Note
Go to the end to download the full example code
Forecasting#
In this example, we present a full forecasting workflow for the Sioux Falls example model.
Imports
from uuid import uuid4
from tempfile import gettempdir
from os.path import join
from aequilibrae.utils.create_example import create_example
import logging
import sys
We create the example project inside our temp folder
fldr = join(gettempdir(), uuid4().hex)
project = create_example(fldr)
logger = project.logger
# We get the project open, and we can tell the logger to direct all messages to the terminal as well
stdout_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(asctime)s;%(levelname)s ; %(message)s")
stdout_handler.setFormatter(formatter)
logger.addHandler(stdout_handler)
Traffic assignment with skimming#
from aequilibrae.paths import TrafficAssignment, TrafficClass
We build all graphs
project.network.build_graphs()
# We get warnings that several fields in the project are filled with NaNs.
# This is true, but we won't use those fields
/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/aequilibrae/project/network/network.py:342: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
df = pd.read_sql(sql, conn).fillna(value=np.nan)
2024-02-25 08:40:05,168;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2024-02-25 08:40:05,197;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2024-02-25 08:40:05,226;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2024-02-25 08:40:05,255;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2024-02-25 08:40:05,283;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2024-02-25 08:40:05,311;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
We grab the graph for cars
graph = project.network.graphs["c"]
# Let's say we want to minimize the free_flow_time
graph.set_graph("free_flow_time")
# 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 computed going through other centroids/centroid connectors
# required for the Sioux Falls network, as all nodes are centroids
graph.set_blocked_centroid_flows(False)
2024-02-25 08:40:05,331;WARNING ; Cost field with wrong type. Converting to float64
We get the demand matrix directly from the project record So let’s inspect what we have in the project
proj_matrices = project.matrices
print(proj_matrices.list())
name file_name ... description status
0 demand_omx demand.omx ... Original data imported to OMX format
1 demand_mc demand_mc.omx ... None
2 skims skims.omx ... Example skim
3 demand_aem demand.aem ... Original data imported to AEM format
[4 rows x 8 columns]
Let’s get it in this better way
demand = proj_matrices.get_matrix("demand_omx")
demand.computational_view(["matrix"])
assig = TrafficAssignment()
# Create the assignment class
assigclass = TrafficClass(name="car", graph=graph, matrix=demand)
# The first thing to do is to add at list of traffic classes to be assigned
assig.add_class(assigclass)
# We set these parameters only after adding one class to the assignment
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 = 0.001
assig.execute() # we then execute the assignment
2024-02-25 08:40:05,619;INFO ; Traffic Class specification
2024-02-25 08:40:05,620;INFO ; {'car': {'Graph': "{'Mode': 'c', 'Block through centroids': False, 'Number of centroids': 24, 'Links': 76, 'Nodes': 24}", 'Matrix': "{'Source': '/tmp/32b3cf7c517e470aae897e460d46ab58/matrices/demand.omx', 'Number of centroids': 24, 'Matrix cores': ['matrix'], 'Matrix totals': {'matrix': 360600.0}}"}}
2024-02-25 08:40:05,620;INFO ; Traffic Assignment specification
2024-02-25 08:40:05,620;INFO ; {'VDF parameters': {'alpha': 'b', 'beta': 'power'}, 'VDF function': 'bpr', 'Number of cores': 4, 'Capacity field': 'capacity', 'Time field': 'free_flow_time', 'Algorithm': 'bfw', 'Maximum iterations': 250, 'Target RGAP': 0.0001}
2024-02-25 08:40:05,622;WARNING ; Cost field with wrong type. Converting to float64
2024-02-25 08:40:05,622;INFO ; bfw Assignment STATS
2024-02-25 08:40:05,622;INFO ; Iteration, RelativeGap, stepsize
2024-02-25 08:40:05,628;INFO ; 1,inf,1.0
2024-02-25 08:40:05,633;INFO ; 2,0.8550751349428284,0.32839952448634563
2024-02-25 08:40:05,638;INFO ; 3,0.4763455007221067,0.18660240547488702
2024-02-25 08:40:05,643;INFO ; 4,0.2355126365951965,0.2411477440291793
2024-02-25 08:40:05,653;INFO ; 5,0.10924072010481088,0.8185470737942447
2024-02-25 08:40:05,659;INFO ; 6,0.1980945227617506,0.14054330572978305
2024-02-25 08:40:05,666;INFO ; 7,0.0668172221544687,0.36171152718899247
2024-02-25 08:40:05,671;INFO ; 8,0.06792122267870587,0.9634685345644044
2024-02-25 08:40:05,678;INFO ; 9,0.10705582933092855,0.13757153109677187
2024-02-25 08:40:05,683;INFO ; 10,0.04038814432034622,0.16094034254279727
2024-02-25 08:40:05,690;INFO ; 11,0.027952481137756675,0.34089282287004663
2024-02-25 08:40:05,698;INFO ; 12,0.03269999206552449,0.546768053302855
2024-02-25 08:40:05,704;INFO ; 13,0.02404097017217699,0.13812236751252946
2024-02-25 08:40:05,710;INFO ; 14,0.021451030909508357,0.19705281508905503
2024-02-25 08:40:05,715;INFO ; 15,0.01711663825927446,0.33993816583363845
2024-02-25 08:40:05,721;INFO ; 16,0.017350824111296542,0.7287610532385275
2024-02-25 08:40:05,730;INFO ; 17,0.02116470546437133,0.08183287977099357
2024-02-25 08:40:05,736;INFO ; 18,0.012464530324249254,0.15115985804759682
2024-02-25 08:40:05,747;INFO ; 19,0.012549789919850798,0.1683404948154094
2024-02-25 08:40:05,753;INFO ; 20,0.011860719789715247,0.539990352272706
2024-02-25 08:40:05,759;INFO ; 21,0.012859165521052577,0.05496659199654951
2024-02-25 08:40:05,770;INFO ; 22,0.007671197552803821,0.061255615573593686
2024-02-25 08:40:05,776;INFO ; 23,0.005529178907230243,0.07401911120607248
2024-02-25 08:40:05,782;INFO ; 24,0.0054667973306647966,0.1917097792435792
2024-02-25 08:40:05,788;INFO ; 25,0.007073668823305794,0.42287206962831864
2024-02-25 08:40:05,797;INFO ; 26,0.009664731222551093,0.9410177051614992
2024-02-25 08:40:05,806;INFO ; 27,0.008756083467130284,0.051726110618755095
2024-02-25 08:40:05,812;INFO ; 28,0.005105221228053531,0.06397929882334376
2024-02-25 08:40:05,822;INFO ; 29,0.0035319062476948794,0.050590904988213295
2024-02-25 08:40:05,828;INFO ; 30,0.0031482926233624992,0.05843748781794705
2024-02-25 08:40:05,835;INFO ; 31,0.003063209044595043,0.09173138967979506
2024-02-25 08:40:05,842;INFO ; 32,0.002664650770772641,0.07094979246383042
2024-02-25 08:40:05,849;INFO ; 33,0.002302802037873577,0.12412864151959871
2024-02-25 08:40:05,858;INFO ; 34,0.002751030256061905,0.12799355702542584
2024-02-25 08:40:05,865;INFO ; 35,0.002125634778302838,0.16620387933942551
2024-02-25 08:40:05,873;INFO ; 36,0.0020994912232001157,0.10282963642083914
2024-02-25 08:40:05,880;INFO ; 37,0.0014407763657240283,0.14492101336869087
2024-02-25 08:40:05,886;INFO ; 38,0.001418044704397894,0.0652968986666325
2024-02-25 08:40:05,895;INFO ; 39,0.0009714813735966395,0.09399257335227715
2024-02-25 08:40:05,895;INFO ; bfw Assignment finished. 39 iterations and 0.0009714813735966395 final gap
Convergence report is easy to see
import pandas as pd
convergence_report = assig.report()
print(convergence_report.head())
iteration rgap alpha warnings beta0 beta1 beta2
0 1 inf 1.000000 1.000000 0.000000 0.0
1 2 0.855075 0.328400 1.000000 0.000000 0.0
2 3 0.476346 0.186602 1.000000 0.000000 0.0
3 4 0.235513 0.241148 1.000000 0.000000 0.0
4 5 0.109241 0.818547 0.607382 0.392618 0.0
volumes = assig.results()
print(volumes.head())
matrix_ab matrix_ba matrix_tot ... PCE_AB PCE_BA PCE_tot
link_id ...
1 4502.545113 NaN 4502.545113 ... 4502.545113 NaN 4502.545113
2 8222.240524 NaN 8222.240524 ... 8222.240524 NaN 8222.240524
3 4622.925028 NaN 4622.925028 ... 4622.925028 NaN 4622.925028
4 5897.692905 NaN 5897.692905 ... 5897.692905 NaN 5897.692905
5 8101.860609 NaN 8101.860609 ... 8101.860609 NaN 8101.860609
[5 rows x 15 columns]
We could export it to CSV or AequilibraE data, but let’s put it directly into the results database
assig.save_results("base_year_assignment")
And save the skims
assig.save_skims("base_year_assignment_skims", which_ones="all", format="omx")
2024-02-25 08:40:05,957;WARNING ; Matrix Record has been saved to the database
Trip distribution#
Calibration#
We will calibrate synthetic gravity models using the skims for TIME that we just generated
import numpy as np
from aequilibrae.distribution import GravityCalibration
Let’s take another look at what we have in terms of matrices in the model
print(proj_matrices.list())
name ... status
0 demand_omx ...
1 demand_mc ...
2 skims ...
3 demand_aem ...
4 base_year_assignment_skims_car ...
[5 rows x 8 columns]
We need the demand
demand = proj_matrices.get_matrix("demand_aem")
# And the skims
imped = proj_matrices.get_matrix("base_year_assignment_skims_car")
We can check which matrix cores were created for our skims to decide which one to use
imped.names
# Where ``free_flow_time_final`` is actually the congested time for the last iteration
['distance_blended', 'distance_final', 'free_flow_time_blended', 'free_flow_time_final']
But before using the data, let’s get some impedance for the intrazonals Let’s assume it is 75% of the closest zone
imped_core = "free_flow_time_final"
imped.computational_view([imped_core])
# 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_view, 0)
# We compute it with a little bit of NumPy magic
intrazonals = np.amin(imped.matrix_view, where=imped.matrix_view > 0, initial=imped.matrix_view.max(), axis=1)
intrazonals *= 0.75
# Then we fill in the impedance matrix
np.fill_diagonal(imped.matrix_view, intrazonals)
Since we are working with an OMX file, we cannot overwrite a matrix on disk So we give a new name to save it
imped.save(names=["final_time_with_intrazonals"])
This also updates these new matrices as those being used for computation As one can verify below
imped.view_names
['final_time_with_intrazonals']
We set the matrices for being used in computation
demand.computational_view(["matrix"])
for function in ["power", "expo"]:
gc = GravityCalibration(matrix=demand, impedance=imped, function=function, nan_as_zero=True)
gc.calibrate()
model = gc.model
# We save the model
model.save(join(fldr, f"{function}_model.mod"))
# We can save the result of applying the model as well
# We can also save the calibration report
with open(join(fldr, f"{function}_convergence.log"), "w") as otp:
for r in gc.report:
otp.write(r + "\n")
Forecast#
We create a set of ‘future’ vectors using some random growth factors. We apply the model for inverse power, as the trip frequency length distribution (TFLD) seems to be a better fit for the actual one.
from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel
from aequilibrae.matrix import AequilibraeData
We compute the vectors from our matrix
origins = np.sum(demand.matrix_view, axis=1)
destinations = np.sum(demand.matrix_view, axis=0)
args = {
"file_path": join(fldr, "synthetic_future_vector.aed"),
"entries": demand.zones,
"field_names": ["origins", "destinations"],
"data_types": [np.float64, np.float64],
"memory_mode": False,
}
vectors = AequilibraeData()
vectors.create_empty(**args)
vectors.index[:] = demand.index[:]
# Then grow them with some random growth between 0 and 10%, and 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#
imped = proj_matrices.get_matrix("base_year_assignment_skims_car")
imped.computational_view(["final_time_with_intrazonals"])
# If we wanted the main diagonal to not be considered...
# ``np.fill_diagonal(imped.matrix_view, np.nan)``
for function in ["power", "expo"]:
model = SyntheticGravityModel()
model.load(join(fldr, f"{function}_model.mod"))
outmatrix = join(proj_matrices.fldr, f"demand_{function}_model.aem")
args = {
"impedance": imped,
"rows": vectors,
"row_field": "origins",
"model": model,
"columns": vectors,
"column_field": "destinations",
"nan_as_zero": True,
}
gravity = GravityApplication(**args)
gravity.apply()
# We get the output matrix and save it to OMX too,
gravity.save_to_project(name=f"demand_{function}_modeled", file_name=f"demand_{function}_modeled.omx")
2024-02-25 08:40:06,638;WARNING ; Matrix Record has been saved to the database
2024-02-25 08:40:06,731;WARNING ; Matrix Record has been saved to the database
We update the matrices table/records and verify that the new matrices are indeed there
proj_matrices.update_database()
print(proj_matrices.list())
name ... status
0 demand_omx ...
1 demand_mc ...
2 skims ...
3 demand_aem ...
4 base_year_assignment_skims_car ...
5 demand_power_modeled ...
6 demand_expo_modeled ...
[7 rows x 8 columns]
IPF for the future vectors#
args = {
"matrix": demand,
"rows": vectors,
"columns": vectors,
"column_field": "destinations",
"row_field": "origins",
"nan_as_zero": True,
}
ipf = Ipf(**args)
ipf.fit()
ipf.save_to_project(name="demand_ipfd", file_name="demand_ipfd.aem")
ipf.save_to_project(name="demand_ipfd_omx", file_name="demand_ipfd.omx")
2024-02-25 08:40:06,805;WARNING ; Matrix Record has been saved to the database
2024-02-25 08:40:06,826;WARNING ; Matrix Record has been saved to the database
<aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7ff1ca5ce730>
df = proj_matrices.list()
Future traffic assignment#
from aequilibrae.paths import TrafficAssignment, TrafficClass
logger.info("\n\n\n TRAFFIC ASSIGNMENT FOR FUTURE YEAR")
2024-02-25 08:40:06,845;INFO ;
TRAFFIC ASSIGNMENT FOR FUTURE YEAR
demand = proj_matrices.get_matrix("demand_ipfd")
# Let's see what is the core we ended up getting. It should be 'gravity'
demand.names
['matrix']
Let’s use the IPF matrix
demand.computational_view("matrix")
assig = TrafficAssignment()
# Creates the assignment class
assigclass = TrafficClass(name="car", graph=graph, matrix=demand)
# The first thing to do is to add at a list of traffic classes to be assigned
assig.add_class(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 = 500
assig.rgap_target = 0.00001
OPTIONAL
# If we want to execute select link analysis on a particular TrafficClass, we set the links we are analyzing.
# The format of the input select links is a dictionary (str: list[tuple]).
# Each entry represents a separate set of selected links to compute. The str name will name the set of links.
# The list[tuple] is the list of links being selected, of the form (link_id, direction), as it occurs in the Graph.
# Direction can be 0, 1, -1. 0 denotes bi-directionality
# For example, let's use Select Link on two sets of links:
select_links = {
"Leaving node 1": [(1, 1), (2, 1)],
"Random nodes": [(3, 1), (5, 1)],
}
# We call this command on the class we are analyzing with our dictionary of values
assigclass.set_select_links(select_links)
assig.execute() # we then execute the assignment
/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/aequilibrae/paths/traffic_class.py:166: UserWarning: Input string name has a space in it. Replacing with _
warnings.warn("Input string name has a space in it. Replacing with _")
2024-02-25 08:40:07,124;INFO ; Traffic Class specification
2024-02-25 08:40:07,125;INFO ; {'car': {'Graph': "{'Mode': 'c', 'Block through centroids': False, 'Number of centroids': 24, 'Links': 76, 'Nodes': 24}", 'Matrix': "{'Source': '/tmp/32b3cf7c517e470aae897e460d46ab58/matrices/demand_ipfd.aem', 'Number of centroids': 24, 'Matrix cores': ['matrix'], 'Matrix totals': {'matrix': 383178.2881905334}}", 'select_links': "{'Leaving node 1': [(1, 1), (2, 1)], 'Random nodes': [(3, 1), (5, 1)]}"}}
2024-02-25 08:40:07,125;INFO ; Traffic Assignment specification
2024-02-25 08:40:07,125;INFO ; {'VDF parameters': {'alpha': 'b', 'beta': 'power'}, 'VDF function': 'bpr', 'Number of cores': 4, 'Capacity field': 'capacity', 'Time field': 'free_flow_time', 'Algorithm': 'bfw', 'Maximum iterations': 250, 'Target RGAP': 0.0001}
2024-02-25 08:40:07,127;WARNING ; Cost field with wrong type. Converting to float64
2024-02-25 08:40:07,127;INFO ; bfw Assignment STATS
2024-02-25 08:40:07,127;INFO ; Iteration, RelativeGap, stepsize
2024-02-25 08:40:07,134;INFO ; 1,inf,1.0
2024-02-25 08:40:07,142;INFO ; 2,0.8751743987728927,0.3191922468344516
2024-02-25 08:40:07,148;INFO ; 3,0.5148943855359133,0.17016548477714577
2024-02-25 08:40:07,154;INFO ; 4,0.2855143074391096,0.1820596440202315
2024-02-25 08:40:07,162;INFO ; 5,0.11073856334472275,0.6577892879280489
2024-02-25 08:40:07,175;INFO ; 6,0.24396663392859413,0.11616466932026379
2024-02-25 08:40:07,182;INFO ; 7,0.11247428071783787,0.17783850478329255
2024-02-25 08:40:07,188;INFO ; 8,0.06137588538719,0.6089654825173924
2024-02-25 08:40:07,194;INFO ; 9,0.12716850206697475,0.14306806185983678
2024-02-25 08:40:07,200;INFO ; 10,0.04574106162423547,0.24382091906899286
2024-02-25 08:40:07,216;INFO ; 11,0.04943815385588104,0.5261606810549172
2024-02-25 08:40:07,226;INFO ; 12,0.06427622418288366,0.10810579529781267
2024-02-25 08:40:07,232;INFO ; 13,0.03806560577953316,0.2055073871686453
2024-02-25 08:40:07,238;INFO ; 14,0.03045913339191789,0.25812762259521393
2024-02-25 08:40:07,244;INFO ; 15,0.03304777381974532,0.72624755983576
2024-02-25 08:40:07,254;INFO ; 16,0.04082283202734483,0.09662643404269156
2024-02-25 08:40:07,266;INFO ; 17,0.019281934449515065,0.14745095544837647
2024-02-25 08:40:07,273;INFO ; 18,0.01901177080820419,0.22525133971951034
2024-02-25 08:40:07,279;INFO ; 19,0.015833844386344893,0.3063268704054275
2024-02-25 08:40:07,286;INFO ; 20,0.020791043512151698,0.08268614595071419
2024-02-25 08:40:07,298;INFO ; 21,0.011253277984943409,0.14753421873879669
2024-02-25 08:40:07,305;INFO ; 22,0.012657159676920448,0.2590761375385592
2024-02-25 08:40:07,312;INFO ; 23,0.01550556110866974,0.059306952458223906
2024-02-25 08:40:07,319;INFO ; 24,0.007058099142771472,0.07120475979481979
2024-02-25 08:40:07,326;INFO ; 25,0.007990573245120638,0.11482251713032551
2024-02-25 08:40:07,333;INFO ; 26,0.009135264412219887,0.3952794536874391
2024-02-25 08:40:07,340;INFO ; 27,0.011736021310491299,0.05713787381943355
2024-02-25 08:40:07,347;INFO ; 28,0.006375029328855654,0.054326060121934104
2024-02-25 08:40:07,354;INFO ; 29,0.0054328734784629,0.06772652355375203
2024-02-25 08:40:07,366;INFO ; 30,0.004394298061508366,0.119013894451448
2024-02-25 08:40:07,373;INFO ; 31,0.005249489323712751,0.31563918840009775
2024-02-25 08:40:07,380;INFO ; 32,0.005711624763590253,0.5102971707532196
2024-02-25 08:40:07,387;INFO ; 33,0.007575864643216441,0.03713870566813292
2024-02-25 08:40:07,394;INFO ; 34,0.004296459580628858,0.05481170990470343
2024-02-25 08:40:07,403;INFO ; 35,0.003782625330466585,0.06258209500389797
2024-02-25 08:40:07,411;INFO ; 36,0.004277308799687251,0.13564639843174994
2024-02-25 08:40:07,418;INFO ; 37,0.003607148763340441,0.09368329179095329
2024-02-25 08:40:07,424;INFO ; 38,0.0026259421869643646,0.13223488325940658
2024-02-25 08:40:07,432;INFO ; 39,0.0026926899181044465,0.14591448172858706
2024-02-25 08:40:07,442;INFO ; 40,0.0029432679259635964,0.3350219160310055
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2024-02-25 08:40:09,659;INFO ; 234,1.523339766097751e-05,0.000940440712619752
2024-02-25 08:40:09,674;INFO ; 235,1.3888996095974238e-05,0.0017103730637431354
2024-02-25 08:40:09,691;INFO ; 236,1.462341016994341e-05,0.0011959697135053944
2024-02-25 08:40:09,707;INFO ; 237,1.4764562759046479e-05,0.0023969453661072514
2024-02-25 08:40:09,730;INFO ; 238,1.6121903821681302e-05,0.002633009842629811
2024-02-25 08:40:09,749;INFO ; 239,1.3476729273031213e-05,0.0014782674968071756
2024-02-25 08:40:09,771;INFO ; 240,1.1842113700450975e-05,0.0037138890374857282
2024-02-25 08:40:09,790;INFO ; 241,1.1250124440696712e-05,0.0012035677683921006
2024-02-25 08:40:09,804;INFO ; 242,7.240299427449257e-06,0.0007300154369215753
2024-02-25 08:40:09,804;INFO ; bfw Assignment finished. 242 iterations and 7.240299427449257e-06 final gap
Now let us save our select link results, all we need to do is provide it with a name In addition to exporting the select link flows, it also exports the Select Link matrices in OMX format.
assig.save_select_link_results("select_link_analysis")
Say we just want to save our select link flows, we can call:
assig.save_select_link_flows("just_flows")
# Or if we just want the SL matrices:
assig.save_select_link_matrices("just_matrices")
# Internally, the save_select_link_results calls both of these methods at once.
# We could export it to CSV or AequilibraE data, but let's put it directly into the results database
assig.save_results("future_year_assignment")
# And save the skims
assig.save_skims("future_year_assignment_skims", which_ones="all", format="omx")
2024-02-25 08:40:09,868;WARNING ; Matrix Record has been saved to the database
We can also plot convergence#
import matplotlib.pyplot as plt
df = assig.report()
x = df.iteration.values
y = df.rgap.values
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(x, y, "k--")
plt.yscale("log")
plt.grid(True, which="both")
plt.xlabel(r"Iterations")
plt.ylabel(r"Relative Gap")
plt.show()
Close the project
project.close()
Total running time of the script: (0 minutes 5.203 seconds)