctg_names.index:
prod_name = sctg_names['Commodity Description'][i]
mat_filtered_sctg = agg_matrix[agg_matrix.sctg2 == i]

m = coo_matrix((mat_filtered_sctg[y], (mat_filtered_sctg['from'], mat_filtered_sctg['to'])),
shape=(num_zones, num_zones)).toarray().astype(np.float64)

mat.matrix[prod_name][:,:] = m[:,:]

mat.close()


## 8.6. Project module¶

Let’s suppose one wants to create project files for a list of 5 cities around the world with their complete networks downloaded from Open Street Maps and place them on a local folder for analysis at a later time.

from aequilibrae.project import Project

cities = ["Darwin, Australia",
"Karlsruhe, Germany",
"London, UK",
"Paris, France",
"Auckland, New Zealand"]

for city in cities:
print(city)
pth = f'd:/net_tests/{city}.sqlite'

p = Project()
p.new(pth)
p.network.create_from_osm(place_name=city)
p.conn.close()
del p


If one wants to load a project and check some of its properties, it is easy:

>>> from aequilibrae.project import Project

>>> p = Project()

# for the modes available in the model
>>> p.network.modes()
['car', 'walk', 'bicycle']

157926

>>> p.network.count_nodes()
793200


## 8.7. Paths module¶

from aequilibrae.paths import allOrNothing
from aequilibrae.paths import path_computation
from aequilibrae.paths.results import AssignmentResults as asgr
from aequilibrae.paths.results import PathResults as pthr


### 8.7.2. Skimming¶

Let’s suppose you want to compute travel times between all zone on your network. In that case, you need only a graph that you have previously built, and the list of skims you want to compute.

from aequilibrae.paths.results import SkimResults as skmr
from aequilibrae.paths import Graph
from aequilibrae.paths import NetworkSkimming

# We instantiate the graph and load it from disk (say you created it using the QGIS GUI
g = Graph()

# You now have to set the graph for what you want
# In this case, we are computing fastest path (minimizing free flow time)
g.set_graph(cost_field='fftime')

# We are also **blocking** paths from going through centroids
g.set_blocked_centroid_flows(block_centroid_flows=True)

# We will be skimming for fftime **AND** distance along the way
g.set_skimming(['fftime', 'distance'])

# We instantiate the skim results and prepare it to have results compatible with the graph provided
result = skmr()
result.prepare(g)

# We create the network skimming object and execute it
# This is multi-threaded, so if the network is too big, prepare for a slow computer
skm = NetworkSkimming(g, result)
skm.execute()


If you want to use fewer cores for this computation (which also saves memory), you also can do it You just need to use the method set_cores before you run the skimming. Ideally it is done before preparing it

result = skmr()
result.set_cores(3)
result.prepare(g)


And if you want to compute skims between all nodes in the network, all you need to do is to make sure the list of centroids in your graph is updated to include all nodes in the graph

from aequilibrae.paths.results import SkimResults as skmr
from aequilibrae.paths import Graph
from aequilibrae.paths import NetworkSkimming

g = Graph()

# Let's keep the original list of centroids in case we want to use it again
orig_centr = g.centroids

# Now we set the list of centroids to include all nodes in the network
g.prepare_graph(g.all_nodes)

# And continue **almost** like we did before
# We just need to remember to NOT block paths through centroids. Otherwise there will be no paths available
g.set_graph(cost_field='fftime', block_centroid_flows=False)
g.set_skimming('fftime')

result = skmr()
result.prepare(g)

skm = NetworkSkimming(g, result)
skm.execute()


Setting skimming after setting the graph is CRITICAL, and the skim matrices are part of the result object.

You can save the results to your place of choice in AequilibraE format or export to OMX or CSV

result.skims.export('path/to/desired/folder/file_name.omx')

result.skims.export('path/to/desired/folder/file_name.csv')

result.skims.copy('path/to/desired/folder/file_name.aem')


### 8.7.3. Traffic assignment¶

A comprehensive example of assignment

from aequilibrae.project import Project
from aequilibrae.paths import TrafficAssignment, TrafficClass
from aequilibrae.matrix import AequilibraeMatrix

assig = TrafficAssignment()

proj = Project()
proj.network.build_graphs()
# Mode c is car
car_graph = proj.network.graphs['c']

# If, for any reason, you would like to remove a set of links from the
# graph based solely on the modes assigned to links in the project file
# This will alter the Graph ID, but everything else (cost field, set of
# centroids and configuration for blocking flows through centroid connectors
#  remains unaltered

mat = AequilibraeMatrix()
# We will only assign one user class stored as 'matrix' inside the OMX file
mat.computational_view(['matrix'])

# Creates the assignment class
assigclass = TrafficClass(g, mat)

# If you want to know which assignment algorithms are available:
assig.algorithms_available()

# If you want to know which Volume-Delay functions are available
assig.vdf.functions_available()

# The first thing to do is to add at list of traffic classes to be assigned
assig.set_classes([assigclass])

# Then we set the volume delay function
assig.set_vdf("BPR")  # This is not case-sensitive

# And its parameters
assig.set_vdf_parameters({"alpha": "alpha", "beta": "beta"})

# If you don't have parameters in the network, but rather global ones
# assig.set_vdf_parameters({"alpha": 0.15, "beta": 4})

# The capacity and free flow travel times as they exist in the graph
assig.set_capacity_field("capacity")
assig.set_time_field("free_flow_time")

# And the algorithm we want to use to assign
assig.set_algorithm('bfw')

# To overwrite the number of iterations and the relative gap intended
assig.max_iter = 250
assig.rgap_target = 0.0001

# To overwrite the number of CPU cores to be used
assig.set_cores(3)

# we then execute the assignment
assig.execute()


### 8.7.4. Assigning traffic on TNTP instances¶

There is a set of well known traffic assignment problems used in the literature maintained on GitHub that is often used for tests, so we will use one of those problems here.

Let’s suppose we want to perform traffic assignment for one of those problems and check the results against the reference results.

The parsing and importing of those networks are not really the case here, but there is online code available for doing that work.

import os
import sys
import numpy as np
import pandas as pd
from aequilibrae.paths import TrafficAssignment
from aequilibrae.paths import Graph
from aequilibrae.paths.traffic_class import TrafficClass
from aequilibrae.matrix import AequilibraeMatrix, AequilibraeData
import matplotlib.pyplot as plt

from aequilibrae import logger
import logging

# We redirect the logging output to the terminal
stdout_handler = logging.StreamHandler(sys.stdout)

# Let's work with Sioux Falls
os.chdir('D:/src/TransportationNetworks/SiouxFalls')
result_file = 'SiouxFalls_flow.tntp'

# Loads and prepares the graph
g = Graph()
g.set_graph('time')
g.cost = np.array(g.cost, copy=True)
g.set_skimming(['time'])
g.set_blocked_centroid_flows(True)

# Loads and prepares the matrix
mat = AequilibraeMatrix()
mat.computational_view(['matrix'])

# Creates the assignment class
assigclass = TrafficClass(g, mat)

# Instantiates the traffic assignment problem
assig = TrafficAssignment()

# configures it properly
assig.set_vdf('BPR')
assig.set_vdf_parameters(**{'alpha': 0.15, 'beta': 4.0})
assig.set_capacity_field('capacity')
assig.set_time_field('time')
assig.set_classes(assigclass)
# could be assig.set_algorithm('frank-wolfe')
assig.set_algorithm('msa')

# Execute the assignment
assig.execute()

# the results are within each traffic class only one, in this case


### 8.7.5. Setting multiple user classes before assignment¶

Let’s suppose one wants to setup a matrix for assignment that has two user classes, red_cars and blue cars for a single traffic class. To do that, one needs only to call the computational_view method with a list of the two matrices of interest. Both matrices need to be contained in the same file (and to be contiguous if an *.aem instead of a *.omx file) however.

mat = AequilibraeMatrix()
mat.computational_view(['red_cars', 'blue_cars'])


### 8.7.6. Advanced usage: Building a Graph¶

Let’s suppose now that you are interested in creating links from a bespoke procedure. For the purpose of this example, let’s say you have a sparse matrix representing a graph as an adjacency matrix

from aequilibrae.paths import Graph
from aequilibrae.project.network import Network
from scipy.sparse import coo_matrix

# original_adjacency_matrix is a sparse matrix where positive values are actual links
# where the value of the cell is the distance in that link

# We create the sparse matrix in proper sparse matrix format

# We create the structure to create the network
all_types = [k._Graph__integer_type,
k._Graph__integer_type,
k._Graph__integer_type,
np.int8,
k._Graph__float_type,
k._Graph__float_type]

# List of all required link fields for a network

# List of all required node fields for a network
# Network.req_node_flds

# List of fields that are reserved for internal workings
# Network.protected_fields

dt = [(t, d) for t, d in zip(all_titles, all_types)]

my_graph = Graph()

my_graph.network['a_node'] = sparse_graph.row
my_graph.network['b_node'] = sparse_graph.col
my_graph.network["distance"] = sparse_graph.data

# If the links are directed (from A to B), direction is 1. If bi-directional, use zeros

# Let's say that all nodes in the network are centroids
list_of_centroids =  np.arange(max(sparse_graph.shape[0], sparse_graph.shape[0])+ 1)
centroids_list = np.array(list_of_centroids)

my_graph.status = 'OK'
my_graph.network_ok = True
my_graph.prepare_graph(centroids_list)


This usage is really advanced, and very rarely not-necessary. Make sure to know what you are doing before going down this route

## 8.8. Trip distribution¶

The support for trip distribution in AequilibraE is not very comprehensive, mostly because of the loss of relevance that such type of model has suffered in the last decade.

However, it is possible to calibrate and apply synthetic gravity models and to perform Iterative Proportional Fitting (IPF) with really high performance, which might be of use in many applications other than traditional distribution.

### 8.8.1. Synthetic gravity application¶

In this example, imagine that you have your demographic information in an sqlite database and that you have already computed your skim matrix.

It is also important to notice that it is crucial to have consistent data, such as same set of zones (indices) in both the demographics and the impedance matrix.

import pandas as pd
import sqlite3

from aequilibrae.matrix import AequilibraeMatrix
from aequilibrae.matrix import AequilibraeData

from aequilibrae.distribution import SyntheticGravityModel
from aequilibrae.distribution import GravityApplication

# We define the model we will use
model = SyntheticGravityModel()

# Before adding a parameter to the model, you need to define the model functional form
model.function = "GAMMA" # "EXPO" or "POWER"

# Only the parameter(s) applicable to the chosen functional form will have any effect
model.alpha = 0.1
model.beta = 0.0001

# Or you can load the model from a file

# We load the impedance matrix
matrix = AequilibraeMatrix()
matrix.computational_view(['distance'])

# We create the vectors we will use
conn = sqlite3.connect('path/to/demographics/database')
query = "SELECT zone_id, population, employment FROM demographics;"

index = df.zone_id.values[:]
zones = index.shape[0]

# You create the vectors you would have
df = df.assign(production=df.population * 3.0)
df = df.assign(attraction=df.employment * 4.0)

# We create the vector database
args = {"entries": zones, "field_names": ["productions", "attractions"],
"data_types": [np.float64, np.float64], "memory_mode": True}
vectors = AequilibraeData()
vectors.create_empty(**args)

# Assign the data to the vector object
vectors.productions[:] = df.production.values[:]
vectors.attractions[:] = df.attraction.values[:]
vectors.index[:] = zones[:]

# Balance the vectors
vectors.attractions[:] *= vectors.productions.sum() / vectors.attractions.sum()

args = {"impedance": matrix,
"rows": vectors,
"row_field": "productions",
"model": model,
"columns": vectors,
"column_field": "attractions",
"output": 'path/to/output/matrix.aem',
"nan_as_zero":True
}

gravity = GravityApplication(**args)
gravity.apply()


### 8.8.2. Iterative Proportional Fitting (IPF)¶

The implementation of IPF is fully vectorized and leverages all the speed of NumPy, but it does not include the fancy multithreading implemented in path computation.

Please note that the AequilibraE matrix used as input is OVERWRITTEN by the IPF

import pandas as pd
from aequilibrae.distribution import Ipf
from aequilibrae.matrix import AequilibraeMatrix
from aequilibrae.matrix import AequilibraeData

matrix = AequilibraeMatrix()

# Here we can create from OMX or load from an AequilibraE matrix.
matrix.create_from_omx(path/to/aequilibrae_matrix, path/to/omxfile)

# The matrix will be operated one (see the note on overwriting), so it does
# not make sense load an OMX matrix

zones = source_vectors.zone.shape[0]

args = {"entries": zones, "field_names": ["productions", "attractions"],
"data_types": [np.float64, np.float64], "memory_mode": True}

vectors = AequilibraEData()
vectors.create_empty(**args)

vectors.productions[:] = source_vectors.productions[:]
vectors.attractions[:] = source_vectors.attractions[:]

# We assume that the indices would be sorted and that they would match the matrix indices
vectors.index[:] = source_vectors.zones[:]

args = {
"matrix": matrix, "rows": vectors, "row_field": "productions", "columns": vectors,
"column_field": "attractions", "nan_as_zero": False}

fratar = Ipf(**args)
fratar.fit()

# We can get back to our OMX matrix in the end
matrix.export(path/to_omx/output)


We only have import for now, and it is likely to not work on Windows if you want the geometries