.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "_auto_examples/other_applications/plot_check_logging.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr__auto_examples_other_applications_plot_check_logging.py: .. _useful-log-tips: Checking AequilibraE's log ========================== AequilibraE's log is a very useful tool to get more information about what the software is doing under the hood. Information such as Traffic Class and Traffic Assignment stats, and Traffic Assignment outputs. If you have created your project's network from OSM, you will also find information on the number of nodes, links, and the query performed to obtain the data. In this example, we'll use Sioux Falls data to check the logs, but we strongly encourage you to go ahead and download a place of your choice and perform a traffic assignment! .. GENERATED FROM PYTHON SOURCE LINES 18-19 Imports .. GENERATED FROM PYTHON SOURCE LINES 19-25 .. code-block:: Python from uuid import uuid4 from tempfile import gettempdir from os.path import join from aequilibrae.utils.create_example import create_example from aequilibrae.paths import TrafficAssignment, TrafficClass .. GENERATED FROM PYTHON SOURCE LINES 26-27 We create an empty project on an arbitrary folder .. GENERATED FROM PYTHON SOURCE LINES 27-30 .. code-block:: Python fldr = join(gettempdir(), uuid4().hex) project = create_example(fldr) .. GENERATED FROM PYTHON SOURCE LINES 31-32 We build our graphs .. GENERATED FROM PYTHON SOURCE LINES 32-39 .. code-block:: Python project.network.build_graphs() graph = project.network.graphs["c"] graph.set_graph("free_flow_time") graph.set_skimming(["free_flow_time", "distance"]) graph.set_blocked_centroid_flows(False) .. rst-class:: sphx-glr-script-out .. code-block:: none /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) .. GENERATED FROM PYTHON SOURCE LINES 40-41 We get our demand matrix from the project and create a computational view .. GENERATED FROM PYTHON SOURCE LINES 41-45 .. code-block:: Python proj_matrices = project.matrices demand = proj_matrices.get_matrix("demand_omx") demand.computational_view(["matrix"]) .. GENERATED FROM PYTHON SOURCE LINES 46-47 Now let's perform our traffic assignment .. GENERATED FROM PYTHON SOURCE LINES 47-62 .. code-block:: Python assig = TrafficAssignment() assigclass = TrafficClass(name="car", graph=graph, matrix=demand) assig.add_class(assigclass) assig.set_vdf("BPR") assig.set_vdf_parameters({"alpha": 0.15, "beta": 4.0}) assig.set_capacity_field("capacity") assig.set_time_field("free_flow_time") assig.set_algorithm("bfw") assig.max_iter = 50 assig.rgap_target = 0.001 assig.execute() .. GENERATED FROM PYTHON SOURCE LINES 64-68 .. code-block:: Python with open(join(fldr, "aequilibrae.log")) as file: for idx, line in enumerate(file): print(idx + 1, "-", line) .. rst-class:: sphx-glr-script-out .. code-block:: none 1 - 2024-02-25 08:40:10,736;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 2 - 2024-02-25 08:40:10,764;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 3 - 2024-02-25 08:40:10,792;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 4 - 2024-02-25 08:40:10,820;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 5 - 2024-02-25 08:40:10,848;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 6 - 2024-02-25 08:40:10,875;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations 7 - 2024-02-25 08:40:10,894;WARNING ; Cost field with wrong type. Converting to float64 8 - 2024-02-25 08:40:11,169;INFO ; Traffic Class specification 9 - 2024-02-25 08:40:11,170;INFO ; {'car': {'Graph': "{'Mode': 'c', 'Block through centroids': False, 'Number of centroids': 24, 'Links': 76, 'Nodes': 24}", 'Matrix': "{'Source': '/tmp/b9e255f6ca5b45cf89fbdeeb06ceafd1/matrices/demand.omx', 'Number of centroids': 24, 'Matrix cores': ['matrix'], 'Matrix totals': {'matrix': 360600.0}}"}} 10 - 2024-02-25 08:40:11,170;INFO ; Traffic Assignment specification 11 - 2024-02-25 08:40:11,170;INFO ; {'VDF parameters': {'alpha': 0.15, 'beta': 4.0}, 'VDF function': 'bpr', 'Number of cores': 4, 'Capacity field': 'capacity', 'Time field': 'free_flow_time', 'Algorithm': 'bfw', 'Maximum iterations': 250, 'Target RGAP': 0.0001} 12 - 2024-02-25 08:40:11,172;WARNING ; Cost field with wrong type. Converting to float64 13 - 2024-02-25 08:40:11,172;INFO ; bfw Assignment STATS 14 - 2024-02-25 08:40:11,172;INFO ; Iteration, RelativeGap, stepsize 15 - 2024-02-25 08:40:11,185;INFO ; 1,inf,1.0 16 - 2024-02-25 08:40:11,190;INFO ; 2,0.8550751349428284,0.32839952448634563 17 - 2024-02-25 08:40:11,195;INFO ; 3,0.4763455007221067,0.18660240547488702 18 - 2024-02-25 08:40:11,201;INFO ; 4,0.2355126365951965,0.2411477440291793 19 - 2024-02-25 08:40:11,210;INFO ; 5,0.10924072010481088,0.8185470737942447 20 - 2024-02-25 08:40:11,218;INFO ; 6,0.1980945227617506,0.14054330572978305 21 - 2024-02-25 08:40:11,223;INFO ; 7,0.0668172221544687,0.36171152718899235 22 - 2024-02-25 08:40:11,230;INFO ; 8,0.06792122267870576,0.9634685345644022 23 - 2024-02-25 08:40:11,238;INFO ; 9,0.10705582933092841,0.13757153109677167 24 - 2024-02-25 08:40:11,245;INFO ; 10,0.04038814432034633,0.16094034254279746 25 - 2024-02-25 08:40:11,254;INFO ; 11,0.027952481137756665,0.3408928228700516 26 - 2024-02-25 08:40:11,259;INFO ; 12,0.03269999206552473,0.5467680533028665 27 - 2024-02-25 08:40:11,266;INFO ; 13,0.024040970172177347,0.13812236751253087 28 - 2024-02-25 08:40:11,271;INFO ; 14,0.021451030909508475,0.19705281508905445 29 - 2024-02-25 08:40:11,277;INFO ; 15,0.017116638259274335,0.33993816583363656 30 - 2024-02-25 08:40:11,282;INFO ; 16,0.017350824111296178,0.7287610532385328 31 - 2024-02-25 08:40:11,290;INFO ; 17,0.02116470546437159,0.08183287977099345 32 - 2024-02-25 08:40:11,298;INFO ; 18,0.012464530324249013,0.15115985804759405 33 - 2024-02-25 08:40:11,306;INFO ; 19,0.01254978991985043,0.16834049481540547 34 - 2024-02-25 08:40:11,311;INFO ; 20,0.011860719789714877,0.5399903522726819 35 - 2024-02-25 08:40:11,317;INFO ; 21,0.012859165521052207,0.05496659199654713 36 - 2024-02-25 08:40:11,326;INFO ; 22,0.007671197552803324,0.06125561557359048 37 - 2024-02-25 08:40:11,331;INFO ; 23,0.005529178907230118,0.07401911120606994 38 - 2024-02-25 08:40:11,342;INFO ; 24,0.005466797330664673,0.19170977924358065 39 - 2024-02-25 08:40:11,348;INFO ; 25,0.00707366882330592,0.42287206962828094 40 - 2024-02-25 08:40:11,358;INFO ; 26,0.009664731222550847,0.941017705161317 41 - 2024-02-25 08:40:11,366;INFO ; 27,0.008756083467129042,0.05172611061874956 42 - 2024-02-25 08:40:11,374;INFO ; 28,0.005105221228052905,0.06397929882333851 43 - 2024-02-25 08:40:11,379;INFO ; 29,0.0035319062476952545,0.050590904988219755 44 - 2024-02-25 08:40:11,392;INFO ; 30,0.0031482926233623735,0.0584374878179482 45 - 2024-02-25 08:40:11,398;INFO ; 31,0.003063209044595418,0.09173138967981329 46 - 2024-02-25 08:40:11,403;INFO ; 32,0.0026646507707727665,0.07094979246384595 47 - 2024-02-25 08:40:11,410;INFO ; 33,0.002302802037873702,0.12412864151965049 48 - 2024-02-25 08:40:11,418;INFO ; 34,0.002751030256062654,0.12799355702548365 49 - 2024-02-25 08:40:11,425;INFO ; 35,0.002125634778303212,0.16620387933954625 50 - 2024-02-25 08:40:11,431;INFO ; 36,0.002099491223202113,0.10282963642099599 51 - 2024-02-25 08:40:11,437;INFO ; 37,0.0014407763657247766,0.14492101336893168 52 - 2024-02-25 08:40:11,446;INFO ; 38,0.0014180447044001398,0.06529689866681382 53 - 2024-02-25 08:40:11,452;INFO ; 39,0.0009714813735980109,0.09399257335249002 54 - 2024-02-25 08:40:11,452;INFO ; bfw Assignment finished. 39 iterations and 0.0009714813735980109 final gap .. GENERATED FROM PYTHON SOURCE LINES 69-103 In lines 1-7, we receive some warnings that our fields name and lane have ``NaN`` values. As they are not relevant to our example, we can move on. In lines 8-9 we get the Traffic Class specifications. We can see that there is only one traffic class (car). Its **graph** key presents information on blocked flow through centroids, number of centroids, links, and nodes. In the **matrix** key, we find information on where in the disk the matrix file is located. We also have information on the number of centroids and nodes, as well as on the matrix/matrices used for computation. In our example, we only have one matrix named matrix, and the total sum of this matrix element is equal to 360,600. If you have more than one matrix its data will be also displayed in the *matrix_cores* and *matrix_totals* keys. In lines 10-11 the log shows the Traffic Assignment specifications. We can see that the VDF parameters, VDF function, capacity and time fields, algorithm, maximum number of iterations, and target gap are just like the ones we set previously. The only information that might be new to you is the number of cores used for computation. If you haven't set any, AequilibraE is going to use the largest number of CPU threads available. Line 12 displays us a warning to indicate that AequilibraE is converting the data type of the cost field. Lines 13-61 indicate that we'll receive the outputs of a *bfw* algorithm. In the log there are also the number of the iteration, its relative gap, and the stepsize. The outputs in lines 15-60 are exactly the same as the ones provided by the function ``assig.report()``. Finally, the last line shows us that the *bfw* assignment has finished after 46 iterations because its gap is smaller than the threshold we configured (0.001). In case you execute a new traffic assignment using different classes or changing the parameters values, these new specification values would be stored in the log file as well so you can always keep a record of what you have been doing. One last reminder is that if we had created our project from OSM, the lines on top of the log would have been different to display information on the queries done to the server to obtain the data. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.830 seconds) .. _sphx_glr_download__auto_examples_other_applications_plot_check_logging.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_check_logging.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_check_logging.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_