Note
Click here to download the full example code
7.15. Forecasting¶
On 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 the project open, 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. Which is true, but we won't use those fields
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,019;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,081;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,143;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,202;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,263;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:354: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "id"] = np.arange(df.shape[0])
2022-11-29 07:41:23,321;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:371: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
df.loc[:, "direction"] = df.direction.values.astype(np.int8)
# we grab the graph for cars
graph = project.network.graphs["c"]
# let's say we want to minimize 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 compute going through other centroids/centroid connectors
# required for the Sioux Falls network, as all nodes are centroids
graph.set_blocked_centroid_flows(False)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2022-11-29 07:41:23,367;WARNING ; Cost field with wrong type. Converting to float64
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:479: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[skim_fields].reset_index()
# We get the demand matrix directly from the project record
# so let's inspect what we have in the project
proj_matrices = project.matrices
proj_matrices.list()
# Let's get it in this better way
demand = proj_matrices.get_matrix("demand_omx")
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 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
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2022-11-29 07:41:23,803;WARNING ; Cost field with wrong type. Converting to float64
2022-11-29 07:41:23,803;INFO ; bfw Assignment STATS
2022-11-29 07:41:23,803;INFO ; Iteration, RelativeGap, stepsize
2022-11-29 07:41:23,814;INFO ; 1,inf,1.0
2022-11-29 07:41:23,824;INFO ; 2,0.8485503636986156,0.3649733931991619
2022-11-29 07:41:23,834;INFO ; 3,0.38139263975960314,0.22983569243524993
2022-11-29 07:41:23,847;INFO ; 4,0.19621280093105328,0.18591303407405754
2022-11-29 07:41:23,857;INFO ; 5,0.09069069564886302,0.709081525570193
2022-11-29 07:41:23,869;INFO ; 6,0.20600049841796414,0.12290139708465252
2022-11-29 07:41:23,882;INFO ; 7,0.0671057020569446,0.3863865464644097
2022-11-29 07:41:23,890;INFO ; 8,0.10307514522959232,0.10930550628245674
2022-11-29 07:41:23,902;INFO ; 9,0.042221488560558955,0.24878058909094228
2022-11-29 07:41:23,911;INFO ; 10,0.05926436280283587,0.15904812211073494
2022-11-29 07:41:23,920;INFO ; 11,0.034539501887818985,0.5180973982981508
2022-11-29 07:41:23,928;INFO ; 12,0.059426522740212366,0.101971242325629
2022-11-29 07:41:23,938;INFO ; 13,0.023239892828420625,0.1780595247621586
2022-11-29 07:41:23,950;INFO ; 14,0.01787378174233466,0.9787872892076548
2022-11-29 07:41:23,959;INFO ; 15,0.04966137112489825,0.08320656230754342
2022-11-29 07:41:23,968;INFO ; 16,0.021382882927382725,0.11517403372477297
2022-11-29 07:41:23,978;INFO ; 17,0.01314154305058098,0.1064036961470442
2022-11-29 07:41:23,987;INFO ; 18,0.009902228306191804,0.10710395852809111
2022-11-29 07:41:23,998;INFO ; 19,0.008834657124558089,0.252467895277828
2022-11-29 07:41:24,006;INFO ; 20,0.010371041656588888,0.6727839455896467
2022-11-29 07:41:24,015;INFO ; 21,0.011090096024277666,0.07468062458005388
2022-11-29 07:41:24,024;INFO ; 22,0.006512685864200749,0.12485018976276105
2022-11-29 07:41:24,033;INFO ; 23,0.00523888532832478,0.06316016036308214
2022-11-29 07:41:24,042;INFO ; 24,0.003949527733957799,0.09717892036982514
2022-11-29 07:41:24,050;INFO ; 25,0.003282221717998643,0.15775902884958926
2022-11-29 07:41:24,059;INFO ; 26,0.0057928324189390375,0.4524765555189632
2022-11-29 07:41:24,069;INFO ; 27,0.006682358714677215,0.7851865434128832
2022-11-29 07:41:24,081;INFO ; 28,0.00599261954468699,0.039098102583274626
2022-11-29 07:41:24,090;INFO ; 29,0.004031364721003652,0.04253017715977122
2022-11-29 07:41:24,099;INFO ; 30,0.00276965205224939,0.02251286694569934
2022-11-29 07:41:24,111;INFO ; 31,0.002484645375580033,0.045801689794576335
2022-11-29 07:41:24,118;INFO ; 32,0.0016385526291291498,0.034295834113305265
2022-11-29 07:41:24,127;INFO ; 33,0.0014956909894299761,0.035391999643557624
2022-11-29 07:41:24,136;INFO ; 34,0.0011355396506463338,0.053589857140533936
2022-11-29 07:41:24,145;INFO ; 35,0.0012151969842906466,0.04757129085244828
2022-11-29 07:41:24,157;INFO ; 36,0.0012393652042090905,0.06536474008117803
2022-11-29 07:41:24,165;INFO ; 37,0.0010684964499824193,0.1070395355360692
2022-11-29 07:41:24,173;INFO ; 38,0.0010899690746481111,0.09864062890245766
2022-11-29 07:41:24,182;INFO ; 39,0.0009886949061956609,0.06027624172548778
2022-11-29 07:41:24,182;INFO ; bfw Assignment finished. 39 iterations and 0.0009886949061956609 final gap
# Convergence report is easy to see
import pandas as pd
convergence_report = assig.report()
convergence_report.head()
volumes = assig.results()
volumes.head()
# 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")
2022-11-29 07:41:24,374;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
proj_matrices.list()
# 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 TFLD seems to be a better fit for the actual one
from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel
from aequilibrae.matrix import AequilibraeData
import numpy as np
# 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% - 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
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")
apply = GravityApplication()
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")
2022-11-29 07:41:25,688;WARNING ; Matrix Record has been saved to the database
2022-11-29 07:41:25,984;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()
proj_matrices.list()
### We now run 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")
2022-11-29 07:41:26,186;WARNING ; Matrix Record has been saved to the database
2022-11-29 07:41:26,314;WARNING ; Matrix Record has been saved to the database
<aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7f26b447e700>
proj_matrices.list()
## Future traffic assignment
from aequilibrae.paths import TrafficAssignment, TrafficClass
logger.info("\n\n\n TRAFFIC ASSIGNMENT FOR FUTURE YEAR")
2022-11-29 07:41:26,386;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 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
assig.execute() # we then execute the assignment
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2022-11-29 07:41:26,828;WARNING ; Cost field with wrong type. Converting to float64
2022-11-29 07:41:26,828;INFO ; bfw Assignment STATS
2022-11-29 07:41:26,828;INFO ; Iteration, RelativeGap, stepsize
2022-11-29 07:41:26,839;INFO ; 1,inf,1.0
2022-11-29 07:41:26,850;INFO ; 2,0.867091292846019,0.32197433867661696
2022-11-29 07:41:26,862;INFO ; 3,0.5055379347296164,0.1627037478897683
2022-11-29 07:41:26,872;INFO ; 4,0.2732097525532206,0.1910691300436256
2022-11-29 07:41:26,884;INFO ; 5,0.10895974064209679,0.6670288618723861
2022-11-29 07:41:26,895;INFO ; 6,0.2280132998697513,0.1356973577069981
2022-11-29 07:41:26,907;INFO ; 7,0.09842433146442472,0.21519314771597273
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2022-11-29 07:41:29,111;INFO ; 204,2.8011181889071648e-05,0.00399365666069393
2022-11-29 07:41:29,122;INFO ; 205,2.2711182934391366e-05,0.003922852046553955
2022-11-29 07:41:29,133;INFO ; 206,2.6051136296827965e-05,0.003800551050046274
2022-11-29 07:41:29,143;INFO ; 207,1.702715544676636e-05,0.0029712653551246596
2022-11-29 07:41:29,155;INFO ; 208,1.963388186770968e-05,0.0024128504285935433
2022-11-29 07:41:29,167;INFO ; 209,1.657865211258389e-05,0.0018822043714706788
2022-11-29 07:41:29,176;INFO ; 210,1.864774016101084e-05,0.0025563704241852265
2022-11-29 07:41:29,187;INFO ; 211,2.1466131156795298e-05,0.0032532715922329127
2022-11-29 07:41:29,197;INFO ; 212,2.4989553903315986e-05,0.003367612215612431
2022-11-29 07:41:29,207;INFO ; 213,2.1378305365136603e-05,0.004862832370926299
2022-11-29 07:41:29,217;INFO ; 214,2.4628105500102207e-05,0.002762003735684758
2022-11-29 07:41:29,227;INFO ; 215,2.0913840900184602e-05,0.004586192457298623
2022-11-29 07:41:29,240;INFO ; 216,2.2359205246886216e-05,0.0035397569011867127
2022-11-29 07:41:29,251;INFO ; 217,2.250481903128926e-05,0.007346239899070144
2022-11-29 07:41:29,262;INFO ; 218,2.8667673311828782e-05,0.0038814622455038313
2022-11-29 07:41:29,273;INFO ; 219,2.0929368253880903e-05,0.0030957728883807285
2022-11-29 07:41:29,286;INFO ; 220,1.9207133758563193e-05,0.0027844673546344855
2022-11-29 07:41:29,297;INFO ; 221,2.2023450491881492e-05,0.006068928493853041
2022-11-29 07:41:29,310;INFO ; 222,2.731361040371244e-05,0.008015357593072148
2022-11-29 07:41:29,321;INFO ; 223,2.5733546216132467e-05,0.0035046647560754863
2022-11-29 07:41:29,334;INFO ; 224,1.9350511542280017e-05,0.004296852682552948
2022-11-29 07:41:29,344;INFO ; 225,2.7455115718827282e-05,0.0036369256993766075
2022-11-29 07:41:29,355;INFO ; 226,1.6294010272226646e-05,0.0016191431053403946
2022-11-29 07:41:29,366;INFO ; 227,1.6077237920752936e-05,0.0015111840301342348
2022-11-29 07:41:29,377;INFO ; 228,1.6409825365756333e-05,0.004374443220268843
2022-11-29 07:41:29,386;INFO ; 229,2.3932822432532796e-05,0.0034885363764988473
2022-11-29 07:41:29,397;INFO ; 230,1.8594430242670553e-05,0.0032880461667760246
2022-11-29 07:41:29,417;INFO ; 231,1.5434620273542705e-05,0.005047880995789397
2022-11-29 07:41:29,427;INFO ; 232,2.0510442647168057e-05,0.004203165500007726
2022-11-29 07:41:29,439;INFO ; 233,2.2624917652185294e-05,0.004779988206168688
2022-11-29 07:41:29,449;INFO ; 234,1.6934708299395492e-05,0.0028128494111883074
2022-11-29 07:41:29,462;INFO ; 235,1.6798408205667308e-05,0.002399479126644229
2022-11-29 07:41:29,475;INFO ; 236,1.6841908872390963e-05,0.0014630474393014473
2022-11-29 07:41:29,488;INFO ; 237,1.5896537784079684e-05,0.0018399091579230297
2022-11-29 07:41:29,498;INFO ; 238,1.4071845398686542e-05,0.0010462617275863871
2022-11-29 07:41:29,509;INFO ; 239,1.2486811978127134e-05,0.0010453403447087811
2022-11-29 07:41:29,519;INFO ; 240,1.2824749595132711e-05,0.0010817421456321057
2022-11-29 07:41:29,532;INFO ; 241,1.3482230379540947e-05,0.0008880148308409236
2022-11-29 07:41:29,542;INFO ; 242,1.1632066216973314e-05,0.0007101506591106019
2022-11-29 07:41:29,554;INFO ; 243,1.3662623236015629e-05,0.0010471917114766075
2022-11-29 07:41:29,566;INFO ; 244,1.4321429467909652e-05,0.001026351324836033
2022-11-29 07:41:29,577;INFO ; 245,1.5396385938630378e-05,0.0009743830437313134
2022-11-29 07:41:29,588;INFO ; 246,1.0186215464835854e-05,0.0010841087545626308
2022-11-29 07:41:29,602;INFO ; 247,1.4136371992919065e-05,0.0010022734160444883
2022-11-29 07:41:29,614;INFO ; 248,9.78877244037103e-06,0.0007947582132790485
2022-11-29 07:41:29,614;INFO ; bfw Assignment finished. 248 iterations and 9.78877244037103e-06 final gap
# 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")
2022-11-29 07:41:29,777;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 7.814 seconds)