From 39142c74c2da82b4ce00553e002fdee11c1c36c9 Mon Sep 17 00:00:00 2001 From: hm-schuhba1 Date: Mon, 29 Apr 2019 09:39:17 +0200 Subject: [PATCH] - Refactor centroid group model to own package - Initialize group already configured in given pedestrians --- .gitignore | 10 + ...navigation_random_pos_with_offset.scenario | 46 +- ...igation_random_pos_without_offset.scenario | 46 +- ...inates_kai_navigation_with_offset.scenario | 46 +- ...tes_kai_navigation_without_offset.scenario | 46 +- ...s_kai_unit_random_pos_with_offset.scenario | 46 +- ...ai_unit_random_pos_without_offset.scenario | 46 +- ..._coordinates_kai_unit_with_offset.scenario | 46 +- ...ordinates_kai_unit_without_offset.scenario | 46 +- ...navigation_random_pos_with_offset.scenario | 47 +- ...igation_random_pos_without_offset.scenario | 47 +- ...inates_kai_navigation_with_offset.scenario | 47 +- ...tes_kai_navigation_without_offset.scenario | 47 +- ...s_kai_unit_random_pos_with_offset.scenario | 47 +- ...ai_unit_random_pos_without_offset.scenario | 47 +- ..._coordinates_kai_unit_with_offset.scenario | 47 +- ...ordinates_kai_unit_without_offset.scenario | 47 +- .../bridge_timeCost_NAVIGATION.scenario | 30 +- .../bridge_timeCost_OBSTACLES.scenario | 30 +- .../bridge_timeCost_QUEUEING.scenario | 30 +- .../scenarios/bridge_timeCost_UNIT.scenario | 30 +- .../scenarios/complex_NAVIGATION_001.scenario | 30 +- .../complex_NAVIGATION_groups_001.scenario | 30 +- .../complex_UNIT_groups_001.scenario | 30 +- ..._vadere_console_with_all_scenario_files.py | 8 +- Tools/Notebooks/GroupModel.ipynb | 404 +++++ Tools/SUQController/tutorial/groupRun.py | 84 ++ .../OSM_calibrationGroup.ipynb | 837 +++++++++++ .../vadereanalysistool/scenario_output.py | 102 +- .../tests/scenario_output_test.py | 127 +- .../bridge_coordinates_kai.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-42-54.117/overlaps.csv | 2 + .../postvis.trajectories | 22 + ...bridge_coordinates_kai_navigation.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-43-08.160/overlaps.csv | 10 + .../postvis.trajectories | 22 + ...dinates_kai_navigation_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-46-13.488/overlaps.csv | 2 + .../postvis.trajectories | 22 + .../bridge_coordinates_kai_origin_0.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-49-31.248/overlaps.csv | 2 + .../postvis.trajectories | 22 + ...ordinates_kai_origin_0_navigation.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-49-48.269/overlaps.csv | 10 + .../postvis.trajectories | 22 + ...ai_origin_0_navigation_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-53-04.555/overlaps.csv | 1 + .../postvis.trajectories | 22 + ...ordinates_kai_origin_0_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-56-18.210/overlaps.csv | 3 + .../postvis.trajectories | 22 + ...bridge_coordinates_kai_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-56-34.297/overlaps.csv | 3 + .../postvis.trajectories | 21 + .../bridge_coordinates_kai.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-57-42.894/overlaps.csv | 2 + .../postvis.trajectories | 22 + .../bridge_coordinates_kai.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-57-42.err/overlaps.csv | 2 + .../postvis.trajectories | 22 + ...bridge_coordinates_kai_navigation.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_13-57-58.997/overlaps.csv | 10 + .../postvis.trajectories | 22 + ...dinates_kai_navigation_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_14-01-07.289/overlaps.csv | 2 + .../postvis.trajectories | 21 + .../bridge_coordinates_kai_origin_0.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_14-04-19.882/overlaps.csv | 2 + .../postvis.trajectories | 22 + ...ordinates_kai_origin_0_navigation.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_14-04-35.721/overlaps.csv | 10 + .../postvis.trajectories | 22 + ...ai_origin_0_navigation_random_pos.scenario | 279 ++++ .../b_2018-11-16_14-08-14.82/overlapCount.txt | 2 + .../b_2018-11-16_14-08-14.82/overlaps.csv | 1 + .../postvis.trajectories | 21 + ...ordinates_kai_origin_0_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_14-11-36.817/overlaps.csv | 3 + .../postvis.trajectories | 22 + ...bridge_coordinates_kai_random_pos.scenario | 279 ++++ .../overlapCount.txt | 2 + .../b_2018-11-16_14-11-53.469/overlaps.csv | 3 + .../postvis.trajectories | 22 + .../bridge_coordinates_kai.scenario | 279 ++++ .../bridge_coordinates_kai.scenario | 279 ++++ .../overlapCount.txt | 2 + .../overlaps.csv | 2 + .../empty.scenario | 181 +++ .../output/corrupt/invalid/overlapCount.txt | 2 + .../output/corrupt/invalid/overlaps.csv | 2 + .../corrupt/invalid/postvis.trajectories | 21 + .../empty.scenario | 181 +++ .../overlapCount.txt | 2 + .../overlaps.csv | 1 + .../postvis.trajectories | 21 + .../empty.scenario | 181 +++ .../overlapCount.txt | 2 + .../overlaps.csv | 1 + .../postvis.trajectories | 21 + .../tests/vadere_project_test.py | 2 +- .../vadereanalysistool/vadere_project.py | 20 +- VadereGui/pom.xml | 20 + .../PostVisualizationConsole.java | 37 +- .../scenarios/groupBaseScenario.scenario | 388 +++++ ...group_OSM_CGM_density_flow_2group.scenario | 244 +++ ...SM_CGM_density_flow_2group_sparse.scenario | 243 +++ ...group_OSM_CGM_density_flow_3group.scenario | 244 +++ ...SM_CGM_density_flow_3group_sparse.scenario | 243 +++ ...group_OSM_CGM_density_flow_4group.scenario | 244 +++ ...SM_CGM_density_flow_4group_sparse.scenario | 243 +++ ...group_OSM_CGM_density_flow_5group.scenario | 244 +++ ...SM_CGM_density_flow_5group_sparse.scenario | 243 +++ .../TestOSMGroup_calibration/vadere.project | 1 + ...11.scenario => groupBaseScenario.scenario} | 167 ++- .../scenarios/rimea_06_corner.scenario | 68 +- .../VadereSimulation-GroupBehavior.scenario | 238 +++ ...imulation-GroupBehavior_no_groups.scenario | 233 +++ .../group_OSM_1Source1Place.scenario | 20 +- .../group_OSM_1Source2Places.scenario | 10 +- .../group_OSM_2Sources1Place.scenario | 10 +- ..._2Sources1Place_2Group_and_3Group.scenario | 10 +- ...OSM_4Source4Place_SEQ_2G_3G_4G_5G.scenario | 289 ++++ ..._4Source4Place_v2_EVD_2G_3G_4G_5G.scenario | 322 ++++ ..._4Source4Place_v2_SEQ_2G_3G_4G_5G.scenario | 322 ++++ .../group_OSM_CGM_classroom_1group.scenario | 1307 +++++++++++++++++ .../group_OSM_CGM_classroom_2group.scenario | 1307 +++++++++++++++++ .../group_OSM_CGM_classroom_3group.scenario | 1307 +++++++++++++++++ .../group_OSM_CGM_classroom_4group.scenario | 1307 +++++++++++++++++ .../group_OSM_CGM_labratory_15group.scenario | 280 ++++ .../group_OSM_CGM_labratory_1group.scenario | 280 ++++ .../group_OSM_CGM_labratory_25group.scenario | 280 ++++ .../group_OSM_CGM_labratory_2group.scenario | 280 ++++ .../group_OSM_CGM_labratory_4group.scenario | 280 ++++ .../group_OSM_long_corr_2Group.scenario | 190 +++ .../group_OSM_long_corr_3Group.scenario | 178 +++ .../group_OSM_long_corr_4Group.scenario | 178 +++ VadereModelTests/TestOSMGroup/vadere.project | 2 +- .../control/GroupSourceController.java | 18 +- .../simulator/control/SourceController.java | 5 +- .../control/TopographyController.java | 2 + .../factory/GroupSourceControllerFactory.java | 6 +- .../entrypoints/cmd/VadereConsole.java | 7 +- .../cmd/commands/ProjectRunSubCommand.java | 64 +- .../models/DynamicElementFactory.java | 7 + .../vadere/simulator/models/MainModel.java | 17 + .../bhm/BehaviouralHeuristicsModel.java | 5 +- .../models/bmm/BiomechanicsModel.java | 6 +- .../models/gnm/GradientNavigationModel.java | 7 +- .../models/groups/AbstractGroupModel.java | 21 + .../models/groups/CentroidGroup.java | 243 --- .../models/groups/CentroidGroupFactory.java | 68 - .../models/groups/CentroidGroupModel.java | 137 -- .../vadere/simulator/models/groups/Group.java | 30 +- .../simulator/models/groups/GroupFactory.java | 2 - .../simulator/models/groups/GroupModel.java | 69 +- .../models/groups/cgm/CentroidGroup.java | 375 +++++ .../models/groups/cgm/CentroidGroupModel.java | 232 +++ .../{ => cgm}/CentroidGroupPotential.java | 8 +- .../{ => cgm}/CentroidGroupSpeedAdjuster.java | 6 +- .../CentroidGroupStepSizeAdjuster.java | 6 +- .../models/osm/OptimalStepsModel.java | 26 +- .../osm/updateScheme/UpdateSchemeOSM.java | 6 +- .../updateScheme/UpdateSchemeSequential.java | 2 + .../models/ovm/OptimalVelocityModel.java | 5 +- .../reynolds/ReynoldsSteeringModel.java | 6 +- .../models/sfm/SocialForceModel.java | 4 +- .../datakey/TimestepGroupPairKey.java | 81 + .../outputfile/GroupPairOutputFile.java | 17 + .../MaxCentroidGroupDistData.java | 51 + .../processor/DataProcessor.java | 15 + .../processor/GroupMemberEuclideanDist.java | 33 + .../processor/GroupMemberPotentialDist.java | 33 + .../GroupMemberSeparatedByObstacle.java | 33 + .../processor/PedestrianGroupIDProcessor.java | 28 +- .../PedestrianGroupMaxDistProcessor.java | 46 + .../PedestrianGroupSizeProcessor.java | 28 +- .../TargetFloorFieldGridProcessor.java | 2 +- .../processor/util/ModelFilter.java | 28 + .../simulator/projects/io/IOOutput.java | 21 +- .../projects/io/TrajectoryReader.java | 15 +- .../group_OSM_1Source1Place.scenario | 2 +- .../control/GroupSourceControllerTest.java | 88 +- ...ourceControllerUsingConstantSpawnRate.java | 16 +- .../groups/cgm/CentroidGroupModelTest.java | 76 + .../models/groups/cgm/CentroidGroupTest.java | 173 +++ .../groups/cgm/PedestrianIdMatcher.java | 51 + .../simulator/projects/ProjectOutputTest.java | 35 +- .../datakey/TimestepGroupPairKeyTest.java | 99 ++ .../AreaDensityVoronoiProcessorTestEnv.java | 1 + .../processor/AreaSpeedProcessorTestEnv.java | 1 + .../EvacuationTimeProcessorTestEnv.java | 1 + .../processor/MaxOverlapProcessorTestEnv.java | 1 + ...estrianEvacuationTimeProcessorTestEnv.java | 1 + .../NumberOverlapsProcessorTestEnv.java | 1 + ...strianDensityCountingProcessorTestEnv.java | 1 + ...estrianEvacuationTimeProcessorTestEnv.java | 7 +- .../PedestrianFlowProcessorTestEnv.java | 1 + ...edestrianLastPositionProcessorTestEnv.java | 1 + .../PedestrianOverlapProcessorTestEnv.java | 1 + .../PedestrianPositionProcessorTestEnv.java | 5 +- .../PedestrianVelocityProcessorTestEnv.java | 1 + ...PedestrianWaitingTimeProcessorTestEnv.java | 1 + .../TargetFloorFieldGridProcessorTestEnv.java | 1 + .../migration/JsonMigrationAssistantTest.java | 10 +- .../utils/CentroidGroupListBuilder.java | 85 ++ .../PedestrianListBuilder.java | 6 +- .../utils/reflection/TestResourceHandler.java | 8 + .../src/org/vadere/state/scenario/Agent.java | 7 + .../scenario/DynamicElementContainer.java | 4 + .../vadere/state/scenario/PedestrianPair.java | 85 ++ .../org/vadere/state/scenario/Topography.java | 43 +- pom.xml | 6 + 227 files changed, 21591 insertions(+), 1250 deletions(-) create mode 100644 Tools/Notebooks/GroupModel.ipynb create mode 100644 Tools/SUQController/tutorial/groupRun.py create mode 100644 Tools/VadereAnalysisTools/Plots/fundamentalDiagrams/OSM_calibrationGroup.ipynb create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-42-54.117/bridge_coordinates_kai.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-42-54.117/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-42-54.117/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-42-54.117/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-43-08.160/bridge_coordinates_kai_navigation.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-43-08.160/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-43-08.160/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-43-08.160/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-46-13.488/bridge_coordinates_kai_navigation_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-46-13.488/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-46-13.488/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-46-13.488/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-31.248/bridge_coordinates_kai_origin_0.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-31.248/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-31.248/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-31.248/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-48.269/bridge_coordinates_kai_origin_0_navigation.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-48.269/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-48.269/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-49-48.269/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-53-04.555/bridge_coordinates_kai_origin_0_navigation_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-53-04.555/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-53-04.555/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-53-04.555/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-18.210/bridge_coordinates_kai_origin_0_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-18.210/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-18.210/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-18.210/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-34.297/bridge_coordinates_kai_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-34.297/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-34.297/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-56-34.297/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.894/bridge_coordinates_kai.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.894/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.894/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.894/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.err/bridge_coordinates_kai.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.err/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.err/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-42.err/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-58.997/bridge_coordinates_kai_navigation.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-58.997/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-58.997/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_13-57-58.997/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-01-07.289/bridge_coordinates_kai_navigation_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-01-07.289/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-01-07.289/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-01-07.289/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-19.882/bridge_coordinates_kai_origin_0.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-19.882/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-19.882/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-19.882/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-35.721/bridge_coordinates_kai_origin_0_navigation.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-35.721/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-35.721/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-04-35.721/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-08-14.82/bridge_coordinates_kai_origin_0_navigation_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-08-14.82/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-08-14.82/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-08-14.82/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-36.817/bridge_coordinates_kai_origin_0_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-36.817/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-36.817/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-36.817/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-53.469/bridge_coordinates_kai_random_pos.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-53.469/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-53.469/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/b_2018-11-16_14-11-53.469/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/bridge_coordinates_kai_2018-10-24_13-45-18.956/bridge_coordinates_kai.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/bridge_coordinates_kai_2018-10-26_16-12-24.505/bridge_coordinates_kai.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/bridge_coordinates_kai_2018-10-26_16-22-18.280/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/bridge_coordinates_kai_2018-10-26_16-22-18.280/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/empty_2018-10-12_12-53-10.262/empty.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/invalid/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/invalid/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/corrupt/invalid/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_13-56-50.397/empty.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_13-56-50.397/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_13-56-50.397/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_13-56-50.397/postvis.trajectories create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_14-12-09.609/empty.scenario create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_14-12-09.609/overlapCount.txt create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_14-12-09.609/overlaps.csv create mode 100644 Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool/tests/testData/s2ucre/output/empty_2018-11-16_14-12-09.609/postvis.trajectories create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/groupBaseScenario.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_2group.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_2group_sparse.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_3group.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_3group_sparse.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_4group.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_4group_sparse.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_5group.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/scenarios/group_OSM_CGM_density_flow_5group_sparse.scenario create mode 100644 VadereModelCalibration/TestOSMGroup_calibration/vadere.project rename VadereModelCalibration/TestOSM_calibration/scenarios/{osm_calibration_minStepSize_0_11.scenario => groupBaseScenario.scenario} (68%) create mode 100644 VadereModelTests/TestOSMGroup/scenarios/VadereSimulation-GroupBehavior.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/VadereSimulation-GroupBehavior_no_groups.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_4Source4Place_SEQ_2G_3G_4G_5G.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_4Source4Place_v2_EVD_2G_3G_4G_5G.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_4Source4Place_v2_SEQ_2G_3G_4G_5G.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_classroom_1group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_classroom_2group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_classroom_3group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_classroom_4group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_labratory_15group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_labratory_1group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_labratory_25group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_labratory_2group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_CGM_labratory_4group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_long_corr_2Group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_long_corr_3Group.scenario create mode 100644 VadereModelTests/TestOSMGroup/scenarios/group_OSM_long_corr_4Group.scenario create mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/AbstractGroupModel.java delete mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/CentroidGroup.java delete mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/CentroidGroupFactory.java delete mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/CentroidGroupModel.java create mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/cgm/CentroidGroup.java create mode 100644 VadereSimulator/src/org/vadere/simulator/models/groups/cgm/CentroidGroupModel.java rename VadereSimulator/src/org/vadere/simulator/models/groups/{ => cgm}/CentroidGroupPotential.java (94%) rename VadereSimulator/src/org/vadere/simulator/models/groups/{ => cgm}/CentroidGroupSpeedAdjuster.java (81%) rename VadereSimulator/src/org/vadere/simulator/models/groups/{ => cgm}/CentroidGroupStepSizeAdjuster.java (92%) create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/datakey/TimestepGroupPairKey.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/outputfile/GroupPairOutputFile.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/procesordata/MaxCentroidGroupDistData.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/processor/GroupMemberEuclideanDist.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/processor/GroupMemberPotentialDist.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/processor/GroupMemberSeparatedByObstacle.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/processor/PedestrianGroupMaxDistProcessor.java create mode 100644 VadereSimulator/src/org/vadere/simulator/projects/dataprocessing/processor/util/ModelFilter.java create mode 100644 VadereSimulator/tests/org/vadere/simulator/models/groups/cgm/CentroidGroupModelTest.java create mode 100644 VadereSimulator/tests/org/vadere/simulator/models/groups/cgm/CentroidGroupTest.java create mode 100644 VadereSimulator/tests/org/vadere/simulator/models/groups/cgm/PedestrianIdMatcher.java create mode 100644 VadereSimulator/tests/org/vadere/simulator/projects/dataprocessing/datakey/TimestepGroupPairKeyTest.java create mode 100644 VadereSimulator/tests/org/vadere/simulator/utils/CentroidGroupListBuilder.java rename VadereSimulator/tests/org/vadere/simulator/{projects/dataprocessing/processor => utils}/PedestrianListBuilder.java (91%) create mode 100644 VadereState/src/org/vadere/state/scenario/PedestrianPair.java diff --git a/.gitignore b/.gitignore index ce186601c..813d45422 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,16 @@ __pycache__/ Tools/VadereAnalysisTools/VadereAnalysisTool/vadereanalysistool.egg-info/ Tools/VadereAnalysisTools/VadereAnalysisTool/build/ Tools/VadereAnalysisTools/VadereAnalysisTool/dist/ +# Jupyter Notebooks +**/.ipynb_checkpoints + +#model test output +VadereModelTests/**/output +VadereModelTests/*_private +VadereModelCalibration/**/output +VadereModelTests/**/legacy +VadereUtils/output/** +VadereModelTests/*_private # Operating system files .DS_Store diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_with_offset.scenario index d0b60107d..5a53c211e 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_with_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_navigation_random_pos_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_without_offset.scenario index 75111071e..5c1a440d4 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_random_pos_without_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_navigation_random_pos_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_with_offset.scenario index c29816b02..8565dd530 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_with_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_navigation_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_without_offset.scenario index a04c7d392..fc7f17e9d 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_navigation_without_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_navigation_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_with_offset.scenario index 673981d2b..528a2e3a8 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_with_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_unit_random_pos_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_without_offset.scenario index 00bd0d2cc..620174ab2 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_random_pos_without_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_unit_random_pos_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_with_offset.scenario index 94b390ee8..942bbcc14 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_with_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_unit_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_without_offset.scenario index d135a83fb..6bcbbbe45 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/2_bridge_coordinates_kai_unit_without_offset.scenario @@ -1,7 +1,7 @@ { "name" : "2_bridge_coordinates_kai_unit_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", "commithash" : "628b018374f404d2aca1afa3483e308428b6ae20", "processWriters" : { "files" : [ { @@ -39,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -56,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -67,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -98,6 +100,7 @@ "fixedSeed" : -5104110572817619091, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -248,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -273,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_with_offset.scenario index 9ae7b55a7..e39a6965f 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_with_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_navigation_random_pos_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_without_offset.scenario index 667a65fe4..ba1faece2 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_random_pos_without_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_navigation_random_pos_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_with_offset.scenario index addc0adeb..40d1997e6 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_with_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_navigation_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_without_offset.scenario index dc38a63d8..e47265da7 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_navigation_without_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_navigation_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "NAVIGATION", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "NAVIGATION", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_with_offset.scenario index eeac261c8..cd15660d2 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_with_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_unit_random_pos_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_without_offset.scenario index c2ea1c515..9b4ae3a1b 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_random_pos_without_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_unit_random_pos_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_with_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_with_offset.scenario index 63885e1e3..e2baedb56 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_with_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_with_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_unit_with_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_without_offset.scenario b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_without_offset.scenario index 1da930a55..db3ea1171 100644 --- a/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_without_offset.scenario +++ b/Tools/ContinuousIntegration/run_orign_translation_test.d/scenarios/bridge_coordinates_kai_unit_without_offset.scenario @@ -1,7 +1,8 @@ { "name" : "bridge_coordinates_kai_unit_without_offset", "description" : "", - "release" : "0.6", + "release" : "0.8", + "commithash" : "warning: no commit hash", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -38,6 +39,23 @@ "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", "attributesModel" : { + "org.vadere.state.attributes.models.AttributesFloorField" : { + "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", + "potentialFieldResolution" : 0.1, + "obstacleGridPenalty" : 0.1, + "targetAttractionStrength" : 1.0, + "timeCostAttributes" : { + "standardDeviation" : 0.7, + "type" : "UNIT", + "obstacleDensityWeight" : 3.5, + "pedestrianSameTargetDensityWeight" : 3.5, + "pedestrianOtherTargetDensityWeight" : 3.5, + "pedestrianWeight" : 3.5, + "queueWidthLoading" : 1.0, + "pedestrianDynamicWeight" : 6.0, + "loadingType" : "CONSTANT" + } + }, "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, @@ -55,7 +73,9 @@ "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ] + "submodels" : [ ], + "minStepLength" : "0.1", + "maxStepDuration" : "1.7976931348623157E308" }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -66,23 +86,6 @@ "intimateSpaceFactor" : 1.2, "personalSpacePower" : 1, "intimateSpacePower" : 1 - }, - "org.vadere.state.attributes.models.AttributesFloorField" : { - "createMethod" : "HIGH_ACCURACY_FAST_MARCHING", - "potentialFieldResolution" : 0.1, - "obstacleGridPenalty" : 0.1, - "targetAttractionStrength" : 1.0, - "timeCostAttributes" : { - "standardDeviation" : 0.7, - "type" : "UNIT", - "obstacleDensityWeight" : 3.5, - "pedestrianSameTargetDensityWeight" : 3.5, - "pedestrianOtherTargetDensityWeight" : 3.5, - "pedestrianWeight" : 3.5, - "queueWidthLoading" : 1.0, - "pedestrianDynamicWeight" : 6.0, - "loadingType" : "CONSTANT" - } } }, "attributesSimulation" : { @@ -97,6 +100,7 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, + "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -247,7 +251,6 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], - "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -272,7 +275,9 @@ "x" : 1.0, "y" : 0.0 } - } + }, + "dynamicElements" : [ ], + "teleporter" : null } } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_NAVIGATION.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_NAVIGATION.scenario index 88a20198a..528ec31a9 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_NAVIGATION.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_NAVIGATION.scenario @@ -1,8 +1,7 @@ { "name" : "bridge_timeCost_NAVIGATION", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -59,23 +59,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "NELDER_MEAD", "varyStepDirection" : false, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "NELDER_MEAD", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : false, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -100,7 +100,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -176,6 +175,7 @@ }, "id" : -1 } ], + "measurementAreas" : [ ], "stairs" : [ ], "targets" : [ { "id" : 1, @@ -230,6 +230,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : -1, "shape" : { @@ -251,6 +252,7 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -260,6 +262,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -275,9 +278,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_OBSTACLES.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_OBSTACLES.scenario index 301b9a1f4..774b3fd64 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_OBSTACLES.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_OBSTACLES.scenario @@ -1,8 +1,7 @@ { "name" : "bridge_timeCost_OBSTACLES", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -59,23 +59,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "NELDER_MEAD", "varyStepDirection" : false, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "NELDER_MEAD", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : false, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -100,7 +100,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -176,6 +175,7 @@ }, "id" : -1 } ], + "measurementAreas" : [ ], "stairs" : [ ], "targets" : [ { "id" : 1, @@ -230,6 +230,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : -1, "shape" : { @@ -251,6 +252,7 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -260,6 +262,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -275,9 +278,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_QUEUEING.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_QUEUEING.scenario index 19e27dcf9..81efb1d17 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_QUEUEING.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_QUEUEING.scenario @@ -1,8 +1,7 @@ { "name" : "bridge_timeCost_QUEUEING", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -59,23 +59,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "NELDER_MEAD", "varyStepDirection" : false, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "NELDER_MEAD", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : false, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -100,7 +100,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -176,6 +175,7 @@ }, "id" : -1 } ], + "measurementAreas" : [ ], "stairs" : [ ], "targets" : [ { "id" : 1, @@ -230,6 +230,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : -1, "shape" : { @@ -251,6 +252,7 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -260,6 +262,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -275,9 +278,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_UNIT.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_UNIT.scenario index e2ca2ad08..63b6ec85a 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_UNIT.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/bridge_timeCost_UNIT.scenario @@ -1,8 +1,7 @@ { "name" : "bridge_timeCost_UNIT", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -59,23 +59,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "NELDER_MEAD", "varyStepDirection" : false, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "NELDER_MEAD", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : false, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -100,7 +100,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -176,6 +175,7 @@ }, "id" : -1 } ], + "measurementAreas" : [ ], "stairs" : [ ], "targets" : [ { "id" : 1, @@ -230,6 +230,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : -1, "shape" : { @@ -251,6 +252,7 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -260,6 +262,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -275,9 +278,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_001.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_001.scenario index 06e76d993..b115c6564 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_001.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_001.scenario @@ -1,8 +1,7 @@ { "name" : "complex_NAVIGATION_001", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -59,23 +59,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "NELDER_MEAD", "varyStepDirection" : true, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "NELDER_MEAD", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : true, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -100,7 +100,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -221,6 +220,7 @@ }, "id" : 5 } ], + "measurementAreas" : [ ], "stairs" : [ { "shape" : { "type" : "POLYGON", @@ -328,6 +328,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : 2, "shape" : { @@ -409,6 +410,7 @@ "groupSizeDistribution" : [ 1.0 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -418,6 +420,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -433,9 +436,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_groups_001.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_groups_001.scenario index 7fa7f9c04..a639e9d1a 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_groups_001.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_NAVIGATION_groups_001.scenario @@ -1,8 +1,7 @@ { "name" : "complex_NAVIGATION_groups_001", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -63,23 +63,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "DISCRETE", "varyStepDirection" : true, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "DISCRETE", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : true, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ "org.vadere.simulator.models.groups.CentroidGroupModel" ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ "org.vadere.simulator.models.groups.cgm.CentroidGroupModel" ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -104,7 +104,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -225,6 +224,7 @@ }, "id" : 5 } ], + "measurementAreas" : [ ], "stairs" : [ { "shape" : { "type" : "POLYGON", @@ -358,6 +358,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : 2, "shape" : { @@ -439,6 +440,7 @@ "groupSizeDistribution" : [ 0.3, 0.4, 0.3 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -448,6 +450,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -463,9 +466,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_UNIT_groups_001.scenario b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_UNIT_groups_001.scenario index f495a5e99..2054f5565 100644 --- a/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_UNIT_groups_001.scenario +++ b/Tools/ContinuousIntegration/run_seed_comparison_test.d/scenarios/complex_UNIT_groups_001.scenario @@ -1,8 +1,7 @@ { "name" : "complex_UNIT_groups_001", "description" : "", - "release" : "0.7", - "commithash" : "warning: no commit hash", + "release" : "0.8", "processWriters" : { "files" : [ { "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.TimestepPedestrianIdOutputFile", @@ -34,7 +33,8 @@ "pedestrianOverlapProcessorId" : 3 } } ], - "isTimestamped" : true + "isTimestamped" : true, + "isWriteMetaData" : false }, "scenario" : { "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel", @@ -63,23 +63,23 @@ "org.vadere.state.attributes.models.AttributesOSM" : { "stepCircleResolution" : 18, "numberOfCircles" : 1, + "optimizationType" : "DISCRETE", "varyStepDirection" : true, + "movementType" : "ARBITRARY", "stepLengthIntercept" : 0.4625, "stepLengthSlopeSpeed" : 0.2345, "stepLengthSD" : 0.036, "movementThreshold" : 0.0, - "optimizationType" : "DISCRETE", - "movementType" : "ARBITRARY", + "minStepLength" : 0.1, + "minimumStepLength" : false, + "maxStepDuration" : 1.7976931348623157E308, "dynamicStepLength" : true, "updateType" : "EVENT_DRIVEN", "seeSmallWalls" : false, - "minimumStepLength" : false, "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid", "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell", "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell", - "submodels" : [ "org.vadere.simulator.models.groups.CentroidGroupModel" ], - "minStepLength" : "0.1", - "maxStepDuration" : "1.7976931348623157E308" + "submodels" : [ "org.vadere.simulator.models.groups.cgm.CentroidGroupModel" ] }, "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : { "pedPotentialIntimateSpaceWidth" : 0.45, @@ -104,7 +104,6 @@ "fixedSeed" : -3213925745664992646, "simulationSeed" : 0 }, - "eventInfos" : [ ], "topography" : { "attributes" : { "bounds" : { @@ -225,6 +224,7 @@ }, "id" : 5 } ], + "measurementAreas" : [ ], "stairs" : [ { "shape" : { "type" : "POLYGON", @@ -358,6 +358,7 @@ "startingWithRedLight" : false, "nextSpeed" : -1.0 } ], + "absorbingAreas" : [ ], "sources" : [ { "id" : 2, "shape" : { @@ -439,6 +440,7 @@ "groupSizeDistribution" : [ 0.3, 0.4, 0.3 ], "dynamicElementType" : "PEDESTRIAN" } ], + "dynamicElements" : [ ], "attributesPedestrian" : { "radius" : 0.195, "densityDependentSpeed" : false, @@ -448,6 +450,7 @@ "maximumSpeed" : 2.2, "acceleration" : 2.0 }, + "teleporter" : null, "attributesCar" : { "id" : -1, "radius" : 0.195, @@ -463,9 +466,8 @@ "x" : 1.0, "y" : 0.0 } - }, - "dynamicElements" : [ ], - "teleporter" : null - } + } + }, + "eventInfos" : [ ] } } \ No newline at end of file diff --git a/Tools/ContinuousIntegration/run_vadere_console_with_all_scenario_files.py b/Tools/ContinuousIntegration/run_vadere_console_with_all_scenario_files.py index 9e7c828ea..200876269 100644 --- a/Tools/ContinuousIntegration/run_vadere_console_with_all_scenario_files.py +++ b/Tools/ContinuousIntegration/run_vadere_console_with_all_scenario_files.py @@ -22,9 +22,9 @@ short_timeout_in_seconds = 2 * 60 def parse_command_line_arguments(): parser = argparse.ArgumentParser(description="Run all scenario files.") parser.add_argument("--scenario", "-s", type=str, nargs="?", help="Run only the given scenario file and not all. E.g. \"VadereModelTests/TestOSM/scenarios/basic_2_density_discrete_ca.scenario\"") - + args = parser.parse_args() - + return args def run_all(): @@ -120,7 +120,7 @@ def run_scenario_files_with_vadere_console(scenario_files, vadere_console="Vader print("Finished scenario file ({:.1f} s): {}".format(wall_time_delta, scenario_file)) passed_scenarios.append(scenario_file) - + except subprocess.TimeoutExpired as exception: print("Scenario file failed: {}".format(scenario_file)) print("-> Reason: timeout after {} s".format(exception.timeout)) @@ -176,7 +176,7 @@ def print_summary(passed_and_failed_scenarios): if __name__ == "__main__": args = parse_command_line_arguments() - + if args.scenario == None: passed_and_failed_scenarios = run_all() else: diff --git a/Tools/Notebooks/GroupModel.ipynb b/Tools/Notebooks/GroupModel.ipynb new file mode 100644 index 000000000..b030de662 --- /dev/null +++ b/Tools/Notebooks/GroupModel.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import vadere_analysis_tool as vat\n", + "import pandas as pd\n", + "import json\n", + "import suqc\n", + "\n", + "def disp(a):\n", + " display(a)\n", + " \n", + "def _finditem(obj, key):\n", + " keys = obj.keys();\n", + " for k in keys:\n", + " if key in k: \n", + " return obj[k]\n", + " \n", + " for k, v in obj.items():\n", + " if isinstance(v,dict):\n", + " item = _finditem(v, key)\n", + " if item is not None:\n", + " return item" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loaded 22 out of 22 output directories. \n" + ] + } + ], + "source": [ + "project = vat.VadereProject ('/home/luca/Programming/vadere/VadereModelTests/TestOSM_Group2')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# classroom evacuation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frames = []\n", + "for name, output in project.output_dirs.items():\n", + " if 'overlap_and_evac.txt' in output.files:\n", + " frame = output.files['overlap_and_evac.txt']()\n", + " frame = frame.assign(scenario=[output.scenario['name']])\n", + " \n", + " overlaps = output.named_files.df_overlaps_csv()\n", + " frame = frame.assign(minOverlap = [overlaps['overlaps'].min()])\n", + " frame = frame.assign(manOverlap = [overlaps['overlaps'].max()]) \n", + " \n", + " frames.append(frame)\n", + " \n", + "df = pd.concat(frames);\n", + "display(df.sort_values('scenario'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# density overlaps" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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scenarioleader_attractiongroup_member_repulsionnumber_of_overlapsbiggest_overlapbiggest_overlap_timestepmean_overlapstd_overlap
7density_flow_2group0.0030.01212920.043463501.00.0106480.006558
1density_flow_3group0.0030.01342950.044777474.00.0107630.006671
8density_flow_3group0.0300.017460.041913479.00.0100560.006655
9density_flow_3group0.0030.017460.041913479.00.0100560.006655
4density_flow_4group0.0030.019910.043716478.00.0108550.007388
10density_flow_4group0.0030.01191500.042055169.00.0107830.006899
2density_flow_5group0.0030.01328400.045957356.00.0109190.006971
11density_flow_5group0.0030.0156290.044704198.00.0107950.007042
0density_flow_5group_clone_10.0030.013670.036354486.00.0097640.006730
3density_flow_5group_clone_10.0030.0110900.039598532.00.0105910.007002
5density_flow_5group_clone_10.0030.017640.035822289.00.0106510.007111
6density_flow_5group_clone_10.0030.0118390.040961402.00.0106620.007371
\n", + "
" + ], + "text/plain": [ + " scenario leader_attraction group_member_repulsion \\\n", + "7 density_flow_2group 0.003 0.01 \n", + "1 density_flow_3group 0.003 0.01 \n", + "8 density_flow_3group 0.030 0.01 \n", + "9 density_flow_3group 0.003 0.01 \n", + "4 density_flow_4group 0.003 0.01 \n", + "10 density_flow_4group 0.003 0.01 \n", + "2 density_flow_5group 0.003 0.01 \n", + "11 density_flow_5group 0.003 0.01 \n", + "0 density_flow_5group_clone_1 0.003 0.01 \n", + "3 density_flow_5group_clone_1 0.003 0.01 \n", + "5 density_flow_5group_clone_1 0.003 0.01 \n", + "6 density_flow_5group_clone_1 0.003 0.01 \n", + "\n", + " number_of_overlaps biggest_overlap biggest_overlap_timestep \\\n", + "7 21292 0.043463 501.0 \n", + "1 34295 0.044777 474.0 \n", + "8 746 0.041913 479.0 \n", + "9 746 0.041913 479.0 \n", + "4 991 0.043716 478.0 \n", + "10 19150 0.042055 169.0 \n", + "2 32840 0.045957 356.0 \n", + "11 5629 0.044704 198.0 \n", + "0 367 0.036354 486.0 \n", + "3 1090 0.039598 532.0 \n", + "5 764 0.035822 289.0 \n", + "6 1839 0.040961 402.0 \n", + "\n", + " mean_overlap std_overlap \n", + "7 0.010648 0.006558 \n", + "1 0.010763 0.006671 \n", + "8 0.010056 0.006655 \n", + "9 0.010056 0.006655 \n", + "4 0.010855 0.007388 \n", + "10 0.010783 0.006899 \n", + "2 0.010919 0.006971 \n", + "11 0.010795 0.007042 \n", + "0 0.009764 0.006730 \n", + "3 0.010591 0.007002 \n", + "5 0.010651 0.007111 \n", + "6 0.010662 0.007371 " + ] + }, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frames = pd.DataFrame(columns=[\n", + " 'scenario',\n", + " 'leader_attraction',\n", + " 'group_member_repulsion',\n", + " 'number_of_overlaps',\n", + " #'smallest_overlap', \n", + " #'smallest_overlap_timestep', \n", + " 'biggest_overlap', \n", + " 'biggest_overlap_timestep',\n", + " 'mean_overlap'\n", + "])\n", + "for name, output in project.output_dirs.items():\n", + " scenario_name = output.get_scenario_name()\n", + " if 'density' in scenario_name and 'overlaps.csv' in output.files:\n", + " attributes = _finditem(output.scenario, 'AttributesCGM')\n", + " overlaps = output.named_files.df_overlaps_csv()\n", + " \n", + " frames = frames.append({\n", + " 'scenario': scenario_name[len('group_OSM_CGM_'):], \n", + " 'number_of_overlaps': len(overlaps['overlaps']),\n", + " #'smallest_overlap': overlaps['overlaps'].min(), \n", + " 'biggest_overlap': overlaps['overlaps'].max(),\n", + " #'smallest_overlap_timestep':overlaps.iloc[overlaps['overlaps'].idxmin()]['timeStep'],\n", + " 'biggest_overlap_timestep': overlaps.iloc[overlaps['overlaps'].idxmax()]['timeStep'],\n", + " 'group_member_repulsion': attributes['groupMemberRepulsionFactor'],\n", + " 'leader_attraction': attributes['leaderAttractionFactor'],\n", + " 'mean_overlap': overlaps['overlaps'].mean(),\n", + " 'std_overlap': overlaps['overlaps'].std()\n", + " },\n", + " ignore_index=True\n", + " )\n", + " \n", + "display(frames.sort_values('scenario'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tools/SUQController/tutorial/groupRun.py b/Tools/SUQController/tutorial/groupRun.py new file mode 100644 index 000000000..927f1f35e --- /dev/null +++ b/Tools/SUQController/tutorial/groupRun.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 + +import os, sys +# This is just to make sure that the systems path is set up correctly, to have correct imports, it can be ignored: +sys.path.append(os.path.abspath(".")) +sys.path.append(os.path.abspath("..")) + +from tutorial.imports import * + +run_local = True + +############################################################################################################### +# Usecase: Set yourself the parameters you want to change. Do this by defining a list of dictionaries with the +# corresponding parameter. Again, the Vadere output is deleted after all scenarios run. + +# Set own values to vary, they don't have to be the same - in the first run acceleration is left to default. +par_var = [{"name": "g2_0_11", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "minStepLength": 0.11}, + {"name": "g2_0_17", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "minStepLength": 0.17}, + {"name": "g2_0_25", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "minStepLength": 0.25}, + {"name": "g2_0_4625", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "minStepLength": 0.4625}, + {"name": "g2_p18", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "stepCircleResolution": 18, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g2_p4", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], "stepCircleResolution": 4, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g2_sievers16b", "sources.[id==-1].groupSizeDistribution": [0.0, 1.0], + "stepLengthIntercept" : 0.235, "stepLengthSlopeSpeed" : 0.302, "minStepLength" : 0.235, + "minimumStepLength" : True, + }, + {"name": "g3_0_11", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "minStepLength": 0.11}, + {"name": "g3_0_17", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "minStepLength": 0.17}, + {"name": "g3_0_25", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "minStepLength": 0.25}, + {"name": "g3_0_4625", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "minStepLength": 0.4625}, + {"name": "g3_p18", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "stepCircleResolution": 18, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g3_p4", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], "stepCircleResolution": 4, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g3_sievers16b", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 1.0], + "stepLengthIntercept" : 0.235, "stepLengthSlopeSpeed" : 0.302, "minStepLength" : 0.235, + "minimumStepLength" : True, + }, + {"name": "g4_0_11", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "minStepLength": 0.11}, + {"name": "g4_0_17", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "minStepLength": 0.17}, + {"name": "g4_0_25", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "minStepLength": 0.25}, + {"name": "g4_0_4625", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "minStepLength": 0.4625}, + {"name": "g4_p18", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "stepCircleResolution": 18, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g4_p4", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], "stepCircleResolution": 4, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g4_sievers16b", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 1.0], + "stepLengthIntercept" : 0.235, "stepLengthSlopeSpeed" : 0.302, "minStepLength" : 0.235, + "minimumStepLength" : True, + }, + {"name": "g5_0_11", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "minStepLength": 0.11}, + {"name": "g5_0_17", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "minStepLength": 0.17}, + {"name": "g5_0_25", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "minStepLength": 0.25}, + {"name": "g5_0_4625", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "minStepLength": 0.4625}, + {"name": "g5_p18", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "stepCircleResolution": 18, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g5_p4", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], "stepCircleResolution": 4, "minStepLength": 0.0, + "minimumStepLength": False}, + {"name": "g5_sievers16b", "sources.[id==-1].groupSizeDistribution": [0.0, 0.0, 0.0, 0.0, 1.0], + "stepLengthIntercept" : 0.235, "stepLengthSlopeSpeed" : 0.302, "minStepLength" : 0.235, + "minimumStepLength" : True, + } + ] + +if __name__ == "__main__": # main required by Windows to run in parallel + + setup = QuickVaryScenario(scenario_path=path2scenario, + parameter_var=par_var, + qoi="density.txt", + model=path2model) + + if run_local: + par_var, data = setup.run(-1) # -1 indicates to use all cores available to parallelize the scenarios + else: + par_var, data = setup.remote(-1) + + print("\n \n ---------------------------------------\n \n") + print("ALL USED PARAMETER:") + print(par_var) + + print("COLLECTED DATA:") + print(data) diff --git a/Tools/VadereAnalysisTools/Plots/fundamentalDiagrams/OSM_calibrationGroup.ipynb b/Tools/VadereAnalysisTools/Plots/fundamentalDiagrams/OSM_calibrationGroup.ipynb new file mode 100644 index 000000000..77ea2cd57 --- /dev/null +++ b/Tools/VadereAnalysisTools/Plots/fundamentalDiagrams/OSM_calibrationGroup.ipynb @@ -0,0 +1,837 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calibration of the Optimal Steps Model\n", + "\n", + "This script is an attempt to recompute the results in silver-2016b page 51. The scenario [scenario](./../../../../VadereModelTests/TestOSM_calibration/rimea_04_calibration_osm.scenario) is based on the RiMEA-Test 4. We use the `Teleporter` to model a circular scenario and the parameter `useFreeSpaceOnly = false` to generate high densities. The following code plots all the necessary diagrams." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "# from vadere_analysis_tool import ScenarioOutput, VadereProject\n", + "from vadereanalysistool import ScenarioOutput, VadereProject\n", + "from scipy.optimize import curve_fit\n", + "import re\n", + "from os.path import join\n", + "\n", + "sns.set(style=\"whitegrid\")\n", + "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.0})\n", + "\n", + "def plotEvolution(project, ending, yval, ylabel, export_prefix=\"\"):\n", + " plots = []\n", + " i = 1\n", + " width = 15* len(project.output_dirs)\n", + " axNum = len(project.output_dirs)\n", + " plt.figure(1, figsize=(width, 10))\n", + " for outStr in project.output_dirs :\n", + " out = project.output_dirs[outStr]\n", + " dataFrames = []\n", + " for fileStr in out.files :\n", + " if fileStr.endswith(ending) :\n", + " ndf = pd.DataFrame();\n", + " df = out.files[fileStr]()\n", + " #df = df[df.velocity > 0]\n", + " ndf['density'] = df.density.astype(float)\n", + " ndf['velocity'] = df.velocity.astype(float)\n", + " ndf['timeStep'] = df.timeStep.astype(int)\n", + " ndf['scenario'] = [out.scenario['name']] * len(df.density)\n", + " dataFrames.append(ndf)\n", + " #concatFrames = pd.concat(dataFrames)\n", + " plt.subplot(axNum, 1, i)\n", + " plt.title(out.scenario['name'])\n", + " plt.xlabel('timeStep')\n", + " plt.ylabel(ylabel)\n", + " sns.lineplot(x='timeStep', y=yval, data=pd.concat(dataFrames))\n", + " i = i + 1\n", + " plt.savefig(\"./\"+export_prefix+\"plotEvolution_\"+ylabel+\".png\", bbox_inches='tight')\n", + "\n", + "\n", + " \n", + " \n", + "def plotFundamentalDiagram(vproject, ending,export_prefix=\"\"):\n", + " dataFrames = []\n", + " for outStr in project.output_dirs :\n", + " out = project.output_dirs[outStr]\n", + " for fileStr in out.files :\n", + " if fileStr.endswith(ending) :\n", + " ndf = pd.DataFrame();\n", + " df = out.files[fileStr]()\n", + " ndf['density'] = df.density.astype(float)\n", + " ndf['velocity'] = df.velocity.astype(float)\n", + " ndf['scenario'] = [out.scenario['name']] * len(df.velocity)\n", + " #ndf = ndf[ndf.density < 7]\n", + " dataFrames.append(ndf)\n", + " concatFrames = pd.concat(dataFrames)\n", + " g = sns.relplot(x=\"density\", y=\"velocity\", hue=\"scenario\", data=concatFrames,\n", + " height=10, aspect=2)\n", + "\n", + "def createFundamentalDiagramScatterPlot(fig, axes, data, scenario_name):\n", + " ndf = pd.DataFrame()\n", + " ndf['density'] = data.density.astype(float)\n", + " ndf['velocity'] = data.velocity.astype(float)\n", + " ndf['scenario'] = scenario_name * len(data.velocity)\n", + " axes.set_xlabel('density')\n", + " axes.set_ylabel('velocity')\n", + " axes.set_xticks([0,1,2,3,4,5,6])\n", + " axes.set_yticks([0,0.5,1,1.5,2,2.5])\n", + " axes.set_xlim(0,6)\n", + " axes.set_ylim(0,2.5)\n", + " axes.scatter(ndf['density'], ndf['velocity'], s=0.7, marker='*', color='#555555')\n", + " wm = plotWeidmann(axes)\n", + " popt, pcov = curve_fit(kladek, ndf['density'], ndf['velocity'], p0=(1.34, 1.913, 5.4))\n", + " print(str(popt[0]) + \",\" + str(popt[1]) + \",\" + str(popt[2]))\n", + " xx = np.linspace(0.1, 6, 1000)\n", + " yy = kladek(xx, *popt)\n", + " axes.plot(xx, yy, '--', c=sns.color_palette().as_hex()[1])\n", + " axes.set_title(scenario_name)\n", + " axes.legend(['Weidmann', 'regression', 'Simulated data'])\n", + "\n", + " \n", + "def plotFundamentalDiagramScatter2(outputs, processor_names, separte_fig, height, width, offset=0, export_path=\"\", offset_gen=None):\n", + " cols = len(processor_names)\n", + " rows = len(outputs)\n", + " print(f\"{height*rows} = {height} * {rows}\")\n", + " print(f\"{width*cols} = {width} * {cols}\")\n", + " \n", + " if not separte_fig:\n", + " fig, axs = plt.subplots(int(rows), int(cols), figsize=(width*cols, height*rows), sharex=False, sharey=True)\n", + " index = 0\n", + " for output in outputs:\n", + " \n", + " if separte_fig:\n", + " fig, axs = plt.subplots(1, int(cols), figsize=(width*cols, height*1), sharex=False, sharey=True)\n", + " axs = [plt.subplot2grid((2,2), (0,0)),\n", + " plt.subplot2grid((2,2), (1,0)),\n", + " plt.subplot2grid((2,2), (1,1))]\n", + " fig.suptitle(output.scenario['name'], fontsize=32)\n", + " \n", + " for processor in processor_names:\n", + " data = output.files[processor]()\n", + " if separte_fig:\n", + " axes= axs[index]\n", + " axes_name = f\"{processor} ({output.output_dir_name})\"\n", + " else:\n", + " axes= axs[int(index / cols), int(index % cols)]\n", + " axes_name = f\"{output.scenario['name']}:{processor} ({output.output_dir_name})\"\n", + "# createFundamentalDiagramScatterPlot(fig, axes, df, f\"{output.scenario['name']}:{processor} ({output.output_dir_name})\")\n", + " ndf = pd.DataFrame()\n", + " ndf['density'] = data.density.astype(float)\n", + " ndf['velocity'] = data.velocity.astype(float)\n", + " ndf['scenario'] = output.scenario['name'] * len(data.velocity)\n", + " axes.set_xlabel('density')\n", + " axes.set_ylabel('velocity')\n", + " axes.set_xticks([0,1,2,3,4,5,6])\n", + " axes.set_yticks([0,0.5,1,1.5,2,2.5])\n", + " axes.set_xlim(0,6)\n", + " axes.set_ylim(0,2.5)\n", + " axes.scatter(ndf['density'], ndf['velocity'], s=0.7, marker='*', color='#555555')\n", + " wm = plotWeidmann(axes)\n", + " popt, pcov = curve_fit(kladek, ndf['density'], ndf['velocity'], p0=(1.34, 1.913, 5.4))\n", + " print(str(popt[0]) + \",\" + str(popt[1]) + \",\" + str(popt[2]))\n", + " xx = np.linspace(0.1, 6, 1000)\n", + " yy = kladek(xx, *popt)\n", + " axes.plot(xx, yy, '--', c=sns.color_palette().as_hex()[1])\n", + " axes.set_title(axes_name)\n", + " axes.legend(['Weidmann', 'regression', 'Simulated data'])\n", + " index = index + 1\n", + " \n", + " if separte_fig:\n", + " index = 0\n", + " txt = f\"--- groupSizeDistribution: {output.scenario['scenario']['topography']['sources'][0]['groupSizeDistribution']} --- \"\n", + " txt += f\"minStepLength: {output.scenario['scenario']['attributesModel']['org.vadere.state.attributes.models.AttributesOSM']['minStepLength']} --- stepCircleResolution: {out.scenario['scenario']['attributesModel']['org.vadere.state.attributes.models.AttributesOSM']['stepCircleResolution']} --- \"\n", + " txt += f\"minimumStepLength: {output.scenario['scenario']['attributesModel']['org.vadere.state.attributes.models.AttributesOSM']['minimumStepLength']} --- \"\n", + " fig.text(.25,-.03,txt, fontsize=24)\n", + " if export_path:\n", + " fig.savefig(join(export_path, f\"{output.output_dir_name}.png\") , bbox_inches='tight')\n", + " \n", + " \n", + " fig.savefig(\"./XXX\"+\".png\", bbox_inches='tight')\n", + " \n", + "def plotFundamentalDiagramScatter(vproject, ending, sep=False, width = 10, height = 5, offset=0, export_prefix=\"\", offset_gen=None):\n", + " dataFrames = []\n", + " index = 0\n", + " cols = 3\n", + " rows = len(vproject.output_dirs) / cols + 1\n", + " if not sep :\n", + " fig, axs = plt.subplots(int(rows), int(cols), figsize=(height*rows, width*cols), sharex=False, sharey=True) \n", + " for outStr in vproject.output_dirs :\n", + " out = vproject.output_dirs[outStr]\n", + " for fileStr in out.files :\n", + " if fileStr.endswith(ending) :\n", + " if not sep : \n", + " axes = axs[int(index / cols), int(index % cols)]\n", + " else :\n", + " fig = plt.figure(1, figsize=(width, height))\n", + " axes = plt.axes()\n", + " fig.add_axes(axes)\n", + " ndf = pd.DataFrame();\n", + " df = out.files[fileStr]()\n", + " if offset_gen is not None:\n", + " offset = offset_gen(out)\n", + " print(f\"ignore to timestep {offset}\")\n", + " print(len(df))\n", + " df = df[offset:] # ignore first n rows defined by offset\n", + " print(len(df))\n", + " plotFundamentalDiagramScatter2(fig, axes, df, f'{out.output_dir_name}: {fileStr}')\n", + " if sep :\n", + " fig.savefig(\"./\"+export_prefix+out.scenario['name']+\"_fundamental_diagram\"+\".png\", bbox_inches='tight')\n", + " plt.show()\n", + " if not sep :\n", + " fig.savefig(\"./\"+export_prefix+vproject.project_name+\"_fundamental_diagrams\"+\".png\", bbox_inches='tight')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{dfdf5}'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 5\n", + "f\"{{dfdf{5}}}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "def plotWeidmann(axes):\n", + " wmaxDensity = 5.4\n", + " wmeanVelocity = 1.34\n", + " wgamma = 1.913\n", + " wx = np.linspace(0.1, wmaxDensity, 100)\n", + " return plotKladek(wx, wmeanVelocity, wgamma, wmaxDensity, axes)\n", + "\n", + "def plotKladek(x, v, gamma, pmax, axes):\n", + " result, = axes.plot(x, kladek(x, v, gamma, pmax), c=sns.color_palette().as_hex()[0])\n", + " return result\n", + "\n", + "def kladek(x, v, gamma, pmax):\n", + " return v * (1 - np.exp(-gamma * (1/x - 1/pmax)))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def filterOutput(project, output_name_re):\n", + " del_keys = [k for k in project.output_dirs if not output_name_re.match(k)]\n", + " for k in del_keys: del project.output_dirs[k]\n", + "\n", + "def getOffset(out):\n", + " postvis_df = out.files['postvis.trajectories']()\n", + " offset = postvis_df[postvis_df.x > 38]['timeStep'].min()\n", + " return offset + 250\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "suq_out='/home/lphex/hm.d/groupModelCalibration/vadere_output'\n", + "out = ScenarioOutput.create_output_from_suq_output(suq_out, 0)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Meassurement methods\n", + "All methods are described in zhang-2011.\n", + "\n", + "## Method A Plots\n", + "The computation of the velocity is slightly different i.e. we use the velocity computed by the current and last foot step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transform data frame\n", + "plotFundamentalDiagramScatter(project, \"aTimeStep.fundamentalDiagram\", True, 6, 6,export_prefix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method B Plots\n", + "This method does not work for this scenario since agents run multiple times through the same measurement area." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method C Plots\n", + "The computation of the velocity is slightly different i.e. we use the velocity computed by the current and last foot step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "plotFundamentalDiagramScatter(project, \"cTimeStep.fundamentalDiagram50m\",\n", + " True, 6, 6, 0,\"Y\",\n", + " offset_gen=lambda x: 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plotFundamentalDiagramScatter(project, \"cTimeStep.fundamentalDiagram2\", True, 6, 6, 0,\"2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# suq_out='/home/lphex/hm.d/groupModelCalibration/vadere_output' # alt\n", + "suq_out='/home/lphex/hm.d/groupModelCalibration_run2/groupModelCalibration_run2/vadere_output'\n", + "outputs = [ScenarioOutput.create_output_from_suq_output(suq_out, i) for i in range(0,28)]\n", + "processors = [f\"cTimeStep.fundamentalDiagram{i}m\" for i in ['35', '50', '70']]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "504 = 18 * 28\n", + "45 = 15 * 3\n", + "1.2141483978075638,6.668737223599294,5.279112674891128\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lphex/PycharmProjects/venvs/venvHM/lib64/python3.7/site-packages/scipy/optimize/minpack.py:787: OptimizeWarning: Covariance of the parameters could not be estimated\n", + " category=OptimizeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6143810193794514,89.35977496321556,73.08342718969978\n", + "0.6822736667194752,56.55036243295984,70.56939928116905\n", + "1.214742271950381,6.487363787015806,5.276577097591886\n", + "0.6195217225759084,88.49629147230948,69.08178954269349\n", + "0.6821817116830807,49.131547052953806,65.01260466111755\n", + "1.2125318013580466,7.330048062499827,4.733105662894167\n", + 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Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.1803533010296785,6.5870882628258265,4.914985548182125\n", + "0.6120983225905631,85.7059623624872,102.05947208177292\n", + "0.6577845737242018,54.831242126327254,68.6874101213361\n", + "1.1123316743518599,7.205464846570427,4.716350572505447\n", + "0.65956436018094,51.889951684849734,64.45835837938998\n", + "0.5713944120312582,51.15032114927871,59.98377054672549\n", + "1.0350061091920015,7.602527859980859,4.803936929188036\n", + "0.7194003876582105,49.72613532226435,70.48161235786787\n", + "0.616453958958365,7.909388205993513,-0.06393198711262205\n", + "1.0028278646410587,8.862047505386244,4.8681099890209145\n", + "0.5764571954136226,83.9713537911971,80.32396695817891\n", + "0.6107827720385651,51.94044554393627,63.66231258669788\n", + "0.48186219075166853,22.089374909279428,4.350452555608176\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Optimal parameters not found: Number of calls to function has reached maxfev = 800.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplotFundamentalDiagramScatter2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexport_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/home/lphex/hm.d/presentations/groupmodel/bilder'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in 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\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/PycharmProjects/venvs/venvHM/lib64/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mcurve_fit\u001b[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfodict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fvec'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mier\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Optimal parameters not found: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 749\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 750\u001b[0m \u001b[0;31m# Rename maxfev (leastsq) to max_nfev (least_squares), if specified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Optimal parameters not found: Number of calls to function has reached maxfev = 800." + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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VV17Jcs74B3ReCgoKMoUy0p1ftkuUKKHy5cubgjCjRo0a6cqVK7p165akO6s+jx8/rm7duik1NVWxsbGm/xo3bqzixYsrKirK9PihQ4dq9uzZatWqlU6dOqXFixfr7bffVps2bTR+/Pgcb9uwdu1aOTs7q3379mZjxsfHKzAwUP/880+WIKNly5amIEySbGxsTH94b968OVfP2cMKDg42e86feOIJ+fr6mtX8+++/KykpSQMGDDAFYZKyrNI1KlasmCkIS01N1Y0bNxQbG6uAgABlZGTozz//zPKYkJAQs0+oa9KkiaQ7qxWNQZh0Z0sWb29vs/qioqJ09epVBQUFKT4+3ux1MK6Sy/zaS3fuX+NH5Rs1b95c0p1tB3LC0dHRFIrcvn1bcXFxio2NVcuWLSVJBw8ezFE/91OiRAndvHnzvu2MgVRGRoYSEhIUGxsrT09PlSxZUocOHTK1M26lM2jQILPvY09PTwUEBGTb/7Bhwx5qXCNfX19TECb97zU1GAxZthsw3geZX5NKlSrpq6++UmhoqNq1a6fevXtr/vz56tOnj7Zv367vv//e1Nb4fZz53so8buY2RnZ2dhbrMBgMCgkJMQs/LdWXndjYWEkybSmUWXJyssUaJcnJyUnJyck5uiYnJydTf/dre/f1Gx9zr34zP1ePe81GPj4+WrhwoWbPnq0xY8bIzc1NcXFx992m6W7Xrl3T/v37FRgYaArCpDvv/y+//LKk/H3/7969u1ng5ejoqCFDhuj27dvaunVrvo1rvN+vXbuWb2MAAAAAQGFAznp/5KzkrBI5a07GNXrYnPVuUVFRunDhgrp27SpnZ2ezc+SsBSOzLIw1G5GzPhxyVgBFFZ90CACFWHR0tOrVq2f6oyK/ValSJcuxUqVKWVwZVKpUKUnSjRs35OzsrFOnTkmSwsPDTR/LfrerV6+afR0YGKjAwEClp6fr5MmT2rFjh77++mutXr1a9vb2FrenuNupU6d069YtUwBiybVr11SzZk3T15ZWIRlXhZ0/f/6+Y+YlS8956dKldePGDdPXMTExkmRxBaSla7l9+7bmzp2ryMhIRUdHy2AwmJ2Pj4/P8pi7VyUaX9/s7onM9Rlf+/Hjx2dpa3T3a1++fPks97Xxj7bMfd/P0qVLtWLFCp08eTLLqvS4uLgc93MvN2/evO+WEpK0Y8cOff755zp48KBSUlKyreVer2fNmjX166+/Wuy/Ro0aDzWukaUVqNm93i4uLpJy9pq89NJL+uabb/TLL7/o2WeflSRTeJuampqlvfFY5oBXktzc3LLcG8Y67q4v8/vQ/RgD4ru/H6Q7AbKlGqU7K8yLFStm+jrzNWU+bmxr7O/utne7+/qNj7HU1thv5ufqca/ZyNXV1fT+HhgYqB49eujZZ59VbGxsjn5GGBm/7yytAH7yySdla2ubr+//1v65k/kTGQAAAACgKCJnJWeVyFnvhZzV+jlrRESEpDtbUd+NnLVgZJaFsWYjcta8Qc4KoKhh0iEAIMeMW6Lm9LiU9Q/LoUOHqnXr1hbbGv+gtdS/p6enPD099eyzz6pjx4769ttv9eGHH95zbOP4rq6umjJlSrZt6tSpc88+8lt6enq25+53fQ9i0qRJWrx4sbp27aqXXnpJrq6ucnBw0JEjRxQWFmZxy5jsVnTnpD7jPTB27FjVq1fPYpu7txDIzT2VnYULF2rSpEkKCAjQoEGDVL58eTk4OOjSpUsaN25cjvu5l5iYGN26dUt+fn73bHfo0CENGzZM1apV05gxY1SlShXTSujRo0fnSS13h0YPOu69nvvszuWk/ooVK8rOzk7Xr183HTO+7pm3fjAybh1x95YR96ovu/s0J/W5urpKuhMQVqxY0exc+fLls2w/krnOzDUar+nSpUuqXr16lrY2NjamNrm5/sz9Ztc28/fR415zdtzd3dWyZUtFRETo/fffz3ZFb3651/t5QRzXGBQb738AAAAAwKNBzpo/yFnJWR/XnPX69evasmWLPDw8LG5LTs5aMDLLwlhzdshZc4ecFUBRxaRDACjEatSoodOnTys1NfWR/8KfW8Y/sGxtbe+5GvZ+XF1dVa1aNR05ckTXr19XuXLl7rlyqHr16jp79qx8fHyybDmQHeOK0cxOnjwpyfLqxJwqXbq0jhw5kuX4w66uMq46PH36tFq0aGF2ztK1REZGyt/fX1OnTjU7ntPtNHLLuDL0iSeeeKjX3pJ7vfaRkZGqXLmy5s2bZxaSZLeC9UGsWrVKktSmTZt7tlu/fr3S09M1b948s3soMTExy4rnzK/n3ffbmTNnclVfbsbNb+fPn1d6errKli1rOmbcMmb//v1ZVugeOHBANjY2Zlvw5CdjKH727FmzbR+Mda5bt07//e9/zYKy//73v7p8+bICAwPN2q5cuVL79+/PEiwdPHhQNWvWNL0X5eb6y5cvL3d3d4vb1Rw4cMCsv6JQ870kJycrPT1dN2/ezHHIY/y+M77XZ3b69GllZGSYfQ/l5v08J6tbc/pz5+5PYHjYcc+dOyfJ+v9TCAAAAACsjZyVnFUiZ80OOav1c9bIyEilpaUpODjY4nly1oKRWRbGmu+FnDXn45KzAiiqLC9TAAAUCt27d1dcXJw+//zzLOfyYkVfXqpfv748PDy0YsUKi7+037592/QLflJSknbv3m2xn7Nnz+rkyZMqU6aM6Y+c4sWLS7K8hcFzzz2njIwMffbZZxb7u3u7CUnavn272R85BoNBX375pSSpffv2puMJCQk6deqUYmNjLfZ9txo1aujWrVs6dOiQ6VhGRoYWLVqUo8dnp1WrVipWrJiWLl2qpKQk0/GLFy9q3bp1Wdrb2tpmuT8SExMfuo7sBAQEqGzZspo3b57FP+KSk5N18+bNB+r7Xq+9ra2tbGxszK719u3bmjdv3gONdbcNGzZo/vz5Kl++vAYMGHDPttmtGv3iiy+yrHhu27atJOnrr782O3f8+HH9/vvvuaoxN+PmlcyfZGiUkZGhadOmSfrf9Ul3wmovLy9t3LjRbOXlpUuXtHHjRjVv3lxubm75UufdmjZtKkkWg5tu3bpJkr766iuz48avu3fvbjrWrl070/dj5lWRW7du1fnz583a5vb6n3nmGZ07d05bt241HUtPT9eSJUvk4uKip556qsjUbOm9W7oTIO3cuVPVqlXL1arSsmXLys/PT9u2bdOJEydMxw0Gg+bOnStJ6tChg+l4bt7P7/U+ZbRu3TpdvHjR9HVqaqoWLVokOzs7s++ZGjVq6MyZM2bPfWpqqpYuXfpA4x44cEDlypWzuM0QAAAAABQl5KzkrBI5q0TOmhfj5oeIiAg5ODioR48eFs+TsxaMzLIw1kzOSs4KAA+DTzoEgEJs0KBB2rZtm2bPnq3Dhw8rICBAjo6OOnnypM6cOZNv4caDsLGx0aeffqrBgwfr2WefVXBwsGrXrq3k5GRFR0dr8+bNevPNNxUUFKSkpCSFhobKw8NDAQEBqlGjhgwGg06fPq3IyEilpKTogw8+MK2qrF27tpydnbVs2TIVK1ZMLi4ucnV1VYsWLdS5c2cFBQVpyZIlOnLkiNq2basyZcro4sWLOnDggKKjo7VlyxazWuvWravBgwdrwIABcnNz05YtW7R9+3b16NHDbHuHzZs3691339WoUaP06quv3vc56NOnjxYuXKhXXnlFgwYNkoODgzZt2vTQHxNfqlQpvf7665o8ebL69eun5557TklJSVqxYoVq1Kihv/76y6x9p06dtHLlSr3xxhtq2bKlrl69qtWrV6t06dIPVUd2ihcvrsmTJ+uVV15R586dFRwcrOrVqys+Pl6nT5/W5s2bNXPmTDVr1izXfXt7e8vW1lZz5sxRXFycihcvripVqsjHx0edO3fWlClTNHz4cHXo0EE3b97U+vXrZW+fu19/Ll26pMjISElSSkqKLly4oKioKB06dEjVq1dXeHh4tlvWGLVv316LFi3S8OHD1bdvXzk4OCgqKkrHjx9XmTJlzNrWqVNHffv21cqVKzVkyBB16NBBsbGxWrZsmerVq6cjR47kaGVdbsfNK//3f/+nmzdvys/PTxUrVtT169e1adMmHTlyRO3atVPnzp3N2r/33nsaNGiQBgwYoIEDB0qSlixZIoPBoHHjxuVLjZa4urqqadOm+uWXX/TOO++YnXv66afVtm1bLVy4UAkJCfL19dWBAwcUERGhZ599Vk2aNDHrx/j9OGTIEHXr1k2XLl3SwoUL9eSTT2rw4MFmfefm+l988UVt2rRJY8aM0fPPPy93d3etX79ehw8f1kcffaQSJUoUmZq/+OILbd++XW3atFHlypUlSSdOnNDatWuVlpamDz744D6veFbvvfeeQkNDNWDAAPXv319ubm7atm2bfv/9d3Xr1s3sEw5y835+r59RRjVr1lTv3r3Vr18/OTs7m56jkSNHmq1GHjBggL7//nsNGTJE/fr1U1pamiIjIy1u+3Ov90dJunXrlvbu3ZvtCnkAAAAAKErIWclZJXJWcta8GTevHTx4UH///be6dOlyz7HIWa2fWRbGmslQ63IMAAAgAElEQVRZyVkB4GEw6RAACjFHR0ctWLBACxYs0Pr16/XZZ5/JyclJ1atXV1BQkLXLy6JevXr69ttv9cUXX2jr1q1asWKFnJ2dVblyZfXs2dP0h4GLi4s++eQTRUVFaevWrbpy5YpSU1NVpkwZ+fv7a+DAgWrevLmp32LFimnq1KmaNm2aPvnkE6Wmpqpp06am/iZOnKhmzZrpm2++0RdffKG0tDS5ubmpfv36GjNmTJY6AwMDVbNmTX3xxRc6c+aMypYtq5EjR2rkyJEPdf1Vq1bVrFmz9Nlnn2n69OkqXbq0evTooeDgYHXp0uWh+h46dKiKFy+uhQsXasqUKapYsaKGDh2qkiVLavz48WZt3333XTk7O2vjxo3asmWLKlasqL59+8rb21tDhgx5qDqy07p1a0VERGju3Llau3a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