From 288287636334c5ad2227c753b13f6d0ba4123e54 Mon Sep 17 00:00:00 2001 From: Sahand Asgarpour <133748876+sahand-asgarpour@users.noreply.github.com> Date: Tue, 10 Sep 2024 11:05:38 +0200 Subject: [PATCH] Feat/518 calculate eal (#531) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * chore: renaming representative_damage_percentile * feat: risk_calculation_factory/base and its inherited classes are created. risk_calculation_mode/year added to the analysis_config * feat: risk_calculation_factory returns the correct RiskCalculationBase classes * feat: self._to_integrate = self.rework_damage_data() added to each risk_calculation class * chore: renaming to _rework_damage_data * feat: minor + creating the risk column in the self.result * feat: aggregate_wl is added to analysis_config_data; can be read from ini and config. * chore: aggregate_wl removed form AnalysisSectionLosses, because aggregate_wl is now part of AnalysisConfigData * chore: self.analysis_config.config_data.aggregate_wl is used in _get_disrupted_criticality_analysis_results * chore: aggregate_wl_suffix added to filter on the criticality_results that have any EV or RP columns with aggregate_wl_suffix of above aggregate_wl. * chore: analysis_config_data_reader updated * chore: analysis_config_data_reader updated * chore: re package imported correctly * chore: re package imported correctly * chore: removed aggregate_wl from test analysis.ini * chore: aggregate_wl added to the config of the test * feat: EventTypeEnum NONE added and analysis.ini is updated for correcting failing tests * feat: aggregate_wl is removed from analysis.ini for correcting failing tests * feat: aggregate_wl is removed from analysis.ini for correcting failing tests * chore: error numbers are reduced. event_type default has become NONE instead of INVALID. The latter used to give error. * chore: getting the return_periods is revised to get the numbers in the column names * chore: cut_from_year rework function is updated to return correct _to_integrate * test: updating test_calc_vlh for the cases with more than one event and different aggregate_wl[:2] * test: resilience_curve updated. Test assertion added. expected_results updated to include the second resilience curve * test: resilience_curve updated and relevant tests are updated * test: TestRiskCalculation created * test: TestRiskCalculation minor change * test: TestRiskCalculation expected data updated * chore: Unused import removed + .gitignor update * Update tests/analysis/losses/resilience_curves/test_resilience_curves.py chore: a number made float Co-authored-by: Ardt Klapwijk <59741981+ArdtK@users.noreply.github.com> * chore: ABC inheritance removed * chore: attributes with type hints are added to the class and removed form __init__ * chore: Unused imports are removed * chore: passing key (k) is simplified * chore: converted the function to an internal method of _get_to_integrate * chore: inplace used instead of reassigning * chore: conditions aggregated for readability * chore: local variable is modified * chore: super.__init__ is removed and _rework_damage_data usage moved to the base class * chore: list made set + commented lines removed * chore: self._to_integrates is returned in the end and not set in the method * chore: asssert exists() added * chore: self._rework_damage_data() moved to _get_to_integrate * chore: self._rework_damage_data() returned * chore: self._rework_damage_data() does not return but set self._to_integrate * chore: cut_from_year test updated + rework_to_integrate sets self._to_integrate * chore: cut_from_year test updated * chore: cut_from_year test updated * chore: cut_from_year test updated * test: factory test is made * test: a fixture is added * feat: get_suffix created for AggregateWlEnum * chore: improvements for a correct encapsulation implementation * chore: module test_risk_calculation_factory created * chore: __post_init__ removed * chore: properties protected * chore: integrate_df_trapezoidal updated to avoid changing self._to_integrate * chore: test updated regarding the protected attributes * chore: get_wl_prefix updated * chore: _ removed for the fixtures * chore: fixtures moved to congtest + factory test in new module * test: get_wl _prefix is added * test: get_wl _prefix is modified to include expected prefixes * test: get_wl _prefix fixed * chore: gitignor updated * chore: minor * chore: minor * chore: usage of abstract classes corrected and dependencies are updated accordingly * Changed get_disruption to calculate_distruption * chore: id test updated * chore: conftest created at the lowest level. id naming also changed * chore: prefix replaced with abbr * chore: properties defined in init * chore: _return_periods property and read-only (annotated) return_periods property * chore: fixture added to the naming and reference * chore: performance_key set once * fix: average renamed (#572) * Fix/573 update rfid c in the graph simple after segmentation (#574) * fix: lanes added * feat: raise error if not all attributes_to_include in the dgf * feat: reverted the raise error if not all attributes_to_include in the dgf * feat: function created and used in nut to update rfid_c * feat: function used in osm_network_wrapper in nut to update rfid_c * Update ra2ce/network/networks_utils.py chore: inline comments changed Co-authored-by: Carles S. Soriano Pérez * chore: apply the comments on a previously made function + using deepcopy() * chore: use of deepcopy is corrected --------- Co-authored-by: Carles S. Soriano Pérez --------- Co-authored-by: Ardt Klapwijk <59741981+ArdtK@users.noreply.github.com> Co-authored-by: Cham8920 <138701847+Cham8920@users.noreply.github.com> Co-authored-by: Carles S. Soriano Pérez --- examples/data/isolated_locations/analyses.ini | 1 - .../multi_link_isolated_locations.csv | 1668 ++++++++--------- .../analysis.ini | 1 - .../analysis.ini | 1 - .../analysis.ini | 1 - .../analysis_config_data.py | 11 +- .../analysis_config_data_reader.py | 11 + .../enums/event_type_enum.py | 1 + ra2ce/analysis/analysis_config_wrapper.py | 3 + ra2ce/analysis/losses/losses_base.py | 164 +- .../losses/risk_calculation/__init__.py | 0 .../risk_calculation/risk_calculation_base.py | 84 + .../risk_calculation_cut_from_year.py | 91 + .../risk_calculation_default.py | 33 + .../risk_calculation_factory.py | 31 + .../risk_calculation_triangle_to_null_year.py | 63 + ra2ce/configuration/config_factory.py | 3 + ra2ce/network/hazard/hazard_overlay.py | 2 +- .../enums/aggregate_wl_enum.py | 4 + .../vector_network_wrapper.py | 7 + ra2ce/network/networks_utils.py | 57 +- ra2ce/ra2ce_handler.py | 3 + .../test_analysis_config_data_validator.py | 2 +- tests/analysis/losses/conftest.py | 16 +- .../test_resilience_curves.py | 6 +- .../losses/risk_calculation/__init__.py | 0 .../losses/risk_calculation/conftest.py | 117 ++ .../risk_calculation/test_risk_calculation.py | 45 + .../test_risk_calculation_factory.py | 30 + tests/analysis/losses/test_losses.py | 13 + tests/conftest.py | 2 - .../network_config_data/test_get_wl_prefix.py | 34 + .../test_data/4_analyses_losses/analyses.ini | 3 - .../acceptance_test_data/analyses.ini | 4 - ...lience_curve.csv => resilience_curves.csv} | 2 +- .../results_test_calc_vlh.csv | 14 +- .../single_link_redundancy_losses_test.csv | 14 +- 37 files changed, 1587 insertions(+), 955 deletions(-) create mode 100644 ra2ce/analysis/losses/risk_calculation/__init__.py create mode 100644 ra2ce/analysis/losses/risk_calculation/risk_calculation_base.py create mode 100644 ra2ce/analysis/losses/risk_calculation/risk_calculation_cut_from_year.py create mode 100644 ra2ce/analysis/losses/risk_calculation/risk_calculation_default.py create mode 100644 ra2ce/analysis/losses/risk_calculation/risk_calculation_factory.py create mode 100644 ra2ce/analysis/losses/risk_calculation/risk_calculation_triangle_to_null_year.py create mode 100644 tests/analysis/losses/risk_calculation/__init__.py create mode 100644 tests/analysis/losses/risk_calculation/conftest.py create mode 100644 tests/analysis/losses/risk_calculation/test_risk_calculation.py create mode 100644 tests/analysis/losses/risk_calculation/test_risk_calculation_factory.py create mode 100644 tests/network/network_config_data/test_get_wl_prefix.py rename tests/test_data/losses/csv_data_for_losses/{resilience_curve.csv => resilience_curves.csv} (74%) diff --git a/examples/data/isolated_locations/analyses.ini b/examples/data/isolated_locations/analyses.ini index 9117733f5..c7192561e 100644 --- a/examples/data/isolated_locations/analyses.ini +++ b/examples/data/isolated_locations/analyses.ini @@ -4,7 +4,6 @@ name = isolated_locations [analysis1] name = multi link isolated locations analysis = multi_link_isolated_locations -aggregate_wl = max threshold = 0.1 weighing = length buffer_meters = 100 diff --git a/examples/data/isolated_locations/output/multi_link_isolated_locations/multi_link_isolated_locations.csv b/examples/data/isolated_locations/output/multi_link_isolated_locations/multi_link_isolated_locations.csv index 6d860df93..325434d21 100644 --- a/examples/data/isolated_locations/output/multi_link_isolated_locations/multi_link_isolated_locations.csv +++ b/examples/data/isolated_locations/output/multi_link_isolated_locations/multi_link_isolated_locations.csv @@ -1,834 +1,834 @@ -building,i_id,index_right0,index_right1,index_right2,osmid,oneway,lanes,name,highway,maxspeed,reversed,length,rfid_c,rfid,junction,bridge,access,ref,width,area,EV1_mi,EV1_ma,EV1_me,EV1_fr,edges_distance,u,v,key,edge_fid,i_type_EV1,i_EV1 -apartments,28,11336236797,372475020,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,68.0,6771,107,None,None,None,None,None,None,,0.0,,0.0,41.4485830587631,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1167,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,60.95163790035938,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1168,4928030436,412942384,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,222.0,3264,107,None,None,None,None,None,None,,0.0,,0.0,59.42107308016944,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1169,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,62.826845780408505,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1170,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,61.02587819919281,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1172,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,60.78201780752823,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1174,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,83.35501868343223,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1175,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,103.19254079000461,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1176,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,84.48331113989899,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1177,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,59.73108491108583,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1178,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,82.86800261334298,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1214,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,81.80297122200481,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1255,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,109.19374774628133,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1256,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.0,2359,107,None,None,None,None,None,None,,0.0,,0.0,91.22846005326983,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1257,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,93.31646590039205,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,1258,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,66.4804187782942,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -industrial,1977,210330108,1575325562,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,74.0,96,107,None,None,None,None,None,None,1.0,1.0,1.0,1.0,57.66285248869221,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2326,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.0,2359,107,None,None,None,None,None,None,,0.0,,0.0,93.80667329472897,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2331,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,86.932067210409,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2333,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,70.80563522588626,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2334,210330115,4928058964,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,102.0,101,107,None,None,None,None,None,None,,0.0,,0.0,92.74317091261656,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2335,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,101.61571252899097,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2336,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,80.43596646928586,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2369,4928058965,386860033,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,16.0,3267,107,None,None,None,None,None,None,,0.0,,0.0,100.82851697786967,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2372,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,104.78483464124727,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2373,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,102.24701980497527,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2375,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,61.52033769818927,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2608,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,101.26985074891664,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -house,2609,372475020,3719788163,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,45.0,406,107,None,None,None,None,None,None,,0.0,,0.0,61.572125041812654,1149635257,1502706294,0,1149635257_1502706294,flooded,1 -apartments,28,11336236797,372475020,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,68.0,6771,107,None,None,None,None,None,None,,0.0,,0.0,41.4485830587631,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1167,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,60.95163790035938,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1168,4928030436,412942384,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,222.0,3264,107,None,None,None,None,None,None,,0.0,,0.0,59.42107308016944,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1169,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,62.826845780408505,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1170,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,61.02587819919281,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1172,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,60.78201780752823,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1174,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,83.35501868343223,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1176,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,84.48331113989899,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1177,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,59.73108491108583,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1178,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,82.86800261334298,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1214,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,81.80297122200481,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1256,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.0,2359,107,None,None,None,None,None,None,,0.0,,0.0,91.22846005326983,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1257,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,93.31646590039205,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,1258,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,49.0,3270,107,None,None,None,None,None,None,,0.0,,0.0,66.4804187782942,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -industrial,1977,210330108,1575325562,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,74.0,96,107,None,None,None,None,None,None,1.0,1.0,1.0,1.0,57.66285248869221,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2326,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.0,2359,107,None,None,None,None,None,None,,0.0,,0.0,93.80667329472897,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2331,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,86.932067210409,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2333,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,70.80563522588626,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2334,210330115,4928058964,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,102.0,101,107,None,None,None,None,None,None,,0.0,,0.0,92.74317091261656,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2336,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,23.0,3266,107,None,None,None,None,None,None,,0.0,,0.0,80.43596646928586,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2375,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,168.0,5706,107,None,None,None,None,None,None,,0.0,,0.0,61.52033769818927,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -house,2609,372475020,3719788163,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,45.0,406,107,None,None,None,None,None,None,,0.0,,0.0,61.572125041812654,1575325577,1575325616,0,1575325577_1575325616,flooded,1 -apartments,30,3341473300,3341501571,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,26.0,1699,717,None,None,None,None,None,None,,0.0,,0.0,75.62738424829605,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,119,3341471690,6122077940,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,20.0,1693,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,36.7538081966273,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,120,3341471688,3341471690,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,17.0,1692,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,33.48572582391713,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,120,3341471687,3341471688,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,18.0,1691,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,33.48572582391713,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,121,8778523988,3341473299,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,25.0,5197,717,None,None,None,None,None,None,,0.0,,0.0,38.503503612097084,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,122,3341473297,8778523988,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,24.0,1697,717,None,None,None,None,None,None,,0.0,,0.0,35.02576943722017,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,123,3341473299,3341473300,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,40.0,1698,717,None,None,None,None,None,None,,0.0,,0.0,37.8990669957061,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,124,8778523988,3341473299,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,25.0,5197,717,None,None,None,None,None,None,,0.0,,0.0,76.69863178959396,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,126,3341473299,3341473300,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,40.0,1698,717,None,None,None,None,None,None,,0.0,,0.0,81.64582875863064,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,409,3341473300,3341501571,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,26.0,1699,717,None,None,None,None,None,None,,0.0,,0.0,35.08301158597704,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,718,3341473303,3341473304,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,14.0,1702,717,None,None,None,None,None,None,,0.0,,0.0,25.888042265619248,2320163251,3341512594,0,2320163251_3341512594,flooded,1 -apartments,30,3341473300,3341501571,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,26.0,1699,717,None,None,None,None,None,None,,0.0,,0.0,75.62738424829605,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,119,3341471690,6122077940,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,20.0,1693,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,36.7538081966273,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,120,3341471688,3341471690,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,17.0,1692,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,33.48572582391713,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,120,3341471687,3341471688,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,18.0,1691,838,None,None,None,None,None,None,1.0,1.0,1.0,1.0,33.48572582391713,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,121,8778523988,3341473299,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,25.0,5197,717,None,None,None,None,None,None,,0.0,,0.0,38.503503612097084,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,122,3341473297,8778523988,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,24.0,1697,717,None,None,None,None,None,None,,0.0,,0.0,35.02576943722017,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,123,3341473299,3341473300,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,40.0,1698,717,None,None,None,None,None,None,,0.0,,0.0,37.8990669957061,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,124,8778523988,3341473299,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,25.0,5197,717,None,None,None,None,None,None,,0.0,,0.0,76.69863178959396,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,126,3341473299,3341473300,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,40.0,1698,717,None,None,None,None,None,None,,0.0,,0.0,81.64582875863064,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,409,3341473300,3341501571,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,26.0,1699,717,None,None,None,None,None,None,,0.0,,0.0,35.08301158597704,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -apartments,718,3341473303,3341473304,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,14.0,1702,717,None,None,None,None,None,None,,0.0,,0.0,25.888042265619248,3341473316,6782510037,0,3341473316_6782510037,flooded,1 -house,37,5950697916,5950696470,0,630145506,True,2,U-Loop,primary,40,False,13.0,3814,549,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,77.28898205664565,3663126491,5950730201,0,3663126491_5950730201,flooded,1 -house,39,5950697916,5950696470,0,630145506,True,2,U-Loop,primary,40,False,13.0,3814,549,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,67.61316058160206,3663126491,5950730201,0,3663126491_5950730201,flooded,1 -house,40,5950697914,5950697916,0,630145506,True,2,U-Loop,primary,40,False,16.0,3812,549,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,72.57597276308937,3663126491,5950730201,0,3663126491_5950730201,flooded,1 -apartments,41,5950697921,5950697918,0,630145506,True,2,U-Loop,primary,40,False,7.0,3819,549,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,62.39248225326637,3663126491,5950730201,0,3663126491_5950730201,flooded,1 -apartments,41,5950697922,5950697921,0,630145506,True,2,U-Loop,primary,40,False,6.0,3820,549,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,62.39248225326637,3663126491,5950730201,0,3663126491_5950730201,flooded,1 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সরণী,primary,None,False,10.0,6993,943,None,None,None,None,None,None,1.0,1.0,1.0,1.0,97.60576851531772,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2463,2320175124,9241328855,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,18.0,1183,949,None,None,None,None,None,None,1.0,1.0,1.0,1.0,55.102306678707684,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2464,10084412525,11612636091,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,5.0,6183,943,None,None,None,None,None,None,1.0,1.0,1.0,1.0,78.97812372743437,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2471,9241328855,10084412525,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,80.0,5229,949,None,None,None,None,None,None,1.0,1.0,1.0,1.0,22.5336052647351,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2472,9241328855,10084412525,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,80.0,5229,949,None,None,None,None,None,None,1.0,1.0,1.0,1.0,30.546660432544332,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2497,2320175124,9241328855,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,18.0,1183,949,None,None,None,None,None,None,1.0,1.0,1.0,1.0,75.7634409407509,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2533,2320175124,9241328855,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,18.0,1183,949,None,None,None,None,None,None,1.0,1.0,1.0,1.0,44.67055210416269,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,2551,11612636091,5798586183,0,444378907,True,2,শহীদ তাজউদ্দিন আহমেদ সরণী,primary,None,False,10.0,6993,943,None,None,None,None,None,None,1.0,1.0,1.0,1.0,32.42303641531694,4965462420,10084412525,0,4965462420_10084412525,isolated,1 -apartments,1987,11595320578,2320176644,0,343245804,True,2,নতুন ইস্কাটন রোড,primary,50,False,54.0,6946,496,None,None,None,None,None,None,1.0,1.0,1.0,1.0,97.58456938780046,2320176644,10010667870,0,2320176644_10010667870,flooded,1 -apartments,2466,4878968349,10010667879,0,343245804,True,2,নতুন ইস্কাটন রোড,primary,50,False,31.0,3019,223,None,None,None,None,None,None,1.0,1.0,1.0,1.0,96.97804048244441,2320176644,10010667870,0,2320176644_10010667870,flooded,1 -apartments,2469,2320176644,4878968349,0,343245804,True,2,নতুন ইস্কাটন রোড,primary,50,False,41.0,1194,223,None,None,None,None,None,None,1.0,1.0,1.0,1.0,92.04064549728764,2320176644,10010667870,0,2320176644_10010667870,flooded,1 -house,2008,3457245797,3457245796,0,338617129,True,2,None,trunk_link,50,False,75.0,2048,11,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,81.9192161088171,123042284,3458900439,0,123042284_3458900439,flooded,1 -house,2010,3457245797,3457245796,0,338617129,True,2,None,trunk_link,50,False,75.0,2048,11,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,90.26913053297477,123042284,3458900439,0,123042284_3458900439,flooded,1 -house,2010,3457245797,3457245796,0,338617129,True,2,None,trunk_link,50,False,75.0,2048,11,None,yes,None,None,None,None,1.0,1.0,1.0,1.0,90.26913053297477,123042284,4965331096,0,123042284_4965331096,flooded,1 -commercial,2031,1990890648,11612554203,0,188459714,True,3,কাউরান বাজার এফডিসি রোড,primary,None,False,58.0,1002,1050,None,None,None,None,None,None,,0.0,,0.0,42.358530640001966,2320175137,11612554202,0,2320175137_11612554202,flooded,1 -commercial,2031,1990890648,11612554203,0,188459714,True,3,কাউরান বাজার এফডিসি রোড,primary,None,False,58.0,1002,1050,None,None,None,None,None,None,,0.0,,0.0,42.358530640001966,11612554208,11618002292,0,11612554208_11618002292,flooded,1 -commercial,2034,2320379573,2320379571,0,240107302,True,None,কাজী নজরুল ইসলাম সরণী,trunk_link,None,False,29.0,1315,75,None,None,None,None,None,None,,0.0,,0.0,80.27527527425016,11612554208,11618002292,0,11612554208_11618002292,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,836879516,1575325577,0,836879516_1575325577,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1149635257,1693014272,0,1149635257_1693014272,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1149635257,1812632823,0,1149635257_1812632823,flooded,1 -apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,41.0,773,139,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1575325577,11175998550,0,1575325577_11175998550,flooded,1 -house,2395,6138976732,6138976731,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,75.0,4179,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,31.855110761277402,373389179,373389925,0,373389179_373389925,flooded,1 -house,2429,6138976738,6138976737,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,46.0,4185,594,None,None,None,R820,None,None,1.0,1.0,1.0,0.015297527034588846,42.32003352510398,373389179,373389925,0,373389179_373389925,flooded,1 -house,2430,7334274309,6138976732,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,38.0,5025,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,59.90091473280302,373389179,373389925,0,373389179_373389925,flooded,1 -house,2432,6138976738,6138976737,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,46.0,4185,594,None,None,None,R820,None,None,1.0,1.0,1.0,0.015297527034588846,59.668157726909854,373389179,373389925,0,373389179_373389925,flooded,1 -house,2395,6138976732,6138976731,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,75.0,4179,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,31.855110761277402,373389925,9944537919,0,373389925_9944537919,flooded,1 -house,2395,6138976732,6138976731,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,75.0,4179,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,31.855110761277402,6138976716,9944537919,0,6138976716_9944537919,flooded,1 -house,2395,6138976732,6138976731,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,75.0,4179,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,31.855110761277402,6138976716,9944537920,0,6138976716_9944537920,flooded,1 -house,2429,6138976738,6138976737,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,46.0,4185,594,None,None,None,R820,None,None,1.0,1.0,1.0,0.015297527034588846,42.32003352510398,373389179,6557044129,0,373389179_6557044129,flooded,1 -house,2430,7334274309,6138976732,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,38.0,5025,697,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,59.90091473280302,373389179,6557044129,0,373389179_6557044129,flooded,1 -house,2432,6138976738,6138976737,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,46.0,4185,594,None,None,None,R820,None,None,1.0,1.0,1.0,0.015297527034588846,59.668157726909854,373389179,6557044129,0,373389179_6557044129,flooded,1 -apartments,2469,2320176644,4878968349,0,343245804,True,2,নতুন ইস্কাটন রোড,primary,50,False,41.0,1194,223,None,None,None,None,None,None,1.0,1.0,1.0,1.0,92.04064549728764,3397973038,10010667870,0,3397973038_10010667870,flooded,1 +building,i_id,index_right0,index_right1,index_right2,osmid,oneway,lanes,name,highway,maxspeed,reversed,length,rfid_c,rfid,avgspeed,time,junction,bridge,access,ref,width,area,EV1_mi,EV1_ma,EV1_me,EV1_fr,edges_distance,u,v,key,edge_fid,i_type_EV1,i_EV1 +apartments,28,11336236797,372475020,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,67.555,6766,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,41.4485830587631,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1167,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,60.95163790035938,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1168,4928030436,412942384,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,221.666,3264,107,50.0,0.004,None,None,None,None,None,None,,0.0,,0.0,59.42107308016944,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1169,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,62.826845780408505,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1170,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,61.02587819919281,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1172,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,60.78201780752823,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1174,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,83.35501868343223,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1175,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,103.19254079000461,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1176,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,84.48331113989899,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1177,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,59.73108491108583,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1178,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,82.86800261334298,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1214,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,81.80297122200481,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1255,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,109.19374774628133,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1256,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.001,2359,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,91.22846005326983,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1257,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,93.31646590039205,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,1258,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,66.4804187782942,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +industrial,1977,210330108,1575325562,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,73.421,96,107,50.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,57.66285248869221,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,40.559,773,139,50.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2326,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.001,2359,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,93.80667329472897,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2331,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,86.932067210409,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2333,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,70.80563522588626,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2334,210330115,4928058964,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,102.283,101,107,50.0,0.002,None,None,None,None,None,None,,0.0,,0.0,92.74317091261656,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2335,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,101.61571252899097,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2336,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,80.43596646928586,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2369,4928058965,386860033,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,15.682,3267,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,100.82851697786967,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2372,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,104.78483464124727,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2373,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,102.24701980497527,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2375,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,61.52033769818927,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2608,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,101.26985074891664,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +house,2609,372475020,3719788163,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,44.647,406,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,61.572125041812654,1149635257,1502706294,0,1149635257_1502706294,flooded,1 +apartments,28,11336236797,372475020,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,67.555,6766,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,41.4485830587631,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1167,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,60.95163790035938,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1168,4928030436,412942384,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,221.666,3264,107,50.0,0.004,None,None,None,None,None,None,,0.0,,0.0,59.42107308016944,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1169,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,62.826845780408505,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1170,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,61.02587819919281,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1172,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,60.78201780752823,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1174,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,83.35501868343223,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1176,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,84.48331113989899,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1177,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,59.73108491108583,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1178,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,82.86800261334298,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1214,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,81.80297122200481,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1256,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.001,2359,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,91.22846005326983,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1257,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,93.31646590039205,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,1258,4928058976,210330115,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,48.607,3270,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,66.4804187782942,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +industrial,1977,210330108,1575325562,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,73.421,96,107,50.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,57.66285248869221,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +apartments,2318,1575325576,1575325575,0,143955381,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,40.559,773,139,50.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,68.02358859514639,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2326,3719788164,4928058976,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,27.001,2359,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,93.80667329472897,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2331,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,86.932067210409,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2333,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,70.80563522588626,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2334,210330115,4928058964,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,102.283,101,107,50.0,0.002,None,None,None,None,None,None,,0.0,,0.0,92.74317091261656,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2336,4928058964,4928058965,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,22.614,3266,107,50.0,0.0,None,None,None,None,None,None,,0.0,,0.0,80.43596646928586,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2375,9997860723,9997860724,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,167.505,5707,107,50.0,0.003,None,None,None,None,None,None,,0.0,,0.0,61.52033769818927,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +house,2609,372475020,3719788163,0,606372019,True,None,বীর উত্তম এ. কে. খন্দকার সড়ক,primary,None,False,44.647,406,107,50.0,0.001,None,None,None,None,None,None,,0.0,,0.0,61.572125041812654,1575325577,1575325616,0,1575325577_1575325616,flooded,1 +apartments,30,3341473300,3341501571,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,26.149,1699,718,40.0,0.001,None,None,None,None,None,None,,0.0,,0.0,75.62738424829605,2320163251,3341512594,0,2320163251_3341512594,flooded,1 +apartments,119,3341471690,6122077940,0,327377093,True,None,শহিদ ক্যাপ্টেন মুনসুর আলী সড়ক,primary,40,False,20.16,1693,839,40.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,36.7538081966273,2320163251,3341512594,0,2320163251_3341512594,flooded,1 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সড়ক,primary,None,False,40.248,4185,593,50.0,0.001,None,None,None,R820,None,None,,0.0,,0.0,43.18153679488567,373389179,6557044129,0,373389179_6557044129,flooded,1 +house,2430,7334274309,6138976732,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,37.904,5026,696,50.0,0.001,None,None,None,R820,None,None,1.0,1.0,1.0,1.0,59.90091473280302,373389179,6557044129,0,373389179_6557044129,flooded,1 +house,2432,6138976738,11993724750,0,654284300,True,2,ধোলাইখাল সড়ক,primary,None,False,40.248,4185,593,50.0,0.001,None,None,None,R820,None,None,,0.0,,0.0,60.97374697568809,373389179,6557044129,0,373389179_6557044129,flooded,1 +apartments,2469,2320176644,4878968349,0,343245804,True,2,নতুন ইস্কাটন রোড,primary,50,False,41.376,1194,223,50.0,0.001,None,None,None,None,None,None,1.0,1.0,1.0,1.0,92.04064549728764,3397973038,10010667870,0,3397973038_10010667870,flooded,1 diff --git a/examples/data/optimal_routes_analysis_with_hazard/analysis.ini b/examples/data/optimal_routes_analysis_with_hazard/analysis.ini index 38e164b4a..dbeed0c6e 100644 --- a/examples/data/optimal_routes_analysis_with_hazard/analysis.ini +++ b/examples/data/optimal_routes_analysis_with_hazard/analysis.ini @@ -4,7 +4,6 @@ name = beira [analysis1] name = multi link origin destination analysis = multi_link_origin_destination -aggregate_wl = max threshold = 0.5 weighing = distance calculate_route_without_disruption = True diff --git a/examples/data/origin_destination_analysis_with_hazard/analysis.ini b/examples/data/origin_destination_analysis_with_hazard/analysis.ini index 833081d6b..be2493915 100644 --- a/examples/data/origin_destination_analysis_with_hazard/analysis.ini +++ b/examples/data/origin_destination_analysis_with_hazard/analysis.ini @@ -4,7 +4,6 @@ name = beira [analysis1] name = multi link origin closest destination analysis = multi_link_origin_closest_destination -aggregate_wl = max threshold = 0.5 weighing = distance calculate_route_without_disruption = True diff --git a/examples/data/origin_destination_analysis_without_hazard/analysis.ini b/examples/data/origin_destination_analysis_without_hazard/analysis.ini index 2cd937c81..d4315157b 100644 --- a/examples/data/origin_destination_analysis_without_hazard/analysis.ini +++ b/examples/data/origin_destination_analysis_without_hazard/analysis.ini @@ -4,7 +4,6 @@ name = beira [analysis1] name = multi link origin closest destination without hazard analysis = multi_link_origin_closest_destination -aggregate_wl = None threshold = None weighing = distance calculate_route_without_disruption = True diff --git a/ra2ce/analysis/analysis_config_data/analysis_config_data.py b/ra2ce/analysis/analysis_config_data/analysis_config_data.py index 8e7a30c5e..d7a601daf 100644 --- a/ra2ce/analysis/analysis_config_data/analysis_config_data.py +++ b/ra2ce/analysis/analysis_config_data/analysis_config_data.py @@ -108,7 +108,6 @@ class AnalysisSectionLosses(AnalysisSectionBase): values_of_time_file: Optional[Path] = None # the redundancy analysis) and the intensities # accessibility analyses - aggregate_wl: AggregateWlEnum = field(default_factory=lambda: AggregateWlEnum.NONE) threshold: float = 0.0 threshold_destinations: float = math.nan uniform_duration: float = math.nan @@ -120,6 +119,13 @@ class AnalysisSectionLosses(AnalysisSectionBase): category_field_name: str = "" save_traffic: bool = False + # risk or estimated annual losses related + event_type: EventTypeEnum = field(default_factory=lambda: EventTypeEnum.NONE) + risk_calculation_mode: RiskCalculationModeEnum = field( + default_factory=lambda: RiskCalculationModeEnum.NONE + ) + risk_calculation_year: int = 0 + @dataclass class AnalysisSectionDamages(AnalysisSectionBase): @@ -139,7 +145,7 @@ class AnalysisSectionDamages(AnalysisSectionBase): climate_period: float = math.nan # road damage representative_damage_percentage: float = 100 - event_type: EventTypeEnum = field(default_factory=lambda: EventTypeEnum.INVALID) + event_type: EventTypeEnum = field(default_factory=lambda: EventTypeEnum.NONE) damage_curve: DamageCurveEnum = field( default_factory=lambda: DamageCurveEnum.INVALID ) @@ -168,6 +174,7 @@ class AnalysisConfigData(ConfigDataProtocol): default_factory=OriginsDestinationsSection ) network: NetworkSection = field(default_factory=NetworkSection) + aggregate_wl: AggregateWlEnum = field(default_factory=lambda: AggregateWlEnum.NONE) hazard_names: list[str] = field(default_factory=list) @property diff --git a/ra2ce/analysis/analysis_config_data/analysis_config_data_reader.py b/ra2ce/analysis/analysis_config_data/analysis_config_data_reader.py index 4c6966337..2588bab95 100644 --- a/ra2ce/analysis/analysis_config_data/analysis_config_data_reader.py +++ b/ra2ce/analysis/analysis_config_data/analysis_config_data_reader.py @@ -129,6 +129,17 @@ def _get_analysis_section_losses(self, section_name: str) -> AnalysisSectionLoss self._parser.get(section_name, "loss_type", fallback=None) ) # losses + _section.event_type = EventTypeEnum.get_enum( + self._parser.get(section_name, "event_type", fallback=None) + ) + _section.risk_calculation_mode = RiskCalculationModeEnum.get_enum( + self._parser.get(section_name, "risk_calculation_mode", fallback=None) + ) + _section.risk_calculation_year = self._parser.getint( + section_name, + "risk_calculation_year", + fallback=_section.risk_calculation_year, + ) _section.traffic_cols = self._parser.getlist( section_name, "traffic_cols", fallback=_section.traffic_cols ) diff --git a/ra2ce/analysis/analysis_config_data/enums/event_type_enum.py b/ra2ce/analysis/analysis_config_data/enums/event_type_enum.py index b1bff725b..20f421e14 100644 --- a/ra2ce/analysis/analysis_config_data/enums/event_type_enum.py +++ b/ra2ce/analysis/analysis_config_data/enums/event_type_enum.py @@ -2,6 +2,7 @@ class EventTypeEnum(Ra2ceEnumBase): + NONE = 0 EVENT = 1 RETURN_PERIOD = 2 INVALID = 99 diff --git a/ra2ce/analysis/analysis_config_wrapper.py b/ra2ce/analysis/analysis_config_wrapper.py index 2c0400b32..885e19409 100644 --- a/ra2ce/analysis/analysis_config_wrapper.py +++ b/ra2ce/analysis/analysis_config_wrapper.py @@ -85,6 +85,9 @@ def from_data_with_network( _new_analysis.config_data.origins_destinations = ( network_config.config_data.origins_destinations ) + _new_analysis.config_data.aggregate_wl = ( + network_config.config_data.hazard.aggregate_wl + ) # Graphs are retrieved from the already configured object _new_analysis.graph_files = network_config.graph_files diff --git a/ra2ce/analysis/losses/losses_base.py b/ra2ce/analysis/losses/losses_base.py index 457196b1a..23bc4d0a8 100644 --- a/ra2ce/analysis/losses/losses_base.py +++ b/ra2ce/analysis/losses/losses_base.py @@ -30,6 +30,10 @@ from ra2ce.analysis.analysis_config_data.analysis_config_data import ( AnalysisSectionLosses, ) +from ra2ce.analysis.analysis_config_data.enums.event_type_enum import EventTypeEnum +from ra2ce.analysis.analysis_config_data.enums.risk_calculation_mode_enum import ( + RiskCalculationModeEnum, +) from ra2ce.analysis.analysis_config_data.enums.traffic_period_enum import ( TrafficPeriodEnum, ) @@ -40,13 +44,15 @@ from ra2ce.analysis.losses.resilience_curves.resilience_curves_reader import ( ResilienceCurvesReader, ) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_factory import ( + RiskCalculationFactory, +) from ra2ce.analysis.losses.time_values.time_values_reader import TimeValuesReader from ra2ce.analysis.losses.traffic_intensities.traffic_intensities_reader import ( TrafficIntensitiesReader, ) from ra2ce.network.graph_files.graph_file import GraphFile from ra2ce.network.hazard.hazard_names import HazardNames -from ra2ce.network.network_config_data.enums.aggregate_wl_enum import AggregateWlEnum from ra2ce.network.network_config_data.enums.road_type_enum import RoadTypeEnum @@ -107,6 +113,8 @@ def __init__( self.output_path = analysis_input.output_path self.hazard_names = analysis_input.hazard_names + self.criticality_analysis = gpd.GeoDataFrame() + self.criticality_analysis_non_disrupted = gpd.GeoDataFrame() self.result = gpd.GeoDataFrame() def _check_validity_analysis_files(self): @@ -130,34 +138,33 @@ def _get_disrupted_criticality_analysis_results( ) else: criticality_analysis = criticality_analysis.drop_duplicates(["u", "v"]) + # ToDO: check hazard overlay with AggregateWlEnum.NONE or INVALID - # filter out all links not affected by the hazard - if self.analysis.aggregate_wl == AggregateWlEnum.NONE: - self.criticality_analysis = criticality_analysis[ - criticality_analysis["EV1_ma"] > self.analysis.threshold - ] - elif self.analysis.aggregate_wl == AggregateWlEnum.MAX: - self.criticality_analysis = criticality_analysis[ - criticality_analysis["EV1_max"] > self.analysis.threshold - ] - elif self.analysis.aggregate_wl == AggregateWlEnum.MEAN: - self.criticality_analysis = criticality_analysis[ - criticality_analysis["EV1_mean"] > self.analysis.threshold - ] - elif self.analysis.aggregate_wl == AggregateWlEnum.MIN: - self.criticality_analysis = criticality_analysis[ - criticality_analysis["EV1_min"] > self.analysis.threshold - ] + aggregate_wl_abbreviation = ( + self.analysis_config.config_data.aggregate_wl.get_wl_abbreviation() + ) + hazard_aggregate_wl_columns = [ + c + for c in criticality_analysis.columns + if (c.startswith("RP") or c.startswith("EV")) + and c.endswith(f"_{aggregate_wl_abbreviation}") + ] + self.criticality_analysis = criticality_analysis[ + ( + criticality_analysis[hazard_aggregate_wl_columns] + > self.analysis.threshold + ).any(axis=1) + ] self.criticality_analysis_non_disrupted = criticality_analysis[ ~criticality_analysis.index.isin(self.criticality_analysis.index) ] # link_id from list to tuple if len(self.criticality_analysis_non_disrupted) > 0: - self.criticality_analysis_non_disrupted[ - self.link_id - ] = self.criticality_analysis_non_disrupted[self.link_id].apply( - lambda x: tuple(x) if isinstance(x, list) else x + self.criticality_analysis_non_disrupted[self.link_id] = ( + self.criticality_analysis_non_disrupted[self.link_id].apply( + lambda x: tuple(x) if isinstance(x, list) else x + ) ) self.criticality_analysis[self.link_id] = self.criticality_analysis[ self.link_id @@ -185,7 +192,7 @@ def _get_range(height: float) -> tuple[float, float]: for range_tuple in _hazard_intensity_ranges: x, y = range_tuple if x <= height <= y: - return (x, y) + return x, y raise ValueError(f"No matching range found for height {height}") def _create_result(vlh: gpd.GeoDataFrame) -> gpd.GeoDataFrame: @@ -236,7 +243,7 @@ def _create_result(vlh: gpd.GeoDataFrame) -> gpd.GeoDataFrame: _check_validity_criticality_analysis() _hazard_intensity_ranges = self.resilience_curves.ranges - events = self.criticality_analysis.filter(regex=r"^EV(?!1_fr)") + events = self.criticality_analysis.filter(regex=r"^(EV|RP)(?!\d+_fr)") # Read the performance_change stating the functionality drop if "key" in self.criticality_analysis.columns: performance_change = self.criticality_analysis[ @@ -295,52 +302,48 @@ def _create_result(vlh: gpd.GeoDataFrame) -> gpd.GeoDataFrame: ) for event in events.columns.tolist(): for _, vlh_row in vehicle_loss_hours.iterrows(): - row_hazard_range = _get_range(vlh_row[event]) - row_connectivity = vlh_row[connectivity_attribute] - row_performance_changes = performance_change.loc[ - [vlh_row[self.link_id]] - ] - if "key" in vlh_row.index: - key = vlh_row["key"] - else: - key = 0 - (u, v, k) = ( - vlh_row["u"], - vlh_row["v"], - key, - ) - # allow link_id not to be unique in the graph (results reliability is up to the user) - # this can happen for instance when a directed graph should be made from an input network - for performance_row in row_performance_changes.iterrows(): - row_performance_change = performance_row[-1][ - f"{self.performance_metric}" + if vlh_row[event] > self.analysis.threshold: + row_hazard_range = _get_range(vlh_row[event]) + row_connectivity = vlh_row[connectivity_attribute] + row_performance_changes = performance_change.loc[ + [vlh_row[self.link_id]] ] - if "key" in performance_row[-1].index: - performance_key = performance_row[-1]["key"] + if "key" in vlh_row.index: + key = vlh_row["key"] else: + key = 0 + (u, v) = (vlh_row["u"], vlh_row["v"]) + # allow link_id not to be unique in the graph (results reliability is up to the user) + # this can happen for instance when a directed graph should be made from an input network + for performance_row in row_performance_changes.iterrows(): + row_performance_change = performance_row[-1][ + f"{self.performance_metric}" + ] performance_key = 0 - row_u_v_k = ( - performance_row[-1]["u"], - performance_row[-1]["v"], - performance_key, - ) - if ( - math.isnan(row_performance_change) and row_connectivity == 0 - ) or row_performance_change == 0: - self._calculate_production_loss_per_capita( - vehicle_loss_hours, row_hazard_range, vlh_row, event - ) - elif not ( - math.isnan(row_performance_change) - and math.isnan(row_connectivity) - ) and ((u, v, k) == row_u_v_k): - self._populate_vehicle_loss_hour( - vehicle_loss_hours, - row_hazard_range, - vlh_row, - row_performance_change, - event, + if "key" in performance_row[-1].index: + performance_key = performance_row[-1]["key"] + row_u_v_k = ( + performance_row[-1]["u"], + performance_row[-1]["v"], + performance_key, ) + if ( + math.isnan(row_performance_change) and row_connectivity == 0 + ) or row_performance_change == 0: + self._calculate_production_loss_per_capita( + vehicle_loss_hours, row_hazard_range, vlh_row, event + ) + elif not ( + math.isnan(row_performance_change) + and math.isnan(row_connectivity) + ) and ((u, v, key) == row_u_v_k): + self._populate_vehicle_loss_hour( + vehicle_loss_hours, + row_hazard_range, + vlh_row, + row_performance_change, + event, + ) vehicle_loss_hours_result = _create_result(vehicle_loss_hours) return vehicle_loss_hours_result @@ -355,7 +358,7 @@ def _get_relevant_link_type( _max_disruption = 0 for _row_link_type in vlh_row[self.link_type_column]: _link_type = RoadTypeEnum.get_enum(_row_link_type) - disruption = self.resilience_curves.get_disruption( + disruption = self.resilience_curves.calculate_disruption( _link_type, row_hazard_range ) if disruption > _max_disruption: @@ -448,9 +451,9 @@ def _calculate_production_loss_per_capita( [vlh_row.name], f"vlh_{trip_type}_{hazard_col_name}" ] = vlh_trip_type_event vlh_total += vlh_trip_type_event - vehicle_loss_hours.loc[ - [vlh_row.name], f"vlh_{hazard_col_name}_total" - ] = vlh_total + vehicle_loss_hours.loc[[vlh_row.name], f"vlh_{hazard_col_name}_total"] = ( + vlh_total + ) def _populate_vehicle_loss_hour( self, @@ -500,9 +503,9 @@ def _populate_vehicle_loss_hour( [vlh_row.name], f"vlh_{trip_type}_{hazard_col_name}" ] = vlh_trip_type_event vlh_total += vlh_trip_type_event - vehicle_loss_hours.loc[ - [vlh_row.name], f"vlh_{hazard_col_name}_total" - ] = vlh_total + vehicle_loss_hours.loc[[vlh_row.name], f"vlh_{hazard_col_name}_total"] = ( + vlh_total + ) @abstractmethod def _get_criticality_analysis(self) -> AnalysisLossesProtocol: @@ -516,4 +519,19 @@ def execute(self) -> gpd.GeoDataFrame: ) self.result = self.calculate_vehicle_loss_hours() + + # Calculate the risk or estimated annual losses if applicable + if ( + self.analysis.event_type == EventTypeEnum.RETURN_PERIOD + and self.analysis.risk_calculation_mode + not in (RiskCalculationModeEnum.INVALID, RiskCalculationModeEnum.NONE) + ): + risk_calculation = RiskCalculationFactory.get_risk_calculation( + risk_calculation_mode=self.analysis.risk_calculation_mode, + risk_calculation_year=self.analysis.risk_calculation_year, + losses_gdf=self.result, + ) + risk = risk_calculation.get_integration_of_df_trapezoidal() + self.result[f"risk_vlh_total"] = risk + return self.result diff --git a/ra2ce/analysis/losses/risk_calculation/__init__.py b/ra2ce/analysis/losses/risk_calculation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/ra2ce/analysis/losses/risk_calculation/risk_calculation_base.py b/ra2ce/analysis/losses/risk_calculation/risk_calculation_base.py new file mode 100644 index 000000000..bf4e8a0ed --- /dev/null +++ b/ra2ce/analysis/losses/risk_calculation/risk_calculation_base.py @@ -0,0 +1,84 @@ +from abc import ABC, abstractmethod + +import geopandas as gpd +import numpy as np +import re + + +class RiskCalculationBase(ABC): + risk_calculation_year: int + losses_gdf: gpd.GeoDataFrame + _return_periods: set[int] + + def __init__(self, risk_calculation_year: int, losses_gdf: gpd.GeoDataFrame): + self.risk_calculation_year = risk_calculation_year + self.losses_gdf = losses_gdf + self._return_periods = self._get_return_periods() + + @property + def return_periods(self): + return self._return_periods + + @property + def _max_return_period(self) -> int: + return max(self.return_periods) + + @property + def _min_return_period(self) -> int: + return min(self.return_periods) + + def _get_return_periods(self) -> set: + # Find the hazard columns; these may be events or return periods + hazard_column = [c for c in self.losses_gdf.columns if c.startswith("RP")] + rps = set( + [float(x.split("_")[0].replace("RP", "")) for x in hazard_column] + ) # set of unique rps + return rps + + def _get_to_integrate(self) -> gpd.GeoDataFrame: + def _extract_columns_by_pattern( + pattern_text: str, gdf: gpd.GeoDataFrame + ) -> set[str]: + """ + Extract column names based on the provided regex pattern. + """ + pattern = re.compile(pattern_text) + columns = { + pattern.search(c).group(1) for c in gdf.columns if pattern.search(c) + } + return columns + + loss_columns = sorted( + _extract_columns_by_pattern( + pattern_text=r"(vlh.*.total)", gdf=self.losses_gdf + ) + ) + loss_columns = [c for c in loss_columns if c.split("_")[1].startswith("RP")] + + _to_integrate = self.losses_gdf[loss_columns].copy() + + _to_integrate.columns = [ + float(c.split("_")[1].replace("RP", "")) for c in _to_integrate.columns + ] + return _to_integrate + + @abstractmethod + def _get_network_risk_calculations(self) -> gpd.GeoDataFrame: + pass + + def get_integration_of_df_trapezoidal(self) -> np.array: + """ + Arguments: + *df* (pd.DataFrame) : + Column names should contain return periods (years) + Each row should contain a set of damages for one object + + Returns: + np. Array : integrated result per row + + """ + _risk_calculations = self._get_network_risk_calculations() + frequencies = sorted(1 / rp for rp in _risk_calculations.columns) + return np.trapz(_risk_calculations.values, frequencies, axis=1) + + diff --git a/ra2ce/analysis/losses/risk_calculation/risk_calculation_cut_from_year.py b/ra2ce/analysis/losses/risk_calculation/risk_calculation_cut_from_year.py new file mode 100644 index 000000000..de6f89a34 --- /dev/null +++ b/ra2ce/analysis/losses/risk_calculation/risk_calculation_cut_from_year.py @@ -0,0 +1,91 @@ +import logging +import geopandas as gpd + +import numpy as np + +from ra2ce.analysis.losses.risk_calculation.risk_calculation_base import ( + RiskCalculationBase, +) + + +class RiskCalculationCutFromYear(RiskCalculationBase): + """ + In this mode, the integration mimics the presence of a flood protection + """ + + def _get_network_risk_calculations(self) -> gpd.GeoDataFrame: + """ + Rework the damage data to make it suitable for integration (risk calculation) in cut_from_year mode + """ + _data_to_integrate = self._get_to_integrate() + if self.risk_calculation_year <= self._min_return_period: + raise ValueError( + """ + RA2CE cannot calculate risk in 'cut_from' mode if + Return period of the cutoff ({}) <= smallest available return period ({}) + Use 'default' mode or 'triangle_to_null_mode' instead. + """.format( + self.risk_calculation_year, self._min_return_period + ) + ) + elif ( + self._min_return_period + < self.risk_calculation_year + < self._max_return_period + ): + if self.risk_calculation_year in self._return_periods: + _dropcols = [ + rp for rp in self._return_periods if rp < self.risk_calculation_year + ] + _data_to_integrate.drop(columns=_dropcols, inplace=True) + else: + # Copy the maximum return period with an infinitely high damage + _data_to_integrate[float("inf")] = _data_to_integrate[ + self._max_return_period + ] + + _data_to_integrate[self.risk_calculation_year] = np.nan + _data_to_integrate.interpolate(method="index", axis=1, inplace=True) + + _dropcols = [ + c + for c in _data_to_integrate.columns + if c < self.risk_calculation_year + ] + _data_to_integrate.drop(columns=_dropcols, inplace=True) + _data_to_integrate = _data_to_integrate.fillna(0) + + _data_to_integrate = _data_to_integrate[ + sorted(_data_to_integrate.columns, reverse=True) + ] + + logging.info( + """Risk calculation runs in 'cut_from' mode. + Assumptions: + - for all return periods > max RP{}, damage = dam_RP{} + - damage at cutoff is linearly interpolated from known damages + - no damage for al RPs > RP_cutoff ({}) + + """.format( + self._max_return_period, + self._max_return_period, + self.risk_calculation_year, + ) + ) + + elif ( + self.risk_calculation_year >= self._max_return_period + ): # cutoff is larger or equal than the largest return period + # risk is return frequency of cutoff + # times the damage to the most extreme event + _data_to_integrate = _data_to_integrate.fillna(0) + _data_to_integrate[self.risk_calculation_year] = _data_to_integrate[ + self._max_return_period + ] + _data_to_integrate[float("inf")] = _data_to_integrate[ + self._max_return_period + ] + _data_to_integrate = _data_to_integrate[ + [self.risk_calculation_year, float("inf")] + ] + return _data_to_integrate diff --git a/ra2ce/analysis/losses/risk_calculation/risk_calculation_default.py b/ra2ce/analysis/losses/risk_calculation/risk_calculation_default.py new file mode 100644 index 000000000..da65d98f0 --- /dev/null +++ b/ra2ce/analysis/losses/risk_calculation/risk_calculation_default.py @@ -0,0 +1,33 @@ +import logging +import geopandas as gpd + +from ra2ce.analysis.losses.risk_calculation.risk_calculation_base import ( + RiskCalculationBase, +) + + +class RiskCalculationDefault(RiskCalculationBase): + def _get_network_risk_calculations(self) -> gpd.GeoDataFrame: + """ + Rework the damage data to make it suitable for integration (risk calculation) in default mode + """ + _data_to_integrate = self._get_to_integrate() + # Copy the maximum return period with an infinitely high damage + _data_to_integrate[float("inf")] = _data_to_integrate[self._max_return_period] + + # Stop integrating at the last known return period, so no further manipulation needed + _data_to_integrate = _data_to_integrate.fillna(0) + + logging.info( + """Risk calculation runs in 'default' mode. + Assumptions: + - for all return periods > max RP{}, damage = dam_RP{} + - for all return periods < min RP{}, damage = 0 + + """.format( + self._max_return_period, + self._max_return_period, + self._min_return_period, + ) + ) + return _data_to_integrate diff --git a/ra2ce/analysis/losses/risk_calculation/risk_calculation_factory.py b/ra2ce/analysis/losses/risk_calculation/risk_calculation_factory.py new file mode 100644 index 000000000..a4983a899 --- /dev/null +++ b/ra2ce/analysis/losses/risk_calculation/risk_calculation_factory.py @@ -0,0 +1,31 @@ +import geopandas as gpd +from ra2ce.analysis.analysis_config_data.enums.risk_calculation_mode_enum import ( + RiskCalculationModeEnum, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_cut_from_year import ( + RiskCalculationCutFromYear, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_default import ( + RiskCalculationDefault, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_triangle_to_null_year import ( + RiskCalculationTriangleToNullYear, +) + + +class RiskCalculationFactory: + @staticmethod + def get_risk_calculation( + risk_calculation_mode: RiskCalculationModeEnum, + risk_calculation_year: int, + losses_gdf: gpd.GeoDataFrame, + ): + if risk_calculation_mode == RiskCalculationModeEnum.DEFAULT: + return RiskCalculationDefault(risk_calculation_year, losses_gdf) + if risk_calculation_mode == RiskCalculationModeEnum.CUT_FROM_YEAR: + return RiskCalculationCutFromYear(risk_calculation_year, losses_gdf) + if risk_calculation_mode == RiskCalculationModeEnum.TRIANGLE_TO_NULL_YEAR: + return RiskCalculationTriangleToNullYear(risk_calculation_year, losses_gdf) + raise NotImplementedError( + "Risk calculation mode {} not yet supported.".format(risk_calculation_mode) + ) diff --git a/ra2ce/analysis/losses/risk_calculation/risk_calculation_triangle_to_null_year.py b/ra2ce/analysis/losses/risk_calculation/risk_calculation_triangle_to_null_year.py new file mode 100644 index 000000000..5610acef4 --- /dev/null +++ b/ra2ce/analysis/losses/risk_calculation/risk_calculation_triangle_to_null_year.py @@ -0,0 +1,63 @@ +import logging +import geopandas as gpd + +from ra2ce.analysis.losses.risk_calculation.risk_calculation_base import ( + RiskCalculationBase, +) + + +class RiskCalculationTriangleToNullYear(RiskCalculationBase): + """ + In this mode, an extra data point with zero damage is added at some distance from the smallest known RP, + and the area of the Triangle this creates is also calculated + """ + + def _get_network_risk_calculations(self) -> gpd.GeoDataFrame: + """ + Rework the damage data to make it suitable for integration (risk calculation) in triangle_to_null_year mode + """ + _data_to_integrate = self._get_to_integrate() + if ( + self.risk_calculation_year >= self._min_return_period + and self.risk_calculation_year != 0 + ): + raise ValueError( + """ + RA2CE cannot calculate risk in 'triangle_to_null' mode if + Return period of the triangle ({}) >= smallest available return period ({}) + Use 'default' mode or 'cut_from' instead. + """.format( + self.risk_calculation_year, self._min_return_period + ) + ) + + if self.risk_calculation_year == 0: + logging.warning( + "Available lane data cannot simply be converted to float/int, RA2CE will try a clean-up." + ) + self.risk_calculation_year = 1 + + _data_to_integrate[float("inf")] = _data_to_integrate[self._max_return_period] + + # At the return period of the self.risk_calculation_year, set all damage values to zero + _data_to_integrate[self.risk_calculation_year] = 0 + + _data_to_integrate = _data_to_integrate.sort_index( + axis="columns", ascending=False + ) # from large to small RP + + _data_to_integrate = _data_to_integrate.fillna(0) + + logging.info( + """Risk calculation runs in 'triangle to null' mode. + Assumptions: + - for all return periods > max RP{}, damage = dam_RP{} + - at the end of the triangle {}, damage = 0 + + """.format( + self._max_return_period, + self._max_return_period, + self.risk_calculation_year, + ) + ) + return _data_to_integrate diff --git a/ra2ce/configuration/config_factory.py b/ra2ce/configuration/config_factory.py index 2ee56415d..c20dd0fd8 100644 --- a/ra2ce/configuration/config_factory.py +++ b/ra2ce/configuration/config_factory.py @@ -125,6 +125,9 @@ def get_analysis_config_without_network( _network_config.config_data.origins_destinations = ( _network_data.origins_destinations ) + _network_config.config_data.hazard.aggregate_wl = ( + _network_data.hazard.aggregate_wl + ) else: logging.error( f"Network configuration not found. Value provided: {_output_network_ini_file}" diff --git a/ra2ce/network/hazard/hazard_overlay.py b/ra2ce/network/hazard/hazard_overlay.py index bda0e9c1b..3a1c7fca2 100644 --- a/ra2ce/network/hazard/hazard_overlay.py +++ b/ra2ce/network/hazard/hazard_overlay.py @@ -267,7 +267,7 @@ def get_hazard_name_table(self) -> pd.DataFrame: all_agg_types = { "max": "maximum", "min": "minimum", - "mean": "average", + "mean": "mean", "fr": "fraction of network segment impacted by hazard", } chosen_agg_types = [ diff --git a/ra2ce/network/network_config_data/enums/aggregate_wl_enum.py b/ra2ce/network/network_config_data/enums/aggregate_wl_enum.py index e05293d61..ee4d0d28c 100644 --- a/ra2ce/network/network_config_data/enums/aggregate_wl_enum.py +++ b/ra2ce/network/network_config_data/enums/aggregate_wl_enum.py @@ -7,3 +7,7 @@ class AggregateWlEnum(Ra2ceEnumBase): MAX = 2 MEAN = 3 INVALID = 99 + + def get_wl_abbreviation(self): + """gets the first two letters of the Enum.name used in hazard overlay, damages and losses analysis.""" + return self.name[:2].lower() diff --git a/ra2ce/network/network_wrappers/vector_network_wrapper.py b/ra2ce/network/network_wrappers/vector_network_wrapper.py index 9b1fede2a..0ea83010b 100644 --- a/ra2ce/network/network_wrappers/vector_network_wrapper.py +++ b/ra2ce/network/network_wrappers/vector_network_wrapper.py @@ -87,6 +87,7 @@ def get_network( graph = self._get_direct_graph_from_vector( gdf=gdf, edge_attributes_to_include=[ + "lanes", "length", "maxspeed", "avgspeed", @@ -98,6 +99,7 @@ def get_network( graph = self._get_undirected_graph_from_vector( gdf, edge_attributes_to_include=[ + "lanes", "length", "maxspeed", "avgspeed", @@ -146,6 +148,10 @@ def get_network( # Check if all geometries between nodes are there, if not, add them as a straight line. graph_simple = nut.add_missing_geoms_graph(graph_simple, geom_name="geometry") + + # Update rfid_c after segmentation, which created more edges n teh complex graph + graph_simple = nut.add_complex_id_to_graph_simple(graph_simple, link_tables[0], "rfid") + logging.info("Finished converting the complex graph to a simple graph") return graph_simple, edges_complex @@ -288,6 +294,7 @@ def _create_graph_from_gdf( nx.Graph: NetworkX graph object with node and edge geometries and specified attributes. """ _networkx_graph = nx.DiGraph(crs=geo_dataframe.crs, approach="primal") + for _, row in geo_dataframe.iterrows(): link_id = row.get(self.file_id, None) link_type = row.get(self.link_type_column, None) diff --git a/ra2ce/network/networks_utils.py b/ra2ce/network/networks_utils.py index 8ac5d460a..622240e49 100644 --- a/ra2ce/network/networks_utils.py +++ b/ra2ce/network/networks_utils.py @@ -20,7 +20,7 @@ """ import itertools import logging -import math +from copy import deepcopy import os import sys import warnings @@ -1480,16 +1480,20 @@ def add_simple_id_to_graph_complex( """ + # {(u,v,k) : 'rfid_c'} obtained_complex_ids = nx.get_edge_attributes( complex_graph, "{}_c".format(new_id) - ) # {(u,v,k) : 'rfid_c'} - simple_ids_per_complex_id = obtained_complex_ids # start with a copy + ) + # start with a copy + simple_ids_per_complex_id = deepcopy(obtained_complex_ids) - for key, value in obtained_complex_ids.items(): # {(u,v,k) : 'rfid_c'} + # {(u,v,k) : 'rfid_c'} + for key, value in obtained_complex_ids.items(): try: + # find simple id belonging to the complex id new_value = complex_to_simple[ value - ] # find simple id belonging to the complex id + ] simple_ids_per_complex_id[key] = new_value except KeyError as e: logging.error( @@ -1580,3 +1584,46 @@ def buffer_geometry( cap_style=cap_stype, ) return gdf + +def add_complex_id_to_graph_simple( + simple_graph: nx.classes.Graph, simple_to_complex, simple_id: str +) -> nx.classes.Graph: + """Adds the appropriate ID of the complex graph to each edge of the simple graph as a new attribute 'rfid_c' + + Arguments: + simple_graph (Graph) : The simple graph, to update its 'rfid_c' + simple_to_complex (dict) : lookup table linking complex to simple graphs + simple_id (str): simple_id attribute to update + + Returns: + simple_graph (Graph) : Same object, with added attribute 'rfid_c' + + """ + + # {(u,v,k) : 'rfid'} + obtained_simple_ids = nx.get_edge_attributes( + simple_graph, f"{simple_id}" + ) + # start with a copy + complex_ids_per_simple_id = deepcopy(obtained_simple_ids) + + # {(u,v,k) : 'rfid'} + for key, value in obtained_simple_ids.items(): + try: + # find simple id belonging to the complex id + new_value = simple_to_complex[ + value + ] + complex_ids_per_simple_id[key] = new_value + except KeyError as e: + logging.error( + "Could not find the simple ID belonging to complex ID %s; value set to None. Full error: %s", + key, + e, + ) + complex_ids_per_simple_id[key] = None + + # Now the format of simple_ids_per_complex_id is: {(u,v,k) : 'rfid} + nx.set_edge_attributes(simple_graph, complex_ids_per_simple_id, f"{simple_id}_c") + + return simple_graph diff --git a/ra2ce/ra2ce_handler.py b/ra2ce/ra2ce_handler.py index 880025a40..7c92f0485 100644 --- a/ra2ce/ra2ce_handler.py +++ b/ra2ce/ra2ce_handler.py @@ -117,6 +117,9 @@ def get_analysis_config() -> AnalysisConfigWrapper | None: _analysis_config.config_data.origins_destinations = ( _handler.input_config.network_config.config_data.origins_destinations ) + _analysis_config.config_data.aggregate_wl = ( + _handler.input_config.network_config.config_data.hazard.aggregate_wl + ) _analysis_config.graph_files = ( _handler.input_config.network_config.graph_files ) diff --git a/tests/analysis/analysis_config_data/test_analysis_config_data_validator.py b/tests/analysis/analysis_config_data/test_analysis_config_data_validator.py index c2acdbfa3..49ba4c5f7 100644 --- a/tests/analysis/analysis_config_data/test_analysis_config_data_validator.py +++ b/tests/analysis/analysis_config_data/test_analysis_config_data_validator.py @@ -95,4 +95,4 @@ def test_validate_headers_fails_when_invalid_value(self): # 3. Verify final expectations. assert not _report.is_valid() - assert len(_report._errors) == 4 + assert len(_report._errors) == 3 diff --git a/tests/analysis/losses/conftest.py b/tests/analysis/losses/conftest.py index 4853d8ddf..4cc74c202 100644 --- a/tests/analysis/losses/conftest.py +++ b/tests/analysis/losses/conftest.py @@ -14,16 +14,16 @@ @pytest.fixture(name="resilience_curves_csv") def get_resilience_curves_csv_filepath() -> Iterator[Path]: _csv_file = test_data.joinpath( - "losses", "csv_data_for_losses", "resilience_curve.csv" + "losses", "csv_data_for_losses", "resilience_curves.csv" ) assert _csv_file.is_file() yield _csv_file @pytest.fixture(name="resilience_curves_data") -def get_resilience_curves_data() -> Iterator[ - list[tuple[RoadTypeEnum, tuple[float, float], list[float], list[float]]] -]: +def get_resilience_curves_data() -> ( + Iterator[list[tuple[RoadTypeEnum, tuple[float, float], list[float], list[float]]]] +): """ Get resilience curve data for testing. @@ -32,7 +32,7 @@ def get_resilience_curves_data() -> Iterator[ """ yield [ (RoadTypeEnum.MOTORWAY, (0.2, 0.5), [3.0, 5.0], [1.0, 0.4]), - (RoadTypeEnum.MOTORWAY, (0.5, 1.2), [2.0, 4.0], [1.0, 0.3]), + (RoadTypeEnum.MOTORWAY, (0.5, 1.2), [6.0, 10.0], [1.0, 0.4]), ] @@ -46,9 +46,9 @@ def get_traffic_intensities_csv_filepath() -> Iterator[Path]: @pytest.fixture(name="traffic_intensities_data") -def get_traffic_intensities_data() -> Iterator[ - dict[tuple[TrafficPeriodEnum, TripPurposeEnum], list[int]] -]: +def get_traffic_intensities_data() -> ( + Iterator[dict[tuple[TrafficPeriodEnum, TripPurposeEnum], list[int]]] +): """ Get traffic intensities data for testing (links 1:5). diff --git a/tests/analysis/losses/resilience_curves/test_resilience_curves.py b/tests/analysis/losses/resilience_curves/test_resilience_curves.py index 837743a5d..f8c271791 100644 --- a/tests/analysis/losses/resilience_curves/test_resilience_curves.py +++ b/tests/analysis/losses/resilience_curves/test_resilience_curves.py @@ -25,7 +25,7 @@ def _get_valid_resilience_curves( RoadTypeEnum.MOTORWAY, (0.2, 0.5), [3.0, 5.0], id="Motorway 0.2" ), pytest.param( - RoadTypeEnum.MOTORWAY, (0.5, 1.2), [2.0, 4.0], id="Motorway 0.5" + RoadTypeEnum.MOTORWAY, (0.5, 1.2), [6.0, 10.0], id="Motorway 0.5" ), ], ) @@ -49,7 +49,7 @@ def test_get_duration_steps( RoadTypeEnum.MOTORWAY, (0.2, 0.5), [1.0, 0.4], id="Motorway 0.2" ), pytest.param( - RoadTypeEnum.MOTORWAY, (0.5, 1.2), [1.0, 0.3], id="Motorway 0.5" + RoadTypeEnum.MOTORWAY, (0.5, 1.2), [1.0, 0.4], id="Motorway 0.5" ), ], ) @@ -72,7 +72,7 @@ def test_get_functionality_loss_ratio( "link_type, hazard_range, expected", [ pytest.param(RoadTypeEnum.MOTORWAY, (0.2, 0.5), 5.0, id="Motorway 0.2-0.5"), - pytest.param(RoadTypeEnum.MOTORWAY, (0.5, 1.2), 3.2, id="Motorway 0.5-1.2"), + pytest.param(RoadTypeEnum.MOTORWAY, (0.5, 1.2), 10.0, id="Motorway 0.5-1.2"), ], ) def test_calculate_disruption( diff --git a/tests/analysis/losses/risk_calculation/__init__.py b/tests/analysis/losses/risk_calculation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/analysis/losses/risk_calculation/conftest.py b/tests/analysis/losses/risk_calculation/conftest.py new file mode 100644 index 000000000..183571c39 --- /dev/null +++ b/tests/analysis/losses/risk_calculation/conftest.py @@ -0,0 +1,117 @@ +from typing import Iterator + +import numpy as np +import pandas as pd +import pytest + +from ra2ce.analysis.analysis_config_data.enums.risk_calculation_mode_enum import ( + RiskCalculationModeEnum, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_cut_from_year import ( + RiskCalculationCutFromYear, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_default import ( + RiskCalculationDefault, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_triangle_to_null_year import ( + RiskCalculationTriangleToNullYear, +) +from tests import test_data + + +@pytest.fixture(name="losses_fixture") +def get_losses_fixture() -> Iterator[pd.DataFrame]: + _csv_results_filepath = test_data.joinpath( + "losses", "csv_data_for_losses", "results_test_calc_vlh.csv" + ) + assert _csv_results_filepath.exists() + _test_calc_vlh = pd.read_csv(_csv_results_filepath) + yield _test_calc_vlh.drop( + columns=[ + "EV1_ma", + "EV2_mi", + "vlh_business_EV1_ma", + "vlh_commute_EV1_ma", + "vlh_business_EV2_mi", + "vlh_commute_EV2_mi", + "risk_vlh_total_default", + "risk_vlh_total_triangle_to_null_year", + "risk_vlh_total_cut_from_year", + ] + ) + + +@pytest.fixture(name="expected_factory_results_fixture") +def get_expected_factory_results_fixture() -> Iterator[dict]: + _expected_factory_results = { + RiskCalculationModeEnum.DEFAULT: { + "class": RiskCalculationDefault, + "_to_integrate": pd.DataFrame( + [ + [5.869500e06] * 3, + [2.540240e05] * 3, + [1.2899164e06] * 3, + [2.2945832e06] * 3, + [1.459500e06] * 3, + [2.533333e02] * 3, + ], + columns=[1000, 100, np.inf], + ), + }, + RiskCalculationModeEnum.CUT_FROM_YEAR: { + "class": RiskCalculationCutFromYear, + "_to_integrate": pd.DataFrame( + [ + [5.869500e06] * 3, + [2.540240e05] * 3, + [1.2899164e06] * 3, + [2.2945832e06] * 3, + [1.459500e06] * 3, + [2.533333e02] * 3, + ], + columns=[np.inf, 1000, 500], + ), + }, + RiskCalculationModeEnum.TRIANGLE_TO_NULL_YEAR: { + "class": RiskCalculationTriangleToNullYear, + "_to_integrate": pd.DataFrame( + [ + [5.869500e06] * 3 + [0], + [2.540240e05] * 3 + [0], + [1.2899164e06] * 3 + [0], + [2.2945832e06] * 3 + [0], + [1.459500e06] * 3 + [0], + [2.533333e02] * 3 + [0], + ], + columns=[np.inf, 1000, 100, 1], + ), + }, + "_return_periods": {100, 1000}, + } + yield _expected_factory_results + + +@pytest.fixture( + params=[ + pytest.param( + (RiskCalculationModeEnum.DEFAULT, 0), + id=RiskCalculationModeEnum.DEFAULT.name, + ), + pytest.param( + (RiskCalculationModeEnum.CUT_FROM_YEAR, 500), + id=RiskCalculationModeEnum.CUT_FROM_YEAR.name, + ), + pytest.param( + (RiskCalculationModeEnum.TRIANGLE_TO_NULL_YEAR, 0), + id=RiskCalculationModeEnum.TRIANGLE_TO_NULL_YEAR.name, + ), + ], + name="risk_calculation_info_fixture", +) +def _get_risk_calculation_info_fixture( + request: pytest.FixtureRequest, +) -> Iterator[tuple[RiskCalculationModeEnum, int]]: + _risk_calculation_info = request.param + assert isinstance(_risk_calculation_info[0], RiskCalculationModeEnum) + assert isinstance(_risk_calculation_info[1], int) + yield _risk_calculation_info diff --git a/tests/analysis/losses/risk_calculation/test_risk_calculation.py b/tests/analysis/losses/risk_calculation/test_risk_calculation.py new file mode 100644 index 000000000..d0a8fab98 --- /dev/null +++ b/tests/analysis/losses/risk_calculation/test_risk_calculation.py @@ -0,0 +1,45 @@ +import pandas as pd +from ra2ce.analysis.analysis_config_data.enums.risk_calculation_mode_enum import ( + RiskCalculationModeEnum, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_factory import ( + RiskCalculationFactory, +) +from tests import test_data + + +class TestRiskCalculation: + def test_risk_calculation_result( + self, + risk_calculation_info_fixture: tuple[RiskCalculationModeEnum, int], + losses_fixture: pd.DataFrame, + ): + # 1. get the expected results + _expected_result = pd.read_csv( + test_data.joinpath( + "losses", "csv_data_for_losses", "results_test_calc_vlh.csv" + ) + ) + + # 2. run + risk_calculation = RiskCalculationFactory.get_risk_calculation( + risk_calculation_mode=risk_calculation_info_fixture[0], + risk_calculation_year=risk_calculation_info_fixture[1], + losses_gdf=losses_fixture, + ) + risk = risk_calculation.get_integration_of_df_trapezoidal() + losses_fixture[ + f"risk_vlh_total_{risk_calculation_info_fixture[0].name.lower()}" + ] = risk + + # 3. Verify final expectations. + pd.testing.assert_frame_equal( + losses_fixture[ + [f"risk_vlh_total_{risk_calculation_info_fixture[0].name.lower()}"] + ], + _expected_result[ + [f"risk_vlh_total_{risk_calculation_info_fixture[0].name.lower()}"] + ], + rtol=1e-0, + atol=1e-2, + ) diff --git a/tests/analysis/losses/risk_calculation/test_risk_calculation_factory.py b/tests/analysis/losses/risk_calculation/test_risk_calculation_factory.py new file mode 100644 index 000000000..93a7edc4b --- /dev/null +++ b/tests/analysis/losses/risk_calculation/test_risk_calculation_factory.py @@ -0,0 +1,30 @@ +import pandas as pd + +from ra2ce.analysis.analysis_config_data.enums.risk_calculation_mode_enum import ( + RiskCalculationModeEnum, +) +from ra2ce.analysis.losses.risk_calculation.risk_calculation_factory import ( + RiskCalculationFactory, +) + + +class TestRiskCalculationFactory: + def test_risk_calculation_factory( + self, + risk_calculation_info_fixture: tuple[RiskCalculationModeEnum, int], + expected_factory_results_fixture: dict, + losses_fixture: pd.DataFrame, + ): + _risk_calculation = RiskCalculationFactory.get_risk_calculation( + risk_calculation_mode=risk_calculation_info_fixture[0], + risk_calculation_year=risk_calculation_info_fixture[1], + losses_gdf=losses_fixture, + ) + + assert isinstance( + _risk_calculation, + expected_factory_results_fixture[risk_calculation_info_fixture[0]]["class"], + ) + assert sorted(_risk_calculation._return_periods) == sorted( + expected_factory_results_fixture["_return_periods"] + ) diff --git a/tests/analysis/losses/test_losses.py b/tests/analysis/losses/test_losses.py index cb1189a9a..baa00ad6b 100644 --- a/tests/analysis/losses/test_losses.py +++ b/tests/analysis/losses/test_losses.py @@ -21,6 +21,7 @@ from ra2ce.analysis.losses.losses_base import LossesBase from ra2ce.analysis.losses.multi_link_losses import MultiLinkLosses from ra2ce.analysis.losses.single_link_losses import SingleLinkLosses +from ra2ce.network.network_config_data.enums.aggregate_wl_enum import AggregateWlEnum from ra2ce.network.network_config_wrapper import NetworkConfigWrapper from tests import test_data @@ -154,6 +155,7 @@ def create_linestring(row): _config_data = AnalysisConfigData() _network_config = NetworkConfigWrapper() + _network_config.config_data.hazard.aggregate_wl = AggregateWlEnum.MAX _valid_analysis_ini = test_data.joinpath("losses", "analyses.ini") _config = AnalysisConfigWrapper.from_data_with_network( _valid_analysis_ini, _config_data, _network_config @@ -220,6 +222,7 @@ def create_linestring(row): "losses", "csv_data_for_losses", "results_test_calc_vlh.csv" ) ) + _expected_result.reset_index(inplace=True) # 3. Verify final expectations. assert isinstance(_result, pd.DataFrame) @@ -228,3 +231,13 @@ def create_linestring(row): _result[["vlh_business_EV1_ma", "vlh_commute_EV1_ma"]], _expected_result[["vlh_business_EV1_ma", "vlh_commute_EV1_ma"]], ) + assert "vlh_business_EV2_mi" in _result + pd.testing.assert_frame_equal( + _result[["vlh_business_EV2_mi", "vlh_EV2_mi_total"]], + _expected_result[["vlh_business_EV2_mi", "vlh_EV2_mi_total"]], + ) + assert "vlh_business_RP100_me" in _result + pd.testing.assert_frame_equal( + _result[["vlh_business_RP100_me", "vlh_RP100_me_total"]], + _expected_result[["vlh_business_RP100_me", "vlh_RP100_me_total"]], + ) diff --git a/tests/conftest.py b/tests/conftest.py index 213e10343..ae6d689dd 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,8 +1,6 @@ from pathlib import Path from typing import Iterator - import pytest - from tests import acceptance_test_data diff --git a/tests/network/network_config_data/test_get_wl_prefix.py b/tests/network/network_config_data/test_get_wl_prefix.py new file mode 100644 index 000000000..d48e499f7 --- /dev/null +++ b/tests/network/network_config_data/test_get_wl_prefix.py @@ -0,0 +1,34 @@ +import pytest + +from ra2ce.network.network_config_data.enums.aggregate_wl_enum import AggregateWlEnum + + +class TestGetWlPrefix: + @pytest.mark.parametrize( + "aggregate_wl, expected_prefix", + [ + pytest.param( + AggregateWlEnum.MAX, + "ma", + id=AggregateWlEnum.MAX.name, + ), + pytest.param( + AggregateWlEnum.MIN, + "mi", + id=AggregateWlEnum.MIN.name, + ), + pytest.param( + AggregateWlEnum.MEAN, + "me", + id=AggregateWlEnum.MEAN.name, + ), + ], + ) + def test_aggregate_wl_prefix( + self, aggregate_wl: AggregateWlEnum, expected_prefix: str + ): + # 1./2. Define test data / Run test + _prefix = aggregate_wl.get_wl_abbreviation() + + # 3. Verify results + assert _prefix == expected_prefix diff --git a/tests/test_data/4_analyses_losses/analyses.ini b/tests/test_data/4_analyses_losses/analyses.ini index a166a56a2..7648a1a8d 100644 --- a/tests/test_data/4_analyses_losses/analyses.ini +++ b/tests/test_data/4_analyses_losses/analyses.ini @@ -11,7 +11,6 @@ save_csv = True [analysis2] name = multi link redundancy test analysis = multi_link_redundancy -aggregate_wl = max threshold = 1 weighing = distance save_gpkg = True @@ -27,7 +26,6 @@ save_csv = True [analysis4] name = multilink origin dest test analysis = multi_link_origin_destination -aggregate_wl = max threshold = 1 weighing = distance save_gpkg = True @@ -36,7 +34,6 @@ save_csv = True [analysis5] name = multilink origin closest dest test analysis = multi_link_origin_closest_destination -aggregate_wl = max threshold = 1 weighing = distance calculate_route_without_disruption = True diff --git a/tests/test_data/acceptance_test_data/analyses.ini b/tests/test_data/acceptance_test_data/analyses.ini index 480f5caf8..322946365 100644 --- a/tests/test_data/acceptance_test_data/analyses.ini +++ b/tests/test_data/acceptance_test_data/analyses.ini @@ -11,7 +11,6 @@ save_csv = True [analysis2] name = multi link redundancy test analysis = multi_link_redundancy -aggregate_wl = max threshold = 1 weighing = distance save_gpkg = True @@ -27,7 +26,6 @@ save_csv = True [analysis4] name = multilink origin dest test analysis = multi_link_origin_destination -aggregate_wl = max threshold = 1 weighing = distance save_gpkg = True @@ -36,7 +34,6 @@ save_csv = True [analysis5] name = multilink origin closest dest test analysis = multi_link_origin_closest_destination -aggregate_wl = max threshold = 1 weighing = distance save_gpkg = True @@ -45,7 +42,6 @@ save_csv = True [analysis6] name = multilink isolated locations analysis = multi_link_isolated_locations -aggregate_wl = max threshold = 1 weighing = distance buffer_meters = 40 diff --git a/tests/test_data/losses/csv_data_for_losses/resilience_curve.csv b/tests/test_data/losses/csv_data_for_losses/resilience_curves.csv similarity index 74% rename from tests/test_data/losses/csv_data_for_losses/resilience_curve.csv rename to tests/test_data/losses/csv_data_for_losses/resilience_curves.csv index 59ed13a08..1d96452a9 100644 --- a/tests/test_data/losses/csv_data_for_losses/resilience_curve.csv +++ b/tests/test_data/losses/csv_data_for_losses/resilience_curves.csv @@ -1,3 +1,3 @@ link_type_hazard_intensity;duration_steps;functionality_loss_ratio motorway_0.2-0.5;(3, 5);(1, 0.4) -motorway_0.5-1.2;(2, 4);(1, 0.3) +motorway_0.5-1.2;(6, 10);(1, 0.4) diff --git a/tests/test_data/losses/csv_data_for_losses/results_test_calc_vlh.csv b/tests/test_data/losses/csv_data_for_losses/results_test_calc_vlh.csv index e1e0e4408..95b4ba478 100644 --- a/tests/test_data/losses/csv_data_for_losses/results_test_calc_vlh.csv +++ b/tests/test_data/losses/csv_data_for_losses/results_test_calc_vlh.csv @@ -1,7 +1,7 @@ -link_id,link_type,vlh_business_EV1_ma,vlh_commute_EV1_ma,vlh_EV1_ma_total -1,motorway,1467375.0,1467375.0,0.0 -2,motorway,70562.5,56450.0,0.0 -3,motorway,196291.6,448666.6,0.0 -4,motorway,458916.6,1835666.6,0.0 -5,motorway,729750.0,729750.0,0.0 -6,motorway,126.66666666666666,126.66666666666666,0.0 +link_id,link_type,vlh_business_EV1_ma,vlh_commute_EV1_ma,vlh_EV1_ma_total,vlh_business_EV2_mi,vlh_commute_EV2_mi,vlh_EV2_mi_total,vlh_business_RP100_me,vlh_commute_RP100_me,vlh_RP100_me_total,vlh_business_RP1000_me,vlh_commute_RP1000_me,vlh_RP1000_me_total,risk_vlh_total_default,risk_vlh_total_triangle_to_null_year,risk_vlh_total_cut_from_year,EV1_ma,EV2_mi,RP100_me,RP1000_me +1,motorway,1467375.0,1467375.0,2934750.0,1467375.0,10467375.0,2934750.0,2934750.0,2934750.0,5869500.0,2934750.0,2934750.0,5869500.0,58695.0,2964097.5,11739.0,0.4,0.4,0.8,0.8 +2,motorway,70562.5,56450.0,127012.0,70562.5,56450.0,127012.0,141125.0,112900.0,254024.0,141125.0,112900.0,254024.0,2540.24,128282.12,580.048,0.4,0.4,0.8,0.8 +3,motorway,196291.6,448666.6,644958.2,196291.6,448666.6,644958.2,392583.2,897333.2,1289916.4,392583.2,897333.2,1289916.4,12899.164,651407.782,2579.8328,0.4,0.4,0.8,0.8 +4,motorway,458916.6,1835666.6,2294583.2,458916.6,1835666.6,2294583.2,458916.6,1835666.6,2294583.2,458916.6,1835666.6,2294583.2,22945.832,1158764.516,4589.1664,0.4,0.4,0.4,0.4 +5,motorway,729750.0,729750.0,1459500.0,729750.0,729750.0,1459500.0,729750.0,729750.0,1459500.0,729750.0,729750.0,1459500.0,14595.0,737047.5,2919,0.4,0.4,0.4,0.4 +6,motorway,126.66666666666666,126.66666666666666,253.33333333333333,126.66666666666666,126.66666666666666,253.33333333333333,126.66666666666666,126.66666666666666,253.33333333333333,126.66666666666666,126.66666666666666,253.33333333333333,2.5333333333333333,127.93333333333332,0.5066666666666666,0.4,0.4,0.4,0.4 diff --git a/tests/test_data/losses/csv_data_for_losses/single_link_redundancy_losses_test.csv b/tests/test_data/losses/csv_data_for_losses/single_link_redundancy_losses_test.csv index 08e9acc58..50bf02c73 100644 --- a/tests/test_data/losses/csv_data_for_losses/single_link_redundancy_losses_test.csv +++ b/tests/test_data/losses/csv_data_for_losses/single_link_redundancy_losses_test.csv @@ -1,7 +1,7 @@ -u,v,link_id,link_type,avgspeed,node_A,node_B,edge_fid,length,alt_length,alt_nodes,diff_length,detour,EV1_ma -0,1,1,motorway,10,0,1,0_1,34728.0,105162.0,"[0, 2, 1]",70434.0,1,0.4 -0,2,2,motorway,10,0,2,0_2,57245.0,70793.0,"[0, 3, 2]",13548.0,1,0.4 -0,3,3,motorway,10,0,3,0_3,50559.0,77479.0,"[0, 2, 3]",26920.0,1,0.4 -1,2,4,motorway,10,1,2,1_2,47917.0,91973.0,"[1, 0, 2]",44056.0,1,0.4 -2,3,5,motorway,10,2,3,2_3,20234.0,107804.0,"[2, 0, 3]",87570.0,1,0.4 -2,4,6,motorway,10,2,3,2_3,20234.0,0,"[]",0,0,0.4 +u,v,link_id,link_type,avgspeed,node_A,node_B,edge_fid,length,alt_length,alt_nodes,diff_length,detour,EV1_ma,EV2_mi,RP100_me,RP1000_me +0,1,1,motorway,10,0,1,0_1,34728.0,105162.0,"[0, 2, 1]",70434.0,1,0.4,0.4,0.8,0.8 +0,2,2,motorway,10,0,2,0_2,57245.0,70793.0,"[0, 3, 2]",13548.0,1,0.4,0.4,0.8,0.8 +0,3,3,motorway,10,0,3,0_3,50559.0,77479.0,"[0, 2, 3]",26920.0,1,0.4,0.4,0.8,0.8 +1,2,4,motorway,10,1,2,1_2,47917.0,91973.0,"[1, 0, 2]",44056.0,1,0.4,0.4,0.4,0.4 +2,3,5,motorway,10,2,3,2_3,20234.0,107804.0,"[2, 0, 3]",87570.0,1,0.4,0.4,0.4,0.4 +2,4,6,motorway,10,2,3,2_3,20234.0,0,"[]",0,0,0.4,0.4,0.4,0.4